Facilitators and Barriers to Medication Adherence Among Stroke

Clinical study

Facilitators and barriers to medication adherence among stroke survivors in India

S.D. Shani a, P.N. Sylaja b,⇑, P. Sankara Sarma c, V. Raman Kutty d

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a Achutha Menon Centre for Health Sciences Studies (AMCHSS), Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Thiruvananthapuram, Kerala, India bDepartment of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Thiruvananthapuram, Kerala 695 011, India c Achutha Menon Centre for Health Sciences Studies (AMCHSS), Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Thiruvananthapuram, Kerala, India d Research Director, Amala Cancer Research Centre, Thrissur 680555, India

article info

Article history: Received 20 December 2020 Accepted 11 March 2021 Keywords: Medication adherence Risk factor control Stroke survivors Life style India

. Introduction

abstract

Strict compliance with medication and life style modification are integral to secondary stroke prevention. This study was undertaken to find out medication adherence among stroke survivors and factors associ ated with it. Cross sectional survey among stroke survivors was conducted. Interview based self-reported medication adherence was defined as consumption at least >80% of their medications for last two weeks, based on last prescription. Structured interview using pretested interview schedule was done to collect other data. Sequential step wise logistic regression analysis was done to find out the facilitators and bar riers to medication adherence. Two hundred and forty stroke survivors (mean age 58.64 ± 10.96 years; 25.4% females) with a mean post-stroke period of 6.65 ± 3.36 months were participated. Overall medica tion adherence was 43.8% (n = 105). Medication adherence was 34.3% (n = 134), 52.6% (n = 190) and 56.7% (n = 224) for antidiabetics, antihypertensives and statins respectively and was associated with risk factor control (Diabetes: Odds Ratio (OR) = 4.85; 95% Confidence Interval (CI) 2.12–11.08, Hypertension: OR = 3.42; 95% CI 1.83–6.4, Dyslipidaemia: OR = 3.88; 95% CI 1.96–4.04). Having daily routine (OR = 2.82; 95% CI 1.52–5.25), perceived need of medication (OR = 2.33; 95% CI 1.04–5.2) and perceived poor state of health (OR = 2.65; 95% CI 1.30–5.40) were facilitators. Memory issues (OR = 0.34; 95% CI 0.16–0.71), side effects (OR = 0.24; 95% CI 0.11–0.42) and financial constraints (OR = 0.46; 95% CI 0.24–0.91) were barriers to medication adherence. Establishing daily routines, periodic reminders, finan cial supports to buy medicines and patient education can enhance medication adherence to prevent future strokes. 2021 Elsevier Ltd. All rights reserved. ence varies from 40 to 85% among stroke survivors [5–9]. The low est ranges are from studies conducted in China and United States Survivors of first ever stroke have an increased chance of stroke recurrence: 11.1% at 1 year; 26.4% at 5 years; and 39.2% at 10 years [1]. Studies have shown that effective secondary stroke prevention is associated with an 80% reduction in risk of early recurrent strokes [2]. But one out of every four strokes are recurrent [3], sug gesting inadequate secondary stroke prevention strategies or lack of adherence to it. Adherence to medication is important in the management of chronic diseases and is a mediator between the treatment and patient outcome. Over 30–50% of medicines prescribed for long term illnesses are not taken as directed [4]. It is a major problem in both developing and developed countries and medication adher

E-mail address: Sylajapn@hotmail.com (P.N. Sylaja). https://doi.org/10.1016/j.jocn.2021.03.019 0967-5868/ 2021 Elsevier Ltd. All rights reserved.

