Ramadan is not associated with increased infection risk in Pakistani and Bangladeshi populations: Findings from controlled interrupted time series analysis of UK primary care data

Background The effect of fasting on immunity is unclear. Prolonged fasting is thought to increase the risk of infection due to dehydration. This study describes antibiotic prescribing patterns before, during, and after Ramadan in a primary care setting within the Pakistani and Bangladeshi populations in the UK, most of whom are Muslims, compared to those who do not observe Ramadan. Method Retrospective controlled interrupted time series analysis of electronic health record data from primary care practices. The study consists of two groups: Pakistanis/Bangladeshis and white populations. For each group, we constructed a series of aggregated, daily prescription data from 2007 to 2017 for the 30 days preceding, during, and after Ramadan, respectively. Findings Controlling for the rate in the white population, there was no evidence of increased antibiotic prescription in the Pakistani/Bangladeshi population during Ramadan, as compared to before Ramadan (IRR: 0.994; 95% CI: 0.988–1.001, p = 0.082) or after Ramadan (IRR: 1.006; 95% CI: 0.999–1.013, p = 0.082). Interpretation In this large, population-based study, we did not find any evidence to suggest that fasting was associated with an increased susceptibility to infection.

Impact of the COVID-19 pandemic on new starts to oral oncology medications in the US

Introduction The COVID-19 pandemic has had a significant impact on healthcare delivery. Although others have documented the impact on new cancer diagnoses, trends in new starts for oncology drugs are less clear. We examined changes in new users of oral oncology medications in the US following COVID-19 stay-at-home orders in 2020 compared to prior years. Methods We examined prescription data for members enrolled with a national pharmacy benefits manager in the US from January 1-October 31 of 2018, 2019, and/or 2020. This is a retrospective, observational study comparing new users per 100,000 members per month for all oral oncology drugs, and separately for breast, lung, and prostate cancer, leukemia, and melanoma oral drugs. We performed a difference-in-differences analysis for change in new users from pre-period (prior to pandemic-induced disruption, January-March), to post-period (following pandemic-induced disruption, April-October), between 2020 and 2019, and 2020 and 2018. Results New oral oncology drug users per 100,000 members per month declined by an additional 11.3% in the 2020 post-period compared to 2019 ( p = 0.048). New oral breast cancer drug starts declined by an additional 14.0% in the 2020 post-period compared to 2019 ( p = 0.040). Similar but non-significant trends were found between 2020 and 2018. No significant differences were found between post-period monthly new starts of leukemia, melanoma, lung or prostate cancer disease-specific oral medications. Conclusions Long-term implications of delays in cancer treatment initiation are unclear, although there is concern that patient outcomes may be negatively impacted.

PrAna: an R package to calculate and visualize England NHS primary care prescribing data

Background The objective of this work to calculate prescribed quantity of an active pharmaceutical ingredient (API) in prescription medications for human use, to facilitate research on the prediction of amount of API released to the environment and create an open-data tool to facilitate spatiotemporal and long-term prescription trends for wider usage. Design We have developed an R package, PrAna to calculate the prescribed quantity (in kg) of an APIs by postcode using England’s national level prescription data provided by National Health Service, for the years 2015–2018. Datasets generated using PrAna can be visualized in a real-time interactive web-based tool, PrAnaViz to explore spatiotemporal and long-term trends. The visualisations can be customised by selecting month, year, API, and region. Results PrAnaViz’s targeted API approach is demonstrated with the visualisation of prescribed quantities of 14 APIs in the Bath and North East Somerset (BANES) region during 2018. Once the APIs list is loaded, the back end retrieves relevant data and populates the graphs based on user-defined data features in real-time. These plots include the prescribed quantity of APIs over a year, by month, and individual API by month, general practice, postcode, and medicinal form. The non-targeted API approach is demonstrated with the visualisation of clarithromycin prescribed quantities at different postcodes in the BANES region. Conclusion PrAna and PrAnaViz enables the analysis of spatio-temporal and long-term trends with prescribed quantities of different APIs by postcode. This can be used as a support tool for policymakers, academics and researchers in public healthcare, and environmental scientist to monitor different group of pharmaceuticals emitted to the environment and for prospective risk assessment of pharmaceuticals in the environment.

