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The unmet need for postacute rehabilitation among medicare observation patients: A single-center study
As the US population ages and becomes increasingly frail, the need for rehabilitation rises. By 2030, an estimated 20% of the population will be 65 years old or older, and almost 10% will be over 75.1 About 20% of hospitalized Medicare patients receive subsequent care in postacute inpatient rehabilitation (PAIR) facilities, accounting for $31 billion in Medicare expenditures in 2014.2 Although the need for rehabilitation will continue to rise, Medicare policy restricts access to it.
Under Medicare policy, PAIR services are covered for certain hospitalized patients but not others. Hospitalized patients are either inpatients, who are billed under Medicare Part A, or outpatients, billed under Part B. When hospital length of stay (LOS) is anticipated to be less than 2 midnights, patients are admitted as outpatients under the term observation status; when longer stays are expected, patients are admitted as inpatients.3 This recently implemented time-based distinction has been criticized as arbitrary, and as potentially shifting many patients from inpatient to outpatient (observation) status.4
The distinction between inpatient and observation status has significant consequences for posthospital care. Medicare Part A covers care in skilled nursing facilities (SNFs) and acute inpatient rehabilitation facilities (IRFs); after hospitalization, inpatients have access to either, without copay. As observation patients are covered under Medicare Part B, they are technically not covered for either service after their hospital stay. IRFs sometimes accept patients from ambulatory and nonacute settings; observation patients may be accepted in rare circumstances, but they pay the Part A deductible ($1288 in 2016) to have the services covered by Medicare. SNF services are never covered for observation patients, and access to this care requires an average out-of-pocket payment of more than $10,503 per beneficiary for a typical SNF stay.5 Given that about 70% of Medicare patients fall below 300% of the federal poverty line,6 the out-of-pocket costs for PAIR services for observation patients can be prohibitive.
Although only 0.75% of community-dwelling Medicare observation patients are discharged to PAIR facilities,7 it is unclear if the need for this care is higher but remains unmet secondary to cost concerns of Medicare beneficiaries. Also unclear is whether observation patients who would benefit from this care but do not receive it end up with poorer health outcomes and therefore use more healthcare services.
The purpose of this study was to estimate the proportion of Medicare observation patients who are admitted from home and receive a recommendation for placement in a PAIR facility, and to determine the ultimate disposition of such patients. We also sought to evaluate the association between recommendation for PAIR placement, LOS, and 30-day hospital revisit rate.
METHODS
The Institutional Review Board of Christiana Care Health System (CCHS) approved this study.
Sample and Design
This was an observational study of community-dwelling Medicare patients admitted under observation status to Delaware’s CCHS, which consists of a 907-bed regional tertiary-care facility in Newark and a 241-bed community hospital in Wilmington. The study period was January 1 to December 31, 2013. We limited our sample to patients treated by hospitalists on hospital wards, as this care constitutes 80% of the care provided to observation patients at CCHS and the majority of care nationally.8 As neither SNF care nor IRF care is covered under Medicare Part B, and both would result in high out-of-pocket costs for Medicare observation patients, we combined them into a single variable, PAIR.
All data were obtained from institutional electronic medical record and administrative data systems. Study inclusion criteria were Medicare as primary insurance, admission to hospital from home, and care received at either CCHS facility. Exclusion criteria were admission from PAIR facility, long-term care facility, assisted-living facility, or inpatient psychiatric facility; death; discharge against medical advice (AMA) or to hospice, non-SNF, or inpatient psychiatric facility; and discovery (during review of case management [CM] notes) of erroneous listing of Medicare as primary insurance, or of inpatient admission (within 30 days before index observation stay) that qualified for PAIR coverage under Medicare Part A.
We reviewed the medical charts of a representative (~30%) sample of the cohort and examined physical therapy (PT) and CM notes to determine the proportions of patients with recommendations for home with no services, home-based PT, possible PAIR, and PAIR. Charts were sorted by medical record number and were reviewed in consecutive order. We coded a patient as having a recommendation for possible PAIR if the PT notes indicated the patient may benefit from PAIR but could have home PT if PAIR placement was not possible. CM notes were also reviewed for evidence of patient or family preference regarding PAIR placement. All questions about PT and CM recommendations were resolved by consensus.
Measures
For the total study sample, we calculated descriptive statistics and frequencies for demographic and administrative variables, including age, sex, race (Caucasian, African American, other), ethnicity (Hispanic/non-Hispanic), ICD-9 (International Classification of Diseases, Ninth Revision) primary diagnosis code, LOS (in hours) for index observation admission, discharge disposition (home with no services, home PT, possible PAIR, PAIR), and 30-day hospital revisit (emergency department, observation, inpatient admission). We used χ2 test, Student t test, and analysis of variance (ANOVA) to test for statistically significant differences in characteristics between the chart review subgroup and the rest of the sample and between the groups with different disposition recommendations from PT notes.
For the chart review subgroup, we used ANOVA to calculate the unadjusted association between PT recommendation and LOS. We then adjusted for potential confounders, using multivariable linear regression with PT recommendation as a predictor and LOS as the outcome, controlling for variables previously associated with increased LOS among observation patients (primary diagnosis category, age, sex).6 We also adjusted for hospitalist group to account for potential variability in care delivery. As LOS was not normally distributed, we calculated the fourth root of LOS, which resulted in a more normal distribution, and used the transformed values in the regression model. We then calculated predicted values from the regression and back-transformed these to obtain adjusted mean values for LOS.
RESULTS
Of the 1417 unique patients who had Medicare as primary insurance and were admitted under observation status to a hospitalist service during the study period (2013), 94 were excluded (Figure). Of the remaining 1323 patients, the majority were 65 years old or older, female, white, and non-Hispanic. The most common ICD-9 diagnoses were syncope and chest pain. Mean LOS was 46.7 hours (range, 0-519 h). Less than 1% of patients were discharged to PAIR. Almost 25% of patients returned to the hospital, either for an emergency department visit or for observation or inpatient stay, within 30 days (Table).
Of the 419 charts reviewed to determine the proportion of patients evaluated by PT, and their subsequent recommendations, 33 were excluded, leaving 386 (92%) for analysis (Figure). There were no significant demographic differences between the patients in the chart review subgroup and the rest of the patients (Appendix). Of the 386 patients whose charts were analyzed, 181 (46.9%) had a PT evaluation, and 17 (4.4%) received a PAIR recommendation (Figure). Of the 17 patients recommended for PAIR, 12 (70.5%) were 65 years old or older, and 1 was discharged to a PAIR facility. Of the 46 patients recommended for home PT, 29 (63%) were discharged home with no services (Table).
PT-evaluated patients had unadjusted mean LOS of 52.2 hours (discharged home with no services), 64.1 hours (home PT or possible PAIR), and 83.1 hours (PAIR) (P = 0.001). With adjustment made for variables previously associated with increased LOS for observation patients, mean LOS for patients recommended for PAIR remained higher than that for patients in the other 2 categories (Table). Patients recommended for PAIR were more likely to return to hospital within 30 days than patients recommended for home PT or possible PAIR and patients discharged home with no services (Table).
Review of CM notes revealed that, of the 17 patients recommended for PAIR, 7 would have accepted PAIR services had they been covered by Medicare, 4 preferred discharge with home health services, and 6 did not provide clear details of patient or family preference.
DISCUSSION
To our knowledge, this is the first study to use chart review to examine the proportion of observation patients who would benefit from PAIR and the relationships among these patients’ rehabilitation needs, dispositions, and outcomes. We tried to be conservative in our estimates by limiting the study population to patients admitted from home. Nevertheless, the potential need for PAIR significantly outweighed the actual use of PAIR on discharge. The study sample was consistent with nationally representative samples of observation patients in terms of proportion of patients admitted from and discharged to facilities7 and the most common ICD-9 diagnoses.9
Physical Therapy Consultations and Observation
Of the 386 patients whose charts were reviewed and analyzed, 17 (4.4%) were evaluated as medically qualifying for and potentially benefiting from PAIR. Although the rate represents a minority of patients, it is 5- to 6-fold higher than the rate of discharge to PAIR, both in our study population and in previous national samples that used administrative data.7 In some cases, the decision not to discharge the patient to PAIR reflected patient and family preference. However, in other cases, patients clearly could have benefited from PAIR and would have gone had it been covered by Medicare. The gap suggests an unmet need for PAIR among a substantial proportion of Medicare beneficiaries for whom the therapy is recommended and wanted.
Efforts to expand coverage for PAIR have been resisted. According to Medicare regulations, beneficiaries qualify for PAIR coverage if they are hospitalized as inpatients for 3 midnights or longer. Days under observation status do not count toward this requirement, even if this status is changed to inpatient.10 The Medicare Payment Advisory Commission (MedPAC) recommendation that time under observation status count toward the Medicare requirement11 has not been accepted,12 in large part because further expansion of PAIR services likely would be unaffordable to Medicare under its payment structure.13 Given our finding that the need for PAIR likely is much higher than previously anticipated, Medicare policy makers should consider broadening access to PAIR while efforts are made to rein in expenditures through payment reform.
One potential area of cost savings is more judicious use of PT evaluation for observation patients, particularly given our finding that the majority of PT consultations resulted in no further recommendations. Efforts to triage PT consultations for appropriateness have had some success, though the literature is scant.14 To improve value for Medicare, healthcare systems, and patients, researchers should rigorously evaluate approaches that maximize
Hospital Length of Stay
Our cohort’s mean hospital stay was longer than averages reported elsewhere,9 likely reflecting our selection of Medicare patients rather than a general medicine population.6 However, our cohort’s adjusted mean hospital stay was significantly longer for patients recommended for PAIR than for patients without PT needs. That out-of-pocket costs for observation patients increase dramatically as LOS goes past 48 hours6 could have significant financial implications for Medicare beneficiaries.
Return Visits
Almost 25% of our observation patients returned to hospital within 30 days. There was a significant trend toward increased rehospitalization among patients recommended for PAIR than among patients with no PT needs.
Policies related to PAIR for observation patients are rooted in the concern that expanded access to services will contribute to overuse of services and higher healthcare costs.15 However, patients who could have benefited from PAIR but were not covered also were at risk for increased healthcare use and costs. A recent study found that more than one fourth of observation patients with repeat observation stays accrued excessive financial liability.16 Researchers should determine more precisely how the cost of coverage for PAIR placement on an index observation admission compares with the cost of subsequent healthcare use potentially related to insufficient supportive care at home.
Study Limitations
Our results must be interpreted within the context of study limitations. First is the small sample size, particularly the subset of patients selected for detailed manual chart review. We were limited in our ability to calculate sample size prospectively because we were unaware of prior work that described the association between PT recommendation and outcomes among observation patients. However, post hoc analysis estimated that a sample size of 181 patients would have been needed to determine a statistically significant difference in 30-day hospital revisit between patients recommended for PAIR and patients with no PT needs with 80% power, which we achieved. Although there are significant limitations to post hoc sample size estimation, we consider our work hypothesis-generating and hope it will lead to larger studies.
We could not account for the potential bias of the physical therapists, whose evaluations could have been influenced by knowledge of patients’ observation status. Our findings could have underestimated the proportion of patients who otherwise would have been recommended for PAIR. Alternatively, therapists could have inaccurately assessed and overstated the need for PAIR. Although we could not account for the therapists’ accuracy and biases, their assessments provided crucial information beyond what was previously obtained from administrative data alone.7,9
Hospital revisits were only accounted for within our hospital system—another potential source of underestimated findings. A significant proportion of patients recommended for home PT were discharged without services, which is counterintuitive, as Medicare covers home nursing services for observation patients. This finding most likely reflects administrative error but probably merits further evaluation.
Last, causality cannot be inferred from the results of a retrospective observational study.
CONCLUSION
As our study results suggest, there is an unmet need for PAIR services for Medicare observation patients, and LOS and subsequent use may be increased among patients recommended for PAIR. Our estimates are conservative and may underestimate the true need for services within this population. Our findings bolster MedPAC recommendations to amend the policies for Medicare coverage of PAIR services for observation patients.
Acknowledgment
The authors thank Paul Kolm, PhD, for statistical support.
Disclosures
Dr. Schwartz reports receiving personal fees from the Agency for Health Research and Quality, Bayer, the Blue Cross Blue Shield Association, Pfizer, and Takeda, all outside the submitted work. Dr. Hicks is supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (grant U54-GM104941; principal investigator Stuart Binder-Macleod, PT, PhD, FAPTA). The other authors have nothing to report.
1. Ortman JM, Velkoff VA, Hogan H. An Aging Nation: The Older Population in the United States (Current Population Reports, P25-1140). Washington, DC: US Census Bureau; 2014. https://www.census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed January 1, 2016.
2. Carter C, Garrett B, Wissoker D. The Need to Reform Medicare’s Payments to Skilled Nursing Facilities Is as Strong as Ever. Washington, DC: Medicare Payment Advisory Commission & Urban Institute; 2015. http://www.urban.org/sites/default/files/publication/39036/2000072-The-Need-to-Reform-Medicare-Payments-to-SNF.pdf. Published January 2015. Accessed January 1, 2016.
3. Cassidy A. The two-midnight rule (Health Policy Brief). HealthAffairs website. http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_133.pdf. Published January 22, 2015. Accessed January 1, 2016.
4. Sheehy AM, Caponi B, Gangireddy S, et al. Observation and inpatient status: clinical impact of the 2-midnight rule. J Hosp Med. 2014;9(4):203-209. PubMed
5. Wright S. Memorandum report: hospitals’ use of observation stays and short inpatient stays for Medicare beneficiaries (OEI-02-12-00040). Washington, DC: US Dept of Health and Human Services, Office of Inspector General; 2013. https://oig.hhs.gov/oei/reports/oei-02-12-00040.pdf. Published July 29, 2013. Accessed January 1, 2016.
6. Hockenberry JM, Mutter R, Barrett M, Parlato J, Ross MA. Factors associated with prolonged observation services stays and the impact of long stays on patient cost. Health Serv Res. 2014;49(3):893-909. PubMed
7. Feng Z, Jung HY, Wright B, Mor V. The origin and disposition of Medicare observation stays. Med Care. 2014;52(9):796-800. PubMed
8. Ross MA, Hockenberry JM, Mutter R, Barrett M, Wheatley M, Pitts SR. Protocol-driven emergency department observation units offer savings, shorter stays, and reduced admissions. Health Aff. 2013;32(12):2149-2156. PubMed
9. Sheehy AM, Graf B, Gangireddy S, et al. Hospitalized but not admitted: characteristics of patients with “observation status” at an academic medical center. JAMA Intern Med. 2013;173(21):1991-1998. PubMed
10. Centers for Medicare & Medicaid Services. Medicare & Your Hospital Benefits. https://www.medicare.gov/Pubs/pdf/11408.pdf. CMS Product 11408. Published 2014. Revised March 2016. Accessed February 6, 2017.
11. Medicare Payment Advisory Commission. Hospital short-stay policy issues. In: Report to the Congress: Medicare and the Health Care Delivery System. Washington, DC: Medicare Payment Advisory Commission; 2015:173-204. http://www.medpac.gov/docs/default-source/reports/chapter-7-hospital-short-stay-policy-issues-june-2015-report-.pdf. Published June 2015. Accessed January 1, 2016.
12. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program: hospital outpatient prospective payment and ambulatory surgical center payment systems and quality reporting programs; short inpatient hospital stays; transition for certain Medicare-dependent, small rural hospitals under the hospital inpatient prospective payment system; provider administrative appeals and judicial review. Final rule with comment period; final rule. Fed Regist. 2015;80(219):70297-70607. PubMed
13. Medicare Payment Advisory Commission. Skilled nursing facility services. In: Report to the Congress: Medicare Payment Policy. Washington, DC: Medicare Payment Advisory Commission; 2015:181-209. http://www.medpac.gov/docs/default-source/reports/chapter-8-skilled-nursing-facility-services-march-2015-report-.pdf. Published March 2015. Accessed January 1, 2016.
14. Hobbs JA, Boysen JF, McGarry KA, Thompson JM, Nordrum JT. Development of a unique triage system for acute care physical therapy and occupational therapy services: an administrative case report. Phys Ther. 2010;90(10):1519-1529. PubMed
15. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and fiscal year 2014 rates; quality reporting requirements for specific providers; hospital conditions of participation; payment policies related to patient status. Final rules. Fed Regist. 2013;78(160):50495-51040. PubMed
16. Kangovi S, Cafardi SG, Smith RA, Kulkarni R, Grande D. Patient financial responsibility for observation care. J Hosp Med. 2015;10(11):718-723. PubMed
As the US population ages and becomes increasingly frail, the need for rehabilitation rises. By 2030, an estimated 20% of the population will be 65 years old or older, and almost 10% will be over 75.1 About 20% of hospitalized Medicare patients receive subsequent care in postacute inpatient rehabilitation (PAIR) facilities, accounting for $31 billion in Medicare expenditures in 2014.2 Although the need for rehabilitation will continue to rise, Medicare policy restricts access to it.
Under Medicare policy, PAIR services are covered for certain hospitalized patients but not others. Hospitalized patients are either inpatients, who are billed under Medicare Part A, or outpatients, billed under Part B. When hospital length of stay (LOS) is anticipated to be less than 2 midnights, patients are admitted as outpatients under the term observation status; when longer stays are expected, patients are admitted as inpatients.3 This recently implemented time-based distinction has been criticized as arbitrary, and as potentially shifting many patients from inpatient to outpatient (observation) status.4
The distinction between inpatient and observation status has significant consequences for posthospital care. Medicare Part A covers care in skilled nursing facilities (SNFs) and acute inpatient rehabilitation facilities (IRFs); after hospitalization, inpatients have access to either, without copay. As observation patients are covered under Medicare Part B, they are technically not covered for either service after their hospital stay. IRFs sometimes accept patients from ambulatory and nonacute settings; observation patients may be accepted in rare circumstances, but they pay the Part A deductible ($1288 in 2016) to have the services covered by Medicare. SNF services are never covered for observation patients, and access to this care requires an average out-of-pocket payment of more than $10,503 per beneficiary for a typical SNF stay.5 Given that about 70% of Medicare patients fall below 300% of the federal poverty line,6 the out-of-pocket costs for PAIR services for observation patients can be prohibitive.
Although only 0.75% of community-dwelling Medicare observation patients are discharged to PAIR facilities,7 it is unclear if the need for this care is higher but remains unmet secondary to cost concerns of Medicare beneficiaries. Also unclear is whether observation patients who would benefit from this care but do not receive it end up with poorer health outcomes and therefore use more healthcare services.
