Alstonia scholaris

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Alstonia scholaris

Alstonia scholaris, a tree that grows 50-80 feet high and belongs to the Apocynaceae family, has a long history of use in traditional and homeopathic medicine, including Ayurvedic medicine in India, where it is known as sapthaparna (Integr. Cancer Ther. 2009;8:273-9), in traditional Chinese medicine (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81), and in traditional medicine in Africa and Australia (Integr. Cancer Ther. 2010;9:261-9). The bark contains the alkaloids ditamine, echitamine (or ditaine), and echitanines; and decoctions or other preparations of the bark have been used to treat gastrointestinal conditions (Grieve M. A Modern Herbal (Vol. 1). New York, Dover Publications, 1971, p. 29). Often called the devil’s tree, the bark of A. scholaris also has been used to treat malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions (such as asthma and bronchitis), helminthiasis, and agalactia (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]).

In the study of A. scholaris most directly pertinent to potential dermatologic treatment, Lee et al. found that ethanolic bark extracts of A. scholaris significantly suppressed retinoid-induced skin irritation in vitro and in vivo, in human HaCat keratinocytes. The investigators identified echitamine and loganin as the primary components likely responsible for the anti-inflammatory effects.

Courtesy Wikimedia Commons/Binh Giang/Public Domain
Alstonia scholaris has a long history of use in traditional and homeopathic medicine.

Data showed that A. scholaris dose-dependently inhibited the all-trans retinoic acid–induced releases of the pro-inflammatory cytokines monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8) in vitro. Also in vitro, A. scholaris extract potently suppressed radiation-induced increases in matrix metalloproteinase-1 (MMP-1). Importantly, in a cumulative irritation patch test, the botanical extract diminished retinol-induced skin irritation while enhancing retinoid activity in blocking MMP-1 expression, which is linked closely to cutaneous aging. The authors concluded that A. scholaris appears to have the dual benefits of decreasing irritation associated with retinoids while augmenting their antiaging impact (Evid. Based Complement. Alternat. Med. 2012;2012:190370).

The leaf extract of A. scholaris has been used to treat cold symptoms and tracheitis, and it has been prescribed in hospitals and approved for commercial over-the-counter sale by the State Food and Drugs Administration of China (SFDA) (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81). The broad range of biological properties associated with A. scholaris has been ascribed to particular constituent categories, including alkaloids, flavonoids, and terpenoids (specifically, phenolic acids) (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). These properties include, but are reportedly not limited to, antioxidant, anticancer, anti-inflammatory, antistress, analgesic, antimutagenic, hepatoprotective, immunomodulatory, and chemopreventive activity (Integr. Cancer Ther. 2010;9:261-9; Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). Antineoplastic effects have been linked directly to phytochemical constituents including echitamine, alstonine, pleiocarpamine, O-methylmacralstonine, macralstonine, and lupeol (Integr. Cancer Ther. 2010;9:261-9).

In 2006, Jagetia and Baliga investigated the anticancer activity of A. scholaris alkaloid fractions in vitro in cultured human neoplastic cell lines. They also conducted in vivo studies in tumor-bearing mice. The in vitro data in HeLa cells revealed a time-dependent rise in antineoplastic activity after 24 hours of exposure (25 mcg/mL). Further, once-daily administration of A. scholaris (240 mg/kg) to tumor-bearing mice yielded dose-dependent remissions, although there were toxic presentations at this dosage. The next-lower dose of 210 mg/kg was found to be most effective, with 20% of the mice surviving for as long as 120 days after tumor cell inoculation, compared with none of the control animals treated with saline (Phytother. Res. 2006;20:103-9).

Using an acute-restraint stress model in mice in 2009, Kulkarni and Juvekar evaluated the effects of stress and the impact of a methanolic extract of A. scholaris bark. Pretreatments with the extract of 100, 250, and 500 mg/kg for 7 days were found to exert significant antistress effects. In addition, nootropic activities were observed, with memory functions clearly enhanced in learning tasks. A. scholaris also was associated with significant antioxidant properties. The extract at 200 mcg/mL exhibited maximum scavenging of the stable radical 1,1-diphenyl-2-picrylhydrazyl at 90.11% and the nitric oxide radical at 62.77% (Indian J. Exp. Biol. 2009;47:47-52).

Later in 2009, Jahan et al. reported on their investigation of potential antioxidant and chemopreventive activity displayed by A. scholaris in a two-stage murine model. Skin carcinogenesis development was initiated in Swiss albino mice through one application of 7, 12-dimethyabenz(a)anthrecene (DMBA) and then promoted two weeks later by repeated application of croton oil three times per week through 16 weeks. The investigators found a lower incidence of tumors, tumor yield, tumor burden, and number of papillomas in mice treated with A. scholaris extract as compared to untreated controls (Integr. Cancer Ther. 2009;8:273-9).

 

 

In 2010, Shang et al. conducted multiple studies using A. scholaris. In the first published report, they assessed the anti-inflammatory and analgesic properties of the ethanolic leaf extract to validate its use in traditional Chinese medicine and modern clinical medicine. The investigators first determined that analgesic activity was conferred as the ethyl acetate and alkaloid fractions significantly diminished acetic acid-induced reactions in mice and, along with the ethanolic extract, reduced xylene-induced ear edema.

The researchers also performed in vivo and in vitro assessments of anti-inflammatory activity again on xylene-induced ear edema and carrageenan-induced air pouch formation in mice, as well as cyclooxygenase (COX)-1, -2 and 5-LOX inhibition.

In the air pouch model, A. scholaris alkaloids were found to have significantly spurred superoxide dismutase activity while lowering nitric oxide, prostaglandin E2, and malondialdehyde levels. In vitro tests, supporting evidence from animal models, showed that the three primary alkaloids isolated from A. scholaris leaves (picrinine, vallesamine, and scholaricine) inhibited the inflammatory mediators COX-1, COX-2, and 5-LOX. The researchers also noted that the in vitro anti-inflammatory assay results reinforced the notion of these alkaloids as the bioactive fraction of the plant (J. Ethnopharmacol. 2010;129:174-81).

In their second published report that year, Shang et al. investigated the antitussive and anti-asthmatic activities of the ethanolic extract, fractions, and chief alkaloids of A. scholaris leaf.

The researchers tested for antitussive effects using ammonia-induced or sulfur dioxide-induced coughing in mice and citric acid-induced coughing in guinea pigs. They evaluated anti-asthmatic activity via histamine-induced bronchoconstriction in guinea pigs. They also measured phenol red volume in murine tracheas to assess expectorant activity.

The data indicated antitussive activity, with significant alkaloid suppression of ammonia-induced coughing frequency in mice. Latency periods of sulfur dioxide-induced cough in mice and citric acid-induced cough in guinea pigs increased, and cough frequency in guinea pigs decreased.

Anti-asthmatic effects, such as suppression of convulsion, were observed in guinea pigs. In the expectorant assessment, tracheal phenol red production was increased. The researchers identified picrinine as the primary alkaloid responsible for these activities (J. Ethnopharmacol. 2010;129:293-8).

In addition, Jahan and Goyal showed that pretreatment with A. scholaris bark extract protected the bone marrow of mice against radiation-induced chromosomal damage and micronuclei induction (J Environ. Pathol. Toxicol. Oncol. 2010;29:101-11).

Conclusion

Despite the dearth of research on A. scholaris, the existing data are intriguing, particularly the findings that A. scholaris may have the capacity to amplify the anti-aging activity of retinoids while blunting their irritating effects. Although more research is needed to determine the dermatologic value of A. scholaris, the pursuit may potentially prove fruitful.

Dr. Baumann is in private practice in Miami Beach. She did not disclose any conflicts of interest. To respond to this column, or to suggest topics for future columns, write to her at [email protected].

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Alstonia scholaris, a tree that grows 50-80 feet high and belongs to the Apocynaceae family, has a long history of use in traditional and homeopathic medicine, including Ayurvedic medicine in India, where it is known as sapthaparna (Integr. Cancer Ther. 2009;8:273-9), in traditional Chinese medicine (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81), and in traditional medicine in Africa and Australia (Integr. Cancer Ther. 2010;9:261-9). The bark contains the alkaloids ditamine, echitamine (or ditaine), and echitanines; and decoctions or other preparations of the bark have been used to treat gastrointestinal conditions (Grieve M. A Modern Herbal (Vol. 1). New York, Dover Publications, 1971, p. 29). Often called the devil’s tree, the bark of A. scholaris also has been used to treat malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions (such as asthma and bronchitis), helminthiasis, and agalactia (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]).

In the study of A. scholaris most directly pertinent to potential dermatologic treatment, Lee et al. found that ethanolic bark extracts of A. scholaris significantly suppressed retinoid-induced skin irritation in vitro and in vivo, in human HaCat keratinocytes. The investigators identified echitamine and loganin as the primary components likely responsible for the anti-inflammatory effects.

Courtesy Wikimedia Commons/Binh Giang/Public Domain
Alstonia scholaris has a long history of use in traditional and homeopathic medicine.

Data showed that A. scholaris dose-dependently inhibited the all-trans retinoic acid–induced releases of the pro-inflammatory cytokines monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8) in vitro. Also in vitro, A. scholaris extract potently suppressed radiation-induced increases in matrix metalloproteinase-1 (MMP-1). Importantly, in a cumulative irritation patch test, the botanical extract diminished retinol-induced skin irritation while enhancing retinoid activity in blocking MMP-1 expression, which is linked closely to cutaneous aging. The authors concluded that A. scholaris appears to have the dual benefits of decreasing irritation associated with retinoids while augmenting their antiaging impact (Evid. Based Complement. Alternat. Med. 2012;2012:190370).

The leaf extract of A. scholaris has been used to treat cold symptoms and tracheitis, and it has been prescribed in hospitals and approved for commercial over-the-counter sale by the State Food and Drugs Administration of China (SFDA) (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81). The broad range of biological properties associated with A. scholaris has been ascribed to particular constituent categories, including alkaloids, flavonoids, and terpenoids (specifically, phenolic acids) (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). These properties include, but are reportedly not limited to, antioxidant, anticancer, anti-inflammatory, antistress, analgesic, antimutagenic, hepatoprotective, immunomodulatory, and chemopreventive activity (Integr. Cancer Ther. 2010;9:261-9; Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). Antineoplastic effects have been linked directly to phytochemical constituents including echitamine, alstonine, pleiocarpamine, O-methylmacralstonine, macralstonine, and lupeol (Integr. Cancer Ther. 2010;9:261-9).

In 2006, Jagetia and Baliga investigated the anticancer activity of A. scholaris alkaloid fractions in vitro in cultured human neoplastic cell lines. They also conducted in vivo studies in tumor-bearing mice. The in vitro data in HeLa cells revealed a time-dependent rise in antineoplastic activity after 24 hours of exposure (25 mcg/mL). Further, once-daily administration of A. scholaris (240 mg/kg) to tumor-bearing mice yielded dose-dependent remissions, although there were toxic presentations at this dosage. The next-lower dose of 210 mg/kg was found to be most effective, with 20% of the mice surviving for as long as 120 days after tumor cell inoculation, compared with none of the control animals treated with saline (Phytother. Res. 2006;20:103-9).

Using an acute-restraint stress model in mice in 2009, Kulkarni and Juvekar evaluated the effects of stress and the impact of a methanolic extract of A. scholaris bark. Pretreatments with the extract of 100, 250, and 500 mg/kg for 7 days were found to exert significant antistress effects. In addition, nootropic activities were observed, with memory functions clearly enhanced in learning tasks. A. scholaris also was associated with significant antioxidant properties. The extract at 200 mcg/mL exhibited maximum scavenging of the stable radical 1,1-diphenyl-2-picrylhydrazyl at 90.11% and the nitric oxide radical at 62.77% (Indian J. Exp. Biol. 2009;47:47-52).

Later in 2009, Jahan et al. reported on their investigation of potential antioxidant and chemopreventive activity displayed by A. scholaris in a two-stage murine model. Skin carcinogenesis development was initiated in Swiss albino mice through one application of 7, 12-dimethyabenz(a)anthrecene (DMBA) and then promoted two weeks later by repeated application of croton oil three times per week through 16 weeks. The investigators found a lower incidence of tumors, tumor yield, tumor burden, and number of papillomas in mice treated with A. scholaris extract as compared to untreated controls (Integr. Cancer Ther. 2009;8:273-9).

 

 

In 2010, Shang et al. conducted multiple studies using A. scholaris. In the first published report, they assessed the anti-inflammatory and analgesic properties of the ethanolic leaf extract to validate its use in traditional Chinese medicine and modern clinical medicine. The investigators first determined that analgesic activity was conferred as the ethyl acetate and alkaloid fractions significantly diminished acetic acid-induced reactions in mice and, along with the ethanolic extract, reduced xylene-induced ear edema.

The researchers also performed in vivo and in vitro assessments of anti-inflammatory activity again on xylene-induced ear edema and carrageenan-induced air pouch formation in mice, as well as cyclooxygenase (COX)-1, -2 and 5-LOX inhibition.

In the air pouch model, A. scholaris alkaloids were found to have significantly spurred superoxide dismutase activity while lowering nitric oxide, prostaglandin E2, and malondialdehyde levels. In vitro tests, supporting evidence from animal models, showed that the three primary alkaloids isolated from A. scholaris leaves (picrinine, vallesamine, and scholaricine) inhibited the inflammatory mediators COX-1, COX-2, and 5-LOX. The researchers also noted that the in vitro anti-inflammatory assay results reinforced the notion of these alkaloids as the bioactive fraction of the plant (J. Ethnopharmacol. 2010;129:174-81).

In their second published report that year, Shang et al. investigated the antitussive and anti-asthmatic activities of the ethanolic extract, fractions, and chief alkaloids of A. scholaris leaf.

The researchers tested for antitussive effects using ammonia-induced or sulfur dioxide-induced coughing in mice and citric acid-induced coughing in guinea pigs. They evaluated anti-asthmatic activity via histamine-induced bronchoconstriction in guinea pigs. They also measured phenol red volume in murine tracheas to assess expectorant activity.

The data indicated antitussive activity, with significant alkaloid suppression of ammonia-induced coughing frequency in mice. Latency periods of sulfur dioxide-induced cough in mice and citric acid-induced cough in guinea pigs increased, and cough frequency in guinea pigs decreased.

Anti-asthmatic effects, such as suppression of convulsion, were observed in guinea pigs. In the expectorant assessment, tracheal phenol red production was increased. The researchers identified picrinine as the primary alkaloid responsible for these activities (J. Ethnopharmacol. 2010;129:293-8).

In addition, Jahan and Goyal showed that pretreatment with A. scholaris bark extract protected the bone marrow of mice against radiation-induced chromosomal damage and micronuclei induction (J Environ. Pathol. Toxicol. Oncol. 2010;29:101-11).

Conclusion

Despite the dearth of research on A. scholaris, the existing data are intriguing, particularly the findings that A. scholaris may have the capacity to amplify the anti-aging activity of retinoids while blunting their irritating effects. Although more research is needed to determine the dermatologic value of A. scholaris, the pursuit may potentially prove fruitful.

Dr. Baumann is in private practice in Miami Beach. She did not disclose any conflicts of interest. To respond to this column, or to suggest topics for future columns, write to her at [email protected].

Alstonia scholaris, a tree that grows 50-80 feet high and belongs to the Apocynaceae family, has a long history of use in traditional and homeopathic medicine, including Ayurvedic medicine in India, where it is known as sapthaparna (Integr. Cancer Ther. 2009;8:273-9), in traditional Chinese medicine (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81), and in traditional medicine in Africa and Australia (Integr. Cancer Ther. 2010;9:261-9). The bark contains the alkaloids ditamine, echitamine (or ditaine), and echitanines; and decoctions or other preparations of the bark have been used to treat gastrointestinal conditions (Grieve M. A Modern Herbal (Vol. 1). New York, Dover Publications, 1971, p. 29). Often called the devil’s tree, the bark of A. scholaris also has been used to treat malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions (such as asthma and bronchitis), helminthiasis, and agalactia (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]).

In the study of A. scholaris most directly pertinent to potential dermatologic treatment, Lee et al. found that ethanolic bark extracts of A. scholaris significantly suppressed retinoid-induced skin irritation in vitro and in vivo, in human HaCat keratinocytes. The investigators identified echitamine and loganin as the primary components likely responsible for the anti-inflammatory effects.

Courtesy Wikimedia Commons/Binh Giang/Public Domain
Alstonia scholaris has a long history of use in traditional and homeopathic medicine.

Data showed that A. scholaris dose-dependently inhibited the all-trans retinoic acid–induced releases of the pro-inflammatory cytokines monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8) in vitro. Also in vitro, A. scholaris extract potently suppressed radiation-induced increases in matrix metalloproteinase-1 (MMP-1). Importantly, in a cumulative irritation patch test, the botanical extract diminished retinol-induced skin irritation while enhancing retinoid activity in blocking MMP-1 expression, which is linked closely to cutaneous aging. The authors concluded that A. scholaris appears to have the dual benefits of decreasing irritation associated with retinoids while augmenting their antiaging impact (Evid. Based Complement. Alternat. Med. 2012;2012:190370).

The leaf extract of A. scholaris has been used to treat cold symptoms and tracheitis, and it has been prescribed in hospitals and approved for commercial over-the-counter sale by the State Food and Drugs Administration of China (SFDA) (J. Ethnopharmacol. 2010;129:293-8; J. Ethnopharmacol. 2010;129:174-81). The broad range of biological properties associated with A. scholaris has been ascribed to particular constituent categories, including alkaloids, flavonoids, and terpenoids (specifically, phenolic acids) (Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). These properties include, but are reportedly not limited to, antioxidant, anticancer, anti-inflammatory, antistress, analgesic, antimutagenic, hepatoprotective, immunomodulatory, and chemopreventive activity (Integr. Cancer Ther. 2010;9:261-9; Chin. J. Integr. Med. 2012 Mar 28 [Epub ahead of print]). Antineoplastic effects have been linked directly to phytochemical constituents including echitamine, alstonine, pleiocarpamine, O-methylmacralstonine, macralstonine, and lupeol (Integr. Cancer Ther. 2010;9:261-9).

In 2006, Jagetia and Baliga investigated the anticancer activity of A. scholaris alkaloid fractions in vitro in cultured human neoplastic cell lines. They also conducted in vivo studies in tumor-bearing mice. The in vitro data in HeLa cells revealed a time-dependent rise in antineoplastic activity after 24 hours of exposure (25 mcg/mL). Further, once-daily administration of A. scholaris (240 mg/kg) to tumor-bearing mice yielded dose-dependent remissions, although there were toxic presentations at this dosage. The next-lower dose of 210 mg/kg was found to be most effective, with 20% of the mice surviving for as long as 120 days after tumor cell inoculation, compared with none of the control animals treated with saline (Phytother. Res. 2006;20:103-9).

Using an acute-restraint stress model in mice in 2009, Kulkarni and Juvekar evaluated the effects of stress and the impact of a methanolic extract of A. scholaris bark. Pretreatments with the extract of 100, 250, and 500 mg/kg for 7 days were found to exert significant antistress effects. In addition, nootropic activities were observed, with memory functions clearly enhanced in learning tasks. A. scholaris also was associated with significant antioxidant properties. The extract at 200 mcg/mL exhibited maximum scavenging of the stable radical 1,1-diphenyl-2-picrylhydrazyl at 90.11% and the nitric oxide radical at 62.77% (Indian J. Exp. Biol. 2009;47:47-52).

Later in 2009, Jahan et al. reported on their investigation of potential antioxidant and chemopreventive activity displayed by A. scholaris in a two-stage murine model. Skin carcinogenesis development was initiated in Swiss albino mice through one application of 7, 12-dimethyabenz(a)anthrecene (DMBA) and then promoted two weeks later by repeated application of croton oil three times per week through 16 weeks. The investigators found a lower incidence of tumors, tumor yield, tumor burden, and number of papillomas in mice treated with A. scholaris extract as compared to untreated controls (Integr. Cancer Ther. 2009;8:273-9).

 

 

In 2010, Shang et al. conducted multiple studies using A. scholaris. In the first published report, they assessed the anti-inflammatory and analgesic properties of the ethanolic leaf extract to validate its use in traditional Chinese medicine and modern clinical medicine. The investigators first determined that analgesic activity was conferred as the ethyl acetate and alkaloid fractions significantly diminished acetic acid-induced reactions in mice and, along with the ethanolic extract, reduced xylene-induced ear edema.

The researchers also performed in vivo and in vitro assessments of anti-inflammatory activity again on xylene-induced ear edema and carrageenan-induced air pouch formation in mice, as well as cyclooxygenase (COX)-1, -2 and 5-LOX inhibition.

In the air pouch model, A. scholaris alkaloids were found to have significantly spurred superoxide dismutase activity while lowering nitric oxide, prostaglandin E2, and malondialdehyde levels. In vitro tests, supporting evidence from animal models, showed that the three primary alkaloids isolated from A. scholaris leaves (picrinine, vallesamine, and scholaricine) inhibited the inflammatory mediators COX-1, COX-2, and 5-LOX. The researchers also noted that the in vitro anti-inflammatory assay results reinforced the notion of these alkaloids as the bioactive fraction of the plant (J. Ethnopharmacol. 2010;129:174-81).

In their second published report that year, Shang et al. investigated the antitussive and anti-asthmatic activities of the ethanolic extract, fractions, and chief alkaloids of A. scholaris leaf.

The researchers tested for antitussive effects using ammonia-induced or sulfur dioxide-induced coughing in mice and citric acid-induced coughing in guinea pigs. They evaluated anti-asthmatic activity via histamine-induced bronchoconstriction in guinea pigs. They also measured phenol red volume in murine tracheas to assess expectorant activity.

The data indicated antitussive activity, with significant alkaloid suppression of ammonia-induced coughing frequency in mice. Latency periods of sulfur dioxide-induced cough in mice and citric acid-induced cough in guinea pigs increased, and cough frequency in guinea pigs decreased.

Anti-asthmatic effects, such as suppression of convulsion, were observed in guinea pigs. In the expectorant assessment, tracheal phenol red production was increased. The researchers identified picrinine as the primary alkaloid responsible for these activities (J. Ethnopharmacol. 2010;129:293-8).

In addition, Jahan and Goyal showed that pretreatment with A. scholaris bark extract protected the bone marrow of mice against radiation-induced chromosomal damage and micronuclei induction (J Environ. Pathol. Toxicol. Oncol. 2010;29:101-11).

Conclusion

Despite the dearth of research on A. scholaris, the existing data are intriguing, particularly the findings that A. scholaris may have the capacity to amplify the anti-aging activity of retinoids while blunting their irritating effects. Although more research is needed to determine the dermatologic value of A. scholaris, the pursuit may potentially prove fruitful.

Dr. Baumann is in private practice in Miami Beach. She did not disclose any conflicts of interest. To respond to this column, or to suggest topics for future columns, write to her at [email protected].

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Alstonia scholaris
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Alstonia scholaris, Apocynaceae family, homeopathic medicine, Ayurvedic medicine in India, alkaloids ditamine, echitamine, echitanines, malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions, asthma, bronchitis, helminthiasis, agalactia, Leslie Baumann
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Alstonia scholaris, Apocynaceae family, homeopathic medicine, Ayurvedic medicine in India, alkaloids ditamine, echitamine, echitanines, malaria, cutaneous diseases, tumors, ulcers, chronic respiratory conditions, asthma, bronchitis, helminthiasis, agalactia, Leslie Baumann
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Early Death or Hospital Readmission

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Influence of neighborhood household income on early death or urgent hospital readmission

Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.

Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.

We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.

METHODS

Study Setting and Data Sources

This population‐based study took place in Ontario, Canada, between April 1, 2003 and March 31, 2009. All hospital and physician care in Ontario is publicly funded. The study used 2 databases, the Discharge Abstract Database and the Registered Persons Database. The Discharge Abstract Database records information about all nonpsychiatric hospitalizations, including dates of hospital admission and discharge, vital status at end of hospitalization, discharge destination (ie, community, nursing home, or chronic hospital), admission urgency, primary and other diagnoses, and postal code of patient's household. The Registered Persons Database captures basic demographic data about all Ontarians, including date of birth and date of death (if applicable), postal code of residence, and average household‐income quintile of postal code, determined by linking the postal code to Statistics Canada geographical units through the Postal Code Conversion File Plus.[13] The Registered Persons Database captures all deaths regardless of the death location (ie, community vs hospital).

