Granada Cancer Registry, Andalusian School of Public Health (EASP), Campus Universitario de Cartuja, C/Cuesta del Observatorio 4, 18011, Granada, Spain.
Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), University of Granada, Granada, Spain.
Popul Health Metr. 2021 Mar 23;19(1):18. doi: 10.1186/s12963-021-00248-1.
Population-based cancer registries are required to calculate cancer incidence in a geographical area, and several methods have been developed to obtain estimations of cancer incidence in areas not covered by a cancer registry. However, an extended analysis of those methods in order to confirm their validity is still needed.
We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio (IMR), the IMR method. Using the previous 15-year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004-2013 for six cancer sites for each sex. Generalized linear mixed models, including a polynomial function for the year of death and smoothing splines for age, were adjusted. Models were fitted under a Bayesian framework based on Markov chain Monte Carlo methods. The IMR method was applied to five scenarios reflecting different assumptions regarding the behavior of the IMR. We compared incident cases estimated with the IMR method to observed cases diagnosed in 2004-2013 in Granada. A goodness-of-fit (GOF) indicator was formulated to determine the best estimation scenario.
A total of 39,848 cancer incidence cases and 43,884 deaths due to cancer were included. The relative differences between the observed and predicted numbers of cancer cases were less than 10% for most cancer sites. The constant assumption for the IMR trend provided the best GOF for colon, rectal, lung, bladder, and stomach cancers in men and colon, rectum, breast, and corpus uteri in women. The linear assumption was better for lung and ovarian cancers in women and prostate cancer in men. In the best scenario, the mean absolute percentage error was 6% in men and 4% in women for overall cancer. Female breast cancer and prostate cancer obtained the worst GOF results in all scenarios.
A comparison with a historical time series of real data in a population-based cancer registry indicated that the IMR method is a valid tool for the estimation of cancer incidence. The goodness-of-fit indicator proposed can help select the best assumption for the IMR based on a statistical argument.
基于人群的癌症登记处需要计算特定地理区域的癌症发病率，已经开发了几种方法来估算未被癌症登记处覆盖的区域的癌症发病率。然而，仍然需要对这些方法进行扩展分析以确认其有效性。
我们根据癌症死亡率数据和发病率-死亡率比（IMR）评估了最常使用的一种估算癌症发病率的方法的有效性，即 IMR 方法。我们利用之前 15 年的癌症死亡率时间序列，为每个性别和六个癌症部位推导出 2004-2013 年期间预期的每年癌症病例数。广义线性混合模型包括死亡年份的多项式函数和年龄的平滑样条，进行了调整。模型是基于马尔可夫链蒙特卡罗方法的贝叶斯框架进行拟合的。IMR 方法应用于五个反映 IMR 行为不同假设的场景。我们将 IMR 方法估计的发病病例数与 2004-2013 年诊断的观察病例数进行了比较。制定了一个拟合优度（GOF）指标来确定最佳估计场景。
共纳入 39848 例癌症发病病例和 43884 例癌症死亡病例。对于大多数癌症部位，观察到的和预测的癌症病例数之间的相对差异小于 10%。IMR 趋势的常数假设为男性的结肠癌、直肠癌、肺癌、膀胱癌和胃癌以及女性的结肠癌、直肠癌、乳腺癌和子宫体癌提供了最佳的 GOF。线性假设更适合女性的肺癌和卵巢癌以及男性的前列腺癌。在最佳情况下，男性和女性的总体癌症的平均绝对百分比误差分别为 6%和 4%。在所有情况下，女性乳腺癌和前列腺癌的 GOF 结果最差。
与基于人群的癌症登记处的历史时间序列真实数据的比较表明，IMR 方法是估算癌症发病率的有效工具。所提出的拟合优度指标可以帮助根据统计论点选择 IMR 的最佳假设。
