Dataset Information

Australian state influenza notifications and school holiday closures in 2019.

ABSTRACT: Background: The impact of school holidays on influenza rates has been sparsely documented in Australia. In 2019, the early winter influenza season coincided with mid-year school breaks, enabling us the unusual opportunity to examine how influenza incidence changed during school holiday closure dates. Methods: The weekly influenza data from five Australian state and one territory health departments for the period of week 19 (mid-May) to week 39 (early October) 2019 were compared to each state's public-school holiday closure dates. We used segmented regression to model the weekly counts and a negative binomial distribution to account for overdispersion due to autocorrelation. The models' goodness-of-fit was assessed by plots of observed versus expected counts, plots of residuals versus predicted values, and Pearson's Chi-square test. The main exposure was the July two-week school holiday period, using a lag of one week. The effect is estimated as a percent change in incidence level, and in slope. Results: School holidays were associated with significant declines in influenza incidence in three states and one territory by between 41% and 65%. Two states did not show evidence of declines although one of those states had already passed its peak by the time of the school holidays. The models showed acceptable goodness-of-fit. The first decline during school holidays is seen in the school aged (5-19 years) population, with the declines in the adult and infant populations being smaller and following a week later. Conclusions: Given the significant and rapid reductions in incidence, these results have important public health implications. Closure or extension of holiday periods could be an emergency option for state governments.

SUBMITTER: Scott AM

PROVIDER: S-EPMC8097737 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundRecently, a small number of studies have suggested that gains in fitness and reductions in body fat achieved during the school term are reversed or stagnate during the holiday period. This may be associated with changed activity patterns. The aim of this study was to compare 24-h activity compositions between school and holiday periods in Australian children.MethodsThe participants in this study were 366 children (53% female, 13.4 ± 2.3 years) who were a subgroup of the 2007 Australian National Children's Nutrition and Physical Activity Survey. Each child recalled use of time on at least one school day, one weekend day and one holiday using the Multimedia Activity Recall for Children and Adults. Composite "in-term" and "holiday" use-of-time profiles were generated by weighting school days by 5, and weekends by 2 where data were available. Difference between holiday and in-term time use was assessed using a compositional multivariate linear model for repeated measures. Subsequent models tested for interaction between time of measurement and socio-economic status or body mass index.ResultsTime use was significantly different between holidays and in-term days (F = 103, p < 0.0001). On holidays, children accumulated 140 min less School-related time, compensated by sleeping 40 min longer, 58 min more Screen Time, and 35 min more Domestic/Social time. Children spent 10 min less in vigorous physical activity, and although sitting time was 33 min/day less during holidays, estimated total daily energy expenditure (TDEE) was 5.4% lower. Differences between holiday and in-term activity compositions did not vary by parental education (F = 1.2, p = 0.25), postcode-level socio-economic status (F = 0.9, p = 0.56) or weight status (F = 1.7, p = 0.07).ConclusionsIn this subsample of a nationally representative survey of Australian children, holidays were characterised by longer sleep and higher TV and videogame time, lower vigorous activity and lower TDEE. Uncompensated by dietary adjustments, these differences would result in an accumulation of about 650 g of fat over a six-week holiday period. Holiday activity patterns may be a promising focus for obesity prevention efforts.

Project description:ObjectiveTo replicate and analyse the information available to UK policymakers when the lockdown decision was taken in March 2020 in the United Kingdom.DesignIndependent calculations using the CovidSim code, which implements Imperial College London's individual based model, with data available in March 2020 applied to the coronavirus disease 2019 (covid-19) epidemic.SettingSimulations considering the spread of covid-19 in Great Britain and Northern Ireland.PopulationAbout 70 million simulated people matched as closely as possible to actual UK demographics, geography, and social behaviours.Main outcome measuresReplication of summary data on the covid-19 epidemic reported to the UK government Scientific Advisory Group for Emergencies (SAGE), and a detailed study of unpublished results, especially the effect of school closures.ResultsThe CovidSim model would have produced a good forecast of the subsequent data if initialised with a reproduction number of about 3.5 for covid-19. The model predicted that school closures and isolation of younger people would increase the total number of deaths, albeit postponed to a second and subsequent waves. The findings of this study suggest that prompt interventions were shown to be highly effective at reducing peak demand for intensive care unit (ICU) beds but also prolong the epidemic, in some cases resulting in more deaths long term. This happens because covid-19 related mortality is highly skewed towards older age groups. In the absence of an effective vaccination programme, none of the proposed mitigation strategies in the UK would reduce the predicted total number of deaths below 200 000.ConclusionsIt was predicted in March 2020 that in response to covid-19 a broad lockdown, as opposed to a focus on shielding the most vulnerable members of society, would reduce immediate demand for ICU beds at the cost of more deaths long term. The optimal strategy for saving lives in a covid-19 epidemic is different from that anticipated for an influenza epidemic with a different mortality age profile.

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.

Dataset Information

Australian state influenza notifications and school holiday closures in 2019.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets