A spatial model of socioeconomic and

environmental determinants of dengue fever in Cali,

Colombia

Abstract. Dengue fever has gradually re-emerged across the global South, particularly affecting

urban areas of the tropics and sub-tropics. The dynamics of dengue fever transmission are

sensitive to changes in environmental conditions, as well as local demographic and

socioeconomic factors. In 2010, the municipality of Cali, Colombia, experienced one of its worst

outbreaks, however the outbreak was not spatially homogeneous across the city. In this paper, we

evaluate the role of socioeconomic and environmental factors associated with this outbreak at the

neighborhood level, using a Geographically Weighted Regression model. Key socioeconomic

factors include population density and socioeconomic stratum, whereas environmental factors

are proximity to both tire shops and plant nurseries and the presence of a sewage system

(R2=0.64). The strength of the association between these factors and the incidence of dengue

fever is spatially heterogeneous at the neighborhood level. The findings provide evidence to

support public health strategies in allocating resources locally, which will enable a better

detection of high risk areas, a reduction of the risk of infection and to strengthen the resilience of

the population.

Keywords: Colombia; Dengue Fever; Environmental and Socioeconomic Determinants; GIS;

Geographically Weighted Regression (GWR).

1. Introduction

Dengue fever is a fast emerging mosquito-borne viral disease of high public health relevance

which is prevalent in many urban and semi-urban environments in the tropics and sub-tropics. It

is transmitted among humans by female mosquitos of the genus Aedes (i.e. Ae. aegypti and Ae.

albopictus) (Messina et al. 2015). The spatial distribution and impact of the disease is determined

by a complex interplay of environmental and socioeconomic factors. Changes in temperature,

precipitation patterns and humidity affect the distribution of these diseases through a number of

effects on mosquito habitats, the vector, vector activity and pathogen development within the

vector (Rogers and Randolph 2006; Semenza et al. 2012). Socioeconomic (e.g. education,

knowledge, behavior, occupation, income, livelihoods, etc.), biological (e.g. age, acquired

immunity, health status) and institutional factors (e.g. access to health care, quality of care,

control and prevention strategies, etc.) contribute to the vulnerability of the population and are

therefore fundamental drivers of disease risk (Bates et al. 2004a; Bates et al. 2004b; Hagenlocher

et al. 2013). Finally, the rapid, uncontrolled expansion of urban environments and increasing

international mobility/air travel has exacerbated the potential of its spread (Gardner and Sarkar

2013; Nunes et al. 2014; Alirol et al. 2011). To date, approximately 4 billion individuals are at

risk of contracting dengue fever, resulting in nearly 400 million worldwide infections per year

(Bhatt et al. 2013; Messina et al. 2015).

In the Americas, despite efforts to eradicate the vector during the 1950s and 1960s (OPS

1960), dengue fever has reemerged as a result of a reduction in surveillance and control

strategies (Dick et al. 2012; Wilson and Chen 2002; Torres and Castro 2007; Guzman and Kouri

2003; Istúriz, Gubler, and Castillo 2000; San Martín et al. 2010). Between the years 2001 and

2007 alone, nearly 4.5 million cases were reported in 30 countries. Examples reported in the

literature include Peru (Chowell et al. 2008), Colombia (Restrepo, Baker, and Clements 2014),

Brazil (Rodriguez-Barraquer et al. 2011; Teixeira et al. 2009; Nogueira, Araújo, and Schatzmayr

2007) and northern Argentina (Vezzani and Carbajo 2008).

Several studies have been conducted in urban environments across the Americas, where

population growth and unplanned urbanization have accelerated the prevalence of dengue fever.

Considerable research efforts have focused on (1) mapping spatial and spatiotemporal disease

patterns and associated risks (Castillo et al. 2011; Delmelle et al. 2013; Getis et al. 2003; Tran et

al. 2004; Rotela et al. 2007; Stoddard et al. 2013; Teixeira et al. 2002; Chiaravalloti-Neto et al.

