
==== Front
Eur J Neurol
Eur J Neurol
10.1111/(ISSN)1468-1331
ENE
European Journal of Neurology
1351-5101
1468-1331
John Wiley and Sons Inc. Hoboken

38953278
10.1111/ene.16395
ENE16395
EJoN-24-0056.R1
Original Article
Stroke
Determinants of adherence to post‐stroke/transient ischemic attack secondary prevention medications: A cohort study
Adherence to post‐stroke medications
Hoarau et al.
Hoarau Damien https://orcid.org/0009-0002-0175-4819
1 2
Ramos Inès 1
Termoz Anne https://orcid.org/0000-0003-4274-0114
1 2
Fernandez Violaine 2
Rambure Marie 1 2
Allemann Samuel S. 3
Derex Laurent https://orcid.org/0000-0002-0909-8900
1 4
Haesebaert Julie 1 2
Schott Anne‐Marie 1 2
Viprey Marie 1 2 marie.viprey@chu-lyon.fr

1 Research on Healthcare Performance RESHAPE, INSERM U1290 Université Claude Bernard Lyon 1 Lyon France
2 Hospices Civils de Lyon Pôle de Santé Publique Lyon France
3 Pharmaceutical Care Research Group University of Basel Basel Switzerland
4 Comprehensive Stroke Center Pierre Wertheimer Hospital, Hospices Civils de Lyon Bron France
* Correspondence
Marie Viprey, 162 avenue Lacassagne, 69424 Lyon Cedex 03, France.
Email: marie.viprey@chu-lyon.fr

02 7 2024
10 2024
31 10 10.1111/ene.v31.10 e1639526 5 2024
09 1 2024
10 6 2024
© 2024 The Author(s). European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

Background

Adherence to post‐stroke secondary prevention medications mitigates recurrence risk. This study aimed to measure adherence to secondary prevention medications during 3 years post‐ischemic stroke/transient ischemic attack, using prescription and dispensing data, and identify factors associated with suboptimal adherence.

Methods

This multicenter, prospective, cohort study involved patients from the STROKE 69 cohort, which included all consecutive patients with suspected acute stroke admitted between November 2015 and December 2016 to any emergency department or stroke center in the Rhône area in France. Prescription data for antihypertensive agents, antidiabetic agents, lipid‐lowering drugs, and antithrombotics were collected. Dispensing data were provided by the French regional reimbursement database. Adherence was calculated using the continuous medication acquisition index. Associations between suboptimal adherence and potential influencing factors across the World Health Organization's five dimensions were explored through univariate and multivariate analyses.

Results

From 1512 eligible patients, 365 were included. Optimal adherence to overall treatment (≥90%) was observed in 61%, 62%, and 65% of patients in the first, second, and third years, respectively. Education level (high school diploma or higher: OR = 3.24, 95% CI [1.49; 7.36]) and depression (Hospital Anxiety and Depression Scale–Depression scores 8–10: OR = 1.90, 95% CI [1.05; 3.44]) were significantly associated with suboptimal adherence.

Conclusions

Overall adherence to secondary prevention medications was fairly good. Having an initial diagnosis of transient ischemic attack, a high level of education, or depression was associated with increased odds of suboptimal adherence, while having a history of heart rhythm disorder was associated with lower odds.

adherence factors
medication adherence
secondary prevention medication
stroke
transient ischemic attack
Agence Nationale de la Recherche 10.13039/501100001665 ANR‐16‐RHUS‐0009 Swiss National Science Foundation 10.13039/501100001711 P2BSP3_178648 Programme de recherche sur la performance du système des soins (DGOS)PREPS16‐0592 source-schema-version-number2.0
cover-dateOctober 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:20.09.2024
Hoarau D , Ramos I , Termoz A , et al. Determinants of adherence to post‐stroke/transient ischemic attack secondary prevention medications: A cohort study. Eur J Neurol. 2024;31 :e16395. doi:10.1111/ene.16395
==== Body
pmcINTRODUCTION

