1. Introduction
According to the United Nations, the elderly population is defined as people who are 60 years or older, and it is predicted to expand by 56% globally between 2015 and 2030, with the most rapid growth in cities of developing countries [
1]. The global population of those aged 60 years were over 382 million in 1980 and increase to 962 million in 2017 worldwide. In Malaysia, the elderly population prevalence is expected to increase from 9.2% in 2015 to 23.6% by year 2050. This is expected to put a significant pressure on the country’s healthcare systems. [
2].
The Malaysian National Health and Morbidity Survey (NHMS) 2015 observed that the disease trend among the elderly has shifted from aging-related diseases to lifestyle-related diseases, such as, hypertension, hypercholesterolemia, chronic obstructive pulmonary disease, and Type 2 Diabetes Mellitus (T2DM) [
2]. T2DM is becoming more common worldwide due to increased life expectancy and lifestyle changes [
3]. Globally, the number is expected to increase from 220 million in 2010 to 300 million by 2025. In Malaysia, according to the latest report from NHMS, showed an increasing prevalence trend of patients with T2DM, from 13.4% in 2015 to 18.3% in 2019 and it is increasing with age. The prevalence was reported to peak at 34.3% among people aged 65 to 69 years old [
4].
The elderly is in poorer health than the youth, as the debilitating effects of numerous diseases are compounded with the physical and social changes associated with ageing. Hence, an increase in the proportion of elderly is associated with an increase in the prevalence of diseases [
5]. More than 60% of adults will have two or more chronic diseases by the age of 65, a condition known as multimorbidity, while more than 25% will have four or more chronic diseases and over 10% will have six or more chronic diseases [
6]. The presence of multimorbidity increases the complexity of therapeutic management and it has a detrimental impact on health outcomes [
7]. Elderly with T2DM pose a particular challenge to clinicians concerning managing medications as they are prescribed more medicines and are exposed to more drug interactions than their non-T2DM counterparts. Lim et al. supported that the number of medications used by elderly with endocrine diseases was higher than those with cardiovascular diseases [
8]. Multiple drugs not only increase the cost and complexity of therapeutic regimens, but also increase the risk of adverse drug reactions [
7]. While nonpharmacological approaches for managing diabetes and its complications are important, medication remains the cornerstone of management. As a result, there are a number of strong factors that favor multiple drugs or polypharmacy in diabetic patients [
7].
Polypharmacy is described as the use of five or more drugs. Other definitions of polypharmacy involve duration of therapy either 90 days to 239 days or ≥240 days or more (‘long term use’) [
9]. Polypharmacy was found to be 45.9% common among urban community-dwelling elderly in Malaysia, with 576 people out of 1256 are using at least five drugs. In total, 499 (86.6%) of those with polypharmacy exposure received five to nine medications, 65 (11.3%) received 10 to 14 medications, and 12 (2.0%) received 15 or more [
8]. Physiological changes in the ageing kidney, memory problems and different treatment regimens all add to the difficulty of therapy [
5]. It raises the likelihood of adverse drug reactions through altering medication absorption, distribution, metabolism and excretion [
10].
There is an increase in the patient’s frailty linked to the number of their medicines as frailty increases by 1.5 times with five or more drugs [
11]. Geriatric syndromes are common and they can be caused by using the inappropriate drugs [
12]. Even mild cognitive impairment might lead to care errors that can result in significant consequences [
13]. Elderly individuals with T2DM are also susceptible to orthostatic hypotension, insomnia and constipation with commonly used drugs, such as opioids, anticholinergic agents and αblockers [
7]. Thus, it is vital to develop strategies to limit the prescription of unnecessary medications in order to improve the QOL of diabetic patients. [
14]. Most studies found that people with diabetes have a lower QOL than those without diabetes [
15]. Thus, understanding and compliance regarding medication are critical for avoiding drug-related issues and subsequently leading to better QOL [
16].
Medication understanding (MU) is described as how patients feel, react and think about their medications [
17]. Patients’ misunderstanding of prescription medication instructions has been recognized as health literacy issue and factor affecting patient’s safety [
18]. Patients frequently misunderstand the correct dose of a prescription as well as the warnings and precautions associated with the medication [
19]. Medication errors frequently result from patients’ unintentional misuse of prescribed medication. Hence, there has been an emphasis on improving provider-patient communication around the topic of medication use. Simplifying prescription regimens, decreasing pill burdens and providing better explanations of the needs for the medications should be targeted for intervention to increase MU in the elderly. Among other factors, health literacy and self-efficacy (SE) have been repeatedly identified as predictors of one’s ability to understand medication instructions [
19].
