A review of the Cochrane COVID-19 Study Register reveals inconsistency in the choice and measurement of SARS-CoV-2 infection outcomes in prevention trials

Introduction

Coronavirus disease 2019 (COVID-19), resulting from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has significant morbidity and mortality. Addressing human-to-human transmission of SARS-CoV-2 is a global research priority.¹ Research to fully understand the modes of transmission of SARS-CoV-2 is ongoing. Studies to prevent the spread of SARS-CoV-2 are increasing, with more than 1000 studies registered between December 2020 and February 2021.² Interventions are wide ranging, including public health measures such as lockdown of countries/cities, closure of schools and public places, social distancing measures and handwashing, interventions in primary and secondary care practice such as the use of personal protective equipment (PPE) and increased time for appointments/interventions, alongside the development of effective vaccines and drugs to prevent disease.

Evidence is accumulating to evaluate the effectiveness of prevention interventions, but limitations in evidence synthesis have been identified because of inconsistent selection, measurement and reporting of outcomes in the studies.^3–5 Whilst studies primarily focus on mitigating the risk of contracting COVID-19, they may measure other outcomes relevant to the population of interest and type of intervention, for example, educational outcomes in studies of school closures⁶ and tolerance/acceptability in studies of PPE use in secondary care.⁵ However, unnecessary heterogeneity in the outcomes measured and reported in COVID-19 prevention trials leads to research waste and inefficiency. For example, a recent commentary highlighted the rarity of randomised trials of public health interventions for COVID-19, which is likely due to the need for unattainably large sample sizes.⁷ Fretheim argues that underpowered trials in public health should be regarded as contributions to the larger body of evidence on COVID-19. However, for underpowered trials to be collectively assessed and synthesised in systematic reviews they need to be measuring the same outcomes.

A solution to this issue is to develop a core outcome set (COS), which is defined as an agreed standardised set of outcomes that should be measured and reported, as a minimum, in all clinical research studies in a specific area of health care to address inconsistencies in outcome measurement.⁸ The COS-COVID-P study, in collaboration with an international and multidisciplinary committee of experts and public contributors, has developed a COS for studies of interventions to prevent SARS-CoV-2 infection. During the COS development process, online workshops were held to consider the evidence on COVID-19 prevention and the issue of outcome heterogeneity. More than 70 key international stakeholders, including clinicians, researchers, public representatives, methodologists, systematic reviewers, guideline developers, and regulatory agency representatives shared their expert opinions. Agreement on the final COS was reached in August 2020. Two outcome domains were identified as being essential to all COVID-19 prevention studies: infection and intervention-specific harms.⁹ How to measure these outcomes now requires consideration and led to this paper.

SARS-CoV-2 infection can be measured through laboratory testing, symptom assessment or a combination of both.² However, there is no single standalone diagnostic test nor agreement on diagnostic symptoms.^10,11 Thus, there is a pressing need to consider how to assess SARS-CoV-2 infection uniformly across studies to optimise data reporting and synthesis. The aim of this study is to review SARS-CoV-2 infection outcomes in registered COVID-19 prevention trials and examine the reported rationales for choice of outcome measurement. Our intention is that this will inform optimal methods for the design of future studies.

Methods

Results

Search results

Our searching of the Cochrane COVID-19 Study Register returned 430 records that were screened for eligibility, of which 231 were excluded. A total of 199 unique prevention trials were identified (Figure 1).

Figure 1. Flow diagram of identified trials.

SARS-CoV-2 infection outcomes

Of the 199 trials, 15 (8%) did not include any SARS-CoV-2 infection outcomes. These 15 studies predominantly evaluated behavioural (n = 8, where interventions were ‘STOP touching your face' mindfulness online training programme; Brief informational infographic; E-E video about COVID hygiene; Mental Imagery; Digital health literacy intervention; e-learning module; Telepsychoeducation with personalized videos; combined video display and live demonstration as training methods to healthcare providers for donning and doffing PPE) or PPE/physical barrier (n = 3) interventions, with the remaining four trials evaluating social distancing, dietary supplements, a medicinal plant and homeopathy. Examples of outcomes recorded in trials that did not include a SARS-CoV-2 infection outcome included frequency of face-touching behaviour in a behavioural intervention trial¹² and contamination of any part of the base clothing or exposed skin of the upper body in a PPE trial.¹³

A total of 268 SARS-CoV-2 infection outcomes were identified across 184 (92%) trials, with infection measured as a primary outcome in 149 (81%) studies. Table 2 provides the outcome definitions recorded. We categorised outcome definitions using the verbatim text available in the registry records. In 118 (64%) trials, a single infection outcome was included. Fifty (27%) trials included two infection outcomes, 14 (8%) included three infection outcomes and two (1%) included four infection outcomes. Examples of multiple infection outcomes within the same trial are provided in Table 2. Table 3 lists the specific outcome definitions across the 184 trials.