Patients with an ischemic stroke (IS) or transient ischemic attack (TIA) face a high immediate and lifelong recurrence risk [1, 2, 3]. Preventive lifestyle measures, such as smoking cessation, weight loss, and physical activity, as well as secondary prevention medications (SPMs) can significantly reduce this risk. The SPMs prescribed often depend on the patient's condition and comorbidities, but generally include four types of drugs: antihypertensive therapies, lipid‐lowering drugs, antithrombotic therapies, and antidiabetics if needed [4, 5, 6]. The effectiveness of these SPMs is highly dependent on medication adherence, which is defined as “the degree or extent of conformity to the recommendations about day‐to‐day treatment by the provider with respect to the timing, dosage, and frequency” [7]. Non‐adherence to SPMs decreases the effectiveness of the treatment and increases the risk of recurrence [8, 9]. A meta‐analysis published in 2019 found that only 64.1% (95% confidence interval [CI]: [57.4%; 70.8%]) of stroke or TIA patients were adherent, which is not optimal to avoid recurrences for these patients [7]. This issue of non‐adherence is complex and multifaceted. The World Health Organization has classified the factors influencing medication adherence into five dimensions: socioeconomic‐related factors, patient‐related factors, health system/health care team‐related factors, condition‐related factors, and therapy‐related factors [10]. A comprehensive understanding of medication adherence necessitates an approach that considers all these dimensions. Given the importance of medication adherence in preventing recurrence, it is crucial to understand these factors and to accurately measure adherence over the long term. Previous studies have varied widely in their methods of measuring adherence [7], and have often relied on self‐reported data, which can be subject to bias and may not accurately reflect actual medication use [11]. In the light of these considerations, our study aimed to provide a more objective measure of medication adherence by combining prescription and dispensing data during 3 years post‐IS/TIA in a population‐based cohort. In addition to measuring adherence, our study also sought to identify factors across the five dimensions associated with suboptimal adherence.

METHODS

Design, setting, sample selection

This multicenter, prospective study involved patients from the population‐based STROKE 69 cohort. The STROKE 69 cohort included all consecutive patients suspected of having an acute stroke admitted between November 2015 and December 2016 to any emergency department, comprehensive stroke center, or primary stroke center in the Rhône area of France, and presenting a symptom‐onset (the last time the patient was seen without neurological deficit) less than 24 hours [12, 13]. Patients were eligible for the present study if they: (1) were admitted during the STROKE 69 study period for an IS or TIA and confirmed by a neurologist based on computed tomography scan or magnetic resonance imaging; (2) were alive 1 year post‐stroke; (3) were not permanently living in a nursing home; (4) were able to take their medication alone; (5) were living in the Rhône area; and (6) did not have communication issues. All these patients were invited to participate in the present study. To be included, a signed informed consent for follow‐up interviews and the extraction of their healthcare consumption data from the national health insurance database and also a successful matching between STROKE 69 data and data from the French national health insurance were required. Adherence reporting was performed in accordance to the ESPACOMP guidelines on medication adherence research (EMERGE) [14].

Prescription data

Prescription data collected included drug name, form, dose, and duration of the prescription for four SPM classes: antihypertensives, oral antidiabetics, lipid‐lowering drugs, and antithrombotics, which include antiplatelets and anticoagulants.

Selection of these medications was made based on SPM prescription guidelines of 2015 from the French national health authority (Haute Autorité en Santé [HAS]) [15], which were consistent with international guidelines at that time [4]. Trained clinical research assistants collected the prescriptions made at the initial hospital discharge for the index IS or TIA from medical files. During the 3‐year follow‐up period, patients were asked to self‐report their drug prescriptions. If these were not initially reported, clinical research assistants made further inquiries during follow‐up interviews at 1, 2, and 3 years post‐IS/TIA to ensure this information, including daily dose, was accurately captured.

Administrative claims data

Administrative claims data for the 3 years following the index IS/TIA (follow‐up window [FUW]) were provided for each patient by the regional reimbursement database (Extraction, Recherches, Analyses pour un Suivi Medico‐Economique [ERASME]). The ERASME database contains information on hospitalization reimbursement and community drug dispensation (without prescription data) for patients affiliated to the French national health insurance system living in the Rhône area [16, 17]. In France, all SPMs are reimbursed by the health insurance system, including low‐dose aspirin as an antiplatelet agent.

