JMIRx med最新文献

{"title":"Real-World Performance of COVID-19 Antigen Tests: Predictive Modeling and Laboratory-Based Validation.","authors":"Miguel Bosch, Dawlyn Garcia, Lindsey Rudtner, Nol Salcedo, Raul Colmenares, Sina Hoche, Jose Arocha, Daniella Hall, Adriana Moreno, Irene Bosch","doi":"10.2196/68376","DOIUrl":"https://doi.org/10.2196/68376","url":null,"abstract":"<p><strong>Background: </strong>Rapid and safe deployment of lateral-flow antigen tests, coupled with uncompromised quality assurance, is critical for outbreak control and pandemic preparedness, yet real-world performance assessment still lacks laboratory and quantitative approaches that remain uncommon in current regulatory science. The approach proposed here can help standardize and accelerate early phase appraisal of antigen tests in preparation for clinical validation.</p><p><strong>Objective: </strong>The aim of this study is to present a quantitative, laboratory-anchored framework that links image-based test line intensities and the population distribution of naked-eye limits of detection (LoD) to a probabilistic prediction of positive percent agreement (PPA) as a function of viral-load-related variables (eg, quantitative real-time polymerase chain reaction [qRT-PCR] cycle thresholds [Cts]). Using dilution-series calibrations and a Bayesian model, the predicted PPA-vs-Ct curve closely tracks the observed PPA in a real-world self-testing cohort.</p><p><strong>Methods: </strong>The proposed methodology combines: (1) a quantitative evaluation of the test signal response to concentrations of target protein and inactive virus or active virus, (2) a statistical characterization of the LoD using the observer's visual acuity of the test band, and (3) a calibration of a gold-standard method (eg, qRT-PCR cycles) against virus concentration. We elaborate these quantitative methods and unfold a Bayesian-based predictive model to describe the real-world performance of the antigen test, quantified by the probability of positive agreement as a function of viral-load variables like qRT-PCR Cts.</p><p><strong>Results: </strong>We applied the methodology by characterizing each brand of COVID-19 antigen test and estimating its real-world probability of agreement with qRT-PCR. We aligned protein and inactivated-virus standard curves at matched signal intensities and fit a linear calibration linking protein to viral concentrations. Using logistic regression, we modeled the PPA as a continuous function of qRT-PCR Ct, then integrated this curve over a predefined reference Ct distribution to obtain the expected sensitivity. This standardization enables consistent performance comparisons across sites.</p><p><strong>Conclusions: </strong>Modeling performance under real-world conditions requires coupling laboratory evaluation with the population's ability to perceive the test's visual signal. We represent observer capability as a probability density function of the LoD over the signal-intensity domain. Rather than reporting bin-based sensitivity, we summarize performance with the PPA as a continuous function of qRT-PCR Ct. Our framework produces PPA-Ct curves by composing (1) normalized signal-to-concentration models from the laboratory, (2) the observer LoD distribution, and (3) a Ct-to-viral-load calibration. The resulting inferences are inherently context-bound-di","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e68376"},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Interventions for the Prevention and Management of Maternal Anemia in the Advent of the COVID-19 Pandemic: Systematic Review and Meta-Analysis.","authors":"John Kyalo Muthuka, Dianna Kageni Mbari-Fondo, Francis Muchiri Wambura, Kelly Oluoch, Japheth Mativo Nzioki, Everlyn Musangi Nyamai, Rosemary Nabaweesi","doi":"10.2196/57626","DOIUrl":"https://doi.org/10.2196/57626","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic presented many unknowns for pregnant women, with anemia potentially worsening pregnancy outcomes due to multiple factors.</p><p><strong>Objective: </strong>This review aimed to determine the pooled effect of maternal anemia interventions and associated factors during the pandemic.