Welfare Regimes and Social Inequalities in Health Dynamics: A Comparative Analysis of Panel Data from Britain, Denmark, Germany and the US
Notice bibliographique
Résumé
Objective: To describe average national trajectories of self-rated health over a 7-year period, identify social determinants of cross-sectional and longitudinal health; and compare cross-national patterns.\nDesign: Prospective nationally representative household panel studies (the US Panel Study of Income Dynamics; British Household Panel Survey; the German Socio-Economic Panel Survey; the Danish panel from the European Community Household Panel Survey).\nSetting: The US, Britain, Germany and Denmark\nParticipants: Household heads and their partners of working age throughout follow-up (US: 4855; Britain: 4365; Germany: 4694; Denmark: 3252).\nMain Outcome Measure: Repeated measures of self-rated health (1995 – 2001). Social indicators include education, occupational class, employment status, income, age, gender, minority status and marital status, all measured in 1994.\nMethods: Latent growth curve models describe average national trajectories of self-rated health and individual differences in these trajectories. Latent factors representing intercept and slope components are extracted from seven annual observations across time for self-rated health, and are conditioned on predictors measured one year prior to baseline. Aging-vector graphs are used to visualize trajectories of self-rated health.\nResults: The vector graphs for the US and Germany show that self-rated health remained relatively stable for young adults, declined as adults became middle aged and then became more stable again. The graphs for Britain and Denmark indicate a steady decline throughout working life. The Danish model indicates an unfavourable trend in self-rated health during a period that experienced a move to monetarism: ratings were lower for persons of a given age in 2001 than for persons of the same age in 1995. Social covariates predicted baseline health in all four countries, with the strength of association consistent with Esping-Andersen’s welfare regime type. The strongest social gradients were seen in the US, while the weakest were seen in Germany and Denmark. Britain occupied a position between these two extremes. Once inequalities in baseline health had been accounted for, there were few determinants of mean health decline. When these did occur, they were in countries classified as liberal welfare states. There was little difference in the aging trajectories for those with advantaged and average social profiles. By contrast, disadvantage has a strong effect on aging trajectories. Differences were already apparent at 25 years of age in the US and Britain and gaps widened with age in all four countries.\nConclusion: National differences in self-rated health trajectories and their social correlates may be attributed, in part, to welfare policies.\nThe paper is forthcoming in the Journal of Community Health and Epidemiology (JECH).\nPeggy McDonough is an Associate Professor in the Dalla Lana School of Public Health at the University of Toronto. Her research interests in social inequalities in health and women’s health have led her recently to incorporate a comparative welfare state dimension in her studies.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».