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AHEAD WP I, II Health and Morbidity Brian Nolan, Richard Layte, Anne Nolan (ESRI) Stanislawa Golinowska, Agnieszka Sowa, Roman Topor-Madry (CASE)

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Presentation on theme: "AHEAD WP I, II Health and Morbidity Brian Nolan, Richard Layte, Anne Nolan (ESRI) Stanislawa Golinowska, Agnieszka Sowa, Roman Topor-Madry (CASE)"— Presentation transcript:

1 AHEAD WP I, II Health and Morbidity Brian Nolan, Richard Layte, Anne Nolan (ESRI) Stanislawa Golinowska, Agnieszka Sowa, Roman Topor-Madry (CASE)

2 AHEAD WP I Aim is to describe and econometrically model health status and health services use by age and socio-economic circumstances Based on data for (most) EU-15 members in the ECHP, To inform analysis of implications of population ageing for health care resources

3 Analysing Health Status Look at how health status measures vary by age across countries, for men and women Multivariate analysis of determinants of health - age, gender, education, marital status, employment status and income Focus is on age “effect” on health before and after socio-economic controls

4 Percentage Reporting Chronic illness by age, Men

5 Predicted probability of reporting a chronic illness (controlling for socio-economic characteristics) by age, Men

6 Key Messages on Age and Health Strong observed relationship between self- reported health status and age for both men and women, but gradient varies a good deal across countries Controlling for socio-economic characteristics substantially reduces age- health relationship –Substantial variation in degree of remaining “age effect” across countries

7 Analysis of Health Services Use Descriptive picture of number of GP visits and hospital nights in year by age across countries, separately for men and women Multivariate analysis of determinants of GP and hospital utilisation, incorporating age, gender, education, marital status, employment status, income and household composition Mulivariate analysis of utilisation also controlling for health status

8 Number of GP visits by age, Men

9 Predicted number of GP visits (controlling for age and socio-economic characteristics) by age, Men

10 Predicted number of GP visits (controlling for age, socio-economic characteristics and health status) by age, Men

11 Predicted number of GP visits by age, All ECHP, Men

12 Key Messages on Age and GP Visits Number of visits increases markedly with age in every country, but much more in some than others Controlling for socio-economic characteristics flattens this relationship, so in some countries very modest age “effect” remains Controlling also for self-reported health status substantially reduces age effect, not now consistently significant in most countries

13 Number of hospital nights by age, Men

14 Predicted hospital nights (controlling for age and socio-economic characteristics) by age, Men

15 Predicted hospital nights (controlling for age, socio-economic characteristics and health status) by age, Men

16 Predicted number of hospital nights by age, All ECHP, Men

17 Key Messages on Age and Hospital Nights Number of nights increases with age but less consistent than GP visits Controlling for socio-economic characteristics flattens this relationship – for some countries few significant age “effect” remain Controlling also for self-reported health status nearly all the age coefficients are insignificant

18 WPI Summary Increasing age is associated with worse self- assessed health in a cross-section; partly due to socio-economic composition Increasing age is also associated with more use of health services, much of this is attributable to socio- economic composition Differences in self-reported health account for most of remaining cross-sectional age-use relationship Substantial variation across countries in age-health and age-service use relationships

19 AHEAD WP II Aim is to analyse health status and use of health services in selected new Member States –Bulgaria, Estonia, Hungary, Poland, Slovakia Uses survey and administrative data, estimates econometric models, and looks at trends over time

20 Share of elderly (65+) in the population

21 Total fertility rate Hungary Poland Slovakia

22 Life expectancy at birth total

23 Infant mortality

24 Epidemiological development Improving health status indicators such as life expectancy and infant mortality since mid-1990s, but significantly behind EU-15 Disease incidence: –Increasing incidence of cancer, –rapid increase in tuberculosis early 1990s (transition crisis), esp. Estonia, Bulgaria –increasing incidence of mental disorders – alcohol-related Health adjusted life expectancy gap vis-à-vis EU-15 8 years, life expectancy gap 5 years

25 % poor, very poor% very good Bulgaria Estonia Hungary Poland Slovakia Self-Assessed Health

