Validity of Self-Reported Diabetes in a Cohort of Thai Adults
Bibliographic record
Abstract
BACKGROUND: Much of South East Asia is experiencing an epidemiological transition. In Thailand, chronic disease has emerged and the prevalence of diabetes has tripled. As part of a large cohort study of the Thai transition to chronic disease, we gathered data on self-reported diabetes. Epidemiological studies commonly ascertain such data by self-report but the validity of this method has not been assessed in Thailand. Therefore, we aimed to investigate the validity of self-reported type 2 diabetes (T2DM) in Thai adults participating in the Thai Cohort Study (TCS).METHODS: Data were collected by mailed questionnaire from adults involved in the TCS, a nationwide community-based longitudinal health study of distance learning adult students enrolled at Sukhothai Thammathirat Open University. Participants were surveyed in 2005, 2009 and 2013. We sampled all participants with self-reported T2DM status (878 cases) for telephone interview with our study physician along with a random selection of 650 participants who self-reported not having diabetes in all three TCS surveys. These physician telephone interviews allowed us to validate self-reported questionnaire responses.RESULTS: Questionnaire self-report of diabetes slightly over-estimated the incidence of T2DM in this cohort; the overall proportion of confirmed T2DM cases was 78%. Participants with a consistent pattern of diabetes reporting at the 2009 and 2013 questionnaire follow-ups had the highest validity of self-reported responses (96%; 95%CI 92.9-99.1).The lowest proportion of confirmed T2DM cases was recorded among participants who reported diabetes in 2009 and not in 2013 (32%)(95%CI 22.6-41.4), mostly young women with transient (gestational) diabetes.CONCLUSIONS: Our results, derived mainly from young, educated Thai adults nationwide, show that self-reported doctor diagnosed T2DM is a feasible and acceptable method for assessing diabetes in epidemiological studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".