{"id":49888,"date":"2023-06-30T10:00:47","date_gmt":"2023-06-30T10:00:47","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=49888"},"modified":"2023-07-11T06:59:09","modified_gmt":"2023-07-11T06:59:09","slug":"analysis-of-fatality-among-covid-19-cases-in-mexican-pregnant-women-a-cross-sectional-study","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol16no2\/analysis-of-fatality-among-covid-19-cases-in-mexican-pregnant-women-a-cross-sectional-study\/","title":{"rendered":"Analysis of Fatality Among COVID-19 Cases in Mexican Pregnant Women: A Cross-Sectional Study"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

In November-December 2019,\nseveral cases of pneumonia of unknown origin were detected in Wuhan, China1<\/sup>.\nAfter genomic analyses, the pathogen responsible was determined and called\nSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Meanwhile, the\ndisease was named Coronavirus Disease 2019 (COVID-19)2<\/sup>. Since then,\nthis disease has spread to the entire world. By November 1, 2021, 246,594,191\ncases and 4,998,784 deaths have been reported in 224 countries3<\/sup>.<\/p>\n\n\n\n

Pregnant women are likely to\nrepresent a high-risk population in the COVID-19 pandemic. Although pregnant\nwomen infected with SARS-CoV-2 are predominantly asymptomatic or present mild\nCOVID-19 -very similar to non-pregnant women4 <\/sup>– they may be at\nhigher risk of severe COVID-19 than the general population, according to\nWastnedge5<\/sup>.<\/p>\n\n\n\n

In a report from seven pregnant\nwomen infected by SARS-CoV-2 in Wuhan, China, all were in the last pregnancy\ntrimester and had contact with the epidemic area, but not with food from the\nseafood market in the same city. The median age was 32 years -spreading between\n29 and 34 years- and none was required to be treated in the ICU6<\/sup>.<\/p>\n\n\n\n

Pregnant women are considered a\ngroup at risk during the pandemic by SARS-CoV-2 due to biological processes\nrelated to pregnancy. It is hypothesized that a shift in the CD4 + cell\npopulation towards the Th2 phenotype, promoting the humor response, mediates\nthe altered inflammatory response to viruses during pregnancy. The Th2 and Th1\nresponse has been implicated in the pathogenesis of severe COVID-197<\/sup>.\nAlso, during pregnancy, a decrease in natural killer (NK) cells occurs8<\/sup>,\nand the NKs participate in virus neutralization9<\/sup>. The effect of the\nNK decreasing in the clinical picture of COVID-19 is unknown. There are other\nimmune effects as progesterone levels10<\/sup>, and alterations in\nToll-like receptors (TLRs)11<\/sup>. It remains to be elucidated whether\nthey have protective or harmful effects on pregnant women with COVID-195<\/sup>.\nBesides, the reduction in total lung capacity and the inability to expel\nsecretions can make pregnant women more susceptible to severe respiratory\ninfections12<\/sup>. <\/p>\n\n\n\n

Pregnancy is also a\nhypercoagulable state, with increased thrombin production and intravascular\ninflammation13<\/sup>. On the other hand, COVID-19 has been associated with\nthromboembolic complications in 31% of the patients reported in Knight M14<\/sup>.<\/p>\n\n\n\n

According to Lopez-Rodriguez15<\/sup>,\nin 29,416 pregnant Mexican women, 39% had a COVID-19-positive diagnosis. They\nfound that the risk of death was three times higher among those positive for\nCOVID-19 than those who tested negative.<\/p>\n\n\n\n

This paper aims to analyze the\ncases of COVID-19 in pregnant women registered in the open and public database\nof the National Epidemiological Surveillance System (NESS) from the General Directorate\nof Epidemiology (GDE) of the Ministry of Health (MH)16<\/sup> and their\nassociation with comorbidities.<\/p>\n\n\n\n

Material and methods<\/strong><\/p>\n\n\n\n

The study was approved by the Research\nEthics Committee from the Salamanca General Hospital, Mexico.<\/p>\n\n\n\n

