Evaluation of school absenteeism data for early outbreak detection, New York City

Performance of discrete wavelet transform-based method in the detection of influenza outbreaks in Iran: An ecological study

Background and Aim

Timely detection of outbreaks is one of the main purposes of the health surveillance system. The presence of appropriate methods in the detection of outbreaks can have an important role in the timely detection of outbreaks. Because of the importance of this issue, this study aimed to assess the performance of discrete wavelet transform (DWT) based methods in detecting influenza outbreaks in Iran from January 2010 to January 2020.

Methods

All registered influenza-positive virus cases in Iran from January 2010 to January 2010 were obtained from the FluNet web base tool, the World Health Organization website. The combination method that includes DWT and Shewhart control chart was used in this study. All analyses were performed using MATLAB software version 2018a Stata software version 15.

Results

The Mean ± SD and median of reported influenza cases from January 2010 to January 2020 was 36 ± 108 and four cases per week. The combination of the DWT and Shewhart control chart with K = 0.25 had the most sensitivity. The most specificity in the detection of nonoutbreak days was seen in the combination of DWT and Shewhart control chart with K = 1.5, K = 1.75, and K = 2, respectively. The combination of DWT and Shewhart control chart with K = 0.5 had the best performance in the detection of outbreaks (sensitivity = 0.64, specificity: 0.90, Youden index: 0.54, and area under the curve [AUC]: 0.77).

Conclusion

The DWT-based method in detecting influenza outbreaks has acceptable performance, but it is recommended that this method's performance be assessed in detecting outbreaks of other infectious diseases.

Monitoring School Absenteeism for Influenza-Like Illness Surveillance: Systematic Review and Meta-analysis

Background

Influenza causes considerable disease burden each year, particularly in children. Monitoring school absenteeism has long been proposed as a surveillance tool of influenza activity in the community, but the practice of school absenteeism could be varying, and the potential of such usage remains unclear.

Objective

The aim of this paper is to determine the potential of monitoring school absenteeism as a surveillance tool of influenza.

Methods

We conducted a systematic review of the published literature on the relationship between school absenteeism and influenza activity in the community. We categorized the types of school absenteeism and influenza activity in the community to determine the correlation between these data streams. We also extracted this correlation with different lags in community surveillance to determine the potential of using school absenteeism as a leading indicator of influenza activity.

Results

Among the 35 identified studies, 22 (63%), 12 (34%), and 8 (23%) studies monitored all-cause, illness-specific, and influenza-like illness (ILI)–specific absents, respectively, and 16 (46%) used quantitative approaches and provided 33 estimates on the temporal correlation between school absenteeism and influenza activity in the community. The pooled estimate of correlation between school absenteeism and community surveillance without lag, with 1-week lag, and with 2-week lag were 0.44 (95% CI 0.34, 0.53), 0.29 (95% CI 0.15, 0.42), and 0.21 (95% CI 0.11, 0.31), respectively. The correlation between influenza activity in the community and ILI-specific absenteeism was higher than that between influenza activity in community all-cause absenteeism. Among the 19 studies that used qualitative approaches, 15 (79%) concluded that school absenteeism was in concordance with, coincided with, or was associated with community surveillance. Of the 35 identified studies, only 6 (17%) attempted to predict influenza activity in the community from school absenteeism surveillance.

Conclusions

There was a moderate correlation between school absenteeism and influenza activity in the community. The smaller correlation between school absenteeism and community surveillance with lag, compared to without lag, suggested that careful application was required to use school absenteeism as a leading indicator of influenza epidemics. ILI-specific absenteeism could monitor influenza activity more closely, but the required resource or school participation willingness may require careful consideration to weight against the associated costs. Further development is required to use and optimize the use of school absenteeism to predict influenza activity. In particular, the potential of using more advanced statistical models and validation of the predictions should be explored.

Cause-specific student absenteeism monitoring in K-12 schools for detection of increased influenza activity in the surrounding community—Dane County, Wisconsin, 2014–2020

Background

Schools are primary venues of influenza amplification with secondary spread to communities. We assessed K-12 student absenteeism monitoring as a means for early detection of influenza activity in the community.

Materials and methods

Between September 2014 and March 2020, we conducted a prospective observational study of all-cause (a-TOT), illness-associated (a-I), and influenza-like illness–associated (a-ILI) absenteeism within the Oregon School District (OSD), Dane County, Wisconsin. Absenteeism was reported through the electronic student information system. Students were visited at home where pharyngeal specimens were collected for influenza RT-PCR testing. Surveillance of medically-attended laboratory-confirmed influenza (MAI) occurred in five primary care clinics in and adjoining the OSD. Poisson general additive log linear regression models of daily counts of absenteeism and MAI were compared using correlation analysis.

Findings

Influenza was detected in 723 of 2,378 visited students, and in 1,327 of 4,903 MAI patients. Over six influenza seasons, a-ILI was significantly correlated with MAI in the community (r = 0.57; 95% CI: 0.53–0.63) with a one-day lead time and a-I was significantly correlated with MAI in the community (r = 0.49; 0.44–0.54) with a 10-day lead time, while a-TOT performed poorly (r = 0.27; 0.21–0.33), following MAI by six days.

Discussion

Surveillance using cause-specific absenteeism was feasible and performed well over a study period marked by diverse presentations of seasonal influenza. Monitoring a-I and a-ILI can provide early warning of seasonal influenza in time for community mitigation efforts.

School Attendance Registers for the Syndromic Surveillance of Infectious Intestinal Disease in UK Children: Protocol for a Retrospective Analysis

Background

Infectious intestinal disease (IID) is common, and children are more likely than adults both to have IID and to transmit infection onto others. Before the introduction of the vaccine, rotavirus was the leading cause of severe childhood diarrhea, with norovirus and Campylobacter predominate pathogens. Public health surveillance of IID is primarily based on health care data, and as such, illness that is managed within the community will often go undetected. School attendance registers offer a novel data set that has the potential to identify community cases and outbreaks of IID that would otherwise be missed by current health surveillance systems. Although studies have explored the role of school attendance registers in the monitoring of influenza among children, no studies have been identified that consider this approach in the surveillance of IID.

Objective

The aim of this study is to explore the role and utility of school attendance registers in the detection and surveillance of IID in children. The secondary aims are to estimate the burden of IID on school absenteeism and to assess the impact of the rotavirus vaccine on illness absence among school-aged children.

Methods

This study is a retrospective analysis of school attendance registers to investigate whether school absences due to illness can be used to capture seasonal trends and outbreaks of infectious intestinal disease among school-aged children. School absences in Merseyside, United Kingdom will be compared and combined with routine health surveillance data from primary care, laboratories, and telehealth services. These data will be used to model spatial and temporal variations in the incidence of IID and to apportion likely causes to changes in school absenteeism trends. This will be used to assess the potential utility of school attendance data in the surveillance of IID and to estimate the burden of IID absenteeism in schools. It will also inform an analysis of the impact of the rotavirus vaccine on disease within this age group.

