Hospital Quality Indicators in Iowa Rural Hospitals

1. Hospital Quality Indicators in Iowa Rural Hospitals Pengxiang (Alex) Li, Marcia M. Ward, Paul James, John E. Schneider 2008 AHRQ Annual Meeting Bethesda, Maryland Support grant: Agency for Healthcare Research and Quality Grant # HS015009

2. Background Hospital quality indicators were used to provide a perspective on hospital quality of care AHRQ Inpatient Quality Indicators (IQIs) AHRQ Patient Safety Indicators (PSIs) Our analyses focus on Acute Myocardial Infarction (AMI) in-hospital mortality (IQI-15) Four PSIs (PSI-5, PSI-6, PSI-7, and PSI-15)

3. Outline Comparison of Iowa urban and rural hospitals on AMI in-hospital mortality James PA, Li P, Ward MM. Myocardial infarction mortality in rural and urban hospitals: Rethinking measures of quality of care. Annals of Family Medicine, 5:105-111, 2007 Association between Critical Access Hospital (CAH) conversion and patient safety indicator performance Li, P., Schneider, J. E. & Ward, M. M., (2007) Effect of Critical Access Hospital Conversion on Patient Safety. Health Services Research, 42 (6): 2089-2108 Exploration of a potential reason of patient safety change associated with CAH conversion Li, P., Schneider, J. E. & Ward, M. M., Effects of Critical Access Hospital Conversion on the Financial Performance of Rural Hospitals Inquiry (in press)

4. How do Iowa urban and rural hospitals compare on AMI in-hospital mortality? James PA, Li P, Ward MM. Myocardial infarction mortality in rural and urban hospitals: Rethinking measures of quality of care. Annals of Family Medicine, 5:105-111, 2007

5. Introduction Observational studies find that the quality of care for myocardial infarction (MI) patients admitted to rural hospitals is substandard (Sheikh 2001, Baldwin 2004) Lower volumes of MI patients in rural hospitals Lacking cardiologists Lacking support services The higher MI mortality rate for rural hospitals is due to Lower volumes, lacking cardiologists and lacking the support services in rural hospitals The higher MI mortality rate for rural hospitals is due to Lower volumes, lacking cardiologists and lacking the support services in rural hospitals

6. Introduction Validity of these observational studies has been questioned Unbalanced comparison groups Patients admitted to rural hospitals tend to be older, poorer, in poorer health, and have greater number of comorbidities (Baldwin 2004, Chen 2000, Frances 2000) Referral patterns of rural provider Empirical study showed that less severe patients were referred to urban hospitals (Metha 1999) Unmeasured confounding may account for differences in patient outcomes

7. Objectives of the study To compare characteristics of MI patients admitted to rural and urban hospitals To examine in-hospital mortality between rural and urban hospitals among MI patients Using traditional risk adjustment techniques (Logistic regression) Using instrumental variable methods (IV) The objective of the study is to compare the in-hospital mortality between patients admitted to rural and urban hospitals using traditional risk adjustment and instrumental variables approachThe objective of the study is to compare the in-hospital mortality between patients admitted to rural and urban hospitals using traditional risk adjustment and instrumental variables approach

8. Methods: Data Discharge data from Iowa State Inpatient Dataset (2002 & 2003) Inclusion criteria A principal diagnosis of MI (ICD-9-CM: 410.01-410.91) Eighteen years or older Exclusion criteria The hospital identification number was missing (n=9) Patient�s whose home county was not in Iowa (n=1,248) Patients� zip code was missing (n=14) Patients� sex was missing (n=1) Our primary analyses also excluded patients discharged or transferred to another short term general hospital for inpatient care (n=1,618) Most of our analyses are based on 12,191 MI patients

9. Methods: Variables Dependent variable In-hospital mortality Independent variables Urban vs Rural hospitals that patients admitted to Urban: 27 hospitals Rural: 89 hospitals Payer: e.g. Medicare, private insurance, self-pay Admission type: e.g. emergency Race Risk adjustment index Charlson comorbidity index All Patient Refined DRGs (APR-DRGs) risk index payer (Medicare, Medicaid, private insurance, self-pay, no charge, other), admission type (emergency, urgent, other), and race (black or not). payer (Medicare, Medicaid, private insurance, self-pay, no charge, other), admission type (emergency, urgent, other), and race (black or not).

