SARS-CoV-2 in a tropical area of Colombia, a remarkable conversion of presymptomatic to symptomatic people impacts public health.

BACKGROUND: The ability of SARS-CoV-2 to remain in asymptomatic individuals facilitates its dissemination and makes its control difficult. OBJECTIVE: To establish a cohort of asymptomatic individuals, change to the symptomatic status, and determine the most frequent clinical manifestations.  METHODS: Between April 9 and August 9, 2020, molecular diagnosis of SARS-CoV-2 infection was confirmed in 154 asymptomatic people in contact with subjects diagnosed with COVID-19. Nasopharyngeal swabs were performed on these people in different hospitals in Córdoba, the Caribbean area of Colombia. The genes E, RdRp, and N were amplified with RT-qPCR. Based on the molecular results and the Cq values, the patients were subsequently followed up through telephone calls to verify their health conditions. RESULTS: Overall, of 154 asymptomatic individuals, 103 (66.9%) remained asymptomatic, and 51 (33.1%) changed to symptomatic. The most frequent clinical manifestations in young people were anosmia and arthralgia. Adults showed cough, ageusia, and odynophagia; in the elderly were epigastralgia, dyspnea, and headache. Mortality was 8%. CONCLUSIONS: A proportion of 33% of presymptomatic individuals was found, of which four of them died. This high rate could indicate a silent transmission, contributing significantly to the epidemic associated with SARS-CoV-2.

Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy.

In many perinatal pharmacoepidemiologic studies, exposure to a medication is classified as "ever exposed" versus "never exposed" within each trimester or even over the entire pregnancy. This approach is often far from real-world exposure patterns, may lead to exposure misclassification, and does not to incorporate important aspects such as dosage, timing of exposure, and treatment duration. Alternative exposure modeling methods can better summarize complex, individual-level medication use trajectories or time-varying exposures from information on medication dosage, gestational timing of use, and frequency of use. We provide an overview of commonly used methods for more refined definitions of real-world exposure to medication use during pregnancy, focusing on the major strengths and limitations of the techniques, including the potential for method-specific biases. Unsupervised clustering methods, including k-means clustering, group-based trajectory models, and hierarchical cluster analysis, are of interest because they enable visual examination of medication use trajectories over time in pregnancy and complex individual-level exposures, as well as providing insight into comedication and drug-switching patterns. Analytical techniques for time-varying exposure methods, such as extended Cox models and Robins' generalized methods, are useful tools when medication exposure is not static during pregnancy. We propose that where appropriate, combining unsupervised clustering techniques with causal modeling approaches may be a powerful approach to understanding medication safety in pregnancy, and this framework can also be applied in other areas of epidemiology.

Attributable Mortality of Ventilator-associated Pneumonia. Replicating Findings, Revisiting Methods.

Rationale: Estimating the impact of ventilator-associated pneumonia (VAP) from routinely collected intensive care unit (ICU) data is methodologically challenging.Objectives: We aim to replicate earlier findings of limited VAP-attributable ICU mortality in an independent cohort. By refining statistical analyses, we gradually tackle different sources of bias.Methods: Records of 2,720 adult patients admitted to Ghent University Hospital ICUs (2013-2017) and receiving mechanical ventilation within 48 hours after admission were extracted from linked Intensive Care Information System and Computer-based Surveillance and Alerting of Nosocomial Infections, Antimicrobial Resistance, and Antibiotic Consumption in the ICU databases. The VAP-attributable fraction of ICU mortality was estimated using a competing risk analysis that is restricted to VAP-free patients (approach 1), accounts for VAP onset by treating it as either a competing (approach 2) or censoring event (approach 3), or additionally adjusts for time-dependent confounding via inverse probability weighting (approach 4).Results: A total of 210 patients (7.7%) acquired VAP. Based on benchmark approach 4, we estimated that (compared with current preventive measures) hypothetical eradication of VAP would lead to a relative ICU mortality reduction of 1.7% (95% confidence interval, -1.3 to 4.6) by Day 10 and of 3.6% (95% confidence interval, 0.7 to 6.5) by Day 60. Approaches 1-3 produced estimates ranging from -0.7% to 2.5% by Day 10 and from 5.2% to 5.5% by Day 60.Conclusions: In line with previous studies using appropriate methodology, we found limited VAP-attributable ICU mortality given current state-of-the-art VAP prevention measures. Our study illustrates that inappropriate accounting of the time dependency of exposure and confounding of its effects may misleadingly suggest protective effects of early-onset VAP and systematically overestimate attributable mortality.

