MOESM3 of Is cancer-related death associated with circadian rhythm?

Additional file 3: Figure S2. Scatter plot of the number of deaths by time in hours (A) and in minutes of the day (B), showing the temporal pattern of death due to ischemic heart disease, Hong Kong, 2008–2016. We found evidence of a unimodal sinusoidal circadian rhythm (periodicity) in the time of cardiac deaths according to the parametric sinusoidal circadian test (Z = 3.97, P = 0.019). Note: Restricted cubic splines using 3 knots were fitted to model the number of deaths in each hour of the day. The resulting spline fit is graphed as a red line

MOESM4 of Is cancer-related death associated with circadian rhythm?

Additional file 4: Figure S3. Scatter plot of the number of deaths by time in hours (A) and in minutes of the day (B), showing the temporal pattern of death due to pneumonia, Hong Kong, 2008–2016. We found no evidence of a unimodal sinusoidal circadian rhythm (periodicity) in the time of pneumonia deaths according to the parametric sinusoidal circadian test (Z = 1.94, P = 0.144). Note: Restricted cubic splines using 3 knots were fitted to model the number of deaths in each hour of the day. The resulting spline fit is graphed as a red line

MOESM1 of Is cancer-related death associated with circadian rhythm?

Additional file 1. Materials and method, and model specification and formulae

MOESM2 of Is cancer-related death associated with circadian rhythm?

Additional file 2: Figure S1. Plot of the distribution of the prevalence ratios of death due to cancer, ischemic heart disease, and pneumonia by time (hour) in a day. 0:00–0:59 am is the reference hour

Additional file 1 of Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research

Robust standard error estimation for generalized linear models. (PDF 104 kb

Additional file 2 of Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research

Stata do file with commented syntax. (PDF 40 kb

Additional file 3 of Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers

Additional file 3. Probability (%) of condition present as single or multiple comorbidity, by deprivation group (rectal cancer). Additional results in complement to those presented in Fig. 3: graphs representing the probability of having any of nine comorbid conditions in rectal cancer patients

Additional file 1 of Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers

Additional file 1. Definition of the fourteen conditions, according to ICD-10 code classification. Table of the fourteen conditions and the ICD-10 code groupings used to define them

Additional file 2 of Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers

Additional file 2. Probability (%) of condition present as single or multiple comorbidity, by deprivation group (lung cancer). Additional results in complement to those presented in Fig. 3: graphs representing the probability of having any of nine comorbid conditions in lung cancer patients

MOESM2 of Bayesian smoothed small-areas analysis of urban inequalities in fertility across 1999–2013

Additional file 2. Supplementary contextual factors of the citie