1 Are Changing Rates of Admission for Chronic Medical Conditions Simply a Reflection of Changes in the Demographics, Health Status and Geographic Migration Patterns of the Elderly? P.O. Box · 3040 Cornwallis Road · Research Triangle Park, NC Phone: · Fax: · · RTI International is a trade name of Research Triangle Institute. Presented at: the AcademyHealth 2004 Annual Research Meeting, San Diego, CA, June 6–8, 2004 Presented by: Nancy McCall, Sc.D.

2 Acknowledgements n Lee Mobley, Ph.D. n Sujha Subramanian, Ph.D. n Erica Brody, M.P.H. n Mary Kapp, M. Phil

3 Research Question n What is the influence of beneficiary sociodemographic and health status characteristics on the rate of growth of ACSC admissions?

4 Data n Rates of ACSC admissions and average health status of the Medicare FFS population l 1992–2000 MQMS Base Analytic Files n Rates of Emergency Room or observation bed stays l 1992–2000 Outpatient SAFS n Estimates of the proportion of the Medicare population with specific attributes of interest l 1992–2000 MQMS Base Denominator Files

5 Methods n Full Year Part A and B Medicare FFS, including deceased n Age 65 and older and residing in U.S. n Approximately 25 million per year n Defined two ACSCs for beneficiaries with diabetes n Health status measured using the PIP-DCG predictive expenditure model n Age-sex Adjusted to July 1, 1999 FFS Population using direct standardization

6 All Cause Hospitalization Rates Trend in Age-Sex Adjusted All Cause Admissions (per thousand) Medicare FFS Beneficiaries: % Change 1992–

7 % Change in Inpatient and Outpatient Age-Sex Adjusted Rates for Eleven Selected ACSCs, InpatientOutpatient Cellulitis Asthma COPD Dehydration CHF Acute diabetic events* Lower Limb PVD Pneumonia Septicemia Stroke UTI

8 Empirical Model ACSC jt = f (OUTPAT jt, SOCIO jt, HEALTH jt, GEO, YEAR, TIME t ) n ACSC jt = rate of inpatient admissions for the specific ACSC in year t and region j, where each state is divided into one MSA and one non-MSA region; n OUTPAT is rate of ER/observation bed stays for the specific ACSC in year t and region j; n SOCIO = a vector of yearly beneficiary demographic characteristics aggregated to each region; n HEALTH = a yearly health status measure aggregated to each region;

9 Empirical Model ACSC jt = f (OUTPAT jt, SOCIO jt, HEALTH jt, GEO, YEAR, TIME t ) n GEO = a set of census region dummy variables; n YEAR = a set of dummy variables for each year 1993–2000 l Interacted with two variables — median age and outpatient rates n TIME is a continuous time variable — 1993…2000

10 Empirical Model n Three chronic ACSCs: l Lower limb peripheral vascular disease (PVD) l COPD l CHF n Independent variables in the SOCIO vector are specified as proportions, except for the median age n Health status is represented as the median of the PIP-DCG risk score of the population for the year. n For PVD, we add the number of Medicare beneficiaries with diabetes in the prior year

11 Trend Analysis: Methods n We pool the cross sections for each year 1993–2000 n We separate inpatient stays from ER/observation bed stays n We aggregate ACSC admissions to MSA and non-MSA regions within states n We tested whether the same relationships exist in MSA and non-MSA subsets of the data, and find they are significantly different

12 Trend Analysis: Methods n We first estimated a simple model with a continuous time variable as the only regressor and found a significant positive trend. n We then stepped in beneficiary demographics, health status and dummy variables for 9 Census divisions n We interacted median age with time and ER/observation bed stays with time to examine whether there are time varying associations

13 Trend Analysis: Time Trend and Variation Explained Inpatient PVD Rate Inpatient COPD Rate Inpatient CHF Rate VariableMSA Non- MSAMSA Non- MSAMSA Non- MSA Continuous Time Variable– Adjusted R 2 40%38%66%75%82%71% Number of Observations

14 Trend Analysis: Demographics Inpatient PVD Rate Inpatient COPD Rate Inpatient CHF Rate VariableMSA Non- MSAMSA Non- MSAMSA Non- MSA Demographics Proportion Died++– Proportion Dual Enrolled in Medicaid–++ Proportion Men++ Proportion Black––+ Proportion with ESRD+–++

15 Trend Analysis: Median Age and Time Inpatient PVD Rate Inpatient COPD Rate Inpatient CHF Rate VariableMSA Non- MSAMSA Non- MSAMSA Non- MSA Median Age of Medicare FFS Population of each study year interacted with Time Dummy for the year 1993 Median Age *1993–– 1994 Median Age *1994–– Median Age *1995+––– Median Age *1996+––– Median Age *1997+–– Median Age *1998+–– Median Age *1999+–– Median Age *2000+––+

16 Trend Analysis: Health Status Inpatient PVD Rate Inpatient COPD Rate Inpatient CHF Rate VariableMSA Non- MSAMSA Non- MSAMSA Non- MSA Health Status Median PIP-DCG Risk Score+++ Lag Number of Diabetics–NI

17 Trend Analysis: ER Visit and Time Inpatient PVD Rate Inpatient COPD Rate Inpatient CHF Rate VariableMSA Non- MSAMSA Non- MSAMSA Non- MSA Year Interacted with ER Visit Rate 1993 ER Visit Rate * ER Visit Rate * ER Visit Rate * ER Visit Rate * ER Visit Rate *1997– ER Visit Rate *1998– ER Visit Rate * ER Visit Rate *2000–+++

18 Trend Analysis: Population Migration Inpatient PVD Rate Inpatient COPD Rate Inpatient CHF Rate VariableMSA Non- MSAMSA Non- MSAMSA Non- MSA Population Migration Patterns Change in Size of Medicare FFS Beneficiary Population from Current Year to Prior Year ––

19 Trend Analysis: Census Divisions Inpatient PVD Rate Inpatient COPD Rate Inpatient CHF Rate VariableMSA Non- MSAMSA Non- MSAMSA Non- MSA Set of Dummy Variables for Each Census Division (New England is Reference Division) Middle Atlantic+++++ East North Central++++ West North Central–++–+ South Atlantic+++ East South Central++++ West South Central++ Mountain––– Pacific–––

20 Conclusions: Trend Analysis n Positive trends in the raw rates over time are substantially explained by demographic- specific factors n Little evidence of substitution of ER for hospitalization for COPD and CHF; some evidence of substitution for lower limb PVD n Rural areas that experienced outbound migration experienced a decline in admission for COPD and CHF

21 Conclusions: Trend Analysis n Observed variation in direction and strength of relationship between explanatory factors and selected chronic conditions suggests that interventions employed to reduction hospitalizations may have to be tailored to the underlying condition n Unexplained geographic variation in hospitalization rates for all three chronic conditions remain