COVID-19 Risk Assessment Tool: Dual application of risk communication and risk governance.
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ABSTRACT:
SUBMITTER: Chatterjee R
PROVIDER: S-EPMC7266604 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The COVID-19 pandemic has exceeded over sixty-five million cases globally. Different approaches are followed to mitigate its impact and reduce its spreading in different countries, but limiting mobility and exposure have been de-facto precautions to reduce transmission. However, a full lockdown cannot be sustained for a prolonged period. An evidence-based, multidisciplinary approach on risk zoning, personal and transmission risk assessment in near real-time, and risk communication would support the optimized decisions to minimize the impact of coronavirus on our lives. This paper presents a framework to assess the individual and regional risk of COVID-19 along with risk communication tools and mechanisms. Relative risk scores on a scale of 100 represent the integrated risk of influential factors. The personal risk model incorporates age, exposure history, symptoms, local risk and existing health condition, whereas regional risk is computed through the actual cases of COVID-19, public health risk factors, socioeconomic condition of the region, and immigration statistics. A web application tool ( http://www.covira.info ) has been developed, where anyone can assess their risk and find the guided information links primarily for Nepal. This study provides regional risk for Nepal, but the framework is scalable across the world. However, personal risk can be assessed immediately from anywhere.
| S-EPMC7729931 | biostudies-literature
Project description:RationaleThe spread of severe acute respiratory syndrome coronavirus-2 has suspended many non-COVID-19 related research activities. Where restarting research activities is permitted, investigators need to evaluate the risks and benefits of resuming data collection and adapt procedures to minimize risk.ObjectivesIn the context of the multicountry Household Air Pollution Intervention (HAPIN) trial, we developed a framework to assess the risk of each trial activity and to guide protective measures. Our goal is to maximize integrity of reseach aims while minimizing infection risk based on the latest understanding of the virus.MethodsWe drew on a combination of expert consultations, risk assessment frameworks, institutional guidance and literature to develop our framework. We then systematically graded clinical, behavioral, laboratory and field environmental health research activities in four countries for both adult and child subjects using this framework.ResultsOur framework assesses risk based on staff proximity to the participant, exposure time between staff and participants, and potential aerosolization while performing the activity. One of of four risk levels, from minimal to unacceptable, is assigned and guidance on protective measures is provided. Those activities which can potentially aerosolize the virus are deemed the highest risk.ConclusionsBy applying a systematic, procedure-specific approach to risk assessment for each trial activity, we can compare trial activities using the same criteria. This approach allows us to protect our participants and research team and to uphold our ability to deliver on the research commitments we have made to our participants, local communities, and funders. The trial is registered with clinicaltrials.gov (NCT02944682).
| S-EPMC7668754 | biostudies-literature
Project description:BackgroundAs COVID-19 makes its way around the globe, each nation must decide when and how to respond. Yet many knowledge gaps persist, and many countries lack the capacity to develop complex models to assess risk and response. This paper aimed to meet this need by developing a model that uses case reporting data as input and provides a four-tiered risk assessment output.MethodsWe used publicly available, country/territory level case reporting data to determine median seeding number, mean seeding time (ST), and several measures of mean doubling time (DT) for COVID-19. We then structured our model as a coordinate plane with ST on the x-axis, DT on the y-axis, and mean ST and mean DT dividing the plane into four quadrants, each assigned a risk level. Sensitivity analysis was performed and countries/territories early in their outbreaks were assessed for risk.ResultsOur main finding was that among 45 countries/territories evaluated, 87% were at high risk for their outbreaks entering a rapid growth phase epidemic. We furthermore found that the model was sensitive to changes in DT, and that these changes were consistent with what is officially known of cases reported and control strategies implemented in those countries.ConclusionsOur main finding is that the ST/DT Model can be used to produce meaningful assessments of the risk of escalation in country/territory-level COVID-19 epidemics using only case reporting data. Our model can help support timely, decisive action at the national level as leaders and other decision makers face of the serious public health threat that is COVID-19.
| S-EPMC7309203 | biostudies-literature
Project description:COVID-19 is now established in Africa, and requires appropriate prioritization of resources and customized control measures. Although there have been lower than predicted number of COVID-19 cases (6,839,159 (3.4% of global cases)) and fatalities (172,413 (4.1% of cases in Africa)) compared to global estimates from other regions, as of 3rd August 2021. The World Health Organization (WHO) has warned that poor mitigation strategies could worsen the current situation in African countries. Several aspects have been attributed to the lower COVID-19 magnitude observed in Africa that include: warmer climate, a youthful population, and previous experience in managing infectious diseases. However, the level of COVID-19 risk of exposure and vulnerability to develop complications varies greatly across the continent. At present, most COVID-19 disease trajectories have been predicted using mathematical models focused solely on demographic factors. We compared the global health security and governance indices in forty-one African countries, with a population of more than 2.5 million, and matched these to the magnitude of COVID-19 burden, to establish whether there is correlation. These findings suggests that more accurate comparisons of responses to COVID-19 can only be made within clusters of African countries that share similar governance and preparedness standards. We conclude that proper implementation of the continental framework on disaster preparedness and management is required for management of the COVID-19 pandemic.
