Economic burden using the value of a statistical life method Economic burden using the economic growth approach

Although these estimates are based on fairly small sample sizes, they are consistent with data from the United States, which reveal that men with chronic disease work 6.1 percent fewer hours and women work 3.9 percent fewer hours than healthy workers (Stuckler et al. 2006). Monetizing absenteeism estimates based on the average wage of full-time workers (estimated to be Int$214 per day for Saudi nationals in 2018 international dollars) (GASTAT 2019) with one or more NCDs listed above reveals the following:

• The total annual costs due to absenteeism in Saudi Arabia are Int$22.5 billion (2018 international dollars), which represent 1.21 percent of GDP in 2018. • This estimate does not take into account presenteeism, inability to have better paid jobs, early retirement, or productivity losses due to time spent caring for someone with NCDs.

Only one study is available for Saudi Arabia that uses a bottom-up approach and assumptions regarding productivity loss from select NCDs, rate of population aging, incidence rate for each disease, and labor market projections (rasmussen, Sweeny, and Sheehan 2015). It estimates that NCDs reduced GDP by at least 6.7 percent in Saudi Arabia in 2015 and predicts that NCDs will reduce GDP by 9.7 percent in 2030. Predictions for 2030 for other countries are similar: Singapore (6.7 percent), Japan (8.5 percent), and the United States (8.5 percent) (rasmussen, Sweeny, and Sheehan 2015).

ECONOMIC BURDEN USING THE VALUE OF A STATISTICAL LIFE METHOD

Cost-of-illness studies such as those described above tend to use market rates for health services and wages to quantify the burden. An alternative paradigm is the value of a statistical life, defined as the marginal rate of substitution between income (or wealth) and mortality risk. Using the VSL method, the value of premature death is inferred from real or hypothetical trade-offs that people willingly make (how much individuals are willing to pay to reduce the risk of death). These trade-offs typically entail taking on greater health risks in exchange for something of value, such as working in a smoke-filled bar or on an Alaskan fishing vessel, both risky occupations, in exchange for a higher salary. This higher salary can be interpreted as a risk premium and can be used to estimate the value of a statistical life.

The main advantage of this approach is that it is most consistent with economic theory (that is, with utility maximization). The cost-of-illness approach accurately quantifies the burden of disease from an accounting perspective, but it does not take into account the changes in utility (value) that individuals may accrue from, say, not having to diet and exercise or the intrinsic value that people place on being alive. An additional advantage is that, unlike the cost-of-illness approach, the VSL approach can be used to generate unique estimates that each individual or set of individuals places on a particular risky scenario. These estimates, if aggregated across individuals, can be interpreted as the total statistical value of the loss due to a condition (for example, diabetes) and may include all direct, indirect, and intangible costs not easily measured, such as pain and suffering and premature mortality.

This approach proceeds as follows (US ePA n.d.). Suppose that 100,000 people are asked how much they would be willing to pay to reduce their individual

risk of dying by 1 in 100,000, or 0.001 percent, over the next year. Since this reduction means that there would be one fewer death expected among the sample over the next year, this is sometimes described as “one statistical life saved.” Now suppose that the average response to this hypothetical question is US$100. Then the total dollar amount that the group would be willing to pay to save one statistical life in a year would be US$100 per person × 100,000 people, or US$10 million. This is an estimate of the value of a statistical life.

Although this approach is intuitively appealing and has been used in policy analyses in a range of fields, from environment to transportion to health, it has several limitations. Primary weaknesses include problems with stated preference questions, such as the one posed above, where responses suffer from people’s inability to differentiate between small changes in risks for rare events as well as from framing issues, hypothetical bias, and oversimplification. revealed preference results often suffer from possible selection bias and the variation in risk perceptions across individuals. For these reasons, results of the VSL vary greatly across studies, with estimates ranging between US$45,000 and US$18.3 million (Viscusi and Masterman 2017).

Studies in other countries apply data from the United States and provide a VSL estimate for Saudi Arabia of US$4.05 million (2015 US dollars) (Viscusi and Masterman 2017). The 2017 Global burden of Diseases study reports 31,682 premature deaths due to NCDs among persons 15–64 years of age in Saudi Arabia (IHMe 2018). Multiplying these two figures values the loss of statistical lives due to premature deaths from NCDs in 2017 at US$128 billion (2015 US dollars). For comparison, a similar calculation for Morocco, which has a slightly larger population, suggests a loss equivalent to only US$30 billion (2015 US dollars).

To produce something more specific to Saudi Arabia, the value of premature mortality due to NCDs in Saudi Arabia is quantified using the VSL method. Using the VSL approach, following the method of Jamison et al. (2013), and defining a standardized mortality unit as a 10−4 increase in the risk of death, the value of this mortality unit is estimated at 1.8 percent of GDP per capita. The VSL estimates are compared with a different method commonly used in the NCD community, wherein healthy life years gained are valued at GDP per capita. The latter approach is referred to as the human capital approach. To implement these two approaches, “avertable” (rather than total) NCD deaths and mortality rates are used, and in the case of the human capital approach, avertable mortality rates are converted into avertable NCD-attributable disability-adjusted life years (DALYs) using predefined empirical relationships. Avertable DALYs are then monetized using GDP estimates. results are presented in table 4.4. The methodology is described briefly in annex 4A. because these methods use different approaches to dealing with the age distribution of avertable deaths and the age distribution of deaths differs for men and for women, the gender differential in the value of avertable mortality also differs between the two methods. The following summarizes the main findings:

• The value of avertable NCD mortality in Saudi Arabia ranges from US$66 billion to US$96 billion using the cause-level analysis focusing on 34,000 avertable NCD deaths. • The value of avertable NCD mortality in Saudi Arabia ranges between US$30 billion and US$49 billion using the risk-factor-level analysis focusing on the 28,000 avertable risk-attributable deaths. • As a share of GDP in Saudi Arabia, these values represent 8.3–12.0 percent and 3.8–6.2 percent of GDP, respectively.