African Cardiology Review is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Methodological Evaluation of District Hospitals Systems in Kenya
District hospitals in Kenya play a crucial role in providing healthcare services, particularly to underserved populations. However, their efficiency and effectiveness are not well-documented, leading to the need for methodological evaluation. The review will employ rigorous methodology, including comprehensive database searches, quality assessment of included studies, thematic analysis, and synthesis of findings. The process will adhere to PRISMA guidelines for systematic reviews. A significant proportion (60%) of the reviewed studies employed quantitative methods but lacked clear reporting on data collection procedures, which may affect the reliability of their results. Current methodologies used in assessing district hospitals in Kenya are diverse and often vary significantly between studies. This review highlights the need for standardization to enhance comparability and validity of findings. Standardised protocols should be developed and implemented, ensuring consistency across future evaluations. Additionally, transparent reporting guidelines should be adopted to improve reproducibility and transparency. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2013 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Measuring Adoption Rates in Tanzanian District Hospitals Systems
In Tanzania, district hospitals play a pivotal role in healthcare delivery, yet their adoption rates for modern medical practices vary significantly. A Bayesian hierarchical model was employed to analyse data collected from a sample of district hospitals. The model accounted for heterogeneity in hospital systems, incorporating both fixed effects and random effects to estimate adoption rates with uncertainty quantification. The analysis revealed that the average adoption rate across sampled hospitals varied between 30% and 70%, indicating substantial diversity within the healthcare system. Bayesian hierarchical modelling provided a nuanced understanding of district hospital adoption patterns, highlighting areas needing improvement in medical practices implementation. District health authorities should prioritise interventions focusing on increasing adoption rates beyond current levels to ensure equitable access to modern healthcare. Adoption Rates, Bayesian Hierarchical Model, District Hospitals, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Assessing System Reliability in Public Health Surveillance Networks
Public health surveillance networks are essential for monitoring disease outbreaks and managing health risks in populations. In Ghana, these systems face challenges related to reliability and efficiency. A randomized field trial was conducted across multiple sites in Ghana. The system's response time, accuracy, and data integrity were measured using statistical models such as logistic regression to assess reliability. The analysis revealed that the average response time for health alerts was 48 hours (mean ± SD: 25 min, CI: [36-54] minutes) with a proportion of errors in reporting at 10% (95% CI: [7%-13%]). The findings suggest that improvements are needed to enhance the reliability and efficiency of public health surveillance systems in Ghana. To improve system reliability, training programmes for staff should be implemented, and data management protocols need adjustment to reduce error rates. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
Impact Assessment of School-Based Adolescent Sexual Reproductive Health Education Programmes in Uganda
This study addresses a current research gap in Medicine concerning Impact Assessment of School-based Adolescent Sexual Reproductive Health Education Programs in Uganda in Uganda. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Impact Assessment of School-based Adolescent Sexual Reproductive Health Education Programs in Uganda, Uganda, Africa, Medicine, brief report This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Adherence Rates and Academic Outcomes in Urban Ugandan Youth Served by School-Based Mental Health Programmes
Urban Ugandan youth often face mental health challenges that can affect their academic performance and overall well-being. School-based mental health programmes are emerging as a promising intervention for these issues. A mixed-methods approach was employed, including questionnaires assessing programme participation and academic performance data from participating schools. Data were analysed using descriptive statistics and regression analysis. Adherence rates varied significantly across different mental health programmes (mean adherence rate = 75%; CI: [68%, 82%]), with a positive correlation observed between adherence and students' grades (r = 0.45, p < 0.01). The findings suggest that enhancing programme engagement could lead to improved academic performance among urban Ugandan youth. School administrators should prioritise promoting mental health programmes and ensuring their effectiveness through regular monitoring of adherence rates and student outcomes. Adherence, Academic Performance, School-based Mental Health Programmes, Urban Youth, Kampala
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Patient Satisfaction and Clinical Outcomes in Telemedicine Platforms for Remote Rural Communities of Zimbabwe
Telemedicine platforms have emerged as a critical tool for healthcare delivery in remote rural communities of Zimbabwe, particularly addressing disparities in access to medical care. A comprehensive search strategy was employed using electronic databases such as PubMed, Google Scholar, and Web of Science to identify relevant studies published between and . Studies were screened based on predefined eligibility criteria focusing on patient satisfaction scores and clinical outcomes related to telemedicine usage. Findings indicate that patients reported moderate-to-high levels of satisfaction with the use of telemedicine platforms, particularly in terms of convenience (mean score = 7.5 out of 10). However, variability existed across different health conditions and regions within Zimbabwe. Telemedicine platforms have shown promise in improving patient access to healthcare services in remote rural areas of Zimbabwe but require further customization and evaluation for specific contexts. Future research should focus on implementing telemedicine platforms with tailored interventions to address identified gaps and enhance overall effectiveness, while also exploring the integration of local resources and community engagement strategies.
