Elliott S. Fisher, MD, MPH; David E. Wennberg, MD, MPH; Thrse A. Stukel, PhD; Daniel J. Gottlieb, MS; F. L. Lucas, PhD; Étoile L. Pinder, MS
Acknowledgments: The authors thank the staff of the Northeast Health Care Quality Foundation for assistance in preparing the Cooperative Cardiovascular Project data.
Disclaimer: The analyses of the Cardiovascular Cooperative Project data were performed under contract number 500-99-NH01, titled Utilization and Quality Control Peer Review Organization for the State of New Hampshire, sponsored by the Centers for Medicare & Medicare Services (formerly the Health Care Financing Administration), Department of Health and Human Services. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. government.
The authors assume full responsibility for the accuracy and completeness of the analyses presented. This article is a direct result of the Health Care Quality Improvement Program initiated by the Centers for Medicare & Medicare Services, which has encouraged identification of quality improvement projects derived from analysis of patterns of care, and therefore required no special funding on the part of this contractor. Ideas and contributions to the authors concerning experience with issues presented are welcomed.
The opinions expressed herein are those of the authors alone and do not necessarily reflect those of the Centers for Medicare & Medicare Services, the Robert Wood Johnson Foundation, or the Department of Veterans Affairs.
Grant Support: By the Robert Wood Johnson Foundation, the National Cancer Institute (CA52192), and the National Institute of Aging (1PO1 AG19783-01).
Potential Financial Conflicts of Interest: None disclosed.
Requests for Single Reprints: Elliott S. Fisher, MD, MPH, Strasenburgh Hall, HB 7251, Dartmouth Medical School, Hanover, NH 03755; VA Outcomes Group, White River Junction Veterans Affairs Medical Center, White River Junction, VT 05001; e-mail, email@example.com.
Current Author Addresses: Dr. Fisher, Mr. Gottlieb, and Ms. Pinder: Strasenburgh Hall, HB 7251, Dartmouth Medical School, Hanover, NH 03755.
Drs. Wennberg and Lucas: Maine Medical Center, 22 Bramhall Street, Portland, ME 04102.
Dr. Stukel: Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada.
Author Contributions: Conception and design: E.S. Fisher, D.E. Wennberg, T.A. Stukel, .L. Pinder.
Analysis and interpretation of the data: E.S. Fisher, D.E. Wennberg, T.A. Stukel, D.J. Gottlieb, F.L. Lucas, .L. Pinder.
Drafting of the article: E.S. Fisher, D.E. Wennberg, T.A. Stukel, D.J. Gottlieb, .L. Pinder.
Critical revision of the article for important intellectual content: E.S. Fisher, D.E. Wennberg, D.J. Gottlieb, F.L. Lucas.
Final approval of the article: E.S. Fisher, D.E. Wennberg, T.A. Stukel, D.J. Gottlieb, F.L. Lucas, .L. Pinder.
Statistical expertise: T.A. Stukel, D.J. Gottlieb.
Obtaining of funding: E.S. Fisher.
Administrative, technical, or logistic support: .L. Pinder.
Collection and assembly of data: E.S. Fisher, D.J. Gottlieb, .L. Pinder.
Fisher E., Wennberg D., Stukel T., Gottlieb D., Lucas F., Pinder É.; The Implications of Regional Variations in Medicare Spending. Part 1: The Content, Quality, and Accessibility of Care. Ann Intern Med. 2003;138:273-287. doi: 10.7326/0003-4819-138-4-200302180-00006
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Published: Ann Intern Med. 2003;138(4):273-287.
The health implications of regional differences in Medicare spending are unknown.
To determine whether regions with higher Medicare spending provide better care.
National study of Medicare beneficiaries.
Patients hospitalized between 1993 and 1995 for hip fracture (n = 614 503), colorectal cancer (n = 195 429), or acute myocardial infarction (n = 159 393) and a representative sample (n = 18 190) drawn from the Medicare Current Beneficiary Survey (19921995).
End-of-life spending reflects the component of regional variation in Medicare spending that is unrelated to regional differences in illness. Each cohort member's exposure to different levels of spending was therefore defined by the level of end-of-life spending in his or her hospital referral region of residence (n = 306).
Content of care (for example, frequency and type of services received), quality of care (for example, use of aspirin after acute myocardial infarction, influenza immunization), and access to care (for example, having a usual source of care).
Average baseline health status of cohort members was similar across regions of differing spending levels, but patients in higher-spending regions received approximately 60% more care. The increased utilization was explained by more frequent physician visits, especially in the inpatient setting (rate ratios in the highest vs. the lowest quintile of hospital referral regions were 2.13 [95% CI, 2.12 to 2.14] for inpatient visits and 2.36 [CI, 2.33 to 2.39] for new inpatient consultations), more frequent tests and minor (but not major) procedures, and increased use of specialists and hospitals (rate ratio in the highest vs. the lowest quintile was 1.52 [CI, 1.50 to 1.54] for inpatient days and 1.55 [CI, 1.50 to 1.60] for intensive care unit days). Quality of care in higher-spending regions was no better on most measures and was worse for several preventive care measures. Access to care in higher-spending regions was also no better or worse.
Regional differences in Medicare spending are largely explained by the more inpatient-based and specialist-oriented pattern of practice observed in high-spending regions. Neither quality of care nor access to care appear to be better for Medicare enrollees in higher-spending regions.
Per capita Medicare spending varies considerably from region to region. The effect of greater Medicare spending on quality of care and access is not known.
Using end-of-life care spending as an indicator of Medicare spending, the researchers categorized geographic regions into five quintiles of spending and examined costs and outcomes of care for hip fracture, colorectal cancer, and acute myocardial infarction. Residents of high-spending regions received 60% more care but did not have better quality or outcomes of care.
Medicare beneficiaries who live in higher Medicare spending regions do not necessarily get better-quality care than those in lower-spending regions.
Health care spending in the United States is expected to increase dramatically in this decade. By 2011, per capita spending is forecast to increase by 49% in real terms, reaching $9216 per capita or 17% of the gross domestic product (1). The likely consequences of such dramatic growth in health care costs include further increases in the numbers of uninsured persons and reduced public and private spending in other sectors of the economy. Spending growth, however, is seen as an inexorable consequence of the aging of the population and advancing technology (2, 3). Moreover, the effectiveness of specific interventions in cardiovascular disease, neonatal care, and cancer treatment has been used to argue that the overall gains from increased spending are worth the costs (3) and that any constraints on the expansion of the specialist workforce or on further spending growth could be harmful (2-4).
