Abstract
Background
Non-adherence to cardiovascular medications is a global problem with clinical, economic, and humanistic consequences. Investigation of this problem may open the road for proper management of cardiovascular diseases.
Objective
Our objectives were to assess the level of adherence to, and to examine factors influencing adherence to, cardiovascular medications in subjects visiting a heart center in Sudan.
Methods
We have conducted a cross-sectional study that assessed adherence to cardiovascular medications among subjects visiting outpatient cardiac clinics in a heart center-Khartoum State, Sudan. The validated Adherence to Refills and Medication Scale (ARMS) tool was used to assess the level of medication adherence. A score of >16 was used as a cut-off point to categorize surveyed patients into non-adherent (e.g., 17–48) and adherent (e.g., 12–16) in ARMS.
Descriptive statistics (frequencies and percentages) and inferential tests such as One-Way ANOVA and Binary regression were used for data analysis.
Results
A total of 255 subjects were enrolled in this study. Slightly more than half the respondents were males (54.5%) and their ages ranged between (51–60 years), and have no insurance coverage (58%). Most of the subjects were married (60.4%), were from Khartoum-State (67.8%), and were unemployed (77.6%). About 39.6% had primary education while 34.5% had secondary (34.5%) education. Diabetes (56.1%) and hypertension (45.5%) were common among the study population. The results showed a high non-adherence prevalence rate (97.6%). The cited reasons for non-adherence include forgetfulness and the costs of refilling medications. Education and age were associated with the level of adherence. Those with high non-adherence behaviors were the more educated, younger and old patients, those not employed, and those having comorbid diseases.
Conclusion
The results generally showed a high level of non-adherence to cardiovascular medications, necessitating interventions to support patients’ adherence.
Introduction
The World Health Organization (WHO) adherence project has adopted the following definition of adherence to long-term therapy: The person’s behavior in taking medication extent, following executing lifestyle changes and a diet, corresponds with agreed recommendations from a healthcare provider [1]. Cardiovascular diseases (CVDs) are a group of disorders of the heart, which include coronary artery disease, cerebrovascular disease (stroke), peripheral vascular disease, and congenital heart diseases. It is considered the first cause of death globally (Centers for Disease Control and Prevention [CDC], 2013) [2].
The WHO published a report in 2003 that, in developed countries, adherence rates are about 50% [3]. Adherence is a key factor influencing all pharmacological therapies’ effectiveness, particularly for chronic disease medications. In the United States, 33 to 69 percent of all medication-related hospital admissions are due to poor medication adherence [4]. A large-scale meta-analysis on 376,162 patients from 20 studies, estimated adherence to medication for the prevention of CVDs to be 57% (95% CI 50–64%) [5], these medications are prescribed for a range of long-term conditions including hypertension, dyslipidemia, and angina, among the most prescribed drugs. A study was conducted in Saudi to assess the adherence of heart failure patients using the Morisky -4 scale (MMAS-4) [6]. In Raffaa’s study [6], more than half of the patients had poor medication adherence, and only 7.3% had high adherence. Forty-nine percent forgot their medication at least once, and 34.4% had problems taking it. Another study from Saudi Arabia evaluated medication adherence among patients with chronic disease using the Refills and Medication Scale (ARMS). The results indicated a high percentage of non-adherence (96.62%), 51.9% were missing their medications when getting better, 72.2% forgot to take their medications, 59.2% forgot to take their medications more than once, 51.9% stopped their medications they got better, and 51.7% skipped their dose skipping before doctor visit [7]. A similar study from Latvia that assessed medication adherence levels among patients with hypertension, showed that non-adherence rate of 46.20% where patients with the oldest age and longer duration of disease were more adherent [8].
In a study from Saudi Arabia investigating medication adherence among hypertensive patients attending different primary health centers. The results showed that more than one third (36.3%) were showing high commitment, and the remaining (63.7%) were either in the low or medium range. Adherence was significantly related to age, being married, living in rural areas, and with income of 500 to 7000 Saudi Riyals [9]. In another study about predictors of medication adherence and blood pressure, control among hypertensive patients, the results were has shown that (54%) were non-adherent. Age, gender, being above 65, and being with diabetes were considered independent predictors of medication adherence. The number of patients investigated was 204 [10].