Study design<\/strong><\/p>\n\n\n\n

It is a cross-sectional and analytical study.<\/p>\n\n\n\n

As a requisite to be tested, pregnant women must meet the operational\ndefinition of suspected cases of respiratory viral infection17<\/sup>. A\nsuspected case of viral disease is considered as anyone who presents fever,\nheadache, cough, or dyspnea (signs of severity), in addition to one of the\nfollowing: myalgias, arthralgias, odynophagia, chills, chest pain, rhinorrhea,\nanosmia, dysgeusia, or conjunctivitis. If a subject meets the operational\ndefinition, the subject undergoes a nasopharyngeal Real-Time Polymerase Chain\nReaction (RT-PCR) test17<\/sup>.<\/p>\n\n\n\n

The variables included in the database are\nthe following: state of residence, age, date of onset of symptoms, date of\nregistration, date of death (if it occurred), hospital or outpatient management,\npneumonia diagnosis, underlying presence of diabetes, Chronic Obstructive\nPulmonary Disease (COPD), asthma, immunosuppression, hypertension,\ncardiovascular disease, chronic kidney disease, obesity, or smoking. The\ndiagnosis tool for COVID-19 was the real-time polymerase chain reaction test\n(RT-PCR)16,17<\/sup>.<\/p>\n\n\n\n

The database does not record gestational age at the time of positive\ndiagnosis for SARS-CoV-2. <\/p>\n\n\n\n

After the approval of the research protocol by the ethics committee for\nresearch, we downloaded the open database of the NESS\/GDE, and we preprocessed\nit to verify the data consistency. The dataset was analyzed in STATA 13.0 \u00ae\n(Stata Corp., College Station, TX, USA). NESS\/GDE database does not include\npersonal data.<\/p>\n\n\n\n

Statistical analysis<\/strong><\/p>\n\n\n\n

Descriptive statistics are presented for\nall the variables. To compare quantitative variables between confirmed COVID-19\ncases, deceased or not, the Student t-test was performed. To compare\ncategorical variables, the Chi-squared was used. Odds Ratio (OR) and 95% confidence\nintervals (95%CI) were calculated to estimate the effect of independent\nvariables on dying from COVID-19. Also, it was calculated the Case Fatality\nRatio (CFR) among pregnant women. In all cases, the corresponding P-values are\npresent. Also, to demonstrate the statistical significance of the results, the\nalpha value was set at 0.05. Statistical analysis was performed in STATA 13.0 \u00ae\n(Stata Corp., College Station, TX, USA).<\/p>\n\n\n\n

Results<\/strong><\/p>\n\n\n\n

Description of the pregnant women population<\/strong><\/p>\n\n\n\n

In the NESS\/GDE database, 42,334 pregnant\nwomen were reported; from them, 13,891 (32.81%) tested positive for SARS-CoV-2\nand 28,443 (67.19%) tested negative16<\/sup>. Characterization of death\ncases <\/p>\n\n\n\n

Both SARS-CoV-2 positive and negative pregnant women had an age range\nof 12 to 49 years and a mean of 28.41 \u00b1 6.13 years for the former and 27.09 \u00b1\n6.36 years for the latter -the difference was statistically significant (P =\n.00001)-. According to the database, the difference between the onset of\nsymptoms and registry into the system, on average, is higher among COVID-19\ncases than among negative ones (P <.05) (Table 1).<\/p>\n\n\n\n

The CFR was 152\/13,739 (1.1%). For confirmed cases, the average age\namong the deceased cases was greater than among those who did not die (P\n<0.05). The average time between the onset of symptoms and registration into\nthe system was greater among those who died. (P <0.05) (Table 1).<\/p>\n\n\n\n

Table 1: Distribution of quantitative variables among deaths and non-deaths by COVID-19 in Mexican pregnant<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Variable<\/strong><\/p>\n<\/td>\n

\n

Confirmed COVID-19 cases<\/strong><\/p>\n<\/td>\n

\n

t-test, df, P-value<\/em><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Deaths <\/strong><\/p>\n<\/td>\n

