AustralieFrV1, PascalFrancis, Curation, bibRecord, 004017

Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study

Identifieur interne : 004017 ( PascalFrancis/Curation ); précédent : 004016; suivant : 004018

Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study

Auteurs : L. Chen [Australie] ; D. J. Magliano [Australie] ; B. Balkau [Australie, France] ; R. Wolfe [Australie] ; L. Brown [Australie] ; A. M. Tonkin [Australie] ; P. Z. Zimmet [Australie] ; J. E. Shaw [Australie]

Source :

Diabetic medicine [ 0742-3071 ] ; 2011.

RBID : Pascal:11-0155765

Descripteurs français

Pascal (Inist)
- Diabète de type 2, Efficacité, Analyse coût efficacité, Obésité, Economie santé, Dépistage, Australie, Epidémiologie, Mode de vie, Santé publique, Coût, Aspect économique, Prédiction, Facteur prédictif, Facteur risque, Risque, Stratégie, Endocrinologie, Etat nutritionnel.
Wicri :
- geographic : Australie.
- topic : Santé publique.

English descriptors

KwdEn :
- Australia, Cost efficiency analysis, Costs, Economic aspect, Efficiency, Endocrinology, Epidemiology, Health economy, Life style, Medical screening, Nutritional status, Obesity, Prediction, Predictive factor, Public health, Risk, Risk factor, Strategy, Type 2 diabetes.

Abstract

Aims To evaluate how to most efficiently screen populations to detect people at high risk of incident Type 2 diabetes and those with prevalent, but undiagnosed, Type 2 diabetes. Methods Data from 5814 adults in the Australian Diabetes, Obesity and Lifestyle study were used to examine four different types of screening strategies. The strategies incorporated various combinations of cut-points of fasting plasma glucose, the non- invasive Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK1) and a modified version of the tool incorporating fasting plasma glucose (AUSDRISK2). Sensitivity, specificity, positive predictive value, screening costs per case of incident or prevalent undiagnosed diabetes identified and intervention costs per case of diabetes prevented or reverted were compared. Results Of the four strategies that maximized sensitivity and specificity, use of the non-invasive AUSDRISK1, followed by AUSDRISK2 in those found to be at increased risk on AUSDRISK1, had the highest sensitivity (80.3%; 95% confidence interval 76.6-84.1 %), specificity (78.1 %; 95% confidence interval 76.9-79.2%) and positive predictive value (22.3%; 95% confidence interval 20.2-24.4%) for identifying people with either prevalent undiagnosed diabetes or future incident diabetes. It required the fewest people (24.1 %; 95% confidence interval 23.0-25.2%) to enter lifestyle modification programmes, and also had the lowest intervention costs and combined costs of running screening and intervention programmes per case of diabetes prevented or reverted. Conclusions Using a self-assessed diabetes risk score as an initial screening step, followed by a second risk score incorporating fasting plasma glucose, would maximize efficiency of identifying people with undiagnosed Type 2 diabetes and those at high risk of future diabetes.

