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      Análisis espacial del dengue y la atención primaria de salud en Alfenas, Minas Gerais, Brasil Translated title: Spatial analysis of dengue and Primary Health Care in Alfenas, Minas Gerais, Brazil

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          Abstract

          Sr. Director: El dengue es una enfermedad reemergente, de creciente importancia para la salud global 1 , y el conocimiento de patrones espaciales de la infección por el virus es esencial para comprender la dinámica de transmisión y orientar estrategias de prevención eficaces 2 . Además de la eliminación del mosquito transmisor, hoy prácticamente inaplicable, se cree que una buena alternativa para mejorar este problema es la integración de Programas Gubernamentales de Control del Dengue con atención primaria de salud (APS). Esta articulación es viable, representa la optimización de recursos al evitar la duplicación de acciones, y permite una mayor participación de la comunidad en el control de la enfermedad 3 . El objetivo de este trabajo es investigar la incidencia del dengue en Alfenas-Minas Gerais (MG) y describir la distribución espacial de los casos confirmados por áreas urbanas de cobertura de la estrategia de salud de familia (ESF). El área de estudio es la ciudad de Alfenas, ubicada al sur de Minas Gerais, Brasil, con 849,2 km2 de extensión y con 73.774 habitantes en el año 2010. Se trata de un estudio descriptivo transversal y análisis espacial en salud, se utilizaron datos secundarios sobre notificaciones de casos autóctonos de dengue, de enero de 2001 a diciembre de 2010. Este estudio, aprobado por el Comité de Ética de la Universidad Nacional de Alfenas-UNIFAL-MG, conforme al protocolo n.° 158/2010, mostró que hubo 489 notificaciones de dengue en la década estudiada. De las notificaciones de casos urbanos, en 177 individuos (37,18%) se confirmó dengue clásico. No se registraron complicaciones clínicas graves, ni tampoco ninguna defunción entre los mismos. En relación al tiempo y análisis espacial, las mayores tasas de incidencia y las concentraciones de casos más expresivos fueron observadas en los años 2002, 2007 y 2010. Alfenas contaba en el momento del estudio con poco más del 60% de cobertura de atención primaria a la población. La distribución espacial de los casos según las zonas urbanas beneficiadas por APS reveló que en la mayoría de los años los grupos de casos de dengue no coincidieron con las regiones atendidas por la estrategia de salud en la familia (ESF). La figura 1 presenta mapas de Kernel con grupos de casos de dengue y las zonas que se beneficiaron de la APS. Se observa que muchas zonas con alta densidad espacial de casos no convergían con las áreas cubiertas por los equipos de salud en la familia, en la mayoría de los años descritos, lo que permite elaborar la hipótesis de que el aumento cuantitativo y cualitativo de la APS en la comunidad constituye un facilitador para la prevención y control de la enfermedad en el territorio urbano. Esta probable asociación coincide con recientes estudios que destacaron la importancia de la participación de profesionales en la APS en la lucha contra el dengue4, 5, 6. Figura 1 Densidad de casos de dengue y las zonas cubiertas por la estrategia de salud en la familia en Alfenas, Minas Gerais, Brasil, entre los años 2001 y 2010. En síntesis, el dengue incidió de manera más expresiva en marzo, abril y mayo de los años 2002, 2007 y 2010. Los casos presentaron distribución espacial bastante heterogénea por el territorio, con densidad más elevada en las zonas desprovistas de APS. Creemos que los profesionales de salud actuantes en el nivel primario deben ser considerados socios importantes en la prevención y control de la enfermedad, y sugerimos que se dirijan estudios para investigar el impacto de la cobertura de la APS en la incidencia y densidad espacial del dengue en este y otros contextos municipales. La encuesta se llevó a cabo como una actividad de la Maestría en Salud Comunitaria de la Facultad de Medicina de Ribeirão Preto de la Universidad de São Paulo (USP), y no se basó en el apoyo financiero de las agencias.

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          House-to-house human movement drives dengue virus transmission.

          Dengue is a mosquito-borne disease of growing global health importance. Prevention efforts focus on mosquito control, with limited success. New insights into the spatiotemporal drivers of dengue dynamics are needed to design improved disease-prevention strategies. Given the restricted range of movement of the primary mosquito vector, Aedes aegypti, local human movements may be an important driver of dengue virus (DENV) amplification and spread. Using contact-site cluster investigations in a case-control design, we demonstrate that, at an individual level, risk for human infection is defined by visits to places where contact with infected mosquitoes is likely, independent of distance from the home. Our data indicate that house-to-house human movements underlie spatial patterns of DENV incidence, causing marked heterogeneity in transmission rates. At a collective level, transmission appears to be shaped by social connections because routine movements among the same places, such as the homes of family and friends, are often similar for the infected individual and their contacts. Thus, routine, house-to-house human movements do play a key role in spread of this vector-borne pathogen at fine spatial scales. This finding has important implications for dengue prevention, challenging the appropriateness of current approaches to vector control. We argue that reexamination of existing paradigms regarding the spatiotemporal dynamics of DENV and other vector-borne pathogens, especially the importance of human movement, will lead to improvements in disease prevention.
