Pooling data (or NOT): an empirical examination of the relationship between maternal education and child morbidity in Central Africa using Demographic and Health Surveys.
6 affiliations
- 1School of Population and Development Sciences, University of Kinshasa, Kinshasa, Congo (the Democratic Republic of the).
- 2University of Zambia, Lusaka, Zambia.
- 3Université du Québec, Rimouski, Quebec, Canada.
- 4Dar es Salaam University College of Education, Universiry of Dar es Salaam, Dar es Salaam, Tanzania, Dar es Salaam, Tanzania, United Republic of.
- 5Arba Minch University, Arba Minch, Ethiopia.
- 6University of Technology and Applied Sciences, Navrongo, Ghana.
Abstract
Introduction
Pooling datasets is increasingly used in global health research to increase statistical power, improve precision and enable cross-country comparisons. Yet, combining data from heterogeneous settings may obscure important contextual differences and lead to conclusions that diverge from those obtained through country-specific analyses. Using maternal education and child morbidity as a case study, this paper examines how pooled vs individual datasets can yield different interpretations, highlighting the implications for research and policy.Methods
We conducted a secondary analysis of Demographic and Health Surveys (DHS) from five Central African countries: the Democratic Republic of Congo, Cameroon, Republic of Congo, Gabon and Chad. The study population included children aged 0-59 months. Child morbidity outcomes-fever, diarrhoea and cough in the 2 weeks preceding the survey-were analysed using descriptive statistics and multivariate logistic regression models applied separately to pooled and country-specific datasets.Results
The final analytic sample comprised 58 079 children under 5 years of age. Educational attainment varied substantially across countries, with 66.0% (96% CI 64.0% to 67.9%) of women in Chad reporting no formal education-far higher than in the other countries. Overall prevalence of cough, diarrhoea and fever was 6.6% (95% CI 6.2% to 7.1%), 19.3% (95% CI 18.6% to 19.9%) and 26.1% (95% CI 25.2% to 26.9%), respectively, with notable cross-country variation. Multivariate analyses revealed that the association between maternal education and child morbidity differed between the pooled dataset and the individual country models, demonstrating how aggregation can mask local heterogeneity.Conclusion
Pooling datasets can be a powerful analytical strategy, particularly for increasing statistical power, improving the precision of estimates and enabling cross-country comparisons. Using DHS data from five Central African countries, this study demonstrates that while pooled analyses identify broad regional patterns, they can mask important country-specific heterogeneity, which is of paramount importance to devise national and local policies.
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BibTeX
@article{dimbuene2026pooling,
title = {{Pooling data (or NOT): an empirical examination of the relationship between maternal education and child morbidity in Central Africa using Demographic and Health Surveys.}},
author = {Zacharie Tsala Dimbuene and Elizabeth T Nyirenda and Philippe Kabore and Janet Kalinga and Negussie Boti Sidamo and Francisca Achaliwie},
journal = {BMJ public health},
year = {2026},
month = oct,
doi = {10.1136/bmjph-2026-004892},
url = {https://europepmc.org/article/MED/42968922},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Pooling data (or NOT): an empirical examination of the relationship between maternal education and child morbidity in Central Africa using Demographic and Health Surveys.
AU - Zacharie Tsala Dimbuene
AU - Elizabeth T Nyirenda
AU - Philippe Kabore
AU - Janet Kalinga
AU - Negussie Boti Sidamo
AU - Francisca Achaliwie
PY - 2026
DA - 2026/10/27
JO - BMJ public health
DO - 10.1136/bmjph-2026-004892
UR - https://europepmc.org/article/MED/42968922
AB - Introduction Pooling datasets is increasingly used in global health research to increase statistical power, improve precision and enable cross-country comparisons. Yet, combining data from heterogeneous settings may obscure important contextual differences and lead to conclusions that diverge from those obtained through country-specific analyses. Using maternal education and child morbidity as a case study, this paper examines how pooled vs individual datasets can yield different interpretations, highlighting the implications for research and policy.Methods We conducted a secondary analysis of Demographic and Health Surveys (DHS) from five Central African countries: the Democratic Republic of Congo, Cameroon, Republic of Congo, Gabon and Chad. The study population included children aged 0-59 months. Child morbidity outcomes-fever, diarrhoea and cough in the 2 weeks preceding the survey-were analysed using descriptive statistics and multivariate logistic regression models applied separately to pooled and country-specific datasets.Results The final analytic sample comprised 58 079 children under 5 years of age. Educational attainment varied substantially across countries, with 66.0% (96% CI 64.0% to 67.9%) of women in Chad reporting no formal education-far higher than in the other countries. Overall prevalence of cough, diarrhoea and fever was 6.6% (95% CI 6.2% to 7.1%), 19.3% (95% CI 18.6% to 19.9%) and 26.1% (95% CI 25.2% to 26.9%), respectively, with notable cross-country variation. Multivariate analyses revealed that the association between maternal education and child morbidity differed between the pooled dataset and the individual country models, demonstrating how aggregation can mask local heterogeneity.Conclusion Pooling datasets can be a powerful analytical strategy, particularly for increasing statistical power, improving the precision of estimates and enabling cross-country comparisons. Using DHS data from five Central African countries, this study demonstrates that while pooled analyses identify broad regional patterns, they can mask important country-specific heterogeneity, which is of paramount importance to devise national and local policies.
SN - 2753-4294
ER -