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Enregistrement W4390095996 · doi:10.3384/9789180754170

Social Inequalities in Child Health : Type 1 Diabetes, Obesity, Cardiovascular Risk Factors and the Role of Self-control

2023· book· en· W4390095996 sur OpenAlexaboutno aff
Pär Andersson White

Notice bibliographique

RevueLinköping University medical dissertations · 2023
Typebook
Langueen
DomaineMedicine
ThématiqueObesity, Physical Activity, Diet
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésObesityType 2 diabetesMedicineInequalitySocial inequalityDiabetes mellitusEnvironmental healthGerontologyEndocrinologyMathematics

Résumé

récupéré en direct d'OpenAlex

The Swedish Commission on Health Inequality defined health inequality as systematic differences in health between groups in society with different social positions. All avoidable socioeconomic health inequalities are unfair, and as stated by WHO's Commission on the Social Determinants of Health, we have a moral obligation to try to reduce them. "Putting these inequities right is a matter of social justice. Reducing health inequities is, for the Commission on Social Determinants of Health, an ethical imperative." This ethical imperative is especially apparent regarding the health of children and adolescents. Children’s right to the highest attainable standard of health is also enshrined in Article 24 of the Convention on the Rights of the Child. To reach the goal of a reduction of health inequalities, research is necessary to describe the social gradients of health. Research is also needed to better understand why these gradients occur. A better understanding and knowledge about health inequalities can lead to policies that reduce these inequalities and ensure children’s right to health. This thesis investigates social inequality in child health using data from a Swedish population-based prospective birth cohort, the All Babies in Southeast Sweden (ABIS) cohort. Social inequality in obesity in the ABIS cohort is also compared with other birth cohorts participating in the Elucidating Pathways to Child Health Inequality (EPOCH) collaboration which includes cohorts from six high-income countries; Sweden, the Netherlands, Canada (one national and one cohort from Quebec), UK, Australia, and USA. In Paper 1 we show that health inequalities in overweight and obesity are detectable already at two years of age and that these inequalities increase during childhood. In adolescents, low socioeconomic status increases the risk of becoming overweight and the risk of components of the metabolic syndrome, including high blood pressure and dyslipidemia (low high-density cholesterol). The level of inequality in obesity in the Swedish ABIS cohort was lower than in the other participating countries in the EPOCH collaboration (Paper 2). Inequality was lower in absolute and relative terms when SES was measured by household income. Inequality was also lower in absolute, but not relative, terms when SES was measured by maternal education. This finding indicates that some of the policies implemented in Sweden may attenuate social inequalities in obesity in children. Examples of such policies with evidence for reducing social inequality in obesity implemented in Sweden include universal preschools and free school meals. This thesis also investigates health inequalities in autoimmune disease (Paper 3). In this study, we found that low socioeconomic status increased the risk of Type 1 Diabetes but not the other autoimmune diseases investigated. Path analysis indicated that part of the increased risk in children with low SES of Type 1 Diabetes might be mediated by a higher body mass index and an elevated risk of serious life events. In the final paper, this thesis tests the hypothesis that differences in maternal and child self-control mediate social inequalities in obesity. Two measures of self-control were used; for mothers, the self-control variable was based on behaviors related to self-control (smoking during pregnancy, smoking during the child’s first year of life, breastfeeding duration, and participating in the ABIS study with biological samples). For the children, the self-control variable was based on questionnaire data on the impulsivity subscale of the Strengths and Difficulties Questionnaire (SDQ). The results showed that the two measures of self-control mediated 87.5 % of the increased risk of obesity at age 19 years in children with low maternal education and 93 % of the risk if maternal BMI was also included in the selfcontrol variable. In the discussion part of this thesis, the conclusions that can be deduced from understanding the mechanisms of social inequality in child health are discussed. A theory with a central role of self-control for health inequality predicts that social inequality will increase without interventions. In an environment with rising numbers of stimuli of the human reward system, stimuli that also have negative long-term consequences (socalled Limbic traps), child and adolescent health, in general, will decrease. Because of the mechanisms related to SES and self-control, children with low SES will be disproportionally affected. The result of this development will be increasing levels of social inequalities in child health. The discussion also includes implications for policies that may improve health and reduce inequalities. These policies should reduce the exposure of children and adolescents to harmful behaviors/limbic traps. Examples of policies that have this effect include universal preschools for all children, free healthy meals in preschools and schools, increased after-school activities for all children, and longer school days for adolescents with increased hours for physical activity, music, and art. Mobile phones and social media restrictions in schools and policies to reduce use at home should also be implemented. Finally, policies should be implemented to reduce residential and school segregation in the community.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,038
Score d'incertitude au seuil0,077

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,004
Études des sciences et des technologies0,0010,002
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,010
Tête enseignante GPT0,240
Écart entre enseignants0,229 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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