Exploring the Impact of Medication Regimen Complexity on Health-Related Quality of Life in Patients with Multimorbidity

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Introduction
Multimorbidity refers to the coexistence of two or more long-term diseases in an individual [1,2]. Its prevalence has risen in recent years [1,2], with evidence in developed countries indicating that more than 40% of the population has at least one chronic disease, with around 25% having more than one [2]. Interestingly, recent evidence suggests that high levels of multimorbidity are present in low-and middle-income countries (LMICs) [3]. Te empirical studies available indicate that multimorbidity is particularly prevalent among elderly individuals, who are often considered the largest users of the healthcare system [4,5]. Te prevalence of multimorbidity can vary greatly depending on various factors, such as setting/location, data sources, and sample characteristics such as age, gender, and socioeconomic class [6][7][8]. For example, a study of primary care patients in the Netherlands found that the prevalence of three chronic conditions increased by 60% from 1985 to 2005, and the prevalence of four or more conditions increased by 300% [8]. A more recent study in the United States found that 23% of participants had multimorbidity [9].
Multimorbidity has a variety of negative impacts on health and well-being. Studies have shown that individuals with multimorbidity have a shorter life expectancy [10], are more likely to be admitted to the hospital, have longer hospital stays [11], and tend to see a greater number of healthcare providers in a given year [11]. In addition, multimorbidity can greatly afect an individual's overall well-being, HRQoL, and ability to function [12]. Reduced physical functioning due to multimorbidity can also contribute to the development of depression and other mental health issues, further exacerbating the challenges of managing multiple chronic conditions [13]. Managing multiple medications, often prescribed for diferent chronic conditions, can be difcult for individuals with multimorbidity [14], leading to a complex medication regimen, poor adherence to treatment, and decreased HRQoL [5,15,16].
Medication regimen complexity (MRC) refers to the various aspects of a patient's medication regimen, including the number of medications prescribed, their dosage forms, dosing frequencies, and usage instructions [17]. Many patients with long-term diseases are often prescribed multiple medications [18,19], making it difcult for them to maintain the same level of commitment to managing their conditions over time [20]. As a result, patients with multimorbidity can experience a complicated medication regimen and reduced HRQoL. However, not all patients experience the burden of treatment in the same way. Tose with multimorbidity are at a higher risk of experiencing MRC-related treatment burden [21]. Factors such as a patient's skills, cognitive and intellectual abilities, and social support can also afect their perception of MRC [21,22]. In addition, mental illness, low health literacy, and limited overlap in the management of multiple conditions can further contribute to a higher treatment burden [21,23].
Multimorbidity is becoming increasingly common in Ethiopia. A study conducted in the current study area found that 44.6% of patients with cardiovascular diseases also had multimorbidity [24]. Tis high prevalence can have a signifcant impact on patients' treatment burden and HRQoL. Tis is particularly concerning in Ethiopia, where health literacy rates are low [25], and studies have shown that individuals who struggle to understand their therapy are more likely to experience MRC [23]. Despite this, the relationship between MRC and HRQoL in patients with multimorbidity is not well understood. Tis lack of understanding is likely due to difculties in accessing and enrolling these patients in research studies. Further research on this topic could lead to the development of interventions that improve health outcomes for this population. To the best of the authors' knowledge, there is limited literature on MRC and HRQoL in patients with multimorbidity in LMICs, particularly in Ethiopia. Terefore, the aim of this study was to assess MRC and investigate its impact on diferent dimensions of HRQoL, including mobility, selfcare, usual activity, pain/discomfort, and anxiety/depression, among patients with multimorbidity in Ethiopia.

Study Design, Setting, and Participants.
A cross-sectional study was conducted at the University of Gondar Comprehensive Specialized Hospital (UOGCSH) located in Gondar Town, Ethiopia. UOGCSH is one of the oldest referral hospitals in the northwest region of the country and receives referrals from a large population, nearly 17 million people [26]. Te study population comprised patients who were aged 18 years or older, had been diagnosed with at least two long-term diseases, and were already on medical treatment for at least six months. However, patients who were in emergency conditions or had conditions that would prevent the administration of the study instruments, such as severe mental illnesses or dementia, were excluded from the study. Te data were collected when patients came for routine check-ups or medication reflls at the outpatient department of the hospital between May 2021 and July 2021.

