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Record W2984908450 · doi:10.1182/blood-2019-125183

Factors Associated with Health Care Resource Utilization in Myeloproliferative Neoplasms: A Population-Based Cost Study

2019· article· en· W2984908450 on OpenAlexaffabout
Aniket Bankar, Haoyu Zhao, Javaid Iqbal, Ruth Coxford, Matthew C. Cheung, Lee Mozessohn, Craig C. Earle, Vikas Gupta

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science CentrePrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicinePopulationPolycythemia veraDisease burdenEssential thrombocythemiaHealth careCohortMyelofibrosisInternal medicinePediatricsEnvironmental healthBone marrow

Abstract

fetched live from OpenAlex

Background: Myeloproliferative neoplasms (MPNs) are chronic myeloid malignancies with markedly heterogeneous disease course, and are associated with underlying inflammatory states that promote development of thrombotic events and acquisition of comorbidities. There is poor understanding of health care resource utilization (HRU) and cost of treatment in patients with MPNs. Objectives:To estimate and compare the HRU and cost of treatment for MPN patients (Essential Thrombocythemia, ET; Polycythemia Vera, PV; and Myelofibrosis, MF) with matched controls, and investigate the impact of patient characteristics and health service factors on the cost of treatment. Study design: Retrospective, population-based, matched-cohort study, using provincial health databases of Ontario's single payer universal health system. Study population: Cases were individuals in the Ontario Cancer Registry, diagnosed with MPN (Total n= 7130; ET, n=3481; PV, n=2618; MF, n=1031), from 2004 to 2016. Controls were individuals in the general population of Ontario, without a diagnosis of MPN. Each case was matched with four controls on age, sex, geographical location, and neighborhood income quintile. Baseline parameters including thrombosis and other comorbidities were collected during two-years prior to the date of MPN diagnosis. The baseline comorbid disease burden was measured using the Aggregated Diagnostic Group (ADG) score with a larger number of ADGs representing a greater comorbid disease burden (https://www.johnshopkinssolutions.com/wp-content/uploads/2014/04/ACG-White-Paper-Applications-Dec-2012.pdf). Main outcome measures:For each case and its controls, direct medical costs were obtained by costing all health care-related resources and expressed as mean per person year costs ((2018 Canadian Dollars, $1 CDN = $0.76 USD) to adjust for variable length of follow-up. Linear regression analysis was performed to assess the impact of baseline factors on the cost of treatment for MPN and represented as rate ratios (95% CI). Results:The mean duration of follow-up in years (cases vs controls) was 3.9 vs 4.3 for ET; 3.9 vs 4.2 for PV and 3.2 vs 4.9 for MF. The total follow-up duration was 27449 person years for all MPN cases, and 124963 person years for all controls. Comorbidities (congestive heart failure, chronic obstructive pulmonary disease, coronary artery disease, stroke, chronic renal failure, chronic liver disease, and pre-diagnosis arterial and venous thromboses were significantly higher in cases as compared to controls (p<0.001). Mean (+SD) ADG score (cases vs controls) were 19.2 vs 10.6 for ET, 19.5 vs 10.9 for PV, and 22.7 vs 11.8 for MF (p<0.001 for all three MPN types). The mean per-patient-year direct medical cost of treatment for patients with ET was $18,840 (2.3 X controls), for PV $18,966 (2.2 X controls) and for MF, $38,147 (4.5 X controls). Factors impacting costs of treatment are summarized in Table 1. In all MPNs, increased health expenditure was associated with older age (>65 years), those who never consulted a specialist in hematology-oncology, and increasing morbidity denoted by ADG score. Patients who were first seen by the specialist >6 months after the diagnosis incurred significantly lesser cost of treatment due to less comorbidity burden as noted by the lower ADG score for patients with >6 months vs <6 months to specialist referral (15 vs 18 for ET, 17 vs 19 for PV and 21 vs 22 for MF). History of venous thrombosis in ET and PV patients predicted higher cost of treatment but not arterial thrombosis due to confounding between arterial thrombosis and higher ADG score. CONCLUSION: MPN patients have substantial higher direct medical cost of treatment compared to matched-controls and have a high co-morbidity burden at diagnosis that significantly predicted higher cost of treatment. After adjusting for co-morbidity, history of venous thrombosis at diagnosis showed significantly higher cost of treatment in ET and PV. In addition, MPN patients who were never referred to a specialist incurred significantly higher cost of treatment. Disclosures Gupta: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Research Funding; Sierra Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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)0.001
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 score0.679
Threshold uncertainty score0.645

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.307
Teacher spread0.262 · 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
Published2019
Admission routes2
Has abstractyes

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