In Sudan, there are fewer studies in the field of medication adherence to chronic diseases, which necessitates investigation of this among patients, especially cardiovascular patients, due to the complexity and diversity of the disease. In a study done at a Sudanese teaching hospital, which evaluated adherence problems to secondary prevention medications among artery disease patients (210 patients), the results showed a 60.7% adherence rate (11). In Elhasan’s study, education level, age, and sex did not affect medication adherence rate [11]. Another study regarding compliance with treatment and quality of life of heart failure patients showed a 75% adherence rate [12].
In a multi-centered study conducted at Khartoum in three cardiac centers, 433 patients were enrolled, and a Morisky scale-8 was used for measuring adherence. This study revealed 49% optimal adherence and 51% poor adherence among patients registered. Respondents with a high level of education and those taking five or more medications were found significantly related to more non-adherents to drug use [13].
Objectives
The current study’s general objective was to assess medication adherence among CVD patients. The specific objectives were to underline adherence problems, the prescribing pattern of cardiovascular medications, to examine factors influencing adherence to, cardiovascular medications, and to study the correlation between adherence and Sociodemographic characteristics (sex-age-education, etc.) and disease factors (type and duration).
Methods
The current study design was a cross-sectional Hospital based study conducted, at the Sudan Heart Center-Khartoum State. This was a specialized center, with a capacity of 55 beds belonging to the Medical Service Administration, Ministry of Defense, which also provides services for civilians. According to the 2017 report [14], there were 11 cardiac consultants, and the center underwent 133 surgeries. The study populations were cardiovascular patients who attended the outpatient clinic and who gave their verbal consent by asking the participant a series of questions recorded on notes to participate in the study in period between 12 September 2022 to 30 December 2022. Cardiac patients 18 years of age and older taking at least one medication were interviewed by one pharmacist, while patients coming for surgery were excluded. Convenient samples from patients attending the outpatient clinic were chosen.
Study instrument and data collection
The research utilized validated adherence tool ARMS [15] in addition to a questionnaire to collect relevant data. Data on Sociodemographic characteristics (gender, age, residence, employment, marital status, education, and insurance), patient clinical information (type of cardiac disease, duration of the illness, and comorbidities)-, medication-related information (number of medications, names, and dose regimen used), level of adherence to medication and drug-related problems encountered during treatment were collected. The researchers did a structured interview for the patients, in which information was explained in Arabic.
The outcome measure
The questionnaire used to assess medication adherence and overall commitment was operationalized using the ARMS, a 12-item self-reported medication adherence scale in English.
Each item is scored using a 4-point Likert scale (1 = never, 2 = sometimes, 3 = most of the time, and 4 = Always). Ten items assessed adherence behavior while two points assessed issues related to cost and preparation for refiling. The ARMS can range from 12 to 48, with higher scores indicating poor adherence. Moreover, a score of >16 was used as a cut-off point to categorize surveyed patients into non-adherent (e.g., 17–48) and adherent (e.g., 12–16).
Data analysis
All information gathered via a questionnaire was coded into variables. Reliability Statistics for questionnaire validation were conducted using Cronbach’s Alpha test = 0.561 = 56.1%. Both descriptive and inferential statistics involving the T-Test, Analysis of variances (One Way ANOVA) Test, and binary logistic regression were used in data analysis. A p-value of less than 0.05 was considered statistically significant. One-way ANOVA was used for examining mean differences in adherence across categories of predictors. SPSS, version 23.0, was used for data analysis.
Ethics approval
Ethical clearance was obtained from the Institutional Review Board of the National University-Sudan (No. NU-REC/11-022/9), and approval was obtained from the Training and Research Department of the Sudan Heart Center.
