sjtg_367 265..282
Urban structure and dengue incidence in
Puntarenas, Costa Rica
Adriana Troyo,
1,2
Douglas O. Fuller,
3
Olger Calderón-Arguedas,
2
Mayra E. Solano
2
and John C. Beier
1,4
1
Global Public Health Program, Department of Epidemiology and Public Health, University of Miami,
Florida, USA
2
Centro de Investigación en Enfermedades Tropicales, Departamento de Parasitología, Facultad de
Microbiología, Universidad de Costa Rica, San José, Costa Rica
3
Department of Geography and Regional Studies, University of Miami, Coral Gables, Florida, USA
4
Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, Florida, USA
Correspondence: Adriana Troyo (email: adriana.troyo@ucr.ac.cr)
Dengue is currently the most important arboviral disease globally and is usually associated with
built environments in tropical areas. Remotely sensed information can facilitate the study of urban
mosquito-borne diseases by providing multiple temporal and spatial resolutions appropriate to
investigate urban structure and ecological characteristics associated with infectious disease. In this
study, coarse, medium and fine resolution satellite imagery (Moderate Resolution Imaging Spec-
trometer, Advanced Spaceborne Thermal Emission and Reflection Radiometer and QuickBird
respectively) and ground-based data were analyzed for the Greater Puntarenas area, Costa Rica for
the years 2002–04. The results showed that the mean normalized difference vegetation index
(NDVI) was generally higher in the localities with lower incidence of dengue fever during 2002,
although the correlation was statistically significant only in the dry season (r =-0.40; p = 0.03).
Dengue incidence was inversely correlated to built area and directly correlated with tree cover
(r = 0.75, p = 0.01). Overall, the significant correlations between dengue incidence and urban
structural variables (tree cover and building density) suggest that properties of urban structure may
be associated with dengue incidence in tropical urban settings.
Keywords: Costa Rica, dengue, normalized difference vegetation index (NDVI), QuickBird,
remote sensing, urban environment
Dengue is the most important arboviral disease in terms of worldwide morbidity and
mortality with an estimated 50 to 100 million cases and 12 000 to 24 000 deaths per
year (WHO, 2002; Gibbons & Vaughn, 2002). The principal mosquito vector, Aedes
aegypti, lives in close association with humans mostly in urban and suburban environ-
ments where larvae commonly develop in water-filled artificial containers such as
drums, buckets, tyres and flower pots (Focks & Chadee, 1997; Gubler, 1998; Calderon-
Arguedas et al., 2004). The recent dissemination of dengue viruses and Ae. aegypti
throughout the tropics has been influenced by such factors as increasing global trade,
migration and travel, population growth and uncontrolled or unplanned urbanization
(Kuno, 1995).
Dengue
1
incidence in Costa Rica is one of the highest in Central America, with over
45 000 reported cases from 2006 to 2008 (close to 26 000 cases in 2007 alone) (PAHO,
n.d.). In comparison, next in ranking over the same period, Honduras and El Salvador
reported slightly over 30 000 and 26 000 cases respectively. Whereas the incidence rate
per 100 000 population for 2007 in Costa Rica was 815, it was 445 in Honduras and 195
in El Salvador (PAHO, n.d.). Although Ae. aegypti and dengue were eradicated from the
country in 1960, the vector recolonized in 1993 and dengue cases were reported soon
after (WHO, 1994). From 1993 to 2008 more than 180 000 cases have been reported
doi:10.1111/j.1467-9493.2009.00367.x
Singapore Journal of Tropical Geography 30 (2009) 265–282
© 2009 The Authors
Journal compilation © 2009 Department of Geography, National University of Singapore and Blackwell Publishing Asia Pty Ltd
(Ministerio de Salud et al., 2004), including almost 38 000 cases in 2005 (Ministerio de
Salud, n.d.). Dengue currently represents the most important vector-borne disease in
Costa Rica.
