Introduction

Schizophrenia is among the serious mental illnesses characterized by a clinical syndrome of variable but profoundly disruptive psychopathology that affects cognition, emotion, perception, and other aspects of the patient’s life, including work, self-care, and the ability to form interpersonal relationships1,2. Globally, it affects approximately 1% of the population and ranks among the top ten causes of long-term disability3. Pharmacological treatment, particularly antipsychotic medications, remains the cornerstone of schizophrenia management, alleviating acute symptoms, preventing relapse, and improving quality of life4,5,6. However, the effectiveness of these medications dependent strongly on patient adherence.

The World Health Organization (WHO) defines adherence as the extent to which a person’s behavior taking medication, following a diet, or executing lifestyle changes corresponds with agreed recommendations from a healthcare provider7. Despite the availability of effective pharmacological treatments, medication non-adherence is a longstanding clinical issue in schizophrenia, with evidence showing that discontinuation increases relapse risk nearly fivefold8. While adherence is often supported in acute care settings, it becomes more challenging in the long-term management of chronic psychiatric illnesses, including schizophrenia9.

Non-adherence is a multifaceted problem influenced by numerous patient-related and systemic factors. Patients’ knowledge, insight into their illness, beliefs about medication efficacy, perceived side effects, social stigma, and past experiences significantly affect adherence behaviors6,10,11. Individuals with severe psychiatric symptoms, cognitive impairments, or fears of side effects are particularly at risk11. Moreover, attitudes of family members and cultural beliefs about mental illness also contribute to negative perceptions of medication use12. Medication non-adherence may manifest in various ways, from complete refusal to inconsistent or partial use of prescribed doses6.

The consequences of non-adherence to antipsychotic medication are profound. It is associated with symptom relapse, re-hospitalization, poor quality of life, strained social relationships, treatment resistance, comorbid conditions, and even increased suicide risk13,14,15. Additionally, schizophrenia is often linked with other comorbidities due to substance abuse, unhealthy lifestyles, and side effects of antipsychotic medications factors that may further discourage adherence16. Globally, about 43.7% of patients with schizophrenia experience re-hospitalizations, with medication non-adherence being a major contributing factor17. This not only affects individual health outcomes but also leads to greater healthcare costs due to increased emergency visits, longer hospital stays, and frequent readmissions18,19.

Several studies have identified individual sociodemographic factors, substance use, perceived stigma, and attitudes toward medications as significant predictors of non-adherence11. Additionally, concerns related to side effects, fear of addiction, and potential drug-drug interactions further hinder medication-taking behavior20,21,22.

In Sub-Saharan Africa (SSA), these challenges are exacerbated by under-resourced healthcare systems, limited access to medications, low mental health literacy, and strong cultural stigma. Due to ongoing political instability, economic constraints, and a shortage of trained mental health professionals, the region faces widespread misdiagnosis, treatment mismanagement, and pervasive medication non-adherence23. Yet, despite the critical importance of this issue, the determinants of non-adherence to antipsychotic medications in SSA remain poorly documented and lack a comprehensive, systematic evaluation.

Understanding psychotropic medication non-adherence and its contributing factors in patients with schizophrenia is essential for developing effective, culturally sensitive interventions tailored to the realities of healthcare delivery in SSA24.

This systematic review and meta-analysis aim to summarize the prevalence of non-adherence to antipsychotic medication regimens in SSA, identify key associated factors, and provide evidence-based recommendations to improve adherence. The findings will support policy makers, healthcare providers, and organizations working with schizophrenia patients to design more effective interventions, enhance patient outcomes, and strengthen mental health care systems across the region.

Method

Protocol and registration

The PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) offer a well-established framework for organizing and reporting systematic reviews and meta-analyses (Supplementary File 1). This review protocol has been registered with the International Registration of Systematic Reviews (PROSPERO)25, a global platform for registering prospective systematic reviews. It was assigned a (PROSPERO; registration number: CRD420251038674).

Study eligibility criteria and study selection

Before retrieving full-text articles, two reviewers (ZDA and CT) assessed the relevant papers based on their titles and abstracts. The full-text articles identified were then evaluated using predefined inclusion and exclusion criteria. Concerns were resolved with the assistance of a third reviewer (TSY). We included cross-sectional and other observational studies that examined non-adherence to antipsychotic medications and/or factors associated with non-adherence to antiepileptic drugs among schizophrenia patients in SSA. All studies conducted at the community or health institution level, published in English before February, 2025 were included, regardless of the type of adherence measurement. All the included studies were carried out in SSA. The review excluded research focusing on populations other than adult patients, as well as case reports, conference abstracts, and duplicate articles.

