E.U. & U.S. Public Policy Forum Social Development

Age, gender and health disparities in Hungary

Author:Chyong-fang Ko Research Fellow

Release Date:2017/09/07

Abstract

  This paper uses World Values Survey data to investigate self-perceived health and its determinants in Hungary. Ordinal logistic regression modelling suggests that age, subjective well-being, and/or level of education and social economic status are significantly associated with self-perceived health. Vital statistics reveal that the life expectancy of Hungarian females at birth is 10% greater than that of their male counterparts (79.1vs. 72.2 in 2013). However, expected healthy years at birth in terms of percentage of the total life expectancy was almost 6 percentage points less for females than for their male counterparts (76.0% vs. 81.8%) in Hungary. This implies that, on average, women live longer, and yet are less healthy than men. Nevertheless, no significant gender difference has been found in self-perceived health in the above mentioned ordinal logistic models of Hungary. These contradictory findings might be associated with other hidden factors which require further investigation.

Introduction

  Gender disparities for life expectancy (LE) and health expectancy (HE) vary across countries and age brackets. Data from the European Union indicate that over the past 50 years, LE at birth has increased by approximately 10 years for both men and women (Eurostat, 2016), with females still outliving males by 5-to-6 years. As to HE, in 2013, HE at birth in EU-28 countries was 61.5 years for females and 61.4 for males—a nonsignificant difference. However, in terms of a percentage of LE, also referred to as HEPCLE (Health expectancy as percentage of life expectancy), in 2013, the HEPCLE for females was 5 percentage points lower than that for males at birth (73.9% vs. 78.9%), 7 percentage points lower at age 50 (51.4% vs. 58.4%), and 7.1 percentage points lower at age 65 (40.4% vs. 47.5%). In other words, females live longer, but spend a greater proportion of their lifespan in an unhealthy state, with the difference increasing with age.

  Many researchers have investigated the causes of these gender differences— whether they are triggered by biological or social factors. While self-perceived health as an indicator of individual health, it is widely acknowledged that greater LE and HE are observed in developed countries, and that within countries, individuals with more education tend to have longer LE and HE (Dahlin and Harkoner 2013). Vlassoff (2007) has suggested that by themselves, biological differences do not adequately explain health behavior, with health outcomes also being dependent on such social and economic factors as gender roles, education, income, household autonomy, living arrangements, and social support, among others. Arber and Cooper (1999) looked at data for over 14,000 men and women 60 years of age and older who were interviewed during the 1992-1994 British General Household Survey. Taking class, income, age, and level of functional disability into account, they found small gaps between genders in terms of both self-perceived health and chronic illnesses.

  In another study, using data from the 1994-1995 Canadian National Population Health Survey, Denton, Prus and Walters (2004) concluded that social structural and psychosocial health determinants were generally of greater importance for women’s health status, while behavioral determinants were generally more important for men. They reported that gender differences in health remain even when controlling for these individual factors.

Demographic Trends in Hungary

  Between 1960 and 2013, LE at birth for Hungarians increased about 10% for males (65.9 to 72.2) and 13% for females (70.2 to 79.1). Females have lived longer than males in that country over the past five decades (by 4.3 years in 1960, and 6.9 years in 2013). At age 65, on average females could expect to outlive males by 1.6 to 4.2 years; at age 80 the expectation was 0.6-1.4 years. However, the ratio of female-to-male LE did not increase with age—it was greater at ages 50 (1.28) and 65 (1.29), and lower at ages 80 (1.16) and at birth (1.11). In other words, the LE gender gap was relatively smaller at the beginning and end of life, implying associations with acquired social factors during various life stages.

  Data indicate that Hungarian females have had HEPCLE values of approximately 6 percentage points less than males since 2005 (70.3% vs. 76.0% in 2005 and 76.0% vs. 81.8% in 2013). The gap was relatively greater at ages 50 (4-9 percentage points difference) and 65 (5-10 percentage points difference), suggesting that women’s health-related disadvantages increase with age.

Data analysis and Results

  Data from the fifth (2005-2009) World Values Survey (WVS) has been used to analyze self-perceptions of heath and determinants in Hungary. WVS surveys support multi-national and cross-cultural comparisons of values and norms for a broad range of topics. Multistage random probability sampling is applied to create samples of persons 15 years of age and older. Approximately 84,000 individuals from 58 countries/regions are included in WVS5. Survey samples include 1,007 Hungarians; 25 Hungarians were excluded due to missing data, resulting in a final sample of 982 Hungarians.

  Self-perceived health (the outcome variable used in this study) was measured by the question: “All in all, how would you describe your state of health these days? Would you say it is 1) very good, 2) good, 3) fair, 4) poor. ”Responses were recoded so that lower numbers indicated worse health and higher numbers better health. A total of eight independent variables were used to examine self-perceived health determinants: chronological age, gender (1, female and 0, male), education (ranging from 1, some primary school to 8, university degree), subjective social class (SES, ranging from 1, lower class to 5, upper class), life satisfaction (ranging from 1, low to 10, high), free choice and control over one’s life (ranging from 1, low to 10, high), household financial satisfaction (ranging from 1, low to 10, high), and happiness (ranging from 1, not at all happy to 4, very happy). An ordinal logistic regression model was used to analyze the determinants.

