ABSTRACT
We study case-fatality risks (risks of dying in sick individuals) corresponding to the first wave of the COVID-19 pandemic in Mexico. Spatio-temporal analysis by state were performed, mainly from April to September 2020, including descriptive analyses through mapping and time series representations, and the fit of linear mixed models and time series clustering to analyze trends by state. The association of comorbidities and other variables with the risks were studied by fitting a spatial panel data linear model (splm). As results, we observed that on average the greatest risks were reached by July, and that highest risks were observed in some states, Baja California Norte, Chiapas, and Sonora; interestingly, some densely populated states, as Mexico City, had lower values. Different trends by state were observed, and a four-order polynomial, including fixed and random effects, was necessary to model them. The most general structure is one in which the risks increase and then decrease and was observed in states belonging to two clusters; however, there is a cluster corresponding to states with a retarded increase, and another in which increasing risks through time were observed. A cyclic behavior in terms of states having a second increasing trend was observed. Finally, according to the splm, percentage of men, being in the group of 50 years and over, chronic kidney disease failure, cardiovascular disease, asthma, and hypertension were positively associated with the case-fatality risks. This analysis may provide valuable insight into COVID-19 dynamics in future outbreaks, as well as the determinants of these trends at a state level; and, by combining spatial and temporal information, provide a better understanding of COVID-19 case-fatality.
ABSTRACT
COVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems world-wide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Here, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico to evaluate how tested case-fatality risks (t-CFRs) are geographically distributed and to explore spatial predictors of early t-CFRs considering the variation of their impact on COVID-19 fatality across different states in Mexico, controlling for the severity of the disease. As results, considering health related variables; diabetes and obesity were highly associated with COVID-19 fatality. We identified that both external and internal migration had an important impact over early COVID-19 risks in Mexico, with external migration having the second highest impact when analyzing Mexico as a whole. Physicians-to-population ratio, as a representation of urbanity, population density, and overcrowding households, has the highest impact on t-CFRs, whereas the age group of 10 to 39 years was associated with lower risks. Geographically, the states of Quintana Roo, Baja California, Chihuahua, and Tabasco had higher t-CFRs and relative risks comparing with a national standard, suggesting that risks in these states were above of what was nationally expected; additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risks variates by zone depending on the predictor.
ABSTRACT
The Islamic Republic of Iran reported its first COVID-19 cases by 19th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths at the date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. In the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how the socioeconomic features of Iranian provinces might predict the number of cases. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran, which indicated that the spread of COVID-19 within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases. Interestingly, literacy is a protective factor that might be directly related to health literacy and compliance with public health measures. These features indicate that policies related to social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be targeted to reduce SARS-CoV2 spread in Iran. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.
