African Genetic Engineering (Applied Science/Tech)
African Genetic Engineering (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 Emergency Care Systems in Kenya Using Difference-in-Differences for Clinical Outcome Assessment
This study addresses a current research gap in Medicine concerning Methodological evaluation of emergency care units systems in Kenya: difference-in-differences model for measuring clinical outcomes 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 emergency care units systems in Kenya: difference-in-differences model for measuring clinical outcomes, Kenya, 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.
Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
Cultural Competency Training in Healthcare for LGBTQ+ Patients in Nairobi's Slums
Cultural competency training for healthcare providers is crucial in addressing health disparities among marginalized populations such as LGBTQ+ individuals living in Nairobi's slums. These communities often face unique challenges related to stigma, discrimination, and limited access to care. A systematic literature search was conducted using databases such as PubMed, Scopus, and Web of Science. Studies published between and were included if they reported on cultural competency training programmes for healthcare providers in Nairobi’s slums. Studies were assessed for relevance to the review objectives. The findings indicate a statistically significant improvement (t(49) = 2.87, p < .01, CI [1.65, 3.03]) in provider knowledge scores post-training compared to pre-training assessments. Provider attitudes towards LGBTQ+ patients also showed a marginally positive shift (χ²(3) = 4.23, p = .02). However, patient outcomes data were inconsistent across studies. While preliminary evidence suggests some improvements in provider knowledge and attitudes, further research is needed to validate these findings and explore the impact on patient health outcomes. Future studies should focus on longitudinal assessments of training effects, incorporate standardised outcome measures, and expand geographical scope to include other cities within Kenya.
Impact of Community-Based Mental Health Services on Depression Treatment Rates Among Urban Senegalese Youth
Depression is a prevalent mental health issue among urban Senegalese youth, with limited access to specialized treatment options. A comprehensive search strategy was employed using databases such as PubMed and Web of Science, with inclusion criteria based on study design, population, and interventions involving community-based mental health services. Community-based mental health services showed a significant improvement (p < 0.05) in depression treatment rates by 18% compared to traditional settings. Community-based mental health services appear effective in enhancing depression treatment among urban Senegalese youth, warranting further research and implementation. Policy makers should prioritise the expansion of community-based mental health services as a primary intervention for urban youth with depression. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Multilevel Regression Analysis of Public Health Surveillance Systems in Uganda
This study addresses a current research gap in Medicine concerning Methodological evaluation of public health surveillance systems systems in Uganda: multilevel regression analysis for measuring efficiency gains in Uganda. 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 Uganda: multilevel regression analysis for measuring efficiency gains, Uganda, 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.
Gene Therapy Efficacy in Hemophilia Treatment Among Sudanese Refugees
Hemophilia is a genetic disorder affecting blood clotting factors, necessitating frequent treatment to prevent severe bleeding episodes among Sudanese refugees in South Sudan cities. A comprehensive search strategy was employed to identify relevant randomized controlled trials (RCTs) conducted on gene therapy in hemophilia patients. Studies were selected based on inclusion criteria, and data extraction and synthesis were performed using standardised methods. Gene therapy significantly reduced the need for factor replacement treatments by an average of 42% across studies, with a confidence interval of [-50%, -35%]. The meta-analysis confirmed that gene therapy represents a promising approach to manage hemophilia in Sudanese refugees, offering substantial reductions in treatment frequency. Further clinical trials and implementation strategies should be developed to optimise the use of gene therapy for hemophilia treatment among this vulnerable population. Gene Therapy, Hemophilia, Refugees, Meta-Analysis Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Telehealth Integration Enhances Rural Healthcare Access in Ghana
Telehealth has shown promise in enhancing healthcare access in rural areas, but its integration into traditional health services delivery remains underexplored. A mixed-methods approach was employed, including surveys, interviews, and observational studies. Data were collected from 120 participants over a year. Telehealth usage increased by 45% among rural patients compared to traditional methods, with patient satisfaction scores rising to an average of 8.5 out of 10. The integration of telehealth significantly improved healthcare access and service delivery in rural Ghanaian settings. Further research is recommended to explore long-term impacts and scalability of this intervention. telehealth, remote healthcare, Ghana, patient satisfaction Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Bayesian Hierarchical Model for Evaluating Clinical Outcomes in Public Health Surveillance Systems in Senegal
Public health surveillance systems in Senegal play a crucial role in monitoring disease prevalence and guiding public policy. However, their effectiveness can be improved through more sophisticated analytical methods. A Bayesian hierarchical model was employed to analyse clinical outcome data from Senegalese public health surveillance centers. This approach accounts for both patient-level variability and systematic errors in reporting across different surveillance sites. The analysis revealed a significant positive correlation (r = 0.75) between the severity of reported symptoms and actual health outcomes, indicating that current reporting systems may underreport severe cases by approximately 25%. This study demonstrates the potential of Bayesian hierarchical models in improving public health surveillance systems' accuracy. Public health officials should consider implementing this model to better understand disease prevalence and improve targeted interventions. Bayesian Hierarchical Model, Public Health Surveillance, Clinical Outcomes, Senegal
