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Enregistrement W2608847808 · doi:10.1097/ede.0000000000000677

Declining US Life Expectancy

2017· letter· en· W2608847808 sur OpenAlexaffabout
Sam Harper, Jay S. Kaufman, Richard Cooper

Notice bibliographique

RevueEpidemiology · 2017
Typeletter
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensNatural Sciences and Engineering Research Council of CanadaMcGill UniversityFonds de Recherche du Québec - SantéMcGill University Health Centre
Organismes subventionnairesnon disponible
Mots-clésLife expectancyDemographyMedicineGerontologyPopulationRace (biology)Cause of deathHealth statisticsDiseasePsychological interventionEnvironmental healthPsychiatryBiologyPathology

Résumé

récupéré en direct d'OpenAlex

To the Editor: In 2015, for the first time in nearly 25 years, life expectancy decreased in the United States.1 The decrease was small—from 78.2 to 78.1 years—but is nevertheless a cause for concern given recent studies2,3 showing adverse trends in mortality (although these studies were limited to whites). Understanding the components of changes in life expectancy, and how they differ across demographic groups, is an important first step toward identifying root causes and potential ameliorative interventions. We abstracted data on deaths and population from the US National Vital Statistics System by age, cause-of-death, and race ethnicity for 2014 and 2015.4 We limited our analysis to non-Hispanic blacks and non-Hispanic whites because of longstanding concerns for black-white differences in life expectancy. We created abridged life tables and used Arriaga’s5 method for decomposing changes in life expectancy by age and cause-of-death. We selected International Classification of Disease, 10th edition codes (Figure) to capture leading causes of death among gender and race groups. We used Stata software (version 14) to analyze the data and do not present measures of precision because the mortality data are available for the entire population.FIGURE: Contribution of cause-of-death groups (International Classification of Diseases, Tenth Revision [ICD-10] categories taken from National Center for Health Statistics list of 113 selected causes of death: Cardiovascular diseases [I00–I78]; Cancers [C00–C97]; Diabetes [E10–E14]; Alzheimer’s disease (G30); Influenza and pneumonia [J09–J18]; Human immunodeficiency virus [B20–B24]; Chronic lower respiratory disease [J40–J47]; Liver disease [K70, K73–K74]; Kidney disease [N00–N07, N17–N19, N25–N27]; Motor vehicle crashes [V02–V04, V09.0, V09.2, V12–V14, V19.0–V19.2, V19.4–V19.6, V20–V79, V80.3–V80.5, V81.0–V81.1, V82.0–V82.1, V83–V86, V87.0–V87.8, V88.0–V88.8, V89.0, V89.2]; Unintentional poisoning [X40–X49]; Suicide [*U03, X60–X84, Y87.0]; Homicide [*U01–*U02, X85–Y09, Y87.1]; All other causes (all other codes). Available from: http://www.cdc.gov/nchs/data/dvs/Part9InstructionManual2011.pdf) to the change in life expectancy between 2014 and 2015, by gender and race ethnicity (Race and Hispanic origin were classified by the funeral director for death certificates and self-reported for population estimates, and were reported separately on the death certificate in accordance with standards set forth by the US Office of Management and Budget).Among non-Hispanic men, life expectancy at birth decreased from 76.6 to 76.5 years for whites and from 72.7 to 72.4 for blacks. For non-Hispanic women, life expectancy decreased from 81.3 to 81.1 years for whites and remained essentially constant (78.5 years) for blacks. The largest absolute increases in age-adjusted death rates between 2014 and 2015 were for Alzheimer’s and cardiovascular disease among women, unintentional poisoning for men, and homicide for black men (eTable 1; https://links.lww.com/EDE/B202). Among white women, increases in cardiovascular and Alzheimer’s disease accounted for 71% of the decrease in life expectancy (Figure). Alzheimer’s disease also made a notable (20%) contribution among white men, but the majority (50%) of the decline was due to unintentional poisoning, in addition to suicide (12%) and motor vehicle crashes (11%). For black men, however, increases in homicide accounted for nearly 60% of the life expectancy decrease, alongside contributions from unintentional poisoning (23%) and motor vehicle crashes (16%). Improvements in cancer survival kept life expectancy from decreasing further in all groups. Analysis by age group showed that the increase in mortality among the oldest group (85 and over) accounted for 49% of the decrease in life expectancy for white women, whereas mortality increases among those 15–44 accounted for 80% and 65% of the decrease for black and white men, respectively (eFigure 1 and eTable2; https://links.lww.com/EDE/B202). The decline in US life expectancy resulted from a heterogeneous group of causes of death, and did not affect all demographic groups equally. Black men lost nearly twice as many years of life expectancy than did white men, and black women showed virtually no change in life expectancy. The increase in cardiovascular disease is worrisome and consistent with other reports of stalling progress in mortality declines, but our estimates show that this primarily affected life expectancy among white women. Considerable attention has also focused on middle-aged whites who have experienced sustained increases in mortality, largely due to the rise in opioid overdose deaths.2,3 However, the rise in homicide, which disproportionately affects young black men, has received little attention. Although the homicide rate has shown impressive declines since peaking in the 1970s, the 2015 increase requires additional investigation.6 If this trend were sustained it could erode the substantial progress made in reducing the black-white life expectancy gap.7 On a more positive note, improvements in cancer survival kept life expectancy from decreasing by more than it otherwise would have. Our analysis used the underlying cause-of-death and may underestimate the contribution of factors involved in multiple causes. Our findings demonstrate that paths to decreased life expectancy differ substantially by gender and race. Sam Harper Jay S. Kaufman Department of Epidemiology Biostatistics & Occupational Health McGill University Montreal, QC, Canada [email protected] Richard S. Cooper Loyola University Chicago Chicago, IL

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)0,012
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Intégrité 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: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,083
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,012
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,005

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 Opus0,298
Tête enseignante GPT0,377
Écart entre enseignants0,079 · 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
GenreCommentaire

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

En bref

Citations12
Publié2017
Routes d'admission2
Résumé présentoui

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