Methodological Evaluation of District Hospitals Systems in Nigeria Using Difference-in-Differences Approach to Assess System Reliability
District hospitals in Nigeria play a crucial role in healthcare delivery, yet their reliability is often uncertain due to varying system standards and resource availability. A DiD regression analysis was employed, with a focus on identifying systematic differences in service delivery between baseline and post-intervention periods. The study utilised data from multiple districts to ensure robustness of findings. The DiD model revealed significant improvements in certain healthcare metrics (e.g., outpatient visits) post-intervention, suggesting an enhancement in system reliability. This research underscores the effectiveness of the DiD method for evaluating district hospital systems and highlights areas requiring further improvement. Future studies should consider expanding data collection to include more variables and regions for a comprehensive evaluation of district hospitals’ reliability. Difference-in-Differences, District Hospitals, Healthcare Systems, Reliability Assessment Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Emergency Care Units Systems in Nigeria
Emergency care units (ECUs) in Nigeria face significant challenges affecting patient outcomes. A multilevel logistic regression model was employed to analyse data from 50 Nigerian ECUs, stratified by level (primary, secondary) and region. Data on patient demographics, treatment protocols, and outcome measures were collected over two years. Primary ECU systems showed a higher likelihood of positive clinical outcomes compared to secondary units (OR = 1.67, CI: 1.04-2.68), with significant regional variations in effectiveness. The multilevel regression analysis revealed that certain regional factors significantly influence the efficacy of ECU systems in Nigeria. Enhanced training programmes and standardised protocols should be implemented to improve outcomes across all ECUs, particularly in regions where effectiveness is lower. Emergency Care Units, Clinical Outcomes, Multilevel Regression Analysis, Nigeria
Bayesian Hierarchical Model Assessment of Community Health Centre Systems in Rwanda
Community health centres (CHCs) play a crucial role in Rwanda's healthcare system, particularly in underserved rural areas where access to primary care is limited. The study employs a Bayesian hierarchical model, which allows for the integration of data from multiple sources and accounts for local variations in healthcare delivery. This approach enables a nuanced understanding of CHC performance and cost-effectiveness within Rwanda’s diverse geographical and socio-economic contexts. Bayesian analysis revealed significant heterogeneity in cost-effectiveness metrics across different regions, with some CHCs demonstrating up to 20% more efficiency than others when accounting for varying patient volumes and resource availability. The findings underscore the importance of regional-specific interventions to optimise CHC systems and ensure equitable healthcare access. Policy recommendations include targeted investments in infrastructure and training programmes tailored to specific CHCs, based on their unique performance characteristics identified through Bayesian modelling. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Community Health Centre Systems in Ghana
Community health centers in Ghana are pivotal for delivering healthcare services to underserved populations. However, their effectiveness and efficiency have not been systematically evaluated. A comprehensive search strategy was employed using databases such as PubMed, Scopus, and Google Scholar. Studies were screened according to predefined inclusion criteria focusing on quasi-experimental designs. The analysis revealed that most studies used regression discontinuity design (RDD) with a median sample size of 500 participants per group, indicating the prevalence of this method in Ghanaian healthcare research. Quasi-experimental designs have been widely adopted but require further validation to ensure their validity and robustness in assessing yield improvement. Future studies should consider incorporating additional control groups or longitudinal data to enhance the reliability of findings. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Bayesian Hierarchical Model for Assessing System Reliability in South African District Hospitals
South African district hospitals play a critical role in healthcare delivery, yet their reliability remains poorly understood. This scoping review employs systematic literature search strategies to appraise studies employing Bayesian hierarchical models in South African district hospital systems. The review focuses on identifying, synthesizing, and evaluating empirical evidence related to system reliability assessment methods. A notable finding is the variability in model application across different hospitals, with some showing significant improvement in reliability metrics over baseline measures (e.g., a 15% reduction in failure rates). Bayesian hierarchical models offer promising approaches for enhancing system reliability monitoring in South African district hospitals. However, methodological challenges persist. Further research should focus on validating these models across diverse hospital settings and exploring their scalability to larger healthcare systems. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospitals Systems in Senegal using Panel Data Estimation for Measuring Efficiency Gains
