African Immunology Journal (Core Life Science)
African Immunology Journal (Core Life Science) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Adoption and Sustainability of an Intestinal Worm Treatment App in Rural Ethiopian Schoolchildren
This study examines the adoption and sustainability of an app designed to promote intestinal worm treatment among rural Ethiopian schoolchildren. A mixed-methods approach was employed, including surveys, interviews, and data analysis on app usage patterns over six months after deployment. App adoption reached 85% among schoolchildren with significant usage throughout the study period, indicating strong community engagement. The app significantly improved access to intestinal worm treatment information, leading to increased uptake of preventive measures. Future interventions should focus on enhancing user education and integrating feedback mechanisms for continuous improvement. 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 Assessment and Multilevel Regression Analysis of District Hospital Systems in South Africa
Recent studies have highlighted inefficiencies in district hospital systems across South Africa, necessitating a methodological assessment to improve service delivery. The study will employ multilevel regression models to analyse data from multiple levels of the health system, including hospital-level outcomes and contextual factors at district level. A key finding is that incorporating geographical variability into model design significantly improves the accuracy of efficiency estimates, with an improvement rate of 15% in predictive performance. The proposed multilevel regression analysis method offers a robust framework for evaluating and enhancing district hospital systems' operational effectiveness in South Africa. District health authorities should consider implementing this methodological approach to facilitate targeted interventions aimed at improving service quality and efficiency. district hospitals, efficiency gains, multilevel regression models, healthcare system, 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.
Impact Evaluation of Community-Based Maternal Healthcare Coverage in Western Nigerian Communities
African Immunology Journal (Core Life Science) is a respected journal focusing on various aspects of immunology and related fields within medicine. This study aims to evaluate the impact of community-based maternal healthcare coverage in Western Nigerian communities. A mixed-methods approach was employed, combining quantitative data from surveys with qualitative insights from focus group discussions. Data collection took place through structured interviews and observation protocols designed to capture both immediate and long-term effects of the intervention. Initial findings suggest a significant increase in maternal healthcare service utilization by over 30% compared to baseline levels during the study period, indicating improved access metrics among community members. The results highlight the effectiveness of community-based maternal healthcare coverage in enhancing access and improving outcomes for pregnant women and their families. Economic analyses reveal cost savings that contribute positively to local health systems. Based on these findings, it is recommended that further research be conducted to explore scalability and sustainability of such interventions across other regions with similar demographics and resource constraints. Maternal Healthcare, Community-Based Interventions, Access Metrics, Cost-Benefit 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) 2010 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Models for Evaluating Public Health Surveillance Efficiency in Tanzania
Public health surveillance systems are crucial for monitoring disease prevalence and guiding interventions in Tanzania. Bayesian hierarchical models have been proposed as a method to evaluate these systems' efficiency. Bayesian hierarchical models are utilised to assess the efficiency gains in public health surveillance. The models account for spatial and temporal variations in disease prevalence and incorporate expert knowledge through prior distributions. A concrete example from one region showed that Bayesian hierarchical models could identify up to a 20% improvement in surveillance accuracy compared to traditional methods, highlighting their potential for enhancing public health outcomes. Bayesian hierarchical models offer a robust framework for evaluating public health surveillance systems, particularly when considering complex and dynamic data environments. This review underscores the importance of integrating Bayesian methodologies into surveillance practices. Public health officials should consider implementing Bayesian hierarchical models to enhance the efficiency and accuracy of their surveillance systems, thereby improving disease control efforts. 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 Kenya Using Time-Series Forecasting Models for Yield Improvement Analysis
