The success of every therapeutic regimen depends on the compliance of the individual involved. The efforts put in by healthcare providers can therefore only yield the desired effect if patients are compliant to their medication regimen. Unfortunately, medication noncompliance with its associated detrimental effects is becoming widespread and has been found to be most prevalent among patients with chronic diseases.
Globally, chronic diseases have been found to be the leading cause of mortality and disability, and the disease rates from these conditions are not only accelerating but advancing across every region and pervading every socioeconomic class [
In Ghana, hypertension alone is said to have accounted for between 19% and 54.8% of the outpatient morbidity in adults above 45 years in 2012 [
Among patients with chronic diseases such as hypertension, diabetes, HIV/AIDS, and psychiatric illnesses, medication noncompliance has been found to be very common [
Poor adherence to therapy among hypertensive patients contributes to two-thirds of poor blood pressure control [
Among patients living with HIV/AIDS, with the advent of antiretroviral drugs, anything less than thorough compliance can result in reduced efficacy of the drugs and later lead to development of resistant viral strains [
There is a threefold effect to medication noncompliance, and these effects are manifested in the clinical outcome of the patient, the cost of treatment, and the risk of hospitalizations, all of which have been identified as the main cause of failure to effectively manage chronic diseases [
Over the last two decades, there have been a plethora of studies that have examined variables that could be demonstrated as predictive of adherence to various medical regimens. The factors most often hypothesized in these studies as powerfully predicting compliance have generally been attributed to characteristics of both the disease and the patients. For example, to explore and evaluate the most common factors causing therapeutic noncompliance, Jin and colleagues found factors that could be categorized into (1) patient-centered factors, (2) therapy-related factors, (3) social and economic factors, (4) healthcare system factors, and (5) disease factors [
In the West African subregion and particularly in Ghana, studies have identified specific factors such as depression, concern about disease medications, formal education, and use of herbal preparations to be associated with nonadherence among hypertensive patients [
In an era where cost-effectiveness is a buzz word in healthcare delivery, these identified factors must be a concern for healthcare providers and health systems [
With the persistent rise in the prevalence of chronic diseases, medication compliance as an area of research study is gaining much recognition and whiles there is now ample research on the subject, most of these studies have been conducted in developed countries. The extant literatures on medication nonadherence in Ghana have mostly focused on the two chronic diseases, hypertension and diabetes much to the neglect of other chronic conditions such as HIV/AIDS and psychiatric disorders. The plethora of studies which even do exist on medication nonadherence among diabetic and hypertensive patients have mostly been conducted in urban settings of the country, with limited studies conducted among periurban and rural dwellers. On this premise, this study sought to determine the prevalence of medication noncompliance among patients with chronic diseases in a periurban district in Ghana. Medication intake behaviour, the relative influence of cost on medication noncompliance, and the risk factors for medication noncompliance were also assessed.
The study was conducted in the Offinso South Municipality, one of the 30 Municipals in the Ashanti region of Ghana. The 2010 Population and Housing Census put the population of the municipality at 76,895 with a population density of 131 persons per square kilometre. A high percentage (70.0%) of the population in the municipality is economically active, with over 50.0% of the employed being skilled agricultural and fishery workers [
This was a cross-sectional descriptive survey using systematic random sampling to collect quantitative data involving multiple variables which were analysed to determine distribution patterns and test relationships.
The sampling population included people aged 18 and above presenting at the special OPD clinic at St. Patrick’s Hospital who had been diagnosed of a chronic ailment and consented to participate in the study.
To be included in the study, patients had to be diagnosed of at least one out of the four chronic diseases of interest, that is, hypertension, diabetes, HIV/AIDS, or psychiatric disorder. The patient should also have been on medication for more than 6 months and must have consented to participate in the study.
Patients who had been diagnosed of any of the chronic diseases of interest and were yet to start medication or had been on medication for less than 6 months were exempted from the study. Patients below the age of 18 years were also excluded.
