MORTALITY AND AIDS DEATHS IN DEVELOPING COUNTRIES
Griffith Feeney <email@example.com> 1999-05-24
Prepared for the Meeting of the Reference Group on HIV/AIDS
Estimates, Modelling and Projections, Geneva, 10-11, June 1999.
Sections 1-6 present some essential background. Section 7 lists
some issues for discussion at the meeting, incorporating the
suggestions contained in emails of April 9 from Bernhard (from
firstname.lastname@example.org) and May 10 from Geoff Garnett.
1.1 First, to estimate the life table survivorship function
and/or various statistics derived from it, e.g., expectation of
life at birth, expectation of life at age 5, or conditional
probability of death by age 65 given survival to age 30.
1.2 Second, to estimate the impact of AIDS deaths on adult
mortality. Perhaps child mortality as well, but this will involve
quite different data and techniques.
1.3 Ideally we would like annual estimates for a series of years
extending from the more or less distant past through the most
recent completed calendar year. In practice we will generally
have to settle for far less.
2.1 Three data collection instruments in general use are: (i)
national population censuses; (ii) large scale, nationally
representative population surveys; (iii) civil registration
systems. Censuses aim to be complete enumerations of all
households and persons in a country and are generally taken once
every ten years. Population surveys of the type indicated may be
taken every 3-5 years. There will often be at least one such
survey in every intercensal period. Civil registration aims to
record information on all vital events in a country as they
occur, with reports for calendar years available annually and
2.2 Non-nationally representative surveys may be available and
can play an important role. The main liability will usually be
that there are few or no alternative sources to compare them to,
making it more difficult to assess their results. Surveys to
estimate adult mortality are necessarily large because death
before old age is a relatively rare event (this is/may be
changing in countries most severely hit by the AIDS epidemic).
2.3 Traditionally, information available from these sources has
been limited to tabulations and contextual information in
published reports. Increasingly, it may now include the computer
files and associated documentation from which the published
tabulations were produced. New tabulations, tailored to the
problem at hand, may then be produced with modest effort. This
possibility, a consequence of extremely rapid information
technology development, is so new that it may be overlooked. It
should not be, though various practical problems will often
render it inoperable in the near future.
3 The following data are useful for calculating/estimating adult
mortality: (i) population by age and sex; (ii) annual deaths by
age, sex and (where available) cause of death; (iii) population
by age and survival of mother/father/siblings. Somewhat more
precise specifications are necessary in application. Deaths
should be classified by time of occurrence, for example, rather
than by time of registration, and information on survival of
siblings will usually be restricted to siblings living at the
time the respondent reached age 15.
4.1 The classical data sources for calculation of national life
tables are one or more population censuses and annual death
registration data. If this data is available and accurate, as is
generally the case in developed countries, life tables may be
calculated with minimal fuss for census years. Obtaining life
tables for intercensal years may be more difficult on account of
migration. These problems will usually be compounded if life
tables are desired for subnational units.
4.2 Most developing countries have taken at least one population
census. Many have taken three or more. Completeness of
enumeration is generally fairly good, though accuracy of age
reporting may be very poor. Many developing countries have some
form of civil registration, but completeness of reporting is
nearly always far too low for the numbers produced to be used
without adjustment. Conditions tending to poor age reporting in
censuses will tend to poor reporting in civil registration as
4.3 Attempts by demographers to estimate life tables and life
table statistics in the absence of (reasonably) complete death
registration statistics go back at least 50 years (Mortara 1949),
yet there are only a few basic approaches: (i) census survival
methods; (ii) use of retrospective reports of deaths from
censuses or surveys; (iii) methods that adjust death registration
data for under reporting on the basis of supplementary census or
survey data; (iv) methods that estimate life table statistics
from census or survey reports of survival of various relatives.
4.4 The original literature on these methods is scattered and
difficult. The United Nations "Manual X" (1983) provides a
unified account of many of the methods but is somewhat dated. A
review of methods for adult mortality estimation is underway in
the United Nations Population Division.
