Data Resource Profile: Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry)

Résumé fourni par la source

FinRegistry is a curated, nationwide, register-based data resource for developing statistical and machine learning models, performing high-throughput epidemiological analyses and deriving outcome-specific prediction models. FinRegistry data are collected across 19 registries covering public health care visits, health conditions, medications, vaccinations, laboratory responses, demographics, familial relations and socioeconomic variables, with decades of follow-up for most registries. FinRegistry includes everyone living in Finland on 1 January 2010, as well as their parents, spouses, children and siblings, comprising a sample size of approximately 7.2 million persons. FinRegistry data are mapped to more than 3000 clinical endpoints defined by leveraging multiple registers and clinical expertise as part of the FinnGen project. The Risteys web portal [https://risteys.finregistry.fi] enables exploration of clinical endpoint definitions, their links to international ontologies and the results of epidemiological analyses to gain insights into disease epidemiology in the Finnish population. Access to FinRegistry is granted via the Finnish Social and Health Data Permit Authority Findata, which provides a clear and transparent application process and delivers the pseudonymized data in a secure computing environment. Nationwide health-related registry data provide comprehensive insights into population health and, combined with other data such as demographics, familial relations and socioeconomic data, enable the exploration of various dimensions of human behaviour and health. With the increasing size and variety of available data, advanced statistical and machine learning methods present novel possibilities for prediction and causal inference.1–3 At the same time, efforts to extract high-quality ‘phenotypes’ from registries, for example clinical endpoints, are needed to provide interpretable results. In this spirit, projects such as the CALIBER initiative have provided curated phenotype definitions based on UK’s primary and secondary health care data.4 Traditionally, the identification of risk factors and the creation of prediction models for diseases have been conducted using a targeted approach where a specific condition, risk factor or medication is studied for its association with a single disease. Several studies4–10 have shown the potential of data-driven approaches in examining the associations of a large number of risk factors and thousands of disease trajectories. These studies have been accompanied by an increasing trend towards making the results publicly available through web portals, to enable the re-use of the results by other researchers. The FinRegistry research project [www.finregistry.fi] seeks to model the complex relationship between health and various risk factors by developing statistical and machine learning models using high-resolution longitudinal registry data. The project is a joint effort led by the Finnish Institute for Health and Welfare (THL) and the Institute for Molecular Medicine Finland (FIMM), University of Helsinki. Access to FinRegistry is granted via the Finnish Social and Health Data Permit Authority Findata, which provides a clear and transparent application process and delivers the data in a secure computing environment. No ethics approval is required but instead, Findata examines the data access requests and grants a fixed-term data permit for processing confidential materials containing personal data under the Act on the Secondary Use of Health and Social Data.11 FinRegistry data are collected, used and stored in accordance with the General Data Protection Regulation. FinRegistry is funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme. FinRegistry data are collected across 19 registries covering the Finnish population’s public health care visits, health conditions, medications, vaccinations, laboratory responses, demographics, familial relations and socioeconomic variables. As in other Nordic countries, the data are collected in nationwide electronic registries.12 The earliest year of data collection varies by the registry, with the Finnish Cancer Registry being the oldest and dating back to 1953. Pseudonymized individual-level data from different registers can be linked together using pseudo-IDs that replace the unique personal identification number assigned to each individual residing in Finland, and familial relations allow the connection of individuals with their close relatives and their respective registry data. Furthermore, including geospatial data (geographical coordinates of the place of residence) enables the integration of open-access geographical data, such as the average environmental pollution of the area. The study population in FinRegistry is fully representative of the Finnish population: FinRegistry covers individuals living in Finland on 1 January 2010 (FinRegistry index persons) as well as their parents, spouses, children and siblings (non-index relatives), with the exception of individuals excluded due to non-disclosure for personal safety reasons. To date, the data comprise 5 339 804 index persons and 1 826 612 non-index relatives, making up a total sample size of approximately 7.2 million individuals. The number of persons included and the years covered by each registry are presented in Figure 1, and more details are available in Supplementary Table S1 (available as Supplementary data at IJE online). Finnish registries included in FinRegistry and the approximate number of unique individuals (in parentheses). Cancer, Finnish Cancer Registry; health care, Care Register for Health Care (Hilmo); congenital malformations, Register of Congenital Malformations; Birth, Medical Birth Register; infectious diseases, Finnish National Infectious Diseases Register; primary health care visits, Register of Primary Health Care Visits (AvoHilmo); laboratory responses, Kanta Laboratory Responses; intensive care, Intensive Care Registry; drug prescriptions, Kanta Prescription Centre and Prescription Archive; vaccinations, Finnish National Vaccination Register and Monitoring of the Vaccination Programme; population, Population Registry; Social assistance, Register of Social Assistance; Social welfare, Care Register for Social Welfare (Social Hilmo) FinRegistry is a unique nationwide registry resource because of: (i) the scale and diversity of data linkage; (ii) extensive quality control, including the generation of curated health register-based clinical endpoints obtained by leveraging multiple registries and clinical expertise as part of the FinnGen project13; and (iii) access to descriptive statistics and high-throughput epidemiological analyses via the Risteys web portal [https://risteys.finregistry.fi]. Furthermore, we are planning to map FinRegistry data to the Observational Medical Outcomes Partnership Common Data Model (OMOP-CMD) as described in the Supplementary Materials (available as Supplementary data at IJE online). FinRegistry data can be broadly categorized into the following partially overlapping categories: (i) health care visits and health conditions; (ii) medications and vaccinations; and (iii) demographics and socioeconomics. Registers included in each category and the years and numbers of persons covered are presented in Supplementary Table S1. A publicly available data dictionary is linked on the FinRegistry website [www.finregistry.fi/finnish-registry-data] and the code for data preprocessing is available on GitHub.14 Health care visits and health conditions comprise extensive data resulting from patient contacts with primary (since 2011 in FinRegistry), secondary (since 1969) and intensive care (since 2020), and cover details on the health care specialty. Additional information is available on psychiatric patients and those with demanding heart diseases (since 1994). Primary and home care information has been collected since 20

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