Factors Associated with Non-Compliance among Patients with Chronic Kidney Disease at the Departmental University Hospital of Borgou and Alibori in Parakou (Benin) ()
1. Introduction
Throughout the world, communicable diseases and epidemics that once accounted for the lion’s share of the burden of morbidity and mortality are gradually giving way to chronic and degenerative diseases and accidents. The major public health burden lies in non-communicable diseases. Over the past two decades, mortality from communicable diseases has fallen, while mortality from non-communicable diseases (NCDs) has risen. In 2022, the World Health Organization (WHO) reported that non-communicable diseases (NCDs) were responsible for 74% of all deaths worldwide, with 17 million people dying each year from an NCD before the age of 70. Most of these deaths occurred in low- and middle-income countries [1] [2] . Chronic kidney disease (CKD) is one of the NCDs that are increasingly attracting the attention of the scientific community, particularly in Benin, where the burden of NCDs is on the rise. In the Nephrology Department of the National Hospital and University Center Hubert Koutoukou Maga (CNHU-HKM) in Cotonou, Benin, it accounted for 91% of admissions in 2018 [3] . One of the main challenges in preventing this condition is to ensure that patients maintain a good level of therapeutic compliance. Compliance with treatment in chronic pathologies fluctuates in the face of the multiplicity of alternative solutions. The proportion of chronically ill patients adhering to their treatment is only 50%, depending on the pathology [4] . Numerous studies have reported that an average of one-third of patients, with all pathologies combined, could be considered totally non-compliant [5] . In Africa, the standard of living in the face of benefit costs is an obstacle to therapeutic compliance in NCDs; the black race remains the least adherent to treatment [6] . In Benin, a study carried out in 2017 by Vigan et al. [7] at the CNHU-HKM in Cotonou showed that the prevalence of non-adherence to treatment was 48.67% among hemodialysis patients. The aim of our study was to evaluate therapeutic compliance and determine the factors associated with therapeutic non-compliance among chronic kidney patients at CHUD/B-A in 2022.
2. Materials and Methods
This descriptive cross-sectional study with analytical aims took place in the Nephrology Department of the Departmental University Hospital of Borgou and Alibori in Benin from December 25, 2022 to March 15, 2023 and covered data from 2017 to 2022. These were patients diagnosed with chronic kidney disease according to the chronic kidney disease classification criteria [8] (KDIGO, 2012). Therapeutic compliance was assessed using the Girerd’s scale at 6 months follow-up, with a threshold of poor compliance greater than or equal to 3.
The minimum expected sample size was determined using the Schwartz formula:
And this minimum size was determined from the prevalence of non-adherence reported by Vigan et al. (48.7%).
N = sample size of population.
p = prevalence of non-adherence of 89.2% reported in Benin by Vigan et al. [7] in 2017.
q = (1 − p) = 10.8%.
α = 5% (first-species risk) hence the value of the centered reduced deviation Z = 1.96.
i = 0.06, desired accuracy for results.
i.e. a minimum size of 267 chronic kidney disease.
All patient records meeting the following inclusion criteria were included in our study: any individual received during the study period, diagnosed with chronic kidney disease according to KDIGO 2012 criteria whatever the stage of the disease, followed up for at least 6 months with regular assessment of Therapeutic compliance according to the Girerd’s scale [9] . Incomplete and unusable chronic kidney disease files were excluded (6 chronic kidney disease).
2.1. Study Variables
· The dependent variable was non-adherence to therapy assessed using the Girerd’s scale [9] .
· The independent variables were:
○ Socio-demographic and economic characteristics: Age, sex, professional status, marital status, income, distance travelled.
○ Clinical data: Time of consultation, previous treatment, reason for consultation, mode of admission, general condition, blood pressure, heart rate, stages of chronic kidney disease, heart rate.
○ Therapeutic data: Quantity of medications.
· Operational definitions of variables were:
○ CKD was diagnosed according to the classification criteria for chronic kidney disease [8] (KDIGO, 2012). Stages 2, 3, 4 and 5 were retained for a glomerular filtration rate (GFR) between 60 and 89 ml/min/1.73 m2, 30 and 59 ml/min/1.73 m2, 15 and 29 ml/min/1.73m2 and less than 15 ml/min/1.73 m2 respectively.
○ The chronic kidney disease epidemiology collaboration (CKD-EPI) equation was used as the basis for calculating GFR [8] .
