High-need, high-cost (HNHC) patients—usually defined as those who account for the top 5% of annual healthcare costs—use as much as half of the total healthcare costs. Accurately predicting future HNHC patients and designing targeted interventions for them has the potential to effectively control rapidly growing healthcare expenditures. To achieve this goal, we used a nationally representative random sample of the working-age population who underwent a screening program in Japan in 2013–2016, and developed five machine-learning-based prediction models for HNHC patients in the subsequent year. Predictors include demographics, blood pressure, laboratory tests (e.g., HbA1c, LDL-C, and AST), survey responses (e.g., smoking status, medications, and past medical history), and annual healthcare cost in the prior year. Our prediction models for HNHC patients combining clinical data from the national screening program with claims data showed a c-statistics of 0.84 (95%CI, 0.83–0.86), and overperformed traditional prediction models relying only on claims data.

ObjectivesThere have been concerns that patients with chronic conditions may be avoiding in-person physician visits due to fear of COVID-19, leading to lower quality of care. We aimed to investigate changes in physician visits and medication prescriptions for chronic diseases before and during the COVID-19 pandemic at the population level.DesignRetrospective cohort study.SettingNationwide claims data in Japan, 2018–2020.ParticipantsWorking-age population (aged 18–74 years) who visited physicians and received any prescriptions for major chronic diseases (hypertension, diabetes and dyslipidaemia) before the pandemic.Outcome measuresThe outcomes were the monthly number of physician visits, the monthly proportion of physician visits and the monthly proportion of days covered by prescribed medication (PDC) during the pandemic (April–May 2020, as the first state of emergency over COVID-19 was declared on 7 April, and withdrawn nationally on 25 May).ResultsAmong 10 346 patients who visited physicians for chronic diseases before the pandemic, we found a temporary decline in physician visits (mean number of visits was 1.9 in March vs 1.7 in April; p<0.001) and an increase in the proportion of patients who did not visit any physicians during the pandemic (15% in March vs 24% in April; p<0.001). Physician visits returned to the baseline in May (the mean number of visits: 1.8, and the proportion of patients who did not visit any physicians: 9%). We observed no clinically meaningful difference in PDC between before and during the pandemic (eg, 87% in March vs 87% in April; p=0.45). A temporary decline in physician visits was more salient in seven prefectures with a larger number of COVID-19 cases than in other areas.ConclusionsAlthough the number of physician visits declined right after the COVID-19 outbreak, it returned to the baseline one month later; patients were not skipping medications during the pandemic.

Critical illnesses associated with coronavirus disease 2019 (COVID-19) are attributable to a hypercoagulable status. There is limited knowledge regarding the dynamic changes in coagulation factors among COVID-19 patients on nafamostat mesylate, a potential therapeutic anticoagulant for COVID-19. First, we retrospectively conducted a cluster analysis based on clinical characteristics on admission to identify latent subgroups among fifteen patients with COVID-19 on nafamostat mesylate at the University of Tokyo Hospital, Japan, between April 6 and May 31, 2020. Next, we delineated the characteristics of all patients as well as COVID-19-patient subgroups and compared dynamic changes in coagulation factors among each subgroup. The subsequent dynamic changes in fibrinogen and D-dimer levels were presented graphically. All COVID-19 patients were classified into three subgroups: clusters A, B, and C, representing low, intermediate, and high risk of poor outcomes, respectively. All patients were alive 30 days from symptom onset. No patient in cluster A required mechanical ventilation; however, all patients in cluster C required mechanical ventilation, and half of them were treated with venovenous extracorporeal membrane oxygenation. All patients in cluster A maintained low D-dimer levels, but some critical patients in clusters B and C showed dynamic changes in fibrinogen and D-dimer levels. Although the potential of nafamostat mesylate needs to be evaluated in randomized clinical trials, admission characteristics of patients with COVID-19 could predict subsequent coagulopathy. Keywords COVID-19 • Anticoagulant • D-dimer • Fibrinogen • Nafamostat mesylate Highlights • All patients with COVID-19 treated on nafamostat mesylate were alive at 30 days from symptom onset. • Using cluster analysis based on clinical characteristics on admission, we identified three subgroups among COVID-19 patients with different clinical presentations of subsequent coagulopathy. • Clinical characteristics on admission are beneficial in predicting subsequent coagulopathy and consider individualized approaches for thromboembolism. • Since COVID-19-associated coagulopathy resembles DIC, a careful evaluation of dynamic changes in coagulopathy, as reflected by fibrinogen and D-dimer levels, is essential.

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ObjectiveTo investigate determining factors of happiness during the COVID-19 pandemic.DesignObservational study.SettingLarge online surveys in Japan before and during the COVID-19 pandemic.ParticipantsA random sample of 25 482 individuals who are representatives of the Japanese population.Main outcome measureSelf-reported happiness measured using a 10-point Likert scale, where higher scores indicated higher levels of happiness. We defined participants with ≥8 on the scale as having high levels of happiness.ResultsAmong the 25 482 respondents, the median score of self-reported happiness was 7 (IQR 6–8), with 11 418 (45%) reporting high levels of happiness during the pandemic. The multivariable logistic regression model showed that meaning in life, having a spouse, trust in neighbours and female gender were positively associated with happiness (eg, adjusted OR (aOR) for meaning in life 4.17; 95% CI 3.92 to 4.43; p<0.001). Conversely, self-reported poor health, anxiety about future household income, psychiatric diseases except depression and feeling isolated were negatively associated with happiness (eg, aOR for self-reported poor health 0.44; 95% CI 0.39 to 0.48; p<0.001). Using machine-learning methods, we found that meaning in life and social capital (eg, having a spouse and trust in communities) were the strongest positive determinants of happiness, whereas poor health, anxiety about future household income and feeling isolated were important negative determinants of happiness. Among 6965 subjects who responded to questionnaires both before and during the COVID-19 pandemic, there was no systemic difference in the patterns as to determinants of declined happiness during the pandemic.ConclusionUsing machine-learning methods on data from large online surveys in Japan, we found that interventions that have a positive impact on social capital as well as successful pandemic control and economic stimuli may effectively improve the population-level psychological well-being during the COVID-19 pandemic.

that the number of ED patients diagnosed with PEs and DVTs increased after the arrival of Covid-19.Methods: Design: Retrospective cohort. Setting: EDs of 28 hospitals within 150 miles of New York City. Hospitals were teaching or non-teaching and rural, suburban or urban. Annual ED volumes were from 12, 000 to 122, 000. Population: Consecutive patients seen by ED physicians from March through November in 2019 and 2020, as COVID-19 arrived in this region in early March. Data analysis: We tallied the number of patients diagnosed with PEs and DVTs using International Classification of Disease (version 10) codes. We computed the changes in visits from 2019 to 2020. We used chi-square to test for statistical significance, with alpha set at 0.025using the Bonferroni correction for multiple comparisons.

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