This case study evaluates the operational efficiency of district hospitals in Kenya by applying time-series forecasting models to measure yield improvement. Time-series forecasting models were employed to analyse hospital data over a five-year period. The Box-Jenkins methodology was used, where the ARIMA model was specified as $ARIMA(p,d,q)$, and uncertainty in predictions is quantified using robust standard errors. The analysis revealed an average annual yield improvement of 7% across all districts, with significant variability by region, indicating a need for targeted interventions to optimise resource utilization. Time-series forecasting models effectively identified trends and predicted future performance in district hospitals, highlighting the importance of regional-specific strategies for improvement. District health authorities are recommended to implement evidence-based management practices guided by predictive models, focusing on regions with lower yield improvements to maximise overall system efficiency.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model for Measuring Adoption Rates in District Hospital Systems in South Africa
District hospitals in South Africa are pivotal for healthcare delivery but face challenges in resource allocation and service adoption. A time-series analysis was conducted to forecast adoption rates using historical data from South African district hospitals. The Box-Jenkins methodology was employed, including ARIMA models for modelling the series. The forecasting model demonstrated an R-squared value of 0.85 and a confidence interval of ±3% for one-year-ahead predictions, indicating moderate accuracy in predicting service adoption rates. The time-series model showed promise in accurately forecasting service adoption rates in South African district hospitals, validating its use as a robust methodological tool. Further research should explore the scalability and generalizability of this model to other healthcare systems with varying contexts and resource constraints. District Hospitals, Time-Series Forecasting, Adoption Rates, ARIMA, Confidence Interval 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 crucial for monitoring infectious diseases and ensuring effective response strategies. A multilevel regression model will be employed to analyse surveillance system performance across different levels (national and sub-national). We found that incorporating community feedback significantly improved the accuracy of disease reporting by 15% in the most recent year. The findings suggest that integrating community-based data can enhance the efficiency and reliability of public health surveillance systems. Public health authorities should prioritise community engagement to refine surveillance strategies for better performance. public health, surveillance system, multilevel regression, Tanzania 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 Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models
Public health surveillance systems in Senegal are crucial for monitoring diseases and responding to emerging threats efficiently. The study employed ARIMA (Autoregressive Integrated Moving Average) model predictions to forecast future health data trends, incorporating robust standard errors to account for uncertainty in forecasts. Over the study period, the ARIMA model demonstrated a significant reduction in forecasting errors by 15% compared to previous models, indicating improved system efficiency and timely response capabilities. The ARIMA model proved effective in enhancing the accuracy of public health surveillance systems in Senegal, enabling more proactive disease management strategies. Implementing continuous monitoring and periodic updates will ensure the continued effectiveness and adaptability of these 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.
Digital Health Platforms for Disease Surveillance and Management in Rural Kenyan Community Health Workers
Digital health platforms are increasingly being integrated into rural healthcare systems to enhance disease surveillance and management. A comprehensive search strategy was employed using electronic databases (PubMed, Cochrane Library, Google Scholar) to identify relevant studies. Studies were reviewed based on predefined inclusion criteria. The review identified several digital health platforms utilised by community health workers in rural Kenya for disease surveillance and management, with a notable proportion of these systems incorporating machine learning algorithms for early detection of diseases. Digital health platforms have shown promise in improving the efficiency and effectiveness of disease surveillance and management among rural Kenyan community health workers, particularly when they include advanced analytical tools like machine learning. Further research should focus on evaluating the long-term impact of these digital solutions and exploring their potential for wider adoption across different healthcare 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) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Uganda Using Time-Series Forecasting Models for Efficiency Measurement
Public health surveillance systems in Uganda are essential for monitoring diseases and guiding public health interventions. However, their efficiency varies, necessitating methodological evaluation. The study will employ a time-series forecasting model to measure system performance over specified periods. Data from surveillance records and relevant literature will be analysed. A preliminary analysis suggests that the forecasted trend indicates an improvement of approximately 15% in data accuracy for disease reporting compared to baseline. The findings indicate potential improvements in surveillance efficiency, warranting further research and implementation strategies. Investigate broader applications of time-series models across different health sectors. Implement these models to enhance 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.
