2012年-CEPS欧洲政策研究中心_Health_and_Morbidity_in_Hungary_12页_137kb

报告摘要

Summary of "Health & Morbidity in Hungary" (ENEPRI Policy Brief No. 2, December 2007)

Core Content

This policy brief provides an analysis of health and morbidity in Hungary, focusing on the relationship between aging, health status, and health service utilisation. It is part of the AHEAD project, which investigates the determinants of health expenditure in the European Union, particularly in relation to aging. The report draws on data from the Hungarian National Health Interview Survey (NHIS) conducted in 2003, which is a representative sample of the adult population.

Main Objectives

To analyse the prevalence of good and poor health and the use of medical services by individuals at different ages.
To identify social and economic factors that influence health status.
To examine health service utilisation patterns among individuals with varying health statuses.
To assess the impact of age and other factors on health status and service utilisation.

Data Sources and Methods

Data Sources

National Health Interview Survey (NHIS) 2003: Conducted by the Johan Béla National Center for Epidemiology, covering 7,000 individuals aged 18 and over from 447 communities.
The survey collected data on:
- Socio-economic status (age, gender, marital status, living conditions, education, occupation, income)
- Self-reported health status and functional capabilities
- Specific morbidity (hypertension, high cholesterol, heart attack, diabetes, anxiety/depression, etc.)
- Medical resource use (GP visits, specialist visits, dental visits, inpatient hospitalisation, drug prescriptions)

Methods

Crosstabulations and Chi-square tests: Used to examine associations between variables and differences across categories.
Odds ratios: Calculated to assess risk factors for poor health.
Logistic regression models: Applied to evaluate the influence of age and other factors on health status and service utilisation.
The reference case for logistic regression was:
- Age-group: 18-34
- Gender: Male
- Living in a township with <1,000 inhabitants
- Education: Primary school only
- Household income: ≤ HUF 50,000/month
- Occupation: Not working
- Household size: 1 or 2 people

Key Findings

3.1 Morbidity in Hungary

Prevalence of diseases: 30% reported high blood pressure, 11% high cholesterol, 4% had a history of heart attack, 8% had diabetes, and 9% reported anxiety or depression.
Age-related trends: The prevalence of all these diseases increases with age.
Gender differences: Women had a significantly higher risk of hypertension, high cholesterol, and anxiety/depression, but lower risk of heart attacks compared to men.
Marital status: Single individuals had the lowest prevalence of diseases, while those separated, divorced, or widowed had the highest.
Household size: Larger households showed lower disease prevalence.
Education: Primary school only had the highest disease prevalence, while higher education correlated with lower prevalence.
Economic activity: Non-workers had higher disease prevalence than those working.

3.2 Social and Economic Factors Affecting Health Status

Self-reported health status: 82% of the population reported average/satisfactory, good, or very good health.
Age: Older people were less likely to report good health.
Gender: Females were less likely to report good health than males (odds ratio of 0.6).
Household size: Larger households were more likely to report good health (odds ratio of 2.4).
Education: Higher education correlated with better health outcomes.
Income: Lower income households were more likely to report poor health.
Occupation: Inactive individuals (not working) were more likely to report poor health.

3.3 Health Service Utilisation

Overall utilisation: Over 70% contacted their GP, 55% used specialist services, and 16% were hospitalised in the last 12 months. Dental visits were low, with only 38% reporting visits.
Age-related trends: GP and inpatient care use increased with age, while dental visits decreased.
Health status: Individuals in poor health had significantly more GP visits (8 times more on average) and higher utilisation of health services.
Gender differences: Females were more likely to use health services than males, but this difference disappeared after age 65.
Education and income: Higher education and income correlated with increased specialist and dentist visits, but lower inpatient hospitalisation.

3.4 Effect of Age and Other Factors on Health Status

Age: A significant negative impact on self-reported health status and a positive impact on health service use.
Gender: Females were less likely to report good health.
Place of living: Rural populations were less likely to report good health.
Marital status: Married or separated/widowed individuals had higher odds of requiring inpatient care.
Household size: Larger households were less likely to use health services.
Education: Higher education correlated with better health and lower service utilisation.
Labour market activity: Inactive individuals were more likely to report poor health and use health services.
Income: Higher income correlated with better health and lower service utilisation.
Self-assessed health status: Poorer health status correlated with higher use of health services.

Conclusions

Hungary's population is aging due to low birth rates, not increased life expectancy.
Aging significantly increases the prevalence of diseases and the likelihood of poor health.
Health service utilisation increases with age and is higher among individuals in poor health.
Social and economic factors, such as gender, education, and income, play a significant role in health status and service use.
The findings highlight the potential for increased health expenditure in the future due to an aging population.
The study also underscores the importance of understanding the interaction between health status, age, and socio-economic characteristics for policy planning.