Sample Size Determination.
Te sample size required for the present study was calculated using the formula for estimating a single population proportion [27]. In this formula, "n" represents the initial sample size, "Z" represents the desired level of confdence (95% confdence interval), "p" represents the estimated proportion of patients with the desired outcome within the study area, and "d" represents the level of precision (5%). As the proportion of patients with the desired outcome was not known a priori, a conservative estimate of 50% was used. Based on these assumptions, an initial sample size of 384 was calculated. To account for the potential nonresponse, a 10% nonresponse rate was added to the sample size, resulting in a fnal sample size of 423.

Data Collection Instruments and Procedure.
To achieve the objectives of the present study, two validated instruments were utilized. Te frst instrument is the Medication Regimen Complexity Index (MRCI), which is a commonly used tool for evaluating the complexity of a medication regimen [28]. It is based on 65 items that take into consideration the dosage form, dosing frequency, and any additional instructions. For each patient, the MRCI score was determined by evaluating three diferent aspects of their medication regimen: dosage formulation, dosing frequency, and additional administration instructions. Each tablet or capsule dosage form that was administered once per day was given a weight of 1, and other dosage formulations and dosing frequencies were assigned increasing weights based on their difculty of administration. Additional administration instructions, such as "break or crush" or "take with food," were also taken into account and given increasing weight based on their level of difculty. Te MRCI score accounted for all prescription and over-thecounter medications for each patient and was interpreted as low MRC (≤4), medium MRC (5-8), or high MRC (>8) based on the fnal score [29][30][31]. Te MRCI tool was translated and validated in Amharic language in a subset of multimorbid patients (diabetes patients) in Ethiopia [31]. Te second instrument utilized in the study is the EuroQol-5 Dimension (EQ-5D) instrument, a widely used generic and multiattribute tool that is employed to evaluate health status and inform decisions on resource allocation in healthcare [32]. With over three decades of experience and translations in over 170 languages [33], it is the most widely used measure of HRQoL. Te EQ-5D-5L, a descriptive system of the EQ-5D, comprises fve dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension has fve levels, ranging from no problems to extreme problems, resulting in 3125 (�5 5 ) possible value sets that range from full health (11111) to extreme problems in all dimensions (55555). Te EQ-5D-Index for Ethiopia was derived using the EQ-5D-5L value set [34]. In addition, the EQ-5D-5L includes the EuroQol-Visual Analogue Scale (EQ-VAS), which measures the patient's self-reported health on a vertical visual analogue scale, with endpoints labeled 100 "Te best health you can imagine" and 0 "Te worst health you can imagine." Te EQ-VAS is a quantitative measure of health outcome that refects the patient's personal judgment.
Data on the demographic and clinical characteristics of the patients, including age, gender, current diagnosis, number of multimorbidity, current medications, duration of illness, dosage formulation, frequency, route of administration, and other relevant clinical information, were collected by thoroughly reviewing the patients' medical records. Te patient's HRQoL data were obtained through faceto-face interviews conducted by trained data collectors.

Outcome Measures.
Te outcome measures of this study include the MRCI score, the EuroQol-5 Dimension Index, the EQ-5D Visual Analogue Scale (EQ-VAS) score, and the EQ-5D dimensions.

Data Analysis.
Te study utilized descriptive statistics to summarize continuous and categorical variables, including the mean with standard deviation for continuous variables and frequency with proportion for categorical variables. Te MRC score was categorized into three levels, low (score ≤4), medium (score 5-8), and high (score>8), for the purpose of comparing mean diferences in the EQ-5D-Index and EQ-VAS score. Te mean EQ-VAS score was calculated by averaging individual patient ratings on a scale of 0-100. To determine the signifcance of diferences between the MRC categories, the study employed Welch's ANOVA test for unequal variance, followed by Games-Howell post hoc analysis to identify the specifc groups responsible for any signifcant diferences. For correlation analysis, the distribution of variables was examined for normality and linear relationship. All analyses were conducted using Statistical Package for the Social Sciences (SPSS) version 26.0 software, with a 95% confdence interval and 5% precision.

