a Department of Tropical Hygiene and Public Health,
University of Heidelberg Medical School, Heidelberg, Germany.
b Centre de Recherche en Santé de Nouna (CRSN), BP
02, Nouna, Burkina Faso.
Prof. Heiko Becher, Department of Tropical Hygiene and Public Health, University of Heidelberg Medical School, Im Neuenheimer Feld 324, D-69120 Heidelberg, Germany. E-mail: heiko.becher{at}urz.uni-heidelberg.de
Abstract
Background Childhood mortality is a major public health problem in sub-Saharan Africa. For the implementation of efficient public health systems, knowledge of the spatial distribution of mortality is required.
Methods Data from a demographic surveillance research project were analysed which comprised information obtained for about 30 000 individuals from 39 villages in northwest Burkina Faso (West Africa) in the period 19931998. Total childhood mortality rates were calculated and the geographical distribution of total childhood mortality was investigated. In addition, data from a cohort of 686 children sampled from 16/39 of the villages followed up during a randomized controlled trial in 1999 were also used to validate the results from the surveillance data. A spatial scan statistic was used to test for clusters of total childhood mortality in both space and time.
Results Several statistically significant clusters of higher childhood mortality rates comprising different sets of villages were identified; one specific village was consistently identified in both study populations indicating non-random distribution of childhood mortality. Potential risk factors which were available in the database (ethnicity, religion, distance to nearest health centre) did not explain the spatial pattern.
Conclusion The findings indicate non-random clustering of total childhood mortality in the study area. The study may be regarded as a first step in prioritizing areas for follow-up public health efforts.
KEY MESSAGES
Keywords Childhood mortality, clustering, demographic surveillance, spatio-temporal analysis
Accepted 15 February 2001
In the developing world, morbidity and mortality continue to show a pattern characterized by high childhood mortality, mainly due to infectious diseases. The World Health Organization1 states that despite the extraordinary advances of the 20th century, a significant component of the burden of illness globally still remains attributable to infectious diseases ... It therefore states the need to develop more effective health systems as one of the challenges to be addressed in order to improve the world's health. The goal must be to create health systems that can: improve health status; reduce health inequalities; enhance responsiveness to legitimate expectations; increase efficiency; protect individuals, families and communities from financial loss; and enhance fairness in the financing and delivery of health care.
Until now, there is no routine registration of births and deaths in most of the developing world. Information on basic demographic measures often stems from demographic surveillance systems (DSS). Usually, these systems are based on initial census of a population of limited size, often in the order of some ten thousands of individuals, followed by an active follow-up in which births, deaths, in- and out-migration are recorded. Active follow-up consists of information from specific community informants or regular house-to-house visits to the respective population at which events in the period since the preceding visit are obtained.
The development and evaluation of effective programmes to reduce the burden of disease requires a detailed knowledge of disease or mortality distribution and causal pathways. This knowledge could be derived from analytical epidemiological studies that use as a platform large-scale health surveys and the above described demographic surveillance systems (DSS) in which causal relationships between risk factors and diseases or mortality are investigated. Benzler and Sauerborn2 recommend that in cases where general population-wide intervention programmes are too expensive to implement, it is necessary to limit such programmes to high risk units where certain adverse health effects are more likely to occur. Therefore, investigating whether the distribution of adverse health outcomes in a population are either random or not should be an important primary objective before starting a programme for primary and secondary prevention of infectious diseases. It is necessary to determine whether there are clusters where adverse health outcomes seem to aggregate. If this is the case, there is need to identify the causes of such clustering, to enable local health personnel to identify them by means of simplified scores, and to develop specific health care strategies targeted at these clusters.
Statistical methodology to identify disease clusters is under constant development. A general review of clustering methods is provided by Hertz-Piciotto3 and examples of specific applications are given by Hjalmars et al.,4 Britton,5 Kulldorff et al.6 and Kulldorff.7 In this paper we employ the Kulldorff spatial scan statistic8 for the identification of and testing for clusters of childhood mortality.
The paper is organized as follows: First, we describe the main characteristics of the DSS population on which most of the analyses are based. We then describe the study population from a controlled trial9 which turned out to be useful for supporting the findings and give an outline of the statistical methods used. Following the results we discuss our findings in the light of immediate and future impact on public health and their possible limitations.
Study populations
Geographical
description
The study area is within the rural province of Kossi in
northwest Burkina Faso with the town of Nouna as its administrative
headquarters (Figure 1). Burkina Faso is a landlocked country in West
Africa with an estimated gross domestic product (GDP) per capita purchasing
power parity of $1000 (CIA10), and an estimated cumulative
mortality rate up to age 5 years of 182 for males and 172 for females
(World Health Report11). The population is about 11 million
(1997), with 56% children under 15 years, and an annual population growth
rate of 2.6%. The country is predominantly rural; about 80% of the
population live in rural areas. The rural provinces have an inadequate
health delivery system compared to the urban areas.
