Health and Economic Consequences of Universal Paid Sick Leave Policies During the COVID-19 Pandemic

ImportanceUniversal paid sick-leave (PSL) policies have been implemented in jurisdictions to mitigate the spread of SARS-CoV-2. However empirical data regarding health and economic consequences of PSL policies is scarce. ObjectiveTo estimate effects of a universal PSL policy in Ontario, Canadas most populous province. DesignAn agent-based model (ABM) to simulate SARS-CoV-2 transmission informed by data from Statistics Canada, health administrative sources, and from the literature. SettingOntario from January 1st to May 1st, 2021. ParticipantsA synthetic population (1 million) with occupation and household characteristics representative of Ontario residents (14.5 million). ExposureA base case of existing employer-based PSL alone versus the addition of a 3-or 10-day universal PSL policy to facilitate testing and self-isolation among workers infected with SARS-CoV-2 themselves or because of infected household members. Main Outcome(s) and Measure(s)Number of SARS-CoV-2 infections and COVID-19 hospitalizations, worker productivity, lost wages, and presenteeism (going to a workplace while infected). ResultsIf a 3- and 10-day universal PSL were implemented over the 4-month study period, then compared with the base-case, the PSL policies were estimated to reduce cumulative SARS-CoV-2 cases by 85,531 (95% credible interval, CrI -2,484; 195,318) and 215,302 (81,500; 413,742), COVID-19 hospital admissions by 1,307 (-201; 3,205) and 3,352 (1,223; 6,528), numbers of workers forgoing wages by 558 (-327;1,608) and 7,406 (6,764; 8,072), and numbers of workers engaged in presenteeism by 24,499 (216; 54,170) and 279,863 (262,696; 295,449). Hours of productivity loss were estimated to be 10,854,379 (10,212,304; 11,465,635) in the base case, 17,446,525 (15,934,321; 18,854,683) in the 3-day scenario, and 26,127,165 (20,047,239; 29,875,161) in the 10-day scenario. Lost wages were $5,256,316 ($4,077,280; $6,804,983) and $12,610,962 ($11,463,128; $13,724,664) lower in the 3 day and 10 day scenarios respectively, relative to the base case. Conclusions and RelevanceExpanded access to PSL is estimated to reduce total numbers of COVID-19 cases, reduce presenteeism of workers with SARS-CoV-2 at workplaces, and mitigate wage loss experienced by workers. Competing interestsThe authors have no competing interests relevant to this article to disclose. FundingSupported by COVID-19 Rapid Research Funding (C-291-2431272-SANDER). This research was further supported, in part, by a Canada Research Chair in Economics of Infectious Diseases held by Beate Sander (CRC-950-232429). The study sponsor had no role in the design, collection, analysis, interpretation of the data, manuscript preparation or the decision to submit for publication. Author ContributionsConceptualization: PP, JDR, BS, DN Data Curation: PP, JDR, BS, DN Formal Analysis: PP, JDR, DN Methodology: PP, JDR, BS, DN Supervision: PP, DN, BS Validation: PP, JDR, BS, DN First Draft: PP, JDR, BS, DN Review and Edit PP, JDR, BS, DN Key pointsO_ST_ABSQuestionC_ST_ABSWhat could be the health and economic consequence of more generous paid sick leave policies in the context of the COVID-19 pandemic? FindingsMore generous policies are estimated to reduce SARS-CoV-2 infections (and thus COVID-19 hospitalizations), lost wages and presence of individuals with infection at workplaces. MeaningMore generous paid sick leave can be a valuable addition to other COVID-19 public health interventions.