among low income under- privileged groups [7–10]. Medication nonadherence is associated with poor control of risk factors, worse outcome [11] and risk of death (OR: 7.99; 6.28–10.18) when com pared to adherent group [12]. Adherence to antihypertensives after stroke have been shown to reduce the incidence of cardiovascular events [13], hospitalisations and health care costs [14]. Studies on factors associated with medication adherence among stroke survivors are mainly from developed countries and they have identified factors like difficulties in taking medications, fear of over medication, lack of knowledge on stroke and medication benefits and perceived discrimination from health care setting as the barriers [15–17]. Support from caregivers and healthcare pro fessionals, fear of recurrence, advanced age, following daily routi nes, fixed dose combination and counselling were facilitators for treatment adherence [8,15,17–19]. The use of alternate medicine S.D. Shani, P.N. Sylaja, P. Sankara Sarma et al. Journal of Clinical Neuroscience 88 (2021) 185–190 is also a reason for stopping modern medicine among stroke sur vivors especially in Asian countries [20]. Causes of non-adherence to treatment are multifactorial and studies exploring multidimensional indicators of adherence to medications intended to prevent recurrent strokes are lacking especially in low and middle income settings [19]. Identifying the modifiable factors which affect adherence to medications to pre vent stroke recurrence can help in planning intervention to improve medication adherence. The results can also inform rele vant policy decision makers to improve stroke care in the community. 2. Objectives of the study The aim of our study was to assess the level of medication adherence among survivors of first episode of stroke, to analyse the control of risk factors among them and to identify the facilita tors and barriers to medication adherence among survivors of first episode of stroke. 3. Methodology A cross sectional survey among hospital based stroke survivors of first episode of stroke within a post stroke period of three months to one year was conducted to find out the medication adherence, risk factor control and facilitators and barriers to med ication adherence among them. This study received Institutional Ethics committee approval (SCT/IEC/1327). Written informed con sent was obtained from all the participants. 3.1. Setting Sree Chitra Tirunal Institute for Medical Sciences and Technol ogy (SCTIMST), Thiruvananthapuram is a tertiary level referral hos pital which admit patients of neurology and cardiology specialities. The comprehensive stroke care unit admits 450–500 patients per year. The patients are followed up in the stroke clinic on an outpa tient basis. 3.2. Population and sample Survivors of first episode of stroke refers to the survivors of diagnosed cases of ischemic stroke, haemorrhagic and transient ischemic attack with evidence of acute infarct. The study popula tion for this cross sectional survey were the survivors of first epi sode of stroke within a post stroke period of three months to one year. The risk factor control is defined as achieving the desired levels of blood pressure, blood sugars and cholesterol. The targets for risk factor control were taken from National Cholesterol Education Pro gram (NCEP) ATP III guidelines [21] for Cholesterol (LDL < 100 and TC (200), American diabetes Association guidelines [22] (FBS 70– 130 and HBAIC < 7) for Diabetes control and ASA secondary stroke prevention guidelines [23] for blood pressure (SBP < 140 and DBP < 90). 3.3. Sample size Sample size for this study was determined based on the follow ing formula: n=za2 p (1-p)/d2, where za = 1.96 (at 95% confidence interval), p = 40% (prevalence of medication adherence among stroke sur vivors [7], d = absolute precision of 7%. A sample size of 185 was calculated using Open Epi. Considering a non-response rate of 20%, the total sample size came out to be 222 participants. To 186 round up, the total sample size estimated for this study was 240 participants. 3.4. Sample selection procedure Survivors of first episode of stroke, aged 18 and above within a period of three months to one year were recruited from the stroke clinic for this survey. The data collection period was February 2019 to August 2019. Comatose, severely disabled [modified Rankin Scale (mRS) score = 5] or having multiple coexisting diseases with life expectancy less than six months were excluded from the study. Data were collected during their follow up visit to stroke clinic. 