Introduction: In India, the proportion of older population is projected to increase from 8% in 2015 to 19% in 2050 and a third of the country's population will be older adults by end of the century. Multimorbidity is common among the elderly and the prevalence increases with age. Chronic conditions are most often present as clusters and it's critical to explore the prevalent pattern of clustering for better public health strategies. Method: A cross-sectional study was conducted among 725 rural older adults (>60 years) in Tigiria block of Odisha, India. Multimorbidity status was assessed using the prior validated MAQ-PC tool. Survey was conducted using android tablets installed with open data kit software. While Euclidean distances using K-means clustering algorithm were used to estimate the similarity or dissimilarity of observations. The optimum numbers of clusters were determined using silhouette method. Data were analyzed using multiple open source packages of R statistical programming software ver-3.6.3. Result: The overall prevalence of multimorbidity was 48.8% of which dyads (25%) were the most common form, followed by triads (15.2%). The prevalence of multimorbidity was higher in females (50.4%) than males (47.4%). The optimal number of clusters was found to be 3. While arthritis alone was a separate cluster, hypertension and acid peptic disease were in another cluster and all the rest conditions were included in the third cluster. Conclusion: The cluster analysis to measure of proximity suggested arthritis, hypertension, and acid peptic disease are the diseases that occur mostly in isolation with the other chronic conditions in the rural elderly.

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Background With an increasing number of older adults in low- and middle-income countries (LMIC), the burden of multimorbidity and functional dependence is on the rise. At the same time, a higher prevalence of elder abuse is observed in these populations. There is scarce evidence on the interplay between elder abuse and multimorbidity with no reports from LMIC settings yet. Present study examined the association of multimorbidity with the risk of elder abuse and its correlates in a rural elderly population of Odisha, India. Methods The data for this study was collected as a part of our AHSETS study comprising of 725 older adults residing in rural Odisha, India. Multimorbidity was assessed by the MAQ PC tool while Hwalek-Sengstock elder abuse screening test (HS-EAST) was used to assess the risk of elder abuse. Functional dependence was measured by the Lawton IADL questionnaire. We used ordinal logistic regression models to identify the correlates of elder abuse and test for mediation by functional dependence. Results Around 48.8 % (95 % CI:45.13–52.53 %) older adults had multimorbidity while 33.8 % (95 % CI:30.35–37.35 %) had some form of dependence. Out of 725, 56.6 % (CI 52.85–60.19 %) were found to be at low-risk elder abuse and 15.9 % (CI 13.27–18.72 %) being at high-risk. The prevalence of higher risk of elder abuse was greater among females, non-literates, widowed persons, those not currently working and those belonging to lower socio-economic strata. The risk of elder abuse was significantly associated with multimorbidity (AOR = 1.68; 95 %CI: 1.11–2.57) and functional dependence (AOR = 2.08; 95 %CI: 1.41–3.06). Additionally, we found a partial mediation mechanism of functional dependency between the pathway of multimorbidity and elder abuse. Conclusions Elder abuse and multimorbidity are emerging as issues of significant concern among rural elderly in Odisha, India. Multimorbidity and functional dependence are associated with significantly higher odds of elder abuse among rural older adults. Further, we report the role of functional dependence as a partial mediator between multimorbidity and elder abuse. Therefore, potential interventions on reducing the economic, physical and care dependence among multimorbid patients may reduce the risk of elder abuse.

This was a population based cross-sectional study carried out to estimate and compare the seroprevalence, hidden prevalence and determine the demographic risk factors associated with SARS-CoV-2 infection among adults in the three largest cities of Odisha, India, and ascertain the association with the progression of the epidemic. The survey carried out in August 2020 in the three largest cities of the state of Odisha, India. Blood samples were collected from the residents using random sampling methods and tested for anti- SARS CoV-2 antibodies using an automated CLIA platform. A total of 4146 participants from the 3 cities of Bhubaneswar (BBS), Berhampur (BAM) and Rourkela (RKL) participated. The female to male participation ratio was 5.9:10 across the three cities. The gender weighted seroprevalence across the three cities was 20.78% (95% CI 19.56–22.05%). While females reported a higher seroprevalence (22.8%) as compared to males (18.8%), there was no significant difference in seroprevalence across age groups. A majority of the seropositive participants were asymptomatic (90.49%). The case to infection ratio on the date of serosurvey was 1:6.6 in BBS, 1:61 in BAM and 1:29.8 in RKL. The study found a high seroprevalence against COVID-19 in urban Odisha as well as high numbers of asymptomatic infections. The epidemic curves had a correlation with the seroprevalence.