BACKGROUND: Short birth interval is an important reproductive health concern in low- and middle-income countries, including Bangladesh, as it may have adverse consequences for both perinatal and maternal health. According to the International Classification of Diseases, Tenth Revision (ICD-10) codes O09.891, O09.892 and O09.893, pregnancies occurring after a short interpregnancy interval are classified as high-risk pregnancies that require appropriate medical supervision. Understanding the risk factors associated with short birth intervals is crucial for designing effective interventions and achieving the maternal and child health targets outlined in the Sustainable Development Goals. Therefore, this study aimed to investigate the prevalence and identify the potential risk factors of short birth interval using a two-level logistic regression (LR) model and the Boruta machine learning (ML) based feature selection method. DESIGN: This was a cross-sectional study. SETTING: The study used secondary data from the nationally representative Bangladesh Demographic and Health Survey (BDHS) 2022. PARTICIPANTS: The study included 11 872 married women of reproductive age (15-49 years) in Bangladesh who reported at least one previous live birth. The demographic, socioeconomic, reproductive and media exposure-related characteristics of the study participants were obtained from the BDHS 2022 dataset. METHODS: A two-level LR model was used to identify the risk factors associated with short birth interval, and the results were presented as adjusted ORs (aORs) with 95% CIs and p values <0.05. Additionally, the Boruta ML method was applied to determine the important predictors of short birth intervals. The overlapping risk factors identified by both the LR model and the Boruta method were considered potential risk factors of short birth intervals among reproductive-aged women in Bangladesh. Subsequently, three widely used ML models were implemented in the study. The performance of each model was evaluated on the test dataset using five evaluation metrics, namely accuracy, precision, recall, F1-score and the area under the receiver operating characteristic curve. RESULTS: In Bangladesh, the prevalence of short birth intervals was 34%. Both the two-level LR model and the Boruta method identified women aged ≤19 years (aOR=6.496, 95% CI 2.900 to 14.551; p<0.001); residence in Sylhet (aOR=2.457, 95% CI 1.956 to 3.086; p<0.001), Chattogram (aOR=1.554, 95% CI 1.279 to 1.887; p<0.001), Dhaka (aOR=1.287, 95% CI 1.059 to 1.565; p=0.011), Mymensingh (aOR=1.394, 95% CI 1.118 to 1.738; p=0.003) and Rangpur (aOR=1.248, 95% CI 1.009 to 1.543; p=0.041) divisions; no education (aOR=1.399, 95% CI 1.078 to 1.816; p=0.011); poorest (aOR=1.364, 95% CI 1.146 to 1.624; p<0.001), poorer (aOR=1.230, 95% CI 1.042 to 1.452; p=0.014) and middle wealth quintile (aOR=1.196, 95% CI 1.022 to 1.399; p=0.025); use of traditional contraceptive methods (aOR=1.615, 95% CI 1.305 to 1.998; p<0.001) and no intention to use contraception (aOR=1.506, 95% CI 1.237 to 1.833; p<0.001); birth order ≥4 (aOR=1.467, 95% CI 1.243 to 1.730; p<0.001); and having more than two children ever born (aOR=2.777, 95% CI 2.455 to 3.141; p<0.001) as potential risk factors for short birth interval among reproductive-aged women in Bangladesh. Among the ML-based models, the extreme gradient boosting model achieved the highest performance, with an accuracy of 66.356% in predicting short birth intervals. CONCLUSION: The findings highlight that women aged ≤19 years, those residing in the Eastern and Central divisions, women with no formal education, lower household wealth quintile, traditional or no intention to use contraceptive methods, higher birth order and having more than two children were potential risk factors for short birth interval. The extreme gradient boosting classifier showed the best performance in predicting short birth intervals among reproductive-aged women in Bangladesh.
Akter et al. (Wed,) studied this question.