Saturday, January 9, 2016: 11:00-12:30
Kai Chong Tong Auditorium, G/F (Jockey Club School of Public Health and Primary Care Building at Prince of Wales Hospital)

Yen Wei Lim, BPharm(Hons), Universiti Sains Malaysia, Kepala Batas, Malaysia and Asrul Shafie, PhD, UNIVERSITI SAINS MALAYSIA, PENANG, Malaysia
Purpose:   Current practice in Malaysia on decision making of new healthcare technologies is made arbitrary without an explicit cost-effectiveness (CE) threshold. This study was mainly done to determine a CE threshold value for healthcare interventions in Malaysia.

Method(s):   A cross-sectional, contingent valuation study was conducted using stratified multistage cluster random sampling technique in the states of Penang, Kedah, Selangor and Kuala Lumpur Federal Territory in Malaysia. Respondents aged between 20–60 years old who can understand either English or Malay language were interviewed face-to-face using pre-designed questionnaires. They were asked for the socioeconomic background, quality of life and their willingness-to-pay (WTP) for a hypothetical scenario (treatment, extended life in terminal illness and life saving situations with three severities and two quality-adjusted life-year (QALY) gained levels – 0.2 QALY and 0.4 QALY). Bidding game technique and double-bounded dichotomous-choice approach were applied in eliciting WTP amount for each respondent. The mean ratio of the amount of WTP for an additional QALY gained was explored by non-parametric Turnbull method and parametric interval regression model. Parametric interval regression model was also used to analyse the factors that affect the CE threshold.

Result(s):   A total of 1100 respondents were approached and the overall response rate was recorded at 92.1%. The CE threshold found from the non-parametric Turnbull method was ranged from MYR 12810 – 22840 (~ USD 4000 – 7000). Using the parametric interval regression model, the CE threshold was estimated to be ranged from MYR 19929 – 28470 (~ USD 6200 – 8900). Key factors that affect the CE threshold were education level, estimated monthly household income and the description of health state scenarios.

Conclusion(s):   The findings in this study support that there is no single value of a QALY. The CE thresholds estimated for Malaysia in this study were found to be lower than the normally used threshold value of one to three times the gross domestic product per capita as recommended by the World Health Organisation.


Y Y Guan, MSc, C Chen, PhD and J S Yoong, PhD, National University of Singapore, Singapore, Singapore

The increasing prevalence of obesity, which is associated with physical inactivity and sedentary lifestyle, continues to increase and pose a substantial economic burden in most developed countries. Many workplace wellness programs have evolved to integrate the use of financial incentives to promote behaviour change and healthier lifestyles. The purpose of the present study was to determine whether tournament-style financial incentive (individual-level competitive prize of S$150) was effective in motivating adults in the workplace to increase daily stair usage over a 6-week period between July and October 2014. This study incorporates behavioural economic principles, a workplace stairs competition and point-of-decision prompts to offer insights on a behavioural reinforcement strategy on physical activity.


Participants (N=41) were randomized to one of the two experimental groups: (i) control group without financial incentives (n=20) or (ii) intervention group with financial incentives (n=21). Data was collected using a self-monitoring steps cum calories tracker app installed into mobile devices of participants in both groups.


The intervention group significantly increased their stair use compared to the control group by a difference of 7,743 steps (95% CI: 2,889 – 12,598, p=0.003). Furthermore, participants in the financial incentive intervention group burned 579 more calories, on average, than the control group (95% CI: 229 – 930, p=0.002). There was no sustained significant effect beyond the 6-week period of intervention.


Participants in the financial incentive intervention group outperformed the control group through increased stair usage, and hence physical activity levels. There is evidence that the use of tournament-style financial incentives can promote the uptake of physical activity, however this effect attenuates over time and is not sustained beyond the period of intervention. Further research should be broadened to include investigating novel mHealth technologies that increase stair use and multicomponent interventions that promote physical activity for a sustained period of time.


Hla Hla Thein, MD, MPH, PhD, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada, Toronto, ON, Canada, Ayaz Hyder, PhD, Ohio State University, Columbus, OH, Norman Marcon, MD, FRCP(C), Department of Medicine, University of Toronto, Toronto, ON, Canada, Tony Godfrey, PhD, School of Medicine, Boston University, Boston, MA and Lincoln Stein, MD, PhD, Ontario Institute for Cancer Research, Toronto, ON, Canada
Purpose: Incidence rates of esophageal adenocarcinoma (EAC) have increased in Canada over the past three decades. Early diagnosis of Barrett’s esophagus (BE), which is a risk factor for developing EAC, may lead early interventions for better clinical outcomes. This study compared health benefits and cost-effectiveness of current (endoscopy+biopsy) and alternative (sponge-based cytological sampling with genetic testing) surveillance strategies to detect early stage of BE and EAC.

