1. 1 Spatial Data and Methods for Socio-Ecological Research P.O. Box 12194 · 3040 Cornwallis Road · Research Triangle Park, NC 27709 Phone: 919-541-7195 · Fax: 919-541-7384 · lmobley@rti.org · www.rti.org RTI International is a trade name of Research Triangle Institute. Methods Workshop Academy Health June 27, 2010 Boston, MA Presented by:Lee Rivers Mobley, PhD Senior Fellow in Health Economics and Spatial Epidemiology RTI International

2. Acknowledgments This work was funded by a National Cancer Institute (NCI) grant (1R01CA126858-01A1), Geospatial Factors & Impacts: Measurement and Use. Thanks to the funding support, multilevel data developed for this work are available to other researchers free of charge. The content is solely the responsibility of the authors and does not necessarily represent the official views of NCI, the National Institutes of Health, RTI International, or Arizona State University (ASU). 2

3. 3 Acknowledgments The work described here could not have been done without the help of the following people: RTI : Lee Mobley (PI)Sujha Subramanian Tzy-Mey KuoMatt Urato Linda AndrewsNneka Ubaka-Blackmoore Laurel ClaytonMatthew Scott Jennifer WellardBrenda Stone-Wiggins ASU GeoDa Center: Luc Anselin (Co-PI)Julia Koschinsky Nancy Lozano-GraciaCharles Schmidt

4. Overview 1.Online availability of FREE! RTI Spatial Impact Factor databases, with 100% fee-for-service (FFS) Medicare breast cancer (BC) and colorectal cancer (CRC) screening rates in small areas, 2001–2006 2.Multilevel analysis methods for spatial data: Managed Care Spillovers and CRC Screening among the FFS Medicare Population in 50 States 3.Spatial regression methods for small area ecological analysis: Barriers to Endoscopic CRC Screening among the FFS Medicare Population, 2001–2006 4

5. 5 1. FREE! RTI Spatial Impact Factor Databases http://rtispatialdata.rti.org

6. FREE! RTI Spatial Impact Factor databases include public use data from various sources at different geographic levels  Crime, HRSA spatial data, CMS data  1990 and 2000 Census data and constructs  Land use mix index  Travel impedance measures (commuting intensity, hilliness, climate)  Social segregation, income dispersion/disparity  Environmental toxins (air, land, water)  Medicare FFS population BC and CRC screening rates, distance to closest provider, 2001–2006  1960, 1970, 1980, 1990, and 2000 GINI indices (income)  POS counts by county and PCSA, 1996–2009  County-level social capital index, 1990, 1997, 2005  Herfindahl indices for hospitals, mammography and endoscopy providers 6

7. Geographic Areas and Upcoming Data  Database contains (1990 and 2000) tract and ZCTA data; (1960–2009) county data; (2000) PCSA and MSSA data  ZCTAs nest inside PCSAs; tracts nest inside counties and MSSAs (California); ZCTAs and PCSAs cross county boundaries  RTI’s relational databases are defined for all 4 areal units, in MS Access TM ; Web-served data by selection criteria and area available soon  Databases are constantly updated and expanded; shapefiles available for tracts, ZCTAs, and PCSAs; crosswalks available  Future data to include American Community Survey and Census 2010 data comparable to variables from the 1990 and 2000 census  Future data to include FFS Medicare BC and CRC screening rates and providers, 2007+ 7

8. Multilevel Analysis Methods for Spatial Data Managed Care Spillovers and CRC Screening among the FFS Medicare Population in 50 States 8

9. What Are Spatial Data?  Spatial data describe a population in local areas. Examples: cancer registry populations, 100% FFS Medicare files, tract-level census long form data  Spatial data can derive from a sample, but only if it is designed to represent the areas under study  Example: BRFSS data—can be used in spatial analysis at the state level, but not the county level; smart BRFSS data—do not represent substate areas, although sample sizes are larger than ordinary BRFSS 9

10. The Basics of Multilevel Modeling  The individual is the unit of analysis in our multilevel modeling, which is used to assess the effects of contextual and individual factors on the probability of cancer screening utilization.  We link contextual variables to claims address geography (may differ from SSN address). Watch out for obsolete ZIP codes – elderly people still use them. – Control for various levels of influence on probability of individual utilization: Neighborhood: census tract, ZCTA, PCSA, MSSA Community/health market: PCSA, MSSA, county  Policy environment: state, region, country 10

