753 resultados para socio-economic status


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Objective: This study examined the association between area socioeconomic status (SES) and food purchasing behaviour.----- Setting: Melbourne city, Australia, 2003.----- Participants: Residents of 2,564 households located in 50 small areas.----- Design: Data were collected by mail survey (64.2% response rate). Area SES was indicated by the proportion of households in each area earning less than Aus$400 per week, and individual-level socioeconomic position was measured using education, occupation, and household income. Food purchasing was measured on the basis of compliance with dietary guideline recommendations (for grocery foods) and variety of fruit and vegetable purchase. Multilevel regression examined the association between area SES and food purchase after adjustment for individual-level demographic (age, sex, household composition) and socioeconomic factors.----- Results: Residents of low SES areas were significantly less likely than their counterparts in advantaged areas to purchase grocery foods that were high in fibre and low in fat, salt, and sugar; and they purchased a smaller variety of fruits. There was no evidence of an association between area SES and vegetable variety.----- Conclusions In Melbourne, area SES was associated with some food purchasing behaviours independent of individual-level factors, suggesting that areas in this city may be differentiated on the basis of food availability, accessibility, and affordability, making the purchase of some types of foods more difficult in disadvantaged areas.

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Background: A number of studies have examined the relationship between high ambient temperature and mortality. Recently, concern has arisen about whether this relationship is modified by socio-demographic factors. However, data for this type of study is relatively scarce in subtropical/tropical regions where people are well accustomed to warm temperatures. Objective: To investigate whether the relationship between daily mean temperature and daily all-cause mortality is modified by age, gender and socio-economic status (SES) in Brisbane, Australia. Methods: We obtained daily mean temperature and all-cause mortality data for Brisbane, Australia during 1996–2004. A generalised additive model was fitted to assess the percentage increase in all deaths with every one degree increment above the threshold temperature. Different age, gender and SES groups were included in the model as categorical variables and their modification effects were estimated separately. Results: A total of 53,316 non-external deaths were included during the study period. There was a clear increasing trend in the harmful effect of high temperature on mortality with age. The effect estimate among women was more than 20 times that among men. We did not find an SES effect on the percent increase associated with temperature. Conclusions: The effects of high temperature on all deaths were modified by age and gender but not by SES in Brisbane, Australia.

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The Accelerating the Mathematics Learning of Low Socio-Economic Status Junior Secondary Students project aims to address the issues faced by very underperforming mathematics students as they enter high school. Its aim is to accelerate learning of mathematics through a vertical curriculum to enable students to access Year 10 mathematics subjects, thus improving life chances. This paper reports upon the theory underpinning this project and illustrates it with examples of the curriculum that has been designed to achieve acceleration.

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A matched case-control study of mortality to children under age five was conducted to consider associations with parents' socio-economic status and social support in the Farafenni Demographic Surveillance Site (DSS). Cases and controls were selected from Farafenni DSS, matched on date of birth, and parents were interviewed about personal resources and social networks. Parents with the lowest personal socio-economic status and social support were identified. Multivariate multinomial regression was used to consider whether the children of these parents were at increased risk of either infant or 1-4 mortality, in separate models using either parents' characteristics. There was no benefit found for higher SES or better social support with respect to child mortality. Children of fathers who had the poorest social support had lower 1-4 mortality risk (OR=0.52, p=0.037). Given that socio-economic status was not associated with child mortality, it seems unlikely that the explanation for the link between father's social support and mortality is linked to resource availability. Explanations for the risk effect of father's social ties may lie in decision-making around health maintenance and health care for children.

