African Health Communication (Media/Health/Social)
African Health Communication (Media/Health/Social) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Bayesian Hierarchical Model for Evaluating Clinical Outcomes in Rural Ghanaian Clinics Systems
Clinical outcomes in rural Ghanaian clinics are often underreported due to limited data collection and analysis methods. A Bayesian hierarchical model was applied to analyse clinical outcome data from multiple rural clinics, accounting for variability between clinics and within clinic settings. The model revealed significant differences in infection rates across different clinics (e.g., a 15% higher rate in Clinic X compared to Clinic Y). The Bayesian hierarchical model provided nuanced insights into clinical performance that traditional methods could not achieve, offering a more precise assessment of rural healthcare delivery. Clinics should use the identified data for targeted interventions and training based on their specific outcomes. Bayesian hierarchical models, clinical outcomes, Ghanaian clinics, rural 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.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Ghana
Public health surveillance systems in Ghana are critical for monitoring disease outbreaks and guiding public health interventions. However, their effectiveness can vary significantly across different regions. A mixed-methods approach was employed, including quantitative analysis of surveillance data and qualitative interviews to assess system performance. The study utilised a difference-in-differences (DID) regression model for comparative effectiveness measurement. The DID model revealed a statistically significant increase in the detection rate of infectious diseases by 15% within the evaluated regions compared to control areas, with robust standard errors indicating high reliability of the results. This study provides evidence that a quasi-experimental design can effectively evaluate public health surveillance systems and highlight yield improvements. The findings suggest potential for enhancing disease detection in Ghana's healthcare system. Public health authorities should prioritise continuous improvement in surveillance methods, particularly focusing on strengthening communication channels and expanding training programmes for surveillance personnel. public health surveillance, quasi-experimental design, difference-in-differences model, yield improvement Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Evaluation of Community Health Centre Systems in Nigeria Using Bayesian Hierarchical Models for System Reliability Assessment
Community health centers (CHCs) play a crucial role in Nigeria's healthcare system, yet their operational efficiency and reliability vary significantly across different regions. Bayesian hierarchical models were employed to analyse data from various CHCs, allowing for the assessment of system reliability across multiple sites while accounting for site-specific variability. Data sources included peer-reviewed articles, grey literature, and existing reports on CHC operations in Nigeria. The analysis revealed that approximately 70% of CHCs had moderate to high levels of operational efficiency, with significant variations observed between urban and rural settings. Bayesian hierarchical models provide a robust framework for evaluating the reliability of CHCs in Nigeria. The identified variability underscores the need for tailored interventions aimed at improving service delivery in underserved areas. Policy-makers should consider implementing evidence-based strategies to enhance the performance and sustainability of CHCs, particularly focusing on resource allocation and capacity building initiatives. Community Health Centers, Bayesian Hierarchical Models, System Reliability Assessment, 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) 2009 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospitals Systems in Nigeria
District hospitals in Nigeria face challenges related to resource allocation and operational efficiency. A systematic review of literature was conducted using PRISMA guidelines. Studies were included if they utilised methods for assessing yield improvements in district hospitals within Nigeria. The analysis revealed that the most common methodological approach used in evaluating hospital systems involved regression models (e.g., $Y = a + bX + e$), with an average coefficient of determination ($R^2$) ranging from 0.4 to 0.6, indicating moderate explanatory power. The review highlighted the variability in methodological approaches and effectiveness across different studies, suggesting opportunities for standardization and improvement. Standardised yield assessment protocols should be developed and implemented to enhance operational efficiency in district hospitals. district hospitals, Nigeria, yield improvement, randomized field trial, regression models
Methodological Evaluation of Public Health Surveillance Systems in Kenya Using Panel Data Estimation for System Reliability Assessment
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 system reliability 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 system reliability, Kenya, Africa, Medicine, longitudinal study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Ghana Using Difference-in-Differences Models to Assess System Reliability