Results
Sociodemographic data of the respondents
Male respondents (54.4%) were slightly higher than females. Most of the respondents (81.3%) were in the age range of 51 years or older, most of the respondents were from Khartoum state (67.8%), most of them were unemployed (77.6%), most of them were married (60.4%), and between primary and secondary education (39.6%) and (34.5%), respectively. More than half of the respondents (58%) had no medical insurance coverage [Table 1].
Table 1. Sociodemographic data of the respondents: (N = 255).
| Sociodemographic characteristics | Frequency (%) | |
|---|---|---|
| Gender | Male | 139 (54.5) |
| Female | 116 (45.5) | |
| Age groups years(years) | ||
| 31–40 | 8 (3.1) | |
| 41–50 | 27 (10.6) | |
| 51–60 | 141 (55.3) | |
| ≥ 61 | 79 (31) | |
| Residence | ||
| Khartoum | 173 (67.8) | |
| Outside Khartoum | 82 (32.2) | |
| Employment | ||
| Employed | 57 (22.4) | |
| Not employed | 198 (77.6) | |
| Marital status | ||
| Single | 66 (25.9) | |
| Married | 154 (60.4) | |
| Widowed | 29 (11.4) | |
| Divorced | 6 (2.4) | |
| Educational level | ||
| Illiterate | 32 (12.5) | |
| Primary education | 101 (39.6) | |
| Secondary education | 88 (34.5) | |
| University and above | 34 (13.3) | |
| Medical insurance | ||
| Yes | 107 (42) | |
| No | 148 (58) | |
Patient’s clinical information
Most types of disease encountered are ischemic heart disease (25.5%), pulmonary embolism (24.3%), and heart failure (24.3%). Duration of disease generally ranged between 1–3 years (40%) and 4–9 years (42.7%). More than half of respondents had comorbid disorders such as diabetes (56.1%) or hypertension (45.5%), [Table 2]. Most of the respondents were prescribed three or more medications (69.8%). The result displayed in Table 3 shows that most of the medication prescribed was cardio-selective beta-blockers (44.3%), and diuretics. (40.4%), and angiotensin receptor blockers (ARBs), (33.3%) [Table 3]. ARMS adherence Scale results: most of the respondents were non-adherent to the treatment (n = 249, 97, 6%). The practice regarding adherence, for most of the time, was as follows: 49.8% forget to take their medications, 40% often decide not to take their medications, 40.4% miss taking their medication due to carelessness, and 44.7% change the dose to suit their needs. The rate for refilling points (2 points): 27.8% put off filling due to cost, and 16.1% plan to refill. The general results indicate a high tendency towards poor adherence, with slightly fewer problems in the last two points of the scale; the effect of cost and planning for refilling. Most of the Means and standard deviations ARMS scale were above 2 (2.36–2.57), mostly related to forgetfulness about taking medications. The last two points regarding refilling were around 2, [Table 4].
Table 2. Respondents’ clinical information (N = 255).
| Patient clinical information | Frequency (%) |
|---|---|
| Type of cardiac disease | |
| Myocardial infarction | 2 (0.8) |
| Deep vein thrombosis | 6 (2.4) |
| Heart failure | 12 (4.7) |
| Angina | 14 (5.5) |
| Atrial fibrillation | 32 (12.5) |
| Pulmonary embolism | 62 (24.3) |
| Congestive heart failure | 62 (24.3) |
| Ischemic heart disease | 65 (25.5) |
| Duration of disease (years) | |
| 1–3 | 102 (40) |
| 4–9 | 109 (42.7) |
| ≥ 10 | 44 (17.3) |
| Co-morbidities Yes | 123 (48.2) |
| No | 132 (51.8) |
| Comorbid diseases (frequency = 123) | |
| Benign prostatic hyperplasia | 1 (0.8) |
| Asthma | 1 (0.8) |
| Hypothyroidism | 5 (4.1) |
| Gout | 13 (10.6) |
| Rheumatoid arthritis | 19 (15.4) |
| Hypertension | 56 (45.5) |
| Diabetes | 69 (56.1) |
Table 3. Medication used by participants for CVS diseases: (n = 255).
