SummaryBackgroundDetailed assessments of mortality patterns, particularly age-specific mortality, represent a crucial input that enables health systems to target interventions to specific populations. Understanding how all-cause mortality has changed with respect to development status can identify exemplars for best practice. To accomplish this, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated age-specific and sex-specific all-cause mortality between 1970 and 2016 for 195 countries and territories and at the subnational level for the five countries with a population greater than 200 million in 2016.MethodsWe have evaluated how well civil registration systems captured deaths using a set of demographic methods called death distribution methods for adults and from consideration of survey and census data for children younger than 5 years. We generated an overall assessment of completeness of registration of deaths by dividing registered deaths in each location-year by our estimate of all-age deaths generated from our overall estimation process. For 163 locations, including subnational units in countries with a population greater than 200 million with complete vital registration (VR) systems, our estimates were largely driven by the observed data, with corrections for small fluctuations in numbers and estimation for recent years where there were lags in data reporting (lags were variable by location, generally between 1 year and 6 years). For other locations, we took advantage of different data sources available to measure under-5 mortality rates (U5MR) using complete birth histories, summary birth histories, and incomplete VR with adjustments; we measured adult mortality rate (the probability of death in individuals aged 15–60 years) using adjusted incomplete VR, sibling histories, and household death recall. We used the U5MR and adult mortality rate, together with crude death rate due to HIV in the GBD model life table system, to estimate age-specific and sex-specific death rates for each location-year. Using various international databases, we identified fatal discontinuities, which we defined as increases in the death rate of more than one death per million, resulting from conflict and terrorism, natural disasters, major transport or technological accidents, and a subset of epidemic infectious diseases; these were added to estimates in the relevant years. In 47 countries with an identified peak adult prevalence for HIV/AIDS of more than 0·5% and where VR systems were less than 65% complete, we informed our estimates of age-sex-specific mortality using the Estimation and Projection Package (EPP)-Spectrum model fitted to national HIV/AIDS prevalence surveys and antenatal clinic serosurveillance systems. We estimated stillbirths, early neonatal, late neonatal, and childhood mortality using both survey and VR data in spatiotemporal Gaussian process regression models. We estimated abridged life tables for all location-years using age-specific death rates. We grouped locations in...

Background On 11 th March 2020, the World Health Organization declared COVID-19 as Pandemic. The estimation of transmission dynamics in the initial days of the outbreak of any infectious disease is crucial to control its spread in a new area. The serial interval is one of the significant epidemiological measures that determine the spread of infectious disease. It is the time interval between the onset of symptoms in the primary and secondary case. Objective The present study aimed at the qualitative and quantitative synthesis of the currently available evidence for the serial interval of COVID-19. Methodology Data on serial intervals were extracted from 11 studies following a systematic review. A meta-analysis was performed to estimate the pooled estimate of the serial interval. The heterogeneity and bias in the included studies were tested by various statistical measures and tests, including I 2 statistic, Cochran's Q test, Egger's test, and Beggs's test. Result The pooled estimate for the serial interval was 5.40 (5.19, 5.61) and 5.19 (4.37, 6.02) days by the fixed and random effects model, respectively. The heterogeneity between the studies was found to be 89.9% by I 2 statistic. There is no potential bias introduced in the meta-analysis due to small study effects. Conclusion The present review provides sufficient evidence for the estimate of serial interval of COVID-19, which can help in understanding the epidemiology and transmission of the disease. The information on serial interval can be useful in developing various policies regarding contact tracing and monitoring community transmission of COVID-19.

