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Risk factors and control of Opisthorchis viverrini in the Lower Mekong Basin:A systematic review
No full textBACKGROUND: Opisthorchis viverrini (OV) is a major public health concern in the Lower Mekong Basin. This study aimed to synthesize all field-based empirical research examining risk factors and control strategies for OV in the Lower Mekong Basin (LMB).METHODS: We performed a systematic review of published literature (1990-2024) on field-based OV studies that examined risk factors and control strategies in LMB. The literature search included two databases: PubMed and Scopus. We included field-based studies that analysed or reported on OV risk factors or control strategies using quantitative or mixed methods and were written in English. We excluded secondary research articles, laboratory-based research, qualitative only research and studies conducted outside LMB. All prospective studies underwent quality assessment using the Newcastle-Ottawa Scale or the Cochrane Risk of Bias tool II prior to final inclusion.RESULTS: We identified 807 citations from PubMed and Scopus. From those, 56 studies were included in the review and three additional studies were identified from citation searches of included studies in the review. Studies were extracted and analysed by research focus. Among the included studies, 45 were conducted in Thailand, 11 in Laos, two in Vietnam, and one in Cambodia. Factors associated with OV infection were explored in 51 studies, and 11 studies reported on control strategies. General education was found to be an important protective factor for OV infection. Consumption of raw or undercooked fish was the most reported risk factor. Anthelmintic treatment was the primary control strategy across studies.CONCLUSION: This review summarises risk factors and control strategies reported in LMB since 1990. We found that sociodemographic, environmental, and economic factors were important predictors of OV infection. Given the multitude of risk factors for infection identified in this study and the complex lifecycle of OV, we recommend a One Health approach, that recognises the interconnectedness of human, animal and environmental health, for future health promotion and control strategies.TRIAL REGISTRATION: PROSPERO registration ID: CRD42022357080.</p
Development and Validation of Prognostic Models for Treatment Response of Patients with B-Cell Lymphoma:Standard Statistical and Machine-Learning Approaches
No full text Background: Achieving a complete response after therapy is an important predictor of long-term survival in lymphoma patients. However, previous predictive models have primarily focused on overall survival (OS) and progression-free survival (PFS), often overlooking treatment response. Predicting the likelihood of complete response before initiating therapy can provide more immediate and actionable insights. Thus, this study aims to develop and validate predictive models for treatment response to first-line therapy in patients with B-cell lymphomas. Methods: The study used 2763 patients from the Lymphoma and Related Diseases Registry (LaRDR). The data were randomly divided into training (n = 2221, 80%) and validation (n = 553, 20%) cohorts. Seven algorithms: logistic regression, K-nearest neighbor, support vector machine, random forest, Naïve Bayes, gradient boosting machine, and extreme gradient boosting were evaluated. Model performance was assessed using discrimination and classification metrics. Additionally, model calibration and clinical utility were evaluated using the Brier score and decision curve analysis, respectively. Results: All models demonstrated comparable performance in the validation cohort, with area under the curve (AUC) values ranging from 0.69 to 0.70. A nomogram incorporating the six variables, including stage, lactate dehydrogenase, performance status, BCL2 expression, anemia, and systemic immune-inflammation index, achieved an AUC of 0.70 (95% CI: 0.65-0.75), outperforming the international prognostic index (IPI: AUC = 0.65), revised IPI (AUC = 0.61), and NCCN-IPI (AUC = 0.63). Decision curve analysis confirmed the nomogram's superior net benefit over IPI-based systems. Conclusions: While our nomogram demonstrated improved discriminative performance and clinical utility compared to IPI-based systems, further external validation is needed before clinical integration. </p
Profiles of health outcomes, healthcare utilization, and cost: A study of older workforce by type of economic activities in India
No full textShipping professionals and the voluntary adoption of state-of-the-art technologies for the fuel-efficient operation of ships: The role of confidence capital
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Exploring teachers’ understanding of personal and social capability in the secondary school environment
No full textStagnancy of under–5 mortality rate in Bangladesh at the beginning of SDG era: challenges and opportunities for a more effective reduction
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