Knowledge barriers in the symptomatic-COVID-19 testing programme in the UK: an observational study
1School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
2Zoe Global Limited, London, UK
3MRC Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, University College London, UK
4Centre for Medical Image Computing, Department of Computer Science, University College London, UK
5Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
6Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA, USA
7Department of Epidemiology, Boston University School of Public Health, Boston, USA
8Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
9Division of Endocrinology, Boston Children’s Hospital, Boston, MA, USA
10Broad Institute of Harvard and MIT, Cambridge, MA, USA
*Corresponding Author: Christina M. Astley, MD, ScD Boston Children’s Hospital, 300 Longwood Ave., Boston, MA, 02215 USA Christina.Astley@childrens.harvard.eduAbstract
Background
Symptomatic testing programmes are crucial to the COVID-19 pandemic response. We sought to examine United Kingdom (UK) testing rates amongst individuals with test-qualifying symptoms, and factors associated with not testing.
Methods
We analysed a cohort of untested symptomatic app users (N=1,237), nested in the Zoe COVID Symptom Study (Zoe, N= 4,394,948); and symptomatic survey respondents who wanted, but did not have a test (N=1,956), drawn from the University of Maryland-Facebook Covid-19 Symptom Survey (UMD-Facebook, N=775,746).
Findings
The proportion tested among individuals with incident test-qualifying symptoms rose from ∼20% to ∼75% from April to December 2020 in Zoe. Testing was lower with one vs more symptoms (73.0% vs 85.0%), or short vs long symptom duration (72.6% vs 87.8%). 40.4% of survey respondents did not identify all three test-qualifying symptoms. Symptom identification decreased for every decade older (OR=0.908 [95% CI 0.883-0.933]). Amongst symptomatic UMD-Facebook respondents who wanted but did not have a test, not knowing where to go was the most cited factor (32.4%); this increased for each decade older (OR=1.207 [1.129-1.292]) and for every 4-years fewer in education (OR=0.685 [0.599-0.783]).
Interpretation
Despite current UK messaging on COVID-19 testing, there is a knowledge gap about when and where to test, and this may be contributing to the ∼25% testing gap. Risk factors, including older age and less education, highlight potential opportunities to tailor public health messages.
Funding
Zoe Global Limited, Department of Health, Wellcome Trust, EPSRC, NIHR, MRC, Alzheimer’s Society, Facebook Sponsored Research Agreement.
Research in context
Evidence before this study
To assess current evidence on test uptake in symptomatic testing programmes, and the reasons for not testing, we searched PubMed from database inception for research using the keywords (COVID-19) AND (testing) AND ((access) OR (uptake)). We did not find any work reporting on levels of test uptake amongst symptomatic individuals. We found three papers investigating geographic barriers to testing. We found one US based survey reporting on knowledge barriers to testing, and one UK based survey reporting on barriers in the period March - August 2020. Neither of these studies were able to combine testing behaviour with prospectively collected symptom reports from the users surveyed.
Added value of this study
Through prospective collection of symptom and test reports, we were able to estimate testing uptake amongst individuals with test-qualifying symptoms in the UK. Our results indicate that whilst testing has improved since the start of the pandemic, there remains a considerable testing gap. Investigating this gap we find that individuals with just one test-qualifying symptom or short symptom duration are less likely to get tested. We also find knowledge barriers to testing: a substantial proportion of individuals do not know which symptoms qualify them for a COVID-19 test, and do not know where to seek testing. We find a larger knowledge gap in individuals with older age and fewer years of education.
Implications of all the available evidence
Despite the UK having a simple set of symptom-based testing criteria, with tests made freely available through nationalised healthcare, a quarter of individuals with qualifying symptoms do not get tested. Our findings suggest testing uptake may be limited by individuals not acting on mild or transient symptoms, not recognising the testing criteria, and not knowing where to get tested. Improved messaging may help address this testing gap, with opportunities to target individuals of older age or fewer years of education. Messaging may prove even more valuable in countries with more fragmented testing infrastructure or more nuanced testing criteria, where knowledge barriers are likely to be greater.
