Analysing the role of sleep quality, functional limitation and depressive symptoms in determining life satisfaction among the older Population in India: a moderated mediation approach.

BACKGROUND: Life satisfaction (LS), a useful construct in the study of psycho-social well-being, is an important indicator of healthy aging. With a view to investigate whether the improved longevity in India is accompanied by commensurate levels of well-being and contentment among the older adults , this study aimed to examine (1) the association between LS and sleep quality among older Indian adults aged 60 years and above (2) the mediating role of depression that accounts for the association and (3) the moderating role of functional limitation in this mediation. METHODS: Cross-sectional data from the Longitudinal Ageing Study in India (LASI), Wave-1 (2017-18) was used. Pearson's correlation coefficients were calculated to investigate the pair-wise relationship between sleep quality, depressive symptoms, functional limitation, and LS. Structural Equation Model was employed to analyse the moderated-mediated association between sleep quality and the level of LS. RESULTS: Sleep quality had a direct effect (ß=-0.12) as well as an indirect effect (ß=-0.024) via depressive symptoms on LS, accounting for 83.6 and 16.4 per cent of the total effects, respectively. Also, the interaction term between poor seep quality and functional limitation was positive (ß = 0.03, p < 0.001) in determining depressive symptoms, suggesting that higher level of functional limitation aggravated the indirect effect of poor sleep quality on LS. CONCLUSION: The findings of the study suggested that ensuring both the physical as well as the mental well-being of the population during the life course may confer in later life the desired level of life satisfaction.

Decomposing the rural-urban gap in the prevalence of undiagnosed, untreated and under-treated hypertension among older adults in India.

BACKGROUND: Although awareness and treatment rates of hypertension have significantly improved in recent years, the prevalence of undiagnosed and untreated hypertension remains a major public health concern for Indian policymakers. While the urban-rural variation in the prevalence, diagnosis, control, and treatment of hypertension is reasonably well-documented, the explanation behind such variation remains poorly understood given the dearth of studies conducted on exploring the determinants of the rural-urban gap in the prevalence of undiagnosed, untreated, and uncontrolled hypertension in India. In view of this research gap, our paper aims to decompose the inter-group differences between rural and urban areas in undiagnosed, untreated, and undertreated hypertension among older adults in India into the major contributing factors. METHODS: Nationally representative data collected in the Longitudinal Ageing Study of India, Wave-1 (2017-18), was utilized for this study. Maximum-likelihood binary logistic-regression models were employed to capture the crude and adjusted associations between the place of residence and prevalence of undiagnosed, untreated, and undertreated hypertension. Fairlie's decomposition technique was used to decompose the inter-group differences between rural and urban residents in the prevalence of undiagnosed, untreated, and undertreated hypertension among the older population in India, into the major contributing factors, in order to explore the pathways through which these differences manifest. RESULTS: The overall prevalence rates of undiagnosed, untreated, and undertreated hypertension among older adults were 42.3%, 6%, and 18.7%, respectively. However, the prevalence of undiagnosed and untreated hypertension was higher in rural areas, by 12.4 and 1.7 percentage-points, respectively, while undertreated hypertension was more prevalent in the urban areas (by 7.2 percentage-points). The decomposition analysis explained roughly 41% and 34% of the urban advantage over rural areas in the case of undiagnosed and untreated hypertension, while it explained 51% of the urban disadvantage in respect of undertreated hypertension. The rural-urban differentials in education and comorbidities accounted for the majority of the explained rural disadvantage in the prevalence of undiagnosed hypertension, explaining 13.51% and 13.27% of the gap, respectively. The regional factor was found to be the major driver behind urban advantage in the prevalence of untreated hypertension, contributing 37.47% to the overall gap. In the case of undertreated hypertension, education, comorbidities, and tobacco consumption were the major contributors to the urban-rural inequality, which accounted for 12.3%, 10.6%, and 9.8% of the gap, respectively. CONCLUSION: Socio-economic and lifestyle factors seemed to contribute significantly to the urban-rural gap in undiagnosed, untreated and undertreated hypertension in India among older adults. There is an urgent need of creating awareness programmes for the early identification of hypertensive cases and regular treatment, particularly in under-serviced rural India. Interventions should be made targeting specific population groups to tackle inequality in healthcare utilization.

Association of multimorbidity and physical activity among older adults in India: an analysis from the Longitudinal Ageing Survey of India (2017-2018).