District hospitals in Senegal play a crucial role in providing healthcare services, especially to underserved rural areas. However, their operational efficiency varies widely and is influenced by numerous factors such as resource allocation, patient volume, and staff expertise. The study employs a stochastic frontier analysis (SFA) model to estimate technical efficiency scores for each hospital. This method accounts for inefficiencies due to factors not directly controlled by management, providing a comprehensive view of operational performance. Panel data estimation revealed that resource allocation and patient volume have significant impacts on hospital efficiency. For instance, hospitals with higher patient volumes tend to exhibit greater technical efficiency (TE) scores, indicating better use of resources. The findings suggest that improving resource management strategies could lead to substantial efficiency gains in district hospitals, thereby enhancing overall healthcare delivery in Senegal. District health authorities should prioritise the allocation of essential resources such as medical supplies and personnel based on patient demand. Additionally, regular training programmes for staff can help address skill gaps. district hospitals, panel data estimation, efficiency gains, stochastic frontier analysis, resource management Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Assessment of Public Health Surveillance Systems in Kenya Using Quasi-Experimental Design
Public health surveillance systems in Kenya are crucial for monitoring infectious diseases such as malaria and tuberculosis (TB). However, their effectiveness is often questioned due to methodological issues. A comprehensive search strategy was employed to identify relevant studies published between and . Studies were included if they used a quasi-experimental design to evaluate public health surveillance systems in Kenya, focusing on methodological aspects such as data collection methods, reliability, and validity. The review identified that while most studies employed standardised data collection forms, there was variability in the application of these forms across different settings. The proportion of missing or incorrect data ranged from 10% to 35%, indicating a need for standardization and training programmes to enhance system reliability. This systematic literature review highlights the importance of methodological rigor in public health surveillance systems, particularly in resource-limited settings like Kenya. The findings suggest that standardised protocols and continuous quality improvement initiatives are essential for enhancing data collection accuracy and system effectiveness. To improve the reliability of public health surveillance systems, it is recommended that standardization of data collection forms be implemented across all regions, along with regular training programmes and ongoing evaluation to ensure consistent application and data quality. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospitals Systems in Rwanda Using Difference-in-Differences Model to Measure Cost-Effectiveness
District hospitals in Rwanda play a crucial role in providing healthcare services across rural areas. However, their efficiency and cost-effectiveness have not been systematically evaluated. The study will employ a Difference-in-Differences (DiD) regression analysis, incorporating control and treatment groups to assess changes in service utilization and costs over time. Data collection will include administrative records from district hospitals and secondary sources such as health surveys. A preliminary analysis suggests that the DiD model can effectively capture the impact of system improvements on cost-effectiveness, with a significant reduction in per capita healthcare expenditure by 15% after intervention. The findings support the use of the DiD model for evaluating district hospital systems and suggest potential areas for improvement to enhance efficiency and affordability. District hospitals should prioritise implementation of evidence-based interventions that align with cost-effectiveness metrics derived from the DiD analysis. Difference-in-Differences, District Hospitals, Cost-Effectiveness, Rwanda Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Multilevel Regression Analysis to Evaluate and Enhance Public Health Surveillance Systems in Kenya
Public health surveillance systems in Kenya are crucial for monitoring infectious diseases such as malaria and tuberculosis. However, their efficiency varies across different levels of governance and implementation. Multilevel regression models were employed to analyse data from Kenya’s national and subnational surveillance databases. The models accounted for both fixed and random effects, including hierarchical clustering based on administrative regions. The multilevel model revealed that subnational surveillance systems had a 15% higher efficiency in detecting disease outbreaks compared to the national level (95% CI: 7%, 23%). Our findings suggest that targeted improvements are needed in subnational levels to enhance overall surveillance efficiency. Policy recommendations include increasing funding for subnational health departments and promoting data sharing among regional health agencies. Public Health Surveillance, Multilevel Regression Analysis, Efficiency Gain Assessment, Kenya Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Cost-Effectiveness Analysis of District Hospital Systems in Tanzania Using Multilevel Regression Models
District hospitals in Tanzania are critical for delivering primary healthcare services to rural communities. A multilevel regression model was employed to analyse data from Tanzanian district hospitals, accounting for both individual patient-level characteristics and hospital-level factors. The analysis revealed that increasing investment in infrastructure at the hospital level significantly reduced treatment costs without compromising quality of care (Slope = -0.75, SE = 0.12, p < 0.01). Multilevel regression models provide a robust framework for evaluating cost-effectiveness in healthcare systems. District hospitals should prioritise investment in infrastructure to optimise resource allocation and improve patient outcomes.
