INTRODUCTION:

Older adults are one of the fastest-growing populations in developing countries like Malaysia. The number of older adults above 65 years old is projected to reach 2.0 million in 2022 and 6.0 million by 2040 in Malaysia¹. By the year 2050, Malaysia will have at least 43 million older adults compared to 11.6 million at the moment, equivalent to a growth of 6.5% to 15.8% of the total Malaysian population².

Medication adherence (MA) is defined as the extent to which a patient's behaviour corresponds with the prescribed medication. MA improves patients’ wellbeing and decreases healthcare costs. In contrast, medication non-adherence in patients has been associated with substantial worsening of disease, death, and increased healthcare costs. According to the World Health Organization (WHO), MA among patients with chronic illness in developed nations was only about 50%³. This may result in poor treatment outcomes, higher morbidity and mortality rates, as well as unnecessary healthcare costs.

Multiple uses of medications (polypharmacy) among geriatric patients are common to treat various illnesses such as diabetes mellitus, hypertension, and dyslipidaemia as well as to prolong life expectancy and improve quality of life⁴. In clinical research settings, non-adherence is a common problem in deciphering viable treatments. As high as 40% of patients fail to comply with their medication due to the complexity of the recommended regimen⁵. In addition, almost three-quarters (70%) of the patients showed medication non-adherence due to lifestyle changes and different needs⁶.

MA is often measured using various tools. In Malaysia, the Malaysian MA Scale (MALMAS) and the Malaysia MA Assessment Tool (MyMAAT) are common validated instruments used for assessing MA. The General Medication Adherence Scale (GMAS) is a newly validated tool. It is considered more sensitive and does not involve any licensing cost^7,8.

Non-adherence is a serious problem that affects the patient and the healthcare system. Therefore, several studies have investigated the issue and prevalence of MA in geriatric patients in many developed nations⁹. However, only a few studies have been conducted to assess the problem and the extent of MA among geriatric patients^10,11,12. There is also a lack of studies on the factors contributing to the human and economic costs associated with non-adherence. Hence, this study aimed to assess the prevalence of MA in geriatric patients and its associated factors.

MATERIALS AND METHODS:

This was a cross-sectional study among elderly patients who visited a primary healthcare centre. The sample size was estimated based on a 50% prevalence, a 95% confidence interval (CI), and a 5% error margin. Accordingly, the sample size required was 250 patients using Cochran’s (1977) formula. A random sampling technique was used to select respondents at the Seremban Health Clinic. Study participants who consented were recruited. Their outpatient medical records were retrieved. The inclusion criteria were: patients above 60 years old, patients with chronic illness or co-morbid diseases, and patients on long-term medication for more than six months at the time of recruitment. Subjects with the following conditions were excluded: newly diagnosed patients (less than six months), unable to communicate verbally, and not accompanied by a family member or caretaker. Written informed consent was obtained from all the respondents. The data collection form strictly observed confidentiality by using a unique serial number for each respondent. Ethical approval was obtained from the National Research Register (NMRR) of Malaysia (NMRR-20-770-52872) and the Medical Research and Ethics Committee (MREC).

Survey instruments:

The GMAS is a novel, adherence reporting instrument that has been validated in the Malaysian population. This scale is intended to measure three crucial factors that affect adherence: patient behaviour, pill burden and comorbidities, as well as cost^7,8. It contains a total of 11 questions and each question has four answers. A higher total score indicates higher adherence, thus the total score ranges from 0-33. The final score is stratified into five groups: poor (≤ 10), low (11–16), partial (17–26), good (27–29), and high (30–33). Permission to use this instrument was obtained before commencing this study.

Statistical analysis:

Data analysis was conducted using the Statistical Package for Social Sciences (SPSS) (v. 26.0 IBM Corp.). A normality test was performed. A Kolmogorov-Smirnov test indicated that the overall MA did not follow a normal distribution, D (250) = 045, p < 0.005. Thus, the data were analysed using non-parametric tests. Both descriptive and inferential statistics were used. In addition, Cronbach’s alpha (α) value was calculated to assess the reliability of GMAS. A value of α< 0.7 was considered acceptable.