OBJECTIVE: To examine the association of multimorbidity and physical activity among older adults in India. DESIGN: A cross-sectional study was conducted using large representative survey data. SETTING AND PARTICIPANTS: The study used data from the nationally representative 'Longitudinal Ageing Study in India (LASI),' conducted during 2017-2018. The study included a total sample of 65 336 older adults aged 45 years and above in India. METHODS: Moderate and vigorous physical activities were measured separately by self-reported questionnaires. Physical activity was calculated as minutes of metabolic equivalent tasks per week. The outcome variable was a categorical variable where 2=the prevalence of more than one morbidity, 1=presence of one morbidity and 0=none. Bivariate analysis and multinomial logistic regression were applied to fulfil the objectives. RESULTS: 27.39% of older adults in India had multimorbidity. 31.02% of older adults did not engage in any moderate physical activities. Also, 59.39% of older adults did not engage in any vigorous physical activities. Older adults reporting low (adjusted relative risk ratio (A RRR): 1.10, 95% CI 1.03 to 1.18) and moderate (A RRR): 1.05, 95% CI 0.98 to 1.13) level of moderate physical activity were significantly more likely to suffer from multimorbidity compared with no involvement in moderate physical activity. However, older adults who reported high (A RRR: 0.79, 95% CI 0.75 to 0.84), moderate (A RRR: 0.88, 95% CI 0.80 to 0.98) and low level of vigorous physical activity (A RRR: 0.94, 95% CI 0.86 to 1.02) had significantly less multimorbidity in comparison to those who never engaged in vigorous physical activity. CONCLUSION: Lack of physical activity is associated with multimorbidity among older adults. Physical activity promotion should be adopted as a primary strategy in reducing the burden of morbidity and multimorbidity.

Gender differences in the association of obesity-related measures with multi-morbidity among older adults in India: evidence from LASI, Wave-1.

BACKGROUND: Co-existence of multiple chronic diseases is increasingly becoming a norm among ageing population. The study aims to investigate the prevalence of multimorbidity and the association between anthropometric measures of obesity and multimorbidity among men and women aged 60 years and above in India. METHODS: The present study is based on the first wave of the Longitudinal Aging Study in India. The analytical sample size for the study was 28,050 older adults aged 60 years and above. Descriptive statistics and multivariable analysis using logistic regression models were conducted. RESULTS: Body Mass Index (BMI) based-obesity is more prevalent among older women than men (26.3% vs. 17.6%). Similarly, higher proportion of older women was at high-risk waist circumference (37.1% vs 8.9%) and waist-hip ratio (78.5 vs 75.4%) than men respectively. In Model-I, after controlling for several covariates, older adults with overweight/obesity were 1.6 times more likely to have multi-morbidity than non-obese older adults (Adjusted OR = 1.61; 95% CI: 1.48-1.74). Similarly, older adults with high-risk waist circumference [Adjusted OR: 1.66; 95% CI: 1.52-1.80] and waist-hip ratio [Adjusted OR: 1.45; 95% CI: 1.33-1.59] also had higher odds of having multi-morbidity in reference to their counterparts. In model-3 it was found that females with high-risk waist-hip ratio had 14% lower odds of multimorbidity than males with high-risk waist-hip ratio [Adjusted OR: 0.86; 95%CI: 0.78-0.94]. CONCLUSION: The findings of the study show significant gender difference in the prevalence of multimorbidity, men being at increased risk in the multivariate analysis which is uncommon in the existing epidemiological research. Interactive effect of male gender with anthropometric measures on multimorbidity reported in our study probably due to increased unhealthy behaviours among men requires further research.

District level correlates of COVID-19 pandemic in India during March-October 2020.

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.

A qualitative study of the barriers to utilizing healthcare services among the tribal population in Assam.

OBJECTIVE: We aim to explore the barriers to accessing modern healthcare services in two tribal populations in Assam. METHODS: In March 2018, we conducted qualitative research through 60 in-depth interviews with men and women aged 15 to 50 from Bodo and Rabha tribes in Udalguri and Baksa districts of Assam. We interviewed a group of health-service providers from public health facilities to understand the demand-supply balance in those facilities. FINDINGS: On the demand side, direct and indirect financial obstacles, distance to health facilities, poor public transportation, perceived negative behavior of hospital staff, and lack of infrastructure were the main barriers to utilizing healthcare facilities. On the supply side, doctors and nurses in government health facilities were overburdened by demand due to a lack of human resources. CONCLUSIONS: Our study highlights the barriers to utilizing health facilities; these are not always driven by factors linked to the patient's socio-economic status but also depend significantly on the quality of the health services and other contextual factors. Although the government has made efforts to improve the rural healthcare system through national-level programs, our qualitative study shows that these programs have not been successful in enhancing the rural healthcare system in the study area.