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Washington State Institute for Public Policy
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Home hazard reduction (general population)

Health Care: Falls Prevention for Older Adults
Benefit-cost methods last updated December 2023.  Literature review updated October 2017.
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Home hazard reduction programs for falls prevention aim to prevent falls by facilitating modifications to the physical environment. In a typical program, participants receive home hazard assessments and assistance with purchasing or installing modifications. In this meta-analysis, most home assessors were registered nurses who made one home visit and offered free installation of modifications. The most common modifications were installing bathroom grab bars, installing step stripping, and stabilizing rugs.

This meta-analysis includes only interventions delivered to community-dwelling older adults. It excludes interventions delivered to participants with a high risk of falling. We analyze home hazard interventions for community-dwelling older adults at high risk separately.
For an overview of WSIPP's Benefit-Cost Model, please see this guide. The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2022). The chance the benefits exceed the costs are derived from a Monte Carlo risk analysis. The details on this, as well as the economic discount rates and other relevant parameters are described in our Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $1 Benefits minus costs ($271)
Participants $0 Benefit to cost ratio ($0.46)
Others $0 Chance the program will produce
Indirect ($87) benefits greater than the costs 17%
Total benefits ($86)
Net program cost ($185)
Benefits minus cost ($271)

The effect size for this outcome indicates an incidence rate ratio (IRR), not a standardized mean difference effect size. An IRR less than one indicates a lower rate of the outcome in the treatment group relative to the comparison group; an IRR greater than one indicates a higher rate of the outcome. The treatment n for this outcome represents person-years.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic in order to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the types of program impacts that were measured in the research literature (for example, crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases. See Estimating Program Effects Using Effect Sizes for additional information.

Adjusted effect sizes are used to calculate the benefits from our benefit cost model. WSIPP may adjust effect sizes based on methodological characteristics of the study. For example, we may adjust effect sizes when a study has a weak research design or when the program developer is involved in the research. The magnitude of these adjustments varies depending on the topic area.

WSIPP may also adjust the second ES measurement. Research shows the magnitude of some effect sizes decrease over time. For those effect sizes, we estimate outcome-based adjustments which we apply between the first time ES is estimated and the second time ES is estimated. We also report the unadjusted effect size to show the effect sizes before any adjustments have been made. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Treatment age No. of effect sizes Treatment N Adjusted effect sizes(ES) and standard errors(SE) used in the benefit - cost analysis Unadjusted effect size (random effects model)
First time ES is estimated Second time ES is estimated
ES SE Age ES SE Age ES p-value
76 2 731 0.998 0.076 76 1.000 0.000 77 0.998 0.981
1In addition to the outcomes measured in the meta-analysis table, WSIPP measures benefits and costs estimated from other outcomes associated with those reported in the evaluation literature. For example, empirical research demonstrates that high school graduation leads to reduced crime. These associated measures provide a more complete picture of the detailed costs and benefits of the program.

2“Others” includes benefits to people other than taxpayers and participants. Depending on the program, it could include reductions in crime victimization, the economic benefits from a more educated workforce, and the benefits from employer-paid health insurance.

3“Indirect benefits” includes estimates of the net changes in the value of a statistical life and net changes in the deadweight costs of taxation.
Detailed Monetary Benefit Estimates Per Participant
Affected outcome: Resulting benefits:1 Benefits accrue to:
Taxpayers Participants Others2 Indirect3 Total
Falls Health care associated with falls $1 $0 $0 $1 $2
Mortality associated with falls $0 $0 $0 $5 $5
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($93) ($93)
Totals $1 $0 $0 ($87) ($86)
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Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $157 2016 Present value of net program costs (in 2022 dollars) ($185)
Comparison costs $0 2016 Cost range (+ or -) 20%
Per-participant cost estimates are based on weighted average costs in the included studies. We estimate staff hours including home visits, transportation, and training. We assume each home assessment visit lasted one hour and required 30 minutes of travel time. We assume follow-up phone calls lasted 20 minutes and follow-up letters required 15 minutes time, on average. For the included study that provided training (Stevens et al., 2001), we include the cost of a two-day training, provider time spent in attendance, and trainer compensation. We also include the cost of home modifications (materials and labor). We use 2016 U.S. Bureau of Labor Statistics (retrieved March 2018) information to estimate Washington State mean wages for home health care registered nurses and for maintenance workers and increase wages by a factor of 1.441 to account for the cost of employee benefits.
The figures shown are estimates of the costs to implement programs in Washington. The comparison group costs reflect either no treatment or treatment as usual, depending on how effect sizes were calculated in the meta-analysis. The cost range reported above reflects potential variation or uncertainty in the cost estimate; more detail can be found in our Technical Documentation.
Benefits Minus Costs
Benefits by Perspective
Taxpayer Benefits by Source of Value
Benefits Minus Costs Over Time (Cumulative Discounted Dollars)
The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in discounted dollars. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.

Citations Used in the Meta-Analysis

Fitzharris, M.P., Day, L., Fildes, B., Lord, S.R., & Gordon, I. (2010). The Whitehorse NoFalls trial: Effects on fall rates and injurious fall rates. Age and Ageing, 39(6), 728-733.

Stevens, M., Holman, C.D.A.J., Bennett, N., & de Klerk, N. (2001). Preventing falls in older people: Outcome evaluation of a randomized controlled trial. Journal of the American Geriatrics Society, 49(11), 1448-1455.