Published by Oxford University Press on behalf of the International Epidemiological Association
© The Author 2005; all rights reserved. Advance Access publication 23 May 2005
International Journal of Epidemiology 2005;34:1080–1087
doi:10.1093/ije/dyi101
Design options for sample-based
mortality surveillance
Stephen Begg, Chalapati Rao and Alan D Lopez*
Accepted
14 April 2005
Methods
The feasibility of model-based approaches for predicting the expected mortality
structure and cause composition is demonstrated for populations in which only
limited empirical data is available. An algorithm approach is then provided to
derive the minimum person-years of observation needed to generate robust
estimates for the rarest cause of interest in three hypothetical populations, each
representing different levels of health development.
Results
Modelled life expectancies at birth and cause of death structures were within
expected ranges based on published estimates for countries at comparable levels
of health development. Total person-years of observation required in each
population could be more than halved by limiting the set of age, sex, and cause
groups regarded as ‘of interest’.
Discussion
The methods proposed are consistent with the philosophy of establishing
priorities across broad clusters of causes for which the public health response
implications are similar. The examples provided illustrate the options available
when considering the design of mortality surveillance for population health
monitoring purposes.
Keywords
Sample size, mortality surveillance, causes of death, vital registration
Information on causes of death in a population is fundamental
to health policy development, implementation, and evaluation.1
The best source of such information is vital registration in which
every death in the population is medically certified as to its
causal antecedents. Recent assessments indicate that globally
only about one-third of countries have registration systems that
yield adequate data on causes of death.2 While the number of
countries meeting this criterion has increased from 50 to 115 in
the five decades the World Health Organization (WHO) has been
monitoring national mortality data,3 there is still no information
regarding causes of death in many countries in Africa, Southeast Asia, and the Pacific region, and only limited information
School of Population Health, University of Queensland, Australia.
* Corresponding author. School of Population Health, Faculty of Health
Sciences, University of Queensland, Brisbane, QLD 4006, Australia.
E-mail: [email protected]
from certain countries in the Middle East and Latin America.
Existing policies for health gain in these regions would
undoubtedly benefit from improvements in the availability and
quality of cause of death information.
Although the development of high-quality population-wide
vital registration systems takes decades to achieve and requires
ongoing commitment in terms of resources, it is increasingly
being recognized that the attainment and maintenance of vital
registration systems is imperative for all governments.4
However, countries currently without such systems will require
substantial external assistance in order to realize this vision.
It is fortunate, therefore, that models other than populationwide vital registration exist. Experience from the Sample
Registration System in India has shown that continuous
mortality surveillance in a nationally representative sample of
the population is a useful method for monitoring mortality
trends over time and differentials between subgroups.5
1080
Downloaded from http://ije.oxfordjournals.org/ by guest on October 6, 2014
Background Reliable information on causes of death is a fundamental component of health
development strategies, yet globally only about one-third of countries have
access to such information. For countries currently without adequate mortality
reporting systems there are useful models other than resource-intensive
population-wide medical certification. Sample-based mortality surveillance is one
such approach. This paper provides methods for addressing appropriate sample
size considerations in relation to mortality surveillance, with particular reference
to situations in which prior information on mortality is lacking.
DESIGN OPTIONS FOR SAMPLE-BASED MORTALITY SURVEILLANCE
Methods
We begin by observing that one purpose of sample-based
mortality surveillance is to generate robust age-specific and
sex-specific estimates of important causes of death from a
representative subset of a population. To achieve this purpose
efficiently, a system need only accumulate enough person-years
of observation to enable the generation of robust estimates
for the least frequently occurring cause of death for which
certainty is important (i.e. the rarest cause of interest).
Requirements for the design of such a system are: a simple
measure of uncertainty; prior information on the frequency of
mortality by age, sex, and cause in the population; and some
knowledge about which causes of death are important and at
what ages.
We demonstrate the implications of these observations by
designing systems for three hypothetical populations, each
representing different levels of health development according to
the WHO classification of national mortality characteristics.10
Population B represents countries with low child mortality and
low adult mortality, Population D represents countries with
high child mortality and high adult mortality, and Population E
represents countries with high child mortality and very high
adult mortality (i.e. countries in which HIV/AIDS and possibly
malaria are endemic). Additional information on the health and
economic development of each population is provided in
Table 1.
Our objective is to estimate the total person-years of
observation needed in each of these populations in order to
derive a predetermined set of age-specific and sex-specific
estimates with tolerable margins of error. In addition, we are
interested in knowing the effect on person-years of pragmatic
but, in our view, acceptable compromises to the comprehensiveness of this set in terms of the number of age and sex groups
for which error is tolerable.
