Resource-constrained Settings Research Articles

Breaking barriers: supporting hematopoietic stem cell transplant program through collaborative radiation therapy service from a physically distant center.

Total body irradiation (TBI) for hematopoietic stem cell transplant (HSCT) has certain distinct advantages, such as uniform dose distribution and lack of drug resistance, but it is not widely available in resource-constrained settings. To overcome the limitations of in-house radiotherapy services in hematology centers, we evaluated the feasibility of conducting HSCT programs in coordination with two physically distant centers using a reduced-intensity TBI protocol. Thirty-two patients with a median age of 20.5years were included in the study. Fifteen patients were diagnosed with aplastic anemia, 10 patients with acute myeloid leukemia (AML), 3 patients with acute lymphocytic leukemia (ALL), and 4 patients with other hematological conditions. Conditioning regimens used were fludarabine plus cyclophosphamide in 29 cases, fludarabine-cytarabine ATG in 2 cases, and busulfan plus fludarabine in 1 case. The TBI dose was 3Gy in 28 cases and 2Gy in 4 cases. Patients were followed monthly after TBI, and the major toxicities were recorded. The median follow-up was 22months. The most common acute complication was acute graft-versus-host disease (GVHD), which occurred in 15.6% of patients. The major late complications were chronic GVHD (9.3%), Cytomegalovirus (CMV) infection (34.3%), and CMV-induced secondary graft failure (6.2%). Seventy-five percent of patients were alive, 21.9% were dead, and 1 patient was lost to follow-up. HSCT based on TBI is feasible even if the center lacks a radiotherapy facility by coordinating with a remote radiotherapy facility. without compromising the patient's outcome.

Unravelling the complexity of research capacity strengthening for health professionals in low- and middle-income countries: A concept analysis.

Research capacity strengthening (RCS) is crucial in enhancing healthcare outcomes, particularly in low- and middle-income countries (LMICs), which face challenges due to limited resources, unequal access to care and the need for evidence-based decision-making. We seek to move beyond a surface-level understanding of RCS, unearthing the core attributes, the factors that precede its implementation and the transformative outcomes it generates within the LMIC healthcare landscape. This study employs the Walker and Avant approach to concept analysis to comprehensively explore the dimensions and attributes of RCS as it pertains to allied and public health professionals in LMICs, propose empirical referents and suggest an operational definition. Ovid MEDLINE, Embase, CINAHL and Cochrane CENTRAL were searched from inception to 27 July 2023, to identify studies on RCS in LMICs. The Walker and Avant approach to concept analysis was selected because it provides a framework for systematically examining and clarifying the meaning and implications of RCS. This method involves a structured process of defining RCS, identifying its attributes, antecedents, consequences and cases, and ultimately providing a clear understanding of its meaning and implications. Identifying empirical referents offers measurable indicators that researchers and policymakers can use to assess the effectiveness of RCS initiatives in LMICs. RCS for health professionals in LMICs involves a sustainable process that equips them with essential research skills, fostering the ability to conduct high-quality research and improve healthcare delivery in resource-constrained settings. RCS aims to empower health professionals to apply evidence-based practices, reduce disparities and enhance the well-being of populations in LMICs. Ultimately, a concept analysis of RCS empowers us to harness the full potential of research to enhance healthcare delivery, improve patient outcomes and advance the well-being of populations worldwide.

Pediatric anesthesia outreach in low and middle-income countries: Models, motives, and moral misalignments.

A lack of anesthesia and surgical capacity leaves approximately 1.7 billion children per annum without access to surgical and anesthetic care. Over the past 50 years, the predominant strategy to address this lack of access has been to provide surgical capacity primarily from high-income countries (HICs) to low and middle-income countries (LMICs) in the form of short-term surgical missions. More recently, the international medical community has recognized the need to build sustainable surgical capacity in resource-constrained settings. This article reviews three models of surgical aid: the vertical model (short-term surgical missions); the horizontal model (system-wide capacity building); and the diagonal model, which is a hybrid of the first two. At their core, medical aid interventions exist on a spectrum ranging from providing surgical capacity to building surgical capacity. The skills, attitudes, and behaviors that drive success in providing medical capacity are fundamentally different from those that drive success in building medical capacity. The root cause of this difference is a shift in the moral duty of the visiting physician from a duty solely to the patient in front of them (based on the primacy of the doctor-patient relationship) to include a duty to the local physicians and the local medical system, and by extension to the next 10 000 patients in need of care. Failure to address the conflicts engendered by this fundamental moral shift risks undermining capacity-building efforts in all models of medical aid.

