Tracking the longitudinal dynamics of symptoms of COVID-19 infection in primary care

Published in Microbiology
Tracking the longitudinal dynamics of symptoms of COVID-19 infection in primary care
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Information on the clinical characteristics of COVID-19 infection is rapidly accumulating but is mostly based on data of hospitalized patients and lacks longitudinal follow-up. As the majority of COVID-19 cases are mild, prospective, longitudinal studies of symptoms of COVID‐19 disease of an unselected population presenting to primary care are needed.

During the first stages of the COVID-19 pandemic, our group collaborated with health maintenance organizations (HMO) in Israel and distributed daily surveys in order to assess the development of symptoms on a population-wide level. Our goals included creating a strategic tool for public-health officials and policymakers that will allow detection of disease spreading zones (1), development of testing strategies (2) and evaluation of the effectiveness of various social-distancing measures (3).  

In this study, we leveraged the data collected in the surveys together with linked electronic health records (EHR) from the second largest HMO in Israel to assess the longitudinal dynamic of clinical symptoms in non-hospitalized individuals. This allowed us to capture both self-reported and physician documented symptoms prior to and throughout the disease course and the overall agreement between the two data sources.  

Overall, we analysed 120,120 primary care visits with recorded symptoms and 1,262,479 Self-reported symptoms surveys. Information on symptoms from either data sources was available for 206,377 individuals, including 2,471 who were tested positive for COVID-19. When comparing individuals who had information from both data sources on the same day,  we found the overall agreement between them was generally low with survey data capturing most of the symptoms more sensitively (an example of the longitudinal course of an individual in our cohort is presented in Figure 1). 

Figure 1 - An example of an individual in our cohort. Symptoms recorded by a physician at a primary care (yellow rectangles) and self-reported symptoms (colored circles) are presented. Red and blue vertical lines represent a positive or negative PCR test for SARS-CoV-2 respectively.

By analysing symptoms longitudinally, we unravel different temporal patterns of self- reported and documented symptoms. Loss of taste and smell 3 weeks prior to testing, either self-reported or EHR-recorded, were the most discriminative symptoms for COVID-19 (Figure 2). Additional discriminative symptoms included self-reported headache and fatigue and a documentation of syncope, rhinorrhea and fever.  Symptoms with long duration after recovery included fatigue, myalgia, runny nose and shortness of breath. 

Figure 2. Loss of taste and smell with respect to different anchor times in COVID-19 cases

Furthermore, analysis of EHR of 21,567 children, from which 862 tested positive to COVID-19, revealed that children had a significantly shorter disease duration compared to adults, and have different prevalence of reported symptoms in primary care. In contrast to adults, where the most prevalent symptoms recorded were cough (11.6%), fever (10.3%), and myalgia (7.7%), the most prevalent symptoms in children were fever (7%), cough (5.5%) and abdominal pain (2.4%). In addition, we found a high prevalence of diagnosis related to emotional disturbance in COVID-19 cases, accounting for 15.9% of the adults and 4.2% of the children. 

Our study provides new insights into the clinical spectrum of COVID-19 infection in primary care. In addition to a better understanding of the longitudinal dynamics of symptoms, their progression and their expected duration, our study highlights symptoms that may alert physicians for the possibility of COVID-19 infection and direct the need for COVID-9 testing and self isolation. In addition, our findings highlight the importance of self-reported surveys in capturing the full spectrum of symptoms experienced by patients. 

https://www.nature.com/articles/s41467-020-20053-y

References

  1. Rossman H, Keshet A, Shilo S, Gavrieli A, Bauman T, Cohen O, et al. A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys. Nat Med. 2020;26(5):634–8.
  2. Shoer S, Karady T, Keshet A, Shilo S, Rossman H, Gavrieli A, et al. A prediction model to prioritize individuals for SARS-CoV-2 test built from national symptom surveys. Med (N Y). 2020 Oct 10;
  3. Keshet A, Gavrieli A, Rossman H, Shilo S, Meir T, Karady T, et al. The effect of a national lockdown in response to COVID-19 pandemic on the prevalence of clinical symptoms in the population. medRxiv. 2020 May 1;

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