Dataset Information

Remaining life expectancy among older people in a rural area of Vietnam: trends and socioeconomic inequalities during a period of multiple transitions.

ABSTRACT:

Background

Better understanding of the trends and disparities in health at old age in terms of life expectancy will help to provide appropriate responses to the growing needs of health and social care for the older population in the context of limited resources. As a result of rapid economic, demographic and epidemiological changes, the number of people aged 60 and over in Vietnam is increasing rapidly, from 6.7% in 1979 to 9.2% in 2006. Life expectancy at birth has increased but not much are known about changes in old ages. This study assesses the trends and socioeconomic inequalities in RLE at age 60 in a rural area in an effort to highlight this vulnerable group and to anticipate their future health and social needs.

Methods

An abridged life table adjusted for small area data was used to estimate cohort life expectancies at old age and the corresponding 95% confidence intervals from longitudinal data collected by FilaBavi DSS during 1999-2006, which covered 7,668 people at age 60+ with 43,272 person-years, out of a total of 64,053 people with 388,278 person-years. Differences in life expectancy were examined according to socioeconomic factors, including socio-demographic characteristics, wealth, poverty and living arrangements.

Results

Life expectancies at age 60 have increased by approximately one year from the period 1999-2002 to 2003-2006. The increases are observed in both sexes, but are significant among females and relate to improvements among those who belong to the middle and upper household wealth quintiles. However, life expectancy tends to decrease in the most vulnerable groups. There is a wide gap in life expectancy according to poverty status and living arrangements, and the gap by poverty status has widened over the study period. The gender gap in life expectancy is consistent across all socioeconomic groups and tends to be wider amongst the more disadvantaged population.

Conclusions

There is a trend of increasing life expectancy among older people in rural areas of Vietnam. Inequalities in life expectancy exist between socioeconomic groups, especially between different poverty levels and also patterns of living arrangements. These inequalities should be addressed by appropriate social and health policies with stronger targeting of the poorest and most disadvantaged groups.

SUBMITTER: Hoi le V

PROVIDER: S-EPMC2803493 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Remaining life expectancy among older people in a rural area of Vietnam: trends and socioeconomic inequalities during a period of multiple transitions.

Hoi Le V le V Phuc Ho D HD Dung Truong V TV Chuc Nguyen T K NT Lindholm Lars L

BMC public health 20091217

<h4>Background</h4>Better understanding of the trends and disparities in health at old age in terms of life expectancy will help to provide appropriate responses to the growing needs of health and social care for the older population in the context of limited resources. As a result of rapid economic, demographic and epidemiological changes, the number of people aged 60 and over in Vietnam is increasing rapidly, from 6.7% in 1979 to 9.2% in 2006. Life expectancy at birth has increased but not muc ...[more]

PMID: 20017933

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Project description:BackgroundSocioeconomic disparities in life expectancy are well-documented in various contexts, including Chile. However, there is a lack of research examining trends in life expectancy inequalities and lifespan variation over time. Addressing these gaps can provide crucial insights into the dynamics of health inequalities.MethodsThis study utilizes data from census records, population surveys, and death certificates to compare the life expectancy and the lifespan variation at age 26 of individuals according to their rank in the distribution of years of education within their own birth cohort. The analysis spans three periods (1991, 2002, and 2017) and focuses on two educational groups: individuals in the first (lowest) quintile and tenth (highest) decile of educational attainment. Changes in life expectancy are disaggregated by major causes of death to elucidate their contributions to overall trends.ResultsConsistent with existing literature, our findings confirm that individuals with lower education levels experience lower life expectancy and higher lifespan variation compared to their more educated counterparts. Notably, by 2017, life expectancy for individuals in the lowest quintile of education has caught up with that of the top decile in 1991, albeit with contrasting trends between genders. Among women, the gap has reduced, while it has increased for males. Moreover, lifespan variation decreased (increased) over time for individuals in the tenth decile (first quintile). The leading causes of death that explain the increase in life expectancy in women and men in the tenth decile as well as women in the first quintile are cardiovascular, cancer, respiratory and digestive diseases. In the case of males in the first quintile, few gains have been made in life expectancy resulting from cancer and a negative contribution is associated with digestive conditions.ConclusionsThis study underscores persistent socioeconomic disparities in life expectancy in Chile, emphasizing the importance of ongoing monitoring of health inequalities across different demographic segments. The gender-specific and educational gradient trends highlight areas for targeted interventions aimed at reducing health disparities and improving overall population health outcomes. Further research is warranted to delve into specific causes of death driving life expectancy differentials and to inform evidence-based policy interventions.

