Disparities in influenza mortality and transmission SEE COMMENTARY related to sociodemographic factors within Chicago in the pandemic of 1918 Kyra H. Grantza,b,1, Madhura S. Ranec,1, Henrik Saljed,e, Gregory E. Glassb,f, Stephen E. Schachterleg, and Derek A. T. Cummingsa,b,d,2 aDepartment of Biology, University of Florida, Gainesville, FL 32611; bEmerging Pathogens Institute, University of Florida, Gainesville, FL 32611; cDepartment of Epidemiology, University of Washington, Seattle, WA 98195; dDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; eMathematical Modelling Unit, Institut Pasteur, Paris 75015, France; fDepartment of Geography, University of Florida, Gainesville, FL 32611; and gEpidemiology, Worldwide Safety & Regulatory, Pfizer Inc., New York, NY 10017 Edited by Burton H. Singer, University of Florida, Gainesville, FL, and approved September 29, 2016 (received for review August 10, 2016) Social factors have been shown to create differential burden of in- increased case fatality rates, and also, they were unable to detect fluenza across different geographic areas. We explored the relationship variation in excess mortality within countries or even finer spatial scales. between potential aggregate-level social determinants and mortality Here, we investigate whether sociodemographic factors con- during the 1918 influenza pandemic in Chicago using a historical tributed to the spatial variation in mortality observed within the dataset of 7,971 influenza and pneumonia deaths. Census tract-level city of Chicago during the 1918 pandemic. To evaluate the spatial social factors, including rates of illiteracy, homeownership, population, and social dependencies of influenza mortality, we digitized his- and unemployment, were assessed as predictors of pandemic mortality torical maps of point-level mortality incidence published shortly in Chicago. Poisson models fit with generalized estimating equations after the pandemic and combined the spatial data with near- (GEEs) were used to estimate the association between social factors contemporaneous US Census data. We constructed a Poisson and the risk of influenza and pneumonia mortality. The Poisson model model with generalized estimating equations (GEEs) model to showed that influenza and pneumonia mortality increased, on average, explore the fine-scale effects of sociodemographic factors, in- by 32.2% for every 10% increase in illiteracy rate adjusted for cluding illiteracy, homeownership, unemployment, and population population density, homeownership, unemployment, and age. We density, on influenza mortality. Illiteracy has been cited as the also found a significant association between transmissibility and closest approximation of socioeconomic and health status (18), population density, illiteracy, and unemployment but not home- whereas high rates of homeownership and low rates of un- ownership. Lastly, analysis of the point locations of reported employment are often considered predictors of improved financial influenza and pneumonia deaths revealed fine-scale spatiotempo- security, access to healthcare resources, and general health status ral clustering. This study shows that living in census tracts with (19). To test the robustness of our results to the possibility that the higher illiteracy rates increased the risk of influenza and pneumo- pandemic virus (or a related virus) infected significant numbers of individuals in the spring of 1918 (referred to as a herald wave), we nia mortality during the 1918 influenza pandemic in Chicago. Our MEDICAL SCIENCES observation that disparities in structural determinants of neigh- used simulations to investigate the impact of an unobserved herald borhood-level health lead to disparities in influenza incidence in wave on the observed sociodemographic relationships. We also this pandemic suggests that disparities and their determinants should remain targets of research and control in future pandemics. Significance influenza | 1918 | transmission | mortality | sociodemographic The pervasiveness of influenza among humans and its rapid spread during pandemics create a false sense that all humans are affected he influenza pandemic of 1918 is one of the most devastating equally. In this work, we show that neighborhood-level social de- Tinfectious disease outbreaks on record, with an estimated death terminants were associated with greater burdens of pandemic in- toll of 50 million worldwide (1). The deadly nature of the H1N1 virus fluenza in 1918 and several other diseases in a major US city. We that caused the pandemic, the unique epidemiological features of the show that literacy, homeownership, and unemployment were as- pandemic, and the pathophysiology of deaths caused by this virus set