Barriers to epilepsy care: Measuring the treatment gap in rural and peri-urban districts in Ghana

Emmanuel Kwame Darkwa; Patrick Adjei; Sabina Asiamah; Elizabeth Awini; Cynthia Sottie; Anthony Godi; John E Williams; Frank Atuguba; Fuseini Sataru; Alexander Adjei; Arjune Sen; Charles R Newton; Anthony Danso-Appiah; J Helen Cross; Josemir W Sander; Albert Akpalu; on behalf of the EPInA investigators

doi:10.1371/journal.pone.0355854

. 2026 Aug 11;21(8):e0355854. doi: 10.1371/journal.pone.0355854

Barriers to epilepsy care: Measuring the treatment gap in rural and peri-urban districts in Ghana

Emmanuel Kwame Darkwa ^1,^2,^3,^‡, Patrick Adjei ^4,^5,^‡, Sabina Asiamah ^1,², Elizabeth Awini ^1,², Cynthia Sottie ², Anthony Godi ⁶, John E Williams ^1,², Frank Atuguba ^1,², Fuseini Sataru ¹, Alexander Adjei ^1,², Arjune Sen ⁷, Charles R Newton ^7,⁸, Anthony Danso-Appiah ^3,⁹, J Helen Cross ¹⁰, Josemir W Sander ^11,^12,^*, Albert Akpalu ^4,^5,^*; on behalf of the EPInA investigators^¶

Editor: Hiroki Annaka¹³

¹Dodowa Health Research Centre, Research and Development Division, Accra, Ghana

²Ghana Health Service, Accra, Ghana

³Department of Epidemiology and Disease Control, School of Public Health, University of Ghana, Legon, Accra, Ghana

⁴University of Ghana Medical School, Accra, Ghana

⁵Korle Bu Teaching Hospital, Korle Bu, Accra, Ghana

⁶Department of Biostatistics, School of Public Health, University of Ghana, Legon, Accra, Ghana

⁷Oxford Epilepsy Research Group, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, United Kingdom

⁸Department of Psychiatry, University of Oxford, United Kingdom

⁹Centre for Evidence Synthesis and Policy, University of Ghana, Legon, Accra, Ghana

¹⁰UCL Great Ormond Street NIHR BRC Institute of Child Health, London, United Kingdom

¹¹UCL Queen Square Institute of Neurology, London, United Kingdom

¹²Department of Neurology, West China Hospital, Sichuan University, Chengdu, China

¹³Niigata University of Health and Welfare: Niigata Iryo Fukushi Daigaku, JAPAN

Competing Interests: The authors have declared that no competing interests exist.

‡ Denotes joint co-authors.

¶ Membership of the EPInA Study Group is listed in the Acknowledgments.

^✉

* E-mail: a_akpalu@yahoo.com (AA); l.sander@ucl.ac.uk (JWS)

Roles

Emmanuel Kwame Darkwa: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

Patrick Adjei: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

Sabina Asiamah: Data curation, Supervision, Writing – review & editing

Elizabeth Awini: Methodology, Project administration, Supervision, Writing – review & editing

Cynthia Sottie: Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

Anthony Godi: Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Writing – review & editing

John E Williams: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Frank Atuguba: Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – review & editing

Fuseini Sataru: Investigation, Methodology, Project administration, Supervision, Writing – review & editing

Alexander Adjei: Investigation, Methodology, Project administration, Supervision, Writing – review & editing

Arjune Sen: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing

Charles R Newton: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – review & editing

Anthony Danso-Appiah: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – review & editing

J Helen Cross: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Software, Supervision, Writing – review & editing

Josemir W Sander: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – review & editing

Albert Akpalu: Conceptualization, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

Hiroki Annaka: Editor

PMCID: PMC13460580 PMID: 42579681

Abstract

Background

Epilepsy is an easily treatable condition, but a substantial treatment gap persists, particularly in low- and middle-income countries. We quantified the epilepsy treatment gap in Ghana.

Materials and methods

We conducted a three-stage population-based study in the Shai Osudoku and Ningo-Prampram districts, embedded within the Dodowa Health and Demographic Surveillance System to estimate prevalence and assess treatment barriers from April 2023 to December 2023.

Results

We found an epilepsy prevalence of 8.84 per 1,000 people (95% CI: 8.00–9.68). A 70% treatment gap was identified, with only 30% of those with a confirmed diagnosis using antiseizure medication. Among untreated people, the most common reason was a lack of diagnostic awareness (37.8%). Barriers to treatment included sociocultural beliefs and misconceptions, stigma, preference for alternative medicine, cost, and antiseizure medication shortages. Significant knowledge gaps were evident, with 36.8% of respondents attributing epilepsy to witchcraft or supernatural causes.

Conclusion

Most people with epilepsy in the area were not receiving adequate biomedical care. The observed treatment gap was associated with multiple factors, including gaps in awareness, sociocultural misconceptions, and economic barriers, suggesting the need for targeted public health interventions to improve epilepsy care and management.

Background

Epilepsy is a manageable condition [1], imposing a significant multidimensional burden worldwide [2–4]. In low- and middle-income countries (LMIC), where 80% of the over 50 million affected people live, the substantial burden is underpinned by a wide treatment gap [2–5]. Most people with epilepsy can achieve seizure freedom and experience improved quality of life with access to first-line antiseizure medications (ASM) costing less than US$10 annually [1,6].

Disparities in epilepsy treatment rates across and within countries mainly result from unequal access to healthcare [7–9]. In resource-limited settings, systemic barriers weaken diagnostic capacity and access to care, increasing the risk of untreated epilepsy. These barriers include disparities in geographic access, fewer specialists, and a lack of advanced diagnostic technology [10–12]. In sub-Saharan Africa (SSA), particularly, these systemic weaknesses are amplified by entrenched cultural misconceptions, as well as the cost and availability of ASM [13–17].

Similar to other LMICs, Ghana has a large epilepsy treatment gap, with most people with epilepsy lacking access to biomedical care [1,6]. Critical knowledge gaps exist regarding the extent and contributing factors nationwide, complicating a nationwide understanding of the issue, enabling targeted interventions, and achieving better outcomes. Quantifying these gaps is crucial to improving treatment rates and outcomes and to developing targeted interventions. We aim to identify barriers and quantify the local epilepsy treatment gap in two districts of Ghana using a comprehensive, three-stage prevalence survey. This study is part of a broader project generating current epidemiological data on epilepsy in southeastern Ghana.

