660 Prevalence of IBD Among Asian Subgroups in a Northern California Managed Care Organization
Bibliographic record
Abstract
INTRODUCTION: The prevalence of inflammatory bowel disease (IBD, ulcerative colitis (UC) and Crohn's disease (CD)) has been estimated to be approximately 400-600 per 100,000 persons in the United States, 3 but this relies on data skewed heavily towards Caucasian populations. Previous studies have suggested that IBD prevalence in Asia is much lower, around 30-60 per 100,000. 7 A recent study highlighted higher rates and more extensive UC in migrant groups than in their home countries. 5 However, the overall trend with IBD being more prevalent in Caucasians compared to Asians has been shown previously. One caveat to this is in South Asians, who have a higher incidence and prevalence of UC compared to the indigenous population of the UK. 5 In this study we seek to further breakdown the prevalence of IBD among Asian subgroups in a Northern California integrated health care delivery system. METHODS: Adults aged 18 years and older diagnosed with IBD between 1/1/2014-12/31/2014 were identified with ICD-9 diagnosis codes for UC (556.x) and CD (555.x). We calculated overall period prevalence of IBD, UC and CD by race/ethnicity, including Asian subtypes. RESULTS: Among 7,766 Kaiser Permanente Northern California (KPNC) members with IBD, the prevalence of IBD was higher in Caucasians compared to Asians (377 vs 125 per 100,000, P < 0.01 Table 1). However, when Asians were further subclassified, the period prevalence among South Asians was significantly higher than not only the other Asian subgroups, but also the Caucasian group (504 vs 377 per 100,000). Breaking up IBD into CD and UC, the period prevalence among Asians continued to be significantly lower than Caucasians (Table 2). However, among South Asians the prevalence of UC is again higher than that of Caucasians (417 vs 208 per 100,000). Interestingly, the prevalence of CD is less among South Asians compared to Caucasians (87 vs 169 per 100,000). CONCLUSION: Our data from a diverse patient population in Northern California show an overall lower prevalence of IBD in Asian compared to Caucasian patients overall but a higher prevalence among South Asians. Our data is consistent with prior Canadian and UK studies. 5 Most American studies combine South Asians into the Asian category, which shows a deceptively decreased prevalence compared to the Caucasian population. Further studies are needed to understand the etiology of this finding and seek to further explore the differences among Asian subpopulations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".