Does self-assessed health measure health?

Doiron, D; Fiebig, DG; Johar, M; Suziedelyte, A

Does self-assessed health measure health?

Doiron, D Fiebig, DG Johar, M Suziedelyte, A

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Publisher:: Routledge
Publication Type:: Journal Article
Citation:: Applied Economics, 2015, 47 (2), pp. 180 - 194
Issue Date:: 2015-01-08

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Field	Value	Language
dc.contributor.author	Doiron, D
dc.contributor.author	Fiebig, DG
dc.contributor.author	Johar, M
dc.contributor.author	Suziedelyte, A
dc.date.accessioned	2014-12-22T05:04:46Z
dc.date.available	2020-05-25T19:10:57Z
dc.date.issued	2015-01-08
dc.identifier.citation	Applied Economics, 2015, 47 (2), pp. 180 - 194
dc.identifier.issn	0003-6846
dc.identifier.uri	http://hdl.handle.net/10453/30751
dc.description.abstract	Despite concerns about reporting biases and interpretation, self-assessed health (SAH) remains the measure of health most used by researchers, in part reflecting its ease of collection and in part the observed correlation between SAH and objective measures of health. Using a unique Australian data set, which consists of survey data linked to administrative individual medical records, we present empirical evidence demonstrating that SAH indeed predicts future health, as measured by hospitalizations, out-of-hospital medical services and prescription drugs. Our large sample size allows very disaggregate analysis and we find that SAH predicts more serious, chronic illnesses better than less serious illnesses. Finally, we compare the predictive power of SAH relative to administrative data and an extensive set of self-reported health measures; SAH does not add to the predictive power of future utilization when the administrative data is included and improves prediction only marginally when the extensive survey-based health measures are included. Clearly there is value in the more extensive survey and administrative health data as well as greater cost of collection.
dc.language	eng
dc.publisher	Routledge
dc.relation.ispartof	Applied Economics
dc.relation.hasversion	Accepted manuscript version	en_US
dc.relation.isbasedon	10.1080/00036846.2014.967382
dc.relation.isreplacedby	10453/37944
dc.relation.isreplacedby	http://hdl.handle.net/10453/37944
dc.rights	"This is an Accepted Manuscript of an article published by Taylor & Francis in Denise Doirona, Denzil G. Fiebiga, Meliyanni Joharb* & Agne Suziedelytec Applied Economics, 2015, 47 (2), pp. 180 – 194 on 08 Jan 2015, available online: http://www.tandfonline.com/doi/abs/10.1080/00036846.2014.967382 10.1080/00036846.2014.967382“	en_US
dc.rights	This is an Author's Accepted Manuscript of an article published in Applied Economics, 2015, 47 (2), pp. 180 - 194. Copyright Routledge, available online at: https://dx.doi.org/10.1080/00036846.2014.967382.	en_US
dc.subject	administrative data
dc.subject	health
dc.subject	health care use
dc.subject	self-assessed health
dc.title	Does self-assessed health measure health?
dc.type	Journal article
utslib.citation.volume	2
utslib.citation.volume	47
pubs.embargo.period	Not known
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics
pubs.organisational-group	/University of Technology Sydney/Strength - Study of Choice
utslib.copyright.status	open_access
pubs.declined	1970-01-01T00:00:00.0+1000
pubs.merge-to	10453/37944
pubs.merge-to	http://hdl.handle.net/10453/37944
pubs.deleted	1970-01-01T00:00:00.0+1000

Abstract:

Despite concerns about reporting biases and interpretation, self-assessed health (SAH) remains the measure of health most used by researchers, in part reflecting its ease of collection and in part the observed correlation between SAH and objective measures of health. Using a unique Australian data set, which consists of survey data linked to administrative individual medical records, we present empirical evidence demonstrating that SAH indeed predicts future health, as measured by hospitalizations, out-of-hospital medical services and prescription drugs. Our large sample size allows very disaggregate analysis and we find that SAH predicts more serious, chronic illnesses better than less serious illnesses. Finally, we compare the predictive power of SAH relative to administrative data and an extensive set of self-reported health measures; SAH does not add to the predictive power of future utilization when the administrative data is included and improves prediction only marginally when the extensive survey-based health measures are included. Clearly there is value in the more extensive survey and administrative health data as well as greater cost of collection.

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http://hdl.handle.net/10453/30751