Cholesterol-lowering effect of statin therapy in a clinical HIV cohort: an application of double propensity score adjustment.

PURPOSE: Propensity score matching (PSM) is often used to estimate the average treatment effect among the treated (ATT) using observational data. We demonstrate how the use of "double propensity score adjustment" can reduce residual confounding and avoid bias due to incomplete matching compared with traditional PSM methods. METHODS: The DC Cohort is an observational clinical HIV cohort in Washington, DC. We compared the mean percent change in non-high-density lipoprotein cholesterol (non-HDL-C) concentration after 3-12 months between participants treated and participants not treated with statin therapy between 2011 and 2018. We conducted traditional PSM procedures (optimal, nearest neighbor, and nearest neighbor caliper matching) and double propensity score adjustment. RESULTS: Among 202 treated and 1252 untreated participants, the ATT was -14.5% (95% CI: -18.4, -10.6) after optimal matching (202 matched pairs; 15/22 covariates balanced), -14.9% (-18.9, -11.0) after nearest neighbor matching (202 matched pairs; 17/22 covariates balanced), and -12.0% (-16.5, -7.5) after nearest neighbor caliper matching (153 matched pairs; 21/22 covariates balanced). After double propensity score adjustment, the ATT was -13.0% (-16.0, -10.1). CONCLUSIONS: In PSM analyses, double propensity score adjustment is a readily accessible alternative approach for estimating ATTs when sufficient covariate balance between treatment groups cannot be achieved without excluding treated participants.

Proteomics and Metabolomics in Kidney Disease, including Insights into Etiology, Treatment, and Prevention.

In this review of the application of proteomics and metabolomics to kidney disease research, we review key concepts, highlight illustrative examples, and outline future directions. The proteome and metabolome reflect the influence of environmental exposures in addition to genetic coding. Circulating levels of proteins and metabolites are dynamic and modifiable, and thus amenable to therapeutic targeting. Design and analytic considerations in proteomics and metabolomics studies should be tailored to the investigator's goals. For the identification of clinical biomarkers, adjustment for all potential confounding variables, particularly GFR, and strict significance thresholds are warranted. However, this approach has the potential to obscure biologic signals and can be overly conservative given the high degree of intercorrelation within the proteome and metabolome. Mass spectrometry, often coupled to up-front chromatographic separation techniques, is a major workhorse in both proteomics and metabolomics. High-throughput antibody- and aptamer-based proteomic platforms have emerged as additional, powerful approaches to assay the proteome. As the breadth of coverage for these methodologies continues to expand, machine learning tools and pathway analyses can help select the molecules of greatest interest and categorize them in distinct biologic themes. Studies to date have already made a substantial effect, for example elucidating target antigens in membranous nephropathy, identifying a signature of urinary peptides that adds prognostic information to urinary albumin in CKD, implicating circulating inflammatory proteins as potential mediators of diabetic nephropathy, demonstrating the key role of the microbiome in the uremic milieu, and highlighting kidney bioenergetics as a modifiable factor in AKI. Additional studies are required to replicate and expand on these findings in independent cohorts. Further, more work is needed to understand the longitudinal trajectory of select protein and metabolite markers, perform transomics analyses within merged datasets, and incorporate more kidney tissue-based investigation.

State of Mental Health and Associated Factors in Nursing Students from Southeastern Iran.

OBJECTIVES: To evaluate the state of mental health and its relation with associated factors among nursing students. METHODS: A cross-sectional study was conducted with 130 students from the Nursing and Midwifery College affiliated to the University of Medical Sciences of Shiraz (Iran). Data was collected through a document that included information on the demographic characteristics, the mean grades of the practical assignments and of the total (practical and theoretical assignments), and the Goldberg Health Questionnaire (GHQ-28) that measures symptoms grouped into four dimensions (somatic symptoms, anxiety and insomnia, social dysfunction, and depression). RESULTS: Most of the participants (65.1%) were women; 5.3% were between 21 and 22 years of age, 84.5% were single, and 33.3% were in the sixth semester; 68.5% of the students had problems with mental health. By dimensions of the GHQ-28, it was found that 7.7% had somatic symptoms, 13.8% symptoms of anxiety and sleep disorders, 52.3% social dysfunction, and 6.2% depression. Males had a higher score of depression than females, and being single was related with higher scores of physical symptoms, anxiety and insomnia, and depression, compared with those who were married. An inverse relationship was found between the GHQ-28 average score and the semester, the grade in practical assignments, and the total grade for physical symptoms and anxiety and insomnia. CONCLUSIONS: There is a high proportion of nursing students with suspected mental health disorder. Some demographic and academic factors are related with the mental health of students and must be kept in mind by the institutions training future nurses.

