Sampling in health geography: reconciling geographical objectives and probabilistic methods. An example of a health survey in Vientiane (Lao PDR).

Abstract

Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods.
We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected a priori determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision.
We applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population.
This paper describes the conceptual reasoning behind the construction of the survey sample and shows that it can be advantageous to choose clusters using reasoned hypotheses, based on both probability and geographical approaches, in contrast to a conventional, random cluster selection strategy.

Full text

Page 1

BioMed Central
Page 1 of 8
(page number not for citation purposes)
Emerging Themes in Epidemiology
Open Access
Methodology
Sampling in health geography: reconciling geographical objectives
and probabilistic methods. An example of a health survey in
Vientiane (Lao PDR)
Julie Vallée*1,2, Marc Souris3, Florence Fournet4, Audrey Bochaton1,2,
Virginie Mobillion1,2, Karine Peyronnie1 and Gérard Salem2
Address: 1Conditions et Territoires d'Emergence des Maladies (UR178), Institut de Recherche pour le Développement (IRD), UR 178, PO. 5992,
Vientiane, Laos, 2Laboratoire Espace, Santé et Territoire, Université Paris X- Nanterre, 200 avenue de la République, 92000 Nanterre, France,
3Conditions et Territoires d'Emergence des Maladies (UR178), Institut de Recherche pour le Développement (IRD), Mahidol University, Bangkok,
Thailand and 4Conditions et Territoires d'Emergence des Maladies (UR 178), Institut de Recherche pour le Développement, BP 182,
Ouagadougou, Burkina Faso
Email: Julie Vallée* - valleej@yahoo.fr; Marc Souris - marc.souris@ird.fr; Florence Fournet - florence.fournet@ird.bf;
Audrey Bochaton - audreybochaton@hotmail.com; Virginie Mobillion - virgimobi@hotmail.com;
Karine Peyronnie - karine.peyronnie@bondy.ird.fr; Gérard Salem - gsalem@ext.jussieu.fr
* Corresponding author
Abstract
Background: Geographical objectives and probabilistic methods are difficult to reconcile in a unique
health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence
with a certain precision, while geographical approaches emphasise the selection of specific areas to study
interactions between spatial characteristics and health outcomes. A sample selected from a small number
of specific areas creates statistical challenges: the observations are not independent at the local level, and
this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that
is appropriate for both geographical and probability methods.
Methods: We used a two-stage selection procedure with a first non-random stage of selection of clusters.
Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would
contain all the variation in health measures in the population. As there was no health information available
before the survey, we selected a priori determinants that can influence the spatial homogeneity of the
health characteristics. This method yields a distribution of variables in the sample that closely resembles
that in the overall population, something that cannot be guaranteed with randomly-selected clusters,
especially if the number of selected clusters is small. In this way, we were able to survey specific areas while
minimising design effects and maximising statistical precision.
Application: We applied this strategy in a health survey carried out in Vientiane, Lao People's
Democratic Republic. We selected well-known health determinants with unequal spatial distribution
within the city: nationality and literacy. We deliberately selected a combination of clusters whose
distribution of nationality and literacy is similar to the distribution in the general population.
Conclusion: This paper describes the conceptual reasoning behind the construction of the survey sample
and shows that it can be advantageous to choose clusters using reasoned hypotheses, based on both
probability and geographical approaches, in contrast to a conventional, random cluster selection strategy.
Published: 1 June 2007
Emerging Themes in Epidemiology 2007, 4:6 doi:10.1186/1742-7622-4-6
Received: 10 October 2006
Accepted: 1 June 2007
This article is available from: http://www.ete-online.com/content/4/1/6
© 2007 Vallée et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Page 2

