Covid-19 vaccination priorities defined on machine learning
DOI:
https://doi.org/10.11606/s1518-8787.2022056004045Keywords:
COVID-19 vaccines, supply & distribution, Immunization Programs, Health Priorities, Machine LearningAbstract
OBJECTIVE: Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS: We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS: Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS: The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers.
References
Instituto Brasileiro de Geografia e Estatística. Projeção da população do Brasil e das Unidades da Federação. Rio de Janeiro: IBGE; 2021 [cited 2021 May 28]. Available from: https://www.ibge.gov.br/apps/populacao/projecao/index.html
Ministério da Saúde (BR). COVID-19 -Painel coronavirus. Brasília, DF; 2021 [cited 2021 Sept 19]. Available from: https://covid.saude.gov.br
GAVI The Vehicle Alliance. Geneva (CH); 2021 [cited 2021 May 20]. Available from: https://www.gavi.org/vaccineswork?gclid=CjwKCAjwg4-EBhBwEiwAzYAlsmWCLaXZc2iUAPeOSYYoc_F5dHZzcylkzw_05Ux4ad5vZNyhRXBJexoCikYQAvD_BwE
The Duke Global Health Innovation Center. Launch and Scale Speedometer. Durham, CN; 2021 [cited 2021 May 20]. Available from: https://launchandscalefaster.org/covid-19/vaccineprocurement
International Federation of Pharmaceutical Manufactures & Associations. Geneva (CH): IFPMA; 2021 [cited 2021 May 20]. Available from: https://www.ifpma.org/wp-content/uploads/2021/03/Airfinity_global_summit_master_final.pdf
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York: Springer; 2008.
Santos HG, Nascimento AF, Izbicki R, Duarte YAO, Chiavegatto Filho ADP. Machine Learning para análises preditivas em saúde. Cad Saude Publica. 2019;35(7):e00050818. https://doi.org/10.1590/0102-311X00050818
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837-45.
Caruana R, Niculescu-Mizil A. Predicting good probabilities with supervised learning. In: ICML '05: proceedings of the 22. International Conference on Machine Learning; 2005 Aug 7-11; Bonn, Germany [cited 2021 May 10]. p. 625-32. Available from: https://doi.org/10.1145/1102351.1102430
Lundberg SM, Lee S. A unified approach to interpreting model predictions. In: 31. Conference on Neural Information Processing Systems (NIPS 2017); 2017 Dec 4-9; Long Beach, USA [cited 2021 May 10]. Available from: https://dl.acm.org/doi/pdf/10.5555/3295222.3295230
World Health Organization. WHO SAGE values framework for the allocation and prioritization of COVID-19 vaccination. Geneva (CH): WHO; 2020 [cited 2021 May 14]. Available from: https://apps.who.int/iris/bitstream/handle/10665/334299/WHO-2019-nCoV-SAGE_Framework-Allocation_and_prioritization-2020.1-eng.pdf?sequence=1&isAllowed=y
National Academies of Sciences, Engineering, and Medicine. Framework for equitable allocation of COVID-19 vaccine. Washington, DC: The National Academies Press; 2020 [cited 2021 May 14] Available from: https://doi.org/10.17226/25917
GOV.UK, Department of Health and Social Care, Joint Committee on Vaccination and Immunization: advice on priority groups for COVID-19 vaccination, 30-December-2020, updated 6 January 2021. London (UK); 2021 [cited 2021 May 14]. Available from: https://www.gov.uk/government/publications/priority-groups-for-coronavirus-covid-19-vaccination-advice-from-the-jcvi-30-december-2020
Ministério da Saúde (BR). 1ª edição - PNO - 16.12.2020.pdf. Brasília, DF; 2021 [cited 2021 May 14]. Available from: https://www.gov.br/saude/pt-br/composicao/secovid/pno_edicoes/1a-edicao-pno-16-12-2020.pdf/view
Tiwari A, Dadhania AV, Ragunathrao VAB, Oliveira ERA. Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI). Sci Total Environ. 2021;773:145650. https://doi.org/10.1016/j.scitotenv.2021.145650
Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KD; COVID-19 Research Consortium, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA. 2020;323(20):2052-9. htpps://doi:10.1001/jama.2020.6775
