Medication Regimen Complexity and Medication Adherence among Patients with Multimorbidity Treated at University of Gondar Compressive Specialized Hospital

doi:10.21203/rs.3.rs-2120076/v1

Download PDF

Research Article

Medication Regimen Complexity and Medication Adherence among Patients with Multimorbidity Treated at University of Gondar Compressive Specialized Hospital

https://doi.org/10.21203/rs.3.rs-2120076/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Assessing the medication regimen complexity and treatment adherence of a patient is important to tailor medication related outcomes of patients with multimorbidity. Therefore, this study aimed to assess medication regimen complexity and level of medication adherence in patients with multimorbidity.

Methods

A hospital-based cross-sectional study was conducted from May to June, 2021. A systematic random sampling technique was used to enroll participants. Descriptive statistics such as mean, percent, and frequency were used to summarize patients' demographic and clinical characteristics. Ordinal logistic regression and Pearson chi-square tests were performed to identify associations between variables with the outcome variable. The Adjusted Odds Ratio (AOR) with 95% (CI) at P < 0.05 was reported for potential determinant factors of adherence level.

Results

A total of 416 subjects were participated in the study. The mean (SD) age was 56.12(± 13.75) years. The majority of the participants (57%) were classified under high medication regimen complexity index (MRCI) score. Nearly half of the patients (49%) were poor adherent to their medication regimens. In the multivariable ordinal regression analysis; number of medications (AOR = 0.63[0.41, 0.97]), income level (AOR = 4.59 [2.14, 9.83]), follow up duration (AOR = 2.31[1.09, 4.86]) and Charlson comorbidity index (CCI) (AOR = 0.36 [0.16, 0.83]) were significantly associated with low level of adherence.

Conclusion

Medication regimens for patients with multimorbidity were high and complex. Almost half of the study populations were classified as non-adherent. Hence, health care professionals and other stakeholders should seek interventions aimed at simplifying drug regimens complexity and improving adherence.

medication regimen complexity

multimorbidity

medication adherence

Ethiopia

Multimorbidity has been defined as the presence of two or more chronic conditions in one individual (1). Multimorbidity have been associated with polypharmacy, high prevalence of treatment conflicts and potentially as a consequence increased risk of medicine-related adverse events (2).

Noncommunicable diseases (NCDs) remain the leading cause of morbidity and mortality worldwide, responsible for 36 million deaths annually, with a rapidly rising prevalence due to population growth and aging (3). Individuals with chronic disease are usually taking medications for the long-term management of chronic diseases and, the benefits of medications in preventing or slowing the progression of the adverse consequences of NCDs, including premature deaths, as well as managing the associated symptoms, are indisputable (4).

The presence of higher-complexity therapies is related to recent changes in the epidemiological profile and the wide availability of drugs on the market, as pharmacotherapy is the main therapeutic strategy to cure and control diseases(5). The complexity of the medication regimen is a term used to describe multiple characteristics of the drug regimen of a patient, beyond just the number of medications. This includes factors such as the number of doses per day, number of units per dose, forms of dosage, and additional guidelines (6).

Different studies have identified a link between the management complexity of a medication regimen and non-adherence(7, 8) as well, complex medication regimens (MRC) are more likely to be error-prone which may impact patient safety and quality of life(9). Medication regimen complexity is, therefore, a major concern for patients with chronic diseases and their caregivers. The complexity of a person’s medication regimen depends on the characteristics of the pharmacotherapy, such as the number of drugs consumed, their pharmaceutical forms, schedules and doses, and additional instructions given by the physician (10). Medication nonadherence is widely recognized as a significant and costly problem. Approximately 30–50% of US adults are not adherent to long-term medications leading to an estimated $100 billion in preventable costs annually(11). Improving medication adherence is a significant aspect of clinical practice and research. Non-adherence to medication is an alarming problem in the United States healthcare system causing an excess cost of over $170 billion annually(12). Non-adherence to prescription medication can lead to an increase in morbidity, mortality, and healthcare cost (13). Adherence to prescribed medications is extremely important to ensure the efficacy of medical treatment regimens and more positive health outcomes. According to WHO data, only about 50% of chronically ill patients adhere strictly to the recommendations in developed countries(14). Missing doses, not taking medications at the correct time, or not following the correct administration instructions can be resulting the patient receiving a suboptimal clinical outcome(13). The adherence for chronic disease scale (ACDS) is designed for surveying adults treated for chronic diseases. This tool not only has to reflect the actual implementation of the treatment plan in terms of pharmacotherapy but also indicates the mechanisms that determine adherence of patients(15). However, it has not been investigated in the study area and there is no any study which assessed medication regimen complexity and level of adherence in patients with multimorbidity. Assessing the medication regimen complexity and treatment adherence of a patient is important to better tailor medication related outcomes of patients living with multimorbidity. Therefore, this study aimed to assess medication regimen complexity and level of medication adherence among patients with multimorbidity.

