Center for Disease Control and Prevention, National Center for HIV/AIDS, Viral Herpatitis, STD, and TB Prevention, Division of HIV/AIDS Prevention, Atlanta, GA 30333, USA.
Public Health Rep. 2011 Sep-Oct;126 Suppl 3(Suppl 3):70-80. doi: 10.1177/00333549111260S312.
We developed a statistical tool that brings together standard, accessible, and well-understood analytic approaches and uses area-based information and other publicly available data to identify social determinants of health (SDH) that significantly affect the morbidity of a specific disease.
We specified AIDS as the disease of interest and used data from the American Community Survey and the National HIV Surveillance System. Morbidity and socioeconomic variables in the two data systems were linked through geographic areas that can be identified in both systems. Correlation and partial correlation coefficients were used to measure the impact of socioeconomic factors on AIDS diagnosis rates in certain geographic areas.
We developed an easily explained approach that can be used by a data analyst with access to publicly available datasets and standard statistical software to identify the impact of SDH. We found that the AIDS diagnosis rate was highly correlated with the distribution of race/ethnicity, population density, and marital status in an area. The impact of poverty, education level, and unemployment depended on other SDH variables.
Area-based measures of socioeconomic variables can be used to identify risk factors associated with a disease of interest. When correlation analysis is used to identify risk factors, potential confounding from other variables must be taken into account.
我们开发了一种统计工具,它结合了标准、可及且充分理解的分析方法,并利用基于区域的信息和其他公开可用的数据,来识别对特定疾病发病率有显著影响的健康社会决定因素(SDH)。
我们指定艾滋病为研究疾病,并使用美国社区调查和国家艾滋病毒监测系统的数据。两个数据系统中的发病率和社会经济变量通过可以在两个系统中识别的地理区域进行关联。使用相关系数和偏相关系数来衡量社会经济因素对特定地理区域艾滋病诊断率的影响。
我们开发了一种易于解释的方法,数据分析师可以使用该方法访问公开数据集和标准统计软件,以识别 SDH 的影响。我们发现,艾滋病诊断率与该地区的种族/民族分布、人口密度和婚姻状况高度相关。贫困、教育水平和失业的影响取决于其他 SDH 变量。
可以使用基于区域的社会经济变量来识别与研究疾病相关的风险因素。在使用相关分析来识别风险因素时,必须考虑其他变量的潜在混杂因素。