https://orcid.org/0000-0003-1218-5826
Retraction
After this article [1] was published, it came to light that the authors did not have permission to use the MMAS®-8 scale [2].
In addition, PLOS identified concerns about the article’s [1] study design and reporting. The MMAS®-8 scale was adapted and translated for this study, but the methods used for the adaptation and translation were not clearly reported. Furthermore, based on information PLOS received it does not appear as though the adapted and translated versions were rigorously evaluated as to their construct validity and reliability. The article reports that a “pretest” was done, but the details of this pretest are unclear and the article does not report the rationale for the low sample size used in this assessment.
The corresponding author provided the following responses to the study design concerns.
- The questionnaire was translated to Amharic and then back translated to English to evaluate the consistency.
- Data were collected using adherence questions reported in [3], and per the authors the reliability of the MMAS®-8 scale was assessed previously in [4, 5].
- A sample size of 5% was used for the pretest.
- For the main study’s sample size, the authors used “the magnitude of the problem as 50%” on the basis that no similar study had been conducted at the study sites previously.
The information received from the authors in post-publication discussions did not fully resolve the study design and permissions concerns. PLOS ONE considers that these are critical issues given the reliance of the study on the MMAS®-8 scale and community standards for survey tool development and validation.
In light of these issues, the PLOS ONE Editors retract this article. We regret that the concerns were not identified and addressed during the article’s pre-publication peer review.
The corresponding author stated that the authors do not agree with retraction. The other authors did not respond directly or could not be reached.
The article was republished at time of retraction to remove contents from the Methods, Results, Conclusions, and Supporting Information that directly relate to the MMAS®-8 work.
Please note that the MMAS®-8 scale (used in this study) was originally published in 2008 [2], but the copyright for the scale was registered by Donald E. Morisky on September 21, 2018 (U.S. Copyright Registration No. TX0008632533/2018-09-21). The MMAS® trademark was also registered by Donald E. Morisky on January 29, 2019 (Reg. No. 5837374). Any reference to “MMAS” in this article [1] is for research fair use purposes.
23 Jun 2023: The PLOS ONE Editors (2023) Retraction: Medication regimen complexity and its association with adherence and blood pressure control among hypertensive patients at selected hospitals of South Gondar Zone: A hospital based cross sectional study. PLOS ONE 18(6): e0282464. https://doi.org/10.1371/journal.pone.0282464 View retraction
Figures
Abstract
Introduction
Despite the availability of effective antihypertensive medications, blood pressure (BP) control is suboptimal. High medication regimen complexity index (MRCI) is known to reduce adherence and may be the reason for poor BP control. However, there is no data in the present study areas. Hence, the aim of this study was to assess MRCI and its association with adherence and BP control among hypertensive patients at selected hospitals of South Gondar Zone.
Methods
A hospital based cross sectional study was conducted from December 1, 2020 to February 30, 2021 at selected hospitals of South Gondar Zone. Medication regimen complexity and adherence was evaluated using 65-item validated tool called MRCI (Text removed at time of retraction. See retraction notice for more information.). Multivariable logistic regression analysis was done to determine the association between predictive and outcome variables.
Results
About 3.3% of participants were classified as having high HTN specific MRCI whereas 34.75% of participants were classified as having high patient level MRCI. (Text removed at time of retraction. See retraction notice for more information.) Being illiterate, and having low HTN MRCI were more likely to have controlled BP in adjusted analyses. On the contrary, (Text removed at time of retraction. See retraction notice for more information.) not having health insurance, and having lower monthly income were less likely to have controlled BP.
