Journal of Pharmacovigilance

Prevalence of Drug-Drug Interactions and Duplicate Therapy in Chronic Patients in Switzerland: A Real-World Data Study

Redona Hafizi^1* and Anja Bauert^{2 1}Department of Partner Relations and Medical Affairs, TOM Medication, Wermatswil, Switzerland
²Department of Partner Relations and Medical Affairs, University of TOP Pharma Pharmacy Witikon, Zurich, Switzerland
^*Correspondence: Redona Hafizi, Department of Partner Relations and Medical Affairs, TOM Medication, Wermatswil, Switzerland, Email:

Received: 26-Sep-2022, Manuscript No. JP-22-18165; Editor assigned: 30-Sep-2022, Pre QC No. JP-22-18165(PQ); Reviewed: 14-Oct-2022, QC No. JP-22-18165; Revised: 21-Oct-2022, Manuscript No. JP-22-18165(R); Published: 28-Oct-2022, DOI: 10.35248/2329-6887.22.10.390

Abbreviations

ACE-I: Angiotensin Converting Enzyme Inhibitor; ADR: Adverse Drug Reactions; ARB: Angiotensin Receptor Blocker; CMS: Content Management System; CNS: Central Nervous System; CVD: Cardiovascular Diseases; DDI: Drug-Drug Interactions; DRP: Drug Related Problems; HIV: Human Immunodeficiency Virus; ICU: Intensive Care Unit; NSAID: Non-Steroid Anti-inflammatory Drugs; OTC: Over the Counter; SD: Standard Deviations; SPSS: Statistical Package for Social Sciences; WHO: World Health Organization.

Introduction

According to the World Health Organization (WHO), in 50% of the cases, medicines are irrationally prescribed or sold. Furthermore, up to half of the chronic illness patients do not adhere to their prescribed treatment regime. Common examples of inappropriate drug use are polypharmacy, inadequate prescription of antibiotics, inappropriate self-medication by patients and the non-adherence to treatment [1]. Polypharmacy is known as the continuous co- prescription of various drugs to treat multiple chronic diseases or symptoms in a patient. Often, these drugs are prescribed using disease-specific guidelines and target specific disease goals without considering comorbidities and medications patients are already taking [2]. Consequently, the higher the number of medications took by chronic patients, the more likely the occurrence of Adverse Drug Reactions (ADR), medication errors, drug-drug interactions, low adherence, higher use of healthcare services and higher mortality risk [3-6].

Apart from health risks, the inappropriate use of medicines presents elevated costs in the healthcare system. In the USA, adverse drug events make it to the top 10 causes of death, and its costs are calculated to range between $30 and $130 billion each year [7]. The same situation has also been observed in other countries like Japan [8], Germany [9] and Italy [10]. Therefore, finding ways to prevent and control patient medication errors is a substantial need.

Harmful DDIs due to polypharmacy are observed and assessed commonly in hospitalized patients and specific diseases [11- 15]. However, until now, no work has studied the real-world polypharmacy patterns in patients by analyzing their intake of prescribed and Over The Counter (OTC) drugs and medication consumption using digital health apps.

In this cross-sectional study, it is described for the first time the distribution of DDIs and polypharmacy based on real-world data from chronic patients in Switzerland using the TOM Medications app. TOM medications is a digital health app that helps patients follow their therapy by reminding them daily when to take the medications, together with additional functionalities for keeping track of their well-being.

Materials and Methods

Study design and sample size

This cross-sectional cohort study is based on the real-time collection of anonymous data from the polypharmacy check analysis of engaged patients in the TOM Medications app. 100 anonymous medication check results were analyzed to describe the drug-drug interaction and duplicate therapy prevalence among TOM’s user base.

The TOM medication app is an app that reminds chronic patients when to take the drug and increases adherence to therapy among human adults in the general population. The medications check is a service provided to TOM users to check for polypharmacy interactions in their therapy. This medication Check is carried out online by Top Pharm Apotheke Witikon, a partner pharmacy, authorized to provide polypharmacy checks for patients.