Adherence measurement

We performed an objective measure of adherence to SPM over an extended follow‐up period by integrating data on prescriptions, medication refills, and hospitalizations [18]. The continuous medication acquisition (CMA) index was utilized to calculate adherence, assessing both the implementation (compliance with the recommended dosing regimen) and persistence (continuation of the regimen for the recommended duration) [19, 20]. The CMA index determines the proportion of days within an observational window that are covered by medication. The method assumes that patients use dispensed medications as directed and complete any oversupply from previous dispensing before starting a new supply. We used CMA6 for calculation of adherence during the first year post‐IS/TIA (from index hospital discharge to 1 year post‐IS/TIA) and CMA7 during the second year and the third year post‐IS/TIA (Table S1) [21, 22]. CMA6 was used for the first year because we did not have dispensing data prior to IS/TIA. For subsequent years of study we were able to consider oversupplies and calculate CMA7. CMA was calculated for each drug taken by the patients and averaged to obtain a composite CMA (cCMA) for each therapeutic class: antihypertensives, lipid‐lowering drugs, antidiabetics (only oral antidiabetic agents), antiplatelets, and anticoagulants. A cCMA was also calculated for the overall SPM treatment. For overall SPM treatment cCMA, rather than considering antiplatelets and anticoagulants separately, we treated them as a combined antithrombotic class in the calculation. Only medication courses prescribed for a minimum of 3 months and with at least one dispensing event during the FUW were included in the calculations to accurately estimate adherence to chronic treatments. We assumed that during hospitalizations, excluding the admission day, the hospital supplied the treatments, and we adjusted the remaining supplies accordingly. After each prescription change (for example, dosage modification), we recalculated the duration of the remaining supply. Patients who died between annual follow‐ups were excluded from subsequent CMA calculations; deceased patients identified at the 2‐year follow‐up were excluded from the second‐ and third‐year calculations, and those identified at the 3‐year follow‐up were excluded from the third‐year calculations. We considered a cCMA≥90% as optimal adherence.

Potential determinants of suboptimal adherence

We explored potential factors influencing adherence across the five dimensions of the World Health Organization's classification. These dimensions include socioeconomic‐related factors, health system/healthcare team‐related factors, patient‐related factors, condition‐related factors, and therapy‐related factors. For socioeconomic‐related factors, we considered sex (referring to the administrative sex as recorded in the French administrative data), age, and education level. Health system/healthcare team‐related factors included the patient's family situation (alone, with a caregiver, or unknown), and whether the patient had a stay in a neurovascular unit or a follow‐up care and rehabilitation unit. Patient‐related factors were assessed using several validated questionnaires. The Beliefs about Medicines Questionnaire‐Specific (BMQ‐Specific) was used to measure patients' beliefs about treatments, with two subscales: the BMQ‐Specific Concerns subscale related to uptake of medications and the BMQ‐Specific Necessity subscale measured the patients' perceived necessity to take their medications. The Hospital Anxiety and Depression Scale was used to assess anxiety and depression, and the Multidimensional Fatigue Inventory (MFI) was used to measure patient fatigue. The modified Rankin Scale was used to assess disability. Condition‐related factors included the initial diagnosis of IS or TIA and the patient's history of smoking. Therapy‐related factors included the patient's history of IS or TIA, diabetes, high blood pressure, dyslipidemia, heart rhythm disorder, and cardiopathy. These factors were collected from medical files as part of the STROKE 69 cohort by trained clinical research assistants or from self‐report questionnaires sent by email to each patient at their inclusion in the present study (at 1 year post‐stroke). Moreover, clinical research assistants conducted telephone follow‐up interviews at 1, 2, and 3 years post‐IS/TIA with patients and ensured that the questionnaires were well understood and asked for information in the case of missing or unclear data. All necessary permissions were obtained for use of questionnaires.

Statistical analyses

Patient characteristics, prescription data, medication adherence, and potential determinants of adherence were described by the mean and standard deviation (SD) or the median and interquartile range for continuous variables, and by the frequency and percentage for categorical variables. Comparison of patient characteristics between those for whom cCMA calculation was performed at 1 year post‐IS/TIA and those who were eligible but not included was performed for mean age, using the Mann–Whitney test, and sex and initial diagnosis using Fisher's exact test. Statistical significance threshold was fixed at 0.017 according to the Bonferroni correction for multiple testing. cCMA for all SPM treatment and for each therapeutic class were described for each year as continuous variables and as a proportion of patients with an optimal medication adherence (cCMA≥90). The nonparametric Cochran's Q test was used to detect differences in medication adherence between the 3 years. The McNemar test was used to detect differences in medication adherence between the first and second year, the second and third year, and the first and third year. Univariate and multivariate logistic regressions were performed to assess the association between SPM adherence as a dichotomic dependent variable and potential determinants of adherence (independent variables). Variables included in the multivariate analysis were age, sex, and factors found to be significant at p < 0.10 from univariate logistic regression. Missing and unknown responses for categorical variables were treated as a distinct category. A sensitivity analysis was performed for the education variable, recoding unknown responses as “no formal educational qualification”. Another sensitivity analysis was conducted by adjusting the threshold for defining optimal adherence from ≥90% to ≥80%. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R 4.2.2 (R Foundation for Statistical Computing).

Statement of ethics

This study was approved by the ethics committee from Tours (France, NIORG0008143) and the French National Commission on data protection (CNIL). The study was registered in ClinicalTrials.gov under the reference NCT03153020.

