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.2014 Apr 2;281(1783):20132438.
doi: 10.1098/rspb.2013.2438. Print 2014 May 22.

Human birth seasonality: latitudinal gradient and interplay with childhood disease dynamics

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Human birth seasonality: latitudinal gradient and interplay with childhood disease dynamics

Micaela Martinez-Bakker et al. Proc Biol Sci..

Abstract

More than a century of ecological studies have demonstrated the importance of demography in shaping spatial and temporal variation in population dynamics. Surprisingly, the impact of seasonal recruitment on infectious disease systems has received much less attention. Here, we present data encompassing 78 years of monthly natality in the USA, and reveal pronounced seasonality in birth rates, with geographical and temporal variation in both the peak birth timing and amplitude. The timing of annual birth pulses followed a latitudinal gradient, with northern states exhibiting spring/summer peaks and southern states exhibiting autumn peaks, a pattern we also observed throughout the Northern Hemisphere. Additionally, the amplitude of United States birth seasonality was more than twofold greater in southern states versus those in the north. Next, we examined the dynamical impact of birth seasonality on childhood disease incidence, using a mechanistic model of measles. Birth seasonality was found to have the potential to alter the magnitude and periodicity of epidemics, with the effect dependent on both birth peak timing and amplitude. In a simulation study, we fitted an susceptible-exposed-infected-recovered model to simulated data, and demonstrated that ignoring birth seasonality can bias the estimation of critical epidemiological parameters. Finally, we carried out statistical inference using historical measles incidence data from New York City. Our analyses did not identify the predicted systematic biases in parameter estimates. This may be owing to the well-known frequency-locking between measles epidemics and seasonal transmission rates, or may arise from substantial uncertainty in multiple model parameters and estimation stochasticity.

Keywords: birth; disease; latitude; measles; seasonality.

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Figures

Figure 1.
Figure 1.
Temporal patterns of birth rates (per 1000 individuals per month) in the USA organized by geographical region, separated into three eras: Pre-Baby Boom (1931–1945), Baby Boom (1946–1965) and Modern Era (1965–2008). The time series for Louisiana is plotted at the top as an example. (Online version in colour.)
Figure 2.
Figure 2.
Spatio-temporal patterns of seasonal birth peak timing and amplitude in the USA. (Top panels) Pre-Baby Boom (1931–1945), (middle panels) Baby Boom (1946–1964) and (bottom panels) Modern Era (1965–2008). Maps depict the latitudinal gradient in the timing of the birth peak. Colours indicate the mean timing of the birth peak for each state. Hatched regions represent states which had significant biannual peaks and are colour-coded based on the timing of their primary annual birth pulse (also see the electronic supplementary material, figures S1 and S2). States shown in white did not exhibit significant periodicity. Regressions show the latitudinal variation in seasonal amplitude, with the colours representing the peak birth timing for the respective period. (Online version in colour.)
Figure 3.
Figure 3.
Northern Hemisphere patterns of seasonal birth pulses colour-coded by region. Birth pulses occurred earlier in the year at northern latitudes. Electronic supplementary material, table S5 provides the details for each country, including the time frame of the data which ranges from the 1960s to 2011. (Online version in colour.)
Figure 4.
Figure 4.
Impact of birth seasonality on childhood disease. (a) Epidemic and skip-year incidence varies with birth peak timing along thex-axis. Solid curve shows the change in epidemic year incidence when birth seasonality is added to the measles model. Dashed curve shows the change in the skip-year. The phase relationship between seasonal births and transmission determines whether birth seasonality has an effect on incidence. The greatest increase in epidemic year incidence is when the birth peak occurs after children return from winter holiday (orange points). A decrease in epidemic year incidence occurs when births peak prior to summer vacation (green points). School terms are noted and vertical arrows mark the timing of incidence peaks during the epidemic year. (inset) Time series from the constant birth model (black), and time series corresponding to the colour-matched points on the main graph. (b) Bifurcation diagram showing the change in epidemic and skip-year peak incidence with increasing birth amplitude. In the absence of birth seasonality, epidemics are biennial. As birth amplitude increases, skip-year incidence increases and epidemic year incidence decreases. When birth amplitude reaches approximately 40% epidemics become annual. Time series in the inset correspond to the points in the main graph; blue time series are biennial, and golden are annual. Arrows denote the birth amplitude observed in Switzerland, Cuba, Egypt, Nigeria, Guinea and Sierra Leone, left to right. Amplitudes for Nigeria, Guinea and Sierra Leone are from reference [49]. (c) Bias inR0 estimates owing to the exclusion of birth seasonality in an SEIR model. Time series were generated using an SEIR model with 28% birth amplitude and a birth peak day of 162 (turquoise), 295 (blue) or 351 (orange). Each time series was fit to the SEIR model with a birth amplitude of 0%. The actualR0 value is shown by the dashed line, and the likelihood profiles show that maximum-likelihood estimate (MLE) ofR0 is either over- or underestimated when birth seasonality is excluded from the model. 95% confidence intervals for MLE are indicated on profiles. (Online version in colour.)
Figure 5.
Figure 5.
Measles cases in New York City. (a) Measles incidence (black) and a stochastic realization using the MLE for each type of birth covariate: seasonal births with a three month lag (blue), seasonal births with a six month lag (green), seasonal births with a nine month lag (yellow) and births with no seasonality (maroon). Legend applies to all of figure 5. (b) The shape of the likelihood surface with respect toR0. The MLER0s are indicated by points, and the values within the standard error of the MLE are represented by horizontal lines. (c) MLE transmission splines for each model. (d) Transmission splines estimated using TSIR [46,47] for each type of birth covariate. The MLEs differed with and without birth seasonality, but the differences in the point estimates were overwhelmed by uncertainty in parameter estimates (b,c). No difference in transmission parameters was observed using the TSIR method. (Online version in colour.)
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