Journal of biological and health sciences http://biotecnia.unison.mx
ISSN: 1665-1456
Correlación espacial de dengue con estado socioeconómico y temperatura terrestre en el noroeste de México
1 Universidad de Sonora, Departamento de Medicina y Ciencias de la Salud. Hermosillo, México.
2 Instituto Tecnológico de Sonora, Departamento de Ciencias del Agua y Medio Ambiente. Ciudad Obregón, México.
3 Universidad de Sonora, Departamento de Matemáticas. Hermosillo, México.
4 El Colegio de Sonora, Centro de Estudios en Salud y Sociedad. Hermosillo, México.
Objective. To characterize the geographic distribution of dengue and to evaluate the spatial autocorrelation with so- cial and climatic determinants at the census-tract level in two medium sized cities in northwestern Mexico. Methods. In this work we apply spatial analysis ecological tools, such as the Moran’s Index and the Local Indicator of Spatial Association (LISA) method, to examine global and local spatial correla- tion between incidence of dengue, with socioeconomic and climatic factors at the census tract-level. For the analysis of the spatial clustering, the Getis-Ord method was used to find statistically significant hot spots in each city. Results. Overall, a global spatial autocorrelation could not be identified, although local clusters of a high dengue incidence, soil surfa- ce temperature ≤ 31 °C and high degree of social marginali- zation coincide. Discussion. We found that at the census-tract level in urban settings, socially disadvantaged populations showed higher clusters of dengue when compared to areas with better socioeconomic conditions. A similar spatial pat- tern was observed in the two study sites, when considering public health conditions and its aggregation with physical attributes using spatial analysis techniques, supporting the application of this technique for a better understanding about the dengue distribution in urban areas.
Objetivo. Caracterizar la distribución geográfica de dengue confirmado y evaluar su autocorrelación espacial con de- terminantes sociales y climáticos a nivel de áreas pequeñas, en dos ciudades del noroeste de México. Métodos. En este trabajo se utilizan herramientas de análisis espacial, como el Índice de Moran y el método de Indicadores Locales de Asociación Espacial (LISA), para examinar la correlación es- pacial, global y local, entre la incidencia de dengue y factores climáticos y socioeconómicos a nivel de área geoestadística básica (AGEB). Para mapear áreas de alto riesgo, se usó el método de agrupamiento espacial de Getis-Ord. Resultados.
Volumen XXV, Número 3
En general, no identificamos una autocorrelación espacial global, aunque agrupamientos locales de una alta incidencia de dengue, temperatura de la superficie terrestre ≤31 °C y un alto grado de marginación social, coinciden. Discusión. Encontramos que a nivel de áreas pequeñas urbanas, po- blaciones con desventaja social agrupan una mayor carga de dengue comparadas con áreas de mejores condiciones socioeconómicas. En los dos sitios de estudio, se encontraron patrones espaciales similares que correlacionaron problemas de salud y características físicas del medio ambiente utilizan- do os análisis espaciales, lo que soporta la utilidad de su uso sistemático para entender mejor la distribución urbana del dengue.
Dengue is a viral infection transmitted from human to human through the bite of the female mosquito Aedes aegypti or Ae. albopictus, present in more than 100 tropical and subtropical countries (Brady et al., 2012). In the last decade there has been a notorious upsurge of the disease burden mainly in Latin America and the Caribbean (Kourí, 2011). In the Ame- ricas, an endemic-epidemic pattern has been described with periodic outbreaks every three to five years, with a transition from an epidemic-endemic status to an only endemic one recently (Montoya et al., 2012).
The Aedes mosquitoes live in close relationship with humans and grow mainly in urban or suburban areas where breeding places and hosts are easily available (Lega et al., 2017). The infection can rapidly spread in a community if the- re are susceptible people, and if there are high densities of the transmitting mosquitoes, therefore the timely control of the latter is considered one of most important actions of pre- vention (Bhatt et al., 2013). A key factor that has determined its status as a public health problem is its growing geographic dispersion associated with a high morbidity, which creates a heavy economic and health burden in endemic countries. More than half of the world’s population are at risk of dengue
*Autor para correspondencia: Gerardo Álvarez-Hernández
e.mail: gerardo.alvarez@unison.mx
infection, with 100 million symptomatic cases reported each year (Brady et al., 2012).
The spatial and temporal distribution of cases and outbreaks of dengue are related to the interplay of physical, socioeconomic, and climatic conditions, particularly in urban zones with high population density (Bhatt et al., 2013; Ze- llweger et al., 2017). Temperature is a key determinant, since the increase in environmental temperature has been obser- ved to decrease the duration of the extrinsic incubation of dengue virus, and increases the frequency of the mosquitoes’ propensity to bite. Additionally, temperatures in the range of 22 to 32°C are associated with a higher risk of dengue and are considered the lower and upper threshold of the dengue risk (Fan et al., 2015; Peña-García et al., 2017). Likewise, rainfall patterns are also powerful drivers of a high density of mos- quitos (Benedum et al., 2018).
