Centre of Infectious Disease Epidemiology and Research (CIDER), University of Cape Town, Cape Town, South Africa
, Biomedical Research & Training Institute and the University of Zimbabwe College of Health Sciences, Harare, Zimbabwe
Medecins sans Frontieres / Public health department: Water, Hygiene and Sanitation Unit, Amsterdam Operational Centre, Amsterdam, Netherlands
NADAR sprl, Geographic Information Systems, Marchin, Belgium
Medecins sans Frontieres / Medical department: Water, Hygiene and Sanitation Unit, Brussels Operational Centre, Brussel, Belgium
Abstract
Background
In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009.
Methods
We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs.
Results
This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 (95% confidence interval=0.660.76). Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%.
Conclusion
This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic.
Background
On the 20th of August 2008, an outbreak of 118 cases was declared at St. Mary’s and Zenenga wards of Chitungwiza, a large urban centre on the outskirts of Harare
Following this initial outbreak in Chitungwiza, a second wave of infections was reported a few months later with numerous wards being affected and a rapid transmission of the infections to the whole city of Harare. This is one of the largest and most extensive outbreaks of cholera yet recorded in Zimbabwe affecting rural and urban areas
In developing countries, cholera is closely related to poor environmental status and lack of basic infrastructure. In this respect, high population densities and poor access to safe water and proper sanitation, along with other environmental conditions, contribute to the spread of cholera in Africa
In 1852, Farr hypothesized a causal relation between the cholera cases in the London epidemic and the elevation, indicating that there was an underlying “natural law” correlating infection with cholera inversely to elevation above high water. Farr stated that the elevation of the soil in London had a more constant relation with mortality from cholera than any other known element. In the same way, his contemporaneous colleague Snow, by mapping cholera deaths by elevation, found a correlation between higher elevations and reduced risk for cholera death
Since 1852 there is some evidence about the association between cholera and elevation, although there is no empirical data explaining this pattern
Methods
We developed an ecological study using secondary data.
Data were drawn from the registry of cholera treatment centers (CTCs) and oral rehydration points (ORP) functioning during the cholera epidemic in Harare and Chitungwiza. Medecins Sans Frontieres, in collaboration with the Department of City Health of the Ministry of Health & Child Welfare, implemented and managed three CTCs, in the Budiriro Polyclinic, in the Beatrice Road Infectious Diseases Hospital and in the city of Chitungwiza. Ten ORPs were functioning in Harare city and one in Chitungwiza.
Data used in this study were the aggregated number of cholera cases by suburb of residence, topographic average elevation of each suburb in meters, and the distance in meters from suburbs’ geometric centre to Chitungwiza (the epicenter of the epidemic).
Population figures by suburb were calculated from the official census of Harare and Chitungwiza, completed in 2002. To estimate the populations’ figures at the time of the epidemic, we employed an average constant annual growth rate of 3%, as estimated by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat
During the epidemic, a cholera case was defined as any patient presenting 3 or more liquid stools and/or vomiting for the last 24 hours
We described the elevation by suburbs and distance of each suburb to the epicenter (Chitungwiza) in meters. Geographical information was integrated into the GIS database as vector polygons. Using ArcGIS ‘zonal statistics’ module, we measured for each polygon average ground elevation and the distance between each suburb centroid (geometric center of the polygon) to Chitungwiza.
To describe the outbreak, we calculated attack rates for each suburb, including 95% exact confidence intervals (CIs). We assumed that the attack rates follow a Poisson distribution
To describe the relation between the cholera cases and elevation, the Pearson’s correlation coefficient (R) between the cholera rates and the mean elevation of each suburb was calculated. Then, a scatter plot was built to show the relation between the observed rates and the average elevation by suburb.
A generalized linear mixed model (under the assumption of a Poisson distribution) was used to model the relation between the risk of cholera by suburb and the elevation in meters of Harare: The model assumed a random intercept, with the number of cholera cases as the dependent variable, the log of the population figures by suburb as the offset, and the suburb’s average elevation as the independent variable
We use prior knowledge about the dynamic and spread of the epidemic to allow for spatial correlation between neighboring suburbs based on the distance in meters to the epicenter (Chitungwiza)
which corresponds to
The derived risk ratio of the model was interpreted as the cholera risk per 100 meters of increase in the elevation, taking into account overdispersion and spatial correlation of the data.
Finally, the predicted rates of cholera by suburb were derived from the model using an Empirical Bayesian estimation. This Empirical Bayesian approach consists of computing a weighted average between the raw rate for each suburb and the regional average, with weights proportional to the underlying population at risk
A sensitivity analysis was developed to confirm the consistency of our findings, consisting of an iterative random selection of suburbs to build the cluster variable for the estimation of the random effect. The aim of this process was to estimate the variability of the coefficient of the elevation, taking our first estimation as reference and proving its consistency
For the GIS component of the analysis (mean elevation and distance to the epicenter), a recent (2008) high resolution satellite image of Harare was used. In order to assure a good integration with field surveys, the satellite image was geometrically corrected to fit a set of control points surveyed with a GPS. This image was used as a spatial reference for all GIS analysis. Information from the Harare’s topographic map (Produced by the Surveyor General of Zimbabwe), was digitized on top of the satellite image using Arcview software. The elevation information was extracted from the SRTM database (a digital terrain model obtained from the Space Shuttle mission). In this database, ground elevation is provided for each 90x90 meter cell of the model. The extent of each urban district was integrated to the GIS database as vector polygons. Using ArcGIS ‘zonal statistics’ module, average ground elevation and the distance between each suburb centroid (geometric center of the polygon) to Chitungwiza were measured for each polygon.
