The Pharmacists' Role in Disease Outbreaks: Navigating the Dynamics of Uncertainty Before, During, and After Disease Outbreaks.
Ontology highlight
ABSTRACT:
SUBMITTER: Guerci J
PROVIDER: S-EPMC7337136 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The COVID-19 pandemic continues to affect the world. Wuhan, the epicenter of the outbreak, underwent a 76-day lockdown. Research has indicated that the lockdown negatively impacted the quality of life of older individuals, but little is known about their specific experiences during the confinement period. Qualitative interviews were conducted with 20 elderly residents of Wuhan, aged 65 to 85, who experienced mandatory isolation throughout the pandemic. The interviews centered around three stages of experiences: the Early Lockdown stage (the first week of lockdown after the government implemented the lockdown policy in January 2020), Infection During Lockdown stage (from February to April 2020 when participants were affected by the lockdown), and the Post-Lockdown stage (after April 2020 when the government lifted the lockdown policy). We found that older adults experienced different core themes during each lockdown stage. In the Early Lockdown stage, they felt nervousness and fear while searching for information. During the Lockdown and Infection Stage, they relied on reciprocal support and adjusted to new lifestyles. In the Post-Lockdown stage, they expressed cautions, trust, and gratitude. The finding highlights the evolving emotions and coping strategies of older adults throughout the lockdown phases. This study has yielded valuable insights into the adaptations of behavior and the importance of social interactions, specifically emphasizing the significance of healthcare among the elderly population.
| S-EPMC10671022 | biostudies-literature
Project description:The current COVID-19 pandemic is the most severe pandemic of the 21st century, on track to having a rising death toll. Beyond causing respiratory distress, COVID-19 may also cause mortality by way of suicide. The pathways by which emerging viral disease outbreaks (EVDOs) and suicide are related are complex and not entirely understood. We aimed to systematically review the evidence on the association between EVDOs and suicidal behaviors and/or ideation. An electronic search was conducted using five databases: Medline, Embase, Web of Science, PsycINFO and Scopus in April 2020. A rapid systematic review was carried out, which involved separately and independently extracting quantitative data of selected articles. The electronic search yielded 2480 articles, of which 9 met the inclusion criteria. Most of the data were collected in Hong Kong (n = 3) and the USA (n = 3). Four studies reported a slight but significant increase in deaths by suicide during EVDOs. The increase in deaths by suicide was mainly reported during the peak epidemic and in older adults. Psychosocial factors such as the fear of being infected by the virus or social isolation related to quarantine measures were the most prominent factors associated with deaths by suicide during EVDOs. Overall, we found scarce and weak evidence for an increased risk of deaths by suicide during EVDOs. Our results inform the need to orient public health policies toward suicide prevention strategies targeting the psychosocial effects of EVDOs. High-quality research on suicide risk and prevention are warranted during the current pandemic. Highlights • Few studies assessed the association between suicidal behaviors or ideation and emerging viral outbreaks.• The literature reports weak evidence for increased suicide rates during emerging viral outbreaks.• Psychosocial factors are associated with deaths by suicide during emerging viral outbreaks.• Suicide prevention strategies are needed during emerging viral outbreaks.
| S-EPMC7531915 | biostudies-literature
Project description:In 2018, the Bill and Melinda Gates Foundation convened over thirty subject matter experts in clinical development, manufacturing, and regulatory assessment to determine how the development and approval of medical countermeasures could be accelerated in the event of Disease X. Disease X is the result of a presently unknown pathogen with epidemic or pandemic potential. A key opportunity to accelerate the scientific assessment and regulatory approval of medical countermeasures exists within efficient navigation of facilitated regulatory pathways. It was identified that not all stakeholders will be able to skillfully navigate the facilitated pathways offered by the various regulatory agencies during a public health emergency. To democratize this knowledge, we have written an overview of the facilitated approaches which have been developed and refined by Stringent Regulatory Authorities and the World Health Organization for the primary assessment of medical products. We discuss the conditions necessary for use of these approaches, scenarios in which certain pathways may be applicable, and the pros and cons of these approaches. We also address opportunities available to developers in, or developers who wish to access, low-income countries that may have nascent regulatory frameworks.