Search strategy

The review aimed to determine the prevalence of antipsychotic medication non-adherence in SSA. Major international databases, including PubMed, Cochrane Library, and Scopus Online, as well as the Google Scholar search engine, were searched alongside Africa-specific databases (AFROLIB, African Index Medicus, and African Journals Online All studies identified before February, 2025 were retrieved and assessed for eligibility. This review employed the PECO search strategy (Population, Exposure, Comparison, and Outcomes).

Population: Patients with schizophrenia in SSA, Exposure: Non-adherence with the treatment regimen (i.e., antipsychotic medications). Comparison: Each study reported the reference group for each associated factor, such as comparing non-adherence among schizophrenia patients who received information about antipsychotic medications to those who did not, as well as non-adherence among schizophrenia patients who experienced side effects to those who did. Outcome: Prevalence of non-adherence and associated factors. We employed the following text keywords: (“schizophrenia” OR “psychotic disorders” OR “mental illness” OR “psychiatric disorders”) AND (“non-compliance” OR “non-adherence” OR “treatment non-compliance” OR “medication non-adherence” OR “treatment adherence” OR “medication adherence”) AND/OR (“Associated Factors” OR “Determinants” OR “Predictors”) AND (“prevalence” OR “frequency” OR “proportion” OR “incidence” OR “occurrence” OR “rate”) AND (“Sub-Saharan Africa” OR “Africa South of the Sahara” OR the specific names of Sub-Saharan African countries such as Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Democratic Republic of Congo, Djibouti, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Ivory Coast, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Togo, Uganda, Tanzania, Zambia, Zimbabwe).

Data abstraction

The data was extracted using a preformatted Microsoft Excel file. Two authors (ZDA and DGD) independently extracted the required data from the articles using a standardized data extraction form. This form was designed to gather relevant information, including authors’ names, publication years, African regions, study designs, settings, participants, sampling methods, sample sizes, prevalence of medication non-adherence, adherence assessment tools used to assess non-adherence, and factors associated with medication non-adherence.

Quality and risk of bias assessment

The Joanna Briggs Institute (JBI) tool for assessing the quality of cross-sectional studies was used to assess the methodological quality of the studies included in this review26. Two authors (ZDA and DAB) independently assessed the quality of the original studies using the JBI criteria. Studies scoring six or more points on a nine-point scale were included in the analysis. The JBI tool evaluates nine essential aspects: the suitability of the sample frame for addressing the target population, the appropriateness of the sampling method for study participants, the adequacy of the sample size, the detailed description of study subjects and settings, the comprehensiveness of data analysis for the identified sample, the use of valid methods to identify the condition, the reliability of condition measurement across all participants, the appropriateness of statistical analysis, and the adequacy of the response rate or proper handling of a low response rate.

Outcomes

Primary outcome

Medication nonadherence: The main outcome of this study was to estimate the pooled prevalence of medication nonadherence among patients with schizophrenia in SSA, which is assessed using validated medication adherence instruments regardless of the type of adherence measurement.

Secondary outcome

Associated factors of medication nonadherence among patients with schizophrenia in SSA.

Data analysis

To estimate the pooled prevalence of medication non-adherence both proportion of medication prevalence and 95% confidence intervals (CIs) were reported. For the associated factors, odds ratios, their logarithms, and the standard errors of these logarithms were computed. Data were initially extracted into Microsoft Excel and subsequently imported into STATA 17.0 for further analysis. A random-effects model was employed to summarize the data, while heterogeneity was assessed using the Q test and the I² statistic. The thresholds for I² indicating low, moderate, substantial, and high heterogeneity were defined as ≤ 25%, 25–50%, 50–75%, and ≥ 75%, respectively. Meta-regression analyses were conducted to assess the impact of continuous and categorical moderator variables (e.g., study year, sample size, study quality) on prevalence of medication non-adherence. Additionally, subgroup analyses were performed based on the study region in Africa and adherence measurement tools to identify potential sources of heterogeneity. Both meta-analysis and narrative analysis were utilized to present the findings. A sensitivity analysis was also carried out to verify that the results were robust against potentially influential decisions. Finally, publication bias was evaluated using a funnel plot and Egger’s regression test; if Egger’s test indicated statistical significance (p < 0.05) or if the funnel plot exhibited asymmetry, publication bias was noted.

