Volume 18, No. 2, 2021
Advancements In Statistical Modeling Of Fertility And Birth Interval Dynamics: Methodologies, Challenges, And Future Directions
Phurailatpam Kamala Devi , Oinam Tomba Singh
Abstract
This review critically examines the advancements in statistical modeling techniques used to analyse fertility and birth interval dynamics. Key methodologies, including survival analysis, Bayesian methods, machine learning, multistate transition models, and spatial statistical techniques, are explored for their ability to capture the complex interactions of biological, socio-economic, and cultural factors influencing fertility. The evolution of these models allowed researchers to better understand temporal aspects of fertility patterns, such as postpartum amenorrhoea, and to uncover region-specific disparities in birth intervals. Despite their contributions, these models faced limitations in addressing issues like computational complexity, data quality, and the integration of qualitative factors. Bayesian and machine learning methods, for example, provided nuanced insights but were often constrained by data and computational demands, particularly in resource-limited settings. Multistate transition and spatial statistical models offered detailed analyses of reproductive processes and geographic disparities but struggled with data accessibility and the integration of socio-cultural dimensions. The review suggests that future research should focus on enhancing model accessibility, integrating qualitative dimensions, and improving their applicability for policy-making, with a particular emphasis on addressing computational and data limitations.
Pages: 3196-3203
Keywords: fertility modeling, statistical techniques, Bayesian methods, machine learning, birth intervals.