Health inequities in influenza transmission and surveillance

Abstract The lower an individual’s socioeconomic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generalizable insights. Here, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social, behavioral, and physiological determinants exacerbates influenza epidemics, and that low socioeconomic status (SES) individuals disproportionately bear the burden of infection. We also identify geographical hotspots of influenza burden in low SES populations, much of which is overlooked in traditional influenza surveillance, and find that these differences are most predicted by variation in susceptibility and access to sickness absenteeism. Our results highlight that the effect of overlapping factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, as targeting public health efforts spatially may be an efficient use of resources to abate inequities. The association between health and socioeconomic prosperity has a long history in the epidemiological literature; addressing health inequities in respiratory infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat. Author summary Health inequities, or increased morbidity and mortality due to social factors, have been demonstrated for respiratory-transmitted infectious diseases, most recently evidenced by disparities in COVID-19 severe cases and deaths. Many potential causes of these inequities have been proposed, but they have not been compared, and we do not understand their mechanistic impacts. Our understanding of these issues is further hindered by epidemiological surveillance, which has been shown to overlook areas of low socioeconomic status. Here, we combine mechanistic and statistical modeling with high volume datasets to disentangle the drivers of respiratory transmitted infectious diseases, and to estimate locations where these health inequities are most severe, using influenza as a case study. We show that low socioeconomic individuals disproportionately bear the burden of influenza infection, and that all proposed factors are synergistic in causing these. Thus, public health intervention that targets any one of these drivers may alleviate other issues, as they are not mutually exclusive. Additionally, we provide geographical hotspots for improved surveillance. This work also demonstrates the imperative need to consider inequities and social drivers in data collection, epidemiological modeling, and public health work, as the most vulnerable populations may also be the most likely to be overlooked.