Target Trial Emulation Using Hospital-Based Observational Data: Demonstration and Application in COVID-19.

Methodological biases are common in observational studies evaluating treatment effectiveness. The objective of this study is to emulate a target trial in a competing risks setting using hospital-based observational data. We extend established methodology accounting for immortal time bias and time-fixed confounding biases to a setting where no survival information beyond hospital discharge is available: a condition common to coronavirus disease 2019 (COVID-19) research data. This exemplary study includes a cohort of 618 hospitalized patients with COVID-19. We describe methodological opportunities and challenges that cannot be overcome applying traditional statistical methods. We demonstrate the practical implementation of this trial emulation approach via clone-censor-weight techniques. We undertake a competing risk analysis, reporting the cause-specific cumulative hazards and cumulative incidence probabilities. Our analysis demonstrates that a target trial emulation framework can be extended to account for competing risks in COVID-19 hospital studies. In our analysis, we avoid immortal time bias, time-fixed confounding bias, and competing risks bias simultaneously. Choosing the length of the grace period is justified from a clinical perspective and has an important advantage in ensuring reliable results. This extended trial emulation with the competing risk analysis enables an unbiased estimation of treatment effects, along with the ability to interpret the effectiveness of treatment on all clinically important outcomes.

Current Challenges with the Use of Test-Negative Designs for Modeling COVID-19 Vaccination and Outcomes.

The widespread testing for SARS-CoV-2 infection has facilitated the use of test-negative designs (TND) for modeling COVID-19 vaccination and outcomes. Despite the comprehensive literature on TND, the use of TND in COVID-19 studies is relatively new and calls for robust design and analysis to adapt to a rapidly changing and dynamically evolving pandemic and to account for changes in testing and reporting practices. In this commentary, we aim to draw the attention of researchers to COVID-specific challenges in using TND as we are analyzing data amassed over more than two years of the pandemic. We first review when and why TND works, and general challenges in TND studies presented in the literature. We then discuss COVID-specific challenges which have not received adequate acknowledgment but may add to the risk of invalid conclusions in TND studies of COVID-19.

Integrating real-world data from Brazil and Pakistan into the OMOP common data model and standardized health analytics framework to characterize COVID-19 in the Global South.

OBJECTIVES: The aim of this work is to demonstrate the use of a standardized health informatics framework to generate reliable and reproducible real-world evidence from Latin America and South Asia towards characterizing coronavirus disease 2019 (COVID-19) in the Global South. MATERIALS AND METHODS: Patient-level COVID-19 records collected in a patient self-reported notification system, hospital in-patient and out-patient records, and community diagnostic labs were harmonized to the Observational Medical Outcomes Partnership common data model and analyzed using a federated network analytics framework. Clinical characteristics of individuals tested for, diagnosed with or tested positive for, hospitalized with, admitted to intensive care unit with, or dying with COVID-19 were estimated. RESULTS: Two COVID-19 databases covering 8.3 million people from Pakistan and 2.6 million people from Bahia, Brazil were analyzed. 109 504 (Pakistan) and 921 (Brazil) medical concepts were harmonized to Observational Medical Outcomes Partnership common data model. In total, 341 505 (4.1%) people in the Pakistan dataset and 1 312 832 (49.2%) people in the Brazilian dataset were tested for COVID-19 between January 1, 2020 and April 20, 2022, with a median [IQR] age of 36 [25, 76] and 38 (27, 50); 40.3% and 56.5% were female in Pakistan and Brazil, respectively. 1.2% percent individuals in the Pakistan dataset had Afghan ethnicity. In Brazil, 52.3% had mixed ethnicity. In agreement with international findings, COVID-19 outcomes were more severe in men, elderly, and those with underlying health conditions. CONCLUSIONS: COVID-19 data from 2 large countries in the Global South were harmonized and analyzed using a standardized health informatics framework developed by an international community of health informaticians. This proof-of-concept study demonstrates a potential open science framework for global knowledge mobilization and clinical translation for timely response to healthcare needs in pandemics and beyond.

Impact of mandatory COVID-19 shelter-in-place order on controlled substance use among rural versus urban communities in the United States.

PURPOSE: Mandatory COVID-19 shelter-in-place (SIP) orders have been imposed to fight the pandemic. They may also have led to unintended consequences of increased use of controlled substances especially among rural communities due to increased social isolation. Using the data from the American Association of Poison Control Centers, this study tests the hypothesis that the poison control centers received higher rates of calls related to exposures to controlled substances from rural counties than they did from urban counties during the SIP period. METHODS: Call counts received by the poison control centers between October 19, 2019 and July 6, 2020 due to exposure to controlled substance (methamphetamine, opioids, cocaine, benzodiazepines, and other narcotics) were aggregated to per-county-per-month-per-10,000 population exposure rates. A falsification test was conducted to reduce the possibility of spurious correlations. FINDINGS: During the study period, 2,649 counties in the United States had mandatory SIP orders. The rate of calls reporting exposure to any of the aforementioned controlled substances among the rural counties was higher (14%; P = .047) relative to the urban counties. This overall increase was due to increases in the rates of calls reporting exposure to opioids (26%; P = .017) and methamphetamine (39%; P = .077). Moreover, the rate of calls reporting exposures at home was also higher among the rural counties (14%; P = .069). CONCLUSION: The mandatory SIP orders may have had an unintended consequence of exacerbating the use of controlled substances at home in rural communities relative to urban communities.