A01	`01`	`1`		`@0 0742-3071`
A02	`01`			`@0 DIMEEV`
A03		`1`		`@0 Diabetic med.`
A05				`@2 28`
A06				`@2 4`
A08	`01`	`1`	`ENG`	`@1 Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study`
A11	`01`	`1`		`@1 CHEN (L.)`
A11	`02`	`1`		`@1 MAGLIANO (D. J.)`
A11	`03`	`1`		`@1 BALKAU (B.)`
A11	`04`	`1`		`@1 WOLFE (R.)`
A11	`05`	`1`		`@1 BROWN (L.)`
A11	`06`	`1`		`@1 TONKIN (A. M.)`
A11	`07`	`1`		`@1 ZIMMET (P. Z.)`
A11	`08`	`1`		`@1 SHAW (J. E.)`
A14	`01`			`@1 Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University @2 Melbourne, Victoria @3 AUS @Z 1 aut. @Z 2 aut. @Z 4 aut. @Z 6 aut.`
A14	`02`			`@1 Baker IDI Heart and Diabetes Institute @2 Melbourne, Vic @3 AUS @Z 1 aut. @Z 2 aut. @Z 3 aut. @Z 7 aut. @Z 8 aut.`
A14	`03`			`@1 Epidemiology of diabetes, obesity and chronic kidney disease over the lifecourse, CESP Centre for Research in Epidemiology and Public Health, U1018 Inserm @2 Villejuif @3 FRA @Z 3 aut.`
A14	`04`			`@1 University Paris-Sud 11, UMRS 1018 @2 Villejuif @3 FRA @Z 3 aut.`
A14	`05`			`@1 National Centre for Social and Economic Modelling, University of Canberra @2 Canberra, ACT @3 AUS @Z 5 aut.`
A20				`@1 414-423`
A21				`@1 2011`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 20274 @5 354000194433410040`
A44				`@0 0000 @1 © 2011 INIST-CNRS. All rights reserved.`
A45				`@0 31 ref.`
A47	`01`	`1`		`@0 11-0155765`
A60				`@1 P`
A61				`@0 A`
A64	`01`	`1`		`@0 Diabetic medicine`
A66	`01`			`@0 GBR`
C01	`01`		`ENG`	@0 Aims To evaluate how to most efficiently screen populations to detect people at high risk of incident Type 2 diabetes and those with prevalent, but undiagnosed, Type 2 diabetes. Methods Data from 5814 adults in the Australian Diabetes, Obesity and Lifestyle study were used to examine four different types of screening strategies. The strategies incorporated various combinations of cut-points of fasting plasma glucose, the non- invasive Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK1) and a modified version of the tool incorporating fasting plasma glucose (AUSDRISK2). Sensitivity, specificity, positive predictive value, screening costs per case of incident or prevalent undiagnosed diabetes identified and intervention costs per case of diabetes prevented or reverted were compared. Results Of the four strategies that maximized sensitivity and specificity, use of the non-invasive AUSDRISK1, followed by AUSDRISK2 in those found to be at increased risk on AUSDRISK1, had the highest sensitivity (80.3%; 95% confidence interval 76.6-84.1 %), specificity (78.1 %; 95% confidence interval 76.9-79.2%) and positive predictive value (22.3%; 95% confidence interval 20.2-24.4%) for identifying people with either prevalent undiagnosed diabetes or future incident diabetes. It required the fewest people (24.1 %; 95% confidence interval 23.0-25.2%) to enter lifestyle modification programmes, and also had the lowest intervention costs and combined costs of running screening and intervention programmes per case of diabetes prevented or reverted. Conclusions Using a self-assessed diabetes risk score as an initial screening step, followed by a second risk score incorporating fasting plasma glucose, would maximize efficiency of identifying people with undiagnosed Type 2 diabetes and those at high risk of future diabetes.