Several studies (Teurlai et al., 2015; Phanitchat et al., 2019; Watts et al., 2020) have analyzed the spatial variability of the incidence of dengue and its relationship with climatic and socioeconomic variables. Overall, a consistent correlation has been found between high incidence rates of the disease and high values of air temperature, rainfall, and humidity; however, also an inverse relation between socioeconomic status and incidence of dengue has been observed (Ogas- hawara et al., 2019).
The increasing use of new methods and tools for spatial analysis, has contributed to improve the understanding of the geographic distribution of dengue and how it is related to the environment. With this in mind, we aim to describe the intraurban spatial distribution of dengue cases, and to ascer- tain its potential correlation with socioeconomic and climate determinants at the census-tract level in Hermosillo and Ciudad Obregón, two endemic medium-size cities located in northwest Mexico.
In this study, an ecological approach using Geographic In- formation Systems (GIS) clustering techniques, and spatially disaggregated health data, are used to examine the spatial correlation between the incidence of dengue and sociode- mographic and climatic determinants in the cities of Hermo- sillo and Ciudad Obregón, in the Mexican state of Sonora, at the northwest of the country. Both cities accounted for 61 % of incidents of dengue cases in Sonora for the period bet- ween 2007-2017. Hermosillo is the largest and most popula- ted urban city in the state, in the year 2020 had an estimated population of 936,263 inhabitants and had 577 urban census tracts, known as Basic Geostatistical Areas (BGA) (Gobierno de México, 2020). Its average annual temperature is 24.9 oC, ranging from 17.4 - 34.0 ºC (Navarro-Estupiñán et al., 2018). Meanwhile, Ciudad Obregón has an estimated population of 436, 484 inhabitants and 254 urban BGA (Gobierno de México, 2020). It has an average annual temperature of 25.1 o C (17.6 - 33.9ºC). Precipitation depends on the influence of the North American monsoon that generates about 70 % of the rainfall during the July to September period for both
cities. In each of the urban areas, since the year 1966 annual pluvial precipitation for Hermosillo is 356 mm and for Ciudad Obregón is 386 mm.
The socioeconomic profile of each BGA, entails small geographical units containing 25-50 urban blocks and averaging 2,500 inhabitants, and was based on the Urban Marginalization Index (UMI) delineated by the National Population Council and Housing, which is widely used in Mexico to categorize socioeconomic status (SES) at aggrega- te levels (Gobierno de México, 2015). The UMI includes data about familial income, degree of schooling, housing quality, overcrowding, and health care insurance among others; the UMI stratifies the SES of each BGA in five socioeconomic deprivation strata (very low, low, intermediate, high and very high). In both cities, most BGA are concentrated in the strata of intermediate, low, and very low marginalization, for Hermosillo 340 (68 %) and for Ciudad Obregón 156 (73 %) of the BGA correspond to such categories.
All study procedures were approved by the Research Ethics Committee of the Department of Medicine and Health Sciences, University of Sonora. This research only included secondary data analysis and was therefore considered with no risk to humans.
To be considered an official incident, a case of dengue must be confirmed by laboratory techniques such as serology (NS1), Enzyme-Linked Immunosorbent Assay (ELISA) for IgG and IgM antibodies, and real-time polymerase chain reaction (RT-PCR), carried out in the Sonora State Laboratory for Pu- blic Health (LESP). Each confirmed case was recorded in the Epidemiological Surveillance System for Vector Transmitted Diseases during the period of January 1st, 2007, to December 31st, 2017. Each eligible case was then georeferenced using the addresses registered in such surveillance platform, and linked geographically with its corresponding BGA; all addres- ses were mapped using Google Earth® including street, num- ber, intersections, and neighborhood and then transferred to a vector file in a GIS. Only cases with a known address were used and this resulted in a loss of 6,343 (6.6 %) confirmed cases that could not be georeferenced.