2015; Siqueira-Junior et al. 2008; Vazquez-Prokopec et al. 2010) and –but to a lesser extent, on

(2) estimating vulnerability to the disease by evaluating the role of socioeconomic and

environmental determinants (Braga et al. 2010; de Mattos Almeida et al. 2007; Hagenlocher et

al. 2013; Kienberger et al. 2013; Honorio et al. 2009).

In Colombia, several measures were adopted to eradicate the presence of the mosquito in the

1950s and 1960s, by means of fumigation and the elimination of mosquito foci (Dick et al.

2012). Although these efforts were tangible for a while, a re-infestation of the Aedes Aegypti

occurred during the 1970s and beyond due to the reduction in surveillance and control program

support (Romero-Vivas, Leake, and Falconar 1998). Since the 1970s, DF has become endemic in

many areas of Colombia and adjacent countries (Messina et al. 2014). Dengue fever is now

considered endemic in certain areas of the country with periodic outbreaks including 1991, 1994,

1998, 2001, 2006, 2010, and 2013 (Restrepo, Baker, and Clements 2014; Ocampo et al. 2014;

Cali 2010; Villegas, Aristizabal, and Rojas 2010). The disease has exhibited an endemo-

epidemic pattern repeating every 2 to 3 years. While the temporal pattern is influenced by the El

Niño oscillation (Eastin et al. 2014), we hypothesize that the variation of rates across the city is

largely driven by environmental and socioeconomic factors.

In this paper, we evaluate the role of socioeconomic and environmental determinants

associated with rates of dengue fever during the 2010 outbreak in the city of Cali, Colombia. We

hypothesize that this association may not be spatially homogeneous (i.e. the dengue fever rate

estimated at one neighborhood is likely to exhibit similarities to rates at nearby neighborhoods)

and that a Geographically Weighted Regression (GWR), which explicitly incorporates the notion

of spatial autocorrelation, better captures this heterogeneity. Our paper is part of a larger,

collaborative effort to (1) map the dynamics of dengue fever in Cali (Kienberger et al. 2013;

Delmelle, Zhu, et al. 2014; Delmelle et al. 2013; Delmelle, Dony, et al. 2014), (2) estimating

vulnerabilities to dengue fever (Hagenlocher et al. 2013) and to (3) strengthen our understanding

of the role played by both environmental and socioeconomic factors (Eastin et al. 2014). The

findings from our work are critical for the planning of preventive measures and inform future

risk assessments.

2 Materials and Methods

2.1 Study area

The city of Cali in Colombia is the third largest metropolitan area in the country with an

estimated 2010 population of 2.3 million (average population density over 4,000/km2). The city

is administratively organized into 22 communes, which correspond to neighborhood groupings (a

total of 340 neighborhoods) with homogenous socioeconomic stratum. Neighborhoods are the

finest scale at which the census reports its socioeconomic and demographic information. Cali is

characterized by a tropical climate which favors annual transmission of dengue (Eastin et al.

2014). In recent decades, the city has experienced unorganized migrations of individuals

displaced by the Fuerzas Armadas Revolucionarias de Colombia (FARC). These populations

have settled in high-density, low income population neighborhoods at the periphery of the city.

These neighborhoods have suffered from unplanned urbanization, including squatter settlements

and contain limited sewer infrastructure with several open-air waste water channels.

2.2 Data pre-processing

We rely on three distinct sources of geospatial information, namely (1) individual dengue

fever cases, (2) socioeconomic and demographic datasets from the 2005 national Colombian

census (provided by the planning department of the city of Cali), and (3) 2010 environmental

variables from the city of Cali.