C02	`01`	`X`		`@0 002A16E`
C02	`02`	`X`		`@0 002A28`
C02	`03`	`X`		`@0 002B21E01A`
C03	`01`	`X`	`FRE`	`@0 Diabète de type 2 @2 NM @5 01`
C03	`01`	`X`	`ENG`	`@0 Type 2 diabetes @2 NM @5 01`
C03	`01`	`X`	`SPA`	`@0 Diabetes de tipo 2 @2 NM @5 01`
C03	`02`	`X`	`FRE`	`@0 Efficacité @5 02`
C03	`02`	`X`	`ENG`	`@0 Efficiency @5 02`
C03	`02`	`X`	`SPA`	`@0 Eficacia @5 02`
C03	`03`	`X`	`FRE`	`@0 Analyse coût efficacité @5 03`
C03	`03`	`X`	`ENG`	`@0 Cost efficiency analysis @5 03`
C03	`03`	`X`	`SPA`	`@0 Análisis costo eficacia @5 03`
C03	`04`	`X`	`FRE`	`@0 Obésité @5 04`
C03	`04`	`X`	`ENG`	`@0 Obesity @5 04`
C03	`04`	`X`	`SPA`	`@0 Obesidad @5 04`
C03	`05`	`X`	`FRE`	`@0 Economie santé @5 05`
C03	`05`	`X`	`ENG`	`@0 Health economy @5 05`
C03	`05`	`X`	`SPA`	`@0 Economía salud @5 05`
C03	`06`	`X`	`FRE`	`@0 Dépistage @5 06`
C03	`06`	`X`	`ENG`	`@0 Medical screening @5 06`
C03	`06`	`X`	`SPA`	`@0 Descubrimiento @5 06`
C03	`07`	`X`	`FRE`	`@0 Australie @2 NG @5 08`
C03	`07`	`X`	`ENG`	`@0 Australia @2 NG @5 08`
C03	`07`	`X`	`SPA`	`@0 Australia @2 NG @5 08`
C03	`08`	`X`	`FRE`	`@0 Epidémiologie @5 09`
C03	`08`	`X`	`ENG`	`@0 Epidemiology @5 09`
C03	`08`	`X`	`SPA`	`@0 Epidemiología @5 09`
C03	`09`	`X`	`FRE`	`@0 Mode de vie @5 11`
C03	`09`	`X`	`ENG`	`@0 Life style @5 11`
C03	`09`	`X`	`SPA`	`@0 Modo de vida @5 11`
C03	`10`	`X`	`FRE`	`@0 Santé publique @5 12`
C03	`10`	`X`	`ENG`	`@0 Public health @5 12`
C03	`10`	`X`	`SPA`	`@0 Salud pública @5 12`
C03	`11`	`X`	`FRE`	`@0 Coût @5 17`
C03	`11`	`X`	`ENG`	`@0 Costs @5 17`
C03	`11`	`X`	`SPA`	`@0 Coste @5 17`
C03	`12`	`X`	`FRE`	`@0 Aspect économique @5 18`
C03	`12`	`X`	`ENG`	`@0 Economic aspect @5 18`
C03	`12`	`X`	`SPA`	`@0 Aspecto económico @5 18`
C03	`13`	`X`	`FRE`	`@0 Prédiction @5 19`
C03	`13`	`X`	`ENG`	`@0 Prediction @5 19`
C03	`13`	`X`	`SPA`	`@0 Predicción @5 19`
C03	`14`	`X`	`FRE`	`@0 Facteur prédictif @5 20`
C03	`14`	`X`	`ENG`	`@0 Predictive factor @5 20`
C03	`14`	`X`	`SPA`	`@0 Factor predictivo @5 20`
C03	`15`	`X`	`FRE`	`@0 Facteur risque @5 21`
C03	`15`	`X`	`ENG`	`@0 Risk factor @5 21`
C03	`15`	`X`	`SPA`	`@0 Factor riesgo @5 21`
C03	`16`	`X`	`FRE`	`@0 Risque @5 22`
C03	`16`	`X`	`ENG`	`@0 Risk @5 22`
C03	`16`	`X`	`SPA`	`@0 Riesgo @5 22`
C03	`17`	`X`	`FRE`	`@0 Stratégie @5 23`
C03	`17`	`X`	`ENG`	`@0 Strategy @5 23`
C03	`17`	`X`	`SPA`	`@0 Estrategia @5 23`
C03	`18`	`X`	`FRE`	`@0 Endocrinologie @5 24`
C03	`18`	`X`	`ENG`	`@0 Endocrinology @5 24`
C03	`18`	`X`	`SPA`	`@0 Endocrinología @5 24`
C03	`19`	`X`	`FRE`	`@0 Etat nutritionnel @5 25`
C03	`19`	`X`	`ENG`	`@0 Nutritional status @5 25`
C03	`19`	`X`	`SPA`	`@0 Estado nutricional @5 25`
C07	`01`	`X`	`FRE`	`@0 Océanie @2 NG`
C07	`01`	`X`	`ENG`	`@0 Oceania @2 NG`
C07	`01`	`X`	`SPA`	`@0 Oceania @2 NG`
C07	`02`	`X`	`FRE`	`@0 Endocrinopathie @5 37`
C07	`02`	`X`	`ENG`	`@0 Endocrinopathy @5 37`
C07	`02`	`X`	`SPA`	`@0 Endocrinopatía @5 37`
C07	`03`	`X`	`FRE`	`@0 Trouble de la nutrition @5 38`
C07	`03`	`X`	`ENG`	`@0 Nutrition disorder @5 38`
C07	`03`	`X`	`SPA`	`@0 Trastorno nutricíon @5 38`
N21				`@1 101`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

