Who is at high risk of poor diet quality? A precision public health nutrition approach using machine learning.

BACKGROUND: Suboptimal diet quality contributes to poor health. Numerous factors at all levels of the socioecological model intersect to influence individuals' diet quality. A precision public health nutrition framework can be used to understand how these factors jointly shape diet quality across several subgroups in the population. This is, however, very challenging to operationalize. OBJECTIVE: The purpose of this study was to assess whether machine learning can be leveraged to operationalize a precision public health nutrition approach by more precisely identifying subgroups with the highest proportion of adults with poor diet quality and the most important predictors of diet quality. METHODS: We conducted a secondary analysis of cross-sectional data from the 2018 and 2019 International Food Policy Study in Canada (n=5,093). A total of 42 candidate predictors within four domains (sociodemographic characteristics and socioeconomic position; food policies and environments; food literacy; health-related practices and indicators) were used. The Healthy Eating Index-2015 (HEI-2015) was used to assess diet quality; tertiles of HEI-2015 scores were defined as the outcome. Conditional inference tree (CIT) and conditional random forest (CRF) analyses were conducted. RESULTS: The CIT partitioned the sample into seven subgroups with different proportions of adults with lower, moderate or higher diet quality using six predictors. The probability of lower diet quality ranged from 16.9% to 49.9% across subgroups. The subgroup with the highest proportion of adults with lower diet quality was characterised by individuals confident in using ≤4 cooking techniques. Based on the CRF model and conditional variable importance, the five most important predictors of diet quality in were: frequency of food label use, confidence in using cooking techniques, perceived general health, household food insecurity status, and health literacy. CONCLUSIONS: This study provides evidence that machine learning approaches can be leveraged to operationalize a precision public health nutrition approach.