Background: During Ramadan, Muslims fast throughout daylight hours. There is a direct link between fasting and increasing incidence of infections. Antibiotic usage for treatment of infections should be based on accurate diagnosis, with the correct dose and dosing regimen for the shortest period to avoid bacterial resistance. This study aimed to evaluate the practices of physicians in prescribing suitable antibiotics for fasting patients and the compliance of the patients in using such antibiotics at regular intervals. Materials and methods: An observational study was carried out during the middle 10 days of Ramadan 2014 in two pharmacies at Baghdad. A total of 34 prescriptions (Rx) for adults who suffered from infections were examined. For each included Rx, the researchers documented the age and sex of the patient, the diagnosis of the case, and the name of the given antibiotic(s) with dose and frequency of usage. A direct interview with the patient was also done, at which each patient was asked about fasting and if he/she would like to continue fasting during the remaining period of Ramadan. The patient was also asked if the physician asked him/her about fasting before writing the Rx. Results: More than two-thirds of participating patients were fasting during Ramadan. Antibiotics were prescribed at a higher percentage by dentists and surgeons, for which a single antibiotic with a twice-daily regimen was the most commonly prescribed by physicians for patients during the Ramadan month. Conclusion: Physicians fail to take patient fasting status into consideration when prescribing antibiotics for their fasting patients. Antibiotics with a twice-daily regimen are not suitable and best to be avoided for fasting patients in Iraq during Ramadan – especially if it occurs during summer months – to avoid treatment failure and provoking bacterial resistance. Keywords: fasting, Ramadan, antibiotics, dosing regimen
This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. Several models were used for comparison with the integrated model, namely Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest Reg
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