Search

Introduction Advancement in the field of gerontology has been concerned with the well-being of older adults in a family setup that is associated with caregiving and support. While family life and well-being are defined by emotion, caregiving, and support activities, dissatisfaction/discontent with living arrangements is a public health concern, which is increasing with a rise in the proportion of the older population in the country. The study examines the association of dissatisfaction with living arrangements with health outcomes among older men and women in India. Methods The present research used data from the 'Building a Knowledge Base on Population Aging in India'. The effective sample size for the analysis was 9181 older adults. Descriptive statistics and bivariate analysis were performed to present the preliminary estimates. For finding the association between various health outcomes over explanatory variables, binary logistic regression model was used separately for men and women. Results About 22.8% of men and 30.8% of women who were living alone were dissatisfied with their present living arrangement. It was revealed that both men and women who were dissatisfied with their present living arrangements had significantly higher odds of experiencing poor self-rated health [OR:4.45, 3.25 ~ 6.09 and OR:3.32, 2.54 ~ 4.34], low psychological health [OR: 2.15, 1.61 ~ 2.86 and OR: 1.99, 1.57 ~ 2.53], low subjective well-being [OR: 3.37, 2.54 ~ 4.45 and OR: 3.03, 2.36 ~ 3.38], low ADL [OR: 1.77, 1.2 ~ 2.62 and OR: 1.59, 1.17 ~ 2.18, low IADL] [OR: 1.32, 1.03 ~ 1.69 and OR: 1.57, 1.24 ~ 1.98] and low cognitive ability [OR: 1.26, 0.98 ~ 1.61 and OR:1.44, 1.13 ~ 1.82] in comparison to their counterpart from men and women respectively. Conclusion It is found that dissatisfaction with the living arrangement of older men and women is negatively associated with major health outcomes. Hence, appropriate policies and programs must be developed to promote increased family care and support and an improved residential environment that would create a feeling of comfort and happiness among older individuals.

Indian agriculture is mostly dependent on monsoon. Poor and irregular rainfall may result in crop failure and food shortage among the vulnerable population. This study examined the variations in drought condition and its association with under age 5 child malnutrition across the districts of India. Using remote sensing and National Family Health Survey (NFHS-4) data, univariate Moran’s I and bivariate local indicator of spatial autocorrelation (LISA) maps were generated to assess the spatial autocorrelation and clustering. To empirically check the association, we applied multivariate ordinary least square and spatial autoregressive models. The study identified highly significant spatial dependence of drought followed by underweight, stunting, and wasting. Bivariate LISA maps showed negative spatial autocorrelation between drought and child malnutrition. Regression results suggest agricultural drought is substantially associated with stunting. An increasing value of drought showed statistical association with the decreasing (β = − 8.251; p value

Spatial hot spots of COVID‐19 infections and fatalities are observed at places exposed to high levels of air pollution across many countries. This study empirically investigates the relationship between exposure to air pollutants that is, sulfur dioxide, nitrogen dioxide, and particulate matter (SO2, NO2, and PM10) and COVID‐19 infection at the smallest administrative level (ward) of Mumbai City in India. The paper explores two hypotheses: COVID‐19 infection is associated with air pollution; the pollutants act as determinants of COVID‐19 deaths. Kriging is used to assess the spatial variations of air quality using pollution data, while information on COVID‐19 are retrieved from the database of Mumbai municipality. Annual average of PM10 in Mumbai over the past 3 years is much higher than the WHO specified standard across all wards; further, suburbs are more exposed to SO2, and NO2 pollution. Bivariate local indicator of spatial autocorrelation finds significant positive relation between pollution and COVID‐19 infected cases in certain suburban wards. Spatial Auto Regressive models suggest that COVID‐19 death in Mumbai is distinctly associated with higher exposure to NO2, population density and number of waste water drains. If specific pollutants along with other factors play considerable role in COVID‐19 infection, it has strong implications for any mitigation strategy development with an objective to curtail the spreading of the respiratory disease. These findings, first of its kind in India, could prove to be significant pointers toward disease alleviation and better urban living., Key Points Significant association between COVID‐19 infection with SO2, NO2, and PM10 reveals spatial hot spots in MumbaiWith increase in NO2 there is substantial increase in COVID‐19 deaths in MumbaiAir quality is an important element to address COVID‐19 management and sustainable urban development