○ Past treatment was the type of treatment used prior to the consultation which could be phytotherapy when it is the practice of phytotherapy; traditional when it is the use of a traditional healer who uses rituals and invokes spirits to heal the patient. This later treatment can be accompanied by phytotherapy and medication when instituted by a health professional;
○ The consultation delay is the time elapsed between the onset of symptoms and the first contact with health services.
○ Blood pressure was measured in a seated subject after 15 minutes of rest, using an automatic digital blood pressure monitor fitted with a cuff (belonging to the list of blood pressure monitors validated by the French Society of Hypertension). It was taken on the bare right arm, and placed on a table with the palm facing upwards. Three measurements were taken at five-minute intervals; the average of the last two measurements was the blood pressure retained for each patient. Blood pressure is considered high when it is greater than or equal to 140/90 mmHg [10] .
○ Heart rate (HR) was considered normal for a number of heartbeats between 60 and 100 beats (bpm) per minute. It was taken 15 minutes after the patient’s arrival, on the bare right arm, placed on a table, palm upwards, with the same automatic digital blood pressure meter fitted with a cuff used for blood pressure measurement.
○ The Girerd’s [9] scale is used to determine therapeutic non-adherence. The items on Girerd’s [9] scale are: forgetting to take medication, running out of medication, delay in taking medication, amnestic stroke, feeling that treatment is doing more harm than good, and feeling that there are too many pills to take. It is validated in a patient when the number of affirmative responses is greater than or equal to 3. A total of 0 indicates good compliance. A total of 1 or 2 indicates a minimal compliance problem, and a total of 3 or more indicates poor compliance. In this study, the first two modalities (good compliance and minimal problem) were categorized as good compliance and the last one (poor compliance) as non-compliance.
○ The number of medications was the amount of oral medication patients were taking on the day of the adherence assessment.
2.2. Data Collection and Analysis
Data were collected using a medical record tabulation form, covering patients’ socio-demographic, economic, clinical and therapeutic variables. Data were analyzed using R 4.3.0 software. Quantitative variables were expressed as means with standard deviations, and qualitative variables as proportions. Chi2 and Fisher tests were used to determine the association between two qualitative variables. Student’s T-test was used to compare means. A statistically significant association was established for a p-value < 0.05. The variables associated with the univariate analysis were introduced into the multivariate analysis model. Potential predictor variables were identified from binary logistic regression analysis, with odds ratios (OR) and their 95% confidence intervals (95% CI) determined.
2.3. Ethical Considerations
The survey protocol was validated by the Public health unit of the Cotonou Faculty of Health Sciences. Collection authorizations were obtained from the aforementioned unit and from the Head of the CHUD/B-A Nephrology Department. The anonymity and confidentiality of the data collected complied with the ethical principles applicable to medical research on human subjects contained in the Declaration of the World Medical Association of Helsinki.
3. Results
From January 1, 2017 to December 31, 2022, a total of 621 new patients were received in the CHUD/B-A Nephrology Department, including 345 cases of chronic kidney disease. Of the 345 cases of chronic kidney disease, 148 had good compliance and 197 had poor compliance.
3.1. Socio-Demographic Characteristics
The mean age of patients was 50 ± 14.9 years (CI 95% [48.4 - 51.6]), with extremes of 13 and 90 years. Patients aged between 40 and 60 were the most numerous (47.2%). Over half the patients (66.7%) were male, giving a sex ratio (M/F) of 2. The majority (36.8%) had no formal education. In terms of monthly income, 163 patients (47.2%) had monthly revenue of between 52,000 - 150,000 FCFA, most of them having come from less than 50 km away (61.7%) (Table 1).
3.2. Clinical and Therapeutic Characteristics of Patients
Some 275 patients, or 79.7% of the total, were admitted with an altered general condition. According to the mode of admission to the department, 65.5% of patients were referred by consultants, the majority of whom (69.9%) were general practitioners. More than half of patients (71.6%) came for consultation at stage 5 of the disease. More than three-quarters (85.5%) of patients had received prior treatment before consulting the nephrology department. Of these, 65.2% had opted for a previous herbal treatment (phytotherapy). All patients (100%) received therapeutic education prior to treatment. The average number of oral medications taken by patients was 4.3 ± 1.7, with extremes of 2 and 9 molecules. Most patients were on 4 molecules. The average number of molecules differed statistically (p = 0.004) between the group of patients with good compliance (4.6 ± 1.7) and those with poor compliance (4.1 ± 1.7) (Table 2).