Application of Data Mining Techniques in Biopsy Interpretation and Staging of Carcinoma Cancer Disease: A Case Study of Northeastern Nigeria

This paper introduces an innovative framework tailored for carcinoma cancer staging in northeastern Nigeria, employing data mining techniques and machine learning algorithms, with analysis conducted using the WEKA toolkit. Given the challenges in healthcare systems, especially in cancer diagnosis and treatment, this study aims to enhance diagnostic precision thorough analysis of biopsy reports. By assessing Support Vector Machine (SVM) and decision tree algorithms using datasets from six tertiary health institutions, the framework demonstrates promising outcomes in accurately determining cancer malignancy stage. SVM exhibits high accuracies in staging breast and prostate cancer, while decision tree algorithms show notable accuracy rates in staging skin cancer. These results highlight the potential of machine learning in advancing cancer diagnosis and treatment planning, particularly in resource-constrained settings, and pave the way for the development of computer-aided diagnostic tools tailored for carcinoma cancer staging in northeastern Nigeria.

Impact of Socioeconomic Factors and Health Insurance Coverage on Out-of-Pocket Health Expenditure among Ghanaian Women: Implications for Health Financing Policy

Background. The cost of health financing is an age-long social challenge in resource-constrained settings. Out-of-pocket (OOP) health spending adversely affects maternal healthcare provisions and use, making health insurance one of the most effective social interventions. Objective. The purpose of this research was to study the effect of OOP health expenditure on socioeconomic factors and health insurance coverage among Ghanaian women. Materials and Methods. Secondary statistical data pulled out from Ghana Demographic and Health Survey (GDHS)—2014 was investigated. A sample of 9,396 women whose ages ranged from 15 to 49 years was studied. We used marginal predictive model to examine the interaction effect between socioeconomic factors and health insurance coverage on OOP health expenditure. The significance level was determined at 5%. Results. Overall, about 41.9% (95% CI: 39.4%−44.4%) of Ghanaian women reported OOP health expenditure for drugs and services. Amongst those covered by health insurance, this was 37.9% (95% CI: 35.3%−44.4%). The marginal interaction effects of OOP health expenditure were higher for women in the lowest neighborhood socioeconomic disadvantage group (42.6%) than those in the highest neighborhood socioeconomic disadvantage group (26.0%) who were covered by health insurance. In addition, the marginal interaction effects of OOP health expenditure were found to be greater among women who had higher education (43.8%) compared with those who had no formal education (34.0%) who were covered by health insurance. Furthermore, the marginal interaction effects of OOP health expenditure were higher for rural (39.0%), than their urban counterpart (36.6.0%) who were under the covering of health insurance. Conclusion. There is a gap in health insurance coverage, which leads to increased OOP health expenditure. The OOP health expenditure effect was more concentrated among high socioeconomic women with health insurance. The healthcare system’s stakeholders should implement policies aimed at eliminating OOP health expenditure for maternal health services.

Environmental reservoirs of multidrug-resistant pseudomonads in a geographical location in Kenya with high community-acquired infections

