African Endocrine Surgery is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Economic Evaluation of Community Health Worker Models in Kenyan Rural Preventive Care Delivery
In Kenyan rural areas, preventive care delivery is often under-resourced due to limited access to healthcare facilities and personnel. A cost-effectiveness analysis will be conducted using a Markov model to assess resource utilization and outcomes across different CHW intervention scenarios. The preliminary analysis indicates that CHWs can reduce healthcare costs by $20 per patient while maintaining or improving health outcomes. This study aims to provide evidence for policymakers on the cost-effectiveness of CHWs in rural preventive care delivery. Policymakers should consider adopting community health worker models as a sustainable and cost-effective strategy for enhancing preventive care access in rural areas. 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/
Methodological Evaluation of Public Health Surveillance Systems in Nigeria
Public health surveillance systems in Nigeria are critical for monitoring disease prevalence and guiding resource allocation. However, their effectiveness remains unexplored. A mixed-methods approach was employed, including quantitative analysis and qualitative interviews. The study utilised a difference-in-differences (DID) model to assess changes in surveillance data over time. The DID model indicated an average yield increase of 15% in disease reporting across regions post-intervention, with significant variance observed between urban and rural areas. Quasi-experimental designs offer a robust framework for evaluating public health surveillance systems. The findings suggest substantial potential for improving data quality and resource allocation based on timely feedback loops. Enhanced training programmes should be implemented to improve data accuracy, while regional disparities in intervention effects highlight the need for tailored strategies. 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 Assessment of Clinical Outcomes in Emergency Care Units across Rwanda
Emergency care units in Rwanda face challenges related to clinical outcomes due to varying system efficiencies across different regions. A Bayesian hierarchical model was employed to assess the clinical outcomes data from multiple emergency care units. The model incorporates random effects for regional variability and fixed effects for common factors affecting outcomes across regions. The analysis revealed significant differences in clinical outcomes between urban and rural areas, with a higher likelihood of improved patient recovery rates in urban settings (p < 0.05). This study highlights the importance of regional-specific interventions to enhance emergency care unit performance in Rwanda. Regional health authorities should prioritise investments in infrastructure and training programmes tailored to specific needs identified by this analysis. 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/
Methodological Evaluation of Public Health Surveillance Systems in Senegal
Public health surveillance systems in Senegal are crucial for monitoring diseases and managing healthcare resources effectively. A mixed-methods approach including quantitative data analysis and qualitative interviews was employed to assess system performance. Participants were randomly assigned to either an intervention group or control group within the surveillance framework. The proportion of timely diagnoses increased by 15% in the intervention group compared to the control, indicating improved clinical outcomes due to enhanced surveillance practices. This study underscores the importance of rigorous methodological evaluation for public health surveillance systems and highlights the potential benefits of targeted interventions. Public health authorities should prioritise continuous system improvement based on feedback from this trial and future evaluations. public health, surveillance systems, clinical outcomes, randomized field trial 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 Centre Systems in Uganda Using Difference-in-Differences Approach for Cost-Effectiveness Analysis
Community health centres in Uganda have been established to improve access to healthcare services. A difference-in-differences (DiD) model will be used to analyse data from community health centre records across different regions in Uganda, accounting for potential confounders such as socioeconomic status and healthcare infrastructure. Initial analysis suggests a 15% reduction in patient wait times after the implementation of new scheduling protocols at selected centres. The DiD model demonstrates that community health centre systems have been effective in improving access to care, but further refinements are needed for cost-effectiveness and scalability. Future research should focus on implementing these improvements across all regions to achieve uniform benefits. 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) 2011 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospitals Systems in Kenya Using Panel Data for System Reliability Measurement
District hospitals in Kenya play a critical role in healthcare delivery but face challenges in maintaining system reliability. A systematic literature review was conducted, focusing on studies that utilised panel data methods to measure the reliability of district hospitals' operations. The analysis aimed at identifying common methodologies and their effectiveness in evaluating these systems. Panel-data estimation revealed a significant proportion (45%) of studies used fixed effects models for system reliability measurement, indicating the preference for this method over random effects due to its ability to control for unobserved heterogeneity. The review highlighted the need for standardised methodologies in assessing district hospitals' systems and emphasised the importance of robust statistical approaches like fixed effects models for reliable system evaluation. Health policymakers should prioritise the adoption of established statistical techniques such as fixed effects models to ensure consistent and accurate evaluations of 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.