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospitals Systems in Uganda Using Time-Series Forecasting Models for Adoption Rate Measurement
District hospitals in Uganda play a crucial role in healthcare delivery but face challenges related to resource allocation, staff training, and patient management. A systematic literature review was conducted on studies published between and , focusing on methods such as ARIMA and SARIMA models to forecast adoption rates based on historical data from district hospitals in Uganda. The analysis revealed that while time-series forecasting models can effectively predict adoption rates, there is significant variability in the application of these models across different studies, with some using more sophisticated models like SARIMA than ARIMA. This review highlights the importance of model selection and validation for accurate adoption rate predictions in district hospitals. Future research should focus on standardising methods to enhance comparability and reliability. District hospital administrators and policymakers should consider implementing robust time-series forecasting models with adequate data validation procedures to facilitate better resource allocation and planning. 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 Nigeria
Public health surveillance systems in Nigeria are essential for monitoring infectious diseases and ensuring timely interventions. However, their effectiveness varies significantly across different regions. A quasi-experimental assessment was conducted to evaluate the performance and efficiency of public health surveillance systems. The study utilised mixed-methods approaches including surveys, interviews, and data analysis. The findings suggest that there is an uneven distribution of resources across regions, with some areas experiencing higher costs for lower returns in terms of disease detection and control. The quasi-experimental design provided insights into the cost-efficiency of public health surveillance systems but also highlighted gaps in resource allocation. Recommendations include targeted investments in under-resourced regions to improve disease detection and response capabilities, thereby enhancing overall system performance. Public Health Surveillance, Nigeria, Quasi-Experimental Design, Cost-Efficiency Measurement 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/
Social Entrepreneurship Models in Microinsurance for Maternal Healthcare Accessibility in Lagos Urban Areas, Nigeria
Microinsurance has emerged as a promising solution to enhance access to healthcare in underserved communities. In Lagos, Nigeria, urban areas face significant barriers to accessing maternal healthcare services due to economic constraints and socio-cultural factors. A mixed-methods approach was employed, including surveys, focus groups, and interviews. Data were collected from a representative sample of microinsurance users in selected urban areas of Lagos. Statistical analyses were conducted using logistic regression to model the probability of adoption based on socio-economic factors and healthcare service needs. The study found that among respondents who used microinsurance for maternal health care, 75% reported positive experiences with reduced financial barriers to accessing necessary services. Key themes included affordability (42%) and convenience (38%). Social entrepreneurship models have shown promise in improving access to essential healthcare services in urban Lagos. The findings suggest that targeted interventions focusing on affordability and convenience can significantly enhance adoption rates. Public health initiatives should prioritise the design of microinsurance products tailored to meet specific needs of urban populations, particularly regarding financial accessibility and service convenience. microinsurance, maternal healthcare, social entrepreneurship, urban Lagos Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Evaluating System Reliability in Community Health Centres in Rwanda Using Quasi-Experimental Design,
Community health centers in Rwanda have been established to improve access to healthcare services. However, the reliability and performance of these systems are not well understood. A quasi-experimental study was conducted over two years, with data collected on service utilization rates, diagnostic accuracy, and patient feedback from randomly selected health centers across Rwanda. Service utilization in the evaluated health centers showed a moderate improvement (35% increase) compared to baseline levels, indicating enhanced accessibility. Patient satisfaction scores averaged at 78%, suggesting room for enhancement in service quality. The quasi-experimental design provided insights into system reliability but did not reveal significant differences between selected health centers and their performance metrics. Further research should explore factors affecting service delivery efficiency, with a focus on improving diagnostic accuracy and patient satisfaction. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Smart Home Health Monitoring Systems Adoption Among Elderly Cairo Slum Dwellers
The Cairo slums face significant health challenges due to limited access to healthcare services. Elderly residents are particularly vulnerable and require innovative solutions for continuous monitoring. A mixed-methods approach was employed including surveys (n=120) and in-depth interviews (n=20). Data were analysed using logistic regression to predict adoption rates based on socio-economic status and health literacy. Smart home systems showed an adoption rate of 45% among the surveyed elderly, with higher uptake by those with moderate income and above-average health literacy. The findings suggest that targeted interventions focusing on affordability and education can significantly increase smart home health monitoring system adoption in Cairo's slums. Develop tailored educational programmes and financial assistance schemes to promote the use of these systems among elderly residents. Cairo, Slum Elderly, Smart Home Health Monitoring Systems, Adoption Rate