BackgroundNeonatal mortality defined as a death during the first 28 days of life and is the most critical phase of child survival. In spite of the strong evidence supporting immediate and long term health benefits of timely initiation of breastfeeding in India, only two-fifths (44%) of children receive breastfeeding within 1 h of birth. This study aims to examine the role of a behavioral factor i.e., timing of initiation of breastfeeding on neonatal deaths.MethodsData from India Human Development Survey-II (IHDS-II), 2011–12, a nationally representative, large scale population-based dataset has been used. Sample Registration System (SRS) has been used to examine the rate of change in Neonatal Mortality Rates from the year 2011 to 2015. District Level Household & Facility Survey (DLHS-4), 2012–2013 and Annual Health Survey(AHS), 2012–13 data have been used to show the district wise distribution of women who have breastfed their child within 1 h of birth. Population Attributable fraction has been computed using binary logistic regression model for various scenarios of breastfeeding within first hour of birth.ResultsLess than one fourth (21%) of children were breastfed within 1 h of birth across the different districts of India, which varies from the lowest 15% in Sarasvati of Uttar Pradesh state to the highest 94.6% in Thiruvananthapuram of Kerala state. Findings suggest when women did not breastfeed their newborn within the 1 h after his birth, the odds of neonatal deaths were increased by nearly threefold (OR 2.93; 95% CI 1.89, 4.53) in comparison with those neonates who have breastfed within 1 h of birth. Population Attributable Risk estimates that the risk of the neonatal deaths could be reduced to a maximum of 15% when all babies would expose to early breastfeeding from the present level of breastfeeding.ConclusionsWe found that timely initiation of breastfeeding is beneficial for child survival within the first 28 days of birth, including all causes of mortality. Therefore, efforts in formulating an effective policy focusing on early initiation of breastfeeding are needed.

Numerous studies have examined the empirical evidence concerning the influence of demographic and socio-economic factors influencing child immunization, but no documentation is available which shows the actual impact of antenatal care (ANC) visits on subsequent child immunization. Therefore, this paper aims to examine the net impact of ANC visits on subsequent utilization of child immunization after removing the presence of selection bias. Nationwide data from India’s latest National Family Health Survey conducted during 2005–06 is used for the present study. The analysis has been carried out in the two separate models, in the first model 1–2 ANC visit and in the second model three or more ANC visits has been compared with no visit. We have used propensity score matching method with a counterfactual model that assesses the actual ANC visits effect on treated (ANC visits) and untreated groups (no ANC visit), and have employed Mantel-Haenszel bounds to examine whether result would be free from hidden bias or not. Using matched sample analysis result shows that child immunization among the groups of women who have completed 1–2 ANC visits and those who had more than two visits was about 13 percent and 19 percent respectively, higher than the group of women who have not made any ANC visit. Findings of nearest neighbor matching with replacement method, which completely eliminated the bias, indicate that selection bias present in data set leads to overestimates the positive effects of ANC visits on child immunization. Result based on Mantel-Haenszel bounds method suggest that if around 19 percent bias would be involved in the result then also we could observe the true positive effect of 1–2 ANC visits on child immunization. This also indicates that antenatal clinics are the conventional platforms for educating pregnant women on the benefits of child immunization.

BackgroundIn India, while the total fertility rate has been declined from 3.39 in 1992–93 to 2.68 in 2005–06, the prevalence of unintended pregnancy is still stagnant over the same period. A review of existing literature shows that within the country, there are variations in fertility preferences between different regions. Also there is a strong argument that the availability of a health facility at the village level plays an important role in reshaping the fertility behavior of women. Keeping in mind the fact that there is no information at the village level (which is the lowest geographical boundary) in the recent round of National Family Health Survey (NFHS-3), the specific objective of this study is to examine the impact of individual and household level variables on unwanted pregnancies without controlling the village level variation. Further, once the village level variation (i.e. unobserved variation) has been controlled, it is necessary to study whether there has been any alteration in the contribution of factors from earlier results of without adjusting the village level variation.MethodsThis paper attempts to examine the associated factors of unwanted pregnancies, without matching the village and after matching the village, by using the matched case–control design. Nationwide data from India’s latest NFHS-3 conducted during 2005–06 was used for the present study. Frequency and pair wise matching has been applied in the present paper and conditional logistic regression analysis was used to work out the models and to find out the factors associated with unwanted pregnancies.ResultsA major finding of this study was that 1:3 case–control study (without matching the village) shows that women belonging to non Hindu/Muslim religion, Scheduled Tribe, women who have experienced child loss and if the previous birth interval is 24 through 36 months were significant predictors of unwanted pregnancy. However, this relationship did not hold significant after village wise matching. Other factors such as Muslim religion, women and their partners with high school education and above, women belonging to the richest wealth index and if the sex of the last child was female, emerge as significant predictors of unwanted pregnancies.ConclusionsThis study clearly underscores the importance of adjusting the village (PSU) level variation in explaining unwanted pregnancies.