Spatial-temporal excess mortality patterns of the 1918–1919 influenza pandemic in Spain

Abstract

BackgroundThe impact of socio-demographic factors and baseline health on the mortality burden of seasonal and pandemic influenza remains debated. Here we analyzed the spatial-temporal mortality patterns of the 1918 influenza pandemic in Spain, one of the countries of Europe that experienced the highest mortality burden.MethodsWe analyzed monthly death rates from respiratory diseases and all-causes across 49 provinces of Spain, including the Canary and Balearic Islands, during the period January-1915 to June-1919. We estimated the influenza-related excess death rates and risk of death relative to baseline mortality by pandemic wave and province. We then explored the association between pandemic excess mortality rates and health and socio-demographic factors, which included population size and age structure, population density, infant mortality rates, baseline death rates, and urbanization.ResultsOur analysis revealed high geographic heterogeneity in pandemic mortality impact. We identified 3 pandemic waves of varying timing and intensity covering the period from Jan-1918 to Jun-1919, with the highest pandemic-related excess mortality rates occurring during the months of October-November 1918 across all Spanish provinces. Cumulative excess mortality rates followed a south–north gradient after controlling for demographic factors, with the North experiencing highest excess mortality rates. A model that included latitude, population density, and the proportion of children living in provinces explained about 40% of the geographic variability in cumulative excess death rates during 1918–19, but different factors explained mortality variation in each wave.ConclusionsA substantial fraction of the variability in excess mortality rates across Spanish provinces remained unexplained, which suggests that other unidentified factors such as comorbidities, climate and background immunity may have affected the 1918–19 pandemic mortality rates. Further archeo-epidemiological research should concentrate on identifying settings with combined availability of local historical mortality records and information on the prevalence of underlying risk factors, or patient-level clinical data, to further clarify the drivers of 1918 pandemic influenza mortality.

Disparities in influenza mortality and transmission related to sociodemographic factors within Chicago in the pandemic of 1918

“…Deaths were more likely to occur within the same week and a few hundred meters than expected given the underlying spatial and temporal clustering, speaking to the importance of neighborhood-level transmission even in a mobile, urban population. Previous studies found significant autocorrelation in influenza mortality across county boroughs in England and Wales (42) and provinces in Spain (20), but this study is unique in showing a spatial dependence in the spread of individual influenza cases during the 1918 pandemic.…”

“…The association between R and population density may be a result of an increased number of effective contacts between individuals in denser population tracts, but the relationship is confounded by the strong associations between population density and other sociodemographic factors and the largely nonsignificant association between population density and weekly R estimates (Tables S7 and S8). Moreover, the increased transmissibility of influenza in more densely populated census tracts did not translate into significantly higher mortality risk, although population density has been linked to pandemic influenza mortality at greater spatial scales (7,11,20).…”

“…This dataset of point-level mortality data and census tract-level demographic data provides an opportunity to investigate pandemic influenza dynamics at a spatial scale rarely examined for the 1918 pandemic. Some studies have assessed influenza dynamics at the state or county level (2,20,21), but temporal and spatial variations have not been widely studied at within-city spatial scales (3). There could be great variations in social status and disease burden across small geographic units, such as census tracts, particularly in a city as large and diverse as Chicago, that might be concealed by analysis at the country or state level.…”

Severe mortality impact of the 1957 influenza pandemic in Chile

“…Underlying population risk factors and socioeconomic conditions were likely more important drivers of pandemic mortality rates than environmental factors. For instance, prior studies have suggested that indigenous groups could have been affected disproportionately from past influenza pandemics …”

Pandemic influenza and socioeconomic disparities: Lessons from 1918 Chicago

“…For example, population density and background death rates explained 70% of the variation in cumulative pandemic excess mortality rates in province-level data from Chile (10). In another study, latitude, population density, and the proportion of children explained about 40% of between-province variation in cumulative excess death rates in Spain (11). Very little, however, has been done to understand the drivers of pandemic-related mortality rates at the fine scale of households or census tracts, an issue tackled by Grantz et al (6).…”