North Africa

FIGURE 5.2

Comparative overview of HCI scores of economies in the Middle East and North Africa

HCI score 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.37 0.4 0.49 0.5 0.510.52 0.54 0.55 0.56 0.580.58 0.59 0.61 0.62 0.66 0.67 0.7

0.1 0 Yemen, Rep. Iraq Egypt, Arab Rep.MoroccoTunisiaAlgeriaLebanon West Bank and GazaJordanKuwaitSaudi ArabiaIran, Islamic Rep.QatarOman United Arab EmiratesBahrain Malta

Source: World Bank 2018. Note: HCI = human capital index.

Health is not the main driver of this low ranking, which is attributed largely to educational outcomes. In Saudi Arabia, 99 percent of children born today will survive to school age, 91 percent of children 15 years of age will survive to 60, and stunting is not much of an issue (World Bank 2018). Instead, a low level of learning is slowing human capital formation in Saudi Arabia. A fouryear-old child in Saudi Arabia can expect to complete 12.4 years of preprimary, primary, and secondary school by age 18. However, when years of schooling are adjusted for quality of learning—that is, how much children actually learn—the 12.4 years of schooling is equivalent to only 8.1 years, a learning gap of 4.3 years (World Bank 2018).

At the same time, it would be flawed to suggest that health does not affect human capital outcomes in Saudi Arabia. The HCI does not include NCDs as key indicators per se. It does, however, include adult survival (until 60) as an indicator that is likely to be affected directly by NCDs. This rate refers to the probability that persons who have reached age 15 will die before reaching age 60 (shown per 1,000 persons).

In order to estimate the impact of NCDs on the HCI score in Saudi Arabia, this section estimates the impact of avertable mortality and risk-attributable mortality (that is, rates that exceed the rates observed in best-performing countries worldwide) on adult survival in Saudi Arabia. Counterfactual patterns of mortality (described further in annex 5A) are used to generate alternative life tables for Saudi Arabia in 2017 using the cause-deleted life table approach (Beltran-Sanchez, Preston, and Canudas-romo 2008). These alternative life tables make it possible to compute the probability of dying between the ages of 15 and 60 (45q15), an input to the HCI. Both a cause-level analysis and a risk factor–level analysis are conducted. The cause-level analysis uses estimates of mortality rates from specific NCD causes, whereas the risk factor–level analysis uses estimates of NCD mortality linked to specific risk factors such as tobacco use and obesity. risk factors account for about two-thirds of NCD deaths in Saudi Arabia, so the burden of risk factor–attributable deaths is the fraction of total avertable deaths. For both the cause-level analysis and the risk factor–level

analysis, these values are compared with the observed 45q15 for Saudi Arabia in 2017 (described further in annex 5B).

For Saudi Arabia, the impact of eliminating avertable NCD deaths and risk-attributable deaths on 45q15 is substantial. For the cause-level analysis, reducing NCD mortality to counterfactual levels would result in a 38 percent reduction in 45q15, with a 48 percent reduction for women and a 33 percent reduction for men. For the risk factor–level analysis, reducing NCD mortality to counterfactual levels would result in a 22 percent reduction in 45q15, with a 29 percent reduction for women and an 18 percent reduction for men. eliminating avertable NCD deaths and risk-attributable deaths would improve the HCI values for Saudi Arabia by 5 percent and 3 percent, respectively. reducing NCD mortality to the counterfactual level used in this analysis would increase the HCI value from 0.58 to 0.61, a 0.03-unit absolute improvement or a 5 percent relative improvement. likewise, reducing risk-attributable mortality would increase the HCI value to 0.60, a 0.02-unit absolute improvement or a 3 percent relative improvement. These modest improvements undersell the benefits of NCD prevention and control and are a result of how the HCI is constructed. eliminating avertable NCD deaths and risk-attributable deaths would ensure that Saudi Arabia would achieve the SDG Target 3.4 for NCDs. The 40q30 indicator (the probability of dying between the ages of 30 and 70) captures cross-country differences in NCD mortality better than 45q15 and is used in SDG Target 3.4, reducing NCD mortality by one-third between 2015 and 2030 (UN 2016). The impact of lower NCD mortality and risk-attributable mortality on 40q30 in Saudi Arabia is estimated using a method similar to the one used for 45q15. For the cause-level analysis, reducing NCD mortality to counterfactual levels would result in a 48 percent reduction in 40q30, with a 57 percent reduction for women and a 44 percent reduction for men. For the risk factor–level analysis, reducing NCD mortality to counterfactual levels would result in a 31 percent reduction in 40q30, with a 40 percent reduction for women and a 26 percent reduction for men. These findings suggest that Saudi Arabia could achieve the SDG Target 3.4 or even exceed it by wide margins by aggressively tackling risk factors (such as smoking) and fully implementing clinical interventions (such as drug therapy for secondary prevention of cardiovascular disease) that can reduce case-fatality in a costeffective manner and reduce age-specific mortality rates to levels observed in high-performing countries.

Figure 5.3 shows the improvements in adult mortality from eliminating avertable NCD-attributable deaths and risk-attributable deaths in Saudi Arabia. The figure shows 45q15 and 40q30, disaggregated by gender, at both the cause level and the risk factor level. The height of each bar is the observed value for Saudi Arabia. For each bar, the light shading (change) reflects the share of 45q15 or 40q30 that could be eliminated if counterfactual mortality levels are achieved (that is, if 45q15 or 40q30 is reduced to counterfactual levels, represented by the height of the dark shading in each bar). The observed values for 45q15 and 40q30 are slightly different in this analysis than those used by the World Bank because this analysis uses the Global Burden of Disease estimates (IHme 2018), while the World Bank uses the World Population Prospects 2019 revision estimates (UN DeSA 2019).