Study design, setting and period

An institutional based cross-sectional study was conducted on patients with multimorbidity at the University of Gondar Comprehensive Specialized Hospital (UoGCSH) from May 01 to June 30, 2021. The hospital is one of the largest teaching hospitals in the country and have been served more than 9 million population in the catchment area.

Study participants and eligibility criteria

The study population consisted of patients with multimorbidity who visited the UoGCSH chronic ambulatory clinic during the study period and who fulfilled the inclusion criteria. Adult patients (18 years and older) who had at least two or more chronic conditions with at least 6 month of follow-up time in the study area were included in the study. While patients who had a serious mental illness with unable to cooperate for the interview and seeking emergency medical attention were excluded in the study. Patients with incomplete medical records due to loss of follow-up or change in follow-up site were also excluded from the study.

Sample size determination and sampling techniques

The sample size was determined by using single population proportion formula:

n = Z²p (1-p) / w²

Where: n, sample size required; w, Marginal error of 5% (w = 0.05); Z, the degree of accuracy required (95%) level of significance = 1.96). P, 0.5(50%) level of regimen complexity, this is because no study was done previously in the study area. Considering 10% for possible non-respondents/incomplete records, the final sample size was 423.

The study participants were selected using systematic random sampling technique form eligible study population. The projected number of patients with multimorbidity in two months follow-up was 2340 so, Ø K = 52 *45/423 = 5.57, approximately 6. Initially, the first study participant was selected using simple random sampling for the starting point. Then, subsequently participants included in the study using the sampling interval through coding of their medical record until sufficient samples were maintained.

Operational definition

Medication regimen complexity (MRC)

patient’s drug regimen, beyond just the number of medications(16).

Incomplete medical chart

is one that fails to tell the patient's story, and lacks clarity, specificity, or completeness which includes, incomplete diagnosis, duration of follow up and medication regimens.

Polypharmacy

defined as, when greater than or equal to 5 drugs are prescribed for the patient.

Patient-level medication regimen complexity index (MRCI)

defined as the overall MRCI, in both prescription and OTC medications.

Adherence status

using Adherence in Chronic Diseases Scale (ACDS). Measured as Total score < 21 points, Low adherence; Total score 21–26 points, medium adherence and Total score > 26 points, High adherence.

Data Collection Instruments and Procedures

The MRCI is a validated commonly used measure to predict the complexity of a medication regimen. It is based on 65 items and takes into account the number of drugs, dosage form, dosing frequency, and extra directions (e.g., break/crush the pill, take at a specific time, and relationship to food/liquid). The instrument consists of three sections related to the route of drug administration, dosing frequency, and additional directions. The sum of the scores of each of the three sections contribute to a complexity index. MRCI was calculated using the Microsoft Access 2013 electronic data capture tool. Level of MRC was divided into three categories; low, moderate and high with the cut-off set at ≤ 4 for low complexity, 5–8 for medium complexity and score > 8 was considered as high complexity (17).

Medication adherence was assessed using the Adherence in Chronic Diseases Scale (ACDS) (15). The questionnaire consists of 7 questions. Questions 1–5 concern the patient’s behavior related to medication while questions 6 and 7 concern the physician-patient relationship. Based on the answer, each item of the scale is awarded 0–4 points. The sum of the total medication adherence score ranged from 0 to 28. The patient was classified as having high adherence to treatment with a total score > 26 points whereas scores of 21–26 and < 21 points, respectively, correspond to medium adherence and low adherence (15).

Relevant information regarding each patient's demographics and clinical data was collected using a structured questionnaire. Data on patients’ clinical characteristics such as current diagnosis, number of multimorbidity, number of Charlson comorbidity index, current medications, duration of treatment, number of medications, dosage formulation, frequency, route of administration, and clinical information were collected by using data abstraction format from patient medical records using the most recent part of the records. Medication adherence was collected through a face-to-face interview by data collator using the Amharic version prepared tool of ACDS. MRCI was calculated using data abstraction format from patient charts and from out of the patient chart including over-the-counter non-prescription drugs.

Data Quality Assurance

Data collectors were trained intensively by the principal investigator (15) on the contents of the questionnaire, data collection methods, and ethical concerns. The filled questionnaire was checked daily for completeness by the principal investigator for uniformity and understandability of the checklist after which modification for its appropriateness and suitability was done. The questionnaire was pre-tested on 21 patients (5%) of patients before the actual data collection and avoided in the final analysis. The reliability and accuracy of the questionnaires were assessed by an independent expert personnel in the area. The Cronbach’s alpha value of medication regimen complexity measurement tool and adherence level measurement tool were 0.87 and 0.845, respectively. The selection of data collectors was based on the educational level and possible familiarity in medical and health research. Data collectors were four BSc nurses and had trained on strict use of study criteria, explanation of study objective, getting of verbal consents from study participants, implementation of sampling technique, uniform interpretation of questions, and the collected data confidentiality.

