BackgroundUnwanted birth is an important public health concern due to its negative association with adverse outcomes of mothers and children as well as socioeconomic development of a country. Although a number of studies have been investigated the determinants of unwanted births through logistic regression analysis, an extensive assessment using path model is lacking. In the current study, we applied path analysis to know the important covariates for unwanted births in Bangladesh.MethodsThe study used data extracted from Bangladesh Demographic and Health Survey (BDHS) 2011. It considered sub-sample consisted of 7,972 women who had given most recent births five years preceding the date of interview or who were currently pregnant at survey time. Correlation analysis was used to find out the significant association with unwanted births. This study provided the factors affecting unwanted births in Bangladesh. The path model was used to determine the direct, indirect and total effects of socio-demographic factors on unwanted births.ResultsThe result exhibited that more than one-tenth of the recent births were unwanted in Bangladesh. The differentials of unwanted births were women’s age, education, age at marriage, religion, socioeconomic status, exposure of mass-media and use of family planning. In correlation analysis, it showed that unwanted births were positively correlated with women age and place of residence and these relationships were significant. On the contrary, unwanted births were inversely significantly correlated with education and social status. The total effects of endogenous variables such as women age, place of residence and use of family planning methods had favorable effect on unwanted births.ConclusionPolicymakers and program planners need to design programs and services carefully to reduce unwanted births in Bangladesh, especially, service should focus on helping those groups of women who were identified in the analysis as being at increased risks of unwanted births- older women, illiterate, low socioeconomic status, early age at marriage and rural poor susceptible women.

The impact of zinc (Zn) sufficiency/supplementation on COVID-19-associated mortality and incidence (SARS-CoV-2 infections) remains unknown. During an infection, the levels of free Zn are reduced as part of “nutritional immunity” to limit the growth and replication of pathogen and the ensuing inflammatory damage. Considering its key role in immune competency and frequently recorded deficiency in large sections of different populations, Zn has been prescribed for both prophylactic and therapeutic purposes in COVID-19 without any corroborating evidence for its protective role. Multiple trials are underway evaluating the effect of Zn supplementation on COVID-19 outcome in patients getting standard of care treatment. However, the trial designs presumably lack the power to identify negative effects of Zn supplementation, especially in the vulnerable groups of elderly and patients with comorbidities (contributing 9 out of 10 deaths; up to >8,000-fold higher mortality). In this study, we have analyzed COVID-19 mortality and incidence (case) data from 23 socially similar European populations with comparable confounders (population: 522.47 million; experiencing up to >150-fold difference in death rates) and at the matching stage of the pandemic (March 12 to June 26, 2020; first wave of COVID-19 incidence and mortality). Our results suggest a positive correlation between populations’ Zn-sufficiency status and COVID-19 mortality [r (23): 0.7893–0.6849, p-value < 0.0003] as well as incidence [r (23):0.8084–0.5658; p-value < 0.005]. The observed association is contrary to what would be expected if Zn sufficiency was protective in COVID-19. Thus, controlled trials or retrospective analyses of the adverse event patients’ data should be undertaken to correctly guide the practice of Zn supplementation in COVID-19.

A very special type of pneumonic disease that generated the COVID-19 pandemic was first identified in Wuhan, China in December 2019 and is spreading all over the world. The ongoing outbreak presents a challenge for data scientists to model COVID-19, when the epidemiological characteristics of the COVID-19 are yet to be fully explained. The uncertainty around the COVID-19 with no vaccine and effective medicine available until today create additional pressure on the epidemiologists and policy makers. In such a crucial situation, it is very important to predict infected cases to support prevention of the disease and aid in the preparation of healthcare service. In this paper, we have tried to understand the spreading capability of COVID-19 in India taking into account of the lockdown period. The numbers of confirmed cases are increased in India and states in the past few weeks. A differential equation based simple model has been used to understand the pattern of COVID-19 in India and some states. Our findings suggest that the physical distancing and lockdown strategies implemented in India are successfully reducing the spread and that the tempo of pandemic growth has slowed in recent days.

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Fert ility plays an important ro le in any demographic transition and total fert ility rate (TFR) is one of the basic measurements of fert ility. Due to non-availability of co mp lete and reliable data, a large nu mber of indirect techniques have been developed to estimate the demographic parameters with incomp lete data. So me of these techniques are based on utilizing the data fro m stable population theory while others are based on the regression technique in which the parameters are estimated through regression equations between the dependent variable which is the TFR and the independent variables which is the socio economic well as demographic variables. In the present paper an indirect method has been proposed to estimate the TFR using regression analysis. In these types of analysis the most serious problem is the choice of predictor variable. If the choice of predictor variab le is good then it gives the better estimate for the dependent variable (TFR). Using new predictor variab le (proposed in this paper), the improved model exp lained about 85 percent of the variation in TFR. The findings reveal that the values of TFR calculated by the present method are quite close to the observed values of the TFR without involving much computational comp lexities at state level for different background characteristics. By using this modified estimate of TFR, the demographers can easily calculate the birth averted for different regions as well as states also.

Domestic violence, when conducted against women, is a type of gender-based violence that negatively impacts a woman's physical and psychological health, causing insecurity, lack of safety, and loss of health and self-worth. Domestic violence is an important consideration for sexual, reproductive, and child health, as it can affect contraceptive behaviors of couples as well as levels of infant mortality. In the present analysis, an attempt has been made to study the relationship between women's experience of domestic violence and couple interaction after controlling for certain socioeconomic and demographic variables using logistic regression. This study looks at data from the National Family Health Survey-III conducted from 2005 to 2006 in Uttar Pradesh, the most populous state of India. Findings reveal that 43% of women suffer from domestic violence in the society as a whole; however, if a couple makes joint decisions in household matters, the prevalence of domestic violence is observed to be 24% less. Education and occupation of women, standard of living, media exposure, and partner's alcoholic behaviors are also found to be possible predictors of domestic violence.

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