Full length article| Volume 20, ISSUE 8, P1131-1138, September 01, 2017

# Determination of Cost-Effectiveness Threshold for Health Care Interventions in Malaysia

Open ArchivePublished:June 02, 2017

## Abstract

### Background

One major challenge in prioritizing health care using cost-effectiveness (CE) information is when alternatives are more expensive but more effective than existing technology. In such a situation, an external criterion in the form of a CE threshold that reflects the willingness to pay (WTP) per quality-adjusted life-year is necessary.

### Objectives

To determine a CE threshold for health care interventions in Malaysia.

### Methods

A cross-sectional, contingent valuation study was conducted using a stratified multistage cluster random sampling technique in four states in Malaysia. One thousand thirteen respondents were interviewed in person for their socioeconomic background, quality of life, and WTP for a hypothetical scenario.

### Results

The CE thresholds established using the nonparametric Turnbull method ranged from MYR12,810 to MYR22,840 (~US $4,000–US$7,000), whereas those estimated with the parametric interval regression model were between MYR19,929 and MYR28,470 (~US $6,200–US$8,900). Key factors that affected the CE thresholds were education level, estimated monthly household income, and the description of health state scenarios.

### Conclusions

These findings suggest that there is no single WTP value for a quality-adjusted life-year. The CE threshold estimated for Malaysia was found to be lower than the threshold value recommended by the World Health Organization.

## Introduction

For many years, clinical evidence has been the only evidence required in deciding how to fund health care interventions or drug reimbursement. Nevertheless, many countries are now considering the cost of drugs as part of the important criteria in decision making because of the finite resources available in the health care sector [
• Barnieh L.
• Manns B.
• Harris A.
• et al.
A synthesis of drug reimbursement decision-making processes in organisation for economic co-operation and development countries.
]. Consequently, economic evaluations have become increasingly important tools to support efficient resource allocation in the health care sector, especially in resource-constrained settings [
• Eichler H.
• Kong S.X.
• Gerth W.C.
• et al.
Use of cost-effectiveness analysis in health-care resource allocation decision-making: How are cost-effectiveness thresholds expected to emerge?.
].
The results of economic evaluations, especially cost-effectiveness analysis and cost-utility analysis, are usually summarized as an incremental cost-effectiveness ratio (ICER). The ICER represents the incremental cost per incremental gain in outcomes of one intervention compared with another. To draw conclusions on the cost-effectiveness (CE) of health care interventions, the ICER is usually compared with a reference value, the CE threshold, sometimes referred to as the ICER threshold or the ceiling threshold [
• Cleemput I.
• Neyt M.
• Thiry N.
• et al.
Threshold Values for Cost-Effectiveness in Health Care.
,
• Thavorncharoensap M.
• Teerawattananon Y.
• Natanant S.
• et al.
Estimating the willingness to pay for a quality-adjusted life year in Thailand: Dose the context of health gain matter?.
].
The CE threshold represents the willingness to pay per quality-adjusted life-year (WTP/QALY) gained and is a vital component of decision making involving economic evaluation [
• Drummond M.F.
• Sculpher M.J.
• Torrance G.W.
• et al.
]. In previous arguments on the importance of an explicit threshold value, Johannesson and Meltzer [
• Johannesson M.
• Meltzer D.
Editorial: some refections on cost-effectiveness analysis.
] claimed that without a CE threshold, cost-effectiveness analysis cannot be considered a proper decision-making tool because it would lack a systematic and universally recognizable decision criterion.
A number of countries such as the United Kingdom, Ireland, and the Slovak Republic have explicitly stated their own threshold values [
• Barnieh L.
• Manns B.
• Harris A.
• et al.
A synthesis of drug reimbursement decision-making processes in organisation for economic co-operation and development countries.
]. For instance, the National Institute for Health and Care Excellence (NICE) in the United Kingdom has set a threshold value of £20,000 to £30,000 per QALY gained [
• Barnieh L.
• Manns B.
• Harris A.
• et al.
A synthesis of drug reimbursement decision-making processes in organisation for economic co-operation and development countries.
,
• Appleby J.
• Devlin N.
• Parkin D.
NICE’s cost-effectiveness threshold. How high should it be?.
,
• Shiroiwa T.
• Sung Y.K.
• Fukuda T.
• et al.
International survey on willingness-to-pay (WTP) for one additional QALY gained: What is the threshold of cost effectiveness?.
]. In Malaysia, however, there is no such explicit threshold value. In current practice, decisions regarding new health care technologies are made without a transparent decision criterion. This situation leaves more room for arbitrariness and ad hoc considerations in the decision-making process. It also prevents the determination of the true opportunity cost of a new medical intervention, which, in turn, imposes inefficiencies and inconsistencies in decision making, and threatens the sustainability of the health care funding system [
• Eichler H.
• Kong S.X.
• Gerth W.C.
• et al.
Use of cost-effectiveness analysis in health-care resource allocation decision-making: How are cost-effectiveness thresholds expected to emerge?.
,
• Donaldson C.
• Baker R.
• Bell S.
• et al.
]. Although the World Health Organization (WHO) has made a generic recommendation for CE threshold in developing countries to take the value of 1 to 3 times the gross domestic product (GDP) per capita per disability-adjusted life-years, such an approach does not accurately reflect the specific needs and the economic and disease burden of the general population in each country. Therefore, establishing a Malaysian CE threshold expressed in terms of cost per QALY is vital, because it will provide a solid criterion for decision making. This study was conducted primarily to determine a CE threshold value for health care interventions in Malaysia. A secondary goal was to identify the factors that affect WTP per QALY.

