COVID-19 Clinical Trials Research Articles

Completion and reporting of COVID-19 clinical trials registered on ClinicalTrials.gov during the first 6 months of the pandemic: cohort study

BackgroundEarly in the COVID-19 pandemic, numerous clinical trials were initiated. Although concerns were raised regarding the quality of the trials, the eventual research output yielded from the trials remains unknown....

Navigating the Landscape of B Cell Mediated Immunity and Antibody Monitoring in SARS-CoV-2 Vaccine Efficacy: Tools, Strategies and Clinical Trial Insights

Correlates of Protection (CoP) are biomarkers above a defined threshold that can replace clinical outcomes as primary endpoints, predicting vaccine effectiveness to support the approval of new vaccines or follow up studies. In the context of COVID-19 vaccination, CoPs can help address challenges such as demonstrating vaccine effectiveness in special populations, against emerging SARS-CoV-2 variants or determining the durability of vaccine-elicited immunity. While anti-spike IgG titres and viral neutralising capacity have been characterised as CoPs for COVID-19 vaccination, the contribution of other components of the humoral immune response to immediate and long-term protective immunity is less well characterised. This review examines the evidence supporting the use of CoPs in COVID-19 clinical vaccine trials, and how they can be used to define a protective threshold of immunity. It also highlights alternative humoral immune biomarkers, including Fc effector function, mucosal immunity, and the generation of long-lived plasma and memory B cells and discuss how these can be applied to clinical studies and the tools available to study them.

Navigating the barriers to multi-regional research administrative data access: Forward thinking solutions

In 2021, aware of both the importance of pan-Canadian data access for advancing health research and the conditions and requirements that make administrative data access onerous, HDRN Canada worked with CATCO the Canadian arm of an international COVID-19 clinical trial to create a roadmap for navigating the barriers to multi-regional administrative data access in Canada. While developing the roadmap, data access request processes were studied in real time for a multi-region study seeking access to link administrative data to clinical trial data, at the individual level. The intent was to move beyond anecdotal evidence and document the specific nature of policy and processes that hamper data access. The case study allowed the study team to define the barriers permitting application of tangible mitigating measures. In 2022, barriers such as the lack of ability to share data across provincial borders became too great to continue the study. Although the roadmap was not completed, insights were gained for waypoints along the path enabling HDRN Canada to make important process improvement to harmonize data access across regions. Solutions to challenges with data access request forms, regional data flows, data sharing partner relationships, data sharing agreements, and REB and project reviews were identified. Recently pan-Canadian initiatives have released important guidance for addressing data access challenges. The adoption of this guidance is paramount to ensure Canada continues to meaningfully contribute to health research. Forward thinking solutions to data access challenges today will establish the necessary infrastructure for a future with minimal data access barriers.

Acceptance of a third COVID-19 vaccine dose, vaccine interchangeability, and clinical trial enrolment among parents of children 12-17 years in Lima, Perú.

To characterize factors associated with parental willingness for their children participation in a COVID-19 vaccine trial, use of different COVID-19 vaccines and acceptance of a third vaccine dose. Parents of children aged 12-17 years in Lima, Perú were asked to complete an online questionnaire via social networks, from November 9, 2021, to April 23, 2022. We calculated crude and adjusted prevalence ratios with 95% confidence intervals to compare factors with the mentioned outcomes. From 523 parents responding, 374 completed the survey. 90.4% would give their children a third vaccine dose, 36.6% would allow their children participation in a COVID-19 vaccine clinical trial, and 33.2% would accept different vaccine brands between doses. Parental belief that COVID-19 vaccine studies met quality standards was associated with acceptance of a third booster dose (adjusted PR 3.25; 95% CI1.57-6.74; p = 0.002), enrolment in a COVID-19 clinical trial (adjusted PR 4.49; 95% CI1.25-16.06; p = 0.02), and acceptance of different COVID-19 vaccine brands between doses (adjusted PR 10.02; 95% CI1.40-71.95; p = 0.02). Most parents would accept a third vaccine booster dose, approximately a third would participate in COVID-19 vaccine trials. Believing COVID-19 vaccines studies fulfilled quality standards was associated with the study outcomes. It is necessary to inform about the rigorous processes for the development of COVID-19 vaccines to generate confidence in parents to accept these vaccine-related outcomes.

