1887
Research Open Access
Like 0

Abstract

Background

Following the SARS-CoV-2 Omicron variant spread, the use of unsupervised antigenic rapid diagnostic tests (self-tests) increased.

Aim

This study aimed to measure self-test uptake and factors associated with self-testing.

Methods

In this cross-sectional study from 20 January to 2 May 2022, the case series from a case–control study on factors associated with SARS-CoV-2 infection were used to analyse self-testing habits in France. A multivariable quasi-Poisson regression was used to explore the variables associated with self-testing among symptomatic cases who were not contacts of another infected individual. The control series from the same study was used as a proxy for the self-test background rate in the non-infected population of France.

Results

During the study period, 179,165 cases who tested positive through supervised tests were recruited. Of these, 64.7% had performed a self-test in the 3 days preceding this supervised test, of which 79,038 (68.2%) were positive. The most frequently reported reason for self-testing was the presence of symptoms (64.6%). Among symptomatic cases who were not aware of being contacts of another case, self-testing was positively associated with being female, higher education, household size, being a teacher and negatively associated with older age, not French by birth, healthcare-related work and immunosuppression. Among the control series, 12% self-tested during the 8 days preceding questionnaire filling, with temporal heterogeneity.

Conclusion

The analysis showed high self-test uptake in France with some inequalities which must be addressed through education and facilitated access (cost and availability) for making it a more efficient epidemic control tool.

Loading

Article metrics loading...

/content/10.2807/1560-7917.ES.2023.28.18.2200781
2023-05-04
2024-04-20
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2023.28.18.2200781
Loading
Loading full text...

Full text loading...

/deliver/fulltext/eurosurveillance/28/18/eurosurv-28-18-2.html?itemId=/content/10.2807/1560-7917.ES.2023.28.18.2200781&mimeType=html&fmt=ahah

