EQ-5D-3L Derived Population Norms for Health Related Quality of Life in Sri Lanka
Identifieur interne : 002B51 ( Main/Merge ); précédent : 002B50; suivant : 002B52EQ-5D-3L Derived Population Norms for Health Related Quality of Life in Sri Lanka
Auteurs : Sanjeewa Kularatna [Australie] ; Jennifer A. Whitty [Australie] ; Newell W. Johnson [Australie] ; Ruwan Jayasinghe [Sri Lanka] ; Paul A. Scuffham [Australie]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
Health Related Quality of Life (HRQoL) is an important outcome measure in health economic evaluation that guides health resource allocations. Population norms for HRQoL are an essential ingredient in health economics and in the evaluation of population health. The aim of this study was to produce EQ-5D-3L-derived population norms for Sri Lanka.
A population sample (n = 780) was selected from four districts of Sri Lanka. A stratified cluster sampling approach with probability proportionate to size was employed. Twenty six clusters of 30 participants each were selected; each participant completed the EQ-5D-3L in a face-to-face interview. Utility weights for their EQ-5D-3L health states were assigned using the Sri Lankan EQ-5D-3L algorithm. The population norms are reported by age and socio-economic variables.
The EQ-5D-3L was completed by 736 people, representing a 94% response rate. Sixty per cent of the sample reported being in full health. The percentage of people responding to any problems in the five EQ-5D-3L dimensions increased with age. The mean EQ-5D-3L weight was 0.85 (SD 0.008; 95%CI 0.84-0.87). The mean EQ-5D-3L weight was significantly associated with age, housing type, disease experience and religiosity. People above 70 years of age were 7.5 times more likely to report mobility problems and 3.7 times more likely to report pain/discomfort than those aged 18-29 years. Those with a tertiary education were five times less likely to report any HRQoL problems than those without a tertiary education. A person living in a shanty was 4.3 more likely to have problems in usual activities than a person living in a single house.
The population norms in Sri Lanka vary with socio-demographic characteristics. The socioeconomically disadvantaged have a lower HRQoL. The trends of population norms observed in this lower middle income country were generally similar to those previously reported in high income countries.
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DOI: 10.1371/journal.pone.0108434
PubMed: 25365171
PubMed Central: 4217723
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Whitty</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Centre for Applied Health Economics, School of Medicine, Griffith University, Brisbane, Queensland, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Applied Health Economics, School of Medicine, Griffith University, Brisbane, Queensland</wicri:regionArea><wicri:noRegion>Queensland</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Population and Social Health Research Programme, Griffith Health Institute, Griffith University, Brisbane, Queensland, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Population and Social Health Research Programme, Griffith Health Institute, Griffith University, Brisbane, Queensland</wicri:regionArea><wicri:noRegion>Queensland</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>School of Pharmacy, The University of Queensland, Brisbane, Queensland, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>School of Pharmacy, The University of Queensland, Brisbane, Queensland</wicri:regionArea><wicri:noRegion>Queensland</wicri:noRegion></affiliation></author><author><name sortKey="Johnson, Newell W" sort="Johnson, Newell W" uniqKey="Johnson N" first="Newell W." last="Johnson">Newell W. 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Whitty</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Centre for Applied Health Economics, School of Medicine, Griffith University, Brisbane, Queensland, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Applied Health Economics, School of Medicine, Griffith University, Brisbane, Queensland</wicri:regionArea><wicri:noRegion>Queensland</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Population and Social Health Research Programme, Griffith Health Institute, Griffith University, Brisbane, Queensland, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Population and Social Health Research Programme, Griffith Health Institute, Griffith University, Brisbane, Queensland</wicri:regionArea><wicri:noRegion>Queensland</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>School