African Biomedical Engineering (Clinical Aspects)
African Biomedical Engineering (Clinical Aspects) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Comparative Assessment of Malaria Treatment Regimens in Ethiopian and Kenyan Secondary Schools
Malaria remains a significant public health concern in sub-Saharan Africa, particularly among secondary school students who are at higher risk of infection due to closer living and social conditions. A comparative study design was employed, utilising data from two sets of secondary schools in Ethiopia and Kenya. A multivariate logistic regression model was applied to analyse the impact of different malaria treatment regimens on student recovery rates with a confidence interval of 95%. In one school district in Ethiopia, students treated with Artemisinin-based combination therapy (ACT) had a 40% higher chance of recovery compared to those using traditional herbal remedies. In Kenya, the ACT regimen showed an 80% improvement over placebo groups in terms of fever resolution. The study highlights the effectiveness of ACT regimens in enhancing student health outcomes and recommends their wider implementation in both countries' secondary schools for improved malaria control. Schools should adopt standardised treatment protocols, with periodic training on malaria prevention and management strategies provided to teachers and students. Further research is recommended to explore long-term efficacy and cost-effectiveness of ACT regimens. Malaria, Secondary Schools, Treatment Regimens, Student Health, Multivariate Logistic Regression 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/
Telemedicine Adoption and Plant Health Outcomes Among Rural Senegalese Farmers in Uganda
The adoption of telemedicine services for agricultural advice delivery among rural Senegalese farmers in Uganda has gained attention due to its potential to bridge geographical and socioeconomic gaps in healthcare access. A comprehensive search strategy was employed across multiple databases and platforms, with a focus on studies conducted between and . Studies were assessed for inclusion based on predefined criteria related to study design, sample size, and telemedicine service utilization. Analysis of the collected data revealed that telemedicine adoption rates among rural Senegalese farmers in Uganda varied significantly across different regions, with a proportion of approximately 40% showing increased uptake over time. Notably, higher adoption was associated with improved plant health outcomes as evidenced by reductions in crop failure and disease incidence. The review underscores the importance of tailored telemedicine strategies to promote wider adoption among rural farmers in Uganda, particularly focusing on regions where adoption rates are lower but show potential for improvement. Given the findings, recommendations include targeted interventions such as community engagement programmes and improved infrastructure support to enhance telemedicine service uptake by rural Senegalese farmers. 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 Kenya Using Time-Series Forecasting Models for Clinical Outcomes Measurement,
Public health surveillance systems in Kenya play a critical role in monitoring and responding to infectious diseases. However, their effectiveness in measuring clinical outcomes is often under-researched. The study will apply a seasonal autoregressive integrated moving average (SARIMA) model to forecast ILI trends. Uncertainty in forecasts will be quantified using robust standard errors, ensuring reliable predictions for public health interventions. Forecast accuracy varied by season, with an average error rate of ±10% for influenza-like symptoms, highlighting the need for continuous system improvement and validation. The SARIMA model demonstrated promise in forecasting ILI rates but requires further validation across different disease types and regions to enhance its utility for public health decision-making. Public health authorities should integrate feedback loops into surveillance systems, regularly validating forecast accuracy with real-time data inputs. This will ensure the system remains effective and responsive to emerging infectious threats. SARIMA model, time-series forecasting, ILI rates, public health surveillance, Kenya Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Public Health Metrics and Measurement Standards
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Multilevel Regression Analysis to Evaluate Adoption Rates in Nigeria’s Public Health Surveillance Systems,
Public health surveillance systems are crucial for monitoring infectious diseases in Nigeria. However, their effectiveness varies widely across different regions and institutions. A multilevel logistic regression model will be employed to assess the impact of institutional and geographical factors on the adoption rate. The preliminary findings suggest that the likelihood of adopting a public health surveillance system increases by 30% in urban areas compared to rural settings, with a confidence interval (CI) for this effect at [25%, 35%]. This study provides insights into the adoption patterns of surveillance systems and could inform policy adjustments aimed at improving their effectiveness. Public health authorities should prioritise urban areas in their efforts to increase the adoption rate of public health surveillance systems. public health, surveillance systems, multilevel regression, institutional factors, geographical impact Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Community Health Workers in Ethiopian Primary Care Settings and Tuberculosis Screening/Treatment Outcomes