References

  1. Peeling RW, Olliaro PL, Boeras DI, Fongwen N. Scaling up COVID-19 rapid antigen tests: promises and challenges. Lancet Infect Dis. 2021;21(9):e290-5.  https://doi.org/10.1016/S1473-3099(21)00048-7  PMID: 33636148 
  2. Mercer TR, Salit M. Testing at scale during the COVID-19 pandemic. Nat Rev Genet. 2021;22(7):415-26.  https://doi.org/10.1038/s41576-021-00360-w  PMID: 33948037 
  3. Smith RL, Gibson LL, Martinez PP, Ke R, Mirza A, Conte M, et al. Longitudinal assessment of diagnostic test performance over the course of acute SARS-CoV-2 infection. J Infect Dis. 2021;224(6):976-82.  https://doi.org/10.1093/infdis/jiab337  PMID: 34191025 
  4. Larremore DB, Wilder B, Lester E, Shehata S, Burke JM, Hay JA, et al. Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening. Sci Adv. 2021;7(1):eabd5393.  https://doi.org/10.1126/sciadv.abd5393  PMID: 33219112 
  5. Peeling RW, Heymann DL, Teo Y-Y, et al. Diagnostics for COVID-19: Moving from pandemic response to control. The Lancet Published Online First. 2022; (December).  PMID: 34942102 
  6. Drain PK. Rapid diagnostic testing for SARS-CoV-2. N Engl J Med. 2022;386(3):264-72.  https://doi.org/10.1056/NEJMcp2117115  PMID: 34995029 
  7. Killingley B, Mann AJ, Kalinova M, Boyers A, Goonawardane N, Zhou J, et al. Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge in young adults. Nat Med. 2022;28(5):1031-41.  https://doi.org/10.1038/s41591-022-01780-9  PMID: 35361992 
  8. Baldanti F, Ganguly NK, Wang G, Möckel M, O’Neill LA, Renz H, et al. Choice of SARS-CoV-2 diagnostic test: challenges and key considerations for the future. Crit Rev Clin Lab Sci. 2022;59(7):445-59.  https://doi.org/10.1080/10408363.2022.2045250  PMID: 35289222 
  9. Bubar KM, Middleton CE, Bjorkman KK, Parker R, Larremore DB. SARS-CoV-2 transmission and impacts of unvaccinated-only screening in populations of mixed vaccination status. Nat Commun. 2022;13(1):2777.  https://doi.org/10.1038/s41467-022-30144-7  PMID: 35589681 
  10. Mina MJ, Parker R, Larremore DB. Rethinking covid-19 test sensitivity a strategy for containment. N Engl J Med. 2020;383(22):e120.  https://doi.org/10.1056/NEJMp2025631  PMID: 32997903 
  11. Mina MJ, Peto TE, García-Fiñana M, Semple MG, Buchan IE. Clarifying the evidence on SARS-CoV-2 antigen rapid tests in public health responses to COVID-19. Lancet. 2021;397(10283):1425-7.  https://doi.org/10.1016/S0140-6736(21)00425-6  PMID: 33609444 
  12. Jean S, Burnham CD, Chapin K, Garner OB, Pant Pai N, Turabelidze G, et al. At-home testing for infectious diseases: The laboratory where you live. Clin Chem. 2021;68(1):19-26.  https://doi.org/10.1093/clinchem/hvab198  PMID: 34969103 
  13. Rader B, Gertz A, Iuliano AD, Gilmer M, Wronski L, Astley CM, et al. Use of at-home COVID-19 tests united states, august 23, 2021march 12, 2022. MMWR Morb Mortal Wkly Rep. 2022;71(13):489-94.  https://doi.org/10.15585/mmwr.mm7113e1  PMID: 35358168 
  14. Kepczynski CM, Genigeski JA, Koski RR, Bernknopf AC, Konieczny AM, Klepser ME. A systematic review comparing at-home diagnostic tests for SARS-CoV-2: Key points for pharmacy practice, including regulatory information. J Am Pharm Assoc (Wash DC). 2021;61(6):666-677.e2.  https://doi.org/10.1016/j.japh.2021.06.012  PMID: 34274214 
  15. Peaper DR, Kerantzas CA, Durant TJS. Advances in molecular infectious diseases testing in the time of COVID-19. Clin Biochem. 2022; (February):S0009-9120(22)00053-4.  PMID: 35181291 
  16. Haute Autorité de Santé (HAS). Avis numero 2021.0089/AC/SEAP du 30 décembre 2021 du collège de la haute autorité de santé relatif à l’extension de l’utilisation des autotests de détection antigénique du SARS-CoV-2 sur prélèvement nasal chez les personnes-contacts. [Opinion No. 2021.0089/AC/SEAP of 30 December 2021 from the college of the Haute Autorité de santé relating to the extension of the use of self-tests for the antigenic detection of SARS-CoV-2 on nasal swabs in contact persons.] Paris: HAS. [Accessed: 04 Apr 2023]. French. Available from: https://www.has-sante.fr/jcms/p_3307279/fr/avis-n-2021-0089/ac/seap-du-30-decembre-2021-du-college-de-la-haute-autorite-de-sante-relatif-a-l-extension-de-l-utilisation-des-autotests-de-detection-antigenique-du-sars-cov-2-sur-prelevement-nasal-chez-les-personnes-contacts
  17. Galmiche S, Charmet T, Schaeffer L, Paireau J, Grant R, Chény O, et al. Exposures associated with SARS-CoV-2 infection in France: A nationwide online case-control study. Lancet Reg Health Eur. 2021;7:100148.  https://doi.org/10.1016/j.lanepe.2021.100148  PMID: 34124709 