Background Pseudomonads are gram negative bacteria and readily form biofilms in the environment, allowing long-term colonization and persistence in sinks, water systems. They pose a risk of life-threatening opportunistic infections in immune-compromised individuals. MDR strains, make treatment increasingly difficult. Environmentally persistent MDR strains are typically problematic within healthcare facilities, however, data on MDR pseudomonad reservoirs in settings with community-acquired infections to inform preventive interventions, in resource-constrained settings is scarce. Here, we determined reservoirs and antibiotic susceptibility of Pseudomonas species in water sources in Kisumu County, Kenya with reported high levels of community acquired pseudomonad infections. Methods We adopted a cross-sectional design, randomly collecting 297 samples from tap heads, sinks, tanks, vendor and household storage containers in six selected sub-locations and one hospital (KCRH). Standard microbiological procedures were used for identification and AST of the isolates. Results We isolated Pseudomonads from 14.1% of the samples collected, predominantly from the community 10.4%. Seven different pseudomonads were identified, with Pseudomonas aeruginosa predominating 6.7% overall, in the community samples 5.7%, and among isolates from water tanks 21.4%. Pseudomonad isolates were 62% non-susceptible to piperacillin, 57% to tigecycline, 24% meropenem, 21% cefepime, 19% levofloxacin and 14% colistin. Carbapenem resistance was mainly detected in P. aeruginosa 80% (8/10) from Milimani sub-location 75% (6/8). 45% of the isolates recovered were MDR, mainly community-associated carbapenem-resistant P. aeruginosa (CRPA) 42%, strains susceptible to colistin. The MDR pseudomonads exhibited high multiple antibiotic resistance indices, ranging from 0.43 to 1. Conclusion This study reveals a higher prevalence of MDR pseudomonads, including CRPA strains in community water sources. These potential conduits of drug resistance present a critical public health threat, especially among immunocompromised. Regular cleaning of water storage facilities, water treatment and implementation of antimicrobial stewardship programs, are required to prevent a rise in AMR and eliminate the environmental reservoirs that put the vulnerable populations at risk.

Febrile disease modeling and diagnosis system for optimizing medical decisions in resource-scarce settings

Febrile diseases are highly prevalent in tropical regions due to elevated humidity and high temperatures. These regions, mainly comprising low- and middle-income countries, often face challenges related to inadequate medical infrastructure and a lack of skilled personnel for accurately diagnosing febrile diseases. Distinguishing one febrile illness from another posed a significant challenge, adding to the complexity of accurate diagnoses. This study developed a multi-symptom multi-disease model to address this challenge, leveraging exploratory data analysis of patient datasets from field studies and the expertise of medical practitioners specializing in tropical diseases. The research investigated the most effective modeling approach for differentiating among 11 febrile illnesses that are prevalent in Nigeria using three intelligent techniques: Extreme Gradient Boost (XGBoost), Fuzzy Cognitive Map (FCM), and Analytic Hierarchy Process (AHP). Comparative analysis demonstrates that AHP surpassed the others, achieving a precision of 84%, recall of 83%, and an F1-score of 84%. Consequently, the AHP technique was integrated into the development of “Febra Diagnostica,” an app aimed at enhancing febrile disease diagnosis in resource-constrained settings. The app was then deployed and utilized in select Nigerian states, offering scalability and empowering frontline health workers in primary health facilities. Febra Diagnostica featured user-friendly interfaces, automated diagnosis and treatment suggestions, streamlined referrals, and provisions for further investigations. Encryption, access control, and multi-factor authentication were some of the security and privacy considerations in the app which gained acceptance from medical experts and adapted to regulatory and ethical policies for smart healthcare systems.

Adaptive Multi-Criteria Selection for Efficient Resource Allocation in Frugal Heterogeneous Hadoop Clusters

Efficient resource allocation is crucial in clusters with frugal Single-Board Computers (SBCs) possessing limited computational resources. These clusters are increasingly being deployed in edge computing environments in resource-constrained settings where energy efficiency and cost-effectiveness are paramount. A major challenge in Hadoop scheduling is load balancing, as frugal nodes within the cluster can become overwhelmed, resulting in degraded performance and frequent occurrences of out-of-memory errors, ultimately leading to job failures. In this study, we introduce an Adaptive Multi-criteria Selection for Efficient Resource Allocation (AMS-ERA) in Frugal Heterogeneous Hadoop Clusters. Our criterion considers CPU, memory, and disk requirements for jobs and aligns the requirements with available resources in the cluster for optimal resource allocation. To validate our approach, we deploy a heterogeneous SBC-based cluster consisting of 11 SBC nodes and conduct several experiments to evaluate the performance using Hadoop wordcount and terasort benchmark for various workload settings. The results are compared to the Hadoop-Fair, FOG, and IDaPS scheduling strategies. Our results demonstrate a significant improvement in performance with the proposed AMS-ERA, reducing execution time by 27.2%, 17.4%, and 7.6%, respectively, using terasort and wordcount benchmarks.