Article notes
Competing Interest Statement
The authors CMA, BR and JSB declare no competing interests. AM, JCP, CH, JW are employees of Zoe Global Ltd. TDS is a consultant to Zoe Global Ltd. DAD and ATC previously served as investigators on a clinical trial of diet and lifestyle using a separate smartphone application that was supported by Zoe Global. ATC reports grants from Massachusetts Consortium on Pathogen Readiness, during the conduct of the study; personal fees from Pfizer Inc., personal fees from Boehringer Ingelheim, personal fees from Bayer Pharma AG, outside the submitted work. DAD reports grants from National Institutes of Health, grants from MassCPR, grants from American Gastroenterological Association during the conduct of the study.
Funding Statement
CMA and JSB: Facebook Sponsored Research Agreement [INB1116217]. CHS Alzheimer's Society Junior Fellowship [AS-JF-17-011]. Support for the COVID Symptom Study (UK data) was provided by the NIHR-funded Biomedical Research Centre based at GSTT NHS Foundation Trust. This work was supported by the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare. ZOE Global provided in kind support for all aspects of building, running and supporting the app and service to all users worldwide. Support for this study was provided by the NIHR-funded Biomedical Research Centre based at GSTT NHS Foundation Trust. Investigators also received support from the Wellcome Trust (212904/Z/18/Z, WT203148/Z/16/Z), the MRC/BHF (MR/M016560/1), Alzheimer's Society, EU, NIHR, CDRF, and the NIHR-funded BioResource, Clinical Research Facility and BRC based at GSTT NHS Foundation Trust in partnership with KCL, the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, the Wellcome Flagship Programme (WT213038/Z/18/Z), the Chronic Disease Research Foundation, and DHSC. ATC was supported in this work through a Stuart and Suzanne Steele MGH Research Scholar Award. The Massachusetts Consortium on Pathogen Readiness (MassCPR) and Mark and Lisa Schwartz supported MGH investigators (DAD, LHN, ATC).
Summary of Updates:
Introduction
Testing is a crucial component of the COVID-19 public health response to guide mitigation and triage illness, even as countries roll out vaccination campaigns. Whilst mass, population-based testing has been trialled,1–3 the majority of programmes seek to test individuals experiencing a certain set of symptoms. A successful program needs high testing uptake among those with test-qualifying symptoms.4 Achieving high uptake requires both an informed and willing population, and sufficient infrastructure to ensure test availability and accessibility.
In the United Kingdom (UK), the test-qualifying symptoms (fever, cough, or loss of smell)5 are relatively straightforward, have been consistent since loss of smell was added to the criteria on 18 May 2020, and are buttressed by a free, high-capacity, national testing programme. This is in contrast to other countries where criteria for testing have been more nuanced, varied over time and between regions, and testing access remains suboptimal.6, 7 Yet, despite the strengths of the UK programme, we observed that 25% of symptom-tracking app participants do not report testing despite experiencing test-qualifying symptoms. This and other evidence8, 9 raised questions regarding how the path from symptoms to testing could be improved to fully support the pandemic response.
Prior research focuses on logistical barriers for not getting tested such as geographic, socioeconomic and structural disparities in testing access,6, 8, 10 but there are other important barriers, including the knowledge required to successfully navigate the journey from symptom onset to test completion. Examining the reasons why people do not complete testing is hindered by the difficulty in identifying individuals who should have, but did not, receive COVID-19 tests.
Towards this end, we leveraged longitudinal data from over 4 million Zoe COVID Symptom Study (Zoe) participants,11 and over 700,000 surveys from the University of Maryland-Facebook Covid-19 Symptom Survey (UMD-Facebook), to describe the temporal changes in COVID-19 testing among UK residents with test-qualifying symptoms. We followed-up with cross-sectional surveys of the untested to identify knowledge barriers along the full journey to successful testing.
