The effects of Covid-19 on the indigenous population of Mexico. A Bayesian spatio-temporal analysis
DOI:
https://doi.org/10.18800/kawsaypacha.202401.A006Keywords:
Covid-19, Incidence Rates, INLA, Indigenous Population, Bayesian Hierarchical Model, Spatio-Temporal Analysis, MexicoAbstract
The aim of this work is to analyze the impact of Covid-19 on indigenous populations in municipalities of Mexico. To analyze this relationship, Bayesian spatio-temporal models are used to capture the complex dynamics of epidemiological transmission in terms of spatial, temporal and joint spatio-temporal dependence. These models have the ability to include covariates, such as the percentage of indigenous population, which makes it possible to quantify the effect that the covariate has on the evolution of the epidemic. Likewise, the models allow us to identify spatio-temporal clusters with high and low incidence rates, showing health inequalities based on the proportion of the indigenous population residing in specific municipalities. Contrary to expectations, the results showed a protective effect on the incidence rate of COVID-19 for the indigenous population. Furthermore, a wide heterogeneity was observed in the distribution of COVID-19 incidence rates by municipality, with significant fluctuations over time. The incidence rates of COVID-19 in indigenous populations were low, which may be due to the fact that the indigenous population predominates in municipalities with low population density, less access to health services, and greater social marginalization. However, it is important to interpret these results with caution due to the high level of observed underreporting of COVID-19 cases found in indigenous populations.
Downloads
References
Alm, E.; Broberg, E. K.; Connor, T.; Hodcroft, E. B.; Komissarov, A. B.; Maurer-Stroh, S.; Melidou, A.; Neher, R. A. & O’Toole. Á., Pereyaslov, D. (2020). Geographical and temporal distribution of SARS-CoV-2 clades y the WHO European region, January to June 2020. Euro Surbveill, 25 (32). DOI: 10.2807/1560-7917.ES.2020.25.32.2001410
Anderson, R. M.; Heesterbeek, H.; Klinkenberg, D. & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet, 395(10228), pp. 931-934. https://doi.org/10.1016/S0140-6736(20)30567-5
Baguelin, M.; Donnelly, C. A.; Riley, S. & Ferguson, N. M. (2020). Report 3: transmissibility of 2019-nCoV. Reference Source, pp. 1-6.
Bello, Á. & Rangel, M. (2002). La equidad y la exclusión de los pueblos indígenas y afrodescendientes en América Latina y el Caribe. Revista de la CEPAL, (76), pp. 39-54. Santiago de Chile: CEPAL.
Bivand, R.; Gómez-Rubio, V. & Rue, H. (2015). Spatial Data Analysis with R-INLA with Some Extensions. Journal of Statistical Software, 63(20), pp. 1-31. https://doi.org/10.18637/jss.v063.i20
Blangiardo, M. & Cameletti, M. (2015). Spatial and Spatio-Temporal Bayesian Models with R-INLA. Chichester, UK: John Wiley & Sons.
China CDC Weekly (2020). The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19)-China, 2020. China CDC Weekly, 2(8), pp. 113-122.
Consejo Nacional de Evaluación de la Política Pública de Desarrollo Social (CONEVAL) (2019). La pobreza en la población indígena de México, 2008-2018. México: CONEVAL. https://www.coneval.org.mx/Medicion/MP/Documents/Pobreza_Poblacion_indigena_2008-2018.pdf
Fernández-Rojas, M. A.; Luna-Ruiz Esparza, M. A.; Campos-Romero, A.; Calva-Espinosa, D. Y.; Moreno-Camacho, J. L.; Langle-Martínez, A. P.; García-Gil, A.; Solís-González, C. J.; Canizalez-Román, A.; León-Sicairos, N: & Alcántar-Fernández, J. (2021). Epidemiology of Covid-19 in Mexico: Symptomatic profiles and presymptomatic people. Int J Infect Dis, 104, pp. 572-579. DOI: 10.1016/j.ijid.2020.12.086
Gamerman, D. & Lopes, H. F. (2006). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. Second Edition. London: Chapman & Hall/CRC Press.
