Estimating the effectiveness of early control measures through school absenteeism surveillance in observed outbreaks at rural schools in Hubei, China

Influenza surveillance systems using traditional and alternative sources of data: A scoping review

OBJECTIVE

While the World Health Organization's recommendation of syndromic sentinel surveillance for influenza is an efficient method to collect high-quality data, limitations exist. Aligned with the Research Recommendation 1.1.2 of the WHO Public Health Research Agenda for Influenza-to identify reliable complementary influenza surveillance systems which provide real-time estimates of influenza activity-we performed a scoping review to map the extent and nature of published literature on the use of non-traditional sources of syndromic surveillance data for influenza.

METHODS

We searched three electronic databases (PubMed, Web of Science, and Scopus) for articles in English, French, and Spanish, published between January 1 2007 and January 28 2022. Studies were included if they directly compared at least one non-traditional with a traditional influenza surveillance system in terms of correlation in activity or timeliness.

FINDINGS

We retrieved 823 articles of which 57 were included for analysis. Fifteen articles considered electronic health records (EHR), 11 participatory surveillance, 10 online searches and webpage traffic, seven Twitter, five absenteeism, four telephone health lines, three medication sales, two media reporting, and five looked at other miscellaneous sources of data. Several articles considered more than one non-traditional surveillance method.

CONCLUSION

We identified eight categories and a miscellaneous group of non-traditional influenza surveillance systems with varying levels of evidence on timeliness and correlation to traditional surveillance systems. Analyses of EHR and participatory surveillance systems appeared to have the most agreement on timeliness and correlation to traditional systems. Studies suggested non-traditional surveillance systems as complements rather than replacements to traditional systems.

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.

Automated Cluster Detection of Health Care-Associated Infection Based on the Multisource Surveillance of Process Data in the Area Network: Retrospective Study of Algorithm Development and Validation

BACKGROUND

The cluster detection of health care-associated infections (HAIs) is crucial for identifying HAI outbreaks in the early stages.

OBJECTIVE

We aimed to verify whether multisource surveillance based on the process data in an area network can be effective in detecting HAI clusters.

METHODS

We retrospectively analyzed the incidence of HAIs and 3 indicators of process data relative to infection, namely, antibiotic utilization rate in combination, inspection rate of bacterial specimens, and positive rate of bacterial specimens, from 4 independent high-risk units in a tertiary hospital in China. We utilized the Shewhart warning model to detect the peaks of the time-series data. Subsequently, we designed 5 surveillance strategies based on the process data for the HAI cluster detection: (1) antibiotic utilization rate in combination only, (2) inspection rate of bacterial specimens only, (3) positive rate of bacterial specimens only, (4) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in parallel, and (5) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in series. We used the receiver operating characteristic (ROC) curve and Youden index to evaluate the warning performance of these surveillance strategies for the detection of HAI clusters.

RESULTS

The ROC curves of the 5 surveillance strategies were located above the standard line, and the area under the curve of the ROC was larger in the parallel strategy than in the series strategy and the single-indicator strategies. The optimal Youden indexes were 0.48 (95% CI 0.29-0.67) at a threshold of 1.5 in the antibiotic utilization rate in combination-only strategy, 0.49 (95% CI 0.45-0.53) at a threshold of 0.5 in the inspection rate of bacterial specimens-only strategy, 0.50 (95% CI 0.28-0.71) at a threshold of 1.1 in the positive rate of bacterial specimens-only strategy, 0.63 (95% CI 0.49-0.77) at a threshold of 2.6 in the parallel strategy, and 0.32 (95% CI 0.00-0.65) at a threshold of 0.0 in the series strategy. The warning performance of the parallel strategy was greater than that of the single-indicator strategies when the threshold exceeded 1.5.

CONCLUSIONS

The multisource surveillance of process data in the area network is an effective method for the early detection of HAI clusters. The combination of multisource data and the threshold of the warning model are 2 important factors that influence the performance of the model.

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

Background

Replacing traditional surveillance with syndromic surveillance is one of the major interests in public health. However, it is unclear whether the number of influenza patients is associated with the number of telephone triages in Japan.

Methods

This retrospective, observational study was conducted over the six-year period between January 2012 to December 2017. We used the dataset of a telephone triage service in Osaka, Japan and the data on influenza patients published from the Information Center of Infectious Disease in Osaka prefecture. Using a linear regression model, we calculated Spearman’s rank-order coefficient and R² of the regression model to assess the relationship between the number of telephone triages for fever and the number of influenza patients in Osaka. Furthermore, we calculated Spearman’s rank-order coefficient and R² between the predicted weekly number of influenza patients from the linear regression model and the actual weekly number of influenza patients for influenza outbreak season (December-April).

