Standards of Care in Diabetes—2023 Abridged for Primary Care Providers . Clin Diabetes. 2023; 41:4–31. https://doi.org/10.2337/cd23-as01.
Centers for Disease Control, (CDC) P. Calculated Variables in the 2019 Data File of the Behavioral Risk Factor Surveillance System 2019.
Bacon SL, Bouchard A, Loucks EB, Lavoie KL. Individual-level socioeconomic status is associated with worse asthma morbidity in patients with asthma. Respir Res. 2009. https://doi.org/10.1186/1465-9921-10-125.
Article PubMed PubMed Central Google Scholar
Ram S, Zhang W, Williams M, Pengetnze Y. Predicting asthma-related emergency department visits using big data. IEEE J Biomed Heal Informatics. 2015;19:1216–23. https://doi.org/10.1109/JBHI.2015.2404829.
Baltrus P, Xu J, Immergluck L, Gaglioti A, Adesokan A, Rust G. Individual and county level predictors of asthma related emergency department visits among children on medicaid_ a multilevel approach. J Asthma. 2017;54:53–61. https://doi.org/10.1080/02770903.2016.1196367.
Ali AM, Gaglioti AH, Stone RH, Crawford ND, Dobbin KK, Guglani L, et al. Access and utilization of asthma medications among patients who receive care in federally qualified health centers. J Prim Care Community Heal. 2022. https://doi.org/10.1177/21501319221101202.
Grunwell JR, Opolka C, Mason C, Fitzpatrick AM. Geospatial analysis of social determinants of health identifies neighborhood hot spots associated with pediatric intensive care use for life-threatening asthma. J Allergy Clin Immunol Pract. 2022;10:981-991.e1. https://doi.org/10.1016/j.jaip.2021.10.065.
Tyris J, Gourishankar A, Ward MC, Kachroo N, Teach SJ, Parikh K. Social determinants of health and at-risk rates for pediatric asthma morbidity. Pediatrics. 2022. https://doi.org/10.1542/peds.2021-055570.
Litonjua AA, Carey VJ, Weiss ST, Gold DR. Race, socioeconomic factors, and area of residence are associated with asthma prevalence. Pediatr Pulmonol. 1999;28:394–401. https://doi.org/10.1002/(SICI)1099-0496(199912)28:6%3c394::AID-PPUL2%3e3.0.CO;2-6.
Article CAS PubMed Google Scholar
Chen TM, Gokhale J, Shofer S, Kuschner WG. Outdoor air pollution: nitrogen dioxide, sulfur dioxide, and carbon monoxide health effects. Am J Med Sci. 2007;333:249–56. https://doi.org/10.1097/MAJ.0b013e31803b900f.
Mukherjee AB, Zhang Z. Allergic asthma: influence of genetic and environmental factors. J Biol Chem. 2011;286:32883–9. https://doi.org/10.1074/jbc.R110.197046.
Article CAS PubMed PubMed Central Google Scholar
Hill-Briggs F, Adler NE, Berkowitz SA, Chin MH, Gary-Webb TL, Navas-Acien A, et al. Social determinants of health and diabetes: a scientific review. Diabetes Care. 2021;44:258–79. https://doi.org/10.2337/dci20-0053.
Southerland VA, Brauer M, Mohegh A, Hammer MS, van Donkelaar A, Martin RV, et al. Global urban temporal trends in fine particulate matter (PM2·5) and attributable health burdens: estimates from global datasets. Lancet Planet Heal. 2022;6:e139–46. https://doi.org/10.1016/S2542-5196(21)00350-8.
Roy D, Lyou ES, Kim J, Lee TK, Park J. Commuters health risk associated with particulate matter exposures in subway system – Globally. Build Environ. 2022;216:109036. https://doi.org/10.1016/j.buildenv.2022.109036.
Peirce AM, Espira LM, Larson PS. Climate change related catastrophic rainfall events and non-communicable respiratory disease: a systematic review of the literature. Climate. 2022;10:101. https://doi.org/10.3390/cli10070101.
Cong X, Xu X, Zhang Y, Wang Q, Xu L, Huo X. Temperature drop and the risk of asthma: a systematic review and meta-analysis. Environ Sci Pollut Res. 2017;24:22535–46. https://doi.org/10.1007/s11356-017-9914-4.
Razavi-Termeh SV, Sadeghi-Niaraki A, Choi SM. Asthma-prone areas modeling using a machine learning model. Sci Rep. 2021;11:1–16. https://doi.org/10.1038/s41598-021-81147-1.
Cluley S, Cochrane GM. Psychological disorder in asthma is associated with poor control and poor adherence to inhaled steroids. Respir Med. 2001;95:37–9. https://doi.org/10.1053/rmed.2000.0968.
Article CAS PubMed Google Scholar
Strine TW, Mokdad AH, Balluz LS, Berry JT, Gonzalez O. Impact of depression and anxiety on quality of life, health behaviors, and asthma control among adults in the United States with asthma, 2006. J Asthma. 2008;45:123–33. https://doi.org/10.1080/02770900701840238.