Citation: Yazie TS, Mengistu WE, Asmare Z, Belete AM, Teshome AA, Yimer YS (2022) Medication regimen complexity and its association with adherence and blood pressure control among hypertensive patients at selected hospitals of South Gondar Zone: A hospital based cross sectional study. PLoS ONE 17(8): e0272717. https://doi.org/10.1371/journal.pone.0272717
Editor: Suman S. Thakur, CCMB: Centre for Cellular and Molecular Biology CSIR, INDIA
Received: November 27, 2021; Accepted: July 25, 2022; Published: August 18, 2022
Copyright: © 2022 Yazie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are available within the paper and its Supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Hypertension (HTN) is defined as an average systolic blood pressure of ≥ 140 mmHg and/or average diastolic blood pressure of ≥ 90 mmHg in adult individuals [1]. The worldwide prevalence of HTN among adults was 31.1% in 2010 and its prevalence was higher in lower and middle-income countries [1]. A systematic review and meta-analysis in Ethiopia revealed that the prevalence of HTN was estimated to be 19.6% in 2014 [2]. HTN is a very important public-health challenge and also the leading reason for the development of cardiovascular disease and premature death worldwide [3]. HTN is managed with a combination of antihypertensive drugs and self-care practices [4,5]. Antihypertensive drugs are known to scale back the speed of cardiovascular complications [4]. Despite the provision of effective drugs, blood pressure (BP) control is suboptimal. In Indiana, 57% of hypertensive patients who received antihypertensive drugs had uncontrolled HTN [6]. Additionally, in Ethiopia a systematic review and meta-analysis of pieces of the literature showed that 48% of hypertensive patients had uncontrolled HTN even if they were taking antihypertensive drugs [7]. The foremost reason for poor BP control is non-adherence to antihypertensive drugs and self-care practices [5,8]. Approximately 43% to 65.5% of hypertensive patients didn’t adhere to prescribed regimens and non-adherence to medications could be a potential contributing factor to the occurrence of concomitant diseases [9]. Several factors such as socio-demographic factors, individual’s knowledge and skills, individual’s beliefs and perceptions, physical/mental ability, quality of healthcare services, cost of treatment, patient resources, provider-patient relationship and communication, and drugs regimen complexity affect medication adherence, and subsequently therapeutic outcomes [10–12].
Medication regimen complexity comprises number of medicines, dosage forms, dosage frequency and other instructions for usage and it’s quantified by medication regimen complexity index (MRCI) [13]. Medication regimen complexity could be one of modifiable factors that affect patients’ adherence to their medication and in turn, it affects their clinical outcomes. Teamwork between pharmacists and other healthcare providers has been effective to simplify complex medication regimens to boost adherence and clinical outcomes [14,15]. A count of prescribed medications could be an easy and common measure of medication regimen complexity. However, medication count doesn’t address other regimen features contributing to complexity, like dosage forms, dose administration frequencies, and additional instructions, so it is unlikely to be a sufficient measure of regimen complexity. Moreover, medication count might not comprise non-prescription medications, which can contribute a significant part to medication complexity in some patients [16]. Median HTN specific MRCI was found to be 3 in one study [17], and 3.39 in another study [18]. Although some studies found that high MRCI had positive association with adherence, most studies found negative association between high MRCI and adherence [19]. By contributing for non-adherence, high MRCI may affect treatment effectiveness and patient safety because of the clinical consequences caused by not taking the medications, the utilization of below or above the recommended dose of medicines [20]. Moreover, the negative impact of non-adherence on blood pressure control has been established [21,22]. In Ethiopia, a big percentage of hypertensive patients (> 24%) were not adhered to their antihypertensive medications and there was high prevalence of uncontrolled hypertension (48%) [7,22,23]. The main contributing factor for uncontrolled hypertension is medication non-adherence [24]. There are limited studies regarding medication regimen complexity, and its association with medication adherence in hypertensive patients in the world. Moreover, there are no previous studies regarding the association of medication regimen complexity with BP control in the world. Therefore, this study is aimed to assess medication regimen complexity and its association with medication adherence and BP control among hypertensive patients.
Methods
Study area
The study will be conducted at 4 selected hospitals in South Gondar Zone of Amhara Region, Ethiopia. South Gondar Zone is one of 11 administrative zones of Amhara region, Ethiopia. It has 18 woredas (13 rural and 5 urban). There are 1 general hospital, 7 primary hospitals and 95 health centers in this zone. According to the 2007 national census conducted by Central Statistical Agency of Ethiopia (CSA), this zone has a total population of 2, 051, 738 and among this, 1, 041, 061 are men [25].
Study design and period
A hospital based cross sectional study was conducted from December1, 2020 to February 30, 2021.
Source and study population
All hypertensive patients on follow-up and attending the selected hospitals were used as a source population. Those patients who got treatment and fulfill the inclusion criteria were the study population.
Sample size and sampling technique
The sample size was calculated using a single population proportion formula as follows
Where n is the desired sample size for pollution, Z is the standard normal distribution set as 1.96 (which corresponds to 95% CI), P value means that we used positive prevalence estimated to maximize the sample size, and W is the degree of accuracy 0.05 desired. There is no previous study regarding MRCI, so a p value of 0.5 was used. Then the sample size was, n = (1.96)2 * 0.5*((1–0.5)/(0.05)2 = 384. By adding 10% non-respondent, the final sample size was 423. By random and purposive sampling method, 3 primary hospitals (Addis Zemen, Mekane Eyesus, Nifas Mewcha) and 1 specialized hospital (Debre Tabor Comprehensive Specialized Hospital) were selected, respectively. Then, using systematic random sampling method and proportional allocation to each selected hospitals, 423 study participants were interviewed and their charts were reviewed. Systematic random sampling was applied by assuming all study population come to each respective study sites from December 1, 2020 –February 30, 2021. The sampling interval was determined by dividing the total population of study sites by sample size of study sites. Finally, the sampling interval was 4, and every 4th of study participants was included until the desired sample was achieved.
