By estimating a cross-correlation coefficient between seasonal IMFs of search trend and temperature (see Table S3 for raw correlation values), Figure 4a shows a linear relationship between latitude and the correlation between temperature and search interests for depression (northern hemisphere: r = −0.686; p<0.001; southern hemisphere: r = 0.871; p<0.8711).
In addition, penalized smoothing splines were used in the models to adjust for nonlinear effects of day of the year, long-term time trend, and temperature.
In the analysis of the association of air pollutants with tHcy, the following variables were chosen a priori as relevant predictors and included in the linear regression analysis: age, sex, body mass index, current cigarette smoking (0, 0 15, or > 15 cigarettes/day), current alcohol consumption (yes or no), current hormone use, day of the year, long-term time trend, and temperature.
Thus, on the Russian Plain isotherms have a north-south trend, and temperatures at each latitude decline from the west toward a cold pole in northeastern Siberia.
By applying the EMD method to isolate the seasonal IMF in search trends and temperature data, Figure 3 shows that the seasonal IMFs of search trends in both countries are negatively correlated with seasonal fluctuations in temperature (USA: r = −0.872, p<0.001; Australia: r = −0.656, p<0.001).
†Adjusted for photochemical oxidant, sex, age, height, day of the week, temporal trends and temperature.
‡Adjusted for nitrogen dioxide, photochemical oxidant, sex, age, height, day of the week, temporal trends and temperature.
Several sensitivity analyses, including the use of robust regressions and of nonparametric methods for fitting time trends and temperature effects in the data, supported these findings.
PM2.5: Particulate matter with a 50% cut-off of aerodynamic diameter ≤2.5 μm *Adjusted for sex, age, height, day of the week, temporal trends and temperature.
To study the effect of latitude on magnitude of seasonality of search trends, we measured the correlation of amplitudes between search trend IMFs and temperature.
After adjusting for seasonality and trend, mortality and temperature show a U-shaped pattern; below a temperature of around 29°C, a decrease in temperature resulted in an increase in mortality, whereas above 29°C, increased temperature resulted in increased mortality.