4.5 Census survival methods rely on two censuses of a population
closed to migration to estimate adult mortality for the
intercensal period, typically 10 years. Infant and child
mortality cannot be estimated by this method (though models may
be used to infer infant and child mortality from adult
mortality). These methods provide excellent results under optimal
conditions, but good results in practical applications are rare.
The migration assumption generally renders them useless for
subnational areas. Problems with the closure to migration
assumption at the national level may sometimes be attenuated by
suitable redefinition of the population, e.g., by considering the
native born population rather than the total population.
Estimates are provided only for a long period in the more or less
distant past. For Zimbabwe at this writing, to take a single
example, the most recent estimates would be for the 1982-1992
intercensal period. In practice these methods will most often be
useful as a means of assessing the quality of census data.
4.6 Population censuses or surveys may include questions on
deaths occurring in enumerated households during the year (or
other period) prior to enumeration. Under optimal conditions
these provide a tabulation of deaths by age and sex of decedent
that may be used as a proxy for death registration data.
Estimates are available only for the year (rarely, longer
periods) preceding the census or survey.
4.7 Numbers of deaths in data of this kind way may be sharply
under or over reported. In the 1989 census of Vietnam, for
example, approximately half of all deaths were omitted (General
Statistical Office 1991:105). Under reporting may result from
reluctance of respondents to report deaths, memory failure, or
misunderstanding of the reference period to which the question
refers. The last may also result in over reporting. Obviously
poor results are less likely to be published, complicating the
assessment of how well such questions work.
4.8 Several methods are available for estimating completeness of
death registration by combining data on registered deaths
classified by age and sex with census or survey information on
population by age and sex. As with census survival, it is
necessary to assume that the population is closed to migration.
This will often be the only data source capable of providing, in
some circumstances and with some extra effort, a time series of
4.9 The original "growth balance" method of Brass requires only a
population age distribution and a corresponding distribution of
deaths by age, which may be taken either from death registration
or from retrospective reports in a census or survey.
Generalizations of this method utilize data from two successive
censuses and intercensal deaths and are capable of estimating
differential completeness of census enumeration (Brass 1979,
Brass and Swamy 1980, Hill 1987).
4.10 Another approach to estimates from the same data is provided
by "extinct generations" methods, in which population at each age
enumerated in a census or survey is compared with population at
the same age estimated by cumulating estimates of deaths at this
and later ages to the cohort in question (Bennett and Horiuchi
4.11 Reports on the survival of relatives may contain information
sufficient to estimate various life table statistics. Most famous
and widely applied is the "Brass method" for estimating child
mortality from census or survey reports on numbers of children
ever born and surviving to women in each age group. Adult female
mortality may be estimated from reports on whether or not the
mothers of persons enumerated are surviving, and also from
reports on whether female siblings of persons enumerated are
surviving. Adult male mortality may be similarly estimated from
reports on survival of fathers and/or brothers.
4.12 Survival of relatives methods may provide information on
mortality trends using the dating procedure of Brass and Bamgboye
(1981). This is likely to be the only source of data on mortality
trends (excepting very long trends, as in successive intercensal
periods) other than civil registration.
5.1 None of the data may be taken at face value. Age is often
misreported, sometimes severely, in censuses, surveys, and civil
registration. Censuses commonly miss a few to several percent or
more of the target population. Civil registration rarely covers
more than a fraction of deaths occurring in the population,
perhaps as little as 25 percent. Coverage is likely to be
selective, e.g., higher in urban than in rural areas. Survival of
relatives may be misreported in various ways, e.g., adopted
children of deceased mothers may report that their (adoptive)
mother is surviving.
5.2 All of the methods make assumptions that will never be
perfectly satisfied and will produce imperfect results (even with
perfect input data) to the extent that actual demographic
conditions depart from assumed conditions. We have much to learn
about the robustness of different methods.
