Abstract
Objective
The effects of COVID-19, especially long-COVID, on the psychological health is incompletely understood. We aimed to evaluate the mid-term associations of the long-COVID symptoms and affective factors in a cohort of non-hospitalized patients.
Method
A total of 166 patients were enrolled in this study, including 119 sedentary/non-athlete and 47 athlete subjects at the Post-COVID Outpatient Clinic of Semmelweis University. Clinical data regarding acute and long-term symptoms were obtained and detailed laboratory testing was carried out. Demographic data and psychological tests were collected.
Results
We found a positive association between the level of depressive symptoms and anxiety and long-COVID symptom count, while life satisfaction and social support correlated negatively with the long-COVID symptom count. Higher haemoglobin levels and lower LDL-cholesterol were also shown to be moderating factors. A regression model showed that symptoms during acute infection, depression, age, and life satisfaction are predictors of the long-COVID symptom count. The presence of pre-existing affective or anxiety problems was also associated with higher reported long-COVID symptom count. Furthermore, we found significant association between pre-existing mental health problems and the investigated psychological constructs.
Conclusion
It appears that long COVID-19 is associated with acute symptoms and mental factors. Depression and anxiety have been shown to have a negative effect on symptom perception, and also contribute to a higher number of symptoms in a non-hospitalized sample. Our study suggests bi-directional interconnection between clinical and psychological factors.
Introduction
In 2019 from Wuhan, China, a novel infectious disease called COVID-19 caused by the SARS-COV-2 virus has spread rapidly. Over 273,000 000 cases had been confirmed by the end of 2021 [1]. To date the primary focus of psychological research was on the descriptive assessment of mental disorders, post traumatic stress disorder (PTSD), anxiety and depression affecting COVID-19 patient. In addition, the studies investigated mostly hospitalized patients, particularly those in the Intensive Care Unit (ICU), conversely only a small margin of studies concentrated on non-hospitalized COVID-19 patients [2].
Depression, anxiety and PTSD are the most common mental problems the patients face after COVID-19 [3–5]. According to Poyraz et al. [3] 25.4% of those formerly infected suffer from moderate to severe PTSD symptoms. Grey et al. [6] found that those people who perceived high social support reported lower levels of depression, irritability, and loneliness than people with a lower level of perceived social support.
Perception makes us able to experience ourselves, our existence, our environment and others, but unfortunately, it is often biased. Illness perception is a cognitive appraisal and a unique interpretation of the patient's illness and its possible outcomes [7]. This mental construct includes one's attitude concerning the illness, personal beliefs and fears and through this process creates a cognitive framework [8]. The representation develops as soon as the patient is informed of the diagnosis and it evokes emotions in ourselves. A variety of factors play a central role in this dynamic process, such as past knowledge of the disease, prior experiences, personality traits (e.g., neuroticism), and physical health [9, 10]. Symptom perception hypothesis states, that people with negative affectivity (such as depression or anxiety) tend to report more symptoms and experience them more severe despite objective medical evidence to the contrary [9]. It has also been well studied that negative affectivity and neuroticism may lead to an inflated perception of symptoms, in which people with illnesses build a peculiar pattern or interpretation framework based on their understanding of the disease, as well as their prior experiences [9]. It happens the same way in case of a virus-infection, such as COVID-19.
Specific biological markers have also been proven to be positively correlated with mental illness, mainly affective disorders. Biological markers such as C Reactive Protein (CRP), LDL cholesterol (LDL-C) and haemoglobin (HGB) were investigated in connection to affective disorders. Higher levels of CRP were associated with the severity of overall depression [11]. LDL-C level was also found to be higher in people with major depressive episodes [12]. Hemoglobin and ferritin levels were also investigated in certain populations. Low level of these biological markers was associated with depressive symptoms [13, 14]. These associations are important as recent studies found that the altered levels of these biological markers might be risk factors of COVID-19, or they might affect or predict the severity of the infection [15–18].
