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Record W2123117470 · doi:10.1186/s12913-014-0532-2

High-cost health care users in Ontario, Canada: demographic, socio-economic, and health status characteristics

2014· article· en· W2123117470 on OpenAlex
Laura C. Rosella, Tiffany Fitzpatrick, Walter P. Wodchis, Andrew Calzavara, Heather Manson, Vivek Goel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBMC Health Services Research · 2014
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsInstitute for Work & HealthInstitute for Clinical Evaluative SciencesToronto Rehabilitation InstitutePublic Health OntarioUniversity of Toronto
FundersOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
KeywordsMedicinePublic healthHealth informaticsMultinomial logistic regressionHealth administrationCommunity healthPopulationPopulation healthHealth careOddsNursing researchDemographySocioeconomic statusGerontologyEnvironmental healthLogistic regressionNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Health care spending is overwhelmingly concentrated within a very small proportion of the population, referred to as the high-cost users (HCU). To date, research on HCU has been limited in scope, focusing mostly on those characteristics available through administrative databases, which have been largely clinical in nature, or have relied on ecological measures of socio-demographics. This study links population health surveys to administrative data, allowing for the investigation of a broad range of individual-level characteristics and provides a more thorough characterization of community-dwelling HCU across demographic, social, behavioral and clinical characteristics. METHODS: We linked three cycles of the Canadian Community Health Survey (CCHS) to medical claim data for the years 2003-2008 for Ontario, Canada. Participants were ranked according to gradients of cost (Top 1%, Top 2-5%, Top 6-50% and Bottom 50%) and multinomial logistic regression was used to investigate a wide range of factors, including health behaviors and socio-demographics, likely associated with HCU status. RESULTS: Using a total sample of 91,223 adults (18 and older), we found that HCU status was strongly associated with being older, having multiple chronic conditions, and reporting poorer self-perceived health. Specifically, in the fully-adjusted model, poor self-rated health (vs. good) was associated with a 26-fold increase in odds of becoming a Top 1% HCU (vs. Bottom 50% user) [95% CI: (18.9, 36.9)]. Further, HCU tended to be of lower socio-economic status, former daily smokers, physically inactive, current non-drinkers, and obese. CONCLUSIONS: The results of this study have provided valuable insights into the broader characteristics of community-dwelling HCU, including unique demographic and behavioral characteristics. Additionally, strong associations with self-reported clinical variables, such as self-rated general and mental health, highlight the importance of the patient perspective for HCU. These findings have the potential to inform policies for health care and public health, particularly in light of increasing decision-maker attention in the sustainability of the health care system, improving patient outcomes and, more generally, in order to achieve the common goal of improving population health outcomes.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.385
Teacher spread0.332 · 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