Study variables
- Dependent variables: Adherence, BP control
- Independent variables: Medication regimen complexity, sex, age, co-morbidity, duration of hypertension treatment, adherence, type of antihypertensive drug, education level, income level, marital status
Inclusion and exclusion criteria
All adult hypertensive patients (≥ 18 years) who came to hospitals for follow up during the study period, treated with antihypertensive drug for at least 6 months, and willing to participate were included. Hypertensive patients who have hearing and cognitive impairment and incomplete chart were excluded. Furthermore, hospitalized patients during data collection period were excluded from the study.
Operational definitions
The Eighth Joint National Committee (JNC-8): It consists of members from different expertise such as in hypertension, primary care, pharmacology, nursing, epidemiology, evidence based medicine and the development and implementation of clinical guidelines in systems of care. JNC-8 evidence based guideline for the management of high blood pressure was prepared by the committee members. This guideline considers age, race and commodities while selecting antihypertensive drugs [26].
- Controlled hypertension: BP < 150/90 mmHg in hypertensive patients aged 60 or older, or BP < 140/90 mmHg in hypertensive patients aged less than 60 years and all ages of hypertensive patients with diabetes mellitus or nondiabetic chronic kidney disease [26].
- Uncontrolled hypertension: BP ≥150/90 mmHg in hypertensive patients aged 60 or older, or BP ≥140/90 mmHg in hypertensive patients aged less than 60 years and all ages of hypertensive patients with diabetes mellitus or nondiabetic chronic kidney disease [26].
- Hypertension specific MRCI: It is defined as the component of the MRCI that only included in antihypertensive medications [13].
- Patient level MRCI: It is defined as the overall MRCI, including antihypertensive medications in addition to all other prescription and OTC medications [13].
- MRCI: With the cut-off set at ≤4 for low complexity, 5–8 for medium complexity, and a score >8 will be considered as high complexity [12].
- (Text removed at time of retraction. See retraction notice for more information. The removed text cited references [27, 28].)
Data collection procedures
Clinical, demographic data and patients’ details of current medications were obtained from the chart. Socioeconomics, over the counter drugs, medication adherence status, and other demographics data that are not available from the chart were collected by interviewing the patients using structured questionnaire. The questionnaire was translated to Amharic language in order to make understandable by the study participants.
Medication complexity index (MRCI) is a validated 65-item tool for quantifying drug regimen complexity based on the quantity of medications, dosage form, dosage frequency, and additional instructions (eg, break/crush the tablet, take at a specified time, and relation to food/liquid) [13]. The 65-item tool consists of three sections related to the route of drug administration (section A), dosing frequency (section B), and additional directions (section C). The sum of the scores of each of the three sections (A+B+C) contributes to a complexity index [12].
(Text removed at time of retraction. See retraction notice for more information. The removed text cited references [27, 28].)
Data quality control
Pretest was done on 5% of the sample size before the actual data collection initiation in Ebinat primary hospital. Data collectors were 4 pharmacists. One data collector was assigned for each study site to interview the participant and to review the patient charts. Data collectors were trained intensively by the principal investigator and 1 co-investigator for 2 days on the contents of the questionnaire, data collection methods, and ethical concerns. The filled questionnaire was checked daily for completeness by the principal investigator.
Data processing and analysis
The data were entered and analyzed using SPSS version 21.0. Descriptive statistics like frequencies for categorical variables and means and Standard deviation (SD) for variables measured on a continuous scale was calculated. Association between predictive variables (regimen complexity, sociodemographic, and clinical data of patients) and dependent variables (adherence and BP control) was determined by using binary logistic regression. Therefore, univariate logistic regression, which is used to analyze the association between an individual independent variable and outcome of interest, was done to compute the crude odds ratio, whereas multivariate logistic regression for analyzing two or more variables with the outcome of interest was also done to compute the adjusted odds ratio (AOR). In univariate analysis, variables with p value < 0.2 were included in multivariate logistic regression and a statistical significance was set at a p value < 0.05.
Ethical consideration
Ethical approval was obtained from Ethical Review Committee of the College of Health Sciences, Debre Tabor University (Approval number: 2044/2020). Official letter of cooperation was presented to the medical director of selected hospitals in order to facilitate data collection process. Informed verbal consent was also obtained from each respondent after explaining the purpose of the study. Participant’s confidentiality was kept by not recording their identifiers on the data collection formats.