An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans

BackgroundRecent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-constrained settings. This study aims to propose an efficient diagnostic deep learning model specifically designed for the classification of intracranial hemorrhage in brain CT scans. MethodOur proposed model utilizes a combination of depthwise separable convolutions and a multi-receptive field mechanism to achieve a trade-off between performance and computational efficiency. The model was trained on RSNA datasets and validated on CQ500 dataset and PhysioNet dataset. ResultThrough a comprehensive comparison with state-of-the-art models, our model achieves an average AUROC score of 0.952 on RSNA datasets and exhibits robust generalization capabilities, comparable to SE-ResNeXt, across other open datasets. Furthermore, the parameter count of our model is just 3 % of that of MobileNet V3. ConclusionThis study presents a diagnostic deep-learning model that is optimized for classifying intracranial hemorrhages in brain CT scans. The efficient characteristics make our proposed model highly promising for broader applications in medical settings.

Vaccine effectiveness against emerging COVID-19 variants using digital health data

BackgroundParticipatory surveillance of self-reported symptoms and vaccination status can be used to supplement traditional public health surveillance and provide insights into vaccine effectiveness and changes in the symptoms produced by an infectious disease. The University of Maryland COVID Trends and Impact Survey provides an example of participatory surveillance that leveraged Facebook’s active user base to provide self-reported symptom and vaccination data in near real-time.MethodsHere, we develop a methodology for identifying changes in vaccine effectiveness and COVID-19 symptomatology using the University of Maryland COVID Trends and Impact Survey data from three middle-income countries (Guatemala, Mexico, and South Africa). We implement conditional logistic regression to develop estimates of vaccine effectiveness conditioned on the prevalence of various definitions of self-reported COVID-like illness in lieu of confirmed diagnostic test results.ResultsWe highlight a reduction in vaccine effectiveness during Omicron-dominated waves of infections when compared to periods dominated by the Delta variant (median change across COVID-like illness definitions: −0.40, IQR[−0.45, −0.35]. Further, we identify a shift in COVID-19 symptomatology towards upper respiratory type symptoms (i.e., cough and sore throat) during Omicron periods of infections. Stratifying COVID-like illness by the National Institutes of Health’s (NIH) description of mild and severe COVID-19 symptoms reveals a similar level of vaccine protection across different levels of COVID-19 severity during the Omicron period.ConclusionsParticipatory surveillance data alongside methodologies described in this study are particularly useful for resource-constrained settings where diagnostic testing results may be delayed or limited.

Operating room black box: GIMMICK or a patient safety tool?

The Operating Room Black Box (ORBB) is a relatively recent technology that provides a comprehensive solution for assessing technical and non-technical skills of the operating team. Originating from aviation, the ORBB enables real-time observation and continuous recording of intraoperative events allowing for an in-depth analysis of efficiency, safety, and adverse events. Its dual role as a teaching tool enhances transparency and patient safety in surgical training. In comparison to traditional methods, like checklists that have limitations, the ORBB offers a holistic understanding of clinical and non-clinical performances that are responsible for intraoperative patient outcomes. It facilitates systematic observation without additional personnel, allowing for review of numerous surgical cases. This review highlights the potential benefits of the ORBB in enhancing patient safety, its role as a surgical training tool, and addresses barriers especially in resource-constrained settings. It signifies a transformative step towards global surgical practices, emphasizing transparency and improved surgical outcomes.

Ductal Stenting in Low-Resource Environments.

Duct-dependent pulmonary circulation has traditionally been addressed by the Blalock-Taussig-Thomas shunts (BTTS). Recently, catheter-based alternatives such as ductal stenting have emerged as a particularly advantageous option, especially in resource-constrained settings. This article delves into the nuances of ductal stenting within low-resource environments, highlighting its relative ease of application, reduced morbidity, and cost-effectiveness as key factors in its favor. Comparisons in mortality between the two procedures are however likely to be confounded by selection biases. Ductal stenting appears to be particularly beneficial for palliating older infants and children with cyanotic congenital heart disease and diminished pulmonary blood flow who present late. Additionally, it serves as a valuable tool for left ventricular training in late-presenting transposition with an intact ventricular septum. A meticulous pre-procedure echocardiographic assessment of anatomy plays a pivotal role in planning access and hardware, with additional imaging seldom required for this purpose. The adaptation of adult coronary hardware has significantly enhanced the technical feasibility of ductal stenting. However, challenges such as low birth weight and sepsis specifically impact the performance of ductal stenting and patient recovery in low-resource environments. There is potential for systematic application of quality improvement processes to optimize immediate and long-term outcomes of ductal stenting. There is also a need to prospectively examine the application of ductal stenting in low-resource environments through multi-center registries.

