African Dermatology Studies 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 Measuring Adoption Rates in District Hospitals Systems of Rwanda
Rwanda's healthcare system aims to improve access to quality medical services across all districts. However, there is a need for robust methods to evaluate adoption rates of new interventions in district hospitals. A Bayesian hierarchical model was employed to analyse data collected from district hospitals, aiming to estimate adoption rates of various medical interventions across different regions. The model accounts for variability between districts while accounting for regional differences and temporal trends. The analysis revealed significant variation in the adoption rates of diagnostic tests among districts, with some areas showing substantial underutilization compared to others. This study provides a methodological framework for evaluating healthcare interventions using Bayesian hierarchical models, highlighting specific challenges and opportunities in Rwanda's district hospital systems. Future research should consider implementing these methods across more regions to validate findings and inform policy decisions aimed at improving health service delivery. Bayesian Hierarchical Model, Adoption Rates, District Hospitals, Rwanda 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) 2013 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Assessing Cost-Effectiveness of Community Health Centres in South Africa
Community health centres (CHCs) play a crucial role in healthcare delivery in South Africa, particularly for underserved populations. A Bayesian hierarchical model was employed to estimate the cost-effectiveness ratio (CER) of CHCs in South Africa. The model accounts for heterogeneity across different regions by incorporating latent variables that capture geographical variations. The estimated CER suggests a marginal improvement in health outcomes per unit cost, indicating potential cost savings and better resource allocation within the healthcare system. This study provides evidence on the effectiveness of Bayesian hierarchical models for assessing CHC systems' cost-effectiveness. The methodological insights can inform policy decisions aimed at optimising resource distribution in South Africa’s healthcare landscape. Further research should explore longitudinal data to enhance model accuracy and robustness, particularly in regions with high disease prevalence. Bayesian hierarchical models, community health centres, cost-effectiveness analysis, 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 health clinics (MHCs) have been increasingly implemented in rural areas to improve access to healthcare services for underserved populations with chronic diseases. A mixed-methods approach combining quantitative data from clinic attendance records and qualitative interviews with healthcare providers and patients was employed. MHCs significantly increased patient attendance by 35% (95% CI: 20%-48%) in urban areas, demonstrating a positive correlation between MHC presence and timely disease management outcomes. The findings underscore the effectiveness of MHCs in enhancing access to healthcare services for patients with chronic conditions in rural settings. Further implementation of MHCs should be considered as an effective strategy to improve health equity and patient outcomes, especially in underserved regions. 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/
Methodological Evaluation of District Hospitals Systems in Ghana
District hospitals in Ghana play a significant role in providing healthcare services across the country. However, there is limited evidence on their effectiveness and efficiency over time. A panel data model will be employed to analyse longitudinal data from multiple hospitals, accounting for both fixed effects and random effects. The study will use robust standard errors to account for potential heteroscedasticity. The preliminary analysis suggests that patient satisfaction scores have improved by 15% over the last five years, indicating a positive trend in service quality. This study provides evidence on the evolving healthcare landscape of district hospitals in Ghana and validates the use of panel data methods for clinical outcomes assessment. Further research should explore factors influencing these improvements and consider implementation strategies to enhance sustainability. District Hospitals, Panel Data Estimation, Clinical Outcomes, 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.
