Neonatal and Postneonatal Death Clustering Among Siblings in Bangladesh: Evidence from Bangladesh Demographic and Health Survey 2017–2018

Despite knowledge of neonatal and postneonatal mortality determinants in Bangladesh, some families continue to lose a larger share of children, a phenomenon known as early-life mortality clustering. This study uses the random intercept Weibull survival regression model to explore the correlation of mortality risk among siblings at the family (or, mother) and community levels. Utilizing the Bangladesh Demographic and Health Survey 2017–2018, we found evidence of death scarring, where children whose previous sibling was not alive at the time of conception had significantly higher odds of neonatal mortality. Moreover, the neonatal (and postneonatal) mortality hazard was highest for children with a birth interval of fewer than 19 months, corresponding to the preceding sibling. The intraclass correlation coefficient's statistically significant values show that neonatal and postneonatal mortality risk is correlated among children of the same family and community. The findings suggest focusing on high-risk families and communities to reduce the mortality level effectively.

A Comparative approach to cluster maternal data using k-means and k-medoid

In our work, we cluster the data collected on pregnancy women through hospitals, into three different categories such as elective LSCS, emergency LSCS and normal delivery. We aim to identify outliers so that we can predict the complications that can occur during pregnancy. These complications can even lead to maternal death. Clustering is performed by the aid of k-means, and k-medoid procedures which are implemented in windows form application to run in Microsoft Visual Studio, a comparison is provided on its performance and efficiency between both the algorithms and also the outlier, i.e. normal delivery cases are detected and depicted. From our work, we can clearly say that K-Means works better when the dataset is between the range of 50 – 100. Suppose the data set is greater than 500 then K-medoid works efficiently. In our work, we are considering various maternity cases. Here we are detecting normal delivery cases as an outlier.

BH3-mimetic toolkit guides the respective use of BCL2 and MCL1 BH3-mimetics in myeloma treatment

BH3 mimetics are promising drugs for hematologic malignancies that trigger cell death by promoting the release of proapoptotic BCL2 family members from antiapoptotic proteins. Multiple myeloma is considered to be a disease dependent mainly on MCL1 for survival, based mostly on studies using cell lines. We used a BH3-mimetic toolkit to study the dependency on BCL2, BCLXL, or MCL1 in malignant plasma cells from 60 patients. Dependencies were analyzed using an unbiased BH3 mimetics cell-death clustering by k-means. In the whole cohort of patients, BCL2 dependency was mostly found in the CCND1 subgroup (83%). Of note, MCL1 dependence significantly increased from 33% at diagnosis to 69% at relapse, suggesting a plasticity of the cellular dependency favoring MCL1 dependencies at relapse. In addition, 35% of overall patient samples showed codependencies on either BCL2/MCL1 or BCLXL/MCL1. Finally, we identified a group of patients not targeted by any of the BH3 mimetics, predominantly at diagnosis in patients not presenting the common recurrent translocations. Mechanistically, we demonstrated that BAK is crucial for cell death induced by MCL1 mimetic A1210477, according to the protection from cell death observed by BAK knock-down, as well as the complete and early disruption of MCL1/BAK complexes on A1210477 treatment. Interestingly, this complex was also dissociated in A1210477-resistant cells, but free BAK was simultaneously recaptured by BCLXL, supporting the role of BCLXL in A1210477 resistance. In conclusion, our study opens the way to rationally use venetoclax and/or MCL1 BH3 mimetics for clinical evaluation in myeloma at both diagnosis and relapse.

