Self-diagnosis of seasonal influenza in a rural primary care setting in Japan: A cross sectional observational study

Importance of History Taking and Physical Examination Before Rapid Laboratory Test for Seasonal Influenza Virus: A Single-Center Multiple Logistic Analysis

Background Despite several rapid influenza diagnostic tests (RIDTs), they are predicting whether a patient has influenza before rapid testing is important. Here, we assessed factors predictive of a positive flu test via RIDTs by combining interviews and physical examination. Methods We analyzed the relationship between interviews and physical findings and results of RIDTs using multivariable logistic regression. Results Two hundred seventy-six children were enrolled throughout the 2018-2019 flu season. Accordingly, 115 patients (41.7%) were positive for flu A. Our logistic regression model identified age, body temperature, and the existence of upper respiratory symptoms as significant factors for predicting positive for RIDTs, with odds ratios (OR) of 1.17 [95% CI (confidence interval): 1.08-1.25]/+Δ1year old, 1.70 (95% CI: 1.27-2.27)/+Δ1 ℃, and 5.08 (95% CI: 2.57-10.00) for respiratory symptoms. In addition, the OR for sick contact was 7.67 (95% CI: 3.96-14.90). Our logistic regression model showed an area under the curve (AUC) of 0.84. History of vaccination was not identified as a significant factor in positive RIDTs. Conclusions The existence of sick contact was associated with a positive flu test via RIDTs. Although RIDTs are an easy and quick method for detecting the flu virus, we should perform the appropriate identification of cases for RIDTs by combining interviews and physical findings.

Patients' perspectives on irritable bowel syndrome: a qualitative analysis based on social media in China

AIM

To explore the perspectives, experience, and concerns of patients with irritable bowel syndrome (IBS) in China.

METHODS

We used data mining to investigate posts shared in Baidu Tieba concerned with IBS; we collected the data through the crawler code, and mined the cleaned data's themes based on Latent Dirichlet allocation (LDA) and the Grounded theory.

RESULTS

We found 5746 network posts related to IBS. LDA analysis generated 20 topics, and grounded theory analysis established eight topics. Combining the two methods, we finally arranged the topics according to five concepts: difficulty in obtaining disease information; serious psychosocial problems; dissatisfied with the treatment; lack of social support; and low quality of life.

CONCLUSION

Social media research improved patient-centric understanding of patients' experiences and perceptions. Our study may facilitate doctor-patient communication and assist in the formulation of medical policies.

Clinical diagnosis of seasonal influenza by physicians: a retrospective observational study

OBJECTIVE

To elucidate the diagnostic accuracy of pretest probability of influenza (%) by physicians and the factors affecting the clinical diagnosis.

DESIGN

Retrospective, single-centre observational study.

SETTING

A community primary care clinic in Japan.

PARTICIPANTS

The participants were recruited from a database of studies conducted during the influenza season from December 2017 to April 2019.

PRIMARY OUTCOME MEASURE

Sensitivity and specificity of the physician's clinical diagnosis of influenza recorded in the medical record as pretest probability.

RESULTS

A total of 335 patients (median age, 31 years; male, 66.6%) were analysed in this study. The area under the curve (AUC) of the physician's pretest probability was 0.77. At a cut-off value of 30%, the sensitivity and negative likelihood ratio were 92.0% (95% CI 86.7 to 95.7) and 0.19 (95% CI 0.11 to 0.33), respectively. At a cut-off value of 80%, the specificity and positive likelihood ratio were 90.8% (95% CI 85.4 to 94.6) and 4.01 (95% CI 2.41 to 6.66), respectively. The AUCs of patients who had and had not taken any medications before visiting the clinic were 0.77 (95% CI 0.69 to 0.85) and 0.78 (95% CI 0.71 to 0.84), respectively. The AUCs of patients with type A and B influenza were 0.78 (95% CI 0.72 to 0.84) and 0.76 (95% CI 0.70 to 0.82), respectively. The AUCs of vaccinated and unvaccinated patients were 0.80 (95% CI 0.72 to 0.88) and 0.76 (95% CI 0.63 to 0.89), respectively. The AUC for patients less than 12 hours after onset was 0.69 (95% CI 0.51 to 0.88), and that for patients aged younger than 6 years was 0.69 (95% CI 0.49 to 0.88).

CONCLUSIONS

The physician's pretest probability of influenza (%) may be useful for both definitive and exclusionary diagnoses within the limits of our study.

Factors associated with children's seasonal influenza diagnosis by their guardians: a prospective observational study in Japan

Introduction Children's influenza diagnosis by their guardians has been reported to be highly accurate, but clinical factors that improve the reliability of a guardian's diagnosis are unclear. Aim To determine the accuracy of guardians' influenza diagnosis of their children, investigate clinical factors that improve the diagnostic accuracy, and determine the additional clinical value of the guardian's diagnosis. Methods A prospective observational study was conducted at a primary care clinic in Japan from December 2017 to April 2019. Pre-examination checklists completed by guardians accompanying children aged Results A total of 112 patient pairs of child (median age, 6 years) and guardian (mother, 81.2%; father, 16.1%; grandmother, 1.8%; other, 0.9%) were included in the analysis. The AUC for guardians' influenza diagnosis was higher in mothers (0.72), as well as pairs with children with a history of influenza (0.72), guardians who were aware of the influenza epidemic (0.71), and unvaccinated children (0.76), than in other guardians. After multivariate analysis, the AUC increased significantly from 0.79 to 0.85. Discussion Guardians' influenza diagnosis for their children was highly accurate. We identified factors that improve the accuracy of the guardians' diagnosis and demonstrated that the guardians' diagnosis can support physicians' diagnostic accuracy.

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

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.

Performance and ease of use of a molecular point-of-care test for influenza A/B and RSV in patients presenting to primary care

Annual influenza epidemics cause substantial morbidity and mortality, and the majority of patients with influenza-like illness present to primary care. Point-of-care influenza tests could support treatment decisions. It is critical to establish analytic performance of these platforms in real-life patient samples before uptake can be considered. We aimed to assess the analytical performance and ease of use of the cobas® Liat® PCR POCT in detecting influenza A/B and RSV in samples collected from patients with influenza-like illness in primary care. Sensitivity and specificity of the cobas® Liat® POCT are calculated in comparison with a commercial laboratory-based PCR test (Fast-Track Respiratory Pathogens 21 Plus kit (Fast-Track Diagnostics)). Samples with discordant results were analysed additionally by the RespiFinder 2Smart (PathoFinder) using an Extended Gold Standard (EGS). Acceptability was scored on a five-point Likert scale as well as a failure mode analysis of the cobas® Liat® POCT was performed. Nasal and oropharyngeal swabs were obtained from 140 children and nasopharyngeal swabs from 604 adults (744 patients). The cobas® Liat® POCT had a sensitivity and specificity of 100% (95% CI 99–100%) and 98.1% (95%CI 96.3–99%) for influenza A, 100% (95% CI 97.7–100%) and 99.7% (95%CI 98.7–99.9%) for influenza B and 100% (95% CI 87.1–100%) and 99.4% (95%CI 98.6–99.8%) for RSV, respectively. According to trained lab technicians, the cobas® Liat® POCT was considered easy-to-use, with a fast turn-around-time. Cobas® Liat® POCT is a promising decentralised test platform for influenza A/B and RSV in primary care as it provides fairly rapid results with excellent analytic performance. Point-of-care influenza tests could support treatment decisions in primary care. Cobas® Liat® POCT is a promising decentralised test platform for influenza A/B and RSV in primary care as it provides fairly rapid results with excellent analytic performance.