11. Mobley, Kuo, and Andrews (2008): A Hybrid Spatial Interaction Model with Levels of Influence 11 Macro/Fundamental or Distal Factors: Distribution of Wealth, Educational Opportunities, and Political Influence; Social and Economic Policies, Institutions, Topography, Climate, Water Supply Personal Health Behavior: Utilization of Healthcare Services Intermediate or Community Social Context: Neighborhood, Workplace, and Housing Conditions; Public Infrastructure and Investment; Police, Enforcement Services, Crime; Health Care System Physical Environment: Community Capacity and Partnership; Land Use Patterns, Transportation Systems, Buildings, Public Resources, Pollution Interpersonal or Proximate Stressors; Social Integration and Support; Psychosocial Factors; Behavioral Settings; Social Relationships; Living Conditions; Neighborhoods and Communities; Neighborhood Watchfulness; Driver Courtesy; Social or Cultural Cohesion; Population Health Behaviors or Norms Individual/Population Enabling/Disabling Personal Disability Personal Resources Type of Health Coverage New Address Marital Status Employment Status Predisposing Age, Sex, Gender Race or Ethnicity Educational Attainment Need Beliefs, Family History Perceived Risk Health Status Health Care System Proximity and Density of Facilities, Physicians Crowding, Scheduling Convenience Personal Physician Managed Care Climate Primary Care Physician Shortage International Medical Graduate Enclave

12. The Importance of Conceptual Models  Conceptual models guide development of hypothesized causal relationships. – They guide development of the full complement of contextual data to model spatial dynamics – They guide specification of the appropriate levels of influence to include in multilevel regression modeling – Do levels of Influence matter? (see Mobley, Kuo, and Andrews, 2008, MCRR) 12

13. Multilevel Modeling: Controlling for Clustering  Multilevel modeling accounts statistically for different levels in the data (whether nested/not).  Different types of multilevel modeling are used for different purposes; all control clustering caused by individuals grouped within larger areas or units, such as schools, providers, states. – Nuisance: Adjust the standard errors of higher level (contextual) variables using the White robust or sandwich estimator – GEE: Imposes a formal correlation structure between observations but models the population average effect across areas, rather than individual area effects; also considered best when the dependent variable is binary – Clustering as a hierarchical construct using random intercepts or coefficients that allow estimation of random effects specific to areas or units and to make unit-specific predictions. 13

14. Multilevel Modeling: Controlling for Clustering in Geospatial Data  GEE is a common choice when the outcome measure of interest is discrete (e.g., binary or count data, possibly from a binomial or Poisson distribution) rather than continuous. – Attractive because it provides a nonparametric, empirical approach that is robust to wrong assumptions about the variance-covariance matrix – Preferred when the number of clusters is large, such as when using geospatial data – Preferred when seeking unbiased population-level inference about the influence of contextual variables, but not interested in the areas themselves as constructs that could be influenced by policy, etc. 14

15. Multilevel Modeling: When to Use What?  When you have spatially representative data, there are many contiguous areas completely covering the geographic space— but these areas are often not of inherent interest.  In this case, the GEE or White or sandwich estimator is best— controls for redundancy and adjusts standard errors so correct inference can be made regarding contextual effects at the population level.  For data such as BRFSS that are spatially representative at the state level, the state may be of inherent interest. In this case, HLM, random, or fixed effects estimators may be desired and used to make state-specific predictions.  For nonrepresentative data, such as BRFSS, the GEE or White or sandwich estimators can be used to understand effects of linked county-level variables, but the interpretation of findings is not generalizable to the population level—findings are conditional on/relevant for the sample only. 15

16. 100% FFS Medicare Population Characteristics  We use extracts of 100% of Medicare claims for all colonoscopy or endoscopy use and link these records to corresponding individuals in 100% Medicare denominator files each year 2001–2005.  We define a cohort of Medicare eligibles aged 65 to 104 in the year 2001, with both Part A and Part B Medicare throughout 2001–2005, who remain alive and living in the same state.  For this cohort, we have demographic information, payer information, original entitlement information, and address (ZIP code from claims). 16

17. CRC Test Use in 50 States: Descriptive Mapping 17

18. CRC Test Use in 50 States: Multilevel Modeling  We use Probit regression of individual choice, using GEE.  We model each state separately and estimate separate regressions for each state, to allow all effect parameters to vary across states and reflect the decentralized, heterogeneous comprehensive cancer control program environment.  We summarize the findings across states by mapping effect parameter estimates for each single covariate: spatial translation of research findings. 18