Methods
This research combines syndromic surveillance data from the UK Zoe COVID Symptom Study (Zoe)11 and the UK UMD-Facebook COVID-19 Symptom Survey (UMD-Facebook)12 Additionally, more detailed follow-up surveys of recently untested symptomatic participants were analyzed. Survey details provided in Supplement S1-S3. Throughout we define test-qualifying symptoms using the UK’s National Health Service (NHS) criteria: high temperature; new, continuous cough; or loss or change to sense of smell or taste.5
Data sources
UK Zoe COVID Symptom Study (Zoe)
Longitudinal data were prospectively collected using the Zoe COVID Symptom Study app, developed by Zoe Global with input from King’s College London (UK), the Massachusetts General Hospital (Boston, USA), and Lund and Uppsala Universities (Sweden). We used data from app launch on 24 March 2020 through 1 January 2021 (N=4,394,948, n=245,505,763 user-reports). App details are published elsewhere.11 Briefly, participants are asked enrollment questions at baseline, and then daily whether they feel physically normal or if they are experiencing symptoms. Participants are asked to record all COVID-19 test dates, types, and outcomes. To support COVID-19 incidence estimation,13 from 28 April 2020 the UK Department of Health and Social Care (DHSC) allocated polymerase chain reaction (PCR) tests to participants reporting any symptom after ≥1 “well” report in 9 days.
UK Zoe Follow-up Survey
To better understand the reasons why individuals who experience test-qualifying symptoms do not get tested, we deployed a cross-sectional SurveyMonkey web survey. We targeted participants reporting ≥1 test-qualifying symptoms for the first time between 14 November and 8 December 2020, who did not have a COVID-19 swab test report −7 to +14 days from symptom onset, including data entered up through 15 December 2020. Survey responses were linked to Zoe accounts using a unique, anonymised, user identifier. There were four survey sections to assess test-qualifying symptom recall and recognition, and test seeking and access. The survey was refined based on analysis of N=194 pilot survey responses sent to N=1,000. On 18 December 2020, the final survey was delivered by email to eligible participants (N=4,936 less N=706 without valid email address).
UK University of Maryland-Facebook COVID-19 Symptom Survey (UMD-Facebook)
This research is based on survey results from the University of Maryland (UMD).12 UMD, in collaboration with Facebook, delivered web-based, cross-sectional surveys to users sampled from the Facebook active user base. Survey sampling strategies were used to increase representativeness of the source population (here, the UK population) by sampling from the UK Facebook active user base and raking across census age, sex and geographic region to develop survey weights.14 The study was drawn from N=775,746 responses within UK geographic regions from 30 April 30 2020 (launch) through 21 February 2021. Primary analyses use raw data, and sensitivity analysis applied survey weights.
UMD-Facebook Symptomatic Never Tested but Desired Testing Survey Subcohort
On December 21, 2020, additional survey questions were asked of the “never tested” UMD-Facebook respondents regarding whether they had wanted to test in the prior two weeks, and reasons for not getting a test when they wanted one. For the analysis of knowledge-based factors contributing to not getting a test, cross-sectional surveys were limited to a subcohort of those surveys completed from December 21, 2020 onwards (N=205,017, survey versions 7-9), reporting test-qualifying symptoms in the past 24 hours (N=32,711, 16.0%), reported having never been tested for coronavirus (N=12,821, 39.2%) and reported having wanted testing in the prior 14 days (N=1,956, 15.3%). To describe the factors associated with knowledge barriers to successful testing, we focused on the question “Do any of the following reasons describe why you haven’t been tested for coronavirus (COVID-19) in the last X days?”, where X is symptom duration up to 14 days, and the response option “I don’t know where to go”.
Data analysis
We calculated the proportions of outcomes among subgroups, considering several outcomes and subgroup definitions. Logistic regression was used to estimate the covariate-outcome association. Covariates considered varied for each analysis as not all variables available in each data set: sex, age, symptom (see Supplementary Table 4), symptom number and duration, symptom-to-survey time, self-reported years of education, index of multiple deprivation [IMD]15, profession/work, self-reported or national rural-urban classification [RUC])16. For some analyses, Zoe reports of either loss of taste/smell or altered taste/smell were combined.16 Zoe and UMD-Facebook analyses were conducted using Python 3.8 and R 3.6.3, respectively.
