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Hospitals Seek Ways to Defuse Angry Doctors
Everyone is prone to an angry outburst from time to time, and doctors are no exception. With well-documented, negative effects on morale, nurse retention, and patient safety, it's safe to say anger issues crop up from time to time among the nearly 40,000 practicing hospitalists throughout the U.S.
A recent article in Kaiser Health News describes efforts by hospitals to deal with physicians' tirades, such as a three-day counseling program developed at Vanderbilt University in Nashville, Tenn.
"All physicians need to be aware that there should be a 'zero tolerance' attitude for disruptive behavior, hospitalists included, and that disruptive behavior undermines a culture of safety, and therefore can put patients in danger," says Danielle Scheurer, MD, MSCR, SFHM, hospitalist and chief quality officer at Medical University of South Carolina in Charleston and physician editor of The Hospitalist.
In 2009, The Joint Commission issued a sentinel alert about intimidating and disruptive behaviors by physicians and the ways in which hospitals can address the issue.
The problem is not unique to any physician specialty, including hospitalists, says Alan Rosenstein, MD, an internist and disruptive behavior researcher based in San Francisco. A physician's training or personality might contribute to angry outbursts, but excessive workloads will cause pressure, stress, and burnout, which can lead to poor behavior.
"Hospitals can no longer afford to look the other way," Dr. Rosenstein says. "I look at physicians as a precious resource. The organizations they're affiliated with need to be more proactive and empathetic, intervening before the problem reaches the stage of requiring discipline through techniques such as coaching and stress management." TH
Visit our website for more information about the impact of workloads on hospitalists.
Everyone is prone to an angry outburst from time to time, and doctors are no exception. With well-documented, negative effects on morale, nurse retention, and patient safety, it's safe to say anger issues crop up from time to time among the nearly 40,000 practicing hospitalists throughout the U.S.
A recent article in Kaiser Health News describes efforts by hospitals to deal with physicians' tirades, such as a three-day counseling program developed at Vanderbilt University in Nashville, Tenn.
"All physicians need to be aware that there should be a 'zero tolerance' attitude for disruptive behavior, hospitalists included, and that disruptive behavior undermines a culture of safety, and therefore can put patients in danger," says Danielle Scheurer, MD, MSCR, SFHM, hospitalist and chief quality officer at Medical University of South Carolina in Charleston and physician editor of The Hospitalist.
In 2009, The Joint Commission issued a sentinel alert about intimidating and disruptive behaviors by physicians and the ways in which hospitals can address the issue.
The problem is not unique to any physician specialty, including hospitalists, says Alan Rosenstein, MD, an internist and disruptive behavior researcher based in San Francisco. A physician's training or personality might contribute to angry outbursts, but excessive workloads will cause pressure, stress, and burnout, which can lead to poor behavior.
"Hospitals can no longer afford to look the other way," Dr. Rosenstein says. "I look at physicians as a precious resource. The organizations they're affiliated with need to be more proactive and empathetic, intervening before the problem reaches the stage of requiring discipline through techniques such as coaching and stress management." TH
Visit our website for more information about the impact of workloads on hospitalists.
Everyone is prone to an angry outburst from time to time, and doctors are no exception. With well-documented, negative effects on morale, nurse retention, and patient safety, it's safe to say anger issues crop up from time to time among the nearly 40,000 practicing hospitalists throughout the U.S.
A recent article in Kaiser Health News describes efforts by hospitals to deal with physicians' tirades, such as a three-day counseling program developed at Vanderbilt University in Nashville, Tenn.
"All physicians need to be aware that there should be a 'zero tolerance' attitude for disruptive behavior, hospitalists included, and that disruptive behavior undermines a culture of safety, and therefore can put patients in danger," says Danielle Scheurer, MD, MSCR, SFHM, hospitalist and chief quality officer at Medical University of South Carolina in Charleston and physician editor of The Hospitalist.
In 2009, The Joint Commission issued a sentinel alert about intimidating and disruptive behaviors by physicians and the ways in which hospitals can address the issue.