3.3. Prevalence of Non-Compliance
Evaluation of the various items on the Girerd’s scale showed that 60.6% of patients were late in taking their medication in relation to the usual times, and 58% mentioned medication breakdowns. The evaluation of therapeutic compliance showed that 197 patients were noncompliant, giving a prevalence of non-compliance equal to 57.1% (Figure 1, Table 3).
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Table 1. Distribution of patients by socio-demographic and economic characteristics, CHUD/B-A 2017-2022.
a1 FCFA = 0.0016$.
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Table 2. Patient distribution by clinical and therapeutic characteristics, CHUD/B-A 2017-2022.
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Table 3. Evaluation of therapeutic non-adherence according to Girerd, CHUD/B-A 2017-2022.
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Figure 1. Classification of therapeutic adherence according to the Girerd’s scale, CHUD/B-A 2017-2022.
3.4. Factors Associated with Non-Compliance
3.4.1. Socio-Demographic and Economic Characteristics
Only two variables had a statistically significant relationship with compliance: monthly income (p = 0.005) and distance travelled (p = 0.01). Patients with a monthly income of more than 300,000 FCFA had an 80% lower risk of poor compliance than patients with an income of less than 52,000 FCFA. Patients from more than 50 km away were 1.8 times less compliant than patients from less than 50 km away. Other characteristics were not associated with compliance at the 5% level (Table 4).
3.4.2. Clinical and Therapeutic Characteristics
Among clinical and therapeutic characteristics, there was a statistically significant relationship between therapeutic compliance and mode of admission (p = 0.012), herbal medicine as previous treatment (p = 0.000), previous traditional treatment (p = 0.006), previous drug-based treatment (p = 0.005) and the number of tablets the patient took orally (p = 0.023).
Referred patients were 1.8 times less compliant than self-referred patients. The risk of poor compliance was reduced by 70% in patients who had opted for phytotherapy prior to consultation, and by 80% in patients who had opted for traditional treatment (80%) prior to consultation. Patients who opted for self-medication and/or a prescription from a healthcare professional were 2 times less compliant. As for the number of oral medications taken, the risk of poor compliance was reduced by 40% in patients taking more than 5 tablets (MRC and adjuvant treatments) (Table 5).
Multivariate analysis showed that the potential predictors of poor compliance in the Nephrology Department of CHUD/B-A in 2023 were monthly income (p = 0.009), phytotherapy (p = 0.004), traditional treatment (p = 0.045), mode of admission (p = 0.001) and number of tablets (p = 0.0042). Low monthly income and referral were risk factors for poor compliance, whereas previous phytotherapy and traditional treatment, and number of oral tablets above 5, were protective factors against poor compliance.
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Table 4. Relationship between patient compliance and socio-demographic and economic characteristics, CHUD/B-A 2017-2022.
a1 FCFA = 0.0016$.
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Table 5. Relationship between patient compliance and clinical and therapeutic characteristics, CHUD/B-A 2017-2022.
Patients with a monthly revenue of less than 52,000 FCFA and between 52,000 - 150,000 FCFA had respective risks of non-compliance of 3.7 [1.2 - 11.7] and 5.0 [1.8 - 14.2] compared with patients with an income of over 300,000 FCFA. Referred patients had 2.3 [1.4 - 3.7] times the risk of non-adherence compared with those who came directly for consultation. Previous herbal and traditional treatments and a high number of drugs were protective factors. They protected patients against non-compliance by 50%, 60% and 40% respectively, compared with those who had not taken any prior treatment (Table 6).
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Table 6. Multivariate model of potential predictors of CKD non-compliance at CHUD/B-A in 2022, CHUD/B-A 2017-2022.
Binary logistic regression: 1 is for the reference/comparison modality; aNon-compliance with treatment; ORb: adjusted odds ratio; c1 FCFA = 0.0016$.
4. Discussion
4.1. Prevalence of Non-Compliance
The prevalence of therapeutic non-adherence was 57.1% at CHUD/B-A in 2022. This result is similar to the one of Yousfi et al. [11] in Tunisia in 2012, who reported prevalences ranging from 20% to 60% and depending on the number of medications taken per day. Similarly, Bettaieb et al. [12] , measuring therapeutic compliance in chronic hemodialysis patients in Tunisia, and Vigan et al. [7] in Cotonou reported prevalences of 57.35% and 48.67% respectively. However, our result was lower than the 89.2% found in Togo by Sabi et al. [13] and higher than the 10.4% reported by Parmier et al. [14] in France in 2012.
