Using administrative data to look at changes in the level and distribution of out-of-pocket medical expenditure: An example using Medicare data from Australia.
- Publisher:
- ELSEVIER IRELAND LTD
- Publication Type:
- Journal Article
- Citation:
- Health Policy, 2017, 121, (4), pp. 426-433
- Issue Date:
- 2017-04
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Hua, X | |
dc.contributor.author | Erreygers, G | |
dc.contributor.author | Chalmers, J | |
dc.contributor.author | Laba, T-L | |
dc.contributor.author | Clarke, P | |
dc.date.accessioned | 2022-10-03T20:38:07Z | |
dc.date.available | 2017-02-03 | |
dc.date.available | 2022-10-03T20:38:07Z | |
dc.date.issued | 2017-04 | |
dc.identifier.citation | Health Policy, 2017, 121, (4), pp. 426-433 | |
dc.identifier.issn | 0168-8510 | |
dc.identifier.issn | 1872-6054 | |
dc.identifier.uri | http://hdl.handle.net/10453/162241 | |
dc.description.abstract | OBJECTIVES: Australia's universal health insurance system Medicare generates very large amounts of data on out-of-pocket expenditure (OOPE), but only highly aggregated statistics are routinely published. Our primary purpose is to develop indices from the Medicare administrative data to quantify changes in the level and distribution of OOPE on out-of-hospital medical services over time. METHODS: Data were obtained from the Australian Hypertension and Absolute Risk Study, which involved patients aged 55 years and over (n=2653). Socio-economic and clinical information was collected and linked to Medicare records over a five-year period from March 2008. The Fisher price and quantity indices were used to evaluate year-to-year changes in OOPE. The relative concentration index was used to evaluate the distribution of OOPE across socio-economic strata. RESULTS: Our price index indicates that overall OOPE were not rising faster than inflation, but there was considerable variation across different types of services (e.g. OOPE on professional attendances rose by 20% over a five-year period, while all other items fell by around 14%). Concentration indices, adjusted for demographic factors and clinical need, indicate that OOPE tends to be higher among those on higher incomes. CONCLUSIONS: A major challenge in utilizing large administrative data sets is to develop reliable and easily interpretable statistics for policy makers. Price, quantity and concentration indices represent statistics that move us beyond the average. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | ELSEVIER IRELAND LTD | |
dc.relation.ispartof | Health Policy | |
dc.relation.isbasedon | 10.1016/j.healthpol.2017.02.003 | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | 1117 Public Health and Health Services, 1605 Policy and Administration | |
dc.subject.classification | Health Policy & Services | |
dc.subject.mesh | Aged | |
dc.subject.mesh | Australia | |
dc.subject.mesh | Cost of Illness | |
dc.subject.mesh | Cross-Sectional Studies | |
dc.subject.mesh | Female | |
dc.subject.mesh | Health Expenditures | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Hypertension | |
dc.subject.mesh | Insurance, Health | |
dc.subject.mesh | Male | |
dc.subject.mesh | Middle Aged | |
dc.subject.mesh | Models, Economic | |
dc.subject.mesh | National Health Programs | |
dc.subject.mesh | Primary Health Care | |
dc.subject.mesh | Universal Health Insurance | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Hypertension | |
dc.subject.mesh | Models, Economic | |
dc.subject.mesh | Cross-Sectional Studies | |
dc.subject.mesh | Cost of Illness | |
dc.subject.mesh | Aged | |
dc.subject.mesh | Middle Aged | |
dc.subject.mesh | Health Expenditures | |
dc.subject.mesh | Insurance, Health | |
dc.subject.mesh | National Health Programs | |
dc.subject.mesh | Primary Health Care | |
dc.subject.mesh | Australia | |
dc.subject.mesh | Female | |
dc.subject.mesh | Male | |
dc.subject.mesh | Universal Health Insurance | |
dc.title | Using administrative data to look at changes in the level and distribution of out-of-pocket medical expenditure: An example using Medicare data from Australia. | |
dc.type | Journal Article | |
utslib.citation.volume | 121 | |
utslib.location.activity | Ireland | |
utslib.for | 1117 Public Health and Health Services | |
utslib.for | 1605 Policy and Administration | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Health | |
pubs.organisational-group | /University of Technology Sydney/Strength - CHERE - Centre for Health Economics Research and Evaluation | |
utslib.copyright.status | open_access | * |
dc.date.updated | 2022-10-03T20:38:05Z | |
pubs.issue | 4 | |
pubs.publication-status | Published | |
pubs.volume | 121 | |
utslib.citation.issue | 4 |
Abstract:
OBJECTIVES: Australia's universal health insurance system Medicare generates very large amounts of data on out-of-pocket expenditure (OOPE), but only highly aggregated statistics are routinely published. Our primary purpose is to develop indices from the Medicare administrative data to quantify changes in the level and distribution of OOPE on out-of-hospital medical services over time. METHODS: Data were obtained from the Australian Hypertension and Absolute Risk Study, which involved patients aged 55 years and over (n=2653). Socio-economic and clinical information was collected and linked to Medicare records over a five-year period from March 2008. The Fisher price and quantity indices were used to evaluate year-to-year changes in OOPE. The relative concentration index was used to evaluate the distribution of OOPE across socio-economic strata. RESULTS: Our price index indicates that overall OOPE were not rising faster than inflation, but there was considerable variation across different types of services (e.g. OOPE on professional attendances rose by 20% over a five-year period, while all other items fell by around 14%). Concentration indices, adjusted for demographic factors and clinical need, indicate that OOPE tends to be higher among those on higher incomes. CONCLUSIONS: A major challenge in utilizing large administrative data sets is to develop reliable and easily interpretable statistics for policy makers. Price, quantity and concentration indices represent statistics that move us beyond the average.
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