RESULTS

Patient characteristics

Among the 2347 patients with an IS or a TIA included in the STROKE 69 cohort, 1512 were eligible for the present study and were invited to participate herein; among these, 365 were included. cCMA was calculated for 341 of these patients in the first year, 251 patients in the second year, 208 patients in the third year, and 199 patients for all 3 years (Figure 1). Some 196 patients (57.5%) were admitted for an IS and 145 (42.5%) for a TIA. The median [IQR] age of the total population was 71 [61–79] years (Table 1). No significant difference was found in the main characteristics between the 341 patients with calculated cCMA at 1 year post‐IS/TIA and the 1171 eligible but not included, except for dyslipidemia (Table S2).

FIGURE 1 Study flowchart. †Ischemic stroke (IS). ‡Transient ischemic attack (TIA). cCMA, composite continuous medication acquisition.

TABLE 1 Characteristics of patients included in first year composite continuous medication acquisition calculations.

Characteristic	Total population (N = 341)	IS (N = 196)	TIA (N = 145)	
Sex, female, n (%)	155 (45)	83 (42)	72 (50)	
Median age, years [IQR]	71 [61–79]	70 [58–78]	71 [63–81]	
Age by class (years), n (%)	
29–60	84 (25)	54 (28)	30 (21)	
60–70	84 (25)	49 (25)	35 (24)	
70–80	100 (29)	62 (32)	38 (26)	
80+	73 (21)	31 (16)	42 (29)	
Education, n (%)	
No formal educational qualification	50 (15)	26 (13)	24 (17)	
Vocational degree a	137 (40)	83 (42)	54 (37)	
High school diploma or higher b	102 (30)	54 (28)	48 (33)	
Unknown	52 (15)	33 (17)	19 (13)	
Family situation, n (%)	
Alone	91 (27)	57 (29)	34 (23)	
With a caregiver	222 (65)	130 (66)	92 (63)	
Unknown	28 (8)	9 (5)	19 (13)	
Stay in a neurovascular unit, n (%)	208 (61)	148 (76)	60 (41)	
Stay in a follow‐up care and rehabilitation unit, n (%)	44 (13)	43 (22)	1 (1)	
BMQ‐Specific, n (%)	
Concerns ≤ Necessity	302 (89)	172 (88)	130 (90)	
Concerns ≥ Necessity	19 (6)	11 (6)	8 (6)	
Unknown	20 (6)	13 (7)	7 (5)	
HAD‐D, n (%)	
7	214 (63)	120 (61)	94 (65)	
8–10	69 (20)	40 (20)	29 (20)	
11	53 (16)	32 (16)	21 (14)	
Unknown	5 (2)	4 (2)	1 (1)	
HAD‐A, n (%)	
7	155 (45)	90 (46)	65 (45)	
8–10	81 (24)	49 (25)	32 (22)	
11	89 (26)	45 (23)	44 (30)	
Unknown	16 (5)	12 (6)	4 (3)	
MFI, n (%)	
No	232 (68)	130 (66)	102 (70)	
Yes	102 (30)	60 (31)	42 (29)	
Unknown	7 (2)	6 (3)	1 (1)	
Rankin score c , n (%)	
0		54 (28)		
1		89 (45)		
≥2		30 (15)		
Unknown		23 (12)		
History of IS or TIA, n (%)	
None	270 (79)	162 (83)	108 (74)	
IS	42 (12)	20 (10)	22 (15)	
TIA	29 (9)	14 (7)	15 (10)	
History of diabetes, n (%)	53 (16)	35 (18)	18 (12)	
History of high blood pressure, n (%)	192 (56)	108 (55)	84 (58)	
History of dyslipidemia, n (%)	127 (37)	79 (40)	48 (33)	
History of heart rhythm disorder, n (%)	40 (12)	25 (13)	15 (10)	
History of cardiopathy, n (%)	33 (10)	18 (9)	15 (10)	
History of smoking, n (%)	80 (23)	50 (26)	30 (21)	
Abbreviations: BMQ, Beliefs about Medicines Questionnaire; HAD‐A, Hospital Anxiety and Depression Scale‐Anxiety; HAD‐D, Hospital Anxiety and Depression Scale‐Depression; IQR, interquartile range; IS, ischemic stroke; MFI, Multidimensional Fatigue Inventory; TIA, transient ischemic attack.

a Corresponding to individuals with a Certificate of Professional Aptitude (CAP) or Professional Studies Certificate (BEP) in the French education system.

b Corresponding to individuals with the Baccalauréat (BAC) or higher in the French education system.

c Rankin scores were collected only for IS patients.

