Julie Rajaratnam and colleagues describe a novel method, called the Corrected Sibling Survival method, to measure adult mortality in countries without good vital registration by use of histories taken from surviving siblings.
For several decades, global public health efforts have focused on the development and application of disease control programs to improve child survival in developing populations. The need to reliably monitor the impact of such intervention programs in countries has led to significant advances in demographic methods and data sources, particularly with large-scale, cross-national survey programs such as the Demographic and Health Surveys (DHS). Although no comparable effort has been undertaken for adult mortality, the availability of large datasets with information on adult survival from censuses and household surveys offers an important opportunity to dramatically improve our knowledge about levels and trends in adult mortality in countries without good vital registration. To date, attempts to measure adult mortality from questions in censuses and surveys have generally led to implausibly low levels of adult mortality owing to biases inherent in survey data such as survival and recall bias. Recent methodological developments and the increasing availability of large surveys with information on sibling survival suggest that it may well be timely to reassess the pessimism that has prevailed around the use of sibling histories to measure adult mortality.
Methods and Findings
We present the Corrected Sibling Survival (CSS) method, which addresses both the survival and recall biases that have plagued the use of survey data to estimate adult mortality. Using logistic regression, our method directly estimates the probability of dying in a given country, by age, sex, and time period from sibling history data. The logistic regression framework borrows strength across surveys and time periods for the estimation of the age patterns of mortality, and facilitates the implementation of solutions for the underrepresentation of high-mortality families and recall bias. We apply the method to generate estimates of and trends in adult mortality, using the summary measure 45q15—the probability of a 15-y old dying before his or her 60th birthday—for 44 countries with DHS sibling survival data. Our findings suggest that levels of adult mortality prevailing in many developing countries are substantially higher than previously suggested by other analyses of sibling history data. Generally, our estimates show the risk of adult death between ages 15 and 60 y to be about 20%–35% for females and 25%–45% for males in sub-Saharan African populations largely unaffected by HIV. In countries of Southern Africa, where the HIV epidemic has been most pronounced, as many as eight out of ten men alive at age 15 y will be dead by age 60, as will six out of ten women. Adult mortality levels in populations of Asia and Latin America are generally lower than in Africa, particularly for women. The exceptions are Haiti and Cambodia, where mortality risks are comparable to many countries in Africa. In all other countries with data, the probability of dying between ages 15 and 60 y was typically around 10% for women and 20% for men, not much higher than the levels prevailing in several more developed countries.
Our results represent an expansion of direct knowledge of levels and trends in adult mortality in the developing world. The CSS method provides grounds for renewed optimism in collecting sibling survival data. We suggest that all nationally representative survey programs with adequate sample size ought to implement this critical module for tracking adult mortality in order to more reliably understand the levels and patterns of adult mortality, and how they are changing.
Please see later in the article for the Editors' Summary
Governments and international health agencies need accurate information on births and deaths in populations to help them plan health care policies and monitor the effectiveness of public-health programs designed, for example, to prevent premature deaths from preventable causes such as tobacco smoking. In developed countries, full information on births and deaths is recorded in “vital registration systems.” Unfortunately, very few developing countries have complete vital registration systems. In most African countries, for example, less than one-quarter of deaths are counted through vital registration systems. To fill this information gap, scientists have developed several methods to estimate mortality levels (the proportion of deaths in populations) and trends in mortality (how the proportion of deaths in populations changes over time) from data collected in household surveys and censuses. A household survey collects data about family members (for example, number, age, and sex) for a national sample of households randomly selected from a list of households collected in a census (a periodic count of a population).
Why Was This Study Done?
To date, global public-health efforts have concentrated on improving child survival. Consequently, methods for calculating child mortality levels and trends from surveys are well-developed and generally yield accurate estimates. By contrast, although attempts have been made to measure adult mortality using sibling survival histories (records of the sex, age if alive, or age at death, if dead, of all the children born to survey respondents' mothers that are collected in many household surveys), these attempts have often produced implausibly low estimates of adult mortality. These low estimates arise because people do not always recall deaths accurately when questioned (recall bias) and because families that have fallen apart, possibly because of family deaths, are underrepresented in household surveys (selection bias). In this study, the researchers develop a corrected sibling survival (CSS) method that addresses the problems of selection and recall bias and use their method to estimate mortality levels and trends in 44 developing countries between 1974 and 2006.
What Did the Researchers Do and Find?
The researchers used a statistical approach called logistic regression to develop the CSS method. They then used the method to estimate the probability of a 15-year-old dying before his or her 60th birthday from sibling survival data collected by the Demographic and Health Surveys program (DHS, a project started in 1984 to help developing countries collect data on population and health trends). Levels of adult mortality estimated in this way were considerably higher than those suggested by previous analyses of sibling history data. For example, the risk of adult death between the ages of 15 and 60 years was 20%–35% for women and 25%–45% for men living in sub-Saharan African countries largely unaffected by HIV and 60% for women and 80% for men living in countries in Southern Africa where the HIV epidemic is worst. Importantly, the researchers show that their mortality level estimates compare well to those obtained from vital registration data and other data sources where available. So, for example, in the Philippines, adult mortality levels estimated using the CSS method were similar to those obtained from vital registration data. Finally, the researchers used the CSS method to estimate mortality trends. These calculations reveal, for example, that there has been a 3–4-fold increase in adult mortality since the late 1980s in Zimbabwe, a country badly affected by the HIV epidemic.
What Do These Findings Mean?
These findings suggest that the CSS method, which applies a correction for both selection and recall bias, yields more accurate estimates of adult mortality in developing countries from sibling survival data than previous methods. Given their findings, the researchers suggest that sibling survival histories should be routinely collected in all future household survey programs and, if possible, these surveys should be expanded so that all respondents are asked about sibling histories—currently the DHS only collects sibling histories from women aged 15–49 years. Widespread collection of such data and their analysis using the CSS method, the researchers conclude, would help governments and international agencies track trends in adult mortality and progress toward major health and development targets.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000260.
This study and two related PLoS Medicine Research Articles by Rajaratnam et al. and by Murray et al. are further discussed in a PLoS Medicine Perspective by Mathers and Boerma
Information is available about the Demographic and Health Surveys
The Institute for Health Metrics and Evaluation makes available high-quality information on population health, its determinants, and the performance of health systems
Grand Challenges in Global Health provides information on research into better ways for developing countries to measure their health status
The World Health Organization Statistical Information System (WHOSIS) is an interactive database that brings together core health statistics for WHO member states, including information on vital registration of deaths; the WHO Health Metrics Network is a global collaboration focused on improving sources of vital statistics