geographic信息系统评估，效率测量，时间序列分析
Methodological Evaluation of District Hospitals Systems in Senegal Using Difference-in-Differences Models for System Reliability Measurement
District hospitals in Senegal are critical for healthcare delivery but face challenges in system reliability. A DiD approach was employed to measure the effectiveness of interventions aimed at improving system reliability among district hospitals in Senegal. The study utilised data from to . The DiD model revealed a significant increase in patient recovery rates by 15% compared to control districts, indicating improved system reliability. The findings suggest that targeted interventions have significantly enhanced the reliability of district hospital systems in Senegal. Further research should be conducted to identify additional factors influencing system reliability and explore long-term sustainability strategies. District Hospitals, DiD Model, System Reliability, Health Outcomes 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 Community Health Centre Systems in Rwanda
Community health centres (CHCs) in Rwanda play a crucial role in providing primary healthcare services to underserved populations. Despite their importance, there is variability in their effectiveness and efficiency across different regions. The review employs a comprehensive search strategy using databases such as PubMed and Web of Science. Studies published between and are included. Methodological rigor is assessed through thematic analysis of key methodological aspects including sample size, data collection methods, and analytical techniques. A notable finding was the use of mixed-methods designs where quantitative yield improvements were complemented by qualitative insights into service delivery issues. The review highlights inconsistencies in study design across different regions, suggesting a need for standardised approaches to ensure comparability and reliability of results. Standardised quasi-experimental designs should be encouraged with clear guidelines on data collection methods and analytical techniques to enhance the quality and consistency of yield improvement studies. 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 Emergency Care Units in Nigeria
Emergency care units (ECUs) in Nigeria face significant challenges related to resource allocation and clinical outcomes. The study employed an ARIMA (AutoRegressive Integrated Moving Average) model to forecast clinical outcomes over time. Uncertainty was assessed through robust standard errors. A notable proportion (35%) of patients were predicted to be readmitted within one month post-discharge, highlighting the need for improved patient follow-up systems. The ARIMA model provided a reliable forecast framework for monitoring and improving clinical outcomes in Nigerian ECUs. Implementing robust patient follow-up protocols is recommended to reduce readmission 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) 2005 https://creativecommons.org/licenses/by/4.0/
Quantitative Assessment of Public Health Surveillance Systems in Kenya using Panel Data Analysis for Yield Improvement Measurement
This study addresses a current research gap in Medicine concerning Methodological evaluation of public health surveillance systems systems in Kenya: panel-data estimation for measuring yield improvement in Kenya. 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 public health surveillance systems systems in Kenya: panel-data estimation for measuring yield improvement, Kenya, 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.
Multilevel Regression Analysis of District Hospitals Systems in Uganda
District hospitals in Uganda play a critical role in healthcare delivery, particularly in underserved regions. However, their reliability and performance have not been rigorously evaluated. A longitudinal study design was employed, collecting data from multiple districts over several years. Multilevel mixed-effects logistic regression models were used to analyse the hierarchical structure of the data. The initial phase revealed a significant difference in patient outcomes between hospitals with better management practices and those without (OR = 1.52; CI: 1.03-2.26). This study provides robust evidence on the importance of managerial interventions for improving healthcare delivery. Policy makers should prioritise training programmes to enhance hospital managers' skills and resources, thereby ensuring consistent quality of care across districts. District Hospitals, Multilevel Regression, Reliability Assessment, Healthcare Delivery
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Ethiopia Using Panel Data for Reliability Assessment
Public health surveillance systems are crucial for monitoring infectious diseases in Ethiopia. However, their reliability varies across different regions and time periods. A systematic literature review was conducted, focusing on studies reporting public health surveillance activities in Ethiopia. Panel-data estimation techniques were employed to analyse the impact of various factors on surveillance reliability. The analysis revealed a significant variation (p < 0.05) in system reliability across different regions and years, with some areas showing lower than expected coverage rates. This study provides insights into the methodological challenges faced by public health surveillance systems in Ethiopia. Further research should focus on implementing standardised data collection methods to improve surveillance accuracy. 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 of Community Health Centre Systems in Nigeria
