Abstract
Purpose
We aimed to test associations of participant-reported Long COVID symptom interference with life activities with Long COVID symptoms, presence of U09.9 Long COVID diagnosis code, demographics, and clinical factors. In a subgroup, we documented coding related to Long COVID and post-exertional malaise in the electronic medical record (EMR).
Methods
Using a cross-sectional analysis (n = 205) of participant data from a Long COVID survey, we tested associations with Chi-square, Fisher’s exact, or Fisher-Freeman-Halton exact statistical tests and Independent Samples T-tests.
Results
Participants were predominately female (67%) with a mean age of 50.9 years. Participants were White (50.0%), African American (47.5%), and Asian (2.5%); 1.5% reported Hispanic ethnicity. 41% of participants reported high Long COVID symptom interference with life activities. Participants who were older (p=.028), were female (p=.002), were obese (p=.049), had worse general health (p<.001), worse physical health (p<.001), had worse mental health (p<.001), and had U09.9 diagnosis (p<.001) were more likely to experience high symptom interference. Among participants with high symptom interference, there were no significant associations with U09.9 diagnosis code. EMR sub-analysis (n = 100) revealed that among participants that reported high symptom interference (n = 39), 64% (n = 25) had a code related to Long COVID.
Conclusion
Although we found discrepancies between self-reported measures and EMR coding, we did not find evidence of demographic biases in diagnosis among participants with high symptom interference.
Clinical trial number
Not applicable.
Keywords: International classification of diseases, Medical records, Post-acute COVID-19 syndrome, Post-exertional malaise, Symptom burden
Introduction
Infection-associated chronic illnesses present major challenges for treatment and research and are associated with societal impacts such as labor force absenteeism and high health care costs [1]. Various mechanisms, such as autoimmune responses, pathogen persistence, end organ damage, and microclots, have been proposed as potential underlying causes of the pathology of infection-associated chronic illness [1]. Around 10–35% of people who are infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) report long-lasting symptoms in a condition known as post-acute sequalae of COVID-19 syndrome (PASC) or Long COVID [2]. Long COVID was defined by the National Academies of Sciences Engineering and Medicine as a chronic condition that occurs after a SARS-CoV-2 infection and is present for at least three months [2]. Long COVID may present as continuous, relapsing and remitting, or progressive with single or multiple symptoms (e.g. shortness of breath, persistent fatigue, and post-exertional malaise) and diagnosable conditions (e.g. interstitial lung disease, cardiovascular disease, and cognitive impairment) [2].
Around 400 million people globally are estimated to have experienced Long COVID with an annual economic cost of 1 trillion dollars [3]. Although Long COVID is acknowledged as a sequalae of a SARS-CoV-2 infection [4], substantial challenges remain for an accurate diagnosis of Long COVID [5, 6]. Clear and consistent diagnostic criteria with accurate clinical reporting are essential to both clinical management and care reimbursement. However, factors such as attribution uncertainty of Long COVID-like symptoms, lack of diagnostic biomarkers, and heterogeneity of symptoms due to a distinct and overlapping pathophysiology are barriers to definitive and consistent diagnosis [6]. Attribution uncertainty is particularly challenging in individuals with co-morbid conditions or functional limitations [6]. A qualitative study of primary care providers confirmed these challenges as well as lengthy pathways to a diagnosis [7].
Several studies have evaluated the uptake and frequency of use of Long COVID diagnostic codes in the United States [8–12] and other countries. [13, 14] These studies also reported characterizations of sociodemographic or clinical characteristics of people with Long COVID diagnostic codes. A wide range of diagnostic codes have been used clinically for Long COVID due to changes in the International Classification of Diseases (ICD) coding options as the pandemic progressed [11] and research assessing Long COVID codes have also utilized a wide variety of codes [8–10]. Older, female, White, people with more comorbidities, and people who were hospitalized or received mechanical ventilation were more likely to have a Long COVID code in electronic medical record based (EMR) studies [10–12, 14]. There have been conflicting findings in the Hispanic population. [10–11] Economic differences in use of the U09.9 code (ICD-10 post COVID-19 condition, unspecified) had been found with people living in countries with high levels of poverty having the lowest use [11]. Clinical symptoms consistently associated with a Long COVID code in the United States include shortness of breath/dyspnea, fatigue, cognitive impairment, and changes in smell or taste. [9, 10] Disparate coding, inconsistent symptom attribution, and fragmented care can be especially problematic for vulnerable populations. This may exacerbate health care inequities and fuel health disparities.
Some studies used diagnostic codes and EMR symptoms to capture a more inclusive Long COVID sample. [9, 12, 13] However, there is an apparent lack of information on the relationships between participant-reported symptoms and severity and Long COVID EMR diagnoses. Ideally, (1) patients recognize a worsening of symptoms or a lack of recovery from a SARS-CoV-2 infection, (2) they report these symptoms clearly and accurately to healthcare providers, (3) who then receive the information as intended, (4) document the concerns as Long COVID concerns, and (5) document a plan of care for the disclosed symptoms. However, this communication pathway is vulnerable to break down at many stages. Patient-reported measures of Long COVID symptoms and function are important because clinical conversations may not materialize into EMR symptom documentation or Long COVID diagnostic coding. Previous studies have confirmed the importance of patient-reported outcomes related to function and quality-of-life within COVID recovery research [15].