Data Management and Statistical Analysis

The collected data were sorted, cleaned, coded, and entered into Epi-data version 4.6.02 and then exported to statistical package for social sciences (SPSS) version 26 software for analysis. Descriptive statistics were used to summarize frequencies, means, and percentages. The ordinal logistic regression analysis was suitable approach and conducted due to the ordinal nature of the outcome variable (low adherence, medium adherence, and High adherence). To choose the appropriate ordinal model for the data, we checked the Proportional Odds (PO) assumptions using a full likelihood ratio test for P-value was < 0.05. Variables with a p-value < = 0.25 in the bivariable analysis were considered for the multivariable analysis. In the multivariable proportional odds model, the AOR with 95% CI at P < 0.05 was reported for potential determinant factors of adherence level. The Pearson's chi-square test of association was used to identify statistically significant association between MRCI and Level of adherence.

Ethical considerations

The proposal had legitimate approval by the institutional review board (IRB) of the University of Gondar, research and ethics committee of the school of pharmacy with a reference number UOG-Sop/129/2021. The ethical clearance was obtained from the school of pharmacy at the University of Gondar before conducting the study. Participants were informed both in written and verbally by explaining the aim and objective of the study and informed them to withdraw at any time if they don’t want to take part in the study. Confidentiality was ensured through making all information being anonymous.

Socio-demographic and Clinical Characteristics of Study Participants

From a total of 423 study approached participants, data of 416 study participants were used for analysis. The mean (± SD) age was 56.12years (± 13.75) (range 18 to 92 years). The majority of them were within the range of 50–64 years of age (38.2%) followed by greater than 65 years of age (31.5%). Around two-thirds (64.2%) of them were females and about three-fourths (76.2%) were married. The greater proportion of study participants were non-schooled (28.1%). Most the participants (65.9%) had governmental health insurance. Among the study participants, more than half (57.7%) had duration of diagnosis and had been taking treatments had been taking treatments for less than 5 years. Nearly two-thirds (64.2%) had a previous history of hospitalization. Based on the number of multimorbidity; the majority of the patients (52.7%) had 2 multimorbidity and around one-third (10.3%) of patients had greater than or equal to 4 multimorbidity (Table 1).

Table 1

Socio-demographic and clinical characteristics of study participants attending chronic care unit at UOGCSH, Ethiopia, 2021(N = 416)
Variable	Category	Frequency	Percentage (%)
Sex	Male Female	149 267	35.85 64.2%
Age	18–29 30–39 40–49 50–64 >+65	14 27 85 159 131	3.4% 6.5% 20.4 38.2% 31.5%
Marital status	Single Married Divorced Widowed	22 317 54 23	5.3% 76.2% 13% 5.5%
Religion	Orthodox Protestant Muslim Catholic	347 10 58 1	83.4% 2.4 13.9 0.2%
Educational status	No formal education Primary education Secondary College and above	117 134 87 78	28.1% 32.2% 20.9% 18.8%
occupation	Farmer Gov’t/ private employed Merchant Housewife Retired	63 93 70 166 24	15.1% 22.4% 16.8% 39.9% 5.8%
Monthly income	< 1500 ETB 1500–3000 ETB 3000–5000 ETB > 5000ETB	128 154 80 54	30.8% 37% 19.2% 13%
Alcohol consumption	Yes No	107 309	25.7% 74.3%
Cigarette smoking	Yes No	9 407	2.2% 97.8%
Residence	Rural Urban	143 273	34.4% 65.6%
Herbal drug use	Yes No	62 354	14.9% 85.1%
Physical exercise	Yes No	53 363	12.7% 87.3%
Source of medication fee	Free Payment	274 142	65.9% 34.1%
Medication administration	Self(autonomous) Relatives (required assistance)	259 157	62.3% 37.7%
Duration of diagnosis	< 5 years	240	57.7%)
	5–10 years	148	35.6%
	> 10 years	28	6.7%)
Duration of treatment	< 5 years	240	57.7%
	5–10 years	153	36.8%
	> 10 years	28	6.7%
Previous hospitalization	No	149	35.8%
Previous hospitalization	Yes	267	64.2%
Number of drugs per patient	< 5	223	53.6%
Number of drugs per patient	≥ 5	193	46.4
Number of Multimorbidity	Patients with 2 MM*	215	51.7%
	Patients with 3 MM*	158	38%
	Patients with > = 4MM*	43	10.3%
CCI	Mild	328	78.8%
	Moderate	56	13.5
	Severe	32	7.7%%

The primary diagnosis of the patients was categorized according to the International Classification of Diseases tenth edition code (ICD). Most of the patients were diagnosed with diseases of the circulatory system (93.5), diseases of the endocrine system (52.95%), and diseases of the respiratory system (13.70%) (Table 2).

Table 2

Most common primary diagnosis of study participants based on ICD -classification
ICD- Classifications	Frequency (%)
Disease of the Circulatory system	388(93.35%)
Disease of the Endocrine system	220(52.95%)
Disease of Respiratory system	57(13.70%)
Disease of the Renal system	27(6.50%)
Disease of the digestive system	21(5%)
Disease of hematology system	17(4.10%)
Disease of the central nerve system	16(3.80%)
Disease of the musculoskeletal system	12(2.9%)
Others	24(5.85%)
Note: Others, Diseases of the eye and adnexa, Diseases of the skin and subcutaneous tissue, Neoplasms, Mental and behavioral disorders, nutritional and electrolyte disorders.

