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DataWatch

A New Way To Compare Health Systems: Avoidable Hospital Conditions In Manhattan And Paris

Abstract

 
Based on a comparison of discharges for avoidable hospital conditions (AHCs), we find that Paris provides greater access to primary care than Manhattan. Age-adjusted AHC rates are more than 2.5 times as high in Manhattan as in Paris. In contrast, the difference in rates of hospital discharge for "marker conditions" are only about 20 percent higher in Manhattan. Rates of discharges for AHCs are higher among residents of low-income neighborhoods in both cities, but the disparity among high- and low-income neighborhoods is more than twice as great in Manhattan. Our analysis highlights the consequences of access barriers to care in Manhattan, particularly among vulnerable residents.

Here we analyze avoidable hospital conditions (AHCs), an indicator of access to primary health care, in Manhattan and Paris, cities that are more alike than their respective countries are (Exhibit 1). The selection of AHCs is only one dimension of health system performance. But in thinking about how health systems can assure access through a combination of health insurance coverage and the availability of primary care doctors and safety-net providers, AHCs are recognized in the literature (in the United States, Canada, Spain, and the United Kingdom) as a valid indicator of access to primary care.⁴

Top A Tale Of Two... AHCs As A Measure... Methods, Definitions, And Data... Study Findings Summary And Policy Implications NOTES

Studies often define New York and Paris as enormous "city-regions."⁵ Consistent with previous studies we have conducted, we focus on their "urban cores": Manhattan and Paris.⁶ Both are centers of medical excellence, with a disproportionate share of hospitals, physicians, and indigent patients in comparison with their surrounding rings. Their per capita rates of physicians and acute hospital beds are virtually the same (Exhibit 1). Both cities are destinations for large immigrant communities from around the world.

Divergent characteristics. Despite their similar characteristics, the cities also diverge with regard to the balance among primary care and specialist physicians (Exhibit 2). Although the primary care system in France might not be as strong as those in other OECD countries, it is much stronger than in Manhattan.⁷ In Manhattan, less than 30 percent of all physicians are in primary care. In Paris, half are in primary care, and there are few financial barriers to care.

In Paris, 61 percent of physicians in private practice do not accept NHI reimbursement as payment in full.¹⁰ If patients choose to consult with physicians who require coinsurance, they are eligible for some coverage under complementary insurance. If this constitutes a financial barrier, they can choose physicians who accept NHI rates as payment in full or can consult physicians at one of fifty health centers located in every arrondissement of the city.¹¹ These centers serve as a safety net for all patients who fall through the cracks, but they are used by a broad segment of Parisians. Even after the extension of NHI in 2000 to the 3–4 percent of Parisians who were previously not covered, illegal immigrants and others still make use of these centers.¹²

The contrast between the two cities is remarkable. In addition to greater income inequality in Manhattan than in Paris, 24 percent of the Manhattan population is uninsured, and gaps in access to primary care exist, despite the presence of a strong safety net, including the largest U.S. public hospital system.¹³ Our comparison of AHCs in Manhattan and Paris can shed light on how health coverage and availability of primary care might affect AHCs.

AHCs As A Measure of Health System Performance

Top A Tale Of Two... AHCs As A Measure... Methods, Definitions, And Data... Study Findings Summary And Policy Implications NOTES

 
Studies have found that the uninsured are more likely than the insured to be admitted to hospitals with AHCs because they are less likely to receive appropriate and timely primary care.¹⁴ Differences in disease prevalence, not in access to care, could explain the differences in hospital discharge rates for AHCs among areas with low and high socioeconomic status (SES).¹⁵ Based on the relevant literature, however, we are confident that the prevalence or severity of disease in a population does not explain the differences.

Unlike discharge rates for AHCs, hospital discharge rates for "marker conditions" are not influenced by access to ambulatory care. These are conditions for which previous use of ambulatory care does not affect the risk of hospitalization. They do not vary greatly by SES and access to primary care. Another contrast to AHCs are hospital discharge rates for referral-sensitive procedures that are not immediately life-threatening and that require referral to a specialist.

Previous research has examined hospital discharge rates of these three categories of hospitalization in major U.S. urban areas. Our study is the first such comparison between Manhattan and Paris.

We expect that AHCs are higher among residents of Manhattan than among Parisians, while rates for referral-sensitive conditions are higher among Parisians. Because residents of Manhattan are less healthy than residents of Paris (Exhibit 1), we also expect that hospital discharge rates for marker conditions are higher in Manhattan, but not as high as the differences among rates of AHCs.