Links toward previous steps (curation, corpus...)

to stream PascalFrancis, to step Corpus: Pour aller vers cette notice dans l'étape Curation :001F20

Links to Exploration step

Pascal:11-0155765

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study</title>
<author><name sortKey="Chen, L" sort="Chen, L" uniqKey="Chen L" first="L." last="Chen">L. Chen</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>6 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Magliano, D J" sort="Magliano, D J" uniqKey="Magliano D" first="D. J." last="Magliano">D. J. Magliano</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>6 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Balkau, B" sort="Balkau, B" uniqKey="Balkau B" first="B." last="Balkau">B. Balkau</name>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
<affiliation wicri:level="1"><inist:fA14 i1="03"><s1>Epidemiology of diabetes, obesity and chronic kidney disease over the lifecourse, CESP Centre for Research in Epidemiology and Public Health, U1018 Inserm</s1>
<s2>Villejuif</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>France</country>
</affiliation>
<affiliation wicri:level="1"><inist:fA14 i1="04"><s1>University Paris-Sud 11, UMRS 1018</s1>
<s2>Villejuif</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>France</country>
</affiliation>
</author>
<author><name sortKey="Wolfe, R" sort="Wolfe, R" uniqKey="Wolfe R" first="R." last="Wolfe">R. Wolfe</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>6 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Brown, L" sort="Brown, L" uniqKey="Brown L" first="L." last="Brown">L. Brown</name>
<affiliation wicri:level="1"><inist:fA14 i1="05"><s1>National Centre for Social and Economic Modelling, University of Canberra</s1>
<s2>Canberra, ACT</s2>
<s3>AUS</s3>
<sZ>5 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Tonkin, A M" sort="Tonkin, A M" uniqKey="Tonkin A" first="A. M." last="Tonkin">A. M. Tonkin</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>6 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Zimmet, P Z" sort="Zimmet, P Z" uniqKey="Zimmet P" first="P. Z." last="Zimmet">P. Z. Zimmet</name>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Shaw, J E" sort="Shaw, J E" uniqKey="Shaw J" first="J. E." last="Shaw">J. E. Shaw</name>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">INIST</idno>
<idno type="inist">11-0155765</idno>
<date when="2011">2011</date>
<idno type="stanalyst">PASCAL 11-0155765 INIST</idno>
<idno type="RBID">Pascal:11-0155765</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">001F20</idno>
<idno type="wicri:Area/PascalFrancis/Curation">004017</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study</title>
<author><name sortKey="Chen, L" sort="Chen, L" uniqKey="Chen L" first="L." last="Chen">L. Chen</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>6 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Magliano, D J" sort="Magliano, D J" uniqKey="Magliano D" first="D. J." last="Magliano">D. J. Magliano</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>6 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Balkau, B" sort="Balkau, B" uniqKey="Balkau B" first="B." last="Balkau">B. Balkau</name>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
<affiliation wicri:level="1"><inist:fA14 i1="03"><s1>Epidemiology of diabetes, obesity and chronic kidney disease over the lifecourse, CESP Centre for Research in Epidemiology and Public Health, U1018 Inserm</s1>
<s2>Villejuif</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>France</country>
</affiliation>
<affiliation wicri:level="1"><inist:fA14 i1="04"><s1>University Paris-Sud 11, UMRS 1018</s1>
<s2>Villejuif</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>France</country>
</affiliation>
</author>
<author><name sortKey="Wolfe, R" sort="Wolfe, R" uniqKey="Wolfe R" first="R." last="Wolfe">R. Wolfe</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>6 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Brown, L" sort="Brown, L" uniqKey="Brown L" first="L." last="Brown">L. Brown</name>
<affiliation wicri:level="1"><inist:fA14 i1="05"><s1>National Centre for Social and Economic Modelling, University of Canberra</s1>
<s2>Canberra, ACT</s2>
<s3>AUS</s3>
<sZ>5 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Tonkin, A M" sort="Tonkin, A M" uniqKey="Tonkin A" first="A. M." last="Tonkin">A. M. Tonkin</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>6 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Zimmet, P Z" sort="Zimmet, P Z" uniqKey="Zimmet P" first="P. Z." last="Zimmet">P. Z. Zimmet</name>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