Medication adherence

Median cCMA for overall treatment was 93%, 94%, and 94% in the first, second, and third years, respectively. Optimal adherence for overall treatment was observed in 61%, 62%, and 65% of patients in the first, second, and third years, respectively (Table 2).

TABLE 2 Composite continuous medication acquisition for overall treatment and by therapeutic class over time.

Included medications	First year	Second year	Third year	
N	Median cCMA [IQR]	Optimal adherenta n (%)	N	Median cCMA [IQR]	Optimal adherents n (%)	N	Median cCMA [IQR]	Optimal adherents n (%)	
Overall treatment b	341	93 [85–98]	209 (61)	251	94 [85–98]	156 (62)	208	94 [86–98]	136 (65)	
Antidiabetics	35	88 [80–98]	16 (46)	27	94 [88–96]	18 (67)	23	96 [83–99]	16 (70)	
Antihypertensives	216	94 [87–99]	145 (67)	164	96 [90–99]	122 (74)	140	97 [92–99]	112 (80)	
Antiplatelets	253	94 [82–99]	156 (62)	136	93 [81–98]	99 (73)	143	93 [83–97]	80 (56)	
Lipid‐lowering drugs	205	96 [86–100]	143 (70)	136	95 [87–99]	94 (69)	111	94 [87–98]	72 (65)	
Anticoagulants	65	99 [95–100]	57 (88)	50	98 [95–99]	47 (94)	52	97 [92–99]	40 (77)	
Abbreviations: cCMA, composite continuous medication acquisition; IQR, interquartile range.

a Patients with a cCMA≥90% were considered as optimal adherents.

b Overall treatment included antihypertensives, lipid‐lowering drugs, antidiabetics, and antithrombotics (including anticoagulants or/and antiplatelets, depending on prescriptions).

Considering patients with available data for all 3 years (n = 199), the proportion of optimal adherents did not vary significantly over time; it was 67% for the first year, 65% for the second year, and 66% for the third year (p = 0.873). For antiplatelets, a significant decrease was observed in the proportion of optimal‐adherents from 92/130 (71%) the first year to 73/130 (56%) the third year (p = 0.0119); for lipid‐lowering drugs, the proportion of optimal‐adherents decreased from 82% the first year to 64% the third year (p = 0.0089); for antihypertensive therapies, there was a trend towards an increase (p = 0.06), and for anticoagulants, there was a trend towards an increase (Table 3).

TABLE 3 Composite continuous medication acquisition for overall treatment and by therapeutic class for patients with data for all 3 years.

Included medications	N	First year	Second year	Third year	
Median cCMA [IQR]	Optimal adherentsa n (%)	Median cCMA [IQR]	Optimal adherents n (%)	Median cCMA [IQR]	Optimal adherents n (%)	
Overall treatment b	199	94 [87–98]	134 (67)	94 [87–98]	130 (65)	94 [86–98]	131 (66)	
Antidiabetics	17	94 [87–99]	11 (65)	94 [88–97]	12 (71)	96 [82–99]	12 (71)	
Antihypertensives	118	95 [88–100]	78 (66)	97 [91–99]	92 (78)	97 [92–99]	92 (78)	
Antiplatelets	130	97 [87–100]	92 (71)	93 [83–98]	77 (59)	93 [84–97]	73 (56)	
Lipid‐lowering drugs	95	97 [92–100]	78 (82)	96 [90–99]	72 (60)	95 [87–98]	61 (64)	
Anticoagulants	43	99 [96–100]	40 (93)	98 [95–99]	41 (95)	98 [93–99]	35 (81)	
Note: Bold values indicate significant decrease in the proportion of optimal‐adherent patients between the first and third year (p < 0.01).

Abbreviations: cCMA, composite continuous medication acquisition; IQR, interquartile range.

a Patients with a cCMA≥90% were considered as optimal adherents.

b Overall treatment included antihypertensives, lipid‐lowering drugs, antidiabetics, and antithrombotics (including anticoagulants or/and antiplatelets, depending on prescriptions).