To address this gap in the literature, we aimed to test the associations between the self-reported variable, Long COVID symptom interference with life activities, with U09.9 code presence (ICD-10 post COVID-19 condition, unspecified), demographics, clinical factors, and Long COVID symptoms. Finally, we augmented our quantitative analysis with an examination of the EMR to document the types of Long COVID and post-exertional malaise coding that were present.
Methods
Design, setting, and sample
The COVID-19 Enterprise Biorepository and Data Registry (Enterprise) was launched in March 2020 at an academic medical center in the Southeastern United States with the first confirmed COVID-19 case at the institution. The protocol allowed for longitudinal follow-up with clinical, self-reported surveys, and biospecimen collection. Patients with COVID-19 of any severity were eligible for Enterprise, but hospitalized individuals and those receiving antibody therapy to prevent hospitalization were oversampled. People with confirmed or suspected COVID cases from January 2020 to January 2023 who were initially recruited during acute infection (Enterprise n = 4,779) were contacted for participation in the Long COVID research study. Among surviving participants, 206 completed a follow-up clinic visit for Long COVID measures from July 2023 to December 2023. The inclusion criteria for the Long COVID study were participation in Enterprise and a willingness to participate. The current cross-sectional study included a sub-sample of the Long COVID study participants who had complete data for our Long COVID symptom interference with life activities measure (n = 205).
Measures
Personal characteristics including age at the time of SARS-CoV-2 infection, body mass index (BMI), diabetes, biological sex, race, and ethnicity were abstracted from the EMR. BMI was dichotomized into a yes/no response for obesity with a BMI of 30 or higher indicating obesity. Self-reported characteristics and symptoms were collected using an online survey adapted from the RECOVER Initiative adult cohort [16]. These characteristics included smoking status, level of education, health insurance status, health status, COVID-19 related questions, and Long COVID symptoms. Health status variables were assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) instruments. General health was assessed using the first item of PROMIS Global Health “In general, would you say your health is?” [17]. Response choices included excellent, very good, good, fair, and poor. Physical and mental health were assessed using PROMIS Global Physical Health and PROMIS Global Mental Health instruments which are subscales of the PROMIS Global Health measure [17, 18]. PROMIS Global Physical Health includes items 3, 6, 7, and 8 of the Global Health measure and ask about physical health, physical activities, fatigue, and pain. PROMIS Global Mental Health includes items 2, 4, 5, 10 of the Global Health measure and asked about quality of life, mental health, satisfaction with social activities, and being bothered by emotional problems. Scores on the PROMIS measure were transformed into standardized T-scores (mean = 50, SD = 10) and treated as continuous variables. Higher total scores indicated better self-perceived health. The PROMIS Global Health measure has demonstrated accuracy in its ability to detect overall health [17].
Long COVID symptom interference with life activities was a participant self-reported measure and an assessment of life activity function with Long COVID. We assessed symptom interference with life activities using an author-developed item “During the past 4 weeks, how much have long-lasting symptoms of COVID-19, sometimes called long covid, interfered with your life?”. The response options were coded into categorical response options (high symptom interference= moderately, quite a bit, or extremely; low symptom interference = not at all or slightly). Before dichotomization, this variable was positively skewed with 44% reporting no symptom interference. The Long COVID symptom interference item had a moderate, statistically significant association with an established functional life activities measure (PROMIS Global Health measure item 9 r = .339, p<.001), providing preliminary evidence of convergent validity for this measure.
Clinical variables included COVID strain, hospitalization, use of supplemental oxygen, whether the participant had a vaccine before their first SARS-CoV-2 infection, number of SARS-CoV-2 infections, and Long COVID symptoms. COVID strain was determined based on the participant-reported date of first infection and the dominant circulating variant at that time. Timelines were based on in-house institutional sequencing and included Novel/Wuhan (January 2020—December 2020), Alpha B.1.1.7 (December 2020-June 2021), Delta B.1.617.2 (June 2021- December 2021), and Omicron B.1.1.529 (December 2021-January 2023). COVID-related acute care data that was abstracted from the EMR included hospitalization at the academic medical center (emergency, inpatient, or no hospital visit) and use of supplemental oxygen (advanced oxygen support, regular oxygen support, and no supplemental oxygen). Advanced oxygen support was defined as the use of CPAP, BiPAP, high flow nasal cannula, ventilator, or ECMO during the COVID-related acute care visit. Regular oxygen support was defined as the use of regular flow nasal cannula. Long COVID symptoms such as fatigue, post-exertional malaise, chronic cough, and dizziness/fainting were assessed using an adapted survey from the RECOVER Initiative adult cohort [15]. The original survey contained 44 Long COVID symptoms, however, only symptoms occurring with a frequency of 20% in the total sample were included in analysis (n = 25 symptoms included). Response options included yes/no for presence or absence of the symptom at the time of the Long COVID study visit. Participants were specifically asked to rate symptoms that appeared with the acute SARS-CoV-2 infection or after to minimize conflation with pre-existing conditions.
U09.9 codes were instituted and were used by medical providers at the study institution beginning October 1, 2021. On March 5, 2024, EMRs for the study sample were queried for U09.9 diagnostic codes using an informatics software tool. The EMR data were then merged with the online survey database. U09.9 data were converted into a dichotomous variable present/absent. The first 100 participants to enter the study were selected, without regard to other characteristics, for an EMR chart review sub-analysis completed on June 26, 2025.
