Location-specific patterns of exposure to recent pre-pandemic strains of influenza A in southern China

Abstract

Variation in influenza incidence between locations is commonly observed on large spatial scales. It is unclear whether such variation occurs on smaller spatial scales and whether it is the result of heterogeneities in population demographics or more subtle differences in population structure and connectivity. Here we show that significant differences in immunity to influenza A viruses among communities in China are not explained by differences in population demographics. We randomly selected households from five randomly selected locations near Guangzhou, China to answer a questionnaire and provide a blood sample for serological testing against five recently circulating influenza viruses. We find a significant reduction in the frequency of detectable neutralization titers with increasing age, levelling off in older age groups. There are significant differences between locations in age, employment status, vaccination history, household size and housing conditions. However, after adjustment, significant variations in the frequency of detectable neutralization titers persists between locations. These results suggest there are characteristics of communities that drive influenza transmission dynamics apart from individual and household level risk factors, and that such factors have effects independent of strain.

Figure 1. Study setup

Panel a shows the spatial distribution of study locations and the transect sector from which they were randomly chosen. Grayscale shows population density with darker colors indicating large population density. Panel b shows the process of participant recruitment.

Figure 2. Neutralization titers by age and strain and study location

The line on each chart is a non-parametric local polynomial regression (LOESS) with a span of 0.9 and gaussian distribution family. All charts in the same column show results for the same strain and all charts in the same row show results for the same location.

Figure 3. Proportion with detectable titers in running 20 year windows by age

Points represent the mid-point of each (overlapping) 20 year age class (point position +/- 10 years). The shaded regions show bootstrapped 95% confidence intervals (1,000 bootstrap iterations), and blacked dotted lines show the predicted proportion with detectable titers from a logistic-regression on a natural-spline of age with 2 degrees of freedom.

Figure 4. Proportions of individuals with detectable neutralization titers across locations

Each point on the off diagonal plots represents the proportion of individuals with detectable titers in the row location (y position) versus the proportion in the column location (x position). In the on diagonal plots the percentage positive in the indicated location is shown for each strain.