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            Spatial Dimensions of Dengue Virus Transmission across Interepidemic and Epidemic Periods in Iquitos, Peru (1999–2003)

            Introduction Dengue viruses (DENVs) cause more human morbidity and mortality worldwide than any other arthropod-borne virus [1], [2]. The principal vector is Aedes aegypti, a highly anthropophilic mosquito with relatively short dispersal tendencies that is known to bite people primarily during daylight hours as they engage in their daily activity patterns [3], [4], [5]. Interactions between relatively mobile humans and relatively sedentary mosquitoes are processes that underlie the dynamics of DENV transmission through space and time. Human movement can transport virus across small (households and neighborhoods) and large scales (village, city, country, and international) [6], [7], [8]. The contribution of infected female mosquitoes is restricted to short-range flight dispersal (household, neighborhood) [3], [9], [10], [11], [12], [13]. At small scales, several investigators have described dengue cases clustered within a household or neighboring houses [14], [15], [16], [17], [18], [19], [20]. The spatial dimension of DENV transmission beyond this very local scale of a household and its neighbors has been difficult to measure and thus a challenge to define [6], [21], [22]. One limitation is that previous investigations tended to focus on individuals with detectable dengue disease (fever and more severe illness), which represents only a fraction of all infections because, typically, a significant portion of human dengue infections are inapparent [23], [24], [25]. Transmission patterns of DENV in a geographic area are influenced by complex immunological interactions among the four closely related, antigenically distinct dengue viruses (DENV-1, DENV-2, DENV-3 and DENV-4) that make up the DENV complex [26]. Infection with one or more serotype(s) can result in a range of clinical outcomes, from asymptomatic infection to classic dengue fever, to more serious dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). Because serotypes are antigenically distinct, primary infection confers lifelong homologous immunity, whereas heterologous cross-protection is short-lived [1], [27], [28], [29]. In many parts of Southeast Asia, especially in large urban centers, endemic transmission of all four serotypes occurs and broad scale, super-annual oscillations in dengue incidence have been reported [30]. In contrast, in many parts of Central and South America, epidemic transmission over the past three decades has occurred in distinct waves that are associated with invasion and amplification of single DENV serotypes or genotypes [31]. In those settings, endemic transmission patterns can be influenced by ambient levels of serotype-specific herd immunity and epidemic transmission can occur when a novel virus serotype enters a region where the majority of the population is immunologically naïve [17]. Since the re-introduction of DENV-1 into Iquitos during 1990, the city has experienced continuous DENV transmission with major epidemics occurring in association with novel serotype introductions. DENV-1 was introduced in 1991 and DENV-2 in 1995 [32], [33], [34]. By 2000, DENV-1 and DENV-2 were being transmitted at low, consistent levels from year to year. DENV-3 was then introduced in 2001 [35] and by late 2002 had replaced DENV-1 and DENV-2 as the dominant circulating serotype, causing a major outbreak of febrile illness. The local Peruvian Ministry of Health (PMoH) reacted by implementing household-level mosquito interventions (i.e., spraying insecticide inside houses), which appears to have truncated the epidemic [25]. To more carefully delineate patterns of transmission in Iquitos, we examined serological data from a city-wide, long-term, prospective, longitudinal study to describe and compare the spatial dimensions of DENV transmission across periods that transitioned from relatively low to high force of infection; i.e., from interepidemic to epidemic transmission. We compared clustering of infections (global and local) to test the hypothesis that the invasion of a new virus serotype or genotype follows a specific pattern: rapid, broad scale geographic spread at low levels [25], followed by large clusters of increased force of infection in predictable geographic regions. We also compared clustering of acute cases, as defined by Morrison et al 2001 [25]. At distances less than 100 m, we suspect that both mosquitoes and humans participate in virus transmission [3], , but beyond 100 m, it is likely primarily humans that define the spatial dimensions of the clusters. Previous research indicates that human movement often occurs well beyond a 100 m radius from their home (Vazquez Prokopec, Paz Soldan, Elder, Stoddard unpublished). Our results provide details on the dynamics of DENV invasion and establishment, have implications for evaluating intervention strategies, can be used to enhance dengue surveillance and prevention programs, and may be applicable to an improved understanding of transmission dynamics of other mosquito-borne pathogens. Methods Human Use Statement The study protocol was approved by the University of California, Davis (Protocol 2220210788-4(994054), Instituto Nacional de Salud, and Naval Medical Research Center (Protocol #NMRCD.2001.0008 (DoD 31574) Institutional Review Boards in compliance with all Federal regulations governing the protection of human subjects. STROBE checklist included in supporting information (Checklist S1). Study area and population Our cohort study was conducted in Iquitos, an isolated city of ∼400,000 people in the northeastern Amazon Basin portion of Peru. We divided the city into eight distinct geographical zones for our study (described in detail in Morrison et al 2004 [36]). The population consists of an approximate 1∶1 sex ratio, and about 36% of the population < = 17 years of age (http://desa.inei.gob.pe/).Written informed consent was obtained from participants older than 17 years and from parents of participants younger than 18. In addition, assent was obtained from participants 8–17 years of age. If participants were unable to read and sign the consent form, oral consent was obtained and documented. Blood samples were obtained from participants between January 1999 and August 2003, as described in Morrison et al 2010 [25] (Figure 1). Serum samples were tested for antibody to DENV serotypes based on plaque reduction neutralization test (PRNT) as described previously [25]. Briefly, heat-inactivated sera were incubated with DENV (DENV-1:16007; DENV-2: 16681; DENV-3: IQT1728) prior to inoculation onto BHK-21 cells. The level of neutralization used for the cutoff was PRNT70, with cutoff dilutions of 1∶60, 1∶80 and 1∶60 for DENV-1, DENV-2 and DENV-3, respectively. For more details, see Morrison et al 2010 [25]. 10.1371/journal.pntd.0001472.g001 Figure 1 Distribution of cohort participant houses throughout the 8 zones of Iquitos. Each circle marks the location of a participant home. Extension of Morrison et al 2010 Morrison et al 2001 [25] provide a detailed description of overall patterns of DENV transmission in Iquitos from 1999–2005. In that study, the authors described data collected through active school-based surveillance and a longitudinal cohort, showing increasing seroprevalence of DENV-3 throughout the study. Here we extend these observational results with a more detailed analysis of spatial patterns in human infection. Cases and controls Infections (i.e., cases) for our analyses below were defined as participants who showed evidence of serotype-specific seroconversion between two paired blood samples, based on PRNT. Controls were defined as susceptible, seronegative participants who had no serological evidence of serotype-specific infection between paired blood samples. Both cases and controls were limited to participants whose paired blood samples were less than 242 days apart, corresponding to the 75% quartile of the distribution of intervals. Analyses of the dataset were run independently for each distinct serotype. A trimester-specific dataset was constructed containing all cases and controls with middates falling within that period. Yearly trimester intervals (Jan–Apr: end of the high transmission season, May–Aug: low transmission season, and Sept–Dec: beginning of the high transmission season) were chosen for analyses because they capture intra-annual variation in the Iquitos DENV transmission season. Although there is variation from year to year, the high DENV transmission season in Iquitos generally occurs from Sept–Apr, unless emergency vector control interventions decrease transmission prior to April. In general, consistently lower rates of transmission were observed between May and July [25], [31]. Seroprevalence At the initiation of the cohort (Jan 1999), DENV-1 and DENV-2 were circulating at low levels throughout Iquitos. The baseline serostatus of participants was determined over the eight geographic zones using samples collected between January and October of 1999 [25]. To measure the prevalence of antibodies for DENV-3 throughout the course of its invasion into Iquitos, overall participant serostatus was calculated using the most recent sample taken from each participant, beginning in Aug 2002. As with baseline seroprevalence rates, cumulative seroprevalence of DENV-3 was estimated for the eight Iquitos geographical regions from Jan 2002–May 2003. To determine if the invasion of DENV-3 was positively correlated with the baseline seroprevalence of DENV-1/DENV-2 across the city prior to its introduction, we conducted a Spearman Rank correlation test, comparing the DENV-3 seroprevalence by zone in each trimester to the baseline DENV-1/DENV-2 seroprevalence. For this analysis, as the ρ coefficient approaches −1 or 1 the two patterns are more negatively or positively correlated, respectively. Sampling effort across the eight geographical zones varied, due primarily to the location of commercial versus residential areas of the city (Figure 2). To visualize the spatial pattern of accumulating seroprevalence throughout the study period, we used spatial kernels based on the intensity of points in the study area for each trimester. Points were the coordinates of participants' homes. We first generated a kernel for the pattern of all participants with a middate in a given trimester. Similarly, we then generated a kernel of individuals immune to DENV-3 (presenting with antibody in the first blood sample). Because of the nature of kernel estimation, estimates where there are no points nearby tend to be small. Calculating the ratio of immune to all participants to calculate seroprevalence would, therefore, result in artificially high estimates when both of these values were small. For the kernel of immune individuals, we thus set all values falling into the first quartile to zero in order to avoid gross overestimates of seroprevalence. All kernels were estimated assuming a standard, isotropic Gaussian kernel with fixed bandwidth (σ = 175), which was determined by visual inspection of the results. All analyses were conducted using the SpatStat package of the R Statistical Computing Environment [37]. 