Pathogens detected from patients with acute respiratory infections negative for SARS-CoV-2, Saitama, Japan, 2020.

Objective: During the coronavirus disease pandemic in Japan, all patients with respiratory symptoms were initially tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study describes the respiratory pathogens detected from patients who tested negative for SARS-CoV-2 at the Saitama Institute of Public Health from January to December 2020. Methods: We performed pathogen retrieval using multiplex real-time polymerase chain reaction on samples from patients with acute respiratory diseases who tested negative for SARS-CoV-2 in Saitama in 2020 and analysed the results by age and symptoms. Results: There were 1530 patients aged 0-104 years (1727 samples), with 14 pathogens detected from 213 patients (245 samples). Most pathogens were human metapneumovirus (25.4%, 54 cases), rhinovirus (16.4%, 35 cases) and Mycoplasma pneumoniae (13.1%, 23 cases). Human metapneumovirus, human coronavirus (but not NL63) and M. pneumoniae were detected in almost all age groups without any significant bias. Seasonal human coronaviruses, human metapneumovirus, M. pneumoniae and several other pathogens were detected until April 2020. Discussion: Multiple respiratory pathogens were circulating during 2020 in Saitama, including SARS-CoV-2 and influenza viruses. We suggest introducing a system that can comprehensively monitor the regional prevalence of all viruses that cause acute respiratory infections.

Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases.

BACKGROUND: Medical costs and the burden associated with cardiovascular disease are on the rise. Therefore, to improve the overall economy and quality assessment of the healthcare system, we developed a predictive model of integrated healthcare resource consumption (Adherence Score for Healthcare Resource Outcome, ASHRO) that incorporates patient health behaviours, and examined its association with clinical outcomes. METHODS: This study used information from a large-scale database on health insurance claims, long-term care insurance, and health check-ups. Participants comprised patients who received inpatient medical care for diseases of the circulatory system (ICD-10 codes I00-I99). The predictive model used broadly defined composite adherence as the explanatory variable and medical and long-term care costs as the objective variable. Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors, and multiple regression analysis to construct ASHRO scores. The ability of discrimination and calibration of the prediction model were evaluated using the area under the curve and the Hosmer-Lemeshow test. We compared the overall mortality of the two ASHRO 50% cut-off groups adjusted for clinical risk factors by propensity score matching over a 48-month follow-up period. RESULTS: Overall, 48,456 patients were discharged from the hospital with cardiovascular disease (mean age, 68.3 ± 9.9 years; male, 61.9%). The broad adherence score classification, adjusted as an index of the predictive model by machine learning, was an index of eight: secondary prevention, rehabilitation intensity, guidance, proportion of days covered, overlapping outpatient visits/clinical laboratory and physiological tests, medical attendance, and generic drug rate. Multiple regression analysis showed an overall coefficient of determination of 0.313 (p < 0.001). Logistic regression analysis with cut-off values of 50% and 25%/75% for medical and long-term care costs showed that the overall coefficient of determination was statistically significant (p < 0.001). The score of ASHRO was associated with the incidence of all deaths between the two 50% cut-off groups (2% vs. 7%; p < 0.001). CONCLUSIONS: ASHRO accurately predicted future integrated healthcare resource consumption and was associated with clinical outcomes. It can be a valuable tool for evaluating the economic usefulness of individual adherence behaviours and optimising clinical outcomes.