Prevalence of potential drug- drug interactions and associated factors among outpatients and inpatients in Ethiopian Hospitals: a systematic review and Meta-analysis of observational studies

DOI: https://doi.org/10.21203/rs.2.20891/v1

Abstract

Background

A very few number of studies are available regarding the evaluation of potential drug- drug interactions in Sub-Saharan Africa. This is also a problem in Ethiopian health care system. Now a days, in Ethiopia polypharmacy is increased due to comorbid conditions in the hospital health care system, a large number of patients are treated in the outpatient setting and also hospitalized and there is a high possibility for drug- drug interactions. Therefore, this study aims to summarize the prevalence of potential drug- drug interactions and associated factors in hospitals, both among hospitalized patients and outpatients in Ethiopia.

Method

Literature search was performed through accessing legitimate databases in PubMed/MEDLINE, Google Scholar and Research Gate for English-language publications. Advanced search strategies were applied in Science Direct and HINARI to identify any additional papers and published reviews and to retrieve relevant findings closely related to prevalence of potential drug- drug interactions and associated factors with it. The search was conducted from August 22-25, 2019 and all published and unpublished articles available online until the day of data collection were considered.

Results

A total of 14 studies were included for systematic review and meta-analysis. From 14 studies, 5761 patients were included and a total of 8717 potential drug- drug interactions were found in 3259 of patients. The prevalence patients with potential drug- drug interactions in Ethiopian Hospitals were found to be 72.2% (95% confidence interval: 59.1%, 85.3%). Based on severity, the prevalence of potential drug- drug interactions were 25.1%, 52.8%, 16.9% and 1.27% for major, moderate, minor potential drug- drug interactions and contraindications respectively. The factors associated with potential drug- drug interactions were related to patient characteristics such as polypharmacy, age, comorbid disease and hospital stay.

Conclusion

There is a high prevalence of potential drug- drug interactions in Ethiopian Hospitals. From this the most prevalent drug- drug interactions were moderate severity, 52.8%. Polypharmacy, age, comorbid disease and hospital stay were the risk factors associated with potential drug- drug interactions.

Background

Drug-drug interactions (DDIs) are types of adverse drug events (ADEs) which can occur when the effect of a drug is altered by another drug that is taken concurrently and results in a qualitative and/or quantitative change in drug action(Stockley’s, 2010).

It can be major, moderate and minor interactions based on its severity. Major DDIs can cause a life threating or a last longing damage. Moderate DDIs call for additional treatment and minor DDIs do not have a significant effect on the therapy (Varma MV, Pang KS, Isoherranen N, 2015).

According to the mechanisms by which drugs interact with each other, DDIs can a classified as pharmaceutical, pharmacokinetic and pharmacodynamics(Bolhuis MS, Panday PN, PrangerAD, 2011).

DDIs may have desirable, over and above undesirable or harmful effects(Varma MV, Pang KS, Isoherranen N, 2015), increase or decrease the efficacy of one drug on another, increase the toxicity of medications or result in treatment failure(Bjornsson T, Callaghan J, Einolf H, 2003; Bolhuis MS, Panday PN, PrangerAD, 2011).

DDI is an emerging threat to public health(Kothari N, 2014) which can occur within a couple of minutes or can take several weeks to develop (Jacob S, 2011). Various studies suggest that cardiovascular patients, Human Immunodeficiency Virus infected patients and psychiatric patients are more often reported with potential DDIs as compared to patients with other diseases. The possible reasons behind include older age, multiple drug regimens, pharmacokinetic and pharmacodynamic nature of drugs used in cardiology, and the influence of heart disease on drug metabolism (Diksis et al, 2019; Behailu Terefe Tesfaye et al, 2017; Haftay Berhane Mezgebe et al, 2017).

Most of DDIs occurred because of inadequate knowledge of prescribers on DDIs or poor recognition of the relevance of DDIs by prescribers(Heininger-Rothbucher D, Bischinger S, Ulmer H, Pechlaner C, Speer G, 2001; Ko Y, Malone DC, Skrepnek GH, Armstrong EP, Murphy JE, Abarca J, Rehfeld RA, Reel SJ, 2008).

When different prescribers prescribe a drug in the treatment of the same patient, the number of prescribed drugs may increase, and it may be difficult for the prescriber to keep track of the prescribed medications. This will lead to an increased risk of potential DDIs(Bjerrum L, Lopez Valcarcel BG, 2008).

