Rapid Epidemiological Analysis of Comorbidities and Treatments as risk factors for COVID-19 in Scotland (REACT-SCOT): A population-based case-control study


Autoři: Paul M. McKeigue aff001;  Amanda Weir aff002;  Jen Bishop aff002;  Stuart J. McGurnaghan aff003;  Sharon Kennedy aff004;  David McAllister aff002;  Chris Robertson aff006;  Rachael Wood aff004;  Nazir Lone aff001;  Janet Murray aff002;  Thomas M. Caparrotta aff003;  Alison Smith-Palmer aff002;  David Goldberg aff002;  Jim McMenamin aff002;  Colin Ramsay aff002;  Sharon Hutchinson aff002;  Helen M. Colhoun aff002
Působiště autorů: Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, Scotland aff001;  Public Health Scotland, Glasgow, Scotland aff002;  Institute of Genetics and Molecular Medicine, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, Scotland aff003;  NHS Information Services Division (Public Health Scotland), Edinburgh, Scotland aff004;  Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland aff005;  Department of Mathematics and Statistics, University of Strathclyde, Glasgow, Scotland aff006;  School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, Scotland aff007
Vyšlo v časopise: Rapid Epidemiological Analysis of Comorbidities and Treatments as risk factors for COVID-19 in Scotland (REACT-SCOT): A population-based case-control study. PLoS Med 17(10): e1003374. doi:10.1371/journal.pmed.1003374
Kategorie: Research Article
doi: 10.1371/journal.pmed.1003374

Souhrn

Background

The objectives of this study were to identify risk factors for severe coronavirus disease 2019 (COVID-19) and to lay the basis for risk stratification based on demographic data and health records.

Methods and findings

The design was a matched case-control study. Severe COVID-19 was defined as either a positive nucleic acid test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the national database followed by entry to a critical care unit or death within 28 days or a death certificate with COVID-19 as underlying cause. Up to 10 controls per case matched for sex, age, and primary care practice were selected from the national population register. For this analysis—based on ascertainment of positive test results up to 6 June 2020, entry to critical care up to 14 June 2020, and deaths registered up to 14 June 2020—there were 36,948 controls and 4,272 cases, of which 1,894 (44%) were care home residents. All diagnostic codes from the past 5 years of hospitalisation records and all drug codes from prescriptions dispensed during the past 240 days were extracted. Rate ratios for severe COVID-19 were estimated by conditional logistic regression. In a logistic regression using the age-sex distribution of the national population, the odds ratios for severe disease were 2.87 for a 10-year increase in age and 1.63 for male sex. In the case-control analysis, the strongest risk factor was residence in a care home, with rate ratio 21.4 (95% CI 19.1–23.9, p = 8 × 10−644). Univariate rate ratios for conditions listed by public health agencies as conferring high risk were 2.75 (95% CI 1.96–3.88, p = 6 × 10−9) for type 1 diabetes, 1.60 (95% CI 1.48–1.74, p = 8 × 10−30) for type 2 diabetes, 1.49 (95% CI 1.37–1.61, p = 3 × 10−21) for ischemic heart disease, 2.23 (95% CI 2.08–2.39, p = 4 × 10−109) for other heart disease, 1.96 (95% CI 1.83–2.10, p = 2 × 10−78) for chronic lower respiratory tract disease, 4.06 (95% CI 3.15–5.23, p = 3 × 10−27) for chronic kidney disease, 5.4 (95% CI 4.9–5.8, p = 1 × 10−354) for neurological disease, 3.61 (95% CI 2.60–5.00, p = 2 × 10−14) for chronic liver disease, and 2.66 (95% CI 1.86–3.79, p = 7 × 10−8) for immune deficiency or suppression. Seventy-eight percent of cases and 52% of controls had at least one listed condition (51% of cases and 11% of controls under age 40). Severe disease was associated with encashment of at least one prescription in the past 9 months and with at least one hospital admission in the past 5 years (rate ratios 3.10 [95% CI 2.59–3.71] and 2.75 [95% CI 2.53–2.99], respectively) even after adjusting for the listed conditions. In those without listed conditions, significant associations with severe disease were seen across many hospital diagnoses and drug categories. Age and sex provided 2.58 bits of information for discrimination. A model based on demographic variables, listed conditions, hospital diagnoses, and prescriptions provided an additional 1.07 bits (C-statistic 0.804). A limitation of this study is that records from primary care were not available.

