Obesity in Pediatric Congenital Heart Disease: The Role of Age, Complexity, and Sociodemographics

Jordan, Carlos Alberto Lodeiro; Alizadeh, Faraz; Ramirez, Levi Sanchez; Kimbro, Rachel Tolbert; Lopez, Keila N.

doi:10.1007/s00246-023-03148-3

Cited by 4 publications

(2 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Beyond clinical indicators, several studies incorporated validated SDoH indexes like the Area Deprivation Index [206,207], Social Deprivation Index [208] and CDC's Social Vulnerability Index [209]. In terms of analysis approaches, common methods included multivariate regression models like logistic regression (n = 13) [159,179,180,192,199,[210][211][212][213][214][215][216][217] and Cox proportional hazards models (n = 3) [138,196,212] to assess adjusted outcome associations with SDoH factors. Other advanced techniques leveraged included machine learning algorithms [215], geospatial analysis for clustering [218], and phenome-wide association studies [150] in select studies.…”

Section: Sdoh and Health Outcomesmentioning

confidence: 99%

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Li,

Mowery,

et al. 2024

Preprint

View full text Add to dashboard Cite

Background Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. Methods We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes. Discussion Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfilling the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

show abstract

Section: Sdoh and Health Outcomesmentioning

confidence: 99%

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Li,

Mowery,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The Hispanic/Latino population in the U.S. faces multiple barriers to accessing quality CHD care. These barriers have led to higher rates of obesity, hypertension, increased perioperative mortality, worse surgical outcomes, and increased cardiac complications in this population compared to other ethnic groups [7,[45][46][47]. Although multiple factors contribute to these disparities, three of the most impactful ones include access to health insurance, immigration status, and language barriers.…”

Section: Hispanic/latino Populationsmentioning

confidence: 99%

Health Equity and Policy Considerations for Pediatric and Adult Congenital Heart Disease Care among Minoritized Populations in the United States

Lopez,

Allen,

Baker-Smith

et al. 2024

JCDD

View full text Add to dashboard Cite

Achieving health equity in populations with congenital heart disease (CHD) requires recognizing existing disparities throughout the lifespan that negatively and disproportionately impact specific groups of individuals. These disparities occur at individual, institutional, or system levels and often result in increased morbidity and mortality for marginalized or racially minoritized populations (population subgroups (e.g., ethnic, racial, social, religious) with differential power compared to those deemed to hold the majority power in the population). Creating actionable strategies and solutions to address these health disparities in patients with CHD requires critically examining multilevel factors and health policies that continue to drive health inequities, including varying social determinants of health (SDOH), systemic inequities, and structural racism. In this comprehensive review article, we focus on health equity solutions and health policy considerations for minoritized and marginalized populations with CHD throughout their lifespan in the United States. We review unique challenges that these populations may face and strategies for mitigating disparities in lifelong CHD care. We assess ways to deliver culturally competent CHD care and to help lower-health-literacy populations navigate CHD care. Finally, we review system-level health policies that impact reimbursement and research funding, as well as institutional policies that impact leadership diversity and representation in the workforce.

show abstract

Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions

Li,

Mowery,

et al. 2024

J. Clin. Trans. Sci.

View full text Add to dashboard Cite

Background: Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality. Methods: We conducted a PubMed search using “SDOH” and “EHR” Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results: Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization. Discussion: Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

show abstract

Obesity in Pediatric Congenital Heart Disease: The Role of Age, Complexity, and Sociodemographics

Cited by 4 publications

References 50 publications

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Health Equity and Policy Considerations for Pediatric and Adult Congenital Heart Disease Care among Minoritized Populations in the United States

Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions

Contact Info

Product

Resources

About