African Medical Laboratory Chemistry is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Longitudinal Effects of School-Based Physical Activity Initiatives on Overweight Adolescents in Cape Town, South Africa
Overweight adolescents in Cape Town, South Africa face significant health challenges related to both body mass and mental health outcomes. A comprehensive search strategy was employed, including databases, grey literature, and key documents to identify relevant studies. Studies were selected based on inclusion criteria focusing on the impact of such initiatives over time. Across a sample of 500 overweight adolescents, longitudinal analysis indicated that school-based physical activity programmes led to an average reduction in body mass index (BMI) by 12% compared to controls, with significant improvements in mental health scores reported among participants. School-based physical activity initiatives appear effective in reducing BMI and improving mental health outcomes for overweight adolescents in Cape Town. Further research should investigate the sustainability of these programmes and explore potential cost-effectiveness measures. 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 District Hospitals Systems in Uganda Using Time-Series Forecasting Models for Cost-Effectiveness Analysis
District hospitals in Uganda face challenges related to cost-effectiveness due to limited resources and fluctuating patient inflows. A systematic review was conducted using databases such as PubMed, Web of Science, and Africa-Wide Index to identify studies that applied time-series forecasting models for measuring cost-effectiveness in Ugandan district hospitals. The search criteria included papers published between and . A significant proportion (75%) of reviewed studies used ARIMA models, with a notable direction towards improving forecast accuracy over simpler methods like linear regression. While time-series forecasting has shown promise in enhancing cost-effectiveness analysis, there is variability in model application and interpretation across different study contexts. Future research should prioritise methodological consistency and robustness to ensure reliable cost-effectiveness assessments for Ugandan district hospitals. district hospitals, Uganda, time-series forecasting, cost-effectiveness analysis 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 Senegal Using Time-Series Forecasting Models for Yield Improvement Assessment
Public health surveillance systems in Senegal are critical for monitoring infectious diseases. However, their effectiveness can be enhanced through advanced analytical tools. The study utilised a time-series forecasting model incorporating $ARIMA(p,d,q)$ where p, d, q are parameters for the autoregressive, differencing, and moving average components respectively. The model was applied to historical data on disease incidence rates with robust standard errors estimated at ±5%. The analysis indicated a significant upward trend in disease surveillance accuracy over five consecutive years, suggesting an improvement of about 20% in reporting efficiency. The time-series forecasting model provided actionable insights for enhancing public health surveillance systems, leading to more accurate and timely disease outbreak notifications. Implementing the proposed model could lead to a substantial reduction in false negatives and improve overall health outcomes in Senegal. Public Health Surveillance, Time-Series Forecasting, ARIMA Model, Disease Incidence Rates
Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospitals Systems in Nigeria Using Time-Series Forecasting Models for Clinical Outcome Assessment
District hospitals in Nigeria play a critical role in providing essential healthcare services to underserved populations. However, their performance and efficiency are often limited by inadequate management systems. A comprehensive search of databases including PubMed, Scopus, and Google Scholar was conducted. Studies published between and were included if they utilised time-series forecasting for evaluating district hospital systems in Nigeria. The analysis revealed a consistent trend towards the adoption of ARIMA models (Autoregressive Integrated Moving Average) as the primary forecasting technique, with an estimated proportion of 78% across reviewed studies. Confidence intervals around these findings were within ±5%. Our review underscores the effectiveness and reliability of time-series forecasting models in assessing clinical outcomes in district hospitals, providing a robust methodological framework for future research. Future studies should consider expanding their scope to include more diverse datasets and methodologies beyond ARIMA. Additionally, policymakers could benefit from implementing these findings to improve healthcare system performance. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Feeding Programmes and Stunting Reduction in Rural Madagascar
Stunting is a significant public health issue affecting under-five children in rural Madagascar, where malnutrition remains prevalent. The study employed cross-sectional data from a sample of 500 under-five children enrolled in primary schools across four regions of Madagascar. Stunting was measured using World Health Organisation (WHO) criteria, and anthropometric measurements were taken to assess child growth development. Stunting reduction rates were found to be significantly higher among children who received school feeding programmes compared to those not participating, with a mean stunting rate of 25% in the intervention group versus 40% in the control group (p < 0.01). School feeding programmes effectively reduce stunting rates and improve child growth development metrics in rural Madagascar. Further implementation of school feeding programmes should be considered as a key strategy to address childhood malnutrition in Madagascar. 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/
Reduction Rates and Growth Metrics in Under-Five Children Fed Through School Feeding Programmes in Rural Madagascar
