Polypharmacy and associated factors among patients with type two diabetes mellitus with comorbidity: a multicenter cross-sectional study in Northwest Ethiopia

Abstract

Background

Patients with Diabetes Mellitus (DM) may have various comorbid illnesses, which necessitates the prescription of multiple drugs. Polypharmacy can raise the risk of adverse drug events, drug interactions, and medication non-adherence, even though using numerous medications is not always inappropriate. The aim of this study was to assess the rate of polypharmacy and associated factors among patients with Type 2 Diabetes Mellitus (T2DM) with comorbidity in Northwest Ethiopia.

Methods

A cross-sectional study was conducted among 409 T2DM patients with comorbidity in Northwest Ethiopia from April 1 to June 30, 2023. A consecutive sampling technique was used to enroll study participants. Data were entered using EpiData version 4.6.0 and analyzed with SPSS version 26. Polypharmacy was determined by reviewing the number and type of medications based on relevant evidence. A multivariable logistic regression model was fitted to identify factors associated with polypharmacy. Variables with a p-value less than 0.05 at a 95% confidence interval were considered statistically significant.

Results

The prevalence of polypharmacy was 59.1%. Duration of illness (AOR = 2.06, 95% CI: 1.14, 3.07) hypoglycemia (AOR = 2.75, 95% CI: 1.45, 5.21) and comorbidity of four, five or more (AOR = 2.19, 95% CI: 1.16, 4.15), (AOR = 3.23, 95% CI: 1.47, 7.09) were significantly associated with polypharmacy.

Conclusion and recommendation

In this study, more than half of the participants were found to be on polypharmacy. Patients with longer duration of illness, hypoglycemia and multiple comorbidities need routine follow-up to reduce the rate of polypharmacy.

Background

Diabetes Mellitus (DM) is one of the most common chronic diseases in the world, and its prevalence is rising as a result of changing lifestyle choices such less physical activity and rising obesity [1]. According to projections, the prevalence of diabetes will increase from 9.3% in 2019 to 10.2% by 2030 and 10.9% by 2045, with low- and middle-income nations accounting for 79.4% of cases [2]. The risk of complications such diabetic nephropathy, neuropathy, cardiovascular disease, stroke, lower limb amputation, and mortality is greatly increased by poorly controlled Type 2 Diabetes Mellitus (T2DM) [3]. Better health outcomes and less financial strain on patients, society, and healthcare systems can result from successfully reducing the progression of disease [4].

Multiple drugs are required to control the coexisting chronic illnesses that many patients with diabetes have, including depression, coronary artery disease, dyslipidemia, hypertension, and chronic kidney disease [5]. Antihypertensive, lipid-lowering agent, and antiplatelet drugs are frequently taken in conjunction with antidiabetic drugs, according to studies on the prescription trends of diabetic patients globally [6].

Descriptive definitions evaluating the appropriateness of drug usage, numerical definitions based on the number of medications taken concurrently, and definitions considering treatment duration are all examples of how the term “polypharmacy” has changed over time and differs throughout studies [7]. The use of five or more medications is the most prevalent definition of polypharmacy, which has historically been linked to harmful consequences despite these differences and it’s estimated prevalence ranges from 57 to 84% [8, 9]. Since polypharmacy increases the risk of adverse drug events, drug–drug interactions, therapy duplication, hospitalization, and medication errors, it is imperative to look into it in diabetic patients. Additionally, polypharmacy can result in poor glycemic control, decreased functional status, a lower quality of life, and higher healthcare expenditures by decreasing adherence to antidiabetic drugs [10, 11].

