Differences In Poor Health Research Articles

Stability and variability in income position over time: exploring their role in self-rated health in Swedish survey data.

BackgroundThe adverse relationship between income and health is well documented, but less is known of how income trajectories, i.e. downward or upward trends in income, determine health. We therefore link longitudinal income information to cross-sectional data on self-rated health and conduct logistic regression models in order to investigate income trends over time and their relationship with health.MethodsThe data, with the exception of income information, are derived from the Swedish Level-of-Living Survey 2000. The information on income was drawn from the income register covering the period 1995 to 2000. We used an age-restricted sample of those 30–64 years of age, and generated a series of models accounting for average income position, lagged income position, income decrease and increase, and annual periods in specific income positions. The analysis was conducted for men and women separately.ResultsApart from the overall association between income and health, we found a similar pattern when including average and lagged income in the model. The analysis of length of time in a specific income position showed substantial sex differences in poor health. Income decrease was more strongly associated with men’s poor health, whereas income increase revealed only weak associations with self-rated health.ConclusionsIt was shown that income changes and the time dimension of income are important for self-rated health. Self-rated health responds to decreases in absolute income and lowered rank position in the income distribution to a greater extent than to income gains over time. Lagged lower income position and its associations with health suggest that socio-economic disadvantages accumulate over time.

Employment status and the prevalence of poor self-rated health. Findings from UK individual-level repeated cross-sectional data from 1978 to 2004

ObjectivesTo assess, using individual level data, how the proportion of people in different employment statuses may have played a role in the prevalence of poor self-rated health from 1978 to...

What we want to know is…

Walsh et al. identify unexplained variations in the distribution of inequalities in three UK cities and propose a ‘Glasgow’ effect. This paper describes the situation in Glasgow, Liverpool and Manchester where the differences in poor health cannot be explained solely by the socio-economic circumstances of the three cities, and show that the situation in Glasgow is considerably worse than other similar cities (Liverpool and Manchester) in England. It is a further development of the previously described ‘Scottish effect’. What is new about this paper is that it goes down to amuch lower level of geography than other authors have been able to, and also uses possibly more sensitive measures of deprivation. However, unlike some analyses, it has been somewhat limited in the selection of locations (three conurbations). Of course what we want to know is, can this be explained and is there any way that the situation can be remedied? The possible explanationswhich are postulated are all very plausible, the gap between high and low incomes, the worse health behaviours in Glasgow compared to other UK cities, the possibility that certain behaviours such as drug misuse have a disproportionate effect on life expectancy, societal breakdown and the level of self-harming, and even vitamin D are also suggested by the authors. Doran et al. used local authorities in a paper published in 2006 as a basis for looking at local authorities that either ‘overachieved’ or ‘underachieved’. In their analysis of local authorities they suggested that the regional context in which local authorities sat could have a greater disproportion of ill health than the measures of

The Swedish labour market in the 1990s: The very last of the healthy jobs?

The economic recession in Sweden in the 1990s influenced several aspects of the labour market, including the psychosocial work environment. This study examined psychosocial working conditions in relation to self-reported ill-health in the 1990s by means of the job strain model. The study was based on two representative cross-sectional samples of Swedish employed men and women in 1991 (n=3,292) and 2000 (n=3,010), together with a panel of employees who were included for both years (n=1,953). The main outcome measures were psychological distress and self-rated poor health. The primary method used was logistic regression. The results suggested that although adverse psychosocial conditions increased during the 1990s, the association with health weakened. However, further analyses showed that poor health increased in most groups with various types of psychosocial conditions, and that reduced relative differences in poor health were discernible. Finally, a longitudinal analysis showed that long-term exposure to and experience of deteriorating psychosocial conditions was most detrimental for health at the end of the 1990s, once health status at baseline had been adjusted for. The increased prevalence of health problems among most groups with various psychosocial conditions during the 1990s, together with reduced relative differences in poor health, might make it more difficult for employers and policy-makers to direct health policies towards specific groups of employees, as the "healthy'' job seems to have disappeared. Another important inference of the results is that psychosocial working conditions seem to be causally related to health.

Social determinants of health inequalities in Bosnia and Herzegovina

To examine the social determinants of inequalities in health in Bosnia and Herzegovina in the post-conflict period, and to test if the relative effects vary across the two entities of the Federation of Bosnia and Herzegovina and the Republika Srpska. Cross-sectional data come from the first wave of the Bosnia and Herzegovina Household Panel Study conducted in 2001, which collected data from 7482 respondents aged 17 years and older based on over 3000 households. Distributions and odds ratios for physical limitations and poor mental well-being were calculated over a number of known social determinants. Multivariate logistic regression and t-tests were used to compare risks across entities within the state of Bosnia and Herzegovina. The prevalence of poor mental well-being and physical limitations was significantly higher in the Republika Srpska. Significant differences in poor mental well-being and physical limitations were observed across most determinants within each entity, but only a few of these relative effects differed between entities. Efforts to tackle absolute differences in poor health between the entities within Bosnia and Herzegovina should be pursued, along with reducing social inequalities.

The impact of individual and area characteristics on urban socioeconomic differences in health and smoking.

In general, poor health and lifestyles occur more frequently among individuals of low socioeconomic status (SES) and in deprived areas. An explanation for the latter may simply be the on average lower SES of residents of these areas. It is possible, however, that living in a deprived area contributes to poor health and lifestyles. This study examines whether such an area-contribution exists in urban settings. Data on health, smoking, and individual SES were collected on 5121 residents of Amsterdam, the Netherlands, by face-to-face interviews (response: 61.4%). Area deprivation was measured by indicators used previously, on 22 areas. Odds ratios (OR) for poor health (poor self-rated health, long-term limitations, health complaints and obesity) and smoking were computed comparing tertiles of area deprivation. All analyses employed multilevel techniques, with residents hierarchically nested within areas. The age- and gender-adjusted prevalences of poor health and smoking are higher in deprived urban areas. Most of the differences in poor health can be explained by the on average lower SES of residents of deprived areas. Only for long-term limitations and obesity, some statistically significant area-differences remain. The higher prevalence of smoking in deprived areas can only partially be explained by the SES of residents. Adverse health status in deprived areas is mainly due to a lower individual SES and not to contextual factors. For smoking, living in a deprived area contributes to a higher prevalence. This shows the necessity of community-based preventive interventions in deprived areas.