5.3 Different methods applied to the same data, or to different
data representing the same population and time period, may
produce very different results. This is good in that it alerts us
that something is wrong; if different methods gave consistently
wrong results we would be likely to draw wrong conclusions
without realizing it. It does pose the question of which of the
available estimates is closest to the truth. Arriving at answers
will in some cases be more difficult than producing the initial
5.4 A high incidence of AIDS deaths poses at least three special
problems: (i) rising rather than falling mortality, (ii)
nonlinear change, and (iii) distortions in the age pattern of
mortality. In the past, one was usually justified in assuming
that mortality risks were falling, or at least not rising. A rise
in retrospectively reported deaths in the months prior to a
census or survey was therefore evidence, for example, that deaths
in the more distant past were under reported--an invalid
inference if deaths have been increasing. Assuming linear change
is often reasonable and very useful as a first approximation, but
this is unacceptable in the face of sharp rises in AIDS deaths.
5.5 Distortions in the age pattern of mortality influence the
validity of inferences based on model age patterns of mortality,
an essential tool in nearly all of adult mortality estimation
methods. To illustrate briefly, census survival and methods based
on incomplete death registration provide estimates of expectation
of life at age 5. Methods based on survival of parents give
estimates conditional probability of death by age 65 given
survival to age 30. These statistics are not directly comparable,
but it is common practice to effect comparison by using the
relation between them that exists in a family of model life
tables. In doing so one assumes that the age pattern in the
population conforms to the age pattern in the model. Distortions
in the age pattern due to unusually high death rates in adult
ages may not wholly invalidate this approach, but they certainly
5.6 Census and survey questions that work well in one country may
not work well elsewhere. Sharp differences are observed even in
the same country over time and between population subgroups.
Children born and surviving questions worked well in the early
censuses of Western Samoa, for example, but broke down in later
censuses (Banister 1979). The same questions worked well for
Malays and Indians in the 1970 census of Malaysia but very poorly
for Chinese. Assumptions made by the various methods will be
valid in some countries and invalid in others.
5.7 Producing useful estimates of adult mortality risks for
developing countries and of the role played by AIDS is a
non-trivial task. For most developing countries, however, it is
probably less difficult than producing good estimates of AIDS
prevalence (to say nothing of incidence). We are likely to have
more data to work with, from a wider variety of sources, with
more historical depth, a wider variety of estimation methods, and
more experience in application.
6.1 Given that we want to produce the best possible estimates of
adult mortality and of AIDS impact for a country, how should we
6.2 Inventory available data. Use primary sources to the greatest
extent possible. Errors tend to slip into secondary sources and
important contextual information (e.g., sample design) will
usually be missing. Inventory digital as well as print sources.
6.3 Apply all (within reason) methods applicable to the available
data and compare results. Be prepared to find discrepancies,
sometimes severe. In some cases the hardest work begins at this
6.4 Attempt to assess errors in available data and the extent to
which the various assumptions of methods are satisfied. Attempt
to assess how different estimates are impacted by errors in the
data and departures of actuality from assumptions. Try to arrive
at a defensible conclusion about best estimates.
6.5 Begin the analysis at the national level. Proceed to
subnational levels if data and resources permit and if the
results promise to justify the effort. Studying data for
subnational levels will often provide insights valuable even if
estimates are desired only for the national population.
6.6 Expect to adapt the analysis and innovate in response to
particular opportunities or problems thrown up by circumstance,
not merely to apply available methods and record their results.
The most important evidence may not result directly from the
application of any off-the-shelf method.
6.7 In Zimbabwe, for example, important evidence of the
demographic impact of the AIDS epidemic comes from death rates
calculated from death registration data, available for 1982,
1986, 1990-92 and 1995. The rise in death rates throughout the
adult ages is far too rapid to be explained by improving
registration completeness. On the other hand, rises in death
rates for the 10-14 age group suggest that improving completeness
has played a role and provide a basis for estimating changing
7.1 Why precisely is the Reference Group concerned with mortality
estimates? What is the importance of mortality estimates in the
work as a whole? Perhaps the same answers will be obvious to all,
but it may be prudent to raise the question.
7.2 Given that it is impossible to exhaustively analyze available
data for all (or even a large number of) developing countries,
how should effort be allocated? Intensive analysis of a small
number of countries is an obvious strategy. What countries? How
to select them? How much diversity or focus?