</p><p><strong>Methods: </strong>Eligible studies were observational and included reproductive-age women receiving anemia-related interventions during the COVID-19 pandemic. Exclusion criteria comprised non-English publications, reviews, editorials, case reports, studies with insufficient data, sample sizes below 50, and those lacking DOIs. A systematic search of PubMed, Scopus, Embase, Web of Science, and Google Scholar identified articles published between December 2019 and August 2022. Risk of bias was evaluated using the Cochrane Risk of Bias 2 tool for randomized trials and the National Institutes of Health's assessment tool for observational studies. Pooled rate ratios (RRs) with 95% CIs were calculated in Review Manager 5.4.1. Synthesis included subgroup analysis, meta-regression, and publication bias checks to assess intervention effectiveness.</p><p><strong>Results: </strong>This meta-analysis included 11 studies with 6129 pregnant women. Of these, 3591 (59%) were in the intervention group and 2538 (41%) were in the comparator group. Effects were recorded for 1921 (53.4%) women in the intervention group and 1350 (53.1%) in the comparator group. The cumulative impact ranged from 23% to 81%, averaging 56%. The initial analysis showed no significant effect on anemia prevention (RR 0.79, 95% CI 0.61-1.02; P=.07), with high heterogeneity (I²=97%). Sensitivity analysis excluding 4 outlier studies improved the effect size to a significant level at 39% (RR 0.61, 95% CI 0.43-0.87; P=.006). Subgroup analysis revealed substantial heterogeneity (I²=87.2%). Intravenous sucrose had a poor impact (RR 1.31, 95% CI 1.17-1.47; P<.001), while medicinal or herbal interventions showed benefit (RR 0.81, 95% CI 0.73-0.90; P=.006). Educational interventions yielded a 28% effect (RR 0.72), medicinal administration 19% (RR 0.81), iron supplementation 17% (RR 0.83), and intravenous ferric carboxylmaltose 15% (RR 0.85; P<.02). Additional sensitivity analysis confirmed a pooled positive effect of 17% (RR 0.83, 95% CI 0.79-0.88; P<.001), with minimal heterogeneity (I²=0%). Regionally, effectiveness was highest in Africa (RR 0.84, 95% CI 0.79-0.89; P<.001). Multicenter studies and those with 2020 data were predictive of better outcomes (RR 0.84 and RR 0.50, respectively). Despite initial heterogeneity and publication bias, interventions showed utility in mitigating maternal anemia in targeted subgroups and regions.</p><p><strong>Conclusions: </strong>Maternal anemia interventions during the COVID-19 pandemic showed modest, context-specific effectiveness, with declining impact from 2020 to 2022. Although high heterogeneity and study inconsi","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e57626"},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Health Monitoring Using 5G Networks: Deep Learning-Based Architecture for Remote Patient Care.","authors":"Iqra Batool","doi":"10.2196/70906","DOIUrl":"10.2196/70906","url":null,"abstract":"<p><strong>Background: </strong>Remote patient monitoring systems face critical challenges in real-time vital sign analysis and secure data transmission.</p><p><strong>Objective: </strong>This study aimed to develop a novel architecture integrating deep learning with 5G networks for real-time vital sign monitoring and prediction.</p><p><strong>Methods: </strong>A hybrid convolutional neural network-long short-term memory model with attention mechanisms was optimized for edge deployment using 5G ultrareliable low-latency communication. The system incorporated end-to-end encryption and HIPAA (Health Insurance Portability and Accountability Act) compliance. Performance was evaluated over 3 months using data from 1000 patients.</p><p><strong>Results: </strong>The system demonstrated superior prediction accuracy and significantly reduced latency compared to existing solutions. Performance remained stable under adverse network conditions and across diverse patient populations, supporting thousands of concurrent monitoring sessions.</p><p><strong>Conclusions: </strong>This framework addresses security, scalability, and robustness requirements for clinical implementation, potentially improving patient outcomes through early detection of deteriorating conditions.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e70906"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12488166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach.","authors":"Anjali Dharmik","doi":"10.2196/75015","DOIUrl":"10.2196/75015","url":null,"abstract":"<p><strong>Background: </strong>SARS-CoV-2, the causative agent of COVID-19, remains a global health concern due to its high transmissibility and evolving variants. Although vaccination efforts and therapeutic advancements have mitigated disease severity, emerging mutations continue to challenge diagnostics and containment strategies. As of mid-February 2025, global test positivity has risen to 11%, marking the highest level in over 6 months, despite widespread immunization efforts. Newer variants demonstrate enhanced host cell binding, increasing both infectivity and diagnostic complexity.