key: cord-0835322-55qm5c12
authors: Najera, Hector; Ortega-Avila, Ana G.
title: Health and Institutional Risk Factors of COVID-19 Mortality in Mexico, 2020
date: 2020-12-09
journal: Am J Prev Med
DOI: 10.1016/j.amepre.2020.10.015
sha: 2d02a2207bc205003ec9a6768c39865f8c64947b
doc_id: 835322
cord_uid: 55qm5c12

Introduction Several studies in developed and developing countries have analyzed the health risk factors associated with coronavirus disease 2019 (COVID-19) mortality. Comorbid diseases are a key explanatory factor behind COVID-19 mortality, but current studies treat comorbidities in isolation, at average-population values, and rarely assess how death risk varies for different health profiles across institutions. Estimating death risk variations for different interactions between comorbid diseases and across healthcare institutions is crucial to gaining a significant depth of understanding in relation to mortality during the pandemic. Methods This study relies on data from approximately half a million people in Mexico (of all recorded cases through August 15, 2020) and on Bayesian estimation to provide a more robust estimate of the combined effect of several comorbidities and institutional inequalities upon COVID-19 mortality. Results The findings of the study illustrate the additive effects of several comorbid diseases with the presence of obesity, diabetes, hypertension, and CKD increasing the mortality risk of COVID-19. There are also variations in risk of death across the heterogeneous Mexican health system. Conclusions This study shows that COVID-19 mortality risk sharply increases in patients with 2 or more comorbid diseases (obesity, diabetes, hypertension, and cardiovascular diseases) in Mexico. However, death risk varied significantly across institutions for patients with the same comorbidity profile.

Coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in December 2019, caused by the virus strain severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 1 Infection manifestations range from no symptoms, to a severe acute respiratory disease that can lead to serious illness or death. 2 International research in developed countries suggests that older age and comorbid diseases such as diabetes, hypertension, cardiovascular disease, and chronic lung and kidney disease are risk factors for severe illness-patients requiring hospitalization, intensive care unit admission, and invasive mechanical ventilation-and death. [3] [4] [5] [6] These risk factors have also been correlated with other similar viral infections, such as influenza H1N1, SARS, and Middle East respiratory syndrome (MERS). 1, 2, 5, 7, 8 Hypertension has been the most prevalent underlying condition among hospitalized COVID-19 patients across different countries. 9 Three meta-analyses found a positive relationship between hypertension and COVID-19 severity, [10] [11] [12] but evidence on the effects of hypertension upon mortality suggest that among non-survivors, it is the most common disease. 13, 14 Cardiovascular disease (CVD) also correlates with the severity of COVID-19 infection. Patients in the U.S. with pre-existing heart disease are more likely to require invasive mechanical ventilation during hospitalization. 15 In China, the second-most prevalent underlying condition among hospitalized COVID patients is CVD. 9, 16 Findings from 2 meta-analyses of Chinese studies support the thesis that the presence of coronary heart disease and CVD increases the risk of developing severe illness almost 3-fold. 12, 13 In addition, 2 small retrospective studies in China suggest that patients with CVD have significantly higher risk of death 14, 17 ; however, CVD does not have an effect in a sample of U.S. COVID-19 patients. 18 Several studies have suggested that diabetes increases the severity of COVID-19. 8 In China, the pooled estimate from 9 studies indicates that it increases the risk of severe illness almost 3fold. 13, 18 Descriptive cross-tabulated data suggest that higher percentages of deaths are prevalent among those patients with a previous diagnosis of diabetes. 12, 14, 19, 20 A meta-analysis of 4 studies finds weak evidence of the effect of chronic kidney disease (CKD), 21 but a pooled-data model in the same study suggests that its effect upon death risk is higher than reported in single studies.

Obesity has also been reported as another critical risk factor. 4 Obese patients studied in China are more likely to progress to severe pneumonia due to COVID-19. 22 A small retrospective cohort study conducted in France indicates that the risk of receiving invasive mechanical ventilation was higher for those patients with a BMI >35kg/m 2 . 23 A large prospective cohort study in the U.S. reported that among people admitted to hospital with COVID-19 (n=5,279), those with a BMI >40 kg/m 2 had a greater risk of critical illness and death. 18 Similarly, another study determined that those aged <60 years with obesity (BMI 30-34.9 kg/m 2 ) were more likely to be admitted to acute and critical care. 24 Many of the extant studies model the effect of comorbid diseases in isolation at averagepopulation values, and the emerging evidence from developing countries regarding COVID-19 risk factors echoes the same approach. This recent research forms an important addition to the literature, as it analyzes data from countries where health and institutional profiles differ significantly from those in developed countries. In Mexico, recent studies have examined risk factors associated with risk of infection, 25 severity, and mortality. [26] [27] [28] [29] This literature is consistent with the international research, but uses data that do not include the approximately 250,000 new cases that occurred during the peak of the pandemic in July-August. Furthermore, Mexico's epidemiological profile includes a high prevalence of obesity (36.1% in adults in 2018 30 ) CVD, and diabetes-the 3 leading causes of mortality, combined with high levels of institutional inequality. 31 Therefore, it is important to conduct more-detailed analyses (using a most recently updated database) of the interactions of comorbidities (in such unequal institutional settings) and their impact on COVID-19 mortality risk.

