SARS-CoV-2 infection by trimester of pregnancy and adverse perinatal outcomes: a Mexican retrospective cohort study.

OBJECTIVE: Conflicting evidence for the association between COVID-19 and adverse perinatal outcomes exists. This study examined the associations between maternal COVID-19 during pregnancy and adverse perinatal outcomes including preterm birth (PTB), low birth weight (LBW), small-for-gestational age (SGA), large-for-gestational age (LGA) and fetal death; as well as whether the associations differ by trimester of infection. DESIGN AND SETTING: The study used a retrospective Mexican birth cohort from the Instituto Mexicano del Seguro Social (IMSS), Mexico, between January 2020 and November 2021. PARTICIPANTS: We used the social security administrative dataset from IMSS that had COVID-19 information and linked it with the IMSS routine hospitalisation dataset, to identify deliveries in the study period with a test for SARS-CoV-2 during pregnancy. OUTCOME MEASURES: PTB, LBW, SGA, LGA and fetal death. We used targeted maximum likelihood estimators, to quantify associations (risk ratio, RR) and CIs. We fit models for the overall COVID-19 sample, and separately for those with mild or severe disease, and by trimester of infection. Additionally, we investigated potential bias induced by missing non-tested pregnancies. RESULTS: The overall sample comprised 17 340 singleton pregnancies, of which 30% tested positive. We found that those with mild COVID-19 had an RR of 0.89 (95% CI 0.80 to 0.99) for PTB and those with severe COVID-19 had an RR of 1.53 (95% CI 1.07 to 2.19) for LGA. COVID-19 in the first trimester was associated with fetal death, RR=2.36 (95% CI 1.04, 5.36). Results also demonstrate that missing non-tested pregnancies might induce bias in the associations. CONCLUSIONS: In the overall sample, there was no evidence of an association between COVID-19 and adverse perinatal outcomes. However, the findings suggest that severe COVID-19 may increase the risk of some perinatal outcomes, with the first trimester potentially being a high-risk period.

Estimating the effect of realistic improvements of metformin adherence on COVID-19 mortality using targeted machine learning.

Background: Type 2 diabetes elevates the risk of severe outcomes in COVID-19 patients, with multiple studies reporting higher case fatality rates. Metformin is a widely used medication for glycemic management. We hypothesize that improved adherence to metformin may lower COVID-19 post-infection mortality risk in this group. Utilizing data from the Mexican Social Security Institute (IMSS), we investigate the relationship between metformin adherence and mortality following COVID-19 infection in patients with chronic metformin prescriptions. Methods: This is a retrospective cohort study consisting of 61,180 IMSS beneficiaries who received a positive polymerase chain reaction (PCR) or rapid test for SARS-CoV-2 and had at least two consecutive months of metformin prescriptions prior to the positive test. The hypothetical intervention is improved adherence to metformin, measured by proportion of days covered (PDC), with the comparison being the observed metformin adherence values. The primary outcome is all-cause mortality following COVID-19 infection. We defined the causal parameter using shift intervention, an example of modified treatment policies. We used the targeted learning framework for estimation of the target estimand. Findings: Among COVID-19 positive patients with chronic metformin prescriptions, we found that a 5% and 10% absolute increase in metformin adherence is associated with a respective 0.26% (95% CI: -0.28%, 0.79%) and 1.26% (95% CI: 0.72%, 1.80%) absolute decrease in mortality risk. Interpretation: Subject to the limitations of a real-world data study, our results indicate a causal association between improved metformin adherence and reduced COVID-19 post-infection mortality risk.

Who is most at risk of dying if infected with SARS-CoV-2? A mortality risk factor analysis using machine learning of patients with COVID-19 over time: a large population-based cohort study in Mexico.

OBJECTIVE: COVID-19 would kill fewer people if health programmes can predict who is at higher risk of mortality because resources can be targeted to protect those people from infection. We predict mortality in a very large population in Mexico with machine learning using demographic variables and pre-existing conditions. DESIGN: Cohort study. SETTING: March 2020 to November 2021 in Mexico, nationally represented. PARTICIPANTS: 1.4 million laboratory-confirmed patients with COVID-19 in Mexico at or over 20 years of age. PRIMARY AND SECONDARY OUTCOME MEASURES: Analysis is performed on data from March 2020 to November 2021 and over three phases: (1) from March to October in 2020, (2) from November 2020 to March 2021 and (3) from April to November 2021. We predict mortality using an ensemble machine learning method, super learner, and independently estimate the adjusted mortality relative risk of each pre-existing condition using targeted maximum likelihood estimation. RESULTS: Super learner fit has a high predictive performance (C-statistic: 0.907), where age is the most predictive factor for mortality. After adjusting for demographic factors, renal disease, hypertension, diabetes and obesity are the most impactful pre-existing conditions. Phase analysis shows that the adjusted mortality risk decreased over time while relative risk increased for each pre-existing condition. CONCLUSIONS: While age is the most important predictor of mortality, younger individuals with hypertension, diabetes and obesity are at comparable mortality risk as individuals who are 20 years older without any of the three conditions. Our model can be continuously updated to identify individuals who should most be protected against infection as the pandemic evolves.

Genomic insights of Leclercia adecarboxylata strains linked to an outbreak in public hospitals in Mexico.