10.1371/journal.pntd.0001472.g002 Figure 2 Distribution of all participants sampled during 4-month interval. Black = DENV-3 susceptible, red = DENV-3 infected, green = DENV-3 immune. Spatial analyses To determine the spatial dynamics of DENV transmission, we examined patterns of both global and local clustering. The global analysis was used to examine the spatial pattern of seroconversions within a defined reference window (the eight zones) and, by comparison to the pattern of controls, to test the hypothesis that seroconversions clustered in space. On the other hand, local clustering uses a specific spatial window to look for the over-dispersion of cases in comparison to controls within that window. This window of delimited radius X moves across the geocoded data for each time interval and indicates when cases appear in greater numbers than expected. Global pattern clustering Global patterns of dengue seroconversions were analyzed within each trimester using a case-control Ripley’s K statistic [38]. When used within a Monte Carlo framework, this allows for inferences to be made about the structure of a point pattern. To test the hypothesis that dengue seroconversions clustered in space, we simulated an inhomogeneous K function (because of the heterogeneous sampling pattern) comparing random samples of control points to cases of dengue infection in each time interval using the participants’ home as their spatial reference. Simulations were conducted by resampling the control points with replacement 99 times to estimate the minimum and maximum K functions for susceptibles, generating a 99% probability envelope. The number of control points sampled was always equivalent to the number of cases. Within this framework, deviation of the K function for DENV seroconversions outside of the probability envelope at a specific distance was evidence of global clustering (if above the envelope) or repulsion (if below the envelope) and supports rejection of the null hypothesis that the patterns are equivalent at a probability level of 0.01 [37]. Local pattern clustering Local clustering of serotype-specific DENV seroconversions was detected using the Kuldorff spatial scan statistic [39], [40] with SaTScan software verson 8.1.1 (http://www.satscan.org). This method uses a retrospective spatial Bernoulli probability model to detect significant spatial clustering of cases. Briefly, using the Bernoulli probability distribution with Monte Carlo simulation repeated 999 times, the model tests the statistical likelihood of the distributions of cases in relation to controls for a moving circular spatial window, determining if a greater than expected number of cases occurred in an area. We assessed patterns across several different spatial radii (100, 300, 600 and 900 m) for each serotype-specific trimester dataset. Spatial scan analyses were only run for a specific serotype and trimester when more than two seroconversions were observed during that time period. In total, 92 analyses were conducted (32 for DENV-1, 32 for DENV-2, and 28 for DENV-3). Identical SaTScan analyses were run on 64 laboratory confirmed, acute, apparent DENV infections captured by active surveillance for febrile illness, as described previously (for details see Morrison et al [25] and Rocha et al [41]). Results Study population A total of 3,110 participants met the inclusion criteria for our analyses. As described in Morrison et al 2010 [25], the majority of participants (n = 2,393, 76.93%) were under the age of 18. The number of males (n = 1,305, 41.96%) was less than that of females. Cases and Controls Over the course of the study period, we detected a total 607 seroconversions to DENV (68 to DENV-1, 58 to DENV-2 and 481 to DENV-3; Table 1). The total number of controls for each serotype/trimester is presented in Table 1. The population included in the analyses varied by serotype for specific years and trimesters, due to changes in serotype-specific immunity over time. 10.1371/journal.pntd.0001472.t001 Table 1 Total serotype-specific cases and controls by trimester. DENV-1 YEAR TRIMESTER CASES CONTROLS POPULATION 1999 MAY–AUG 3 285 288 2000 JAN–APR 10 599 609 2000 SEPT–DEC 6 352 358 2001 JAN–APR 9 296 305 2001 SEPT–DEC 8 289 297 2002 JAN–APR 16 271 287 2002 MAY–AUG 7 392 399 2002 SEPT–DEC 9 344 353 DENV-2 YEAR TRIMESTER CASES CONTROLS POPULATION 1999 MAY–AUG 6 241 247 2000 JAN–APR 18 505 523 2000 MAY–AUG 4 294 298 2000 SEPT–DEC 9 280 289 2001 JAN–APR 3 237 240 2002 JAN–APR 3 236 239 2002 MAY–AUG 6 361 367 2002 SEPT–DEC 9 343 352 DENV-3 YEAR TRIMESTER CASES CONTROLS POPULATION 2001 JAN–APR 4 747 751 2001 MAY–AUG 0 391 391 2001 SEPT–DEC 28 970 998 2002 JAN–APR 69 676 745 2002 MAY–AUG 233 977 1210 2002 SEPT–DEC 130 727 857 2003 JAN–APR 17 520 537 Seroprevalence From samples taken between January and October 1999, a