The extent of polypharmacy and patients’ experiences with medications are increasingly recognized as key components of medication-related quality of life. While achieving optimal therapeutic outcomes and meeting patients’ drug-related needs remain central goals, minimizing unnecessary polypharmacy and enhancing the medication experience are now at the forefront of pharmaceutical care [12]. Understanding the degree of polypharmacy among patients with DM is particularly important, as it is a growing concern linked to potentially inappropriate prescribing and negative health outcomes. Additionally, evaluating polypharmacy from both clinical and public health perspectives is essential, as it may serve as an indicator of inappropriate prescribing and an increased risk of adverse drug reactions. To the best of our knowledge, based on the literature reviewed, the prevalence and determinants of polypharmacy have not been specifically studied among patients with T2DM with comorbidity in Ethiopia. Thus, the aim of this study was to assess the rate of polypharmacy and its associated factors among patients with T2DM with comorbidity in Northwest Ethiopia.

Materials and methods

Study setting, period and design

A multicenter cross-sectional study was conducted from April 1 to June 30, 2023, at selected government general hospitals in Northwest Ethiopia. Injibara General Hospital (IGH), Shegaw Motta General Hospital (SMGH), and Finote Selam General Hospital (FSGH) were chosen by lottery. FSGH is located in the town of Finote Selam, 387 km from Addis Ababa; IGH is situated 435 km away in Injibara; and SMGH is 450 km away in Motta. Collectively, these hospitals provide comprehensive care for patients with DM and have dedicated chronic outpatient clinics, serving a catchment population of approximately 3.6 million people.

Population, inclusion and exclusion criteria

The source population for this study consisted of all adult patients with T2DM and comorbid conditions who were receiving follow-up care at selected general hospitals in Northwest Ethiopia. The study population specifically included adult T2DM patients with comorbidities who attended follow-up visits at these hospitals during the study period. Eligibility criteria were: age 18 years or older, a confirmed diagnosis of T2DM with at least one comorbid condition, and currently receiving treatment. Patients were excluded if they had severe illness, psychiatric disorders that impaired their ability to respond to verbal questions (as indicated by a score of 3 out of 3 on the insight assessment tool), and those with incomplete medical records.

Sample size determination and sampling technique

The sample size was calculated using a single population proportion formula as follows:

where n is the desired sample size, Z is the typical normal distribution set at 1.96 (which corresponds to 95% CI), the p-value signifies that positive prevalence was utilized in calculating the optimal sample size, and d is the degree of accuracy 0.05 required (a marginal error is 0.05). Since the current study was carried out at a multicenter facility, we calculated the sample size using a proportion of 50% in order to obtain a more representative sample. Therefore, . Consequently, the total computed sample size was 422 after adding a 10% non-response rate.

Sampling technique and procedure

A consecutive sampling technique was used to select participants form the hospitals. To ensure representativeness, the final sample size was proportionally distributed across the selected hospitals. The number of T2DM patients at each hospital was determined using data from the previous three months of follow-up visits. Accordingly, 125, 140, and 157 participants were allocated to FSGH, IGH, and SMGH, respectively. All T2DM patients who met the inclusion criteria and attended follow-up appointments during the data collection period were approached consecutively until the required sample size was reached.

Study variables

Polypharmacy was the main outcome variable. The independent variables included participants’ sociodemographic characteristics (such as sex, marital status, residence, and health insurance), clinical-related factors (including duration of illness, body mass index, DM-related admissions, DM-related complications, hypoglycemia, and the number of comorbid conditions), as well as substance use.

Operational definitions

Current substance use

According to the Alcohol, Smoking, and Substance Involvement Screening Tool (ASSIST), using alcohol, khat, or cigarettes for non-medical purposes during the last three months [13].

Polypharmacy

The World Health Organization (WHO) defines polypharmacy as the consistent use of five or more medications [14].

Data collection instrument and procedures and quality control

A structured questionnaire, adapted from previous studies [11, 15] was used to suit the study setting and the sociodemographic characteristics of the participants. To ensure accuracy and consistency, the questionnaire was first translated into the local Amharic language and then back-translated into English. Information on sociodemographic characteristics, history of DM education, and substance use was collected through face-to-face interviews. Clinical and medication-related data—including duration of illness, family history of DM, Body Mass Index (BMI) category, DM-related hospital admissions, complications, hypoglycemia, number of comorbid conditions, DM treatment, and polypharmacy—were obtained by reviewing participants’ medical records.