Medication Regimen Complexity and Adherence Levels

The MRCI score was stratified by low, medium, and high regimen complexity. The patient-level MRCI was ranged from 2 to 20; and majority of the participants, 57% with 95% CI (52.4, 61.8) were showed having high complexity score (Fig. 1).

The mean score of patient-level medication regimen complexity index (PMRCI) was 9.73(9.73 ± 3.38). The total number of medications used for multimorbidity management were 1980. Nonetheless, 53.6% and 46.4% of patients had been taking < 5 and ≥ 5 drugs, respectively. The mean (± SD) number of drugs used per patient was 4.47 ± 1. While dosage forms, dosing frequency, and additional directions are the main subgroups within the pMRCI and dosing frequency contributed most to the MRCI across all of the three sections of pMRCI. Commonly prescribed drug classes were cardiovascular drugs (95%) followed by Endocrine drugs (50.5%) (Table 3).

Table 3

Most common therapeutic class of the medication
Therapeutic class of the drugs	Frequency (%)
Cardiovascular drugs	395(95%)
Endocrine drugs	210(50.5%)
Analgesic and anti-pyretic	141(33.9%)
Gastrointestinal drugs	87(20.9%)
Respiratory drugs	61(14.7%)
Hematology Drugs	40(9.6%)
Others	42(10%)

Note: Others, Chemotherapy (antibiotic), Central nerve system drugs, musculoskeletal drugs, vitamin and mineral drugs

The Adherence in Chronic Disease Scale (ACDS) showed that majority of patients had low adherence level. 21% of participants; 95% CI [16.8, 24.5] were highly adherent, and 30% and 49% of patients had medium and low adherent level to their treatment regimens (Table 4).

Table 4

Level of Adherence Among patients with multimorbidity individuals at UOGCSH, 2021 (N = 416)
Total level of adherence	Cut-off point	Frequency(n)	Percentage (%)
Low adherence	Total score < 21 point	205	49%
Medium adherence	Total score 21–26	125	30%
High adherence	Total score > 26 point	86	21%

Predictors of Adherence

As the multivariable ordinal regression analysis showed, the income level, duration of follow-up, the number of CCI and the number of drugs were the only predictors of medication adherence in multimorbidity patients. the odds of having lower adherence levels among multimorbid individuals whose income level less than 1500 Ethiopian birr, 1500–3000, 3000–5000 were increased by 4.59 times (AOR = 4.591[2.14, 9.83]), 3.325 times (AOR = 3.32, [1.631, 6.779]), 2.698 times (AOR = 2.698[1.263, 5.762]) compared to those who had monthly income level greater than 5000 Ethiopian birr, respectively. Regarding to duration of follow-up, the odds of lower level of adherence status was increased in patients who had 5–10 years and greater than 10 years of follow-up duration than patients who had who had less than 5 years follow-up duration; (AOR = 1.115 [0.729, 1.706]) and (AOR = 2.313 [1.099, 4.868]), respectively. In addition, the odds of being lower level of adherence among individuals who had moderate and severe CCI were decreased by 12% (AOR = 0.88 [0.49, 1.58]) and 64% (AOR = 0.36 [0.15, 0.83]), respectively. The odds of having a lower level of adherence among patients with multimorbid who had > 5 numbers of medication were decreased by 27% (AOR = 0.63 [0.41, 0 .97]) compared to those who had < 5 number of medication (Table 5).

Table 5

Univariable and multivariable ordinal logistic regression analysis of predictors of medication adherence among study participants attending chronic care at UOGCSH, Ethiopia, 2021(N = 416).
Factors		COR (95%CI)	P-value	AOR (95% CI)	P-value
Gender	Male	1		1
Gender	Female	1.79(1.22,2,63)	0.003	1.45(0.93, 2.25)	0.097
Level of education	No formal education	1.05(0.615,1.841)	0.843	0.86(0.43,1.74)	0.683
	Primary education	1.26(0.74,2.14)	0.391	0.85(0.45,1.59)	0.641
	Secondary	1.44(0.87,2.57)	0.22	1.1(0.60,2.12)	0.715
	College and above	1		1
Residence	Urban	1		1
Residence	Rural	0.67(0.45,0.98)	0.04	0.80(.50, 1.27)	0.355
Monthly Income level	< 1500	3.5(1.86,6.67)	<0.001	4.59(2.14,9.83)	P < 0.001*
	1500–3000	3.18(1.667,6.0720)	0.000	3.32(1.63,6.77)	P < 0.001*
	3000–5000	2.25(1.11,4.56)	0.025	2.69(1.26,5.76)	0.010*
	> 5000	1		1
Alcohol history	Yes	0.64(0.42,0.98)	0.042	0.79(0.4, 1.27)	0.346
Alcohol history	No	1		1
Traditional medicine use	Yes	0.67(0.39,1.15)	0.144	0.79(0.45,1.39)	0.417
Traditional medicine use	No	1		1
Cost of healthcare service	Free	0.73(0.4,1.06)	0.101	0.7(0.511,1.21)	0.277
Cost of healthcare service	Paid	1		1
Managing medicine	Autonomous	1.65(1.12,2.42)	0.010	1.37(0.89,2.09)	0.142
Managing medicine	Required assistance	1		1
Duration of follow-up	< 5 years	1		1
	5–10 years	1.07(0.73,1.59)	0.707	1.11(0.729, 1.70)	0.613
	> 10 years	1.83(0.95,3.55)	0.078	2.31(1.0, 4.86)	0.027*
Number of CCI	Mild	1		1
	Moderate	0.81(0.47, 1.40)	0.453	0.88(0.49, 1.56)	0.679
	severe	0.37(0.17,0.80)	0.012	0.36(0.15, 0.82)	0.016*
Number of medications	< 5	1		1
Number of medications	≥	0.48(0.33,0.7)	0.001	0.63(0.41,0.971)	0.036*