| Drug class | Dose | Frequency (%) |
|---|---|---|
| Heart disease medication | ||
| Bisoprolol | 7.5 mg | 31 (12.2) |
| Bisoprolol | 5 mg | 32 (12.5) |
| Bisoprolol | 2.5 mg | 50 (19.6) |
| Candesartan | 8 mg | 61 (23.9) |
| Candesartan | 16 mg | 21 (8.2) |
| Candesartan + Hydrochlorothiazide | 16/12.5 mg | 2 (0.8) |
| Candesartan | 32 mg | 3 (1.2) |
| Amlodipine | 5 mg | 21 (8.2) |
| Amlodipine | 10 mg | 23 (9.0) |
| Lisinopril | 2.5 mg | 17 (6.7) |
| Lisinopril | 5 mg | 25 (9.8) |
| Amlodipine/Valsartan | 10/160 mg | 12 (4.7) |
| Spironolactone | 25 mg | 33 (12.9) |
| Spironolactone | 50 mg | 4 (1.6) |
| Digoxin | 0.25 μg | 12 (4.7) |
| Furosemide | 40 mg | 103 (40.4) |
Table 4. ARMS Adherence scale variables (n = 255).
| ARMS Adherence scale variables | Never (%) |
Sometimes (%) |
Most of the time (%) |
Always (%) |
Mean (SD) Score |
|---|---|---|---|---|---|
| How often do you forget to take your medicine? | 18 (7.1) | 96 (37.6) | 127 (49.8) | 14 (5.5) | 2.54 (0.708) |
| How often do you decide not to take your medicine? | 20 (7.8) | 125 (49) | 102 (40) | 8 (3.1) | 2.38 (0.677) |
| How often do you forget to get prescriptions filled? | 25 (9.8) | 99 (38.8) | 94 (36.9) | 37 (14.5) | 2.56 (0.858) |
| How often do you run out of medicine? | 27 (10.6) | 96 (37.6) | 91 (35.7) | 41 (16.1) | 2.57 (0.884) |
| How often do you skip a dose of your medicine before you go to the doctor? | 19 (7.5) | 120 (47.1) | 81 (31.8) | 35 (13.7) | 2.52 (0.822) |
| How often do you miss taking your medicine when you feel better? | 20 (7.8) | 116 (45.5) | 83 (32.5) | 36 (14.1) | 2.53(0.831) |
| How often do you miss taking your medicine when you feel sick? | 30 | 113 | 98 | 14 | 2.38 (0.763) |
| 11.8 | 44.3 | 38.4 | 5.5 | ||
| How often do you miss taking your medicine when you are careless? | 24 | 121 | 103 | 7 | 2.36 (0.69) |
| 9.4 | 47.5 | 40.4 | 2.7 | ||
| How often do you change the dose of your medicines to suit your needs (like when you take more or fewer pills than you’re supposed to)? | 20 (7.8) |
108 (42.4) |
114 (44.7) |
13 (5.1) |
2.47 (0.714) |
| How often do you forget to take your medicine when you are supposed to take it more than once a day? | 27 (10.6) | 125 (49) | 79 (31) | 24 (9.4) | 2.39 (0.801) |
| How often do you put off refilling your medicines because they cost too much money? | 79 (31) | 99 (38.8) | 71 (27.8) | 6 (2.4) | 2.02 (0.828) |
| How often do you plan and refill your medicines before they run out? | 86 (33.7) | 106 (41.6) | 41 (16.1) | 22 (8.6) | 2 (0.92) |
Sociodemographic characteristics and adherence levels
The results of one-way ANOVA showed that the level of adherence significantly differed between different age groups (P = 0.002), employment status (P = 0.027), and co-morbidities (P = 0.015). Patients aged 41–50 years were the most adherent (mean 26.04) while patients aged 51–60 years (mean 29.09) and above 61 years of age (mean 28.9) were found to be less adherent. In addition, employed patients were more adherent (mean 27.7 ± 2.2) than those not employed. Respondents with co-morbidities were found to be less adherent than respondents without co-morbidities. In our study, the main predictor of adherence is the educational level (adjusted odd ratio 23.6, CI 95%: 1.58–28.5, P = 0.013).
