The Hidden Asthma Epidemic in Immigrant Subpopulations
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Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsLaura CorlinLaura Corlin is an environmental health student in the Department of Civil and Environmental Engineering at Tufts University School of Engineering in Boston, MA. Her research and advocacy focus on characterizing and mitigating health disparities.Doug BruggeDoug Brugge is a professor in the Department of Public Health and Community Medicine at Tufts University School of Medicine in Boston, MA. He became interested in the causes of asthma when his students noticed that foreign-born Chinese immigrant children were less likely to have asthma than U.S.-born children Chinese children.Notes1. Centers for Disease Control and Prevention, “Asthma—BRFSS 201—Table L1 Adult Self-Reported Lifetime Asthma Prevalence Rate (Percent) and Prevalence (Number) by State or Territory,” 2013, http://www.cdc.gov/asthma/brfss/2011/tablel1.htm (accessed September 21, 2013).2. K. B. Weiss, P. J. Gergen, and T. A. Hodgson, “An Economic Evaluation of Asthma in the United States,” New England Journal of Medicine 326 (1992): 862–66. doi:10.1056/NEJM199203263261304; H. Rappaport, “The Direct Expenditures and Indirect Costs Associated With Treating Asthma in the United States,” Journal of Allergy Therapy 03 (2012). doi:10.4172/2155-6121.1000118; S. B. L. Barnett and T. A. Nurmagambetov, “Costs of Asthma in the United States: 2002–2007,” Journal of Allergy and Clinical Immunology 127 (2011): 145–52. doi:10.1016/j.jaci.2010.10.020; Inflation Calculator: Bureau of Labor Statistics, n.d., http://www.bls.gov/data/inflation_calculator.htm (accessed September 21, 2013).3. S. L. Bacon, A. Bouchard, E. B. Loucks, and K. L. Lavoie, “Individual-Level Socioeconomic Status Is Associated With Worse Asthma Morbidity in Patients With Asthma,” Respiratory Research 10 (2009): 1–8. doi:10.1186/1465-9921-10-125; E. D. Boudreaux, S. D. Emond, S. Clark, and J. Camargo, “Acute Asthma Among Adults Presenting to the Emergency Department: The Role of Race/Ethnicity and Socioeconomic Status,” Chest Journal 124 (2003): 803–12. doi:10.1378/chest.124.3.803; A. Farfel, A. Tirosh, E. Derazne, B. Z. Garty, and A. Afek, “Association Between Socioeconomic Status and the Prevalence of Asthma,” Annals of Allergy, Asthma, and Immunology 104 (2010): 490–95. doi:10.1016/j.anai.2010.03.019; H.-Z. Law, E. Oraka, and D. M. Mannino, “The Role of Income in Reducing Racial and Ethnic Disparities in Emergency Room and Urgent Care Center Visits for Asthma—United States, 2001–2009,” Journal of Asthma 48 (2011): 405–13. doi:10.3109/02770903.2011.565849; V. W. Persky et al., “Relationships of Race and Socioeconomic Status with Prevalence, Severity, and Symptoms of Asthma in Chicago School Children,” Annals of Allergy, Asthma, and Immunology 81 (1998), 266–271. doi:10.1016/S1081-1206(10)62824-4.4. J. Northridge, O. F. Ramirez, and J. A. 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Sporik et al., “Mite, Cat, and Cockroach Exposure, Allergen Sensitisation, and Asthma in Children: A Case-Control Study of Three Schools,” Thorax 54 (1999): 675–80. doi:10.1136/thx.54.8.6758. J. J. K. Jaakkola, B.-F. Hwang, and N. Jaakkola, “Home Dampness and Molds, Parental Atopy, and Asthma in Childhood: A Six-Year Population-Based Cohort Study,” Environmental Health Perspectives 113 (2005): 357–61. doi:10.1289/ehp.72429. J.-P. Zock, D. Jarvis, C., Luczynska, J. Sunyer, and P. Burney, “Housing Characteristics, Reported Mold Exposure, and Asthma in the European Community Respiratory Health Survey,” Journal of Allergy and Clinical Immunology 110 (2002): 285–92. doi:10.1067/mai.2002.12638310. J. E. Clougherty, J. I. Levy, H. P. Hynes, and J. D. Spengler, “A Longitudinal Analysis of the Efficacy of Environmental Interventions on Asthma-Related Quality of Life and Symptoms Among Children in Urban Public Housing,” Journal of Asthma 43 (2006): 335–43. doi:10.1080/02770900600701408; J. I. 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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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it