Results

There were 465,971 patients with influenza, and the number of telephone triages for fever was 420,928 among 1,065,628 total telephone triages during the study period. Our analysis showed that the Spearman rank-order coefficient was 0.932, and R² and adjusted R² were 0.869 and 0.842, respectively. The Spearman rank-order coefficient was 0.923 (P<0.001) and R² was 0.832 in December-April (P<0.001).

Conclusion

We revealed a positive relationship in this population between the number of influenza patients and the number of telephone triages for fever.

Cost-effectiveness analysis of (Nursery) School Absenteeism Surveillance System

BACKGROUND

Our earlier report reported that the (Nursery) School Absenteeism Surveillance System ((N)SASSy) can decrease numbers of patients. This study evaluates (N)SASSy's cost-effectiveness.

METHODS

A social perspective is taken for economic evaluation. For simplicity, 8,000 yen is assumed for direct medical costs. We assume the home health care duration to be 6 days, with 30 000 yen as the indirect opportunity cost of family nursing. Benefit-cost ratios are used as indicators of cost-effectiveness.

RESULTS

By multiplying the disease burden per patient by the reduced number of patients, the (N)SASSy effect was estimated as 206.9 billion yen, with 95% confidence interval of [67.3,346.6] billion yen. The total cost attributable to (N)SASSy throughout Japan is expected to be 2.63 billion yen. The benefit-cost ratio is expected to be approximately 60.

CONCLUSIONS

The estimated benefit-cost ratio is much higher than that for the routine immunization of children.

Infection Control in Nursery Schools and Schools Using a School Absenteeism Surveillance System

Infection control in nursery schools and schools is important for community health and the health of children. In Japan, caregivers of children or students usually report the absence due to illness to their attending nurseries or schools, including symptoms and diagnosed diseases. The (Nursery) School Absenteeism Surveillance System, (N)SASSy, covers about 60% of schools and 40% of nurseries in Japan. In this paper, we evaluated the benefits of (N)SASSy as an infection control measure by a public health center. Mito Public Health Center (MPHC) covers 58 nurseries and 186 schools, as of May 2015, and called the nurseries and/or schools to confirm the situation, in case of aberration detected through (N)SASSy. The outcome was defined as the proportion of cluster avoidance by advice from MPHC. A cluster was identified, when the number of patients at the same facility with the same symptom or diagnosed disease was greater than ten during the prior seven days. During the study period (April 2015-March 2016), MPHC advised 85 times, and clusters were avoided 82 times (96.5%). The proportion of cluster avoidance was 100% for fever, enterohemorrhagic Escherichia coli infection, respiratory syncytial virus infection, or streptococcal pharyngitis infection. The proportion of cluster avoidance for diarrhea, vomiting or gastroenteritis infection, mumps, hand-foot-mouth disease (HFMD), and influenza was 78.8, 50.0, 20.0, and 6.7%, respectively. In conclusion, advice from a public health center given by phone based on information from (N)SASSy will be helpful for reducing the number of clusters of infectious diseases, except for HFMD and influenza.

Association of influenza outbreak in each nursery school and community in a ward in Tokyo, Japan

In nursery schools, influenza outbreaks have occurred every year. However, influenza characteristics of its diffusion among nursery schools, within each nursery school, and among classes of different ages in nursery schools remains unclear. This paper presents an examination of these matters using the Nursery School Absenteeism Surveillance System (NSASSy). All nursery schools in ward A in Tokyo introduced to the NSASSy in 2015. The study period was November 2015 through March 2016. The data of influenza patients were extracted from NSASSy. We examined four definitions of 'starting date of community outbreak' (SDCO) of influenza: 1) the first recorded day of influenza patients (SDCO1), 2) the last day of influenza patients recorded for two consecutive days (SDCO2), 3) three consecutive days (SDCO3), and 4) four consecutive days (SDCO4). We evaluated those four definitions by duration of the initial case at each nursery school from SDCO and evaluated the proportion of nursery schools at which the initial case occurred before SDCO. The average durations of initial cases at respective nursery schools from SDCO1-4 were 40.3, 26.3, 23.1 and 13.3 days. The respective proportions of nursery schools at which the initial case occurred before SDCO1-4 were 3.1%, 6.4%, 9.4% and 40.6%. Results demonstrate that SDCO3 is an appropriate definition of SDCO. Robustness checks for other areas, seasons, and population size constitute the next challenge for research in this area.

