Differences In Self-rated Health Research Articles

Effects of an Intervention on Black Family Incomes on Self-Rated Health and Obesity in Black and White Adolescents and Young Adults.

Structural racism contributes to health disparities between U.S. non-Hispanic Black and non-Hispanic white populations by differentially distributing resources used to maintain health. Policies that equitably redistribute resources may mitigate racialized health disparities. Using National Longitudinal Study of Adolescent to Adult Health data and time-to-event parametric g-formula methods, we investigate a hypothetical intervention to reduce Black-white family income inequities on racialized differences in self-rated health (N=11,312) and obesity (N=10,547). We first intervene to increase individual Black family incomes by $11,000, creating Black-white equity in median incomes in 1995. Then, we measure social multiplier effects by additionally increasing county-level Black median household incomes by $11,000. By Wave 4, individual, direct effects models comparing Black intervention to Black control groups show no risk differences in self-rated health (RD=-0.009; 95% CI: -0.026, 0.008) or obesity (RD=0.003; 95% CI: -0.017, 0.023). Social multiplier effects models suggestively reduce Black-white inequalities in obesity by increasing obesity in white intervention versus white control groups (RD=0.050=; 95% CI: -0.011, 0.110), but exacerbate Black-white disparities in self-rated health by reducing self-rated health in Black intervention versus white control groups (RD=0.184; 95% CI: 0.018, 0.351). In this cohort, income transfers may not reduce racialized disparities in obesity and self-rated health.

Self-Rated Health among Older Adults: Longitudinal Analyses Examining Sex Differences across Different Birth Cohorts and Educational Levels

Introduction: Given the known female disadvantage in physical and mental health, this study aimed to investigate sex differences in self-rated health (SRH) among older adults, considering the longitudinal course by age, birth cohort, and educational level. Methods: Data from birth cohort 1911–1937 with baseline age 55–81 years (n = 3,107) and birth cohort 1938–1947 with baseline age 55–65 years (n = 1,002) from the Longitudinal Aging Study Amsterdam (LASA) were used. Mixed model analyses were used to examine sex differences in SRH (RAND General Health Perception Questionnaire [RAND-GHPQ], range 0–16) over the age course, testing for effect modification by the birth cohort and educational level (low, middle, high). Results: For both sexes, a decline in SRH was seen with increasing age. Over the age course, there was no significant sex difference in SRH within the older (1911–1937) birth cohort (0.13 lower score on SRH for women compared to men, 95% CI: −0.35 to 0.09) and only a small sex difference in the more recent (1938–1947) birth cohort (0.35 lower score on SRH for women compared to men [95% CI: −0.69 to −0.02], p = 0.04). There was no significant cohort difference in the size of the sex difference (p = 0.279). Those with a higher level of education reported a higher SRH, but between educational levels, there was no significant difference in the size of the sex difference in SRH. Discussion: In this study, no relevant sex difference in SRH over the age course was observed among older adults. Future research on SRH trajectories by sex during aging should take health-related, cognitive, psychosocial, and behavioral factors into account.

Sex and age differences in self-reported immune fitness

Studies have reported sex and age differences in self-rated health. On average, women rate their health as being poorer compared to men, and older individuals report poorer health than younger individuals. The current study evaluated sex and age differences for self-reported immune fitness, i.e. the capacity of the body to respond to health challenges (such as infections) by activating an appropriate immune response in order to promote health and prevent and resolve disease. Data from different survey studies (N = 8586) were combined for the current analyses. N = 8064 participants (93.3%) completed the single-item scale to assess momentary immune fitness (mean (Standard deviation, SD) age of 32.4 (16.7) years old, range: 18 to 103, 68.0% women) and N = 4263 participants (49.7%) completed the Immune Status Questionnaire (ISQ) to assess past year's immune fitness (mean (SD) age of 40.9 (17.1) years old, range: 18 to 103, 61.1% women). The analyses revealed that women rated their momentary and past year's immune fitness significantly lower than men (p < 0.001). A small but significant decline in momentary immune fitness when aging was found (r = −0.073, p < 0.001). In contrast, past year's immune fitness steadily improved with progressing age (r = 0.295, p < 0.001), and for each age group the difference from the 18–24 years old group was statistically significant (p < 0.001). When using age as covariate, the sex differences in immune fitness remained significant for both momentary immune fitness (p < 0.001) and past year's immune fitness (p < 0.001). In conclusion, women report a poorer momentary and past year's immune fitness than men. The sex effects in immune fitness are robust and seen across all age groups except the elderly. A relative stable momentary immune fitness was found across the age groups. However, past year's immune fitness (assessments with the ISQ) improved with age. This observation may be related to the fact that the studies comprised convenience samples. Therefore, the observed age effects should be interpreted with caution and require further investigation in nationally representative samples.