Pharmacological inhibition of CK2 by silmitasertib mitigates sepsis-induced circulatory collapse, thus improving septic outcomes in mice

Casein kinase II (CK2) has recently emerged as a pivotal mediator in the propagation of inflammation across various diseases. Nevertheless, its role in the pathogenesis of sepsis remains unexplored. Here, we investigated the involvement of CK2 in sepsis progression and the potential beneficial effects of silmitasertib, a selective and potent CK2α inhibitor, currently under clinical trials for COVID-19 and cancer. Sepsis was induced by caecal ligation and puncture (CLP) in four-month-old C57BL/6OlaHsd mice. One hour after the CLP/Sham procedure, animals were assigned to receive silmitasertib (50 mg/kg/i.v.) or vehicle. Plasma/organs were collected at 24 h for analysis. A second set of experiments was performed for survival rate over 120 h. Septic mice developed multiorgan failure, including renal dysfunction due to hypoperfusion (reduced renal blood flow) and increased plasma levels of creatinine. Renal derangements were associated with local overactivation of CK2, and downstream activation of the NF-ĸB-iNOS-NO axis, paralleled by a systemic cytokine storm. Interestingly, all markers of injury/inflammation were mitigated following silmitasertib administration. Additionally, when compared to sham-operated mice, sepsis led to vascular hyporesponsiveness due to an aberrant systemic and local release of NO. Silmitasertib restored sepsis-induced vascular abnormalities. Overall, these pharmacological effects of silmitasertib significantly reduced sepsis mortality. Our findings reveal, for the first time, the potential benefits of a selective and potent CK2 inhibitor to counteract sepsis-induced hyperinflammatory storm, vasoplegia, and ultimately prolonging the survival of septic mice, thus suggesting a pivotal role of CK2 in sepsis and silmitasertib as a novel powerful pharmacological tool for drug repurposing in sepsis.

Ethical and informative trials: How the COVID-19 experience can help to improve clinical trial design

During the COVID-19 pandemic, the race to find an effective vaccine or treatment saw an ‘extraordinary number’ of clinical trials being conducted. While there were some key success stories, not all trials produced results that informed patient care. There was a significant amount of waste in clinical research during the pandemic which is said to have hampered an evidence-based response. Conducting trials which could have been predicted to fail to answer the research question (e.g. because they are not large enough to provide a definitive result) is not only a waste of resources but also a breach of research participants’ trust and a violation of research ethics. The issues seen in COVID-19 clinical trials are symptomatic of a wider trial design crisis where many trials do not provide informative results. This paper examines the roles of key stakeholders in delivering ethical and informative trials and whether guidance published by ‘The Good Clinical Trials Collaborative’ could be used to align key stakeholder groups and enable a joined-up approach to improve clinical trial design.

COVID-19 clinical trials: who is likely to participate and why?

Aim: To identify factors associated with willingness to participate in a COVID-19 clinical trial and reasons for and against participating. Materials & methods: We surveyed Massachusetts (MA, USA)residents online using the Dynata survey platform and via phone using random digit dialing between October andNovember 2021. Respondents were asked to imagine they were hospitalized with COVID-19 and invited to participate in a treatment trial. We assessed willingness to participate by asking, "Which way are you leaning" and why. We used multivariate logistic regression to model factors associated with leaning toward participation. Open-ended responses were analyzed using conventional content analysis. Results: Of 1071 respondents, 65.6% leaned toward participating. Multivariable analyses revealed college-education (OR: 1.59; 95% CI: 1.11, 2.27), trust in the healthcare system (OR: 1.32; 95% CI: 1.10, 1.58) and relying on doctors (OR: 1.77; 95% CI: 1.45, 2.17) and family or friends (OR: 1.31; 95% CI: 1.11, 1.54) to make health decisions were significantly associated with leaning toward participating. Respondents with lower health literacy (OR: 0.57; 95% CI: 0.36, 0.91) and who identify as Black (OR: 0.40; 95% CI: 0.24, 0.68), Hispanic (OR: 0.61; 95% CI: 0.38, 0.98), or republican (OR: 0.61; 95% CI: 0.38, 0.97) were significantly less likely to lean toward participating. Common reasons for participating included helping others, benefitting oneself and deeming the study low risk. Common reasons for leaning against were deeming the study high risk, disliking experimental treatments and not wanting to be a guinea pig. Conclusion: Our finding that vulnerable individuals and those with lower levels of trust in the healthcare system are less likely to be receptive to participating in a COVID-19 clinical trial highlights that work is needed to achieve a healthcare system that provides confidence to historically disadvantaged groups that their participation in research will benefit their community.

