risk of infection and hospitalization by covid-19 in mexico: a case … · 2020. 5. 24. · 1 risk...

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Risk of infection and hospitalization by Covid-19 in Mexico:

a case-control study

Jaime Berumen1*, Max Schmulson1, Jesús Alegre-Díaz1, Guadalupe Guerrero2, Jorge Larriva-Sahd3, Gustavo Olaiz4, Rosa María Wong-Chew5, Carlos Cantú-Brito6, Ana Ochoa-Guzmán6, Adrián Garcilazo-Ávila6, Carlos González-Carballo6, Erwin Chiquete6

1. Unidad de Medicina Experimental, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico. 2. Hospital General de México, Dr. Eduardo Liceaga, Mexico City, Mexico. 3. Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro Mexico. 4. Centro de Investigación en políticas, población y salud, Facultad de Medicina, Universidad Nacional Autónoma de Mexico, Mexico City, Mexico. 5. División de Investigación, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico. 6. Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.

*Corresponding authors: Dr. Jaime Berumen. Unidad de Medicina Experimental, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico. E-mail: [email protected] Dr. Erwin Chiquete. Department of Neurology. Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán. Vasco de Quiroga #15, Col. Belisario Dominguez Sección XVI, Delegacion Tlalpan C.P. 14080, Ciudad de Mexico. Phone: (55) 5485 1328. E-mail: [email protected]

ABSTRACT

Objective. During the onset of a novel epidemic, there are public health priorities

that need to be estimated, such as risk factors for infection, hospitalization, and

clinical severity to allocate resources and issue health policies. In this work we

calculate the risk of infection and hospitalization by Covid-19 conferred by

demographic, lifestyle, and co-morbidity factors. Material and methods. This is a

case-control study including the tested individuals for SARS-Cov-2 by RT-PCR

All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprintthis version posted May 26, 2020. ; https://doi.org/10.1101/2020.05.24.20104414doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

mailto:[email protected]

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officially reported by the Health Secretary of Mexico from January 01 to May 8, 2020

(102,875 subjects). Demographic (sex, age, foreign and immigrant status, native

speaking, place of residence), life-style (smoking), and co-morbidities [diabetes,

obesity, high blood pressure (HBP), asthma, immunosuppression, chronic

obstructive pulmonary disease (COPD), cardiovascular disease other than HBP,

chronic kidney disease (CKD), and other not specified diseases (other diseases)]

variables were included in this study. The risk of infection and hospitalization

conferred by each variable was calculated with univariate (ULR) and multivariate

(MLR) logistic regression models. Results. The place of residence (OR=4.91 living

in Tijuana City), followed by advanced age (OR=6.71 in 61-70 years-old), suffering

from diabetes (OR=1.87) or obesity (OR=1.61), being male (OR=1.55), having HBP

(OR=1.52), and notoriously being indigenous (OR=1.49) conferred a higher risk of

becoming infected by SARS-CoV-2 in Mexico. Unexpectedly, we found that having

asthma (OR=0.63), immunosuppression (OR=0.65) or smoking (OR=0.85) are

protective factors against infection, while suffering from COPD does not increase the

risk for SARS-CoV-2 infection. In contrast, advanced age (OR=11.6 in ≥ 70 years-

old) is the main factor for hospitalization due to Covid-19, followed by some co-

morbidities, mainly diabetes (OR=3.69) and HBP (OR=2.79), being indigenous

(OR=1.89), male sex (OR=1.67) and the place of residence (OR=4.22 for living in

Juarez City). Unlike the protective risk against infection, immunosuppression

(OR=2.69) and COPD (OR=3.63), contribute to the risk of being hospitalized, while

having asthma (OR=0.7) also provides protection against hospitalization.

Conclusions. In addition to confirming that older age, diabetes, HBP and obesity

are the main risk of infection and hospitalization by Covid-19, we found that being



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indigenous, immunosuppression, smoking and asthma protect against infection, and

the latter also against hospitalization.

Key Words: Covid-19, Risk Factors, Age, Diabetes, Obesity, Hospitalization

INTRODUCTION

Coronavirus disease-2019 (COVID-19) the name provided to the new severe acute

respiratory syndrome-coronavirus-2 (SARS-CoV-2) that broke in the city of Wuhan,

Province of Hubei in China, in December 2019, was declared a pandemic by the

World Health Organization (WHO) on March 11th, 2020.1-3 During the emergence

of a novel epidemic, there are public health priorities that need to be estimated such

as risk factors for infection, hospitalization and clinical severity to allocate resources

and to issue health policies.4, 5 In Wuhan-China, the risk of symptomatic infection

increased with age at ~4% per year among adults aged 30–60 years;5 while in a

systematic review, age, sex, previous hospital admissions, comorbidity data, and

social determinants of health, were determinants of hospital admission.4, 6 In another

systematic review and metanalysis of ten papers including 76,993 patients, high

blood pressure (HBP), cardiovascular diseases, diabetes mellitus, smoking, chronic