Study Population

This study used patients from a previous analysis that internally validated an index to predict the risk of 30‐day death or urgent readmission.[12] This analysis included a simple random sample of 250,000 adult Ontarians (age >18 years) who were discharged from the hospital to the community between April 1, 2003 and March 31, 2009. These medical and surgical hospitalizations were sampled from the Discharge Abstract Database described above. Psychiatric admissions were excluded because their hospitalizations are captured in a distinct database; obstetrical admissions were also excluded because they have a very low risk of 30‐day death or readmission. We randomly chose 1 index admission per person to ensure that the patient was the unit of analysis.

For the present study, we selected all patients from the previous analysis who were discharged from the hospital in 2006. This year was chosen because the SES indicator we used in the study (average household‐income quintile) was measured during the 2006 Canadian Census and would be most accurate for patients discharged in that year. The present study also limited patients to those with a valid postal code, because this was required to link patients to their neighborhood and their household‐income quintile.

Study Outcome

The study outcome was all‐cause death or urgent readmission within 30 days of discharge from hospital. We combined death with urgent readmission to avoid potential biases that could occur when measuring associations between risk factors and urgent readmission; in analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.

We linked to the Registered Patients Database to determine each person's 30‐day death status. We linked to the Discharge Abstract Database to determine if patients had been urgently readmitted to any hospital within 30 days of discharge. All deaths were considered regardless of cause. All urgent (ie, nonscheduled) readmissions were included regardless of the reason for admission. Urgent status was determined by the urgency field in the Discharge Abstract Database, for which data abstractors are instructed to classify all nonscheduled admissions as urgent; these admissions frequently include those admitted after presenting to the emergency department.

Study Covariates: Readmission Risk and Neighborhood Household‐Income Quintile

In our primary analysis, we quantified the risk of 30‐day death or urgent readmission using an internally validated index, the LACE+ index: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E).[12] The LACE+ index predicts the risk of 30‐day all‐cause death or urgent readmission for nonpsychiatric and nonobstetrical admissions. This index includes patient age, sex, comorbidities, and previous hospital and emergency‐department utilization; admission urgency; hospital type; total length of stay (LOS) and days in hospital awaiting placement; and hospitalization diagnostic risk.[14] The index quantified outcome risk as a score that ranged from 17 to 114. It was very discriminatory (C statistic, 77.1%) and was well calibrated (the observed and expected outcome risk was statistically distinct in only 2 of 14 risk groups that contained <2% of the population). The LACE+ quintiles were defined using score distribution from the entire 20032009 cohort.[12]

We used neighborhood income quintile as 1 measure of patient SES. Neighborhood income quintile was calculated by Statistics Canada using the Income Per Person Equivalent (IPPE) determined from the 2006 Canadian census.[13] The IPPE was calculated as total household income divided by the Single Persons Equivalent, which reflects decreased costs per person (and therefore increased available income per household occupant) in households having greater numbers of people. Within each dissemination area (each contains 400700 people), the average IPPE was calculated. Then, within each region (delineated by the Census Metropolitan Area, the Census Agglomeration, or provincial residual areas), dissemination areas were ranked by their average IPPE and then categorized into quintiles. These household‐income quintiles, therefore, are community‐specific and ensure that neighborhood household incomes are categorized based on comparisons within the same community. As such, the income thresholds for quintile categorization will vary between regions. We linked each patient's postal code to their dissemination area using the Postal Code Conversion File Plus[13] to determine their neighborhood income quintile.

Analysis

We described the patient cohort by readmission status. We categorized the expected risk of 30‐day death or urgent readmission to hospital (as determined by the LACE+ score) into quintiles. We used the 2 test and the test for trend to determine the association of these risk quintiles and SES quintiles with observed rates of 30‐day death or urgent readmission. The Cochran‐Mantel‐Haenszel test was used to determine the association of household‐income quintile and outcome risk after adjusting for LACE+ quintile.

To determine how the association between income quintile and outcome changes with increase adjustment, we constructed a series of logistic‐regression models that contained household‐income quintile and the sequential addition of components of the LACE+ score. For each model, we measured the influence of these added covariates on the association between household‐income quintile and early death or urgent readmission. We used orthogonal parameterization (which facilitates the comparison of parameter estimates in a regression model) to measure linear trends in the association of the income quintiles with outcomes.

RESULTS

The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).

Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.

Description of Study Patients by 30‐Day Death or Urgent Readmission Status
VariableValueNo Death/Readmission, n=38,189Death/Readmission, n=2,638Overall, N=40,827
  • NOTE: Abbreviations: ALC, alternate level of care (indicating a patient who does not currently require hospitalization but is awaiting alternate living arrangements, such as nursing home); CMG, Case Mix Group; ED, emergency department; IQR, interquartile range; LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score (C), and emergency‐department use (E); LOS, length of stay; SD, standard deviation. The Charlson index measures number and severity of patient comorbidities.[20] Nonteaching hospitals having <100 beds were classified as small. The CMG score[14] quantifies the independent probability that particular admission types are followed by early death or urgent readmission.

Mean age (SD), y 57.39 (18.3)67.17 (17.2)58.02 (18.4)
Female sex 20,04452.5%1,29148.9%21,33552.3%
Charlson index028,90875.7%1,23846.9%30,14673.8%
 145011.7%36213.7%4,81211.8%
 22,6687.0%42716.2%3,0957.6%
 3+2,1635.7%61123.2%2,7746.8%
ED visits in previous 6 moths024,59964.4%1,21045.9%25,80963.2%
 1211,26229.5%1,00838.2%12,27030.1%
 3+2,3286.1%42015.9%2,7486.7%
Urgent hospitalizations, previous year033,72988.3%1,79668.1%35,52587.0%
 13,4259.0%52519.9%3,9509.7%
 1+1,0352.7%31712.0%1,3523.3%
Elective hospitalizations, previous year035,98894.2%2,38990.6%38,37794.0%
 11,9985.2%2138.1%2,2115.4%
 2+2030.5%361.4%2390.6%
Hospital typeNonteaching, large20,55453.8%1,33450.6%21,88853.6%
 Nonteaching, small5,23913.7%48718.5%572614.0%
 Teaching12,39632.5%81731.0%13,21332.4%
Urgent admit 23,76962.2%2,22384.3%25,99263.7%
LOS rounded to nearest day, median (IQR) 3 (26)5 (311)3 (26)
Any hospital days on ALC06461.7%1274.8%7731.9%
CMG score of index admission027,25771.4%1,59460.4%28,85170.7%
 1+5,21813.7%94835.9%6,16615.1%
 <05,71415.0%963.6%5,81014.2%
LACE+ score of index admission, median (IQR) 31 (1848)61 (4175)32 (1951)
Household‐income quintile1 (poorest)7,79820.4%62123.5%8,41920.6%
 27,81220.5%58622.2%8,39820.6%
 37,55719.8%48418.3%8,04119.7%
 47,56119.8%50019.0%8,06119.7%
 5 (richest)7,46119.5%44716.9%7,90819.4%

Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.

Household Income and Risk of 30‐Day Death or Urgent Readmission

People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, 2=27.4, P<0.0001; Mantel‐Haenszel trend 2=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (2=19.8, df=1, P<0.0001).

Risk of 30‐Day Postdischarge Death or Urgent Readmission by Household Income and Predicted Risk
 Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range) 
 1 (1416) [A]2 (1727)3 (2839)4 (4056)5 (57114) [B]Income Quintile Overall [C]
  • NOTE: Abbreviations: LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score, C), and emergency‐department use (E). Risk of death or urgent readmission was summarized by the LACE+ score[12] divided into quintiles, with higher score indicating higher risk. Income quintile used neighborhood average household income, with higher score indicating higher household income. The uppercase letters in brackets indicate table columns and rows discussed in the text of the article. Each cell presents the number of people who died or were urgently readmitted (numerator) over the number of people at risk (denominator).

Income quintile      
1 (poorest)18/1,485 (1.2%)42/1,667 (2.5%)65/1,635 (4.0%)117/1,722 (6.8%)379/1,910 (19.8%)621/8,419 (7.4%)
221/1,627 (1.3%)39/1,665 (2.3%)65/1,598 (4.1%)130/1,808 (5.2%)331/1,700 (19.5%)586/8,398 (7.0%)
318/1,761 (1.0%)33/1,665 (2.0%)63/1,568 (4.0%)96/1,499 (6.4%)274/1,548 (17.7%)484/8,041 (6.0%)
427/1,851 (1.5%)42/1,698 (2.4%)57/1,585 (3.6%)110/1,548 (6.1%)264/1,379 (19.1%)500/8,061 (6.2%)
5 (richest)20/1,864 (1.1%)32/1,736 (1.8%)60/1,468 (4.1%)107/1,525 (7.0%)228/1,315 (17.3%)447/7,908 (5.6%)
Risk quintile overall [D]104/8,588 (1.2%)188/8,431 (2.2%)310/7,854 (4.0%)560/8,102 (6.9%)1476/7,852 (18.8%)2,638/40,827 (6.5%)

However, household income was also strongly associated with LACE+ scores (2=240, P<0.0001; Mantel‐Haenszel trend 2=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).

The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (2=2703, P<0.0001; Mantel‐Haenszel trend 2=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel 2=5.9, df=4, P=0.21).

We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).

The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.

Figure 1
The incremental influence of important factors on the association of neighborhood income quintile with early death or urgent readmission. This figure presents results from a series of logistic‐regression models having death or urgent readmission within 30 days of discharge from hospital as the outcome. Each plot presents the adjusted OR (horizontal axis) relative to the poorest income quintile, 1, for income quintiles 2 through 5 (the wealthiest quintile). Other covariates entered into the model are presented on the left side, with all (except the final model containing LACE alone) being cumulative, so that the model adding patient sex (“ Sex”) also contains patient age (the variable above). Each point estimate is flanked by 95% CIs. The P value for linear trend over the income quintiles is presented on the right. Abbreviations: ALC, alternate level of care; CI, confidence interval; CMG, Case Mix Group; LACE , length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E); OR, odds ratio.

DISCUSSION

Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.

Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.

We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.

In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Acknowledgment

Disclosure: Nothing to report.

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References
  1. Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
  2. Lynch J, Smith GD, Harper S, et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):599.
  3. Bottle A, Aylin P, Majeed A. Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406414.
  4. Howell S, Coory M, Martin J, Duckett S. Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
  5. Silverstein MD, Qin H, Mercer SQ, Fong J, Haydar Z. Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363372.
  6. Amarasingham R, Moore BJ, Tabak YP, et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48(11):981988.
  7. Billings J, Mijanovich T. Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):16431654.
  8. Burns R, Nichols LO. Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389393.
  9. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211219.
  10. Boult C, Dowd B, McCaffrey D, Boult L, Hernandez R, Krulewitch H. Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811817.
  11. Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:164.
  12. Walraven C, Wong J, Forster AJ. LACE+ index: extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine. 2012;6(2):8089.
  13. Wilkins RH. PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
  14. Walraven C, Wong J, Forster AJ. Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80e89.
  15. Coleman EA, Williams MV. Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287290.
  16. Walraven C, Wong J, Forster AJ, Hawken S. Predicting post‐discharge death or readmission: deterioration of model performance in a population having multiple admissions per patient [published online ahead of print November 19, 2012]. J Eval Clin Pract. doi: 10.1111/jep.12012.
  17. Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 24692475.
  18. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111122.
  19. Walraven C, Jennings A, Taljaard M, et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067E1072.
  20. Charlson ME, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):12451251.
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Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.

Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.

We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.

METHODS

Study Setting and Data Sources

This population‐based study took place in Ontario, Canada, between April 1, 2003 and March 31, 2009. All hospital and physician care in Ontario is publicly funded. The study used 2 databases, the Discharge Abstract Database and the Registered Persons Database. The Discharge Abstract Database records information about all nonpsychiatric hospitalizations, including dates of hospital admission and discharge, vital status at end of hospitalization, discharge destination (ie, community, nursing home, or chronic hospital), admission urgency, primary and other diagnoses, and postal code of patient's household. The Registered Persons Database captures basic demographic data about all Ontarians, including date of birth and date of death (if applicable), postal code of residence, and average household‐income quintile of postal code, determined by linking the postal code to Statistics Canada geographical units through the Postal Code Conversion File Plus.[13] The Registered Persons Database captures all deaths regardless of the death location (ie, community vs hospital).

Study Population

This study used patients from a previous analysis that internally validated an index to predict the risk of 30‐day death or urgent readmission.[12] This analysis included a simple random sample of 250,000 adult Ontarians (age >18 years) who were discharged from the hospital to the community between April 1, 2003 and March 31, 2009. These medical and surgical hospitalizations were sampled from the Discharge Abstract Database described above. Psychiatric admissions were excluded because their hospitalizations are captured in a distinct database; obstetrical admissions were also excluded because they have a very low risk of 30‐day death or readmission. We randomly chose 1 index admission per person to ensure that the patient was the unit of analysis.

For the present study, we selected all patients from the previous analysis who were discharged from the hospital in 2006. This year was chosen because the SES indicator we used in the study (average household‐income quintile) was measured during the 2006 Canadian Census and would be most accurate for patients discharged in that year. The present study also limited patients to those with a valid postal code, because this was required to link patients to their neighborhood and their household‐income quintile.

Study Outcome

The study outcome was all‐cause death or urgent readmission within 30 days of discharge from hospital. We combined death with urgent readmission to avoid potential biases that could occur when measuring associations between risk factors and urgent readmission; in analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.

We linked to the Registered Patients Database to determine each person's 30‐day death status. We linked to the Discharge Abstract Database to determine if patients had been urgently readmitted to any hospital within 30 days of discharge. All deaths were considered regardless of cause. All urgent (ie, nonscheduled) readmissions were included regardless of the reason for admission. Urgent status was determined by the urgency field in the Discharge Abstract Database, for which data abstractors are instructed to classify all nonscheduled admissions as urgent; these admissions frequently include those admitted after presenting to the emergency department.

Study Covariates: Readmission Risk and Neighborhood Household‐Income Quintile

In our primary analysis, we quantified the risk of 30‐day death or urgent readmission using an internally validated index, the LACE+ index: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E).[12] The LACE+ index predicts the risk of 30‐day all‐cause death or urgent readmission for nonpsychiatric and nonobstetrical admissions. This index includes patient age, sex, comorbidities, and previous hospital and emergency‐department utilization; admission urgency; hospital type; total length of stay (LOS) and days in hospital awaiting placement; and hospitalization diagnostic risk.[14] The index quantified outcome risk as a score that ranged from 17 to 114. It was very discriminatory (C statistic, 77.1%) and was well calibrated (the observed and expected outcome risk was statistically distinct in only 2 of 14 risk groups that contained <2% of the population). The LACE+ quintiles were defined using score distribution from the entire 20032009 cohort.[12]

We used neighborhood income quintile as 1 measure of patient SES. Neighborhood income quintile was calculated by Statistics Canada using the Income Per Person Equivalent (IPPE) determined from the 2006 Canadian census.[13] The IPPE was calculated as total household income divided by the Single Persons Equivalent, which reflects decreased costs per person (and therefore increased available income per household occupant) in households having greater numbers of people. Within each dissemination area (each contains 400700 people), the average IPPE was calculated. Then, within each region (delineated by the Census Metropolitan Area, the Census Agglomeration, or provincial residual areas), dissemination areas were ranked by their average IPPE and then categorized into quintiles. These household‐income quintiles, therefore, are community‐specific and ensure that neighborhood household incomes are categorized based on comparisons within the same community. As such, the income thresholds for quintile categorization will vary between regions. We linked each patient's postal code to their dissemination area using the Postal Code Conversion File Plus[13] to determine their neighborhood income quintile.

Analysis

We described the patient cohort by readmission status. We categorized the expected risk of 30‐day death or urgent readmission to hospital (as determined by the LACE+ score) into quintiles. We used the 2 test and the test for trend to determine the association of these risk quintiles and SES quintiles with observed rates of 30‐day death or urgent readmission. The Cochran‐Mantel‐Haenszel test was used to determine the association of household‐income quintile and outcome risk after adjusting for LACE+ quintile.

To determine how the association between income quintile and outcome changes with increase adjustment, we constructed a series of logistic‐regression models that contained household‐income quintile and the sequential addition of components of the LACE+ score. For each model, we measured the influence of these added covariates on the association between household‐income quintile and early death or urgent readmission. We used orthogonal parameterization (which facilitates the comparison of parameter estimates in a regression model) to measure linear trends in the association of the income quintiles with outcomes.

RESULTS

The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).

Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.

Description of Study Patients by 30‐Day Death or Urgent Readmission Status
VariableValueNo Death/Readmission, n=38,189Death/Readmission, n=2,638Overall, N=40,827
  • NOTE: Abbreviations: ALC, alternate level of care (indicating a patient who does not currently require hospitalization but is awaiting alternate living arrangements, such as nursing home); CMG, Case Mix Group; ED, emergency department; IQR, interquartile range; LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score (C), and emergency‐department use (E); LOS, length of stay; SD, standard deviation. The Charlson index measures number and severity of patient comorbidities.[20] Nonteaching hospitals having <100 beds were classified as small. The CMG score[14] quantifies the independent probability that particular admission types are followed by early death or urgent readmission.

Mean age (SD), y 57.39 (18.3)67.17 (17.2)58.02 (18.4)
Female sex 20,04452.5%1,29148.9%21,33552.3%
Charlson index028,90875.7%1,23846.9%30,14673.8%
 145011.7%36213.7%4,81211.8%
 22,6687.0%42716.2%3,0957.6%
 3+2,1635.7%61123.2%2,7746.8%
ED visits in previous 6 moths024,59964.4%1,21045.9%25,80963.2%
 1211,26229.5%1,00838.2%12,27030.1%
 3+2,3286.1%42015.9%2,7486.7%
Urgent hospitalizations, previous year033,72988.3%1,79668.1%35,52587.0%
 13,4259.0%52519.9%3,9509.7%
 1+1,0352.7%31712.0%1,3523.3%
Elective hospitalizations, previous year035,98894.2%2,38990.6%38,37794.0%
 11,9985.2%2138.1%2,2115.4%
 2+2030.5%361.4%2390.6%
Hospital typeNonteaching, large20,55453.8%1,33450.6%21,88853.6%
 Nonteaching, small5,23913.7%48718.5%572614.0%
 Teaching12,39632.5%81731.0%13,21332.4%
Urgent admit 23,76962.2%2,22384.3%25,99263.7%
LOS rounded to nearest day, median (IQR) 3 (26)5 (311)3 (26)
Any hospital days on ALC06461.7%1274.8%7731.9%
CMG score of index admission027,25771.4%1,59460.4%28,85170.7%
 1+5,21813.7%94835.9%6,16615.1%
 <05,71415.0%963.6%5,81014.2%
LACE+ score of index admission, median (IQR) 31 (1848)61 (4175)32 (1951)
Household‐income quintile1 (poorest)7,79820.4%62123.5%8,41920.6%
 27,81220.5%58622.2%8,39820.6%
 37,55719.8%48418.3%8,04119.7%
 47,56119.8%50019.0%8,06119.7%
 5 (richest)7,46119.5%44716.9%7,90819.4%

Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.

Household Income and Risk of 30‐Day Death or Urgent Readmission

People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, 2=27.4, P<0.0001; Mantel‐Haenszel trend 2=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (2=19.8, df=1, P<0.0001).

Risk of 30‐Day Postdischarge Death or Urgent Readmission by Household Income and Predicted Risk
 Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range) 
 1 (1416) [A]2 (1727)3 (2839)4 (4056)5 (57114) [B]Income Quintile Overall [C]
  • NOTE: Abbreviations: LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score, C), and emergency‐department use (E). Risk of death or urgent readmission was summarized by the LACE+ score[12] divided into quintiles, with higher score indicating higher risk. Income quintile used neighborhood average household income, with higher score indicating higher household income. The uppercase letters in brackets indicate table columns and rows discussed in the text of the article. Each cell presents the number of people who died or were urgently readmitted (numerator) over the number of people at risk (denominator).

Income quintile      
1 (poorest)18/1,485 (1.2%)42/1,667 (2.5%)65/1,635 (4.0%)117/1,722 (6.8%)379/1,910 (19.8%)621/8,419 (7.4%)
221/1,627 (1.3%)39/1,665 (2.3%)65/1,598 (4.1%)130/1,808 (5.2%)331/1,700 (19.5%)586/8,398 (7.0%)
318/1,761 (1.0%)33/1,665 (2.0%)63/1,568 (4.0%)96/1,499 (6.4%)274/1,548 (17.7%)484/8,041 (6.0%)
427/1,851 (1.5%)42/1,698 (2.4%)57/1,585 (3.6%)110/1,548 (6.1%)264/1,379 (19.1%)500/8,061 (6.2%)
5 (richest)20/1,864 (1.1%)32/1,736 (1.8%)60/1,468 (4.1%)107/1,525 (7.0%)228/1,315 (17.3%)447/7,908 (5.6%)
Risk quintile overall [D]104/8,588 (1.2%)188/8,431 (2.2%)310/7,854 (4.0%)560/8,102 (6.9%)1476/7,852 (18.8%)2,638/40,827 (6.5%)

However, household income was also strongly associated with LACE+ scores (2=240, P<0.0001; Mantel‐Haenszel trend 2=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).

The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (2=2703, P<0.0001; Mantel‐Haenszel trend 2=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel 2=5.9, df=4, P=0.21).

We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).

The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.

Figure 1
The incremental influence of important factors on the association of neighborhood income quintile with early death or urgent readmission. This figure presents results from a series of logistic‐regression models having death or urgent readmission within 30 days of discharge from hospital as the outcome. Each plot presents the adjusted OR (horizontal axis) relative to the poorest income quintile, 1, for income quintiles 2 through 5 (the wealthiest quintile). Other covariates entered into the model are presented on the left side, with all (except the final model containing LACE alone) being cumulative, so that the model adding patient sex (“ Sex”) also contains patient age (the variable above). Each point estimate is flanked by 95% CIs. The P value for linear trend over the income quintiles is presented on the right. Abbreviations: ALC, alternate level of care; CI, confidence interval; CMG, Case Mix Group; LACE , length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E); OR, odds ratio.

DISCUSSION

Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.

Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.

We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.

In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Acknowledgment

Disclosure: Nothing to report.

Socioeconomic status (SES) classifies people according to occupation, prior education, or income.[1] Socioeconomic status has been associated with several population‐health outcomes, albeit with geographically inconsistent results.[2] If lower SES is associated with higher readmission rates, then further studies could be done to determine which specific socioeconomic factors are potentially modifiable and whether the provision of additional resources could allay the increased risk associated with those factors.

Nine studies have examined the association between SES and readmissions.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies varied extensively in methodologies, SES measures, and results. However, results from 1 of these studies[11] were particularly notable given the study's significant association between lower household income and increased risk of acute readmission in a publicly funded, open‐access healthcare system. Given the implications of these results, an accurate and explicit assessment of the association between SES measures and the risk of adverse postdischarge outcomes is important.

We recently developed a model that accurately predicts the risk of 30‐day death or urgent readmission using administrative data.[12] This model did not directly control for any SES factors. In this study, we determined if a commonly used SES measurehousehold‐income quintilewas associated with the risk of early death or urgent readmission after controlling for factors known to influence this outcome.

METHODS

Study Setting and Data Sources

This population‐based study took place in Ontario, Canada, between April 1, 2003 and March 31, 2009. All hospital and physician care in Ontario is publicly funded. The study used 2 databases, the Discharge Abstract Database and the Registered Persons Database. The Discharge Abstract Database records information about all nonpsychiatric hospitalizations, including dates of hospital admission and discharge, vital status at end of hospitalization, discharge destination (ie, community, nursing home, or chronic hospital), admission urgency, primary and other diagnoses, and postal code of patient's household. The Registered Persons Database captures basic demographic data about all Ontarians, including date of birth and date of death (if applicable), postal code of residence, and average household‐income quintile of postal code, determined by linking the postal code to Statistics Canada geographical units through the Postal Code Conversion File Plus.[13] The Registered Persons Database captures all deaths regardless of the death location (ie, community vs hospital).

Study Population

This study used patients from a previous analysis that internally validated an index to predict the risk of 30‐day death or urgent readmission.[12] This analysis included a simple random sample of 250,000 adult Ontarians (age >18 years) who were discharged from the hospital to the community between April 1, 2003 and March 31, 2009. These medical and surgical hospitalizations were sampled from the Discharge Abstract Database described above. Psychiatric admissions were excluded because their hospitalizations are captured in a distinct database; obstetrical admissions were also excluded because they have a very low risk of 30‐day death or readmission. We randomly chose 1 index admission per person to ensure that the patient was the unit of analysis.

