Hrev_master [page 56] [Healthcare in Low-resource Settings 2022; 10:10694] Non adherence to treatment and the associated factors in patients with epilepsy in Southern Ethiopia Kebede Abebe,1 Birrie Deresse,2 Keneni Gutema Negeri3 1Department of Internal Medicine, School of Medicine, College of Medicine and Health Sciences, Arba Minch University, Arba Minch; 2Neurology Unit, Department of Internal Medicine, Faculty of Medical Sciences, College of Medicine and Health Sciences, Hawassa University, Hawassa; 3School of Public Health, Health System Management and Policy Unit, College of Medicine and Health Sciences, Hawassa University, Hawassa, Ethiopia Abstract Adherence to antiepileptic drug therapy in people with epilepsy is critical for seizure control. Poor adherence to epilepsy treat- ment, on the other hand, is recognized as a worldwide problem, particularly in devel- oping countries such as Ethiopia. As a result, the current study seeks to ascertain patients’ adherence to antiepileptic medica- tions and the factors that influence it at Hawassa University Comprehensive Specialized Hospital in Southern Ethiopia. From February 1 to October 15, 2017, 187 people with epilepsy who were on follow- up at Hawassa University Comprehensive Specialized Hospital underwent a hospital- based retrospective medical review. Adherence was measured using Morisky Medication Scale-8. Data was entered and analyzed using the soft ware Statistical package for social sciences version 20. The results were summarized using cross-tabu- lations and frequency tables. While binary logistic regression was used to analyze fac- tors associated with adherence to antiepileptic drug therapy, significance was declared at p<0.05. According to the find- ings of the current study, about nineteen percent of the study participants were non- adherent to their treatment. Compared to patients with monthly income of less than 1000.00 ETB, those who earn>3000.00 ETB [AOR=0.164, 95% CI (0.038: 0.702)] and those with monthly income between 2000 and 3000 ETB [AOR=0.110, 95%CI (0.026:0.461)] [AOR=0.110, 95% CI (0.026:0.461)] are less likely associated with non-adherent to antiepileptic drugs (P<0.05). Likewise, patients who perceived epilepsy as psychiatric disorder [AOR= 0.250, 95%CI (0.087: 0.716)] compared to those who perceive it as neurologic, and those patients with seizure free period of less than one year [AOR= 0.206, 95%CI(0.076:0.562)] compared to those with seizure free period of more than one year are found to be less non adherent (p<0.05). Introduction Epilepsy, one of the most common neu- rological diseases worldwide, is a chronic brain disorder that affects people of all ages. It affects approximately 50 million people worldwide, with approximately 80% of those affected living in low- and middle- income countries.1 Adherence to antiepileptic drug therapy is necessary for effective seizure control and for ensuring that changes in patients treatment outcomes can be attributed to the recommended regimen.2 Medication non-adherence continues to be a major source of concern for both health care providers and patients due to the nega- tive effects it has on therapeutic outcomes.3 Poor adherence to Antiepileptic Drug (AED) therapy has been reported to be as low as 20% and as high as 80%,4 which is associated with poor epilepsy control, with a reported prevalence of 21-45%.5 In patients with uncontrolled seizures due to poor adherence to AEDs, non-adher- ence to medication regimen accounts for significant worsening of disease, death, increased health-care costs, and impaired productivity (e.g. missing school and work).6 Because of the limited health-care system in developing countries, the magni- tude and impact of poor adherence are expected to be greater than in industrialized countries.2 Even with the best available treatment regimen, more than 30% of People With Epilepsy (PWE) do not achieve complete seizure control. The fail- ure of such a large proportion of PWE to have controlled seizures is attributed to poor adherence to medication(s).7 While non-adherence is a problem with many determinants, there is a scarcity of lit- erature on the subject in Ethiopia in general and Hawassa Comprehensive Specialized Hospital (HCSH) in particular. As a result, the purpose of this study is to examine non- adherence to treatment and the factors that