Cost-effectiveness and economic investment to eliminate chronic hepatitis C in Mexico.

In July 2020, the Mexican Government initiated the National Program for Elimination of Hepatitis C (HCV) under a procurement agreement, securing universal, free access to HCV screening, diagnosis and treatment for 2020-2022. This analysis quantifies the clinical and economic burden of HCV (MXN) under a continuation (or end) to the agreement. A modelling and Delphi approach was used to evaluate the disease burden (2020-2030) and economic impact (2020-2035) of the Historical Base compared to Elimination, assuming the agreement continues (Elimination-Agreement to 2035) or terminates (Elimination-Agreement to 2022). We estimated cumulative costs and the per-patient treatment expenditure needed to achieve net-zero cost (the difference in cumulative costs between the scenario and the base). Elimination is defined as a 90% reduction in new infections, 90% diagnosis coverage, 80% treatment coverage and 65% reduction in mortality by 2030. A viraemic prevalence of 0.55% (0.50-0.60) was estimated on 1st January 2021, corresponding to 745,000 (95% CI 677,000-812,000) viraemic infections in Mexico. The Elimination-Agreement to 2035 would achieve net-zero cost by 2023 and accrue 31.2 billion in cumulative costs. Cumulative costs under the Elimination-Agreement to 2022 are estimated at 74.2 billion. Under Elimination-Agreement to 2022, the per-patient treatment price must decrease to 11,000 to achieve net-zero cost by 2035. The Mexican Government could extend the agreement through 2035 or reduce the cost of HCV treatment to 11,000 to achieve HCV elimination at net-zero cost.

Probability of hospitalisation and death among COVID-19 patients with comorbidity during outbreaks occurring in Mexico City.

Background: We compared the probability of hospitalization and death caused by COVID-19 in patients with comorbidities during three periods defined for this study: first-wave (FW), interwave period (IP), and second-wave (SW) observed in Mexico City. Methods: In this registry-based study, we included individuals over 20 years of age. During the FW (symptomatic), the IP, and the SW (symptomatic and asymptomatic), participants were diagnosed using nasopharyngeal swabs. Symptomatic individuals with risk factors for serious disease or death were referred to the hospital. SARS-CoV-2 infection was defined by RT-qPCR in all hospitalized patients. All data were added to the SISVER database. Bayesian analysis and False Discovery Rate were used for further evaluation. Results: The study included 2 260 156 persons (mean age of 43.1 years). Of these, 8.6% suffered from DM, 11.6% arterial hypertension, and 9.7% obesity. Of the total, 666 694 persons tested positive (29.5%). Of the infected persons, a total of 85 587 (12.8%) were hospitalized: 24 023 in the FW; 16 935 in the IP, and 44 629 in the SW. Of the hospitalized patients, there were 42 979 deaths (50.2%), in the FW, 11 964 (49.8%), in the IP, 6794 (40.1%), and in the SW 24 221 (54.3%). The probability of death among individuals hospitalized with or without comorbidities increased consistently in all age groups. A significant increase in the Fatality Rate was observed in individuals with comorbidities (1.36E-19< = FDR< = 3.36E-2). A similar trend was also observed in individuals without comorbidities (1.03E-44< = FDR< = 5.58E-4). Conclusions: The data from this study show a considerable increase in the number of detected cases of infection between the FW and SW. In addition, 12.8% of those infected were hospitalized for severe COVID-19. A high mortality rate was observed among hospitalized patients (>50%). An age-dependent probability of death was observed with a positive trend in hospitalized patients with and without comorbidities.

Time-Dependent Changes of Laboratory Parameters as Independent Predictors of All-Cause Mortality in COVID-19 Patients.

Independent predictors of mortality for COVID-19 patients have been identified upon hospital admission; however, how they behave after hospitalization remains unknown. The aim of this study is to identify clinical and laboratory parameters from admission to discharge or death that distinguish survivors and non-survivors of COVID-19, including those with independent ability to predict mortality. In a cohort of 266 adult patients, clinical and laboratory data were analyzed from admission and throughout hospital stay until discharge or death. Upon admission, non-survivors had significantly increased C reactive protein (CRP), neutrophil count, neutrophil to lymphocyte ratio (NLR) (p < 0.0001, each), ferritin (p < 0.001), and AST (aspartate transaminase) (p = 0.009) compared to survivors. During the hospital stay, deceased patients maintained elevated CRP (21.7 mg/dL [admission] vs. 19.3 [hospitalization], p = 0.060), ferritin, neutrophil count and NLR. Conversely, survivors showed significant reductions in CRP (15.8 mg/dL [admission] vs. 9.3 [hospitalization], p < 0.0001], ferritin, neutrophil count and NLR during hospital stay. Upon admission, elevated CRP, ferritin, and diabetes were independent predictors of mortality, as were persistently high CRP, neutrophilia, and the requirement of invasive mechanical ventilation during hospital stay. Inflammatory and clinical parameters distinguishing survivors from non-survivors upon admission changed significantly during hospital stay. These markers warrant close evaluation to monitor and predict patients' outcome once hospitalized.