These forecasts and the policy prescriptions that depend on them do not take into account the dramatic regional variations in spending and medical practice observed across the United States (5-8). For example, age-, sex-, and race-adjusted spending for traditional (fee-for-service) Medicare in 1996 was $8414 per enrollee in the Miami, Florida, region compared with $3341 in the Minneapolis, Minnesota, region (9). The greater-than-twofold differences observed across U.S. regions are not due to differences in the prices of medical services (7, 10) or to apparent differences in average levels of illness or socioeconomic status (10-12). Rather, they are due to the overall quantity of medical services provided and the relative predominance of internists and medical subspecialists in high-cost regions (2, 13).
The implications for health and health care of these regional differences in resources and spending, although directly relevant to current policy debates, remain relatively unexplored (14). The financial implications are clear: Savings of up to 30% of Medicare spending might be possible, and the Medicare Trust Fund would remain solvent into the indefinite future (10). However, remarkably little is known about whether the increased spending in high-cost regions results in better care or improved health. Although recent studies have found no improvement in mortality (12, 15, 16), they have been criticized because of weak designs (most were cross-sectional and ecologic), inadequate individual-level measures to control for potential differences in case mix, insufficient clinical detail on the process of care to allow inferences on potential causal pathways to be drawn, and limited outcome measures. We designed a research project to address these concerns.
We present our findings in two articles. This article, Part 1, provides an overview of the study design and addresses the question, What are the differences in the content, quality, and accessibility of care across U.S. regions that differ in per capita Medicare spending? The second article, Part 2, asks, Do regions with higher Medicare spending achieve better health outcomes and improved patient satisfaction?
One approach to determining whether the increased spending in some U.S. regions leads to better care and better outcomes would be to conduct a randomized trial. This would ensure that assignment to the treatment and control groups (those receiving more and less spending) was independent of patient characteristics. Logistic barriers to such a trial, however, would be substantial. We therefore conducted a cohort study in four parallel cohorts using a natural randomization approach (17), in which one or more exposure variables allowed assignment of patients into treatment groups (different levels of average spending), as would a randomized trial. An overview of the design is provided in Figure 1.
Overview of study design.
Because some of the regional differences in Medicare spending are due to differences in illness levels (enrollees in Louisiana are sicker than those in Colorado) and price (Medicare pays more for the same service in New York than in Iowa), we could not use Medicare spending itself as the exposure. We therefore assigned U.S. hospital referral regions (HRRs), and thus the cohort members residing within them, to different exposure levels. We did this by using the End-of-Life Expenditure Index (EOL-EI), a measure reflecting the component of regional variation in Medicare spending that is due to physician practice rather than regional differences in illness or price. Because regional differences in end-of-life spending are unrelated to underlying illness levels, it is reasonable to consider residence in HRRs with different end-of-life spending as a random event. The index was calculated as standardized spending on hospital and physician services provided to a reference cohort distinct from the study cohorts: Medicare enrollees in their last 6 months of life.
We confirmed that the exposure used to assign the HRRs achieved the goals of natural randomization: 1) Study samples assigned to different levels of the exposure [the EOL-EI] were similar in baseline health status, and 2) the actual quantity of services delivered to the individuals within the study samples nevertheless differed substantially across exposure levels and was highly correlated with average per capita Medicare spending in the HRRs. We then followed the cohort members for up to 5 years after study enrollment and compared the processes of care (Part 1) and health outcomes (Part 2) across HRRs assigned to different exposure levels.
We sought study samples that would be similarly ill across regions based on the occurrence of an incident illness (acute myocardial infarction [MI], hip fracture, colorectal cancer) or in which we had excellent data for case-mix adjustment (acute MI, Medicare Current Beneficiary Survey [MCBS] sample). We restricted the eligible population to Medicare enrollees between the ages of 65 and 99 years who, at the time of study enrollment, were eligible for both Medicare Parts A and B and were not enrolled in a Medicare health maintenance organization (HMO).
The acute MI cohort was drawn from patients included in the Cooperative Cardiovascular Project, which identified from billing records a national sample of Medicare beneficiaries who were discharged after acute MI between February 1994 and November 1995 (18). We excluded patients with an unconfirmed acute MI (with the same criteria as in previous studies ) and included only the first episode of acute MI for a given patient. The hip fracture and colorectal cancer cohorts were selected based on a first admission between 1993 and 1995 for a primary diagnosis of hip fracture or colorectal cancer with resection, using the same International Classification of Diseases, Ninth Revision, Clinical Modification codes as in earlier work (20). Hospitalization rates for acute MI, hip fracture, and colorectal cancer vary little across regions (21), and patients with incident cases of these conditions are likely to be similarly ill in different communities (20). We excluded patients with a previous hospitalization for the same diagnosis in the year before their index stay. The general population cohort was drawn from the access-to- care component of the MCBS, a continuous panel survey that is representative of the Medicare population (22). Our inclusion criteria are detailed in the Appendix.
Trained abstractors working in the Cooperative Cardiovascular Project obtained characteristics of patients in the acute MI cohort from the medical record (18). Quality of the chart review process was monitored by random reabstractions, and percentage agreement was generally very high (93.3% to 94.8%) (23). Missing data for clinical variables were handled by including a specific categorical variable for patients with each missing variable (for example, admission blood pressure missing). Income was defined based on ZIP code of residence by using 1990 U.S. Census data.
For the hip fracture and colorectal cancer cohorts, we coded the presence of specific comorbid conditions based on diagnoses recorded on the discharge abstract, as was done in previous work (24, 25). Cancer stage was classified as distant versus local or regional because this classification has been found to correspond most closely to reported stage, according to analyses of linked MedicareSurveillance, Epidemiology, and End Results data (26). Data from the 1990 U.S. Census, measured at the level of the ZIP code, were used to provide measures of income, education, disability status, urban or rural residence, employment, marital status, and Hispanic origin. For all three chronic disease cohorts, we used American Hospital Association data to characterize the hospital teaching status (27) and the Medicare claims files of patients' index hospitals to determine the volume of cases of hip fracture, colorectal cancer, and acute MI treated per year.
Data collection and preparation procedures for the MCBS are described elsewhere (22). Because not all respondents completed all survey items, analyses of utilization, access to care, satisfaction, and survival are based on slightly different numbers of respondents (Appendix, Section C). Among the patient attributes, responses were most likely to be missing for income (5%); patients for whom income data were missing were recoded to the lowest-income group after analyses showed them to be similar in other attributes and survival to other patients in that group. The MCBS includes responses from proxies, which represented a maximum of 8.0% of our initial cohort. There were no differences in the proportion of proxy responses across the quintiles of spending, and excluding proxy responses did not alter our findings.
We used information from Medicare enrollment files to determine the percentage of all Medicare enrollees in each HRR who were enrolled in HMOs, to assign patients to one of nine major regions of the country, to determine whether patients had moved within an HRR or across HRRs within 1 or 2 years before their index admission, and to determine when patients should be censored based on loss of Medicare fee-for-service coverage (for utilization analyses) or based on relocation from their original HRR.