  18. Charmet T, Schaeffer L, Grant R, Galmiche S, Chény O, Von Platen C, et al. Impact of original, B.1.1.7, and B.1.351/P.1 SARS-CoV-2 lineages on vaccine effectiveness of two doses of COVID-19 mRNA vaccines: Results from a nationwide case-control study in France. Lancet Reg Health Eur. 2021;8:100171.  https://doi.org/10.1016/j.lanepe.2021.100171  PMID: 34278372 
  19. Grant R, Charmet T, Schaeffer L, Galmiche S, Madec Y, Von Platen C, et al. Impact of SARS-CoV-2 Delta variant on incubation, transmission settings and vaccine effectiveness: Results from a nationwide case-control study in France. Lancet Reg Health Eur. 2022;13:100278.  https://doi.org/10.1016/j.lanepe.2021.100278  PMID: 34849500 
  20. Gourieroux C, Monfort A, Trognon A. Pseudo maximum likelihood methods: Applications to poisson models. Econometrica. 1984;52(3):701-20.  https://doi.org/10.2307/1913472 
  21. Wooldridge JM. M-estimation, nonlinear regression, and quantile regression. In: Econometric analysis of cross section and panel data. Cambridge: The MIT Press; 2010. 397-468. [Accessed: 23 Jan 2023]. Available from: http://www.jstor.org/stable/j.ctt5hhcfr.17
  22. Wooldridge JM. Count, fractional, and other nonnegative responses. In: Econometric analysis of cross section and panel data. Cambridge: The MIT Press; 2010. 723-76. [Accessed: 15 Jan 2023]. Available from: http://www.jstor.org/stable/j.ctt5hhcfr.24
  23. Blackburn ML. The relative performance of poisson and negative binomial regression estimators. Oxf Bull Econ Stat. 2015;77(4):605-16.  https://doi.org/10.1111/obes.12074 
  24. Leeb H, Pötscher BM. Model selection and inference: Facts and fiction. Econom Theory. 2005;21(1).  https://doi.org/10.1017/S0266466605050036 
  25. Vach W, Blettner M. Biased estimation of the odds ratio in case-control studies due to the use of ad hoc methods of correcting for missing values for confounding variables. Am J Epidemiol. 1991;134(8):895-907.  https://doi.org/10.1093/oxfordjournals.aje.a116164  PMID: 1670320 
  26. Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338(jun29 1):b2393-3.  PMID: 19564179 
  27. Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40-9.  https://doi.org/10.1002/mpr.329  PMID: 21499542 
  28. Rubin D. Multiple imputation for nonresponse in surveys. Hoboken, N.J: Wiley-Interscience 2004.
  29. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing 2021. Available from: https://www.R-project.org/
  30. van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in r. J Stat Softw. 2011;45(3):1-67.  https://doi.org/10.18637/jss.v045.i03 
  31. Hughes RA, Heron J, Sterne JAC, Tilling K. Accounting for missing data in statistical analyses: multiple imputation is not always the answer. Int J Epidemiol. 2019;48(4):1294-304.  https://doi.org/10.1093/ije/dyz032  PMID: 30879056 
  32. Lee KJ, Tilling KM, Cornish RP, Little RJA, Bell ML, Goetghebeur E, et al. Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework. J Clin Epidemiol. 2021;134:79-88.  https://doi.org/10.1016/j.jclinepi.2021.01.008  PMID: 33539930 
  33. Zeileis A, Hothorn T. Diagnostic checking in regression relationships. R News. 2002;2:7-10. Available from: https://CRAN.R-project.org/doc/Rnews/
  34. Zeileis A, Köll S, Graham N. Various versatile variances: An object-oriented implementation of clustered covariances in R. J Stat Softw. 2020;95(1):1-36.  https://doi.org/10.18637/jss.v095.i01 
  35. Zeileis A. Object-oriented computation of sandwich estimators. J Stat Softw. 2006;16(9):1-16.  https://doi.org/10.18637/jss.v016.i09 
  36. Santé publique France. Comment évolue l’adhésion des français aux mesures de prévention contre la covid-19? Résultats de la vague 33 de l’enquête CoviPrev. [How is French people’s adherence to preventive measures against Covid-19 changing? Results of wave 33 of the CoviPrev survey]. Paris: Santé publique France. [Accessed: 4 Apr 2023]. French. Available from: https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/documents/enquetes-etudes/comment-evolue-l-adhesion-des-francais-aux-mesures-de-prevention-contre-la-covid-19-resultats-de-la-vague-33-de-l-enquete-coviprev
  37. Serdic L. Autotests: Pourquoi sont-ils beaucoup moins chers en grande surface qu’en pharmacie? [Self-tests: why are they much cheaper in supermarkets than in pharmacies?]. Toulouse: La Dépêche: 2021. [Accessed: 4 Apr 2023]. French. Available from: https://www.ladepeche.fr/2021/12/31/autotests-pourquoi-ils-sont-beaucoup-moins-chers-en-grande-surface-quen-pharmacie-10021356.php
