Risk Factor Associated with Acute Otitis External at ENT Department, Mahosot Hospital, Lao PDR / Lao Medical Journal

Background and rationale@#Acute otitis externa is a disease that significantly affects the life and health of people, mostly caused by bacteria, fungi, virus and irritation from chemicals. Besides that, it can also be caused by trauma such as ear picking, spinning ears, swimmer’s ear and wiping the ear too hard, and if not treated in time serious complications are possible. @*Objective@#To study the risk factors associated with acute otitis externa at the ENT Department, Mahosot Hospital.@*Methodology@#This study was cross-sectional, descriptive study among patients with ear-ache and acute otitis externa. Data were collected by using questionnaire interviewed face to face and by ear examination. The data were entered into Epi-data and analyzed by using SPSS software.@*Results@#Of 186 participants, 62.9% had otitis externa, with a mean (95%CI) age of 32 (01-76) years. The commonest age group was those aged less than 15 years old with 29%. Males were 1.88 times more likely to present with otitis externa than females. Occupational groups at risk of disease are farmers/laborers/ housewife/others and those living in rural areas was 2.7 times having acute otitis externa. Those who had swum or had travelled in the forest and ear picking had a higher frequency of acute otitis externa (1.6 times) and patients who regularly cleaned their ears with a cotton swab were 1.39 times of having otitis externa and some patients who used hair clip for ear picking had a higher frequency of acute otitis externa (2.44 times).@*Conclusion@#Most of patients have common clinical manifestations of which are tinnitus, earache, swelling or redness of the ears, hearing loss and itching. Demographic characteristics, history, and ear health care behaviors are associated with acute otitis external.

Comparing the potential effectiveness of interventions against coronavirus 2019 outbreak in the Lao PDR: a mathematical modeling approach / Lao Medical Journal

@#This is a policy brief article on the prediction of Covid-19 outbreak and its prevention and control for the possible second wave in the Lao PDR. Compartmental dynamic modeling was created to reflect the natural history of Covid-19. This included susceptible, symptomatic and asymptomatic states and recovery or death. The simulation was done for one year and with two scenarios: 1) high transmission level (R0=5.2) and 2) mid -transmission level (R0=2.0). The model output showed that the size of the outbreak depended on the transmission level, and could reach to 85% of the Lao population with high transmission scenario. However, disease burden was predicted to be smaller with the interventions. Among these, voluntary home quarantine was found to be the most effective, but the predication reverses in the mid-level transmission scenario. Social distancing is much more effective. If there are imported COVID-19 cases, a new wave could occur in two weeks to 2 months, depending on the size of pandemic and efficacy of the rest of interventions. Mid-level lockdown would result in new epidemic starting by July 2020, but the number of infected people would be less if travel bans and social distancing are maintaining. Only high-level lockdown would be able to stop community transmission in the country.

Signal Detection of Imipenem Compared to Other Drugs from Korea Adverse Event Reporting System Database

PURPOSE: To detect signals of adverse drug events after imipenem treatment using the Korea Institute of Drug Safety & Risk Management-Korea adverse event reporting system database (KIDS-KD). MATERIALS AND METHODS: We performed data mining using KIDS-KD, which was constructed using spontaneously reported adverse event (AE) reports between December 1988 and June 2014. We detected signals calculated the proportional reporting ratio, reporting odds ratio, and information component of imipenem. We defined a signal as any AE that satisfied all three indices. The signals were compared with drug labels of nine countries. RESULTS: There were 807582 spontaneous AEs reports in the KIDS-KD. Among those, the number of antibiotics related AEs was 192510; 3382 reports were associated with imipenem. The most common imipenem-associated AE was the drug eruption; 353 times. We calculated the signal by comparing with all other antibiotics and drugs; 58 and 53 signals satisfied the three methods. We compared the drug labelling information of nine countries, including the USA, the UK, Japan, Italy, Switzerland, Germany, France, Canada, and South Korea, and discovered that the following signals were currently not included in drug labels: hypokalemia, cardiac arrest, cardiac failure, Parkinson's syndrome, myocardial infarction, and prostate enlargement. Hypokalemia was an additional signal compared with all other antibiotics, and the other signals were not different compared with all other antibiotics and all other drugs. CONCLUSION: We detected new signals that were not listed on the drug labels of nine countries. However, further pharmacoepidemiologic research is needed to evaluate the causality of these signals.

Signal Detection of Adverse Drug Reaction of Amoxicillin Using the Korea Adverse Event Reporting System Database

We conducted pharmacovigilance data mining for a β-lactam antibiotics, amoxicillin, and compare the adverse events (AEs) with the drug labels of 9 countries including Korea, USA, UK, Japan, Germany, Swiss, Italy, France, and Laos. We used the Korea Adverse Event Reporting System (KAERS) database, a nationwide database of AE reports, between December 1988 and June 2014. Frequentist and Bayesian methods were used to calculate disproportionality distribution of drug-AE pairs. The AE which was detected by all the three indices of proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) was defined as a signal. The KAERS database contained a total of 807,582 AE reports, among which 1,722 reports were attributed to amoxicillin. Among the 192,510 antibiotics-AE pairs, the number of amoxicillin-AE pairs was 2,913. Among 241 AEs, 52 adverse events were detected as amoxicillin signals. Comparing the drug labels of 9 countries, 12 adverse events including ineffective medicine, bronchitis, rhinitis, sinusitis, dry mouth, gastroesophageal reflux, hypercholesterolemia, gastric carcinoma, abnormal crying, induration, pulmonary carcinoma, and influenza-like symptoms were not listed on any of the labels of nine countries. In conclusion, we detected 12 new signals of amoxicillin which were not listed on the labels of 9 countries. Therefore, it should be followed by signal evaluation including causal association, clinical significance, and preventability.