We used two approaches to determine cohort members' exposure to different levels of Medicare spending in their regions of residence. Previous research has shown that the dramatic differences in end-of-life treatment across U.S. regions are highly predictive of differences in total spending (13, 28) but are not due to differences in case mix or patient preferences (29). Our primary measure of exposure was the EOL-EI, which was calculated as age-sex-raceadjusted spending (measured with standardized national prices) on hospital and physician services provided to Medicare enrollees in their last 6 months of life in each of the 306 U.S. HRRs from mid-1994 to 1997, excluding any members of the study cohorts (Appendix, Sections D and E).
Although Medicare enrollees identified 6 months before death are identical in terms of their risk for death (which is 100%), they may differ in other ways across HRRs (for example, in illness levels unrelated to risk for death). We therefore repeated the major analyses with an alternative exposure measurean Acute Care Expenditure Index (AC-EI)which was based on differences across HRRs in risk-adjusted spending during an acute care episode, calculated as follows. For each of the four study cohorts, we determined age-, sex-, race, and illness-adjusted spending on physician and hospital services (measured with standardized national prices) provided during the first 6 months after index hospitalization across the 306 HRRs. The AC-EI subsequently used to assign each cohort to different exposure levels was the average of the age-sex-race-illnessadjusted spending during the acute episode of care in the other cohorts (Appendix, Section F).
The EOL-EI measures regional differences in practice at the end of life, while the AC-EI measures regional differences in practice during acute illness (or exacerbation of a chronic illness). Both measures were highly predictive of average age-sex-raceadjusted Medicare spending at the HRR level (r = 0.81 for the EOL-EI and 0.79 for the AC-EI in the acute MI cohort) and of regional differences in utilization. Both exposure measures produced similar results, so we present our findings on utilization based on the EOL-EI. (A sensitivity analysis of our mortality analyses using the AC-EI is presented in Part 2.) For many analyses, we grouped HRRs into quintiles of increasing exposure to Medicare spending based on the EOL-EI.
We used 100% Part A and B Medicare claims for all four cohorts to determine rates of specific physician and hospital services within the first year of follow-up and a 5% sample to examine aggregate physician utilization over the full 5 years of follow-up. The major categories of physician services provided to the Medicare population (MCBS) were defined by using Medicare's BerensonEggers Type of Service classification (cms.hhs.gov/data/betos/default.asp). The acute MI chart review allowed us to describe the proportion of patients in defined clinical subgroups who received specific medications or interventions during the initial hospitalization. Analyses of the quality of care were restricted to patients who were ideal candidates for each therapy (that is, patients with any absolute or potential contraindication to the treatment were excluded), as in the original description of the Cardiovascular Cooperative Project (18). The in-person interviews for the MCBS included specific questions about the types of visits received during the past year, waiting times at these visits, receipt of specific preventive services, and access to care.
All analyses used the patient as the unit of analysis and measured other attributes at progressively higher levels of aggregation where appropriate (ZIP code of residence, hospital of index admission, and HRR of residence). To test whether patients' baseline characteristics differed across HRRs with differing EOL-EI levels, we used logistic regression at the individual level with the attribute (for example, age 65 to 74 years) as a dichotomous dependent variable and the HRR-level EOL-EI as the independent variable. To assess the aggregate impact of any differences in individual attributes on average baseline risk for death across regions of increasing EOL-EI, we used logistic regression to determine each individual's predicted 1-year risk for death as a function of his or her baseline characteristics. The models had modest to excellent predictive ability (c-statistics were 0.61 for the colorectal cancer cohort, 0.68 for the hip fracture cohort, 0.77 for the acute MI cohort, and 0.82 for the MCBS cohort). We used these models to determine the average predicted risk for death across quintiles of EOL-EI. We then determined average predicted 1-year risk for death in each HRR and tested for an association across HRRs using logistic regression.
For the analyses of aggregate utilization, cohort members were censored (no longer followed) if they lost Medicare insurance (either Part A or B eligibility), enrolled in an HMO, or moved out of the original HRR of residence. The average amount of hospital and physician services provided to each cohort member across quintiles of the EOL-EI was calculated by using standardized national prices (Appendix, Section D) and was calculated separately for the acute episode (index admission to 6 months for the three chronic disease cohorts) and for 6 months to 5 years (chronic disease cohorts) or for 5 years (MCBS cohort). To control for baseline differences, we used linear regression, weighting by follow-up time, with the log-transformed utilization of hospital and physician services per person-year as the dependent variable for the individual and the quintile of end-of-life spending as the primary independent variable. Coefficients and confidence intervals were back-transformed to provide estimates of the adjusted relative rates of utilization in the highest compared with the lowest quintile for each cohort.
We analyzed only the first year of follow-up (for which we had 100% physician claims) to compare rates of specific physician services across quintiles. We used Poisson regression to calculate adjusted relative rates of specific services (dependent variable) in quintile 5 compared with quintile 1, controlling for baseline differences (independent variables) (30). We then computed pooled relative rates across the cohorts, using a weighted average of the individual regression coefficients for each cohort and weighting by the inverse of the variance (31).
The specific independent and dependent variables included in each regression are described in Appendix Table 5. We used the REG routine of Stata 6.0 (Stata Corp., College Station, Texas) to perform all regression analyses. For the analyses of the MCBS cohort, we used SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina) to account for sampling weights and the two-stage design.
The 306 U.S. HRRs were assigned to quintiles of Medicare spending based on the primary exposure measure, the EOL-EI, which averaged $14 644 in quintile 5 (the highest-spending quintile) and $9074 in quintile 1 (the lowest-spending quintile) (Figure 2). Average age-sex-raceadjusted per capita Medicare spending was highly correlated with EOL-EI (r = 0.81); per capita Medicare spending was $6304 in quintile 5 and $3922 in quintile 1. Residents of the highest quintile received 61% more Medicare resources than those in the lowest quintile whether measured by the EOL-EI or by average Medicare spending. Quintiles with a higher expenditure index had more hospital beds and physicians, a relative predominance of large hospitals and teaching hospitals, and a higher proportion of urban residents. The percentage of Medicare beneficiaries enrolled in HMOs was higher in both the lowest and highest quintiles than in the middle quintiles.
Average per capita Medicare spending, health care resource levels, and other key attributes of U.S. hospital referral regions according to quintiles of spending.
Tables 1, 2, 3, and 4 present selected characteristics of each study cohort in each quintile and a test for trend across HRRs that had a higher EOL-EI. Because of the large sample sizes, many differences in the chronic disease cohorts were statistically significant. Notable differences were found in racial composition (more black patients in higher quintiles) and income (higher quintiles had more enrollees in the highest and lowest income categories). Smaller differences across quintiles were apparent in age, sex, comorbid conditions, and cancer stage. For the acute MI cohort, patients in the highest quintile had a higher prevalence of nonQ-wave infarctions and congestive heart failure but were less likely to have creatine kinase levels over 1000 IU/L. For the MCBS cohort, residents of HRRs in the quintiles with a higher EOL-EI were more likely to report being in fair or poor health but were less likely to live in a facility. Few differences were found, however, in activities of daily living or instrumental activities of daily living, smoking, or reported chronic conditions.