sociated with cumulative influenza mortality as well as measures this pandemic apart from its successors and its predecessors alike (1). of the speed of transmission using a unique dataset describing the Although there is evidence of substantial geographic variation in home location and week of death of individuals who died during mortality rates across countries (2) and even within cities (3), studies the influenza pandemic in 1918. Our results suggest that, similar to of the 1918 pandemic in the United States (4, 5), the United King- other infectious diseases, social disparities should be a focus of dom (6, 7), Europe (8), South America (9–11), and Asia (12, 13) research and public health response in future pandemics. focus mainly on differences in onset of the pandemic, age-specific Author contributions: K.H.G., M.S.R., S.E.S., and D.A.T.C. designed research; K.H.G., M.S.R., mortality, and transmission of the virus across large geographic re- S.E.S., and D.A.T.C. performed research; H.S. contributed new reagents/analytic tools; gions. Few studies have explored the effects of sociodemographic K.H.G., M.S.R., S.E.S., and D.A.T.C. analyzed data; and K.H.G., M.S.R., H.S., G.E.G., S.E.S., and factors on differential mortality during the 1918 pandemic, despite D.A.T.C. wrote the paper. the known impacts of social inequalities on disease progression and The authors declare no conflict of interest. outcomes through decreased access to healthcare, overcrowding, This article is a PNAS Direct Submission. comorbidities associated with lower resources, lower nutritional sta- Freely available online through the PNAS open access option. tus, and a poor understanding of control measures because of lack of Data deposition: The data reported in this paper have been deposited in the Dryad education (14–17). One such study found that excess mortality from Digital Repository, https://datadryad.org (10.5061/dryad.48nv3). 1918 to 1920 was negatively associated with per-head income in many See Commentary on page 13557. countries (2). The authors attributed this association to a combina- 1K.H.G. and M.S.R. contributed equally to this work. tion of community factors, such as healthcare access, and individual 2To whom correspondence should be addressed. Email: [email protected]. factors, including comorbidities and nutritional status. However, they This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. were unable to separate an impact on incidence of infection from 1073/pnas.1612838113/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1612838113 PNAS | November 29, 2016 | vol. 113 | no. 48 | 13839–13844 Downloaded by guest on September 25, 2021 N incidence in the central and southcentral regions of the city, whereas the northern, western, and southernmost areas of the city experienced a lower disease burden (Fig. 1). The average 3.5 0 3.5 km case fatality rate city-wide was 17.5% during the pandemic pe- riod, although this rate is likely to be an overestimate because of underreporting of nonfatal cases. Cumulative mortality Relationship with Sociodemographic Factors. Fig. 2 shows the per 1000 weekly point pattern mortality overlaid on a map of illiteracy 0.00 - 2.38 rates by census tracts. The first deaths were observed in census 2.39 - 4.48 tracts with higher illiteracy rates. As the epidemic progressed, 4.49 - 9.32 the disease spread to the neighboring tracts but remained con- centrated in the high-illiteracy areas. 9.33 - 20.59 A Poisson model fit with GEE was used to estimate average mortality per census tract with robust variance. The clustering variable in the model was the census tract, and the correlation in data in each census tract was assumed to be unstructured. As- suming correlation within census tracts, we analyzed association between mortality and illiteracy rate, homeownership, un- employment, and population density. The model also accounted for any unknown correlations between the outcomes and was offset by the total underlying population of each census tract. Fig. 1. Cumulative incidence of influenza and pneumonia mortality per The multivariate models were also adjusted for age, because 1,000 by census tracts during the 7 wk of the epidemic in Chicago in 1918. pandemic mortality was known to vary by age (1). The three empty regions represent the three census tracts for which there The univariate and multivariate analyses’ estimates for risk were no demographic data that were excluded from analysis. The south- ratios of mortality rate per census tract are presented in Table 1. ernmost empty region is a lake. Univariate analysis found that illiteracy rate, homeownership, and unemployment were independently statistically associated expanded our analysis to investigate the relationship