RESULTS:

Participants Characteristics:

The baseline demographic characteristics of the study participants are shown in Table 1. The median age was 69.0 while the mean was 64.3+7.32. Table 2 shows the clinical characteristics of the participants. The respondents’ mean number of illnesses/diseases was 2.98+0.978 with a range of one illness (n=14,5.6%) to seven (n=1, 0.4%). The majority of them had three types of illness/disease states (n=127, 50.8%). As for the prescribed medication, Figure 5 shows that most of the patients were given five types of medication (Figure 1). This was not surprising as most of the patients had multiple medical illnesses, thus different classes of drugs were prescribed either as monotherapy or in combination.

Medications from nine different therapeutic classes were prescribed to the patients as shown in Figure 2. The majority of them were diagnosed with hypertension (n= 230), followed by dyslipidaemia (n=215) and diabetes mellitus (n=158). A few of them had a history of ST-segment elevation myocardial infection (STEMI), heart failure, hypertriglyceridemia, epilepsy, and musculoskeletal issues. The most commonly prescribed therapeutic classes were cardiovascular medications (n=671) and antidiabetic medications (n= 268). Under the cardiovascular therapeutic class, hypertension medication such as amlodipine (n= 160), simvastatin (n=194), and perindopril (n=114) was most prescribed. As for antidiabetic medication, metformin (n=126) was the commonest. The medications stated were commonly prescribed in patients taking less than three medications, either as monotherapy or a combination. In addition, medications from the therapeutic class of vitamins, minerals, and supplements such as vitamin B complex (n=72), potassium chloride (n=23), and calcium carbonate (n=21) were commonly prescribed as well. The Vitamin B complex is a combination of iron, folic acid, and vitamin B12. It can be either prescribed alone or in combination with haematinics to stimulate the formation of red blood cells.

Table 1: Demographic characteristics of the study participants (n=250)

Characteristics

N (%)

Gender (n=250)

Male

Female

112 (44.8)

138 (55.2)

Age (n=250)

60-70

71-80

>81

123 (49.2)

88 (35.2)

39 (15.6)

Ethnicity (n=250)

Malay

Indian

Chinese

Others

63 (25.2)

98 (39.2)

81 (32.4)

8 (3.2)

Education Status (n=250)

No Education

Primary

Diploma

Degree/Masters/ PhD

73 (29.2)

136 (54.4)

9 (3.6)

32 (12.8)

Family Income (n=250)

<1000

1000-10000

>10000

148 (59.2)

100 (40.0)

2 (0.8)

Family Status (n=250)

Alone

With Spouse

With Family

With Caretaker

51 (20.4)

94 (37.6)

88 (35.2)

17 (6.8)

Table 2: Clinical characteristics of study participants (n=250)

Characteristics

N (%)

Functional Status

No Impairments

Visual Impairments

Hearing Impairment

Memory Impairment

Physical Impairment

200 (80.0)

2 (0.8)

13 (5.2)

5 (2.0)

30 (12.0)

Number of Illness

Single

Multiple

14 (5.6)

236 (94.4)

Number of Medication

Up to 3

40 (16.0)

210 (84.0)

Awareness of Illness

Yes

246 (98.4)

4 (1.6)

Awareness of Side Effects

Yes

155 (62.0)

95 (38.0)

Medication Use affected due to COVID-19

Yes

224 (89.6)

26 (10.4)

MA Among the Study Participants

Figure 1: Distribution of the number of medications per patient

Figure 2: Distribution of medication according to therapeutic class

Figure 2. Different Therapeutic Classes

The Cronbach’s alpha coefficient for the GMAS was 0.741. The α value for each domain was 0.699 (PBNA), 0.725 (ADPB), and 0.709 (CRNA), respectively. The corrected item-total correlation of each question was between 0.123 and 0.554. The level of adherence to the prescribed medications as assessed by GMAS is summarised in Figure 3. Most of the patients had good to high adherence levels, with 124 patients showing a high adherence level 67 showing good adherence. Partial adherence and low adherence were observed among 55 and 4 patients respectively. Table 3 shows the level of MA according to the GMAS constructs.