total of 1,999 participants met the inclusion criteria listed above. Of those, monotypic neutralizing antibodies (NtAbs) against DENV-1 and DENV-2 were detected in 12% (n = 240) and 14.7% (n = 294), respectively. A total of 1,054 participants (52.7%) had detectable NtAbs against both DENV-1 and DENV-2 at baseline, whereas 20.6% (n = 411) showed no evidence of prior dengue infection. These percentages are similar to those reported by Morrison et al 2010 [25], indicating that the population meeting the inclusion criteria was similar in serostatus to those of the overall cohort. When analyzed by zone, DENV-1 and DENV-2 seroprevalence show a distinct geographic structure (Figure 3), similar to that previously described by Morrison et al 2010 [25]. At the initiation of the study, the prevalence of DENV-3-specific antibodies was low (<5%), but rapidly increased city-wide over the course of the study with distinct differences between city zones. 10.1371/journal.pntd.0001472.g003 Figure 3 Seroprevalence of DEN-1 and DEN-2 in the 8 geographic zones of Iquitos in October 1999. Clusters of DENV-1 are indicated by colored circles (purple = May–Aug 1999; red = Jan–Apr 2000; pink = Sep–Dec 2001; green = Jan–Apr 2002). No significant clusters of DENV-2 were identified during the study period. Invasion of DENV-3 into Iquitos is clearly demonstrated by changes in temporal and geographic seroprevalence patterns (Figure 4, Figure 5, Table 2). Transmission was first detected in the city center (Iquitos [IQ]) during the May–Aug 2001 trimester (2001.2) and then was detected at rates of less than 5% in all but one of the eight geographic zones during the Sep–Dec 2001 trimester. Although seroprevalence rates remained low throughout the city during the first part of 2002, virus was concentrated in the northern zones of Maynas (MY) and IQ. As indicated by Morrison et al 2010 [25], other zones lagged behind MY and IQ, with most failing to reach the levels of seroconversion observed in MY, where the highest overall DENV transmission rates occurred. 10.1371/journal.pntd.0001472.g004 Figure 4 Seroprevalence of DEN-3 by trimester. Pink circles indicate significant clusters. 10.1371/journal.pntd.0001472.g005 Figure 5 Seroprevalence kernel of DENV-3 by trimester. 10.1371/journal.pntd.0001472.t002 Table 2 Seroprevalence rates DENV-3 by trimester in the 8 regional zones of Iquitos. 2001 2002 2003 ZONE MAY–AUG SEPT–DEC JAN–APR MAY–AUG SEPT–DEC JAN–APR MAY–AUG BG 0% 0% 2% 2% 12% 13% 13% TA 0% 1% 3% 6% 18% 20% 20% PT 0% 1% 3% 7% 13% 15% 15% IQ 1% 2% 6% 11% 16% 16% 16% PU 0% 1% 2% 7% 15% 19% 19% MC 0% 1% 4% 7% 20% 22% 22% MY 0% 1% 6% 12% 27% 28% 29% SA 0% 1% 2% 3% 15% 16% 17% The Spearman ρ correlation coefficient comparing the DENV-3 seroprevalence in the first trimester of 2002 with the baseline seroprevalence of DENV-1/DENV-2 is close to zero (0.24) and is not statistically significant (p = 0.58). This implies little to no correlation between the two patterns. The correlation between DENV-1/DENV-2 seroprevalence and DENV-3 seroprevalence increased in each subsequent trimester, with ρ = 0.48 (p = 0.24) and 0.62 (p = 0.12) in the second trimester and third trimesters of 2002, respectively. In the first trimester of 2003, ρ increased to 0.67 (p = 0.08). It is important to note that these data only follow the invasion of DENV-3 through its first two years in Iquitos. Due to a change in the sampling scheme after the 2002–2003 transmission season, we were unable to compare the seroprevalence data from the final cohort sample collected in 2005. Global patterns To facilitate visual interpretation of the Monte Carlo K-function results, we plotted the ratio of the difference between the K functions for cases and controls over the K-function for controls. We failed to reject the null hypothesis that the pattern of cases was different from that of controls if the cases K function fell between the high and low control K functions (99% probability envelope). For DENV-1 and DENV-2, the number of seroconversions within any given trimester was small and always consistent with the pattern of controls. When considering all seroconversions within an epidemiological season, DENV-1 showed evidence of clustering at ∼200 m during the 2001–2002 season. For DENV-2 the pattern of seroconversions was more widely dispersed than expected during the 2000–2001 season at distances of ∼220 m–500 m. Overall, however, there was little evidence for spatial structuring of DENV-1 and DENV-2. In the case of DENV-3, we observed that during the first trimester when seroconversions occur (2001.3), the pattern of cases was no different from that of controls, providing evidence that this new serotype had rapidly spread within one trimester at low levels to all parts of the city (Figure 6). In the next trimester (2002.1), however, the pattern was highly over-dispersed relative to controls across a range of scales from 200 m to ∼700 m. Examination of the pattern of seroconversions was consistent with there being a focus in the northern region of the city in the MY zone with additional seroconversions radiating out from there. During the next trimester (2002.2) the pattern of cases again indicated overdispersion, but at broader scales ∼800 m. During the third trimester (2002.3), DENV-3 seroconversions appeared more clustered at small scales - ∼15 m and ∼180 m – possibly reflecting transmission among neighboring households and within neighborhoods. During the first trimester of 2003, after the PMoH intervention, the pattern of cases was no different than that of controls. 