The purpose of this study was to estimate the proportion of Medicare observation patients who are admitted from home and receive a recommendation for placement in a PAIR facility, and to determine the ultimate disposition of such patients. We also sought to evaluate the association between recommendation for PAIR placement, LOS, and 30-day hospital revisit rate.
METHODS
The Institutional Review Board of Christiana Care Health System (CCHS) approved this study.
Sample and Design
This was an observational study of community-dwelling Medicare patients admitted under observation status to Delaware’s CCHS, which consists of a 907-bed regional tertiary-care facility in Newark and a 241-bed community hospital in Wilmington. The study period was January 1 to December 31, 2013. We limited our sample to patients treated by hospitalists on hospital wards, as this care constitutes 80% of the care provided to observation patients at CCHS and the majority of care nationally.8 As neither SNF care nor IRF care is covered under Medicare Part B, and both would result in high out-of-pocket costs for Medicare observation patients, we combined them into a single variable, PAIR.
All data were obtained from institutional electronic medical record and administrative data systems. Study inclusion criteria were Medicare as primary insurance, admission to hospital from home, and care received at either CCHS facility. Exclusion criteria were admission from PAIR facility, long-term care facility, assisted-living facility, or inpatient psychiatric facility; death; discharge against medical advice (AMA) or to hospice, non-SNF, or inpatient psychiatric facility; and discovery (during review of case management [CM] notes) of erroneous listing of Medicare as primary insurance, or of inpatient admission (within 30 days before index observation stay) that qualified for PAIR coverage under Medicare Part A.
We reviewed the medical charts of a representative (~30%) sample of the cohort and examined physical therapy (PT) and CM notes to determine the proportions of patients with recommendations for home with no services, home-based PT, possible PAIR, and PAIR. Charts were sorted by medical record number and were reviewed in consecutive order. We coded a patient as having a recommendation for possible PAIR if the PT notes indicated the patient may benefit from PAIR but could have home PT if PAIR placement was not possible. CM notes were also reviewed for evidence of patient or family preference regarding PAIR placement. All questions about PT and CM recommendations were resolved by consensus.
Measures
For the total study sample, we calculated descriptive statistics and frequencies for demographic and administrative variables, including age, sex, race (Caucasian, African American, other), ethnicity (Hispanic/non-Hispanic), ICD-9 (International Classification of Diseases, Ninth Revision) primary diagnosis code, LOS (in hours) for index observation admission, discharge disposition (home with no services, home PT, possible PAIR, PAIR), and 30-day hospital revisit (emergency department, observation, inpatient admission). We used χ2 test, Student t test, and analysis of variance (ANOVA) to test for statistically significant differences in characteristics between the chart review subgroup and the rest of the sample and between the groups with different disposition recommendations from PT notes.
For the chart review subgroup, we used ANOVA to calculate the unadjusted association between PT recommendation and LOS. We then adjusted for potential confounders, using multivariable linear regression with PT recommendation as a predictor and LOS as the outcome, controlling for variables previously associated with increased LOS among observation patients (primary diagnosis category, age, sex).6 We also adjusted for hospitalist group to account for potential variability in care delivery. As LOS was not normally distributed, we calculated the fourth root of LOS, which resulted in a more normal distribution, and used the transformed values in the regression model. We then calculated predicted values from the regression and back-transformed these to obtain adjusted mean values for LOS.
RESULTS
Of the 1417 unique patients who had Medicare as primary insurance and were admitted under observation status to a hospitalist service during the study period (2013), 94 were excluded (Figure). Of the remaining 1323 patients, the majority were 65 years old or older, female, white, and non-Hispanic. The most common ICD-9 diagnoses were syncope and chest pain. Mean LOS was 46.7 hours (range, 0-519 h). Less than 1% of patients were discharged to PAIR. Almost 25% of patients returned to the hospital, either for an emergency department visit or for observation or inpatient stay, within 30 days (Table).
Of the 419 charts reviewed to determine the proportion of patients evaluated by PT, and their subsequent recommendations, 33 were excluded, leaving 386 (92%) for analysis (Figure). There were no significant demographic differences between the patients in the chart review subgroup and the rest of the patients (Appendix). Of the 386 patients whose charts were analyzed, 181 (46.9%) had a PT evaluation, and 17 (4.4%) received a PAIR recommendation (Figure). Of the 17 patients recommended for PAIR, 12 (70.5%) were 65 years old or older, and 1 was discharged to a PAIR facility. Of the 46 patients recommended for home PT, 29 (63%) were discharged home with no services (Table).
PT-evaluated patients had unadjusted mean LOS of 52.2 hours (discharged home with no services), 64.1 hours (home PT or possible PAIR), and 83.1 hours (PAIR) (P = 0.001). With adjustment made for variables previously associated with increased LOS for observation patients, mean LOS for patients recommended for PAIR remained higher than that for patients in the other 2 categories (Table). Patients recommended for PAIR were more likely to return to hospital within 30 days than patients recommended for home PT or possible PAIR and patients discharged home with no services (Table).
Review of CM notes revealed that, of the 17 patients recommended for PAIR, 7 would have accepted PAIR services had they been covered by Medicare, 4 preferred discharge with home health services, and 6 did not provide clear details of patient or family preference.
DISCUSSION
To our knowledge, this is the first study to use chart review to examine the proportion of observation patients who would benefit from PAIR and the relationships among these patients’ rehabilitation needs, dispositions, and outcomes. We tried to be conservative in our estimates by limiting the study population to patients admitted from home. Nevertheless, the potential need for PAIR significantly outweighed the actual use of PAIR on discharge. The study sample was consistent with nationally representative samples of observation patients in terms of proportion of patients admitted from and discharged to facilities7 and the most common ICD-9 diagnoses.9
Physical Therapy Consultations and Observation
Of the 386 patients whose charts were reviewed and analyzed, 17 (4.4%) were evaluated as medically qualifying for and potentially benefiting from PAIR. Although the rate represents a minority of patients, it is 5- to 6-fold higher than the rate of discharge to PAIR, both in our study population and in previous national samples that used administrative data.7 In some cases, the decision not to discharge the patient to PAIR reflected patient and family preference. However, in other cases, patients clearly could have benefited from PAIR and would have gone had it been covered by Medicare. The gap suggests an unmet need for PAIR among a substantial proportion of Medicare beneficiaries for whom the therapy is recommended and wanted.
Efforts to expand coverage for PAIR have been resisted. According to Medicare regulations, beneficiaries qualify for PAIR coverage if they are hospitalized as inpatients for 3 midnights or longer. Days under observation status do not count toward this requirement, even if this status is changed to inpatient.10 The Medicare Payment Advisory Commission (MedPAC) recommendation that time under observation status count toward the Medicare requirement11 has not been accepted,12 in large part because further expansion of PAIR services likely would be unaffordable to Medicare under its payment structure.13 Given our finding that the need for PAIR likely is much higher than previously anticipated, Medicare policy makers should consider broadening access to PAIR while efforts are made to rein in expenditures through payment reform.
One potential area of cost savings is more judicious use of PT evaluation for observation patients, particularly given our finding that the majority of PT consultations resulted in no further recommendations. Efforts to triage PT consultations for appropriateness have had some success, though the literature is scant.14 To improve value for Medicare, healthcare systems, and patients, researchers should rigorously evaluate approaches that maximize
Hospital Length of Stay
Our cohort’s mean hospital stay was longer than averages reported elsewhere,9 likely reflecting our selection of Medicare patients rather than a general medicine population.6 However, our cohort’s adjusted mean hospital stay was significantly longer for patients recommended for PAIR than for patients without PT needs. That out-of-pocket costs for observation patients increase dramatically as LOS goes past 48 hours6 could have significant financial implications for Medicare beneficiaries.
Return Visits
Almost 25% of our observation patients returned to hospital within 30 days. There was a significant trend toward increased rehospitalization among patients recommended for PAIR than among patients with no PT needs.
Policies related to PAIR for observation patients are rooted in the concern that expanded access to services will contribute to overuse of services and higher healthcare costs.15 However, patients who could have benefited from PAIR but were not covered also were at risk for increased healthcare use and costs. A recent study found that more than one fourth of observation patients with repeat observation stays accrued excessive financial liability.16 Researchers should determine more precisely how the cost of coverage for PAIR placement on an index observation admission compares with the cost of subsequent healthcare use potentially related to insufficient supportive care at home.
Study Limitations
Our results must be interpreted within the context of study limitations. First is the small sample size, particularly the subset of patients selected for detailed manual chart review. We were limited in our ability to calculate sample size prospectively because we were unaware of prior work that described the association between PT recommendation and outcomes among observation patients. However, post hoc analysis estimated that a sample size of 181 patients would have been needed to determine a statistically significant difference in 30-day hospital revisit between patients recommended for PAIR and patients with no PT needs with 80% power, which we achieved. Although there are significant limitations to post hoc sample size estimation, we consider our work hypothesis-generating and hope it will lead to larger studies.
We could not account for the potential bias of the physical therapists, whose evaluations could have been influenced by knowledge of patients’ observation status. Our findings could have underestimated the proportion of patients who otherwise would have been recommended for PAIR. Alternatively, therapists could have inaccurately assessed and overstated the need for PAIR. Although we could not account for the therapists’ accuracy and biases, their assessments provided crucial information beyond what was previously obtained from administrative data alone.7,9
Hospital revisits were only accounted for within our hospital system—another potential source of underestimated findings. A significant proportion of patients recommended for home PT were discharged without services, which is counterintuitive, as Medicare covers home nursing services for observation patients. This finding most likely reflects administrative error but probably merits further evaluation.
Last, causality cannot be inferred from the results of a retrospective observational study.
CONCLUSION
As our study results suggest, there is an unmet need for PAIR services for Medicare observation patients, and LOS and subsequent use may be increased among patients recommended for PAIR. Our estimates are conservative and may underestimate the true need for services within this population. Our findings bolster MedPAC recommendations to amend the policies for Medicare coverage of PAIR services for observation patients.
Acknowledgment
The authors thank Paul Kolm, PhD, for statistical support.
Disclosures
Dr. Schwartz reports receiving personal fees from the Agency for Health Research and Quality, Bayer, the Blue Cross Blue Shield Association, Pfizer, and Takeda, all outside the submitted work. Dr. Hicks is supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (grant U54-GM104941; principal investigator Stuart Binder-Macleod, PT, PhD, FAPTA). The other authors have nothing to report.
As the US population ages and becomes increasingly frail, the need for rehabilitation rises. By 2030, an estimated 20% of the population will be 65 years old or older, and almost 10% will be over 75.1 About 20% of hospitalized Medicare patients receive subsequent care in postacute inpatient rehabilitation (PAIR) facilities, accounting for $31 billion in Medicare expenditures in 2014.2 Although the need for rehabilitation will continue to rise, Medicare policy restricts access to it.
Under Medicare policy, PAIR services are covered for certain hospitalized patients but not others. Hospitalized patients are either inpatients, who are billed under Medicare Part A, or outpatients, billed under Part B. When hospital length of stay (LOS) is anticipated to be less than 2 midnights, patients are admitted as outpatients under the term observation status; when longer stays are expected, patients are admitted as inpatients.3 This recently implemented time-based distinction has been criticized as arbitrary, and as potentially shifting many patients from inpatient to outpatient (observation) status.4
The distinction between inpatient and observation status has significant consequences for posthospital care. Medicare Part A covers care in skilled nursing facilities (SNFs) and acute inpatient rehabilitation facilities (IRFs); after hospitalization, inpatients have access to either, without copay. As observation patients are covered under Medicare Part B, they are technically not covered for either service after their hospital stay. IRFs sometimes accept patients from ambulatory and nonacute settings; observation patients may be accepted in rare circumstances, but they pay the Part A deductible ($1288 in 2016) to have the services covered by Medicare. SNF services are never covered for observation patients, and access to this care requires an average out-of-pocket payment of more than $10,503 per beneficiary for a typical SNF stay.5 Given that about 70% of Medicare patients fall below 300% of the federal poverty line,6 the out-of-pocket costs for PAIR services for observation patients can be prohibitive.
Although only 0.75% of community-dwelling Medicare observation patients are discharged to PAIR facilities,7 it is unclear if the need for this care is higher but remains unmet secondary to cost concerns of Medicare beneficiaries. Also unclear is whether observation patients who would benefit from this care but do not receive it end up with poorer health outcomes and therefore use more healthcare services.
The purpose of this study was to estimate the proportion of Medicare observation patients who are admitted from home and receive a recommendation for placement in a PAIR facility, and to determine the ultimate disposition of such patients. We also sought to evaluate the association between recommendation for PAIR placement, LOS, and 30-day hospital revisit rate.
METHODS
The Institutional Review Board of Christiana Care Health System (CCHS) approved this study.
Sample and Design
This was an observational study of community-dwelling Medicare patients admitted under observation status to Delaware’s CCHS, which consists of a 907-bed regional tertiary-care facility in Newark and a 241-bed community hospital in Wilmington. The study period was January 1 to December 31, 2013. We limited our sample to patients treated by hospitalists on hospital wards, as this care constitutes 80% of the care provided to observation patients at CCHS and the majority of care nationally.8 As neither SNF care nor IRF care is covered under Medicare Part B, and both would result in high out-of-pocket costs for Medicare observation patients, we combined them into a single variable, PAIR.
All data were obtained from institutional electronic medical record and administrative data systems. Study inclusion criteria were Medicare as primary insurance, admission to hospital from home, and care received at either CCHS facility. Exclusion criteria were admission from PAIR facility, long-term care facility, assisted-living facility, or inpatient psychiatric facility; death; discharge against medical advice (AMA) or to hospice, non-SNF, or inpatient psychiatric facility; and discovery (during review of case management [CM] notes) of erroneous listing of Medicare as primary insurance, or of inpatient admission (within 30 days before index observation stay) that qualified for PAIR coverage under Medicare Part A.
We reviewed the medical charts of a representative (~30%) sample of the cohort and examined physical therapy (PT) and CM notes to determine the proportions of patients with recommendations for home with no services, home-based PT, possible PAIR, and PAIR. Charts were sorted by medical record number and were reviewed in consecutive order. We coded a patient as having a recommendation for possible PAIR if the PT notes indicated the patient may benefit from PAIR but could have home PT if PAIR placement was not possible. CM notes were also reviewed for evidence of patient or family preference regarding PAIR placement. All questions about PT and CM recommendations were resolved by consensus.
Measures
For the total study sample, we calculated descriptive statistics and frequencies for demographic and administrative variables, including age, sex, race (Caucasian, African American, other), ethnicity (Hispanic/non-Hispanic), ICD-9 (International Classification of Diseases, Ninth Revision) primary diagnosis code, LOS (in hours) for index observation admission, discharge disposition (home with no services, home PT, possible PAIR, PAIR), and 30-day hospital revisit (emergency department, observation, inpatient admission). We used χ2 test, Student t test, and analysis of variance (ANOVA) to test for statistically significant differences in characteristics between the chart review subgroup and the rest of the sample and between the groups with different disposition recommendations from PT notes.
For the chart review subgroup, we used ANOVA to calculate the unadjusted association between PT recommendation and LOS. We then adjusted for potential confounders, using multivariable linear regression with PT recommendation as a predictor and LOS as the outcome, controlling for variables previously associated with increased LOS among observation patients (primary diagnosis category, age, sex).6 We also adjusted for hospitalist group to account for potential variability in care delivery. As LOS was not normally distributed, we calculated the fourth root of LOS, which resulted in a more normal distribution, and used the transformed values in the regression model. We then calculated predicted values from the regression and back-transformed these to obtain adjusted mean values for LOS.
RESULTS
Of the 1417 unique patients who had Medicare as primary insurance and were admitted under observation status to a hospitalist service during the study period (2013), 94 were excluded (Figure). Of the remaining 1323 patients, the majority were 65 years old or older, female, white, and non-Hispanic. The most common ICD-9 diagnoses were syncope and chest pain. Mean LOS was 46.7 hours (range, 0-519 h). Less than 1% of patients were discharged to PAIR. Almost 25% of patients returned to the hospital, either for an emergency department visit or for observation or inpatient stay, within 30 days (Table).
Of the 419 charts reviewed to determine the proportion of patients evaluated by PT, and their subsequent recommendations, 33 were excluded, leaving 386 (92%) for analysis (Figure). There were no significant demographic differences between the patients in the chart review subgroup and the rest of the patients (Appendix). Of the 386 patients whose charts were analyzed, 181 (46.9%) had a PT evaluation, and 17 (4.4%) received a PAIR recommendation (Figure). Of the 17 patients recommended for PAIR, 12 (70.5%) were 65 years old or older, and 1 was discharged to a PAIR facility. Of the 46 patients recommended for home PT, 29 (63%) were discharged home with no services (Table).
PT-evaluated patients had unadjusted mean LOS of 52.2 hours (discharged home with no services), 64.1 hours (home PT or possible PAIR), and 83.1 hours (PAIR) (P = 0.001). With adjustment made for variables previously associated with increased LOS for observation patients, mean LOS for patients recommended for PAIR remained higher than that for patients in the other 2 categories (Table). Patients recommended for PAIR were more likely to return to hospital within 30 days than patients recommended for home PT or possible PAIR and patients discharged home with no services (Table).
Review of CM notes revealed that, of the 17 patients recommended for PAIR, 7 would have accepted PAIR services had they been covered by Medicare, 4 preferred discharge with home health services, and 6 did not provide clear details of patient or family preference.
DISCUSSION
To our knowledge, this is the first study to use chart review to examine the proportion of observation patients who would benefit from PAIR and the relationships among these patients’ rehabilitation needs, dispositions, and outcomes. We tried to be conservative in our estimates by limiting the study population to patients admitted from home. Nevertheless, the potential need for PAIR significantly outweighed the actual use of PAIR on discharge. The study sample was consistent with nationally representative samples of observation patients in terms of proportion of patients admitted from and discharged to facilities7 and the most common ICD-9 diagnoses.9
Physical Therapy Consultations and Observation
Of the 386 patients whose charts were reviewed and analyzed, 17 (4.4%) were evaluated as medically qualifying for and potentially benefiting from PAIR. Although the rate represents a minority of patients, it is 5- to 6-fold higher than the rate of discharge to PAIR, both in our study population and in previous national samples that used administrative data.7 In some cases, the decision not to discharge the patient to PAIR reflected patient and family preference. However, in other cases, patients clearly could have benefited from PAIR and would have gone had it been covered by Medicare. The gap suggests an unmet need for PAIR among a substantial proportion of Medicare beneficiaries for whom the therapy is recommended and wanted.
