Seroprevalence of SARS-CoV-2 and assessment of epidemiologic determinants in Portuguese municipal workers.

OBJECTIVES: To assess the seroprevalence of SARS-CoV-2 antibodies in municipal employees of Northern Portugal during the first pandemic wave (May-June 2020) and its association with potentially related risk factors for infection. MATERIAL AND METHODS: The authors assessed municipal employees of 2 cities in Northern Portugal, in whom serological tests to SARS-CoV-2 and an epidemiological survey were applied. The authors assessed the proportion of individuals presenting IgM and/or IgG antibodies to SARS-CoV-2, and evaluated the association between having positive serological test results, epidemiologic variables and clinical presentations. Reported symptoms were evaluated on their sensitivity, specificity, and predictive values. RESULTS: The authors assessed 1696 employees, of whom 22.0% were firefighters, 10.4% were police officers, 10.3% were maintenance workers, and 8.1% were administrative assistants. The seroprevalence of SARS-CoV-2 infection was 2.9% (95% CI: 2.1-3.7%). Administrative assistants comprised the professional group with highest seroprevalence of SARS-CoV-2 (OR = 1.9 in the comparison with other occupational groups, 95% CI: 0.8-4.3, p = 0.126). The seroprevalence of SARS-CoV-2 infection among those who were in direct contact with COVID-19 patients in their professional activity was 3.9%, compared to 2.7% among those who were not in direct contact with such patients (OR = 1.5, 95% CI: 0.8-2.8, p = 0.222). The highest risk of infection was associated with the presence of a confirmed SARS-CoV-2 infection in the household (OR = 17.4, 95% CI: 8.3-36.8, p < 0.001). Living with a healthcare professional was not associated with a higher risk of infection (OR = 1.0, 95% CI: 0.4-2.5, p = 0.934). Anosmia/ dysgeusia was the symptom with the highest positive predictive value (52.2%, 95% CI: 31.8-72.6, p < 0.001) and specificity (99.3%, 95% CI: 98.9-99.7, p < 0.001), while cough was the most prevalent symptom among SARS-CoV-2 seropositive participants (36%). CONCLUSIONS: The authors observed a SARS-CoV-2 seroprevalence of 2.9% among assessed municipal employees. Anosmia/dysgeusia was the COVID-19 symptom which displayed the highest positive predictive value and specificity. Int J Occup Med Environ Health. 2022;35(3):297-307.

COVID-19 surveillance data quality issues: a national consecutive case series.

OBJECTIVES: High-quality data are crucial for guiding decision-making and practising evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese epidemiological surveillance dataset, our study aims to assess COVID-19 data quality issues and suggest possible solutions. SETTINGS: On 27 April 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On 4 August, an updated dataset (DGSAugust) was also obtained. PARTICIPANTS: All COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June. PRIMARY AND SECONDARY OUTCOME MEASURES: Data completeness and consistency. RESULTS: DGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (eg, 4075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (eg, the variable 'underlying conditions' had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily. CONCLUSIONS: Important quality issues of the Portuguese COVID-19 surveillance datasets were described. These issues can limit surveillance data usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed-for example, simplification of data entry processes, constant monitoring of data, and increased training and awareness of healthcare providers-as low data quality may lead to a deficient pandemic control.