Note: CCI, Charlson comorbidity index; * statistically significant at P< 0.05

Association between MRCI and Adherence level

The Pearson's chi-square test association was used to identify statistically significant association between MRCI and level of adherence, and the result revealed that there was a statistically significant association between MRCI and Level of adherence (P = 0.003). Cross tabulation presents the distributions of two categorical variables simultaneously, with the intersections of the categories of the variables appearing in the cells of the (Table 6).

Table 6

Association of MRCI and Level of adherence using Pearson chi-square test among study participants attending chronic care at UOGCSH, Ethiopia, 2021(N = 416)
		Level of Adherence				P-Value
MRCI level		Low	Medium	High	Total	P-Value
	Low	4	2	3	9	0.003
	Medium	73	46	50	169
	High	128	77	33	238
	Total	205	125	86	416

This study provided important information about the Medication regimen complexity index (MRCI) and medication adherence level among patients with multimorbidity. Evaluating medication regimen complexity has tried to determine its magnitude as well as its relationship with the level of medication adherence among multimorbid individuals. As these patient groups were at high risk of having high regimen complexity and low level of adherence, due to the presence of multiple chronic comorbidities and polypharmacy for the long durations; close follow-up of these patients could be warranted.

To the best of investigators knowledge, this is the first study that assessed MRCI and its association with medication adherence that account for the complexity of multiple medication intake in the daily life for patients with multimorbidity in low-income sub-Saharan African countries such as Ethiopia. Several studies also have investigated the factors associated with adherence to chronic medication (18, 19)

In the current study, MRCI complexity scores were stratified by low, medium, and high regimen complexity. The majority of participants had high complexity score. Dosing frequency contributed most to the MRCI across all of the three sections of pMRCI followed by dosage form and additional instruction. Studies revealed that the complexity of pharmacotherapy is composed of multiple features of the prescribed regimen including the number of different drugs in the treatment, number of doses of each drug per day, number of per-dose dosage units, the total number of doses per day and drug interactions with food (20). Consistently with the current study, a previous study (21) also showed that a comparable number of patients were in the same medication complexity burden score. In contrast, the current finding was higher than previous study done in Brazil (22). This discrepancy may be due to use of different cut-off points, the treatment approach of clinicians in line with guidelines, and the difference in socio-demographics of patients.

The current study has also demonstrated adherence level of patients to their medications based on the Adherence in Chronic Diseases Scale (ACDS), and the finding revealed that significant number of participants with multimorbidity had low level of adherence to their medications. Similarly, earlier studies done in Saudi Arabia (23), South India (24) and South Australia (25) showed that a significant number of study subjects have been in the level of low medication adherence. However, the finding was vary with the study done in Spain (26), which revealed that significant proportion of the study participants were in the range of high adherent level of medications. This variation potentially might be due to poor patients’ medication knowledge in the resource-limited setting and negative perceptions about medication, fear of side effects and high medication costs in patients with polypharmacy. In addition to this attributed to differences in the study setting, methods used to measure medication adherence, quality of service in the health facilities and physicians' approach to their patients that could bring differences in patients’ attitudes toward their medication.

The present study has identified the association of participants’ socio-demographic and clinical variables with the level of adherence. Consistently with the previous studies (27–31), the multivariable ordinal logistic model showed that monthly income, duration of the disease, Charlson comorbidity index (CCI) and the number of medications were found to have significant association with level of medication adherence in the current study. The Person Chi Square also showed that there is a significant association between medication regimen complexity and level of adherence. In contrast, a study in China (32) showed that age of the participants and alcohol drinking status were associated with level of adherence of medications. This difference could be attributed to the heterogeneity of the factors and relates to diseases, drugs and behavior that may affect the patient’s taking the drugs. For example, the treatment approach of clinicians and patients’ lifestyles may have a pivotal impact on the variability which might be relate with environmental and socio-cultural differences.