Methods

Setting

The study was conducted in the Shai-Osudoku and Ningo-Prampram districts, located in the Greater Accra region and bordering each other along Ghana’s southeastern coast (see Fig 1) [18]. They share several characteristics, including a mix of Ga-Dangme, Ewe, and Akan ethnic groups. Shai-Osudoku is mainly rural and agrarian. It contains a network of health facilities, including a modern district referral hospital, three health centres, eight Community-Based Health Planning Services (CHPS) compounds, and four private facilities. In contrast, Ningo-Prampram is a peri-urban area with a more diverse economy, including one polyclinic, three health centres, ten CHPS, and twenty-six private facilities, located along the coast. Both districts are located within the boundaries of the Dodowa Health and Demographic Surveillance Site (DHDSS), which monitors population and health trends and serves as a sampling frame for research and interventions [19–21].

Study design

We conducted a community cross-sectional survey using a door-to-door data collection approach, including a matched case-control study. Using the DHDSS database, we enumerated long-term residents to quantify the prevalence of epilepsy and the treatment gap. This work is a component of the Epilepsy Pathway Innovation in Africa (EPInA) project (https://epina.web.ox.ac.uk/home). It aims to improve the quality of life for people with epilepsy in SSA by addressing all aspects of the treatment pathway: prevention, diagnosis, treatment, and awareness. The project began on 1 October, 2019 and ended on 31 January, 2025.

Participant recruitment and screening

We undertook significant preparatory work before conducting the door-to-door survey. The initial step was to develop and validate a method for case identification. The team reviewed existing tools to create a contextually relevant 17-item screening tool. After translating it into the local languages, we tested and confirmed its effectiveness at multiple health facilities across the districts and a major referral hospital. In a formal validation, the tool was administered to 70 people with epilepsy and 70 controls in Dangme, and to 50 people with epilepsy and 50 controls in Asante Twi [23]. The screening tool demonstrated acceptable sensitivity and specificity for population-based identification of epilepsy. Full validation results are reported elsewhere [23].

Concurrently, the team worked to reduce stigma associated with epilepsy and secure community acceptance by consulting with local stakeholders. Subsequently, research assistants with community experience underwent a 10-day neurologist-led training to standardize knowledge of seizures, terminology, and questionnaire administration using role-play and instructional videos. The training ensured that the screening questions were administered consistently and uniformly. We determined the prevalence of epilepsy through a systematic three-stage door-to-door survey. The three-stage approach consisted of population screening, detailed individual assessment, and clinical confirmation by specialists.

Three contact attempts were made before classifying households as unavailable. The protocol involved a progressive screening process, culminating in a clinical evaluation. Stage 1 involved a broad screening process using a 7-item checklist, in which a single positive response constituted a potential case. Stage 2 used a 10-item questionnaire. Lastly, clinical confirmation, supported by a physician or neurologist’s assessment and an electroencephalogram (EEG), was used to determine the type of epilepsy. We operationalized “lifetime epilepsy” as confirmed epilepsy at any time before the survey, and “active epilepsy” as treatment or seizures within the past year [10,24].

We used a census approach within the DHDSS to cover eligible residents in the Shai-Osudoku and Ningo-Prampram surveillance areas. The combined DHDSS population of 136,727 served as the sampling frame. The flow of participant recruitment and the three-stage screening process have been reported elsewhere [22].

Following identification, we compared people with confirmed active epilepsy (“cases”) to people without epilepsy (“controls”). We entered an algorithm into the DHDSS database that identified 421 cases and accurately matched them to controls by age and sex. As there were many potential controls, the matching was random. Eventually, the groups consisted of 366 people with epilepsy and 312 controls.

Data collection

Android tablets running the KoboCollect app (version 2023.2.4) were used to collect data. In addition to the socio-demographic and economic information collected from participants, a modified questionnaire adapted from a previous study [25] was used to compile a comprehensive clinical profile for cases. The information collected included seizure details, risk factors, lifestyle factors, treatment history, and barriers to accessing care. Age-specific questionnaires were administered, with caregivers responding on behalf of children or individuals with cognitive impairments. Cases were interviewed at health facilities, and controls at their homes.

Definitions

Epilepsy treatment gap: Treatment status was determined by self-report of any ASM use within the 30 days preceding the survey, irrespective of dose, adherence, or seizure control. The treatment gap was estimated as the percentage of those with active epilepsy who did not report ASM use during this period. This operational definition captures treatment access, although it may not fully distinguish between untreated epilepsy, undertreatment, interrupted treatment, or poor adherence; these nuances should be considered when interpreting the estimates.

Prevalence of epilepsy is the proportion of individuals in a defined population who have epilepsy at a specified point in time or period.

Treatment barriers are factors that limit access to, initiation of, or continuity of appropriate epilepsy care, including individual, family, health system, economic, cultural, and geographic influences.

The diagnostic gap refers to the proportion of people with epilepsy who have not been correctly identified or diagnosed by the health system.

The Therapeutic gap refers to the proportion of people with epilepsy who are diagnosed but do not receive effective and sustained treatment.

Treatment adherence refers to the extent to which a person with epilepsy takes antiepileptic medication as prescribed in terms of dose, frequency, and duration.

Appropriate treatment refers to evidence-based antiepileptic therapy prescribed at an adequate dose and duration, tailored to seizure type, syndrome, comorbidities, and local guidelines.

Active epilepsy: a history of epilepsy with at least one seizure in the past 12 months and/or current use of antiseizure medication

Statistical analysis

We processed and analyzed the data using a predefined statistical workflow. Data were cleaned in KoboToolbox and Microsoft Excel, and analyses were conducted in Stata(Stata/SE 17; StataCorp, College Station, TX, USA). Clinical characteristics, treatment history, and perceived barriers were summarized using descriptive statistics and reported as frequencies and percentages. Comparisons between districts were performed using Pearson’s chi-square test for categorical variables. Fisher’s exact test was applied where expected cell counts were small (<5). We estimated the crude prevalence of epilepsy by dividing the number of clinically confirmed cases by the total number of individuals screened. We used inverse probability weighting to adjust for attrition bias. Weights were derived from logistic regression models predicting stage-to-stage participation based on DHDSS demographic variables. The weights accounted for the probability of completing each screening stage, with covariates including age, sex, district, and screening-stage positivity. To account for potential household and community-level clustering inherent in the door-to-door sampling design, cluster-robust standard errors were estimated for all comparative analyses. Potential confounding by age and sex was controlled via exact matching during the case-control selection phase. The treatment gap was estimated as the proportion of active epilepsy cases that did not receive appropriate treatment [5,7] based on the operational definition of treatment status. We estimated p-values and set statistical significance at p < 0.05.

