κ_i,t and γ_i,t plotted as a function of 25(OH)D level for various parameter combinations.

<p>a) <i>κ</i>_i,t plotted for different combinations of <i>λ</i> and <i>η</i>. b) <i>γ_i,t</i> plotted for <i>λ</i> = 20 <i>ng/ml</i> and <i>η</i> = 10 <i>ng/ml</i> and different levels of <i>φ</i>. c) <i>γ_i,t</i> plotted for <i>λ</i> = 20 <i>ng/ml</i> and <i>η</i> = 2 <i>ng/ml</i> and different levels of <i>φ</i>.</p

Test of the effect of stochasticity within the SIRS model on well-matched simulations verified with New York state P&I mortality data.

<p>a) The 10 best-fit parameter combinations for the SIRS model forced with observed New York school calendar (Shaman et al., 2010: Table S5), observed New York absolute humidity (Shaman et al., 2010: Table S2), and northeastern U.S. vitamin D metabolite levels (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020743#pone-0020743-t003" target="_blank">Table 3</a>) were each run an additional 100 times, each time with different random seeding. Histograms of correlations with 1972–2002 New York state observed excess P&I mortality are shown. The green line indicates the correlation of an optimally phased sine function with annual periodicity with 1972–2002 New York state observed excess P&I mortality (<i>r</i> = 0.80). b) As in a), but for the 10 best-fit simulations using 1972–2002 daily average New York absolute humidity and daily interpolated northeastern U.S. vitamin D metabolite levels.</p

1993–1995 average monthly mean and standard deviation of 25-hydroxy-vitamin D levels for the Great Lakes and Northeast U.S. regions.

<p>The Great Lakes includes the states of Illinois, Indiana, Iowa, Michigan, Minnesota, Montana, and Wisconsin. The Northeast includes the states of Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, West Virginia, Vermont and the District of Columbia.</p

Parameter combinations for the 10 best-fit simulations for the Great Lakes region as validated with Illinois P&I mortality data.

<p>3000 simulations were performed at each site with the parameters <i>L</i> (mean duration of immunity), <i>D</i> (mean infectious period), <i>φ</i> (vitamin D scaling), and <i>R</i>₀^* (the basic reproduction number if <i>γ_i,t</i> = 1) randomly chosen from within specified ranges. Parameters <i>λ</i> (inflection point) and <i>η</i> (inflection point slope) were fixed at and . Best-fit simulations were selected based on RMS error after scaling the 31-year mean daily infection number to the 31-year mean observed daily excess P&I mortality rate.</p

Relative Risks for the Association between Pre-diagnosis BMI and Colorectal Cancer-Specific and All-cause Mortality.

<p>Association between pre-diagnosis BMI and colorectal cancer-specific mortality and all-cause mortality.</p

Best-fitting SIRS model simulation for the northeastern U.S. with parameters λ and η fixed at and .

<p>Other parameters are shown in the top line of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020743#pone-0020743-t003" target="_blank">Table 3</a>. The 31-year simulated mean daily infection number has been scaled to the observed 1972–2002 mean daily excess P&I mortality rate for New York state.</p

Prospective cohort studies of pre-diagnosis BMI (kg/m²) and survival outcomes in colorectal cancer (CRC) patients.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120706#pone.0120706.t001" target="_blank">Table 1</a> is a summary of pre-diagnosis studies. This assessment examined the following items: clarity of BMI measurement before diagnosis, adjustment for intermediate factors (e.g., age, stage and tumor differentiation), duration of follow-up, study endpoints (colorectal cancer-specific mortality and overall mortality), representativeness of the exposed cohort, and adequacy of follow-up of cohorts.</p><p>Abbreviations: BMI, body mass index; RR, risk ratio; CI, confidence interval; CRC, colorectal cancer; RC, rectal cancer; CC, colon cancer; PMH, post-menopausal hormone</p><p>Prospective cohort studies of pre-diagnosis BMI (kg/m²) and survival outcomes in colorectal cancer (CRC) patients.</p

Prospective cohort studies of post-diagnosis BMI (kg/m²) and survival outcomes in colorectal cancer patients.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120706#pone.0120706.t002" target="_blank">Table 2</a> is a summary of post-diagnosis studies. This assessment examined the following items: clarity of BMI measurement after diagnosis, adjustment for intermediate factors (e.g., age, stage and tumor differentiation), duration of follow-up, study endpoints (colorectal cancer-specific mortality and overall mortality), representativeness of the exposed cohort, and adequacy of follow-up of cohorts.</p><p>Abbreviations: BMI, body mass index; RR, risk ratio; CI, confidence interval; CRC, colorectal cancer; RC, rectal cancer; CC, colon cancer; PMH, post-menopausal hormone</p><p>Prospective cohort studies of post-diagnosis BMI (kg/m²) and survival outcomes in colorectal cancer patients.</p

Flow diagram of the selection process for this meta-analysis.

<p>Details of the selection process are presented.</p

Relative Risks for the Association between Post-diagnosis BMI and Colorectal Cancer-specific and All-cause Mortality.

<p>Association between post-diagnosis BMI and colorectal cancer-specific mortality and all-cause mortality.</p