Results

This study has received ethical approval from the University of Liverpool Research Ethics Committee (reference number 1819). Use of general practice data has been approved for the evaluation of rotavirus vaccination in Merseyside by NHS Research Ethics Committee, South Central-Berkshire REC Reference 14/SC/1140.

Conclusions

This study is unique in considering whether school attendance registers could be used to enhance the surveillance of IID. Such data have multiple potential applications and could improve the identification of outbreaks within schools, allowing early intervention to reduce transmission both within and outside of school settings. These data have the potential to act as an early warning system, identifying infections circulating within the community before they enter health care settings. School attendance data could also inform the evaluation of vaccination programs, such as rotavirus and, in time, norovirus.

International Registered Report Identifier (IRRID)

DERR1-10.2196/30078

Alcohol and drug use as factors for high-school learners' absenteeism in the Western Cape

Background

School absenteeism has been studied in detail in relation to health risk behaviours using cross sectional studies.

Aim

The aim of this longitudinal study was to examine the association amongst alcohol, drug use and high-school learners’ absenteeism.

Setting

This study was set in the Western Cape.

Methods

Data were collected at three separate time points from 2950, 2675 and 2230 grade 8 learners aged 13–18 years old on school absenteeism, alcohol and drug use and sociodemographic characteristics. Associations between school absenteeism, alcohol and cannabis and sociodemographic factors use were examined using descriptive and chi-square analyses. Binary logistic regression was performed using generalised linear mixed model analyses.

Results

Results revealed that 9.3% of the learners were absent for 2 weeks in the 15 weeks of the school year. Alcohol consumption (X² = 34.1, p < 0.001; odds ratio [OR]: 1.64 (1.38–1.94), p < 0.001) and smoking cannabis (X² = 49.9, p < 0.001; OR: 2.01 (1.65–2.45), p < 0.001) were associated with school absenteeism at bivariate and multivariate analyses. Furthermore, alcohol (OR: 1.42 (1.06–1.89), p < 0.05) and cannabis (OR: 1.57 (1.11–2.22), p < 0.05) use remained robust in predicting learners school absenteeism after adjusting for age, sex and socioeconomic status.

Conclusion

These findings suggest that alcohol consumption and smoking cannabis are contemporary factors associated with school absenteeism. Therefore, interventions to ensure learners’ consistent attendance to school should integrate prevention of alcohol and cannabis use.

The Oregon Child Absenteeism Due to Respiratory Disease Study (ORCHARDS): Rationale, objectives, and design

BACKGROUND

Influenza viruses pose significant disease burdens through seasonal outbreaks and unpredictable pandemics. Existing surveillance programs rely heavily on reporting of medically attended influenza (MAI). Continuously monitoring cause-specific school absenteeism may identify local acceleration of seasonal influenza activity. The Oregon Child Absenteeism Due to Respiratory Disease Study (ORCHARDS; Oregon, WI) implements daily school-based monitoring of influenza-like illness-specific student absenteeism (a-ILI) in kindergarten through Grade 12 schools and assesses this approach for early detection of accelerated influenza and other respiratory pathogen transmission in schools and surrounding communities.

METHODS

Starting in September 2014, ORCHARDS combines automated reporting of daily absenteeism within six schools and home visits to school children with acute respiratory infection (ARI). Demographic, epidemiological, and symptom data are collected along with respiratory specimens. Specimens are tested for influenza and other respiratory viruses. Household members can opt into a supplementary household transmission study. Community comparisons are possible using a pre-existing and highly effective influenza surveillance program, based on MAI at five family medicine clinics in the same geographical area.

RESULTS

Over the first 5 years, a-ILI occurred on 6634 (0.20%) of 3,260,461 student school days. Viral pathogens were detected in 64.5% of 1728 children with ARI who received a home visit. Influenza was the most commonly detected virus, noted in 23.3% of ill students.

CONCLUSION

ORCHARDS uses a community-based design to detect influenza trends over multiple seasons and to evaluate the utility of absenteeism for early detection of accelerated influenza and other respiratory pathogen transmission in schools and surrounding communities.

Predicting virologically confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons

Background

Children are important in community‐level influenza transmission. School‐based monitoring may inform influenza surveillance.

Methods

We used reported weekly confirmed influenza in Allegheny County during the 2007 and 2010‐2015 influenza seasons using Pennsylvania's Allegheny County Health Department all‐age influenza cases from health facilities, and all‐cause and influenza‐like illness (ILI)‐specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all‐cause and illness‐specific absence rates, calendar week, average weekly temperature, and relative humidity, using four cross‐validations.

Results

School districts reported 2 184 220 all‐cause absences (2010‐2015). Three one‐season studies reported 19 577 all‐cause and 3012 ILI‐related absences (2007, 2012, 2015). Over seven seasons, 11 946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE) = 0.94, 0.98, 0.99). K‐5 grade‐specific absence models had lowest mean absolute errors (MAE) in cross‐validations. ILI‐specific absences performed marginally better than all‐cause absences in two years, adjusting for other covariates, but markedly worse one year.

Conclusions

Our findings suggest seasonal models including K‐5th grade absences predict all‐age‐confirmed influenza and may serve as a useful surveillance tool.

School-based surveillance of acute infectious disease in children: a systematic review

BACKGROUND

Syndromic surveillance systems are an essential component of public health surveillance and can provide timely detection of infectious disease cases and outbreaks. Whilst surveillance systems are generally embedded within healthcare, there is increasing interest in novel data sources for monitoring trends in illness, such as over-the-counter purchases, internet-based health searches and worker absenteeism. This systematic review considers the utility of school attendance registers in the surveillance of infectious disease outbreaks and occurrences amongst children.

METHODS

We searched eight databases using key words related to school absence, infectious disease and syndromic surveillance. Studies were limited to those published after 1st January 1995. Studies based in nursery schools or higher education settings were excluded. Article screening was undertaken by two independent reviewers using agreed eligibility criteria. Data extraction was performed using a standardised data extraction form. Outcomes included estimates of absenteeism, correlation with existing surveillance systems and associated lead or lag times.

RESULTS

Fifteen studies met the inclusion criteria, all of which were concerned with the surveillance of influenza. The specificity of absence data varied between all-cause absence, illness absence and syndrome-specific absence. Systems differed in terms of the frequency of data submissions from schools and the level of aggregation of the data. Baseline rates of illness absence varied between 2.3-3.7%, with peak absences ranging between 4.1-9.8%. Syndrome-specific absenteeism had the strongest correlation with other surveillance systems (r = 0.92), with illness absenteeism generating mixed results and all-cause absenteeism performing the least well. A similar pattern of results emerged in terms of lead and lag times, with influenza-like illness (ILI)-specific absence providing a 1-2 week lead time, compared to lag times reported for all-cause absence data and inconsistent results for illness absence data.

CONCLUSION

Syndrome-specific school absences have potential utility in the syndromic surveillance of influenza, demonstrating good correlation with healthcare surveillance data and a lead time of 1-2 weeks ahead of existing surveillance measures. Further research should consider the utility of school attendance registers for conditions other than influenza, to broaden our understanding of the potential application of this data for infectious disease surveillance in children.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO 2019 CRD42019119737.