Introduction: Intergenerational Transmissions of Infant Mortality using the Intermediate Data Structure (IDS)

It has previously been shown that infant mortality clusters in a subset of families, a phenomenon which was observed in historical populations as well as contemporary developing countries. A transmission of death clustering across generations has also been shown in Belgium, but it is unknown whether such effects are specific to the studied context or are also found in other areas. The current article introduces a special issue devoted to analysing intergenerational transmissions of infant mortality across the maternal line in Belgium, the Netherlands, northern and southern Sweden, and Norway. Taking advantage of the Intermediate Data Structure (IDS), the five empirical studies created datasets for analysis and ran statistical models using exactly the same programs, which are also published within the special issue. These works are the first set of studies using the IDS on several databases for comparative purposes. Consistent results across the studied contexts were shown: transfers of infant mortality across the maternal line were seen in all five areas. In addition, the works have shown that there are large advantages of adopting the IDS for historical demographic research. The structure has in fact allowed researchers to conduct studies which were fully comparable, transparent and replicable.

Intergenerational Transfers in Infant Mortality in Southern Sweden, 1740-1968

Studies conducted in historical populations and developing countries have evidenced the existence of clustering in infant deaths, which could be related to genetic inheritance, early life exposures, and/or to social and cultural factors such as education, socioeconomic status or parental care. A transmission of death clustering has also been found across generations. This paper is one of five studies that analyses intergenerational transmissions in infant mortality by using a common program to create the dataset for analysis and run the statistical models with data stored in the Intermediate Data Structure. The results of this study show that in five rural parishes in Scania, the southernmost province of Sweden, during the years 1740-1968 infant mortality was transmitted across generations. Children whose maternal grandmothers experienced two or more infant deaths had higher risks of dying in infancy. The results remained consistent when restricting the sample only to cases where the grandmother had been observed for her entire reproductive history or when controlling for socioeconomic status. When running sex specific models, significant effects of the number of infant deaths of the grandmother were observed for girls but not for boys.

Program for Studying Intergenerational Transmissions in Infant Mortality Using the Intermediate Data Structure (IDS)

Studies conducted in historical populations and developing countries have evidenced the existence of clustering in infant deaths, which could be related to genetic inheritance and/or to social and cultural factors such as education, socioeconomic status or parental care. A transmission of death clustering has also been found across generations. One way of expanding the knowledge on intergenerational transfers in infant mortality is by conducting comparable studies across different populations. The Intermediate Data Structure (IDS) was developed as a strategy aimed at simplifying the collecting, storing and sharing of historical demographic data. The current work presents two programs that were developed in STATA to construct a dataset for analysis and run statistical models to study intergenerational transfers in infant mortality using databases that are stored in the IDS. The programs use information stored in the IDS tables and after elaborating such information produce Excel files with results. They can be used with any longitudinal database constructed from church books, civil registers, or population registers.

CASTE DIFFERENTIALS IN DEATH CLUSTERING IN CENTRAL AND EASTERN INDIAN STATES

SummaryThis study assessed caste differentials in family-level death clustering, linked survival prospects of siblings (scarring) and mother-level unobserved heterogeneity affecting infant mortality risk in the central and eastern Indian states of Jharkhand, Madhya Pradesh, Odisha and Chhattisgarh. Family-level infant death clustering was examined using bivariate analysis, and the linkages between the survival prospects of siblings and mother-specific unobserved heterogeneity were captured by applying a random effects logit model in the selected Indian states using micro-data from the National Family Health Survey-III (2005–06). The raw data clustering analysis showed the existence of clustering in all four states and among all caste groups with the highest clustering found in the Scheduled Castes of Jharkhand. The important factor from the model that increased the risk of infant deaths in all four states was the causal effect of a previous infant death on the risk of infant death of the subsequent sibling, after controlling for mother-level heterogeneity and unobserved factors. The results show that among the Scheduled Castes and Scheduled Tribes, infant death clustering is mainly affected by the scarring factor in Jharkhand and Madhya Pradesh, while mother-level unobserved factors were important in Odisha and both (scarring and mother-level unobserved factors) were key factors in Chhattisgarh. Similarly, the Other Caste Group was mainly influenced by the scarring factor only in Odisha, mother-level unobserved factors in Jharkhand and Chhattisgarh and both (scarring and mother-level unobserved factors) in Madhya Pradesh. From a government policy perspective, these results would help in identifying high-risk clusters of women among all caste groups in the four central and eastern Indian states that should be targeted to address maternal and child health related indicators.