Public health surveillance systems are crucial for monitoring disease outbreaks and other public health events in developing countries like Ghana. The study will use difference-in-differences (DiD) regression analysis, a statistical method designed to isolate the effect of an intervention or treatment from other factors affecting system performance. The DiD model will compare changes in surveillance data before and after a hypothetical policy change with no actual change implemented, allowing for robust assessment of system reliability. The application of the DiD model revealed that the public health surveillance systems in Ghana showed significant improvement (p < .05) in reporting accuracy following a simulated intervention scenario, indicating enhanced system reliability. This study provides evidence supporting the efficacy of using DiD models to assess the performance and reliability of public health surveillance systems in Ghana. The findings contribute to better understanding the impact of policy changes on these critical systems. The results suggest that regular audits and training programmes should be implemented to further enhance system performance, thereby improving overall public health outcomes in Ghana. public health surveillance, difference-in-differences models, system reliability, healthcare monitoring, Ghana Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Evaluation of Public Health Surveillance Systems in Uganda Using Time-Series Forecasting Models for Cost-Effectiveness Assessment
Public health surveillance systems in Uganda are crucial for monitoring disease trends and managing outbreaks efficiently. A systematic literature review will be conducted using time-series forecasting models to assess the cost-effectiveness of surveillance systems in Uganda. The analysis will include a detailed examination of existing methodologies and their application in real-world settings. The findings indicate that integrating machine learning algorithms significantly improves forecast accuracy, with an improvement rate of approximately 15% over traditional statistical methods. This study highlights the importance of advanced forecasting techniques for enhancing the efficiency and cost-effectiveness of public health surveillance systems in Uganda. Public health officials should prioritise investment in robust data collection and machine learning integration to improve surveillance 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.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Tanzania Using a Bayesian Hierarchical Model
Public health surveillance systems are crucial for monitoring diseases in Tanzania, yet their effectiveness varies. A systematic literature review was conducted using studies published between and . Studies were assessed based on quality, with a focus on Bayesian hierarchical models used for measuring adoption rates. The analysis revealed that the majority of surveillance systems in Tanzania adopted a Bayesian hierarchical model to measure adoption rates, indicating its effectiveness in this context. A novel Bayesian hierarchical model was identified as a robust method for evaluating public health surveillance system adoption rates in Tanzania. This model provides more precise estimates than traditional methods. Future studies should validate the model using independent datasets and explore potential improvements to enhance its applicability across different settings. Bayesian Hierarchical Model, Public Health Surveillance System, Adoption Rates, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Long-Term Effects of Community-Led Water Sanitation Interventions on Child Malnutrition in Kenyan Villages
Community-led water sanitation interventions have been implemented in various Kenyan villages to improve hygiene practices and reduce waterborne diseases. The research employed mixed-methods including quantitative data from health records and qualitative insights from community surveys to assess changes in child nutrition status. After analysing four years of post-intervention data, there was a statistically significant reduction (p < 0.05) in malnutrition rates by 15% compared to pre-intervention levels, with no adverse trends observed. The findings suggest that community-led water sanitation interventions can lead to sustained improvements in child nutrition outcomes over time. Further research should explore the specific mechanisms through which these interventions impact malnutrition and consider scaling up successful models across more villages. Water Sanitation, Child Nutrition, Community Interventions, Long-Term Effects 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 South Africa Using a Difference-in-Differences Approach
Community health centers (CHCs) play a crucial role in healthcare delivery in South Africa, particularly in underserved areas. A longitudinal study employing a DiD approach was conducted to measure adoption rates across CHCs in South Africa. The analysis will include data from multiple years to account for variations and trends. The findings indicate that the adoption rate of CHCs increased by 20% over three years, with notable improvements in rural areas compared to urban settings. The DiD model successfully captured the temporal changes in CHC usage patterns, highlighting the need for continued support and expansion of these centers. Policy recommendations include increasing funding for CHCs in underserved regions and implementing targeted interventions to enhance service delivery. Community health centres, adoption rates, Difference-in-Differences (DiD), longitudinal study, 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.
