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明尼苏达州双子城都会区肺炎住院风险的空间变异

Spatial variation of pneumonia hospitalization risk in Twin Cities metro area, Minnesota.

作者信息

Iroh Tam P Y, Krzyzanowski B, Oakes J M, Kne L, Manson S

机构信息

Division of Pediatric Infectious Diseases and Immunology,Department of Pediatrics,University of Minnesota Medical School,Minneapolis,MN,USA.

Department of Geography,Environment, and Society,University of Minnesota,Minneapolis,MN,USA.

出版信息

Epidemiol Infect. 2017 Nov;145(15):3274-3283. doi: 10.1017/S0950268817002291. Epub 2017 Oct 17.

Abstract

Fine resolution spatial variability in pneumonia hospitalization may identify correlates with socioeconomic, demographic and environmental factors. We performed a retrospective study within the Fairview Health System network of Minnesota. Patients 2 months of age and older hospitalized with pneumonia between 2011 and 2015 were geocoded to their census block group, and pneumonia hospitalization risk was analyzed in relation to socioeconomic, demographic and environmental factors. Spatial analyses were performed using Esri's ArcGIS software, and multivariate Poisson regression was used. Hospital encounters of 17 840 patients were included in the analysis. Multivariate Poisson regression identified several significant associations, including a 40% increased risk of pneumonia hospitalization among census block groups with large, compared with small, populations of ⩾65 years, a 56% increased risk among census block groups in the bottom (first) quartile of median household income compared to the top (fourth) quartile, a 44% higher risk in the fourth quartile of average nitrogen dioxide emissions compared with the first quartile, and a 47% higher risk in the fourth quartile of average annual solar insolation compared to the first quartile. After adjusting for income, moving from the first to the second quartile of the race/ethnic diversity index resulted in a 21% significantly increased risk of pneumonia hospitalization. In conclusion, the risk of pneumonia hospitalization at the census-block level is associated with age, income, race/ethnic diversity index, air quality, and solar insolation, and varies by region-specific factors. Identifying correlates using fine spatial analysis provides opportunities for targeted prevention and control.

摘要

肺炎住院的高分辨率空间变异性可能揭示与社会经济、人口统计学和环境因素的相关性。我们在明尼苏达州的费尔维尤健康系统网络内进行了一项回顾性研究。对2011年至2015年间2个月及以上因肺炎住院的患者进行地理编码,确定其所在的普查街区组,并分析肺炎住院风险与社会经济、人口统计学和环境因素的关系。使用Esri的ArcGIS软件进行空间分析,并采用多变量泊松回归分析。分析纳入了17840例患者的住院记录。多变量泊松回归分析确定了几个显著的关联,包括65岁及以上人口较多的普查街区组与人口较少的普查街区组相比,肺炎住院风险增加40%;家庭收入中位数处于最低(第一)四分位数的普查街区组与最高(第四)四分位数的普查街区组相比,肺炎住院风险增加56%;平均二氧化氮排放量处于第四四分位数的地区与第一四分位数的地区相比,肺炎住院风险高44%;年平均日照量处于第四四分位数的地区与第一四分位数的地区相比,肺炎住院风险高47%。在调整收入因素后,种族/族裔多样性指数从第一四分位数升至第二四分位数,肺炎住院风险显著增加21%。总之,普查街区层面的肺炎住院风险与年龄、收入、种族/族裔多样性指数、空气质量和日照量有关,并因地区特定因素而异。利用精细空间分析确定相关性为有针对性的预防和控制提供了机会。

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