This study uses state public health department data to describe trends in COVID-19 hospitalizations before and after state executive stay-at-home orders issued in March and April 2020 in Colorado, Minnesota, Ohio, and Virginia, and compares projected vs observed admissions to estimate associations between the orders and hospital admissions.
In analyses of the effectiveness of response measures to the outbreak of coronavirus disease 2019 (COVID-19), most studies have used the number of confirmed cases or deaths. However, case count is a conservative estimate of the actual number of infected individuals in the absence of community-wide serologic testing. Death count is a lagging metric and insufficient for proactive hospital capacity planning. A more valuable metric for assessing the effects of public health interventions on the health care infrastructure is hospitalizations.1 As of April 18, 2020, governors in 42 states had issued statewide executive “stay-at-home” orders to help mitigate the risk that COVID-19 hospitalizations would overwhelm their state’s health care infrastructure. This study assessed the association between these orders and hospitalization trends.
MethodsIn March 2020, we began collecting data on cumulative confirmed COVID-19 hospitalizations from each state’s department of health website on a daily basis.2 Among states issuing a statewide stay-at-home order, we identified states with at least 7 consecutive days of cumulative hospitalization data for COVID-19 (including patients currently hospitalized and those discharged) before the stay-at-home order date and at least 17 days following the order date. Because the median incubation period of COVID-19 was reported to be 4 to 5.1 days3,4 and the median time from first symptom to hospitalization was found to be 7 days,5 we hypothesized that any association between stay-at-home orders and hospitalization rates would become evident after 12 days (median effective date). States included in this sample were Colorado, Minnesota, Ohio, and Virginia. Among the 4 states meeting the inclusion criteria, the earliest date with data on hospitalizations was March 10. All states were observed through April 28. We fit the best exponential growth function to cumulative hospitalization data in each state for dates up to and including the median effective date of that state’s stay-at-home order. We computed 95% prediction bands on the exponential fit line to determine if the observed number of hospitalizations fell within the interval. We then examined whether the observed cumulative hospitalizations for dates after the median effective date deviated from the projected exponential growth in cumulative hospitalizations. In an additional analysis, a linear growth function was fit to cumulative hospitalization data for dates up to and including the median effective date, and goodness of fit was assessed with an R2 comparison. All analyses were performed using Microsoft Excel version 14.1.
ResultsIn all 4 states, cumulative hospitalizations up to and including the median effective date of a stay-at-home order closely fit and favored an exponential function over a linear fit (R2 = 0.973 vs 0.695 in Colorado; 0.965 vs 0.865 in Minnesota; 0.98 vs 0.803 in Ohio; 0.994 vs 0.775 in Virginia) (Table). However, after the median effective date, observed hospitalization growth rates deviated from projected exponential growth rates with slower growth in all 4 states. Observed hospitalizations consistently fell outside of the 95% prediction bands of the projected exponential growth curve (Figure).
Table. Cumulative Hospitalizations Due to COVID-19 in Colorado, Minnesota, Ohio, and Virginia, March 10 Through April 28, 2020. State Fitting perioda Stay-at-home issue date Median effective date Cumulative hospitalizations Best exponential fit: ln(y) = ln(a) + bt Linear fit: y = ct On first day of reporting On April 28 ln(a) (95% CI) b (95% CI) R2 c (95% CI) R2 Colorado March 10-April 6 March 26 April 6 2 2671 1.28 (1.02-1.54) 0.24 (0.22-0.25) 0.973 30.89 (25.28-36.5) 0.695 Minnesota March 19-April 8 March 28 April 8 7 912 2.02 (1.8-2.24) 0.19 (0.17-0.21) 0.965 9.993 (8.86-11.12) 0.865 Ohio March 17-April 4 March 24 April 4 17 3340 2.94 (2.75-3.13) 0.23 (0.21-0.24) 0.98 38.23 (32.78-43.67) 0.803 Virginia March 19-April 10 March 30 April 10 19 2165 2.77 (2.69-2.85) 0.178 (0.172-0.184) 0.994 23.31 (19.74-26.9) 0.775 Figure. Projected vs Observed COVID-19 Hospitalizations Before and After Stay-at-Home Orders, March 10 Through April 28, 2020.Blue lines indicate observed cumulative hospitalizations (including those currently hospitalized and those discharged) up to each day; select values are displayed for clarity. Dashed red lines begin on the first day of available reporting by each state and are the best-fit exponential curves for cumulative hospitalizations for the fitting period: first day of reporting up to and including the median effective date (panel A: y = 3.5829 exp(0.23599t), R2 = 0.9734; B: y = 7.521 exp(0.1876t), R2 = 0.96445; C: y = 18.8482 exp(0.2268t), R2 = 0.9798; D: y = 15.932 exp(0.1397t), R2 = 0.99444). Shaded regions indicate the 95% prediction bands of the exponential growth curves. Because the median incubation period of coronavirus disease 2019 (COVID-19) was reported to be 4 to 5.1 days3,4 and the median time from first symptom to hospitalization was found to be 7 days,5 it was hypothesized that any association between stay-at-home orders and hospitalization rates would become evident after 12 days (median effective date).
For example, Minnesota’s residents were mandated to stay at home starting March 28. On April 13, 5 days after the median effective date, the cumulative projected hospitalizations were 988 and the actual hospitalizations were 361. In Virginia, projected hospitalizations 5 days after the median effective date were 2335 and actual hospitalizations were 1048.
DiscussionIn 4 states with stay-at-home orders, cumulative hospitalizations for COVID-19 deviated from projected best-fit exponential growth rates after these orders became effective. The deviation started 2 to 4 days sooner than the median effective date of each state’s order and may reflect the use of a median incubation period for symptom onset and time to hospitalization to establish this date. Other factors that potentially decreased the rate of virus spread and subsequent hospitalizations include school closures, social distancing guidelines, and general pandemic awareness. In addition, economic insecurity and loss of health insurance during the pandemic may have also decreased hospital utilization. Limitations of the study include that these other factors could not be modeled in the analysis and that data on only 4 states were available.
Section Editor: Jody W. Zylke, MD, Deputy Editor.
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