Abstract PO4-10-10: NAVYA-AI enabled intervention to increase real-world guideline compliant care: Improving NGS testing in breast cancer

Abstract BACKGROUND: Navya-AI is a validated online cancer informatics solution that combines artificial intelligence based analysis of the guidelines and evidence, along with asynchronous expert review. Navya-AI releases preliminary system-generated opinions for patients whose treatment plans fit high confidence based on NCCN Guidelines and prior expert reviews. Prior research (SABCS 2014-2018 and ASCO 2021 showed: 1) 97% concordance of Navya-AI predictions with an academic medical center in India and in the US 2) 80% of patients implement treatment concordant with Navya-AI recommendations on the ground 3) Navya’s NCCN and evidence-based treatment plans reduce the patient waiting times for an expert opinion by an average by 3.5 days. Next generation sequencing (NGS) is an expensive but necessary method for identifying patients for risk reduction and therapy selection in breast cancer (BC). NGS testing can be over-used or under-used, and compliance with NCCN guidelines on patient selection is especially important in resource-constrained settings and Low Middle Income Countries (LMIC) such as India. The aim of this study is to find the physician compliance to NCCN guidelines on NGS testing in India, and how Navya-AI can identify the opportunity for improved care, and improve compliance with guidelines for optimal care. METHODS: From January 2022 to May 2023, Breast Cancer patients receiving Navya-AI treatment plan (based on guidelines and live expert-review) were analyzed. Patients who could afford NGS testing and targeted therapies, and who met the Enhanced tier of NCCN resource stratified guidelines for their care centers were identified. Their records were screened by Navya-AI to assess if they met NCCN criteria for NGS testing for risk reduction or for treatment selection (young age, positive family history, triple negative breast cancer etc.). Treating physicians were then analyzed if they had ordered NGS appropriately, or had missed an opportunity for NGS (undertreatment), or had over-ordered NGS (overtreatment).RESULTS: 521 BC patients who could afford NGS tests received a Navya-AI plan during this period. Of these, 85% were Indian patients (415/521) and were analyzed. Patients were diverse with respect to age,cancer stage, family history of cancer and histology: Age < 35: 6.7%, 35-50: 33%, 51-65: 43.76%, >65: 16.51%; early stage: 21.5%, locally advanced stage: 29.56%,metastatic stage: 47.79% and benign disease: 1%; positive family history of cancer: 37.35%, triple negative BC: 21.9%. Of 415 indian breast cancer patients who could afford testing, NGS testing was indicated in 69.64% (289/415) as per NCCN guidelines. The treating physician was compliant with NCCN guidelines in only 47.95% (199/415) of the cases: 19.2% (80/415) of the time, NGS was indicated and the treating physician ordered the same; 28.67% (119/415) of the time, NGS was not indicated, and the treating physician did not order NGS. The remaining 52% were not compliant with NCCN. Under-treatment was present in 50.36% (209/415) of cases, and was the vast majority of fallouts. Over-treatment was only present in 1.69% (7/415). In all 50.63% (209/415) cases, where NGS was not ordered by the treating oncologist, but was indicated, and affordable to the patient, Navya requested the patient to discuss the risks/benefits of NGS testing with their treating oncologist. CONCLUSION: NGS testing in BC patients has significant impact on risk-reduction, genetic counseling, choice of surgery, and treatment options for adjuvant therapy. Missed opportunities for NGS testing in more than 50% of the patients who can afford the testing and resultant therapies, points to a signficant area where compliance with guidelines and expert opinions can impact outcomes. A 'Technological Earthshot" that significantly increases adoption of guideline-based care is the first and an easy step towards 'Cancer Moonshots'. Citation Format: RA Badwe, Premal Thaker, Farzana Begum, Mayukh Acherjee, Gitika Srivastava, Naresh Ramarajan, Shona Nag, Sudeep Gupta, Seema Gulia, Jaya Ghosh, Bhawna Sirohi. NAVYA-AI enabled intervention to increase real-world guideline compliant care: Improving NGS testing in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-10-10.

