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Record W2251490213 · doi:10.1155/2014/279794

Financial Impact of Health Care‐associated Infections: When Money Talks

2014· article· en· W2251490213 on OpenAlexaffabout
Louis Valiquette, Claire Nour Abou Chakra, Kevin B. Laupland

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of CalgaryRoyal Inland HospitalUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineHealth careInfection controlIncidence (geometry)PneumoniaHealth spendingEnvironmental healthEmergency medicineDisease controlAntibiotic resistanceIntensive care medicineAntibioticsInternal medicineHealth servicesPopulation

Abstract

fetched live from OpenAlex

A ccording to the Canadian Institute for Health Information, health spending in Canada was projected to reach $211 billion in 2013 (versus $207 billion in 2012), corresponding to $5,988 per person (1).Overall, this represents 11.2% of Canada's gross domestic product.Approximately 60% of total health spending is directed to hospitals (30%), drugs (16%) and physicians (15%).Although it is difficult to estimate, the proportion of this spending attributed to the management of nosocomial infections, overuse and/or misuse of antimicrobials, and infections due to multidrug-resistant bacteria is significant.Despite the availability of efficient strategies targeting each of these aspects, large-scale progress has not been demonstrated.In a recent meta-analysis, Zimlichman et al (2) included 26 studies and used incidence estimates from the National Healthcare Safety Network of the Centers for Disease Control and Prevention, formerly known as the National Nosocomial Infections Surveillance (NNIS).They estimated the costs and excess length of stay (xLOS) associated with significant health care-associated infections (HAIs).On a per-case basis, central lineassociated bloodstream infections (CLABSIs) were found to be the most costly (US$45,814 [2012]; xLOS 10.4 days), followed by ventilatorassociated pneumonia (US$40,144; xLOS 13.1 days) and surgical site infections (US$20,785; xLOS 11.2 days).When caused by methicillinresistant Staphylococcus aureus (MRSA), both the cost and xLOS of surgical site infections increased by 105%; CLABSI cost increased by 22% and CLABSI xLOS increased by 51%.These results highlight the importance of strategies to control bacterial antibiotic resistance.The three most significant antibiotic-resistant bacteria found in Canadian centres are MRSA, vancomycin-resistant enterococci (VRE) and extended-spectrum beta-lactamase (ESBL)-producing organisms.A summary of a literature review is shown in Table 1.Most studies were from different states in the United States (US), and used retrospective cohorts from administrative databases and various analyses with various levels of sophistocation.Very few cost assessments were found for Canada: according to a systematic review (3), MRSA infection cost the Canadian health system between $54 million and $110 million (2005 CAD$) (direct attributable health care cost per year) including infection, colonization and infrastructure.The average cost per patient for MRSA infection was estimated to be $12,216 (range $6,878 to $17,553) (3).In a conference publication, Muller et al (4) showed that $10 million in expenses were needed to control an MRSA outbreak in Toronto (Ontario) in 2006 to 2007, increasing hospital cost per patient by 35%.In studies from the US, Filice et al (5) estimated adjusted mean cost of medical services for MRSA in patients with low Charlson's score (0 to 3) to be $51,252 (2007 US$) (95% CI $46,041 to $56,464) versus $30,158 (95% CI $27,092 to $33,225) for methicillin-sensitive Staphylococcus aureus, and up to $84,436 (95% CI $79,843 to $89,029) versus $59,245 (95% CI $56,016 to $62,473) for a high Charlson's score (≥4).Shorr et al (6) did not find significant differences between MRSA and methicillinsensitive Staphylococcus aureus in a crude analysis, but estimates of cost were both high ($70,028 versus $71,186).Only one study assessed the cost of VRE; performed in Vancouver, British Columbia (7), it involved a large sample (n=1292) and showed Financial impact of health care-associated infections:When money talks

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)0.008
metaresearch head score (Gemma)0.039
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 score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.004
Scholarly communication0.0110.009
Open science0.0010.004
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0170.002

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 head0.094
GPT teacher head0.433
Teacher spread0.339 · 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

Citations27
Published2014
Admission routes2
Has abstractyes

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