Analysis
Descriptive statistics were used to describe the sample. Chi-square, Fisher’s exact, or Fisher-Freeman-Halton exact tests were used to test the associations between categorical variables. Fisher’s exact tests were used for dichotomous variables that had at least one cell with a low expected count. Fisher-Freeman-Halton exact tests were used when at least one variable had more than two response options and at least one cell had a low expected count. Independent Samples t-tests were used to test associations between categorical and continuous variables. Two-sided tests were used with a p-value < 0.05 denoting statistical significance. IBM SPSS Statistics version 30.0.0.0 IBM Corp 2024 was used for analysis. A pairwise deletion approach was used, whereby participants with missing data on specific variables were excluded only from analyses that required those variables.
We tested associations between (1) symptom interference with life activities with U09.9 diagnosis, demographics, and clinical variables; (2) U09.9 diagnosis with demographics and clinical variables (for the high interference group only); and (3) symptom interference with life activities with Long COVID symptoms. We restricted our U09.9 diagnosis assessment to the high symptom interference group, to identify variables that were associated with U09.9 diagnosis in a subgroup of individuals with a high need for clinical identification and intervention. Variables with significant bivariate correlations were modeled using logistic regressions to adjust for confounding variables and identify independent predictors. Logistic regressions were performed to test associations between (1) symptom interference with life activities dependent variable (DV) and significant factors (age, sex, obesity, physical health, mental health, U09.9 diagnosis) with full sample n = 193 (12 missing cases), (2) U09.9 diagnosis (DV) and factors trending towards significance (Obesity, number of SARS-CoV-2 infections, COVID vaccine before first infection) with high symptom interference sample only n = 84 (none missing), (3) symptom interference with life activities (DV) and significant symptoms (excessive dry mouth, fatigue, vision, nerve problems, weakness in arms or legs, pain in muscles, post-exertional malaise, shortness of breath, excessive thirst, joint pain, any pain in body, swelling, chronic cough, bladder problems, problems thinking or concentrating, and sleep problems) with full sample n = 195 (10 missing cases). The symptom numbness and tingling was excluded from the model due to collinearity.
Finally, among a subgroup of the first 100 participants enrolled, EMR problem and diagnosis lists were examined in their entirety for any codes related to Long COVID, fatigue, and post-exertional malaise. Abstraction involved identifying the binary presence or absence of specific diagnostic codes directly from EMR. All abstractions were completed by the same individual (JV). In this context, formal intra-rater reliability testing (e.g., kappa) is not standard practice and was not conducted. Data were collected on the code number, title, and group (Long COVID, fatigue, or post-exertional malaise). Selection was not random, however, it was completed without respect to characteristics other than order. The abstractor was not blinded to symptom interference status or U09.9 diagnosis presence from the study survey. The COVID-19 Enterprise Biorepository and Data Registry study received approval from the university’s Institutional Review Board (IRB-300005127) and adhered to ethical research practices.
Results
Participant demographics
A total of 205 individuals were included in the sample. All participants had at least one SARS-CoV-2 infection between January 2020 and January 2023. Participants were largely female (67%) with a mean age of 50.9 ± 14.6 years. Race distribution of participants included: White (50.0%), African American (47.5%), and Asian (2.5%). A small proportion of all participants reported a Hispanic ethnicity (1.5%). Participants were predominately insured (97%) and educated with a high school diploma or higher (94%). 41% of all participants reported high Long COVID symptom interference with life activities and 31% had a U09.9 diagnosis in their chart.
Symptom interference, demographics, and clinical factors
The results of the associations between Long COVID symptom interference with life activities, demographics, and clinical characteristics are listed in Table 1 (n = 205). Participants with older age (p=.028), females (p=.002), with obesity (p=.049), with worse general health (p<.001), worse physical health (p<.001), worse mental health (p<.001), and with a U09.9 Long COVID diagnosis in their medical record (p<.001) were more likely to experience high Long COVID symptom interference with life activities. A higher proportion of African Americans (52%) reported high symptom interference with life activities compared to Whites (46%), however, this difference was not statistically significant (p=.546). Based on the bivariate correlations, we then conducted a multivariable logistic regression using symptom interference with life activities as the dependent variable. When controlling for all other significant factors (U09.9 diagnosis present in EMR, age, sex, obesity, general health, physical, and mental health), U09.9 diagnosis- present (OR = 2.40, 95% CI [1.15–5.02] p=.020), sex- female (OR = 2.31, 95% CI [1.07–4.99], p=.034), and physical health- per 1 unit difference in T-score (OR = 0.905, 95% CI [0.850–0.964], p=.002) were still associated with symptom interference with life activities. For goodness of fit, the Hosmer-Lemeshow Test (6.98, p = .539) provided evidence of good calibration. The concordance (c) statistic, which assesses discriminative ability, was 0.80, 95% CI (0.738–0.863). In summary, having a formal Long COVID diagnosis code, being female, and experiencing poorer physical health were the key independent factors predicting greater interference of Long COVID symptoms with daily life activities, even when accounting for age, obesity, and overall general and mental health status.
Table 1.
