Search

Background Discrimination is an important determinant of negative mental health outcomes. This study determined the association between the experience of COVID-19-related discrimination and psychological distress among healthcare workers (HCWs) in Japan. Methods This cross-sectional study conducted a health survey among 5703 HCWs of six national medical and research centers in Japan from October 2020 to March 2021. COVID-19-related discrimination was defined either when participants or their family members were badmouthed or when they felt discriminated against in some way. We used the Kessler Psychological Distress Scale (K6) to assess the presence of severe psychological distress (≥ 13 points). We used logistic regression models to examine the association between discrimination and psychological distress. We also identified factors associated with discrimination. Results Of the participants, 484 (8.4%) reported COVID-19-related discrimination and 486 (8.5%) had severe psychological distress. HCWs who were female vs. male (adjusted odds ratio [AOR] = 1.41, 95% confidence interval [CI] = 1.28–1.55), had high vs. low viral exposure (AOR = 2.31, 95% CI = 1.81–2.93), and worked for 11 or more hours/day vs. 8 or less hours/day (AOR = 1.42, 95% CI = 1.35–1.49) were more likely to have experienced COVID-19-related discrimination. The AOR (95% CI) of severe psychological distress was 1.83 (1.29–2.59) among those who experienced discrimination. In the stratified analysis by sociodemographic and job-related factors, all the interactions did not reach statistical significance (p for interaction > 0.20). Conclusion Experience of COVID-19-related discrimination was associated with severe psychological distress among HCWs. During the pandemic, effective measures should be taken to prevent the development of negative mental health outcomes in HCWs who experience discrimination.

We assessed the consistency of seropositive results of three rapid immunoassays (Kits A, B, and C) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) compared to highly accurate serological tests (Abbott and Roche) among healthcare workers in a hospital in Tokyo. The seroprevalence of SARS-CoV-2 immunoglobulin G was 0.41%, 2.36%, and 0.08% using Kits A, B, and C, respectively. Of the 51 samples that were seropositive on any rapid test, all were seronegative on both the Abbott and the Roche assays. Given that the seroprevalence of SARS-CoV-2 immunoglobulin G varied widely according to the choice of rapid test and the rapid test results were inconsistent with the results of highly accurate tests, the diagnostic accuracy of rapid serological tests for SARS-CoV-2 should be assessed before introducing these tests for point-of-care testing or surveillance.

The objective of this study was to investigate the association between adherence to healthy lifestyles (as measured by the healthy lifestyle index [HLI]) and depressive symptoms among staff members at a large national medical institution in Tokyo, Japan, during the coronavirus disease 2019 (COVID-19) pandemic. The study sample consisted of 1228 staff members aged between 21 and 73 years who participated in a cross-sectional survey conducted in July 2020. We constructed the HLI by assigning 1 point to each healthy lifestyle factor: normal body mass index, sufficient physical activity, non-smoking status, non-to-moderate alcohol consumption, and sufficient sleep duration. The multivariate adjusted odds ratios (95% confidence interval) for depressive symptoms were 1.00 (reference), 0.71 (0.46-1.11), 0.66 (0.43-1.01), and 0.56 (0.33-0.94) for participants with HLI scores of 0 to 2, 3, 4, and 5, respectively (P for trend = 0.032). The present study suggests the role of healthy lifestyles in mental health among hospital staff working during the pandemic.

This study investigated the sex-associated difference in the impact of obesity on antibody response to a COVID-19 vaccine.This study included 2,435 health care workers who received two doses of the BioNTech, Pfizer (BNT162b2) vaccine and participated in a serological survey, during which they were tested for anti-SARS-CoV-2 spike immunoglobin G (IgG) antibodies and asked for information on height, weight, and vaccination history via a questionnaire. Multivariable linear regression analysis was used to estimate the geometric mean titers (GMT) of antibodies for each sex and BMI category.The relationship between BMI and anti-SARS-CoV-2 spike IgG titers markedly differed by sex (p value for interaction = 0.04). Spike IgG antibody titers tended to decrease with increasing BMI in men (p value for trend = 0.03); GMT (95% CI) were 6,093 (4,874-7,618) and 4,655 (3,795-5,708) for BMI18.5 and ≥30 kg/mHigher BMI was associated with lower titers of SARS-CoV-2 spike antibodies in men, but not in women, suggesting the need for careful monitoring of vaccine efficacy in men with obesity, who are at high risk of severe COVID-19 outcomes.

