Linking of primary care records to census data to study the association between socioeconomic status and cancer incidence in Southern Europe: a nation-wide ecological study

Background Area-based measures of economic deprivation are seldom applied to large medical records databases to establish population-scale associations between deprivation and disease. Objective To study the association between deprivation and incidence of common cancer types in a Southern European region. Methods Retrospective ecological study using the SIDIAP (Information System for the Development of Research in Primary Care) database of longitudinal electronic medical records for a representative population of Catalonia (Spain) and the MEDEA index based on urban socioeconomic indicators in the Spanish census. Study outcomes were incident cervical, breast, colorectal, prostate, and lung cancer in 2009–2012. The completeness of SIDIAP cancer recording was evaluated through linkage of a geographic data subset to a hospital cancer registry. Associations between MEDEA quintiles and cancer incidence was evaluated using zero-inflated Poisson regression adjusted for sex, age, smoking, alcoholism, obesity, hypertension, and diabetes. Results SIDIAP sensitivity was 63% to 92% for the five cancers studied. There was direct association between deprivation and lung, colorectal, and cervical cancer: incidence rate ratios (IRR) 1.82 [1.64–2.01], IRR 1.60 [1.34–1.90], IRR 1.22 [1.07–1.38], respectively, comparing the most deprived to most affluent areas. In wealthy areas, prostate and breast cancers were more common: IRR 0.92 [0.80–1.00], IRR 0.91 [0.78–1.06]. Adjustment for confounders attenuated the association with lung cancer risk (fully adjusted IRR 1.16 [1.08–1.25]), reversed the direction of the association with colorectal cancer (IRR 0.90 [0.84–0.95]), and did not modify the associations with cervical (IRR 1.27 [1.11–1.45]), prostate (0.74 [0.69–0.80]), and breast (0.76 [0.71–0.81]) cancer. Conclusions Deprivation is associated differently with the occurrence of various cancer types. These results provide evidence that MEDEA is a useful, area-based deprivation index for analyses of the SIDIAP database. This information will be useful to improve screening programs, cancer prevention and management strategies, to reach patients more effectively, particularly in deprived urban areas

Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research

Berta Raventós,1,2,* Sergio Fernández-Bertolín,1,* María Aragón,1 Erica A Voss,3– 5 Clair Blacketer,3– 5 Leonardo Méndez-Boo,6 Martina Recalde,1 Elena Roel,1,2 Andrea Pistillo,1,7 Carlen Reyes,1 Sebastiaan van Sandijk,8 Lars Halvorsen,9 Peter R Rijnbeek,4,5 Edward Burn,1,10 Talita Duarte-Salles1,4 1Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 2Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Barcelona, Spain; 3Janssen Pharmaceutical Research and Development, Titusville, NJ, USA; 4Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands; 5OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA; 6Sistemes d’Informació dels Serveis d’Atenció Primària (SISAP), Institut Català de la Salut, Barcelona, Spain; 7Universitat Pompeu Fabra, Barcelona, Spain; 8Odysseus Data Services s.r.o., Prague, Czech Republic; 9edenceHealth NV, Kontich, Belgium; 10Centre for Statistics in Medicine, University of Oxford, Oxford, UK*These authors contributed equally to this workCorrespondence: Talita Duarte-Salles, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Gran Via Corts Catalanes, 587 àtic, Barcelona, 08007, Spain, Tel +34935824342, Email [email protected]: The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population.Patients and Methods: We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022.Results: After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died.Conclusion: We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.Keywords: electronic health records, medical ontologies, secondary data use, common data model, OMO