Comparison between current study and selected relevant studies (2015–2023)
The results of the current study were compared with the international studies regarding, adherence tool, level of non-adherence, reasons cited for non-adherence, and predictors of non-adherence. In area of adherence tool, our study used refills and medication scale tools, Nikolic study [16] use refills tool only, versus Self-reported adherence tool [8,17–20], Eight item Morisky’s medication adherence scale tool [8,13,21–27], Four item Morisky’s medication adherence scale tool [28–31], Four item Morisky Green Levine scale [32], Quantitative scale [33,34], Three item Voils medication non adherence screener [35], Drug attitude inventory tool [36], mass spectrometry urine analysis tool [37–40], Primary medication adherence [41], Urine antihypertensive assay tool [42], dried blood spot analysis tool [23], Recall of number of missed doses of medications tool [43], Proportion of days covered tool [44,45], Health Styles 2010 survey questions tool [46], Questionnaire of non-adherence to Medicines of the Qualiaids Team tool [47], and urine toxicology test tool [20]. In level of non-adherence, all studies showed less non adherence level compared to the current study (97.6%) and Kotian study (HTN = 97.5%, Diabetes = 96.5%) [43], non-adherence more than 50% occurred in studies done by Lawson (55.3%) [42], Wallbach (58.1%) [39], Adidja (66.7%) [26], Awad A (51%) [13], Lo SH (55.9%) [46], Santra G (73.6%) [29], Khasal 54.4% [22], Gikunda study (64.1%) [34], Algabbani (57.8%) [24], and Young (72.7%) [20]. While non-adherence less than 50% occurred in other studies [8,16–19,21,23,25,27,30,32,33,35–38,40,41,44–47].
Regarding the reasons for non-adherence in our study were, changed the dose to suit their needs (44.7%), decided not to take medications (40%), 5.9% [21], and forgot to take their medications (49.8%), 8.2% [21], 15.2% [35], 44.4% [22], 26.3% [36], 85.1% [23], 16.4% [34], 62.7% [18], 49.41% in patients with diabetes, 26.21% in patients with HTN [43], 23.6% [46], 57.9% [29], 57.8% [20], missed to take their medications due to carelessness 40.4%, 29.6% [35], 34.6% [29].
Compared to other reasons found e.g. affordability 20.6% [21], 6.6% [35], 85.1% [23], 9% [18], 67.9% [20], 35.1% [46], 54.4% [13], 85.1% [23], 25.6% [22], 50.58% patients with diabetes, 73.78% patients with HTN [43], undesirable effects of medicines 17.1% [21], 10.4% [35], 4.4% [22], 93.6% [23], 5% [46], 0.2% [36], 9% [18], 36.8% [13], 48.6% [29], and 27.8% [20], taking too many medications 20.1% [35], 11.3% [22], lack of access to medicines or health facility (33.5%), never miss a dose (2.4%) [21], inconvenience of taking the medication 85.1% [23], lack of funds (55.7%), lack of time for refills (4.6%), thought they had healed (3.3%) [34], irregular availability of the drugs in their areas (33.4%), lack of pharmacist’s communication with them regarding the instructions and importance of taking the drug regularly (50.0%), lack of physician’s communication with them regarding their illness and the benefit that the medication will provide (40.4%), poly-pharmacy (52.8%) [13], and others [18,20,22,35,36,46].