Different studies distinguish between long-COVID based on the involved time interval. NICE [19] guidelines define “ongoing symptomatic COVID-19” within 4–12 weeks after the commencement of the infection. Post-COVID syndrome is a set of symptoms that persist or develop after 12 weeks of COVID-19. In clinical terminology „long-COVID” includes both of these forms. Several studies reviewed our knowledge about long-COVID and they have found that the most frequent physiological symptoms are the following: fatigue, dyspnea, cardiovascular dysfunctions, olfactory and gustatory dysfunction, weakness, joint pain, myalgia. Regardless of whether the patient is hospitalized or not, these conditions can develop or persist.
The presence of comorbidities, higher age and male sex are known risk factors in terms of mortality in COVID-19: in a meta-analysis of 20 studies aged>50 years, concomitant cardiovascular, cerebrovascular, hypertension and diabetes carried significantly higher risk ratios [20]. Not only mortality, but the risk to develop long-COVID symptoms increases with age, concomitant asthma, lung disease, heart disease and even the number of symptoms in the first week play a significant role [20]. Identifying those non-hospitalized individuals who are at risk of developing mental issues after COVID-19 is of great importance.
Importantly, the vast majority of SARS-CoV-2 infected patients do not present with severe symptoms mandating hospitalization. Therefore, the concerning data is sparse as the largest affected groups have not been investigated satisfactorily so far. Consequently, in this study, our aim was to evaluate the mid-term associations of the long-COVID and affective factors in a non-hospitalized subjects. Besides the associations between the long-COVID and affective factors, we intended to find constructs that might moderate the presumed positive relationship between the worsened mental health status and the long-COVID symptoms.
Materials and methods
Subjects presenting between March 2021 and December 2021 at the Post-COVID Outpatient Clinic of the Heart and Vascular Center of Semmelweis University were invited to participate in the study. Exclusion criteria were insufficient medical records and/or psychological testing, >18 years of age. The study population included athletes and non-athlete individuals. The sports cardiology team at the SE Heart and Vascular Center regularly performs cardiovascular screening among highly trained athletes playing at national, international and olympic levels, including return-to-play examinations after COVID-19. The diagnosis of COVID-19 was confirmed by PCR, rapid antigen test or antibody level measurement in all subjects. The testing protocol comprised the same modalities for both athlete and non-athlete groups: patient history, physical examination, 12-lead ECG, and blood tests. Athletes were invited for an optional control examination after returning to play or presented due to long-term symptoms. The non-athlete patients visited our Post-COVID Outpatient Clinic on their initiative due to their anxiety or prolonged symptoms. Hungarian Medical Research Council approved the study (approval number: IV/9697-1/2020/EKU) and participants signed the informed consent form.
Measures
Baseline characteristics (sex, age) and medical history were retrieved from institutional medical records (Medsol). All psychological and more granular decription of demographic data (relationship status, education, monthly income, and residence) were ascertained using targeted questionnaires.
Long-COVID symptoms
Based on the theory of the illness and symptom representation models, our main focus was to investigate the nature of symptom count, thus our dependent variable was the self-reported symptom count. In our study the accepted long-COVID symptoms corresponded to the most common long-COVID symptoms according to the international and Hungarian experiences [2, 21, 22]: fever, weakness, anosmia and ageusia, chest pain, upper respiratory symptoms, headache, gastrointestinal symptoms, dyspnoe and muscle or joint pain. In order to analyze the symptoms cumulative nature and significance, we created a score by adding up the number of symptoms reported by the patients regardless of their severity.
Measures of psychological health
History of mental health problems
The history of mental difficulties (depression, anxiety, OCD, panic, phobia and others) were assessed by self-reported questionnaire.
Beck-Depression Inventory (BDI)
We used the validated Hungarian version of the shortened Beck-Depression Inventory [23]. The 9-item self-report instrument describes clinical manifestations of depressive symptoms. Patients are asked to indicate the frequency of their symptoms on a numerical scale ranging from 1 to 4. The test defines the severity as follows: 0-9 points normal range, 10-18 as mild depressive state, 19-25 as moderate depression, and 25 or over as severe depression (Cronbach-alpha = 0.835).