Pluvial precipitation and temperature data were obtained for both cities from the Mexican National Water Commission and the Mexican Weather Service regional office. We used records from 1966 to 2018 retrieved from the single climate stations located within the city limits. Historically. in both cities 70 % of the precipitation falls during the summer months of July and September. In addition, both cities have experienced an increase in air temperatures in the same period, and an average of about three heat waves per year for Hermosillo and five for Ciudad Obregón (Navarro-Estupiñán et al., 2018). As a result of the limited number of climate stations within the city limits, many cities like our study sites are
unable to create spatial maps of air temperature and rainfall variability. Notwithstanding, the land’s surface temperature (LST) has been measured using remote sensing applications through various satellites with different spatial and temporal resolutions (Tsou et al., 2017; Yang et al., 2017). The satellites measure the radiation coming from the Earth-Ocean-Earth- Atmosphere systems, including the solar radiation reflected and the infrared radiation emitted (Sorek-Hamer et al., 2016). This allows the use of LST as a proxy for air temperature values in locations with no climate stations, and provides a global understanding about climatic conditions favorable for dengue outbreaks. Thus, Landsat 5 and Landsat 8 satellites’ remote sensing images were used to estimate LST. Three thermal bands at a resolution of 30 m were used with images available approximately every 16 days at 11:00 am local time (Navarro-Estupiñán et al., 2020). To calculate LST, Landsat 5 data was used for the 2007-2012 period, and Landsat 8 ima- ges for the years 2013-2017. A total of 354 satellite images were downloaded and used to create a map of the historic average of LST spatial distribution for both cities (Figure 1).
Descriptive statistics were used to compare gender and age group of selected cases, according to SES of their BGA of re- sidency for both cities. Statistical differences were examined through Chi-squared test using the SPSS 23 program; values of p < 0.05 were considered as statistically significant. Given that the population data of each BGA was obtained from
the 2010 national population census, and that our dengue incidences were estimated for the 2011-2017 period, we calculated the softened cumulative incidence rate (CIR) of dengue per 10,000 inhabitants, based on the Bayes empirical method with the use of the GeoDa ™ program version 1.14.0, and thus avoiding overestimating the incidence of dengue cases given the heterogeneity of the population size of each BGA.
The global spatial correlation between the dengue CIR and each BGA UMI was evaluated using the Moran I Index, in which a value close to 0 indicates a random pattern, a value of -1 indicates that the data are too dispersed and a value close to 1 indicates that there is a positive spatial correlation. For the local bivariate analysis, the Local Indicators of Spatial Association (LISA) method was used (Celemin, 2009). All the above was conducted using the GeoDa™ program version 1.14.0, where the value of statistical significance was p≤0.05. To examine the correlation between dengue CIR, UMI,
and LST, we set a temperature threshold lower than 30.6 °C as well as a CIR higher than 90.5 cases per 10,000 inhabitants for Hermosillo. A LST threshold lower than 31.4 °C and CIR higher than 37.6 per 10,000 inhabitants were used for Ciudad Obre- gón. According to previous studies, a temperature threshold located in the 20th percentile indicates an ideal scenario for the development of Aedes mosquitoes (Fan et al., 2015; Peña- García et al., 2017). Furthermore, for the analysis of the spatial
Figure 1. Annual average of LST spatial distributions from 2007-2017 for: (a) Hermosillo and (b) Ciudad Obregón.
Figura 1. Promedio anual de la distribución espacial de la temperatura de la superficie terrestre (LST) del periodo 2007-2017 para:
(a) Hermosillo y (b) Ciudad Obregón.
local grouping, the Getis-Ord clustering method was used to find statistically significant “hot and cold” spots within the limits of each city. The Gi* is a Z score, with large range of values, where high scoring implies a more intense clustering (hot spots), and negative values mean cold spots (Peeters et al., 2015).
The final study sample comprised 5 921 confirmed dengue cases, 4 924 (83.0 %) corresponded to the city of Hermosillo and 997 (17.0 %) to Ciudad Obregón. No significant differen- ces were observed in the distribution of cases neither by sex nor age strata, although the highest proportion (30.9 %) of cases occurred in subjects aged 30 to 49 years. When dengue cases were stratified by the UMI of each BGA, the highest percentage corresponded to the very low stratum for social marginalization in both cities (Table I). For Hermosillo, 340 BGA accounting for 94 % of the population, showed better welfare but the highest CIR (78.4 per 10 000 inhabitants) of dengue as well. The opposite was observed for Ciudad Obregón, because although 156 BGA accounting for 97 % of its population were classified as less vulnerable, the highest incidence (35.5 per 10,000 inhabitants) corresponded to BGA with a high degree of social marginalization (Table II).
The distribution of cases in Hermosillo was spread-out across the entire city, albeit the largest volume occurred in the north side, ranging between 220 and 273 cases per km2. Similarly, in Ciudad Obregón, the dengue cases were distri- buted throughout the city, but the density per km2 showed small areas of concentration, discretely more evident in the central and southern areas of the city, with a density per km2 between 77 and 96 cases (Figure 2).
The highest smoothed CIR found in Hermosillo was 310 per 10 000, while for Ciudad Obregón was considerably lower (62 per 10,000 inhabitants). The higher dengue CIR corres- ponded to BGA located in the northeast and downtown in Hermosillo (figure 3a), meanwhile for Ciudad Obregón the higher CIR values were dispersedly pinpointed across the city as shown in figure 3b.