Ethical considerations

Ethical approval was obtained from the Ghana Health Service Ethics Review Committee (GHS-ERC 022/08/20) and the Dodowa Health Research Centre Institutional Review Board (DHRCIRB 95/08/20. Participants provided written informed consent. Community consent was secured through local leaders. Written informed consent was obtained from adults. For minors, parents provided assent. Interviews were conducted in the preferred languages (Dangme, Twi, and English) with interpreters as needed. Data were anonymized pre-analysis. Those with epilepsy were registered for continuous clinical follow-up.

Results

Prevalence of epilepsy

The initial screening covered 65,177 people across both districts, of whom 617 were identified as suspected cases at stage 1 (Table 1). The prevalence of suspected epilepsy at Stage 1 varied significantly between districts, with a higher proportion observed in Shai-Osudoku (1.3%) than in Ningo-Prampram (0.6%) (p < 0.001). During Stage 2 screening, 544 individuals were evaluated, of whom 467 were suspected of having epilepsy. Of the 435 individuals who underwent clinical assessment, 421 were confirmed to have epilepsy. However, no significant district differences were observed at Stage 2 screening (85.3% vs 86.8%; p = 0.634) or in confirmed epilepsy cases following clinical assessment (96.6% vs 97.1%; p = 0.766). Not all attrition in screening stage transitions reflected loss to follow-up. Of the 544 participants assessed at Stage 2, 72 (13.2%) screened negative and were excluded from Stage 3 clinical assessment as false positives from the initial broad screening tool, consistent with the multi-stage design. Overall, the attrition-adjusted prevalence of epilepsy was 8.84 cases per 1,000 people.

Table 1. Three-stage screening for epilepsy.

	District, N (%)
	Ningo-Prampram	Shai-Osudoku	Total	P-value*
Stage 1 screening	36,965	28,212	65,177
Suspected cases	237 (0.6)	380 (1.3)	617 (0.9)	<0.001
Stage 2 screening	204	340	544
Suspected cases	177 (86.8)	290 (85.3)	467 (85.8)	0.634
Stage 3 screening	176	285	461
Suspected cases	172 (97.7)	263 (92.3)	435 (94.4)	0.014
Confirmed cases within those still suspected	167 (97.1)	254 (96.6)	421 (96.8)	0.766

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* P-values were derived using Pearson’s chi-square test for categorical variables. Fisher’s exact test was applied where expected cell counts were <5.

Epilepsy treatment coverage and gap

The epilepsy treatment gap was 70.3% as only 29.7% of people with confirmed epilepsy were taking ASM in the month before the survey (Table 2). A marked difference in ASM uptake was observed between the districts, with a higher rate in Ningo-Prampram (35.3%) than in Shai-Osudoku (26%). However, this did not differ significantly between the districts (p = 0.183). Most (93.6%) of those on treatment were receiving prescriptions from physicians or nurses. Similarly, no statistically significant district-level differences were observed in ASM prescribers (p = 0.387). The most common ASMs used were Carbamazepine (66.4%) and Phenobarbital (19.2%). A significant proportion of cases (11.2%) did not know the names of their medications, and this was markedly higher in Shai-Osudoku (16.6%) than in Ningo-Prampram (5.1%). ASM effectiveness was good, with a 72% reduction in seizure frequency, and an additional 10% of people reported being seizure-free since starting treatment. However, 11% reported their medication was ineffective. Adherence was a significant challenge, with 43.2% of participants missing doses in the past month, with higher nonadherence rates in Shai-Osudoku (45.5%).

Table 2. Treatment history and associated factors of epilepsy.

	District, N (%)
	Ningo-Prampram	Shai-Osudoku	Total	P-value**
Amongst confirmed cases,	167	254	421
Those on treatment for at least the past 1 month	59 (35.3)	66 (26.0)	125(29.7)	0.183
For those on treatment,
Prescribers of the medication*
Doctor/Nurse	56 (94.9)	61 (92.4)	117(93.6)	0.387
Relative/Friend	2 (3.4)	3 (4.5)	5 (4.0)
Pharmacy attendant/Drug store owner	1 (1.7)	0 (0.0)	1 (0.8)
Other	0 (0.0)	2 (3.0)	2 (1.6)
Types of ASM
Carbamazepine	39 (66.1)	44 (66.7)	83 (66.4)
Phenobarbital	14 (23.7)	10 (15.2)	24 (19.2)
Sodium Valproate	2(3.4)	1(1.5)	3 (2.4)
Phenytoin	1(1.7)	0 (0.0)	1 (0.8)
Others (Unknown)	3(5.1)	11(16.6)	14 (11.2)
Effect of this medication on seizures
Seizures have stopped	8 (13.6)	5 (7.6)	13 (10.4)	0.579
Seizures have reduced	42 (71.2)	49 (74.2)	91 (72.8)
No effect	6 (10.2)	8 (12.1)	14 (11.2)
Seizures have got worse	0 (0.0)	2 (3.0)	2 (1.6)
Don’t know	3 (5.1)	2 (3.0)	5 (4.0)
Sources of ASM*
Pharmacy	53 (89.8)	59 (89.4)	112 89.6)	0.487
Roadside drug store/Mobile drug vendor	0 (0.0)	1 (1.5)	1 (0.8)
Donation from family/well-wishers	1 (1.7)	3 (4.5)	4 (3.2)
Other	5 (8.5)	3 (4.5)	8 (6.4)
Missed taking medication within the past 1 month	24 (40.7)	30 (45.5)	54 (43.2)	0.59
For those not on treatment,	108	188	296
Reasons for not being on treatment*
Not aware they have epilepsy	19 (17.6)	93 (49.5)	112(37.8)	<0.001
Don’t think ASM are effective	25 (23.1)	37 (19.7)	62 (20.9)
ASM are too costly	30 (27.8)	36 (19.1)	66 (22.3)
ASM are not available in pharmacy	3 (2.8)	2 (1.1)	5 (1.7)
Fear of side-effects	3 (2.8)	1 (0.5)	4 (1.4)
Other	45 (41.7)	35 (18.6)	80 (27.0)
Within confirmed cases,	167	254	421
Ever taken traditional medicine for epilepsy	108 (64.7)	177(69.7)	285(67.7)	0.282
Ever consulted a priest/pastor/man of God for prayers or deliverance from epilepsy	138 (82.6)	179(70.5)	317(75.3)	0.005
Perceive overall management of their epilepsy as adequate	31 (18.6)	35 (13.8)	66 (15.7)	0.187
Perceived causes of their epilepsy*
Family/genetic trait	5 (3.0)	14 (5.5)	19 (4.5)	0.082
Illness	37 (22.2)	41 (16.1)	78 (18.5)
Previous injury	6 (3.6)	4 (1.6)	10 (2.4)
Spirits/curses	65 (38.9)	90 (35.4)	155(36.8)
Don’t know	61 (36.5)	106 41.7)	167(39.7)
Other	14 (8.4)	11 (4.3)	25 (5.9)
Person/caregiver believes that they can be cured of epilepsy
No	10 (6.0)	5 (2.0)	15 (3.6)	0.016
Yes	129 (77.2)	221(87.0)	350(83.1)
Don’t know	28 (16.8)	28 (11.0)	56 (13.3)
For those who believe they can be cured
What they think is the best option for them to be cured
Medication	83 (64.3)	139(62.9)	222(63.4)	0.632
Surgery	1 (0.8)	1 (0.5)	2 (0.6)
Prayers	19 (14.7)	41 (18.6)	60 (17.1)
Spirituality	4 (3.1)	12 (5.4)	16 (4.6)
Other	4 (3.1)	7 (3.2)	11 (3.1)
Don’t know	18 (14.0)	21 (9.5)	39 (11.1)