Interfacility Transfer of Medicare Beneficiaries With Acute Type A Aortic Dissection and Regionalization of Care in the United States.

BACKGROUND: The feasibility and effectiveness of delaying surgery to transfer patients with acute type A aortic dissection-a catastrophic disease that requires prompt intervention-to higher-volume aortic surgery hospitals is unknown. We investigated the hypothesis that regionalizing care at high-volume hospitals for acute type A aortic dissections will lower mortality. We further decomposed this hypothesis into subparts, investigating the isolated effect of transfer and the isolated effect of receiving care at a high-volume versus a low-volume facility. METHODS: We compared the operative mortality and long-term survival between 16 886 Medicare beneficiaries diagnosed with an acute type A aortic dissection between 1999 and 2014 who (1) were transferred versus not transferred, (2) underwent surgery at high-volume versus low-volume hospitals, and (3) were rerouted versus not rerouted to a high-volume hospital for treatment. We used a preference-based instrumental variable design to address unmeasured confounding and matching to separate the effect of transfer from volume. RESULTS: Between 1999 and 2014, 40.5% of patients with an acute type A aortic dissection were transferred, and 51.9% received surgery at a high-volume hospital. Interfacility transfer was not associated with a change in operative mortality (risk difference, -0.69%; 95% CI, -2.7% to 1.35%) or long-term mortality. Despite delaying surgery, a regionalization policy that transfers patients to high-volume hospitals was associated with a 7.2% (95% CI, 4.1%-10.3%) absolute risk reduction in operative mortality; this association persisted in the long term (hazard ratio, 0.81; 95% CI, 0.75-0.87). The median distance needed to reroute each patient to a high-volume hospital was 50.1 miles (interquartile range, 12.4-105.4 miles). CONCLUSIONS: Operative and long-term mortality were substantially reduced in patients with acute type A aortic dissection who were rerouted to high-volume hospitals. Policy makers should evaluate the feasibility and benefits of regionalizing the surgical treatment of acute type A aortic dissection in the United States.

Compreendendo os métodos de escore de propensão na pesquisa clínica / Understanding the methods of propensity score in clinical research

Os métodos de escore de propensão são a probabilidade de um sujeito receber um tratamento condicional em um conjunto de características de base (confundidores), sendo usado para comparar pacientes com distribuição similar de fatores de confusão, de modo que a diferença nos resultados forneça estimativa imparcial do efeito do tratamento. Esta revisão mostra os conceitos básicos dos escore de propensão e fornece orientação na implementação de métodos de propensão, além de outros, como estratificação, ponderação e ajuste de covariáveis, tornando-se uma guia prático para o clínico

The propensity score methods are the probability of a subject receiving conditional treatment on a set of baseline characteristics (confounders), and are used to compare patients with similar confounding distributions, so that the difference in results provides an unbiased estimate of the treatment effect. This review shows the basic concepts of propensity scores, and provides guidelines for the implementation of propensity methods, and others based on it, such as stratification, weighting, and adjustment of covariables, becoming a practical guide for the clinician

Air Pollution and Suicide in Mexico City: A Time Series Analysis, 2000-2016.

The association between air pollution and suicide has recently been under examination, and the findings continue to be contradictory. In order to contribute evidence to this still unresolved question, the objective of the present study was to evaluate the association between air quality and daily suicides registered in Mexico City (MC) between 2000 and 2016. Air quality was measured based on exposure to particulate matter under 2.5 and 10 micrometers (µm) (PM2.5 and PM10, respectively), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2), adjusting for weather variables (air temperature and relative humidity), and holidays. To this end, an ecologic time series analysis was performed using a Poisson regression model conditioned by time and stratified by gender and age groups. Models were also generated to explore the lagged and accumulative effects of air pollutants, adjusted by weather variables. The effects of the pollutants were very close to the null value in the majority of the models, and no accumulative effects were identified. We believe these results, in this case, no evidence of a statistical association, contribute to the current debate about whether the association between air pollution and suicide reported in the scientific literature reflects an actual effect or an uncontrolled confounding effect.

[The propensity score method and it's application].

