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Health Services Research & Development

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2015 HSR&D/QUERI National Conference Abstract

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1091 — Geographic Variation and Community Attributes Affecting VA Mental Health Appointment Wait Times

Bollinger MJ, South Texas Veterans Health Care System, Audie L. Murphy VAMC, San Antonio TX; Finley EP, South Texas Veterans Health Care System, Audie L. Murphy VAMC, San Antonio TX; Pugh MJ, South Texas Veterans Health Care System, Audie L. Murphy VAMC, San Antonio TX; Kazanis W, South Texas Veterans Health Care System, Audie L. Murphy VAMC, San Antonio TX;

In response to the Veterans Choice Act and related efforts to reduce wait times for Veterans' health care services, we explored geographical variation in delayed access to mental health care and potential explanatory variables at the community level.

We extracted wait-times data for mental health appointments for all, established and new patients from the VHA Support Service Center (VSSC) and geographic data from the Health Resources and Services Administration (HRSA) Area Health Resources Files (AHRF). We aggregated data at the system (market)-level, calculated a weight matrix to measure the "nearness" of spatial units, then calculated the Moran's I statistic to identify significant clustering of wait times. Finally, we estimated a spatial regression model with correction for spatial dependency. Variables in the spatial regression model included supply and demand indicators for mental health care and local community variables associated with care seeking and health care outcomes.

A significant global Moran's I value of 0.7 indicated a strong spatial relationship in wait time across all groups. Local Moran's I values indicated the presence of geographic areas with high (hot spots) and low (cold spots) VA wait times. Results of the spatial auto-regression (SAR) models across patient groups show that local environmental variables, including availability of community mental health care providers, whether a location is a retirement destination, household income, population density, percentage uninsured in area, percentage of veterans with a disability, and percentage of veterans in the population, significantly predict local wait times (p-value < 0.01).

Our analysis identified considerable geographic variation in VA mental health appointment wait times and points to community-level explanatory factors. We further identified certain community environments that contribute to excessive wait times, likely requiring additional efforts to alleviate stress on the VA mental health system of care.

This research identifies locations where unmet mental health service need is problematic as well as community attributes associated with longer wait-times. Our results can inform more targeted implementation of the Veterans Choice Act and other efforts to increase Veterans' access to mental health care in areas where existing health care systems are under stress.