Research ethics and permissions

This study is conducted using anonymous health-related personal data from TOM users. Users of the TOM Medications app do not undergo any registration process or have the possibility to insert personal data (e.g. Name or E-mail). Therefore, TOM users are anonymous at every step of the process. According to the Swiss association of the research ethics committee in the "Guidance for basic Research", this does not fall within the scope of the Human Research Act (HRA), and thus it does not require an ethics committee approval.

Study design and sample size

Polypharmacy check data used in this study were collected from TOM Medications Content Management System (CMS). The study population consisted of 100 analyzed polypharmacy checks.

Data collection

Each TOM user’s medication cabinet was analyzed for the occurrence of the DDIs with the Propharma X system provided from HCI solutions (Bern, Switzerland). The DDIs were classified based on their relevance on 5 classes: Class 1 (contraindicated interactions), class 2 (major interactions avoid or use an alternative), class 3 (moderate, monitor or modify treatment), class 4 (minor, caution advised), and class 5 (observation). Subsequently, the identified DDIs were double-checked the Medscape drug interactions checker to analyze the mechanism of action of the drug-drug interactions into pharmacodynamic, pharmacokinetic or unknown, and Epocrates online. The results of the analysis were recorded in the data extraction sheet (Excel and SPSS database) used for this research study. Both the databases are considered reliable clinical support tools with sufficient sensitivity to provide dependable medical information.

Data and statistical analysis

Descriptive analysis for variables was calculated using Statistical Package for Social Sciences SPSS version 26, and the results from the analysis were expressed in mean and standard deviations for continuous variables and in percentages for categorical variables.

The Chi-Square test of associations was used to determine potential associated or relationships between the predictor variable (e.g. duplicate therapy) and the outcome variable (i.e. DDIs). Significant relationships identified with the Chi-Square test of associations were then fitted in logistic regression models to identify the factors associated with DDIs and duplicate therapy. The logistic regression models included the number of medications and the presence of DDIs or duplicate therapy. The analyses were carried out with SPSS for Mac, version 26 (SPSS, Chicago, IL). The significance level for statistical tests, the p-values, was set at p<0.05.

Results

Four hundred and five (405) medicines were taken by 100 patients included in this study with an average (SD) of 4.05 ± 1.226 medication intakes per user.

Participants in our study used a combination of several formulations of medicines, including tablets, inhalators (nebulizers or nasal sprays), liquid formulations, parenteral injections, and ophthalmological preparations. The most frequent formulation used by TOM users was oral formulations with 90.9%, followed by liquid preparations (3.7%) and inhalators (2.7%). It is shown the distribution of pharmaceutical formulations in our study group in Figure 1.

Figure 1: Distribution of medicines by pharmaceutical formulation.

The most frequently used medicines were pantoprazole (n=23) and aspirin cardio (n=15), taken by 22% and 15% of our study population, respectively. These medicines were followed by paracetamol (n=11) and metformin (n=11), used by 11.11% of patients included in this study. On a higher level, when classifying by therapeutic class, the five most used drugs belong to anti- inflammatory and rheumatoid products (11.6%), antithrombotic medicines (7.4%), and drugs for related acid disorders (6.9%), antidiabetics (5.7%) and antihypertensives (5.2%). A descriptive frequency table of all used medicines can be found in Table S1.

Prevalence of potential Drug-Drug Interactions (DDIs)

45 potential DDIs were detected in 34% of the study population, with an average (SD) of 1.32 ± 0.589 per patient. DDIs were then classified into five relevant classes based on the severity of the detected interaction. Our analysis showed that 6.7% of the analyzed polypharmacy checks had at least one DDI classifiable as class 1 (the combination is contraindicated due to severe life-threatening consequences), whereas 11.1% were identified as class 2 (major interactions, where one of the medicines should be discontinued), and 51.1% of the DDIs had at least one class 3 drug combination that needs clinical monitoring and dose adjustment to reach optimal therapy related outcomes. Similar distribution patterns were also seen in the confirmed DDIs with the two online drug databases, Medscape drug-drug interaction checker and Epocrates online, shown in Tables S2 and S3 and Table 1.