19. Spatial Translation of Research Findings: Impact of Poverty in One’s Neighborhood 19

20. Spatial Translation of Research Findings: Impact of Poor/No Elderly English-Language Ability in One’s Neighborhood 20

21. Spatial Regression Methods for Small Area Ecological Analysis Barriers to Endoscopic CRC Screening among the FFS Medicare Population, 2001–2006 21

22. Background  Endoscopy is essential for early detection or prevention of CRC and both reduced morbidity and increased survival among CRC victims.  In the US, less than 39% of CRC cases are diagnosed at an early stage, and less than 55% of the over-50 population have received CRC screening within the past 10 years.  CRC screening is covered by Medicare (but requires co-pays), and coverage was expanded in 2001 to include colonoscopy, but utilization varies widely across areas and time. 22

23. Proportion of FFS Medicare Enrollees with Colonoscopy or Sigmoidoscopy Utilization, by Age Group, 2001–2006 23 Colonoscopy UseSigmoidoscopy Use

24. Background  This paper examines spatial competition in the diffusion of a newer endoscopic technology (colonoscopy) that has greater power to detect anomalies leading to CRC than an older technology (sigmoidoscopy) but costs more.  Sigmoidoscopy is more cost-effective (~$250, $15 co-pay versus $3,000, $300 co-pay) but is less clinically effective than colonoscopy.  Managed care firms have historically favored more cost-effective technologies, especially since they have no guarantee of long-term enrollment loyalty. 24

25. The Medicare Study Population  The sample is the entire population of Medicare eligibles, aged 65+ in each year 2001–2006.  Sample members with CRC screening have both Parts A and B of traditional Medicare FFS coverage, allowing free choice of provider and coverage for endoscopic services (subject to co-pays and deductibles). Claims were extracted from 100% Medicare physician carrier files. Small-area screening rates were derived (proportions).  Small-area demographic data are from the 100% denominator files and include all eligibles. We derive proportions for age, race/ethnicity, sex, whether dually eligible, proportion ever enrolled in Medicare HMOs, or missing Part B coverage for outpatient services (no claims). 25

26. Medicare Subgroups by Type of Insurance Coverage (MCBS, 2002) 26 Subset of the population with Supplemental Insurance in addition to traditional FFS Medicare: 61% Subset of population with Parts A and B Medicare only (traditional FFS Medicare): 8.75% Subset of the population with traditional FFS Medicare coverage, where Part B is provided by state Medicaid supplement (dually eligible): 17% Subset of the population with Part A Medicare only (no Part B coverage for outpatient services): 0.5% Subset of the population with Medicare HMO coverage instead of traditional FFS Medicare; no coverage allowed for Supplemental: 13.5%

27. The Distribution of Various Medicare-eligible Insurance Groups Is Not Uniform across US Regions (upper quartiles presented) 27

28. Research Focus: Spatial Diffusion of Endoscopy Services (Colonoscopy and Sigmoidoscopy)  The purchase of endoscopy equipment will depend on the expected return on investment, which is a function of market conditions: – market size (density), – affluence and insurance coverage of the elderly, – elderly demographic characteristics, and – health market conditions: competition of managed care plans and endoscopy providers, and prevalence of these. 28

29. Research Focus: Spatial Diffusion of Endoscopy Services (Colonoscopy and Sigmoidoscopy)  Sigmoidoscopy has lower fixed costs so easier to establish in less dense/urban markets; performing fewer services is adequate to recoup costs.  Sigmoidoscopy is less costly to perform and has much lower out-of-pocket costs and about the same time-costs for patients. Areas where elderly are poorer or less well insured (duals and no Part B) are expected to show higher sigmoidoscopy than colonoscopy use.  Managed care favors treatments with a lower cost- effectiveness ratio, so areas with higher Medicare HMO penetration are expected to encourage sigmoidoscopy and discourage colonoscopy use. 29

30. Spatial Spillovers in Endoscopy Services  The change in availability of colonoscopy or sigmoidoscopy services is reflected in the change in colonoscopy or sigmoidoscopy use rates in areas (counties) over time.  Spatial spillovers are expected to occur in area utilization rates when the supply of endoscopy services in one market is affected by its supply in neighboring markets. Because of the high fixed costs, adoption by a neighbor impacts the investor’s ability to realize a favorable return on investment quickly. 30