Results

Search results

A total of 2,504 articles were initially retrieved from the electronic database. After removing 850 duplicate records 1,654 articles remained for screening. Based on title and abstract review 1,345 articles were excluded, leaving 309 for full-text assessment. Of these, 211 articles were excluded due to differing primary endpoints (Reason 1), and 114 were removed due to inappropriate results (Reason 2). Finally, 16 studies met the inclusion criteria and were included in the systematic review and meta-analysis (Fig. 1).

Fig. 1
figure 1

Flowchart of study selection for systematic review and meta-analysis of pooled prevalence of medication nonadherence in patients with schizophrenia and associated factors in SSA.

Full size image

Description of studies

Table 1 shows the characteristics of the studies included in the meta-analysis, which examined antipsychotic medication non-adherence among patients with schizophrenia across various regions in SSA. The studies were conducted in Ethiopia, Nigeria, Ghana, Rwanda, Uganda, and Eritrea, with the majority (nine studies) from East Africa and the rest from West Africa. Sample sizes varied significantly, ranging from 120 to 893 participants. All studies employed a cross-sectional design, with most using the Morisky Medication Adherence Scale (MMAS) as the assessment tool, while two studies utilized self-reported questionnaires (SRQ). Response rates were generally high, ranging from 80% to 100%. The prevalence of medication non-adherence varied considerably, with the lowest rate reported in Uganda (16.3%) and the highest in Ghana (98%).

Based on the study objectives, several studies focused on assessing the prevalence and associated factors of antipsychotic medication non-adherence among patients with schizophrenia. These include research conducted by Erkiso Shumi Mamo et al.27, Fethia Mohammed et al.28, Fasil Bayafers Tamene et al.29, Minale Tareke et al.30, Mulugeta Nega et al.31, Shimelis Girma et al.32, Jombo Henry Effiong et al.33, and Moses Kule et al.34. Additionally, studies by Tadele Eticha et al.35, Schadrack Ntirenganya et al.36, Merhawi Bahta et al.37, Maigari Yusufu Taru et al.38, and Irene A. Kretchy et al.39 explored factors influencing non-adherence, including the role of caregivers’ psychological distress and the extent of non-adherence. Two studies, conducted by Adegoke Oloruntoba Adelufosi et al.40 and Oluseun P. Ogunnubi et al.41, examined the relationship between medication adherence and quality of life while also reporting the prevalence of non-adherence. Finally, a study by Sharon Ashong et al.42 focused on medication use patterns, therapeutic monitoring, and the influence of side effects on adherence behavior, also reporting non-adherence prevalence. Different adherence measurement tools were employed, with the Morisky Medication Adherence Scale (MMAS) being the most commonly used in its various versions (MMAS-4, MMAS-8, and MMAS-10). Other studies utilized the Medication Adherence Rating Scale (MARS) and Self-Reported Questionnaires (SRQ). The definitions of non-adherence differed across studies, with most MMAS-based studies considering a score below a specific threshold (e.g., MMAS ≤ 2 or MMAS ≤ 5) as indicative of non-adherence. However, some studies using MARS or SRQ did not clearly specify their non-adherence criteria.

Table 1 Characteristics of studies included in the meta-analysis on non-adherence with treatment regimen and its associated factors among schizophrenia patients in SSA.
Full size table

The pooled prevalence of medication nonadherence in patients with schizophrenia in Sub-Saharan Africa

A total of sixteen studies were included to estimate the prevalence of non-adherence to antipsychotic medication among patients with schizophrenia in SSA. Using the random-effects model, the pooled prevalence of medication non-adherence in this population was 45.30% (95% CI: 29.57–61.04%). Substantial heterogeneity was observed across the studies (I2 = 99.51%, p < 0.001) Fig. 2.

Fig. 2
figure 2

Forest plot of the pooled prevalence of medication nonadherence among patients with schizophrenia in SSA.

