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TEMPORAL MODELS FOR ESTIMATION AND SHORT-TERM FORECASTING OF NEONATAL MORTALITY RATES IN SUB-SAHARAN AFRICA.

Katherine R Paulson1, Geir-Arne Fuglstad2, Zehang Richard Li3

  • 1Department of Biostatistics, University of Washington.

The Annals of Applied Statistics
|April 23, 2026
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Summary
This summary is machine-generated.

Accurate neonatal mortality rate (NMR) forecasting is crucial for low- and middle-income countries. The penalized spline model shows the least bias and best short-term forecast performance for NMR trends.

Keywords:
Bayesianautoregressiveneonatal mortality ratepenalized splinerandom walktemporal latent effects

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Area of Science:

  • Global Health
  • Demography
  • Biostatistics

Background:

  • Neonatal mortality rates (NMRs) in low- and middle-income countries require accurate estimation and forecasting.
  • Understanding temporal trends in NMR is vital for policy and program evaluation, especially where vital registration is weak.
  • Existing temporal models lack comprehensive evaluation for NMR estimation using household survey data.

Purpose of the Study:

  • To evaluate and compare the performance of various temporal models for estimating and forecasting NMRs in low- and middle-income countries.
  • To identify the most statistically robust models for NMR analysis using household survey data.
  • To assess progress towards Sustainable Development Goals (SDG) targets for neonatal mortality in sub-Saharan Africa.

Main Methods:

  • Utilized Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) data from 41 sub-Saharan African countries.
  • Estimated and forecasted national-level NMRs from 1970-2030 using random walk, auto-regressive, penalized spline, natural spline, and logit-linear latent temporal models.
  • Conducted out-of-sample analyses and simulation studies to examine model statistical behavior and performance.

Main Results:

  • The second-order random walk and penalized spline models exhibited the least bias in NMR estimation.
  • The penalized spline model demonstrated superior out-of-sample performance and narrower forecast intervals for short-term predictions.
  • An estimated 6 or fewer of the 41 countries are projected to meet the SDG target of 12 neonatal deaths per 1000 live births by 2030.

Conclusions:

  • The penalized spline and second-order random walk models are recommended for NMR estimation and forecasting in data-limited settings.
  • Current trends suggest most sub-Saharan African countries will not achieve the SDG target for neonatal mortality by 2030.
  • Improved modeling approaches are essential for effective public health planning and intervention for neonatal survival.