Remotely sensed data, together with geographical information systems (GIS), have
been used to study vector-borne diseases, mostly in ex-urban settings (Hay et al., 1997;
Bergquist, 2001; Correia et al., 2004). The study of vector-borne diseases in urban
environments poses particular challenges owing to urban spatial heterogeneity and
structural complexity, complex movement of hosts and vectors, and anthropogenic
creation of vector habitats. The launch of commercial imaging satellites such as IKONOS
and QuickBird offers new opportunities to assess urban habitats for disease vectors by
providing very high spatial resolutions (1 m and 0.62 m respectively) for identification
of city blocks, individual roads, trees, roadways, buildings and rooftops (Jensen &
Cowen, 1999). While such imagery produces a fine-scale representation of urban
environments, near-nadir observations are relatively infrequent compared to other
orbital sensors that possess coarse spatial resolution such as the Advanced Very High
Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectrometer
(MODIS). These latter instruments enable the study of seasonal factors, including
humidity, vegetation greenness and temperature, that control physiological variables
related to vector and pathogen phenology (Goetz et al., 2000; Tatem et al., 2004; Hay
et al., 2006).
Few studies have used satellite imagery to investigate environmental factors asso-
ciated with dengue fever. Recent studies involving remote sensing for dengue surveil-
lance have employed coarse spatial resolution data from AVHRR (Peterson et al., 2005;
Rogers et al., 2006; Kolivras, 2006), as well as medium resolution imagery and land
use/cover maps derived from Landsat Enhanced Thematic Mapper+ (ETM+;30m
spatial resolution) (Nakhapakorn & Tripathy, 2005; van Benthem et al., 2005) and SPOT
(20 m spatial resolution) (Tran & Raffy, 2005). Using Landsat ETM+, spatial determi-
nants of dengue infection were studied in specific rural and peri-urban areas (van
Benthem et al., 2005). For other mosquito-borne diseases such as malaria, data obtained
from very high resolution multispectral bands have been used to study disease risk
(Sithiprasasna et al., 2005) and anopheline larval habitats (Mushinzimana et al., 2006;
Jacob et al., 2006). However, studies that have used satellite imagery with very high
spatial resolution (<5 m) in multispectral bands to assess Ae. aegypti habitats within
urban areas appear to be lacking.
In this study, relevant spatial and seasonal determinants of dengue incidence were
investigated for the Greater Puntarenas area, Costa Rica, for the years 2002–04. The
approach involved a series of exploratory data analyses of static urban structural fea-
tures (houses and other buildings, roads, parks, and so on) as well as dynamically
changing variables (such as greenness and rainfall) derived through remote sensing
and ground data. The choice of variables was informed by a number of factors includ-
ing the likelihood of obtaining acceptable classification accuracies for relevant urban
objects such as trees, houses and paved surfaces, as well literature on modelling and
epidemiological analyses that incorporated static and dynamic spatial variables to
explain spatial patterns of dengue incidence and spread (Nakhapakorn & Tripathy,
2005; Tran & Raffy, 2005; Kolivras, 2006). Thus, the purpose of our study was three-
fold. First, to obtain basic spatial information on the urban environment of Greater
Puntarenas using satellite and ground-based data. Second, to correlate this informa-
tion to epidemiological data gathered by local public health authorities. And third, to
explore relationships between specific urban structural metrics and disease parameters
266 Adriana Troyo et al.
to further our understanding of urban features that may favour the spread and per-
sistence of dengue fever in the tropics.
Materials and methods
Study site background
Our study focused on the Greater Puntarenas area of Puntarenas Province, Costa Rica,
which is a term used to refer to the area comprising the districts of Puntarenas,
Chacarita, El Roble and Barranca (Figure 1). Populated areas within districts are mapped
as localities (localidades), the smallest unit within the health system, which in Costa
Rica is jointly managed by the Ministry of Health and Social Security Bureau. The
provincial capital is centred in Puntarenas City, an urban area encompassing a narrow
peninsula (Puntarenas District) and nearby mainland areas (Chacarita District) on the
Pacific coast. Within approximately 20 km
2
, census data from 2000 indicate a popula-
tion close to 100 000 people with approximately 20 000 houses (INEC, 2002). Because
the main government offices, commercial buildings and amenities are located here,
much of the population of the Greater Puntarenas study area and even the province is
concentrated in Puntarenas City. Most of the area is classified as urban (>95 per cent),
although habitations vary greatly in size density and construction quality (INEC, 2002).
