Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Predictive Modelling for Stillbirths and Neonatal Deaths in Sub-Saharan Africa

Domaine:

healthcare

Type de record:

softwaredatasetpaper
Créateur:
AkuVicBouWan
Éditeur:
DadWaiLawHanson, Claudia
Éditeur:
Zenodo
Hôte:avatar

Initial release of the reproducible analytical pipeline for data harmonisation and predictive modelling of stillbirths and neonatal deaths in sub-Saharan Africa (SSA), integrating seven contributing studies: the Action Leveraging Evidence to Reduce Perinatal Mortality and Morbidity trial (ALERT), the Every Newborn-INDEPTH study (EN-INDEPTH), the Preterm Birth Initiative (PTBi), the Pregnancy Care Integrating Translational Science Everywhere cohort (PRECISE), the WHO Multi-Country Survey on Maternal and Newborn Health (WHOMCS), the Neonatal Care Outcomes Project Study (NCOPS), and Demographic and Health Surveys (DHS). The unified dataset contains 5,996,390 birth records from 66 countries.

The pipeline implements a structured five-stage harmonisation framework: (1) ethical data acquisition and governance; (2) variable mapping across 13 harmonised domains using standardised domain-prefix naming conventions; (3) value standardisation and recoding using structured case-when logic with regular-expression pattern matching; (4) linkage of environmental and climate data from ERA5, CHIRPS, SRTM, ACAG and MODIS sources at 99.3% completeness; and (5) quality assurance including range validation, cross-tabulations and logical consistency checking. The modelling pipeline benchmarks classical statistical methods (logistic regression, generalised estimating equations), ensemble machine learning (Random Forest, XGBoost, LightGBM, CatBoost) and exploratory deep learning (multilayer perceptrons) across four prediction scenarios and two primary outcomes. Interpretability analysis uses SHapley Additive exPlanations (SHAP) values throughout.

The methodology is documented in Data Harmonisation Documentation Version 6.1 and Statistical Analysis Plan Version 1 (February 2026), both publicly deposited on the Open Science Framework prior to commencement of model development analyses.

Funding: Wellcome Trust Accelerator Award 314747/Z/24/Z, London School of Hygiene and Tropical Medicine. The funders had no role in study design, development, or preparation of the code.

Related resources:

  • OSF project: https://osf.io/ptf7x/overvi…
  • Harmonisation methodology: https://osf.io/a5rkz/overvi…
  • Statistical Analysis Plan: https://osf.io/djz5c/overvi…
  • Published protocol: https://doi.org/10.12688/we…
  • Wellcome grant: https://wellcome.org/resear…

Visit

doi.org

Tags

stillbirthneonatal deathsub-Saharan Africapredictive modellingdata harmonisationmachine learningperinatal healthLMIClow- and middle-income countriesALERT+7

Licenses

info:eu-repo/semantics/openAccessMIT Licensehttps://opensource.org/licenses/MIT

Similaires

jakuze2/Predictive-modelling-stillbirths-neonatal-deaths-SSAStatistical Analysis Plan: Predictive Modelling of Stillbirths and Neonatal Deaths in Sub-Saharan Africa Using Classical, Machine Learning, and AI Approaches (Version 1, February 2026)Direct maternal morbidity and the risk of pregnancy-related deaths, stillbirths, and neonatal deaths in South Asia and sub-Saharan Africa: A population-based prospective cohort study in 8 countriesThe potential for aflatoxin predictive risk modelling in sub-Saharan Africa: a reviewEnding Neonatal Deaths From Hypothermia in Sub-Saharan Africa: Call for Essential Technologies Tailored to the Context

jakuze2/Predictive-modelling-stillbirths-neonatal-deaths-SSA

Reproducible analytical pipeline for data harmonisation and predictive modelling of stillbirths and

Statistical Analysis Plan: Predictive Modelling of Stillbirths and Neonatal Deaths in Sub-Saharan Africa Using Classical, Machine Learning, and AI Approaches (Version 1, February 2026)

This component contains the pre-specified Statistical Analysis Plan (SAP) governing the development,

Direct maternal morbidity and the risk of pregnancy-related deaths, stillbirths, and neonatal deaths in South Asia and sub-Saharan Africa: A population-based prospective cohort study in 8 countries

Background Maternal morbidity occurs several times more frequently than mortality, yet data on morb

The potential for aflatoxin predictive risk modelling in sub-Saharan Africa: a review

This review presents the current state of aflatoxin risk prediction models and their potential for v

Ending Neonatal Deaths From Hypothermia in Sub-Saharan Africa: Call for Essential Technologies Tailored to the Context

Neonatal death represents a major burden in Sub-Saharan Africa (SSA), where the main conditions trig