Local variations in the timing of RSV epidemics

Noveroske, Douglas; Warren, Joshua L.; Pitzer, Virginia E.; Weinberger, Daniel M

doi:10.1186/s12879-016-2004-2

Cited by 13 publications

(16 citation statements)

References 14 publications

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“…Our results suggest that population density is a stronger predictor of epidemic peak timing than of incidence rates, even after adjustment for IMD. Similar to the results of a RSV epidemic study in Connecticut,20 we found that higher population density (residents per km 2 ) was associated with an earlier peak timing. Area-level socioeconomic deprivation had an inverted U-shaped association with peak timing, which may be indicative of shared factors between CCGs on the extremes of socioeconomic deprivation.…”

Section: Discussionsupporting

confidence: 88%

“…We used robust SEs and expressed the rate in terms of region, year and harmonic functions capturing seasonal variations in weeks (see box 1). 19 20 We adjusted for year of admission to account for the annual increasing trend in bronchiolitis admission rates 2…”

Section: Methodsmentioning

confidence: 99%

“…We included the harmonic functions as specified above and allowed for clustering at the CCG level via random effects for the intercept and the harmonic function parameters 20. Using the estimated harmonic function coefficients, we calculated CCG-specific average annual peak week as true0P^i=52.14∗false(arctan⁡false(false(β^1+η^1ifalse)/false(β^2+η^2ifalse)false)/2πfalse)+1 , where i indicates CCG 20 21. The CCG-specific departures from the model intercept (ie, the random effects for the intercept, η0i ) across England were exponentiated to obtain CCG-specific incidence rate ratios (IRRs) relative to the population mean 22 23…”

Section: Methodsmentioning

confidence: 99%

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Geospatial and seasonal variation of bronchiolitis in England: a cohort study using hospital episode statistics

Lewis

Stavola

Hardelid

2020

Thorax

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BackgroundRates of hospital admissions for bronchiolitis vary seasonally and geographically across England; however, seasonal differences by area remain unexplored. We sought to describe spatial variation in the seasonality of hospital admissions for bronchiolitis and its association with local demographic characteristics.MethodsSingleton children born in English National Health Service hospitals between 2011 and 2016 (n=3 727 013) were followed up for 1 year. Poisson regression models with harmonic functions to model seasonal variations were used to calculate weekly incidence rates and peak timing of bronchiolitis admissions across English regions and clinical commissioning groups (CCGs). Linear regression was used to estimate the joint association of population density and deprivation with incidence and peak timing of bronchiolitis admissions at the CCG level.ResultsBronchiolitis admission rates ranged from 30.9 per 1000 infant-years (95% CI 30.4 to 31.3) in London to 68.7 per 1000 (95% CI 67.9 to 69.5) in the North West. Across CCGs, there was a 5.3-fold variation in incidence rates and the epidemic peak ranged from week 49.3 to 52.2. Admission rates were positively associated with area-level deprivation. CCGs with earlier peak epidemics had higher population densities, and both high and low levels of deprivation were associated with earlier peak timing.ConclusionsApproximately one quarter of the variation in admission rates and two-fifths of the variation in peak timing of hospital admissions for bronchiolitis were explained by local demographic characteristics. Implementation of an early warning system could help to prepare hospitals for peak activity and to time public health messages.

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Section: Discussionsupporting

confidence: 88%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Geospatial and seasonal variation of bronchiolitis in England: a cohort study using hospital episode statistics

Lewis

Stavola

Hardelid

2020

Thorax

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“…29 US-based research, on the other hand, found a negative association between the proportion of the population from a black ethnic group (correlated with socioeconomic position of the population) and seasonal peak timing at the ZIP-code level. 36 We have previously investigated the relationship between socioeconomic deprivation and seasonality by geographical area, finding a 3-week difference in peak timing across Clinical Commissioning Groups (CCGs) in England. 37 In this study, CCGs with both low and high IMD scores were associated with earlier peak timing of bronchiolitis, after adjustment for population density.…”

