How do Doctors Decide When to Prescribe Antibiotics in Upper Respiratory Tract Infections?

Purpose: To examine the predictive features which doctors use when prescribing antibiotics in upper respiratory tract infections (URTIs). Method: This is a cross sectional, prospective study done in a teaching university primary care centre in Kuala Lumpur from June to August 2000. Twelve primary care practitioners participated in the study. Each practitioner was asked to record clinical data and prescriptions given to twenty consecutive patients with URTIs using a structured questionnaire for each patient

Are Doctors in the University-Based Primary Care Clinic More Evidence-Based?

Letter to the editor

Do Primary Care Doctors Behave the Same in Antibiotic Prescribing for Upper Respiratory Tract Infections?

Purpose: To compare the extent of using an evidence-based approach in managing upper respiratory tract infections (URTIs) among primary care doctors from two different government clinic settings in Malaysia. Method: This is a cross sectional, prospective study carried out in a teaching university primary care centre in Kuala Lumpur (KL) where doctors are constantly exposed to continuing medical education (CME) and seven health clinics in Seremban, Malaysia where doctors have less exposure to CME in the year 2000. Twelve primary care practitioners in KL and 13 in Seremban participated in the study. Each practitioner was asked to record clinical data and prescriptions given to twenty consecutive patients with URTIs using a structured questionnaire for each patient. The extent of usage of an evidence-based approach in managing URTIs among practitioners was assessed

Prevalence and determinants of anxiety and depression in end stage renal disease (ESRD). A comparison between ESRD patients with and without coexisting diabetes mellitus

Objective: To compare anxiety and/or depressive symptoms between patients with end-stage renal disease with and without comorbid diabetes and identify factors associated with symptoms of distress in this population. Methods: Data from two studies (conducted between 2010 and 2014) were pooled. A total of 526 patients on hemodialysis (68.8% with diabetes) completed the Hospital Anxiety and Depression Scale (HADS). Elevated symptoms were defined as HADS-Anxiety or HADS-Depression ≥ 8. Univariate and multivariate logistic regressions were used to estimate associations between diabetic status, and other socio-demographic and clinical factors with baseline clinical anxiety and depression. Results: A total of 233 (45.4%) reported elevated anxiety symptoms and 256 (49.9%) reported elevated depressive symptoms sufficient for caseness. Rates were not different between patients with and without diabetes. Risk for clinical depression was higher in patients who were single/unpartnered (OR = 1.828), Chinese vs. Malay (OR = 2.05), or had lower albumin levels (OR = 0.932). None of the parameters were associated with anxiety caseness. Conclusion: Sociocultural factors rather than comorbid burden may help identify patients at risk for depression. The high rates of anxiety and depression underlie the importance for monitoring and intervention in dialysis care

Lost in translation: how can education about dementia be effectively integrated into medical school contexts? A realist synthesis

\ua9 Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. OBJECTIVES: The prevalence of dementia in both community and hospital settings requires a clinical workforce that is skilled in diagnosis and management of the condition to competently care for patients. Though evidence of successful educational interventions about dementia exists, effective translation into medical school curricula is the exception rather than the norm. DESIGN: We adopted a realist synthesis approach following Realist And MEta-narrative Evidence Syntheses: Evolving Standards (RAMESES) guidelines to answer the following questions: (1) what are the barriers to integrating effective interventions about dementia into medical school curricula and (2) where they are successfully delivered, what are the contextual factors that allow for this enactment? DATA SOURCES: We searched PubMed, Embase, CINAHL and PsycINFO using the MesH terms Schools, Medical; Students, Medical; Education, Medical AND Neurocognitive disorders or the closest possible set of terms within each database. ELIGIBILITY CRITERIA: Undergraduate or graduate entry medical school programme, teaching and learning focussing on dementia, evaluating student outcomes (satisfaction, knowledge, skills, attitudes or behaviours), interventions described clearly enough to classify teaching method, any research design (quantitative and qualitative), English language. DATA EXTRACTION AND SYNTHESIS: We used a shared spreadsheet to enter key information about eligible studies and the reasons for excluding studies that did not fit eligibility criteria. We extracted descriptive data about the nature of educational interventions and narrative information as to barriers and facilitators to implementing those interventions. RESULTS: Our initial literature search identified 16 relevant papers for review. Systematic extraction of data informed the development of an initial programme theory (IPT) structured around four contextual barriers: \u27culture\u27, \u27concern for patient welfare\u27, \u27student attitudes\u27 and \u27logistics\u27 with associated facilitatory mechanisms embed medical education about dementia. CONCLUSIONS: We outline the process of generating our IPT, including overlap with Cultural Historical Activity Theory. We outline our intention to refine our programme theory through ongoing review of the evidence base and collaboration with stakeholders, with the aim of finalising a model for successful integration of dementia education

Assessment of the variability of airborne contamination levels in an intensive care unit over a 24 hour period

