MP21-06 ONE SIZE DOES NOT FIT ALL: VARIATIONS BY ETHNICITY IN DEMOGRAPHIC CHARACTERISTICS OF MEN SEEKING FERTILITY TREATMENT ACROSS NORTH AMERICA
Bibliographic record
Abstract
You have accessJournal of UrologyInfertility: Epidemiology & Evaluation I (MP21)1 Sep 2021MP21-06 ONE SIZE DOES NOT FIT ALL: VARIATIONS BY ETHNICITY IN DEMOGRAPHIC CHARACTERISTICS OF MEN SEEKING FERTILITY TREATMENT ACROSS NORTH AMERICA Andrew Chen, Keith Jarvi, Katherine Lajkosz, James Smith, Kirk Lo, Ethan Grober, Jared Bieniek, Robert Brannigan, Victor Chow, Trustin Domes, James Dupree, Marc Goldstein, Jason Hedges, James Hotaling, Edmund Ko, Peter Kolettis, Ajay Nangia, Jay Sandlow, David Shin, Aaron Spitz, J Trussell, Scott Zeitlin, Armand Zini, and Mary Samplaski Andrew ChenAndrew Chen More articles by this author , Keith JarviKeith Jarvi More articles by this author , Katherine LajkoszKatherine Lajkosz More articles by this author , James SmithJames Smith More articles by this author , Kirk LoKirk Lo More articles by this author , Ethan GroberEthan Grober More articles by this author , Jared BieniekJared Bieniek More articles by this author , Robert BranniganRobert Brannigan More articles by this author , Victor ChowVictor Chow More articles by this author , Trustin DomesTrustin Domes More articles by this author , James DupreeJames Dupree More articles by this author , Marc GoldsteinMarc Goldstein More articles by this author , Jason HedgesJason Hedges More articles by this author , James HotalingJames Hotaling More articles by this author , Edmund KoEdmund Ko More articles by this author , Peter KolettisPeter Kolettis More articles by this author , Ajay NangiaAjay Nangia More articles by this author , Jay SandlowJay Sandlow More articles by this author , David ShinDavid Shin More articles by this author , Aaron SpitzAaron Spitz More articles by this author , J TrussellJ Trussell More articles by this author , Scott ZeitlinScott Zeitlin More articles by this author , Armand ZiniArmand Zini More articles by this author , and Mary SamplaskiMary Samplaski More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002006.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: While over 50% of infertility cases have a male component, there is scant data on what factors are associated with males seeking fertility evaluation. We aim to evaluate the impact of race and ethnicity on male reproductive history and care. METHODS: Anonymous patient surveys were collected at 22 North American fertility centers from 01/2014-01/2020. Domains included demographics, fertility history, and fertility-related medical, social, and procedure history. Patients were grouped by race, with differences in categorical and continuous outcomes assessed using Fisher’s Exact test and Mann-Whitney U test. RESULTS: A total of 6462 men were surveyed, of which 3320 (51%) were White, 1302 (20%) Asian/Indo-Canadian/Indo-American, 392 (6%) Black, 67 (1%) Indian/Native, 8 (0%) Native Hawaiian/Other Pacific Islander, 1373 (21%) identified as “other”, and 122 (1.9%) did not respond. White males were more likely to seek male factor evaluation sooner (3.5 vs 3.8 years, p<0.001), have older partners (33.3 vs 32.9 years, p=0.008), use exogenous testosterone (1.3% vs 0.6%, p<0.003), use steroids (1.3% vs 0.6%, p=0.007) and to have had a vasectomy (8.4% vs 2.9%, p<0.001) as compared to other races. Black males were more likely to be older than other races (38.0 vs 36.5 years, p<0.001), seek male factor evaluation later (4.8 vs 3.6±4.4 years, p<0.001), less likely to have had a vasectomy (3.3% vs 5.9%, p=0.033), less likely to have partners that underwent intrauterine insemination (IUI) (8.2% vs 12.6%, p=0.009). Asian/Indo-Canadian/Indo-American patients were more likely to be younger (36.1 vs 36.7 years, p=0.012), with younger partners (32.8 vs 33.2 years, p=0.021), less likely to have had a vasectomy (1.2% vs 6.9%, p<0.001), and more likely to have had partners that underwent IUI or in vitro fertilization (IVF) (14.2% vs 11.9%, p=0.024 and 8.0% vs 6.3%, p=0.021, respectively). Native/Indians were more likely to wait longer before pursuing evaluation (5.1 vs 3.6 years, p=0.035) and more likely to have had a vasectomy (13.4% vs 5.7%, p=0.014). CONCLUSIONS: This is the first data looking at racial differences for males undergoing male fertility evaluation by a reproductive urologist. Racial differences exist, and a better recognition and understanding of these, in conjunction with societal and biologic factors can guide personalized care. This is an opportunity to better understand and address disparities in access to fertility care. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e348-e348 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Andrew Chen More articles by this author Keith Jarvi More articles by this author Katherine Lajkosz More articles by this author James Smith More articles by this author Kirk Lo More articles by this author Ethan Grober More articles by this author Jared Bieniek More articles by this author Robert Brannigan More articles by this author Victor Chow More articles by this author Trustin Domes More articles by this author James Dupree More articles by this author Marc Goldstein More articles by this author Jason Hedges More articles by this author James Hotaling More articles by this author Edmund Ko More articles by this author Peter Kolettis More articles by this author Ajay Nangia More articles by this author Jay Sandlow More articles by this author David Shin More articles by this author Aaron Spitz More articles by this author J Trussell More articles by this author Scott Zeitlin More articles by this author Armand Zini More articles by this author Mary Samplaski More articles by this author Expand All Advertisement Loading ...
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.024 |
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".