0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Contribution |

The Relationship Between Managed Care Insurance and Use of Lower-Mortality Hospitals for CABG Surgery FREE

Lars C. Erickson, MD, MPH; David F. Torchiana, MD; Eric C. Schneider, MD, MSc; Jane W. Newburger, MD, MPH; Edward L. Hannan, PhD
[+] Author Affiliations

Author Affiliations: Department of Cardiology, Children's Hospital (Drs Erickson and Newburger), Departments of Pediatrics (Drs Erickson and Newburger) and Surgery (Dr Torchiana), Harvard Medical School, Division of Cardiovascular Surgery, Massachusetts General Hospital (Dr Torchiana), Department of Health Policy and Management, Harvard School of Public Health (Dr Schneider), and Division of General Medicine, Brigham and Women's Hospital (Dr Schneider), Boston, Mass; and the Department of Health Policy, Management and Behavior, State University of New York at Albany (Dr Hannan).


JAMA. 2000;283(15):1976-1982. doi:10.1001/jama.283.15.1976.
Text Size: A A A
Published online

Context Explicit information about the quality of coronary artery bypass graft (CABG) surgery has been available for nearly a decade in New York State; however, the extent to which managed care insurance plans direct enrollees to the lowest-mortality CABG surgery hospitals remains unknown.

Objective To compare the proportion of patients with managed care insurance and fee-for-service (FFS) insurance who undergo CABG surgery at lower-mortality hospitals.

Design A retrospective cohort study of CABG surgery discharges from 1993 to 1996, using New York Department of Health databases and multivariate analysis to estimate the use of lower-mortality hospitals by patients with different types of health insurance.

Setting Cardiac surgical centers in New York, of which 14 were classified as lower-mortality hospitals (mean rate, 2.1%) and 17 were classified as higher-mortality hospitals (mean rate, 3.2%).

Patients A total of 58,902 adults older than 17 years who were hospitalized for CABG surgery. Patients were excluded if their CABG surgery was combined with any valve procedure or left ventricular aneurysm resection or if they were younger than 65 years and enrolled in Medicare FFS or Medicare managed care.

Main Outcome Measure Probability of a patient receiving CABG surgery at a lower-mortality hospital.

Results Compared with patients with private FFS insurance (n=18,905), patients with private managed care insurance (n=7169) and Medicare managed care insurance (n=880) were less likely to receive CABG surgery at a lower-mortality hospital (relative risk [RR] of surgery at a lower-mortality hospital compared with patients with private FFS insurance, 0.77; 95% confidence interval [CI], 0.74-0.81; P<.001; and RR, 0.61; 95% CI, 0.54-0.70; P<.001, respectively, after controlling for multiple potential confounding factors). Patients with Medicare FFS insurance used lower-mortality hospitals at rates more similar to those with private FFS insurance (n=31,948; RR, 0.95; 95% CI, 0.91-0.98; P=.004).

Conclusions Patients in New York State with private managed care and Medicare managed care insurance were significantly less likely to use lower-mortality hospitals for CABG surgery compared with patients with private FFS insurance.

Managed care health plans offer a package of health care benefits to their enrollees on a prepaid basis, assuming financial risk for the covered services. Plans manage this risk in many ways. For instance, they may selectively contract with a restricted set of clinicians and hospitals. For acute care, prices and other contract terms are negotiated in advance, and health plans may require patients to use hospitals under contract with the plan or to pay more if they choose a hospital outside the health plan network.

Financial risk provides a strong incentive for health plans to select low-priced hospitals. However, health plans should also consider quality of care when contracting with hospitals, especially if explicit data on quality are available. Health plans that ignore publicly available quality data are at risk of adverse publicity about their contracting decisions. A recent study by Escarce et al1 found that managed care patients in California were more likely than insured non–managed care patients to use hospitals with lower-than-expected mortality rates for coronary artery bypass graft (CABG) surgery, but they found no difference between these 2 types of patient groups in Florida. Data on CABG surgery mortality were not publicly available in either setting, making it unlikely that health plans explicitly considered mortality rates in their CABG surgery contracting decisions.

