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RAND Roybal Center for Health Policy Simulation

Tools for Efficient Allocation of Falls Prevention Resources

Objectives

Develop decision-support tools for policy makers to identify needs and gaps in falls prevention and to assist in best allocation of resources to prevent falls.

1. Develop a geographic information system (GIS) based tool (called GIS tool thereafter) that provides mapping and spatial analysis capabilities for identifying statistically significant clusters and spatial relationships among multiple variables related to falls and prevention planning.
2. Develop a cost-effectiveness analysis tool (called the CE tool thereafter) to help decision makers analyze and compare various interventions in order to allocate fall prevention resources cost-effectively.
3. Use the tools to enhance long-term planning for the projected number of older adults at risk for falls, for workforce planning, and for capacity building among community-based organizations and governmental agencies.

Progress to Date

1. The GIS tool
   Using this year of funding, we have added new data, and analytical and reporting capabilities to the tool. We received 10 years of data from the California Department of Health Office of Statewide Health Planning and Development (OSHPD) on patient discharges from all hospitals in the state. This data has been incorporated into the database to map the place-of-residence at the zip code level of all seniors who were hospitalized for a fall injury. These data will be used to provide evidence on where falls occur at the local level and the ability to look at possible spatial correlations with other demographic and environmental variables. We have also added a spatial cluster detection capability to the tool to determine where fall events have clustered over time at the zip code level. These "hotspots", detected by a spatial scan statistic, identify areas of interest for implementing fall prevention programs and evaluating cost-effectiveness.
   Several new output and reporting capabilities were added to the tool to allow users to select areas of interest and produce output in the form of demographic descriptions and charts for the area selected. The purpose for adding the cluster detection and reporting capabilities were to allow decision-makers to prioritize among the many possible areas in which fall prevention resources could be allocated, and to focus the cost-effective analysis of implementing a fall prevention program in the identified area. We believe this type of small-area targeting of communities/areas that contribute substantially to the total number of fall injuries will be more effective in reducing falls among seniors overall.
2. The CE tool
   We replaced the nominal values with empirical evidence in our CE Tool to make accurate and sensible estimation of the cost and effectiveness of various interventions. On the basis of evidence review work done by the Southern California Evidence-based Practice Center (SCEPC), we conducted a comprehensive updated literature review of fall-prevention articles published after 2001. We then developed an Excel spreadsheet to synthesize the economic evaluation evidence and derived empirical values for the cost parameters in the tool. The literature review also provides the evidence table for our meta-analysis in estimating the effectiveness parameters. However, several limitations are encountered during the data collection and parameterization, which is documented elsewhere.
   Our meta-analysis assessed the relative effectiveness of different types of interventions to prevent falls. Our work updates the SCEPC's findings by incorporating the new evidence published after 2001. We performed the meta-analysis analysis separately for the incident rate ratio and risk ratio between the intervention group and control group. We then fitted a meta-regression to assess the relative effectiveness of various intervention types (e.g., exercise, risk assessment). In addition, we assessed the effects of the interventions by settings (community versus long-term care facilities) by age groups. These analyses allowed us to improve our CE tool by incorporating the evidence-based effectiveness estimates.

Policy Tools

1. A GIS tool for enhancing falls surveillance and prevention planning.
2. A decision-analytic tool to compare cost-effectiveness of fall-prevention interventions.

Conference Presentation

Wu S, Kramer BJ, Sarkisyan S, Overton, A. Is an Infrastructure in Place to Support Fall Prevention in California? Gerontological Society of American Annual Meeting, Orlando, FL, November 18 to 22, 2005.

Invited Talks

Enhancing Falls Surveillance and Community Planning Activities with a Geographic Information System based Tool. Fall Prevention Center of Excellence Advisory Board Meeting, University of Southern California, October 2005.

Tools for Efficient Allocation of Fall-Prevention Resources. Center for Health Policy and Center for Primary Care and Outcomes Research, Stanford University, March 2006.

A Look at the Future. California Senior Injury Prevention Conference. Organized by Alameda County Public Health, Center for Injury Prevention, and Senior Injury Prevention Partnership, Orange County, CA, May 2006.

Next Steps

We will turn our attentions to our objective #3 — using the tools to enhance long-term planning for fall prevention. Specifically, we will use the GIS tool to identify high-risk zones for fall injuries and project geographic-based fall epidemics by population growth in California. For the CE tool, we will perform uncertainty analysis to determine the effects of uncertainty of the numerous CE parameters on the cost-effectiveness estimates. This analysis will allow us to simplify the model structure to make it more feasible in validation with substance experts and published studies. Then the tool can be used for CE estimation and to inform key parameters for further research. In addition to the Roybal funding, we will seek additional grant support for further tool development (e.g. developing user-interface, combining the two tools into one that allows assessing cost-effectiveness of potential interventions in specific geographic contexts) and dissemination of the tools.