A major assumption behind the obesity model application is that the body mass index (BMI) measure indicates adult nutritional status. (BMI is a person’s weight in kilograms divided by his or her height in meters squared.) The US National Institutes of Health (NIH) now defines normal weight, overweight, and obesity according to BMI. A second assumption is that changes in BMI reflect changes in overall levels of obesity. Thus, predictions of future numbers of people with high BMIs are used to assess the effectiveness of obesity prevention programs and, accordingly, provide the effectiveness component of a cost-effectiveness analysis. In addition, the predictions will indicate to both policy makers and the community at large the potential scale of the obesity epidemic and whether hypothetical interventions are likely to be effective in limiting the epidemic.
The first step in the process was to assign the baseline height and weight for adults by age and sex from the 2007 IFLS (hereafter “IFLS2007”) to the baseline synthetic population.14 Because the Indonesian synthetic population does not contain BMI, height, and weight as characteristics assigned to each person, we used the IFLS2007 survey, which does contain measures of BMI, height, and weight for each survey respondent.
We summarized these key variables by age, sex, and urban or rural location to determine the mean, maximum, minimum, and standard deviation values by demographic covariate. Also, we summarized the distribution of weight Table 3.2 Demographic characteristics applied as parameters to the Indonesian synthetic population
Characteristic Source of estimate Geospatial level of estimate Demographics of synthetic
population
IPUMS 201027 Regency (i.e., a political subdivision of a province)
Household location LandScan 201228 1 kilometer cell unit
Height IFLS200714 Province
Weight IFLS200714 Province
Sources: International Public Use Microdata Series (IPUMS 2010)27; LandScan28; Indonesia Family Life Survey 2007 (IFLS2007).14
in each BMI range (using the WHO eight-category BMI standard definitions shown in Table 3.3). Then we assigned weight to each synthetic person based on the distributions by age, sex, and urban/rural location. Next, based on the assigned weight for each individual, we estimated and assigned a BMI value to each individual. Having assigned a BMI and weight value for each individual, we then solved for height and assigned a height to each synthetic person.
The simulation phase assigned adult persons an annual weight change in kilograms. The model assumed height to be constant for each individual man or woman for the duration of the simulation. The subject’s age, sex, and BMI determine which weight distribution to use in this calculation. In this case, we used the weight trajectories for this calculation derived from US data.29 We took the baseline weight value for men and women and simulated an annual weight change every year. Every year, each individual’s weight change was sampled from a normal distribution with a mean annual weight change and standard deviation estimated from IFLS2007 by sex and urban/rural location. The yearly percentage changes were assumed to be between −0.5 BMI units and 2.0 BMI units and no greater than 10 percent of the current weight. The minimum BMI was set to 16.
Finally, we introduced a behavioral intervention to reduce the level of obesity in the adult Indonesian population. A literature search revealed that scant evidence exists for obesity interventions within Indonesia. Because of this lack of evidence, our intervention assumed the structure of a general Table 3.3 The international classification of adult underweight, overweight, and obesity according to BMI
Octile Classification Body mass index (BMI) (kg/m2) range
1 Severe thinness < 16.00 2 Moderate thinness 16.00–16.99 3 Mild thinness 17.00–18.49 4 Normal 18.50–24.99 5 Pre-obese 25.00–29.99 6 Obese class I 30.00–34.99 7 Obese class II 35.00–39.99
8 Obese class III More than 40
behavioral treatment based on an example reported by LeBlanc and colleagues that targeted behavioral interventions involving overweight or obese US adults.30 The advantage of this assumption is that the authors examined a broad array of interventions, ranging from physical activity to cognitive therapy.30 The resulting effect sizes were moderate: a 3-kilogram loss (2–4 kilograms, 95 percent confidence interval [CI]) between 12 and 18 months. We simulated their reported effect size for 1 year.
To implement an intervention, a subset of the “simulated” persons participate and as a consequence reduce their BMI by 3 kilograms (2–4, 95 percent CI) for 12 months during the intervention phase. After the intervention and maintenance period, the pre-intervention weight trajectory resumes. Over time, changes to nutritional state occur as changes to BMI occur. When BMI levels dictate, persons move into new nutritional categories. As time advances, subjects move to new BMI categories according to the change in their BMI projections. Table 3.4 summarizes parameter estimates and statistical model assumptions used in the obesity model.
Table 3.4 Parameters and sources of estimates for the obesity model
Parameter Source of parameter estimate Statistical model assumption Explanatory variables Baseline height
and weight
Indonesia Family Life
Survey14 Sample from normal distribution based on explanatory variables
Age, sex, urban/ rural status Weight
trajectories
National Longitudinal
Survey of Youth29 Annual mean change Sex and BMI category Intervention
efficacy
Evidence-based interventions for improvement of maternal and child nutrition: what can be done and at what cost?26
Annual mean change Initial BMI category
BMI = body mass index.
Sources: Indonesia Family Life Survey 2007 (IFLS2007)14; National Longitudinal Survey of Youth (NLSY79), Table 329; US Preventive Services Task Force.30