A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico

Submitted: 18 November 2021
Accepted: 10 March 2022
Published: 24 March 2022
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spatio-temporal analysis of the first wave of the coronavirus (COVID-19) pandemic in Mexico (April to September 2020) was performed by state. Descriptive analyses through diagrams, mapping, animations and time series representations were carried out. Greater risks were observed at certain times in specific regions. Various trends and clusters were observed and analysed by fitting linear mixed models and time series clustering. The association of co-morbidities and other variables were studied by fitting a spatial panel data linear model (SPLM). On average, the greatest risks were observed in Baja California Norte, Chiapas and Sonora, while some other densely populated states, e.g., Mexico City, had lower values. The trends varied by state and a four-order polynomial, including fixed and random effects, was necessary to model them. The most common risk development was observed in states belonging to two clusters and consisted of an initial increase followed by a decrease. Some states presented cluster configurations with a retarded risk increase before the decrease, while the risk increased throughout the time of study in others. A cyclic behaviour with a second increasing trend was also observed in some states. The SPLM approach revealed a positive significant association with respect to case fatality risk between certain groups, such as males and individuals aged 50 years and more, and the prevalence of chronic kidney disease, cardiovascular disease, asthma and hypertension. The analysis may provide valuable insight into COVID-19 dynamics applicable in future outbreaks, as well as identify determinants signifying certain trends at the state level. The combination of spatial and temporal information may provide a better understanding of the fatalities due to COVID-19.

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Aghabozorgi S, Shirkhorshidi AS, Wah TY, 2015. Time-series clustering - a decade review. Inf Syst 53:16-38. DOI: https://doi.org/10.1016/j.is.2015.04.007
Ahasan R, Hossain MM, 2021. Leveraging GIS and spatial analysis for informed decision-making in COVID-19 pandemic. Health Policy Technol 10:7-9. DOI: https://doi.org/10.1016/j.hlpt.2020.11.009
Amdaoud M, Arcuri G, Levratto N, 2021. Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe. Eur J Health Econ 22:629-42. DOI: https://doi.org/10.1007/s10198-021-01280-6
Antonio-Villa NE, Fernandez-Chirino L, Pisanty-Alatorre J, Mancilla-Galindo J, Kammar-García A, Vargas-Vázquez A, González-Díaz A, Fermín-Martínez CA, Márquez-Salinas A, Guerra EC, Bahena-López JP, Villanueva-Reza M, Márquez-Sánchez J, Jaramillo-Molina ME, Gutiérrez-Robledo LM, Bello-Chavolla OY, 2021. Comprehensive evaluation of the impact of sociodemographic inequalities on adverse outcomes and excess mortality during the coronavirus disease 2019 (COVID-19) pandemic in Mexico City. Clin Infec Dis 74:785-92. DOI: https://doi.org/10.1093/cid/ciab577
Argoty-Pantoja AD, Robles-Rivera K, Rivera-Paredez B, Salmerón J, 2021. COVID-19 fatality in Mexico’s indigenous populations. Public Health 193:69-75. DOI: https://doi.org/10.1016/j.puhe.2021.01.023
Baltagi BH, Song SH, Koh W, 2003. Testing panel data regression models with spatial error correlation. J Econom 117:123-50. DOI: https://doi.org/10.1016/S0304-4076(03)00120-9
Baltagi BH, Song SH, Jung B, Koh W, 2007. Testing for serial correlation, spatial autocorrelation and random effects using panel data. J Econom 140:5-51. DOI: https://doi.org/10.1016/j.jeconom.2006.09.001
Barquera S, Hernández-Barrera L, Trejo-Valdivia B, Shamah T, Campos-Nonato I, Rivera-Dommarco J, 2020. Obesity in Mexico, prevalence and trends in adults. Ensanut 2018-19. Salud Publica Mex 62:682-92. DOI: https://doi.org/10.21149/11630
