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1.
J Epidemiol Community Health ; 71(12): 1152-1160, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-28993471

RESUMO

BACKGROUND: Social patterning of dietary-related diseases may partly be explained by population disparities in children's diets. This study aimed to determine which early life socioeconomic factors best predict dietary trajectories across childhood. METHODS: For waves 2-6 of the Baby (B) Cohort (ages 2-3 to 10-11 years) and waves 1-6 of the Kindergarten (K) Cohort (ages 4-5 to 14-15 years) of the Longitudinal Study of Australian Children, we constructed trajectories of dietary scores and of empirically derived dietary patterns. Dietary scores, based on the Australian Dietary Guidelines, summed children's consumption frequencies of seven groups of foods or drinks over the last 24 hours. Dietary patterns at each wave were derived using factor analyses of 12-16 food or drink items. Using multinomial logistic regression analyses, we examined associations of baseline single (parental education, remoteness area, parental employment, income, food security and home ownership) and composite (socioeconomic position and neighbourhood disadvantage) factors with adherence to dietary trajectories. RESULTS: All dietary trajectory outcomes across both cohorts showed profound gradients by composite socioeconomic position but not by neighbourhood disadvantage. For example, odds for children in the lowest relative to highest socioeconomic position quintile being in the 'never healthy' relative to the 'always healthy' score trajectory were OR=16.40, 95% CI 9.40 to 28.61 (B Cohort). Among the single variables, only parental education consistently predicted dietary trajectories. CONCLUSION: Child dietary trajectories vary profoundly by family socioeconomic position. If causal, reducing dietary inequities may require researching underlying pathways, tackling socioeconomic inequities and targeting health promoting interventions to less educated families.


Assuntos
Dieta , Comportamento Alimentar , Determinantes Sociais da Saúde , Fatores Socioeconômicos , Adolescente , Austrália/epidemiologia , Criança , Pré-Escolar , Dieta/classificação , Dieta/economia , Inquéritos sobre Dietas , Escolaridade , Feminino , Promoção da Saúde , Humanos , Renda , Estudos Longitudinais , Masculino
2.
J Epidemiol Community Health ; 71(8): 817-826, 2017 08.
Artigo em Inglês | MEDLINE | ID: mdl-28490476

RESUMO

BACKGROUND: Inflammation plays a central role in cardiometabolic disease and may represent a mechanism linking low socioeconomic status (SES) in early life and adverse cardiometabolic health outcomes in later life. Accumulating evidence suggests an association between childhood SES and adult inflammation, but findings have been inconsistent. METHODS: We conducted a systematic review and meta-analysis of observational studies to quantify the association between childhood (age <18 years) SES and the inflammatory marker C reactive protein (CRP) in adulthood. Studies were identified in Medline and Embase databases, and by reviewing the bibliographies of articles published from 1946 to December 2015. Study-specific estimates were combined into meta-analyses using random-effects models. RESULTS: 15 of 21 eligible studies (n=43 629) were ultimately included in two separate meta-analyses. Compared with those from the most advantaged families, participants from the least advantaged families had 25% higher CRP levels (ratio change in geometric mean CRP: 1.25; 95% CI 1.19 to 1.32) in minimally adjusted analyses. This finding was attenuated by the inclusion of adult body mass index (BMI) in adjusted models, suggesting BMI has a strong mediating role in CRP levels. CONCLUSIONS: We observed an inverse association between childhood SES and adulthood CRP, potentially mediated through BMI. Investigating how childhood SES is associated with childhood BMI and CRP would provide insight into the effective timing of social and clinical interventions to prevent cardiometabolic disease.


Assuntos
Proteína C-Reativa/análise , Classe Social , Adolescente , Adulto , Idoso , Biomarcadores , Criança , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Adulto Jovem
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