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Burden and Prevalence of Prognostic Factors for Severe COVID-19 In European Journal of Epidemiology (2020) 35:401–409 https://doi.org/10.1007/s10654-020-00646-z COVID-19 Burden and prevalence of prognostic factors for severe COVID‑19 in Sweden Katalin Gémes1 · Mats Talbäck1 · Karin Modig1 · Anders Ahlbom1 · Anita Berglund1 · Maria Feychting1 · Anthony A. Matthews1 Received: 18 April 2020 / Accepted: 5 May 2020 / Published online: 18 May 2020 © The Author(s) 2020 Abstract The World Health Organization and European Centre for Disease Prevention and Control suggest that individuals over the age of 70 years or with underlying cardiovascular disease, cancer, chronic obstructive pulmonary disease, asthma, or dia- betes are at increased risk of severe COVID-19. However, the prevalence of these prognostic factors is unknown in many countries. We aimed to describe the burden and prevalence of prognostic factors of severe COVID-19 at national and county level in Sweden. We calculated the burden and prevalence of prognostic factors for severe COVID-19 based on records from the Swedish national health care and population registers for 3 years before 1st January 2016. 9,624,428 individuals were included in the study population. 22.1% had at least one prognostic factor for severe COVID-19 (2,131,319 individuals), and 1.6% had at least three factors (154,746 individuals). The prevalence of underlying medical conditions ranged from 0.8% with chronic obstructive pulmonary disease (78,516 individuals) to 7.4% with cardiovascular disease (708,090 individuals), and the county specifc prevalence of at least one prognostic factor ranged from 19.2% in Stockholm (416,988 individu- als) to 25.9% in Kalmar (60,005 individuals). We show that one in fve individuals in Sweden is at increased risk of severe COVID-19. When compared with the critical care capacity at a local and national level, these results can aid authorities in optimally planning healthcare resources during the current pandemic. Findings can also be applied to underlying assump- tions of disease burden in modelling eforts to support COVID-19 planning. Keywords COVID-19 · Prognostic factors · Burden of disease · Prevalence Introduction such as cardiovascular disease, high blood pressure, cancer, chronic obstructive pneumatic/pulmonary disease (COPD), As of 28th April 2020, the number of confrmed COVID-19 asthma, and diabetes, are considered to be at high risk of cases surpassed 2.9 million worldwide, and the number of developing severe symptoms of COVID-19, requiring in- deaths due to the disease reached 200,000 [1]. In Sweden, hospital care [1, 2]. These recommendations are mainly diagnosed COVID-19 cases surpassed 18,000 and related based on studies from China and Italy, and generally show deaths reached 2200 at the same date. Guidelines from the that once infected, individuals with at least one of these World Health Organization and the European Centre for Dis- prognostic factors are more likely to generate severe dis- ease Prevention and Control suggest that individuals aged ease, requiring hospitalization and a resulting higher risk 70 years and older or with an underlying medical condition of mortality [3–8]. Governments around the world have, therefore, recommended that individuals with at least one of these factors self-isolate for prolonged periods of time Electronic supplementary material The online version of this to not only reduce the risk contracting severe COVID-19, article (https ://doi.org/10.1007/s1065 4-020-00646 -z) contains but also prevent any sudden increase in demand for critical supplementary material, which is available to authorized users. care in hospitals, which could overwhelm health systems. If * Anthony A. Matthews the pandemic developed to afect a large proportion of the [email protected] population, then critical care capacity could become satu- rated. However, the prevalence of these prognostic factors 1 Unit of Epidemiology, Institute of Environmental Medicine, for severe COVID-19 are to a large extent unknown in many Karolinska Institutet, Stockholm, Sweden Vol.:(0123456789)1 3 402 K. Gémes et al. countries. Knowledge of the distribution of individuals con- Study population sidered to be at high risk of severe COVID-19, coupled with the capacity of the health care system, would allow clear We identifed all people living in Sweden on 31st December strategic planning. 