3.5. Data collection tools and techniques Pretested structured interview schedule was used to collect data. The interview schedule included sections to collect sociode mographic data, family support, financial support for medications, health care seeking behaviour, access to health care and relation ship with health care provider. Details regarding stroke survivor’s belief about treatment and disease and presence of anxiety and depressive mood were also assessed. Compliance with other recommended health advice like follow ing a healthy diet, physical activity, tobacco abstinence and limit ing alcohol consumption were also assessed. A healthy diet was defined consumption of approximately 350-400gms of vegetables a day, one medium sized fruit or two small sized fruit per day and avoiding extra salts, sweets and fried foods. Recommended physical activity was at least 30 min of moderate physical activity like walking for at least 3 days a week. Standard instruments were used to measure blood pressure, weight, height and waist circumference (WC). Clinical information sheet was used to collect data from medical records which included stroke related data, history of diabetes, hypertension, dys lipidaemia, their treatment and blood investigation results of fast ing blood sugar, glycosylated haemoglobin, low density lipoprotein and total cholesterol. Modified Rankin scale was used to measure physical disability at the time of follow up. Medication intake history of previous 14 days prior to recruit ment was collected through face to face interview with patients and their primary care giver. The last given prescription from our hospital was used to identify the medications prescribed for the patients. The patients and the care givers were asked to fill the check list of medication intake prior to 14 days of recruitment. The check list included the name of medication and frequency. Medication intake history was taken for antiplatelets, antihyper tensives, antidiabetics, statin and anticoagulants. This medication intake history was compared with the prescription and medication adherence was calculated for each category of medication. Self reported medication adherence was defined as consumption of at least >80% of their medications for last two weeks, based on their last prescription. Medication adherence was calculated for five cat egories of medication; antiplatelets, antihypertensives, antidiabet ics, statin and anticoagulants. Overall adherence was defined adherence to all the categories of medication prescribed. The self-reported reasons for non-adherence were also collected from non-adherers. 3.6. Analysis For meeting the first objective, the proportion of patients who had consumed at least >80% of their medications for last two weeks, based on their last prescription was reported. To find out the risk factor control, the proportion of patients who achieved the targeted level of diabetic control, hypertension control and dyslipidaemia control were analysed. Separate models S.D. Shani, P.N. Sylaja, P. Sankara Sarma et al. Journal of Clinical Neuroscience 88 (2021) 185–190 were created to find out the effect of medication adherence on risk factor control, adjusted for the following: physical activity, healthy diet, current smoking and alcohol use. Adjusted odds ratio and 95% confidence interval were reported. Bivariate analysis with medication adherence as outcome vari able and patient’s characteristics was done to find out facilitators and barriers to medication adherence. Odds Ratio (OR) with 95% Confidence Interval (CI) was reported. Those variables have statis tical significance < 0.1 were considered for regression analysis. Table1 Characteristics of the study participants. Variables Categories n (%) Age 45 years > 45 years 31 (12.9) 209 (87.1) Sex Male Female 179 (74.6) 61 (25.4) Place of residence Rural Urban 184 (76.7) 56 (23.3) Sequential stepwise logistic regression analysis was done in the following steps. House hold monthly income (Rupees) <1500 1500–5000 5000 88 (36.7) 83(34.6) 69(28.8) Step1- The variables were categorized into five groups. Sociode mographic variables included age, sex, ownership of vehicle, belongs to poor households and place of residence. The second cat egory of variables are treatment access, availability of medicine in Education of patient Up to 10th standard Graduate professional Occupation of patient Manual labourer Skilled worker Professional Unemployed 175(72.9) 50 (20.8) 15(6.3) 141(58.8) 77 (32.1) 14(5.3) 8 (3.3) the nearest medical shop and buying medicines for one month. Number of family members 4 > 4 97(40.4) 143 (59.6) Stroke related variables included stroke severity at ictus, stroke severity at recruitment and presence of memory issues. Variables System of treatments followed for stroke Modern medicine Ayurveda Homeopathy 238(99.2) 15(6.3) 2(0.8) related to patients belief about treatment and disease were per ceived need of medication, experiencing side effects, fear of depen BMI Under weight Normal Over weight Obese 2(0.8) 60(25) 147 (61.3) 31(12.9) dence to medications, belief that medicine prevents recurrence, simple treatment regimen and perceived poor state of health. Separate logistic regressions were built including age and sex in each model, corresponding to each group of predictors resulting in five models. Those variables having significance level < 0.1 in Diabetes Yes No 154(61.2) 86 (38.8) Hypertension Yes No 213(88.6) 27 (11.4) Dyslipidemia Yes No 232(96.7) 8(3.3) Type of stroke Ischemic Hemorrhagic TIA 201(83.8) 30 (12.5) 9(3.8) each model were considered for the second step. Step2- Those variables having significance level < 0.1 from each Post stroke period 3–6 months 6–9 months 9– 12 months 141(58.8) 31 (12.9) 68(28.3) category were selected for regression analysis at second step. The variables were availability of medicine in the nearest medical shop, NIHSS At ictus At recruitment Mean (SD) 6.5(5.9) 1.2(2.2) mRS at recruitment 1.2(1.3) buying medicines for one month, continuing physiotherapy at home, having a daily routine, NIHSS < 4 at review, presence of Waist circumference (cm) 97.34(9.5) memory problem, experiencing side effects, belief that medicine prevents recurrence and perceived poor state of health. A multiple logistic regression model was created including all the variables plus age and sex. Step3- Final model was created by backward elimination from step 2, with alpha level set to 0.05. 