</p><p><strong>Objective: </strong>This study aimed to evaluate the effectiveness of deep transfer learning in delivering a rapid, accurate, and mutation-resilient COVID-19 diagnosis from medical imaging, with a focus on scalability and accessibility.</p><p><strong>Methods: </strong>An automated detection system was developed using state-of-the-art convolutional neural networks, including VGG16 (Visual Geometry Group network-16 layers), ResNet50 (residual network-50 layers), ConvNeXtTiny (convolutional next-tiny), MobileNet (mobile network), NASNetMobile (neural architecture search network-mobile version), and DenseNet121 (densely connected convolutional network-121 layers), to detect COVID-19 from chest X-ray and computed tomography (CT) images.</p><p><strong>Results: </strong>Among all the models evaluated, DenseNet121 emerged as the best-performing architecture for COVID-19 diagnosis using X-ray and CT images. It achieved an impressive accuracy of 98%, with a precision of 96.9%, a recall of 98.9%, an F1-score of 97.9%, and an area under the curve score of 99.8%, indicating a high degree of consistency and reliability in detecting both positive and negative cases. The confusion matrix showed minimal false positives and false negatives, underscoring the model's robustness in real-world diagnostic scenarios. Given its performance, DenseNet121 is a strong candidate for deployment in clinical settings and serves as a benchmark for future improvements in artificial intelligence-assisted diagnostic tools.</p><p><strong>Conclusions: </strong>The study results underscore the potential of artificial intelligence-powered diagnostics in supporting early detection and global pandemic response. With careful optimization, deep learning models can address critical gaps in testing, particularly in settings constrained by limited resources or emerging variants.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e75015"},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perception and Impact of White Spot Lesions in Young People Undergoing Orthodontic Treatment and Their Guardians: Protocol for a Mixed Methods Study.","authors":"Amaar Obaid Hassan, Janine Doughty, Jayne Harrison","doi":"10.2196/60213","DOIUrl":"10.2196/60213","url":null,"abstract":"<p><strong>Background: </strong>White spot lesions (WSLs) are white marks that can form on teeth during orthodontic treatment with fixed appliances and become apparent once they are removed. About half of people who have fixed appliance treatment get WSLs. They are usually caused by poor toothbrushing around the brace. Although there have been studies that have investigated the prevention and treatment of WSL, there remain uncertainties about what young people and their parents or guardians know or feel about them. A Cochrane review concluded that patient-reported outcomes have been overlooked in WSL prevention studies.</p><p><strong>Objective: </strong>The aim of this study is to explore young people's and their parents'/guardians' perceptions, attitudes, and feelings toward WSLs using a mixed methods study.</p><p><strong>Methods: </strong>This is a mixed methods study. Part 1 is a cross-sectional survey using a web-based survey questionnaire and images of pretreatment malocclusions and postorthodontic WSLs of varying severity (mild, moderate, severe). Part 2 will involve one-to-one, semistructured interviews, using open-ended questions with young people and their parents/guardians. Participants will be recruited from patients aged 11-15 years before, during, or after undergoing orthodontic treatment at Liverpool University Dental Hospital and their parents/guardians. Part 1 (quantitative) will use a Likert scale with the option of free text comments. Data will be analyzed using descriptive statistics. Agreement between participants will be analyzed using the κ statistic. Part 2 (qualitative) will be analyzed using a modified framework analysis approach; the outcomes will be presented as themes. Transcripts from the qualitative interview will be analyzed using inductive thematic analysis. Once the qualitative and quantitative data have been analyzed, we will combine the two datasets and compare them for convergence or divergence. We will aim for a sample size of at least 100 participant and parent/guardian pairs for Part 1 and 30 interviewees for Part 2. Ethical approval was granted in November 2024. The Sponsor Permission to Proceed notification was received in January 2025.