Tools for Efficient Allocation of Fall-Prevention Resources

1. Tools for Efficient Allocation of Fall-Prevention Resources Shinyi Wu, PhD Adrian Overton, MPA RAND Roybal Center for Health Policy Simulation March 10, 2006

2. Outline of the Talk • A brief introduction of RAND Roybal Center for Health Policy Simulation • Rationale and evidence of fall prevention for older people • A decision-analytic tool to compare cost-effectiveness of fall-prevention interventions • A geographic information system (GIS) based tool to enhance falls surveillance and prevention planning

3. RAND Roybal Center for Health Policy Simulation • Director: Dana Goldman • One of 10 centers established by the NIA: • to translate promising social and behavioral research findings into programs, practices, and policies that will improve the lives of older people and the capacity of society to adapt to societal aging. • Created in October, 2004 • To develop better policy models: • to understand the consequences of biomedical developments and social forces for health, health spending, and health care delivery.

4. The Center’s Specific Aims • Research new methods for forecasting disease, functional status, and health expenditures of older populations, and develop decisionmaking tools based on these methods to support better health investments. • Assess how new and existing medical interventions affect the health, functional status, and spending of older cohorts, and their implications for Medicare and Medicaid and society-at-large. • Assess how demographic and public health trends – including obesity, diabetes, and smoking – affect future outcomes for the elderly and society-at-large.

5. Ongoing Pilot Projects • Application pilots – to apply simulation models to assess the value of new interventions or treatments to both prevent or mitigate undesirable outcomes • Tools for Efficient Allocation of Fall—Prevention Resources (Shinyi Wu) • The Lifetime Burden of Chronic Disease Among the Elderly (Geoffrey Joyce) • Nursing Home Workforce Dynamics and Quality of Care (John Engberg) • The Value of Pharmaceutical Innovations for the Elderly: The Case of Antidepressants (Pinar Karaca-Mandic)

6. Ongoing Pilot Projects • Development pilots – to develop simulation models to better predict health, spending, functional status and other outcomes • Health and Medical Spending of the Near Elderly (Federico Girosi) • The Consequences of Obesity for Older Americans (Darius Lakdawalla) • Eligibility for Comprehensive End of life Services: Developing and Piloting a Method (Joanne Lynn)

7. Ongoing Pilot Projects • Research pilots – to examine the determinants of health, spending, and functional status of the elderly and near elderly • Functional Status, Health, and Health Care Costs among the Elderly (Hao Yu) • Adverse Selection, Population Aging, and the Market for Supplementary Health Insurance (Nicole Maestas) • Rising Medicare Expenditures for the oldest Medicare Beneficiaries (Melinda Beeuwkes Buntin)

8. Tools for Efficient Allocation of Fall-Prevention Resources

9. Acknowledgement • Funded by National Institute on Aging • Collaborators: • Southern California Evidence-base Practice Center (SCEPC) • Fall Prevention Center of Excellence (FPCE) http://www.stopfalls.org • Project team: • Gordon Bitko • Jianglai Zhang • Yuyan Shi

10. Why A Policy Tool? • Over the past decade, there has been an explosion in the availability of systematic reviews and meta-analyses that have critically examined evidence of health interventions. • However, such information may not be applied directly to the complex processes of policymaking and resource allocation. • Evidence is summarized at the level of the individual • Resource decisions are made at the population-level

11. Evidence-based Health Policy • A conceptual framework proposed by Dobrow et al.* • Evidence • Context • The interaction between evidence and context • A policy tool to translate the evidence from the individual-clinical level to the population-policy level to assist policymakers in making evidence-based health policy and in maximizing the efficiency of resource allocation. *Dobrow MJ et al. Evidence-based health policy: context and utilization. Social Science and Medicine, 2004; 58:207-17.

12. What's an Elderly Person's Greatest Fear? • Being a crime victim? • Isolation? • Running out of money? • Falling down and fracturing a hip? • Death? • Losing friends? • Running out of Bingo cards? www.theinternetparty.org/

13. Sure, all of these things worry our senior citizens, but… • FallsFrom anecdotal evidence only, we believe many people over 65 think falling is their greatest fear.  That is a legitimate concern because one in three Americans over 65 experiences a debilitating fall each year. • One reason it's so serious is that the elderly have brittle bones that break easily.  Another concern is for seniors who live alone.  They're afraid that if they fall in the tub or bedroom they wouldn't be discovered for days. http://www.theinternetparty.org/commentary/c_s.php?section_type=com&td=20020510000105

14. Background • Fall is the number one cause of injury among adults ages 65 and older: • More than one-third (12 millions) fall each year • Nearly 27,000 people died from fall-related injuries in 2003 • 20% to 30% suffer moderate to sever injuries such as hip fractures or head traumas • Among people 75+, those who fall are four to five times more likely to be admitted to a long- term care facility for a year or longer

15. Threats and Opportunities • Falls and fall-related injuries impose an enormous burden on individuals, society, and to the nation’s health care system. • As the population of the United States ages, the negative impact of falls continues to increase. • Yet many falls, and fall-related injuries, can be prevented with existing knowledge and technology.