In contrast, New York was the first and one of the few states to provide information on CABG surgery, including risk-adjusted mortality rates, to the public,2 allowing health plans, at least in theory, to explicitly consider quality of care when contracting with hospitals to provide CABG surgery. To explore the relationship between insurance type and patterns of hospital use, we compared the probabilities that patients with managed care insurance and patients with fee-for-service (FFS) insurance would undergo CABG surgery at lower-mortality hospitals in New York State, where quality-of-care information is available.

Population

We examined CABG surgery discharges in New York State for the years 1993 through 1996, using records from New York State annual hospital discharge databases.36 These legislatively mandated databases included patient age, race, ethnicity, insurance type, ZIP code, hospital of admission, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes for all patients aged 18 years or older who underwent CABG surgery in these years. We selected New York State residents who underwent CABG surgery (ICD-9-CM codes 36.1, 36.10-36.17) at 1 of 31 CABG surgery hospitals in New York State and had 1 of the following primary health insurance types: private FFS, private managed care, Medicare FFS, or Medicare managed care. Single codes represented each insurance type except private FFS, which included codes 06 (Blue Cross), 08 (commercial insurance company), and 15 (self-insured company). Private managed care was defined as code 11 (health maintenance organization); no codes existed for preferred provider organizations or other variations of private managed insurance. We excluded patients who underwent CABG surgery combined with any valve procedure or left ventricular aneurysm resection. Because they represent such a heterogeneous group, patients younger than 65 years who were enrolled in either Medicare FFS or Medicare managed care programs were also excluded.

Designation of Lower- and Higher-Mortality Hospital Groups

Rather than ranking hospitals separately for each year, we ranked hospitals into lower- and higher-mortality groups based on volume-weighted average adjusted mortality rates to simplify the analysis and improve interpretability of results. These rates, published by the New York Department of Health,710 are adjusted for multiple demographic and medical risk factors, including age, sex (for years 1995-1996 only), body surface area, hemodynamic status, medical comorbidities, severity of atherosclerotic process, measures of ventricular function, and history of open-heart surgery.

Based on these averages, we divided hospitals into lower- and higher-mortality groups using a mortality rate cut point that allocated about half of the patients to each group. The lower-mortality hospital group included 14 hospitals, constituting 49.8% of the total number of patients, with a mean adjusted mortality rate of 2.1% (range, 1.2%-2.5%) and a mean annual case volume of 561 (range, 189-1528). The higher-mortality hospital group included 17 hospitals, with a mean adjusted mortality rate of 3.2% (range, 2.5%-5.1%) and a mean annual case volume of 466 (range, 63-1019).

This method of ranking hospitals appeared to be fairly stable from year to year. The published hospital adjusted mortality rates averaged for the first 2 years of the study correlated highly with those for the last 2 years (Pearson r=0.53; P=.002). Rankings derived from these rates correlated significantly as well (Spearman r=0.37; P=.04). Thus, at least in theory, insurers could use this type of rating to predict hospital mortality rates in future years.

Analysis of Use of Lower-Mortality Centers

The outcome of interest was the probability that a patient underwent a CABG operation at 1 of the lower-mortality hospitals. The independent variable of interest was insurance type. In addition, models controlled for age, sex, urgent/emergent admission, and the presence of medical comorbidities, including diabetes, chronic renal failure, congestive heart failure, systemic hypertension, and chronic obstructive pulmonary disease. Age was represented as a categorical variable with 4 values: younger than 55 years, 55 to 64 years, 65 to 75 years, and older than 75 years. Other independent variables, determined using the 1990 US census,11 were median family income for the patient's ZIP code of residence and designation of the patient's residential area as urban. Median family income for ZIP code was represented as a categorical variable in quartiles.

Because the location of a patient's residence may be correlated with both insurance status and the choice of hospital,1215 our analysis also used 2 factors to account for the impact of distance on the use of lower-mortality centers. These factors were the distance to the nearest lower-mortality center and the distance to the nearest higher-mortality center. Both were based on the distances from the geographic centroids16 of the hospital and patient residential ZIP codes because street addresses were not included in the available data. Such "straight-line" methods of estimating geographic accessibility through geographic longitude and latitude data have been shown to correlate closely with estimated travel times17 using actual driving routes.14 These distance factors were log-transformed prior to analysis based on the observation in our data set and in other similar studies18 that such transformation resulted in a closer fit to the relationship between distance and use of a lower-mortality hospital than did actual distance and several other transformations investigated.

