Burden of Benign Prostatic Hyperplasia (BPH) in Low- and Middle-Income Countries in Sub-Saharan Africa (SSA)
Bibliographic record
Abstract
Background: Benign prostatic hyperplasia (BPH) is the leading cause of lower urinary tract symptoms (LUTSs) in men, with a histological prevalence that increases significantly with age. While extensive research on BPH has been conducted in high-income countries (HICs), limited information exists regarding its burden in low- and middle-income countries (LMICs), especially in Sub-Saharan Africa (SSA). Understanding the prevalence, impact on quality of life, and management practices in these regions is crucial for developing effective healthcare policies and improving patient outcomes. Objectives: This scoping review aims to collate the existing literature on the burden of BPH in LMICs, focusing on prevalence rates, quality-of-life impact, and management practices. The goal is to provide a comprehensive overview that informs healthcare strategies in resource-constrained settings. Methods: A scoping review methodology was employed, following the framework proposed by Arksey and O’Malley. The review encompassed five stages: identifying the research question, identifying relevant studies, study selection, charting the data, and collating, summarizing, and reporting the results. A comprehensive search of electronic databases, including Google Scholar, PubMed, Scopus, and Embase, was conducted using specific search terms related to BPH and LMICs. The inclusion criteria were studies conducted in LMICs (limited to SSA); reporting on the prevalence, impact, or management of BPH; and published in English. Results: The review included studies that reported the wide-ranging prevalence rates of BPH in LMICs, highlighting significant regional variations. The impact of BPH on quality of life was profound, with many men experiencing significant bother and reduced daily functioning due to LUTSs. The management practices in these regions often differed from those in HICs, with limited access to pharmacological treatments and surgical interventions. The economic burden of BPH in LMICs was also notable, with many patients facing financial barriers to accessing effective care. Conclusions: BPH poses a significant health and economic burden in LMICs, particularly in SSA. The prevalence of the condition is high, and its impact on quality of life is substantial. There is an urgent need for improved diagnostic and management strategies tailored to the resources available in these settings. Policymakers should consider integrating BPH management into broader healthcare initiatives to enhance patient outcomes and reduce the overall burden of the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".