tools for efficient allocation of fall-prevention resources

Tools for Efficient Allocation of Fall-Prevention Resources

Shinyi Wu, PhD

Adrian Overton, MPA

RAND Roybal Center for Health Policy Simulation

March 10, 2006

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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

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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.

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The Center’s Specific Aims

1. 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.

2. 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.

3. Assess how demographic and public health trends – including obesity, diabetes, and smoking – affect future outcomes for the elderly and society-at-large.

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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)

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• 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)

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• 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)

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Tools for Efficient Allocation of Fall-Prevention Resources

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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

http://www.stopfalls.org/

http://www.stopfalls.org/

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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


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.


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/

http://www.theinternetparty.org/


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


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


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.


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


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


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


Results

• 40 articles that met all inclusion in Chang’s article.

• 13 new articles were identified for the meta-analysis

pooled estimates of effect of fall prevention intervention

# pairs adjusted IRR (95%CI) # pairs adjusted RR (95%CI)

Chang et al.(2004) 30 0.80, (0.72 to 0.88) 26 0.88, (0.82 to 0.95)up to date (cumulative) 36 0.76, (0.68 to 0.84) 38 0.86, (0.82 to 0.91)

participants who fell at least onceMonthly rate of falling

Pooled Estimate


Table 1 Meta-regression estimates of effect of individual intervention components

interventions type Chang et al (2004) up to date (cumulative) Chang et al (2004) up to date (cumulative)

Multi-factorial 0.63 (0.49 to 0.83) 0.63 (0.54 to 0.73) 0.82 (0.72 to 0.94) 0.83 (0.76 to 0.89)Exercise 0.86 (0.73 to 1.01) 0.83 (0.73 to 0.95) 0.86 (0.75 to 0.99) 0.88 (0.80 to 0.96)Environmental modif 0.85 (0.65 to 1.11) 0.90 (0.71 to 1.14) 0.90 (0.77 to 1.05) 0.92 (0.78 to 1.07)Education 0.33 (0.09 to 1.30) 0.33 (0.09 to 1.27) 1.28 (0.95 to 1.72) 0.94 (0.74 to 1.20)

Single-factorial* 0.84 (0.75 to 0.94) 0.89 (0.83 to 0.96)

* all three types of single factorial interventions combined to be compared with multi-factorial intervention

Monthly rate of falling adjusted IRR (95%CI)

Participants who fell at least once adjusted RR (95%CI)

Meta-regression: Effect by Intervention Types


Meta-regression: Effect by Settings and Age Groups

Table 2 Meta-regression estimates of effect of other covariates

# comparison pair adjusted IRR (95%CI) # comparison pair adjusted RR (95%CI)Settingcommunity or home 29 0.74 (0.65 to 0.82) 30 0.86 (0.81 to 0.92)long-term care facilities 7 0.83 (0.66 to 1.04) 8 0.85 (0.77 to 0.95)

age group<70 2 0.90 (0.55 to 1.50) 1 1.16 (0.70 to 1.96)70-80 23 0.75 (0.65 to 0.85) 25 0.87 (0.81 to 0.93)80+ 10 0.75 (0.62 to 0.91) 12 0.85 (0.77 to 0.93)

Monthly rate of falling participants who fell at least once


Decision Analysis Modeling Tool Demonstration

GIS Mapping Tool Demonstration


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


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


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


Identify Trends in Raw Fall Rate

Fall Hospitalizations by Age Group

65-69 65-69 65-69 65-69 65-69

70-74 70-74 70-74 70-74 70-74

75-79 75-79 75-79 75-79 75-79

80-84 80-84 80-8480-84 80-84

0

10

20

30

40

50

60

1999 2000 2001 2002 2003

Year

Fa

lls p

er

10

00

• 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.


Falls among females significantly higher than Males

0

10

20

30

40

50

60

70

65 - 69 70 - 74 75 - 79 80 - 84 85+

FemalesMales

0

5

10

15

20

25

30

35

1999 2000 2001 2002 2003

Females

Males


Map Tool Interface

Menu-driven interface for visualizing data

Form fill-in dialogs for setting map parameters


Automated Map Creation


*Dobrow MJ et al. Evidence-based health policy: context and utilization. Social Science and Medicine, 2004; 58:207-17.

How are females aged 70-74 distributed?


Calculation of Non-Fatal Fall Rates -California Population 65 Years or More

6565 65

65

PopFalls i

i

ii Pop

Falls

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

10006565

PopFalls

i

i


Average Fall Rate 1999 - 2003


Avg. Standardized Fall Rate (1999-2003)


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


Healthcare Workers by Place of Work


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


Identify areas w/high proportion of females aged 70-74 and high standardized fall rate with low access to potential

providers

Assess Multivariate Spatial Relationships in Data


Exploratory Spatial Analysis: Zoom to County with high fall rate and low access to explore

spatial distribution of potential sites and seniors


Create New Map of County of Interest


Detailed Mapping of Potential Providers


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.


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


Web-based Interface for Public Use

Questions?

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