contribute to it in patients with epilepsy at HCSH in southern Ethiopia. Materials and Methods Study area and period This study was conducted in Hawassa University Comprehensive Specialized Hospital of Hawassa at Southern Ethiopia, Healthcare in Low-resource Settings 2022; volume 10:10694 Correspondence: Keneni Gutema Negeri, School of Public Health, Health System Management and Policy Unit, College of Medicine and Health Sciences, Hawassa University, P.O.Box. 1560 Hawassa, Ethiopia. Tel.: 251911424467; Fax: 046-2208755 Email: kenenigut2000@yahoo.com Key words: Adherence; epilepsy; antiepileptic drug; seizure control. Conflict of interest: The authors declare no conflict of interest. Funding: There is no fund received from external source for this study Availability of data and materials: All data generated or analyzed during this study are included in this published article. Ethics approval and consent to participate: The study was approved by the Institutional Review Board (IRB) of the College of Medicine and Health Sciences of Hawassa University. Written informed consent was obtained from participants of the study after the objective of the study has been explined to them. Additionally, each of the respondents were assured about the confidentiality of the information they provided as well as their right to withdraw at any time during participa- tion. Informed consent: Written informed consent was obtained from a legally authorized repre- sentative(s) for anonymized patient informa- tion to be published in this article. Received for publication: 21 June 2022. Revision received: 1 October 2022. Accepted for publication: 1 October 2022. This work is licensed under a Creative Commons Attribution 4.0 License (by-nc 4.0). ©Copyright: the Author(s), 2022 Licensee PAGEPress, Italy Healthcare in Low-resource Settings 2022; 10:10694 doi:10.4081/hls.2022.10694 Publisher's note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affili- ated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guar- anteed or endorsed by the publisher. Non -co mmerc ial us e o nly from February 01, to October 15, 2017 using a cross-sectional study design. Data was explored from a retrospective patient chart review. The sample size was deter- mined using a single population proportion formula with a 95% confidence interval as follows: n = (zα/2)2pq / d2 where n = sample size Z α/2= 1.96 (Z α/2 is the 95% confidence interval) P = estimated prevalence, 30% (taking the non-adherence rate from the study conduct- ed in Amanuel Specialized Mental Hospital.8 q = 1-p and d = margin of error (5%) Substituting all values the calculated sample size was resulted to be 322. However, the total number of PWE hav- ing follow up at HUCSH was small (esti- mated from registration log-book is 400), finite population correction formula9 was used to calculate the exact sample size (nf) as follows: nf = n*N/n+N-1 where nf = adjusted estimated sample size N = population size Substituting the values, the calculated sample size is 178. With consideration of 10% non-response rate, the final sample size was set to be 195. Systematic random sampling method, set to be every other patient, was used to recruit the samples for the study in each day of the data collection process. The Validated 8-item, MMAS-8, a self reporting tool was used to assess the patient’s adherence level to AED therapy in this study. The MMAS-8 is a generic self- reported, medication-taking behavior scale, validated for hypertension but used for a wide variety of medical conditions, which is the latest version of the scale, with a good internal consistency.10 It consists of eight items focusing on past medication use pat- terns. A higher score indicates high level of self-reported adherence. Adherence level is to be categorized as high (Score: 8), medi- um (Score: 6 and 7) and low (Score: <6). But for ease of analysis in the current study it was categorized in to non adherence (low score) and adherence (medium and higher score). A data abstraction form was prepared to extract pertinent information from patients’ chart. The form contained information on diagnosis, prescribed drugs, years of follow up and treatment, age at first seizure, seizure free period, adverse effects, and seizure control pattern. The questionnaire was pretested using 5% of the sample size by the investigators