Emerging Themes in Epidemiology 2007, 4:6http://www.ete-online.com/content/4/1/6
Page 2 of 8
(page number not for citation purposes)
Background
Geography is an independent scientific approach, while
statistics simply constitutes a body of methods and tools
that can be employed by scientists from various fields of
research. Probabilistic statistics can be used to estimate a
variable's prevalence in a given population with a certain
precision. However, analysing survey data in this way can
become complicated when the sample is selected using a
geographical approach. The geographical approach often
favours the study of specific areas, deliberately chosen to
enable analysis of processes and interactions that are key
to the understanding of particular health behaviours and
spatial disparities. Cluster samples chosen from a small
number of specific areas raise an important statistical
challenge, in that the non-independence of observations
within clusters has an impact on the statistical validity of
the sample at the global level. Waldo Tobler showed in his
first law of geography that "everything is related to every-
thing else, but near things are more related than far
things" [1]. Therefore, statistical methods for analysing
spatial data have to take into consideration spatial
arrangements, and the resulting correlations between
observations, in order to provide accurate and meaningful
conclusions [2]. This article suggests a method for the rec-
onciliation of probabilistic statistical methods and geo-
graphical objectives in a unique health survey in
Vientiane, the capital of Lao People's Democratic Repub-
lic (Lao PDR). In this method, areas are selected so that
respondents are as representative of the general popula-
tion as possible, whilst still enabling the study of health
spatial interactions at the local level.
Problem statement
A need for meaningful data for public health and for
health geography
The geographical approach calls for selecting specific
places from where health information about large seg-
ments of the population can be acquired, in order to study
interactions between people living in the same place.
Selection of specific areas allows precise descriptions of
the environment (e.g., the ecological landscape, medical
equipment availability, markets, relevant policies, and so
on) and its relationship with health, which would be very
difficult to assess for a whole city. It is interesting to select
some relevant territories where it is possible to study
health spatial disparities, to explore interactions between
people and places and to gain a better understanding of
spatial organisation in a given society. Additionally, to
analyse health spatial disparities, geography researchers
often distinguish between the effects of "context" (e.g.,
area or group properties) and "composition" (characteris-
tics of individuals living in different areas) in contextual
and multilevelanalyses; these analyses therefore require
datasets including individuals nested within areas or
neighbourhoods [3]. In conclusion, to conduct geograph-
ical analyses (spatial interaction, spatial correlation, con-
textual and multilevel analysis), researchers need to carry
out a health survey in specific places where people and
their neighbours can be interviewed.
At the same time, it would be useful if the study also pro-
duced an estimate of prevalence for each important health
variable, and identified health-seeking behaviours and
individual risk factors. Such findings help inform public
health policy decisions. Indeed, as the Asian Develop-
ment Bank noted regarding Lao PDR, "there is an urgent
need for a nationwide survey of household sanitation in
urban areas. This can be a sample survey as long as the
sample size is representative" [4]. Therefore, the results of
a survey, even one in which data are collected in specific
areas, must also be representative for the whole area.
Stratification can improve the representativeness of a sam-
ple by reducing sampling errors, and can make variance
estimates more precise [5]. If surveyed individuals were
chosen in every stratum by simple random sampling, we
would have achieved a good representative sample of the
stratum population; however, if this is done, geographical
approaches that examine how individuals interact with
their wider environment are not possible [6]. In a health
survey recently carried out in Lao PDR, we needed to
define and select some relevant territories where it was
possible to study health spatial disparities, to explore
interactions between people and places and to gain a bet-
ter understanding of spatial organisation in a given soci-
ety.
Conventional random cluster selection
Design effects
The most commonly used spatial sampling method is
cluster sampling: the studied area is divided into units,
and a selection is then randomly chosen. Within each
unit, individuals are ideally chosen by simple random
sampling. Cluster sampling economises on time, budgets
and energy; it is often done primarily for these practical
reasons, as in the Expanded Programme on Immunisation
(EPI) [7], because it is less expensive than simple random
sampling when the population is dispersed. Cluster
designs can also be useful in geographical approaches,
because they allow for the study of specific places and ter-
ritories. However, such designs are often less precise than
simple random sampling, due to the homogeneity of indi-
viduals within clusters. There may be good reasons why
individuals' behaviour within a small area is similar:
"Why should we expect independence in spatial observa-
tions (...)? All our efforts to understand spatial patterns,
structure and process have indicated the lack of independ-
ence (...) of things in time and space" [8]. With cluster
sampling, every member of the population has an equal
chance of selection, but individuals with similar charac-

