Ludden Thomas M, Taylor Yhenneko J, Simmons Laura K, Smith Heather A, de Hernandez Brisa Urquieta, Tapp Hazel, Furuseth Owen J, Dulin Michael F
Department of Family Medicine, Atrium Health, Charlotte, NC, 28207, USA.
Center for Outcomes Research and Evaluation (CORE), Atrium Health, Charlotte, NC, 28203, USA.
J Prim Prev. 2018 Apr;39(2):171-190. doi: 10.1007/s10935-018-0505-z.
Hispanic immigrant communities across the U.S. experience persistent health disparities and barriers to primary care. We examined whether community-based participatory research (CBPR) and geospatial modeling could systematically and reproducibly pinpoint neighborhoods in Charlotte, North Carolina with large proportions of Hispanic immigrants who were at-risk for poor health outcomes and health disparities. Using a CBPR framework, we identified 21 social determinants of health measures and developed a geospatial model from a subset of those measures to identify neighborhoods with large proportions of Hispanic immigrant populations at risk for poor health outcomes. The geospatial model included four measures-poverty, English ability, acculturation and violent crime-which comprised our Hispanic Health Risk Index (HHRI). We developed a Primary Care Barrier Index (PCBI) to determine (1) how well the HHRI correlated with a statistically derived composite measure incorporating all 21 measures identified through the CBPR process as being associated with access to primary care; (2) whether the HHRI predicted primary care access as well as the statistically-derived composite measure in a statistical model; and (3) whether the HHRI identified similar neighborhoods as the statistically derived composite measure. We collapsed 17 of the 21 social determinants using principal components analysis to develop the PCBI. We determined the correlation of each index with inappropriate emergency department (ED) visits, a proxy for primary care access, using logistic generalized estimating equations. Results from logistic regression models showed positive associations of both the HHRI and the PCBI with the use of the ED for primary care treatable conditions. Enhanced by the knowledge of the local community, the CBPR process with geospatial modeling can guide the multi-tiered validation of social determinants of health and identify neighborhoods that are at-risk for poor health outcomes and health disparities.
美国各地的西班牙裔移民社区长期面临健康差距和初级保健障碍。我们研究了基于社区的参与性研究（CBPR）和地理空间建模是否能够系统且可重复地找出北卡罗来纳州夏洛特市那些有很大比例西班牙裔移民面临健康状况不佳和健康差距风险的社区。利用CBPR框架，我们确定了21项健康措施的社会决定因素，并从这些措施的一个子集中开发了一个地理空间模型，以识别有很大比例西班牙裔移民人口面临健康状况不佳风险的社区。该地理空间模型包括四项指标——贫困、英语能力、文化适应和暴力犯罪——它们构成了我们的西班牙裔健康风险指数（HHRI）。我们制定了初级保健障碍指数（PCBI），以确定：（1）HHRI与通过CBPR过程确定的与获得初级保健相关的所有21项指标的统计衍生综合指标的相关性有多好；（2）在统计模型中，HHRI预测初级保健可及性的能力是否与统计衍生综合指标一样好；（3）HHRI识别出的社区是否与统计衍生综合指标识别出的社区相似。我们使用主成分分析对21项社会决定因素中的17项进行汇总，以制定PCBI。我们使用逻辑广义估计方程确定每个指数与不适当的急诊科就诊（初级保健可及性的一个替代指标）之间的相关性。逻辑回归模型的结果显示，HHRI和PCBI与因初级保健可治疗疾病而使用急诊科均呈正相关。借助当地社区的知识，结合地理空间建模的CBPR过程可以指导对健康的社会决定因素进行多层次验证，并识别出面临健康状况不佳和健康差距风险的社区。