Bello-Chavolla OY, Bahena-López JP, Antonio-Villa NE, Vargas-Vázquez A, González-Díaz A, Márquez-Salinas A, Fermín-Martínez CA, Naveja JJ, Aguilar-Salinas CA, 2020. Predicting Mortality Due to SARS-CoV-2: A Mechanistic Score Relating Obesity and Diabetes to COVID-19 Outcomes in Mexico. J Clin Endocrinol Metab 105:2752-61. DOI: https://doi.org/10.1210/clinem/dgaa346
Bello-Chavolla OY, González-Díaz A, Antonio-Villa NE, Fermín-Martínez CA, Márquez-Salinas A, Vargas-Vázquez A, Bahena-López JP, García-Peña C, Aguilar-Salinas CA, Gutiérrez-Robledo LM, 2021. Unequal impact of structural health determinants and comorbidity on COVID-19 severity and lethality in older mexican adults: considerations beyond chronological aging. J Gerontol A Biol Sci Med Sci 76:e52-9. DOI: https://doi.org/10.1093/gerona/glaa163
Bourdin S, Jeanne L, Nadou F, Noiret G, 2021. Does lockdown work? A spatial analysis of the spread and concentration of COVID-19 in Italy. Regional Stud 55:1182-93. DOI: https://doi.org/10.1080/00343404.2021.1887471
Elliott P, Wartenberg D, 2004. Spatial epidemiology: current approaches and future challenges. Environ Health Perspect 112:998-1006. DOI: https://doi.org/10.1289/ehp.6735
Franch-Pardo I, Napoletano BM, Rosete-Verges F, Billa L, 2020. Spatial analysis and GIS in the study of COVID-19. A review. Sci Total Environ 739:140033. DOI: https://doi.org/10.1016/j.scitotenv.2020.140033
Gobierno de la Ciudad de México, 2020. Casos a nivel nacional asociados a COVID-19 para la CDMX. Retrieved from Datos Abiertos Ciudad de México. Available from: https://datos.cdmx.gob.mx/explore/dataset/casos-asociados-a-covid-19/table/
Hausman JA, 1978. Specification tests in econometrics. Econometrica 46:1251-71. DOI: https://doi.org/10.2307/1913827
Huyser KR, Yang TC, Yellow-Horse AJ, 2021. Indigenous Peoples, concentrated disadvantage, and income inequality in New Mexico: a ZIP code-level investigation of spatially varying associations between socioeconomic disadvantages and confirmed COVID-19 cases. J Epidemiol Community Health 75:1044-9. DOI: https://doi.org/10.1136/jech-2020-215055
INEGI, 2020. Características de las Defunciones Registradas en México durante 2019. Retrieved from Instituto Nacional de Estadística y Geografía (INEGI), Mexico. Available from: https://www.inegi.org.mx/contenidos/saladeprensa/boletines/2020/EstSociodemo/DefuncionesRegistradas2019.pdf Accessed: October 29, 2020.
INEGI, 2021. Características de las Defunciones Registradas en México durante Enero a Agosto de 2020. Instituto Nacional de Estadística y Geogragía (INEGI), Mexico. Accessed: January 27, 2021.
INFOBAE, 2021. Coronavirus en México al 26 de junio: suman más de 2 millones y medio de contagios. Retrieved from INFOBAE. Available from: https://www.infobae.com/america/mexico/2021/06/26/coronavirus-en-mexico-al-26-de-junio-mas-de-2-millones-y-medio-de-contagios/ Accessed: June 26, 2021.
Levratto N, Amdaoud M, Arcuri G, 2020. Covid-19: analyse spatiale de l’influence des facteurs socio-économiques sur la prévalence et les conséquences de l’épidémiedans les départementsfrançais. EconomiX Working Papers, University of Paris Nanterre, EconomiX 2020-4. Available from: https://economix.fr/pdf/dt/2020/WP_EcoX_2020-4.pdf
Mas JF, 2021. Spatio-temporal dataset of COVID-19 outbreak in Mexico. Data Brief 35:106843. DOI: https://doi.org/10.1016/j.dib.2021.106843
Mercado CEG, Lawpoolsri S, Sudathip P, Kaewkungwal J, Khamsiriwatchara A, Pan-ngum W, Yimsamran S, Lawawirojwong S, Ho K, Ekapirat N, Maude RR, Wiladphaingern J, Carrara VI, Day NPJ, Dondorp AM, Maude RJ, 2019. Spatiotemporal epidemiology, environmental correlates, and demography of malaria in Tak Province, Thailand (2012-2015). Malar J 18:240. DOI: https://doi.org/10.1186/s12936-019-2871-2
Millo G, Piras G, 2012. splm: Spatial Panel Data Models in R. J Stat Soft 47:1-38. DOI: https://doi.org/10.18637/jss.v047.i01
Mizumoto K, Tariq A, Roosa K, Kong J, Yan P, Chowell G, 2019. Spatial variability in the reproduction number of Ebola virus disease, Democratic Republic of the Congo, January-September 2019. Euro Surveill 24:1900588. DOI: https://doi.org/10.2807/1560-7917.ES.2019.24.42.1900588
