Quantifying the impact of social groups and vaccination on inequalities in infectious diseases using a mathematical model

Munday, James D.; Hoek, Albert Jan van; Edmunds, W. John; Atkins, Katherine E.

doi:10.1186/s12916-018-1152-1

Cited by 21 publications

(19 citation statements)

References 47 publications

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“…Mathematical modeling studies of social disparities in influenza burden have used a simulation approach [27][28][29] and have focused on the effects of material deprivation (i.e. lack of access from income, education, and employment) or social deprivation (i.e.…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…Such studies are important in uncovering the mechanistic explanations of influenza disparities, but have been limited in their geographical extent, or by the use of proxy measures. For example, [ 27 , 29 ] consider phenomenological variation in social contact rates without empirical evidence linking vulnerable groups to that variation, thus limiting insights on the mechanisms that lead to influenza disparities; [ 28 , 29 ] focus on dynamics within specific cities, limiting generalizability.…”

Section: Introductionmentioning

confidence: 99%

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Health inequities in influenza transmission and surveillance

2021

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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 and healthcare-based 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, and 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-transmitted infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat.

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Section: Plos Computational Biologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Health inequities in influenza transmission and surveillance

2021

View full text Add to dashboard Cite

show abstract

“…Mathematical modeling studies of social disparities in influenza burden have used a simulation approach [27, 28, 29] and have focused on the effects of material deprivation (i.e. lack of access from income, education, employment) or social deprivation (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Such studies are important in uncovering the mechanistic explanations of influenza disparities, but have been limited in their geographical extent, or by the use of proxy measures. For example, [27, 29] consider phenomenological variation in social contact rates without empirical evidence linking vulnerable groups to that variation, thus limiting insights on the mechanisms that lead influenza disparities; [28, 29] focus on dynamics within specific cities, limiting generalizability.…”

Section: Introductionmentioning

confidence: 99%

Health inequities in influenza transmission and surveillance

Zipfel

Bansal

2020

Preprint

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The lower an individual's socio-economic 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 generating 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 SES individuals disproportionately bear the burden of infection. We also identify geographical hotspots of disproportionate influenza burden in low SES populations, and find that these differences are most predicted by variation in healthcare utilization and susceptibility. The negative association between health and socio-economic 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 to the entire population. Our results highlight that the effect of overlapping behavioral social, and physiological 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.

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“…Analysis of the initial weeks of the vaccination programme in England highlights emerging inequalities in vaccine coverage with lower rates in ethnic minorities, people living in areas of higher deprivation, and those with severe mental illness or learning disabilities ( 4 ). This is concerning but unsurprising as previous research has shown that universal interventions can increase inequality because of the differential uptake between communities ( 5 , 6 ).…”

mentioning

confidence: 97%