Even though prescribing of multiple drugs for one patient may be logical and necessary practice for patients particularly those who have comorbid disease, physicians should take into account the incidence of potential DDIs for patients taking multiple drugs(Grattagliano I, Portincasa P, D’Ambrosio G, Palmieri VO, 2010).The incidence of potential DDIs is close to 40% in patients taking 5 drugs, and exceeds 80% in patients taking seven or more drugs(Grattagliano I, Portincasa P, D’Ambrosio G, Palmieri VO, 2010; Kapp PA, 2013).

DDIs are more prevalent in patients receiving a combination two or more drugs(Astrand E, Astrand B, Antonov K, 2007; Juurlink DN, Mamdani M, Kopp A, Laupacis A, 2003) and more frequent in patients who are elder, hospitalized for a longer period of time, and/or receive more drugs per day(Janković SM, Pejčić AV, Milosavljevic MN, 2018; Obreli-Neto PR, Nobili A, de Oliveira Baldoni A, 2012; Romagnoli KM, Nelson SD, Hines L, 2017).

Even though the concomitant use of a combination of drugs often increases therapeutic effectiveness, certain combinations are harmful(Teixeira J, Crozatti M, Santos C, 2012). But all potential DDIs aren’t clinically significant(Goldberg RM, Mabee J, Chan L, 1996).

Clinically significant DDIs may cause a potential harm to patients, harmful outcomes and resulting in an estimated cost of more than $1 billion per year to governmental health care system expenditure(Qorraj-Bytyqi H, Hoxha R, Krasniqi S, Bahtiri E, 2012).The risk of DDI rose from 13% for patients taking two medications to 82% for patients taking seven or more medications(Cristiano Moura C, Acurcio F, 2009).

Hospitalized patients are more likely to be affected by DDIs because of severe and multiple illnesses, comorbid conditions, chronic therapeutic regimens, poly-pharmacy and frequent modification in therapy(Zwart-van-Rijkom JEF, Uijtendaal EV, Ten Berg MJ, Van Solinge WW, 2009). Among hospitalized patients, elderly patients are at higher risk of potential DDIs and occurrence of potential DDIs ranges from 3 to 69%, depending on the specific area and population. This increased prevalence was found to be related to presence of multiple chronic illnesses, use of multiple medications and altered pharmacokinetics in the elderly patients(Wang JK, Herzog NS, Kaushal R, Park C, Mochizuki C, 2007).

Some studies report that hospitalized patients receive an average of 10 different drugs(Zopf Y, Rabe C, Neubert A, Hahn A, 2008). The greater the severity of the patient’s disease the higher the number of drugs prescribed, and the greater the likelihood of adverse drug interactions happened(Joshua L, Devi P, 2009).

In addition to elder patients, Hospitalized pediatric patients face higher risk of drug induced problems due to wide-ranging of patient ages and body-weights, limited physiologic reserve, medications dosing errors and inaptitude to properly communicate with healthcare workers(Wang JK, Herzog NS, Kaushal R, Park C, Mochizuki C, 2007).

Generally, the risk factors that are associated with potential DDIs are age, increased number of drugs (poly-pharmacy), multiple prescribers, comorbid conditions, chronic therapeutic regimens, and frequent modification in therapy and hospitalization (Kapp PA, 2013).

Studies have suggested that drug use can be improved and potential DDIs can be prevented by better communication among patients, physicians, and pharmacists(Carter BL, Lund BC, Hayase N, 2002). In addition to this, DDIs can be prevented by avoiding multiple drug treatment (poly-pharmacy) and weighing the potential benefits of drug combinations against the risk of the occurrence of clinically significant DDIs.

A very few number of studies are available regarding the evaluation of potential DDIs in Sub-Saharan region of Africa(Lubinga SJ, 2011). This is also a problem in Ethiopian health care system.

In Ethiopia, now a days polypharmacy is increased due to comorbid conditions in the hospital health care system(Berha AB, 2018; Sisay M., Mengistu G., Molla B., 2017), a large number of patients are hospitalized and there is a high possibility for DDIs. Furthermore, due to economic problems, the probability of monitoring patients with comorbid diseases using sophisticated instruments is not feasible causing the patient to DDIs.