The problem is not unique to any physician specialty, including hospitalists, says Alan Rosenstein, MD, an internist and disruptive behavior researcher based in San Francisco. A physician's training or personality might contribute to angry outbursts, but excessive workloads will cause pressure, stress, and burnout, which can lead to poor behavior.
"Hospitals can no longer afford to look the other way," Dr. Rosenstein says. "I look at physicians as a precious resource. The organizations they're affiliated with need to be more proactive and empathetic, intervening before the problem reaches the stage of requiring discipline through techniques such as coaching and stress management." TH
Visit our website for more information about the impact of workloads on hospitalists.
VIDEO: Five Reasons You Should Attend HM13 in Washington, D.C.
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.
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.
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.
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].
Early Death or 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.
| Variable | Value | No Death/Readmission, n=38,189 | Death/Readmission, n=2,638 | Overall, N=40,827 | |||
|---|---|---|---|---|---|---|---|
| |||||||
| Mean age (SD), y | 57.39 (18.3) | 67.17 (17.2) | 58.02 (18.4) | ||||
| Female sex | 20,044 | 52.5% | 1,291 | 48.9% | 21,335 | 52.3% | |
| Charlson index | 0 | 28,908 | 75.7% | 1,238 | 46.9% | 30,146 | 73.8% |
| 1 | 450 | 11.7% | 362 | 13.7% | 4,812 | 11.8% | |
| 2 | 2,668 | 7.0% | 427 | 16.2% | 3,095 | 7.6% | |
| 3+ | 2,163 | 5.7% | 611 | 23.2% | 2,774 | 6.8% | |
| ED visits in previous 6 moths | 0 | 24,599 | 64.4% | 1,210 | 45.9% | 25,809 | 63.2% |
| 12 | 11,262 | 29.5% | 1,008 | 38.2% | 12,270 | 30.1% | |
| 3+ | 2,328 | 6.1% | 420 | 15.9% | 2,748 | 6.7% | |
| Urgent hospitalizations, previous year | 0 | 33,729 | 88.3% | 1,796 | 68.1% | 35,525 | 87.0% |
| 1 | 3,425 | 9.0% | 525 | 19.9% | 3,950 | 9.7% | |
| 1+ | 1,035 | 2.7% | 317 | 12.0% | 1,352 | 3.3% | |
| Elective hospitalizations, previous year | 0 | 35,988 | 94.2% | 2,389 | 90.6% | 38,377 | 94.0% |
| 1 | 1,998 | 5.2% | 213 | 8.1% | 2,211 | 5.4% | |
| 2+ | 203 | 0.5% | 36 | 1.4% | 239 | 0.6% | |
| Hospital type | Nonteaching, large | 20,554 | 53.8% | 1,334 | 50.6% | 21,888 | 53.6% |
| Nonteaching, small | 5,239 | 13.7% | 487 | 18.5% | 5726 | 14.0% | |
| Teaching | 12,396 | 32.5% | 817 | 31.0% | 13,213 | 32.4% | |
| Urgent admit | 23,769 | 62.2% | 2,223 | 84.3% | 25,992 | 63.7% | |
| LOS rounded to nearest day, median (IQR) | 3 (26) | 5 (311) | 3 (26) | ||||
| Any hospital days on ALC | 0 | 646 | 1.7% | 127 | 4.8% | 773 | 1.9% |
| CMG score of index admission | 0 | 27,257 | 71.4% | 1,594 | 60.4% | 28,851 | 70.7% |
| 1+ | 5,218 | 13.7% | 948 | 35.9% | 6,166 | 15.1% | |
| <0 | 5,714 | 15.0% | 96 | 3.6% | 5,810 | 14.2% | |
| LACE+ score of index admission, median (IQR) | 31 (1848) | 61 (4175) | 32 (1951) | ||||
| Household‐income quintile | 1 (poorest) | 7,798 | 20.4% | 621 | 23.5% | 8,419 | 20.6% |
| 2 | 7,812 | 20.5% | 586 | 22.2% | 8,398 | 20.6% | |
| 3 | 7,557 | 19.8% | 484 | 18.3% | 8,041 | 19.7% | |
| 4 | 7,561 | 19.8% | 500 | 19.0% | 8,061 | 19.7% | |
| 5 (richest) | 7,461 | 19.5% | 447 | 16.9% | 7,908 | 19.4% | |
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 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] | |
| ||||||
| 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%) |
| 2 | 21/1,627 (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%) |
| 3 | 18/1,761 (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%) |
| 4 | 27/1,851 (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.