The epidemiological dataset used in this paper was provided by the Health Secretariat of

the Municipality of the city of Cali, based on the Colombian Public Health Surveillance System

(SIVIGILA). A total of 11,760 cases were reported in the year 2010 at local health facilities. A

case is defined as a patient identified by a doctor at a health facility with dengue fever symptoms

and confirmed via lab work. The doctor files a form which is logged into the country’s

epidemiological surveillance system (SIVIGILA). Using the residential address of each patient,

we geocoded 9,404 cases which were then aggregated at the neighborhood level due the

difference in scale between the case data and the socioeconomic data (data is reported at the

individual level while socioeconomic data are reported at neighborhood level). For each

neighborhood, rates of dengue fever were estimated by dividing the aggregated counts by the

total population (range: 0 to 4.86 per 100 individuals). Neighborhoods representing parks and

military installations were removed, resulting in a revised sample of 9,287 dengue cases across

323 neighborhoods.

Several authors have isolated the role played by socioeconomic and environmental

determinants on the variation of dengue fever in urban areas. Although conclusions may vary by

locations, there exists a certain level of consensus. Greater population density (Hsueh, Lee, and

Beltz 2012; Khormi and Kumar 2011; de Mattos Almeida et al. 2007), higher levels of

urbanization (Wu et al. 2009), larger proportion of elderly individuals (Wu et al. 2009; Siqueira-

Junior et al. 2008), lower level of educational attainment and income (Siqueira-Junior et al.

2008; Koyadun, Butraporn, and Kittayapong 2012; de Mattos Almeida et al. 2007), proximity of

canals and ditches (Chiu et al. 2014) and poor household conditions (Khormi and Kumar 2011)

have been associated with higher rates of dengue fever. In this paper, we use a set of 36

environmental, socioeconomic and demographic variables motivated by the aforementioned

studies and also based on discussion with local experts (Hagenlocher et al. 2013). These

variables, which are all standardized between zero and one, are organized according to a risk and

social vulnerability framework (Table 1), where “hazard” variables are those factors influencing

the spread and distribution of the pathogen and disease transmitting vector (here: the mosquito),

and “social vulnerability” refers to the pre-disposition of the population to be negatively affected

by the disease as a result of their susceptibility and their lack of resilience, which comprises the

lacking capacity to anticipate infection, to cope with infection or to recover from infection

(Hagenlocher et al. 2013; Hagenlocher and Castro 2015).

To estimate the potential exposure of individuals in each neighborhood to the presence of

mosquitos in parks and parkways, rivers, tire shops, plant nurseries, water pumps and cemeteries

we used a kernel density estimation (KDE) approach, reflecting the density (i.e. probability

surface) of those features across the study area. A total of 9 cemeteries, 49 plant nurseries, 23

water pumping stations and 499 tire shops were geocoded, while 4 rivers (Cali, Aguacatal,

Cauca and Jarillon) were hand digitized; all served as an input for the KDE technique. To

compute proximity to rivers and green areas such as parks and parkways (linear and polygon

features), we converted those features to gridded points and used them as an input for the KDE

procedure. Zonal statistics at the neighborhood level were then implemented in a GIS to

summarize the average KDE value of the “hazard” variable for each of these neighborhoods.

<Table 1 around here>

2.3 Spatial autocorrelation

We use the global Moran’s I statistic (Moran 1948) to test the hypothesis that dengue fever

rates are not stationary across the study area. Its local version - the Local Moran’s I statistic

(Anselin 1995), maps local clusters of unusually high rates. The statistic evaluates the difference

in rate from one neighborhood i to its surrounding neighborhoods j. Positive [negative] values of

the statistic are indicative of a homogeneous cluster of high [low] rates. Both global and local

Moran’s I statistic are estimated in Geoda (Anselin, Syabri, and Kho 2006).

2.4 Geographically Weighted Regression

Global regression models such as the Ordinary Least Squares (OLS) capture the average

strength and significance of the predictor variables, but makes the assumption that rates at a

neighborhood i are independent of rates at neighboring units j and that the residuals are normally

distributed. As such, OLS does not consider small-scale spatial variation and spatial

autocorrelation in the residuals are indicative that those assumptions are violated (Anselin 2002).