</p><p><strong>Results: </strong>Funding for the study was secured in May 2024. Recruitment started on February 2, 2025. As of August 31st, 2025, seventy five participant pairs have been recruited.</p><p><strong>Conclusions: </strong>The study will increase understanding of the impact WSLs have on oral health-related quality of life and the decision-making of young people and their parents/guardians.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e60213"},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12431786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges in Implementing a Mobile AI Chatbot Intervention for Depression Among Youth on Psychiatric Waiting Lists: Randomized Controlled Study Termination Report.","authors":"Junichi Fujita, Yuichiro Yano, Satoru Shinoda, Noriko Sho, Masaki Otsuki, Akira Suda, Mizuho Takayama, Tomoko Moroga, Hiroyuki Yamaguchi, Mio Ishii, Tomoyuki Miyazaki","doi":"10.2196/70960","DOIUrl":"https://doi.org/10.2196/70960","url":null,"abstract":"<p><strong>Background: </strong>The mental health of children and adolescents is a growing public health concern, with increasing rates of depression and anxiety impacting their emotional, social, and academic well-being. In Japan, access to timely psychiatric care is limited, leading to extended waiting periods that can range from 3 months to a year. Artificial intelligence (AI)-driven chatbots, such as emol (Emol Inc) that integrates acceptance and commitment therapy, show potential as digital solutions to support young patients during these waiting times. The AI chatbot emol was selected based on a comprehensive review of Japanese mental health technology apps, including in-person evaluations with company representatives.</p><p><strong>Objective: </strong>This exploratory parallel-group randomized controlled trial examined the feasibility of using an AI chatbot emol with pediatric and adolescent individuals on psychiatric waiting lists.</p><p><strong>Methods: </strong>Participants aged 12-18 years were recruited from 4 hospitals in Kanagawa Prefecture and randomly assigned to either an intervention group, receiving 8 weekly chatbot sessions, or a control group, receiving standard mental health information. The primary outcome was the change in scores on the 9-item Patient Health Questionnaire from pre- to postintervention. Secondary assessments, such as voice and writing pressure analysis, provided additional engagement metrics, with data collected at baseline, during the intervention, and at week 9.</p><p><strong>Results: </strong>Of the 96 eligible individuals on psychiatric waiting lists, 8 expressed interest and 3 provided initial consent. However, all participants subsequently withdrew or were excluded, resulting in no evaluable data for analysis. Low engagement may have been influenced by the perceived irrelevance of digital tools, complex protocols, and privacy concerns.</p><p><strong>Conclusions: </strong>Significant barriers to engagement suggest that digital interventions may need simpler protocols and trusted environments to improve feasibility. Future studies could test these interventions in supportive settings, like schools or community centers, to enhance accessibility and participation among youth.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e70960"},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapidly Benchmarking Large Language Models for Diagnosing Comorbid Patients: Comparative Study Leveraging the LLM-as-a-Judge Method.","authors":"Peter Sarvari, Zaid Al-Fagih","doi":"10.2196/67661","DOIUrl":"10.2196/67661","url":null,"abstract":"<p><strong>Background: </strong>On average, 1 in 10 patients die because of a diagnostic error, and medical errors represent the third largest cause of death in the United States. While large language models (LLMs) have been proposed to aid doctors in diagnoses, no research results have been published comparing the diagnostic abilities of many popular LLMs on a large, openly accessible real-patient cohort.</p><p><strong>Objective: </strong>In this study, we set out to compare the diagnostic ability of 18 LLMs from Google, OpenAI, Meta, Mistral, Cohere, and Anthropic, using 3 prompts, 2 temperature settings, and 1000 randomly selected Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital admissions. We also explore improving the diagnostic hit rate of GPT-4o 05-13 with retrieval-augmented generation (RAG) by utilizing reference ranges provided by the American Board of Internal Medicine.