Background: Substance use-defined by the consumption of alcohol, smoking, and smokeless tobacco-has been identified as a serious health hazard in India. Understanding the socioeconomic correlates of substance use in Northeast India (NEI) will provide crucial input for public health policies in this region. Objectives: To examine the socioeconomic correlates of substance consumption among male adults of NEI. Data and methods: We analyzed nationally representative data from the National Family Health Survey (NFHS-4) 2015-2016. We examined the prevalence and frequency of smoking, using smokeless tobacco products, alcohol consumption, and other substance use among 14,555 men in the 15 to 54-year age group. We carried out binary logistic regressions to investigate the socioeconomic correlates of substance use among male adults in NEI. Results: Substance use was significantly higher among the male adults of NEI than among those from elsewhere in the country (any substance i.e. smoking, smokeless tobacco and alcohol use in India: 50.03% vs. NEI: 70.83%). The frequency of smoking and alcohol consumption is also higher in NEI compared to the rest of the country. In total, there are about 10.2 million substance users, of which 6.7 million are from the state of Assam. About 44.38% of adolescents (aged between 15 and 19) use at least one type of substance. Substance use plateaus in the 25 to 49 age group before beginning to decline among users aged between 50 and 54 years. The likelihood of substance use goes down with increasing education and wealth in NEI. Among social groups, Scheduled Tribe adults have the highest likelihood of using any substance. Conclusion: The intensity of substance consumption in NEI is very high. These unhealthy behaviors are more prevalent among socioeconomically disadvantaged populations. This calls for intensive research to better understand the dynamics of substance availability and use in NEI. Furthermore, strong policy measures are needed to reduce substance use among vulnerable groups.

Background COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19’s infection ratio (IR) is essential for formulating policies and interventions. Objective The present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India’s socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level. Data and methods We used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio. Results The results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran’s- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15–59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts. Conclusion The COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.

Background The number of patients with coronavirus infection (COVID-19) has amplified in India. Understanding the district level correlates of the COVID-19 infection ratio (IR) is essential for formulating policies and intervention. Objectives The present study examines the association between socioeconomic and demographic characteristics of India's population and the COVID-19 infection ratio at the district level. Data and Methods Using crowdsourced data on the COVID-19 prevalence rate, we analyzed state and district level variation in India from March 14 to July 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out a regression analysis to highlight the district level demographic, socioeconomic, infrastructure, and health-related correlates of the COVID-19 infection ratio. Results The results showed that the IR is 42.38 per one hundred thousand population in India. The highest IR was observed in Andhra Pradesh (145.0), followed by Maharashtra (123.6), and was the lowest in Chhattisgarh (10.1). About 80 percent of infected cases and 90 percent of deaths were observed in nine Indian states (Tamil Nadu, Andhra Pradesh, Telangana, Karnataka, Maharashtra, Delhi, Uttar Pradesh, West Bengal, and Gujarat). Moreover, we observed COVID-19 cold-spots in central, northern, western, and north-eastern regions of India. Out of 736 districts, six metropolitan cities (Mumbai, Chennai, Thane, Pune, Bengaluru, and Hyderabad) emerged as the major hotspots in India, containing around 30 percent of confirmed total COVID-19 cases in the country. Simultaneously, parts of the Konkan coast in Maharashtra, part of Delhi, the southern part of Tamil Nadu, the northern part of Jammu & Kashmir were identified as hotspots of COVID-19 infection. Moran's- I value of 0.333showed a positive spatial clustering level in the COVID-19 IR case over neighboring districts. Our regression analysis found that district-level population density (β: 0.05, CI:004-0.06), the percent of urban population (β:3.08, CI: 1.05-5.11), percent of Scheduled Caste Population (β: 3.92, CI: 0.12-7.72),and district-level testing ratio (β: 0.03, CI: 0.01-0.04) are positively associated with the prevalence of COVID-19. Conclusion COVID-19 cases were heavily concentrated in 9 states of India. Several demographic, socioeconomic, and health-related variables are correlated with the COVID-19 prevalence rate. However, after adjusting the role of socioeconomic and health-related factors, the COVID-19 infection rate was found to be more rampant in districts with a higher population density, a higher percentage of the urban population, and a higher percentage of deprived castes and with a higher level of testing ratio. The identified hotspots and correlates in this study give crucial information for policy discourse. Keywords COVID-19, socioeconomic, co-morbidity, geographical, hot-cold spot, districts, India.

AbstractsPurposeThe prevalence of substance use among tribal adolescents in north-east India is higher than that of the rest of India.ObjectiveThe study aimed to investigate the association between social network measures and substance use among male tribal adolescents in the West district of Tripura, North-East India.MethodsWe used data on 12-19-year-old tribal adolescents (N=340) from a primary cross-sectional survey in selected schools in the study area. We carried out bivariate and logistic regression analysis to establish the association between substance use and social network.ResultsOut of the total sample 340, about 27.65% reported smoking, 26.18% reported using smokeless tobacco, and 30.59% reported drinking alcohol; 35.29% reported using any of these substances. The substance use status of social network members was highly correlated to the substance use status among adolescents. The odds of substance consumption among adolescents increase with having a friend who smokes (OR = 6.152, 95% CI = 1.80–21.09), having friends who instigate to smoke (OR = 5.41, 95% CI = 1.86–15.74), and having friends who say smoking as a sign of masculinity(OR = 5.19, 95% CI = 1.4–18.22). Adolescents were more likely to smoke when their family member uses a substance (OR = 3.39, 95% CI = 1.5– 7.4,p= 0.002) and who spent time with friends with the same behaviour (OR = 2.66, 95% CI= 1.5–4.5,p≤ 0.000).ConclusionsIntervention is needed to address adolescents’ substance use habits and members belonging to close social networks.