4. Results 4.1. Participant’s characteristics Stroke survivors (n = 240; mean age 58.64 ± 10.96 years; 25.4% females) with mean years of education 9.78 ± 3.77 constituted the sample. Ischemic stroke patients constitute 83.8% and mean post stroke period was 6.65 ± 3.36 months. Rural inhabitants were 76.7%. Among the participants 32.1% belonged to the category of poor households based on official government classification. Man ual laborers were 58.8%; 32.1% were skilled workers, 5.3% were professionals and rest were unemployed. The work or employment status of 44.6% were affected by stroke. Nearly 5.4% had to go on long leave and 6.3% lost their jobs. The other characteristics of the study population is given in Table 1. The mean cost of medication per week was ₹377.96 (minimum 100; maximum 2000). Majority (68.5%) had medical insurance which covered only their inpatient treatment expenditure, in which 84.8% had insurance provided from government agencies. Only 26.7% (n = 64) had any form of assistance to cover the expen diture of their prescribed medications after discharge. Of them 40.6% were getting reimbursement for their outpatient care and 59.4% were getting free medications from government hospitals. Modern medicine was followed for the treatment of stroke by 99.17% while 6.25% followed Ayurveda treatment also. The mean distance to nearby hospital was 3.4 ± 2.6 km and that of a medical shop was 2.5 ± 1.9 km. >50% of the patients had a last medical 187 NIHSS – National Institute Health Stroke Scale, mRS –Modified Rankin Scale. check-up within 1–3 months. Usual check-up was done in nearby government hospital by 51.7% while around 32% had gone to physicians’ private clinics. >90% of the participants had good rela tionship with treating physician and had not experienced any com munication problem in terms of language or understanding. 4.2. Medication adherence Overall medication adherence was 43.8% (N = 240). Lowest adherence was seen for antidiabetic medication 34.3% (n = 134). Adherence to antiplatelets and anticoagulants were nearly same [62.4% (n = 196) and 61.9% (n = 21)]. Adherence to antihyperten sives and statins were 52.6% (n = 190) and 56.7% (n = 224) respec tively. The self-reported reasons for nonadherence (n = 135) were forgetfulness (41.5%), experiencing side effects (28.9%), perceived lack of need (8.1%), journeys (27.4%) and not being able to buy medicine for one month (62.2%). 4.3. Risk factor control and medication adherence Patients with Diabetes, hypertension and dyslipidaemia consti tuted 64.2%, 88.8% and 96.7%. The prevalence of smoking and alco hol use were very low among stroke survivors. Only 4.2% were current smokers and 3.8% were using alcohol. More than half of the patients were following the recommended physical activity (57.6%), but a healthy diet was followed by only 12.9%. Optimal level of low density lipoprotein was present in 63.3% (n = 207). Among the 228 patients in which value of total serum cholesterol was available, desirable level of total serum cholesterol was achieved in 92.1%. Diabetic control was achieved in 26% (n = 154), desired level of blood pressure was achieved in 36.2% (n = 213), lipid control was achieved in 72.9% (n = 207). Multiple S.D. Shani, P.N. Sylaja, P. Sankara Sarma et al. Journal of Clinical Neuroscience 88 (2021) 185–190 logistic regression analysis revealed medication adherence was associated with risk factor control when adjusted for physical activity, healthy diet, current smoking and alcohol use (Table 2). 4.4. Facilitators and barriers to medication adherence Strokes survivor aged 45 years (58.1%), females (52.5%) and those had an education of higher secondary and above (47.7%) had higher medication adherence compared to others. Profession als had highest medication adherence (64.3%) but it was least among manual labourers (41.6%). People belonging to poor house hold had low adherence compared to others (33.8% Vs 48.5%). Medication adherence was lesser in patients with a post stroke duration of 9–12 months (41.2%) compared to patients with post stroke duration of 3–6 months (44.7%). Family support or treating physician did not had any significant influence on medication adherence. The participants characteristics and its relationship with medication adherence (p value <0.1 and their odds ratios) are summarised in Table 3. In the final model, availability of medicine in the nearest med ical shop, having a daily routine and perceived poor state of health were facilitators and memory issues, minimum neurological defi cits at recruitment and experiencing side effects were barriers to medication adherence. The final model with odds ratios is dis played in Table 4. 