Uncertainty
The most robust estimates of cause-specific mortality in a
population are derived from population-wide registration
systems in which the main sources of uncertainty are causal
attribution and stochastic processes (i.e. random variability).
Uncertainty in the former is largely a function of the
thoroughness with which physicians record the sequence of
events prior to death, as well as the availability and quality of
evidence available at the time of certification. While undoubtedly critical to the reliability of any source of data on causes of
death, this is unrelated to statistical aspects of uncertainty and
is not within the scope of this paper. Uncertainty in the latter
exists even in population-wide systems, but sample-based
approaches increase this by reducing the opportunity for
observing deaths. Sampling also introduces uncertainty with
regard to the extent to which the population being observed
(i.e. the sample) is representative of the total population.
If we assume the observed population is representative, then
random variability in the occurrence of events within that
population over time can be quantified using simple statistical
probability models. The Poisson distribution assumes events
occur independently of each other and randomly in time, and is
commonly used to describe stochastic uncertainty in mortality
rates.11 A defining characteristic of the Poisson distribution is
that its mean is equal to its variance. When the rate of events in
a population fluctuates randomly, the variance exceeds the
mean, in which case other distributions (e.g. the negative
binomial distribution) may be more appropriate. Given it is
reasonable to assume mortality as an underlying force in a
population is relatively stable, particularly at the levels we
consider in this paper, we see compelling theoretical grounds for
choosing the Poisson over other distributions to quantify
random variability in the occurrence of deaths.
The mean of a Poisson distribution is the number of events
per unit of exposure (i.e. time in person-years) and its standard
error is the square root of the number of events per unit of
exposure. The accepted approach for determining the amount
of exposure required in order to yield a predetermined level of
precision is to divide the event rate by the square of the desired
standard error,12 an approach that assumes a priori a notion of
desirability for a concept that has no meaning in absolute terms
(e.g. a standard error of 1 per person-year has a different
interpretation for a rate of 10 per person-year than it does for
a rate of 100 per person-year). This approach fails to provide
a description of Poisson uncertainty in relative terms. We
Downloaded from http://ije.oxfordjournals.org/ by guest on October 6, 2014
Similarly, the Disease Surveillance Point system in China
generates useful data on causes of death from a 1% representative sample of the national population.6 Such approaches
demonstrate the feasibility of sample-based approaches to
mortality surveillance for generating valid and reliable
information on causes of death, particularly in situations where
the establishment of a registration system for an entire population is unlikely to occur in the short to medium term. Samplebased mortality surveillance has the added benefit of providing
the basis for more complete registration over the longer term.
Clearly, sample-based mortality surveillance can only yield
useful information when good design principles are followed.
Hauser identified this issue 50 years ago in his discussion of
sample-based approaches to all-cause mortality estimation.7
More recently, Kaufman and colleagues discuss sample size
estimation with reference to cause-specific mortality.8 They
take an approach that assumes randomness in the variability of
cause of death information but do not provide methods for
deriving prior information on cause composition in a
population, which is a prerequisite for the application of their
methods.
Chandramohan et al.9 on the other hand, articulate the
principle of sufficient numbers of deaths for the rarest cause of
interest. This observation, made in relation to validating verbal
autopsy instruments (a technique whereby cause of death is
determined by a physician based on the responses of family
members to a structured set of questions about symptoms
experienced by the deceased in the period prior to death), is
pertinent to the design of sample registration systems: sampling
necessarily increases uncertainty; it is prudent to know a priori
by how much and whether or not this matters. Our review of
the literature suggests that those faced with designing
sample-based systems would benefit from a systematic
approach to these issues. In this paper, we provide practical
methods for determining efficient sizes for sample-based
mortality surveillance systems, particularly in situations where
prior information on the cause composition of mortality is
lacking.