Opportunities and Challenges for Developing Syndromic Surveillance Systems for the Detection of Social Epidemics

This commentary explores the potential and challenges of developing syndromic surveillance systems with the ability to more rapidly detect epidemics of addiction, poverty, housing instability, food insecurity, social isolation and other social determinants of health (SDoH). Epidemiologists tracking SDoH heavily rely on expensive government surveys released annually, delaying for months if not years the timely detection of social epidemics, defined as sudden, rapid or unexpected changes in social determinants of population health. Conversely, infectious disease syndromic surveillance is an effective early warning tool for epidemic diseases using various types of non-traditional epidemiological data from emergency room chief complaints to search query data. Based on such experience, novel social syndromic surveillance systems for early detection of social epidemics with health implications are not only possible but necessary. Challenges to their widespread implementation include incorporating disparate proprietary data sources and database integration. Significantly more resources are critically needed to address these barriers to allow for accessing, integrating and rapidly analyzing appropriate data streams to make syndromic surveillance for social determinants of health widely available to public health professionals.

Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development

Background

The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms.

Objective

Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework.

Methods

We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review.

Results

The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms.

Conclusions

The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.

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.

Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system

Background

School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics.

Methods

Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness.

Results

All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827.

Conclusions

School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.

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.

Using Google Trends to Examine the Spatio-Temporal Incidence and Behavioral Patterns of Dengue Disease: A Case Study in Metropolitan Manila, Philippines

Dengue is a major public health concern and an economic burden in the Philippines. Despite the country's improved dengue surveillance, it still suffers from various setbacks and needs to be complemented with alternative approaches. Previous studies have demonstrated the potential of Internet-based surveillance such as Google Dengue Trends (GDT) in supplementing current epidemiological methods for predicting future dengue outbreaks and patterns. With this, our study has two objectives: (1) assess the temporal relationship of weekly GDT and dengue incidence in Metropolitan Manila from 2009⁻2014; and (2) examine the health-seeking behavior based on dengue-related search queries of the population. The study collated the population statistics and reported dengue cases in Metropolitan Manila from respective government agencies to calculate the dengue incidence (DI) on a weekly basis for the entire region and annually per city. Data processing of GDT and dengue incidence was performed by conducting an 'adjustment' and scaling procedures, respectively, and further analyzed for correlation and cross-correlation analyses using Pearson's correlation. The relative search volume of the term 'dengue' and top dengue-related search queries in Metropolitan Manila were obtained and organized from the Google Trends platform. Afterwards, a thematic analysis was employed, and word clouds were generated to examine the health behavior of the population. Results showed that weekly temporal GDT pattern are closely similar to the weekly DI pattern in Metropolitan Manila. Further analysis showed that GDT has a moderate and positive association with DI when adjusted or scaled, respectively. Cross-correlation analysis revealed a delayed effect where GDT leads DI by 1⁻2 weeks. Thematic analysis of dengue-related search queries indicated 5 categories namely; (a) dengue, (b) sign and symptoms of dengue, (c) treatment and prevention, (d) mosquito, and (e) other diseases. The majority of the search queries were classified in 'signs and symptoms' which indicate the health-seeking behavior of the population towards the disease. Therefore, GDT can be utilized to complement traditional disease surveillance methods combined with other factors that could potentially identify dengue hotspots and help in public health decisions.

A Smart Card-Based Electronic School Absenteeism System for Influenza-Like Illness Surveillance in Hong Kong: Design, Implementation, and Feasibility Assessment

Background

School-aged children have the highest incidence of respiratory virus infections each year, and transmission of respiratory viruses such as influenza virus can be a major concern in school settings. School absenteeism data have been employed as a component of influenza surveillance systems in some locations. Data timeliness and system acceptance remain as key determinants affecting the usefulness of a prospective surveillance system.

Objective

The aim of this study was to assess the feasibility of implementing an electronic school absenteeism surveillance system using smart card–based technology for influenza-like illness (ILI) surveillance among a representative network of local primary and secondary schools in Hong Kong.

Methods

We designed and implemented a surveillance system according to the Protocol for a Standardized information infrastructure for Pandemic and Emerging infectious disease Response (PROSPER). We employed an existing smart card–based education and school administration platform for data capture, customized the user interface, and used additional back end systems built for other downstream surveillance steps. We invited local schools to participate and collected absenteeism data by the implemented system. We compared temporal trend of the absenteeism data with data from existing community sentinel and laboratory surveillance data.

Results

We designed and implemented an ILI surveillance system utilizing smart card–based attendance tracking approach for data capture. We implemented the surveillance system in a total of 107 schools (including 66 primary schools and 41 secondary schools), covering a total of 75,052 children. The system successfully captured information on absences for 2 consecutive academic years (2012-2013 and 2013-2014). The absenteeism data we collected from the system reflected ILI activity in the community, with an upsurge in disease activity detected up to 1 to 2 weeks preceding other existing surveillance systems.

Conclusions

We designed and implemented a novel smart card technology–based school absenteeism surveillance system. Our study demonstrated the feasibility of building a large-scale surveillance system riding on a routinely adopted data collection approach and the use of simple system enhancement to minimize workload implication and enhance system acceptability. Data from this system have potential value in supplementing existing sentinel influenza surveillance for situational awareness of influenza activity in the community.

Web-Based Surveillance of Illness in Childcare Centers

School absenteeism is an inefficient and unspecific metric for measuring community illness and does not provide surveillance during summertime. Web-based biosurveillance of childcare centers may represent a novel way to efficiently monitor illness outbreaks year-round. A web-based biosurveillance program ( sickchildcare.org ) was created and implemented in 4 childcare centers in a single Michigan county. Childcare providers were trained to report sick children who required exclusion or had parent-reported absences due to illness. Deidentified data on age range, number of illnesses, and illness categories were collected. Weekly electronic reports were sent to the county public health department. Data for reports were gathered beginning in December 2013 and were summarized using descriptive statistics. A total of 385 individual episodes of illness occurred during the study period. Children with reported illness were infants (16%, n = 61), toddlers (38%, n = 148), and preschoolers (46%, n = 176). Illness categories included: fever (30%, n = 116), gastroenteritis (30%, n = 115), influenzalike illness (8%, n = 32), cold without fever (13%, n = 51), rash (7%, n = 26), conjunctivitis (1%, n = 3), ear infection (1%, n = 5), and other (10%, n = 37). The majority of reports were center exclusions (55%, n = 214); others were absences (45%, n = 171). The detection of a gastroenteritis outbreak by web-based surveillance during winter 2013-14 preceded county health reports by 3 weeks; an additional outbreak of hand-foot-mouth disease was detected during June 2014 when standard school-based surveillance was not available. Web-based biosurveillance of illness in childcare centers represents a novel and feasible method to detect disease trends earlier and year-round compared to standard school-based disease surveillance.

