The relationship between seasonal influenza and telephone triage for fever: A population-based study in Osaka, Japan

Role and effectiveness of telephone hotlines in outbreak response in Africa: A systematic review and meta-analysis

BACKGROUND

In Africa, little is known about the role of telephone hotlines in outbreak response. We systematically reviewed the role and effectiveness of hotlines on outbreak response in Africa.

METHOD

We used the Cochrane handbook and searched five databases. The protocol was registered on PROSPERO (CRD42021247141). Medline, Embase, PsycINFO, Global Health and Web of Science were searched from 30 June 2020 to August 2020 for studies on the use of telephone hotlines in outbreak response in Africa published between January 1995 and August 2020. The search was also repeated on 16 September 2022. Data on effectiveness (alerts generated, cases confirmed) were extracted from peer-reviewed studies. Meta-analysis of alerts generated, and proportion of cases confirmed was done using the random effects model. The quality of studies was assessed using the Joanna Briggs Institute (JBI) tools. The heterogeneity and publication bias were assessed using the Galbraith and funnel plots, respectively.

RESULTS

Our search yielded 1251 non-duplicate citations that were assessed. 41 full texts were identified, and 21 studies were included in the narrative synthesis, while 12 were included in the meta-analysis. The hotlines were local (seven studies) or national (three studies). A combination of a local and national hotline was used in one study. The hotlines were set up for unusual respiratory events (one study), polio (one study), Ebola (10 studies), COVID-19 (two studies), malaria (one study), influenza-like illnesses (ILI) (one study) and rift valley fever in livestock (one study). Hotlines were mainly used for outbreak surveillance at the local level. A total of 332,323 alerts were generated, and 67,658 met the case definition, corresponding to an overall pooled proportion of alerts generated(sensitivity) of 38% (95%CI: 24-52%). The sensitivity was 41% (95% CI: 24-59%) for local hotlines and 26%(95%CI:5-47%) for national hotlines. Hotlines were also used for surveillance of rift valley fever in livestock (one study) vaccination promotion (one study), death reporting (five studies), rumour tracking and fighting misinformation (two studies) and community engagement (five studies). The studies were of low to moderate quality with high publication bias and heterogeneity(I2 = 99%). The heterogeneity was not explained by the sample size.

CONCLUSION

These data suggest that telephone hotlines can be effective in outbreak disease surveillance in Africa. Further implementation research is needed to scale up telephone hotlines in rural areas.

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil

BACKGROUND

Telehealth has been widely used for new case detection and telemonitoring during the COVID-19 pandemic. It safely provides access to health care services and expands assistance to remote, rural areas and underserved communities in situations of shortage of specialized health professionals. Qualified data are systematically collected by health care workers containing information on suspected cases and can be used as a proxy of disease spread for surveillance purposes. However, the use of this approach for syndromic surveillance has yet to be explored. Besides, the mathematical modeling of epidemics is a well-established field that has been successfully used for tracking the spread of SARS-CoV-2 infection, supporting the decision-making process on diverse aspects of public health response to the COVID-19 pandemic. The response of the current models depends on the quality of input data, particularly the transmission rate, initial conditions, and other parameters present in compartmental models. Telehealth systems may feed numerical models developed to model virus spread in a specific region.

OBJECTIVE

Herein, we evaluated whether a high-quality data set obtained from a state-based telehealth service could be used to forecast the geographical spread of new cases of COVID-19 and to feed computational models of disease spread.

METHODS

We analyzed structured data obtained from a statewide toll-free telehealth service during 4 months following the first notification of COVID-19 in the Bahia state, Brazil. Structured data were collected during teletriage by a health team of medical students supervised by physicians. Data were registered in a responsive web application for planning and surveillance purposes. The data set was designed to quickly identify users, city, residence neighborhood, date, sex, age, and COVID-19-like symptoms. We performed a temporal-spatial comparison of calls reporting COVID-19-like symptoms and notification of COVID-19 cases. The number of calls was used as a proxy of exposed individuals to feed a mathematical model called "susceptible, exposed, infected, recovered, deceased."

RESULTS

For 181 (43%) out of 417 municipalities of Bahia, the first call to the telehealth service reporting COVID-19-like symptoms preceded the first notification of the disease. The calls preceded, on average, 30 days of the notification of COVID-19 in the municipalities of the state of Bahia, Brazil. Additionally, data obtained by the telehealth service were used to effectively reproduce the spread of COVID-19 in Salvador, the capital of the state, using the "susceptible, exposed, infected, recovered, deceased" model to simulate the spatiotemporal spread of the disease.

CONCLUSIONS

Data from telehealth services confer high effectiveness in anticipating new waves of COVID-19 and may help understand the epidemic dynamics.

Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review

Background

Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings.

Methods

We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist.

Results

Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments’ data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians’ reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches.

Conclusions

Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs’ method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.

Telephone Triage for Emergency Patients Reduces Unnecessary Ambulance Use: A Propensity Score Analysis With Population-Based Data in Osaka City, Japan

Background

Telephone triage service in emergency care has been introduced around the world, but the impact of this service on the emergency medical service (EMS) system has not been fully revealed. The aim of this study was to evaluate the effect of telephone triage service for emergency patients on decreasing unnecessary ambulance use by analysis with propensity score (PS) matching.