As a result, potential DDIs causing serious risk to patient health. Therefore, this study attempted to review and quantitatively estimate the prevalence of potential DDIs and associated risk factors in hospitals, both among inpatients and outpatients in Ethiopia.

Methods

Study protocol

The identification of records, screening of titles and abstracts as well as evaluation of eligibility of full texts for final inclusion was conducted in accordance with the Preferred Reporting Items for Systematic review and Meta-analysis (PRISMA) flow diagram. PRISMA checklist was also strictly followed while conducting this systematic review and meta-analysis (additional file 1: Table 1)(Liberati, 2009).

Table 1
Quality assessment of included studies in the review
Studies
Total scores
Quality
Gunasekaran et al, 2016
9
Moderate
Behailu Terefe Tesfaye et al, 2017
12
High
Diksis et al, 2019
12
High
Chelkeba L et al, 2013
12
High
B.Akshaya Srikanth et al., 2014
12
High
Admassie, et al, 2013
10
High
Henok Getachew et al, 2016
12
High
Teka et al, 2016
12
High
Zeru Gebretsadik et al, 2017
11
High
Haftay Berhane Mezgebe, 2015
11
High
Teklay et al, 2014
11
High
Yesuf TA, et al, 2017
10
High
Tesfaye and Nedi, 2017
11
High
Kibrom et al, 2018
11
High

Inclusion and exclusion criteria

Inclusion criteria

  • Observational studies addressing prevalence of potential DDIs and conducted in Ethiopia were included(prospective, retrospective and descriptive crossectional studies)
  • All male and female patients in any age(pediatrics, adults, and geriatric) and admitted to hospital wards or visited outpatients were included
  • All published articles without time limit were included
  • Patients who had any disease and admitted to hospital wards or visited outpatients
  • Studies which were published in English language and provided sufficient data for the review were included

Exclusion criteria

  • Studies that were conducted outside of Ethiopia were excluded
  • Articles with missing or insufficient outcomes were also excluded.
  • Drug interactions with herbs, diseases, and nutrients were excluded
  • Studies that were conducted in primary health care settings

Search strategy and data sources

Literature search was performed through accessing legitimate databases in PubMed/MEDLINE, Google Scholar and Research Gate for English-language publications. Advanced search strategies were applied in Science Direct and HINARI to identify any additional papers and published reviews and to retrieve relevant findings closely related to prevalence of potential DDIs and associated factors with DDIs among outpatients and inpatients in Ethiopian Hospitals.

The search was conducted with the aid of carefully selected search-words without specification in time. “Prevalence”, “occurrence”, “potential DDIs”, “associated factors” and “Ethiopia” were the search words used in this review and meta-analysis. AND/OR words were used for the identification of the articles. The search was conducted from August 3–25, 2019 and all published articles available online until the day of data collection were considered.

Data Extraction

A standardized data extraction form was prepared in Microsoft Excel by the investigators and important information which were related to study characteristics (Region, Study area, Author, Year of publication, study design, Pathology, Target population, Study setting, Interaction data base, Number of patients, Number of patients with DDIs, and lists of medications that caused the interactions) and outcome of interest (Prevalence of DDIs (%), Potential DDIs (major, moderate and minor) and associated factors of DDIs) were extracted.

Fourteen studies were selected based on their abstract, inclusion and exclusion criteria. Studies were searched, identified and screened from different search engines which are published in English language. Out of a total of 69 articles gained, 32 were from Google scholar, 15 were from PubMed, 22 were from Research Gate (Fig. 1)

Quality assessment

The quality of selected studies was performed. All selected studies was reviewed according to 12 criteria adapted from a previous study(Nabovati E., Vakili-Arki H., Taherzadeh Z., Reza Hasibian M. & Eslami A., 2014). Each criteria is related to a quality assessment criterion with score 0 or 1 and the total quality scores ranged from 0 to 12 (scores 0 to 6 = poor quality, 7 to 9 scores = moderate quality, 10 to 12 points = high quality) (Table 1).

Outcome measurements

The outcome measure in this review and meta-analysis is the prevalence of potential DDIs. It is primarily aimed to assess the pooled estimates of potential DDIs in the Hospitals of Ethiopia. This study has also two secondary outcome measures: Associated risk factors for potential DDIs and number of potential DDIs (major, moderate and minor) in Ethiopian Hospitals.

Data processing and statistical analysis

The relevant data were extracted from included studies using format prepared in Microsoft Excel.

