| Cohort Studies (prospective or retrospective) |
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Results easily communicated to policy makers and stakeholders
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Can estimate burden of COVID-19 in a population and potentially measure the impact of vaccination
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Easier to interpret when done early when limited vaccine supply
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Can potentially be used to study asymptomatic or mildly symptomatic infections
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Vaccination status difficult to determine in retrospective cohorts without good vaccination records
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Rt if outcome of interest is uncommon such as severe COVID 19
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May be expensive, especially if prospective
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If prospective, possible ethical dilemma in following unvaccinated persons who are recommended for vaccination
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High |
Could be undertaken in certain situations such as among healthcare workers, in institutionalized settings, Health Maintenance Organizations or sentinel hospitals with electronic medical records, or in well circumscribed outbreaks |
| Case-Control (CaCo) Studies |
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Efficient as requires smaller sample size, as focus on identifying cases rather than following a large population with few cases
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Less expensive than cohort studies
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Most people familiar with case-control design
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Need to choose controls to reflect the population from which cases arise, in terms of exposure to virus and vaccination coverage
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Vaccinated persons may be more likely to seek, or have access to, health care and become cases, biasing towards reduced VE
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Misclassification of vaccination status greater compared to cohort studies, especially prospective cohort studies
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Moderate |
Controls should be enrolled at same time as case enrolled in changing incidence setting. |
| Test-Negative Design (TND) Case-Control Studies |
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Reduces bias of differences in healthcare seeking behavior and access by vaccine status
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All cases and controls seek care at same facilities, potentially decreasing differences in access to vaccines and community-level confounders
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Vaccination status often obtained before results of laboratory tests available, minimizing diagnostic bias
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Can use existing surveillance platforms, such as those for influenza
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Logistics are simplified, less resource intensive
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False negative misclassification more likely than CaCo as both cases and controls have COVID-19-like illness.
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Test-negative controls more likely to be tested for exacerbation of an underlying illness (e.g., COPD), that is an indication for COVID-19 vaccination leading to increased VE.
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Cases and controls need to be matched or the analysis needs to be adjusted by time
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Does not remove confounding from common predictors of vaccination and exposure to infection, such as being in a priority group by age or occupation
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Moderate |
Probably most efficient and least biased study design for VE studies of COVID-19 disease in most settings. |
| Screening Method |
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Markedly reduced expenses since relies on available coverage data and leverages ongoing disease surveillance
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Do not have to collect data among non-cases since uses vaccine coverage surveys
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Estimation of expected number of cases who are vaccinated (I.e., breakthrough cases)
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Coverage survey data may not be representative of population from which cases are being collected (e.g. differences in healthcare access and healthcare seeking behavior)
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Vaccination status may come from administrative data rather than surveys raising concerns about validity of coverage estimate
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Must have vaccine status of all reported cases
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Unable to adjust for individual level covariates
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Minimal |
Rapid rollout makes coverage estimate moving target; disaggregation of coverage data by target populations is difficult. Could be used to determine expected number of cases among vaccinated. |
| Regression Discontinuity Design |
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Defining the ”neighborhood” around cut-off value for vaccination can be challenging
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Potentially small sample size
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Spillover vaccination among those outside cut-off
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Herd protection among unvaccinated
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Age cut-offs for vaccination may change rapidly depending on vaccine availability.
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Moderate |
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