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*Multiple responses allowed

** P-values were derived using Pearson’s chi-square test for categorical variables. Fisher’s exact test was applied where expected cell counts were <5.

Barriers to treatment and perceptions of epilepsy

The main obstacle to treatment was a lack of awareness of the diagnosis (37.8%), especially in Shai-Osudoku (49.5%). Other obstacles included medication costs, doubts about effectiveness, stigma, limited healthcare access, and a preference for alternative medicine.

Significant knowledge gaps persist in the understanding of epilepsy. A higher proportion of participants (39.7%) o were unsure of what caused epilepsy. A substantial portion (36.8%) attributed epilepsy to witchcraft or supernatural forces, while a smaller group (18.5%) correctly identified it as a medical condition.

Among confirmed epilepsy cases, significantly more participants in Ningo-Prampram had ever consulted a priest/pastor/man of God for prayers or deliverance than in Shai-Osudoku (82.6% vs 70.5%; p = 0.005). Similarly, beliefs about whether epilepsy could be cured also differed significantly between districts (p = 0.016), with a higher proportion in Shai-Osudoku reporting that it could be cured (87.0% vs 77.2%). However, no significant district-level differences were observed in perceived causes of epilepsy (p = 0.082) or preferred treatment options among participants who believed epilepsy could be cured (p = 0.632). Most (83%) believed the condition was treatable. Of these, 63% viewed ASM as the most effective treatment, while 17% considered prayer as most effective. The treatment history and associated factors are presented in Table 2.

Discussion

We examined epilepsy management within the Shai-Osudoku and Ningo-Prampram districts in Ghana. The treatment gap, defined as the proportion of people with epilepsy who were not on ASM during the reference period, was high and broadly consistent with estimates from other SSA settings [15,26,27]. However, comparisons should be interpreted cautiously because definitions of the treatment gap vary across studies, with some incorporating treatment adequacy, continuity of care, or effective biomedical management rather than recent ASM use alone. Consequently, the gap we report captures recent access to medication. This may overestimate the proportion of individuals receiving adequate care when compared to studies that define the gap based on seizure control or treatment appropriateness. We also observed geographic disparities in untreated epilepsy, with a marked gap in the predominantly rural Shai-Osudoku district. This difference is consistent with reports of higher treatment gaps in rural areas [8].

Several factors were associated with the observed treatment gap, and these were multifaceted. Lack of awareness was frequently reported and was associated with the diagnostic gap observed in this study, consistent with findings among people with epilepsy in a suburban area of Southeast Nigeria [27]. These observations align with evidence suggesting that low awareness is associated with reduced access to care in many rural African communities [28,29].

The substantial treatment gap we observed, which reflects recent ASM use, is consistent with ongoing challenges in epilepsy care delivery reported in Ghana and across SSA [5,16,17]. In areas like Shai-Osudoku, this gap persists despite an existing network of health facilities and may be associated with multiple barriers along the treatment pathway. These include limited knowledge among primary healthcare workers, delayed early treatment-seeking, insufficient numbers of specialists, weak diagnostic capacity and inadequate diagnostic tools. These factors may be associated with the undermanagement of epilepsy in these settings. Reducing the treatment gap in Ghana and similar SSA contexts may require urgent, coordinated action across the public health, community, and policy sectors to expand treatment coverage. In particular, targeted training programmes for primary healthcare workers could play an important role in improving diagnostic accuracy and enabling the initiation of treatment [16].

Beyond individual and cultural barriers, health system weaknesses were also associated with the treatment gap. Primary healthcare facilities in the study area often lack personnel trained in epilepsy diagnosis and management; essential ASMs are available inconsistently, and referral pathways are underdeveloped. Supply-chain disruptions and limited procurement budgets may be associated with drug stock-outs, particularly in the more rural Shai-Osudoku district. Task-sharing models, in which non-specialist health workers are trained to diagnose and manage epilepsy under supervision, have shown promise in comparable low-resource settings and may be associated with reduced workforce constraints [30]. Strengthening primary care capacity, improving ASM procurement and distribution, and integrating epilepsy care into existing health programmes have been proposed as strategies to address these structural barriers [31].

Consistent with many African reports, our findings suggest that deeply ingrained cultural beliefs may be associated with epilepsy treatment-seeking behaviours [14,16,17]. Attributing epilepsy to spiritual causes, such as witchcraft or curses, was associated with a greater reliance on traditional medicine and spiritualists. This perspective often co-exists with biomedical care, which may be viewed as complementary rather than primary [13]. Such pluralistic health-seeking was associated with delays in seeking biomedical treatment, interruptions in care or nonadherence to prescribed medications. Reliance on traditional methods may also be associated with epilepsy-related stigma, which, in turn, may be associated with healthcare-seeking behaviour [15]. These underscore the need for public health interventions that integrate culturally sensitive, community-based education to improve epilepsy awareness, reduce stigma, and engage with existing belief systems [17]. Rather than dismissing traditional community structures, interventions could foster collaborative pathways that respect pluralistic care models and may help facilitate effective care.

Dependence on alternative care was also associated with financial barriers, with almost a quarter of participants citing high ASM costs, followed by pharmacy stock-outs, particularly in remote areas. This situation was further associated with reports that some traditional practitioners actively discredit the effectiveness of biomedical treatment. These findings are consistent with evidence from Kenya that high medication costs and limited ASM availability are important barriers to treatment access [26,32]. Such barriers may be associated with nonadherence or even treatment discontinuation [16,17]. In resource-limited settings, such as Ghana and other SSA contexts, strengthening health systems and expanding affordable, consistent ASM access, particularly in rural areas, may be associated with improved care continuity.