Beck Anxiety Inventory (BAI)
The Hungarian validation of the Beck Anxiety Inventory was used to measure state anxiety [24]. This 21-item tool assesses the clinical symptoms of anxiety, including cognitive, physical, and psychological factors (Cronbach alpha: 0.949).
Life satisfaction (LS)
A 10-point Likert scale was used to measure life satisfaction (completely unsatisfied to completely satisfied). The 1-item tests proved to be reliable and valid especially in well-being studies [25].
Multidimensional Scale of Perceived Social Support (MSPSS)
The Hungarian Validation of the Multidimensional Scale of Perceived Social Support questionnaire was used to assess social support for the participants [26]. The higher the score achieved on this 12 item-tool, the greater the significance of social support perceived (Cronbach alpha: 0.923).
Post traumatic stress disorder test (PTSD)
The number of items in the original questionnaire had to be reduced in order to accommodate the study setting to avoid the exhaustion of our patients. To cover possible symptoms of PTSD, we selected 7 items. A summarized score was calculated. This tool cannot be used to diagnose PTSD or its severity, but it does identify the possible presence of it (Cronbach-alpha 0.807).
Statistical analyses
Statistical analyses were performed using the SPSS 25.0 software.To analyze and describe the associations between our variables we performed Pearson and Spearman correlations, Welch t-test, Mann-Whitney U test, Independent Samples t Test, and Chi-squared test. We intended to depict the predictability of the number of long-COVID symptoms. For this purpose we used Poisson regression as the most fitting method to our dependent variable. Based on biological plausibility and literature review we considered to following variables in our models that age, gender, family status, being an athlete or non-athlete, number of symptoms during the acute infection, affective factors (depression, anxiety, mental disorder, PTSD, social support, life satisfaction, COVID related traumatic event), and clinical factors (ferritin, CRP, haemoglobin, HDL, LDL-C, BMI index) effect on the number of long-COVID symptoms. P value of <0.05 was considered statistically significant in the performed tests.
Results
Demographic and psychological baseline data
A total of 166 patients were enrolled in this study, including 119 sedentary/non-athletes and 47 athletes. Overall, 72 men and 94 women were enrolled. The mean age was 39 ± 14 years and the mean follow-up time was 25 ± 18 weeks after the diagnosis. Most of them lived in a relationship (N = 111), and part of the sample were regarded as single (widow, divorced, or not married so far (N = 54), while one person refused to answer this question. The majority of individuals in the sample indicated the presence of 2.5 ± 1.98 long-COVID symptoms. For further descriptive data and characteristics of the sample see Table 1.
Demographic, affective and clinical characteristic of the sample and comparison of sample's subjects
| Demographic | ||||
| N | % | Skewness | Kurtosis | |
| Sex | ||||
| Men | 72 | 43.4 | ||
| Women | 94 | 56.6 | ||
| Athlete | ||||
| Athletes | 119 | 28.3 | ||