Section: Discussionmentioning

confidence: 99%

Is socioeconomic position associated with bronchiolitis seasonality? A cohort study

Lewis

Stavola

Hardelid

2020

J Epidemiol Community Health

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BackgroundUnderstanding differences in the seasonality of bronchiolitis can help to plan the timing of interventions. We quantified the extent to which seasonality in hospital admissions for bronchiolitis is modified by socioeconomic position.MethodsUsing Hospital Episode Statistics, we followed 3 717 329 infants born in English National Health Service hospitals between 2011 and 2016 for 1 year. We calculated the proportion of all infant admissions due to bronchiolitis and the incidence rate of bronchiolitis admissions per 1000 infant-years, according to year, month, age, socioeconomic position and region. We used harmonic Poisson regression analysis to assess whether socioeconomic position modified bronchiolitis seasonality.ResultsThe admission rate for bronchiolitis in England increased from 47.4 (95% CI 46.8 to 47.9) to 58.9 per 1000 infant-years (95% CI 58.3 to 59.5) between 2012 and 2016. We identified some variation in the seasonality of admissions by socioeconomic position: increased deprivation was associated with less seasonal variation and a slightly delayed epidemic peak. At week 50, the risk of admission was 38% greater (incidence rate ratios 1.38; 95% CI 1.35 to 1.41) for infants in the most deprived socioeconomic group compared with the least deprived group.ConclusionThese results do not support the need for differential timing of prophylaxis or vaccination by socioeconomic group but suggest that infants born into socioeconomic deprivation should be considered a priority group for future interventions. Further research is needed to establish if the viral aetiology of bronchiolitis varies by season and socioeconomic group, and to quantify risk factors mediating socioeconomic deprivation and bronchiolitis rates.

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“…In temperate northern climates, RSV primarily circulates between the months of November and April. However, when a seasonal epidemic begins varies from location to location . Hospitalization season begins earlier and persists longer than the beginning and end of bronchiolitis season for the general population, making understanding of seasonal timing imperative to reducing bronchiolitis rates among high‐risk groups.…”

Section: Introductionmentioning

confidence: 99%

Estimating seasonal onsets and peaks of bronchiolitis with spatially and temporally uncertain data

Pugh

Heaton

Hartman

et al. 2019

Statistics in Medicine

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RSV bronchiolitis (an acute lower respiratory tract viral infection in infants) is the most common cause of infant hospitalizations in the United States (US). The only preventive intervention currently available is monthly injections of immunoprophylaxis. However, this treatment is expensive and needs to be administered simultaneously with seasonal bronchiolitis cycles in order to be effective. To increase our understanding of bronchiolitis timing, this research focuses on identifying seasonal bronchiolitis cycles (start times, peaks, and declinations) throughout the continental US using data on infant bronchiolitis cases from the US Military Health System Data Repository. Because this data involved highly personal information, the bronchiolitis dates in the dataset were "jittered" in the sense that the recorded dates were randomized within a time window of the true date. Hence, we develop a statistical change point model that estimates spatially varying seasonal bronchiolitis cycles while accounting for the purposefully introduced jittering in the data. Additionally, by including temperature and humidity data as regressors, we identify a relationship between bronchiolitis seasonality and climate. We found that, in general, bronchiolitis seasons begin earlier and are longer in the southeastern states compared to the western states with peak times lasting approximately 1 month nationwide.

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Local variations in the timing of RSV epidemics

Cited by 13 publications

References 14 publications

Geospatial and seasonal variation of bronchiolitis in England: a cohort study using hospital episode statistics

Geospatial and seasonal variation of bronchiolitis in England: a cohort study using hospital episode statistics

Is socioeconomic position associated with bronchiolitis seasonality? A cohort study

Estimating seasonal onsets and peaks of bronchiolitis with spatially and temporally uncertain data

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