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Factors associated with seasonal influenza self-diagnosis: a prospective observational study in Japan.

ABSTRACT: This prospective observational study, conducted at a community clinic in Japan during the influenza season, from December 2017 to April 2018 aimed to investigate the accuracy of factors used for influenza self-diagnosis. Data were collected from pre-examination checklists issued to patients with suspected influenza and electronic medical records. Receiver operating characteristic (ROC) curve analysis was performed using a rapid influenza diagnostic test as the reference standard, and 2?×?2 contingency tables were analysed at each cut-off point. We analysed data from 290 patients (72.8% males, median age: 38 years, interquartile range: 26-50 years). The area under the ROC curve (AUC) for patients who were aware of other patients presumed to have influenza within close proximity was 0.74 (95% confidence interval (CI): 0.66-0.82). The AUCs for patients with a history of influenza, unvaccinated status, cough, or nasal discharge were 0.68 (95% CI: 0.60-0.75), 0.66 (95% CI: 0.59-0.73), 0.67 (95% CI: 0.59-0.75), and 0.70 (95% CI: 0.62-0.78), respectively. The sensitivity, specificity and positive likelihood ratio at a 90% cut-off point was 19.5% (95% CI: 13.5-26.6%), 94.1% (95% CI: 88.7-97.4%) and 3.31 (95% CI: 1.57-6.98). The sensitivity, specificity and negative likelihood ratio at a 10% cut-off point was 95.5% (95% CI: 90.9-98.2%), 9.6% (95% CI: 5.2-15.8%) and 0.48 (95% CI: 0.20-1.16). After multivariate logistic regression analysis, the AUC increased significantly from 0.77 (95% CI: 0.70-0.83) to 0.81 (95% CI: 0.76-0.86) when self-diagnosis-related information was added to basic clinical information. We identified factors that improve the accuracy and validity of influenza self-diagnosis. Appropriate self-diagnosis could contribute to the containment efforts during influenza epidemics and reduce its social and economic burden.

SUBMITTER: Maita H

PROVIDER: S-EPMC7090047 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Factors associated with seasonal influenza self-diagnosis: a prospective observational study in Japan.

Maita Hiroki H Kobayashi Tadashi T Akimoto Takashi T Matsuoka Fumihiko F Osawa Hiroshi H Kato Hiroyuki H

NPJ primary care respiratory medicine 20200323 1

This prospective observational study, conducted at a community clinic in Japan during the influenza season, from December 2017 to April 2018 aimed to investigate the accuracy of factors used for influenza self-diagnosis. Data were collected from pre-examination checklists issued to patients with suspected influenza and electronic medical records. Receiver operating characteristic (ROC) curve analysis was performed using a rapid influenza diagnostic test as the reference standard, and 2 × 2 conti ...[more]

PMID: 32251292

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Project description:BackgroundPredicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions.ObjectiveThe aim of this study was to develop predictive models for influenza vaccination behavior among children in China.MethodsWe obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance.ResultsA total of 2383 participants were included in the study; 83.2% of these children (n=1982) were <5 years old and 6.6% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen κ value. The LASSO and logistic regression models were well-calibrated.ConclusionsThe developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes.

Project description:Emergency rooms (ERs) generally assign a preliminary diagnosis to patients, who are then hospitalised and may subsequently experience a change in their lead diagnosis (cDx). In ERs, the cDx rate varies from around 15% to more than 50%. Among the most frequent reasons for diagnostic errors are cognitive slips, which mostly result from faulty data synthesis. Furthermore, physicians have been repeatedly found to be poor self-assessors and to be overconfident in the quality of their diagnosis, which limits their ability to improve. Therefore, some of the clinically most relevant research questions concern how diagnostic decisions are made, what determines their quality and what can be done to improve them. Research that addresses these questions is, however, still rare. In particular, field studies that allow for generalising findings from controlled experimental settings are lacking. The ER, with its high throughput and its many simultaneous visits, is perfectly suited for the study of factors contributing to diagnostic error. With this study, we aim to identify factors that allow prediction of an ER's diagnostic performance. Knowledge of these factors as well as of their relative importance allows for the development of organisational, medical and educational strategies to improve the diagnostic performance of ERs.We will conduct a field study by collecting diagnostic decision data, physician confidence and a number of influencing factors in a real-world setting to model real-world diagnostic decisions and investigate the adequacy, validity and informativeness of physician confidence in these decisions. We will specifically collect data on patient, physician and encounter factors as predictors of the dependent variables. Statistical methods will include analysis of variance and a linear mixed-effects model.The Bern ethics committee approved the study under KEK Number 197/15. Results will be published in peer-reviewed scientific medical journals. Authorship will be determined according to ICMJE guidelines.The study protocol Version 1.0 from 17 May 2015 is registered in the Inselspital Research Database Information System (IRDIS) and with the IRB ('Kantonale Ethikkomission') Bern under KEK Number 197/15.

Project description:ObjectiveThe onset of acute myocardial infarction (AMI) shows characteristic circadian variations involving a definite morning peak and a less-defined night-time peak. However, the factors influencing the circadian patterns of AMI onset and their influence on morning and night-time peaks have not been fully elucidated.Design, setting and participantsAn analysis of patients registered between 1998 and 2008 in the Osaka Acute Coronary Insufficiency Study, which is a prospective, multicentre observational study of patients with AMI in the Osaka region of Japan. The present study included 7755 consecutive patients with a known time of AMI onset.Main outcomes and measuresA mixture of two von Mises distributions was used to examine whether a circadian pattern of AMI had uniform, unimodal or bimodal distribution, and the likelihood ratio test was then used to select the best circadian pattern among them. The hierarchical likelihood ratio test was used to identify factors affecting the circadian patterns of AMI onset. The Kaplan-Meier method was used to estimate survival curves of 1-year mortality according to AMI onset time.ResultsThe overall population had a bimodal circadian pattern of AMI onset characterised by a high and sharp morning peak and a lower and less-defined night-time peak (bimodal p<0.001). Although several lifestyle-related factors had a statistically significant association with the circadian patterns of AMI onset, serum triglyceride levels had the most prominent association with the circadian patterns of AMI onset. Patients with triglyceride ?150?mg/dL on admission had only one morning peak in the circadian pattern of AMI onset during weekdays, with no peaks detected on weekends, whereas all other subgroups had two peaks throughout the week.ConclusionsThe circadian pattern of AMI onset was characterised by bimodality. Notably, several lifestyle-related factors, particularly serum triglyceride levels, had a strong relation with the circadian pattern of AMI onset.Trial registration numberUMIN000004575.

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Factors associated with seasonal influenza self-diagnosis: a prospective observational study in Japan.

Publications

Factors associated with seasonal influenza self-diagnosis: a prospective observational study in Japan.

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