Summary Background COVID-19 is spreading rapidly in India and other parts of the world. Despite the Indian Government's efforts to contain the disease in the affected districts, cases have been reported in 627 (98%) of 640 districts. There is a need to devise a tool for district-level planning and prioritisation and effective allocation of resources. Based on publicly available data, this study reports a vulnerability index for identification of vulnerable regions in India on the basis of population and infrastructural characteristics. Methods We computed a composite index of vulnerability at the state and district levels based on 15 indicators across the following five domains: socioeconomic, demographic, housing and hygiene, epidemiological, and health system. We used a percentile ranking method to compute both domain-specific and overall vulnerability and presented results spatially with number of positive COVID-19 cases in districts. Findings A number of districts in nine large states—Bihar, Madhya Pradesh, Telangana, Jharkhand, Uttar Pradesh, Maharashtra, West Bengal, Odisha, and Gujarat—located in every region of the country except the northeast, were found to have high overall vulnerability (index value more than 0·75). These states also had high vulnerability according to most of the five domains. Although our intention was not to predict the risk of infection for a district or a state, we observed similarities between vulnerability and the current concentration of COVID-19 cases at the state level. However, this relationship was not clear at the district level. Interpretation The vulnerability index presented in this paper identified a number of vulnerable districts in India, which currently do not have large numbers of COVID-19 cases but could be strongly impacted by the epidemic. Our index aims to help planners and policy makers effectively prioritise regions for resource allocation and adopt risk mitigation strategies for better preparedness and responses to the COVID-19 epidemic. Funding None.

BackgroundThis study evaluates an eight-session behavior change health intervention with women’s self-help groups (SHGs) aimed to promote healthy maternal and newborn practices among the more socially and economically marginalized groups.MethodsUsing a pre-post quasi-experimental design, a total of 545 SHGs were divided into two groups: a control group, which received the usual microcredit intervention; and an intervention group, which received additional participatory training around maternal, neonatal, and child health issues. Women members of SHGs who had a live birth in the 12 months preceding the survey were surveyed on demographics, practices around maternal, neonatal and child health (MNCH), and collectivization. Outcome effects were assessed using difference-in-difference (DID) methods.ResultsWomen from the SHGs with health intervention, relative to controls over time (time 1 to time 2), were more likely to: use contraceptive methods (DID: 9 percentage points [pp], p<0.001), have institutional delivery (DID: 9pp, p<0.05), practice skin-to-skin care (DID: 17pp, p<0.05), delay bathing for 3 or more days (DID: 19pp, p<0.001), initiate timely breastfeeding (DID: 21pp, p<0.001), exclusively breastfeed the child (DID: 27pp, p<0.001), and provide age-appropriate immunization (DID: 9pp, p<0.001). Additionally, women from the SHGs with health intervention when compared to the control group over time were more likely to report: collective efficacy (DID: 17pp, p<0.001), support through accompanying SHG members for antenatal care (DID: 8pp, p<0.05), receive a visit from SHG member within 2 days post-delivery (DID: 32pp, p<0.001), and receive reproductive, maternal, neonatal and child health information from an SHG member (DID: 45pp, p<0.001).ConclusionFindings demonstrate that structured participatory communication on MNCH with women’s groups improve positive health practices. In addition, SHGs can reach a substantial proportion of women while providing an avenue for pregnant women and young mothers to be assisted by others in learning and practicing healthy behaviors, thus building social cohesion on health.

No abstract

No abstract

ObjectiveTo assess the effects of new-born care intervention through self-help groups in improving new-born healthcare practices in rural India.MethodsA quasi-experimental design was used to evaluate behavioral change intervention integrated in >25,000 microfinance-based self-help groups in rural Bihar. Three rounds of cross-sectional surveys were conducted to understand the impact of intervention on new-born healthcare practices by talking to women who delivered a baby in the last 6 months.ResultsIntervention groups showed greater improvement than control groups in the timely initiation of breastfeeding (adjusted odds ratio (AOR) = 6.3, 95% CI: 2.8, 14.3), exclusive breastfeeding on day 1 (AOR = 4.3, 95% CI: 1.9, 9.9), initiation of skin-to-skin care (AOR = 1.9, CI: 1.0, 3.8), and delayed bathing (AOR = 2.8, 95% CI: 1.4, 5.9) with greater effect of on home deliveries where clinical care is often absent.ConclusionSharing messages on appropriate new-born practices through self-help groups improve new-born care practices.

