An Economic Evaluation of Physical Activity and Implications for Its Promotion
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המחקר בונה שני מודלים של ניתוח באמצעות עץ החלטות מבוסס על קוהורט מבוגרים בריאים בני 40 שנה: Cost-minimisation analysis (CMA) וגם Cost-utility analysis (CUA) . המודלים מורכבים מנתונים אשר נאספו מן הספרות המקומית והבינלאומית על עלויות המחלה , התחלואה, איכות חיים והמידה שבה פעילות גופנית משפיעה על גורמים אלה. עולה כי ניתן להפחית כ 117,000 $ מההוצאות הרפואיות אצל אדם פעיל, לעומת אדם לא פעיל. אם כל הקוהורט בארץ היה מגיע לרמות המומלצות של פ"ג הרי יכולנו לצפות לחיסכון בסך 9.5 מיליארד דולר אמריקאי לאורך חייו של הקוהורט. . אם מניחים שפרמטרים אלה תקפים לקוהורט בני 20-65 ומכפילים את המספרים באוכלוסיית ישראל מגיעים לחיסכון שנתי של כ 10 $ מיליארד. זה אומר שאפילו אם רק 1% נהיים פעילים ניתן להשקיע כ-90 $ מיליון לשנה ולראות חיסכון.

The quality adjusted life year (QALY) combines quality and quantity of life, with one QALY being equivalent to one life year spent in full health. QALY‘s suffer from a number of limitations but they still currently provide the best methodology available for comparing outcomes from different health care interventions.

Outcomes of CUA are therefore expressed as quantity (duration) of health status multiplied by the quality of life (utility) of that health status.

An intervention is considered to cost-effective if it costs less and generates more QALYs than the standard treatment. This is called dominance. It may also be judged to be cost-effective if the incremental cost-effectiveness ratio (ICER) falls below a certain threshold. The incremental cost-effectiveness ratio is calculated as the estimated difference in cost between the competing interventions/treatment divided by the difference in QALY's gained. A QALY of $50,000/QALY gained has historically been deemed an acceptable threshold but today there is evidence supporting thresholds of $100,000 - $300,00 per QALY gained (Ubel et al 2003, Braithwaite et al 2008). This is notwithstanding debate on the value of any threshold at all (Weinstein, 2008). In order to make the strongest case possible for the promotion of physical activity, this dissertation uses the conservative $50,000 threshold.

4. Thesis Goals and Objectives

Goal

To calculate medical cost reduction engendered by physical activity on an individual level and, based thereon, to infer threshold parameter values in terms of cost and efficiency whereby investing in interventions for the promotion of physical activity on a population level is cost-effective.

General Objective

Use a decision-tree analysis model to determine the absolute savings expected from a physically active compared to a sedentary individual and thereafter to establish the cost-effectiveness of investing in promoting physical activity versus a laissez-faire strategy for a cohort of healthy adults aged 40.

The measures of the effectiveness of physical activity are:

1. Reductions in disease incidence.

2. Gains in life expectancy.

3. Gains in quality of life.

Conditions included in the decision tree are:

 Coronary Heart Disease

 Cerebrovascular Attacks

 Type 2 Diabetes Mellitus

 Colon Cancer

 Breast cancer (for women)

 Dementia

 Musculoskeletal disorders (arthritis and hip fracture)

Results of the CUA are expressed as an incremental cost-effectiveness ratio (ICER), that is, the ratio of change in costs to the change in effects. This represents the additional cost of one unit of outcome gained a healthcare intervention or strategy, when compared to its comparator. The formula is represented thus:

ICER = (COSTnew strategy – COSTcurrent practice) / (EFFECTnew strategy – EFFECTcurrent practice)

Quality Adjusted Life Year‘s (QALY‘s) are calculated by multiplying years lived with a condition by the utility of that condition. For a cohort of healthy 40yr olds a combination of healthy life expectancy and life expectancy with disease is necessary. It was assumed that the utility of life before disease onset is 1.0 such that overall QALY‘s can be expressed as:

(Years between age 40 and disease onset * 1) + (Life expectancy at diagnosis * disease-specific utility)