| S-EPMC8380308 | biostudies-literature
Project description:To make informed COVID-19 related decisions, individuals need information about their personal risks and how those risks may vary with specific demographic and health characteristics. The Fight COVID Milwaukee web-based risk assessment tool allows for assessment of COVID-19 mortality risk as a function of personal and neighborhood characteristics. The purpose of this study is to explore public understanding of this risk assessment tool and risk perception through community focus groups. Individuals were recruited from the general adult population in Milwaukee County, Wisconsin, USA, to participate in nine online focus groups where the risk assessment tool was presented for feedback. Three main themes were identified in the focus groups regarding the web-based risk assessment tool: some challenges in accessibility, variable ease of understanding, and personal usefulness but uncertain value for others. This paper explores how members of the community interpret individual risk assessments and life expectancy estimations, and how these vary with age, gender, race/ethnicity, socioeconomic status, and pre-existing comorbidities.
| S-EPMC10494421 | biostudies-literature
Project description:BackgroundSome of the most vexing issues with the COVID-19 pandemic were the inability of facilities and events, such as schools and work areas, to track symptoms to mitigate the spread of the disease. To combat these challenges, many turned to the implementation of technology. Technology solutions to mitigate repercussions of the COVID-19 pandemic include tools that provide guidelines and interfaces to influence behavior, reduce exposure to the disease, and enable policy-driven avenues to return to a sense of normalcy. This paper presents the implementation and early evaluation of a return-to-work COVID-19 symptom and risk assessment tool. The system was implemented across 34 institutions of health and education in Alabama, including more than 174,000 users with over 4 million total uses and more than 86,000 reports of exposure risk between July 2020 and April 2021.ObjectiveThis study aimed to explore the usage of technology, specifically a COVID-19 symptom and risk assessment tool, to mitigate exposure to COVID-19 within public spaces. More specifically, the objective was to assess the relationship between user-reported symptoms and exposure via a mobile health app, with confirmed COVID-19 cases reported by the Alabama Department of Public Health (ADPH).MethodsThis cross-sectional study evaluated the relationship between confirmed COVID-19 cases and user-reported COVID-19 symptoms and exposure reported through the Healthcheck web-based mobile application. A dependent variable for confirmed COVID-19 cases in Alabama was obtained from ADPH. Independent variables (ie, health symptoms and exposure) were collected through Healthcheck survey data and included measures assessing COVID-19-related risk levels and symptoms. Multiple linear regression was used to examine the relationship between ADPH-confirmed diagnosis of COVID-19 and self-reported health symptoms and exposure via Healthcheck that were analyzed across the state population but not connected at the individual patient level.ResultsRegression analysis showed that the self-reported information collected by Healthcheck significantly affects the number of COVID-19-confirmed cases. The results demonstrate that the average number of confirmed COVID-19 cases increased by 5 (high risk: β=5.10; P=.001), decreased by 24 (sore throat: β=-24.03; P=.001), and increased by 21 (nausea or vomiting: β=21.67; P=.02) per day for every additional self-report of symptoms by Healthcheck survey respondents. Congestion or runny nose was the most frequently reported symptom. Sore throat, low risk, high risk, nausea, or vomiting were all statistically significant factors.ConclusionsThe use of technology allowed organizations to remotely track a population as it is related to COVID-19. Healthcheck was a platform that aided in symptom tracking, risk assessment, and evaluation of status for admitting individuals into public spaces for people in the Alabama area. The confirmed relationship between symptom and exposure self-reporting using an app and population-wide confirmed cases suggests that further investigation is needed to determine the opportunity for such apps to mitigate disease spread at a community and individual level.
| S-EPMC10410382 | biostudies-literature
Project description:Background: As of August 11, 2020, Coronavirus disease 2019 (COVID-19) has infected 19,936,210 persons and led to 732,499 deaths worldwide. The impact has been immense, and with no vaccine currently available, the best way to protect our communities is health education. We developed a brief COVID-19 knowledge test for health educators that can be used to assess deficits in clients' understanding of the disease. Methods: COVID-19 Knowledge Test items were developed by the research team and administered to participants. An alternate-choice item format was selected for the knowledge test, and data analysis was based on an American sample of 273 respondents. A detailed analysis of the data was conducted with classical test theory and Rasch analysis. Findings: The final instrument was found to be a unidimensional measure of COVID-19 knowledge. Results provided evidence for absolute model fit and model fit for individual items. All items included on the scale were monotonically increasing and split-half reliability was considered acceptable. Total test information revealed that the test is suitable for individuals with low to average knowledge of COVID-19. Interpretation: Rasch analysis provides support for the COVID-19 Knowledge Test to be used as an assessment tool for health educators. The final version of the test consists of 34 high-quality test items that can be administered in <10 min. Normative data and suggested cutoff scores are also provided.