| S-EPMC7584587 | biostudies-literature
Project description:Device-aided therapies (DAT), which include deep brain stimulation and pump-based continuous dopaminergic stimulation with either levodopa or apomorphine, are among the major advances in the clinical management of Parkinson's disease (PD). Although DAT are being increasingly offered earlier in the disease course, their classical indication remains advanced PD. Theoretically, every patient should be offered transition to DAT when faced with refractory motor and nonmotor fluctuations and functional decline. Worldwide clinical reality is far from these ideal, and, therefore, question the "real-world" equal opportunity of access to DAT for PD patients with advanced PD-even within a single health care system. Differences in access to care, referral pattern (timing and frequency), as well as physician biases (unconscious/implicit or conscious/explicit bias), and patients' preferences or health-seeking behaviour are to be considered. Compared to DBS, little information is available concerning infusion therapies, as well as neurologists' and patients' attitudes towards them. This viewpoint aims to be thought-provoking and to assist clinicians in moving through the process of DAT selection, by including in their decision algorithm their own biases, patient perspective, ethical concerns as well as the current unknowns surrounding PD prognosis and DAT-related long-term side effects for a given patient.
| S-EPMC10645670 | biostudies-literature
Project description:BackgroundDengue virus outbreaks are increasing in number and severity worldwide. Viral transmission is assumed to require a minimum time period of viral replication within the mosquito midgut. It is unknown if alternative transmission periods not requiring replication are possible.MethodsWe used a mouse model of dengue virus transmission to investigate the potential of mechanical transmission of dengue virus. We investigated minimal viral titres necessary for development of symptoms in bitten mice and used resulting parameters to inform a new model of dengue virus transmission within a susceptible population.FindingsNaïve mice bitten by mosquitoes immediately after they took partial blood meals from dengue infected mice showed symptoms of dengue virus, followed by mortality. Incorporation of mechanical transmission into mathematical models of dengue virus transmission suggest that this supplemental transmission route could result in larger outbreaks which peak sooner.InterpretationThe potential of dengue transmission routes independent of midgut viral replication has implications for vector control strategies that target mosquito lifespan and suggest the possibility of similar mechanical transmission routes in other disease-carrying mosquitoes.FundingThis study was funded by grants from the National Health Research Institutes, Taiwan (04D2-MMMOST02), the Human Frontier Science Program (RGP0033/2021), the National Institutes of Health (1R01AI143698-01A1, R01AI151004 and DP2AI152071) and the Ministry of Science and Technology, Taiwan (MOST104-2321-B-400-016).
| S-EPMC10382859 | biostudies-literature
Project description:Reinforcement learning models of the basal ganglia map the phasic dopamine signal to reward prediction errors (RPEs). Conventional models assert that, when a stimulus predicts a reward with fixed delay, dopamine activity during the delay should converge to baseline through learning. However, recent studies have found that dopamine ramps up before reward in certain conditions even after learning, thus challenging the conventional models. In this work, we show that sensory feedback causes an unbiased learner to produce RPE ramps. Our model predicts that when feedback gradually decreases during a trial, dopamine activity should resemble a "bump," whose ramp-up phase should, furthermore, be greater than that of conditions where the feedback stays high. We trained mice on a virtual navigation task with varying brightness, and both predictions were empirically observed. In sum, our theoretical and experimental results reconcile the seemingly conflicting data on dopamine behaviors under the RPE hypothesis.
| S-EPMC8930519 | biostudies-literature
Project description:Internists are well-positioned to play significant roles in recognizing and responding to epidemics, outbreaks, and bioterrorist attacks. They see large numbers of patients with various health problems and may be the patients' only interaction with the medical community for symptoms resulting from infectious diseases and injuries from radiation, chemicals, and/or burns. Therefore, Internists must understand early warning signs of different bioterrorist and infectious agents, proper reporting channels and measures, various ways that they can assist the public health response, and roles of different local, state, and federal agencies. In addition, it is important to understand effects of a public health disaster on clinic operations and relevant legal consequences.
| S-EPMC1824729 | biostudies-other
Project description:Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.
| S-EPMC5465899 | biostudies-literature
Project description:Amid the epidemic outbreaks such as COVID-19, a large number of patients occupy inpatient and intensive care unit (ICU) beds, thereby making the availability of beds uncertain and scarce. Thus, elective surgery scheduling not only needs to deal with the uncertainty of the surgery duration and length of stay in the ward, but also the uncertainty in demand for ICU and inpatient beds. We model this surgery scheduling problem with uncertainty and propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost. Our model is developed using fuzzy sets whereas the proposed algorithm is based on the differential evolution algorithm and heuristic rules. We set up experiments based on data and expert experience respectively. A comparison between the fuzzy model and the crisp (non-fuzzy) model proves the usefulness of the fuzzy model when the data is not sufficient or available. We further compare the proposed model and algorithm with several extant models and algorithms, and demonstrate the computational efficacy, robustness, and adaptability of the proposed framework.
| S-EPMC9742073 | biostudies-literature
Project description:In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.
| S-EPMC6075790 | biostudies-literature