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Regional Variation in Heart Failure Hospitalizations: Biology, Barrier, or Bias?

2008· article· en· W1597302165 on OpenAlexaboutno aff
Vasiliki V. Georgiopoulou, Javed Butler

Bibliographic record

VenueCongestive Heart Failure · 2008
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHeart failureComorbidityPopulationMortality rateDiabetes mellitusEmergency medicineDiseaseIntensive care medicinePediatricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

The worsening heart failure (HF) epidemic is upon us! It is estimated that more than 5 million persons in the United States have HF and more than 550,000 are diagnosed annually.1 HF is the primary reason for 12 to 15 million office visits, 6.5 million hospital days, and more than 53,000 deaths annually, with a readmission rate as high as 50% within 6 months of discharge.1 During the past decade, the annual number of hospitalizations has increased to more than 1 million for HF as a primary diagnosis.1 HF remains the most common Medicare diagnosis-related group, and more Medicare dollars are spent on HF than on any other diagnosis.1 HF hospitalization marks a fundamental change in the progression of the disease process because the mortality rates in the year following HF hospitalization are significantly higher compared with other conditions. Indeed, HF hospitalization has been shown to be the single most important risk factor for mortality in these patients.2 Also, HF hospitalization remains the most important driver of cost of care, and it is estimated that the vast majority of the nearly $30 billion spent for the treatment of these patients is related to recurrent hospitalization costs. The worsening HF hospitalizations are usually attributed to worsening comorbidity profile in the population (eg, diabetes and obesity) and the growing segment of the elderly in the population. In other words, most of the worsening HF hospitalizations are ascribed to biology. In this issue of Congestive Heart Failure, however, Zhang and Watanabe-Galloway3 present an interesting report that suggests the possibility of other factors that may be at least partially responsible for the worsening HF hospitalization epidemic. Assessing the data from the National Hospital Discharge Survey, they examined the secular trends and regional variation in hospitalization rates for HF. As noted by other investigators, they report that the HF hospitalization rate increased significantly between 1995 and 2004. What was interesting, however, was that there was a significant regional variation within the United States with respect to changes in HF hospitalization rate. The trend in increased hospitalization was particularly marked in the West (from 10.3 per 10,000 population in 1995 to 17.0 per 10,000 population in 2004; P<.001) and South regions (from 21.9 per 10,000 population in 1995 to 27.6 per 10,000 population in 2004; P<.001). The regional variations for HF hospitalization were associated with the number of primary care physicians per 10,000 population, regional income level, and the proportion of patients with Medicare payment. Could the regional differences be biologically mediated? One can think of several hypothetical reasons for the differences observed that could be related to “regional biological” causes (eg, regions with higher altitude and low ambient oxygen, regions with higher allergies and pulmonary complications, or areas of the country with heavy snowfall and more need for physical exertion in suboptimal environmental conditions). These reasons cannot explain the results presented in this report, however. Another possibility is the higher proportion of the elderly in specific regions, although this issue is unimportant because the rise was primarily seen in persons aged 35 to 64 years. Could barriers be the root cause of these results? It can be postulated that places with a higher proportion of indigent persons or uninsured or immigrant populations do not have access to routine and timely outpatient care. These persons may therefore be either more likely to develop HF decompensation without outpatient intervention or may use emergency room services for partly routine medical needs. In this study, however, the higher hospitalization rate was related to more, not fewer, physicians. Although this somewhat superficial look does not rule out access and barrier to care as reasons for the regional variations, it certainly raises interesting questions. Are there regional biases based on physician? This is certainly possible. For other cardiovascular conditions it has been well documented that significant variations in procedure rates after acute myocardial infarction exist between North and South regions of the United States4 and between the United States and Canada and that these variations are largely explained by the physician supply and managed care infiltration. This investigation may suggest a similar dynamic. Even if regional differences are explained by physician biases, however, it does not reflect appropriateness (ie, is the rise in HF hospitalization rate in certain regions appropriate or inappropriate?). Could it be that the slower rise is inappropriately slow? The study by Zhang and Watanabe-Galloway was designed neither to answer that question nor provide data to start exploring this issue. However, since it seems that regional variations in both the absolute HF hospitalization rates and the relative changes over time may be related to factors beyond biology of the disease, it is important to understand these dynamics. If there are regions with inappropriately high hospitalization rates, this needs to be corrected, as the cost of care in the United States has grown at an unsustainable rate. More important, if the regions with lower HF hospitalization rates are inappropriately low, this is an even more important issue that needs remedy since it suggests poor patient care. This study provides initial data in this respect, but much more work lies ahead. Disclosures: None.

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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)0.120
metaresearch head score (Gemma)0.283
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: none
Teacher disagreement score0.120
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.283
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.006
Science and technology studies0.0020.006
Scholarly communication0.0050.005
Open science0.0040.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.042
GPT teacher head0.294
Teacher spread0.252 · 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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Citations2
Published2008
Admission routes1
Has abstractyes

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