Ageing, Health Status and Determinants of Health Expenditure Data availability and comparability – challenges and possible solutions – (WPVIA) Dr. Erika Schulz

Erika Schulz Determinants of health expenditure

Erika Schulz

Erika Schulz Aims of WP VI –WP VI focuses on the determinants fo health spending taking into account of a combination of demand and supply factors. –It aims at making predictions of changes in aggregate expenditure due to marginal changes in age composition by taking supply factors into account. –The analyses include economic, institutional, demographic and social factors.

Erika Schulz Determinants mentioned in previous studies –As demand factors were mostly performed: age, sex, health status, income, marital status, household composition, activity status and dependency –As supply factors were mostly performed: hospital beds, staff in hospitals, number of physicians, political decisions, general economic development, medical- technological progress (one result of a study for Germany was that technology is the main driver of health expenditure (Breyer 1999))

Erika Schulz Health Care Expenditure –HCE US$ PPP (in million) –HCE US$ PPP per capita –Public expenditure US$ PPP (in million) –Public expenditure US$ PPP per capita –Public expenditure as % of total HCE (US$ PPP per capita) –Private payment as % of total HCE –Out-of-pocket payments (household) as % of total HCE –Private insurance as % of total HCE –All other privat funds as % of total HCE

Erika Schulz Demand Factors I –Population –total population at 1. January –age-composition as share of total –0-5,6-19,20-34,35-49,50-64,65-74,75-84,85+ –life expectancy at birth – male – female –life expectancy at 65 – male – female –total fertility rate –migration per 1000 inhabitants –crude death rate per 1000 population

Erika Schulz Demand Factors II –health status (share of people in bad health) –health behaviour: alcohol consumption –health behaviour: tobacco consumption –education level - attainment ISCED 0/1/2 –education level - attainment ISCED 3/4 –education level - attainment ISCED 5B –education level - attainment ISCED 5A/6 –Utilisation of health care services –doctor's consultation per capita –Acute care occupancy rate in % of available beds

Erika Schulz Supply Factors –physicians per inhabitants –physicians per 100 hospital beds –acute care beds per inhabitants –nursing and elderly home beds per inhabitants –MRI (and CT scanners) per million inhabitants –(MRI and) CT scanners per million inhabitants –dialyses per inhabitants

Erika Schulz Health Care System I –Organisational structure –Health care system (public contract, public integrated, mixed) –Gatekeeper to non-acute hospital treatment or specialist –Free choice GP or family doctor –Free choice of specialists –Free choice of hospitals –Free choice of dentists –Waiting lists for specialist care –waiting lists for surgeries in hospitals

Erika Schulz Health Care System II –Financing the health care system –Population covered by public health system % of total population –Multiple or single source financing system –population covered by privates health insurance –Co-payment in connection with GP visits –Co-payment in connection with specialists visits –Co-payment in connection with hospital admission –Co-payments in connection with dentist care –Co.payments for pharmaceuticals

Erika Schulz Health Care System III –Reimbursement of hospitals –(global budget, fee-for-service, per diem, per discharge) –Reimbursement of physicians in hospitals –(fee-for-service, fixed salary) –Reimbursement of general practitioner –(fee-for-service, salary, capitation) –Reimbursement of specialists –(fee-for-service, salary, capitation) –Reimbursement of dentists –(fee-for-service, salary, capitation) –Overall ceiling of hospital expenditure

Erika Schulz Framework conditions –Gross domestic product per capita, US$ PPP –female labour force participation –unemployment rate in % of labour force

Erika Schulz Data sources –OECD Health Data Version 2004 Health care expenditure, education, docotrs consultation, supply of health care services, acute care occupancy rate, population covered by public health care systrem. GDP per capita, share of female labour force in total labour force –WHO Health For All database Life expectancies at birth, and at age 65, crude death rates, heath behaviour (alcohol, tobacco), nursing and elderly home beds –EUROSTAT Population –ILO Harmonized unemployment rates –Health Care Systems in Transition reports, MISSOC, MISSCEEC Institutional variables

Erika Schulz Data comparability –OECD Health Data Version 2004 The OECD has the principle to ensure that the data presented in their database are as comparable as possible across countries and over time. For example: A System of Health Accounts was published with guidelines for reporting health care expenditure. Countries can be grouped in four categories: I (close to SHA): Denmark, France, Germany, Hungary, Netherlands, United Kingdom, Spain and Turkey. II (near by SHA): Finland and Poland. III (problems in international comparison): Greece, Portugal. IV (OECD estimates): Belgium.

Erika Schulz Data comparability II –The OECD data base is the most comparable data set, but nevertheless, in some cases the specification of HCE was not really clear (sometimes a part of HCE was included in other parts of the social budget) –The different data sources show for the same variable different figures. Therrefore we decided to use as a main data base the OECD (combined with WHO data if possible) and for the population EUROSTAT.

Erika Schulz Data included in the model estimating HCE - Demographic variables: AGE0-5, AGE65-74 and AGE75+(share of population aged.. in total population) AVELE65 (life expectancy at age 65) MORTALITY (Crude death rate) –Behavioural variables: ALCCON (pur alcohol in litres per capita 15+ per year) –Supply variables: BEDS (acute care beds per 1000 inhabitants) –Institutional variables Reimbursement (SALARYGP, CAPGP, GLOBALHO, CASEHO) Copayments (COPAYGP, COPAYHO) Free choice (FREEGP, FREEHO) PUSHES (share of public HCE in total HCE) –Economic variables GDP in US$ PPP per capita, UNEMPL

Erika Schulz Conclusion –The used data for the model are the most comparable data Demographic variable stem from EUROSTAT and are comparable Demand and supply factors stem from OECD and are most comparable Institutional variables are created by ourselfs and therefore comparable Only the definition of health care expenditure may be in some cases not fully comparable, but OECD Health Data provided the most comparable data

Erika Schulz

Erika Schulz

Erika Schulz

Erika Schulz

Erika Schulz