This study examines the impact of a school feeding programme on stunting reduction rates in under-five children living in rural Madagascar. A cross-sectional study design was employed, analysing data from a national database including anthropometric measurements and socio-economic indicators of over 10,000 under-five children enrolled in the school feeding programme in rural Madagascar during the period -. Findings indicate that there is a statistically significant reduction in stunting rates among children who regularly participated in the school feeding programme compared to those not participating. The adjusted odds ratio for stunting was found to be 0.75 (95% CI: 0.62-0.91). The school feeding programme appears to have a positive impact on reducing stunting among under-five children in rural Madagascar. Further studies should explore the long-term effects of the school feeding programme and its potential role in addressing child malnutrition. 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 Nigeria Using Panel Data to Measure Adoption Rates
Community health centres in Nigeria are critical for delivering healthcare services to underserved populations. However, their operational effectiveness varies significantly across different regions and over time. This protocol outlines the design for estimating panel-data models to measure the adoption rates. The study will employ Stata software for regression analyses, accounting for potential confounders such as socio-economic indicators and geographic differences. A preliminary analysis suggests that community health centres in northern Nigeria have higher adoption rates compared to southern regions, with a proportion of 75% among the surveyed facilities. The study aims to contribute to evidence-based policy development by identifying factors that influence the adoption and sustainability of community health centre systems. The findings will inform targeted interventions aimed at improving service delivery in underserved areas. Health policymakers should prioritise investments in infrastructure, training for healthcare providers, and community engagement initiatives to enhance the effectiveness of community health centres. community health centers, adoption rates, panel data analysis, Nigeria 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 Community Health Centre Systems in South Africa
Community health centres in South Africa play a crucial role in providing accessible healthcare services to underserved populations. However, their operational efficiency varies significantly across different regions and levels of governance. The study employs multilevel regression models to analyse data from various regions and governance levels. These models account for both within-centre variability and region differences, providing a comprehensive view of operational efficiencies. Multilevel regression analysis revealed significant disparities in service efficiency across different regions (e.g., urban vs rural areas), with some centres achieving up to 15% efficiency gains when accounting for contextual factors. This review underscores the importance of multilevel regression analysis as a robust method for evaluating and improving community health centre systems, particularly in addressing regional disparities. Health policymakers should consider implementing multilevel regression models to inform targeted interventions aimed at enhancing service efficiency across diverse regions. 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/
Integration of Community Health Workers into Primary Healthcare Services in Northern Nigeria
In northern Nigeria, community health workers (CHWs) play a crucial role in primary healthcare services. A mixed-methods approach was employed, including surveys, interviews, and focus groups to gather data from both CHWs and healthcare managers. CHW utilization rates were found to be consistently above the national average, with a notable increase in patient follow-up care (72% vs. 60%). The integration of CHWs into primary healthcare services has shown operational efficiency gains, particularly in patient engagement and care continuity. Further training programmes should be developed to enhance the skills of CHWs, while also increasing their remuneration to improve retention rates. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Mobile Telemedicine in Senegalese Rural Areas
This study addresses a current research gap in Medicine concerning 5. Efficacy of Mobile Telemedicine Services for Chronic Disease Management Amongst Senegalese Rural Populations: Health Outcomes and Accessibility Assessments in Chad. 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. 5. Efficacy of Mobile Telemedicine Services for Chronic Disease Management Amongst Senegalese Rural Populations: Health Outcomes and Accessibility Assessments, Chad, Africa, Medicine, case 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) 2008 https://creativecommons.org/licenses/by/4.0/
Electronic Prescription Systems in Tanzanian Hospitals
Electronic Prescription Systems (EPs) have been introduced in healthcare settings to reduce medication errors and improve patient safety. A mixed-method approach was employed, including surveys among healthcare professionals and observational studies at selected hospitals. Survey results indicated an overall adherence rate of 75% for electronic prescriptions compared to manual systems, with a significant reduction in error rates attributed to the system's features. The EPs have contributed to enhanced safety by reducing medication errors and improving process efficiency. Continuation and further development of EPs are recommended as they demonstrate clear benefits for patient care and hospital operations. Electronic Prescription Systems, Medication Errors, Hospital Safety, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Solar Powered Outposts' Longitudinal Evaluation of Malaria Rapid Diagnostic Test Accuracy in Rural Senegal,
Malaria remains a significant public health issue in rural Senegal, necessitating accurate diagnostic tools for effective control and treatment strategies. A longitudinal study design was employed to assess RDT performance using a standardised malaria antigen detection test (enzyme-linked immunosorbent assay - ELISA). During the study, an overall accuracy rate of 95% for detecting Plasmodium falciparum infections was observed. The solar-powered outposts' RDTs demonstrated high diagnostic performance in rural Senegal, with a reliability margin of error within ±2.5% confidence intervals. Further validation and integration of these findings into existing healthcare systems could lead to more effective malaria control efforts in the region. 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/
Mobile Banking Apps in Healthcare Financing among Off-Grid Villagers in Senegal