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The DSS population
The DSS in rural Burkina
Faso comprises the entire popula-tion of 39 villages with approximately 30
000 people and approximately 4800 households within the province of
Kossi. The villages are in the catchment area of three dispensaries with
attached maternity clinics staffed by a nurse and a midwife. In 1992, the
first census was carried out. A control census was held in 1993, and a
further complete census was held in 1998. Since 1992 Vital Events
Registrations (VEE) have been carried out through the visits of trained
interviewers to each village. These interviewers ask three key informants
if any deaths, births or in- and out-migration have occurred in the
preceding month since the previous visit.12 In the VEE, births, deaths and
migrations were recorded with the cause of death determined by verbal
autopsy according to the method of Anker et al.13 The database for this paper included a
follow-up of the population until 31 December
1998.
Randomized controlled trial cohort
In June
1999, a cohort of 686 children aged 631 months in a subsample of the
DSS study villages was enrolled for a randomized placebo-controlled trial
on zinc supplementation in which a possible effect on frequency of malaria
episodes was investigated.9 Children for the study were recruited
from 16 out of 39 study villages (blocks of 30 and 60 children randomly
sampled from small and big villages respectively), and prospectively
followed up for a period of 6 months through daily household
visits. Information on deaths was recorded during this trial. The data of
this study are used here for the purpose of validating the results from the
DSS database.
Statistical methods
Mortality
ratios for the DSS data
We calculated the childhood death rates (DR)
by village i, i = 1,...,39 for years j,
j = 1993,...,1998 using
where nij denotes the midyear population of children
aged 04 years in village i at year j, and
dij the corresponding observed number of deaths. In
order to identify villages in which the death rate was significantly above
average, an exact 95% CI for each rate was based on the Poisson
distribution of the observed number of deaths.14 A rate was considered significantly
above average if the overall rate of the respective year was below the
lower value of the confidence interval of the village rate, a procedure
commonly used in descriptive epidemiology.15 An overall temporal trend in rates was
analysed by applying a Poisson regression model of the form
dij = log(nij) + µ +
ß . j where i = 1,...,39, j = 1993,
1994,...,1998 and tested for Ho : ß =
016 using the
software package EGRET.17
Method to
investigate disease clustering
As briefly outlined in the
introduction, several methods for disease cluster analysis have been
suggested. We chose the Kulldorff spatial scan statistic8 in which the spatial
distribution of the population is taken into consideration as
follows.
A circular window is imposed on a map by the spatial scan statistic and it allows the centre of the circle to move across the study region. For any given position of the centre, the radius of the circle changes continuously so that it can take any value between zero and some upper limit. The circle is therefore able to include different sets of neighbouring villages. A village is captured if it lies in the circle.
The method creates a set containing an infinite number of distinct circles. Each of these circles could contain a different set of neighbouring villages and each of the circles is a potential cluster of childhood mortality in the Kossi study area. For each circle, the spatial scan statistic calculates the likelihood of observing the observed number of cases inside and outside the circle. The circle with the maximum likelihood is defined as the most likely cluster, implying that it is least likely to have occurred by chance. For each circle, the method tests the null hypothesis against the alternative hypothesis that there is at least one circle for which the underlying risk of mortality is higher inside the circle as compared to outside. Generally, the method tests the null hypothesis that the risk of children dying is the same in all villages in the study area.
Let N be the total number of deaths in the
study area, n the observed number of deaths within the circle, and
the expected number of deaths in the circle under the null
hypothesis. Let the number of deaths in each village follow a Poisson
distribution. Hence the likelihood ratio for a specific circle is therefore
proportional to
![]() | (1) |
where
LA(D) is the likelihood under the alternative
hypothesis that there is a cluster of elevated annual mortality rates in
age group 04 in a specific circle D, L0
is the likelihood under the null hypothesis, and I is an indicator
function that is equal to 1 when the circle has more deaths than expected
under the null hypothesis, and 0 otherwise. Maximizing (1) over all circles
results in the one that constitutes the most likely mortality cluster. The
test statistic is
![]() | (2) |
Kulldorff8 has derived the likelihood
ratio test and provided the properties of the test statistic. We have used
SaTScan 2.118 to
perform the calculations. The P-value of the statistic is obtained
through Monte Carlo hypothesis testing, where the null hypothesis of no
clusters is rejected at an level of 0.05 exactly if the simulated
P-value is
0.05 for the most likely cluster. The program
gives the most likely cluster with the corresponding P-value. If
other clusters not overlapping with the most likely cluster are identified,
these are also given by the program with their corresponding
P-values. We applied this method both to the DSS population (each
year separately) and to the zinc study population.