5. Discussion This study revealed that adherence to medications intended to control risk factors and prevent stroke recurrence was very low as 43.8% in our setting. The reported medication adherence varies widely in studies ranging from 40% to 85% [5–10]. This present study being done in a low and middle income country supports the findings of studies done among low and underprivileged groups [7,10]. In a study conducted using validated adherence measure in a low income setting found out an adherence level of 40% among stroke survivors [7]. This study used consumption of at least >80% of their prescribed medication during last two weeks. Our study result is similar to the study done using validated mea sure. We could reduce the recall bias as we had assessed the med ication intake status of last two weeks only. Availability of medication in the nearest medical shop and buy ing medicine for one month were facilitators to medication adher ence. Those people had financial constraints expressed not being able to buy medicine for one month, which led to missing of doses till they bought next time. Studies done among low income group had identified high cost of medication and difficulties to access health care as barriers to medication adherence [7,10]. Presence of neurological deficits and perceived poor state of health were associated with higher adherence. The presence of neurological impairment can be a confounder for factors like mem ory issues, perceived poor state of health and having a daily rou tine. However, the confounding caused by neurological Table 2 Adjusted models for medication adherence and risk factor control. impairment on medication adherence has been accounted for in logistic regression analysis. The initial step in the process of devel opment of medication adherence behaviour is perceived need of medicine. The disability caused by stroke and subsequent per ceived poor state of health thus remained a major facilitator for medication adherence. A systematic review of psychological deter minants of medication adherence also revealed perceived medica tion necessity is a major facilitator to medication adherence [24]. Following daily routine facilitated medication adherence, as previ ously reported [18]. The most common self- reported reason for non-adherence was simple forgetfulness and when they remember it would have been the time for next dose. Our study showed memory issues and expe riencing side effects as major barriers to medication adherence. Studies done in both developing and developed countries showed similar results [11,15]. We collected self-reported data on memory issues and side effects, which remains one of our limitations. The identified factors negatively affected adherence among low income groups were mainly related to health care system such as inadequate continuity of care, high cost of medication, poor com munication from care provider, difficulty in accessing care, per ceived discrimination and low trust with treating doctor [7,11]. But studies done in developed countries identified fear of side effects, forgetfulness, higher education and increased quality of life as barriers to medication adherence [15,25,26]. Our study yielded mixed results. The effect of age, gender, income and education on medication adherence was inconsistent and varied in studies conducted in dif ferent settings. In our present study, on bivariate analysis there was a significant difference between medication adherence among people belonging to poor households, but when adjusted for other factors the difference was not significant. A study done in a devel oped country among stroke survivors also found out no effect of income on medication adherence [26]. We did not get any signifi cant difference between medication adherence among those who had financial support to buy medications or free medications avail able and who did not have such facility. A systematic review of 21 studies showed almost similar level of poor medication adherence among patients with free medication and with different payment schemes [27]. Gender and medication adherence is still controver sial with contrasting evidence [25,26]. We found higher adherence among females and those with higher education and professionals but for these variables the difference was not significant. In con trast to some qualitative study findings, we could not get any sig nificant difference in adherence among those have care giver support or not [11,15]. Our study revealed poor control of risk factors among stroke survivors. Previous studies brought out the similar results [11,28]. None of the studies assessed the effect of medication adherence and following other health recommendation on risk fac tor control. We could prove that medication adherence is indepen dently associated with risk factor control. There is no substitute for medication adherence. Risk Factors On treatment Adherence# Risk Factor control* ORa 95% CI n % n % n % Lower limit Upper limit Diabetes (N = 154) 134 87.07 46 34.3 40 26 4.85 2.12 11.08 Hypertension (N = 213) 190 89.05 100 52.6 77 36.2 3.4 1.83 6.4 Dyslipidaemia (N = 232) 224 93.97 127 56.7 95 64.6 3.88 1.96 4.04 Abbreviations: OR- Odds Ratio, CI- Confidence Interval *Outcome variables; Diabetic control-Among patients with diabetes FBS 70–130 and HBAIC > 7, Hypertension control SBP < 140 and DBP < 90 among hypertensives and LDL < 100 and TC < 200 among patients with dyslipidaemia. #Adherence to corresponding categories of medication was taken. ORa –adjusted OR (Adjusted for physical activity, healthy diet, current smoking and alcohol use).