  38. Green MA, García-Fiñana M, Barr B, Burnside G, Cheyne CP, Hughes D, et al. Evaluating social and spatial inequalities of large scale rapid lateral flow SARS-CoV-2 antigen testing in COVID-19 management: An observational study of Liverpool, UK (November 2020 to January 2021). Lancet Reg Health Eur. 2021;6:100107.  https://doi.org/10.1016/j.lanepe.2021.100107  PMID: 34002172 
  39. Holden TM, Richardson RAK, Arevalo P, Duffus WA, Runge M, Whitney E, et al. Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020. BMC Public Health. 2021;21(1):1105.  https://doi.org/10.1186/s12889-021-11177-x  PMID: 34107947 
  40. French CE, Denford S, Brooks-Pollock E, Wehling H, Hickman M. Low uptake of COVID-19 lateral flow testing among university students: a mixed methods evaluation. Public Health. 2022;204:54-62.  https://doi.org/10.1016/j.puhe.2022.01.002  PMID: 35176622 
  41. Smith LE, Potts HW, Amlôt R, Fear NT, Michie S, Rubin GJ. Who is engaging with lateral flow testing for COVID-19 in the UK? The COVID-19 Rapid Survey of Adherence to Interventions and Responses (CORSAIR) study. BMJ Open. 2022;12(2):e058060.  https://doi.org/10.1136/bmjopen-2021-058060  PMID: 35144956 
  42. Griffiths D, Leder K, Collie A. Sociodemographic indicators of COVID-19 testing amongst working-age Australians. Health Promot J Austr. 2021;32(2):361-4.  https://doi.org/10.1002/hpja.472  PMID: 33723869 
  43. Rader B, Astley CM, Sy KTL, Sewalk K, Hswen Y, Brownstein JS, et al. Geographic access to United States SARS-CoV-2 testing sites highlights healthcare disparities and may bias transmission estimates. J Travel Med. 2020;27(7):taaa076.  https://doi.org/10.1093/jtm/taaa076  PMID: 32412064 
  44. Graham MS, May A, Varsavsky T, Sudre CH, Murray B, Kläser K, et al. Knowledge barriers in a national symptomatic-COVID-19 testing programme. PLOS Glob Public Health. 2022;2(1):e0000028.  https://doi.org/10.1371/journal.pgph.0000028  PMID: 36962066 
  45. Vandentorren S, Smaïli S, Chatignoux E, Maurel M, Alleaume C, Neufcourt L, et al. The effect of social deprivation on the dynamic of SARS-CoV-2 infection in France: a population-based analysis. Lancet Public Health. 2022;7(3):e240-9.  https://doi.org/10.1016/S2468-2667(22)00007-X  PMID: 35176246 
  46. Saravolatz LD, Depcinski S, Sharma M. Molnupiravir and Nirmatrelvir-Ritonavir: Oral Coronavirus Disease 2019 Antiviral Drugs. Clin Infect Dis. 2023;76(1):165-71.  https://doi.org/10.1093/cid/ciac180  PMID: 35245942 
  47. World Health Organisation (WHO). Use of SARS-CoV-2 antigen-detection rapid diagnostic tests for COVID-19 self-testing INTERIM GUIDANCE. Geneva: WHO; 2022.Available from: https://apps.who.int/iris/bitstream/handle/10665/352348/WHO-2019-nCoV-Ag-RDTs-Self-testing-Web-annex-E-2022.1-eng.pdf
  48. European Centre for Disease Prevention and Control (ECDC). ECDC technical report - considerations on the use of self-tests for COVID-19 in the EU/EEA. Stockholm: ECDC; 2021.Available from: https://www.ecdc.europa.eu/sites/default/files/documents/Considerations-for-the-use-of-self-tests-for-COVID-19-in-the-EU-EEA_0.pdf
  49. Beauté J, Adlhoch C, Bundle N, Melidou A, Spiteri G. Testing indicators to monitor the COVID-19 pandemic. Lancet Infect Dis. 2021;21(10):1344-5.  https://doi.org/10.1016/S1473-3099(21)00461-8  PMID: 34450053 
  50. Pullano G, Di Domenico L, Sabbatini CE, Valdano E, Turbelin C, Debin M, et al. Underdetection of cases of COVID-19 in France threatens epidemic control. Nature. 2021;590(7844):134-9.  https://doi.org/10.1038/s41586-020-03095-6  PMID: 33348340 
  51. Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet. 2022;23(9):547-62.  https://doi.org/10.1038/s41576-022-00483-8  PMID: 35459859 
  52. Drews CD, Greeland S. The impact of differential recall on the results of case-control studies. Int J Epidemiol. 1990;19(4):1107-12.  https://doi.org/10.1093/ije/19.4.1107  PMID: 2083997 
  53. Coughlin SS. Recall bias in epidemiologic studies. J Clin Epidemiol. 1990;43(1):87-91.  https://doi.org/10.1016/0895-4356(90)90060-3  PMID: 2319285 
  54. Sackett DL. Bias in analytic research. J Chronic Dis. 1979;32(1-2):51-63.  https://doi.org/10.1016/0021-9681(79)90012-2  PMID: 447779 
  55. Bethlehem J. Selection bias in web surveys. Int Stat Rev. 2010;78(2):161-88.  https://doi.org/10.1111/j.1751-5823.2010.00112.x 
  56. Lu H, Cole SR, Howe CJ, Westreich D. Toward a clearer definition of selection bias when estimating causal effects. Epidemiology. 2022;33(5):699-706.  https://doi.org/10.1097/EDE.0000000000001516  PMID: 35700187 
/content/10.2807/1560-7917.ES.2023.28.18.2200781
Loading

Data & Media loading...

Supplementary data

Submit comment
Close
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error