Bandura (1986) defined SE as people’s judgements of their capabilities to organize and execute courses of action required to attain designated types of performances. SE is a significant indicator of adherence in all types of diseases [
20]. Multi-morbid primary care patients with lower SE and higher disease load have lower QOL [
21]. Awareness of SE levels among individuals with multi-morbidity may aid health professionals identify patients who require additional self-management support. Providing chronic disease self-management support has been hailed as a hallmark of good care. Higher SE may lead to enhanced QOL in multi-morbidity and promote positive behavioral health [
21,
22]. Low SE will lead to a decline in an individual’s differential association, differential reinforcement, imitation and definition. A decline in these social parameters will further lead to a decline in medications adherence and SE [
23]. Higher SE was linked to better glycemic control, medication adherence, self-care and mental health-related QOL [
24].
Research looking into the association of MU and SE had been conducted in other countries with various findings reported. Potentially, a study that is inclusive of factors known to impact diabetic QOL as a result of poor MU and SE may provide better knowledge on how MU and SE may affect diabetic QOL in the elderly. Bowen et al. also reported a relationship between QOL and SE. They propose that person with higher SE dealing with their diabetes had better QOL, and findings are in line with previous studies on the impact of SE on QOL [
25]. QOL is a significant health outcome in its own right, serving as the ultimate goal of all health interventions. Most studies found that persons with diabetes have a lower QOL than people without diabetes, particularly in terms of physical functioning and wellbeing. QOL measures should be used to coordinate and evaluate treatment interventions [
15].
Patients with T2DM have been shown to have a positive correlation between SE and QOL. MU, on the other hand, leads to better adherence, which indirectly improves QOL. In measuring SE in MU, it may be useful to ascertain changes in patient’s confidence related to medication use [
19]. To our knowledge, no research has been completed on the relationship between SE in MU or MUSE with QOL in elderly with T2DM on polypharmacy. Therefore, the objective of this study was to determine the level of SE in MU and QOL, the correlation between SE in MU and QOL and factors associated with QOL in elderly with T2DM on polypharmacy.
2. Materials and Methods
2.1. Study Design and Setting
A cross-sectional study was carried out at an institutional Primary Care Specialist Clinic (PCSC) in Selangor from December 2019 to November 2020. Services provided here include walk-in clinics for health screening, acute ailments, and appointment-based clinics for follow-up of chronic diseases. In addition, the clinic has access to radiological imaging, laboratory and referral services to other specialties. An average of 80 patients attending this clinic per day. We selected this clinic because it was located in an urban area with heavy patients’ load, including elderly patients with T2DM. This PCSC was run by family medicine specialists, consultants and postgraduate doctors pursuing a Master’s degree in family medicine from the institution. The vast majority of the patients are from Klang Valley.
2.2. Sampling Frame
The study population were elderly patients with T2DM on polypharmacy who received care at the institutional PCSC. The inclusion criteria were patients aged ≥60 years old, intact cognitive function, have been diagnosed with T2DM for at least one-year duration, received follow up care at institutional PCSC for at least once within the last one year, on polypharmacy which is ≥5 medications and was able to read and communicate in either Malay or English language.
2.3. Sampling Method
Convenience sampling of elderly who attended the institutional PCSC for follow-up were screened for eligibility through their medical record system. The researcher approached and explained to patients while waiting for their clinic consultation. Later, the researcher invited those eligible to participate in the study using inclusion and exclusion criteria.
2.4. Study Tools
Mini-Cog is a brief measure for assessing cognitive impairment in our participants as one of the inclusion criteria is intact cognitive function. It was developed and validated by Borson et al. with a sensitivity of 99% and specificity of 93%. It consists of a 3-item recall (1 point for each word) and clock drawing (2 points for a normal clock). The 3-item recall and clock drawing scores together with a total score of ≥3 indicate a lower likelihood of dementia [
26].
A standardized form was used to collect sociodemographic and clinical data information. Part A consisted of age, gender, ethnic group, marital status, educational level and household income. Part B: clinical data included the number of prescribed medications, duration of T2DM, other co-morbidities and modalities of treatment, whether it is ‘pills’, a mix of ‘pills and insulin’ or ‘combined with other modalities’ such as Meter Dose Inhaler (MDI). Part C consisted of two sets of questionnaires that had been translated and validated: The Self Efficacy in Medication Understanding (MUSE) Malay version (27) and the Revised Version of Diabetic Quality of Life (RVDQOL-13) Malay version (28).