The study has several limitations. First, households that were unavailable for screening were excluded after three contact attempts, but this may still have introduced attrition bias. Second, the multi-stage screening design resulted in decreasing numbers at each stage. Not all reductions across screening stages represented attrition, as some participants screened negative at Stage 2 and were appropriately excluded from further neurological assessment. However, loss to follow-up among eligible participants may have introduced some uncertainty in prevalence and treatment gap estimates. Despite the application of inverse probability weighting, residual attrition bias may remain if individuals lost to follow-up differed systematically from completers in terms of healthcare access, stigma, or seizure severity. Such differences could influence estimates of prevalence and the treatment gap.

Thirdly, diagnostic misclassification is possible. The screening tool, although locally validated [23], may have limited sensitivity for non-convulsive epilepsies, and some seizure mimics may have been misclassified as epilepsy. However, efforts were made to mitigate this with physician confirmation of cases. Fourth, several key variables-including ASM adherence, seizure frequency, and treatment effectiveness were based on self-report. This introduces potential recall bias and social desirability bias. Objective measures such as serum drug levels were not obtained due to logistical constraints. Fifth, the cross-sectional design precludes causal inference. Lastly, resource constraints limited the capacity to conduct independent validation of prevalence and treatment gap estimates, and findings may not be fully generalizable beyond the districts.

Conclusion

We identified a substantial epilepsy treatment gap in Ghana, underlined by a confluence of awareness gaps, sociocultural, and economic hurdles. Improving this requires a multidisciplinary approach beyond a purely medical model. Sustainable management, aligned with global frameworks such as the WHO Mental Health Gap Action Programme (mhGAP) and the Intersectoral Global Action Plan on Epilepsy (IGAP), can improve outcomes. Key strategies include community education, primary healthcare worker training, policy reforms and collaborative engagement with traditional healers. Integrating epilepsy care into primary health systems through task-sharing, as recommended by IGAP, is critical. Lastly, robust ASM supply-chain mechanisms must be implemented.

Supporting information

S1 File. EPInA Ghana survey households.

(CSV)

pone.0355854.s001.csv^{(4.4MB, csv)}

S2 File. EPInA Ghana survey members.

(CSV)

pone.0355854.s002.csv^{(29.9MB, csv)}

Acknowledgments

We thank the heads of the various facilities, especially the Medical Superintendent at the Shai-Osudoku District Hospital, Dr Kennedy Brightson, and their staff, for their assistance, as well as the staff and management of Prampram Poly Clinic and Ningo Health Centre. We acknowledge the contributions of the Bureau of Ghana Languages for the forward translations and Jevetic Education Services for the back translations of the epilepsy screening questionnaires. We are also grateful to the research assistants of the Dodowa Health Research Centre for their assistance with data collection. We are thankful to the chiefs and opinion leaders of Shai Osudok and Ningo Prampram, as well as the study participants and their caregivers, without whom this study would not have been possible. We are also thankful to Drs Nana Akua Acheampong, Samuel Mensah, Hayford Ayertey Tetteh, and Fiifi Duodu for their help with the clinical assessments. JHC holds an endowed chair at UCL Great Ormond Street Institute of Child Health; her research is supported by the National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital.JWS is based at the NIHR University College London Hospitals Biomedical Research Centre, which is sponsored by the UK Department of Health. He receives research support from the Marvin Weil Epilepsy Research Fund, the National Health Research Institute, and the Academy of Medical Sciences. PA is the lead researcher on the EPInA project in Ghana.

EPInA Study Group†: Patrick Adjei, Albert Akpalu, Sabina Asiamah, Gershim Asiki, Mercy Atieno, Dan Bhwana, Mary Bitta, Neerja Chowdhary, J Helen Cross, Emmanuel Darkwa, Timothy Denison, Tarun Dua, Tony Godi, F. Simone Grassi, Samuel Iddi, Daniel Nana Yaw, Abankwah Junior, Symon Kariuki, Henrika Kimambo, Thomas Kwasa, Sloan Mahone, Gergana Manolova, William Matuja, David McDaid, Bruno Mmbando, Daniel Mtai Mwanga, Damazo Twebaze Kadengye, Dorcas Muli, Frederick Murunga Wekesah, Vivian Mushi, Charles R. Newton, Guillaume Pages, Peter Otieno, Ley Sander, Arjune Sen, Cynthia Sottie, Isolide Sylvester, Sonia Vallentin, Richard Walker, Stella Waruinge.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The UK National Institute for Health and Care Research (NIHR 200134) funded this project through Official Development Assistance (ODA). The views expressed in this publication are those of the authors and not necessarily those of the National Health Service (NHS), the NIHR, or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0355854.r001

Decision Letter 0

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10 May 2026

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The authors failed to state in Methods section that the study was granted ethics committee approval, although the approval letters were shown under "Supporting Information".

Although the statistical analysis was performed appropriately and rigorously, the authors failed to state in the text under "Results" which statistical method they used (eg, Fisher's exact test, chi-square test). They also failed to state some important results in the text and only displayed them on two tables.

There are no other issues beside those I mentioned in my comments to authors.

Thank you.

Nura Alkali

Reviewer #2: This is an important and policy-relevant community-based epilepsy epidemiology study from southeastern Ghana addressing treatment gap in sub-Saharan Africa.

The strengths are the large population denominator, staged screening design, focus on treatment barriers, and integration of sociocultural determinants with treatment access data.

The study could be considerably strengthened by addressing the following

1. Treatment gap definition and operationalization

The manuscript defines the epilepsy treatment gap appropriately in conceptual terms; however, its operational definition within the study requires clarification.

In practice, treatment status appears to rely largely on reported ASM use in the preceding month, which may not adequately distinguish between untreated epilepsy, undertreatment, interrupted treatment, or poor adherence. Greater clarity regarding how ‘appropriate treatment’ was assessed would strengthen the validity and interpretability of the treatment-gap estimates.

2. Cross-sectional design and causal interpretation

Several sections of the discussion imply causal relationships between sociocultural beliefs, awareness deficits, and treatment gaps. However, given the cross-sectional observational design, the findings should be interpreted as associations rather than evidence of causality. The discussion would benefit from greater caution, using terms such as ‘associated with’ or ‘may contribute to’ rather than implying direct causal effects.