Table 2. SARS-CoV-2 infection outcome definitions, by number of infection outcomes per trial.

	Number of trials (% of 184)
One SARS-CoV-2 infection outcome per trial1	118 (64.1)
Testing alone	49 (26.6)
Non-specific term for infection	32 (17.4)
Symptoms alone	16 (8.7)
Positive on both testing AND symptoms	15 (8.2)
Positive on either testing OR symptoms	6 (3.3)
Two SARS-CoV-2 infection outcomes per trial2	50 (27.2)
Testing alone (2 versions)	7 (3.8)
Testing alone; Non-specific term for infection	3 (1.6)
Testing alone; Symptoms alone	9 (4.9)
Testing alone; Testing AND symptoms	12 (6.5)
Testing alone; Testing OR symptoms	3 (1.6)
Symptoms alone; Non-specific term for infection	7 (3.8)
Testing AND symptoms; Non-specific term for infection	2 (1.1)
Symptoms alone; Testing AND symptoms	3 (1.6)
Symptoms alone; Testing OR symptoms	2 (1.1)
Testing AND symptoms (2 versions)	2 (1.1)
Three SARS-CoV-2 infection outcomes per trial3	14 (7.6)
Testing alone (3 versions)	1 (0.5)
Testing alone (2 versions); Symptoms alone	2 (1.1)
Testing alone (2 versions); Testing AND symptoms	3 (1.6)
Testing alone; Symptoms alone; Testing AND symptoms	2 (1.1)
Testing alone; Symptoms alone; Non-specific term for infection	1 (0.5)
Testing alone; Testing AND symptoms; Testing OR symptoms	1 (0.5)
Testing alone; Testing AND symptoms (2 versions)	2 (1.1)
Symptoms alone; Testing AND symptoms (2 versions)	2 (1.1)
Four SARS-CoV-2 infection outcomes per trial	2 (1.1)
Testing alone; Symptoms alone; Testing AND symptoms (2 versions)	1 (0.5)
Testing AND symptoms (3 versions); Testing OR symptoms	1 (0.5)

1 Note that when the same COVID outcome definition was measured at multiple time points or analysed in multiple ways (e.g. as a binary outcome AND a time to event outcome), these repetitions were considered as a single COVID outcome for the purpose of this review.

2 Example of two infection outcomes: ‘Number of people turning symptomatic’ and ‘Number of people turning Covid19 positive’ (see: http://www.ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=44065).

3 Example of three infection outcomes: ‘positive serology or RT-PCR for COVID-19 up until day 28’, ‘positive serology at day 28’ and ‘symptoms of COVID-19’ (see: https://clinicaltrials.gov/ct2/show/NCT04342156).

4 Example of four infection outcomes: ‘Number of symptomatic infections confirmed by SARS-CoV-2 (COVID-19)’, ‘Number of asymptomatic infections confirmed by SARS-CoV-2 (COVID-19)’, ‘Severity of SARS-CoV-2 (COVID-19) infection’ and ‘Duration of symptoms of COVID-19 coronavirus infection’ (see: https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001530-35/ES).

Table 3. SARS-CoV-2 infection outcome definitions.

