Public awareness of melioidosis in Thailand and potential use of video clips as educational tools.

Melioidosis causes more than 1,000 deaths in Thailand each year. Infection occurs via inoculation, ingestion or inhalation of the causative organism (Burkholderia pseuodmallei) present in soil and water. Here, we evaluated public awareness of melioidosis using a combination of population-based questionnaire, a public engagement campaign to obtain video clips made by the public, and viewpoints on these video clips as potential educational tools about the disease and its prevention

A retrospective review of the management of acute infections and the indications for antibiotic prescription in primary care in Northern Thailand

Introduction Antibiotic use in low-income and middle-income countries continues to rise despite the knowledge that antibiotic overuse can lead to antimicrobial resistance. There is a paucity of detailed data on the use of antibiotics in primary care in low-resource settings. Objective To describe the presentation of acute infections and the indications for antibiotic prescription. Design A 2-year retrospective review of routinely collected data. Setting All 32 primary care units in one district in northern Thailand. Participants Patients attending primary care with a history of fever, documented temperature, International Statistical Classification of Diseases 10 code for infection or prescribed a systemic antibiotic. Patients attending after the initiation of a study on C-reactive protein testing in four centres were excluded. Outcome measures The proportion of patients prescribed an antibiotic and the frequency of clinical presentations. Results 762 868 patients attended the health centres, of whom 103 196 met the inclusion criteria, 5966 were excluded resulting in 97 230 attendances consisting of 83 661 illness episodes. 46.9% (39 242) of the patients were prescribed an antibiotic during their illness. Indications for antibiotic prescription in the multivariable logistic regression analysis included male sex (adjusted OR (aOR) 1.21 (95% CI 1.16 to 1.28), p37.5°C (aOR 1.24 (95% CI 1.03 to 1.48), p=0.020). 77.9% of the presentations were for respiratory-related problems, of which 98.6% were upper respiratory tract infections. The leading infection diagnoses were common cold (50%), acute pharyngitis (18.9%) and acute tonsillitis (5%) which were prescribed antibiotics in 10.5%, 88.7% and 87.1% of cases, respectively. Amoxicillin was the most commonly prescribed antibiotic. Conclusions Nearly half of the patients received an antibiotic, the majority of whom had a respiratory infection. The results can be used to plan interventions to improve the rational use of antibiotics. Further studies in private facilities, pharmacies and dental clinics are required

Using a Web-Based Application to Define the Accuracy of Diagnostic Tests When the Gold Standard Is Imperfect

Background: Estimates of the sensitivity and specificity for new diagnostic tests based on evaluation against a known gold standard are imprecise when the accuracy of the gold standard is imperfect. Bayesian latent class models (LCMs) can be helpful under these circumstances, but the necessary analysis requires expertise in computational programming. Here, we describe open-access web-based applications that allow non-experts to apply Bayesian LCMs to their own data sets via a user-friendly interface. Methods/Principal Findings: Applications for Bayesian LCMs were constructed on a web server using R and WinBUGS programs. The models provided (http://mice.tropmedres.ac) include two Bayesian LCMs: the two-tests in two-population model (Hui and Walter model) and the three-tests in one-population model (Walter and Irwig model). Both models are available with simplified and advanced interfaces. In the former, all settings for Bayesian statistics are fixed as defaults. Users input their data set into a table provided on the webpage. Disease prevalence and accuracy of diagnostic tests are then estimated using the Bayesian LCM, and provided on the web page within a few minutes. With the advanced interfaces, experienced researchers can modify all settings in the models as needed. These settings include correlation among diagnostic test results and prior distributions for all unknown parameters. The web pages provide worked examples with both models using the original data sets presented by Hui and Walter in 1980, and by Walter and Irwig in 1988. We also illustrate the utility of the advanced interface using the Walter and Irwig model on a data set from a recent melioidosis study. The results obtained from the web-based applications were comparable to those published previously. Conclusions: The newly developed web-based applications are open-access and provide an important new resource for researchers worldwide to evaluate new diagnostic tests

A retrospective review of the management of acute infections and the indications for antibiotic prescription in primary care in Northern Thailand

Introduction Antibiotic use in low-income and middle-income countries continues to rise despite the knowledge that antibiotic overuse can lead to antimicrobial resistance. There is a paucity of detailed data on the use of antibiotics in primary care in low-resource settings. Objective To describe the presentation of acute infections and the indications for antibiotic prescription. Design A 2-year retrospective review of routinely collected data. Setting All 32 primary care units in one district in northern Thailand. Participants Patients attending primary care with a history of fever, documented temperature, International Statistical Classification of Diseases 10 code for infection or prescribed a systemic antibiotic. Patients attending after the initiation of a study on C-reactive protein testing in four centres were excluded. Outcome measures The proportion of patients prescribed an antibiotic and the frequency of clinical presentations. Results 762 868 patients attended the health centres, of whom 103 196 met the inclusion criteria, 5966 were excluded resulting in 97 230 attendances consisting of 83 661 illness episodes. 46.9% (39 242) of the patients were prescribed an antibiotic during their illness. Indications for antibiotic prescription in the multivariable logistic regression analysis included male sex (adjusted OR (aOR) 1.21 (95% CI 1.16 to 1.28), p<0.001), adults (aOR 1.77 (95% CI 1.57 to 2), p<0.001) and a temperature >37.5°C (aOR 1.24 (95% CI 1.03 to 1.48), p=0.020). 77.9% of the presentations were for respiratory-related problems, of which 98.6% were upper respiratory tract infections. The leading infection diagnoses were common cold (50%), acute pharyngitis (18.9%) and acute tonsillitis (5%) which were prescribed antibiotics in 10.5%, 88.7% and 87.1% of cases, respectively. Amoxicillin was the most commonly prescribed antibiotic. Conclusions Nearly half of the patients received an antibiotic, the majority of whom had a respiratory infection. The results can be used to plan interventions to improve the rational use of antibiotics. Further studies in private facilities, pharmacies and dental clinics are required

