Prodromal clinical, demographic, and socio-ecological correlates of asthma in adults: a 10-year statewide big data multi-domain analysis

References

• Vos T, Allen C, Arora M, Barber RM, Bhutta ZA, Brown A, Carter A. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016;388:1545–1602. doi: 10.1016/S0140-6736(16)31678-6.
   PubMed Web of Science ®Google Scholar
• Nurmagambetov T, Kuwahara R, Garbe P. The economic burden of asthma in the United States, 2008-2013. Ann Am Thorac Soc. 2018;15:348–356. doi: 10.1513/AnnalsATS.201703-259OC.
   PubMed Web of Science ®Google Scholar
• Steppuhn H, Langen U, Keil T, Scheidt-Nave C. Chronic disease co-morbidity of asthma and unscheduled asthma care among adults: results of the national telephone health interview survey German Health Update (GEDA) 2009 and 2010. Prim Care Respir J. 2014;23:22–29. doi: 10.4104/pcrj.2013.00107.
   PubMedGoogle Scholar
• Sumino K, O’Brian K, Bartle B, Au DH, Castro M, Lee TA. Coexisting chronic conditions associated with mortality and morbidity in adult patients with asthma. J Asthma 2014;51:306–314. doi: 10.3109/02770903.2013.879881.
   PubMed Web of Science ®Google Scholar
• Howard R, Rattray M, Prosperi M, Custovic A. Distinguishing asthma phenotypes using machine learning approaches. Curr Allergy Asthma Rep. 2015;15:38. doi: 10.1007/s11882-015-0542-0.
   PubMed Web of Science ®Google Scholar
• Svenningsen S, Nair P. Asthma endotypes and an overview of targeted therapy for asthma. Front Med. 2017;4:158.
   Google Scholar
• Lötvall J, Akdis CA, Bacharier LB, Bjermer L, Casale TB, Custovic A, Lemanske Jr RF, Wardlaw AJ, Wenzel SE, Greenberger PA. Asthma endotypes: a new approach to classification of disease entities within the asthma syndrome. J Allergy Clin Immunol. 2011;127:355–360.
   PubMed Web of Science ®Google Scholar
• Leung JM, Sin DD. Asthma-COPD overlap syndrome: pathogenesis, clinical features, and therapeutic targets. BMJ 2017;358:j3772. http://www.bmj.com/lookup/doi/10.1136/bmj.j3772
   PubMedGoogle Scholar
• Postma DS, Rabe KF. The asthma–COPD overlap syndrome. N Engl J Med. 2015;373:1241–1249. doi: 10.1056/NEJMra1411863.
   PubMed Web of Science ®Google Scholar
• Löwhagen O. Diagnosis of asthma - new theories. J Asthma 2015;52:538–544. http://www.tandfonline.com/action/journalInformation?journalCode=ijas20http://informahealthcare.com/jas
   PubMed Web of Science ®Google Scholar
• Wildfire JJ, Gergen PJ, Sorkness CA, Mitchell HE, Calatroni A, Kattan M, Szefler SJ, Teach SJ, Bloomberg GR, Wood RA, Development and validation of the composite asthma severity index - an outcome measure for use in children and adolescents. J Allergy Clin Immunol. 2012;129:694–701. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3294274/pdf/nihms350008.pdf doi: 10.1016/j.jaci.2011.12.962.
   PubMed Web of Science ®Google Scholar
• Bateman ED, Buhl R, O'Byrne PM, Humbert M, Reddel HK, Sears MR, Jenkins C, Harrison TW, Quirce S, Peterson S, Eriksson G. Development and validation of a novel risk score for asthma exacerbations: the risk score for exacerbations. J Allergy Clin Immunol. 2015;135:1457–1464.e4. doi: 10.1016/j.jaci.2014.08.015.
   PubMed Web of Science ®Google Scholar
• Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, Himes BE, Levin AM, Mathias RA, Hancock DB, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43:887–892.
   PubMed Web of Science ®Google Scholar
• Zhu Z, Lee PH, Chaffin MD, Chung W, Loh P-R, Lu Q, Christiani DC, Liang L. A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat Genet. 2018;50:857–864. doi: 10.1038/s41588-018-0121-0.
   PubMed Web of Science ®Google Scholar
• Sinharoy A, Mitra S, Mondal P. Socioeconomic and environmental predictors of asthma-related mortality. J Environ Public Health 2018;2018:1–7. doi: 10.1155/2018/9389570.
   Web of Science ®Google Scholar
• Prosperi MC, Marinho S, Simpson A, Custovic A, Buchan IE. Predicting phenotypes of asthma and eczema with machine learning. BMC Med Genom. 2014;7:S7. doi: 10.1186/1755-8794-7-S1-S7.
   PubMed Web of Science ®Google Scholar
• U.S. Census Bureau. U.S. Census Bureau QuickFacts: Florida; United States. https://www.census.gov/quickfacts/fact/table/fl,US/PST045217
   Google Scholar
• Agency for Healthcare Research and Quality. HCUP-US Home Page [Internet]; HCUP. www.hcup-us.ahrq.gov/.
