Temporal cross-correlation between influenza-like illnesses and invasive pneumococcal disease in The Netherlands

Abstract

Background: While the burden of community-acquired pneumonia and invasive pneumococcal disease (IPD) is still considerable, there is little insight in the factors contributing to disease. Previous research on the lagged relationship between respiratory viruses and pneumococcal disease incidence is inconclusive, and studies correcting for temporal autocorrelation are lacking.

Objectives: To investigate the temporal relation between influenza-like illnesses (ILI) and IPD, correcting for temporal autocorrelation.

Methods: Weekly counts of ILI were obtained from the Sentinel Practices of NIVEL Primary Care Database. IPD data were collected from the Dutch laboratory-based surveillance system for bacterial meningitis from 2004 to 2014. We analysed the correlation between time series, pre-whitening the dependent time series with the best-fit seasonal autoregressive integrated moving average (SARIMA) model to the independent time series. We performed cross-correlations between ILI and IPD incidences, and the (pre-whitened) residuals, in the overall population and in the elderly.

Results: We found significant cross-correlations between ILI and IPD incidences peaking at lags -3 overall and at 1 week in the 65+ population. However, after pre-whitening, no cross-correlations were apparent in either population group.

Conclusion: Our study suggests that ILI occurrence does not seem to be the major driver of IPD incidence in The Netherlands.

Keywords: SARIMA; cross-correlation; filtering; influenza; pneumococci; pre-whitening; respiratory tract infections.

Figure 1

Schematic of databases used for weekly ILI and IPD incidence calculations

Figure 2

Schematic of pre‐whitening approach for cross‐correlation analysis. Both time series are transformed to stabilize the variance; the best‐fit SARIMA model to the independent variable is then used to filter the dependent variable. The white noise of the best‐fit SARIMA and the residuals of the filtered dependent time series are cross‐correlated. Lags with a significant correlation will indicate the time lag between the independent and dependent time series

Figure 3

Weekly incidence of influenza‐like illnesses and invasive pneumococcal disease recorded in the Dutch population 2004‐2014 (A) across all age groups (B) aged 65 years and over

Figure 4

Cross‐correlation functions between weekly ILI and IPD incidences (A) in the whole population (B) for elderly (65+) population; pre‐whitened cross‐correlation functions for same respective groups in (C)—whole population and (D)—elderly, on log‐transformed data. The 95% confidence intervals around 0 are indicated by the dashed blue lines