小儿哮喘管理的预测因素:利用地理决定因素确定可操作的结果。

Enbal Shacham,Stephen E Scroggins,Adam Gilmore,Jane Cheng,Rosalinda Nava
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引用次数: 0

摘要

背景儿童哮喘仍然是美国青少年中最常见的慢性疾病之一。空气污染物等地理决定因素已被确认在哮喘的发展和恶化中发挥作用。本研究的目的是确定小儿哮喘恶化事件的地理空间预测因素,并优先考虑住房补救资源。纳入标准为年龄在 21 岁及以下、在 2019 年 1 月至 2021 年 12 月期间至少有过一次与哮喘相关的就诊经历的 51 557 名成员。样本包括诊断、年龄、门诊和急诊就诊次数以及家庭住址。空间分析还包括研究期间最近监测站的空气质量指数、住宅与主干道的距离以及住宅与生产场所的距离。哮喘相关就诊次数较多的公共住房居民更有可能居住在距离工业生产地点 4 公里以内(P < .001)、距离主要道路较近(P < .001)以及空气质量较差的天数较多(P < .001)。建模结果表明,这些因素也可显著预测与哮喘相关的医疗就诊次数的增加。结论:本研究的结果与之前将哮喘与空气质量差联系起来的研究结果一致,并进一步强调了公共住房、主要道路和生产场所为邻近社区带来的一些叠加性和潜在的指数挑战。这项研究可以为环境干预措施提供指导,包括公共住房检查频率、社区宣传和制定沟通策略,以减少各社区与哮喘有关的经历。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictors of Pediatric Asthma Management: Identifying Actionable Results With Geographic Determinants.
BACKGROUND Pediatric asthma remains one of the most prominent chronic health conditions among US youth. Geographic determinants such as air pollutants have been identified as playing a role in asthma development and exacerbation. The purpose of this study was to determine geospatial predictors of pediatric asthma exacerbation events and to prioritize housing remediation resources. METHODS Electronic medical records were abstracted from a health plan in Southern California. The inclusion criteria that created a sample of 51 557 members were those aged 21 years and younger, who had at least 1 asthma-related encounter between January 2019 and December 2021. Diagnoses, age, number of clinic and emergency department visits, and home addresses were included. The air quality index from the closest monitoring station during the study period, residential distance from a primary roadway, and residential distance from manufacturing sites were included in the spatial analysis. RESULTS The average number of asthma-related clinic visits was 2 across the sample. Individuals with more asthma-related clinic visits residing in public housing were more likely to live within 4 km of industrial manufacturing locations (P < .001), reside closer to a major roadway (P < .001), and experience a higher number of poor air quality days (P < .001). Modeling results show these factors were also significantly predictive of an increase of asthma-related health care encounters. CONCLUSIONS The findings of this study were consistent with previous studies linking asthma and poor air quality and further highlighted some of the additive and potentially exponential challenges that public housing, major roadways, and manufacturing sites provide communities in their proximity. This research can guide environmental interventions, including the frequency of public housing inspections, community outreach, and the development of communication strategies, to reduce asthma-related experiences across neighborhoods.
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