While accumulating evidence suggests a protective role of healthy diet against depression, evidence on this issue is limited among healthcare workers combating COVID-19 pandemic. The aim of this study was to determine the cross-sectional association between frequency of balanced meal consumption and depressive symptoms among Japanese hospital workers during the COVID-19 pandemic.Participants were 2,457 workers of the National Center for Global Health and Medicine who responded to a questionnaire survey in October or December 2020. Depressive symptoms were assessed using the patient health questionnaire-9 (PHQ-9). The number of days per week of eating two or more balanced meals was categorized into four from ≤1 day/week to daily. The association between frequency of balanced meal consumption and depressive symptoms was assessed using logistic regression analysis, with adjustment for lifestyle and COVID-19-related factors.The prevalence of depressive symptoms was 14.8%. The odds of depressive symptoms increased with decreasing frequency of balanced meal consumption. The multivariable-adjusted odds ratios (95% confidence intervals) of depressive symptoms were 1.00 (reference), 1.09 (0.75-1.58), 1.62 (1.17-2.24), and 2.21 (1.54-3.17) for balanced meal consumption categories of daily, 4-5 days/week, 2-3 days/week, and ≤1 day/week, respectively (P for trend0.001).Our results suggest that infrequent consumption of well-balanced meal is associated with increased depressive symptoms among hospital workers during the COVID-19 pandemic.

BackgroundThe effect of heat-not-burn (HNB) tobacco product use and moderate alcohol drinking on immunogenicity to coronavirus disease (COVID-19) vaccines remain elusive. This study aimed to examine the association of tobacco product use and alcohol consumption with anti-SARS-CoV-2 spike IgG antibody titers after the BNT162b2 vaccine.MethodsParticipants were 3,457 fully vaccinated healthcare workers in the 4 national centers for advanced medical and research in Japan. Smoking status and alcohol consumption were assessed via a questionnaire, and anti-SARS-CoV-2 spike IgG titers were measured by chemiluminescent enzyme immunoassay using serum collected on the median of 64 days after the second vaccination. Multilevel linear regression models were used to estimate the geometric mean titers (GMT) and the ratios of means (RoM) between groups.ResultsOf vaccinated participants, 99.5% (3,440/3,457) were seropositive. Compared with never-smokers (GMT=119), IgG antibody titers were significantly lower among HNB tobacco users (including those who also smoked cigarettes) (GMT=105; RoM=0.88 [95%CI: 0.78–0.99]) and exclusive cigarettes smokers (GMT=96; RoM=0.81 [95%CI: 0.71–0.92]). Compared with non-drinkers of alcohol (GMT=123), alcohol drinkers consuming go/day (GMT=114; RoM=0.93 [95%CI: 0.88–0.98]), 1–1.9 go/day (GMT=105; RoM=0.85 [95%CI: 0.79–0.93]), and ≥2 go/day (GMT=101; RoM=0.82 [95%CI: 0.72–0.94]) had significantly lower antibody titers (P for trendgo/day of alcohol consumption, and then they gradually decreased.ConclusionsResults suggest that in addition to conventional cigarette smoking and heavy alcohol drinking, use of HNB tobacco products and moderate alcohol drinking may be predictors of lower immunological response to COVID-19 vaccine.Key MessagesEpidemiological evidence regarding the association of smoking status and alcohol drinking with COVID-19 vaccine-induced antibody levels is scarce.Users of heat-not-burn (HNB) tobacco products, as well as cigarettes smokers, had lower antibody titers than never-smokers.Not only high-dose but moderate-dose alcohol intake was also associated with decreased vaccine-induced antibody levels.HNB tobacco product use and moderate alcohol drinking may be modifiers of COVID-19 vaccine-induced immunogenicity.

Obesity may downregulate vaccine-induced immunogenicity, but the epidemiological evidence for the COVID-19 vaccine is limited, and the sex-associated difference is unknown. It was observed that a higher body mass index was associated with lower titers of spike IgG antibodies against SARS-CoV-2 in men but not in women.

The effect of heated tobacco products (HTPs) use and moderate alcohol drinking on immunogenicity to coronavirus disease (COVID-19) vaccines remain elusive. This study aimed to examine the association of tobacco product use and alcohol consumption with anti-SARS-CoV-2 spike IgG antibody titers after the BNT162b2 vaccine. Participants were 3433 healthcare workers receiving two vaccine doses in the 4 national centers for advanced medical and research in Japan. Smoking status and alcohol consumption were assessed via a questionnaire, and anti-SARS-CoV-2 spike IgG titers were measured with chemiluminescent enzyme immunoassay using serum collected on the median of 64 days after the second vaccination. Multilevel linear regression models were used to estimate the geometric mean titers (GMT) and the ratios of means (RoM) between groups with adjustment for covariates. Compared with never-smokers (GMT = 118), IgG antibody titers were significantly lower among HTPs users (including those who also smoked cigarettes) (GMT = 105; RoM = 0.89 [95%CI: 0.78-0.99]) and exclusive cigarettes smokers (GMT = 98; RoM = 0.81 [95%CI: 0.71-0.92]). Compared with non-drinkers of alcohol (GMT = 123), alcohol drinkers consuming1 go/day (GMT = 113; RoM = 0.93 [95%CI: 0.88-0.98]), 1-1.9 go/day (GMT = 104; RoM = 0.85 [95%CI: 0.78-0.93]), and ≥ 2 go/day (GMT = 103; RoM = 0.84 [95%CI: 0.74-0.96]) had significantly lower antibody titers (P for trend0.01). Spline analysis showed a large reduction of antibody until around 1 go/day of alcohol consumption, and then they gradually decreased. Results suggest that in addition to conventional cigarette smoking and heavy alcohol drinking, HTPs use and moderate alcohol drinking may be predictors of lower immunological response to COVID-19 vaccine.

