Rainfall drives hydrocephalus in East Africa

Clinical article

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Hydrocephalus is one of the most common brain disorders in children throughout the world. The majority of infant hydrocephalus cases in East Africa appear to be postinfectious, related to preceding neonatal infections, and are thus preventable if the microbial origins and routes of infection can be characterized. In prior microbiological work, the authors noted evidence of seasonality in postinfectious hydrocephalus (PIH) cases.


The geographical address of 696 consecutive children with PIH who were treated over 6 years was fused with satellite rainfall data for the same time period. A comprehensive time series and spatiotemporal analysis of cases and rainfall was performed.


Four infection-onset peaks were found to straddle the twice-yearly rainy season peaks, demonstrating that the infections occurred at intermediate levels of rainfall.


The findings in this study reveal a previously unknown link between climate and a neurosurgical condition. Satellite-derived rainfall dynamics are an important factor in driving the infections that lead to PIH. Given prior microbial analysis, these findings point to the importance of environmental factors with respect to preventing the newborn infections that lead to PIH.

Abbreviations used in this paper:NOAA = National Oceanic and Atmospheric Administration; PIH = postinfectious hydrocephalus.

Article Information

Address correspondence to: Steven J. Schiff, M.D., Center for Neural Engineering, W311 Millennium Science Complex, Penn State University, University Park, Pennsylvania 16802. email: sschiff@psu.edu.

Please include this information when citing this paper: published online July 6, 2012; DOI: 10.3171/2012.5.PEDS11557.

© AANS, except where prohibited by US copyright law.



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    Panel Aa shows a satellite 0.1° × 0.1° grid overlain on the geographic districts of Uganda. Monthly rainfall was averaged within each district, and patients with PIH were assigned to a district. Satellite data are missing for January and February of 2000, and these data were set to 0. These 77 districts were mapped to a 10 × 10 adjacency matrix in panel Ab, keeping at least 1 or more neighboring districts in contact to facilitate display of spatiotemporal analysis. The 23 black squares are empty. The rainfall by month from each district is shown overlain in panel Ba, and the spectrogram demonstrates a strong twice-yearly frequency of rainy seasons. Birth, febrile illness, and surgery dates are shown in panels Bb, Bc, and Bd, respectively, and their spectrograms are complex. Note that the time axes for the spectrograms are shortened to 1.5–4.5 years, reflecting the resolution within the 3-year sliding windows used. Summing the total rainfall (in mm) for each district over all 6 years reflects the high levels of rainfall across Uganda in panel Ca and the more complex spatial distribution of cases over all 6 years in panel Cb (a base 10 log scale is used for cases to facilitate the display). A linear regression of total rainfall to cases (using febrile illness dates, for comparison later) in panel Cc demonstrates a modest fit (coefficient of determination, R2 = 0.44), and the residuals of this fit steadily increase as total rainfall increases, indicating that the relationship between rain and cases over geography is not a simple one explained by total rainfall. A regression by birth dates is nearly identical (R2 = 0.43).

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    Average global time series (univariate) are shown in panels A and B, and spatiotemporal analysis in panels C–E. After averaging rainfall and summing cases across districts by month, their cross-correlations are shown in panel A. Confidence bounds were calculated as described in Methods, representing twice the SD of surrogate correlations displayed as the dotted line bootstrap confidence limits in panel A. All correlations greater than these confidence limits are potentially significant, especially where consecutive significant excursions are seen. The correlation with rainfall is greatest for febrile illness dates, and the surgery dates are offset from febrile illness and birth dates by several months, as expected. The coherograms of the data from panel A are shown in B, where a prominent band at 4 cycles per year emerges from each of the data sets. The full data sets for each district were decomposed using a singular value decomposition, and the eigenvalues (statistical importance of the eigenimages) and eigenimages (most statistically common pattern to the data, ordered by the eigenvalues from largest to smallest) are shown for rainfall and febrile illness dates in panel C. There is a prominent first mode, which represents the average total rainfall or case numbers, followed by second and third modes displaying fluctuations about the mean. These modes were used as filters to form reconstructed univariate time series in panel D (products of eigenvalue, modal amplitude, and eigenimage, and summing to reconstruct data), where it is seen that there were no correlations between the first rainfall mode and case data. Testing all combinations of the first 6 modes, we find the largest correlations by summing modes 2 and 3, where febrile illness dates demonstrate larger correlations with rainfall than birth dates, and no correlation in these modes is seen for surgery dates. In panel E we show coherograms from the sum of modes 2 and 3, demonstrating that there is very substantial correlation between birth and febrile illness dates and that febrile illness dates correlate more strongly with surgery dates (in the correlation sums at arbitrary lags [Csum]) than do the birth dates. C0 = absolute value of the total correlation at 0 time lag when greater than the 2-SD confidence limit; Csum = sum of the total significant correlation at all lags.

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    Histograms for all district case data per month (5544 samples) shown as a function of rainfall per month for febrile illness, birth dates, and surgery dates in panels Aa–Ac. The cases are strongly peaked at intermediate levels of rainfall. Normal distribution fits are shown, which indicate that the birth and febrile illness months have almost identical means, whereas the surgery mean rainfall values are offset. A histogram of monthly rainfall is shown in panel Ba, along with monthly cases based on febrile illness, birth, and surgery dates in panels Bb–Bd. Tests of nonuniformity based on Shannon entropy show that each case histogram is significantly nonuniform with respect to monthly rainfall, with febrile dates being the most nonuniform (p = 0.0066). Note the 4 peaks per year in monthly febrile cases flanking the 2 peaks in rainfall in April and October. Rainfall for each district was then narrow-band filtered from 1.5 to 2.5 Hz by using a fifth-order Chebyshev filter applied so as not to distort phase in panel Ca, and a Hilbert transform was used to assign phase from 0 to 4π to represent yearly cycles in panel Cb. The average rainfall per phase was fit by a sine wave in panel Da, and the histograms of case data are shown in panels Db–Dd. Again, assigning phase values rather than calendar months demonstrates that febrile illness occurs with 4 peaks per year, straddling the phase peaks, which coincide with peak rainfall months.



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