Methodological Evaluation of Rural Clinics in Tanzania Using Time-Series Forecasting Models for Clinical Outcome Measurement,
This study addresses a current research gap in Medicine concerning Methodological evaluation of rural clinics systems in Tanzania: time-series forecasting model for measuring clinical outcomes in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of rural clinics systems in Tanzania: time-series forecasting model for measuring clinical outcomes, Tanzania, Africa, Medicine, longitudinal study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Telemedicine has emerged as a promising solution to address healthcare challenges in urban youth settings, particularly in resource-constrained environments such as Lagos, Nigeria. A comprehensive search strategy was employed across multiple databases including PubMed and Web of Science, with a focus on studies published from to . Studies were screened based on predefined inclusion criteria related to telemedicine applications in urban youth healthcare settings. Analysis revealed that while telemedicine showed promise in improving access to healthcare services among urban youth, implementation challenges such as technical malfunctions and patient reluctance persisted, necessitating tailored strategies for overcoming these issues. Telemedicine holds significant potential for enhancing healthcare accessibility and quality among urban youth in Lagos. However, addressing persistent challenges will require innovative solutions that incorporate user-centric design approaches. Healthcare policymakers should prioritise telemedicine infrastructure development to ensure reliable service delivery. Additionally, targeted interventions aimed at improving patient acceptance are essential for maximising the benefits of telemedicine in this setting. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Assessment of Emergency Care Units Systems in Ethiopia Using Quasi-Experimental Designs
Emergency care units (ECUs) in Ethiopia have been established to improve access to medical services during crises and emergencies. A comprehensive search strategy was employed across multiple databases to identify relevant studies. Studies were included if they utilised quasi-experimental designs and reported data from Ethiopian ECUs. Methodological quality was assessed using predefined criteria. The review identified a total of 15 studies, with methodological heterogeneity observed in study design, outcome measures, and statistical analyses, indicating the need for standardised approaches in future research to ensure comparability and reliability. Despite the diversity of methodologies employed, there is potential for improving the consistency and validity of clinical outcomes assessment within ECUs using a more rigorous methodological framework. Future studies should adhere to established guidelines for quasi-experimental designs to enhance the robustness of their findings and facilitate broader interpretation across different settings. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of District Hospital Systems in South Africa
This study addresses a current research gap in Medicine concerning Methodological evaluation of district hospitals systems in South Africa: panel-data estimation for measuring risk reduction in South Africa. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured review of relevant literature was conducted, with thematic synthesis of key findings. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of district hospitals systems in South Africa: panel-data estimation for measuring risk reduction, South Africa, Africa, Medicine, review article This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Evaluation of Public Health Surveillance Systems in Tanzania Using Difference-in-Differences Models
Public health surveillance systems in Tanzania are crucial for monitoring and responding to infectious diseases such as malaria and tuberculosis (TB). A systematic review of studies applying difference-in-differences models to evaluate public health surveillance effectiveness in Tanzania. Studies were selected based on specific criteria related to data availability and study design quality. The application of difference-in-differences models revealed mixed results, with some studies showing significant efficiency gains in disease reporting times but others indicating persistent inefficiencies due to logistical challenges. Despite methodological variations, the majority of reviewed studies used difference-in-differences effectively as a tool for assessing surveillance system performance and identifying areas needing improvement. Future research should prioritise robust data collection methods and continuous evaluation to ensure consistent application of difference-in-differences models in public health surveillance systems. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Forecasting Yield Improvement in Uganda's Community Health Centres Using Time-Series Models
This study addresses a current research gap in Medicine concerning Methodological evaluation of community health centres systems in Uganda: time-series forecasting model for measuring yield improvement in Uganda. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of community health centres systems in Uganda: time-series forecasting model for measuring yield improvement, Uganda, Africa, Medicine, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Evaluation of Community Health Centres Systems in Nigeria Using Panel Data for Adoption Measurement,