		Number of trials with ≥1 outcome (% out of 184)	Number of outcomes (% out of 268)
Non-specific term for infection (no explicit test/symptoms detailed)		45 (24.5)	45 (16.8)
	• COVID • SARS-Cov-2 infection • infected	29 (15.8)	29 (10.8)
	• confirmed • documented	16 (8.7)	16 (6.0)
Confirmed with testing alone		96 (52.2)	110 (41.0)
	• positivity (positive disease/test)	13 (7.1)	13 (4.9)
	• antigen test	1 (0.5)	1 (0.4)
	• swab	2 (1.1)	2 (0.7)
	• lab confirmation	9 (4.9)	9 (3.4)
	• microbiologically confirmed	1 (0.5)	1 (0.4)
	• molecular test	1 (0.5)	1 (0.4)
	• RT-PCR	36 (19.6)	36 (13.4)
	• serology (antibodies, seroconversion, seroprevalence)	22 (12.0)	23 (8.6)
	• RT-PCR OR serology	16 (8.7)	16 (6.0)
	• test OR seroconversion	2 (1.1)	2 (0.7)
	• virology OR serology	3 (1.6)	3 (1.1)
	• molecular OR serological test	2 (1.1)	2 (0.7)
	• RT-PCR AND SARS-Cov-2 test	1 (0.5)	1 (0.4)
Symptoms alone		45 (24.5)	45 (16.8)
	• symptomatic1,2	40 (21.7)	40 (14.9)
	• clinical evidence	2 (1.1)	2 (0.7)
	• clinical disease without laboratory confirmation	1 (0.5)	1 (0.4)
	• confirmed via online questionnaire	1 (0.5)	1 (0.4)
	• confirmed OR probable	1 (0.5)	1 (0.4)
Testing OR symptoms		13 (7.1)	13 (4.9)
	• lab confirmation OR symptomatic • positive test OR symptomatic	2 (1.1)	2 (0.7)
	• asymptomatic OR symptomatic	4 (2.2)	4 (1.5)
	• microbiological test OR clinical features	1 (0.5)	1 (0.4)
	• RT-PCR OR symptomatic	4 (2.2)	4 (1.5)
	• RT-PCR OR serology OR symptomatic	1 (0.5)	1 (0.4)
	• molecular confirmation OR symptomatic	1 (0.5)	1 (0.4)
Testing AND symptoms		46 (25.0)	55 (20.5)
	• asymptomatic	2 (1.1)	2 (0.7)
	• lab confirmation AND asymptomatic	3 (1.6)	3 (1.1)
	• lab confirmation AND symptomatic	8 (4.3)	8 (3.0)
	• microbiologically confirmed AND symptomatic	1 (0.5)	1 (0.4)
	• microbiologically AND lab confirmed AND symptomatic	1 (0.5)	1 (0.4)
	• RT-PCR AND symptomatic	21 (11.4)	21 (7.8)
	• (RT-PCR OR serology) AND asymptomatic	1 (0.5)	1 (0.4)
	• (RT-PCR OR serology) AND symptomatic	3 (1.6)	3 (1.1)
	• (RT-PCR OR molecular test) AND symptomatic	2 (1.1)	2 (0.7)
	• (serology OR lab confirmed) AND symptomatic	2 (1.1)	2 (0.7)
	• serology AND asymptomatic	1 (0.5)	1 (0.4)
	• serology AND symptomatic	2 (1.1)	2 (0.7)
	• (serology AND symptomatic) OR RT-PCR	1 (0.5)	1 (0.4)
	• virologically-confirmed AND symptomatic	2 (1.1)	2 (0.7)
	• test AND symptomatic	4 (2.2)	4 (1.5)
	• molecular confirmation AND symptomatic	1 (0.5)	1 (0.4)

1 Includes one study that specified individual symptoms only.

2 Note that “symptomatic” includes references to clinical confirmation.

Forty-five (25%) trials defined infection in non-specific terms (e.g. “COVID”, “SARS-CoV-2 infection”, “infected”, “confirmed”), without specifying how infection would be/was determined (i.e. via a specific test to be undertaken or symptoms to be recorded). In 32 of these trials (17% of all included trials), the non-specific infection term was the only infection outcome listed. Figure 2 displays the number of trials with non-specific COVID outcomes by month of registration. Of the 45 trials with non-specific COVID outcomes, 23 (51%) trials were registered within the first three months (March to May 2020) of the COVID-19 outbreak being declared a global pandemic.

Figure 2. Trials with ≥1 non-specific SARS-CoV-2 infection outcome by month of registration (out of 184 trials with at least one SARS-CoV-2 infection outcome).

Left-hand axis - number of trials. Right-hand axis - % of total number of trials.

