ArticlePloS one2020
Modelling zero-truncated overdispersed antenatal health care count data of women in Bangladesh.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Parental Education and Children Ever Born Among Women in Bangladesh: Evidence From a Nationally Representative Survey.Public health challenges · 2026Article
- Factors Influencing Timing of First Antenatal Care Visit Among Bangladeshi Women: Application of Accelerated Failure Time Models Using Demographic Health Survey 2022.Health science reports · 2026Article
- Robustness of zero truncated negative binomial models over traditional models with application to analysing factor association to length of ICU stay in a middle-income country.BMC research notes · 2026Article
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- Maternal health inequity in Somalia: A multivariate analysis of poverty and service utilization using nationwide cross-sectional survey data.Public health in practice (Oxford, England) · 2025Article
- Beyond averages: dissecting urban-rural disparities in skilled antenatal care utilization in Bangladesh - a conway-maxwell-poisson regression analysis.BMC pregnancy and childbirth · 2025Article
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- Does dropout from school matter in taking antenatal care visits among women in Bangladesh? An application of marginalized poisson-poisson mixture model.BMC pregnancy and childbirth · 2022Article
- Determinants of Antenatal Care Visits Among Pregnant Women in Somalia: A Zero-Inflated Poisson Regression Analysis of the First-Ever Somali Health and Demographic Survey 2020 Datasets.Women's health reports (New Rochelle, N.Y.)Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Overdispersion in count data analysis is very common in many practical fields of health sciences. Ignorance of the presence of overdispersion in such data analysis may cause misleading inferences and thus lead to incorrect interpretations of the results. Researchers should account for the consequences of overdispersion and need to select the correct choice of models for the analysis of such data. In this paper, Generalized Linear Models (GLMs) are applied in modelling and analysis of antenatal care (ANC) count data extracted from the Bangladesh Demographic and Health Survey (BDHS) 2014. Pearson chi-square and different score tests are used to investigate the effect of overdispersion in the analysis. Overdispersion is found to be significant in the antenatal health care count data and so appropriate modelling is used to produce valid inferences for the regression parameters. The zero-truncated negative binomial regression (0-NBR) is found to be the best choice for analysing such data while excluding zero counts. Study findings reveal that place of residence, order of birth, exposure to mass media, wealth index and education of mother have significant impacts on the ANC status of women during pregnancy in Bangladesh.
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