Risk Indicators Improve the Prescription Quality of Drugs with Anticholinergic Properties in Nursing Homes

Aim: The objective of this study was to assess the impact of a collaborative therapeutic optimization program on the rate of potentially inappropriate prescription of drugs with anticholinergic properties in nursing homes. Methods: Quasi-experimental study in 37 nursing homes in France. The intervention included the use of quality indicators for prescriptions combined with educational sessions and dedicated materials for nursing home staff (unlimited access to study material for staff, including nurses, general practitioners, pharmacists). Indicators were calculated based on routine data collected from an electronic pill dispenser system. The primary outcome was the presence of at least one prescription containing ≥1 drug from a list of 12 drugs with anticholinergic properties. A difference-in-differences analysis was conducted at 18 months as well as propensity score weighting to minimize any potential indication bias. A generalized estimating equation model estimated the probability of being prescribed at least one target drug at any time during a 9-month period for each resident. Results: In total, 33 nursing homes (intervention group: n = 10; control group: n = 23) were included, totalling 8137 residents. There was a decrease in the use of drugs with anticholinergic properties over time in both groups, as well as a decline in the intervention group compared to the control group (Odds Ratio: 0.685, 95% CI: 0.533, 0.880; p < 0.01) that was attributable to the intervention. An estimated 49 anticholinergic properties drug prescriptions were avoided by the intervention. Conclusion: This study found that an intervention based on indicators derived from routine prescription data was effective in reducing use of drugs with anticholinergic properties prescriptions in nursing homes.

Use of State Sequence Analysis in Pharmacoepidemiology: A Tutorial

While state sequence analysis (SSA) has been long used in social sciences, its use in pharmacoepidemiology is still in its infancy. Indeed, this technique is relatively easy to use, and its intrinsic visual nature may help investigators to untangle the latent information within prescription data, facilitating the individuation of specific patterns and possible inappropriate use of medications. In this paper, we provide an educational primer of the most important learning concepts and methods of SSA, including measurement of dissimilarities between sequences, the application of clustering methods to identify sequence patterns, the use of complexity measures for sequence patterns, the graphical visualization of sequences, and the use of SSA in predictive models. As a worked example, we present an application of SSA to opioid prescription patterns in patients with non-cancer pain, using real-world data from Italy. We show how SSA allows the identification of patterns in prescriptions in these data that might not be evident using standard statistical approaches and how these patterns are associated with future discontinuation of opioid therapy.

Data Mining and Analysis of the Compatibility Law of Traditional Chinese Medicines Based on FP-Growth Algorithm

The compatibility law of prescriptions is the core link of TCM theory of “theory, method, prescription and medicine,” which is of great significance for guiding clinical practice, new drug development and revealing the scientific connotation of TCM theory, and is also one of the hot spots and difficulties of TCM modernization research. How to efficiently analyze the frequency of drug use, core combination, and association rules between drugs in prescription is a basic core problem in the study of prescription compatibility law. In this paper, a systematic study was made on the compatibility rules of traditional Chinese antiviral classical prescriptions and the mechanism of traditional Chinese medicine molecules. FP-growth algorithm was used to analyze association rules of 961 classical prescriptions collected and to explore the compatibility rules of traditional Chinese antiviral classical prescriptions. In terms of compatibility law of traditional Chinese antiviral prescriptions, this paper studied the compatibility law of traditional Chinese antiviral prescriptions based on the FP-growth algorithm and made exploratory research on the compatibility law information of 961 traditional classical antiviral prescriptions. Firstly, FP tree was constructed based on the classic recipe data set. Then, frequent item set rules were established, and association rules contained in FP tree were extracted. Finally, the frequency and association rules of antiviral TCM prescriptions were analyzed according to dosage forms (decoction, pill, paste, and ingot). The results show that the FP-growth algorithm adopted in this paper has excellent algorithm performance and strong generalization and robustness in the screening and mining of large-scale prescription data sets, which can provide important processing tools and technical methods for the study of the compatibility rule of traditional Chinese medicine prescriptions.

Rational Prescription Pattern of Antibiotics: A Quantitative Analysis Based on Khwaja Yunus Ali Medical College and Hospital (KYAMCH), Sirajganj, Bangladesh

Antibiotics, alternatively known as antibacterial drugs, prevent or reduce the development of germs. A decade has been added to the life expectancy of human beings since the discovery of antibiotics. Antibiotic overuse can result in resistance to a wide spectrum of diseases and bacteria. Antibiotic utility is being jeopardized by the rise of resistance. There aren't enough innovative agents to deal with the problem of resistant strains. The current study targeted to highlight the current status of antibiotic use.The study was designed as a prescription-based survey where the medicines in prescriptions were checked containing antibiotics, whether the drugs were prescribed rationally or not. The study was conducted from February to July 2018 at Khwaja Yunus Ali Medical College and Hospital, Bangladesh. Patient’s data were collected through review of patient medical records and prepared questionnaires. 100 people were interviewed, and their prescriptions were captured as photos and then checked for rationality.The antibiotics are prescribed in the group of 10 to 30 years, 31 to 50 years and more than 50 years of age.The survey demonstrated that 46% of patients know about antibiotics partially, about 74% of patients fulfill their entire course of medication and the rest of the patients stop taking medication after feeling better. Only 21% of patients knew about antibiotic resistance, whereas 37% of patients only heard about antibiotic resistance. According to the age group from low to high, 92.9%, 91.67%, 86.36% prescriptions were rational; 2.4%, 2.78%, 4.55% prescriptions were contraindicated and 4.7%, 5.56%, 9.1% prescriptions where medicines interacted with other non-antibiotic drugs, respectively. The overall rational prescription is 91%, whereas 3% of prescriptions are contraindicated and 6% of prescriptions showed interaction between antibiotics and other drugs (non-antibiotics).The study concluded that lack of knowledge and awareness of patients and inaccurate prescription data by physicians are two key factors that contribute to irrational antibiotic usage.

