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Record W3047592323 · doi:10.1377/hlthaff.2019.01637

Food Insecurity Is Associated With Higher Health Care Use And Costs Among Canadian Adults

2020· article· en· W3047592323 on OpenAlexafffundabout
F. K. Men, Craig Gundersen, Marcelo L. Urquía, Valerie Tarasuk

Bibliographic record

VenueHealth Affairs · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of ManitobaManitoba HealthVale (Canada)University of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineOddsHealth careEnvironmental healthPublic healthAcute careFood insecurityMedical Expenditure Panel SurveyOdds ratioFood securityAmbulatory careGerontologyLogistic regressionNursingHealth insurance

Abstract

fetched live from OpenAlex

Food insecurity predicts poorer health, yet how it relates to health care use and costs in Canada remains understudied. Linking data from the Canadian Community Health Survey to hospital records and health care expenditure data, we examined the association of food insecurity with acute care hospitalization, same-day surgery, and acute care costs among Canadian adults, adjusting for sociodemographic characteristics. Compared with fully food-secure adults, marginally, moderately, and severely food-insecure adults presented 26 percent, 41 percent, and 69 percent higher odds of acute care admission and 15 percent, 15 percent, and 24 percent higher odds of having same-day surgery, respectively. Conditional on acute care admission, food-insecure adults stayed from 1.48 to 2.08 more days in the hospital and incurred $400-$565 more per person-year in acute care costs than their food-secure counterparts, with this excess cost representing 4.4 percent of total acute care costs. Programs reducing food insecurity, such as child benefits and public pensions, and policies enhancing access to outpatient care may lower health care use and costs.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
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 head0.119
GPT teacher head0.369
Teacher spread0.250 · 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.

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

Citations60
Published2020
Admission routes3
Has abstractyes

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