Applying the zero-inflated Poisson model with random effects to detect abnormal rises in school absenteeism indicating infectious diseases outbreak

Records of absenteeism from primary schools are valuable data for infectious diseases surveillance. However, the analysis of the absenteeism is complicated by the data features of clustering at zero, non-independence and overdispersion. This study aimed to generate an appropriate model to handle the absenteeism data collected in a European Commission granted project for infectious disease surveillance in rural China and to evaluate the validity and timeliness of the resulting model for early warnings of infectious disease outbreak. Four steps were taken: (1) building a 'well-fitting' model by the zero-inflated Poisson model with random effects (ZIP-RE) using the absenteeism data from the first implementation year; (2) applying the resulting model to predict the 'expected' number of absenteeism events in the second implementation year; (3) computing the differences between the observations and the expected values (O-E values) to generate an alternative series of data; (4) evaluating the early warning validity and timeliness of the observational data and model-based O-E values via the EARS-3C algorithms with regard to the detection of real cluster events. The results indicate that ZIP-RE and its corresponding O-E values could improve the detection of aberrations, reduce the false-positive signals and are applicable to the zero-inflated data.

Epidemiological Characteristics and Spatial-Temporal Clusters of Mumps in Shandong Province, China, 2005-2014

Mumps presents a serious threat to public health in China. We conducted a descriptive analysis to identify the epidemiological characteristics of mumps in Shandong Province. Spatial autocorrelation and space-time scan analyses were utilized to detect spatial-temporal clusters. From 2005 to 2014, 115745 mumps cases were reported in Shandong, with an average male-to-female ratio of 1.94. Mumps occurred mostly in spring (32.17% of all reported cases) and in children aged 5 to 9 (40.79% of all reported cases). The Moran’s I test was significant and local indicators of spatial autocorrelation (LISA) analysis revealed significant spatial clusters with high incidence. The results showed that the mid-west of Shandong Province and some coastal regions (Qingdao City and Weihai City) were high-risk areas, particularly in the center of the Jining City and the junction of Dongying City, Binzhou City and Zibo City. The results could assist local and national public health agencies in formulating better public health strategic planning and resource allocation.

The short-term association between meteorological factors and mumps in Jining, China

BACKGROUND

An increasing trend of the incidence of mumps has been observed in a few developing countries in recent years, presenting a major threat to children's health. A few studies have examined the relationship between meteorological factors and mumps with inconsistent findings.

METHODS

The daily data of meteorological variables and mumps from 2009 to 2013 were obtained from Jining, a temperate inland city of China. A generalized additive model was used to quantify the association between meteorological factors and mumps based on the exposure-response relationship.

RESULTS

A total of 8520 mumps cases were included in this study. We found a nonlinear relationship of daily mean temperature, sunshine duration and relative humidity with mumps, with an approximately linear association for mean temperature above 4°C (excess risk (ER) for 1°C increase was 2.72%, 95% confidence interval (CI): 2.38%, 3.05% on the current day), for relative humidity above 54%, the ER for 1% increase was -1.86% (95% CI: -2.06%, -1.65%) at lag day 14; and for sunshine duration higher than 5h/d, the ER for per 1h/d increase was12.91% (95% CI: 11.38%, 14.47%) at lag day 1. While we found linear effects for daily wind speed (ER: 2.98%, 95% CI: 2.71%, 3.26% at lag day 13).

CONCLUSIONS

This study suggests that meteorological factors might be important predictors of incidence of mumps, and should be considered in its control and prevention.

Dengue Contingency Planning: From Research to Policy and Practice

BACKGROUND

Dengue is an increasingly incident disease across many parts of the world. In response, an evidence-based handbook to translate research into policy and practice was developed. This handbook facilitates contingency planning as well as the development and use of early warning and response systems for dengue fever epidemics, by identifying decision-making processes that contribute to the success or failure of dengue surveillance, as well as triggers that initiate effective responses to incipient outbreaks.

METHODOLOGY/PRINCIPAL FINDINGS

Available evidence was evaluated using a step-wise process that included systematic literature reviews, policymaker and stakeholder interviews, a study to assess dengue contingency planning and outbreak management in 10 countries, and a retrospective logistic regression analysis to identify alarm signals for an outbreak warning system using datasets from five dengue endemic countries. Best practices for managing a dengue outbreak are provided for key elements of a dengue contingency plan including timely contingency planning, the importance of a detailed, context-specific dengue contingency plan that clearly distinguishes between routine and outbreak interventions, surveillance systems for outbreak preparedness, outbreak definitions, alert algorithms, managerial capacity, vector control capacity, and clinical management of large caseloads. Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed.

CONCLUSIONS/SIGNIFICANCE

Most countries do not have comprehensive, detailed contingency plans for dengue outbreaks. Countries tend to rely on intensified vector control as their outbreak response, with minimal focus on integrated management of clinical care, epidemiological, laboratory and vector surveillance, and risk communication. The Technical Handbook for Surveillance, Dengue Outbreak Prediction/ Detection and Outbreak Response seeks to provide countries with evidence-based best practices to justify the declaration of an outbreak and the mobilization of the resources required to implement an effective dengue contingency plan.

