Evaluating the Effect of Ethnicity on the Risk of Venous Thromboembolism (VTE): A Systematic Review
Bibliographic record
Abstract
Abstract Background Studies of hospital discharge data and large observational cohorts show that the incidence of venous thromboembolism (VTE) varies by race. We sought to determine the incidence of VTE in each of the following ethnic groups Caucasians, Africans, Asians, and Hispanics. Methods A systematic literature search strategy was used to identify potential studies in MEDLINE, EMBASE, and CENTRAL using an OVID interface. The methodological quality of eligible studies was assessed according to Newcastle-Ottawa Quality Assessment Scale. The primary outcome measure was to identify the incidence/prevalence of VTE of each ethnic group available for the study in the context of a population based study. Results Out of 3418 potential studies, 19 met our inclusion criteria (Table). Of these, 9 studies were population based studies: 5 reported VTE incidence per 100,000 person-years (PY), 2 measured the standardized incidence ratio, and 2 European studies assessed VTE rate according to country of origin but not ethnicity (data not shown). In addition, 7 studies measured the hospital incidence rate of VTE, and 3 assessed VTE prevalence. Data could not be pooled due to marked heterogeneity including varied periods of study. VTE incidence per 100,000 PY was found to be between 162 and 439 in Caucasians, 143 and 746 in Africans, 3.2 and 16.6 in Asians. Hospital incidence of VTE per 100,000 PY was found to be between 21 and 131 in Caucasians, 22 and 155 in Africans, 2 and 26 in Asians, 33.1 and 71 in American Indians/Alaskan Indians, and 9 in Hispanics. Conclusions Our findings suggest a wide variation in reported incidence rates for VTE among different ethnic groups, even within the same group. In general, studies in Asian populations suggest a lower incidence of VTE. A marked heterogeneity of study designs, population settings prevent drawing firm conclusions. A significant risk of bias cannot be excluded for several studies. Further studies assessing concurrently the risk of VTE among different ethnicities in the same geographical area are needed. Table 1. Summary of the included studies. Study Country Time Period f/u duration Sample size VTE incidence /Prevalence (95% CI) Zakai, 2014 US 439,090 person year 51,149 Zakai: ARIC US 1987-1996 15,792 (I) Cauc.: 166 (1.48-1.86) Afr.: 259 (2.21-3.03) Zakai: CHS US 1989-1997 5,888 (I) Cauc.: 439 (3.13-6.16) Afr.: 746 (4.68-11.90) Zakai: REGARDS, Southeast area US 2003-2007 30,239 (I) Cauc.: 162 (1.26-2.07) Afr.: 224 (1.72-2.92) Zakai: REGARDS, Rest of US US 2003-2007 30,239 (I) Cauc.: 205 (1.58-2.66) Afr.: 143 (1.02-2.00) Deitelzweig, 2010 US 2002 46,652 (P) per 100,000 persons Cauc.: M 457, F NA Afr.: M 584, F NA Hisp.: M 94, F 93 Others: M 329, F 345 2005 46,652 (P) per 100,000 persons Cauc.: M 643, F 446 Afr.: M 784, F 444 Hisp.: M 149, F 154 Others: M 285, F 297 DeMonaco, 2008 US 1997 37,892 (HI) Cauc.: 44 Afr.: 53 2001 37,892 (HI) Cauc.: 56 Afr.: 60 Heit, 2010 US 2003-2009 2,397 (P) N(%) Cauc.: Total= 1381 (69) Afr.: Total=272 (69) N(%) Cauc.: VTE = 551 (27) Afr.: VTE = 75 (19) Hooper, 2003 US 1980-1996 (HI) American Indians/Alaskan Indians: 33.1 Jang, 2011 Korea 2004 (I) Asians: VTE: 8.83, DVT: 3.91, PE: 3.74 2008 (I) Asians: VTE: 13.8, DVT: 5.31 , PE: 7.01 Kitamukai, 2003 Japan 2000 (I) Asians: 3.2 (29.2-33.9) Klatsky, 2000 US 1978-1994 1,822,302 total person years 128,934 (HI) Cauc.: 21 Afr.: 22 Asians: 2 Hisp.: 9 Others (unspecified): 15 Lee, 2010 Taiwan 2001 & 2002 11,566 person year Crude Incidence per 100,000 person year: Asians: VTE=15.9 Liu, 2002 China 1997-2000 (I) Asians: 16.6 Sakuma, 2009 Japan 2006 (I) Asians: PE= 6.19 (51.7- 72.1) DVT= 11.5 (98.2-132.9) Schneider, 2006 US 1998-2000 (HI) Cauc.: M 36.47 ; F 37.96 Afr.: M 53.64 ; F 61.53 Stein, Am J Med. 2004 US 1990-1999 (HI) Cauc.: VTE= 122 Afr.: VTE= 134 Asians: VTE= 23 Stein, Arch Intern Med. 2004 US 1996-2001 (HI) Cauc.: 131 Afr.: 155 American Indians and Alaskan Indians: 71 Tan, 2007 Singa-pore 1998-2001 271 pairs of cases and controls (P) N(%) Asians: 32.1% (29.0-35.3) White, 2005 US 1996 21,002 (SIR): Cauc.: 103 (101-105) Afr.: 138 (132-145) Asians: 29 (27-32) Hisp.: 61 (59-64) Others: 72 (65-80) White, 1998 US 1991-1994 17991 (SIR): Cauc.: 23 Afr.: 29.3 Asians: 6 Hisp.: 13.9 Abbreviations. f/u: follow up; CI: confidence interval; (I): incidence per 100,000 PY; (P): prevalence; (HI): Hospital Incidence per 100,000 PY; (SIR): Standardized Incidence Rate; M:male; F: female; N: total number. Cauc. Caucasians, Afr. Africans, Hisp. hispanics Disclosures Lazo-Langner: Pfizer: Honoraria; Bayer: Honoraria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".