Early stages of the COVID-19 pandemic introduced a change in communication norms in regard to well-being. People traversed through different forms of communication to adapt to policies and regulations that limited in-person interactions to prevent the spread of the COVID-19 virus. Social interactions have been found to be an innate human need, important to one’s health and well-being. The study looked at the relationship between socializing and well-being during the state of the COVID-19 pandemic. Socializing variables consisted of remote and in-person socializing which in-person socializing was divided into two distinct categories. In-person socializing was divided into in-person safe socializing, indicating socializing that was safe from the risk of contracting the virus, and in-person unsafe socializing which indicates that socializing was at risk of contracting the virus. Additionally, the current study also investigated how age moderates this relationship between socializing and well-being. SEM analyses reported that in-person unsafe socializing has a significant positive association with well-being outcomes: anxiety and depression which indicate high levels of anxiety and depression with increased in-person unsafe socializing. The study also found remote socializing to have a significant positive association with the well-being outcome: positive affect, indicating increased levels of positive affect with increased remote socializing. Regression analyses looked at moderation by age, finding no significant interactions of age between socializing and well-being. Findings suggest the beneficial role of remote socializing and although remote socializing cannot replace in-person interactions, it serves as a supplemental resource during unpredictable events such as the COVID-19 pandemic.

Significant health inequalities exist between different castes and ethnic communities in India, and identifying the roots of these inequalities is of interest to public health research and policy. Research on caste-based health inequalities in India has historically focused on general, government-defined categories, such as “Scheduled Castes,” “Scheduled Tribes,” and “Other Backward Classes.” This method obscures the diversity of experiences, indicators of well-being, and health outcomes between castes, tribes, and other communities in the “scheduled” category. This study analyzes data on 699,686 women from 4,260 castes, tribes and communities in the 2015-2016 Demographic and Health Survey of India to: (1) examine the diversity within and overlap between general, government-defined community categories in both wealth, infant mortality, and education, and (2) analyze how infant mortality is related to community category membership and socioeconomic status (measured using highest level of education and household wealth). While there are significant differences between general, government-defined community categories (e.g., scheduled caste, backward class) in both wealth and infant mortality, the vast majority of variation between communities occurs within these categories. Moreover, when other socioeconomic factors like wealth and education are taken into account, the difference between general, government-defined categories reduces or disappears. These findings suggest that focusing on measures of education and wealth at the household level, rather than general caste categories, may more accurately target those individuals and households most at risk for poor health outcomes. Further research is needed to explain the mechanisms by which discrimination affects health in these populations, and to identify sources of resilience, which may inform more effective policies.