Results
Socio-demographics and clinical characteristics of the study participants
A total of 423 patients with hypertension who met the inclusion criteria were included in the final data analysis. The mean age of the study participants was 58.48 years with a SD of 12.96. Among the study participants, women comprised the higher proportion (59.8%). The higher percentages of participants (58.6%) were unable to read and write. The mean (±SD) duration of hypertension treatment of the patients was 3.98 ±3.61 years ranging from half a year to thirty seven years, and 49.4% of the participants had at least one more disease in addition to hypertension. Details of other characteristics are available in Table 1.
Medication regimen complexity, adherence and blood pressure control
Hypertension-specific MRCI ranged from 2 to 12.5; more than half (55.8%) was categorized as low complexity, 40.9% as moderate complexity, and 3.3% as high complexity. Patient level MRCI ranged from 2 to 28; approximately one-fifth (20.5%) was categorized as low complexity, 44.7% as moderate complexity, and 34.8% as high complexity. (Text removed at time of retraction. See retraction notice for more information.) Regarding to blood pressure control, the mean systolic and diastolic blood pressure reading were 132.34 (±18.21 SD) and 82.11 (+9.26 SD) mmHg, respectively. And majority of respondents (52.2%) were categorized as having good blood pressure control (Table 2).
(Text removed at time of retraction. See retraction notice for more information.)
(Text removed at time of retraction. See retraction notice for more information.) (Table 3).
Determination of the association between medication regimen complexity and other variables with blood pressure control
Having low hypertension-specific MRCI was four fold more likely to have controlled blood pressure level compared with having high hypertension-specific MRCI (AOR = 4.38, 95% CI: 1.08–17.84, P = 0.039). From other variables, (Text removed at time of retraction. See retraction notice for more information.) and not having health insurance were negatively related to controlled blood pressure in adjusted analyses (Text removed at time of retraction. See retraction notice for more information.) AOR = 0.47, 95% CI: 0.27–0.82, P = 0.008, respectively). Additionally, having monthly income of less than 1500 birr, and from 1500 to less than 2500 birr were negatively related to controlled blood pressure in adjusted analyses (AOR = 0.41, 95% CI: 0.22–0.78, P = 0.006; AOR = 0.57, 95% CI: 0.33–0.99, P = 0.044, respectively). In contrast, participants who were unable to write and read were three times more likely to have controlled blood pressure compared to participants who had higher education level (AOR = 3.34, 95% CI: 1.05–10.65, P = 0.041). However, patient level regimen complexity, and other variables (sex, age, marital status, doing aerobic exercise, employment status and having co-morbidity) were not significantly related to blood pressure control in adjusted analyses (Table 4).
Discussion
The current study aimed to determine MRCI and its association with adherence, and blood pressure control. This study used a validated 65 item MRCI tool for computing medication regimen complexity among patients with hypertension. As far as we know, there was no such type of study in Africa before, so this study was the first study in Africa. About 3% and 35% of patients in this study had high hypertension-specific MRCI and high patient level MRCI, respectively according to a category of the MRCI score. The mean (±SD), and median (range) of hypertension MRCI was 4.66 ± 1.75, and 4 (2–12.5), respectively. This result was higher than the findings from the previously done researches using 65-item MRCI as a complexity measuring tool [17,18].
Before the coming of 65-item MRCI, medication regimen complexity was computed employing a simple count of medicines within which it fails to catch many components of medicines leads to not adequately showing medication regimen complexity [16]. A validated tool called MRCI which is a 65-item instrument was employed in our study to determine medication regimen complexity which will be computed from data collected from patient’s medical chart [13]. Number of drugs, drug dosage forms, dosage frequency, and other instructions contribute to medication regimen complexity levels. In the present study, the prevalence of high regimen complexity was higher in patient-level complexity compared with hypertension-specific complexity, One might argue that the total patient-level regimen complexity should reveal the level of antihypertensive regimen complexity since patient-level MRCI contains the hypertension-specific MRCI. Nevertheless, the scoring could be affected by the gathering of other prescriptions and non-prescription drugs which frequently outshines the antihypertensive component. Therefore, a high patient-level MRCI might not surely arise from a high hypertension-specific MRCI. Hence, patient level MRCI includes all prescriptions and non-prescription medications, which is critical to judge, even when a selected disease treatment goes to be addressed. Earlier study showed that patient-level MRCI scores were greater than disease-specific scores for every patient group [17].
(Text removed at time of retraction. See retraction notice for more information. The removed text cited references [29–34].)