Antibiotic use for typhoid in India: Estimation from private sector prescription data during 2013-15

Background India's typhoid burden estimates are based on a limited number of population-based studies and data from a grossly incomplete disease surveillance system. In this study, we estimated the total and sex-and age-specific antibiotic prescription rates for typhoid. Methods We used systematic antibiotic prescription by private sector primary care physicians in India. We categorized antibiotics using the WHO classification system and calculated the prescription for various classes of antibiotics. Results We analyzed 671 million prescriptions for the three-year period (2013-2015), of which an average of 8.98 million antibiotic prescriptions per year was for typhoid, accounting for 714 prescriptions per 100,000 population. Combination antibiotics are the preferred choice of prescribers in the adult age group, while cephalosporins are the preferred choice in children and young age. The prescription rate decreased from 792/100,000 in 2013 to 635 in 2015. Conclusion We report a higher rate of antibiotic prescription for typhoid using prescription data, indicating a higher disease burden than previously estimated. Quinolones are still widely used in monotherapy, and children less than 10 years account for more than a million cases annually, which calls for a routine vaccination program.

Predictors of Polypharmacy Among Elderly Patients in China: The Role of Decision Involvement, Depression, and Taking Chinese Medicine Behavior

Introduction: The prevalence of polypharmacy is gradually increasing in geriatrics, which may contribute to adverse effects, such as potential drug–drug and drug–disease interactions. These side effects remain an important challenge in patient safety, which has a significant impact on mortality and incidence rate.Aims: Therefore, this study aims to understand the epidemiology of polypharmacy and identify factors that have an impact on the management of potentially inappropriate prescribing.Methods: This study is a cross-sectional study, analyzing the prescription data from 720 hospitalized patients aged 50+ with a random cluster sampling method. We used inverse probability treatment weighting (IPTW) method to group and match polypharmacy and non-polypharmacy patients, and logistic regression was conducted to explore the factors associated with polypharmacy.Results: The prevalence of polypharmacy accounted for 50.14% among the old patients in this study. Female patients (67.34%) have more polypharmacy than male patients, and key predictors associated with polypharmacy in the logistic regression model included the following: domicile (AOR = 0.63, 95% CI 0.42–0.95), annual income (AOR = 0.38, 95% CI 0.20–0.70), the number of chronic diseases (AOR = 3.68, 95% CI 2.69–5.06), taking Chinese medicine (AOR = 1.70, 95% CI 1.22–2.36), decision involvement (AOR = 1.49 95% CI 1.10–2.03), and depression (AOR = 1.42, 95% CI 1.03–1.96).Conclusion: Polypharmacy is common among the participants with chronic diseases in Hubei province, China. The study emphasizes that gerontology practitioners should be prudent in applying clinical guidelines to provide personalized, comprehensive assessment of decision making of prescriptions, especially in socioeconomically deprived areas.

Capture - recapture based study on the completeness of smear positive pulmonary tuberculosis reporting in southwest Iran during 2016

Background Tuberculosis (TB) is one of the ten leading causes of death in infectious diseases and one of the ten leading causes of death in the world. For any TB control program, a valid surveillance is essential. In order to assess the status of the assessment, the quality of the record and the completeness of reporting should be assessed. The purpose of this study was to investigate the completeness of smear positive pulmonary tuberculosis reporting in Ahvaz, south west of Iran. Methods This cross-sectional study was conducted in 2016 in Ahvaz, southwest Iran. The study was conducted through a three-source Capture recapture method by collecting laboratory, hospital, physician prescription data; including patient referral to the health care center, prescriptions of patients receiving anti-tuberculosis drugs and prescriptions of medical TB diagnostic laboratories, and laboratory prescriptions. Percentage, mean and standard deviation were used to describe the variables. Data analysis was performed using log-linear model in Rcapture package R software. Results Generally, 134 new cases of smear-positive pulmonary tuberculosis patients were reported through three sources from urban and rural regions during 2016. Pulmonary tuberculosis was reported through three sources from urban and rural regions during 2016. The most common age group was 25 to 44 years and 79.1% of the patient were man. The overall prevalence of new cases of smear-positive pulmonary tuberculosis was in persons that lived urban areas (97.8%). The completeness of reporting the disease estimated by log-linear model was 87.5% and the incidence rate was estimated to be 11.8 disease per 100,000 persons. Completeness of reporting of laboratory, hospital and physician resources were 79%, 30% and 16.3%, respectively. Conclusions The present study shows the necessity of evaluating the quality, completeness and linkage between data. Linking between data sources can improve the accuracy and completeness of TB surveillance.