obesity

TABLE 5.4 Summary of findings: Direct medical costs attributable to overweight and obesity

Current health expenditure (CHEs) as percentage of gross domestic product (GDP) in 2017a

GDP in 2018 (2018 international $)a 6%

$2,714,546,976,703.24

Current health expenditure (2018 international $) (CHEs (%) × GDP) $173,731,006,509

Overweight/obesity burden Overweight/obesity burden as a % of current health expenditure $11,291,476,821

7%

Overweight/obesity burden as a % of GDP 0.42%

Sources: Table 5.2 and table 5.3. Original table for this publication. a. Current health expenditure (percent) estimates were obtained from National Health Accounts, Saudi Arabia, 2018, obtained from the Saudi Health Council.

obesity account for as much as 83 percent of cases of type 2 diabetes (Flegal, Panagiotou, and Graubard 2015), 44 percent of coronary heart disease (Birmingham et al. 1999; Flegal, Panagiotou, and Graubard 2015), 10 percent of ischemic stroke (Asia Pacific Cohort Studies Collaboration 2007; Birmingham et al. 1999), 10 percent of asthma (Dal Grande et al. 2009; Tonorezos et al. 2008), 13 percent of breast cancer (Birmingham et al. 1999; Flegal, Panagiotou, and Graubard 2015), and 15 percent of colon cancer incidence (Arnold et al. 2015; Birmingham et al. 1999; Flegal, Panagiotou, and Graubard 2015). However, these total cost estimates are conservative, since costs for hypertension, dyslipidemia, endometrial cancer, and several other NCDs are not included because of a lack of available data. By comparison, a recently published Organisation for Economic Co-operation and Development (OECD) report using an alternative method reported that Saudi Arabia will spend about 7 percent of its annual health expenditure on overweight/obesity between 2020 and 2050 (Cecchini and vuik 2019). The United Nations (UN) Interagency Task Force on NCDs (2018) for Saudi Arabia reports a direct cost burden of 0.84 percent of GDP, which is slightly greater than the estimate here of 0.42 percent. Both the OECD and the UN reports rely on dynamic models that are further discussed in this chapter’s section “Estimating the Economic Burden Using the Economic Growth Approach Method.”

An alternative to using the epidemiologic approach is to use an econometric model. However, this requires individual-level data on both the outcome of interest and a person’s BMI. As shown in equation 5.1, if these data are available, the model can be estimated using the outcome of interest (for example, days missed from work) as the dependent variable and indicators for overweight and obesity as the key independent variables, with controls for other variables that may influence the outcome and be correlated with a person’s weight, such as age, education, or sex:

days absent from work = α + β1 (Overweight) +β2 (Obese) + ε (5.1)

In equation 5.1, α represents days missed from work for normal-weight individuals, and the coefficients on the overweight (β1 and obesity (β2) variables represent the incremental burden imposed by the average individual in these weight classes relative to normal-weight individuals. This approach can be applied to multiple categories of direct and indirect costs, from different types

of medical expenditure to various aspects of human capital losses, such as absenteeism or presenteeism. Compared to the epidemiological approach, the econometric approach relies on fewer assumptions. However, it requires more granular data that are not easily accessible in Saudi Arabia. For this reason, published estimates for the costs of medical care in Saudi Arabia using this approach could not be found in the published literature.

As an example of what is possible, using the 2013 Saudi Health Interview Survey (MOH and IHME 2013), the econometric approach was applied to estimate the increase in absenteeism days (that is, workdays missed due to illness or injury) resulting from excess weight. The 2013 Saudi Health Interview Survey (Saudi nationals only) includes questions on height and weight (to quantify BMI) and on days missed from work over the past 12 months because of illness or injury. These questions were used to quantify the incremental costs of absenteeism. Using responses to the days-missed question as the dependent variables in an individual level regression analysis allowed for quantifying the increase in days missed from work for those with excess weight.

The data set excluded underweight individuals and defined a normal-weight reference group based on World Health Organization (WHO) definitions: normal weight (18.5 kg/m2 ≤ BMI < 25 kg/m2), overweight (25 kg/m2 ≤ BMI < 30 kg/m2), and obesity (BMI ≥ 30 kg/m2). A linear regression model (ordinary least squares) was then used to estimate the incremental days missed at work per year by respondents within each BMI category while controlling for demographics. Finally, the estimates were monetized based on the average wage of full-time workers (estimated to be $214 per day for Saudi nationals, in 2018 international dollars) and the number of overweight and obese employees in the population. The regression results are presented in table 5.5.

TABLE 5.5 Absenteeism costs for overweight and obese employees in Saudi Arabia, 2018

BMI CLASSa SAMPLE SIZE (N = 4,030) INCREMENTAL DAYS MISSED, ADJUSTED (OLS ESTIMATES) (N = 3,148)c,d INDIRECT COSTS ASSOCIATED WITH DISEASE CATEGORY (2018 INTERNATIONAL $)

N PERCENTb COEFF 95% CI COST PER CASEe TOTAL COSTf

Normal weight: 18.5 ≤ BMI < 25 976 24.2 Reference category Reference category

Overweight: 25 ≤ BMI < 30 1,517 37.6 0.13 −0.84 1.11 $28.64 $3,321,626,300

Obese: BMI ≥ 30 1,339 33.2 1.07 0.06 2.08 $229.68 $23,511,460,330

Total cost $26,833,086,630

Data sources: a. MOH and IHME 2013. One hundred thirty-six individuals (3.3 percent) had missing or out-of-range weight or height information. Reported weight of < 35 kg or > 400 kg and reported height of <110 cm were defined as out of range. b. MOH and IHME 2013. Percentages were calculated with respect to the total number of employed individuals ages 15–64 years (n = 4,030). Percentages do not total to 100 percent because of the presence of underweight individuals and missing and out-of-range height and weight observations. c. MOH and IHME 2013. Sixty-two individuals (1.5 percent) were underweight and were excluded from the analysis. d. MOH and IHME 2013. Adjusted models include the following covariates: age, gender, education level, and marital status. e. Average wage of full-time workers is estimated to be $214 per day for Saudi Nationals in 2018 international $. This is based on (1) wage data reported by General Statistics Authority (Bulletin, Labour Market Q1 2019), www.stats.gov.sa/sites/default/files/labour_market_q1_2019_en.pdf, and (2) countryspecific inflation rates and purchasing power parity exchange rates reported by the World Bank, https://data.worldbank.org/indicator/PA.NUS. PPP?locations=SA. f. Total cost = cost per case × prevalence rate × number of employed Saudis in the age group 15–64 years as of Q1 2019. The number of employed persons was obtained from General Statistics Authority (Bulletin, Labour Market Q1 2019), www.stats.gov.sa/sites/default/files/labour_market_q1_2019 _en.pdf. Note: CI = confidence interval; OLS = ordinary least squares; BMI = body mass index.