The MUSE is a brief, valid and reliable research questionnaire which can be used in clinical practice and research to evaluate patients’ understanding and use of prescription medication. The MUSE provides a more general approach to measuring self-efficacy in medication use than existing disease- or context-specific measures. MUSE assesses self-efficacy in medication use and it also emphasizing the importance of patient medication understanding. This scale was developed originally from two subscales of the CASE-Cancer measure [
27]; these subscales were supplemented with additional items intended to reflect participants’ understanding of and confidence in taking their prescription medications. The MUSE Malay version [
28] was chosen to measure this study population’s understanding and SE levels because of the language used and its good reliability (Cronbach’s α of 0.89). The self-administered questionnaire consisted of an 8-item scale to assess patient’s self-efficacy in learning about and taking medications with a four-point Likert scale, with 1 = strongly disagree, 2 = slightly disagree, 3 = slightly agree, and 4 = strongly agree. Overall, the scores ranged from 8 to 32, with higher scores indicating greater levels of medication understanding.
The RVDQOL-13 Malay version is a self-administered questionnaire comprising 13 items with three domains measuring diabetic patients’ QOL (DQOL) [
29]. The three domains include ‘satisfaction’, ‘impact’ and ‘worry’. The Malay version of RVDQOL-13 has good composite reliability for each domain; “satisfaction” domain showed highest composite reliability of 0.922, followed by “worry” domain (0.794) and “impact” domain (0.781). Response choices of satisfaction are scored on a five-point Likert scale, with responses of 1 = very satisfied, 2 = moderately satisfied, 3 = neither satisfied nor dissatisfied, 4 = moderately dissatisfied and 5 = very dissatisfied in with a range score from 6 to 30, worry domain are scored from 1 = never, 2 = sometimes, 3 = often, 4 = frequently and “impact” domain are scored from 5 = always with range score from 4 to 20 and 3 to 15, respectively, giving a total score ranging from 13 to 65. The author proposed the score for each domain and the total score to be converted to percentage. Higher total scores indicate poorer quality of life.
2.5. Sample Size Calculation
Few sample sizes were calculated based on a few different prevalence according to the objectives. All the sample sizes were calculated using the Single Proportion formula based on the study’s objective. As a conclusion, the highest sample size was taken according to the variation in medication understanding among elderly (62%) by Spiers et al. [
30]. The confidence interval was taken as 95%, power 80%; the minimum sample required for the study was 321 patients. Considering a 10% non-response and non-eligible rate, this study aimed to approach at least 353 participants.
2.6. Data Collection and Study Procedure
The patients who attended their follow-up were screened for eligibility using the medical record system. The researcher approached eligible patients in the waiting area after they had their registration numbers. They were given a patient information leaflet outlining the study and its objectives. Consent was then obtained from those who were interested in participating and met the inclusion and exclusion criteria, which included intact cognitive function as measured by the Mini-Cog. Only one investigator was trained and involved in the study procedures before the conduct of the study to minimize variability in the method of data collection.
2.7. Questionnaires Administration
Participants were given a set of questionnaires containing sociodemographic and clinical characteristics, MUSE Malay and RVDQOL-13 Malay versions. Both verbal and written instructions were given on how to complete the questionnaires. They were asked to circle or mark the options that best suited them. Should any queries arise, participants were encouraged to seek clarification from the investigator at any time. Participants took an average of 10–20 min to complete the questionnaires. They handed the questionnaires to the researcher once they were finished, who double-checked the answers for completeness.
Figure 1 illustrates the conduct of the study.
2.8. Definition of Terms
The elderly was divided into three age groups. The young-old (60–69 years old), middle-old (70–79 years old), and very-old (over 80 years old). According to the Malaysian educational system, education levels are classified as follows: no formal education, primary school (standard 1–6; ages 7–12), secondary school (form 1–5; age 13–17), and tertiary education (college or university) [
31]. Household incomes were divided into three categories: low (B40) with a monthly household income of <RM 4850, middle (M40) with income between RM 4,850 and RM 10, 959, and high (T20) with a monthly household income of >RM 10960 [
32].
2.9. Statistical Analyses
Data were analyzed using the Statistical Package for the Social Science (SPSS) version 26.0 (IBM). All continuous variables were described as median (IQR) and number (n) and percentages (%) for dichotomous or nominal data. The levels of MUSE and DQOL were analyzed using median (IQR) as the data were not normally distributed. Relationships between MUSE and DQOL were analyzed using Spearman’s correlation test. Factors associated with DQOL amongst the study population were analyzed by simple linear regression (SLR) followed by multiple linear regression (MLR). Variables with a p value of less than 0.25 by SLR were then included in the MLR. A p value of less than 0.05 was considered statistically significant in the MLR.