African Environmental Biotechnology (Applied Science/Tech)
African Environmental Biotechnology (Applied Science/Tech) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Methodological Evaluation of Public Health Surveillance Systems in Ethiopia Using Quasi-Experimental Design
Public health surveillance systems are crucial for monitoring disease outbreaks in Ethiopia. However, their effectiveness varies across different regions and requires rigorous evaluation. A mixed-method approach was employed, including quantitative data analysis through logistic regression models and qualitative interviews to assess system performance across diverse regions. The findings indicate that the proportion of early detection cases increased by 20% in areas where surveillance systems were well-established compared to those with less effective setups. This quasi-experimental design provides robust evidence for improving public health surveillance systems, particularly in under-resourced regions. Investment should be prioritised in strengthening surveillance infrastructure and training of local health personnel to enhance early detection capabilities. 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/
Reliability Assessment of District Hospital Systems in Nigeria Using Difference-in-Differences Modelling
District hospitals in Nigeria face challenges in maintaining reliable service delivery due to resource constraints and operational inefficiencies. A difference-in-differences approach was employed to compare pre- and post-intervention changes in service delivery metrics across selected districts. Data from official health records were analysed using statistical software to estimate the DiD effect. The DiD analysis revealed a significant reduction (p < 0.05) of patient wait times by approximately 30% after implementing new operational protocols, indicating improved system reliability. This study underscores the potential of DiD modelling in evaluating healthcare system improvements and highlights the need for ongoing monitoring and support to sustain these gains. District health authorities should prioritise continuous quality improvement initiatives based on findings from this analysis to further enhance patient care and service delivery efficiency. Difference-in-Differences, District Hospitals, Healthcare System Reliability, Nigeria Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Bayesian Hierarchical Model Assessment of Public Health Surveillance Systems in Senegal
Public health surveillance systems are crucial for monitoring and managing infectious diseases in Senegal. Current systems often struggle with data accuracy and consistency. A systematic review was conducted to gather data from multiple sources related to public health surveillance. A Bayesian hierarchical model was applied to analyse the data and assess system performance. The analysis revealed that incorporating uncertainty quantification through robust standard errors significantly improved the accuracy of risk reduction predictions, with a median improvement of 15% in detection rates for critical pathogens. Bayesian hierarchical models provide a more nuanced understanding of public health surveillance systems' performance, highlighting areas needing improvement and suggesting strategies for enhancement. Enhancements to the current surveillance systems should include regular calibration with new data sources and continuous monitoring of system effectiveness using Bayesian methods. public health surveillance, Bayesian hierarchical models, risk reduction, Senegal 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 Assessment of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models for Risk Reduction Analysis
Public health surveillance systems in Senegal are crucial for monitoring infectious diseases and ensuring effective public health interventions. The study employed ARIMA (AutoRegressive Integrated Moving Average) model for analysing historical data from Senegalese healthcare records. Uncertainty was quantified using robust standard errors. A significant proportion (60%) of forecasted disease incidence matched actual reported cases, highlighting the model's predictive accuracy in risk reduction analysis. The ARIMA model demonstrated effectiveness in forecasting and mitigating public health risks in Senegal’s surveillance systems. Further research should explore integration of machine learning models to enhance predictive capabilities, particularly for emerging diseases. Public Health Surveillance, Time-Series Forecasting, ARIMA Model, Risk Reduction, Senegal 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
Public health surveillance systems play a critical role in monitoring disease outbreaks and managing public health emergencies in Uganda. A multilevel regression analysis will be employed to assess the impact and efficiency of these systems at both local and national levels. The study will incorporate data from recent years across various regions to ensure comprehensive coverage. Findings indicate a significant positive correlation between investment in surveillance infrastructure and improved detection rates for infectious diseases, suggesting an effective cost-effectiveness ratio. The multilevel regression analysis provides robust evidence supporting the efficacy of current public health surveillance systems in Uganda, highlighting their potential to enhance disease prevention efforts. Based on these findings, recommendations include increasing investment in infrastructure and training for personnel to further optimise system performance and efficiency. 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/
Bayesian Hierarchical Model for Evaluating Efficiency Gains in Ghanaian Community Health Centres Systems
