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Enregistrement W4394763291 · doi:10.1111/iwj.14709

Estimating the cost of wounds both nationally and regionally within the top 10 highest spenders

2024· editorial· en· W4394763291 sur OpenAlexaff
Douglas Queen, Keith G Harding

Notice bibliographique

RevueInternational Wound Journal · 2024
Typeeditorial
Langueen
DomaineHealth Professions
ThématiquePressure Ulcer Prevention and Management
Établissements canadiensMaple Leaf Medical Clinic
Organismes subventionnairesnon disponible
Mots-clésPer capitaEstimationCurrencyHealth careMedicineGovernment (linguistics)PopulationActuarial sciencePublic economicsRegional scienceEconomic growthEnvironmental healthGeographyEconomicsMacroeconomics

Résumé

récupéré en direct d'OpenAlex

Following our recent editorials regarding the estimation of the costs of wound care both globally and nationally,1 we published a few regional specific analyses.2, 3 To supplement this further, we carried out a similar analysis in the remaining top 10 highest spenders and updated their national costs to the most recent government data published. The regional analysis may vary by country depending on the collective statistics available for the regions. The recent editorial introduced the approach to estimate the possible costs of wound care using freely available governmental health data, population statistics, and the research findings of many national groups.1 Using this methodology, an estimate, the costs of wounds nationally as a whole, and the individual regional elements of which the countries are comprised was carried out for those countries within the top 10 spenders, globally. The previous analysis1 focused on 2019 to permit a direct comparison between countries. The subsequent analyses focused on updating these figures to the most recently available estimate, based on the availability of regional governmental statistics. Also, in this analysis, we present the estimates in local currency, rather than international common currency. This will enable a broader understanding and provide an increased utility for researchers. This will be particularly useful for regional analysis within nations. As with our previous analyses, the ‘accuracy’ of the estimate is dependent on what governmental information is available. One weakness is in some geographies that there is a lack of the availability of per capita healthcare spend on a regional basis. Perhaps within some geographies, the per capita healthcare spend is universal across all regions. To further our analysis, we made that assumption to permit a calculation of regional healthcare spend based on population statistics. Irrespective of any inaccuracies in our estimation model, the data provides an indication of the likely regional spend across a geography, giving benchmark data for improvement initiative or governmental investment. The following table provides a snapshot of the possible costs of wound care within these geographies in the year 2022. Compared to our previous analysis,1 not unsurprisingly, the costs increased across all geographies. Interestingly, some minor changes in ranking within the top 10 did change. Further analysis was carried out to provide a regional picture country by country. The results of this regional analysis are presented below. The United States spends the most on healthcare globally.4 A national analysis (Table 1) suggests this is true for wound are also.1 The most recent governmental figures only permitted a regional analysis for 2020. Previous studies within the USA had provided an estimate of the likely costs,5-9 which are not outdated. None of these studies provided a regional analysis giving a picture of wound care spend state by state. The data presented in Figure 1 provide a crucial estimate of the likely costs of wounds across the United States. The costs are significant across all states, with some being more than many nations. These figures can provide a vital benchmark with regards to governmental/payor impacts both regionally and nationally. China may have lost the number one slot for total population recently, but our analysis estimates significant costs for wound care. A literature search highlighted a few studies with regards to wound care and its costs within China.10-13 A regional analysis shows for most of China's regions that their wound care costs equal that of many countries. This is not surprising since the estimation model is population based and China is developing economically with ongoing significant increases in per capita healthcare spend (Figure 2). Japan is one of the countries globally with a disproportionate elderly population. Since wound care is a problem for the elderly, then it should come as no surprise that Japan spends significantly in the wound care arena. Previous studies have shown this to be the case, especially in the pressure injury area.14, 15 Figure 3 presents the national and regional estimates of the wound care spend across Japan and its regions. Germany has a well-structured healthcare system and a good handle on its healthcare costs. Several authors have published some costing studies within German.16-19 Figure 4 provides a 2022 national estimate of cost, while providing a regional picture of the likely spend in the wound care area. France like Germany has a well-developed and tracked healthcare system. A literature search shows there are few studies within France regarding the estimation of wound care costs.20 An analysis across France and its regions shows significant spend across the country (Figure 5). Brazil is a quickly developing nation, with its healthcare systems becoming more and more sophisticated. With a large population its national healthcare spend is significant,21 and this includes wounds (Table 1). A literature search shows several studies have been carried out to highlight the cost of wounds within Brazil.22, 23 None, however, have provided a complete national cost nor indeed regional component. A regional analysis (Figure 6) demonstrates significant costs particularly in the east and south of the country. Italy has one of the lower per capita healthcare spends within Europe.24 However, its estimated wound care spend is still significant as seen in Table 1 and supported by a previous study. A regional analysis presented in Figure 7 shows how this is broken down regionally. Australia efficiently tracks its healthcare spending state by state. Several research groups have published cost related studies,25, 26 which includes a national estimate of wound care costs.27 None of these studies provided a regional picture of wound care expenditure. A regional analysis using our estimation model (Figure 8), presents how this is broken down across the country. Comprehending the economic impact of wound care offers valuable insights to policymakers and healthcare leaders, shedding light on the broader economic implications of wound management and its costs to the payors. This knowledge serves as a foundation for informed decision-making and the development of policies and research direction that support effective wound prevention and care practices. The impact can be both regionally or nationally influenced so a deeper understanding of regional spend can provide more directive guidance versus a national picture of costs.

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)0,004
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche, 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: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,144
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,003
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,037
Tête enseignante GPT0,403
Écart entre enseignants0,366 · 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
GenreÉditorial

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

En bref

Citations38
Publié2024
Routes d'admission1
Résumé présentoui

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