Related Experiment Video

Updated: Jun 23, 2025

A Treatment Package without Escape Extinction to Address Food Selectivity
04:23

A Treatment Package without Escape Extinction to Address Food Selectivity

Published on: August 21, 2015

11.5K

Disparities and Biases in Food Insecurity Screening Among Admitted Children

Mary M Orr1, Adolfo L Molina1, Cassandra N Smola1

  • 1Division of Pediatric Hospital Medicine, Department of Pediatrics, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama.

Hospital Pediatrics
|June 20, 2024
PubMed

Insights

Screening hospitalized children for food insecurity (FI) using electronic medical records (EMR) showed disparities. Younger, non-White, and uninsured children were screened less often, highlighting potential implementation bias in FI screening.

Area of Science:

  • Pediatric Health
  • Public Health Nutrition
  • Health Informatics

Background:

  • Food insecurity (FI) is a growing concern for hospitalized patients, yet optimal screening methods for children remain unclear.
  • Electronic medical record (EMR) systems offer potential for integrated screening, but their effectiveness in pediatric inpatient settings requires evaluation.

Purpose of the Study:

  • To assess the outcomes of a brief, EMR-embedded screening tool for food insecurity (FI) among pediatric inpatients.
  • To identify demographic factors associated with screening completion and positive FI identification in hospitalized children.

Main Methods:

  • A cross-sectional study analyzed 31,553 pediatric inpatient encounters from August 2020 to September 2022.
  • The Hunger Vital Sign, a validated 2-question screen, was administered verbally within the EMR nursing intake form.
  • Statistical analyses examined screening proportions and positive FI rates, considering covariates like age, sex, race, ethnicity, language, and insurance.

Main Results:

  • 81.7% of pediatric patients were screened for FI; 3.4% screened positive.
  • Younger (0-2 years), non-White, and uninsured children were significantly less likely to be screened (P < .001).
  • Higher FI prevalence was observed in older, non-White, Hispanic, non-English speaking, and non-privately insured patients (P < .001).

Conclusions:

  • EMR-embedded FI screening tools, despite aiming for universality, exhibited implementation bias.
  • Screening disparities suggest that vulnerable pediatric populations may be missed, hindering timely intervention.
Abstract

More Related Videos

Assessment of Child Anthropometry in a Large Epidemiologic Study09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

27.1K
A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

3.8K

Related Concept Videos

Anorexia Nervosa01:28

Anorexia Nervosa

Anorexia nervosa is a complex and severe eating disorder characterized by an intense fear of weight gain, an unrelenting pursuit of thinness, and a distorted body image. It often leads to dangerously low body weight relative to an individual's age and height. This disorder is marked by significant physical and psychological consequences, making it one of the most life-threatening psychiatric illnesses.
Symptoms and Physical Effects
Individuals with anorexia nervosa commonly exhibit extreme...
59
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
238
Confirmation Biases01:31

Confirmation Biases

The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
5.5K