Page 3

Emerging Themes in Epidemiology 2007, 4:6 http://www.ete-online.com/content/4/1/6
Page 3 of 8
(page number not for citation purposes)
teristics are more likely to be surveyed. Cluster sampling
necessarily has a design effect, making it less statistically
robust than simple random sampling [5]. This effect var-
ies between areas and even within the same area, and it
can vary depending on the question. The size of the design
effect can be calculated after the study as the variance
obtained from the cluster sample divided by the variance
that would have been obtained with a simple random
sample of equal number.
Number of selected clusters
The statistical precision of cluster sampling can be
improved by increasing the number of clusters. At the
extreme, when all of the clusters are sampled, one effec-
tively has a stratified sample, typically associated with
increased levels of precision. At the other extreme, if only
one cluster is selected, results would be specific to that
cluster and not necessarily generalizable to the global
population. To improve representativeness of the overall
sample, it is best to select many areas, even if this means
sampling fewer individuals within each area [9]. It is often
hard to find the best compromise between the number of
clusters and the number of individuals per cluster. It is
interesting both to study the heterogeneity of health char-
acteristics between different areas and to study the homo-
geneity of characteristics in the same area. The ideal
approach would be to survey a large population in a large
number of areas. However, when budgetary constraints
must be taken into account, one must often choose the
number of clusters according to the following criteria:
1. The number of clusters must be sufficiently large that
statistical precision at the population level is adequate,
and spatial comparisons remain possible, and
2. The number of clusters must be sufficiently small for
the survey to remain logistically feasible, and for geo-
graphical analyses to be performed properly at the local
scale.
The argument against random selection of clusters
Random cluster selection does not necessarily mean that
the sample of clusters is representative of the whole pop-
ulation, especially when the number of clusters sampled
is small; in fact, the design effect is highly dependent on
this number. The variance obtained by cluster sampling
can be reduced by selecting areas that are as internally het-
erogeneous (i.e. have a full range of variability within
them) as possible and as externally homogeneous (i.e. are
as similar to one another) as possible. The ideal situation,
in terms of statistical precision, would be that each area
was a microcosm of the entire population and, therefore,
was perfectly representative, in terms of variability, of the
overall population [9]. However, this solution is in oppo-
sition to geographical objectives, in that the geographic
approach (as in the spatial statistics approach) views pop-
ulation homogeneity as interesting in itself. Herein lies
the difficulty: the design effect could be reduced by choos-
ing similar areas with high internal heterogeneity, but
other than the fact that it is impossible in practice to iden-
tify such an area, this would be in contradiction with the
objectives of health geography.
To reconcile probabilistic methods and geographical
objectives, we propose purposeful, rather than random,
selection of a group of clusters that together could contain
all the variability of the overall population.
How do we select clusters to best reproduce the
population distribution of variables?
To improve the reliability of our sample, consisting of a
small number of clusters, we have to choose the best com-
bination of n clusters, such that respondents are represent-
ative of population heterogeneity (with regards to, in this
case, health variables). Since no health information is
available before the survey, a priori variables that can
influence the spatial homogeneity of health characteristics
in the studied area have to be determined. The notion of
resemblance is subjective, but it aims to ensure that any
two given populations resemble each other in terms of the
phenomenon being researched. The choice of n clusters is
thus based on reasoned hypotheses and on the specific
research objectives. Cluster selection is therefore concep-
tually derived from a set of definite hypotheses, which is
necessarily different from the hypotheses another group
of researchers might have. Among every available varia-
ble, we select well-known health determinants (e.g. age,
nationality, ethnic origin, education, occupation, etc.)
and keep some variables (v1; v2...vn) with unequal spatial
distribution within the studied area. It is possible to check
the survey results after the research has been carried out
for a posteriori relevance of the given variables as health
determinants.
To select the combination of n clusters whose composi-
tion is most similar to the composition of the overall stud-
ied area, it is first necessary to list all the possible
combinations of n clusters (without repetition) among N
clusters existing in the studied area. Usually, we obtain a
large number of combinations:
Where Cb is the number of possible combinations of n
clusters without repetition; N is the total number of clus-
ters existing in the studied area and n is the number of
clusters we want to select.
CbC
N
Nnn
n
N
==
×
! (
−
!
)!