There exist several techniques to incorporate spatial effects, such as the spatial regression (see

for instance Anselin 2009 for a summary) or the Geographically Weighted Regression (GWR)

technique, developed by Fotheringham, Brunsdon, and Charlton (2003). Unlike OLS or spatial

regression that provide global estimates, GWR is a local regression technique that is used to

measure how the strength of the relationships among the dependent and explanatory variables

differ from location to location. In essence, for each location, GWR pools data from adjacent

neighborhoods to conduct a local regression, resulting in an estimate of local regression

coefficients for each of the predictor variables. Using dengue fever rate as the dependent

variable, the GWR model can be expressed as follows:

�̂�(𝑖) = b0(i) + ∑ bk(𝑖)Xk(𝑖)Nk=1 + 𝜀(𝑖) (1)

with �̂�(𝑖) the predicted dengue fever rate at neighborhood i, 𝑏0(𝑖) the local intercept, bk(𝑖) the

coefficient of the kth variable at location i (k=1…N) and 𝜀(𝑖) the error term. GWR creates an

equation for every neighborhood, and its coefficients are calibrated using information from

nearby neighborhoods j. The importance of neighborhood j is weighted by the distance

separating neighborhood i and j. The ideal number of neighbors is determined by minimizing the

Akaike Information Criterion (AIC). Because each neighborhood has its own regression

equation, coefficients are allowed to vary across the study area. GWR results in a map of

coefficients, allowing to asses where each factor is a strong predictor of dengue fever, which is

particularly informative for public health prevention. Positive [negative] variable coefficients

(𝑏𝑘 < 0) suggest that an increase [decrease] in the amount of variable k, increases [decreases]

rates of dengue fever. Local regression coefficients are estimated in GWR 4.0 and visualized in

ArcGIS (ESRI, Redlands, USA). The GWR also results in a local coefficient of determination

R2, mapping the local strength of the model.

We use an OLS forward stepwise approach to select the variables entering the regression

model. Following this approach, variables are added sequentially until the coefficient of

determination, R2 does not increase significantly anymore. Significant variables are used in the

GWR model. We compare regression results to the ones obtained using an OLS approach.

3. Results

3.1Dengue fever rates

Table 2 and Figure 1(b) illustrates the rates of dengue fever per 100 individuals in the city

of Cali, suggesting substantial variation and the presence of a strong spatial autocorrelation

(Moran’s I = 0.34). Lower rates are observed in the eastern and northeastern region of the city

(numbers 4 and 5), while maximum rates of 4.86 cases per 100 individuals are observed both in

the center of town and the city’ southernmost neighborhoods.

<Figure 1 around here>

Following a local Moran’s I approach, clusters of higher rates are identified including 19

neighborhoods, which are grouped into homogeneous clusters numbered 1 through 3. Located in

the old core of the city and characterized by its strong economic activity, cluster 1 exhibits rates

ranging from 1.2 to 4.2 cases per 100 individuals. Although not as densely populated, individuals

are more at risk due to the presence of transient individuals. Cluster 2 (min: 1.3, max: 2.5 cases

per 100 individuals) is located on a military base area (Ejercito Tercera División) containing

several military housing complexes. Control processes for spraying of the mosquito are not the

same as in the rest of the city. Cluster 3 (min: 0.7, max: 4.86 cases per 100 individuals), which is

located in the southern edge of the city, is experiencing less housing development, but there is a

risk that transient population may import the virus into the area.

A total of 48 neighborhoods are characterized by lower rates and those are highlighted in

red in Figure 1. The population in region 4 is characterized by a low income status while a lack

of supply of health facilities reduces proper and timely access to care. The combination of these

two factors may contribute to underreporting. Low rates in region 5 were also attributed to

intensified spraying campaigns conducted by the Health Municipality1. In comparison to region

4, region 5 is safer in regards to criminal activity and as a result, control strategies can be

conducted more frequently.