Lorlatinib in the second line and beyond for ALK positive lung cancer: real-world data from resource-constrained settings

BackgroundALK-positive lung cancers are known to have favorable responses with oral tyrosine kinase inhibitors. Lorlatinib is an approved treatment option post first and second-line ALK inhibitors and is now also in first line. We present a retrospective observational study of the safety and efficacy of patients receiving Lorlatinib in second-line and beyond.MethodsWe conducted a retrospective observational study of ALK-positive patients who received Lorlatinib post-progression or intolerance to initial therapy at the Medical Oncology department. The patients who were started on Lorlatinib between January 2018 to December 2019 were included. The patients underwent routine blood and radiological evaluation every two to three months.ResultsA total of 38 patients received Lorlatinib in the specified period. The median age was 48 years (range 23–68), with 53% of patients being male, 37% having comorbidities; the most common being hypertension and diabetes and 79% of patients were of ECOG-PS1. Twenty-two patients (58%) had received two prior TKIs. The most common sites of metastasis before starting Lorlatinib were brain (55%) and bone (53%). All patients except one received prior whole-brain radiotherapy with 4 receiving radiation twice. The median follow-up period was 49 months (95% CI: 46.4–51.6). Eighty-four percent showed disease control with median progression-free survival (PFS) and overall survival (OS) of 16 months (95% CI 5.4–26.6) and 22 months (95% CI 9.9–34.1) respectively. Twelve patients died without documented progression. Five out of twelve with documented progression had brain involvement while six had lung involvement. Twelve out of twenty-four patients who progressed received subsequent chemotherapy. The most common grade 3 and above toxicities were hypercholesterolemia and hypertriglyceridemia. Three (7.8%) patients required dose reduction.ConclusionThis real-world data confirms the efficacy of Lorlatinib in the second line and beyond with adverse effects matching that of registration studies.

Validation of new invasive digital hemoglobinometer for hemoglobin estimation as point-of-care device among pregnant women in a facility setting India

Background: Anemia poses a significant health challenge for pregnant women (PW), and accurate and timely diagnosis is essential for effective management. Objective: The objective was to assess the diagnostic validity of the new digital hemoglobinometer for hemoglobin against laboratory-based hematology autoanalyzer among PW. Methodology: The study was conducted at a secondary-level healthcare facility among 204 pregnant women to be sampled conveniently, their sociodemographic and iron intake data collected, and hemoglobin levels assessed using a digital hemoglobinometer (Device A) and a hematology analyzer. Specificity, sensitivity, PPV, NPV, diagnostic accuracy, and method agreement were evaluated via Bland-Altman analysis and Lin's concordance correlation coefficient. Results: The proportion of anemia using the Device A was 64.7% while the hematology analyzer reported a proportion of 52.9%. Device A showed a sensitivity of 97.22%, specificity of 80.30%, and diagnostic accuracy of 86.3%, with substantial agreement indicated by Cohen's kappa coefficient (kappa = 0.72) and the weighted kappa coefficient for different grades of anemia was 0.67. Bland-Altman analysis revealed a mean difference (bias) of -0.28 (± 0.5) between the two methods, with limits of agreement at -1.24 and 0.68. Lin's concordance correlation coefficient of absolute agreement was 0.91. Conclusion: The DH showed high sensitivity and diagnostic accuracy for anemia detection in PW, with substantial agreement with the hematology analyzer. It offers a convenient and rapid alternative for POC hemoglobin estimation in resource-constrained settings.

Harnessing synergy between biomedical and electrical engineering: A comparative analysis of healthcare advancement in Nigeria and the USA

This review paper explores the intersection of biomedical and electrical engineering and its potential for revolutionizing healthcare systems, with a comparative focus on Nigeria and the USA. By examining the collaborative efforts between these two disciplines, we aim to identify key opportunities and challenges in leveraging their synergy to enhance healthcare delivery, particularly in resource-constrained settings like Nigeria. Through a comprehensive analysis of current trends, technological innovations, and policy frameworks, this paper offers insights into pathways for bridging the gap between research advancements and practical implementation, ultimately facilitating the development of more effective and accessible healthcare solutions.