Page 4

Emerging Themes in Epidemiology 2007, 4:6http://www.ete-online.com/content/4/1/6
Page 4 of 8
(page number not for citation purposes)
It is then necessary to calculate, for each combination of n
clusters, the mean squared difference between the propor-
tion of v1 (we call it Pv1) in each of the n clusters (c1;
c2...cn) and the mean proportion of v1 calculated at the
studied area level (Pv1area).
Mean squared difference = [(Pv1 c1- Pv1area)2 + (Pv1 c2-
Pv1area)2 + (Pv1 c3- Pv1area)2 +...+ (Pv1 cn- Pv1area)2 ]/n
This mean squared difference is compared with the vari-
ance of v1 calculated at the studied area level. Cluster com-
binations are retained where the mean difference
corresponds with the variance calculated for the studied
area. The same steps are followed with every selected var-
iable (v2...vn). Among the large number of possible com-
binations of n clusters, few combinations are obtained,
whose variability of different selected variables is very
similar to the variability calculated at the studied area
level. This procedure enables clusters to be selected that
have a composition of a priori health determinants that is
similar to the composition of the a priori health determi-
nant in the overall studied area. With this procedure, we
hope to reduce design effects and gain statistical precision
while surveying only a few clusters.
Applications in Vientiane health survey
We applied this form of clustered sampling to a health
survey conducted in Vientiane, Lao PDR.
Health survey in Vientiane
The main objective of the research programme, entitled
"Urbanization, Governance and Spatial Disparities of
Health in Vientiane", is to describe and analyse the organ-
isation of urban areas (including geographical, social, cul-
tural, political, environmental, and behavioural variables)
as sources of intra-urban health inequalities. The urban-
ised area of Vientiane is spread over 148 villages ('ban' in
Lao) comprising approximately 277,000 inhabitants in
2005 [10]. In Lao PDR, the "village" (ban) is the smallest
administrative, religious and political unit in both rural
and urban areas. The spatial division into villages reflects
political, administrative and social reality and, as census
data are available at the village level, we decided to keep
the village as the reference unit for the survey: a cluster
thus corresponds to a village. In 2005, an average of
approximately 1870 people lived in a Vientiane City vil-
lage (interquartile range: 1080 – 2311). It is likely that the
increased urbanisation of the capital has led to wide dis-
parities in health, but as little health information exists,
there is no way to know what kind of health problems the
population encounters and how people seek healthcare.
To provide health data and to analyse health spatial dis-
parities in Vientiane, the French Research Institute for
Development (Institut de Recherche pour le Développe-
ment – IRD) carried out a health survey within the city in
collaboration with the Lao Ministry of Health, the
National Institute of Public Health, the Faculty of Medical
Sciences, the Francophone Institute of Tropical Medicine
(Institut Francophone de Médecine Tropicale – IFMT) and
the Microbiology Laboratory in Mahosot Hospital. Ethical
approval for this survey was obtained from the Lao
National Ethics Committee for Health Research in Lao
PDR.
Two age groups were selected: children (aged from six
months to less than six years) and adults (aged 35 years
and above). Data were collected in February and March
2006 through household and individual questionnaires.
Household questionnaires collected data on house loca-
tion and description, living conditions, incomes, commu-
nity bonds and demographic data on every member of the
household. Individual questionnaires collected demo-
graphic data and socio-economic information, urban life-
style variables, behavioural risk factors, health status data,
and healthcare-seeking behaviours. Health status was
measured through medical examination and investiga-
tions (weight, height, temperature, blood pressure, dental
examination and blood samples from a fingerprick to
study diabetes, anaemia and communicable diseases).
Healthcare-seeking behaviour was examined through
questions about type and gravity of health problem, local
health structure, price, quality, and satisfaction with
health care services.
With these data, we aimed to: (i) compare levels of mor-
bidity in different urban areas; (ii) identify appropriate
urban scales for recognising health disparities; (iii) detect
hotspots of morbidity using exploratory spatial data anal-
ysis; and (iv) measure the impacts of both social and
urban contexts using multilevel analyses.
Use of urban stratification
As the main objective of this programme was to study the
relationship between urbanisation and health, the first
stage of this health survey required us to set the spatial
limits of the urbanised area of Vientiane and to stratify
this defined area [10]. To set the limits of an urban area, it
was more relevant to use a variety of census-based and
GIS-based indicators rather than a single common indica-
tor such as population density. We selected 13 different
indicators describing urbanisation in the urban area of
Vientiane: the proportion of built-up area; density of the
population; changes in the built-up surface area between
1981 and 1999; the proportion of public infrastructure
buildings; the proportion of trade buildings; the number
of markets nearby; distance to the city centre via the road
network; the average distance of every building to the road
network; access to running water, electricity and toilets;
the proportion of concrete houses; and the proportion of
the population involved with agricultural activities. These