Table 1: Frequency of potential DDIs.

Methylphenidate, Trazodone and Mirtazapine were the most frequent medicines involved in 8.88% of the DDIs (n=4). They were followed closely by metformin, ibuprofen, bisoprolol and irbesartan hydrochlorothiazide and amlodipine valsartan hydrochlorothiazide involved in 7.14% of the DDIs (n=3). Figure 2 presents an overview of the prescription frequency and the incidence of DDIs (Figure 2). Interestingly, 8 out of 43 drugs (17.7%) were involved in DDIs and interacted with more than one drug in the same therapy. The drugs involved in more than one DDI in the same therapy were clarithromycin, trazodone, mirtazapine, methylphenidate, bupropion hydrochloride, and irbesartan hydrochlorothiazide (Table S4).

Figure 2: Overview of the frequency of prescribed medicines and their involvement in DDIs. For every drug involved in a drug- drug interaction, the frequency of their prescription in the overall study population and the frequency of involvement in drug- drug interaction were assessed. Note: () Frequency of prescription, ( ) Frequency of DDI interaction.

The mechanism of action involved in the interactions was pharmacodynamic in 61% (n=18) of the cases, pharmacokinetic in 4.2% (n=2) and unknown in 31% (n=9) of the time. Table S5 presents a summary of interacting medicine pairs identified in this research study with a probability of clinical significance (moderate, major, and contraindicated).

To determine clinical severity on a deeper level, the organs affected physiologically by the occurrence of DDIs were analyzed. Our results show that 51.1% of the DDIs affected the cardiovascular system, 22.2% of the DDIs affected the nervous system, and 13.3% affected the endocrine system. Only 2% of the DDIs affect multiple organs in the organisms, causing an anaphylactic effect in the body (Figure 3).

Figure 3: Distribution of DDIs effects among physiological organ systems. Note: ( ) Anaphylaxis, ( ) Cardiovascular system, ( ) Nervous system, ( ) Endocrine system, ( ) Digestive system, ( ) Hepatic portal system, ( ) Miscellaneous.

Subsequently, the clinical effects of the moderate, major, and contraindicated DDIs were studied. By analyzing such interactions, an understanding is built about the symptoms and quality-of-life effects which patients experience when taking these medications together over time. Hypotension (12.6%) was the most prevalent experienced symptom resulting from the potential DDIs, followed by bradycardia (9.2%), hyperglycemia (8%) and Central Nervous System (CNS) depression (6.9%). This number correlates with the affected organ systems distribution shown above, where the cardiovascular system was the most affected, followed by the nervous and endocrine systems (Figure 4).

Figure 4: Frequency of potential DDI Symptoms. The incidence of symptoms that possibly result from the present DDI events were evaluated and presented above.

Prevalence of duplicate therapy

Apart from the potential DDIs in our study populations, the unwanted duplicate therapy prevalence was analyzed. Duplicate therapy is a common practice of prescribing two or more medications for the same purpose, indication or similar pharmacological activities without a clear distinction of when one agent should be administered over the other [16]. While sometimes a combination of drugs is deliberately prescribed to improve the patients' health, taking the same medication twice can result in toxic side effects and a reduction in their well-being.

Our analysis shows that 33% of our study group were subject to duplicate therapy. A more detailed analysis of the data showed that 33.3% of the positive duplicate therapy cases were unwanted duplicate therapies, and 45.5% were potentially unnecessary duplicate therapies. According to our results, only in 21.2% the combination therapy is wanted (Table S6).

Further, indications for which drugs were mostly taken in duplicate therapies were analysed. In 42.4% of the cases, duplicate therapies were taken for the treatment of cardiovascular diseases, followed closely by drugs taken for the management of pain symptoms in 30.3% of the cases, and nervous system disease and diabetes with 9.1% (Figure 5).

Figure 5: Distribution of possible indications in duplicate therapy.

When classified according to the therapeutic class, the antihypertensive drugs make up 30.1% of the medications used in duplicate therapy in our study populations. The second highest intake of medications belongs to the non-steroid anti-inflammatory drugs (NSAIDs) with 24.2%, and the antithrombotic agents are the third group with 12.1% (Figure 6).

