Explore the vast range of titles available.

Knowledge of a patient’s medication is important in treating hyperlipidemia; however, little is known about this in practice. We carried out a repeated cross-sectional study to analyze a nationally representative sample of US adult statin users from the National Health and Nutrition Examination Survey, 1999–2018. We used medication bottle checks and self-reported survey data to estimate the percentage of individuals who are unaware of their hypercholesterolemia, type of medication, or how to take their medication. We used logistic regression to examine their characteristics. We included 8798 statin users; however, 17.6% were unaware of their hypercholesterolemia or statin use. Being older, male, non-Hispanic Black, taking a wider range of prescription medications, and previous diabetes or cardiovascular disease diagnosis were associated with lack of awareness. Serum low-density lipoprotein cholesterol level was lower among those lacking awareness (85.5 vs. 100.7 mg/dL; p < 0.001). Many of those unaware of drug type had been given little information about statins; 34.0% had no diagnosis of diabetes or cardiovascular disease, and of these, 27.1% were >75 years old. Roughly one in six lacked awareness, but no association was found with hypercholesterolemia control. Healthcare providers should ascertain a patient’s understanding and consider the risks and benefits of statin medication.

Globally, unjustified medication use during pregnancy, a critical phase in human life, is a threat that compromises the safety of both, the mother and the child. We aim to investigate the prevalence of over-the-counter (OTC) or non-prescription and prescription medication use during pregnancy in women from the city of Riyadh, Saudi Arabia, the level of prior knowledge, and the sources of their information about medication hazard/safety. A cross-sectional study was performed using a self-administered questionnaire for 287 pregnant women visiting King Saud Medical City (KSMC) - outpatient departments for routine antenatal care during 3 months (1st Mar-31st May 2021). The questionnaire was developed by Navaro et al with 4 sections: socio-demographic data, medication use during pregnancy, level of knowledge, and relevant sources of information. The participants had a mean age of 32.21 years ± 6.41 (SD), and gestational age of 23.67 weeks ± 8.47. About 76.66% of them reported using medication during their current pregnancy: predominantly prescribed (86.36%). Women who used medication during pregnancy were slightly older; the mean difference was 1.97 years (95% CI 0.23-3.71) (P=0.027). Women living in an urban environment as compared with rural had a higher prevalence of medication use (79.01% vs 52%) (P=0.002). Overall, 58.19% reported using non-prescribed medications during pregnancy, with analgesics as the most frequently used class (70.30%). The mild nature of the illnesses and availability of an old prescription and information from pharmacists were the main reasons for self-medication. About 40.77% denied receiving any information about medication use during pregnancy. The prevalence of the medication use during pregnancy in our population is alarmingly high. Analgesics were the most frequently used. Lack of adequate information from treating physicians appears to be contributory to self-medication during this critical time.

Student drug use surveys provide an essential source of information about the prevalence and frequency of drug use, associated harms, socio-demographic correlates, and identification of high-risk groups in a youth population. Materials and Methods: The study was carried out among medical students studying in the Shridevi Institute of Medical Sciences and Research Hospital, Tumkur, by questionnaire-based interview. Drug Utilization; Self-medication; Prescription Medication INTRODUCTION The World Health Organization addressed drug utilization as the marketing, distribution, prescription, and use of drugs in society, with special emphasis on the resulting medical, social, and economic consequences. [1] Studies on the process of drug utilization focus on the factors related to the prescribing, dispensing, administering, and taking of medication, and its associated events, covering the medical and non-medical determinants of drug utilization, the effects of drug utilization as well as studies of how drug utilization relates to the effects of drug use, beneficial, or adverse. [...]it is essential to ensure that the drug used should match the burden of diseases and essential needs. [4] Student drug use surveys (SDUS) provide an essential source of information about the prevalence and frequency of substance use, associated harms, socio-demographic correlates, and identification of high-risk groups in a youth population. From 2006 to 2009, the SDUS working group composed of representatives from 9 of the 13 provinces and territories as well as national representation from the Office of Research and Surveillance in the Controlled Substances and Tobacco Directorate at Health Canada, was tasked with developing a set of core...

