Prevalence and factors associated with potential drug-drug interactions in prescriptions presented at private pharmacies in Mbarara city, southwestern Uganda

doi:10.21203/rs.3.rs-2747207/v1

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Prevalence and factors associated with potential drug-drug interactions in prescriptions presented at private pharmacies in Mbarara city, southwestern Uganda

https://doi.org/10.21203/rs.3.rs-2747207/v1

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published 02 Jan, 2024

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Background: Drug-drug interactions (DDIs) influence the effectiveness of medication and thus determine the treatment outcomes of diseases managed with pharmacotherapy. This study aimed to determine the prevalence, severity, and factors associated with potential drug-drug interactions in prescriptions presented at private pharmacies in Mbarara city.

Methods: DDIs were identified and classified basing on risk and severity using Lexicomp drug interaction database. STATA version 13 was used to analyze the collected data. Descriptive statistics were used to summarize the severity of potential DDIs identified. Bivariate and multivariate logistic regression was employed to identify different factors associated with the presence of potential DDIs.

Results: A total of 295 prescriptions from 18 private pharmacies were studied and the prevalence of clinically significant potential DDIs was 37.6%. About half (149, 50.5%) of the patients were females, the majority (199, 67.5%) were adults 18-59 years of age whereas most (208, 70.5%) had a comorbid condition. Over one half (162, 54.9%) of the prescriptions were received from hospitals and majority of the prescriptions had 4 drugs prescribed (n=175, 59.32%). Having one or more comorbidities and prescribing of therapeutic drug categories including anti-fungal, anti-hypertensives, analgesics, or corticosteroids were significantly associated with potential DDIs.

Conclusions: The prevalence of potential drug-drug interactions in outpatient setting in Mbarara city was high and majority of the potential DDIs were of moderate severity. Having 1 or more comorbidities and prescribing of therapeutic drug categories including anti-fungals, anti-hypertensives, analgesics, or corticosteroids were significantly associated with potential DDIs.

Prevalence

Potential drug − drug interactions

Risk factors

Mbarara

Southwestern Uganda

A drug-drug interaction (DDI) is defined as a pharmacokinetic or pharmacodynamic influence of drugs on each other, which may result in undesired effects such as reduced effectiveness or increased toxicity [1]. According to the Food and Drug Authority, drug-drug interactions occur when two or more drugs react with each other and this may make the interacting drugs less effective, cause unexpected side effects or increase the action of a particular drug [2]. Different drugs are often used concurrently with others to achieve a desired therapeutic goal and treat coexisting diseases or multi-morbidity conditions [3]. The presence of multi-morbidity increases the possibility of patients using many drugs concurrently, and this is associated with a high risk of potential drug-drug interactions [4, 5]. The concurrent use of many drugs (4 or more) is referred to as polypharmacy [6].

DDIs can be classified into two main groups i.e., pharmacokinetic DDIs and pharmacodynamic DDIs. Pharmacokinetic DDIs involve interference with the absorption, distribution, metabolism and excretion of one drug by another, whereas pharmacodynamic DDIs may be due to: direct effect of a drug on function of the receptor of another, interference with a biological or physiological control process and additive/opposed pharmacological effect of one drug on another. The toxic effects of DDIs may be adverse drug events that can be severe enough to necessitate hospitalization and increase hospital stay. Drug-drug interactions may also lead to poor health outcomes, as well as increased costs and utilization of healthcare service [7, 8].

In all health care systems, there is need for information on the occurrence of potential DDIs, knowledge of their associated factors and tools to identify them. These assist prescribers and other members of healthcare teams in predicting, identifying, and managing potential drug-drug interactions so as to minimize damages caused by them [9].

There are several factors that contribute to the occurrence of potential DDIs, including the number of medications prescribed at any given time, therapeutic drug classes in prescriptions, the patient's gender, age, number of prescribers involved, the presence of comorbidities, coinfections, and the availability of potential DDI identification tools [10].

From real-world data, there is proof of high prevalence of potential drug-drug interactions worldwide. Nearly 74,000 emergency room visits and 195,000 hospitalizations in the USA every year are caused by drug-drug interactions, resulting from the ineffectiveness of current approaches to DDI identification [11, 12].

Similarly, overall prevalence of potential DDIs in developing countries is high, ranging from 23–86% according to reports from hospital settings in Uganda, Ethiopia, Pakistan and Iran. Additionally, Uganda has a high overall prevalence of potential DDIs of 89.3% [13]. According to a study conducted by Lubinga and Uwiduhaye in 2011, 23% of in-patient prescriptions in Mbarara Regional Referral Hospital had potential DDIs [7].