3.2 Geographically Weighted Regression

Following an OLS stepwise regression, we identified six contributing socioeconomic and

environmental predictors (or risk factors) based on the 36 geospatial variables that significantly

contributed to the 2010 dengue fever outbreak (R2=0.295). These are proximity to tire shops,

proximity to plant nurseries, proportion of households with sewage system, proportion of

indigenous population, socioeconomic stratum2 and population density. We analyzed

collinearity metrics among the six variables (all variance inflation factor values were less than

1.632), suggesting no collinearity among the selected variables. Among those 6 variables, the

strongest correlation was between relative population density and socioeconomic stratum (r=-

0.541, p<0.01) and between relative population density and relative proportion of households

with sewage (r=-.292, p<0.01); individuals residing in neighborhood of higher socio-economic

stratum were more likely to have a sewage system and those neighborhoods also had lower

population density. We modified our OLS model to include interaction terms (population

density* socio-economic stratum) and (population density * proportion of households with

sewage system), however this did not improve our OLS model fit (in terms of R2: 0.201 and

0.182, respectively) from 0.295 (with the 6 variables mentioned earlier).

The spatial distribution of the six final variables that were used in the GWR is mapped in

Figure 2, while Table 2 summarizes the range of the regression coefficients for each predictor

variables.

<Figure 2 and Table 2 around here>

Our GWR model explains 64% of the variance among dengue fever rates (29.5% in a

standard OLS). Figure 3 illustrates the local variation of coefficients (𝑏𝑘) used in the GWR

procedure, and can be interpreted as follows: for each variable, a red [blue] colored region

suggests that an increase in the amount of that variable will increase [decrease] rates of dengue

fever. We use contour maps of varying sizes to map the standard error of the estimates for each

predictor variable in Figure 3(a)-(f), suggesting substantial greater error in the peripheral

neighborhoods. On the same Figure, local R2 values suggest an uneven coefficient of

determination across the city.

<Figure 3 around here>

1 Personal communication with J.H. Rojas, epidemiologist in the Secretaria de Salud Municipal, Cali. 2 Neighborhoods are classified based on socioeconomic strata into six classes, six being the highest.

In the hazard category, the association between dengue fever rates and the proximity to

tire shops is generally positive (𝑏𝑡𝑖𝑟𝑒 = 0.0023), but that trend declines eastward. Second, the

proximity to plant nurseries (𝑏𝑛𝑢𝑟𝑠 = 0.0071) exhibits a strong localized impact in the central

part of the city, coinciding with clusters 1 on Figure 1b (the southern part of the city also exhibits

a strong localized impact, but its standard error is very large). Third, a higher proportion of

households equipped with a sewage system is likely to reduce dengue fever rates (𝑏𝑠𝑒𝑤 =

−0.0073); however this trend is particularly true in areas south of the city (especially areas

around cluster 2 in Figure 1b). Neighborhoods in the southern part of the city are more modern

with a better sewage system and we hypothesize that individuals residing in this area may

maintain better sanitary practices of not keeping water containers than if they had lived in an

older neighborhood with a similar stratification. In the social vulnerability category, higher

proportions of indigenous population and higher socioeconomic stratum has a modest, yet

significant impact on the rate (𝑏𝑖𝑛𝑑 = 0.0122, 𝑏𝑠𝑡𝑟𝑎𝑡 = 0.0023), while higher population density

has a slight negative impact (𝑏𝑝𝑜𝑝 = −0.0067). The coefficient reflecting the proportion of

indigenous people in the local population demonstrates substantial variation from the northern

part of the city (slightly negative association) to the south (stronger positive association). Higher

socioeconomic strata in the center part of the city was associated with lower levels of dengue

fever rates (around cluster 1, Figure 1b). Interestingly, in the southern part of the city, houses are

typically bigger with relatively larger yards, which may provide a suitable habitat for the vector

to breed. Finally, we observe that population density has a slight negative impact on dengue

fever rates, but exhibits little spatial variation. Some of the densely populated neighborhoods in

the eastern part of Cali have a high concentration of Afro-Colombian population, which has been

suggested to be less susceptible to the virus (Villegas et al. 2010).