3. Diagnostic ascertainment and epilepsy misclassification

The multistage screening design with physician confirmation is a major strength; however, additional details regarding validation of the screening tool would improve methodological rigor. Information on sensitivity, specificity, and diagnostic reliability is lacking. Furthermore, community-based surveys in low-resource settings may underdetect nonconvulsive epilepsies and may overdetect seizure mimics such as syncope or psychogenic nonepileptic events.

4. Attrition bias and representativeness

A substantial proportion of suspected cases did not complete all stages of assessment, raising the possibility of attrition bias despite the use of inverse probability weighting. Individuals lost during follow-up may systematically differ in healthcare access, stigma, or seizure severity, potentially influencing both prevalence and treatment-gap estimates. This limitation should be discussed in greater depth.

5. Reliance on self-reported data

Several important variables, including medication adherence, seizure control, treatment effectiveness, and health-seeking behavior, relied primarily on participant self-report. This introduces the possibility of recall bias and social desirability bias, particularly in relation to adherence and perceived seizure outcomes. The limitations associated with self-reported treatment data should therefore be emphasized more explicitly.

6. Interpretation of sociocultural beliefs

The discussion appropriately highlights the importance of sociocultural beliefs in epilepsy care pathways; however, some interpretations risk oversimplifying culturally embedded explanatory models as mere misinformation. Greater engagement with literature on pluralistic healthcare systems and culturally informed health-seeking behaviors would strengthen the discussion and provide a more balanced interpretation of traditional and spiritual beliefs.

7. Limited health-system analysis

Although the manuscript identifies important barriers such as medication cost, drug shortages and limited specialist access, the health-system analysis remains relatively underdeveloped.

Additional discussion of primary healthcare capacity, ASM supply-chain challenges, workforce limitations, and economic barriers would strengthen the policy relevance of the findings.

8. Statistical analysis and reporting

The statistical methods are generally appropriate; however, additional detail regarding handling of clustering, missing data, and potential confounders would improve transparency.

9.References

The reference list is generally appropriate and includes several authoritative sources on epilepsy epidemiology, treatment gaps, and sub-Saharan African epilepsy care. The strong use of regional African literature is a notable strength and provides relevant contextual grounding for the findings.

However, the manuscript would benefit from inclusion of more recent global policy and implementation-science references, particularly WHO mhGAP and IGAP frameworks, task-sharing models, and literature addressing explanatory models of health seeking in epilepsy.

In addition, at least one reference seems irrelevant :

Reference 13:

“Chowdhury et al. Chronic riluzole treatment increases glucose metabolism…”

All the references should be reviewed for relevance and consistency.

10 Recommedations

The recommendations would benefit from stronger linkage to existing implementation frameworks such as WHO mhGAP and IGAP initiatives, as well as evidence from task-sharing epilepsy-care models in similar low-resource settings.

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Reviewer #1: Yes: Nura Hamidu Alkali

Reviewer #2: No

**********

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PLoS One. 2026 Aug 11;21(8):e0355854. doi: 10.1371/journal.pone.0355854.r002

Author response to Decision Letter 1

3 Jul 2026

PONE-D-26-03687

Barriers to Epilepsy Care: Measuring the Treatment Gap in Rural and Peri-Urban Districts in Ghana

PLOS One

Response to editorial comments

We thank the Editor for these important observations and have revised the manuscript accordingly.

Comment 1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: Thank you. We have revised the manuscript to conform to PLOS ONE formatting requirements, including file naming, manuscript structure, figure presentation, and title page formatting.

Comment 2-5: Journal Requirement - Funding statement, NIHR expansion, and role of funders

Response: Thank you for this observation. We have removed funding-related text from the Acknowledgments section and from the manuscript and updated the Funding Statement accordingly. We have also expanded the acronym “NIHR” as National Institute for Health and Care Research.

We have revised the funding statement: “The UK National Institute for Health and Care Research (NIHR 200134) funded this project through Official Development Assistance (ODA). The views expressed in this publication are those of the authors and not necessarily those of the National Health Service (NHS), the NIHR, or the Department of Health and Social Care”.

Comment 6: Data availability

Response: Thank you. We confirm that the minimal anonymized dataset underlying the findings was prepared and uploaded as Supporting Information files in the initial submission.

Comment 7: Your ethics statement should only appear in the Methods section of your manuscript.

Response: Thank you for this observation. We have revised the manuscript accordingly by moving the ethics statement section to the Methods section of the manuscript and removing it from other sections to ensure consistency with the journal requirements. The necessary changes have been made in the manuscript (Page 12, lines 239-248).

Comment 8: Please ensure that you refer to Figure 1 in your text as, if accepted, production will need this reference to link the reader to the figure.

Response: Thank you for this observation. We have now appropriately cited Figure 1 in the main text of the manuscript to ensure proper linkage between the text and the figure during production.

Comment 9: We note that Figure 1 in your submission contains map images which may be copyrighted.

Response: Thank you for this important observation. The maps presented in Figure 1 were generated by the authors using geographic coordinates collected from the study area and administrative shapefiles obtained from the respective district assemblies (local authority). No copyrighted Google Maps, Google Earth, Street View images, or other proprietary satellite imagery were used in the preparation of the figure. This figure has been published by the same authors elsewhere (1). This has now been properly referenced (Page 7, line 132).

1. Darkwa EK, Asiamah S, Awini E, Sottie C, Godi A, Williams JE, et al. The prevalence and determinants of epilepsy in Ghana: A population‐based study in two districts using a three‐stage approach. Epilepsia. 2026.

Response to Reviewers

We thank the reviewers for their useful comments, and we have revised the manuscript as such

Reviewer # 1

Comment: The authors failed to state in the Methods section that the study was granted ethics committee approval, although the approval letters were shown under "Supporting Information".

Response: Thank you for this comment. The ethics approval statement was included in the original manuscript; however, it was inadvertently placed before the References section. We have now revised the manuscript by moving the ethics statement to the Methods section, where it is more appropriately positioned in line with the journal requirements. The necessary changes have been made in the manuscript (Page 12, lines 239-248).

Comment: Although the statistical analysis was performed appropriately and rigorously, the authors failed to state in the text under "Results" which statistical method they used (eg, Fisher's exact test, chi-square test). They also failed to state some important results in the text and only displayed them on two tables.

Response: Thank you. We have revised the manuscript accordingly by explicitly stating the statistical tests used in the analyses within the Methods section, including the appropriate tests applied for comparisons between groups (Page 11, lines 224-226).

In addition, key findings previously presented only in the tables have now been incorporated into the narrative text of the Results section to improve clarity and interpretation. The necessary changes have been made in the manuscript (Pages 12-13 and 14, lines 256-258, 261-266, 279-281, and lines 298-305).

