Data and analysis code forSex differences in early and late neonatal all-cause mortality and disease burden in Kazakhstan, 2010–2023: A population-based analysis of Global Burden of Disease estimates

This repository contains the analytic dataset and R code supporting the statistical analyses reported in the associated study of sex-specific trends in neonatal mortality and disease burden in Kazakhstan using Global Burden of Disease (GBD) 2023 estimates. The analytic dataset includes annual estimates for Kazakhstan from 2010 to 2023 for the early neonatal (0–6 days) and late neonatal (7–27 days) periods. Data are stratified by sex (male, female, and both sexes) and include deaths, disability-adjusted life years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs), expressed using the GBD age-specific rate metric. Point estimates and corresponding lower and upper 95% uncertainty bounds are provided. The accompanying R script reproduces the principal statistical analyses and numerical results reported in the manuscript and supplementary materials. These include estimates and percentage changes between 2010 and 2023; estimated annual percentage changes (EAPCs) from log-linear regression; male-to-female rate ratios with approximate uncertainty intervals; segmented regression and Davies tests for changes in temporal trends; residual autocorrelation diagnostics; and projections to 2030 using log-linear regression as the primary forecasting model, with linear regression and exponential smoothing (ETS) as sensitivity models. Model assessment includes prediction intervals, AIC and BIC where applicable, and rolling-origin out-of-sample RMSE. The deposited datasets represent the original data and the analysis-ready data used to generate the reported statistical results. Code used solely for graphical rendering and manuscript figure formatting is not included; the numerical data underlying the figures are contained in the deposited analytic dataset. The source estimates were obtained from the Institute for Health Metrics and Evaluation (IHME), Global Burden of Disease Study 2023. The repository is intended to facilitate transparency, reproducibility, and independent verification of the statistical analyses reported in the associated publication.