ArticleIJID regions2022
Title: Factors associated with time lag between symptom onset and reporting in the first epidemic wave of COVID-19 in Osaka, Japan.
Article in IJID regions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Article
- Surveillance of SARS-CoV-2 in sewage from buildings housing residents with different vulnerability levels.The Science of the total environment · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Longer reporting lags after symptom onset reportedly exert a substantial impact on onward transmission, increasing outbreak probability. Our study investigated the risk factors associated with reporting lag. Methods: Using active epidemiological surveillance data for all symptomatic cases reported in Osaka Prefecture during the first wave of the coronavirus disease 2019 (COVID-19) epidemic (February 1-May 13, 2020), multivariable regression analyses were implemented to estimate the effects of exposure variables on reporting lag, by controlling for potential confounders. Results: Cases in their 30s showed a longer reporting lag than cases ≥ 80 years old. Cases who lived in areas with a high COVID-19 incidence demonstrated a longer reporting lag. Cases with a history of visiting a nightlife district also showed longer reporting lag than cases without such a history. Healthcare workers and cases with immunodeficiency both displayed shorter reporting lags than others. Conclusion: Identifying newly infected cases as soon as possible and increased testing capacity for all age groups, and for individuals with a history of visiting high infection-risk areas, represented important measures in shortening reporting lags in the first wave period. The evidence from this study may provide lessons for controlling future emerging diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.