Table 1. Characteristics of the Hip Fracture Cohort according to Level of Medicare Spending in Hospital Referral Region of Residence
Table 2. Characteristics of the Colorectal Cancer Cohort according to Level of Medicare Spending in Hospital Referral Region of Residence
Table 3. Characteristics of the Acute Myocardial Infarction Cohort according to Level of Medicare Spending in Hospital Referral Region of Residence
Table 4. Characteristics of the Medicare Current Beneficiary Survey Cohort according to Level of Medicare Spending in Hospital Referral Region of Residence
Tables 1, 2, 3, and 4 also present the average predicted risk for death in each quintile and a formal test of trend assessing whether HRRs with a higher EOL-EI had higher predicted mortality at baseline. A higher expenditure index was associated with an increased predicted risk for death for the AMI cohort, a lower predicted risk for death for the hip fracture cohort, and no significant difference in predicted mortality across spending levels for the colorectal cancer or MCBS cohorts. These data indicate that the burden of illness in these cohorts is similar across HRRs that differ by more than 60% in both EOL-EI and, as grouped according to the EOL-EI, average per capita Medicare spending.
Figure 3 displays the aggregate utilization of hospital and physician services by the study cohorts across quintiles. The data presented exclude the initial acute episode of care for the acute MI, hip fracture, and colorectal cancer cohorts because utilization rates were, as expected, similar during this period. Differences across the cohorts are apparent: Within each quintile, the MCBS cohort received the least care while the acute MI cohort received the most care. Within each cohort, however, patients who resided in regions with a higher EOL-EI received more medical care. After adjustment for baseline differences in health status, overall use of hospital and physician services was between 52% higher (MCBS cohort) and 77% higher (acute MI cohort) in the highest compared with the lowest quintile. For each of the cohorts, approximately half of the spending within a quintile was for acute inpatient hospital care and half was for physician services (data not shown).
Per capita utilization of hospital and physician services during follow-up by study cohorts.
Figure 4 characterizes the content of physician services provided to the Medicare population (MCBS) across quintiles. Only a small proportion of total physician services in any quintile was for major surgical procedures, and the overall rate of major surgery was relatively constant across quintiles. Evaluation and management services (visits), tests, radiology services, and minor procedures made up the vast majority of physician activity and explained the differences in physician practice found across the quintiles.
Utilization of physician services across quintiles of spending for the Medicare Current Beneficiary Survey cohort, 19921996.
Figure 5 provides detailed information on differences in the specific services provided to the chronic disease cohorts across HRRs with differing spending levels. We present the pooled relative rates and 95% CIs for all three chronic disease cohorts combined in quintile 5 (highest expenditure index) compared with quintile 1 (lowest expenditure index) because the relative use rates across quintiles were similar for each cohort. For example, although pulmonary function tests were performed twice as frequently overall in patients with acute MI than in those with hip fracture, they were performed nearly three times more often in the highest quintile than in the lowest quintile in all three cohorts (Appendix Table 11).
Relative rate and 95% CIs of specific services provided to cohort members residing in the highest quintile of Medicare spending compared with those residing in the lowest quintile for the three chronic disease cohorts combined.
Rates of major procedures differed little across quintiles and were sometimes lower and sometimes higher in quintile 5. Rates of several minor, relatively nondiscretionary procedures (skin laceration repair, breast biopsy) were also similar across quintiles.
As was found in the general population sample, differences in utilization across quintiles were largely due to increased use of evaluation and management services and associated tests, imaging and minor procedures, and use of the hospital as a site of care. Rates of outpatient physician office visits averaged 1.27 (95% CI, 1.26 to 1.28) times higher in quintile 5 than in quintile 1. Inpatient visits, however, were 2.13 (CI, 2.12 to 2.14) times higher, and inpatient specialist consultations were 2.36 (CI, 2.33 to 2.39) times higher. The proportion of patients seeing more than 10 different physicians during the first year after their index admission was 2.97 (CI, 2.84 to 3.11) times higher in quintile 5. More frequent physician contact was associated with more frequent use of diagnostic tests and minor procedures. Patients in quintile 5 spent more time in the hospital (rate ratio, 1.52 [CI, 1.50 to 1.54]) and in the intensive care unit (rate ratio, 1.55 [CI, 1.50 to 1.60]).
Some of the most dramatic differences were found in rates of services provided to severely ill patients. For example, among patients in their last 6 months of life, intensive care unit days were 2.28 (CI, 2.18 to 2.38) times higher in quintile 5 than in quintile 1 and the use of vena cava filters, feeding tubes, and emergency intubation were all more than 2.3 times as frequent in quintile 5 as in quintile 1.
Regions with higher expenditure indices did not provide better quality of care on most measures (Table 5). Among patients in whom the specific treatment was recommended, patients with acute MI in the highest quintile were no more likely to receive acute reperfusion, were less likely to receive aspirin at admission or discharge and angiotensin-converting enzyme inhibitors in the setting of a low ejection fraction, and were more likely to receive -blockers. Because some preventive services (for example, influenza vaccination) may be provided in nonreimbursed settings, the results for preventive services are based on patient reports from the MCBS. Although mammography was performed as frequently in high as in low quintiles, influenza and pneumococcal immunization and Papanicolaou smears were provided less frequently in HRRs with higher expenditure indices. Lack of better-quality care in HRRs in the highest quintile was not related to the greater predominance of teaching or large hospitals (Figure 6). Major teaching hospitals had somewhat higher quality on several of the measures, but the differences in quality across quintiles were small and inconsistent.
Table 5. Quality of Care according to Level of Medicare Spending in Hospital Referral Region of Residence
Percentage of patients in the acute myocardial infarction cohort who received the specified therapy (among ideal candidates), according to type of hospital and quintile of Medicare spending.PP
Although the absolute differences in access to care were small, the findings suggest a general pattern of slightly lower access to care in HRRs with higher expenditure indices (Table 6). Patients with acute MI who lived in regions with higher expenditure indices were significantly less likely to receive exercise testing and angiography, and a slightly smaller percentage saw a physician within 30 days of discharge. Larger differences emerged in the type of specialists seen during the first 30 days: Those in HRRs with lower expenditure indices were more likely to see family practitioners, and those in HRRs with higher expenditure indices were more likely to see medical subspecialists.