We identified univariate predictors of use of lower-mortality centers using 2 × 2 χ2 tests for categorical variables. In the case of categorical variables with more than 2 possible values, each value was compared with a reference value (eg, the youngest age group). The Wilcoxon rank sum test was used to determine differences among insurance types for all continuous variables, none of which were normally distributed.

We then used a stepwise logistic regression model to estimate the multivariate-adjusted odds of use of lower-mortality centers, considering each of the independent variables listed herein, including the 2 distance factors. Because the rate of the outcome (proportion of patients in lower-mortality hospitals) exceeded 10%, we transformed odds ratios (ORs) to approximate relative risks (RRs) according to the method described by Zhang and Yu.19

Two hospitals did not report patient race or ethnicity for the study period, so these variables were not entered into the models. However, we did investigate the relationships among insurance type, hospital used, and race/ethnicity by repeating the analysis separately for patients coded as non-Hispanic white and those coded as nonwhite or Hispanic. We also performed supplementary analyses by stratifying patients according to age, urgency of admission, and residence in New York City and by varying the threshold for inclusion in the lower-mortality hospital group. In addition, we examined the distribution of patients among individual hospitals in 4 delimited regions of the state, including Buffalo, Rochester, Long Island, and New York City.

Among 58,902 adults admitted for CABG surgery between 1993 and 1996, most patients were male (71%), non-Hispanic white (87%), and Medicare FFS beneficiaries (54%). Total inpatient mortality was 2.8%. Hypertension (52%) and diabetes (27%) were the most commonly coded comorbidities. Urgent/emergent admissions constituted 51% of all cases. Table 1 summarizes the demographic characteristics according to insurance group.

Table Graphic Jump LocationTable 1. Characteristics of the Study Population*

Unadjusted analyses (Table 2) revealed that patients with private managed care, Medicare FFS, and Medicare managed care insurance were less likely than patients with private FFS insurance to undergo CABG surgery at lower-mortality centers. Other factors associated with less frequent use of lower-mortality centers included female sex, nonwhite race/ethnicity, lower median family income for ZIP code, urgent/emergent admission, diabetes, and chronic renal failure. In contrast, patients with congestive heart failure, hypertension, and chronic obstructive pulmonary disease were more likely to use lower-mortality hospitals. On average, patients undergoing surgery at a lower-mortality hospital lived closer to a lower-mortality center (15.4 vs 24.6 miles; P<.001) and patients undergoing surgery at a higher-mortality hospital lived closer to a higher-mortality center (38.9 vs 13.9 miles; P<.001).

Table Graphic Jump LocationTable 2. Categorical Covariate Predictors of Utilization of Lower-Mortality Hospitals*

Table 2 also shows coefficients and 95% confidence intervals (CIs) for categorical covariates in the final multivariate model. Also included in the final model were the 2 distance variables, discussed herein. Compared with patients with private FFS insurance, patients with private managed care insurance were less likely to undergo CABG surgery at a lower-mortality hospital (RR, 0.77; 95% CI, 0.74-0.81; P<.001), as were patients with Medicare managed care insurance (RR, 0.61; 95% CI, 0.54-0.70; P<.001). After controlling for other factors, patients with Medicare FFS insurance used lower-mortality hospitals at a rate similar to those with private FFS insurance (RR, 0.95; 95% CI, 0.91-0.98; P=.004). As in the univariate analysis, patients who were female, lived in lower ZIP code median family income groups, were admitted urgently or emergently, or had a code recorded for either diabetes or chronic renal failure were less likely to be admitted to a lower-mortality center, although the magnitude of the difference was small in some cases. In contrast, those older than 75 years and those who had a code recorded for heart failure, hypertension, or chronic obstructive pulmonary disease had a greater probability of admission to a lower-mortality center. Increasing distance from the nearest lower-mortality center reduced the odds of admission to a lower-mortality hospital (for a 0.1-log increase in distance, OR, 0.76; 95% CI, 0.76-0.77; P<.001), but increasing distance to the nearest higher-mortality hospital increased the odds of admission to a lower-mortality center (for a 0.1-log increase in distance, OR, 1.45; 95% CI, 1.44-1.46; P<.001).

