Spatio-temporal interaction with disease mapping

Markov chain Monte Carlo methods are used to estimate mortality rates under a Bayesian hierarchical model. Spatial correlations are introduced to examine spatial effects relative to both regional and regional changes over time by groups. A special feature of the models is the inclusion of longitudinal variables which will describe temporal trends in mortality or incidences for different population groups. Disease maps are used to illustrate the role of different parameters in the model and pinpointing areas of interesting patterns. The methods are demonstrated by male cancer mortality data from the state of Missouri during 1973-1992. Of special interest will be the geographic variations in the trend of lung cancer mortality over the recent past. Marginal posterior distributions are used to examine effects due to spatial correlations and age difference in temporal trends. Numerical results from the Missouri data show that although spatial correlations exist, they do not have a large effect on the estimated mortality rates.     

Proportional hazards models and age–period–cohort analysis of cancer rates

Age–period–cohort (APC) analysis is widely used in cancer epidemiology to model trends in cancer rates. We develop methods for comparative APC analysis of two independent cause‐specific hazard rates assuming that an APC model holds for each one. We construct linear hypothesis tests to determine whether the two hazards are absolutely proportional or proportional after stratification by cohort, period, or age. When a given proportional hazards model appears adequate, we derive simple expressions for the relative hazards using identifiable APC parameters. To demonstrate the utility of these new methods, we analyze cancer incidence rates in the United States in blacks versus whites for selected cancers, using data from the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. The examples illustrate that each type of proportionality may be encountered in practice. Published in 2010 by John Wiley & Sons, Ltd.     

Age–space–time CAR models in Bayesian disease mapping

Mortality counts are usually aggregated over age groups assuming similar effects of both time and region, yet the spatio‐temporal evolution of cancer mortality rates may depend on changing age structures. In this paper, mortality rates are analyzed by region, time period and age group, and models including space–time, space–age, and age–time interactions are considered. The integrated nested Laplace approximation method, known as INLA, is adopted for model fitting and inference in order to reduce computing time in comparison with Markov chain Monte Carlo (McMC) methods. The methodology provides full posterior distributions of the quantities of interest while avoiding complex simulation techniques. The proposed models are used to analyze prostate cancer mortality data in 50 Spanish provinces over the period 1986–2010. The results reveal a decline in mortality since the late 1990s, particularly in the age group [65,70), probably because of the inclusion of the PSA (prostate‐specific antigen) test and better treatment of early‐stage disease. The decline is not clearly observed in the oldest age groups. Copyright © 2016 John Wiley & Sons, Ltd.     

Cervical cancer mortality in the Netherlands

In this study cervical cancer mortality figures for the period 1936-1985 were evaluated. Trends in age-specific rates were analysed by separating the variations attributable to age at death, period of birth and period of death. Cervical cancer mortality has been decreasing since 1960 and this decline has become more rapid since about 1975. Organized screening, which began in 1976 could be responsible for the acceleration in the decline, but since the decline was already evident before screening was started, other factors must play a role. It seemed likely that a fall in incidence or an improvement in early diagnosis were responsible for the decline. Cohort analysis showed an increased risk for younger age groups but since the observations were derived from few five-year age-specific mortality rates no firm conclusions about future trends can be made.     

Esophageal cancer mortality: Relationship with alcohol intake and cigarette smoking in Italy

This paper examines changes with time in age-adjusted mortality from esophageal cancer for the years 1950–1981, in relation to changes in smoking habits and alcohol consumption. In both sexes the age-adjusted death rates have shown no marked time variation. Instead in the same period there have been marked temporal variations in consumption of alcohol and tobacco which are considered risk factors associated with esophageal cancer.The male cohort variation seems to indicate some fluctuations in mortality before 1921 and a progressive increase after this year. In females the death rates are very low and the cohort variation is pratically constant.The progressive increase of cohort variation in esophageal cancer mortality for men born after 1921 coincides with a progressive increase in hard alcohol consumption. In the same period there has also been a progressive increase in tobacco consumption but this begun at the turn of this century.     

