Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach.
Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach.
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DOI：
10.1186/1471-2458-11-644
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发表时间：
2011-08-12
影响因子：
4.5
通讯作者：
Odoi A
中科院分区：
文献类型：
--
作者：
Pedigo A;Aldrich T;Odoi A
中风和心肌梗死（MI）是美国严重的公共卫生负担。这些负担因地理位置而异，美国东南部报告的死亡风险最高。虽然这些差异已经在州和县一级进行了调查，但对较低地理水平（如社区）的风险差异知之甚少。因此，本研究的目的是调查东田纳西州阿巴拉契亚地区中风和心肌梗死死亡风险的空间模式，以确定风险最高的社区。根据田纳西州卫生部的要求，免费获得了1999-2007年期间的卒中和MI死亡率数据，并将其汇总到人口普查区（社区）水平。死亡率风险采用直接法进行年龄标准化。为了调整空间自相关性、群体异质性和方差不稳定性，使用空间经验贝叶斯技术平滑标准化风险。使用空间扫描统计学确定高风险的空间集群，使用离散泊松模型调整年龄并使用5%的扫描窗口。使用999个Monte Carlo排列进行显著性检验。Logistic模型被用来调查邻里水平的社会经济和人口预测所确定的空间集群。有3，824例中风死亡和5，018例MI死亡。确定了死亡率风险极高的社区。年卒中死亡风险范围为0 - 182/100，000人群（中位数：55.6），而年MI死亡风险范围为0 - 243/100，000人群（中位数：65.5）。在28%和32%的社区中，中风和MI死亡风险分别超过了67.5和85.5的国家风险。分别确定了6个和10个显著（p < 0.001）卒中和MI死亡率高风险的空间集群。属于中风和MI死亡率高风险集群的社区往往有高比例的低教育程度的人口。这些用于识别不同社区之间死亡风险差异的方法对于识别高风险社区和指导旨在解决健康差异和改善人口健康的人口健康计划是有用的。
Stroke and myocardial infarction (MI) are serious public health burdens in the US. These burdens vary by geographic location with the highest mortality risks reported in the southeastern US. While these disparities have been investigated at state and county levels, little is known regarding disparities in risk at lower levels of geography, such as neighborhoods. Therefore, the objective of this study was to investigate spatial patterns of stroke and MI mortality risks in the East Tennessee Appalachian Region so as to identify neighborhoods with the highest risks. Stroke and MI mortality data for the period 1999-2007, obtained free of charge upon request from the Tennessee Department of Health, were aggregated to the census tract (neighborhood) level. Mortality risks were age-standardized by the direct method. To adjust for spatial autocorrelation, population heterogeneity, and variance instability, standardized risks were smoothed using Spatial Empirical Bayesian technique. Spatial clusters of high risks were identified using spatial scan statistics, with a discrete Poisson model adjusted for age and using a 5% scanning window. Significance testing was performed using 999 Monte Carlo permutations. Logistic models were used to investigate neighborhood level socioeconomic and demographic predictors of the identified spatial clusters. There were 3,824 stroke deaths and 5,018 MI deaths. Neighborhoods with significantly high mortality risks were identified. Annual stroke mortality risks ranged from 0 to 182 per 100,000 population (median: 55.6), while annual MI mortality risks ranged from 0 to 243 per 100,000 population (median: 65.5). Stroke and MI mortality risks exceeded the state risks of 67.5 and 85.5 in 28% and 32% of the neighborhoods, respectively. Six and ten significant (p < 0.001) spatial clusters of high risk of stroke and MI mortality were identified, respectively. Neighborhoods belonging to high risk clusters of stroke and MI mortality tended to have high proportions of the population with low education attainment. These methods for identifying disparities in mortality risks across neighborhoods are useful for identifying high risk communities and for guiding population health programs aimed at addressing health disparities and improving population health.
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