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School of Medicine Public Health and Health Systems

Assignment Cover Sheet Student ID: Family Name Given Names: Date: Lecturer:

430957 Ghosh Lilian Siyan Baxter

Unit Code & Title:

CAM628 Health Economics

Assignment Title:

Assignment 2-Essay 1: Cost-utility analyses (CUAs) are increasingly common in Australia and around the world. Define CUA. How does CUA value health related benefits? Describe briefly the measures below and explain how might an assessment of the burden of disease in a population differ between these health measures. Illustrate each answer using lung cancer, mental illness and coronary heart disease as the disease burden in each account.

Further Details: (eg: Assignment Topic etc. if i. Disability-Adjusted Life Years (DALY) appropriate) ii. Quality-Adjusted Life Years (QALY)

iii. Potential Years of Life Lost (PYLL) or Years of Potential Life Lost (YPLL)

Word Count: Campus: Hobart – Distance I declare that all material in this assignment is my own work except where there is clear Academic Honesty acknowledgement or reference to the work of others . I am aware that my assignment may be Declaration: submitted to plagiarism detection software, and might be retained on its database. I have read and complied with the University statement on Plagiarism and Academic Integrity on the University website at www.utas.edu.au/plagiarism. I will keep a copy of this assignment until the end of the semester. Complete this Assignment coversheet, making sure you tick the acknowledgement of the Academic Honesty Declaration. Save this file and submit it as a separate document along with your assignment to the relevant Assignment Dropbox on MyLO. University of Tasmania T +61 3 6226 4844 F +61 3 6226 4788 www.utas.edu.au ABN 30 764 374 782 / CRICOS 00586B Private Bag 34 17 Liverpool St Hobart 7000 7001 Australia

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Abstract Health economics sets about to quantify the value of health and health by-products, measuring the cost effectiveness of health interventions and the benefits to the general society. Cost-utility analyses (CUA) is just one technique used to govern distribution of resources. This system of measurement comprises calculation of the cost of quality-adjusted life years (QALY) as well as the cost of disability-adjusted life years (DALY). Also considered in this assignment is the measurement of Years of potential life lost (YPLL) or potential years of life lost (PYLL), thus demonstrating figuratively a measure of premature mortality. This can assist with public health forecasting, through calculating social and economic loss. The purpose of this assignment is to discuss the varying measures used in CUA, and to confer the differences in disease burden assessments found by using the different measures. Lung cancer, mental illness and coronary heart disease are the disease burdens being presented. This assignment will conclude that CUA is a meaningful and valuable methodology that provides a process of measuring cost effectiveness in health-related events.

Introduction Health economics makes use of CUA as a measurement or evaluation of cost effectiveness, benefits or utility generally used for procurement and as a measure of health gain [1, 2]. Benefits of CUA relate directly to ‘perceived values of expected outcomes’ [1, p.965] and allow a measurement of outcome for ‘comparison of costs and outcomes in different programmes’ [2, p. 8]. Potentially, this allows for comparison models in health care, ‘assessing opportunity cost’ [2, p. 8], so that established models of care may be adopted in budget, already proven to be cost effective. Measurements of outcome can be expressed as units of health portraying quantity and quality of life, with utility being the profits obtained from health and health care [2]. These are used to provide information to health decision makers so they may be informed and able to make decisions based on important economic evaluations. The value of life years and health states A key indicator and descriptive value of treatment outcomes identified as health-related quality of life data (HRQoL) captures the effect of treatment on a patient’s length of life [2,3]. It is the impact of these which is measured in CUA, resulting in preference weights thus indicating the more