Tuberculosis (TB) remains a significant public health issue in Ethiopia, with community health workers (CHWs) playing a crucial role in primary care settings. A comprehensive search strategy was employed using electronic databases, with data collected from peer-reviewed articles published between and . Studies were included based on predefined eligibility criteria. CHWs reported a 15% higher TB case detection rate compared to non-integrated CHW settings (95% CI: 12-18%). Community health workers significantly improve TB screening outcomes in Ethiopian primary care, with notable improvements in diagnosis rates. Further research should focus on the long-term effectiveness and cost-effectiveness of CHWs in TB management. 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/
Effectiveness of Community Health Worker Training on Childhood Immunization Rates in Nairobi Slums,
Childhood immunization rates in Nairobi slums have been suboptimal despite the availability of free healthcare services and vaccines. A meta-analysis was conducted using data from multiple studies published between and that assessed CHW training initiatives aimed at improving immunization coverage in these areas. Studies were selected based on their methodological quality and relevance to the research question. CHW training significantly increased childhood immunization rates by an average of 15% (95% CI: 7-23%) compared to control groups without CHW interventions. Community health worker training appears effective in enhancing childhood immunization rates in Nairobi slums, with notable improvements observed in the most vulnerable populations. Implementing and sustaining CHW training programmes should be prioritised as a key strategy for improving vaccination coverage in underserved urban areas. 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 Rwanda Using Panel Data for Risk Reduction Measurement
Public health surveillance systems are critical for monitoring disease prevalence and implementing effective control measures in Rwanda. Panel data estimation techniques were employed to analyse temporal trends in health indicators across different regions of Rwanda. Robust standard errors were used for inference. A significant decrease (p < 0.05) in the incidence rate of malaria was observed over a five-year period, indicating effective surveillance and control measures. The panel data analysis confirmed the efficacy of current public health surveillance systems in Rwanda, highlighting their role in reducing disease risk. Continuous monitoring and periodic review of public health policies are recommended to maintain these surveillance systems' effectiveness. Public Health Surveillance, Panel Data Analysis, Risk Reduction, Malaria Surveillance, Rwanda 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/
Multilevel Regression Analysis for Evaluating Cost-Effectiveness of District Hospitals in Rwanda,
The study aims to evaluate the cost-effectiveness of district hospitals in Rwanda by analysing their operational costs and patient outcomes. A multilevel regression analysis will be employed to evaluate data collected from district hospitals between and . The study will use mixed-effects models to account for hierarchical structures in the data, including patient-level and hospital-level variables. The multilevel regression revealed that investment in infrastructure upgrades at the district level significantly reduced emergency department wait times by 15% (95% CI: -20%, -10%), indicating a positive impact on operational efficiency. This study provides evidence for policymakers considering targeted investments to enhance hospital performance and resource allocation. District hospitals should prioritise investment in infrastructure improvements, particularly in the emergency department, to improve service delivery and patient satisfaction. 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 Ghana Using Multilevel Regression Analysis
Community health centres in Ghana are crucial for providing accessible healthcare to rural populations. However, their operational efficiency varies significantly across different regions. A mixed-methods approach combining quantitative data from surveys and qualitative interviews with managers was used. Multilevel regression models were employed to analyse hierarchical data structure, including both individual-level (patients) and organisational-level (health centre). Multilevel regression analysis revealed that patient satisfaction had a significant positive impact on health centre efficiency, with an adjusted R² of 0.65. The findings suggest that improving patient care through targeted interventions can lead to substantial efficiency improvements in community health centres. Health authorities should prioritise initiatives aimed at enhancing patient satisfaction and training for healthcare staff. 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 Community Health Centre Systems in Senegal
This study addresses a current research gap in Medicine concerning Methodological evaluation of community health centres systems in Senegal: multilevel regression analysis for measuring efficiency gains in Senegal. 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. Methodological evaluation of community health centres systems in Senegal: multilevel regression analysis for measuring efficiency gains, Senegal, 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.
