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What is the INDEPTH Network?
The International Network for the Demographic
Evaluation of Populations and their Health
(INDEPTH) Network is an umbrella organization for
a group of independent health research centres operating
health and demographic surveillance system
(HDSS) sites in low- and middle-income countries
(LMICs). Founded in 1998, it brought together a
number of existing HDSS sites, and since then has
encouraged newer HDSS sites to join.1
The purpose of this Editorial is to set the scene for a
series of profiles from INDEPTH HDSS member sites,
the first examples of which are published in this edition
of IJE.2–5 All these profiles will follow a set pattern,
to facilitate a systematic understanding of the multiplicity
of HDSS sites involved in the Network and the
various ways in which they are operated by their parent
institutions. This Editorial therefore, follows the same
general pattern as the individual profiles, but seeks to
explore the epidemiological basis on which the HDSSs
operate in general, and the role of the Network, rather
than dealing with site-specific issues.
At the central level, the INDEPTH Network operates
from its base in Accra, Ghana, as an international
NGO and is also registered as a not-for-profit entity
in the USA. The emphasis on the Network’s position
as a Southern-led and -based organization was an
important founding tenet, and this is very welcome
in a world where vestiges of colonialism still occasionally
surface in relation to health data and policy.
Day-to-day operations are led by the Executive
Director (O.S.), and governance and oversight are provided
by an international Board of Trustees and a
Scientific Advisory Committee (chaired by P.B.).
Why was the INDEPTH Network
set up and what does it cover
now?
The raison d’eˆtre behind the emergence of the Network
was the apparently intractable lack of reliable
population-based data on health across many LMICs
in Africa, Asia and Oceania. Recognizing that there
are no quick fixes in terms of achieving universal individual
registration of populations in LMICs,6 the
Network represents a medium-term attempt to break
the link between material and data poverty.7
Epidemiology in many LMICs suffers from a dual
lack of reliable population data and human capacity
to make use of them. The immediate consequence is
that health policy making often lacks its essential evidence
base, with the possible effect of failing to use
scarce resources effectively in some of the world’s
poorest countries.
There are considerable global disparities in terms of
epidemiological research output per population.
Figure 1 shows the countries of the world shaded
by a crude measure of this, namely the number of
PubMed hits for a search on (‘epidemiology’ and
<country4) per 1000 population. Much of Africa
and Asia falls under the level of 0.05 per 1000, corresponding
to rates which represent less than onetwentieth
of some of the world’s leading countries
in terms of epidemiological output. Superimposed on
the map in Figure 1 are the current 43 HDSS sites run
by 36 member centres of the INDEPTH Network.
Although the locations of these sites are somewhat
serendipitous, rather than being strategically planned,
it is evident that there is considerable coverage
across the areas of the world that lack substantial
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Published by Oxford University Press on behalf of the International Epidemiological Association
 The Author 2012; all rights reserved.
International Journal of Epidemiology 2012;41:579–588
doi:10.1093/ije/dys081
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Where are the INDEPTH HDSSs?
From the outset, the INDEPTH Network has operated
by accepting as members already functioning independent
health research centres that run HDSSs.
Therefore, the Network has little influence over the
locations or geographical distribution of member
HDSS sites. However, since the concept of an HDSS
would be somewhat irrelevant in countries with universal
population registration, in practice there is
self-selection of site locations in places where the
lack of other reliable population-based data justifies
the considerable effort involved in launching an
HDSS. As is evident from Figure 1, this means that
HDSS sites are located across Africa, Asia and
Oceania, but by no means randomly. Several countries
contain multiple HDSS sites, whereas many epidemiologically
poor countries contain none.
What populations are covered by
the HDSSs and how are they
followed up?
HDSSs set out to collect epidemiological data (risks,
exposures and outcomes) within a defined population
on a longitudinal basis. In terms of Pearce’s classification
scheme for epidemiological study designs,8 this
places HDSSs as representing ‘the most comprehensive
approach since they use all of the available information
on the source population over the risk period’.
