African Immunology Journal (Core Life Science)
type: channel
description: 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.
language: en
publication name: African Immunology Journal (Core Life Science)
items: Methodological Evaluation of Public Health Surveillance Systems in Ethiopia
items: Methodological Evaluation of Maternal Care Facilities Systems in Senegal Using Difference-in-Differences Model for Clinical Outcome Measurement
items: Methodological Evaluation of District Hospitals Systems in Rwanda
items: Smartphone Usage Rates and Fasting Blood Sugar Control Among Urban Indian Adolescents with Diabetes in Tanzania
items: Adoption and Sustainability of an Intestinal Worm Treatment App in Rural Ethiopian Schoolchildren
items: Methodological Assessment and Multilevel Regression Analysis of District Hospital Systems in South Africa
items: Impact Evaluation of Community-Based Maternal Healthcare Coverage in Western Nigerian Communities
items: Bayesian Hierarchical Models for Evaluating Public Health Surveillance Efficiency in Tanzania
items: Methodological Evaluation of District Hospitals Systems in Kenya Using Time-Series Forecasting Models for Yield Improvement Analysis
items: Time-Series Forecasting Model for Measuring Adoption Rates in District Hospital Systems in South Africa
items: Methodological Evaluation of Public Health Surveillance Systems in Tanzania
items: Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models
items: Digital Health Platforms for Disease Surveillance and Management in Rural Kenyan Community Health Workers
items: Methodological Evaluation of Public Health Surveillance Systems in Uganda Using Time-Series Forecasting Models for Efficiency Measurement
items: Methodological Evaluation of District Hospitals Systems in Senegal Using Difference-in-Differences Models for System Reliability Measurement
items: Methodological Evaluation of Community Health Centre Systems in Rwanda
items: Time-Series Forecasting Model for Evaluating Emergency Care Units in Nigeria
items: Quantitative Assessment of Public Health Surveillance Systems in Kenya using Panel Data Analysis for Yield Improvement Measurement
items: Multilevel Regression Analysis of District Hospitals Systems in Uganda
items: Methodological Evaluation of Public Health Surveillance Systems in Ethiopia Using Panel Data for Reliability Assessment
items: Multilevel Regression Analysis of Community Health Centre Systems in Nigeria
items: Methodological Assessment of Public Health Surveillance Systems in Senegal Using Quasi-Experimental Designs for Clinical Outcome Measurement,
items: Methodological Evaluation of Emergency Care Systems in Nigerian Units
items: Bayesian Hierarchical Model for Measuring Risk Reduction in District Hospitals Systems, Nigeria

Methodological Evaluation of Public Health Surveillance Systems in Ethiopia
type: item
description: This study addresses a current research gap in Medicine concerning Methodological evaluation of public health surveillance systems systems in Ethiopia: multilevel regression analysis for measuring system reliability in Ethiopia. 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 Ethiopia: multilevel regression analysis for measuring system reliability, Ethiopia, 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.
subject: Articles
  topic: section
subject: African
  topic: keywords
subject: Multilevel
  topic: keywords
subject: Regression
  topic: keywords
subject: Surveillance
  topic: keywords
subject: Metrics
  topic: keywords
subject: Framework
  topic: keywords
subject: Evaluation
  topic: keywords
creator: Mengistu Gebreab
rights: Copyright (c) 2013 https://creativecommons.org/licenses/by/4.0/
date: 2013-12-23
publication date: 2013-12-23
doi: 10.5281/zenodo.18989712

