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BackgroundThe burden of disability and chronic morbidity among the elderly has been increasing substantially in India in recent years. Yet, the use of nationally representative data to investigate the relationship between chronic morbidity and reported disability in the country has been minimal. The objective of this study is twofold: i) to quantify the association between chronic morbidities and overall disabilities in the activities of daily living (ADLs) among elderly people in India, and ii) to understand how various chronic morbidities influence individual ADLs, specifically, walking, toileting and dressing.MethodsWe used data from the India Human Development Survey-II (IHDS-II) as a basis for this study. We computed the Katz Index of independence in ADL to examine the burden of disability among the elderly. Ordered logistic regression was carried out to examine the effect of chronic morbidities on: i) the disability index (where 0 = no disability; 1 = disability in 1 or 2 ADLs; and 2 = disability in 3 ADLs), and ii) disabilities in three ADLs in the population over-60 years of age in India.ResultsThe percentage of people scoring lower Katz index (indicating severe and mild disability) in at least one of the three ADLs is very high in India (17.91% for males and 26.21% for females). Irrespective of the type of ADL, the Katz score is lower in elderly females than in elderly males. Elderly people who are illiterate and belong to the poorest wealth quintile report lower Katz scores in ADL. Both bivariate and multivariate analyses confirm that all three types of chronic morbidities are positively and significantly associated with a disability condition in the ADLs. Yet, the effects of morbidities vary greatly according to the type of disability. For instance, while diabetes affect walking (OR: 2.56; 95% CI: 2.29–2.86), and toileting (OR: 2.63; 95% CI: 2.26–3.07), high blood pressure mainly affects walking (OR: 2.29, 95% CI: 2.09–2.5) and dressing disabilities (OR: 2.13, 95% CI: 1.84–2.46).ConclusionsChronic morbidity is a decisive factor in old age disability. It is crucial to reduce chronic morbidity in a timely way to minimise the enormous associated burden of disability.

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.

Introduction: The burden of non-communicable diseases (NCDs) is a major public health concern across the world. Various initiatives have tried to address these with varying degrees of success. Objective: The objective is to assess and collate existing evidence in implementation research done in India on three broad domains of NCDs namely, cardiovascular diseases (CVD), diabetes mellitus (DM), and mental health (MH) in India. Materials and methods: Three systematic review protocols have been drafted to explore and collate extant evidence of implementation research on cardiovascular diseases, diabetes mellitus, and mental health in India, in accordance with the PRISMA-P statement. Academic databases including PubMed, Embase and Science Direct will be searched. Search strategies will be formulated in iterative processes and in accordance with the formats that are specific to the databases that will be searched. In addition, grey literature and non-academic databases will also be explored. Data extracted from the selected studies will be analysed and a narrative summary of the selected articles, using the SWiM (Synthesis without meta-analysis) guidelines will be produced. Intended Outcomes: The outputs of these systematic reviews could help in a better understanding of implementation research gaps and also how to address them. Apart from giving insights into how healthcare initiatives for CVDs, diabetes and mental health could be implemented in a better way, the study could also advocate the need to build and consolidate capacity for implementation research in the country.