Bayesian transition models for ordinal longitudinal outcomes.

Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.

Temporal trends and characteristics associated with racial, ethnic, and sex representation in COVID-19 clinical trials: A systematic review and meta-analysis

BackgroundEarly in the pandemic, extensive attention was cast on limited inclusion of historically underrepresented patient populations in COVID-19 clinical trials. How diverse representation improved following these initial reports remains unclear. MethodsPubMed, Embase and the Cochrane Library were searched (through April 2024) for US-based COVID-19 trials. Utilizing random-effects, we compared expected proportions of trial participants from racial and ethnic groups and of female sex between trials enrolling primarily in 2020 versus primarily 2021–2022. Meta-regression was performed to assess associations between trial characteristics and group representation. ResultsWe retrieved 157 studies comprising 198,012 participants. White (2020: 63.1% [95% CI, 60.8%–67.3%]; 2021–2022: 73.8% [95% CI, 71.5%–76.0%]) and female representation (2020: 46.1% [95% CI, 44.7%–47.4%)]; 2021–2022: 51.1% [95% CI, 49.3%–52.8%) increased across enrollment periods. Industry-sponsored trials were associated with higher White (coefficient, 0.10 [95% CI, 0.03–0.18]) and Hispanic or Latinx representation (coefficient, 0.16 [95% CI, 0.08–0.25]) and lower Asian (coefficient, −0.03 [95% CI, −0.06– –0.003]) and female representation (coefficient, −0.03 [95% CI, −0.07– –0.002]). Outpatient trials were associated with higher White (coefficient, 0.20 [95% CI, 0.13–0.26]) and female representation (coefficient, 0.16 [95% CI, 0.13–0.18]), and lower Black representation (coefficient, −0.10 [95% CI, −0.10– –0.08]). ConclusionsDespite improved female representation in COVID-19 trials over time, there was no clear increase in non-White representation. Trial characteristics such as primary sponsor, clinical setting, and intervention type correlate with representation of specific demographic groups and should be considered in future efforts to improve participant diversity.

The association of COVID-19 vaccination and menstrual health: A period-tracking app-based cohort study

BackgroundIn initial COVID-19 clinical trials, menstrual health was not formally monitored, yet anecdotal reports of menstruation changes surfaced on social media. This study aims to assess the association between COVID-19 vaccines and menstruation using Clue, a period-tracking application. Study designA survey assessing demographics, menstrual health, stress levels, and COVID-19 vaccination was sent to Clue users between 12/7/2021 and 2/9/2022. Inclusion criteria were (1) 18 years or older (2) currently menstruating (3) not pregnant or breastfeeding since 1/2020. Menstrual data was collected for each participant. Users with cycle lengths more than 90 days were excluded. Cycle lengths were calculated for the 6-month average pre-vaccination (PRIOR), the cycle during which vaccination was administered (DURING), the cycle following DURING (AFTER1), and the cycle following AFTER1 (AFTER2). For periods, individuals were stratified based on whether vaccination was received during their menstrual period (DURING). Period lengths were additionally calculated for the 6-month average pre-vaccination (PRIOR), the first period following vaccination (AFTER1), and the period following AFTER1 (AFTER2). For unvaccinated participants, an index date (4/1/2022) was used to similarly designate menstrual cycles and periods. For each participant, cycle length changes for DURING, AFTER1, and AFTER2 compared to PRIOR were determined. Student’s t-test compared the mean of these changes between vaccinated and unvaccinated groups. ResultsOf 7,559 participants, 6,897 (91 %) were vaccinated. Compared to PRIOR, individuals vaccinated during their menstrual period demonstrated a statistically significant increase in the DURING period length, but not AFTER1 (p = 0.463) and AFTER2 (p = 0.692). No statistically significant changes were observed in period lengths of those vaccinated in between periods or in cycle lengths overall. ConclusionA small but statistically significant change in period length was observed only in individuals vaccinated for COVID-19 during their menstrual period. Providers can better counsel menstruating individuals to reduce vaccine misinformation.