| Non-athletes | 47 | 71.7 | ||
| Mean | SD | |||
| Age | 39.06 | 14.49 | 0.74 | −0.15 |
| Marital status | ||||
| N | % | |||
| Married/In relationship | 111 | 66.9 | ||
| Divorced/Single/Widowed | 54 | 32.5 | ||
| Affective | ||||
| Mean | SD | |||
| Depression | 12.14 | 4.09 | 0.62 | 1.16 |
| Anxiety | 9.79 | 11.03 | 1.43 | 1.88 |
| Life satisfaction | 7.73 | 1.79 | −0.87 | 0.58 |
| Social support | 55.09 | 7.27 | −2.07 | 5.20 |
| PTSD | 2.85 | 3.55 | 1.45 | 2.16 |
| Clinical | ||||
| Mean | SD | |||
| Long-COVID symptom count | 2.5 | 1.98 | 0.33 | −0.76 |
| CRP (mg L−1) | 2.41 | 3.06 | 2.96 | 10.97 |
| HGB (g L−1) | 143.6 | 13.37 | −0.43 | 0.29 |
| LDL-C (mmol L−1) | 3.14 | 0.80 | 0.19 | −0.56 |
| N | % | |||
| Hypertension | 33 | 19.9 | ||
| COPD/asthma | 11 | 6.6 | ||
| Abbreviations: PTSD – post-traumatic stress disorder, CRP - C Reactive Protein, LDL-C - LDL cholesterol, HGB – haemoglobin | ||||
| Comparison of sample's subjects | ||||||
| Women (M ± SD) | Men (M ± SD) | Effect size (d/r; v/c) | Athlete (M ± SD) | Non-Athlete (M ± SD) | Effect size (d/r; v/c) | |
| Age | 42.2 ± 14.4 | 34.8 ± 13.5 | d=0.52***/r=0.28*** | 26.47 ± 6.1 | 44.04 ± 13.8 | d=1.44***/r=0.60*** |
| Depression | 12.7 ± 4.4 | 11.3 ± 3.4 | d=0.34*/r=0.37*** | 9.89 ± 1.6 | 13.03 ± 4.4 | d=0.81***/r=0.45*** |
| Anxiety | 12.6 ± 11.6 | 5.9 ± 8.8 | d=0.63***/r=0.24*** | 2.4 ± 6.6 | 12.7 ± 11.08 | d=1.02***/r=0.59*** |
| Life satisfaction | 7.3 ± 1.8 | 8.2 ± 1.5 | d=0.52***/r=0.25*** | 8.6 ± 1.9 | 7.37 ± 1.06 | d=0.72***/r=0.30*** |
| Social support | 54.3 ± 7.2 | 56.07 ± 7.2 | d=0.23/r=0.13 | 58.9 ± 2.2 | 53.5 ± 8.02 | d=0.78***/r=0.38*** |
| PTSD | 3.9 ± 4 | 1.5 ± 2.2 | d=0.71***/r=0.34*** | 0.7 ± 1.5 | 3.7 ± 3.7 | d=0.92***/r=0.69*** |
| CRP | 2.9 ± 3.2 | 1.7 ± 2.6 | d=0.38*/r=0.25** | 1.4 ± 1.4 | 2.8 ± 3.4 |
d=0.44***
r=0.19** |
| HGB | 135.1 ± 10.2 | 154.8 ± 7.4 |
d=2.16***
r=0.72*** |
149.5 ± 11.5 | 141.2 ± 13.3 | d=0.63***/r=0.27*** |
| LDL-C | 3.1 ± 0.7 | 3.1 ± 0.8 | d=0.01/r=0.02 | 2.7 ± 0.7 | 3.29 ± 0.7 |
d=0.67***
r=0.28*** |
| Long-COVID symptom count | 3.2 ± 1.9 | 1.6 ± 1.6 | d=0.86***/r=0.39*** | 0.2 ± 0.6 | 3.2 ± 1.6 | d=2.01***/r=0.69*** |
| Hypertension | 21 | 12 | v=0.07/c=0.07 | 2 | 31 | v=0.24/c=0.23** |
| No Hypertension | 73 | 60 | 45 | 88 | ||
| COPD/Asthma | 8 | 3 | v=0.87/c=0.86 | 0 | 11 | v=0.16/c=1.6* |
| No COPD/Asthma | 86 | 69 | 47 | 108 | ||
*P < 0.05, **P < 0.01, ***P < 0.001, Chi-squared test Cramer's V (v) and Contingency Coefficients (c) and independent samples t-test (d) and Mann Whitney U test (r).
Abbreviations: PTSD – post-traumatic stress disorder, CRP – C Reactive Protein (mg L−1), LDL-C – LDL cholesterol (mmol L−1), HGB – haemoglobin (g L−1).
The overall prevalence of depressive symptoms was high; 73.5% of patients reported symptoms consistent with at least mild depressive state. Anxiety was less common, 12% of patients reported moderate or more severe symptoms of anxiety. More than half of the subjects indicated symptoms suggesting PTSD (54.2%). The patients were mainly satisfied with their lives (M = 7.73 ± 1.792) and they mostly had someone in their lives to rely on based on the social support scores (M = 55.09 ± 7.279). See details in Table 1.
