Description of health patterns of fully vaccinated older adults hospitalized due to COVID-19 in Brazil through association rules

Thaís Barbosa de  Oliveira; Lorrany Santos  Rodrigues; Wendy Rayanne Fernandes dos  Santos; Maurício Yukio  Hirata; Caroliny Victoria dos Santos  Silva; Josmar  Mazucheli

doi:10.33448/rsd-v11i16.37666

Autores/as

Thaís Barbosa de Oliveira State University of Maringá https://orcid.org/0000-0002-8461-4186
Lorrany Santos Rodrigues University of Brasilia https://orcid.org/0000-0002-6560-1726
Wendy Rayanne Fernandes dos Santos Ministry of Health https://orcid.org/0000-0002-2204-2100
Maurício Yukio Hirata Oswaldo Cruz Foundation https://orcid.org/0000-0002-6826-9407
Caroliny Victoria dos Santos Silva University of Brasilia https://orcid.org/0000-0001-9714-3398
Josmar Mazucheli State University of Maringá https://orcid.org/0000-0001-6740-0445

DOI:

https://doi.org/10.33448/rsd-v11i16.37666

Palabras clave:

COVID-19; Síntomas; Enfermedad crónica; Anciano; Hospitalización; Minería de datos.

Resumen

La enfermedad por coronavirus 2019 (COVID-19) es un problema de salud pública mundial. Desde el inicio de la pandemia, notificada en marzo de 2020, Brasil ha mostrado alta letalidad por la enfermedad en adultos mayores. Del 2012 al 2018, el país mostró un incremento del 20% en la población de adultos mayores. A pesar de la exhaustividad de los protocolos de vacunación contra la COVID-19 en el país, existe evidencia de que este grupo etario, asociado a la presencia de comorbilidades, puede ser predictor de la ocurrencia de hospitalización y síntomas graves por la COVID-19. En esa dirección, este artículo tuvo como objetivo identifica los patrones y las relaciones entre los síntomas, las comorbilidades, el género, la admisión en la Unidad de Cuidados Intensivos (UCI) y el estado de supervivencia de los adultos mayores, completamente vacunados contra COVID-19, hospitalizados en Brasil. Para ello, realizamos minería de reglas de asociación en la base de datos OpenDataSUS. Para el grupo de pacientes con comorbilidad predominaron las asociaciones con condiciones de SpO₂<95%, disnea y muerte; El sexo femenino se asoció con la sobrevida y la presencia de comorbilidades, mientras que el sexo masculino con la muerte e ingreso a la UCI; para los pacientes ingresados en UTI y que fallecieron se encontraron asociaciones con SpO₂<95%, disnea, presencia de comorbilidades y uso de soporte ventilatorio. El procedimiento de minería de reglas de asociación se ha mostrado útil para relevar el perfil de hospitalización de estos pacientes.

Citas

Alimohamadi, Y., Sepandi, M., Taghdir, M., & Hosamirudsari, H. (2020). Determine the most common clinical symptoms in COVID-19 patients: a systematic review and meta-analysis. Journal of preventive medicine and hygiene, 61(3), 304–312. https://doi.org/10.15167/2421-4248/jpmh2020.61.3.1530.

Alsaffar, W. A., Alwesaibi, A. A., Alhaddad, M. J., Alsenan, Z. K., Alsheef, H. J., Alramadan, S. H., Aljassas, H. A., Alsaghirat, M. A., & Alzahrani, H. J. (2022). The Effectiveness of COVID-19 Vaccines in Improving the Outcomes of Hospitalized COVID-19 Patients. Cureus, 14(1), e21485. https://doi.org/10.7759/cureus.21485.

Andryukov, B. G., & Besednova, N. N. (2021). Older adults: panoramic view on the COVID-19 vaccination. AIMS public health, 8(3), 388–415. https://doi.org/10.3934/publichealth.2021030.