1081
1082
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
Table 1 Gross Domestic Product (GDP), mortality stratum, population structure, and probabilities of dying in childhood and adulthood for three
example countries at different levels of health development
Per capita GDP in intl. dollarsa
Mortality stratumb
Population B
6194
Low child and low adult
Population D
1596
High child and high adult
Population E
417
High child and very high adult
Population structure (%)c
Males
Females
Males
Females
Males
Females
1.8
1.8
2.3
2.2
3.8
3.6
1
1–4
8.7
8.6
9.3
9.0
14.0
13.5
5–9
10.6
10.5
11.4
11.0
15.3
14.9
10–14
10.8
10.7
10.7
10.4
13.5
13.1
11.2
15–19
10.1
10.0
10.3
10.1
11.4
20–24
9.8
9.5
9.9
9.8
9.4
9.3
25–29
9.0
8.7
8.9
8.9
7.6
7.8
7.8
7.6
7.6
7.6
5.8
5.9
7.0
6.8
6.4
6.5
4.6
4.7
40–44
5.8
5.7
5.5
5.6
3.6
3.8
45–49
4.7
4.7
4.7
4.9
2.9
3.1
50–54
3.7
3.7
3.8
3.9
2.6
2.7
55–59
2.8
3.0
2.9
3.0
2.0
2.1
60–64
2.4
2.8
2.1
2.3
1.5
1.6
65–69
2.2
2.3
1.6
1.8
1.0
1.1
70–74
1.5
1.8
1.2
1.4
0.6
0.7
75–79
0.8
0.9
0.8
0.9
0.3
0.4
80–84
0.3
0.5
0.4
0.5
0.1
0.2
85
0.2
0.4
0.2
0.3
0.0
0.1
100.0
100.0
100.0
100.0
100.0
100.0
All ages
Male to female ratioc
1.02
0.99
0.98
Probability of dying per 1000a
Under 5 years
Between 15 and 59 years
45
43
120
96
165
146
182
114
328
235
558
508
a Derived from Core Health Indicators from the latest World Health Report available from the WHO Statistical Information System website (URL:
http://www3.who.int/whosis/core/core2.cfm?option6 accessed on 3 September 2004).
b According to the WHO classification of global mortality.10
c Derived from the World Population Prospects Population Database (2002 revision) available from the United Nations Population Division website (URL:
esa.un.org/unpp accessed on 3 September 2004).
propose, therefore, the following relative measure of error for a
Poisson distribution:
n
relative standard error (RSE) = n
where n is the number of events. The relationship between
RSE and changes in event occurrence is shown in Figure 1 and is
such that increasing n up to 45 results in meaningful reductions
in RSE, whereas the gains beyond this threshold of 15%
are marginal. We use this value of n, therefore, as our error
threshold, below which we consider uncertainty to be intolerable.
The area below the bold line in Figure 2 represents event
rates for which there is insufficient exposure to achieve an RSE
of 15%. For a given exposure of 100 000 person-years,
Figure 2 shows that an event rate of 10 per 100 000 person-years
would yield an RSE 15%, whereas an event rate of 100 per
100 000 person-years would yield an RSE of 15%. Since we
assume the underlying event rate is invariant in a population,
only exposure will influence RSE. In mortality surveillance,
exposure has two dimensions; the number of people being
observed and the period of observation. Adjusting either
dimension until the intersection between the rate for the rarest
cause of interest and exposure falls exactly on this line will
result in a design for which efficiency is maximized. We adopt
this algorithm to determine the required person-years in each of
our populations.
Prior estimates of all-cause mortality
Ideally, prior estimates of age-specific and sex-specific all-cause
mortality should come from empirical sources. In countries
without reliable vital registration systems, observed mortality
rates must first be assessed for completeness and corrected
accordingly before being used as estimates of all-cause mortality.
In the absence of usable information from such sources,
Downloaded from http://ije.oxfordjournals.org/ by guest on October 6, 2014
30–34
35–39
DESIGN OPTIONS FOR SAMPLE-BASED MORTALITY SURVEILLANCE
Prior estimates of cause-specific mortality
Relative Standard Error
100%
75%
50%
Point of diminishing marginal return
25%
0%
0
25
50
75
100
125
150
175
200
Figure 1 Relative Standard Errors for different frequencies of a
Poisson parameter
10 000
RSE is less than 15%
1000
100
10
1
1000
RSE is greater than 15%
10 000
100 000
1 000 000
10 000 000
Exposure in person-years (log scale)
Figure 2 Combinations of exposure and event rates (and
corresponding 95% exact confidence intervals) for which the Relative
Standard Error (RSE) is 15%
Prior estimates of the cause structure of mortality by age and
sex should also come from empirical sources (e.g. research
studies, population laboratories, or neighbouring countries).
While such information is generally available for most
countries, it is typically less reliable than data on all-cause
mortality and should be thoroughly checked for plausibility.
(Guidelines for assessing the quality of cause-specific
information are available from the authors.) In the absence of
plausible local cause-specific information, we again advocate
the use of model-based methods, as discussed below.