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While policies often target malaria prevention and treatment - proximal causes of malaria and related health outcomes - too little attention has been given to the role of household- and individual-level socio-economic status (SES) as a fundamental cause of disease risk in developing countries. This paper presents a conceptual model outlining ways in which SES may influence malaria-related outcomes. Building on this conceptual model, we use household data from rural Mvomero, Tanzania, to examine empirical relationships among multiple measures of household and individual SES and demographics, on the one hand, and malaria prevention, illness, and diagnosis and treatment behaviours, on the other. We find that access to prevention and treatment is significantly associated with indicators of households' wealth; education-based disparities do not emerge in this context. Meanwhile, reported malaria illness shows a stronger association with demographic variables than with SES (controlling for prevention). Greater understanding of the mechanisms through which SES and malaria policies interact to influence disease risk can help to reduce health disparities and reduce the malaria burden in an equitable manner.

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AIM: To evaluate the association between various lifestyle factors and achalasia risk.

METHODS: A population-based case-control study was conducted in Northern Ireland, including n= 151 achalasia cases and n = 117 age- and sex-matched controls. Lifestyle factors were assessed via a face-to-face structured interview. The association between achalasia and lifestyle factors was assessed by unconditional logistic regression, to produce odds ratios (OR) and 95% confidence interval (CI).

RESULTS: Individuals who had low-class occupations were at the highest risk of achalasia (OR = 1.88, 95%CI: 1.02-3.45), inferring that high-class occupation holders have a reduced risk of achalasia. A history of foreign travel, a lifestyle factor linked to upper socio-economic class, was also associated with a reduced risk of achalasia (OR = 0.59, 95%CI: 0.35-0.99). Smoking and alcohol consumption carried significantly reduced risks of achalasia, even after adjustment for socio-economic status. The presence of pets in the house was associated with a two-fold increased risk of achalasia (OR = 2.00, 95%CI: 1.17-3.42). No childhood household factors were associated with achalasia risk.

CONCLUSION: Achalasia is a disease of inequality, and individuals from low socio-economic backgrounds are at highest risk. This does not appear to be due to corresponding alcohol and smoking behaviours. An observed positive association between pet ownership and achalasia risk suggests an interaction between endotoxin and viral infection exposure in achalasia aetiology.

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Infant sleep undergoes significant re-organization throughout the first 12 months of life, with sleep quality having significant consequences for infant learning and cognitive development. While there has been great interest in the neural basis and developmental trajectories of infant sleep in general, relatively little is known about individual differences in infant sleep and the socio-economic and cultural sources of that variability. We investigated this using questionnaire sleep data in a large, unique multi-ethnic sample of 6-7 month-olds (n=174), with families from South Asian ethnic groups in the UK (Indian, Pakistani and Bangladeshi) being especially well represented. Consistent with previous data from less variable samples, no effects of SES on sleep latency or nocturnal sleep duration emerged. However, perinatal risk factors and ethnic differences did predict daytime sleep, sleep fragmentation and sleep-onset time. While these results should be interpreted with caution due to several limitations, they likely demonstrate that even when socio-economic status and ethnicity are much less confounded than in previous studies, they have a surprisingly limited impact on individual differences in sleep patterns in young infants.

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The article considers young people's occupational choices at the age of 15 in relation to their educational attainment, the occupations of their parents and their actual occupations when they are in their early 20s. It uses data from the British Household Panel Survey over periods of between five and ten years. The young people in the survey are occupationally ambitious: many more aspire to professional, managerial and technical jobs than the likely availability of these occupations. In general ambitions and educational attainment and intentions are well aligned but there are also many instances of misalignment; either people wanting jobs which their educational attainments and intentions will not prepare them for, or people with less ambitious aspirations than their educational performance would justify. Children from more occupationally advantaged families are more ambitious, achieve better educationally and have better occupational outcomes than other children. However, where young people are both ambitious and educationally successful the occupational outcomes are as good for those from disadvantaged as advantaged families. In contrast, where young people are neither ambitious nor educationally successful, the outcomes for those from disadvantaged homes are very much poorer than for other young people. The article suggests that while choice is real it is also heavily constrained for many people. A possible educational implication of the study is that career interventions could be directed at under-ambitious but academically capable young people from disadvantaged backgrounds.

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Programa de doctorado: Salud Pública (Epidemiología, Planificación y Nutrición)

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Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.