Characteristics of the sample by Long COVID symptom interference with life activities
| Characteristic | Total (n = 205) |
High Symptom Interference (n = 84) |
Low Symptom Interference (n = 121) |
P-value | Adjusted OR (95% CI) | ||
|---|---|---|---|---|---|---|---|
| U09.9 Diagnosis Code Present in Medical Record | 63(31%) | 37(44%) | 26(22%) | < 0.001 |
2.40 (1.15–5.02) |
||
| Demographics | |||||||
| Age |
50.9yrs ± 14.6 (17–81) |
53.6yrs ± 14.3 (23–81) |
49.0 yrs ± 14.5 (17–81) |
0.028 |
1.02 (0.997 − 1.05) |
||
| Sex | Male | 67(33%) | 17(20%) | 50(41%) | 0.002 | -- | |
| Female | 138(67%) | 67(80%) | 71(59%) |
2.31 (1.07–1.05) |
|||
| Race1 | White | 99(50%) | 38(46%) | 61(53%) | 0.546†† | -- | |
| Black/ African American | 94(48%) | 43(52%) | 51(43%) | -- | |||
| Asian | 5(2%) | 2(2%) | 3(3%) | -- | |||
| Ethnicity2 | Hispanic | 3(2%) | 0(0%) | 3(3%) | 0.269† | -- | |
| Non-Hispanic | 191(99%) | 80(100%) | 111(97%) | -- | |||
| Education3 | 6th to 8th grade | 2(1%) | 0(0%) | 2(2%) | 0.601†† | -- | |
|
9th to 12th grade, no diploma |
10(5%) | 4(5%) | 6(5%) | -- | |||
| High School Graduate/GED | 31(15%) | 13(16%) | 18(15%) | -- | |||
| Some College/ Technical School | 76(38%) | 36(43%) | 40(34%) | -- | |||
| Bachelor’s Degree | 43(21%) | 17(21%) | 26(22%) | -- | |||
| Advanced Degree | 39(19%) | 13(16%) | 26(22%) | -- | |||
| Health & Clinical Factors | |||||||
| Health Insurance4 | Private | 109(57%) | 44(56%) | 65(57%) | 0.811†† | -- | |
| Public | 59(31%) | 25(32%) | 34(30%) | -- | |||
| Private & Public | 20(10%) | 8(10%) | 12(10%) | -- | |||
| Uninsured | 5(3%) | 1(1%) | 4(4%) | -- | |||
| General Health5 | Excellent | 20(10%) | 2(3%) | 18(16%) | < 0.001†† |
0.434 (0.078-2.41) |
|
| Very Good | 47(24%) | 16(20%) | 31(27%) | -- | |||
| Good | 72(37%) | 25(31%) | 47(41%) |
0.400 (0.148 − 1.08) |
|||
| Fair | 49(25%) | 31(29%) | 18(16%) |
0.808 (0.239 − 2.73) |
|||
| Poor | 7(4%) | 6(8%) | 1(1%) |
1.39 (0.098-19.7) |
|||
| Diabetes (Type I and Type II) | 64(31%) | 29(35%) | 35(29%) | 0.395 | -- | ||
| Smoker6 | 30(16%) | 8(10%) | 22(19%) | 0.084 | -- | ||
| Obesity7 | 127(62%) | 59(70%) | 68(57%) | 0.049 |
0.841 (0.406 − 1.74) |
||
| Physical Health- PROMIS score8 | 46.7 ± 9.57 (23.5–67.7) |
41.7 ± 8.65 (23.5–61.9) |
50.2 ± 8.61 (29.6–67.7) |
< 0.001 |
0.905 (0.850-0.964) |
||
| Mental Health- PROMIS score9 |
48.7 ± 8.63 (28.4–67.6) |
45.6 ± 8.36 (28.4–67.6) |
50.8 ± 8.21 (31.3–67.6) | < 0.001 |
1.00 (0.947 − 1.06) |
||
| Number of SARS-CoV-2 Infections |
1.6 inf ± 0.82 (1–4) |
1.7 inf ± 0.86 (1–4) |
1.5 inf ± 0.78 (1–4) |
0.101 | -- | ||
| Hospitalization | Emergency | 4(2%) | 1(1%) | 3(3%) | 0.733†† | -- | |
| Inpatient | 90(44%) | 39(46%) | 51(42%) | -- | |||
| No Hospital Visit | 111(54%) | 44(52%) | 67(55%) | -- | |||
| Supplemental Oxygen (O2) | Advanced O2 | 35(17%) | 19(23%) | 16(13%) | 0.173 | -- | |
| Regular O2 | 44(22%) | 15(18%) | 29(24%) | -- | |||
| No O2 | 126(62%) | 50(60%) | 76(63%) | -- | |||
| COVID Strain | Alpha | 46(22%) | 21(25%) | 25(21%) | 0.318 | -- | |
| Delta | 57(28%) | 24(29%) | 33(27%) | -- | |||
| Novel/Wuhan | 60(29%) | 27(32%) | 33(27%) | -- | |||
| Omicron | 42(21%) | 12(14%) | 30(25%) | -- | |||
| COVID Vaccine Before First Infection | 44(22%) | 15(18%) | 29(24%) | 0.295 | -- | ||
Rounding to the nearest whole percentage and valid percents were used; yrs=years; inf= SARS-CoV-2 infections; † Indicates a Fisher’s exact test p-value; †† Indicates a Fisher-Freeman-Halton exact test; Race1=missing 7 responses n = 198; Ethnicity2=missing 11 n = 194; Education3=missing 4 n = 201;Health Insurance4=missing 12 n = 193; General Health5= missing 10 n = 195; Smoker6=missing 12 n = 193; Obesity7=missing 1 n = 204;Physical Health8=missing 10 n = 195; Mental Health9=missing 11 n = 194; Regression model was adjusted for the following covariates: U09.9 diagnosis present in EMR, age, sex, obesity, general health, physical, and mental health
U09.9 diagnosis, demographics, and clinical factors
When looking at a subsample of participants (n = 84) who reported high Long COVID symptom interference with life activities, none of the demographic nor clinical factors were significantly associated with the presence of a U09.9 diagnosis in the medical record (Table 2). Based on the bivariate correlations, we then conducted a multivariable logistic regression using the U09.9 diagnosis variable as the dependent variable. When controlling for all other factors that were trending towards significance (number of SARS-CoV-2 infections, obesity, vaccine before first infection), none were associated with a U09.9 code in the high symptom interference group.
