Evidence map›Paper›PMID 35162344›Full record

ArticleInternational journal of environmental research and public health2022

Critical Periods, Critical Time Points and Day-of-the-Week Effects in COVID-19 Surveillance Data: An Example in Middlesex County, Massachusetts, USA.

Ryan B Simpson, Brianna N Lauren, Kees H Schipper, James C McCann, Maia C Tarnas, Elena N Naumova

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Food Insecurity across Age Groups in the United States during the COVID-19 Pandemic.International journal of environmental research and public health · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs.International statistical review = Revue internationale de statistique · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ryan B SimpsonDivision of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA.ORCID 0000-0002-1375-5387
Brianna N LaurenDivision of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA.
Kees H SchipperDivision of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA.ORCID 0000-0002-2695-1969
James C McCannDivision of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA.
Maia C TarnasDivision of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA.ORCID 0000-0003-1470-313X
Elena N NaumovaDivision of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA.ORCID 0000-0002-9562-4734

Funding

National Institute of Food and Agriculture 2020-38420-30724Office of the Director of National Intelligence 2017-17072100002Tufts University DISC
6 · The paper itself

Abstract

Critical temporal changes such as weekly fluctuations in surveillance systems often reflect changes in laboratory testing capacity, access to testing or healthcare facilities, or testing preferences. Many studies have noted but few have described day-of-the-week (DoW) effects in SARS-CoV-2 surveillance over the major waves of the novel coronavirus 2019 pandemic (COVID-19). We examined DoW effects by non-pharmaceutical intervention phases adjusting for wave-specific signatures using the John Hopkins University's (JHU's) Center for Systems Science and Engineering (CSSE) COVID-19 data repository from 2 March 2020 through 7 November 2021 in Middlesex County, Massachusetts, USA. We cross-referenced JHU's data with Massachusetts Department of Public Health (MDPH) COVID-19 records to reconcile inconsistent reporting. We created a calendar of statewide non-pharmaceutical intervention phases and defined the critical periods and timepoints of outbreak signatures for reported tests, cases, and deaths using Kolmogorov-Zurbenko adaptive filters. We determined that daily death counts had no DoW effects; tests were twice as likely to be reported on weekdays than weekends with decreasing effect sizes across intervention phases. Cases were also twice as likely to be reported on Tuesdays-Fridays (RR = 1.90-2.69 [95%CI: 1.38-4.08]) in the most stringent phases and half as likely to be reported on Mondays and Tuesdays (RR = 0.51-0.93 [0.44, 0.97]) in less stringent phases compared to Sundays; indicating temporal changes in laboratory testing practices and use of healthcare facilities. Understanding the DoW effects in daily surveillance records is valuable to better anticipate fluctuations in SARS-CoV-2 testing and manage appropriate workflow. We encourage health authorities to establish standardized reporting protocols.

Indexed as

COVID-19COVID-19 TestingHumansMassachusettsPandemicsSARS-CoV-2COVID-19data reliabilityday-of-the-week effectsKolmogorov-Zurbenko filtersnonpharmaceutical interventionsprecision public healthstringency

Identifiers

PMID35162344
PMCPMC8835321

What OpenQuestion holds

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Registered trials

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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.