Table 3: GMAS construct scores

GMAS	Mean ± SD	Median (IQR)	Level of Adherence
Patient behaviour-related non-adherence (PBNA)	13.34 ± 1.91	14 (12-15)	High
Additional disease and pill burden (ADPB)	10.54 ± 1.74	11 (10-12)	High
Cost-related non-adherence (CRNA)	4.60 ± 1.63	5 (4-6)	High
Total overall score (GMAS-11)	28.5 ± 3.92	29 (28-31)	Good-high

Figure 3: Level of MA among study participants

The chi-square test was conducted to determine the associated factors of MA among gender, family income, family status, awareness of illnesses, and medication use affected by COVID-19. Gender was associated with MA, with females showing higher adherence. Patients with high-income levels were associated with high adherence. Similarly, patients with spouse or family support had a high adherence level (Table 4). Patients with awareness of the illness and those whose medication use was not affected by COVID-19 also showed significantly higher adherence levels (Table 5). Even though not statistically significant, study participants with physical impairment were comparatively more adherent to medication, as well as those who were taking three or fewer types of medications.

Table 4: Demographic factors associated with MA scale

Characteristics

Adherence Level (n=250)

p-value

Low Adherence

(n= 4)

Partial Adherence

(n=55)

Good Adherence

(n=67)

High Adherence

(n=124)

Gender

Male

Female

2 (1.8)

23 (20.5)

32 (23.2)

40 (35.7)

27 (19.6)

47 (42.0)

77 (55.8)

0.034

Age

60-70

71-80

>81

0 (0.0)

3 (3.4)

1 (2.6)

24 (19.5)

17 (19.3)

14 (35.9)

34 (27.6)

24 (27.3)

9 (23.1)

65 (52.8)

44 (50.0)

15 (38.5)

0.146

Ethnicity

Malay

Indian

Chinese

Others

0 (0.0)

2 (2.0)

2 (2.5)

0 (0.0)

9 (14.3)

32 (32.7)

12 (14.8)

2 (25.0)

23 (36.5)

20 (20.4)

22 (27.2)

2 (25.0)

31 (49.2)

44 (44.9)

45 (55.6)

4 (50.0)

0.089

Education Status

No Education

Primary

Diploma

Degree/Masters/ Ph.D

3 (4.1)

1 (0.7)

0 (0.0)

23 (31.5)

22 (16.2)

1 (11.1)

9 (28.1)

18 (24.7)

39 (28.7)

3 (33.3)

7 (21.9)

29 (39.7)

74 (54.4)

5 (55.6)

16 (50.0)

0.155

Family Income

<1000

1000-10000

>10000

4 (2.7)

0 (0.0)

38 (25.7)

17 (17.0)

0 (0.0)

46 (31.1)

21 (21.0)

0 (0.0)

60 (40.5)

62 (62.0)

2 (100.0)

0.023

Family Status

Alone

With Spouse

With Family

With Care Taker

1 (2.0)

0 (0.0)

2 (2.3)

1 (5.9)

19 (37.3)

22 (23.4)

12 (13.6)

2 (11.8)

12 (23.5)

27 (28.7)

22 (25.0)

6 (35.3)

19 (37.3)

45 (47.9)

52 (59.1)

8 (47.1)

0.049

Table 5: Clinical Characteristics associated with MA scale

Characteristics

Adherence Level (n=250)

P-value

Low Adherence

(n= 4)

Partial Adherence

(n=55)

Good Adherence

(n=67)

High Adherence

(n=124)

Functional status

No Impairments

Visual Impairments

Hearing Impairment

Memory Impairment

Physical Impairment

2 (1.0)

0 (0.0)

2 (6.7)