Full size image

Subgroup and meta-regression analysis

The selected studies showed considerable heterogeneity (I² = 99.51%, p < 0.001), indicating that the variability among studies exceeded the expected level. Consequently, the overall pooled prevalence estimate of medication non-adherence among patients with schizophrenia in SSA was inconsistent. To address this, a random effects model was used for the pooled estimate. In addition, meta-regression and subgroup analyses were conducted to investigate possible causes of heterogeneity. Factors such as sample size, response rate, country, study region in Africa, study period, publication year and adherence assessment tool were included in the meta-regression. Among these, the study region in Africa and the adherence assessment tool were identified as significant factors for the observed heterogeneity Table 2.

Table 2 Meta-regression analysis of the studies based on sample size, response rate, country, study regions in Africa, study period, publication year, and tool used to assess adherence.
Full size table

Subgroup analysis was conducted based on the type of instrument used to assess non-adherence and the African region that the studies were conducted. In East Africa, the pooled prevalence of medication non-adherence was 38.53% (95% CI: 29.26–47.80), with substantial heterogeneity (I² = 97.35%, p < 0.001). In contrast, the pooled prevalence in West Africa was higher at 56.62% (95% CI: 29.81–83.43), also with considerable heterogeneity (I² = 99.50%, p < 0.001) (Fig. 3). However, the test for subgroup differences (Q = 1.56, p = 0.21) indicated no statistically significant difference in the pooled prevalence of antipsychotic medication non-adherence between the East and West African subgroups.

Fig. 3
figure 3

Subgroup analysis based on regions in Africa for medication nonadherence among patients with schizophrenia in SSA.

Full size image
Fig. 4
figure 4

Subgroup analysis based on the instrument used to estimate antipsychotic medication non-adherence among patients with schizophrenia in SSA.

Full size image

Subgroup analysis was also conducted based on the type of instrument used to assess the magnitude of non-adherence to antipsychotic medication among patients with schizophrenia. Fourteen studies used the Morisky Medication Adherence Scale (MMAS), while two studies used the Self-Reported Questionnaire (SRQ). The pooled prevalence of antipsychotic medication non-adherence among patients with schizophrenia was 41.56% (95% CI: 34.18–48.93) for studies using the MMAS and 71.15% (95% CI: 18.24–124.07) for studies using the SRQ (Fig. 4). However, the test for subgroup differences (Q = 1.18, p = 0.28) indicated no statistically significant difference in the pooled prevalence of antipsychotic medication non-adherence between the two instruments used to assess non-adherence.

A Galbraith plot was also created to identify studies that differed significantly from others. However, the plot showed no significant heterogeneity as all studies were within the 95% confidence interval (shaded area) (Fig. 5).

Fig. 5
figure 5

Galbraith plot of random effect modal.

Full size image

Sensitivity analysis

A leave-one-out meta-analysis was conducted to evaluate the impact of each study on the pooled prevalence of medication non-adherence among patients with schizophrenia. The results indicated that excluding any single study did not alter the overall estimate, as the pooled prevalence remained within the confidence interval, demonstrating that no individual study significantly influenced the findings or statistical significance (Fig. 6).

Fig. 6
figure 6

Leave-one-out sensitivity analysis.

Full size image

Publication bias

Although the funnel plot showed asymmetry, Egger’s test did not indicate significant publication bias in reporting the prevalence of medication non-adherence among schizophrenia patients in SSA. The statistical analysis confirmed no significant bias, with a P-value of 0.3864 (Fig. 7).

Fig. 7
figure 7

Publication bias assessment plot for included studies.

Full size image

Factors associated with medication non-adherence in patients with schizophrenia in SSA

Of the 16 studies included in this review, 11 reported various factors associated with antipsychotic medication non-adherence among patients with schizophrenia in SSA. These factors were diverse and described differently across the studies (Table 3). To address these variations, we included eight selected factors in our meta-analysis: alcohol use, extrapyramidal symptoms (EPS), substance use, polypharmacy, khat use, perceived stigma, lack of family support, and negative attitudes toward medication. These factors were chosen because they were consistently reported by multiple studies in a comparable manner.