When assessing the global spatial autocorrelation in the city of Hermosillo between dengue CIR and the UMI of each BGA, we did not observe a significant value for the Moran’s Index (I= -0.040). However, when we used the LISA method, a significant spatial autocorrelation was found in BGA with a high incidence of dengue cases and high degree of social marginalization, particularly in the northern parts of the city. Likewise, in Ciudad Obregón the Moran’s I was 0.033, meaning that there is no significant autocorrelation at the global scale. However, the local bivariate analysis showed
Table 1. Sociodemographic characteristics of the study subjects, by site of residency.
Tabla 1. Características sociodemográficas de los sujetos de estudio, por si- tio de residencia.
N (%) Variable Hermosillo Ciudad Total P# Obregón | ||||
Sex | ||||
̶ Male ̶ Female | 2,077 (42.2) 2847 (57.8) | 422 (42.3) 575 (57.7) | 2,499 (42.2) 3,422 (57.8) | 0.932 |
Age group (years) | ||||
<10 | 331 (6.7) | 113 (11.3) | 444 (7.5) | |
̶ 10 -19 | 995 (20.2) | 234 (23.4) | 1,229 (20.7) | |
̶ 20 to 29 | 995 (20.2) | 224 (22.5) | 1,219 (20.6) | |
0.000* | ||||
̶ 30 to 49 | 1,559 (31.7) | 272 (27.4) | 1,831 (30.9) | |
̶ 50 to 59 | 578 (11.7) | 83 (8.3) | 661 (11.2) | |
̶ 60 and over | 466 (9.5) | 71 (7.1) | 537 (9.1) | |
BGA degree of social marginalization & | ||||
̶ Very High | 29 (0.6) | 2 (0.2) | 31 (0.5) | |
̶ High | 126 (2.6) | 22 (2.2) | 148 (2.5) | |
̶ Intermediate | 1,199 (24.4) | 226 (22.7) | 1,425 (24.1) | 0.246 |
̶ Low | 1,614 (32.8) | 323 (32.4) | 1,937 (32.7) | |
̶ Very Low | 1,956 (39.7) | 424 (42.5) | 2,380 (40.2) | |
#Based on a chi square test *Statistically significant at 95 % confidence level.
& Basic Geostatistical Area-level of socialmarginalization is based on the Urban Marginalization Index from the National Council of Population and Housing.
Table 2. Dengue incidence by degree of social marginalization at the Basic Geostatistical Area-level in the study cities.
Tabla 2. Incidencia de dengue por grado de marginación social a nivel de Área Geoestadística Básica en las ciudades de estudio.
Marginalization stratum | Hermosillo | Ciudad Obregón | ||
Incidence* | IQR | Incidence* | IQR | |
Very High (n = 10) | 42.8 | 26.8-50.4 | 31.2 | 30.0-32.2 |
High (n = 18) | 53.2 | 33.2-68.9 | 35.5 | 26.9-44.1 |
Intermediate (n = 92) | 65.9 | 36.0-85.7 | 32.6 | 28.8-37.1 |
Low (n = 118) | 78.4 | 40.4-93.0 | 34.6 | 27.6-38.6 |
Very Low (n = 209) | 62.7 | 31.2-78.4 | 31.7 | 28.5-34.1 |
*Smoothed cumulative incidence rate per 10 000 habitants. IQR=Interquartile Range.
Figure 2. Spatial distribution of the density of dengue cases for (a, b) Hermosillo and (c, d) Ciudad Obregón, Sonora, Mexi- co.
Figura 2. Distribución espacial de la densidad de casos de dengue para (a, b) Hermosillo y (c, d) Ciudad Obregón, Sonora, México.
Figure 3. Cumulative incidence rate of dengue at the Basic Geostatistical Area-level for (a) Hermosillo and (b) Ciudad Obregón, Sonora, Mexico.
Figura 3. Incidencia acumulada de dengue a nivel de Área Geoestadística Básica para (a) Hermosillo y (b) Ciudad Obregón, Sonora, México.
three areas with a high incidence and a high UMI (figures 4a and 4b).
Finally, by using the Getis-Ord statistic we found a significant (p <0.01) spatial correlation between the dengue CIR, with the UMI and LST, identifying an area of high risk (“hot spot”) clearly defined, and delimited in both cities. For Hermosillo, the hot spots were located at the central and northern side, where we observed a correlation between an incidence of dengue > 90.5 cases per 10,000 inhabitants, an LST below 30.6 °C, and BGA with high degree of social mar- ginalization. Meanwhile, in Ciudad Obregón three hot spots with statistical significance were found, they were located at the northwest, northeast and southwest sectors of the city; in these “hot spots” a high incidence of dengue coincides > 37.6 per 10,000 inhabitants, a temperature of the land surface lower than 31.4 °C and BGA with high social marginalization as shown in figure 5.