The study aimed to examine differences in medication use among midlife women from different cultural origins and to identify socio-demographic, health, and lifestyle characteristics associated with prescribed and non-prescribed medication use. Face-to-face interviews with women aged 45–64 years were conducted during 2004–2006 within three population groups: long-term Jewish residents (LTJR), immigrants from the former Soviet Union after 1989, and Arab women. The survey instrument included current use of medications and way of purchasing (with/without prescription). The level of prescribed and non-prescribed medication use was categorized as taking none, taking 1–2, and taking 3 or more medications. The rates of medication use were 59.5% for prescribed medication and 47% for non-prescribed medications. Differences between the minority groups and LTJR were observed mainly for cardiovascular, vitamins, supplements, and hormonal medications. The analyses showed significantly lower use of prescribed medications among immigrants and of non-prescribed medications among Arab women after taking into account health and socioeconomic indicators. Increased use of prescribed and non-prescribed medications was associated with worse health status and older age. Education was associated with increased use of non-prescribed medications. The disparities in pharmaceutical care may be linked to barriers in access to health care and to cultural preferences among minorities.

Background In Uganda, many people self-medicate and the practice raises important questions about access to healthcare, patient choices, and the increasing prevalence of antimicrobial resistance. This systematic review and meta-analysis investigated the prevalence and factors associated with self-medication in Uganda. Methods We searched Scopus, PubMed, and Embase databases, WHO AFRO, UNIPH registries, and Google Scholar search engine from inception to November 2024 using the algorithm “Self-Medication” AND “Uganda”. Twenty-two eligible studies were included while adhering to the preferred reporting items for systematic reviews and meta-analysis (PRISMA). Results A total of 9113 participants were represented across different demographics and regions of Uganda. Our analysis revealed a 55.63% (95%CI [40.40; 70.66] pooled prevalence of self-medication in Uganda. Antibiotics are the commonly self-medicated drugs and ease of access to medications, perceived cost effectiveness, long hospital waiting time, home storage of drugs (leftovers), and perceptions of minor illnesses were key contributors to self-medication behaviour. Conclusion At least 1 in 2 Ugandans self-medicate and antibiotics constitute the dominant self-medicated drugs compounding the situation in an era of antimicrobial resistance. Awareness campaigns on the dangers of self-medication will be timely.

Background Direct-to-consumer (DTC) marketing of pharmaceuticals is controversial, yet effective. Little is known relating patterns of medication use to patient responsiveness to DTC. Methods We conducted a secondary analysis of data collected in national telephone survey on knowledge of and attitudes toward DTC advertisements. The survey of 1081 U.S. adults (response rate = 65%) was conducted by the Food and Drug Administration (FDA). Responsiveness to DTC was defined as an affirmative response to the item: \"Has an advertisement for a prescription drug ever caused you to ask a doctor about a medical condition or illness of your own that you had not talked to a doctor about before?\" Patients reported number of prescription and over-the-counter (OTC) medicines taken as well as demographic and personal health information. Results Of 771 respondents who met study criteria, 195 (25%) were responsive to DTC. Only 7% respondents taking no prescription were responsive, whereas 45% of respondents taking 5 or more prescription medications were responsive. This trend remained significant (p trend .0009) even when controlling for age, gender, race, educational attainment, income, self-reported health status, and whether respondents \"liked\" DTC advertising. There was no relationship between the number of OTC medications taken and the propensity to discuss health-related problems in response to DTC advertisements (p = .4). Conclusion There is a strong cross-sectional relationship between the number of prescription, but not OTC, drugs used and responsiveness to DTC advertising. Although this relationship could be explained by physician compliance with patient requests for medications, it is also plausible that DTC advertisements have a particular appeal to patients prone to taking multiple medications. Outpatients motivated to discuss medical conditions based on their exposure to DTC advertising may require a careful medication history to evaluate for therapeutic duplication or overmedication.