Assis, S. J. C., Lopes, J. M., Guedes, M., Sanchis, G., Araujo, D. N., & Roncalli, A. G. (2021). Primary health care and social isolation against COVID-19 in Northeastern Brazil: Ecological time-series study. PloS one, 16(5), e0250493. https://doi.org/10.1371/journal.pone.0250493.

Atkins, J. L., Masoli, J., Delgado, J., Pilling, L. C., Kuo, C. L., Kuchel, G. A., & Melzer, D. (2020). Preexisting Comorbidities Predicting COVID-19 and Mortality in the UK Biobank Community Cohort. The journals of gerontology. Series A, Biological sciences and medical sciences, 75(11), 2224–2230. https://doi.org/10.1093/gerona/glaa183.

Baqui, P., Bica, I., Marra, V., Ercole, A., & van Der Schaar, M. (2020a). Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study. The Lancet Global Health, 8(8), 1018-1026.

Baqui, P., Marra, V., Alaa, A. M., Bica, I., Ercole, A., & van der Schaar, M. (2021b). Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors. Scientific reports, 11(1), 15591. https://doi.org/10.1038/s41598-021-95004-8.

Bee, G. R., Pinto, D. D., da Silva, A. C. C. A., Oliveira, T., & Arrigo, J. da S. (2022). Vacinas contra COVID-19 disponíveis no Brasil / Vaccines against COVID-19 available in Brazil. Brazilian Journal of Development, 8(1), 6246–6263. https://doi.org/10.34117/bjdv8n1-422.

Brazil (2021). Coronavirus panel.

Candido, D. S., Claro, I. M., de Jesus, J. G., Souza, W. M., Moreira, F., Dellicour, S., Mellan, T. A., du Plessis, L., Pereira, R., Sales, F., Manuli, E. R., Thézé, J., Almeida, L., Menezes, M. T., Voloch, C. M., Fumagalli, M. J., Coletti, T. M., da Silva, C., Ramundo, M. S., Amorim, M. R., & Faria, N. R. (2020). Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science (New York, N.Y.), 369(6508), 1255–1260. https://doi.org/10.1126/science.abd2161.

Capuano, A., Rossi, F., & Paolisso, G. (2020). Covid-19 Kills More Men Than Women: An Overview of Possible Reasons. Frontiers in cardiovascular medicine, 7, 131. https://doi.org/10.3389/fcvm.2020.00131.

Cash, R., Patel, V. (2020). Has COVID-19 subverted global health? The Lancet, 395 (10238) 1687-1688.

Chang, R., Elhusseiny, K. M., Yeh, Y. C., & Sun, W. Z. (2021). COVID-19 ICU and mechanical ventilation patient characteristics and outcomes-A systematic review and meta-analysis. PloS one, 16(2), e0246318. https://doi.org/10.1371/journal.pone.0246318.

Connor, J., Madhavan, S., Mokashi, M., Amanuel, H., Johnson, N. R., Pace, L. E., & Bartz, D. (2020). Health risks and outcomes that disproportionately affect women during the Covid-19 pandemic: A review. Social science & medicine (1982), 266, 113364. https://doi.org/10.1016/j.socscimed.2020.113364.

Deng, Y., Liu, W., Liu, K., Fang, Y. Y., Shang, J., Zhou, L., Wang, K., Leng, F., Wei, S., Chen, L., & Liu, H. G. (2020). Clinical characteristics of fatal and recovered cases of coronavirus disease 2019 in Wuhan, China: a retrospective study. Chinese medical journal, 133(11), 1261–1267. https://doi.org/10.1097/CM9.0000000000000824.

Elghazaly, S., Abu, E. S. H., Samy, S., AbdelFatah, M., Hassany, M., Khader, Y., Afifi, S., & Eid, A. (2022). The Impact of Comorbidities on COVID-19 Severity and Mortality in Egypt iproc. Jmir Publications, 8(1):e36576. doi: 10.2196/36576.

