COVID-19 Breakthrough Infections and Transmission Risk: Real-World Data Analyses from Germany’s Largest Public Health Department (Cologne)

Background and Methods: Vaccination is currently considered the most successful strategy for combating the SARS-CoV-2 virus. According to short-term clinical trials, protection against infection is estimated to reach up to 95% after complete vaccination (≥14 days after receipt of all recommended COVID-19 vaccine doses). Nevertheless, infections despite vaccination, so-called breakthrough infections, are documented. Even though they are more likely to have a milder or even asymptomatic course, the assessment of further transmission is highly relevant for successful containment. Therefore, we calculated the real-world transmission risk from fully vaccinated patients (vaccination group, VG) to their close contacts (CP) compared with the risk from unvaccinated reference persons matched according to age, sex, and virus type (control group = CG) utilizing data from Cologne’s health department. Results: A total of 357 breakthrough infections occurred among Cologne residents between 27 December 2020 (the date of the first vaccination in Cologne) and 6 August 2021. Of the 979 CPs in VG, 99 (10.1%) became infected. In CG, 303 of 802 CPs (37.8%) became infected. Factors promoting transmission included non-vaccinated status (β = 0.237; p < 0.001), male sex (β = 0.079; p = 0.049), the presence of symptoms (β = −0.125; p = 0.005), and lower cycle threshold value (β = −0.125; p = 0.032). This model explained 14.0% of the variance (corr. R2). Conclusion: The number of transmissions from unvaccinated controls was three times higher than from fully vaccinated patients. These real-world data underscore the importance of vaccination in enabling the relaxation of stringent and restrictive general pandemic control measures.

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Application of Real-World Data to External Control Groups in Oncology Clinical Trial Drug Development

Frontiers in Oncology ◽

10.3389/fonc.2021.695936 ◽

2022 ◽

Vol 11 ◽

Author(s):

Timothy A. Yap ◽

Ira Jacobs ◽

Elodie Baumfeld Andre ◽

Lauren J. Lee ◽

Darrin Beaupre ◽

...

Keyword(s):

Clinical Trials ◽

Drug Development ◽

Real World ◽

External Control ◽

Control Group ◽

Precision Oncology ◽

Efficacy And Safety ◽

Control Groups ◽

Real World Data ◽

World Data

Randomized controlled trials (RCTs) that assess overall survival are considered the “gold standard” when evaluating the efficacy and safety of a new oncology intervention. However, single-arm trials that use surrogate endpoints (e.g., objective response rate or duration of response) to evaluate clinical benefit have become the basis for accelerated or breakthrough regulatory approval of precision oncology drugs for cases where the target and research populations are relatively small. Interpretation of efficacy in single-arm trials can be challenging because such studies lack a standard-of-care comparator arm. Although an external control group can be based on data from other clinical trials, using an external control group based on data collected outside of a trial may not only offer an alternative to both RCTs and uncontrolled single-arm trials, but it may also help improve decision-making by study sponsors or regulatory authorities. Hence, leveraging real-world data (RWD) to construct external control arms in clinical trials that investigate the efficacy and safety of drug interventions in oncology has become a topic of interest. Herein, we review the benefits and challenges associated with the use of RWD to construct external control groups, and the relevance of RWD to early oncology drug development.

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Border control and SARS-CoV-2: an opportunity for generating highly policy-relevant, real-world evidence

Journal of Travel Medicine ◽

10.1093/jtm/taab037 ◽

2021 ◽

Author(s):

Jacob Burns ◽

Ani Movsisyan ◽

Eva A Rehfuess ◽

Jan M Stratil

Keyword(s):

Real World ◽

Evidence Base ◽

Control Measures ◽

Border Control ◽

Combined Effects ◽

Repeated Testing ◽

Real World Data ◽

World Data ◽

Real World Evidence ◽

Study Type

We propose a study type that would contribute to the evidence base related to border control measures. Over a study period during which arriving travellers are quarantined, repeated testing and/or screening at regular intervals would provide real-world data on the relative and combined effects of various screening and testing measures.

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A91 ECONOMIC EVALUATION OF A FECAL MICROBIOTA TRANSPLANTATION PROGRAM FOR RECURRENT CLOSTRIDIOIDES DIFFICILE INFECTION IN ALBERTA

Journal of the Canadian Association of Gastroenterology ◽

10.1093/jcag/gwab002.089 ◽

2021 ◽

Vol 4 (Supplement_1) ◽

pp. 63-65

Author(s):

D Y Yang ◽

T Mullie ◽

H Sun ◽

L Russell ◽

B Roach ◽

...

Keyword(s):

