Risk factors for SARS-CoV-2 infection, hospitalisation, and death in Catalonia, Spain: a population-based cross-sectional study

  1. Judit Villar-García
  2. Rosa María Vivanco-Hidalgo
  3. Montse Clèries
  4. Elisenda Martinez
  5. David Monterde
  6. Pol Perez-Sust
  7. Luis Garcia-Eroles
  8. Carol Sais
  9. Montse Moharra
  10. Emili Vela

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

OBJECTIVE

To identify the different subpopulations that are susceptible for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and hospitalisation or death due to coronavirus disease 2019 (COVID-19) in Catalonia, Spain.

DESIGN

Cross-sectional study.

SETTING

Data collected from the Catalan Health Surveillance System (CatSalut) in Catalonia, a region of Spain.

PARTICIPANTS

Using data collected between 1 March and 1 June 2020, we conducted the following comparative analyses: people infected by SARS-CoV-2 (328 892) vs Catalonia’s entire population (7 699 568); COVID-19 cases who required hospitalisation (37 638) vs cases who did not require hospitalisation (291 254); and COVID-19 cases who died during the study period vs cases who did not die during the study period (12 287).

MAIN OUTCOME MEASURES

Three clinical outcomes related to COVID-19 (infection, hospitalisation, or death). We analysed sociodemographic and environment variables (such as residing in a nursing home) and the presence of previous comorbidities.

RESULTS

A total of 328 892 cases were considered to be infected with SARS-CoV-2 (4.27% of total population). The main risk factors for the diagnostic were: female gender (risk ratio [RR] =1.49; 95% confidence interval [95% CI] =1.48-1.50), age (4564 years old; RR=1.02; 95% CI=1.01-1.03), high comorbidity burden (GMA index) (RR=3.03; 95% CI=2.97-3.09), reside in a nursing home (RR=11.82; 95% CI=11.66-11.99), and smoking (RR=1.06; 95% CI=1.05-1.07). During the study period, there were 37 638 (11.4 %) hospitalisations due to COVID-19, and the risk factors were: male gender (RR=1.45; 95% CI=1.43-1.48), age > 65 (RR=2.38; 95% CI=2.28-2.48), very low individual income (RR=1.03; 95% CI=0.97-1.08), and high burden of comorbidities (GMA index) (RR=5.15; 95% CI=4.89-5.42). The individual comorbidities with higher burden were obesity (RR=1.23; 95% CI=1.20-1.25), chronic obstructive pulmonary disease (RR=1.19; 95% CI=1.15-1.22), heart failure (RR=1.19; 95% CI=1.16-1.22), diabetes mellitus (RR=1.07; 95% CI=1.04-1.10), and neuropsychiatric comorbidities (RR=1.06; 95% CI=1.03-1.10). A total of 12 287 deaths (3.73%) were attributed to COVID-19, and the main risk factors were: male gender (RR=1.73; 95% CI=1.67-1.81), age > 65 (RR=37.45; 95% CI=29.23-47.93), residing in a nursing home (RR=9.22; 95% CI=8.81-9.65), and high burden of comorbidities (GMA index) (RR=5.25; 95% CI=4.60-6.00). The individual comorbidities with higher burden were: heart failure (RR=1.21; 95% CI=1.16-1.22), chronic kidney disease (RR=1.17; 95% CI=1.13-1.22), and diabetes mellitus (RR=1.10; 95% CI=1.06-1.14). These results did not change significantly when we considered only PCR-positive patients.

CONCLUSIONS

Female gender, age between 45 to 64 years old, high burden of comorbidities, and factors related to environment (nursing home) play a relevant role in SARS-CoV-2 infection and transmission. In addition, we found risk factors for hospitalisation and death due to COVID-19 that had not been described to date, including comorbidity burden, neuro-psychiatric disorders, and very low individual income. This study supports interventions for transmission control beyond stratify-and-shield strategies focused only on protecting those at risk of death. Future COVID-19 studies should examine the role of gender, the burden of comorbidities, and socioeconomic status in disease transmission, and should determine its relationship to workplaces, especially healthcare centres and nursing homes.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.08.26.20182303: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study protocol was approved by the Ethical Review Board of IMIM (Code 2020/9368, Barcelona, Spain).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and limitations: In this study, data were collected from the Catalan Health Surveillance System, a large healthcare database that includes a myriad of information on Catalan residents. This broad array of data, including an index for measuring the comorbidity burden, allowed us to gain a global perspective of the issues discussed, as opposed to working with just a very specific group (e.g. hospitalised patients with COVID-19). This database includes sociodemographic and other comorbidities that have not been prioritized in previous studies, and that have clearly been shown to play a role in SARS-CoV-2 transmission, and thus in disease control. Nevertheless, a limitation of this study is its descriptive nature, although we were indeed able to build a predictive model. A major drawback was the lack of PCR confirmation for most SARS-CoV-2 infected patients: the scarcity of tests during the study period precluded an extensive robust validation of all results. In addition, the fast-changing epidemiologic definitions of COVID-19 also worked against our analysis. Finally, since ours was a near-real-time analysis, hospitalisation notifications are likely to have been underestimated.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.08.26.20182303v1 on medRxiv