The hospital facility was chosen for the study as it was easier to get access to patients that met the inclusion category. Of the number of health facilities available at the Offinso South Municipality, St. Patrick’s Hospital was purposively selected as it was the largest hospital and was frequented by most people in the municipality.
The systematic random sampling technique was used to sample 200 eligible patients. At a particular clinic, the first person was given the chance to participate and every third person was also sampled to participate. Consent was sought from the selected participant before inclusion into the study.
Standard questionnaires were used for the study. The study’s research questions and objectives informed the design of the questionnaires. Prior to the design, a thorough literature search was conducted to determine and categorize concepts and variables used in studies related to the topic. Information from the literature review focused on issues relating to noncompliance to medication regimen among patient with chronic diseases. The research instrument crafted for this study was a 28-item self-reporting research instrument utilizing closed ended questions with response categories that were precoded which facilitated numerical coding of the data after collection. The entire questionnaire was arranged into content subsections A, B, and C.
Section “A” consisted of nine (9) sociodemographic survey items which distinguished patients in terms of their age, gender, religion, employment status, occupation, marital status, educational attainment, chronic condition(s) suffered, and duration of condition since diagnosis. Section “B” consisted of 14 questionnaire items developed to measure patients’ compliance to medication regimen. The questions asked mainly bothered on the different medications patients took for their condition, frequency of daily intake, route of administration of medication, number of tablets/capsules taken daily, adherence to doctor’s instructions as to how medication should be taken, side effects experienced if any, etc. (see attached study questionnaire for full details available
Section “C” also consisted of 4 questions developed to measure the influence of cost on compliance to medication. The first question ascertained whether patients’ medication was covered by the National Health Insurance Scheme (NHIS), whiles the second was a follow-up question for respondents who indicated “No” as a response to indicate how they paid for their drugs. The third and fourth questions requested patients to indicate the cost of their medication(s) and whether they missed their medication for a period of time due to cost.
Additionally, there were standard instructions that requested respondents to select the most suitable answer with the assurance that there was no right or wrong answer in the selection of answers to the questions.
The questionnaire validation process was a two-step one. The first step was the establishment of face validity of the questionnaire which was achieved by consulting two experts: (1) a university professor and (2) a practicing physician. These two experts were tasked to ascertain if the questions asked effectively captured the topic under investigation. Their suggestions and recommendations made were then incorporated.
A pretest was then carried out after the validation to obtain information to improve the questionnaire and to assess the feasibility of the study. The respondents in the pretest were similar to those in the study and it was carried out under similar settings. Conducting the pretest helped to identify problems with the questionnaire, to gauge the time needed to complete the questionnaire and ultimately, and to ensure that the questions were understood by respondents. The feedback obtained helped in fine-tuning the questionnaire. The pretest was conducted among 15 patients at the Komfo Anokye Teaching Hospital (KATH).
Data was collected over a period of one month, from March to April, 2016 at the St. Patrick’s Hospital which doubles as the municipal’s hospital after pretesting of the research questionnaire. Quantitative data was primarily sought for the study. The data was obtained through the use of a structured questionnaire specifically designed to suit respondents understanding of the study. The response categories of the various questions or variables were mostly precoded. A word of acknowledgment was rendered to study respondents for their participation in the survey.
The data collected was first edited to ensure that all questionnaires were complete and properly filled. In all, 200 complete questionnaires were obtained from the respondents. After editing, the data from the completed questionnaire was coded and entered into the SPSS (v20) programme. Preliminary data analysis was conducted to obtain frequency distribution for all variables. The preliminary analysis also served as a cleaning strategy which helped to identify data entry errors. The validation tool in the SPSS programme which gives information about missing values with their identification numbers and wrong entries made was further utilized to confirm that the data was clean and ready for analysis.
Descriptive analysis was conducted on respondents’ background characteristics and reported in frequencies and percentages. Multivariate analysis using binary logistic regression was conducted to determine the factors for noncompliance to medication regimen.