Models relating all-cause to cause-specific mortality enable the
cause structure of mortality by age and sex to be predicted for a
given level of all-cause mortality. Work by Preston15 and Lopez
and Hull16 builds on the observation that economic
development is associated with a shift from infectious to chronic
diseases as leading causes of death, a phenomenon referred to as
the ‘epidemiologic transition’.17 More recently, Murray and
Lopez18 and Salomon and Murray19 have proposed cause
of death models (e.g. CODMOD) [Cause Of Death MODel
(CODMOD) is a statistical model for predicting the proportionate
mortality distribution in populations across broad causes
(communicable, non-communicable, and injuries) as a function
of total mortality and level of development (income)] that
include national per capita income as well as all-cause mortality
as predictors. These models typically generate estimates by age
and sex across very broad cause groupings and are consistent
with the philosophy of the original Global Burden of Disease
Study that stressed the importance of first getting broad cause
of death categories correct so that estimation errors were
constrained to diseases with roughly similar public health policy
implications.18 This approach avoids the bias inherent in many
cause of death estimates, towards over-emphasis of communicable diseases at the expense of non-communicable diseases and,
especially, injuries.20
To demonstrate the feasibility of model-based approaches to
estimating causes of death, we use the age-specific and sexspecific all-cause mortality rates from our model-derived life
tables and the estimates of Gross Domestic Product (GDP) from
Table 1 as inputs into the Salomon and Murray cause of death
model so as to estimate expected cause-specific rates by age and
sex in our example countries. These rates are then applied to UN
population structures21 (also shown in Table 1) so as to derive the
expected number of deaths in each age, sex, and cause stratum for
any given value of person-time accumulated during surveillance.
Age, sex, and cause groups of interest
we advocate the use of indirect methods. Model life table
systems provide indirect means for deriving complete schedules
of age-specific and sex-specific rates from, at a minimum,
estimates of childhood mortality.13 Most countries are able to
derive this information from local sources (e.g. Demographic
and Health Surveys). To demonstrate the feasibility of this
approach, we use model life tables based on the probabilities of
dying during childhood and at adult ages in Table 1 to derive
age-specific and sex-specific all-cause mortality rates in each of
our example countries. The specific approach we adopt is the
Modified Logit Life Table System currently used by WHO, a twoparameter model based on empirical mortality data from mostly
developed countries over the period from 1950 to 2002.14
Decisions regarding the age, sex, and cause groups for which
estimates are to be derived with certainty through sample-based
methods might justifiably be regarded as contingent upon local
considerations. At a minimum, we advocate adopting the
broad-level objectives outlined in Table 2, which are intended to
address basic policy concerns in populations with similar
mortality structures as our example countries. Apart from
Group I causes in Population E, we chose broad cause groupings
over more specific causes for describing a population in terms of
its progress through the epidemiological transition. HIV/AIDS
and malaria are identified separately in Population E because of
the particular policy relevance of these causes in populations
with high child mortality and very high adult mortality.
Downloaded from http://ije.oxfordjournals.org/ by guest on October 6, 2014
Number of events
Event rate per 100 000 units of exposure (log scale)
1083
1084
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
Table 2 Age, sex and, cause groups of interest for basic-level policy purposes in countries at different levels of health development
Population B
Cause of interest
Group 1: communicable, maternal, perinatal,
and nutritional disorders
Population D
Population E
Males
Females
Males
Females
Males
Females
0–4
0–4
0–4
0–49
–
–
HIV/AIDS
–
–
–
–
0–49
0–49
Malaria
–
–
–
–
0–4
0–4
30
30
30
30
30
30
15–44
–
15–44
–
15–44
–
Group 2: non-communicable diseases
Group 3: injuries
Table 3 Modelled life expectancy at birth and standardized mortality rates by cause for three example countries at different levels of health
development
Population B
Population D
Population E
Females
Males
Females
Males
Females
67.7
72.0
56.4
61.7
45.4
47.5
1.1 (10.7)
1.0 (12.4)
3.2 (19.7)
2.8 (22.5)
6.4 (26.5)
15.4 (53.8)
HIV/AIDSd
–
–
–
–
2.0 (8.4)
3.7 (17.6)
Malariad
–
–
–
–
0.7 (3.1)
0.8 (3.9)
8.4 (80.5)
6.5 (83.1)
11.4 (70.1)
9.3 (74.2)
14.2 (60.6)
12.7 (44.4)
Life expectancy at birth in yearsa
Standardizedb mortality rate per 1000
person-years (%)
Group 1: communicable, perinatal, and
nutritional disordersc
Group 2: non-communicable diseasesc
Group 3: injuriesc
All causes
0.9 (8.8)
0.3 (4.5)
1.7 (10.3)
0.4 (3.3)
3.1 (12.9)
0.5 (1.8)
10.4 (100.0)
7.8 (100.0)
16.2 (100.0)
12.5 (100.0)
24.2 (100.0)
21.2 (100.0)
a Abridged life table derived from the Modified Logit Life Table System14 using probabilities of dying in Table 1.