Why is there a gap in self-rated health among people with hypertension in Zambia? A decomposition of determinants and rural‒urban differences

BackgroundHypertension affects over one billion people globally and is one of the leading causes of premature death. Low- and middle-income countries, especially the sub-Saharan Africa region, bear a disproportionately higher share of hypertension globally. Recent evidence shows a steady shift in the burden of hypertension from more affluent and urban populations towards poorer and rural communities. Our study examined inequalities in self-rated health (SRH) among people with hypertension and whether there is a rural‒urban gap in the health of these patients. We then quantified factors driving the health gap. We also examined how much HIV accounts for differences in self-rated health among hypertension patients due to the relationship between HIV, hypertension and health in sub-Saharan Africa.MethodsWe utilized the Zambia Household Health Expenditure and Utilization Survey for data on SRH and other demographic and socioeconomic controls. District HIV prevalence information was from the Zambia Population-Based HIV Impact Assessment (ZAMPHIA) survey. We applied the Linear Probability Model to assess the association between self-rated health and independent variables as a preliminary step. We then used the Blinder-Oaxaca decomposition to identify self-rated health inequality between urban and rural patients and determine determinants of the health gap between the two groups.ResultsAdvanced age, lower education and low district HIV prevalence were significantly associated with poor health rating among hypertension patients. The decomposition analysis indicated that 45.5% of urban patients and 36.9% of rural patients reported good self-rated health, representing a statistically significant health gap of 8.6%. Most of the identified health gap can be attributed to endowment effects, with education (73.6%), district HIV prevalence (30.8%) and household expenditure (4.8%) being the most important determinants that explain the health gap.ConclusionsUrban hypertension patients have better SRH than rural patients in Zambia. Education, district HIV prevalence and household expenditure were the most important determinants of the health gap between rural and urban hypertension patients. Policies aimed at promoting educational interventions, improving access to financial resources and strengthening hypertension health services, especially in rural areas, can significantly improve the health of rural patients, and potentially reduce health inequalities between the two regions.

Objective and subjective measures of air pollution and self-rated health: the evidence from Chile.

The literature exploring individual differences in self-rated health has grown fast in recent years. Self-rated health (SRH) is a good indicator of general health status. This empirical study explores the association between outdoor air pollution and SRH in Chile. This type of analysis is infrequent in Latin America. We used objective and subjective air pollution measures. The first corresponds to PM2.5, and the latter to the perception of a high level of air pollution. Drawing on data from two independent and repeated nationwide surveys over the period 2006-2017 at the individual level in Chile, we performed repeated cross-sectional analyses for each year of survey application. Ordered Logit (OL) and Logit (L) multivariate models were used to investigate the association between SRH and air pollution measures, considering other socioeconomic and demographic covariates. We found that the higher isthe level of air pollution, the lower the SRH in Chile, regardless of whether air pollution is physically measured or perceived by respondents. The results were consistent over the years in the sign and significance of regression coefficients using two surveys and two forms of the outcome variable. Our findings add evidence that air pollution is a relevant determinant of SRH. In addition, they show that subjective measures of air pollution can be as reliable as physical measures in the analysis of the association between air pollution and human health.