For the present study, we selected all patients from the previous analysis who were discharged from the hospital in 2006. This year was chosen because the SES indicator we used in the study (average household‐income quintile) was measured during the 2006 Canadian Census and would be most accurate for patients discharged in that year. The present study also limited patients to those with a valid postal code, because this was required to link patients to their neighborhood and their household‐income quintile.

Study Outcome

The study outcome was all‐cause death or urgent readmission within 30 days of discharge from hospital. We combined death with urgent readmission to avoid potential biases that could occur when measuring associations between risk factors and urgent readmission; in analyses having readmission as the sole outcome, the categorization of early deaths that occur prior to readmission as nonevents could minimize the importance of factors (such as severe comorbidities or patient age) that are associated with both early death and readmission.

We linked to the Registered Patients Database to determine each person's 30‐day death status. We linked to the Discharge Abstract Database to determine if patients had been urgently readmitted to any hospital within 30 days of discharge. All deaths were considered regardless of cause. All urgent (ie, nonscheduled) readmissions were included regardless of the reason for admission. Urgent status was determined by the urgency field in the Discharge Abstract Database, for which data abstractors are instructed to classify all nonscheduled admissions as urgent; these admissions frequently include those admitted after presenting to the emergency department.

Study Covariates: Readmission Risk and Neighborhood Household‐Income Quintile

In our primary analysis, we quantified the risk of 30‐day death or urgent readmission using an internally validated index, the LACE+ index: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E).[12] The LACE+ index predicts the risk of 30‐day all‐cause death or urgent readmission for nonpsychiatric and nonobstetrical admissions. This index includes patient age, sex, comorbidities, and previous hospital and emergency‐department utilization; admission urgency; hospital type; total length of stay (LOS) and days in hospital awaiting placement; and hospitalization diagnostic risk.[14] The index quantified outcome risk as a score that ranged from 17 to 114. It was very discriminatory (C statistic, 77.1%) and was well calibrated (the observed and expected outcome risk was statistically distinct in only 2 of 14 risk groups that contained <2% of the population). The LACE+ quintiles were defined using score distribution from the entire 20032009 cohort.[12]

We used neighborhood income quintile as 1 measure of patient SES. Neighborhood income quintile was calculated by Statistics Canada using the Income Per Person Equivalent (IPPE) determined from the 2006 Canadian census.[13] The IPPE was calculated as total household income divided by the Single Persons Equivalent, which reflects decreased costs per person (and therefore increased available income per household occupant) in households having greater numbers of people. Within each dissemination area (each contains 400700 people), the average IPPE was calculated. Then, within each region (delineated by the Census Metropolitan Area, the Census Agglomeration, or provincial residual areas), dissemination areas were ranked by their average IPPE and then categorized into quintiles. These household‐income quintiles, therefore, are community‐specific and ensure that neighborhood household incomes are categorized based on comparisons within the same community. As such, the income thresholds for quintile categorization will vary between regions. We linked each patient's postal code to their dissemination area using the Postal Code Conversion File Plus[13] to determine their neighborhood income quintile.

Analysis

We described the patient cohort by readmission status. We categorized the expected risk of 30‐day death or urgent readmission to hospital (as determined by the LACE+ score) into quintiles. We used the 2 test and the test for trend to determine the association of these risk quintiles and SES quintiles with observed rates of 30‐day death or urgent readmission. The Cochran‐Mantel‐Haenszel test was used to determine the association of household‐income quintile and outcome risk after adjusting for LACE+ quintile.

To determine how the association between income quintile and outcome changes with increase adjustment, we constructed a series of logistic‐regression models that contained household‐income quintile and the sequential addition of components of the LACE+ score. For each model, we measured the influence of these added covariates on the association between household‐income quintile and early death or urgent readmission. We used orthogonal parameterization (which facilitates the comparison of parameter estimates in a regression model) to measure linear trends in the association of the income quintiles with outcomes.

RESULTS

The original cohort contained 250,000 people, of which 40,827 people (16.3%) were included in the present study (208,995 were excluded because patients were discharged in years other than 2006; 178 were excluded because of invalid postal codes).

Patients are described in Table 1. Patients were middle‐aged and had few documented chronic comorbidities. Of the patients, 37% had been to the emergency department and 12% had been admitted urgently. Most admissions were to large, nonteaching hospitals with a median LOS of 3 days.

Description of Study Patients by 30‐Day Death or Urgent Readmission Status
VariableValueNo Death/Readmission, n=38,189Death/Readmission, n=2,638Overall, N=40,827
  • NOTE: Abbreviations: ALC, alternate level of care (indicating a patient who does not currently require hospitalization but is awaiting alternate living arrangements, such as nursing home); CMG, Case Mix Group; ED, emergency department; IQR, interquartile range; LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score (C), and emergency‐department use (E); LOS, length of stay; SD, standard deviation. The Charlson index measures number and severity of patient comorbidities.[20] Nonteaching hospitals having <100 beds were classified as small. The CMG score[14] quantifies the independent probability that particular admission types are followed by early death or urgent readmission.

Mean age (SD), y 57.39 (18.3)67.17 (17.2)58.02 (18.4)
Female sex 20,04452.5%1,29148.9%21,33552.3%
Charlson index028,90875.7%1,23846.9%30,14673.8%
 145011.7%36213.7%4,81211.8%
 22,6687.0%42716.2%3,0957.6%
 3+2,1635.7%61123.2%2,7746.8%
ED visits in previous 6 moths024,59964.4%1,21045.9%25,80963.2%
 1211,26229.5%1,00838.2%12,27030.1%
 3+2,3286.1%42015.9%2,7486.7%
Urgent hospitalizations, previous year033,72988.3%1,79668.1%35,52587.0%
 13,4259.0%52519.9%3,9509.7%
 1+1,0352.7%31712.0%1,3523.3%
Elective hospitalizations, previous year035,98894.2%2,38990.6%38,37794.0%
 11,9985.2%2138.1%2,2115.4%
 2+2030.5%361.4%2390.6%
Hospital typeNonteaching, large20,55453.8%1,33450.6%21,88853.6%
 Nonteaching, small5,23913.7%48718.5%572614.0%
 Teaching12,39632.5%81731.0%13,21332.4%
Urgent admit 23,76962.2%2,22384.3%25,99263.7%
LOS rounded to nearest day, median (IQR) 3 (26)5 (311)3 (26)
Any hospital days on ALC06461.7%1274.8%7731.9%
CMG score of index admission027,25771.4%1,59460.4%28,85170.7%
 1+5,21813.7%94835.9%6,16615.1%
 <05,71415.0%963.6%5,81014.2%
LACE+ score of index admission, median (IQR) 31 (1848)61 (4175)32 (1951)
Household‐income quintile1 (poorest)7,79820.4%62123.5%8,41920.6%
 27,81220.5%58622.2%8,39820.6%
 37,55719.8%48418.3%8,04119.7%
 47,56119.8%50019.0%8,06119.7%
 5 (richest)7,46119.5%44716.9%7,90819.4%

Death or urgent readmission within 30 days occurred in 2638 people (6.5%) (Table 1). Outcome risk increased with age; in males; as comorbidities increased; with greater numbers of emergency‐department visits, urgent admissions, and previous elective admissions; when index admissions were emergent; with longer hospital LOS and increased number of alternate level of care days; and as the diagnostic risk (measured as the Case Mix Group [CMG] score)[14] increased. Outcome risk increased as income quintile became poorer.

Household Income and Risk of 30‐Day Death or Urgent Readmission

People were evenly divided among the income quintiles (Table 2). By itself, household‐income quintile was significantly associated with the risk of early death or urgent hospital readmission (Table 2, column C, 2=27.4, P<0.0001; Mantel‐Haenszel trend 2=24.3, P<0.0001). In the poorest quintile, 7.4% of people had an outcome, compared with 5.6% in the richest quintile (2=19.8, df=1, P<0.0001).

Risk of 30‐Day Postdischarge Death or Urgent Readmission by Household Income and Predicted Risk
 Risk Quintile of 30‐Day Death or Readmission (LACE+ Points Range) 
 1 (1416) [A]2 (1727)3 (2839)4 (4056)5 (57114) [B]Income Quintile Overall [C]
  • NOTE: Abbreviations: LACE+, length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson comorbidity index score, C), and emergency‐department use (E). Risk of death or urgent readmission was summarized by the LACE+ score[12] divided into quintiles, with higher score indicating higher risk. Income quintile used neighborhood average household income, with higher score indicating higher household income. The uppercase letters in brackets indicate table columns and rows discussed in the text of the article. Each cell presents the number of people who died or were urgently readmitted (numerator) over the number of people at risk (denominator).

Income quintile      
1 (poorest)18/1,485 (1.2%)42/1,667 (2.5%)65/1,635 (4.0%)117/1,722 (6.8%)379/1,910 (19.8%)621/8,419 (7.4%)
221/1,627 (1.3%)39/1,665 (2.3%)65/1,598 (4.1%)130/1,808 (5.2%)331/1,700 (19.5%)586/8,398 (7.0%)
318/1,761 (1.0%)33/1,665 (2.0%)63/1,568 (4.0%)96/1,499 (6.4%)274/1,548 (17.7%)484/8,041 (6.0%)
427/1,851 (1.5%)42/1,698 (2.4%)57/1,585 (3.6%)110/1,548 (6.1%)264/1,379 (19.1%)500/8,061 (6.2%)
5 (richest)20/1,864 (1.1%)32/1,736 (1.8%)60/1,468 (4.1%)107/1,525 (7.0%)228/1,315 (17.3%)447/7,908 (5.6%)
Risk quintile overall [D]104/8,588 (1.2%)188/8,431 (2.2%)310/7,854 (4.0%)560/8,102 (6.9%)1476/7,852 (18.8%)2,638/40,827 (6.5%)

However, household income was also strongly associated with LACE+ scores (2=240, P<0.0001; Mantel‐Haenszel trend 2=209, P<0.0001). The number of people in the lowest‐risk quintile increased with income, from 1485 in the poorest quintile to 1864 in the richest quintile (Table 2, column A). In contrast, the number of high‐risk people progressively decreased with income, from 1910 in the poorest quintile to 1315 in the richest quintile (Table 2, column B).

The LACE+ quintile was very strongly associated with outcome risk, as shown in Table 2, row D (2=2703, P<0.0001; Mantel‐Haenszel trend 2=2102, P<0.0001). Within each LACE+ stratum, the risk of death or urgent readmission did not appear to consistently change with income quintile. After adjusting for LACE+ scores, income quintile was no longer associated with 30‐day death or readmission (Cochran‐Mantel‐Haenszel 2=5.9, df=4, P=0.21).

We found no nonlinear associations between household‐income quintile and 30‐day death or readmission after adjusting for the LACE+ score. In addition, the association between LACE+ quintile and outcome did not vary significantly by household‐income quintile (P value for interaction term in logistic regression model=0.5582).

The association between income quintile and 30‐day death or urgent readmission decreased when incrementally controlling for other covariates in the LACE+ model (Figure 1). By itself, all income quintiles except 2 were significantly distinct from the poorest income quintile. The addition of patient age, sex, and hospital type had little effect on the association between income and outcomes. The addition of index admission urgency shifted all point estimates toward unity (Figure 1). Associations between income and death or readmission then remained relatively stable until the addition of number of urgent admissions in the previous year (Figure 1). The subsequent addition of number of emergency visits and comorbidities resulted in none of the income quintiles being statistically distinct from the poorest quintile, as well as a nonsignificant linear trend over the quintiles.

Figure 1
The incremental influence of important factors on the association of neighborhood income quintile with early death or urgent readmission. This figure presents results from a series of logistic‐regression models having death or urgent readmission within 30 days of discharge from hospital as the outcome. Each plot presents the adjusted OR (horizontal axis) relative to the poorest income quintile, 1, for income quintiles 2 through 5 (the wealthiest quintile). Other covariates entered into the model are presented on the left side, with all (except the final model containing LACE alone) being cumulative, so that the model adding patient sex (“ Sex”) also contains patient age (the variable above). Each point estimate is flanked by 95% CIs. The P value for linear trend over the income quintiles is presented on the right. Abbreviations: ALC, alternate level of care; CI, confidence interval; CMG, Case Mix Group; LACE , length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency‐department use (E); OR, odds ratio.

DISCUSSION

Our study shows that the risk of 30‐day death or urgent readmission was higher in people from lower‐income neighborhoods. However, this risk appears to be explained by patient‐level factors that are known to be associated with bad postdischarge outcomes. After accounting for these factors with the LACE+ index, we found no notable changes in the risk of early death or urgent readmission with SES as measured with average neighborhood household income.

Nine previous studies have measured the association between various SES measures and hospital readmission in disparate populations.[3, 4, 5, 6, 7, 8, 9, 10, 11] These studies were done in the United States,[5, 6, 8, 9, 10] the United Kingdom,[3, 7] Australia,[4] and Canada.[11] They used a range of SES indicators (from area‐level measures of household income[5] or deprivation[3] to personal education and income)[8, 9, 10] in diverse patient populations (from a random sample of all hospitalizations[3] to people with disabilities living in New York City)[15] and very different time horizons (capturing hospital readmissions that occurred from within 30 days[5] to 4 years).[10] Of these 9 studies, 5 found no independent association between their SES measure and readmission,[5, 6, 8, 9, 10] and 2 included SES in their final regression model but did not present the modelmaking it impossible to determine if SES significantly influenced outcomes.[3, 15] One study found that the risk of hospital readmission independently increased as a composite measure of area‐level social and economic indicators decreased.[4] A Canadian study[11] measured neighborhood income quintile and showed, after adjusting for patient sex, comorbidities, LOS variance, and previous admissions, that the odds of acute, nonpsychiatric readmission within 30 days of discharge were approximately 10% higher in the lowest versus the highest SES quintile. The ability of this model to adjust for important confounders when associating SES and risk of readmission is uncertain because the model fit was not reported.

Several factors could explain the difference between our study and the previous Canadian analysis showing significantly higher adjusted risk of readmission in patients from the lowest versus the highest SES quintile.[11] First, our analysis had a slightly different outcome, combining early death with urgent readmission (rather than the latter alone). We believe that this combination is important to avoid biased results when associating patient factors with readmission risk.[14] Second, our unit of analysis was the patient, whereas in the previous analysis it was the hospitalization.[11] A recent analysis by our group found that this distinction can change the results on analyses in early postdischarge outcomes.[16] In the present analysis, different results could occur if patients with multiple readmissions were disproportionately prevalent in low‐income neighborhoods. Third, our analysis was limited to Ontario rather than the entire country. Finally, and we believe most importantly, we used a validated model to control for risk of poor outcomes soon after discharge from hospital. Our analysis shows that this risk was strongly associated with neighborhood income (Table 2). This suggests that the association between SES and bad postdischarge outcomes could be explained by factors that independently increase the risk of these outcomes. Adequately controlling for these covariates would then remove variation in readmission risk by SES. We believe that these results highlight the importance of adequately controlling for potential confounders.

We believe that our results are reassuring but not definitive. We found no indication that, in Ontario, people from poorer neighborhoods are systematically more likelyafter considering factors that are known to be associated with early death or urgent readmissionto have a worse outcome early after their discharge from hospital. However, patient income and other SES measures could be associated with early death or readmission for several reasons. First, our study used average neighborhood income quintiles to quantify SES. It is possible that other SES measures (such as education or social deprivation) or patient‐level SES indicators could be significantly associated with early death or readmission.[17, 18] Second, we previously found that approximately only 25% of hospital readmissions are potentially avoidable.[19] Further study is required to determine if patient SES independently influences potentially avoidable hospital readmissions. Third, we cannot be certain how our results might generalize to health populations outside of Ontario. Specifically, SES might play a more important role in regions without universal healthcare in which community‐based healthcare resources that could decrease readmission risk, such as medications or physician follow‐up, are unavailable to those without health insurance coverage. Finally, we found notable confounding between neighborhood income quintile and factors known to be independently associated with early death or urgent readmission (Figure 1). This was especially prominent with index admission urgency, number of previous urgent admissions and emergency visits, and patient comorbidities. These factors have a much stronger association with early death or readmission than neighborhood income quintile. If low neighborhood income actually results in urgent hospital admission, emergency‐department visits, and comorbidities, then the inclusion of these covariates in the model could obscure the influence of neighborhood income on early death or readmission.

In summary, our study found that neighborhood income was not associated with early death or urgent readmission independent of known risk factors. Our analysis indicates that focusing resources on patients in lower‐income neighborhoods is unlikely to change the risk of early postdischarge adverse events. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Acknowledgment

Disclosure: Nothing to report.

References
  1. Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
  2. Lynch J, Smith GD, Harper S, et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):599.
  3. Bottle A, Aylin P, Majeed A. Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406414.
  4. Howell S, Coory M, Martin J, Duckett S. Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
  5. Silverstein MD, Qin H, Mercer SQ, Fong J, Haydar Z. Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363372.
  6. Amarasingham R, Moore BJ, Tabak YP, et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48(11):981988.
  7. Billings J, Mijanovich T. Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):16431654.
  8. Burns R, Nichols LO. Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389393.
  9. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211219.
  10. Boult C, Dowd B, McCaffrey D, Boult L, Hernandez R, Krulewitch H. Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811817.
  11. Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:164.
  12. Walraven C, Wong J, Forster AJ. LACE+ index: extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine. 2012;6(2):8089.
  13. Wilkins RH. PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
  14. Walraven C, Wong J, Forster AJ. Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80e89.
  15. Coleman EA, Williams MV. Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287290.
  16. Walraven C, Wong J, Forster AJ, Hawken S. Predicting post‐discharge death or readmission: deterioration of model performance in a population having multiple admissions per patient [published online ahead of print November 19, 2012]. J Eval Clin Pract. doi: 10.1111/jep.12012.
  17. Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 24692475.
  18. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111122.
  19. Walraven C, Jennings A, Taljaard M, et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067E1072.
  20. Charlson ME, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):12451251.
References
  1. Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
  2. Lynch J, Smith GD, Harper S, et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):599.
  3. Bottle A, Aylin P, Majeed A. Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406414.
  4. Howell S, Coory M, Martin J, Duckett S. Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
  5. Silverstein MD, Qin H, Mercer SQ, Fong J, Haydar Z. Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363372.
  6. Amarasingham R, Moore BJ, Tabak YP, et al. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010;48(11):981988.
  7. Billings J, Mijanovich T. Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):16431654.
  8. Burns R, Nichols LO. Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389393.
  9. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211219.
  10. Boult C, Dowd B, McCaffrey D, Boult L, Hernandez R, Krulewitch H. Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811817.
  11. Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:164.
  12. Walraven C, Wong J, Forster AJ. LACE+ index: extension of a validated index to predict early death or unplanned readmission following hospital discharge using administrative data. Open Medicine. 2012;6(2):8089.
  13. Wilkins RH. PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
  14. Walraven C, Wong J, Forster AJ. Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80e89.
  15. Coleman EA, Williams MV. Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287290.
  16. Walraven C, Wong J, Forster AJ, Hawken S. Predicting post‐discharge death or readmission: deterioration of model performance in a population having multiple admissions per patient [published online ahead of print November 19, 2012]. J Eval Clin Pract. doi: 10.1111/jep.12012.
  17. Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 24692475.
  18. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111122.
  19. Walraven C, Jennings A, Taljaard M, et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067E1072.
  20. Charlson ME, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):12451251.
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Address for correspondence and reprint requests: Carl van Walraven, MD, MSc, Ottawa Hospital Research Institute, Administrative Services Building, 1053 Carling Ave, First Floor, Room 1003, Ottawa ON K1Y 4E9; Telephone: 613–761‐4903; Fax: 613–761‐5492; E‐mail: [email protected]
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Hospitalists Can’t Ignore Rise in Carbapenem-Resistant Enterobacteriaceae (CRE) Infections

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Hospitalists Can’t Ignore Rise in Carbapenem-Resistant Enterobacteriaceae (CRE) Infections

Neil Fishman, MD, associate chief medical officer at the University of Pennsylvania Health System in Philadelphia, sounds like a football coach when he says the best way to fight carbapenem-resistant Enterobacteriaceae (CRE) infections is with a good defense. Hospitalists and others should focus on contact precautions, hand hygiene, removing gowns and gloves before entering new rooms, and even suggest better room cleanings when trying to prevent the spread of CRE, he says. In fact, he has worked with SHM leadership for years to engage hospitalists about the “critical necessity of antimicrobial stewardship.”

“They’re all critical to prevent transmission,” says Dr. Fishman, who chairs the CDC’s Health Infection Control Practices Advisory Committee. “That’s part of the things that can be done in the here and now to try to prevent people from getting infected with these organisms. It’s what the CDC calls ‘detect and prevent.’”

Dr. Fishman’s suggestions echo findings in a new CDC report that shows a threefold increase in the proportion of Enterobacteriaceae bugs that proved resistant to carbapenem in the past decade. The data, in the CDC’s Morbidity and Mortality Weekly Report, showed the proportion of reported Enterobacteriacae that were CRE infections jumped to 4.2% in 2011 from 1.2% in

2001, according to data from the National Nosocomial Infection Surveillance system.

“It is a very serious public health threat,” says co-author Alex Kallen, MD, MPH, a medical epidemiologist and outbreak response coordinator in the CDC’s Division of Healthcare Quality Promotion. “Maybe it’s not that common now, but with no action, it has the potential to become much more common—like a lot of the other MDROs [multidrug-resistant organisms] that hospitalists see regularly. [Hospitalists] have a lot of control over some of the things that could potentially lead to increased transmission.”

Part of the problem, Dr. Fishman says, is a lack of antibiotic options. Polymyxins briefly showed success against the bacteria, but performance is waning. Dr. Fishman estimates it will be up to eight years before a new antibiotic to combat the infection is in widespread use.

Listen to Dr. Fishman discuss the history of treating CRE infections and importance of antimicrobial stewardship.

Both he and Dr. Kallen say hospitalists can help reduce the spread of CRE through antibiotic stewardship, review of detailed patient histories to ferret out risk factors, and dedication to contact precautions and hand hygiene.

Dr. Kallen notes hospitalists also can play a leadership role in coordinating efforts for patients transferring between hospitals and other institutions (i.e. skilled nursing or assisted-living facilities). Part of being that leader is refusing to dismiss possible CRE cases.

“If you’re a place that doesn’t see this very often, and you see one, that’s a big deal,” Dr. Kallen says. “It needs to be acted on aggressively. Being proactive is much more effective than waiting until it’s common and then trying to intervene.” TH

Richard Quinn is a freelance writer in New Jersey.

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Neil Fishman, MD, associate chief medical officer at the University of Pennsylvania Health System in Philadelphia, sounds like a football coach when he says the best way to fight carbapenem-resistant Enterobacteriaceae (CRE) infections is with a good defense. Hospitalists and others should focus on contact precautions, hand hygiene, removing gowns and gloves before entering new rooms, and even suggest better room cleanings when trying to prevent the spread of CRE, he says. In fact, he has worked with SHM leadership for years to engage hospitalists about the “critical necessity of antimicrobial stewardship.”

“They’re all critical to prevent transmission,” says Dr. Fishman, who chairs the CDC’s Health Infection Control Practices Advisory Committee. “That’s part of the things that can be done in the here and now to try to prevent people from getting infected with these organisms. It’s what the CDC calls ‘detect and prevent.’”

Dr. Fishman’s suggestions echo findings in a new CDC report that shows a threefold increase in the proportion of Enterobacteriaceae bugs that proved resistant to carbapenem in the past decade. The data, in the CDC’s Morbidity and Mortality Weekly Report, showed the proportion of reported Enterobacteriacae that were CRE infections jumped to 4.2% in 2011 from 1.2% in

2001, according to data from the National Nosocomial Infection Surveillance system.

“It is a very serious public health threat,” says co-author Alex Kallen, MD, MPH, a medical epidemiologist and outbreak response coordinator in the CDC’s Division of Healthcare Quality Promotion. “Maybe it’s not that common now, but with no action, it has the potential to become much more common—like a lot of the other MDROs [multidrug-resistant organisms] that hospitalists see regularly. [Hospitalists] have a lot of control over some of the things that could potentially lead to increased transmission.”

Part of the problem, Dr. Fishman says, is a lack of antibiotic options. Polymyxins briefly showed success against the bacteria, but performance is waning. Dr. Fishman estimates it will be up to eight years before a new antibiotic to combat the infection is in widespread use.

