Post-COVID-19-associated morbidity in children, adolescents, and adults: A matched cohort study including more than 157,000 individuals with COVID-19 in Germany

Abstract

Background Long-term health sequelae of the Coronavirus Disease 2019 (COVID-19) are a major public health concern. However, evidence on post-acute COVID-19 syndrome (post-COVID-19) is still limited, particularly for children and adolescents. Utilizing comprehensive healthcare data on approximately 46% of the German population, we investigated post-COVID-19-associated morbidity in children/adolescents and adults. Methods and findings We used routine data from German statutory health insurance organizations covering the period between January 1, 2019 and December 31, 2020. The base population included all individuals insured for at least 1 day in 2020. Based on documented diagnoses, we identified individuals with polymerase chain reaction (PCR)-confirmed COVID-19 through June 30, 2020. A control cohort was assigned using 1:5 exact matching on age and sex, and propensity score matching on preexisting medical conditions. The date of COVID-19 diagnosis was used as index date for both cohorts, which were followed for incident morbidity outcomes documented in the second quarter after index date or later.Overall, 96 prespecified outcomes were aggregated into 13 diagnosis/symptom complexes and 3 domains (physical health, mental health, and physical/mental overlap domain). We used Poisson regression to estimate incidence rate ratios (IRRs) with 95% confidence intervals (95% CIs). The study population included 11,950 children/adolescents (48.1% female, 67.2% aged between 0 and 11 years) and 145,184 adults (60.2% female, 51.1% aged between 18 and 49 years). The mean follow-up time was 236 days (standard deviation (SD) = 44 days, range = 121 to 339 days) in children/adolescents and 254 days (SD = 36 days, range = 93 to 340 days) in adults. COVID-19 and control cohort were well balanced regarding covariates. The specific outcomes with the highest IRR and an incidence rate (IR) of at least 1/100 person-years in the COVID-19 cohort in children and adolescents were malaise/fatigue/exhaustion (IRR: 2.28, 95% CI: 1.71 to 3.06, p < 0.01, IR COVID-19: 12.58, IR Control: 5.51), cough (IRR: 1.74, 95% CI: 1.48 to 2.04, p < 0.01, IR COVID-19: 36.56, IR Control: 21.06), and throat/chest pain (IRR: 1.72, 95% CI: 1.39 to 2.12, p < 0.01, IR COVID-19: 20.01, IR Control: 11.66). In adults, these included disturbances of smell and taste (IRR: 6.69, 95% CI: 5.88 to 7.60, p < 0.01, IR COVID-19: 12.42, IR Control: 1.86), fever (IRR: 3.33, 95% CI: 3.01 to 3.68, p < 0.01, IR COVID-19: 11.53, IR Control: 3.46), and dyspnea (IRR: 2.88, 95% CI: 2.74 to 3.02, p < 0.01, IR COVID-19: 43.91, IR Control: 15.27). For all health outcomes combined, IRs per 1,000 person-years in the COVID-19 cohort were significantly higher than those in the control cohort in both children/adolescents (IRR: 1.30, 95% CI: 1.25 to 1.35, p < 0.01, IR COVID-19: 436.91, IR Control: 335.98) and adults (IRR: 1.33, 95% CI: 1.31 to 1.34, p < 0.01, IR COVID-19: 615.82, IR Control: 464.15). The relative magnitude of increased documented morbidity was similar for the physical, mental, and physical/mental overlap domain. In the COVID-19 cohort, IRs were significantly higher in all 13 diagnosis/symptom complexes in adults and in 10 diagnosis/symptom complexes in children/adolescents. IRR estimates were similar for age groups 0 to 11 and 12 to 17. IRs in children/adolescents were consistently lower than those in adults. Limitations of our study include potentially unmeasured confounding and detection bias. Conclusions In this retrospective matched cohort study, we observed significant new onset morbidity in children, adolescents, and adults across 13 prespecified diagnosis/symptom complexes, following COVID-19 infection. These findings expand the existing available evidence on post-COVID-19 conditions in younger age groups and confirm previous findings in adults. Trial registration ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT05074953.

Incident autoimmune diseases in association with a SARS-CoV-2 infection: A matched cohort study

“…We used information on preexisting chronic conditions as available from 2019 health records to adjust for potential confounders in the relationship of exposure (COVID-19) and incident autoimmune diseases. The approach is similar to a previous study [16].…”

“…We conducted a matched cohort study based on routine health care data as applied in a previous study [16]. In the present analysis, we compared the rates of newly diagnosed autoimmune disease between individuals with and without documented SARS-CoV-2 infection.…”

“…The underlying data sources were set up for the “Post-COVID-19 Monitoring in Routine Health Insurance Data” (POINTED) consortium [16] to study the long-lasting effects of the COVID-19 pandemic in Germany. The POINTED consortium is coordinated by the Center for Evidence-Based Healthcare (ZEGV) at the TU Dresden and consists of the German National Public Health Authority, the Robert Koch Institute, health research institutes and statutory health insurances.…”

The Greifswald Post COVID Rehabilitation Study and Research (PoCoRe)–Study Design, Characteristics and Evaluation Tools

“…Existing cohort studies that aim to assess long-term symptoms after COVID-19 disease partly include much higher numbers of cases, but are mostly based either on self-report only or on the use of electronic health records, often follow the participants for no longer than a year and do not necessarily have as broad a multidisciplinary approach as the one used in our study [ 49 , 50 , 51 , 52 , 53 , 54 ]. Population-based approaches make an important contribution to the investigation of (long-term) symptoms associated with PCS.…”

“…The advantage of the large samples of such studies is countered by the lack of complementary medical examinations, such as those collected in our study, which could allow further considerations on the pathogenesis of symptom persistence. These limitations also apply to population-based approaches using large datasets of healthcare data [ 53 ]. Other existing studies have also collected medical data but usually focused on a specific research area.…”