The statistical software used for this study was Stata v.11.2 and the module ArcMAP of ArcGIS (ESRI, v. 9.2) software for spatial and geographical representation.
Results
During this cholera outbreak, CTCs and ORPs in Harare and Chitungwiza registered and cared for 19,422 persons who met the case definition. The epidemiological description of the outbreak including the number of cases, attack rates and rate ratios by suburb is presented in Table
Suburbs
Average
Elevation
in meters
Distance to
Chitungwiza
in meters
Quintiles of the
distance to Chitungwiza
(Q1Q5)
Cases
Population
Attack rates
per 1,000 people
(95% CI)
Rate ratios
(95% CI)
Hopley
1471
9095
1
541
5994
90.3 (82.898.2)
7.00 (6.437.63)
Waterfalls
1457
12523
1
548
7347
74.6 (68.481.1)
5.79 (5.326.30)
Mbare
1452
16562
2
2138
92219
23.2 (22.224.2)
1.80 (1.721.88)
Budiriro
1415
20484
3
2536
109545
23.2 (22.224.1)
1.80 (1.721.87)
Glen ViewGlen Norah
1420
14731
1
4583
234353
19.6 (19.020.1)
1.52 (1.471.57)
Highfield
1433
15069
1
824
48713
16.9 (15.818.1)
1.31 (1.221.41)
Hatfield
1504
15857
1
355
28959
12.3 (11.013.6)
0.95 (0.861.06)
Chitungwiza
1425
0
0
3710
321782
11.5 (11.112.0)
0.89 (0.860.93)
Mufakose
1419
23106
3
568
52921
10.7 (9.811.6)
0.83 (0.770.91)
Dzivarasekwa
1440
28724
5
435
43302
10.0 (9.111.0)
0.78 (0.710.86)
Tafara
1568
24962
4
712
72737
9.8 (9.010.5)
0.76 (0.700.82)
City Centre
1489
21155
3
123
12609
9.8 (8.111.6)
0.76 (0.630.90)
Kuwadzana
1434
26513
4
759
109137
7.0 (6.47.4)
0.54 (0.500.58)
Epworth
1516
18323
2
824
130267
6.3 (5.96.7)
0.49 (0.460.53)
Kambuzuma
1439
20673
3
183
29796
6.1 (5.27.1)
0.48 (0.410.55)
Mt. Pleasant
1495
30789
5
32
5368
6.0 (4.18.4)
0.47 (0.330.65)
Malbourg
1473
32445
5
47
9600
4.9 (3.56.5)
0.38 (0.290.51)
Rugare
1445
18944
2
61
12718
4.8 (3.76.1)
0.37 (0.290.48)
Milton Park
1471
23612
4
21
4405
4.8 (2.97.3)
0.37 (0.240.57)
Avondale
1491
26243
4
47
10613
4.4 (3.25.8)
0.34 (0.260.46)
Borrowdale
1498
32400
5
63
14746
4.3 (3.25.4)
0.33 (0.260.42)
Eastlea
1495
21810
3
29
6991
4.1 (2.75.9)
0.32 (0.220.46)
Southerton
1450
17951
2
42
10385
4.0 (3.05.5)
0.31 (0.230.42)
Belvedere
1465
21532
3
30
10353
2.9 (1.94.1)
0.22 (0.160.32)
Mabelreign
1484
28900
5
16
6065
2.6 (1.54.2)
0.20 (0.130.33)
Greendale
1538
22662
3
31
14661
2.1 (1.43.0)
0.16 (0.120.23)
Arcadia
1480
17880
2
12
5681
2.1 (1.13.7)
0.16 (0.090.29)
Warren Park
1456
23702
4
100
57341
1.7 (1.42.1)
0.14 (0.110.16)
Highlands
1521
26462
4
12
6545
1.8 (0.93.2)
0.14 (0.080.25)
Hillside
1485
20116
2
8
4806
1.7 (0.73.2)
0.13 (0.060.26)
Tynwald
1460
26891
5
32
27398
1.2 (0.71.6)
0.09 (0.060.13)
All Suburbs



19422
1507359
12.9 (12.713.1)
1
Distribution of cholera cases and average elevation by suburb in Harare, 20082009 (n= 19,422 cholera cases). (1a) Distribution of cholera cases by suburb (1b) Distribution of cholera cases and average elevation by suburb.
