Measures
The online survey included questions on socio-demographic characteristics, medical information, HRQoL, fatigue and cognition. Socio-demographic characteristics included age, sex and educational level (categorized in three categories: low, middle and high education) [
18].
Patients were classified according to three diagnosis groups based on their self-reported diagnosis: chronic Q-fever (CQ), Q-fever fatigue syndrome (QFS), and patients who experience QFS-like disease (QLD). Furthermore, medical data included hospitalization (yes/no) during acute infection.
Patients were asked to report their HRQoL on the EQ-5D-5L and visual analogue scale (EQ VAS). The EQ-5D-5L consists of five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression [
14]. Each dimension is operationalized in one item with five response levels: no problems, slight problems, some problems, severe problems and extreme problems/unable to [
13]. The EQ VAS consists of one question where respondents rate their health state on a scale from 0 (worst imaginable health state) to 100 (best imaginable health state), and is often used to determine to what extent the EQ-5D captures HRQoL [
17].
The survey also informed on problems with fatigue, using the subscale ‘subjective experience of fatigue’, of the Checklist Individual Strength (CIS) [
19]. The CIS fatigue scale is a validated instrument that consists of eight items that inform on different aspects of fatigue [
19,
20]. Response options consist of a Likert scale from ‘Yes’ (score 1) to ‘No’ (score 7), with no description in words of the answer options in between score 1 and 7. Scores were recoded so that a score 7 always indicated fatigue. The CIS fatigue score was categorized in two groups: no to moderate fatigue (CIS fatigue score < 35), and severe fatigue (CIS fatigue score ≥ 35) [
21]. Since some fatigue complaints are common in a general population, and therefore, not necessarily indicative of more problems than ‘normal’, no to moderate fatigue was considered one group [
22].
In addition, a frequently used item on cognitive problems in EQ-5D-5L format was included in the survey, informing on problems with memory/understanding/coherence/thinking. The item has been tested in multiple studies on its psychometric performance [
23,
24]. The wording of the item was: ‘Cognition, such as memory, understanding, concentration, thinking’. The answer options were the same as the answer options of the EQ-5D-5L items. Degree of cognitive problems was categorized in two groups: no cognitive problems (score 1), and cognitive problems (score 2–5). We will refer to this cognition item as the cognition dimension.
Data analyses
Data analyses were performed using SPSS version 25 (Statistical Product and Service Solutions, Chicago, Illinois, USA). Respondents were included in the analyses if the EQ-5D, EQ VAS, CIS fatigue items and the cognition dimension had been completed.
Socio-demographic characteristics were presented for the whole population and for subgroups based on fatigue and cognitive problems. Distributional effect was determined by defining the proportion of perfect health profiles among all observed health profiles. A health profile was formed by combining the responses to the EQ-5D into a profile, e.g. ‘13252’. A perfect health profile consisted of ‘no problems’ on all EQ-5D dimensions (‘11111’), and a higher proportion of perfect health profiles indicated more ceiling effect. Utility scores were calculated using the Dutch value set for the EQ-5D-5L [
25].
Mean and standard deviation from utility scores and EQ VAS were compared between groups based on the presence of severe fatigue, cognitive problems, or both.
A head-to-head comparison was performed between dimensions of the EQ-5D-5L and fatigue. The percentage of respondents with corresponding answers on fatigue and on EQ-5D dimensions was assessed. Corresponding answers referred to reporting no or mild problems on the fatigue scale in combination with reporting no problems on the EQ-5D dimension, or the opposite (severe fatigue in combination with reporting problems on the EQ-5D dimension). The same assessment was performed for the cognition dimension with each EQ-5D dimension. However, since the cognition dimension is designed in a similar way as the EQ-5D dimensions, corresponding answers were defined as reporting no problems on the cognition dimension in combination with no problems on the EQ-5D dimension, and reporting problems on the cognition dimension in combination with problems on the EQ-5D dimension. Furthermore, dominance of dimensions was analyzed by determining whether problems on the fatigue or cognition dimension were always associated with problems on another dimension.
Convergent validity between the EQ-5D dimensions with the CIS fatigue score and the cognition dimension was determined using Spearman rank correlation coefficient. A correlation of 0.1–0.29 was considered weak, 0.3–0.49 moderate, and ≥ 0.5 was considered strong [
26]. For fatigue, a moderate to strong correlation was expected for all dimensions, because both physical and mental problems are likely to be worsened by fatigue [
27]. For cognition, a strong correlation was expected with usual activities, as usual activities require concentration (for example for work), which is likely to be strongly related to cognitive problems [
28]. In addition, a moderate to strong correlation was expected with pain/discomfort, as already identified in previous studies [
29,
30], and with anxiety/depression, because especially mental problems are likely to be affected by cognitive problems. Moreover, anxiety/depression could lead to cognitive impairment [
31].
Explanatory power of the EQ-5D for fatigue and cognition was determined using multiple linear regression analyses, as the assumptions of linear regression were met, to gain insight in the association, if any, between fatigue and cognition and the EQ-5D dimensions. Dummy variables were created for each response level for each EQ-5D dimension, except for ‘no problems’, which was used as reference category. Explained variance was reported for the full model, and unstandardized beta were reported for independent variables with a statistically significant effect (p < 0.05).
Furthermore, explanatory power of the EQ-5D (with fatigue/cognition) for EQ VAS was also determined using multiple linear regression analyses, to gain insight in the added value of fatigue and cognition to the measurement of HRQoL, measured with the EQ VAS. Dummy variables for each response level, except for ‘no problems’, were used as independent variables.