Method(s): We developed a microsimulation model to track individual-level health trajectories accounting for demographic and lifestyle risk factors for developing BE, medication use, screening and surveillance for BE and treatment algorithms for each stage of BE and EAC. Model calibration and validation was performed under the assumption of endoscopy+biopsy surveillance strategy. We compared clinical outcomes, including differences in EAC incidence rate, the number of new EAC cases averted and the number of surveillance procedures per patient-year for BE patients before diagnosis of EAC under two surveillance strategies and used cost-utility analysis to estimate lifetime costs (2014 Canadian dollars), health benefits (quality-adjusted life years, QALYs), and incremental cost-effectiveness ratios. In addition, we undertook extensive sensitivity analysis and value-of-information analysis to determine the expected value of perfect information (EVPI) at different willingness-to-pay values. 

Result(s): Compared to endoscopy+biopsy, sponge-based surveillance with genetic testing reduced overall EAC incidence by 71% and new EAC cases by 26%. Total incremental costs and health benefits (discounted at 5% annually) for sponge-based surveillance with genetic testing under 100% uptake and 11% higher sensitivity than the endoscopy+biopsy strategy for detecting BE with dysplasia were $186,152,124 and 556,511 QALYs gained with a corresponding ICER with 95% confidence interval of $334($307-$366) per QALY gained. Our results were sensitive to parameters related to sensitivity, cost and uptake of sponge-based surveillance with genetic testing.

Conclusion(s): Based on our model, sponge-based surveillance with genetic testing is cost-effective and may reduce incidence of EAC if it is widely taken up in clinical practice. Our results provide novel insights for clinicians, patients, and decision-makers evaluating non-endoscopic surveillance methods in the BE population. These insights should be helpful in designing optimal strategies to reduce the burden of BE and EAC among individuals, health care systems and society.


Thaison Tong, MSc, John Brazier, PhD and Praveen Thokala, PhD, University of Sheffield, Sheffield, United Kingdom
Purpose: The practice of health technology assessment (HTA) has traditionally been dominated by cost effectiveness analyses (CEA) using quality adjusted life years (QALYs) as the measure of benefit. However, a review of decisions by NICE and other HTA agencies revealed that other criteria are being used during evaluuation of medical technologies. Does the implicit use of these other criteria than cost-effectiveness benefit the legitimacy, consistency and transparency of the HTA decisions? Is there is a need for a more explicit and formal approach. In other words, how should these other criteria be considered in HTA decisions?

Method(s): We present two alternative methodological  frameworks to undertake economic evaluation using a wider perspective. Dementia will be used as a case study, as it has a big impact on health, social care and informal caregivers. We will present a whole system micro simulation model for dementia to include costs and consequences on all the sectors. Then, we present the data gathered on impact of dementia interventions from literature and analysis of routine data available to us. The impact of different dementia interventions are estimated using the data on costs and outcomes input into the model. The model will then be operationalised using two economic frameworks: a) extending a cost-per-QALY approach and b) cost consequence analysis (CCA) with Multi-Criteria Decision Analysis (MCDA) approach. 

Result(s): Extending the cost-per-QALY approach to incorporate other criteria requires recalculation of the cost effectiveness threshold - this is because if different ‘benefit function' than QALYs is used then the threshold needs to be recalculated taking into account the displaced 'benefit function' i.e adjusted cost per ‘benefit function threshold. However, this can lead to consistency as all the things are explicitly included in a CEA framework.  MCDA can also be used to guide decision makers in understanding the trade-offs between values that may be conflicting and can be done a case-by-case basis. Many different tools and techniques are available under the general heading of MCDA ranging from fully quantitative MCDA models to more deliberative MCDA approaches.

Conclusion(s): In certain HTA problems, there may be a need for incorporating other important considerations than just QALYs. There a number of frameworks available for broadening economic evaluation and the choice between different methods may depend on the need for consistency, scale of the problem and the stakeholders.