4.2. Factors Associated with Patient Compliance
In our study, the factors associated with NOT in CKD patients were: income, mode of admission, number of drugs, and previous treatment with herbs or traditional healers. Our results are similar to those of Seck et al. [15] , who reported that factors contributing to poor compliance included adverse drug reactions and high drug costs.
4.2.1. Monthly Income and Treatment Accessibility
Patients with a monthly income of less than 52,000 FCFA and between 52,000 - 150,000 FCFA were respectively 3.7 times and 5 times more likely to be non-compliant with treatment than patients with an income of over 300,000 FCFA. This may be explained by the difficulty of meeting the direct and indirect costs of treatment for patients with modest incomes. Financial capacity affects the entire treatment process: consultation, complementary examinations, purchase of medication, compliance with follow-up appointments, logistics, etc. Income thus determines therapeutic compliance, as reported in a study by Higuchi [16] on access to care for the treatment of chronic diseases in low- and middle-income countries. The author identified high medical costs as a direct consequence of poor compliance with treatment.
4.2.2. Admission Mode
Referred patients were 2.3 times more likely to be non-compliant than those admitted directly. This finding can be explained by several factors. Firstly, voluntary request for care in a specialized nephrology department through direct admission, usually after failure of other therapies, is a sign of patient awareness and action. On the other hand, admission on referral, after referral by a healthcare professional and a richly nourished awareness campaign, may signal for the patient the hope of a cure, which is quickly stifled by the true and sometimes alarming therapeutic education delivered for chronic kidney patients. Finally, it is important to question the quality of the information provided upstream of the referral, and the multidisciplinary collaboration offered to patients who now have to deal with several medical expenses simultaneously. It is likely that overlapping appointments and the high cost of treatment by several specialists are at the root of therapeutic non-compliance. The distance travelled by referred patients could also be a factor in non-adherence, given the cost of transport; bearing in mind that most referred patients lived in rural areas.
4.2.3. Past Treatments
Phytotherapy and traditional treatment as previous therapies were protective factors against non-adherence. This could be explained either by patients’ awareness of the failure of these practices in response to the symptoms of chronic kidney disease, or by a feeling of invulnerability acquired as a result of these treatments, which would henceforth authorize the patient to resort more serenely to so-called more modern therapies. Added to all this is the fear of dialysis at the final stage of the disease evolution, which could raise the patient’s awareness and improve compliance. Therapeutic education carried out at each check-up by healthcare staff as part of their follow-up of patients with CKD could be an important element in the return to good therapeutic compliance. As such, this result could signify the success of awareness-raising messages shared within the department.
4.2.4. Number of Drugs in the Patient Treatment Regimen
According to the results of our study, a high number of molecules protects against non-adherence to therapy, and thus improves compliance. Each additional molecule in a patient’s regimen represents an additional disorder or dysfunction to be corrected. This could have a deterrent effect on patients, especially in view of the therapeutic education they receive. As the kidneys are involved in the elimination of patients, those already suffering from CKD are at risk of progressing to the terminal stage, with the possibility of treatment by hemodialysis. What’s more, adjuvant molecules or those prescribed in response to symptoms improve clinical conditions and give patients renewed hope, making them even more compliant. Family pressure and support, linked to the fear aroused by the large quantity of drugs to be used, could also explain this result. Family and friends would see this as a worsening of the patient’s condition, requiring more rigorous monitoring of medication intake to avoid complications or even premature death.
4.3. Limitations of Study
Our study did not enable us to assess compliance with treatment in terms of follow-up appointments, prescription and paraclinical test renewals, hygienic and dietary measures, psychosocial aspects or quality of life. For a chronic disease with long-term treatment, the evaluation of these parameters could provide a holistic assessment of the handicaps to therapeutic compliance. A qualitative assessment of the reasons for poor compliance was also lacking.
5. Conclusion
Non-adherence to treatment is a major problem among patients with chronic kidney disease at CHUD/B-A while referred patients with low incomes were less compliant. From diagnosis to treatment, including the mode of admission, communication and therapeutic education were essential for good treatment compliance. So, adherence to treatment can be improved if work is carried out at the time of diagnosis, before referral and at the time of treatment initiation. Hence, the raising of awareness could be more developed among patients and those around them about the benefits of good treatment compliance and keeping follow-up appointments.
Acknowledgements
Our thanks go to the staff of CHUD/B-A and colleagues from the Public Health Unit of the Faculty of Health Sciences (FSS).
Authors’ Contributions
All authors have contributed to the correction of the document.