Testing was the only means employed for determining infection in 57 (31%) trials, whereas relying on symptoms alone to define infection occurred in 16 (9%). All other trials included multiple infection outcomes, defined in different ways (Table 2). Where one of the infection outcomes was to be determined by testing alone (n = 96), this was most commonly by RT-PCR (n = 36, 20%) and serology (n = 22, 12%) tests. Where infection was defined on the basis of symptoms alone (e.g. symptomatic COVID-19) (n = 45), the number of symptoms and definitions varied (Tables 4 and 5). Some trials included outcomes defined by combinations of testing and/or symptoms: 13 (7%) using either testing or symptoms to determine infection; 46 (25%) specifying both testing and symptoms must be positive to define infection (e.g. RT-PCR and symptomatic).

Table 4. “Symptomatic” definitions provided for SARS-CoV-2 infection outcomes.

“Symptomatic” definitions

https://clinicaltrials.gov/ct2/show/NCT04318015 “Symptomatic infection rate by COVID-19 defined as cough, dyspnea, fever, myalgia, arthralgias or rhinorrhea along with a positive COVID-19 real-time polymerase chain reaction test”

https://pubmed.ncbi.nlm.nih.gov/33068425 “The definition of Covid-19-compatible symptoms was based on guidance from the US Council for State and Territorial Epidemiologists (Appendix)13. Specifically, probable disease was defined as having cough, shortness of breath, or difficulty breathing, OR two or more of the following symptoms: fevers, chills, rigors, myalgia, headache, sore throat, new olfactory and taste disorders. Possible disease was defined as one or more COVID-19-compatible symptoms. Three blinded infectious diseases physicians independently adjudicated cases of symptomatic participants based on the above criteria.”

http://www.ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=43105 “defined as self-reported fever or cough, or shortness of breath, or respiratory distress, or runny or blocked nose plus a positive PCR test and/or antibody test”

https://clinicaltrials.gov/ct2/show/NCT04321174 “fever, cough or other respiratory/systemic symptoms (including but not limited to fatigue, myalgias, arthralgias, shortness of breath, sore throat, headache, chills, coryza, nausea, vomiting, diarrhea) by day 14 in a patient with laboratory confirmed infection, combined with microbiologic confirmation of COVID-19 infection in the participant”

https://clinicaltrials.gov/show/NCT04377646 “Any COVID-19 related symptoms (cough, fever, headache, vomiting, nausea, dyspnea, diarrhea, smell disorder, conjunctivitis, dizziness)”

https://clinicaltrials.gov/show/NCT04360122 “any symptoms suggestive of COVID19: fever, plus at least one sign or symptom of respiratory disease including cough, shortness of breath, respiratory distress/failure, runny/blocked nose, plus Laboratory: Sampling of the following will be withdrawn from all participants at beginning and end of the study: COVID 19 IgM (for recent infection) or IgG or (old infection)”

https://clinicaltrials.gov/show/NCT04445428 “Episodes with self-reported infectious disease morbidity suspected to be caused by COVID (three or more of the following: fever, cough, sore thought, extreme fatigue, loss of smell/taste).”

https://clinicaltrials.gov/show/NCT04452643 “Incidence of subjects with COVID-19, defined by the presence of: Fever, Any of the respiratory signs and/or symptoms: cough, dyspnea, respiratory failure, runny nose/nasal obstruction, Positive test for SARS-COV-2 (PCR or serology)”

https://clinicaltrials.gov/show/NCT04461379 “disease defined as positive SARS-Cov-2 test (serology), plus fever (using self-reported questionnaire), or at least one sign or symptom of respiratory disease including cough, shortness of breath, respiratory distress/failure (using self-reported questionnaire)”

https://clinicaltrials.gov/show/NCT04470427 “The participant must have experienced at least TWO of the following systemic symptoms: Fever (≥ 38°C), chills, myalgia, headache, sore throat, new olfactory and taste disorder(s)”

https://clinicaltrials.gov/show/NCT04471766 “Self-reported main symptoms of COVID-19 (three or more - fever, cough, fatigue, shortness of breath, loss of smell/taste)”

https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-002287-31/ES: “Examine the incidence of typical symptoms of SARS-CoV-2 infection: fever, cough, headache, arthromyalgia, pharyngeal pain, dyspnea, diarrhea, vomiting, abdominal pain, anosmia”

https://clinicaltrials.gov/show/NCT04437693 “COVID-19 related symptoms, that may include: Temperature over 37.8 degrees Celsius, Shortness of Breath, Cough”

https://clinicaltrials.gov/show/NCT04614948 “Mild COVID-19 includes: Fever, sore throat, malaise, headache, muscle pain, gastrointestinal symptoms, cough, chest congestion, runny nose, wheezing, skin rash, eye irritation or discharge, or chills, without shortness of breath or dyspnea.”