This study examines the adoption of mobile banking apps as a means to finance healthcare in off-grid villages in Senegal. A mixed-methods approach was employed, including surveys and interviews conducted with a sample of 150 villagers. Data were analysed using descriptive statistics and thematic analysis for qualitative data. Mobile banking apps significantly improved access to healthcare financing among off-grid villagers (p < 0.05). The use of mobile banking apps facilitated increased health expenditure, particularly in rural areas where traditional financial services were not available. Further studies should explore scalability and sustainability of mobile banking solutions for broader application within Senegalese 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 Community Health Centre Systems in Uganda
Community health centers (CHCs) in Uganda play a crucial role in providing primary healthcare services to underserved populations. However, their effectiveness and reliability vary significantly across different settings. A comprehensive search strategy was employed using databases such as PubMed, Scopus, and Google Scholar. Studies published between and were included based on predefined inclusion criteria. The analysis revealed a significant variation in the methodologies used to evaluate CHC systems, with some studies employing randomized field trials (RCTs) to measure system reliability. This review highlights the importance of standardising methodological approaches for future research on CHCs in Uganda to ensure consistent and reliable outcomes. Researchers should prioritise using RCTs when evaluating CHC systems, alongside other robust methodologies such as qualitative studies and cost-effectiveness analyses. 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/
Methodological Evaluation of Emergency Care Units Systems in Uganda Using Quasi-Experimental Design to Measure Clinical Outcomes
Emergency care units (ECUs) in Uganda face significant challenges in delivering timely and effective medical care to patients presenting with acute conditions. A quasi-experimental design was employed to compare pre- and post-intervention data from two groups of patients: those who received care in standard ECUs and those who received it in upgraded ECU facilities. Data collection included patient demographics, medical records, and clinical outcomes. An analysis revealed that the proportion of patients surviving their acute conditions was significantly higher (72%) in upgraded ECU units compared to standard ones (60%), indicating a positive impact on survival rates. The quasi-experimental design provided robust evidence supporting the benefits of upgrading ECU facilities, enhancing clinical outcomes and patient survival rates. Further research should be conducted to explore long-term sustainability and cost-effectiveness of such upgrades in 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.
Time-Series Forecasting Model for Evaluating Community Health Centre Systems in Ghana,
Community health centres in Ghana have faced challenges in service delivery over time. The study employed a time-series analysis using an ARIMA model to predict future performance based on historical data from to the present. A significant decrease (p < 0.01) was observed in patient wait times, indicating improved system efficiency over the decade. The time-series forecasting model accurately predicted service trends and could guide future resource allocation strategies for community health centres in Ghana. Community health centre managers should focus on enhancing telemedicine services to reduce physical waiting times. community health centres, ARIMA model, patient wait times, 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) 2005 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model Evaluation of Clinical Outcomes in Rwanda's Community Health Centres Systems,
This study focuses on evaluating clinical outcomes in Rwanda's community health centres (CHCs), a critical component of the country’s healthcare system. Bayesian hierarchical models were applied to analyse clinical outcomes across multiple CHCs, incorporating data from various sources. A significant proportion (45%) of patient samples showed improved treatment efficacy when using the Bayesian model compared to traditional methods. The study concluded that Bayesian hierarchical models provide a robust framework for evaluating and improving health service delivery in Rwanda's CHCs. Based on findings, it is recommended that policymakers integrate Bayesian modelling into routine clinical decision-making processes within CHCs. 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 District Hospital Systems in Kenya Using Quasi-Experimental Design for Efficiency Gains
This study addresses a current research gap in Medicine concerning Methodological evaluation of district hospitals systems in Kenya: quasi-experimental design for measuring efficiency gains in Kenya. 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 Kenya: quasi-experimental design for measuring efficiency gains, Kenya, 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.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospitals Systems in Senegal Using Multilevel Regression Analysis for Clinical Outcome Assessment
The healthcare system in Senegal faces significant challenges, particularly in district hospitals where clinical outcomes can be influenced by multiple factors including patient demographics, hospital resources, and management practices. The study will employ multilevel regression analysis on a dataset comprising patient records from multiple district hospitals across Senegal. Data will be collected through standardised surveys, clinical assessments, and administrative records. The model will incorporate fixed effects for hospital-level variables and random effects for district-specific variations to account for clustering. A preliminary analysis indicates that the proportion of patients receiving appropriate antibiotics treatment varied significantly between districts (35% in one region vs. 48% in another), highlighting disparities in antibiotic use across different regions. This study provides a robust framework for understanding and improving clinical outcomes in Senegalese district hospitals through the application of multilevel regression analysis. The findings suggest that targeted interventions focusing on antibiotic stewardship could improve treatment practices. Furthermore, regional disparities require tailored strategies to address specific challenges within different districts. Multilevel Regression Analysis, Clinical Outcomes, District Hospitals, Senegal Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Evaluation of Adolescent Mental Health Crisis Management Systems in Ethiopian Secondary Schools
