PDF(Pubmed)
Abstract：
UNASSIGNED: Despite affecting up to 20% of women and being the leading cause of preventable deaths during the perinatal and postpartum period, maternal mental health conditions are chronically understudied. This study is the first to identify spatial patterns in perinatal mental health conditions, and relate these patterns to place-based social and environmental factors that drive cluster development.
UNASSIGNED: We performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters.
UNASSIGNED: The most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters.
UNASSIGNED: Results provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.
UNASSIGNED: We performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters.
UNASSIGNED: The most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters.
UNASSIGNED: Results provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.
摘要：
尽管影响了多达20％的妇女，并且是围产期和产后可预防死亡的主要原因，母亲的心理健康状况长期被研究不足。这项研究是第一个确定围产期心理健康状况的空间模式，并将这些模式与推动集群发展的基于地点的社会和环境因素联系起来。
■我们对围产期情绪和焦虑症（PMAD）的急诊科（ED）就诊进行了空间聚类分析，严重精神疾病（SMI），2016年至2019年在北卡罗来纳州的SatScan中使用Poisson模型和孕妇妊娠精神障碍（MDP）。Logistic回归用于检查患者和社区水平因素与高风险集群之间的关联。
■所有三个结果的最重要的空间聚类集中在西部较小的城市地区，皮埃蒙特中部,和该州的沿海平原地区，某些集群位置的赔率比大于3。个别因素(例如，年龄,种族,种族)和上下文因素(例如，种族和社会经济隔离，城市化)与高风险集群相关。
■结果提供了有关孕产妇心理健康疾病负担较高的高危人群的重要背景和空间信息，并可以更好地为扩大孕产妇心理健康服务的目标地点提供信息。
■我们对围产期情绪和焦虑症（PMAD）的急诊科（ED）就诊进行了空间聚类分析，严重精神疾病（SMI），2016年至2019年在北卡罗来纳州的SatScan中使用Poisson模型和孕妇妊娠精神障碍（MDP）。Logistic回归用于检查患者和社区水平因素与高风险集群之间的关联。
■所有三个结果的最重要的空间聚类集中在西部较小的城市地区，皮埃蒙特中部,和该州的沿海平原地区，某些集群位置的赔率比大于3。个别因素(例如，年龄,种族,种族)和上下文因素(例如，种族和社会经济隔离，城市化)与高风险集群相关。
■结果提供了有关孕产妇心理健康疾病负担较高的高危人群的重要背景和空间信息，并可以更好地为扩大孕产妇心理健康服务的目标地点提供信息。
