Mental Health Trends in India from 2020 to 2022: Association with Financial Stress, Food Insecurity, and COVID-19-related Illness Concerns (preprint)

This study examines the impact of pandemic-related worries on mental health in the Indian general adult population from 2020 to 2022. Using data from the Global COVID-19 Trends and Impact Survey (N = 2,576,174 respondents aged >= 18 years in India; an average weekly sample size of around 25,000), it explores the associations between worry variables (namely financial stress, food insecurity, and COVID-19-related health worries) and self-reported symptoms of depression and nervousness. The statistical analysis was conducted using complete cases only (N = 747,996). Our analysis used survey-weighted models, focusing on the three pandemic-related worries as the exposures, while also adjusting for various other covariates, including demographics and calendar time. The study finds significant associations between these worries and mental health outcomes, with financial stress being the most significant factor affecting both depression (adjusted odds ratio: 2.36, 95% confidence interval: [2.27, 2.46]) and nervousness (adjusted odds ratio: 1.91, 95% confidence interval: [1.81, 2.01]) during the first phase of the study period (June 27, 2020, to May 19, 2021). The fully adjusted models also identify additional factors related to mental health, including age, gender, residential status, geographical region, occupation, and education. Moreover, the research highlights that males and urban residents had higher odds ratios for self-reported mental health problems regarding the worry variables than females and rural residents, respectively. Furthermore, the study reveals a rise in the prevalence of self-reported depression and nervousness and their association with COVID-19-related health worries during the lethal second wave of the pandemic in May 2021 compared to the onset of the pandemic. This study shows that social media platforms like Facebook can deploy surveys to a large number of participants globally and can be useful tools in capturing mental health trends and uncovering associations during a public health crisis.

Exploring the big data paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS) (preprint)

Selection bias poses a challenge to statistical inference validity in non-probability surveys. This study compared estimates of the first-dose COVID-19 vaccination rates among Indian adults in 2021 from a large non-probability survey, COVID-19 Trends and Impact Survey (CTIS), and a small probability survey, the Center for Voting Options and Trends in Election Research (CVoter), against benchmark data from the COVID Vaccine Intelligence Network (CoWIN). Notably, CTIS exhibits a larger estimation error (0.39) compared to CVoter (0.16). Additionally, we investigated the estimation accuracy of the CTIS when using a relative scale and found a significant increase in the effective sample size by altering the estimand from the overall vaccination rate. These results suggest that the big data paradox can manifest in countries beyond the US and it may not apply to every estimand of interest.