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Record W4323350630 · doi:10.1093/jcag/gwac036.182

A182 PREDICTING HIGH DIRECT HEALTHCARE COSTS IN PEDIATRIC PATIENTS WITH INFLAMMATORY BOWEL DISEASE IN THE FIRST YEAR FOLLOWING DIAGNOSIS

2023· article· en· W4323350630 on OpenAlexafffundabout
E Kuenzig, R Duchen, T D Walters, David R. Mack, A M Griffiths, C N Bernstein, Gilaad G. Kaplan, Anthony Otley, W Yu, X Wang, Jun Guan, Stephen Fung, Eric I. Benchimol

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of ManitobaResearch ManitobaDalhousie UniversitySickKids FoundationUniversity of TorontoAgricultural Research Institute of OntarioUniversity of OttawaUniversity of CalgaryInstitute for Clinical Evaluative Sciences
FundersJanssen CanadaTakeda CanadaAbbVie CanadaSandoz CanadaPfizer CanadaDairy Farmers of OntarioAmgen CanadaAmgenPfizerEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineHealth careInflammatory bowel diseaseDiseaseLogistic regressionPercentileCrohn's diseasePediatricsInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background The incidence of inflammatory bowel disease (IBD) continues to rise rapidly among Canadian children. The care of children results in higher direct healthcare costs than adults with IBD. It is imperative that we identify individuals who will become the highest-cost users of the health system in order to intervene early and decrease the individual- and system-level burden of IBD. Purpose To develop a predictive-model for high-cost health system users and (2) identify factors associated with high-cost healthcare use. Method Incident cases of IBD diagnosed ≤17y residing in Ontario and enrolled in the Canadian Children IBD Network (CIDsCaNN) between Dec 31 2013 and Jan 31 2019 were linked deterministically using health card number to health administrative data. Using a validated algorithm, direct healthcare costs accumulated between the 31st and 365th day after diagnosis were calculated using data from CIDsCaNN (medications) and health administrative data (health system encounters, including surgery). A predictive model was created to determine high-cost (≤25th percentile) and medium-cost (26th to 75th) users, compared to low-cost users (>75th) using ordinal logistic regression. Potential predictive variables were determined a priori based on clinical significance and magnitude of univariable association, based on sample size-informed degrees of freedom. Variables from CIDsCaNN data included: IBD type, age at diagnosis, sex, first line of therapy (steroids, aminosalicylates, exclusive enteral nutrition; yes or no, not mutually exclusive), disease activity (severe vs. moderate vs. none/mild based on PUCAI [UC] or wPCDAI [Crohn’s]). Predictive variables from the health administrative data included: rural/urban residence, hospitalization at diagnosis, intestinal resection or colectomy within 3 months of diagnosis, emergency department visit ±1 month of diagnosis, and a mental health encounter within the first year following diagnosis. Anti-TNF treatment was excluded from models due to the strong correlation with the outcome (direct costs). Overall model fit was estimated with a c-statistic. Result(s) Among the 487 (57% Crohn’s) children included in the study, the mean (sd) direct costs accumulated between the 31st and 365th days following IBD diagnosis was $14,451 (14,665). The mean cost among high-cost users was $33,533 (16,530); medium-cost users, $11,038 (5322); low-cost users, $2530 (831). The predictive model identified high-cost users of the health system with acceptable model fit (c-statistic 0.69). The relative contribution of individual variables, as measured by odds ratio (OR), is reported in the Table. Image Conclusion(s) The direct healthcare costs of pediatric IBD are substantial. Children with IBD who become high-cost users of the health system were identifiable using characteristics at diagnosis (e.g., need for mental health care, emergency visits, older age). Further research should assess whether interventions in patients at-risk for becoming high-cost users may help to reduce costs. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding; Ontario Academic Health Sciences Centres Alternate Funding Plan Innovation Fund Disclosure of Interest E. Kuenzig: None Declared, R. Duchen: None Declared, T. Walters Grant / Research support from: Janssen, Abbvie, Psfizer, Ferring, Amgen, Consultant of: Janssen, Abbvie, Psfizer, Ferring, Amgen, D. Mack: None Declared, A. Griffiths Grant / Research support from: Abbvie, Consultant of: Abbvie, Amgen, BristolMyersSquibb, Janssen, Lilly, Takeda, Speakers bureau of: Abbvie, Janssen, Takeda, C. Bernstein Grant / Research support from: Research grants from Abbvie Canada, Amgen Canada, Pfizer Canada, and Sandoz Canada and contract grants from Janssen, Abbvie and Pfizer, Consultant of: Abbvie Canada, Amgen Canada, Bristol Myers Squibb Canada, JAMP Pharmaceuticals, Janssen Canada, Pfizer Canada, Sandoz Canada, Takeda, Speakers bureau of: Abbvie Canada, Janssen Canada, Pfizer Canada and Takeda Canada, G. Kaplan Grant / Research support from: Ferring, Consultant of: AbbVie, Janssen, Pfizer, Amgen, Sandoz, Pendophram, and Takeda, Speakers bureau of: AbbVie, Janssen, Pfizer, Amgen, Sandoz, Pendophram, and Takeda, A. Otley Grant / Research support from: Research support: AbbVie Global. Research site: AbbVie, Pfizer, Eli-Lily, Janssen, Consultant of: AbbVie Canada, W. Yu: None Declared, X. Wang: None Declared, J. Guan: None Declared, S. Fung: None Declared, E. Benchimol Consultant of: McKesson Canada, Dairy Farmers of Ontario (unrelated to medications used to treat inflammatory bowel disease)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.450
Threshold uncertainty score0.895

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.282
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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".

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Citations3
Published2023
Admission routes3
Has abstractyes

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