  Results from ordinal logistic regression models indicate a statistically significant association between age and self-perceived health (Model 1); however, the association with gender was nonsignificant when age was controlled for (Model 2) (Table 1). When social economic factors such as education and SES were included in the model, the association with gender remained nonsignificant. It is interesting to find that education and SES were highly correlated with each other (r=0.51), and both were statistically significant at p<0.05 in Model 3.

  According to the Model 4 results, almost all of the subjective well-being variables (life satisfaction, life control, financial satisfaction and happiness) were significant when controlled for age, gender and socioeconomic factors. When subjective well-being variables were included in the model, SES became nonsignificant, while education continued to exert strong positive impacts on self-perceived health. This suggests that regardless of gender, Hungarians who were younger, well-educated, and with better subjective senses of well-being tended to have better self-perceived health.

Discussion and Conclusions

  The motivation for this paper was to use vital statistics and World Values Survey data to analyze LE, HE, HEPCLE, and self-perceived health and its determinants among Hungarian WVS respondents. Vital statistics reveal that women live longer than men, yet suffer from poorer long-term health in both countries. However, according to the WVS data, no significant relationship between gender and self-perceived health was found when age was controlled for.

  Ordinal logistic regression model data indicate significant associations between self-perceived health and both socioeconomic factors and subjective well-being. When other variables were controlled for, a significant correlation was noted between education and health, but not with SES. Further, significant correlations were found between all subjective well-being indicators (life satisfaction, life choice, financial satisfaction and happiness) and self-perceived health. Unexpectedly, gender was not significantly associated with self-perceived health among the Hungarian respondents when age, socioeconomic factors, and subjective well-being were controlled for.

  Due to data limitations, it was not possible to determine if the minor gender differences in self-perceived health were the result of similar chronic conditions among males and females, and therefore it is not possible to confirm Case and Paxson’s (2005) assertion that “women and men with the same chronic conditions have the same self-perceived health,” nor is it possible to refute assertions of gender differences in self-perceived health when controlling for social factors. Future researchers may be interested in investigating whether men and women suffer similar consequences in terms of health and illness in order to clarify this health-survival paradox.

 

Table 1 Ordinal logistic regression results for self-perceived health

 

Model 1

Model 2

Model 3

Model 4

Model 5

 

estimate      (P-value)

estimate       (P-value)

estimate       (P-value)

estimate       (P-value)

estimate      (P-value)

Odds ratio estimate

Intercept 4

1.46***

(0.18)

1.51***

(0.19)

-0.31

(0.30)

-3.31***

(0.42)

-3.21***

(0.40)

------

Intercept 3

3.90***

(0.22)

3.95***

(0.23)

2.22***

(0.31)

-0.51

(0.41)

-0.39

(0.39)

------

Intercept 2

6.19***

(0.27)

6.24***

(0.28)

4.63***

(0.34)

2.14***

(0.41)

2.27***

(0.40)

------

Age

-0.07***

(0.004)

-0.07***

(0.004)

-0.07***

(0.004)

-0.07***

(0.005)

-0.07***

(0.005)

0.932

Gender

(female = 1,

male = 0)

 

-0.09

(0.12)

-0.13

(0.12)

-0.08

(0.13)

-0.07

(0.13)

0.937

Education

 

 

0.13***

(0.03)

0.12***

(0.04)

0.12***

(0.03)

1.127

SES

 

 

0.44***

(0.09)

0.08

(0.10)

----

------

Life satisfaction

 

 

 

0.10*

(0.04)

0.10*

(0.04)

1.103

Life choice

 

 

 

0.10**

(0.03)

0.10**

(0.03)

1.107

Financial satisfaction

 

 

 

0.10**

(0.04)

0.11**

(0.04)

1.115

Happy

 

 

 

0.69***

(0.11)

0.70***

(0.11)

2.022

N

982

982

963

963

982

Model 5 Goodness-of-fit test of overall model (Likelihood Ratio): Chi-square=28.9133, df=14, p-value=0.0107.

       ***P<0.001, **P<0.01, *P<0.05, #P<0.10.

References

Arber, S. & Cooper, H. (1999). Gender differences in health in later life: the new paradox? Social Science & Medicine, 48: 61-76.

Case, A., & Paxson, C. (2005). Sex differences in morbidity and mortality. Demography, 42, 2: 139-214.

Dahlin, J., & Härkönen, J. (2013). Cross-national differences in the gender gap in subjective health in Europe: Does country-level gender equality matter? Social Science & Medicine, 98: 24-28.

Denton, M., Prus, S., & Walters, V. (2004). Gender differences in health: A Canadian study of the psychosocial, structural and behavioural determinants of health. Social Science & Medicine, 58: 2585-2600.

Eurostat (2016). Mortality and life expectancy statistics. Retrieved January 10, 2016, from http://ec.europa.eu/eurostat/statistics-explained/index.php/Mortality_and_life_expectancy_statistics

Vlassoff, C. (2007). Gender differences in determinants and consequences of health and illness. Journal of Health, Population and Nutrition, 25(1): 47-61.

(This article is abridged from the following paper:Ko, Chyong-fang (2016). A Comparative Study of Age, Health and Gender: Hungary and Taiwan. In Janos Vandor & Judit Beke (eds.) The Current Issues of Economic and Social Integration in Hungary and Taiwan (pp. 239-255). Budapest, Hungary.)

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