Background

Occupation 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.

Objective

This 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 setting

States 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.

Methods

Using 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.

Results

The 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).

Conclusions

Our 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.

中文翻译：

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采用弹性网络回归分析儿童虐待和忽视率的职业和生态决定因素的县级分析

背景

职业是工人身体和行为健康风险的一个已知决定因素，但大多数先前的研究都集中在失业、就业不足和工作满意度上，以了解虐待儿童的风险。

客观的

这项县级研究 (n = 278) 调查了加利福尼亚、科罗拉多、明尼苏达、俄勒冈和新墨西哥的职业和儿童虐待率与社区福祉之间的关系。

参与者和设置

之所以选择各州，是因为在 2015 年至 2020 年的五年跨度内，有可比的、公开的县级关于经证实的儿童虐待和忽视率的数据。

方法

使用美国人口普查局美国社区调查数据，我们收集了 13 个职业中就业人口的百分比。另外五个社区健康指标来自县健康排名和路线图。由于解释变量的相互关系，弹性网络线性回归用于变量选择。线性回归被用来模拟与虐待儿童率正相关的个别行业。

结果

弹性网模型选择了十个重要变量来解释儿童虐待率。重要的职业部门是农业、林业、渔业 (AFF)、制造、批发、零售、金融和教育。重要的社区指标包括住房、伤害死亡和心理健康不佳天数。在未经调整的模型中，只有 AFF 和零售业显示出更高的儿童虐待率，并且这些职业的劳动力比例增加（AFF：β = 0.03 SE = 0.01，p = 0.02；零售业：β = 0.09 SE = 0.04，p = 0.02）。

结论

我们的研究结果表明，具有较大 AFF 和零售业务的县的群体层面影响存在较高的儿童虐待率。鉴于大量先前对县域经济的研究注意到某些就业类型与文化态度、教育机会、区域偏见和其他未测量变量之间存在密切关联，未来的研究应将个人层面的数据纳入多层次框架。