Introduction

The present is made to advise about the situation of the COVID pandemics in México.

The goal is get the factors that affect to the mexican population and that can be prevented in order to provide tools that help to improve mitigate the impact of the pandemics and improve the health of the population in general.

After this analysis is made is provided a model to predict the risk of a given person once he/she has contracted COVID this is made as an exercise and must not be considered as a realistic tool, thought it was made in a formal way, cause the conditions of propagation has been varying due the vaccination, confinement conditions and other factors.

To do this this document is based in the data fetched from Dirección General de Epidemiología

COVID testing impact on this document.

One thing to consider is the reliability of the information with concern to the COVID tests made by the mexican government. It is known that no government or methodology can in a 100% say the number of people infected with COVID as many of the cases are not informed or not symptomatic. The mexican government has been doing more tests as the pandemics has been propagating in mexican ground and this has not been homogeneous at least concerning the time.

This can be seen in the following figure.

So, it is not valid make comparisons with other countries that have been doing more or less tests, this document will not compare the measures that depend on the reported cases with other countries. Instead it will use the hospitalized and confirmed people admitted for COVID disease.

Data cleaning

For this analysis the data that is not utterly defined and can be misclassified is deleted. For example in many cases the data can fall in categories like “NO APLICA” or “SE IGNORA”, considering this like a lack of certainty and confusion these records are deleted.

Main factors in mortality and severe disease cases of COVID.

This document analyses the factors shown in the files issued by the DGE trying to pointing when they are clearly a cause of having a risk because the COVID disease.

GENDER

As of 2021-09-11 in México has been a total of 3483846 with a total of 551985 people admitted in hospitals and 265118 deceased. The proportions are shown below.

Male Female Male proportion Female Proportion
Total cases 1745719 1738127 50.109 49.891
Total interned 324862 227123 58.853 41.147
Total deceased 164507 100611 62.05 37.95

The lethality rate for México is: \[ lethality=\frac{deceased}{total}*100=\frac{265118}{3483846}*100=7.61 \% \] The lethality by gender is:

male=9.423 %

female=5.788 %

Doing a binomial test in order to asure the correlation between gender and lethality:

## 
##  Exact binomial test
## 
## data:  total.male.deceased and total.male.cases
## number of successes = 2e+05, number of trials = 2e+06, p-value <2e-16
## alternative hypothesis: true probability of success is not equal to 0.0761
## 95 percent confidence interval:
##  0.0938 0.0947
## sample estimates:
## probability of success 
##                 0.0942

The confidence interval and the p-value suggest that there is a correlation between gender and lethality. With a chi squared test we have:

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data.covid.positives$fallecido and data.covid.positives$SEXO
## X-squared = 16367, df = 1, p-value <2e-16

Both test shows a difference in the lethality, so is plausible say that the lethality is bigger for men.

This graph shows the evolution of the deceases in the time.

Tabaquism

Based on the Encuesta Nacional de Consumo de Drogas, Alcohol y Tabaco 2016-2017 there are 14.9 million people who smoke in México (that is more or less 11.8% of the population). Tabaquism is known as a factor in many respiratory deficiencies that are correlated to 60,000 deceases by year EN MÉXICO, CASI 60 MIL MUERTES AL AÑO POR CONSUMO DE TABACO. As a probable factor in the impact of COVID this analysis is made.

The lethality rate for smokers is:

\[ \frac{19768}{235946}*100=8.378 \% \]

The lethality rate for smokers is closer that in other factors, doing the binomial test:

## 
##  Exact binomial test
## 
## data:  total.smokers.deceased and total.smokers
## number of successes = 19768, number of trials = 2e+05, p-value <2e-16
## alternative hypothesis: true probability of success is not equal to 0.0761
## 95 percent confidence interval:
##  0.0827 0.0849
## sample estimates:
## probability of success 
##                 0.0838

The p-value falls to 1e-6 and the general lethality rate (7.61) is outside the confidence interval, even so, the causality of deceases is less than in other causes. Doing the chi squared test for the lethality:

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data.covid.positives$fallecido and data.covid.positives$TABAQUISMO
## X-squared = 212, df = 1, p-value <2e-16

Let’s analyze the hospitalization rate.

\[ \frac{40171}{235946}*100=17.026 \% \]

Doing the binomial and chi test:

## 
##  Exact binomial test
## 
## data:  total.smokers.interned and total.smokers
## number of successes = 40171, number of trials = 2e+05, p-value <2e-16
## alternative hypothesis: true probability of success is not equal to 0.158
## 95 percent confidence interval:
##  0.169 0.172
## sample estimates:
## probability of success 
##                   0.17
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data.covid.positives$fallecido and data.covid.positives$TABAQUISMO
## X-squared = 212, df = 1, p-value <2e-16

The range for general hospitalization (0.158) falls barely outside the confidence interval.

As the ATS inform suggest (Cigarette Smoking and COVID-19: A Complex Interaction)[https://www.atsjournals.org/doi/10.1164/rccm.202005-1646LE] the interaction between smoking and COVID is complex and requiers a further analysis. It seems is not as clear the relation between lethality for COVID and smoking. Besides there is a relation between obesity and tabaquism that breakes the balance. This relation will be treated in another document.

Asthma

The lethality rate for people with asthma is:

\[ \frac{4606}{69754}*100=6.603 \% \]

The binomial test for lethality shows the following:

## 
##  Exact binomial test
## 
## data:  total.asthma.deceases and total.asthma.cases
## number of successes = 4606, number of trials = 69754, p-value <2e-16
## alternative hypothesis: true probability of success is not equal to 0.0761
## 95 percent confidence interval:
##  0.0642 0.0679
## sample estimates:
## probability of success 
##                  0.066

The lethality rate is less than in the general population (7.61) and the binomial test suggest that the negative correlation is truth.

The data suggest a NEGATIVE RELATIONSHIP BETWEEN ASTHMA AND COVID, having a minor rate in the lethality for people with asthma. Though sounding weird this can be explained considering that the therapeutics for asthma could protect people against COVID The Impact of COVID-19 on Patients with Asthma.

We can confirm trying to get an estimated of the severe COVID cases using the hospitalization data:

\[ \frac{10892}{69754}*100=15.615 \% \]

## 
##  Exact binomial test
## 
## data:  total.asthma.interned and total.asthma.cases
## number of successes = 10892, number of trials = 69754, p-value = 0.1
## alternative hypothesis: true probability of success is not equal to 0.158
## 95 percent confidence interval:
##  0.153 0.159
## sample estimates:
## probability of success 
##                  0.156

Again the hospitalization rate is less than the general (15.844).

So, we can conclude that is worth do further investigation about the effects of the asthma teurapeuthics against COVID.