of Pharmacy, The University of Queensland, Brisbane, Queensland, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>School of Pharmacy, The University of Queensland, Brisbane, Queensland</wicri:regionArea><wicri:noRegion>Queensland</wicri:noRegion></affiliation></author><author><name sortKey="Johnson, Newell W" sort="Johnson, Newell W" uniqKey="Johnson N" first="Newell W." last="Johnson">Newell W. Johnson</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Population and Social Health Research Programme, Griffith Health Institute, Griffith University, Brisbane, Queensland, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Population and Social Health Research Programme, Griffith Health Institute, Griffith University, Brisbane, Queensland</wicri:regionArea><wicri:noRegion>Queensland</wicri:noRegion></affiliation></author><author><name sortKey="Jayasinghe, Ruwan" sort="Jayasinghe, Ruwan" uniqKey="Jayasinghe R" first="Ruwan" last="Jayasinghe">Ruwan Jayasinghe</name><affiliation wicri:level="1"><nlm:aff id="aff4"><addr-line>Faculty of Dental sciences, University of Peradeniya, Peradeniya, Sri Lanka</addr-line></nlm:aff><country xml:lang="fr">Sri Lanka</country><wicri:regionArea>Faculty of Dental sciences, University of Peradeniya, Peradeniya</wicri:regionArea><wicri:noRegion>Peradeniya</wicri:noRegion></affiliation></author><author><name sortKey="Scuffham, Paul A" sort="Scuffham, Paul A" uniqKey="Scuffham P" first="Paul A." last="Scuffham">Paul A. Scuffham</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Centre for Applied Health Economics, School of Medicine, Griffith University, Brisbane, Queensland, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Applied Health Economics, School of Medicine, Griffith University, Brisbane, Queensland</wicri:regionArea><wicri:noRegion>Queensland</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Population and Social Health Research Programme, Griffith Health Institute, Griffith University, Brisbane, Queensland, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Population and Social Health Research Programme, Griffith Health Institute, Griffith University, Brisbane, Queensland</wicri:regionArea><wicri:noRegion>Queensland</wicri:noRegion></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Health Related Quality of Life (HRQoL) is an important outcome measure in health economic evaluation that guides health resource allocations. Population norms for HRQoL are an essential ingredient in health economics and in the evaluation of population health. The aim of this study was to produce EQ-5D-3L-derived population norms for Sri Lanka.</p></sec><sec><title>Method</title><p>A population sample (n = 780) was selected from four districts of Sri Lanka. A stratified cluster sampling approach with probability proportionate to size was employed. Twenty six clusters of 30 participants each were selected; each participant completed the EQ-5D-3L in a face-to-face interview. Utility weights for their EQ-5D-3L health states were assigned using the Sri Lankan EQ-5D-3L algorithm. The population norms are reported by age and socio-economic variables.</p></sec><sec><title>Results</title><p>The EQ-5D-3L was completed by 736 people, representing a 94% response rate. Sixty per cent of the sample reported being in full health. The percentage of people responding to any problems in the five EQ-5D-3L dimensions increased with age. The mean EQ-5D-3L weight was 0.85 (SD 0.008; 95%CI 0.84-0.87). The mean EQ-5D-3L weight was significantly associated with age, housing type, disease experience and religiosity. People above 70 years of age were 7.5 times more likely to report mobility problems and 3.7 times more likely to report pain/discomfort than those aged 18-29 years. Those with a tertiary education were five times less likely to report any HRQoL problems than those without a tertiary education. A person living in a shanty was 4.3 more likely to have problems in usual activities than a person living in a single house.</p></sec><sec><title>Conclusion</title><p>The population norms in Sri Lanka vary with socio-demographic characteristics. The socioeconomically disadvantaged have a lower HRQoL. 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Begum</name></author><author><name sortKey="Harper, C" uniqKey="Harper C">C Harper</name></author><author><name sortKey="Whitty, Ja" uniqKey="Whitty J">JA Whitty</name></author><author><name sortKey="Scuffham, Pa" uniqKey="Scuffham P">PA Scuffham</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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