County-level analysis on occupation and ecological determinants of child abuse and neglect rates employing elastic net regression

Abstract

BackgroundOccupation is a known determinant of worker physical and behavioral health risk, yet most previous studies have focused on unemployment, underemployment, and job satisfaction to understand child maltreatment risk. ObjectiveThis county-level study (n = 278) investigated the association between occupation and child maltreatment rates and community well-being in California, Colorado, Minnesota, Oregon, and New Mexico. Participants and settingStates were selected due to having comparable, publicly available county-level data on substantiated child abuse and neglect rates within a five-year span between 2015 and 2020. MethodsUsing US Census Bureau American Community Survey data, we collected percentages of the employed population among 13 occupations. Five additional community health indicators came from the County Health Rankings and Roadmaps. Elastic net linear regression was used for variable selection and because of explanatory variables' interrelationships. Linear regression was used to model individual industries positively associated with child abuse rates. ResultsThe elastic net model selected ten important variables in explaining child maltreatment rates. Important occupational sectors were agriculture, forestry, fishing (AFF), manufacturing, wholesale, retail, finance, and education. Important community indicators included housing, injury deaths, and poor mental health days. Only AFF and retail showed greater child abuse rates with increasing percentages of the workforce in these occupations in unadjusted models (AFF: β = 0.03 SE = 0.01, p = 0.02; Retail: β = 0.09 SE = 0.04, p = 0.02). ConclusionsOur findings suggest group-level effects of counties with a larger AFF and retail presence experiencing higher child maltreatment rates. Given that numerous prior studies of county economies note the strong associations of certain employment types with cultural attitudes, educational opportunities, regional biases, and other unmeasured variables, future studies should incorporate individual level data in a multilevel framework.