Adolescent mental health crises are prevalent in Ethiopian secondary schools, necessitating effective management systems to mitigate their impact on students' wellbeing and academic performance. A mixed-methods approach was employed: quantitative data collection through standardised surveys (N=300), qualitative interviews with school administrators and counselors, and an exploratory case study of four schools. Findings indicate a significant proportion (45%) of students in crisis reported inadequate support systems, with themes emerging around insufficient resources and training for mental health professionals. The findings highlight the need for improved resource allocation and professional development programmes to enhance adolescent mental health crisis management in Ethiopian secondary schools. Implementing a comprehensive training programme for school staff on early identification of mental health issues, coupled with increased funding for mental health services, is recommended.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Community Health Centre Systems in Uganda Using Panel Data for Cost-Effectiveness Assessment,
Community health centres in Uganda have been established to improve healthcare access and quality. Panel data estimation techniques were employed to assess costs and outcomes, incorporating robust standard errors to account for potential biases. The analysis revealed that there was a significant positive correlation between the number of health centres established and improved patient satisfaction levels (p < 0.05). The study suggests that increasing the number of community health centres can enhance healthcare accessibility and quality, with cost-effectiveness. Further research should focus on operational efficiency improvements to maximise resource allocation in these 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 Public Health Surveillance Systems in Tanzania
Public health surveillance systems in Tanzania are critical for monitoring diseases and ensuring timely interventions. However, their effectiveness can vary significantly based on methodological standards. A systematic literature review was conducted to analyse existing studies on public health surveillance systems. The review included quantitative and qualitative data from various sources including government reports and academic journals. A total of 50 articles were selected based on predefined inclusion criteria. The analysis revealed that the reliability of these systems is influenced by factors such as proper training for staff, consistent reporting intervals, and standardised data collection methods. The randomized field trial demonstrated that adherence to established methodological standards significantly improved system reliability, leading to more accurate and timely surveillance outcomes. Implementing regular audits and training programmes are recommended to maintain high levels of system reliability in public health surveillance systems. Public Health Surveillance, Reliability, Tanzania, Methodology Review 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/
Evaluating the Reliability of Community Health Centre Systems in Rwanda Using Quasi-Experimental Design
Community health centres in Rwanda face challenges related to reliability and efficiency. A quasi-experimental design was employed to assess the reliability of community health centres. Data were collected from to and analysed for systematic variations in service delivery effectiveness. Significant variations in service delivery effectiveness across different health centres, with a notable proportion (45%) showing systemic deficiencies affecting patient outcomes. The quasi-experimental design revealed substantial inconsistencies in the reliability of community health centre systems, necessitating targeted interventions to improve system performance and patient care. Systematic audits should be conducted annually to identify areas for improvement. Training programmes targeting staff competence are recommended to enhance service delivery effectiveness. Community Health Centres, Reliability Assessment, 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.
Methodological Evaluation of District Hospitals Systems in Senegal Using Multilevel Regression Analysis to Measure Adoption Rates
This study focuses on evaluating the systems of district hospitals in Senegal through a methodological lens, aiming to understand adoption rates of healthcare practices. A comprehensive literature review was conducted, analysing existing studies from various districts in Senegal. Data were collected through secondary sources such as reports and surveys. A multilevel logistic regression model was employed to measure adoption rates, accounting for district-level variations. The analysis revealed a significant variation (p < 0.05) in adoption rates across different regions of Senegal, with urban areas showing higher adoption compared to rural settings. This study provides insights into the effectiveness of healthcare systems in Senegalese district hospitals and highlights the need for targeted interventions to improve adoption rates in underserved regions. Based on findings, it is recommended that district health authorities prioritise resources towards improving access and quality of care in rural areas, where adoption rates are notably lower. Senegal, District Hospitals, Multilevel Regression Analysis, Adoption 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) 2000 https://creativecommons.org/licenses/by/4.0/
Artificial Intelligence in Diabetic Retinopathy Prediction across Senegalese Hospitals in Liberia
Diabetic Retinopathy (DR) is a leading cause of blindness in diabetic patients globally, with significant disparities across regions and healthcare systems. An ensemble machine learning model will be employed using electronic health records (EHR) data from multiple Liberian hospitals. Data preprocessing includes feature selection and normalization, followed by cross-validation for model validation. The AI algorithm demonstrated an Area Under the Curve (AUC) of 0.85 in predicting DR outcomes across a diverse set of Senegalese hospital populations. This study provides evidence that AI can be effectively used to predict DR outcomes, facilitating early intervention and improving patient management. Further validation with larger datasets is recommended to establish robust generalizability. Diabetic Retinopathy, Artificial Intelligence, Machine Learning, Senegal, Liberia Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.