Estimating subnational under-five mortality rates using a spatio-temporal Age-Period-Cohort model

Subnational estimates of under-five mortality rates (U5MRs) are a vital statistic for the United Nations to reduce mortality inequalities between high-income and Low-and-Middle Income Countries (LMICs). Current methods of modelling U5MR in LMICs smooth across trends in age and year of death, but not birth-cohort, to reduce uncertainty in estimates caused by data-sparsity. Using survey data from Kenya, we innovatively apply an Age-Period-Cohort model which accounts for spatial trends and the complex survey design of the data to estimate subnational U5MRs in Kenya. After validating our results against current methods, the inclusion of cohort can provide new insights into U5MRs. We ensure our method is flexible and can be applied to other LMICs.

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Publication Details

Journal
Spatial and Spatio-temporal Epidemiology
Published
2024-12-13
DOI
https://doi.org/10.1016/j.sste.2024.100708
Citations
3
Primary Topic
Insurance, Mortality, Demography, Risk Management
Type
article
Field-Weighted Citation Impact
5.12
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article

Estimating subnational under-five mortality rates using a spatio-temporal Age-Period-Cohort model

Jonathan Wakefield, Theresa Smith, John Paige, Connor Gascoigne
3 citations
Spatial and Spatio-temporal Epidemiology
Insurance, Mortality, Demography, Risk Management
5.12
article

Estimating subnational under-five mortality rates using a spatio-temporal Age-Period-Cohort model

Jonathan Wakefield, Theresa Smith, John Paige, Connor Gascoigne
article en
3 citations

Abstract

Subnational estimates of under-five mortality rates (U5MRs) are a vital statistic for the United Nations to reduce mortality inequalities between high-income and Low-and-Middle Income Countries (LMICs). Current methods of modelling U5MR in LMICs smooth across trends in age and year of death, but not birth-cohort, to reduce uncertainty in estimates caused by data-sparsity. Using survey data from Kenya, we innovatively apply an Age-Period-Cohort model which accounts for spatial trends and the complex survey design of the data to estimate subnational U5MRs in Kenya. After validating our results against current methods, the inclusion of cohort can provide new insights into U5MRs. We ensure our method is flexible and can be applied to other LMICs.

Spatial and Spatio-temporal EpidemiologyVol. 52
University of Washington (US), Norwegian University of Science and Technology (NO), Imperial College London (GB), University of Bath (GB)
Good health and well-being
Openalex Percentile: Top 4%
Insurance, Mortality, Demography, Risk Management
5.12
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