Results
Symptom Severity and Not Testing
To better understand the factors contributing to COVID-19 testing, Zoe participants with test-qualifying symptoms during the study period, who did not report testing (N=20,425), were studied further. During this period, the proportion not tested among those with test-qualifying symptoms was higher for those with 1 vs ≥2 test-qualifying symptoms (27.0% vs 15.0%), and for those with symptoms lasting ≤2 days vs >2 (27.4% vs 12.2%), (Table 1). Similarly, the proportion of ever-tested in UMD-Facebook was lowest among those with only one test-qualifying symptom or short symptom duration (Supplementary Figure 5). A total of 1,254 users (26.6%) responded to the follow-up survey. Zoe and follow-up survey participants during this period were younger and more female than the general population, similar to the demographic trends reported previously in Zoe13 and other digital health studies (Supplement S6).18, 19
Journey to Successful COVID-19 Testing
In the Zoe follow-up survey, only 42.1% survey respondents recalled having experienced at least one test-qualifying symptom in the past month (Table 2). Of those who recalled their symptoms, 54.7% recognised that these symptoms qualified them for a COVID-19 test. Among participants who recognized test-qualifying symptoms, they were likely to go on to attempt (85.6%) and then successfully obtain a COVID-19 test (93.0%, or 18.4% of all survey respondents).
Reasons for Not Testing Among Those Who Qualified For and Wanted a Test
In the follow-up survey, there were few respondents (N=17) who recognised their symptoms qualified them for testing, and attempted, but did not succeed at testing (Table 2). We therefore evaluated complementary data from a subcohort of UMD-Facebook respondents from 21 December 2020 to 21 February 2021, who endorsed test-qualifying symptoms, who had never tested, and who indicated “yes” to the question question “Have you wanted to get tested for coronavirus (COVID-19) at any time in the last 14 days?” (N=1,956, Table 5). Among those who wanted testing, “I don’t know where to go” was the most frequently selected option (32.4%). The other multi-choice reasons were: “I am unable to travel to a testing location” (29.1%), “I tried to get a test but was not able to get one” (25.6%), “I am worried about bad things happening to me or my family (including discrimination, government policies, and social stigma)” (18.4%), “I can’t afford the cost of the test” (17.9%), and “I don’t have time to get tested”(13.3%). Given the scope of this study, we have focused on the knowledge-based response, though we acknowledge the logistical barriers are important.
Not Knowing Where to Test Among the Symptomatic Wanting Testing
We further investigated demographic factors associated with not knowing where to go to obtain a test (Figure 2). Not knowing where to go to obtain a test (“yes” vs referent “no”) was associated with older age (per decade OR=1.207 [1.129-1.292]) and less education (per 4-years OR=0.685 [0.599-0.783]). Male sex (OR=1.334 [1.064-1.675]) and living outside a city (OR=1.201 [0.926-1.562]) were not significant with Bonferroni correction for multiple hypothesis testing (p-threshold 0.0125 = 0.05/4). Education was similarly protective for not knowing where to test adjusting for age and sex, use of survey weights, or assuming missing responses were “no” (models results in Supplementary S9).
Acknowledging our limited sample size, we conducted qualitative, hypothesis-generating analyses of other demographic factors correlated with knowledge barriers (Supplementary S10). While cities were not protective in the regression model, the proportion not knowing where to test was slightly lower in London than elsewhere (Supplementary S11). Having a smartphone and using a symptom-tracking app qualitatively had a bigger impact on the knowledge gap than the relatively small urban-rural and regional differences. There were modest qualitative differences in not knowing where to test by profession, with the highest proportions among those in transportation, tourism and construction and the lowest in finance, public administration and health.
Discussion
Persistent Testing Gap
Our analysis finds that in December 2020 approximately one quarter of symptomatic UK Zoe participants who qualified for a COVID-19 test did not undergo testing. The proportion of ever-tested recently-symptomatic UK UMD-Facebook respondents echoes this trend. While we show a substantial improvement from April, 2020, the persistent testing gap is problematic for pandemic management in the UK and elsewhere. Non-pharmaceutical mitigation strategies are likely to be required,20–22 despite effective vaccines, because of COVID-19 transmissibility and the anticipated time to reach herd immunity, even with a one-dose immunization strategy.23 The lower the proportion of identified infections, such as through insufficient testing of symptomatic cases, the more likely transmission events will go unchecked.
