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Enregistrement W2898657834 · doi:10.1111/add.14485

Substance use and the objectives of current global health frameworks: measurement matters

2018· editorial· en· W2898657834 sur OpenAlexaff
Kevin D. Shield, Jürgen Rehm

Notice bibliographique

RevueAddiction · 2018
Typeeditorial
Langueen
DomaineBusiness, Management and Accounting
ThématiqueGlobal Public Health Policies and Epidemiology
Établissements canadiensPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
Organismes subventionnairesnon disponible
Mots-clésSubstance useCurrent (fluid)PsychologyEnvironmental healthMedicinePsychiatryEngineering

Résumé

récupéré en direct d'OpenAlex

Current global health frameworks emphasize the prevention of premature mortality, placing equal importance on all premature deaths, regardless of the age at death, and ignoring both non-premature deaths and disability. Health benchmarks should be based on summary health indicators, such as disability adjusted life years lost or health adjusted life expectancy, where data on the prevalence of disabling conditions exist, or on reductions in years of life lost or improvements in life expectancies for countries where data on the prevalence of disabling conditions do not exist. Current global health frameworks emphasize premature mortality prevention. The United Nations’ Sustainable Development Goals 2030 aim to reduce by one-third premature (defined as death before age 70) mortality from non-communicable diseases (NCDs) 1. The World Health Organization's Global Action Plan for the Prevention and Control of NCDs 2013–2020 has the target of a 25% relative reduction by 2025 in premature mortality due to cardiovascular diseases, cancer, diabetes and chronic respiratory diseases 2. Given that there are limited resources allocated to health issues 3, we argue that major global goals should be formulated differently, taking into consideration either non-fatal health outcomes by using summary health measures or, where this is not feasible, differentially weighting death at diffent ages with measures such as years of life lost to premature mortality. Equal valuation of deaths regardless of the age at which death occurs disregards the unequal health loss caused by deaths among people relatively younger in age 4, e.g. by alcohol and illicit drug use 5-7. In contrast, tobacco use has a relatively larger impact among people older in age 6. Therefore, program planning based on reductions in premature mortality may not represent the most effective method of improving population health, especially with respect to tobacco, alcohol and illicit drugs. Data from the Global Burden of Disease study were used to illustrate the harms resulting from premature mortality in 2016 in China, India, Brazil, South Africa, the United Kingdom and the United States 6 (see Fig. 1). Globally, a large proportion of deaths are not considered premature (47.6% of all deaths occurred among people 70 years of age and older). The percentage of all non-premature deaths was higher for the high-income countries (United Kingdom and the United States) compared to the lower- and upper middle-income countries of Brazil, China, India and South Africa. Additionally, infant mortality (at < 5 years of age) represented 9.1% of all deaths globally, with infant mortality being more common in low- and middle-income countries. The impact of tobacco, alcohol and illicit drug use on mortality differs by age 8. The distribution of deaths attributable to tobacco was skewed towards those older in age; 48.7, 28.7 and 14.2% of deaths caused by tobacco, alcohol and illicit drugs, respectively, occurred among people 70 years of age and older. Tobacco caused more premature deaths (3.7 million) compared to alcohol (2.0 million) or illicit drugs (0.4 million). Of the premature deaths caused by tobacco, the majority occurred among people 40–69 years of age (93.3%) compared to alcohol (81.0%) or illicit drugs (65.2%). Furthermore, tobacco, alcohol and illicit drugs had a larger impact on premature mortality among adults (15 years of age and older) compared to people 0–14 years of age. This was due to these substances being predominately used by adults 9 (i.e. most people aged 0–14 do not experience first-hand substance-attributable health effects). For low- and middle-income countries, where infant mortality is high, a focus on the health effects of tobacco, alcohol and drugs may be less of a priority than infant mortality. However, in those countries where the use of tobacco, alcohol and illicit drugs is prevalent during pregnancy, public health gains among infants can be achieved through targeted and generalized population health policies 10, 11. In high-income countries, deaths attributable to alcohol and illicit drugs occur relatively younger in age (due mainly to cirrhosis, poisonings often driven by opioid overdose deaths and other injuries) compared to deaths attributable to tobacco, thereby negatively impacting life expectancies (to the point of stagnation and reversals) 4, 12 and giving rise to health inequalities 7, 13. If increasing life expectancies is a goal, prevention of these deaths should be a priority over those deaths that occur relatively later in life due to tobacco use (e.g. cardiovascular and cancer deaths). Given differences in the distribution of deaths by age, countries may have vastly different health priorities, which are not reflected in measures of premature mortality. The above reasoning becomes more important if social inequalities are taken into consideration, as lower social status is linked to mortality and other health consequences at younger ages 14. The use of alternative health indicators as benchmarks, such as a reduction in years of life lost (YLL) due to premature mortality and increases in life expectancies, captures the unequal health loss caused by deaths at different ages. However, for global comparisons, YLL are derived using the reference standard life expectancy estimated based on the lowest observed mortality rate at each age (in populations greater than 5 million) 15. Therefore, changes in YLL may not be appropriate country-specific health targets. Furthermore, YLL and life expectancy changes will not capture health loss due to disability. Disability-adjusted life years (DALYs) lost and health-adjusted life expectancy (HALE) include non-fatal health outcomes. However, DALYs lost and HALE require data on the prevalence of disabling conditions (in many countries such data do not exist), and these health indicators can be conceptually difficult to understand. It has been hypothesized that reductions in premature mortality are strongly correlated with similar reductions in the number of YLL due to premature mortality and morbidity 16, thereby justifying the use of premature mortality as a proxy measure of health loss; however, no analysis of this hypothesis exists to our knowledge. Furthermore, it should be noted that YLL and DALYs lost treat all years of life lost equally, despite the age at which the loss occurs. The utility of this practice has been questioned (see 17). Tobacco, alcohol and illicit drugs cause numerous disabling chronic illnesses which are not fatal (e.g. dependence, as well as depression caused by alcohol) 5. Thus, summary health indicators are needed to measure the full health impacts of tobacco, alcohol and illicit drugs (see Fig. 1). Thus, current global health frameworks based on premature mortality are flawed and may lead to non-optimal health strategies. Instead, health benchmarks should be based on improvements in summary health indicators, such as DALYs lost or HALE, where data on the prevalence of disabling conditions exist, or on reductions in YLL or improvements in life expectancies for countries where data on the prevalence of disabling conditions do not exist. 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)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,019
Score d'incertitude au seuil0,985

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
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 Opus0,035
Tête enseignante GPT0,322
Écart entre enseignants0,287 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations4
Publié2018
Routes d'admission1
Résumé présentoui

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