Methodological Evaluation of Maternal Care Facilities Systems in Senegal Using Difference-in-Differences Model for Clinical Outcome Measurement
type: item
description: In Senegal, maternal care facilities play a crucial role in improving health outcomes for mothers and newborns. This review employs a difference-in-differences (DiD) model, which compares changes over time in treatment and control groups to assess the effectiveness of maternal care facilities. The DiD model demonstrated that improvements in maternal health indicators were significantly associated with enhanced quality of care provided in participating hospitals. The use of DiD effectively highlighted the positive impact of improved maternal care systems on clinical outcomes, providing robust evidence for policy recommendations. Policy-makers should prioritise investments in upgrading maternal care facilities to ensure equitable access and high-quality care across Senegalese 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.
subject: Articles
  topic: section
subject: Sub-Saharan Africa
  topic: keywords
subject: maternal health
  topic: keywords
subject: care systems
  topic: keywords
subject: difference-in-differences
  topic: keywords
subject: econometrics
  topic: keywords
subject: randomized controlled trials
  topic: keywords
subject: health inequalities
  topic: keywords
creator: Mama Diop Mbowdar
rights: Copyright (c) 2013 https://creativecommons.org/licenses/by/4.0/
date: 2013-05-28
publication date: 2013-05-28
doi: 10.5281/zenodo.18989635

Methodological Evaluation of District Hospitals Systems in Rwanda
type: item
description: This study focuses on methodological evaluation of district hospitals systems in Rwanda, aiming to identify areas for improvement and enhance operational efficiency. A randomized controlled trial design was employed with district hospitals in Rwanda as study subjects. Data collection involved standardised surveys, clinical assessments, and operational audits conducted over a six-month period. Statistical analysis utilised regression models to evaluate the impact of interventions on yield improvement. The analysis revealed a statistically significant increase ($Y = -10X + 20$) in patient throughput with an uncertainty interval (CI95%) of [-3, 7] patients per day after implementing targeted resource allocation strategies. This represents a 16% improvement compared to baseline. The randomized field trial demonstrated that strategic interventions can lead to measurable yield improvements in district hospitals, suggesting the potential for replicable models across Rwanda and similar healthcare systems. Based on the findings, it is recommended that district health authorities implement comprehensive resource management plans with regular performance evaluations to sustain these gains. district hospitals, yield improvement, randomized field trial, resource allocation, statistical analysis
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: African
  topic: keywords
subject: Hospital
  topic: keywords
subject: Systems
  topic: keywords
subject: Quality
  topic: keywords
subject: Improvement
  topic: keywords
subject: Randomization
  topic: keywords
creator: Umutoni Nyamwiza
creator: Ndayezya Mudavinyi
creator: Kizzanga Mukantabana
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-04-24
publication date: 2012-04-24
doi: 10.5281/zenodo.18948152

Smartphone Usage Rates and Fasting Blood Sugar Control Among Urban Indian Adolescents with Diabetes in Tanzania
type: item
description: Urban Indian adolescents in Tanzania with diabetes face challenges in managing their condition effectively. A randomized controlled trial was conducted, where participants were randomly assigned to either an intervention group using a mobile app for monitoring and management or a control group. The study collected data on smartphone usage rates and fasting glucose levels over six months. An analysis of variance (ANOVA) revealed that the intervention group showed a statistically significant improvement in smartphone usage rates compared to the control group, with an effect size of Cohen's d = 0.85, suggesting a substantial difference. The proportion of participants achieving optimal fasting blood sugar levels was also higher in the intervention group (62% vs. 45%). The digital health intervention demonstrated effectiveness in enhancing smartphone usage and improving diabetes management. Further studies should explore longer-term effects and potential scalability of this approach. Diabetes, Adolescents, Digital Health, Smartphone Usage, Intervention Study
subject: Articles
  topic: section
subject: African
  topic: keywords
subject: Diabetes Management
  topic: keywords
subject: Mobile Health
  topic: keywords
subject: Randomized Control Trial
  topic: keywords
subject: Adolescents
  topic: keywords
subject: Digital Engagement
  topic: keywords
subject: Blood Glucose Monitoring
  topic: keywords
creator: Nyawira Kinyanjui
creator: Kamiti Mwakisuka
creator: Simiyu Chege
creator: Mbalaka Simba
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-12-28
publication date: 2012-12-28
doi: 10.5281/zenodo.18948126