Mutl J, Pfaffermayr M, 2011. The Hausman test in a Cliff and Ord panel model. Econom J 14:48-76. DOI: https://doi.org/10.1111/j.1368-423X.2010.00325.x
Neelon B, Mutiso F, Mueller NT, Pearce JL, Benjamin-Neelon SE, 2021. Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States. PLoS One 16:1-17. DOI: https://doi.org/10.1371/journal.pone.0248702
PAHO, 2020. COVID-19 and comorbidities in the Americas: Background information. Retrieved from Pan American Health Organization (PAHO). Accessed: July 29, 2020. Available from: https://www.paho.org/es/documentos/covid-19-comorbilidades-americas-antecedentes
Pearce N, 2000. The ecological fallacy strikes back. J Epidemiol Community Health 54:326-7. DOI: https://doi.org/10.1136/jech.54.5.326
Pebesma E, 2012. spacetime: Spatio-Temporal Data in R. J Stat Soft 51:1-30. DOI: https://doi.org/10.18637/jss.v051.i07
Ramírez-Aldana R, Gomez-Verjan JC, Bello-Chavolla OY, García-Peña C, 2021. Spatial epidemiological study of the distribution, clustering, and risks factors associated with early COVID-19 mortality in Mexico. PLoS One 16:e0254884. DOI: https://doi.org/10.1371/journal.pone.0254884
Salcido A, 2021. A lattice gas model for infection spreading: Application to the COVID-19 pandemic in the Mexico City Metropolitan Area. Results Physics 20:103758. DOI: https://doi.org/10.1016/j.rinp.2020.103758
Sannigrahi S, Pilla F, Basu B, Basu AS, Molter A, 2020. Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustain Cities Soc 62:102418. DOI: https://doi.org/10.1016/j.scs.2020.102418
Sardá-Espinosa A, 2017. Comparing time-series clustering algorithms in R using the dtwclust package. Semantic Scholar, 1-45. Retrieved from Semantic. Available from: https://www.semanticscholar.org/paper/Comparing-Time-Series-Clustering-Algorithms-in-R-Sarda-Espinosa/a46ec863bbf3e179de4e7ccedd205a96ab1ca64f
Sardá-Espinosa A, 2019. Time-series clustering in R using the dtwclust package. The R Journal 11:1-22. DOI: https://doi.org/10.32614/RJ-2019-023
Schmitt-Grohé S, Teoh K, Uribe M, 2020. Covid-19: Testing Inequality in New York City. National Bureau of Economic Research Inc., 27019. DOI: https://doi.org/10.3386/w27019
Ministry of Health, 2020. Datos Abiertos Bases Históricas. Dirección General de Epidemiología. (Secretaría de Salud). Available from: https://www.gob.mx/salud/documentos/datos-abiertos-bases-historicas-direccion-general-de-epidemiologia
Shaweno D, Karmakar M, Alene KA, Ragonnet R, Clements ACA, Trauer JM, Denholm JT, McBryde ES, 2018. Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review. BMC Med 16:193. DOI: https://doi.org/10.1186/s12916-018-1178-4
Tatem AJ, 2018. Innovation to impact in spatial epidemiology. BMC Med 16:209. DOI: https://doi.org/10.1186/s12916-018-1205-5
Verbeke G, Molenberghs G, 2000. Linear mixed models for longitudinal data. Springer, Berlin, Germany. DOI: https://doi.org/10.1007/978-1-4419-0300-6
Wikle CK, Zammit-Mangion A, Cressie N, 2019. Spatio-temporal statistics with R. Chapman and Hall, CRC Press, Boca Raton, FL, USA. DOI: https://doi.org/10.1201/9781351769723
WHO, 2020a. Coronavirus disease (COVID-19) pandemic. World Health Organization (WHO), Geneva, Switzerland. Available from: https://www.who.int/es/emergencies/diseases/novel-coronavirus-2019
WHO, 2020b. WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. World Health Organization (WHO), Geneva, Switzerland. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 Accessed: March 11, 2020.
WHO, 2020c. COVID-19 Weekly epidemiology update. World Health Organization (WHO), Geneva, Switzerland. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20201020-weekly-epi-update-10.pdf Accessed: October 20, 2020.

How to Cite

Ramìrez-Aldana , R., Gomez-Verjan, J. C., Bello-Chavolla , O. Y., & Naranjo, L. (2022). A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico. Geospatial Health, 17(s1). https://doi.org/10.4081/gh.2022.1054