Beate Jahn, PhD, UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, Hall in Tyrol, Austria, Annette Conrads-Frank, PhD, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria, Gaby Sroczynski, MPH, Dr.PH, Institute of Public Health, Medical Decision Making and HTA, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T./Innsbruck, Austria, Ursula Rochau, MD, MSc, UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and HTA, Department of Public Health and HTA/ ONCOTYROL - Center for Personalized Cancer Medicine, Area 4 HTA and Bioinformatics, Hall in Tyrol/ Innsbruck, Austria, Günther Zauner, PhD, dwh GmbH, simulation services / DEXHELPP (Decision Support for Health Policy and Planning), Vienna, Austria, Michael Gyimesi, MSc, Austrian Public Health Institute, Vienna, Austria, Niki Popper, PhD, dwh Simulation Services /Technical University Vienna, Institute for Analysis and Scientific Computing / DEXHELPP (Decision Support for Health Policy and Planning), Vienna, Austria and Uwe Siebert, Prof., MD, MPH, MSc, ScD, UMIT, Dept. Public Health&HTA/ ONCOTYROL, Area 4 HTA&Bioinformatics/ Harvard T.H. Chan School Public Health, Center for Health Decision Science, Dept. Health Policy&Management/ Harvard Medical School, Institute for Technology Assessment&Dept. Radiology, Hall in Tyrol/ Innsbruck/ Boston, Austria
Purpose: Population models have become a common tool to explicitly consider population dynamics or changes when guiding decision making for health or social care policies. Applications range from prediction of burden of disease, over demand for (old age) care to economic evaluations of specific treatments or public health interventions. In our project DEXHELPP (Decision Support for Health Policy and Planning), we focus on population models, suitable modelling techniques and methodological challenges. The goal of this systematic review is to increase the insight of health policy researchers in population modelling.

Method(s): We performed a systematic review on population models, focusing on the development and application for health policy questions. We identified existing models and systematically extracted structured information. The information was summarized in evidence tables and narrative comparisons. We present goals, modeling techniques, general model characteristics, model specification, model parameter estimation as well as advantages and shortcomings of chosen approaches.

Result(s): The term ‘population model’ is not used consistently. It refers to both models applied to study population dynamics and models investigating the impact of interventions on populations. Population models consider open (dynamic) rather than closed cohorts. In general, populations can be projected into the future using micro- or macro simulations, time can be continuous or discrete, and a modular structure can allow studying several diseases and applications. Comprehensive population models that have been applied for several research questions exist, for example, in Canada (POHEM), Sweden (SESIM), Australia (APPSIM) or Austria (GEPOC).

The found models are often microsimulation models. Reported challenges are: data shortage, calibration, complexity and related time and resource demands as well as difficulties understanding the outcomes. Successful microsimulation projects require continuity and resources to allow multiple applications and updates on a long perspective.


Population models are applied to inform health policy decisions. Applications still require better data, opportunities for data linkage and long-term perspectives of funding. Research should focus on continued methodological improvement for developing and applying complex population microsimulations.

The research project DEXHELPP (Decision Support for Health Policy and Planning: Methods, Models and Technologies based on Existing Health Care Data) is in the frame of COMET-Competence Centers for Excellent Technologies. DEXHELPP is supported by BMVIT, BMWFW and the state Vienna. The COMET program is transacted by the FFG.


Sorapop Kiatpongsan, MD, PhD, Chulalongkorn University, Bangkok, Thailand, Krittinee Nuttavuthisit, PhD, Sasin Graduate Institute of Business Administration of Chulalongkorn University, Bangkok, Thailand and Michael I. Norton, PhD, Harvard Business School, Boston, MA


To compare Thais' estimated and ideal distributions of governmental spending on healthcare services for the rich and poor.



The survey was conducted by face-to-face interviews in Thailand from February to April 2015. A nationally representative, probability-based, random sample of Thais (N = 3,500) was asked to estimate the distribution of governmental spending on healthcare services for Thais in each of the five income quintiles. Respondents then reported their ideal distributions: how they thought the government should allocate healthcare spending to rich and poor Thais.

We compared estimated and ideal distributions to each other, and to the actual distributions of healthcare spending for the rich and poor. We focused our analyses on government healthcare spending on the richest (top 20%) and poorest (bottom 20%) groups.

Results were analyzed in aggregate and stratified by participants' gender, age, education, and income. Statistical significance was determined at p < 0.05.



Three thousand and five hundred participants completed the interviews. The response rate was 72.4% (3,500 out of 4,833). Mean age was 41.1 years and half of participants (50.8%) were female.

Respondents underestimated how much of the healthcare budget (i.e., proportion of total healthcare budget) was spent on the richest group (21.9% versus 54.8%) and preferred that a smaller proportion be used for the richest group than their estimate (12.0% versus 21.9%), p < 0.001 for both comparisons. Respondents overestimated how much of the healthcare budget was spent on the poorest group (21.1% versus 5.7%) and preferred that a larger proportion be used for the poorest group compared to their estimate (33.1% versus 21.1%), p < 0.001 for both comparisons. See Figure 1 for full results.

Importantly, respondents preferred that the government spend a significantly higher proportion of healthcare budget on the poorest group compared to the richest group (33.1% versus 12.0%), p < 0.001 – the exact opposite pattern of current spending on the poorest (5.7%) and richest (54.8%).

We found similar results in all subgroup analyses regardless of respondents' gender, age, education and income.



Thais underestimate the disparities in governmental healthcare spending on the rich and poor. They prefer that the government allocate a higher proportion of healthcare budget to the poor and that the proportion for the poor is larger than the proportion for the rich.