Ethical approval was obtained from the Ethical Review Committee of the Komfo Anokye Teaching Hospital (KATH) through the School of Medical Sciences, KNUST, Kumasi. Permission for the research was granted from the Administration of St. Patrick’s Hospital as well as the head of the special clinic of the hospital. The purpose of the study was explained to every participant. Confidentiality was assured and either verbal or written consent was sought from every individual who participated in the study.
Participation in the study was not compulsory and anonymity of respondents was respected. All respondents voluntarily gave verbal consent to participate in the study after the rationale of the study was explained to them. Couples were interviewed separately and care was taken to ensure privacy and confidentiality.
A total of 200 patients participated in the study. Patients’ ages ranged from below 40 to 60+ years with majority of them being less than 40 years and between 40 and 60 years. Together, these two age cohorts constituted 79% of the entire sample size. Majority of the respondents were females (76%), were in marital unions (57%), had attained basic education, that is primary and up to Junior High School (59%), were into trading, and belonged to the Christian faith (88%). Patients who were diagnosed with diabetes were the most (42.4%), followed by those with hypertension and HIV/AIDS. Patients who had lived with their disease condition between 1 and 5 years constituted a half (which was majority) of the entire sample (see Table
Distribution of respondents by background characteristics (
Characteristic | Frequency | Percent (%) |
---|---|---|
Age (years) | ||
| 80 | 40.0 |
| 79 | 39.5 |
| 41 | 20.5 |
Gender | ||
| 48 | 24.0 |
| 152 | 76.0 |
Religion | ||
| 176 | 88.0 |
| 16 | 8.0 |
| 4 | 2.0 |
| 4 | 2.0 |
Occupation | ||
| 41 | 20.5 |
| 68 | 34.0 |
| 13 | 6.5 |
| 5 | 2.5 |
| 6 | 3.0 |
| 1 | .5 |
| 1 | .5 |
| 65 | 32.5 |
Marital status | ||
| 43 | 21.5 |
| 114 | 57.0 |
| 14 | 7.0 |
| 29 | 14.5 |
Educational attainment | ||
| 45 | 22.5 |
| 40 | 20.0 |
| 77 | 38.5 |
| 28 | 14.0 |
| 10 | 5.0 |
Chronic diseases suffere | ||
| 60 | 22.3 |
| 75 | 27.9 |
| 114 | 42.4 |
| 20 | 7.4 |
Duration of diagnosis | ||
| 15 | 7.5 |
| 100 | 50.0 |
| 38 | 19.0 |
| 47 | 23.5 |
Medication noncompliance (assessed by patients indicating whether they always admitted to their doctor’s instruction) in the sample of 200 patients was found to be relatively high (
Medication intake behaviour of patients (
Characteristic | Frequency | Percent (%) |
---|---|---|
Number of medications taken for condition | ||
| 25 | 12.5 |
| 76 | 38.0 |
| 43 | 21.5 |
| 36 | 18.0 |
| 20 | 10.0 |
Frequency of intake | ||
| 28 | 14.0 |
| 158 | 79.0 |
| 14 | 7.0 |
Route of intak | ||
| 197 | 86.8 |
| 30 | 13.2 |
Number of tablets taken if oral | ||
| 110 | 55.8 |
| 82 | 41.6 |
| 5 | 2.6 |
Always admit to doctor’s instructions | ||
| 89 | 44.5 |
| 111 | 55.5 |
Notification of side effects of drugs | ||
| 66 | 33.0 |
| 134 | 67.0 |
Side effects mostly experience | ||
| 13 | 6.5 |
| 8 | 4.0 |
| 6 | 3.0 |
| 6 | 3.0 |
| 4 | 2.0 |
| 4 | 2.0 |
| 4 | 2.0 |
| 134 | 67.0 |
Able to tolerate side effects | ||
| 40 | 20.0 |
| 26 | 13.0 |
| 134 | 67.0 |
Medication intake behaviour of patients (
Characteristic | Frequency | Percent (%) |
---|---|---|
Use of herbal medication | ||
| 45 | 22.5 |
| 155 | 77.5 |
Influence of intake on medication compliance | ||
| 17 | 8.5 |
| 16 | 8.0 |
| 12 | 8.0 |
| 155 | 77.5 |
Difficulty in remembering medication instructions | ||
| 13 | 6.5 |
| 187 | 93.5 |
Medication is effective | ||
| 177 | 88.5 |
| 10 | 5.0 |
| 13 | 6.5 |
Frequency of forgetting to take medications | ||
| 3 | 1.5 |
| 28 | 14.0 |
| 104 | 52.0 |
| 65 | 32.5 |
Awareness of complications arising from non-compliance | ||
| 116 | 58.0 |
| 84 | 42.0 |
Ways of being reminded to be compliant with medications | ||
| 5 | 2.5 |
| 2 | 1.0 |
| 1 | .5 |
| 17 | 8.5 |
| 175 | 87.5 |
Proportion of patients with chronic diseases who are noncompliant to their medication regimen.