If disparities among discharge rates for AHCs reflect only differences in the use of hospitals as a site of care, we would expect higher discharge rates for AHCs in Paris than in Manhattan (Exhibit 1).

Finally, we expect that after age, race, sex, severity of illness, and area of residence are controlled for, people with no health insurance and Medicaid recipients have higher incidence of AHCs than people with private coverage in Manhattan. We also expect that blacks and Hispanics have higher incidence of AHCs. In Paris, we expect that after age, sex, and severity of illness are controlled for, people living in arrondissements with the lowest median household incomes and in arrondissements with the lowest levels of education have higher incidence of AHCs, but we expect that differences are not as great in Paris as they are in Manhattan.

Methods, Definitions, And Data Sources

Top A Tale Of Two... AHCs As A Measure... Methods, Definitions, And Data... Study Findings Summary And Policy Implications NOTES

 
To calculate hospital discharge rates for AHCs, we used the definition of AHCs developed by Joel Weissman and colleagues, as validated by previous studies.¹⁶ For "marker conditions," we calculated discharge rates for appendicitis, gastrointestinal (GI) obstruction, and hip fractures; and for "referral-sensitive procedures," we examined lower-extremity joint replacements and organ transplants.¹⁷

We calculated rates of these three indicators for age-adjusted cohorts, employing the direct standardization method using the 2000 U.S. standard population to obtain adjustment weights.¹⁸ We present age-adjusted rates for each city for neighborhoods above and below median income, and we used difference-of-means tests to examine the statistical significance of the differences.

We relied on existing administrative boundaries and data availability to define neighborhoods. For Manhattan, we used the twelve community districts for our ecological comparisons and ZIP codes in the logistic regression analysis. For Paris, we examined the twenty arrondissements.

In addition to our city- and neighborhood-level comparisons, we present results from multiple logistic regression models for each city.¹⁹ These estimate the effects of both individual and neighborhood characteristics on the probability of hospitalization for AHCs.

For Manhattan, our model estimated the probability that a person is hospitalized with an AHC. The primary independent variables were age, sex, race/ethnicity, primary payers, and number of diagnoses on the record (as a measure of severity of illness). The model also controlled for a number of the neighborhood variables at the ZIP code level: income quartile, physician density, and dummy variables for ZIP codes in which more than 15 percent of the households are linguistically isolated and more than 40 percent of the adult population does not have a high school degree. We ran a model with secondary payers and interactive terms relating race and ZIP code, income and race, and insurance. These variables did not change the results, so we dropped them from the final model.

For Paris, the individual independent variables include age, sex, number of diagnoses, and the French hospital index of severity of illness (IGS-2); the neighborhood variables at the arrondissement level include indicators for income quartile, physician density, and an education dummy variable for arrondissements in which more than 40 percent of the adult population does not have a baccalaureate degree.²⁰ The Paris model does not include race/ethnicity or "linguistic isolation," because the French census does not include such indicators. Nor does the model have a measure for "payer source," because the Programme de médicalisation des systèmes d’information (PMSI—the Ministry of Health’s Hospital Reporting System) does not include this information. Although French NHI includes multiple payers, the benefit packages and reimbursement levels are virtually identical, so there is no meaningful difference among them.

Because observations on individuals from the same neighborhood might be correlated, we tested for bias attributable to unobserved neighborhood-level heterogeneity by estimating the models with a dummy variable for each ZIP code or arrondissement as a replacement for neighborhood-level variables. The parameter estimates for the individual characteristics were not appreciably different from those generated by these models.²¹

To ensure an adequate number of hospital discharges and procedures for statistically meaningful comparisons and to reduce the likelihood of having an annual anomaly affect the results, we calculated averages over a three-year period (1999–2001) for each city.

Data sources. Hospital data for Manhattan are from the Statewide Planning and Research Cooperative System (SPARCS), which includes information for all Manhattan residents discharged from all nonfederal hospitals in the state, excluding the population cared for in Veterans Affairs (VA) hospitals. For Paris, data are from the PMSI, which centralizes discharge data by patients’ diagnosis, procedure, age, and residence. The PMSI includes data from all hospitals (public and private) having more than 100 beds, thus possibly excluding a small number of discharges for AHCs in Paris. The city-level hospital discharge data are for residents of both cities, regardless of whether they were hospitalized within or outside these cities.

In calculating the hospital discharge rates, we relied on French and U.S. census data to calculate our population denominators: the number of adults age eighteen and older living in each city. For income, we used a similar measure of pretax median household income, by neighborhood. For the logistic regressions, we calculated neighborhood income quartiles based on currencies in each city.

