Impact of Environment and Social Gradient on Leptospira Infection in Urban Slums

Leptospirosis, a life-threatening zoonotic disease, has become an important urban slum health problem. Epidemics of leptospirosis now occur in cities throughout the developing world, as the growth of slum settlements has produced conditions for rat-borne transmission of this disease. In this prevalence survey of more than 3,000 residents from a favela slum community in Brazil, Geographical Information System (GIS) and modeling approaches identified specific deficiencies in the sanitation infrastructure of slum environments—open sewers, refuse, and inadequate floodwater drainage—that serve as sources for Leptospira transmission. In addition to the environmental attributes of the slum environment, low socioeconomic status was found to independently contribute to the risk of infection. These findings indicate that effective prevention of leptospirosis will need to address the social factors that produce unequal health outcomes among slum residents, in addition to improving sanitation

Contexto sócio-econômico e percepção da saúde bucal em uma população de adultos no Rio de Janeiro, Brasil: uma análise multinível

Um problema dos delineamentos ecológicos é o viés de agregação. Uma alternativa é selecionar estruturas contextuais de menor tamanho e maior homogeneidade interna. Nós comparamos diferentes estruturas geográficas de dados, com a finalidade de estimar efeitos de variáveis sócio-econômicas contextuais na chance de saúde bucal percebida ruim. As análises foram baseadas em um delineamento misto, que compreendeu os participantes dos censos Pró-Saúde I e II, residentes no Município do Rio de Janeiro, Brasil, em 1999 (n = 2.426). Os indicadores do contexto de residência dos participantes foram extraídos do Censo Demográfico da Unidade Federativa do Rio de Janeiro de 1991. Em estudos epidemiológicos com estruturas hierárquicas de dados e desfechos categóricos, a utilização do coeficiente de partição de variância permite analisar a origem da variabilidade do desfecho em relação aos níveis hierárquicos. Neste estudo, a partição geográfica de menor tamanho (setor censitário) foi a melhor unidade contextual de análise para explicar a variabilidade da saúde bucal percebida. Atributos sócio-econômicos individuais revelaram maior poder explicativo para a variação na saúde bucal percebida do que os contextos sócio-econômicos das áreas de residência

Spatiotemporal determinants of urban Leptospirosis transmission:four-year prospective cohort study of slum residents in Brazil

Background Rat-borne leptospirosis is an emerging zoonotic disease in urban slum settlements for which there are no adequate control measures. The challenge in elucidating risk factors and informing approaches for prevention is the complex and heterogeneous environment within slums, which vary at fine spatial scales and influence transmission of the bacterial agent. Methodology/Principal Findings We performed a prospective study of 2,003 slum residents in the city of Salvador, Brazil during a four-year period (2003–2007) and used a spatiotemporal modelling approach to delineate the dynamics of leptospiral transmission. Household interviews and Geographical Information System surveys were performed annually to evaluate risk exposures and environmental transmission sources. We completed annual serosurveys to ascertain leptospiral infection based on serological evidence. Among the 1,730 (86%) individuals who completed at least one year of follow-up, the infection rate was 35.4 (95% CI, 30.7–40.6) per 1,000 annual follow-up events. Male gender, illiteracy, and age were independently associated with infection risk. Environmental risk factors included rat infestation (OR 1.46, 95% CI, 1.00–2.16), contact with mud (OR 1.57, 95% CI 1.17–2.17) and lower household elevation (OR 0.92 per 10m increase in elevation, 95% CI 0.82–1.04). The spatial distribution of infection risk was highly heterogeneous and varied across small scales. Fixed effects in the spatiotemporal model accounted for the majority of the spatial variation in risk, but there was a significant residual component that was best explained by the spatial random effect. Although infection risk varied between years, the spatial distribution of risk associated with fixed and random effects did not vary temporally. Specific “hot-spots” consistently had higher transmission risk during study years. Conclusions/Significance The risk for leptospiral infection in urban slums is determined in large part by structural features, both social and environmental. Our findings indicate that topographic factors such as household elevation and inadequate drainage increase risk by promoting contact with mud and suggest that the soil-water interface serves as the environmental reservoir for spillover transmission. The use of a spatiotemporal approach allowed the identification of geographic outliers with unexplained risk patterns. This approach, in addition to guiding targeted community-based interventions and identifying new hypotheses, may have general applicability towards addressing environmentally-transmitted diseases that have emerged in complex urban slum settings. Author Summary Leptospirosis is a rat-borne infectious disease that occurs worldwide, predominantly among vulnerable populations, such as urban slum communities with poor sanitation infrastructure. However, urban slums are complex local settings, where transmission of the disease varies over space and time, and the factors that influence this risk difference are unknown. An improved understanding of the environmental and social factors that modify the risk of this infection is needed in order to guide interventions to reduce the disease burden. We recruited a cohort of 2003 community residents of a high- risk urban slum in Salvador, Brazil. We followed them for a four-year period to understand yearly variation in individual and spatial risk factors for infection using spatiotemporal statistical modeling techniques. Our findings suggest that environmental factors related to topology such as household elevation and inadequate sewage drainage systems increase the risk of transmission in the slum microenvironment by promoting contact with mud contaminated with the pathogenic leptospiral bacteria, and that individual characteristics such as age and gender increase risk through behaviors that lead to increased exposures to a contaminated environment. Through this technique, we also identified local geographic areas where the risks are not well explained by these factors. This will help generate new hypotheses and identify intervention strategies for targeted prevention of leptospirosis in urban slum populations

