ABSTRACT
Background and Objectives@#The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards.@*Methods@#A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated. @*Results@#With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices. @*Conclusions@#qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.
Subject(s)
Tuberculosis , Diagnostic Imaging , Deep LearningABSTRACT
ObjectiveCOVID-19 appears to have caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with high-income countries, possibly because of differing demographics, socio-economics, surveillance, and policy responses. Here, we investigate the role of multiple factors on COVID-19 dynamics in the Philippines, a LMIC that has had a relatively severe COVID-19 outbreak. MethodsWe applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a "Minimum Health Standards" policy, MHS) to represent the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and interventions. FindingsPopulation age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. Several of the fitted epidemiological parameters were consistent with those reported in high-income settings. The model indicated that MHS reduced the probability of transmission per contact by 15-26%. The February 2021 case detection rate was estimated at [~]9%, population recovered at [~]12%, and scenario projections indicated high sensitivity to MHS adherence. ConclusionsCOVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and MHS adherence, and the epidemic can be understood within a similar framework as for high-income settings. Continued compliance with low-cost MHS should allow the Philippines to maintain epidemic control until vaccines are widely distributed, but disease resurgence could occur due to low population immunity and detection rates.
ABSTRACT
@#<p style="text-align: justify;"><strong>BACKGROUND AND OBJECTIVE:</strong> With an aim of developing an effective disease monitoring and surveillance of dengue fever, this study intends to analyze the spatial distribution of dengue incidences in the National Capital Region (NCR), across four years of reported dengue cases.<br /><strong>MATERIALS AND METHODS:</strong> Data used was provided by the Department of Health (DOH) consisting of all reported dengue cases in NCR from 2010-2013. For mapping and visualization, a shapefile of NCR was made readily available by www.philgis.org. Both Moran's I and Kulldorff's spatial scan statistics (SaTScan) were used to identify clusters across the same time period.<br /><strong>RESULTS AND CONCLUSION:</strong> The analyses identified significant clustering of dengue incidence and revealed that the northern cities of NCR, such as Caloocan, Malabon, Navotas and Valenzuela, exhibited high spatial autocorrelation using local Moran's I and Kulldorff's SaTScan. A temporal analysis of the results also suggested movement in increased dengue incidence through time, from the northwest cities to the northeast cities. Presence of spatial autocorrelation in dengue incidence suggests possible enhancements of early detection schemes for dengue surveillance. Moreover, the results of these analyses will be of interest to both policymakers and health experts in providing a basis for which they can properly allocate resources for the prevention and treatment of dengue fever.</p>
