Prioritizing nutrition interventions for low-income clients receiving public health nurses’ home visiting services: a latent class analysis study of Omaha System data

Author:

Lee Jiwoo1,Austin Robin R1ORCID,Mathiason Michelle A1,Monsen Karen A1ORCID

Affiliation:

1. School of Nursing, University of Minnesota , Minneapolis, Minnesota, USA

Abstract

Abstract Objective This study aimed to identify phenotypes of nutritional needs of home-visited clients with low income, and compare overall changes in knowledge, behavior, and status of nutritional needs before and after home visits by identified phenotypes. Materials and methods Omaha System data collected by public health nurses from 2013 to 2018 were used in this secondary data analysis study. A total of 900 low-income clients were included in the analysis. Latent class analysis (LCA) was used to identify phenotypes of nutrition symptoms or signs. Score changes in knowledge, behavior, and status were compared by phenotype. Results The five subgroups included Unbalanced Diet, Overweight, Underweight, Hyperglycemia with Adherence, and Hyperglycemia without Adherence. Only the Unbalanced Diet and Underweight groups showed an increase in knowledge. No other changes in behavior and status were observed in any of the phenotypes. Discussion and conclusions This LCA using standardized Omaha System Public Health Nursing data allowed us to identify phenotypes of nutritional needs among home-visited clients with low income and prioritize nutrition areas that public health nurses may focus on as part of public health nursing interventions. The sub-optimal changes in knowledge, behavior, and status suggest a need to re-examine the intervention details by phenotype and develop strategies to tailor public health nursing interventions to effectively meet the diverse nutritional needs of home-visited clients.

Funder

Eunice Kennedy Shriver National Institute of Child Health and Human Development

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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