Variant-of-concern-attributable health and health system-related outcomes: a population-level propensity-score matched cohort study

BackgroundAs the transmission of SARS-CoV-2 variants intensifies globally, the burden of COVID-19 on the already strained health systems is becoming increasingly concerning. While there is growing literature on the effects of various variants-of-concern (VOC) on increased transmission, the extent to which VOCs may lead to more severe disease remains debated. MethodsIn the current analysis, we use a population-based propensity-score matched cohort study of all incident laboratory-confirmed COVID-19 cases with VOC testing in Ontario, Canada to estimate healthcare resource use and health outcomes attributable to VOCs introduced to Ontario between January 1 and April 9, 2021, relative to the previously circulating wild-type strain. ResultsWe find that VOCs are associated with a higher odds of hospitalisation (odds ratio [OR], 2.25; 95% confidence interval [CI], 2.10-2.40) and ICU admission (OR, 3.31; 95%CI, 2.84-3.86); as well as with a higher odds of mortality for both the general COVID-19 population (OR 1.75; 1.47-2.09) and hospitalised cases (OR, 1.62; 95%CI, 1.23-2.15). ConclusionTaken together, these findings suggest that health systems may face increased demand for healthcare resources as VOCs predominate worldwide in view of low global vaccination coverage.

The Humoral Response to the BNT162b2 Vaccine in Hemodialysis Patients

ImportanceHemodialysis patients have an exceptionally high mortality from COVID-19 and this patient population often has a poor response to vaccinations. Randomized controlled trials for COVID-19 vaccines included few patients with kidney disease, therefore vaccine immunogenicity is uncertain in this population. ObjectiveEvaluate the SARS-CoV-2 antibody response in chronic hemodialysis patients following one versus two doses of BNT162b2 COVID-19 vaccination compared to health care worker controls and convalescent serum. DesignProspective observational cohort study. SettingSingle centre study in Toronto, Ontario, Canada. Participants142 in-centre hemodialysis patients and 35 health care worker controls. ExposureBNT162b2 (Pfizer-BioNTech) COVID-19 vaccine. Main Outcomes and MeasuresSARS-CoV-2 IgG antibodies to the spike protein (anti-spike), receptor binding domain (anti-RBD), and nucleocapsid protein (anti-NP) were measured in 66 hemodialysis patients receiving one vaccine dose following a public health policy change, 76 patients receiving two vaccine doses, and 35 health care workers receiving two vaccine doses. ResultsDetectable anti-NP suggestive of natural SARS-CoV-2 infection was detected in 15/142 (11%) of patients at baseline while only three patients had prior RT-PCR confirmed COVID-19. Two additional patients contracted COVID-19 after receiving two doses of vaccine. In patients receiving a single BNT162b2 dose, seroconversion occurred in 53/66 (80%) for anti-spike and 35/66 (55%) for anti-RBD by 28 days post dose, but only 15/66 (23%) and 4/66 (6%), respectively attained a robust response as defined by reaching the median level of anti-spike and anti-RBD in convalescent serum from COVID-19 survivors. In patients receiving two doses of BNT162b2 vaccine, seroconversion occurred in 69/72 (96%) for anti-spike and 63/72 (88%) for anti-RBD by 2 weeks following the second dose while 52/72 (72%) and 43/76 (41%) reached the median convalescent serum level of anti-spike and anti-RBD, respectively. In contrast, 35/35 (100%) of health care workers exceeded the median level of anti-spike and anti-RBD found in convalescent serum 2-4 weeks after the second dose. Conclusions and RelevanceThis study confirms poor immunogenicity 28 days following a single dose of BNT162b2 vaccine in the hemodialysis population, supporting adherence to recommended vaccination schedules, and avoiding delay of the second dose in these at-risk individuals. Key PointsO_ST_ABSQuestionC_ST_ABSWhat is the serologic response to the BNT162b2 COVID-19 vaccine in hemodialysis patients? FindingsIn this prospective observational study, humoral response was compared in 66 hemodialysis patients sampled 28 days after receipt of one dose of vaccine to 76 patients who received two doses of vaccine sampled 14 days after the second dose. Among those receiving one dose, 6% had anti-RBD response above the median level of convalescent serum versus 41% who received two doses. MeaningGiven that hemodialysis patients exhibit a poor humoral response to a single dose of BNT162b2 vaccine, the second dose should not be delayed.