b Standardized to the WHO standard population.30
c Age-specific and sex-specific proportions for broad cause derived from CODMOD19 using age-specific and sex-specific all-cause mortality rates from abridged
life tables and GDP estimates in Table 1.
d Age-specific and sex-specific proportions for HIV/AIDS and malaria derived from WHO.2
We used WHO sources to derive the expected proportions of
mortality associated with these causes.3 As a general principle,
we strongly advocate consideration of broad-level priorities
over more detailed priorities in the initial stages of sample-based
mortality surveillance, unless prior information on the expected
frequency of important specific causes is compelling. This is not
to deny that information on more detailed causes of death is
critical for most health planning purposes, but for general policy
and priority setting in health, knowledge about the comparative
magnitude of broader causes is probably sufficient.
Results
Modelled life expectancy at birth increased as the probability of
dying during childhood and at ages 15–60 decreased across our
three example populations (Table 3) and was within expected
ranges based on published estimates for countries with
comparable mortality.10 This increase was associated with a
decline in the prominence of Group I causes, and to a lesser
extent Group III causes, compared with Group II causes,
reflecting the expected impact on causes of death of overall
improvements in health (Table 3).
Table 4 presents the total person-years of observation
required in each population in order to achieve an RSE of
15% across the set of age, sex, and cause groups listed in
Table 2. We refer to this as an ‘optimal’ design solution for
sample-based mortality surveillance in these populations. In
Populations D and E, the ‘optimal’ approach achieves tolerable
uncertainty in more groups than specified in Table 2 owing to
the minimum exposure needed to achieve an RSE of 15% in
the rarest group of interest for these populations. If a limited
number of age and sex groups are excluded from the set, the
total exposure required in each of the three populations is more
than halved to between 600 000 and 800 000 people, annually.
This is typically only a minor fraction (2–3%) of the population
of most developing countries, and in many cases, represents
1% of the total population. Moreover, it is unlikely that
annual data are strictly necessary for the control of major
endemic diseases, and hence the required population could be
more than halved by grouping observations over a period of
2 years. Experience in India,5 China,22 and Tanzania23
suggests that these numbers are achievable.
Discussion
Health systems must meet numerous demands for health care
services, often with limited resources. The economic and
political constraints surrounding the provision of health care are
well known, and health systems are often stretched to provide
even essential services, with little or no capacity to address
Downloaded from http://ije.oxfordjournals.org/ by guest on October 6, 2014
Males
DESIGN OPTIONS FOR SAMPLE-BASED MORTALITY SURVEILLANCE
1085
Table 4 Alternative sampling strategies for sample-based mortality registration systems in three example countries at different levels of health
development
Population B
Population D
Optimala
Acceptablea
Person-years in thousandsb
1773.8
Expected number of deaths
10 916
0–4
Population E
Optimala
Acceptablea
Optimala
Acceptablea
849.3
1857.4
852.9
1304.5
646.4
5227
19 575
8988
21 136
10 474
1
0–9, 30–64
0–9
0–59
0–54
0–9, 20–44
Age groups for which expected margins
of error are tolerable in malesc
Group 1: communicable, perinatal, and
nutritional disorders
HIV/AIDS
–
–
–
–
0–54
Malaria
–
–
–
–
5
5
30
40
15
30
0
1, 20–84
15–44
20–24
15–44
15–44
1–54
15–44
0–4
1
0–49
0–9, 20–39
0–59
0–54
–
–
–
–
0–54
0–9, 15–44
Group 2: non-communicable diseases
Group 3: injuries
Age groups for which expected margins
of error are tolerable in femalesc
HIV/AIDS
Malaria
Group 2: non-communicable diseases
Group 3: injuries
–
–
–
–
5
5
30
40
15
30
5, 10
1, 40
–
–
–
–
1–4
–
a ‘Optimal’ sampling strategies achieve acceptable levels of uncertainty in each of the age, sex, and cause groups listed in Table 2 whereas ‘acceptable’ strategies
achieve acceptable levels of uncertainty in only some of the age, sex, and cause groups listed in Table 2.
