AustralieFrV1, PascalFrancis, Checkpoint, bibRecord, 001905

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

Identifieur interne : 001905 ( PascalFrancis/Checkpoint ); précédent : 001904; suivant : 001906

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.

Affiliations:

Australie, France

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Le document en format XML

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<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>
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<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>
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<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>
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<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>
<affiliations><list><country><li>Australie</li>
<li>France</li>
</country>
</list>
<tree><country name="Australie"><noRegion><name sortKey="Chen, L" sort="Chen, L" uniqKey="Chen L" first="L." last="Chen">L. Chen</name>
</noRegion>
<name sortKey="Balkau, B" sort="Balkau, B" uniqKey="Balkau B" first="B." last="Balkau">B. Balkau</name>
<name sortKey="Brown, L" sort="Brown, L" uniqKey="Brown L" first="L." last="Brown">L. Brown</name>
<name sortKey="Chen, L" sort="Chen, L" uniqKey="Chen L" first="L." last="Chen">L. Chen</name>
<name sortKey="Magliano, D J" sort="Magliano, D J" uniqKey="Magliano D" first="D. J." last="Magliano">D. J. Magliano</name>
<name sortKey="Magliano, D J" sort="Magliano, D J" uniqKey="Magliano D" first="D. J." last="Magliano">D. J. Magliano</name>
<name sortKey="Shaw, J E" sort="Shaw, J E" uniqKey="Shaw J" first="J. E." last="Shaw">J. E. Shaw</name>
<name sortKey="Tonkin, A M" sort="Tonkin, A M" uniqKey="Tonkin A" first="A. M." last="Tonkin">A. M. Tonkin</name>
<name sortKey="Wolfe, R" sort="Wolfe, R" uniqKey="Wolfe R" first="R." last="Wolfe">R. Wolfe</name>
<name sortKey="Zimmet, P Z" sort="Zimmet, P Z" uniqKey="Zimmet P" first="P. Z." last="Zimmet">P. Z. Zimmet</name>
</country>
<country name="France"><noRegion><name sortKey="Balkau, B" sort="Balkau, B" uniqKey="Balkau B" first="B." last="Balkau">B. Balkau</name>
</noRegion>
<name sortKey="Balkau, B" sort="Balkau, B" uniqKey="Balkau B" first="B." last="Balkau">B. Balkau</name>
</country>
</tree>
</affiliations>
</record>

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

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