The data collection tool consisted of three sections. The first section covered the sociodemographic characteristics of the participants, including sex, age, marital status, residence, educational level, occupation, and health insurance status. The second section focused on clinical and medication-related factors, such as duration of illness, family history of DM, BMI category, DM-related hospital admissions and complications, history of hypoglycemia, number and type of comorbid illnesses, DM education, DM treatment, and polypharmacy. The third section assessed substance use. For this purpose, the ASSIST—developed and validated by the WHO [13] —was used to screen for the use of psychoactive substances.

The consistency, clarity, and usability of the data collection instrument were evaluated through a pretest conducted at IGH on 22 participants, representing 5% of the total study population, prior to the actual data collection period. Data obtained from the pretest were excluded from the final analysis. Data collectors received one day of training, which covered the objectives of the study, the data collection tools, and ethical considerations related to the data collection process.

Data processing and analysis

After cleaning, coding, and entering the data into EpiData version 4.6.0, analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 26. Descriptive statistics, including frequencies, percentages, means, and standard deviations (SD), were used to examine the data distribution. Bivariate and multivariable binary logistic regression models were employed to identify factors associated with polypharmacy. Model fitness was assessed using the Hosmer–Lemeshow test, which yielded a p-value of 0.724, indicating a good fit. Multicollinearity was checked, with the maximum Variance Inflation Factor (VIF) below 5, suggesting no significant multicollinearity. Variables with a p-value less than 0.25 in the bivariate analysis were included in the multivariable model. The strength of associations was measured by calculating Odds Ratios (OR) with 95% Confidence Intervals (CI). A p-value less than 0.05 was considered statistically significant.

Results

Sociodemographic characteristics among patients with type two diabetes mellitus with comorbidity

Out of 422 individuals approached, 409 eligible respondents participated in the study, yielding a response rate of 96.9%. The majority of participants were female (57.9%), with a mean age of 56.89 ± 16.1 years. More than two-thirds (69.4%) were married, and the majority (60.4%) resided in rural areas. Approximately half (52.8%) of the respondents had no formal education, and nearly half (42.3%) were farmers. Additionally, nearly one-third (31.1%) of participants did not have health insurance coverage (Table 1).

Table 1.

Sociodemographic characteristics among patients with type two diabetes mellitus with comorbidity (n = 409)

Variables	Categories	Frequency (percentage)
Sex	Male	172 (42.1)
	Female	237 (57.9)
Age	Mean (± SD)	56.89 (± 16.1)
Marital status	Single	26 (6.4)
	Married	284 (69.4)
	Divorced	62 (15.1)
	Widowed	37 (9.1)
Residence	Urban	161 (39.6)
	Rural	248 (60.4)
Educational level	No formal education	216 (52.8)
	Primary (1–8 grade)	92 (22.5)
	Secondary (9–12 grade)	55 (13.4)
	College degree and above	46 (11.3)
Occupation	Government employee	73 (17.8)
	Private employee	65 (15.9)
	Farmer	173 (42.3)
	Daily labor	20 (4.9)
	Housewife	78 (19.1)
Health insurance	Yes	282 (68.9)
	No	127 (31.1)

SD: Standard deviation

Clinical and medication related variables among patients with type two diabetes mellitus with comorbidity

Regarding clinical-related variables, more than one-third of the respondents (38.4%) had been living with the illness for over ten years, and nearly one-third (30.1%) reported a family history of diabetes mellitus (DM). About one-fourth (27.1%) of participants were overweight, and 71.1% had a history of DM-related hospital admissions. Nearly half (44.9%) of the patients had DM-related complications, and 16.6% had experienced hypoglycemia. One-third (33.1%) of respondents had three comorbid conditions, with hypertension and dyslipidemia being the most common. Close to one-fourth (22.1%) reported substance use, and more than half (59.6%) had not received diabetes education.