Overall, our findings in the two dengue endemic cities with the largest population in Sonora, Mexico, indicate that although there is no global spatial correlation between the incidence rate of the disease and contextual socioeconomic conditions, there is a local significant correlation at the census-tract level between occurrence of incident cases of
dengue and socioeconomic characteristics of the physical space. It drew attention that BGA with medium, low, and very low social marginalization presented the highest dengue in- cidence, which is consistent with previous research showing a similar pattern of the disease in urban settings (Fuentes- Vallejo, 2017).
The lack of linear correlation between dengue incidence and socioeconomic conditions of peripheral spaces in both cities, can be explained by the fact that more than 90 % of population resides in BGA considered as medium to very low degree of social marginalization. In spite of that, the LISA analysis showed areas of local clusters in such neighbor- hoods that coincided with a high incidence of dengue with difficulties connecting to the health network, high poverty rates, and other indicators of social vulnerability, which has also been documented in previous Latin-American studies (Mena et al., 2011; do Carmo et al., 2020). Thus, a potential ecological fallacy derived from inaccuracies when measuring socioeconomic attributes of the physical space at the census- tract level, may flaw our findings. To overcome this problem, a smaller scale such as blocks of socioeconomic indicators can be approached, or a more specific classification for cities in the north part of the country that have dengue cases but where the social vulnerability categories appear lower when compared to other localities in the south of the country.
Several social determinants of health are consistently
Figure 4. Bivariate local spatial clustering at the Basic Geostatistical Area-level, between incidence of dengue and the index of urban marginalization, for (a) Hermosillo and (b) Ciudad Obregón Sonora, Mexico.
Figura 4. Agrupamientos espaciales locales bivariados a nivel de Área Geoestadística Básica entre la incidencia de dengue y el índice de marginación urbana, para (a) Hermosillo, y (b) Ciudad Obregón, Sonora, México.
Figure 5. Hot and cold spot areas with 90 %, 95 % and 99 % confidence that combine incidence of dengue, marginalization index and land surface temperature for (a) Hermosillo and (b) Ciudad Obregón, Sonora, Mexico.
Figura 5. Áreas frías y calientes con confianza de 90 %, 95 % y 99 % que combinan la incidencia de dengue, índice de margin- ación social y temperatura de la superficie terrestre para (a) Hermosillo y (b) Ciudad Obregón, Sonora, México.
related with the incidence of dengue, such as unplanned urbanization, urban slums, overcrowding, and shortage of running water, sewage, and waste management systems, fostering local transmission of the dengue virus in urban scenarios (Kikuti et al., 2015; Teurlai et al., 2015; Watts et al., 2020; Kolimenakis et al., 2021). Our results are consistent with previous Mexican studies, as we observed a negative rela- tionship between higher incidence rates of dengue and less social welfare at the aggregate level, indicating that better socioeconomic collective development would mitigate the occurrence of the disease in urban areas with limited resour- ces (Escobar-Mesa y Gómez-Dantés, 2015; Reyes-Castro et al., 2017a).
Our results also showed local clusters in BGAs with high dengue incidence rates but low degree of social marginaliza- tion, which could be potentially explained by other social va- riables not included in this study (e.g., population density), as well as climatic conditions of the physical space. In our study, by integrating socioeconomic variables and incidence rates of dengue into a single map, we assess correlations between LST (physical environment) and UMI (social context) finding statistically significant hotspots where a high incidence of the disease coincided with a high surface temperature of the soil, and a high degree of social marginalization. Our findings showed intraurban heterogeneity and overlap with the spatial distribution of infant mortality (Alvarez et al., 2009) and tuberculosis (Alvarez et al., 2010) previously examined at the census-tract level in Hermosillo, and are consistent with
recent scientific evidence depicting a similar spatial cluste- ring of arboviral diseases in nine cities located in southern Mexico (Dzul-Manzanilla et al., 2021). Nonetheless, the inci- dence heterogeneity of dengue that we observed could be explained by a differential in the mosquito density and its spatial distribution as well. It is accepted that heterogeneity in entomological indices (i.e., Breteau index) are related with differential risk of dengue incidence, with higher dengue cases in areas with higher density of mosquitoes, regardless the implementation of control strategies (Peña-García et al., 2016). Entomological data from systematic surveillance should be routinely integrated when assessing spatial distri- bution of dengue cases.