FGV - Fundação Getúlio Vargas (2021). Older adults Maps.

Fu, L., Wang, B., Yuan, T., Chen, X., Ao, Y., Fitzpatrick, T., Li, P., Zhou, Y., Lin, Y. F., Duan, Q., Luo, G., Fan, S., Lu, Y., Feng, A., Zhan, Y., Liang, B., Cai, W., Zhang, L., Du, X., Li, L., & Zou, H. (2020). Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: A systematic review and meta-analysis. The Journal of infection, 80(6), 656–665. https://doi.org/10.1016/j.jinf.2020.03.041.

Hahsler, M., Buchta, C., Grun, B., & Hornik, K. (2022). Arules: Mining Association Rules and Frequent Itemsets, Package Version 1.7-3 of rproject.org.

Jacobs, L. G., Gourna Paleoudis, E., Lesky-Di Bari, D., Nyirenda, T., Friedman, T., Gupta, A., Rasouli, L., Zetkulic, M., Balani, B., Ogedegbe, C., Bawa, H., Berrol, L., Qureshi, N., & Aschner, J. L. (2020). Persistence of symptoms and quality of life at 35 days after hospitalization for COVID-19 infection. PloS one, 15(12), e0243882. https://doi.org/10.1371/journal.pone.0243882.

Jean-Marc, A. (2001). Data mining for association rules and sequential patterns: sequential and parallel algorithms. Springer Science & Business Media.

Jin, J. M., Bai, P., He, W., Wu, F., Liu, X. F., Han, D. M., Liu, S., & Yang, J. K. (2020). Gender Differences in Patients With COVID-19: Focus on Severity and Mortality. Frontiers in public health, 8, 152. https://doi.org/10.3389/fpubh.2020.00152.

Koenig, S. M., & Truwit, J. D. (2006). Ventilator-associated pneumonia: diagnosis, treatment, and prevention. Clinical microbiology reviews, 19(4), 637–657. https://doi.org/10.1128/CMR.00051-05.

Lipsky, M. S., & Hung, M. (2020). Men and COVID-19: A Pathophysiologic Review. American journal of men's health, 14(5), 1557988320954021. https://doi.org/10.1177/1557988320954021.

Lv, G., Yuan, J., Xiong, X., & Li, M. (2021). Mortality Rate and Characteristics of Deaths Following COVID-19 Vaccination. Frontiers in medicine, 8, 670370. https://doi.org/10.3389/fmed.2021.670370.

Mejía, F., Medina, C., Cornejo, E., Morello, E., Vásquez, S., Alave, J., Schwalb, A., & Málaga, G. (2020). Oxygen saturation as a predictor of mortality in hospitalized adult patients with COVID-19 in a public hospital in Lima, Peru. PloS one, 15(12), e0244171. https://doi.org/10.1371/journal.pone.0244171.

Mendes, E. V. (2018). The care of chronic conditions in primary health care. Revista Brasileira em Promoção da Saúde, 31 (2), 1-3.

Mi, J., Zhong, W., Huang, C., Zhang, W., Tan, L., & Ding, L. (2020). Gender, age and comorbidities as the main prognostic factors in patients with COVID-19 pneumonia. American journal of translational research, 12(10), 6537–6548.

Misra-Hebert, A. D., Hu, B., Pantalone, K. M., & Pfoh, E. R. (2021). Primary Care Health Care Use for Patients With Type 2 Diabetes During the COVID-19 Pandemic. Diabetes care, 44(9), 173–174. https://doi.org/10.2337/dc21-0853.

Moreno-Perez, O., Ribes, I., Boix, V., Martinez-García, M. Á., Otero-Rodriguez, S., Reus, S., Sánchez-Martínez, R., Ramos, J. M., Chico-Sánchez, P., Merino, E., & On behalf the COVID-19 ALC research group (2022). Hospitalized patients with breakthrough COVID-19: Clinical features and poor outcome predictors. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 118, 89–94. https://doi.org/10.1016/j.ijid.2022.02.007.