Medical Advice for Sick-reported Students (MASS) in intermediate vocational education schools: design of a controlled before-and-after study

Background

School absenteeism, including medical absenteeism, is associated with early school dropout and may result in physical, mental, social and work-related problems in later life. Especially at intermediate vocational education schools, high rates of medical absenteeism are found. In 2012 the Dutch intervention ‘Medical Advice for Sick-reported Students’ (MASS), previously developed for pre-vocational secondary education, was adjusted for intermediate vocational education schools. The aim of the study outlined in this paper is to evaluate the effectiveness of the MASS intervention at intermediate vocational education schools in terms of reducing students’ medical absenteeism and early dropping out of school. Additionally, the extent to which biopsychosocial and other factors moderate the effectiveness of the intervention will be assessed.

Methods

A controlled before-and-after study will be conducted within Intermediate Vocational Education schools. Schools are allocated to be an intervention or control school based on whether the schools have implemented the MASS intervention (intervention schools) or not (control schools). Intervention schools apply the MASS intervention consisting of active support for students with medical absenteeism provided by the school including a consultation with the Youth Health Care (YHC) professional if needed. Control schools provide care as usual. Data will be collected by questionnaires among students in both groups meeting the criteria for extensive medical absenteeism (i.e. ‘reported sick four times in 12 school weeks or for more than six consecutive school days’ at baseline and at 6 months follow-up). Additionally, in the intervention group a questionnaire is completed after each consultation with a YHC professional, by both the student and the YHC professional. Primary outcome measures are duration and cumulative incidence of absenteeism and academic performances. Secondary outcome measures are biopsychosocial outcomes of the students.

Discussion

It is hypothesized that implementing the MASS intervention including a referral to a YHC professional on indication, will result in a lower level of medical absenteeism and a lower level of school drop outs among intermediate vocational education students compared to students receiving usual care. The study will provide insight in the effectiveness of the intervention as well as in factors moderating the intervention’s effectiveness.

Trial registration

Nederlands Trial Register NTR5556. Date of clinical trial registration: 29-Oct-2015.

Illness absenteeism rates in primary and secondary schools in 2013–2014 in England: was there any impact of vaccinating children of primary-school age against influenza?

A phased introduction of routine influenza vaccination of healthy children was recommended in the UK in 2012, with the aim of protecting both vaccinated children and the wider population through reducing transmission. In the first year of the programme in 2013–2014, 4- to 11-year-olds were targeted in pilot areas across England. This study assesses if this was associated with school absenteeism, an important societal burden of influenza. During the spring 2014 term when influenza predominantly circulated, the proportion of absence sessions due to illness was compared between vaccination pilot and non-pilot areas for primary schools (to measure overall impact) and secondary schools (to measure indirect impact). A linear multilevel regression model was applied, adjusting for clustering within schools and potential school-level confounders, including deprivation, past absenteeism, and ethnicity. Low levels of influenza activity were reported in the community in 2013–2014. Primary schools in pilot areas had a significantly adjusted decrease in illness absenteeism of 0·05% relative to non-pilot schools; equivalent to an average of 4 days per school. In secondary schools, there was no significant indirect impact of being located in a pilot area on illness absenteeism. These insights can be used in conjunction with routine healthcare surveillance data to evaluate the full benefits of such a programme.

Missouri K-12 school collection and reporting of school-based syndromic surveillance data: a cross sectional study

BACKGROUND

School participation in collecting and reporting syndromic surveillance (SS) data to public health officials and school nurses' attitudes regarding SS have not been assessed.

METHODS

An online survey was sent to Missouri Association of School Nurses members during the 2013/2014 school year to assess whether K-12 schools were collecting and reporting SS data. Z-scores were used to assess collection versus reporting of SS indicators. Logistic regressions were used to describe factors predicting nurses' collection and reporting of SS indicators: all-cause absenteeism, influenza-like illness and gastrointestinal illness. Univariate predictors were assessed with Chi-Squares.

RESULTS

In total, 133 school nurses participated (33.6 % response rate). Almost all (90.2 %, n = 120) collect at least one SS indicator; half (49.6 %, n = 66) report at least one. Schools are collecting more SS data than they are reporting to the health department (p < .05 for all comparisons). Determinants of school nurses' collection of SS data included perceived administrative support, and knowledge of collecting and analyzing SS data. The strongest predictive factors for reporting SS data were the perception that the health department was interested in SS data and being approached by the health department to collect SS data.

CONCLUSION

Schools are collecting SS indicators at a relatively high rate, yet less than half of the data is reported to public health officials. Findings from this study indicate that public health officials can increase access to school-based SS data by approaching schools about collecting and reporting this important data.

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.

Impact of influenza vaccination on respiratory illness rates in children attending private boarding schools in England, 2013-2014: a cohort study

Several private boarding schools in England have established universal influenza vaccination programmes for their pupils. We evaluated the impact of these programmes on the burden of respiratory illnesses in boarders. Between November 2013 and May 2014, age-specific respiratory disease incidence rates in boarders were compared between schools offering and not offering influenza vaccine to healthy boarders. We adjusted for age, sex, school size and week using negative binomial regression. Forty-three schools comprising 14 776 boarders participated. Almost all boarders (99%) were aged 11-17 years. Nineteen (44%) schools vaccinated healthy boarders against influenza, with a mean uptake of 48·5% (range 14·2-88·5%). Over the study period, 1468 respiratory illnesses were reported in boarders (5·66/1000 boarder-weeks); of these, 33 were influenza-like illnesses (ILIs, 0·26/1000 boarder-weeks) in vaccinating schools and 95 were ILIs (0·74/1000 boarder-weeks) in non-vaccinating schools. The impact of vaccinating healthy boarders was a 54% reduction in ILI in all boarders [rate ratio (RR) 0·46, 95% confidence interval (CI) 0·28-0·76]. Disease rates were also reduced for upper respiratory tract infections (RR 0·72, 95% CI 0·61-0·85) and chest infections (RR 0·18, 95% CI 0·09-0·36). These findings demonstrate a significant impact of influenza vaccination on ILI and other clinical endpoints in secondary-school boarders. Additional research is needed to investigate the impact of influenza vaccination in non-boarding secondary-school settings.

Assessing the use of hospital staff influenza-like absence (ILA) for enhancing hospital preparedness and national surveillance

Background

Early warning and robust estimation of influenza burden are critical to inform hospital preparedness and operational, treatment, and vaccination policies. Methods to enhance influenza-like illness (ILI) surveillance are regularly reviewed. We investigated the use of hospital staff ‘influenza-like absences’ (hospital staff-ILA), i.e. absence attributed to colds and influenza, to improve capture of influenza dynamics and provide resilience for hospitals.

Methods

Numbers and rates of hospital staff-ILA were compared to regional surveillance data on ILI primary-care presentations (15–64 years) and to counts of laboratory confirmed cases among hospitalised patients from April 2008 to April 2013 inclusive. Analyses were used to determine comparability of the ILI and hospital-ILA and how systems compared in early warning and estimating the burden of disease.

Results

Among 20,021 reported hospital-ILA and 4661 community ILI cases, correlations in counts were high and consistency in illness measurements was observed. In time series analyses, both hospital-ILA and ILI showed similar timing of the seasonal component. Hospital-ILA data often commenced and peaked earlier than ILI according to a Bayesian prospective alarm algorithm. Hospital-ILA rates were more comparable to model-based estimates of ‘true’ influenza burden than ILI.