In addition to contextual social marginalization, indi- vidual determinants of welfare such as lower educational level, low family income, among others, contribute to the use of fewer protective barriers (e.g., use of repellants, lack of insecticide pavilions) against the transmitting mosquito. Traditional interventions (e.g., application of larvicides, spa- tial fumigation) have been found to be less effective in these populations (Reyes-Castro et al., 2017b), and may contribute to the weakening of interventions to prevent and control the transmitting mosquito Aedes at the neighborhood level. Simultaneous statistical modeling of individual and contex- tual variables, both biological (i.e., fluctuations in dengue incidence and mosquitoes density) and socioeconomic, such as that used in the multilevel analysis, can help to improve our understanding about the interaction of multiple deter-
minants of the social and physical space on the individual risk of dengue, moreover considering the changes in the variance of dengue cases, within and between groups and over time (Diez-Roux et al., 2005; Ordóñez-Sierra et al., 2021). Despite the consistency and reproducibility of findings from different studies to analyze the spatial pattern of den- gue as well as other health events, the clustering techniques are mainly based in registered cases, while socioeconomic and climatic variables used in previous studies were analyzed separately. The combination of different techniques from the spatial analysis are urgent in local contexts similar to ours, not only to search for scientific answers but to be implemented for policymakers and health personnel dealing with intraur- ban growth of Aedes-borne diseases. Recognition about the applicability of different spatial analysis tools for dengue risk stratification and the design of interventions to prevent and control of mosquito populations, must be accompanied by substantive improvements in the quality of the secondary data sources used routinely in the national health systems, mainly those for epidemiological reports and entomological
monitoring.
In this sense, our research has limitations already poin- ted out in similar Mexican studies (Dzul-Manzanilla et al., 2021; Cortés-Escamilla et al., 2022). Our analyses were based in registered confirmed cases, which may flaw identifying additional hotspots in local areas with less access to health care interventions for the timely diagnosis of the disease. We also had limited data to characterize the effect of urban heat islands and their associated dangers, mostly because air tem- perature data on land depend on the number of climatic sta- tions and these are missing in many cities across the country (Navarro-Estupiñán et al., 2020). Because we used a Bayesian approach to calculate the cumulative incidence of dengue at the census-tract level, our estimate may be biased impeding to carry out inferences about population dynamics, so new research needs to consider additional information about the implicit conditions involved in dengue transmission such as geometric projections of population and mosquito density. Other limitation of our study is the use of satellite images, which prevented to accurately measure the moments of mosquito heavier activity throughout the day. Future re- search on this topic can use historical climate datasets freely available in some internet sites (www.worldclim.org).
We found that at the census-tract level disadvantaged popu- lations suffer a higher burden of dengue when compared to areas with better socioeconomic conditions. Although in the study sites the disease does not show a global spatial correla- tion, and there is a heterogeneous pattern throughout both cities, conditions of high social marginalization and a terres- trial temperature below 31 °C were positively correlated with a higher incidence of dengue at the census-tract level.
The use of spatial clustering analysis methods and tools can be useful to characterize the distribution of dengue cases in small areas in endemic localities. Its practicality
and low-cost application can help to detect high-risk areas of dengue and thus plan cost-effective strategies aimed to protect vulnerable populations.
We thank to the Sonora Health Ministry for providing us with the morbidity data used in this study.
All the authors declare they have no conflict of interests.
Álvarez, G., Lara, F., Harlow, S.D. and Denman, C.A. 2009. Mortalidad infantil y marginación urbana: análisis espacial de su relación en una ciudad de tamaño medio del noroeste mexicano. Rev Panam Salud Pública. 26(1):31-38.
Álvarez-Hernández, G., Lara-Valencia, F., Reyes-Castro, P. and Rascón-Pacheco, R.A. 2010. An analysis of spatial and socioeconomic determinants of tuberculosis in Hermosillo, Sonora. 2000-2006. Int J Tub Lung Dis 14 (6): 708-713.
Benedum, C.M., Seidahmed, O.M.E., Eltahir, E.A.B. and Markuzon,
N. 2018. Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore. PLoS Negl Trop Dis 12 (12):e0006935 https://doi.org/10.1371/pntd.0006935
Bhatt, S., Gething, P.W., Brady, O.J., Messina, J.P., Farlow, A.W., Moyes, C.L., et al. 2013. The global distribution and burden of dengue. Nature 496: 504-507. https://doi.org/10.1038/ nature12060
Brady, O.J., Gething, P.W., Bhatt, S., Messina, J.P., Brownstein, J.S., Hoen, A.G., et al. 2012. Refining the Global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl Trop Dis 6, 1-15 https://doi.org/10.1371/journal. pntd.0001760
Celemín, J.P. 2009. Autocorrelación espacial e indicadores locales de asociación espacial. Importancia, estructura y aplicación. Rev Univ Geogr 18: 11-31.
