Transcript Tools for Efficient Allocation of Fall

Tools for Efficient Allocation of
Fall-Prevention Resources
Shinyi Wu, PhD
Adrian Overton, MPA
RAND Roybal Center for Health Policy Simulation
March 10, 2006
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 costeffectiveness 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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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)
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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)
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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
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Why A Policy Tool?
• Over the past decade, there has been an explosion
in the availability of systematic reviews and metaanalyses 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 populationlevel
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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.
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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/
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Sure, all of these things worry our senior citizens,
but…
• Falls
From 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=co
m&td=20020510000105
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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 longterm care facility for a year or longer
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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.
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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 fallprevention interventions to
determine those that best
meet their needs; in particular, those most likely to
maximize the impact of limited resources
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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
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Evidence Review and Meta-Analysis
• Previous Study:
– Chang et al. “Interventions for the prevention of
falls in older adults: systematic review and metaanalysis 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
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Results
• 40 articles that met all inclusion in Chang’s article.
• 13 new articles were identified for the meta-analysis
Pooled Estimate
pooled estimates of effect of fall prevention intervention
Monthly rate of falling
# pairs
adjusted IRR (95%CI)
Chang et al.(2004)
up to date (cumulative)
30
36
0.80, (0.72 to 0.88)
0.76, (0.68 to 0.84)
participants who fell at least once
# pairs
adjusted RR (95%CI)
26
38
0.88, (0.82 to 0.95)
0.86, (0.82 to 0.91)
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Meta-regression:
Effect by Intervention Types
Table 1 Meta-regression estimates of effect of individual intervention components
Monthly rate of falling
adjusted IRR (95%CI)
Participants who fell at least once
adjusted RR (95%CI)
interventions type
Chang et al (2004)
up to date (cumulative) Chang et al (2004)
up to date (cumulative)
Multi-factorial
Exercise
Environmental modif
Education
0.63 (0.49 to 0.83)
0.86 (0.73 to 1.01)
0.85 (0.65 to 1.11)
0.33 (0.09 to 1.30)
0.63 (0.54 to 0.73)
0.83 (0.73 to 0.95)
0.90 (0.71 to 1.14)
0.33 (0.09 to 1.27)
0.83 (0.76 to 0.89)
0.88 (0.80 to 0.96)
0.92 (0.78 to 1.07)
0.94 (0.74 to 1.20)
Single-factorial*
0.82 (0.72 to 0.94)
0.86 (0.75 to 0.99)
0.90 (0.77 to 1.05)
1.28 (0.95 to 1.72)
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
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Meta-regression:
Effect by Settings and Age Groups
Table 2 Meta-regression estimates of effect of other covariates
Monthly rate of falling
participants who fell at least once
# comparison pair adjusted IRR (95%CI) # comparison pair adjusted RR (95%CI)
Setting
community or home
long-term care facilities
29
7
0.74 (0.65 to 0.82)
0.83 (0.66 to 1.04)
30
8
0.86 (0.81 to 0.92)
0.85 (0.77 to 0.95)
age group
<70
70-80
80+
2
23
10
0.90 (0.55 to 1.50)
0.75 (0.65 to 0.85)
0.75 (0.62 to 0.91)
1
25
12
1.16 (0.70 to 1.96)
0.87 (0.81 to 0.93)
0.85 (0.77 to 0.93)
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Decision Analysis Modeling Tool
Demonstration
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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
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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
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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
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Identify Trends in Raw Fall Rate
Fall Hospitalizations by Age Group
60
Falls per 1000
80-84
80-84
80-84
80-84
80-84
50
•
Falls per 1000 persons aged 65+
40
•
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.
30
75-79
75-79
75-79
75-79
75-79
20
70-74
70-74
70-74
70-74
70-74
65-69
65-69
65-69
65-69
65-69
10
0
1999
2000
2001
2002
2003
Year
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Falls among females significantly higher than Males
70
35
60
30
50
25
Females
Males
Females
40
20
30
15
20
10
10
5
0
0
65 - 69
70 - 74
75 - 79
80 - 84
85+
Males
1999
2000
2001
2002
2003
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Map Tool Interface
Menu-driven interface for
visualizing data
Form fill-in dialogs for
setting map parameters
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Automated Map Creation
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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.
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Calculation of Non-Fatal Fall Rates California Population 65 Years or More
Average Fall Rate =
Falls 65 1000
Pop65
i
i
SFR =
   Falls65 

 i


Falls 65i     Pop65  Pop65i 




 i

• 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
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Average Fall Rate 1999 - 2003
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Avg. Standardized Fall Rate (1999-2003)
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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
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Healthcare Workers by Place of Work
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Where are high number of seniors and
low potential access to providers?
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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
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Exploratory Spatial Analysis: Zoom to County
with high fall rate and low access to explore
spatial distribution of potential sites and seniors
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Create New Map of County of Interest
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Detailed Mapping of Potential Providers
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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.
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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
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Web-based Interface for Public Use
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Questions?