To better understand the complexity of adherence to multiple medications and in daily life, we need measures that can detect differences in adherence; between medications, individual persons and over time. An increasing number of patients with multimorbidity means that more and more patients are faced with complex medication regimens. Some of them including; the number of drugs, low-income level, duration of follow up and, the number of CCI were the only predictors of medication adherence in multimorbidity patients have been consistently shown to have a significant association (25, 32–35) On the other hand, participants’ age, level of education and social drug use (19, 36–38) were also associated with poor adherence. The difference could be due to variability in the study participants, adherence measurement tool, health care systems and policies, and knowledge, skill and patient care approaches of the health care professionals. This in turn is because; non-adherence is not limited to medication alone. It can also take many other forms, which include the failure to keep appointments, to follow recommended dietary or other lifestyle changes and to follow other aspects of treatment or recommended preventive health practices, which all can be the reason for the differences.

Consistently with the findings of the present study, another research conducted in Korea Hospital (39) showed that lower household income was significantly associated with increased odds of low level of adherence. The associated factor identified in this study was also largely consistent with findings with a previous large-scale territory-wide study using patient health records in the Chinese population (40) and Italian (41). Lower household income might be the reason of medication noncompliance due to affordability issues in patients with multimorbidity. However, studies done in Iran (42) showed that income was no significant association with medication adherence. We know that financial situation is an important determinant of health which negatively affecting health outcomes and contributing to health inequities. Patients with a lower income had a low level of adherence and low quality of life because most multimorbidity patients are medical treatment dependent and unable to afford treatment costs. Medication adherence is adversely affected by various factors such as patient-centered, therapy-related, social and economic, disease, and health care system factors (31). Hence, the identification of specific barriers for each patient and designing appropriate prevention strategies are indispensable to mitigate medication adherence.

In this finding treated with several medications (polypharmacy) was also significantly associated with non-adherence to medication. This is in line with the finding from Mangalore, in India (43). The association could be justifying those patients with polypharmacy may have poor medication adherence due to affordability issue and because of adverse effects related with potential drug-drug interactions. Therefore, patients with polypharmacy would be in close follow-up during in their course overtreatment. Medication non-adherence can happen potentially in patients with comorbidity have been taking polypharmacy for longer period of follow-up time. The numerous barriers to the effective use of medications in resources limited settings include; poor communication between the patient and physician, inappropriate and knowledge gap on medications, fears of adverse events, long-term therapy, polypharmacy, cost and access barrier (44).

Although medication regimen complexity and medication adherence are different outcome measures, it is believed that medication regimen complexity has been associated with non-adherence. The current study also disclosed that there is statistical relationship between medication regimen complexity and medication adherence level. Patients with multimorbidity have potentially treated with polypharmacy, which can be the reason of increased medication regimen complexity burden, in turn it could be result in poor medication adherence of patients with multimorbidity. Hence, patients with multimorbidity who have been treated with poly pharmacy could assess their level of medication regimen complexity and adherence level.

Strength and Limitation of the Study

Considering the recent burden of patients with multimorbidity; the present study has highlighted the level of medication regimen complexity and adherence, which is the first study in the study setting attempted to explore the burden of medication regimen complexity and its association with adherence in patients with multimorbidity management.

There were some limitations to the present study that need to be considered. First, it is a cross-sectional study design, whereby claims about the directionality of the causal relationship between the dependent and independent variables cannot be verified. Secondly, MRCI was calculated using only what was captured in the patients’ medical chart. As a result, any medications or instructions not recorded were missed out. Another limitation was the extent of generalizability may be limited, since it was a single-centered study. Despite the above limitations, we hope it might be a bench mark for the coming researchers in study area with larger population and follow-up study design. The finding from the current study may use as an input for clinicians, researchers and any stakeholders to their interest and context.

In this study, there was a high medication regimen complexity index. Overall, average adherence to regularly prescribed medications in multimorbidity individuals was suboptimal, with almost half of the study population classified as non-adherent. The number of drugs, income level, duration of follow-up and the number of CCI were identified predictors for a level of medication adherence in multimorbid individuals. Medication adherence is critical to improving chronic disease outcomes and reducing health care costs.

ACDS, Adherence of Chronic Disease Scale; ADE, Adverse Drug Event; CCI, Charlson Comorbidity Index; MCC, Multiple Chronic Conditions; MRC, Medication Regimen Complexity; MRCI, Medication Regimen Complexity Index; NCD, Non-Communicable Disease; pMRCI, Personal Medication Regimen Complexity Index; UoGCSH, University of Gondar Comprehensive Specialized Hospital

Ethics approval and consent to participate: Ethical clearance was obtained from the Ethical Review Committee of School of Pharmacy, University of Gondar. The respondents were informed about the purpose of the study and their consent to participate was obtained.

Availability of data and materials: All relevant data is present in the manuscript. But the remaining relevant documents are available from corresponding author up on reasonable request.

Authors contribution: All authors substantially contributed either to the concept or design of the work; or acquisition, analysis, interpretation of data and drafted the manuscript or revised. All authors approved the final manuscript.

Competing interests: The authors reported there is no conflicts of interest.

Funding: No financial support was gained to conduct this study.