2014 and alive on 1st January 2016. Several models have been produced to support COVID-19 planning in countries across the world [9–12]. Many of these Identifcation of prognostic factors for severe models are based on the assumption that disease severity COVID‑19 increases with age, but they do not account for an increased risk of severe disease in individuals with underlying medical We based our decision on the prognostic factors for severe conditions. This is usually because age stratifed burden of COVID-19 on the guidelines from the World Health Organi- disease at a local level is rarely available. Even when this zation and European Centre for Disease Prevention and Con- information is available, data from which it originates can be trol [1, 2], which were age 70 years and older, cardiovascular obtained from a sample of the population rather than from disease, cancer, COPD, severe asthma, and diabetes. Age the whole population. If the sample is not representative was calculated at 31st December 2015. An individual was of the population at large, results may be biased. In order then identifed as having an underlying medical condition to build clear robust models that will provide trustworthy if they had a diagnosis in either the Inpatient or Outpatient estimates of the extent to which the infection will impact Register (as primary or secondary diagnosis) or the Cancer populations, we need reliable estimates on the underlying Register within 3 years prior to 1st January 2016. If data prevalence of medical conditions suggesting high risk of were available, we also identifed related dispensations of severe disease. prescriptions from the Prescribed Drug Register within the The unified Swedish healthcare and register system same time period. We used International Statistical Clas- provides a unique opportunity to calculate the burden and sifcation of Diseases and Related Health Problems version prevalence of prognostic factors for severe COVID-19. 10 codes (ICD-10) to identify a diagnosis and Active Thera- This knowledge will help both healthcare capacity planning peutic Chemical codes (ATC) to identify the dispensation of and provide further data that can be applied to underlying a prescription. The ICD-10 and ATC codes for underlying assumptions for models that support planning worldwide. medical conditions were: cardiovascular disease (I20-I99), We therefore aimed to use Swedish register data to describe cancer (C00-C75, combined with morphology codes indi- the prevalence of prognostic factors of severe COVID-19 at cating malignant behavior), COPD (J41-J44), severe asthma national and county level in Sweden. (J45), and diabetes (E10, E11, E13, E14, O24; ATC: A10). Analysis Methods We initially calculated the burden (raw number) and preva- Data sources lence (proportion) of all individuals living in Sweden in relation to their sex, age (1–9, 10–19, 20–29, 30–39, 40–49, We used data from the Swedish national health care and 50–59, 60–69, 70–79, 80+), county of residence (21 coun- population registers linked at an individual level using the ties), if they had a predetermined prognostic factors for unique personal identifcation number of all residents in severe COVID-19, and if they had at least one, two, or three Sweden [13]. Disease burden was based on diagnoses and of these prognostic factors. We then calculated the burden date of hospitalization or visits from the National Inpatient and prevalence of the fve underlying medical conditions Register and Outpatient Specialist Care Register, and soci- across each age group, and the burden and prevalence of odemographic characteristics such as age, sex, county of each of the six prognostic factors individually in each of the residence were obtained from the Total Population Register 21 counties across the whole of Sweden. We also calculated [14, 15]. We also used the Cancer Register to identify malig- the age group stratifed burden and prevalence of each of the nant tumors, and the Swedish Prescribed Drug Register to fve underlying medical conditions in each county. Finally, identify prescriptions dispensed by individuals and further we calculated the burden and prevalence of individuals with our understating of disease burden [16, 17]. These data were at least one, two, and three prognostic factors for severe originally aggregated as part of a study on comorbidities in COVID-19 in each county. cancer risk and survival. 1 3 403 Burden and prevalence of prognostic factors for severe COVID-19 in Sweden Sensitivity analyses Table 1 Characteristics of the study population Characteristics N (%) We repeated all above analyses using a look back period of 1, 5 and 10 years prior to 1st January 2016 to defne occur- Total 9,624,428 (100.0) rence of disease in the registers, rather than 3 years. We Female 4,814,553 (50.0) also repeated the main analysis, but restricted cardiovas- Age (years) cular disease and cancer diagnoses to more severe disease 1–9 1,041,469 (10.8) (restricted cardiovascular disease to ICD-10 codes: I20.0, 10–19 1,046,237 (10.9) I21–22, I24–28, I30–46, I50, I60–69, I71–72; restricted can- 20–29 1,306,883 (13.6) cer codes to exclude non-melanoma skin cancers). 30–39 1,203,053 (12.5) 40–49 1,302,755 (13.5) Patient involvement 50–59 1,220,303 (12.7) 60–69 1,146,021 (11.9) No patients were involved in setting the research question 70–79 856,218 (8.9) or the outcome choices, nor were they involved in devel- 80+ 501,489 (5.2) oping plans for design or implementation of the study.
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