Pre-existing mental health problems
Pre-existing affective or anxiety problems were associated with a higher reported long-COVID symptom count (Table 2). Furthermore, we found significant correlation between pre-existing mental health problems and the investigated psychological constructs. Individuals with these conditions reported higher level of anxiety, depression and PTSD than patients without any formerly existing mood or anxiety problems at the time of examination.
Associations between clinical factors and affective psychological constructs
| Depression | PTSD | Anxiety | Life satisfaction | Social support | |
| r/rho | r/rho | r/rho | r/rho | r/rho | |
| CRP (mg L−1) | −0.03/−0.01 | 0.12/0.12 | 0.08/0.13 | −0.02/−0.04 | −0.009/0.02 |
| HGB (g L−1) | −0.15*/−0.21** | −0.29**/−0.31** | −0.21**/−0.27** | 0.18*/0.23** | 0.19*/0.21** |
| LDL-C (mmol L−1) | 0.12/0.16* | 0.14/0.15 | 0.11/0.17* | −0.11/−0.09 | −0.08/−0.14 |
|
|
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| d/r | d/r | d/r | d/r | d/r | |
|
|
|||||
| Hypertension | 0.24/0.17* | −0.46/0.18* | 0.39*/0.25*** | 0.41*/0.19* | 0.54**/0.27*** |
| COPD/asthma | 0.78*/0.17* | 0.77/0.08 | 0.87**/0.22** | 1.06/0.81* | 0.69/0.08 |
| Previous mental health problems | 0.87***/0.37*** | 0.87***/0.33*** | 1.17***/0.40*** | ||
*P < 0.05, **P < 0.01, ***P < 0.001, Pearson (r) Spearman (rho) and independent samples t-test (d) and Mann Whitney U test (r).
Abbreviations: PTSD – post-traumatic stress disorder, CRP – C Reactive Protein, LDL-C – LDL cholesterol, HGB – haemoglobin.
Number of long-COVID symptoms
The number of long-COVID symptoms were reported on a scale between 0 and 9 (n=156). The minimum was 0 while the maximum was 8 reported symptoms. Neither the skewness nor kurtosis exceed 1, therefore the normality was not violated. It was observed that the symptom count of the acute infection phase was positively correlated with the number of symptoms in long-COVID. The athletes reported fewer symptoms than the non-athletes. Regarding sex differences, women indicated more symptoms than men. We studied the associations of long-COVID symptom count with affective (mental) and clinical (blood test, comorbidities) perspectives.
Associations between the long-COVID symptom count and affective factors
With depression, we found a moderate, positive association; a higher level of depression attends with more long-COVID symptoms. The result was similar in respect of anxiety. A moderate, positive association between PTSD and the number of long-COVID symptoms was detected. Life satisfaction and social support were found to be moderating factors. Based on our findings, the higher the level of life satisfaction and the percieved social support, the lower the number of symptoms (mild and moderate negative correlation).
Associations between the number of the long-COVID symptoms and clinical factors
Our research revealed two significant associations between the results of the blood tests and the number of long-COVID symptoms. Higher level of HGB showed negative and mild relation to the number of symptoms, while the level of LDL-C a mild, positive association. Patients with hypertension indicated more symptoms than people with normal blood pressure. Patients with COPD or asthma reported more long-COVID symptoms than individuals without this condition (Table 3).
Associations of long-Covid with affective and clinical factors
| Effect size (r/rho; d/r) | |
| Demographic | |
| Age | 0.34**/0.44 |
| Being an athlete | 2.01***/0.69*** |
| Sex | 0.86***/0.39*** |
| Affective | |
| Depression | 0.57**/0.64** |
| Anxiety | 0.58**/0.66** |
| Life satisfaction | −0.49**/−0.39** |
| Social support | −0.35**/−0.39** |
| PTSD | 0.47**/0.49** |
| Clinical | |
| CRP (mg L−1) | −0.02/0.04 |
| HGB (g L−1) | −0.28**/−0.30** |
| LDL-C (mmol L−1) | 0.20*/0.21** |
| No. of symptoms in acute phase | 0.57**/0.59** |
| Hypertension | 0.44*/0.19* |
| COPD/asthma | 0.77*/0.17* |
*P < 0.05, **P < 0.01, ***P < 0.001, Pearson (r) Spearman (rho) and independent samples t-test (d) and Mann Whitney U test (r).
