skip to main content
Washington State Institute for Public Policy
Back Button

Home hazard reduction (high-risk population)

Health Care: Falls Prevention for Older Adults
Benefit-cost methods last updated December 2017.  Literature review updated October 2017.
Open PDF
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. Among studies included in this analysis, assessors from a variety of healthcare professions (e.g. occupational therapists, registered nurses, or physiotherapists) made two visits on average to recommend and assist with modifications. The most common modifications were removing mats and rugs, elevating toilets, and providing non-slip bathmats. Some participants received assistive devices, such as a rolling walker. Participants were typically recruited while inpatients at a hospital or geriatric clinic and were typically selected due to a high risk for falls (e.g. chronic conditions or functional deterioration).

This meta-analysis includes only interventions delivered to community-dwelling older adults with a high risk of falling. We classify participants as high risk if they were selected for falls risk factors or if they were recruited from an inpatient setting. We analyze home hazard interventions for a general population of community-dwelling older adults separately.
BENEFIT-COST
META-ANALYSIS
CITATIONS
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2016). 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 $584 Benefits minus costs $3,198
Participants $74 Benefit to cost ratio $11.05
Others $91 Chance the program will produce
Indirect $2,768 benefits greater than the costs 100 %
Total benefits $3,516
Net program cost ($318)
Benefits minus cost $3,198
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
Benefits from changes to:1 Benefits to:
Taxpayers Participants Others2 Indirect3 Total
Health care associated with falls $584 $74 $91 $292 $1,040
Labor market earnings associated with falls $0 $0 $0 $2,635 $2,635
Adjustment for deadweight cost of program $0 $0 $0 ($159) ($159)
Totals $584 $74 $91 $2,768 $3,516
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $318 2016 Present value of net program costs (in 2016 dollars) ($318)
Comparison costs $0 2016 Cost range (+ or -) 40 %
Per-participant cost estimates are based on weighted average costs in the included studies. We estimate staff hours including home visits, transportation, telephone contacts, meetings, and training. If staff time is unreported by authors, we assume home visits lasted one hour and required 30 minutes of travel time; follow-up phone calls lasted 20 minutes; meetings lasted 30 minutes; and follow-up letters required 15 minutes time, on average. For the included study that provided training (Campbell et al., 2005), 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) and assistive devices. We use 2016 U.S. Bureau of Labor Statistics information (retrieved March 2018) to estimate Washington State mean wages for the providers represented in the studies, including occupational therapists, registered nurses, physical therapists, healthcare social workers, medical secretaries, physicians, and maintenance workers. We 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.
Estimated Cumulative Net Benefits Over Time (Non-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 non-discounted dollars to simplify the “break-even” point from a budgeting perspective. 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.

^^WSIPP does not include this outcome when conducting benefit-cost analysis for this program.

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.

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
Fall-related hospitalization^^ 75 1 30 0.000 0.344 75 n/a n/a n/a 0.000 1.000
Falls 75 3 498 0.586 0.091 75 1.000 0.000 76 0.586 0.001

Citations Used in the Meta-Analysis

Campbell, A.J., Robertson, M.C., La Grow, S.J., Kerse, N.M., Sanderson, G.F., Jacobs, R.J., . . . Hale, L.A. (2005). Randomised controlled trial of prevention of falls in people aged ≥75 with severe visual impairment: the VIP trial. BMJ, 331(7520), 817.

Cumming, R.G., Thomas, M., Szonyi, G., Salkeld, G., O'Neill, E., Westbury, C., & Frampton, G. (1999). Home visits by an occupational therapist for assessment and modification of environmental hazards: A randomized trial of falls prevention. Journal of the American Geriatrics Society, (47) 12, 1397-1402.

Nikolaus, T., & Bach, M. (2003). Preventing falls in community-dwelling frail older people using a home intervention team (HIT): Results from the randomized Falls-HIT Trial. Journal of the American Geriatrics Society, 51 (3), 300-305.

Pardessus, V., Puisieux, F., Di Pompeo, C., Gaudefroy, C., Thevenon, A., & Dewailly, P. (2002). Benefits of home visits for falls and autonomy in the elderly: a randomized trial study. American Journal of Physical Medicine & Rehabilitation, 81 (4), 247-252.