Figure 6: Frequency of therapeutic classes in duplicate therapy. The proportion of the used medicines in the positive duplicate therapy analysis was analysed and shown in the graph above.

The Pearson Chi-Square test revealed a strongly significant positive association between the patients taking a duplicate therapy and the possibility of a DDI being present as well (Tables 2 and S7).

Table 2: Pearson chi-square and likelihood ratio estimates.

The number of prescribed medications was strongly associated with an increased probability of DDIs with an odds ratio of 2.044 (95% CI 1.33-3.133, p=0.01). Contrary to the association identified with the Pearson Chi-Square test, our logistic regression model did not identify duplicate therapy as a strong variable for an increased number of DDIs (Table 3). Neither the medication number nor the DDIs were significantly associated with an increased probability of a duplicate therapy (Table S8).

Table 3: Logistic regression model of predictors of DDIs.

Discussion

This study sheds light on the real-world prevalence of duplicate therapy and drug-drug interactions in Switzerland and its link to polypharmacy in the general swiss population using digital health tools. Our results show that DDIs were prevalent in our polypharmacy study population with a frequency of 34%. Furthermore, 17.8% of patients with prevalent DDIs experience serious life-threatening side effects, whereas, for the majority, 51.1% of dose adjustments of the medicines and close monitoring are recommended to benefit from the therapy.

The most frequent drugs involved in DDIs belonged to the antihypertensives (15.9%), psychoanaleptics (15.9%), NSAIDs (12.5%) and ACE-I/ARB (9.1%) therapeutic classes. Interestingly, these are similar to our study's therapeutic classes involved in duplication therapy. Antihypertensives (30.3%) were the highest class being prescribed as duplicate therapy, followed by NSAIDs (21.2%). When comparing the distribution of the therapeutic class in our study's entire medication usage, an interesting picture is seen. While NSAIDs are the highest taken medication class from all participants in this study (11.6%), antihypertensives are the 5th most frequently used class (5.2%), and psychoanaleptics (4.7%) and ACE-I/ARB (4.7%) are positioned lower. There is a higher likelihood of antihypertensives being the most predisposed therapeutic class to be involved in DDIs and duplicate therapies. This fact has been previously reported in a cross-sectional investigation. Drug-Related Problems (DRP) affected 50% of the patients and were most frequently encountered for "Drugs for the Cardiovascular System" and caused by inappropriate duplication of the therapeutic group or active ingredient. Our data show a similar pattern of DDI and duplicate therapy distribution [17].

Similarly, a study analyzing the effect of potential DDIs and polypharmacy in the Caribbean showed that polypharmacy and the presence of hypertension were associated with a higher risk of DDIs [18]. Patients suffering from cardiovascular diseases show higher rates of DDIs presence. This finding is vital since cardiovascular diseases are the leading cause of deaths, with 17.9% (32%) million people dying from cardiovascular diseases globally [19]. It can be possible that duplicate therapy and DDIs have their share in the high morbidity and mortality rate of CVD patients.

The prevalence of DDIs in our study group is 34% which is a similar rate to other published findings [20,21]. Our results with 17.8% of the population carrying serious life-threatening DDIs are higher than the 4.4% of the Danish patients [22] and 3.8% of HIV-infected patients in Spain [23], but much lower than the 69% of the American pediatric patients admitted to ICUs [24]. These differences can be explained by the difference in the study population groups. In the Danish population, only elderly patients and their prescription database are studied. However, patients have access in their day-to-day activities to many over the counter drugs which can interact with the prescribed drugs. In our analysis, we show that NSAIDs make up 12.5% of the medicines that are involved in DDIs. NSAIDs are commonly sold as OTC drugs without prescription in every pharmacy.

Regarding the two other studies, the difference lies in the different population groups. One of the groups above consists of HIV patients, who, due to their immunocompromised system are not a good representative of the general population. The second study is focused only on pediatric patients admitted to the intensive care unit and is not representable of TOM’s study population, where most of the patients suffer cardiovascular diseases, diabetes, and central nervous system diseases. Nevertheless, our results are similar to the 18.2% of the severe DDIs seen in the emergency department patients in the Caribbean, where 18.2% of the patients, both old and young, presented potential severe DDIs in their therapy.