Analyses of pooled estimate of outcome measures i.e. Prevalence of potential DDIs, as well as for subgroup analysis were done by Open Meta Analyst advanced software. Der Simonian and Laird’s random effects model were used by considering clinical heterogeneity among studies. Heterogeneity of studies was assessed using I2 statistics. CMA version-3 software was used for publication bias assessment. The presence of publication bias was evaluated by using Egger’s regression tests and presented with funnel plots of standard error and precision with Logit event rate. A statistical test with a P value less than 0.05 (one tailed) was considered significant(Begg CB, 1994; Egger M, Davey Smith G, Schneider M, 1997).

Results

Article search results

A total of 69 articles were identified through the search strategy. After duplication was removed, 49 articles were remained for screening. From these, 30 articles were excluded by their titles and abstracts. The remaining 19 articles were then evaluated as per predetermined eligibility criteria for inclusion. Five articles were also excluded with justification. Finally, a total of 14 full-text articles which passed the eligibility criteria and quality assessment were included for final review and analysis (Fig. 1).

General characteristics of the included studies

A total of 14 studies were included for systematic review and meta-analysis and important information which were related to study characteristics were presented in Table 2. All studies employed were observational cross-sectional study designs i.e. six retrospective CS; three prospective CS and five CS design. The year of publication of included studies ranges from 2013 to 2019. The study included a wide range of population characteristics (pediatric, adult and geriatric patients). Regarding geographic distribution, 14 studies were obtained from three regions and one city administration (Addis Ababa). The studies included all types of disease which had been treated in medical ward and outpatient setting.

Table 2
General characteristics of studies included for systematic review and Meta-analysis
Region
Study area
Author and publication year
Study design
Pathology
Target population
Study setting
Interaction data base
Oromia
Middle East Ethiopia, Adama
Gunasekaran et al, 2016
Retrospective CS
All
All hospitalized patients
All wards
Medscape online
 
South East of AA, Bishoftu
Behailu Terefe Tesfaye et al, 2017
CS
HIV/AIDS
All HIV infected patients
ART Clinic
Meds cape online & Drug.com
 
South West Ethiopia, Jimma
Diksis et al, 2019
Prospective CS
Cardiac disorder
Cardiac adult patients
Medical ward
Micromedex 3.0 DRUG-REAX®
   
Chelkeba L et al, 2013
CS
Cardiac disorder
Patients on CV medication in OPD
Cardiac clinic
Micromedex 2 ®
Amhara
North West Ethiopia, Gondar
B.Akshaya Srikanth et al., 2014
Prospective CS
All
All hospitalized patients
Medical ward
www.drugs.com
   
Admassie, et al, 2013
Retrospective CS
All
All hospitalized patients
Inpatients and Out patients
Micromedex2®
   
Henok Getachew et al, 2016
Retrospective CS
All
All hospitalized pediatric patients
Pediatric ward
Micromedex 2
Tigray
Northern Ethiopia
Teka et al, 2016
CS
All
All hospitalized elder patients
Medical ward
Micromedex® 2.0
   
Zeru Gebretsadik et al, 2017
Retrospective CS
All
All patients who come for medical service
Outpatient pharmacy
Micromedex® 2.0
   
Haftay Berhane Mezgebe, 2015
Retrospective CS
Psychiatric illness
Patients with psychiatric illness
Psychiatric unit
Micromedex 2.0 Drug-Reax®
   
Teklay et al, 2014
Prospective CS
DVT
Patients on warfarin therapy
Medical ward
Micromedex® online
   
Yesuf TA, et al, 2017
CS
All
All hospitalized patients
Medical ward
Micromedex 2 ®
AA
TASH
Tesfaye and Nedi, 2017
CS
All
All hospitalized patients
Medical ward
Medscape online
 
SPHMMC
Kibrom et al, 2018
Retrospective CS
All
Adult patients
Medical ward
Micromedex 3.0 DRUG-REAX®

Nine articles analyzed patients with all type of pathologies without focusing on any specific disease, 2 articles analyzed patients with cardiac disorder, 1article studied HIV patients and1 article analyzed patients with psychiatric disorders.

Nine articles studied DDIs in inpatient ward (7 articles in medical ward; 1 article in pediatric ward; 1 article in all wards); four articles studied DDIs in outpatient setting (ART Clinic, Cardiac clinic, Psychiatric unit, and Outpatient pharmacy) and one articles studied in Inpatients and Outpatient setting.

