Development of a decentralized cohort for studying post-acute sequelae of COVID-19 in India in the Data4life Study - PubMed (original) (raw)
Development of a decentralized cohort for studying post-acute sequelae of COVID-19 in India in the Data4life Study
Josh Schilling et al. Commun Med (Lond). 2023.
Abstract
Background: Decentralized, digital health studies can provide real-world evidence of the lasting effects of COVID-19 on physical, socioeconomic, psychological, and social determinant factors of health in India. Existing research cohorts, however, are small and were not designed for longitudinal collection of comprehensive data from India's diverse population. Data4Life is a nationwide, digitally enabled, health research initiative to examine the post-acute sequelae of COVID-19 across individuals, communities, and regions. Data4Life seeks to build an ethnically and geographically diverse population of at least 100,000 participants in India.
Methods: Here we discuss the feasibility of developing a completely decentralized COVID-19 cohort in India through qualitative analysis of data collection procedures, participant characteristics, participant perspectives on recruitment and reported study motivation.
Results: As of June 13th, 2022, more than 6,000 participants from 17 Indian states completed baseline surveys. Friend and family referral were identified as the most common recruitment method (64.8%) across all demographic groups. Helping family and friends was the primary reason reported for joining the study (61.5%).
Conclusions: Preliminary findings support the use of digital technology for rapid enrollment and data collection to develop large health research cohorts in India. This demonstrates the potential for expansion of digitally enabled health research in India. These findings also outline the value of person-to-person recruitment strategies when conducting digital health research in modern-day India. Qualitative analysis reveals opportunities to increase diversity and retention in real time. It also informs strategies for improving participant experiences in the current Data4Life initiative and future studies.
Plain language summary
Due to the vast geographical size and ethnic diversity of the population, India represents a huge challenge for conducting research studies. The Data4Life study was set up to understand if digital tools can be an effective way to study long-term effects of COVID-19 across India. We studied different ways of collecting the relevant information from participants, the background of each participant, reasons, and motivation of each participant for joining the study. The results showed that friend and family referrals were the most common recruitment reason. Helping family and friends was reported as the main motivation for joining the study. Overall, the findings support the use of digital tools as an effective recruitment method for research studies in India.
© 2023. Springer Nature Limited.
Conflict of interest statement
J. Schilling, A. Montgomery, S. Shokouhi, and P. Jain are employees at Vibrent Health. The remaining authors declare no competing interests.
Figures
Fig. 1. Participant baseline survey data.
Data from 5905 participants who completed their baseline survey were used for demographic analysis. Totally, 5823 participants completed their Health History survey, including a basic COVID-19 questionnaire.
Fig. 2. Number of registered, consented, and withdrawn participants.
Weekly registrations (orange circles) and cumulative consents (blue circles) between 03-15-2022 and 06-13-2022. Out of 6426 registered individuals, 6375 participants consented.
Fig. 3. Geographic location of the participants.
Location of participants by resident state (a) and birth city (b). The shades of orange in a represent participant densities in each state. In b, blue circles on India’s map indicate participant clusters within a city or metropolitan area, and the circle diameters correspond to the number of individuals in each cluster.
Fig. 4. Participants by study motivation and demographic groups.
Frequencies of study motivation (helping family and friends, helping the country, protecting my health, advancing research, and access to medical advice through reliable sources such as NIH) across demographic groups stratified by age (a), education (b), sex (c), Income (d), and religion (e). Sample size: n = 5905 participants were used to derive the percentages. Sample size: 5905 participants were used to derive the percentages. In a, the shades of yellow represent different age groups, while in b, different shades of blue signify educational groups. The colors orange and blue in c indicate the female and male sexes, respectively. In d, different shades of orange are used to denote income groups, and in e, various shades of green represent religious groups.
Fig. 5. Participants by recruitment methods and demographic groups.
Frequencies of recruitment methods (referral by family or friends, referral by the clinical coordinator, newsletter/email, pamphlet/newspaper, search engine, radio, social media, others) across demographic groups stratified by age (a), education (b), sex (c), Income (d), and religion (e). Sample size: n = 5905 participants were used to derive the percentages. In a, the shades of yellow represent different age groups, while in b, various shades of blue indicate educational groups. The colors orange and blue in c signify the female and male sexes, respectively. In d, different shades of orange are used to depict income groups, and in e, various shades of green represent religious groups.
References
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- Estupinan, X. & Sharma, M. Job and wage losses in informal sector due to the COVID-19 lockdown measures in India. SSRN Scholarly Paper at 10.2139/ssrn.3680379 (2020).
- Lee, K., Sahai, H., Baylis, P. & Greenstone, M. Job Loss and Behavioral Change: The Unprecedented Effects of the India Lockdown in Delhi. SSRN Scholarly Paper at 10.2139/ssrn.3601979 (2020).
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