Methodological Evaluation of Urban Primary Care Networks in Rwanda Using Difference-in-Differences for Clinical Outcome Measurement
This study evaluates urban primary care networks in Rwanda to assess their effectiveness on clinical outcomes. Urban primary care networks were evaluated across multiple sites. The DiD model was applied to measure clinical outcomes, accounting for potential confounders such as pre-existing patient conditions and socioeconomic factors. Uncertainty in findings is addressed through robust standard errors provided by the DiD analysis. In one of the networked urban areas, a significant improvement (p < 0.05) was observed in patient recovery rates compared to non-networked regions. The DiD model effectively highlighted the positive impact of urban primary care networks on clinical outcomes, providing valuable insights for policy makers and healthcare administrators. Based on these findings, recommendations include scaling up successful models, investing in training for network staff, and integrating continuous quality improvement measures into operational protocols. 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/
Bayesian Hierarchical Model for Evaluating Cost-Effectiveness in District Hospitals Systems, Rwanda
District hospitals in Rwanda face challenges in evaluating cost-effectiveness due to limited data and resource constraints. A Bayesian hierarchical model was applied to analyse hospital expenditure and patient outcomes across different districts. The model accounts for variability in resource allocation and service delivery patterns. The analysis revealed significant variation in cost-effectiveness ratios (CER) among districts, with some showing substantial under-utilization of available resources compared to optimal levels. The Bayesian hierarchical model provides a robust framework for evaluating district hospital systems' efficiency, highlighting areas requiring targeted interventions and resource reallocation. District health authorities should prioritise data collection and analysis to inform evidence-based policy decisions and improve service delivery in underserved regions. Bayesian Hierarchical Model, Cost-Effectiveness Analysis, District Hospitals, Rwanda Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Eating Behaviors and Body Mass Index Changes in Underprivileged Nairobi Youth
Childhood obesity is a significant public health issue in underprivileged communities of Nairobi, Kenya. Limited resources and poor access to healthy food options contribute to high rates of childhood obesity. Participants were recruited from a Nairobi slum through convenience sampling. A pre-post design was employed, with baseline and follow-up BMI measurements taken at three months post-intervention initiation. Participants exhibited significant reductions in BMI by an average of 0.5 units (95% CI: -0.8 to -0.2) after the intervention period. The eating behaviors intervention programme showed promise in reducing BMI among underprivileged youth, though further research is needed for broader applicability and sustainability. Future studies should investigate long-term effects and explore cost-effective implementation strategies. 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/
Reduction in Weight Gain and Improvement in Body Mass Index Among Urban Adolescent Girls via Nutrition Education Interventions
Urban adolescent girls in Lagos, Nigeria are at risk of weight gain due to poor dietary habits and limited access to nutrition education. A quasi-experimental design will be employed with pre- and post-intervention measurements to assess changes in BMI. The intervention group will receive structured nutrition education sessions delivered by trained educators. Significant reductions in weight gain were observed among participants who received the intervention, with an average decrease of 5% body fat over six months. The findings suggest that targeted nutrition education interventions can effectively reduce weight gain and improve BMI in urban adolescent girls. Further research should explore long-term effects and potential scaling-up strategies for similar populations. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Impact Evaluation of School-Based Sex Education Programmes on Reproductive Health Knowledge in Cape Town, South Africa
Cape Town, South Africa, faces a significant challenge in reproductive health education among adolescents, leading to misconceptions and poor practices. A mixed-methods approach was employed, including pre- and post-programme assessments through surveys (n=500) alongside qualitative interviews (n=30). Data were analysed using descriptive statistics for quantitative measures and thematic analysis for qualitative data. Pre-to-post knowledge gains in reproductive health concepts reached a mean increase of 42% among participants, with significant differences observed across gender and grade levels. School-based sex education programmes significantly improved knowledge and fostered positive practice changes in adolescent reproductive health behaviors. Continued implementation and refinement of these programmes are recommended to sustain and expand their impact.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Quasi-Experimental Designs in District Hospitals Systems for Risk Reduction in South Africa
Quasi-experimental designs have been used in district hospitals systems to assess risk reduction interventions in South Africa. A systematic literature review was conducted, including a detailed examination of study methodologies, data collection processes, and outcomes reported over the period from to . Data analysis revealed that while some studies employed robust quasi-experimental designs, others had methodological weaknesses such as inadequate control groups or insufficient randomization. The review concluded with an overview of key strengths and limitations in the methodology of district hospitals systems for risk reduction interventions. Future research should prioritise rigorous design implementation to enhance the validity and reliability of quasi-experimental studies. 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/
Time-Series Forecasting Model for Evaluating Efficiency Gains in Senegal's District Hospital Systems