Table 2.
Demographic and clinical factors associations with the presence of a U09.9 diagnosis in EMR among participants reporting high symptom interference with life activities
| Characteristic | Total (n = 84) |
U09.9 Diagnosis Present (n = 37) |
U09.9 Diagnosis Absent (n = 47) |
P-value | Adjusted OR (95% CI) | |
|---|---|---|---|---|---|---|
| Demographics | ||||||
| Age |
53.6yrs ± 14.3 (23–81) |
53.1yrs ± 14.5 (23–79) |
54.0yrs ± 14.2 (23–81) |
0.777 | -- | |
| Sex | Male | 17(20%) | 6(16%) | 11(23%) | 0.416 | -- |
| Female | 67(80%) | 31(84%) | 36(77%) | -- | ||
| Race1 | White | 38(45%) | 17(46%) | 21(46%) | 0.333†† | -- |
| Black/ African American | 43(51%) | 18(49%) | 25(54%) | -- | ||
| Asian | 2(2%) | 2(5%) | 0(0%) | -- | ||
| Ethnicity2 | Hispanic | 0(0%) | 0(0%) | 0(0%) | -- | -- |
| Non-Hispanic | 80(100%) | 37(100%) | 43(100%) | -- | ||
| Education3 | 6th to 8th grade | 0(0%) | 0(0%) | 0(0%) | 0.702†† | -- |
|
9th to 12th grade, no diploma |
4(5%) | 2(6%) | 2(4%) | -- | ||
| High School Graduate/GED | 13(16%) | 5(14%) | 8(17%) | -- | ||
| Some College/ Technical School | 36(43%) | 13(36%) | 23(49%) | -- | ||
| Bachelor’s Degree | 17(21%) | 9(25%) | 8(17%) | -- | ||
| Advanced Degree | 13(16%) | 7(19%) | 6(13%) | -- | ||
| Health & Clinical Factors | ||||||
| Health Insurance4 | Private | 44(56%) | 19(58%) | 25(56%) | 0.202†† | -- |
| Public | 25(32%) | 12(36%) | 13(29%) | -- | ||
| Private & Public | 8(10%) | 1(3%) | 7(16%) | -- | ||
| Uninsured | 1(1%) | 1(3%) | 0(0%) | -- | ||
| General Health5 | Excellent | 2(3%) | 0(0%) | 2(5%) | 0.761†† | -- |
| Very Good | 16(20%) | 7(19%) | 9(21%) | -- | ||
| Good | 25(31%) | 11(31%) | 14(32%) | -- | ||
| Fair | 31(39%) | 15(42%) | 16(36%) | -- | ||
| Poor | 6(8%) | 3(8%) | 3(7%) | -- | ||
| Diabetes (Type I and Type II) | 29(35%) | 13(35%) | 16(34%) | 0.917 | -- | |
| Smoker6 | 8(10%) | 2(6%) | 6(13%) | 0.277 | -- | |
| Obesity | 59(70%) | 30(81%) | 29(62%) | 0.054 |
2.67 (0.958 − 7.45) |
|
| Physical Health- PROMIS score7 | 41.7 ± 8.65 (23.5–61.9) |
40.9 ± 9.3 (26.7–61.9) |
42.4 ± 8.15 (23.5–61.9) |
0.462 | -- | |
| Mental Health- PROMIS score8 |
45.6 ± 8.36 (28.4–67.6) |
46.0 ± 9.59 (28.4–67.6) |
45.3 ± 7.34 (28.4–59.0) | 0.702 | -- | |
| Number of SARS-CoV-2 Infections |
1.73 inf ± 0.86 (1–4) |
1.9 inf ± 1.0 (1–4) |
1.6 inf ± 0.68 (1–3) |
0.116 |
1.52 (0.885 − 2.60) |
|
| Hospitalization | Emergency | 1(1%) | 1(3%) | 0(0%) | 0.377†† | -- |
| Inpatient | 39(46%) | 15(41%) | 24(51%) | -- | ||
| No Hospital Visit | 44(52%) | 21(57%) | 23(49%) | -- | ||
| Supplemental Oxygen (O2) | Advanced O2 | 19(23%) | 9(24%) | 10(21%) | 0.326 | -- |
| Regular O2 | 15(18%) | 4(11%) | 11(23%) | -- | ||
| No O2 | 50(60%) | 24(65%) | 26(55%) | -- | ||
| COVID Strain | Alpha | 21(25%) | 7(19%) | 14(30%) | 0.714 | -- |
| Delta | 24(29%) | 11(30%) | 13(28%) | -- | ||
| Novel/Wuhan | 27(32%) | 13(35%) | 14(30%) | -- | ||
| Omicron | 12(14%) | 6(16%) | 6(13%) | -- | ||
| COVID Vaccine Before First Infection | 15(18%) | 7(19%) | 8(17%) | 0.051 |
1.21 (0.373 − 3.90) |
|
Rounding to the nearest whole percentage and valid percents were used; yrs=years; inf= SARS-CoV-2 infections; † Indicates a Fisher’s exact test p-value; †† Indicates a Fisher-Freeman-Halton exact test; Race1=missing 1 n = 83; Ethnicity2=missing 4 n = 80; Education3=missing1 n = 83; Health Insurance4=missing 6 n = 78; General Health5=missing 4 n = 80; Smoker6=missing 5 n = 79; Physical Health7=missing 4 n = 80; Mental Health8=missing 5 n = 79; Regression model was adjusted for the following covariates: number of SARS-CoV-2 infections, obesity, and vaccine before first infection
Symptom interference with life activities and long COVID symptoms
