This article aims to evaluate the sociodemographic determinants of ultra-processed foods (UPF) consumption in the Brazilian population ≥ 10 years of age. The study used data from the personal and resident food consumption module of the Family Budget Surveys, grouping foods according to the NOVA classification of food processing. The classification and regression tree (CART) was used to identify the factors determining the lowest to highest percentage participation of UPF in the Brazilian population. UPF accounted for 37·0 % of energy content in 2017–2018. In the end, eight nodes of UPF consumption were identified, with household situation, education in years, age in years and per capita family income being the determining factors identified in the CART. The lowest consumption of UPF occurred among individuals living in rural areas with less than 4 years of education (23·78 %), while the highest consumption occurred among individuals living in urban areas, < 30 years of age and with per capita income ≥ US$257 (46·27 %). The determining factors identified in CART expose the diverse pattern of UPF consumption in the Brazilian population, especially conditions directly associated with access to these products, such as penetration in urban/rural regions. Through the results of this study, it may be possible to identify focal points for action in policies and actions to mitigate UPF consumption.

This paper adopts an actuarial approach to identify the risk factors of government-funded maternal out-of-hospital costs in Australia, with a focus on women who experience adverse birth outcomes. We use a two-phase modelling methodology incorporating both classification and regression trees and generalised linear models on a data set that links administrative and longitudinal survey data from a large sample of women, to address maternal out-of-hospital costs. We find that adverse births are a statistically significant risk factor of out-of-hospital costs in both the delivery and postnatal periods. Furthermore, other significant cost risk factors are in-vitro fertilisation, specialist use, general practitioner use, area of residence and mental health factors (including anxiety, intense anxiety, postnatal depression and stress about own health) and the results vary by perinatal sub-period and the patient’s private health insurance status. We highlight these differences and use the results as an evidence base to inform public policy. Mental health policy is identified as a priority area for further investigation due to the dominance of these factors in many of the fitted models.

We investigate an actuarial approach to identifying the factors impacting government-funded maternal hospital costs in Australia, with a focus on women who experience adverse birth outcomes. We propose a two-phase modelling methodology that adopts actuarial methods from typical insurance claim cost modelling and extends to other statistical techniques to account for the large volume of covariates available for modelling. Specifically, Classification and Regression Trees and generalised linear mixed models are employed to analyse a data set that links longitudinal survey and administrative data from a large sample of women. The results show that adverse births are a statistically significant risk factor affecting maternal hospital costs in the antenatal and delivery periods. Other significant cost risk factors in the delivery period include mode of delivery, private health insurance status, diabetes, smoking status, area of residence and onset of labour. We demonstrate the efficacy of using actuarial techniques in non-traditional areas and highlight how the results can be used to inform public policy.

In understanding decreases in demand after exposure to media-induced food scares, aggregate data are almost exclusively presented without taking into consideration potential confounding variables. However, a better approach may be to use an experimental design coupled with targeting homogeneous willingness-to-pay (WTP) subgroups based on similarities in behavioral, psychological, and demographic characteristics of those who are most vulnerable to food scare information. This is accomplished through experimental economics and an analysis strategy called a classification and regression tree (CART). A stigma framework—which guides conceptual understanding of effects of media-induced food scares—suggests controlling contextual variables to better approximate ceteris paribus. To this end, we conducted an experiment that exposed people to information about mad cow disease and then asked them to bid their willingness-to-pay for an actual hamburger. The CART found distinct homogeneous WTP subgroups of individuals that could be used by government and industry professionals to create interventions to reduce potential consumer concern and producer losses.

In epidemiological studies of community-acquired pneumonia (CAP) that utilize administrative data, cases are typically defined by the presence of a pneumonia hospital discharge diagnosis code. However, not all such hospitalizations represent true CAP cases. We identified 3991 hospitalizations during 1997–2005 in a managed care organization, and validated them as CAP or not by reviewing medical records. To improve the accuracy of CAP identification, classification algorithms that incorporated additional administrative information associated with the hospitalization were developed using the classification and regression tree analysis. We found that a pneumonia code designated as the primary discharge diagnosis and duration of hospital stay improved the classification of CAP hospitalizations. Compared to the commonly used method that is based on the presence of a primary discharge diagnosis code of pneumonia alone, these algorithms had higher sensitivity (81–98%) and positive predictive values (82–84%) with only modest decreases in specificity (48–82%) and negative predictive values (75–90%).