Study Findings

Top A Tale Of Two... AHCs As A Measure... Methods, Definitions, And Data... Study Findings Summary And Policy Implications NOTES

 
City-level differences. We found that for people age eighteen and older, the age-adjusted discharge rate for AHCs in Manhattan is more than two and a half times that of Paris. This is much greater than the differences found among large U.S. cities.²² In contrast, discharge rates for marker conditions are only about 20 percent higher in Manhattan than in Paris. Discharge rates for referral-sensitive procedures are identical in these cities (Exhibit 3).

View larger version (21K): [in this window] [in a new window]   EXHIBIT 3 Hospital Discharges For Avoidable Hospital Conditions (AHCs), Marker Conditions, And Referral-Sensitive Procedures, Manhattan And Paris, 1999–2001

In Manhattan, the relationships among median household income, by ZIP code of residence, race, and insurance status on AHCs, are statistically significant and large. The odds of AHC discharges are about 29 percent higher among blacks and 47 percent higher among Hispanics than whites. The odds of AHC discharges for uninsured people are about 82 percent greater than for people with private insurance. The odds are 39 percent higher among Medicaid recipients and 21 percent higher among Medicare beneficiaries than among people with private coverage.

Limitations of analysis and alternative explanations. We cannot account directly for any effect of differences in disease prevalence on discharge rates for AHCs, but previous research suggests that this is unlikely to explain the differences we observed. For example, Ady Oster and Andrew Bindman argue that higher discharge rates for AHCs among African Americans and Medicaid patients do "not appear to be explained by either the differences in disease prevalence or disease severity."²³ Similarly, James Laditka and colleagues find that higher discharge rates for AHCs among African Americans and Hispanics, compared with non-Hispanic whites, are not attributable to differences in disease prevalence.²⁴ The notion that underlying prevalence of disease is unlikely to explain differences in these rates is reinforced further by the work of John Wennberg, who finds that population illness rates do not explain hospitalization rates.²⁵

Similarly, the race and insurance effects we observed in Manhattan and the sex effects noted for both cities could be the result of patients’ compliance or care-seeking behavior. It is also possible that physicians’ behavior influences these rates.²⁶ Despite this concern, others suggest that physician practice style is unlikely to explain the differences we observed.²⁷ Our data did not permit us to test these alternative hypotheses.

Finally, although our use of dummy variables is an accepted technique when both individual and group-level variables are included in the same model, we plan to refine this analysis by using multilevel modeling in future work on Manhattan and inner London.²⁸

Summary And Policy Implications

Top A Tale Of Two... AHCs As A Measure... Methods, Definitions, And Data... Study Findings Summary And Policy Implications NOTES

 
In Manhattan and Paris, AHCs are related to neighborhood-level income. Women have much lower odds of being admitted to a hospital for AHCs in both cities, which is consistent with well-known sex differences in the use of ambulatory care—higher among women than men.²⁹

Despite these similarities, Manhattan stands out in comparison to Paris. Although age-adjusted discharge rates for marker conditions are only slightly higher, age-adjusted rates of discharge for AHCs are more than two and a half times as high in Manhattan as in Paris. Since the aggregate discharge rate is higher in Paris (Exhibit 1), this is striking. Our findings therefore provide strong evidence that the health care system in Paris, which provides better access to primary care, performs better in terms of admitting far fewer patients for AHCs.

Also, although AHCs are related to neighborhood-level income in both cities, the magnitude of the disparity among high- and low-income neighborhoods is higher by a factor of two in Manhattan than in Paris. The findings from our analysis of Manhattan data are consistent with those of other studies and suggest that the higher rates of AHCs in Manhattan are explained by multiple barriers to care, including race and ethnicity, income of residence, sex, and insurance status. Medicare beneficiaries, Medicaid recipients, and the uninsured are all more likely than the privately insured to be hospitalized for AHCs. Blacks and Hispanics are also more likely to be hospitalized for AHCs than non-Hispanic whites.

Because of inadequate insurance coverage in the United States, lack of timely, effective primary care often results in unnecessary illness, loss of productivity, and costly hospitalizations. It is difficult to quantify the magnitude of unnecessary illness and loss of productivity. But our analysis documents some important consequences of access barriers in a city known for its strong health care safety net. In Manhattan, the magnitude of AHCs is considerably higher than in Paris, where all residents benefit from an NHI system that has effectively eliminated financial barriers to care. Although our findings suggest that insurance is only one of several barriers to care in Manhattan, it is clearly an important one.