Among fourteen studies analyzed, six different databases were used to detect potential interactions. About half of the studies used Micromedex® 2.0 data base system (7 articles; 50.0%), 2 articles (14.2%) used Medscape online, 2 articles (14.2%) used Micromedex® 3.0 data base system. The other three articles used Medscape online and drug.com, Drug.com and Micromedex online (Table 2)

Study outcome measures

Prevalence of potential DDIs

Prevalence and number of potential DDIs for each studies is presented in Table 3.

Table 3
Studies of prevalence of potential DDIs in included articles
Region
Author
Pathology
Target population
Study setting
No. of patients
No. of patients with DDIs
Prevalence patients with DDIs (%)
No. of potential DDIs
Major
Moderate
Minor
Unknown& Contraindication
Oromia
Gunasekaran et al, 2016
All
All hospitalized patients
All wards
300
267
89.00
62
95
110
 
 
Behailu Terefe Tesfaye et al, 2017
HIV/AIDS
All HIV infected patients
ART Clinic
350
350
100.00
2
1767
662
 
 
Diksis et al, 2019
Cardiac disorder
Cardiac adult patients
Medical ward
200
195
97.50
316
441
210
 
 
Chelkeba L et al, 2013
Cardiac disorder
Patients on CV medication in OPD
Cardiac clinic
322
297
92.24
88
200
9
 
Amhara
B.Akshaya Srikanth et al., 2014
All
All hospitalized patients
Medical ward
100
78
78.00
53
253
107
 
 
Admassie, et al, 2013
All
All hospitalized patients
Inpatients and Out patient
2180
711
32.61
127
1020
177
Contraindication = 11
 
Henok Getachew et al, 2016
All
All hospitalized pediatric patients
Pediatric ward
384
176
45.83
40
201
152
 
Tigray
Teka et al, 2016
All
All hospitalized elder patients
Medical ward
140
87
62.14
46
36
0
Contraindication = 5
 
Zeru Gebretsadik et al, 2017
All
All patients who come for medical service
Outpatient pharmacy
596
275
46.14
34
210
87
unknown = 22
 
Haftay Berhane Mezgebe
Psychiatric illness
Patients with psychiatric illness
Psychiatric unit
216
176
81.48
198
232
22
Contraindication = 13
 
Teklay et al
DVT
Patients on warfarin therapy
Medical ward
133
132
99.25
118
310
0
 
 
Yesuf TA, et al
All
All hospitalized patients
Medical ward
204
135
53.43
150
36
0
Contraindication = 80
AA
Tesfaye and Nedi
All
All hospitalized patients
Medical ward
252
197
78.17
94
385
240
 
 
Kibrom et al
All
Adult patients
Medical ward
384
209
54.43
105
157
32
Contraindication = 2

From 14 studies, the pooled prevalence of patients with potential DDIs in Ethiopian Hospitals were found to be 72.2% with 95% CI between 59.1% and 85.3%). Figure 2 showed heterogeneity across studies were high (I2 = 99.78%, p < 0.001).

Based on the severity of DDIs, the pooled prevalence of potential DDIs were 25.1%, 52.8%, 16.9% and 1.27% for major, moderate, minor potential DDIs and contraindications respectively. Figures 3, 4 and 5 showed heterogeneity across studies were high.

Based on the mechanisms of DDIs involved, seven studies documented well but the remaining seven studies didn’t document well the mechanisms of DDIs (Table 4).

Table 4
Studies of prevalence of DDIs according to the mechanisms involved in Ethiopian Hospitals
Authors
Mechanism of DDIs
Pharmacokinetic
Pharmacodynamics
Unknown
Gunasekaran et al, 2016
164(61.42%)
101(37.83%)
2(0.75%)
Behailu Terefe Tesfaye et al, 2017
1059(43.56%)
1335(54.92%)
37(1.52%)
Diksis et al, 2019
245(25.34%)
574(59.36%)
148(15.3%)
Henok Getachew et al, 2016
197(50.13%)
181 (46.06%)
15(3.82%)
Yesuf TA, et al, 2017
142(53.38%)
124(46.62%)
0(0.0%)
Tesfaye and Nedi, 2017
358(49.79%)
321(44.65%)
40(5.56%)
Kibrom et al, 2018
142(47.97%)
87(29.39%)
67(22.6%)

Factors associated with potential DDIs

The factors associated with potential DDIs were related to patient characteristics (Table 5).