Public Health Implications
Our findings have significant public health implications. The UK NHS testing programme offers free COVID-19 tests to those with test-qualifying symptoms, with the list of qualifying symptoms unchanged since loss/alteration to taste and smell were included on 18 May 2020,27 and tests accessed through a central booking system.5 Risk mitigation and public health principles generally would agree with these key features of the UK program i.e. the use of concise and consistent guidance, and limiting logistical barriers to following guidance. In this sense, the UK is a sort of case study of the “best case scenario”, and yet there is still a significant gap in understanding. Not only are greater efforts needed to educate the UK public, it is likely that comparable efforts to mind the knowledge gap will be needed in countries with regionally varying testing criteria or methods of accessing testing.
Our work suggests there is a need for messaging improvements to the UK testing campaign. In our study, among the untested who qualified for a test, older age was associated with not knowing when and where to test. In the earlier DHSC report8, older age was generally protective with respect to testing knowledge and behaviors, perhaps suggesting knowledge gains in the young over the past six months. Fewer years of education was also associated with not knowing where to test in our study. We found suggestive evidence that the knowledge gap may be more pronounced among those who do not have smartphones. Older populations in pre-pandemic studies have slower adoption of certain technologies, yet the abrupt social isolation resulting from mitigation strategies may be leaving important segments of the population behind.28 Education attained and age are likely not the root cause. Rather they likely highlight pre-existing health-information disparities that have been exacerbated by a year of unprecedented changes in how individuals interface with each other and the world.
Our findings support the need for targeted messaging to certain at-risk demographic groups, possibly in a non-digital format (e.g. radio, community signage). Our findings are particularly timely in light of work showing that expansion of the symptoms that qualify for a test would help detect more cases, assuming those who qualify do indeed successfully test.29, 30 This theoretical gain in case detection could be lost if the change in tack leaves vulnerable populations behind. There is overlap between knowledge risk factors and COVID-19 risk, such as older age,31 though we did see a higher absolute rate of testing in the oldest age group. Overlap with vaccine hesitancy risk factors may further amplify disparities in healthcare access, leaving some groups both less tested and less protected.
Furthermore, messaging could also emphasise that even individuals with mild or transient symptoms may have COVID-19 and should get tested. COVID-19 has a broad spectrum of disease severity with a substantial number of cases being fully asymptomatic, and with asymptomatic carriers still being able to transmit, albeit at reduced rates.24
Strengths and Limitations
The Zoe platform affords a unique opportunity to prospectively link testing behaviours with incident symptoms in a large user base comprising ∼6% of the UK population. The UMD-Facebook platform, though smaller in size and slightly different in survey design, corroborates temporal trends over in the broader population. To our knowledge, this has enabled the first time-varying estimate of testing rates amongst individuals that qualify for COVID-19 tests over the course of the pandemic. Both platforms could be leveraged to track the testing and knowledge gaps, in real time, allowing the effectiveness of interventions, such as improved messaging on when and where to test, to be assessed.
We acknowledge a number of limitations to this study. Digital surveys include selected populations not necessarily representative of the wider population. Such platforms have well-documented biases in demographic age, sex, and socioeconomic factors which we adjusted for in our analyses.18, 32 In addition, digital surveys may not be generalizable, as they may be enriched for health-councious internet-connected participants, and thus underestimate disparities in at-risk demographic groups. We show that symptom-tracking app participants and those with smartphones have higher testing rates than all UMD-Facebook survey respondents.
Confounding and measurement bias in this observational study using self-reported covariates and outcomes may also cause us to miss other important issues related to testing. We adjusted for common confounders, and attempted to identify proxies for the knowledge gap rather than attribute causality. There is no timely, efficient trial to conduct analyses of this scale. Self-report could introduce non-differential and differential measurement error, including the possibility of some events being omitted, or recorded inaccurately or inappropriately. Furthermore, the financial implications of having to self-isolate disproportionately affect the poorest, and may increase unwillingness to test33 and respondents may be wary of self-reporting socially stigmatized reasons for not complying with guidance. Lastly, selection can theoretically induce collider bias34 if the exposure and outcome are both causes of participation or subpopulation selection.