With respect to medication-related variables, nearly one-third (32.3%) of participants were taking both insulin and oral hypoglycemic agents (OHAs), while the majority (59.1%) were on polypharmacy (Table 2).

Table 2.

Clinical and medication related variables among patients with type two diabetes mellitus with comorbidity (n = 409)

Variables	Categories	Frequency (percentage)
Duration of the illness	< 5 year	86 (21.0)
	5–10 year	166 (40.6)
	> 10 year	157 (38.4)
Family history of DM	Yes	123 (30.1)
	No	286 (69.9)
BMI category	Underweight	46 (11.2)
	Normal	166 (40.6)
	Overweight	111 (27.1)
	Obese	86 (21.1)
DM related admission	Yes	291 (71.1)
	No	118 (28.9)
DM related complication	Yes	184 (44.9)
	No	225 (55.1)
Hypoglycemia	Yes	68 (16.6)
	No	341 (83.4)
Number of comorbid illnesses	Two	158 (39.1)
	Three	135 (33.1)
	Four	69 (16.5)
	Five and above	47 (11.3)
Type of comorbid illness	Hypertension	306 (74.8)
	Dyslipidemia	254 (62.1)
	Cardiac disorders	69 (16.8)
	Musculoskeletal disorder	56 (13.7)
	Renal disorders	35 (8.5)
	Eye disorders	29 (7.1)
	Others*	58 (14.1)
Substance use	Yes	93 (22.1)
	No	316 (77.9)
DM education	Yes	166 (40.4)
	No	243 (59.6)
DM treatment	OHA	114 (27.8)
	Insulin	162 (39.8)
	Insulin + OHA	133 (32.3)
Polypharmacy	Yes	241 (59.1)
	No	168 (40.9)

DM: diabetes mellitus, BMI: body mass index, OHA: oral hypoglycemic agent

*Others; thyroid disorders, retroviral infection, asthma, gastro-intestinal disorders, bacterial infection, skin disorders, depression

Factors associated with polypharmacy among patients with type two diabetes mellitus with comorbidity

Following multivariable logistic regression analysis, polypharmacy was found to be significantly associated with duration of illness, hypoglycemia, and the number of comorbid illnesses. Patients with a duration of illness greater than 10 years were 2.06 times more likely to be on polypharmacy compared to those with a duration of less than 5 years (AOR = 2.06, 95% CI: 1.14, 3.07). Similarly, patients who had experienced hypoglycemia were 2.75 times more likely to be on polypharmacy than those without hypoglycemia (AOR = 2.75, 95% CI: 1.45, 5.21). Furthermore, participants with four and five or more comorbid conditions were 2.19 and 3.23 times more likely, respectively, to be on polypharmacy compared to those with two comorbid illnesses (AOR = 2.19, 95% CI: 1.16, 4.15; AOR = 3.23, 95% CI: 1.47, 7.09) (Table 3).

Table 3.

Factors associated with polypharmacy among patients with type two diabetes mellitus with comorbidity (n = 409)