When we restricted the sample to patients living within 25 miles of both a lower-mortality and higher-mortality hospital, the results were similar. Compared with patients with private FFS insurance, patients with private managed care insurance were less likely to undergo CABG surgery at a lower-mortality hospital (RR, 0.62; 95% CI, 0.58-0.66; P<.001), as were patients with Medicare managed care insurance (RR, 0.57; 95% CI, 0.48-0.66; P<.001) and, to a smaller degree, Medicare FFS insurance (RR, 0.92; 95% CI, 0.88-0.96; P<.001).

Table 3 shows the sensitivity of these results to changes in the cut point for inclusion in the lower-mortality group at between 38% and 68%, which was as close as the data would allow to division at the upper and lower tertiles. Regardless of the threshold, patients with private managed care or Medicare managed care insurance were significantly less likely than patients with private FFS insurance to use a lower-mortality center. Medicare FFS estimates of use were slightly lower than those for private FFS at all cut points as well.

Table Graphic Jump LocationTable 3. Supplementary Multivariate Analyses*

In subgroups stratified by region (New York City vs upstate New York and Long Island), admission type (nonurgent vs urgent or emergent), age, and patient race/ethnicity, patients with private managed care and Medicare managed care insurance were significantly less likely to use lower-mortality centers than patients with private FFS insurance (Table 3). Patients with Medicare FFS insurance had rates of use much closer to those with private FFS insurance, with nonsignificant differences in several patient subgroups (New York City, nonurgent admissions, and non-Hispanic white race/ethnicity).

When we examined 4 delimited areas (Buffalo, Rochester, New York City, and Long Island), patients with private managed care and, particularly, Medicare managed care insurance were virtually excluded from many of the hospitals in a given area (Table 4). For example, of 3 hospitals in the Buffalo area, 99% of patients in Medicare managed care plans were admitted to a single (higher-mortality) hospital that otherwise accounted for only 59% of that region's caseload. Those admitted to hospitals in Long Island were restricted to the 3 hospitals with the highest adjusted mortality rates among 5 hospitals. Medicare managed care patients admitted to hospitals in New York City were admitted to 8 of 14 hospitals, and 46% were admitted to a single higher-mortality hospital that otherwise accounted for only 11% of the region's caseload.

In all, 4 (13%) of 31 hospitals admitted no CABG surgery patients with private managed care insurance. Fourteen (45%) admitted no patients with Medicare managed care plans. Restricting the sample to patients admitted to the 17 hospitals that admitted at least 1 patient with Medicare managed care insurance did not significantly change the results of multivariate analysis. Compared with patients with private FFS insurance, the corrected odds of being admitted to a lower-mortality center were still lower for patients with private managed care (RR, 0.66; 95% CI, 0.62-0.71; P<.001) and Medicare managed care (RR, 0.64; 95% CI, 0.56-0.73; P<.001) insurance, but to a lesser extent for Medicare FFS insurance (RR, 0.92; 95% CI, 0.87-0.97; P=.002).

To investigate the possible role of lag-time effects in these results, we repeated the analysis examining only patients admitted in the second half of the study (1995-1996), using public data from the first half of the study (1993-1994) to designate lower-mortality centers. Compared with patients with private FFS insurance, those with private managed care insurance were less likely to use a lower-mortality center (RR, 0.69; 95% CI, 0.65-0.73; P<.001), as were patients with Medicare managed care (RR, 0.49; 95% CI, 0.40-0.58; P<.001) and Medicare FFS (RR, 0.75; 95% CI, 0.69-0.80; P<.001) insurance.

Over the period of the present study, patients in New York State with managed care insurance were significantly less likely to undergo CABG surgery at a hospital with lower CABG mortality compared with patients with FFS insurance. This finding remained significant within categories of age, race, urgency, and region. The findings were not sensitive to the threshold used to define lower-mortality hospitals, nor did they appear to be a manifestation of a differential lag time in the recognition of changing hospital outcomes.

Some insight into the mechanisms of these differences in hospital use patterns can be gained by examining insurance-specific use on a hospital-by-hospital basis. Patients with managed care insurance and, particularly, managed Medicare insurance were often excluded from many lower-mortality hospitals entirely, implicating relatively powerful disincentives, such as use restrictions set by insurance companies, rather than differences in patient or referring physician preferences. Such restrictions could include removing a hospital from a plan's preferred provider list or requiring a significant patient copayment for the use of that hospital.

