Trend of determinants of birth interval dynamics in Bangladesh

Abstract

BackgroundThe distribution of birth intervals can be used to draw attention to important characteristics of dynamics of fertility process. The main objective of this paper is to examine the effects of socioeconomic, demographic and proximate determinants on the length of birth intervals of women of Bangladesh and also to see whether the effects are changed over the years.MethodsBirth intervals can be considered as correlated time-to-event data because two or more birth intervals could correspond to a single mother. Moreover, women from the same neighborhood usually share certain unobserved characteristics, which may also lead to correlated time-to-event data (birth interval). A parametric random effect (frailty) model is used to analyze correlated birth interval data obtained from three Bangladesh Demographic and Health Surveys (BDHS 2004, 2007, and 2011).ResultsThe results show that alongside different socioeconomic, demographic determinants, unobserved community and mother effects have considerable impact on birth interval in Bangladesh. However, the effects of different factors on birth interval changes in a small scale over the duration of 2004–2011.ConclusionsEfficient policy is a priority for promoting longer birth spacing and achieving a decline in fertility.

Associated factors of pregnancy spacing among women of reproductive age Group in South of Iran: cross-sectional study

“…The current study indicates that couples who were satisfied with their economic status tended to have longer pregnancy spacing. This result is in accordance with the result of the study that showed low income families tended to have more children with shorter pregnancy spacing [ 25 ]. This might be because the families with higher economic status usually had higher education status, too.…”

Guidance for practitioners on the choices of software implementation for frailty models: Simulations and an application in determining the birth interval dynamics

“…That means birth intervals are considered as time-to-event data. Among the important covariates for modeling birth intervals that were reported in earlier studies (Khan et al, 2016;Mahmood et al, 2013;Chakraborty et al, 1996), those available in BDHS data were selected for this study. Web- Table 14.1 describes summary statistics of the covariates under consideration.…”

“…However, these software implementations of frailty models differ with respect to types of regression models used (e.g., parametric and semiparametric), the assumed base-line hazard and frailty distributions for the estimation of the respective parameters as well as the optimization technique. In real-life applications, where true baseline hazard and frailty distributions are generally unknown, the choice of a parametric or a non-parametric baseline hazard function, as well as the assumed frailty distributions in the frailty model, is rather arbitrary (Khan et al, 2016;Mahmood et al, 2013). The ramifications of the potential violation of this assumption on the parameters associated with the survival model covariates ( 's) and frailties (✓) are hard to assess empirically.…”

“…Using the same scenarios, we will also assess the impact of the varying the number of clusters, percentage of censoring, varying heterogeneity parameter and compare the findings with the existing literature. We will then use these simulation findings to guide our choice of appropriate frailty model in an empirical context of determining the birth interval dynamics in Bangladesh (Khan et al, 2016).…”

Erratum to: Trend of determinants of birth interval dynamics in Bangladesh

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