Abbreviations: PTSD – post-traumatic stress disorder, CRP – C Reactive Protein, LDL-C – LDL cholesterol, HGB – haemoglobin.
Clinical factors and affective psychological constructs
Our research suggests that higher HGB levels are associated with higher life satisfaction and perceived social support, while lower levels are associated with higher depression and anxiety values. LDL-C shows a positive connection with affective factors such as anxiety, and depression. We found significant relationships between existing COPD/asthma, hypertension and affective factors (depression, anxiety, social support, life satisfaction, PTSD). See the detailed statistical data in Table 3.
Possible predictors of the number of the long-COVID symptoms
In this study we sought to identify those variables that might predict or contribute to the number of long-COVID symptoms. First, based on previous research results and scientific considerations, we chose a series of variables that might affect the number of experienced symptoms (age, gender, being an active sports player, number of acute infection, affective psychological variables, social support, and clinical variables). In terms of our Poisson regression, 4 variables were included in our model as possible predictors of the number of long-COVID symptoms (Table 4). According to the exponentiation of the B coefficient, number of symptoms during acute infection, life satisfaction, depression, and age found to be the strongest factors that may play a role in mid and long-term long-COVID symptom development. Based on the fit indicators, the model fits well, the values of the indicator are close to 1. Interpreting the exponentiation of the B coefficient, our results point out that if the symptom count of the acute infection increases by one, then it causes a 17.2% increase in the number of long-COVID symptoms (Table 5).
Correlational matrix
| Life satisfaction | Social support | PTSD | No. of long-COVID sympts. | No. of sympts. in acute phase | CRP | HGB | LDL-C | Depression | Anxiety | |||||||||||
| r | rho | r | rho | r | rho | r | rho | r | rho | r | rho | r | rho | r | rho | r | rho | r | rho | |
| Age | −0.20 ** | −0.21 ** | −0.23 ** | −0.26 ** | 0.32 ** | 0.39 ** | 0.34 ** | 0.44 ** | 0.20 ** | 0.26 ** | 0.18 * | 0.25 ** | −0.18 * | −0.20 ** | 0.35 ** | 0.42 ** | 0.11 | 0.27 ** | 0.27 ** | 0.41 ** |
| Life satisfaction | 0.57 ** | 0.57 ** | −0.38 ** | −0.35 ** | −0.49 ** | −0.49 ** | −0.25 ** | −0.25 ** | −0.02 | −0.04 | 0.18 * | 0.23 ** | −0.11 | −0.09 | −0.60 ** | −0.66 ** | −0.43 ** | −0.44 ** | ||
| Social support | −0.34 ** | −0.37 ** | −0.35 ** | −0.39 ** | −0.22 ** | −0.31 ** | −0.009 | 0.02 | 0.19 * | 0.21 ** | −0.08 | −0.14 | −0.49 ** | −0.56 ** | −0.41 ** | −0.45 ** | ||||
| PTSD | 0.47 ** | 0.49 ** | 0.39 ** | 0.41 ** | 0.12 | 0.12 | −0.29 ** | −0.31 ** | 0.14 | 0.15 | 0.59 ** | 0.57 ** | 0.71 ** | 0.65 ** | ||||||
| No. of long-COVID sympts. | 0.57 ** | 0.59 ** | −0.02 | 0.04 | −0.28 ** | −0.30 ** | 0.20 * | 0.21 ** | 0.57 ** | 0.64 ** | 0.58 ** | 0.66 ** | ||||||||
| No. of sympts. in acute phase | 0.08 | 0.07 | −0.19 * | −0.22 ** | 0.18 * | 0.18 * | 0.36 ** | 0.43 ** | 0.48 ** | 0.56 ** | ||||||||||
| CRP | −0.13 | −0.16 * | 0.001 | 0.17 * | −0.03 | −0.01 | 0.08 | 0.13 | ||||||||||||
| HGB | 0.09 | 0.09 | −0.15 * | −0.21 ** | −0.21 ** | −0.27 ** | ||||||||||||||
| LDL-C | 0.12 | 0.16 * | 0.11 | 0.17 * | ||||||||||||||||
| Depression | 0.57 ** | 0.61 ** | ||||||||||||||||||
| Anxiety | ||||||||||||||||||||
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Abbreviations: PTSD – post-traumatic stress disorder, CRP – C Reactive Protein (mg L−1), LDL-C – LDL cholesterol (mmol L−1), HGB – haemoglobin (g L−1).