How do we reconcile our finding that New York State managed health plans appeared to ignore risk-adjusted mortality rates in contracting decisions with the finding that patients in California health plans appeared more likely to use lower-mortality hospitals?1 The absence of public data on risk-adjusted mortality in California makes it unlikely that mortality rates played a significant, if any, role in health plans' decision making. In a commentary to the study by Escarce et al, Hannan20 suggests that another reason for the difference between California and Florida may be that, because California has no certificate-of-need system, numerous low-volume hospitals with high mortality rates perform CABG surgery. Because low volumes make contracting unattractive, managed care plans in California avoid sending their patients to the highest-mortality hospitals. This indirect effect of CABG surgical volume would not be present in New York, where the certificate-of-need program dictates that all CABG surgery hospitals have high surgical volumes.

Our study was possible because of the New York Hospital Discharge Datasets (SPARCS), which are large, independently administered data sets maintained by the New York Department of Health. Administrative data sets have certain disadvantages relative to prospectively collected clinical data sets, particularly with respect to the consistency and precision with which variables used in risk adjustment are defined and collected.2124 With respect to use of lower-mortality hospitals, however, illness severity is less relevant than other potential confounders, such as urgency of the admission, patient socioeconomic status, and the geographic relationship between the residence of the patient and area hospitals. In the present study, we were able to control for each of these variables, except that an ecological proxy, ZIP code median family income, was the only measure of socioeconomic status available. The use of this type of ecological variable has major drawbacks,25,26 but in the present study it allowed some adjustment for affluence, as it has in other studies on access to medical care.27,28 Despite the strong relationship of race/ethnicity to socioeconomic status,2931 we did not include it as a covariate in our main analysis both because of significant missing data and because of limitations on the interpretation of racial and ethnic information.3234 Nonetheless, in a supplementary analysis that stratified by race/ethnicity, the effects of insurance type on the use of lower-mortality centers was generally consistent with those noted in the nonstratified model.

It must be noted that the results of the present study may not generalize beyond New York State. Because the nature of managed care, the availability of hospital quality information, and the types of hospitals performing CABG surgery vary substantially from state to state, these relationships could differ in other parts of the country.

Despite these limitations, our findings suggest that explicit monitoring of the process and outcomes of care could play an important role in identifying problems with the quality of care for managed care enrollees. The role of managed care organizations in the reduced use of lower-mortality centers among their beneficiaries is likely to be complex and multifactorial. Plans may enter into relationships with hospitals largely on the basis of anticipated costs and may create incentives for primary care providers and patients to use lower-cost centers as well. It is also possible that lower-mortality centers may themselves be unwilling to contract with managed care organizations, if they expect better remuneration from other payers. Meanwhile, by limiting patient choices, managed care organizations may prevent patients and their advocates from taking full advantage of available information about hospital quality. This could inadvertently stifle incentives for hospitals to compete on the quality of care. Additional studies on the impact of quality information on health plans' contracting decisions will be important as price competition among health plans becomes more intense.

