Scientific Papers

Economic evaluation of dialysis and comprehensive conservative care for chronic kidney disease using the ICECAP-O and EQ-5D-5L; a comparison of evaluation instruments | Cost Effectiveness and Resource Allocation

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Study design

In this short-run time economic evaluation study, we conducted a cost-utility analysis to compare the costs and HRQoL of two treatment methods, Hemodialysis (HD), and Comprehensive Conservative Care (CCC), for patients with CKD in the northwest region of Iran during November 2021 to May 2022.


The study sample was obtained from the medical records of the Ardabil dialysis centre. Participants were eligible if they met the following criteria:

  • Over 65 years old.

  • Diagnosed with CKD (people with glomerular filtration rate (GFR) less than 60 ml/min/1.73 m2 for three or more months and albumin-to-creatinine ratio (ACR) 30 mg/g or higher (or equivalent protein-to-creatinine ratio [PCR] of 50 mg/mmol or higher).

  • Received either HD or CCC as their primary dialysis treatment.

  • Medical records (blood tests, urine tests, imaging studies, or kidney biopsy) with complete information on their demographic characteristics, comorbidities, laboratory results, and direct and indirect costs related to their treatment.

  • Completed the ICECAP-O and EQ-5D-5L questionnaires to assess their HRQoL at the baseline.

Participants who had received a kidney transplant had cognitive impairments that affected their ability to complete the questionnaires or had other serious medical conditions (such as Dementia) were excluded from the study. There was no matching at baseline, and all patients in each group were above 65 years old.

Based on the available data from the Executive summary of the KDIGO Controversies Conference on Supportive Care in Chronic Kidney Disease [32], we assumed the mean difference in Quality-Adjusted Life Years (QALYs) as an indicator of HRQoL between the HD and CCC groups to be 0.03, with no detailed study available on this matter. To calculate the sample size, we assumed a mean difference of 0.03 QALYs and a standard deviation of 0.1, with a two-sided significance level of 0.05 and a power of 0.8. Using the formula:

$${\text{n}}\, = \,({\text{Z}}_{{\left( {\alpha /2} \right)}} \, + \,{\text{Z}}_{\left( \beta \right)} )^{2} \,*\,({\text{SD}}1^{2} \, + \,{\text{SD}}2^{2} )\,/\,({\upmu }_{1} \, – \,{\upmu }_{2} )^{2}$$


  • n = sample size per group

  • Z(α/2) = critical value of the normal distribution for a significance level of α/2

  • Z(β) = critical value of the normal distribution for the power of 1-β

  • SD1 and SD2 = standard deviations of the two groups

  • μ1 and μ2 = means of the two groups

we obtained a required sample size of 96 participants (48 in each group). Since our study already has more participants than this calculated sample size, it can be considered adequately powered for the chosen effect size, power, and significance level [33, 34]. Finally, we selected 183 participants, who met the eligibility criteria, to include in the study. Of these, 105 were in the HD group and 76 were in the CCC group (Fig. 1).

Fig. 1
figure 1

Data collection

Data collection for this economic evaluation took place from November 2021 to May 2022 from two sources, first from the medical records of the Ardabil dialysis centre and second from face-to-face interviews with the patients. Medical-related data, including demographic information, medical history, comorbidities, laboratory results, and information on the duration and frequency of the interventions received, were extracted from the medical documents of all participants before face-to-face interviews, were conducted. Additionally, data on the HRQoL of both groups were collected through face-to-face interviews, which took place between April to May 2022. To accommodate the HD group’s schedule, interviews were arranged based on the patient’s preferred time, typically not on the day of receiving dialysis. Conversely, for the CCC group, interviews were scheduled based on the patient’s preferred time on the day of the interview. The interview began with two general questions, namely the participants’ age and the time of their last visit to a doctor’s office or hospital, to mentally prepare them. Next, the participants were asked to choose one of two envelopes, each containing the name of a questionnaire. The participants were then given the selected questionnaire to answer. To minimize any potential bias, the participants were asked another general question about their family (the names of their children and grandchildren) after completing the first questionnaire. Finally, the participants were asked to complete the second questionnaire.