Community health centre systems in Nigeria have faced challenges over the past two decades, particularly in terms of service delivery and patient outcomes. A longitudinal study design will be employed to collect data from multiple years at both individual patient levels and aggregated centre-level metrics. Multilevel regression models incorporating fixed effects for geographical regions and random effects for health centres will be used to account for potential confounders. Analysis of the multilevel regression model indicates a significant reduction in infectious disease incidence by 15% (95% CI: -20%, -10%) when considering all factors, including patient demographics and centre-specific interventions. The study highlights the importance of community health centres in addressing health inequalities and underscores the need for consistent monitoring and supportive policies to enhance their effectiveness. Policy makers should prioritise funding for infrastructure improvements and training programmes for healthcare workers within these centres. Additionally, regular evaluations and feedback loops with stakeholders are essential for continuous improvement. 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 Assessment of Public Health Surveillance Systems in Senegal Using Quasi-Experimental Designs for Clinical Outcome Measurement,
Public health surveillance systems in Senegal are crucial for monitoring diseases and implementing effective interventions. However, their methodological rigor varies, raising concerns about the accuracy of clinical outcome measurements. A comprehensive search strategy was employed to identify relevant studies from to . Studies were assessed using predefined criteria for methodological rigor and the quality of quasi-experimental design applied in measuring clinical outcomes. The review identified a significant proportion (35%) of studies used complex statistical models, including multivariate regression ($y = β_0 + β_1X$), which enhanced the accuracy of outcome measurements. However, only 20% reported robust standard errors, indicating potential variability in results. The analysis highlights both strengths and limitations in the current methodological practices within Senegal’s public health surveillance systems. Enhanced reporting of statistical methods and uncertainty measures is recommended to improve the credibility and reliability of clinical outcome measurements. public health surveillance, quasi-experimental design, Senegal, clinical outcomes, methodological rigor
Methodological Evaluation of Emergency Care Systems in Nigerian Units
Emergency care units in Nigeria often face challenges related to inadequate staffing, equipment, and procedures, leading to suboptimal clinical outcomes for patients. A randomized controlled trial will be conducted, with eligible units randomly assigned to either intervention or control groups. Data on patient outcomes such as mortality rate and complication frequency will be collected using a validated scoring system. Statistical analysis will employ logistic regression models to assess the impact of interventions on clinical outcomes. Initial data suggests that implementation of standardised protocols led to a significant reduction in complications (p < 0.05), with an average decrease in mortality rates by 20% compared to baseline. The study concludes that implementing standardised emergency care systems can lead to better clinical outcomes and reduced patient complications, warranting further widespread adoption. Nigerian healthcare authorities are recommended to adopt the findings of this study to improve emergency care units across the country. Emergency Care Systems, Clinical Outcomes, Randomized Field Trial, 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) 2000 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Measuring Risk Reduction in District Hospitals Systems, Nigeria
District hospitals in Nigeria face significant challenges in managing patient care risks effectively. A Bayesian hierarchical model was employed to analyse data from multiple districts. This approach accounts for variability across hospitals while estimating the effectiveness of implemented interventions on reducing patient care risks. The analysis revealed that specific risk reduction measures in one district led to a 20% reduction in hospital-acquired infections, indicating the potential impact of targeted healthcare improvements. This study validates the utility of Bayesian hierarchical models for assessing and optimising risk management strategies in district hospitals. Further research is recommended to validate these findings across different regions. Healthcare policymakers should consider implementing the identified risk reduction measures based on this model, alongside continuous monitoring and adaptation of interventions. Bayesian Hierarchical Model, District Hospitals, Risk Reduction, Nigeria, Healthcare Delivery Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.