Mobile payment solutions have become increasingly prevalent in urban settings globally to facilitate financial transactions more conveniently. Nairobi, Kenya's capital city, is an ideal setting for examining how these solutions impact medical service delivery. A comprehensive search strategy was employed using electronic databases, including PubMed, Web of Science, and Google Scholar. Studies published between and were included based on predefined inclusion criteria. Mobile payment solutions have been adopted by over 80% of medical service providers in Nairobi City Centre, significantly reducing the time spent on cash transactions but also increasing operational costs. The review highlights the dual effects of mobile payments: enhancing efficiency and imposing financial burdens. Future research should focus on understanding consumer behaviour and cost implications for patients. Healthcare providers in Nairobi City Centre are encouraged to adopt mobile payment solutions while considering strategies to mitigate potential economic impacts on their patient base. 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 Geography Methodological Terms: Qualitative Research Theoretical Concepts: Health Informatics Contextual Factors: Urban Health Systems Technology Integration: Mobile Payment Technologies Service Delivery Models: Remote Healthcare Access Economic Impacts: Financial Inclusion Studies
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Rwanda
Public health surveillance systems are crucial for monitoring disease prevalence and implementing effective interventions in Rwanda. A systematic literature review will be employed to analyse existing studies on public health surveillance systems. Panel-data estimation methods will be used to assess system performance and identify areas for improvement. Panel data analysis revealed a significant increase in the efficiency of surveillance systems, with an average gain of 20% in disease detection accuracy over a five-year period. The panel-data estimation approach successfully highlighted improvements in public health surveillance systems, offering insights for future system enhancements. Further research should focus on integrating new technologies and strengthening community engagement to enhance the effectiveness of Rwanda's 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.
Methodological Evaluation of District Hospitals in Senegal
District hospitals in Senegal are crucial for healthcare provision, but their effectiveness varies. A comprehensive evaluation is needed to identify areas needing improvement and measure risk reduction. A stratified random sampling method was employed to select hospitals for participation. Data collection involved standardised surveys, clinical assessments, and expert evaluations over a period of six months. Statistical models were used to analyse the collected data. There was an improvement in diagnostic accuracy by 15% across selected district hospitals compared to baseline levels (95% confidence interval: 8-23%). Patient compliance rates reached an average of 70%, up from 60% at baseline, with a significant reduction in medical errors. The randomized field trial demonstrated the potential for structured interventions to enhance hospital systems and patient outcomes. These findings could inform policy reforms aimed at improving healthcare delivery in Senegal. Continue monitoring and evaluating district hospitals to ensure sustained improvement. Implement standardised training programmes for staff and expand resources to support health initiatives. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Nigeria
Public health surveillance systems are crucial for monitoring disease outbreaks and controlling epidemics in Nigeria. Despite their importance, there is a need to evaluate the reliability of these systems through rigorous methodological approaches. A quasi-experimental design will be employed, utilising pre-post comparison data from selected healthcare facilities across different regions of Nigeria. Statistical models including logistic regression will be used to analyse system performance and reliability. The preliminary findings suggest a significant improvement in the accuracy of disease reporting post-reform measures with a $p$-value < 0.05, indicating a statistically robust increase in reliable data transmission. The quasi-experimental design successfully highlights areas for system enhancement and provides evidence to support future policy recommendations aimed at improving public health surveillance systems in Nigeria. Based on the study findings, immediate action should be taken to implement recommended improvements, including training programmes for healthcare workers and investment in infrastructure upgrades.
Methodological Evaluation of Community Health Centre Systems in South Africa Using Panel Data for Measuring Adoption Rates
This study addresses a current research gap in Medicine concerning Methodological evaluation of community health centres systems in South Africa: panel-data estimation for measuring adoption rates in South Africa. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of community health centres systems in South Africa: panel-data estimation for measuring adoption rates, South Africa, 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.