The factors associated with antipsychotic medication non-adherence among patients with schizophrenia in SSA are summarized in Table 4. Alcohol use was significantly associated with non-adherence, with an AOR of 2.696 (95% CI: 1.213–5.991, P = 0.0149), although moderate heterogeneity was observed (I² = 62.99%). Similarly, extrapyramidal symptoms (EPS) showed a strong and significant association with non-adherence (AOR = 3.945, 95% CI: 1.836–8.475, P = 0.0004), while moderate heterogeneity was noted (I² = 61.22%) across four studies. Furthermore, substance use (AOR = 2.303, 95% CI: 1.429–3.712, P = 0.0006) and perceived stigma (AOR = 2.576, 95% CI: 1.729–3.840, P < 0.0001) were also significantly associated with non-adherence, with moderate (I² = 46.05%) and low (I² = 36.32%) heterogeneity, respectively.

In addition, polypharmacy showed a significant association with non-adherence (AOR = 2.146, 95% CI: 1.556–2.959, P < 0.0001), and notably, no heterogeneity was found (I² = 0.00%) among three studies. On the other hand, khat use (AOR = 1.182, 95% CI: 0.275–5.081, P = 0.8222) and negative attitudes toward medication (AOR = 1.163, 95% CI: 0.230–5.890, P = 0.8554) were not significantly associated with non-adherence; however, both factors exhibited high heterogeneity, with I² values of 94.67% and 88.89%, respectively. Moreover, lack of family support was significantly associated with non-adherence (AOR = 2.014, 95% CI: 1.333–3.043, P = 0.0009), while minimal heterogeneity was observed (I² = 17.23%) across two studies. Moreover, Publication bias was assessed using Egger’s test. Significant bias was detected for EPS (P = 0.0085), substance use (P = 0.0209), khat use (P = 0.0027), and negative attitudes toward medication (P = 0.0195). In contrast, no significant publication bias was observed for alcohol use, polypharmacy, perceived stigma, or lack of family support.

Table 3 Factors associated with non-adherence to antipsychotic medications among patients with schizophrenia in studies included in this review.
Full size table
Table 4 Summarized factor associated with antipsychotic medication non-adherence among patients with schizophrenia in SSA.
Full size table

Discussion

This systematic review and meta-analysis aimed to estimate the pooled prevalence of non-adherence to antipsychotic medication regimens in Sub-Saharan Africa (SSA), identify key associated factors, and provide evidence-based recommendations to improve adherence.

A total of 16 cross-sectional studies involving 5,366 participants with sample sizes ranging from 120 to 893 were retrieved and analyzed from various SSA countries. The findings from the studies examined in this review highlight several key factors that Contribute to the non-adherence of antipsychotic drugs in schizophrenia patients in SSA countries. Substance use and EPS were identified in four studies, polypharmacy, khat use and negative attitude towards drug in three studies, alcohol use, perceived stigma and no family support were also identified in two studies.

Medication non-adherence is a widespread challenge across various chronic conditions, including insulin use in diabetes, antihypertensive medications in hypertension43, antiretroviral therapy in HIV44, and statins in hyperlipidemia45. However, non-adherence may pose an even greater concern in psychiatry compared to general medicine, due to the complexity of psychiatric illnesses and the unique barriers faced by individuals with mental health disorders46.

In this study the pooled prevalence of medication non-adherence among patients with schizophrenia in SSA was found to be 45.30% (95% CI: 29.57–61.04%). This finding is comparable to results from a previous comprehensive review that assessed the prevalence and risk factors for medication non-adherence in patients with schizophrenia, which reported a non-adherence rate of 49.5%47. Similarly, a systematic review and meta-analysis conducted in India reported a non-adherence rate of 37%48. Moreover, it slightly exceeds the rates typically observed in high-income countries (HICs), where non-adherence generally ranges from 35%49.

Regarding specific countries, the prevalence of medication non-adherence was lowest in Uganda (16.3%)34 and highest in Ghana (98%)42. This observed variation may be partly explained by the differences in the measurement tools used. In Uganda, the MMAS, a structured and validated tool specifically designed to assess medication-taking behavior, was utilized. This instrument tends to provide a more accurate and conservative estimate of non-adherence. In contrast, in Ghana, SRQ were used, which may be more prone to reporting biases and overestimation.

Importantly, non-adherence to antipsychotic medications has been associated with significant clinical and economic impacts. A retrospective study conducted in Sweden, involving 861 patients, showed that non-adherence to antipsychotic medication shortly after discharge was associated with early rehospitalization50. Furthermore, secondary analyses of data from national survey of psychiatric morbidity in the UK emphasized that non-adherence increases to the already considerable costs of managing schizophrenia51. Therefore, non-adherence can negatively affect patients’ health and functioning and impose substantial financial burdens on healthcare systems and society at large.