The second finding in our study shows that the prevalence of duplicate therapy in our study populations is 33%. This number is higher than 11.1% of a real-world study performed in Catalonia, and the 2.5% of the REPOSI study hospitalized elderly in Italy [25,26]. The difference in prevalence with the studies performed in Catalonia is explained with the fact that they only included geriatric patients and the contraindicated duplication therapies in their study. Our results show that 21.2% of the analyzed duplication therapies could be wanted combinations. Regarding the REPOSI study, hospitalized old patients were included in the study. Patients using the TOM app are not hospitalized and are not in continuous observation by the nurses and doctors. Nevertheless, our finding is very similar to 39% observed in an American primary care study [27], which presents a closer resemblance to the conditions of chronic patients in our study group.

NSAIDs are the drugs leading the category of unwanted duplicate therapy, which comes with no surprise since they are easy to access drugs bought without a prescription, even in high doses. Several studies showed that although NSAIDs are generally considered safe can so easily; patients performing self-medication without control can easily exceed the daily dose limit for each drug [28- 30]. According to Schiffman et al. in a one-week diary study, in 24.1% of the cases, patients Exceeded the Daily Dose (EDD) for these drugs, and EDD was associated with deviations from detailed dosing directions. These patients exhibited an attitude of "choosing my own dose" and not starting with the lowest dose, as well as poor knowledge of the recommended 1-time and 24-hours doses. Following the findings in these studies, it is also possible for patients in our study group to exceed the daily dose of NSAIDs or pain killers.

Our study shows an association between polypharmacy and a higher odds ratio of the occurrence of the DDIs, but not with the presence of duplicate therapy. This association was shown in several studies on various diseases. One study showed that even mild polypharmacy increases the risk of dementia in older adults and that the presence of DDIs is responsible for this effect in 70.4% of the cases [31]. On the contrary, the only study that could not show a significant association between polypharmacy and DDIs was done in Korean patients diagnosed with cancer. However, the same study showed a significant association between polypharmacy and the hospitalization rate, which could be due to the presence of unidentified DDIs. Further, our regression model could not show higher odds of duplicate therapy when a higher number of medicines are taken. While the Pearson correlation showed an association between DDIs and duplicate therapy, it was impossible to fit a significant logistic regression model for our correlation between the DDIs and duplicate therapy. Such an association has been seen in a study performed in Thailand among the elderly in primary care, where duplicate drug classes accounted for the highest proportion of potential DDIs [32]. One possible explanation could be the small sample size we have included in this study; it could be that more than 100 patients need to be included and fit into the model to establish such a correlation.

The results of this study draw attention to an important concern of the healthcare system. While treating critical diseases and making patients healthy is vital, it is equally important not to deteriorate their health and well-being due to inappropriate medication management practices. Data shows that in Germany, it is estimated that 16000 to 25000 patients die yearly due to polyreaction [33], compared to only 2700 people who die in car accidents. Furthermore, studies in Switzerland, Finland and Sweden estimated that 3%-5% of all deaths could be traced back to unwanted medication events [34]. Several studies have analyzed the possibility of a better medication management system and its impact on reducing DDIs and duplicate therapies. A study where medical students performed in-home medication reviews showed that 86% of elderly patients were prescribed at least one inappropriate drug. Due to the medication reviews, changes to therapy were made to 57% of the study population. This resulted in a better health outcome for these patients and could improve patient safety [35]. While this approach proved to bring significant results, it should also be considered whether the healthcare system has the human capital capacity to rely on these approaches. While since the 80s, a repeat prescription card (also known as the pink card) has been suggested, it has not been implemented so far [36]. Such a card would prevent the duplication of drugs, thus helping to control both polypharmacy and iatrogenic disease.