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.
- Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
- , , , et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):5–99.
- , , . Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406–414.
- , , , . Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
- , , , , . Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363–372.
- , , , 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):981–988.
- , . Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):1643–1654.
- , . Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389–393.
- , , , et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.
- , , , , , . Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811–817.
- Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:1–64.
- , , . 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):80–89.
- . PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
- , , . Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80–e89.
- , . Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287–290.
- , , , . 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.
- , , , . Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 2469–2475.
- , . Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111–122.
- , , , et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , , , . Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251.
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.
| Variable | Value | No Death/Readmission, n=38,189 | Death/Readmission, n=2,638 | Overall, N=40,827 | |||
|---|---|---|---|---|---|---|---|
| |||||||
| Mean age (SD), y | 57.39 (18.3) | 67.17 (17.2) | 58.02 (18.4) | ||||
| Female sex | 20,044 | 52.5% | 1,291 | 48.9% | 21,335 | 52.3% | |
| Charlson index | 0 | 28,908 | 75.7% | 1,238 | 46.9% | 30,146 | 73.8% |
| 1 | 450 | 11.7% | 362 | 13.7% | 4,812 | 11.8% | |
| 2 | 2,668 | 7.0% | 427 | 16.2% | 3,095 | 7.6% | |
| 3+ | 2,163 | 5.7% | 611 | 23.2% | 2,774 | 6.8% | |
| ED visits in previous 6 moths | 0 | 24,599 | 64.4% | 1,210 | 45.9% | 25,809 | 63.2% |
| 12 | 11,262 | 29.5% | 1,008 | 38.2% | 12,270 | 30.1% | |
| 3+ | 2,328 | 6.1% | 420 | 15.9% | 2,748 | 6.7% | |
| Urgent hospitalizations, previous year | 0 | 33,729 | 88.3% | 1,796 | 68.1% | 35,525 | 87.0% |
| 1 | 3,425 | 9.0% | 525 | 19.9% | 3,950 | 9.7% | |
| 1+ | 1,035 | 2.7% | 317 | 12.0% | 1,352 | 3.3% | |
| Elective hospitalizations, previous year | 0 | 35,988 | 94.2% | 2,389 | 90.6% | 38,377 | 94.0% |
| 1 | 1,998 | 5.2% | 213 | 8.1% | 2,211 | 5.4% | |
| 2+ | 203 | 0.5% | 36 | 1.4% | 239 | 0.6% | |
| Hospital type | Nonteaching, large | 20,554 | 53.8% | 1,334 | 50.6% | 21,888 | 53.6% |
| Nonteaching, small | 5,239 | 13.7% | 487 | 18.5% | 5726 | 14.0% | |
| Teaching | 12,396 | 32.5% | 817 | 31.0% | 13,213 | 32.4% | |
| Urgent admit | 23,769 | 62.2% | 2,223 | 84.3% | 25,992 | 63.7% | |
| LOS rounded to nearest day, median (IQR) | 3 (26) | 5 (311) | 3 (26) | ||||
| Any hospital days on ALC | 0 | 646 | 1.7% | 127 | 4.8% | 773 | 1.9% |
| CMG score of index admission | 0 | 27,257 | 71.4% | 1,594 | 60.4% | 28,851 | 70.7% |
| 1+ | 5,218 | 13.7% | 948 | 35.9% | 6,166 | 15.1% | |
| <0 | 5,714 | 15.0% | 96 | 3.6% | 5,810 | 14.2% | |
| LACE+ score of index admission, median (IQR) | 31 (1848) | 61 (4175) | 32 (1951) | ||||
| Household‐income quintile | 1 (poorest) | 7,798 | 20.4% | 621 | 23.5% | 8,419 | 20.6% |
| 2 | 7,812 | 20.5% | 586 | 22.2% | 8,398 | 20.6% | |
| 3 | 7,557 | 19.8% | 484 | 18.3% | 8,041 | 19.7% | |
| 4 | 7,561 | 19.8% | 500 | 19.0% | 8,061 | 19.7% | |
| 5 (richest) | 7,461 | 19.5% | 447 | 16.9% | 7,908 | 19.4% | |
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 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] | |
| ||||||
| 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%) |
| 2 | 21/1,627 (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%) |
| 3 | 18/1,761 (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%) |
| 4 | 27/1,851 (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.