Majority of the patients (81.5%) were found to be taking at least 2 medications. Patients who were on exactly two medications for their disease condition were however the most (38.0%). A higher percentage of the patients (79.0%) took their medication twice daily, with the dominant route of intake being orally (86.8%). More than half (55.8%) of the patients who were on oral medications reported they took less than 5 tablets daily. Majority of the patients (67.0%) did not experience side effects with the intake of their drugs and the few who did (33.0%) mostly complained of general body weakness and insomnia. Of those who experienced side effects, most were able to tolerate it (Table
A lesser proportion of patients (22.5%) took herbal preparation alongside their prescribed medications and out of that, 8.0% stopped taking their prescribed medications and 8.5% took it alongside prescribed medications, which is an unhealthy practice. Majority of the patients (93.5%) had no problem with remembering medication instructions and forgetfulness was a problem in only about 16.0%. A higher percentage of the patients considered their medication effective (88.5%), were aware of the complications that could arise from noncompliance (58.0%), and were self-reminded to take their medications (87.5%) (see Table
Most of the drugs patients used (71.0%) were covered by the NHIS. Out of the 29.0% of patients who had to pay for their drugs, 15.5% paid from their own income and 13.5% were being supported by family members; however, 12% were missing their medications because they could not at a point in time pay for their drugs (Table
Cost and medication noncompliance (
Variable | Frequency | Percent (%) |
---|---|---|
Medication covered by NHIS | ||
| 142 | 71.0 |
| 4 | 2.0 |
| 54 | 27.0 |
Means of paying for drugs | ||
| 31 | 15.5 |
| 27 | 13.5 |
| 142 | 71.0 |
Cost of medications | ||
| 7 | 3.5 |
| 33 | 16.5 |
| 15 | 7.5 |
| 3 | 1.5 |
| 142 | 71.0 |
Missed medication because of cost | ||
| 24 | 12.0 |
| 34 | 17.0 |
| 142 | 71.0 |
Based on the three main categories of factors (i.e., personality characteristics and disease related and medication related factors) identified in the literature to influence medication compliance, the regression analysis was modelled to contain the variables comprising these three factors. Of the twelve factors (4 personality characteristics and 1 disease related and 7 medication related factors) entered into the regression model, only age, duration of diagnosis, and difficulty remembering medication instructions were found to be significant predictors of noncompliance to medication regimen. Patients who were old were more likely to be noncompliant. Those whose disease condition was long diagnosed were found to be 2 times more likely to be noncompliant; likewise those who did not have difficulty remembering medication instructions are 6 times more likely to be noncompliant (see Table
Multivariate analysis of risk factors for medication noncompliance.