| S-EPMC7676894 | biostudies-literature
Project description:ObjectivesThis study sought to identify coronavirus disease 2019 (COVID-19) risk communication materials distributed in Jamaica to mitigate the effects of the disease outbreak. It also sought to explore the effects of health risk communication on vulnerable groups in the context of the pandemic.MethodsA qualitative study was conducted, including a content analysis of health risk communications and in-depth interviews with 35 purposively selected elderly, physically disabled, persons with mental health disorders, representatives of government agencies, advocacy and service groups, and caregivers of the vulnerable. Axial coding was applied to data from the interviews, and all data were analyzed using the constant comparison technique.ResultsTwelve of the 141 COVID-19 risk communication messages directly targeted the vulnerable. All participants were aware of the relevant risk communication and largely complied. Barriers to messaging awareness and compliance included inappropriate message medium for the deaf and blind, rural location, lack of Internet service or digital devices, limited technology skills, and limited connection to agencies that serve the vulnerable.ConclusionThe vulnerable are at increased risk in times of crisis. Accessibility of targeted information was inadequate for universal access to health information and support for vulnerable persons regardless of location and vulnerability.
| S-EPMC8446589 | biostudies-literature
Project description:BackgroundAccurate assessment of COVID-19 severity in the community is essential for patient care and requires COVID-19-specific risk prediction scores adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms, and risk factors, we aimed to develop and validate two COVID-19-specific risk prediction scores. Remote COVID-19 Assessment in Primary Care-General Practice score (RECAP-GP; without peripheral oxygen saturation [SpO2]) and RECAP-oxygen saturation score (RECAP-O2; with SpO2).MethodsRECAP was a prospective cohort study that used multivariable logistic regression. Data on signs and symptoms (predictors) of disease were collected from community-based patients with suspected COVID-19 via primary care electronic health records and linked with secondary data on hospital admission (outcome) within 28 days of symptom onset. Data sources for RECAP-GP were Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP-RSC) primary care practices (development set), northwest London primary care practices (validation set), and the NHS COVID-19 Clinical Assessment Service (CCAS; validation set). The data source for RECAP-O2 was the Doctaly Assist platform (development set and validation set in subsequent sample). The two probabilistic risk prediction models were built by backwards elimination using the development sets and validated by application to the validation datasets. Estimated sample size per model, including the development and validation sets was 2880 people.FindingsData were available from 8311 individuals. Observations, such as SpO2, were mostly missing in the northwest London, RCGP-RSC, and CCAS data; however, SpO2 was available for 1364 (70·0%) of 1948 patients who used Doctaly. In the final predictive models, RECAP-GP (n=1863) included sex (male and female), age (years), degree of breathlessness (three point scale), temperature symptoms (two point scale), and presence of hypertension (yes or no); the area under the curve was 0·80 (95% CI 0·76-0·85) and on validation the negative predictive value of a low risk designation was 99% (95% CI 98·1-99·2; 1435 of 1453). RECAP-O2 included age (years), degree of breathlessness (two point scale), fatigue (two point scale), and SpO2 at rest (as a percentage); the area under the curve was 0·84 (0·78-0·90) and on validation the negative predictive value of low risk designation was 99% (95% CI 98·9-99·7; 1176 of 1183).InterpretationBoth RECAP models are valid tools to assess COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored and SpO2 is available, RECAP-O2 is useful to assess the need for treatment escalation.FundingCommunity Jameel and the Imperial College President's Excellence Fund, the Economic and Social Research Council, UK Research and Innovation, and Health Data Research UK.
| S-EPMC9333950 | biostudies-literature
Project description:The need for a biological disease risk assessment method to prevent the contagion of these diseases, particularly among healthcare personnel, is crucial. Therefore, this study aimed to develop and validate a biological risk assessment tool for biological agents among hospital personnel under COVID-19 conditions. This cross-sectional study was performed on 301 employees in two hospitals. Firstly, we identified the items affecting the contagion of biological agents. Then, we computed the weight of the items using the Fuzzy Analytical Hierarchy Process (FAHP) method. We used the identified items and the estimated weights in the next step to develop a predictive equation. The outcome of this tool was the risk score of biological disease contagion. After that, we used the developed method to evaluate the biological risk of the participants. The ROC curve was also used to reveal accuracy of developed method. In this study, 29 items were identified and categorized into five dimensions, including environmental items, ventilation items, job items, equipment-related items, and organizational items. The weights of these dimensions were estimated at 0.172, 0.196, 0.255, 0.233, and 0.144, respectively. The final weight of items was used to develop a predictive equation. The area under ROC curves (AUC) was also calculated as 0.762 (95% CI: 0.704, 0.820) (p<0.001). The tools developed using these items had acceptable diagnostic accuracy for predicting the risk of biological diseases in health care. Therefore, one can apply it in identifying persons exposed to dangerous conditions.
| S-EPMC10228796 | biostudies-literature