Ethics Approval and Consent to Participate.
Ethical clearance for the study was granted by the Ethical Review Committee of School of Pharmacy, University of Gondar. Prior to conducting the interviews, participants were provided with information regarding the background and purpose of the study. Participants who were able to read and write provided their informed consent by signing the consent form themselves. For those who were unable to read or write, the interviewer assisted them in providing their consent through thumbprinting. All information obtained through the interviews was kept confdential, and participant identifers were not used.

Sociodemographic and Clinical Characteristics.
A total of 416 participants were included in the study, with a response rate of 98.3%. Te participants' ages ranged from 18 to 92 years, with a mean age of 56.12 ± 13.75 years. Te majority of the participants, 273 (65.6%), live in Gondar town. A signifcant proportion of the participants had either no formal education (n = 117, 28.1%) or primary school education (n = 134, 32.2%). At the time of the study, the majority of patients had been diagnosed with two long-term diseases 215 (51.7%) and they had a duration of illness of less than fve years (n=240, 57.7%). In addition, nearly half of the patients, 193 (46.4%), were prescribed fve or more drugs during the study period, with a statistically signifcant diference across the levels of MRC (P < 0.001). Overall, more than half of the patients, 238 (57.2%), had a high level of MRC (Table 1).

Long-Term Diseases and Teir Treatment.
According to the International Classifcation of Diseases (ICD), the majority of patients (n = 388, 93.3%) were diagnosed with circulatory system diseases. Tis was followed by endocrine system disease (n = 220, 52.9%) and respiratory system disease (n = 57, 13.7%). Te complete list of chronic diseases and their associated medications are available in the supplement (Supplementary fle). All patients diagnosed with endocrine (n = 220, 100%) and respiratory system diseases (n = 57, 100%), as well as almost all patients diagnosed with circulatory system diseases (n = 379, 97.7%), had either medium or high levels of MRC (Figure 1). Te most commonly prescribed drug classes were cardiovascular (n = 395, 95.0%), followed by endocrine (n = 210, 50.5%) and analgesics and antipyretics (n = 141, 33.9%). Similarly, patients who were prescribed endocrine and cardiovascular drugs (n = 386, 97.7%) had either medium or high level of medication regimen complexity (Figure 2).

Medication Regimen Complexity and Health-Related
Quality of Life. Te overall mean MRCI score was 9.73 ± 3.38, indicating that the overall complexity of the medication  regimen was high. Pain/discomfort and anxiety/depression problems were highly prevalent in these patients, with only 7.5% and 9.6% reporting no problems in these domains, respectively. Among patients who reported having at least some problems with pain/discomfort and anxiety/depression on the EQ-5D-5L, the majority had level 2 and level 3 problems. Te majority of patients also reported problems with self-care (52.6%) and usual activities (72.8%), while most patients (53.8%) reported no mobility problems ( Figure 3). Overall, patients with high regimen complexity reported "severe" and "unable/extreme" levels more frequently than the other groups. Tere was a statistically signifcant weak negative correlation between the MRCI score and the mean EQ-5D-5L index (r � −0.175; P < 0.001), as well as between the MRCI score and the EQ-VAS score (r � −0.151; P � 0.002). Tere was also a statistically signifcant diference in the mean EQ-5D-5L index (P � 0.001) and EQ-VAS score (P � 0.001) across MRC levels ( Table 2).