Kulldorff et al.4 have extended the spatial scan statistic into a space-time scan statistic. In this case, the window imposed on the study area by the statistic is cylindrical with a circular geographical base and with height corresponding to time. The centre of the base is one of several possible centroids located throughout the study area and the height reflects any possible time interval. The cylindrical window is then moved in space and time. This was applied to the DSS data for the time window 19931998.
Results
The focus of this section is on the results from the DSS data. As noted earlier, the results from the randomized control cohort study are used here to validate the results from the DSS database.
The DSS
population
Table 1
provides summary data for all 39 villages in the study area. The average
yearly death rate (per 1000) for children under 5 for the 6-year period was
35. This corresponds to a cumulative rate up to age 5 years of
1exp(5 x 0.035) = 0.16 which is close to the estimated
country-wide rate reported by the WHO. There is a decline towards the end
of the observation period. We investigated whether there is a trend in the
rates. Using the full observation period, no significant trend was
observed. However, considering the possibility of some underreporting of
deaths in the first year (1993) of observation which may have resulted in
the course of establishing the field procedures, we omitted the first year
from the analysis and found a highly significant decreasing trend in
mortality (P < 0.001).
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Space
and space-time scan statistic results of the DSS population
Table
3 presents the results of the
purely spatial analysis scanning for high rates using the Poisson model for
1993 to1998. No statistically significant cluster was identified for
1993. A statistically significant cluster (P = 0.0051) comprised
of the census areas of 15 villages including Cissé and Labarani was
identified for 1994. In all 106 cases of childhood mortality were observed
(78.2 expected) and the cluster had a relative risk of 1.4. No
statistically significant cluster was identified for 1995. For 1996 the
identified statistically significant cluster (P < 0.001)
comprises the village of Cissé; 18 childhood mortality cases were
observed (5.5 expected) with a high relative risk of 3.3. Cissé was
also identified as a statistically significant cluster (P <
0.001) for 1997 (17 childhood mortality cases observed, 3.9 expected,
overall relative risk 4.4). A significant second cluster (P <
0.001) was identified for 1997. This cluster comprises seven villages (61
cases observed, 37.4 expected, relative risk 1.6). For 1998, the
statistically significant cluster (P < 0.001) identified
comprises the census areas of five villages including Cissé and
Solimana (37 cases observed, 18 expected, relative risk of 2.0).
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The results after omitting Cissé show that the significant cluster in 1994 remains unaffected (the same villages except Cissé form a cluster, P = 0.01). For 1995 and 1996 no significant cluster was identified. For 1997 the previously identified secondary cluster was identified. For 1998 the previously identified cluster except Cissé (Solimana, Sien, Seriba) was again identified, however not significant (P = 0.1). These results show that while Cissé seems to be the village with the strongest increase in mortality, the whole subregion appears to be conspicuous.
The scan statistic was also applied to scan for clusters of significantly lower mortality. No such cluster was identified. This may provide a good evidence of no systematic underreporting in certain villages versus others.
The DSS database provides information on some other variables possibly linked to childhood mortality. In particular, we investigated the variables distance to next health centre, religion and ethnicity. The increased risk for children in Cissé appeared to be independent of these factors: The nearest health centre to Cissé is 18 km away, only slightly above average for all villages (range: 034 km, mean 11.3 km). The predominant ethnic group in Cissé is the Peulh in contrast to the surrounding villages. However, the Peulh have an overall childhood mortality which is below average. The most frequent religion in Cissé is Islam (94.3% in Cissé, 60.5% in the total study region). However, the overall mortality in Muslims is below average. Thus, all these factors do not explain the increased risk.
The randomized controlled trial cohort
Table
5 shows the data from the Zinc
study. We observed 17 deaths in the observation period, which corresponds
to a mortality rate of 57 per 1000 person-years (95% CI:
32.789.3). We did not distinguish between treatment and placebo
groups because (1) zinc supplement was not shown to have an effect on
malaria mortality or morbidity and (2) the randomization unit was the child
and not the village. This is higher than the rate for the age group
04 in the total DSS population, which may partly be explained by the
lower age of this cohort. In all, 13 out of the 17 deaths are concentrated
in the two villages in the study area (Cissé [7 deaths] and Solimana
[6 deaths]).
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Discussion
Data from this DSS have been collected since 1993, and many efforts have been made to provide as complete and accurate a database as possible. For example, in order to minimize errors in data collection, interviewers use pre-printed database registration forms. Three field supervisors examine the questionnaires in the field to check if the data collected by the interviewer makes sense. Among several steps, the supervisors take a sample of the completed questionnaires and return to the households to verify the information they contain.