\n

Non-deaths<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

(n=152) <\/strong><\/p>\n<\/td>\n

\n

 (n=13,739)<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Age (years)<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

6.42, 13889, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

  Minimum to maximum<\/strong><\/p>\n<\/td>\n

\n

16 to 49<\/p>\n<\/td>\n

\n

12 to 49<\/p>\n<\/td>\n<\/tr>\n

\n

  Mean \u00b1 S<\/strong><\/p>\n<\/td>\n

\n

31.59 \u00b1 6.89<\/p>\n<\/td>\n

\n

28.38 \u00b1 6.12<\/p>\n<\/td>\n<\/tr>\n

\n

Time between beginning of symptoms and date of registry (days)<\/strong><\/p>\n<\/td>\n

 <\/td>\n <\/td>\n\n

4.96, 13899, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

  Minimum to maximum<\/strong><\/p>\n<\/td>\n

\n

0 to 16<\/p>\n<\/td>\n

\n

0 to 39<\/p>\n<\/td>\n<\/tr>\n

\n

  Mean \u00b1 S<\/strong><\/p>\n<\/td>\n

\n

4.90 \u00b1 3.55<\/p>\n<\/td>\n

\n

3.68 \u00b1 3.06<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Abbreviations: df degree of freedom<\/p>\n\n\n\n

Between pregnant women positive for COVID-19, the CFR by age group\nwere: 12-19 years, 0.44%, 20-29 years, 0.78%, 30-39 years, 1.36%, and 40-49\nyears, 3.74%.<\/p>\n\n\n\n

The predominant age group among the deceased cases was 30-39 years\n(47.37%). Meanwhile, in the non-deceased, those aged 20 to 29 years (51.63%) (P\n<0.05) predominated. <\/p>\n\n\n\n

Associated risk factors for lethality<\/strong><\/p>\n\n\n\n

Among the deceased cases, hospitalized patients predominated (93.42%).\nCompared with the non-deceased, where most subjects received outpatient\nmanagement (82.27%) (P <0.05). Among the deceased, 76.97% had pneumonia.\nAmong the non-deceased, only 17.73% had it (P <0.05). A statistically\nsignificant relationship with death (P <0.05) was found for diabetes, COPD,\nasthma, immunosuppression, hypertension, chronic kidney disease, obesity, and\nsmoking (Table 2).<\/p>\n\n\n\n

 Table 2: Relationship between variables and deaths by COVID-19 in Mexican pregnant women.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 <\/p>\n<\/td>\n

\n

Confirmed COVID-19 cases<\/strong><\/p>\n<\/td>\n

\n

X2<\/sup>, df, P-value<\/em><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Deaths<\/strong><\/p>\n<\/td>\n

\n

Non-deaths<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

n<\/strong><\/p>\n<\/td>\n

\n

%<\/strong><\/p>\n<\/td>\n

\n

n  <\/strong><\/p>\n<\/td>\n

\n

%<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Age group (years)<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

47.86, 3, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   12 to 19<\/strong><\/p>\n<\/td>\n

\n

4<\/p>\n<\/td>\n

\n

2.63<\/p>\n<\/td>\n

\n

897<\/p>\n<\/td>\n

\n

6.53<\/p>\n<\/td>\n<\/tr>\n

\n

   20 to 29<\/strong><\/p>\n<\/td>\n

\n

56<\/p>\n<\/td>\n

\n

36.84<\/p>\n<\/td>\n

\n

7094<\/p>\n<\/td>\n

\n

51.6<\/p>\n<\/td>\n<\/tr>\n

\n

   30 to 39<\/strong><\/p>\n<\/td>\n

\n

72<\/p>\n<\/td>\n

\n

47.37<\/p>\n<\/td>\n

\n

5233<\/p>\n<\/td>\n

\n

38.1<\/p>\n<\/td>\n<\/tr>\n

\n

   40 to 49<\/strong><\/p>\n<\/td>\n

\n

20<\/p>\n<\/td>\n

\n

13.16<\/p>\n<\/td>\n

\n

515<\/p>\n<\/td>\n

\n

3.75<\/p>\n<\/td>\n<\/tr>\n

\n

Care type<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

569.84, 1, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Hospitalized<\/strong><\/p>\n<\/td>\n

\n

142<\/p>\n<\/td>\n

\n

93.42<\/p>\n<\/td>\n

\n

2436<\/p>\n<\/td>\n

\n

17.7<\/p>\n<\/td>\n<\/tr>\n

\n

   Ambulatory<\/strong><\/p>\n<\/td>\n

\n

10<\/p>\n<\/td>\n

\n

6.58<\/p>\n<\/td>\n

\n

11303<\/p>\n<\/td>\n

\n

82.3<\/p>\n<\/td>\n<\/tr>\n

\n

Pneumonia   <\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

1000.03, 1, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

117<\/p>\n<\/td>\n

\n

76.97<\/p>\n<\/td>\n

\n

952<\/p>\n<\/td>\n

\n

6.93<\/p>\n<\/td>\n<\/tr>\n

\n

   No<\/strong><\/p>\n<\/td>\n

\n

35<\/p>\n<\/td>\n

\n

23.03<\/p>\n<\/td>\n

\n

12787<\/p>\n<\/td>\n

\n

93.1<\/p>\n<\/td>\n<\/tr>\n

\n

Diabetes<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

74.04,  1, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

20<\/p>\n<\/td>\n

\n

13.16<\/p>\n<\/td>\n

\n

414<\/p>\n<\/td>\n

\n

3.01<\/p>\n<\/td>\n<\/tr>\n

\n

   No<\/strong><\/p>\n<\/td>\n

\n

130<\/p>\n<\/td>\n

\n

85.53<\/p>\n<\/td>\n

\n

13313<\/p>\n<\/td>\n

\n

96.9<\/p>\n<\/td>\n<\/tr>\n

\n

Chronic Obstructive Pulmonary Disease<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

*-4.17, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

21<\/p>\n<\/td>\n

\n

0.15<\/p>\n<\/td>\n<\/tr>\n

\n

   No <\/strong><\/p>\n<\/td>\n

\n

149<\/p>\n<\/td>\n

\n

98.03<\/p>\n<\/td>\n

\n

13706<\/p>\n<\/td>\n

\n

99.8<\/p>\n<\/td>\n<\/tr>\n

\n

Asthma<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

28.15, 1, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

5<\/p>\n<\/td>\n

\n

3.29<\/p>\n<\/td>\n

\n

279<\/p>\n<\/td>\n

\n

2.03<\/p>\n<\/td>\n<\/tr>\n

\n

   No <\/strong><\/p>\n<\/td>\n

\n

145<\/p>\n<\/td>\n

\n

95.39<\/p>\n<\/td>\n

\n

13450<\/p>\n<\/td>\n

\n

97.9<\/p>\n<\/td>\n<\/tr>\n

\n

Immunosuppression<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

17.70, 1, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

1.32<\/p>\n<\/td>\n

\n

91<\/p>\n<\/td>\n

\n

0.66<\/p>\n<\/td>\n<\/tr>\n

\n

   No<\/strong><\/p>\n<\/td>\n

\n

148<\/p>\n<\/td>\n

\n

97.37<\/p>\n<\/td>\n

\n

13632<\/p>\n<\/td>\n

\n

99.2<\/p>\n<\/td>\n<\/tr>\n

\n

Hypertension <\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

35.74, 1, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

15<\/p>\n<\/td>\n

\n

9.87<\/p>\n<\/td>\n

\n

362<\/p>\n<\/td>\n

\n

2.63<\/p>\n<\/td>\n<\/tr>\n

\n

   No<\/strong><\/p>\n<\/td>\n

\n

136<\/p>\n<\/td>\n

\n

89.47<\/p>\n<\/td>\n

\n

13366<\/p>\n<\/td>\n

\n

97.3<\/p>\n<\/td>\n<\/tr>\n

\n

Cardiovascular Disease<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

*-1.38, 0.17<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

59<\/p>\n<\/td>\n

\n

0.43<\/p>\n<\/td>\n<\/tr>\n

\n

   No<\/strong><\/p>\n<\/td>\n

\n

150<\/p>\n<\/td>\n

\n

98.68<\/p>\n<\/td>\n

\n

13669<\/p>\n<\/td>\n

\n

99.5<\/p>\n<\/td>\n<\/tr>\n

\n

Chronic Kidney Disease<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

35.09, 1, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

3<\/p>\n<\/td>\n

\n

1.97<\/p>\n<\/td>\n

\n

39<\/p>\n<\/td>\n

\n

0.28<\/p>\n<\/td>\n<\/tr>\n

\n

   No<\/strong><\/p>\n<\/td>\n

\n

147<\/p>\n<\/td>\n

\n

96.71<\/p>\n<\/td>\n

\n

13687<\/p>\n<\/td>\n

\n

99.6<\/p>\n<\/td>\n<\/tr>\n

\n

Obesity   <\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

29.76, 1, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

26<\/p>\n<\/td>\n

\n

17.11<\/p>\n<\/td>\n

\n

1026<\/p>\n<\/td>\n

\n

7.47<\/p>\n<\/td>\n<\/tr>\n

\n

   No<\/strong><\/p>\n<\/td>\n

\n

125<\/p>\n<\/td>\n

\n

82.24<\/p>\n<\/td>\n

\n

12706<\/p>\n<\/td>\n

\n

92.5<\/p>\n<\/td>\n<\/tr>\n

\n

Smoking<\/strong><\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n

\n

27.27, 1, <0.001<\/p>\n<\/td>\n<\/tr>\n

\n

   Yes<\/strong><\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

1.32<\/p>\n<\/td>\n

\n

262<\/p>\n<\/td>\n

\n

1.91<\/p>\n<\/td>\n<\/tr>\n

\n

   No<\/strong><\/p>\n<\/td>\n

\n

148<\/p>\n<\/td>\n

\n

97.37<\/p>\n<\/td>\n

\n

13467<\/p>\n<\/td>\n

\n

98<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Notes: * Z for two proportions<\/p>\n\n\n\n

Using the logistic regression model that included all the variables\nwith a statistically significant relationship with death, an effect on dying\nwas detected for the age group from 40 to 49 years (OR 4.97 95%CI 1.77-17.85).\nThe difference in days between the onset of symptoms and system registration is\nin the border area. The type of outpatient care has a protective effect on\ndying (OR 0.04 95%CI 0.02-0.09). Meanwhile, Pneumonia shows a high association\n(OR 8.68 95%CI 5.72-13.6). Diabetes, asthma, immunosuppression, hypertension,\nchronic kidney disease, and obesity did not affect death. As seen in the\nvariance inflation factor (VIF), these variables may be highly correlated.\nHence, they have little influence on the model. Smoking has little effect on\ndeath due to COVID-19 among pregnant women (Table 3). We fitted a model by\nomitting diabetes, immunosuppression, hypertension, and chronic kidney disease\n-those with higher VIF, different age groups, and non-statistically significant\neffects-. The Bayesian Information Criteria decreased from 1217.52 in the\noriginal model to 1190.26. The likelihood ratio test showed a statistically\nsignificant difference between the models. Nevertheless, the qualitative\nresults were the same.<\/p>\n\n\n\n

Table 3: Logistic regression model including all independent variables with death in Mexican pregnant women<\/strong>.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Deaths<\/strong><\/p>\n<\/td>\n

\n

OR<\/strong><\/p>\n<\/td>\n

\n

(95% CI) adjusted<\/strong><\/p>\n<\/td>\n

\n

VIF<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Age group<\/strong><\/p>\n<\/td>\n

 <\/td>\n <\/td>\n <\/td>\n<\/tr>\n
\n

12-19<\/strong><\/p>\n<\/td>\n

\n

Reference group<\/p>\n<\/td>\n

\n

————–<\/p>\n<\/td>\n

 <\/td>\n<\/tr>\n
\n

20 \u2013 29<\/strong><\/p>\n<\/td>\n

\n

1.67<\/p>\n<\/td>\n

\n

( 0.66 – 5.68 )<\/p>\n<\/td>\n

\n

8.97<\/p>\n<\/td>\n<\/tr>\n

\n

30 \u2013 39 <\/strong><\/p>\n<\/td>\n

\n

2.34<\/p>\n<\/td>\n

\n

( 0.93 – 7.92 )<\/p>\n<\/td>\n

\n

9.39<\/p>\n<\/td>\n<\/tr>\n

\n

40 \u2013 49 <\/strong><\/p>\n<\/td>\n

\n

4.97<\/p>\n<\/td>\n

\n

( 1.77 – 17.85 )<\/p>\n<\/td>\n

\n

4.55<\/p>\n<\/td>\n<\/tr>\n

\n

Difference in days <\/strong><\/p>\n<\/td>\n

\n

1.04<\/p>\n<\/td>\n

\n

( 0.99 – 1.09 )<\/p>\n<\/td>\n

\n

1.02<\/p>\n<\/td>\n<\/tr>\n

\n

Care type<\/strong><\/p>\n<\/td>\n

\n

0.04<\/p>\n<\/td>\n

\n

( 0.02 – 0.09 )<\/p>\n<\/td>\n

\n

1.27<\/p>\n<\/td>\n<\/tr>\n

\n

Pneumonia<\/strong><\/p>\n<\/td>\n

\n

8.68<\/p>\n<\/td>\n

\n

( 5.72 – 13.6 )<\/p>\n<\/td>\n

\n

1.22<\/p>\n<\/td>\n<\/tr>\n

\n

Diabetes<\/strong><\/p>\n<\/td>\n

\n

0.98<\/p>\n<\/td>\n

\n

( 0.89 – 1.04 )<\/p>\n<\/td>\n

\n

9.56<\/p>\n<\/td>\n<\/tr>\n

\n

Chronic Obstructive Pulmonary Disease<\/strong><\/p>\n<\/td>\n

\n

1.07<\/p>\n<\/td>\n

\n

( 1.04 – 1.11 )<\/p>\n<\/td>\n

\n

2.66<\/p>\n<\/td>\n<\/tr>\n

\n

Asthma<\/strong><\/p>\n<\/td>\n

\n

1.05<\/p>\n<\/td>\n

\n

( 1 – 1.1 )<\/p>\n<\/td>\n

\n

4.48<\/p>\n<\/td>\n<\/tr>\n

\n

Immunosuppression<\/strong><\/p>\n<\/td>\n

\n

0.95<\/p>\n<\/td>\n

\n

( 0.84 – 1.03 )<\/p>\n<\/td>\n

\n

49.11<\/p>\n<\/td>\n<\/tr>\n

\n

Hypertension<\/strong><\/p>\n<\/td>\n

\n

0.97<\/p>\n<\/td>\n

\n

( 0.87 – 1.02 )<\/p>\n<\/td>\n

\n

16.02<\/p>\n<\/td>\n<\/tr>\n

\n

Chronic kidney disease<\/strong><\/p>\n<\/td>\n

\n

0.99<\/p>\n<\/td>\n

\n

( 0.87 – 1.06 )<\/p>\n<\/td>\n

\n

32.98<\/p>\n<\/td>\n<\/tr>\n

\n

Obesity<\/strong><\/p>\n<\/td>\n

\n

0.96<\/p>\n<\/td>\n

\n

( 0.87 – 1.01 )<\/p>\n<\/td>\n

\n

1.68<\/p>\n<\/td>\n<\/tr>\n

\n

Smoking<\/strong><\/p>\n<\/td>\n

\n

1.05<\/p>\n<\/td>\n

\n

( 1.01 – 1.09 )<\/p>\n<\/td>\n

\n

1.91<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Notes: VIF Variance inflation factor<\/p>\n\n\n\n