Project description:BackgroundSmoking inequalities in Turkey were previously demonstrated in an early stage of the smoking epidemic model. This paper aimed to assess the trends for socioeconomic inequalities in smoking in Turkey over the years in the context of the smoking epidemic model using data from the Global Adult Tobacco Survey (GATS) Turkey 2008-2012-2016.MethodsCross-sectional data were analyzed to calculate the association of smoking with, wealth, education, occupation and place of residence using age-standardized prevalence rates, odds ratios, relative index of inequality (RII) and slope index of inequality (SII). The analysis was performed separately for age groups (younger: 20-39 years/older: 40 and above years) and sex.ResultsYounger women with higher wealth and older women with higher wealth and education smoked more. For both age groups, smoking was increased for working class and urban women. Relative wealth inequalities in smoking narrowed and then showed a reversal for younger women (RII2008 = 3.37; 95% CI:1.64-3.40; RII2012 = 2.19; 95% CI:1.48-3.24; RII2016 = 0.80; 95% CI:0.58-1.10, p-for trend < 0.0001). Relative educational inequalities in smoking for older women also showed a narrowing (RII2008 = 21.45; 95% CI:11.74-39.19; RII2012 = 15.25; 95% CI:9.10-25.55; and RII2016 = 5.48; 95% CI:3.86-7.78, p-for trend < 0.0001). For older women, a similar narrowing was observed for wealth (RII2008 = 3.94; 95% CI:2.38-6.53; RII2012 = 2.79; 95% CI:1.80-4.32; and RII2016 = 1.34; 95% CI:0.94-1.91, p-for trend = 0.0001). The only significant trend for absolute inequalities was for younger women by wealth. This trend showed a narrowing and then a reversal (SII2008 = 0.14; 95% CI:0.09-1.20; SII2012 = 0.12; 95% CI:0.06-0.18; and SII2016 = -0.05; 95% CI:-0.12-0.02, p-for trend = 0.0001). Unlike women, smoking in men showed inverse associations for wealth and education, although not statistically confirmed for all years. Smoking was increased in working classes and unemployed men in 2012 and 2016. Inequalities did not show a trend in relative and absolute terms for men.ConclusionsFor smoking inequalities in Turkey, a transition to the next stage was observed, although the previously defined Southern European pattern also existed. Low socioeconomic women deserve special attention as well as stressors at work and drivers of smoking at urban settings.

Project description:ImportanceExamining life expectancy by county allows for tracking geographic disparities over time and assessing factors related to these disparities. This information is potentially useful for policy makers, clinicians, and researchers seeking to reduce disparities and increase longevity.ObjectiveTo estimate annual life tables by county from 1980 to 2014; describe trends in geographic inequalities in life expectancy and age-specific risk of death; and assess the proportion of variation in life expectancy explained by variation in socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors.Design, setting, and participantsAnnual county-level life tables were constructed using small area estimation methods from deidentified death records from the National Center for Health Statistics (NCHS), and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Measures of geographic inequality in life expectancy and age-specific mortality risk were calculated. Principal component analysis and ordinary least squares regression were used to examine the county-level association between life expectancy and socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors.ExposuresCounty of residence.Main outcomes and measuresLife expectancy at birth and age-specific mortality risk.ResultsCounties were combined as needed to create stable units of analysis over the period 1980 to 2014, reducing the number of areas analyzed from 3142 to 3110. In 2014, life expectancy at birth for both sexes combined was 79.1 (95% uncertainty interval [UI], 79.0-79.1) years overall, but differed by 20.1 (95% UI, 19.1-21.3) years between the counties with the lowest and highest life expectancy. Absolute geographic inequality in life expectancy increased between 1980 and 2014. Over the same period, absolute geographic inequality in the risk of death decreased among children and adolescents, but increased among older adults. Socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors explained 60%, 74%, and 27% of county-level variation in life expectancy, respectively. Combined, these factors explained 74% of this variation. Most of the association between socioeconomic and race/ethnicity factors and life expectancy was mediated through behavioral and metabolic risk factors.Conclusions and relevanceGeographic disparities in life expectancy among US counties are large and increasing. Much of the variation in life expectancy among counties can be explained by a combination of socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors. Policy action targeting socioeconomic factors and behavioral and metabolic risk factors may help reverse the trend of increasing disparities in life expectancy in the United States.