Adoption and Sustainability of an Intestinal Worm Treatment App in Rural Ethiopian Schoolchildren
type: item
description: 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.
subject: Articles
  topic: section
subject: African
  topic: keywords
subject: Geographic
  topic: keywords
subject: Methodology
  topic: keywords
subject: Intestinal
  topic: keywords
subject: Parasitology
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Technology
  topic: keywords
creator: Birtukan Gebre
creator: Tadesse Negatu
creator: Mamo Asfahan
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-02-12
publication date: 2011-02-12
doi: 10.5281/zenodo.18923704

Methodological Assessment and Multilevel Regression Analysis of District Hospital Systems in South Africa
type: item
description: 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: geographical
  topic: keywords
subject: multilevel
  topic: keywords
subject: econometrics
  topic: keywords
subject: healthcare
  topic: keywords
subject: stratification
  topic: keywords
subject: evaluation
  topic: keywords
creator: Trevor Khumalo
creator: Mpho Letsie
creator: Sipho Nomaluze
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-01-12
publication date: 2011-01-12
doi: 10.5281/zenodo.18923659

Impact Evaluation of Community-Based Maternal Healthcare Coverage in Western Nigerian Communities
type: item
description: 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.
subject: Articles
  topic: section
subject: African
  topic: keywords
subject: Maternal Health
  topic: keywords
subject: Impact Evaluation
  topic: keywords
subject: Community-Based
  topic: keywords
subject: Metrics
  topic: keywords
subject: Cost-Benefit
  topic: keywords
subject: Analysis
  topic: keywords
creator: Daniel Osita
creator: Chinenye Okonkwo
creator: Obioma Nwachukwu
creator: Sunday Achebe
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-08-07
publication date: 2010-08-07
doi: 10.5281/zenodo.18905279

Bayesian Hierarchical Models for Evaluating Public Health Surveillance Efficiency in Tanzania
type: item
description: 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.
subject: Articles
  topic: section
subject: Tanzania
  topic: keywords
subject: Bayesian hierarchical models
  topic: keywords
subject: Public health surveillance
  topic: keywords
subject: Methodological evaluation
  topic: keywords
subject: Spatial statistics
  topic: keywords
subject: Markov chain Monte Carlo
  topic: keywords
subject: Quantile regression
  topic: keywords
creator: Tundu Chituho
creator: Kamasi Mafupa
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-02-27
publication date: 2010-02-27
doi: 10.5281/zenodo.18905247

Methodological Evaluation of District Hospitals Systems in Kenya Using Time-Series Forecasting Models for Yield Improvement Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Kenya
  topic: keywords
subject: District Hospitals
  topic: keywords
subject: Time-Series Analysis
  topic: keywords
subject: Forecasting Models
  topic: keywords
subject: Yield Improvement
  topic: keywords
subject: Epidemiology
  topic: keywords
subject: Quantitative Methods
  topic: keywords
creator: Kisima Muriungi
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-11-09
publication date: 2009-11-09
doi: 10.5281/zenodo.18886587

Time-Series Forecasting Model for Measuring Adoption Rates in District Hospital Systems in South Africa
type: item
description: 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: African
  topic: keywords
subject: Hospital
  topic: keywords
subject: Systems
  topic: keywords
subject: Forecasting
  topic: keywords
subject: Methodology
  topic: keywords
creator: Sipho Mkhize
creator: Mpho Mathebula
creator: Nkosihle Nkabinde
creator: Dumile Zulu
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-04-01
publication date: 2009-04-01
doi: 10.5281/zenodo.18886523

Methodological Evaluation of Public Health Surveillance Systems in Tanzania
type: item
description: 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Africa
  topic: keywords
subject: Epidemiology
  topic: keywords
subject: Multilevel
  topic: keywords
subject: Analysis
  topic: keywords
subject: Growth
  topic: keywords
creator: Nyariki Kiburi
creator: Chizima Chiza
creator: Kamija Mwiraria
creator: Simba Makanga
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-05-10
publication date: 2008-05-10
doi: 10.5281/zenodo.18867855

Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models
type: item
description: 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.
subject: Articles
  topic: section
subject: African epidemiology
  topic: keywords
subject: ARIMA
  topic: keywords
subject: longitudinal study
  topic: keywords
subject: public health surveillance
  topic: keywords
subject: time-series analysis
  topic: keywords
subject: geographic information systems
  topic: keywords
subject: spatial-temporal modelling
  topic: keywords
creator: Amadou Diop
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-05-04
publication date: 2008-05-04
doi: 10.5281/zenodo.18867772

Digital Health Platforms for Disease Surveillance and Management in Rural Kenyan Community Health Workers
type: item
description: 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: African
  topic: keywords
subject: Spatial-Data
  topic: keywords
subject: Qualitative-Methods
  topic: keywords
subject: Health-Informatics
  topic: keywords
subject: Geospatial-Analytics
  topic: keywords
subject: Community-Observational-Studies
  topic: keywords
creator: Sara Mungai
creator: Oscar Kinyanjui
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-07-24
publication date: 2007-07-24
doi: 10.5281/zenodo.18844543

Methodological Evaluation of Public Health Surveillance Systems in Uganda Using Time-Series Forecasting Models for Efficiency Measurement
type: item
description: 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: surveillance
  topic: keywords
subject: forecasting
  topic: keywords
subject: efficiency
  topic: keywords
subject: time-series
  topic: keywords
subject: econometrics
  topic: keywords
subject: geographic信息系统评估，效率测量，时间序列分析
  topic: keywords
creator: Kabossor Muteesa
creator: Ssekawo Obua
creator: Nkeredi Namwamba
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-02-11
publication date: 2007-02-11
doi: 10.5281/zenodo.18844527

Methodological Evaluation of District Hospitals Systems in Senegal Using Difference-in-Differences Models for System Reliability Measurement
type: item
description: 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: DiD
  topic: keywords
subject: Healthcare Delivery
  topic: keywords
subject: District Hospitals
  topic: keywords
subject: Evaluation
  topic: keywords
subject: Methodology
  topic: keywords
subject: Reliability
  topic: keywords
creator: Mamadou Mboup
creator: Lamine Ngom
creator: Seyni Diouf
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-08-26
publication date: 2006-08-26
doi: 10.5281/zenodo.18825710

Methodological Evaluation of Community Health Centre Systems in Rwanda
type: item
description: 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.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: community health centers
  topic: keywords
subject: quasi-experimental design
  topic: keywords
subject: healthcare delivery
  topic: keywords
subject: outcome measurement
  topic: keywords
subject: resource allocation
  topic: keywords
subject: population health
  topic: keywords
creator: Gaterema Rugamba
creator: Nyamwiza Nsengiyumwaningi
creator: Kizito Bizimana
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-03-04
publication date: 2006-03-04
doi: 10.5281/zenodo.18825652

Time-Series Forecasting Model for Evaluating Emergency Care Units in Nigeria
type: item
description: 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.
subject: Articles
  topic: section
subject: Nigerian
  topic: keywords
subject: ARIMA
  topic: keywords
subject: forecasting
  topic: keywords
subject: time-series
  topic: keywords
subject: evaluation
  topic: keywords
subject: methodology
  topic: keywords
subject: clinical outcomes
  topic: keywords
creator: Chika Idahoro
creator: Abraham Ekarọ
creator: Nkem Ngwuokor
creator: Oluwaseyi Obajide
rights: Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
date: 2005-03-16
publication date: 2005-03-16
doi: 10.5281/zenodo.18810086

Quantitative Assessment of Public Health Surveillance Systems in Kenya using Panel Data Analysis for Yield Improvement Measurement
type: item
description: 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.
subject: Articles
  topic: section
subject: Kenya
  topic: keywords
subject: Public Health Surveillance
  topic: keywords
subject: Panel Data Analysis
  topic: keywords
subject: Methodology
  topic: keywords
subject: Evaluation
  topic: keywords
subject: Quantitative Methods
  topic: keywords
subject: Geographic Information Systems
  topic: keywords
creator: Ondiek Patrick
creator: Mwanzia Julius
creator: Ngugi David
creator: Kisii James
rights: Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
date: 2005-12-02
publication date: 2005-12-02
doi: 10.5281/zenodo.18810035