10. Methods: Traditional Analytic Approach (Logistic Regression) Univariate analyses of group comparisons Chi-square tests for dichotomous data ANOVAs for continuous data Logistic regressions for multiple regression analyses

11. Methods: Pitfalls with Logistic Regression Using administrative inpatient data, one cannot control all patients� risk factors (e.g. severity of illness) If unmeasured variables are related to selection of the hospital, the estimates of the hospital-specific contribution to mortality will be biased. For example, elderly MI patients with severe comorbid conditions, which are unmeasured in administrative data, might prefer to remain in the rural hospitals. As a result, a higher risk-adjusted mortality rate in rural hospitals might simply be due to more severe patients in rural hospitals. Because we use administrative data and we are not able to fully control patient characteristics, which may lead to some problems for the estimation using traditional approach.Because we use administrative data and we are not able to fully control patient characteristics, which may lead to some problems for the estimation using traditional approach.

12. Approaches to Minimize Bias Collect all the relevant patient-level variables: very costly Randomized controlled trial Not feasible to this study Instrumental variable (IV) estimation An econometric technique which enables us to obtain unbiased estimates of treatment effects in observational studies An example: Wehby (2006) found that using the logistic regression model, early initiation of prenatal care is associated with a higher probability of low birth weight (LBW) Unmeasured confounders: women at a higher risk demand more (or early) prenatal care compared to those at lower risk. IV estimations showed that early time to prenatal care initiation is associated with a lower probability of LBW. The biased estimates are due to the correlation between omitted variables and the choice of hospitals. Thus, if we can exclude omitted variables by collecting all relevant variables or using RCT to enforce the hospital choice unrelated to omitted variables, we are able to achieve unbiased estimates.The biased estimates are due to the correlation between omitted variables and the choice of hospitals. Thus, if we can exclude omitted variables by collecting all relevant variables or using RCT to enforce the hospital choice unrelated to omitted variables, we are able to achieve unbiased estimates.

13. The Instrumental Variable (IV) estimation IVs are used to achieve a �pseudo-randomization� The instrumental variable technique can extract variation in the focal variable (rural hospital selection) that is unrelated to unmeasured confounders, and employ this variation to estimate the causal effect on an outcome Assumptions for IV(s) IV(s) should correlate with treatment variable (choice of rural hospital) IV(s) should not be correlated with the unmeasured confounders

14. Methods: Instrumental Variable Technique Instrumental Variable = Patients� distance to the nearest urban hospital The distances between each patient�s home and all urban hospitals in Iowa were obtained by calculating the distances between the centroids of each patient�s resident zip code and all urban hospitals� zip codes. Similar to Brooks (2003) approach, instrumental variables in the study are dummy variables that group patients based on the their distance to the nearest urban hospital. The validity of IV estimation depends on the choices of IVs For example, if you can decide patients� choice of hospital by flipping coins, it will be the best instrument. The validity of IV estimation depends on the choices of IVs For example, if you can decide patients� choice of hospital by flipping coins, it will be the best instrument.

15. Methods: IV Technique: First assumption Patients who live closer to an urban hospital are more likely to choose an urban hospital than those who live farther away. Partial F-statistics for the IVs in the first stage regression Small values of first-stage F-statistics imply failure of assumption 1 Rule of thumb: F>10 indicates good association (Staiger 1997)

16. Methods: IV Technique Second Assumption: Distance to the nearest urban hospital is not associated with the severity or pre-morbid risks of patients with MI Descriptive comparison between two groups of patients classified by IV If the instrument is independent of the unmeasured confounders, it should also be independent of observed risk factors (e.g. age, and comorbidity index). Over-identifying restrictions tests The null hypothesis is that the IV is not correlated with unmeasured confounders

17. Methods: IV Technique To examine the robustness of our findings: We used a range of patients� groups for the instrumental variable (2, 4, 8, and 12 groups). We varied the independent variables. The syslin two-stage least squares (2SLS) procedure in SAS 9.1 was used to do IV estimation. The validity of IV estimation depends on the choices of IVs For example, if you can decide patients� choice of hospital by flipping coins, it will be the best instrument. The validity of IV estimation depends on the choices of IVs For example, if you can decide patients� choice of hospital by flipping coins, it will be the best instrument.