16. Purpose of the Project • The goal of this translational research is to provide policy makers, program planners, and interventionists decision support tools: • to identify local needs, gaps and opportunities to reduce falls and fall related injuries among people age 65+ • to compare effective fall- prevention interventions to determine those that best meet their needs; in particular, those most likely to maximize the impact of limited resources

17. Approaches • Evidence base: • Updated systematic evidence review and meta-analysis built on the work of the SCEPC • CE analytic model development: • Consulting subject matter experts in FPCE to understand the structure of fall-prevention problem and the inter-relationships among the many different parameters that affect cost and effectiveness of an intervention program • Using Analytica to develop the decision-analytic model • GIS tool development: • Obtaining publicly available geo-coded data on population demographics, fall epidemiology, workforce etc. • Using a mapping and spatial analysis software ESRI ArcGIS to develop a customized GIS tool to enhance falls surveillance and prevention planning

18. Evidence Review and Meta-Analysis • Previous Study: • Chang et al. “Interventions for the prevention of falls in older adults: systematic review and meta-analysis of randomized clinical trials” BMJ Vol. 328, 2004 • Update the results from this meta-analysis by including studies published from 2001 to present: • Quantitatively assess the overall effectiveness of intervention to prevent falls • Further assess the effects of different intervention components • Examine the influence of other covariates such as settings and age on effectiveness

19. Results • 40 articles that met all inclusion in Chang’s article. • 13 new articles were identified for the meta-analysis PooledEstimate

20. Meta-regression: Effect by Intervention Types

21. Meta-regression: Effect by Settings and Age Groups

22. Decision Analysis Modeling Tool Demonstration

23. GIS Mapping Tool Demonstration

24. Purpose of the GIS Tool Support resource allocation decision-making of senior fall prevention planning community by supporting: • Development and identification of decision evaluation criteria using exploratory data analysis methods • Exploratory analysis and visualization of contextual factors and relationship to fall risk • Neighborhood-level targeting of interventions

25. System Design Goals • Use of GIS map interface for interacting with and exploring large amount of data. • GIS: geographic information system • Store, manage, analyze, and model spatial data • Integrates spatial data with relational database • Utilize spatial data analysis methods that facilitate small-area comparisons and visualization of spatial relationships • Use readily available data in a user-friendly interactive tool

26. Mapping Database • Spatial database component • Census geography – TIGER/Line files • InfoUSA database of health providers • DHS data on falls by residence location • Population Projections 2000 – 2050 • Census SF1 Population data 2000

27. Identify Trends in Raw Fall Rate • Falls per 1000 persons aged 65+ • Assess fall hospitalization trends over space and time • Falls increasing with age • Falls among seniors 75 years and older are significantly different from all other age groups.

28. Falls among females significantly higher than Males

29. Map Tool Interface Menu-driven interface for visualizing data Form fill-in dialogs for setting map parameters

30. Automated Map Creation

31. How are females aged 70-74 distributed? *Dobrow MJ et al. Evidence-based health policy: context and utilization. Social Science and Medicine, 2004; 58:207-17.

32. Calculation of Non-Fatal Fall Rates -California Population 65 Years or More Average Fall Rate = SFR = • Falls65 is number of falls among 65 years or more population in County i • Pop65 is total population 65 years or more in County i • SFR (Standardized Fall Rate) is the ratio of falls among 65 years or more population in County i divided by the expected number of falls for 65 plus pop. in County i

33. Average Fall Rate 1999 - 2003

34. Avg. Standardized Fall Rate (1999-2003)

35. Are there gaps in access to potential fall prevention resources? • Potential Accessibility = ratio of seniors to healthcare workers • Pros: widely used and easy to interpret • Cons:doesn’t account for travel outside admin defined area • Other spatial methods for measuring potential accessibility

36. Healthcare Workers by Place of Work

37. Where are high number of seniors and low potential access to providers?

38. Assess Multivariate Spatial Relationships in Data Identify areas w/high proportion of females aged 70-74 and high standardized fall rate with low access to potential providers

39. Exploratory Spatial Analysis: Zoom to County with high fall rate and low access to explore spatial distribution of potential sites and seniors

40. Create New Map of County of Interest

41. Detailed Mapping of Potential Providers

42. Assess Populations Within 30-min. Drive Time of Potential Fall Prevention Program Site • Overlay with small-area populations and provider locations • Compute accessibility ratio using network service area for each tract centroid • Assess cost-effectiveness of locating program at specific locations.

43. Automated Zoning • GIS facilitates automated construction of comparably sized areas • equalize senior population in area • create new map layer for analysis • Handles spatial heterogeneity in data • Many optimized heuristic methods available: • automated zoning procedure • simulated annealing • optimized zone partitioning

44. Web-based Interface for Public Use

45. Questions?