Page 5

Emerging Themes in Epidemiology 2007, 4:6http://www.ete-online.com/content/4/1/6
Page 5 of 8
(page number not for citation purposes)
indicators are derived from the 1999 aerial photographs
processed in "Atlas Infographique de Vientiane" [11] and
the 1995 census from the Lao National Statistical Center.
Using a hierarchical classification, we differentiated Vien-
tiane's villages into three strata of decreasing level of
urbanisation (central zone, first urbanised belt and sec-
ond urbanised belt) with 25, 67 and 56 villages in each
stratum respectively (Figure 1) [10]. The proportions of
the population living in these three strata in 2005 are une-
qual: 11.7% in the central zone, 40.7% in the first urban-
ised belt and 47.7% in the second belt. It was therefore
important to stratify by degree of urbanisation if we
wished to survey a sufficient number of individuals in
each stratum, especially in the central zone where the pop-
ulation was smallest.
Number of villages to survey
Budgetary limitations affected the overall sampling size:
2000 adults and 2000 children for the whole city (or 666
adults and 666 children in each urban stratum). This
allowed for a 95% CI of +/- 2.3% around a prevalence of
10% at the stratum scale, and +/-1.3% around a preva-
lence of 10% at the city scale. In this calculation, we have
not considered the design effect. The value of the design
effect (which differs between variables within the same
survey) is difficult to estimate during survey preparation,
and is very dependent on the selection of clusters. We
planned to survey the same number of individuals in
every village so that comparison could be done with the
same precision. According to the size of the village popu-
lation, we fixed a sample size of 27 villages (nine per
urban stratum), with 74 adults and 74 children to be sam-
pled in each village. This corresponds to a mean sampling
rate of 1/5.6 for adults and 1/2.4 for children, based on
the list of households created in December 2005 in every
one of the 27 selected villages.
Selection of villages
For logistical convenience, we decided to re-group these
27 villages into nine groups of three adjacent villages,
such that only one medical centre was needed for every
three adjacent villages. Using a contiguity matrix, we listed
in each stratum all the possible groups of three adjacent
villages and produced all the combinations of three
groups of three adjacent villages without repetition. We
obtained a very large number of combinations (table 1),
from which we selected the combination of nine villages
that was most representative of the stratum in both urban
and health terms.
Urban representativeness
Among the combinations of three groups of three adja-
cent villages, we pre-selected combinations that were rep-
resentative of urban characteristics. Despite already
having stratified on degree of urbanisation, the small
number of studied villages (nine) in each stratum still did
not guarantee a good representation of urban characteris-
tics within the stratum itself. To solve this problem, we
used our hierarchical classification of urban characteris-
tics to distinguish further sub-strata of urbanisation
within each urban stratum, and defined three substrata in
the central zone, four in the first urbanised belt and five in
the second urbanised belt (Figure 2). For each possible
combination of nine villages, we calculated the propor-
tions of villages belonging to the different urban sub-
strata, and the mean of differences between these propor-
tions and those for the whole stratum. We pre-selected
every combination of nine villages for which the mean of
the differences was lower than 5% (table 1).
Health representativeness
Among every available variable in the 1995 census, we
selected two well-known health determinants with une-
qual spatial distribution within the city proper: national-
ity (proportion of people who do not have Lao
nationality) and the level of education (proportion of
people who were literate, in any language). The spatial
heterogeneity of these two variables in Vientiane could
influence spatial health distribution (Figures 3 and 4). As
Urban stratification in Vientiane
Figure 1
Urban stratification in Vientiane. 3 areas of decreasing
degree of urbanization can be distinguished in Vientiane: a
central zone, a first belt and a second belt of urbanization.