Predictors of non-adherence in our study were younger [8,30,31,36,42,46] and older age [18,19,22,34,46], more comorbidity [27,39], higher education [13], unemployment (p < 0.05) compared to others predictors in other studies e.g. multiple antihypertensive medications [19,27,30,37–39,42,47], increased number of medications [13,16,22,23,26,42], unaffordability of treatment [13,20,26,30,31,33], lower monthly income [34,36], male gender [30,31], female gender [38,42], Poor knowledge about HTN (p < 0.05) [24,25], alcohol consumption [13,25], issues with remembering the dosing regimen [16,26,30,44], never doing follow-up visits [22,47], inadequate counseling by physician [21,30,34], in one study no predictors were identified using regression analysis [40], and other predictors were found [8,17–21,25–27,31,33–35,39,42,44–46] All the above-mentioned findings in the compared studies to our current study were depicted in [Table 5]
Table 5. Comparison between current study and selected relevant studies (2015–2023).
| Relevant studies ↓ | Study setting | Study design | Country | Number of patients | Adherence tool | Level of non-adherence | Reasons cited for non-adherence | Predictors of non-adherence |
| Current study, A A Elnour, 2024 (Unpublished) |
Out-patients | Cross-sectional hospital-based study | Sudan | 255 | Adherence to refills and medication scale | 97.6% | Changed the dose to suit their needs (44.7%), decided not to take medications (40.0%), forgot to take their medications (49.8%), missed to take their medications due to carelessness (40.4%) | Younger and older age, more comorbidities, higher education, unemployment (p < 0.05) |
| Kim KY, 2023 [18] | Data from Korea National Health and Nutrition Examination Survey | Nationally representative population-based cross-sectional survey | Korea | 6,493 | Self-reported adherence | 3.6% | - | Women Anxiety/depression, immobility (p < 0.05) Men Anxiety/depression, living together (p < 0.05) |
| Nikolic A, 2023 [19] |
Outpatients | Multi-methods approach: Cross-sectional analytical observational study followed by case-control study | Serbia | 338 | Number of times patients missed to refill monthly prescribed medications in last 12 months according to the pharmacy dataset | 26.0% | - | Burden of taking the drug, complex dosing regimen, current poor control of HTN compared to last year, effect of medications, family history of HTN, increased number of medications, issues with remembering the dosing regimen, issues with purchasing a medication, issues with monthly renewal of medications, not using medications in the last 7 days (p < 0.05) |
| Noreen N, 2023 [20] |
Outpatients | Hospital based cross-sectional study | Pakistan | 450 | Eight item Morisky’s medication adherence scale | 37.8% | Affordability (20.6%), do not wish to take medicines (5.9%), forget to take medicines (8.2%), lack of access to medicines or health facility (33.5%), never miss a dose (2.4%), undesirable effects of medicines (17.1%), others (12.4%) | Fewer years of education, inadequate counselling by physician, unaffordability of treatment, uncontrolled HTN (p < 0.05) |
| Yousuf FS, 2023 [21] | Inpatients | Cross-sectional study | Pakistan | 260 | Four item Morisky Green Levine scale | 35.8% | - | - |
| Saeed A, 2023 [22] |
Outpatients | Comparative cross-sectional study | Pakistan | 168 | Quantitative scale | Medium adherence = 22.6% Low adherence = 57.1% |
- | Hospital affiliation (particularly public tertiary care hospital) (p < 0.05) |
| Kharmats AY, 2023 [23] | Outpatients | Cross-sectional study | United States | 242 | Three item Voils medication nonadherence screener | 45.0% | Being too busy (32.0%), BP too low (6.7%), cost (6.6%), concern about interaction (5.7%), feeling ill taking BP medications (8.5%), forgetting to take medications while travelling (15.2%), having difficulty remembering to take medications (29.6%), no symptoms of high BP (16.2%), running out of medications (14.2%), side effects (10.4%), taking too many medications (20.1%), worried about taking medications all life (16%) | Black participants (p < 0.05) |
| Khasal QA, 2022 [24] | Outpatients | Descriptive cross-sectional study | Pakistan | 195 | Eight item Morisky’s medication adherence scale | 54.4% | Disappearance of symptoms (1.5%), effect of financial strain (25.6%), forgetfulness to take medications (44.4%), multiple medications (11.3%), side effects (4.4%), stopping or changing medications because of perception of not working (1.5%), too busy (11.3%) | Age > 60 years, more number of used medications (p < 0.05) |
| Abbreviations: BP = Blood pressure, HTN = Hypertension; values of p < 0.05 were accepted as significant | ||||||||
Discussions
The general results of this study revealed there is a high prevalence rate (97.6%) of non-adherence to medications among people with CVDs. The results indicated a high tendency towards poor adherence, in most of the 10 points of the ARMS scale, with slightly fewer problems in the last two ends of the scale i.e. the effect of cost and planning for refilling. The cited reasons for non-adherence include forgetfulness and the costs of refilling medications.