Shortage of information concerning DDIs in resource-limited countries like Uganda results in more DDI related damage than in developed countries [14]. Despite understanding the role played by DDIs in therapeutic outcomes, there was limited information regarding their prevalence in outpatient settings in Uganda. To the best of our knowledge, the prevalence of potential DDIs in Southwestern Uganda was last documented in a 2008 study on inpatients at Mbarara Regional Referral Hospital. There was no study that explored potential DDIs in outpatient settings in Mbarara city. This study aimed to determine the prevalence, severity, and factors associated with potential drug-drug interactions in outpatient settings in Mbarara city.

A cross-sectional study was performed in 18 randomly selected NDA licensed private human pharmacies in Mbarara city. All prescriptions and clinical information from them were collected prospectively as they were presented at the pharmacies for filling. No medication surveillance or patient follow up was conducted and no interaction between the researchers and the patients occurred. Informed consent and permission were sought from the Pharmacy managers to collect data from the prescriptions that they were receiving at their premises.

All prescriptions that were valid basing on the presence of the prescriber’s name and signature/stamp plus health facility name and address were included for this study. Prescriptions having no drug, having only one drug prescribed, having only topical skin care products prescribed, those that were illegible and all that were written before the month of July 2022 were excluded from the study. The study protocol was approved by the Department of Pharmacy of Mbarara University of Science and technology (MUST). All medical data and patient demographic data were collected from the presented prescriptions including sex, age, diagnosis, comorbidities, drug regimens prescribed, and level of health care where the prescription was written. Prescriber’s details and patient specific information were not recorded for purposes of confidentiality.

All patient prescriptions were scanned for potential DDIs utilizing the Lexicomp drug interaction database. The database identifies the risk of drugs interacting, mechanism of drug interaction, severity, reliability rating, and outlines the clinical management of the drug interaction. Basing on the risk of the interaction, the DDIs are categorized as A, B, C, D and X whereas basing on severity, they are categorized as minor, moderate and major. Only potential DDIs of clinical significance i.e., risk category C, D and X according to Lexicomp drug interaction database were identified and considered for this study. The prevalence of potential DDIs was calculated as the ratio of the number of prescriptions with at least one potential DDI to the total number of prescriptions assessed, expressed as a percentage.

Bivariate and multivariate logistic regression analysis was performed to identify risk factors associated with the occurrence of clinically significant potential DDIs. The outcome variable was the incidence of at least one clinically significant potential DDI per prescription. At bivariate analysis, we analyzed categorical variables using cross tabulations, Crude Odds Ratios (COR) and chi-square test to assess for the association between patient demographic and clinical characteristics (age, sex, number of comorbidities, number of drugs prescribed, level of health care and therapeutic drug categories prescribed) with the outcome variable. Exposure variables with bivariate p values less than 0.2 were included in the multivariate logistic regression model. We performed a stepwise and backward selection procedure to determine the final parsimonious model of independent factors associated with our outcome of interest. Confounding and interaction were assessed and the final model checked for goodness of fit using the Hosmer Lemeshow test. The Adjusted Odds Ratios (AOR) and their corresponding P values were tabulated. A P value < = 0.05 was considered to be statistically significant. Statistical data analysis was performed using STATA version 13.

Sociodemographic and clinical characteristics of patients

In this study we included a total of 295 patient prescriptions, each prescription for a different patient, presented to 18 community pharmacies in Mbarara city. About half (149, 50.5%) of the patients were females, the majority (199, 67.5%) were adults 18–59 years of age whereas most (208, 70.5%) had a comorbid condition. Over one half (162, 54.9%) of the prescriptions were received from hospitals and majority of the prescriptions had 4 drugs prescribed (n = 175, 59.32%). These results have been summarized in Table 1.

Drug Characteristics

The prescriptions studied had different therapeutic classes of drugs prescribed and these included; anti-bacterial agents (n = 182, 61.69%), analgesic agents (n = 98, 33.22%), anti-hypertensive agents (n = 55, 18.64%), corticosteroids (n = 35, 11.86%), anti-diabetic agents (n = 21, 7.12%), anti-fungal agents (n = 20, 6.78%), and the others category of drugs (n = 187, 63.39%). The classes of drugs that were grouped as others had very low incidence in the prescriptions and these included; anti-coagulants, anti-convulsants, anti-acids, anti-psychotics, anti-epileptics, anti-viral agents, anti-depressants among others.

