Grantz Kyra H, Rane Madhura S, Salje Henrik, Glass Gregory E, Schachterle Stephen E, Cummings Derek A T
Department of Biology, University of Florida, Gainesville, FL 32611.
Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611.
Proc Natl Acad Sci U S A. 2016 Nov 29;113(48):13839-13844. doi: 10.1073/pnas.1612838113. Epub 2016 Nov 21.
Social factors have been shown to create differential burden of influenza across different geographic areas. We explored the relationship between potential aggregate-level social determinants and mortality during the 1918 influenza pandemic in Chicago using a historical dataset of 7,971 influenza and pneumonia deaths. Census tract-level social factors, including rates of illiteracy, homeownership, population, and unemployment, were assessed as predictors of pandemic mortality in Chicago. Poisson models fit with generalized estimating equations (GEEs) were used to estimate the association between social factors and the risk of influenza and pneumonia mortality. The Poisson model showed that influenza and pneumonia mortality increased, on average, by 32.2% for every 10% increase in illiteracy rate adjusted for population density, homeownership, unemployment, and age. We also found a significant association between transmissibility and population density, illiteracy, and unemployment but not homeownership. Lastly, analysis of the point locations of reported influenza and pneumonia deaths revealed fine-scale spatiotemporal clustering. This study shows that living in census tracts with higher illiteracy rates increased the risk of influenza and pneumonia mortality during the 1918 influenza pandemic in Chicago. Our observation that disparities in structural determinants of neighborhood-level health lead to disparities in influenza incidence in this pandemic suggests that disparities and their determinants should remain targets of research and control in future pandemics.
社会因素已被证明会在不同地理区域造成流感负担的差异。我们利用7971例流感和肺炎死亡病例的历史数据集,探讨了1918年芝加哥流感大流行期间潜在的总体社会决定因素与死亡率之间的关系。普查区层面的社会因素,包括文盲率、自有住房率、人口和失业率,被评估为芝加哥大流行死亡率的预测因素。使用与广义估计方程(GEE)拟合的泊松模型来估计社会因素与流感和肺炎死亡风险之间的关联。泊松模型显示,在调整了人口密度、自有住房率、失业率和年龄后,文盲率每增加10%,流感和肺炎死亡率平均增加32.2%。我们还发现传播性与人口密度、文盲率和失业率之间存在显著关联,但与自有住房率无关。最后,对报告的流感和肺炎死亡病例的地点分析揭示了精细尺度的时空聚集。这项研究表明,在1918年芝加哥流感大流行期间,生活在文盲率较高的普查区会增加流感和肺炎死亡的风险。我们观察到邻里层面健康的结构决定因素的差异导致了此次大流行中流感发病率的差异,这表明差异及其决定因素应继续成为未来大流行研究和防控的目标。