Catálogo de publicaciones - libros

Introduction: Why do we visualize data and what is this book about? The introductory chapter describes the three rationales to visualize data: exploration, confirmation and presentation, and discusses the developments in computer hardware, software and connectivity that were instrumental for the recent increased interest in visualizing data.

The second chapter specifies how a Lexis diagram is constructed and shows that cohorts are depicted on the 45 line. It briefly discusses the so-called identification problem of standard methods of age-, period-, and cohort analysis and explains how those effects look like in the Lexis diagram. The chapter concludes with a brief history of the depiction of population dynamics in three dimensions.

This chapter describes the major data sets used in this monograph: The Human Mortality Database, data from the National Center for Health Statistics of the United States for the analysis of causes of death, and the individual-level, longitudinal data of the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute of the United States. The latter is used to illustrate the dynamics of cancer survival.

The chapter on observed death rates illustrates why demographic rates need to be adjusted by the number of person years lived and shows surface maps of such “raw” death rates for a few selected national populations. One can easily see that random fluctuations can turn out be problematic for smaller populations as they may lead to misinterpretations.

The surface maps of the previous chapter showed that random fluctuations can be quite large. The present chapter on smoothing mortality data explains how we smoothed the observed mortality with P-splines. We illustrate our smoothing results with the same set of countries as in the previous chapter for unsmoothed data.

Surface maps of unsmoothed and smoothed mortality data have been used widely before. In this chapter, we present surface plots of rates of mortality improvement (“ROMI”), which are the derivative of age-specific mortality with respect to time. They have been introduced rather recently. By showing a large set of surface maps for countries from the Human Mortality Database, we argue that those ROMI plots are better able to detect period and cohort effects than standard mortality surface maps.

This chapter shows that ROMI plots, as presented in the previous chapter, can not only be employed for mortality from all-causes but also for cause-specific mortality. They allow us to demonstrate that the slow increase in life expectancy among women in the United States during the 1980s and 1990s can not be attributed to heart diseases or stroke. Instead, mortality from respiratory diseases and from lung cancer, the latter featuring a pronounced cohort effect, suppressed faster gains in life expectancy.

Rates of mortality improvement, as presented in the previous two chapters, are an excellent tool to illustrate mortality dynamics. They can not be directly translated to contributions to changes in life expectancy. Using Arriaga’s decomposition approach, we plot maps for selected countries from the Human Mortality Database, showing which ages contributed most to the respective change in life expectanc over time.

Many causes of death have a clear seasonal pattern. Circulatory and respiratory diseases, which comprise about half of all deaths, have a peak in winter and lowest mortality during summer. Data by cause of death, age, and calendar time (month and year) provided by the National Center for Health Statistics allow an analysis how seasonality has changed over age and time. Using a two-dimensional decomposition and smoothing approach, we show how the amplitude and the phase of selected causes of death have developed since the late 1950s.

While previous chapters focused only on the event of death, this chapter investigates the dynamics over age and time for the duration between being diagnosed with a specific cancer and death. We use five year survival as our indicator of survival in general, disease-specific survival and relative survival for selected cancer sites such as breast cancer, colorectal cancer, lung cancer or pancreatic cancer. The major impact of the stage of the tumor at the time of diagnosis for survival is illustrated using stage 1 and stage 4 of colorectal cancer as an example.