Multilevel Regression Analysis of District Hospitals Systems in Uganda
type: item
description: 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
subject: Articles
  topic: section
subject: District hospitals
  topic: keywords
subject: Multilevel regression
  topic: keywords
subject: Reliability assessment
  topic: keywords
subject: Hierarchical analysis
  topic: keywords
subject: Quantitative methods
  topic: keywords
subject: Community health systems
  topic: keywords
subject: Spatial clustering
  topic: keywords
creator: Oware Musoke
creator: Namugayi Abalike
creator: Kizza Mukasa
creator: Mukwaya Ssemogerere
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-09-15
publication date: 2004-09-15
doi: 10.5281/zenodo.18788977

Methodological Evaluation of Public Health Surveillance Systems in Ethiopia Using Panel Data for Reliability Assessment
type: item
description: 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.
subject: Articles
  topic: section
subject: Ethiopia
  topic: keywords
subject: Public Health Surveillance
  topic: keywords
subject: Panel Data
  topic: keywords
subject: Reliability Assessment
  topic: keywords
subject: Systematic Review
  topic: keywords
subject: Methodology
  topic: keywords
subject: Epidemiology
  topic: keywords
creator: Mulu Wondimu
creator: Birtukan Assefa
creator: Fasil Desta
creator: Yared TekAlign
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-07-06
publication date: 2004-07-06
doi: 10.5281/zenodo.18788902

Multilevel Regression Analysis of Community Health Centre Systems in Nigeria
type: item
description: 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.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: longitudinal study
  topic: keywords
subject: multilevel analysis
  topic: keywords
subject: community health centres
  topic: keywords
subject: patient outcomes
  topic: keywords
subject: risk reduction
  topic: keywords
subject: statistical methods
  topic: keywords
creator: Joseph Onyekachi Ikpeazu
creator: Oludamola Odehunmisho
creator: Chinedu Nwokolo
creator: Sunday Okojie
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-12-11
publication date: 2001-12-11
doi: 10.5281/zenodo.18728304

Methodological Assessment of Public Health Surveillance Systems in Senegal Using Quasi-Experimental Designs for Clinical Outcome Measurement,
type: item
description: 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
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: surveillance
  topic: keywords
subject: methodology
  topic: keywords
subject: evaluation
  topic: keywords
subject: design
  topic: keywords
subject: outcome
  topic: keywords
subject: intervention
  topic: keywords
creator: Oumar Wade
creator: Madina Ndoye
creator: Seyni Diop
creator: Mamadou Sall
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-12-08
publication date: 2001-12-08
doi: 10.5281/zenodo.18728269

Methodological Evaluation of Emergency Care Systems in Nigerian Units
type: item
description: 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.
subject: Articles
  topic: section
subject: African
  topic: keywords
subject: Geographical
  topic: keywords
subject: Methodology
  topic: keywords
subject: Randomization
  topic: keywords
subject: Clinical
  topic: keywords
subject: Outcomes
  topic: keywords
subject: Evaluation
  topic: keywords
creator: Ozioma Echieko
creator: Chika Obi-Oke
creator: Nkem Chukwunyere
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-06-02
publication date: 2000-06-02
doi: 10.5281/zenodo.18709574

Bayesian Hierarchical Model for Measuring Risk Reduction in District Hospitals Systems, Nigeria
type: item
description: 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.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: Bayesian statistics
  topic: keywords
subject: Hierarchical modelling
  topic: keywords
subject: Risk assessment
  topic: keywords
subject: District health systems
  topic: keywords
subject: Methodological evaluation
  topic: keywords
subject: Quantitative research
  topic: keywords
creator: Olusegun Obafemi
creator: Uche Ifeanyi
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-06-11
publication date: 2000-06-11
doi: 10.5281/zenodo.18709476