## Methods

### Study Design and Samples

A cross-sectional, contingent valuation survey was conducted between December 1, 2012, and December 31, 2014. A sample size of 608 was required to detect a minimum difference of 0.05 between health states at a 0.05 significance level and with 0.80 statistical power. To account for the 40% of nonresponse expected in population survey, the sample size was increased to 1000. Respondents were interviewed in person for 10 to 20 minutes. All respondents chosen were Malaysian adults aged between 20 and 60 years and able to understand either English or Malay. The questionnaire was available in both languages. Both the English and Malay versions of questionnaire were tested and validated in pilot studies to ensure that there is no translational bias. The questionnaire was first designed in English and translated into Malay using the standardized patient-reported outcomes translation procedure [
• Wild D.
• Grove A.
• Martin M.
• et al.
Principles of good practice for the translation and cultural adaptation process for patient-reported outcomes (PRO) measures: report of the ISPOR Task Force for Translation and Cultural Adaptation.
]. Forward translation was done by native English speakers who resided in Malaysia and came from a medical background with experience in translating/managing the translation of patient-reported outcome measures. Both languages were used in questionnaire development because most Malaysians younger than 60 years are literate in at least one of the two languages. Malay is the official language of Malaysia, whereas English is offered as a compulsory second language subject as part of the national education syllabus. The country has an adult literacy rate of up to 93.1% [
UNICEF
]. During the interview process, respondents were given the flexibility of choosing their language of preference during the survey. As such, the risk of selection bias because of language was expected to be low.
Stratified multistage cluster sampling was used on the basis of the sampling frame provided by the Population and Housing Census of Malaysia [
Department of Statistics Malaysia
Population and Housing Census of Malaysia.
]. Three states and a federal territory in Peninsular Malaysia, namely, Penang, Kedah, Selangor, and Kuala Lumpur, were clustered into four regions. The samples were then allocated to each region on the basis of the total population of each region. After this step, 20 enumeration blocks were selected from each region. In the third stage of stratification, 120 and 66 living quarters were selected in each city and each rural area, respectively, in proportion to the 65% urban dwellers nationally. Full-time students were excluded because their financial dependency might bias the valuation of WTP.

### Study Instrument

The study questionnaire was developed by a group of practitioners and academics in HTAsiaLink, a network of health technology assessment organizations in Asia. This collaborative study was conducted simultaneously in three other member countries: Korea, Japan, and Thailand.
Each questionnaire was divided into four parts (Appendix 1 in Supplemental Materials). Part 1 consisted of 11 items on respondents’ socioeconomic background (sex, age, ethnicity, educational level, occupation, marital status, number of household members, monthly household income, status in the household, presence of health problem, and private health insurance). Part 2 consisted of an assessment of the respondents’ current health state using the three-level EuroQol five-dimensional questionnaire (EQ-5D) and valuation of their current and hypothetical health states. During this part of the study, each respondent was asked to imagine being in one of the seven hypothetical health states (Appendix A in Supplemental Materials). The health state descriptions were derived from the EQ-5D definitions. They were chosen to represent “mild,” “moderate,” or “severe” health conditions as well as “extended life for terminal illness” and “life-saving intervention for immediate death” [
EuroQol Group
EuroQol—a new facility for the measurement of health-related quality of life.
]. Health states with a utility value of more than 0.70 were categorized as mild conditions, whereas those with utility values of 0.35 to 0.70 and less than 0.35 were classified as moderate and severe health conditions, respectively [
• Szende A.
• Oppe S.
• Devlin N.
EQ-5D Value Sets: Inventory, Comparative Review and User Guide.
,
• Shafie A.A.
EuroQol 5-dimension measure in Malaysia.
]. This gave rise to two mild health states (11121 and 11212), two moderate health states (11323 and 22222), and one severe health state (22232) (Fig. 1).
The terminal health state group was represented by two scenarios: one involving extended life for terminal illness and the other involving life-saving intervention for immediate death situations. Both versions shared the same hypothetical severe health state (22232) but the valuation scenarios being described were different. Each respondent valued only one hypothetical health state during the interview. Descriptions of the hypothetical health states were illustrated on a separate color-printed card that was used by the interviewers. Utilities of the current and hypothetical health states were measured using the three-level EQ-5D and the visual analogue scale (VAS) [
• Shafie A.A.
• Hassali M.A.
• Liau S.Y.
A cross-sectional validation study of EQ-5D among the Malaysian adult population.
,
• Yusof F.A.
• Goh A.
• Azmi S.
Estimating an EQ-5D value set for Malaysia using time trade off and visual analogue scale methods.
].
Part 3 consisted of a contingent valuation exercise in which each respondent was asked for the amount he or she was willing to pay for a scenario involving the hypothetical health state selected in part 2 (Fig. 2) with two QALY gained levels, 0.2 QALY and 0.4 QALY. Keeping small QALY gains would enable “health losses” to be considered by the respondents such that WTP values are subjected to “budget constraints” to avoid extreme WTP values [
• Donaldson C.
• Baker R.
• Bell S.
• et al.
].
A bidding game technique and a double-bounded dichotomous choice approach were applied in eliciting the maximum WTP value for each respondent. In each version of the questionnaire, respondents were asked to place a value on a hypothetical health state scenario on the basis of a certain starting bidding amount. The starting bidding amounts were calculated in proportion to the Malaysian GDP per capita in 2010. To test and control for anchoring effect commonly associated with contingent valuation, the starting bidding values were varied at 5%, 10%, 20%, 40%, 80%, or 120% of the GDP per capita. This yielded six different starting bidding amounts (MYR1,300, MYR2,600, MYR5,200, MYR10,400, MYR20,800, and MYR31,000). Respondents were randomly assigned to one of the six starting bidding amounts by rolling a dice.
The bidding amount was adjusted on the basis of the answers provided by respondents. If the answer was “yes” for the first bid, the bidding amount was increased by one level in the subsequent bid. If respondents, however, answered “no” for the first bid, the second bidding amount was decreased by one level. Respondents who accepted both bids were asked for their maximum WTP in an open-ended question. Nevertheless, respondents who rejected both bids were asked whether they were willing to pay a small amount. If the answer was “yes,” they were asked an open-ended question about the maximum amount they were willing to pay, which should be lower than the minimum offered bid. Respondents who were not willing to pay even a small amount were directed to indicate their reasons. In part 4, respondents were asked to provide their feedback on the survey’s difficulty regarding the VAS used and on the contingent valuation exercise, each rated on a five-point Likert scale.

### Data Management and Statistical Analysis

In this study, the mean WTP amount was estimated using both the nonparametric Turnbull method and the parametric interval regression analysis. To obtain the mean value of WTP per QALY, the WTP values derived from the bidding amounts were divided by the amount of utility gained. Protest zero cases were determined and were excluded from the analysis. All the statistical analyses were performed using STATA 9.0 (StataCorp, College Station, TX) and SAS 9.3 (SAS Institute Inc., Cary, NC).