The aims of this study are 2-fold. First, it sets out to examine how the interaction effects of noncommunicable diseases affect mortality risk by analyzing a larger sample of COVID-19 patients and using more robust statistical modelling. Second, it aims to estimate how the risk varies across institutions for different comorbidity profiles.

Patient data came from official records of all confirmed COVID-19 cases and deaths in Mexico (1) sociodemographic information (age, sex, ethnic background), geographic data (state and municipality of residence), patient's ancillary variables (asthma, smoking, immunosuppressants, and other illnesses), and information on the time of diagnosis and healthcare intervention, to control for time dependency. Table 1 and Appendix Table 1 describe these variables, and Figure   1 displays the coefficients from the model.

A hierarchical Bayesian modeling approach was implemented to estimate the adjusted effects of both comorbid diseases and institutions on COVID-19 mortality risk. Two reasons underpin the selection of this approach. First, both large differences in population across states and municipalities and unknown contextual factors might influence the point estimates from a standard model. Hierarchical models result in better estimates that model uncertainty using random effects (i.e., intercepts for states and municipalities) and produce partially pooled estimates (i.e., shrink the point estimates toward average-population values). 33 Therefore, statelevel differences are conditional on the model. Second, random effects from hierarchical Bayesian models are more robust than those from maximum likelihood estimation, and the Bayesian model relies on the Hamiltonian Monte Carlo algorithm, 34 which outperforms standard maximum likelihood estimators when using large and complex models in terms of both speed and accuracy. 33 A 3-level hierarchical model (states, municipalities, and individuals) with a Bernoulli distribution was fitted to the data (Appendix). The model uses weak priors ( ) for all variables included in the model 33 and was estimated in R, version 4.0.2 using Rstan in combination with the brms() package. 35, 36 A key objective of this paper was to obtain the interaction effect of comorbidities. However, the interaction of binary variables is difficult to interpret, especially for interactions with ≥3 There was also a clear relationship between the institution offering medical attention and death risk. Patients at private clinics and within the navy health system (SEMAR) had lower death risks relative to those in the open public health system (SSA). Yet, people receiving care at public local state hospitals, the army (SEDENA), and the 2 major social security systems (IMSS and ISSSTE) had higher death risks. These findings reflect large inter-institutional inequalities in Mexico, and these effects are explored further in the following subsections.

After adjusting for individual-level factors, the state-level intercepts from the model suggest that the COVID-19-related death risk varied considerably across states and municipalities (Appendix Figure 4 ). Around 12% of the unexplained variance was due to municipal differences and 8% due to state-level differences. This result indicates that there are other sources of risk across the Mexican territory affecting patients' chances of overcoming the illness. States with lower average death risk tend to perform better across several key indicators including GDP per capita, infrastructure quality, and lower multidimensional poverty than some of those with higher average death risk. However, the pattern is not conclusive, and this hypothesis requires further exploration in order to assess the contextual factors that explain interstate differences.

A second objective of the analysis was to estimate adjusted probabilities for different population profiles. These probabilities draw on the hierarchical model and were calculated to explore how interactions between the main risk factors can increase patient risk. The probabilities below consider the average value of the other variables so that these estimates approximate the mean probability for the typical sample population. which, according to these findings, was 3.5 times higher for this second group compared with those without them. Figure 3 shows the estimated probabilities for each healthcare subsector. The estimates approximate 2 types of populations: typical and high risk (Methods section). The results show that there was substantial variation across institutions. The probability of death ranged between 5% and almost 10% across institutions for the average population (medium risk). These values almost tripled once high health risks were included; however, the effect was much higher for patients at primary social security institutions (IMSS and ISSSTE) and local public hospitals (SSA). The lowest probability was for those patients in the private sector, whereas the probability was nearly the same as for relatively healthy patients at the IMSS.

The study aimed to examine, after adjusting by different individual-level characteristics, how The study did not model the effect of state or municipal-level effects, so research is required to cover this weakness. Another important consideration is the contrasting finding from this study in relation to the international literature (mostly Chinese studies), namely that CVD disease is not associated with mortality, as noted by Petrilli et al. 18 in the U.S. A potential explanation for this similarity is that both countries share similar health profiles, defined in part by the obesity epidemic and health inequalities, which apparently seem to cancel out the potential effect of CVD on mortality. This requires further research with an even larger sample size to ensure that CDV has no role in increasing mortality in COVID-19 patients. Finally, this study is limited in that it did not look at intra-institutional variations. Empirical research on this matter is fundamental to understanding more precisely the performance of public health institutions.

This study shows that COVID-19 mortality risk sharply increases in patients with >2 comorbid diseases (obesity, diabetes, hypertension, and cardiovascular diseases) in Mexico. However, the paper shows that death risk varies significantly across institutions for patients with the same comorbidity profile. Hence, it is very likely that 2 otherwise similar patients will have different outcomes if they are treated in different institutions. The study of the source of these variations in risk across institutions is central to understand the impact of differences in resources and provision upon individuals' mortality. 

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The data used in this study are open, public, and free. The authors did not receive specific funding for this research. All authors contributed equally to the writing and analysis. The raw data and R-code will be made available upon request.No financial disclosures were reported by the authors of this paper.