Evaluating the World Health Organization's SkinNTDs App as a Training Tool for Skin Neglected Tropical Diseases in Ghana and Kenya: Cross-Sectional Study.

Neglected tropical diseases (NTDs) affect over 1.5 billion people worldwide, primarily impoverished populations in low- and middle-income countries. Skin NTDs, a significant subgroup, manifest primarily as skin lesions and require extensive diagnosis and treatment resources, including trained personnel and financial backing. The World Health Organization has introduced the SkinNTDs app, a mobile health tool designed to train and be used as a decision support tool for frontline health care workers. As most digital health guidelines prioritize the thorough evaluation of mobile health interventions, it is essential to conduct a rigorous and validated assessment of this app. This study aims to assess the usability and user experience of World Health Organization SkinNTDs app (version 3) as a capacity-building tool and decision-support tool for frontline health care workers. A cross-sectional study was conducted in Ghana and Kenya. Frontline health care workers dealing with skin NTDs were recruited through snowball sampling. They used the SkinNTDs app for at least 5 days before completing a web-based survey containing demographic variables and the user version of the Mobile Application Rating Scale (uMARS), a validated scale for assessing health apps. A smaller group of participants took part in semistructured interviews and one focus group. Quantitative data were analyzed using SPSS with a 95% CI and P≤.05 for statistical significance and qualitative data using ATLAS.ti to identify attributes, cluster themes, and code various dimensions that were explored. Overall, 60 participants participated in the quantitative phase and 17 in the qualitative phase. The SkinNTDs app scored highly on the uMARS questionnaire, with an app quality mean score of 4.02 (SD 0.47) of 5, a subjective quality score of 3.82 (SD 0.61) of 5, and a perceived impact of 4.47 (SD 0.56) of 5. There was no significant association between the app quality mean score and any of the categorical variables examined, according to Pearson correlation analysis; app quality mean score vs age (P=.37), sex (P=.70), type of health worker (P=.35), country (P=.94), work context (P=.17), frequency of dealing with skin NTDs (P=.09), and dermatology experience (P=.63). Qualitative results echoed the quantitative outcomes, highlighting the ease of use, the offline functionality, and the potential utility for frontline health care workers in remote and resource-constrained settings. Areas for improvement were identified, such as enhancing the signs and symptoms section. The SkinNTDs app demonstrates notable usability and user-friendliness. The results indicate that the app could play a crucial role in improving capacity building of frontline health care workers dealing with skin NTDs. It could be improved in the future by including new features such as epidemiological context and direct contact with experts. The possibility of using the app as a diagnostic tool should be considered. RR2-10.2196/39393.

Beyond the fractures: A comprehensive Comparative analysis of Affordable and Accessible laboratory parameters and their coefficients for prediction and Swift confirmation of pulmonary embolism in high-risk orthopedic patients

BackgroundPulmonary embolism (PE) poses a significant challenge in diagnosis and treatment, particularly in high-risk patient populations such as those hospitalized for orthopedic reasons. This study explores the predictive and diagnostic potential of laboratory parameters in identifying PE among orthopedic patients. ObjectivesThe purpose of this study was to determine whether selected (inexpensive and readily available) laboratory parameters and their coefficients can be used to diagnose pulmonary embolism and whether they are applicable in predicting its occurrence. Material and methodsSelected laboratory parameters were determined twice in 276 hospitalized orthopedic patients with suspected PE: PLT, MPV, NEU, LYM, D-dimer, troponin I, age-adjusted D-dimer and their coefficients. Depending on the angio-CT results, patients were divided into groups. Selected popular laboratory coefficients were calculated and statistically analyzed. Optimal cutoff points were determined for the above laboratory tests and ROC curves were plotted. ResultsD-dimer/troponin I [p = 0.008], D-dimer [p = 0.001], age-adjusted D-dimer [p = 0.007], NLR/D-dimer [p = 0.005] and PLR [p = 0.021] are statistically significant predictors of PE. D-dimer/troponin I [p < 0.001], troponin I [p = 0.005] and age-adjusted D-dimer [p = 0.001] correlated with the diagnosis of PE after the onset of clinical symptoms. ConclusionsIn the context of orthopedic patients, cost-effective laboratory parameters, particularly the D-dimer/troponin I ratio and age-adjusted D-dimer, exhibit considerable potential in predicting and diagnosing PE. These findings suggest that combining readily available laboratory tests with clinical observation can offer a viable and cost-effective diagnostic alternative, especially in resource-constrained settings. Further studies with larger and diverse patient populations are recommended to validate these results.

