Presented at the AcademyHealth 2004 Annual Research Meeting, San Diego, CA, June 6, 2004

1. Spatial Analysis of Healthcare Markets: Separating the Signal from the Noise in ACSC Admission Rates Presented at the AcademyHealth 2004 Annual Research Meeting, San Diego, CA, June 6, 2004 Presented by: Lee R. Mobley, PhD Co-Authors: Elisabeth Root, MA Nancy McCall, ScD Sujha Subramanian, PhD Mary Kapp, MPhil Barbara Gage, PhD P.O. Box 12194 · 3040 Cornwallis Road · Research Triangle Park, NC 27709Phone: 919-541-7195 · Fax: 919-541-7384 · lmobley@rti.org · www.rti.org RTI International is a trade name of Research Triangle Institute.

2. Overview • This paper examines the association between geographic or market-level supply and demand factors and market-level rates of three ambulatory care sensitive conditions (ACSCs): • chronic obstructive pulmonary disease (COPD) • congestive heart failure (CHF), and • lower limb peripheral vascular diseases (PVD) Study Population: Medicare FFS beneficiaries over two time periods: mid nineties and latter nineties. Markets Definition: 306 Hospital Referral Regions from the Dartmouth Atlas Project

3. Spatial Market Analysis: Motivation • In the previous market-level analysis, we found that Census division effects were significant even after controlling for demographic and disease severity factors, which suggests that there may be important characteristics of places that are omitted in the analysis. • Market-specific factors that impact access or continuity of care may be important variables to examine in explaining ACSC admission rates. • Medicare policy changes in the latter nineties may have impacted these market-specific effects.

4. CHF Rates in HRRs, 1995–1997

5. CHF Rates in HRRs, 1998–2000

6. COPD Rates in HRRs, 1995–1997

7. COPD Rates in HRRs, 1998–2000

8. PVD Rates in HRRs, 1995–1997

9. PVD Rates in HRRs, 1998–2000

10. Access and Utilization • Access to care and utilization of health services is impacted by many factors: • Population characteristics • Health system characteristics • Local area market characteristics • Our conceptual model combines these three elements to show the pathways to realized demand (actual utilization)

11. Methods: Conceptual Model of Access (Khan and Bhardwaj, 1994; WHO, 2000)

12. Methods: Empirical Challenge • The intervening factors that may be so important in determining elderly utilization are difficult to measure (proxy: population density). • Other important missing variables are practice patterns and/or health behaviors that may vary significantly from place to place, yet may be similar in local regions (spillovers). • The empirical challenge is to find a model specification that accounts for these missing variables so that their omission does not impart bias on other parameters of interest.

13. Methods • Three ACSCs were examined: PVD, COPD, CHF; 3-year rates were constructed for 1995–1997 and 1998–2000 (ACSC admission/all beneficiaries in 1,000s). • We combined inpatient and ER/observation bed stays. • Beneficiary and county-level data were aggregated to HRRs, yielding 306 observations in an early period and 306 in a late period. • SpaceStat software was used to estimate a spillovers spatial regression model

14. Methods • Primary Data Sources for explanatory variables • CMS Enrollment Data File and claims Files • CMS Provider of Service (OSCAR) file • CMS Medicare Managed Care Penetration file • AMA Physician Masterfile • AHA County Hospital File • US Census of Populations • Interstudy • AARP

15. Methods • Sociodemographic Characteristics • Proportion of elderly in poverty (1989,1999) • Proportion of county that are elderly • Proportion of sample Black • Proportion of sample male • Proportion of sample >80 • Proportion of sample dual enrolled • Proportion of sample who died • Proportion of elderly with supplemental insurance

16. Methods • Health Status Characteristics • Median PIP-DCG score • Proportion of sample with ESRD • Proportion of sample with diabetes

17. Methods • Market Characteristics • M+C Penetration • Proportion of population in private HMOs • Home health visits per Medicare insured • Medicare admissions to SNFs • Hospital inpatient occupancy of staffed beds • Number of non-Federal practicing MDs • Number of FTE, hospital-based RNs • Number of SNFs • Number of HHAs • Number of Hospices • Number of Rural Health Clinics

18. Methods • Market Characteristics • Number of hospitals with outreach programs • Number of hospitals with assisted living programs • Number of hospitals with rehabilitation programs • Number of hospitals with transportation • Number of hospitals with home health services

19. Methods • Access Proxies • Proportion of the population who said they didn’t visit a physician due to cost • Proportion of the population who reported problems accessing a primary care provider

20. Methods • We estimate the ecological model on data from two separate cross sections to assess whether factor effects changed over a time • We indirectly examine the influence of SNF and HH payment reforms on the market rate of ACSC hospitalizations

21. Empirical Findings • Beneficiary characteristics explain most of the market-level variation in ACSC admissions. • Poverty among the elderly has become an increasingly important predictor of all three ACSCs over time — the large, positive association with COPD and CHF increases (doubles in magnitude) over time. • The mean proportion of the elderly in poverty declined nationally between 1989 and 1999.

22. Empirical Findings: Demographic Factors • Places with higher proportions of the oldest-old have lower COPD rates (and increasingly so over time) and lower CHF rates (and diminishing over time) • Places with higher proportions of beneficiaries who died had higher COPD and CHF rates, fairly stable over time. • Places with higher proportions of black beneficiaries had lower PVD and COPD rates • Places with greater proportions of diabetics and ESRD had higher PVD rates and more home health visits per beneficiary (magnitude doubled over time)

23. Empirical Findings: Market Factors • Places with more SNFs exhibit higher COPD and CHF admit rates – but stable over time. • No association between number of HHAs and ACSC admit rates but number of HHA visits positively associated with PVD hospitalizations and increasingly so over time. • Places with more hospital-based rehabilitation programs have lower CHF and COPD rates – smaller effect over time. • Managed care penetration of Medicare market has no change in effect over time for CHF and PVD and modest negative effect for COPD in later period

24. Other Factors • Inpatient hospital occupancy rate was positively associated with PVD rates in the early period, and negatively associated with CHF rates in the later period. • Places with higher HMO penetration in the private market show lower PVD and COPD hospitalization rates in the early period. • Places with higher proportions of the elderly holding supplemental insurances show lower COPD hospitalization rates in the early period and higher CHF rates in the later period.

25. Other Factors • Places where higher proportions of the population ‘didn’t visit a doctor because of cost’ showed positive association with COPD rates in the later period. • The numbers of physicians and registered nurses, and the statewide measure of physician shortage, were surprisingly insignificant in these models. • Other supply variables such as hospital services and other post-acute care services were also surprisingly silent.

26. Empirical Findings: Spatial Spillovers Spatial Lag Model: Ri = jI wijRj +  Xi + ui  Is the measure of strength of spatial spillovers The estimate of  is 0.164 for PVD, >0.50 for COPD, and >0.40 for CHF For PVD, spillovers are local (2 closest HRRs) while for COPD and CHF, spillovers are regional (6 closest HRRs) • Given the spatial clustering observed in COPD and CHF, geographically-targeted interventions may be possible.

27. CHF & COPD Rates ('98–'00) and Elderly in Poverty (1999)

28. Change in CHF & COPD Rates ('95–'97) to ('98–'00), and Change in Elderly Poverty Rate