The implementation of a vaccine against COVID-19 is one of the most important health strategies to mitigate the spread of the disease. The objective of this study was to estimate the prevalence of the intention to be vaccinated against COVID-19 and its predictors in older Peruvian adults. This is a cross-sectional study, where information was collected through an online survey regarding vaccination intention of the participants, as well as sociodemographic and psychological variables. A multiple regression analysis was applied to identify predictors of intention to be vaccinated against COVID-19. We evaluated 245 participants, who had a mean age of 72.74 years old (SD = 6.66). 65.5% of these older adults expressed a high likelihood of accepting vaccination, while 20.9% expressed a low likelihood of accepting vaccination, and 13.6% were hesitant. Eleven predictors were identified that explained 66.69% of the intention to vaccinate against COVID-19. This identified place of residence, perceived likelihood of contracting COVID-19, severity of previous infection with COVID-19, fear of the disease, previous refusal of a vaccine, concerns about vaccine sales and speculation, and trust toward vaccines against COVID-19, as the main predictors. Our results show that confidence in vaccines and previous vaccine refusal are relevant predictors of intention to vaccinate against COVID-19 in older adults; these findings may be useful to guide the development of campaigns for the immunization of this vulnerable group in the current pandemic.

I t has been observed that COVID-19 infection decreases in high-altitude regions (Arias-Reyes et al., 2020; Segovia-Juarez et al., 2020). However, the interaction between altitude and COVID-19 infection has not been examined in a follow-up study since the first case reported in each region. Therefore, our aim was to evaluate the influence of altitude and population density on the evolution of case fatality rate (CFR) and prevalence of COVID-19 in Peru. An ecological study was carried out. The observation units were the geographic regions. The dependent variables were the COVID-19 CFR (deaths per 100 COVID-19 cases) and prevalence (cases per 1,000 inhabitants), covariables were the altitude (meters) and population density (inhabitants per km 2). The number of COVID-19-positive cases and deaths were obtained from the COVID-19 National Open Data

Introduction: Only 3 types of coronavirus cause aggressive respiratory disease in humans (MERS-Cov, SARS-Cov-1, and SARS-Cov-2). It has been reported higher infection rates and severe manifestations (ICU admission, need for mechanical ventilation, and death) in patients with comorbidities such as diabetes mellitus (DM). For this reason, this study aimed to determine the prevalence of diabetes comorbidity and its associated unfavorable health outcomes in patients with acute respiratory syndromes for coronavirus disease according to virus types. Methods: Systematic review of literature in Pubmed/Medline, Scopus, Web of Science, Cochrane, and Scielo until April of 2020. We included cohort and cross-sectional studies with no restriction by language or geographical zone. The selection and extraction were undertaken by 2 reviewers, independently. The study quality was evaluated with Loney’s instrument and data were synthesized by random effects model meta-analysis. The heterogeneity was quantified using an I2 statistic. Funnel plot, Egger, and Begg tests were used to evaluate publication biases, and subgroups and sensitivity analyses were performed. Finally, we used the GRADE approach to assess the evidence certainty (PROSPERO: CRD42020178049). Results: We conducted the pooled analysis of 28 studies (n = 5960). The prevalence analysis according to virus type were 451.9 diabetes cases per 1000 infected patients (95% CI: 356.74-548.78; I2 = 89.71%) in MERS-Cov; 90.38 per 1000 (95% CI: 67.17-118.38) in SARS-Cov-1; and 100.42 per 1000 (95% CI: 77.85, 125.26 I2 = 67.94%) in SARS-Cov-2. The mortality rate were 36%, 6%, 10% and for MERS-Cov, SARS-Cov-1, and SARS-Cov-2, respectively. Due to the high risk of bias (75% of studies had very low quality), high heterogeneity ( I2 higher than 60%), and publication bias (for MERS-Cov studies), we down rate the certainty to very low. Conclusion: The prevalence of DM in patients with acute respiratory syndrome due to coronaviruses is high, predominantly with MERS-Cov infection. The unfavorable health outcomes are frequent in this subset of patients. Well-powered and population-based studies are needed, including detailed DM clinical profile (such as glycemic control, DM complications, and treatment regimens), comorbidities, and SARS-Cov-2 evolution to reevaluate the worldwide prevalence of this comorbidity and to typify clinical phenotypes with differential risk within the subpopulation of DM patients.

No abstract

In this study, we aimed to assess the relationship between tuberculosis case rate and COVID-19 case fatality rate (CFR) among districts within a tuberculosis-endemic metropolitan area. We analyzed data from 43 districts in Lima, Peru. We used districts as the units of observation. Linear regressions were used to investigate the relationship between COVID-19 CFRs and tuberculosis case rates. The mean COVID-19 CFR in each district for reporting Weeks 5-32 was used as the dependent variable. Independent variable was the mean rate of confirmed pulmonary tuberculosis cases for 2017-2019 period. Analyses were adjusted by population density, socioeconomic status, crowded housing, health facility density, and case rates of hypertension, diabetes mellitus, and HIV infection. The mean COVID-19 CFR in Lima was 4.0% ± 1.1%. The mean tuberculosis rate was 16.0 cases per 10,000 inhabitants. In multivariate analysis, tuberculosis case rate was associated with COVID-19 CFR (β = 1.26; 95% confidence interval: 0.24-2.28; p = .02), after adjusting for potential confounders. We found that Lima districts with a higher burden of tuberculosis exhibited higher COVID-19 CFRs, independent of socioeconomic, and morbidity variables.