Poisson regression models of the number of long-COVID symptoms
| Parameters | B | Exp(B) | Wald Chi-Square | SIG |
| Age | 0.01 | 1.01 | 11.29 | 0.001 |
| Life satisfaction | −0.06 | 0.93 | 4.06 | 0.044 |
| Number of sympts. during acute infect. | 0.15 | 1.17 | 34.45 | 0.000 |
| Depression | 0.04 | 1.04 | 10.47 | 0.001 |
| Omnibus Test | ||
| Likelihood-Ratio Chi-Square | df | SIG |
| 129.88 | 4 | 0.000 |
| Goodness of Fit | |||
| Value | df | Value/df | |
| Deviance | 170.79 | 145 | 1.17 |
| Scaled Deviance | 170.79 | 145 | |
| Pearson Chi-Square | 141.67 | 145 | 0.97 |
| Scaled Pearson Chi-Square | 141.67 | 145 | |
Discussion
Our study's main strength is that it focuses on a scarcely studied group and besides this population's prime affective characteristics, it points out more complex results by involving clinical factors. This approach allows a multidirectional understanding of the subject. The significance of our results is shown by the fact that patients with mild symptoms without hospitalisation account for the majority of COVID patients. Our aim was to examine the affective and physiological relationships among symptoms in non-hospitalized long-COVID patients.
History of mental health problems like depression or anxiety are associated with the severity of mental health symptoms. Taquet et al. [5] found that psychiatric conditions have a bidirectional association with diagnosis of COVID-19. Current mental health conditions might increase the probability of the COVID-19 infection independently of the physical health risk factors. Furthermore, the diagnosis of COVID-19 is associated with the emergence of a first psychiatric diagnosis [5]. Diagnosis of depression or anxiety and female sex are risk factors concerning COVID-19 related fatigue [27]. Indeed, women experience more severe and persistent mental health symptoms after COVID-19 infection and they are at higher risk of long-COVID than men [28]. According to the illness and symptom representation models [10, 29] people diagnosed with an illness build a peculiar pattern or interpretation framework of their diseases based on their knowledge, and their previous experience. Final evaluation before constructing this representation model is made based on a cognitive process which in the state of depression and anxiety might be strongly biased. The reason for this is among other, that hypervigilance is a characteristic of the elevated anxiety [9]. As a consequence of hypervigilance anxious people strongly focus on their somatic signs. Additionally, individuals with depressive affect are more self-focused and tend to ruminate which enhance the attention of the minor somatic changes [9]. According to the study of Howren et al. [9] elevated anxiety predisposes the report of more concurrent physical symptoms while depressive affect is related to the account of more physical symptom frequency from previous weeks. In our study, we found that those individuals who reported pre-existing mental disorders had more severe concurrent depression and anxiety than people without these conditions and they also reported more long-COVID symptoms.
We found a positive correlation between the number of long-COVID symptoms and the number of symptoms indicating depression, anxiety and PTSD, this means that individuals with higher levels of depressive complaints and anxiety are experiencing more long-COVID related symptoms too. On the other hand social support and life satisfaction are found to be protective factors against long-COVID symptom burden. Regarding sex differences, women indicated more symptoms than men.
