Appendix Figure 1. Age-specific annual female breast cancer incidence(red)/mortality(blue)
in Hong Kong, and fitted values (lines) from the age-period-cohort model. Gray lines
represent the 95% credible intervals around the respective projected incidence/mortality for
2011 to 2025.
Appendix Figure 2. Mean parity number (1960-2011), and median childbearing age for first
birth in Hong Kong (1971-2011).
Appendix Figure 3. Estimated 1-(M/I) ratio based on the age-period-cohort models for breast
cancer mortality and incidence, 1976-2025.
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Appendix Table 1. Jointpoint analysis of breast cancer incidence and mortality rates, ages 20
or above, 1976-2010, Hong Kong
Trend 1
Incidence
Mortality
Years
1976-1993
1976-1993
Trend 2
sAPC
1.24*
0.35*
Years
1993-2010
1993-2010
sAPC
2.14*
-0.39*
OAPC
1.69*
-0.02*
sAPC = segmented annual percent change
OAPC = overall annual percent change
* Statistically significant, p-value < 0.05
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Appendix 1
Data sources
We obtained age-specific breast cancer incidence, death data and mid-year population figures
for the years 1976-2010 from the HK Cancer Registry, the Death Registry and the Census
and Statistics Department, respectively. The Hong Kong Cancer Registry, an accredited
member of the International Association of Cancer Registries, is a population-based registry
covering the entire HK resident population (6.86 million people in mid 2010). Information on
newly diagnosed cases is collected from both the private and public service sectors (mainly
through departments of clinical and radiation oncology and histopathology), from the Hong
Kong Death Registry which is a population-based registry covering all registered deaths for
the Hong Kong population, and from voluntary notification by medical practitioners. The
completeness and quality of data from both the Hong Kong Cancer Registry and the Hong
Kong Death Registry has been reported to be good, with over 95% coverage for most
cancers.30
Bayesian age-period-cohort model
We modeled breast cancer incidence and mortality using age-period-cohort APC models3,4,7,
which decomposes incidence and mortality rates over time by the three determinants. This
method enabled us to estimate the relative contributions of age of diagnosis/death, period of
diagnosis/death and birth cohort effects on the disease risk and mortality. In the models, we
restricted our interpretation on the second order change (i.e., inflection point), due to inherent
identification problem3,4,7. The identifiability problem was one of the practical issues in
estimation in the APC models because of the inter-dependency of three effects. For instance,
year of birth cohort was derived from age at diagnosis/death and period at diagnosis/death.
In the Bayesian APC model, we specified the second-order Gaussian autoregressive priors in
which the initial expected value of each effect was based on an extrapolation from its two
previous predecessors. Based on the fitted model, we projected future incidence/mortality in
three 5-year periods up to 2025. We estimated the model parameters using Markov Chain
Monte Carlo (MCMC) simulations with five concurrent chains starting at different initial
values since comparison of multiple chains enabled us to discern convergence. We used the
criteria R-hat to monitor convergence.31 On the basis of the values of R-hat, we discarded the
first 5,000 samples as a burn-in period, and then took a further 20,000 samples from the
posterior distributions. The parameter estimates and the derived rates were summarized in
terms of posterior means and 95% credible intervals. The model goodness-of-fit was
measured by the posterior mean deviance D.32 To compare models, the deviance information
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criterion (DIC) was estimated so as to adjust the posterior mean deviance with the number of
parameters in the model. A smaller DIC implies a better fit.
All analyses were implemented using Joinpoint 4.0.1, R version 3.1.0 and WinBUGS version
1.4.
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