obstructive pulmonary disease (COPD), malignancy, and chronic kidney disease

(CKD), were among the most prevalent underlying diseases among hospitalized

COVID-19 patients.6 In a report by the Centers for Disease Control (CDC) among

7,162 cases with COVID-19 in the United States that were analyzed on March 28th,

more than a third of patients had one or more underlying health condition, of which

the most commonly reported were diabetes mellitus, chronic lung disease, and

cardiovascular diseases.5 In another metanalysis, older age and a history of



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smoking were related to progression and deterioration compared to an improvement

and stabilization of the COVID-19 illness.7 The presence of obesity is also emerging

as a risk factor with a six-fold increase risk for severe COVID-19 illness, as well as

for mechanical intubation, especially in patients with metabolic associated fatty liver

disease.8 Obesity appears to be shifting the severity of COVID-19 to younger ages.9

Finally, immunosuppression following renal transplantation, and in patients with

cancer, is a risk factor for severe disease, although, not necessarily for a worst

prognosis.10 In fact, adult and children patients with immunosuppression seem to

have a favorable disease course, as compared to the general population, suggesting

a protective role of a weaker immune response.11

COVID-19 hit Mexico on February 27th, 2020 with an in imported case of a subject

that traveled to Italy; while the first death was reported on March 18th.12 Currently,

the 10.2% mortality rate for COVID-19 in Mexico is one of the highest in the world.12

However, risk factors for infection and hospitalization, are almost unknown for this

country. Therefore, we aimed at investigating such risk factors from a registry of the

Health Secretary of Mexico that included the total number of subjects that have been

tested with real-time reverse transcriptase-polymerase chain reaction (RT-PCR

assay) of nasopharyngeal swabs for SARS-CoV-2.13 We have hypothesized that

older aged, presence of chronic health issues including obesity, diabetes, asthma,

COPD, CKD, HBP and cardiovascular disorders, as well as smoking, are risk factors

for infection and hospitalization for COVID-19 in Mexico. In addition, we also wanted

to determine if immunosuppression, would be a protective factor as it has been

previously suggested, and to explore if the indigenous population in Mexico would

be at a similar risk for COVID-19 as the rest of the population.



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MATERIALS AND METHODS

Design, study population and setting. This is a case-control study including the

tested individuals for SARS-Cov-2 by RT-PCR officially reported by the Health

Secretary of Mexico from January 01 to May 8, 2020 (102,875 subjects).13 This

registry collects demographic and clinical information from the 475 Respiratory

Disease Monitoring Units (USMER) of the Viral Respiratory Disease Epidemiological

Surveillance System, located in health centers or hospitals throughout the country

and includes all national health systems (IMSS, ISSSTE, SS, SEDENA, SEMAR,

others). The information contained in that database corresponds only to the data

obtained from the epidemiological study of suspected viral respiratory disease at the

time the person is interrogated in those medical units. According to the clinical

diagnosis of admission, it was considered whether the patient could be managed

ambulatorily or had to be hospitalized. However, the database does not include

clinical symptomatology or evolution during the stay in the medical units.

No informed consent was obtained as this was not an interventional study nor a

direct survey of the study subjects. This study was based solely on the analysis of

a public national registry of subjects that have been tested for SARS-CoV-2 RT-PCR

in Mexico. In addition, no personal identifications are present in this dataset

therefore there is no bridge in the privacy of the study subjects. However, the

Research and Ethics Committee of the Faculty of Medicine of the Universidad

Nacional Autónoma de México was consulted, and they replied that ethical approval

was not required.

.



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Main outcome measures. Demographic (sex, age, foreign and immigrant status,

native speaking, place of residence), life-style (smoking), and co-morbidities

[diabetes, obesity, HBP, asthma, immunosuppression, COPD, cardiovascular

disease other than HBP, CKD, and other not specified diseases (other diseases)]

variables were included in this study. Speaking indigenous languages/dialects was

used as a surrogate marker of indigenous Mexican population.

For sex, women were considered as the reference group. Age was grouped by age-

ranges and patients 20 years-old or younger were considered the reference group.

For place of residence, all metropolitan areas with more than 1 million inhabitants

were included, and individuals from the rest of the country's cities were considered

the reference group. For all other variables that include a yes or no answer, the “no”

response was considered as the reference value. The risk of infection or

hospitalization conferred by each variable was calculated with univariate (ULR) and

multivariate (MLR) logistic regression models.

Statistical analysis

Numerical variables were described with medians and interquartile range (IQR) or

means and standard deviations (SD). The significance of differences between the

groups (cases and controls) was assessed with the Mann-Whitney U-test or the t-

test. The association of significant variables with infection or hospitalization was

explored using ULR and MLR logistic regression models. The association was

expressed as the odd ratio (OR) and 95% confidence interval (CI), and the

contribution to the variability of be infected or hospitalized was expressed as

adjusted r2. Variables with p<0.2 in the ULR analysis were considered for entry in

MLR models. Confounders were defined as those variables for which the percentage



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difference of β coefficient between the adjusted and non-adjusted variables in the

MLR model were higher than 10% (p>0.1). All statistical tests were two-sided. The

statistical analyses were conducted using SPSS version 20 software (SPSS Inc.,

Chicago, IL, USA).

RESULTS

Demographic and clinical characteristics of studied population.

A total of 102,875 registered individuals tested for SARS-CoV-2 RT-PCR, were

included in the study; 31,522 (30.6%) were positive and 71,353 (69.4) had a negative

result. The demographic characteristics of the cases and controls are presented in

Table 1. Positivity for SARS-CoV-2 was higher in men than women (35.2% vs.

25.9%, p<0.001), and increases progressively with age, from 10.6% in individuals in

the ≤20 year-old group, up to 44.2% in the 61-70 year-old group (p<0.001).

Interestingly, the percentage of positivity was higher in native Mexican population

than in the rest of individuals (39.5% vs. 30.5%, p<0.001), and much lower in

foreigners than in nationals (19.4% vs. 30.8%, p<0.001). On the other hand,

positivity in the US-border cities (Tijuana and Juarez) and Cancun, about 60% of the

individuals that were explored, was higher than that in Mexico City (38.6%), the

State of Mexico (44.5%), and the rest of the country (25.9%). The positivity-rate in

individuals with co-morbidities such as diabetes, obesity and hypertension was much

higher than the one in subjects without these co-morbidities, mainly with diabetes

(43% vs. 28.7%, p<0.001); and it is striking that in asthmatic or individuals with

immunosuppression, as well as in smokers, the opposite was found, a lower infection

rate compared to those with these factors (see Table 1).



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Risk of infection with the virus SARS-Cov-2 in Mexico.

Interestingly, the place of residence was the factor that conferred the greatest risk to

be infected by the SARS-CoV-2. Places with the highest risk were Tijuana

(OR=4.91, CI=4.48-5.38, p<0.001), Cancun (OR=4.3, CI=3.84-4.81, p<0.001) and

Juarez (OR=3.86, CI=3,31-4,5, p<0.001), followed by the metropolitan area of the

State of Mexico (OR=2.3, CI=2.21-2.39, p<0.001) and Mexico City (OR=1.8,

CI=1.74-1.86, p<0.001). Notoriously, people living in Monterrey and Guadalajara

have a 3.5 to 3.9 times lower risk of becoming infected than those living in the rest

of the country (Table 2). The place of residence explains 9% of the variability of the

risk of infection. The age was the second most important risk factor for infection

(Table 2). The greatest risk starts in the group of 21-30 years-old, relative to

the group of 20 years or younger, with an OR=2.31 (CI=2.13-2.51, p<0.001), and

then, progressively increases as the age range rises to the age group of 61-70 years-

old, which has a 6.71 times higher risk of becoming infected than young people aged

≤20 years-old. Then, in those over 70 years-old, the risk of infection decreases

slightly (OR=4.95, CI=4.53-5.41, p<0.001). Despite conferring a high risk, age only

contributes 5.8% of the total variability of the risk of infection. Also, men are 1.55

times more at risk of becoming infected than women, although this factor only

explains 1.4% of the risk variability. Interestingly, indigenous subjects were 1.49

times more at risk of becoming infected than the rest of the population, however, this

factor contributes very little to the variability of the risk of infection (0.1%). On the

other hand, foreigners and immigrants in Mexico had a lower risk of becoming

infected by the SARS-CoV-2 (Table 2). Individuals with diabetes (OR=1.87, IC=1.81-

1.94, p<0.001), obesity (OR=1.61, IC=1.55-1.66, p<0.001) or HBP (OR=1.52,



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IC=1.47-1.57, p<0.001) were at higher risk of infection than those without it. Among

them, diabetes contributed the most to the variability of the risk of infection (1.5%),

and the three factors together contribute only 3.3%. It is interesting to note that

patients with asthma (OR=0.63, IC=0.59-0.68, p<0.001) or immunosuppression

(OR=0.65, IC=0.59-0.72, p<0.001), as well as smokers (OR=0.85, IC=0.82-0.89,

p<0.001), had a lower risk of becoming infected by SARS-CoV-2 virus. In addition,

COPD did not contribute to the risk of infection, and cardiovascular diseases other

than HBP, very marginally decreased it, while CKD increased the risk of infection.

In the multivariate analysis, in which variables with a p≤0.2 in the URL models were

entered, all variables but three (immigrant, HBP, CKD) remained in the model. Very

noticeable, all the variables that together contributed to the risk of infection, only

explained 18.4% of the variability of the risk of infection. This suggests that

sociocultural variables, not included in the analysis, such as occupation, means of

transportation, and compliance with containment measures could be much more

important than the factors analyzed in this work to explain the risk of infection.

Risk of hospitalization by Covid-19 in Mexico.