Conclusions

Hospital-ILA appears to have the potential to be a robust, yet simple syndromic surveillance method that could be used to enhance estimates of disease burden and early warning, and assist with local hospital preparedness.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-0789-z) contains supplementary material, which is available to authorized users.

Exploring national surveillance for health-related workplace absenteeism: lessons learned from the 2009 influenza A pandemic

BACKGROUND

During the 2009 influenza A (H1N1) virus pandemic, the Centers for Disease Control and Prevention did a pilot study to test the feasibility of using national surveillance of workplace absenteeism to assess the pandemic's impact on the workplace to plan for preparedness and continuity of operations and to contribute to health awareness during the emergency response.

METHODS

Population-based and sentinel worksite approaches were used. Monthly measures of the 1-week prevalence of health-related absenteeism among full-time workers were estimated using nationally representative data from the Current Population Survey. Enhanced passive surveillance of absenteeism was conducted using weekly data from a convenience sample of sentinel worksites.

RESULTS

Nationally, the pandemic's impact on workplace absenteeism was small. Estimates of 1-week absenteeism prevalence did not exceed 3.7%. However, peak workplace absenteeism was correlated with the highest occurrence of both influenza-like illness and influenza-positive laboratory tests.

CONCLUSIONS

Systems for monitoring workplace absenteeism should be included in pandemic preparedness planning.

Syndromic surveillance of influenza activity in Sweden: an evaluation of three tools

An evaluation was conducted to determine which syndromic surveillance tools complement traditional surveillance by serving as earlier indicators of influenza activity in Sweden. Web queries, medical hotline statistics, and school absenteeism data were evaluated against two traditional surveillance tools. Cross-correlation calculations utilized aggregated weekly data for all-age, nationwide activity for four influenza seasons, from 2009/2010 to 2012/2013. The surveillance tool indicative of earlier influenza activity, by way of statistical and visual evidence, was identified. The web query algorithm and medical hotline statistics performed equally well as each other and to the traditional surveillance tools. School absenteeism data were not reliable resources for influenza surveillance. Overall, the syndromic surveillance tools did not perform with enough consistency in season lead nor in earlier timing of the peak week to be considered as early indicators. They do, however, capture incident cases before they have formally entered the primary healthcare system.

p-value approximations for spatial scan statistics using extreme value distributions

Spatial scan statistics are widely applied to identify spatial clusters in geographic disease surveillance. To evaluate the statistical significance of detected clusters, Monte Carlo hypothesis testing is often used because the null distribution of spatial scan statistics is not known. A drawback of the method is that we have to increase the number of replications to obtain accurate p-values. Gumbel-based p-value approximations for spatial scan statistics have recently been proposed and evaluated for Poisson and Bernoulli models. In this study, we examine the use of a generalized extreme value distribution to approximate the null distribution of spatial scan statistics as well as the Gumbel distribution. Through simulation, p-value approximations using extreme value distributions for spatial scan statistics are assessed for multinomial and ordinal models in addition to Poisson and Bernoulli models.

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

BACKGROUND

School absenteeism is a common data source in syndromic surveillance, which allows for the detection of outbreaks at an early stage. Previous studies focused on its correlation with other data sources. In this study, we evaluated the effectiveness of control measures based on early warning signals from school absenteeism surveillance in rural Chinese schools.

METHODS

A school absenteeism surveillance system was established in all 17 primary schools in 3 adjacent towns in the Chinese region of Hubei. Three outbreaks (varicella, mumps, and influenza-like illness) were detected and controlled successfully from April 1, 2012, to January 15, 2014. An impulse susceptible-exposed-infectious-recovered model was used to fit the epidemics of these three outbreaks. Moreover, it simulated the potential epidemics under interventions resulting from traditional surveillance signals. The effectiveness of the absenteeism-based control measures was evaluated by comparing the simulated datasets.

RESULTS

The school absenteeism system generated 52 signals. Three outbreaks were verified through epidemiological investigation. Compared to traditional surveillance, the school absenteeism system generated simultaneous signals for the varicella outbreak, but 3 days in advance for the mumps outbreak and 2-4 days in advance for the influenza-like illness outbreak. The estimated excess protection rates of control measures based on early signals were 0.0%, 19.0-44.1%, and 29.0-37.0% for the three outbreaks, respectively.

CONCLUSIONS

Although not all outbreak control measures can benefit from early signals through school absenteeism surveillance, the effectiveness of early signal-based interventions is obvious. School absenteeism surveillance plays an important role in reducing outbreak spread.

Real-time monitoring of school absenteeism to enhance disease surveillance: a pilot study of a mobile electronic reporting system

Background

School absenteeism is a common source of data used in syndromic surveillance, which can eventually be used for early outbreak detection. However, the absenteeism reporting system in most schools, especially in developing countries, relies on a paper-based method that limits its use for disease surveillance or outbreak detection.

Objective

The objective of this study was to develop an electronic real-time reporting system on school absenteeism for syndromic surveillance.

Methods

An electronic (Web-based) school absenteeism reporting system was developed to embed it within the normal routine process of absenteeism reporting. This electronic system allowed teachers to update students' attendance status via mobile tablets. The data from all classes and schools were then automatically sent to a centralized database for further analysis and presentation, and for monitoring temporal and spatial patterns of absent students. In addition, the system also had a disease investigation module, which provided a link between absenteeism data from schools and local health centers, to investigate causes of fever among sick students.

Results

The electronic school absenteeism reporting system was implemented in 7 primary schools in Bangkok, Thailand, with total participation of approximately 5000 students. During May-October 2012 (first semester), the percentage of absentees varied between 1% and 10%. The peak of school absenteeism (sick leave) was observed between July and September 2012, which coincided with the peak of dengue cases in children aged 6-12 years being reported to the disease surveillance system.

Conclusions

The timeliness of a reporting system is a critical function in any surveillance system. Web-based application and mobile technology can potentially enhance the use of school absenteeism data for syndromic surveillance and outbreak detection. This study presents the factors that determine the implementation success of this reporting system.

Guess who's not coming to dinner? Evaluating online restaurant reservations for disease surveillance

Background

Alternative data sources are used increasingly to augment traditional public health surveillance systems. Examples include over-the-counter medication sales and school absenteeism.

Objective

We sought to determine if an increase in restaurant table availabilities was associated with an increase in disease incidence, specifically influenza-like illness (ILI).

Methods

Restaurant table availability was monitored using OpenTable, an online restaurant table reservation site. A daily search was performed for restaurants with available tables for 2 at the hour and at half past the hour for 22 distinct times: between 11:00 am-3:30 pm for lunch and between 6:00-11:30 PM for dinner. In the United States, we examined table availability for restaurants in Boston, Atlanta, Baltimore, and Miami. For Mexico, we studied table availabilities in Cancun, Mexico City, Puebla, Monterrey, and Guadalajara. Time series of restaurant use was compared with Google Flu Trends and ILI at the state and national levels for the United States and Mexico using the cross-correlation function.