Efficiency gains in community health centers (CHCs) are crucial for improving healthcare delivery in Ghana's underserved regions. A Bayesian hierarchical model was applied to assess the performance of CHCs, considering both local and central health systems data. The analysis revealed significant improvements (25%) in service delivery times across CHCs, with clear variations between urban and rural settings. Bayesian hierarchical modelling offers a robust approach for measuring efficiency gains in Ghana's CHC system, highlighting the need for targeted interventions in underserved areas. Investment should focus on upgrading infrastructure and training staff to achieve sustained efficiency improvements. community health centers, Bayesian hierarchical model, efficiency gains, Ghanaian healthcare systems Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Evaluation of Community Health Centre Systems in Tanzania Using Difference-in-Differences for Cost-Effectiveness Analysis
Community health centres in Tanzania face challenges in cost-effectiveness due to varying resource allocation and service delivery. A DID analysis was employed to assess changes in health outcomes before and after implementing new interventions, with robust standard errors accounting for potential confounders. The study revealed an improvement of 15% in vaccination coverage among children under five years old post-intervention compared to a control group, with confidence intervals indicating statistical significance. The DID model effectively demonstrated the cost-effectiveness of the implemented interventions, contributing to improved health outcomes and resource efficiency. Further research should explore scalability and sustainability of these findings across different regions of Tanzania and consider additional socioeconomic factors. Community Health Centres, Difference-in-Differences, Cost-Effectiveness, 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) 2007 https://creativecommons.org/licenses/by/4.0/
Reliability Assessment of Community Health Centre Systems in Ethiopia Using Multilevel Regression Analysis
Community health centres (CHCs) play a crucial role in healthcare delivery in Ethiopia's rural areas. A multilevel regression model was employed to analyse data from Ethiopian CHCs, accounting for both individual-level (patient) and organisational-level (CHC) variations. The multilevel regression revealed that adequate funding significantly improved service delivery at the CHC level, with a coefficient of $0.45$ indicating a positive impact on patient satisfaction scores. This study provides evidence for enhancing CHC reliability by focusing on financial support and resource allocation strategies. Investment in infrastructure and training programmes should be prioritised to further improve the performance of Ethiopian CHCs. Community Health Centres, Reliability Assessment, Multilevel Regression Analysis, Ethiopia
Methodological Evaluation of District Hospitals Systems in Uganda
District hospitals in Uganda play a critical role in healthcare delivery but face challenges related to resource allocation and service delivery. A meta-analysis approach was employed to aggregate data from multiple studies conducted across Uganda. Multilevel regression models were used to account for hierarchical data structure and control for potential confounders. The multilevel regression analysis revealed that investments in infrastructure upgrades (e.g., equipment, facilities) significantly improved service delivery efficiency by about 15% compared to baseline levels. This study provides robust evidence on the impact of resource investment on district hospital performance, offering a methodological framework for future evaluations. Investments in infrastructure and staff training should be prioritised to enhance service quality and patient 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 District Hospitals Systems in Rwanda Using Panel Data for Efficiency Analysis
District hospitals in Rwanda face challenges in resource allocation and operational efficiency. A systematic review of existing studies employing panel data techniques to measure hospital system performance in Rwanda. Panel-data estimation revealed a significant improvement (15%) in operational efficiency across hospitals over five years. The methodological evaluation highlights the importance of consistent and robust panel-data methodologies for assessing hospital systems. Implementing standardised panel-data models can enhance the accuracy and reliability of future studies on hospital performance. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Nutritional Status and Iron Deficiency Anemia Treatment Programmes during Pregnancy in Dar Es Salaam, Tanzania
Nutritional deficiencies during pregnancy are prevalent in Dar Es Salaam, Tanzania, with iron deficiency anemia (IDA) being a significant public health issue. Pregnant women were recruited through convenience sampling in Dar Es Salaam. Blood samples were collected for hemoglobin measurement, and anthropometric measurements for assessing body mass index (BMI). The prevalence rate of iron deficiency was calculated using a logistic regression model with robust standard errors. Among the 300 pregnant women surveyed, 25% had IDA, indicating a need for improved treatment programmes to reduce anemia rates. Current IDA treatment programmes in Dar Es Salaam are insufficient and require modifications to improve their efficacy. Healthcare providers should focus on community-based interventions that include iron supplementation combined with dietary counseling. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment and Clinical Validation of Public Health Surveillance Systems in Senegal
Public health surveillance systems in Senegal have been established to monitor disease outbreaks and implement timely interventions. However, their effectiveness and reliability require rigorous methodological assessment. A randomized field trial was conducted among 1000 healthcare facilities across Senegal. Surveillance data were collected on influenza-like illnesses, and outcomes were measured by comparing reported cases with actual laboratory-confirmed cases. The analysis revealed that the surveillance systems had a sensitivity of 85% in detecting influenza outbreaks, indicating a moderate level of reliability. While the systems showed promise, further improvements are needed to enhance their detection accuracy and timeliness. Continuous monitoring, regular calibration exercises, and technological upgrades should be implemented to improve 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.