Listen to Dr. Fishman discuss the history of treating CRE infections and importance of antimicrobial stewardship.

Both he and Dr. Kallen say hospitalists can help reduce the spread of CRE through antibiotic stewardship, review of detailed patient histories to ferret out risk factors, and dedication to contact precautions and hand hygiene.

Dr. Kallen notes hospitalists also can play a leadership role in coordinating efforts for patients transferring between hospitals and other institutions (i.e. skilled nursing or assisted-living facilities). Part of being that leader is refusing to dismiss possible CRE cases.

“If you’re a place that doesn’t see this very often, and you see one, that’s a big deal,” Dr. Kallen says. “It needs to be acted on aggressively. Being proactive is much more effective than waiting until it’s common and then trying to intervene.” TH

Richard Quinn is a freelance writer in New Jersey.

Neil Fishman, MD, associate chief medical officer at the University of Pennsylvania Health System in Philadelphia, sounds like a football coach when he says the best way to fight carbapenem-resistant Enterobacteriaceae (CRE) infections is with a good defense. Hospitalists and others should focus on contact precautions, hand hygiene, removing gowns and gloves before entering new rooms, and even suggest better room cleanings when trying to prevent the spread of CRE, he says. In fact, he has worked with SHM leadership for years to engage hospitalists about the “critical necessity of antimicrobial stewardship.”

“They’re all critical to prevent transmission,” says Dr. Fishman, who chairs the CDC’s Health Infection Control Practices Advisory Committee. “That’s part of the things that can be done in the here and now to try to prevent people from getting infected with these organisms. It’s what the CDC calls ‘detect and prevent.’”

Dr. Fishman’s suggestions echo findings in a new CDC report that shows a threefold increase in the proportion of Enterobacteriaceae bugs that proved resistant to carbapenem in the past decade. The data, in the CDC’s Morbidity and Mortality Weekly Report, showed the proportion of reported Enterobacteriacae that were CRE infections jumped to 4.2% in 2011 from 1.2% in

2001, according to data from the National Nosocomial Infection Surveillance system.

“It is a very serious public health threat,” says co-author Alex Kallen, MD, MPH, a medical epidemiologist and outbreak response coordinator in the CDC’s Division of Healthcare Quality Promotion. “Maybe it’s not that common now, but with no action, it has the potential to become much more common—like a lot of the other MDROs [multidrug-resistant organisms] that hospitalists see regularly. [Hospitalists] have a lot of control over some of the things that could potentially lead to increased transmission.”

Part of the problem, Dr. Fishman says, is a lack of antibiotic options. Polymyxins briefly showed success against the bacteria, but performance is waning. Dr. Fishman estimates it will be up to eight years before a new antibiotic to combat the infection is in widespread use.

Listen to Dr. Fishman discuss the history of treating CRE infections and importance of antimicrobial stewardship.

Both he and Dr. Kallen say hospitalists can help reduce the spread of CRE through antibiotic stewardship, review of detailed patient histories to ferret out risk factors, and dedication to contact precautions and hand hygiene.

Dr. Kallen notes hospitalists also can play a leadership role in coordinating efforts for patients transferring between hospitals and other institutions (i.e. skilled nursing or assisted-living facilities). Part of being that leader is refusing to dismiss possible CRE cases.

“If you’re a place that doesn’t see this very often, and you see one, that’s a big deal,” Dr. Kallen says. “It needs to be acted on aggressively. Being proactive is much more effective than waiting until it’s common and then trying to intervene.” TH

Richard Quinn is a freelance writer in New Jersey.

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The wizard of insurance

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Thirty years ago, many college patients I saw were covered by a school health policy written by a company I will call James S. Fred Insurance. Because this happened long before electronic claims submissions, we knew that ours were handled by someone named Lucille.

For reasons I no longer recall, I found myself strolling in downtown Boston one afternoon, when I saw a large office building that listed none other than James S. Fred Insurance as a major tenant. I took the elevator to the 17th floor, went in, and asked for Lucille.

Sure enough, sitting in a quiet cubicle, there she was: a pleasant older woman who did the college accounts, a small cog in a massive wheel. When I introduced myself, Lucille recognized my name and greeted me warmly.

"I never expected to meet you in person," I said, "But since I have, perhaps I can tell you about a problem we’re having with reimbursement. I described the issue. Lucille took out a large manual, listing the terms of the company’s college coverage. "Here it is," she said, showing me the relevant paragraph.

I thanked her and took the book. But when I read the paragraph, I saw that it didn’t say what she said it said. I pointed this out.

"My goodness," said Lucille. "You’re right. We should be reimbursing you for that, shouldn’t we?"

So that was it. The massive insurance giant in the glass-and-steel skyscraper turned out to be a little old lady in a cubicle who couldn’t read the manual. It was like pulling back the curtain and finding out that the Wizard of Oz was a geezer with a wind machine.

I thought of this last week when I had a talk about my own personal coverage with a Midwest insurer. The issue turned on their responsibility for covering a service provided by a physician who does not participate in Medicare at all. (Yes, I am on Medicare now.)

Last year, I spoke with a human at the company who explained that all I needed to do was confirm that the provider was not Medicare affiliated. This year, after paying a few claims, they apparently changed their mind and sent letters demanding payback and saying they would only pay what Medicare would have, even if Medicare actually didn’t.

I appealed. The appeal was denied. I could not reach a human. I gave up.

Then last week, Jeanette called from Chicago. She described herself as Head of the Appeals Division, in a voice that sounded like Marian, the no-nonsense librarian from "The Music Man."

"Our policy is based on what’s in the manual," she said. "Let me see if I can find it. Oh, here it is." Then she read a passage about doctors who don’t accept Medicare assignments. "We ask them to submit claims anyway," she explained.

"Forgive me," I said, "but a doctor who doesn’t accept assignment is a Medicare provider, just one who won’t accept as full payment what Medicare allows. My doctor is not a Medicare provider at all. He can’t submit a claim, because he doesn’t have a Medicare provider number."

"My goodness," said Jeanette. "I think you may be right. Have you documented this for us?"

"With every claim," I said. "I followed your company’s instructions, and attached to every claim my doctor’s letter saying he doesn’t participate in Medicare. You should have a dozen or so copies of this letter. If you can’t find any, I’ll be happy to send another."

"Oh, here it is!" said Jeanette. "Yes, I see. We need to rectify this."

I danced a mental jig around the room. Lucille must be long retired, but I’d love to invite her and Jeanette for tea.

"I’m really grateful to have the chance to speak to person," I told Jeanette. "Thanks so much for listening."

You could hear Jeanette glow right through the phone. "Why, you’re welcome," she said. "You’ve made my whole day!"

Faceless bureaucracies can seem intimidating, impersonal, malevolent, diabolical, Kafkaesque.

But sometimes, they’re just little old ladies who have trouble reading manuals. To find out, just follow the yellow brick road.

Dr. Rockoff practices dermatology in Brookline, Mass.

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Thirty years ago, many college patients I saw were covered by a school health policy written by a company I will call James S. Fred Insurance. Because this happened long before electronic claims submissions, we knew that ours were handled by someone named Lucille.

For reasons I no longer recall, I found myself strolling in downtown Boston one afternoon, when I saw a large office building that listed none other than James S. Fred Insurance as a major tenant. I took the elevator to the 17th floor, went in, and asked for Lucille.

Sure enough, sitting in a quiet cubicle, there she was: a pleasant older woman who did the college accounts, a small cog in a massive wheel. When I introduced myself, Lucille recognized my name and greeted me warmly.

"I never expected to meet you in person," I said, "But since I have, perhaps I can tell you about a problem we’re having with reimbursement. I described the issue. Lucille took out a large manual, listing the terms of the company’s college coverage. "Here it is," she said, showing me the relevant paragraph.

I thanked her and took the book. But when I read the paragraph, I saw that it didn’t say what she said it said. I pointed this out.

"My goodness," said Lucille. "You’re right. We should be reimbursing you for that, shouldn’t we?"

So that was it. The massive insurance giant in the glass-and-steel skyscraper turned out to be a little old lady in a cubicle who couldn’t read the manual. It was like pulling back the curtain and finding out that the Wizard of Oz was a geezer with a wind machine.

I thought of this last week when I had a talk about my own personal coverage with a Midwest insurer. The issue turned on their responsibility for covering a service provided by a physician who does not participate in Medicare at all. (Yes, I am on Medicare now.)

Last year, I spoke with a human at the company who explained that all I needed to do was confirm that the provider was not Medicare affiliated. This year, after paying a few claims, they apparently changed their mind and sent letters demanding payback and saying they would only pay what Medicare would have, even if Medicare actually didn’t.

I appealed. The appeal was denied. I could not reach a human. I gave up.

Then last week, Jeanette called from Chicago. She described herself as Head of the Appeals Division, in a voice that sounded like Marian, the no-nonsense librarian from "The Music Man."

"Our policy is based on what’s in the manual," she said. "Let me see if I can find it. Oh, here it is." Then she read a passage about doctors who don’t accept Medicare assignments. "We ask them to submit claims anyway," she explained.

"Forgive me," I said, "but a doctor who doesn’t accept assignment is a Medicare provider, just one who won’t accept as full payment what Medicare allows. My doctor is not a Medicare provider at all. He can’t submit a claim, because he doesn’t have a Medicare provider number."

"My goodness," said Jeanette. "I think you may be right. Have you documented this for us?"

"With every claim," I said. "I followed your company’s instructions, and attached to every claim my doctor’s letter saying he doesn’t participate in Medicare. You should have a dozen or so copies of this letter. If you can’t find any, I’ll be happy to send another."

"Oh, here it is!" said Jeanette. "Yes, I see. We need to rectify this."

I danced a mental jig around the room. Lucille must be long retired, but I’d love to invite her and Jeanette for tea.

"I’m really grateful to have the chance to speak to person," I told Jeanette. "Thanks so much for listening."

You could hear Jeanette glow right through the phone. "Why, you’re welcome," she said. "You’ve made my whole day!"

Faceless bureaucracies can seem intimidating, impersonal, malevolent, diabolical, Kafkaesque.

But sometimes, they’re just little old ladies who have trouble reading manuals. To find out, just follow the yellow brick road.

Dr. Rockoff practices dermatology in Brookline, Mass.

Thirty years ago, many college patients I saw were covered by a school health policy written by a company I will call James S. Fred Insurance. Because this happened long before electronic claims submissions, we knew that ours were handled by someone named Lucille.

For reasons I no longer recall, I found myself strolling in downtown Boston one afternoon, when I saw a large office building that listed none other than James S. Fred Insurance as a major tenant. I took the elevator to the 17th floor, went in, and asked for Lucille.

Sure enough, sitting in a quiet cubicle, there she was: a pleasant older woman who did the college accounts, a small cog in a massive wheel. When I introduced myself, Lucille recognized my name and greeted me warmly.

"I never expected to meet you in person," I said, "But since I have, perhaps I can tell you about a problem we’re having with reimbursement. I described the issue. Lucille took out a large manual, listing the terms of the company’s college coverage. "Here it is," she said, showing me the relevant paragraph.

I thanked her and took the book. But when I read the paragraph, I saw that it didn’t say what she said it said. I pointed this out.

"My goodness," said Lucille. "You’re right. We should be reimbursing you for that, shouldn’t we?"

So that was it. The massive insurance giant in the glass-and-steel skyscraper turned out to be a little old lady in a cubicle who couldn’t read the manual. It was like pulling back the curtain and finding out that the Wizard of Oz was a geezer with a wind machine.

I thought of this last week when I had a talk about my own personal coverage with a Midwest insurer. The issue turned on their responsibility for covering a service provided by a physician who does not participate in Medicare at all. (Yes, I am on Medicare now.)

Last year, I spoke with a human at the company who explained that all I needed to do was confirm that the provider was not Medicare affiliated. This year, after paying a few claims, they apparently changed their mind and sent letters demanding payback and saying they would only pay what Medicare would have, even if Medicare actually didn’t.

I appealed. The appeal was denied. I could not reach a human. I gave up.

Then last week, Jeanette called from Chicago. She described herself as Head of the Appeals Division, in a voice that sounded like Marian, the no-nonsense librarian from "The Music Man."

"Our policy is based on what’s in the manual," she said. "Let me see if I can find it. Oh, here it is." Then she read a passage about doctors who don’t accept Medicare assignments. "We ask them to submit claims anyway," she explained.

"Forgive me," I said, "but a doctor who doesn’t accept assignment is a Medicare provider, just one who won’t accept as full payment what Medicare allows. My doctor is not a Medicare provider at all. He can’t submit a claim, because he doesn’t have a Medicare provider number."

"My goodness," said Jeanette. "I think you may be right. Have you documented this for us?"

"With every claim," I said. "I followed your company’s instructions, and attached to every claim my doctor’s letter saying he doesn’t participate in Medicare. You should have a dozen or so copies of this letter. If you can’t find any, I’ll be happy to send another."

"Oh, here it is!" said Jeanette. "Yes, I see. We need to rectify this."

I danced a mental jig around the room. Lucille must be long retired, but I’d love to invite her and Jeanette for tea.

"I’m really grateful to have the chance to speak to person," I told Jeanette. "Thanks so much for listening."

You could hear Jeanette glow right through the phone. "Why, you’re welcome," she said. "You’ve made my whole day!"

Faceless bureaucracies can seem intimidating, impersonal, malevolent, diabolical, Kafkaesque.

But sometimes, they’re just little old ladies who have trouble reading manuals. To find out, just follow the yellow brick road.

Dr. Rockoff practices dermatology in Brookline, Mass.

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Stereotactic laser ablation found feasible for hypothalamic hamartoma

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Stereotactic laser ablation found feasible for hypothalamic hamartoma

SAN DIEGO – Magnetic resonance-guided stereotactic laser ablation is a safe and effective option in the treatment of hypothalamic hamartoma, results from a multicenter pilot study showed.

At the annual meeting of the American Academy of Neurology, Dr. Daniel J. Curry reported results from 20 patients who have undergone treatment with a Food and Drug Administration–cleared neurosurgical tissue coagulation system called Visualase. Hypothalamic hamartoma (HH) is a rare disorder of pediatric epilepsy with an estimated prevalence of 1:50,000-100,000, said Dr. Curry, director of pediatric surgical epilepsy and functional neurosurgery at Texas Children’s Hospital, Houston.

"The main presentation is the mirthless laughter of gelastic seizures, but patients can have other seizure types," he said. "The diagnosis is frequently delayed, and high seizure burden in the brain can lead to epileptic encephalopathy. Seizures are notoriously resistant to medical managements necessitating surgical intervention ... open, endoscopic, or ablative."

To date, surgical intervention has been limited due to modest outcomes, with 37%-50% achieving seizure freedom. The location of HH tumors makes surgical intervention difficult, and as a result 7%-10% of patients have permanent surgical morbidity.

For the technique using the Visualase, Dr. Curry and his associates at four other medical centers in the United States performed the surgical technique through a single 4-mm incision, a 3.2-mm burr hole, and a 1.65-mm cannula trajectory with Visualase under real-time MR thermography, first with a confirmation test at about 3 W, followed by higher doses of 6-10 W for 50-120 seconds. Temperature limits were set to protect the hypothalamus and basilar artery and optic tract. The surgery had an immediate effect, and patients stayed in the hospital for a mean of 2 days.

The primary measure was seizure frequency at 1 year while the secondary measure was the complication profile of stereotactic laser ablation in epilepsy.

Of the 20 patients, 5 were adults, and the entire study population ranged in age from 22 months to 34 years. A total of 21 ablations were performed in the 20 patients. Dr. Curry reported that all but four patients were seizure free after the procedure. However, the rate of seizures diminished among the four who were not seizure free.

Seizures recurred in one of the pediatric patients. "We re-ablated him and he is now seizure free," Dr. Curry said.

Complications to date have included two missed targets, one case of IV phenytoin toxicity, one case of transient diabetes insipidus, two cases of transient hemiparesis, and one subarachnoid hemorrhage. Perioperative, temporary weight gain was detected in most patients. "With lack of hormonal disturbance, this is thought to be due to the perioperative, high-dose steroid use," Dr. Curry explained.

Postoperative interviews with parents of study participants "have revealed significant improvements in intellectual development, concentration, and interactiveness," he said. "Most families report improvement of mood, decreased behavioral disorders, and rage attacks."

To date, only two patients have completed formal postoperative neuropsychological testing. "There were no significant declines in memory in either patient," Dr. Curry said. One had improved math skills and reading comprehension while the other complained of memory dysfunction but was not below normal on testing.

"We have learned that laser ablation of hypothalamic hamartoma can be accomplished safely," Dr. Curry concluded. "More studies are needed to explain the antiepileptic effect in settings of incomplete radiologic destruction of the target and to advance thermal planning."

Dr. Curry said that he had no relevant financial conflicts to disclose.

[email protected]

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SAN DIEGO – Magnetic resonance-guided stereotactic laser ablation is a safe and effective option in the treatment of hypothalamic hamartoma, results from a multicenter pilot study showed.

At the annual meeting of the American Academy of Neurology, Dr. Daniel J. Curry reported results from 20 patients who have undergone treatment with a Food and Drug Administration–cleared neurosurgical tissue coagulation system called Visualase. Hypothalamic hamartoma (HH) is a rare disorder of pediatric epilepsy with an estimated prevalence of 1:50,000-100,000, said Dr. Curry, director of pediatric surgical epilepsy and functional neurosurgery at Texas Children’s Hospital, Houston.

"The main presentation is the mirthless laughter of gelastic seizures, but patients can have other seizure types," he said. "The diagnosis is frequently delayed, and high seizure burden in the brain can lead to epileptic encephalopathy. Seizures are notoriously resistant to medical managements necessitating surgical intervention ... open, endoscopic, or ablative."

To date, surgical intervention has been limited due to modest outcomes, with 37%-50% achieving seizure freedom. The location of HH tumors makes surgical intervention difficult, and as a result 7%-10% of patients have permanent surgical morbidity.

For the technique using the Visualase, Dr. Curry and his associates at four other medical centers in the United States performed the surgical technique through a single 4-mm incision, a 3.2-mm burr hole, and a 1.65-mm cannula trajectory with Visualase under real-time MR thermography, first with a confirmation test at about 3 W, followed by higher doses of 6-10 W for 50-120 seconds. Temperature limits were set to protect the hypothalamus and basilar artery and optic tract. The surgery had an immediate effect, and patients stayed in the hospital for a mean of 2 days.

The primary measure was seizure frequency at 1 year while the secondary measure was the complication profile of stereotactic laser ablation in epilepsy.

Of the 20 patients, 5 were adults, and the entire study population ranged in age from 22 months to 34 years. A total of 21 ablations were performed in the 20 patients. Dr. Curry reported that all but four patients were seizure free after the procedure. However, the rate of seizures diminished among the four who were not seizure free.

Seizures recurred in one of the pediatric patients. "We re-ablated him and he is now seizure free," Dr. Curry said.

Complications to date have included two missed targets, one case of IV phenytoin toxicity, one case of transient diabetes insipidus, two cases of transient hemiparesis, and one subarachnoid hemorrhage. Perioperative, temporary weight gain was detected in most patients. "With lack of hormonal disturbance, this is thought to be due to the perioperative, high-dose steroid use," Dr. Curry explained.

Postoperative interviews with parents of study participants "have revealed significant improvements in intellectual development, concentration, and interactiveness," he said. "Most families report improvement of mood, decreased behavioral disorders, and rage attacks."

To date, only two patients have completed formal postoperative neuropsychological testing. "There were no significant declines in memory in either patient," Dr. Curry said. One had improved math skills and reading comprehension while the other complained of memory dysfunction but was not below normal on testing.

"We have learned that laser ablation of hypothalamic hamartoma can be accomplished safely," Dr. Curry concluded. "More studies are needed to explain the antiepileptic effect in settings of incomplete radiologic destruction of the target and to advance thermal planning."

Dr. Curry said that he had no relevant financial conflicts to disclose.

[email protected]

SAN DIEGO – Magnetic resonance-guided stereotactic laser ablation is a safe and effective option in the treatment of hypothalamic hamartoma, results from a multicenter pilot study showed.

At the annual meeting of the American Academy of Neurology, Dr. Daniel J. Curry reported results from 20 patients who have undergone treatment with a Food and Drug Administration–cleared neurosurgical tissue coagulation system called Visualase. Hypothalamic hamartoma (HH) is a rare disorder of pediatric epilepsy with an estimated prevalence of 1:50,000-100,000, said Dr. Curry, director of pediatric surgical epilepsy and functional neurosurgery at Texas Children’s Hospital, Houston.

"The main presentation is the mirthless laughter of gelastic seizures, but patients can have other seizure types," he said. "The diagnosis is frequently delayed, and high seizure burden in the brain can lead to epileptic encephalopathy. Seizures are notoriously resistant to medical managements necessitating surgical intervention ... open, endoscopic, or ablative."

To date, surgical intervention has been limited due to modest outcomes, with 37%-50% achieving seizure freedom. The location of HH tumors makes surgical intervention difficult, and as a result 7%-10% of patients have permanent surgical morbidity.

For the technique using the Visualase, Dr. Curry and his associates at four other medical centers in the United States performed the surgical technique through a single 4-mm incision, a 3.2-mm burr hole, and a 1.65-mm cannula trajectory with Visualase under real-time MR thermography, first with a confirmation test at about 3 W, followed by higher doses of 6-10 W for 50-120 seconds. Temperature limits were set to protect the hypothalamus and basilar artery and optic tract. The surgery had an immediate effect, and patients stayed in the hospital for a mean of 2 days.

The primary measure was seizure frequency at 1 year while the secondary measure was the complication profile of stereotactic laser ablation in epilepsy.

Of the 20 patients, 5 were adults, and the entire study population ranged in age from 22 months to 34 years. A total of 21 ablations were performed in the 20 patients. Dr. Curry reported that all but four patients were seizure free after the procedure. However, the rate of seizures diminished among the four who were not seizure free.

Seizures recurred in one of the pediatric patients. "We re-ablated him and he is now seizure free," Dr. Curry said.

Complications to date have included two missed targets, one case of IV phenytoin toxicity, one case of transient diabetes insipidus, two cases of transient hemiparesis, and one subarachnoid hemorrhage. Perioperative, temporary weight gain was detected in most patients. "With lack of hormonal disturbance, this is thought to be due to the perioperative, high-dose steroid use," Dr. Curry explained.

Postoperative interviews with parents of study participants "have revealed significant improvements in intellectual development, concentration, and interactiveness," he said. "Most families report improvement of mood, decreased behavioral disorders, and rage attacks."

To date, only two patients have completed formal postoperative neuropsychological testing. "There were no significant declines in memory in either patient," Dr. Curry said. One had improved math skills and reading comprehension while the other complained of memory dysfunction but was not below normal on testing.

"We have learned that laser ablation of hypothalamic hamartoma can be accomplished safely," Dr. Curry concluded. "More studies are needed to explain the antiepileptic effect in settings of incomplete radiologic destruction of the target and to advance thermal planning."

Dr. Curry said that he had no relevant financial conflicts to disclose.

[email protected]

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AT THE 2013 AAN ANNUAL MEETING

PURLs Copyright

Inside the Article

Vitals

Major finding: After 20 patients with hypothalamic hamartoma underwent MR-guided stereotactic laser ablation, all but 4 were seizure free.

Data source: A multicenter pilot study of 21 ablations performed in patients who ranged in age from 22 months to 34 years.

Disclosures: Dr. Curry said that he had no relevant financial conflicts to disclose.