Often, we assume in the United States that the existence of public hospitals, community health centers, and other safety-net institutions enables the uninsured to obtain the care they need.³⁰ An important implication of our analysis is that we should not devote disproportionate attention to strengthening the health care safety net at the expense of extending health insurance coverage.³¹

Editor's Notes

 
Michael Gusmano (mkg2104{at}columbia.edu) is an assistant professor of health policy and management at Columbia University, New York City, and codirector of the World Cities Project, International Longevity Center–USA, also in New York. Victor Rodwin is director of the World Cities Project and professor of health policy and management at the Wagner School, New York University. Daniel Weisz is a research associate with the World Cities Project.

The authors thank the Robert Wood Johnson Foundation and the French Ministry of Health for financial support; and Robert Butler, Pierre Chauvin, Dhiman Das, Françoise Forette, Marc Joubert, Frank Lopresti, Diane Slama-Lequet, Yongzhao Shao, Alfred Spira, and Florence Veber for other assistance.

NOTES

Top A Tale Of Two... AHCs As A Measure... Methods, Definitions, And Data... Study Findings Summary And Policy Implications NOTES

 

1. V. Rodwin, "Comparative Analysis of Health Systems in Wealthy Nations," in Health Care Delivery in the United States, 8th ed., ed. A. Kovner and J. Knickman (New York: Springer, 2005), 162–209; and R. Klein, "Strategies for Comparative Social Policy Research," in Health and Welfare States of Britain, ed. A. Williamson and G. Room (London: Heineman Educational Books, 1983).
2. Organization for Economic Cooperation and Development, OECD Health Data, 2004 (Paris: OECD, 2004).
3. M.K. Gusmano and V.G. Rodwin, "Health Services Research and the City," chap. 16 in Handbook of Urban Health, ed. D. Vlahov and S. Galea (New York: Springer, 2005), 295–316.
4. A.D. Brown et al., "Hospitalization for Ambulatory Care–Sensitive Conditions: A Method for Comparative Access and Quality Studies using Routinely Collected Statistics," Canadian Journal of Public Health 92, no. 2 (2001): 155–159[Web of Science][Medline]; C. Casanova and B. Starfield, "Hospitalizations of Children and Access to Primary Care: A Cross-National Comparison," International Journal of Health Services 25, no. 2 (1995): 283–294; and C. Sanderson and J. Dixon, "Conditions for Which Onset or Hospital Admission Is Potentially Preventable by Timely and Effective Ambulatory Care," Journal of Health Services Research and Policy 5, no. 4 (2000): 222–230.
5. A. Scott, Global City-Regions: Trends, Theory, Policy (New York: Oxford University Press, 2001).
6. Our earlier work includes L. Neuberg and V.G. Rodwin, "Neighborhood Matters—Infant Mortality Rates in Four Cities: London, Manhattan, Paris, and Tokyo," Indicators 2, no. 1 (2002/03): 15–38; V.G. Rodwin and L. Neuberg, "Infant Mortality and Income in Four World Cities: New York, London, Paris, and Tokyo," American Journal of Public Health 95, no. 1 (2005): 86–90[Abstract/Free Full Text]; and D. Weisz, M.K. Gusmano, and V.G. Rodwin, "Gender and the Treatment of Heart Disease in Older Persons in the United States, France, and England: A Comparative, Population-based View of a Clinical Phenomenon," Gender Medicine 1, no. 1 (2004): 29–40.
7. J. Macinko, B. Starfield, and L. Shi, "The Contribution of Primary Care Systems to Health Outcomes within Organization for Economic Cooperation and Development (OECD) Countries, 1970–1998," Health Services Research 38, no. 3 (2003): 831–865.[CrossRef][Web of Science][Medline]
8. V.G. Rodwin, "The Health Care System under French National Health Insurance: Lessons for Health Reform in the United States," American Journal of Public Health 93, no. 1 (2003): 31–37.[Abstract/Free Full Text]
9. V.G. Rodwin and C. Le Pen, "Health Care Reform in France: The Birth of State-Led Managed Care," New England Journal of Medicine 351, no. 22 (2004): 2259–2262.[Free Full Text]
10. Caisse primaire d’assurance maladie, "Démographie des professionels de santé parisiens" (Unpublished, 2002).
11. Florence Veber, health adviser to the mayor of Paris, personal communication, 5 April 2005.
12. On 1 January 2000, when NHI in France was extended to all those who previously fell through the cracks, the most disenfranchised population in Paris became eligible for coverage under NHI and received complementary coverage, as well, to cover all up-front out-of-pocket payments.