An individually adjusted approach for communicating epidemiological results on health and lifestyle to patients

If scientific research on modifiable risk factors was more accessible to the general population there is a potential to prevent disease and promote health. Mobile applications can automatically combine individual characteristics and statistical models of health to present scientific information as individually tailored visuals, and thus there is untapped potential in incorporating scientific research into apps aimed at promoting healthier lifestyles. As a proof-of-concept, we develop a statistical model of the relationship between Self-rated-health (SRH) and lifestyle-related factors, and a simple app for conveying its effects through a visualisation that sets the individual as the frame of reference. Using data from the 6th (n = 12 981, 53.4% women and 46.6% men) and 7th (n = 21 083, 52.5% women and 47.5% men) iteration of the Tromsø population survey, we fitted a mixed effects linear regression model that models mean SRH as a function of self-reported intensity and frequency of physical activity (PA), BMI, mental health symptoms (HSCL-10), smoking, support from friends, and HbA1c ≥ 6.5%. We adjusted for socioeconomic and demographic factors and comorbidity. We designed a simple proof-of-concept app to register relevant user information, and use the SRH-model to translate the present status of the user into suggestions for lifestyle changes along with predicted health effects. SRH was strongly related to modifiable health factors. The strongest modifiable predictors of SRH were mental health symptoms and PA. The mean adjusted difference in SRH between those with 10-HSCL index = 1.85 (threshold for mental distress) and HSCL-10 = 1 was 0.59 (CI 0.61–0.57). Vigorous physical activity (exercising to exhaustion ≥ 4 days/week relative to sedentary) was associated with an increase on the SRH scale of 0.64 (CI 0.56–0.73). Physical activity intensity and frequency interacted positively, with large PA-volume (frequency ⨯ intensity) being particularly predictive of high SRH. Incorporating statistical models of health into lifestyle apps have great potential for effectively communicating complex health research to a general audience. Such an approach could improve lifestyle apps by helping to make the recommendations more scientifically rigorous and personalised, and offer a more comprehensive overview of lifestyle factors and their importance.

The sex difference in self-rated health among older Turkish and Moroccan migrants in the Netherlands: an exploratory study of contributing determinants

BackgroundAlthough being a woman and having a migration background are strong predictors of poor self-rated health among (older) adults, research on the sex difference in self-rated health among (older) migrants remains limited. This study therefore aims to investigate this topic and explore the contributing role of determinants of self-rated health.MethodsCross-sectional data from 360 Turkish-Dutch and Moroccan-Dutch adults aged 55–65 as part of the Longitudinal Aging Study Amsterdam (LASA) were used. Self-rated health (good versus poor) was measured by a single item question. Univariate age-adjusted logistic regression analysis was used to investigate the sex difference in self-rated health and the contribution of sex differences in sensitivity (strength of the association) and/or exposure (prevalence) to socio-demographic, social, lifestyle or health-related determinants of self-rated health.ResultsWomen had a 0.53 times lower odds (95%CI:0.40–0.82, p = 0.004) on good self-rated health compared to men. Women more often having a lower education level, living alone and having a higher prevalence of depressive symptoms, chronic diseases and especially functional limitations contributed to the lower self-rated health among women. In contrast, men were more sensitive to the impact of memory complaints, depressive symptoms, visual difficulties and functional limitations.ConclusionsOlder Turkish-Dutch and Moroccan-Dutch women have a significant lower self-rated health compared to men. Women having a higher exposure to both socio-demographic and health-related determinants of self-rated health, which contributed to the sex difference. Future research should take these differences in self-rated health and determinants between women and men into account when investigating health among older migrants.

