Dengue infection in Paracambi, State of Rio de Janeiro, 1990-1995

Cunha, Rivaldo Venâncio da; Maspero, Renato; Miagostovich, Marize Pereira; Araújo, Eliane Saraiva Machado de; Luz, Daniele da C.; Nogueira, Rita Maria Ribeiro; Schatzmayr, Hermann G.

doi:10.1590/s0037-86821997000500005

Cited by 10 publications

(8 citation statements)

References 10 publications

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“…Waterman et al . (1985) found no significant difference in sex during the investigation of a dengue epidemic in Puerto Rico, whereas in several epidemics in Brazil, the attack rates in females were higher (da Cunha et al . 1997, 1999; Vasconcelos et al .…”

Section: Discussionmentioning

confidence: 98%

Risk factors associated with an outbreak of dengue fever in a favela in Fortaleza, north‐east Brazil

Heukelbach

Oliveira

Kerr

et al. 2001

Tropical Med Int Health

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SummaryTo increase the effectiveness of ongoing anti-dengue control measures, we conducted a case±control study in a favela in Fortaleza (north-east Brazil) during an outbreak of dengue fever. Cases were de®ned according to the national guidelines for dengue control as well as based on the detection of IgM-dengue antibodies, and 34 cases and 34 controls were investigated. Signi®cant risk factors were: living in a street perpendicular to the beach on which the favela is situated (P < 0.0001), an interval > 30 days since the last visit of the vector control agent (P 0.001), receptacles in the garden or courtyard (P 0.001), plants with temporary water pools on the property, gutter to collect rainwater, uncovered water storage container (all P 0.02), and no waste collection (P 0.03). Socio-economic variables were not associated with dengue fever. The probable starting point of the epidemic was an uncovered water tank on the roof of the house adjacent to the index case. From there, the outbreak spread uphill parallel to the prevailing direction of the wind. Thus, the chronological and spatial evolution of the epidemic could have been forecast after the ®rst cases had occurred. This example of investigative epidemiology in an operational setting shows that targeted intervention leading to increased effectiveness of control measures is possible.keywords dengue, Aedes aegypti, epidemic, risk factor, Brazil

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Section: Discussionmentioning

confidence: 98%

Risk factors associated with an outbreak of dengue fever in a favela in Fortaleza, north‐east Brazil

Heukelbach

Oliveira

Kerr

et al. 2001

Tropical Med Int Health

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“…To increase the sample, we invited additional residents from nearby houses, reaching a total of 171 participating households (19 in Higienópolis, 93 in Tubiacanga and 59 in Palmares), with 337 individuals (44 in Higienópolis, 162 in Tubiacanga and 131 in Palmares). Since previous studies reported lower seropositive rates in the younger age classes [16],[18],[19], we concentrated our sample effort in the age group of 1–20 years old to increase the chance of detecting seroconversion events [30]. However, due to problems related to participant refusal, particularly for small children in the urban area, older people were included as well, to increase the sample size.…”

Section: Methodsmentioning

confidence: 99%

“…In the neighbor city of Niterói, 55% of schoolchildren were positive in 1988, and 66% in 1992 (after the arrival of DENV-2) [17],[18]. In Paracambi, another neighbor city, 29.2% schoolchildren were positive in 1997 [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Evaluation and Modeling of Dengue Seroprevalence and Vector Density in Rio de Janeiro, Brazil

et al. 2009

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BackgroundRio de Janeiro, Brazil, experienced a severe dengue fever epidemic in 2008. This was the worst epidemic ever, characterized by a sharp increase in case-fatality rate, mainly among younger individuals. A combination of factors, such as climate, mosquito abundance, buildup of the susceptible population, or viral evolution, could explain the severity of this epidemic. The main objective of this study is to model the spatial patterns of dengue seroprevalence in three neighborhoods with different socioeconomic profiles in Rio de Janeiro. As blood sampling coincided with the peak of dengue transmission, we were also able to identify recent dengue infections and visually relate them to Aedes aegypti spatial distribution abundance. We analyzed individual and spatial factors associated with seroprevalence using Generalized Additive Model (GAM).Methodology/Principal FindingsThree neighborhoods were investigated: a central urban neighborhood, and two isolated areas characterized as a slum and a suburban area. Weekly mosquito collections started in September 2006 and continued until March 2008. In each study area, 40 adult traps and 40 egg traps were installed in a random sample of premises, and two infestation indexes calculated: mean adult density and mean egg density. Sera from individuals living in the three neighborhoods were collected before the 2008 epidemic (July through November 2007) and during the epidemic (February through April 2008). Sera were tested for DENV-reactive IgM, IgG, Nested RT-PCR, and Real Time RT-PCR. From the before–after epidemics paired data, we described seroprevalence, recent dengue infections (asymptomatic or not), and seroconversion. Recent dengue infection varied from 1.3% to 14.1% among study areas. The highest IgM seropositivity occurred in the slum, where mosquito abundance was the lowest, but household conditions were the best for promoting contact between hosts and vectors. By fitting spatial GAM we found dengue seroprevalence hotspots located at the entrances of the two isolated communities, which are commercial activity areas with high human movement. No association between recent dengue infection and household's high mosquito abundance was observed in this sample.Conclusions/SignificanceThis study contributes to better understanding the dynamics of dengue in Rio de Janeiro by assessing the relationship between dengue seroprevalence, recent dengue infection, and vector density. In conclusion, the variation in spatial seroprevalence patterns inside the neighborhoods, with significantly higher risk patches close to the areas with large human movement, suggests that humans may be responsible for virus inflow to small neighborhoods in Rio de Janeiro. Surveillance guidelines should be further discussed, considering these findings, particularly the spatial patterns for both human and mosquito populations.

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“…One of the few studies that indirectly show some correlation between the house index and the number of dengue cases is the work by Cunha et al (1997), in which it is suggested, although it is not demonstrated, a relationship between low number of cases and low house index in a community of Rio de Janeiro. In Brazil, an alternative approach to the so-called container indexes (German et al 1980, Bang et al 1981 for larval surveillance was proposed in order to optimize the logistics of its operation.…”

mentioning

confidence: 99%