For this analysis, the risk of being hospitalized was investigated once the subjects

were positive for SARS-Cov-2 RT-PCR (n=31,522). Among them, only 40.3%

(n=12,690) were hospitalized. In general, the factors that contributed to the risk of

infection also contributed to the risk of hospitalization, with notable exceptions and

variations (Table 3). For example, the risk of hospitalization conferred by age, began

in the 41-50 years-old group (OR=2.98, CI=2.47-3.59, p<0.001), considering the

MLR analysis, and rapidly increased to the >70 years (OR=11.6, CI=9.48-14.19,



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p<0.001). Unlike the risk of infection, for hospitalization, age contributed much more

than the city of residence (18% vs. 4.8%). In fact, age is the main contributing factor

for being hospitalized. Living in Juarez confers the highest risk of being hospitalized

(OR=4.22, CI=3.38-5.26, p<0.001), followed by the State of Mexico (OR=1.95,

CI=1.83-2.08, p<0.001) and Cancun (OR=1.77, CI=1.53-2.04, p<0.001). In contrast,

living in Mexico City, Monterrey and Guadalajara, confer lower risk for hospitalization

than living in the rest of the country.

Positive individuals for SARS-CoV-2 who speak an indigenous language are almost

twice as likely to be hospitalized (OR=1.89, IC=1.57-2.28, p<0.001), suggesting that

they consult at a more severe disease stage. Diabetes (OR=3.69, CI=3.48-3.92,

p<0.001), contributes more than HBP (OR=2.79, CI=2.64-2.95, p<0.001), and HBP,

more than obesity (OR=1.47, CI=1.39-1.55, p<0.001) on the risk of being

hospitalized.

On the other hand, factors that protected against infection, like immunosuppression

(OR=2.69, CI=2.26-3.2, p<0.001) or other cardiovascular diseases (OR=2.45,

CI=2.14-2.81, p<0.001), or that do not contribute to the risk of infection, such as

COPD (OR=3.63, CI=3.1-4.24, p<0.001) and CKD (OR=4.24, CI=3.61-4.97,

p<0.001), conferred a very high risk for hospitalization. Notoriously, being asthmatic

not only protected against infection but also against being hospitalized (OR=0.7,

CI=0.61-0.8, p<0.001). In the multivariate analysis all factors remained in the model,

except being an immigrant, other cardiovascular diseases, and smoking. The latter

conferred a marginal risk for hospitalization in the ULR model. The overall r2 value

obtained in the MLR model was 0.277, indicating that there are other factors



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explaining hospitalization, such as the severity of the disease at the time of diagnosis

of Covid-19, data that were not recorded in the analyzed registry.

DISCUSSION

Key notes.

In the current study we have found that place of residence, followed by advanced

age, suffering from diabetes, obesity, being male, having BPH, and notoriously being

indigenous conferred a higher for infection with SARS-CoV-2 in Mexico.

Unexpectedly, we found that having asthma, immunosuppression or smoking are

protective factors against infection, while COPD does not increase the risk for SARS-

CoV-2. In contrast, advanced age is the main factor for hospitalization for Covid-19,

followed by some co-morbidities, mainly diabetes, HBP, being indigenous, male sex

and place of residence. Unlike protecting against infection, immunosuppression and

COPD, were risk factors for hospitalization, while asthma was a protective factor.

Strengths and weaknesses.

The strengths of this work include the large size of the sample studied, the sampling

of individuals at the national level in Mexico, and the analysis of the most important

demographic data and comorbidities. However, important weaknesses are the lack

of information on the outcomes of the disease and sociocultural data that may be

important for the risk of infection, such as occupation, use of mass transit,

attendance at mass events, and adherence to containment measures in the country.

Discussion of findings and contrast with literature.

It is interesting to note that the greatest risk of infection and hospitalization is

conferred by cities with much trade with the USA (Tijuana and Juarez) and the port



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of Cancun, which is a massive gateway for foreign tourism to Mexico, both from the

USA and Europe. This suggests that at these sites, at the beginning of the pandemic,

there was a higher proportion of Covid-19 cases that were imported from abroad and

were higher than in other regions of the country, subsequently favoring the pandemic

expansion into the region. The metropolitan area, in the center of Mexico, including

Mexico City and the State of Mexico, is the second most common place of residence

conferring a risk of infection by SARS-Cov-2. However, while people living in Mexico

City are less at risk of hospitalization, people living in the State of Mexico have 2-

times greater risk than those living in the rest of the country, suggesting that by the

time they get the RT-PCR test, the disease is much more advanced.

Because of the age distribution of cases, the Covid-19 pandemic has been

characterized as an illness of adults.14, 15 Since the initial reports, data on individuals

below the age of 20 have been scarce.16 Efforts to detect infection in children have

been able to identify only a few and most of them present mild symptoms or are

asymptomatic and are identified during epidemiological surveillance.17 The age

disparities in observed cases could be explained by children having lower

susceptibility to infection, lower propensity to show clinical symptoms, or both.18 This

same study estimated that clinical symptoms occur in 25% of infected subjects within

the 10-19 age group, rising to 76% in those over 70 years.18 In Mexico, the infection

distribution according to age is similar to that in China and other countries. Table 1

shows that only 2.6% of all the tests have been performed in people younger than

20, and only 10% were positive. In contrast, close to 40% of individuals older than

60 that were tested, were positive. Furthermore, the risk of becoming infected

increased with age, starting from early ages (Table 2). The above data may be



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related to comorbidities (HBP, diabetes and obesity) that start earlier in life in the

Mexican population.19 However, this needs to be closely followed because the risk

is severe for the age group over 40. On the other hand, the risk of hospitalization

follows a well-established dose-response gradient with age, starting at 41 years of

age and jumping by decade to almost double for every age group, as shown in Table

3. This pattern has been observed in other countries as well.20

According to sex, men are more susceptible to viral infections and worst clinical

prognosis,21 eliciting lower amounts of interferon alpha (IFN-α) in response to toll-

like receptor (TLR) 7 ligands but also higher amounts of the immunosuppressive

cytokine interleukin (IL)-10 after stimulation with TLR8 and TLR9 ligands or viruses.

More specifically, in a large meta-analysis of Covid-19 a higher incidence in men

was reported.14 In fact, results from the randomized effects-model meta-analysis

revealed a higher male incidence (i.e., 60%) of Covid-19. This proclivity in men to

acquire the COVID 19 infection confirm previous accounts for MERS-COV and

SARS-COV.22, 23 Furthermore, a fatal outcome of Covid-19 infection in men, is

consistently found throughout series. Present observations are in line with the notion

that males are prone to COVID-19 infection, clinical deterioration and death.22, 24-26

Notably, indigenous ethnicity emerged as a risk factor for SARS-CoV-2 infection and

the need for hospitalization in this large dataset. However, the fact that indigenous

ethnicity is a risk factor for relevant clinical outcomes is a finding that should be

investigated in detail.26 We have recently described indigenous ethnicity is also a

risk factor for COVID-19 death.25 It is possible that factors that limit access to

healthcare may explain the emergence of this novel risk factor, such as the

availability of hospital and ICU beds, dedicated healthcare personnel, medical



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supplies, as well as geographic distance and time to travel from their communities

to hospitals, among other factors. This will be the subject of future analysis.

Globally, patients with SARS-CoV-2 and diabetes or metabolic syndrome had an

increased death-rate.27, 28 Considering that the burden of diabetes in Mexico is very

important increasing the general risk of mortality in at least five fold for every cause,23

is a crucial factor that together with obesity might explain the lower age-threshold for

the Covid-19 mortality found in this country (>41 years-old); in contrast with other

countries reporting increased risk for mortality in those older than 60 or 65.7 The

process causing damage to kidneys and heart,29, 30 might be more lethal in Mexican

diabetics. The above may be explained by the frequent presence of uncontrolled

levels of glucose and in general, without SARS-CoV-2, they are 30 times more at

risk than the rest of the population, to die of kidney failure.31 This excess risk causes

a larger burden during the pandemic to the already overloaded ICU services and

demands for new standards to define the best criteria to manage diabetics in

Mexico.26

In the current study, obesity was the second most important risk factor for SARS-

Cov-2 infection after obesity, however it was not such an important factor for

hospitalization. The higher risk for infection is in agreement with findings from China,

in which obesity remained a significant risk factor even after adjusting for age, sex,

smoking, diabetes, HBP, and dyslipidemia.8 However, the fact that in Mexico obesity

was less important than other risk factors for hospitalization, is somewhat different

than other reports.22, 32-35 The connection between obesity and COVID-19 is beyond

the scope of the current paper but factors such as attenuation of the immune system

and chronic inflammation, have been implicated.22 Also, binding of SARS-CoV-2 to



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the angiotensin converting enzyme 2 receptor (ACE2) occurs at low cytosolic pH

values. In the presence of diabetes and conditions such as HBP, obesity, old age,

and smoking, cytosolic pH is low, thus the virus may easily enter the cell by attaching

to ACE2.36 Nevertheless, the higher risk for COVID-19 infection among diabetic and

obese subjects in Mexico is an important factor considering that in 2017, this country

was number five in population suffering from diabetes in the world, with 12.0 million

subjects affected, and has one of the highest obesity prevalence-rates, 32.4%

among the 15-74 year-old population-group.37, 38 Furthermore, these findings are of

great importance for implementing public health strategies for preventing infection

with SARS-CoV-2 in this population.9

The Centers for Disease Control and Prevention (CDC) guidelines state that patients

with moderate to severe asthma could have a greater risk for severe Covid-19

illness.27 In fact asthmatics are at risk for more severe outcomes with other virus

infections. It is known that they have a delay innate anti-viral immune responses,

with deficiency in lung cell interferon , and responses related to higher asthma

exacerbation severity. 28, 29, 39 Recommendations for asthmatics during the Covid-19

pandemic are to continue their asthma medication to prevent exacerbations and be

lifesaving.40 Interestingly, in our study, asthma showed a decreased risk for Covid-

19 infection and hospitalization. In Covid-19, the evolution to a critical stage is

associated to a cytokine storm, so it could be possible that a regular use of low

dosage of corticosteroids or anti-inflammatory drugs, could prevent inflammation

and decrease the risk of a critical illness.41 In addition, COPD did not increase the

risk for Covid-19 infection in Mexico, however, it increased the risk for hospitalization.

This is consistent with other studies that reported COPD as a risk factor for a more



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severe COVID-19 illness.42, 43 In addition, the lower risk for infection conferred by

immunosuppression herein found, is in contrast with the higher risk for

hospitalization of this factor. As mentioned before, clinical deterioration of Covid-19

has been associated to an overactive immune response, and based on this

hypothesis, several immunosuppressants are currently studied as potential

treatments for severe Covid-19.44 Some authors hypothesized that a moderately

reduced immune response may play a favorable role in the severity of the diseases

due to a lack of significant increase in IL-6.45

One of the most intriguing issues is that smoking/nicotine theoretically should be a

risk factor for SARS-CoV-2 infection since nicotine affects the ACE2 receptors.46

However, our results suggest that smoking is a protective mechanism for Covid-19

infection. It has been published that there is an increased ACE2 expression in the

airways of current smokers and those with COPD.33, 47, 48 Studies indicate that ACE2

is likely to be the host-receptor for SARS-CoV-2,49 but also is involved in the

regulation of the renin-angiotensin system (RAS).50 Most importantly, ACE2 could

have a double mechanism, both protective and pathogenic roles within RAS

pathways, and its direct mechanisms in cells, remains unknown.50 It has been

hypothesized that nicotine, in other health conditions, could influence the cytokine

response.51 Nicotine is usually confounded with cigarette smoking, since it is the

most common mechanism to access the drug. Therefore, public health specialists

ought to consider it as a negative risk factor, since smoking is a health hazard.52

However, empirical data shows otherwise; nicotine regardless of the pathway utilized

to deliver it, may be a protector for the general population,53 including for Covid-19

infection. It is clear that promoting smoking in the population would make no sense,



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however nicotine could be provided as a pharmaceutical product in patches and

other forms to protect against Covid-19.

Conclusions

Based on the public registry of the Secretary of Health of Mexico of all the subject

that have been tested with RT-PCR for SARS-CoV-2, we have confirmed that older

age, diabetes, HBP and obesity are the main risk of infection and hospitalization for

Covid-19. Notably, indigenous ethnicity emerged as a risk factor for SARS-CoV-2

infection and the need for hospitalization in this large dataset. In addition, having

immunosuppression, smoking and asthma are protective factors against infection,

and the latter one also protects against hospitalization. These findings are important

for establishing public health policies and allocate healthcare resources during this

pandemic.

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Table 1. Demographic and clinical characteristic of studied population (n=102,875)*

Variable Value

Covid-19 confirmed % (n)

Hospitalized for Covid-19 % (n)

No (n= 71,353)

Yes (n= 31,522)

No (n= 18,832)

Yes (n= 12,690)

Demographic and Life-style factors

Gender Woman 74.1 (37,411) 25.9 (13,081) 66.9 (8,748) 33.1 (4,333)

Man 64.8 (33,942) 35.2 (18,441)f 54.7 (10,084) 45.3 (8,357)f

Age

<= 20 years 89.4 (6,880) 10.6 (813) 82.5 (671) 17.5 (142)

21-30 years 78.5 (14,602) 21.5 (3,990) 84.1 (3,355) 15.9 (635)

31-40 years 72.1 (17,917) 27.9 (6,923) 76.8 (5,316) 23.2 (1,607)

41-50 years 65.5 (14,087) 34.5 (7,425) 61.4 (4,556) 38.6 (2,869)

51-60 years 59.2 (9,089) 40.8 (6,257) 47.3 (2,957) 52.7 (3,300)

61-70 years 55.8 (4,698) 44.2 (3,727) 34.5 (1,286) 65.5 (2,441)

>70 years 63.1 (4,080) 36.9 (2,387)f 28.9 (691) 71.1 (1,696)f

Foreign No 69.2 (70,354) 30.8 (31,281) 59.6 (18,652) 40.4 (12,629)

Yes 80.6 (999) 19.4 (241)f 74.7 (180) 25.3 (61)f

Native speaking No 69.5 (70,657) 30.5 (31,067) 60 (18,631) 40 (12,436)

Yes 60.5 (696) 39.5 (455)f 44.2 (201) 55.8 (254)f

Immigrant No 69.3 (71,188) 30.7 (31,483) 59.7 (18,803) 40.3 (12,680)