Propensity score method, as an analytical strategy for adjusting multiple covariates, has been widely used in observational comparative effectiveness research. This paper introduces this method covered basic principles, case illustration and software implementation, in order to help readers understand propensity score method, apply it correctly in their researches, improve the efficiency of data utilization, and enhance the level of statistical analysis.

Restriction of Pharmacoepidemiologic Cohorts to Initiators of Medications in Unrelated Preventive Drug Classes to Reduce Confounding by Frailty in Older Adults.

Nonexperimental studies of the effectiveness of seasonal influenza vaccine in older adults have found 40%-60% reductions in all-cause mortality associated with vaccination, potentially due to confounding by frailty. We restricted our cohort to initiators of medications in preventive drug classes (statins, antiglaucoma drugs, and ß blockers) as an approach to reducing confounding by frailty by excluding frail older adults who would not initiate use of these drugs. Using a random 20% sample of US Medicare beneficiaries, we framed our study as a series of nonrandomized "trials" comparing vaccinated beneficiaries with unvaccinated beneficiaries who had an outpatient health-care visit during the 5 influenza seasons occurring in 2010-2015. We pooled data across trials and used standardized-mortality-ratio-weighted Cox proportional hazards models to estimate the association between influenza vaccination and all-cause mortality before influenza season, expecting a null association. Weighted hazard ratios among preventive drug initiators were generally closer to the null than those in the nonrestricted cohort. Restriction of the study population to statin initiators with an uncensored approach resulted in a weighted hazard ratio of 1.00 (95% confidence interval: 0.84, 1.19), and several other hazard ratios were above 0.95. Restricting the cohort to initiators of medications in preventive drug classes can reduce confounding by frailty in this setting, but further work is required to determine the most appropriate criteria to use.

The propensity score method and it′s application / 中华预防医学杂志

Propensity score method, as an analytical strategy for adjusting multiple covariates, has been widely used in observational comparative effectiveness research. This paper introduces this method covered basic principles, case illustration and software implementation, in order to help readers understand propensity score method, apply it correctly in their researches, improve the efficiency of data utilization, and enhance the level of statistical analysis.

Aplicación de grafos acíclicos dirigidos en la evaluación de un set mínimo de ajuste de confusores: un complemento al modelamiento estadístico en estudios epidemiológicos observacionales / Directed acyclic graphs in statistical modelling of epidemiological studies

Background: Confusion in observational epidemiological studies distorts the relationship between exposure and event. "Step by step" regression models, diverts the decision to a statistical algorithm with little causal basis. Directed Acyclic Graphs (DAGs), qualitatively and visually assess the confusion. They can complement the decision on confounder control during statistical modeling. Aim: To evaluate the minimum set of confounders to be controlled in a cause-effect relationship with the use of "step-by-step regression" and DAGs, in a study of arsenic exposure. Material and Methods: We worked with data from Cáceres et al., 2010 in 66 individuals from northern Chile. The interindividual variability in the urinary excretion of dimethyl arsenic acid attributable to the GSTT1 polymorphism was estimated. A causal DAG was constructed using DAGitty v2.3 with the list of variables. A multiple linear regression model with the step-by-step backwards methodology was carried out. Results: The causal diagram included 12 non-causal open pathways. The minimum adjustment set corresponded to the variables "sex", "body mass index" and "fish and seafood ingest". Confusion retention of the multivariate model included normal and overweight status, gender and the interaction between "water intake" and GSTT1. Conclusions: The use of DAG prior to the modeling would allow a more comprehensive, coherent and biologically plausible analysis of causal relationships in public health.

An overview of confounding. Part 1: the concept and how to address it.

Confounding is an important source of bias, but it is often misunderstood. We consider how confounding occurs and how to address confounding using examples. Study results are confounded when the effect of the exposure on the outcome, mixes with the effects of other risk and protective factors for the outcome. This problem arises when these factors are present to different degrees among the exposed and unexposed study participants, but not all differences between the groups result in confounding. Thinking about an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe helps to distinguish confounders from other differences. In an actual study, an observed unexposed population is chosen to stand in for the unobserved parallel universe. Differences between this substitute population and the parallel universe result in confounding. Confounding by identified factors can be addressed analytically and through study design, but only randomization has the potential to address confounding by unmeasured factors. Nevertheless, a given randomized study may still be confounded. Confounded study results can lead to incorrect conclusions about the effect of the exposure of interest on the outcome.

An overview of confounding. Part 2: how to identify it and special situations.