The Association Between Self-Surveillance and Self-Rated Health in College Women: A Mechanistic Approach

Although women outlive men, women report poorer self-rated physical health over much of the lifespan. Objectification theory may offer insight into this gender difference. Objectifying culture promotes self-surveillance, which encourages prioritizing the body’s appearance over its physical functioning and well-being. As women may engage in self-surveillance more so than men, this sequence of events may help explain this gender difference in self-rated health. However, the few studies examining this idea show an inconsistent relationship between self-surveillance and self-rated health. Nonetheless, self-surveillance may promote body shame and diminished awareness of internal bodily states. As these factors are associated with poor self-rated health, self-surveillance may predict poor self-rated health via these mechanisms. In study 1 ( N = 166 undergraduate women), self-surveillance was unrelated to self-rated health. However, body shame and low interoceptive awareness mediated this relationship. In study 2 ( N = 347 undergraduate women and men), self-surveillance correlated negatively with self-rated health for female participants but not male participants. Female participants reported poorer self-rated health, and this effect was serially mediated by self-surveillance, body shame, and low body responsiveness. These studies suggest a potential avenue by which self-surveillance may explain the gender difference in self-rated health in college samples, providing an important basis for future longitudinal investigations in more diverse samples. Moreover, these results suggest that interventions targeting body shame and low internal awareness may be used to promote good self-rated health as well as to prevent disease and poor health behaviors related to self-rated health.

Case report Germany

Abstract Health data in Germany is collected through official statistics, registries, health insurance and population-based health information surveys. These data are used for monitoring progress of the Sustainable Development Goal on Good Health and Well-being (SDG3). According to the Sustainable Development Report 2022, Germany has achieved an overall high-performance score; however, there are still two health indicators which are stagnating or back-warding: “Gap in self-reported health status by income” and “Gap in life expectancy at birth among regions”. In order to illustrate regional inequalities in health, the Robert Koch Institute has developed the German Index of Socioeconomic Deprivation, which merges the dimensions of education, employment and income. It has been estimated that life expectancy between the least and most deprived district in Germany in 2017 differed by 6.0 years among men and by 3.2 years among women. In addition, data on all-cause premature mortality in Germany between 1992 and 2016 showed that while 13% of women and 27% of men from the lowest income group died before reaching the age of 65, this was the case for 8% of women and 14% of men from the highest income group. Self-rated health (SRH) according to income showed in Germany 2019 the largest gap (31% points) compared to other high-income OECD countries. Data from the German Health Update 2019/2020 also showed differences in SRH according to educational background. These differences were more pronounced among women (54% vs. 82%) than men (64% vs. 81%) and largely persisted across all age groups. Unfavorable SRH is an indicator known to be associated with increased morbidity and health-related limitations in daily life as well as a predictor of premature mortality. In Germany progress towards achieving SDG3 has been made; however, it is important to identify vulnerable groups and health inequalities in order to develop target-oriented interventions.

Hurt on Both Sides: Political Differences in Health and Well-Being during the COVID-19 Pandemic.

Republicans and conservatives report better self-rated health and well-being compared to Democrats and liberals, yet they are more likely to reside in geographic areas with heavy COVID-19 morbidity and mortality. This harmed health on "both sides" of political divides, occurring in a time of rapid sociopolitical upheaval, warrants the revisiting of psychosocial mechanisms linked to political health differences. Drawing on national Gallup data (early 2021), we find that predicted differences in health or well-being vary substantially by ideology, party, voting behavior, and policy beliefs, with model fit depending on how politics are measured. Differences in self-rated health, psychological distress, happiness, trouble sleeping, and delayed health care tend to reveal worse outcomes for Democrats or liberals. Such differences often are reduced to insignificance by some combination of mastery, meritocratic beliefs, perceived social support, and COVID-19-related exposures and attitudes. Policy beliefs predict health differences most robustly across outcomes and mechanism adjustments.