Predicting the Ophthalmic Surgical Backlog as a Result of the COVID-19 Pandemic: A population-based study and microsimulation model to inform surgical recovery plans

BackgroundJurisdictions worldwide ramped down ophthalmic surgeries to mitigate the effects of coronavirus disease 2019 (COVID-19), creating a global surgical backlog. We sought to predict the long-term impact of COVID-19 on ophthalmology surgical care delivery. MethodsThis is a population-based and a microsimulation modelling study. Provincial administrative data from January 2019 to May 2021 was used to estimate the backlog size and wait-times following the COVID-19 pandemic. For the post-pandemic recovery phase, we estimated the resources required to clear the backlog of patients accumulated on the waitlist during the pandemic. ResultsA total of 56,923 patients were on the waitlist in the province of Ontario awaiting non-emergency ophthalmic surgery as of March 15, 2020. The number of non-emergency surgeries performed in the province decreased by 45-98% from March to May 2020, and 48-80% from April to May 2021 compared to the same months in 2019. By 2 years and 3 years, the overall estimated number of patients awaiting surgery grew by 129% and 150%, respectively. The estimated mean wait-time for patients for all subspecialty surgeries increased to 282 (SD 91) in March 2023 compared to 94 days (SD 97) in 2019. The provincial monthly additional resources required to clear the backlog by March 2023 was estimated to be a 34% escalation from the pre-pandemic volumes (4,626 additional surgeries). InterpretationThe magnitude of the ophthalmic surgical backlog from COVID-19 has important implications for the recovery phase. The estimates from this microsimulation modelling can be adapted to other jurisdictions to assist with recovery planning for vision saving surgeries.

Using Administrative Data to Incorporate Age and Sex-Dependent Resource Use for COVID-19 Acute Care Resource Use Simulations in Ontario, Canada

As the COVID-19 pandemic has progressed, more local data has become available, enabling a more granular modeling approach. In March 2020, we developed a COVID-19 Resource Estimator (CORE) model to estimate the acute care resource use in Ontario, Canada. In this paper, we describe the evolution of CORE2.0 to incorporate age, sex, and time-dependent acute care resource use, length of stay, and mortality to simulate hospital occupancy. Demographics (e.g., age and sex) of infected cases are informed by 4-month averages between March-June, and July-October using 10-year age groups. The probability of hospitalization, ICU admission, and requiring mechanical ventilation are all age and sex-dependent. LOS for each acute care level ranges from 5.7 to 16.15 days in the ward, 6.5 to 10.7 days in the ICU without ventilation, and 14.8 to 21.6 days on the ventilator, depending on month of infection. We calibrated some LOS components to reported ward and ICU occupancy between June 15 and October 31, 2020. Furthermore, we demonstrate the use of CORE2.0 for a regional analysis of Region of Waterloo, Ontario, Canada to simulate the ward bed, ICU bed, and ventilator occupancies for 30 days starting December 2020 for three case trajectory scenarios. Moving forward, this model has become highly flexible and customizable to data updates, and can better inform acute care planning and public measures as the pandemic progresses.

The potential impact of School Closure Relative to Community-based Non-pharmaceutical Interventions on COVID-19 Cases in Ontario, Canada

ImportanceResurgent COVID-19 cases have resulted in the re-institution of nonpharmaceutical interventions, including school closure, which can have adverse effects on families. Understanding the impact of schools on the number of incident and cumulative COVID-19 cases is critical for decision-making. ObjectiveTo determine the quantitative effect of schools being open or closed relative to community-based nonpharmaceutical interventions on the number of COVID-19 cases. DesignAn agent-based transmission model. SettingA synthetic population of one million individuals based on the characteristics of the population of Ontario, Canada. ParticipantsMembers of the synthetic population clustered into households, neighborhoods or rural districts, cities or a rural region, day care facilities, classrooms - primary, elementary or high school, colleges or universities and workplaces. ExposureSchool reopening on September 15, 2020, versus schools remaining closed under different scenarios for nonpharmaceutical interventions. Main Outcome and MeasuresIncident and cumulative COVID-19 cases between September 1, 2020 and October 31, 2020. ResultsThe percentage of infections among students and teachers acquired within schools was less than 5% across modelled scenarios. Incident case numbers on October 31, 2020, were 4,414 (95% credible interval, CrI: 3,491, 5,382) and 4,740 (95% CrI 3,863, 5,691), for schools remaining closed versus reopening, respectively, with no other community-based nonpharmaceutical intervention; 714 (95%, CrI: 568, 908) and 780 (95% CrI 580, 993) for schools remaining closed versus reopening, respectively, with community-based nonpharmaceutical interventions implemented; 777 (95% credible CrI: 621, 993) and 803 (95% CrI 617, 990) for schools remaining closed versus reopening, respectively, applied to the observed case numbers in Ontario in early October 2020. Contrasting the scenarios with implementation of community-based interventions versus not doing so yielded a mean difference of 39,355 cumulative COVID-19 cases by October 31, 2020, while keeping schools closed versus reopening them yielded a mean difference of 2,040 cases. Conclusions and relevanceOur simulations suggest that the majority of COVID-19 infections in schools were due to acquisition in the community rather than transmission within schools and that the effect of school reopening on COVID-19 case numbers is relatively small compared to the effects of community-based nonpharmaceutical interventions. KEY POINTSO_ST_ABSQuestionC_ST_ABSWith resurgence of COVID-19, reinstitution of school closure remains a possibility. Given the harm that closures can cause to children and families, the expected quantitative effect of school reopening or closure on incident and cumulative COVID-19 case numbers is an important consideration. FindingRelative to community-based nonpharmaceutical interventions, school closure resulted in a small change in COVID-19 incidence trajectories and cumulative case counts. MeaningCommunity-based interventions should take precedence over school closure.