and necessary modifications were done. The collected data were crosschecked by the investigators and supervisors. Data was collected by trained nurses who trained for two days on how to collect the necessary data from charts. The collect- ed data were checked for completeness, cleaned prior to data entry and then entered using Epi Info™ version 3.1. Data analysis was carried out using Statistical Package for Social Sciences program version 20. Descriptive statistics like frequency and percentage were used to summarize charac- teristics and related information of the vari- ables. The Cross-tabular form of descriptive statistics was carried out to relate each vari- able to non-adherence and seizure control as a univariate. A Chi-square (c2) test was used to see the significance of association of categorical variables to non-adherence. From the univariate analysis, the variables with a p-value of <0.20 were considered as candidate for further analysis using multi- variable binary logistic regression method so that to assess the predictability of the independent variables of non-adherence. An estimate of Odds Ratios (OR) with the cor- responding 95% Confidence Intervals (CI) was used to determine the significant fac- tors. and p-values. The association was declared significant at p<0.05. Ethical clearance was approved by the Institutional Review Board (IRB) of the College of Medicine and Health Sciences of Hawassa University. A letter of support was asked from chief clinical director of HUCSH. Data was collected after permis- sion was asked from the study subjects fol- lowing a brief discussion with the people with epilepsy about the purpose and impor- Article Table 1. Scio-demographic characteristics of PWE having follow- up at HUCSH, February 01-October 15, 2017. Variables Number % Age in years 15-24 63 34 25-34 71 38 35-44 31 16 45-60 17 9 >60 5 2.5 Sex Male 98 52 Female 89 48 Marital Status Single 96 51 Married 87 47 divorced 2 1 Widowed 2 1 Place of residence Rural 45 24 Urban 142 76 Educational Status No formal education 14 8 Primary 51 27 Secondary 62 32 Tertiary 60 32 Occupation Student 44 24 Goernment Employed 43 23 Merchant 27 14 Farmer 19 10 House wife 18 10 Day laborer 31 20 Ethnicity Sidama 82 44 Amhara 25 13 Oromo 28 15 wolayta 24 13 Guraghe 13 7 Others 15 8 Religion Protestant 105 56 Orthodox 60 32 Muslim 20 11 Others 2 1 Monthly income in ETB 1000-1999 44 23.5 2000-2999 41 22 >3000 42 22.5 Unknown 15 8 [Healthcare in Low-resource Settings 2022; 10:10694] [page 57] Non -co mmerc ial us e o nly tance of the study and was collected from those who were willing to participate. Operational definition and defini- tion of terms Adherence to antiepileptic drugs: The extent to which the patient follows medica- tion instructions. For this study; Adherent: MMAS-8 score of ≥6, Non-adherent: MMAS-8 score of <6 Controlled seizure: Seizure free for ≥1 year. Results Socio-demographic characteristics of the participants Of the sought total sample size 187 epileptic patients card medical record was reviewed (96% response rate). Of all partic- ipants, 98(52%) were male. The majority (38%) of them was in the age group between 25-34 years of age. Fifty one per- cent of the participants reported to be unmarried; comparable with those who got married (47%). The urban dwellers com- prised the large proportion (76%). The edu- cational background of majority of the study participants was found to be sec- ondary and tertiary schools, 32% for both. The majority of them reported to be stu- dents; with comparable proportion of those who responded to be employed, 24 and 23% respectively. Financially, majority of them reported a monthly income of <1000 and 1000-1999, in ETBs accounting for 24% and 22% respectively (Table 1). Clinical characteristics of the study subjects According to the current analysis, in 90 patients (58%) the onset of the first epilep- tic seizure occurred between the age of 16 to 30. The majority of them (57.5%) had >5 years of follow up duration at HUCSH. Duration of illness (epilepsy) was >10 years for 42% of them. The highest proportion of them (56%) had seizure free period of less than one year. In this study, non-adherence level by MMAS-8 was found to 35 (18.5%) as detailed in Table 2. Patterns of antiepileptic drugs use On the bases of therapy with AED, monotherapy was found to be the most