When looking at associations between Long COVID symptom interference with life activities and Long COVID symptoms reported (Table 3) (n = 195), the following symptoms were strongly associated with higher symptom interference: excessive dry mouth, fatigue, vision problems, nerve problems, weakness in arms or legs, pain in muscles, numbness and tingling, post-exertional malaise, shortness of breath, excessive thirst, pain in joints, and pain in any part of body (p<.001). Swelling of legs (p=.002), chronic cough (p=.003), bladder problems (p=.005), problems thinking (p=.007), and problems with sleep (p=.031) were also associated with high symptom interference with life activities. Based on the bivariate correlations, we then conducted a multivariable logistic regression using symptom interference with life activities as the dependent variable and excluded the symptom numbness and tingling due to collinearity. When controlling for all other statistically significant Long COVID symptoms, dry mouth (OR = 10.9, 95% CI [1.6–74.0], p=.015) and fatigue (OR = 3.91, 95% CI [3.9–11.9], p=.017) were still associated with symptom interference with life activities. For goodness of fit, the Hosmer-Lemeshow Test (7.6, p = .269) provided evidence of good calibration. The c-statistic was 0.784, 95% CI (0.717–0.852). In summary, having symptoms of excessive dry mouth and fatigue were the key independent factors predicting greater interference of Long COVID symptoms with daily life activities, even when accounting for all other statistically significant Long COVID symptoms.
Table 3.
Long COVID symptoms by symptom interference with life activities
| Long COVID Symptom | Total (n = 195) |
High Symptom Interference (n = 84) |
Low Symptom Interference (n = 121) |
P-value | Adjusted OR (95% CI) |
|---|---|---|---|---|---|
| Excessively dry mouth | 31(16%) | 25(31%) | 6(5%) | < 0.001 |
10.85 (1.59–73.95) |
| Fatigue | 52(27%) | 35(44%) | 17(15%) | < 0.001 |
3.91 (1.28–11.96) |
| Vision problems (blurry, light sensitivity, difficulty reading or focusing, floaters, flashing lights, “snow”) | 50(26%) | 34(43%) | 16(14%) | < 0.001 |
2.19 (0.840 − 5.72) |
| Nerve problems (tremor, shaking, abnormal movements, numbness, tingling, burning, can’t move part of body, new seizures) | 33(17%) | 25(31%) | 8(7%) | < 0.001 |
2.81 (0.781-10.09) |
| Weakness in arms or legs | 32(16%) | 24(30%) | 8(7%) | < 0.001 |
0.995 (0.283 − 3.50) |
| Pain in muscles | 44(23%) | 30(38%) | 14(12%) | < 0.001 |
1.93 (0.729 − 5.11) |
| Numbness, tingling, burning | 31(16%) | 23(29%) | 8(7%) | < 0.001 | -- |
| Post-Exertional Malaise (Symptoms worse after even minor physical or mental effort) | 28(14%) | 21(26%) | 7(6%) | < 0.001 |
2.28 (0.598 − 8.70) |
| Shortness of breath | 36(19%) | 25(31%) | 11(10%) | < 0.001 |
0.760 (0.214 − 2.70) |
| Excessive thirst | 31(16%) | 22(28%) | 9(8%) | < 0.001 |
0.269 (0.040-1.81) |
| Pain in joints | 75(39%) | 42(53%) | 33(29%) | < 0.001 |
1.58 (0.720 − 3.48) |
| Pain in any part of your body (include headache) | 45(23%) | 28(35%) | 17(15%) | < 0.001 |
0.546 (0.173 − 1.72) |
| Swelling of your legs | 24(12%) | 17(21%) | 7(6%) | 0.002 |
1.19 (0.351 − 4.03) |
| Persistent (chronic) cough | 23(12%) | 16(20%) | 7(6%) | 0.003 |
1.70 (0.468 − 6.21) |
| Bladder problems (incontinence, trouble passing urine or emptying bladder) | 20(10%) | 14(18%) | 6(5%) | 0.005 |
1.03 (0.264 − 4.03) |
| Problems thinking or concentrating (“brain fog”) | 53(27%) | 30(38%) | 23(20%) | 0.007 |
0.916 (0.328 − 2.56) |
| Problems with sleep | 50(26%) | 27(34%) | 23(20%) | 0.031 |
0.488 (0.150 − 1.58) |
| Feeling faint, dizzy, “goofy”; difficulty thinking soon after standing up from a sitting or lying position | 34(17%) | 19(24%) | 15(13%) | 0.053 | -- |
| Head pain/headache | 43(22%) | 23(29%) | 20(17%) | 0.060 | -- |
| Gastrointestinal (belly) symptoms (feeling full or vomiting after eating, diarrhea, constipation) | 32(16%) | 17(21%) | 15(13%) | 0.128 | -- |
| Pain in back/spine | 59(30%) | 29(36%) | 30(26%) | 0.129 | -- |
| Pain in feet | 31(16%) | 16(20%) | 15(13%) | 0.191 | -- |
| Hair loss | 28(14%) | 14(18%) | 14(12%) | 0.297 | -- |
| Problems with anxiety, depression, stress, or trauma-related symptoms like nightmares or grief | 40(21%) | 18(23%) | 22(19%) | 0.567 | -- |
| Problems with hearing (hearing loss, ringing in ears) | 31(16%) | 14(18%) | 17(15%) | 0.610 | -- |
Rounding to the nearest whole percentage; Regression model was adjusted for the following covariates: excessive dry mouth, fatigue, vision, nerve problems, weakness in arms or legs, pain in muscles, post-exertional malaise, shortness of breath, excessive thirst, joint pain, any pain in body, swelling, chronic cough, bladder problems, problems thinking or concentrating, and sleep problems
