Department of Health Administration, Pfeiffer University, 2880 Slater Road, Suite 100, Morrisville, NC, 27560, USA.
Disabil Health J. 2017 Oct;10(4):518-524. doi: 10.1016/j.dhjo.2017.01.003. Epub 2017 Feb 16.
People with disabilities tend to be at risk for secondary conditions. There is a need for comprehensive disability and health databases, including geographic information systems to evaluate trends in health, functioning, and employment.
We evaluated county levels in morbidity and mortality across the Southeastern United States using spatial regression, examining 2015 trends in accordance with Healthy People 2020 objectives.
We merged 2015 National County Health Rankings and the 2015 Social Security Administration's Report on SSDI Beneficiaries, all for n = 1387 Southeastern U.S. county units. We used GeoDa to regress health and disability multivariable models for the dependent variable, age-adjusted Years of Potential Life Lost (YPLL) per 100,000 population.
The principal Health/Demographic multivariable model of factors impacting YPLL yielded an adjusted R = 0.743 (F = 188.3, p < 0.001) with percentage physically inactive, preventable hospital stays, percentage diabetics, and low college attendance figuring prominently. A Socioeconomic/Demographic multivariable model impacting YPLL yielded R = 0.631 (F = 156.0, p < 0.001), with disability and percentage unemployment being major associated variables.
For the Southeastern U.S., counties with higher prevalence of SSDI disability workers correlated with significantly higher YPLL and poorer health outcomes. The research augments CDC Disability and Health GIS systems to measure Healthy People 2020 outcomes for persons with disabilities nationwide. Spatial regression represents a robust approach for improved analysis of geographic data for population health measures.
残疾人往往面临继发疾病的风险。需要建立全面的残疾和健康数据库，包括地理信息系统，以评估健康、功能和就业趋势。
我们使用空间回归评估了美国东南部各县的发病率和死亡率，根据《健康人民 2020》目标，检查了 2015 年的趋势。
我们合并了 2015 年国家县健康排名和 2015 年社会安全管理局的 SSDI 受益人报告，共涉及美国东南部 1387 个县单位。我们使用 GeoDa 对健康和残疾多变量模型进行回归，因变量为每 10 万人中调整后的潜在生命损失年数（YPLL）。
主要的健康/人口多变量模型影响 YPLL，调整后的 R = 0.743（F = 188.3，p < 0.001），其中身体不活跃、可预防住院、糖尿病患者和低大学入学率的比例显著。影响 YPLL 的社会经济/人口多变量模型产生的 R = 0.631（F = 156.0，p < 0.001），残疾和失业率是主要相关变量。
对于美国东南部，SSDI 残疾工人比例较高的县与显著较高的 YPLL 和较差的健康结果相关。这项研究增强了疾病预防控制中心残疾和健康地理信息系统，以衡量全国残疾人士的《健康人民 2020》成果。空间回归是一种强大的方法，可以改进人口健康措施的地理数据分析。