Introduction: The objective of this study was to evaluate the variability in the dynamics and levels of airborne contamination within a hospital ICU in order to establish an improved understanding of the extent to which airborne bioburden contributes to cross-infection of patients. Microorganisms from the respiratory tract or skin can become airborne by coughing, sneezing and periods of increased activity such as bed changes and staff rounds. Current knowledge of the clinical microflora is limited however it is estimated that 10-33% of nosocomial infections are transmitted via air. Methods: Environmental air monitoring was conducted in Glasgow Royal Infirmary ICU, in the open ward and in patient isolation rooms. A sieve impactor air sampler was used to collect 500 L air samples every 15 minutes over 10 hour (08:00-18:00 h) and 24 hour (08:00-08:00 h) periods. Samples were collected, room activity logged and the bacterial contamination levels were recorded as CFU/m3 of air. Results: A high degree of variability in levels of airborne contamination was observed over the course of a 10 hour day and a 24 period in a hospital ICU. Counts ranged from 12-510 CFU/m3 over 24 hours in an isolation room occupied for 10 days by a patient with C. difficile infection. Contamination levels were found to be lowest during the night and in unoccupied rooms, with an average value of 20 CFU/m3. Peaks in airborne contamination showed a direct relation to an increase in room activity. Conclusions: This study demonstrates the degree of airborne contamination that can occur in an ICU over a 24 hour period. Numerous factors were found to contribute to microbial air contamination and consideration should be given to potential improved infection control strategies and decontamination technologies which could be deployed within the clinical environment to reduce the airborne contamination levels, with the ultimate aim of reducing healthcare-associated infections from environmental sources

Evaluation of the airborne contamination levels in an intensive care unit over a 24 hour period

Airborne transmission of infectious microorganisms poses a critical threat to human health, particularly in the clinical setting where it is estimated that 10-33% of nosocomial infections are spread via the air. Within the clinical environment, microorganisms originating from the human respiratory tract or skin can become airborne by coughing and sneezing, and periods of increased activity such as bed and dressing changes, movement, staff rounds and visiting hours. Current knowledge of the clinical airborne microflora is limited and there is uncertainty surrounding the contribution of airborne microorganisms to the transmission of nosocomial infection. This study aims to establish an improved understanding of the variability in the dynamics and levels of airborne microbial contamination within an operational intensive care unit (ICU). Methods Environmental monitoring of airborne contamination levels was conducted in Glasgow Royal Infirmary ICU, in the open ward and in both occupied and unoccupied patient isolation rooms. Monitoring was performed using a sieve impactor air sampler, with 500 L air samples collected every 15 minutes over 10 hour (08:00-18:00 h) and 24 hour (08:00-08:00 h) periods. Samples were collected on tryptone soya agar (TSA) plates, and the bacterial contamination levels were recorded as CFU/m3 of air. An activity log was also collated over the 10 hour and 24 hour sampling periods in order to record any activity occurring in the ward/room that might contribute to spikes in airborne contamination levels. Results Results highlight the degree of variability in levels of airborne contamination over the course of both a working day and a 24 hour period in a hospital ICU. A high degree of variability was observed across the 24 hour period, with counts ranging from 12-510 CFU/m3 in one study in an occupied patient room. Peaks in airborne contamination showed a direct relation to an increase in room activity. Monitoring found contamination levels to be lower overall during the night, and in unoccupied isolation rooms, with an average value of 20 CFU/m3. The highest counts were observed in an isolation room occupied for 10 days by a patient with C. difficile infection which generated an average microbial load of 104 CFU/m3 and a peak value of 510 CFU/m3. Discussion This study has demonstrated the degree of airborne contamination that can occur in the ICU environment over a 24 hour period. Numerous factors were found to contribute to the microbial air contamination levels, including patient status, length of room occupation, time of day and room activity, and further work is required to establish the extent to which this airborne bioburden contributes to cross-infection of patients

Simple model for decay of superdeformed nuclei

Recent theoretical investigations of the decay mechanism out of a superdeformed nuclear band have yielded qualitatively different results, depending on the relative values of the relevant decay widths. We present a simple two-level model for the dynamics of the tunneling between the superdeformed and normal-deformed bands, which treats decay and tunneling processes on an equal footing. The previous theoretical results are shown to correspond to coherent and incoherent limits of the full tunneling dynamics. Our model accounts for experimental data in both the A~150 mass region, where the tunneling dynamics is coherent, and in the A~190 mass region, where the tunneling dynamics is incoherent.Comment: 4 page

Spreading Width for Decay out of a Superdeformed Band

The attenuation factor F responsible for the decay out of a superdeformed (SD) band is calculated with the help of a statistical model. This factor is given by 1/F = (1 + Gamma(down) / Gamma(S)). Here, Gamma(S) is the width for the collective E2 transition within the superdeformed band, and Gamma(down) is the spreading width which describes the mixing between a state in the SD band and the normally deformed (ND) states of equal spin. The attenuation factor F is independent of the statistical E1 decay widths Gamma(N) of the ND states provided that the Gamma(N) are much larger than both Gamma(down) and Gamma(S). This condition is generically met. Previously measured values of F are used to determine Gamma(down).Comment: Submitted to Physical Review Letter