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Enregistrement W3033236746 · doi:10.1142/9789811212413_0005

How Much Do Countries Spend on Primary Care in the Americas?

2020· book-chapter· en· W3033236746 sur OpenAlexaboutno aff
Camilo Cid, Claudia Pescetto, James Fitzgerald, Amalia del Riego

Notice bibliographique

RevueWorld Scientific series in global healthcare economics and public policy · 2020
Typebook-chapter
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Systems and Reforms
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPer capitaHealth carePublic expenditurePublic healthDeveloping countryEconomic growthGeographyBusinessDemographic economicsMedicineEnvironmental healthEconomicsPopulationPublic financeNursing

Résumé

récupéré en direct d'OpenAlex

The following sections are included:Estimating expenditure on the first level of care or primary care is not an easy task. There is a need to differentiate between primary health care (PHC) as an overarching approach to the organization and operation of health systems and the first level of care or primary care, which refers to a level of care in the provision of health services. Improved resolution capacity of the first level of care to expand access to comprehensive, quality health services is required to advance toward universal access to health and universal health coverage, hence the need to measure how much countries are spending in the first level of care.Results for 13 countries in the Americas chosen in this study indicate that the expenditure in the first level of care or primary care, as a percentage of public expenditure in health, presents a high variability, fluctuating between 12.5% in the United States and 44.2% in El Salvador. Despite the methodological differences found, we estimated a 24% median considering each country as one observation.When the indicator is compared to total health expenditure per capita for each country, two main groups and two outliers (Cuba and the US) are identified: the first (Bolivia, El Salvador, and Jamaica), with high spending in the first level of care as a percentage of total public expenditure in health (over 38%) but with low total expenditure per capita (less than int$550); the second (Argentina, Barbados, Brazil, Chile, Costa Rica, Mexico, and Uruguay), with spending in the first level of care as a percentage of total public expenditure in health (ranging 20–25%) with a high per capita expenditure (ranging 1, 000–2, 000 dollars).When comparing spending in the first level of care as a percentage of public expenditure in health, with the public expenditure in health as a percentage of GDP, three sets of countries are clearly identified: first, countries that invest between 20% and 25% in the first level of care and over 4% public expenditure in health as a percentage of GDP (Jamaica, Brazil, Chile, Costa Rica, Argentina, Uruguay, and Canada); countries with higher levels of investment in the first level of care, between 25% and 45%, but lower public expenditure in health as a percentage of GDP; and USA and Cuba are outliers, with low investment in first level of care in the first and very high investment in the second.Data found for other regions show a median of 16% of expenditure in the first level of care as a percentage of total public expenditure for the OECD countries, and for a sample of middle- and upper-middle-income countries, a median of 25% for spending in the first level of care as a percentage of total public expenditure.Our results show the need for a more comprehensive study and the promotion of an international definition of or first level of care and that further analysis is needed. For the case of countries in the Americas, there is a clear need also to standardize what is first level of care to allow for a more systematic monitoring and measurement that could be comparable across countries.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Communication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,863
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,048
Tête enseignante GPT0,272
Écart entre enseignants0,224 · 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'étudeThéorique ou conceptuel
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

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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