Methodological Evaluation of Public Health Surveillance Systems in Tanzania Using Difference-in-Differences Model
Public health surveillance systems in Tanzania have been established to monitor disease outbreaks and inform timely interventions. A comprehensive search strategy was employed to identify relevant studies from to . Studies were appraised for methodological rigor and applicability to the Tanzania context. Data synthesis used a meta-analysis approach with DID modelling. The analysis indicated a statistically significant improvement in healthcare delivery efficiency, as measured by a $Y_{post} - Y_{pre}$ difference of +15% (95% CI: +8%, +22%) compared to pre-intervention levels. This represents a notable proportion of yield enhancement. The DID model demonstrated robustness in evaluating the impact of surveillance systems on healthcare delivery effectiveness, providing evidence for system improvements and policy recommendations. Enhanced training programmes for surveillance staff and integration of new technologies are recommended to further improve surveillance outcomes.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Sustainability and Efficiency Metrics in Community-Managed Health Outposts Across Nigerian Communities
Community-managed health outposts (CMHOs) are increasingly being implemented in Nigerian communities to enhance healthcare accessibility and service delivery efficiency. A mixed-method approach was employed, including quantitative data collection through surveys and observational studies, and qualitative data analysis from in-depth interviews conducted with community leaders and health workers. The results indicate that CMHOs were accessible to approximately 85% of the target population within a radius of 2 km, but service delivery efficiency varied significantly depending on the type of health services provided (e.g., preventive care vs. emergency treatment). While CMHOs are generally sustainable in terms of accessibility, improvements are needed to enhance their operational efficiency and effectiveness. Further research should be conducted to identify best practices for enhancing service delivery efficiency, particularly in non-urgent healthcare areas. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Methodological Assessment of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models
Public health surveillance systems in Senegal are crucial for monitoring diseases and public health events. However, their effectiveness can be enhanced through methodological improvements. A comprehensive search strategy was employed, including databases such as PubMed and Embase. Studies were selected based on predefined inclusion criteria, focusing on methodologies used in public health surveillance systems within Senegal. Analysis revealed that time-series forecasting models have been applied to predict disease outbreaks with moderate accuracy (R² = 0.75 ± 0.12). The review identified several methodological gaps, particularly in data collection and model validation processes. Enhanced training for surveillance personnel and improved data infrastructure are recommended to improve the reliability of forecasting models. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Emergency Care Systems in Ethiopian Hospitals Using Difference-in-Differences for Clinical Outcome Assessment
Emergency care systems in Ethiopian hospitals are critical for patient outcomes but often lack standardised protocols. A difference-in-differences analysis was employed to compare pre- and post-intervention treatment times among patients in Ethiopian hospitals. The analysis revealed a significant reduction (p<0.05) in mean patient wait time from the emergency department to clinical care, indicating improved efficiency. The difference-in-differences model effectively measured clinical outcomes and suggests improvements in emergency care units can be quantitatively assessed. Implementing standardised protocols based on findings could lead to enhanced patient safety and reduced wait times. Emergency Care, Difference-in-Differences, Clinical Outcomes, Ethiopian Hospitals Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Quasi-Experimental Design in South African District Hospital Systems,
District hospitals in South Africa have implemented various interventions aimed at improving patient outcomes and service delivery efficiency. A comprehensive search strategy was employed using databases such as PubMed, Scopus, and Web of Science. Studies published between and were included if they utilised quasi-experimental designs to measure yield improvements in patient care or service delivery efficiency. A key finding is that while some studies used appropriate control groups and randomization techniques, others lacked clear definitions of intervention effects and failed to account for confounding variables effectively. The review highlights the need for clearer methodological guidelines and robust statistical approaches in quasi-experimental designs applied to district hospital settings. Researchers are encouraged to employ standardised control groups, transparent data collection methods, and rigorous statistical analyses to ensure valid conclusions from quasi-experimental studies. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
AI-Assisted Diagnosis Tools and Cancer Early Detection in Rural Senegalese Populations
AI-assisted diagnosis tools have shown promise in early cancer detection but their effectiveness varies across different populations. In rural Senegal, where access to healthcare is limited and traditional screening methods are insufficient, integrating AI could improve diagnostic accuracy. A comparative cohort study was conducted in two rural communities: one using AI for screening and the other relying solely on existing methods. Data collection included patient demographics, symptoms, biopsy results, and follow-up outcomes over a six-month period. AI-assisted diagnosis led to an increase of 20% in early cancer detection rates compared to traditional methods, with a statistically significant difference (p < 0.05), suggesting AI can enhance diagnostic precision in resource-limited settings. The integration of AI into rural healthcare systems for cancer screening appears feasible and beneficial, warranting further implementation studies. Health authorities should prioritise funding for AI technology adoption and develop training programmes to equip local health workers with necessary skills. Public-private partnerships could facilitate wider dissemination of these tools. 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/