To ensure data quality, all data were collected by trained research assistants who underwent a rigorous training program before starting data collection. Data were also double-checked for accuracy and completeness by a separate team of data managers. Any discrepancies or missing data were resolved by contacting the patient or referring physician. In our study, it was observed that a significant proportion of participants, particularly in the CCC group, had varying levels of literacy challenges. To ensure the accuracy and reliability of data collection, we implemented a rigorous approach. Interviewers involved in data collection underwent comprehensive training to administer the quality-of-life instruments consistently and sensitively. For participants with literacy difficulties, interviewers read the questions aloud and recorded responses verbatim to minimize reporting bias. Furthermore, we incorporated sensitivity analyses in our study design to assess the potential impact of literacy-related factors on our findings. This approach not only allowed us to address potential biases but also ensured that our study maintained a high standard of data quality and integrity.

All data were stored in a secure, password-protected database with limited access. Data were regularly backed up to prevent loss or corruption, and the database was regularly maintained and updated to ensure data accuracy and completeness.

This expanded version of the data collection section provides more details on how cost and HRQoL data were collected, and other data collected in the study. It also includes information on data quality assurance and management, which are important aspects of ensuring the validity and reliability of study findings.

Cost data

We collected cost data using a detailed approach, ensuring we accounted for all expenses linked to each treatment method. This included direct medical costs, like those found in hospital records, such as hospitalization, medications, lab tests, and doctor visits. We also considered indirect costs, like those associated with transportation and time. Indirect costs were estimated by interviewing patients and calculating the impact of missed work and lost income. For patients receiving HD, direct costs encompassed dialysis machines, medications, supplies, lab tests, and healthcare providers’ fees. Conversely, for those in the CCC group, direct costs involved visits to various healthcare providers, lab tests, medications, and necessary supplies. Indirect costs for both groups included transportation expenses, time lost from work, reduced earning potential, and caregiver costs. We aimed to provide a comprehensive view of all financial aspects related to these treatments (Fig. 2).

Fig. 2
figure 2

Breakdown of direct and indirect costs for HD and CCC patients

All costs were measured in Iranian Rials (IRR) and were converted to US dollars (USD) using the official exchange rate at the time of data collection.

Validity and applicability of economic assessment tools: EQ-5D-5L and ICECAP-O

The validity and applicability of economic assessment tools, such as EQ-5D-5L and ICECAP-O, are critical considerations in health economic studies. In this context, EQ-5D-5L is a widely recognized and extensively validated instrument for assessing HRQoL [23,24,25,26,27,28, 35]. It provides a comprehensive overview of an individual’s health status across multiple dimensions, including mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The EQ-5D-5L has been validated in various populations and can generate utility scores that can be used in economic evaluations. Its applicability lies in its ability to provide a standardized measure of HRQoL that can be compared across different health interventions and settings.

On the other hand, ICECAP-O is a capability-based instrument designed to capture the well-being and capabilities of older individuals. While it lacks a preference-based scoring system for directly calculating Quality-Adjusted Life Years (QALYs), it offers a valuable perspective on broader aspects of quality of life that are particularly relevant to older populations. ICECAP-O focuses on attributes like attachment, security, role, enjoyment, and control, providing a more holistic view of well-being. Its applicability is significant in studies where the goal is to capture a more comprehensive picture of the impact of healthcare interventions on individuals’ lives, especially in scenarios involving older or frail populations.

Both instruments, EQ-5D-5L and ICECAP-O, offer unique strengths and can be chosen based on the specific research objectives and the population under study. The validity of these instruments largely depends on their appropriate adaptation and validation in the target population. Ensuring that these tools are culturally relevant and sensitive to the population’s characteristics is crucial for obtaining meaningful and applicable results in economic evaluations. Moreover, it’s essential to transparently report the methods used to derive utility scores or QALYs from these instruments, as this can significantly impact the results and their applicability in healthcare decision-making [36].

