Leading Predictors of COVID-19-Related Poor Mental Health in Adult Asian Indians: An Application of Extreme Gradient Boosting and Shapley Additive Explanations.

During the COVID-19 pandemic, an increase in poor mental health among Asian Indians was observed in the United States. However, the leading predictors of poor mental health during the COVID-19 pandemic in Asian Indians remained unknown. A cross-sectional online survey was administered to self-identified Asian Indians aged 18 and older (N = 289). Survey collected information on demographic and socio-economic characteristics and the COVID-19 burden. Two novel machine learning techniques-eXtreme Gradient Boosting and Shapley Additive exPlanations (SHAP) were used to identify the leading predictors and explain their associations with poor mental health. A majority of the study participants were female (65.1%), below 50 years of age (73.3%), and had income ≥ $75,000 (81.0%). The six leading predictors of poor mental health among Asian Indians were sleep disturbance, age, general health, income, wearing a mask, and self-reported discrimination. SHAP plots indicated that higher age, wearing a mask, and maintaining social distancing all the time were negatively associated with poor mental health while having sleep disturbance and imputed income levels were positively associated with poor mental health. The model performance metrics indicated high accuracy (0.77), precision (0.78), F1 score (0.77), recall (0.77), and AUROC (0.87). Nearly one in two adults reported poor mental health, and one in five reported sleep disturbance. Findings from our study suggest a paradoxical relationship between income and poor mental health; further studies are needed to confirm our study findings. Sleep disturbance and perceived discrimination can be targeted through tailored intervention to reduce the risk of poor mental health in Asian Indians.

Factors associated with COVID-19-related mental health among Asian Indians in the United States.

Background: In the United States, the COVID-19 pandemic has caused increased mental health symptoms and mental illness. Specific subgroups such as Asian Indians in the US have also been subject to additional stressors due to unprecedented loss of lives in their home country and increased Asian hate due to the misperception that Asians are to be blamed for the spread of the SARS-CoV-2. Objective: We examined the various factors including discrimination associated with COVID-19-related mental health symptoms among Asian Indians. Methods: We administered an online survey between May 2021 and July 2021 using convenient and snowball sampling methods to recruit Asian Indian adults (age > 18 years, N = 289). The survey included questions on mental health and the experience with unfair treatment in day-to-day life. Descriptive analysis and logistic regressions were performed. Results: Overall, 46.0% reported feeling down, depressed, or lonely and feeling nervous, tense, or worried due to the COVID-19 pandemic; 90.0% had received at least one dose of vaccination and 74.7% reported some form of discrimination. In the fully-adjusted logistic regression, age (AOR = 0.95; 95%CI- 0.92, 0.97;p < 0.01) and general health (AOR=0.84; 95%CI- 0.73, 0.97; p < 0.015) were negatively associated with mental health symptoms. Participants who experienced discrimination were more likely (AOR=1.26; 95%CI- 1.08, 1.46; p < 0.01) to report mental health symptoms. Conclusion: In this highly vaccinated group of Asian Indians discriminatory behaviors were associated with mental health symptoms suggesting the need for novel institutional level policy responses to reduce anti-Asian racism.

Trajectories and Determinants of Physical Activity during COVID-19 Pandemic: A Population-Based Study of Middle-Aged and Elderly Individuals in The Netherlands.

Physical inactivity is a major public health problem, and there are concerns this might have increased during the COVID-19 pandemic. We aimed to identify distinct trajectories of physical activity over a 6-week period after the first restrictive measures and to explore determinants of these trajectories in a population-based cohort of middle-aged and elderly in the Netherlands (n = 5777). We observed that at least 59% of participants did not meet the World Health Organization recommendations for physical activity. Using latent class trajectory analyses over three time points, we identified five distinct trajectories, including four steady trajectories at different levels (very low, low, medium and high) and one increasing trajectory. Using multinomial logistic regression analyses, we observed that, compared to the 'steadily high' trajectory, participants in the 'steadily very low' trajectory were more often older, lower educated, reporting poorer physical health, more depressive symptoms, consuming a less healthy diet, smoking, and lower alcohol use, and were less often retired. A similar pattern of determinants was seen for those in the increasing trajectory, albeit with smaller effect sizes. Concluding, we observed low levels of physical activity that generally remained during the pandemic. The determinants we described can help identify groups that require additional preventive interventions.

External validity of phase III trials on vaccines against SARS-CoV-2 to a middle-aged and elderly Western European population.

Initial results from various phase-III trials on vaccines against SARS-CoV-2 are promising. For proper translation of these results to clinical guidelines, it is essential to determine how well the general population is reflected in the study populations of these trials. This study was conducted among 7162 participants (age-range: 51-106 years; 58% women) from the Rotterdam Study. We quantified the proportion of participants that would be eligible for the nine ongoing phase-III trials. We further quantified the eligibility among participants at high risk to develop severe COVID-19. Since many trials were not explicit in their exclusion criterion with respect to 'acute' or 'unstable preexisting' diseases, we performed two analyses. First, we included all participants irrespective of this criterion. Second, we excluded persons with acute or 'unstable preexisting' diseases. 97% of 7162 participants was eligible for any trial with eligibility for separate trials ranging between 11-97%. For high-risk individuals the corresponding numbers were 96% for any trial with separate trials ranging from 5-96%. Importantly, considering persons ineligible due to 'acute' or 'unstable pre-existing' disease drastically dropped the eligibilities for all trials below 43% for the total population and below 36% for high-risk individuals. The eligibility for ongoing vaccine trials against SARS-CoV-2 can reduce by half depending on interpretation and application of a single unspecified exclusion criterion. This exclusion criterion in our study would especially affect the elderly and those with pre-existing morbidities. These findings thus indicate the difficulty as well as importance of developing clinical recommendations for vaccination and applying these to the appropriate target populations. This becomes especially paramount considering the fact that many countries worldwide have initiated their vaccination programs by first targeting the elderly and most vulnerable persons.