A County-Level Examination of the Relationship Between HIV and Social Determinants of Health: 40 States, 2006-2008.
A County-Level Examination of the Relationship Between HIV and Social Determinants of Health: 40 States, 2006-2008.
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DOI：
10.2174/1874613601206010001
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
R S
中科院分区：
文献类型：
--
作者：
Z G;M L;H I H;X H;X G;R S
健康的社会决定因素（SDH）是影响不健康或危险行为的社会和身体因素。健康的社会决定因素可以通过行为影响和有限的预防和医疗保健机会来影响感染传染病（如艾滋病毒）的机会。我们分析了健康的社会决定因素和艾滋病毒诊断率之间的关系，以更好地了解美国不同人群之间的比率差异。利用国家艾滋病监测数据和美国社区调查数据，我们在县一级研究了健康变量的社会决定因素之间的关系（例如，白人比例、收入不平等）和艾滋病毒诊断率（2006-2008年平均值）。对1，560个县的数据分析显示，艾滋病毒诊断率与收入不平等（皮尔逊相关系数ρ = 0.40）和未婚-年龄>15岁的比例（ρ = 0.52）之间存在显著的正相关。白人比例与死亡率呈显著负相关（ρ = -0.67）。健康指标和比率的种族社会决定因素之间的相关性很低。总体而言，艾滋病毒诊断率随着收入不平等和未婚比例的增加而增加，随着白人比例的增加而下降。这些数据反映出非白人中艾滋病毒的流行率较高。虽然统计相关性是中等的，确定和了解这些健康变量的社会决定因素可以帮助有针对性的预防工作，以帮助降低艾滋病毒的诊断率。未来的分析需要确定单身比例的增加是否反映了同性恋和双性恋男性的增加。
Social determinants of health (SDH) are the social and physical factors that can influence unhealthy or risky behavior. Social determinants of health can affect the chances of acquiring an infectious disease – such as HIV – through behavioral influences and limited preventative and healthcare access. We analyzed the relationship between social determinants of health and HIV diagnosis rates to better understand the disparity in rates between different populations in the United States. Using National HIV Surveillance data and American Community Survey data at the county level, we examined the relationships between social determinants of health variables (e.g., proportion of whites, income inequality) and HIV diagnosis rates (averaged for 2006-2008) among adults and adolescents from 40 states with mature name-based HIV surveillance. Analysis of data from 1,560 counties showed a significant, positive correlation between HIV diagnosis rates and income inequality (Pearson correlation coefficient ρ = 0.40) and proportion unmarried – ages >15 (ρ = 0.52). There was a significant, negative correlation between proportion of whites and rates (ρ = -0.67). Correlations were low between racespecific social determinants of health indicators and rates. Overall, HIV diagnosis rates increased as income inequality and the proportion unmarried increased, and rates decreased as proportion of whites increased. The data reflect the higher HIV prevalence among non-whites. Although statistical correlations were moderate, identifying and understanding these social determinants of health variables can help target prevention efforts to aid in reducing HIV diagnosis rates. Future analyses need to determine whether the higher proportion of singles reflects higher populations of gay and bisexual men.