18. Results: Table 1: Baseline characteristics of MI patients* admitted to rural and urban hospitals

19. Results: Table 2: Baseline characteristics of MI patients transferred out of rural hospitals or staying in rural hospitals This result is consistent with Metha 1999�s findingThis result is consistent with Metha 1999�s finding

20. Results: Table 3: Odds ratios of in-hospital mortality* among MI patients admitted to urban hospitals or to rural hospitals, using logistic regression models (n=12,191)

21. Results: Table 4: Characteristics among MI patients grouped by distance to the nearest urban hospital We are going to use distance to the nearest urban hospitals as the IV. This table is used to test whether it is a good candidate for IV. Two assumptions: Highly correlated with the variable in interest Not correlated with the omitted variable This table is like the table1 for RCT to examine balance between two groups assigned by randomization. These two groups are much more balanced than rural and urban hospital comparison groupWe are going to use distance to the nearest urban hospitals as the IV. This table is used to test whether it is a good candidate for IV. Two assumptions: Highly correlated with the variable in interest Not correlated with the omitted variable This table is like the table1 for RCT to examine balance between two groups assigned by randomization. These two groups are much more balanced than rural and urban hospital comparison group

22. Results: Table 5: Instrumental variable estimates of the difference of in-patient mortality between urban and rural hospitals

23. Results: Sensitivity analyses Repeat analyses in different samples Excluding transferred in MI patients Three-year state inpatient datasets (2001 to 2003) Different IV estimation method Two-stage residual inclusion method to account for the endogeneity in nonlinear (logistic) model Bivariate Probit model (using Stata 9.0) The results are consistent with IV estimation in Table 5

24. Discussion This study confirms earlier studies MI patients admitted to rural hospitals were older and sicker than their urban counterparts Traditional models all indicate significantly higher in-hospital mortality for those admitted to rural hospitals

25. Discussion Our findings suggest that the traditional logistic regression models are biased Admissions to rural or urban hospitals are likely to be confounded by unmeasured patient variables Referral patterns in rural hospitals Younger and less sick patients are transferred to urban hospitals The clinical judgment about transfer of rural senior patients with MI may rely on different criteria It cannot be generalized to those patient bypassing rural hospitals patients Our results cannot be generalized to those patients in rural areas bypassing rural hospitalsIt cannot be generalized to those patient bypassing rural hospitals patients Our results cannot be generalized to those patients in rural areas bypassing rural hospitals

26. Discussion Patient preferences are likely to play a significant role in transfer decisions for older MI patients May reflect personal choice or existing serious comorbidities Serious cases may choose to remain close to home The transfer patterns may reflect rural doctors respecting their patients� wishes Using in-hospital MI mortality to measure quality of care in rural hospitals is problematic.

27. Limitations of the study The results of the IV estimation can only be generalized to patients for whom distance affects their choice The conclusion cannot be applied to MI patients bypassing rural hospitals and seeking care in urban hospitals The findings for hospitals in one state may not generalize to other states . Analyses of in-hospital mortality rates may not generalize to mortality rates after hospitalization.

28. Conclusions Mortality from MI in rural Iowa hospitals is not higher when controlled for unmeasured confounders. Current risk-adjustment models may not be sufficient when assessing hospitals that perform different functions within the healthcare system. Unmeasured confounding is a significant concern when comparing heterogeneous and undifferentiated populations. Given that current methods to monitor quality may be flawed without adequate controls of selection bias with unmeasured confounders, we believe that this study calls into question conventional wisdom about the quality of care delivered in rural hospitals. Given that current methods to monitor quality may be flawed without adequate controls of selection bias with unmeasured confounders, we believe that this study calls into question conventional wisdom about the quality of care delivered in rural hospitals.

29. Did conversion to Critical Access Hospital (CAH) status affect patient safety indicator performance? Li, P., Schneider, J. E. & Ward, M. M., (2007) Effect of Critical Access Hospital Conversion on Patient Safety. Health Services Research, 42 (6): 2089-2108

30. Background In order to protect small, financially vulnerable rural hospitals, the Medicare Rural Hospital Flexibility Program of the 1997 Balanced Budget Act allowed hospitals meeting certain criteria to convert to critical access hospitals (CAH) This changed their Medicare reimbursement mechanism from prospective (PPS) to cost-based One objective of the policy was to increase the quality of care in these hospitals

31. Timeframe for Conversion to CAH

32. Patient Safety

33. 4 PSIs and Composite AHRQ recommends suppressing the estimates if fewer than 30 cases are in the denominator Only five patient safety indicators are able to provide PSI measures for all rural Iowa hospitals PSI-5: foreign body left during procedure PSI-6: iatrogenic pneumothorax PSI-7: selected infections due to medical care PSI-15: accidental puncture or laceration PSI-16: transfusion reaction Too rare to provide variability to differentiate hospitals in Iowa A composite patient safety variable was created by summing the four PSIs (PSI-5, PSI-6, PSI-7, and PSI-15).

34. Number of Hospitals Having Better or Worse Performance after CAH Conversion

35. Cross-sectional Analyses Cross-sectional comparisons showed that CAHs had better performance than rural PPS hospitals on 4 of the 5 PSI measures. However, the difference in patient safety indicators might be due to differences in patient mix, hospital characteristics besides CAH conversion, and differences in markets and environment.

