چکیده: (137 مشاهده)
Background: Child mortality is a significant public health issue in Indonesia, influenced by various factors. Understanding key predictors is crucial for effective interventions. This study analyses predictors of child mortality using secondary data from the 2017 Indonesia Demographic Health Survey (IDHS) and evaluates the predictive ability of birth size and birth order through Receiver Operating Characteristic (ROC( analysis.
Method: It was a cross-sectional study using secondary data from 2017 IDHS. A total of 13,946 respondents were recruited by multistage stratified random sampling. This study only included one child per mother to clarify the analysis and avoid confusion. Accordingly, the last-born children under five were chosen as the 2017 IDHS collects the most complete and detailed information about the most recent child. The data were analysed using bivariate and multivariate techniques to examine associations between child mortality and factors such as birth size, birth order, maternal education, and socioeconomic status. The ROC curve assessed the model's predictive performance. The data for this study were analysed using Statistical Package for Social Sciences (SPSS) version 21 (IBM, USA). The significance level was set at P<0.05.
Results: Significant predictors included maternal education (p-value: <0.001; OR: 3.750; 95% CI: 1.23-11.34), birth size (p-value: <0.001; OR: 0.130; 95% CI: 0.03-0.44), and birth order (p-value: <0.001; OR: 0.168; 95% CI: 0.09.0.30). Multivariate analysis showed that larger birth sizes were associated with higher mortality odds than very small children (p-value: <0.005; OR: 5,964; 95% CI: 1,698-20,942). First and second-born children had higher mortality risks (p-value: <0.001; OR: 5.209; 95% CI: 2.861-9.483). The ROC analysis revealed modest predictive ability, with area under the curve (AUC) values of 0.611 for birth order and 0.582 for birth size.
Conclusion: Birth size and birth order significantly predict child mortality, but their predictive power is limited. Expanding models to include maternal health and socioeconomic factors is necessary. Public health efforts should enhance prenatal care, particularly for early-born children, and address disparities to improve child survival outcomes.
Method: It was a cross-sectional study using secondary data from 2017 IDHS. A total of 13,946 respondents were recruited by multistage stratified random sampling. This study only included one child per mother to clarify the analysis and avoid confusion. Accordingly, the last-born children under five were chosen as the 2017 IDHS collects the most complete and detailed information about the most recent child. The data were analysed using bivariate and multivariate techniques to examine associations between child mortality and factors such as birth size, birth order, maternal education, and socioeconomic status. The ROC curve assessed the model's predictive performance. The data for this study were analysed using Statistical Package for Social Sciences (SPSS) version 21 (IBM, USA). The significance level was set at P<0.05.
Results: Significant predictors included maternal education (p-value: <0.001; OR: 3.750; 95% CI: 1.23-11.34), birth size (p-value: <0.001; OR: 0.130; 95% CI: 0.03-0.44), and birth order (p-value: <0.001; OR: 0.168; 95% CI: 0.09.0.30). Multivariate analysis showed that larger birth sizes were associated with higher mortality odds than very small children (p-value: <0.005; OR: 5,964; 95% CI: 1,698-20,942). First and second-born children had higher mortality risks (p-value: <0.001; OR: 5.209; 95% CI: 2.861-9.483). The ROC analysis revealed modest predictive ability, with area under the curve (AUC) values of 0.611 for birth order and 0.582 for birth size.
Conclusion: Birth size and birth order significantly predict child mortality, but their predictive power is limited. Expanding models to include maternal health and socioeconomic factors is necessary. Public health efforts should enhance prenatal care, particularly for early-born children, and address disparities to improve child survival outcomes.
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