Unlike many epidemiological study designs, in
which study participants are somehow selected to represent
particular population subgroups, HDSSs generally
set out to cover a real-life population and see
what happens epidemiologically over a period of
years and even decades. Issues of representativity
and sampling are nevertheless critical considerations
for all HDSSs, and need to be considered at the
outset, when often little is known about potential
target populations. Many HDSSs have started from
intentions of covering an area that is at least subjectively
thought to be typical of wider areas, maybe up
to national levels. A chicken-and-egg situation arises,
however, in that the motivation for having an HDSS
is driven by a recognized lack of population-based
health data, so that at the outset, very little may be
known about candidate areas and maybe even less
about the wider situation. There are no simple solutions
to this conundrum.
Even after identifying a target area for an HDSS,
there are a number of possible design considerations.
A range of different sampling strategies can be used
within the target area, that have both epidemiological
and practical implications.9 In practical terms, one
important consideration is whether the final population
is defined as being within a contiguous area or in
a collection of small areas (e.g. discrete villages)
within a wider area. This has important logistic implications
in terms of organizing and maintaining
on-going surveillance, as well as affecting the definition
of migration events (see below). The independent
INDEPTH HDSSs naturally include a mixture of
approaches to initially identifying target areas, withinarea
sampling and population contiguity.
The overall size of the population within an HDSS is
a further important factor, as is the case in any epidemiological
study. However, an HDSS is not a classic
sample survey, and so determining the size of the
Figure 1 Countries of the world classified by PubMed citations for (‘epidemiology’ and <country4) per 1000 population,
also showing the location of 43 HDSS site members of the INDEPTH Network (white dots)
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target population is not straightforward. Size is of
course driven by considerations of the rarest
event(s) of interest, which for most HDSSs are
mortality-related outcomes. If specific causes of mortality
are of particular concern, then the overall population
size needs to be based on numbers relating to
the nth ranked cause of interest.10 Current INDEPTH
member HDSS sites range in population size from
tens of thousands up to around a quarter of a million.
In most HDSSs the overall numbers are driven by
mortality outcomes, with the result that surveillance
of particular more common outcomes (such as morbidity
and social measures) may in some situations be
more effectively undertaken using a sample drawn
from within the overall HDSS population.
During the life of the INDEPTH Network, the
technological and methodological possibilities for obtaining
and using geographical data have advanced
considerably, to the point where recording the latitude
and longitude of every residential unit, and
other salient features, in an HDSS using global positioning
system (GPS) technology have become
commonplace.
Once an HDSS population is defined, an initial detailed
census is usually undertaken to capture details
of all residents and the social units in which they live.
This usually involves assigning unique identifiers to
all the residents and social units encountered in the
census, using a numbering system that has sufficient
capacity for expansion to reflect the addition of future
residents and social units. It is not simple to arrive at
generic definitions of social units across cultures and
traditions, and individual HDSSs have to handle these
issues in ways that make sense for their own context,
both for physical structures (housing) and groups
of inhabitants (families). INDEPTH has tried to standardize
definitions as far as possible by publishing a
resource kit for HDSS design on its website. This initial
census then forms the basis of a database system
that is updated on a regular basis to reflect the dynamic
cohort of people living within the HDSS, as
conceptualized in Figure 2. An important consideration
is to determine the modality of the regular
update rounds. Since HDSSs operate by definition in
populations that are not otherwise enumerated, and
generally have weak infrastructures, the norm is that
local staff have to be recruited to undertake regular
update visits to all the social units in the defined
area. This forms a major component of the ongoing
effort of running an HDSS, and consequently issues
such as the frequency of update rounds need to be
considered very carefully. Different INDEPTH HDSSs
use various update frequencies, from one to four
annual rounds. Certain types of events, e.g. neonatal
mortality, are likely to be particularly sensitive to
recall bias, which in turn is related to update frequency.
Thus, it tends to be the case that more frequent
updates are needed in high mortality or high
migration settings, whereas in societies that are more
stable, or at later stages of demographic transition,
less frequent updates may prove adequate.
What is being measured and how
are the HDSS databases
constructed?
Having set up an HDSS, the next challenge is to track
the progress of the dynamic cohort shown in Figure 2
by regularly updating a series of core parameters, detailed
below. Naturally, the operation of an HDSS is
not confined only to these core activities, and most
HDSSs will have specific agendas defining what other
parameters they may need to handle, e.g. in relation
to the epidemiology of specific diseases, the execution
of clinical trials, monitoring the effectiveness of
health systems and other important issues that can
be built onto the basic HDSS platform.