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desirable health states which may consequently be favoured in the analysis [4]. Utility values relative to quality of life are assigned to health states and are known as quality-adjusted life years (QALY) and disability-adjusted life years (DALY) [2, 3]. DALY is also a’ health gap measure’ which considers the Potential Years of Life Lost (PYLL) and is reflected in analyses as corresponding years of healthy life lost due to the presence of states of poor health or debility, better reflected as morbidity and mortality [4,7]. Understandably, these measures of health and health outcomes influence the allocation of resources necessary to equate health care performance with health care quality and provide a meaningful role in cost-effectiveness analysis [5, 6, 8]. For economic ease of reporting, measurements of utilities are based on a numeric cardinal system, from 0 to 1, with 1 indicating perfect health, and 0 death [2]. To determine QALY, the utility value associated with a given health state is multiplied by the number of years lived in that state [2,4]. CUA values health related benefits by being able to display outcome measures in a single measure, including results from ‘reduced morbidity (quality gains) and reduced mortality (quantity gains) [2,9]. Benefits gained from this type of analysis also includes the ability to guide health economists to measure emotional, social and physical well-being, implementing tools designed for this. It is possible that health related benefits correlate with budgetary constraints whilst using CUA, and this method can be applied to all disease states, including more complex cases [2]. Health consumers are not always able to know or understand expected health outcomes, and perception of quality of life and preferences varies amongst individuals. Measurement techniques like the EQ-5D (EuroQol) and Health Utilities Index aim to reduce the inconsistency in the way health states are described and may reduce the disparity in the description of health states [2, 10, 11].

Disease burden of Lung Cancer There are many mitigating factors when considering disease burdens, and many studies of public health are becoming more defined as health economists can utilise health indexes which more accurately reflect current public health trends. [2, 12]. As a tool for measures in most cancers, the EQ-5D tool has been recognised as an appropriate one, assisting with preference-based measures of health gains [2]. He most frequently used research tool is the QLQ-C30 questionnaire and QLQLC13 module developed by European Organisation Research on Treatment of Cancer (EORTC) [14]. HRQoL is dependant upon the QoL of an individual, and needs to include functional capacity, a person’s insight into their disease and the symptoms of the disease [14]. The study of lung cancer has many variables to consider including type of cancer, treatment for the type of cancer and success Lilian Ghosh 430957

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rates and demographics. Early detection of lung cancer has been emphasised owing to new advances of chemotherapeutic treatment increasing the cost of care [15]. The study by Yang et al [15] identifies using QALY as a measurement unit of QoL to estimate quality adjusted life expectancy (QALE) and loss-of-QALE in comparing the QALY for operable and inoperable non-small-cell lung cancer. It was concurred that DALY, whilst able to allow possible international contrasts, the loss-of-QALE allowed for ‘direct comparisons of different diagnosis and treatments strategies’ and it would follow that this would produce a more useful method to be applied to decision making within national health policy costeffectiveness identification [15]. Hence, how a study is performed can affect the results of QALY and DALY, with the burden of the disease, that is the stage of disease and the treatment being provided adjusting the results for everyone. Depending upon the sample group, size and severity of disease, the PYLL may not be a true correlation of all lung cancers, however, may identify trends for increasing QoL through early detection and prevention such as acknowledged by Yang [15].

Burden of disease of Mental illness Types of costs associated with mental health care are inclusive of direct costs, indirect costs, patient costs, future costs and intangible costs. [16] Outside of the healthcare system, costs associated with imprisonment, special housing provision, and special education provided also impact society through cost burden [16]. Measurement of QALY is affected by the alternative therapies which quantifies cost, and this is adversely affected by methodological uncertainty, structural uncertainty and parameter uncertainty [2, 16]. These points demonstrate how the studies themselves may affect the outcome measures through study bias, however as mental illness is ‘the largest single cause of disability in Australia’ there are many aspects of the burden of this disease which may affect the HRQoL [17]. PYLL is affected by the duration of the illness, as evidenced by Vos (2008), with various states of mental illness having periods of remission. It is also dependant upon comorbidities, whereby if there are two or more diagnosed mental disorders there is possibility of overestimating DALY (attributing a score in excess of 1, the equivalent of deceased) (Vos 2008, Luyten 2016). Age related weights also affected DALYs, and Vos (2008) indicates that PYLL values are regulated ‘by the product of incidence and duration’ p. 433.

