Data source and results for "The human cost of delaying interventions to reduce obesity: a modeling study using taxes in Mexico"

Variables in "Baseline sample ENSANUT 2020-2022.csv"

Name
Variable

id
Identifier for each individual in the data.

est_var
Strata for the estimation of variances, accounting for survey design.

svy_weights
Complex survey weight after adjustment to match the report of the National Population Council of Mexico (CONAPO) 

code_upm
Identifier of the primary sampling unit.

sex
Sex of the individual (``male'' or ``female'').

age
Age (yrs).

body_weight
Measured body weight (kg).

height
Measured height (cm).

bmi
Body mass index, estimated before the simulation process (kg/m2).

year
Year (= 2021).

obes
Indicator of obesity (1 = yes, 0 = no).

svy_weghts_og
Complex survey weight without adjustments

year_og
Indicates if the individuals belong to the ENSANUT 2020, 2021, or 2022

Results

The files "Results Linear.zip" and "Results Nordpred.zip" contain results of BMI (BMI.csv), obesity (Obesity.csv), survey weights adjusted for mortality (Population.csv), and deaths (Deaths.csv) by individual, year and scenario. Each csv has the same number of rows as the baseline sample (n = 26,854; it is possible to merge them by column; they are sorted). The first column of the csvs is the age of the individual in the baseline (age_in_2021). The other columns are the years of simulation. The values in the column "2021" are the same as those in the baseline sample (bmi in the BMI.csv, obesity in the Obesity.csv, and survey weights in the Population.csv).  For example, the following table shows the first four subjects in the "Population.csv" for the years 2021-2027 in the Status quo Scenario.

age_in_2021
2021
2022
2023
2024
2025
2026
2027

62
793.456475
779.43512
769.105823
758.09231
746.353933
733.863597
718.331278

30
2947.77704
2933.43957
2922.22167
2911.63185
2901.20043
2890.612
2883.24558

49
1335.5641
1326.41111
1319.94571
1313.17373
1305.96443
1298.2321
1288.65234

62
3818.15146
3750.68001
3700.97493
3647.97736
3591.49172
3531.38761
3484.58945

The first individual has 62 years old in 2021 and represents about 793 people, as in the "svy_weights" variable in the baseline sample. Then, in 2022, the same individual represent a lower number of individuals: 779. That is because we adjusted the survey weight to consider mortality. The details are presented in the main manuscript.

Population and deaths by year

To get the population by year for a given scenario, use the file "Population.csv" of the scenario and sum by column. Similar to deaths, using the file "Deaths.csv".

Obesity prevalence and verage BMI, and

To get the obesity prevalence or the average BMI by year for a given scenario, use each column of the file "Population.csv" as weights.

For example, in R:

Obes_prev_in_2025 = weighted.mean(x = Obesity[, "2025"], w = Population[, "2025"])

Average_BMI_in_2025 = weighted.mean(x = BMI[, "2025"], w = Population[, "2025"]).