Yes 80.9 (165) 19.1 (39)f 74.4 (29) 25.6 (10)a

Place of residence

Rest of the country 74.1 (37,853) 25.9 (13,206) 63.1 (8,331) 36.9 (4,875)

Mexico City 61.4 (13,875) 38.6 (8,705) 66.5 (5,792) 33.5 (2,913)

State of Mexico 55.5 (6,758) 44.5 (5,418) 46.7 (2,531) 53.3 (2,887)

Monterrey 90.9 (5,656) 9.1 (563) 83.3 (469) 16.7 (94)

Guadalajara 91.7 (3,859) 8.3 (348) 69 (240) 31 (108)

Puebla 65.1 (1,182) 34.9 (634) 50.2 (318) 49.8 (316)

Tijuana 36.9 (756) 63.1 (1,295) 41.6 (539) 58.4 (756)

Juarez City 42.6 (291) 57.4 (392) 28.8 (113) 71.2 (279)

Queretaro 76.7 (605) 23.3 (184) 63.6 (117) 36.4 (67)

Cancun 40 (518) 60 (777)f 49.2 (382) 50.8 (395)f

Smoking No 69 (64,180) 31 (28,774) 60.1 (17,286) 39.9 (11,488)

Yes 72.3 (7,173) 27.7 (2,748)f 56.3 (1,546) 43.7 (1,202)f

Co-morbidities

Diabetes No 71.3 (63,566) 28.7 (25,638) 65.6 (16,827) 34.4 (8,811)

Yes 57 (7,787) 43 (5,884)f 34.1 (2,005) 65.9 (3,879)f

Obesity No 71.1 (61,238) 28.9 (24,912) 61.7 (15,371) 38.3 (9,541)

Yes 60.5 (10,115) 39.5 (6,610)f 52.4 (3,461) 47.6 (3,149)f

HBP No 71 (60,386) 29 (24,699) 65.2 (16,093) 34.8 (8,606)

Yes 61.6 (10,967) 38.4 (6,823)f 40.1 (2,739) 59.9 (4,084)f

Asthma No 69 (67,777) 31 (30,508) 59.5 (18,145) 40.5 (12,363)

Yes 77.9 (3,576) 22.1 (1,014)f 67.8 (687) 32.2 (327)f

COPD No 69.3 (69,550) 30.7 (30,764) 60.5 (18,607) 39.5 (12,157)

Yes 70.4 (1,803) 29.6 (758)a 29.7 (225) 70.3 (533)f

Immunosuppression No 69.2 (69,435) 30.8 (30,966) 60.2 (18,632) 39.8 (12,334)

Yes 77.5 (1,918) 22.5 (556)f 36 (200) 64 (356)f

Cardiovascular diseases

No 69.3 (69,110) 30.7 (30,625) 60.4 (18,488) 39.6 (12,137)

Yes 71.4 (2,243) 28.6 (897)c 38.4 (344) 61.6 (553)f

CKD No 69.4 (69,692) 30.6 (30,733) 60.6 (18,622) 39.4 (12,111)



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Yes 67.8 (1,661) 32.2 (789)b 26.6 (210) 73.4 (579)f

Other diseases No 69 (67,671) 31 (30,334) 60 (18,205) 40 (12,129)

Yes 75.6 (3,682) 24.4 (1,188)f 52.8 (627) 47.2 (561)f HBP= High blood pressure, COPD= chronic obstructive pulmonary disease, CKD= chronic kidney disease *The percentage was calculated by the totals of each line a. p>0.05, b. p<0.05, c. p<0.01, d. p<0.001, e. p<0.001, f. p<0.0001



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Table 2. Risk of infection with the virus SARS-Cov-2 in Mexico (n=102,875)