Background Many patients with rheumatoid arthritis (RA) require a trial of multiple biologic disease-modifying anti-rheumatic drugs (bDMARDs) to control their disease. With the availability of several bDMARD options, the history of bDMARDs may provide an alternative approach to understanding subphenotypes of RA. The objective of this study was to determine whether there exist distinct clusters of RA patients based on bDMARD prescription history to subphenotype RA. Methods We studied patients from a validated electronic health record-based RA cohort with data from January 1, 2008, through July 31, 2019; all subjects prescribed ≥ 1 bDMARD or targeted synthetic (ts) DMARD were included. To determine whether subjects had similar b/tsDMARD sequences, the sequences were considered as a Markov chain over the state-space of 5 classes of b/tsDMARDs. The maximum likelihood estimator (MLE)-based approach was used to estimate the Markov chain parameters to determine the clusters. The EHR data of study subjects were further linked with a registry containing prospectively collected data for RA disease activity, i.e., clinical disease activity index (CDAI). As a proof of concept, we tested whether the clusters derived from b/tsDMARD sequences correlated with clinical measures, specifically differing trajectories of CDAI. Results We studied 2172 RA subjects, mean age 52 years, RA duration 3.4 years, and 62% seropositive. We observed 550 unique b/tsDMARD sequences and identified 4 main clusters: (1) TNFi persisters (65.7%), (2) TNFi and abatacept therapy (8.0%), (3) on rituximab or multiple b/tsDMARDs (12.7%), (4) prescribed multiple therapies with tocilizumab predominant (13.6%). Compared to the other groups, TNFi persisters had the most favorable trajectory of CDAI over time. Conclusion We observed that RA subjects can be clustered based on the sequence of b/tsDMARD prescriptions over time and that the clusters were correlated with differing trajectories of disease activity over time. This study highlights an alternative approach to consider subphenotyping of patients with RA for studies aimed at understanding treatment response.

The problem of unaffordable prescription medications in the United States is complex and can result in poor patient adherence to therapy, worse clinical outcomes, and high costs to the healthcare system. While providers are aware of the financial burden of healthcare for patients, there is a lack of actionable price transparency at the point of prescribing. Real-time prescription benefit (RTPB) tools are new electronic clinical decision support tools that retrieve patient- and medication-specific out-of-pocket cost information and display it to clinicians at the point of prescribing. The rise in US healthcare costs has been a major driver for efforts to increase medication price transparency, and mandates from the Centers for Medicare & Medicaid Services for Medicare Part D sponsors to adopt RTPB tools may spur integration of such tools into electronic health records. Although multiple factors affect the implementation of RTPB tools, there is limited evidence on outcomes. Further research will be needed to understand the impact of RTPB tools on end results such as prescribing behavior, out-of-pocket medication costs for patients, and adherence to pharmacologic treatment. We review the terminology and concepts essential in understanding the landscape of RTPB tools, implementation considerations, barriers to adoption, and directions for future research that will be important to patients, prescribers, health systems, and insurers.

Background Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. Methods We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class. Results Our proposed fusion-based model performs significantly better than the best traditional model (F 1 -score [95% CI]: 0.67 [0.64–0.69] vs. 0.45 [0.42–0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. Conclusions BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.

Using simulated patients to mimic 9 established noncommunicable and infectious diseases, we assessed ChatGPT’s performance in treatment recommendations for common diseases in low- and middle-income countries. ChatGPT had a high level of accuracy in both correct diagnoses (20/27, 74%) and medication prescriptions (22/27, 82%) but a concerning level of unnecessary or harmful medications (23/27, 85%) even with correct diagnoses. ChatGPT performed better in managing noncommunicable diseases than infectious ones. These results highlight the need for cautious AI integration in health care systems to ensure quality and safety.