Decomposition and comparative analysis of health inequities between the male and female older adults in China: a national cross-sectional study

BackgroundThis study aimed to examine the factors influencing self-rated health (SRH) among Chinese older adults by gender differences and provide suggestions and theoretical references to help make policies for older adults’ health concerns by government agencies.MethodsChinese Longitudinal Health Longevity Survey (CLHLS) in 2018 was adopted, the chi-squared test and the logistic regression analysis were performed to analyse self-rated health reported by Chinese female and male older adults and its influencing factors. In addition, Fairlie decomposition analysis was performed to quantify the contribution level of different influencing factors.ResultsAmong older adults, males (48.0%) reported a significantly higher level of good self-rated health than females (42.3%). Residence, body mass index (BMI), self-reported income, smoking, drinking, exercise, and social activity were the factors that influenced SRH reported by male and female respondents, with age, marital status and education reaching the significance level only in women. The Fairlie decomposition model can explain the underlying reasons for 86.7% of the gender differences in SRH, with self-reported income (15.3%), smoking (32.7%), drinking (42.5%), exercise (17.4%), social activity (15.1%) and education (-14.6%) being the major factors affecting gender differences in SRH.ConclusionsThe study results can help promote the implementation of the Healthy China Initiative, inform intervention measures, and offer new proposals on creating policies for older adults’ health issues by the Chinese government to improve health equity.

Urban-rural health disparities in Brazil: Do sociodemographic attributes play a role?

The debate over urban-rural health disparities in Latin American countries is disputed, and this relationship may not be as straightforward as initially thought. In this study, we investigate whether individual-level self-rated health (SRH) varies across urban and rural populations in the country and how sociodemographic attributes are associated with such differences. We relied on data from the Brazilian National Health Survey (n = 78,806), a nationwide household-based survey conducted in 2019 that is representative of Brazil, geopolitical macro-regions, states, and 27 state capitals. Logistic regression models were used to test for differences in SRH between urban and rural inhabitants. The findings suggest that Brazilian adults living in rural areas are at a higher risk of perceiving their health as poor than their urban counterparts. The urban-rural health disparities are significant and influenced by sociodemographic attributes, highlighting the importance of developing public health interventions to improve health outcomes in these populations.

Economic development, intergenerational mobility, and health in South Korea

A growing number of studies reported the association between social mobility and health. However, few studies investigated whether the association varies by age group. Drawing on the economic environment that facilitated social mobility in South Korea, we postulate each age group had a different extent of social mobility, which would vary with the extent of economic growth and affect the association between social mobility and health. We used data from KDI National Happiness Survey 2018 and measured perceived mobility using respondents’ perceived social position and their parents’ social position. We examined whether social mobility was associated with self-rated health and psychological well-being. The upwardly mobile individuals were more likely than the stable ones to report ‘happy’. Such a positive association between upward mobility and happiness was consistently found when the sample was restricted to the ages 30–59 and 40–49. For self-rated health, the downwardly mobile individuals were less likely to report good health. However, no significant difference in self-rated health was found after the youngest and oldest age groups were excluded. We found that perceived social mobility was strongly associated with psychological well-being rather than self-rated health. Moreover, we found a stronger association between upward mobility and happiness among the aged 40–49, who had the largest proportion of upwardly mobile individuals and spent their adolescence during rapid economic growth. The findings underscore the importance of the economic and social context in which individuals perceive their social position and shape their well-being.

Working Conditions and Racial and Ethnic Disparities in Self-rated Health.

The aims of the study are to examine racial and ethnic differences in occupational physical demands, substantive complexity, time pressure, work hours, and establishment size and to assess whether working conditions contribute to racial and ethnic differences in self-rated health. We used 2017 and 2019 Panel Study of Income Dynamics data for 8439 adults. Using path models, we examined working conditions among Black, Latino, and White workers and explored whether those conditions mediated racial and ethnic differences in incident poor self-rated health. Some working conditions disproportionately affected Black workers (high physical demands, low substantive complexity), Latino workers (low substantive complexity, small establishments), and White workers (time pressure). Time pressure predicted worse self-rated health; there was no evidence that the working conditions studied mediated racial/ethnic differences. Working conditions vary by racial and ethnic group; some predict worse health.

Values, Health and Well-Being of Young Europeans Not in Employment, Education or Training (NEET).