Table 6. Access to Care according to Level of Medicare Spending in Hospital Referral Region of Residence
In HRRs with higher expenditure indices, a slightly smaller proportion of patients in the general population (MCBS) reported having a usual source of care. Some differences in the site of physician visits were seen, with more frequent outpatient and office visits in regions with high expenditure indices. Waiting times for emergency department, outpatient facility, and office visits were significantly longer in higher-spending regions. Finally, on the basis of one of three traditional measures of access (having a problem but not seeing a physician), HRRs with a higher expenditure index provided significantly worse access to care.
We conducted a cohort study in four distinct samples of Medicare enrollees to compare the content, quality, and accessibility of care across 306 U.S. HRRs with substantially different spending levels. The primary exposure variable in this study, the EOL-EI, was intended to measure the component of regional variation in Medicare spending that is unrelated to regional differences in illness or price. The goal was to ensure assignment of HRRs (and the patients within them) to treatment groups that were similar in baseline health status but that differed in subsequent treatment. The validity of the approach was confirmed by our finding that illness levels in each of the four study cohorts differed little across quintiles but that health care utilization rates and spending (for all four study samples) increased steadily and substantially as the expenditure index for a given HRR increased. Regardless of the measure used to characterize spending, residents of the highest-spending quintile received about 60% more care than residents of the lowest-spending quintile.
We compared the content, quality, and accessibility of care across regions with different levels of spending. The differences in spending across HRRs were largely due to more frequent use of the hospital as a site of care, more frequent physician visits, greater use of medical subspecialists, and more frequent diagnostic tests and minor procedures. The quality of care was no better in higher-spending regions, and access to care was slightly worse on most measures.
Our analysis has several limitations. First, we had only a limited number of measures of quality and access and studied only four cohorts. The consistency of our findings across cohorts and measures, however, strengthens the causal inferences that can be drawn. Other large studies have also documented substantial differences in resource use that are unrelated to quality of care (32-35).
Second, we must address concerns about potential unmeasured differences in health status. It is highly unlikely that the 60% differences in utilization observed across quintiles of spending could be due to residual confounding by unmeasured illness levels. In each of the four cohorts, we found that patients' predicted risk for death differed little across regions of differing spending levels. Moreover, crude and adjusted utilization analyses yielded nearly identical results. To account for the greater than 60% difference, therefore, an unmeasured confounder would have to be a more powerful predictor of utilization than those we measured (including self-assessed health status or the severity of an acute MI) and at the same time not be correlated with the available measures (because crude and adjusted analyses yielded similar results). The nearly 60% increased utilization observed in higher-spending regions was found in every subgroup of the study samples (Appendix Tables 12, 13, and 14).
Third, our findings cannot prove that the strong association we observed between capacity (the supply of hospital beds and medical specialists) and utilization is entirely causal. Differences in the malpractice environment could contribute to the differences in practice we observed. State-level differences in malpractice, however, are associated with a less than 10% difference in utilization (36). In addition, while it is possible that Medicare enrollees in high-spending regions prefer a more specialist-intensive pattern of practice, neither preferences nor greater fears of malpractice provide a compelling justification for the differences in public expenditures.
Previous research, however, suggests a causal relationship between supply and utilization. Use of physician services is strongly associated with the local workforce composition (7) and supply (9). Chronically ill patients are more likely to receive care in the hospital in communities with more beds (8, 12, 20, 37). In addition, the local bed supply, rather than patient preferences, explained the differences in end-of-life care among patients in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT) (29). Finally, it has been shown that physicians adapt their admission and discharge decisions to the availability of intensive care unit beds, admitting more patients with less severe illness and extending length of stay when more beds are available (38). It appears likely that physicians in all regions are simply managing their patients with available resources and that inpatient management and subspecialist consultation are easier in regions where these resources are readily available.
Regional differences in Medicare spending are due almost entirely to use of discretionary services that are sensitive to the local supply of physicians and hospital resources: more frequent physician visits, greater use of specialists, and greater use of the hospital and intensive care unit as sites of care. Policymakers and purchasers concerned with resurgent growth in health care spending will need to focus on these supply-sensitive services. As we discuss in greater detail in Part 2, however, our study provides little guidance on the potential impact of reducing the use of such services, and caution is warranted as policies are developed to control health care spending.
Nevertheless, for the Medicare population, it appears that neither greater local availability of physicians and hospital beds nor the more inpatient-based and specialist-oriented pattern of practice that result are associated with improved access to care, better-quality care, or (as is reported in Part 2) better health outcomes or satisfaction. These findings call into question the notion that additional growth in health care spending is primarily driven by advances in science and technology and that spending more will inevitably result in improved quality of care.
The Appendix was developed to provide interested readers with additional detail on the methods of the study as well as supplementary findings referred to in the body of the papers that could not be included there because of space constraints. Section B provides an expanded discussion of the rationale for our study design and its relationship to instrumental variables analysis. Section C describes in greater detail our study populations, exclusions applied, and data quality. Section D describes in detail the rationale behind the approach and the methods used to calculate spending and utilization rates using measures free of bias that could be introduced because of differences in wages, prices, or policy payments to physicians or hospitals. Section E describes in greater detail the End-of-Life Expenditure Index (EOL-EI), the primary exposure used in the analysis, including the study population within which it was calculated and how members of each study cohort were excluded from the sample used to calculate the index used as the exposure for that cohort. Section F describes the motivation, methodology, and results of our sensitivity analysis using the Acute Care Expenditure Index.
In addition, the Appendix also includes supplementary tables that present additional detail on individual patient attributes (Appendix Tables 1, 2, 3, and 4), a table that lists specifically which variables are included in each of the major models used in the analyses (Appendix Table 5), the main models examining survival (Appendix Tables 6, 7, 8, and 9) and change in functional status (Appendix Table 10), a table presenting specific procedure rates for each chronic disease cohort and for all three cohorts combined (Appendix Table 11), and tables summarizing overall health care utilization rates across quintiles for each chronic disease cohort (Appendix Tables 12, 13, and 14).