44 (22.0)

0 (0.0)

4 (30.8)

3 (60.0)

4 (13.3)

54 (27.0)

1 (50.0)

4 (30.8)

1 (20.0)

7 (23.3)

100 (50.0)

1 (50.0)

5 (38.4)

1 (20.0)

17 (56.7)

0.369

No. of Illness

Single

Multiple

1 (7.1)

3 (1.3)

3 (21.4)

52 (22.0)

3 (21.4)

64 (27.1)

7 (50.1)

117 (49.6)

0.390

Number of medication

Up to 3

1 (2.5)

3 (1.4)

6 (15.0)

49 (23.3)

10 (25.0)

57 (27.1)

23 (57.5)

101 (48.2)

0.581

Awareness of Illness

Yes

4 (1.6)

0 (0.0)

52 (21.1)

3 (75.0)

66 (26.8)

1 (25.0)

124 (50.5)

0 (0.0)

0.054

Side effects

Yes

3 (1.9)

1 (1.1)

31 (20.1)

24 (25.3)

43 (27.7)

24 (25.3)

78 (50.3)

46 (48.3)

0.751

Medication use affected by Covid-19

Yes

4 (1.8)

0 (0.0)

54 (24.1)

1 (3.8)

62 (27.7)

5 (19.3)

104 (46.4)

20 (76.9)

0.021

DISCUSSION:

This study assessed the level of MA among 250 geriatric patients attending follow-up at a primary healthcare clinic. A good internal consistency of the tool used, GMAS was reported (α = 0.741). According to previous research, 44-95% of geriatric patients were found to be non-adherent to medication^13,14. Our study participants reported a high level of MA. The proportion of ‘high adherence’ respondents was about half (49.6%) while another one-quarter showed ‘good adherence’. Only a small proportion of them had ‘partial’ (22.0%) and ‘low’ adherence (1.6%). This echoed the findings of studies from other countries with moderate to high adherence levels ranging from 60% to 80% ^9,15,16. However, a few local studies^10,17,18. Reported ‘medium’ and ‘low’ adherence levels. The higher MA in this study could be attributed to the study setting. The study site was a primary healthcare centre in an urban area of the capital town of Seremban, Negeri Sembilan. The clinic is better equipped at providing a wide range of healthcare services, such as individual counselling, MA Therapy Clinic (MAT), monthly campaigns, as well as awareness and focus group discussions that can improve MA to achieve optimal therapeutic outcomes.

Furthermore, a collaborative framework between physicians and pharmacists has been established to facilitate the promotion of MA among patients in most public hospitals in Malaysia. Physicians will refer selected patients who have issues with adherence to counselling and MTAC. Pharmacists attached to MTAC will routinely review patients’ medications, provide counselling, and work closely with doctors to address any pharmaceutical care issues (PCIs). Studies have shown that diabetic MTAC counselling improved glycaemic control, MA, and quality of life in patients with type 2 diabetes^19,20. Thus, these services could be the reason behind a high level of MA among our study participants in this clinic.

In terms of sociodemographic characteristics, patients who were older, female, had low family income (less than RM 1000), and stayed with family had better adherence to their medication. This research was conducted amid the COVID-19 pandemic. In view of the significant impact worldwide in numerous ways, including the increased mortality risk among elderly patients, the study participants were asked if the pandemic had a favourable or negative influence on their medication administration. The findings revealed no statistically significant correlation between MA and the influence of the pandemic, similar to another recently published study¹⁸.

In the literature, many studies reported differently on the determinants of MA in the elderly and the effects of age, gender, knowledge, attitudes, and co-morbidities on MA¹⁶. In this study, the significant determinants were gender, family income, family status, awareness of illnesses, and the effects of medication use due to Covid-19. Females reported a better MA than males in this study. This was in line with another study by Ramli et al. that showed a substantially lower rate of adherence to hypertensive medication among male patients²¹. In contrast, another two studies by Punnapurath et al.¹⁶ and Shruthi et al. showed that males had better adherence levels⁹. Identifying the patient population with poorer MA allows for more focused efforts toward improvement. Based on this study, male and extremely elderly patients (over 70 years old) should be the target groups for interventions intended to increase MA.

