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Record W4409662759 · doi:10.1097/aud.0000000000001661

Health Service Use in Children With Mild Bilateral and Unilateral Hearing Loss

2025· article· en· W4409662759 on OpenAlexaffabout
Elizabeth M. Fitzpatrick, Eunjung Na, Marie Pigeon, Janet Olds, Lamia Hayawi, Bahar Rafinejad-Farahani, Doug Coyle, Isabelle Gaboury, Andrée Durieux-Smith, Flora Nassrallah, JoAnne Whittingham

Bibliographic record

VenueEar and Hearing · 2025
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsUniversité de SherbrookeChildren's Hospital of Eastern OntarioAgricultural Research Institute of OntarioUniversity of Ottawa
Fundersnot available
KeywordsHearing lossMedicinePopulationHealth careAudiologyInterquartile rangeCohortPediatricsFamily medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: The number of children identified early with mild bilateral and unilateral hearing loss (MUHL) has increased over the past 3 decades due to population-based newborn hearing screening initiatives. Early identification involves additional hearing-related services for these children in the early years. Despite the growing number of children, little information exists regarding their use of health care services. We examined overall health care utilization for this population of children with hearing loss in a Canadian pediatric center as well as the factors associated with audiology and early intervention service utilization. DESIGN: As part of a longitudinal MUHL research program, we examined health care utilization in a population-based cohort of 182 children with MUHL who were identified in one Canadian pediatric center from 2014 to 2018 and followed up to 6 years. Audiologic characteristics were collected prospectively, and health care utilization data were collected retrospectively through administrative databases. Descriptive statistics were used to summarize health care encounters. We used negative binomial regression models to examine the relationship between several clinical factors including age of diagnosis, degree, and laterality (unilateral/mild bilateral) of hearing loss, use of hearing technology, developmental concerns, and services used in audiology and early intervention. RESULTS: The 182 children were diagnosed at a median age of 4.1 months (interquartile range: 1.9, 55.7) and mean follow-up time was 48.6 (SD: 20.0) months. A total of 9867 hospital encounters were recorded in the medical chart including 2247 audiology, 3429 early intervention, and 701 Ear Nose and Throat service encounters. For audiology services, health care utilization (rate of visits per month of follow-up) was related to whether hearing loss was mild bilateral or unilateral, use of hearing aid(s), progressive hearing loss, developmental concerns, and age of diagnosis. Children with mild bilateral hearing loss had 68% more visits compared with children with unilateral hearing loss. Children with hearing aid(s) had 86%more visits than those without amplification. During the study period, 68.1% of children had at least one early intervention visit. In multivariable regression, after controlling for time followed, earlier age at diagnosis, bilateral hearing loss, use of hearing aid(s), progressive hearing loss, more severe hearing loss, and developmental concerns were all significantly associated with more early intervention service utilization. CONCLUSIONS: Our findings provide a comprehensive profile of hearing-related services provided to a population-based cohort of early-identified children with MUHL. Children with mild bilateral loss required more audiology services than those with unilateral hearing loss. Two-thirds of the children with MUHL utilized some early intervention services. Use of hearing aid(s), bilateral hearing loss, progressive hearing loss, and earlier age of diagnosis result in more service utilization for both audiology and early intervention. Understanding the intensity of care use among various subgroups of children with hearing loss can shed light on the impact of these hearing losses and inform resource planning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.291
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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