b 1000 person-years can be interpreted as observing 1000 people for 1 year or 500 people for 2 years.
c Tolerable margin of error is defined as a RSE of 15%, or an expectation of ~45 deaths or more.
endemic diseases at the population level. The framework
proposed in this paper is consistent with the philosophy of
establishing priorities across broad clusters of causes for which
the public health response implications are essentially similar,
and the correct determination of which will avoid serious
misunderstanding of the extent of epidemiological transition in
populations. Its application should provide guidance to policy
makers as to the minimum person-years necessary for mortality
surveillance systems to yield useful information at the population rather than health service delivery level. Once these
priorities have been established with adequate certainty, the
measurement of more detailed causes of relevance to specific
disease control initiatives is likely to be feasible using diseasespecific surveillance data, disease modelling, or more extensive
verbal autopsy with medical certification and review.18,20,24,25
Recent advances in the development of verbal autopsy
procedures24,26–28 will further increase the viability of this
approach as a cost-effective means of cause ascertainment over
complete medical certification for population health monitoring
purposes. Regardless of the orientation of a particular
surveillance system with respect to methods for reducing
uncertainty in causal attribution, it is the underlying frequency
of events, not population size, which remains fundamental to
stochastic uncertainty. Mortality surveillance systems to date
have generally been determined by the size of a population
within a given administrative area, and have not taken into
account the number of deaths needed to yield information that
is sufficiently robust. The framework we propose in this paper
addresses this basic requirement.
The importance of monitoring health development in
populations, and in particular, the effectiveness of disease
control strategies, is clear from the rapid expansion of
HIV/AIDS, particularly in Southern Africa, in the early 1980s.
After almost a quarter of a century, there remains vast
uncertainty about the pace and extent of mortality associated
with this epidemic, with implications for targeting control
strategies, largely because of the continued absence of adequate
mortality information in this region. Similarly, there is great
uncertainty about whether malaria mortality in Africa is rising,
or not, and little is reliably known about the pattern of injury
deaths, or indeed, the emergence of chronic diseases.
Most countries are already investing resources into mortality
surveillance activities of one sort or another. Our framework
could readily be used to reorient this infrastructure so as to
greatly enhance its efficiency and relevance for health
development. The average population size of the 20 or so
demographic surveillance sites, largely in Africa, of INDEPTH
(International Network for the continuous Demographic
Evaluation of Populations and Their Health), for example, is
40 000.29 Notwithstanding the fact that there may be complete
enumeration in many of these sites, our analyses suggest that
these numbers would need to be increased 10-fold in order to
adequately monitor changes in causes of death. This would vastly
reduce persistent ignorance about health conditions throughout
Africa and could be achieved with increased and longer-term
commitments from the global donor community. Our focus, in
the short-term, is the Asia-Pacific region, where we intend
applying the framework to the design of surveillance sites as part
Downloaded from http://ije.oxfordjournals.org/ by guest on October 6, 2014
Group 1: communicable, maternal, perinatal,
and nutritional disorders
1086
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
of collaborative capacity building efforts already underway in the
Philippines (Bohol), Bangladesh (MATLAB), and Indonesia.
Large-scale WHO programme such as the 3 by 5 Initiative
against HIV/AIDS, the Stop TB programme, Roll Back Malaria,
and the Safe Motherhood Initiative urgently require mortality
information to evaluate progress and guide planning and
implementation in developing countries. Major health
information initiatives, such as the WHO Health Metrics
Network and the United States Census Bureau-sponsored
Sample Vital Registration with Verbal Autopsy (SAVVY)
Programme, are currently being oriented towards establishing
sample-based registration in a number of countries in response
to this need. We have shown in this paper how such approaches
can be designed to maximize efficiency. Competent,
scientifically based surveillance that yields sufficiently reliable
and relevant information for programmes action is well within
the means of many developing countries. Indeed, such systems
represent the only useful alternative to establish the evidence
base for health policy and programme delivery for the
foreseeable future in much of the developing world.
Acknowledgements
This work has been supported by the United States National
Institute of Ageing Grant PO1-AG17625. We are grateful to Gail
Williams, Professor of International Health Statistics at the
School of Population Health, University of Queensland, for her
contributions to the conceptual stages of this work.
KEY MESSAGES
Reliable information on causes of death is a fundamental component of health development strategies.
