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Life Tables for 191 Countries: Data, Methods and Results

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Life Tables for 191 Countries: Data, Methods and Results LIFE TABLES FOR 191 COUNTRIES: DATA, METHODS AND RESULTS Alan D Lopez Joshua Salomon Omar Ahmad Christopher JL Murray Doris Mafat GPE Discussion Paper Series: No.9 EIP/GPE/EBD World Health Organization 1 I Introduction The life table is a key summary tool for assessing and comparing mortality conditions prevailing in populations. From the time that the first modern life tables were constructed by Graunt and Halley during the latter part of the 17th century, life tables have served as a valuable analytical tool for demographers, epidemiologists, actuaries and other scientists. The basic summary measure of mortality from the life table, the expectation of life at birth, is widely understood by the general public and trends in life expectancy are closely monitored as the principal measure of changes in a population's health status. The construction of a life table requires reliable data on a population's mortality rates, by age and sex. The most reliable source of such data is a functioning vital registration system where all deaths are registered. Deaths at each age are related to the size of the population in that age group, usually estimated from population censuses, or continuous registration of all births, deaths and migrations. The resulting age-sex-specific death rates are then used to calculate a life table. While the legal requirement for the registration of deaths is virtually universal, the cost of establishing and maintaining a system to record births and deaths implies that reliable data from routine registration is generally only available in the more economically advanced countries. Reasonably complete national data to calculate life tables in the late 1990s was only available for about 65 countries, covering about one-third of the deaths estimated to have occurred in 1999. In the absence of complete vital registration, sample registration or reliable information on mortality in childhood has been used, together with indirect demographic methods, to estimate life tables (1). This approach has been greatly facilitated by the availability of reliable estimates of child mortality in many countries of the developing world during the 1980s and 1990s under the Demographic and Health Surveys Programme. Several international agencies and other demographic centres routinely prepare national mortality estimates or life table compilations as part of their focus on sectoral monitoring. Thus, UNICEF have periodically reviewed available data on child mortality to assess progress with child survival targets and to evaluate interventions (2). A recent update of trends in child mortality during the 1990s has also just been completed (3). Three agencies or organizations, the United Nations Population Division, the World Bank and the United States Census Bureau have all produced international compilations of life tables, and in the case of the Population Division at least, continue to update them biennially. These various studies generally rely on the same data sources - censuses, surveys and vital registration - but can produce quite different results due to differences in the timing of data availability, differences in judgement about whether or how the basic data should be adjusted, and differences in estimation techniques and choice of models. In all cases, estimation of life tables for the majority of countries still involves choosing a model life table approach and applying this to observed data, usually on child mortality, to estimate a full life table. 2 Careful review of these existing approaches suggests that all have some limitations. For example, in the latest United Nations demographic assessment carried out in 1998, the Republic of Korea and the Democratic Peoples Republic of Korea were assigned the same overall population life expectancy (72 years) for 1995-2000 and only marginally different child mortality rates (in absolute terms), despite evidence of dramatically different social and economic circumstances in the 1990s which would affect relative survival prospects in the two countries. Indeed, Robinson et al have estimated that crude deaths rates doubled between 1995 and 1997 as a result of the severe food crisis in the DPR of Korea during this period (4). Other difficulties relate to the timing of assessments. For example, the latest UN demographic assessment for Russia was prepared with data from the mid 1990s when adult mortality had only just peaked, after rising by 70% since 1987. As a result, the United Nations projections of mortality to the end of the decade greatly exceed the likely number of deaths, especially in middle age, following the abrupt reversal of death rates which commenced in 1995. In other cases the cause of discrepancies is not clear. In Japan, for example, the United Nations projections for 1999 suggest an annual total of 1.05 million deaths, about 100,000 more than the latest figure (913,000 for 1997) from vital registration (see Figure 1). Figure 1. Number of deaths reported to WHO, Japan, 1950-1997 1,200,000 1,000,000 800,000 Males Females 600,000 Both sexes UN estimate for 400,000 1999 - Both sexes 200,000 - Year In India, adjusting the SRS system for underreporting of adult mortality, estimated at 13-14% in 1999 (5), yields an estimate of 10.1 million deaths in 1999, or 1.4 million more than the 1998 UN Population Assessment (6). Differences such as these are not insignificant and have major implications for the monitoring, evaluation and reorientation of public health programmes in countries as well as at a global level. While it would obviously be desirable to develop a single set of life tables for all countries of the world, technical judgement, data availability and the timing of periodic assessments will continue to vary. Given WHO's needs for annual life table estimates as part of the continuous assessment of health system performance, and a preference for a model life table system based on the Brass logit system, rather than other families of model life tables (7), WHO has constructed a new set of life tables, the first results of which, for 1999, are reported in this paper. The paper begins with a brief review of the sources, types and quality of the data available. We examine the different sources of data and the problems and difficulties involved in using them in generating life tables. We also provide a brief review of the two main approaches used by WHO to estimate the parameters of the Brass logit system (a, b) for each country in 1999. For 3 countries with a long series of vital registration data, lagged-time series analysis was used. For all other countries, a and b were estimated from either shorter time series of vital registration data or from survey or surveillance data on child and adult mortality. In the latter case, the new WHO model life table system (see Working Paper N° 8 in this series) was applied to generate life tables for 1999. Much of the remainder of the paper is dedicated to a discussion of how the basic demographic input for the method, levels of 5qo and 45q15, were estimated for countries. A brief summary of the major findings is provided at the end of the paper, and detailed country-specific life tables for WHO's 191 Member States are given in an Appendix. II Data Availability and Evaluation II.1 Vital Registration Data Ideally, life tables should be constructed from a long historical series of mortality data from vital registration where the deaths and population of the de jure (or defacto) population-at-risk are entirely covered by the system. In order to compute life tables for a given year (i.e.1999) for which vital registration of deaths is not yet available for administrative reasons, short term projections are required from the latest available year. This will require an adequate time series of data, with at least 15-20 years of mortality statistics. Appendix A shows the availability of vital registration data on mortality at the World Health Organization which could be used for life table estimation. The basic criteria used in selecting countries for the time-series analysis, are availability of historical data (1) of good quality as judged by the internal consistency of the data as well as proportion of the population covered, (2) with no more than 5 year gap in the most recent period, and (3) with at least 10 observations to allow for a more robust projection. Following a review of the quality of the vital registration data, the following countries were deemed to have data suitable for projection. These include: Argentina, Australia, Austria, Barbados, Belgium, Bulgaria, Canada, Chile, Costa Rica, Cuba, Denmark, Finland, France, Germany, Greece, Hungary. Iceland, Ireland, Israel, Italy, Japan, Malta, Mauritius, Mexico, Netherlands, New Zealand, Poland, Portugal, Romania, Singapore, Spain, Sweden, Switzerland, Trinidad & Tobago, UK, USA and Venezuela. Other countries with a time series of data were rejected for failing one or more of the above criteria. They include: Armenia, Azerbaijan, Belarus, Estonia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Russian Federation and Uruguay. In addition to these countries, a further 40 or so countries had vital registration data of sufficient completeness for some years in the 1990s to permit the estimation of 5qo and 45q15. However, in several cases, adjustments were made to the vital registration data before the application of the Brass logit approach to estimate a and b. Essentially, these 50 or so countries can be divided into the following categories in terms of data adjustments. Category 1 Countries with complete or virtually complete registration of deaths for one or more years in the 1990s. Of these, several (including those mentioned above), had enough time points of vital registration data to estimate a trend in a and b using simple linear regression. Corrections for underreporting were made where necessary based on the DHS or other information.
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