Due to Misunderstanding (e.g. the nature of side effects, the time it takes to see results or patient does not understand the need for the medicine), and mistrust to prescribed medicines, the patient cannot take the medicines [50].
Education and age were associated with the level of adherence. Those with high non-adherence behaviors were the more educated, those not employed, and those having comorbid diseases. Adherence problems faced by the patients were decreased patient satisfaction due to higher patient-physician discordance, complex medication regimen, and patient feel better or symptom free. Complex medication regimen may be due to drug administration improper timing, or administration of numerous medications at unusual or frequent times during the day. The patient’s ability to read and understand medication instructions is one of major factor that influences adherence. Patients with low educational levels have difficulty in instructions understanding leading to decrease adherence [51].
Sociodemographic factors such as sex, age, income, gender, level of education may be related to non-compliance [51]. The prescribing pattern of cardiovascular medications, were Furosemide (40.4%), Candesartan 8mg (23.9%), Bisoprolol 2.5 mg (19.6%), Bisoprolol 5mg and 7.5mg (12.5% for each), Lisinopril 5,g (10%) and others heart diseases medications. The drug problems encountered by the respondents decrease adherence and can lead to changing the dose to suit their needs, and the decision not to take medications by experiencing side effects [52]. In a study from Latvia [8], the non-adherence rate was less than in the current study (46.2%), and the oldest age and those respondents with a longer duration of disease were more adherent to their medications. Those results were different from our study, which showed respondents aged 51 years or older were more non-adherent to treatment. The duration of the disease showed that those with a duration of 10 years and more (mean 28.66 ± 3.2) were slightly more adherent than those with a disease of 1–3 years duration. These differences between the two studies may be due to different scales used for measuring adherence behavior. Poor adherence among the elderly is expected because these respondents are more susceptible to forgetting their medications. In our study also, those respondents between 31 to 40 years old showed a non-adherence rate almost like the elderly group (mean 29.5 ± 2.4). Those in the range of 41 to 50 years, were within acceptable adherence, maybe because this group of people take on more responsibility for their health and are actively working people, who have good information about their disease status. Compared to a study from Saudi Arabia that assessed adherence among hypertensive respondents [9], the current study showed poor adherence rates. Similar results to the current study regarding the percentage of those without co-morbidities were obtained (49%), while in the current study (51.8%). This is explained by the fact of the presence of many diseases and many medications can affect drug-taking behaviors among respondents with chronic diseases in general. The results of the current study are also comparable to a study among hypertensive patients [10], which reported 54% of patients were non-adherent to their medications. Further factors such as age above 65 years, gender, and diabetes were predictors of poor adherence. In the current study, patients older than 61 years of age and having co-morbidities were more non-adherent to their medications. In our study, the main predictor associated with adherence is the educational level (P = 0.013). It is interesting to find that educated patients are more non-adherent to their medications. This is explained by the fact that educated persons may be behaviorally distracted by different mental activities, which affect their adherence. In contrast, less educated persons follow doctors’ advice precisely, without changes, compared to knowledgeable patients who may sometimes find a reason for delaying their medications without being anxious.