https://clinicaltrials.gov/show/NCT04614948 “COVID-19 symptoms consistent with those defined by the US FDA harmonized case definition at the time of finalization of this protocol: fever or chills, cough, shortness of breath or difficulty breathing, fatigue, muscle or body aches, headache, new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, diarrhea.”

https://clinicaltrials.gov/show/NCT04350931 “COVID 19 infection in the form of: fever, dry cough, fatigue, & dyspnea”

https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=12175 “defined as I. fever (using a self-reported questionnaire), plus II. at least one sign or symptom of respiratory disease including cough, runny/blocked nose (using self-reported questionnaire)”

https://clinicaltrials.gov/show/NCT04534803 “fever (as documented in EHR) or at least one sign or symptom of respiratory disease including cough, shortness of breath, respiratory distress/failure (as documented in EHR)”

https://clinicaltrials.gov/show/NCT04349228 “The rate of COVID19 infections is defined by the occurrence of the clinical signs below: Cough, Dyspnea, Fever, Myalgia, Arthralgia, Rhinorrhea, Anosmia, Asthenia, Fatigability”

https://clinicaltrials.gov/show/NCT04452643 “WHO definition”

https://clinicaltrials.gov/show/NCT04526821 “Severity of SARS-CoV-2 infection (asymptomatic, mild, moderate, severe infection), assessed by the World Health Organization definitions”

Table 5. Symptoms included in definitions of SARS-CoV-2 infection outcomes.

	Trial reference #
Symptom	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	Frequency of inclusion in any definition (n = 19)
Fever	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	19
Cough	Y	Y	Y	Y	Y	Y	Y	Y	Y		Y	Y	Y	Y	Y	Y	Y	Y	Y	18
Dyspnoea/shortness of breath/Difficulty breathing/ respiratory distress/respiratory failure	Y	Y	Y	Y	Y	Y		Y	Y		Y	Y	Y	Y	Y	Y		Y	Y	16
Rhinorrhoea/coryza/runny or blocked nose	Y		Y	Y		Y		Y						Y	Y		Y		Y	9
Loss of smell/anosmia/new olfactory/smell disorder		Y			Y		Y			Y	Y	Y			Y				Y	8
Fatigue/extreme fatigue/malaise				Y			Y				Y			Y	Y	Y			Y	7
Headache		Y		Y	Y					Y		Y		Y	Y					7
Myalgia/muscle pain	Y	Y		Y						Y				Y	Y				Y	7
Sore throat		Y		Y			Y			Y				Y	Y					6
Chills		Y		Y						Y				Y	Y					5
Loss of taste/new taste disorder		Y					Y			Y	Y				Y					5
Gastrointestinal symptoms/nausea/abdominal pain				Y	Y							Y		Y	Y					5
Vomiting				Y	Y							Y			Y					4
Diarrhoea				Y	Y							Y			Y					4
Arthralgias	Y			Y															Y	3
Conjunctivitis/eye irritation/discharge					Y									Y						2
Rigors		Y																		1
Dizziness					Y															1
Arthromyalgia												Y								1
Pharyngeal pain												Y								1
Chest congestion														Y						1
Wheezing														Y						1
Skin rash														Y						1
Asthenia																			Y	1

Discussion

We have reviewed COVID-19 prevention trials that were registered in the first nine months of the pandemic and examined in detail their rationale, selection and method of measuring SARS-CoV-2 infection. Of the 199 included trials, 268 SARS-CoV-2 infection outcomes were identified, across 184 studies. Of these, almost one fifth did not define how infection would be measured in the register entry, with the remaining studies specifying a combination of testing alone, symptoms alone, or testing and/or symptoms. Very few trials provided a rationale for the choice of how infection would be measured. The observed heterogeneity of outcome measurement of SARS-CoV-2 infection in COVID-19 prevention studies limits effective evidence synthesis and scientific comparison of interventions, which has enormous consequences for decision-making at an individual and public health level. We recommend that studies provide a rationale for choice of infection measurement and, if possible, that uniformity is agreed internationally on how to measure SARS-CoV-2 infection in trials of prevention interventions.