Appendix Table 1. Characteristics of the Hip Fracture Cohort according to Level of Medicare Spending in Hospital Referral Region of Residence
Appendix Table 2. Characteristics of the Colorectal Cancer Cohort according to Level of Medicare Spending in Hospital Referral Region of Residence
Appendix Table 3. Characteristics of the Acute Myocardial Infarction Cohort according to Level of Medicare Spending in Hospital Referral Region of Residence
Appendix Table 4. Characteristics of the Medicare Current Beneficiary Survey Cohort according to Level of Medicare Spending in Hospital Referral Region of Residence
Appendix Table 5. Summary of Variables Used in Cohort Analyses
Appendix Table 6. Survival Model for the Hip Fracture Cohort
Appendix Table 7. Survival Model for the Colorectal Cancer Cohort
Appendix Table 8. Survival Model for the Acute Myocardial Infarction Cohort
Appendix Table 9. Survival Model for the Medicare Beneficiary Survey Cohort
Appendix Table 10. Models Testing the Association between the End-of-Life Expenditure Index and Change in Scores on the Health Activities and Limitations Index
Appendix Table 11. Specific Services Provided to Chronic Disease Cohorts during First Year of Follow-up
Appendix Table 12. Unadjusted Utilization Rates of Hospital and Physician Services, by Specified Subgroups of the Hip Fracture Cohort
Appendix Table 13. Unadjusted Utilization Rates of Hospital and Physician Services, by Specified Subgroups of the Colorectal Cancer Cohort
Appendix Table 14. Unadjusted Utilization Rates of Hospital and Physician Services, by Specified Subgroups of the Acute Myocardial Infarction Cohort
As is discussed in the overview of the study design in Parts 1 and 2, the ideal approach to addressing the study questionwhether the increased spending observed in some regions of the United States leads to better care or outcomeswould be to carry out a randomized trial. However, such a trial would be difficult and would probably end up answering a slightly different question (depending on the intervention under study).
The field of economic research has addressed this problem through approaches that attempt to create a natural randomization through what is termed instrumental variables analysis. The key notion is that an exposure is identified that allows the study sample to be assigned to different treatment groups in a way that assures that those in different treatment groups are similar in terms of attributes that might affect the outcome (that is, that case mix is similar in the groups). They are nonetheless treated differently.
A good example of this type of natural randomization comes from a study of how serving in the Vietnam War affected the probability of suicides and vehicular deaths (39). Clearly, comparing suicide rates for Vietnam veterans and nonveterans would be statistically suspect, since the underlying characteristics of the two groups would be expected to differ by so much. Draft lottery numbers, chosen randomly on the basis of ones birthday, were used as a natural randomization to place men into the treatment group, those most likely to be sent to Vietnam, and the control group, those least likely to be sent. This method qualified as an instrument because it fulfilled the two [intuitive] requirements of an instrumental variable: 1) It was highly correlated with the exposure variable, which was serving in Vietnam, and 2) it was plausibly uncorrelated with the underlying mental health of the population (or, more formally, with any unmeasured differences in the populations). In other words, any differences in suicide and accident rates between the two groups were very likely to have been the result of serving (or not serving) in Vietnam, and not individual risks for suicide or poor driving. The article by Hearst and colleagues, like our articles, took a reduced-form approach to the problem. In other words, they compared what they called draft eligible (the treatment group) with draft ineligible (the control group).
By the same token, in our papers, we compared outcomes of people living in areas where the health system displays a more aggressive approach to end-of-life care with those of people living in areas where the health system displays a less aggressive approach. We have no a priori reason for believing that these populations in these regions should differ in their underlying health status, but they are treated differently.
Why didnt we use the formal instrumental variables approach, in which we would predict how much an additional $1000 in Medicare spending affects survival? There are three main reasons. First, we are interested primarily in the direction and general magnitude of effect, rather than in the cost of achieving that effect. We recognize that if increased expenditures across regions result in improved health outcomes, knowing the magnitude of the effect of an additional 10% increase in regional spending on survival and functional status for Medicare patients would be important for policy research. If we find no association or that higher spending is associated with lower survival, however, the precise estimate of the coefficient (in terms of dollars) is relatively unimportant. Second, instrumental variables analysis is able to provide unbiased estimates only in certain settings, one of which is a linear model. Our need to use Cox proportional-hazards regression for our mortality analyses precluded a formal instrumental variables analysis using currently developed statistical tools. Finally, it is important to recognize that the fundamental limitation of instrumental variables analysis would remain. One cannot prove that one has a perfect instrument.
We therefore presented our analysis as an observational study. We recognize that unmeasured confounding remains a possibility, but we nevertheless believe that our findings represent a major advance over previous research and that our conclusions that residence in higher-spending regions does not cause improved quality, access to care, or survival (and may cause worse survival) are sound.
For all three study cohorts, we restricted the eligible population to Medicare enrollees between the ages of 65 and 99 years who, at the time of their index admission, were eligible for both Medicare Parts A and B and were not enrolled in a health maintenance organization (HMO).
The acute myocardial infarction (MI) cohort was drawn from the patients included in the Cooperative Cardiovascular Project, which identified from billing records a national sample of Medicare beneficiaries with discharges for acute MI that occurred between February 1994 and November 1995 (40). We excluded patients with an unconfirmed acute MI (using the same criteria as in previous studies ) and included only the first episode of acute MI for a given patient. Characteristics of the acute MI cohort were obtained from the medical record by trained abstractors working in the Health Care Financing Administrations Cooperative Cardiovascular Project. They collected extensive data on predefined variables, including presentation characteristics (location of MI, cardiac rhythm, blood pressure, shock, and whether cardiopulmonary resuscitation was performed), initial laboratory values, the presence of comorbid conditions, and functional status before admission. Quality of the chart review process was monitored by random reabstractions; percentage agreement was generally very high (93.3% to 94.8%) (42). Demographic information available through the administrative databases was virtually complete (for example, age, sex, ethnicity, date of death) and is believed to be highly accurate. Clinical variables had some missing values; we created an additional categorical variable (for example, missing creatine kinase level) where appropriate.
We used Medicares 100% national MedPAR files to identify the first admission between 1993 and 1995 for patients with a primary diagnosis of hip fracture or colorectal cancer with resection, using the same International Classification of Diseases, Ninth Revision, Clinical Modification codes as in earlier work (43). Hospitalization rates for these conditions vary little across regions, and incident cases are likely to be similarly ill in different communities. We excluded patients with a previous hospitalization for the same diagnosis in the year before their index stay. Characteristics of the hip fracture and colorectal cancer cohorts were ascertained from claims data and U.S. Census data. Age, sex, race, and date of death were all ascertained from Medicares denominator file (44). We coded the presence or absence of specific comorbid conditions by using diagnoses recorded on the discharge abstract as in previous work (43, 45). Colorectal cancer stage was defined by using the diagnoses recorded on the discharge abstract and classified as distant versus local or regional because this classification has been found to correspond most closely to reported stage according to analyses of linked Medicare-Surveillance, Epidemiology, and End Results data (46). Data from the 1990 U.S. Census, measured at the level of the ZIP code, were used to provide measures of income, education, disability status, urban or rural residence, employment, marital status, and Hispanic origin. Fewer than 1% of cohort members were missing these census variables. For those with missing values, we assigned the average of the value for other members of the study cohort residing in the same hospital referral region (HRR).