The main economic activities are related to the port, tourism, fishing and commerce
(Impoinvil et al., 2007). Although much of the population in Puntarenas City are settled
communities, there is a small percentage of migrants constantly moving to and from
other parts of Costa Rica and also neighbouring Nicaragua.
Puntarenas City is the site of dengue reintroduction to Costa Rica in 1993 (WHO,
1994) and the disease has been endemic in the area since. From 2002 to 2005, more
than 7000 cases of dengue were reported for the Greater Puntarenas area (Ministerio de
Salud, n.d.); most cases reported after 2000 have been caused by the DEN-1 serotype.
Compared with other health regions of the country, the Central Pacific Region of the
Ministry of Health, which Greater Puntarenas falls under, registers some of the higher
social burdens: the poverty level is 26.4 per cent and the unemployment rate of 6.8 per
cent is the second highest in the country. The climate is moist tropical: mean minimum
N
Gulf of Nicoya
Barranca
El Roble
Chacarita
Puntarenas
N
0
50 100 150 km
NICARAGUA
PANAMA
Caribbean
Sea
North Pacific Ocean
Greater
Puntarenas
Puntarenas
Province
COSTA RICA
SAN JOSE
Downtown Puntarenas City centre
Boundary of districts
Boundary of health localitie s
Legend
Puntarenas City and the 10 localities
included in the QuickBird imagery
Other 20 health localities in Greater
Puntarenas
0
123
4km
Figure 1. Map showing the location of the 30 health localities – 10 in Puntarenas City included in the
QuickBird imagery and analyses, the remaining 20 included in ASTER and MODIS images and analyses – in
the Greater Puntarenas study area, Puntarenas Province, Costa Rica.
Urban structure and dengue in Costa Rica 267
and maximum daily temperatures are 22°C and 32°C respectively, with a marked wet
season (May to mid-November) and a dry season (mid-November to April). Combined
with the tropical environmental characteristics, relatively populous Puntarenas City is a
potentially high-risk area for vector-borne diseases.
Population data and dengue case reports
The weekly number of dengue cases reported in 2002, 2003 and 2004, and data on the
number of households, estimated population and line drawings of the geographical
boundaries for each of the 30 localities in Greater Puntarenas were obtained from the
Central Pacific Region Directorate of the Ministry of Health located in downtown
Puntarenas. Each locality has at least one small clinic and/or basic health team that
collects weekly surveillance and case data (as diagnosed by qualified physicians) and
reports this to central clinics (that come under the Social Security Bureau), which then
group the data and send it to the Regional Directorate of the Ministry of Health. House
density (houses per km
2
) was determined for each locality. Dengue incidence data per
100 population was calculated as:
total number of cases reported in a year or season total population
()
[[]
∗100
The total population was assumed to be constant during all three years. To assess local
climate and weather conditions during those same years, daily observations on rainfall,
maximum, minimum and mean temperatures for Puntarenas City were obtained from
the headquarters of the National Meteorological Institute in San Jose.
Information derived from satellite imagery
Seasonality and vegetation greenness was evaluated at a monthly time scale during
2002 using the enhanced vegetation index (EVI) obtained from MODIS (500 m spatial
resolution). The EVI provides greater sensitivity to changes in vegetation greenness than
other widely used vegetation indices (VIs) as it reduces atmospheric and background
effects that may introduce significant errors in VI time series. EVI is given as:
EVI NIR red NIR red blue=−
[]
+× −× +
[]
GCCL
12
where NIR (near infrared), red and blue correspond to the surface reflectance for the
respective band, L = 1 and is the canopy background adjustment, C
1
= 6 and C
2
= 7.5 and
are coefficients of the aerosol resistance term, and G = 2.5 and is the gain factor (Huete
et al., 2002).