S.D. Shani, P.N. Sylaja, P. Sankara Sarma et al. Journal of Clinical Neuroscience 88 (2021) 185–190

Table 3 Bivariate association between characteristics of stroke survivors and medication adherence. Variables Categories Total participants N = 240 Adherence* Yes (n = 105) OR 95% CI for OR

Age 45 >45(R) Sex Male Female(R) 31 209 179 61 12.9 87.1 74.6 25.4 18 87 73 32 58.1 41.6 40.8 52.5 1.94 0.90 4.17 0.62 0.35 1.12 Place of residence Urban Rural (R) 56 23.3 31 55.4 184 76.7 74 40.2 1.84 1.01 3.37 Ownership of vehicle Yes No (R) Belongs to poor households No Yes (R) 135 105 77 163 56.3 43.8 33.1 67.9 66 39 26 79 48.9 37.1 33.8 48.5 1.62 0.96 2.72 0.54 0.31 0.95 Availability of medicines in the nearest medical shop Yes No (R) 156 84 65 35 80 25 51.3 29.8 2.48 1.41 4.36 Buy medicine for one month Yes 106 44.2 55 51.9 No (R) 134 55.8 50 37.3 NIHSS at ictus Mild stroke( 4) Moderate 114 47.5 43 37.7 to severe(>4)(R) 126 52.5 72 57.1 156 65 61 39.1 MRS 2 >2 (R) 84 35 44 52.4 Memory issues Yes 92 38.3 34 37.0 No (R) 148 61.7 71 48.0 223 92.9 93 41.7 NIHSS at recruitment Mild stroke 17 7.1 12 70.6 ( 4) Moderate to severe (>4) (R) Perceived poor state of health Yes 78 32.5 43 55.1 No (R) 162 67.5 62 38.3 Treatment regimen Simple 174 72.5 85 48.9 Complex (R) 66 27.5 20 30.3 Fear of dependence to treatment Yes 83 34.6 30 36.1 No (R) 157 65.4 75 47.8 Perceived need of medicine Yes 190 79.2 93 48.9 No (R) 50 20.8 12 24 Believes that medicine prevent recurrence Yes 138 57.5 73 52.9 No (R) 102 42.5 32 31.4 1.81 1.08 3.04 0.63 0.37 1.05 0.58 0.34 1.00 0.64 0.37 1.08 0.30 0.10 0.88 1.98 1.15 3.43 2.20 1.20 4.02 0.62 0.36 1.07 3.04 1.50 6.17 2.46 1.44 4.2 Experiencing side effects Yes No (R) 68 28.3 13 19.1 172 71.7 92 53.5 49.2 66 55.9 Having a daily routine Yes No (R) 118 50.8 39 32 122 Healthy diet Yes No (R) 31 12.9 18 58.1 209 87.1 87 41.6 Continuing physiotherapy Yes No (R) 116 48.4 58 50 124 51.6 47 37.9 0.21 0.11 0.40 2.70 1.60 4.57 1.94 0.90 4.17 1.64 0.98 2.74