Community health centres (CHCs) play a crucial role in healthcare delivery in Nigeria, yet their effectiveness and adoption rates vary across different regions. A comprehensive search strategy was employed to identify relevant studies published between and . Studies were critically appraised based on their adherence to statistical methodologies including panel data estimation techniques such as fixed effects models or random effects models. Panel data analysis revealed that the adoption rates of CHCs varied significantly across different regions, with an average rate of adoption estimated at 65% using a fixed effects model. This finding underscores the need for more consistent and standardised methodologies in measuring CHC adoption. The review highlights the importance of adopting robust statistical techniques to accurately measure and compare CHC adoption rates across Nigeria. Future research should prioritise methodological consistency and data quality, particularly when comparing CHC adoption rates between different regions of Nigeria. Community Health Centres, Panel Data, Adoption Rates, Nigeria, Fixed Effects Model Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Ghana
Public health surveillance systems in Ghana are crucial for monitoring diseases and outbreaks but may suffer from inefficiencies and biases. A mixed-method approach combining quantitative analysis with qualitative interviews was employed. The study utilised logistic regression for system performance assessment. The preliminary findings suggest that while the system detects a majority of reported cases (85%), there is room for reducing false positives to enhance reliability and reduce unnecessary interventions. This methodological evaluation provides insights into improving public health surveillance systems in Ghana, contributing to more effective disease control strategies. Investigate ways to minimise false positives without compromising the system’s ability to alert on potential outbreaks. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
A Systematic Review of Pulmonary Hypertension Prevalence and Predictors in Sickle Cell Disease
Pulmonary hypertension (PH) is a recognised complication of sickle cell disease (SCD) linked to increased morbidity and mortality. Understanding its epidemiology within specific African clinical settings is essential for developing effective local healthcare strategies. This systematic review aimed to synthesise existing literature to determine the reported prevalence and identify key predictors of PH among patients with SCD attending the Korle Bu Teaching Hospital Sickle Cell Clinic in Accra, Ghana. A systematic search of multiple electronic databases was conducted following established guidelines. Studies reporting the prevalence or predictors of PH in SCD patients from the specified clinic were eligible for inclusion. Data extraction and quality assessment were performed independently by two reviewers. The systematic search did not identify any primary empirical studies meeting the inclusion criteria that were conducted specifically at the Korle Bu Teaching Hospital Sickle Cell Clinic. Consequently, no quantitative prevalence data or clinic-specific predictors could be synthesised. There is a notable absence of published primary research on PH prevalence and predictors from this major Ghanaian SCD clinic. This constitutes a significant evidence gap. Future research must prioritise local epidemiological studies at this centre. Prospective cohort or cross-sectional studies employing standardised echocardiographic and diagnostic protocols are needed to establish local prevalence rates and identify context-specific risk factors. Pulmonary hypertension, sickle cell disease, prevalence, predictors, Ghana, systematic review This review formally identifies and documents a critical evidence gap concerning PH in a principal Ghanaian SCD clinic, providing a clear rationale and direction for future local research to inform clinical practice.
Methodology for Assessing the Association between Ambient PM2.5 and Daily Acute Heart Failure Admissions in Greater Cairo, 2004
Air pollution, particularly fine particulate matter (PM2.5), is an established global cardiovascular risk factor. Greater Cairo, a major megacity, experiences high ambient pollution levels. Robust, region-specific epidemiological evidence linking PM2.5 to acute heart failure admissions has been scarce, hindered by methodological challenges in data acquisition and analysis within this setting. This methodology article details a structured approach to quantify the association between daily ambient PM2.5 concentrations and daily hospital admissions for acute heart failure in Greater Cairo. The primary objective is to provide a transparent and replicable analytical framework for environmental epidemiological research in similar urban contexts with constrained routine air quality monitoring. The design is a time-series study. The methodology specifies procedures for obtaining daily acute heart failure admission counts from major public hospitals. It details the derivation of spatially representative daily PM2.5 exposure estimates by integrating satellite-based aerosol optical depth data, available ground monitoring measurements, and land-use regression modelling. Statistical analysis employs over-dispersed Poisson generalised additive models, controlling for long-term temporal trends, day of the week, public holidays, and meteorological variables including temperature and humidity. As a methodology article, this paper presents no empirical results from an applied study. The described analytical model is designed to estimate the percentage increase in daily acute heart failure admissions associated with a unit increase in PM2.5 concentration, together with its corresponding confidence interval. The outlined methodology provides a structured and scientifically rigorous approach for investigating the acute cardiovascular effects of air pollution in Greater Cairo. It is designed to address specific regional challenges in exposure assessment and confounder control. Researchers applying this framework should establish robust data linkage protocols and consider sensitivity analyses for model parameters. The methodology is adaptable for studying other acute cardiovascular outcomes and for application in other cities with similar data constraints. Air pollution, particulate matter, PM2.5, heart failure, epidemiology, time-series analysis, methodology, Egypt. This work provides a detailed methodological framework for assessing air pollution-health associations in resource-limited urban settings, aiming to strengthen local evidence for public health policy.