Project description:BackgroundIndia experienced a rapid economic boom between 1991 and 2007. However, this economic growth has not translated into improved nutritional status among young Indian children. Additionally, no study has assessed the trends in social disparities in childhood undernutrition in the Indian context. We examined the trends in social disparities in underweight and stunting among Indian children aged less than three years using nationally representative data.MethodsWe analyzed data from the three cross-sectional rounds of National Family Health Survey of India from 1992, 1998 and 2005. The social factors of interest were: household wealth, maternal education, caste, and urban residence. Using multilevel modeling to account for the nested structure and clustering of data, we fit multivariable logistic regression models to quantify the association between the social factors and the binary outcome variables. The final models additionally included age, gender, birth order of child, religion, and age of mother. We analyzed the trend by testing for interaction of the social factor and survey year in a dataset pooled from all three surveys.ResultsWhile the overall prevalence rates of undernutrition among Indian children less than three decreased over the 1992-2005 period, social disparities in undernutrition over these 14 years either widened or stayed the same. The absolute rates of undernutrition decreased for everyone regardless of their social status. The disparities by household wealth were greater than the disparities by maternal education. There were no disparities in undernutrition by caste, gender or rural residence.ConclusionsThere was a steady decrease in the rates of stunting in the 1992-2005 period, while the decline in underweight was greater between 1992 and 1998 than between 1998 and 2005. Social disparities in childhood undernutrition in India either widened or stayed the same during a time of major economic growth. While the advantages of economic growth might be reaching everyone, children from better-off households, with better educated mothers appear to have benefited to a greater extent than less privileged children. The high rates of undernutrition (even among the socially advantaged groups) and the persistent social disparities need to be addressed in an urgent and comprehensive manner.

Project description:BackgroundThe proportion of older people is increasing rapidly in Vietnam. The majority of the elderly live in rural areas. Their health status is generally improving but this is less pronounced among the most vulnerable groups. The movement of young people for employment and the impact of other socioeconomic changes leave more elderly on their own and with less family support. This study aims to assess the daily care needs and their socioeconomic determinants among older people in a rural setting.MethodsIn 2007, people aged 60 years and older, living in 2,240 households, were randomly selected from the FilaBavi Demographic Surveillance System (DSS). They were interviewed using structured questionnaires to assess needed support in activities of daily living (ADLs). Individuals were interviewed about the presence of chronic illnesses that had been diagnosed by a physician. Participant socioeconomic characteristics were extracted from the FilaBavi repeat census. The repeat census used a repeat of the same survey methods and questions as the original FilaBavi DSS. Distributions of study participants by socioeconomic group, supports needed, levels of support received, types of caregivers, and the ADL index were described. Multivariate analyses were performed to identify socioeconomic determinants of the ADL index.ResultsThe majority of older people do not need of support for each specific ADL item. Dependence in instrumental or intellectual ADLs was more common than for basic ADLs. People who need total help were less common than those who need some help in most ADLs. Over three-fifths of those who need help receive enough support in all ADL dimensions. Children and grandchildren are the main caregivers. Age group, sex, educational level, marital status, household membership, working status, household size, living arrangement, residential area, household wealth, poverty status, and chronic illnesses were determinants of daily care needs in old age.ConclusionsAlthough majority of older people who needed help received enough support in daily care, the need of care is more demanded in disadvantaged groups. Future community-based, long-term elderly care should focus on instrumental and intellectual ADLs among the general population of older people, and on basic ADLs among those with chronic illnesses. Socioeconomic determinants of care needs should be addressed in future interventions.

Dataset Information

Remaining life expectancy among older people in a rural area of Vietnam: trends and socioeconomic inequalities during a period of multiple transitions.

Background

Methods

Results

Conclusions

Publications

Remaining life expectancy among older people in a rural area of Vietnam: trends and socioeconomic inequalities during a period of multiple transitions.

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