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.

How predictive of SARS-CoV-2 infection are clinical characteristics at presentation among individuals with COVID-like symptoms treated at the Mexican Institute of Social Security.

BACKGROUND: The COVID-19 pandemic has progressed rapidly, with the emergence of new virus variants that pose challenges in treating infected individuals. In Mexico, four epidemic waves have been recorded with varying disease severity. To understand the heterogeneity in clinical presentation over time and the sensitivity and specificity of signs and symptoms in identifying COVID-19 cases, an analysis of the changes in the clinical presentation of the disease was conducted. AIM: To analyze the changes in the clinical presentation of COVID-19 among 3.38 million individuals tested for SARS-CoV-2 at the Mexican Social Security Institute (IMSS) from March 2020 to October 2021 and evaluate the predictivity of signs and symptoms in identifying COVID-19 cases. METHODS: A retrospective analysis of clinical presentation patterns of COVID-19 among individuals treated at IMSS was performed, contrasting the signs and symptoms among SARS-CoV-2-positive individuals with those who tested negative for the virus but had respiratory infection symptoms. The sensitivity and specificity of each sign and symptom in identifying SARS-CoV-2 infection were estimated. RESULTS: The set of signs and symptoms reported for COVID-19-suspected patients treated at IMSS were not highly specific for SARS-CoV-2 positivity. The signs and symptoms exhibited variability based on age and epidemic wave. The area under the receiver operating characteristic (ROC) curve was 0.62 when grouping the five main symptoms (headache, dyspnea, fever, arthralgia, and cough). Most of the individual symptoms had ROC values close to 0.5 (16 out of 22 between 0.48 and 0.52), indicating non-specificity. CONCLUSIONS: The results highlight the difficulty in making a clinical diagnosis of COVID-19 due to the lack of specificity of signs and symptoms. The variability of clinical presentation over time and among age groups highlights the need for further research to differentiate whether the changes are due to changes in the virus, who is becoming infected, or the population, particularly with respect to prior infection and vaccination status.

Evolution of differences in clinical presentation across epidemic waves among patients with COVID-like-symptoms who received care at the Mexican Social Security Institute.

Background: Timely monitoring of SARS-CoV-2 variants is crucial to effectively managing both prevention and treatment efforts. In this paper, we aim to describe demographic and clinical patterns of individuals with COVID-19-like symptoms during the first three epidemic waves in Mexico to identify changes in those patterns that may reflect differences determined by virus variants. Methods: We conducted a descriptive analysis of a large database containing records for all individuals who sought care at the Mexican Social Security Institute (IMSS) due to COVID-19-like symptoms from March 2020 to October 2021 (4.48 million records). We described the clinical and demographic profile of individuals tested (3.38 million, 32% with PCR and 68% with rapid test) by test result (positives and negatives) and untested, and among those tested, and the changes in those profiles across the first three epidemic waves. Results: Individuals with COVID-19-like symptoms were older in the first wave and younger in the third one (the mean age for those positive was 46.6 in the first wave and 36.1 in the third wave; for negatives and not-tested, the mean age was 41 and 38.5 in the first wave and 34.3 and 33.5 in the third wave). As the pandemic progressed, an increasing number of individuals sought care for suspected COVID-19. The positivity rate decreased over time but remained well over the recommended 5%. The pattern of presenting symptoms changed over time, with some of those symptoms decreasing over time (dyspnea 40.6 to 14.0%, cough 80.4 to 76.2%, fever 77.5 to 65.2%, headache 80.3 to 78.5%), and some increasing (odynophagia 48.7 to 58.5%, rhinorrhea 28.6 to 47.5%, anosmia 11.8 to 23.2%, dysgeusia 11.2 to 23.2%). Conclusion: During epidemic surges, the general consensus was that any individual presenting with respiratory symptoms was a suspected COVID-19 case. However, symptoms and signs are dynamic, with clinical patterns changing not only with the evolution of the virus but also with demographic changes in the affected population. A better understanding of these changing patterns is needed to improve preparedness for future surges and pandemics.