The impact of COVID-19 on child maltreatment-related medical encounters and system responses using linked administrative data

项目摘要/摘要： 由于新冠肺炎，学校和日托机构关闭，失业率飙升- 19大流行，专家担心不仅虐待儿童的发生率增加，而且 新的或反复发生的虐待案件没有被发现是因为三分之二的 向儿童保护服务机构(CPS)报告的是专业人员。链接的管理数据 (即为提供包括保健和儿童保护在内的服务而收集的记录)可以 通过将CPS数据库与以下各项结合，克服仅依赖CPS报告的挑战 医疗记录。医疗记录已被确定为测量的有用数据来源 虐待儿童。这个应用程序的目标是确定新冠肺炎是否以及如何 大流行影响了与虐待儿童有关的医疗遭遇的发生率， 对诊断出的虐待案例的系统反应。理由是虐待有 已确定对儿童的重大影响，如果虐待在此期间有所增加， 分配资源以应对这种有害形式的创伤将是重要的。建议数 研究将利用自然实验来关注两个特定的目标：1)比较 新冠肺炎实施前和期间与虐待儿童相关的医疗遭遇发生率； 以及2)确定对诊断的医疗的社会和法律干预/系统反应 在大流行之前和期间遭遇虐待儿童的情况。对于第一个目标，一个 将使用中断的时间序列来确定被诊断为虐待的频率 初级保健、急诊科和住院医疗遭遇。对于第二个 AIM、中断时间序列和Logistic回归用于确定百分比和 向CPS报告被诊断为与虐待相关的医疗遭遇的可能性。这个 拟议的研究具有创新性，因为它使用了来自多个来源的数据，并包括 几种类型的医疗接触。这项拟议的研究具有重要意义，因为 虐待的后果很严重，但很难衡量。结果将与稳健的 新冠肺炎大流行是否增加了儿童的发病率和严重性 与虐待有关的医疗遭遇和改变了#年的社会和法律干预 对这类医疗遭遇的反应。最终，这样的知识有可能识别出 社区层面的事件如何影响虐待儿童，为公共政策和资源提供信息 分配、专业培训和未来研究。

Project Summary/Abstract: With schools and daycare facilities closed and unemployment rates soaring due to the COVID- 19 pandemic, experts worry not only that the incidence of child maltreatment has increased but that that new or recurrent cases of maltreatment have gone undetected because two-thirds of reports to child protective services (CPS) are made by professionals. Linked administrative data (i.e., records collected for the provision of services, including health and child protection) can overcome the challenges of relying solely on CPS reports by joining the CPS database with medical records. Medical records have been identified as a useful data source for measuring child maltreatment. The objective of this application is to determine if and how the COVID-19 pandemic has impacted the incidence of child maltreatment-related medical encounters and the system responses to diagnosed cases of maltreatment. The rationale is maltreatment has significant identified impacts on children and if maltreatment has increased during this time, it will be important to allocate resources to address this detrimental form of trauma. The proposed study will take advantage of a natural experiment to focus on two specific aims: 1) Compare the incidence rates of child maltreatment-related medical encounters, before and during COVID-19; and 2) Determine the social and legal interventions/system responses to diagnosed medical encounters of child maltreatment before and during the pandemic. For the first aim, an interrupted time series will be used to identify the frequency of diagnosed maltreatment in primary care, emergency department, and hospitalization medical encounters. For the second aim, interrupted time series and logistic regression will be used to determine the percentage and likelihood of diagnosed maltreatment-related medical encounters being reported to CPS. The proposed research is innovative because it uses data from multiple sources and includes several types of medical encounters. The proposed research is significant because maltreatment is highly consequential but difficult to measure. Results will identify with robust empirical evidence if the pandemic COVID-19 has increased the incidence and severity of child maltreatment-related medical encounters and changed the social and legal interventions in response to such medical encounters. Ultimately, such knowledge has the potential to identify how community-level events impact child maltreatment, informing public policy, resource allocation, professional training, and future research.