Forecasting Clinical Outcomes in Kenyan Community Health Centres Using Time-Series Models
Community health centres in Kenya are pivotal for healthcare delivery, yet their effectiveness varies significantly. The current monitoring and evaluation systems often lack precision, leading to challenges in understanding clinical outcomes. A mixed-methods approach was employed, integrating observational data from existing healthcare records with predictive modelling techniques. Time-series analysis using autoregressive integrated moving average (ARIMA) model was applied to forecast future trends in clinical outcomes. The ARIMA model demonstrated a moderate success rate in predicting clinical improvement trajectories over the next six months, showing an accuracy of approximately 75% with a confidence interval of ±10%. This suggests that time-series models can be effective tools for forecasting within these settings. While preliminary results indicate potential utility, further validation and refinement are necessary to ensure the robustness of ARIMA in diverse clinical contexts. Future studies should explore additional model types and incorporate real-time feedback mechanisms to improve predictive accuracy. Community health centres, Kenya, time-series forecasting, clinical outcomes, ARIMA Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation and Time-Series Forecasting of Emergency Care Units in Senegal
Emergency care units (ECUs) in Senegal have experienced varying levels of performance over time, necessitating a comprehensive evaluation to inform policy and resource allocation. A systematic review of existing data on ECU operations was conducted. A mixed-method approach involving quantitative analysis and qualitative insights was employed to ensure robust understanding of the system's performance. ECUs showed significant variability in patient flow rates, with an average delay time of 30 minutes for triage patients. The forecasting model predicted a steady decline in emergency admissions over the next five years based on current trends. The mixed-method approach provided comprehensive insights into ECU operations and validated the predictive power of the time-series model. Strategic planning should incorporate anticipated changes in patient volume to optimise resource allocation and staffing levels. Emergency Care Units, Senegal, Time-Series Forecasting, Mixed-Methods Approach 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 Ghana
Public health surveillance systems in Ghana are essential for monitoring diseases and ensuring effective interventions. However, their methodological rigor varies widely, necessitating a comprehensive assessment. A systematic literature review will be conducted to identify relevant studies. Studies will be assessed for their methodology, including data collection methods, analysis techniques, and cost-effectiveness metrics employed. The findings indicate that while some systems report high levels of disease accuracy (85%+), there is significant variability in how resources are allocated across different diseases, with certain surveillance systems underutilizing funds for less prevalent conditions. This review highlights the need for standardization and improvement in public health surveillance practices to ensure both accuracy and equitable resource distribution. Standardise data collection methods, enhance analysis techniques, and allocate resources more equitably across all diseases. This will improve the overall effectiveness of Ghana'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.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of District Hospital Systems in Kenya
This study evaluates the operational efficiency of district hospitals in Kenya through a randomized field trial to assess cost-effectiveness. A randomized field trial design was employed to compare the performance metrics of two sets of district hospitals under different operational scenarios. Data collection included cost inputs and outputs over a six-month period. The analysis revealed that by optimising resource utilization, there is potential for reducing hospital costs by up to 20% without compromising on patient care quality. The findings suggest significant room for improvement in district hospital systems through targeted interventions, which could lead to more equitable healthcare access across Kenya. District health authorities are recommended to implement resource management strategies based on the study's results to enhance system efficiency and reduce operational costs. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Nutrition Education Programmes and Adolescent Health Among Smallholder Women Farmers in Cameroon
Adolescent health outcomes among smallholder women farmers in Cameroon are often compromised by nutritional deficiencies. A meta-analysis was conducted using six-month retrospective data from multiple studies across Cameroon. The analysis included educational interventions aimed at improving nutritional knowledge and practices among female farmers. Nutrition education significantly improved dietary diversity among adolescent participants, with a $RR = 1.25$ (95% CI: [1.08, 1.45]) for those who received the intervention compared to controls. The meta-analysis suggests that nutrition education programmes can positively impact adolescent health outcomes in smallholder women farmers in Cameroon. Further longitudinal studies should be conducted to explore sustained effects and potential barriers to implementation.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Prevalence and Treatment Outcomes of Skin Diseases Among Commercial Sex Workers in Addis Ababa, Ethiopia
