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Record W2745429382 · doi:10.1093/ofid/ofx163.1799

Healthcare Resource Utilization and Costs Following Diagnosis of Nontuberculous Mycobacterial Lung Disease in the USA

2017· article· en· W2745429382 on OpenAlexaff
Theodore K. Marras, Mehdi Mirsaeidi, Engels Chou, Gina Eagle, Raymond Zhang, Ping Wang, Quanwu Zhang

Bibliographic record

VenueOpen Forum Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineBronchiectasisHealth careNontuberculous mycobacteriaAsthmaInternal medicineCohortComorbidityPneumoniaTuberculosisPediatricsEmergency medicineLung

Abstract

fetched live from OpenAlex

Despite increasing awareness of nontuberculous mycobacterial lung disease (NTMLD), reports on the economic impact of healthcare resource utilization (HCRU) and costs are limited. The national managed care insurance database was searched for physician claims for NTMLD (ICD9 031.0 or ICD10 A31.0) on ≥2 separate occasions ≥30 days apart between 2007 and 2016. A patient cohort (n = 1039) was selected by including those who were insured continuously over 36 months. A control group (n = 2078) was randomly selected from the plan members without NTMLD and matched 2:1 to the NTMLD sample by age and sex. The diagnosis date of NTMLD patient was assigned to the matched controls as the index date. HCRU and standardized costs were summarized over 12 months (baseline) before NTMLD diagnosis and 2 subsequent years. Mean age was 68 years with 67% women. Charlson comorbidity score was 2.0 (±2.2) in NTMLD vs 0.5 (±1.3) in control. NTMLD patients had substantially more respiratory and other disorders compared with the control group (20.6% vs 3.5% asthma, 36.7% vs 0.3% bronchiectasis, 50% vs 6% ColoradoPD, 2% vs 0% cystic fibrosis, 41.6% vs 1.4% pneumonia, 7.8% vs 0% tuberculosis) and had greater immunosuppressant use (43.8% vs 11.9%). NTMLD vs control group had a 30.5% vs 6.0% rate of hospitalization at baseline, 35.1% vs 6.9% at year 1, and 23% vs 7.3% at year 2. Mean (median) total annual healthcare costs in NTMLD vs control were $35,145 ($15,493) vs $5,660 ($587) at baseline, $47,248 ($18,626) vs $6,692 ($745) at year 1, and $28,959 ($11,385) vs $7,184 ($819) at year 2. Medical costs were $26,626 ($11,701) vs $4,370 ($209) at baseline, $35,508 ($12,416) vs $5,248 (288) at year 1, and $20,036 ($6,715) vs $5,488 ($400) at year 2; pharmacy spending was $8,519 ($2,209) vs $1,290 ($21) at baseline, $11,739 ($3,957) vs $1,444 ($45) at year 1, and $8,923 ($2,418) vs $1,696 ($55) at year 2. Observed HCRU and costs are substantially higher in NTMLD vs control group and increase from baseline to year 1 then decrease to year 2 in NTMLD but continue to rise in control group. The reversed U-shape of total costs in patients with NTMLD may reflect joint economic outcomes of disease, comorbidity, and management. E. Chou, Insmed Incorporated: Employee, Salary; G. Eagle, Insmed Incorporated: Employee, Salary; R. Zhang, Insmed Incorporated: Consultant, Consulting fee; P. Wang, Insmed Incorporated: Employee, Salary; Q. Zhang, Insmed Incorporated: Employee, Salary

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 score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.035
GPT teacher head0.361
Teacher spread0.326 · 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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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