EMR sub-analysis
EMR examination in a subsample of 100 participants revealed that 41% of participants had a code related to Long COVID in their medical chart. Long COVID codes included 4361655018 (Snomed ID- Chronic post-COVID-19 syndrome), B94.8 (ICD-10 Sequelae of other specified infectious and parasitic diseases), J45.998 (ICD-10 Other asthma), R05.3 (ICD-10 Chronic cough), R05.8 (ICD-10 Other specified cough), R06.09 (ICD-10 other forms of dyspnea), R43.0 (ICD-10 Other disturbances of smell and taste loss of smell), R43.8 (ICD-10 Other disturbances of smell and taste), R51.9 (ICD-10 Headache, unspecified), and U09.9 (ICD-10 Post COVID-19 condition, unspecified). Among participants that reported high symptom interference (n = 39), 64% (n = 25) had a code related to Long COVID. Among participants that reported low symptom interference (n = 61), 74% (n = 45) had no code related to Long COVID. Codes for fatigue were reported for 42% of participants. Fatigue codes included 139690015 (Snomed ID- fatigue), 780.79 (ICD-9 Other malaise and fatigue), and R53.83 (ICD-10 other fatigue). Codes for post-exertional malaise or chronic fatigue were reported for 15% of participants. Among participants that self-reported having the symptom of post-exertional malaise, 33% had a code in their EMR related to post-exertional malaise or chronic fatigue. Malaise and chronic fatigue codes included 780.79 (ICD-9 Other malaise and fatigue), R53.81 (ICD-10 Other malaise), R53.82 (ICD-10 Chronic fatigue, unspecified), R53.83 (ICD-10 other fatigue [with a provider designation of activity intolerance]), and R68.89 (ICD-10 Other general symptoms and signs [with a provider designation of exercise intolerance]).
Discussion
We aimed to assess the associations between Long COVID symptom interference with life activities, presence of a U09.9 diagnostic code in the EMR, demographics, and COVID-related clinical characteristics. We found that 31% of participants had a U09.9 diagnosis in their EMR and 41% had a U09.9 or some other type of Long COVID code in their EMR. Among participants reporting high Long COVID symptom interference with life activities, only 44% had a U09.9 code in their EMR and 64% had a U09.9 code or some other code related to Long COVID. Only 33% of participants that reported post-exertional malaise had a code related to chronic fatigue or malaise in their chart. The mismatch of self-reported measures and EMR coding in this study provides preliminary evidence for the need for participant-reported outcomes in studies using EMR diagnostic codes, reinforcing the importance of patient-reported measures in this population. Factors such as patients lack of disclosure of Long COVID symptoms with their medical provider, the provider’s need to more consistently document Long COVID codes in the EMR, diagnostic uncertainty, insurance coding pressures, provider awareness of Long COVID codes, and fragmentation of documentation across intra- and inter-healthcare systems could all impact the patient-report of Long COVID symptoms to documentation discrepancy.
Out of all the demographic and clinical factors, U09.9 diagnosis (OR = 2.40, 95% CI [1.15–5.02] p=.020), female sex (OR = 2.31, 95% CI [1.07–4.99], p=.034), and worse physical health (OR = 0.905, 95% CI [0.850–0.964], p=.002) remained associated with high symptom interference with life activities after controlling for all significant variables. Similarly, when controlling for all other statistically significant Long COVID symptoms, dry mouth (OR = 10.9, 95% CI [1.6–74.0], p=.015) and fatigue (OR = 3.91, 95% CI [3.9–11.9], p=.017) were still associated with high symptom interference with life activities. These findings indicate the groups that may be more likely to experience Long COVID symptom interference with life activities. Within the high Long COVID symptom interference group, we did not find major demographic or clinical differences between people with and without Long COVID diagnoses. Although we found discrepancies between self-reported participant measures and EMR coding, we did not find evidence of substantial demographic biases in diagnosis.