Distribution of cholera cases and average elevation by suburb in Harare, 20082009 (n= 19,422 cholera cases). (1a) Distribution of cholera cases by suburb (1b) Distribution of cholera cases and average elevation by suburb.
This pattern of the spatial distribution of cholera cases correlates positively with the average elevation of Harare suburbs. The computed Pearson’s correlation coefficient between the cholera observed rates and the mean elevation by suburb shows a significant negative relationship, which means that at higher elevation, the number of cholera cases was lower (
Figure
Observed rates and predicted cholera rates based on the generalized linear mixed model by elevation and suburb in Harare, 20082009 (n= 19,422 cholera cases). (2a) Observed cholera rates (2b) Predicted cholera rates based on the generalized linear mixed model. Q1Q5 represent the quintiles relating to the distance to Chitungwiza and the random intercept in the model.
Observed rates and predicted cholera rates based on the generalized linear mixed model by elevation and suburb in Harare, 20082009 (n= 19,422 cholera cases). (2a) Observed cholera rates (2b) Predicted cholera rates based on the generalized linear mixed model. Q1Q5 represent the quintiles relating to the distance to Chitungwiza and the random intercept in the model.
Taking into account the overdispersion and the spatial correlation, the modeled relationship between the risk of cholera and the average elevation shows the same pattern (lower elevation, higher risk), with higher rate ratios for the suburbs that are located nearest to the cholera epicenter (Chitungwiza, Figure
Discussion
This study shows a spatial pattern of the distribution of cholera cases during the epidemic in Harare. This pattern is characterized by an increase of cholera risk for each 100 meters of drop in elevation; lowest suburbs had the highest cholera risk.
During the cholera epidemic, Zimbabwe was in economic crisis: the health care systems had become dysfunctional, water supplies were irregular and sanitation systems had collapsed. The reason for this was a lack of maintenance of the system, with frequent power interruptions affecting pumping stations
A similar pattern could have happened in Haiti where the epicenter of the epidemic had been located in the Artibonite area, which is a plain where rivers and surface water have high risk of contamination compared with mountains, situation that reinforces the external validity of our finding
The diversity of regions affected by cholera epidemics during the last twenty years in Africa and the multiplicity of risk factors need to be highlighted; however, lack of water and sanitation facilities are the most common factors involved in the origin of an epidemic
Our finding could be affected by the fact that data are based on estimated suburb population, and we recognize that the estimates may not reflect the real figures during the epidemic. However, we tried to take into account the natural average growth of the population to work with most realistic population estimates possible. The internal validity could be affected for data overdispersion and spatial correlation; however we used a mixed model with a random intercept based on a neighborhood variable which is defined via the distance to the epicenter. In introducing a random intercept, suburbs within a cluster are allowed to be dependent. Finally, to confirm whether the choice of our neighborhood cluster for the mixed effect was stable, a sensitivity analysis was developed fitting the model with many other choices of clusters specifying the dependency of suburbs (random choice, choice based on visual aspects and also a second hierarchy). Even though this methodological approach is not the classic way to account for spatial correlation, the standard errors of our estimations allow for suburbs correlation within each of the five groups (spatial correlation), relaxing the usual requirement that the observations are independent.
Our study focuses only on topographic elevation, but we have to highlight that there were other historical, geographical, social, and environmental risk factors previously published, that explained the spatial pattern of the Harare epidemic. Specifically, the fact that Tafara, a high elevated suburb, had high cholera rates, could be explained by the dynamics of population movement because there is a big concentration of bus stops and informal markets offering service to the travelers moving to Mozambique. Bus stops and informal markets, residential housing, water supplies and sewage disposal system were previously identified as risk factors related with the cholera epidemic in Harare, 20082009
Despite that fact, and to our knowledge, this study could be the first attempt to model the relationship between cholera risk and elevation. Furthermore, the sensitivity analysis developed reinforces the validity of our final model confirming the inverse relationship between cholera risk and elevation.
Conclusion
In conclusion, it has been shown that the distribution of cholera cases by suburb in Harare during the outbreak of 20082009 followed an identifiable spatial pattern characterized by a lower cholera risk when the average elevation in meters by suburb increased by 100 meters. During the rainy season, in high populated urban areas, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns. This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Further studies are needed to explore the link between elevation, water supplies and sewage disposal systems. The identification of this spatial pattern could guide public health action to orientate further preparedness interventions in Harare, aiming to prevent a new cholera epidemic.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
MALF and PM developed the concept and design of the study; JF, YB, MALF and PM acquired the data; MALF and MS developed analyses; MALF carried out the analyses, and all authors interpreted the data; MALF and MS wrote the manuscript. All authors drafted the manuscript, revised critically the content, and gave technical support and conceptual advice. Finally, all authors read and approved the final manuscript. MALF is the guarantor of the paper.
Acknowledgements
We would like to thank Ariane Bauernfeind, Henry Gray and Dionioso Herrera Guibert for their collaboration and support.
Prepublication history
The prepublication history for this paper can be accessed here: