Purpose: Children should attend well child visits (WCVs) during early childhood so that developmental disorders may be identified as early as possible, so treatment can begin. The aim of this research was to determine if rurality impacts access to WCV during early childhood, and if altering rurality measurement methods impacts outcomes.
Design And Methods: We utilized a longitudinal correlational design with early childhood data gathered from the Virginia All Payer Claims Database, which contains claims data from Medicaid and the majority of Virginia commercial insurance payers (n = 6349). WCV attendance was evaluated against three rurality metrics: a traditional metric using Rural-Urban Commuting Area codes, a developed land variable, and a distance to care variable, at a zip code level.
Results: Two of the rurality methods revealed that rural children attend fewer WCVs than their urban counterparts, (67% vs. 50% respectively, using a traditional metric; and a 0.035 increase in WCV attendance for every percent increase in developed land). Differences were attenuated by insurance payer; children with Medicaid attend fewer WCVs than those with private insurance.
Conclusions: Young children in rural Virginia attend fewer WCVs than their non-rural counterparts, placing them at higher risk for missing timely developmental disorder screenings. The coronavirus disease pandemic has been associated with an abrupt and significant reduction in vaccination rates, which likely indicates fewer WCVs and concomitant developmental screenings. Pediatric nurses should encourage families of young children to develop a plan for continued WCVs, so that early identification of developmental disorders can be achieved.
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http://dx.doi.org/10.1016/j.pedn.2020.12.005 | DOI Listing |
BMC Oral Health
January 2025
Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway.
Background: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifying crucial health behaviours within mother-child pairs.
Methods: For the analysis, we utilized a representative sample of 724 mothers with children under six years in Bangladesh.
Crim Behav Ment Health
January 2025
Institute of Psychology, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany.
Background: This article is dedicated to David Farrington who was a giant in criminology and, in particular, a pioneer in studying developmental pathways of delinquent and antisocial behaviour. Numerous studies followed his work. Systematic reviews of his and others' research described between two and seven (mainly 3-5) trajectories.
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