</p><p><strong>Methods: </strong>We evaluated the diagnostic ability of 21 LLMs, using an LLM-as-a-judge approach (an automated, LLM-based evaluation) on MIMIC-IV patient records, which contain final diagnostic codes. For each case, a separate assessor LLM (\"judge\") compared the predictor LLM's diagnostic output to the true diagnoses from the patient record. The assessor determined whether each true diagnosis was inferable from the available data and, if so, whether it was correctly predicted (\"hit\") or not (\"miss\"). Diagnoses not inferable from the patient record were excluded from the hit rate analysis. The reported hit rate was defined as the number of hits divided by the total number of hits and misses. The statistical significance of the differences in model performance was assessed using a pooled z-test for proportions.</p><p><strong>Results: </strong>Gemini 2.5 was the top performer with a hit rate of 97.4% (95% CI 97.0%-97.8%) as assessed by GPT-4.1, significantly outperforming GPT-4.1, Claude-4 Opus, and Claude Sonnet. However, GPT-4.1 ranked the highest in a separate set of experiments evaluated by GPT-4 Turbo, which tended to be less conservative than GPT-4.1 in its assessments. Significant variation in diagnostic hit rates was observed across different prompts, while changes in temperature generally had little effect. Finally, RAG significantly improved the hit rate of GPT-4o 05-13 by an average of 0.8% (P<.006).</p><p><strong>Conclusions: </strong>While the results are promising, more diverse datasets and hospital pilots, as well as close collaborations with physicians, are needed to obtain a better understanding of the diagnostic abilities of these models.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e67661"},"PeriodicalIF":0.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Thermal and Vibration Changes on Automated External Defibrillator Circuit Boards: Finite Element Analysis Study.","authors":"Saidi Olayinka Olalere","doi":"10.2196/53208","DOIUrl":"10.2196/53208","url":null,"abstract":"<p><strong>Background: </strong>An automated external defibrillator (AED) is a device that is used to prevent sudden death by delivering an electrical shock to restore the heart rhythm when experiencing cardiac arrest.</p><p><strong>Objective: </strong>This study was performed to analyze the vibration and thermal changes experienced by an AED medical device when exposed to shocks caused by patients' reactions, vibrations from mobile and air ambulances, and heat changes due to the battery component on the circuit board.</p><p><strong>Methods: </strong>Basically, AED is made from plastic, with the external parts containing the display, buttons, pad socket, and speaker, while the internal part entails the circuit boards comprising components such as resistors, capacitors, inductors, and integrated circuits, among others. In this study, the AED was modeled with the Ansys Workbench 2020 and calibrated based on static and dynamic loading to verify the static displacement and determine the first set of five frequencies obtained based on the unprestressed conditions.</p><p><strong>Results: </strong>Using the prestressed analysis with modifications, the next set of frequencies was obtained with an error margin of 0.0003% between each frequency. The modeled circuit board was used to examine the vibration and dynamic analysis for the rigid board. Similarly, thermal analysis was conducted on the modeled circuit board with the battery serving as the heat source. The rate of dissipation of heat around the board and its effect on the circuit components was evaluated.</p><p><strong>Conclusions: </strong>The modeled circuit board was reinforced with more support structures to mitigate the deformation effect. The deformation peaked at 33.172 mm, with minimum deformation at the edges of the board. Components with greater height, such as capacitors, experienced more pronounced deformation. Therefore, it is suggested that flat capacitors of lesser height would be suitable for future designs. Additionally, significant heat dissipation from the battery suggests a need for better dissipation pathways.