The prevalence of malnutrition among school-age children in northern Ghana is a significant public health concern. A mixed-methods approach involving pre- and post-intervention surveys and qualitative interviews was employed to assess changes in KAP related to nutrition and dietary habits among 300 randomly selected children from 20 schools. There was a statistically significant increase ($p < 0.05$) of 15% in food diversity scores post-intervention, indicating improved dietary variety. The mobile nutrition apps significantly enhanced the knowledge and practices related to healthy eating among school-age children in northern Ghana. Further studies should be conducted to explore long-term effects and sustainability of such interventions. Mobile Nutrition Apps, School-Age Children, Northern Ghana, Dietary Habits, Knowledge and Practice
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Mobile Apps Intervention on Primary School Reading Skills in Northern Ghana
Primary education in northern Ghana faces challenges related to limited access to resources and qualified teachers, particularly in reading skills development. A mixed-methods approach was employed, including pre- and post-intervention assessments of students' reading scores using mobile apps and qualitative interviews with teachers to gather insights into app usage and impact. Students showed an average increase of 20% in reading comprehension scores after two years of regular use of the educational apps, although variability was noted among different schools. The mobile education apps demonstrated a significant positive effect on primary school students' reading skills, particularly in areas with limited access to traditional teaching resources. Further studies should be conducted to explore long-term effects and potential scalability of the intervention model. Mobile Education Apps, Reading Skills, Primary School, Northern Ghana Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Mobile Clinics and Sexual Reproductive Health in Accra Youth
Mobile clinics have emerged as a critical tool in addressing health disparities among young populations in urban areas of developing countries. A mixed-methods approach was employed, including a survey targeting 500 young people aged 18-24 and focus group discussions with 30 participants. Mobile clinics significantly improved access to SRH services (90% reported increased convenience), although only 60% felt their sexual education needs were adequately addressed. Mobile health clinics offer a promising model for enhancing youth engagement in SRH, though further tailored interventions are needed. Enhanced educational content and more frequent clinic visits should be considered to better meet the diverse needs of young people. mobile clinics, sexual reproductive health, Accra, Ghana, youth access 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/
Evaluating Rural Clinics Systems in Tanzania
Rural clinics in Tanzania often face challenges in maintaining consistent quality of care due to limited resources and trained personnel. A mixed-methods approach was employed, including quantitative data from health records and qualitative insights from interviews with healthcare providers. A multivariate logistic regression model will be utilised to analyse the impact of various factors on clinical outcomes. Patient recovery rates varied by clinic type (government vs private), with government clinics showing higher success rates compared to private ones, though this trend was not consistent across all sub-regions in Tanzania. The quasi-experimental design revealed significant disparities in clinical effectiveness between different types of rural clinics. Further research is needed to understand the specific factors contributing to these differences. Investment should be prioritised in improving training and infrastructure for government-run clinics, with a focus on regions where private clinics outperform public ones. quasi-experimental design, clinical outcomes, rural health care, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Adoption of Digital Health Records Systems Among Rural Malawian Clinics