Healthcare Cost ◽

Real World ◽

Fecal Microbiota Transplantation ◽

Fecal Microbiota ◽

Control Group ◽

Real World Data ◽

World Data ◽

The Cost ◽

The Impact ◽

Over Time

Abstract Background Fecal microbiota transplantation (FMT) is the most effective therapy for recurrent C. difficile infection. Although studies using statistical modeling have shown FMT to be cost-effective, real-world data is lacking. Aims To assess the impact of FMT program on the healthcare cost of recurrent C. difficile infections using real-world data from Alberta’s public healthcare system. Methods C. difficile infection patients were identified through provincial laboratory database with positive C. difficile results in Edmonton, Alberta between 2009–16. If an initial positive test was followed by ≧2 positive tests within 183 days, an individual was categorized as recurrent C. difficile infection (RCDI). Otherwise, non-recurrent C. difficile infection (non-RCDI) was assigned. Since the Edmonton FMT program was established in 2013, patients were further divided into pre-FMT (2009–12) and post-FMT (2013–16) eras. This divided patients into four study groups as outlined in Table 1. Administrative data, including inpatient stays, ambulatory or emergency room visits, outpatient prescriptions, and physician billings, were extracted. A cost of $389 was assigned to each FMT procedure to account for cost of donor screening and sample preparation. A difference in differences (DID) approach, a tool which estimates the effect of a treatment by comparing outcome difference between treatment group and control group over time, was used to analyze the impact of FMT program on the cost of RCDI. Non-RCDI patients were used as control group to account for changes in treatment costs over time. Ordinary least squares regression, with log-transformed healthcare cost as the dependent variable, was used for the analysis. Results 4717 non-RCDI and 548 RCDI patients were identified and divided into the 4 groups (Table 1). RCDI patients were significantly older than non-RCDI patients (71.13 vs 62.49; P < 0.001). After adjusting for differences in age, sex, and baseline healthcare utilization, cost for RCDI patients were significantly lower relative to costs for non-RCDI patients in the post-FMT era. Cost of non-RCDI increased by $5,300.08 between the pre- and post-FMT eras, while the cost of RCDI decreased by $7,654.92 in the same time frame (Table 2). FMT program was estimated to have saved $12,954 annually for RCDI patients at mean age, sex, and baseline cost of our overall sample. Conclusions Our data suggest that the healthcare cost of RCDI has decreased with the introduction of an FMT program. Funding Agencies Alberta Health Services, University of Alberta Hospital Foundation

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Real world effects of COPD medications: a cohort study with validation against RCT results

European Respiratory Journal ◽

10.1183/13993003.01586-2020 ◽

2020 ◽

pp. 2001586

Author(s):

Kevin Wing ◽

Elizabeth Williamson ◽

James R Carpenter ◽

Lesley Wise ◽

Sebastian Schneeweiss ◽

...

Keyword(s):

Cohort Study ◽

Propensity Score ◽

Drug Treatment ◽

Real World ◽

Treatment Effects ◽

Control Group ◽

Real World Data ◽

Exacerbation Rate ◽

Active Comparator ◽

World Data

Real-world data provide the potential for generating evidence on drug treatment effects in groups excluded from trials, but rigorous, validated methodology for doing so is lacking. We investigated whether non-interventional methods applied to real-world data could reproduce results from the landmark TORCH COPD trial.We performed a historical cohort study (2000–2017) of COPD drug treatment effects in the UK Clinical Practice Research Datalink (CPRD). Two control groups were selected from CPRD by applying TORCH inclusion/exclusion criteria and 1:1 matching to TORCH participants: control group 1- people with COPD not prescribed fluticasone propionate-salmeterol (FP-SAL), control group 2- people with COPD prescribed salmeterol (SAL). FP-SAL exposed groups were then selected from CPRD by propensity-score matching to each control group. Outcomes studied were COPD exacerbations, death from any cause and pneumonia.2652 FP-SAL exposed people were propensity-score matched to 2652 FP-SAL unexposed people while 991 FP-SAL exposed people were propensity-score matched to 991 SAL exposed people. Exacerbation rate ratio was comparable to TORCH for FP-SAL versus SAL (0.85, 95% CI 0.74–0.97 versus 0.88, 0.81–0.95) but not for FP-SAL versus no FP-SAL (1.30, 1.19–1.42 versus 0.75, 0.69–0.81). Active comparator results were also consistent with TORCH for mortality (hazard ratio 0.93, 0.65–1.32 versus 0.93, 0.77–1.13) and pneumonia (risk ratio 1.39, 1.04–1.87 versus 1.47, 1.25–1.73).We obtained very similar results to the TORCH trial for active comparator analyses, but were unable to reproduce placebo-controlled results. Application of these validated methods for active comparator analyses to groups excluded from RCTs provides a practical way for contributing to the evidence base and supporting COPD treatment decisions.

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Controlling the Covid-19 Pandemic without Killing the Economy: About Data Driven Decision Making with a Data Model Assessing Local Transmission Risk

Journal of Public Health International ◽

10.14302/issn.2641-4538.jphi-20-3621 ◽

2020 ◽

Vol 3 (2) ◽

pp. 22-29

Author(s):

Manfred Stapff

Keyword(s):

Public Health ◽

At Risk ◽

Real World ◽

Virus Transmission ◽

Transmission Risk ◽

Real World Data ◽

World Data ◽

Protective Actions ◽

Populations At Risk ◽

Long Term Impact

Context In the face of further waves of the COVID-19 pandemic, it becomes essential to find a balance between protective actions to guard public health and restrictive measures which can collapse our economy. Background As a basis for public health decisions, officials still rely on metrics that were helpful in the beginning of the pandemic but are now not precise enough for a focused and targeted approach to keep the spread of the infection under control. This can lead to public mistrust, “pandemic tiredness”, and can cause unnecessary damage to the economy without having the desired protective effect on public health. Methods This article discusses various metrics, their advantages and caveats, and it provides suggestions for use in a more targeted and risk-based approach, as an alternative to the current “general lock-down” practice. It suggests the notion of including a concept of “risk contacts per area” to better describe the possibility of virus transmission than currently published metrics do. The article also suggests specific analyses of real-world data for the identification of populations at risk for severe courses of COVID-19 to allow more targeted protective actions. Discussion Data currently used to describe the COVID-19 pandemic lack important parameters like population density and the local likelihood of potentially infectious contacts. The currently often used “all or nothing” approach of shut-down orders needs to be replaced by more sophisticated tactics considering individual local exposure risks and need to be balanced towards metrics on economic short-term and long-term impact. In addition, smart analyses of real-world data may contribute to the effective protection of individuals at risk.

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