Antibody profiling of COVID-19 patients reveals antibodies and autoantibodies that may be associated with faster recovery from COVID-19

Abstract COVID-19 infection exhibits a broad spectrum of clinical outcomes and a combination of host and viral factors interact to drive variability in COVID-19 outcomes. While some studies showed severe COVID-19 patients were more likely to have neutralizing autoantibodies to type-I IFNs relative to the general population, other studies showed preexisting immunity to endemic coronaviruses could be beneficial. This suggests comprehensive evaluation of antibodies and autoantibodies associated with COVID-19 outcomes could reveal important host factors contributing to disease outcomes. Here we profiled antibodies to 87 bacterial and viral proteins, including SARS-CoV-2, and autoantibodies to 383 immune- and autoimmune-associated antigens using the Oncimmune serological profiling technology. A total of 460 patients from two COVID-19 clinical trials (COVACTA, NCT04320615, and MARIPOSA, NCT04363736) were used in this study, and baseline levels of antibodies and autoantibodies in serum samples were associated with COVID-19 outcomes. We observed higher autoantibodies to type-I IFNs and some chemokines were associated with longer time to hospital discharge (TTHD), while higher autoantibodies to IL-6 and IL-36A led to faster TTHD. High levels of antibodies to endemic coronaviruses and seasonal influenza were also associated with faster TTHD. These findings suggest preexisting host antibody and autoantibody repertoire could provide some protection from the more severe consequences of COVID-19.

COVID-19 clinical trial participation and awareness in Texas

ABSTRACT The COVID-19 pandemic required the rapid development of COVID-19 vaccines and treatments, necessitating quick yet representative clinical trial enrollment to evaluate these preventive measures. However, misinformation around the COVID-19 pandemic and general concerns about clinical trial participation in the U.S. hindered clinical trial enrollment. This study assessed awareness of, willingness to participate in, and enrollment in COVID-19 vaccine and treatment clinical trials in Texas. A quota sample of 1,089 Texas residents was collected online from June – July 2022. Respondents were asked if they were aware of, willing to participate in, and had enrolled in clinical trials for COVID-19 vaccines or treatments. Overall, 45.8% of respondents reported being aware of clinical trials for COVID-19 treatments or vaccines, but only 21.7% knew how to enroll and only 13.2% had enrolled in a COVID-19 clinical trial. Respondents with bachelor’s or graduate degrees were more likely to be aware of clinical trials, more likely to have enrolled in trials, and more willing to participate in treatment trials. Women were less willing to participate and less likely to have enrolled in COVID-19 clinical trials than men. Respondents aged 55 years and older were more willing to participate, but less likely to have enrolled in COVID-19 clinical trials than 18-to-24-year-olds. Common reasons given for not participating in clinical trials included concerns that COVID-19 treatments may not be safe, government distrust, and uncertainty about what clinical trial participation would entail. Substantial progress is needed to build community awareness and increase enrollment in clinical trials.

A Systematic Analysis of COVID-19 Clinical Trials Registered in the Clinical Trials Registry of India.