Youth unemployment is a problem that undermines young people's health and well-being and is also a concern for their immediate communities and society. Human values predict health-related behaviour; however, this relation is very little studied and not examined earlier among NEET (not in employment, education or training) young people. This study aimed to explore the association between four higher-order human values (conservation, openness to change, self-enhancement, self-transcendence), self-rated health (SRH) and subjective well-being (SW) among NEET young men and women (n = 3842) across European regions. Pooled European Social Survey data from 2010-2018 were used. First, we run linear regression analysis stratified by European socio-cultural regions and gender. Then, multilevel analyses by gender with interactions were performed. The results show expected variation in value profiles across genders and regions and corresponding differences in SRH and SW. Significant associations between values and SRH and SW were found among both genders and across the regions; however, the results did not entirely confirm the expectations about the "healthiness" of specific values. More likely, prevailing values in societies, such as the social norm to work, might shape these associations. This study contributes to a deeper understanding of the factors affecting NEETs' health and well-being.

Factors Associated with Self-Rated Health among Korean Elderly.

South Korea has the fastest growing aging population in the world, and the Korean older adult population has been reported to have poor self-rated health (SRH). This cross-sectional descriptive survey aimed to examine the SRH and associated factors among the Korean older adult population using the 2019 Korea National Health and Nutrition Examination Survey (KNHANES) data. The differences in SRH according to socioeconomic, health-related, and mental health-related factors and their relationships were analyzed using chi-square tests and logistic regression, using data from 1,521 older adults in Korea from the 8th KNHANES. Self-rated health was higher among older adults with a high family income (odds ratio [OR]=1.58, 95% CI: 1.05-2.36), those who finished elementary school or lower as opposed to those with a middle school diploma (OR=0.52, 95% CI:0.29-0.95), those with a low body mass index (BMI) compared to those with a higher BMI (OR=0.43, 95% CI: 0.21-0.88), those with no unmet health needs (OR=2.00, 95% CI: 1.31-3.06), those with better diet-related circumstances (OR=1.98, 95% CI: 1.15-3.39), those with lower stress (OR=1.77, 95% CI: 1.28-2.44), those with no suicidal ideation (OR=2.92, 95% CI: 1.75-4.87), and those without a history of psychiatric counseling (OR=2.10, 95% CI: 1.02-4.34). The findings highlight the importance of health behaviors in promoting SRH among older adults in Korea, and emphasize the need for developing and implementing educational programs that reflect these findings.

СОЦИАЛЬНЫЕ РАЗЛИЧИЯ В СОСТОЯНИИ ЗДОРОВЬЯ РОССИЯН: РОЛЬ МАТЕРИАЛЬНЫХ, ПСИХОСОЦИАЛЬНЫХ И ПОВЕДЕНЧЕСКИХ ФАКТОРОВ

Significance. Social inequality in the health status is a major public health challenge. The underlying mechanisms are yet to be well understood. This is especially true for Russia. The purpose of the study is to analyze the association between the social status and self-rated health, as well as to study the contribution of various material, psychosocial and behavioral factors with due regard to their interrelations, to social inequality in the health status in Russia. Material and methods. The study is based on data from the Comprehensive observation of living conditions conducted by the Federal State Statistics Service (Rosstat) in 2018. Social status was measured by the level of education. The direct and indirect contributions of three material, four psychosocial, and two behavioral factors to social differences in self-rated health were studied using logistic regression methods. Results. Given the socio-demographic characteristics, lower levels of education are associated with higher health risks. Out of the three groups of factors, material factors contribute to social inequality the most: the relationship between education and health decreased by 45-48% when material factors were included, with the inclusion of psychosocial factors - by 8-15%, and with behavioral factors included - by 5-13%. Part of the contribution of material factors is associated with psychosocial factors, while the other part - with the behavioral ones. Psychosocial factors contribute to social inequality, in part through behavioral factors. The inclusion of all possible explanatory factors has reduced the association between education and health by 52-62%. The sequential inclusion of individual factors made it possible to establish that such factors as employment, income level, impossibility of meeting with relatives and friends, and alcohol consumption make the main contribution to the explanation of the relationship between health and education. Conclusions. The results highlight the need to address material problems first, especially low incomes and unemployment, which can significantly reduce social inequity in the health status.