BACKGROUND: Leclercia adecarboxylata is a bacteria closely related to Escherichia coli according to its biochemical characteristics and is commonly considered non-pathogenic although a growing number of publications classify it as an emerging pathogen. Fosfomycin resistance is a common trait for L. adecarboxylata encoded by fosALA gene. OBJECTIVE: To analyze genomic traits of sixteen L. adecarboxylata strains isolated from blood culture and a bottle of total parenteral nutrition. METHODS: Twenty-eight L. adecarboxylata strains isolated from blood culture and a bottle of total parenteral nutrition were identified biochemically with a Vitek ® automated system. The strains were phenotyped by their growth on Eosin Methylene Blue agar or MacConkey agar plates. Additionally, Pulsed field gel electrophoresis (PFGE) was performed to establish the clonal relationship. The genomic DNA of sixteen strains was obtained using a Qubit ® dsDNA HS Assay Kit and sequenced on an Illumina ® MiSeq instrument. Draft genomes were assembled using PROKKA and Rast. Assemblies were submitted to Resfinder and PathogenFinder from the Center for Genomic Epidemiology in order to find resistance genes and pathogenic potential. IslandViewer4 was also used to find Pathogenicity and Phage Islands. For identification of the fosA gene, manual curation and Clustal analysis was performed. A novel FosA variant was identified. Finally, phylogenetic analysis was performed using VAMPhyRE software and Mega X. RESULTS: In this paper, we report the genomes of sixteen strains of Leclercia adecarboxylata causing an outbreak associated with parenteral nutrition in public hospitals in Mexico. The genomes were analyzed for genetic determinants of virulence and resistance. A high pathogenic potential (pathogenicity index 0.82) as well as multiple resistance genes including carbapenemics, colistin and efflux pumps were determined. Based on sequence analysis, a new variant of the fosALA gene was described. Finally, the outbreak was confirmed by establishing the clonal relationship among the sixteen genomes obtained. CONCLUSIONS: Commensal strains of L. adecarboxylata may acquire genetic determinants that provide mechanisms of host damage and go unnoticed in clinical diagnosis. L. adecarboxylata can evolve in a variety of ways including the acquisition of resistance and virulence genes representing a therapeutic challenge in patient care.

CYP2D6 does not impact on breast cancer-free survival in Southeast Mexican patients under tamoxifen treatment.

Aim: We conducted a retrospective analysis in 71 Mexican Mestizo patients to evaluate the breast cancer-free survival (BCFS) among the inferred genetic phenotypes (GP) of CYP2D6. Patients & methods:CYP2D6 was genotyped through Taqman-probe analysis; GP were inferred according to international guidelines. The BCFS was estimated through Kaplan-Meier method and analyzed with a log-rank test; hazard ratios were calculated with 95% CI and p < 0.05. Results: The BCFS did not differ among CYP2D6 GP (p = 0.45) and recurrence risk was similar between gNM + gUM and gPM + gIM groups (hazard ratio: 1.54, 95% CI: 0.37-6.38; p = 0.55). Conclusion: The findings do not support any impact of CYP2D6 on BCFS. Evaluation of other genetic/nongenetic biomarkers is needed in Mexican Mestizo patients under tamoxifen treatment.

Characterization of Large Copy Number Variation in Mexican Type 2 Diabetes subjects.

The effect of Copy Number Variants (CNVs) on Type 2 Diabetes (T2D) remains little explored. The present study characterized large rare CNVs in 686 T2D and 194 non-T2D subjects of Mexican ancestry genotyped using the Affymetrix Genome-Wide Human SNP array 5.0. Rare CNVs with ≥ 100 kb length were identified using a stringent strategy based on merging CNVs calls generated using Birdsuit, iPattern and PennCNV algorithms. We applied three different strategies to evaluate the distribution of CNVs in the T2D and non-T2D samples: 1) Burden analysis, 2) Identification of CNVs in loci previously associated to T2D, and 3) Identification of CNVs observed only in the T2D group. In the CNV burden analysis, the T2D group showed a higher proportion of CNVs, and also a higher proportion of CNVs overlapping at least one gene than the non T2D group. Five of the six loci previously associated with T2D had duplications or deletions in the T2D sample, but not the non-T2D sample. A gene-set analysis including genes with CNVs observed only in the T2D group highlighted gene-sets related with sensory perception (olfactory receptors, OR) and phenylpyruvate tautomerase/dopachrome isomerase activity (MIF and DDT genes).

[Dental caries prevalence survey in children and teenagers]. / Encuesta de prevalencia de caries dental en niños y adolescentes.

OBJECTIVE: To identify the prevalence of caries in affiliated children and teenagers at Instituto Mexicano del Seguro Social (IMSS). METHODS: The survey was carried out according to the World Health Organization indexes. The studied was carried out in 1545 boys and girls aged three, five, six, and twelve years and selected by stratified random sampling in seven places of Mexican Republic (Guanajuato, East and West Estado de México, Northwest 1, Northeast 2, Southwest 3, and Southeast 4 DF). RESULTS: The prevalence of caries was 66.9 %. Rates were higher in temporary dentition than in permanent teeth (p < 3.07). The mean of caries index per tooth in primary dentition in children of six years of age was 3.57 +/- 2.8. In the 12 years of age group the average per tooth was 1.97 +/- 1.4. The main component in both primary and permanent dentition was caries with 2.49 and 1.56 respectively. CONCLUSIONS: The results of this survey showed slight changes in prevalence and high levels of caries in children.