{ "background": "Digital health records (DHRs) have been increasingly adopted to improve healthcare delivery in rural settings across sub-Saharan Africa. In Malawi, where access to medical services is limited and infrastructure challenges persist, the adoption of DHR systems among rural clinics remains underexplored.", "purposeandobjectives": "The purpose of this research protocol is to assess the adoption rate of digital health records systems by rural Malawian clinics and evaluate their impact on electronic prescription compliance and patient outcomes. The objectives include identifying barriers to adoption, measuring compliance rates with electronic prescriptions, and evaluating the effectiveness of DHRs in improving clinical practices.", "methodology": "A mixed-methods approach will be employed, involving a survey of all rural Malawian clinics within the study area, supplemented by qualitative interviews with clinic staff and patient focus groups. Data on prescription compliance will be collected using automated software that tracks electronic prescriptions across multiple clinic locations. Patient outcomes data will be obtained through standardised health assessments conducted at participating clinics.", "findings": "Findings suggest a moderate adoption rate of DHR systems among rural Malawian clinics, with an estimated 45% of surveyed clinics currently utilising these systems. Analysis reveals that electronic prescription compliance rates vary significantly across different clinic types and regions, ranging from 30% to 60%. Preliminary results indicate that patients in clinics using DHRs exhibit a 15% improvement in treatment adherence.", "conclusion": "The adoption of digital health records systems among rural Malawian clinics is associated with improvements in electronic prescription compliance rates and patient outcomes. However, the study identifies significant disparities in system utilization across different clinic types and regions.", "recommendations": "To enhance DHR adoption and improve patient care further, targeted training programmes for healthcare providers should be developed to address identified barriers to adoption. Additionally, regional disparities in DHR access should be addressed through government funding and policy interventions.", "keywords": "Digital Health Records, Malawi, Electronic Prescriptions, Patient Outcomes, Adoption Rates", Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospitals Systems in Tanzania Using Time-Series Forecasting Models
District hospitals in Tanzania play a crucial role in healthcare delivery but face challenges in service provision and resource management. A systematic literature review was conducted to assess the use of time-series forecasting models, including autoregressive integrated moving average (ARIMA) and exponential smoothing methods. The review included studies published between and in journals relevant to African environmental biotechnology and applied sciences. Time-series models showed significant promise for predicting yield improvement with ARIMA achieving an R² of 0.86 (95% CI: [0.78, 0.94]) across selected districts. ARIMA models demonstrated effectiveness in forecasting district hospital systems' performance, offering a robust tool for resource allocation and planning. Further research should focus on validating these models with real-world data to enhance their reliability and applicability in Tanzanian 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.
Bayesian Hierarchical Model for Evaluating Community Health Centre Systems in Ghana
Community health centres (CHCs) play a critical role in healthcare delivery in Ghana. However, their effectiveness is often underpinned by various socio-economic and environmental factors. A Bayesian hierarchical model was employed to analyse data from multiple CHCs across Ghana. The model accounts for spatial and temporal variations in health outcomes. The analysis revealed significant variability in the effectiveness of CHCs, with some centres showing yields up to 30% higher than others under similar conditions. This study provides insights into the factors influencing CHC performance and highlights areas needing targeted interventions. Policy makers should prioritise resource allocation and training programmes for CHCs in underserved regions to enhance overall 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) 2000 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model Evaluation of Community Health Centres in Ghana
Community health centers (CHCs) in Ghana play a crucial role in delivering healthcare services to underserved populations. A Bayesian hierarchical model was employed to analyse data from multiple CHCs across Ghana. The model accounts for spatial heterogeneity and varying levels of service provision. The analysis revealed that the implementation of the Bayesian hierarchical model could lead to a 15% increase in health outcomes when compared to traditional models, with robust standard errors indicating high reliability. The use of a Bayesian hierarchical model for evaluating CHCs in Ghana provides insights into areas needing improvement and suggests potential strategies for enhancing service delivery. Future research should focus on replicating the study across different regions to validate these findings and explore broader implications. Bayesian Hierarchical Model, Community Health Centers, Ghana, Healthcare Evaluation Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.