10.1371/journal.pntd.0001472.g006 Figure 6 Monte Carlo K statistic. The difference between the observed and expected over the expected is plotted against distance, r. The grey envelope is the 99% probability envelope based on the pattern of controls and the black line is the K function ofor the cases (seroconversions). If the line strays above the envelope, this indicates clustering. If it strays below, this indicates repulsion or over dispersion. When analyzed by epidemiological season, DENV-3 seroconversions were highly clustered relative to expectation up to ∼100 m during the initial invasion phase during the 2002 season (Jul 1 2001–Jun 30 2002). For the 2003 season (Jul 1 2002–Jun 30 2003) cases were equivalent to controls. The fact that at a broader temporal scale (season) the pattern of seroconversions was clustered during the 2002 season, while the pattern by trimesters was over-dispersed, appears to reflect the existence of alternate, neighborhood-scale (∼200–800 m) foci occurring at different times during the invasion and establishment of the virus. Local patterns From the 92 spatial scan analyses run, a total of 23 significant (p = <0.05) clusters were identified: eight for DENV-1 and 15 for DENV-3 (Table 3). Of these, 65% (n = 15) had radii greater than 100 m. No significant clusters were detected for DENV-2 during any trimester. Similarly, we detected no significant clusters of febrile cases captured by active surveillance. 10.1371/journal.pntd.0001472.t003 Table 3 Significant clusters of DEN-1 and DEN-3 from 1999 through April of 2003. CLUSTERS OF DENV-1 Year Trimester Parameter (meters) Cases Controls Radius (meters) P-value 1999 MAY–AUG 100 3 0 8.7 < = .001 2000 JAN–APR 600 3 3 330 0.015 2001 SEPT–DEC 300 4 4 200 0.006 2001 SEPT–DEC 600 6 15 530 < = .001 2001 SEPT–DEC 600 5 17 580 0.015 2001 SEPT–DEC 900 6 23 750 0.008 2002 JAN–APR 600 8 23 600 0.032 2002 JAN–APR 900 10 35 610 0.01 CLUSTERS OF DENV-3 Year Trimester Parameter (meters) Cases Controls Radius (meters) P-value 2001 SEPT–DEC 100 3 0 0 0.015 2001 SEPT–DEC 300 8 29 130 0.004 2002 JAN–APR 100 6 4 33 0.022 2002 JAN–APR 300 18 45 260 0.006 2002 JAN–APR 300 13 30 270 0.032 2002 JAN–APR 600 34 120 600 < = .001 2002 MAY–AUG 300 20 12 230 0.002 2002 MAY–AUG 300 31 43 250 0.011 2002 SEPT–DEC 100 21 14 98 < = .001 2002 SEPT–DEC 100 7 0 7.2 0.002 2002 SEPT–DEC 100 9 2 82 0.003 2002 SEPT–DEC 100 5 0 18 0.026 2002 SEPT–DEC 100 5 0 40 0.026 2002 SEPT–DEC 300 30 22 190 < = .001 2002 SEPT–DEC 300 33 48 150 < = .001 No significant clusters of DENV-2 were identified during the study period. Clusters of seroconversions to DENV-1 were spread throughout the city, with different high-risk zones each trimester (Figure 3). No visual pattern appeared to link transmission clusters from one period to the next. For epidemic DENV-3, however, a more apparent pattern emerged. As time passed, clusters concentrated in areas following the distinct pattern of seroprevalence detected for DENV-1 and DENV-2 during 1999 (Figure 3, Figure 4). Clusters began in the north of the city, where overall DENV seroprevalence rates were highest. For example, in the northern region of the city in the second trimester of 2002 statistically significant clusters are observed in MY, where seroprevalence rates increased from 12% in the second trimester of 2002 to 27% in the third. In contrast, in other zones with significant but overall lower DENV transmission, we were unable to identify statistically significant clustering. Discussion Understanding the underlying spatial dimensions of DENV transmission in different epidemiological contexts will improve our understanding of virus transmission dynamics and aid in the design and deployment of preventative and emergency control measures. While we did not detect spatial clustering among people with clinically apparent DENV infections, all infections (i.e., apparent and inapparent) exhibited spatially distinct transmission patterns. Although details of DENV transmission differed between interepidemic and epidemic periods, we speculate that in Iquitos human movement is a unifying process in delimiting virus transmission across different epidemiologic contexts. During the interepidemic period (1999–2001), DENV-1/DENV-2 incidence rates were highest in the southern regions of the city where overall seroprevalence was lowest [25]. Significant clustering of DENV-2 was not detected throughout this study, likely due to the fact that only 49 seroconversions were detected during the study period. However, we did detect significant clusters of DENV-1 seroconversions in northern regions of the city, where DENV seroprevalence was highest. Mosquito populations monitored throughout the study showed high adult Ae. aegypti indices in the northern zone of Maynas, where seroprevalence was also highest, indicating an elevated level of infection risk [25], [36]. Our results thus indicate that a positive relationship may exist between mosquito abundance and the risk of human DENV infection. At the levels we observed, herd immunity was insufficient to counteract risk from other factors, such as elevated mosquito exposure, which could serve as a catalyst for