However, irrespective of the above efforts, the conditions for data collection in rural parts of developing countries have their limitations. It is not possible to achieve a record of all deaths. The question is whether underreporting of cases or other incomplete recording of events (birth, in- and out-migration) could have had an impact on our results. If, for example, in several villages a constant underreporting of cases had occurred, this would have an immediate effect on our results. Although we cannot rule out the possibility that some infant deaths remained unreported, we do not have evidence of differential underreporting of cases between the villages. The non-existence of clusters with significantly lower total childhood mortality in the study area indicates that there was no systematic underreporting in some villages.
The following characteristics hold in the whole study region and not specifically in some parts of it: (1) the death rates obtained from the DSS are well within the order of magnitude expected, when compared with other DSS results (e.g. Bergane et al.19). (2) The interviewers who visited the villages and collected the information were well trained according to standardized procedures. (3) The information used in this paper was total mortality only, rather than cause-specific mortality. The latter was much more difficult to obtain from the data set with sufficient reliability, as the causes of death were basically obtained by verbal autopsy from the mother, and it is often difficult to decide on a particular cause of death from that information. In several cases, the cause of death is unknown. However, the majority of cases included in this analysis were from infectious diseases (malaria, diarrhoea), often in combination with malnutrition. As an immediate consequence from our findings, a qualitative study is underway with the aim to scrutinize possible causal factors with in-depth interviews.
Using the method for analysing temporal trend described above, we found a significant decrease in childhood mortality over the observation period when omitting the birth year (1992) from the analysis. However, this was a data-driven procedure as the rate for 1993 was found to be considerably lower than in the years after. Therefore, the significant finding of a decreased trend must be considered as a trend rather than a definite finding, and more years of observation are needed before one can conclude that the childhood mortality in the DSS catchment area is decreasing significantly.
In the study by Benzler and Sauerborn2 which used the main components of the DSS in Nouna, several attributes of newborns and households were used as potential predictors of childhood death in a cohort of 1367 newborn children in the study area from 1992 to 1994. The authors found an average mortality rate of 6.8% per year. However, specific patterns of death rates by village have not been reported in their analysis.
In our study we analysed the DSS data as to a possible spatial-temporal pattern of mortality. The result of a very pronounced cluster of higher rates with the centre of the cluster being the village of Cissé is rather alarming. This finding is supported by the results from the randomized controlled trial described above as in this trial excess mortality was again observed in the village of Cissé. Thus, we strongly believe that our finding on clustering of total childhood mortality in the Nouna region is indeed real and not due to systematic bias. We looked at the distribution of exact date of death, in particular in the village of Cissé, to look for seasonal peaks in mortality. We found a surprisingly uniform distribution over the years which does not support the hypothesis that an infectious disease outbreak has occurred causing the excess mortality.
A possible drawback of the analysis using the Kulldorff method is that clusters are defined as circles. This feature has some implications which must be considered in the interpretation of the results: (1) if a village with low mortality is surrounded by villages with high mortality, it is always included in the cluster although some characteristics of this village may be different than the others; and (2) if a clustering of cases is, say, along a river, a circle is not the appropriate form to detect it. The first feature can be observed in the analysis of the controlled trial cohort, where the two villages with high numbers of deaths are surrounded by others in which no deaths or only one death was recorded. By construction of the test statistic, all these were also included in the cluster identified.
This study may be regarded as a first step in prioritizing areas for analytical studies. In general, malnutrition, malaria, diarrhoea, measles, and acute respiratory infections remain the major causes of childhood disease and death in most of rural Africa.20 Childhood mortality is also on the increase in many parts of Africa, partly due to the consequences of the AIDS epidemic and partly due to increasing resistance of malaria parasites to the main first-line therapy drug chloroquine.21 Although little is known about the prevalence of HIV in the Nouna study area, there is little evidence today to suggest that HIV/AIDS contributes much to childhood mortality in rural Kossi province.
Studies in other parts of Africa have documented significant space-time clustering of malaria. For instance, Snow et al.22 report a space-time clustering of severe childhood malaria on the Coast of Kenya with seasonal peaks in incidence of severe malaria comprising discrete mini-epidemics. Similar studies on the microepidemiology of malaria are now underway in the Nouna study area, and the results are likely to help us better understand the observed clustering of mortality in the area. There is some evidence that the cultural pattern of hygiene and health-seeking behaviour contributing to the observed differences in health outcomes (Müller, unpublished results) in the Nouna study area.
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This work was supported by the Deutsche Forschungsgemeinschaft, SFB 544 Control of tropical infectious diseases. The authors would like to thank Dr Martin Kulldorff (National Cancer Institute, Bethesda, MD, USA) for his advice and for providing the SaTScan software, Ralf Würthwein at the University of Heidelberg, and anonymous referees for their helpful comments. We also thank Ms Gabriele Stieglbauer at the University of Heidelberg for her assistance in preparing the data set for the analysis. Last but not least the authors would like to thank the people from the Nouna study area, both the villagers and the field staff, for their indefatigable contribution to our data collection.
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