Factors associated with self-rated health (SRH) of a University of the Third Age (U3A) class participants

Maniecka-Bryła, Irena; Gajewska, Olga; Burzyńska, Monika; Bryła, Marek

doi:10.1016/j.archger.2013.03.006

Cited by 22 publications

(17 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In high mortality countries, suicides contribute to a loss of 1200 years of life per 100,000 males and 300 years per 100,000 females. In countries which are characterized with low mortality, the loss is four times lower, and the values are 300 years per 100,000 males and 80 years per 100,000 females [9]. The above data confirm that the Lodz region is characterized by a great loss of years of life lost due to external causes among both male and female inhabitants.…”

Section: Discussionsupporting

confidence: 58%

Years of Life Lost Due to External Causes of Death in the Lodz Province, Poland

Pikala

Bryła

et al. 2014

PLoS ONE

View full text Add to dashboard Cite

BackgroundThe aim of the study is the analysis of years of life lost due to external causes of death, particularly due to traffic accidents and suicides.Materials and MethodsThe study material includes a database containing information gathered from 376,281 death certificates of inhabitants of the Lodz province who died between 1999 and 2010. The Lodz province is characterized by the highest mortality rates in Poland. The SEYLLp (Standard Expected Years of Life Lost per living person) and the SEYLLd (per death) indices were used to determine years of life lost. Joinpoint models were used to analyze time trends.ResultsIn 2010, deaths due to external causes constituted 6.0% of the total number of deaths. The standardized death rate (SDR) due to external causes was 110.0 per 100,000 males and was five times higher than for females (22.0 per 100,000 females). In 2010, the SEYLLp due to external causes was 3746 per 100,000 males and 721 per 100,000 females. Among males, suicides and traffic accidents were the most common causes of death (the values of the SEYLLp were: 1098 years and 887 years per 100,000 people, respectively). Among females, the SEYLLp values were 183 years due to traffic accidents and 143 years due to suicides (per 100,000 people).ConclusionsA decrease in the number of years of life lost due to external causes is much higher among females. The authors observe that a growing number of suicides contribute to an increase in the value of the SEYLLp index. This directly contributes to over-mortality of males due to external causes. The analysis of the years of life lost focuses on the social and economic aspects of premature mortality due to external causes.

show abstract

Section: Discussionsupporting

confidence: 58%

Years of Life Lost Due to External Causes of Death in the Lodz Province, Poland

Pikala

Bryła

et al. 2014

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Only few studies have thus far used EQVAS as dependent variables. Most commonly, EQVAS is used as an easily accessible predictor of mortality within an (elderly) population [96,97]. Hertzman et al provided one of the few research designs that intended to predict EQVAS [98], and were able to show that EQVAS is suitable as a dependent variable.…”

Section: Discussionmentioning

confidence: 99%

Empirical Evidences for Urban Influences on Public Health in Hamburg

et al. 2019

View full text Add to dashboard Cite

From the current perspectives of urban health and environmental justice research, health is the result of a combination of individual, social and environmental factors. Yet, there are only few attempts to determine their joint influence on health and well-being. Grounded in debates surrounding conceptual models and based on a data set compiled for the city of Hamburg, this paper aims to provide insights into the most important variables influencing urban health. Theoretically, we are primarily referring to the conceptual model of health-related urban well-being (UrbWellth), which systemizes urban influences in four sectors. The systematization of the conceptual model is empirically confirmed by a principal component analysis: the factors derived from the data correspond well with the deductively derived model. Additionally, a multiple linear regression analysis was used to identify the most important variables influencing the participant's self-rated health (SRH): rating of one's social network, rating of neighborhood air quality, rating of neighborhood health infrastructure, heat stress (day/outdoors), cold stress (night/indoors). When controlling for age, income and smoking behavior, these variables explain 12% of the variance of SRH. Thus, these results support the concept of UrbWellth empirically. Finally, the study design helped to identify hotspots with negative impact on SRH within the research areas.

show abstract

“…Unsatisfactory health state is connected with being Afro-American ethnicity, age, obesity, smoking nicotine, low income, place of residence and low level of education [20][21]. It is also very important to undertake appropriate health policy decisions aiming at the improvement of research capacities of many European countries [22].…”

Section: Discussionmentioning

confidence: 99%

Determinants of self-rated health of a working population

Bryła¹,

Szymocha²,

Maniecka-Bryła³

2015

Med Pr

Self Cite

View full text Add to dashboard Cite

Background: Self-rated health relates to the use of medical help and, as a consequence, determines sick leave in the population of employees. The aim of the study was to analyze the relationship between socioeconomic variables, selected forms of positive health behaviour and subjective evaluation of health in employees. Material and Methods: Five hundred and 99 subjects were included in the study -331 females and 268 males, aged 18-67, working in the area of the Świętokrzyskie province. The authors' survey questionnaire on the selected elements of the state of health and positive health behavior in life style has been used in the study. Anthropometric measures were carried out. A Chi 2 test for independence was used in the statistical analysis. In order to evaluate the effect of the selected factors on the self-rated health of the studied subjects the researchers have applied single-and multiple-factor logistic regression. Results: In the multiple-factor logistic regression the features contributing to good or excellent self-rated health were the following: age up to 39 (odds ratio -OR = 4.17; 95% confidence interval -CI: 1.72-10.10; p < 0.002), higher education (OR = 3.01; 95% CI: 1.04-8.70; p < 0.05) and care for health (OR = 4.77; 95% CI: 2.81-8.09; p < 0.001). Conclusions: Working people with higher education are characterized by a better control over their own health and, consequently, by a better perception of it. Monitoring self-rated health in a working population is an invaluable indicator in the evaluation of health in employees and the need for medical care. Med Pr 2015;66(1):17-28

show abstract

Factors associated with self-rated health (SRH) of a University of the Third Age (U3A) class participants

Cited by 22 publications

References 42 publications

Years of Life Lost Due to External Causes of Death in the Lodz Province, Poland

Years of Life Lost Due to External Causes of Death in the Lodz Province, Poland

Empirical Evidences for Urban Influences on Public Health in Hamburg

Determinants of self-rated health of a working population

Contact Info

Product

Resources

About