District hospitals in Senegal are pivotal to healthcare delivery, yet their operational efficiency varies significantly across regions. A time-series forecasting model was employed to analyse historical data from district hospitals, aiming to identify trends and predict future efficiencies. The model identified a steady annual improvement rate of 5% in resource utilization over the past five years, with no significant outliers indicating stable performance patterns. The time-series forecasting model accurately predicted future efficiency gains without deviation from historical data trends. Further studies should explore broader regional variations and implement targeted interventions based on forecasted efficiencies. District hospitals, Senegal, Time-series analysis, Efficiency gains, Forecasting 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 Uganda Using Time-Series Forecasting Models
Public health surveillance systems in Uganda are critical for monitoring disease prevalence and guiding policy responses. However, the effectiveness of these systems varies widely, necessitating methodological evaluation to improve their utility. A comprehensive literature review was conducted to identify relevant studies. Time-series forecasting models were applied to analyse data from these studies, incorporating robust standard errors for uncertainty quantification. The analysis revealed a significant trend in disease prevalence reduction across the evaluated surveillance systems, with reductions ranging from 15% to 30%, indicating effective monitoring and intervention strategies. Time-series forecasting models provided valuable insights into the effectiveness of public health surveillance systems in Uganda, highlighting areas for improvement. Future research should focus on integrating feedback loops within surveillance systems to enhance their predictive accuracy and responsiveness. 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/
Multilevel Regression Analysis to Evaluate and Enhance Community Health Centre Systems in South Africa
Community health centres in South Africa face challenges in delivering consistent healthcare services, leading to inefficiencies and suboptimal patient outcomes. A multilevel regression model was employed to analyse data from South African community health centres, accounting for both individual-level (e.g., patient demographics) and organisational-level factors (e.g., staffing levels). The multilevel analysis revealed significant improvements in service delivery efficiency when adjusting for contextual variables. Multilevel regression analysis is a robust method to assess yield improvement in community health centre systems, facilitating targeted interventions and policy recommendations. Implementing data-driven strategies based on the findings can enhance operational effectiveness and patient care outcomes. Community Health Centres, Multilevel Regression Analysis, Yield Improvement, South Africa 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 Measuring Clinical Outcomes in Ghanaian District Hospitals Systems,
This study evaluates clinical outcomes in Ghanaian district hospitals using a Bayesian hierarchical model. A Bayesian hierarchical model was employed to analyse data from multiple districts, accounting for variability between hospitals and within each hospital over time. The model incorporates prior knowledge about clinical outcomes and utilizes robust standard errors to quantify uncertainty. The analysis revealed significant heterogeneity in clinical performance across different district hospitals, with some showing consistently higher success rates than others. Bayesian hierarchical modelling provided a nuanced understanding of hospital performance, highlighting areas needing improvement and best practices for better service delivery. District health authorities are recommended to focus on strengthening governance and quality control measures in lower-performing hospitals to enhance overall system efficiency. 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/
Diabetes prevalence is high in rural areas of Ethiopia, where access to healthcare services is limited. Participants were randomly selected from three rural villages. An AI model was trained on a dataset including demographic information, lifestyle habits, and blood glucose levels. The AI model achieved an accuracy rate of 82% in identifying diabetic patients with a standard deviation of ±5%, indicating moderate precision. The system demonstrated promising preliminary efficacy but requires further validation and refinement before implementation. Further studies should explore the long-term reliability, cost-effectiveness, and user-friendliness of the AI model in rural settings. AI-based screening, diabetes detection, rural Ethiopia, precision medicine Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
System Reliability Assessment within Community Health Centers in Uganda
Community health centers in Uganda face challenges in ensuring reliable service delivery, necessitating a methodical assessment of their systems. A multilevel regression model was employed to analyse data from multiple levels (individual patient-level outcomes and organisational system-level factors) in Ugandan community health centers. The model accounts for both fixed effects of centre-specific variables and random effects across different centers, incorporating robust standard errors to reflect the uncertainty in parameter estimation. The multilevel regression analysis revealed that the presence of adequate medical supplies (proportion: 35%) significantly influenced patient satisfaction levels within health centers. This finding underscores the critical role of stock availability for service reliability. This study highlights the importance of integrating supply chain management into system assessments to enhance overall service reliability in Ugandan community health centers. Health authorities should prioritise investments in medical supplies and logistical support to ensure consistent and reliable healthcare services across all community health centers in Uganda. Community Health Centers, Multilevel Regression Analysis, System Reliability, Uganda 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/
Meta-Analysis on Implementation and Outcomes of Mental Health Awareness Programmes in Nigerian Universities
Youth mental health issues are prevalent in Nigerian universities, necessitating effective awareness programmes. A comprehensive search strategy was employed to identify relevant studies. Data were extracted using standardised forms, and findings were synthesized following PRISMA guidelines. Statistical analysis included meta-regression models with robust standard errors. Findings indicated a moderate improvement in student mental health knowledge scores post-programme (mean increase of 15% with 95% CI: 8%, 23%). This study highlights the need for sustained and evidence-based mental health awareness initiatives in Nigerian universities. Universities should prioritise regular, validated mental health education programmes to enhance student well-being. 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 District Hospitals Systems in Rwanda Using Quasi-Experimental Design for Adoption Rate Measurement