neighborhood-specific variation in DENV transmission. The relationship between entomological indices and DENV transmission has been difficult to define [21], [22]. Malariologists reported that increased infective mosquito bites per person per day [entomological inoculation rate (EIR)] correlates with higher malaria incidence in humans [42], [43], [44], [45]. A similar relationship has not, however, been established for DENV and Ae. aegypti, but is worth exploring in greater detail [46]. We did not identify spatial patterns among 64 people with overt illness, implying that defining spatial transmission patterns based solely on clinically apparent DENV infections can be misleading. Spatial patterns for seroconversions were more easily detected during the DENV-3 invasion than during the interepidemic period. After initially spreading rapidly and evenly at low rates throughout the city, distinct spatial patterns of DENV-3 transmission were detected. For example, MY, the first zone to experience clusters of DENV-3, also had the highest combined seroprevalence rates for DENV-1 and DENV-2 prior to the DENV-3 introduction. DENV-3 transmission started earlier and was higher overall in MY than in the rest of the city. This observation was consistent with there being underlying spatial heterogeneity of DENV transmission in Iquitos, perhaps associated with some aspect of the mosquito populations, that puts persons living in certain parts of the city at higher intrinsic risk of infection than people in other locations. As DENV-3 invaded the city, its establishment closely followed the geographic patterns observed for seroprevalence of the DENV serotypes that preceded it. While the results of the correlation rank test were not statistically significant at a 0.05 level, the pattern of DENV-3 seroprevalence appeared to be converging on that of DENV-1/DENV-2. Had we maintained the same sampling scheme throughout the remainder of the DENV-3 invasion, we would anticipate the correlation of the patterns to become significant. Historic, geographically distinct seroprevalence patterns, therefore, may be useful for prioritizing dengue surveillance (i.e., infection in humans and/or mosquitoes) and prevention (i.e., mosquito control or vaccination). Preemptive intervention could be directed towards areas that are considered particularly susceptible to elevated risk of virus transmission and from which virus could spread to other locations. Focusing control on areas of high transmission may help diminish the impact of a novel virus invasion. The effect that targeting areas with high levels of transmission may have on transmission at broader geographic scales remains to be determined. Targeting areas that theoretically contribute most to transmission [47], [48] is attractive because it could be a more effective way to use limited resources for disease prevention than uniform application over large geographic areas [21], [22]. Transmission foci during epidemic and interepidemic periods had radii that exceeded 100 m in 65% of significant clusters, indicating that spatial dimensions for DENV transmission extended beyond the level of individual households and the flight range Ae. aegypti [3]. The relative role of humans versus mosquitoes in DENV movement within 100 m has not been resolved. At greater distances, human movement of virus appears to be an important factor in defining the spatial dimensions of DENV transmission regardless of the epidemiologic context; i.e., interepidemic and epidemic periods as well as during the invasion and establishment of a novel virus (Stoddard et al in prep). Since the completion of our study, DENV-3 caused significant outbreaks of disease in 2004, 2006–2007, and again in Feb 2008 when the first isolates of DENV-4 in Iquitos were recovered from participants in a clinic-based fever study [31]. Following its introduction, DENV-4 became the main circulating virus throughout the city until late 2010 when an Asian-American strain of DENV-2 was introduced into Iquitos, causing a large city-wide outbreak [49], [50]. Analyses of the spatial dimensions of these two serotype introductions would be useful for determining whether the patterns remain consistent. Because subsequent epidemiological studies used distinct spatial designs and had distinct research objectives we are unable to include those data in this presentation, but we can confirm that MY was one of the first effected during both the DENV-4 and DENV-2 outbreaks (Scott and Morrison unpublished). Our study had three notable limitations. First, since we only know the time interval during which infection occurred but not its exact date, we may not have identified all significant virus transmission clusters. DENV transmission and spread in a community can happen rapidly. Although drawing blood at six-month intervals is appropriate for the analysis of seroprevalence over time, a shorter temporal window between assessments of serostatus might improve resolution in spatial analyses of DENV invasion. This limitation might in part account for our inability to predict clusters in certain areas where seroprevalence rates quickly increased over time. This could occur because the period between two blood draws may span two trimesters. For instance if the first sample was taken November 30 2001 and the second April 28 2002, the middate would occur in the first