Reviewer # 2

Comments 1: Treatment gap definition and operationalization

The manuscript defines the epilepsy treatment gap appropriately in conceptual terms; however, its operational definition within the study requires clarification. In practice, treatment status appears to rely largely on reported ASM use in the preceding month, which may not adequately distinguish between untreated epilepsy, undertreatment, interrupted treatment, or poor adherence. Greater clarity regarding how ‘appropriate treatment’ was assessed would strengthen the validity and interpretability of the treatment-gap estimates.

Response: Thank you for this important observation. We have revised the manuscript to provide greater clarity on the operational definition of the epilepsy treatment gap used in this study. Specifically, we have expanded the Methods section to explain how treatment status and ‘appropriate treatment’ were assessed, including the role of recent anti-seizure medication (ASM) use, treatment continuity, and clinical history obtained during the assessment. We have also clarified the limitations associated with relying on self-reported ASM use in the preceding month, particularly regarding the distinction between untreated epilepsy, undertreatment, interrupted treatment, and poor adherence (Pages 10, lines 194-199, Page 20, lines 392-397).

Comment 2: Cross-sectional design and causal interpretation

Response: Thank you. We agree that, given the cross-sectional observational design of this study, causal inferences cannot be established. The Discussion section has been carefully revised to avoid causal language and to ensure that findings are interpreted as associations rather than evidence of direct causality. Terms implying causal relationships, such as “led to,” “resulted in,” or “caused,” have been replaced with more appropriate cautious language such as “was associated with,” “may contribute to,” or “could partly explain” (Pages 17, 18 and 19, lines 322-334 and 350-372).

Comment 3: Diagnostic ascertainment and epilepsy misclassification

Response: Thank you for acknowledging the strength of the multistage screening design. We appreciate this important comment. In response, we have included additional details in the manuscript regarding the validation of the screening tool and provided an appropriate reference to the validation study conducted before this household survey to strengthen the methodological rigor of the paper (Page 8, lines 151-155).

We also acknowledge the potential limitations of community-based epilepsy surveys in low-resource settings, including the possible under detection of nonconvulsive epilepsies and the over-detection of seizure mimics such as syncope or psychogenic nonepileptic events. To minimize these risks, we implemented a multistage screening process, employed trained fieldworkers for standardized data collection, and ensured neurologist confirmation of suspected epilepsy cases at the final stage of assessment. These measures were intended to improve diagnostic accuracy and reduce the likelihood of misclassification. Relevant clarifications have now been incorporated into the Methods and Discussion sections of the revised manuscript (Pages 7 and 20, lines 160-163 and 388-391).

Comment 4: Attrition bias and representativeness

Response: Thank you. We acknowledge that loss to follow-up across screening stages could have introduced bias and led to underestimation of epilepsy prevalence and treatment gap estimates. To address this, we applied attrition-adjusted prevalence estimation using inverse probability weighting (IPW) to account for participants lost at Stages 2 and 3. Sampling weights were derived from the inverse probability of progression through each screening stage, based on observed characteristics and response patterns. These weights were incorporated into prevalence calculations to reduce attrition bias, improve representativeness of confirmed epilepsy cases, and strengthen the robustness of the estimates. This limitation has now been discussed in greater detail

(Pages 20, lines 380-387).

Comment 5: Reliance on self-reported data

Response: Thank you. We agree that reliance on self-reported data for key variables such as medication adherence, seizure control, treatment effectiveness, and health-seeking behaviour may introduce recall bias and social desirability bias. In response, we have strengthened the Discussion/Limitations section to more explicitly acknowledge these potential sources of bias and their possible influence on the interpretation of treatment-related findings. We now clarify that treatment-related information was primarily based on participant or caregiver self-report, which may have affected the accuracy of reported adherence, seizure outcomes, and care-seeking patterns. These limitations have now been explicitly discussed in the revised manuscript (Page 20, lines 392-397).

Comment 6: Interpretation of sociocultural beliefs

Response: Thank you. We appreciate the reviewer’s emphasis on the need for a more balanced interpretation of sociocultural beliefs within epilepsy care pathways. We agree that traditional and spiritual beliefs should not be framed solely as misinformation, as health-seeking behaviours in many low-resource settings often occur within pluralistic healthcare systems where biomedical, traditional, and spiritual approaches may coexist and shape care pathways. In response, we have revised the Discussion section to adopt a more nuanced interpretation of culturally embedded explanatory models and strengthened the discussion by incorporating relevant literature on pluralistic healthcare systems and culturally informed health-seeking behaviours. (Pages 18, lines 350-363).

Comment 7: Limited health-system analysis

Although the manuscript identifies important barriers such as medication cost, drug shortages and limited specialist access, the health-system analysis remains relatively underdeveloped.

Additional discussion of primary healthcare capacity, ASM supply-chain challenges, workforce limitations, and economic barriers would strengthen the policy relevance of the findings.

Response: Thank you. We agree that expanding the health-system perspective would strengthen the policy relevance of the findings. In response, we have revised the Discussion section to provide a more comprehensive interpretation of the structural and health-system barriers that may influence epilepsy diagnosis, treatment access, and continuity of care. Specifically, we have expanded the discussion to include primary healthcare capacity for epilepsy management, anti-seizure medication (ASM) supply-chain and availability challenges, workforce limitations including restricted access to specialist neurological care. (Page 18, lines 340-349).

Comment 8: Statistical analysis and reporting

The statistical methods are generally appropriate; however, additional detail regarding handling of clustering, missing data, and potential confounders would improve transparency.

Response: Thank you. To account for potential household and community-level clustering inherent in the door-to-door sampling design, cluster-robust standard errors were calculated for all comparative analyses. Potential confounding by age and sex was controlled through exact matching during the case-control selection phase (Page 10 and 11, lines 228-235).

Comment 9: References

Response: Thank you for the suggestions. We have revised the manuscript to include more recent global policy and implementation-science references, particularly the WHO Mental Health Gap Action Programme (mhGAP), the Intersectoral Global Action Plan on Epilepsy and Other Neurological Disorders (IGAP 2022–2031), task-sharing approaches, and literature on explanatory models that influences health-seeking behaviour in epilepsy.(Page 18. Lines 344-349).

Comment 10: Recommendations

Response: Thank you for this valuable comment. We have strengthened the recommendations section by explicitly linking our findings to existing implementation frameworks, including the WHO Mental Health Gap Action Programme (mhGAP) and the Intersectoral Global Action Plan on Epilepsy and Other Neurological Disorders (IGAP 2022–2031). (Page 21, lines 408-417).