fre- quently prescribed treatment modality (84%), with Phenobarbitone being pre- scribed for the majority of the participants (59%), followed by phenytoin (18%). Among combination therapy, Phenobarbitone with phenytoin was com- monly prescribed (11%). Duration of the use of AED was documented >5 years for majority of the study participants (63%; Table 3). Factors associated with non adher- ence to antiepileptic drugs In this study, of the potential factors identified by bivariate logistic regression analysis, participants monthly income, knowledge about epilepsy (neurologic, hereditary, psychiatric or evil spirit) and seizure control status were found to be sig- nificant factors associated with non-adher- ence to antiepileptic drugs (p<0.05). Accordingly, patients with monthly income of >3000.00ETB were about 16% less like- ly to be non adherent than those who earn <1000.00ETB [AOR=0.164, 95% CI (0.038, 0.702)]. Likewise, patients who’s monthly income is between 2000 and 3000 ETB were about 11% [AOR=0.110, 95%CI (0.026:0.461)] less likely to be non adherent than patients with monthly income of <1000ETB. Patients who perceived epilep- sy as psychiatric disorder were 25% less likely to be non adherent than those who perceive epilepsy as a neurologic disorder [AOR= 0.250, 95%CI (0.087, 0.716)]. Patients with seizure free period of less than one year are about 21% less likely [AOR= 0.206,95%CI(0.076,0.562)] to be non adherent than those with seizure free period more than one year (Table 4). Discussion This study aimed to assess non adher- ence and the associated factors among PWE at HUCSH in south Ethiopia. The Validated 8-item Morisky Medication Adherence Scale (MMAS-8), a self reporting tool, was Article Table 2. Clinical characteristics of People with epilepsy on follow-up at HUCSH, February 01 to October 15, 2017. Variables Number % Age at onset of first seizure (n=187) ≤5 years 15 10 6-15 years 57 30 16-30 years 90 58 >30 years 25 13 Duration of follow up at HUCSH (n=187) 1-2 years 21 11 3-5 years 59 32 >5 years 107 57.5 Duration of illness (epilepsy) (n=187) ≤5 years 43 23 6-10 years 66 35 >10 years 78 42 Seizure free period (n=187) <1 year 105 56 1-2 years 59 31 ≥3 years 23 13 Co-morbidities (n=12) Hypertension 6 3.2 HIV 2 1.1 HIV and Stroke 1 0.5 Stroke 3 1.6 Adverse effect(s) (n=187) No 131 70 Yes 56 30 Adherence (by MMAS-8) ≥6 152 81 <6 35 18.5 TOTAL 187 100 Table 3. Patterns of Antiepileptic drugs use by peoples with epilepsy at HUCSH, February 01-October 15, 2017. Variables Number % Type of AED used Phenobarbitone 110 59 Phenytoin 34 18 Carbamazepine 13 7 Phenobarbitone and Phenytoin 20 11 Phenobarbitone and Carbamazepine 4 2 Phenytoin and Carbamazepine 6 3 The no. of AED used One drug 157 84 Two drugs 30 16 Duration of AED use/treatment 1-2 years 27 14 3-5 years 43 23 >5 years 117 63 [page 58] [Healthcare in Low-resource Settings 2022; 10:10694] Non -co mmerc ial us e o nly [Healthcare in Low-resource Settings 2022; 10:10694] [page 59] used in this study which revealed a non- adherence rate of 18.5%. In contrast to this, higher percentage of non-adherence rate was documented in previous studies; like in Yirgalem Hospital, Ethiopia 68%,5 Northern Nigeria 67.4%,11 Ambo Hospital, Ethiopia 53.8%12 and Dessie referral hospi- tal (34.1%).13 The probable reason for the discrepancies could be due to the difference in sampling methods, and operational defi- nitions for non-adherence to AEDs in which the current study considered only low score (<6 MMAS-8) excluding medium scores. Besides, as one would expect, in self report non adherence measures, patients could overvalue their adherence and report low non adherence to AEDs. Patients with higher monthly incomes (>3000ETB) were less likely to be non- adherent. This was consistent with the study in India.14 Patients with better income might have better opportunity for education in turn awareness towards the adherence, In the current study patients who perceive epilepsy as a psychiatric disorder were less likely to be non-adherent. While such per- ception is reported in previous studies,15,16 the possible reason for being more adherent to the medication could be their better awareness on psychiatric disorders in which adherence is important to control the dis- ease.17 Patients with uncontrolled seizure (seizure free period of less than one year) are