Determinants of adherence

Socioeconomic‐related factors such as sex and age did not show significant association with suboptimal adherence in both univariate and multivariate analyses. Education level showed a significant association in the multivariate analysis, with individuals holding a high school diploma or higher (odds ratio [OR] = 3.24, 95% CI [1.49; 7.36], p = 0.004) and those with unknown education status (OR = 3.41, 95% CI [1.43; 8.45], p = 0.007) showing increased odds of suboptimal adherence. When coding unknown responses as “no formal educational qualification”, there was no longer any significant association (Table S3). No significant associations were found with health system/healthcare team‐related factors. Among patient‐related factors, the Hospital Anxiety and Depression Scale‐Depression (HAD‐D) scores showed significant association in the multivariate analysis. Patients with scores of 8–10 (OR = 1.90, 95% CI [1.05; 3.44], p = 0.033) and those with unknown scores (OR = 10.55, 95% CI [1.35; 220.61], p = 0.046) had increased odds of suboptimal adherence. A history of smoking showed a trend towards increased odds of suboptimal adherence, but this was not statistically significant in multivariate analysis (OR = 1.62, 95% CI [0.90; 2.91], p = 0.104). In condition‐related factors, an initial diagnosis of IS compared with TIA was associated with lower odds of suboptimal adherence (OR = 0.49, 95% CI [0.30; 0.78], p = 0.003). Among therapy‐related factors, a history of heart rhythm disorder was associated with lower odds of suboptimal adherence (OR = 0.34, 95% CI [0.14; 0.78], p = 0.016; Table 4).

TABLE 4 Factors associated with suboptimal adherence during first year post‐ischemic stroke/transient ischemic attack.

Factor (N = 341)	Univariate analysis OR [95% CI]	Multivariate analysis OR [95% CI]	P‐value	
Socioeconomic‐related factors	
Sex (female)	0.90 [0.58; 1.40]	0.99 [0.61; 1.60]	0.954	
Age, years	
29–60	–			
60–70	0.57 [0.30; 1.07]	0.74 [0.37; 1.46]	0.387	
70–80	0.88 [0.49; 1.59]	1.22 [0.63; 2.37]	0.551	
80+	0.79 [0.42; 1.49]	0.98 [0.47; 2.04]	0.960	
Education	
No formal educational qualification	–			
Vocational degree a	1.26 [0.63; 2.63]	1.61 [0.76; 3.55]	0.225	
High school diploma or higher b	2.29 [1.12; 4.86]	3.24 [1.49; 7.36]	0.004	
Unknown	2.38 [1.06; 5.52]	3.41 [1.43; 8.45]	0.007	
Health system/healthcare team‐related factors	
Family situation	
Alone	‐			
With a caregiver	0.86 [0.52; 1.43]			
Unknown	2.04 [0.87; 4.89]			
Stay in a neurovascular unit	1.03 [0.66; 1.61]			
Stay in a follow‐up care and rehabilitation unit	0.71 [0.35; 1.37]			
Patient‐related factors	
BMQ‐Specific	
Concerns ≤ Necessity	‐			
Concerns ≥ Necessity	1.44 [0.56; 3.68]			
Unknown	0.86 [0.32; 2.17]			
HAD‐D	
≤7	–			
8–10	1.73 [1.00; 3.01]	1.90 [1.05; 3.44]	0.033	
≥11	1.24 [0.66; 2.29]	1.31 [0.66; 2.57]	0.438	
Unknown	7.57 [1.10; 149.48]	10.55 [1.35; 220.61]	0.046	
HAD‐A	
≤7	–			
8–10	1.01 [0.58; 1.77]			
≥11	1.55 [0.91; 2.65]			
Unknown	1.41 [0.48; 4.00]			
History of smoking	1.72 [1.03; 2.85]	1.62 [0.90; 2.91]	0.104	
Condition‐related factors	
Initial diagnosis of IS (TIA as reference)	0.55 [0.35; 0.85]	0.49 [0.30; 0.78]	0.003	
MFI	
No	‐			
Yes	1.12 [0.69; 1.80]			
Unknown	2.22 [0.48; 11.50]			
Therapy‐related factors	
History of IS or TIA	
None	‐			
TIA	0.81 [0.35; 1.78]			
IS	0.95 [0.48; 1.84]			
History of diabetes	1.51 [0.83; 2.73]			
History of high blood pressure	0.98 [0.63; 1.53]			
History of dyslipidemia	0.89 [0.57; 1.40]			
History of heart rhythm disorder	0.42 [0.18; 0.88]	0.34 [0.14; 0.78]	0.016	
History of cardiopathy	1.56 [0.75; 3.21]			
Notes: P‐value corresponds to p‐value from multivariate analysis. Bold values indicate statistical significance for the corresponding model (p‐value < 0.05). Suboptimal adherence is defined by a composite continuous medication acquisition (cCMA)<90% for overall treatment. Results are given for cCMA values in the first year. The multivariate analysis included the following variables: sex, age, initial diagnosis, education level, history of smoking, history of heart rhythm disorder, and HAD‐D.

