Survey design and data collection
Data were collected in Europe (France, Germany, Spain, Italy, the UK), the USA and China during three periods between June 2012 and February 2018, using the Adelphi Real World Disease Specific Programme™ (DSP) for CKD. DSPs are large, real-world, cross-sectional, multi-country surveys of physicians and their consulting patients in clinical practice .
Physicians were identified from publicly available lists and invited to participate in the DSP following completion of a short screening questionnaire, if they met the following eligibility criteria:
- Were nephrologists, endocrinologists, cardiologists, hematologists or primary care physicians.
- Were actively involved in managing the treatment of patients with CKD.
- In a typical month, saw a specified minimum number of patients with different stages of CKD (the required number varied between data collection periods and depended on their specialty).
Participating physicians were asked to complete a Patient Record Form (PRF) for twelve patients with CKD stage 3a or above, with a specified number of patients at each stage of CKD (the numbers required at each stage varied depending on the data collection period and the physician’s specialty). EGFR was used for both diagnosis and staging of CKD, with eGFR ≥90, 60–89, 45–59, 30–44, 15–29 and <15 ml/min/1.73m2 representing stages 1, 2, 3a, 3b, 4 and 5, respectively . PRFs were completed for consecutively consulting patients at each stage of CKD until the quota of patients for that stage had been reached. Information recorded in the PRF included demographics; disease characteristics and history; current Hb level; concomitant conditions; current treatment and treatment history. Patients for whom a PRF was completed were also invited to complete a patient self-completion form (PSC), which included complementary information on CKD history to that recorded on the PRF, as well as a number of well-established patient-reported outcomes (PRO) instruments.
Data were collected according to market research guidelines; hence, no source validation was possible or required. Patient and physician identities were not known to the research team; no identifiers were recorded for the patients, and PRFs and PSCs for each patient were linked by unique numeric codes preprinted on the forms.
HRQoL was assessed using the generic EQ–5D–3L measure of health status and the disease-specific Kidney Disease Quality of Life (KDQOL–36) instrument. Productivity was also assessed using the Work Productivity and Activity Impairment (WPAI) questionnaire.
The EQ–5D–3L is a generic instrument often used routinely in healthcare systems to assess patient health status before and after an intervention . It comprises 5 individual items and a 20 cm vertical visual analog scale (VAS) [25, 26]. The individual items ask the respondent to indicate the level of problems related to mobility, self-care, and usual activities (e.g. work, study, housework, family or leisure activities), and the severity of pain/discomfort and anxiety/depression experienced (if any). Each item provides a score ranging from 1 to 3; a single health utility index score is generated using a country-specific algorithm that provides a number, with 1 indicating perfect health, 0 death and <0 worse than death . Patients indicate their general health status on the day that they complete the EQ–5D–3L by drawing a line on the VAS to provide a score ranging from 0 (worst imaginable health state) to 100 (best imaginable health state).
The KDQOL–36 is one of the most-commonly used disease-specific instruments in studies showing HRQoL impairment in CKD [28, 29, 30, 31]. It comprises 36 items, including the generic 12-Item Short-Form Health Survey (SF–12) to provide 2 summary scores assessing impact on the physical and mental dimensions of HRQoL, and a further 24 items to provide 3 disease-specific subscales [32, 33]:
- The SF–12 Physical Component Summary (PCS) is calculated from all 12 items in the SF–12.
- The SF–12 Mental Component Summary (MCS) is calculated from all 12 items in the SF–12.
- The symptoms and problems with kidney disease subscale is calculated from 12 items each describing a symptom of kidney disease. Patients are asked to what extent they were bothered by each of these during the past 4 weeks, with 5 response options ranging from “Not at all bothered” to “Extremely bothered.”
- The effects of kidney disease on daily life subscale is calculated from 8 items describing ways in which kidney disease can impact a range of issues, such as a patient’s ability to work around the house, and their personal appearance. Patients are asked to what extent they are bothered by each of these, with 5 response options ranging from “Not at all bothered” to “Extremely bothered”.
- The burden of kidney disease subscale is calculated from 4 statements related to the impact of kidney disease on the patient and their family. Patients indicate their agreement with the statements by choosing from 5 response options ranging from “Definitely true” to “Definitely false”.
The SF–12 PCS and MCS are calculated as the sum of scores following conversion into standardized values. The disease-specific subscales are scored by transforming all items to a score in the range 0 to 100 and averaging across the items. Higher scores indicate better HRQoL in all cases.
The WPAI is an instrument to assess the impact of disease on work productivity and daily activities over the past 7 days. Its use has been completed by patients in a large number of studies and a wide range of disease areas ; as it is not disease-specific, it can be used to compare productivity impact across diseases. The WPAI comprises 6 items and results in the generation of 4 scores, each expressed as a percentage of work time missed or a percentage impairment :
- Absenteeism (work time missed due to impairment): calculated as hours missed as a percentage of total work hours using: patient-reported hours missed during the 7-day recall period / (patient-reported hours worked during the 7-day recall period + patient-reported hours missed during the 7-day recall period) x 100);
- Presenteeism (ability to function at work while being impaired): calculated as a percentage using: patient-reported impact of CKD on productivity at work during the 7-day recall period recorded on a scale of 0 (no impact) to 10 (prevented me from working) x 10;
- Overall work impairment: calculated as a percentage using: patient-reported hours worked, patient-reported hours missed and patient-reported impact of CKD on productivity during the 7-day recall period, applying an algorithm described on the WPAI website ; and
- Total activity impairment: calculated as a percentage using: patient-reported impact of CKD on productivity in regular unpaid activities during the 7-day recall period recorded on a scale of 0 (no impact) to 10 (prevented activities) x 10.
Means and standard deviations were calculated for continuous variables, and frequency counts and percentages for categorical variables. Descriptive analyses were performed for the total survey population and stratified by Hb level, geographical region, and CKD stage. For the generic EQ–5D–3L, Hb levels of <8 g/dL, 8-<10 g/dL, 10–12 g/dL, and >12 g/dL were used; for the KDQOL–36 and WPAI, Hb levels of <10 g/dL, 10–12 g/dL, and >12 g/dL were used. CKD stages were 3a non-dialysis dependent (NDD), 3b NDD, 4 NDD, 5 NDD, and dialysis-dependent (DD).
The non-parametric Spearman’s rank correlation test was used to assess the correlation of Hb level with EQ–5D–3L utility index and domains, EQ–5D–3L VAS, SF–12 PCS, SF–12 MCS scores and the three subscales from the KDQOL–36. To adjust for potential confounding, linear regression analyses were performed on EQ–5D–3L VAS, SF–12 PCS, SF–12 MCS and the three subscales from the KDQOL–36 as the outcome variables; independent variables included were Hb level (continuous), CKD stage, Hb and CKD stage interacted, sex, common comorbidities (diabetes, heart failure, stroke) and CV risk. Logistic regression analysis was performed with the same independent variables and EQ–5D–3L utility index score (classified as ≥0.8 and <0.8) as the outcome variable. Exploratory analyses compared KDQOL–36 and WPAI scores for patients with Hb <8 g/dL with those for patients with higher Hb levels.
Patients who had completed a PSC, and for whom current CKD stage and Hb level were available were included in the analysis. Patients with no Hb level reported were included in the descriptive analysis of demographics and disease characteristics, but excluded from all other analyses. Missing data were not imputed but remained missing; therefore, the base of patients for analysis could vary between variables, and is reported for each analysis. All descriptive and exploratory analyses were conducted using IBM SPSS Data Collection Survey Reporter version 6 or later and all statistical testing was conducted in Stata v15.1 .