The influencing factors of discrimination against recovered Coronavirus disease 2019 (COVID-19) patients in China: a national study. Issue 1 (31st January 2022) - Liu, Rugang; Nicholas, Stephen; Leng, Anli; Qian, Dongfu; Maitland, Elizabeth; Wang, Jian
ABSTRACT: Background: Over 26 million recovered COVID-19 patients will suffer from discrimination in work, education and social interactions. We analyzed the determinants of discrimination against recovered COVID-19 patients and suggest policy recommendations to reduce such discrimination. Methods: Twenty-seven Chinese cities were selected randomly based on their geographical location and GDP rank. One hundred adults were interviewed in each city with an equal number of men and women and three urban residents for every two rural residents. A multiple ordered logistic regression model was used to assess the associations between potential determinants and the COVID-19 discrimination level. Results: Of 2377 participants, 79.76% displayed discrimination toward recovered COVID-19 patients. The female discrimination level was 1.25 times that of males; the discrimination level increased with age; and was occupation-specific, with physicians' (OR = 0.352) and students' (OR = 0.553) discrimination level lower than that of farmers. The discrimination level of participants from the central regions was 1.828 times, and the eastern region 1.504 times, that of participants from western region. The participants' discrimination level was lower when they scored higher in transmission knowledge, prevention knowledge and other COVID-19 knowledge, treatment methods and quarantine time. Conclusion: Sex, age, occupation, infections of relatives and friends, regions and scores on COVID-19 knowledge were determinants of discrimination level against recovered COVID-19 patients. In contrast with qualitative studies, our quantitative study recommends targeted education campaigns, focusing on physicians, women, older people and certain occupations. Only the COVID-19 vaccination program for the whole population will resolve the COVID-19 discrimination problem.