Overview

This database contains original calculations of death rates and life tables for national populations, as well as the raw data used in constructing those tables. The raw data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources. A complete statement of our methodology is contained in the methods protocol.

Documentation that is specific to an individual population (including data sources) is provided through links within each country section. The Methods Protocol and documentation for specific populations are available in portable document format (PDF). In order to read these files, you must have Adobe Acrobat Reader installed on your computer. The reader may be downloaded for free from the Adobe homepage.

Here we provide just an overview of the kinds of data available for each country or area in the collection. Although the form and format of the raw data for each population may be different, all quantities estimated as part of this database project are presented here in a uniform manner.

Our process for computing mortality rates and life tables can be described in terms of six steps, corresponding also to six data types that are available through the database. Details are provided in the methods protocol. Here is just an overview of the process:

1. Births. Annual counts of live births by sex are collected for each population over the longest possible time period. These counts are used mainly for making population estimates at younger ages.
   

2. Deaths. Death counts are collected at the finest level of detail available. If raw data are aggregated, uniform methods are used to estimate death counts by completed age (i.e., age-last-birthday at time of death), calendar year of death, and calendar year of birth.
   

3. Population size. Estimates of population size on January 1st of each year are either obtained from another source or are derived as part of this project from census data plus birth and death statistics.

4. Exposure-to-risk. Estimates of the population exposed to the risk of death during some age-time interval are based on annual (January 1st) population estimates, with a small correction that reflects the timing of deaths within the interval.
   

5. Death rates. Death rates are always a ratio of the death count in some interval of age and time, to an estimate of the exposure-to-risk in the same interval.
   

6. Life tables. To build a life table, probabilities of death are computed from death rates. These probabilities are used to construct life tables, which include life expectancies and other useful indicators of mortality and longevity.

The data presented here have been corrected for gross errors detected in the information we have collected. For example, a processing error whereby 3,800 becomes 38,000 in a published statistical table would be obvious in most cases, and it would be corrected. However, we have not attempted to correct the data for systematic age misstatement (misreporting of age) or coverage errors (over- or under-enumeration of people or events). It is possible to find evidence of both age heaping (an attraction for round ages) and age exaggeration in these data. Some available studies assess the completeness of census coverage or death registration in the various countries, and more work is needed in this area. However, in developing the database thus far, we did not consider it feasible or desirable to attempt corrections of this sort, especially since it would be impossible to correct the data by a uniform method across all countries.

Populations are included here if there is a well-founded belief that the coverage of their census and vital registration systems is relatively high, and thus, that fruitful analyses by both specialists and non-specialists should be possible with these data. Nevertheless, all users should be attentive to issues of data quality. In general, the degree of age heaping in these data varies by the time period and population considered, but it is usually no burden to scientific analysis. In most cases it is sufficient to analyze data in five-year age groups in order to avoid the false impressions created by this particular form of age misstatement. Age exaggeration, on the other hand, is a more insidious problem. Our approach is guided by the conventional wisdom that age reporting in death registration systems is typically more reliable than in census counts or official population estimates. For this reason, we derive population estimates at older ages from the death counts themselves, employing extinct cohort methods. Such methods eliminate some, but certainly not all, of the biases in old-age mortality estimates due to age exaggeration.

A key goal of this project is to follow a uniform set of procedures for each population. This approach does not guarantee the cross-national comparability of the data. Rather, it ensures only that we have not introduced biases by our own manipulations. The collaborators in this project agreed on a strict methods protocol before commencing calculations for each country. Our desire for uniformity had to face the challenge that raw data come in a variety of formats (for example, 1-year versus 5-year age groups). Our general approach to this problem is that the available raw data are used first to estimate two quantities: 1) the number of deaths by completed age, year of birth, and year of death; and 2) population estimates by single years of age on January 1 of each year. For each population, these calculations are performed separately by sex. From these two pieces of information, we compute death rates and life tables in a variety of age-time configurations (for women and men separately, and for the total population).

It is reasonable to ask whether a single procedure is the best method for treating the data from a variety of populations. Here, two points must be considered. First, our uniform methodology is based on procedures that were developed separately, though following similar principles, for various countries and by different researchers. Earlier methods were synthesized by choosing what we considered the best among alternative procedures and by eliminating superficial inconsistencies. The second point is that a uniform procedure is possible only because we have not attempted to correct the data for reporting and coverage errors. Although some general principles could be followed, such problems would have to be addressed individually for each population.

Although we adhere strictly to a uniform procedure, the data for each population also receive significant individualized attention. Each country or area is assigned to one of three research teams and then to an individual researcher, who takes personal responsibility for assembling and checking the data for errors. In addition, the person assigned to each country/area checks our data against other available sources. These procedures help to assure a high level of data quality, but assistance from database users in identifying problems is always appreciated!