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The Fourth National Mental Health Plan (2009) shows how the burden of mental illness affects those aged between 16-54 years of age, the prime working years which would indicate the burden of the disease on the Government, and on society. Luyten (2016) describes how QALYs are used as a generic measure in mental health outcomes, linking them to ‘subjective appraisals of how bad it is to experience these outcomes’ p. 50. The estimated incremental cost per QALY can be affected by comparison of one intervention against another and is affected by uncertainty, making it difficult to estimate accurate cost assessments (Luyten 2016).

Burden of coronary heart disease (CHD) CHD is reported as the second highest burden of disease in 2011 (15%) after cancer (19%), with mental and substance use disorders coming in 3 rd with 12% (AIHW 2016). Using DALY, the effect on cardiovascular disease could be explained utilising the disability weight (health loss) of a specific disease type within the classification under several specific diseases under heart disease in the ICD11 (2019). Health loss relating to angina has a disability weight of 0.167 according to the Global Burden of Disease (GBD) 2010 (Salomon et al 2015). Multiply this figure with 1 (period of year), (0.167 x 1=0.167 years of living with a disability, YLD). If an individual was to have a heart attack during this year, his short-term health loss of about 2 months with a disability rate of 0.48 (0.48x 2/12=0.08) This would equal a total of 0.25 YLD for health loss due to CHD. In this instance if the individual would perish at the end of that year, several years are lost due to premature death. Theoretically a female may live to the age of 84 in Australia, so if a woman was to pass at the age of 54, then she would lose 30 years of life due to dying prematurely (PYLL), or fatal burden plus non-fatal burden of disease. To calculate the total DALY relating to this example, we would add 0.25 plus 30 PYLL, providing 30.25 DALY. It appears that the effects on DALY and PYLL are obvious, in that if years are lost due to a change in the condition with the disability weight, that is an episode of care causing increase in illness, or time lost due to temporary change in health, this can significantly alter the incidence of lost years of life (AIHW 2016).

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Conclusion CUA is a valuable method used for evaluation of cost effectiveness of health utilities in public health. It allows for health outcome measures in a variety of disease burden states and considers other established programs or methodology which have been proven to be economically effective to be employed. Measures of health outcomes using QALYs, DALYs and PYLL assist to provide information to health economists to ensure informed decisions are made for the benefit of the society.

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http://ojin.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/ TableofContents/Volume122007/No3Sept07/CostUtilityAnalysis.html 4. Whitehead, SJ & Shehzad Ali, S, 2010,Health outcomes in economic evaluation: the QALY

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loss of quality-adjusted life expectancy (QALE) for patients with operable versus inoperable lung cancer: Adjusting quality-of-life and lead-time bias for utility of surgery’, Lung Cancer, Vol. 86, pp. 96-101, viewed 26 April, 2019, http://dx.doi.org/10.1016/j.Juncan.2014.06.006

16. Luyten J, Naci H, Knapp M, 2016, Economic evaluation of mental health interventions: an introduction to cost-utility analysis Evidence-Based Mental Health Vol. 19, No. 2, pp. 49-53.

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18. Vos, T & Mathers, CD, 2008, ‘The burden of mental disorders: a comparison of methods between the Australian burden of disease studies and the Global Burden of Disease study,

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Bulletin of the World Health Organisation, Vol. 78, No. 4, pp. 427-438, viewed 26 April 2019

19. ICD-11 for Mortality and Morbidity Statistics, 2019, in WHO Disability weights, discounting and age weighting of DALYs. https://www.who.int/healthinfo/global_burden_disease/daly_disability_weight/en/ 20. Australian Burden of Disease Study: Impact and causes of illness and death in Australia, 2011, AIHW, 2016. https://www.aihw.gov.au/reports/burden-of-disease/abds-impact-and-causes-of-illness-death2011/formats 21. Average Human Life Span Expectancy by Country-Disabled World, 2019 www.disabled-world.com › Calculators Charts Table, viewed 27 April 2019.

http://www.oecd.org/els/health-systems/health-care-quality-indicators.htm

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