Background Despite a variety of programs developed to control inappropriate antibiotic prescribing for viral infections, antibiotics are still prescribed excessively for Respiratory Tract Infections (RTI). The patient’s expectation to receive an antibiotic often influences the clinician’s decision and can lead to inappropriate antibiotic prescriptions. Our objective was to investigate the changes in patient expectations over time when presenting with symptoms of a respiratory infection. Methods We performed a systematic review of patient’s expectation to receive antibiotics for RTIs. Two reviewers independently evaluated the collected studies based on inclusion and exclusion criteria. Our search initially identified 12 070 studies, of which 321 studies were eligible for full text review and 37 articles were selected for final evaluation. Meta‐regression analysis was used to evaluate the association between patient expectations and different years. Heterogeneity was evaluated using the Q statistic. Results Patient expectations (effect size) were pooled using a random effects model. The effect‐equality test showed heterogeneity among studies (Q = 3304.23, df = 40, P < 0.0001, k = 40, τ2 = 0.63). Meta‐regression results revealed that there is a significant linear negative relationship (B = −1.8374, P < 0.05) between patient expectation and year of data collection, at the global level. A similar finding is observed for the subset of studies conducted outside United States (U.S.) (B = −1.2411, P < 0.1). However, there is no discernible trend for patient expectation in the U.S. or among children and adult subgroups. Also, no significant differences are observed between the patient expectations when considering different age groups. Conclusion The trend of patient expectation for receiving antibiotics for RTIs is declining over time on a global level and also outside the U.S.
Background: Opioid addiction and overdose rates are reaching unprecedented levels in the U.S., with around 47,736 overdose deaths in 2017. Many stakeholders affect the opioid epidemic, including government entities, healthcare providers and policymakers, and opioid users. Simulation and conceptual modeling can help us understand the dynamics of the opioid epidemic by simplifying the real world and informing policymakers about different health interventions that could reduce the deaths caused by opioid overdose in the United States every year. Objectives: To conduct a scoping review of simulation and conceptual models that propose policies capable of controlling the opioid epidemic. We demonstrate the strengths and limitations of these models and provide a framework for further improvement of future decision support tools. Methods: Using the methodology of a scoping review, we identified articles published after 2000 from eight electronic databases to map the literature that uses simulation and conceptual modeling in developing public health policies to address the opioid epidemic. Results: We reviewed 472 papers of which 14 were appropriate for inclusion. Each used either system dynamics simulation modeling, mathematical modeling, conceptual modeling, or agent-based modeling. All included studies tested and proposed strategies to improve health outcomes related to the opioid epidemic. Factors considered in the models included physicians prescribing opioids, trafficking, users recruiting new users, and doctor shopping; no model investigated the impact of age and spatial factors on the dynamics of the epidemic. Key findings from these studies were (1) prevention of opioid initiation is better than treatment of opioid addiction, (2) the analysis of an intervention’s impact should include both benefits and harms, and (3) interventions with short-term benefits might have a counterproductive impact on the epidemic in long run. Conclusions: While most studies examined the role of prescription opioids and trafficking on this epidemic, the transition of patients from prescription opioid use to nonprescription use including heroin and synthetic opioids such as fentanyl impacts the system significantly and results in an epidemic with quite different characteristics than what it had a decade ago. We recommend including the impact of age and geographic location on the opioid epidemic using modeling methods.
We concluded that our device facilitates the Gow-Gates technique and increases its success rate irrespective of the gender of the patient, the side of the mandible being injected, and the experience of the administrator who uses the instrument.
Rationale, aims, and objectives: Inappropriate antibiotic prescribing is still a major concern that can lead to devastating outcomes including antibiotic resistance. This study aimed to simulate the antibiotic prescribing behaviour by providers for acute respiratory tract infections (ARTIs) and to evaluate the impact of patient expectation, provider's perception of patient's expectation to receive a prescription, and patient's risk for bacterial infection, on the decision to prescribe. Methods:We developed a unique system dynamics (SD) simulation model based on the significant factors that impact the interaction between provider and patient during visits for ARTIs and the decision to prescribe antibiotics. In order to validate the model for different age groups and regions in the United States, we used the sample of 53 000 ARTI patient visits made at outpatient settings between 1993 and 2015, based on the National Ambulatory Medical Care Survey (NAMCS).Results: Simulation results reveal that physician diagnosis for prescribing antibiotics is based on physician's experience from their prior prescribing behaviour, their perception of patient's infection risk, and patient's expectation to receive antibiotics.Also, there are some variations depending on patient's age and residential region. The simulation analysis also depicts the decreasing trend in patient's expectation over the past two decades for most age groups and regions. Conclusions:Given the high number of unnecessary prescriptions for ARTI, we found that policies are needed to influence provider's prescribing behaviour through patient's expectation and provider's perception regarding those expectations. Our simulation framework can further be used by policymakers to design and evaluate interventions that may modify the interaction between health providers and patients to optimize antibiotic prescriptions among ARTI patients for different regions and age groups.
In maxillofacial traumatic patients, CBCT seems to be a better and cost-effective technique for detecting hidden foreign bodies than other routine techniques.
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