Multitemporal EVI data were obtained from a series of co-registered image tiles
downloaded from the US Geological Survey EROS Data Center server (http://
edcimswww.cr.usgs.gov/pub/imswelcome/). In addition, four cloud-free scenes from
the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (15 m
spatial resolution) acquired during the dry and wet seasons respectively of 2002 were
obtained, and the individual multispectral bands were georeferenced using a Landsat
image of 30 m spatial resolution obtained from the Global Landcover Facility (http://
glcf.umiacs.umd.edu/index.shtml). The normalized difference vegetation index (NDVI)
was calculated from the ASTER multispectral bands (NDVI = [NIR - red]/[NIR + red]),
and the mean NDVI was extracted for each of the 30 localities using Idrisi Kilimanjaro
software (Eastman, 2004).
There were no vector layers available for the localities in Greater Puntarenas pre-
vious to this study. Therefore, polygon topology was built using CartaLinx software
268 Adriana Troyo et al.
(ClarkLabs, 1999) by manually digitizing each of the 30 health localities from the
boundaries provided by the Ministry of Health’s Regional Directorate and both ASTER
and QuickBird imagery (Figure 1). The resulting layer provided the areas for each
locality as well as the polygons required to extract data from the satellite imagery and
derived maps. All image processing and GIS operations, unless otherwise stated, were
performed using Idrisi Kilimanjaro (Eastman, 2004).
Two cloud-free QuickBird scenes available for March 2002 and October 2003 were
mosaicked to produce one single high-resolution image (2.4 m and 0.6 m spatial reso-
lution in the multispectral and panchromatic bands respectively), which included all
the 10 localities of Puntarenas City. There were no single scenes available from very
high-resolution sensors that included the total area and had acceptable image quality.
Most of the localities are limited by natural barriers including open water and man-
groves, so changes in urbanization during 2002–03 are assumed to be minimal. The
scenes obtained were individually georeferenced to increase their spatial accuracy (RMS
= 2.9 m and 3.1 m for the 2002 and 2003 scenes, respectively) by using 38 ground
control points obtained with a hand held global positioning system (GPS; Garmin
GPSmap 76S). Accuracy of each ground control point was improved further by taking
the mean of three GPS readings acquired at least 5 hours apart and at a series of road
intersections readily visible in the QuickBird panchromatic and multispectral bands.
Semiautomated land cover maps were produced from the QuickBird scenes by
applying supervised image classifiers to each QuickBird image, and the resultant clas-
sified images were mosaicked. Classification algorithms included the maximum likeli-
hood (MLC) and backpropagation artificial neural network (BPANN) implemented in
Idrisi Kilimanjaro software (Eastman, 2004). GIS operators were applied to the final
classified products to extract data at the locality level. The panchromatic image provided
a set of mutually exclusive training and validation points for the automated image
classifiers. Once the land use/cover maps were obtained, accuracy was assessed by visual
interpretation of points selected at random from the original panchromatic QuickBird
scene. The proportion of built area and tree cover was extracted for the 10 localities of
the Greater Puntarenas area included in the QuickBird imagery and FRAGSTATS soft-
ware (McGarigal et al., 2002) was used to extract several spatial metrics of the classified
built and tree cover areas. Specific metrics were selected to assess the spatial dispersion
and clustering of various urban features: total class area, number of patches, patch
density, percentage of landscape, percentage of like adjacencies, patch cohesion index,
clumpiness index and connectance index.
The QuickBird images were also classified using the object-oriented classification of
eCognition software (Baatz et al., 2004), which generally improves the classification of
image objects in built environments relative to pixel-based classifiers (Tarantino, 2004;
Carleer & Wolff, 2006). Segmentation for each image was performed for level 1 at scale
parameter = 20, shape factor = 0.3 and compactness = 0.7; a level 2 segmentation was
performed at the same scale parameter but using the spectral difference mode. The level
1 scale parameter determined the size of the objects (corresponds to the maximum
allowed heterogeneity) (Baatz et al., 2004), while the relatively low shape and high
compactness factors (scaled 0–1) favoured segmentation of the many small and diverse
structures in this urban setting. The level 2 segmentation using spectral difference
merged contiguous objects that differed in less than the specified scale parameter (Baatz
et al., 2004), allowing for the formation of larger objects but maintaining the smaller
ones if the spectral difference was large. Samples for different objects were selected from
the level 2 segmentation of the scenes for a hierarchical classification scheme and the
Urban structure and dengue in Costa Rica 269
