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Initial studies have reported an increase in the incidence of mental health problems during the early months of COVID-19. Longitudinal studies of changes in mental health undertaken in Low and Middle Income countries during the pandemic remains an under-researched area. The current study examines changes in mental health among adult residents of metropolitan cities of India, a middle-income country reporting the second-highest COVID cases and third-highest fatalities, during the pandemic. Data was collected, based on a telephonic survey using the internationally accepted abridged Depression Anxiety Stress schedule (DASS-21), in August and September 2020 and July-August 2021. The sample size is 994. The data was analysed using an ordered logit model. At the onset of the pandemic, high levels of anxiety, stress and depression prevailed; their levels reduced after one year. Respondents who have experienced a decline in economic fortunes, have family members with pre-existing co-morbidity or had COVID in the family are significantly less likely to report improvement in mental health; less-educated respondents are also vulnerable. Specific sub-groups, identified as at risk, need monitoring and continued provisioning of tailor-made mental health services addressing their specific needs. Relief measures targeting economically affected households are also required. [ABSTRACT FROM AUTHOR]

Background: The causal impact of income shocks on mental well-being in developing countries is an under-researched area. The COVID-19 pandemic, along with the economic recession caused by the lockdowns to reduce transmission, provides a natural experimental setting to examine the causal impact of a decline in monthly per capita expenditure (MPCE) on mental health of the general population in India during the pandemic. Aim: To evaluate the impact of income shocks on mental health of adults in metro cities during the COVID-19 pandemic. Materials and Methods: The data were collected using the abridged version of the Depression Anxiety Stress Schedule administered through a telephonic survey on adult residents of six metropolitan cities in September-August 2020 and July-August 2021. Results: In the present study, 994 adults participated from six metropolitan cities. Average treatment effects were estimated using Propensity Score Matching. The mean normalized scores are significantly higher for respondents whose MPCE had fallen (treated) vis-à-vis respondents whose MPCE had remained same or increased (control): anxiety (0.21 for treated vs -0.19 for control), stress (0.16 vs -0.14), and depression (0.04 vs -0.19). Propensity score matching reveals that the normalized scores for anxiety, stress, and depression were 33 (95% confidence intervals, CI: 20.0-46.7), 25 (95% CI: 12.9-36.9), and 36 (95% CI: 18.6-53.1) higher among the treated group vis-a-vis control group. The ATET was 34 (95% CI: 18.9-48.9), 26 (95% CI: 10.1-42.9), and 32 (955 CI: 12.3-50.7) for these three outcomes, respectively. The post-estimation tests indicated that the results are valid. Conclusions: The study advocates that policies to ensure income security should be made an integral part of the response packages to tackle pandemics like COVID-19. [ABSTRACT FROM AUTHOR]

Most studies on intimate partner violence (IPV) and its drivers have focused on individual-and household-level characteristics of the victim. Recent studies have acknowledged that it is a community-level phenomenon, using spatial analytical methods to analyze community-level determinants of IPV and its geographic dimensions. Such studies provide mixed evidence on the impact of different factors and need to be supplemented by similar studies—particularly in South Asian countries where IPV is common. The present study examines district-level variations in the incidence of various forms of IPV and identifies its determinants in India, a fast-growing South Asian country with poor gender indicators. The study combines data from the National Family Health Survey, District Level Household Survey, and the decadal Census. It applies spatial analytical methods such as the Global Moran's I, Getis-ord statistic, and Multivariate Local Geary to determine the nature of the spatial distribution of different categories of IPV. Spatial regression models are used to identify the community-level predictors of each category of IPV. The study finds non-random overlapping spatial clusters in the eastern part of India. The study also finds that neighborhoods characterized by low empowerment levels, and with a high child sex ratio, road connectivity, and proportion of socially marginalized groups are more likely to exhibit high levels of all types of IPV—although the impact of these determinants varies across districts. Furthermore, spill-overs in the incidence of IPV between neighboring districts are also observed. The study concludes by recommending the use of localized policies, rather than broad national or state policies, in reducing IPV. [ABSTRACT FROM AUTHOR]