Weekly fraction of MD attributable to all subtypes together and individual influenza subtypes.

<p>Most seasons were dominated by either influenza A/H1N1 or A/H3N2, with little of the other subtype. Nearly all seasons also showed a smaller contribution from influenza B.</p

Modeling results.

<p>Modeling results.</p

Observed MD compared with model-predicted MD.

<p>MD counts were modeled in a negative binomial generalized linear model with an identity link. Independent variables included a sinusoidal term for seasonal variation, linear and quadratic time trends (modeled using Legendre polynomials), autoregressive terms with MD lagged 1−3 weeks, SAIH lagged 1 week, and terms to allow influenza to have a changing effect on MD over time (effect modification).</p

Confirmed cases of 2009 pandemic A/H1N1 influenza by location and hospitalization status, the associated weights used to calculate the weighted risk ratio (shown as %), and the cumulative incidence risk ratio (95% confidence intervals) of male versus female cases by hospitalization status and age group for locations where sex was known.

<p>Confirmed cases of 2009 pandemic A/H1N1 influenza by location and hospitalization status, the associated weights used to calculate the weighted risk ratio (shown as %), and the cumulative incidence risk ratio (95% confidence intervals) of male versus female cases by hospitalization status and age group for locations where sex was known.</p

Differences by sex in cases that were not hospitalized.

<p><b>A Men not hospitalized.</b> The smoothed risk ratio of cases among men who were not hospitalized in a single year age group compared to the overall risk in all age groups. Smoothed curves were created by a locally weighted polynomial regression with fixed bandwidth of 4. The single year of age weighted risk ratios used to create the smoothed curve are plotted as open circles and the 95% confidence bounds are shaded. The inset figure shows the truncated WRR from 0 to 29 years of age while the larger figure focuses on the ages from 30–100. <b>B Men not hospitalized.</b> SiZer plot of the second derivative of the weighted risk ratio by age among men who were not hospitalized. <b>C Women not hospitalized.</b> The smoothed risk ratio of cases among women who were not hospitalized in a single year of age compared to the overall risk in all female age groups. Smoothed curves were created by a locally weighted polynomial regression with fixed bandwidth of 4. The inset figure shows the truncated WRR from 0 to 29 years of age while the larger figure focuses on the ages from 30–100. The single year of age weighted risk ratios used to create the smoothed curve are plotted as open circles and the 95% confidence bounds are shaded. <b>D Women not hospitalized.</b> SiZer plot of the second derivative of the weighted risk ratio by age among women who were not hospitalized.</p

Laboratory confirmed hospitalized cases.

<p><b>A</b> The smoothed risk ratio of laboratory confirmed hospitalized cases in a single year age group compared to the overall risk in all age groups. Smoothed curves for each location were created by a locally weighted polynomial regression with fixed bandwidth of 4. <b>B</b> The smoothed weighted risk ratio (WRR) of laboratory confirmed hospitalized cases in a single year compared to the risk in all age groups combined using a fixed bandwidth of 4. The single year of age WRR used to create the smoothed curve are plotted as open circles and the 95% confidence bounds are shaded. The inset figure shows the truncated WRR from 0 to 29 years of age while the larger figure focuses on the ages from 30–100. <b>C</b> SiZer plot of the first derivative of the WRR by age. The X axis represents age while the Y axis corresponds to the log of the bandwidth (h). For example, log(0.6) corresponds to the fixed bandwidth of 4 used to create Figures <i>A</i> and <i>B</i> and a black horizontal line identifies this bandwidth. The shading corresponds to the significance and direction of the slope (first derivative) of the WRR by age: red is significantly decreasing, purple is possibly zero, blue is significantly increasing, and light grey represents areas where there is insufficient data to generate a smoothed curve. The grid lines correspond to 1 year of age intervals. <b>D</b> SiZer plot of the second derivative of the WRR by age, where the shading corresponds to that described for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042328#pone-0042328-g001" target="_blank">Figure 1C</a>.</p

Peak risk ratio by location, age and hospitalization status.¹

<p>Peak risk ratio by location, age and hospitalization status.^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042328#nt103" target="_blank">1</a>}</p

Average χ² statistics for LT versus other approaches in simulated data.

<p>For each statistic we display average results across 1,000,000 simulations, for various effect sizes <i>γ</i>. All statistics are χ²(1 dof). Logistic regression with an interaction term (G+GxE) values been converted from χ²(2 dof) to the equivalent χ²(1 dof) value. At an effect size of 0 all statistics give the expected value under the null. OR LBMI is the odds ratio computed from cases with BMI = 24. OR HBMI is the odds ratio for cases with BMI = 35.</p

Inferred covariates and effect sizes on the liability scale.

<p>LT model is the liability threshold model for each disease with parameters estimated using the LTPub method. For diseases with multiple covariates, models with all covariates and each covariate separately are given. %Variance Explained is the fraction of variance explained on the liability scale in the study data for each of the covariates in each of the diseases when all covariates are used in the model, and is specific to the distribution of covariates in each particular study. BMI30 is a binary variable, which is 1 if an individual's BMI is greater than 30 and 0 otherwise. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p

Summary statistics across all datasets.

<p>The sum of each of the test statistics across all of the SNPs in each of the diseases. LTPub vs LogR is the % increase of LTPub compared to LogR. It has a median value of 16%. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p