6.1. Cost-minimisation analysis

6.1.1. CMA Base case

6.2.2. CUA one-way sensitivity analyses

As in the cost-minimisation analysis, disease probabilities, costs and utilities were not significant deciding factors as identical parameters were used in all arms of the model. Important potential differences were assessed as:

 The cost of Enabling

 The proportion of people being physically active under each scenario

 The proportion of each sex out of the active population

 The degree to which physical activity increases life expectancy and quality

 The degree to which PA decreases probability of disease

 The degree to which PA decreases disease cost

Cost of Enabling

For lifetime costs of enabling ranging until approximately $5,000 per person the Enabling option is dominant. Up until $7,500 per person, per lifetime the Enabling option still remains below the $50,000/QALY mark, increasing to over $90,000/QALY at $50,000.

If in 2008 there were 82,000 Israelis aged 40 (Central Bureau of Statistics), over US$400 million could be spent on promoting physical activity and still yield a net cost saving while improving the health status of this cohort. If one is prepared to spend $50,000 per QALY gained this would entail an investment of close to USD1 billion over the lifetime of this cohort or $25 million per year (undiscounted).

7. Discussion

This paper reinforces current knowledge regarding the benefits of physical activity. A single person can save close to $120,000 in health costs over a lifetime or $3,000 a year, by initiating this lifestyle change. If we assume that parameters are largely unchanged for a cohort aged 20-65 years we can multiply these figures by the over 3.2 million adults in Israel and see a potential annual saving of close to $10 billion. Even if only 1% becomes active one could invest $90 million a year and still realise a saving. This is keeping in mind that savings are underestimated as many conditions are not included in the model and a person can suffer from more than one chronic disease. A ―back-of-the envelope‖ calculation (see Appendix H) which ignores these constraints and shows a per-person saving of closer to $200,000 – albeit not an order of magnitude greater but still important, particularly since it still excludes conditions for which at least one parameter was missing and it does not take into consideration evidence that disease management is cheaper for physically active people.

As can be seen, the model makes a strong case for facilitating physical activity in a cohort of healthy 40yr olds. Differences in benefits and cost-benefit ratios between the "Enabling" and "Laissez-faire" arms were highly significant with p ≈ 0.. In the base case, using mean parameter values, promoting physical activity costs less than $600 per QALY gained, 50% of all iterations resulted in Enabling being both less costly and more beneficial (dominant) compared to Laissez-faire. 

The incremental cost-effectiveness ratio is less than $10,000 making investing in PA far below even the most stringent willing-to-pay threshold in use today. The model does prove to be sensitive to variation in key model parameters such as cost of intervention, its effectiveness, baseline active population as well as degree of disease risk and cost reduction. Change in utility and proportion of females were not significant deciding factors.

Annemans and others have shown that the results would be even more dramatic for older populations and those already at risk of chronic disease (hyperlipidaemia, hypertension, metabolic syndrome) as well as showing benefit for younger, healthier cohorts as well.

These results are largely compatible with results from similar studies:

Annemans et al report a narrower but similar ICER‘s range of €2,000-15,000/QALY gained ($1500 - $11,000 based on mid-2007 exchange rate) for physical activity versus inactivity study. This difference can be attributed partly to a more limited condition set which would tend to reduce the uncertainty in the model and partly to use of a Markov model which allows for annual changes in risk as well as discounting.

The NICE cost-effectiveness analysis (2006) and Cobiac et al (2009), on the other hand, reported more favourable results. However, Cobiac et al assigned greater health benefits accruing from PA than those used in this model. Furthermore, intervention costs in the NICE study are low (maximum $544 per participant) and are accrued on an individual basis instead of a population-wide basis. The Cobiac study included both individual-based as well as population-based programmes which would also tend to decrease intervention costs. Lastly, both of these studies they measured effectiveness in terms of change in average community physical activity levels. This allows for the accumulation of benefits to sedentary people becoming inadequately active as well as inadequately active people becoming more active whereas this dissertation, like Annemans et al, calculates intervention effectiveness in terms of percentage of the sedentary population becoming adequately active.

It is hoped that the results of this study add to the growing literature giving weight to arguments that will convince policy-makers that investing in a national programme to promote physical activity is a worthwhile use of public funds as it has proved to be cost-effective in most scenarios and may even show to be cost-saving.