The usefulness of school-based syndromic surveillance for detecting malaria epidemics: experiences from a pilot project in Ethiopia

Background

Syndromic surveillance is a supplementary approach to routine surveillance, using pre-diagnostic and non-clinical surrogate data to identify possible infectious disease outbreaks. To date, syndromic surveillance has primarily been used in high-income countries for diseases such as influenza -- however, the approach may also be relevant to resource-poor settings. This study investigated the potential for monitoring school absenteeism and febrile illness, as part of a school-based surveillance system to identify localised malaria epidemics in Ethiopia.

Methods

Repeated cross-sectional school- and community-based surveys were conducted in six epidemic-prone districts in southern Ethiopia during the 2012 minor malaria transmission season to characterise prospective surrogate and syndromic indicators of malaria burden. Changes in these indicators over the transmission season were compared to standard indicators of malaria (clinical and confirmed cases) at proximal health facilities. Subsequently, two pilot surveillance systems were implemented, each at ten sites throughout the peak transmission season. Indicators piloted were school attendance recorded by teachers, or child-reported recent absenteeism from school and reported febrile illness.

Results

Lack of seasonal increase in malaria burden limited the ability to evaluate sensitivity of the piloted syndromic surveillance systems compared to existing surveillance at health facilities. Weekly absenteeism was easily calculated by school staff using existing attendance registers, while syndromic indicators were more challenging to collect weekly from schoolchildren. In this setting, enrolment of school-aged children was found to be low, at 54 %. Non-enrolment was associated with low household wealth, lack of parental education, household size, and distance from school.

Conclusions

School absenteeism is a plausible simple indicator of unusual health events within a community, such as malaria epidemics, but the sensitivity of an absenteeism-based surveillance system to detect epidemics could not be rigorously evaluated in this study. Further piloting during a demonstrated increase in malaria transmission within a community is recommended.

A cost-effectiveness analysis of three components of a syndromic surveillance system for the early warning of epidemics in rural China

BACKGROUND

Syndromic surveillance systems (SSSs) collect non-specific syndromes in early stages of disease outbreaks. This makes an SSS a promising tool for the early detection of epidemics. An Integrated Surveillance System in rural China (ISSC project), which added an SSS to the existing Chinese surveillance system for the early warning of epidemics, was implemented from April 2012 to March 2014 in Jiangxi and Hubei Provinces. This study aims to measure the costs and effectiveness of the three components of the SSS in the ISSC project.

METHODS

The central measures of the cost-effectiveness analysis of the three components of the syndromic surveillance system were: 1) the costs per reported event, respectively, at the health facilities, the primary schools and the pharmacies; and 2) the operating costs per surveillance unit per year, respectively, at the health facilities, the primary schools and the pharmacies. Effectiveness was expressed by reporting outputs which were numbers of reported events, numbers of raw signals, and numbers of verified signals. The reported events were tracked through an internal data base. Signal verification forms and epidemiological investigation reports were collected from local country centers for disease control and prevention. We adopted project managers' perspective for the cost analysis. Total costs included set-up costs (system development and training) and operating costs (data collection, quality control and signal verification). We used self-designed questionnaires to collect cost data and received, respectively, 369 and 477 facility and staff questionnaires through a cross-sectional survey with a purposive sampling following the ISSC project. All data were entered into Epidata 3.02 and exported to Stata for descriptive analysis.

RESULTS

The number of daily reported events per unit was the highest at pharmacies, followed by health facilities and finally primary schools. Variances existed within the three groups and also between Jiangxi and Hubei. During a 15-month surveillance period, the number of raw signals for early warning in Jiangxi province (n = 36) was nine times of that in Hubei. Health facilities and primary schools had equal numbers of raw signals (n = 19), which was 9.5 times of that from pharmacies. Five signals were confirmed as outbreaks, of which two were influenza, two were chicken pox and one was mumps. The cost per reported event was the highest at primary schools, followed by health facilities and then pharmacies. The annual operating cost per surveillance unit was the highest at pharmacies, followed by health facilities and finally primary schools. Both the cost per reported event and the annual operating cost per surveillance unit in Jiangxi in each of the three groups were higher than their counterparts in Hubei.

CONCLUSIONS

Health facilities and primary schools are better sources of syndromic surveillance data in the early warning of outbreaks. The annual operating costs of all the three components of the syndromic surveillance system in the ISSC Project were low compared to general government expenditures on health and average individual income in rural China.