Variable Value Univariate Logistic regression modelsa

OR (95% CI) p-value r2

Demographic and Life-style factors

Gender* Female 1

0.014 Male 1.55 (1.51-1.6) 4.7E-228

Age groups*

≤ 20 years 1

0.058

21-30 years 2.31 (2.13-2.51) 3.5E-92

31-40 years 3.27 (3.03-3.53) 9.7E-196

41-50 years 4.46 (4.13-4.82) 0.0E+00

51-60 years 5.83 (5.38-6.31) 0.0E+00

61-70 years 6.71 (6.17-7.31) 0.0E+00

>70 years 4.95 (4.53-5.41) 8.2E-275

Foreign* No 1

0.001 Yes 0.54 (0.47-0.62) 2.2E-17

Native speaking* No 1

0.001 Yes 1.49 (1.32-1.67) 6.3E-11

Immigrant No 1

0.0002 Yes 0.53 (0.38-0.76) 4.4E-4

Place of residence*

Rest of the Country 1

0.09

Mexico City 1.8 (1.74-1.86) 4.8E-261

State of Mexico 2.3 (2.21-2.39) 0.0E+00

Monterrey 0.29 (0.26-0.31) 1.8E-168

Guadalajara 0.26 (0.23-0.29) 4.5E-125

Puebla 1.54 (1.39-1.7) 1.1E-17

Tijuana 4.91 (4.48-5.38) 1.3E-252

Juarez City 3.86 (3.31-4.5) 3.8E-67

Queretaro 0.87 (0.74-1.03) 1.1E-01b

Cancun 4.3 (3.84-4.81) 2.2E-141

Smoking* No 1

0.001 Yes 0.85 (0.82-0.89) 2.4E-11

Co-morbidities

Diabetes* No 1

0.015 Yes 1.87 (1.81-1.94) 1.03E-244

Obesity* No 1

0.01 Yes 1.61 (1.55-1.66) 2.322E-161

High blood pressure No 1

0.008 Yes 1.52 (1.47-1.57) 8.8E-132

Asthma* No 1

0.002 Yes 0.63 (0.59-0.68) 3.1E-37

Immunosuppression* No 1

0.001 Yes 0.65 (0.59-0.72) 8.4E-19

COPD No 1

0 Yes 0.95 (0.87-1.04) >0.1

Cardiovascular disease*

No 1 0.00009

Yes 0.9 (0.83-0.98) 1.0E-2

Chronic kidney disease No 1

0.00004 Yes 1.08 (0.99-1.17) 8.9E-2

Others* No 1 0.001



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Yes 0.72 (0.67-0.77) 5E-22 COPD=Chronic obstructive pulmonary disease

a. r2 = Nagelkerke method. The p-values were calculated with the Omnibus test for r2, and Wald test for ORs. In the

multivariate analysis r2 = 0.184, and only the variables with a p≤0.1 in the ULR models were introduced in the MLR model. The variables with an asterisk remained in the MLR model. b. These variables had a p>0.05 in the MLR model.



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Table 3. Risk of hospitalization by Covid-19 in Mexico (n=31,522)

Variable Value Univariate Logistic regression modelsa

OR (95 CI) p-value r2 Demographic and Life-style factors

Gender* Female 1

0.02 Male 1.67 (1.6-1.75) 3.5E-104

Age groups*

≤ 20 years 1

0.18

21-30 years 0.89 (0.73-1.09) 2.7E-01

31-40 years 1.43 (1.18-1.73) 2.2E-04b

41-50 years 2.98 (2.47-3.59) 2.9E-30

51-60 years 5.27 (4.37-6.36) 1.7E-67

61-70 years 8.97 (7.39-10.88) 1.1E-109

>70 years 11.6 (9.48-14.19) 1.3E-125

Foreign* No 1

0.001 Yes 0.5 (0.37-0.67) 3.2E-06

Native speaking* No 1

0.002 Yes 1.89 (1.57-2.28) 1.9E-11

Immigrant No 1

0.0002 Yes 0.51 (0.25-1.05) 6.8E-02

Place of residence*

Rest of the Country 1

0.048

Mexico City 0.86 (0.81-0.91) 1.8E-07

State of Mexico 1.95 (1.83-2.08) 7.8E-93

Monterrey 0.34 (0.27-0.43) 7.8E-21

Guadalajara 0.77 (0.61-0.97) 2.5E-02

Puebla 1.7 (1.45-1.99) 8.0E-11

Tijuana 2.4 (2.13-2.69) 2.3E-49

Juarez City 4.22 (3.38-5.26) 3.3E-37

Queretaro 0.98 (0.72-1.32) 8.9E-01b

Cancun 1.77 (1.53-2.04) 1.4E-14

Smoking No 1

0.001 Yes 1.17 (1.08-1.27) 9.9E-05

Co-morbidities

Diabetes* No 1

0.081 Yes 3.69 (3.48-3.92) 0.0E+00

Obesity* No 1

0.008 Yes 1.47 (1.39-1.55) 7.2E-43

High blood pressure* No 1

0.057 Yes 2.79 (2.64-2.95) 5.0E-292

Asthma* No 1

0.001 Yes 0.7 (0.61-0.8) 1.4E-07

immunosuppression* No 1

0.006 Yes 2.69 (2.26-3.2) 1.3E-28

COPD* No 1

0.012 Yes 3.63 (3.1-4.24) 7.9E-58

Cardiovascular disease No 1

0.007 Yes 2.45 (2.14-2.81) 7.8E-38

Chronic kidney disease*

No 1 0.016

Yes 4.24 (3.61-4.97) 1.9E-70



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Others* No 1

0.001 Yes 1.34 (1.2-1.51) 6.6E-07

COPD=Chronic obstructive pulmonary disease

a. r2 = Nagelkerke method. The p-values were calculated with the Omnibus test for r2, and Wald test for ORs. In the

multivariate analysis r2 = 0.277, and only the variables with a p≤0.1 in the ULR models were introduced in the MLR model. The variables with an asterisk remained in the MLR model. b. These variables had a p>0.05 in the MLR model.



https://doi.org/10.1101/2020.05.24.20104414

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