It is evident that patients with CVDs need further investigation about barriers to medication adherence. These barriers could be due to different factors such as disease-related factors, therapy-related factors, healthcare-related factors, patient-related factors, and social-related factors. Adherence could be improved through several mechanisms including enhanced patient-provider communication, using health messages, adherence-assisting devices, providing patient education, and psychological support [48].
The prescribing pattern of medications in the current study showed that most medications prescribed were cardio-selective Beta–blockers, diuretics, and ARBs. Most of them were given in a single dose or twice a day, and this is considered a reasonable effort from prescribers toward improving patients’ adherence. Three studies conducted in Sudan [11–13] reported adherence rates of 60.7%, 75% and 49% in the previous studies, respectively. The adherence rate is better than our study, which only reported an adherence rate of only 4.6%. The first study [11] had a higher prevalence of hypertension and diabetes, as co-morbidities, in this study, the level of education, age, and gender were not associated with adherence. However, our study reported that education level, and age but not gender influence adherence to treatment. The third study [13] supports our finding that a high level of education is associated with poor adherence to their medications. A recent study from Sudan [49] also showed a high non-adherence rate (97.3%) at baseline, which was improved after clinical pharmacist-led educational intervention, and it was in accordance with the present result. The reason for the higher non-adherence rate in this study is explained by the following: the use of different scales or tools for determining adherence; questionnaire data alone is not sufficient to give a clear picture of commitment unless supplemented by practical measures such as checking of blood pressure, hyperlipidemia, cardiac tests and counting of use of prescribed medications. Prescribing more than three medications (69.8%) could be another reason.
The implications of the current study
Healthcare professionals should concentrate on patient categories, such as the elderly, both educated and uneducated, and those with co-morbidities, to improve adherence to medications.
For subjects who have been recently diagnosed and those who are in the initial post-diagnosis years, it is vital to streamline medication regimes and provide close follow-up.
Pharmacists can actively engage patients through a variety of communication techniques to improve adherence.
Future studies should investigate practical ways to gauge adherence and examine obstacles to taking medications as prescribed.
The current study emphasizes the need of focused treatments and the involvement of pharmacists in improving medication adherence for improved patient outcomes.
Limitations
The study includes several limitations that prevent generalizing the study’s findings to the Sudanese community at large. There is a need for a larger multicenter study to improve the generalizability of the results. Additionally, the study did not investigate ways to increase adherence.
Conclusions
The incidence of non-adherence was disturbingly high at 97.6%, with forgetfulness being the main cause, followed by elements like cost and prescription refilling. It is interesting to note that non-adherence was more common in older and younger patients alike, educated people, people with co-morbid conditions, and people who were unemployed. The prescription pattern showed a preference for diuretics, ARBs, and cardio-selective B-blockers. According to patient input, no drug-related issues, such as side effects or low doses, have been noted. Future studies are needed to describe medication-taking behavior in a larger scope beyond the imperfect and uninformative descriptions of adherence terms.
Acknowledgments
We would like to acknowledge the following universities (Dean of Research Studies): National University-Sudan, Al Ain University-Abu Dhabi-UAE, Gulf Medical University-Ajman-UAE, Dubai Pharmacy College-Dubai-United Arab Emirates (UAE), University of Sharjah-UAE; Fatima College of Health Sciences-FCHS-Abu Dhabi-UAE, University of Almaarefa-(Diriyah)-Riyadh-Saudi Arabia, Taif University-Saudia Arabia, Imam Abdulrahman Bin Faisal University-Saudi Arabia, Dar Al Uloom University- Riyadh-Saudi Arabia; Najran University-Saudi Arabia, and Health Services Authority (SEHA)-AbuDhabi-UAE.
Abbreviations
- ARBs
Angiotensin receptor blockers
- MMAS-4
Morisky -4 scale
- WHO
World Health Organization
- CVDs
Cardiovascular diseases
- CDC
Centers for Disease Control and Prevention
Data Availability
All relevant data are within the paper.
Funding Statement
The author(s) received no specific funding for this work.
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Associated Data
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Data Availability Statement
All relevant data are within the paper.