The Clinical Trials Registry - India (CTRI) database is a registry of various trials conducted in India and this study scrutinized the studies registered for COVID-19 from the database to detect patterns in trial design, appraising the target regions of therapiesand comprehending the terrain of research endeavors. This was a cross-sectional study that analyzed the registered trials for COVID-19 between March 2020 and September 2023. A trial search was conducted on the CTRI database to include all types of studies registered for COVID-19 with keywords like "COVID" and "coronavirus" and studies conducted on conditions other than COVID-19 were excluded. The data regarding study characteristics were noted under various sections in a preformed proforma. A total of 807 trials were taken for final analysis and there were about 344 prospective and 260 retrospective interventional trials, 35 prospective and 165 retrospective observational studies, and two prospective and one retrospective post-marketing surveillance study. The majority of the studies had duration under 12 months (91%). The maximum number of studies were registered from AYUSH (Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homeopathy) and allied therapies (n = 283), with about 104 types of interventions, followed by the drug category having 119 trials registered and about 57 types of interventions. Kabasura Kudineer and yoga in the AYUSH category, molnupiravir, colchicine, and favipiravir in the drug category, and tocilizumaband convalescent plasma among biologics were some common interventions used. The majority of trials did not mention the trial phase and declared it as not applicable (54%), whereas 15% were registered as phase 2 and 13% as phase 3. About 54% of the studies were randomized and randomized parallel-group design (20%) was the most common study design. Only 6% of the trials were post-graduate thesis and the majority of the trials (n = 535) denied sharing their individual participant data. Only 0.86% and 0.61% of the trials were terminated and suspended, respectively, denoting proper design and conduct of the trials. In the CTRI database, the majority of trials were prospective interventional studies, with a predominance of AYUSH therapies and drug interventions. Common interventions included Kabasura Kudineer and yoga in AYUSH, and molnupiravir, colchicine, and favipiravir in drugs. Most studies had durations under 12 months and randomized parallel-group design was the most common study design. The intention to use and promote an indigenous system of medicine looks promising in the absence of any definite therapy. A minute number of registered suspended and terminated trials might bea positive picture of meticulously designed and executed trials even during a pandemic situation in India.

An assessment of the informative value of data sharing statements in clinical trial registries

BackgroundThe provision of data sharing statements (DSS) for clinical trials has been made mandatory by different stakeholders. DSS are a device to clarify whether there is intention to share individual participant data (IPD). What is missing is a detailed assessment of whether DSS are providing clear and understandable information about the conditions for data sharing of IPD for secondary use.MethodsA random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions).ResultsBetween the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing (“assessor”) (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as “gold standard” for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated.ConclusionThe study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS.Trial registrationThe protocol for the study was pre-registered on ZENODO (https://zenodo.org/record/7064624#.Y4DIAHbMJD8).

A clustering and graph deep learning-based framework for COVID-19 drug repurposing

Drug repurposing (or repositioning) is the process of finding new therapeutic uses for drugs already approved by drug regulatory authorities (e.g., the Food and Drug Administration (FDA) and Therapeutic Goods Administration (TGA)) for other diseases. This involves analysing the interactions between different biological entities, such as drug targets (genes/proteins and biological pathways) and drug properties, to discover novel drug–target or drug–disease relations. Machine learning and deep learning models have successfully analysed complex heterogeneous data with applications in the biomedical domain, and have also been used for drug repurposing. This study presents a novel unsupervised machine learning framework that utilizes a graph-based autoencoder for multi-feature type clustering on heterogeneous drug data. The dataset consists of 438 drugs, of which 224 are under clinical trials for COVID-19 (category A). The rest are systematically filtered to ensure the safety and efficacy of the treatment (category B). The framework solely relies on reported drug data, including its pharmacological properties, chemical/physical properties, interaction with the host, and efficacy in different publicly available COVID-19 assays. Our machine-learning framework revealed three clusters of interest and provided recommendations featuring the top 15 drugs for COVID-19 drug repurposing, which were shortlisted based on the predicted clusters that were dominated by category A drugs. Our framework can be extended to support other datasets and drug repurposing studies with the availability of our open-source code.

Community-Based Organizations Leading Research Efforts: Preliminary Findings from the Chicagoland CEAL Program's COVID-19 Vaccine Uptake and Intention Survey.

To foster community engaged research in the communities most impacted by COVID-19, the National Institutes of Health (NIH) formed 21 teams of Community Engagement Alliance Against COVID-19 Disparities (CEAL). The national CEAL initiative developed a Common Survey to investigate attitudes and behaviors to the COVID-19 vaccine and clinical trials. This article describes survey implementation at the Chicagoland CEAL Program (CCP). This community-based participatory research project was the result of a strong collaboration between academic institutions, and a community-based non-profit health equity-focused partner organization. The survey implementation was developed and refined with strong input from CHWs, participants, and staff in the partner organizations and institutions. Survey data were collected with Qualtrics, a web-based survey tool. Survey implementation resulted in data collection for 852 participants during the period 12/18/2021-02/18/2023. Excluding participants on the basis of missing data resulted in a sample of 690, 601 of which (87.10%) indicated that they had received at least one dose or intended to get vaccinated. Overall, 54 (7.83%) respondents reported that they had not received the vaccine and were not planning to. Hard to reach populations present two unique challenges in emerging infectious disease events. Reaching populations vulnerable to poor outcomes with vaccines was essential to addressing the COVID-19 pandemic. Additionally, learning about barriers and hesitancy toward vaccine uptake is difficult in these communities. CCP's partnership of five academic institutions, a community research center, and a community-based non-profit health equity-focused organization shows what is possible when traditional models of research and inquiry are reconsidered for community-based participatory research. Results shown here are drawn from a collaboratively designed and implemented survey, collected in person, with over 90% completion.