<author><name sortKey="Shaw, J E" sort="Shaw, J E" uniqKey="Shaw J" first="J. E." last="Shaw">J. E. Shaw</name>
<affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</inist:fA14>
<country>Australie</country>
</affiliation>
</author>
</analytic>
<series><title level="j" type="main">Diabetic medicine</title>
<title level="j" type="abbreviated">Diabetic med.</title>
<idno type="ISSN">0742-3071</idno>
<imprint><date when="2011">2011</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt><title level="j" type="main">Diabetic medicine</title>
<title level="j" type="abbreviated">Diabetic med.</title>
<idno type="ISSN">0742-3071</idno>
</seriesStmt>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Australia</term>
<term>Cost efficiency analysis</term>
<term>Costs</term>
<term>Economic aspect</term>
<term>Efficiency</term>
<term>Endocrinology</term>
<term>Epidemiology</term>
<term>Health economy</term>
<term>Life style</term>
<term>Medical screening</term>
<term>Nutritional status</term>
<term>Obesity</term>
<term>Prediction</term>
<term>Predictive factor</term>
<term>Public health</term>
<term>Risk</term>
<term>Risk factor</term>
<term>Strategy</term>
<term>Type 2 diabetes</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Diabète de type 2</term>
<term>Efficacité</term>
<term>Analyse coût efficacité</term>
<term>Obésité</term>
<term>Economie santé</term>
<term>Dépistage</term>
<term>Australie</term>
<term>Epidémiologie</term>
<term>Mode de vie</term>
<term>Santé publique</term>
<term>Coût</term>
<term>Aspect économique</term>
<term>Prédiction</term>
<term>Facteur prédictif</term>
<term>Facteur risque</term>
<term>Risque</term>
<term>Stratégie</term>
<term>Endocrinologie</term>
<term>Etat nutritionnel</term>
</keywords>
<keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Australie</term>
</keywords>
<keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Santé publique</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Aims To evaluate how to most efficiently screen populations to detect people at high risk of incident Type 2 diabetes and those with prevalent, but undiagnosed, Type 2 diabetes. Methods Data from 5814 adults in the Australian Diabetes, Obesity and Lifestyle study were used to examine four different types of screening strategies. The strategies incorporated various combinations of cut-points of fasting plasma glucose, the non- invasive Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK1) and a modified version of the tool incorporating fasting plasma glucose (AUSDRISK2). Sensitivity, specificity, positive predictive value, screening costs per case of incident or prevalent undiagnosed diabetes identified and intervention costs per case of diabetes prevented or reverted were compared. Results Of the four strategies that maximized sensitivity and specificity, use of the non-invasive AUSDRISK1, followed by AUSDRISK2 in those found to be at increased risk on AUSDRISK1, had the highest sensitivity (80.3%; 95% confidence interval 76.6-84.1 %), specificity (78.1 %; 95% confidence interval 76.9-79.2%) and positive predictive value (22.3%; 95% confidence interval 20.2-24.4%) for identifying people with either prevalent undiagnosed diabetes or future incident diabetes. It required the fewest people (24.1 %; 95% confidence interval 23.0-25.2%) to enter lifestyle modification programmes, and also had the lowest intervention costs and combined costs of running screening and intervention programmes per case of diabetes prevented or reverted. Conclusions Using a self-assessed diabetes risk score as an initial screening step, followed by a second risk score incorporating fasting plasma glucose, would maximize efficiency of identifying people with undiagnosed Type 2 diabetes and those at high risk of future diabetes.</div>
</front>
</TEI>
<inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0742-3071</s0>
</fA01>
<fA02 i1="01"><s0>DIMEEV</s0>
</fA02>
<fA03 i2="1"><s0>Diabetic med.</s0>
</fA03>
<fA05><s2>28</s2>
</fA05>
<fA06><s2>4</s2>
</fA06>
<fA08 i1="01" i2="1" l="ENG"><s1>Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study</s1>
</fA08>
<fA11 i1="01" i2="1"><s1>CHEN (L.)</s1>
</fA11>
<fA11 i1="02" i2="1"><s1>MAGLIANO (D. J.)</s1>
</fA11>
<fA11 i1="03" i2="1"><s1>BALKAU (B.)</s1>
</fA11>
<fA11 i1="04" i2="1"><s1>WOLFE (R.)</s1>
</fA11>
<fA11 i1="05" i2="1"><s1>BROWN (L.)</s1>
</fA11>
<fA11 i1="06" i2="1"><s1>TONKIN (A. M.)</s1>
</fA11>
<fA11 i1="07" i2="1"><s1>ZIMMET (P. Z.)</s1>
</fA11>
<fA11 i1="08" i2="1"><s1>SHAW (J. E.)</s1>
</fA11>
<fA14 i1="01"><s1>Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University</s1>