#### Nonparametric Turnbull analysis

The nonparametric Turnbull method was used to estimate the survivor distribution characterizing WTP responses. The Turnbull method makes minimal assumptions about the distribution of WTP and estimates the mean and median WTP as lower bound estimates. The estimated lower bound of WTP is distributed asymptotically normal. Because of the independence of the true underlying distribution, the lower bound estimate of WTP is appealing. In addition, this estimate offers a conservative lower bound for all non-negative distributions of WTP. In practice, it represents the minimum expected WTP for all distributions of WTP defined from 0 to infinity [
• Haab T.C.
• McConnell K.E.
Referendum models and negative willingness to pay: alternative solutions.
,
• Haab T.C.
• McConnell K.E.
Valuing Environmental and Natural Resources: The Econometrics of Non-Market Valuation.
]. Given that tj is the bidding amount to price j, M* represents the pooled price ranges, and Fj* is the pooled probability of a “no” response to price j, the lower bound estimate of WTP can be calculated by the following equation:
$ELB(WTP)=∑j=0M⁎tj(Fj⁎+1−Fj⁎).$

#### Parametric interval regression analysis with Weibull distribution

Because of its distribution-free property, the drawback of the nonparametric Turnbull method is that it provides estimates of only a fraction of the distribution that falls into particular intervals defined by the lower and upper dollar thresholds used. Moreover, this nonparametric approach does not provide any point estimates, such as a median, and it cannot handle covariate analysis. To address these problems, a parametric interval regression model was used. We assumed that WTP distribution follows a Weibull distribution because of its non-negative distribution and goodness of fit to our sample data [
• Carson R.T.
• Wilks L.
• Imber D.
Valuing the preservation of Australia’s Kakadu Conservation Zone.
]. The cumulative density function of the Weibull distribution is defined as follows:
$F(z;θ;σ)=1–exp(–[z/σ]θ),$

where z is the WTP, θ is the shape parameter, and σ is the scale parameter.
To assess the factors that affected respondents’ WTP per QALY, all the sociodemographic characteristics (such as sex, age, ethnicity, educational level, marital status, and monthly household income), utility measures, and valuation scenarios (such as treatment, life-extending, and immediate death scenario) were included in the interval regression model as the potential explanatory variables. They were then removed using backward-elimination method, for items that were not significant at 25% level [
• Carson R.T.
• Wilks L.
• Imber D.
Valuing the preservation of Australia’s Kakadu Conservation Zone.
,
• Shiroiwa T.
• Igarashi A.
• Fukuda T.
• et al.
WTP for a QALY and health states: More money for severer health states?.
].

## Results

One thousand one hundred respondents were approached and the overall response rate was recorded at 92.1%, with a total of 1013 respondents aged between 20 and 60 years being interviewed face-to-face. Their sociodemographic characteristics are presented in Table 1.
Table 1Sociodemographic characteristics of the Malaysian population
CharacteristicMean ± SD or N (%)Malaysian population
Data for Malaysian populations were obtained from the Department of Statistics, Malaysia, in the Population Distribution and Basic Demographic Characteristics Report 2010. Data shown are from Malaysian citizens and were updated in 2010.
(%)
Age (y)39 ± 11.5
Sex
Male363 (35.8)50.7
Female650 (64.2)49.3
Ethnicity
Malay778 (76.8)67.4
Chinese136 (13.4)24.6
Indian97 (9.6)7.3
Others2 (0.2)0.7
Education
Primary school or less117 (11.5)
Secondary school644 (63.6)
Bachelor’s degree224 (22.1)
Higher than bachelor’s degree28 (2.8)
Occupational sector
Part-time student20 (2.0)
Pensioner55 (5.4)
Housewife274 (27.1)
Unemployed64 (6.3)
In full-time employment600 (59.2)
Marital status
Single213 (21.0)35.1
Married758 (74.8)59.6
Divorced/separated10 (1.0)0.8
Widowed32 (3.2)4.5
Number of household members4.8 (1.9)
Monthly household income
Data for monthly household income were obtained from the Department of Statistics, Malaysia, in the Malaysia Household Income and Basic Amenities Survey Report [22].
<MYR50034 (3.4)1.2
MYR500–999125 (12.3)6.2
MYR1,000–2,999432 (42.6)45.4
MYR3,000–4,999223 (22.0)23.1
MYR5,000–10,000163 (16.1)18.2
>MYR10,00036 (3.6)5.9
Status in household
Head of the household306 (30.2)
Spouse of the head of the household459 (45.3)
Son/daughter of the head of the household215 (21.2)
Parent of the head of the household6 (0.6)
Relative of the head of the household17 (1.7)
Others10 (1.0)
Health problem
Yes230 (22.7)
No783 (77.3)
Private health insurance
Yes398 (39.3)
No615 (60.7)
Hypothetical health state scenario
Treatment scenario with mild health state 11121151 (14.9)
Treatment scenario with mild health state 11212154 (15.2)
Treatment scenario with moderate health state 11323152 (15.0)
Treatment scenario with moderate health state 22222149 (14.7)
Treatment scenario with severe health state 22332154 (15.2)
Extended life for terminal illness128 (12.6)
Life saving for immediate death125 (12.3)
English344 (34.0)
Malay669 (66.0)
Duration of interview (min)
English8.7 ± 2.8
Malay8.6 ± 2.8
Data for Malaysian populations were obtained from the Department of Statistics, Malaysia, in the Population Distribution and Basic Demographic Characteristics Report 2010. Data shown are from Malaysian citizens and were updated in 2010.
Data for monthly household income were obtained from the Department of Statistics, Malaysia, in the Malaysia Household Income and Basic Amenities Survey Report
Department of Statistics Malaysia
Malaysia Household Income and Basic Amenities Survey Report.
.
In the measurement of health status and utility, most of the respondents reported no problem to all the dimensions in the EQ-5D. For moderate problem scenarios, pain/discomfort was reported more by the respondents (n = 186 [18.4%]) when compared with other dimensions. None of the respondents experienced extreme problem in mobility, self-care, and usual activities except for pain/discomfort and anxiety/depression. Figure 3 shows a summary of the responses reported by respondents to the current health status described in the EQ-5D description.
In the VAS valuation, the mean score of current health state was estimated at 81.6, with an SD of 14.5. The responses to five different hypothetical health states in VAS valuation are summarized in Appendix B in Supplemental Materials. The mean score of VAS shows a strong inverse correlation to the severity of health state descriptions. This indicates a considerable agreement that most of the people will place a lower utility score when facing a severe health state.

