Individual, household, and socio-economic predictors of COVID-19 testing and vaccination outcomes: a whole population linked data analysis

ObjectiveThe COVID-19 pandemic has exposed numerous social inequities in health outcomes across nations, despite early warnings of their potential. Poorer outcomes for ethnic minorities, and to a lesser extent Indigenous peoples, have been widely reported, and may be explained in part by household conditions and poorer socio-economic circumstances. This study aimed to identify the effects of diverse individual and social conditions on COVID-19 outcomes within the New Zealand population. MethodWe conducted a whole population analysis of the association between individual (e.g. ethnicity, disability status), household (e.g. household composition), and socio-economic (e.g. crowding, housing quality, income) factors and four COVID-19 health outcomes – infection, hospitalisation, mortality, and vaccination status. We constructed variables of interest from linked administrative data in New Zealand’s Integrated Data Infrastructure, and examined associations within the 2018 Census usually resident population (analysis Ns = 518,571 - 3,216,696). ResultsAnalyses showed that Māori (the Indigenous people of New Zealand) and Pacific peoples, an ethnic minority, experienced worse outcomes than other New Zealanders across all COVID-19 outcomes. These ethnic group differences were persistent, yet reduced, when accounting for household and socio-economic variables. Factors including disability status, high housing mobility, poor quality housing, and household crowding were also widely predictive of worse outcomes. ConclusionEthnic inequity in a range of COVID-19 outcomes is present in a country where the overall impacts of the pandemic were relatively limited. Individual, household, and socio-economic factors are associated with diverse COVID-19 outcomes, and policy amenable factors may help explain the presence of ethnic inequities.

Authors

Institutions

Publication Details

Journal
International Journal for Population Data Science
Published
2024-09-10
DOI
https://doi.org/10.23889/ijpds.v9i5.2637
Primary Topic
Vaccine Coverage and Hesitancy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Individual, household, and socio-economic predictors of COVID-19 testing and vaccination outcomes: a whole population linked data analysis

Nicole Satherley, Andrew Sporle
International Journal for Population Data Science
Vaccine Coverage and Hesitancy
article

Individual, household, and socio-economic predictors of COVID-19 testing and vaccination outcomes: a whole population linked data analysis

Nicole Satherley, Andrew Sporle
article en

Abstract

ObjectiveThe COVID-19 pandemic has exposed numerous social inequities in health outcomes across nations, despite early warnings of their potential. Poorer outcomes for ethnic minorities, and to a lesser extent Indigenous peoples, have been widely reported, and may be explained in part by household conditions and poorer socio-economic circumstances. This study aimed to identify the effects of diverse individual and social conditions on COVID-19 outcomes within the New Zealand population. MethodWe conducted a whole population analysis of the association between individual (e.g. ethnicity, disability status), household (e.g. household composition), and socio-economic (e.g. crowding, housing quality, income) factors and four COVID-19 health outcomes – infection, hospitalisation, mortality, and vaccination status. We constructed variables of interest from linked administrative data in New Zealand’s Integrated Data Infrastructure, and examined associations within the 2018 Census usually resident population (analysis Ns = 518,571 - 3,216,696). ResultsAnalyses showed that Māori (the Indigenous people of New Zealand) and Pacific peoples, an ethnic minority, experienced worse outcomes than other New Zealanders across all COVID-19 outcomes. These ethnic group differences were persistent, yet reduced, when accounting for household and socio-economic variables. Factors including disability status, high housing mobility, poor quality housing, and household crowding were also widely predictive of worse outcomes. ConclusionEthnic inequity in a range of COVID-19 outcomes is present in a country where the overall impacts of the pandemic were relatively limited. Individual, household, and socio-economic factors are associated with diverse COVID-19 outcomes, and policy amenable factors may help explain the presence of ethnic inequities.

International Journal for Population Data ScienceVol. 9(5)
University of Auckland (NZ)
Openalex Percentile: Top 81%
Vaccine Coverage and Hesitancy
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.