Thinking out of the box: revisiting health surveillance based on medical records.

Despite the considerable advances in the last years, the health information systems for health surveillance still need to overcome some critical issues so that epidemic detection can be performed in real time. For instance, despite the efforts of the Brazilian Ministry of Health (MoH) to make COVID-19 data available during the pandemic, delays due to data entry and data availability posed an additional threat to disease monitoring. Here, we propose a complementary approach by using electronic medical records (EMRs) data collected in real time to generate a system to enable insights from the local health surveillance system personnel. As a proof of concept, we assessed data from São Caetano do Sul City (SCS), São Paulo, Brazil. We used the "fever" term as a sentinel event. Regular expression techniques were applied to detect febrile diseases. Other specific terms such as "malaria," "dengue," "Zika," or any infectious disease were included in the dictionary and mapped to "fever." Additionally, after "tokenizing," we assessed the frequencies of most mentioned terms when fever was also mentioned in the patient complaint. The findings allowed us to detect the overlapping outbreaks of both COVID-19 Omicron BA.1 subvariant and Influenza A virus, which were confirmed by our team by analyzing data from private laboratories and another COVID-19 public monitoring system. Timely information generated from EMRs will be a very important tool to the decision-making process as well as research in epidemiology. Quality and security on the data produced is of paramount importance to allow the use by health surveillance systems.

Seroprevalence of anti-SARS-CoV-2 among blood donors in Rio de Janeiro, Brazil.

OBJECTIVE To estimate the seroprevalence of antibodies to SARS-CoV-2 among blood donors in the state of Rio de Janeiro, Brazil. METHODS Data were collected on 2,857 blood donors from April 14 to 27, 2020. This study reports crude prevalence of antibodies to SARS-CoV-2, population weighted prevalence for the state, and prevalence adjusted for test sensitivity and specificity. Logistic regression models were used to establish the correlates of SARS-CoV-2 prevalence. For the analysis, we considered collection period and site, sociodemographic characteristics, and place of residence. RESULTS The proportion of positive tests for SARS-Cov-2, without any adjustment, was 4.0% (95%CI 3.3-4.7%), and the weighted prevalence was 3.8% (95%CI 3.1-4.5%). We found lower estimates after adjusting for test sensitivity and specificity: 3.6% (95%CI 2.7-4.4%) for the non-weighted prevalence, and 3.3% (95%CI 2.6-4.1%) for the weighted prevalence. Collection period was the variable most significantly associated with crude prevalence: the later the period, the higher the prevalence. Regarding sociodemographic characteristics, the younger the blood donor, the higher the prevalence, and the lower the education level, the higher the odds of testing positive for SARS-Cov-2 antibody. We found similar results for weighted prevalence. CONCLUSIONS Our findings comply with some basic premises: the increasing trend over time, as the epidemic curve in the state is still on the rise; and the higher prevalence among both the youngest, for moving around more than older age groups, and the less educated, for encountering more difficulties in following social distancing recommendations. Despite the study limitations, we may infer that Rio de Janeiro is far from reaching the required levels of herd immunity against SARS-CoV-2.

Seroprevalence of anti-SARS-CoV-2 among blood donors in Rio de Janeiro, Brazil

ABSTRACT OBJECTIVE To estimate the seroprevalence of antibodies to SARS-CoV-2 among blood donors in the state of Rio de Janeiro, Brazil. METHODS Data were collected on 2,857 blood donors from April 14 to 27, 2020. This study reports crude prevalence of antibodies to SARS-CoV-2, population weighted prevalence for the state, and prevalence adjusted for test sensitivity and specificity. Logistic regression models were used to establish the correlates of SARS-CoV-2 prevalence. For the analysis, we considered collection period and site, sociodemographic characteristics, and place of residence. RESULTS The proportion of positive tests for SARS-Cov-2, without any adjustment, was 4.0% (95%CI 3.3-4.7%), and the weighted prevalence was 3.8% (95%CI 3.1-4.5%). We found lower estimates after adjusting for test sensitivity and specificity: 3.6% (95%CI 2.7-4.4%) for the non-weighted prevalence, and 3.3% (95%CI 2.6-4.1%) for the weighted prevalence. Collection period was the variable most significantly associated with crude prevalence: the later the period, the higher the prevalence. Regarding sociodemographic characteristics, the younger the blood donor, the higher the prevalence, and the lower the education level, the higher the odds of testing positive for SARS-Cov-2 antibody. We found similar results for weighted prevalence. CONCLUSIONS Our findings comply with some basic premises: the increasing trend over time, as the epidemic curve in the state is still on the rise; and the higher prevalence among both the youngest, for moving around more than older age groups, and the less educated, for encountering more difficulties in following social distancing recommendations. Despite the study limitations, we may infer that Rio de Janeiro is far from reaching the required levels of herd immunity against SARS-CoV-2.

Seroprevalence of IgG and IgM anti-SARS-CoV-2 among voluntary blood donors in Rio de Janeiro, Brazil

Background: In Brazil, mathematical models for derivingestimates and projections of COVID-19 cases have been developed without data on asymptomatic SARS-CoV-2 infection. We estimated the seroprevalence of antibodies to SARS-CoV-2 among blood donors in the State of Rio de Janeiro. Methods: Data were collected on 2,857 blood donors from April 14 to 27, 2020. We report the crude prevalence of antibodies to SARS-CoV-2, the weighted prevalence by the total state population, and adjusted prevalence estimates for test sensitivity and specificity. To establish the correlates of SARS-CoV-2 prevalence, we used logistic regression models. The analysis included period and site of blood collection, sociodemographic characteristics, and place of residence. Results: The proportion of SARS-Cov-2 positive tests without any adjustment was 4.0% (95% CI 3.3-4.7%), and the weighted prevalence was 3.8% (95% CI 3.1-4.5%). Further adjustment by test sensitivity and specificity produced lower estimates, 3.6% (95% CI 2.7-4.4%) and 3.3% (95% CI 2.6-4.1%), respectively. The variable most significantly associated with the crude prevalence was the period of blood collection: the later the period, the higher the prevalence. Regarding socio-demographic characteristics, the younger the blood donors, the higher the prevalence, and the lower the educational level, the higher the odds of a positive SARS-Cov-2 antibody. Similar results were found for the weighted prevalence. Discussion: Although our findings resulted from a convenience sample, they match some basic premises: the increasing trend over time, since the epidemic curve in the state is still on the rise; the higher prevalence among the youngest who are more likely to circulate; and the higher prevalence among the less educated as they have more difficulties in following the social distancing recommendations. Despite the study limitations, it is possible to infer that protective levels of natural herd immunity to SARS-CoV-2 are far from being reached in Rio de Janeiro. (AU)