To estimate sex-specific prevalence and associated socio-economic, demographic, and lifestyle risk factors of hypertension in India. We used data from the National Family Health Survey (NFHS-4) of 2015–16. The analysis based on 6,99,686 women (15–49 years) and 1,12,122 men (15–54 years) whose blood pressure (BP) were measured during the survey. Bivariate distribution was used to show the prevalence of hypertension and, maps were used to present its spatial patterns. Logistic regression model was used to identify sex-specific association between risk factors and hypertension. Results show that the overall prevalence of hypertension was 16.32% among men and 11.56% among women. We also found that the prevalence of hypertension across selected socio-economic, demographic and lifestyle background characteristics and in a majority of the states was higher among men compared to women. Odds ratios from logistic regression analysis direct sex-related differences in risk factors. Hypertension increases with an increase in age and the risk is higher among older women (AOR, 5.58; 95% CI, 5.16–6.03 for women aged 40–49 and AOR, 4.24; 95% CI, 3.94–4.57 for men aged 50–54) compared to men. Education, types of jobs (specially technical, administrative and managerial), marital status and non-vegetarian diet were significantly associated with hypertension in men. While other than age; non-working, consumption of alcohol, and being a diabetic was found to be major risk factors for this disease among women. There are sex-related differences in prevalence as well as risk factors of hypertension in India. In order to prevent early developments of hypertension, awareness related to changing lifestyles such as a diet rich in fruits, vegetables as well as screening to control BP should be promoted among youths and adults in India. The study also recommends sex-specific approaches in health infrastructure and policies besides increasing public awareness.

Objectives Official data on birth is important to monitor the specific targets of SDGs. About 2.7 million children under age five years do not have official birth registration document in India. Unavailability of birth registration document may deprive the children from access to government-aided essential services such as fixed years of formal education, healthcare, and legal protection. This study examines the effect of socioeconomic, demographic and health care factors on birth registration in India. We also examined the spatial pattern of completeness of birth registration that could be useful for district level intervention. Methods We used data from the National Family Health Survey (NFHS-4), 2015–16. We carried out the descriptive statistics and bivariate analysis. Besides, we used multilevel binary logistic regression to identify significant covariates of birth registration at the individual, district, and state levels. We used GIS software to do spatial mapping of completeness of birth registration at district level. Results The birth registration level was lower than national average (80.21%) in the 254 districts. In Uttar Pradesh, 12 out of 71 districts recorded lower than 50% birth registration. Also, some districts from Arunachal Pradesh, J&K, and Rajasthan recorded lower than 50% birth registration. We also found a lower proportion of children are registered among children of birth order three and above (62.83%) and rural resident (76.62%). Children of mothers with no formal education, no media exposure, poorest wealth quintile, OBC and muslims religion have lower level of birth registration. Multilevel regression result showed 25 percent variation in birth registration lie between states while the remaining 75 percent variation lie within states. Moreover, children among illiterate mother (AOR = 0.57, CI [0.54, 0.61], p<0.001), Muslims households (AOR = 0.90, CI [0.87, 0.94], p<0.001), and poorest wealth quintile (AOR = 0.38, CI [0.36, 0.41], p<0.001) showed lower odds for child’s birth registration. Conclusion We strongly suggest linking the birth registration facilities with health institutions.

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Background: Since the COVID-19 pandemic hit Indian states at varying speed, it is crucial to investigate the geographical pattern in COVID-19. We analyzed the geographical pattern of COVID-19 prevalence and mortality by the phase of national lockdown in India. Method: Using publicly available compiled data on COVID-19, we estimated the trends in new cases, period-prevalence rate (PPR), case recovery rate (CRR), and case fatality ratio (CFR) at national, state and district level. Findings: The age and sex are missing for more than 60 percent of the COVID-19 patients. There is an exponential increase in COVID-19 cases both at national and sub-national levels. The COVID-19 infected has jumped about 235 times ( from 567 cases in the pre-lockdown period to 1,33,669 in the fourth lockdown); the average daily new cases have increased from 57 in the first lockdown to 6,482 in the fourth lockdown; the average daily recovered persons from 4 to 3,819; the average daily death from 1 to 163. From first to the third lockdown, PPR (0.04 to 5.94), CRR (7.05 to 30.35) and CFR (1.76 to 1.89) have consistently escalated. At state-level, the maximum number of COVID-19 cases is found in the states of Maharashtra, Tamil Nadu, Delhi, and Gujarat contributing 66.75 percent of total cases. Whereas no cases found in some states, Kerela is the only state flattening the COVID-19 curve. The PPR is found to be highest in Delhi, followed by Maharastra. The highest recovery rate is observed in Kerala, till second lockdown; and in Andhra Pradesh in third lockdown. The highest case fatality ratio in the fourth lockdown is observed in Gujarat and Telangana. A few districts viz. like Mumbai (96.7); Chennai (63.66) and Ahmedabad (62.04) have the highest infection rate per 100 thousand population. Spatial analysis shows that clusters in Konkan coast especially in Maharashtra (Palghar, Mumbai, Thane and Pune); southern part from Tamil Nadu (Chennai, Chengalpattu and Thiruvallur), and the northern part of Jammu & Kashmir (Anantnag, Kulgam) are hot-spots for COVID-19 infection while central, northern and north-eastern regions of India are the cold-spots. Conclusion: India has been experiencing a rapid increase of COVID-19 cases since the second lockdown phase. There is huge geographical variation in COVID-19 pandemic with a concentration in some major cities and states while disaggregated data at local levels allows understanding the geographical disparity of the pandemic, the lack of age-sex information of the COVID-19 patients forbids to investigate the individual pattern of COVID-19 burden. Keyword: COVID-19; India; Case Fatality Rate; Case Recovery Rate; Period Prevalence Rate; Geographical variation

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