COVID-19 Demographics, Acute Care Resource Use and Mortality by Age and Sex in Ontario, Canada: Population-based Retrospective Cohort Analysis

BackgroundUnderstanding resource use for COVID-19 is critical. We conducted a population-based cohort study using public health data to describe COVID-19 associated age- and sex-specific acute care use, length of stay (LOS), and mortality. MethodsWe used Ontarios Case and Contact Management (CCM) Plus database of individuals who tested positive for COVID-19 in Ontario from March 1 to September 30, 2020 to determine age- and sex-specific hospitalizations, intensive care unit (ICU) admissions, invasive mechanical ventilation (IMV) use, LOS, and mortality. We stratified analyses by month of infection to study temporal trends and conducted subgroup analyses by long-term care residency. ResultsDuring the observation period, 56,476 COVID-19 cases were reported (72% < 60 years, 52% female). The proportion of cases shifted from older populations (> 60 years) to younger populations (10-39 years) over time. Overall, 10% of individuals were hospitalized, of those 22% were admitted to ICU, and 60% of those used IMV. Mean LOS for individuals in the ward, ICU without IMV, and ICU with IMV was 12.8, 8.5, 20.5 days, respectively. Mortality for individuals receiving care in the ward, ICU without IMV, and ICU with IMV was 24%, 30%, and 45%, respectively. All outcomes varied by age and decreased over time, overall and within age groups. InterpretationThis descriptive study shows acute care use and mortality varying by age, and decreasing between March and September in Ontario. Improvements in clinical practice and changing risk distributions among those infected may contribute to fewer severe outcomes among those infected with COVID-19.

Potential magnitude of COVID-19-induced healthcare resource depletion in Ontario, Canada

BackgroundThe global spread of coronavirus disease 2019 (COVID-19) continues in several jurisdictions, causing significant strain to healthcare systems. The purpose of our study is to predict the impact of the COVID-19 pandemic on patient outcomes and the healthcare system in Ontario, Canada. MethodsWe developed an individual-level simulation to model the flow of COVID-19 patients through the Ontario healthcare system. We simulated different combined scenarios of epidemic trajectory and healthcare capacity. Outcomes include numbers of patients needing admission to the ward, Intensive Care Unit (ICU), and requiring ventilation; days to resource depletion; and numbers of patients awaiting resources and deaths associated with limited access to resources. FindingsWe demonstrate that with effective early public health measures system resources need not be depleted. For scenarios considering late or ineffective implementation of physical distancing, health system resources would be depleted within 14-26 days. Resource depletion was also avoided or delayed with aggressive measures to rapidly increase ICU, ventilator, and acute care hospital capacity. InterpretationWe found that without aggressive physical distancing measures the Ontario healthcare system would have been inadequately equipped to manage the expected number of patients with COVID-19, despite the rapid capacity increase. This overall lack of resources would have led to an increase in mortality. By slowing the spread of the disease via ongoing public health measures and having increased healthcare capacity, Ontario may have avoided catastrophic stresses to its health care system.