Conclusions

We have shown that, along with older age and male sex, severe COVID-19 is strongly associated with past medical history across all age groups. Many comorbidities beyond the risk conditions designated by public health agencies contribute to this. A risk classifier that uses all the information available in health records, rather than only a limited set of conditions, will more accurately discriminate between low-risk and high-risk individuals who may require shielding until the epidemic is over.

Klíčová slova:

Age groups – Cardiovascular disease risk – COVID 19 – Diabetes mellitus – Medical risk factors – Scotland – Type 2 diabetes risk – Virus testing


Zdroje

1. Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: Prospective observational cohort study. BMJ (Clinical research ed). 2020;369: m1985. doi: 10.1136/bmj.m1985 32444460

2. McGoogan C, Steafel E. Why are young, healthy people dying of coronavirus? The symptoms to look out for. The Telegraph. 2020.

3. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet (London, England). 2020;395: 1054–1062. doi: 10.1016/S0140-6736(20)30566-3 32171076

4. Grasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, et al. Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. JAMA. 2020;323: 1574–1581. doi: 10.1001/jama.2020.5394 32250385

5. Guan W-j, Ni Z-y, Hu Y, Liang W-h, Ou C-q, He J-x, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine. 2020. doi: 10.1056/NEJMoa2002032 32109013

6. Niedzwiedz CL, O’Donnell CA, Jani BD, Demou E, Ho FK, Celis-Morales C, et al. Ethnic and socioeconomic differences in SARS-CoV2 infection in the UK Biobank cohort study. medRxiv. 2020; 2020.04.22.20075663.

7. Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. OpenSAFELY: Factors associated with COVID-19 death in 17 million patients. Nature. 2020; 1–11.

8. Alvarez-Madrazo S, McTaggart S, Nangle C, Nicholson E, Bennie M. Data Resource Profile: The Scottish National Prescribing Information System (PIS). International Journal of Epidemiology. 2016;45: 714–715. doi: 10.1093/ije/dyw060 27165758

9. NHS. Who’s at higher risk from coronavirus. NHS. [cited 2020 Oct 2]. https://www.nhs.uk/conditions/coronavirus-covid-19/people-at-higher-risk/whos-at-higher-risk-from-coronavirus/.

10. Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. Springer Science & Business Media; 2000.

11. Breslow NE, Day NE, Halvorsen KT, Prentice RL, Sabai C. Estimation of multiple relative risk functions in matched case-control studies. American Journal of Epidemiology. 1978;108: 299–307. doi: 10.1093/oxfordjournals.aje.a112623 727199

12. McKeigue P. Quantifying performance of a diagnostic test as the expected information for discrimination: Relation to the C-statistic. Statistical Methods in Medical Research. 2019;28: 1841–1851. doi: 10.1177/0962280218776989 29978758

13. Ho FK, Celis-Morales CA, Gray SR, Katikireddi SV, Niedzwiedz CL, Hastie C, et al. Modifiable and non-modifiable risk factors for COVID-19: Results from UK Biobank. medRxiv. 2020; 2020.04.28.20083295.

14. Ghebreyesus TA. WHO Director-General’s opening remarks at the media briefing on COVID-19–20 March 2020. [cited 2020 Oct 2]. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---20-march-2020.

15. McKeigue PM, Colhoun HM. Evaluation of "stratify and shield" as a policy option for ending the COVID-19 lockdown in the UK. medRxiv. 2020;[preprint]: 2020.04.25.20079913.

16. Bunnik BAD van, Morgan ALK, Bessell P, Calder-Gerver G, Zhang F, Haynes S, et al. Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown. medRxiv. 2020;[preprint]: 2020.05.04.20090597.


Článek vyšel v časopise

PLOS Medicine


2020 Číslo 10

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