Variables	Categories	Polypharmacy		95% CI		P-value
		Yes	No	COR	AOR
Sex	Male	107	65	0.79 (0.53, 1.18)	0.72 (0.45, 1.15)	0.174
	Female	134	103	1	1
Marital status	Unmarried	77	48	1.29 (0.83, 1.99)	1.42 (0.86, 2.36)	0.166
	Married	164	120	1	1
Residence	Urban	100	61	0.77 (0.51, 1.15)	0.85 (0.55, 1.34)	0.504
	Rural	141	107	1	1
Health insurance	No	69	58	0.76 (0.49, 1.16)	0.89 (0.56, 1.43)	0.633
	Yes	172	110	1	1
Duration of illness	> 10 year	97	60	1.87 (1.08, 3.25)	2.06 (1.14, 3.07)	0.015
	5–10 year	91	75	0.70 (0.41, 1.18)	0.77 (0.44, 1.35)	0.371
	< 5 year	53	33	1	1
BMI category	Obese	51	35	1.45 (0.71, 2.99)	1.31 (0.59, 2.93)	0.500
	Over weight	70	41	1.71 (0.85, 3.42)	1.57 (0.73, 3.38)	0.241
	Normal	97	69	1.41 (0.73, 2.71)	1.36 (0.66, 2.81)	0.402
	Under weight	23	23	1	1
DM related admission	Yes	184	107	1.51 (0.98, 2.33)	1.43 (0.89, 2.30)	0.133
	No	57	61	1	1
DM related complication	Yes	122	62	1.42 (0.95, 2.12)	1.50 (0.96, 2.32)	0.07
	No	119	106	1	1
Hypoglycemia	Yes	50	18	2.61 (1.43, 4.76)	2.75 (1.45, 5.21)	0.002
	No	191	150	1	1
Number of co-morbid illness	Five and above	35	12	2.91 (1.41, 6.03)	3.23 (1.47, 7.09)	0.003
	Four	47	22	2.13 (1.18, 3.87)	2.19 (1.16, 4.15)	0.016
	Three	80	55	1.45 (0.91, 2.31)	1.45 (0.87, 2.41)	0.145
	Two	79	79	1	1
DM education	No	137	106	0.77 (0.51, 1.15)	0.74 (0.47, 1.17)	0.202
	Yes	104	62	1	1
Substance use	Yes	59	34	1.35 (0.83, 2.19)	1.18 (0.69, 1.99)	0.538
	No	182	134	1	1

DM: diabetes mellitus, BMI: body mass index

* P < 0.05, AOR: adjusted odds ratio, CI: confidence interval, COR: crude odds ratio, bold figures; statistically significant variables

Discussion

Polypharmacy not only increases the pill burden for patients but also elevates the risk of drug-drug and drug-food interactions [6]. The aim of this study was to assess the rate of polypharmacy and associated factor among patients with T2DM with comorbidity in Northwest Ethiopia. The findings revealed that 59.1% of patients were on polypharmacy. A duration of illness exceeding ten years, a history of hypoglycemia, and the presence of four or more comorbid conditions were significantly associated with polypharmacy.

In this study, the rate of polypharmacy among T2DM patients with comorbidity was 59.1% (95% CI: 55.5, 65.0) which is comparable with the previous studies from Italy (57.1%) [15] and Brazil (56.5%) [16]. However, this study was lower than study conducted in Portugal (72.09%) [17], Saudi Arabia (78%) [11] and Oman (83.1%) [18]. The variance could be related to disparities in sociodemographic, cultural and economic variations. Difference in sample size and inclusion criteria plus changes in healthcare systems, clinical environments, and treatment modalities could also play an effect.

In the current study, no significant associations were observed between sociodemographic variables and polypharmacy, aligning with findings from previous findings [19, 20]. Conversely, other studies have identified associations between polypharmacy and factors such as female gender [21], older age [12] and being employed [22]. These inconsistencies may be explained by methodological differences, including variations in study populations and the inclusion and exclusion criteria, which could have substantially influenced the outcomes.

Patients with duration of illness greater than 10 years were more likely to be on polypharmacy compared to those with duration of illness less than 5 years. This finding aligns with previous evidence [15]. This could be due to people with T2DM are more likely to develop complications and comorbidities for longer periods of time, which often necessitate the use of additional medications. These conditions include arterial hypertension, cardiovascular disorders, and hyperlipidemia, all of which are associated with a higher risk of requiring antihypertensives, antihyperlipidemics, and antiplatelets, which are frequently prescribed in conjunction with antidiabetic medications [23].