COVID-19 Clinical Trials: Are Adaptive Methodologies the Solution?

COVID-19 Clinical Trials: Are Adaptive Methodologies the Solution?

Priority index for critical Covid-19 identifies clinically actionable targets and drugs

While genome-wide studies have identified genomic loci in hosts associated with life-threatening Covid-19 (critical Covid-19), the challenge of resolving these loci hinders further identification of clinically actionable targets and drugs. Building upon our previous success, we here present a priority index solution designed to address this challenge, generating the target and drug resource that consists of two indexes: the target index and the drug index. The primary purpose of the target index is to identify clinically actionable targets by prioritising genes associated with Covid-19. We illustrate the validity of the target index by demonstrating its ability to identify pre-existing Covid-19 phase-III drug targets, with the majority of these targets being found at the leading prioritisation (leading targets). These leading targets have their evolutionary origins in Amniota (‘four-leg vertebrates’) and are predominantly involved in cytokine-cytokine receptor interactions and JAK-STAT signaling. The drug index highlights opportunities for repurposing clinically approved JAK-STAT inhibitors, either individually or in combination. This proposed strategic focus on the JAK-STAT pathway is supported by the active pursuit of therapeutic agents targeting this pathway in ongoing phase-II/III clinical trials for Covid-19.

Collaborative sourcing for the health sector derived from a Covid-19 clinical trial

La logística hospitalaria, relaciona los procesos de aprovisionamiento, almacenamiento y distribución de medicamentos y demás insumos requeridos para el manejo clínico de pacientes. Este trabajo es presenta el diseño de una estrategia de abastecimiento colaborativo de medicamentos que permitieran la realización de un estudio clínico de COVID-19, minimizando fallas en el suministro, costos, tiempos y garantizando siempre su disponibilidad en los centros de salud participantes. Este trabajo se realizó en tres fases: la primera fue un análisis del contexto y una revisión de las normas de cada una de las IPS- instituciones prestadoras de salud participantes, en la segunda fase se diseñaron los protocolos y manuales para los procesos de aprovisionamiento y gestión de inventarios, finalmente se implementaron durante el desarrollo del estudio clínico. Como resultado de este trabajo se presenta un modelo colaborativo de compras y de gestión de inventarios, conformado por tres laboratorios de productos farmacéuticos, un centro de mezclas y diez IPS. Igualmente, se diseñaron los procesos para la gestión y trazabilidad de inventarios de forma colaborativa. En última instancia, estos resultados se pueden utilizar para evaluar la coordinación de toda la cadena de suministro hospitalaria - CSH en un escenario de pandemia.

The Polk COVID-19 and Flu Response clinical trial: COVID-19 antibody positivity higher in Black EMS workers

This study reports on a countywide assessment of COVID-19 positivity among emergency medical services workers. Participants completed a short survey of demographic factors and co-morbidities prior to obtaining COVID-19 and influenza testing to analyze the prevalence of these two respiratory viruses that EMS workers, being the first on the scene, are at special risk for, especially in the early days of the pandemic. A total of 7% had a positive COVID-19 antibody test. Although Black EMS personnel only comprised 9% of the cohort, they were significantly more likely to test positive for COVID-19 (P=0.0012). Black race remained a statistically significant predictor in a multivariate model that included the age, sex, BMI, number of hours of direct patient contact, whether the medic was symptomatic, presence of co-morbidities and whether or not the medic had received a flu shot prior (P=0.0008, 95% CI 0.0621-0.2354). This clinical trial (NCT04682132) ) was registered at clinicaltrials.gov, a resource provided by the U.S. National Library of Medicine.