Escarce JJ, Horn RL, Pauly MV, Williams SV, Shea JA, Chen W. Health maintenance organizations and hospital quality for coronary artery bypass surgery.  Med Care Res Rev.1999;56:340-362.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1990-1992. Albany: New York State Dept of Health; 1993.
New York State Department of Health.  SPARCS 1993 Inpatient Data Files. Albany: New York State Dept of Health; 1994.
New York State Department of Health.  SPARCS 1994 Inpatient Data Files. Albany: New York State Dept of Health; 1995.
New York State Department of Health.  SPARCS 1995 Inpatient Data Files. Albany: New York State Dept of Health; 1996.
New York State Department of Health.  SPARCS 1996 Inpatient Data Files. Albany: New York State Dept of Health; 1997.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1991-1993Albany: New York State Dept of Health; 1995.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1992-1994Albany: New York State Dept of Health; 1996.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1993-1995Albany: New York State Dept of Health; 1997.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1994-1996Albany: New York State Dept of Health; 1998.
Bureau of the Census.  Census of Population and Housing, 1990 [CD-ROM]. Washington, DC: Bureau of the Census; 1992. Summary Tape File 3.
Finlayson SR, Birkmeyer JD, Tosteson AN, Nease Jr RF. Patient preferences for location of care: implications for regionalization.  Med Care.1999;37:204-209.
Williams AP, Schwartz WB, Newhouse JP, Bennett BW. How many miles to the doctor?  N Engl J Med.1983;309:958-963.
Goodman DC, Fisher E, Stukel TA, Chang C. The distance to community medical care and the likelihood of hospitalization: is closer always better?  Am J Public Health.1997;87:1144-1150.
McGuirk MA, Porell FW. Spatial patterns of hospital utilization: the impact of distance and time.  Inquiry.1984;21:84-95.
Bureau of the Census.  ZIP Code Centers With Longitude and Latitude ValuesWashington, DC: Bureau of the Census; 1995. Available at: http://www.census.gov/.
Phibbs CS, Luft HS. Correlation of travel time on roads versus straight line distance.  Med Care Res Rev.1995;52:532-542.
Luft HS, Garnick DW, Mark DH.  et al.  Does quality influence choice of hospital?  JAMA.1990;263:2899-2906.
Zhang J, Yu KF. What's the relative risk? a method of correcting the odds ratio in cohort studies of common outcomes.  JAMA.1998;280:1690-1691.
Hannan EL. Commentary.  Med Care Res Rev.1999;56:363-372.
Jencks SF, Williams DK, Kay TL. Assessing hospital-associated deaths from discharge data: the role of length of stay and comorbidities.  JAMA.1988;260:2240-2246.
Iezzoni LI. Data sources and implications: administrative data bases. In: Iezzoni LI, ed. Risk Adjustment for Measuring Health Care Outcomes. Ann Arbor, Mich: Health Administration Press; 1994:119-175.
Iezzoni LI. Assessing quality using administrative data.  Ann Intern Med.1997;127(8 pt 2):666-674.
Hannan EL, Racz M, Jollis JG, Peterson ED. Using Medicare claims data to assess provider quality for CABG surgery: does it work well enough?  Health Serv Res.1997;31:659-678.
Poole C. Ecologic analysis as outlook and method.  Am J Public Health.1994;84:715-716.
Susser M. The logic in ecological, I: the logic of analysis.  Am J Public Health.1994;84:825-829.
Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines.  Annu Rev Public Health.1997;18:341-378.
Alexander GC, Sehgal AR. Barriers to cadaveric renal transplantation among blacks, women, and the poor.  JAMA.1998;280:1148-1152.
National Center for Health Statistics.  Trends in Indian Health—1993. Rockville, Md: US Dept of Health and Human Services; 1993.
National Center for Health Statistics.  Health, United States, 1993Hyattsville, Md: US Public Health Service; 1994.
Lillie-Blanton M, Parsons PE, Gayle H, Dievler A. Racial differences in health: not just black and white, but shades of gray.  Annu Rev Public Health.1996;17:411-448.
Schulman KA, Rubenstein LE, Chesley FD, Eisenberg JM. The roles of race and socioeconomic factors in health services research.  Health Serv Res.1995;30(1 pt 2):179-195.
Nickens HW. The role of race/ethnicity and social class in minority health status.  Health Serv Res.1995;30(1 pt 2):151-162.
LaVeist TA. Beyond dummy variables and sample selection: what health services researchers ought to know about race as a variable.  Health Serv Res.1994;29:1-16.

Figures

Tables

Table Graphic Jump LocationTable 1. Characteristics of the Study Population*
Table Graphic Jump LocationTable 2. Categorical Covariate Predictors of Utilization of Lower-Mortality Hospitals*
Table Graphic Jump LocationTable 3. Supplementary Multivariate Analyses*