31. 31 Spatial Spillovers in Endoscopy Services: The Empirical Model The spatial lag model is specified as follows: E* =  WE + X  + u where E* is the endoscopy use rate in community I, and the  WE term is the spatial lag term reflecting endoscopy use rates in surrounding neighboring communities, identified by the spatial weights matrix W. X are other explanatory variables in the ecological model. Each community’s endoscopy use rate is determined in part by the use rates in surrounding counties, and the spatial lag parameter (  ) reflects the degree of interdependence in these spatial spillovers.

32. Spatial Spillovers in Endoscopy Services: The Estimating Equations  The data are county-level aggregates.  The dependent variable/outcome is proportion of FFS-enrolled elderly who used either colonoscopy or sigmoidoscopy in the county.  The two technologies have separate estimating equations.  Each technology’s equation is estimated twice: early (2001–2003) and late (2004–2006) period  Spatial Seemingly Unrelated Regression is used to assess significance of changes in effect parameters over time. 32

33. Study Hypotheses to be Tested H1:Where Medicare managed care is more prevalent, there will be negative association with colonoscopy and positive association with sigmoidoscopy use rates. H2:Where there is greater competition among Medicare managed care plans, there will be a positive association with both types of services. H3:Small areas that are less urban, have lower insurance rates for Part B coverage, and have higher rates of dual eligibility coverage will have higher sigmoidoscopy use and lower colonoscopy use rates. H4:To the extent that minorities have lower socioeconomic status than whites, disparities in use between whites and minorities are expected to be lower for sigmoidoscopy than for colonoscopy use. 33

34. County Area-Level Covariates  Proportions by age group, race/ethnicity, sex, dual eligibility, no Part B status  Average distance to closest substitute provider  Density of endoscopy providers and oncologists  Medicare managed care penetration and competition  Endoscopy provider competition  Population density 34

35. Findings  Counties with higher Medicare managed care penetration have higher sigmoidoscopy use and lower colonoscopy use.  Medicare managed care competition has no significant effect in either model.  Managed care impacts seem to favor the older, more cost-effective technology, irrespective of the amount of competition among managed care plans.  Endoscopy provider competition is associated with significantly higher use of both types of service. 35

36. Findings  Areas with higher Medicare managed care competition do not show significant interactions, suggesting an optimal decline in use with advancing age.  Sigmoidoscopy use is significantly higher in markets with greater proportions of elderly without Part B insurance coverage, while the opposite is true for colonoscopy.  Sigmoidoscopy use is not affected by the proportion of poor elderly (dually eligible), but colonoscopy use is significantly lower in poorer areas. 36

37. Findings  Disparities in use exist between Hispanics and American Indians versus whites for both technologies, showing lower use by minorities.  These disparities are smaller and decline significantly over time for sigmoidoscopy use.  Colonoscopy use shows disparities between African Americans and whites that are favorable to African Americans; there are higher colonoscopy use rates in areas with higher proportions of African Americans. 37

38. Conclusions  This paper provides the first nationally representative evidence to date regarding the impact of Medicare managed care spillovers on the availability, diffusion, and utilization of endoscopic services for CRC screening in the entire Medicare FFS population.  Medicare coverage for the newer technology was expanded about the same time that efforts to expand penetration by Medicare managed care plans occurred.  We conclude that managed care spillovers have slowed the diffusion of the newer, more costly but more effective colonoscopy technology, relative to the older, more cost-effective sigmoidoscopy technology. 38

39. How Spatial Regression Impacts the Conclusions  The spatial lag parameter was statistically significant and rather large for both technologies and time periods.  Using ordinary regression would mean estimating a mis-specified model.  The omitted spatial lag effect in an ordinary least squares (OLS) model would result in omitted variables bias on the coefficient estimates.  The degree of bias (i.e., spatial multiplier bias) is reflected in the expression 1/1-lag parameter estimate. Our estimate is ~ 0.5, so the bias would inflate OLS estimates to about twice their actual size.  Spatial multiplier bias impacts the magnitude but not the sign of the effect. 39

40. 40 RTI Spatial Impact Factor Databases http://rtispatialdata.rti.org GeoDa Software for spatial analysis http://geodacenter.asu.edu/software FREE!