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Enregistrement W1597302165 · doi:10.1111/j.1751-7133.2008.00010.x

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

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

Notice bibliographique

RevueCongestive Heart Failure · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueHeart Failure Treatment and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineHeart failureComorbidityPopulationMortality rateDiabetes mellitusEmergency medicineDiseaseIntensive care medicinePediatricsInternal medicineEnvironmental health

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,392
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,042
Tête enseignante GPT0,294
Écart entre enseignants0,252 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2008
Routes d'admission1
Résumé présentoui

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