Controlling duplication therapy is suitable not only for the patient but also for the healthcare system in general. A study performed in Japan showed that the cost of unwanted duplicated drugs was found to be 0.7% of the total drug cost. The estimated expenses for these unwanted duplicative medications were in the range of 5.2%- 7.2% billion yen (37.8%-52.3% million $), which could be saved if the unwanted duplicative medications were eliminated nationwide [37]. Digital health advancements hold great promise in developing to improve medication management and reduce the costs of the healthcare system [38,39].

Conclusion

Altogether, our data that the prevalence of drug-drug interaction and unwanted duplicate therapy is relatively high in a real-world scenario in Switzerland. This results in unwanted effects on patients’ health outcomes, and its cost is a burden for the Swiss healthcare system. Furthermore, our study uses a unique way of assessing the prevalence in the real world using digital tools, which could impact the development of digital strategies to prevent drug- drug interactions, protect chronic patients from medical errors, and improve their well-being.

Author Contributions

R.H. designed the statistical tests; A.B. generated the data. R.H. analyzed the data; R.H. wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported financially from TOM Medications (Innovation 6 AG).

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors declare that all data supporting the findings of this study are available within this paper or within the Supplementary File and can be obtained from the corresponding author on request.

Conflicts of Interest

A.B. is an employee of Toppharm Apotheke Witikon, R.H. is an employee of TOM Medications (Innovation 6 AG). Otherwise, the authors declare no conflict of interest.

REFERENCES

1. WHO. The world medicines situation 2nd ed. 2004.

   [Crossref], [Google Scholar]

2. Marengoni A, Pasina L, Concoreggi C, Martini G, Brognoli F, Nobili A, et al. Understanding adverse drug reactions in older adults through drug-drug interactions. Eur J Intern Med. 2014; 25(9):843-846.

   [Crossref], [Google Scholar], [Pubmed] 

3. Nobili A, Pasina L, Tettamanti M, Lucca U, Riva EM, Marzona I, et al. Potentially severe drug interactions in elderly outpatients: Results of an observational study of an administrative prescription database. J Clin Pharm Ther. 2009; 34(4):377-386.

   [Crossref], [Google Scholar], [Pubmed]

4. Maher RL, Hanlon J, Hajjar ER. Clinical consequences of polypharmacy in elderly. Expert Opin Drug Saf. 2014; 13(1):57-65.

   [Crossref], [Google Scholar], [Pubmed]

5. Weng MC, Tsai CF, Sheu KL, Lee YT, Lee HC, Tzeng SL, et al. The impact of number of drugs prescribed on the risk of potentially inappropriate medication among outpatient older adults with chronic diseases. QJM. 2013; 106(11):1009-1015.

   [Crossref], [Google Scholar], [Pubmed]

6. Menditto E, Gimeno MA, Moreno JA, Poblador PB, Aza PSM, Orlando V, et al. Patterns of multimorbidity and polypharmacy in young and adult population: Systematic associations among chronic diseases and drugs using factor analysis. PLoS One. 2019; 14(2):e0210701.

   [Crossref], [Google Scholar], [Pubmed]

7. White TJ, Arakelian A, Rho JP. Counting the costs of drug-related adverse events. Pharmacoeconomics. 1999; 15(5):445-458.

   [Crossref], [Google Scholar], [Pubmed]

8. Katsuno H, Tachi T, Matsuyama T, Sugioka M, Aoyama S, Osawa T, et al. Evaluation of the direct costs of managing adverse drug events in all ages and of avoidable adverse drug events in older adults in Japan. Front Pharmacol. 2021; 12:761607.

   [Crossref], [Google Scholar], [Pubmed]

9. Heider D, Matschinger H, Meid AD, Quinzler R, Adler JB, Gunster C, et al. Health service use, costs, and adverse events associated with potentially inappropriate medication in old age in Germany: Retrospective matched cohort study. Drugs Aging. 2017; 34(4):289-301.

   [Crossref], [Google Scholar], [Pubmed]

10. Mucherino S, Casula M, Galimberti F, Guarino I, Olmastroni E, Tragni E, et al. The Effectiveness of interventions to evaluate and reduce healthcare costs of potentially inappropriate prescriptions among the older adults: A systematic review. Int J Environ Res Public Health. 2022; 19(11):6724.