Galectin-3 as a potential prognostic biomarker of severe COVID-19 in SARS-CoV-2 infected patients.

Severe COVID-19 is associated with a systemic hyperinflammatory response leading to acute respiratory distress syndrome (ARDS), multi-organ failure, and death. Galectin-3 is a ß-galactoside binding lectin known to drive neutrophil infiltration and the release of pro-inflammatory cytokines contributing to airway inflammation. Thus, we aimed to investigate the potential of galectin-3 as a biomarker of severe COVID-19 outcomes. We prospectively included 156 patients with RT-PCR confirmed COVID-19. A severe outcome was defined as the requirement of invasive mechanical ventilation (IMV) and/or in-hospital death. A non-severe outcome was defined as discharge without IMV requirement. We used receiver operating characteristic (ROC) and multivariable logistic regression analysis to determine the prognostic ability of serum galectin-3 for a severe outcome. Galectin-3 levels discriminated well between severe and non-severe outcomes and correlated with markers of COVID-19 severity, (CRP, NLR, D-dimer, and neutrophil count). Using a forward-stepwise logistic regression analysis we identified galectin-3 [odds ratio (OR) 3.68 (95% CI 1.47-9.20), p < 0.01] to be an independent predictor of severe outcome. Furthermore, galectin-3 in combination with CRP, albumin and CT pulmonary affection > 50%, had significantly improved ability to predict severe outcomes [AUC 0.85 (95% CI 0.79-0.91, p < 0.0001)]. Based on the evidence presented here, we recommend clinicians measure galectin-3 levels upon admission to facilitate allocation of appropriate resources in a timely manner to COVID-19 patients at highest risk of severe outcome.

The Evolutionary Landscape of SARS-CoV-2 Variant B.1.1.519 and Its Clinical Impact in Mexico City.

The SARS-CoV-2 pandemic is one of the most concerning health problems around the globe. We reported the emergence of SARS-CoV-2 variant B.1.1.519 in Mexico City. We reported the effective reproduction number (Rt) of B.1.1.519 and presented evidence of its geographical origin based on phylogenetic analysis. We also studied its evolution via haplotype analysis and identified the most recurrent haplotypes. Finally, we studied the clinical impact of B.1.1.519. The B.1.1.519 variant was predominant between November 2020 and May 2021, reaching 90% of all cases sequenced in February 2021. It is characterized by three amino acid changes in the spike protein: T478K, P681H, and T732A. Its Rt varies between 0.5 and 2.9. Its geographical origin remain to be investigated. Patients infected with variant B.1.1.519 showed a highly significant adjusted odds ratio (aOR) increase of 1.85 over non-B.1.1.519 patients for developing a severe/critical outcome (p = 0.000296, 1.33-2.6 95% CI) and a 2.35-fold increase for hospitalization (p = 0.005, 1.32-4.34 95% CI). The continuous monitoring of this and other variants will be required to control the ongoing pandemic as it evolves.

Direct or Collateral Liver Damage in SARS-CoV-2-Infected Patients.

Liver injury can result from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with more than one-third of COVID-19 patients exhibiting elevated liver enzymes. Microvesicular steatosis, inflammation, vascular congestion, and thrombosis in the liver have been described in autopsy samples from COVID-19 patients. Several factors, including direct cytopathic effect of the virus, immune-mediated collateral damage, or an exacerbation of preexisting liver disease may contribute to liver pathology in COVID-19. Due to its immunological functions, the liver is an organ likely to participate in the viral response against SARS-CoV-2 and this may predispose it to injury. A better understanding of the mechanism contributing to liver injury is needed to develop and implement early measures to prevent serious liver damage in patients suffering from COVID-19. This review summarizes current reports of SARS-CoV-2 with an emphasis on how direct infection and subsequent severe inflammatory response may contribute to liver injury in patients with and without preexisting liver disease.