26 Influences on Self-Assessed Health Bulgaria EstoniaHungaryPolandSlovakia SexHypothesis: women have lower probability of good health status ConfirmedNot significantConfirmed Not significant AgeHypothesis: probability of good health status decreases with age Confirmed Place of livingHypothesis: probability of good health status lower for rural population ConfirmedNot significantN/aConfirmedNot significant Marital statusHypothesis: single are less likely in poor health N/aNot significantN/aConfirmed Number of persons in HHHypothesis: probability of being in good is health higher for bigger households N/aConfirmedN/aConfirmedN/a Education levelHypothesis: persons with higher education levels are more likely in good health N/aConfirmed Labour market activity Hypothesis: inactive are more likely in poor health N/aConfirmed Income in categoriesHypothesis: probability of being in good health increases with income N/aConfirmed N/a

27 Health status – summary Poorer health status related to: Old Age Sex – being female Labour market inactivity Low education level Rural population Good health status related to: Higher education level Higher income Not living single

28 Primary care utilization EstoniaHungaryPoland SexHypothesis: women are more likely to use primary care services Confirmed AgeHypothesis: probability of primary care utilization increases with age Confirmed Place of livingHypothesis: rural population is more likely to use primary care ConfirmedN/aRural population is less likely Marital statusHypothesis: single are less likely to use primary care Not significantN/aNo differences Number of persons in HH Hypothesis: probability of primary care utilization is lower for bigger households Not significantConfirmedNo differences Education levelHypothesis: persons with higher education levels are less likely to use primary care Not significantN/aConfirmed Labour market activity Hypothesis: inactive are more likely to use primary care ConfirmedN/aConfirmed Income in categoriesHypothesis: probability of primary care utilization decreases with income Increases with incomeN/aConfirmed Health statusHypothesis: probability of primary care utilization increases with poorer health status Confirmed

29 Specialist care utilization EstoniaHungaryPoland SexHypothesis: women are more likely to use specialist care Confirmed AgeHypothesis: probability of specialist care utilization increases with age Middle aged are more likely Younger are more likely Place of livingHypothesis: rural population is more likely to use specialist care ConfirmedNot significantUrban is more likely Marital statusHypothesis: single are less likely to use specialist care Single are more likelyConfirmed Number of persons in HH Hypothesis: probability of specialist care utilization is lower for bigger households Not significantN/aConfirmed Education levelHypothesis: persons with higher education levels are more likely to use specialist care Confirmed Labour market activityHypothesis: inactive are more likely to use specialist care ConfirmedN/aConfirmed Income in categoriesHypothesis: probability of specialist care utilization increases with income ConfirmedN/aConfirmed Health statusHypothesis: probability of specialist care utilization increases with poorer health status Confirmed

30 Hospital care utilization EstoniaHungaryPoland SexHypothesis: women are more likely to use hospital care Not significantN/aConfirmed AgeHypothesis: probability of hospital care utilization increases with age No differencesN/aYounger are more likely Place of livingHypothesis: rural population is more likely to use hospital care Not significantN/aUrban is more likely Marital statusHypothesis: single are less likely to use hospital care Not significantConfirmed Number of persons in HH Hypothesis: probability of hospital care utilization is lower for bigger households Not significantN/aConfirmed Education levelHypothesis: persons with higher education levels are less likely to use hospital care Not significantN/aConfirmed Labour market activityHypothesis: inactive are more likely to use hospital care Not significantN/aConfirmed Income in categoriesHypothesis: probability of hospital care utilization increases with income No differencesN/aConfirmed Health statusHypothesis: probability of hospital care utilization increases with poorer health status Confirmed

31 Medical services utilization - summary GP more frequent utilization: Poor health status Old Age Sex – being female Labour market inactivity Higher income and education confirmed only in Poland Specialist more frequent utilization: Sex – being female Higher education level Poor health status Labour market inactivity Higher income Hospital more frequent utilization: Poorer health status Higher income, higher education and inactivity confirmed only in Poland

32 Health services utilization: Key Trends Hospital utilisation – the biggest driver of health care costs, 40%-50% of total Increasing hospital admissions in Hungary, Poland and Bulgaria: Per Bulgaria Estonia Hungary Poland Slovakia Role of primary care still being developed


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