Prevalence Of Potential Ddis

Out of the 295 prescriptions that were assessed, n = 111 (37.64%) had at least one clinically significant potential DDI i.e., potential DDI of risk category C, D or X. This has been illustrated in Fig. 1.

Table 1

Sociodemographic and clinical characteristics of patients
Variables	Category	No. (%)
Age	Paediatrics (0–17 years)	45 (15.25)
	Adults (18–59 years)	199 (67.46)
	Elderly (≥ 60 years)	51 (17.29)
Gender	Male	146 (49.49)
Gender	Female	149 (50.51)
Number of comorbidities	0	208 (70.51)
Number of comorbidities	≥ 1	87 (29.49)
Level of healthcare	Clinic/Medical centre	99 (35.56)
	HC II	4 (1.36)
	HC III	19 (6.44)
	HC IV	11 (3.73)
	Hospital	162 (54.92)
Number of drugs prescribed	< 4	120 (40.46)
Number of drugs prescribed	≥ 4	175 (59.32)

Frequency And Classification Of Potential Ddis

A total of 216 potential DDIs of different risk categories were identified from the 295 prescriptions that were assessed; category C (n = 178, 82.41%) followed by category D (n = 34, 15.74%), and category X (n = 4, 1.85%).

In severity categories, most (181, 83.8%) of the DDIs were moderate, followed by major (31, 14.35%) and minor (4, 1.85%) as shown in Table 2.

Bi-variate Logistic Regression

A total of 14 independent variables (factors) were analyzed in bivariate logistic regression. Only 9 out of the 14 factors qualified for multivariate logistic regression analysis having had P-values of < 0.2. The factors that qualified for multivariate logistic regression analysis included age (18–59) years, age ≥ 60, number of comorbidities, number of drugs prescribed, presence of anti-fungals, presence of antibacterials, presence of anti-hypertensives, presence of analgesics and presence of corticosteroids in a prescription. However, number of drugs prescribed was later excluded from the multivariate logistic regression model because it exhibited collinearity with the number of comorbidities.

Table 2

Potential DDI risk and severity categories
Classification	Category	No. (%)
Risk	C	178 (82.41)
	D	34 (15.74)
	X	4 (1.85)
Severity	Minor	4 (1.85)
	Moderate	181 (83.8)
	Major	31 (14.35)

Multivariate Logistic Regression

The multivariate logistic regression analysis revealed only 5 out of the 8 factors considered for multivariate analysis to be associated with potential DDIs. It showed that patients with one or more comorbidities were about 3 and a half times more likely to have prescriptions with potential DDIs compared to those with no comorbidity. Patients who were prescribed anti-fungals were 3.69 times more likely to have potential DDIs in their prescriptions compared to those who were not prescribed antifungals. Patients who were prescribed anti-hypertensives were 3.82 times more likely to have potential DDIs in their prescriptions compared to those who were not prescribed anti-hypertensive medication.

Additionally, patients who were prescribed analgesics were 4.9 times more likely to have potential DDIs in their prescriptions compared to those who were not prescribed analgesics. Finally, the patients who were prescribed corticosteroids were 5.2 times more likely to have potential DDIs in their prescriptions compared to those who were not prescribed corticosteroids. Table 3 summarizes the results from bivariate and multivariate logistic regression analysis of the factors associated with potential drug-drug interactions in prescriptions presented at private pharmacies in Mbarara city.

Prevalence of potential DDIs

In this study, we obtained 295 prescriptions from 18 private pharmacies in Mbarara city, each contributing at least 16 prescriptions which we considered for analysis. We found that the prevalence of potential drug-drug interactions was 37.64%. This prevalence is comparable with 34.4% that was reported by a study in Romania and 33.5% reported by a cohort study in Kenya [15, 16].

The current prevalence, however, is considerably higher than 22.65% that was reported by a large cohort study of older community-dwelling adults in Kenya [17]. This could be because the study considered only drug-drug interactions involving cardiovascular and CNS drugs leaving out other drug categories. Our result of 37.64% prevalence of potential drug-drug interactions is also higher compared to 18.7% reported from a study in patients attending a specialist HIV outpatient clinic in Kampala, Uganda [18]. The difference in prevalence here is probably due to the fact that the study only considered DDIs involving ART drugs leaving out other drug categories. Sensitization of prescribers on the use of available DDI identification tools like Lexicomp database can be of great benefit towards reducing the prescribing of potentially interacting drugs, thus lower the prevalence.

