982 resultados para Socioeconomic determinants


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Objective : To investigate the relationship between socioeconomic status (SES) and reported perceptions of quality of life (QOL) in a cross-sectional population-based analysis of a representative sample of Australian men.

Methods : In 917 randomly recruited men aged 24–92 years, we measured QoL in the domains of physical health, psychological health, environment and social relationships, using the Australian World Health Organization Quality of Life Instrument (WHOQOL-BREF). Residential addresses were cross-referenced with Australian Bureau of Statistics 2006 census data to ascertain SES. Participants were categorised into lower, mid, or upper SES based on the Index of Relative Socioeconomic Disadvantage and Advantage (IRSAD), the Index of Economic Resources (IER), and the Index of Education and Occupation (IEO). Lifestyle and health information was self-reported.

Results : Males of lower SES reported poorer satisfaction with physical health (OR=0.6, 95%CI 0.4–0.9, p=0.02), psychological health (OR=0.4, 95%CI 0.3–.7, <0.001) and environment (OR=0.5, 95%CI 0.3–0.7, p<0.001), although not social relationships (p=0.59). The poorest QOL for each domain was observed in the lower and upper SES groups, representing an inverse U-shaped pattern of association; however, statistical significance was only observed for psychological  health (OR=0.5, 95%CI 0.4–0.7, p<0.001). These relationships were similar for IEO  and IER.

Conclusions : Men from lower and upper SES groups have lower QOL compared to their counterparts in the mid SES group.

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Aim
To examine the emotional well-being of severely obese Australians with type 2 diabetes, along with markers of social and economic disadvantage, using the Diabetes MILES – Australia dataset.
Methods
Diabetes MILES – Australia was a national survey of 3338 adults with diabetes that focused on psychosocial issues; 1795 had type 2 diabetes and reported BMI. We extracted data regarding depression (PHQ-9), anxiety (GAD-7), obesity- and diabetes- related comorbidities, and demographics. The severely obese group (SOG) (BMI ≥ 35; median BMI = 41.6) constituted 530 (30%) of the type 2 diabetes respondents and was matched with 530 controls (CG) (BMI < 35; median BMI = 28.2). Within- and between- group trends were examined.
Results
The SOG had higher depression scores (median (IQR) 6.0 (3–12)) than CG (5.0 (2–10)); p < 0.001, and were more likely to report moderate-severe depressive symptoms (37% versus 27%; p < 0.001). The groups did not differ on anxiety. The SOG, compared with the CG, were more likely to live alone (21% versus 17%), receive a disability pension (21% versus 15%), earn ≤$40.000/year (51% versus 41%; all p < 0.05), and were less likely to be employed (46% versus 53%), university or higher educated (17% versus 26%), or have health insurance (50% versus 60%; all p ≤ 0.01). Moderate-severe depression was positively associated with cumulative stressors of severe obesity, socioeconomic disadvantage, and obesity- and diabetes- related comorbidity.
Conclusions
Severely obese people living with type 2 diabetes have cumulative stressors related to health, disability, demographic and socioeconomic factors, and impaired emotional well-being.

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Objectives:
Cardiovascular (CVD) mortality disparities 
between rural/regional and urban-dwelling residents of Australia are persistent. Unavailability of biomedical CVD risk factor data has, until now, limited efforts to understand the causes of the disparity. This study aimed to further investigate such disparities.

Design
Comparison of (1) CVD risk measures between a regional (Greater Green Triangle Risk Factor Study (GGT RFS, cross-sectional study, 2004–2006) and an urban population (North West Adelaide Health Study (NWAHS, longitudinal cohort study, 2004–2006); (2) Australian Bureau of Statistics (ABS) CVD mortality rates between these and other Australian regions; and (3) ABS CVD mortality rates by an arealevel indicator of socioeconomic status, the Index of Relative Socioeconomic Disadvantage (IRSD).
Setting
Greater Green Triangle (GGT, Limestone Coast, Wimmera and Corangamite Shires) of South-Western Victoria and North-West Adelaide (NWA).
Participants:
1563 GGT RFS and 3036 NWAHS stage 2 participants (aged 25–74) provided some information (self-administered questionnaire +/−anthropometric and biomedical measurements).
Primary and secondary outcome measures:
Age-group specific measures of absolute CVD risk, ABS CVD mortality rates by study group and Australian Standard Geographical Classification (ASGC) region.
Results:
Few significant differences in CVD risk between the study regions, with absolute CVD risk ranging from approximately 5% to 30% in the 35–39 and 70–74 age groups, respectively. Similar mean 2003–2007 (crude) mortality rates in GGT (98, 95% CI 87 to 111), NWA (103, 95% CI 96 to 110) and regional Australia (92, 95% CI 91 to 94). NWA mortality rates exceeded that of other city areas (70, 95% CI 69 to 71). Lower measures of socioeconomic status were associated with worse CVD outcomes regardless of geographic location.
Conclusions:
Metropolitan areas do not always have better CVD risk factor profiles and outcomes than rural/regional areas. Needs assessments are required for different settings to elucidate relative contributions of the multiple determinants of risk and appropriate cardiac healthcare strategies to improve outcomes.

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This study highlights the sensitivity of capital structure determinants in each sector within the ensembles of Malaysia Listed Companies. Based on pooled OLS, fixed effect and Generalized Method of Moments analysis, the findings revealed that capital structure determinants vary across sectors due to its nature or characteristics.

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In this paper we propose a cross-sectional model of the determinants of asset price bubbles. Using 589 firms listed on the NYSE, we find conclusive evidence that trading volume and share price volatility have statistically significant effects on asset price bubbles. However, evidence from sector-based stocks is mixed. We find that for firms belonging to electricity, energy, financial, and banking sectors, and for the smallest size firms, trading volume has a statistically significant and positive effect on bubbles. We do not discover any robust evidence of a statistically significant effect of share price volatility on bubbles at the sector-level.

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We address credit cycle dependent sovereign credit risk determinants. In our model, the spread determinants' magnitude is conditional on an unobservable endogenous sovereign credit cycle as represented by the underlying state of a Markov regime switching process. Our explanatory variables are motivated in the tradition of structural credit risk models and include changes in asset prices, interest rates, implied market volatility, gold price changes and foreign exchange rates. We examine daily frequency variations of U.S. dollar denominated Eurobond credit spreads of four major Latin American sovereign bond issuers (Brazil, Colombia, Mexico and Venezuela) with liquid bond markets during March 2000 to June 2011. We find that spread determinants are statistically significant and consistent with theory, while their magnitude remarkably varies with the state of the credit cycle. Crisis states are characterized by high spread change uncertainty and high sensitivities with respect to the spread change determinants. We further document that not only changes of local currencies, but also changes of the Euro with respect to the U.S. dollar are significant spread drivers and argue that this is consistent with the sovereigns' ability to pay.

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This paper examines whether the drivers of economic growth are the same as those for genuine progress in the case of South Korea. Using data covering the period 1970–2005, the paper first constructs a Genuine Progress Indicator (GPI). An empirical model is then specified and estimated using growth in GDP per capita and growth in the GPI per capita as dependent variables. Results indicate that while physical capital, research and development, exports, and inflation are all important in determining growth in GDP per capita, only physical capital is a driver of genuine progress. These findings highlight the need for policymakers to identify and target other determinants of genuine progress to improve the well-being of South Koreans, rather than focus attention on traditional sources of economic growth.