Results

Differences in restaurant use were observed across sampling times and regions. We also noted similarities in time series trends between data on influenza activity and restaurant use. In some settings, significant correlations greater than 70% were noted between data on restaurant use and ILI trends.

Conclusions

This study introduces and demonstrates the potential value of restaurant use data for event surveillance.

Comparison of 3 school-based influenza surveillance indicators: lessons learned from 2009 pandemic influenza A (H1N1)--Denver Metropolitan Region, Colorado

CONTEXT

Early in the 2009 pandemic influenza A (H1N1) experience, children aged 5 to 17 years were determined to be disproportionately affected compared with recent influenza seasons.

OBJECTIVE

To characterize the pandemic among school-aged children, to enable timely influenza outbreak identification, and to determine which school-based influenza surveillance indicator correlated most closely with a laboratory-based standard influenza indicator (standard) and, therefore, might be most useful for future school-based influenza surveillance.

DESIGN

: During the 2009-2010 school year, we monitored students using 3 different surveillance indicators: (1) all-cause absenteeism, (2) influenza-like illness (ILI)-related absenteeism, (3) and ILI-related school health office visits. Thresholds were set for each indicator to identify individual school outbreaks. Each surveillance indicator was compared with the standard, confirmed influenza cases among hospitalized patients.

SETTING

Tri-County (Denver metropolitan area), Colorado.

PARTICIPANTS

Prekindergarten through 12th-grade students in public schools.

MAIN OUTCOME MEASURES

Correlation coefficients comparing each influenza surveillance indicator with the standard and graphs comparing weekly rates for each influenza surveillance indicator or weekly outbreak counts with the standard.

RESULTS

Correlation between the surveillance indicators and the standard varied greatly. All-cause absenteeism correlated most poorly with the standard (Pearson's r = 0.33) and ILI-related health office visits correlated moderately well (r = 0.63). Influenza-like illness-related absenteeism correlated best (r = 0.92) and could be improved (r = 0.97) by shifting ILI-absenteeism data later by 1 week. Graphs of weekly rates or weekly outbreak counts also illustrated that ILI-related absenteeism correlated best with the standard.

CONCLUSIONS

For influenza surveillance among school-aged children, when feasible, we recommend using ILI-related absenteeism, which correlated best and its rate peaked more than 1 week sooner than the standard. The other 2 surveillance indicators might be useful in certain situations, such as when resources are limited.

Description of a school nurse visit syndromic surveillance system and comparison to emergency department visits, New York City

OBJECTIVES

We compared school nurse visit syndromic surveillance system data to emergency department (ED) visit data for monitoring illness in New York City schoolchildren.

METHODS

School nurse visit data recorded in an electronic health record system are used to conduct daily surveillance of influenza-like illness, fever-flu, allergy, asthma, diarrhea, and vomiting syndromes. We calculated correlation coefficients to compare the percentage of syndrome visits to the school nurse and ED for children aged 5 to 14 years, from September 2006 to June 2011.

RESULTS

Trends in influenza-like illness correlated significantly (correlation coefficient = 0.89; P < .001) and 72% of school signals occurred on days that ED signaled. Trends in allergy (correlation coefficient = 0.73; P < .001) and asthma (correlation coefficient = 0.56; P < .001) also correlated and school signals overlapped with ED signals on 95% and 51% of days, respectively. Substantial daily variation in diarrhea and vomiting visits limited our ability to make comparisons.

CONCLUSIONS

Compared with ED syndromic surveillance, the school nurse system identified similar trends in influenza-like illness, allergy, and asthma syndromes. Public health practitioners without school-based surveillance may be able to use age-specific analyses of ED syndromic surveillance data to monitor illness in schoolchildren.

Potential use of school absenteeism record for disease surveillance in developing countries, case study in rural Cambodia

BACKGROUND

Disease surveillance allows prospective monitoring of patterns in disease incidence in the general community, specific institutions (e.g. hospitals, elderly care homes), and other important population subgroups. Surveillance activities are now routinely conducted in many developed countries and in certain easy-to-reach areas of the developing ones. However due to limited health resources, population in rural area that consisted of the most the vulnerable groups are not under surveillance. Cheaper alternative ways for disease surveillance were needed in resource-limited settings.

METHODS AND FINDINGS

In this study, a syndromic surveillance system using disease specific absenteeism rates was established in 47 pre-schools with 1,417 students 3-6 y of age in a rural area of Kampot province, Cambodia. School absenteeism data were collected via short message service. Data collected between 1st January and 31st December 2012 was used for system evaluation for future potential use in larger scale. The system appeared to be feasible and acceptable in the rural study setting. Moderate correlation was found between rates of school absenteeism due to illness and the reference data on rates of attendance at health centers in persons <16 y (maximum cross-correlation coefficient = 0.231 at lag = -1 week).

CONCLUSIONS

School absenteeism data is pre-existing, easily accessible and requires minimum time and resources after initial development, and our results suggest that this system may be able to provide complementary data for disease surveillance, especially in resource limited settings where there is very little information on illnesses in the community and traditional surveillance systems are difficult to implement. An important next step is to validate the syndromic data with other forms of surveillance including laboratory data.

Knowledge, attitudes, and practices of school personnel regarding influenza, vaccinations, and school outbreaks

BACKGROUND

School personnel are important for communicating with parents about school vaccination programs and recognizing influenza outbreaks. This study examined knowledge, attitudes, and practices of school personnel regarding seasonal and 2009 H1N1 influenza, vaccinations, and school outbreak investigations.

METHODS

Data were analyzed from survey interviews of 58 elementary and middle school personnel in 2010.

RESULTS

Principals, assistant principals, and nurses have higher knowledge than front office clerks regarding seasonal (odds ratio [OR]: 2.50, 95% confidence interval [CI]: 1.15-5.42) and 2009 H1N1 influenza (OR: 2.04, 95% CI: 1.19-3.71). During 2009-2010, 63.8 and 19.0% of school personnel received seasonal and 2009 H1N1 influenza vaccine, respectively. Personnel were more likely to be vaccinated against seasonal influenza if they believed the vaccine was safe (OR: 2.26, 95% CI: 1.21-4.19). Of those unvaccinated against 2009 H1N1, 48.9% also cited safety concerns. While every principal, assistant principal, and nurse received both infectious diseases and outbreak trainings, only 42.5 and 27.5% of clerks received these trainings, respectively (p < .001), and 30% of clerks believed outbreak recognition was not their responsibility.

CONCLUSION

The level of knowledge regarding influenza illness, vaccination, and outbreaks among subjects was low overall. Education of school personnel may improve school vaccination programs and control of influenza outbreaks.

