Socioeconomic and environmental determinants of asthma prevalence: a cross-sectional study at the U.S. County level using geographically weighted random forests

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  CAS  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

Beuther DA. Recent insight into obesity and asthma. Curr Opin Pulm Med. 2010;16:64–70. https://doi.org/10.1097/MCP.0b013e3283338fa7.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

Singh J. Centers for disease control and prevention. Indian J Pharmacol. 2004;36:268–9. https://doi.org/10.1097/jom.0000000000001045.

Article  Google Scholar 

Pavlov YL. Random forests. Berlin: Springer; 2019. https://doi.org/10.4324/9781003109396-5.

Book  Google Scholar 

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.

Article  Google Scholar 

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.

Article  CAS  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  CAS  Google Scholar 

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.

Article  Google Scholar 

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.

Article 

留言 (0)

沒有登入
gif