Using a general practice research database to assess the spatio-temporal COVID-19 risk.

Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 9918300889006676 Publication Model: Electronic Cited Medium: Internet ISSN: 2731-4553 (Electronic) Linking ISSN: 27314553 NLM ISO Abbreviation: BMC Prim Care Subsets: MEDLINE

Original Publication: [London] : BioMed Central Ltd., [2022]-

COVID-19*/epidemiology ; Spatio-Temporal Analysis* ; General Practice*/statistics & numerical data ; Databases, Factual*; Humans ; Male ; Female ; Middle Aged ; Adult ; Aged ; Belgium/epidemiology ; Adolescent ; Young Adult ; Incidence ; SARS-CoV-2 ; Registries/statistics & numerical data ; Comorbidity ; Electronic Health Records/statistics & numerical data ; Aged, 80 and over

Background: In Flanders, general practitioners (GPs) were among the first ones to collect data regarding COVID-19 cases. Intego is a GPs' morbidity registry in primary care with data collected from the electronic medical records from a sample of general practices. The Intego database contain elaborate information regarding patient characteristics, such as comorbidities. At the national level, the Belgian Public Health Institute (Sciensano) recorded all test-confirmed COVID-19 cases, but without other patient characteristics.
Methods: Spatio and spatio-temporal analyses were used to analyse the spread of COVID-19 incidence at two levels of spatial aggregation: the municipality and the health sector levels. Our study goal was to compare spatio-temporal modelling results based on the Intego and Sciensano data, in order to see whether the Intego database is capable of detecting epidemiological trends similar to those in the Sciensano data. Comparable results would allow researchers to use these Intego data, and their wealth of patient information, to model COVID-19-related processes.
Results: The two data sources provided comparable results. Being a male decreased the odds of having COVID-19 disease. The odds for the age categories (17,35], (35,65] and (65,110] of being a confirmed COVID-19 case were significantly higher than the odds for the age category [0,17]. In the Intego data, having one of the following comorbidities, i.e., chronic kidney disease, heart and vascular disease, and diabetes, was significantly associated with being a COVID-19 case, increasing the odds of being diagnosed with COVID-19.
Conclusion: We were able to show how an alternative data source, the Intego data, can be used in a pandemic situation. We consider our findings useful for public health officials who plan intervention strategies aimed at monitor and control disease outbreaks such as that of COVID-19.
(© 2024. The Author(s).)

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3M190682 Internal Funds KU Leuven

Keywords: COVID-19; Comorbidities; Spatio-temporal methods

Date Created: 20240521 Date Completed: 20240522 Latest Revision: 20240523

20240523

PMC11106891

10.1186/s12875-024-02423-3

38773431