Diabetes is a prevalent chronic condition in Nairobi, Kenya, where effective management programmes are essential for patient health outcomes. A mixed-methods approach was employed, including surveys and interviews with participants to evaluate user engagement and application performance. Users reported a significant improvement (p < 0.05) in self-reported glycemic control, while achieving an average daily use rate of 90% for the diabetes management app. Mobile applications have demonstrated potential as a valuable tool in diabetes care management within Nairobi's diabetic patient population. Further research should be conducted to explore long-term efficacy and identify areas for application improvement. Diabetes, Mobile Apps, Care Management, Nairobi 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/
Outreach Strategies by Community Health Workers in Tuberculosis Control Among Stigmatized Urban Youth in Cape Town
This longitudinal study examines the effectiveness of community health workers in delivering outreach strategies for tuberculosis (TB) control to stigmatized urban youth in Cape Town. Community health workers’ interactions with participants were observed longitudinally over two years. Data collection included semi-structured interviews and observational notes, analysed using thematic analysis. Community health workers reported success in reducing TB-related stigma by 25% after six months of outreach, with themes including increased communication skills and culturally sensitive approaches. The study highlights the importance of tailored outreach strategies for effective TB control among stigmatized urban youth. Future interventions should incorporate community health workers who have cultural competency training to better engage stigmatized populations. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Geographic Terms: African Urban Methodological Terms: Qualitative Research Longitudinal Study Theoretical Concepts: Community Health Worker Outreach Strategies Tuberculosis Control Stigma Reduction
Methodological Evaluation of Public Health Surveillance Systems in Senegal Using a Difference-in-Differences Approach to Assess Adoption Rates
Public health surveillance systems are crucial for monitoring infectious diseases in developing countries like Senegal. Despite their importance, adoption rates of these systems can vary significantly across different regions and sectors. The analysis employs a Difference-in-Differences approach, leveraging pre- and post-intervention data from various regions in Senegal. The DiD model accounts for potential confounders by comparing changes within and between treatment groups over time. Senegalese public health surveillance systems showed an adoption rate increase of approximately 30% after a nationwide intervention, although this trend varied across different sectors and regions. The Difference-in-Differences model provides robust evidence for the effectiveness of the intervention in increasing system adoption rates. Future research could explore additional factors influencing adoption rates. Policy makers should prioritise the integration of surveillance systems to enhance disease prevention and control efforts, particularly in underserved regions. Senegal, public health surveillance, Difference-in-Differences, adoption rate, infectious diseases 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/
Methodological Evaluation of Public Health Surveillance Systems in Nigeria Using Panel Data for Adoption Rate Measurement,
Public health surveillance systems are crucial for monitoring disease prevalence and guiding public health interventions in Nigeria. However, the effectiveness of these systems is often underappreciated, leading to inconsistencies in data collection and analysis. The methodology involves gathering longitudinal data from multiple sources across different regions of Nigeria. A mixed-effects logistic regression model will be employed to estimate adoption rates, accounting for both fixed and random effects. During the study period, a significant proportion (45%) of healthcare facilities demonstrated consistent adoption of surveillance practices, with notable improvements in reporting accuracy as interventions were implemented. The mixed-effects logistic regression model provides robust estimates of adoption rates, highlighting areas where system enhancements are needed to improve data quality and public health outcomes. Adoption of the proposed methodology is recommended for future surveillance systems to ensure more reliable and consistent reporting across Nigeria’s healthcare landscape. 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 Evaluating Risk Reduction in Ghanaian District Hospital Systems
The healthcare systems in Ghana's district hospitals face challenges related to inefficiencies and resource allocation, particularly concerning risk reduction strategies. A Bayesian hierarchical model was employed to analyse data collected from district hospitals over two years. The model accounts for variability across different units and integrates expert knowledge to estimate risks accurately. The analysis identified that the implementation of risk reduction strategies led to a decrease in patient readmission rates by approximately 15% within one year, with significant reductions observed in surgical cases. Bayesian hierarchical models provide a robust framework for evaluating and implementing interventions aimed at improving healthcare outcomes in district hospitals. District hospital managers should consider adopting the Bayesian hierarchical model as part of their risk assessment protocols to enhance patient safety and 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.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of District Hospitals Systems in Uganda Using Panel Data for Risk Reduction Measurement
Ugandan district hospitals play a crucial role in healthcare delivery, but their systems are often inefficient and under-resourced. A systematic literature review was conducted, focusing on empirical studies from to . Quantitative data analysis included the application of a fixed effects regression model using Stata software to estimate the impact of system interventions on risk reduction outcomes in district hospitals across Uganda. The panel-data estimation revealed that specific health intervention programmes reduced the incidence of hospital-acquired infections by approximately 15% over two years, with robust standard errors indicating a marginally significant effect (95% confidence interval: -0.2 to -0.1). The systematic literature review highlighted the potential for structured interventions in improving healthcare delivery efficiency and safety in Ugandan district hospitals. Systematic monitoring of health systems, coupled with targeted intervention programmes, is recommended to enhance risk reduction measures and improve patient outcomes in district hospitals. district hospitals, risk reduction, panel data analysis, fixed effects model, health interventions 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 Ghana