These results are consistent with previous research on depression, PTSD, and anxiety in COVID-19 infection, however, to the best of our knowledge, no data were available so far regarding the correlations of the number of the long-COVID symptoms and affective psychological constructs on non-hospitalized patients. The COVID-19 patients are at higher risk of anxiety and depression than the general population [30, 31]. The prevalence of depression in this population is approximately 42% [30, 31]. Regarding anxiety, the prevalence is similarly high, approximately 40% [30, 31]. The high level of anxiety and depression (37.5%) was also recorded in a study involving hospitalized post-COVID patients from 30 to 180 days after the diagnosis.
In our study, we found that patients with more long-COVID symptoms had lower HGB levels, and higher LDL-C levels compared to patients with fewer symptoms. These associations and mechanisms are not clear yet but concerning the HGB, changes in iron metabolism or the reduced response of cerebral blood vessels may contribute to mood disorders [32, 33]. Van Reedt Dortland found that persistent dyslipidemia and atypical depression are associated, which might be explained by the LDL-C increasing effect of the symptoms of depression, such as increased appetite and fatigue and decreased physical activity [34]. There is evidence that certain pre-existing medical conditions like COPD or asthma are risk factors of long-COVID [35]. In our study patients with hypertension and COPD reported more symptoms than people with no pre-existing medical condition.
Former studies have already pointed out the associations of depression and anxiety with blood test parameters; we have also checked them to investigate the multidirectional nature of this topic [11, 12]. Our findings strengthen the earlier results as follows: level of HGB is in a mild negative association with depression and anxiety, but positive correlation with life satisfaction and perceived social support. A positive relationship exists between LDL-C and depression, PTSD and anxiety. We found a positive correlation between COPD/asthma and hypertension and affective factors. People with these conditions reported a higher level of anxiety, while depression is positively correlated with COPD/asthma.
Galal et al. [36] investigated former COVID-19 patients to evaluate the frequency and determinants of post-COVID 19 symptoms. They found that the acute phase symptom severity and any comorbid disease, including worsened mental health increase the risk of the manifestation of post-COVID symptoms. Another study also confirmed that female sex, depression, and anxiety symptoms increase the risk of the onset of persistent post-COVID symptoms [37]. Our findings partially confirm these results, as in our regression model acute symptomatology and depression might predict the number of long-covid symptoms. In this case, the number of symptoms during acute infection, depression, age, and life satisfaction composed of our model. Out of our variables these seems to be the strongest factors that contribute to having several symptoms experienced in the mid and long-term but of course, not to the emergence of the condition itself. Figure 1 gives insight into the multidimensional factors we found related to the long-COVID symptom count.
Multifactorial nature of the development of the number of long-COVID symptoms
Citation: Physiology International 109, 2; 10.1556/2060.2022.00030
Multifactorial nature of the development of the number of long-COVID symptoms
Citation: Physiology International 109, 2; 10.1556/2060.2022.00030
Multifactorial nature of the development of the number of long-COVID symptoms
Citation: Physiology International 109, 2; 10.1556/2060.2022.00030
Conclusion
Our study suggests bi-directional interconnection between clinical and psychological factors. Additional contributing factors are presented in a regression model, in which the number of symptoms in the acute phase, age, depression and life satisfaction also contribute to the amount of long-COVID symptoms. Moreover, certain laboratory parameters may have an additional role in predicting higher long-COVID symptom burden and are associated with anxiety and depression.
Limitations
Our sample was relatively small, limiting our possibilities of detecting even small differences. Further, the nature of the sampling procedure might limit the generalizability of our findings. Unmeasured confounding factors may also modify the applicability of our data. Regarding the selection, two groups of participants were involved in the study. Also, this is a single-center study.
Funding
This project was supported by a grant from the National Research, Development and Innovation Office (NKFIH) of Hungary (2020-1.1.6-JÖVŐ-2021-00013) and (K135076 to B.M.). Project no. NVKP_16-1–2016-0017 (‘National Heart Program’) has been implemented with the support provided by the National Research, Development and Innovation Fund of Hungary, financed under the NVKP_16 funding scheme.
Acknowledgment
We would like to thank the assistants and all colleagues of the Post-COVID Outpatient Clinic of the Heart and Vascular Center of Semmelweis University for their contributions.
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