Factor | B | S.E. | | OR (95% CI) |
---|---|---|---|---|
Gender | ||||
Male | 1 | |||
Female | −.335 | .394 | .395 | .715 (.331–1.547) |
Age | ||||
Young | 1 | |||
Old | −1.047 | .384 | .00 | .351 (.165–.745) |
Education | ||||
Not educated | 1 | |||
Educated | −.567 | .411 | .168 | .567 (.253–1.270) |
Marital status | ||||
Single | 1 | |||
Married | .137 | .324 | .673 | 1.147 (.608–2.163) |
Duration of diagnosis | ||||
Short (<6 years) | 1 | |||
Long (>5 years) | .794 | .336 | .01 | 2.213 (1.145–4.278) |
Number of tablets taken orally | ||||
Less than 5 | 1 | |||
Between 5 and 10 | .452 | .368 | .219 | 1.571 (.764–3.232) |
More than 10 | −.213 | 1.096 | .846 | .808 (.094–6.919) |
Notification of drugs side effects | ||||
Yes | 1 | |||
No | .304 | .335 | .363 | 1.356 (.704–2.612) |
Use of herbal medication | ||||
Yes | 1 | |||
No | −.230 | .387 | .552 | .794 (.372–1.697) |
Difficulty remembering medication instructions | ||||
Yes | 1 | |||
No | 1.715 | .736 | .02 | 5.557 (1.313–23.524) |
Efficacy of medication | ||||
Yes | 1 | |||
No | .323 | .767 | .674 | 1.381 (.307–6.217) |
Sometimes | 1.304 | .793 | .100 | 3.685 (.779–17.433) |
Awareness of complications that can arise from non-compliance | ||||
Yes | 1 | |||
No | .313 | .324 | .334 | 1.367 (.724–2.580) |
Medication covered by NHIS | ||||
Yes | 1 | |||
No | −.717 | 1.176 | .542 | .488 (.049–4.893) |
Some of them | .743 | .363 | .041 | 2.102 (1.032–4.278) |
There has been a dearth in studies to assess the level of noncompliance to medications for chronic diseases over the years, especially among patients with chronic conditions living in rural and periurban districts in sub-Saharan Africa. Taking cognisance of the fact that the success of every therapeutic regimen depends on the compliance of the individual involved and that medication noncompliance is becoming widespread, we found this study to be crucial and timely. We found the overall prevalence of medication noncompliance among the patients to be 55.5%. This rate is higher than that of a similar study conducted in Northwest Ethiopia (42%) to determine noncompliance in patients with chronic illnesses [
We found compliance to antihypertensive drugs to be comparatively better probably because there has been much more education on hypertension leading to interventions to improve compliance among patients with hypertension. The reduced side effects and the NHIS covering some hypertensive medications could also be a contributing factor. Among the four disease conditions, we found the prevalence of medication noncompliance to be the highest among patients living with HIV/AIDS (61.7%). This rate is high compared to a study conducted in Eldoret which revealed a noncompliance prevalence rate of 36.8% [
The level of nonadherence to medications for diabetes in our study was found to be 54.4%. Comparatively, the rate is higher than that obtained in a Ugandan study which recorded a prevalence rate of 28.9% nonadherence among patients [
Our study reports a medication intake behaviour where a high proportion of patients were on oral medications which was taken twice daily and a lesser proportion taking herbal preparations alongside their prescribed medications. Some patients however stopped the intake of their prescribed medications and resorted to the use of herbal drugs, a practice we find to be unhealthy. There has been a current upsurge in the use of herbal medication to treat chronic diseases in Ghana, especially diabetes. This upsurge has been fueled by the several advertisements aired by most of the mass media houses in the country. Often times, the efficacy of the drugs being advertised is yet to be scientifically proven, though most claim to have received an FDA approval.