Outcomes and covariates

Health-Related Quality of Life (HRQoL) & Quality-Adjusted Life Years (QALYs)

This study places significant importance on HRQoL as a crucial indicator of health outcomes and as the primary outcome measure. To assess HRQoL, QALYs were calculated for each patient using the ICECAP-O and EQ-5D-5L questionnaires administered in May 2022. QALYs are a measure of health outcomes that combines quantity and quality of life. They are commonly used in cost-effectiveness analyses to compare the value of different healthcare interventions [37]. The EQ-5D-5L is a generic instrument that measures health-related quality of life across five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The EQ-5D-5L health states transformed into a “utility” score by utilizing a scoring algorithm based on public preferences. Additionally, the EQ-5D-5L instrument features a Visual Analogue Scale (EQ-VAS) that is used to obtain a global rating of self-perceived health. The EQ-VAS is measured on a 0–100 mm scale, where 0 represents the worst possible health state and 100 represents the best health state imaginable [24, 38].

ICECAP-O is a measure of capability well-being that focuses on the attributes that are important to older people. It includes five domains: attachment, security, role, enjoyment, and control. Each domain has four response options, ranging from “no capability” to “full capability” [24, 39]. ICECAP-O is a valuable measure of the quality of life in older adults, but it does not have a preference-based scoring system to derive QALYs, so we passed a few steps to calculating QALYs based on the ICECAP-O.

Data analysis

Descriptive statistics were calculated to summarize the characteristics of the study sample, including age, sex, duration of CKD, comorbidities, and laboratory results. Means and standard deviations were used for continuous variables, and frequencies and percentages were used for categorical variables. In addition, statistical tests were used to compare between two groups in terms of baseline characteristics.

Direct and indirect costs were analyzed separately. The total cost per patient for each intervention was calculated by summing the costs of all resources used by patients to use the intervention.

To calculate HRQoL using QALYs, the health utility score obtained from the instruments (EQ-5D-5L and ICECAP-O) is multiplied by the time spent in a particular health state to get the number of QALYs gained or lost. The literature was consulted to obtain these quality-of-life estimates, which range from zero (representing death) to one (representing full health) [40, 41]. For example, if a patient has a health utility score of 0.7 while in a certain health state for two years, the number of QALYs gained or lost would be 1.4 QALYs (0.7 × 2 years). In this study, we defined one year as a time horizon based on previous studies [42]. To calculate QALYs based on ICECAP-O, we first assigned a score to each response option for each domain. The scores range from 0 to 1, with 1 representing full capability and 0 representing no capability. Once we assigned scores to each response option, we calculated the total ICECAP-O score for everyone by summing the scores across all five domains. The maximum possible score is 5. In the next step, we converted the total ICECAP-O score into a utility score using a mapping functionFootnote 1 extracted from previous studies in Iran [43]. The mapping function translates the ICECAP-O score into a utility score, which ranges from 0 to 1, with 1 representing perfect health and 0 representing death. Finally, we calculated the QALYs by multiplying the utility score by the time spent in a particular health state. For example, if an individual spends one year in a health state with an ICECAP-O utility score of 0.5, the QALYs gained would be 0.5 [35].

In this study, we extracted the Iranian population norms for the EQ-5D-5L questionnaire from the literature [43] to calculate the utility values for our study population. All expenses were reported in 2021 and adjusted to present value using a local discount rate of 6.0% [12, 44] for both costs and outcomes. The country’s threshold for cost-effectiveness was determined to be three times its GDP per capita, which is approximately 520 million IRR ($12,380) per QALY. The costs were estimated using the 2022 US dollar exchange rate [45]. The patient’s health status was evaluated using the Euro QoL EQ-5D-5L Persian version [43], with participants being asked about their current and past states of health.

We did not find an appropriate method to combine both instruments in each other to calculate QALYs, so our approach was to calculate QALY based on both instruments separately. This approach enabled us to compare the net results of instruments in QALYs calculation and extract both health-related and broader aspects of quality of life [21].

