Are Meteorological Parameters Associated with Acute Respiratory Tract Infections?

Prel, Jean‐Baptist du; Puppe, Wolfram; Gröndahl, Britta; Knuf, Markus; Weigl, Franziska; Schaaff, Franziska; Schmitt, Heinz-Josef

doi:10.1086/605435

Cited by 200 publications

(229 citation statements)

References 37 publications

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“…It is expected that this might overemphasize a little bit the tail ends of the epidemic, and it requires a season‐specific factor to calculate iMAARI from the number of confirmed influenza viruses. Second, as we are lacking a time series of data on RSV (and other respiratory pathogens) and as RSV seasons may overlap with influenza epidemics,25 we might overestimate the burden of influenza to a certain extent. However, data from the above mentioned study in four European countries found that in two of the four countries, RSV was not a significant term in the model (explaining MAILI), and in two others, the model attributed only 11% and 13%, respectively, to RSV 14.…”

Section: Discussionmentioning

confidence: 99%

Estimation of influenza‐attributable medically attended acute respiratory illness by influenza type/subtype and age, Germany, 2001/02–2014/15

Heiden

Buchholz

2016

Influenza Resp Viruses

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BackgroundThe total burden of influenza in primary care is difficult to assess. The case definition of medically attended “acute respiratory infection” (MAARI) in the German physician sentinel is sensitive; however, it requires modelling techniques to derive estimates of disease attributable to influenza. We aimed to examine the impact of type/subtype and age.MethodsData on MAARI and virological results of respiratory samples (virological sentinel) were available from 2001/02 until 2014/15. We constructed a generalized additive regression model for the periodic baseline and the secular trend. The weekly number of influenza‐positive samples represented influenza activity. In a second step, we distributed the estimated influenza‐attributable MAARI (iMAARI) according to the distribution of types/subtypes in the virological sentinel.ResultsSeason‐specific iMAARI ranged from 0.7% to 8.9% of the population. Seasons with the strongest impact were dominated by A(H3), and iMAARI attack rate of the pandemic 2009 (A(H1)pdm09) was 4.9%. Regularly the two child age groups (0‐4 and 5‐14 years old) had the highest iMAARI attack rates reaching frequently levels up to 15%‐20%. Influenza B affected the age group of 5‐ to 14‐year‐old children substantially more than any other age group. Sensitivity analyses demonstrated both comparability and stability of the model.ConclusionWe constructed a model that is well suited to estimate the substantial impact of influenza on the primary care sector. A(H3) causes overall the greatest number of iMAARI, and influenza B has the greatest impact on school‐age children. The model may incorporate time series of other pathogens as they become available.

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Section: Discussionmentioning

confidence: 99%

Estimation of influenza‐attributable medically attended acute respiratory illness by influenza type/subtype and age, Germany, 2001/02–2014/15

Heiden

Buchholz

2016

Influenza Resp Viruses

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“…It can therefore be concluded that the seasonal variation of ARIs caused by these pathogens can be explained by meteorological influences. 38 RSV infections appear to have a correlation with an increase atmospheric humidity. Omer et al, 39 studied 2878 children with 748 RSV(+) cases using a multivariate analysis with a lag of 8 days and the occurrence of rain.…”

Section: Pollutants and Child Healthmentioning

confidence: 97%

“…26,27 In relation to viral diseases, Martins et al(2002) investigated the effects caused by atmospheric pollution on the morbidity of pneumonia and influenza in the elderly, concluding that O 3 and SO 2 are directly related to these diseases, with an increase in the number of cases at health services. 28 Prel et al 38 studied the correlation with climate factors in a sample composed of 3461 children hospitalized for acute respiratory diseases. There was a positive correlation between Adenovirus (hAdV), type A and B influenza (FLU A and FLUB) and respiratory syncytial virus (RSV) and temperature, and between rhinovirus (hRV) and relative air humidity.…”

Section: Pollutants and Child Healthmentioning

confidence: 99%

Do pollution and climate influence respiratory tract infections in children?

Passos

Gazeta

Felgueiras

et al. 2014

Rev. Assoc. Med. Bras.

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“…The model's prediction accuracy is inadequate for practical use [185]. du Prel et al [194] developed an autoregressive integrated moving average model to predict the biweekly number of RSV hospitalizations, with an R 2 of 65% indicating low prediction accuracy.…”

Section: Predicting Community Rsv Activitymentioning

confidence: 99%

A systematic review of predictive modeling for bronchiolitis

Luo

Nkoy

Gesteland

et al. 2014

International Journal of Medical Informatics

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Purpose: Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases.Methods: A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process.Search results were limited to human subjects, the English language, and children (birth-18 years). Results:The literature search returned 2,312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain. Conclusions:Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models.

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Are Meteorological Parameters Associated with Acute Respiratory Tract Infections?

Abstract: Seasonality of certain ARI pathogens can be explained by meteorological influences. The model presented herein is a first step toward predicting annual RSV epidemics using weather forecast data.

Cited by 200 publications

References 37 publications

Estimation of influenza‐attributable medically attended acute respiratory illness by influenza type/subtype and age, Germany, 2001/02–2014/15

Estimation of influenza‐attributable medically attended acute respiratory illness by influenza type/subtype and age, Germany, 2001/02–2014/15

Do pollution and climate influence respiratory tract infections in children?

A systematic review of predictive modeling for bronchiolitis

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