A cancer survival model that takes sociodemographic variations in "normal" mortality into account: comparison with other models

STUDY OBJECTIVES: Sociodemographic differentials in cancer survival have occasionally been studied by using a relative survival approach, where all cause mortality among persons with a cancer diagnosis is compared with that among similar persons without such a diagnosis ("normal" mortality). One should ideally take into account that this "normal" mortality not only depends on age, sex, and period, but also various other sociodemographic variables. However, this has very rarely been done. A method that permits such variations to be considered is presented here, as an alternative to an existing technique, and is compared with a relative survival model where these variations are disregarded and two other methods that have often been used. DESIGN, SETTING, AND PARTICIPANTS: The focus is on how education and marital status affect the survival from 12 common cancer types among men and women aged 40-80. Four different types of hazard models are estimated, and differences between effects are compared. The data are from registers and censuses and cover the entire Norwegian population for the years 1960-1991. There are more than 100 000 deaths to cancer patients in this material. MAIN RESULTS AND CONCLUSIONS: A model for registered cancer mortality among cancer patients gives results that for most, but not all, sites are very similar to those from a relative survival approach where educational or marital variations in "normal" mortality are taken into account. A relative survival approach without consideration of these sociodemographic variations in "normal" mortality gives more different results, the most extreme example being the doubling of the marital differentials in survival from prostate cancer. When neither sufficient data on cause of death nor on variations in "normal" mortality are available, one may well choose the simplest method, which is to model all cause mortality among cancer patients. There is little reason to bother with the estimation of a relative-survival model that does not allow sociodemographic variations in "normal" mortality beyond those related to age, sex, and period. Fortunately, both these less data demanding models perform well for the most aggressive cancers.     

基于年龄-时期-队列模型的中国膀胱癌发病死亡趋势分析

目的 分析1990一2019年中国膀胱癌发病死亡情况及长期变化趋势。方法 利用全球疾病负担（Global Burden of Disease,GBD）数据库,分析1990一2019年中国膀胱癌发病和死亡情况,应用 Joinpoint 软件分析发病率和死亡率的时间变化趋势,计算年度变化百分比和平均年度变化百分比。构建年龄-时期-队列模型,分析年龄、时期和出生队列对膀胱癌发病及死亡趋势变化的影响。结果 2019年膀胱癌发病率、标化发病率分别为7.03/10万和5.16/10万,2019年膀胱癌死亡率、标化死亡率分别为2.82/10万和2.24/10万。1990一2019年中国膀胱癌标化发病率有升高趋势,平均年度变化百分比为1.5%,标化死亡率有降低趋势,平均年度变化百分比为-0.5%。 年龄-时期-队列模型结果显示,发病与死亡的净漂移值分别为1.96%和-1.21%。 发病与死亡风险随年龄的增加而增高,发病的风险随时期的增加而升高,死亡的风险随时期的增加而降低。队列效应亦表现不同,出生越晚的人群发病风险高,死亡风险低。结论 1990一2019 年中国膀胱癌标化发病率呈上升趋势,发病和死亡风险随年龄升高而升高,并且近年代出生人群发病风险升高,提示近年代出生的老年人群应作为膀胱癌防治工作的重点人群。     

Year of birth and mortality caused by diabetes mellitus under 45 years: England and Wales 1927-1986

It has been suggested that mortality caused by diabetes under 45 years of age could be used to monitor the effectiveness of health care for people with diabetes. We report an age-period-cohort analysis of mortality from diabetes which was designed to evaluate effects of short- and longer-term influences on diabetes mortality. Mortality increased with age and in general decreased with advancing time period, but in the age group 40-44 years mortality showed a steep rise between the years 1952 and 1972 which was greater in men than women (increment: 13.1-29.5 per million in men; 9.8-17.9 per million in women). This increment was associated with a cohort effect which was significant in men but not in women. The relation of these findings to the results of prospective studies reported from Denmark are discussed. In this chronic disease contemporary mortality results from a number of influences acting over many years so that interpretation of this proposed indicator would not be straightforward.     

Short-term cancer mortality projections: a comparative study of prediction methods

This paper provides a systematic comparison of cancer mortality and incidence projection methods used at major national health agencies. These methods include Poisson regression using an age-period-cohort model as well as a simple log-linear trend, a joinpoint technique, which accounts for sharp changes, autoregressive time series and state-space models. We assess and compare the reliability of these projection methods by using Canadian cancer mortality data for 12 cancer sites at both the national and regional levels. Cancer sites were chosen to provide a wide range of mortality frequencies. We explore specific techniques for small case counts and for overall national-level projections based on regional-level data. No single method is omnibus in terms of superior performance across a wide range of cancer sites and for all sizes of populations. However, the procedures based on age-period-cohort models used by the Association of the Nordic Cancer Registries tend to provide better performance than the other methods considered. The exception is when case counts are small, where the average of the observed counts over the recent 5-year period yields better predictions.