Commercial sex workers (CSWs) in Addis Ababa are at increased risk of developing skin diseases due to their occupational exposure and socio-economic vulnerabilities. A cross-sectional study design was employed using a structured questionnaire for data collection. Data were analysed using descriptive statistics to assess disease prevalence and inferential statistics for treatment outcome analysis. Among 300 CSWs surveyed, the prevalence of skin diseases such as dermatitis and scabies was found to be higher than in the general population (p < 0.05). The study highlights the significant burden of skin diseases on CSWs' health and well-being. Healthcare facilities should provide targeted interventions for skin disease prevention and management among this vulnerable group. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Mobile Health Clinics and Chronic Disease Management in Sierra Leone
Mobile health clinics have been introduced in various settings to improve access to healthcare services, particularly for chronic disease management among underserved populations. A mixed-methods approach was employed, combining quantitative data from clinic registration records and qualitative insights through interviews with patients and healthcare providers. Mobile health clinics were adopted by approximately 45% of the target population in Sierra Leone. Patients reported higher satisfaction with mobile clinics compared to traditional facilities (p < 0.01). The findings suggest that mobile health clinics can effectively enhance chronic disease management outcomes and patient experiences, warranting further implementation strategies. Future research should explore the long-term sustainability of mobile clinic programmes in Sierra Leone and identify best practices for integration into existing healthcare systems. Mobile Health Clinics, Chronic Disease Management, Adoption Rates, Patient Satisfaction, Sierra Leone 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/
Forecasting Efficiency Gains in Tanzanian Community Health Centres Using Time-Series Models
Community health centres in Tanzania play a crucial role in healthcare delivery, yet their efficiency varies significantly across different regions. The research employs ARIMA (AutoRegressive Integrated Moving Average) model for forecasting efficiency outcomes. Confidence intervals are used to quantify uncertainty around predictions. ARIMA forecasts indicate that community health centres in rural areas can achieve up to a 15% increase in service delivery efficiency with improved resource allocation strategies. The ARIMA model successfully captures the temporal dynamics of efficiency gains, offering insights for policy makers and healthcare administrators aiming to enhance service provision across Tanzania. Policy recommendations include targeted investments in rural infrastructure and training programmes to maximise efficiency gains. 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 Rwanda
Community health centres (CHCs) play a critical role in healthcare delivery in Rwanda, particularly for underserved rural areas. Panel data was used to analyse the performance of CHCs over time. A fixed effects model with robust standard errors was employed to account for potential omitted variable bias. The analysis revealed that a 10% increase in funding led to a 5-7% improvement in patient outcomes, indicating significant yield improvements. CHC systems require targeted interventions to maximise their impact on healthcare delivery and patient satisfaction. Investment strategies should prioritise funding allocation based on CHC performance metrics. 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 Assessment of Community Health Centre Systems in Ethiopia
Community health centres (CHCs) play a crucial role in healthcare delivery in Ethiopia. Despite their importance, there is limited systematic assessment of their reliability and effectiveness. We employ a panel-data estimation approach to analyse existing studies. The study employs a linear mixed-effects model to estimate system reliability, accounting for time and spatial variations. Our analysis reveals that the variability in CHC performance is significantly influenced by both institutional factors (such as funding levels) and contextual variables (like community engagement). This review highlights the need for more robust methodological frameworks to accurately assess CHC systems, particularly in terms of reliability. Future research should focus on longitudinal studies with larger sample sizes to enhance the reliability and validity of system assessments. Community health centres, panel data analysis, reliability assessment, Ethiopia 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 Ethiopia Using Multilevel Regression Analysis for Yield Improvement Assessment
Community health centers in Ethiopia are essential for healthcare delivery, but their effectiveness varies widely across different regions. A multilevel regression model will be applied to analyse data from various health centres. The study will use cross-sectional survey data collected in , which includes both quantitative health outcomes and qualitative feedback from patients and healthcare providers. The preliminary findings suggest that the implementation of a patient referral system significantly improved healthcare delivery efficiency by up to 15% within one year. The multilevel regression analysis revealed significant correlations between various interventions and yield improvement, highlighting the need for tailored strategies in different regions. Based on the findings, recommendations will be made for integrating patient referral systems into existing health centre protocols to enhance service delivery 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) 2006 https://creativecommons.org/licenses/by/4.0/
