Diagnostic accuracy
Diabetes prevalence and risk-score accuracy
Open access · cc by · source: Europe PMC
Hindustani Surinamese had ~26% diabetes prevalence; a clinical risk score showed moderate-to-good AUCs (~0.74–0.80) across ethnic groups.
Study at a glance
- Design
- Cross-sectional — SUNSET Amsterdam population sample; ethnicity-stratified screening criteria
- N
- N=1434 · 339 Hindustani Surinamese, 605 African Surinamese, 490 Dutch
- Population
- Amsterdam adults aged 35–60 (Hindustani Surinamese, African Surinamese, Dutch)
- Outcome
- Diabetes prevalence and risk-score AUC / numbers-needed-to-screen by ethnicity
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Prevalence was 25.6%, 12.7%, and 6.8% respectively. Risk-score AUCs were 0.74, 0.80, and 0.78 with NNS 3, 5, and 7.
Methodology
Cross-sectional population sample compared known/new diabetes prevalence among Hindustani Surinamese, African Surinamese, and Dutch adults and tested screening criteria including a risk score.
Limitations
Cross-sectional Amsterdam-based sample may not generalise globally; score cut-offs trade sensitivity vs workload.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
In GP care, CA125 looks accurate but a positive rarely means ovarian cancer when disease is rare.
In GP care, CA125 ≥35 U/ml for ovarian cancer showed sensitivity about 77%, specificity about 93.8%, and AUC about 0.92, but PPV was only about 10.1% because incidence was about 0.9%—illustrating prevalence’s grip on predictive value.
Scope note — related lesson — risk-score AUCs with number-needed-to-screen trade-offs
Limits the claim's scope: a different population, assay, or outcome.
Diabetes risk scores trade accuracy against how many people you must screen locally.
Diabetes risk scores in an Amsterdam sample showed AUCs about 0.74–0.80 with numbers-needed-to-screen of 3–7 depending on the outcome definition—accuracy metrics tied to local prevalence and cut-offs.
Evidence for the claim as stated.
Binary classification papers in this set use related logistic/ROC machinery for detection rather than for a single exposure AOR. Among 50,780 English primary-care CA125 tests, ovarian-cancer incidence was 0.9%; at ≥35 U/ml, sensitivity was 77%, specificity 93.8%, AUC 0.92, and PPV only 10.1%. In Amsterdam, diabetes prevalence was 25.6%, 12.7% and 6.8% across three ethnic groups, with risk-score AUCs 0.74, 0.80 and 0.78 and numbers needed to screen of 3, 5 and 7.
Evidence for the claim as stated.
Diagnostic papers optimise a cutoff, not an exposure odds ratio. CA125's PPV of 10.1% at a guideline threshold is a rare-disease problem; the diabetes score's NNS of 3–7 is a screening-workload problem that tracks prevalence, not AUC. Those operating-point numbers are not interchangeable with an immunization AOR of 3.10.
Evidence for the claim as stated.
A diabetes risk score can have similar AUCs across ethnic groups while the workload to find one case changes. In an Amsterdam sample, diabetes prevalence was 25.6%, 12.7% and 6.8% among Hindustani Surinamese, African Surinamese and Dutch adults; risk-score AUCs were 0.74, 0.80 and 0.78, with numbers needed to screen of 3, 5 and 7.
Evidence for the claim as stated.
The three papers optimise different operating points because prevalence and harm of error differ. CA125's PPV of 10.1% at a guideline cutoff is a primary-care problem of rare disease; the diabetes score's NNS of 3–7 is a screening-workload problem; StEP's 95%/93% pinprick figures come from a high-prevalence specialist sample where the target is pain subtype, not cancer. A cutoff that is 'accurate' in one setting is not portable to the others.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
Diagnostic papers optimise a cutoff, not an exposure odds ratio. CA125's PPV of 10.1% at a guideline threshold is a rare-disease problem; the diabetes score's NNS of 3–7 is a screening-workload problem that tracks prevalence, not AUC. Those operating-point numbers are not interchangeable with an immunization AOR of 3.10.
The three papers optimise different operating points because prevalence and harm of error differ. CA125's PPV of 10.1% at a guideline cutoff is a primary-care problem of rare disease; the diabetes score's NNS of 3–7 is a screening-workload problem; StEP's 95%/93% pinprick figures come from a high-prevalence specialist sample where the target is pain subtype, not cancer. A cutoff that is 'accurate' in one setting is not portable to the others.
- Supports · How well does CA125 find ovarian cancer in GP care?
- Supports · StEP pain-subtype assessment
History
When this study was placed
Dated entries from the concept change log — when this paper was added or removed as support, challenge, or qualifier on a claim.
Placed as a scope qualifier on Diagnostic Accuracy
In GP care, CA125 ≥35 U/ml for ovarian cancer showed sensitivity about 77%, specificity about 93.8%, and AUC about 0.92, but PPV was only about 10.1% because incidence was about 0.9%—illustrating prevalence’s grip on predictive value.
Placed as supporting evidence on Diagnostic Accuracy
Diabetes risk scores in an Amsterdam sample showed AUCs about 0.74–0.80 with numbers-needed-to-screen of 3–7 depending on the outcome definition—accuracy metrics tied to local prevalence and cut-offs.
Related papers in this topic
Same topic cluster — not a recommendation engine.