Estimation of Influenza Incidence by Age in the 2011/12 Seasons in Japan using SASSy

Objective

Introduction

Methods

Results

Conclusions

[Syndromic surveillance: review and prospect of a promising concept]

Syndromic surveillance appeared in the field of public health surveillance in the late 90's. Initially proposed for public health identification of bioterrorism events, the method failed to provide convincing evidence of its usefulness and potential benefits. The definition which is proposed today by the Centers for Disease Control and Prevention (CDC) of Atlanta is the most commonly accepted. It defines syndromic surveillance as an automatic process that goes from registration to transfer of data recorded within the framework of a professional rather than public health goal. Systems operating today have integrated a public health approach through routine surveillance procedures with a broader focus than bioterrorism, implying active participation of the official public health surveillance structures. Syndromic surveillance offers several advantages including quick access to a large volume of data in real time, no extra-work for data registration and construction of a historical dataset useful as an historical baseline. Nevertheless, the limitations of this type of surveillance should not be forgotten (sometimes limited sensitivity, specificity, important technical burden…). Today, recorded experience shows that there is no opposition between syndromic surveillance and classical surveillance. On the contrary, they should be presented as complementary procedures. Syndromic surveillance should be analyzed from a temporal perspective, examining its short-term use as an alert mechanism, mid-term use for constitution of historical time series, and long-term use for a description of human health in the 21st century.

Evaluation of factors affecting variations in influenza A/H1N1 history in university students, Japan

Although the natural history of H1N1 has been found to vary among patients, little is known about the factors that affect these variations. Infected patients with an extended infection history may shed virus longer and spread infection. To further clarify these variations, we evaluated the natural history of H1N1 infection in 324 university students using a descriptive epidemiological method and analyzed factors affecting the natural history of infection. The median times from infection to fever development and from fever development to cure were 2 days (range 0-8 days) and 5 days (range 1-12 days), respectively, and the median time not attending classes was 5 days (range, 1-13 days). Variations in H1N1 natural history were associated with both environmental and individual factors, including route of infection, grade, gender, epidemic period, respiratory and gastrointestinal symptoms and headache. Steps affecting these factors may help control variations in H1N1 natural history and may enhance infection control measures.

School absenteeism as an adjunct surveillance indicator: experience during the second wave of the 2009 H1N1 pandemic in Quebec, Canada

Background

A school absenteeism surveillance system was implemented in the province of Quebec, Canada during the second wave of the 2009 H1N1pandemic. This paper compares this surveillance approach with other available indicators.

Method

All (3432) elementary and high schools from Quebec were included. Each school was required to report through a web-based system any day where the proportion of students absent for influenza-like illness (ILI) exceeded 10% of current school enrolment.

Results

Between October 18 and December 12 2009, 35.6% of all schools met the 10% absenteeism threshold. This proportion was greater in elementary compared to high schools (40% vs 19%) and in smaller compared to larger schools (44% vs 22%). The maximum absenteeism rate was reached the first day of reporting or within the next two days in 55% and 31% of schools respectively. The first reports and subsequent peak in school absenteeism provincially preceded the peak in paediatric hospitalization by two and one weeks, respectively. Trends in school surveillance otherwise mirrored other indicators.

Conclusion

During a pandemic, school outbreak surveillance based on a 10% threshold appears insufficient to trigger timely intervention within a given affected school. However, school surveillance appears well-correlated and slightly anticipatory compared to other population indicators. As such, school absenteeism warrants further evaluation as an adjunct surveillance indicator whose overall utility will depend upon specified objectives, and other existing capacity for monitoring and response.

The effect of school dismissal on rates of influenza-like illness in New York City schools during the spring 2009 novel H1N1 outbreak

BACKGROUND

The effects of individual school dismissal on influenza transmission have not been well studied. During the spring 2009 novel H1N1 outbreak, New York City implemented an individual school dismissal policy intended to limit influenza transmission at schools with high rates of influenza-like illness (ILI).

METHODS

Active disease surveillance data collected by the New York City Health Department on rates of ILI in schools were used to evaluate the impact. Sixty-four schools that met the Health Department's criteria for considering dismissal were included in the analysis. Twenty-four schools that met criteria subsequently dismissed all classes for approximately 1 school week. A regression model was fit to these data, estimating the effect of school dismissal on rates of in-school ILI following reconvening, adjusting for potential confounders.

RESULTS

The model estimated that, on average, school dismissal reduced the rate of ILI by 7.1% over the entire average outbreak period. However, a large proportion of in-school ILI occurred before dismissal criteria were met. A separate model estimated that school absenteeism rates were not significantly affected by dismissal.

CONCLUSION

Results suggest that individual school dismissal could be considered in situations where schools have a disproportionate number of high-risk students or may be unable to implement recommended preventive or infection control measures. Future work should focus on developing more sensitive indicators of early outbreak detection in schools and evaluating the impact of school dismissal on community transmission.

Enhanced communication and coordination in the public health surveillance component of the Cincinnati Drinking Water Contamination Warning System

Effective communication and coordination are critical when investigating a possible drinking water contamination incident. A contamination warning system is designed to detect water contamination by initiating a coordinated, effective response to mitigate significant public health and economic consequences. This article describes historical communication barriers during water contamination incidents and discusses how these barriers were overcome through the public health surveillance component of the Cincinnati Drinking Water Contamination Warning System, referred to as the "Cincinnati Pilot." By enhancing partnerships in the public health surveillance component of the Cincinnati Pilot, information silos that existed in each organization were replaced with interagency information depots that facilitated effective decision making.

The new school absentees reporting system for pandemic influenza A/H1N1 2009 infection in Japan

Objective

To evaluate the new Japanese School Absentees Reporting System for Infectious Disease (SARSID) for pandemic influenza A/H1N1 2009 infection in comparison with the National epidemiological Surveillance of Infectious Disease (NESID).

Methods

We used data of 53,223 students (97.7%) in Takamatsu city Japan. Data regarding school absentees in SARSID was compared with that in NESID from Oct 13, 2009 to Jan 12, 2010.

Results

Similar trends were observed both in SARSID and NESID. However, the epidemic trend for influenza in SARSID was thought to be more sensitive than that in NESID.

Conclusion

The epidemic trend for influenza among school-aged children could be easily and rapidly assessed by SARSID compared to NESID. SARSID might be useful for detecting the epidemic trend of influenza.

Effectiveness of Non-pharmaceutical Interventions in Controlling an Influenza A Outbreak in a School, Thailand, November 2007

Non-pharmaceutical interventions are often recommended as a component of integrated control measures for pandemic influenza, but the effectiveness needs to be evaluated. An outbreak of influenza A (H1N1) in northern Thailand in November 2007 offered opportunity to evaluate these interventions. An investigation was conducted to describe the outbreak, evaluate effectiveness of non-pharmaceutical interventions and assess surge capacity of health agencies. A descriptive study was conducted by interviewing students and personnel in a school. We characterized transmission of the virus in this outbreak and explored effects of control measures. We identified that 44% of the students and teachers developed influenza during the 19-day outbreak. Non-pharmaceutical interventions including school closure, setting up a field hospital and community health education were implemented. These measures possibly limited the outbreak spreading to other schools nearby. Surveillance and preparedness plans could be strengthened to respond to pandemic and inter-pandemic influenza by using non-pharmaceutical interventions.

