Wasimul Bari

The differentials and determinants of perinatal mortality in rural Bangladesh.

Objective: In Bangladesh, the perinatal mortality is very high. This study examined the differentials and determinants of perinatal mortality in rural Bangladesh. Methods: The study was based on the prospective data on maternal morbidity collected by the Bangladesh Institute of Research for Promotion of Essential and Reproductive Health and Technologies (BIRPERHT). This prospective study was conducted during the period November 1992 to December 1993. In this study, the factors associated with perinatal deaths were examined, employing differential and multivariate analyses. Results: It was found that assisted delivery caused higher risks of stillbirth and death among live births during the first week of life. It was also observed that five or more pregnancies, prior to the index pregnancy, were positively associated with perinatal death. Conclusion: If the delivery is assisted or there are complications in delivery, then it is likely that the incidence of perinatal mortality will increase sharply. If the newborn baby was given colostrum, then perinatal mortality decreased to a great extent.

Factors associated with delivery complications in rural Bangladesh

Objectives There are only a few studies on maternal morbidity, delivery complications and maternal mortality in Bangladesh. This study analyzes data from a follow-up study conducted by the Bangladesh Institute of Research for Health and Technologies (BIRPERHT) on maternal morbidity in rural Bangladesh in 1993. Methods A total of 1020 pregnant women were interviewed in the follow-up component of the study. The survey collected information on socioeconomic and demographic characteristics, pregnancy-related care and practice, morbidity during the period of follow-up as well as in the past, information concerning complications at the time of delivery and during the postpartum period. For the purpose of this study, we selected 993 pregnant women with at least one antenatal follow-up. Both bivariate and multivariate analyses were conducted to identify the potential risk factors for complication during delivery and duration of labor. Results and conclusions It appears that complications during the antenatal period can result in various complications at the time of delivery. Some of the important findings are: hemorrhage during the antenatal period increases the risk of excessive hemorrhage during delivery, the risk of obstructed labor increases significantly if abdominal pain is observed during the antenatal period, prolonged labor appears to be significantly higher for the first pregnancy, and pregnancies suffering from abdominal pain during pregnancy tend to have a higher risk of prolonged labor during delivery. The duration of labor appears to be negatively associated with the number of previous pregnancies, being longest for the first pregnancies. The duration of labor pain is significantly higher for the respondents who reported the index pregnancy as undesired, and, similarly, the respondents who were reported to be involved with gainful employment would have a shorter duration of labor pain than those having no involvement with gainful employment.

“Impact of stature on non-communicable diseases: evidence based on Bangladesh Demographic and Health Survey, 2011 data”

BackgroundIn this paper, an attempt has been made to explore the relationship between height and occurrence of the non-communicable diseases such as diabetes and hypertension.MethodsFor the purpose of analysis, Bangladesh Demographic and Health Survey (BDHS), 2011 data was used. Bivariate analysis along with a Chi-square test was performed to examine association between height and diseases. To measure the impact of stature on diabetes and hypertension, three different logistic regression models (Model I: considering only quartiles of height, Model II: covariates of model I along with demographic variables and Model III: covariates of model II along with clinical variable) were considered.ResultsOccurrence of diabetes and hypertension was found to be inversely related with the height of participants. This inverse association was statistically significant for all three models. After controlling the demographic and clinical variables simultaneously, the odds ratio for highest quartile compared to the lowest quartile was 0.82 with 95% confidence interval (0.69, 0.98) for diabetes; whereas it was 0.72 with 95% confidence interval (0.55, 0.95) for hypertension.ConclusionsFindings of this paper indicate that persons with shorter stature are substantially more likely to develop diabetes as well as hypertension. The occurrence of non-communicable diseases like diabetes and hypertension can be reduced by controlling genetic and non-genetic (early-life and childhood) factors that may influence the height.

A determinant-based criterion for working correlation structure selection in generalized estimating equations.