Overall, the local coefficient of determination R2(i) suggests a strong predictive power in

the central and southern part of the city (coinciding with cluster 1 and 3 in Figure 1b). Lower

R2(i) values in the southwestern part of the city (cluster 2 in Figure 1b) indicates a poorer

regression fit. We came to realize that this area coincides with military barracks around the

Ejército Tercera División. This area suffered from a large number of dengue fever cases in 2010,

possibly owing to the presence of military personnel living in close proximity from one another.

The area also houses important universities (Arquelogico Univalle, Universidad Santiago De

Cali and the Universidad Cooperativa de Colombia Sede Cali). Proximity to schools (including

universities) and military barracks were not included in our model.

Figures 4(a)-(b) summarize the spatial distribution of residuals and predicted dengue

fever rates3, respectively. Overall, residuals are confined in the range [-1.17 to 1.39] cases per

100 individuals with a mean nearly equal to 0. (compared to [-0.0145, 0.0336] following the

standard OLS). Although the spatial pattern in the predicted rates is higher than in the observed

rates (Moran’s I = 0.54) -likely due the GWR smoothing effects, there is no apparent clustering

3 For comparison purposes legend breaks and color schemes of Figure 4b were kept similar to the ones of

Figure 1b (observed rates).

in the residuals (Moran’s I = 0.005, compared to 0.133 for the standard OLS), indicative of a

random pattern (Table 2). Following a local Moran’s I on the predicted rates, higher rates are

identified in 28 neighborhoods (14 of those clusters matched with the 19 from the observed

rates) and lower rates in 62 neighborhoods (44 of those clusters match with the 48 from the

observed rates).

<Figure 4 around here>

4. Discussion Considering both environmental and socioeconomic factors as predictors of dengue

outbreaks was previously highlighted by other studies (Mondini and Chiaravalloti-Neto 2008;

Hsueh, Lee, and Beltz 2012; Koyadun, Butraporn, and Kittayapong 2012). While the community

seems to agree on the importance of environmental factors impacting the spread and distribution

of the vector and the pathogen, the choice and selection of socioeconomic variables reflecting the

vulnerability of the population, seems often to be context and scale specific, largely driven by

data availability. A generalization of the role played by socioeconomic determinants may be

challenging; however the findings of our work exhibit similarities with previous literature, such

as the role of population density (Hsueh, Lee, and Beltz 2012; Khormi and Kumar 2011; de

Mattos Almeida et al. 2007; Koyadun, Butraporn, and Kittayapong 2012) and socioeconomic

status (Siqueira-Junior et al. 2008; Koyadun, Butraporn, and Kittayapong 2012; de Mattos

Almeida et al. 2007). Finally, our study contributes to a growing body of recent literature

underlining the usefulness of regression techniques which incorporates spatial effects in the

prediction of dengue fever rates (Khormi and Kumar 2011; Lin and Wen 2011; Hsueh, Lee, and

Beltz 2012; Baharuddin, Suhariningsih, and Ulama 2014).

Our results underline the need of taking into account both environmental factors favoring

local transmission as well as those socioeconomic factors contributing to social vulnerability of

the population when assessing risks associated with vector-borne diseases. Interestingly, the six

factors identified as statistically significant predictors of dengue fever and their relationship with

dengue coincided with four factors proposed by local domain experts (n =4) during an earlier

study in the same area (Hagenlocher et al. 2013), suggesting that control strategies should

incorporate some of the dengue predictors explored in this paper. Rates of dengue fever were

shown to have a non-stationary relationship with the six covariates, further underlining the need

to incorporate spatial effects in predictive models, as these may reflect locally varying

determinants not captured in traditional regression models. GWR metrics (R2, range of residuals

and autocorrelation in the residuals) were better than when using the standard OLS approach.