Acknowledgments: We would like to thank the University of Gondar for funding this research project. We are also very grateful to data collectors and the study participants.

Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. The Lancet. 2012;380(9836):37-43.
Roughead EE, Vitry AI, Caughey GE, Gilbert AL. Multimorbidity, care complexity and prescribing for the elderly. Aging health. 2011;7(5):695-705.
Makovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. Multimorbidity and quality of life: Systematic literature review and meta-analysis. Ageing research reviews. 2019;53:100903.
Katusiime B, Corlett S, Reeve J, Krska J. Measuring medicine-related experiences from the patient perspective: a systematic review. Patient related outcome measures. 2016;7:157.
Ferreira JM, Galato D, Melo AC. Medication regimen complexity in adults and the elderly in a primary healthcare setting: determination of high and low complexities. Pharmacy practice. 2015;13(4).
Cobretti MR, Page RL, II SAL, Deininger KM, Ambardekar AV, Lindenfeld J, et al. Medication regimen complexity in ambulatory older adults with heart failure. Clinical interventions in aging. 2017;12:679.
Mansur N, Weiss A, Beloosesky Y. Looking beyond polypharmacy: quantification of medication regimen complexity in the elderly. The American journal of geriatric pharmacotherapy. 2012;10(4):223-9.
Witticke D, Seidling HM, Lohmann K, Send AF, Haefeli WE. Opportunities to reduce medication regimen complexity. Drug safety. 2013;36(1):31-41.
Ghidei L, Simone MJ, Salow MJ, Zimmerman KM, Paquin AM, Skarf LM, et al. Aging, antiretrovirals, and adherence: a meta analysis of adherence among older HIV-infected individuals. Drugs & aging. 2013;30(10):809-19.
Advinha AM, de Oliveira-Martins S, Mateus V, Pajote SG, Lopes MJ. Medication regimen complexity in institutionalized elderly people in an aging society. International journal of clinical pharmacy. 2014;36(4):750-6.
Osterberg L, Blaschke T. Adherence to medication N Engl J Med (Vol. 353, pp. 487-497). United States. 2005.
Fischer MA, Stedman MR, Lii J, Vogeli C, Shrank WH, Brookhart MA, et al. Primary medication non-adherence: analysis of 195,930 electronic prescriptions. Journal of general internal medicine. 2010;25(4):284-90.
Peterson AM, Takiya L, Finley R. Meta-analysis of trials of interventions to improve medication adherence. American Journal of Health-System Pharmacy. 2003;60(7):657-65.
Sabaté E, Sabaté E. Adherence to long-term therapies: evidence for action: World Health Organization; 2003.
Kubica A, Kosobucka A, Michalski P, Pietrzykowski Ł, Jurek A, Wawrzyniak M, et al. The Adherence in Chronic Diseases Scale—a new tool to monitor implementation of a treatment plan. Folia Cardiol. 2017;12(1):19-26.
George J, Phun Y-T, Bailey MJ, Kong DC, Stewart K. Development and validation of the medication regimen complexity index. Annals of Pharmacotherapy. 2004;38(9):1369-76.
Yeh A, Shah-Manek B, Lor KB. Medication regimen complexity and A1C goal attainment in underserved adults with type 2 diabetes: a cross-sectional study. Annals of Pharmacotherapy. 2017;51(2):111-7.
Kassahun T, Gesesew H, Mwanri L, Eshetie T. Diabetes related knowledge, self-care behaviours and adherence to medications among diabetic patients in Southwest Ethiopia: a cross-sectional survey. BMC endocrine disorders. 2016;16(1):28.
Ambaw AD, Alemie GA, SM WY, Mengesha ZB. Adherence to antihypertensive treatment and associated factors among patients on follow up at University of Gondar Hospital, Northwest Ethiopia. BMC public health. 2012;12:282.
Melchiors AC, Correr CJ, Fernández-Llimos F. Tradução e validação para o português do Medication Regimen Complexity Index. Arquivos brasileiros de cardiologia. 2007;89(4):210-8.
Patel AY, Shah P, Flaherty JH. Number of medications is associated with outcomes in the elderly patient with metabolic syndrome. Journal of geriatric cardiology: JGC. 2012;9(3):213.
Barreto MdS, Reiners AAO, Marcon SS. Knowledge about hypertension and factors associated with the non-adherence to drug therapy1. Revista latino-americana de enfermagem. 2014;22:491-8.
Khayyat SM, Mohamed MM, Khayyat SMS, Alhazmi RSH, Korani MF, Allugmani EB, et al. Association between medication adherence and quality of life of patients with diabetes and hypertension attending primary care clinics: a cross-sectional survey. Quality of Life Research. 2019;28(4):1053-61.
Atal S, Ray A, Ahmed SN, Singh P, Fatima Z, Sadasivam B, et al. Medication adherence, recall periods and factors affecting it: A community‐based assessment on patients with chronic diseases in urban slums. International Journal of Clinical Practice. 2021:e14316.
McCaffrey N, Kaambwa B, Currow DC, Ratcliffe JJH, outcomes qol. Health-related quality of life measured using the EQ-5D–5L: South Australian population norms. 2016;14(1):1-12.
