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Record 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 on OpenAlexaboutno aff
Pär Andersson White

Bibliographic record

VenueLinköping University medical dissertations · 2023
Typebook
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsObesityType 2 diabetesMedicineInequalitySocial inequalityDiabetes mellitusEnvironmental healthGerontologyEndocrinologyMathematics

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.240
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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