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.
| Variable | Value | No Death/Readmission, n=38,189 | Death/Readmission, n=2,638 | Overall, N=40,827 | |||
|---|---|---|---|---|---|---|---|
| |||||||
| Mean age (SD), y | 57.39 (18.3) | 67.17 (17.2) | 58.02 (18.4) | ||||
| Female sex | 20,044 | 52.5% | 1,291 | 48.9% | 21,335 | 52.3% | |
| Charlson index | 0 | 28,908 | 75.7% | 1,238 | 46.9% | 30,146 | 73.8% |
| 1 | 450 | 11.7% | 362 | 13.7% | 4,812 | 11.8% | |
| 2 | 2,668 | 7.0% | 427 | 16.2% | 3,095 | 7.6% | |
| 3+ | 2,163 | 5.7% | 611 | 23.2% | 2,774 | 6.8% | |
| ED visits in previous 6 moths | 0 | 24,599 | 64.4% | 1,210 | 45.9% | 25,809 | 63.2% |
| 12 | 11,262 | 29.5% | 1,008 | 38.2% | 12,270 | 30.1% | |
| 3+ | 2,328 | 6.1% | 420 | 15.9% | 2,748 | 6.7% | |
| Urgent hospitalizations, previous year | 0 | 33,729 | 88.3% | 1,796 | 68.1% | 35,525 | 87.0% |
| 1 | 3,425 | 9.0% | 525 | 19.9% | 3,950 | 9.7% | |
| 1+ | 1,035 | 2.7% | 317 | 12.0% | 1,352 | 3.3% | |
| Elective hospitalizations, previous year | 0 | 35,988 | 94.2% | 2,389 | 90.6% | 38,377 | 94.0% |
| 1 | 1,998 | 5.2% | 213 | 8.1% | 2,211 | 5.4% | |
| 2+ | 203 | 0.5% | 36 | 1.4% | 239 | 0.6% | |
| Hospital type | Nonteaching, large | 20,554 | 53.8% | 1,334 | 50.6% | 21,888 | 53.6% |
| Nonteaching, small | 5,239 | 13.7% | 487 | 18.5% | 5726 | 14.0% | |
| Teaching | 12,396 | 32.5% | 817 | 31.0% | 13,213 | 32.4% | |
| Urgent admit | 23,769 | 62.2% | 2,223 | 84.3% | 25,992 | 63.7% | |
| LOS rounded to nearest day, median (IQR) | 3 (26) | 5 (311) | 3 (26) | ||||
| Any hospital days on ALC | 0 | 646 | 1.7% | 127 | 4.8% | 773 | 1.9% |
| CMG score of index admission | 0 | 27,257 | 71.4% | 1,594 | 60.4% | 28,851 | 70.7% |
| 1+ | 5,218 | 13.7% | 948 | 35.9% | 6,166 | 15.1% | |
| <0 | 5,714 | 15.0% | 96 | 3.6% | 5,810 | 14.2% | |
| LACE+ score of index admission, median (IQR) | 31 (1848) | 61 (4175) | 32 (1951) | ||||
| Household‐income quintile | 1 (poorest) | 7,798 | 20.4% | 621 | 23.5% | 8,419 | 20.6% |
| 2 | 7,812 | 20.5% | 586 | 22.2% | 8,398 | 20.6% | |
| 3 | 7,557 | 19.8% | 484 | 18.3% | 8,041 | 19.7% | |
| 4 | 7,561 | 19.8% | 500 | 19.0% | 8,061 | 19.7% | |
| 5 (richest) | 7,461 | 19.5% | 447 | 16.9% | 7,908 | 19.4% | |
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 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] | |
| ||||||
| 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%) |
| 2 | 21/1,627 (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%) |
| 3 | 18/1,761 (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%) |
| 4 | 27/1,851 (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.