### WTP Measures

The results for the Turnbull estimates and the probability of respondents responding “no” to each of the bid values for 0.2 QALY and 0.4 QALY gained are presented in Table 2. The responses to the bidding amounts of MYR20,800, MYR31,000, and MYR39,000 violate the monotonicity assumption for a standard distribution function for scenarios involving both 0.2 QALY and 0.4 QALY gained. The responses to these bidding amounts were pooled with the responses for MYR10,400 bids to maintain monotonicity. The probability of mass point estimates is presented in the last two columns. Besides the responses to the aforementioned bidding amounts, responses to all other bidding amounts satisfy the monotonicity assumption.
Table 2Turnbull nonparametric estimator for 0.2 QALY and 0.4 QALY gained
0.2 QALY0.4 QALY
Unrestricted estimateTurnbull estimateUnrestricted estimateTurnbull estimate
tj (MYR)Tj
Data were derived from long format.
Nj
Data were derived from long format.
Fj (= Nj/Tj)Fj
Data were derived from long format.
fj
Data were derived from long format.
tj (MYR)Tj
Data were derived from long format.
Nj
Data were derived from long format.
Fj (= Nj/Tj)Fj
Data were derived from long format.
fj
Data were derived from long format.
700155660.4260.4260.426700147500.3400.3400.340
1,300196940.4800.4800.0541,300183680.3720.3720.031
2,6003181560.4910.4910.0112,6003251310.4030.4030.031
5,2002811540.5480.5480.0575,2003001500.5000.5000.097
10,4003132080.6650.6120.06410,4003261980.6070.5750.075
20,8002201460.664Pooled backPooled back20,8002461580.642Pooled backPooled back
31,00042130.310Pooled backPooled back31,00054190.352Pooled backPooled back
39,0003350.152Pooled backPooled back39,0003340.121Pooled backPooled back
39,000+1.0001.0000.38839,000+1.0001.0000.425
MYR, Malaysian Ringgit; QALY, quality-adjusted life-year; Fj, probability of a negative response to price j; Fj*, pooled probability of a negative response to price j; fj*, difference within Fj* (fj* = Fj*Fj*˗1); Nj, number of respondents rejecting the choice; tj, bidding amount for price j; Tj, total number of respondents.
Data were derived from long format.

## Acknowledgment

We thank the HTAsiaLink members who provided their expert opinions and technical support in this study: Dr Yot Teerawatthaon, Dr Montarat Thavorncharoensap, Dr Jeonghoon Ahn, Professor Takashi Fukuda, Dr Takeru Shiroiwa, and Dr Ataru Igarashi.
Source of financial support: This study was funded by Universiti Sains Malaysia via the Accelerated Programme for Excellence Delivering Excellence Grant (grant no. 1002/PFARMASI/910330). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, and writing and publishing the report.

## Supplementary material

• Supplementary material

• Supplementary material

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