Using a web-based application to define the accuracy of diagnostic tests when the gold standard is imperfect.

BACKGROUND: Estimates of the sensitivity and specificity for new diagnostic tests based on evaluation against a known gold standard are imprecise when the accuracy of the gold standard is imperfect. Bayesian latent class models (LCMs) can be helpful under these circumstances, but the necessary analysis requires expertise in computational programming. Here, we describe open-access web-based applications that allow non-experts to apply Bayesian LCMs to their own data sets via a user-friendly interface. METHODS/PRINCIPAL FINDINGS: Applications for Bayesian LCMs were constructed on a web server using R and WinBUGS programs. The models provided (http://mice.tropmedres.ac) include two Bayesian LCMs: the two-tests in two-population model (Hui and Walter model) and the three-tests in one-population model (Walter and Irwig model). Both models are available with simplified and advanced interfaces. In the former, all settings for Bayesian statistics are fixed as defaults. Users input their data set into a table provided on the webpage. Disease prevalence and accuracy of diagnostic tests are then estimated using the Bayesian LCM, and provided on the web page within a few minutes. With the advanced interfaces, experienced researchers can modify all settings in the models as needed. These settings include correlation among diagnostic test results and prior distributions for all unknown parameters. The web pages provide worked examples with both models using the original data sets presented by Hui and Walter in 1980, and by Walter and Irwig in 1988. We also illustrate the utility of the advanced interface using the Walter and Irwig model on a data set from a recent melioidosis study. The results obtained from the web-based applications were comparable to those published previously. CONCLUSIONS: The newly developed web-based applications are open-access and provide an important new resource for researchers worldwide to evaluate new diagnostic tests

ACORN (A Clinically-Oriented Antimicrobial Resistance Surveillance Network): a pilot protocol for case based antimicrobial resistance surveillance

Background: Antimicrobial resistance (AMR) / drug resistant infections (DRIs) are a major global health priority. Surveillance data is critical to inform infection treatment guidelines, monitor trends, and to assess interventions. However, most existing AMR / DRI surveillance systems are passive and pathogen-based with many potential biases. Addition of clinical and patient outcome data would provide considerable added value to pathogen-based surveillance. Methods: The aim of the ACORN project is to develop an efficient clinically-oriented AMR surveillance system, implemented alongside routine clinical care in hospitals in low- and middle-income country settings. In an initial pilot phase, clinical and microbiology data will be collected from patients presenting with clinically suspected meningitis, pneumonia, or sepsis. Community-acquired infections will be identified by daily review of new admissions, and hospital-acquired infections will be enrolled during weekly point prevalence surveys, on surveillance wards. Clinical variables will be collected at enrolment, hospital discharge, and at day 28 post-enrolment using an electronic questionnaire on a mobile device. These data will be merged with laboratory data onsite using a flexible automated computer script. Specific target pathogens will be Streptococcus pneumoniae, Staphylococcus aureus, Salmonella spp., Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii. A bespoke browser-based app will provide sites with fully interactive data visualisation, analysis, and reporting tools. Discussion: ACORN will generate data on the burden of DRI which can be used to inform local treatment guidelines / national policy and serve as indicators to measure the impact of interventions. Following development, testing and iteration of the surveillance tools during an initial six-month pilot phase, a wider rollout is planned

Input and output screen for the simplified interface of the three-tests in one-population model (Walter and Irwig model) provided on the website (http://mice.tropmedres.ac).

<p>See text for details.</p

Input and output screen for the simplified interface of the two-tests in two-population model (Hui and Walter model) provided on the website (http://mice.tropmedres.ac).

<p>See text for details. </p

Schematic diagram of the web-based application (http://mice.tropmedres.ac).

<p>(A) Users input the data set and settings into a table provided on the webpage, (B) The central web server invisibly transforms the data set and settings inputted into multiple text files suitable for the statistical software, and automatically runs the Bayesian latent class models (LCM) using the R and WinBUGS programs. (C) The results estimated by Bayesian LCM are provided on the webpage within few minutes. </p

Knowledge about risk factors for common infectious diseases in Thailand (n = 4,203).

<p>Knowledge about risk factors for common infectious diseases in Thailand (n = 4,203).</p