less likely to be non-adherent. This is not unexpected as most patients with epilepsy prefer adherence to AEDs to reduce or even combat their seizure fre- quency effectively.18 Like any other study, the current study is not without limitation. One obvious limi- tation is the use of self-reported MMAS-8 method for adherence assessment that might have caused over estimation of adher- ence status. The cross-sectional nature of the study didn’t allow for follow up obser- vation, an approach with better design to assess risk factors associated with non- adherence status, consequently, in the cur- rent study design, there is generally no evi- dence of a temporal relationship between non adherence and factors associated with it while such evidence is more stronger. However these limitations do not invalidate the findings. Conclusions The non-adherence rate was discovered to be 18.5%. Monthly income, knowledge of epilepsy, and seizure control status were found to have a statistically significant rela- tionship with AED adherence. As a result, the hospital should devise strategies to improve current adherence levels. References 1. WHO. Epilepsy [Internet]. WHO 2019. Accessed 13 August 2019. Available from: https://www.who.int/news- room/fact-sheets/detail/epilepsy 2. WHO. Adherence to long-term thera- pies: evidence for action. 2003. Available from: https://apps.who.int/iris/handle/10665/4 2682 3. Munger MA, Van Tassell BW, LaFleur J. Medication nonadherence: an unrec- ognized cardiovascular risk factor. Med Gen Med 2007;9:58. 4. Buck D, Jacoby A, Baker G, Chadwick D. Factors influencing compliance with antiepileptic drug regimes. Seizure 1997;6:87-93. 5. Hasiso T, Desse T. Adherence to treat- ment and factors affecting adherence of epileptic patients at Yirgalem General Hospital, Southern Ethiopia: A prospec- tive cross-sectional study. PLoS One 2016;11:e0163040. 6. Getachew H, Dekema N, Awol S, et al. Medication adherence in epilepsy and potential risk factors associated with non adherence in tertiary care teaching hospital in southwest Ethiopia. Gaziantep Med J 2014;20:59. 7. Sweileh W, Ihbesheh M, Jarar I, et al. Self-reported medication adherence and treatment satisfaction in patients with epilepsy. Epilepsy Behav 2011;21:301- 5. 8. Beyene M, Engidawork E. Adherence and treatment outcome among epileptic patients of follow-up at Amanuel Specialized Mental Hospital, Ethiopia. Ethiopian Pharmaceut J 2018;33:53. 9. Naing L, Winn T, Rusli BN. Sample size calculator for prevalence studies. Arch Orofac Sci 2006;1:9-14. 10. Morisky D, Ang A, Krousel-Wood M, Ward H. Predictive validity of a medi- cation adherence measure in an outpa- tient setting. J Clin Hypert 2008;10: 348-54. 11. Johnbull O, Farounbi B, Adeleye A, et al. Evaluation of factors influencing medication adherence in patients with epilepsy in rural communities of Kaduna State, Nigeria. Neurosci Med 2011;02:299-305. 12. Tefera G, Woldehaimanot T, Angamo M. Poor treatment outcomes and associ- ated factors among epileptic patients at Ambo Hospital, Ethiopia. Gaziantep Article Table 4. Factors associated with non adherence to treatment in patients with epilepsy HUCSH, February 01-October 15, 2017. Adherence status to AED No (%) Yes (%) COR (95% CI) AOR(95%CI) Monthly income <1000ETB 3 (6.4) 44 (93.6) 1 1 1000-1999ETB 5 (10.2) 44 (89.8) 0.600 0.135 2.665 0.761 .159 3.635 2000-3000ETB 6 (34.8) 30 (65.2) 0.128 0.034 0.477** 0.110 0.026 0.461** >3000ETB 11 (24.43) 34 (75.6) 0.211 0.054 0.815* 0.164 0.038 0.702* Knowledge about epilepsy Neurologic 7 (12.7) 48 (87.3) 1 1 Hereditary (1) 3 (9.11) 30 (90.9) 1.458 0.350 6.078 1.404 0.304 6.490 Psychiatric (2) 24 (25.5) 70 (74.5) 0.425 0.170 1.066 0.250 0.087 0.716* Evil sprit (3) 1 (20.0) 4 (80.0) 0.583 0.057 5.998 0.605 0.048 7.704 Whether Epilepsy is Yes 25(23.1) 83 (76.9) 0.481 0.216 1.071 0.630 0.251 1.580 controlled by No 10 (12.7) 69(87.3) 1 1 modern drug Seizure control status Controlled (seizure free period ≥1yr) 7 (8.5) 75 (91.5) 1 1 Uncontrolled (seizure free period of <1yr) 28 (26.7) 77 (73.3) 0.257 0.106 0.623** 0.206 0.076 0.562** Number of AEDs used Monotherapy 27 (17.2) 130 (82.8) 1 1 Dual therapy 8 (26.7) 22(73.3) 0.571 0.230 1.418 0.747 0.252 2.213 Note: * is statistically significant at p<0.05, ** is statistically significant at p<0.01. 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