The heterogeneity of outcome measurement identified in the current study is not uncommon. A recent methodological systematic review revealed significant heterogeneity in the outcome measurement instruments used in trials evaluating therapeutic interventions for COVID-19, thus highlighting the need for greater consistency.¹⁸ However, such heterogeneity is not restricted to COVID-19 research studies. A multiplicity of outcome measurement instruments has recently been reported in areas such as delirium, hearing loss, chronic pelvic pain, and breast reconstruction, with studies concluding the need for consensus on outcomes and their associated measures for use in future clinical trials in these disease areas.^19–22

To our knowledge, this is the first study to examine how SARS-CoV-2 infection outcomes are measured in registered COVID-19 prevention trials. However, there are some methodological limitations. Only the Cochrane COVID-19 Study Register https://covid-19.cochrane.org/ was searched to identify COVID-19 prevention trials, which may limit the number of relevant studies identified. The Cochrane register, however, is updated regularly and includes studies identified from six data sources, including ClinicalTrials.gov. Furthermore, although we did not specifically search each of the data sources, we used the trial URL provided in the Cochrane Study Register entry to access the original trial registry entry. Another limitation is the lack of detail about infection outcome definitions reported in the trial registry entries. Where readily available via links within the registry, protocols and published papers were also examined. However, due to resource limitations we did not manually search for protocols and full text papers corresponding to the trials included in this review, meaning some detail on outcome definitions may have been missed and may be readily available in trial protocols thus potentially changing the results of this study. Trialists may have a rationale for their choice of outcome measurements, but this was not included in the registry entry.

The COS-COVID-P study recommended SARS-CoV-2 infection be measured in all COVID-19 prevention trials, regardless of the intervention and setting.⁹ We found 92% of trials included SARS-CoV-2 infection as an outcome; however, very few reported the rationale for SARS-CoV-2 infection outcome selection and measurement. From the limited details we have regarding rationale, decisions often appear to be based on accessibility of testing and feasibility. This is understandable and will likely be due to circumstances specific to the setting and country where the trial is being conducted. Going forward, we advise trialists to be transparent about their rationale for the definition of SARS-CoV-2 infection outcomes used when registering and reporting their trials, to improve understanding of the context of their research. The trial registration should include both the rationale for outcome measurement, along with details on how infection will be analysed. In addition, we understand trialists will collect as much data as they consider relevant, but we recommend they develop a coherent statistical analysis plan (SAP) to ensure transparency and reproducibility.²³

In the current review, we found two thirds of the studies evaluating behaviour interventions, such as those intended to increase handwashing or SMS text prompts to wear masks, did not include any SARS-CoV-2 infection outcomes. Trials of simple behavioural interventions may be unlikely to measure infection, as their purpose is to modify an individual’s behaviour rather than to directly prevent an individual from acquiring COVID-19. This is in contrast with those of complex interventions that include a behavioural component (e.g. donning standardised PPE versus specialised PPE; or hand washing with soap versus using alcohol wipes) where the overarching aim is still likely to be prevention of SARS-CoV-2 infection.

Alongside SARS-CoV-2 infection, the COS-COVID-P study also led to the inclusion of intervention-specific harms in the COS for all COVID-19 prevention research studies.⁹ The current review did not examine this aspect. Determining these harm outcomes requires agreement amongst those with expertise in the specific interventions being tested. It may be that the harms are similar within certain intervention types, e.g. drug prophylaxis and vaccines. However, expert opinion will be necessary to determine the relevance of such an approach, but consistency will be important to avoid research waste and to facilitate evidence synthesis in which the results of studies of similar interventions are compared, contrasted and, where appropriate, combined. There may also be a need for COS for studies in specific settings. For example, a COS evaluating interventions for the prevention of COVID-19 transmission in care homes (COS-COVID-PCARE) is currently under development. Building on the COS-COVID-P study, the COS-COVID-PCARE study is proposing to develop a supplementary specific module for COVID-19 prevention in care homes.²⁴

Conclusion

We have described the variation in the selection and measurement of SARS-CoV-2 infection outcomes, with illustrated examples from on-going trials across various settings and interventions. As knowledge about COVID-19 increases over time, this is leading to improvements in the design of studies and reporting. Nevertheless, our findings highlight the need for clarity, transparency and standardisation of measures of SARS-CoV-2 infection. It is now timely to highlight these issues, to improve the collection of infection data to inform key public health decisions.

Data availability