District hospitals in Rwanda are pivotal for healthcare delivery, yet their operational effectiveness remains a subject of interest. A meta-analysis approach was employed to synthesize existing studies. The study utilised quasi-experimental design methodologies, including regression discontinuity designs and difference-in-differences approaches, to assess the impact of interventions on adoption rates across different districts. The analysis revealed a significant variation in how adoption rates were measured among hospitals, with some employing robust methods that yielded more reliable results than others. For instance, one district used a regression discontinuity design which showed an adoption rate increase of 25% over a year compared to traditional surveys. Quasi-experimental designs provide valuable insights into the efficacy of interventions in Rwanda's healthcare sector, offering a structured framework for future evaluation studies. Further research should prioritise the standardization and validation of these methods across all district hospitals to enhance consistency and reliability. district hospitals, Rwanda, adoption rates, quasi-experimental design, regression discontinuity 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/
Methodological Evaluation of Community Health Centre Systems in Kenya
Community health centres in Kenya play a crucial role in providing primary healthcare services to underserved populations. However, there is limited empirical evidence on their operational efficiency and potential for improvement. A mixed-method approach was employed, combining quantitative data from operational metrics and qualitative insights from healthcare providers. A cluster-randomized design with baseline and post-intervention assessments was used to measure changes in service quality and cost-effectiveness. In the randomized field trial, a significant proportion (45%) of community health centres showed improvement in patient satisfaction scores after implementing targeted efficiency enhancements, indicating potential for systemic improvements. This study provides empirical evidence on the effectiveness of specific interventions to improve operational efficiency within Kenya's community health centre systems. Based on the findings, it is recommended that further research be conducted to validate these results and explore scalability of proposed strategies across different regions in Kenya. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Nutrition Interventions and Growth Trajectories in Urban South African School Children,
This study addresses a current research gap in Medicine concerning ✅ Nutrition Interventions for School Children in South African Urban Areas: Growth Trajectories of Underprivileged Students in South Africa. 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. ✅ Nutrition Interventions for School Children in South African Urban Areas: Growth Trajectories of Underprivileged Students, South Africa, Africa, Medicine, intervention 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) 2001 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Evaluating Cost-Effectiveness of Public Health Surveillance Systems in Nigeria,
Public health surveillance systems in Nigeria have been established to monitor disease outbreaks and implement control measures efficiently. A Bayesian hierarchical linear regression model was employed to analyse data from Nigeria's public health surveillance system. The model accounts for varying levels of effectiveness across different regions and integrates cost data with surveillance outcomes. The model revealed that the surveillance systems in northern Nigeria were more effective than those in southern regions, with a likelihood ratio test indicating significant differences (p < 0.05). This study provides insights into the effectiveness of public health surveillance systems in Nigeria and highlights the need for targeted interventions to improve system performance. Future research should focus on developing cost-effective strategies that can be implemented across different regions of Nigeria, based on this model’s 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 Assessment of Yield Improvement in District Hospitals Systems, Senegal
District hospitals in Senegal are facing challenges in providing adequate healthcare services to their populations. The current systems lack a comprehensive evaluation framework that can effectively measure and improve service yield. A Bayesian hierarchical model was constructed to analyse hospital data from Senegal. The model accounts for variability between districts and within each district's healthcare services, incorporating prior knowledge about system efficiency. The model identified significant disparities in service yield across different districts, with some areas reporting yields up to 30% below expected levels. The Bayesian hierarchical model provides a robust tool for assessing hospital performance and identifying systemic issues that need addressing. Future research should explore the implementation of this model at larger scales. District health authorities in Senegal are encouraged to use the proposed model as an evaluation framework, alongside targeted interventions to improve service yield. Bayesian hierarchical models, district hospitals, healthcare systems, yield improvement, Senegal 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) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Community Health Centres Systems in Senegal
Community health centres in Senegal are pivotal for providing primary healthcare services to underserved populations. A longitudinal study employing mixed-methods, including quantitative data collection from electronic health records (EHR) and qualitative interviews with patients and healthcare providers. A multivariate regression model will be used to assess the relationship between service quality indicators and patient satisfaction scores. Eleven percent of patients reported improved clinical outcomes after receiving services at community health centres, indicating a positive trend in utilisation and efficacy. The quasi-experimental design successfully measured clinical outcomes without compromising data integrity or participant privacy. Further research should explore the long-term impact on public health metrics and potential for scaling up service delivery models. Community Health Centres, Senegal, Clinical Outcomes, Quasi-Experimental Design Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.