ArticlePloS one2024
Insight into the main determinants of the struggle against overweight and obesity in Chile: Use of random forest techniques and econometric models.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Overweight and obesity are considered the greatest public health problem in this emerging country, which worldwide has the second-highest percentage of overweight people among its population. The objective of this work was to analyse to what extent factors traditionally used in the study of overweight and obesity (such as sociodemographic and behavioural) and new variables proposed in the literature (such as stress, financial stress and emotional support) explain this disease in the adult population of Chile. Data were obtained from the III National Health Survey (ENS) administered by the Ministry of Health of Chile in 2017. The ENS collected a large amount of data with extensive geographic coverage. The survey comprised 4 questionnaires with a total of 576 questions, which were applied to a representative sample of the population in Chile. A double complementary methodological approach was adopted. A random forest (RF) classification model was used, and based on the results obtained, an econometric model of the censored dependent variable, specifically the Heckman sample selection model, was specified and estimated. The RF results allowed, for each of the factors considered in the research, the selection of variables with the greatest power to classify the individuals in the sample on the basis of nutritional state (normal weight, overweight or obese). Subsequently, the estimation of the parameters of the Heckman model made it possible to quantify the variables that most affected overweight and obesity. Most of the variables that make up the factors were found to be significant. Interestingly, psychosocial variables effectively influence overweight and obesity. In addition, the results for reviewing nutritional information and reviewing food warnings allow us to reflect on the impact that recent food policies have had on the Chilean population. The combination of RF and an econometric model allowed us to capitalize on the strength of both models to better explain the complex phenomenon of overweight and obesity. This approach allowed us to more accurately confirm the impact of traditional factors on overweight and obesity but to show also that other psychosocial factors are relevant and should be consider in future studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.