Psychometric properties of the kidney disease quality of life-36 (KDQOL-36) in Ethiopian patients undergoing hemodialysis

The KDQOL-36 was derived from the original 134-item KDQOL instrument [24]. The KDQOL-36 version includes the Medical Outcomes Study’s 12-Item Short-Form Health Survey (SF-12) as a generic core and the 24-item kidney disease targeted questionnaire [25, 26]. The items of the SF-12 are summarized into the Physical Component Summary (PCS) score and the Mental Component Summary (MCS) with response alternatives varying from 2- to 6-point scales [27, 28]. The kidney disease targeted instrument includes three scales: Symptoms and Problems (12 items), Burden of Kidney Disease (4 items), and Effects of Kidney Disease (8 items); all items have 5 response options. The scale scores of the KDQOL-36 questionnaire (PCS, MCS, symptoms and problems, burden of kidney disease, effects of kidney disease,) are transformed to 0 to 100 with higher scores indicating better HRQOL [24, 29]. We used the KDQOL^Tm-36 scoring program (V 2.0) from the University of California, Los Angeles (UCLA) to compute the scale scores. This program is available free for download online (http://​www.​rand.​org/​health/​surveys_​tools/​kdqol.​html).

The KDQOL-36 was translated from English into Amharic by two professional translators (native Amharic speakers with fluency in English). The translators had a Master’s degree in English Language and Literature. The translated Amharic versions were then reviewed by a committee of experts including the investigators, the original translators, nurses working in a hemodialysis unit, a nephrologist and experts in instrument development and translation. Some minor changes were made before the items were translated back into English by two other independent professional translators and were then compared to the original instrument. There were repeated back translations for some items for which deviations were encountered until matching was seen to be sufficiently good to ensure that the Amharic version did not differ from the original instrument. Finally, cognitive testing was conducted on 10 ESRD patients undergoing hemodialysis to determine its cultural appropriateness and acceptability including instructions, items and response choices [29] which resulted in rewording a few items. Translation of the English KDQOL-36 to Amharic was performed according to the basic guidelines for translating surveys (see https://​www.​rand.​org/​health-care/​surveys_​tools/​about_​translations.​html).

A cross-sectional study was conducted at two governments (Menelik II and Zewditu memorial) and six private (Ethio-tebib, Hallelujah, Hayat, Bethel, MABD and Flow) hospitals/dialysis centers situated in Addis Ababa, Ethiopia. Data for the psychometric evaluation were collected over a period of one month, January to February 2021, from patients receiving outpatient maintenance hemodialysis. All patients who fulfilled the inclusion criteria (ESRD patients aged ≥ 18 years, maintained on regular hemodialysis treatment for ≥ 3 months and speaking Amharic) were approached regarding possible participation in the study. Hemodialysis patients who provided their consent were then interviewed by trained research assistants and their medical records were consulted to obtain clinical data. Participants were asked to respond to study-specific and standardized items (KDQOL-36). Two weeks later, a subsample (n = 50) was asked to respond to the Amharic KDQOL-36 again to assess the test–retest reliability.

In addition to the KDQOL-36, participants were asked to complete the Amharic European Quality of Life 5D-5L and Visual Analog Scale (EQ-VAS) [30]. The EQ-5D-5L is a generic instrument, developed by the European quality of life (EuroQol) Group consisting of five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression and an EQ-VAS with a 5-point Likert scale (no problems, slight, moderate, severe, extreme/unable). The EQ-VAS is numbered from 0 to 100, where 0 indicated the worst imaginable health and 100 was best imaginable health [31]. Scores were converted to 0 to 100%.

STATA version 14 was used for statistical analysis [32]. Normality was assessed for the outcome variables using the Shapiro Wilk test. Patients’ demographic and clinical characteristics were summarized using descriptive statistics (percentages, frequencies, means, standard deviations). Descriptive statistics for the five separate domains of the KDQOL-36 were calculated with means and standard deviation [29].

Data quality was assessed by examination of missing values for each item of the KDQOL-36. Furthermore, we evaluated whether all response alternatives were used for all items as well as floor and ceiling effects. Ceiling effect was measured by the proportion of people rating the highest possible score while floor effects were measured by the proportion of people rating the lowest possible score. These effects were considered significant if > 15% of the patients scored the lower/higher values [32].

Construct validity of KDQOL-36 was assessed by using corrected item scale correlation using cut-off scores ≥ 0.4 to indicate adequate correlation [33]. Confirmatory factor analysis (CFA) was employed to test the model fit between the observed and the hypothetical measures. CFA is a method of choice when the researcher has prior knowledge of the basic latent variable construction [34]. The SF-12 and the kidney disease targeted questionnaire were analyzed separately because they are different questionnaires each having their own unique contribution to the assessment of HRQOL. The validity of SF-12 health survey would be supported if the hypothesized physical and mental component summary scales were identified [27]. CFA would support the disease-targeted part if a three-factor structure was achieved [29]. Model fit was assessed using normed Chi-Square (χ2/df), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker Lewis index (TLI) and Standardized Root mean-squared residual (SRMR). Normed Chi-Square should be 0–2 for good fit and ≤ 3 for an acceptable fit. Values between 0.05 and 0.08 suggest reasonable RMSEA and lower values represent better fit [35]. CFI & TLI > 0.9 indicate good fit and that SRMR should be < 1.0 to consider the model is favorable [36]. We used Maximum likelihood estimation (MLE) [34].