Associação entre suporte social e autoavaliação de saúde na meia-idade: seriam os homens mais afetados que as mulheres?

Abstract: Social support from family and friends is recognized as an important social determinant of health, given its protective effects on individuals’ physical and mental well-being. While most studies have focused on older adults, investigating midlife health is equally crucial since middle-aged individuals are also susceptible to the harmful health outcomes of inadequate social support from friends and family. This study contributes to the debate by examining whether social support is associated with self-rated health among middle-aged Brazilian adults and how this relationship varies between men and women. Using data from the nationwide Brazilian National Health Survey conducted in 2019, logistic regression models were employed to assess differences in self-rated health, accounting for confounding factors. The sample comprised 31,926 middle-aged adults, of which 52.5% were women. The overall prevalence of poor self-rated health was 40.7%, with a significant difference between men and women. Results from this study suggest that having no friends or family members to rely on, both during good and challenging times, was associated with poorer self-rated health. However, the strength of this association differs by gender, with social support from friends playing a more critical role in women’s self-rated health. On the other hand, family support was associated with male self-rated health, particularly for men with three or more family members they can rely on. Future studies should consider cultural and contextual factors to better understand other dimensions of social support and its association with midlife health.

Self-rated health of Brazilian women of childbearing age: a cross-sectional study.

To investigate sociodemographic factors, non-communicable diseases and conditions, and behavioral risk factors associated with negative self-rated health among Brazilian women of childbearing age. Cross-sectional study with 26,071 Brazilian women of reproductive age. Estimated prevalence of self-rated health according to sociodemographic characteristics, non-communicable diseases and conditions, and behavioral risk factors. Poisson regression was used to estimate adjusted and unadjusted prevalence ratios. Occurrence of two or more of the diseases and conditions presented a prevalence of negative self-rated health almost three times higher than none. There was a positive association between negative self-rated health and older age groups, lower education, black or brown skin color/race, living in the north and northeast regions, physical inactivity, being a smoker, and presence of one or more of the diseases and conditions. There are differences in self-rated health, reflecting social inequalities.

Decomposition analysis of health inequalities between the urban and rural oldest-old populations in China: Evidence from a national survey

The number of Chinese oldest-old (aged 80+) is growing rapidly and some studies have shown that the health status is unequal among older persons in different regions. However, to the best of our knowledge, no study to date has analyzed health inequalities among the oldest-old in urban and rural areas in China. This study therefore aimed to examine the correlation between health inequalities among the oldest-old in urban and rural areas of China. From the 8th wave of the Chinese Longitudinal Health Longevity Survey (CLHLS), we selected 8124 oldest-old participants who met the requirements of the study. Chi-square tests were used to analyze the distribution characteristics of indicators and a logistic model was performed to determine the factors associated with different self-rated health (SRH). The Fairlie model was adopted to decompose the causes and related contributions to health inequality. Our results found that of the Chinese oldest-old, 46.57% were in good health. Urban residents reported significantly better SRH than rural residents (50.17% vs. 45.13%). Variables associated with good and poor SRH had different distribution characteristics. The logistic model suggested that marital status, alcohol consumption, and annual income were important factors underlying the SRH differences. Our decomposition analysis indicated that 76.64% of the SRH differences were caused by observational factors, and validated that the difference in SRH between urban and rural areas was significantly (P<0.05) associated with exercise status (45.44%), annual income (37.64%), social activity status (3.75%), age (-5.27%), and alcohol consumption (-2.66%). Therefore, socioeconomic status and individual lifestyle status were the main factors underlying the health inequality between urban and rural Chinese oldest-old.