Analyzing Overdispersed Antenatal Care Count Data in Bangladesh: Mixed Poisson Regression with Individual-Level Random Effects (original) (raw)
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Clinical Epidemiology and Global Health, 2020
Objectives Antenatal care has several benefits for expecting mothers and birth outcomes and this study attempts to investigate the prevalence and estimate the risk factors of antenatal care visits utilization in Bangladesh. Material and methods In our study, using the data set BDHS 2014 we have fitted the Poisson Regression Model and Negative Binomial models. Results The most significant factors are respondent's residents (IRR = 0.845, CI: .778, .918), wealth index (IRR = 1.326, CI: 1.186, 1.482), respondent education (IRR = 1.753, CI: 1.461, 2.104) and media access (IRR = 1.235, CI: 1.125, 1.355) with a p-value P < 0.001. Our study found a strong association between the place of residence of the respondents and ANC received by the respondents. In the case of model selection, the Negative Binomial Regression Model had the least -2logL (18647.236), AIC (18695.237) and BIC (18848.690). Conclusion The results of our study strongly demonstrate that the factors respondent's education level, wealth index, respondent's residents, media access and birth order number have great influence in the utilization of antenatal care services. This study also showed that the Negative Binomial Regression Model was the better fitted in identifying factors associated with ANC visits in Bangladesh.
BMC Pregnancy and Childbirth
Background There exists a lack of research in explaining the link between dropout from school and antenatal care (ANC) visits of women during pregnancy in Bangladesh. The aim of this study is to investigate how the drop out from school influences the ANC visits after controlling the relevant covariates using an appropriate count regression model. Methods The association between the explanatory variables and the outcome of interest, ANC visits, have been performed using one-way analysis of variance/independent sample t-test. To examine the adjusted effects of covariates on the marginal mean of count data, Marginalized Poison-Poisson mixture regression model has been fitted. Results The estimated incidence rate of antenatal care visits was 10.6% lower for the mothers who were not continued their education after marriage but had at least 10 years of schooling (p-value <0.01) and 20.2% lower for the drop-outed mothers (p-value <0.01) than the mothers who got continued their educat...
Multidisciplinary Science Journal
Maternal mortality and morbidity reduction constitute policy priorities, facilitated by prenatal care and World Health Organization (WHO)-endorsed antenatal care (ANC) utilization during pregnancy. Progress in Bangladesh is hindered as only 47% of expectant women participated in a minimum of four ANC sessions according to the latest maternal mortality survey. This study, utilizing 2018 Bangladesh Demographic and Health Survey (BDHS) data, undertakes an assessment of the socioeconomic determinants influencing the utilization or non-utilization of ANC services. Additionally, the study investigates socioeconomic factors significantly impacting the attainment of the WHO-recommended four or more ANC sessions. A Hurdle Negative Binomial Model is employed to ascertain ANC risk variables and their frequency, while the utilization characteristics of WHO ANC services are discerned through the Binary Logistic Regression Model. Noteworthy among the statistically significant determinants influen...
Poisson Regression Modeling Generalized in Maternal Mortality Cases in Aceh Tamiang Regency
BAREKENG: Jurnal Ilmu Matematika dan Terapan
Maternal Mortality Rate (MMR) is the number of maternal deaths due to the process of pregnancy, childbirth, and postpartum which is used as an indicator of women's health degrees. The number of maternal deaths in Aceh Tamiang Regency in 2021 is a discrete random variable distributed by Poisson. The purpose of this study is to find out what poisson regression model is generalized in the case of MMR in Aceh Tamiang Regency in 2021 and what factors affect the AKI in Aceh Tamiang Regency in 2021. The research data was obtained from the Aceh Tamiang District Health Office. This type of research is quantitative by using the Generalized Poisson Regression method. The data used are maternal mortality rates and data on factors affecting MMR in Aceh Tamiang Regency in 2021. Influencing factors are the percentage of visits by pregnant women in K1 , percentage of visits by pregnant women K4 , percentage of maternity assistance by health workers , TT immunization of pregnant women , pregnant...
