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

ABSTRACT: 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.

SUBMITTER: Ward MA

PROVIDER: S-EPMC6729058 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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

Ward Madeline A MA Stanley Anu A Deeth Lorna E LE Deardon Rob R Feng Zeny Z Trotz-Williams Lise A LA

BMC public health 20190905 1

<h4>Background</h4>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.<h4>Methods</h4>Daily absenteeism percenta ...[more]

PMID: 31488092

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Project description:To analyze absenteeism among healthcare workers (HCWs) at a large Italian hospital and to estimate the increase in absenteeism that occurred during seasonal flu periods.Retrospective observational study.The absenteeism data were divided into three "epidemic periods," starting at week 42 of one year and terminating at week 17 of the following year (2010-2011, 2011-2012, 2012-2013), and three "non-epidemic periods," defined as week 18 to week 41 and used as baseline data. The excess of the absenteeism occurring among HCWs during periods of epidemic influenza in comparison with baseline was estimated. All data, obtained from Hospital's databases, were collected for each of the following six job categories: medical doctors, technical executives (i.e., pharmacists), nurses and allied health professionals (i.e., radiographers), other executives (i.e., engineers), nonmedical support staff, and administrative staff. The HCWs were classified by: in and no-contact; vaccinated and unvaccinated.5,544, 5,369, and 5,291 workers in three years were studied. The average duration of absenteeism during the epidemic periods increased among all employees by +2.07 days/person (from 2.99 to 5.06), and the relative increase ranged from 64-94% among the different job categories. Workers not in contact with patients experienced a slightly greater increase in absenteeism (+2.28 days/person, from 2.73 to 5.01) than did employees in contact with patients (+2.04, from 3.04 to 5.08). The vaccination rate among HCWs was below 3%, however the higher excess of absenteeism rate among unvaccinated in comparison with vaccinated workers was observed during the epidemic periods (2.09 vs 1.45 days/person).The influenza-related absenteeism during epidemic periods was quantified as totaling more than 11,000 days/year at the Italian hospital studied. This result confirms the economic impact of sick leave on healthcare systems and stresses on the necessity of encouraging HCWs to be immunized against influenza.

Project description:BackgroundInfluenza 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.ObjectiveThe aim of this paper is to determine the potential of monitoring school absenteeism as a surveillance tool of influenza.MethodsWe 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.ResultsAmong 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.ConclusionsThere 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.

Project description:BackgroundSchool closure is a common mitigation strategy during severe influenza epidemics and pandemics. However, the effectiveness of this strategy remains controversial. In this study, we aimed to explore the effectiveness of school closure on seasonal influenza epidemics in provincial-level administrative divisions (PLADs) with varying urbanization rates in China.MethodsThis study analyzed influenza surveillance data between 2010 and 2019 provided by the Chinese National Influenza Center. Taking into consideration the climate, this study included a region with 3 adjacent PLADs in Northern China and another region with 4 adjacent PLADs in Southern China. The effect of school closure on influenza transmission was evaluated by the reduction of the effective reproductive number of seasonal influenza during school winter breaks compared with that before school winter breaks. An age-structured Susceptible-Infected-Recovered-Susceptible (SIRS) model was built to model influenza transmission in different levels of urbanization. Parameters were determined using the surveillance data via robust Bayesian method.ResultsBetween 2010 and 2019, in the less urbanized provinces: Hebei, Zhejiang, Jiangsu and Anhui, during school winter breaks, the effective reproductive number of seasonal influenza epidemics reduced 14.6% [95% confidential interval (CI): 6.2-22.9%], 9.6% (95% CI: 2.5-16.6%), 7.3% (95% CI: 0.1-14.4%) and 8.2% (95% CI: 1.1-15.3%) respectively. However, in the highly urbanized cities: Beijing, Tianjin and Shanghai, it reduced only 5.2% (95% CI: -0.7-11.2%), 4.1% (95% CI: -0.9-9.1%) and 3.9% (95% CI: -1.6-9.4%) respectively. In China, urbanization is associated with decreased proportion of children and increased social contact. According to the SIRS model, both factors could reduce the impact of school closure on seasonal influenza epidemics, and the proportion of children in the population is thought to be the dominant influencing factor.ConclusionsEffectiveness of school closure on the epidemics varies with the age structure in the population and social contact patterns. School closure should be recommended in the low urbanized regions in China in the influenza seasons.

Project description: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.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.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.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.

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

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

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