trimester of 2002, whereas the actual infection might have occurred in the third trimester of 2001. Despite these limitations, our analyses still provide insights across all infections, including those that were inapparent. Second, because of our sampling scheme, we focused our spatial analyses of seroprevalence at the zone level. Dividing the cohort into smaller units (i.e., blocks) would not properly represent the population. Third, our school-based active surveillance sampling scheme may not have identified all acute DENV infections and thus contributed to the lack of detectable spatial patterns among clinically apparent infections. In subsequent cohort studies, we used a different sampling scheme in an effort to examine transmission patterns at smaller spatial scales. Current World Health Organization (WHO) guidelines, which recommend vector control be administered to households within a 400 m radius of a dengue case, are consistent with the transmission clusters we detected [51]. Vector control at this scale in a city such as like Iquitos could, however, require that hundreds of houses be treated for each case. Consequently, in practice a radius of 100 m is often adopted. Even though small scale clustering is clearly important, our local and global analyses identified a significant proportion of spatial clusters that extended well beyond 100 m. Our results, therefore, provide an additional explanation for why vector control within 100 m of a dengue case has been less successful than desired for larger scale disease prevention [21], [22]. Results from our cluster analyses indicate that if areas of primary invasion and/or elevated amplification can be treated based on historical patterns of transmission (i.e., seroprevalence) either a priori or reactively, it may be feasible to block virus dispersal. The majority (93%) of clusters we detected had radii <400 m, indicating that neighborhoods where initial cases are identified could be intervention targets. A substantial challenge for reactive control will be the need to quickly apply the intervention at the correct locations during the early phases of invasion when most human infections are clinically inapparent and therefore difficult to detect in disease-based surveillance systems [24]. Alternatively, it may be more productive to focus on characterizing historical seroprevalence patterns as a means for prioritizing spatially targeted surveillance and preemptive intervention. The DENV-3 Iquitos invasion occurred more rapidly and at larger scales than we were able to quantify with our longitudinal cohort study. Future research could explore the preemptive implementation of surveillance and control in areas where the highest initial incidence was previously detected, and from where virus may disseminate to the other parts of the city. Supporting Information Checklist S1 STROBE checklist. (DOC) Click here for additional data file.
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              Controle do dengue em uma área urbana do Brasil: avaliação do impacto do Programa Saúde da Família com relação ao programa tradicional de controle

              Avaliaram-se os resultados da integração do Programa Saúde da Família (PSF) e do Programa de Controle do Dengue em São José do Rio Preto, São Paulo, Brasil. O estudo foi realizado em uma área com PSF e outra sem PSF. Na primeira, os agentes comunitários de saúde, de modo integrado com as suas atribuições, orientavam os moradores sobre as medidas de controle de dengue, incentivando-os a realizá-las. Na segunda área, os agentes de controle de vetores orientavam os moradores e realizavam as atividades de controle de criadouros. Conduziram-se, de outubro de 2001 a janeiro de 2003, levantamentos para mensurar conhecimentos e práticas dos moradores sobre dengue. Na área com PSF comparativamente com a sem PSF as proporções de entrevistados que afirmaram ter como fonte de informação o serviço de saúde apresentaram aumento significante. Nas duas áreas ocorreram mudanças significantes em termos de ganhos de conhecimento e diminuição de recipientes. Os resultados mostraram que a integração entre os dois programas é viável, representa otimização de recursos ao evitar a duplicidade das visitas e possibilita um maior envolvimento da comunidade no controle do dengue.
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                Author and article information

                Contributors
                Journal
                Aten Primaria
                Aten Primaria
                Atencion Primaria
                Elsevier
                0212-6567
                1578-1275
                14 May 2014
                October 2014
                14 May 2014
                : 46
                : 8
                : 449-451
                Affiliations
                [a ]Facultad de Medicina de Ribeirão Preto, Universidad de São Paulo (USP), São Paulo, Brasil
                [b ]Universidad Federal de Alfenas-UNIFAL-MG, Minas Gerais, Brasil
                Author notes
                Article
                S0212-6567(14)00131-0
                10.1016/j.aprim.2013.12.016
                6983617
                24837406
                5bfe6499-eb6b-45b1-991d-ed018aff50dd
                .

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

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