The study has several strengths, namely, that self-report of function with Long COVID are compared to EMR diagnostic coding. Additionally, an examination of the EMR for Long COVID codes other than U09.9 provides a comprehensive view of coding for this condition. Post-exertional malaise coding, which does not have an ICD-10 code, was examined and can provide valuable insights for the standardization of post-exertional malaise reporting.
This study has several limitations. The medical record data and codes evaluated were restricted to one institution. People may have sought emergency care for an acute SARS-CoV-2 infection at one institution yet received primary or internal medicine care at other institutions. Therefore, the prevalence of Long COVID reported from a single EMR system may be an underestimation. The data are cross sectional which restricts any causal inferences for the associations. Additionally, the regression analyses were data-driven and exploratory with a modest sample size and may not adequately capture the complexity of real-world phenomena in this population. Self-reported measures of COVID-related events may be affected by recall bias. The validity and reliability of the symptom interference with life activities variable has not been well established; however, functional interference with daily life activities is a salient and patient-centered outcome that is essential for capturing the lived experience of Long COVID. Although participants were specifically asked to rate symptoms that appeared with the acute SARS-CoV-2 infection or after to minimize conflation with pre-existing conditions, the rating of symptoms related to pre-existing or new non-Long COVID conditions is possible. Broader generalizability of the observations are limited by the sample size and the small proportion of participants included (206 of 4,779 in the parent registry) for this Long COVID study. This was driven by a combination of budget limitations in the number of follow-up visits and willingness to participate. Of note, the prevalence of Black/African Americans in our sample was higher than the national average and other studies using Long COVID national samples [19]. Additionally, the study was carried out at an academic medical center which may have EMR system resource advantages that may not be available in non-academic and community health care settings. Our sample was also drawn from a large proportion of hospitalized patients and maybe sicker than in Long COVID studies with outpatient or community-based populations.
Future research should test these associations longitudinally, assessing patient-provider communications that occur around Long COVID, and assessing the impact of discrepancies between self-report and EMR documentation on treatment outcomes. Improvements in infection-associated chronic illness documentation in EMR’s are necessary to advance the quality of clinical care and research in these areas. This is particularly important for infection-associated chronic illnesses, since they tend to be under-reported, under-funded for research, and met with skepticism [1]. Additionally, research has found people living with Long COVID face many frustrating barriers in healthcare communication [20]. Increased training efforts and quality improvement initiatives for patient-provider communications and documentation around these conditions are essential.
In conclusion, our findings revealed that only 64% of participants with high Long COVID symptom interference had any EMR code related to Long COVID. The mismatch of self-reported measures and EMR coding reinforces the need for participant-reported outcomes in studies using EMR diagnostic codes. However, we did not find evidence of demographic biases in diagnosis among participants with high symptom interference.
Acknowledgements
We would like to acknowledge the patients, staff, and family members who supported this research while managing the perils of the COVID pandemic. Their time and efforts made this publication possible.
Abbreviations
- BMI
Body mass index
- COVID
Severe acute respiratory syndrome coronavirus 2
- EMR
Electronic medical record
- Enterprise
COVID-19 Enterprise Biorepository and Data Registry
- ICD
International Classification of Diseases
- PROMIS
Patient-Reported Outcomes Measurement Information System
Author contributions
All authors made substantial contributions to the work, assisted with drafting and revisions, approved the version to be published, and have agreed to be accountable for all aspects of the work. JKV contributed to the study concept and design, analysis and interpretation of data, preparation/revision of manuscript. EBL contributed to the study concept and design, acquisition of study subjects and/or data, analysis and interpretation of data, preparation/revision of manuscript. CRH contributed to the study concept and design, analysis and interpretation of data, preparation/revision of manuscript. APM contributed to the study concept and design, analysis and interpretation of data, preparation/revision of manuscript. RJ contributed to the study concept and design, analysis and interpretation of data, preparation/revision of manuscript. FEL contributed to the study concept and design, funding, acquisition of study subjects and/or data, analysis and interpretation of data, preparation/revision of manuscript. NE contributed to the study concept and design, acquisition of study subjects and/or data, analysis and interpretation of data, preparation/revision of manuscript.
Funding
This publication was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under Award Number T32HD071866 (JV) and the National Institute of Allergy and Infectious Diseases under Award Number U19-AI42737-04S1 (FL). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The COVID-19 Enterprise Biorepository and Data Registry study received approval from the University of Alabama at Birmingham’s Institutional Review Board (IRB-300005127) and adhered to ethical research practices as set forth by the Declaration of Helsinki. Verbal informed consent was obtained from all participants in the study.
Consent for publication
Not applicable.
Competing interests
JKV has no conflicts of interest to declare that are relevant to the content of this article. EBL reports research funding (to the institution) from Amgen Inc and personal fees from Pfizer for consulting and University of Pittsburgh for serving on a data and safety monitoring board unrelated to the content of this article. CRH has no conflicts of interest to declare that are relevant to the content of this article. APM has no conflicts of interest to declare that are relevant to the content of this article. RJ has no conflicts of interest to declare that are relevant to the content of this article. FEL has no conflicts of interest to declare that are relevant to the content of this article. NE has no conflicts of interest to declare that are relevant to the content of this article.
Footnotes
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.