Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
Division of Population Health, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, MS S107-6, Atlanta, GA 30341 (
Prev Chronic Dis. 2023 May 11;20:E37. doi: 10.5888/pcd20.230004.
Local data are increasingly needed for public health practice. County-level data on disabilities can be a valuable complement to existing estimates of disabilities. The objective of this study was to describe the county-level prevalence of disabilities among US adults and identify geographic clusters of counties with a higher or lower prevalence of disabilities.
We applied a multilevel logistic regression and poststratification approach to geocoded 2018 Behavioral Risk Factor Surveillance System data, Census 2018 county-level population estimates, and American Community Survey 2014-2018 poverty estimates to generate county-level estimates for 6 functional disabilities and any disability type. We used cluster-outlier spatial statistical methods to identify clustered counties.
Among 3,142 counties, median estimated prevalence was 29.5% for any disability and differed by type: hearing (8.0%), vision (4.9%), cognition (11.5%), mobility (14.9%), self-care (3.7%), and independent living (7.2%). The spatial autocorrelation statistic, Moran's I, was 0.70 for any disability and 0.60 or greater for all 6 types of disability, indicating that disabilities were highly clustered at the county level. We observed similar spatial cluster patterns in all disability types except hearing disability.
The results suggest substantial differences in disability prevalence across US counties. These data, heretofore unavailable from a health survey, may help with planning programs at the county level to improve the quality of life for people with disabilities.
公共卫生实践越来越需要当地数据。县级残疾数据可以是现有残疾估计数的宝贵补充。本研究的目的是描述美国成年人的县级残疾流行率，并确定残疾流行率较高或较低的县的地理聚类。
我们应用多水平逻辑回归和后分层方法对地理编码的 2018 年行为风险因素监测系统数据、2018 年人口普查县级人口估计数以及 2014-2018 年美国社区调查贫困估计数进行了分析，以生成 6 种功能残疾和任何残疾类型的县级估计数。我们使用聚类异常空间统计方法来识别聚类县。
在 3142 个县中，任何残疾的中位数估计患病率为 29.5%，且因类型而异：听力（8.0%）、视力（4.9%）、认知（11.5%）、行动能力（14.9%）、自我护理（3.7%）和独立生活（7.2%）。空间自相关统计量 Moran's I 为任何残疾的 0.70，为所有 6 种残疾类型的 0.60 或更高，表明残疾在县级高度聚类。除听力残疾外，我们在所有残疾类型中都观察到了类似的空间聚类模式。
结果表明，美国各县的残疾流行率存在显著差异。这些数据来自健康调查，迄今为止尚不可用，可能有助于规划县级方案，以提高残疾人士的生活质量。