Bosutinib finds its place in the CML treatment paradigm

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Drug therapy of chronic myeloid leukemia (CML) used to be simple. Or rather, it was narrow and not very effective. For a long time all we had was interferon alpha (IFN-alpha) and hydoxyurea, which failed to protect most patients from progression to the blastic phase. As a result, allotransplant, although associated with high mortality, was the treatment of choice for all eligible patients. Then imatinib came along and replaced a simple but poor choice with a simple but good choice for drug therapy. Now, 12 years later, the drug therapy space for CML is populated by 5 different tyrosine kinase inhibitors (TKIs; imatinib, dasatinib, nilotinib, bosutinib, and ponatinib) and omacetaxine (previously known as homoharringtonine) in addition to IFN-alpha and hydoxyurea. Navigating this space is a challenge, especially for hematologists and oncologists who don’t have the privilege of specializing. The drug at issue is bosutinib, which has been approved for treating adults “with chronic, accelerated, or blast phase Philadelphia chromosome-positive (Ph) CML with resistance or intolerance to prior therapy,” but it has not received approval for frontline therapy. A combined phase 1/2 study demonstrated a 41% cumulative rate of complete cytogenetic response (CCyR) in patients with chronic phase CML with resistance to or intolerance of imatinib who were treated with bosutinib; progressionfree and overall survival at 2 years were 79% and 92%, respectively, with better results for patients with intolerance compared with patients with resistance. The results are quite comparable with those of nilotinib or dasatinib in the same setting.1-3 In contrast, only 24% of patients on bosutinib achieved CCyR if they had prior exposure to dasatinib or nilotinib in addition to imatinib, which is also similar to the results with dasatinib or nilotinib in the third line,4 although follow-up is shorter. Only 2 BCRABL1 kinase mutations confer resistance to bosutinib: the multiresistant T315I mutations and V299L.5

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Drug therapy of chronic myeloid leukemia (CML) used to be simple. Or rather, it was narrow and not very effective. For a long time all we had was interferon alpha (IFN-alpha) and hydoxyurea, which failed to protect most patients from progression to the blastic phase. As a result, allotransplant, although associated with high mortality, was the treatment of choice for all eligible patients. Then imatinib came along and replaced a simple but poor choice with a simple but good choice for drug therapy. Now, 12 years later, the drug therapy space for CML is populated by 5 different tyrosine kinase inhibitors (TKIs; imatinib, dasatinib, nilotinib, bosutinib, and ponatinib) and omacetaxine (previously known as homoharringtonine) in addition to IFN-alpha and hydoxyurea. Navigating this space is a challenge, especially for hematologists and oncologists who don’t have the privilege of specializing. The drug at issue is bosutinib, which has been approved for treating adults “with chronic, accelerated, or blast phase Philadelphia chromosome-positive (Ph) CML with resistance or intolerance to prior therapy,” but it has not received approval for frontline therapy. A combined phase 1/2 study demonstrated a 41% cumulative rate of complete cytogenetic response (CCyR) in patients with chronic phase CML with resistance to or intolerance of imatinib who were treated with bosutinib; progressionfree and overall survival at 2 years were 79% and 92%, respectively, with better results for patients with intolerance compared with patients with resistance. The results are quite comparable with those of nilotinib or dasatinib in the same setting.1-3 In contrast, only 24% of patients on bosutinib achieved CCyR if they had prior exposure to dasatinib or nilotinib in addition to imatinib, which is also similar to the results with dasatinib or nilotinib in the third line,4 although follow-up is shorter. Only 2 BCRABL1 kinase mutations confer resistance to bosutinib: the multiresistant T315I mutations and V299L.5

Drug therapy of chronic myeloid leukemia (CML) used to be simple. Or rather, it was narrow and not very effective. For a long time all we had was interferon alpha (IFN-alpha) and hydoxyurea, which failed to protect most patients from progression to the blastic phase. As a result, allotransplant, although associated with high mortality, was the treatment of choice for all eligible patients. Then imatinib came along and replaced a simple but poor choice with a simple but good choice for drug therapy. Now, 12 years later, the drug therapy space for CML is populated by 5 different tyrosine kinase inhibitors (TKIs; imatinib, dasatinib, nilotinib, bosutinib, and ponatinib) and omacetaxine (previously known as homoharringtonine) in addition to IFN-alpha and hydoxyurea. Navigating this space is a challenge, especially for hematologists and oncologists who don’t have the privilege of specializing. The drug at issue is bosutinib, which has been approved for treating adults “with chronic, accelerated, or blast phase Philadelphia chromosome-positive (Ph) CML with resistance or intolerance to prior therapy,” but it has not received approval for frontline therapy. A combined phase 1/2 study demonstrated a 41% cumulative rate of complete cytogenetic response (CCyR) in patients with chronic phase CML with resistance to or intolerance of imatinib who were treated with bosutinib; progressionfree and overall survival at 2 years were 79% and 92%, respectively, with better results for patients with intolerance compared with patients with resistance. The results are quite comparable with those of nilotinib or dasatinib in the same setting.1-3 In contrast, only 24% of patients on bosutinib achieved CCyR if they had prior exposure to dasatinib or nilotinib in addition to imatinib, which is also similar to the results with dasatinib or nilotinib in the third line,4 although follow-up is shorter. Only 2 BCRABL1 kinase mutations confer resistance to bosutinib: the multiresistant T315I mutations and V299L.5

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Onco-bracketology? March Madness meets today’s practice

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I have just returned from the Oncology Practice Summit, the annual conference for practice-based oncologists and midlevels, which was hosted by COMMUNITY ONCOLOGY and its sister publications, THE JOURNAL OF SUPPORTIVE ONCOLOGY (JSO) and THE ONCOLOGY REPORT, in Las Vegas. During my flight to the conference, I noticed that there was a certain buzz among the passengers, which I naturally assumed was about our oncology meeting. But as I looked around, I realized that not only was I the only passenger who was wearing a tie, I was also the only one who had knocked back less than one drink. The frenzy was about the first weekend of the NCAA’s March Madness, and the pervasive enthusiasm among the passengers revolved around the wellknown “science” of bracketology, in which basketball enthusiasts take all 64 teams in the tournament and try to predict which team will win each match as the teams work their way down to the Final Four and ultimately, to the winner. President Obama had already said that his pick was Indiana (we know now how that turned out — sorry Indiana), but the amateur handicappers on the plane were still sifting through the teams’ records and the coaches’ and individual players’ strengths and weakness to bet (upon their arrival in Las Vegas) on which team would ultimately prevail. Once in Las Vegas, we managed to have our conference despite the March Madness mayhem, and in the course of the meeting, the term bracketology took on an oncology-tinged relevance for me. Bear with me.

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I have just returned from the Oncology Practice Summit, the annual conference for practice-based oncologists and midlevels, which was hosted by COMMUNITY ONCOLOGY and its sister publications, THE JOURNAL OF SUPPORTIVE ONCOLOGY (JSO) and THE ONCOLOGY REPORT, in Las Vegas. During my flight to the conference, I noticed that there was a certain buzz among the passengers, which I naturally assumed was about our oncology meeting. But as I looked around, I realized that not only was I the only passenger who was wearing a tie, I was also the only one who had knocked back less than one drink. The frenzy was about the first weekend of the NCAA’s March Madness, and the pervasive enthusiasm among the passengers revolved around the wellknown “science” of bracketology, in which basketball enthusiasts take all 64 teams in the tournament and try to predict which team will win each match as the teams work their way down to the Final Four and ultimately, to the winner. President Obama had already said that his pick was Indiana (we know now how that turned out — sorry Indiana), but the amateur handicappers on the plane were still sifting through the teams’ records and the coaches’ and individual players’ strengths and weakness to bet (upon their arrival in Las Vegas) on which team would ultimately prevail. Once in Las Vegas, we managed to have our conference despite the March Madness mayhem, and in the course of the meeting, the term bracketology took on an oncology-tinged relevance for me. Bear with me.

I have just returned from the Oncology Practice Summit, the annual conference for practice-based oncologists and midlevels, which was hosted by COMMUNITY ONCOLOGY and its sister publications, THE JOURNAL OF SUPPORTIVE ONCOLOGY (JSO) and THE ONCOLOGY REPORT, in Las Vegas. During my flight to the conference, I noticed that there was a certain buzz among the passengers, which I naturally assumed was about our oncology meeting. But as I looked around, I realized that not only was I the only passenger who was wearing a tie, I was also the only one who had knocked back less than one drink. The frenzy was about the first weekend of the NCAA’s March Madness, and the pervasive enthusiasm among the passengers revolved around the wellknown “science” of bracketology, in which basketball enthusiasts take all 64 teams in the tournament and try to predict which team will win each match as the teams work their way down to the Final Four and ultimately, to the winner. President Obama had already said that his pick was Indiana (we know now how that turned out — sorry Indiana), but the amateur handicappers on the plane were still sifting through the teams’ records and the coaches’ and individual players’ strengths and weakness to bet (upon their arrival in Las Vegas) on which team would ultimately prevail. Once in Las Vegas, we managed to have our conference despite the March Madness mayhem, and in the course of the meeting, the term bracketology took on an oncology-tinged relevance for me. Bear with me.

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Get ready now for 2014 Medicare ACO program

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The Centers for Medicare and Medicaid Services has just announced key dates for the 2014 Medicare Shared Savings Program application cycle – and although the upcoming Jan. 1, 2014, start date for the MSSP seems far off, physicians should start organizing now.

Physician interest in participating is mounting, as physician-led accountable care organizations are emerging as leaders in improving quality while eradicating waste. In fact, there are now more physician-run ACOs than any other model (see chart below).

Physicians see opportunity

The MSSP has embraced the accountable care concept to improve the quality of care for Medicare fee-for-service beneficiaries. Eligible providers and suppliers may participate in the MSSP by creating or participating in an ACO. The MSSP rewards ACOs that lower their rate of growth in health care costs while meeting quality performance standards.

On Jan. 10, 2013, the Centers for Medicare and Medicaid Services (CMS) announced that 106 new organizations were selected to participate in the program. That’s in addition to the 87 ACOs approved in July 2012 and the 27 selected in April 2012 – bringing the total to 220 ACOs selected to participate in the MSSP. Early evidence indicates that these ACOs are decreasing costs while improving clinical outcomes.

For many of those ACOs, Medicare will be just the beginning. Private insurers such as Aetna, UnitedHealth Group, Humana, Cigna, and most Blue Cross plans are contracting with ACOs to care for more patients. Many state Medicaid programs have moved or are considering moving to accountable care.

These multiple streams of shared savings will be generated through the same ACO infrastructure needed for the MSSP, encouraging more physician-owned ACOs to form.

With the rise of ACOs, "providers are doing things in a positive way rather than a reactive way. We are seeing the beginnings of a tsunami," noted Dr. Michael Cryer, national medical director at employee benefits consultancy Aon Hewitt, in a New York Times article ("Small-picture approach flips medical economics," March 12, 2012).

According to a recent study by consulting firm Oliver Wyman entitled "The ACO Surprise," roughly 10% of the U.S. population, or from 25 million to 31 million patients, are being served by ACOs. "Successful ACOs won’t just siphon patients away from traditional providers. They will change the rules of the game," the report’s authors conclude.

Don’t miss these 2013 deadlines

CMS has just released its 2013 application cycle for 2014 (see table). The time to act is now. It will take time to understand ACOs and enlist a critical mass of informed and committed primary care providers. Though the notice of intent ("NOI") is not binding, failure to file in May is binding – you are barred from applying. Likewise, you must obtain your user ID by May 31.

The application is not hard, but it basically reflects your ACO game plan. You must be organized, have a focused care plan, and complete the application by the end of July – much earlier than last year’s deadline.

Bottom line: Do not let the start date lull you into procrastination.

Let’s have a closer look at some of the things that must be covered in the application. In addition to a culture of teamwork, patient engagement, and alignment of financial incentives, which are chief among the eight essential elements necessary for a successful ACO ("The essential elements of an ACO," Internal Medicine News, Oct. 1, 2012, p. 38), the MSSP application requires:

• Compliance with the required definitions of "ACO applicant" and "participant."

• A certification that the ACO, its ACO-provider participants, and its ACO providers/suppliers have agreed to become accountable for the quality, cost, and overall care of the Medicare fee-for-service beneficiaries assigned to the ACO.

• Establishment of a governing body.

• Implementation of a comprehensive compliance plan.

• Execution of an ACO Participation Agreement.

In addition, certain organizational milestones should be reached in advance of the application. In particular, planning for a successful ACO requires identification of a physician-champion, completion of a feasibility analysis, implementation of sufficient information technology, and internal reporting on quality and cost metrics. As in any entrepreneurial pursuit, timing is critical, and delay equates to lost potential.

Given that primary care providers are the only providers mandated for inclusion in the MSSP, it is apparent that CMS expects primary care to drive ACO value via prevention and wellness; chronic disease management; care transitions and navigation; reduced hospitalizations; and multispecialty care coordination of complex patients.

ACOs, in one form or another, are sure to be permanent fixtures in American health care, as the nation’s economy and its residents eagerly await the benefits stemming from primary care–driven innovation.

 

 

Opportunity knocks – get going!

For more information about the Medicare Shared Savings Program, click here.

Mr. Bobbitt is a senior partner and head of the Health Law Group at the Smith Anderson law firm in Raleigh, North Carolina. He has many years’ experience assisting physicians in forming integrated delivery systems. He has spoken and written nationally to primary care physicians on the strategies and practicalities of forming or joining ACOs. This article is meant to be educational and does not constitute legal advice. For additional information, readers may contact the author ([email protected] or 919-821-6612). Mr. McNeill is a practicing attorney pursuing his LLM at Duke University, currently focusing on accountable care.

Medicare Shared Savings Program deadlines
Key dates for Jan. 1, 2014, start:

Notice of intent (NOI)
accepted
May 1-31, 2013
CMS user ID forms acceptedMay 1-31, 2013
Applications acceptedJuly 1-31, 2013
Application approval or
denial decision
Fall 2013
Start date for MSSP ACOJan. 1, 2014

Source: Centers for Medicare and Medicaid Services

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The Centers for Medicare and Medicaid Services has just announced key dates for the 2014 Medicare Shared Savings Program application cycle – and although the upcoming Jan. 1, 2014, start date for the MSSP seems far off, physicians should start organizing now.

Physician interest in participating is mounting, as physician-led accountable care organizations are emerging as leaders in improving quality while eradicating waste. In fact, there are now more physician-run ACOs than any other model (see chart below).

Physicians see opportunity

The MSSP has embraced the accountable care concept to improve the quality of care for Medicare fee-for-service beneficiaries. Eligible providers and suppliers may participate in the MSSP by creating or participating in an ACO. The MSSP rewards ACOs that lower their rate of growth in health care costs while meeting quality performance standards.

On Jan. 10, 2013, the Centers for Medicare and Medicaid Services (CMS) announced that 106 new organizations were selected to participate in the program. That’s in addition to the 87 ACOs approved in July 2012 and the 27 selected in April 2012 – bringing the total to 220 ACOs selected to participate in the MSSP. Early evidence indicates that these ACOs are decreasing costs while improving clinical outcomes.

For many of those ACOs, Medicare will be just the beginning. Private insurers such as Aetna, UnitedHealth Group, Humana, Cigna, and most Blue Cross plans are contracting with ACOs to care for more patients. Many state Medicaid programs have moved or are considering moving to accountable care.

These multiple streams of shared savings will be generated through the same ACO infrastructure needed for the MSSP, encouraging more physician-owned ACOs to form.

With the rise of ACOs, "providers are doing things in a positive way rather than a reactive way. We are seeing the beginnings of a tsunami," noted Dr. Michael Cryer, national medical director at employee benefits consultancy Aon Hewitt, in a New York Times article ("Small-picture approach flips medical economics," March 12, 2012).

According to a recent study by consulting firm Oliver Wyman entitled "The ACO Surprise," roughly 10% of the U.S. population, or from 25 million to 31 million patients, are being served by ACOs. "Successful ACOs won’t just siphon patients away from traditional providers. They will change the rules of the game," the report’s authors conclude.

Don’t miss these 2013 deadlines

CMS has just released its 2013 application cycle for 2014 (see table). The time to act is now. It will take time to understand ACOs and enlist a critical mass of informed and committed primary care providers. Though the notice of intent ("NOI") is not binding, failure to file in May is binding – you are barred from applying. Likewise, you must obtain your user ID by May 31.

The application is not hard, but it basically reflects your ACO game plan. You must be organized, have a focused care plan, and complete the application by the end of July – much earlier than last year’s deadline.

Bottom line: Do not let the start date lull you into procrastination.

Let’s have a closer look at some of the things that must be covered in the application. In addition to a culture of teamwork, patient engagement, and alignment of financial incentives, which are chief among the eight essential elements necessary for a successful ACO ("The essential elements of an ACO," Internal Medicine News, Oct. 1, 2012, p. 38), the MSSP application requires:

• Compliance with the required definitions of "ACO applicant" and "participant."

• A certification that the ACO, its ACO-provider participants, and its ACO providers/suppliers have agreed to become accountable for the quality, cost, and overall care of the Medicare fee-for-service beneficiaries assigned to the ACO.

• Establishment of a governing body.

• Implementation of a comprehensive compliance plan.

• Execution of an ACO Participation Agreement.

In addition, certain organizational milestones should be reached in advance of the application. In particular, planning for a successful ACO requires identification of a physician-champion, completion of a feasibility analysis, implementation of sufficient information technology, and internal reporting on quality and cost metrics. As in any entrepreneurial pursuit, timing is critical, and delay equates to lost potential.

Given that primary care providers are the only providers mandated for inclusion in the MSSP, it is apparent that CMS expects primary care to drive ACO value via prevention and wellness; chronic disease management; care transitions and navigation; reduced hospitalizations; and multispecialty care coordination of complex patients.

ACOs, in one form or another, are sure to be permanent fixtures in American health care, as the nation’s economy and its residents eagerly await the benefits stemming from primary care–driven innovation.

 

 

Opportunity knocks – get going!

For more information about the Medicare Shared Savings Program, click here.

Mr. Bobbitt is a senior partner and head of the Health Law Group at the Smith Anderson law firm in Raleigh, North Carolina. He has many years’ experience assisting physicians in forming integrated delivery systems. He has spoken and written nationally to primary care physicians on the strategies and practicalities of forming or joining ACOs. This article is meant to be educational and does not constitute legal advice. For additional information, readers may contact the author ([email protected] or 919-821-6612). Mr. McNeill is a practicing attorney pursuing his LLM at Duke University, currently focusing on accountable care.

Medicare Shared Savings Program deadlines
Key dates for Jan. 1, 2014, start:

Notice of intent (NOI)
accepted
May 1-31, 2013
CMS user ID forms acceptedMay 1-31, 2013
Applications acceptedJuly 1-31, 2013
Application approval or
denial decision
Fall 2013
Start date for MSSP ACOJan. 1, 2014

Source: Centers for Medicare and Medicaid Services

The Centers for Medicare and Medicaid Services has just announced key dates for the 2014 Medicare Shared Savings Program application cycle – and although the upcoming Jan. 1, 2014, start date for the MSSP seems far off, physicians should start organizing now.

Physician interest in participating is mounting, as physician-led accountable care organizations are emerging as leaders in improving quality while eradicating waste. In fact, there are now more physician-run ACOs than any other model (see chart below).

Physicians see opportunity

The MSSP has embraced the accountable care concept to improve the quality of care for Medicare fee-for-service beneficiaries. Eligible providers and suppliers may participate in the MSSP by creating or participating in an ACO. The MSSP rewards ACOs that lower their rate of growth in health care costs while meeting quality performance standards.

On Jan. 10, 2013, the Centers for Medicare and Medicaid Services (CMS) announced that 106 new organizations were selected to participate in the program. That’s in addition to the 87 ACOs approved in July 2012 and the 27 selected in April 2012 – bringing the total to 220 ACOs selected to participate in the MSSP. Early evidence indicates that these ACOs are decreasing costs while improving clinical outcomes.

For many of those ACOs, Medicare will be just the beginning. Private insurers such as Aetna, UnitedHealth Group, Humana, Cigna, and most Blue Cross plans are contracting with ACOs to care for more patients. Many state Medicaid programs have moved or are considering moving to accountable care.

These multiple streams of shared savings will be generated through the same ACO infrastructure needed for the MSSP, encouraging more physician-owned ACOs to form.

With the rise of ACOs, "providers are doing things in a positive way rather than a reactive way. We are seeing the beginnings of a tsunami," noted Dr. Michael Cryer, national medical director at employee benefits consultancy Aon Hewitt, in a New York Times article ("Small-picture approach flips medical economics," March 12, 2012).

According to a recent study by consulting firm Oliver Wyman entitled "The ACO Surprise," roughly 10% of the U.S. population, or from 25 million to 31 million patients, are being served by ACOs. "Successful ACOs won’t just siphon patients away from traditional providers. They will change the rules of the game," the report’s authors conclude.

Don’t miss these 2013 deadlines

CMS has just released its 2013 application cycle for 2014 (see table). The time to act is now. It will take time to understand ACOs and enlist a critical mass of informed and committed primary care providers. Though the notice of intent ("NOI") is not binding, failure to file in May is binding – you are barred from applying. Likewise, you must obtain your user ID by May 31.

The application is not hard, but it basically reflects your ACO game plan. You must be organized, have a focused care plan, and complete the application by the end of July – much earlier than last year’s deadline.

Bottom line: Do not let the start date lull you into procrastination.

Let’s have a closer look at some of the things that must be covered in the application. In addition to a culture of teamwork, patient engagement, and alignment of financial incentives, which are chief among the eight essential elements necessary for a successful ACO ("The essential elements of an ACO," Internal Medicine News, Oct. 1, 2012, p. 38), the MSSP application requires:

• Compliance with the required definitions of "ACO applicant" and "participant."

• A certification that the ACO, its ACO-provider participants, and its ACO providers/suppliers have agreed to become accountable for the quality, cost, and overall care of the Medicare fee-for-service beneficiaries assigned to the ACO.

• Establishment of a governing body.

• Implementation of a comprehensive compliance plan.

• Execution of an ACO Participation Agreement.

In addition, certain organizational milestones should be reached in advance of the application. In particular, planning for a successful ACO requires identification of a physician-champion, completion of a feasibility analysis, implementation of sufficient information technology, and internal reporting on quality and cost metrics. As in any entrepreneurial pursuit, timing is critical, and delay equates to lost potential.

Given that primary care providers are the only providers mandated for inclusion in the MSSP, it is apparent that CMS expects primary care to drive ACO value via prevention and wellness; chronic disease management; care transitions and navigation; reduced hospitalizations; and multispecialty care coordination of complex patients.

ACOs, in one form or another, are sure to be permanent fixtures in American health care, as the nation’s economy and its residents eagerly await the benefits stemming from primary care–driven innovation.

 

 

Opportunity knocks – get going!

For more information about the Medicare Shared Savings Program, click here.

Mr. Bobbitt is a senior partner and head of the Health Law Group at the Smith Anderson law firm in Raleigh, North Carolina. He has many years’ experience assisting physicians in forming integrated delivery systems. He has spoken and written nationally to primary care physicians on the strategies and practicalities of forming or joining ACOs. This article is meant to be educational and does not constitute legal advice. For additional information, readers may contact the author ([email protected] or 919-821-6612). Mr. McNeill is a practicing attorney pursuing his LLM at Duke University, currently focusing on accountable care.

Medicare Shared Savings Program deadlines
Key dates for Jan. 1, 2014, start:

Notice of intent (NOI)
accepted
May 1-31, 2013
CMS user ID forms acceptedMay 1-31, 2013
Applications acceptedJuly 1-31, 2013
Application approval or
denial decision
Fall 2013
Start date for MSSP ACOJan. 1, 2014

Source: Centers for Medicare and Medicaid Services

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Conflict between randomized and registry trials

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A recent spate of observational or registry analyses have challenged conventional wisdom derived from randomized clinical trials (RCTs). Both RCTs and registries have inherent flaws, but both provide important information in regard to drug efficacy in the search for "truth."

RCTs examine therapeutic effects in highly selected patient populations by focusing on one clinical entity, thereby excluding many patients with comorbidities that could influence or blunt the effect of the intervention. In a sense, RCTs do not represent the real-world expression of disease, since diseases rarely exists in isolation.

Registry trials collect large numbers of patients with a particular diagnosis within a large database. They include unselected patients and examine the effect of therapy in one disease regardless of comorbidities and are subject to both doctor and patient bias and confounding by comorbidities like chronic renal and pulmonary disease and, above all, are not randomized. Using a contemporary analogy, RCTs are a rifle shot whereas registries are more of a shotgun blast.

There have been two recent important targets for clinical research in heart failure. One is the search for better therapy for heart failure patients with preserved ejection fraction (HFPEF). The other is a search for drugs or devices that can provide added benefit to contemporary therapy for heart failure with reduced ejection fraction (HFREF)

The observation that many HFPEF patients develop heart failure despite current therapy with renin angiotensin aldosterone system (RAAS) antagonists and beta-blockers has led to a search for better therapy. RCTs with newer agents. including focused therapy with new RAAS antagonists, have failed to affect mortality in HFPEF (Lancet 2003;362:759-66). In contrast, a recent publication using the Swedish Heart Failure Registry (JAMA 2012;308:2108-17) found that patients treated with RAAS antagonists benefited compared with patients not taking them. The failure of the newer drugs to reach significance was attributed to flawed patient selection in RCTs that led to lower mortality rates and rendered the trials underpowered.

