Background Late sequelae of COVID-19 have been reported; however, few studies have investigated the time-course or incidence of late new COVID-19-related health conditions (post-COVID conditions) after COVID-19 diagnosis. Studies distinguishing post-COVID conditions from late conditions caused by other etiologies are lacking. Using data from a large administrative all-payer database, we assessed the type, association, and timing of post-COVID conditions following COVID-19 diagnosis. Methods Using the Premier Healthcare Database Special COVID-19 Release (PHD-SR) (release date, October 20, 2020) data, during March–June 2020, 27,589 inpatients and 46,857 outpatients diagnosed with COVID-19 (case-patients) were 1:1 matched with patients without COVID-19 through the 4-month follow-up period (control-patients) by using propensity score matching. In this matched-cohort study, adjusted odds ratios were calculated to assess for late conditions that were more common in case-patients compared with control-patients. Incidence proportion was calculated for conditions that were more common in case-patients than control-patients during 31–120 days following a COVID-19 encounter. Results During 31–120 days after an initial COVID-19 inpatient hospitalization, 7.0% of adults experienced at least one of five post-COVID conditions. Among adult outpatients with COVID-19, 7.7% experienced at least one of ten post-COVID conditions. During 31–60 days after an initial outpatient encounter, adults with COVID-19 were 2.8 times as likely to experience acute pulmonary embolism as outpatient control-patients and were also more likely to experience a range of conditions affecting multiple body systems (e.g. nonspecific chest pain, fatigue, headache, and respiratory, nervous, circulatory, and gastrointestinal system symptoms) than outpatient control-patients. Children with COVID-19 were not more likely to experience late conditions than children without COVID-19. Conclusions These findings add to the evidence of late health conditions possibly related to COVID-19 in adults following COVID-19 diagnosis and can inform health care practice and resource planning for follow-up COVID-19 care.

Using a hierarchical model with an adjustment for sample selection, we estimate the overweight prevalence for adults, by states, using data from the Third National Health and Nutrition Examination Survey (NHANES III). A two‐stage hierarchical model was selected to account for geographic variability of outcomes and to model possible overdispersion of estimates due to cluster sampling. We compare our model‐based estimates with design‐based estimates at the national level and obtain excellent agreement. We also provide a check of our model at the state level by comparing estimates with design‐based and synthetic estimates. Copyright © 1999 John Wiley & Sons, Ltd.

No abstract