Hall, P. A.; Sheeran, P.; Fong, G. T.; Cheah, C. S.L.; Oremus. M.; Liu-Ambrose, T.; Sakib, M. N.; Butt, Z. A.; Ayaz, H.; Jandu, N. & Morita, P. P. (2021). Biobehavioral aspects of the Covid-19 pandemic: A review. Psychosom Med, 83(4), pp. 309-321. DOI: 10.1097/PSY.0000000000000932
Hernández Bringas, H. (2020). Covid-19 en México: Un perfil sociodemográfico. Notas de Población, (111), pp. 105-132.
Hernández-Flores, M. d. l. L.; Escobar-Sánchez, J.; Paredes-Zarco, J. E.; Franyuti Kelly, G. A. & Carranza-Ramírez, L. (2020). Prediction and potentially explicit spread of Covid-19 in Mexico´s megacity North periphery. Health Care,8(4), p. 453. https://doi.org/10.3390/healthcare8040453
INEGI (2020). Estadísticas a propósito del día internacional de los pueblos indígenas. Comunicado de prensa número 392/2020, 7 de agosto.
Liu, Y. & Rocklöv, J. (2021). The reproductive number of Delta variant of SARS-CoV-2 is far higher compared to the ancestral SARS-CoV-2 virus. J Travel Med, 28(7). doi: 10.1093/jtm/taab124.
Liu, Y.; Gayle, A, A.; Wilder-Smith, A. & Rocklöv, J. T. (2020). The reproductive number of Covid-19 is higher compared to SARS coronavirus. J Travel Med, 27(2). doi: 10.1093/jtm/taaa021.
Marmot, M.; Allen, J.; Goldblatt, P.; Herd, E. & Morrison, J. (2020). Build back fairer: The Covid-19. The pandemic, socioeconomic and health inequalities in England. England: The Health Foundation/Institute of Health Equity.
Martino, S. & Rue, H. (2008). Implementing Approximate Bayesian Inference using Integrated Nested Laplace Approximation: a manual for the inla program.
Namasivayam, V.; Jain, A.; Agrawal, V.; Prakash, R.; Dehury, B.; Becker, M.; Blanchard, J.; Isac, S. & Prasad, A. M. (2021). Understanding the prevalence and geographic heterogeneity of SARS CoV 2 infection: Findings of the first serosurvey in Uttar Pradesh, India. J Epidemiol Glob Health, 11(4), pp. 364-376. doi: 10.1007/s44197-021-00012-6.
Partida, B. V. & García, G. V. (2018). Proyecciones de la población de México y de las entidades federativas 2016-2050. México: CONAPO.
Pelcastre-Villafuerte, B. E.; Meneses-Navarro, S.; Sánchez-Domínguez, M.; Meléndez-Navarro, D. & Freyermuth-Enciso, G. (2020). Condiciones de salud y uso de servicios en pueblos indígenas de México. Salud Pública de México, 62(6), pp. 810-819. https://doi.org/10.21149/11861
Phan, T. (2020). Genetic diversity and evolution of SARS-CoV-2. Invect Genet Evol, 81, p. 104260.
R Core Team (2016). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
Ramírez-Aldana, R.; Gomez-Verjan, J. C.; Bello-Chavolla, O. Y. & García-Peña, C. (2021). Spatial epidemiological study of the distribution, clustering, and risk factors associated with early Covid-19 mortality in Mexico. Plos One, 16(7), e0254884. doi: 10.1371/journal.pone.0254884
Riebler, A.; Sørbye, S. H.; Simpson, D. & Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25(4), pp. 1145-1165. DOI: 10.1177/0962280216660421
Rue, H., Martino, S. & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2), pp. 319-392.
Schrödle, B. & Held, L. (2011). Spatio-temporal disease mapping using INLA. Environmetrics, 22(6), pp. 725-734.