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e53208"},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Willingness to Pay for the COVID-19 Vaccine and Its Correlates in Bangladesh: Cross-Sectional Study.","authors":"Mohammad Bellal Hossain, Md Zakiul Alam, Md Syful Islam, Shafayat Sultan, Md Mahir Faysal, Sharmin Rima, Md Anwer Hossain, Abdullah Al Mamun, Abdullah-Al- Mamun","doi":"10.2196/69827","DOIUrl":"10.2196/69827","url":null,"abstract":"<p><strong>Background: </strong>The Government of Bangladesh offers COVID-19 vaccines at no cost; however, sustaining this free vaccination program for a large population poses significant challenges. Thus, assessing the willingness to pay (WTP) for the COVID-19 vaccine is essential for understanding potential pricing strategies, subsidy requirements, and vaccine demand.</p><p><strong>Objective: </strong>This study aimed to assess the prevalence of WTP for the COVID-19 vaccine and its correlates.</p><p><strong>Methods: </strong>A cross-sectional design was used to collect data from 1497 respondents through web-based platform and face-to-face interviews. Multivariable logistic regression was used to analyze the correlates of the WTP.</p><p><strong>Results: </strong>The results showed that 772 of 1497 (51.6%) participants were willing to pay for the COVID-19 vaccine, with a median of 300 BDT (IQR 150-500 BDT; a currency exchange rate of 1 BDT=US $0.008 is applicable). The WTP was significantly higher among individuals with a graduate degree (adjusted odds ratio [aOR] 1.98, 95% CI 1.14-3.45) or master's and MPhil or PhD-level education (aOR 1.93, 95% CI 1.07-3.48) and those with higher knowledge about the vaccine (aOR 1.09, 95% CI 1.02-1.15), positive behavioral practices (aOR 1.11, 95% CI 1.06-1.17), stronger subjective norms regarding COVID-19 vaccine (aOR 1.25, 95% CI 1.08-1.46), and higher anticipated regret of getting infected with COVID-19 (aOR 1.17, 95% CI 1.04-1.32). Conversely, WTP was lower among participants with negative attitudes toward vaccines (aOR 0.91, 95% CI 0.88-0.95) and high perceived behavioral control regarding COVID-19 vaccination (aOR 0.86, 95% CI 0.76-0.96; P=.006).</p><p><strong>Conclusions: </strong>With nearly half of the respondents unwilling to pay, this study highlights the need to improve vaccine-related knowledge and enhance income-based affordability to increase WTP. Health promotion efforts should focus on disseminating knowledge about vaccines and addressing negative perceptions. Additionally, a subsidized program for low-income groups can help mitigate financial barriers and promote equitable access to vaccines.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e69827"},"PeriodicalIF":0.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356522/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144859972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Mobile Forms in Low-Resource Areas for Population Health Surveys: Interview and Field Test Study.","authors":"Alexander Davis, Aidan Chen, Milton Chen, James Davis","doi":"10.2196/53715","DOIUrl":"10.2196/53715","url":null,"abstract":"<p><strong>Background: </strong>Population health surveys are an important tool to effectively allocate limited resources in low-resource communities. In such an environment, surveys are often done by the local population with pen and paper. Data thus collected are difficult to tabulate and analyze.</p><p><strong>Objective: </strong>The objective of this study was to evaluate the viability and efficiency of mobile forms as an alternative to paper-based surveys in a specific low-resource setting.</p><p><strong>Methods: </strong>We conducted pilot interviews with 53 local surveyors in the Philippines to assess their initial attitudes toward mobile forms. We then built software that can generate mobile forms that are easy to use, capable of working offline, and able to track key metrics such as time to complete questions. Our mobile form was field-tested in 3 locations in the Philippines with 33 surveyors collecting health survey responses from 266 participants.</p><p><strong>Results: </strong>In the pilot phase, we found that 32 out of 53 (60%) of the local surveyors preferred mobile forms over paper. After field-testing, the number of surveyors preferring mobile forms increased to 25 out of 33 (76%) after just using the form a few times. The mobile forms overall demonstrated enhanced efficiency in data collection and usability over paper surveys.</p><p><strong>Conclusions: </strong>Our findings indicate that mobile forms are a viable method to conduct large-scale population health surveys in this low-resource environment.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"6 ","pages":"e53715"},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144823351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}