Table 1.
Key model input parameters
| Input parameters | Mean value | 95% CI | Distribution (se) | Data source and assumptions e |
|---|---|---|---|---|
| Population estimates | Number of boys and girls aged 4 until 12 years of age | CBS Statline. | ||
| Intervention effect estimate | Bartelink et al. (2019) | |||
| Relative effect after 2 years of intervention in children aged 4–12 years | BMI z-score | Assumption: full effect maintenance over lifetime a | ||
| HPSF: −0.083 | [−0.15;-0.02] | Gamma (0.08) | ||
| PAS: −0.066 | [−0,13;-0.00] | Gamma (0.09) | ||
| BMI (standard deviation 2.55 kg/m2) | ||||
| HPSF: −0.21 | [− 0.38;-0.05] | Gamma (0.08) | ||
| PAS: −0.17 | [−0.33;-0.00] | Gamma (0.09) | ||
| SES-specific 2-year relative effects | BMI z-score | Bartelink et al. (2019). Converted to BMI effects with standard deviation of 2.55 (based on the study sample at baseline). | ||
| HPSF vs control | ||||
| low SES: −0.103 | [−0.22;-0.02] | Gamma (0.16) | ||
| middle SES: −0.049 | [−0.16;-0.06] | Gamma (0.14) | ||
| high SES: −0.063 | [−0.18;-0.05] | Gamma(0.15) | ||
| PAS vs control | ||||
| low SES: −0.067 | [− 0.18;-0.05] | Gamma (0.15) | ||
| middle SES: −0.056 | [−0.18;-0.06] | Gamma (0.16) | ||
| high SES: −0.051 | [−0.16;-0.06] | Gamma (0.14) | ||
| Effect maintenance scenarios | Oosterhoff et al. (2020) | |||
|
1.Constant- and decreasing effects after primary school Maintenance factor uncontrolled environment |
HPSF: 0.22 | [0.04;0.39] | Lognormal (0.09) | |
| PAS: 0.22 | [0.06;0.37] | Lognormal (0.08) | ||
|
2. Increasing- and decreasing effects after primary school Relative BMI effect with household multiplier |
||||
| HPSF: −0.30 | [−0.42;-0.18] | Gamma(0.06) | ||
| PAS: −0.19 | [−0.27;-0.12] | Gamma (0.04) | ||
|
3. Increasing effects Maintenance factor household maintainer |
||||
| HPSF: 1.67 | [1.48;1.85] | Lognormal (0.09) | ||
| PAS: 1.10 | [1.01;1.19] | Lognormal (0.05) | ||
| Intervention cost estimate | Oosterhoff et al. (2019) | |||
| Net intervention costs, societal perspectiveb | HPSF: €153 per year (€0.96 per day) (2016) | Fixed | ||
| PAS: €346 per year (€2.16 per day) (2016) | Fixed | |||
| Net intervention costs, healthcare perspective b | HPSF: €715 per year (€4.47 per day) (2016) | Fixed | ||
| Childhood and adolescence | ||||
| Weight status | ||||
| Cut-off values of overweight and obesity (kg/m2) | Fixed | Cole et al. (2000) | ||
| BMI distribution Dutch children | Age and sex-specific values for skewness and variation | Schönbeck et al. (2011) | ||
| Health-related quality of life | ||||
| Utility weights | Normal weight: 0.85 | [0.84;0.87] | Beta (0.01) | Brown et al. (2018) |
| Overweight: 0.83 | [0.81;0.85] | Beta (0.01) | ||
| Obesity: 0.82 | [0.79;0.84] | Beta (0.01) | ||
| Health resource use | ||||
| Average number of GP visits / year | 59.6% children visiting GP * 6.7 visits / year | Fixed | Statline (n.d.) | |
| Average number of specialist visits / year | 27.0% children visiting GP * 9.7 visits / year | Fixed | Statline (n.d.) | |
| Ratio of HC costs for overweight vs. normal weight | 1 | Fixed | Gortmaker et al. [based on Table A.3.2] | |
| Ratio of HC costs for obesity vs. normal weight | 1.22 | [1.21;1.22] | Lognormal (0.00) | |
| Cost price per GP visit b | €34 | Fixed | Zorginstituut Nederland (2015) | |
| Cost price per specialist visit b | €94 | Fixed | Zorginstituut Nederland (2015) | |
| School absenteeism | ||||
| Median number of school absenteeism days / year c | 5.0 | Gamma (3.26) | Additional analysis based on data collection as described by Willeboordse et al. (2016) | |
| Ratio of absenteeism for overweight vs. normal weight | 1.27 | [1.03;1.56] | Lognormal (0.14) | An et al. (2017) |
| Ratio of absenteeism for obesity vs. normal weight | 1.54 | [1.33;1.78] | Lognormal (0.11) | |
| Cost price per school absenteeism day b | €27 | Fixed | Drost et al. (2014) | |
| Adulthood | ||||
| Weight status a | Normal weight, overweight, obesity | Log-odds | Fifth Dutch Growth Study. Schönbeck et al. (2009) | |
| Chronic diseases d |
Obesity related diseases: acute myocardial infarction, coronary heart disease, stroke, renal, colorectal, breast, prostate, and endometrium cancer, diabetes mellitus, hip, knee arthritis, and low back pain. Indirect-related diseases: Chronic obstructive pulmonary disease, lung, stomach, esophagus, larynx, bladder, pancreas, and oral cavity cancer |
Prevalence: log-oddsIncidence: lognormal |
RIVM Chronic Disease Model. Hoogenveen et al. (2010), van Baal et al. (2006) |
|
| Adulthood | ||||
| Health-related quality of life | ||||
| Utility weights (for chronic disease) | Fixed | Dutch Burden of Disease Study. Melse et al. (2000) | ||
| Health resource use & unit costs | ||||
| Disease healthcare costs | Fixed | Dutch Cost of Illness Study. Slobbe et al. (2006) | ||
| Productivity costs | ||||
| Sick leave days | Overweight women: 3.64 | Fixed | Lehnert et al. (2014) | |
| Overweight men: 0 | ||||
| Obese women: 5.19 | ||||
| Obese men: 3.48 | ||||
| Net labour participation | 72.2% | Fixed | CBS Statline (2017) | |
| Working hours per week | 31.4 (6.28 per day / 5 days a week) | Fixed | CBS Statline (2017) | |
| Productivity costs / hour b | €36 | Fixed | Zorginstituut Nederland (2015) |
Notes: BMI z-score Body mass index standardized score, CI Confidence interval, GP General practitioner, HC Healthcare, HPSF The Healthy Primary School of the Future, HRQOL Health-related quality of life, PAS The Physical Activity School, QALY Quality-adjusted life year
a In the adulthood model, the uncertainty of the intervention effect was incorporated by including the overweight and obesity prevalence rates at young adulthood as probabilistic parameters. This uncertainty parameter reflected the boundaries of the 95% confidence interval of the intervention effect on BMI. The overweight and obesity prevalence rates at 20 years of age were included as multivariate normal distributions with a perfect correlation
b Updated to 2018 prices
c The analysis was based on crossectional data (baseline year). Regression analysis with a Poisson distribution was used to reflect the count data. The effect of weight status (normal weight [reference level], overweight and obesity) on school absenteeism days was analysed. Analysis were additionally adjusted for sex, grade, school type, socioeconomic status and ethnicity
d We used coupled sets of random draws for the prevalence, incidence and mortality for the chronic diseases in the probabilistic sensitivity analysis
e References can be found in Additional File 3