Efforts to expand coverage for PAIR have been resisted. According to Medicare regulations, beneficiaries qualify for PAIR coverage if they are hospitalized as inpatients for 3 midnights or longer. Days under observation status do not count toward this requirement, even if this status is changed to inpatient.10 The Medicare Payment Advisory Commission (MedPAC) recommendation that time under observation status count toward the Medicare requirement11 has not been accepted,12 in large part because further expansion of PAIR services likely would be unaffordable to Medicare under its payment structure.13 Given our finding that the need for PAIR likely is much higher than previously anticipated, Medicare policy makers should consider broadening access to PAIR while efforts are made to rein in expenditures through payment reform.
One potential area of cost savings is more judicious use of PT evaluation for observation patients, particularly given our finding that the majority of PT consultations resulted in no further recommendations. Efforts to triage PT consultations for appropriateness have had some success, though the literature is scant.14 To improve value for Medicare, healthcare systems, and patients, researchers should rigorously evaluate approaches that maximize
Hospital Length of Stay
Our cohort’s mean hospital stay was longer than averages reported elsewhere,9 likely reflecting our selection of Medicare patients rather than a general medicine population.6 However, our cohort’s adjusted mean hospital stay was significantly longer for patients recommended for PAIR than for patients without PT needs. That out-of-pocket costs for observation patients increase dramatically as LOS goes past 48 hours6 could have significant financial implications for Medicare beneficiaries.
Return Visits
Almost 25% of our observation patients returned to hospital within 30 days. There was a significant trend toward increased rehospitalization among patients recommended for PAIR than among patients with no PT needs.
Policies related to PAIR for observation patients are rooted in the concern that expanded access to services will contribute to overuse of services and higher healthcare costs.15 However, patients who could have benefited from PAIR but were not covered also were at risk for increased healthcare use and costs. A recent study found that more than one fourth of observation patients with repeat observation stays accrued excessive financial liability.16 Researchers should determine more precisely how the cost of coverage for PAIR placement on an index observation admission compares with the cost of subsequent healthcare use potentially related to insufficient supportive care at home.
Study Limitations
Our results must be interpreted within the context of study limitations. First is the small sample size, particularly the subset of patients selected for detailed manual chart review. We were limited in our ability to calculate sample size prospectively because we were unaware of prior work that described the association between PT recommendation and outcomes among observation patients. However, post hoc analysis estimated that a sample size of 181 patients would have been needed to determine a statistically significant difference in 30-day hospital revisit between patients recommended for PAIR and patients with no PT needs with 80% power, which we achieved. Although there are significant limitations to post hoc sample size estimation, we consider our work hypothesis-generating and hope it will lead to larger studies.
We could not account for the potential bias of the physical therapists, whose evaluations could have been influenced by knowledge of patients’ observation status. Our findings could have underestimated the proportion of patients who otherwise would have been recommended for PAIR. Alternatively, therapists could have inaccurately assessed and overstated the need for PAIR. Although we could not account for the therapists’ accuracy and biases, their assessments provided crucial information beyond what was previously obtained from administrative data alone.7,9
Hospital revisits were only accounted for within our hospital system—another potential source of underestimated findings. A significant proportion of patients recommended for home PT were discharged without services, which is counterintuitive, as Medicare covers home nursing services for observation patients. This finding most likely reflects administrative error but probably merits further evaluation.
Last, causality cannot be inferred from the results of a retrospective observational study.
CONCLUSION
As our study results suggest, there is an unmet need for PAIR services for Medicare observation patients, and LOS and subsequent use may be increased among patients recommended for PAIR. Our estimates are conservative and may underestimate the true need for services within this population. Our findings bolster MedPAC recommendations to amend the policies for Medicare coverage of PAIR services for observation patients.
Acknowledgment
The authors thank Paul Kolm, PhD, for statistical support.
Disclosures
Dr. Schwartz reports receiving personal fees from the Agency for Health Research and Quality, Bayer, the Blue Cross Blue Shield Association, Pfizer, and Takeda, all outside the submitted work. Dr. Hicks is supported by an Institutional Development Award from the National Institute of General Medical Sciences of the National Institutes of Health (grant U54-GM104941; principal investigator Stuart Binder-Macleod, PT, PhD, FAPTA). The other authors have nothing to report.
1. Ortman JM, Velkoff VA, Hogan H. An Aging Nation: The Older Population in the United States (Current Population Reports, P25-1140). Washington, DC: US Census Bureau; 2014. https://www.census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed January 1, 2016.
2. Carter C, Garrett B, Wissoker D. The Need to Reform Medicare’s Payments to Skilled Nursing Facilities Is as Strong as Ever. Washington, DC: Medicare Payment Advisory Commission & Urban Institute; 2015. http://www.urban.org/sites/default/files/publication/39036/2000072-The-Need-to-Reform-Medicare-Payments-to-SNF.pdf. Published January 2015. Accessed January 1, 2016.
3. Cassidy A. The two-midnight rule (Health Policy Brief). HealthAffairs website. http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_133.pdf. Published January 22, 2015. Accessed January 1, 2016.
4. Sheehy AM, Caponi B, Gangireddy S, et al. Observation and inpatient status: clinical impact of the 2-midnight rule. J Hosp Med. 2014;9(4):203-209. PubMed
5. Wright S. Memorandum report: hospitals’ use of observation stays and short inpatient stays for Medicare beneficiaries (OEI-02-12-00040). Washington, DC: US Dept of Health and Human Services, Office of Inspector General; 2013. https://oig.hhs.gov/oei/reports/oei-02-12-00040.pdf. Published July 29, 2013. Accessed January 1, 2016.
6. Hockenberry JM, Mutter R, Barrett M, Parlato J, Ross MA. Factors associated with prolonged observation services stays and the impact of long stays on patient cost. Health Serv Res. 2014;49(3):893-909. PubMed
7. Feng Z, Jung HY, Wright B, Mor V. The origin and disposition of Medicare observation stays. Med Care. 2014;52(9):796-800. PubMed
8. Ross MA, Hockenberry JM, Mutter R, Barrett M, Wheatley M, Pitts SR. Protocol-driven emergency department observation units offer savings, shorter stays, and reduced admissions. Health Aff. 2013;32(12):2149-2156. PubMed
9. Sheehy AM, Graf B, Gangireddy S, et al. Hospitalized but not admitted: characteristics of patients with “observation status” at an academic medical center. JAMA Intern Med. 2013;173(21):1991-1998. PubMed
10. Centers for Medicare & Medicaid Services. Medicare & Your Hospital Benefits. https://www.medicare.gov/Pubs/pdf/11408.pdf. CMS Product 11408. Published 2014. Revised March 2016. Accessed February 6, 2017.
11. Medicare Payment Advisory Commission. Hospital short-stay policy issues. In: Report to the Congress: Medicare and the Health Care Delivery System. Washington, DC: Medicare Payment Advisory Commission; 2015:173-204. http://www.medpac.gov/docs/default-source/reports/chapter-7-hospital-short-stay-policy-issues-june-2015-report-.pdf. Published June 2015. Accessed January 1, 2016.
12. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program: hospital outpatient prospective payment and ambulatory surgical center payment systems and quality reporting programs; short inpatient hospital stays; transition for certain Medicare-dependent, small rural hospitals under the hospital inpatient prospective payment system; provider administrative appeals and judicial review. Final rule with comment period; final rule. Fed Regist. 2015;80(219):70297-70607. PubMed
13. Medicare Payment Advisory Commission. Skilled nursing facility services. In: Report to the Congress: Medicare Payment Policy. Washington, DC: Medicare Payment Advisory Commission; 2015:181-209. http://www.medpac.gov/docs/default-source/reports/chapter-8-skilled-nursing-facility-services-march-2015-report-.pdf. Published March 2015. Accessed January 1, 2016.
14. Hobbs JA, Boysen JF, McGarry KA, Thompson JM, Nordrum JT. Development of a unique triage system for acute care physical therapy and occupational therapy services: an administrative case report. Phys Ther. 2010;90(10):1519-1529. PubMed
15. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and fiscal year 2014 rates; quality reporting requirements for specific providers; hospital conditions of participation; payment policies related to patient status. Final rules. Fed Regist. 2013;78(160):50495-51040. PubMed
16. Kangovi S, Cafardi SG, Smith RA, Kulkarni R, Grande D. Patient financial responsibility for observation care. J Hosp Med. 2015;10(11):718-723. PubMed
1. Ortman JM, Velkoff VA, Hogan H. An Aging Nation: The Older Population in the United States (Current Population Reports, P25-1140). Washington, DC: US Census Bureau; 2014. https://www.census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed January 1, 2016.
2. Carter C, Garrett B, Wissoker D. The Need to Reform Medicare’s Payments to Skilled Nursing Facilities Is as Strong as Ever. Washington, DC: Medicare Payment Advisory Commission & Urban Institute; 2015. http://www.urban.org/sites/default/files/publication/39036/2000072-The-Need-to-Reform-Medicare-Payments-to-SNF.pdf. Published January 2015. Accessed January 1, 2016.
3. Cassidy A. The two-midnight rule (Health Policy Brief). HealthAffairs website. http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_133.pdf. Published January 22, 2015. Accessed January 1, 2016.
4. Sheehy AM, Caponi B, Gangireddy S, et al. Observation and inpatient status: clinical impact of the 2-midnight rule. J Hosp Med. 2014;9(4):203-209. PubMed
5. Wright S. Memorandum report: hospitals’ use of observation stays and short inpatient stays for Medicare beneficiaries (OEI-02-12-00040). Washington, DC: US Dept of Health and Human Services, Office of Inspector General; 2013. https://oig.hhs.gov/oei/reports/oei-02-12-00040.pdf. Published July 29, 2013. Accessed January 1, 2016.
6. Hockenberry JM, Mutter R, Barrett M, Parlato J, Ross MA. Factors associated with prolonged observation services stays and the impact of long stays on patient cost. Health Serv Res. 2014;49(3):893-909. PubMed
7. Feng Z, Jung HY, Wright B, Mor V. The origin and disposition of Medicare observation stays. Med Care. 2014;52(9):796-800. PubMed
8. Ross MA, Hockenberry JM, Mutter R, Barrett M, Wheatley M, Pitts SR. Protocol-driven emergency department observation units offer savings, shorter stays, and reduced admissions. Health Aff. 2013;32(12):2149-2156. PubMed
9. Sheehy AM, Graf B, Gangireddy S, et al. Hospitalized but not admitted: characteristics of patients with “observation status” at an academic medical center. JAMA Intern Med. 2013;173(21):1991-1998. PubMed
10. Centers for Medicare & Medicaid Services. Medicare & Your Hospital Benefits. https://www.medicare.gov/Pubs/pdf/11408.pdf. CMS Product 11408. Published 2014. Revised March 2016. Accessed February 6, 2017.
11. Medicare Payment Advisory Commission. Hospital short-stay policy issues. In: Report to the Congress: Medicare and the Health Care Delivery System. Washington, DC: Medicare Payment Advisory Commission; 2015:173-204. http://www.medpac.gov/docs/default-source/reports/chapter-7-hospital-short-stay-policy-issues-june-2015-report-.pdf. Published June 2015. Accessed January 1, 2016.
12. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program: hospital outpatient prospective payment and ambulatory surgical center payment systems and quality reporting programs; short inpatient hospital stays; transition for certain Medicare-dependent, small rural hospitals under the hospital inpatient prospective payment system; provider administrative appeals and judicial review. Final rule with comment period; final rule. Fed Regist. 2015;80(219):70297-70607. PubMed
13. Medicare Payment Advisory Commission. Skilled nursing facility services. In: Report to the Congress: Medicare Payment Policy. Washington, DC: Medicare Payment Advisory Commission; 2015:181-209. http://www.medpac.gov/docs/default-source/reports/chapter-8-skilled-nursing-facility-services-march-2015-report-.pdf. Published March 2015. Accessed January 1, 2016.
14. Hobbs JA, Boysen JF, McGarry KA, Thompson JM, Nordrum JT. Development of a unique triage system for acute care physical therapy and occupational therapy services: an administrative case report. Phys Ther. 2010;90(10):1519-1529. PubMed
15. Centers for Medicare & Medicaid Services (CMS), HHS. Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and fiscal year 2014 rates; quality reporting requirements for specific providers; hospital conditions of participation; payment policies related to patient status. Final rules. Fed Regist. 2013;78(160):50495-51040. PubMed
16. Kangovi S, Cafardi SG, Smith RA, Kulkarni R, Grande D. Patient financial responsibility for observation care. J Hosp Med. 2015;10(11):718-723. PubMed
© 2017 Society of Hospital Medicine
Insurance Status and Hospital Care
With about 1 in 5 working‐age Americans (age 18‐64 years) currently uninsured and a large number relying on Medicaid, adequate access to quality health care services is becoming increasingly difficult.1 Substantial literature has accumulated over the years suggesting that access and quality in health care are closely linked to an individual's health insurance status.211 Some studies indicate that being uninsured or publicly insured is associated with negative health consequences.12, 13 Although the Medicaid program has improved access for qualifying low‐income individuals, significant gaps in access and quality remain.2, 5, 11, 1419 These issues are likely to become more pervasive should there be further modifications to state Medicaid funding in response to the unfolding economic crisis.
Although numerous studies have focused on insurance‐related disparities in the outpatient setting, few nationally representative studies have examined such disparities among hospitalized patients. A cross‐sectional study of a large hospital database from 1987 reported higher risk‐adjusted in‐hospital mortality, shorter length of stay (LOS), and lower procedure use among uninsured patients.9 A more recent analysis, limited to patients admitted with stroke, reported significant variation in hospital care associated with insurance status.15 Other studies reporting myocardial infarction registry and quality improvement program data are biased by the self‐selection of large urban teaching hospitals.1618 To our knowledge, no nationally representative study has focused on the impact of insurance coverage on hospital care for common medical conditions among working‐age Americans, the fastest growing segment of the uninsured population.
To address this gap in knowledge, we analyzed a nationally representative hospital database to determine whether there are significant insurance‐related disparities in in‐hospital mortality, LOS, and cost per hospitalization for 3 common medical conditions among working‐age adults, and, if present, to determine whether these disparities are due to differences in disease severity and comorbidities, and whether these disparities are affected by the proportion of uninsured and Medicaid patients receiving care in each hospital.
Methods
Design and Subjects
We examined data from the 2005 Nationwide Inpatient Sample (NIS), a nationally representative database of hospital inpatient stays maintained by the Agency for Healthcare Research and Quality (AHRQ) as part of the Healthcare Cost and Utilization Project (HCUP).20, 21 The NIS is a stratified probability sample of 20% of all US community hospitals, including public hospitals, academic medical centers, and specialty hospitals. Long‐term care hospitals, psychiatric hospitals, and alcoholism/chemical‐dependency treatment facilities are excluded. The 2005 NIS contains data on 7,995,048 discharges from 1054 hospitals located in 37 States and is designed to be representative of all acute care discharges from all US community hospitals.21
We identified discharges with a principal diagnosis of acute myocardial infarction (AMI), stroke, and pneumonia using International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9‐CM) codes specified in the AHRQ definitions of Inpatient Quality Indicators (Supporting Information Appendix).22 These 3 conditions are among the leading causes of noncancer inpatient deaths in patients under 65 years old,23 and evidence suggests that high mortality may be associated with deficiencies in the quality of inpatient care.24
We confined our analysis to patients 18 to 64 years of age, since this population is most at risk of being uninsured or underinsured.25 We excluded pregnant women because they account for an unusually high proportion of uninsured discharges and were relatively few in our cohort.26 In addition, we excluded patients transferred to another acute care hospital and patients missing payer source and discharge disposition. Our study protocol was approved by the Partners Human Research Committee.
Study Variables
We categorized insurance status as privately insured, uninsured, Medicaid, or Medicare. We defined privately insured patients as those having either Blue Cross or another commercial carrier listed as the primary payer and uninsured patients as those having either no charge or self‐pay listed as the primary payer.27 Other governmental payer categories were noted to share several characteristics with Medicare patients and comprised only a small proportion of the sample, and were thus included with Medicare. In order to account for NIS's sampling scheme and accurately apply sample weights in our analysis, we used Medicare as a separate category. However, since Medicare patients age 18 to 64 years represent a fundamentally different population that is primarily disabled or very ill, only results of privately insured, uninsured, and Medicaid patients are reported.
We selected in‐hospital mortality as the outcome measure and LOS and cost per hospitalization as measures of resource utilization. The NIS includes a binary indicator variable for in‐hospital mortality and specifies inpatient LOS in integers, with same‐day stays coded as 0. NIS's cost estimates are based on hospital cost reports submitted to the Centers for Medicare and Medicaid Services. To test the validity of our cost analyses, we performed parallel analyses using hospital charges as a measure of utilization (charges include hospital overhead, charity care, and bad debt). The resulting adjusted ratios differed little from cost ratios and we opted to report only the details of our cost analyses.
In order to assess the independent association between insurance status and the outcome measures listed above, we selected covariates for inclusion in multivariable models based on the existing literature. Patient covariates included: age group (18‐34 years, 35‐49 years, 50‐64 years), sex (male/female), race/ethnicity (non‐Hispanic white, non‐Hispanic black, Hispanic, other, missing), median income by zip code of residence (less than $37,000, $37,000‐$45,999, $46,000‐$60,999, $61,000 or more), admission through the emergency department (yes/no), admission on a weekend (yes/no), measures of disease severity, and comorbidity indicators. Measures of disease severity specific to each outcome are assigned in the NIS using criteria developed by Medstat (Medstat Disease Staging Software Version 5.2, Thomson Medstat Inc., Ann Arbor, MI). Severity is categorized into 5 levels, with a higher level indicating greater risk. We recorded comorbidities for each patient in our sample using HCUP Comorbidity Software, Version 3.2 (
Hospital covariates included: bed size (small, medium, large), ownership/control (private, government, private or government), geographic region (northeast, midwest, south, west), teaching status (teaching, non‐teaching), and the proportion of uninsured and Medicaid patients (combined) admitted to each hospital for AMI, stroke, or pneumonia. The actual number of hospital beds in each bed size category varies according to a hospital's geographic region and teaching status.27 Ownership/control, geographic region, and teaching status are assigned according to information from the American Hospital Association Annual Survey of Hospitals. The proportion of uninsured and Medicaid patients admitted to each hospital was found to have a nonmonotonic relationship with the outcomes being assessed and was thus treated as a 6‐level categorical variable with the following levels: 0% to 10%, 11% to 20%, 21% to 30%, 31% to 40%, 41% to 50%, and 51% to 100%.
Statistical Analysis
Summary statistics were constructed at the patient level and differences in proportions were evaluated with the chi‐square test. We employed direct standardization, using the age and sex distribution of the entire cohort, to compute age‐standardized and sex‐standardized estimates for each insurance group and compared them using the chi‐square test for in‐hospital mortality and t test for log transformed LOS and cost per hospitalization. For each condition, we developed multivariable logistic regression models for in‐hospital mortality and multivariable linear regression models for log transformed LOS and cost. The patient was the unit of analysis in all models.