*Outcome variable; Abbreviations: OR- Odds Ratio, CI- Confidence Interval; (R) –Reference category.

Final model of facilitators and barriers to medication adherence among stroke survivors. Variables UnadjustedOR 95% CI AdjustedOR 95% CI Lower limit Upper limit Lower limit Upper limit Age 45 1.94 0.90 4.17 1.340 0.54 3.35 Sex- Male 0.62 0.35 1.12 0.60 0.3 1.21 Availability of medicines in the nearest medical shop 2.48 1.41 4.36 2.36 1.23 4.52 Buy medicines for one month 1.81 1.08 3.04 1.84 0.99 3.45 Having a daily routine 2.70 1.60 4.57 2.81 1.5 5.26 Mild stroke ( 4) at recruitment 0.3 0.10 0.86 0.19 0.05 0.73 Memory issues 0.64 0.37 1.08 0.34 0.16 0.71 Experiencing side effects 0.21 0.11 0.40 0.2 0.1 0.42 Perceived poor state of health 1.98 1.15 3.43 2.65 1.3 5.4 Abbreviations: OR- Odds Ratio, CI- Confidence Interval; Outcome variable- medication adherence.

Continuous access and availability of medications is prerequi site for uninterrupted consumption. Relevant policy decisions can be made to ensure continuous access and availability of medication locally. Since forgetfulness is the common reason identified in almost all the settings, periodic reminders in the form of care giver reminding or through text messages can help. Another major area of intervention is forming strong habit of medication intake. Incor 189 porating medication intake into daily routines can enhance adher ence. At every clinic visit people should be screened and asked for side effects of medication. Misconceptions about side effects of medications should be corrected by awareness programs. Future studies to assess the effect of habit training and routinisation, motivational counselling and periodic reminders on medication adherence will be worthwhile. S.D. Shani, P.N. Sylaja, P. Sankara Sarma et al. Journal of Clinical Neuroscience 88 (2021) 185–190 6. Strengths and limitations of the study All the interviews were conducted one to one by the principal investigator only which reduced the chance of interviewer bias. Since all patients were meticulously followed up in the stroke clinic, blood reports of all these patients were available at the time of data collection. In this study we could assess the adherence to each category of medication (antidiabetic, antihypertensive and statins) separately and correlate with achieving corresponding risk factor control targets. One of the major limitations of this study is that we did not use validated scale to assess medication adherence. We used self-reported medication adherence, defined as consump tion of at least >80% of their medications for last two weeks, based on their last prescription. Consumption of >80% medication is widely accepted as optimal and is used in most of the clinical trials too. We selected two weeks period to collect the medication intake history to reduce the recall bias and the tendency of patients to be more adherent to the instructions around the days of follow up. But there is still a chance of recall bias while eliciting medication intake history for 14 days prior to the date of interview. The data on memory issues and side effects were also collected based on self report only. 7. Conclusion Medication adherence among survivors of first episode of stroke at three months to one year was suboptimal. Medication adher ence was independently associated with risk factor control. The major causes of nonadherence were memory issues, experiencing side effects and financial constraints. Local availability of medici nes, having a daily routine and presence of neurological deficits facilitated medication adherence. These findings have implications for developing preventive strategies and improving stroke care in the community. Source of funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Competing Interest The authors declare that they have no known competing finan cial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement We acknowledge Dr. Ravi Prasad Varma and Dr Jissa VT for their suggestions while preparing the study protocol and all the partic ipants of the study and staff of comprehensive stroke care program.

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