Wu Z, McGoogan JM. Characteristics of and important lessons from the Coronavirus Disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239-42. https://doi.org/10.1001/jama.2020.2648
Ranzani OT, Leonardo SLB, Gelli JGM, Marchesi JF, Baião F, Hamacher S, et al. Characterization of the first 250 000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. Lancet Respir Med. 2021;9(4):407-18. https://doi.org/10.1016/S2213-2600(20)30560-9
Zhou Y, Yang Q, Chi J, Dong B, Lv W, Shen L, et al. Comorbidities and the risk of severe or fatal outcomes associated with coronavirus disease 2019: a systematic review and meta-analysis. Int J Infect Dis. 2020;99:47-56. https://doi.org/10.1016/j.ijid.2020.07.029
Del Sole F, Farcomeni A, Loffredo L, Carnevale R, Munichelli D, Vicario T, et al. Features of severe COVID-19: a systematic review and meta-analysis. Eur J Clin Invest. 2020;50(10):e13378. https://doi.org/10.1111/eci.13378
Peckham H, Gruijter NM, Raine C, Radziszewska A, Ciurtin C, Wedderburn LR, et al. Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission. Nat Commun. 2020;11:6317. https://doi.org/10.1038/s41467-020-19741-6
Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis. 2020;94:91-5. https://doi.org/10.1016/j.ijid.2020.03.017
Fadini GP, Morieri ML, Boscari F, Fioretto P, Maran A, Busetto L, et al. Newly-diagnosed diabetes and admission hyperglycemia predict COVID-19 severity by aggravating respiratory deterioration. Diabetes Res Clin Pract. 2020;168:108374. https://doi.org/10.1016/j.diabres.2020.108374
Khan MMA, Khan MN, Mustagir MG, Rana J, Islam MS, Kabir MI, et al. Effects of underlying morbidities on the occurrence of deaths in COVID-19 patients: a systematic review and meta-analysis. J Glob Health. 2020;10(2):020503. https://doi.org/10.7189/jogh.10.020503
Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, et al. Risk factors of critical & mortal COVID-19 cases: a systematic literature review and meta-analysis. J Infect. 2020;81(2):e16-e25. https://doi.org/10.1016/j.jinf.2020.04.021
Matsushita K, Ding N, Kou M, Hu X, Chen M, Gao Y, et al. The relationship of COVID-19 severity with cardiovascular disease and its traditional risk factors: a systematic review and meta-analysis. Glob Heart. 2020;15(1):64. https://doi.org/10.5334/gh.814
Saini KS, Tagliamento M, Lambertini M, McNally R, Romano M, Leone M, et al. Mortality in patients with cancer and coronavirus disease 2019: a systematic review and pooled analysis of 52 studies. Eur J Cancer. 2020;139:43-50. https://doi.org/10.1016/j.ejca.2020.08.011
Gerwen M, Alsen M, Little C, Barlow J, Naymagon L, Tremblay D, et al. Outcomes of patients with hypothyroidism and COVID-19: a retrospective cohort study. Front Endocrinol (Lausanne). 2020;11:565. https://doi.org/10.3389/fendo.2020.00565
Brix TH, Hegedüs L, Hallas J, Lund LC. Risk and course of SARS-CoV-2 infection in patients treated for hypothyroidism and hyperthyroidism. Lancet Diabetes Endocrinol. 2021;9(4):197-9. https://doi.org/10.1016/S2213-8587(21)00028-0
Oh SM, Skendelas JP, Macdonald E, Bergamini M, Goel S, Choi J, et al. On-admission anemia predicts mortality in COVID-19 patients: a single center, retrospective cohort study. Am J Emerg Med. 2021;48:140-7. https://doi.org/10.1016/j.ajem.2021.03.083
Wang Q, Xu R, Volkow ND. Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States. World Psychiatry. 2021;20(1):124-30. https://doi.org/10.1002/wps.20806
Kates OS, Haydel BM, Florman SS, Rana MM, Chaudhry ZS, Ramesh MS, et al. COVID-19 in solid organ transplant: a multi-center cohort study. Clin Infect Dis. 2020 Aug 7:ciaa1097. https://doi.org/10.1093/cid/ciaa1097. Epub ahead of print.
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Copyright (c) 2022 Renato Camargos Couto, Tania Moreira Grillo Pedrosa, Luciana Moreira Seara, Carolina Seara Couto, Vitor Seara Couto, Karla Giacomin, Ana Claudia Couto de Abreu
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