Similar discordance was observed between RCT and registry data in patients with HFREF who were treated with aldosterone antagonist (AA) in addition to contemporary RAAS antagonists and beta-blocker therapy. Using the Medicare database (JAMA 2012;308:2097-107), the investigators failed to observe any treatment benefit of AA on mortality that had previously been reported (N. Engl. J. Med. 1999;341:709-17). They did observe a decrease in rehospitalization for heart failure associated with an increase in rehospitalization for hyperkalemia. The authors attributed the reported benefit in the RCT to the exclusion of older and diabetic patients in addition to those with renal impairment, who were included in the registry analysis and reflected the real world of HFREF.

One registry study examining the benefit of ICDs in heart failure patients (JAMA 2013;309:55-62) from the analysis by the National Cardiovascular Registry did support the mortality benefit observed in the RCT (N. Engl. J. Med. 2002; 346:877-83).

As RCTs have developed over the last half-century, they have changed from investigations of therapeutic concepts to assessments of the efficacy of new and, often, expensive drugs. Much of this search has been supported by the pharmaceutical and device industries, which are intent on more focused research because of their concern about the "noise" generated by comorbidities that could obscure the benefit of their product. As a result, RCTs have identified lower-risk, homogeneous patient populations that may not reflect the real-world experience. Nevertheless, registry studies suffer from the major effect of bias, which is influenced by the physicians’ therapeutic choices and can distort the observed outcome. Unfortunately, the search for "truth" in clinical research remains often out of our reach.

Dr. Goldstein, medical editor of Cardiology News, is professor of medicine at Wayne State University and division head emeritus of cardiovascular medicine at Henry Ford Hospital, both in Detroit. He is on data safety monitoring committees for the National Institutes of Health and several pharmaceutical companies.

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A recent spate of observational or registry analyses have challenged conventional wisdom derived from randomized clinical trials (RCTs). Both RCTs and registries have inherent flaws, but both provide important information in regard to drug efficacy in the search for "truth."

RCTs examine therapeutic effects in highly selected patient populations by focusing on one clinical entity, thereby excluding many patients with comorbidities that could influence or blunt the effect of the intervention. In a sense, RCTs do not represent the real-world expression of disease, since diseases rarely exists in isolation.

Registry trials collect large numbers of patients with a particular diagnosis within a large database. They include unselected patients and examine the effect of therapy in one disease regardless of comorbidities and are subject to both doctor and patient bias and confounding by comorbidities like chronic renal and pulmonary disease and, above all, are not randomized. Using a contemporary analogy, RCTs are a rifle shot whereas registries are more of a shotgun blast.

There have been two recent important targets for clinical research in heart failure. One is the search for better therapy for heart failure patients with preserved ejection fraction (HFPEF). The other is a search for drugs or devices that can provide added benefit to contemporary therapy for heart failure with reduced ejection fraction (HFREF)

The observation that many HFPEF patients develop heart failure despite current therapy with renin angiotensin aldosterone system (RAAS) antagonists and beta-blockers has led to a search for better therapy. RCTs with newer agents. including focused therapy with new RAAS antagonists, have failed to affect mortality in HFPEF (Lancet 2003;362:759-66). In contrast, a recent publication using the Swedish Heart Failure Registry (JAMA 2012;308:2108-17) found that patients treated with RAAS antagonists benefited compared with patients not taking them. The failure of the newer drugs to reach significance was attributed to flawed patient selection in RCTs that led to lower mortality rates and rendered the trials underpowered.

Similar discordance was observed between RCT and registry data in patients with HFREF who were treated with aldosterone antagonist (AA) in addition to contemporary RAAS antagonists and beta-blocker therapy. Using the Medicare database (JAMA 2012;308:2097-107), the investigators failed to observe any treatment benefit of AA on mortality that had previously been reported (N. Engl. J. Med. 1999;341:709-17). They did observe a decrease in rehospitalization for heart failure associated with an increase in rehospitalization for hyperkalemia. The authors attributed the reported benefit in the RCT to the exclusion of older and diabetic patients in addition to those with renal impairment, who were included in the registry analysis and reflected the real world of HFREF.

One registry study examining the benefit of ICDs in heart failure patients (JAMA 2013;309:55-62) from the analysis by the National Cardiovascular Registry did support the mortality benefit observed in the RCT (N. Engl. J. Med. 2002; 346:877-83).

As RCTs have developed over the last half-century, they have changed from investigations of therapeutic concepts to assessments of the efficacy of new and, often, expensive drugs. Much of this search has been supported by the pharmaceutical and device industries, which are intent on more focused research because of their concern about the "noise" generated by comorbidities that could obscure the benefit of their product. As a result, RCTs have identified lower-risk, homogeneous patient populations that may not reflect the real-world experience. Nevertheless, registry studies suffer from the major effect of bias, which is influenced by the physicians’ therapeutic choices and can distort the observed outcome. Unfortunately, the search for "truth" in clinical research remains often out of our reach.

Dr. Goldstein, medical editor of Cardiology News, is professor of medicine at Wayne State University and division head emeritus of cardiovascular medicine at Henry Ford Hospital, both in Detroit. He is on data safety monitoring committees for the National Institutes of Health and several pharmaceutical companies.

A recent spate of observational or registry analyses have challenged conventional wisdom derived from randomized clinical trials (RCTs). Both RCTs and registries have inherent flaws, but both provide important information in regard to drug efficacy in the search for "truth."

RCTs examine therapeutic effects in highly selected patient populations by focusing on one clinical entity, thereby excluding many patients with comorbidities that could influence or blunt the effect of the intervention. In a sense, RCTs do not represent the real-world expression of disease, since diseases rarely exists in isolation.

Registry trials collect large numbers of patients with a particular diagnosis within a large database. They include unselected patients and examine the effect of therapy in one disease regardless of comorbidities and are subject to both doctor and patient bias and confounding by comorbidities like chronic renal and pulmonary disease and, above all, are not randomized. Using a contemporary analogy, RCTs are a rifle shot whereas registries are more of a shotgun blast.

There have been two recent important targets for clinical research in heart failure. One is the search for better therapy for heart failure patients with preserved ejection fraction (HFPEF). The other is a search for drugs or devices that can provide added benefit to contemporary therapy for heart failure with reduced ejection fraction (HFREF)

The observation that many HFPEF patients develop heart failure despite current therapy with renin angiotensin aldosterone system (RAAS) antagonists and beta-blockers has led to a search for better therapy. RCTs with newer agents. including focused therapy with new RAAS antagonists, have failed to affect mortality in HFPEF (Lancet 2003;362:759-66). In contrast, a recent publication using the Swedish Heart Failure Registry (JAMA 2012;308:2108-17) found that patients treated with RAAS antagonists benefited compared with patients not taking them. The failure of the newer drugs to reach significance was attributed to flawed patient selection in RCTs that led to lower mortality rates and rendered the trials underpowered.

Similar discordance was observed between RCT and registry data in patients with HFREF who were treated with aldosterone antagonist (AA) in addition to contemporary RAAS antagonists and beta-blocker therapy. Using the Medicare database (JAMA 2012;308:2097-107), the investigators failed to observe any treatment benefit of AA on mortality that had previously been reported (N. Engl. J. Med. 1999;341:709-17). They did observe a decrease in rehospitalization for heart failure associated with an increase in rehospitalization for hyperkalemia. The authors attributed the reported benefit in the RCT to the exclusion of older and diabetic patients in addition to those with renal impairment, who were included in the registry analysis and reflected the real world of HFREF.

One registry study examining the benefit of ICDs in heart failure patients (JAMA 2013;309:55-62) from the analysis by the National Cardiovascular Registry did support the mortality benefit observed in the RCT (N. Engl. J. Med. 2002; 346:877-83).

As RCTs have developed over the last half-century, they have changed from investigations of therapeutic concepts to assessments of the efficacy of new and, often, expensive drugs. Much of this search has been supported by the pharmaceutical and device industries, which are intent on more focused research because of their concern about the "noise" generated by comorbidities that could obscure the benefit of their product. As a result, RCTs have identified lower-risk, homogeneous patient populations that may not reflect the real-world experience. Nevertheless, registry studies suffer from the major effect of bias, which is influenced by the physicians’ therapeutic choices and can distort the observed outcome. Unfortunately, the search for "truth" in clinical research remains often out of our reach.

Dr. Goldstein, medical editor of Cardiology News, is professor of medicine at Wayne State University and division head emeritus of cardiovascular medicine at Henry Ford Hospital, both in Detroit. He is on data safety monitoring committees for the National Institutes of Health and several pharmaceutical companies.

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Recruiting Hospital Patients for Research

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Randomized controlled trials (RCTs) generally provide the most rigorous evidence for clinical practice guidelines and quality‐improvement initiatives. However, 2 major shortcomings limit the ability to broadly apply these results to the general population. One has to do with sampling bias (due to subject consent and inclusion/exclusion criteria) and the other with potential differences between participants and eligible nonparticipants. The latter may be of particular importance in trials of behavioral interventions (rather than medication trials), which often require substantial participant effort.

First, individuals who provide written consent to participate in RCTs of behavioral interventions typically represent a minority of those approached and therefore may not be representative of the target population. Although the consenting proportion is often not disclosed, some estimate that only 35%50% of eligible subjects typically participate.[1, 2, 3] These estimates mirror the authors' prior experience with a 55.2% consent rate among subjects approached for a Medicare quality‐improvement behavioral intervention.[3] Though the literature is sparse, it suggests that eligible individuals who decline to participate in either interventions or usual care may differ from participants in their perception of intervention risks and effort[4] or in their levels of self‐efficacy or confidence in recovery.[5, 6] Relatively low enrollment rates mean that much of the population remains unstudied; however, evidence‐based interventions are often applied to populations broader than those included in the original analyses.

Additionally, although some nonparticipants may correctly decide that they do not need the assistance of a proposed intervention and therefore decline to participate, others may inappropriately judge the intervention's potential benefit and applicability when declining. In other words, electing to not participate in a study, despite eligibility, may reflect more than a refusal of inconvenience, disinterest, or desire to contribute to knowledge; for some individuals it may offer a proxy statement about health knowledge, personal beliefs, attitudes, and needs, including perceived stress,[5] cultural relevance,[7, 8] and literacy/health literacy.[9, 10] Characterizing these patients can help us to modify recruitment approaches and improve participation so that participants better represent the target population. If these differences also relate to patients' adherence to care recommendations, a more nuanced understanding could improve ways to identify and engage potentially nonadherent patients to improve health outcomes.

We hypothesized that we could identify characteristics that differ between behavioral‐intervention participants and eligible nonparticipants using a set of screening questions. We proposed that these characteristics, including constructs related to perceived stress, recovery expectation, health literacy, insight, and action into advance care planning and confusion by any question, would predict the likelihood of consenting to a behavioral intervention requiring substantial subject engagement. Some of these characteristics may relate to adherence to preventive care or treatment recommendations. We did not specifically hypothesize about the distribution of demographic differences.

METHODS

Study Design

Prospective observational study conducted within a larger behavioral intervention.

Screening Question Design

We adapted our screening questions from several previously validated surveys, selecting questions related to perceived stress and self‐efficacy,[11] recovery expectations, health literacy/medication label interpretation,[12] and discussing advance directives (Table 1). Some of these characteristics may relate to adherence to preventive care or treatment programs[13, 14] or to clinical outcomes.[15, 16]

Screening Questions
Screening QuestionAdapted From Original Validated QuestionSourceConstruct
In the last week, how often have you felt that you are unable to control the important things in your life? (Rarely, sometimes, almost always)In the last month, how often have you felt that you were unable to control the important things in your life? (Never, almost never, sometimes, fairly often, very often)Adapted from the Perceived Stress Scale (PSS‐14).[11]Perceived stress, self‐efficacy
In the last week, how often have you felt that difficulties were piling up so high that you could not overcome them? (Rarely, sometimes, almost always)In the last month, how often have you felt difficulties were piling up so high that you could not overcome them? (Never, almost never, sometimes, fairly often, very often)Adapted from the Perceived Stress Scale (PSS‐14).[11]Perceived stress, self‐efficacy
How sure are you that you can go back to the way you felt before being hospitalized? (Not sure at all, somewhat sure, very sure) Courtesy of Phil Clark, PhD, University of Rhode Island, drawing on research on resilience. Similar questions are used in other studies, including studies of postsurgical recovery.[29, 30, 31]Recovery expectation, resilience
Even if you have not made any decisions, have you talked with your family members or doctor about what you would want for medical care if you could not speak for yourself? (Yes, no) Based on consumer‐targeted materials on advance care planning. http://www.agingwithdignity.org/five‐wishes.php; http://www.nhqualitycampaign.org/files/emmpguides/6_AdvanceCarePlanning_TAW_Guide.pdfAdvance care planning
(Show patient a picture of prescription label.) How many times a day should someone take this medicine? (Correct, incorrect)(Show patient a picture of ice cream label.) If you eat the entire container, how many calories will you eat? (Correct, incorrect)Adapted from Pfizer's Clear Health Communication: The Newest Vital Sign.[12]Health literacy

Prior to administering the screening questions, we performed cognitive testing with residents of an assisted‐living facility (N=10), a population that resembles our study's target population. In response to cognitive testing, we eliminated a question not interpreted easily by any of the participants, identified wording changes to clarify questions, simplified answer choices for ease of response (especially because questions are delivered verbally), and moved the most complicated (and potentially most embarrassing) question to the end, with more straightforward questions toward the beginning. We also substantially enlarged the image of a standard medication label to improve readability. Our final tool included 5 questions (Table 1).

The final instrument prompted coaches to record patient confusion. Additionally, the advance‐directive question included a refused to answer option and the medication question included unable to answer (needs glasses, too tired, etc.), a potential marker of low health literacy if used as an excuse to avoid embarrassment.[17]

Setting

We recruited inpatients at 5 Rhode Island acute‐care hospitals, including 1 community hospital, 3 teaching hospitals, and a tertiary‐care center and teaching hospital, ranging from 174 beds to 719 beds. Recruitment occurred from November 2010 to April 2011. The hospitals' respective institutional review boards approved the screening questions.

Study Population

We recruited a convenience sample of consecutively identified hospitalized Medicare fee‐for‐service beneficiaries, identified as (1) eligible for the subsequent behavioral intervention based on inpatient census lists and (2) willing to discuss an offer for a home‐based behavioral intervention. The behavioral intervention, based on the Care Transitions Intervention and described elsewhere,[3, 18] included a home visit and 2 phone calls (each about 1 hour). Coaches used a personal health record to help patients and/or caregivers better manage their health by (1) being able to list their active medical conditions and medications and (2) understanding warning signs indicating a need to reach out for help, including getting a timely medical appointment after hospitalization. The population for the present study included individuals approached to discuss participation in the behavioral intervention who also agreed to answer the screening questions.

Inclusion/Exclusion Criteria

We included hospitalized Medicare fee‐for‐service beneficiaries. We excluded patients who were current long‐term care residents, were to be discharged to long‐term or skilled care, or had a documented hospice referral. We also excluded patients with limited English proficiency or who were judged to have inadequate cognitive function, unless a caregiver agreed to receive the intervention as a proxy. We made these exclusions when recruiting for the behavioral intervention. Because we presented the screening questions to a subset of those approached for the behavioral intervention, we did not further exclude anyone. In other words, we offered the screening questions to all 295 people we approached during this study time period (100%).

Screening‐Question Study Process

Coaches asked patients to answer the 5 screening questions immediately after offering them the opportunity to participate in the behavioral intervention, regardless of whether or not they accepted the behavioral intervention. This study examines the subset of patients approached for the behavioral intervention who verbally consented to answer the screening questions.

Data Sources and Covariates

We analyzed primary data from the screening questions and behavioral intervention (for those who consented to participate), as well as Medicare claims and Medicaid enrollment data. We matched screening‐question data from November 2010 through April 2011 with Medicare Part A claims from October 2010 through May 2011 to calculate 30‐day readmission rates.

We obtained the following information for patients offered the behavioral intervention: (1) responses to screening questions, (2) whether patients consented to the behavioral intervention, (3) exposure to the behavioral intervention, and (4) recruitment date. Medicare claims data included (1) admission and discharge dates to calculate the length of stay, (2) index diagnosis, (3) hospital, and (4) site of discharge. Medicare enrollment data provided information on (1) Medicaid/Medicare dual‐eligibility status, (2) sex, and (3) patient‐reported race. We matched data based on patient name and date of birth. Our primary outcome was consent to the behavioral intervention. Secondarily, we reviewed posthospital utilization patterns, including hospital readmission, emergency‐department use, and use of home‐health services.

Statistical Analysis

We categorized patients into 2 groups (Figure 1): participants (consented to the behavioral intervention) and nonparticipants (eligible for the behavioral intervention but declined to participate). We excluded responses for those confused by the question (no response). For the response scales never, sometimes, almost always and not at all sure, somewhat sure, very sure, we isolated the most negative response, grouping the middle and most positive responses (Table 2). For the medication‐label question, we grouped incorrect and unable to answer (needs glasses, too tired, etc.) responses. We compared demographic differences between behavioral intervention participants and nonparticipants using 2 tests (categorical variables) and Student t tests (continuous variables). We then used multivariate logistic regression to analyze differences in consent to the behavioral intervention based on screening‐question responses, adjusting for demographics that differed significantly in the bivariate comparisons.

Figure 1
Study population.
Association Between Consent to Behavioral Intervention and Screening Question Response, by Question (N=260)
Screening‐Question ResponseAdjusted OR (95% CI)P Value
  • NOTE: Abbreviations: CI, confidence interval; OR, odds ratio; Ref, reference.

  • Significant at P<0.05. Results do not add up to 260 responses in all questions due to the exclusion of confused by question from each response set.

In the last week, how often have you felt that you are unable to control the important things in your life?  
Out of control (Almost always)0.35 (0.14‐0.92)0.034a
In control (Sometimes, rarely)1.00 (Ref)
In the last week, how often have you felt that difficulties were piling up so high that you could not overcome them?  
Overwhelmed (Almost always)0.41 (0.16‐1.07)0.069
Not overwhelmed (Sometimes, rarely)1.00 (Ref)
How sure are you that you can go back to the way you felt before being hospitalized?  
Not confident (Not sure at all)0.17 (0.06‐0.45)0.001a
Confident (Somewhat sure, very sure)1.00 (Ref)
Even if you have not made any decisions, have you talked with your family members or doctor about what you would want for medical care if you could not speak for yourself?  
No0.45 (0.13‐1.64)0.227
Yes1.00 (Ref)
How many times a day should someone take this medicine? (Show patient a medication label)  
Incorrect answer3.82 (1.12‐13.03)0.033a
Correct answer1.00 (Ref)
Confused by any question?  
Yes0.11 (0.05‐0.24)0.001a
No1.00 (Ref)

The authors used SAS version 9.2 (SAS Institute, Inc., Cary, NC) for all analyses.

RESULTS

Of the 295 patients asked to complete the screening questions, 260 (88.1%) consented to answer the screening questions and 35 (11.9%) declined. More than half of those who answered the screening questions consented to participate in the behavioral intervention (160; 61.5%) (Figure 1). When compared with nonparticipants, participants in the behavioral intervention were younger (25.6% age 85 years vs 40% age 85 years, P=0.028), had a longer average length of hospital stay (7.9 vs 6.1 days, P=0.008), were more likely to be discharged home without clinical services (35.0% vs 23.0%, P=0.041), and were unevenly distributed between the 5 recruitment‐site hospitals, coming primarily from the teaching hospitals (P<0.001) (Table 3). There were no significant differences based on race, sex, dual‐eligible Medicare/Medicaid status, presence of a caregiver, or index diagnosis.

Patient Characteristics by Behavioral Intervention Consent Status (N=260)
Patient CharacteristicsDeclined (n=100)Consented (n=160)P Value
  • NOTE: Abbreviations: CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; MI, myocardial infarction; SD, standard deviation.

  • Significant at P<0.05.

  • Eligible for both Medicare and Medicaid benefits.

  • Discharged home with no planned clinical services, as opposed to being discharged to home health care, hospice, or skilled care. Hospitals 35 are teaching hospitals

Male, n (%)34 (34.0)52 (32.5)0.803
Race, n (%)   
White94 (94.0)151 (94.4)0.691
Black2 (2.0)5 (3.1)
Other4 (4.0)4 (2.5)
Age, n (%), y   
<6517 (17.0)23 (14.4)0.028a
657414 (14.0)42 (26.3)
758429 (29.0)54 (33.8)
8540 (40.0)41 (25.6)
Dual eligible, n (%)b11 (11.0)24 (15.0)0.358
Caregiver present, n (%)17 (17.0)34 (21.3)0.401
Length of stay, mean (SD), d6.1 (4.1)7.9 (4.8)0.008a
Index diagnosis, n (%)   
Acute MI3 (3.0)6 (3.8)0.806
CHF6 (6.0)20 (12.5)0.111
Pneumonia7 (7.0)9 (5.6)0.572
COPD6 (6.0)6 (8.8)0.484
Discharged home without clinical services, n (%)c23 (23.0)56 (35.0)0.041a
Hospital site   
Hospital 115 (15.0)43 (26.9)<0.001a
Hospital 220 (20.0)26 (16.3)
Hospital 315 (15.0)23 (14.4)
Hospital 42 (2.0)48 (30.0)
Hospital 548 (48.0)20 (12.5)

Patients who identified themselves as being unable to control important things in their lives were 65% less likely to consent to the behavioral intervention than those in control (odds ratio [OR]: 0.35, 95% confidence interval [CI]: 0.14‐0.92), and those who did not feel confident about recovering were 83% less likely to consent (OR: 0.17, 95% CI: 0.06‐0.45). Individuals who were confused by any question were 89% less likely to consent (OR: 0.11, 95% CI: 0.05‐0.24). Individuals who answered the medication question incorrectly were 3 times more likely to consent (OR: 3.82, 95% CI: 1.12‐13.03). There were no significant differences in consent for feeling overwhelmed (difficulties piling up) or for having discussed advance care planning with family members or doctors.

We had insufficient power to detect significant differences in posthospital utilization (including hospital readmission, emergency‐department use, and receipt of home health), based on screening‐question responses (data not shown).

DISCUSSION

We find that patients who declined to participate in the behavioral intervention (eligible nonparticipants) differed from participants in 3 important ways: perceived stress, recovery expectation, and health literacy. As hypothesized, patients with higher perceived stress and lower recovery expectation were less likely to consent to the behavioral intervention, even after adjusting for demographic and healthcare‐utilization differences. Contrary to our hypothesis, patients who incorrectly answered the medication question were more likely to consent to the intervention than those who correctly answered.

Characterizing nonparticipants and participants can offer important insight into the limitations of the research that informs clinical guidelines and behavioral interventions. Such characteristics could also indicate how to better engage patients in interventions or other aspects of their care, if associated with lower rates of adherence to recommended health behaviors or treatment plans. For example, self‐efficacy (closely related to perceived stress) and hopelessness regarding clinical outcomes (similar to low recovery expectation in the present study) are associated with nonadherence to medication plans and other care in some populations.[5, 6] Other more extreme stress, like that following a major medical event, has also been associated with a lower rate of adherence to medication regimens and a resulting higher rate of hospital readmission and mortality.[19, 20] People with low health literacy (compared with adequate health literacy) are more likely to report being confused about their medications, requesting help to read medication labels and missing appointments due to trouble reading reminder cards.[9] Identifying these characteristics may assist providers in helping patients address adherence barriers by first accurately identifying the root of patient issues (eg, where the lack of confidence in recovery is rooted in lack of resources or social support), then potentially referring to community resources where possible. For example, some states (including Rhode Island, this study's location) may have Aging and Disability Resource Centers dedicated to linking elderly people with transportation, decision support, and other resources to support quality care.

The association between health literacy and intervention participation remains uncertain. Our question, which assessed interpretation of a prescription label as a health‐literacy proxy, may have given patients insight into their limited health literacy that motivated them to accept the subsequent behavioral intervention. Others have found that lowerhealth literacy patients want their providers to know that they did not understand some health words,[9] though they may be less likely to ask questions, request additional services, or seek new information during a medical encounter.[21] In our study, those who correctly answered the medication‐label question were almost mutually exclusive from those who were otherwise stressed (12% overlap; data not shown). Thus, patients who correctly answer this question may correctly realize that they do not need the support offered by the behavioral intervention and decline to participate. For other patients, perceived stress and poor recovery expectations may be more immediate and important determinants of declination, with patients too stressed to volunteer for another task, even if it involves much‐needed assistance.