In order to elucidate the contribution of disease severity and comorbidities and the proportion of uninsured and Medicaid patients admitted to each hospital, we fitted 3 sequential models for each outcome measure: Model 1 adjusted for patient sociodemographic characteristics and hospital characteristics with the exception of the covariate for the proportion of uninsured and Medicaid patients, Model 2 adjusted for all covariates in the preceding model as well as patients' comorbidities and severity of principal diagnosis, and Model 3 adjusted for all previously mentioned covariates as well as the proportion of uninsured and Medicaid patients admitted to each hospital. We excluded patients who died during hospitalization from the models for LOS and cost. We exponentiated the effect estimates from the log transformed linear regression models so that the adjusted ratio represents the percentage change in the mean LOS or mean cost.
To determine whether the association between patients' insurance status and in‐hospital mortality was modified by seeking care in hospitals treating a smaller or larger proportion of uninsured and Medicaid patients, we entered an interaction term for insurance status and proportion of uninsured and Medicaid patients in the final models (Model 3) for our primary outcome of in‐hospital mortality. However, since no significant interaction was found for any of the 3 conditions, this term was removed from the models and results from the interaction models are not described. In order to assess model specification for the linear regression models, we evaluated the normality of model residuals and found that these were approximately normally distributed. Lastly, we attempted to test the robustness of our analyses by creating fixed effects models that controlled for hospital site but were unable to do so due to the computational limitations of available software packages that could not render fixed effects models with more than 1000 hospital sites.
For all variables except race/ethnicity, data were missing for less than 3% of patients, so we excluded these individuals from adjusted analyses. However, race/ethnicity data were missing for 29% of the sample and were analyzed in 3 different ways, namely, with the missing data treated as a separate covariate level, with the missing data removed from the analysis, and with the missing data assigned to the majority covariate level (white race). The results of our analysis were unchanged no matter how the missing values were assigned. As a result, missing values for race/ethnicity were treated as a separate covariate level in the final analysis.15 Sociodemographic characteristics of patients with missing race/ethnicity information were similar to those with complete data.
We used SUDAAN (Release 9.0.1, Research Triangle Institute, NC) to account for NIS's sampling scheme and generalized estimating equations to adjust for the clustering of patients within hospitals and hospitals within sampling strata.29 In order to account for NIS's stratified probability sampling scheme, SUDAAN uses Taylor series linearization for robust variance estimation of descriptive statistics and regression parameters.30, 31 We present 2‐tailed P values or 95% confidence intervals (CIs) for all statistical comparisons.
Results
Patient and Hospital Characteristics
The final cohort comprised of 154,381 patients discharged from 1018 hospitals in 37 states during calendar year 2005 (Table 1). This cohort was representative of 755,346 working‐age Americans, representing approximately 225,947 cases of AMI (29.9%), 151,812 cases of stroke (20.1%), and 377,588 cases of pneumonia (50.0%). Of these patients, 47.5% were privately insured, 12.0% were uninsured, 17.0% received Medicaid, and 23.5% were assigned to Medicare. Compared with the privately insured, uninsured and Medicaid patients were generally younger, less likely to be white, more likely to have lower income, and more likely to be admitted through the emergency department. Of the 1018 hospitals included in our study, close to half (44.3%) were small, with bedsize ranging from 24 to 249. A large number of hospitals were located in the South (39.9%), and 14.9% were designated teaching hospitals.
| Characteristic* | Privately insured (n = 73,256) | Uninsured (n = 18,531) | Medicaid (n = 26,222) |
|---|---|---|---|
| |||
| Principal diagnosis (%) | |||
| Acute myocardial infarction | 36.7 | 31.2 | 19.7 |
| Stroke | 20.6 | 23.7 | 19.9 |
| Pneumonia | 42.7 | 45.2 | 60.4 |
| Age group (%) | |||
| 18‐34 years | 6.8 | 13.0 | 13.7 |
| 35‐49 years | 27.6 | 36.9 | 33.2 |
| 50‐64 years | 65.7 | 50.1 | 53.2 |
| Male sex (%) | 59.3 | 62.3 | 46.6 |
| Race or ethnicity (%) | |||
| White | 55.7 | 41.5 | 38.0 |
| Black | 7.6 | 14.8 | 16.6 |
| Hispanic | 4.8 | 10.5 | 10.4 |
| Other race | 3.6 | 4.7 | 5.2 |
| Missing | 28.4 | 29.0 | 29.7 |
| Median income by ZIP code (%) | |||
| <$37,000 | 21.5 | 36.7 | 43.0 |
| $37,000‐$45,999 | 25.2 | 27.8 | 27.1 |
| $46,000‐$60,999 | 26.3 | 20.3 | 17.6 |
| $61,000 | 24.8 | 11.5 | 8.4 |
| Emergency admission (%) | 63.3 | 75.6 | 72.9 |
| Weekend admission (%) | 24.5 | 26.2 | 25.1 |
| Hospital bed size (%) | |||
| Small | 8.9 | 10.3 | 11.4 |
| Medium | 24.0 | 22.3 | 25.9 |
| Large | 67.1 | 67.5 | 62.8 |
| Hospital control (%) | |||
| Private | 33.8 | 34.8 | 34.4 |
| Government (nonfederal) | 6.7 | 9.7 | 8.3 |
| Private or government | 59.5 | 55.5 | 57.3 |
| Hospital region (%) | |||
| Northeast | 17.4 | 12.5 | 17.6 |
| Midwest | 25.7 | 19.4 | 20.9 |
| South | 39.5 | 56.8 | 42.4 |
| West | 17.4 | 11.3 | 19.2 |
| Teaching hospital (%) | 41.7 | 43.8 | 43.3 |
Compared with privately insured patients, a larger proportion of uninsured and Medicaid patients had higher predicted mortality levels (Table 2). Medicaid patients had a disproportionately higher predicted LOS, whereas predicted resource demand was higher among privately insured patients. Hypertension (48%), chronic pulmonary disease (29.5%), and uncomplicated diabetes (21.5%) were the 3 most common comorbidities in the study cohort, with a generally higher prevalence of comorbidities among Medicaid patients.
| Characteristic* | Privately insured (n = 73,256) | Uninsured (n = 18,531) | Medicaid (n = 26,222) |
|---|---|---|---|
| |||
| Medstat disease staging (%) | |||
| Mortality level 1 | 50.8 | 45.4 | 36.7 |
| Mortality level 2 | 44.0 | 49.1 | 56.7 |
| Mortality level 3 | 5.3 | 5.5 | 6.7 |
| Length of stay level 1 | 66.8 | 71.6 | 53.8 |
| Length of stay level 2 | 28.5 | 24.5 | 39.3 |
| Length of stay level 3 | 4.8 | 3.8 | 6.9 |
| Resource demand level 1 | 45.2 | 54.2 | 48.5 |
| Resource demand level 2 | 40.5 | 34.2 | 39.2 |
| Resource demand level 3 | 14.2 | 11.7 | 12.3 |
| Coexisting medical conditions (%) | |||
| Congestive heart failure | 4.7 | 4.8 | 10.1 |
| Valvular disease | 2.8 | 2.0 | 2.7 |
| Pulmonary circulation disease | 0.8 | 0.6 | 1.5 |
| Peripheral vascular disease | 3.2 | 2.2 | 3.2 |
| Paralysis | 1.2 | 0.8 | 3.5 |
| Other neurological disorders | 2.4 | 1.9 | 7.3 |
| Chronic pulmonary disease | 23.6 | 22.4 | 37.7 |
| Uncomplicated diabetes | 19.6 | 18.6 | 23.4 |
| Complicated diabetes | 3.3 | 2.1 | 4.9 |
| Hypothyroidism | 5.6 | 2.7 | 4.7 |
| Renal failure | 3.0 | 1.9 | 5.6 |
| Liver disease | 1.6 | 2.5 | 4.4 |
| Peptic ulcer disease | <0.5 | <0.5 | <0.5 |
| AIDS | 0.1 | 0.1 | 0.4 |
| Lymphoma | 1.1 | 0.4 | 0.6 |
| Metastatic cancer | 2.1 | 0.7 | 2.2 |
| Non‐metastatic solid tumor | 1.5 | 0.8 | 2.1 |
| Collagen vascular diseases | 2.3 | 0.9 | 2.3 |
| Coagulopathy | 2.7 | 2.4 | 3.4 |
| Obesity | 10.3 | 8.2 | 9.3 |
| Weight loss | 1.6 | 1.8 | 3.3 |
| Fluid and electrolyte disorders | 18.3 | 19.4 | 23.8 |
| Chronic blood loss anemia | 0.6 | 0.6 | 0.8 |
| Deficiency anemias | 8.6 | 8.5 | 13.4 |
| Alcohol abuse | 3.3 | 9.8 | 8.3 |
| Drug abuse | 1.9 | 10.2 | 9.8 |
| Psychoses | 1.5 | 1.9 | 6.8 |
| Depression | 7.2 | 4.8 | 9.9 |
| Hypertension | 48.0 | 44.1 | 45.7 |
In‐Hospital Mortality
Compared with the privately insured, age‐standardized and sex‐standardized in‐hospital mortality for AMI and stroke was significantly higher for uninsured and Medicaid patients (Table 3). Among pneumonia patients, Medicaid recipients had significantly higher in‐hospital mortality compared with privately insured and uninsured patients.
| Privately Insured | Uninsured | Medicaid | |
|---|---|---|---|
| |||
| In‐hospital mortality, rate per 100 discharges (SE) | |||
| Acute myocardial infarction | 2.22 (0.10) | 4.03 (0.31)* | 4.57 (0.34)* |
| Stroke | 7.49 (0.27) | 10.46 (0.64)* | 9.89 (0.45)* |
| Pneumonia | 1.75 (0.09) | 1.74 (0.18) | 2.48 (0.14)* |
| Length of stay, mean (SE), days | |||
| Acute myocardial infarction | 4.17 (0.06) | 4.46 (0.09) | 5.85 (0.16) |
| Stroke | 6.37 (0.13) | 7.15 (0.25) | 9.28 (0.30) |
| Pneumonia | 4.89 (0.05) | 4.64 (0.10) | 5.80 (0.08) |
| Cost per episode, mean (SE), dollars | |||
| Acute myocardial infarction | 21,077 (512) | 19,977 (833) | 22,452 (841) |
| Stroke | 16,022 (679) | 14,571 (1,036) | 18,462 (824) |
| Pneumonia | 8,223 (192) | 7,086 (293) | 9,479 (271) |
After multivariable adjustment for additional patient and hospital characteristics, uninsured AMI and stroke patients continued to have significantly higher in‐hospital mortality compared with the privately insured (Table 4). Among pneumonia patients, Medicaid recipients persisted in having significantly higher in‐hospital mortality than the privately insured.
| Model 1* | Model 2 | Model 3 | |
|---|---|---|---|
| |||
| In‐hospital mortality, adjusted odds ratio (95% CI) | |||
| Acute Myocardial Infarction | |||
| Uninsured vs. privately insured | 1.59 (1.35‐1.88) | 1.58 (1.30‐1.93) | 1.52 (1.24‐1.85) |
| Medicaid vs. privately insured | 1.83 (1.54‐2.18) | 1.22 (0.99‐1.50) | 1.15 (0.94‐1.42) |
| Stroke | |||
| Uninsured vs. privately insured | 1.56 (1.35‐1.80) | 1.50 (1.30‐1.73) | 1.49 (1.29‐1.72) |
| Medicaid vs. privately insured | 1.32 (1.15‐1.52) | 1.09 (0.93‐1.27) | 1.08 (0.93‐1.26) |
| Pneumonia | |||
| Uninsured vs. privately insured | 0.99 (0.81‐1.21) | 1.12 (0.91‐1.39) | 1.10 (0.89‐1.36) |
| Medicaid vs. privately insured | 1.41 (1.20‐1.65) | 1.24 (1.04‐1.48) | 1.21 (1.01‐1.45) |
| Length of stay, adjusted ratio (95% CI)| | |||
| Acute Myocardial Infarction | |||
| Uninsured vs. privately insured | 1.00 (0.98‐1.02) | 1.00 (0.98‐1.02) | 1.00 (0.98‐1.02) |
| Medicaid vs. privately insured | 1.17 (1.14‐1.21) | 1.07 (1.05‐1.09) | 1.07 (1.05‐1.09) |
| Stroke | |||
| Uninsured vs. privately insured | 1.06 (1.02‐1.10) | 1.08 (1.04‐1.11) | 1.07 (1.04‐1.11) |
| Medicaid vs. privately insured | 1.30 (1.26‐1.34) | 1.17 (1.14‐1.20) | 1.17 (1.14‐1.20) |
| Pneumonia | |||
| Uninsured vs. privately insured | 0.95 (0.93‐0.97) | 0.96 (0.94‐0.99) | 0.96 (0.94‐0.98) |
| Medicaid vs. privately insured | 1.15 (1.13‐1.17) | 1.04 (1.03‐1.06) | 1.04 (1.03‐1.06) |
| Cost per episode, adjusted ratio (95% CI)| | |||
| Acute Myocardial Infarction | |||
| Uninsured vs. privately insured | 0.97 (0.95‐0.99) | 0.99 (0.97‐1.00) | 0.99 (0.97‐1.00) |
| Medicaid vs. privately insured | 1.01 (0.98‐1.04) | 0.99 (0.97‐1.01) | 0.99 (0.97‐1.01) |
| Stroke | |||
| Uninsured vs. privately insured | 0.97 (0.93‐1.02) | 1.00 (0.96‐1.03) | 1.00 (0.97‐1.03) |
| Medicaid vs. privately insured | 1.17 (1.13‐1.21) | 1.06 (1.04‐1.09) | 1.06 (1.04‐1.09) |
| Pneumonia | |||
| Uninsured vs. privately insured | 0.95 (0.92‐0.97) | 0.98 (0.96‐1.00) | 0.98 (0.96‐1.00) |
| Medicaid vs. privately insured | 1.17 (1.15‐1.19) | 1.05 (1.04‐1.07) | 1.05 (1.04‐1.07) |
LOS
Among AMI and stroke patients, age‐standardized and sex‐standardized mean LOS was significantly longer for the uninsured and Medicaid recipients compared with the privately insured (Table 3). Among pneumonia patients, the uninsured had a slightly shorter mean LOS compared with the privately insured whereas Medicaid recipients averaged the longest LOS.
These insurance‐related disparities in LOS among pneumonia patients persisted after multivariable adjustment (Table 4). Among AMI patients, only Medicaid recipients persisted in having a significantly longer LOS than the privately insured. Among stroke patients, both the uninsured and Medicaid recipients averaged a longer LOS compared with the privately insured.
Cost per Episode
For all 3 conditions, the uninsured had significantly lower age‐standardized and sex‐standardized costs compared with the privately insured (Table 3). However, Medicaid patients had higher costs than the privately insured for all three conditions, significantly so among patients with stroke and pneumonia.
These insurance‐related disparities in costs persisted in multivariable analyses (Table 4). The uninsured continued to have lower costs compared with the privately insured, significantly so for patients with AMI and pneumonia. Among stroke and pneumonia patients, Medicaid recipients continued to accrue higher costs than the uninsured or privately insured.
Discussion
In this nationally representative study of working‐age Americans hospitalized for 3 common medical conditions, we found that insurance status was associated with significant variations in in‐hospital mortality and resource use. Whereas privately insured patients experienced comparatively lower in‐hospital mortality in most cases, mortality risk was highest among the uninsured for 2 of the 3 common causes of noncancer inpatient deaths. Although previous studies have examined insurance‐related disparities in inpatient care for individual diagnoses and specific populations, no broad overview of this important issue has been published in the past decade. In light of the current economic recession and national healthcare debate, these findings may be a prescient indication of a widening insurance gap in the quality of hospital care.
There are several potential mechanisms for these disparities. For instance, Hadley et al.9 reported significant underuse of high‐cost or high‐discretion procedures among the uninsured in their analysis of a nationally representative sample of 592,598 hospitalized patients. Similarly, Burstin et al.10 found that among a population of 30,195 hospitalized patients with diverse diagnoses, the uninsured were at greater risk for receiving substandard care regardless of hospital characteristics. These, and other similar findings,7, 8, 19 are suggestive of differences in the way uninsured patients are generally managed in the hospital that may partly explain the disparities reported herein.
More specifically, analyses of national registries of AMI have documented lower rates of utilization of invasive, potentially life‐saving, cardiac interventions among the uninsured.16, 17 Similarly, a lower rate of carotid endarterectomy was reported among uninsured stroke patients from an analysis of the 2002 NIS.15 Other differences in inpatient management unmeasured by administrative data, such as the use of subspecialists and allied health professionals, may also contribute.32 Unfortunately, limitations in the available data prevented us from being able to appropriately address the important issue of insurance related differences in the utilization of specific inpatient procedures.
These disparities may also be indicative of differences in severity of illness that are not captured fully by the MedStat disease staging criteria. The uninsured might have more severe illness at admission, either due to the presence of more advanced chronic disease or delay in seeking care for the acute episode. AMI and stroke are usually the culmination of longstanding atherosclerosis that is amenable to improvement through timely and consistent risk‐factor modification. Not having a usual source of medical care,6, 33 inadequate screening and management of known risk‐factors,3, 34 and difficulties in obtaining specialty care5 among the uninsured likely increases their risk of being hospitalized with more advanced disease. The higher likelihood of being admitted through the emergency department19 and on weekends9 among the uninsured lends credence to the possibility of delays in seeking care. All of these are potential mediators of higher AMI and stroke mortality in uninsured patients.
Finally, these mortality differences could also be due to the additional risks imposed by poorly managed comorbidities among uninsured patients. Although we controlled for the presence of comorbidities in our analysis, we lacked data about the severity of individual comorbidities. A recent study reported significant lapses in follow‐up care after the onset of a chronic condition in uninsured individuals under 65 years of age.34 Other studies have also documented insurance related disparities in the care of chronic diseases3, 35 that were among the most common comorbidities in our cohort.
Most of the reasons for insurance‐related disparities noted above for the uninsured are also applicable to Medicaid patients. Differences in the intensity of inpatient care,7, 8, 1519 limited access to health care services,2, 14 unmet health needs,5 and suboptimal management of chronic medical conditions35 were also reported for Medicaid patients in prior research. These factors likely contributed to the higher in‐hospital mortality in this patient population, evidenced by the sequential decrease in odds after adjusting for comorbidities and disease severity. Medicaid patients hospitalized for stroke were noted to have significantly longer LOS, which could plausibly be due to difficulties with arranging appropriate discharge disposition; the higher likelihood of paralysis among these patients15 would likely necessitate a higher frequency of rehabilitation facility placement. The higher costs for Medicaid patients with stroke and pneumonia may potentially be the result of these patients longer LOS. Although cost differences between the uninsured and privately insured were statistically significant, these were not large enough to be of material significance.