Cortés-Escamilla, A., Roche, B., Rodríguez-López, H.M., Lopez-Gatell-Ramírez, H. and Alpuche-Aranda, C. 2022. Spatiotemporal patterns of dengue and zika incidence during the 2015-2018 outbreak of Zika in Mexico. Salud Publica Mex 64: 478-487 https://doi.org/10.21149/13584
do Carmo, R.F., Silva Jr., J.V.J., Pastor, A.F. and Freire-deSouza,
C.D. 2020. Spatiotemporal dynamics, risk areas, and social determinants of dengue in northeastern Brazil, 2014-2017: an ecological study. Infect Dis Poverty 9:153 https://doi. org/10.1186/s40249-020-00772-6
Diez-Roux, A.V. and Aiello, A. 2005. Multilevelanalysisofinfectious diseases. J Infect Dis 191: S25-S33. Doi:10.1086/425288
Dzul-Manzanilla, F., Correa-Morales, F., Che-Mendoza, A., Palacio- Vargas, J., Sánchez-Tejeda, G., González-Roldán, J.F., et al. 2021. Identifying urban hotspots of dengue, chikungunya and Zika transmission in Mexico to support risk stratification efforts: a spatial analysis. Lancet Planet Health 5: e277-e285. Escobar-Mesa, J. and Gómez-Dantés, H. 2015. Determinantes de la transmisión de dengue en Veracruz: un abordaje ecológico para su control. Salud Publica Mex. 45, 43-53. https://dx.doi.
org/10.1590/S0036-36342003000100006
Fan, J., Wei, W., Bai, Z., Fan, C., Li, S., Liu, Q., et al. 2015. A systematic
review and meta-analysis of dengue risk with temperature change. Int J Environ Res Public Health 12: 1-15. https://doi. org/10.3390/ijerph120100001
Fuentes-Vallejo, M. 2017. Space and space-time distributions of dengue in a hyper-endemic urban space: the case of Girardot, Colombia. BMC Infect Dis 17:512 https://doi. org/10.1186/s12879-017-2610-7
Gobierno de México. 2015. Índice de marginación (carencias poblacionales) por localidad, municipio y entidad. Sonora, 2015. [Internet] México. Consejo Nacional de Población y Vivienda (CONAPO). [Consultado: Octubre 1, 2023] Disponible en:https://datos.gob.mx/busca/dataset/indice- de-marginacion-carencias-poblaciones-por-localidad- municipio-y-entidad
Gobierno de México. 2020. XII Censo Nacional de Población y Vivienda. [Internet] México. Instituto Nacional de Estadística, Geografía e Informática. Sonora. [Consultado: Septiembre 1, 2023] Disponible en: https://www.inegi.org.mx/app/scitel/ Default?ev=10
Kikuti, M., Cunha, G.M., Paploski, I.A.D., Kasper, A.M., Silva, M.O., Tavares, A.S., et al. 2015. Spatial distribution of dengue in a Brazilian urban slum setting: role of socioeconomic gradient in disease risk. PLoS Negl Trop Dis 21 (9):e0003937 https:// doi.org/10.1371/journal.pntd.0003937
Kolimenakis, A., Heinz, S., Wilson, M.L., Winkler, V., Yakob, L., Michaelikis, A. et al. 2021. The role of urbanisation in the spread of Aedes mosquitoes and the diseases they transmit—a systematic review. PLoS Negl Trop Dis 15 (9):e0009631 https://doi.org/10.1371/journal.pntd.0009631 Kourí, G. 2011. El dengue, un problema creciente de salud en las Américas. Rev Cuba Salud Publica 37: 616-618. https://doi.
org/10.1590/S1020-49892006000300001
Lega, J., Brown, H.E., Barrera, R., Editor, S. and Ritchie, S. 2017. Aedes aegypti (Diptera :Culicidae) abundance model improved with relative humidity and precipitation-driven egg hatching. J Med Entomol 54: 1375-1384. https://doi. org/10.1093/jme/tjx077
Mena, N., Troyo, A., Bonilla-Carrión, R. and Rica, C. 2011. Factores asociados con la incidencia de dengue en Costa Rica. Rev Panam Salud Pública 29(4): 234-242.