Persons in this study were participants in the access to care component of the Medicare Current Beneficiary Survey (MCBS), a continuous panel survey that is representative of the Medicare population (47). Participants are selected by using a stratified multistage geographic sample design, with oversampling of aged and disabled beneficiaries. Respondents are interviewed in both community settings and health facilities. The access to care component entails annual interviews with respondents and collects information on demographic characteristics, health insurance, health status and functioning, access to care, and satisfaction with services. Response rates to the survey have been high (48): Of the 14 530 initially asked to participate, 83.3% agreed to the interviews. Medicare claims data are available for all participants who are not enrolled in HMOs. Data collection and preparation procedures are described elsewhere (47).
We selected for inclusion in the survival analysis all MCBS participants older than age 65 years with an initial interview between 1991 and 1996, excluding HMO members and those not eligible for Medicare Part A or Part B (n = 23 902). The analysis of utilization were also done on essentially the same cohort (n = 23 498) but excluded several hundred patients because of incomplete utilization data. The analyses of baseline characteristics, access, and satisfaction excluded those with interviews in 1991 because key variables were missing for that year. The study population for analysis of baseline characteristics consisted of 18 190 patients. Analysis of decline in functional status was further restricted to those with at least 1 year of follow-up (n = 15 556).
Demographic data included age, race, sex, marital status, education, household income, and urban residence. Insurance coverage was coded into four mutually exclusive categories, as in others work (49). Health status variables included self-assessed health, activities of daily living, instrumental activities of daily living, other functional impairments, a list of reported medical conditions, whether a patient was bedridden, facility residence, and smoking status. Questions on access to care included having a usual source of care, having a usual physician, having trouble getting care, delaying care because of cost, having a serious problem and not seeing a physician, as well as receiving specific preventive services. Respondents who had received medical care were asked the site or sites of care and how long they had waited to receive care. Satisfaction with medical care was assessed by using the questions used to evaluate care in previous analyses of the Medicare population (50).
We used the Health Activities and Limitations Index (HALex) to characterize participants functional status. The HALex was developed by the National Center for Health Statistics to provide a national measure of years of healthy life that can be calculated using the responses to the National Health Interview Survey. The HALex assesses health on a continuum ranging from death (0.0) to the best possible health state (1.0). Each individual is assigned to 1 of 30 unique health states based on his or her self-perceived health (five levels) and degree of activity limitation (six levels). Multiattribute utility theory was used to develop the scoring algorithm (51). First, the best and worst states of each dimension (when examined independently) were assigned the values of 1 and 0, respectively. The distance between each response level for each dimension (activity limitation and self-perceived health) was then defined by using correspondence analysis to maximize the correlation between the two dimensions and thereby define the values for the intermediate responses on each scale. Finally, after the corners of the distribution were anchored by using utilities derived from the Health Utilities Index Mark I (52), a multiplicative model was then used to assign scores to each of the 30 unique health states. A detailed description of the methods is available elsewhere (53) and at www.cdc.gov/nchs/data/statnt/statnt07.pdf.
The MCBS includes the questions required to calculate a HALex score, but because elderly participants are not asked about limitations in their major activity, only 20 of the 30 cells are used to score their responses, as in other analyses of the elderly. Several studies have reported on the construct validity of the HALex and found that the direction of effects of other patient attributes on HALex scores are as hypothesized (54). Our own models further confirm the construct validity of this measure. For example, the impact of increasing age on functional status can be seen in model A (Appendix Table 10). In model B, which includes interactions between year and age, sex, and race categories, older individuals face a significantly increased risk for decline in HALex scores over up to 3 years. In model C, which includes interaction terms between year and the chronic conditions, it can be seen that both Alzheimer disease and, to a lesser extent, Parkinson disease are associated with a significantly more rapid decline in functional status than other chronic conditions. All these effects appear plausible.
To further validate the use of HALex scores, we compared the impact of chronic conditions on MCBS participants HALex scores with the impact of similar chronic conditions on physical component summary scores derived from the Medical Outcomes Study Short-Form 36 (55). We could not make a perfect head-to-head comparison because the wording of the questions in each survey was not identical and the MCBS survey included questions about chronic conditions not included in the Medical Outcomes Study survey. Nevertheless, when we compared the coefficients derived from age- and sex-adjusted models for the specific chronic conditions included in both data sets, we found a strong correlation overall (r = 0.77) and in the rank order of the impact of the conditions on functional status (r = 0.74) (Appendix Table 15).
Appendix Table 15. Impact of Chronic Conditions on Functional Status Scores
All of our utilization analyses in which dollar amounts are reported were based on measures of expenditures that have been purged of regional differences in prices or policy payments because the use of actual payments would introduce a bias. Actual reimbursements for hospital and physician services vary substantially according to geographic region due to wage, price, and policy differences (such as subsidies for the costs of medical education). To develop a measure of Medicare spending that was free of regional differences in price and policy payments, we followed the general approach developed by the Medicare Prospective Payment Commission in an earlier report (56) to calculate spending as follows. For inpatient hospital services, we based our measure on the diagnosis-related group (DRG) weight. All DRGs are assigned a relative weight proportional to the average national cost for Medicare patients within that DRG compared to the average cost for all Medicare patients. We converted DRG weights to dollars by multiplying the weight times the national average DRG price for 1996 ($3799). The measure reflects average national resource use for this condition. Hospital spending was defined as the sum of all DRG weights for an individual during a specified period times the DRG price. For physician services, we used the Resource-Based Relative Value Scale that forms the basis of the current Medicare physician fee schedule (57). Relative value units (RVUs) are assigned to each physician service to reflect physician work and the associated practice expense. For services included in the physician fee schedule, we assigned the total RVU value for the specific service from the Medicare fee schedule. For services not included in the fee schedule (primarily laboratory services), we calculated an RVU equivalent by dividing either the standard national price (laboratory services) or the median national allowed charge (for physician services without an RVU in the fee schedule) by the average 1996 factor ($36.14) used to convert RVUs to dollars. When DRG weights and RVUs are used, the measure of spending treats the value of a given service equally regardless of where the service is performed in the country. The measure removes the effect of any geographic differences in prices, wages, and policy payments.
Physician spending was defined as the sum of all RVUs for a given beneficiary during a specified period times the conversion factor. Aggregate spending for an individual is calculated in dollars and equals the sum of hospital spending and physician spending.
We used the definition of HRRs developed for the Dartmouth Atlas of Health Care, which is based on where patients travel to receive cardiovascular surgery and neurosurgery (58). More than 90% of Medicare enrollees live in HRRs where over 80% of residents care is delivered by providers within the HRR (58).
To identify a reference population who should be similarly ill across regions (at least in terms of their risk for death), we used the Medicare denominator file to identify all Medicare beneficiaries who died during the 3.5-year period between 1 July 1994 and 31 December 1997, were between 65.5 and 100 years of age at the time of death, were not enrolled in an HMO during their last 6 months of life, and were eligible for Medicare Part A (hospital insurance) and Part B (physician) coverage. We used the entire sample for analyses of hospital utilization. To measure use of physician services, we used the subset that was included in the 5% national sample (44), as in previous work (59), because complete Medicare Part B files were available to us only for that sample.