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.
- Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
- , , , et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):5–99.
- , , . Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406–414.
- , , , . Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
- , , , , . Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363–372.
- , , , 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):981–988.
- , . Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):1643–1654.
- , . Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389–393.
- , , , et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.
- , , , , , . Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811–817.
- Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:1–64.
- , , . 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):80–89.
- . PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
- , , . Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80–e89.
- , . Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287–290.
- , , , . 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.
- , , , . Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 2469–2475.
- , . Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111–122.
- , , , et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , , , . Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251.
- Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press; 1995.
- , , , et al. Is income inequality a determinant of population health? Part 1: A systematic review. Milbank Q. 2004;82(1):5–99.
- , , . Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006;99(8):406–414.
- , , , . Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009;9:96.
- , , , , . Risk factors for 30‐day hospital readmission in patients ≥65 years of age. Proc (Bayl Univ Med Cent). 2008;21(4):363–372.
- , , , 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):981–988.
- , . Improving the management of care for high‐cost Medicaid patients. Health Aff (Millwood). 2007;26(6):1643–1654.
- , . Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991;6(5):389–393.
- , , , et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–219.
- , , , , , . Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811–817.
- Canadian Institute for Health Information. All‐Cause Readmission to Acute Care and Return to the Emergency Department. Ottawa, ON: Canadian Institute for Health Information; 2012:1–64.
- , , . 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):80–89.
- . PCCF Plus version 5E user's guide. Ottawa ON: Statistics Canada; 2009;82F0086‐XDB.
- , , . Derivation and validation of diagnostic score based on case‐mix groups to predict 30‐day death or urgent readmission. Open Medicine. 2012;6(3):e80–e89.
- , . Executing high‐quality care transitions: a call to do it right. J Hosp Med. 2007;2(5):287–290.
- , , , . 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.
- , , , . Patient self‐management of chronic disease in primary care. JAMA. 2002;288(19): 2469–2475.
- , . Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001;55(2):111–122.
- , , , et al. Incidence of potentially avoidable hospital readmissions and its relationship to all‐cause urgent readmissions. CMAJ. 2011;183(14):E1067–E1072.
- , , , . Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251.
Copyright © 2013 Society of Hospital Medicine
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
“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.
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
“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.
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
“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.
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.
The wizard of insurance
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.
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.
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.
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.
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.
AT THE 2013 AAN ANNUAL MEETING
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
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
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
Onco-bracketology? March Madness meets today’s practice
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.
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.
Get ready now for 2014 Medicare ACO program
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 accepted | May 1-31, 2013 |
| Applications accepted | July 1-31, 2013 |
| Application approval or denial decision | Fall 2013 |
| Start date for MSSP ACO | Jan. 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 accepted | May 1-31, 2013 |
| Applications accepted | July 1-31, 2013 |
| Application approval or denial decision | Fall 2013 |
| Start date for MSSP ACO | Jan. 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 accepted | May 1-31, 2013 |
| Applications accepted | July 1-31, 2013 |
| Application approval or denial decision | Fall 2013 |
| Start date for MSSP ACO | Jan. 1, 2014 |
Source: Centers for Medicare and Medicaid Services


