The relationship between community public health, behavioral health service accessibility, and mass incarceration.
The relationship between community public health, behavioral health service accessibility, and mass incarceration.
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DOI：
10.1186/s12913-022-08306-6
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发表时间：
2022-07-29
影响因子：
2.8
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中科院分区：
文献类型：
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社区医疗保健服务可及性与监禁之间的关系是一个重要但尚未被广泛理解的现象。社区行为健康和刑事法律系统是分开对待的，这就产生了对抗大规模监禁和扩大现有服务的竞争需求。因此，行为健康服务、人口统计和社区因素与监禁率之间的关系没有得到很好的解决。了解监禁的潜在驱动因素，包括获得社区服务的机会，对于减少进入法律体系和减少再犯是必要的。这项研究确定了县级人口、社会经济、医疗保健服务的可用性/可及性以及预测美国人均监狱人口的刑事法律特征。每年有超过1000万人通过美国监狱，增加了应对这一挑战的紧迫性。为我们的模型选择变量是分阶段进行的。该研究首先确定潜在的描述符，然后使用机器学习技术选择非共线性变量来预测县监狱的人均人口。然后将Beta回归应用于美国所有3141个县的全国可用数据，以确定预测县监狱人口规模的因素。数据来源包括维拉研究所的监禁数据库、罗伯特·伍德·约翰逊基金会的县健康排名和路线图、统一犯罪报告和美国人口普查。人均精神病医生减少(z-score = -2.16; p =。031)，医疗补助支付的药物治疗比例较低(-3.66;p <。0.001)，人均医疗费用较高（5.71;p < 0.001）。0.001)，一个月内身体不健康天数较高(8.6;p <。001)，高中毕业率较低(-4.05;p <。0.001)，较小的县规模（-2.66,p = 0.008; -2.71, p = 0.007；中型和大型县分别与小型县相比）和更多的人均警察（8.74,p < 0.001）。001)与较高的人均监狱人口有关。在控制了其他因素后，暴力犯罪率并不能预测监禁率。人口较少、高中未毕业人口比例较高、健康相关问题较多、社区治疗服务较少的县，人均监狱人口可能更高。增加获得服务的机会，包括精神卫生服务提供者，以及提高药物治疗和保健的可负担性，可能有助于降低监禁率。
The relationship between healthcare service accessibility in the community and incarceration is an important, yet not widely understood, phenomenon. Community behavioral health and the criminal legal systems are treated separately, which creates a competing demand to confront mass incarceration and expand available services. As a result, the relationship between behavioral health services, demographics and community factors, and incarceration rate has not been well addressed. Understanding potential drivers of incarceration, including access to community-based services, is necessary to reduce entry into the legal system and decrease recidivism. This study identifies county-level demographic, socioeconomic, healthcare services availability/accessibility, and criminal legal characteristics that predict per capita jail population across the U.S. More than 10 million individuals pass through U.S. jails each year, increasing the urgency of addressing this challenge. The selection of variables for our model proceeded in stages. The study commenced by identifying potential descriptors and then using machine learning techniques to select non-collinear variables to predict county jail population per capita. Beta regression was then applied to nationally available data from all 3,141 U.S. counties to identify factors predicting county jail population size. Data sources include the Vera Institute’s incarceration database, Robert Wood Johnson Foundation’s County Health Rankings and Roadmaps, Uniform Crime Report, and the U.S. Census. Fewer per capita psychiatrists (z-score = -2.16; p = .031), lower percent of drug treatment paid by Medicaid (-3.66; p < .001), higher per capita healthcare costs (5.71; p < .001), higher number of physically unhealthy days in a month (8.6; p < .001), lower high school graduation rate (-4.05; p < .001), smaller county size (-2.66, p = .008; -2.71, p = .007; medium and large versus small counties, respectively), and more police officers per capita (8.74; p < .001) were associated with higher per capita jail population. Controlling for other factors, violent crime rate did not predict incarceration rate. Counties with smaller populations, larger percentages of individuals that did not graduate high school, that have more health-related issues, and provide fewer community treatment services are more likely to have higher jail population per capita. Increasing access to services, including mental health providers, and improving the affordability of drug treatment and healthcare may help reduce incarceration rates.
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DOI：
10.1016/s2468-2667(20)30283-8
发表时间：
2021-04
期刊：
The Lancet. Public health
影响因子：
--
作者：
Kajeepeta S;Mauro PM;Keyes KM;El-Sayed AM;Rutherford CG;Prins SJ
通讯作者：
Prins SJ
影响因子：
1.9
作者：
Gao YN
通讯作者：
Gao YN
DOI：
10.1186/s13012-021-01095-2
发表时间：
2021-03-29
期刊：
Implementation science : IS
影响因子：
--
作者：
Johnson JE;Viglione J;Ramezani N;Cuellar AE;Hailemariam M;Rosen R;Breno A;Taxman FS
通讯作者：
Taxman FS
影响因子：
4.9
作者：
Baldwin, LM;Patanian, MM;Hart, LG
通讯作者：
Hart, LG
影响因子：
12.7
作者：
Kajeepeta, Sandhya;Rutherford, Caroline G.;Prins, Seth J.
通讯作者：
Prins, Seth J.