<s2>Melbourne, Victoria</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>6 aut.</sZ>
</fA14>
<fA14 i1="02"><s1>Baker IDI Heart and Diabetes Institute</s1>
<s2>Melbourne, Vic</s2>
<s3>AUS</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
</fA14>
<fA14 i1="03"><s1>Epidemiology of diabetes, obesity and chronic kidney disease over the lifecourse, CESP Centre for Research in Epidemiology and Public Health, U1018 Inserm</s1>
<s2>Villejuif</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</fA14>
<fA14 i1="04"><s1>University Paris-Sud 11, UMRS 1018</s1>
<s2>Villejuif</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</fA14>
<fA14 i1="05"><s1>National Centre for Social and Economic Modelling, University of Canberra</s1>
<s2>Canberra, ACT</s2>
<s3>AUS</s3>
<sZ>5 aut.</sZ>
</fA14>
<fA20><s1>414-423</s1>
</fA20>
<fA21><s1>2011</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA43 i1="01"><s1>INIST</s1>
<s2>20274</s2>
<s5>354000194433410040</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2011 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>31 ref.</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>11-0155765</s0>
</fA47>
<fA60><s1>P</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Diabetic medicine</s0>
</fA64>
<fA66 i1="01"><s0>GBR</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>Aims To evaluate how to most efficiently screen populations to detect people at high risk of incident Type 2 diabetes and those with prevalent, but undiagnosed, Type 2 diabetes. Methods Data from 5814 adults in the Australian Diabetes, Obesity and Lifestyle study were used to examine four different types of screening strategies. The strategies incorporated various combinations of cut-points of fasting plasma glucose, the non- invasive Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK1) and a modified version of the tool incorporating fasting plasma glucose (AUSDRISK2). Sensitivity, specificity, positive predictive value, screening costs per case of incident or prevalent undiagnosed diabetes identified and intervention costs per case of diabetes prevented or reverted were compared. Results Of the four strategies that maximized sensitivity and specificity, use of the non-invasive AUSDRISK1, followed by AUSDRISK2 in those found to be at increased risk on AUSDRISK1, had the highest sensitivity (80.3%; 95% confidence interval 76.6-84.1 %), specificity (78.1 %; 95% confidence interval 76.9-79.2%) and positive predictive value (22.3%; 95% confidence interval 20.2-24.4%) for identifying people with either prevalent undiagnosed diabetes or future incident diabetes. It required the fewest people (24.1 %; 95% confidence interval 23.0-25.2%) to enter lifestyle modification programmes, and also had the lowest intervention costs and combined costs of running screening and intervention programmes per case of diabetes prevented or reverted. Conclusions Using a self-assessed diabetes risk score as an initial screening step, followed by a second risk score incorporating fasting plasma glucose, would maximize efficiency of identifying people with undiagnosed Type 2 diabetes and those at high risk of future diabetes.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>002A16E</s0>
</fC02>
<fC02 i1="02" i2="X"><s0>002A28</s0>
</fC02>
<fC02 i1="03" i2="X"><s0>002B21E01A</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Diabète de type 2</s0>
<s2>NM</s2>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Type 2 diabetes</s0>
<s2>NM</s2>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Diabetes de tipo 2</s0>
<s2>NM</s2>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Efficacité</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Efficiency</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Eficacia</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Analyse coût efficacité</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Cost efficiency analysis</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Análisis costo eficacia</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Obésité</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Obesity</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Obesidad</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Economie santé</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Health economy</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Economía salud</s0>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Dépistage</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Medical screening</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Descubrimiento</s0>
<s5>06</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Australie</s0>
<s2>NG</s2>