Social units
Keeping track of social units is a challenging issue,
since it involves both physical structures (that can be
newly built, in existence or be demolished) and the
family groups associated with physical structures
(that can migrate in or out as complete groups, or
particular individuals can migrate to join or leave a
group). In some cultures the physical structures may
be large and complex compounds, perhaps housing up
to 100 people and possibly containing subunits based
on a polygamous social structure. At the other end of
the spectrum, nuclear families may occupy small, discrete
dwellings. Many HDSSs also aim to gather data
on socio-economic status, often reflected by a basket
of parameters including details of the physical structure,
as well as owning traditional and modern assets.
Births
Capturing details of new births is a critical function of
any HDSS, since births form a major part of new entrants
to the cohort and are critical to any analyses of
fertility. In some settings, traditional behaviours
Dynamic cohort
(updated through regular cycles)
time
Death Out-migration
ENTRY
EXIT
Baseline
census
t0
Dynamic population cohort
(updated in regular visit cycles)
Birth In-migration
Figure 2 Conceptual structure of the dynamic cohort
model used by INDEPTH Health and Demographic
Surveillance System (HDSS) sites
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around childbirth (e.g. going to stay at the maternal
grandmother’s residence for the birth and neonatal
period) may make births more difficult to record accurately.
There is a particular difficulty around detecting
early neonatal deaths, and separating these
reliably from intra-partum stillbirths, and this becomes
more difficult with less frequent update
rounds.
Migrations
Tracking details of migration patterns is one of the
most complex areas in HDSSs, fundamentally comprising
people moving into the surveillance area,
within the area and out of the area. Many of these
complexities are reflected in INDEPTH’s monograph
on migration.11 Every type of migration needs to be
defined by rules (involving duration, intent, destination,
etc.) which are appropriate to the population
concerned. Some communities experience regular
patterns of seasonal migration, related to employment
or agricultural production. The possibility of multiple
moves per individual over a period of time must be
incorporated, and a further challenge can be the reliable
re-identification of an individual on in-migration
as being the same person who previously moved out.
The design of an HDSS site in terms of the contiguity
of the surveyed population is also important, since
local moves in a non-contiguous population may be
classified as in- and out-migrations, whereas similar
moves in a contiguous area would amount to
within-site migrations.
Deaths
Deaths, documented by age and sex, are a critical
outcome measure for every HDSS and, in addition
to reporting basic mortality rates, are an essential
component in formulating life tables and other demographic
measures for HDSS populations. As noted
above, one of the most difficult issues involves reliably
identifying early neonatal deaths.
Causes of death
Identifying the causes of death is a much more difficult
issue in populations where most deaths do not
occur in health facilities. The only realistic approach
to attributing the cause of death is by carrying out
verbal autopsy (VA) interviews with relatives or caretakers
of deceased individuals, and then using those
data to arrive at a likely cause of death. The INDEPTH
Network was closely associated with developing a
WHO standard instrument for VA interviews.12 In
many HDSSs, interpretation of the VA data was
done by giving the VA data to local physicians,
often more than one per case, in order to arrive at a
consensus cause. However, this is an expensive and
time consuming process that is gradually being superseded
for most purposes by the application of
computer-based probabilistic models.13 INDEPTH is
currently part of a new round of VA tool development
in conjunction with WHO, which aims to simplify and
shorten the VA process, as well as moving the scope
of VA beyond research settings into non-enumerated
populations.
Databases
Maintaining a database that reflects all the details of
the population in a dynamic cohort is one of the most
demanding tasks for most HDSSs, and a range of different
approaches are used. The longitudinal nature of
the HDSS data demands the use of relational database
management systems (RDBMS) to handle the
considerable volume of data involved over long periods
of time. The basic principles of implementing an
RDBMS for an HDSS have not changed fundamentally
since the 1980s, when one of the longeststanding
INDEPTH member HDSS sites made the
transition to an RDBMS system.14 However, appropriate
hardware and software resources have progressed
through several generations of development in the
meantime, and that is reflected in the current range
of implementations across the INDEPTH Network.