    [Crossref], [Google Scholar], [Pubmed]

11. Khandeparkar A, Rataboli PV. A study of harmful drug-drug interactions due to polypharmacy in hospitalized patients in Goa medical college. Perspect Clin Res. 2017; 8(4):180.

    [Crossref], [Google Scholar], [Pubmed]

12. Wolff J, Hefner G, Normann C, Kaier K, Binder H, Hiemke C, et al. Polypharmacy and the risk of drug-drug interactions and potentially inappropriate medications in hospital psychiatry. Pharmacoepidemiol Drug Saf. 2021; 30(9):1258-1268.

    [Crossref], [Google Scholar], [Pubmed]

13. Franchini M, Pieroni S, Fortunato L, Molinaro S, Liebman M. Poly-pharmacy among the elderly: Analyzing the co-morbidity of hypertension and diabetes. Curr Pharm Des. 2015; 21(6):791-805.

    [Crossref], [Google Scholar], [Pubmed]

14. Magni LR, Ferrari C, Barlati S, Ridolfi ME, Prunetti E, Vanni G, et al. Psychopharmacological treatment in borderline personality disorder: A pilot observational study in a real-world setting. Psychiatry Res. 2021; 295:113556.

    [Crossref], [Google Scholar], [Pubmed]

15. Unlu O, Levitan EB, Reshetnyak E, Kneifati-Hayek J, Diaz I, Archambault A, et al. Polypharmacy in older adults hospitalized for heart failure. Circ Heart Fail. 2020; 13(11):e006977.

    [Crossref], [Google Scholar], [Pubmed]

16. Huynh I, Rajendran T. Therapeutic duplication on the general surgical wards. BMJ Open Qual. 2021; 10(3):e001363.

    [Crossref], [Google Scholar], [Pubmed]

17. Wang HY, Yeh MK, Ho CH, Hu MK, Huang YB. Cross-sectional investigation of drug-related problems among adults in a medical center outpatient clinic: Application of virtual medicine records in the cloud. Pharmacoepidemiol Drug Saf. 2017; 26(1):71-80.

    [Crossref], [Google Scholar], [Pubmed]

18. Dookeeram D, Bidaisee S, Paul JF, Nunes P, Robertson P, Maharaj VR, et al. Polypharmacy and potential drug-drug interactions in emergency department patients in the Caribbean. Int J Clin Pharm. 2017; 39(5):1119-1127.

    [Crossref], [Google Scholar], [Pubmed]

19. WHO. Cardiovascular Diseases. 2021.

20. Hong S, Lee JH, Chun EK, Kim KI, Kim JW, Kim SH, et al. Polypharmacy, inappropriate medication use, and drug interactions in older Korean patients with cancer receiving first-line palliative chemotherapy. Oncologist. 2020; 25(3):e502-511.

    [Crossref], [Google Scholar], [Pubmed]

21. Halloran MO, Boyle C, Kehoe B, Bagkeris E, Mallon P, Post FA, et al. Polypharmacy and drug-drug interactions in older and younger people living with HIV: The POPPY study. Antivir Ther. 2019; 24(3):193-201.

    [Crossref], [Google Scholar], [Pubmed]

22. Rosholm JU, Bjerrum L, Hallas J, Worm J, Gram LF. Polypharmacy and the risk of drug-drug interactions among Danish elderly. A prescription database study. Dan Med Bull. 1998; 45(2):210-213.

    [Crossref], [Google Scholar], [Pubmed]

23. Lopez-Centeno B, Badenes-Olmedo C, Mataix-Sanjuan A, McAllister K, Bellon JM, Gibbons S, et al. Polypharmacy and drug-drug interactions in people living with human immunodeficiency virus in the region of Madrid, Spain: A population-based study. Clin Infect Dis. 2020; 71(2):353-362.

    [Crossref], [Google Scholar], [Pubmed]

24. Dai D, Feinstein JA, Morrison W, Zuppa AF, Feudtner C. Epidemiology of polypharmacy and potential drug-drug interactions among pediatric patients in intensive care units of US Children’s Hospitals. Pediatr Crit Care Med. 2016; 17(5):e218.

