Factors Associated With Nutritional Risk Among Homebound Older Adults With Depressive Symptoms

This study used the Evans model of public health determinants to identify factors associated with nutritional risk in older adults.

The Evans model domains (physical and mental well-being, social/environmental statuses, individual choice, and economic security) were measured in a sample of homebound older adults. Regularized logistic regression analysis with LASSO penalty function was used to determine the strongest domain of the Evans model. Using traditional logistic regression, individual variables across all domains were compared to identify the significant predictors.

Older adults receiving home meal services were referred to the study by community program staff.

Participants included 164 homebound older adults (age > 60) who endorsed at least one gateway symptom of depression.

Measurements: Nutritional risk was determined using the Mini Nutritional Assessment. Domains of the Evans model were measured using the MAI Medical Condition Checklist, items from the IADL scale, the Structured Clinical Interview for DSM-IV Axis I Disorders, the Duke Social Support Index, living arrangements, marital status, the Alcohol Use Disorders Identification Test, items from the SCID Screening Module, and a self-report of perceived financial security.

Poor mental well-being, defined by a diagnosis of major depressive disorder, was identified as the strongest Evans model domain in the prediction of nutritional risk. When each variable was independently evaluated across domains, instrumental support (Wald’s Z=-2.24, p=0.03) and a history of drug use (Wald’s Z=-2.40, p=0.02) were significant predictors.

The Evans model is a useful conceptual framework for understanding nutritional health, with the mental domain found to be the strongest domain predictor of nutritional risk. Among individual variables across domains, having someone to help with shopping and food preparation and a history of drug use were associated with lower nutritional risk. These analyses highlight potential targets of intervention for nutritional risk among older adults.