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

Background School 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. Methods We 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. Results At 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. Conclusions School 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.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study (Supplementary Information) Elizabeth T Chin * † , Benjamin Q Huynh † , Nathan C Lo, Trevor Hastie and Sanjay Basu * Correspondence: etchin@stanford.edu Full list of author information is available at the end of the article † Equal contributor

Economic analysis
Here we describe how we obtained county-level estimates of childcare costs and wages. We use state-level child care costs from CCAoA and adjust them to countylevel by applying the ratio between state-level and county-level fair market rents from HUD. We calculate state-level rents from HUD by taking population-weighted averages of county rents. To estimate the number of healthcare workers with children at the county-level, we take the state-level proportion of healthcare workers with children from IPUMS and apply it to the county-level number of healthcare workers from ACS. We then calculate the county-level cost of providing child care to healthcare workers by multiplying child care costs by the proportion of healthcare workers with children.
For estimating county-level wages, some counties with low populations had redacted wages to preserve anonymity. We used multiple imputation by chained equations to impute these cases. To get all county-level wages, we multiplied the number of healthcare workers (by occupation group and sex) by their subgrouprespective county-level median wages.

Robustness checks
Here we provide sensitivity analyses and robustness checks of our estimates across various parameters. Table S2 shows different estimates of unmet child care needs based on different assumptions for determining child care needs. Table S3 Shows different estimates of school closure effectiveness based on varying the basic reproduction number from 2 to 6 when holding the reproduction number constant across all states and when varying the basic reproduction number using state-specific estimates across the mean and 95% confidence intervals. Figure S1 displays the cut off points of rho and delta for 70% of counties to reach ω > 1, where ρ = {0.5, 0.6, 0.7, 0.8, 0.9, 1} is the proportion of those with unmet child care needs who go on to be absent from work and δ = {1, 1.1, 1.2, 1.3, 1.4} is the increased cost for emergency child care compared to normal costs. Even under pessimistic parameter assumptions (ρ = 0.6, δ = 1.4), 70% of counties can still afford partial child care subsidies (70%) over bearing absenteeism costs. Figure S2 shows that the rurality proportions across counties remains relatively constant across values of ρ, suggesting county characteristics do not change across parameter changes.
The list of occupation codes through the American Community Survey that were used to categorize essential workers is included as Additional file 3: Table S1: Occupation codes for essential worker classification. Figure S1: Sensitivity analysis of parameter thresholds for 70% of counties to reach ω > 1. Colored lines indicate different levels of subsidization rates. Figure S2: Proportion of counties with higher rates of lost wages due to absenteeism than costs of child care (ω > 1) across ρ = {0.5, 0.6, 0.7, 0.8, 0.9, 1.}. Bars are shaded based on the level of rurality of counties.

Additional maps
Absenteeism, complication factors, and wages Figure S3: County-level comparison of percent of healthcare worker households with unmet childcare needs and cardiovascular disease mortality (deaths per 100,000 people). Counties with confidence interval sizes in the 90th percentile or below are shown. Figure S4: County-level comparison of percent unmet childcare needs and ω. Counties with confidence interval sizes in the 90th percentile or below are shown. Figure S5: County-level comparison of percent of healthcare worker households with unmet child care needs and effectiveness of school closures using estimated reduction of peak ICU bed demand. Counties with confidence interval sizes in the 90th percentile or below are shown. No within-state normalization used.      Table S4: Regression output for models on diabetes, cardiovascular disease, percent rural, and controls.