Sociodemographic and Appointment Factors Affecting Missed Opportunities to Provide Neonatal Ultrasound Imaging

Sociodemographic factors and Child Opportunity Index disparities associated with missed care opportunities in pediatric patients with lymphoma and leukemia referred for FDG-PET/CT

BACKGROUND

Little data exists on the association of missed care opportunities (MCOs) in children referred for nuclear medicine/nuclear oncology imaging examinations and socioeconomic disparities.

OBJECTIVE

To determine the prevalence of MCOs in children with lymphoma/leukemia scheduled for fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) and the impact of sociodemographic factors and Child Opportunity Index (COI).

MATERIALS AND METHODS

Retrospective analysis of MCOs in children with lymphoma/leukemia scheduled for FDG-PET/CT (2012 to 2022) was performed. In univariate analysis, patient, neighborhood, and appointment data were assessed across MCOs and completed appointments. Logistic regression evaluated independent effects of patient-, neighborhood-, and appointment-level factors with MCOs. Two-sided P-value < .05 was considered statistically significant.

RESULTS

In 643 FDG-PET/CT appointments (n = 293 patients; median age 15 years (IQR 11.0-17.0 years); 37.9% female), there were 20 MCOs (3.1%) involving 16 patients. Only 8.2% appointments involved Black/African American non-Hispanic/Latino patients, yet they made up a quarter of total MCOs. Patients living in neighborhoods with very low or low COI experienced significantly higher MCOs versus zip codes with very high COI (6.9% vs. 0.8%; P = 0.02). Logistic regression revealed significantly increased likelihood of MCOs for patients aged 18 to 21 [odds ratio (OR) 4.50; 95% CI 1.53-13.27; P = 0.007], Black/African American non-Hispanic/Latino (OR 3.20; 95% CI 1.08-9.49; P = 0.04), zip codes with very low or low COI (OR 9.60; 95% CI 1.24-74.30; P = 0.03), and unknown insurance status.

CONCLUSION

Children with lymphoma/leukemia, living in zip codes with very low or low COI, and who identified as Black/African American non-Hispanic/Latino experienced more MCOs. Our study supports the need to address intersecting sociodemographic, neighborhood, and health system factors that will improve equitable access to necessary healthcare imaging for children.

Analysis of missed clinic visits, preferred languages, and telemedicine in a pediatric gastroenterology practice

OBJECTIVES

Previous studies have demonstrated a relationship between socioeconomic disparities and missed clinic visits (MCV). However, the relationship between patient-preferred language and MCVs, particularly with respect to telemedicine, remains relatively underexplored. We sought to characterize the associations between MCV and patient-level predictors, including preferred language, in a large single-center pediatric gastroenterology, hepatology, and nutrition practice.

METHODS

This retrospective longitudinal cohort study included all missed or completed outpatient visits in the Gastroenterology, Hepatology, and Nutrition Programs at Boston Children's Hospital from January 1, 2016 to May 20, 2022. Univariate and multivariate hierarchical generalized linear mixed models were employed to identify associations between visit- and patient-level predictors and an MCV outcome.

RESULTS

A total of 300,201 visits from 70,710 patients residing in Massachusetts were included. Univariate analyses revealed higher MCV odds for Hispanic patients and those from areas with the highest Social Vulnerability Index (SVI), and these odds increased with telemedicine (Hispanic in-person odds ratio [OR] 5.21 [(95% confidence interval) 4.93-5.52] vs. telemedicine OR 8.79 [7.85-9.83]; highest SVI in-person OR 5.28 [4.95-5.64] vs. telemedicine OR 7.82 [6.84-8.96]). Controlled multivariate analyses revealed that among six language groups, only Spanish language preference was associated with higher MCV odds, which increased with telemedicine (Spanish in-person adjusted OR [aOR] 1.35 [1.24-1.48] vs. telemedicine aOR 2.1 [1.83-2.44]).

CONCLUSIONS

Patients preferring Spanish experience unique barriers to care beyond those faced by other language preference groups, and telemedicine may exacerbate these barriers.

Socioeconomic and Clinical Demography of Dental Missed Care Opportunities

INTRODUCTION

Missed care opportunities (MCOs) contribute to poor health outcomes, and pediatric dental patients are particularly vulnerable; identifying associated patient characteristics will help inform development of targeted interventional programs.

OBJECTIVE

To assess socioeconomic and demographic disparities associated with MCOs among children in an urban pediatric hospital's dental clinic. MCOs lead to a lack of continuous care and increased emergent needs, so understanding MCOs is required to achieve equitable pediatric dental health.

METHODS

A retrospective 2-y (2019-2020) cohort of MCOs in children 1 to 17 y old, with scheduled dental visits. MCOs were defined as appointments not attended or canceled and not rescheduled prior to initial scheduled visit. Multivariable mixed-effects logistic regression models with patient-level clustering assessed the associations of demographics, neighborhood-level socioeconomic factors (using social vulnerability index [SVI]), and clinic characteristics with MCOs.

RESULTS

Of 30,095 visits, 30.9% were MCOs. Multivariable logistic regression estimated increased likelihood of MCOs in Black/non-Hispanic (odds ratio [OR], 1.20; 95% confidence interval [CI], 1.09-1.32) and Hispanic (OR, 1.18; 95% CI, 1.06-1.31) patients, patients with public insurance (OR, 1.25; 95% CI, 1.15-1.36) or no insurance (OR, 1.46; 95% CI, 1.15-1.85), patients with complex chronic conditions (OR, 1.11; 95% CI, 1.03-1.19), visits scheduled during the COVID-19 pandemic (OR, 9.48; 95% CI, 8.89-10.11), appointments with wait days over 21 d (OR, 4.07; 95% CI, 3.49-4.74), and children from neighborhoods of high social vulnerability (75th percentile SVI) (OR, 1.08; 95% CI, 1.01-1.16).

CONCLUSIONS

Children with highest dental MCOs were from neighborhoods with high SVI, had public insurance, and were from marginalized populations. MCOs contribute to inequities in overall health; hence, interventions that address barriers related to characteristics associated with pediatric dental MCOs are needed.

KNOWLEDGE TRANSFER STATEMENT

Missed care opportunities contribute to poor health outcomes; identifying associated patient characteristics will help inform development of targeted interventional programs. Providing these findings to stakeholders will better impart understanding access barriers and drive research and program development. Dissemination of this information in the form of altering appointment practices will better accommodate specific patient population needs.

Imaging Artificial Intelligence: A Framework for Radiologists to Address Health Equity, From the AJR Special Series on DEI

Artificial intelligence (AI) holds promise for helping patients access new and individualized health care pathways while increasing efficiencies for health care practitioners. Radiology has been at the forefront of this technology in medicine; many radiology practices are implementing and trialing AI-focused products. AI also holds great promise for reducing health disparities and promoting health equity. Radiology is ideally positioned to help reduce disparities given its central and critical role in patient care. The purposes of this article are to discuss the potential benefits and pitfalls of deploying AI algorithms in radiology, specifically highlighting the impact of AI on health equity; to explore ways to mitigate drivers of inequity; and to enhance pathways for creating better health care for all individuals, centering on a practical framework that helps radiologists address health equity during deployment of new tools.