ASSESSING THE IMPACT OF THE COVID-19 LOCKDOWNS: RESULTS AND IMPLICATIONS FROM A MODELLING APPLICATION IN TWO MEDITERRANEAN CITIES

Activity restrictions implemented to control the spread of the COVID-19 virus imposed a significant effect on the air quality of cities across the world. The initiative of the World Meteorological Organisation/Global Atmospheric Watch for studying effects of the 2020 COVID-19 lockdowns on air quality has produced two sets of analysis results for cities across the world, based on observational data and modelling, respectively. The modelling study aims to evaluate the modelling tools in a regime involving significant changes of activity, and at the same provide insights on the effect of selectively reduced emissions on the chemistry and composition of urban pollutants. For most of the cities, a reduction on NOx average concentrations between 11% and 70% was calculated for the lockdown period, while PM10 was reduced by 8% up to 35% in a good agreement with measured reductions observed during the 2020 lockdown period compared to the corresponding period of 2019. Taking advantage of an operational Air Quality Modelling System, which is in continuous application in the cities of Thessaloniki and Nicosia, the contribution of sectoral emissions and the role of meteorology over the observed concentration reductions was assessed. The study reveals that in both cities, observed reductions of urban PM2.5, PM10 and NOx concentration patterns can be mainly attributed to the corresponding emissions reductions in the transport and heating sectors, while O3 is strongly affected by titration near the city centre. At the same time, meteorological patterns appear to strongly influence and even mask these effects in terms of daily averages, while the impact of imposed large-scale boundary conditions on the modelling results can also be significant. © British Crown Copyright (2022)

Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US

Analyzing the causal impact of different policies in reducing the spread of COVID-19 is of critical importance. The main challenge here is the existence of unobserved confounders (e.g., vigilance of residents) which influence both the presence of policies and the spread of COVID-19. Besides, as the confounders may be time-varying, it is even more difficult to capture them. Fortunately, the increasing prevalence of web data from various online applications provides an important resource of time-varying observational data, and enhances the opportunity to capture the confounders from them, e.g., the vigilance of residents over time can be reflected by the popularity of Google searches about COVID-19 at different time periods. In this paper, we study the problem of assessing the causal effects of different COVID-19 related policies on the outbreak dynamics in different counties at any given time period. To this end, we integrate COVID-19 related observational data covering different U.S. counties over time, and then develop a neural network based causal effect estimation framework which learns the representations of time-varying (unobserved) confounders from the observational data. Experimental results indicate the effectiveness of our proposed framework in quantifying the causal impact of policies at different granularities, ranging from a category of policies with a certain goal to a specific policy type. Compared with baseline methods, our assessment of policies is more consistent with existing epidemiological studies of COVID-19. Besides, our assessment also provides insights for future policy-making. © 2022 ACM.

Counter-factual Analysis of On-Line Math Tutoring Impact on Low-income High School Students

The importance of understanding on-line tutoring impact has increased dramatically, especially after the COVID-19 pandemic. However, deep causal concepts about on-line tutoring are still lacking, especially on economically disadvantaged students. This paper is an observational study that targets low-income high school students in Saudi Arabia with high failing risk. The paper aims at (1) finding on-line math tutoring impact on needy students who already took tutoring, and (2) identifying and characterizing students that need tutoring to pass. We use observational data collected in a student registration process to build two models: (1) a Bayesian multi-level regression causal model, then (2) a counter-factual model. Results show that the models gave statistically significant estimates. In model 1, the average causal impact of maximum tutoring minutes on the math mark was +4.9 (out of 100). In model 2, the counter-factual maximum impact on tutored students was +5.3. We also estimate that only 1.9% of students needed the tutoring to avoid failing (2.8% of the enrolled), and we show their characteristics. © 2021 IEEE.

Microbiological Decontamination of Air and Surfaces Due to Nanosecond Discharges

Presented observational data indicate that a significant number of infections with the SARS-CoV-2 coronavirus occur by air without direct contact with the source, in addition, in a tangibly long time interval. It is noticed that atmospheric precipitations help to cleanse the air from pollution and at the same time from viruses, reducing non-contact infections. These facts additionally actualize the problem of optimal microbiological decontamination of air and surfaces. In order to optimize microbiological sterilization, a thermodynamic approach is applied. It is shown that irreversible chemical oxidation reactions are the shortest way to achieve sterility, they being capable of providing one hundred percent reliability of decontamination. It is established that oxygen is optimal as an oxidant, including ecologically, because it and all of its reactive forms harmoniously fit into natural exchange cycles. The optimal way to obtain reactive oxygen species for disinfection is the use of low-temperature (“cold”) plasma, which provides energy-efficient generation of oxidative reactive forms - atomic oxygen (O), ozone (O3), hydroxyl radical (⋅OH), hydrogen peroxide (H2O2), superoxide (O2 −), singlet oxygen O2(a1Δg). Due to the short lifetime for most of the above forms outside the plasma applicator, remoted from the plasma generator objects should be sterilized with ozone (O3), the minimum lifetime of which is quite long (several minutes). It is substantiated that microwave method of generating oxygen plasma is optimal for energy efficient ozone production. A modular principle of generation is proposed for varying the productivity of ozone generating units over a wide range. The module is developed on the basis of an adapted serial microwave oven, in which a non-self-sustaining microwave discharge is maintained due to ionizations produced by radionuclides-emitters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19.

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.