December 3, CONTEXT Although the pervasiveness of domestic violence against women in Bangladesh is well documented, specific risk factors, particularly those that can be affected by policies and programs, are not well understood. METHODS Insurveys, in-depth interviews and small group discussions were conducted with married women from six Bangladeshi villages to examine the types and severity of domestic violence, and to explore the pathways through which women's social and economic circumstances may influence their vulnerability to violence in marriage.
This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The burden of cardiovascular disease CVD is increasing in low-to-middle income countries.
We examined how socioeconomic and demographic characteristics may be associated with CVD risk factors and healthcare access in such countries. Results also revealed significant positive associations between paid employment and waist circumference and systolic blood pressure.
Healthcare access did not differ significantly by socioeconomic position. Women reported significantly higher mean waist circumference than men. Our results suggest that socioeconomic position and demographic characteristics impact CVD risk factors and healthcare access in FSM.
This understanding may help decision-makers tailor population-level policies and programs. The Pohnpei data provides a baseline; subsequent population health surveillance data might define trends. Introduction Population-based surveillance—the ongoing systematic collection, analysis, and interpretation of health data—is critical for providing information on which to base policy; prioritize resources; guide program planning, evaluation, and research; and protect and promote population health [ 1 ].
In LMIC, the burden of cardiovascular disease CVD is increasing at faster rates than those experienced by high-income countries in previous decades, elevating the need to strengthen country-level surveillance [ 23 ].
Research evidence, primarily from high-income countries, shows an inverse association for indicators of socioeconomic position, measured by education, income, or employment status, with CVD risk factors [ 56 ].
Although evidence is limited, most LMIC might follow a similar pattern [ 78 ]. Providing better evidence on the impact of socioeconomic position on CVD risk factors in LMICs may help decision-makers tailor policy and programmatic interventions to fit conditions in these countries.
InPohnpei State, Federated States of Micronesia FSM Figure 1implemented a population-based surveillance survey to measure chronic disease risk factors among adults. While survey reports provide weighted analysis as a whole and by subgroup i.
In an effort to increase understanding of CVD risk factors within the population, the FSM Department of Health and Social Affairs requested assistance in broadening the analysis of the available population-based dataset. This study describes the exploratory analysis of the association between socioeconomic position, measured by education, income, and employment status with CVD risk factors i.
After training, field staff collected data using standardized procedures and protocols. To ensure population representativeness, the created sample for this study included the entire STEPS Pohnpei dataset [ 9 ].
Variables Used for Analyses 2. Socioeconomic Position We used self-reported educational attainment, estimated annual household income, and employment status as indicators of socioeconomic position.
Education is a widely used indicator of socioeconomic position, as formal education is usually completed by early adulthood and therefore remains stable across the adult lifespan. Using years of schooling reported and FSM education system levels, we categorized study participants into one of three groups based on educational attainment: Income and employment status are also widely used as indicators of socioeconomic position as each can provide access to health-promotion resources through ability to pay or employer-provided insurance that can contribute toward better health outcomes [ 6 ].
We categorized study participants into one of three groups by estimated annual household income: We categorized study participants into one of three groups by employment status: Covariates We included sex, age, and place of residence as covariates.
Sex and age may impact the association between socioeconomic position and CVD risk factors, mirroring variations in both biological and social influences across societies [ 610 ]. We categorized study participants into one of four age groups: This greater risk may be attributable to lifestyle changes in urban populations [ 4 ].
Using census enumeration codes, we categorized study participants by place of residence as either urban or rural, defining urban as residing in one of two developed municipalities within Pohnpei. Behavioral Risk Factors Modifiable behavioral risk factors known to increase CVD risk include tobacco use, inadequate fruit and vegetable consumption, and physical inactivity [ 4 ].
Healthcare Access Socioeconomic position is also linked to healthcare access, which can be measured by potential i.
BP, height, weight, waist circumference, fasting blood glucose, and fasting blood lipids [ 4 ]. Detailed methodology is available at http: We used the mean of the two most recent BP measurements to create systolic and diastolic BP variables.
BMI and Waist Circumference.
Diabetes and Blood Lipids. Variables for obesity and diabetes were not included for pregnant women. We applied sex-age structure survey weights standardized to the FSM census for Pohnpei to provide results representative of the adult Pohnpeian population aged 25—64 years.
After data cleaning and recoding, we completed descriptive analysis for all variables. Our analysis included chi-square with Rao-Scott adjustment and one-way analysis of variance with post hoc pairwise comparisons, using Bonferroni adjustment criterion, to determine the associations between socioeconomic position and demographic characteristics with selected CVD risk factors and healthcare access.
Mean fruit and vegetable consumption and fasting blood glucose were excluded from the analysis of variance.
This was because examination of normal Q-Q plots showed that residuals for these variables were not normally distributed, thereby violating the assumptions required for analysis of variance.Journal of Cancer Prevention & Current Research Demographic, Clinical and Area-based Socioeconomic Factors Associated with Glioblastoma Multiforme.
Socioeconomic factors associated with women's condoning of domestic violence were age, wealth, education level, and living area.
In particular, younger age and low educational attainment were key factors associated with violence-supportive attitudes, and . Abstract. Immunization remains one of the most successful and cost-effective public health interventions worldwide.
The purpose of this study was to examine the individual and socioeconomic factors that influence childhood immunization coverage in Nigeria. Many demographic, socioeconomic, and behavioral risk factors predict mortality in the United States. However, very few population-based longitudinal studies are able to investigate simultaneously the impact of a variety of social factors on mortality.
Demographic, socioeconomic and lifestyle factors associated with branched chain amino acid intake in elderly adults, ISA Capital, São Paulo, Brazil, Variables Leucine. associated with rural geographical place of residence, the reverse has also been established.
The purpose of this study was to examine the association between socioeconomic and demographic factors and under-5 mortality in KwaZulu-Natal, South Africa.
DESIGN: The study was cross-sectional, and utilises Census secondary .