Geographic and economic influences on benralizumab prescribing for severe asthma in Japan.

Benralizumab, a monoclonal antibody targeting IL-5 receptors, reduces exacerbations and oral corticosteroid requirements for severe, uncontrolled eosinophilic asthma. In Japan, geographic disparities in asthma outcomes suggest differential prescribing and access. This study aimed to quantify regional prescribing variations for benralizumab nationwide. Using Japan's National Database (NDB) of insurance claims (2009-2019), benralizumab standardized claim ratios (SCRs) were calculated for 47 prefectures. Correlations between SCRs and other biologics' SCRs, economic variables like average income, and physician densities were evaluated through univariate analysis and multivariate regressions. Income-related barriers to optimal prescribing were examined. Wide variation emerged in benralizumab SCRs, from 40.1 to 184.2 across prefectures. SCRs strongly correlated with omalizumab (r = 0.61, p < 0.00001) and mepolizumab (r = 0.43, p = 0.0024). Average monthly income also positively correlated with benralizumab SCRs (r = 0.45, p = 0.0016), whereas lifestyle factors were insignificant. Respiratory specialist density modestly correlated with SCRs (r = 0.29, p = 0.047). In multivariate regressions, average income remained the most robust predictor (B = 0.74, p = 0.022). Benralizumab SCRs strongly associate with income metrics more than healthcare infrastructure/population factors. Many regions show low SCRs, constituting apparent prescribing gaps. Access barriers for advanced asthma therapies remain inequitable among Japan's income strata. Addressing affordability alongside specialist allocation can achieve better prescribing quality and asthma outcomes.

Improved diagnostic accuracy with three lung tumor markers compared to six-marker panel.

Background: Combining multiple tumor markers increases sensitivity for lung cancer diagnosis in the cost of false positive. However, some would like to check as many as tumor markers in the fear of missing cancer. We though to propose a panel of fewer tumor markers for lung cancer diagnosis. Methods: Patients with suspected lung cancer who simultaneously underwent all six tests [carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA), squamous cell carcinoma-associated antigen (SCC), neuron-specific enolase (NSE), pro-gastrin-releasing peptide (ProGRP), and sialyl Lewis-X antigen (SLX)] were included. Tumor markers with significant impact on the lung cancer in a logistic regression model were included in our panel. Area under the curve (AUC) was compared between our panel and the panel of all six. Results: We included 1,733 [median 72 years, 1,128 men, 605 women, 779 (45%) confirmed lung cancer]. Logistic regression analysis suggested CEA, CYFRA, and NSE were independently associated with the lung cancer diagnosis. The panel of these three tumor markers [AUC =0.656, 95% confidence interval (CI): 0.630-0.682, sensitivity 0.650, specificity 0.662] had better (P<0.001) diagnostic performance than six tumor markers (AUC =0.575, 95% CI: 0.548-0.602, sensitivity 0.829, specificity 0.321). Conclusions: Compared to applying all six markers (at least one marker above the upper limit of normal), the panel with three markers (at least one marker above the upper limit of normal) led to a better predictive value by lowering the risk of false positives.

Whole-genome sequencing predicting phenotypic antitubercular drug resistance: meta-analysis.

BACKGROUND: For simultaneous prediction of phenotypic drug susceptibility test (pDST) for multiple anti-tuberculosis drugs, the whole genome sequencing (WGS) data can be analyzed using either catalogue-based approach, wherein one causative mutation suggests resistance, (e.g., WHO catalog) or non-catalogue-based approach using complicated algorithm (e.g., TB-profiler, machine learning). The aim was to estimate the predictive ability of WGS-based tests with pDST as the reference, and to compare the two approaches. METHODS: Following the systematic literature search, the diagnostic test accuracies for 14 drugs were pooled using a random-effect bivariate model. RESULTS: Out of 779 articles, 44 articles with 16,821 specimens for meta-analysis and 13 articles not for meta-analysis were adopted. The areas under summary receiver operating characteristic curve suggested "excellent" (0.97-1.00) for 2 drugs (isoniazid 0.975, rifampicin 0.975), "very good" (0.93-0.97) for 8 drugs (pyrazinamide 0.946, streptomycin 0.952, amikacin 0.968, kanamycin 0.963, capreomycin 0.965, para-aminosalicylic acid 0.959, levofloxacin 0.960, ofloxacin 0.958), and "good" (0.75-0.93) for 4 drugs (ethambutol 0.926, moxifloxacin 0.896, ethionamide 0.878, prothionamide 0.908). The non-catalogue-based and catalogue-based approaches had similar ability for all drugs. CONCLUSION: WGS accurately identifies isoniazid and rifampicin resistance. For most drugs, positive WGS results reliably predict pDST positive. The two approaches had similar ability.