Page 6

Emerging Themes in Epidemiology 2007, 4:6http://www.ete-online.com/content/4/1/6
Page 6 of 8
(page number not for citation purposes)
spatial distribution of non-Lao people (mainly Vietnam-
ese and Chinese) was particularly heterogeneous in the
central zone, it was important to control the choice of
nine villages to ensure that we did not only select villages
with a higher proportion of foreigners than in the central
zone. Equally, given that the spatial distribution of liter-
acy was particularly heterogeneous in the first and second
belt of urbanisation, we wished also to ensure that the
selected villages would not be so alike in terms of literacy.
As described earlier, we calculated, for every combination
of three groups of three adjacent villages, the mean
squared difference of the proportion of those with Lao
nationality and compared it with the variance of the pro-
portion of those with Lao nationality calculated at the
stratum level. We followed the same steps with the pro-
portion of literacy. After this two-step procedure, we
obtained a few combinations in each stratum whose vari-
ability of nationality and education was very similar to the
variability calculated at the stratum level. We chose from
among these the combination villages for which there
were no logistical problems (such as requiring political
authorisation to carry out research there). This procedure
provided three combinations of nine villages (Figure 5).
Table 2 summarises the characteristics (obtained from the
1995 census) of these three combinations in comparison
with the corresponding urban stratum. As a rough guide,
1928 people lived on average in every 27 selected villages,
Spatial distribution of proportion with Lao nationality in 1995
Figure 3
Spatial distribution of proportion with Lao national-
ity in 1995. The majority of non-Lao people (mainly Viet-
namese and Chinese) live in the central zone of Vientiane.
Table 1: Number of combinations in every urban stratum
Central zoneFirst urbanised beltSecond urbanised belt
Number of villages
Number of possible groups of 3 adjacent villages
Number of possible combinations of 3 groups of 3 adjacent villages
Number of possible combinations of 3 groups of 3 adjacent villages which
respect proportions of urbanization types.
25
102
46 828
6 819
67
209
999 140
10 851
56
92
60 225
2 408
12 urban sub-strata in Vientiane: 3 sub-strata in the central zone, 4 in the first urbanised belt and 5 in the second urban-ised belt
Figure 2
12 urban sub-strata in Vientiane: 3 sub-strata in the central
zone, 4 in the first urbanised belt and 5 in the second urban-
ised belt.

Page 7

Emerging Themes in Epidemiology 2007, 4:6http://www.ete-online.com/content/4/1/6
Page 7 of 8
(page number not for citation purposes)
with a minimum of 562 and a maximum of 4513 inhab-
itants per village (2005).
Random selection of respondents in every selected village
To obtain a sample in every selected village as reliably as
possible (not only statistically but also spatially) respond-
ents needed to be selected randomly. With the help of vil-
lage authorities, we created a sampling frame in each
village and then selected households to survey at random.
Within each selected household, a maximum of one adult
and one child were allowed to participate in the survey.
The probability of household selection was proportional
to the number of eligible people in the household.
Discussion
In developing this sampling frame, the main difficulty we
encountered was the lack of data available on the popula-
tion. We needed to ascertain accurate information about
some health determinants and about their spatial reparti-
Table 2: Characteristics (in 1995 census) of selected combinations in comparison with the corresponding urban stratum
Lao nationality Literate people
Proportion (%)Variance* Proportion (%)Variance*
Central zone27 villages
9 selected villages
67 villages
9 selected villages
56 villages
9 selected villages
89.9
89.4
98.3
98.1
99.5
99.7
0.00424
0.00441
0.00050
0.00040
0.00012
0.00001
77.7
76.4
79.1
77.4
73.8
70.5
0.00118
0.00117
0.00198
0.00196
0.00325
0.00341
First urbanised belt
Second urbanised belt
* For 9 selected villages, variance corresponds to the mean difference (high squared) between the proportion in every 9 villages and the mean
proportion calculated at the layer level.
Spatial distribution of proportion of literate people in 1995
Figure 4
Spatial distribution of proportion of literate people in
1995. Spatial distribution of literate people is particularly
heterogeneous in the first and second belt of urbanization.
27 selected villages for health survey
Figure 5
27 selected villages for health survey. In every selected
village (clustered), around 74 adults and 74 children were
planned to be interviewed.