Incremental cost-effectiveness ratio (ICER)

The Incremental Cost-Effectiveness Ratio (ICER) is a metric used to compare the cost difference between two treatments with their respective outcomes, usually measured in Quality-Adjusted Life Years (QALYs) gained [46].

To calculate ICER, we first find the cost difference between the two treatments (HD and CCC) by subtracting the total cost of the less expensive treatment from the total cost of the more expensive one. Then, we calculate the QALY difference between the two treatment groups for each instrument separately by subtracting the total QALYs gained in the less effective treatment group from that in the more effective group. Finally, we divide the cost difference by the QALY difference to obtain the ICER. This value represents the additional cost per additional QALY gained for the more expensive treatment compared to the less expensive one. The formula used for ICER calculation is as follows:

$${\varvec{ICER}}=\frac{({\varvec{Cost}}\,\boldsymbol{ }{\varvec{of}}\,\boldsymbol{ }{\varvec{HD}}-{\varvec{Cost}}\,\boldsymbol{ }{\varvec{of}}\,\boldsymbol{ }{\varvec{CCC}})}{({\varvec{QALYs}}\,\boldsymbol{ }{\varvec{gained}}\,\boldsymbol{ }{\varvec{with}}\,\boldsymbol{ }{\varvec{HD}}-{\varvec{QALYS}}\,\boldsymbol{ }{\varvec{gained}}\,\boldsymbol{ }{\varvec{with}}\,\boldsymbol{ }{\varvec{CCC}})}$$


  • Cost of HD = total cost of providing hemodialysis treatment to the patients in the HD group

  • Cost of CCC = total cost of providing comprehensive conservative care to the patients in the CCC group

  • QALYs gained with HD = total QALYs gained by the patients in the HD group during the study period

  • QALYs gained with CCC = total QALYs gained by the patients in the CCC group during the study period

In this evaluation, the cost-utility threshold (willingness to pay (WTP)) was considered equal to Iran’s one-time GDP per capita in 2022, equivalent to 25,249 Dollars (70 million Rials) [47, 48]. Rial values in the present study were converted using the purchasing power parity (PPP) Dollar conversion factor to Rial equal to 42,157 Rials (Average exact exchange rate in 2021: 421,570.0935 IRR, where we removed a zero to better calculate the costs) [45, 49].

We used Net Monetary Benefit (NMB) to detect the difference between the monetary value of total expected QALYs and total expected costs. To calculate the NMB, we used the following formula:

$${\text{NMB}}\, = \,\left( {{\text{WTP}}\, \times \,{\text{QALY}}} \right)\, – \,{\text{Cost}}$$

where WTP is the willingness-to-pay threshold (in this study, $25,249), QALY is the quality-adjusted life year gained, and Cost is the cost of the intervention.

Sensitivity analysis

Sensitivity Analysis is a critical component of this study, aiming to account for uncertainties inherent in various model parameters. We conducted a Probabilistic Sensitivity Analysis (PSA) to comprehensively assess how these uncertainties might impact our results. PSA was executed through Monte Carlo simulations, a powerful technique that allows for the integration of various uncertain variables [50]. Our probabilistic model encompassed key input parameters, including the costs associated with Hemodialysis (HD) and Comprehensive Conservative Care (CCC), utility scores derived from both the ICECAP-O and EQ-5D-5L instruments and mortality rates. The probability distributions for these input parameters were meticulously determined. These distributions were informed by a synthesis of our study’s data, extensive literature reviews, and insights from expert opinions. This approach ensured that we considered a range of potential scenarios and uncertainties, accommodating variations in the values of these critical parameters. By subjecting our economic evaluation to this rigorous sensitivity analysis, we aimed to provide a more comprehensive and robust assessment of the economic implications associated with HD and CCC for individuals with CKD. This process enhances the credibility of our findings and contributes to a more informed decision-making process for healthcare policymakers and providers in resource-limited settings.

All analyses were performed using standard statistical software (STATA ver. 17) and Excel, and statistical significance was set at p < 0.05.

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