Fernandez-Lazaro CI, García-González JM, Adams DP, Fernandez-Lazaro D, Mielgo-Ayuso J, Caballero-Garcia A, et al. Adherence to treatment and related factors among patients with chronic conditions in primary care: a cross-sectional study. BMC family practice. 2019;20(1):1-12.
Emrani Z, Akbari Sari A, Zeraati H, Olyaeemanesh A, Daroudi R. Health-related quality of life measured using the EQ-5D–5 L: population norms for the capital of Iran. Health and Quality of Life Outcomes. 2020;18(1):108.
Alefishat E, A SJ. Factors affecting health-related quality of life among hypertensive patients using the EQ-5D tool. 2020;74(9):e13532.
Peters M, Kelly L, Potter CM, Jenkinson C, Gibbons E, Forder J, et al. Quality of life and burden of morbidity in primary care users with multimorbidity. Patient related outcome measures. 2018;9:103-13.
Gnjidic D, Le Couteur DG, Hilmer SN. Discontinuing drug treatments. BMJ (Clinical research ed). 2014;349:g7013.
de Vries ST, Keers JC, Visser R, de Zeeuw D, Haaijer-Ruskamp FM, Voorham J, et al. Medication beliefs, treatment complexity, and non-adherence to different drug classes in patients with type 2 diabetes. Journal of psychosomatic research. 2014;76(2):134-8.
de Figueiredo RM, Sinkoc VM, Tomazim CC, Gallani MCBJ, Colombrini MRCJRL-AdE. Adherence of AIDS patients to treatment with antiretroviral drugs in a university hospital: difficulties observed and suggestions of interventions. 2001;9(4):50-5.
Buchholz I, Janssen MF, Kohlmann T, Feng YS. A Systematic Review of Studies Comparing the Measurement Properties of the Three-Level and Five-Level Versions of the EQ-5D. PharmacoEconomics. 2018;36(6):645-61.
Zhang S, Meng L, Qiu F, Yang JD, Sun S. Medication-related risk factors associated with health-related quality of life among community-dwelling elderly in China. Patient preference and adherence. 2018;12:529-37.
Audi G, Korologou A, Koutelekos I, Vasilopoulos G, Karakostas K, Makrygianaki K, et al. Factors Affecting Health Related Quality of Life in Hospitalized Patients with Heart Failure. Cardiol Res Pract. 2017;2017:4690458-.
Khayyat SM, Mohamed MMA, Khayyat SMS, Hyat Alhazmi RS, Korani MF, Allugmani EB, et al. Association between medication adherence and quality of life of patients with diabetes and hypertension attending primary care clinics: a cross-sectional survey. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation. 2019;28(4):1053-61.
Yuvaraj K, Gokul S, Sivaranjini K, Manikandanesan S, Murali S, Surendran G, et al. Prevalence of medication adherence and its associated factors among patients with noncommunicable disease in rural Puducherry, South India - A facility-based cross-sectional study. Journal of family medicine and primary care. 2019;8(2):701-5.
Walsh CA, Bennett KE, Wallace E, Cahir C. Identifying Adherence Patterns Across Multiple Medications and Their Association With Health Outcomes in Older Community-Dwelling Adults With Multimorbidity. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2020;23(8):1063-71.
Cho J, Shin S, Jeong YM, Lee E, Lee EJIJoER, Health P. The effect of regimen frequency simplification on provider order generation: A quasi-experimental study in a Korean hospital. 2021;18(8):4086.
Wong MC, Liu J, Zhou S, Li S, Su X, Wang HH, et al. The association between multimorbidity and poor adherence with cardiovascular medications. International journal of cardiology. 2014;177(2):477-82.
Gnjidic D, Le Couteur DG, Hilmer SNJB. Discontinuing drug treatments. British Medical Journal Publishing Group; 2014.
Zare F, Ameri H. Health-related quality of life and its associated factors in patients with type 2 diabetes mellitus. 2020;8:2050312120965314.
Maddigan SL, Farris KB, Keating N, Wiens CA, Johnson JA. Predictors of older adults' capacity for medication management in a self-medication program: a retrospective chart review. Journal of aging and health. 2003;15(2):332-52.
Kardas P, Lewek P, Matyjaszczyk M. Determinants of patient adherence: a review of systematic reviews. Frontiers in pharmacology. 2013;4:91.

No competing interests reported.

succinctsummary.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Medication Regimen Complexity and Medication Adherence among Patients with Multimorbidity Treated at University of Gondar Compressive Specialized Hospital

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Method And Materials

Study design, setting and period

Study participants and eligibility criteria

Sample size determination and sampling techniques

Operational definition

Data Collection Instruments and Procedures

Data Quality Assurance

Data Management and Statistical Analysis

Ethical considerations

Results

Socio-demographic and Clinical Characteristics of Study Participants

Medication Regimen Complexity and Adherence Levels

Predictors of Adherence

Association between MRCI and Adherence level

Discussion

Strength and Limitation of the Study

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1