Comparative Effectiveness of Two Models of Depression Services Quality Improvement in Health and Community Sectors.

OBJECTIVE: The effectiveness of community coalition building and program technical assistance was compared in implementation of collaborative care for depression among health care and community sector clients. METHODS: In under-resourced communities, within 93 programs randomly assigned to coalition building (Community Engagement and Planning) or program technical assistance (Resources for Services) models, 1,018 clients completed surveys at baseline and at six, 12, or 36 months. Regression analysis was used to estimate intervention effects and intervention-by-sector interaction effects on depression, mental health-related quality of life, and community-prioritized outcomes and on services use. RESULTS: For outcomes, there were few significant intervention-by-sector interactions, and stratified findings suggested benefits of coalition building in both sectors. For services use, at 36 months, increases were found for coalition building in primary care visits, self-help visits, and appropriate treatment for community clients and in community-based services use for health care clients. CONCLUSIONS: Relative to program technical assistance, community coalition building benefited clients across sectors and shifted long-term utilization across sectors.

A Community-Partnered, Participatory, Cluster-Randomized Study of Depression Care Quality Improvement: Three-Year Outcomes.

OBJECTIVE: Community Partners in Care, a community-partnered, cluster-randomized trial with depressed clients from 93 Los Angeles health and community programs, examined the added value of a community coalition approach (Community Engagement and Planning [CEP]) versus individual program technical assistance (Resources for Services [RS]) for implementing depression quality improvement in underserved communities. CEP was more effective than RS in improving mental health-related quality of life, reducing behavioral health hospitalizations, and shifting services toward community-based programs at six months. At 12 months, continued evidence of improvement was found. This study examined three-year outcomes. METHODS: Among 1,004 participants with depression who were eligible for three-year follow-up, 600 participants from 89 programs completed surveys. Multiple regression analyses estimated intervention effects on poor mental health-related quality of life and depression, physical health-related quality of life, behavioral health hospital nights, and use of services. RESULTS: At three years, no differences were found in the effects of CEP versus RS on depression or mental health-related quality of life, but CEP had modest effects in improving physical health-related quality of life and reducing behavioral health hospital nights, and CEP participants had more social- and community-sector depression visits and greater use of mood stabilizers. Sensitivity analyses with longitudinal modeling reproduced these findings but found no significant differences between groups in change from baseline to three years. CONCLUSIONS: At three years, CEP and RS did not have differential effects on primary mental health outcomes, but CEP participants had modest improvements in physical health and fewer behavioral health hospital nights.

USE OF EXPERT RATINGS AS SAMPLING STRATA FOR A MORE COST-EFFECTIVE PROBABILITY SAMPLE OF A RARE POPULATION.

We consider situations in which externally observable characteristics allow experts to quickly categorize individual households as likely or unlikely to contain a member of a rare target population. This classification can form the basis of disproportionate stratified sampling such that households classified as "unlikely" are sampled at a lower rate than those classified as "likely," thereby reducing screening costs. Design weights account for this approach and allow unbiased estimates for the target population.We demonstrate that with sensitivity and specificity of expert classification at least 70%, and ideally at least 80%, our approach can economically increase effective sample size for a rare population. We develop heuristics for implementing this approach and demonstrate that sensitivity drives design effects and screening costs whereas specificity only drives the latter. We demonstrate that the potential gains from this approach increase as the target population becomes rarer. We further show that for most applications, unlikely strata should be sampled at 1/6 to ½ the rate of likely strata.This approach was applied to a survey of Cambodian immigrants in which the 82% of households rated "unlikely" were sampled at » the rate as "likely" households, reducing screening from 9.4 to 4.0 approaches per complete. Sensitivity and specificity were 86% and 91% respectively. Weighted estimation had a design effect of 1.26 so screening costs per effective sample size were reduced 47%. We also note that in this instance, expert classification appeared to be uncorrelated with survey outcomes of interest among eligibles.