No abstract

Background Intraindividual coexistence of anthropometrically defined undernutrition and “metabolic obesity”, characterised by presence of at least one abnormal cardiometabolic risk factor, is rarely investigated in young children and adolescents, particularly in Low-and-Middle-Income-Countries undergoing rapid nutrition transition. Methods Prevalence of biomarkers of metabolic obesity was related to anthropometric and socio-demographic characteristics in 5-19 years old participants from the population-based Comprehensive National Nutrition Survey in India (2016-2018). The biomarkers, serum lipid-profile (total cholesterol (TC), low density lipoprotein (LDL), high density lipoprotein (HDL) and triglycerides), fasting glucose, and glycosylated hemoglobin (HbA1C), and all jointly were analysed in 22567, 23192, 25962 and 19143 participants, respectively. Results Overall (entire dataset), the prevalence of abnormalities was low (4.3-4.5%) for LDL and TC, intermediate for dysglycemia (10.9-16.1%), and high for HDL and triglycerides (21.725.8%). Proportions with ≥1 abnormal metabolic obesity biomarker(s) were 56.2% overall, 54.2% in thin (BMI-for-age <-2SD) and 59.3% in stunted (height-for-age <-2SD) participants. Comparable prevalence was evident in mild undernutrition (-1 to -2 SD). Clustering of two borderline abnormalities occurred in one-third, warranting active life-style interventions. Metabolic obesity prevalence increased with BMI-for-age. Among those with metabolic obesity, only 9% were overweight/obese (>1SD BMI-for-age). Among poor participants, triglyceride, glucose and HDL abnormalities were higher. Conclusions A paradoxical, counter-intuitive prevalence of metabolic obesity biomarker(s) exists in over half of anthropometrically undernourished and normal-weight Indian children and adolescents. There is a crucial need for commensurate investments to address overnutrition along with undernutrition. Nutritional status should be characterized through additional reliable biomarkers, instead of anthropometry alone.

Introduction Preanalytical conditions are critical for blood sample integrity and poses challenge in surveys involving biochemical measurements. A cross sectional study was conducted to assess the stability of select biomarkers at conditions that mimic field situations in surveys. Material and methods Blood from 420 volunteers was exposed to 2 – 8 °C, room temperature (RT), 22 – 30 °C and > 30 °C for 30 min, 6 hours, 12 hours and 24 hours prior to centrifugation. After different exposures, whole blood (N = 35) was used to assess stability of haemoglobin, HbA1c and erythrocyte folate; serum (N = 35) for assessing stability of ferritin, C-reactive protein (CRP), vitamins B12, A and D, zinc, soluble transferrin receptor (sTfR), total cholesterol, high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), tryglicerides, albumin, total protein and creatinine; and plasma (N = 35) was used for glucose. The mean % deviation of the analytes was compared with the total change limit (TCL), computed from analytical and intra-individual imprecision. Values that were within the TCL were deemed to be stable. Result Creatinine (mean % deviation 14.6, TCL 5.9), haemoglobin (16.4%, TCL 4.4) and folate (33.6%, TCL 22.6) were unstable after 12 hours at 22-30°C, a temperature at which other analytes were stable. Creatinine was unstable even at RT for 12 hours (mean % deviation: 10.4). Albumin, CRP, glucose, cholesterol, LDL, triglycerides, vitamins B12 and A, sTfR and HbA1c were stable at all studied conditions. Conclusion All analytes other than creatinine, folate and haemoglobin can be reliably estimated in blood samples exposed to 22-30°C for 12 hours in community-based studies.