Table 5
Associated factors for potential DDIs
Factors
Description
No of prescribed drugs
(Poly pharmacy)
Patients taking three or more than three concomitant drugs are at higher risk of the occurrence potential DDIs(Admassie et al, 2013;B.AkshayaSrikanth et al, 2014)
There is association of the occurrence of one or more potential DDIs with the number of medications prescribed per patient who took more than four medications (Kibrom et al, 2018)
Polypharmacy(five or more medications) is an important factor which leads to potential DDIs(Diksis et al, 2019;ZeruGebretsadik et al, 2017;Teka et al, 2016;Henokgetachew et al, 2016;Yesuf TA et al, 2017;Tesfaye and Nedi, 2017)
Co-morbid disease
Co-morbid condition independently increased the potential DDIs almost 2-folds(Yesuf TA et al, 2017)
Age
Older age were found to be predisposing factors for the occurrence of DDI(Admassie et al, 2013;Teka et al, 2016;Diksis et al, 2019;Zeru Gebretsadik et al, 2017)
Potential DDIs were occurring more frequently in age group of 2–6 years than any other age group of pediatric population (Henok Getachew et al, 2016)
Hospital stay
The chance of taking multiple drugs increases with longer stays(greater than or equal to seven) in the hospital, which in turn increases the risk for potential DDIs(Diksis et al, 2019)
INR value
Increase in international normalized ratio value was found to be strongly associated with DDI and hence risk of bleeding (Teklay et al, 2014)

Common interacting drug-combinations

Most common contraindications, major and moderate DDIs are presented in Table 6.

Table 6
Most common contraindication, major and moderate DDIs identified in the included studies
Drug interaction pairs
Number of interactions
Severity
Potential risk
Clarithromycin + simvastatin
6
Contraindication
Increased risk of myopathy or rhabdomyolysis
Chlorpromazine + Thioridazine
4
Contraindication
Risk of an irregular heartbeat which may belief threatening
Clarithromycin ciprofloxacin
1
Contraindication
Increased risk of QT interval prolongation
Aspirin + clopidogrel
160
Major
Bleeding
Aspirin + enalapril
157
Major
Renal dysfunction
Spironolactone + enalapril
101
Major
Hyperkalemia
Omeprazole + clopidogrel
56
Major
Decrease effect of clopidogrel and increased risk for thrombosis
Spironolactone + digoxin
47
Major
Increased the risk of digoxin toxicity
Heparin + aspirin
38
Major
Increased risk of bleeding
Aspirin + furosemide
173
Moderate
Fluid retention
Haloperidol + Trihexphenidyl
74
Moderate
Decrease effect ofTrihexphenidyl
Enalapril + Furosemide
59
Moderate
Postural hypotension (first dose)
Simvastatin + azithromycin
39
Moderate
Increased risk of rhabdomyolysis

Sensitivity and subgroup analyses

There was no any significant change on the degree of heterogeneity even if an attempt was done to exclude the expected outliers as well as one or more of the studies from analysis. Therefore, fourteen studies were included for the meta-analysis. Subgroup analysis also conducted based on Region and Study setting. Subgroup analysis based on region revealed that the highest prevalence of potential DDIs were observed at Oromia Region, 94.9% (95% CI: 90.3–99.5%) followed by Tigray Region with prevalence of 68.6% (95% CI: 42.6–94.5%) (Fig. 6).

Subgroup analysis based on study setting revealed that the highest prevalence of potential DDIs were observed at outpatient: 80.0% (95% CI: 58.9–101.1% followed by inpatient: 73.2% (95% CI: 60.8–85.7% and inpatient and outpatient setting: 32.6% (95% CI: 30.6–34.6%).

Univariate meta-regression for prevalence of potential DDIs revealed that sampling distribution is a source of heterogeneity (regression coefficient = 7.36; p-value = 0.0067) (Fig. 7)

Publication bias

Funnel plots of standard error with logit effect size i.e event rate supplemented by statistical tests confirmed that there is no evidence of publication bias on studies reporting prevalence of potential DDIs and associated factors in Ethiopian Hospitals because there is no higher concentration of studies on one side of the mean than the other at the bottom of the plot (Fig. 8)

Discussion

This systematic review and meta-analysis aimed to review and summarize the prevalence of potential DDIs and associated factors with it by reviewing and quantitatively summarizing the evidences available in Ethiopia regarding potential DDIs. It was conducted and attempted to analyze 14 original studies addressing prevalence of potential DDIs in Ethiopia. From all included studies, 5761 patients were included for pooled estimation of the primary outcome. A total of 8717 potential DDI was found in 3259 of patients. This indicated that 2.67 potential DDIs were found in one patient.