Supplementary Comments (word document)

Comment 1:

1. Under “Methods – Study setting”, lines 114-118 (Page 5):

The authors listed 16 health facilities in Shai-Osudoku District, but only stated “fewer health facilities” for Ningo-Prampram District. There is a need to mention the health facilities in Ningo-Prampra

Attachment

Submitted filename: Response _ Reviewers Plos One.pdf

pone.0355854.s004.pdf^{(324.5KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0355854.r003

Decision Letter 1

Hiroki Annaka

21 Jul 2026

Dear Dr. Akpalu,

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The reviewer has requested minor revisions. Please make the necessary changes.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Authors have addressed all my earlier comments and suggestions.

1. The authors now stated in the methods section that they had ethical approval for the study.

2. The authors have mentioned the statistical tests used in the study.

3. The authors have now listed the number of facilities in the Ningo-Prampram District

4. The authors have provided the exact start and end dates of the study.

5. The authors have satisfactorily explained the seemingly high attrition rate.

6. The authors have corrected the formatting error at line 193 (page 9).

Reviewer #2: The authors have addressed the majority of the reviewers’ comments and the manuscript has improved substantially. I have only a few remaining comments.

1. Treatment gap definition: Although the operational definition has been clarified, the reported treatment gap reflects recent ASM use rather than treatment adequacy. This distinction should remain explicit when interpreting the findings and comparing them with other studies.

2. Interpretation of findings: The discussion should continue to avoid causal language. Given the cross-sectional design, factors such as lack of awareness, sociocultural beliefs, and reliance on traditional medicine should be described as being associated with, rather than causing, the treatment gap.

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PLoS One. 2026 Aug 11;21(8):e0355854. doi: 10.1371/journal.pone.0355854.r004

Author response to Decision Letter 2

24 Jul 2026

Manuscript ID: PONE-D-26-03687R1 : Barriers to Epilepsy Care: Measuring the Treatment Gap in Rural and Peri-Urban Districts in Ghana

Response to Reviewers

Dear Hiroki Annaka,

We thank the Academic Editor and reviewers for their evaluation of our revised manuscript and for their feedback. We appreciate that the reviewers found the manuscript to have improved. We have carefully considered the remaining comments and revised the manuscript accordingly. Our reactions are provided below. We have used the opportunity to address minor issues identified in the manuscript in track changes.

Reviewer #1

We thank the reviewer for the positive assessment of our manuscript and appreciate the time and effort invested in reviewing our work.

Reviewer #2

We thank the reviewer for the positive evaluation of our revised manuscript and for the remaining suggestions. We have now addressed each comment.

Comment 1:Treatment gap definition: Although the operational definition has been clarified, the reported treatment gap reflects recent ASM use rather than treatment adequacy. This distinction should remain explicit when interpreting the findings and comparing them with other studies.

Response: Thank you for this observation. We agree that our estimate reflects recent antiseizure medication (ASM) use and should not be interpreted as a measure of treatment adequacy. We have revised the Discussion to explicitly state that the treatment gap reported in this study is based on recent ASM use reflecting lack of current treatment rather than the overall adequacy or quality of epilepsy management. We have also emphasized this distinction when comparing our findings with previous studies that may have used different operational definitions of the treatment gap. (Page 16, lines 301-310)

Comment 2: Interpretation of findings: The discussion should continue to avoid causal language. Given the cross-sectional design, factors such as lack of awareness, sociocultural beliefs, and reliance on traditional medicine should be described as being associated with, rather than causing, the treatment gap.

Response: We appreciate this comment. We have carefully reviewed the Discussion and replaced any remaining causal wording with language appropriate for a cross-sectional study. Throughout the manuscript, statements implying causation have been revised to describe these factors as being associated with the treatment gap rather than causing it. This ensures that the interpretation of our findings accurately reflects the observational nature of the study. (Page 16-19, line 300-367).

Sincerely,

Prof. Albert Akpalu, on behalf of the author team

Attachment

Submitted filename: Response to Reviewers 24 July 2026.pdf

pone.0355854.s005.pdf^{(151.5KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0355854.r005

Decision Letter 2

Hiroki Annaka

27 Jul 2026

Barriers to Epilepsy Care: Measuring the Treatment Gap in Rural and Peri-Urban Districts in Ghana

PONE-D-26-03687R2

Dear Dr. Akpalu,

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Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0355854.r006

Acceptance letter

Hiroki Annaka

PONE-D-26-03687R2

PLOS One

Dear Dr. Akpalu,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. EPInA Ghana survey households.

(CSV)

pone.0355854.s001.csv^{(4.4MB, csv)}

S2 File. EPInA Ghana survey members.

(CSV)

pone.0355854.s002.csv^{(29.9MB, csv)}

Attachment

Submitted filename: PLOS ONE Manus-Review Work.docx

pone.0355854.s003.docx^{(21.8KB, docx)}

Attachment

Submitted filename: Response _ Reviewers Plos One.pdf

pone.0355854.s004.pdf^{(324.5KB, pdf)}

Attachment

Submitted filename: Response to Reviewers 24 July 2026.pdf

pone.0355854.s005.pdf^{(151.5KB, pdf)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

Barriers to epilepsy care: Measuring the treatment gap in rural and peri-urban districts in Ghana

Emmanuel Kwame Darkwa

Patrick Adjei

Sabina Asiamah

Elizabeth Awini

Cynthia Sottie

Anthony Godi

John E Williams

Frank Atuguba

Fuseini Sataru

Alexander Adjei

Arjune Sen

Charles R Newton

Anthony Danso-Appiah

J Helen Cross

Josemir W Sander

Albert Akpalu

Roles

Abstract

Background

Materials and methods

Results

Conclusion

Background

Methods

Setting

Fig 1. Map of Ghana highlighting the two study districts of Shai-Osudoku and Ningo-Prampram within the Greater Accra Region, including major towns, roads, and neighboring districts.

Study design

Participant recruitment and screening

Data collection

Definitions

Statistical analysis

Ethical considerations

Results

Prevalence of epilepsy

Table 1. Three-stage screening for epilepsy.

Epilepsy treatment coverage and gap

Table 2. Treatment history and associated factors of epilepsy.

Barriers to treatment and perceptions of epilepsy

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Hiroki Annaka

Roles

Author response to Decision Letter 1

Decision Letter 1

Hiroki Annaka

Roles

Author response to Decision Letter 2

Decision Letter 2

Hiroki Annaka

Roles

Acceptance letter

Hiroki Annaka

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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