Discussion<\/strong><\/p>\n\n\n\n

The CFR reported by Padilla18<\/sup>\nfor the Mexican population on May 15, 2020 (10.59%) was higher than the one for\nthe sample in this study (1.1%). Padilla18<\/sup>, also mentions that\n68.14% of the deaths were in males and 49.57% in those over 60 years.<\/p>\n\n\n\n

Table 2 reports a relationship between the study variables and death\nfrom COVID-19, except for cardiovascular disease. When quantifying the effect\nof these variables, only the age group, the difference in days between the\nonset of symptoms and the registration in the system, the type of care,\npneumonia, COPD, asthma, and smoking affected death (Table 3).<\/p>\n\n\n\n

Of nine pregnant women with COVID-19 and pneumonia, none developed\nsevere pneumonia or died, as reported by Cheng19<\/sup>. In the sample of\nMexican pregnant women, 18.56% were hospitalized, 7.70% developed pneumonia,\nand 1.09% died (Table 2). In a series of 116 cases of pregnant women with\nCOVID-19, 8% developed pneumonia20<\/sup>, like the results in this study.\nYu21<\/sup>, reported 124 maternal deaths in Brazil between February and\nJune 2020. At the same time, Mexico reported seven maternal deaths22<\/sup>.\nThe increase in maternal mortality from COVID-19 in Brazil may be due to a\ndecreased availability of health professionals or a lack of intensive care\nunits.<\/p>\n\n\n\n

There is no evidence that pregnant women are more susceptible to\nSARS-CoV-223<\/sup>.<\/p>\n\n\n\n

In the analysis of the relationship between different variables with\ndying from COVID-19 was found that age group, hospitalization, pneumonia,\ndiabetes, COPD, asthma, immunosuppression, hypertension, chronic kidney\ndisease, obesity, and smoking are related to death from COVID-19 (P <0.05)\n(Table 2). Nevertheless, these relationships are lost when analyzing the logistic\nregression model that included all the variables associated with death from\nCOVID-19.<\/p>\n\n\n\n

The age group of 40-49 years shows an effect on dying from COVID-19 (OR\n= 4.97, 95% CI 1.61 -15.36). The age group from 40 to 49 years had the higher\nCFR, 3.74%. The difference in days between the onset of symptoms and the date\nof registry in the NESS\/DGE system is at the limit of statistical significance\n(OR = 1.04, 95% CI 1.00-1.09). Pneumonia is the one variable with the highest\neffect on dying (OR = 8.68, 95% CI 5.64 – 13.37). COPD, asthma, and smoking\nshow ORs close to 1 (Table 3), which indicates a slight effect.<\/p>\n\n\n\n

Pneumonia showed the highest OR on death from COVID-19 (Table 3),\nconsistent with previous reports concerning children and adults, where the\nprincipal cause of death was pneumonia.<\/p>\n\n\n\n

The underlying pathologies that aggravate COVID-19 in the general\npopulation seem to have minor effects on the severity of COVID-19 in pregnant\nwomen.<\/p>\n\n\n\n

Pregnant women have been considered a risk group for COVID-19. In the\ndatabase analyzed, the co-morbidities did not play a statistically significant\nrole in death while integrating them in a multivariable logistic regression\nmodel, except for COPD and asthma. Also, health care workers must monitor the\ndevelopment of pneumonia because it is significantly related to death. A\nsubstantial limitation of this study is the absence of data about gestational\nage. This variable could play a confounding role in mortality. It is worth\nnoting that some deaths occurred among pregnant women with ambulatory care. It\nshould be addressed in further research. Due to the limitations of the\ndatabase, it was not possible in this study. <\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

As in the general population, older age and\npneumonia relate to death. The co-morbidities had a low prevalence among\npregnant women. Since the reproductive age usually ends at 50, pregnant women\nhave two protective factors for COVID-19 detected in the literature: being\nyoung and woman. They have been two inherent confounding factors in this study.<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/p>\n\n\n\n

There are no conflict of interest<\/p>\n\n\n\n

Funding Source<\/strong><\/p>\n\n\n\n

There is no funding Sources<\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

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