Citation

Godoy-Garraza L, Campos S, Walrath C. Health Serv. Outcomes Res. Methodol. 2021; 21(4): 510-526.

Copyright

(Copyright © 2021, Kluwer Academic Publishers)

DOI

10.1007/s10742-021-00242-y

PMID

unavailable

Abstract

Evaluating the impact of suicide prevention programs is more challenging in smaller counties, where reliable estimates of suicide rates are difficult to obtain. This includes rural and tribal communities disproportionately burdened by suicide. We describe a novel application of a spatiotemporal model developed for disease mapping to assess the impact of suicide prevention in small areas. As an example, we use small counties exposed to the Garrett Lee Smith (GLS) program. Specifically, the impact of suicide prevention programming on suicide-related hospital use among youth between 2008 and 2018 was explored with this novel method in a sample of rural counties across 10 states. While, on average, suicide-related hospital use was close to what would be expected in the absence of the program, there was considerable variation across counties. For example, among a group of counties in South Dakota, there was a substantial decrease in suicide-related hospital use among youth for several years after the start of exposure. Subsequent exploration suggested that implementing a high proportion of longer trainings was associated with membership in this favorably impacted group. The illustrated approach can be used to estimate the impact of interventions targeting other outcomes that are relevant but relatively infrequent. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Language: en

Keywords

human; Arizona; suicide; Suicide; New Mexico; Massachusetts; poverty; hospitalization; Oregon; unemployment; African American; Kentucky; Washington; rural area; health program; Hispanic; health insurance; social status; ethnic difference; reliability; race difference; health care utilization; Caucasian; Michigan; spatiotemporal analysis; mathematical analysis; Article; South Dakota; Bayes theorem; Nebraska; Small-area analysis; Nevada; Disease mapping; Black person; program effectiveness; mortality rate; household income; machine learning; Machine learning; Impact evaluation