Population‐based prevalence surveys during the Covid‐19 pandemic: A systematic review
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Abstract
Population‐based prevalence surveys of Covid‐19 contribute to establish the burden of infection, the role of asymptomatic and mild infections in transmission, and allow more precise decisions about reopen policies. We performed a systematic review to evaluate qualitative aspects of these studies, assessing their reliability and compiling practices that can influence the methodological quality. We searched MEDLINE, EMBASE, bioRxiv and medRxiv, and included cross‐sectional studies using molecular and/or serological tests to estimate the prevalence of Covid‐19 in the general population. Survey quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies. A correspondence analysis correlated methodological parameters of each study to identify patterns related to higher, intermediate and lower risks of bias. The available data described 37 surveys from 19 countries. The majority were from Europe and America, used antibody testing, and reached highly heterogeneous sample sizes and prevalence estimates. Minority communities were disproportionately affected by Covid‐19. Important risk of bias was detected in four domains: sample size, data analysis with sufficient coverage, measurements in standard way and response rate. The correspondence analysis showed few consistent patterns for high risk of bias. Intermediate risk of bias was related to American and European studies, municipal and regional initiatives, blood samples and prevalence >1%. Low risk of bias was related to Asian studies, nationwide initiatives, reverse‐transcriptase polymerase chain reaction tests and prevalence <1%. We identified methodological standards applied worldwide in Covid‐19 prevalence surveys, which may assist researchers with the planning, execution and reporting of future population‐based surveys.
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SciScore for 10.1101/2020.10.20.20216259: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources MEDLINE (accessed via PubMed) MEDLINEsuggested: (MEDLINE, RRID:SCR_002185)PubMedsuggested: (PubMed, RRID:SCR_004846)Excerpta Medica dataBASE (EMBASE), bioRxiv, and medRxiv databases were searched using the following controlled vocabulary heading and terms: “seroprevalence”, “prevalence”, “serology”, “immunoassay”, EMBASEsuggested: (EMBASE, RRID:SCR_001650)bioRxivsuggested: (bioRxiv, RRID:SCR_003933)Results from OddPub: We did not detect open data. We also did not detect open …
SciScore for 10.1101/2020.10.20.20216259: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources MEDLINE (accessed via PubMed) MEDLINEsuggested: (MEDLINE, RRID:SCR_002185)PubMedsuggested: (PubMed, RRID:SCR_004846)Excerpta Medica dataBASE (EMBASE), bioRxiv, and medRxiv databases were searched using the following controlled vocabulary heading and terms: “seroprevalence”, “prevalence”, “serology”, “immunoassay”, EMBASEsuggested: (EMBASE, RRID:SCR_001650)bioRxivsuggested: (bioRxiv, RRID:SCR_003933)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We observed that important limitations of the studies were the low sample size and the low response rate (Figure 4). These factors influence heavily on reliable prevalence estimates (53). Moreover, the recruitment by letter, by mail or online may play a significant role in reducing the response rate and inadequately address the target population (54,55). For example, in the Icelandic study (27), the authors discussed the small variation in the prevalence estimates between open invitation and random selection recruitments. However, the random selection methods were not detailed and the sample size to detect the estimated prevalence was not adequate (<2,529 individuals) (56). In the Slovenian study (31), despite being considered nationwide, the sample size was 1,366, which represented ∼7x less than necessary (10,179) (56) and there was no management of the low response rate (<50%). Some authors seem to have not been concerned with managing this issue because even though the response rate was low, there was still an adequate sample (23,35,36,40,42). Repeated cross-sectional studies featured a widely distinct prevalence estimate on each round (29,36,49,50). This trend might be caused by the ascending curve of infected people, following the epidemic’s natural course. Therefore, there was a need for different sample sizes for each period. Unfortunately, some studies did not yield adequate sample size in all rounds (49, 50). The same proportion of studies validated their methods int...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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