Montoya, R.H., Diego, J., Zambrano, B., Dayan, G.H. and Dick,
O.B. 2012. Review: The history of dengue outbreaks in the Americas. Am J Trop Med Hyg 87: 584-593. https://doi. org/10.4269/ajtmh.2012.11-0770
Navarro-Estupiñán, J., Robles-Morúa, A., Vivoni, E.R., Zepeda, J., Montoy,a J. and Verduzco, V.V. 2018. Observed trends and future projections of extreme heat events in Sonora, Mexico. Int J Climatol 38 (14): 5158-5181. https://doi.org/10.1002/ joc.5719
Navarro-Estupiñan, J., Robles-Morua, A., Díaz-Caravantes, R. and Vivoni, E.R. 2020. Heat risk mapping through spatial analysis of remotely-sensed data and socioeconomic vulnerability in Hermosillo, México. Urban Climate 31, 100576. https://doi. org/10.1016/j.uclim.2019.100576
Ordoñez-Sierra, G., Sarmiento-Senior, D., Jaramillo-Gomez, J.F., Giraldo, P., Porras-Ramírez, A. and Olano, V.A. 2021. Multilevel analysis of social, climatic, and entomological factors that influenced dengue occurrence in three municipalities in Colombia. One Health 12:100234 https://doi.org/10.1016/j. onehlt.2021.100234
Ogashawara, I., Li, L. and Madriñán, M.J.M. 2019. Spatial‐
temporal assessment of environmental factors related to dengue outbreaks in São Paulo, Brazil. Geohealth 3 (8): 202-
217. https://doi.org/10.1029/2019GH000186
Peña-García, V.H., Triana-Chávez, O., Mejía-Jaramillo, A.M., Díaz, F.J., Gómez-Palacio, A. and Arboleda-Sánchez, S. 2016. Infection rates by dengue virus in mosquitoes and the influence of temperature may be related to different endemicity patterns in three Colombian cities. Int J Environm Res Public Health 13, 7. https://doi.org/10.3390/ ijerph13070734
Peña-García, V.H., Triana-Chávez, O. and Arboleda-Sánchez,
S. 2017. Estimating effects of temperature on dengue transmission in Colombian cities. Ann Glob Health 83 (4): 509-518. https://doi.org/10.1016/j.aogh.2017.10.011
Peeters, A., Zude, M., Käthner, J., Ünlü, M., Kanber, R., Hetzroni,
A. et al. 2015. Getis–Ord’s hot- and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data. Comput Electron Agric 111: 140-150. https://doi. org/10.1016/j.compag.2014.12.011
Phanitchat, T., Zhao, B., Haque, U., Pientong, C., Ekalaksananan, T., Aromseree, S., et al. 2019. Spatial and temporal patterns of dengue incidence in northeastern Thailand 2006–2016. BMC Infect Dis 3:1-12. https://doi.org/10.1186/s12879-019- 4379-3
Reyes-Castro, P.A., Harris, R.B., Brown, H.E., Christopherson, G.L., Ernst, K.C. 2017a. Spatio-temporal and neighborhood characteristics of two dengue outbreaks in two arid cities of Mexico. Acta Trop 167: 174-82. http://dx.doi.org/10.1016/ jactatropica.2017.01.001
Reyes-Castro, P.A., Luque-Castro, A.L., Díaz-Caravantes, R., Walker, K.R., Hayden, M.H. and Ernst, K.C. 2017b. Outdoor spatial spraying against dengue: a false sense of security among inhabitants of Hermosillo, Mexico. PLoS Negl Trop Dis 11 (5): e0005611 https://doi.org/10.1371/journal.pntd.0005611
Sorek-Hamer, M., Just, A.C. and Kloog, I. 2016. Satellite remote sensing in epidemiological studies. Curr Opin Pediatr 28 (2): 228-34 doi:10.1097/MOP.0000000000000326
Teurlai, M., Menkes, C.E., Cavarero, V., Degallier, N., Descloux, E., Grageon, J.P., et al. 2015. Socio-economic and climate factors associated with dengue fever spatial heterogeneity: a worked example in New Caledonia. PLoS Neg Trop Dis 9 (12):e0004211 doi:10.1371/journal.pntd.0004211
Tsou, J., Zhuang, J., Li, Y. and Zhang, Y. 2017. Urban heat island assessment using the Landsat 8 data: a case study in Shenzhen and Hong Kong. Urban Sci 1 (1): 10. https://doi. org/10.3390/urbansci1010010
Watts, M.J., Kotsila, P., Mortyn, P.G., i Monteys, V.S., Brancati,
C.U. 2020. Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico. Int J Health Geograph 19, 44 https://doi.org/10.1186/s12942-020-00241-1
Yang, C., He, X., Yan, F., Yu, L., Bu, K. and Yang, J. 2017. Mapping the influence of Land use / Land cover changes on the urban heat island effect — a case study of Changchun, China. Sustainability 9 (2): 312. https://doi.org/10.3390/su9020312 Zellweger, R.M, Cano, J., Mangeas, M., Taglioni, F., Mercier, A., Despinoy, M., et al. 2017. Socioeconomic and environmental determinants of dengue transmission in an urban setting: an ecological study in Nauméa, New Caledonia. Plos Negl Trop