Likewise, patients with hypoglycemia were more likely to be on polypharmacy than those without hypoglycemia, which aligns with previous literature [15, 24]. This may be because people with DM frequently have decreased renal function, which impacts drug elimination and makes them more vulnerable to prolonged, potentially fatal hypoglycemia. Additionally, drug-induced hypoglycemia is more likely to occur in patients with polypharmacy. Although sulfonylureas are frequently linked to hypoglycemia, angiotensin-converting enzyme inhibitors and nonselective beta-adrenoceptor antagonists are also known to increase the risk of hypoglycemia [25]. Hence, close monitoring and a comprehensive treatment approach is crucial to reduce the risk of polypharmacy.

Additionally, respondents with four or more comorbid illnesses were more likely to be on polypharmacy compared to those with two comorbid conditions, which is consistent with findings from previous studies [23, 26, 27]. The potential explanation for this is that polypharmacy increases the likelihood of adverse drug events, therapy duplication, and suboptimal glycemic control, which can lead to an increased risk of hospitalization and a poor quality of life. polypharmacy has been linked in certain studies to a considerable increase in re-hospitalization rates upon discharge [28] which exacerbates other comorbid illnesses. Regular medication reviews are crucial to evaluate the continued necessity of treatments, support deprescribing when appropriate—especially for drugs with limited ongoing benefit—and promote patient-centred care that aligns treatment with individual goals, preferences, and quality of life, ultimately helping to lessen the impact of polypharmacy.

Strength and limitation of the study

Despite being multicenter and having a sizable sample size, which improves generalizability, the cross-sectional design of this study makes it more difficult to determine causal links between polypharmacy and the independent factors. Furthermore, individuals may have underreported or misrepresented their substance use habits, according to social desirability bias, which could have affected the assessment of substance use.

Conclusion

In this study, more than half of the participants were found to be on polypharmacy. Respondents with longer duration of illness, hypoglycemia, and multiple comorbid illnesses need prompt screening and careful follow-up to reduce the rate of polypharmacy. The causal association between polypharmacy and its possible contributing factors should be investigated in future studies.

Abbreviations

AOR

Adjusted Odds Ratio

ASSIST

Alcohol Smoking and Substance Involvement Screening Test

BMI

Body Mass Index

CI

Confidence Interval

COR

Crude Odds Ratio

DM

Diabetes Mellitus

FSHG

Finote Selam General Hospital

IGH

Injibara General Hospital

OHA

Oral Hypoglycemic Agent

SD

Standard Deviation

SMGH

Shegaw Motta General Hospital

SPSS

Statistical Package for Social Sciences

T2DM

Type 2 Diabetes Mellitus

WHO

World Health Organization

Author contributions

Fasil Bayafers Tamene: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing– original draft, Writing– review & editing. Tirsit Ketsela Zeleke: Methodology, Visualization, Formal analysis, Writing– review & editing. Akalu Fetene Desalew: Formal analysis, Project administration, Visualization, Writing– review & editing. Getachew Yitayew Tarekegn: Formal analysis, Project administration, Methodology, Writing– review & editing. Ashenafi Kibret Sendekie: Formal analysis, Methodology, Writing– original draft, Writing– review & editing. Selamawit Mengstu Tafere: Methodology, Data curation, Supervision, Writing– review & editing. Mekdes Kiflu: Formal analysis, Project administration, Writing– review & editing. Tilaye Arega Moges: Investigation, Supervision, Writing– original draft, Writing– review & editing. Fisseha Nigussie Dagnew: Methodology Project administration, Visualization Writing– review & editing. Samuel Agegnew Wondm: Formal analysis, Methodology, Project administration, Visualization, Writing– review & editing. All authors have read and approved the final version of the manuscript.

Funding

The authors declare that no financial support was received for the research, authorship, and/or publication of this article.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the ethics review committee of the Department of Pharmacy, Debre Markos University (Ref. No: DP/303/01/2023). Written informed consent was obtained from all participants. Steps were taken to protect participants’ privacy, and personal identifiers were not included in the collected data. The study followed the principles stated in the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial number

Not applicable.

AI declaration

We have used ChatGPT 3.5 to enhance the language and grammar of the manuscript.