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High school intervention for influenza biology and epidemics/pandemics: impact on conceptual understanding among adolescents.

ABSTRACT: Understanding real-life issues such as influenza epidemiology may be of particular interest to the development of scientific knowledge and initiation of conceptual changes about viruses and their life cycles for high school students. The goal of this research project was to foster the development of adolescents' conceptual understanding of viruses and influenza biology. Thus, the project included two components: 1) pre- and posttests to determine students' conceptions about influenza biology, epidemics/pandemics, and vaccination; and 2) design an intervention that supports conceptual change to promote improvements in influenza knowledge based on these primary conceptions. Thirty-five female students from a high school biology class participated in a series of instructional activities and pre- and posttest assessments. Results from the pretest indicated that high school students exhibit a limited understanding of concepts related to viruses. Six weeks after an intervention that promoted active learning, results from a posttest showed that conceptions about influenza are more accurately related to the provided scientific knowledge. Although adolescents have nonscientific models to explain influenza biology, we showed that a carefully designed intervention can affect students' knowledge as well as influence the implementation of health education programs in secondary schools.

SUBMITTER: Dumais N

PROVIDER: S-EPMC2649650 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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High school intervention for influenza biology and epidemics/pandemics: impact on conceptual understanding among adolescents.

Dumais Nancy N Hasni Abdelkrim A

CBE life sciences education 20090101 1

Understanding real-life issues such as influenza epidemiology may be of particular interest to the development of scientific knowledge and initiation of conceptual changes about viruses and their life cycles for high school students. The goal of this research project was to foster the development of adolescents' conceptual understanding of viruses and influenza biology. Thus, the project included two components: 1) pre- and posttests to determine students' conceptions about influenza biology, ep ...[more]

PMID: 19255137

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Project description:The novel coronavirus is part of a series of infectious disease outbreaks that include: Ebola, Avian influenza, Middle East respiratory syndrome coronavirus, and Influenza A. This paper addresses the question of how do these epidemics and pandemics affect income inequality in countries around the world during the first two decades of this century. To achieve its objective, the paper develops a model that indicates a positive association between these health crises and income inequality. To empirically test our theoretical predictions, the paper explores the effect on the Gini coefficient of a dummy variable that indicates the occurrence of an epidemic or a pandemic in a country in a given year and the number of deaths per 100,000. To properly address potential endogeneity, we implement a Three-Stage-Least Squares technique. The estimation shows that the number of deaths per 100,000 population variable has a statistically significant positive effect on the Gini coefficient, especially when we incorporate COVID-19 data. This suggests that not only the occurrence, but also the health consequences of COVID-19 have a significant and economically important effect on income inequality.BackgroundThe purpose of the study is to examine the effect of epidemics and pandemics on income inequality. This has important implications as the outcome of this study can guide policymakers into implementing policies that can mitigate the economic consequences of these health crises.MethodsThe study is a cross country analysis using fixed effects estimation. To address potential endogeneity and determine causality, the paper uses the Three-Stage-Least-Squares estimation.ResultsThe paper finds that the number of epidemic deaths per 100,000 population variable has a statistically significant positive effect on the Gini coefficient, especially when we incorporate COVID-19 data.ConclusionsThe paper finds that it is not only the occurrence of an epidemic, captured by the epidemics dummy variable, but also the health consequences, captured by the number of deaths per 100,000 population, that have a significant effect on income inequality. This is especially the case when we incorporate COVID-19 in our analysis.Trial registrationNot Applicable.

Project description:BackgroundRapid data sharing can maximize the utility of data. In epidemics and pandemics like Zika, Ebola, and COVID-19, the case for such practices seems especially urgent and warranted. Yet rapidly sharing data widely has previously generated significant concerns related to equity. The continued lack of understanding and guidance on equitable data sharing raises the following questions: Should data sharing in epidemics and pandemics primarily advance utility, or should it advance equity as well? If so, what norms comprise equitable data sharing in epidemics and pandemics? Do these norms address the equity-related concerns raised by researchers, data providers, and other stakeholders? What tensions must be balanced between equity and other values?MethodsTo explore these questions, we undertook a systematic scoping review of the literature on data sharing in epidemics and pandemics and thematically analyzed identified literature for its discussion of ethical values, norms, concerns, and tensions, with a particular (but not exclusive) emphasis on equity. We wanted to both understand how equity in data sharing is being conceptualized and draw out other important values and norms for data sharing in epidemics and pandemics.ResultsWe found that values of utility, equity, solidarity, and reciprocity were described, and we report their associated norms, including researcher recognition; rapid, real-time sharing; capacity development; and fair benefits to data generators, data providers, and source countries. The value of utility and its associated norms were discussed substantially more than others. Tensions between utility norms (e.g., rapid, real-time sharing) and equity norms (e.g., researcher recognition, equitable access) were raised.ConclusionsThis study found support for equity being advanced by data sharing in epidemics and pandemics. However, norms for equitable data sharing in epidemics and pandemics require further development, particularly in relation to power sharing and participatory approaches prioritizing inclusion. Addressing structural inequities in the wider global health landscape is also needed to achieve equitable data sharing in epidemics and pandemics.

Project description: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.

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High school intervention for influenza biology and epidemics/pandemics: impact on conceptual understanding among adolescents.

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High school intervention for influenza biology and epidemics/pandemics: impact on conceptual understanding among adolescents.

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