This study relies on several assumptions that may affect the validity of our results. First,

although the set of input variables selected in this study are those most often considered in

dengue risk assessments, several important variables are missing due to a lack of data

availability. Among others, these include intra-urban mobility and migration patterns, quality of

the health care system, treatment seeking behavior of different social groups as well as extent

and coverage of dengue control programs (Eisen and Lozano-Fuentes 2009). Lower local R2(i)

values in some neighborhoods of the city need to be further investigated, for instance by the

inclusion of additional variables. Second, we did not differentiate among the features that make

up the “hazard” variables. The size and cleanliness of a tire shop or a plant nursery may impact

the presence of the mosquito and increase the risk of dengue fever. Third, both “hazard” and

“vulnerability” variables used in this study were drawn from different years. Fourth, our

geocoding process used the place of residence of each patient diagnosed with dengue fever, and

this location may not be the place where the patient was infected (Vazquez-Prokopec et al.

2009).

Despite these limitations, our approach demonstrated several strengths. First, the

methodological framework is transferable to urban environments in other regions (e.g. Asia,

Africa, etc.) and at different spatial scales (depending on data availability), as well as to different

mosquito-borne diseases influenced by a similar complex interplay of environmental and

socioeconomic factors (e.g. malaria, chikungunya, Zika fever, etc.). A second major strength lies

in the comprehensive selection of potential risk factors/predictors (n = 36), representing those

factors that are most commonly used in dengue risk assessments. Finally, the regression

technique used in this paper incorporated spatial effects and reduced the spatial pattern in the

residuals.

5. Conclusions Using a Geographically Weighted Regression (GWR) model we demonstrated the role of

socioeconomic and environmental factors associated with a dengue outbreak that affected the

city of Cali, Colombia, in 2010. The results presented in this paper make two important

contributions. First, both environmental factors impacting the spread and distribution of the

vector and pathogen as well as socioeconomic factors shaping social vulnerability need to be

considered in disease risk assessments. Second, the contribution of these factors varies spatially,

thus demanding approaches that are able to capture this variability. Our findings may not have

been identified by traditional regression analysis. This further underlines the importance of

small-scale level studies.

Our findings also have deep ramifications for health policy and decision-making, since

interventions aiming at reducing 2016) risk need to be targeted locally to optimize health

outcomes and addressing the specific local conditions that ultimately result in high risk levels

(Ndeffo-Mbah, 2016) We suggest that efforts in control strategies and prevention be greater in

areas where both environmental and socioeconomic factors are known to pose a significant risk.

For instance, educating owners of tire shops and nurseries to monitor for the presence of larvae’s

could curb the spread of mosquitos, while increasing the number of households with sewage

system is also likely to reduce the potential for vector breeding.

We recognize that further research will be needed to evaluate whether the factors identified

in this paper as significant predictors of dengue fever at an urban scale (1) are also valid for other

urban environments with similar characteristics, (2) generalizable to coarser scales and (3)

whether our findings (i.e. relationships) would hold during other epidemic years. Additionally, it

would be worth investigating whether local strategies which were implemented to eradicate the

vector (and associated prevention campaigns) were beneficial in reducing the number of cases in

subsequential years. Further, models incorporating climate factors such as mean monthly

temperature, rainfall or humidity will likely provide better dengue fever estimates. This is

particularly important at national and regional scales, where spatial variations in climatic

conditions manifest. It would be interesting to compare the results of our approach with a mixed-

effect model where the study region is included as a random effect. The merits of other

spatially-explicit models are worth investigating, such as (1) the spatial regression (Anselin,

Syabri, and Kho 2006), (2) count regressions such as in Linard et al. (2007) who combine count

data with generalized linear models and (3) the geographically weighted Poisson regression

(Nakaya et al. 2005). These approaches are likely to further reduce the problem of spatial

dependence.

Acknowledgment The authors would like to thank Jorge H. Rojas, epidemiologist at the Secretaría de Salud

Pública Municipal in Cali, Colombia for his assistance with the manuscript.

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List of Figures

Figure 1: Aggregated counts of 2010 reported dengue fever cases for the city of Cali, Colombia

in (a) and associated rates per 100 individuals in (b). Clusters of high rates are identified using

the Local Moran’s I and are outlined in black.

Figure 2: Spatial distribution of the socioeconomic and environmental variables selected in the

GWR model (standardized from 0 to 1), arranged according to the hazard & vulnerability

framework.

Figure 3: Local coefficients for the six explanatory variables used in the GWR model (a-f) and

associated standard error (SE), where a thinner line indicates a smaller SE. The figure to the

upper right summarizes the local R2.

Figure 4: Residuals and predicted dengue fever rates, in (a) and (b), respectively. Higher values

are determined using a Local Moran’s I statistic, and highlighted in black. Use observed rates in

Figure 1b for comparison.

List of Tables

Table 1. List of socioeconomic, demographic and environmental variables used in this study. All

variables were standardized in the [0, 1] interval.

Table 2: Coefficients and model performance for the GWR model. The Moran’s I statistic

reflects the global and local autocorrelation in predicted rates and associated residuals.

Figure 1

Figure 2

Figure 3

Figure 4

Table 1

Year Source Year Source

HAZARD SOCIAL VULNERABILITY

Environmental factors Lack of resilience

Relative proximity to parks 2010 Cali Households with phone (%) 2005 Census

Relative proximity to rivers 2010 Cali Individuals who can read/write (%) 2005 Census

Relative proximity to tire shops 2010 Cali Individuals who cannot read/write (%) 2005 Census

Relative proximity to water pumps 2010 Cali Individuals with no education (%) 2005 Census

Relative proximity to cemeteries 2010 Cali Individuals with low education (%) 2005 Census

Relative proximity to plant nurseries 2010 Cali Individuals with medium education (%) 2005 Census

Individuals with high education (%) 2005 Census

SOCIAL VULNERABILITY Mean hospital density (km2) 2010 Cali

Susceptibility Travel time to hospital 2010 Cali

Population density (km2) 2005 Census Walking distance to hospital 2010 Cali

Density of occupied households (per km2) 2005 Census Employed individuals (%) 2005 Census

Individuals of age 0-4 (%) 2005 Census Unemployed individuals (%) 2005 Census

Individuals of age 5-14 (%) 2005 Census Retired individuals (%) 2005 Census

Individuals of age 15-29 (%) 2005 Census Doing housework (%) 2005 Census

Individuals of age >30 (%) 2005 Census

Indigeneous population (%) 2005 Census

White population (%) 2005 Census Sources

Black population (%) 2005 Census Cali: City of Cali

Individuals with disabilities (%) 2005 Census Census: 2005 national Colombian census

Households with sewer (%) 2005 Census

Households with water (%) 2005 Census

Stratum (estrata) 2005 Census

Table 2

COEFFICIENTS Beta(min) Beta(max) Beta(avg)

HAZARD/DISEASE

proximity to tire shops -0.0271 0.0179 0.0023

proximity to plant nurseries -0.0057 0.0276 0.0071

proportion of housing with sewage -0.0308 0.0003 -0.0073

SOCIAL VULNERABILITY

proportion of indegeneous people -0.0117 0.2870 0.0122

SES stratum (estrata) -0.0049 0.0179 0.0023

population density -0.0162 0.0071 -0.0067

intercept -0.0076 0.0098 0.0069

MODEL PERFORMANCE

R2

residuals (range)

residuals (mean)

Moran's I (residuals)

Moran's I (predicted)

AIC -2694.00

0.6446

(-0.01177; 0.013941)

-0.000026

0.005

0.541