The Tools for Integrated Management of Childhood Illness (TIMCI) study protocol: a multi-country mixed-method evaluation of pulse oximetry and clinical decision support algorithms.

Effective and sustainable strategies are needed to address the burden of preventable deaths among children under-five in resource-constrained settings. The Tools for Integrated Management of Childhood Illness (TIMCI) project aims to support healthcare providers to identify and manage severe illness, whilst promoting resource stewardship, by introducing pulse oximetry and clinical decision support algorithms (CDSAs) to primary care facilities in India, Kenya, Senegal and Tanzania. Health impact is assessed through: a pragmatic parallel group, superiority cluster randomised controlled trial (RCT), with primary care facilities randomly allocated (1:1) in India to pulse oximetry or control, and (1:1:1) in Tanzania to pulse oximetry plus CDSA, pulse oximetry, or control; and through a quasi-experimental pre-post study in Kenya and Senegal. Devices are implemented with guidance and training, mentorship, and community engagement. Sociodemographic and clinical data are collected from caregivers and records of enrolled sick children aged 0-59 months at study facilities, with phone follow-up on Day 7 (and Day 28 in the RCT). The primary outcomes assessed for the RCT are severe complications (mortality and secondary hospitalisations) by Day 7 and primary hospitalisations (within 24 hours and with referral); and, for the pre-post study, referrals and antibiotic. Secondary outcomes on other aspects of health status, hypoxaemia, referral, follow-up and antimicrobial prescription are also evaluated. In all countries, embedded mixed-method studies further evaluate the effects of the intervention on care and care processes, implementation, cost and cost-effectiveness. Pilot and baseline studies started mid-2021, RCT and post-intervention mid-2022, with anticipated completion mid-2023 and first results late-2023. Study approval has been granted by all relevant institutional review boards, national and WHO ethical review committees. Findings will be shared with communities, healthcare providers, Ministries of Health and other local, national and international stakeholders to facilitate evidence-based decision-making on scale-up.Study registration: NCT04910750 and NCT05065320.

Barriers to accessing care for cardiometabolic disorders in Malawi: partners as a source of resilience for people living with HIV

BackgroundPeople living with HIV (PLWH) are at increased risk of cardiometabolic disorders (CMD). Adequate access to care for both HIV and CMD is crucial to improving health outcomes; however, there is limited research that have examined couples’ experiences accessing such care in resource-constrained settings. We aimed to identify barriers to accessing CMD care among PLWH in Malawi and the role of partners in mitigating these barriers.MethodsWe conducted a qualitative investigation of barriers to CMD care among 25 couples in Malawi. Couples were eligible if at least one partner was living with HIV and had hypertension or diabetes (i.e., the index patient). Index patients were recruited from HIV care clinics in the Zomba district, and their partners were enrolled thereafter. Interviews were conducted separately with both partners to determine barriers to CMD care access and how partners were involved in care.ResultsParticipants framed their experiences with CMD care by making comparisons to HIV treatment, which was free and consistently available. The main barriers to accessing CMD care included shortage of medications, cost of tests and treatments, high cost of transportation to health facilities, lengthy wait times at health facilities, faulty or unavailable medical equipment and supplies, inadequate monitoring of patients’ health conditions, some cultural beliefs about causes of illness, use of herbal therapies as an alternative to prescribed medicine, and inadequate knowledge about CMD treatments. Partners provided support through decision-making on accessing medical care, assisting partners in navigating the healthcare system, and providing financial assistance with transportation and treatment expenses. Partners also helped manage care for CMD, including communicating health information to their partners, providing appointment reminders, supporting medication adherence, and supporting recommended lifestyle behaviors.ConclusionsCouples identified many barriers to CMD care access, which were perceived as greater challenges than HIV care. Partners provided critical forms of support in navigating these barriers. With the rise of CMD among PLWH, improving access to CMD care should be prioritized, using lessons learned from HIV and integrated care approaches. Partner involvement in CMD care may help mitigate most barriers to CMD care.