In generalized estimating equations (GEE), the correlation between the repeated observations on a subject is specified with a working correlation matrix. Correct specification of the working correlation structure ensures efficient estimators of the regression coefficients. Among the criteria used, in practice, for selecting working correlation structure, Rotnitzky-Jewell, Quasi Information Criterion (QIC) and Correlation Information Criterion (CIC) are based on the fact that if the assumed working correlation structure is correct then the model-based (naive) and the sandwich (robust) covariance estimators of the regression coefficient estimators should be close to each other. The sandwich covariance estimator, used in defining the Rotnitzky-Jewell, QIC and CIC criteria, is biased downward and has a larger variability than the corresponding model-based covariance estimator. Motivated by this fact, a new criterion is proposed in this paper based on the bias-corrected sandwich covariance estimator for selecting an appropriate working correlation structure in GEE. A comparison of the proposed and the competing criteria is shown using simulation studies with correlated binary responses. The results revealed that the proposed criterion generally performs better than the competing criteria. An example of selecting the appropriate working correlation structure has also been shown using the data from Madras Schizophrenia Study.

Non-Linear Effects of Continuous Predictors on Malnutrition Status of Children in Bangladesh

Background: To explore non-linear effects of continuous covariates on response variable, generalized additive model (GAM) has now been extensively used. The main aim of this study is to explore the non-linear effect of some selected continuous covariates on malnutrition status of children in Bangladesh using logistic additive model. Methods: To determine the factors of malnutrition status of children in Bangladesh, data are excluded from Bangladesh Demographic and Health survey 2014 (BDHS 2014). The anthropometric index of nutrition status, stunting is used as binary response variable and several demographic, socio-economic and health related variables are used as covariates. Bivariate analysis and additive logistic regression model is used to uncover the non-linear effect of included continuous variables as well as effect of categorical covariates on stunting in the study. Results: The analysis exposes that mother’s current age, mother’s BMI and child’s age have significant non-linear impact on stunting of children. Conclusion: The analysis suggests that to improve nutrition status of children in Bangladesh, the government should take necessary steps targeting women in higher and lower age groups and women with higher and lower BMI. Moreover, the children after 20 months should be properly taken care of for achieving better nutritional health. Keyword: Child’s age, Generalized additive regression model, Malnutrition, Mother’s age, Mother’s BMI, Non-linear effect, Stunting.

Fine and Gray competing risk regression model to study the cause-specific under-five child mortality in Bangladesh

BackgroundThe cause-specific under-five mortality of Bangladesh has been studied by fitting cumulative incidence function (CIF) based Fine and Gray competing risk regression model (1999). For the purpose of analysis, Bangladesh Demographic and Health Survey (BDHS), 2011 data set was used.MethodsThree types of mode of mortality for the under-five children are considered. These are disease, non-disease and other causes. Product-Limit survival probabilities for the under-five child mortality with log-rank test were used to select a set of covariates for the regression model. The covariates found to have significant association in bivariate analysis were only considered in the regression analysis.ResultsPotential determinants of under-five child mortality due to disease is size of child at birth, while gender of child, NGO (non-government organization) membership of mother, mother’s education level, and size of child at birth are due to non-disease and age of mother at birth, NGO membership of mother, and mother’s education level are for the mortality due to other causes.ConclusionFemale participation in the education programs needs to be increased because of the improvement of child health and government should arrange family and social awareness programs as well as health related programs for women so that they are aware of their child health.

Log-Logistic Proportional Odds Model for Analyzing Infant Mortality in Bangladesh.

Log-logistic parametric survival regression model has been used to find out the potential determinants of infant mortality in Bangladesh using the data extracted from Bangladesh Demographic and Health Survey, 2011. First, nonparametric product-limit approach has been used to examine the unadjusted association between infant mortality and covariate of interest. It is found that maternal education, membership of nongovernmental organizations, age of mother at birth, sex of child, size of child at birth, and place of delivery play an important role in reducing the infant mortality, adjusting relevant covariates.

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

Background: The 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. Methods: Birth 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). Results: The 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. Conclusions: Efficient policy is a priority for promoting longer birth spacing and achieving a decline in fertility.

Observation-Driven Model for Zero-Inflated Daily Counts of Emergency Room Visit Data

Time series data with excessive zeros frequently occur in medical and health studies. To analyze time series count data without excessive zeros, observation-driven Poisson regression models are commonly used in the literature. As handling excessive zeros in count data is not straightforward, observation-driven models are rarely used to analyze time series count data with excessive zeros. In this paper an observation-driven zero-inflated Poisson (ZIP) model for time series count data is proposed. This approach can accommodate an autoregressive serial dependence structure which commonly appears in time series. The estimation of the model parameters by using the quasi-likelihood estimating equation approach is discussed. To estimate the correlation parameters of the dependence structure, a moment approach is used. The proposed methodology is illustrated by applying it to a data set of daily emergency room visits due to bronchitis.