Discussion
Tis study looked at the relationship between MRC and HRQoL in patients with multiple long-term diseases in a low-income environment. Complexity in medication regimens is an increasingly recognized concept that can have a negative impact on patient outcomes. Te study used the MRCI to evaluate regimen complexity, and the authors note that this is the frst study to connect MRCI and HRQoL in this study population. Previous research has found that polypharmacy, or the use of multiple medications, is associated with HRQoL [35][36][37]. However, the authors note that medication count alone is not an adequate measure of complexity, as it does not take into account other factors such as dosage forms, dosing frequency, and usage instructions [28,38]. Patients may, for example, use tablets, creams, or patches, each with its own set of dosing instructions [38]. In addition, the medication count may not include over-the-counter (OTC) medications, which can also contribute to complexity in some individuals [28]. Te study highlights the importance of considering complexity in medication regimens when evaluating patients with multiple chronic diseases and the negative impact it can have on their HRQoL.
Te present study assessed MRC using a validated measuring instrument called the MRCI, which is a 65-item instrument that can be computed using data from the patient's medical record. Te level of complexity is determined by factors such as the number of medications, dosage frequency, additional instructions, and dosage forms [28]. Te MRCI instrument has various potential clinical applications for patients with multimorbidity [38], but more research is needed. One potential clinical intervention is simplifying patients' regimens, such as switching from a twice-daily drug regimen to a once-daily drug regimen [38]. However, it is unclear if lowering regimen complexity improves clinically important health outcomes, such as adherence, readmission, and hospitalization [38]. In addition, the MRCI does not take into account the fnancial burden associated with drugs, which is a signifcant concern for patients with multimorbidity and limited income, particularly in lowincome settings [13].
Te study fndings indicate that the majority (57.2%) of patients with multimorbidity had a high treatment regimen complexity. Te mean MRCI score, which is a measure of complexity, was considerably higher than that reported in a previous study from Spain (9.7 versus 6.9) [39]. Tis diference in regimen complexity can be explained in part by the higher number of long-term conditions (2 or more versus 1) and the average number of prescribed medications (5 versus 3) in the current study population. Other studies from Australia [38] and the World Health Organization [40] also suggest that patients with multimorbidity tend to have more complex management regimens and polypharmacy and that the complexity of a medication regimen is usually correlated with the number of prescriptions.
In this study, the complexity of patients' treatment regimens was evaluated using the MRCI. Te scores ranged from 2 to 19, with 2.2% of patients' regimens classifed as low complexity, 40.6% as medium complexity, and 57.2% as high complexity. Te most important factor in determining complexity was the frequency of dosing, followed by the dosage form and additional instructions. Factors such as the number of drugs in the regimen, the number of doses per day, drug-drug, or drug-food interactions also contributed to complexity. Te study found that patients with multiple chronic diseases, particularly those related to the circulatory, endocrine, and respiratory systems, had a medium to high level of regimen complexity. Tis highlights the importance of carefully reviewing and documenting medication use in these patients, as certain prescriptions for those conditions such as insulin, salbutamol inhaler, beclomethasone inhaler,   and propylthiouracil may signifcantly contribute to the overall complexity due to the increased frequency of medication administration, complicated dosage forms, and special instructions.
Te current study also found a signifcant negative relationship between treatment regimen complexity and HRQoL, which is consistent with previous research that has used the MRCI as a measure of complexity [39]. Tis association suggests that as the complexity of treatment regimens increases, patients report lower HRQoL. Tis was refected in the decrease in the mean EQ-5D-Index and EQ-VAS scores as treatment regimen complexity increased. Specifcally, patients with high treatment regimen complexity reported more problems in all dimensions of the EQ-5D, including mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Overall, these fndings highlight the importance of addressing medication-related issues in order to improve HRQoL for patients with multimorbidity.
Tis study is the frst to investigate the relationship between treatment regimen complexity and HRQoL in Ethiopian patients with multiple chronic conditions. Te study employed validated instruments and had a sufcient sample size, but it is limited by being conducted in a single setting and basing the MRCI on what was documented in the patient's medical records, which may have resulted in a weak correlation. Terefore, the fndings should be interpreted with these limitations in mind.

Conclusion
Tis study found that MRC is prevalent among patients with multiple chronic conditions, with 57.2% of patients having high complexity. Tis complexity was found to be signifcantly associated with worse HRQoL. Patients with high complexity reported more problems in areas such as mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Terefore, chronic disease management programs should focus on assessing patients' medications and implementing strategies to simplify regimens, such as reducing dosing frequencies. Future studies are needed to determine the causal association between regimen complexity and HRQoL.

Data Availability
Te materials and data used to support the fndings of this study are available from the corresponding author upon request.

Conflicts of Interest
Te authors declare that they have no conficts of interest.

Authors' Contributions
BMG and ATK conceived the study, drafted and revised the study proposal, prepared data collection instruments, supervised data collection, performed data analysis and interpretation, drafted the manuscript, revised, and approved submission of the manuscript.