    [Crossref], [Google Scholar] [Pubmed]

25. Troncoso-Marino A, Lopez-Jimenez T, Roso-Llorach A, Villen N, Amado-Guirado E, Guisado-Clavero M, et al. Medication-related problems in older people in Catalonia: A real-world data study. Pharmacoepidemiol Drug Saf. 2021; 30(2):220-228.

    [Crossref], [Google Scholar], [Pubmed]

26. Pasina L, Djade CD, Tettamanti M, Franchi C, Salerno F, Corrao S, et al. Prevalence of potentially inappropriate medications and risk of adverse clinical outcome in a cohort of hospitalized elderly patients: results from the REPOSI Study. J Clin Pharm Ther. 2014; 39(5):511-515.

    [Crossref], [Google Scholar], [Pubmed]

27. Vejar MV, Makic MB, Kotthoff-Burrell E. Medication management for elderly patients in an academic primary care setting: A quality improvement project. J Am Assoc Nurse Pract. 2015; 27(2):72-78.

    [Crossref], [Google Scholar], [Pubmed]

28. Kaufman DW, Kelly JP, Battista DR, Malone MK, Weinstein RB, Shiffman S. Exceeding the daily dosing limit of nonsteroidal anti‐inflammatory drugs among ibuprofen users. Pharmacoepidemiol Drug Saf. 2018; 27(3):322-331.

    [Crossref], [Google Scholar], [Pubmed]

29. Shiffman S, Battista DR, Kelly JP, Malone MK, Weinstein RB, Kaufman DW. Exceeding the maximum daily dose of acetaminophen with use of different single-ingredient OTC formulations. J Am Pharm Assoc. 2018; 58(5):499-504.

    [Crossref], [Google Scholar], [Pubmed]

30. Shiffman S, Battista DR, Kelly JP, Malone MK, Weinstein RB, Kaufman DW. Prevalence of exceeding maximum daily dose of paracetamol, and seasonal variations in cold‐flu season. Br J Clin Pharmacol. 2018; 84(6):1250-1257.

    [Crossref], [Google Scholar], [Pubmed]

31. Trevisan C, Limongi F, Siviero P, Noale M, Cignarella A, Manzato E, et al. Mild polypharmacy and MCI progression in older adults: The mediation effect of drug-drug interactions. Aging Clin Exp Res. 2021; 33(1):49-56.

    [Crossref], [Google Scholar], [Pubmed]

32. Vatcharavongvan P, Puttawanchai V. Potentially inappropriate medications among the elderly in primary care in Thailand from three different sets of criteria. Pharm Pract (Granada). 2019; 17(3).

    [Crossref], [Google Scholar], [Pubmed]

33. Basil Wegener. Up to 25,000 drug deaths. South German newspaper GmbH. 2010.

34. Grandt D, Lappe V, Schubert I, Drug report Barner health insurance. 2018; 1:13.

35. Willis JS, Hoy RH, Jenkins WD. In-home medication reviews: a novel approach to improving patient care through coordination of care. J Community Health. 2011; 36(6):1027-1031.

    [Crossref], [Google Scholar], [Pubmed]

36. Barlow MA. Repeat prescription card-'pink'card. S Afr Med J. 1981; 59(8):276.

    [Google Scholar], [Pubmed]

37. Kinoshita H, Kobayashi Y, Fukuda T. Duplicative medications in patients who visit multiple medical institutions among the insured of a corporate health insurance society in Japan. Health Policy. 2008; 85(1):114-123.

    [Crossref], [Google Scholar], [Pubmed]

38. Clauson KA, Polen HH, Marsh WA. Clinical decision support tools: Performance of personal digital assistant versus online drug information databases. Pharmacotherapy. 2007; 27(12):1651-1658.

    [Crossref], [Google Scholar], [Pubmed]

39. McHugh ML. The chi-square test of independence. Biochem Med (Zagreb). 2013; 23(2):143-149.

    [Crossref], [Google Scholar], [Pubmed]