Published 17 March 2009, doi:10.1136/bmj.b973
Cite this as: BMJ 2009;338:b973

Editorials

Screening for type 2 diabetes in primary care

The QDScore is a useful computer based screening tool

In the linked study (doi:10.1136/bmj.b880), Hippisley-Cox and colleagues present a new score that was developed and validated in a large prospective cohort to estimate the risk of acquiring type 2 diabetes over a 10 year period. This score is unique in that it considers social deprivation and ethnicity in a standardised algorithm.¹ The advantage of the QDScore is that it systematically uses electronically recorded medical data available in clinical practice. The algorithm does not require laboratory tests and can be used in clinical settings and for self assessment.

Several risk prediction tools based on the risk factors for type 2 diabetes have been developed.² Measures of performance of a screening tool should consider accuracy, usefulness, acceptability, practicability, and cost.^{3 4} The QDScore was highly accurate—predicted risk and observed risk correlated well (97%). Moreover, the area under the receiver operating characteristics curve (ROC-AUC) of the QDScore—an important parameter for evaluating the accuracy and discriminatory value of a diagnostic test—exceeded 0.83. Some diabetes risk scores, such as the Finnish Diabetes risk score,⁵ perform better on this parameter, but differences in study designs and populations make it inappropriate to compare ROC-AUCs.

A strength of this study is that it prospectively analysed 11 million patients registered by 551 general practices. Two key factors affect the interpretation of the results—whether all people with diabetes were excluded at baseline and whether all cases were detected at the end of follow-up. As in normal clinical practice, both depend on the quality of diagnosis by the doctor, the documentation, and the healthcare environment.⁶ Some cases would have been undiagnosed, but this disadvantage was minimised by the high number of follow-up visits.

The QDScore could be implemented in primary care practices that use electronic medical records to identify patients with an increased risk of diabetes; this might lead to earlier diagnosis and intervention.

One factor, however, restricts the usefulness and acceptability of this score—computer access is essential. This may not be a problem in the developed world, because computers and internet access are widely available, but it may be difficult for people in most developing countries.

Hippisley-Cox and colleagues assessed the risk of diabetes in various ethnic groups living in England and Wales. This is an important and necessary new approach for predicting the risk of diabetes considering the degree of migration in the Western world and the global challenge of the diabetes epidemic.⁷ Recent evidence indicates that migration to a new society may increase the risk for diabetes and depression.⁸ Furthermore, Chinese and South Asian immigrants now living in the UK will have different lifestyles to their relatives still living at home. Therefore, in addition to genetic influences, social stress and lifestyle change probably contributed to the striking differences in the QDScore seen between ethnic groups in this study.

Incorporation of the QDScore into practice computer programmes would not increase doctors’ daily workload, but it would be useful because doctors will not always know the complete medical history of their patients and will not identify all people at increased risk of diabetes. However, accurate and standardised risk stratification is a challenge, and follow-up studies are needed to assess the success of the QDScore.

Authorities such as the European Association for the Study of Diabetes (EASD), the European Society of Cardiology (ESC), and the International Diabetes Federation (IDF) consensus group, as well as two large European funded projects—DE-PLAN⁹ and IMAGE¹⁰—have recommended the use of a risk prediction algorithm in primary care in Europe. The QDScore will be a useful tool to help achieve these goals.

Cite this as: BMJ 2009;338:b973

¹ Department of Medicine III, Medical Faculty Carl Gustav Carus of the Technical University Dresden, 01307 Dresden, Germany

Research, doi:10.1136/bmj.b880

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Competing interests: None declared.

Provenance and peer review: Commissioned; not externally peer reviewed.

References

1. Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 2009;338:b880.[Abstract/Free Full Text]
2. Schwarz PE, Li J, Lindstrom J, Tuomilehto J. Tools for predicting the risk of type 2 diabetes in daily practice. Horm Metab Res 2009;41:86-97.[CrossRef][Medline]
3. WHO. Screening for type 2 diabetes: report of a world health organization and international diabetes federation meeting. 2003. www.who.int/diabetes/publications/en/screening_mnc03.pdf.
4. Gillies CL, Lambert PC, Abrams KR, Sutton AJ, Cooper NJ, Hsu RT, et al. Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis. BMJ 2008;336:1180-5.[Abstract/Free Full Text]
5. Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 2003;26:725-31.[Abstract/Free Full Text]
6. Rothe U, Muller G, Schwarz PE, Seifert M, Kunath H, Koch R, et al. Evaluation of a diabetes management system based on practice guidelines, integrated care and continuous quality management in a federal state of Germany: a population-based approach to health care research. Diabetes Care 2008;31:863-8.[Abstract/Free Full Text]
7. Schwarz PE, Reimann M, Li J, Bergmann A, Licinio J, Wong ML, et al. The metabolic syndrome—a global challenge for prevention. Horm Metab Res 2007;39:777-80.[CrossRef][Web of Science][Medline]
8. Bornstein SR, Schuppenies A, Wong ML, Licinio J. Approaching the shared biology of obesity and depression: the stress axis as the locus of gene-environment interactions. Mol Psychiatry 2006;11:892-902.[CrossRef][Web of Science][Medline]
9. Schwarz PE, Lindstrom J, Kissimova-Scarbeck K, Szybinski Z, Barengo NC, Peltonen M, et al. The European perspective of type 2 diabetes prevention: diabetes in Europe—prevention using lifestyle, physical activity and nutritional intervention (DE-PLAN) project. Exp Clin Endocrinol Diabetes 2008;116:167-72.[CrossRef][Web of Science][Medline]
10. Schwarz PE, Muylle F, Valensi P, Hall M. The European perspective of diabetes prevention. Horm Metab Res 2008;40:511-4.[CrossRef][Web of Science][Medline]

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