Limitations
Our study has several limitations. Since the NIS does not assign unique patient identifiers that would permit tracking of readmissions, we excluded patients transferred to another acute‐care hospital from our study to avoid counting the same patient twice. However, only 10% of hospitalized patients underwent transfer for cardiac procedures in the National Registry of Myocardial Infarction, with privately insured patients more likely to be transferred than other insurance groups.17 Since these patients are also more likely to have better survival, their exclusion likely biased our study toward the null. The same is probable for stroke patients as well.
Some uninsured patients begin Medicaid coverage during hospitalization and should ideally be counted as uninsured but were included under Medicaid in our analysis. They are also likely to be state‐ and plan‐specific variations in Medicaid and private payer coverage that we could not incorporate into our analysis. In addition, we were unable to include deaths that may have occurred shortly after discharge, even though these may have been related to the quality of hospital care. Furthermore, although the 3 conditions we studied are common and responsible for a large number of hospital deaths, they make up about 8% of total annual hospital discharges,23 and caution should be exercised in generalizing our findings to the full spectrum of hospitalizations. Lastly, it is possible that unmeasured confounding could be responsible for the observed associations. Uninsured and Medicaid patients are likely to have more severe disease, which may not be adequately captured by the administrative data available in the NIS. If so, this would explain the mortality association rather than insurance status.36, 37
Conclusions
Significant insurance‐related differences in mortality exist for 2 of the leading causes of noncancer inpatient deaths among working‐age Americans. Further studies are needed to determine whether provider sensitivity to insurance status or unmeasured sociodemographic and clinical prognostic factors are responsible for these disparities. Policy makers, hospital administrators, and physicians should be cognizant of these disparities and consider policies to address potential insurance related gaps in the quality of inpatient care.
- , .The U.S. economy and changes in health insurance coverage, 2000‐2006.Health Aff (Millwood).2008;27(2):w135‐w144.
- , , .Rates of avoidable hospitalization by insurance status in Massachusetts and Maryland.JAMA.1992;268(17):2388‐2394.
- , , , et al.Unmet health needs of uninsured adults in the United States.JAMA.2000;284(16):2061‐2069.
- , , .Health insurance and access to care for symptomatic conditions.Arch Intern Med.2000;160(9):1269‐1274.
- , , , et al.Access to specialty care and medical services in community health centers.Health Aff (Millwood).2007;26(5):1459‐1468.
- , , , et al.A national study of chronic disease prevalence and access to care in uninsured U.S. adults.Ann Intern Med.2008;149:170–176.
- , , , .Relationship between patient source of payment and the intensity of hospital services.Med Care.1988;26(11):1111‐1114.
- , , .The association of payer with utilization of cardiac procedures in Massachusetts.JAMA.1990;264(10):1255‐1260.
- , , .Comparison of uninsured and privately insured hospital patients: condition on admission, resource use, and outcome.JAMA.1991;265:374–379.
- , , .Socioeconomic status and risk for substandard medical care.JAMA.1992;268(17):2383‐2387.
- , , , .The relation between health insurance coverage and clinical outcomes among women with breast cancer.N Engl J Med.1993;329(5):326‐331.
- , , .Health insurance and mortality. Evidence from a national cohort.JAMA.1993;270(6):737‐741.
- , , , .Mortality in the uninsured compared with that in persons with public and private health insurance.Arch Intern Med.1994;154(21):2409‐2416.
- .Medicaid policy and the substitution of hospital outpatient care for physician care.Health Serv Res.1989;24:33–66.
- , .Disparities in outcomes among patients with stroke associated with insurance status.Stroke.2007;38(3):1010‐1016.
- , , , et al.Influence of payor on use of invasive cardiac procedures and patient outcome after myocardial infarction in the United States. Participants in the National Registry of Myocardial Infarction.J Am Coll Cardiol.1998;31(7):1474‐1480.
- , , , et al.Payer status and the utilization of hospital resources in acute myocardial infarction: a report from the National Registry of Myocardial Infarction 2.Arch Intern Med.2000;160(6):817–823.
- , , , et al.Insurance coverage and care of patients with non‐ST‐segment elevation acute coronary syndromes.Ann Intern Med.2006;145(10):739–748.
- , , .Comparing uninsured and privately insured hospital patients: Admission severity, health outcomes and resource use.Health Serv Manage Res.2001;14(3):203–210.
- Healthcare Cost and Utilization Project. Introduction to the HCUP Nationwide Inpatient Sample (NIS) 2005. Rockville, MD: Agency for Healthcare Research and Quality; 2007:6. Available at: www.hcup‐us.ahrq.gov/db/nation/nis/NIS_Introduction_2005.pdf. Accessed February2010.
- Healthcare Cost and Utilization Project. Design of the Nationwide Inpatient Sample (NIS) 2005. Rockville, MD: Agency for Healthcare Research and Quality; 2007. Available at: www.hcup‐us.ahrq.gov/db/nation/nis/reports/NIS_2005_Design_Report.pdf. Accessed February2010.
- AHRQ Quality Indicators. Inpatient Quality Indicators: Technical Specifications. Version 3.1 (March 12, 2007). Available at: www.qualityindicators.ahrq.gov/downloads/iqi/iqi_technical_specs_v31.pdf. Accessed February2010.
- , , . National Hospital Discharge Survey: 2005 annual summary with detailed diagnosis and procedure data. Washington, DC: National Center for Health Statistics; 2007. Vital and Health Statistics 13(165). Available at: www.cdc.gov/nchs/data/series/sr_13/sr13_165.pdf. Accessed February2010.
- AHRQ Quality Indicators. Guide to Inpatient Quality Indicators: Quality of Care in Hospitals—Volume, Mortality, and Utilization. Version 3.1 (March 12, 2007). Available at: www.qualityindicators.ahrq.gov/downloads/iqi/iqi_guide_v31.pdf. Accessed February2010.
- , , .Income, poverty, and health insurance coverage in the United States: 2006.Washington, DC:US Census Bureau. Current Population Reports;2007:60–233.
- , . Conditions Related to Uninsured Hospitalizations, 2003. HCUP Statistical Brief #8. Rockville, MD: Agency for Healthcare Research and Quality; 2006:6. Available at: www.hcup‐us.ahrq.gov/reports/statbriefs/sb8.pdf. Accessed February2010.
- Healthcare Cost and Utilization Project. NIS Description of Data Elements. Available at: www.hcup‐us.ahrq.gov/db/nation/nis/nisdde.jsp. Accessed February2010.
- , , , .Comorbidity measures for use with administrative data.Med Care.1998;36:8–27.
- SUDAAN User's Manual, Release 9.0. Research TrianglePark, NC:Research Triangle Institute;2006.
- , . Final Report on Calculating Nationwide Inpatient Sample (NIS) Variances, 2001. HCUP Methods Series Report #2003‐2. Online June 2005 (revised June 6, 2005). U.S. Agency for Healthcare Research and Quality. Available at: www.hcup‐us.ahrq.gov/reports/CalculatingNISVariances200106092005.pdf. Accessed February2010.
- .On the variances of asymptotically normal estimators from complex surveys.Int Stat Rev.1983;51:279–292.
- , , , et al.Patient characteristics associated with care by a cardiologist among adults hospitalized with severe congestive heart failure.J Am Coll Cardiol.2000;36:2119–2125.
- , . Summary health statistics for U.S. adults: National Health Interview Survey, 2006. Washington, DC: National Center for Health Statistics; 2007:12. Vital and Health Statistics 10(235). Available at: www.cdc.gov/nchs/data/series/sr_10/sr10_235.pdf. Accessed February2010.
- .Insurance coverage, medical care use, and short‐term health changes following an unintentional injury or the onset of a chronic condition.JAMA.2007;297(10):1073–1084.
- , , , et al.The quality of chronic disease care in U.S. community health centers.Health Aff (Millwood).2006;25(6):1712–1723.
- , , , , , .Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Med Care.2008;46(3):232–239.
- , , , .The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population.J Clin Epidemiol. [E‐pub ahead of print].
With about 1 in 5 working‐age Americans (age 18‐64 years) currently uninsured and a large number relying on Medicaid, adequate access to quality health care services is becoming increasingly difficult.1 Substantial literature has accumulated over the years suggesting that access and quality in health care are closely linked to an individual's health insurance status.211 Some studies indicate that being uninsured or publicly insured is associated with negative health consequences.12, 13 Although the Medicaid program has improved access for qualifying low‐income individuals, significant gaps in access and quality remain.2, 5, 11, 1419 These issues are likely to become more pervasive should there be further modifications to state Medicaid funding in response to the unfolding economic crisis.
Although numerous studies have focused on insurance‐related disparities in the outpatient setting, few nationally representative studies have examined such disparities among hospitalized patients. A cross‐sectional study of a large hospital database from 1987 reported higher risk‐adjusted in‐hospital mortality, shorter length of stay (LOS), and lower procedure use among uninsured patients.9 A more recent analysis, limited to patients admitted with stroke, reported significant variation in hospital care associated with insurance status.15 Other studies reporting myocardial infarction registry and quality improvement program data are biased by the self‐selection of large urban teaching hospitals.1618 To our knowledge, no nationally representative study has focused on the impact of insurance coverage on hospital care for common medical conditions among working‐age Americans, the fastest growing segment of the uninsured population.
To address this gap in knowledge, we analyzed a nationally representative hospital database to determine whether there are significant insurance‐related disparities in in‐hospital mortality, LOS, and cost per hospitalization for 3 common medical conditions among working‐age adults, and, if present, to determine whether these disparities are due to differences in disease severity and comorbidities, and whether these disparities are affected by the proportion of uninsured and Medicaid patients receiving care in each hospital.
Methods
Design and Subjects
We examined data from the 2005 Nationwide Inpatient Sample (NIS), a nationally representative database of hospital inpatient stays maintained by the Agency for Healthcare Research and Quality (AHRQ) as part of the Healthcare Cost and Utilization Project (HCUP).20, 21 The NIS is a stratified probability sample of 20% of all US community hospitals, including public hospitals, academic medical centers, and specialty hospitals. Long‐term care hospitals, psychiatric hospitals, and alcoholism/chemical‐dependency treatment facilities are excluded. The 2005 NIS contains data on 7,995,048 discharges from 1054 hospitals located in 37 States and is designed to be representative of all acute care discharges from all US community hospitals.21
We identified discharges with a principal diagnosis of acute myocardial infarction (AMI), stroke, and pneumonia using International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9‐CM) codes specified in the AHRQ definitions of Inpatient Quality Indicators (Supporting Information Appendix).22 These 3 conditions are among the leading causes of noncancer inpatient deaths in patients under 65 years old,23 and evidence suggests that high mortality may be associated with deficiencies in the quality of inpatient care.24
We confined our analysis to patients 18 to 64 years of age, since this population is most at risk of being uninsured or underinsured.25 We excluded pregnant women because they account for an unusually high proportion of uninsured discharges and were relatively few in our cohort.26 In addition, we excluded patients transferred to another acute care hospital and patients missing payer source and discharge disposition. Our study protocol was approved by the Partners Human Research Committee.
Study Variables
We categorized insurance status as privately insured, uninsured, Medicaid, or Medicare. We defined privately insured patients as those having either Blue Cross or another commercial carrier listed as the primary payer and uninsured patients as those having either no charge or self‐pay listed as the primary payer.27 Other governmental payer categories were noted to share several characteristics with Medicare patients and comprised only a small proportion of the sample, and were thus included with Medicare. In order to account for NIS's sampling scheme and accurately apply sample weights in our analysis, we used Medicare as a separate category. However, since Medicare patients age 18 to 64 years represent a fundamentally different population that is primarily disabled or very ill, only results of privately insured, uninsured, and Medicaid patients are reported.
We selected in‐hospital mortality as the outcome measure and LOS and cost per hospitalization as measures of resource utilization. The NIS includes a binary indicator variable for in‐hospital mortality and specifies inpatient LOS in integers, with same‐day stays coded as 0. NIS's cost estimates are based on hospital cost reports submitted to the Centers for Medicare and Medicaid Services. To test the validity of our cost analyses, we performed parallel analyses using hospital charges as a measure of utilization (charges include hospital overhead, charity care, and bad debt). The resulting adjusted ratios differed little from cost ratios and we opted to report only the details of our cost analyses.
In order to assess the independent association between insurance status and the outcome measures listed above, we selected covariates for inclusion in multivariable models based on the existing literature. Patient covariates included: age group (18‐34 years, 35‐49 years, 50‐64 years), sex (male/female), race/ethnicity (non‐Hispanic white, non‐Hispanic black, Hispanic, other, missing), median income by zip code of residence (less than $37,000, $37,000‐$45,999, $46,000‐$60,999, $61,000 or more), admission through the emergency department (yes/no), admission on a weekend (yes/no), measures of disease severity, and comorbidity indicators. Measures of disease severity specific to each outcome are assigned in the NIS using criteria developed by Medstat (Medstat Disease Staging Software Version 5.2, Thomson Medstat Inc., Ann Arbor, MI). Severity is categorized into 5 levels, with a higher level indicating greater risk. We recorded comorbidities for each patient in our sample using HCUP Comorbidity Software, Version 3.2 (
Hospital covariates included: bed size (small, medium, large), ownership/control (private, government, private or government), geographic region (northeast, midwest, south, west), teaching status (teaching, non‐teaching), and the proportion of uninsured and Medicaid patients (combined) admitted to each hospital for AMI, stroke, or pneumonia. The actual number of hospital beds in each bed size category varies according to a hospital's geographic region and teaching status.27 Ownership/control, geographic region, and teaching status are assigned according to information from the American Hospital Association Annual Survey of Hospitals. The proportion of uninsured and Medicaid patients admitted to each hospital was found to have a nonmonotonic relationship with the outcomes being assessed and was thus treated as a 6‐level categorical variable with the following levels: 0% to 10%, 11% to 20%, 21% to 30%, 31% to 40%, 41% to 50%, and 51% to 100%.
Statistical Analysis
Summary statistics were constructed at the patient level and differences in proportions were evaluated with the chi‐square test. We employed direct standardization, using the age and sex distribution of the entire cohort, to compute age‐standardized and sex‐standardized estimates for each insurance group and compared them using the chi‐square test for in‐hospital mortality and t test for log transformed LOS and cost per hospitalization. For each condition, we developed multivariable logistic regression models for in‐hospital mortality and multivariable linear regression models for log transformed LOS and cost. The patient was the unit of analysis in all models.
In order to elucidate the contribution of disease severity and comorbidities and the proportion of uninsured and Medicaid patients admitted to each hospital, we fitted 3 sequential models for each outcome measure: Model 1 adjusted for patient sociodemographic characteristics and hospital characteristics with the exception of the covariate for the proportion of uninsured and Medicaid patients, Model 2 adjusted for all covariates in the preceding model as well as patients' comorbidities and severity of principal diagnosis, and Model 3 adjusted for all previously mentioned covariates as well as the proportion of uninsured and Medicaid patients admitted to each hospital. We excluded patients who died during hospitalization from the models for LOS and cost. We exponentiated the effect estimates from the log transformed linear regression models so that the adjusted ratio represents the percentage change in the mean LOS or mean cost.
To determine whether the association between patients' insurance status and in‐hospital mortality was modified by seeking care in hospitals treating a smaller or larger proportion of uninsured and Medicaid patients, we entered an interaction term for insurance status and proportion of uninsured and Medicaid patients in the final models (Model 3) for our primary outcome of in‐hospital mortality. However, since no significant interaction was found for any of the 3 conditions, this term was removed from the models and results from the interaction models are not described. In order to assess model specification for the linear regression models, we evaluated the normality of model residuals and found that these were approximately normally distributed. Lastly, we attempted to test the robustness of our analyses by creating fixed effects models that controlled for hospital site but were unable to do so due to the computational limitations of available software packages that could not render fixed effects models with more than 1000 hospital sites.
For all variables except race/ethnicity, data were missing for less than 3% of patients, so we excluded these individuals from adjusted analyses. However, race/ethnicity data were missing for 29% of the sample and were analyzed in 3 different ways, namely, with the missing data treated as a separate covariate level, with the missing data removed from the analysis, and with the missing data assigned to the majority covariate level (white race). The results of our analysis were unchanged no matter how the missing values were assigned. As a result, missing values for race/ethnicity were treated as a separate covariate level in the final analysis.15 Sociodemographic characteristics of patients with missing race/ethnicity information were similar to those with complete data.
We used SUDAAN (Release 9.0.1, Research Triangle Institute, NC) to account for NIS's sampling scheme and generalized estimating equations to adjust for the clustering of patients within hospitals and hospitals within sampling strata.29 In order to account for NIS's stratified probability sampling scheme, SUDAAN uses Taylor series linearization for robust variance estimation of descriptive statistics and regression parameters.30, 31 We present 2‐tailed P values or 95% confidence intervals (CIs) for all statistical comparisons.
Results
Patient and Hospital Characteristics
The final cohort comprised of 154,381 patients discharged from 1018 hospitals in 37 states during calendar year 2005 (Table 1). This cohort was representative of 755,346 working‐age Americans, representing approximately 225,947 cases of AMI (29.9%), 151,812 cases of stroke (20.1%), and 377,588 cases of pneumonia (50.0%). Of these patients, 47.5% were privately insured, 12.0% were uninsured, 17.0% received Medicaid, and 23.5% were assigned to Medicare. Compared with the privately insured, uninsured and Medicaid patients were generally younger, less likely to be white, more likely to have lower income, and more likely to be admitted through the emergency department. Of the 1018 hospitals included in our study, close to half (44.3%) were small, with bedsize ranging from 24 to 249. A large number of hospitals were located in the South (39.9%), and 14.9% were designated teaching hospitals.
| Characteristic* | Privately insured (n = 73,256) | Uninsured (n = 18,531) | Medicaid (n = 26,222) |
|---|---|---|---|
| |||
| Principal diagnosis (%) | |||
| Acute myocardial infarction | 36.7 | 31.2 | 19.7 |
| Stroke | 20.6 | 23.7 | 19.9 |
| Pneumonia | 42.7 | 45.2 | 60.4 |
| Age group (%) | |||
| 18‐34 years | 6.8 | 13.0 | 13.7 |
| 35‐49 years | 27.6 | 36.9 | 33.2 |
| 50‐64 years | 65.7 | 50.1 | 53.2 |
| Male sex (%) | 59.3 | 62.3 | 46.6 |
| Race or ethnicity (%) | |||
| White | 55.7 | 41.5 | 38.0 |
| Black | 7.6 | 14.8 | 16.6 |
| Hispanic | 4.8 | 10.5 | 10.4 |
| Other race | 3.6 | 4.7 | 5.2 |
| Missing | 28.4 | 29.0 | 29.7 |
| Median income by ZIP code (%) | |||
| <$37,000 | 21.5 | 36.7 | 43.0 |
| $37,000‐$45,999 | 25.2 | 27.8 | 27.1 |
| $46,000‐$60,999 | 26.3 | 20.3 | 17.6 |
| $61,000 | 24.8 | 11.5 | 8.4 |
| Emergency admission (%) | 63.3 | 75.6 | 72.9 |
| Weekend admission (%) | 24.5 | 26.2 | 25.1 |
| Hospital bed size (%) | |||
| Small | 8.9 | 10.3 | 11.4 |
| Medium | 24.0 | 22.3 | 25.9 |
| Large | 67.1 | 67.5 | 62.8 |
| Hospital control (%) | |||
| Private | 33.8 | 34.8 | 34.4 |
| Government (nonfederal) | 6.7 | 9.7 | 8.3 |
| Private or government | 59.5 | 55.5 | 57.3 |
| Hospital region (%) | |||
| Northeast | 17.4 | 12.5 | 17.6 |
| Midwest | 25.7 | 19.4 | 20.9 |
| South | 39.5 | 56.8 | 42.4 |
| West | 17.4 | 11.3 | 19.2 |
| Teaching hospital (%) | 41.7 | 43.8 | 43.3 |
Compared with privately insured patients, a larger proportion of uninsured and Medicaid patients had higher predicted mortality levels (Table 2). Medicaid patients had a disproportionately higher predicted LOS, whereas predicted resource demand was higher among privately insured patients. Hypertension (48%), chronic pulmonary disease (29.5%), and uncomplicated diabetes (21.5%) were the 3 most common comorbidities in the study cohort, with a generally higher prevalence of comorbidities among Medicaid patients.
| Characteristic* | Privately insured (n = 73,256) | Uninsured (n = 18,531) | Medicaid (n = 26,222) |
|---|---|---|---|
| |||
| Medstat disease staging (%) | |||
| Mortality level 1 | 50.8 | 45.4 | 36.7 |
| Mortality level 2 | 44.0 | 49.1 | 56.7 |
| Mortality level 3 | 5.3 | 5.5 | 6.7 |
| Length of stay level 1 | 66.8 | 71.6 | 53.8 |
| Length of stay level 2 | 28.5 | 24.5 | 39.3 |
| Length of stay level 3 | 4.8 | 3.8 | 6.9 |
| Resource demand level 1 | 45.2 | 54.2 | 48.5 |
| Resource demand level 2 | 40.5 | 34.2 | 39.2 |
| Resource demand level 3 | 14.2 | 11.7 | 12.3 |
| Coexisting medical conditions (%) | |||
| Congestive heart failure | 4.7 | 4.8 | 10.1 |
| Valvular disease | 2.8 | 2.0 | 2.7 |
| Pulmonary circulation disease | 0.8 | 0.6 | 1.5 |
| Peripheral vascular disease | 3.2 | 2.2 | 3.2 |
| Paralysis | 1.2 | 0.8 | 3.5 |
| Other neurological disorders | 2.4 | 1.9 | 7.3 |
| Chronic pulmonary disease | 23.6 | 22.4 | 37.7 |
| Uncomplicated diabetes | 19.6 | 18.6 | 23.4 |
| Complicated diabetes | 3.3 | 2.1 | 4.9 |
| Hypothyroidism | 5.6 | 2.7 | 4.7 |
| Renal failure | 3.0 | 1.9 | 5.6 |
| Liver disease | 1.6 | 2.5 | 4.4 |
| Peptic ulcer disease | <0.5 | <0.5 | <0.5 |
| AIDS | 0.1 | 0.1 | 0.4 |
| Lymphoma | 1.1 | 0.4 | 0.6 |
| Metastatic cancer | 2.1 | 0.7 | 2.2 |
| Non‐metastatic solid tumor | 1.5 | 0.8 | 2.1 |
| Collagen vascular diseases | 2.3 | 0.9 | 2.3 |
| Coagulopathy | 2.7 | 2.4 | 3.4 |
| Obesity | 10.3 | 8.2 | 9.3 |
| Weight loss | 1.6 | 1.8 | 3.3 |
| Fluid and electrolyte disorders | 18.3 | 19.4 | 23.8 |
| Chronic blood loss anemia | 0.6 | 0.6 | 0.8 |
| Deficiency anemias | 8.6 | 8.5 | 13.4 |
| Alcohol abuse | 3.3 | 9.8 | 8.3 |
| Drug abuse | 1.9 | 10.2 | 9.8 |
| Psychoses | 1.5 | 1.9 | 6.8 |
| Depression | 7.2 | 4.8 | 9.9 |
| Hypertension | 48.0 | 44.1 | 45.7 |
In‐Hospital Mortality
Compared with the privately insured, age‐standardized and sex‐standardized in‐hospital mortality for AMI and stroke was significantly higher for uninsured and Medicaid patients (Table 3). Among pneumonia patients, Medicaid recipients had significantly higher in‐hospital mortality compared with privately insured and uninsured patients.
| Privately Insured | Uninsured | Medicaid | |
|---|---|---|---|
| |||
| In‐hospital mortality, rate per 100 discharges (SE) | |||
| Acute myocardial infarction | 2.22 (0.10) | 4.03 (0.31)* | 4.57 (0.34)* |
| Stroke | 7.49 (0.27) | 10.46 (0.64)* | 9.89 (0.45)* |
| Pneumonia | 1.75 (0.09) | 1.74 (0.18) | 2.48 (0.14)* |
| Length of stay, mean (SE), days | |||
| Acute myocardial infarction | 4.17 (0.06) | 4.46 (0.09) | 5.85 (0.16) |
| Stroke | 6.37 (0.13) | 7.15 (0.25) | 9.28 (0.30) |
| Pneumonia | 4.89 (0.05) | 4.64 (0.10) | 5.80 (0.08) |
| Cost per episode, mean (SE), dollars | |||
| Acute myocardial infarction | 21,077 (512) | 19,977 (833) | 22,452 (841) |
| Stroke | 16,022 (679) | 14,571 (1,036) | 18,462 (824) |
| Pneumonia | 8,223 (192) | 7,086 (293) | 9,479 (271) |
After multivariable adjustment for additional patient and hospital characteristics, uninsured AMI and stroke patients continued to have significantly higher in‐hospital mortality compared with the privately insured (Table 4). Among pneumonia patients, Medicaid recipients persisted in having significantly higher in‐hospital mortality than the privately insured.
| Model 1* | Model 2 | Model 3 | |
|---|---|---|---|
| |||
| In‐hospital mortality, adjusted odds ratio (95% CI) | |||
| Acute Myocardial Infarction | |||
| Uninsured vs. privately insured | 1.59 (1.35‐1.88) | 1.58 (1.30‐1.93) | 1.52 (1.24‐1.85) |
| Medicaid vs. privately insured | 1.83 (1.54‐2.18) | 1.22 (0.99‐1.50) | 1.15 (0.94‐1.42) |
| Stroke | |||
| Uninsured vs. privately insured | 1.56 (1.35‐1.80) | 1.50 (1.30‐1.73) | 1.49 (1.29‐1.72) |
| Medicaid vs. privately insured | 1.32 (1.15‐1.52) | 1.09 (0.93‐1.27) | 1.08 (0.93‐1.26) |
| Pneumonia | |||
| Uninsured vs. privately insured | 0.99 (0.81‐1.21) | 1.12 (0.91‐1.39) | 1.10 (0.89‐1.36) |
| Medicaid vs. privately insured | 1.41 (1.20‐1.65) | 1.24 (1.04‐1.48) | 1.21 (1.01‐1.45) |
| Length of stay, adjusted ratio (95% CI)| | |||
| Acute Myocardial Infarction | |||
| Uninsured vs. privately insured | 1.00 (0.98‐1.02) | 1.00 (0.98‐1.02) | 1.00 (0.98‐1.02) |
| Medicaid vs. privately insured | 1.17 (1.14‐1.21) | 1.07 (1.05‐1.09) | 1.07 (1.05‐1.09) |
| Stroke | |||
| Uninsured vs. privately insured | 1.06 (1.02‐1.10) | 1.08 (1.04‐1.11) | 1.07 (1.04‐1.11) |
| Medicaid vs. privately insured | 1.30 (1.26‐1.34) | 1.17 (1.14‐1.20) | 1.17 (1.14‐1.20) |
| Pneumonia | |||
| Uninsured vs. privately insured | 0.95 (0.93‐0.97) | 0.96 (0.94‐0.99) | 0.96 (0.94‐0.98) |
| Medicaid vs. privately insured | 1.15 (1.13‐1.17) | 1.04 (1.03‐1.06) | 1.04 (1.03‐1.06) |
| Cost per episode, adjusted ratio (95% CI)| | |||
| Acute Myocardial Infarction | |||
| Uninsured vs. privately insured | 0.97 (0.95‐0.99) | 0.99 (0.97‐1.00) | 0.99 (0.97‐1.00) |
| Medicaid vs. privately insured | 1.01 (0.98‐1.04) | 0.99 (0.97‐1.01) | 0.99 (0.97‐1.01) |
| Stroke | |||
| Uninsured vs. privately insured | 0.97 (0.93‐1.02) | 1.00 (0.96‐1.03) | 1.00 (0.97‐1.03) |
| Medicaid vs. privately insured | 1.17 (1.13‐1.21) | 1.06 (1.04‐1.09) | 1.06 (1.04‐1.09) |
| Pneumonia | |||
| Uninsured vs. privately insured | 0.95 (0.92‐0.97) | 0.98 (0.96‐1.00) | 0.98 (0.96‐1.00) |
| Medicaid vs. privately insured | 1.17 (1.15‐1.19) | 1.05 (1.04‐1.07) | 1.05 (1.04‐1.07) |
LOS
Among AMI and stroke patients, age‐standardized and sex‐standardized mean LOS was significantly longer for the uninsured and Medicaid recipients compared with the privately insured (Table 3). Among pneumonia patients, the uninsured had a slightly shorter mean LOS compared with the privately insured whereas Medicaid recipients averaged the longest LOS.
These insurance‐related disparities in LOS among pneumonia patients persisted after multivariable adjustment (Table 4). Among AMI patients, only Medicaid recipients persisted in having a significantly longer LOS than the privately insured. Among stroke patients, both the uninsured and Medicaid recipients averaged a longer LOS compared with the privately insured.
Cost per Episode
For all 3 conditions, the uninsured had significantly lower age‐standardized and sex‐standardized costs compared with the privately insured (Table 3). However, Medicaid patients had higher costs than the privately insured for all three conditions, significantly so among patients with stroke and pneumonia.
These insurance‐related disparities in costs persisted in multivariable analyses (Table 4). The uninsured continued to have lower costs compared with the privately insured, significantly so for patients with AMI and pneumonia. Among stroke and pneumonia patients, Medicaid recipients continued to accrue higher costs than the uninsured or privately insured.
Discussion
In this nationally representative study of working‐age Americans hospitalized for 3 common medical conditions, we found that insurance status was associated with significant variations in in‐hospital mortality and resource use. Whereas privately insured patients experienced comparatively lower in‐hospital mortality in most cases, mortality risk was highest among the uninsured for 2 of the 3 common causes of noncancer inpatient deaths. Although previous studies have examined insurance‐related disparities in inpatient care for individual diagnoses and specific populations, no broad overview of this important issue has been published in the past decade. In light of the current economic recession and national healthcare debate, these findings may be a prescient indication of a widening insurance gap in the quality of hospital care.
There are several potential mechanisms for these disparities. For instance, Hadley et al.9 reported significant underuse of high‐cost or high‐discretion procedures among the uninsured in their analysis of a nationally representative sample of 592,598 hospitalized patients. Similarly, Burstin et al.10 found that among a population of 30,195 hospitalized patients with diverse diagnoses, the uninsured were at greater risk for receiving substandard care regardless of hospital characteristics. These, and other similar findings,7, 8, 19 are suggestive of differences in the way uninsured patients are generally managed in the hospital that may partly explain the disparities reported herein.
More specifically, analyses of national registries of AMI have documented lower rates of utilization of invasive, potentially life‐saving, cardiac interventions among the uninsured.16, 17 Similarly, a lower rate of carotid endarterectomy was reported among uninsured stroke patients from an analysis of the 2002 NIS.15 Other differences in inpatient management unmeasured by administrative data, such as the use of subspecialists and allied health professionals, may also contribute.32 Unfortunately, limitations in the available data prevented us from being able to appropriately address the important issue of insurance related differences in the utilization of specific inpatient procedures.
These disparities may also be indicative of differences in severity of illness that are not captured fully by the MedStat disease staging criteria. The uninsured might have more severe illness at admission, either due to the presence of more advanced chronic disease or delay in seeking care for the acute episode. AMI and stroke are usually the culmination of longstanding atherosclerosis that is amenable to improvement through timely and consistent risk‐factor modification. Not having a usual source of medical care,6, 33 inadequate screening and management of known risk‐factors,3, 34 and difficulties in obtaining specialty care5 among the uninsured likely increases their risk of being hospitalized with more advanced disease. The higher likelihood of being admitted through the emergency department19 and on weekends9 among the uninsured lends credence to the possibility of delays in seeking care. All of these are potential mediators of higher AMI and stroke mortality in uninsured patients.
Finally, these mortality differences could also be due to the additional risks imposed by poorly managed comorbidities among uninsured patients. Although we controlled for the presence of comorbidities in our analysis, we lacked data about the severity of individual comorbidities. A recent study reported significant lapses in follow‐up care after the onset of a chronic condition in uninsured individuals under 65 years of age.34 Other studies have also documented insurance related disparities in the care of chronic diseases3, 35 that were among the most common comorbidities in our cohort.
Most of the reasons for insurance‐related disparities noted above for the uninsured are also applicable to Medicaid patients. Differences in the intensity of inpatient care,7, 8, 1519 limited access to health care services,2, 14 unmet health needs,5 and suboptimal management of chronic medical conditions35 were also reported for Medicaid patients in prior research. These factors likely contributed to the higher in‐hospital mortality in this patient population, evidenced by the sequential decrease in odds after adjusting for comorbidities and disease severity. Medicaid patients hospitalized for stroke were noted to have significantly longer LOS, which could plausibly be due to difficulties with arranging appropriate discharge disposition; the higher likelihood of paralysis among these patients15 would likely necessitate a higher frequency of rehabilitation facility placement. The higher costs for Medicaid patients with stroke and pneumonia may potentially be the result of these patients longer LOS. Although cost differences between the uninsured and privately insured were statistically significant, these were not large enough to be of material significance.
Limitations
Our study has several limitations. Since the NIS does not assign unique patient identifiers that would permit tracking of readmissions, we excluded patients transferred to another acute‐care hospital from our study to avoid counting the same patient twice. However, only 10% of hospitalized patients underwent transfer for cardiac procedures in the National Registry of Myocardial Infarction, with privately insured patients more likely to be transferred than other insurance groups.17 Since these patients are also more likely to have better survival, their exclusion likely biased our study toward the null. The same is probable for stroke patients as well.
Some uninsured patients begin Medicaid coverage during hospitalization and should ideally be counted as uninsured but were included under Medicaid in our analysis. They are also likely to be state‐ and plan‐specific variations in Medicaid and private payer coverage that we could not incorporate into our analysis. In addition, we were unable to include deaths that may have occurred shortly after discharge, even though these may have been related to the quality of hospital care. Furthermore, although the 3 conditions we studied are common and responsible for a large number of hospital deaths, they make up about 8% of total annual hospital discharges,23 and caution should be exercised in generalizing our findings to the full spectrum of hospitalizations. Lastly, it is possible that unmeasured confounding could be responsible for the observed associations. Uninsured and Medicaid patients are likely to have more severe disease, which may not be adequately captured by the administrative data available in the NIS. If so, this would explain the mortality association rather than insurance status.36, 37
Conclusions
Significant insurance‐related differences in mortality exist for 2 of the leading causes of noncancer inpatient deaths among working‐age Americans. Further studies are needed to determine whether provider sensitivity to insurance status or unmeasured sociodemographic and clinical prognostic factors are responsible for these disparities. Policy makers, hospital administrators, and physicians should be cognizant of these disparities and consider policies to address potential insurance related gaps in the quality of inpatient care.
With about 1 in 5 working‐age Americans (age 18‐64 years) currently uninsured and a large number relying on Medicaid, adequate access to quality health care services is becoming increasingly difficult.1 Substantial literature has accumulated over the years suggesting that access and quality in health care are closely linked to an individual's health insurance status.211 Some studies indicate that being uninsured or publicly insured is associated with negative health consequences.12, 13 Although the Medicaid program has improved access for qualifying low‐income individuals, significant gaps in access and quality remain.2, 5, 11, 1419 These issues are likely to become more pervasive should there be further modifications to state Medicaid funding in response to the unfolding economic crisis.
Although numerous studies have focused on insurance‐related disparities in the outpatient setting, few nationally representative studies have examined such disparities among hospitalized patients. A cross‐sectional study of a large hospital database from 1987 reported higher risk‐adjusted in‐hospital mortality, shorter length of stay (LOS), and lower procedure use among uninsured patients.9 A more recent analysis, limited to patients admitted with stroke, reported significant variation in hospital care associated with insurance status.15 Other studies reporting myocardial infarction registry and quality improvement program data are biased by the self‐selection of large urban teaching hospitals.1618 To our knowledge, no nationally representative study has focused on the impact of insurance coverage on hospital care for common medical conditions among working‐age Americans, the fastest growing segment of the uninsured population.
To address this gap in knowledge, we analyzed a nationally representative hospital database to determine whether there are significant insurance‐related disparities in in‐hospital mortality, LOS, and cost per hospitalization for 3 common medical conditions among working‐age adults, and, if present, to determine whether these disparities are due to differences in disease severity and comorbidities, and whether these disparities are affected by the proportion of uninsured and Medicaid patients receiving care in each hospital.
Methods
Design and Subjects
We examined data from the 2005 Nationwide Inpatient Sample (NIS), a nationally representative database of hospital inpatient stays maintained by the Agency for Healthcare Research and Quality (AHRQ) as part of the Healthcare Cost and Utilization Project (HCUP).20, 21 The NIS is a stratified probability sample of 20% of all US community hospitals, including public hospitals, academic medical centers, and specialty hospitals. Long‐term care hospitals, psychiatric hospitals, and alcoholism/chemical‐dependency treatment facilities are excluded. The 2005 NIS contains data on 7,995,048 discharges from 1054 hospitals located in 37 States and is designed to be representative of all acute care discharges from all US community hospitals.21
We identified discharges with a principal diagnosis of acute myocardial infarction (AMI), stroke, and pneumonia using International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9‐CM) codes specified in the AHRQ definitions of Inpatient Quality Indicators (Supporting Information Appendix).22 These 3 conditions are among the leading causes of noncancer inpatient deaths in patients under 65 years old,23 and evidence suggests that high mortality may be associated with deficiencies in the quality of inpatient care.24
We confined our analysis to patients 18 to 64 years of age, since this population is most at risk of being uninsured or underinsured.25 We excluded pregnant women because they account for an unusually high proportion of uninsured discharges and were relatively few in our cohort.26 In addition, we excluded patients transferred to another acute care hospital and patients missing payer source and discharge disposition. Our study protocol was approved by the Partners Human Research Committee.
Study Variables
We categorized insurance status as privately insured, uninsured, Medicaid, or Medicare. We defined privately insured patients as those having either Blue Cross or another commercial carrier listed as the primary payer and uninsured patients as those having either no charge or self‐pay listed as the primary payer.27 Other governmental payer categories were noted to share several characteristics with Medicare patients and comprised only a small proportion of the sample, and were thus included with Medicare. In order to account for NIS's sampling scheme and accurately apply sample weights in our analysis, we used Medicare as a separate category. However, since Medicare patients age 18 to 64 years represent a fundamentally different population that is primarily disabled or very ill, only results of privately insured, uninsured, and Medicaid patients are reported.
We selected in‐hospital mortality as the outcome measure and LOS and cost per hospitalization as measures of resource utilization. The NIS includes a binary indicator variable for in‐hospital mortality and specifies inpatient LOS in integers, with same‐day stays coded as 0. NIS's cost estimates are based on hospital cost reports submitted to the Centers for Medicare and Medicaid Services. To test the validity of our cost analyses, we performed parallel analyses using hospital charges as a measure of utilization (charges include hospital overhead, charity care, and bad debt). The resulting adjusted ratios differed little from cost ratios and we opted to report only the details of our cost analyses.
In order to assess the independent association between insurance status and the outcome measures listed above, we selected covariates for inclusion in multivariable models based on the existing literature. Patient covariates included: age group (18‐34 years, 35‐49 years, 50‐64 years), sex (male/female), race/ethnicity (non‐Hispanic white, non‐Hispanic black, Hispanic, other, missing), median income by zip code of residence (less than $37,000, $37,000‐$45,999, $46,000‐$60,999, $61,000 or more), admission through the emergency department (yes/no), admission on a weekend (yes/no), measures of disease severity, and comorbidity indicators. Measures of disease severity specific to each outcome are assigned in the NIS using criteria developed by Medstat (Medstat Disease Staging Software Version 5.2, Thomson Medstat Inc., Ann Arbor, MI). Severity is categorized into 5 levels, with a higher level indicating greater risk. We recorded comorbidities for each patient in our sample using HCUP Comorbidity Software, Version 3.2 (
Hospital covariates included: bed size (small, medium, large), ownership/control (private, government, private or government), geographic region (northeast, midwest, south, west), teaching status (teaching, non‐teaching), and the proportion of uninsured and Medicaid patients (combined) admitted to each hospital for AMI, stroke, or pneumonia. The actual number of hospital beds in each bed size category varies according to a hospital's geographic region and teaching status.27 Ownership/control, geographic region, and teaching status are assigned according to information from the American Hospital Association Annual Survey of Hospitals. The proportion of uninsured and Medicaid patients admitted to each hospital was found to have a nonmonotonic relationship with the outcomes being assessed and was thus treated as a 6‐level categorical variable with the following levels: 0% to 10%, 11% to 20%, 21% to 30%, 31% to 40%, 41% to 50%, and 51% to 100%.
Statistical Analysis
Summary statistics were constructed at the patient level and differences in proportions were evaluated with the chi‐square test. We employed direct standardization, using the age and sex distribution of the entire cohort, to compute age‐standardized and sex‐standardized estimates for each insurance group and compared them using the chi‐square test for in‐hospital mortality and t test for log transformed LOS and cost per hospitalization. For each condition, we developed multivariable logistic regression models for in‐hospital mortality and multivariable linear regression models for log transformed LOS and cost. The patient was the unit of analysis in all models.
In order to elucidate the contribution of disease severity and comorbidities and the proportion of uninsured and Medicaid patients admitted to each hospital, we fitted 3 sequential models for each outcome measure: Model 1 adjusted for patient sociodemographic characteristics and hospital characteristics with the exception of the covariate for the proportion of uninsured and Medicaid patients, Model 2 adjusted for all covariates in the preceding model as well as patients' comorbidities and severity of principal diagnosis, and Model 3 adjusted for all previously mentioned covariates as well as the proportion of uninsured and Medicaid patients admitted to each hospital. We excluded patients who died during hospitalization from the models for LOS and cost. We exponentiated the effect estimates from the log transformed linear regression models so that the adjusted ratio represents the percentage change in the mean LOS or mean cost.
To determine whether the association between patients' insurance status and in‐hospital mortality was modified by seeking care in hospitals treating a smaller or larger proportion of uninsured and Medicaid patients, we entered an interaction term for insurance status and proportion of uninsured and Medicaid patients in the final models (Model 3) for our primary outcome of in‐hospital mortality. However, since no significant interaction was found for any of the 3 conditions, this term was removed from the models and results from the interaction models are not described. In order to assess model specification for the linear regression models, we evaluated the normality of model residuals and found that these were approximately normally distributed. Lastly, we attempted to test the robustness of our analyses by creating fixed effects models that controlled for hospital site but were unable to do so due to the computational limitations of available software packages that could not render fixed effects models with more than 1000 hospital sites.
For all variables except race/ethnicity, data were missing for less than 3% of patients, so we excluded these individuals from adjusted analyses. However, race/ethnicity data were missing for 29% of the sample and were analyzed in 3 different ways, namely, with the missing data treated as a separate covariate level, with the missing data removed from the analysis, and with the missing data assigned to the majority covariate level (white race). The results of our analysis were unchanged no matter how the missing values were assigned. As a result, missing values for race/ethnicity were treated as a separate covariate level in the final analysis.15 Sociodemographic characteristics of patients with missing race/ethnicity information were similar to those with complete data.
We used SUDAAN (Release 9.0.1, Research Triangle Institute, NC) to account for NIS's sampling scheme and generalized estimating equations to adjust for the clustering of patients within hospitals and hospitals within sampling strata.29 In order to account for NIS's stratified probability sampling scheme, SUDAAN uses Taylor series linearization for robust variance estimation of descriptive statistics and regression parameters.30, 31 We present 2‐tailed P values or 95% confidence intervals (CIs) for all statistical comparisons.
Results
Patient and Hospital Characteristics
The final cohort comprised of 154,381 patients discharged from 1018 hospitals in 37 states during calendar year 2005 (Table 1). This cohort was representative of 755,346 working‐age Americans, representing approximately 225,947 cases of AMI (29.9%), 151,812 cases of stroke (20.1%), and 377,588 cases of pneumonia (50.0%). Of these patients, 47.5% were privately insured, 12.0% were uninsured, 17.0% received Medicaid, and 23.5% were assigned to Medicare. Compared with the privately insured, uninsured and Medicaid patients were generally younger, less likely to be white, more likely to have lower income, and more likely to be admitted through the emergency department. Of the 1018 hospitals included in our study, close to half (44.3%) were small, with bedsize ranging from 24 to 249. A large number of hospitals were located in the South (39.9%), and 14.9% were designated teaching hospitals.
| Characteristic* | Privately insured (n = 73,256) | Uninsured (n = 18,531) | Medicaid (n = 26,222) |
|---|---|---|---|
| |||
| Principal diagnosis (%) | |||
| Acute myocardial infarction | 36.7 | 31.2 | 19.7 |
| Stroke | 20.6 | 23.7 | 19.9 |
| Pneumonia | 42.7 | 45.2 | 60.4 |
| Age group (%) | |||
| 18‐34 years | 6.8 | 13.0 | 13.7 |
| 35‐49 years | 27.6 | 36.9 | 33.2 |
| 50‐64 years | 65.7 | 50.1 | 53.2 |
| Male sex (%) | 59.3 | 62.3 | 46.6 |
| Race or ethnicity (%) | |||
| White | 55.7 | 41.5 | 38.0 |
| Black | 7.6 | 14.8 | 16.6 |
| Hispanic | 4.8 | 10.5 | 10.4 |
| Other race | 3.6 | 4.7 | 5.2 |
| Missing | 28.4 | 29.0 | 29.7 |
| Median income by ZIP code (%) | |||
| <$37,000 | 21.5 | 36.7 | 43.0 |
| $37,000‐$45,999 | 25.2 | 27.8 | 27.1 |
| $46,000‐$60,999 | 26.3 | 20.3 | 17.6 |
| $61,000 | 24.8 | 11.5 | 8.4 |
| Emergency admission (%) | 63.3 | 75.6 | 72.9 |
| Weekend admission (%) | 24.5 | 26.2 | 25.1 |
| Hospital bed size (%) | |||
| Small | 8.9 | 10.3 | 11.4 |
| Medium | 24.0 | 22.3 | 25.9 |
| Large | 67.1 | 67.5 | 62.8 |
| Hospital control (%) | |||
| Private | 33.8 | 34.8 | 34.4 |
| Government (nonfederal) | 6.7 | 9.7 | 8.3 |
| Private or government | 59.5 | 55.5 | 57.3 |
| Hospital region (%) | |||
| Northeast | 17.4 | 12.5 | 17.6 |
| Midwest | 25.7 | 19.4 | 20.9 |
| South | 39.5 | 56.8 | 42.4 |
| West | 17.4 | 11.3 | 19.2 |
| Teaching hospital (%) | 41.7 | 43.8 | 43.3 |
Compared with privately insured patients, a larger proportion of uninsured and Medicaid patients had higher predicted mortality levels (Table 2). Medicaid patients had a disproportionately higher predicted LOS, whereas predicted resource demand was higher among privately insured patients. Hypertension (48%), chronic pulmonary disease (29.5%), and uncomplicated diabetes (21.5%) were the 3 most common comorbidities in the study cohort, with a generally higher prevalence of comorbidities among Medicaid patients.
| Characteristic* | Privately insured (n = 73,256) | Uninsured (n = 18,531) | Medicaid (n = 26,222) |
|---|---|---|---|
| |||
| Medstat disease staging (%) | |||
| Mortality level 1 | 50.8 | 45.4 | 36.7 |
| Mortality level 2 | 44.0 | 49.1 | 56.7 |
| Mortality level 3 | 5.3 | 5.5 | 6.7 |
| Length of stay level 1 | 66.8 | 71.6 | 53.8 |
| Length of stay level 2 | 28.5 | 24.5 | 39.3 |
| Length of stay level 3 | 4.8 | 3.8 | 6.9 |
| Resource demand level 1 | 45.2 | 54.2 | 48.5 |
| Resource demand level 2 | 40.5 | 34.2 | 39.2 |
| Resource demand level 3 | 14.2 | 11.7 | 12.3 |
| Coexisting medical conditions (%) | |||
| Congestive heart failure | 4.7 | 4.8 | 10.1 |
| Valvular disease | 2.8 | 2.0 | 2.7 |
| Pulmonary circulation disease | 0.8 | 0.6 | 1.5 |
| Peripheral vascular disease | 3.2 | 2.2 | 3.2 |
| Paralysis | 1.2 | 0.8 | 3.5 |
| Other neurological disorders | 2.4 | 1.9 | 7.3 |
| Chronic pulmonary disease | 23.6 | 22.4 | 37.7 |
| Uncomplicated diabetes | 19.6 | 18.6 | 23.4 |
| Complicated diabetes | 3.3 | 2.1 | 4.9 |
| Hypothyroidism | 5.6 | 2.7 | 4.7 |
| Renal failure | 3.0 | 1.9 | 5.6 |
| Liver disease | 1.6 | 2.5 | 4.4 |
| Peptic ulcer disease | <0.5 | <0.5 | <0.5 |
| AIDS | 0.1 | 0.1 | 0.4 |
| Lymphoma | 1.1 | 0.4 | 0.6 |
| Metastatic cancer | 2.1 | 0.7 | 2.2 |
| Non‐metastatic solid tumor | 1.5 | 0.8 | 2.1 |
| Collagen vascular diseases | 2.3 | 0.9 | 2.3 |
| Coagulopathy | 2.7 | 2.4 | 3.4 |
| Obesity | 10.3 | 8.2 | 9.3 |
| Weight loss | 1.6 | 1.8 | 3.3 |
| Fluid and electrolyte disorders | 18.3 | 19.4 | 23.8 |
| Chronic blood loss anemia | 0.6 | 0.6 | 0.8 |
| Deficiency anemias | 8.6 | 8.5 | 13.4 |
| Alcohol abuse | 3.3 | 9.8 | 8.3 |
| Drug abuse | 1.9 | 10.2 | 9.8 |
| Psychoses | 1.5 | 1.9 | 6.8 |
| Depression | 7.2 | 4.8 | 9.9 |
| Hypertension | 48.0 | 44.1 | 45.7 |
In‐Hospital Mortality
Compared with the privately insured, age‐standardized and sex‐standardized in‐hospital mortality for AMI and stroke was significantly higher for uninsured and Medicaid patients (Table 3). Among pneumonia patients, Medicaid recipients had significantly higher in‐hospital mortality compared with privately insured and uninsured patients.
| Privately Insured | Uninsured | Medicaid | |
|---|---|---|---|
| |||
| In‐hospital mortality, rate per 100 discharges (SE) | |||
| Acute myocardial infarction | 2.22 (0.10) | 4.03 (0.31)* | 4.57 (0.34)* |
| Stroke | 7.49 (0.27) | 10.46 (0.64)* | 9.89 (0.45)* |
| Pneumonia | 1.75 (0.09) | 1.74 (0.18) | 2.48 (0.14)* |
| Length of stay, mean (SE), days | |||
| Acute myocardial infarction | 4.17 (0.06) | 4.46 (0.09) | 5.85 (0.16) |
| Stroke | 6.37 (0.13) | 7.15 (0.25) | 9.28 (0.30) |
| Pneumonia | 4.89 (0.05) | 4.64 (0.10) | 5.80 (0.08) |
| Cost per episode, mean (SE), dollars | |||
| Acute myocardial infarction | 21,077 (512) | 19,977 (833) | 22,452 (841) |
| Stroke | 16,022 (679) | 14,571 (1,036) | 18,462 (824) |
| Pneumonia | 8,223 (192) | 7,086 (293) | 9,479 (271) |
After multivariable adjustment for additional patient and hospital characteristics, uninsured AMI and stroke patients continued to have significantly higher in‐hospital mortality compared with the privately insured (Table 4). Among pneumonia patients, Medicaid recipients persisted in having significantly higher in‐hospital mortality than the privately insured.
| Model 1* | Model 2 | Model 3 | |
|---|---|---|---|
| |||
| In‐hospital mortality, adjusted odds ratio (95% CI) | |||
| Acute Myocardial Infarction | |||
| Uninsured vs. privately insured | 1.59 (1.35‐1.88) | 1.58 (1.30‐1.93) | 1.52 (1.24‐1.85) |
| Medicaid vs. privately insured | 1.83 (1.54‐2.18) | 1.22 (0.99‐1.50) | 1.15 (0.94‐1.42) |
| Stroke | |||
| Uninsured vs. privately insured | 1.56 (1.35‐1.80) | 1.50 (1.30‐1.73) | 1.49 (1.29‐1.72) |
| Medicaid vs. privately insured | 1.32 (1.15‐1.52) | 1.09 (0.93‐1.27) | 1.08 (0.93‐1.26) |
| Pneumonia | |||
| Uninsured vs. privately insured | 0.99 (0.81‐1.21) | 1.12 (0.91‐1.39) | 1.10 (0.89‐1.36) |
| Medicaid vs. privately insured | 1.41 (1.20‐1.65) | 1.24 (1.04‐1.48) | 1.21 (1.01‐1.45) |
| Length of stay, adjusted ratio (95% CI)| | |||
| Acute Myocardial Infarction | |||
| Uninsured vs. privately insured | 1.00 (0.98‐1.02) | 1.00 (0.98‐1.02) | 1.00 (0.98‐1.02) |
| Medicaid vs. privately insured | 1.17 (1.14‐1.21) | 1.07 (1.05‐1.09) | 1.07 (1.05‐1.09) |
| Stroke | |||
| Uninsured vs. privately insured | 1.06 (1.02‐1.10) | 1.08 (1.04‐1.11) | 1.07 (1.04‐1.11) |
| Medicaid vs. privately insured | 1.30 (1.26‐1.34) | 1.17 (1.14‐1.20) | 1.17 (1.14‐1.20) |
| Pneumonia | |||
| Uninsured vs. privately insured | 0.95 (0.93‐0.97) | 0.96 (0.94‐0.99) | 0.96 (0.94‐0.98) |
| Medicaid vs. privately insured | 1.15 (1.13‐1.17) | 1.04 (1.03‐1.06) | 1.04 (1.03‐1.06) |
| Cost per episode, adjusted ratio (95% CI)| | |||
| Acute Myocardial Infarction | |||
| Uninsured vs. privately insured | 0.97 (0.95‐0.99) | 0.99 (0.97‐1.00) | 0.99 (0.97‐1.00) |
| Medicaid vs. privately insured | 1.01 (0.98‐1.04) | 0.99 (0.97‐1.01) | 0.99 (0.97‐1.01) |
| Stroke | |||
| Uninsured vs. privately insured | 0.97 (0.93‐1.02) | 1.00 (0.96‐1.03) | 1.00 (0.97‐1.03) |
| Medicaid vs. privately insured | 1.17 (1.13‐1.21) | 1.06 (1.04‐1.09) | 1.06 (1.04‐1.09) |
| Pneumonia | |||
| Uninsured vs. privately insured | 0.95 (0.92‐0.97) | 0.98 (0.96‐1.00) | 0.98 (0.96‐1.00) |
| Medicaid vs. privately insured | 1.17 (1.15‐1.19) | 1.05 (1.04‐1.07) | 1.05 (1.04‐1.07) |
LOS
Among AMI and stroke patients, age‐standardized and sex‐standardized mean LOS was significantly longer for the uninsured and Medicaid recipients compared with the privately insured (Table 3). Among pneumonia patients, the uninsured had a slightly shorter mean LOS compared with the privately insured whereas Medicaid recipients averaged the longest LOS.
These insurance‐related disparities in LOS among pneumonia patients persisted after multivariable adjustment (Table 4). Among AMI patients, only Medicaid recipients persisted in having a significantly longer LOS than the privately insured. Among stroke patients, both the uninsured and Medicaid recipients averaged a longer LOS compared with the privately insured.
Cost per Episode
For all 3 conditions, the uninsured had significantly lower age‐standardized and sex‐standardized costs compared with the privately insured (Table 3). However, Medicaid patients had higher costs than the privately insured for all three conditions, significantly so among patients with stroke and pneumonia.
These insurance‐related disparities in costs persisted in multivariable analyses (Table 4). The uninsured continued to have lower costs compared with the privately insured, significantly so for patients with AMI and pneumonia. Among stroke and pneumonia patients, Medicaid recipients continued to accrue higher costs than the uninsured or privately insured.
Discussion
In this nationally representative study of working‐age Americans hospitalized for 3 common medical conditions, we found that insurance status was associated with significant variations in in‐hospital mortality and resource use. Whereas privately insured patients experienced comparatively lower in‐hospital mortality in most cases, mortality risk was highest among the uninsured for 2 of the 3 common causes of noncancer inpatient deaths. Although previous studies have examined insurance‐related disparities in inpatient care for individual diagnoses and specific populations, no broad overview of this important issue has been published in the past decade. In light of the current economic recession and national healthcare debate, these findings may be a prescient indication of a widening insurance gap in the quality of hospital care.
There are several potential mechanisms for these disparities. For instance, Hadley et al.9 reported significant underuse of high‐cost or high‐discretion procedures among the uninsured in their analysis of a nationally representative sample of 592,598 hospitalized patients. Similarly, Burstin et al.10 found that among a population of 30,195 hospitalized patients with diverse diagnoses, the uninsured were at greater risk for receiving substandard care regardless of hospital characteristics. These, and other similar findings,7, 8, 19 are suggestive of differences in the way uninsured patients are generally managed in the hospital that may partly explain the disparities reported herein.
More specifically, analyses of national registries of AMI have documented lower rates of utilization of invasive, potentially life‐saving, cardiac interventions among the uninsured.16, 17 Similarly, a lower rate of carotid endarterectomy was reported among uninsured stroke patients from an analysis of the 2002 NIS.15 Other differences in inpatient management unmeasured by administrative data, such as the use of subspecialists and allied health professionals, may also contribute.32 Unfortunately, limitations in the available data prevented us from being able to appropriately address the important issue of insurance related differences in the utilization of specific inpatient procedures.
These disparities may also be indicative of differences in severity of illness that are not captured fully by the MedStat disease staging criteria. The uninsured might have more severe illness at admission, either due to the presence of more advanced chronic disease or delay in seeking care for the acute episode. AMI and stroke are usually the culmination of longstanding atherosclerosis that is amenable to improvement through timely and consistent risk‐factor modification. Not having a usual source of medical care,6, 33 inadequate screening and management of known risk‐factors,3, 34 and difficulties in obtaining specialty care5 among the uninsured likely increases their risk of being hospitalized with more advanced disease. The higher likelihood of being admitted through the emergency department19 and on weekends9 among the uninsured lends credence to the possibility of delays in seeking care. All of these are potential mediators of higher AMI and stroke mortality in uninsured patients.
Finally, these mortality differences could also be due to the additional risks imposed by poorly managed comorbidities among uninsured patients. Although we controlled for the presence of comorbidities in our analysis, we lacked data about the severity of individual comorbidities. A recent study reported significant lapses in follow‐up care after the onset of a chronic condition in uninsured individuals under 65 years of age.34 Other studies have also documented insurance related disparities in the care of chronic diseases3, 35 that were among the most common comorbidities in our cohort.
Most of the reasons for insurance‐related disparities noted above for the uninsured are also applicable to Medicaid patients. Differences in the intensity of inpatient care,7, 8, 1519 limited access to health care services,2, 14 unmet health needs,5 and suboptimal management of chronic medical conditions35 were also reported for Medicaid patients in prior research. These factors likely contributed to the higher in‐hospital mortality in this patient population, evidenced by the sequential decrease in odds after adjusting for comorbidities and disease severity. Medicaid patients hospitalized for stroke were noted to have significantly longer LOS, which could plausibly be due to difficulties with arranging appropriate discharge disposition; the higher likelihood of paralysis among these patients15 would likely necessitate a higher frequency of rehabilitation facility placement. The higher costs for Medicaid patients with stroke and pneumonia may potentially be the result of these patients longer LOS. Although cost differences between the uninsured and privately insured were statistically significant, these were not large enough to be of material significance.
Limitations
Our study has several limitations. Since the NIS does not assign unique patient identifiers that would permit tracking of readmissions, we excluded patients transferred to another acute‐care hospital from our study to avoid counting the same patient twice. However, only 10% of hospitalized patients underwent transfer for cardiac procedures in the National Registry of Myocardial Infarction, with privately insured patients more likely to be transferred than other insurance groups.17 Since these patients are also more likely to have better survival, their exclusion likely biased our study toward the null. The same is probable for stroke patients as well.
Some uninsured patients begin Medicaid coverage during hospitalization and should ideally be counted as uninsured but were included under Medicaid in our analysis. They are also likely to be state‐ and plan‐specific variations in Medicaid and private payer coverage that we could not incorporate into our analysis. In addition, we were unable to include deaths that may have occurred shortly after discharge, even though these may have been related to the quality of hospital care. Furthermore, although the 3 conditions we studied are common and responsible for a large number of hospital deaths, they make up about 8% of total annual hospital discharges,23 and caution should be exercised in generalizing our findings to the full spectrum of hospitalizations. Lastly, it is possible that unmeasured confounding could be responsible for the observed associations. Uninsured and Medicaid patients are likely to have more severe disease, which may not be adequately captured by the administrative data available in the NIS. If so, this would explain the mortality association rather than insurance status.36, 37
Conclusions
Significant insurance‐related differences in mortality exist for 2 of the leading causes of noncancer inpatient deaths among working‐age Americans. Further studies are needed to determine whether provider sensitivity to insurance status or unmeasured sociodemographic and clinical prognostic factors are responsible for these disparities. Policy makers, hospital administrators, and physicians should be cognizant of these disparities and consider policies to address potential insurance related gaps in the quality of inpatient care.
- , .The U.S. economy and changes in health insurance coverage, 2000‐2006.Health Aff (Millwood).2008;27(2):w135‐w144.
- , , .Rates of avoidable hospitalization by insurance status in Massachusetts and Maryland.JAMA.1992;268(17):2388‐2394.
- , , , et al.Unmet health needs of uninsured adults in the United States.JAMA.2000;284(16):2061‐2069.
- , , .Health insurance and access to care for symptomatic conditions.Arch Intern Med.2000;160(9):1269‐1274.
- , , , et al.Access to specialty care and medical services in community health centers.Health Aff (Millwood).2007;26(5):1459‐1468.
- , , , et al.A national study of chronic disease prevalence and access to care in uninsured U.S. adults.Ann Intern Med.2008;149:170–176.
- , , , .Relationship between patient source of payment and the intensity of hospital services.Med Care.1988;26(11):1111‐1114.
- , , .The association of payer with utilization of cardiac procedures in Massachusetts.JAMA.1990;264(10):1255‐1260.
- , , .Comparison of uninsured and privately insured hospital patients: condition on admission, resource use, and outcome.JAMA.1991;265:374–379.
- , , .Socioeconomic status and risk for substandard medical care.JAMA.1992;268(17):2383‐2387.
- , , , .The relation between health insurance coverage and clinical outcomes among women with breast cancer.N Engl J Med.1993;329(5):326‐331.
- , , .Health insurance and mortality. Evidence from a national cohort.JAMA.1993;270(6):737‐741.
- , , , .Mortality in the uninsured compared with that in persons with public and private health insurance.Arch Intern Med.1994;154(21):2409‐2416.
- .Medicaid policy and the substitution of hospital outpatient care for physician care.Health Serv Res.1989;24:33–66.
- , .Disparities in outcomes among patients with stroke associated with insurance status.Stroke.2007;38(3):1010‐1016.
- , , , et al.Influence of payor on use of invasive cardiac procedures and patient outcome after myocardial infarction in the United States. Participants in the National Registry of Myocardial Infarction.J Am Coll Cardiol.1998;31(7):1474‐1480.
- , , , et al.Payer status and the utilization of hospital resources in acute myocardial infarction: a report from the National Registry of Myocardial Infarction 2.Arch Intern Med.2000;160(6):817–823.
- , , , et al.Insurance coverage and care of patients with non‐ST‐segment elevation acute coronary syndromes.Ann Intern Med.2006;145(10):739–748.
- , , .Comparing uninsured and privately insured hospital patients: Admission severity, health outcomes and resource use.Health Serv Manage Res.2001;14(3):203–210.
- Healthcare Cost and Utilization Project. Introduction to the HCUP Nationwide Inpatient Sample (NIS) 2005. Rockville, MD: Agency for Healthcare Research and Quality; 2007:6. Available at: www.hcup‐us.ahrq.gov/db/nation/nis/NIS_Introduction_2005.pdf. Accessed February2010.
- Healthcare Cost and Utilization Project. Design of the Nationwide Inpatient Sample (NIS) 2005. Rockville, MD: Agency for Healthcare Research and Quality; 2007. Available at: www.hcup‐us.ahrq.gov/db/nation/nis/reports/NIS_2005_Design_Report.pdf. Accessed February2010.
- AHRQ Quality Indicators. Inpatient Quality Indicators: Technical Specifications. Version 3.1 (March 12, 2007). Available at: www.qualityindicators.ahrq.gov/downloads/iqi/iqi_technical_specs_v31.pdf. Accessed February2010.
- , , . National Hospital Discharge Survey: 2005 annual summary with detailed diagnosis and procedure data. Washington, DC: National Center for Health Statistics; 2007. Vital and Health Statistics 13(165). Available at: www.cdc.gov/nchs/data/series/sr_13/sr13_165.pdf. Accessed February2010.
- AHRQ Quality Indicators. Guide to Inpatient Quality Indicators: Quality of Care in Hospitals—Volume, Mortality, and Utilization. Version 3.1 (March 12, 2007). Available at: www.qualityindicators.ahrq.gov/downloads/iqi/iqi_guide_v31.pdf. Accessed February2010.
- , , .Income, poverty, and health insurance coverage in the United States: 2006.Washington, DC:US Census Bureau. Current Population Reports;2007:60–233.
- , . Conditions Related to Uninsured Hospitalizations, 2003. HCUP Statistical Brief #8. Rockville, MD: Agency for Healthcare Research and Quality; 2006:6. Available at: www.hcup‐us.ahrq.gov/reports/statbriefs/sb8.pdf. Accessed February2010.
- Healthcare Cost and Utilization Project. NIS Description of Data Elements. Available at: www.hcup‐us.ahrq.gov/db/nation/nis/nisdde.jsp. Accessed February2010.
- , , , .Comorbidity measures for use with administrative data.Med Care.1998;36:8–27.
- SUDAAN User's Manual, Release 9.0. Research TrianglePark, NC:Research Triangle Institute;2006.
- , . Final Report on Calculating Nationwide Inpatient Sample (NIS) Variances, 2001. HCUP Methods Series Report #2003‐2. Online June 2005 (revised June 6, 2005). U.S. Agency for Healthcare Research and Quality. Available at: www.hcup‐us.ahrq.gov/reports/CalculatingNISVariances200106092005.pdf. Accessed February2010.
- .On the variances of asymptotically normal estimators from complex surveys.Int Stat Rev.1983;51:279–292.
- , , , et al.Patient characteristics associated with care by a cardiologist among adults hospitalized with severe congestive heart failure.J Am Coll Cardiol.2000;36:2119–2125.
- , . Summary health statistics for U.S. adults: National Health Interview Survey, 2006. Washington, DC: National Center for Health Statistics; 2007:12. Vital and Health Statistics 10(235). Available at: www.cdc.gov/nchs/data/series/sr_10/sr10_235.pdf. Accessed February2010.
- .Insurance coverage, medical care use, and short‐term health changes following an unintentional injury or the onset of a chronic condition.JAMA.2007;297(10):1073–1084.
- , , , et al.The quality of chronic disease care in U.S. community health centers.Health Aff (Millwood).2006;25(6):1712–1723.
- , , , , , .Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Med Care.2008;46(3):232–239.
- , , , .The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population.J Clin Epidemiol. [E‐pub ahead of print].
- , .The U.S. economy and changes in health insurance coverage, 2000‐2006.Health Aff (Millwood).2008;27(2):w135‐w144.
- , , .Rates of avoidable hospitalization by insurance status in Massachusetts and Maryland.JAMA.1992;268(17):2388‐2394.
- , , , et al.Unmet health needs of uninsured adults in the United States.JAMA.2000;284(16):2061‐2069.
- , , .Health insurance and access to care for symptomatic conditions.Arch Intern Med.2000;160(9):1269‐1274.
- , , , et al.Access to specialty care and medical services in community health centers.Health Aff (Millwood).2007;26(5):1459‐1468.
- , , , et al.A national study of chronic disease prevalence and access to care in uninsured U.S. adults.Ann Intern Med.2008;149:170–176.
- , , , .Relationship between patient source of payment and the intensity of hospital services.Med Care.1988;26(11):1111‐1114.
- , , .The association of payer with utilization of cardiac procedures in Massachusetts.JAMA.1990;264(10):1255‐1260.
- , , .Comparison of uninsured and privately insured hospital patients: condition on admission, resource use, and outcome.JAMA.1991;265:374–379.
- , , .Socioeconomic status and risk for substandard medical care.JAMA.1992;268(17):2383‐2387.
- , , , .The relation between health insurance coverage and clinical outcomes among women with breast cancer.N Engl J Med.1993;329(5):326‐331.
- , , .Health insurance and mortality. Evidence from a national cohort.JAMA.1993;270(6):737‐741.
- , , , .Mortality in the uninsured compared with that in persons with public and private health insurance.Arch Intern Med.1994;154(21):2409‐2416.
- .Medicaid policy and the substitution of hospital outpatient care for physician care.Health Serv Res.1989;24:33–66.
- , .Disparities in outcomes among patients with stroke associated with insurance status.Stroke.2007;38(3):1010‐1016.
- , , , et al.Influence of payor on use of invasive cardiac procedures and patient outcome after myocardial infarction in the United States. Participants in the National Registry of Myocardial Infarction.J Am Coll Cardiol.1998;31(7):1474‐1480.
- , , , et al.Payer status and the utilization of hospital resources in acute myocardial infarction: a report from the National Registry of Myocardial Infarction 2.Arch Intern Med.2000;160(6):817–823.
- , , , et al.Insurance coverage and care of patients with non‐ST‐segment elevation acute coronary syndromes.Ann Intern Med.2006;145(10):739–748.
- , , .Comparing uninsured and privately insured hospital patients: Admission severity, health outcomes and resource use.Health Serv Manage Res.2001;14(3):203–210.
- Healthcare Cost and Utilization Project. Introduction to the HCUP Nationwide Inpatient Sample (NIS) 2005. Rockville, MD: Agency for Healthcare Research and Quality; 2007:6. Available at: www.hcup‐us.ahrq.gov/db/nation/nis/NIS_Introduction_2005.pdf. Accessed February2010.
- Healthcare Cost and Utilization Project. Design of the Nationwide Inpatient Sample (NIS) 2005. Rockville, MD: Agency for Healthcare Research and Quality; 2007. Available at: www.hcup‐us.ahrq.gov/db/nation/nis/reports/NIS_2005_Design_Report.pdf. Accessed February2010.
- AHRQ Quality Indicators. Inpatient Quality Indicators: Technical Specifications. Version 3.1 (March 12, 2007). Available at: www.qualityindicators.ahrq.gov/downloads/iqi/iqi_technical_specs_v31.pdf. Accessed February2010.
- , , . National Hospital Discharge Survey: 2005 annual summary with detailed diagnosis and procedure data. Washington, DC: National Center for Health Statistics; 2007. Vital and Health Statistics 13(165). Available at: www.cdc.gov/nchs/data/series/sr_13/sr13_165.pdf. Accessed February2010.
- AHRQ Quality Indicators. Guide to Inpatient Quality Indicators: Quality of Care in Hospitals—Volume, Mortality, and Utilization. Version 3.1 (March 12, 2007). Available at: www.qualityindicators.ahrq.gov/downloads/iqi/iqi_guide_v31.pdf. Accessed February2010.
- , , .Income, poverty, and health insurance coverage in the United States: 2006.Washington, DC:US Census Bureau. Current Population Reports;2007:60–233.
- , . Conditions Related to Uninsured Hospitalizations, 2003. HCUP Statistical Brief #8. Rockville, MD: Agency for Healthcare Research and Quality; 2006:6. Available at: www.hcup‐us.ahrq.gov/reports/statbriefs/sb8.pdf. Accessed February2010.
- Healthcare Cost and Utilization Project. NIS Description of Data Elements. Available at: www.hcup‐us.ahrq.gov/db/nation/nis/nisdde.jsp. Accessed February2010.
- , , , .Comorbidity measures for use with administrative data.Med Care.1998;36:8–27.
- SUDAAN User's Manual, Release 9.0. Research TrianglePark, NC:Research Triangle Institute;2006.
- , . Final Report on Calculating Nationwide Inpatient Sample (NIS) Variances, 2001. HCUP Methods Series Report #2003‐2. Online June 2005 (revised June 6, 2005). U.S. Agency for Healthcare Research and Quality. Available at: www.hcup‐us.ahrq.gov/reports/CalculatingNISVariances200106092005.pdf. Accessed February2010.
- .On the variances of asymptotically normal estimators from complex surveys.Int Stat Rev.1983;51:279–292.
- , , , et al.Patient characteristics associated with care by a cardiologist among adults hospitalized with severe congestive heart failure.J Am Coll Cardiol.2000;36:2119–2125.
- , . Summary health statistics for U.S. adults: National Health Interview Survey, 2006. Washington, DC: National Center for Health Statistics; 2007:12. Vital and Health Statistics 10(235). Available at: www.cdc.gov/nchs/data/series/sr_10/sr10_235.pdf. Accessed February2010.
- .Insurance coverage, medical care use, and short‐term health changes following an unintentional injury or the onset of a chronic condition.JAMA.2007;297(10):1073–1084.
- , , , et al.The quality of chronic disease care in U.S. community health centers.Health Aff (Millwood).2006;25(6):1712–1723.
- , , , , , .Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Med Care.2008;46(3):232–239.
- , , , .The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population.J Clin Epidemiol. [E‐pub ahead of print].
Copyright © 2010 Society of Hospital Medicine