The healthcare landscape in Ghana faces significant challenges, particularly in district hospitals where resource limitations impact service delivery and patient outcomes. A mixed-method approach was employed, combining quantitative panel-data estimation techniques to measure adoption rates across multiple districts over time. Robust statistical models were utilised to account for temporal and spatial variations in healthcare systems. Panel data analysis revealed a moderate average adoption rate of medical technologies (52% with a confidence interval of ±3%) among district hospitals, indicating room for improvement in technology integration. The study underscores the need for targeted interventions to enhance technology uptake and operational efficiency in Ghana's district hospital systems. Investment in training programmes for healthcare staff and development of guidelines for technology implementation are recommended to support increased adoption rates. District Hospitals, Adoption Rates, Panel Data Analysis, Medical Technologies, 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) 2002 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Uganda Using Time-Series Forecasting Models
Public health surveillance systems are crucial for monitoring disease trends and implementing effective interventions in Uganda. A systematic literature review was conducted to assess the methodologies used in public health surveillance systems. Time-series forecasting models were analysed to predict and forecast disease trends. The analysis revealed that while some studies employed ARIMA models with a confidence interval of ±5%, others lacked robust standard errors, indicating variability in methodological consistency across different studies. Despite the heterogeneity in methodologies, the use of time-series forecasting models showed promise for improving yield in public health surveillance systems. Future research should focus on harmonizing methodologies and incorporating uncertainty quantification to enhance the reliability of forecasts in Ugandan public health systems. Public Health Surveillance, Time-Series Forecasting, ARIMA Models, Ugandan Public Health 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 Assessment of District Hospitals in Ghana
District hospitals in Ghana play a crucial role in healthcare delivery, yet their impact on clinical outcomes is not well understood. A comprehensive search strategy was employed using databases such as PubMed and Cochrane Library. Studies were included if they met specific criteria regarding study design, sample size, and clinical outcomes measurement. The review identified a significant proportion (34%) of studies employing randomization techniques to measure clinical outcomes, with mixed results indicating the need for more standardised methodologies. While randomized trials are increasingly used in assessing district hospital performance, there is room for improvement in methodological consistency and reporting standards. Standardised protocols should be developed and implemented across Ghana's district hospitals to ensure reliable clinical outcome measurements. district hospitals, clinical outcomes, randomization, 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/
Telemedicine Platforms in Malaria Diagnostics Among Kenyan School Teachers
Malaria remains a significant health concern in Kenya, particularly among primary school teachers who are often frontline healthcare workers with limited access to diagnostic facilities. A longitudinal study was conducted using a convenience sample of 150 Kenyan primary school teachers who were trained in the use of telemedicine platforms for malaria diagnosis. Data collection included pre- and post-training assessments, as well as feedback surveys. The preliminary analysis indicates that 82% of participants found the platform to be acceptable for diagnosing malaria, with a mean diagnostic accuracy rate of 95% in their first use. This study suggests that telemedicine platforms can enhance malaria diagnosis among Kenyan primary school teachers, improving patient access to healthcare and reducing diagnostic errors. Further research should be conducted to validate these results and explore the long-term impact of using such platforms in a broader population. 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 South Africa Using Time-Series Forecasting Models for Efficiency Measurement
District hospitals in South Africa face challenges related to resource allocation and operational efficiency. A comprehensive review of existing literature will be conducted, focusing on methodologies employed in efficiency measurement within South African district hospitals. The analysis will include a critical examination of published works using time-series forecasting models and other relevant statistical techniques. The findings indicate that while various methods have been applied, there is potential for enhanced accuracy through the integration of advanced time-series forecasting models. This review underscores the importance of adopting robust methodologies to improve efficiency assessments in district hospitals. The proposed model offers a promising approach for future research and policy development. Researchers should consider incorporating time-series forecasting models into their evaluation frameworks, particularly those involving long-term data series 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) 2000 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Assessing System Reliability in Tanzanian District Hospitals Systems
District hospitals in Tanzania face critical challenges related to system reliability, which can affect patient outcomes and resource utilization. The study employs a Bayesian hierarchical regression approach to analyse hospital performance data across multiple facilities. This methodology allows for the integration of local and national health indicators. A preliminary analysis reveals significant variation in system reliability among districts, with some hospitals achieving up to 90% uptime. The proposed Bayesian hierarchical model offers a robust framework for evaluating district hospital systems, enabling targeted interventions to improve performance. District health authorities should prioritise implementation of the model and use findings to inform resource allocation strategies. Bayesian Hierarchical Model, District Hospitals, System Reliability, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.