This mixed-methods cross-sectional study assessed the prevalence of adherence to anti-seizure medications (ASMs), identified factors associated with adherence, and explored patient experiences among persons living with epilepsy attending a community-based epilepsy clinic in Buikwe District and Mukono General Hospital in central Uganda. The research aimed to inform strategies to improve ASM adherence and seizure control in these settings.
The study was conducted at a monthly community-based epilepsy clinic in Buikwe District and at the weekly epilepsy clinic at Mukono General Hospital. Both facilities draw patients from overlapping communities in Buikwe, Mukono, and surrounding areas and use similar clinical follow-up procedures, medication regimens, and patient counseling. The community clinic is operated in collaboration with Butabika National Referral Hospital and local partners; Mukono General Hospital is a government facility that provides free ASMs.
Participants were persons living with epilepsy aged 15 years and older who attended these clinics regularly. Facility records indicated increasing clinic attendance prior to the study.
A cross-sectional mixed-methods design was used between 5 April and 20 July 2024. The quantitative component enrolled 277 participants, a sample size determined using a modified Kish-Leslie formula for cross-sectional studies. Structured questionnaires captured socio-demographic and treatment-related information. The qualitative component comprised 20 in-depth interviews to explore personal experiences and contextual factors influencing medication-taking behavior.
The most commonly prescribed ASMs at both sites were phenytoin, carbamazepine, phenobarbitone, sodium valproate, and lamotrigine. Medication adherence was measured using the standardized 8-item Morisky Medication Adherence Scale, a self-report instrument widely used to assess adherence behavior.
The overall prevalence of adherence to ASMs was 66.5% according to the Morisky measure. Multivariable modified Poisson regression identified several factors significantly associated with adherence:
Earning a modest monthly income was associated with higher adherence (adjusted prevalence ratio [aPR] 1.29; 95% CI 1.13–1.53).
Receiving an inadequate treatment dosage was associated with lower adherence (aPR 0.80; 95% CI 0.64–0.92).
Waiting three or more hours at the facility was associated with lower adherence (aPR 0.79; 95% CI 0.61–0.91).
Being aged 30 to 34 years was associated with lower adherence compared with other age groups (aPR 0.71; 95% CI 0.53–0.95).
The study pooled participants from the two clinics into a single cohort because the sites serve similar populations, use comparable treatment regimens and follow-up procedures, and baseline characteristics and adherence patterns did not differ meaningfully between sites.
Thematic analysis of the 20 in-depth interviews identified multiple domains shaping adherence behavior:
Fear of stigmatization: participants described concern about community and family stigma related to epilepsy and its treatment.
Fear of injury and loss of life: anxiety about seizure-related harm motivated some patients to adhere to ASMs to reduce seizure risk.
Social support: family or community support influenced patients’ ability and willingness to take medications regularly.
Financial burden: costs related to transport, clinic attendance, or obtaining medicines affected adherence for some participants.
Individual motivation: personal belief in treatment importance and self-discipline were cited as facilitators.
Misconceptions and limited awareness: incorrect beliefs about ASMs and limited knowledge about their effectiveness reduced adherence for some patients.
These qualitative themes provide context for the quantitative associations observed, such as the role of financial resources and facility-level barriers in shaping medication-taking.
About two-thirds of participants were adherent to ASMs in this cohort. Factors that increased adherence included modest monthly income, while inadequate dosing, prolonged facility wait times, and being aged 30–34 years were associated with lower adherence. Qualitative data highlighted modifiable social and health-system barriers, including stigma, financial constraints, misconceptions, and long clinic waits.
The authors recommend implementing strategies to strengthen adherence, including incorporating a medication adherence tool into routine patient care to systematically identify and support patients at risk of non-adherence. Other implications—drawn directly from reported findings—emphasize addressing clinic flow to reduce waiting times, ensuring appropriate dosing, and targeting education to correct misconceptions and raise awareness about ASM effectiveness.
Strengths of the study include a mixed-methods approach combining validated self-report measurement (8-item Morisky scale) with qualitative interviews to capture lived experience. The two clinic sites were pooled because of similar patient populations and care processes; the authors report no meaningful baseline differences between sites.
Limitations reported in the manuscript include the cross-sectional design, which precludes causal inference, and reliance on self-reported adherence, which may be subject to reporting bias. The manuscript and supporting information contain the relevant study data as stated by the authors.
Funding for the research was provided by the National Institute of Neurological Disorders and Stroke (NIH award D43NS118560-01). The authors declared no competing interests.