Chronic Disease Disparities by County Economic Status and Metropolitan Classification, Behavioral Risk Factor Surveillance System, 2013
Chronic Disease Disparities by County Economic Status and Metropolitan Classification, Behavioral Risk Factor Surveillance System, 2013
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DOI：
10.5888/pcd13.160088
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发表时间：
2016-09-01
影响因子：
5.5
通讯作者：
Barker, Lawrence
中科院分区：
文献类型：
--
作者：
Shaw, Kate M.;Theis, Kristina A.;Barker, Lawrence
种族/民族差异已被广泛研究。然而，地理位置和经济状况对特定健康结果的综合影响研究较少。本研究的目的是检验1)美国慢性疾病患病率在县域经济地位和都市分类上的差异，以及2)经济地位的社会梯度。探讨了高血压、关节炎、健康状况不佳与县域经济状况的关系。方法采用2013年行为危险因素监测系统数据。县域经济状况通过使用失业、贫困和人均市场收入数据进行分类。在控制社会人口统计和其他协变量的同时，我们使用多变量逻辑回归来评估经济状况与高血压、关节炎和自评健康之间的关系。结果最贫困县的高血压、关节炎和健康状况不佳的患病率分别比最富裕县高9%、13%和15%。在我们控制了协变量后，贫困县仍然具有较高的研究条件患病率。结论我们发现，即使在我们控制了已知的危险因素之后，贫困县居民的健康状况不佳的发生率也高于富裕县。此外，随着县域经济的改善，健康状况不佳的发生率也有所下降。研究结果表明，贫困县将受益于有针对性的公共卫生干预措施、更好地获得卫生保健服务以及改善食品和建筑环境。
IntroductionRacial/ethnic disparities have been studied extensively. However, the combined influence of geographic location and economic status on specific health outcomes is less well studied. This study's objective was to examine 1) the disparity in chronic disease prevalence in the United States by county economic status and metropolitan classification and 2) the social gradient by economic status. The association of hypertension, arthritis, and poor health with county economic status was also explored.MethodsWe used 2013 Behavioral Risk Factor Surveillance System data. County economic status was categorized by using data on unemployment, poverty, and per capita market income. While controlling for sociodemographics and other covariates, we used multivariable logistic regression to evaluate the relationship between economic status and hypertension, arthritis, and self-rated health.ResultsPrevalence of hypertension, arthritis, and poor health in the poorest counties was 9%, 13%, and 15% higher, respectively, than in the most affluent counties. After we controlled for covariates, poor counties still had a higher prevalence of the studied conditions.ConclusionWe found that residents of poor counties had a higher prevalence of poor health outcomes than affluent counties, even after we controlled for known risk factors. Further, the prevalence of poor health outcomes decreased as county economics improved. Findings suggest that poor counties would benefit from targeted public health interventions, better access to health care services, and improved food and built environments.