References

Escarce JJ, Horn RL, Pauly MV, Williams SV, Shea JA, Chen W. Health maintenance organizations and hospital quality for coronary artery bypass surgery.  Med Care Res Rev.1999;56:340-362.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1990-1992. Albany: New York State Dept of Health; 1993.
New York State Department of Health.  SPARCS 1993 Inpatient Data Files. Albany: New York State Dept of Health; 1994.
New York State Department of Health.  SPARCS 1994 Inpatient Data Files. Albany: New York State Dept of Health; 1995.
New York State Department of Health.  SPARCS 1995 Inpatient Data Files. Albany: New York State Dept of Health; 1996.
New York State Department of Health.  SPARCS 1996 Inpatient Data Files. Albany: New York State Dept of Health; 1997.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1991-1993Albany: New York State Dept of Health; 1995.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1992-1994Albany: New York State Dept of Health; 1996.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1993-1995Albany: New York State Dept of Health; 1997.
New York State Department of Health.  Coronary Artery Bypass Surgery in New York State 1994-1996Albany: New York State Dept of Health; 1998.
Bureau of the Census.  Census of Population and Housing, 1990 [CD-ROM]. Washington, DC: Bureau of the Census; 1992. Summary Tape File 3.
Finlayson SR, Birkmeyer JD, Tosteson AN, Nease Jr RF. Patient preferences for location of care: implications for regionalization.  Med Care.1999;37:204-209.
Williams AP, Schwartz WB, Newhouse JP, Bennett BW. How many miles to the doctor?  N Engl J Med.1983;309:958-963.
Goodman DC, Fisher E, Stukel TA, Chang C. The distance to community medical care and the likelihood of hospitalization: is closer always better?  Am J Public Health.1997;87:1144-1150.
McGuirk MA, Porell FW. Spatial patterns of hospital utilization: the impact of distance and time.  Inquiry.1984;21:84-95.
Bureau of the Census.  ZIP Code Centers With Longitude and Latitude ValuesWashington, DC: Bureau of the Census; 1995. Available at: http://www.census.gov/.
Phibbs CS, Luft HS. Correlation of travel time on roads versus straight line distance.  Med Care Res Rev.1995;52:532-542.
Luft HS, Garnick DW, Mark DH.  et al.  Does quality influence choice of hospital?  JAMA.1990;263:2899-2906.
Zhang J, Yu KF. What's the relative risk? a method of correcting the odds ratio in cohort studies of common outcomes.  JAMA.1998;280:1690-1691.
Hannan EL. Commentary.  Med Care Res Rev.1999;56:363-372.
Jencks SF, Williams DK, Kay TL. Assessing hospital-associated deaths from discharge data: the role of length of stay and comorbidities.  JAMA.1988;260:2240-2246.
Iezzoni LI. Data sources and implications: administrative data bases. In: Iezzoni LI, ed. Risk Adjustment for Measuring Health Care Outcomes. Ann Arbor, Mich: Health Administration Press; 1994:119-175.
Iezzoni LI. Assessing quality using administrative data.  Ann Intern Med.1997;127(8 pt 2):666-674.
Hannan EL, Racz M, Jollis JG, Peterson ED. Using Medicare claims data to assess provider quality for CABG surgery: does it work well enough?  Health Serv Res.1997;31:659-678.
Poole C. Ecologic analysis as outlook and method.  Am J Public Health.1994;84:715-716.
Susser M. The logic in ecological, I: the logic of analysis.  Am J Public Health.1994;84:825-829.
Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines.  Annu Rev Public Health.1997;18:341-378.
Alexander GC, Sehgal AR. Barriers to cadaveric renal transplantation among blacks, women, and the poor.  JAMA.1998;280:1148-1152.
National Center for Health Statistics.  Trends in Indian Health—1993. Rockville, Md: US Dept of Health and Human Services; 1993.
National Center for Health Statistics.  Health, United States, 1993Hyattsville, Md: US Public Health Service; 1994.
Lillie-Blanton M, Parsons PE, Gayle H, Dievler A. Racial differences in health: not just black and white, but shades of gray.  Annu Rev Public Health.1996;17:411-448.
Schulman KA, Rubenstein LE, Chesley FD, Eisenberg JM. The roles of race and socioeconomic factors in health services research.  Health Serv Res.1995;30(1 pt 2):179-195.
Nickens HW. The role of race/ethnicity and social class in minority health status.  Health Serv Res.1995;30(1 pt 2):151-162.
LaVeist TA. Beyond dummy variables and sample selection: what health services researchers ought to know about race as a variable.  Health Serv Res.1994;29:1-16.

Letters

CME
Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 44

Related Content

Customize your page view by dragging & repositioning the boxes below.

See Also...
Articles Related By Topic
Related Collections
PubMed Articles
JAMAevidence.com