COVID-19 patient accounts of illness severity, treatments and lasting symptoms

Abstract

First-person accounts of COVID-19 illness and treatment can complement and enrich data derived from electronic medical or public health records. With patient-reported data, it is uniquely possible to ascertain in-depth contextual information as well as behavioral and emotional responses to illness. The Novel Coronavirus Illness Patient Report (NCIPR) dataset includes complete survey responses from 1,584 confirmed COVID-19 patients ages 18 to 98. NCIPR survey questions address symptoms, medical complications, home and hospital treatments, lasting effects, anxiety about illness, employment impacts, quarantine behaviors, vaccine-related behaviors and effects, and illness of other family/household members. Additional questions address financial security, perceived discrimination, pandemic impacts (relationship, social, stress, sleep), health history, and coping strategies. Detailed patient reports of illness, environment, and psychosocial impact, proximal to timing of infection and considerate of demographic variation, is meaningful for understanding pandemic-related public health from the perspective of those that contracted the disease.

Fig. 1

A schematic overview of the study design and data collection workflow.

Fig. 2

Overview of illness severity in the N = 1,584 COVID-19 quality validated sample. Fever peak and length are given only for those that endorsed having had a fever while infected (N = 877).

Fig. 3

COVID-19 infected sample demographics N = 1,584. MacArthur Ladder responses are only available for those that responded after March 29, 2021 (N = 614), as this question was added between the two recruitment invitations.

Fig. 4

Geographical location of COVID-19 patient survey respondents. Geographical information was only available for those that provided zip code data (N = 697).

Fig. 5

Overview of technical validation workflow and number of cases excluded at each step. 2,212 cases are included on the data release. 1,584 are coded as having passed all technical validation criteria. Abbreviations: date of birth, DOB; incompatible, INCOMP; implausible, IMPLAU. Data available via Open Science Framework (OSF).