Bias, RMSE of the estimated excess hospitalization rates from the observed hospitalization rates with laboratory confirmed influenza infections.

<p>Note. QAIC, quasi-Akaike information criterion; QBIC, quasi-Bayesian information criterion; PACF, partial autocorrelation function; GCV, generalized cross validation; RMSE, root-mean-square error.</p

Excess Winter Mortality and Cold Temperatures in a Subtropical City, Guangzhou, China

<div><p>Background</p><p>A significant increase in mortality was observed during cold winters in many temperate regions. However, there is a lack of evidence from tropical and subtropical regions, and the influence of ambient temperatures on seasonal variation of mortality was not well documented.</p> <p>Methods</p><p>This study included 213,737 registered deaths from January 2003 to December 2011 in Guangzhou, a subtropical city in Southern China. Excess winter mortality was calculated by the excess percentage of monthly mortality in winters over that of non-winter months. A generalized linear model with a quasi-Poisson distribution was applied to analyze the association between monthly mean temperature and mortality, after controlling for other meteorological measures and air pollution.</p> <p>Results</p><p>The mortality rate in the winter was 26% higher than the average rate in other seasons. On average, there were 1,848 excess winter deaths annually, with around half (52%) from cardiovascular diseases and a quarter (24%) from respiratory diseases. Excess winter mortality was higher in the elderly, females and those with low education level than the young, males and those with high education level, respectively. A much larger winter increase was observed in out-of-hospital mortality compared to in-hospital mortality (45% vs. 17%). We found a significant negative correlation of annual excess winter mortality with average winter temperature (r_s=-0.738, P=0.037), but not with air pollution levels. A 1 °C decrease in monthly mean temperature was associated with an increase of 1.38% (95%CI:0.34%-2.40%) and 0.88% (95%CI:0.11%-1.64%) in monthly mortality at lags of 0-1 month, respectively.</p> <p>Conclusion</p><p>Similar to temperate regions, a subtropical city Guangzhou showed a clear seasonal pattern in mortality, with a sharper spike in winter. Our results highlight the role of cold temperature on the winter mortality even in warm climate. Precautionary measures should be strengthened to mitigate cold-related mortality for people living in warm climate.</p> </div

A map of Guangzhou showing the location of Guangzhou weather station (marked by triangles) and seven air pollution monitoring stations (marked by a star).

<p>The six urban districts included in the present study are labeled by number 1-6.</p

Daily mean temperatures (blue dots) and daily number of all-cause deaths (red dots) in Guangzhou, China.

<p>The line represents monthly average and winter months are highlighted in grey.</p

Bias, Standard error and RMSE of influenza coefficients estimated from the best-fit models selected by different criteria.

<p>Note: Lines of QAIC and QBIC are overlapping when the degrees of freedom (<i>df</i>) range from 2 to 10 per year. Abbreviations: QAIC, quasi-Akaike information criterion; QBIC, quasi-Bayesian information criterion; PACF, partial autocorrelation function; GCV, generalized cross validation; RMSE, root-mean-square error.</p

The dose-response relationship between average monthly temperature and monthly mortality using a natural spline function with a degree freedom of 3.

<p>The dose-response relationship between average monthly temperature and monthly mortality using a natural spline function with a degree freedom of 3.</p

Percentage difference of estimated excess hospitalization rates from the observed admission rates of influenza cases during 2003−2008.

<p>Note: Percentage difference = 100%× (estimated excess hospitalization rate – observed rate)/observed rate.</p

Weekly observed all-cause mortality (black line) and simulated mortality data (green lines).

<p>Data were generated (A) under the assumption of low seasonal variation with the degree of freedom for trend set at 1 per year, or (B) under the assumption of high seasonal variation with the degrees of freedom for trend set at 10 per year.</p

Excess risk (ER) % in the never/seldom exercise group, and difference in excess risk (ΔER)% of mortality associated with each 10% increase in influenza intensity for low/moderate and frequent exercise relative to the never/seldom exercise.

<p>ER% were assessed by Poisson regression, and ΔER% were assessed by multinomial logistic regression, negative values indicate odds reduction vs never/seldom exercise (P-values: ^*<0.05 ^**<0.01 ^***<0.001 ^****<0.0001); ‘Never/Seldom’ means adults who never exercised or exercised less than once per month, ‘Low/Moderate’ means adults who exercised at least once per month to three times per week, and ‘Frequent’ means adults who exercised four times or more per week</p

Meteorological conditions and air pollution concentrations in 1998

<p>Meteorological conditions and air pollution concentrations in 1998</p