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Record W3155936974 · doi:10.55016/ojs/sppp.v8i1.42526

Addendum to “Bending the Medicare Cost Curve in 12 Months or Less”: AHS Analysis for Sample of Pure North Seniors (55-plus)

2015· article· en· W3155936974 on OpenAlexaffabout
Daniel J. Dutton, J.C. Herbert Emery

Bibliographic record

VenueThe School of Public Policy Publications · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAddendumSample (material)DemographyStatisticsMathematicsGerontologyMedicineEconometricsChemistryPolitical scienceSociologyChromatography

Abstract

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As part of our analysis in the paper published in January 2015, “Bending the Medicare Cost Curve in 12 Months or Less: How Preventative Health Care Can Yield Significant Near-Term Savings for Acute Care in Alberta,”1 we had carried out analyses of sub-groups of interest, such as workers at Canadian Natural Resources Ltd. (CNRL) and seniors (participants aged 55-plus) that, for reasons of length, we did not include in the published paper. The details for the data, the models estimated, the statistics calculated and the sample inclusion and exclusion restrictions are described in the full paper that was peer reviewed. This addendum discusses the results of the analysis of the sample of seniors (participants aged 55 and up at the time of joining Pure North, n=5,516, made up of 2,758 Pure North participants and 2,758 age- and sex-matched controls). The models estimated are described on pages 9 and 10 of the published paper. Persisting participants are Pure North joiners who have a 25OHD (vitamin D blood serum) measure at the time of joining and one year later. We interpret participants with two 25OHD one year apart as persisting in the Pure North program but we do not infer the degree of adherence to the program. The In-Clinic Seniors Program (ICP) sub-sample of Pure North senior participants had over 90 per cent persistence in the program for at least one year. For this sub-sample, relative to the frequency of hospital and emergency department visits of the ICP seniors program participants and matched controls in the year prior to joining the program, the program reduces hospital visits for seniors in the program by 22 per cent, emergency department visits by 34 per cent and avoids 22 per cent of annual health-care costs. For the 68 per cent of the full sample of Pure North participants aged 55 and over who we can confirm persisted in the program for at least one year, relative to the frequency of hospital and emergency department visits of the program participants and matched controls in the year prior to joining the program, the program reduces hospital visits for seniors persisting in the program by 39 per cent and emergency department visits by 24 per cent. These reductions in health care system contacts result in public health-care expenditures avoided of 35 per cent per year. These magnitudes are comparable to what we calculated for the overall and Vital 2.2 samples in the full 2015 report.Not accounted for in those direct health-care costs avoided is the relief that preventative care can provide to the medical treatment system. Scaled to the population level, the reductions observed in the Pure North seniors sample would represent at least six per cent fewer visits to Alberta emergency departments per year and reduce the need for hospital beds by at least six per cent in the Alberta hospital system. In terms of freed-up hospital beds, this is equivalent to adding another Foothills Medical Centre to the Alberta health-care system.

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)0.006
metaresearch head score (Gemma)0.055
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.122
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0040.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1220.021

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 head0.192
GPT teacher head0.352
Teacher spread0.160 · 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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Citations0
Published2015
Admission routes2
Has abstractyes

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