To ensure that regional differences in wages, prices, and policy payments did not bias our measure of regional differences in spending, we used standardized national prices (as described in Section D).
The reference populationall Medicare enrollees who died between mid-1994 and 1997includes members of the study cohorts who died during this interval. Although they represent a small percentage of the reference population, we wished to avoid the possibility of spurious correlations (sicker hip fracture patients in a given region would have higher expenditures and might be more likely to die). We therefore calculated an overall EOL-EI including all enrollees that was used to prepare Figure 2 in Part 1 and to map the regions. For each study population, however, we calculated a specific EOL-EI for use in the survival analyses (for which even a small bias could be problematic) that excluded from the reference population members of that cohort. There were thus four EOL-EIs. (Because <1% of the population were excluded, these measures were extremely highly correlated and resulted in nearly identical quintiles.) The EOL-EI was calculated as age-sex-race-adjusted spending (using the standardized national prices) on physician and hospital services by the reference population in each HRR. We sorted HRRs in order of increasing intensity and divided them into quintiles of approximately equal population size, based on the entire Medicare population older than 65 years of age.
Because of concern that our primary exposure (the EOL-EI) may not have fully accounted for differences in population characteristics in different regions, we developed an alternative measure and repeated the analyses using this measure. Although the ideal measure would be risk-adjusted differences in total Medicare spending, we know of no way to calculate such a measure using currently available data. An alternative was to define study populations in which we were reasonably confident in our case-mix measures. Given the probable similarity of the cohorts at baseline across regions, and the high quality of the risk-adjustment data for short-term mortality (for example, 6 months), we decided to use as our alternative exposure measure the regional differences in risk-adjusted 6-month utilization in the complementary cohorts as our measure of the exposure. We describe this approach, and our findings, in the sections that follow.
We performed four parallel analyses, one for each of our cohorts. The regional spending measure for each cohort was developed using the other cohorts, as shown in Appendix Table 16. The expenditure index was developed by using a linear regression model. To determine risk-adjusted expenditures, we used the following equation:
in which Uij is the total hospital and physician resource use per person in the first 6 months of follow-up by patient i in HRR j; ZI is a vector of patient covariates, including demographic (age, sex, race, income), severity (for example, stage), and comorbidity measures; is the effects of patient-level factors on utilization; Wj is the coefficient estimating regional intensity in HRR j; j is a set of HRR-level indicator variables [1 to 306]; and vij are patient-level error terms. The regression model is run with no intercept. The expenditure index used for the colorectal cancer cohort, for example, is the average of the coefficients j for the specific HRR generated from the hip fracture and acute MI regressions. We chose the first 6 months of utilization because the risk measures available in the data sets, especially for the acute MI cohort, are clearly most appropriate for this interval. The index for each study population was the weighted average of the coefficients for the specific HRR from each of the relevant models. We then repeated the key analyses related to survival: 1) comparing average predicted 1-year mortality rate across quintiles of the expenditure index; 2) comparing risk-adjusted utilization during both the first 6 months after the original hospitalization (where utilization rates should be relatively similar, given that all patients in the three hospitalized cohorts had an index hospitalization), and after the first 6 months of follow-up [where the most dramatic differences in utilization were seen]; and 3) comparing survival across quintiles and in a model in which the expenditure index was included as a continuous variable.
Appendix Table 16. Reference Populations Used To Calculate the Acute Care Expenditure Index for Each Cohort
The first questionwhether individuals residing in HRRs classified as higher- and lower-spending have similar baseline risk factors for 1-year mortalityis addressed below. The results are similar to those with the EOL-EI. Average risk for death was flat for both hip fracture and colorectal cancer, increased for the acute MI cohort, and decreased for the MCBS sample (Appendix Table 17).
Appendix Table 17. Average Predicted Mortality Rate across Quintiles of the Acute Care Expenditure Index
As in the analyses using the EOL-EI, risk-adjusted utilization rates increased across regions with higher levels of the Acute Care Expenditure Index, with a consistent but small increase during the first 6 months and a dramatic difference apparent after the acute episode. (It is important to recall that the first 6-month analysis includes the index hospitalization, which all three chronic disease cohorts experienced, resulting in smaller relative differences.) The results are similar to the findings using the EOL-EI, except in the hip fracture and colorectal cancer cohorts. In the current analyses, the ratio of utilization rates in the highest to lowest quintiles was somewhat lower than in the original analyses (1.42 vs. 1.75 and 1.58 vs. 1.75) (Appendix Table 18).
Appendix Table 18. Ratio of Risk-Adjusted Utilization Rates for Each Cohort in the Specified Quintile of Medicare Spending to Spending in the Lowest-Cost Regions
Further analyses indicated that the range of spending rates was probably lower across quintiles of the Acute Care Expenditure Index because the two cohorts in which the risk-adjusted expenditure index were developed for the hip fracture cohort were comparatively small, introducing greater measurement error.
Finally, we repeated the survival models (Appendix Table 19). The findings are similar but not identical to those presented in Part 2. Instead of the findings of statistically significant coefficients showing a small increase in the risk for death in the highest quintiles (and in the continuous models that are the appropriate test for trend), the analyses with the Acute Care Expenditure index are essentially flat.
Appendix Table 19. Association between Acute Care Expenditure Index in Hospital Referral Region of Residence and Cohort-Specific Risk-Adjusted Long-Term Mortality Rates (Sensitivity Analysis)
In summary, we found that our overall results using the new expenditure index were similar to the findings using the EOL-EI, especially if it is considered that our essential message is that there are dramatic differences in utilization across regions of increasing Medicare expenditures, that these utilization differences are not explained by underlying illness rates, and that the increased utilization is not associated with any gain in life expectancy. The relative consistency of these findings across the cohorts strengthens our confidence in this inference.
At the same time, because the findings are not identical, it may be worth considering a closely related question: Which measure is better? It could be argued that the EOL-EI is better because 1) it has less measurement error because it was calculated using much larger sample sizes; 2) it may be a better measure of the propensity of physicians in a region for overuse; and 3) it leads to slightly better stratification of HRRs into regions of higher and lower spending.
The argument for the Acute Care Expenditure Index based on first 6-month cohort-specific use is the following: 1) It may allow for better adjustment for possible differences in illness across regions of differing spending levels; and 2) it may be a better measure of regional differences in the propensity of physicians to provide extra care to patients with specific, clear-cut needs (for example, in the acute phase of an injury or illness).
We cannot know which measure is right or gives the better answer. The new index suggests that even when regions are stratified according to differences in how they treat patients during an acute illness episode, however, those regions that take the more intensive approach do not achieve consistently better survival.
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