36. Multivariable Analyses We used multivariable Generalized Estimating Equations (GEE) models and sensitivity analyses to control for the impact of patient case mix, market variables, and time trend. GEE models showed that CAH conversion was associated with significant better performance in PSI-6, PSI-7, PSI-15 and composite PSI. Findings were robust among sensitivity analyses using different samples and different methods

37. Conclusions CAH conversion in rural hospitals resulted in enhanced performance in PSIs We speculate that the likely mechanism involved an increase in financial resources following CAH conversion to cost-based reimbursement for Medicare patients

38. How did Critical Access Hospital conversion affect rural hospital financial condition? Li, P., Schneider, J. E. & Ward, M. M., Effects of Critical Access Hospital Conversion on the Financial Performance of Rural Hospitals Inquiry (in press)

39. Objectives To study the effects of CAH conversion on Iowa rural hospitals� operating revenue, cost, and profit margin

40. Study Sample and Study design Sample Eight year (1997-2004) panel data for 89 Iowa rural hospitals (rural PPS hospitals and CAHs) Unit of analysis is hospital-year Study design Quasi-experimental designs that use both control groups and pretests Panel data regression with fixed hospital effects

41. Models Ad hoc models: Revenueit=f(CAHit,Pjt,Yit,Xit) Costit=f(CAHit,Wjt,Yit,Xit) Marginit=f(CAHit,Wjt, Pjt,Yit, Xit) Variables: CAHit: hospital status (CAH or rural PPS) for ith hospital in year t Pit: output prices for ith hospital in year t Wit: input prices for ith hospital in year t Yit: output volume for ith hospital in year t Xit: other variables for ith hospital in year t that empirically affect dependent variables

42. CAH variables One dummy variable CAH=1, if the hospital is in CAH status Three dummy variables CAH1it=1, if the hospital is in the first year of CAH status, otherwise CAH1it=0 CAH2it=1, if the hospital is in the second year of CAH status, otherwise CAH2it=0 CAH3it=1, if the hospital is in CAH status for more than 2 years, otherwise CAH3it=0 Comparison group: Rural PPS

43. Other covariates Pit: output prices for ith hospital in year t Medicare Part A (hospital) adjusted average per capita cost (AAPCC) as proxy of hospital output price (county level) Wit: input prices for ith hospital in year t Hourly wages for registered nurses (county level) Yit: output volume for ith hospital in year t Total number of acute discharges, total number of outpatient visits, and average length of stay of acute discharges The squared and cubed output measures and interaction terms will be included

44. Others Xit: other variables for ith hospital in year t that empirically affect dependent variables Hospital size (number of beds) Hospital case-mix Hospital mean DRG weight, percent of emergency visits, and percent of Medicare and Medicaid days among acute inpatient days Variables reflecting the hospital market (we assumed the county to be the relevant geographic market of hospital services.) Herfindahl-Hirschman Index (HHI), per capita income, and population density in the county in which the hospital is located Year dummy variables which will adjust the effects of unmeasured, time-specific factors Revenue and expense functions were log transformed

45. Data Sources Iowa Hospital Association Profiles Iowa State Inpatient datasets Area Resource File Centers for Medicare and Medicaid Services American Hospital Association Annual Survey Database Bureau of Labor Statistics

46. Result:Table 1: Changes in rural hospital patient care revenue, expense, and operating margin associated with CAH conversion, 1998-2004

47. Table 2: Changes in rural hospital patient care revenue, expense, and operating margin during the first, second and third plus years of CAH conversion, 1998-2004

48. Results Operating revenue No change in the first year of conversion (paid an interim rate) Significant increases since the second year of CAH conversion Operating expenses CAH conversion is associated with significant increase in hospital operating expenses Hospitals increase expenses in the first year of conversion Operating Margin Significant drop in the first year of conversion Significant increase since the second year of conversion Sensitivity analyses showed similar results

49. Conclusions CAH conversion in rural hospitals resulted in better patient safety. Rural hospital CAH conversion was associated with significant increases in hospital operating revenues, expenses and margins

50. Summary: Limitations of measures In-hospital mortality Substantial unmeasured confounders Patient Safety Indicators Only small number of indicators can be applied to rural hospitals Changes of indicators might reflect changes in coding or reporting in administrative data We need hospital quality indicators specifically for rural hospitals

51. Thank you Questions? Contact information Pengxiang (Alex) Li University of Pennsylvania penli@mail.med.upenn.edu