   Google Scholar
• Slee VN. The International Classification of Diseases: ninth revision (ICD-9). Ann Int Med. 1978;88:424–426. doi: 10.7326/0003-4819-88-3-424.
   PubMed Web of Science ®Google Scholar
• Cooke CR, Joo MJ, Anderson SM, Lee TA, Udris EM, Johnson E, Au DH. The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease. BMC Health Serv Res. 2011;11:11–37. doi: 10.1186/1472-6963-11-37.
   PubMed Web of Science ®Google Scholar
• Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi J-C, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43:1130–1139.
   PubMed Web of Science ®Google Scholar
• US Census Bureau. American Community Survey (ACS) [Internet]. Census.gov. https://www.census.gov/programs-surveys/acs/.
   Google Scholar
• Air data: air quality data collected at outdoor monitors across the US. EPA, Environmental Protection Agency. https://www.epa.gov/outdoor-air-quality-data.
   Google Scholar
• Honaker J, King G. What to do about missing values in time-series cross-section data. Am J Pol Sci 2010;54:561–581.
   Web of Science ®Google Scholar
• Li L, Zhang X, Xue M. Explaining information gain and information gain ratio in information theory. ICIC Express Lett. 2013;7:2385–2391.
   Google Scholar
• Rodriguez-Perez M, Gonzalez-Dominguez J, Mataran L, Garcia-Perez S, Salvatierra D. Association of HLA-DR5 with mucocutaneous lesions in patients with rheumatoid arthritis receiving gold sodium thiomalate. J Rheumatol. 1994;21:41–43.
   PubMed Web of Science ®Google Scholar
• Nembrini S, König IR, Wright MN. The revival of the Gini importance?. Bioinformatics 2018;34:3711–3718. doi: 10.1093/bioinformatics/bty373.
   PubMed Web of Science ®Google Scholar
• Nadeau C, Bengio Y. Inference for the generalization error. Mach Learn. 2003;52:239–281. doi: 10.1023/A:1024068626366.
   Web of Science ®Google Scholar
• R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria; 2017. https://www.R-project.org/
   Google Scholar
• Ghoorah K, De Soyza A, Kunadian V. Increased cardiovascular risk in patients with chronic obstructive pulmonary disease and the potential mechanisms linking the two conditions. A review. Cardiol Rev. 2013;21:196. doi: 10.1097/CRD.0b013e318279e907.
   PubMed Web of Science ®Google Scholar
• Bhatt SP, Dransfield MT. Chronic obstructive pulmonary disease and cardiovascular disease. Transl Res. 2013;162:237. doi: 10.1016/j.trsl.2013.05.001.
   PubMed Web of Science ®Google Scholar
• Ali Z, Hansen AV, Ulrik CS. Exacerbations of asthma during pregnancy: impact on pregnancy complications and outcome. J Obstet Gynaecol. 2016;36:455. doi: 10.3109/01443615.2015.1065800.
   PubMedGoogle Scholar
• Murphy VE, Schatz M. Asthma in pregnancy: a hit for two. Eur Respir Rev. 2014;23:64–68.
   PubMedGoogle Scholar
• Wang G, Murphy VE, Namazy J, Powell H, Schatz M, Chambers C, Attia J, Gibson PG. The risk of maternal and placental complications in pregnant women with asthma: a systematic review and meta-analysis. J Matern Neonatal Med. 2014;27:934–942.
   PubMed Web of Science ®Google Scholar
• NCEI. National Centers for Environmental Information [Internet]. 2015, pp. 1–5. https://www.ncei.noaa.gov/access.
   Google Scholar
• Lee Y, Bang H, Kim DJ. How to establish clinical prediction models. Endocrinol Metab. 2016;31:38. doi: 10.3803/EnM.2016.31.1.38.
   Google Scholar
• Goldstein BA, Navar AM, Pencina MJ, Ioannidis JPA. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Informatics Assoc. 2017;24:198–208.
   PubMed Web of Science ®Google Scholar
• Yang CL, To T, Foty RG, Stieb DM, Dell SD. Verifying a questionnaire diagnosis of asthma in children using health claims data. BMC Pulm Med 2011;11:52. doi: 10.1186/1471-2466-11-52.
   PubMed Web of Science ®Google Scholar
• Bui DS, Lodge CJ, Burgess JA, Lowe AJ, Perret J, Bui MQ, Bowatte G, Gurrin L, Johns DP, Thompson BR, et al. Childhood predictors of lung function trajectories and future COPD risk: a prospective cohort study from the first to the sixth decade of life. Lancet Respir Med 2018;6:535–544.
   PubMed Web of Science ®Google Scholar
• Owens L, Laing IA, Zhang G, Le Souëf PN. Infant lung function predicts asthma persistence and remission in young adults. Respirology 2017;22:289–294. doi: 10.1111/resp.12901.
   PubMed Web of Science ®Google Scholar
• Pennington AF, Strickland MJ, Klein M, Zhai X, Russell AG, Hansen C, Darrow LA. Measurement error in mobile source air pollution exposure estimates due to residential mobility during pregnancy. J Expo Sci Environ Epidemiol. 2017;27:513–520. doi: 10.1038/jes.2016.66.
   PubMed Web of Science ®Google Scholar