These include implementations built on proprietary
RDBMS systems such as Microsoft FoxProTM,
Microsoft AccessTM and Structured Query Language
(SQL), as well as generic systems made available for
the use of HDSS sites, such as the Household
Registration System from the Population Council,15
subsequently re-engineered as the paperless SQLbased
‘Open-HDS’. As commercial hardware and software
specifications move on (e.g. Microsoft’s decision
to cease supporting FoxProTM), long-term HDSS operations
are sometimes forced to migrate their database
operations onto new platforms, which is not a trivial
matter for long-term databases linked to live
surveillance.
Ethical issues
Running an HDSS over a long period raises a range of
ethical issues that are different in some respects from
those pertaining to many epidemiological studies. In
the first place, the core HDSS data on vital events that
are routinely collected in an HDSS population tend to
be considered as research data, and subject to research
ethics approval and informed consent, even
though in countries that implement universal vital
registration, it is regarded as a civic duty or even a
legal obligation to provide such data. But, however
population data are viewed, there are essential standards
of confidentiality and anonymity that must be
safeguarded. In HDSS data, there are three particularly
critical types of data in this respect. Individual
identities (whether by name or some other identifier)
have to be protected at all stages of the process—from
field interviewers observing adequate standards of
confidentiality through database systems (and their
backups) being held securely, to not revealing identifiers
in any data sharing or outputs. Closely coupled
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with this, since HDSSs now commonly collect the
GPS locations of households, it is important to also
regard these data as confidential, since in principle
they can be used to identify and locate households,
and thereby their residents. Anonymizing GPS data is
a much more difficult issue than simply removing
names from a database.16 Third, HDSS databases typically
accumulate a large volume of personal, often
medical, data (such as HIV status) that are sensitive
and must be kept confidential.
Key findings and publications
Outputs from the INDEPTH Network mentioned here
comprise those that are based on data from more
than one HDSS site, or which make external comparisons.
The individual HDSS site profile papers will provide
further details of site-specific outputs. The
INDEPTH Network website (www.indepth-network.
org) provides information about the Network, its
organization and current activities.
One of the clear strengths of a network such as
INDEPTH is its potential to collate data from
member HDSS sites into outputs that enable systematic
comparisons to be made. The first major
INDEPTH output was a monograph published in
2002 that outlined basic HDSS concepts and gave details
of 22 HDSS site members at that time.17 Two
further monographs relating to health equity in
small areas18 and migration11 followed in 2005 and
2009, respectively. In a different format, using a supplement
in an open-access journal, three sets of
multi-site papers were published in 2009–10. The
first related to cross-site findings on noncommunicable
disease risk factors from a group of
INDEPTH member HDSS sites in Asia.19–27 The
second related to mortality clustering across a range
of INDEPTH member HDSS sites28–36 and the third to
results from eight INDEPTH member HDSS sites,
which participated in the WHO–SAGE programme
on ageing.37–46 The latter Supplement represented an
innovation for the INDEPTH Network with the combined
dataset used for the analyses also being published
online together with the papers. Publications
based on these public-domain data are now
emerging.47
A number of other papers have considered particular
issues at the Network level.48–53 In addition, there
have been some outputs that have involved inter-site
collaborations but not included wide representation
across the Network.54–59 In some cases, multiple
INDEPTH members are also members of other research
networks such as the RTS,S Clinical Trials
Partnership60 and the Alpha Network.61 Several
other studies have made comparisons between HDSS
data from single INDEPTH HDSS sites and other
sources.62–65
Future analysis plans
As well as the substantial and continuing volume of
outputs from individual HDSS sites, the INDEPTH
Network will continue to produce multi-site outputs
in particular topic areas. Current priorities include
comparative assessments of fertility and cause-specific
mortality patterns, as well as retrospective analyses of
HDSS data against correspondingly timed weather
data, which offer insights into the possible future
population effects of changes in climatic conditions.
Strengths and weaknesses
HDSS sites represent an inherently strong epidemiological
design, giving considerably greater analytical
scope than can be achieved from e.g. cross-sectional
approaches. However, the resources required to run
an HDSS effectively are very considerable, particularly
since the greatest gaps in health data are generally
found in more logistically challenged environments.
Not least this makes it very difficult for many HDSS
sites to recruit and retain highly competent personnel,
particularly those with experience in database management
and epidemiological analysis, with the result
that HDSS sites sometimes find it difficult to maximize
their outputs.
A recurrent issue that arises in considering HDSS
data is how the site populations are, or are not, representative
of the wider surrounding populations.
Although this does not pose any technical issues in
terms of analysing data within an HDSS site, it is of
concern when it comes to interpreting HDSS data into
wider epidemiological and policy arenas. There are no
simple solutions to this issue, since HDSSs are always
located in places where little is known about the surrounding
population. It is possible to make comparisons
with other data sources, such as national
censuses and cluster sample surveys,62–65 but these
sources come with their own disadvantages such as
greater recall bias, and hence it is very difficult to
attribute causes to observed differences. An empirical
investigation into this issue used Swedish national
data from 1925, a time when Sweden shared many
characteristics with contemporary LMICs.66 This
showed that the majority of individual counties
could have been taken as adequately representative
of the national population, and the less representative
counties were self-evidently so (including the capital
city and the most remote regions). Although this does
not offer any absolute evidence about the representativity
of INDEPTH member HDSS sites, it suggests
that it is not reasonable to assume by default that
HDSS populations are unrepresentative.
The diversity observed across the INDEPTH member
HDSS sites is a further source of both strength and
weakness. As discussed earlier, there has never been
any master plan for establishing HDSS site.
My aim here is to describe the essential nature of the
INDEPTH Network as a background to detailed profiles
of constituent member HDSS sites. Although all
those sites have important differences, the huge
volume of detailed individual data generated across
Africa, Asia and Oceania by the Network constitutes
a unique resource of great value to demographers,
epidemiologists and health planners.

Korogwe Health and Demographic Surveillance System (HDSS) covers 14 villages in Korogwe district, Tanga Region, north-eastern Tanzania. It is coordinated by National Institute for Medical Research (NIMR), Tanga Centre.

The primary aim of the Korogwe HDSS is to generate health and population related information in an area without a system for routine collection of vital events. It also to generate data for planning and evaluating clinical trials and other interventions which are either currently being or will be undertaken in this platform. Priority areas are malaria, HIV/AIDS, TB and Health system research.

Prior to its establishment, village selection was done in October 2005 followed by meetings with village leaders and the entire community to obtain community consent for participation in the study. Thereafter, a baseline census was conducted using field workers (enumerators) who were recruited from the same area as part of community engagement and participation. The enumerators were trained on how to conduct interviews and fill in the information in baseline census questionnaires and other study tools. The baseline census which was conducted in November 2005 helped to obtain background information of the study population before a longitudinal surveillance system was established in January 2006.

Routine updates of demographic data including deaths, births, changes in marital status, and migration is done 3 times annually.

Verbal autopsies are conducted to establish the causes of deaths /reported through regular HDSS rounds. In collecting such data, a VA questionnaire is usually administered to parents/close relatives of the deceased / within a period of 2 - 6 weeks from the date of death by a trained field worker Other research activities undertaken in this platform included: Social economic status and coverage of expanded program on immunization (Epi-coverage) which were assessed between January and April 2006 and Malariometric surveys in the 14 villages between October 2006 and June 2007. Monitoring of malaria febrile illness in the community is ongoing since January 2006, where a passive case detection of cases using Community-owned Resource Persons residing in the same areas is implemented in six of the villages.

Management of HDSS data is done using HRS2 software for demographic data and Microsoft Access for other types of data. The databases are linked by a unique identifier which is a HDSS personal identification number.

Supervision of field activities involves regular visits by a supervisor, surprise visit by the supervisor and/scientist, accompanied interviews (field worker conducts interviews in the presence of a supervisor/scientist), re-interviews (revisiting the household by the supervisor to re-interview the respondents), monthly meetings and training at the end of each round.

By December 2012, the HDSS area is expected to expand to cover 36 more villages with estimated population of over 63,000, which will bring the number of villages to 50 and a population of over 91,000 people.