Abbreviations: BMQ, Beliefs about Medicines Questionnaire; CI, confidence interval; HAD‐A, Hospital Anxiety and Depression Scale‐Anxiety; HAD‐D, Hospital Anxiety and Depression Scale‐Depression; IS, ischemic stroke; MFI, Multidimensional Fatigue Inventory; OR, odds ratio; TIA, transient ischemic attack.

a Corresponding to individuals with a certificate of professional aptitude (CAP) or professional studies certificate (BEP) in the French education system.

b Corresponding to individuals with the Baccalauréat (BAC) or higher in the French education system.

Sensitivity analysis conducted by adjusting the threshold for defining optimal adherence from ≥90 to ≥80% showed that for patients with a high school diploma or higher, the OR decreased from 3.24 (95% CI [1.49; 7.36]) to 2.87 (95% CI [1.05; 9.26]), with a lower, but still not significant, p‐value (p = 0.053). Similarly, for unknown education, the OR slightly decreased with a still‐significant p‐value (p = 0.048). The HAD score's influence (8–10) lost significance using the ≥80% threshold. For history of heart rhythm disorder, the odds ratio slightly decreased with a near significant p‐value (p = 0.054). Initial stroke diagnosis (IS vs. TIA) maintained its effects across thresholds with minor changes in the odds ratio and p‐value (Table S4).

DISCUSSION

In the present study there was fairly good adherence; nearly two‐thirds of patients were considered optimal adherents in the first‐year post IS/TIA. Furthermore, the median cCMA for overall treatment was similar to that reported by Dalli et al. who found a median proportion of days covered (PDC) above 85% for each SPM [23].

Overall adherence was stable over time, with no significant variation in the proportion of optimal adherents over the 3 years following IS/TIA. However, adherence varied over time when examined by therapeutic class. For antiplatelets, a significant decrease was observed in the proportion of optimal adherents, from 71% in the first year to 56% in the third year. This decrease could be due to side effects or a lessened perceived need for antiplatelets, as their benefits do not result in noticeable symptom relief or clear changes in physical or biological markers. Despite this observed decrease, the adherence was higher than that reported previously; Rohde et al. reported that only 30.2% had a PDC ≥80% in first year post‐stroke [24], and Dalli et al. reported 66% adherent patients in the first year post‐IS/TIA [23].

For lipid‐lowering drugs, the proportion of optimal adherents decreased from 82% in the first year to 64% in the third year. This could be partially explained by intolerance to statins, estimated to be 9% in a previous study [25], but contrasts with that reported by Chung et al. who found 66% optimal adherents at 6 months post‐stroke (using the Morisky scale) and that reported by Yeo et al. who found 29% adherents in the first year post‐stroke (using PDC) [26, 27].

The present study identified several determinants associated with suboptimal adherence. Among socioeconomic‐related factors, individuals holding a high school diploma or higher had a greater odds of suboptimal adherence. This finding was somewhat counterintuitive, as we initially hypothesized that higher education may be associated with optimal adherence. However, it is also possible that patients with a high education level may have a high level of concerns about medicines, which is known to negatively influence medication adherence [28]. Unfortunately, due to the low number of patients with a high level of education among those with a BMQ‐Specific Concerns score higher than the BMQ‐Specific Necessity score, we could not perform further analysis. Interestingly, similar associations were found by Tiili et al., who reported an association between tertiary education and poor self‐reported adherence to oral anticoagulation [29]. Furthermore, Dalli et al. reported that more advantaged patients were more likely not to be supplied with antihypertensives and antithrombotics [23]. Patients with an unknown education level were also more likely to be suboptimal adherents. It is plausible that these patients had a low education level, as in previous investigations, such as the study reported by Tsiampalis et al. [30], have indicated that it can be a determinant of missing responses. Recoding unknown responses to no “formal educational qualification” dissipated any significant association, making it harder to conclude on the presence or absence of any true association between education level and adherence.

Among patient‐related factors, moderate depression (HAD‐D score of 8–10) was significantly associated with suboptimal adherence, but not severe depression (HAD‐D scores ≥11), suggesting a nonlinear relationship between depression and adherence. The high number of patients with unknown HAD‐D score, who were also more likely to be suboptimal adherents, could indicate other unexplored barriers that might contribute to both their non‐response and suboptimal adherence. However, this may also be due to the low number of severely depressed patients; further research is needed to better understand the relationship between depression severity and medication adherence.

Among condition‐related factors, patients initially diagnosed with IS had lower odds of suboptimal adherence, possibly due to a greater interest in treatment adherence to avoid recurrence, which is supported by findings from a qualitative study performed on a subgroup of patients included in the present study [31].

Among therapy‐related factors, a history of heart rhythm disorder was associated with lower odds of suboptimal adherence. This is in line with results reported by Chan et al., but contrasts with that reported by Bushnell et al., who described that having a heart rhythm disorder is negatively associated with persistence [32, 33].

