Swedish Covid-19 Investigation for Future Insights – a Population Epidemiology Approach using Register Linkage (SCIFI-PEARL)
Short description
SCIFI-PEARL is a nationwide linked multi-register, regularly updated, observational study for timely response over time to scientific questions regarding SARS-Cov-2 (severe acute respiratory syndrome coronavirus 2) and the COVID-19 (Coronavirus disease 2019) pandemic. For this purpose the study has relatively broad scientific aims that cover diverse aspects of pandemic, COVID-19 and vaccine impacts.
Through its coverage of the entire Swedish population, the SCIFI-PEARL database captures all Swedish residents with Covid-19 on an ongoing basis, and links to a broad range of national and regional healthcare data for a comprehensive longitudinal view of the Covid-19 pandemic. By combining high-quality national registers with a limited data lag through continuous repeated linkage and updating, the project brings timely and internationally relevant data for epidemiological research on SARS-CoV-2 and the pandemic.
Background
In December 2019 the novel coronavirus SARS-CoV-2 appeared for the first time in Wuhan, causing the disease now named COVID-19. Very quickly COVID-19 evolved into a large-scale pandemic that has been spreading to most countries in the world. To understand and combat the pandemic, scientifically sound information is key - about patients and their risk factors and prognosis, about long-term effects after the disease, as well as the broader impact of the pandemic and healthcare efforts including vaccinations.
In response to the Covid-19 pandemic, we designed and initiated a nationwide linked multi-register, regularly updated, observational study for timely response to urgent scientific questions. The SCIFI-PEARL (Swedish Covid-19 Investigation for Future Insights - a Population Epidemiology Approach using Register Linkage) project was formed.
This project seeks to answer many scientific questions with register data, including questions such as :
- What are the characteristics of Covid-19 patients and how do they differ from a normal population?
- Which underlying diseases or treatments increase patients' risk of Covid-19 or unfavourable prognosis?
- What other factors increase or modify the risk?
- What aspects of comorbidity, treatments or medical history increase the risk of long-term (1-2 years) consequences of Covid-19 infection?
- Can we develop predictive models that help predict onset or severe prognosis?
- What is the effect and possible side effects of covid-19 vaccines in the population?
- How has the COVID-19 pandemic and vaccination programmes affected drug use and healthcare in individuals and society?
How does register research work within COVID-19?
In the SCIFI-PEARL project, Covid-19 patients and the whole Swedish population are used for various statistical analysis methods including case-cohort design and prediction modeling. Via the national personal ID (personnummer), data from different health databases are linked, and the data are pseudonymized for research (personal ID is removed and replaced by an anonymous study ID).
Data in the study include:
Data from national and regional health registers :
- Covid-19 test data
- vaccination data
- national patient register
- 2 regional health databases
- national prescribed drug register
- national cancer register
- social insurance data (e.g. sick leave)
The project also includes data from quality registers relevant to Covid-19, including registers related to :
- heart disease
- lung disease
- diabetes
- emergency care
- intensive care
- infectious diseases
and sociodemographic data about the study population from Statistics Sweden.
The project with its aims and design has ethical approval from the Swedish Ethical Review Authority (2020-01800 with subsequent amendments).
The study database
The study database is being updated regularly (every 1-3 months), to provide answers to current research questions within the approved scientific aims, on an ongoing basis with up-to-date information. Various epidemiological methods, including descriptive statistics, regression modelling and machine learning methods, are used for data analysis, with adjustment for confounding factors as appropriate. Results are generated, presented and published on a continuous basis, based on the updated source data.
Examples for current research and analyses within the SCIFI-PEARL project
- Research about COVID-19 vaccination effectiveness and side effects (RECOVAC)
- Research on Post-Covid Condition (PCC), also called Long COVID
- Socioeconomic aspects of COVID-19 risk
- COVID-19 risk in relation to certain medications
- COVID-19 risk in people with different diseases (e.g. asthma, COPD, transplanted patients)
- COVID-19 risk in elderly
- Pandemic effects on drug use and healthcare utilization
Project members
Publications
Register epidemiology
The SCIFI-PEARL project is part of the research group in registry epidemiology and is affiliated to the School of Public Health and Community Medicine at the University of Gothenburg.