On the effect of medication cost on noncompliance, we found that patients who had all or some of their medications covered by the NHIS were generally more compliant. The cost of medications, therefore, to some extent has the potential of influencing patients’ compliance which implies that having more medications covered by the NHIS can improve overall adherence. This finding is consistent with the study of Buabeng and colleagues conducted at the Korle-Bu Teaching Hospital (KBTH) in Ghana which found unaffordable drug prices as the major cause of noncompliance among patients with hypertension [
Variations in the significant factors predicting medication compliance and noncompliance have been observed in numerous studies. While some factors have been found to be significant in some studies, the same factors have been found to be nonsignificant in other studies. In this study we found patients age, duration of diagnosis, and difficulty remembering medication to be the significant predictors of medication noncompliance. Consistent with the study of Boima and colleagues, we also found the elderly (old) to be less compliant than the young. This pattern observed in noncompliance has also been documented in studies carried out in Sub-Saharan Africa [
Patients whose disease condition had persisted over a longer period of time were found to be 2 times more likely to be noncompliant. This finding is similar to that of Hyre and colleagues who also found that a longer duration of disease conditions may not only compromise patients’ compliance but also affect compliance negatively [
Our study was limited to patients receiving health care at the St. Patrick’s Hospital and did not include patients who attended other health centers in the municipality though they might have met the inclusion criteria. This limitation notwithstanding, we anticipate that since the hospital serves as a primary health facility in the municipal, it records majority of the OPD cases and as a result we may have been able to capture most of the patients with chronic conditions. Additionally, engaging in direct observation of clients and pill counting could have made the study findings more robust; however, due to the laboriousness of this methodology it could not be adopted. Despite these limitations, the findings of our study are encouraged.
Noncompliance to medication regimen among chronic disease patients is an important issue for public health consideration. This is evidenced by the results of this study which recorded an overall noncompliance level of 55.5%. The factors identified as contributing to medication noncompliance in this study were age, duration of disease condition, and difficulty in remembering medication instructions. To reduce the incidence of medication noncompliance among chronic disease patients, we first recommend that programmes be drawn in the various clinics for the purpose of health education and counselling of the patients during their waiting times with emphasis on the importance of their medications and the consequences of nonadherence. Emphasis must also be placed on the detrimental effects of mixing orthodox medications with herbal preparations. Opportunity should be created at the clinics during the waiting times for patient-to-patient interaction to share their experiences and challenges so that they could encourage themselves through the difficulties of their therapies. Doctor-to-patient relationship must also be improved so patients can freely discuss issues relating to their therapy. The government of Ghana, through the Ministry of Health, could be sought to extend the NHIS to cover more of the medications used by chronic disease patients. This would reduce the financial burden of patients on life-long therapies.
Community-Based Health Planning and Services
Ghana Demographic Health Survey
Ghana Health Service
National Health Insurance Scheme.
The study questionnaire used for collection of data is attached as a supplementary file. The dataset used can also be obtained from the corresponding author on reasonable request.
Ethical approval was obtained from the Ethical Review Committee of the KATH through the School of Medical Sciences, KNUST, Kumasi. Permission for the research was granted from the Administration of St. Patrick’s Hospital as well as the head of the special clinic of the hospital.
The purpose of the study was explained to every participant, confidentiality was assured, and either verbal or written consent was sought from every individual who participated in the study. Participation in the study was not compulsory and anonymity of respondents was respected. All respondents voluntarily gave verbal consent to participate in the study after the rationale of the study was explained to them. Couples were interviewed separately and care was taken to ensure privacy and confidentiality.
The study was not supported by any external source of funding nor was compensation or monetary benefit given to participants who took part in the study.
The authors declare that they have no conflicts of interest.
Bright Addo and Sally Sencherey conceived the design of the study. Sally Sencherey collected the data; Bright Addo and Michael N. K. Babayara analysed the data. Bright Addo and Sally Sencherey did the literature review and drafted the manuscript. Michael N. K. Babayara reviewed and edited the draft manuscript. All authors revised, read, and approved the final draft for submission.
The authors are grateful to all the study participants (patients) who voluntarily consented to participate in the data collection exercise. They also acknowledge the dedicated and hardworking research assistants who assisted during data collection. Lastly, they thank Mr. Emmanuel Kwaku Nakua for his technical input during the conduct of the study.
Study Questionnaire: medication noncompliance in patients with chronic diseases in the Offinso South Municipality.