Risk categories and severity of potential DDIs

The risk category describes relevance and urgency needed for clinical action to prevent the effects of the drug-drug interactions. Lexicomp classifies risk category as A, B, C, D and X. This study only considered interactions of risk category C, D and X which are described as being clinically relevant. Severity on the other hand describes the impact of the drug-drug interaction on the patient’s condition/health [19]. All severity categories were considered for this study.

Of the 216 potential DDIs identified, 82.41% were of risk category C, 15.74% of risk category D and 1.85% of risk category X.

We found that DDIs of moderate severity were the most prevalent. This finding is comparable to the results of a number of previous studies that were done in Jordan, Pakistan, Switzerland and Ethiopia [8, 20–23].

Factors associated with potential DDIs

Knowledge of factors associated with potential drug-drug interactions is of great importance in healthcare systems. It enables prescribers and other members of healthcare teams to predict, identify, and minimize their occurrence, thus reduce the adverse outcomes.

Table 3

Bi-variate and multivariate analysis of factors associated with the occurrence of potential DDIs
Exposure Variables	Category	Outcome (Presence of pDDI)		Crude Odds Ratios (95% CI)	P value	Adjusted Odds Ratios (95% CI)	P value
Exposure Variables	Category	Yes	No	Crude Odds Ratios (95% CI)	P value	Adjusted Odds Ratios (95% CI)	P value
Gender	Female	53	96	ref
Gender	Male	58	88	1.19 (0.74,1.91)	0.462
Age	< 18 yrs	8	37	ref
	18–59 yrs	75	124	2.80 (1.24,6.33)	0.014	1.67 (0.68,4.11)	0.268
	≥ 60 yrs	28	23	5.63 (2.19,14.45)	< 0.001	2.64 (0.89,7.84)	0.080
Level of Health care	Clinic	36	63	ref
	HC II	2	2	1.75 (0.24,12.96)	0.584
	HC III	6	13	0.81 (0.28,2.31)	0.690
	HC IV	5	6	1.46 (0.42,5.12)	0.556
	Hospital	62	100	1.09 (0.65,1.82)	0.757
No. Of Comorbidities	0	65	143	ref
No. Of Comorbidities	≥ 1	46	41	2.45 (1.48,4.12)	0.001	3.47 (1.86,6.47)	< 0.001
No. Of drugs prescribed	< 4	32	88	ref
No. Of drugs prescribed	≥ 4	79	96	2.81 (1.37,3.74)	0.001
Anti-fungal	No	99	176	ref
Anti-fungal	Yes	12	8	2.6 (1.05,6.74)	0.038	3.69 (1.31,10.36)	0.013
Antibacterials	No	53	60	ref
Antibacterials	Yes	58	124	0.53 (0.33,0.86)	0.01	0.73 (0.38,1.39)	0.342
Anti-hypertensive	No	79	161	ref
Anti-hypertensive	Yes	32	23	2.84 (1.56,5.17)	0.001	3.82 (1.93,7.58)	< 0.001
Analgesics	No	56	141	ref
Analgesics	Yes	55	43	3.22 (1.94,5.34)	< 0.001	4.90 (2.68,8.95)	< 0.001
Corticosteroids	No	88	172	ref
Corticosteroids	Yes	23	12	3.75 (1.78,7.88)	0.001	5.20 (2.25,12.00)	< 0.001
Anticonvulsants	No	99	169	ref
Anticonvulsants	Yes	12	15	1.37 (0.61,3.04)	0.444
Minerals and Vitamins	No	91	152	ref
Minerals and Vitamins	Yes	20	32	1.04 (0.56,1.93)	0.891
Anti-acids	No	93	149	ref
Anti-acids	Yes	18	35	0.82 (0.44,1.54)	0.544
Others	No	67	120	ref
Others	Yes	44	64	1.23 (0.76,2.00)	0.402

In our study, the factors that were significantly associated with presence of potential DDIs included having one or more comorbid conditions and taking any of analgesics, antifungals, antihypertensives or corticosteroids concurrently with other drugs. Having one or more comorbidities was a risk factor for the occurrence of potential DDIs. This is in agreement with findings of a prospective study conducted in Bangalore. Another study conducted in India also reported an association between number of comorbidities and potential drug-drug interactions [24, 25].

Our study revealed that prescriptions that contained analgesics were 4.9 times more likely to have a potential DDI compared to those that didn’t contain an analgesic prescribed. Studies done by Moore et al. (2015) and Kardas et al. (2021) specifically considering potential DDIs of analgesic drugs support the positive correlation between prescription of analgesics and the presence of potential DDIs [26, 27].