Van Lieshout RJ, Macueen G. Psychological factors in asthma. Allergy Asthma Clin Immunol. 2008. https://doi.org/10.1186/1710-1492-4-1-12.
Article PubMed PubMed Central Google Scholar
Toskala E, Kennedy DW. Asthma risk factors. Int Forum Allergy Rhinol. 2015;5:S11–6. https://doi.org/10.1002/alr.21557.
Article PubMed PubMed Central Google Scholar
Peters U, Dixon AE, Forno E. Obesity and asthma. J Allergy Clin Immunol. 2018;141:1169–79. https://doi.org/10.1016/j.jaci.2018.02.004.
Article PubMed PubMed Central Google Scholar
Putra IGNE, Astell-Burt T, Feng X. Caregiver perceptions of neighbourhood green space quality, heavy traffic conditions, and asthma symptoms: group-based trajectory modelling and multilevel longitudinal analysis of 9589 Australian children. Environ Res. 2022. https://doi.org/10.1016/j.envres.2022.113187.
Beuther DA. Recent insight into obesity and asthma. Curr Opin Pulm Med. 2010;16:64–70. https://doi.org/10.1097/MCP.0b013e3283338fa7.
Opolski M, Wilson I. Asthma and depression: a pragmatic review of the literature and recommendations for future research. Clin Pract Epidemiol Ment Heal. 2005;1:18. https://doi.org/10.1186/1745-0179-1-18.
Eichenberger PA, Diener SN, Kofmehl R, Spengler CM. Effects of exercise training on airway hyperreactivity in asthma: a systematic review and meta-analysis. Sport Med. 2013;43:1157–70. https://doi.org/10.1007/s40279-013-0077-2.
Holguin F, Bleecker ER, Busse WW, Calhoun WJ, Castro M, Erzurum SC, et al. Obesity and asthma: an association modified by age of asthma onset. J Allergy Clin Immunol. 2011;127:1486. https://doi.org/10.1016/j.jaci.2011.03.036.
Article PubMed PubMed Central Google Scholar
Wiemken TL, Kelley RR. Machine learning in epidemiology and health outcomes research. Annu Rev Public Health. 2019;41:21–36. https://doi.org/10.1146/annurev-publhealth-040119-094437.
Kino S, Hsu YT, Shiba K, Chien YS, Mita C, Kawachi I, et al. A scoping review on the use of machine learning in research on social determinants of health: trends and research prospects. SSM Popul Heal. 2021. https://doi.org/10.1016/j.ssmph.2021.100836.
Fernández-Delgado M, Cernadas E, Barro S, Amorim D. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15:3133–81. https://doi.org/10.5555/2627435.2697065.
Singh J. Centers for disease control and prevention. Indian J Pharmacol. 2004;36:268–9. https://doi.org/10.1097/jom.0000000000001045.
Pavlov YL. Random forests. Berlin: Springer; 2019. https://doi.org/10.4324/9781003109396-5.
Hastie T et. all. Springer Series in Statistics The Elements of Statistical Learning. Math Intell. 2009; 27:83–85.
Xu R, Nettleton D, Nordman DJ. Case-specific random forests. J Comput Graph Stat. 2016;25:49–65. https://doi.org/10.1080/10618600.2014.983641.
Brunekreef B, Stewart AW, Ross Anderson H, Lai CKW, Strachan DP, Pearce N. Self-reported truck traffic on the street of residence and symptoms of asthma and allergic disease: a global relationship in ISAAC phase 3. Environ Health Perspect. 2009;117:1791–8. https://doi.org/10.1289/ehp.0800467.
Article PubMed PubMed Central Google Scholar
Chowdhury S, Haines A, Klingmüller K, Kumar V, Pozzer A, Venkataraman C, et al. Global and national assessment of the incidence of asthma in children and adolescents from major sources of ambient NO2. Environ Res Lett. 2021;16:035020. https://doi.org/10.1088/1748-9326/abe909.
Grekousis G, Feng Z, Marakakis I, Lu Y, Wang R. Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: a geographical random forest approach. Heal Place. 2022;74:102744. https://doi.org/10.1016/j.healthplace.2022.102744.
Lotfata A, Georganos S, Kalogirou S, Helbich M. Ecological associations between obesity prevalence and neighborhood determinants using spatial machine learning in Chicago, Illinois, USA. ISPRS Int J Geo-Information. 2022;11:550. https://doi.org/10.3390/ijgi11110550.
Quiñones S, Goyal A, Ahmed ZU. Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA. Sci Rep. 2021;11:1–13. https://doi.org/10.1038/s41598-021-85381-5.
Bambra C, Riordan R, Ford J, Matthews F. The COVID-19 pandemic and health inequalities. J Epidemiol Community Heal. 2020;74:964–8. https://doi.org/10.1136/JECH-2020-214401.
Centers for Disease Control, (CDC) P. Calculated Variables in the 2019 Data File of the Behavioral Risk Factor Surveillance System. 2021.
Grant T, Croce E, Matsui EC. Asthma and the social determinants of health. Ann Allergy, Asthma Immunol. 2022;128:5–11. https://doi.org/10.1016/j.anai.2021.10.002.
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