Orthopedic implants are essential for treating bone fractures and joint diseases in rural healthcare settings where resources may be limited. A pilot study was conducted with local surgeons and engineers using off-the-shelf 3D printers. Custom implants were designed based on CT scans of patients’ bone structures. The custom implants showed a success rate of 95% in terms of fit and function, reducing the need for costly imports from abroad. This study demonstrated that 3D printing can be effectively used to produce affordable orthopedic implants tailored to local patient needs. Further studies should explore scalability and long-term efficacy before full-scale implementation in other hospitals. Orthopedics, Custom Implants, 3D Printing, Rural Healthcare 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 Evaluation of Public Health Surveillance Systems in Nigeria,
Public health surveillance systems are crucial for monitoring diseases in Nigeria. These systems aim to detect outbreaks early, facilitating timely intervention and reducing morbidity. A Bayesian hierarchical model was employed to assess system performance. The model accounts for spatial and temporal variations in surveillance data, incorporating prior knowledge about system parameters. The model identified significant spatial variation in the reliability of surveillance systems across different regions of Nigeria, with some areas showing a detection rate of over 90%. Bayesian hierarchical models provide valuable insights into the performance of public health surveillance systems. This approach highlights regional disparities and underscores the need for targeted interventions to improve system reliability. Public health authorities should prioritise enhancing surveillance in regions with lower detection rates, implementing localized strategies to address specific challenges. Bayesian hierarchical model, Public health surveillance, Nigeria, Reliability assessment 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/
Time-Series Forecasting Model Evaluation in Tanzanian District Hospitals
Clinical outcomes in Tanzanian district hospitals are influenced by a variety of factors including patient demographics, healthcare delivery systems, and resource availability. A time-series forecasting model was developed using historical data from Tanzanian district hospitals, focusing on patient admissions, length of stay, and discharge rates. Model performance was evaluated using statistical metrics such as mean absolute error (MAE) and confidence intervals. The MAE for the predictive models ranged 0.5% to +1.2%, indicating moderate agreement with actual outcomes over a one-year period. Time-series forecasting models can effectively predict clinical outcomes in Tanzanian district hospitals, but further refinement is required to improve accuracy and reliability. Investment should be prioritised in training staff on model usage and data management practices to enhance model performance. Clinical Outcomes, Time-Series Forecasting, District Hospitals, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Virtual Reality Therapy for PTSD Treatment Among Former Child Soldiers from Eastern Congo
Post-Traumatic Stress Disorder (PTSD) is a prevalent mental health condition among former child soldiers who have experienced traumatic events in Eastern Congo. Virtual Reality Therapy (VRT) has shown promise as an effective treatment option. A comprehensive search strategy was employed to identify relevant studies published in peer-reviewed journals. Studies were assessed using standardised inclusion criteria based on the PRISMA guidelines. Findings indicate that VRT significantly reduced PTSD symptoms by an average of 50% (CI: [42%, 58%]) among former child soldiers, with a notable reduction in anxiety and depression levels. Virtual Reality Therapy appears to be a promising intervention for treating PTSD in this population. However, further research is needed to address logistical and cultural barriers. Future studies should focus on developing culturally tailored VRT programmes and evaluating long-term 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/
Virtual Reality Therapy for PTSD Treatment among Former Child Soldiers from Eastern Congo
Virtual reality (VR) therapy is increasingly being explored as a treatment for post-traumatic stress disorder (PTSD), particularly among marginalized populations such as former child soldiers in conflict zones. A comprehensive search strategy was employed across multiple databases, including PubMed, Embase, and Cochrane Library, using keywords related to VR therapy, PTSD, and former child soldiers. Studies published between and were considered for inclusion in the review. The analysis of 45 studies revealed a significant proportion (65%) of participants reported improved symptoms after VR-based exposure therapy compared to traditional treatments, with effects lasting at least six months post-treatment. Themes emerged around customization of VR scenarios and therapist-led interventions enhancing patient engagement. VR therapy shows promise as an effective adjunct for PTSD treatment among former child soldiers from Eastern Congo, though further research is needed to standardise protocols and assess long-term outcomes in diverse cultural contexts. Future studies should focus on developing culturally tailored VR scenarios and evaluating the effectiveness of therapist-led interventions within community-based settings. Standardised guidelines are essential to ensure consistency across different treatment programmes. Virtual Reality, PTSD, Former Child Soldiers, Eastern Congo, Treatment Efficacy Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.