Factors contributing to non-adherence can generally be grouped into treatment-related factors, patient-specific factors, healthcare system influences, and socio-economic conditions32. In this systematic review and meta-analysis, treatment-related factors such as EPS and polypharmacy were strongly associated with non-adherence. Among patient-specific factors, both substance use and alcohol use showed significant associations with non-adherence. This finding in line with results from a national survey of psychiatrists managing schizophrenia in U.S. cohorts52. Such substances may disrupt medication routines, impair judgment, or exacerbate psychiatric symptoms, ultimately leading to treatment discontinuation.

Regarding healthcare system influences, this systematic review and meta-analysis revealed that lack of family support was significantly associated with antipsychotic non-adherence. A study involving patients with first-episode psychosis found that social and family support had a consistent influence on medication adherence, with significant monthly correlations observed over a six-month period, emphasizing the importance of family involvement in promoting adherence53.

Under socio-economic conditions, this systematic review and meta-analysis demonstrated that perceived stigma was also significantly associated with non-adherence to antipsychotic medications. Comparable findings were reported in both low-income and high-income countries, including Ethiopia and Turkey, where stigma continues to present a major barrier to psychiatric care and has a negative impact on treatment adherence54,55.

The included studies demonstrated substantial heterogeneity (I2 = 99.51%, p < 0.001), suggesting that the variability across included studies. To address this, a random-effects model was applied, and meta-regression was conducted to explore potential sources heterogeneity. Factors such as sample size, response rate, country, African sub-region, study period, publication year, and the type of adherence assessment tool were examined. Among these, the African sub-region and the adherence assessment tool emerged as significant contributors to heterogeneity, with p-values of 0.022 and 0.026, respectively, possibly due to regional healthcare differences and variability in measurement methods. Similar findings have been reported in prior systematic reviews from low-resource settings, where high heterogeneity (I² > 90%) in medication non-adherence estimates was mainly attributed to differences in national health system56, and adherence assessment methods56,57. Despite this heterogeneity, sensitivity analysis confirmed that no single study disproportionately influenced the pooled prevalence.

Furthermore, assessment for publication bias revealed no significant bias (p = 0.3864). Although some asymmetry was observed in the funnel plot, Egger’s test confirmed the absence of statistically significant publication bias in the reporting of medication non-adherence prevalence among schizophrenia patients in SSA, suggesting that the observed asymmetry could be due to chance or study heterogeneity rather than true bias58.

The subgroup analysis of the current systematic review and meta-analysis revealed that there was no significant difference between reports of pooled prevalence of non-adherence to antipsychotic medication regimens.

Strength and limitations of the study

In this systematic review and meta-analysis, an extensive database search was conducted, leading to the inclusion of a substantial number of primary studies. A standardized tool was used to assess the quality of these studies, and all included articles met the required criteria. However, some limitations of the current study should be noted. Certain potential predictors of medication non-adherence such as living in rural areas, co-morbid depression, cost of medication, lack of insight into illness, and being single were excluded from the analysis due to being reported in only a limited number of studies. Additionally, variables like duration of treatment and satisfaction with outpatient care lacked standardized categorization across the literature. These inconsistencies, along with limited data availability, may have prevented a clear understanding of their impact on medication adherence.

Conclusion

This systematic review and meta-analysis revealed a high prevalence (45.30%) of non-adherence to antipsychotic medications among patients with schizophrenia in Sub-Saharan Africa, underscoring a significant public health concern. The review identified key associated factors including EPS, polypharmacy, substance and alcohol use, lack of family support, and perceived stigma. These findings emphasize the multifaceted nature of medication non-adherence in this context, influenced by individual, treatment-related, and socio-environmental factors.

To improve adherence, targeted interventions should address these modifiable risk factors. Clinicians should prioritize minimizing medication side effects and simplifying treatment regimens where possible. Integrated care models that involve family support systems and provide education about mental health can also enhance adherence. Moreover, community-based strategies to reduce stigma and improve awareness about schizophrenia are essential. Policymakers and healthcare providers must collaborate to strengthen mental health infrastructure and ensure consistent follow-up, especially in resource-limited settings. Further longitudinal studies are recommended to explore causal relationships and evaluate the effectiveness of context-specific interventions.