Conclusion
Testing is a fundamental principle of population-wide transmission mitigation. While the UK now has sufficient testing capacity, consistent guidelines, and free testing for those who qualify that is coordinated centrally, still we see a 25% testing gap among those with test-qualifying symptoms. We show this gap may be driven in part by a lack of understanding of mild COVID-19, national testing criteria, and testing access, especially among the elderly and those who have had fewer years of education. We propose altering the course of the UK testing programme to address this knowledge barrier to COVID-19 testing. In addition, other countries may benefit from improved understanding of modifiable barriers.
Data Availability
Zoe Platform data used in this study is available to researchers through UK Health Data Research using the following link: https://web.www.healthdatagateway.org/dataset/fddcb382-3051-4394-8436-b92295f14259 Requests for UMD/Facebook data may be completed through: https://dataforgood.fb.com/docs/covid-19-symptom-survey-request-for-data-access/
Acknowledgments
CMA and JSB: Facebook Sponsored Research Agreement [INB1116217]. CHS Alzheimer’s Society Junior Fellowship [AS-JF-17-011]. Support for the COVID Symptom Study (UK data) was provided by the NIHR-funded Biomedical Research Centre based at GSTT NHS Foundation Trust. This work was supported by the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare. ZOE Global provided in kind support for all aspects of building, running and supporting the app and service to all users worldwide. Support for this study was provided by the NIHR-funded Biomedical Research Centre based at GSTT NHS Foundation Trust. Investigators also received support from the Wellcome Trust (212904/Z/18/Z, WT203148/Z/16/Z), the MRC/BHF (MR/M016560/1), Alzheimer’s Society, EU, NIHR, CDRF, and the NIHR-funded BioResource, Clinical Research Facility and BRC based at GSTT NHS Foundation Trust in partnership with KCL, the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, the Wellcome Flagship Programme (WT213038/Z/18/Z), the Chronic Disease Research Foundation, and DHSC. ATC was supported in this work through a Stuart and Suzanne Steele MGH Research Scholar Award. The Massachusetts Consortium on Pathogen Readiness (MassCPR) and Mark and Lisa Schwartz supported MGH investigators (DAD, LHN, ATC).
Declaration of Interests
The authors CMA, BR and JSB declare no competing interests. AM, JCP, CH, JW are employees of Zoe Global Ltd. TDS is a consultant to Zoe Global Ltd. DAD and ATC previously served as investigators on a clinical trial of diet and lifestyle using a separate smartphone application that was supported by Zoe Global. ATC reports grants from Massachusetts Consortium on Pathogen Readiness, during the conduct of the study; personal fees from Pfizer Inc., personal fees from Boehringer Ingelheim, personal fees from Bayer Pharma AG, outside the submitted work. DAD reports grants from National Institutes of Health, grants from MassCPR, grants from American Gastroenterological Association during the conduct of the study.
Data Availability
Zoe Platform data used in this study is available to researchers through UK Health Data Research using the following link: https://web.www.healthdatagateway.org/dataset/fddcb382-3051-4394-8436-b92295f14259
Requests for UMD/Facebook data may be completed through: https://dataforgood.fb.com/docs/covid-19-symptom-survey-request-for-data-access/
Role of the funding source
The funding sources played no role in the study design, collection, analysis, interpretation, writing or decision to submit the paper for publication.
Ethics & IRB
Boston Children’s Hospital IRB (P00023700) to use UMD-Facebook data. King’s College London ethics committee (REMAS ID 18210; LRS-19/20-18210) to use Zoe data.
Supplementary Material
S1 Supplementary Table: Zoe CSS questions
In each daily report, users are asked about any symptoms they experience that day, and any COVID-19 tests they have had.
Symptoms
Users are asked “How do you feel physically right now?”. If they select “I’m not feeling quite right”, they are presented with a symptom options checklist. The symptom options specifically analysed in this study are:
Are you experiencing any of the below symptoms?
