RESUMEN

OBJECTIVE: Academic achievement varies according to gestational age but it is unclear whether achievement varies within 'term' (37-41 weeks gestation) or for 'post-term' births (≥42 weeks). We examined gestational age from preterm to post-term against a national minimum standard for academic achievement in population data. DESIGN: Literacy and numeracy data of 8-year-old South Australian grade 3 children in 2008-2010 were linked to routinely collected perinatal data (N=28 155). RESULTS: Longer gestation from 23 to 45 weeks was associated with lower risk of poor literacy and numeracy. Adjusted relative risks for being at or below national minimum standard ranged from 1.12 (95% CI 1.03 to 1.22) for 'late preterm' (32-36 weeks) for numeracy, to 1.84 (95% CI 1.48 to 2.30) for 'early preterm' (23-31 weeks) for writing. Within term, every additional week of gestational age was associated with small decreased risks of poor literacy and numeracy (eg, relative risks for poor numeracy 1.10, 95% CI 1.01 to 1.20 for 37 weeks). Population-attributable fractions for poor achievement were highest among children born 'early term' (37-39 weeks) due to their higher population prevalence. CONCLUSIONS: Shorter gestational age was associated with increased risk of poor literacy/numeracy. While children born 'early term' experience only between 1% and 10% increased risk, they constitute a larger proportion of children with poor educational achievement than preterm children, and thus are important to consider for supportive interventions to improve population-level achievement gains. The seemingly lower risk for post-term children showed large error estimates and warrants further consideration within even larger populations.

Asunto(s)

RESUMEN

Every 3 years, the Australian Government conducts a developmental census across the entire population of children in their first year of full-time schooling (median age 5 years). The first developmental census was conducted in 2009, including 261,147 children, and in 2012 data were collected on 289,973 children-representing 97.5% and 96.5% of the estimated eligible population, respectively. The questionnaire is completed by teachers on the basis of at least 1 month's knowledge of the child, including aspects of physical, social, emotional, language and cognitive development, as well as data on special needs. Teachers are also asked to include details of the child's care arrangements and attendance in early education programmes in the years preceding school. Demographic and geographical data are recorded at the individual and area levels.

Asunto(s)

RESUMEN

OBJECTIVES: Early child development may have important consequences for inequalities in health and well-being. This paper explores population level patterns of child development across Australian jurisdictions, considering socioeconomic and demographic characteristics. DESIGN: Census of child development across Australia. SETTING AND PARTICIPANTS: Teachers complete a developmental checklist, the Australian Early Development Index (AEDI), for all children in their first year of full-time schooling. Between May and July 2009, the AEDI was collected by 14 628 teachers in primary schools (government and non-government) across Australia, providing information on 261 147 children (approximately 97.5% of the estimated 5-year-old population). OUTCOME MEASURES: Level of developmental vulnerability in Australian children for five developmental domains: physical well-being, social competence, emotional maturity, language and cognitive skills and communication skills and general knowledge. RESULTS: The results show demographic and socioeconomic inequalities in child development as well as within and between jurisdiction inequalities. The magnitude of the overall level of inequality in child development and the impact of covariates varies considerably both between and within jurisdiction by sex. For example, the difference in overall developmental vulnerability between the best-performing and worst-performing jurisdiction is 12.5% for males and 7.1% for females. Levels of absolute social inequality within jurisdictions range from 8.2% for females to 12.7% for males. CONCLUSIONS: The different mix of universal and targeted services provided within jurisdictions from pregnancy to age 5 may contribute to inequality across the country. These results illustrate the potential utility of a developmental census to shed light on the impact of differences in universal and targeted services to support child development by school entry.

RESUMEN

BACKGROUND: Previous research points to differences between predictors of intention to screen for colorectal cancer (CRC) and screening behavior, and suggests social ecological factors may influence screening behavior. The aim of this study was to compare the social cognitive and social ecological predictors of intention to screen with predictors of participation. METHODS: People aged 50 to 74 years recruited from the electoral roll completed a baseline survey (n = 376) and were subsequently invited to complete an immunochemical faecal occult blood test (iFOBT). RESULTS: Multivariate analyses revealed five predictors of intention to screen and two predictors of participation. Perceived barriers to CRC screening and perceived benefits of CRC screening were the only predictor of both outcomes. There was little support for social ecological factors, but measurement problems may have impacted this finding. CONCLUSIONS: This study has confirmed that the predictors of intention to screen for CRC and screening behaviour, although overlapping, are not the same. Research should focus predominantly on those factors shown to predict participation. Perceptions about the barriers to screening and benefits of screening are key predictors of participation, and provide a focus for intervention programs.

Asunto(s)