Our study findings showed that prescriptions that contained anti-fungal agents concomitantly prescribed with other agents were 3.7 times more likely to have a potential DDI compared to those that did not contain any anti-fungal agent prescribed. This finding agrees with the results of two studies conducted in haematological patients in 2014 and 2017 [28, 29].

Our study also detected a positive correlation between the prescription of anti-hypertensives and presence of potential DDIs, with the prescriptions containing anti-hypertensive agents being 3.82 times more likely to have a potential DDI compared to those without anti-hypertensives. Similarly, a longitudinal analysis of anti-hypertensive drug interactions in a Medicaid population study reported a positive correlation between the prescription of anti-hypertensives and presence of potential DDIs [30].

Prescription of corticosteroids was also significantly associated with potential drug-drug interactions. Prescriptions containing corticosteroids were 5 times more likely to contain potential DDIs compared to those that had no corticosteroids prescribed. There are very few previous studies that considered analysis of the association between corticosteroids and potential DDIs, among which is a recent study about the risks of potential DDIs in COVID-19 patients treated with corticosteroids [31]. This study reported that the addition of corticosteroids to background therapies resulted in dramatic increase in the number of DDIs classified as moderate, which is similar to our study findings. In light of the above findings, prescribers should be more vigilant when prescribing drug categories including analgesics, antifungals, antihypertensives and corticosteroids so as to minimize potential DDIs.

Strength Of The Study

This study included prescriptions of patients of all ages, all disease categories, and from all levels of healthcare. This allowed us to asses for the association of a wider range of factors with potential DDIs in the outpatient setting which most previous individual studies did not analyse.

Limitations Of The Study

Despite the strengths, our study had some limitations. For one, the study was not powered enough to detect the statistical significance of some of the factors. This was probably because the calculated sample size was inadequate. The study was conducted in an urban setting only, thus its results may not be representative or generalizable for the whole southwestern Uganda which also has a significant number of rural areas. The study was conducted for a short time period and there was no prior research about the topic in an outpatient setting in southwestern Uganda, which limited our ability to determine an accurate/appropriate sample size. Our study was limited to assessment of prescriptions rather than patients, thus no follow-up on patients was made to report the impact of potential DDIs on them. Therefore, we could only assess potential DDIs as we were unable to tell from prescription notes conclusively whether a DDI or its consequences actually occurred or not.

The prevalence of potential drug-drug interactions in prescriptions received by private pharmacies in Mbarara city was high with majority of them being moderate in severity. Having 1 or more comorbidities and prescribing of therapeutic drug categories including anti-fungals, anti-hypertensives, analgesics, or corticosteroids concurrently with other drugs were significantly associated with potential DDIs.

ART

Antiretroviral therapy

DDIs

Drug-drug interactions

CNS

Central Nervous System

HC e.g.

HCIII

Health Centre III (three)

HIV

Human Immunodeficiency Virus.

Acknowledgements

We acknowledge the Department of Pharmacy, Mbarara University of Science and Technology for granting us the opportunity to learn and practice research proposal development, implementation, analysis and reporting of results, and for the approval to conduct the research. We also appreciate the management of the different private pharmacies which permitted us to conduct our data collection at their premises. In a special way, we do thank our parents for the financial support rendered to us throughout the course of this research.

Authors’ contributions

LAP conceived the idea, participated in concept development, protocol writing, data collection, data analysis and developed the first draft of the manuscript. OBD participated in concept development, protocol writing, data collection and analysis. KK participated in concept development, protocol writing, data collection and analysis. MF participated in concept development, protocol writing, data collection and analysis. TYM mentored the team, supervised concept development, protocol writing and data analysis, participated in review and editing of the protocol and manuscript. The author(s) read and approved the final manuscript.

Funding

The authors received no financial support to conduct this study and used personal resources.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to patient privacy or ethical restrictions.

Ethics approval and consent to participate

This study was approved by the Department of Pharmacy, Mbarara University of Science and Technology. The informed consent was obtained from all managers of the selected private human pharmacies that were considered for this study. All methods were performed in accord ance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

There are no competing interests in this work.

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No competing interests reported.

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First submitted to journal
28 Mar, 2023

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Prevalence and factors associated with potential drug-drug interactions in prescriptions presented at private pharmacies in Mbarara city, southwestern Uganda

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Results

Sociodemographic and clinical characteristics of patients

Drug Characteristics

Prevalence Of Potential Ddis

Frequency And Classification Of Potential Ddis

Bi-variate Logistic Regression

Multivariate Logistic Regression

Discussion

Prevalence of potential DDIs

Risk categories and severity of potential DDIs

Factors associated with potential DDIs

Strength Of The Study

Limitations Of The Study

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1