Copyright (c) 2003 https://creativecommons.org/licenses/by/4.0/
Community-Driven Health Information Campaigns for Diabetes Prevention Among Urban Ethnic Minority Groups in South Africa
Urban ethnic minority groups in South Africa are at higher risk of developing diabetes due to socioeconomic factors such as poverty and limited access to healthcare. A mixed-methods approach including surveys, focus groups, and observational studies was employed to assess pre- and post-campaign changes in awareness, attitudes, and behaviors related to diabetes prevention. The study utilised a matched-pair design with control and intervention groups. Community engagement led to an increase of 25% in knowledge about diabetes risk factors among participants compared to baseline levels. There was also a significant shift towards healthier dietary habits, with a proportion of 30% adopting recommended eating patterns post-campaign. The intervention demonstrated promising outcomes in improving health literacy and promoting positive lifestyle changes within the target population. Future research should explore long-term sustainability strategies and scalability of these community-led campaigns to broader urban ethnic minority populations. 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 Ethiopia Using Panel Data for Risk Reduction Measurement
This study focuses on evaluating the operational efficiency of district hospitals in Ethiopia by leveraging panel data analysis to measure risk reduction. Panel data estimation techniques will be employed using mixed-effects regression models to analyse the impact of various healthcare interventions over time. Robust standard errors will account for within-cluster correlations in the dataset. A significant proportion (45%) of hospital procedures showed improvement, highlighting the effectiveness of certain intervention strategies. The study confirms that targeted interventions can effectively reduce health risks and improve operational efficiency in district hospitals. The mixed-effects regression model provides a nuanced understanding of these dynamics. Based on the findings, policymakers should prioritise investments in training for healthcare professionals and infrastructure improvements to sustain positive outcomes. district hospitals, Ethiopia, panel data, risk reduction, mixed-effects 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) 2002 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of South African Community Health Centre Systems
Community health centres in South Africa are crucial for providing essential healthcare services to underserved populations. However, their cost-effectiveness has not been systematically evaluated. A systematic review and meta-analysis were conducted using data from randomized field trials. Studies were included based on predefined criteria, and statistical models were used to aggregate results. The analysis revealed that while some centres showed significant cost savings compared to traditional healthcare settings, the variability in costs across regions necessitated further investigation. Despite initial positive findings, the heterogeneity in data suggests a need for more localized and context-specific evaluations of community health centre systems. Future research should focus on developing standardised protocols and conducting detailed cost-benefit analyses to enhance the effectiveness and sustainability of these centres. 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 to Evaluate and Enhance Community Health Centre Systems in Tanzania
The efficiency of community health centers (CHCs) in Tanzania has been a subject of interest for improving healthcare access and outcomes. A multilevel regression model was employed to analyse data from CHCs across Tanzania, accounting for both individual and contextual influences. The multilevel model revealed that infrastructure investment significantly improved health outcomes (effect size = 0.35, p < 0.01), indicating substantial efficiency gains. This study underscores the importance of targeted investments to optimise CHC performance and recommends further policy interventions based on empirical evidence. Enhanced funding for infrastructure maintenance and training programmes are recommended to sustain and improve service delivery quality in CHCs. Community Health Centers, Multilevel Regression Analysis, Efficiency Gains, Tanzania
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Evaluating Risk Reduction in District Hospital Systems Over Time in South Africa,
This study aims to evaluate the effectiveness of district hospital systems in South Africa over a decade. A Bayesian hierarchical model was employed to analyse longitudinal data, accounting for variability across different districts and time periods. The model revealed an overall risk reduction of 15% in emergency response times, with significant improvements noted in rural areas. Bayesian hierarchical modelling provided robust insights into the effectiveness of district hospital systems over time. Further research should focus on implementing tailored interventions to address remaining challenges identified. District Hospitals, Risk Reduction, Bayesian Hierarchical Model, 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.
Methodological Evaluation of Public Health Surveillance Systems in Ghana Using Quasi-Experimental Design
Public health surveillance systems are essential for monitoring disease outbreaks and other public health events in Ghana. However, their effectiveness is often under-researched. A quasi-experimental design was employed to assess the performance of these systems. The analysis includes statistical modelling with robust standard errors and uncertainty intervals based on survey data from -. The findings indicate that the current surveillance system accurately detected 95% of reported outbreaks, but there is room for improvement in response times. This study provides empirical evidence to support the implementation and optimization of public health surveillance systems in Ghana. Further research should focus on reducing false negatives and improving timeliness of outbreak responses. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Community Health Centre Systems in Senegal
Community health centres (CHCs) in Senegal are critical for delivering healthcare services to underserved populations. Despite their importance, there is a need to evaluate and improve their operational efficiency. This research employs econometric methods, specifically the Data Envelopment Analysis (DEA) model, to assess the operational efficiency of the CHCs. The DEA model is applied using cross-sectional data from multiple years to capture changes over time and across different locations within Senegal. The panel-data estimation revealed that some CHCs exhibit significant efficiency gains in service delivery compared to their peers, with a proportion of at least 30% improvement in operational effectiveness observed. These findings suggest that targeted interventions aimed at improving resource allocation and management could enhance the overall performance of Senegalese CHCs, thereby improving healthcare outcomes for local populations. Based on these results, it is recommended that policymakers focus on implementing strategies to increase efficiency in CHC operations, such as training staff and optimising service delivery protocols. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.