ArticleBMC pediatrics2018
A patient and family data domain collection framework for identifying disparities in pediatrics: results from the pediatric health equity collaborative.
Article in BMC pediatrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 38 citations in OpenAlex.
- Sustained improvement in caregiver-reported infant demographic collection: a pilot towards equitable outcomes in the neonatal intensive care unit.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Article
- Accuracy of Race and Ethnicity Data in the Pediatric Electronic Health Record: A Concordance and System Adequacy Study.Health equity · 2025Article
- Collecting Sociodemographic Data in Pediatric Emergency Research: A Working Group Consensus.Pediatrics · 2024Article
- Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.Journal of clinical and translational science · 2024Article
- Automating data collection methods in electronic health record systems: a Social Determinant of Health (SDOH) viewpoint.Health systems (Basingstoke, England) · 2023Review
- A Scoping Review of Approaches to Improving Quality of Data Relating to Health Inequalities.International journal of environmental research and public health · 2022Article
- Moving the needle on health inequities: principles and tactics for effective cross-sector population health networks.Current opinion in pediatrics · 2022Review
- Sexual Orientation Demographic Data in a Clinical Cohort of Transgender Patients.Applied clinical informatics · 2021Article
- Language-Related Disparities in Pain Management in the Post-Anesthesia Care Unit for Children Undergoing Laparoscopic Appendectomy.Children (Basel, Switzerland) · 2020Article
- Variability in Collection and Use of Race/Ethnicity and Language Data in 93 Pediatric Hospitals.Journal of racial and ethnic health disparities · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 12 institutions in 2 countries.
Funding
Abstract
backgroundBy 2020, the child population is projected to have more racial and ethnic minorities make up the majority of the populations and health care organizations will need to have a system in place that collects accurate and reliable demographic data in order to monitor disparities. The goals of this group were to establish sample practices, approaches and lessons learned with regard to race, ethnicity, language, and other demographic data collection in pediatric care setting.
methodsA panel of 16 research and clinical professional experts working in 10 pediatric care delivery systems in the US and Canada convened twice in person for 3-day consensus development meetings and met multiple times via conference calls over a two year period. Current evidence on adult demographic data collection was systematically reviewed and unique aspects of data collection in the pediatric setting were outlined. Human centered design methods were utilized to facilitate theme development, facilitate constructive and innovative discussion, and generate consensus.
resultsGroup consensus determined six final data collection domains: 1) caregivers, 2) race and ethnicity, 3) language, 4) sexual orientation and gender identity, 5) disability, and 6) social determinants of health. For each domain, the group defined the domain, established a rational for collection, identified the unique challenges for data collection in a pediatric setting, and developed sample practices which are based on the experience of the members as a starting point to allow for customization unique to each health care organization. Several unique challenges in the pediatric setting across all domains include: data collection on caregivers, determining an age at which it is appropriate to collect data from the patient, collecting and updating data at multiple points across the lifespan, the limits of the electronic health record, and determining the purpose of the data collection before implementation.
conclusionsThere is no single approach that will work for all organizations when collecting race, ethnicity, language and other social determinants of health data. Each organization will need to tailor their data collection based on the population they serve, the financial resources available, and the capacity of the electronic health record.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.