Contribution of Socio-Demographic Factors on Antenatal Care in Bangladesh: Modeling Approach
Public Health Research, 2015
Background: Antenatal care (ANC) is essential for both mother and child health well-being. The risk of maternal mortality and morbidity as well as neonatal deaths can be reduced substantially through regular and proper antenatal care taken and delivery under safe and hygienic conditions. An attempt has been made to disclose how many times antenatal care (ANC) was taken by the pregnant mother and to find out the contribution of socio-demographic factors on ANC. Moreover, an effort is concentrated to find out a functional relationship between number of visits of ANC and the respondents. Data and methods: Data and necessary information of 4,921 reproductive women were obtained from the Bangladesh Demographic and Health Survey (BDHS) 2007. Multiple classification analysis (MCA) was used to identify the most important determinants of number of antenatal visit. Furthermore, negative exponential model was also employed here. Results: The results reveal that majority (37.83%) women have not...
Analysis Of Overdispersed Count Data By Poisson Model
2021
Lack assumption that commonly happens in Poisson model is over-dispersion. Over-dispersion is a condition in which the variance value is larger than mean of response variable. The aim of this research is to analyze Poisson models, i.e. Poisson Regression (POI), Zero-Inflated Poisson Regression (ZIP), Generalized Poisson Regression (GP) and Zero-Inflated Generalized Poisson Regression (ZIGP) of over-dispersion data. The data used in this research is Indonesian Demographic and Health Survey (SKDI) Data in 2017. Total number of 17.212 families with response variable of child mortality in these families become the objects of the study. The estimator of parameter model is Maximum likelihood estimator (MLE). The results analysis of those four models aforementioned above show that over-dispersion case causes the usage of POI model becomes less appropriate, while GP model can be used for over-dispersion case, however if the case of over-dispersion is caused by zero excess in the data, GP wi...
Malnourished Children in Bangladesh : Application of the Generalized Poisson Regression Model
2019
Background: Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. Methods: The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of ...
Biometrical Journal, 2013
Count data are very common in health services research, and very commonly the basic Poisson regression model has to be extended in several ways to accommodate several sources of heterogeneity: (i) an excess number of zeros relative to a Poisson distribution, (ii) hierarchical structures, and correlated data, (iii) remaining "unexplained" sources of overdispersion. In this paper, we propose hierarchical zero-inflated and overdispersed models with independent, correlated, and shared random effects for both components of the mixture model. We show that all different extensions of the Poisson model can be based on the concept of mixture models, and that they can be combined to account for all different sources of heterogeneity. Expressions for the first two moments are derived and discussed. The models are applied to data on maternal deaths and related risk factors within health facilities in Mozambique. The final model shows that the maternal mortality rate mainly depends on the geographical location of the health facility, the percentage of women admitted with HIV and the percentage of referrals from the health facility.
On Zero-Inflated Hierarchical Poisson Models with Application to Maternal Mortality Data
International Journal of Mathematics and Mathematical Sciences
Count outcomes are commonly encountered in health sector data. The occurrence of count outcomes that exhibit many zeros has necessitated the extension of the ubiquitous Poisson regression model to accommodate the zero inflation and overdispersion as a result of the extra dispersion. We explored different extensions of the Poisson model including mixed models within the generalized linear mixed model framework to account for the repeated measurement of outcomes. These models are applied to maternal mortality data from fifty-six health facilities in four regions of Ghana. The objective is to identify factors associated with maternal mortality. The best-fitting model, the zero-inflated Poisson generalized linear mixed model, revealed that maternal mortality in hospital facilities is influenced by the number of referrals (into and out) of the hospital facility, number of antenatal visits exceeding four, number of midwives, and number of medical doctors at the facility. To be able to ach...