Methods

This study was a retrospective observational study, and the study period was the 4 years from January 2016 to December 2019. We included cases for which ambulances were dispatched from the Osaka Municipal Fire Department (OMFD). The primary outcome of this study was unnecessary ambulance use. We calculated a PS by fitting a logistic regression model to adjust for 10 variables that existed before use of the telephone triage service. To ensure the robustness of this analysis, we used not only PS matching but also a multivariable logistic regression model and regression model with PS as a covariate.

Results

This study included 868,548 cases, of which 8,828 (1.0%) used telephone triage services and 859,720 (99.0%) did not use this service. Use of the telephone triage service was inversely associated with the occurrence of unnecessary ambulance use in multivariate logistic regression model (adjusted OR 0.453, 95% CI 0.405–0.506) and multivariate logistic regression model with PS as a covariate (adjusted OR 0.514, 95% CI 0.460–0.574). In the PS matching model, we also revealed same results (crude OR 0.487, 95% CI 0.425–0.588).

Conclusions

In this study, we were able to statistically evaluate the effectiveness of telephone triage service already in use by the public using the statistical method with PS. As a result, it was revealed that the use of a telephone triage service was associated with a lower proportion of unnecessary ambulance use in a metropolitan area of Japan.

An Association of Influenza Epidemics in Children With Mobile App Data: Population-Based Observational Study in Osaka, Japan

Background

Early surveillance to prevent the spread of influenza is a major public health concern. If there is an association of influenza epidemics with mobile app data, it may be possible to forecast influenza earlier and more easily.

Objective

We aimed to assess the relationship between seasonal influenza and the frequency of mobile app use among children in Osaka Prefecture, Japan.

Methods

This was a retrospective observational study that was performed over a three-year period from January 2017 to December 2019. Using a linear regression model, we calculated the R² value of the regression model to evaluate the relationship between the number of “fever” events selected in the mobile app and the number of influenza patients ≤14 years of age. We conducted three-fold cross-validation using data from two years as the training data set and the data of the remaining year as the test data set to evaluate the validity of the regression model. And we calculated Spearman correlation coefficients between the calculated number of influenza patients estimated using the regression model and the number of influenza patients, limited to the period from December to April when influenza is prevalent in Japan.

Results

We included 29,392 mobile app users. The R² value for the linear regression model was 0.944, and the adjusted R² value was 0.915. The mean Spearman correlation coefficient for the three regression models was 0.804. During the influenza season (December–April), the Spearman correlation coefficient between the number of influenza patients and the calculated number estimated using the linear regression model was 0.946 (P<.001).

Conclusions

In this study, the number of times that mobile apps were used was positively associated with the number of influenza patients. In particular, there was a good association of the number of influenza patients with the number of “fever” events selected in the mobile app during the influenza epidemic season.

Cross-sectional telephone surveys as a tool to study epidemiological factors and monitor seasonal influenza activity in Malta

Background

Seasonal influenza has major implications for healthcare services as outbreaks often lead to high activity levels in health systems. Being able to predict when such outbreaks occur is vital. Mathematical models have extensively been used to predict epidemics of infectious diseases such as seasonal influenza and to assess effectiveness of control strategies. Availability of comprehensive and reliable datasets used to parametrize these models is limited. In this paper we combine a unique epidemiological dataset collected in Malta through General Practitioners (GPs) with a novel method using cross-sectional surveys to study seasonal influenza dynamics in Malta in 2014–2016, to include social dynamics and self-perception related to seasonal influenza.

Methods

Two cross-sectional public surveys (n = 406 per survey) were performed by telephone across the Maltese population in 2014–15 and 2015–16 influenza seasons. Survey results were compared with incidence data (diagnosed seasonal influenza cases) collected by GPs in the same period and with Google Trends data for Malta. Information was collected on whether participants recalled their health status in past months, occurrences of influenza symptoms, hospitalisation rates due to seasonal influenza, seeking GP advice, and other medical information.

Results

We demonstrate that cross-sectional surveys are a reliable alternative data source to medical records. The two surveys gave comparable results, indicating that the level of recollection among the public is high. Based on two seasons of data, the reporting rate in Malta varies between 14 and 22%. The comparison with Google Trends suggests that the online searches peak at about the same time as the maximum extent of the epidemic, but the public interest declines and returns to background level. We also found that the public intensively searched the Internet for influenza-related terms even when number of cases was low.

Conclusions

Our research shows that a telephone survey is a viable way to gain deeper insight into a population’s self-perception of influenza and its symptoms and to provide another benchmark for medical statistics provided by GPs and Google Trends. The information collected can be used to improve epidemiological modelling of seasonal influenza and other infectious diseases, thus effectively contributing to public health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11862-x.

Emergency medicine in Japan: past, present, and future

Quite a few changes and challenges have arisen in society in general as technology has advanced and the aging population has increased. These can lead to the recognition of the shortcomings of a society’s traditional systems and the various changes that are needed, especially in providing emergency medical care. A super-aged society has been developing in Japan, and the emergency care system needs to change according to these new demographics and society’s needs. The focus has been shifting from critical care and trauma to medical and surgical conditions involving the elderly. Challenges in triage, ambulance diversion, and staffing are discussed in this review. Possible solutions currently underway, such as a public helpline, smartphone app system, coordination by designated hospitals, and universal coverage/government support, are discussed as future directions. Emergency medicine in Japan needs to develop in a more flexible way to meet the upcoming robust challenges of the changing demographics.