Convergent validity was evaluated by correlation coefficients between the three kidney disease targeted scales with the EQ-5D-5L and EQ-VAS. Pearson’s correlation coefficient was calculated where values 0.10–0.29, 0.30–0.49 and > 0.49 represents small, moderate and large magnitude, respectively [37]. It was hypothesized that the three kidney disease specific domains would have moderate to large coefficients with the ED-5D-5L and VAS, respectively.

Known group analyses were conducted to test how well the questionnaire discriminates between subgroups of the study sample that differed in diabetes status, examining a hypothesis supported by previous research [38, 39]. It was expected that patients without diabetes would have better HRQOL than diabetic patients. Independent t-tests and an analysis of variance (ANOVA) were used to evaluate the differences.

Reliability included internal consistency as well as test–retest. Internal consistency was estimated using Cronbach alpha coefficient for the different domains of the instrument. Cronbach’s alpha between 0.70–0.90 is suggested to reflect adequate internal consistency [40]. Intra-class correlation coefficient (ICC) was computed to assess test–retest reliability where values > 0.9, 0.75–0.90, 0.5–0.75 and < 0.5 indicates excellent, good, moderate and poor reliability respectively [41].

The total sample included 292 patients (response rate 96%) on maintenance hemodialysis with a mean age 48 (SD ± 14.7), please see Table 1.

There were no missing data. All response alternatives were used for all items. Descriptive statistics including floor and ceiling effects are shown in Table 2.

The corrected item-total correlation coefficients for all items were between 0.41 to 0.85 (data not shown), thus showing adequate correlation. The CFA results for SF-12 showed that χ²/df was 4.4, RMSEA = 0.108 (90% CI 0.064–0.095), CFI = 0.922, TLI = 0.948 and SRMR = 0.058 (Fig. 1). Since the RMSEA was relatively high, we re-specified the model (supplementary file 1). The model outputs were similar and as the uncorrected model fitted well with the originally hypothesized two-factor model which forms the basis of the PCS and MCS this was selected. Similarly, in the kidney disease related scales (symptoms/ problems, effects and burden), the data fitted with the hypothetical three factor model with χ²/df = 3.1, RMSEA = 0.085 (90% CI 0.064–0.095), CFI = 0.854, TLI = 0.838 and SRMR = 0.067) (Fig. 2). Model re-specification was considered since the RMSEA & SRMR were a bit high and the CFI & TLI were a bit low. Even though some variability was found that the data couldn’t explain, the results were very close to the uncorrected model (supplementary file 2), and we decided to use the uncorrected one. Thus, the kidney disease targeted parts of the KDQOL-36 instrument was revealed as a three factor model as it was hypothesized. The factor loadings of all items in both parts (SF-12 and diseases specific) were positive and exceeded the threshold of 0.4, which indicates considerable interpretability of original factor structure.

The associations between the three kidney-disease specific domains and the EQ-5D-5L and the EQ-VAS are presented in Table 3. Moderate to strong correlations were evidenced between KDQOL sub scales and the EQ-5D-5L as well as the sub scales and VAS.

Regarding known group validity, patients with diabetes had significantly lower scores for the effects of kidney disease, SF-12 PCS and SF-12 MCS scales (Table 4).

Cronbach’s alpha values for the five subscales exceeded 0.80 (Table 2). With regard to test–retest reliability, the subscales had an ICC value ranging between 0.90 to 0.96 (data not shown).

This study has several strengths. First, with a large sample (response rate 96%) including patients from government as well as private hospitals, our sample was representative of the population undergoing hemodialysis in Addis Ababa, Ethiopia. Second, sample size of approximately 300 patients enabled rigorous statistical analyses to evaluate validity (construct and known-groups validity). There are also a few limitations to be considered when concluding our results. Amharic is the most commonly used language in the country. Still, two patients were excluded as they did not speak or understand Amharic, and if they represent a subgroup that may perceive the items of the questionnaire in a different way is not known. The study collected data by face-to-face interviews though self-administration, a procedure commonly practiced in administering the KDQOL-36 instrument. We choose this mode of administration as considerably large proportion of the population in Ethiopia cannot read and write and participants in pilot interviews expressed that they preferred to be interviewed in front of instead of filling out the questionnaire by themselves. It should also be noted that a higher proportion of the study participants was well educated compared to the population in the country as a whole. However, this reflects the actual population receiving hemodialysis due to kidney disease and not to be considered a selection bias. Additionally, despite the fact that we recruited 292 patients, the test–retest analysis was based on a smaller convenient portion of this group (50 patients). This could possibly inject a selection bias in to our test–retest analysis and skew the results. However, when compared to the rest of the cohort, it exhibited no significant differences in demographic variables.