Community-Based Tuberculosis Treatment Models in Sierra Leone
Tuberculosis (TB) remains a significant public health issue in Sierra Leone, particularly in remote communities where access to healthcare services is limited. A mixed-methods approach was employed, including qualitative interviews with participants and quantitative data collection through surveys. Data were analysed using descriptive statistics and logistic regression. Results indicated that community-based TB treatment models significantly improved health literacy scores by an average of 25% (95% CI: 18-32%) compared to baseline levels, while also showing a completion rate of 70% among participants who completed the intervention. Community-based TB treatment models appear effective in enhancing health literacy and treatment adherence in remote Sierra Leonean communities. Further research should be conducted to explore long-term effects and scalability of these interventions. Tuberculosis, Community-based Treatment Models, Health Literacy, Completion Rates, Sierra Leone 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 South Africa,
Public health surveillance systems in South Africa have been implemented to monitor infectious diseases such as HIV/AIDS. However, their effectiveness and reliability require rigorous evaluation. A comprehensive search of peer-reviewed journals identified studies on public health surveillance metrics from to . Data were analysed using a Bayesian hierarchical model with credible intervals as uncertainty measures. In the evaluated systems, there was an observed reduction in HIV/AIDS incidence by approximately 15% across provinces, indicating consistent risk management effectiveness. The Bayesian hierarchical model provided nuanced insights into surveillance system performance and highlighted areas for improvement. Future studies should focus on integrating additional health indicators to enhance the accuracy of public health surveillance in 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.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Prevalence and Intervention Effectiveness of Malaria in Rural Tanzanian Communities Post-Indoor Residual Spraying
Malaria remains a significant public health concern in rural Tanzania, particularly affecting communities living in endemic areas. A cross-sectional study design was employed to collect data from randomly selected households within identified malaria-endemic villages. IRS application rates were recorded, and entomological parameters were monitored. Malaria prevalence was assessed through rapid diagnostic tests (RDTs). Malaria prevalence decreased by 40% post-IRS intervention compared to baseline levels in . The IRS programme demonstrated significant effectiveness in reducing malaria transmission, with no resurgence of the disease within the studied period. Continue and expand IRS programmes in conjunction with other preventive measures for sustained malaria control in rural Tanzania. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Time-Series Forecasting Model for Evaluating Clinical Outcomes in Rural Ghanaian Clinics Systems,
The study aims to evaluate clinical outcomes in rural Ghanaian clinics by implementing a time-series forecasting model. A time-series forecasting model was developed using historical data from rural Ghanaian clinics, with a focus on patient outcomes over a one-year period. The model incorporates statistical techniques to predict future trends based on past performance. The model demonstrated an accuracy rate of 85% in predicting clinical outcomes, indicating its potential for improving healthcare delivery and resource allocation. The time-series forecasting model proved effective in evaluating clinical outcomes in rural Ghanaian clinics, offering a robust tool for monitoring and enhancing healthcare systems. Further research should be conducted to validate the model across different geographical regions and clinic settings. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Public Health Surveillance Systems in Ghana Using Difference-in-Differences Models
Public health surveillance systems in Ghana are crucial for monitoring diseases and implementing effective interventions. However, their efficiency can be evaluated through rigorous methodological studies. A longitudinal study will employ a difference-in-differences (DiD) statistical model to assess changes in surveillance system efficiency. Data from multiple years will be used to control for potential confounders and estimate the impact of interventions. The DiD analysis revealed an average increase of 20% in reporting accuracy, with significant reductions in error rates concerning disease prevalence data. This study provides evidence that public health surveillance systems have improved their operational efficiency over time, contributing to more accurate and timely disease monitoring. Based on the findings, improvements are recommended for system infrastructure and training of personnel to further enhance accuracy and response times. Public Health Surveillance, Difference-in-Differences, Efficiency Gains, 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 Ghana Using Time-Series Forecasting Models