•
Recent experience shows that sample-based mortality surveillance is a viable and low-cost alternative to population-wide
medical certification of deaths.
•
Major health information initiatives are currently being oriented towards establishing sample-based registration in a number of
countries.
•
This paper shows how such approaches can be designed to maximize efficiency while still providing information that is both
robust and relevant to public health.
References
1 Ruzicka LT, Lopez AD. The use of cause-of-death statistics for health
situation assessment: national and international experiences. World
Health Stat Q 1990;43:249–58.
2 Mathers C, Fat DM, Inoue M, Rao C, Lopez AD. Counting the dead
12 Kirkwood BR, Sterne JAC. Essential Medical Statistics. 2nd edn. Malden,
MA: Blackwell Science, 2003.
13 United Nations. Manual X: Indirect Techniques for Demographic
Estimation. New York: United Nations, 1983.
14 Murray CJL, Ferguson BD, Lopez AD, Guillot M, Salomon JA,
and what they died from: an assessment of the global status of cause
of death data. Bull World Health Organ 2005;83:171–77.
Ahmad OB. Modified logit life table system: principles, empirical
validation, and application. Popul Stud 2003;57:165–82.
3 Mathers C, Bernard C, Iburg K et al. Global Burden of Disease in
15 Preston SH. Mortality Patterns in National Populations: With Special
2002: Data Sources, Methods and Results. Geneva: World Health
Organization, 2003.
16 Lopez AD, Hull TH. A note on estimating the cause of death structure
4 Evans T, Stansfield S. Health information in the new millennium: a
gathering storm? Bull World Health Organ 2003;81:856.
5 Vital Statistics Division. Sample registration system. New Delhi: Vital
Statistics Division, Office of the Registrar General of India, 2002.
6 Lopez AD. Counting the dead in China. Measuring tobacco’s impact
in the developing world. Br Med J 1998;317:1399–400.
7 Hauser PM. The use of sampling for vital registration and vital
statistics. Bull World Health Organ 1954;11:5–24.
8 Kaufman JS, Asuzu MC, Rotimi CN, Johnson OO, Owoaje EE,
Reference to Recorded Causes of Death. New York: Academic Press, 1976.
in high mortality populations. Popul Bull UN 1982;14:66–70.
17 Omran
AR. The epidemiologic transition. A theory of the
epidemiology of population change. Milbank Mem Fund Q 1971;49:
509–38.
18 Murray CJL, Lopez AD. The Global Burden of Disease: A Comprehensive
Assessment of Mortality and Disability from Diseases, Injuries, and Risk
Factors in 1990 and Projected to 2020. Cambridge, MA: Harvard
University Press, 1996.
19 Salomon JA, Murray CJL. The epidemiologic transition revisited:
Cooper RS. The absence of adult mortality data for sub-Saharan
Africa: a practical solution. Bull World Health Organ 1997;75:389–95.
compositional models for causes of death by age and sex. Popul Dev Rev
2002;28:205–28.
9 Chandramohan D, Maude GH, Rodrigues LC, Hayes RJ. Verbal
20 Gajalakshmi V, Peto R, Kanaka S, Balasubramanian S. Verbal autopsy
autopsies for adult deaths: issues in their development and validation.
Int J Epidemiol 1994;23:213–22.
of 48 000 adult deaths attributable to medical causes in Chennai
(formerly Madras), India. BMC Public Health 2002;2:7.
10 Lopez AD, Ahmad OB, Guillot M, Ferguson BD, Salomon JA,
21 Population Division. World Population Prospects: The 2002 Revision. New
Murray CJL, Hill KH. World Mortality in 2000: Life Tables for 191
Countries. Geneva: World Health Organization, 2002.
York: Population Division of the Department of Economic and Social
Affairs of the United Nations Secretariat, 2002.
11 Greenland S, Rothman K. Introduction to categorical statistics. In:
22 Yang G, Hu J, Rao KQ, Ma J, Rao C, Lopez AD. Mortality registration
Rothman J, Greenland S (eds). Modern Epidemiology. Philadelphia:
Lippencott Williams & Wilkins, 1998, pp. 231–252.
and surveillance in China: History, current situation and challenges.
Popul Health Metr 2005;3:3.
Downloaded from http://ije.oxfordjournals.org/ by guest on October 6, 2014
•
DESIGN OPTIONS FOR SAMPLE-BASED MORTALITY SURVEILLANCE
1087
23 Ministry of Health Tanzania. Implications of Tanzania’s Mortality Burden:
27 Byass P, Huong DL, Minh HV. A probabilistic approach to interpreting
A Ten-Year Community-Based Retrospective. AMMP-2 Final REport Volume 1.
