Indian Ocean Dipole and Cryptosporidiosis in Australia: Short-Term and Nonlinear Associations

The Indian Ocean Dipole (IOD) is a global climate phenomenon that influences the spread of human infectious diseases through climate extremes including droughts and floods. The Dipole Mode Index (DMI), which measures the strength of the IOD, is one of the main indicators of rainfall variability across Australia. Using an ecological, time-series approach we examined the short-term and nonlinear relationship between the DMI and weekly cryptosporidiosis reported from 2001 to 2012 across the temperate, subtropical, and tropical climate zones in Australia, controlling for season, long-term trends, and cryptosporidiosis counts from the past week. The association of DMI with cryptosporidiosis was nonlinear and varied in the short term and by climatic zone. Including cryptosporidiosis counts from the previous week improved model fit in all three zones and modified the DMI-disease relationship in the subtropical and temperate regions. In the temperate zone, a 0.1 unit increase in an extreme positive DMI was associated with a higher risk of reported cryptosporidiosis [Relative Risk (RR) 1.23 (95% confidence interval (CI), 1.00–1.52)], compared to the risk associated with mean DMI. This methodology shows the potential for quantifying the short-term and nonlinear response of infections like cryptosporidiosis with climate variability. These findings also suggest that future models that account for lagged disease effects may better represent the time varying environmental exposure-disease relationship. The expected increases in the frequency of positive DMI events will likely result in decreased rainfall across temperate Australia, with potential implications for public health

Disease specific, regional monthly seasonality indices.

<p>Months and seasons shown refers to month/seasons of the northern hemisphere (i.e. January = month 1) and adjusted by six months for the southern hemisphere (i.e. January = month 7). Seasons are December, January, February (winter), March, April, May (spring), June, July, August (summer), September, October, November (autumn). CI of plus and minus one standard deviation is plotted. (A-campylobacteriosis, B-salmonellosis, C-VTEC, D-cryptosporidiosis, E-giardiasis).</p

Synchronisation between Dengue Incidence in Bangkok and in the Rest of Thailand

<div><p>The incidence series are square root transformed, and all series are normalised.</p> <p>(A) Wavelet coherence computed based on the Morlet wavelet function between dengue incidence in Bangkok and in the rest of Thailand; colours as in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020106#pmed-0020106-g001" target="_blank">Figure 1</a>B. The white dashed lines show the α = 5% significance level computed based on 1,000 bootstrapped series.</p> <p>(B) Oscillating components computed with the wavelet transform in the 2–3-y period band (colours as in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020106#pmed-0020106-g001" target="_blank">Figure 1</a>A).</p> <p>(C) Oscillating components computed with the wavelet transform in the 0.8–1.2-y period band (colours as in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020106#pmed-0020106-g001" target="_blank">Figure 1</a>A).</p> <p>In (B) and (C) the black line shows the time evolution of the instantaneous time delay in months (Δ<i>T</i>) between the oscillating components of the two incidence time series.</p></div

Flow chart illustrating criteria for study selection.

<p>The grey boxes represent the three major steps relating to article selection process. N is the total number of papers found across all three database searches; the suffixes represent the disease (Ca-Campylobacteriosis, Sa-Salmonellosis, V-VTEC, Cr-Cryptosporidiosis, Gi-Giardiasis). This order is maintained throughout the diagram.</p

Disease specific overall monthly seasonality indices with plotted confidence intervals.

<p>Months and seasons shown refers to month/seasons of the northern hemisphere (i.e. January = month 1) and adjusted by six months for the southern hemisphere (i.e. January = month 7). Seasons are December, January, February (winter), March, April, May (spring), June, July, August (summer), September, October, November (autumn). CI of plus and minus one standard deviation is plotted. (A-campylobacteriosis, B-salmonellosis, C-VTEC, D-cryptosporidiosis, E-giardiasis).</p

Quantifying the nitrate levels in bottled water in New Zealand

No description supplie

Monthly variation in crude mortality and monthly (30-day) mortality rates per 100,000 from January 1980 to December 2000

<p><b>Copyright information:</b></p><p>Taken from "Trends and determinants of excess winter mortality in New Zealand: 1980 to 2000"</p><p>http://www.biomedcentral.com/1471-2458/7/263</p><p>BMC Public Health 2007;7():263-263.</p><p>Published online 24 Sep 2007</p><p>PMCID:PMC2174476.</p><p></p

Annual variation in estimated winter:non-winter all-cause mortality rate ratios from 1980 to 2000

<p><b>Copyright information:</b></p><p>Taken from "Trends and determinants of excess winter mortality in New Zealand: 1980 to 2000"</p><p>http://www.biomedcentral.com/1471-2458/7/263</p><p>BMC Public Health 2007;7():263-263.</p><p>Published online 24 Sep 2007</p><p>PMCID:PMC2174476.</p><p></p

Descriptive statistics for the disease and climatic variables in New Zealand, during 1997–2008.

*<p>Average monthly incidence /100000 population.</p

Time series of raw and log transformed monthly incidence (after differencing) of campylobacteriosis (A-B), salmonellosis (C-D), cryptosporidiosis (E-F), and giardiasis (G-H) in New Zealand, 1997-2008.

<p>Time series of raw and log transformed monthly incidence (after differencing) of campylobacteriosis (A-B), salmonellosis (C-D), cryptosporidiosis (E-F), and giardiasis (G-H) in New Zealand, 1997-2008.</p