The frequency with which patients were confused by the questions merits further comment and may also be driven by stress. Though each question seeks to identify the impact of a specific construct (Table 1), being confused by any question may reflect a more general (or subacute) level of cognitive impairment or generalized low health literacy not limited to the applied numeracy of the medication‐label question. We excluded confused responses to demonstrate more clearly the impact of each individual construct.

The impact of these characteristics may be affected by study design or other characteristics. One of the few studies to examine (via RCT) how methods affect consent found that participation decreased with increasing complexity of the consent process: written consent yielded the lowest participation, limited written consent was higher, and verbal consent was the highest.[10] Other tactics to increase consent include monetary incentives,[22] culturally sensitive materials,[7] telephone reminders,[23] an opt‐out instead of opt‐in approach,[23] and an open design where participants know which treatment they are receiving.[23] We do not know how these tactics relate to the characteristics captured in our screening questions, although other characteristics we measured, such as patients' self‐identified race, have been associated with intervention participation and access to care,[8, 24, 25] and patients who perceive that the benefit of the intervention outweighs expected risks and time requirements are more likely to consent.[4] We intentionally minimized the number of screening questions to encourage participation. The high rate of consent to our screening questions compared with consent to the (more involved) behavioral intervention reveals how sensitive patients are to the perceived invasiveness of an intervention.

We note several limitations. First, overall generalizability is limited due to our small sample size, use of consecutive convenience sampling, and exclusion criteria (eg, patients discharged to long‐term or skilled nursing care). And, these results may not apply to patients who are not hospitalized; hospitalized patients may have different motivations and stressors regarding their involvement in their care. Additionally, although we included as many people with mild cognitive impairment as possible by proxy through caregivers, we excluded some that did not have caregivers, potentially undermining the accuracy of how cognition impacts the choice to accept the behavioral intervention. Because researchers often explicitly exclude individuals based on cognitive impairment, differences between recruited subjects and the population at large may be particularly high among elderly patients, where up to half of the eligible population may be affected by cognitive impairment.[26] Further research into successfully engaging caregivers as a way to reach otherwise‐excluded patients with cognitive impairment can help to mitigate threats to generalizability. Finally, our screening questions are based on validated questions, but we rearranged our question wording, simplified answer choices, and removed them from their original context. Thus, the questions were not validated in our population or when administered in this manner. Although we conducted cognitive testing, further validity and reliability testing are necessary to translate these questions into a general screening tool. The medication‐label question also requires revision; in data collection and analysis, we assume that patients who were unable to answer (needs glasses, too tired, etc.) were masking an inability to respond correctly. Though the use of this excuse is cited in the literature,[17] we cannot be certain that our treatment of it in these screening questions is generalizable. Generalizability also applies to how we group responses. Isolating the most negative response (by grouping the middle answer with the most positive answer) most specifically identifies individuals more likely to need assistance and is therefore clinically pertinent, but this also potentially fails to identify individuals who also need help but do not choose the more extreme answer. Further research to refine the screening questions might also consider the timeframe of the perceived stress questions (past week rather than past month); this timeframe may be specific to the acute medical situation rather than general or unrelated perceived stress. Though this study cannot test this hypothesis, individuals with higher pre‐illness perceived stress may be more interested in addressing the issues that were stressors prior to acute illness, rather than the offered behavioral intervention. Additionally, some of the questions were highly correlated (Q1 and Q2) and indicate a potential for shortening the screening questionnaire.

Still, these findings further the discussion of how to identify and consent hospitalized patients for participation in behavioral interventions, both for research and for routine clinical care. Researchers should specifically consider how to engage individuals who are stressed and are not confident about recovery to improve reach and effectiveness. For example, interventions should prospectively collect data on stress and confidence in recovery and include protocols to support people who are positively identified with these characteristics. These characteristics may also offer insight into improving patient and caregiver engagement; more research is needed into characteristics related to patients' willingness to seek assistance in care. We are not the first to suggest that characteristics not observed in medical charts may impact patient completion or response to behavioral interventions,[27, 28] and considering differences between participants and eligible nonparticipants in clinical care delivery and interventions can strengthen the evidence base for clinical improvements, particularly related to patient self‐management. The implications are useful for both practicing clinicians and larger systems examining the comparativeness of patient interventions and generalizing results from RCTs.

Acknowledgments

The authors thank Phil Clark, PhD, and the SENIOR Project (Study of Exercise and Nutrition in Older Rhode Islanders) research team at the University of Rhode Island for formulating 1 of the screening questions, and Marissa Meucci for her assistance with the cognitive testing and formative research for the screening questions.

Disclosures

The analyses on which this study is based were performed by Healthcentric Advisors under contract HHSM 5002011‐RI10C, titled Utilization and Quality Control Peer Review for the State of Rhode Island, sponsored by the Centers for Medicare and Medicaid Services, US Department of Health and Human Services. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. The authors report no conflicts of interest.

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References
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Randomized controlled trials (RCTs) generally provide the most rigorous evidence for clinical practice guidelines and quality‐improvement initiatives. However, 2 major shortcomings limit the ability to broadly apply these results to the general population. One has to do with sampling bias (due to subject consent and inclusion/exclusion criteria) and the other with potential differences between participants and eligible nonparticipants. The latter may be of particular importance in trials of behavioral interventions (rather than medication trials), which often require substantial participant effort.

First, individuals who provide written consent to participate in RCTs of behavioral interventions typically represent a minority of those approached and therefore may not be representative of the target population. Although the consenting proportion is often not disclosed, some estimate that only 35%50% of eligible subjects typically participate.[1, 2, 3] These estimates mirror the authors' prior experience with a 55.2% consent rate among subjects approached for a Medicare quality‐improvement behavioral intervention.[3] Though the literature is sparse, it suggests that eligible individuals who decline to participate in either interventions or usual care may differ from participants in their perception of intervention risks and effort[4] or in their levels of self‐efficacy or confidence in recovery.[5, 6] Relatively low enrollment rates mean that much of the population remains unstudied; however, evidence‐based interventions are often applied to populations broader than those included in the original analyses.

Additionally, although some nonparticipants may correctly decide that they do not need the assistance of a proposed intervention and therefore decline to participate, others may inappropriately judge the intervention's potential benefit and applicability when declining. In other words, electing to not participate in a study, despite eligibility, may reflect more than a refusal of inconvenience, disinterest, or desire to contribute to knowledge; for some individuals it may offer a proxy statement about health knowledge, personal beliefs, attitudes, and needs, including perceived stress,[5] cultural relevance,[7, 8] and literacy/health literacy.[9, 10] Characterizing these patients can help us to modify recruitment approaches and improve participation so that participants better represent the target population. If these differences also relate to patients' adherence to care recommendations, a more nuanced understanding could improve ways to identify and engage potentially nonadherent patients to improve health outcomes.

We hypothesized that we could identify characteristics that differ between behavioral‐intervention participants and eligible nonparticipants using a set of screening questions. We proposed that these characteristics, including constructs related to perceived stress, recovery expectation, health literacy, insight, and action into advance care planning and confusion by any question, would predict the likelihood of consenting to a behavioral intervention requiring substantial subject engagement. Some of these characteristics may relate to adherence to preventive care or treatment recommendations. We did not specifically hypothesize about the distribution of demographic differences.

METHODS

Study Design

Prospective observational study conducted within a larger behavioral intervention.

Screening Question Design

We adapted our screening questions from several previously validated surveys, selecting questions related to perceived stress and self‐efficacy,[11] recovery expectations, health literacy/medication label interpretation,[12] and discussing advance directives (Table 1). Some of these characteristics may relate to adherence to preventive care or treatment programs[13, 14] or to clinical outcomes.[15, 16]

Screening Questions
Screening QuestionAdapted From Original Validated QuestionSourceConstruct
In the last week, how often have you felt that you are unable to control the important things in your life? (Rarely, sometimes, almost always)In the last month, how often have you felt that you were unable to control the important things in your life? (Never, almost never, sometimes, fairly often, very often)Adapted from the Perceived Stress Scale (PSS‐14).[11]Perceived stress, self‐efficacy
In the last week, how often have you felt that difficulties were piling up so high that you could not overcome them? (Rarely, sometimes, almost always)In the last month, how often have you felt difficulties were piling up so high that you could not overcome them? (Never, almost never, sometimes, fairly often, very often)Adapted from the Perceived Stress Scale (PSS‐14).[11]Perceived stress, self‐efficacy
How sure are you that you can go back to the way you felt before being hospitalized? (Not sure at all, somewhat sure, very sure) Courtesy of Phil Clark, PhD, University of Rhode Island, drawing on research on resilience. Similar questions are used in other studies, including studies of postsurgical recovery.[29, 30, 31]Recovery expectation, resilience
Even if you have not made any decisions, have you talked with your family members or doctor about what you would want for medical care if you could not speak for yourself? (Yes, no) Based on consumer‐targeted materials on advance care planning. http://www.agingwithdignity.org/five‐wishes.php; http://www.nhqualitycampaign.org/files/emmpguides/6_AdvanceCarePlanning_TAW_Guide.pdfAdvance care planning
(Show patient a picture of prescription label.) How many times a day should someone take this medicine? (Correct, incorrect)(Show patient a picture of ice cream label.) If you eat the entire container, how many calories will you eat? (Correct, incorrect)Adapted from Pfizer's Clear Health Communication: The Newest Vital Sign.[12]Health literacy

Prior to administering the screening questions, we performed cognitive testing with residents of an assisted‐living facility (N=10), a population that resembles our study's target population. In response to cognitive testing, we eliminated a question not interpreted easily by any of the participants, identified wording changes to clarify questions, simplified answer choices for ease of response (especially because questions are delivered verbally), and moved the most complicated (and potentially most embarrassing) question to the end, with more straightforward questions toward the beginning. We also substantially enlarged the image of a standard medication label to improve readability. Our final tool included 5 questions (Table 1).

The final instrument prompted coaches to record patient confusion. Additionally, the advance‐directive question included a refused to answer option and the medication question included unable to answer (needs glasses, too tired, etc.), a potential marker of low health literacy if used as an excuse to avoid embarrassment.[17]

Setting

We recruited inpatients at 5 Rhode Island acute‐care hospitals, including 1 community hospital, 3 teaching hospitals, and a tertiary‐care center and teaching hospital, ranging from 174 beds to 719 beds. Recruitment occurred from November 2010 to April 2011. The hospitals' respective institutional review boards approved the screening questions.

Study Population

We recruited a convenience sample of consecutively identified hospitalized Medicare fee‐for‐service beneficiaries, identified as (1) eligible for the subsequent behavioral intervention based on inpatient census lists and (2) willing to discuss an offer for a home‐based behavioral intervention. The behavioral intervention, based on the Care Transitions Intervention and described elsewhere,[3, 18] included a home visit and 2 phone calls (each about 1 hour). Coaches used a personal health record to help patients and/or caregivers better manage their health by (1) being able to list their active medical conditions and medications and (2) understanding warning signs indicating a need to reach out for help, including getting a timely medical appointment after hospitalization. The population for the present study included individuals approached to discuss participation in the behavioral intervention who also agreed to answer the screening questions.

Inclusion/Exclusion Criteria

We included hospitalized Medicare fee‐for‐service beneficiaries. We excluded patients who were current long‐term care residents, were to be discharged to long‐term or skilled care, or had a documented hospice referral. We also excluded patients with limited English proficiency or who were judged to have inadequate cognitive function, unless a caregiver agreed to receive the intervention as a proxy. We made these exclusions when recruiting for the behavioral intervention. Because we presented the screening questions to a subset of those approached for the behavioral intervention, we did not further exclude anyone. In other words, we offered the screening questions to all 295 people we approached during this study time period (100%).

Screening‐Question Study Process

Coaches asked patients to answer the 5 screening questions immediately after offering them the opportunity to participate in the behavioral intervention, regardless of whether or not they accepted the behavioral intervention. This study examines the subset of patients approached for the behavioral intervention who verbally consented to answer the screening questions.

Data Sources and Covariates

We analyzed primary data from the screening questions and behavioral intervention (for those who consented to participate), as well as Medicare claims and Medicaid enrollment data. We matched screening‐question data from November 2010 through April 2011 with Medicare Part A claims from October 2010 through May 2011 to calculate 30‐day readmission rates.

We obtained the following information for patients offered the behavioral intervention: (1) responses to screening questions, (2) whether patients consented to the behavioral intervention, (3) exposure to the behavioral intervention, and (4) recruitment date. Medicare claims data included (1) admission and discharge dates to calculate the length of stay, (2) index diagnosis, (3) hospital, and (4) site of discharge. Medicare enrollment data provided information on (1) Medicaid/Medicare dual‐eligibility status, (2) sex, and (3) patient‐reported race. We matched data based on patient name and date of birth. Our primary outcome was consent to the behavioral intervention. Secondarily, we reviewed posthospital utilization patterns, including hospital readmission, emergency‐department use, and use of home‐health services.

Statistical Analysis

We categorized patients into 2 groups (Figure 1): participants (consented to the behavioral intervention) and nonparticipants (eligible for the behavioral intervention but declined to participate). We excluded responses for those confused by the question (no response). For the response scales never, sometimes, almost always and not at all sure, somewhat sure, very sure, we isolated the most negative response, grouping the middle and most positive responses (Table 2). For the medication‐label question, we grouped incorrect and unable to answer (needs glasses, too tired, etc.) responses. We compared demographic differences between behavioral intervention participants and nonparticipants using 2 tests (categorical variables) and Student t tests (continuous variables). We then used multivariate logistic regression to analyze differences in consent to the behavioral intervention based on screening‐question responses, adjusting for demographics that differed significantly in the bivariate comparisons.

Figure 1
Study population.
Association Between Consent to Behavioral Intervention and Screening Question Response, by Question (N=260)
Screening‐Question ResponseAdjusted OR (95% CI)P Value
  • NOTE: Abbreviations: CI, confidence interval; OR, odds ratio; Ref, reference.

  • Significant at P<0.05. Results do not add up to 260 responses in all questions due to the exclusion of confused by question from each response set.

In the last week, how often have you felt that you are unable to control the important things in your life?  
Out of control (Almost always)0.35 (0.14‐0.92)0.034a
In control (Sometimes, rarely)1.00 (Ref)
In the last week, how often have you felt that difficulties were piling up so high that you could not overcome them?  
Overwhelmed (Almost always)0.41 (0.16‐1.07)0.069
Not overwhelmed (Sometimes, rarely)1.00 (Ref)
How sure are you that you can go back to the way you felt before being hospitalized?  
Not confident (Not sure at all)0.17 (0.06‐0.45)0.001a
Confident (Somewhat sure, very sure)1.00 (Ref)
Even if you have not made any decisions, have you talked with your family members or doctor about what you would want for medical care if you could not speak for yourself?  
No0.45 (0.13‐1.64)0.227
Yes1.00 (Ref)
How many times a day should someone take this medicine? (Show patient a medication label)  
Incorrect answer3.82 (1.12‐13.03)0.033a
Correct answer1.00 (Ref)
Confused by any question?  
Yes0.11 (0.05‐0.24)0.001a
No1.00 (Ref)

The authors used SAS version 9.2 (SAS Institute, Inc., Cary, NC) for all analyses.

RESULTS

Of the 295 patients asked to complete the screening questions, 260 (88.1%) consented to answer the screening questions and 35 (11.9%) declined. More than half of those who answered the screening questions consented to participate in the behavioral intervention (160; 61.5%) (Figure 1). When compared with nonparticipants, participants in the behavioral intervention were younger (25.6% age 85 years vs 40% age 85 years, P=0.028), had a longer average length of hospital stay (7.9 vs 6.1 days, P=0.008), were more likely to be discharged home without clinical services (35.0% vs 23.0%, P=0.041), and were unevenly distributed between the 5 recruitment‐site hospitals, coming primarily from the teaching hospitals (P<0.001) (Table 3). There were no significant differences based on race, sex, dual‐eligible Medicare/Medicaid status, presence of a caregiver, or index diagnosis.

Patient Characteristics by Behavioral Intervention Consent Status (N=260)
Patient CharacteristicsDeclined (n=100)Consented (n=160)P Value
  • NOTE: Abbreviations: CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; MI, myocardial infarction; SD, standard deviation.

  • Significant at P<0.05.

  • Eligible for both Medicare and Medicaid benefits.

  • Discharged home with no planned clinical services, as opposed to being discharged to home health care, hospice, or skilled care. Hospitals 35 are teaching hospitals

Male, n (%)34 (34.0)52 (32.5)0.803
Race, n (%)   
White94 (94.0)151 (94.4)0.691
Black2 (2.0)5 (3.1)
Other4 (4.0)4 (2.5)
Age, n (%), y   
<6517 (17.0)23 (14.4)0.028a
657414 (14.0)42 (26.3)
758429 (29.0)54 (33.8)
8540 (40.0)41 (25.6)
Dual eligible, n (%)b11 (11.0)24 (15.0)0.358
Caregiver present, n (%)17 (17.0)34 (21.3)0.401
Length of stay, mean (SD), d6.1 (4.1)7.9 (4.8)0.008a
Index diagnosis, n (%)   
Acute MI3 (3.0)6 (3.8)0.806
CHF6 (6.0)20 (12.5)0.111
Pneumonia7 (7.0)9 (5.6)0.572
COPD6 (6.0)6 (8.8)0.484
Discharged home without clinical services, n (%)c23 (23.0)56 (35.0)0.041a
Hospital site   
Hospital 115 (15.0)43 (26.9)<0.001a
Hospital 220 (20.0)26 (16.3)
Hospital 315 (15.0)23 (14.4)
Hospital 42 (2.0)48 (30.0)
Hospital 548 (48.0)20 (12.5)

Patients who identified themselves as being unable to control important things in their lives were 65% less likely to consent to the behavioral intervention than those in control (odds ratio [OR]: 0.35, 95% confidence interval [CI]: 0.14‐0.92), and those who did not feel confident about recovering were 83% less likely to consent (OR: 0.17, 95% CI: 0.06‐0.45). Individuals who were confused by any question were 89% less likely to consent (OR: 0.11, 95% CI: 0.05‐0.24). Individuals who answered the medication question incorrectly were 3 times more likely to consent (OR: 3.82, 95% CI: 1.12‐13.03). There were no significant differences in consent for feeling overwhelmed (difficulties piling up) or for having discussed advance care planning with family members or doctors.

We had insufficient power to detect significant differences in posthospital utilization (including hospital readmission, emergency‐department use, and receipt of home health), based on screening‐question responses (data not shown).

DISCUSSION

We find that patients who declined to participate in the behavioral intervention (eligible nonparticipants) differed from participants in 3 important ways: perceived stress, recovery expectation, and health literacy. As hypothesized, patients with higher perceived stress and lower recovery expectation were less likely to consent to the behavioral intervention, even after adjusting for demographic and healthcare‐utilization differences. Contrary to our hypothesis, patients who incorrectly answered the medication question were more likely to consent to the intervention than those who correctly answered.

Characterizing nonparticipants and participants can offer important insight into the limitations of the research that informs clinical guidelines and behavioral interventions. Such characteristics could also indicate how to better engage patients in interventions or other aspects of their care, if associated with lower rates of adherence to recommended health behaviors or treatment plans. For example, self‐efficacy (closely related to perceived stress) and hopelessness regarding clinical outcomes (similar to low recovery expectation in the present study) are associated with nonadherence to medication plans and other care in some populations.[5, 6] Other more extreme stress, like that following a major medical event, has also been associated with a lower rate of adherence to medication regimens and a resulting higher rate of hospital readmission and mortality.[19, 20] People with low health literacy (compared with adequate health literacy) are more likely to report being confused about their medications, requesting help to read medication labels and missing appointments due to trouble reading reminder cards.[9] Identifying these characteristics may assist providers in helping patients address adherence barriers by first accurately identifying the root of patient issues (eg, where the lack of confidence in recovery is rooted in lack of resources or social support), then potentially referring to community resources where possible. For example, some states (including Rhode Island, this study's location) may have Aging and Disability Resource Centers dedicated to linking elderly people with transportation, decision support, and other resources to support quality care.

The association between health literacy and intervention participation remains uncertain. Our question, which assessed interpretation of a prescription label as a health‐literacy proxy, may have given patients insight into their limited health literacy that motivated them to accept the subsequent behavioral intervention. Others have found that lowerhealth literacy patients want their providers to know that they did not understand some health words,[9] though they may be less likely to ask questions, request additional services, or seek new information during a medical encounter.[21] In our study, those who correctly answered the medication‐label question were almost mutually exclusive from those who were otherwise stressed (12% overlap; data not shown). Thus, patients who correctly answer this question may correctly realize that they do not need the support offered by the behavioral intervention and decline to participate. For other patients, perceived stress and poor recovery expectations may be more immediate and important determinants of declination, with patients too stressed to volunteer for another task, even if it involves much‐needed assistance.

The frequency with which patients were confused by the questions merits further comment and may also be driven by stress. Though each question seeks to identify the impact of a specific construct (Table 1), being confused by any question may reflect a more general (or subacute) level of cognitive impairment or generalized low health literacy not limited to the applied numeracy of the medication‐label question. We excluded confused responses to demonstrate more clearly the impact of each individual construct.

The impact of these characteristics may be affected by study design or other characteristics. One of the few studies to examine (via RCT) how methods affect consent found that participation decreased with increasing complexity of the consent process: written consent yielded the lowest participation, limited written consent was higher, and verbal consent was the highest.[10] Other tactics to increase consent include monetary incentives,[22] culturally sensitive materials,[7] telephone reminders,[23] an opt‐out instead of opt‐in approach,[23] and an open design where participants know which treatment they are receiving.[23] We do not know how these tactics relate to the characteristics captured in our screening questions, although other characteristics we measured, such as patients' self‐identified race, have been associated with intervention participation and access to care,[8, 24, 25] and patients who perceive that the benefit of the intervention outweighs expected risks and time requirements are more likely to consent.[4] We intentionally minimized the number of screening questions to encourage participation. The high rate of consent to our screening questions compared with consent to the (more involved) behavioral intervention reveals how sensitive patients are to the perceived invasiveness of an intervention.

We note several limitations. First, overall generalizability is limited due to our small sample size, use of consecutive convenience sampling, and exclusion criteria (eg, patients discharged to long‐term or skilled nursing care). And, these results may not apply to patients who are not hospitalized; hospitalized patients may have different motivations and stressors regarding their involvement in their care. Additionally, although we included as many people with mild cognitive impairment as possible by proxy through caregivers, we excluded some that did not have caregivers, potentially undermining the accuracy of how cognition impacts the choice to accept the behavioral intervention. Because researchers often explicitly exclude individuals based on cognitive impairment, differences between recruited subjects and the population at large may be particularly high among elderly patients, where up to half of the eligible population may be affected by cognitive impairment.[26] Further research into successfully engaging caregivers as a way to reach otherwise‐excluded patients with cognitive impairment can help to mitigate threats to generalizability. Finally, our screening questions are based on validated questions, but we rearranged our question wording, simplified answer choices, and removed them from their original context. Thus, the questions were not validated in our population or when administered in this manner. Although we conducted cognitive testing, further validity and reliability testing are necessary to translate these questions into a general screening tool. The medication‐label question also requires revision; in data collection and analysis, we assume that patients who were unable to answer (needs glasses, too tired, etc.) were masking an inability to respond correctly. Though the use of this excuse is cited in the literature,[17] we cannot be certain that our treatment of it in these screening questions is generalizable. Generalizability also applies to how we group responses. Isolating the most negative response (by grouping the middle answer with the most positive answer) most specifically identifies individuals more likely to need assistance and is therefore clinically pertinent, but this also potentially fails to identify individuals who also need help but do not choose the more extreme answer. Further research to refine the screening questions might also consider the timeframe of the perceived stress questions (past week rather than past month); this timeframe may be specific to the acute medical situation rather than general or unrelated perceived stress. Though this study cannot test this hypothesis, individuals with higher pre‐illness perceived stress may be more interested in addressing the issues that were stressors prior to acute illness, rather than the offered behavioral intervention. Additionally, some of the questions were highly correlated (Q1 and Q2) and indicate a potential for shortening the screening questionnaire.

Still, these findings further the discussion of how to identify and consent hospitalized patients for participation in behavioral interventions, both for research and for routine clinical care. Researchers should specifically consider how to engage individuals who are stressed and are not confident about recovery to improve reach and effectiveness. For example, interventions should prospectively collect data on stress and confidence in recovery and include protocols to support people who are positively identified with these characteristics. These characteristics may also offer insight into improving patient and caregiver engagement; more research is needed into characteristics related to patients' willingness to seek assistance in care. We are not the first to suggest that characteristics not observed in medical charts may impact patient completion or response to behavioral interventions,[27, 28] and considering differences between participants and eligible nonparticipants in clinical care delivery and interventions can strengthen the evidence base for clinical improvements, particularly related to patient self‐management. The implications are useful for both practicing clinicians and larger systems examining the comparativeness of patient interventions and generalizing results from RCTs.

Acknowledgments

The authors thank Phil Clark, PhD, and the SENIOR Project (Study of Exercise and Nutrition in Older Rhode Islanders) research team at the University of Rhode Island for formulating 1 of the screening questions, and Marissa Meucci for her assistance with the cognitive testing and formative research for the screening questions.

Disclosures

The analyses on which this study is based were performed by Healthcentric Advisors under contract HHSM 5002011‐RI10C, titled Utilization and Quality Control Peer Review for the State of Rhode Island, sponsored by the Centers for Medicare and Medicaid Services, US Department of Health and Human Services. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. The authors report no conflicts of interest.

Randomized controlled trials (RCTs) generally provide the most rigorous evidence for clinical practice guidelines and quality‐improvement initiatives. However, 2 major shortcomings limit the ability to broadly apply these results to the general population. One has to do with sampling bias (due to subject consent and inclusion/exclusion criteria) and the other with potential differences between participants and eligible nonparticipants. The latter may be of particular importance in trials of behavioral interventions (rather than medication trials), which often require substantial participant effort.

First, individuals who provide written consent to participate in RCTs of behavioral interventions typically represent a minority of those approached and therefore may not be representative of the target population. Although the consenting proportion is often not disclosed, some estimate that only 35%50% of eligible subjects typically participate.[1, 2, 3] These estimates mirror the authors' prior experience with a 55.2% consent rate among subjects approached for a Medicare quality‐improvement behavioral intervention.[3] Though the literature is sparse, it suggests that eligible individuals who decline to participate in either interventions or usual care may differ from participants in their perception of intervention risks and effort[4] or in their levels of self‐efficacy or confidence in recovery.[5, 6] Relatively low enrollment rates mean that much of the population remains unstudied; however, evidence‐based interventions are often applied to populations broader than those included in the original analyses.

Additionally, although some nonparticipants may correctly decide that they do not need the assistance of a proposed intervention and therefore decline to participate, others may inappropriately judge the intervention's potential benefit and applicability when declining. In other words, electing to not participate in a study, despite eligibility, may reflect more than a refusal of inconvenience, disinterest, or desire to contribute to knowledge; for some individuals it may offer a proxy statement about health knowledge, personal beliefs, attitudes, and needs, including perceived stress,[5] cultural relevance,[7, 8] and literacy/health literacy.[9, 10] Characterizing these patients can help us to modify recruitment approaches and improve participation so that participants better represent the target population. If these differences also relate to patients' adherence to care recommendations, a more nuanced understanding could improve ways to identify and engage potentially nonadherent patients to improve health outcomes.

We hypothesized that we could identify characteristics that differ between behavioral‐intervention participants and eligible nonparticipants using a set of screening questions. We proposed that these characteristics, including constructs related to perceived stress, recovery expectation, health literacy, insight, and action into advance care planning and confusion by any question, would predict the likelihood of consenting to a behavioral intervention requiring substantial subject engagement. Some of these characteristics may relate to adherence to preventive care or treatment recommendations. We did not specifically hypothesize about the distribution of demographic differences.

METHODS

Study Design

Prospective observational study conducted within a larger behavioral intervention.

Screening Question Design

We adapted our screening questions from several previously validated surveys, selecting questions related to perceived stress and self‐efficacy,[11] recovery expectations, health literacy/medication label interpretation,[12] and discussing advance directives (Table 1). Some of these characteristics may relate to adherence to preventive care or treatment programs[13, 14] or to clinical outcomes.[15, 16]

Screening Questions
Screening QuestionAdapted From Original Validated QuestionSourceConstruct
In the last week, how often have you felt that you are unable to control the important things in your life? (Rarely, sometimes, almost always)In the last month, how often have you felt that you were unable to control the important things in your life? (Never, almost never, sometimes, fairly often, very often)Adapted from the Perceived Stress Scale (PSS‐14).[11]Perceived stress, self‐efficacy
In the last week, how often have you felt that difficulties were piling up so high that you could not overcome them? (Rarely, sometimes, almost always)In the last month, how often have you felt difficulties were piling up so high that you could not overcome them? (Never, almost never, sometimes, fairly often, very often)Adapted from the Perceived Stress Scale (PSS‐14).[11]Perceived stress, self‐efficacy
How sure are you that you can go back to the way you felt before being hospitalized? (Not sure at all, somewhat sure, very sure) Courtesy of Phil Clark, PhD, University of Rhode Island, drawing on research on resilience. Similar questions are used in other studies, including studies of postsurgical recovery.[29, 30, 31]Recovery expectation, resilience
Even if you have not made any decisions, have you talked with your family members or doctor about what you would want for medical care if you could not speak for yourself? (Yes, no) Based on consumer‐targeted materials on advance care planning. http://www.agingwithdignity.org/five‐wishes.php; http://www.nhqualitycampaign.org/files/emmpguides/6_AdvanceCarePlanning_TAW_Guide.pdfAdvance care planning
(Show patient a picture of prescription label.) How many times a day should someone take this medicine? (Correct, incorrect)(Show patient a picture of ice cream label.) If you eat the entire container, how many calories will you eat? (Correct, incorrect)Adapted from Pfizer's Clear Health Communication: The Newest Vital Sign.[12]Health literacy

Prior to administering the screening questions, we performed cognitive testing with residents of an assisted‐living facility (N=10), a population that resembles our study's target population. In response to cognitive testing, we eliminated a question not interpreted easily by any of the participants, identified wording changes to clarify questions, simplified answer choices for ease of response (especially because questions are delivered verbally), and moved the most complicated (and potentially most embarrassing) question to the end, with more straightforward questions toward the beginning. We also substantially enlarged the image of a standard medication label to improve readability. Our final tool included 5 questions (Table 1).

The final instrument prompted coaches to record patient confusion. Additionally, the advance‐directive question included a refused to answer option and the medication question included unable to answer (needs glasses, too tired, etc.), a potential marker of low health literacy if used as an excuse to avoid embarrassment.[17]

Setting

We recruited inpatients at 5 Rhode Island acute‐care hospitals, including 1 community hospital, 3 teaching hospitals, and a tertiary‐care center and teaching hospital, ranging from 174 beds to 719 beds. Recruitment occurred from November 2010 to April 2011. The hospitals' respective institutional review boards approved the screening questions.

Study Population

We recruited a convenience sample of consecutively identified hospitalized Medicare fee‐for‐service beneficiaries, identified as (1) eligible for the subsequent behavioral intervention based on inpatient census lists and (2) willing to discuss an offer for a home‐based behavioral intervention. The behavioral intervention, based on the Care Transitions Intervention and described elsewhere,[3, 18] included a home visit and 2 phone calls (each about 1 hour). Coaches used a personal health record to help patients and/or caregivers better manage their health by (1) being able to list their active medical conditions and medications and (2) understanding warning signs indicating a need to reach out for help, including getting a timely medical appointment after hospitalization. The population for the present study included individuals approached to discuss participation in the behavioral intervention who also agreed to answer the screening questions.

Inclusion/Exclusion Criteria

We included hospitalized Medicare fee‐for‐service beneficiaries. We excluded patients who were current long‐term care residents, were to be discharged to long‐term or skilled care, or had a documented hospice referral. We also excluded patients with limited English proficiency or who were judged to have inadequate cognitive function, unless a caregiver agreed to receive the intervention as a proxy. We made these exclusions when recruiting for the behavioral intervention. Because we presented the screening questions to a subset of those approached for the behavioral intervention, we did not further exclude anyone. In other words, we offered the screening questions to all 295 people we approached during this study time period (100%).

Screening‐Question Study Process

Coaches asked patients to answer the 5 screening questions immediately after offering them the opportunity to participate in the behavioral intervention, regardless of whether or not they accepted the behavioral intervention. This study examines the subset of patients approached for the behavioral intervention who verbally consented to answer the screening questions.

Data Sources and Covariates

We analyzed primary data from the screening questions and behavioral intervention (for those who consented to participate), as well as Medicare claims and Medicaid enrollment data. We matched screening‐question data from November 2010 through April 2011 with Medicare Part A claims from October 2010 through May 2011 to calculate 30‐day readmission rates.

We obtained the following information for patients offered the behavioral intervention: (1) responses to screening questions, (2) whether patients consented to the behavioral intervention, (3) exposure to the behavioral intervention, and (4) recruitment date. Medicare claims data included (1) admission and discharge dates to calculate the length of stay, (2) index diagnosis, (3) hospital, and (4) site of discharge. Medicare enrollment data provided information on (1) Medicaid/Medicare dual‐eligibility status, (2) sex, and (3) patient‐reported race. We matched data based on patient name and date of birth. Our primary outcome was consent to the behavioral intervention. Secondarily, we reviewed posthospital utilization patterns, including hospital readmission, emergency‐department use, and use of home‐health services.

Statistical Analysis

We categorized patients into 2 groups (Figure 1): participants (consented to the behavioral intervention) and nonparticipants (eligible for the behavioral intervention but declined to participate). We excluded responses for those confused by the question (no response). For the response scales never, sometimes, almost always and not at all sure, somewhat sure, very sure, we isolated the most negative response, grouping the middle and most positive responses (Table 2). For the medication‐label question, we grouped incorrect and unable to answer (needs glasses, too tired, etc.) responses. We compared demographic differences between behavioral intervention participants and nonparticipants using 2 tests (categorical variables) and Student t tests (continuous variables). We then used multivariate logistic regression to analyze differences in consent to the behavioral intervention based on screening‐question responses, adjusting for demographics that differed significantly in the bivariate comparisons.

Figure 1
Study population.
Association Between Consent to Behavioral Intervention and Screening Question Response, by Question (N=260)
Screening‐Question ResponseAdjusted OR (95% CI)P Value
  • NOTE: Abbreviations: CI, confidence interval; OR, odds ratio; Ref, reference.

  • Significant at P<0.05. Results do not add up to 260 responses in all questions due to the exclusion of confused by question from each response set.

In the last week, how often have you felt that you are unable to control the important things in your life?  
Out of control (Almost always)0.35 (0.14‐0.92)0.034a
In control (Sometimes, rarely)1.00 (Ref)
In the last week, how often have you felt that difficulties were piling up so high that you could not overcome them?  
Overwhelmed (Almost always)0.41 (0.16‐1.07)0.069
Not overwhelmed (Sometimes, rarely)1.00 (Ref)
How sure are you that you can go back to the way you felt before being hospitalized?  
Not confident (Not sure at all)0.17 (0.06‐0.45)0.001a
Confident (Somewhat sure, very sure)1.00 (Ref)
Even if you have not made any decisions, have you talked with your family members or doctor about what you would want for medical care if you could not speak for yourself?  
No0.45 (0.13‐1.64)0.227
Yes1.00 (Ref)
How many times a day should someone take this medicine? (Show patient a medication label)  
Incorrect answer3.82 (1.12‐13.03)0.033a
Correct answer1.00 (Ref)
Confused by any question?  
Yes0.11 (0.05‐0.24)0.001a
No1.00 (Ref)

The authors used SAS version 9.2 (SAS Institute, Inc., Cary, NC) for all analyses.

RESULTS

Of the 295 patients asked to complete the screening questions, 260 (88.1%) consented to answer the screening questions and 35 (11.9%) declined. More than half of those who answered the screening questions consented to participate in the behavioral intervention (160; 61.5%) (Figure 1). When compared with nonparticipants, participants in the behavioral intervention were younger (25.6% age 85 years vs 40% age 85 years, P=0.028), had a longer average length of hospital stay (7.9 vs 6.1 days, P=0.008), were more likely to be discharged home without clinical services (35.0% vs 23.0%, P=0.041), and were unevenly distributed between the 5 recruitment‐site hospitals, coming primarily from the teaching hospitals (P<0.001) (Table 3). There were no significant differences based on race, sex, dual‐eligible Medicare/Medicaid status, presence of a caregiver, or index diagnosis.

Patient Characteristics by Behavioral Intervention Consent Status (N=260)
Patient CharacteristicsDeclined (n=100)Consented (n=160)P Value
  • NOTE: Abbreviations: CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; MI, myocardial infarction; SD, standard deviation.

  • Significant at P<0.05.

  • Eligible for both Medicare and Medicaid benefits.

  • Discharged home with no planned clinical services, as opposed to being discharged to home health care, hospice, or skilled care. Hospitals 35 are teaching hospitals

Male, n (%)34 (34.0)52 (32.5)0.803
Race, n (%)   
White94 (94.0)151 (94.4)0.691
Black2 (2.0)5 (3.1)
Other4 (4.0)4 (2.5)
Age, n (%), y   
<6517 (17.0)23 (14.4)0.028a
657414 (14.0)42 (26.3)
758429 (29.0)54 (33.8)
8540 (40.0)41 (25.6)
Dual eligible, n (%)b11 (11.0)24 (15.0)0.358
Caregiver present, n (%)17 (17.0)34 (21.3)0.401
Length of stay, mean (SD), d6.1 (4.1)7.9 (4.8)0.008a
Index diagnosis, n (%)   
Acute MI3 (3.0)6 (3.8)0.806
CHF6 (6.0)20 (12.5)0.111
Pneumonia7 (7.0)9 (5.6)0.572
COPD6 (6.0)6 (8.8)0.484
Discharged home without clinical services, n (%)c23 (23.0)56 (35.0)0.041a
Hospital site   
Hospital 115 (15.0)43 (26.9)<0.001a
Hospital 220 (20.0)26 (16.3)
Hospital 315 (15.0)23 (14.4)
Hospital 42 (2.0)48 (30.0)
Hospital 548 (48.0)20 (12.5)

Patients who identified themselves as being unable to control important things in their lives were 65% less likely to consent to the behavioral intervention than those in control (odds ratio [OR]: 0.35, 95% confidence interval [CI]: 0.14‐0.92), and those who did not feel confident about recovering were 83% less likely to consent (OR: 0.17, 95% CI: 0.06‐0.45). Individuals who were confused by any question were 89% less likely to consent (OR: 0.11, 95% CI: 0.05‐0.24). Individuals who answered the medication question incorrectly were 3 times more likely to consent (OR: 3.82, 95% CI: 1.12‐13.03). There were no significant differences in consent for feeling overwhelmed (difficulties piling up) or for having discussed advance care planning with family members or doctors.

We had insufficient power to detect significant differences in posthospital utilization (including hospital readmission, emergency‐department use, and receipt of home health), based on screening‐question responses (data not shown).

DISCUSSION

We find that patients who declined to participate in the behavioral intervention (eligible nonparticipants) differed from participants in 3 important ways: perceived stress, recovery expectation, and health literacy. As hypothesized, patients with higher perceived stress and lower recovery expectation were less likely to consent to the behavioral intervention, even after adjusting for demographic and healthcare‐utilization differences. Contrary to our hypothesis, patients who incorrectly answered the medication question were more likely to consent to the intervention than those who correctly answered.

Characterizing nonparticipants and participants can offer important insight into the limitations of the research that informs clinical guidelines and behavioral interventions. Such characteristics could also indicate how to better engage patients in interventions or other aspects of their care, if associated with lower rates of adherence to recommended health behaviors or treatment plans. For example, self‐efficacy (closely related to perceived stress) and hopelessness regarding clinical outcomes (similar to low recovery expectation in the present study) are associated with nonadherence to medication plans and other care in some populations.[5, 6] Other more extreme stress, like that following a major medical event, has also been associated with a lower rate of adherence to medication regimens and a resulting higher rate of hospital readmission and mortality.[19, 20] People with low health literacy (compared with adequate health literacy) are more likely to report being confused about their medications, requesting help to read medication labels and missing appointments due to trouble reading reminder cards.[9] Identifying these characteristics may assist providers in helping patients address adherence barriers by first accurately identifying the root of patient issues (eg, where the lack of confidence in recovery is rooted in lack of resources or social support), then potentially referring to community resources where possible. For example, some states (including Rhode Island, this study's location) may have Aging and Disability Resource Centers dedicated to linking elderly people with transportation, decision support, and other resources to support quality care.

The association between health literacy and intervention participation remains uncertain. Our question, which assessed interpretation of a prescription label as a health‐literacy proxy, may have given patients insight into their limited health literacy that motivated them to accept the subsequent behavioral intervention. Others have found that lowerhealth literacy patients want their providers to know that they did not understand some health words,[9] though they may be less likely to ask questions, request additional services, or seek new information during a medical encounter.[21] In our study, those who correctly answered the medication‐label question were almost mutually exclusive from those who were otherwise stressed (12% overlap; data not shown). Thus, patients who correctly answer this question may correctly realize that they do not need the support offered by the behavioral intervention and decline to participate. For other patients, perceived stress and poor recovery expectations may be more immediate and important determinants of declination, with patients too stressed to volunteer for another task, even if it involves much‐needed assistance.

The frequency with which patients were confused by the questions merits further comment and may also be driven by stress. Though each question seeks to identify the impact of a specific construct (Table 1), being confused by any question may reflect a more general (or subacute) level of cognitive impairment or generalized low health literacy not limited to the applied numeracy of the medication‐label question. We excluded confused responses to demonstrate more clearly the impact of each individual construct.

The impact of these characteristics may be affected by study design or other characteristics. One of the few studies to examine (via RCT) how methods affect consent found that participation decreased with increasing complexity of the consent process: written consent yielded the lowest participation, limited written consent was higher, and verbal consent was the highest.[10] Other tactics to increase consent include monetary incentives,[22] culturally sensitive materials,[7] telephone reminders,[23] an opt‐out instead of opt‐in approach,[23] and an open design where participants know which treatment they are receiving.[23] We do not know how these tactics relate to the characteristics captured in our screening questions, although other characteristics we measured, such as patients' self‐identified race, have been associated with intervention participation and access to care,[8, 24, 25] and patients who perceive that the benefit of the intervention outweighs expected risks and time requirements are more likely to consent.[4] We intentionally minimized the number of screening questions to encourage participation. The high rate of consent to our screening questions compared with consent to the (more involved) behavioral intervention reveals how sensitive patients are to the perceived invasiveness of an intervention.

We note several limitations. First, overall generalizability is limited due to our small sample size, use of consecutive convenience sampling, and exclusion criteria (eg, patients discharged to long‐term or skilled nursing care). And, these results may not apply to patients who are not hospitalized; hospitalized patients may have different motivations and stressors regarding their involvement in their care. Additionally, although we included as many people with mild cognitive impairment as possible by proxy through caregivers, we excluded some that did not have caregivers, potentially undermining the accuracy of how cognition impacts the choice to accept the behavioral intervention. Because researchers often explicitly exclude individuals based on cognitive impairment, differences between recruited subjects and the population at large may be particularly high among elderly patients, where up to half of the eligible population may be affected by cognitive impairment.[26] Further research into successfully engaging caregivers as a way to reach otherwise‐excluded patients with cognitive impairment can help to mitigate threats to generalizability. Finally, our screening questions are based on validated questions, but we rearranged our question wording, simplified answer choices, and removed them from their original context. Thus, the questions were not validated in our population or when administered in this manner. Although we conducted cognitive testing, further validity and reliability testing are necessary to translate these questions into a general screening tool. The medication‐label question also requires revision; in data collection and analysis, we assume that patients who were unable to answer (needs glasses, too tired, etc.) were masking an inability to respond correctly. Though the use of this excuse is cited in the literature,[17] we cannot be certain that our treatment of it in these screening questions is generalizable. Generalizability also applies to how we group responses. Isolating the most negative response (by grouping the middle answer with the most positive answer) most specifically identifies individuals more likely to need assistance and is therefore clinically pertinent, but this also potentially fails to identify individuals who also need help but do not choose the more extreme answer. Further research to refine the screening questions might also consider the timeframe of the perceived stress questions (past week rather than past month); this timeframe may be specific to the acute medical situation rather than general or unrelated perceived stress. Though this study cannot test this hypothesis, individuals with higher pre‐illness perceived stress may be more interested in addressing the issues that were stressors prior to acute illness, rather than the offered behavioral intervention. Additionally, some of the questions were highly correlated (Q1 and Q2) and indicate a potential for shortening the screening questionnaire.

Still, these findings further the discussion of how to identify and consent hospitalized patients for participation in behavioral interventions, both for research and for routine clinical care. Researchers should specifically consider how to engage individuals who are stressed and are not confident about recovery to improve reach and effectiveness. For example, interventions should prospectively collect data on stress and confidence in recovery and include protocols to support people who are positively identified with these characteristics. These characteristics may also offer insight into improving patient and caregiver engagement; more research is needed into characteristics related to patients' willingness to seek assistance in care. We are not the first to suggest that characteristics not observed in medical charts may impact patient completion or response to behavioral interventions,[27, 28] and considering differences between participants and eligible nonparticipants in clinical care delivery and interventions can strengthen the evidence base for clinical improvements, particularly related to patient self‐management. The implications are useful for both practicing clinicians and larger systems examining the comparativeness of patient interventions and generalizing results from RCTs.

Acknowledgments

The authors thank Phil Clark, PhD, and the SENIOR Project (Study of Exercise and Nutrition in Older Rhode Islanders) research team at the University of Rhode Island for formulating 1 of the screening questions, and Marissa Meucci for her assistance with the cognitive testing and formative research for the screening questions.

Disclosures

The analyses on which this study is based were performed by Healthcentric Advisors under contract HHSM 5002011‐RI10C, titled Utilization and Quality Control Peer Review for the State of Rhode Island, sponsored by the Centers for Medicare and Medicaid Services, US Department of Health and Human Services. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. The authors report no conflicts of interest.

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  27. Brazier JE, Dixon S, Ratcliffe J. The role of patient preferences in cost‐effectiveness analysis: a conflict of values? Pharmacoeconomics. 2009;27(9):705712.
  28. Fan VS, Gaziano JM, Lew R, et al. A comprehensive care management program to prevent chronic obstructive pulmonary disease hospitalizations. Ann Intern Med. 2012;156(10):673683.
  29. Flood AB, Lorence DP, Ding J, McPherson K, Black NA. The role of expectations in patients' reports of post‐operative outcomes and improvement following therapy. Med Care. 1993;31:10431056.
  30. Borkan JM, Quirk M. Expectations and outcomes after hip fracture among the elderly. Int J Aging Hum Dev. 1992;34:339350.
  31. Petrie KJ, Weinman J, Sharpe N, Buckley J. Role of patients' view of their illness in predicting return to work and functioning after myocardial infarction: longitudinal study. BMJ. 1996;312:11911194.
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  28. Fan VS, Gaziano JM, Lew R, et al. A comprehensive care management program to prevent chronic obstructive pulmonary disease hospitalizations. Ann Intern Med. 2012;156(10):673683.
  29. Flood AB, Lorence DP, Ding J, McPherson K, Black NA. The role of expectations in patients' reports of post‐operative outcomes and improvement following therapy. Med Care. 1993;31:10431056.
  30. Borkan JM, Quirk M. Expectations and outcomes after hip fracture among the elderly. Int J Aging Hum Dev. 1992;34:339350.
  31. Petrie KJ, Weinman J, Sharpe N, Buckley J. Role of patients' view of their illness in predicting return to work and functioning after myocardial infarction: longitudinal study. BMJ. 1996;312:11911194.
Issue
Journal of Hospital Medicine - 8(4)
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Journal of Hospital Medicine - 8(4)
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208-214
Page Number
208-214
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Recruiting hospitalized patients for research: How do participants differ from eligible nonparticipants?
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Recruiting hospitalized patients for research: How do participants differ from eligible nonparticipants?
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Address for correspondence and reprint requests: Stefan Gravenstein, MD, MPH, Case Western Reserve University, 11100 Euclid Avenue, Mailstop HAN6095, Cleveland, OH 44106; Telephone: 401–528‐3200; Fax: 401–528‐3210; E‐mail: [email protected]
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