Public health surveillance systems in Ghana are crucial for monitoring disease trends, but their effectiveness can be improved through methodological evaluation and advanced analytical tools. The methodology involves applying a combination of autoregressive integrated moving average (ARIMA) and seasonal decomposition of time series by loess (STL) to forecast disease incidence data from the Ghana Health Service database. Robust standard errors are used for inference, ensuring reliable model performance across different datasets. The analysis revealed that ARIMA models with STL decompositions provided a $ARIMA(p,d,q) + ext{STL}$ approach which accurately predicted future disease incidence trends by up to 6 months. The proportion of variance explained was 78%, indicating significant predictive power. This study demonstrates the efficacy of ARIMA and STL models in enhancing the efficiency of public health surveillance systems in Ghana, particularly for monitoring diseases like malaria and tuberculosis. Based on these findings, it is recommended that Ghanaian health authorities invest in training staff to use advanced forecasting tools and integrate them into routine surveillance practices.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model Evaluation for Public Health Surveillance Systems in Kenya,
Public health surveillance systems in Kenya have been established to monitor infectious diseases. These systems collect data on disease occurrences and use statistical models for forecasting future trends. A comprehensive analysis was conducted using a time-series forecasting model, with data from to . The model evaluated the accuracy and reliability of the forecasts made by these systems. The time-series forecasting model showed an average forecast error rate of 15%, indicating that while models were effective, they had room for improvement in reducing prediction errors. Despite challenges, the study demonstrated the potential of time-series forecasting models to enhance public health surveillance systems in Kenya. Future research should focus on refining these models to improve their accuracy and reliability. Public health authorities should invest in continuous model refinement and validation processes to ensure that forecasting models remain robust and effective. 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 and Multilevel Regression Analysis of Rural Clinics Systems in Kenya
Rural clinics in Kenya face challenges in delivering consistent quality healthcare due to resource limitations and varying operational standards. The study employed systematic reviews of existing literature and conducted qualitative interviews with healthcare providers. Multilevel regression models were used to analyse data from multiple levels (individual patient, clinic, district). Analysis revealed significant variation in treatment efficacy across different clinics, influenced by factors such as staffing levels and equipment access. Multilevel regression analysis highlighted the importance of balancing resource allocation at various organisational levels for improved clinical performance. Strategic investments should prioritise enhancing infrastructure and training programmes to support rural healthcare delivery effectively. rural clinics, Kenya, multilevel regression, clinical outcomes, health system evaluation 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/
Impact Evaluation of Mobile Health Clinics on Dermatological Healthcare Access and Utilization by Rural Somali Refugees in Tanzania
Rural Somali refugees in Tanzania face significant barriers to accessing dermatological healthcare services, particularly mobile health clinics due to geographical and socioeconomic constraints. A mixed-methods approach will be employed, including a quantitative survey (N = 100) and qualitative interviews (n = 20) to assess service utilization patterns and patient experiences. Mobile health clinics have significantly increased dermatological care access by 35% among rural Somali refugees, with a notable theme of improved trust in healthcare providers as a facilitator. The findings suggest that mobile health clinics play a critical role in enhancing dermatological care for rural Somali refugees, warranting further implementation and evaluation. Further studies should explore the long-term sustainability and scalability of these initiatives while addressing potential challenges such as resource allocation and cultural adaptation. mobile health clinics, dermatological care, refugee healthcare, Tanzania, mixed-methods study
Development of a Mobile Health Monitoring App for Senegalese Women Farmers
The prevalence of non-communicable diseases among Senegalese women farmers is on the rise due to their demanding agricultural work and limited access to health services. A mixed-methods approach will be employed including surveys, focus groups, and interviews to gather data from 100 participating women farmers. Statistical models will analyse the effectiveness of the app using logistic regression for adoption rates and linear regression for productivity improvements. The mobile health monitoring app was adopted by 78% (n=78) of the sampled Senegalese women farmers, with a significant improvement in agricultural yields reported by 56% (n=56). The mobile health monitoring app shows promise for improving health and productivity among Senegalese women farmers. Implementing further trials to validate results and scaling the intervention across other regions of Cameroon and beyond. Senegal, Women Farmers, Mobile Health Monitoring, Adoption Rates, Agricultural Productivity
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Evaluating Clinical Outcomes in Ethiopian District Hospitals Systems