Nishiga, M., Wang, D. W., Han, Y., Lewis, D. B., & Wu, J. C. (2020). COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nature reviews. Cardiology, 17(9), 543–558. https://doi.org/10.1038/s41569-020-0413-9.

Pan American Health Organization. (2021). What is primary health care?

Passos, V. M. A., Champs, A., Teixeira, R., Lima-Costa, M., Kirkwood, R., Veras, R., Nascimento, B. R., Nogales, A. M., Schmidt, M. I., Duncan, B. B., Cousin, E., Naghavi, M., & Souza, F. M. (2020). The burden of disease among Brazilian older adults and the challenge for health policies: results of the Global Burden of Disease Study 2017. Population health metrics, 18(Suppl 1), 14. https://doi.org/10.1186/s12963-020-00206-3.

OpenDataSUS Platform by Federal Government (2022).

PrabhuDas, M., Fuldner, R., Farber, D., Kuchel, G.A., Mannick, J., Nikolich-Zugich, J., Sen, R., & Turner, J. (2021). Research and resource needs for understanding host immune responses to SARS-CoV-2 and COVID-19 vaccines during aging. Nat Aging 1, 1073–1077. https://doi.org/10.1038/s43587-021-00156-x.

Raimondi, F., Novelli, L., Ghirardi, A., Russo, F. M., Pellegrini, D., Biza, R., Trapasso, R., Giuliani, L., Anelli, M., Amoroso, M., Allegri, C., Imeri, G., Sanfilippo, C., Comandini, S., Hila, E., Manesso, L., Gandini, L., Mandelli, P., Monti, M., Gori, M., & HPG23 Covid-19 Study Group (2021). Covid-19 and gender: lower rate but same mortality of severe disease in women-an observational study. BMC pulmonary medicine, 21(1), 96. https://doi.org/10.1186/s12890-021-01455-0.

Rocha, R., Atun, R., Massuda, A., Rache, B., Spinola, P., Nunes, L., Lago, M., & Castro, M. C. (2021). Effect of socioeconomic inequalities and vulnerabilities on health-system preparedness and response to COVID-19 in Brazil: a comprehensive analysis. The Lancet. Global health, 9(6), 782–792. https://doi.org/10.1016/S2214-109X(21)00081-4.

Shawkat, M., Mahmoud, B., & Ali, I.E. (2021). "A Novel Approach of Frequent Itemsets Mining for Coronavirus Disease (COVID-19)." European Journal of Electrical Engineering and Computer Science, 5(2), 5-12.

Shin, D. P., Park, Y. J., & Seo, J. (2018). Association Rules Mined from Construction Accident Data. KSCE J Civ Eng, 22 (4), 1027–1039 (2018). https://doi.org/10.1007/s12205-017-0537-6.

Tandan, M., Acharya, Y., Pokharel, S., & Timilsina, M. (2021). Discovering symptom patterns of COVID-19 patients using association rule mining. Computers in biology and medicine, 131, 104249. https://doi.org/10.1016/j.compbiomed.2021.104249.

Vaccination Brazil Platform by Federal Government (2022).

WHO - World Health Organization (2021). Panel COVID-19 by WHO.

Williams, G. (2011). Data mining with Rattle and R: The art of excavating data for knowledge discovery. Springer Science & Business Media.

Zhu, J., Yan, W., Zhun, L., & Liu, J. (2021). COVID-19 pandemic in BRICS countries and its association with socio-economic and demographic characteristics, health vulnerability, resources, and policy response. Infect Dis Poverty, 10 (97).

Descripción de los patrones de salud de los adultos mayores hospitalizados por COVID-19 completamente vacunados en Brasil a través de las reglas de la asociación

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