The COVID-related perceptions in India and the USA : a comparative analysis

Published: 23 February 2021| Version 2 | DOI: 10.17632/hp3pdpjgrs.2


We used an online survey for the comparative analyses of various metrics of COVID-related perceptions such as stress, knowledge, and preventive behaviors between India and the US based participants. The working hypothesis was that the Indians would have higher COVID-related stress, perceived threat, and lesser knowledge, compared to the Americans. We further hypothesized that, higher perceived threat would encourage Indians than Americans, to better adhere to the preventive guidelines as well as better accept hydroxychloroquine and COVID-19 vaccine. The survey questionnaire was built and sent out via red cap. The primary outcome of the study was the difference in knowledge, stress, preventive behaviors, and perceived threat between Indian and the US based participants. The study participants were divided into categories based on several sociodemographic factors such as age, education level, gender, and family income. We also asked whether the participant was a healthcare worker (HCW) (yes vs. no), had a family member with COVID-19 (yes vs. no). A new variable was computed to estimate perceived threat based on age, HCW, chances of having severe COVID infection, state of COVID in his area. We used 'factor analyses' to abbreviate the metrics on stress, knowledge, preventive behavior, and perceived risk into normalized combined nominal scores (0-10), respectively. We received 962 responses and among them 242 and 531 participants were from the India or the USA, respectively. The Indian participants were predominantly males and young, while the US based participants were mostly females and middle-aged. Indian participants had lower knowledge, higher stress and acceptance of preventive guidelines, compared to the Americans.


Steps to reproduce

The basic structure in the excel document is unchanged from the original downloaded CSV file from the Redcap. The following variables were re-coded or added to facilitate study analyses. A. Recoding: All the re-coded variables were marked in green needed to be recoded for correct answers. 1) Child, hosp_rate, hosp_rate, death_rate, incubation, corona, animal, screening, climate, sunlight, nasal_spray, hot_beverages, baby_wipes: response 0 in the codebook is correct and response 1 is incorrect. 2) vaccine_pcv_flu: response 1-3 in the codebook were incorrect response, and response 4 in the codebook was correct response. 3) mask_gloves_2: response 0 in the codebook is correct and response 1 is incorrect. 4) social_distance_feet, isolation: response 1,3,4 in the codebook were incorrect, and response 2 was correct. 5) corona live: response 1,3 in the codebook were incorrect. 6) spread: response 3,4 in the codebook were incorrect, and response 1,2 were correct. B. Addition: Few other variables were computed and highlighted in yellow. For example, we used factor analyses to consolidate stress, preventive behavior, perceived threat and knowledge scores into normalized nominal scores. C. Reorganize categorization: We regrouped educational categories. Middle school and high school were categorized as <high school.


Penn State College of Medicine