Jointly estimating subnational mortality for multiple populations

BACKGROUND: Understanding patterns in mortality across subpopulations is essential for local health policy decision-making. One of the key challenges of subnational mortality rate estimation is the presence of small populations and zero or near zero death counts. When studying differences between su...

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Main Authors: Ameer Dharamshi, Monica Alexander, Celeste Winant, Magali Barbieri
Format: Article
Language:English
Published: Max Planck Institute for Demographic Research 2025-01-01
Series:Demographic Research
Subjects:
Online Access:https://www.demographic-research.org/articles/volume/52/3
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author Ameer Dharamshi
Monica Alexander
Celeste Winant
Magali Barbieri
author_facet Ameer Dharamshi
Monica Alexander
Celeste Winant
Magali Barbieri
author_sort Ameer Dharamshi
collection DOAJ
description BACKGROUND: Understanding patterns in mortality across subpopulations is essential for local health policy decision-making. One of the key challenges of subnational mortality rate estimation is the presence of small populations and zero or near zero death counts. When studying differences between subpopulations, this challenge is compounded as the small populations are further divided along socioeconomic or demographic lines. OBJECTIVE: We aim to develop a model to estimate subnational age-specific mortality rates that accounts for the dependencies in mortality experiences across subpopulations. METHODS: We develop a Bayesian hierarchical principal components-based model that shows correlations across subpopulations. RESULTS: We test this approach in a simulation study and also use the model to estimate age- and sex-specific mortality rates for counties in the United States. The model performs well in validation exercises and the US estimates suggest substantial variation in mortality trends over time across geographic lines. CONTRIBUTION: Our proposed model jointly estimates age-specific mortality rates for multiple subpopulations at the subnational level. By sharing information across subpopulations, our model improves on previous approaches that treat subpopulations as independent. Additionally, we demonstrate that ancillary correlation parameters are a useful tool for studying the convergence and divergence of mortality patterns over time.
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spelling doaj-art-ed27d3436e604f57b2c90861b5d1273d2025-08-20T02:06:35ZengMax Planck Institute for Demographic ResearchDemographic Research1435-98712025-01-015237111010.4054/DemRes.2025.52.36455Jointly estimating subnational mortality for multiple populationsAmeer Dharamshi0Monica Alexander1Celeste Winant2Magali Barbieri3University of WashingtonUniversity of TorontoUniversity of California, BerkeleyInstitut National d'Études Démographiques (INED)BACKGROUND: Understanding patterns in mortality across subpopulations is essential for local health policy decision-making. One of the key challenges of subnational mortality rate estimation is the presence of small populations and zero or near zero death counts. When studying differences between subpopulations, this challenge is compounded as the small populations are further divided along socioeconomic or demographic lines. OBJECTIVE: We aim to develop a model to estimate subnational age-specific mortality rates that accounts for the dependencies in mortality experiences across subpopulations. METHODS: We develop a Bayesian hierarchical principal components-based model that shows correlations across subpopulations. RESULTS: We test this approach in a simulation study and also use the model to estimate age- and sex-specific mortality rates for counties in the United States. The model performs well in validation exercises and the US estimates suggest substantial variation in mortality trends over time across geographic lines. CONTRIBUTION: Our proposed model jointly estimates age-specific mortality rates for multiple subpopulations at the subnational level. By sharing information across subpopulations, our model improves on previous approaches that treat subpopulations as independent. Additionally, we demonstrate that ancillary correlation parameters are a useful tool for studying the convergence and divergence of mortality patterns over time. https://www.demographic-research.org/articles/volume/52/3Bayesian hierarchical modelestimationjoint estimationmortality ratesprincipal components analysissubnationalUS counties
spellingShingle Ameer Dharamshi
Monica Alexander
Celeste Winant
Magali Barbieri
Jointly estimating subnational mortality for multiple populations
Demographic Research
Bayesian hierarchical model
estimation
joint estimation
mortality rates
principal components analysis
subnational
US counties
title Jointly estimating subnational mortality for multiple populations
title_full Jointly estimating subnational mortality for multiple populations
title_fullStr Jointly estimating subnational mortality for multiple populations
title_full_unstemmed Jointly estimating subnational mortality for multiple populations
title_short Jointly estimating subnational mortality for multiple populations
title_sort jointly estimating subnational mortality for multiple populations
topic Bayesian hierarchical model
estimation
joint estimation
mortality rates
principal components analysis
subnational
US counties
url https://www.demographic-research.org/articles/volume/52/3
work_keys_str_mv AT ameerdharamshi jointlyestimatingsubnationalmortalityformultiplepopulations
AT monicaalexander jointlyestimatingsubnationalmortalityformultiplepopulations
AT celestewinant jointlyestimatingsubnationalmortalityformultiplepopulations
AT magalibarbieri jointlyestimatingsubnationalmortalityformultiplepopulations