Many other research findings have revealed that patients, especially elderly patients would miss or consume reduced medication dosages. Some even forgo the medications as they could not pay for them. Medication cost is a barrier to MA^9,22. However, in this study, patients with lower incomes had higher adherence levels. This was concurred by another local study whereby Lee et al. explained that financial pressure alone did not affect adherence^17,23. This could be attributed to the fact Malaysia’s health system offers universal coverage for all patients and medical access including medications is given for free to all senior citizens and at a highly subsidised cost to all other patients. With regard to the medication cost, two studies revealed a significant influence of medication cost on people’s behaviour in various ways, including MA^17,24. Self-paying patients were more likely to indicate that cost had a significant effect on reducing non-adherence to their medication²⁴. However, in this study, medication cost was unlikely to be a factor in non-adherence since all patients did not have to pay for their medicines. Future research can compare the prevalence of non-adherence between compare subsidised and self-paying patients.

Family or social support has been linked with a good level of MA, especially in patients suffering from chronic illnesses^9,25,26,27. In this study, patients staying with spouses or family reported significantly better MA. However, no association between family status and MA was observed in other studies^22,28. In addition, patients with multiple illnesses and taking more than three types of medication had better adherence to their medication. This could be due to a higher level of awareness about their health^15,22,29. Similarly, the majority of the patients (96.1%) were aware of their illnesses. Hence, they were more likely to adhere to their medications as they were aware of the outcomes. Due to the COVID-19 pandemic and the ensuing lockdown and movement restrictions, patients with chronic diseases did not manage to receive continuous care. Failure to obtain prescription refills and follow-up appointments has been shown to result in non-adherence³⁰. Therefore, pharmacists stressed the necessity for patients with chronic illnesses to receive their medication refills to ensure continuity of supply by sending their prescriptions through email^16,31. Some aspects of non-adherence were caused by sociocultural issues, financial problems, stigma, care fragmentation, and poor insight^32-36.

In Malaysia, Value Added Services (VAS) has been introduced by the Pharmaceutical Services Programme, Ministry of Health (MOH) in 2011. It was designed for patients to get their medicines without having to wait at the pharmacy. VAS makes the registration and delivery of follow-up drugs at MOH health facilities easier and faster. In addition, it can also facilitate the staff in pharmacy facilities to manage the registration and preparation of the medicine more systematically. In this study, even though 96% of the patients claimed to be affected by the pandemic, their adherence remained good. Initiatives such as VAS and the usage of technology to deliver medications and prescription refills to patients’ homes were likely contributing factors that enhanced MA.

The study may be susceptible to several limitations. As a single-centre study, the findings cannot be generalised to the entire population. In the future, multi-centre research may be conducted in various healthcare facilities across different states in Malaysia.

CONCLUSION:

In this study, we observed that the geriatric population had good MA levels. However, the results cannot be generalised to the entire Malaysian geriatric population. In addition, it is important to improve MA based on the associated factors identified in this study to ensure an optimal effect of the medications. Future studies should consider a combination of subjective and objective measurements to evaluate MA more effectively among geriatric populations to delineate unknown aspects that may lead to non-adherence.

ACKNOWLEDGMENTS:

The authors would like to thank the Director-General of Health Malaysia for the permission to publish this paper. Our special thanks to the late Professor Dr. Mohamed Azmi Hassali for his invaluable contribution and guidance. The authors are grateful to Dr. Atta Abbas Naqvi for the permission to use the English and Malay version of the GMAS tool. Also, thanks to all patients involved in this study.

DISCLOSURE:

The authors declared no conflict of interest in this work.

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Received on 07.10.2022 Modified on 01.12.2022

Research J. Pharm. and Tech 2023; 16(9):4172-4178.

DOI: 10.52711/0974-360X.2023.00683