13. D. Sandman et al., The Commonwealth Fund Survey of Health Care in New York City (New York: Commonwealth Fund, March 1998).
14. J. Billings, G.M. Anderson, and L.S. Newman, "Recent Findings on Preventable Hospitalizations," Health Affairs 15, no. 3 (1996): 239–249[Abstract]; G. Pappas et al., "Potentially Avoidable Hospitalizations: Inequalities in Rates between U.S. Socioeconomic Groups," American Journal of Public Health 87, no. 5 (1997): 811–816[Abstract/Free Full Text]; and M.L. Parchman and S. Culler, "Primary Care Physicians and Avoidable Hospitalizations," Journal of Family Practice 39, no. 2 (1994): 123–128.
15. J. Blustein, K. Hanson, and S. Shea, "Preventable Hospitalizations and Socioeconomic Status," Health Affairs 17, no. 2 (1998): 177–189.[Abstract]
16. The Weissman definition includes the following conditions: pneumonia, congestive heart failure, asthma, cellulitis, perforated or bleeding ulcer, pyelonephritis, diabetes with ketoacidosis or coma, ruptured appendix, malignant hypertension, hypokalemia, immunizable conditions, and gangrene. J.S. Weissman, C. Gatsonis, and A.M. Epstein, "Rates of Avoidable Hospitalization by Insurance Status in Massachusetts and Maryland," Journal of the American Medical Association 268, no. 17 (1992): 2388–2394. Studies validating this definition include L. Backus et al., "Effect of Managed Care on Preventable Hospitalization Rates in California," Medical Care 40, no. 4 (2002): 315–324[CrossRef][Web of Science][Medline]; Pappas et al., "Potentially Avoidable Hospitalizations"; and Parchman and Culler, "Primary Care Physicians and Avoidable Hospitalizations."
17. Billings et al., "Recent Findings."
18. R.J. Klein and C.A. Schoenborn, "Age Adjustment using the 2000 Projected U.S. Population," Healthy People Statistical Notes 20 (Atlanta: Centers for Disease Control and Prevention, January 2001).
19. We ran separate regression equations, instead of pooling the hospital data and including a dummy variable for each city, because we did not have identical covariates for each city. The PMSI does not include information about payer or race/ethnicity.
20. The French baccalaureate degree is the U.S. equivalent of completing high school and a first year of college.
21. In addition to examining models with dummy variables, we used STATA (version 8) to examine the variance inflation factor (VIF), as a test of collinearity (STATA command: collin). Since the VIF is less than 10 for all of our independent variables, we concluded that the correlations among them are not causing unacceptable biases. For further discussion of VIF, see W.H. Greene, Econometric Analysis, 4th ed. (Upper Saddle River, N.J.: Prentice-Hall, 2000).
22. J.D. Billings and R.M. Weinick, Monitoring the Health Care Safety Net, Book I: A Data Book for Metropolitan Areas (Rockville, Md.: Agency for Healthcare Research and Quality, 2003).
23. A. Oster and A.B. Bindman, "Emergency Department Visits for Ambulatory Care Sensitive Conditions: Insights into Preventable Hospitalizations," Medical Care 41, no. 2 (2003): 198–207.[CrossRef][Web of Science][Medline]
24. J.N. Laditka, S.B. Laditka, and M.P. Mastanduno, "Hospital Utilization for Ambulatory Care Sensitive Conditions: Health Outcome Disparities Associated with Race and Ethnicity," Social Science and Medicine 57, no. 8 (2003): 1429–1441.
25. J.E. Wennnberg, "Population Illness Rates Do Not Explain Population Hospitalization Rates," MedicalCare 25, no. 4 (1987): 354.
26. Weissman et al., "Rates of Avoidable Hospitalization," 2393.
27. See M. Komaromy et al., "Physician Practice Style and Rates of Hospitalization for Chronic Medical Conditions," Medical Care 34, no. 6 (1996): 594–609.[CrossRef][Web of Science][Medline]
28. It is impossible to run a multilevel model for Paris using the PMSI because there are not enough observations for the level 2 (group) variables for a hierarchical linear model.
29. C.F. Muller, Health Care and Gender (New York: Russell Sage Foundation, 1990).
30. L.D. Brown, "Comparing Health Systems in Four Countries: Lessons for the United States," American Journal of Public Health 93, no. 1 (2003): 52–56.[Abstract/Free Full Text]
31. P. Cunningham and J. Hadley, "Expanding Care versus Expanding Coverage: How to Improve Access to Care," Health Affairs 23, no. 4 (2004): 234–244.[Abstract/Free Full Text]

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