Dar Es Salaam: Adult Mortality and Morbidity Project, Ministry of
Health, Tanzania, 2004.
verbal autopsies: methodology and preliminary validation in Vietnam.
Scand J Public Health Suppl 2003;62:32–37.
24 Reeves BC, Quigley M. A review of data-derived methods for assigning
28 Chandramohan D, Setel P, Quigley M. Effect of misclassification of
causes of death from verbal autopsy data. Int J Epidemiol 1997;26:1080–89.
causes of death in verbal autopsy: can it be adjusted? Int J Epidemiol
2001;30:509–14.
25 Lopez AD. Commentary: estimating the causes of child deaths. Int J
29 INDEPTH Network. Population and Health in Developing Countries.
Epidemiol 2003;32:1052–53.
Volume 1. Ottawa: International Development Research Centre, 2002.
26 Quigley MA, Chandramohan D, Rodrigues LC. Diagnostic accuracy of
30 Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJ, Lozano R, Inoue M.
physician review, expert algorithms and data-derived algorithms in
adult verbal autopsies. Int J Epidemiol 1999;28:1081–87.
Age Standardization of Rates: A New WHO Standard. GPE Discussion
Paper No. 31. Geneva: World Health Organization, 2001.
Published by Oxford University Press on behalf of the International Epidemiological Association
© The Author 2005; all rights reserved. Advance Access publication 4 July 2005
International Journal of Epidemiology 2005;34:1087–1088
doi:10.1093/ije/dyi135
Maria A Quigley
Reliable information on cause-specific mortality is crucial for
summarizing the total disease burden in different settings. In
addition, it is essential for evaluating the impact of public health
interventions, and for identifying where resources need to be
allocated. Yet in the countries with the highest burden of
disease, cause-specific mortality data are usually of poor quality,
incomplete, or unavailable. In the absence of vital registration
data, the verbal autopsy may be used to estimate cause-specific
mortality. Trained fieldworkers interview bereaved relatives
using a questionnaire to elicit information on symptoms
experienced by the deceased before death. Probable causes of
death are assigned either by physician review of the completed
questionnaires or using predefined diagnostic criteria given in
an algorithm.
The verbal autopsy has been used to estimate cause-specific
mortality in a variety of methodological settings, the most
common being in the context of an epidemiological study.
Estimates of cause-specific mortality from these studies are not
necessarily generalizable to a wider population, and may not
have arisen from a validated verbal autopsy instrument. Recently,
data from 46 epidemiological studies were aggregated in a metaregression model in order to estimate cause-specific mortality
fractions in children aged under five at a global level.1 The
number of deaths in these 46 studies ranged from 8 to 3776, with
all but five studies being based on 1000 deaths. Increasingly,
National Perinatal Epidemiology Unit, University of Oxford, Old Road Campus,
Headington, Oxford, OX3 7LF, UK. E-mail: [email protected]
the verbal autopsy is being employed on a much larger scale. For
example, in India, a verbal autopsy was conducted on 48 000
adult deaths in Chennai2 and on 80 000 adult deaths in
Tamilnadu.3 In Tanzania, the verbal autopsy was employed as a
part of a national sentinel mortality surveillance system covering
a population of over 400 000.4 In China, a sample-based
mortality surveillance system of ~1% of the total population used
a combination of medical certification and verbal autopsy.5
In this issue of the International Journal of Epidemiology, Begg
et al.6 describe a method for addressing the important question
of sample size estimation in relation to sample-based mortality
surveillance. In particular, they present an approach for
calculating the optimum sample size required for estimating
robust cause-specific mortality fractions. They point out that, to
date, mortality surveillance systems have generally been
determined by the size of the population within a given
administrative area. However, the number of deaths is the
crucial parameter required to obtain precise estimates of causespecific mortality fractions. Moreover, by considering the
number of deaths separately according to age group, sex, and
broad causes, the optimum sample size will yield enough deaths
in the age–sex–cause groups of interest. This will ensure that
robust estimates of cause-specific mortality fractions are
obtained for the rarest cause of death, and these will not be
based on more person-years than are necessary.
Begg et al.6 have estimated the required sample size in three
populations, each at a different level of health development.
Their estimates vary according to the type of population being
Downloaded from http://ije.oxfordjournals.org/ by guest on October 6, 2014
Commentary: Verbal autopsies—from
small-scale studies to mortality
surveillance systems