Regarding health system/healthcare team‐related factors, no significant association with suboptimal adherence was identified. This suggests that factors within this dimension may not play a substantial role in influencing adherence in the study cohort, or that such influences were not captured.

The study has certain limitations. The first is that we assumed that patients took their medication as prescribed, which could potentially lead to an overestimation of adherence. Despite this, French administrative claims data, which we used, have been reported to have good concordance with self‐reported drug use for the studied drug classes [34]. In addition, patient inclusion was hindered by non‐consent and unsuccessful linkage between SROKE 69 patients and the ERASME database, resulting in a modest sample size of 365 among the 1512 eligible. This may have reduced the statistical power of the analyses. However, the analysis found no significant difference in the main characteristics (except for dyslipidemia) between patients for whom cCMA calculation was performed at 1 year post‐IS/TIA and those who were eligible but not included. Maintaining patient participation over the 3‐year period was another challenge, with 100 non‐respondents in the second year and 144 in the third year. We hypothesize that respondents who maintained their participation for 3 years may have had better adherence than non‐respondents, which could mean that the adherence reported herein may be somewhat higher than in overall IS/TIA patient population. The threshold for optimal adherence, set at ≥90% in this study, was another key point. This value was chosen as it was both achievable and associated with the best clinical outcomes, such as survival or IS/TIA recurrence, in the light of the insights provided by Dalli et al. [23]. A sensitivity analysis performed by reducing the threshold for optimal adherence moderately affected some observed associations. However, the overall results remained quite robust despite these adjustments.

In conclusion, the study found that overall adherence to SPM after an IS/TIA was fairly good and remained stable over the following 3 years. Having an initial diagnosis of TIA, a high level of education, or depression was associated with increased odds of suboptimal adherence, while having a history of heart rhythm disorder was associated with lower odds. In clinical practice, seeking the presence of these factors may help healthcare providers to better identify patients at risk of non‐adherence. In terms of future research, the findings reported herein highlight the need for further investigation such as qualitative studies to explore the reasons for non‐adherence in specific patient groups, or intervention studies to test strategies for improving adherence. Overall, addressing factors associated with suboptimal adherence could lead to improved patient outcomes, including reduced risk of stroke recurrence and improved survival.

AUTHOR CONTRIBUTIONS

DH, VF, MR, SSA, LD, JH, AMS and MV contributed to the writing of the original draft. AT, VF, MR and MV contributed to data curation. IR, AT, VF, MR, SSA, LD, JH, AMS and MV contributed to the methodology. DH, IR, AT, VF, AMS and MV contributed to the formal analysis. AT, LD and MV contributed to the investigation. AT, JH, AMS and MV contributed to the funding acquisition. LD, JH, AMS and MV contributed to conceptualization and supervision. LD, AMS and MV contributed to the project administration. All authors contributed toward critically revising the paper, gave final approval of the version published, and agree to be accountable for all espects of the work.

FUNDING INFORMATION

This work was supported by public funding from the Programme de recherche sur la performance du système des soins (PREPS 16‐0592) and performed within the framework of RHU MARVELOUS (ANR‐16‐RHUS‐0009) UCBL, as part of the program ‘Investissements d'Avenir’ operated by the French National Research Agency (ANR). SA received funding from the Swiss National Science Foundation for an ‘Early Postdoc. Mobility’ fellowship (Grant No. P2BSP3_178648).

CONFLICT OF INTEREST STATEMENT

The authors report no conflicts of interest in this work.

Supporting information

Tables S1–S4.

ACKNOWLEDGMENTS

We thank Estelle Bravant, Nathalie Perreton, Amine Chakir, Héla Kerd, Ouazna Tassa, Johanna Vivard, Audrey Maurin, Elodie Castelletta, Adèle Perrin, Julie Martin, Jeanice Amiot, Marine Barral, Guillaume Pinte, Cécile Foukoun‐Matchikou, Aurélie Rochefolle, Marie‐Anne Cerfon, Audrey Baroan, Karim Tazarourte, Norbert Nighoghossian, Laurent Derex, Serkan Cakmak, Sylvie Meyran, Bruno Ducreux, Christelle Pidoux, Thomas Bony, Marion Douplat, Véronique Potinet, Alain Sigal, Annaëlle Testud for their efficient participation in data collection, entry, management, and support for analysis. We also thank the Direction régionale du service médical Rhône‐Alpes (DRSM) and the regional directorate of the French National Health Insurance for access to their databases (Extraction, Recherches, Analyses pour un Suivi Médico‐Économique [ERASME]). We thank Philip Robinson for editing the English language version of the manuscript.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.
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