Sebastião, C.S.; Neto, Z.; Martinez, P.; Jandondo, D.; Antonio, J.; Galangue, M.; Carvalho, M d.; David, K.; Miranda, J.; Afonso, P.; Inglês, L.; Rivas Carrelero, R.; Neto de VasconcelosJ. & Morais, J. (2021). Sociodemographic characteristics and risk factors related to SARS-CoV-2 infection in Luanda, Angola. Plos One, 16(3), e0249249. https://doi.org/10.1371/journal.pone.0249249
Secretaría de Salud (2020). Datos Abiertos. Dirección General de Epidemiología. Recuperado de Gobierno de México: https://www.gob.mx/salud/documentos/datos-abiertos-152127
Sepúlveda, J.; Bronfman, M.; Embriz, A.; Esparza, R.; Gómez, J.; Lezana, M. A.; Ortiz, M.; Tapia, R, & Zolla, C. (1993). La salud de los pueblos indígenas. México: Secretaría de Salud/Instituto Nacional Indigenista.
Sharafifi, Z.; Asmarian, N.; Hoorang, S. & Mousavi, A. (2018). Bayesian spatio-temporal analysis of stomach cancer incidence in Iran, 2003-2010. Stoch Environ Res Risk Assess (32), pp. 2943-2950. DOI: 10.1007/s00477-018-1531-3.
Sharma, S. V.; Chuang, R.; Rushing, M.; Naylor, B.; Ranjit, N.; Pomeroy, M. & Markham, C. (2020). Social determinants of health related need during Covid-19 among low income households with children. Prev Chronic Dis, 17. http://dx.doi.org/10.5888/pcd17.200322
Sigler, T.; Mahmuda, S.; Kimpton, A.; Loginova, J.; Wohland, P.; Charles-Edwards, E. & Corcoran, J. (2021). The socio-spatial determinants of Covid-19 diffusion: The impact of globalization, settlement characteristics and population. Globalization and Health, 17, p. 56. https://doi.org/10.1186/s12992-021-00707-2
Suárez, V.; Suárez Quesada, M.; Oros Ruiz, S. & Ronquillo de Jesús, E. (2020). Epidemiology of COVID-19 in Mexico: From the 27th of February to the 30th of April 2020. Rev Clin Esp, 220(8), pp. 463-471.
Vázquez Sandrin, G. & Quezada, M. F. (2015). Los indígenas autoadscritos de México en el censo 2010: ¿revitalización étnica o sobreestimación censal? Papeles de población, 21(86), pp. 171-218. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-74252015000400007&lng=es&tlng=es.
WHO (11 de febrero de 2020a). WHO Director-General’s remarks at the media briefing on 2019-nCoV. https://www.who.int/dg/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020
WHO (30 de enero de 2020b). Pneumonia of unknown cause-China. https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/
WHO (11 de marzo de 2020c). WHO Director-General’s opening remarks at the media briefing on COVID-19. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
WHO (22 de febrero de 2020d). Coronavirus disease 2019 (COVID-19) Situation Report-40. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200229-sitrep-40-covid-19.pdf?sfvrsn=849d0665_2
Wong D. W. S. & Yun, L. (2020). Spreading of Covid-19: Density matters. Plos One, 15(12). https://doi.org/10.1371/journal.pone.0242398
Worldometer (2022). Coronavirus Upday. Covid-19 coronavirus pandemic Worldometer [Internet]. Consultado el 14 de enero, 2021. https://www.worldometers.info/coronavirus/
Wu, F.; Zhao, S.; Yu, B.; Chen, Y. M.; Wang, W.; Song, Z. G.; Hu, Y.; Tao, Z. W.; Tian, J. H.; Pei, Y. Y.; Yuan, M. L.; Zhang, Y. L.; Dai, F. H.; Liu, Y.; Wang, Q. M.; Zheng, J. J.; Xu, L.; Holmes, E. C. & Zhang, Y. Z. (2020). A new coronavirus associated whit human respiratory disease in China. Nature, 579(7803), pp. 265-269. doi: 10.1038/s41586-020-2008-3
Zhou. Y.; Zhang, Z.; Tian, J. & Xiong, S. (2020). Risk factors associated with disease progression in a cohort of patients infected with the 2019 Novel coronavirus. Ann Palliat Med, 9(2), pp. 428-436. oi: 10.21037/apm.2020.03.26