Page 8

Publish with BioMed Central and every
scientist can read your work free of charge
"BioMed Central will be the most significant development for
disseminating the results of biomedical research in our lifetime."
Sir Paul Nurse, Cancer Research UK
Your research papers will be:
available free of charge to the entire biomedical community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central
yours — you keep the copyright
Submit your manuscript here:
http://www.biomedcentral.com/info/publishing_adv.asp
BioMedcentral
Emerging Themes in Epidemiology 2007, 4:6http://www.ete-online.com/content/4/1/6
Page 8 of 8
(page number not for citation purposes)
tion. For the Vientiane survey, only urban data from 1999
with aerial photographs, demographic data from the 1995
census, and the number of inhabitants per village from
2005 census were available. Precise demographic data
from the 2005 census (such as literacy, nationality, access
to electricity, water, and latrine access) were not yet avail-
able during the preparation for this survey.
For the health survey in Vientiane, we adopted a two-stage
selection procedure with a first non-random stage of selec-
tion of clusters: we chose clusters that would be represent-
ative of the urban and health variability of the global
population. Conventional random cluster sampling is cer-
tainly statistically appropriate when the number of clus-
ters is large. However, when only a small number of
clusters are sampled in order to correspond to geographic
objectives and/or to logistical needs, it becomes statisti-
cally more appropriate to choose clusters instead of ran-
domly select them. Where this method is used, the choice
of clusters should be based on reasoned hypotheses and
on the specific research objectives. A modified clustered
design with a first non-random stage of cluster selection
can provide appropriate information both to study health
spatial interactions and to estimate other health variables,
such as prevalence, at the city level.
Competing interests
The author(s) declare that they have no competing inter-
ests.
Authors' contributions
JV performed the sample design, organized the Vientiane
health survey and drafted the manuscript; MS and FF took
part in the reasoning associated with the construction of
the survey sample and corrected the manuscript. MS con-
tributed also to the conception of the sample design; AB,
VM made comments about geographical approach and
organized Vientiane health survey; KP and GS participated
to survey organization. All authors read and approved the
final manuscript.
Acknowledgements
This survey was supported by Institut de Recherche pour le Dévelopement
(IRD) and French Embassy in Laos. We thank those who helped us carry
out this survey: Lao Ministry of Health, the National Institute of Public
Health, the Faculty of Medical Sciences, the Francophone Institute of Trop-
ical Medicine and the Wellcome Trust project, Microbiology Laboratory in
Mahosot Hospital. We thank Daniel Benoît (IRD Representative in Lao
PDR) and Jean-Paul Gonzalez (IRD-UR 178 Director) for their scientific and
logistical support. We really appreciate the assistance of Paul Newton, John
Cotton and Ulf Winan who corrected the manuscript. We thank too Jean
Gary and Stéphane Rican for their help in sample design preparation.
References
1.Tobler WR: A Computer Model Simulating Urban Growth in
the Detroit Region. Economic Geography 1970, 46:234-240.
2.Waller L, Gotway C: Applied Spatial Statistics for Public Health Data
New York: Wiley; 2004.
Diez Roux A: Investigating Neighborhood and Area Effects on
Health. American Journal of Public Health 2001, 91(11):1783-1789.
Asian Development Bank (ADB) & MCTPC, Dept of Hous-
ing and Urban Planning. Lao Urban Data Book, Development Indica-
tors for the Urban Areas of Lao PDR, Draft Final report 2003.
Cochran WG: Sampling Techniques 3rd edition. New York: Wiley;
1977.
Curtis S: Health and Inequality. In Geographical perspectives Sage
Publications Ltd: London; 2004.
Hendersen RH, Sundarset T: Clustered sampling to assess
immunization coverage: a review experience with a simpli-
fied method. Bull WHO 1982, 60:253-260.
Gould P: Is statistic inferens the geographical name for a wild
goose? Economic geography 1970, 46:439-448.
Zelin A, Stubbs R: Clustered Sampling: A False Economy? Inter-
national Journal of Market Research 2005, 47(5):501-522.
Vallée J: Espace urbanisé et périmètres urbains, une délimita-
tion complexe. Vientiane, développement urbain et patrimoine, Keophi-
lavan Aphaylat, Pierre Clément, Charles Goldblum et Christian Taillard
(édit.), les Cahiers de l'Ipraus, Editions Recherches, Paris, forthcoming in
2007.
Rossi G, Tissandier P, Inthiphone B: Atlas infographique de Vientiane,
Vientiane, Programme de formation-recherche en coopération inter-univer-
sitaire, Université Nationale du Laos, Université de Bordeaux III 2003.
3.
4.
5.
6.
7.
8.
9.
10.
11.