Confounding biases study results when the effect of the exposure on the outcome mixes with the effects of other risk and protective factors for the outcome that are present differentially by exposure status. However, not all differences between the exposed and unexposed group cause confounding. Thus, sources of confounding must be identified before they can be addressed. Confounding is absent in an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe. In an actual study, an observed unexposed population represents the unobserved parallel universe. Thinking about differences between this substitute population and the unexposed parallel universe helps identify sources of confounding. These differences can then be represented in a diagram that shows how risk and protective factors for the outcome are related to the exposure. Sources of confounding identified in the diagram should be addressed analytically and through study design. However, treating all factors that differ by exposure status as confounders without considering the structure of their relation to the exposure can introduce bias. For example, conditions affected by the exposure are not confounders. There are also special types of confounding, such as time-varying confounding and unfixable confounding. It is important to evaluate carefully whether factors of interest contribute to confounding because bias can be introduced both by ignoring potential confounders and by adjusting for factors that are not confounders. The resulting bias can result in misleading conclusions about the effect of the exposure of interest on the outcome.

An Assessment of the Cox Proportional Hazards Regression Model for Epidemiologic Studies.

The basic assumptions of the Cox proportional hazards regression model are rarely questioned. This study addresses whether hazard ratio, i.e., relative risk (RR), estimates using the Cox model are biased when these assumptions are violated. We investigated also the dependence of RR estimates on temporal exposure characteristics, and how inadequate control for a strong, time-dependent confounder affects RRs for a modest, correlated risk factor. In a realistic cohort of 500,000 adults constructed using the National Cancer Institute Smoking History Generator, we used the Cox model with increasing control of smoking to examine the impact on RRs for smoking and a correlated covariate X. The smoking-associated RR was strongly modified by age. Pack-years of smoking did not sufficiently control for its effects; simultaneous control for effect modification by age and time-dependent cumulative exposure, exposure duration, and time since cessation improved model fit. Even then, residual confounding was evident in RR estimates for covariate X, for which spurious RRs ranged from 0.980 to 1.017 per unit increase. Use of the Cox model to control for a time-dependent strong risk factor yields unreliable RR estimates unless detailed, time-varying information is incorporated in analyses. Notwithstanding, residual confounding may bias estimated RRs for a modest risk factor.

Selecting Shrinkage Parameters for Effect Estimation: The Multi-Ethnic Study of Atherosclerosis.

We present a method for improving estimation in linear regression models in samples of moderate size, using shrinkage techniques. Our work connects the theory of causal inference, which describes how variable adjustment should be performed with large samples, with shrinkage estimators such as ridge regression and the least absolute shrinkage and selection operator (LASSO), which can perform better in sample sizes seen in epidemiologic practice. Shrinkage methods reduce mean squared error by trading off some amount of bias for a reduction in variance. However, when inference is the goal, there are no standard methods for choosing the penalty "tuning" parameters that govern these tradeoffs. We propose selecting the penalty parameters for these shrinkage estimators by minimizing bias and variance in future similar data sets drawn from the posterior predictive distribution. Our method provides both the point estimate of interest and corresponding standard error estimates. Through simulations, we demonstrate that it can achieve better mean squared error than using cross-validation for penalty parameter selection. We apply our method to a cross-sectional analysis of the association between smoking and carotid intima-media thickness in the Multi-Ethnic Study of Atherosclerosis (multiple US locations, 2000-2002) and compare it with similar analyses of these data.

Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies.

In longitudinal studies, if the time-dependent covariates are affected by the past treatment, time-dependent confounding may be present. For a time-to-event response, marginal structural Cox models are frequently used to deal with such confounding. To avoid some of the problems of fitting marginal structural Cox model, the sequential Cox approach has been suggested as an alternative. Although the estimation mechanisms are different, both approaches claim to estimate the causal effect of treatment by appropriately adjusting for time-dependent confounding. We carry out simulation studies to assess the suitability of the sequential Cox approach for analyzing time-to-event data in the presence of a time-dependent covariate that may or may not be a time-dependent confounder. Results from these simulations revealed that the sequential Cox approach is not as effective as marginal structural Cox model in addressing the time-dependent confounding. The sequential Cox approach was also found to be inadequate in the presence of a time-dependent covariate. We propose a modified version of the sequential Cox approach that correctly estimates the treatment effect in both of the above scenarios. All approaches are applied to investigate the impact of beta-interferon treatment in delaying disability progression in the British Columbia Multiple Sclerosis cohort (1995-2008).