By applying the Bayesian network method to the clinical registry database J-DREAMS (Japan Diabetes compREhensive database project based on an Advanced electronic Medical record System), we have developed a reference model that summarizes the exploration of the patient group's state and facilitates a bird's-eye view. This visualization method would help registry researchers to screen the registry database.

Medication alert systems have been implemented worldwide. The purpose of this study is evaluation of the medication alert systems from a clinical perspective. We surveyed physicians with regard to their reactions to the medication alerts. We collected the revised prescription information and assessed risk avoidance in all cases. The system reviewed 51,006 prescriptions and produced 16,718 physician alerts related to 13,823 prescriptions over the course of 1 month. We identified 45 prescriptions that were revised following the alert and four cases in which patient treatment may have been discontinued or adverse drug events (ADEs) may have occurred if the alerts had not been issued. We demonstrated that the system prevented these potential medication errors. This study adopted a clinical perspective and demonstrated that a real-time alert system can contribute to prevention of ADEs.

This study presents a prediction-based approach to determine thresholds for a medication alert in a computerized physician order entry. Traditional static thresholds can sometimes lead to physician's alert fatigue or overlook potentially excessive medication even if the doses are belowthe configured threshold. To address this problem, we applied a random forest algorithm to develop a prediction model for medication doses, and applied a boxplot to determine the thresholds based on the prediction results. An evaluation of the eight drugs most frequently causing alerts in our hospital showed that the performances of the prediction were high, except for two drugs. It was also found that using the thresholds based on the predictions would reduce the alerts to a half of those when using the static thresholds. Notably, some cases were detected only by the prediction thresholds. The significance of the thresholds should be discussed in terms of the trade-offs between gains and losses; however, our approach, which relies on physicians' collective experiences, has practical advantages.

The purpose of this study was to develop a new alerting and recommender system for preventing medication errors. In recent years, alerting systems have been widely implemented, but because these systems apply a same static threshold for all patients in all cases, they produce excessive alerts and subject physicians to "alert fatigue". We believe that the most commonly-written prescription for a patient's status is the safest one. From this standpoint, we developed a real-time case-based medication alerting and recommender system linked to a database of past prescriptions. When a physician issues his or her prescription, our system dynamically compares it with past ones for similar patients in the database. An analysis of the 10 most frequently-used drugs in the University of Tokyo Hospital revealed that our system reduced the number of false alerts compared to the traditional static alert method. Our system contributes to the creation of alerts that are appropriate for patients' clinical conditions and based on physicians' empirical discretion.

The International Classification of Diseases 10th version (ICD-10) is one of the standard and most important disease classifications. Since computerized ICD-10 coding systems have drawn a great deal of attention in the medical field, a great number of different coding systems have been proposed. This paper proposes a hybrid architecture of different coding systems. First, given an input disease name, three coding systems output codes with their confidence scores. A C4.5-based system selector then selects the best output by using both input statistics and the confidence score from each system. The experimental results demonstrated that the selector significantly boosts the overall performance (+3.4 points).

Radiology reports are written primarily in natural language. Automated extraction of malignant findings from narrative reports is an important technique for clinical support or alert generation for physicians. This paper proposes a method for automatically extracting malignant findings from narrative radiological reports written in Japanese. First, sentences are parsed and a medical attribute of each phrase is determined. Next, sub-trees related to radiological findings are extracted from a dependency tree using medical attributes. Finally, the malignant findings in each sub tree are extracted with their positive or negative assertions, each of which is determined by the multiplication of pos/neg signs along a path in a sub-tree. The recall and precision for the extraction of malignant findings with their positive or negative assertions were 76% and 91% respectively. The experimental results showed the validity of the proposed method for extracting malignant findings with correct assertions.

Although researchers have shown increasing interest in extracting/classifying semantic relations, most previous studies have basically relied on lexical patterns between terms. This paper proposes a novel way to accomplish the task: a system that captures a physical size of an entity. Experimental results revealed that our proposed method is feasible and prevents the problems inherent in other methods.