Incorporating geospatial capacity within clinical data systems to address social determinants of health

Linking electronic health record (EHR) systems with community information systems (CIS) holds great promise for addressing inequities in social determinants of health (SDH). While EHRs are rich in location-specific data that allow us to uncover geographic inequities in health outcomes, CIS are rich in data that allow us to describe community-level characteristics relating to health. When meaningfully integrated, these data systems enable clinicians, researchers, and public health professionals to actively address the social etiologies of health disparities.This article describes a process for exploring SDH by geocoding and integrating EHR data with a comprehensive CIS covering a large metropolitan area. Because the systems were initially designed for different purposes and had different teams of experts involved in their development, integrating them presents challenges that require multidisciplinary expertise in informatics, geography, public health, and medicine. We identify these challenges and the means of addressing them and discuss the significance of the project as a model for similar projects.

Absenteeism in schools during the 2009 influenza A(H1N1) pandemic: a useful tool for early detection of influenza activity in the community?

Certain influenza outbreaks, including the 2009 influenza A(H1N1) pandemic, can predominantly affect school-age children. Therefore the use of school absenteeism data has been considered as a potential tool for providing early warning of increasing influenza activity in the community. This study retrospectively evaluates the usefulness of these data by comparing them with existing syndromic surveillance systems and laboratory data. Weekly mean percentages of absenteeism in 373 state schools (children aged 4-18 years) in Birmingham, UK, from September 2006 to September 2009, were compared with established syndromic surveillance systems including a telephone health helpline, a general practitioner sentinel network and laboratory data for influenza. Correlation coefficients were used to examine the relationship between each syndromic system. In June 2009, school absenteeism generally peaked concomitantly with the existing influenza surveillance systems in England. Weekly school absenteeism surveillance would not have detected pandemic influenza A(H1N1) earlier but daily absenteeism data and the development of baselines could improve the timeliness of the system.

Development of a web-based survey for monitoring daily health and its application in an epidemiological survey

BACKGROUND

Early detection of symptoms arising from exposure to pathogens, harmful substances, or environmental changes is required for timely intervention. The administration of Web-based questionnaires is a potential method for collecting information from a sample population.

OBJECTIVE

The objective of our study was to develop a Web-based daily questionnaire for health (WDQH) for symptomatic surveillance.

METHODS

We adopted two different survey methods to develop the WDQH: an Internet panel survey, which included participants already registered with an Internet survey company, and the Tokyo Consumers' Co-operative Union (TCCU) Internet survey, in cooperation with the Japanese Consumers' Co-operative Union, which recruited participants by website advertising. The Internet panel survey participants were given a fee every day for providing answers, and the survey was repeated twice with modified surveys and collection methods: Internet Panel Survey I was conducted every day, and Internet Panel Survey II was conducted every 3 days to reduce costs. We examined whether the survey remained valid by reporting health conditions on day 1 over a 3-day period, and whether the response rate would vary among groups with different incentives. In the TCCU survey, participants were given a fee only for initially registering, and health information was provided in return for survey completion. The WDQH included the demographic details of participants and prompted them to answer questions about the presence of various symptoms by email. Health information collected by the WDQH was then used for the syndromic surveillance of infection.

RESULTS

Response rates averaged 47.3% for Internet Panel Survey I, 42.7% for Internet Panel Survey II, and 40.1% for the TCCU survey. During a seasonal influenza epidemic, the WDQH detected a rapid increase in the number of participants with fever through the early aberration reporting system.

CONCLUSIONS

We developed a health observation method based on self-reporting by participants via the Internet. We validated the usefulness of the WDQH by its practical use in syndromic surveillance.

Non-invasive sample collection for respiratory virus testing by multiplex PCR

Background

Identifying respiratory pathogens within populations is difficult because invasive sample collection, such as with nasopharyngeal aspirate (NPA), is generally required. PCR technology could allow for non-invasive sampling methods.

Objective

Evaluate the utility of non-invasive sample collection using anterior nare swabs and facial tissues for respiratory virus detection by multiplex PCR.

Study design

Children aged 1 month–17 years evaluated in a pediatric emergency department for respiratory symptoms had a swab, facial tissue, and NPA sample collected. All samples were tested for respiratory viruses by multiplex PCR. Viral detection rates were calculated for each collection method. Sensitivity and specificity of swabs and facial tissues were calculated using NPA as the gold standard.

Results

285 samples from 95 children were evaluated (92 swab-NPA pairs, 91 facial tissue-NPA pairs). 91% of NPA, 82% of swab, and 77% of tissue samples were positive for ≥ 1 virus. Respiratory syncytial virus (RSV) and human rhinovirus (HRV) were most common. Overall, swabs were positive for 74% of virus infections, and facial tissues were positive for 58%. Sensitivity ranged from 17 to 94% for swabs and 33 to 84% for tissues. Sensitivity was highest for RSV (94% swabs and 84% tissues). Specificity was ≥95% for all viruses except HRV for both collection methods.

Conclusions

Sensitivity of anterior nare swabs and facial tissues in the detection of respiratory viruses by multiplex PCR varied by virus type. Given its simplicity and specificity, non-invasive sampling for PCR testing may be useful for conducting epidemiologic or surveillance studies in settings where invasive testing is impractical or not feasible.

Alert system to detect possible school-based outbreaks of influenza-like illness

To evaluate the usefulness of school absentee data in identifying outbreaks as part of syndromic surveillance, we examined data collected from public schools in Miami-Dade County, Florida, USA. An innovative automated alert system captured information about school-specific absenteeism to detect and provide real-time notification of possible outbreaks of influenza-like illness.

Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance

BACKGROUND

A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics.

METHODOLOGY/PRINCIPAL FINDINGS

Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003-2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99.

CONCLUSIONS/SIGNIFICANCE

Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance.

Increased emergency department chief complaints of fever identified the influenza (H1N1) pandemic before outpatient symptom surveillance

OBJECTIVE

To determine whether a sentinel clinic network or an emergency department (ED) was more timely in identifying the 2009 influenza A (H1N1) pandemic.

METHODS

All reasons for presenting to the adult regional medical ED were coded online by admission secretaries, without the aid of medical personnel. Increased influenza activity defined by weekly chief complaints of fever was compared with activity defined by the Israel Center for Disease Control (viral surveillance as well as a large sentinel clinic network).

RESULTS

Influenza activity during the pandemic increased in the ED 2 weeks before outpatient sentinel clinics. During the pandemic, maximal ED activity was much higher than in previous seasons. Maximal activity during the past 5 years correlated with the timeliness of the chief complaint of fever in identifying the onset of epidemics.

CONCLUSION

Chief complaint of fever in the ED can be a sensitive marker of increased influenza activity and might replace the use of sentinel clinics.

Timeliness of syndromic influenza surveillance through work and school absenteeism

In this paper, we investigate the usefulness of work and school absenteeism surveillance as an early warning system for influenza. In particular, time trends in daily absenteeism rates collected during the A(H1N1)2009 pandemic are compared with weekly incidence rates of influenza-like illness (ILI) obtained from the Belgian Sentinel General Practitioner (SGP) network. The results indicate a rise in absenteeism rates prior to the onset of the influenza epidemic, suggesting that absenteeism surveillance is a promising tool for early warning of influenza epidemics. To convincingly conclude on the usefulness of absenteeism data for early warning, additional data covering several influenza seasons is needed.