The overall prevalence of patients with potential DDIs in Ethiopia was found to be 72.2% (95%CI: 59.1%, 85.3%). Based on the severity of DDIs, the pooled prevalence of potential DDIs were 25.1%, 52.8%, 16.9% and 1.27% for major, moderate, minor potential DDIs and contraindications respectively. These potential DDIs are more likely to produce negative outcomes. The analysis showed high prevalence which indicates the countries drug-drug interactions unstudied problem in the Ethiopians Hospitals.

The review showed that all DDIs studies in Ethiopia assessed potential DDIs, while no study was performed on actual DDIs. This may be due to identifying actual DDIs is much more complicated than potential DDIs.

The analysis showed that the occurrence of potential DDIs in inpatient and outpatient settings reported by studies (inpatient: 73.2% (95% CI: 60.8–85.7%; outpatient: 80.0% (95% CI: 58.9–101.1%; inpatient and outpatient setting: 32.6% (95% CI: 30.6–34.6%). The high incidence of DDIs may be associated with high number of drugs per prescription that was observed in individual studies. The prevalence of potential drug-drug interactions in outpatient setting is higher than the inpatient setting because in this review ART Clinic, Cardiac clinic, Psychiatric unit, and Outpatient pharmacy were considered as an outpatient setting.

Similarly, this review showed almost all HIV infected patients treated in outpatient setting (Behailu Terefe Tesfaye et al, 2017), 97.5% of adult patients with heart diseases treated in inpatient ward (Diksis et al, 2019) and 92.23% cardiac disorder patients treated in the outpatient setting (Chelkeba L et al, 2013) were susceptible to DDIs. High number of prescribed drugs and prescribing of drugs with many potential DDIs may cause the high occurrence of potential DDIs in this group of patients. One finding in a developed country showed that 80% of hospitalized patients with heart diseases were susceptible to DDIs(Kohler GI, Bode-Boger SM, Busse R, Hoopmann M, Welte T, 2000).

In the review studies showed that patient age and polypharmacy were the most reported associated factors with the occurrence of potential DDIs. Similarly, the finding from a review in a developed country highlighted these risk factors(Espinosa-Bosch M, Santos-Ramos B, Gil-Navarro MV & Marin-Gil R, 2012). Many studies had emphasized that the high occurrence of potential DDIs in old age is due to physiological changes related to age, comorbid diseases and a high rate of medication use.

The limitation of this review and meta-analysis were the drug-drug interactions found were only potential and doesn’t address the actual DDIs because of lack of studies. Some of the studies included in the review and meta-analysis had small sample sizes. These might have led to bias. Another limitation of this review were Egger’s test funnel plots revealed as there is no publication bias but this estimation may not be accurate as small studies are included for the review and there are studies which has small size.

Conclusion

This review and meta-analysis had considerable clinical heterogeneity so it should be considered with caution. The prevalence patients with potential DDIs in Ethiopian Hospitals were found to be high i.e. 72.2% (95% CI: 59.1%, 85.3%). From this the most prevalent DDIs were moderate severity, 52.8%. In this review polypharmacy, age, comorbid disease and hospital stay were the risk factors associated with potential DDIs.

Abbreviations

ADEs:Adverse Drug Events; ART:Antiretroviral Therapy; CI:Confidence Interval; CMA:Comprehensive Meta-Analysis; CS:Crossectional study; DDIs:Drug- Drug Interactions; PRISMA:Preferred Reporting Items for Systematic Review and Meta-Analysis

Declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

All authors agreed to publish this research article

Availability of data and materials

All data are available in the manuscript

Competing interests

No conflict of interest

Funding

This research article did not receive any fund from any funding agency.

Authors' contributions

WA designed the study. WA and GA collected scientific studies, assessed the quality of the study, extracted and analyzed the data. AI commented the review. WA also prepared the manuscript for publication. All authors have read and approved the manuscript

Acknowledgment

We would like to thank the author and reference that we had used.

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