<s5>08</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Australia</s0>
<s2>NG</s2>
<s5>08</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Australia</s0>
<s2>NG</s2>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Epidémiologie</s0>
<s5>09</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Epidemiology</s0>
<s5>09</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Epidemiología</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Mode de vie</s0>
<s5>11</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Life style</s0>
<s5>11</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Modo de vida</s0>
<s5>11</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Santé publique</s0>
<s5>12</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Public health</s0>
<s5>12</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Salud pública</s0>
<s5>12</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Coût</s0>
<s5>17</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Costs</s0>
<s5>17</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Coste</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Aspect économique</s0>
<s5>18</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Economic aspect</s0>
<s5>18</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Aspecto económico</s0>
<s5>18</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Prédiction</s0>
<s5>19</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Prediction</s0>
<s5>19</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Predicción</s0>
<s5>19</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Facteur prédictif</s0>
<s5>20</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Predictive factor</s0>
<s5>20</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Factor predictivo</s0>
<s5>20</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Facteur risque</s0>
<s5>21</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Risk factor</s0>
<s5>21</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Factor riesgo</s0>
<s5>21</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Risque</s0>
<s5>22</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Risk</s0>
<s5>22</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Riesgo</s0>
<s5>22</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Stratégie</s0>
<s5>23</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Strategy</s0>
<s5>23</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Estrategia</s0>
<s5>23</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Endocrinologie</s0>
<s5>24</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Endocrinology</s0>
<s5>24</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Endocrinología</s0>
<s5>24</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Etat nutritionnel</s0>
<s5>25</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Nutritional status</s0>
<s5>25</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Estado nutricional</s0>
<s5>25</s5>
</fC03>
<fC07 i1="01" i2="X" l="FRE"><s0>Océanie</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="X" l="ENG"><s0>Oceania</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="X" l="SPA"><s0>Oceania</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="02" i2="X" l="FRE"><s0>Endocrinopathie</s0>
<s5>37</s5>
</fC07>
<fC07 i1="02" i2="X" l="ENG"><s0>Endocrinopathy</s0>
<s5>37</s5>
</fC07>
<fC07 i1="02" i2="X" l="SPA"><s0>Endocrinopatía</s0>
<s5>37</s5>
</fC07>
<fC07 i1="03" i2="X" l="FRE"><s0>Trouble de la nutrition</s0>
<s5>38</s5>
</fC07>
<fC07 i1="03" i2="X" l="ENG"><s0>Nutrition disorder</s0>
<s5>38</s5>
</fC07>
<fC07 i1="03" i2="X" l="SPA"><s0>Trastorno nutricíon</s0>
<s5>38</s5>
</fC07>
<fN21><s1>101</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Wicri/Asie/explor/AustralieFrV1/Data/PascalFrancis/Curation

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 004017 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Curation/biblio.hfd -nk 004017 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Wicri/Asie
   |area=    AustralieFrV1
   |flux=    PascalFrancis
   |étape=   Curation
   |type=    RBID
   |clé=     Pascal:11-0155765
   |texte=   Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study
}}

This area was generated with Dilib version V0.6.33.
Data generation: Tue Dec 5 10:43:12 2017. Site generation: Tue Mar 5 14:07:20 2024

	Serveur d'exploration sur les relations entre la France et l'Australie
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration sur les relations entre la France et l'Australie

Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study

Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri