University of Texas Medical Branch, Galveston, TX, USA.
University of Pretoria, South Africa.
Child Abuse Negl. 2024 Aug;154:106897. doi: 10.1016/j.chiabu.2024.106897. Epub 2024 Jun 12.
Street-migration of children is a global problem with sparse multi-level or longitudinal data. Such data are required to inform robust street-migration prevention efforts.
This study analyzes longitudinal cohort data to identify factors predicting street-migration of children - at caregiver- and village-levels.
Kenyan adult respondents (n = 575; 20 villages) actively participated in a community-based intervention, seeking to improve factors previously identified as contributing to street-migration by children.
At two time points, respondents reported street-migration of children, and variables across economic, social, psychological, mental, parenting, and childhood experience domains. Primary study outcome was newly reported street-migration of children at T2 "incident street-migration", compared to households that reported no street-migration at T1 or T2. For caregiver-level analyses, we assessed bivariate significance between variables (T1) and incident street-migration. Variables with significant bivariate associations were included in a hierarchical logistical regression model. For community-level analyses, we calculated the average values of variables at the village-level, after excluding values from respondents who indicated an incident street-migration case to reduce potential outlier influence. We then compared variables between the 5 villages with the highest incidence to the 15 villages with fewer incident cases.
In regression analyses, caregiver childhood experiences, psychological factors and parenting behaviors predicted future street-migration. Lower village-aggregated depression and higher village-aggregated collective efficacy and social curiosity appeared significantly protective.
While parenting and economic strengthening approaches may be helpful, efforts to prevent street migration by children should also strengthen community-level mental health, collective efficacy, and communal harmony.
儿童街头迁移是一个全球性问题，其多层面或纵向数据稀缺。需要此类数据来为有力的街头迁移预防工作提供信息。
本研究分析纵向队列数据，以确定在照顾者和村庄层面预测儿童街头迁移的因素。
肯尼亚成年受访者（n=575；20 个村庄）积极参与了一项基于社区的干预措施，旨在改善先前确定的导致儿童街头迁移的因素。
在两个时间点，受访者报告了儿童的街头迁移情况，以及经济、社会、心理、心理、育儿和儿童经历领域的变量。主要研究结果是在 T2 时报告的新的街头迁移儿童（“新发生的街头迁移”）与在 T1 或 T2 时报告没有街头迁移的家庭相比。对于照顾者层面的分析，我们评估了变量（T1）与新发生的街头迁移之间的双变量显著性。具有显著双变量关联的变量被纳入分层逻辑回归模型。对于社区层面的分析，我们在排除报告新发生街头迁移案例的受访者的值后，计算了村庄层面变量的平均值，以减少潜在的异常值影响。然后，我们将 5 个发病率最高的村庄与发病率较低的 15 个村庄进行了比较。
在回归分析中，照顾者的童年经历、心理因素和育儿行为预测了未来的街头迁移。较低的村庄聚集抑郁和较高的村庄聚集集体效能和社会好奇心表现出明显的保护作用。
虽然育儿和经济增强方法可能会有所帮助，但预防儿童街头迁移的工作还应加强社区层面的心理健康、集体效能和社区和谐。