Prospective Study of Leptospirosis Transmission in an Urban Slum Community: Role of Poor Environment in Repeated Exposures to the <i>Leptospira</i> Agent

<div><p>Background</p><p>Leptospirosis has emerged as an urban health problem as slum settlements have rapidly spread worldwide and created conditions for rat-borne transmission. Prospective studies have not been performed to determine the disease burden, identify risk factors for infection and provide information needed to guide interventions in these marginalized communities.</p><p>Methodology/Principal Findings</p><p>We enrolled and followed a cohort of 2,003 residents from a slum community in the city of Salvador, Brazil. Baseline and one-year serosurveys were performed to identify primary and secondary <i>Leptospira</i> infections, defined as respectively, seroconversion and four-fold rise in microscopic agglutination titers. We used multinomial logistic regression models to evaluate risk exposures for acquiring primary and secondary infection. A total of 51 <i>Leptospira</i> infections were identified among 1,585 (79%) participants who completed the one-year follow-up protocol. The crude infection rate was 37.8 per 1,000 person-years. The secondary infection rate was 2.3 times higher than that of primary infection rate (71.7 and 31.1 infections per 1,000 person-years, respectively). Male gender (OR 2.88; 95% CI 1.40–5.91) and lower per capita household income (OR 0.54; 95% CI, 0.30–0.98 for an increase of $1 per person per day) were independent risk factors for primary infection. In contrast, the 15–34 year age group (OR 10.82, 95% CI 1.38–85.08), and proximity of residence to an open sewer (OR 0.95; 0.91–0.99 for an increase of 1 m distance) were significant risk factors for secondary infection.</p><p>Conclusions/Significance</p><p>This study found that slum residents had high risk (>3% per year) for acquiring a <i>Leptospira</i> infection. Re-infection is a frequent event and occurs in regions of slum settlements that are in proximity to open sewers. Effective prevention of leptospirosis will therefore require interventions that address the infrastructure deficiencies that contribute to repeated exposures among slum inhabitants.</p></div

Geographical information system and spatial–temporal statistics for monitoring infectious agents in hospital: a model using Klebsiella pneumoniae complex

Abstract Background The emergence and spread of antimicrobial resistance and infectious agents have challenged hospitals in recent decades. Our aim was to investigate the circulation of target infectious agents using Geographic Information System (GIS) and spatial–temporal statistics to improve surveillance and control of healthcare-associated infection and of antimicrobial resistance (AMR), using Klebsiella pneumoniae complex as a model. Methods A retrospective study carried out in a 450-bed federal, tertiary hospital, located in Rio de Janeiro. All isolates of K. pneumoniae complex from clinical and surveillance cultures of hospitalized patients between 2014 and 2016, identified by the use of Vitek-2 system (BioMérieux), were extracted from the hospital's microbiology laboratory database. A basic scaled map of the hospital’s physical structure was created in AutoCAD and converted to QGis software (version 2.18). Thereafter, bacteria according to resistance profiles and patients with carbapenem-resistant K. pneumoniae (CRKp) complex were georeferenced by intensive and nonintensive care wards. Space–time permutation probability scan tests were used for cluster signals detection. Results Of the total 759 studied isolates, a significant increase in the resistance profile of K. pneumoniae complex was detected during the studied years. We also identified two space–time clusters affecting adult and paediatric patients harbouring CRKp complex on different floors, unnoticed by regular antimicrobial resistance surveillance. Conclusions In-hospital GIS with space–time statistical analysis can be applied in hospitals. This spatial methodology has the potential to expand and facilitate early detection of hospital outbreaks and may become a new tool in combating AMR or hospital-acquired infection

<i>Leptospira</i> infection rates among 1,585 participants of the slum community cohort according to gender.

<p>Abbreviations: CI: confidence intervals adjusted according to design effect, PY: person-years of follow-up.</p>a<p>Rates expressed as infections per 1,000 person-years.</p>b<p>Primary infection was defined as an increase in the microscopic agglutination test (MAT) titer for any of the tested serovars from zero in the first test to at least 50 in the second test.</p>c<p>Secondary infection was defined as an increase in the MAT of four-fold from an initial titer ≥25.</p

Age-specific attack rates for A) overall <i>Leptospira</i> infection according to gender [light grey bars, male; dark grey bars, female], and B) primary and secondary <i>Leptospira</i> infection rates [open bars, primary infection; black bars, secondary infection].

<p>Whiskers indicate the 95% Confidence Interval for the rate, adjusted for the survey design. The x axes refer to age groups in years. Rates in y axes are shown as infections per 1,000 person-years</p

Multivariate risk factors for primary and secondary infection among cohort participants.

<p>Abbreviations: OR, odds ratio; CI, confidence interval.</p

Selected univariate risk factors for <i>Leptospira</i> infection among cohort participants.

<p>Abbreviations: No., number; %, percentage; IQR, interquartile range; OR, odds ratio; CI, confidence interval.</p