The contribution of complex medication regimens to non-adherence may be more than the total number of drugs taken do. Therapeutic regimen features like dosage frequency, dosage forms, and other instructions could significantly affect adherence principally in hypertensive patients [19]. However, as far as we know, medication regimen features are not taken into account as a possible factors affecting adherence in the globe including Ethiopia.
(Text removed at time of retraction. See retraction notice for more information. The removed text cited references [6, 35, 36].)
Our study found that 52.2% of patients had controlled blood pressure according to the Eighth Joint National Committee (JNC8) criteria [26]. This finding is higher than the results of previous studies in different part of Ethiopia with the same cut off points for the definition of controlled blood pressure [23,37]. The disagreement of findings may be due to difference in sample size, socio-demographic characteristics, and lifestyle practice of study participants. In study conducted at Debre Tabore General Hospital, some patients were smokers, percentage of female patients (51.2% versus 60.8%) and the sample size was lower (n = 346 versus 423) than the sample size of our study. The study conducted in Addis Ababa had some patients who smoke lower percentage of female patients than the present study. On the other hand, the result of the current study is lower than the findings of previous studies in Ethiopia [38,39]. The discrepancy of these findings may be justified by the difference in magnitude of co-morbidity, socio-demographic characteristics, and lifestyle practice of study participants. The percentage of female patients and mean age was lower in studies done at Gondar University Hospital, presence of comorbid illness and illiterate patients were lower in study done at Nekemte compared to the current study. Plus the extent of practicing recommended life style factors may vary from community to community as well as individual to individual.
High MRCI may affect treatment effectiveness and patient safety possibly by contributing to non-adherence. Non-adherence could be explained by not taking the medications, the use of under or overdoses of medications [20]. Moreover, the negative impacts of non-adherence on blood pressure control have been established [21,22]. In line with this idea, low hypertension specific MRCI was significantly associated with controlled blood pressure. In this study, patients with low hypertension specific MRCI was four times more likely to have controlled blood pressure compared with patients with high hypertension specific MRCI (AOR = 4.38, 95% CI: 1.08–17.84, P = 0.039). (Text removed at time of retraction. See retraction notice for more information.) This finding is in line with the findings of other studies [24,38,39].
Our study found that having monthly income of less than 1500 birr, and from 1500 to less than 2500 birr were less likely to have controlled blood pressure compared to having higher monthly income (AOR = 0.41, 95% CI: 0.22–0.78, P = 0.006; AOR = 0.57, 95% CI: 0.33–0.99, P = 0.044, respectively). (Text removed at time of retraction. See retraction notice for more information. The removed text cited reference [30].) In addition, in our study, those not having health insurance were less likely to have controlled blood pressure compared to those having health insurance (AOR = 0.47, 95% CI: 0.27–0.82, P = 0.008). This result is similar with the result of another study done in United States of America [40].
It is logical as education level increases, adherence to medications, and self-care practices could be increased and in turn, these lead to improvement of blood pressure control. In contrast to this logic, our study showed that those unable to write and read were more likely to have controlled blood pressure compared to those having higher education level (AOR = 3.34, 95% CI: 1.05–10.65, P = 0.041). This finding is supported by the result of another study conducted in Addis Ababa [37].
The crucial components of medication regimen complexity are the type of dosage form, the number of medications per day, dose frequency, and other instructions. But these crucial components have not been sufficiently addressed in the earlier studies. Furthermore, medication regimen complexity was not taken into account as an influential factor in achieving optimal adherence and blood pressure control. Therefore, being the first research in Africa that calculates medication regimen complexity and its association with adherence and blood pressure control by employing a validated instrument is the strength of the present study. Nevertheless, our research has a limitation of that MRCI was calculated using only medications ordered and recorded in the patients’ medical chart. Therefore, any medicines or directions not documented were missed.
Conclusion
Among participants, 3.3% had high hypertension-specific medication regimen complexity. The prevalence of high patient level MRCI was found to be higher than that of high hypertension-specific MRCI. (Text removed at time of retraction. See retraction notice for more information.) Low hypertension specific MRCI and being unable to write and read were significantly associated and more likely to have controlled blood pressure in adjusted analyses. (Text removed at time of retraction. See retraction notice for more information.) Carefully organized information about benefit of adherence and the negative impact of non-adherence should be provide for patients in order to improve adherence level and controlled blood pressure. Simplification of a complex medication regimen for patients with hypertension should be sought by physicians and pharmacists to improve blood pressure control.
Acknowledgments
We would like to express our deepest gratitude to Debre Tabor University for providing support to conduct the research. Moreover, we would like to forward our thanks to data collectors and hospitals where data were collected for their support to complete this study.
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