Evidence map›Paper›PMID 32664927›Full record

ArticleBMC medicine2020

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study.

Elizabeth T Chin, Benjamin Q Huynh, Nathan C Lo, Trevor Hastie, Sanjay Basu

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
0.8field-weighted citation impact, top 20% of its field
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

14 citing papers in PubMed, 26 citations in OpenAlex.

  1. Review
  2. Article
  3. Spatial distributions in disaster risk vulnerability for people with disabilities in the U.S.International journal of disaster risk reduction : IJDRR · 2023
    Article
  4. Effects of COVID-19 Financial and Social Hardships on Infants' and Toddlers' Development in the ECHO Program.International journal of environmental research and public health · 2023
    Article
  5. Article
  6. Article
  7. Review
  8. Disaster medicine and public health preparedness · 2022
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. A COVID-19 Risk Assessment for the US Labor Force.medRxiv : the preprint server for health sciences · 2020
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 2 countries.

Elizabeth T ChinDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA. etchin@stanford.edu.ORCID 0000-0001-5774-4236
Benjamin Q HuynhDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Nathan C LoDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA.
Trevor HastieDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Sanjay BasuCenter for Primary Care, Harvard Medical School, Boston, MA, USA.
Stanford University · USHarvard University · USStanford Medicine · USUniversity of California, San Francisco · US

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

backgroundSchool closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.

methodsWe provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.

resultsAt the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.

conclusionsSchool closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.

Indexed as

AbsenteeismSchoolsBetacoronavirusChildChild CareComputer SimulationCoronavirus InfectionsCOVID-19Feasibility StudiesForecastingGeographyHealth PersonnelHealth WorkforceHumansIntensive Care UnitsNeeds AssessmentAbsenteeismChild careCOVID-19Geographic disparitiesGeospatialSchool closuresSimulation study

Identifiers

PMID32664927
PMCPMC7360472
OpenAlexW3043424307

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.