7.3 How much emphasis should be put on death registration data?
Incomplete and selective reporting of deaths pose serious
problems, but civil registration alone is capable of providing
information annually (and indeed, monthly) for subnational as
well as national units. How can we assess changing completeness
7.4 How much emphasis should be put on estimates based on reports
of survival of relatives? There may be serious problems with
accuracy of reports, but these methods do not require any
assumption about migration, may be applied (with some caveats) at
the subnational as well as national level, and may provide
information on trends. In the absence of civil registration data
they may be the only source of data on trends.
7.5 What can/should be done to obtain more data for estimating
adult mortality? There are numerous options for exploiting
existing data. (i) Most obviously, ensuring that all available
published data is available for analysis (many reports are hard
to obtain); this is less trivial than it ought to be. (ii)
Securing the use of tabular data produced but unpublished by
national statistical offices (there usually is some, and it may
be very valuable, e.g., unpublished death registration data for
Zimbabwe). (iii) Urging/supporting the the production of special
tabulations from unit record data files available in national
statistical offices or elsewhere (e.g., tabulation of data
available in DHS sibling histories). (iv) Urge inclusion of
pertinent questions in upcoming surveys and 2000 round population
census schedules (with follow through/support for tabulation and
7.6 How do we measure impact of HIV/AIDS on adult mortality? What
is the role of cause of death data, traditional or other? What
about inferences based on changing age pattern or trend of
7.7 How to overcome the special problems caused by large and
rapidly changing numbers of AIDS deaths? Cf. 5.5 above.
7.8 Under what conditions can we use mortality data to adjust
estimates of prevalence? I should think the answer is "under much
the same conditions as epimodel allows us to estimate (however
imperfectly) infections and incidence from prevalence data, and
by similar methods", but elaboration is required.
7.9 What can be done to learn more about the way in which
available methods respond to data errors and departures of actual
from assumed conditions? Do particular data/methods perform
better than others in the presence of large numbers of AIDS
deaths. We need to know more about sensitivity and robustness to
move from discrepant estimates from various data sources and
methods to best estimates, in general as well as with respect to
the special problems raised by AIDS mortality.
7.10 What can be done to incorporate AIDS mortality into existing
model life tables? One simple approach would be to express
age-specific death rates as the sum of rates in an existing model
and a factor k (say) times a standard pattern of adult AIDS
deaths, k a parameter that may be increased to capture increased
7.11 How to approach the issue of AIDS mortality among children?
The nature of available data and methods suggests that this
should be approached separately from the question of AIDS
mortality among adults.
7.12 What about progression rates for adults and children? This
is certainly a mortality issue, but not one that the data sources
and methods noted above help us answer.
Banister, Judith. 1979. Census questions on fertility and child
mortality: Problems with questionnaire design. Asian and Pacific
Census Forum 6(1):5-8.
Bennett, N.G., and Shiro Horiuchi. 1981. Estimating the
completeness of death registration in a closed population.
Population Studies 47(2):207-221.
Brass, William. 1979. A procedure for comparing mortality
measures calculated from intercensal survival with the
corresponding estimates from registered deaths. Asian and Pacific
Census Forum 6(2):5-7.
Brass, William and E.A. Bamgboye. 1981. The time location of
reports of survivorship estimates for maternal and paternal
orphanhood and the ever-widowed. Working Paper No. 81-1, London
School of Hygiene and Tropical Medicine, University of London.
Brass, William, and Subramania Swamy. 1980. Measurement of death
registration completeness using the growth balance procedure
applied to data from India. Asian and Pacific Census Forum
General Statistical Office [Vietnam]. 1991. Vietnam Population
Census - 1989: Detail Analysis and Sample Results. General
Statistical Office, Hanoi.
Hill, K. 1987 Estimating census and death registration
completeness. Asian and Pacific Census Forum 1(3):8-13.
Mortara, G. 1949. Methods of using census statistics for the
calculation of life tables and other demographic measures. United
Nations, New York.
United Nations. 1983. Manual X: Indirect Techniques for
Demographic Estimation. United Nations, New York.
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