Prevalence, characteristics, and predictors of healthcare workers with COVID-19 infection in an urban district in Malaysia

Abstract

Introduction: healthcare workers (HCWs) are at high risk of acquiring COVID-19 occupational transmission and subsequently, exposing patients and others. This study aimed to determine the prevalence and examine the characteristics and predictors of HCWs with COVID-19 infection in a Malaysian district. Methods: this is a cross-sectional study of HCWs working at Cheras District Health Office, with COVID-19 infection from 1st January to 31st October 2021. Data was obtained from the Occupational Safety and Health Unit which included variables of basic sociodemography, type of disease acquisition; healthcare-acquired (HA) or community-acquired (CA), and management outcome. Data was analysed descriptively and cases with type of disease acquisition were compared using logistic regression. Results: the prevalence of HCWs with COVID-19 was 17.4%. Majority aged 30-39, female gender and Malay ethnicity (51.7%, 60% and 91.7% respectively). Main comorbidities included hypertension (3.3%), diabetes mellitus (3.3%), both hypertension and diabetes mellitus (2.5%) and obesity (4.2%). Smokers, pregnant mothers and non-immunized made up only small proportions (4.2%, 4.2%, and 4% respectively). Paramedics were the most infected proportion (68.4%). About one third of cases managed COVID-19 patients directly (37.5%). Similar proportion had HA infection (29.2%). Smaller proportion (12.8%) needed hospitalization. The early source of infection was HA (January-April). Later, the trend shifted towards CA (May-October). Male gender (OR 3.22, 95% CI = 1.43-7.29, p<0.05), smoker (OR 10.84, 95% CI = 1.17-100.77, p<0.05), and those who manage COVID-19 cases were more likely to acquire occupational COVID-19 infection (OR 2.28, 95% CI = 1.02-5.09, p<0.05). Conclusion: continuous occupational infectious disease control measures is necessary to reduce the disease burden. Future research on HCWs with COVID-19 infection with larger scale is recommended to determine the final model for predictors of infection.