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社区参与式调查方法研究 2020-2021 年大流行对边缘化社区的影响,马萨诸塞州。

Community-Engaged Survey Approach to Pandemic Impacts on Marginalized Communities, Massachusetts, 2020-2021.

机构信息

All of the authors are with the Massachusetts Department of Public Health, Boston.

出版信息

Am J Public Health. 2024 Oct;114(S7):S599-S609. doi: 10.2105/AJPH.2024.307800. Epub 2024 Aug 28.

Abstract

To describe how an innovative, community-engaged survey illuminated previously unmeasured pandemic inequities and informed health equity investments. The methodological approach of Massachusetts' COVID-19 Community Impact Survey, a cross-sectional online survey, was driven by key health equity principles: prioritizing community engagement, gathering granular and intersectional data, capturing root causes, elevating community voices, expediting analysis for timeliness, and creating data-to-action pathways. Data collection was deployed statewide in 11 languages from 2020 to 2021. The embedded equity principles resulted in a rich data set and enabled analyses of populations previously undescribed. The final sample included 33 800 respondents including unprecedented numbers of populations underrepresented in traditional data sources. Analyses indicated that pandemic impacts related to basic needs, discrimination, health care access, workplace protections, employment, and mental health disproportionately affected these priority populations, which included Asian American/Pacific Islanders and parents. Equity-centered data approaches allow for analyses of populations previously invisible in surveillance data, enable more equitable public health action, and are both possible and necessary to deploy in state health departments. (. 2024;114(S7):S599-S609. https://doi.org/10.2105/AJPH.2024.307800).

摘要

描述一项创新的、社区参与的调查如何揭示以前未测量到的大流行病不平等现象,并为健康公平投资提供信息。马萨诸塞州 COVID-19 社区影响调查是一项横断面在线调查,其方法学方法受到以下关键健康公平原则的驱动:优先考虑社区参与、收集详细和交叉数据、捕捉根本原因、提升社区声音、加快分析以实现及时性,并创建数据到行动的途径。数据收集工作于 2020 年至 2021 年在全州以 11 种语言进行。嵌入式公平原则产生了丰富的数据,并能够对以前未描述的人群进行分析。最终样本包括 33800 名受访者,其中包括在传统数据源中代表性不足的人群数量前所未有。分析表明,与基本需求、歧视、医疗保健获取、工作场所保护、就业和心理健康相关的大流行病影响不成比例地影响了这些优先人群,其中包括亚裔美国人/太平洋岛民和父母。以公平为中心的数据方法允许对监测数据中以前看不见的人群进行分析,促进更公平的公共卫生行动,并且在州卫生部门中部署这种方法是可行且必要的。(。2024;114(S7):S599-S609。https://doi.org/10.2105/AJPH.2024.307800)。

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