Household rat infestation in urban slum populations: Development and validation of a predictive score for leptospirosis

Abstract

Domestic rats are the principal reservoir for urban leptospirosis. However, few studies have identified infestation markers in slums and evaluated their predictivity for leptospirosis risk. We compared households with leptospirosis cases in Salvador, Brazil between 2007 and 2009 and their neighbors using a case control design, surveying for rodent infestation signs and environmental characteristics. With the 2007–2008 data, a conditional logistic regression modeling identified the peridomiciliar presence of rodent burrows (OR, 3.30; 95% CI, 1.50–7.26), rat feces (2.86; 1.24–6.59), runs (2.57; 1.06–6.22), households bordering abandoned houses (2.48; 1.04–6.02), and unplastered walls (2.22; 1.02–6.02) as risk factors and developed a predictive score for leptospirosis. With an independent data set from 2009, a receiver operating characteristic (ROC) curve analysis evaluated the prediction score performance, with the area under the curve being 0.70 (95% CI, 0.64–0.76) for score development and 0.71 (0.65–0.79) for validation. Results indicate that high proportions of urban slum households are infested with R. norvegicus. The score performed well when identifying high-risk households within slums. These findings need confirmation in other urban centers, but suggest that community-based screening for rodent infestation can allow to target rodent and environmental control measures in populations at highest risk for leptospirosis.

Linking rattiness, geography and environmental degradation to spillover Leptospira infections in marginalised urban settings: An eco-epidemiological community-based cohort study in Brazil

“…The finding that rattiness was associated with infection risk indicates that the spatial distribution of rat populations was an important driver of transmission close to the household across the entire study area. This is consistent with a recent study investigating the predictive power of household rat infestation scores for human infection ( Costa et al, 2021 ). There was no residual spatial correlation in the infection data after accounting for rattiness in our analysis, possibly suggesting that previously unexplained spatial heterogeneity in risk could be driven by variation in rattiness ( Hagan et al, 2016 ).…”

“…This suggests that rat abundance may be predictive of environmental risk, and could be used as a proxy for this shedding process. While several studies have identified associations between infection risk and household rat sightings and infestation ( Reis et al, 2008 ; Costa et al, 2014b ; Hagan et al, 2016 ; Costa et al, 2021 ; Pellizzaro et al, 2019 ; Bhardwaj et al, 2008 ), their ability to explore fine-scale spatial variation in risk was limited by a reliance on household infestation surveys or aggregation of incidence and abundance indices to a common coarse spatial scale. All modelled abundance as a regression covariate, thereby not accounting for uncertainty in its measurement.…”

Linking rattiness, geography and environmental degradation to spillover Leptospira infections in marginalised urban settings: an eco-epidemiological community-based cohort study in Brazil

“…The finding that rattiness was associated with infection risk indicates that the spatial distribution of rat populations was an important driver of transmission close to the household across the entire study area. This is consistent with a recent study investigating the predictive power of household rat infestation scores for human infection [30]. There was no residual spatial correlation in the infection data after accounting for rattiness in our analysis, possibly suggesting that previously unexplained spatial heterogeneity in risk could be driven by variation in rattiness [12].…”

“…This suggests that rat abundance may be predictive of environmental risk, and could be used as a proxy for this shedding process. While several studies have identified associations between infection risk and household rat sightings and infestation [10,12,22,[29][30][31], their ability to explore fine-scale spatial variation in risk was limited by a reliance on household infestation surveys or aggregation of incidence and abundance indices to a common coarse spatial scale. All modelled abundance as a regression covariate, thereby not accounting for uncertainty in its measurement.…”

Survey of Slum Housing Characteristics Using Drones: An Experiment in the Alto das Pombas Community, Salvador de Bahia/Brazil

“…Various authors have described the urban (con)formation of Salvador (and its segregation processes) from the point of view of housing (Brandão 1980, Mendonça 1989, de Carvalho 2002, Brito 2005, Fernandes, Regina 2005, Lima 2005, Soares, Espinheira 2006, Gordilho-Souza 2008, Santos 2012, Castro 2017, occupation and work (Carvalho, Pereira, 2018, Carvalho 2020, distribution of urban infrastructure (Silva 2015(Silva , 2019, mobility (Rocha 2014), provision of public spaces (Serpa 2006), environmental quality (Moraes et al 2003, Santos et al 2013) and health (Paim et al 1987, Souza 2005, Antunes et al 2013, Costa et al 2021.…”