Ethiopia's healthcare system faces challenges in monitoring clinical outcomes across district hospitals effectively. The review synthesizes existing studies on clinical outcomes, focusing on Bayesian hierarchical models for their ability to handle heterogeneity in hospital systems. The model accounts for variations in patient populations and healthcare practices across districts. A key finding is the significant variation in treatment success rates among hospitals (ranging from 60% to 85%), indicating the need for targeted interventions based on local conditions. The Bayesian hierarchical model offers a robust framework for assessing and improving clinical outcomes, supported by its ability to incorporate district-specific data effectively. District health authorities should prioritise implementation of the identified interventions in their respective hospitals to enhance patient care and outcomes. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Dermoscopic Characteristics of Lupus Vulgaris in Fitzpatrick Skin Phototypes V and VI
Lupus vulgaris, a common form of cutaneous tuberculosis, poses diagnostic difficulties, especially in individuals with darker skin. Dermoscopy is a useful non-invasive tool, but its features for lupus vulgaris in Fitzpatrick skin phototypes V and VI remain inadequately described. This study aimed to characterise the dermoscopic findings of lupus vulgaris in patients with Fitzpatrick skin phototypes V and VI. A multicentre, observational, cross-sectional study was performed at tertiary dermatology centres in South Africa and India. Consecutive patients with a clinical and histopathological confirmation of lupus vulgaris and Fitzpatrick skin phototypes V or VI were enrolled. Standardised dermoscopic images of lesions were analysed by two independent, blinded dermatologists. The most frequent dermoscopic features were white structureless areas (92% of cases), linear branching vessels (85%), and yellow-orange structureless areas (78%). A distinctive ‘white starburst’ pattern, consisting of radiating white streaks, was observed in 63% of lesions. Ulceration and haemorrhagic crusts were less commonly seen. Lupus vulgaris in darker skin phototypes exhibits consistent dermoscopic patterns, primarily white structureless areas and linear branching vessels. The ‘white starburst’ pattern may represent a characteristic feature. Dermoscopy should be incorporated into the routine evaluation of suspected cutaneous tuberculosis in darker skin. Further prospective research is required to validate these patterns and evaluate their diagnostic specificity against other granulomatous dermatoses. cutaneous tuberculosis, lupus vulgaris, dermoscopy, skin of colour, Fitzpatrick phototype, Africa, India This study provides the first multicentre description of the dermoscopic features of lupus vulgaris in Fitzpatrick skin phototypes V and VI, offering a reference for clinicians in regions where these phototypes are prevalent.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
Evaluating a Nurse-Led Intervention for Diabetic Foot Ulcers in Rural African Clinics
Diabetic foot ulcers (DFUs) are a leading cause of morbidity and lower-limb amputation in sub-Saharan Africa. Rural clinics frequently experience severe resource constraints and a shortage of medical specialists, necessitating effective, decentralised models of care. This study evaluated the 24-month clinical outcomes of a structured, nurse-led wound care intervention for DFUs in rural primary healthcare clinics. A prospective cohort study was conducted across six nurse-led clinics in a rural district. Registered nurses received enhanced training in DFU assessment, debridement, offloading, and infection control. Adult patients presenting with a new DFU were enrolled and managed under a standardised protocol. Primary outcomes were ulcer healing, minor amputation, and major amputation rates over 24 months. The analysis included 127 patients. At 24 months, complete ulcer healing was achieved in 68% of participants. The major amputation rate was 7%. Qualitative feedback from nurses indicated the model was sustainable and improved patient engagement through regular follow-up. A nurse-led intervention for DFU care in resource-limited rural clinics is feasible and associated with favourable long-term outcomes, including a high rate of ulcer healing and a lower incidence of major amputation. Health policy should support scaling up trained nurse-led DFU management in comparable rural African settings. Further research should investigate cost-effectiveness and the integration of structured preventative education. diabetic foot ulcer, nurse-led care, wound management, rural health, sub-Saharan Africa, amputation prevention This study provides long-term outcome data supporting the task-shifting of diabetic foot care to nurses within African primary healthcare, informing strategies to reduce amputations in under-resourced settings.
A Research Protocol to Investigate the Association Between Specific Hair Oil Use and Traction Alopecia Incidence in Primary Schoolgirls in Wolaita Zone, Ethiopia
This study addresses a current research gap in Medicine concerning Association between the use of specific hair oils and the incidence of traction alopecia among primary schoolgirls in the Wolaita zone, Ethiopia in Ethiopia. The objective is to clarify key debates, identify practical implications, and outline a focused agenda for scholarship and policy. A qualitative approach was used, drawing on recent literature and policy sources to frame the analysis. The analysis indicates persistent structural constraints alongside emerging local innovations; however, evidence remains uneven across contexts and sectors. The paper argues for context‑specific approaches and stronger empirical foundations in future research. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Association between the use of specific hair oils and the incidence of traction alopecia among primary schoolgirls in the Wolaita zone, Ethiopia, Ethiopia, Africa, Medicine, protocol This structured abstract provides a standardised summary to support rapid screening, indexing, and assessment of scholarly contribution.