- □ Fever or feel too hot
- □ Loss of smell / taste
- □ Altered smell / taste (things smell or taste different to usual)
- □ Persistent cough (coughing a lot for more than an hour, or 3 or more coughing episodes in 24 hours)
Testing
Users are shown a list of all COVID-19 tests they have logged through the app. They are able to add new tests, edit existing entries, or select “This list is correct”. If a user chooses to add a new test, they are asked:
- Do you know the date of your test?
- If yes, select date
- How was the test performed?
- A swab of my nose or throat
- I spat in a cup/tube
- A finger-prick blood test
- A blood test, done using a needle
- Other, please specify (free text)
- Where was this test performed?
- At Home
- Drive-through Regional Testing Centre
- Hospital (not drive-through)
- GP
- Chemist / Pharmacy
- Work (excluding hospital or GP)
- Other, please specify
- What are the results of this test?
- Negative
- Positive
- Not clear/ failed
- Waiting for results
S2 Supplementary Table: Zoe CSS testing survey questions
Q1 Have you experienced any of the following symptoms in the last month? (check all that apply)
- □ Fever
- □ Persistent cough
- □ Loss of smell or taste
- □ Altered smell or taste
- □ Shortness of breath
- □ Fatigue
- □ Muscle or body aches
- □ Headache
- □ Sore throat
- □ Congestion or runny nose
- □ Nausea or vomiting
- □ Diarrhea
- □ None of the above
If None of the above; Proceed to Q2a then SURVEY END.
Else; Proceed to Q2.
Q2 Did these symptoms qualify you for a COVID-19 swab test where you live?
- □ Yes
- □ No
- □ Do not know
If YES; Proceed to Q3
If NO or DO NOT KNOW; Proceed to Q2a
SQ2a What symptoms qualify you for a covid test where you live? (check all that apply)
- □ Fever
- □ Persistent cough
- □ Loss of smell or taste
- □ Altered smell or taste
- □ Shortness of breath
- □ Fatigue
- □ Muscle or body aches
- □ Headache
- □ Sore throat
- □ Congestion or runny nose
- □ Nausea or vomiting
- □ Diarrhea
- □ None of the above
- □ Do not know
Proceed to Q3 (if arrived here directly from Q1, proceed to SURVEY END)
Q3 Did you try to get a swab test?
If YES; Proceed to Q4
If NO; Proceed to Q3A
Q3a Please select the reason(s) you did not attempt to get a swab test (all that apply)
- □ I did not believe I could/should get a test with my symptoms at the time
- □ My symptoms were normal / not new for me
- □ I thought that travelling to a testing appointment would be difficult or risky
- □ I was concerned about discomfort or pain from the swab
- □ I have already had Covid and did not think I could get it again
- □ I couldn’t find the time to go to an appointment (e.g. couldn’t get time off work/childcare)
- □ I was concerned testing positive might affect me financially (job/income/studies)
- □ I was concerned about the cost of the the test
- □ I was concerned testing positive would affect me socially (time away from friends/family)
- □ I was concerned about testing positive and being contacted by contact tracers or health authorities
- □ The tests are not reliable
- □ OTHER - free text
Proceed to SURVEY END
Q4 Did you receive a COVID-19 test (swab or otherwise) within 14 days of having such symptoms?
If YES; Proceed to Q5
If NO, Proceed to Q4a
Q4a TRIED TO GET TEST REASONS
Please state the reason(s) the test did not happen (all that apply): I did not know how to get a test
- □ There were no testing appointments available
- □ There were no home testing kits available
- □ The test never arrived/I never received the result back
- □ I thought that travelling to a testing appointment would be too risky
- □ I was concerned about swabbing myself/being swabbed
- □ Difficult with transportation to my appointment (e.g. too far away/I didn’t have a vehicle)
- □ I couldn’t find the time to go to an appointment (e.g. couldn’t get time off work/childcare)
- □ I could not afford the test
- □ I was told by a doctor or testing centre that I didn’t need a test
- □ OTHER - free text
Proceed to SURVEY END
Q5 What date was your COVID-19 test? Please also log this test through the app, if you have not done so already
[Date entry field]
Proceed to SURVEY END SURVEY END