Leptospirosis: a waterborne zoonosis influenced by local hydrology and climate

Maria C. Vega, Jacob Opadeyi University of the West Indies, Trinidad and Tobago

mariacvc@hotmail.com

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Leptospirosis Incidence in Trinidad (1.78 per 100000 pop).

Reported cases

Lab confirmed cases

Carrier urine

Leptospires in environment

Direct contact transmission

Contaminated water

Reservoir Indirect contact transmission

Transmission and risk factors Environmental factors and leptospirosis

Floods Rain

Poor sanitation Soil type

Land use change

Land cover change

Clinical manifestations

This research uses a multidisciplinary approach towards the quantitative analysis, assessment, modelling and mapping of the risk of Leptospirosis a zoonotic disease of public health importance, currently reported in the Caribbean region and specifically in Trinidad & Tobago.

Methods Study site

Leptospirosis occurs either in urban or rural areas worldwide causing endemic and epidemic. Leptospires survive for long periods in humid environments. It is considered a seasonal disease5. Natural carriers, such as rats, can be symptom free and discard the bacteria via urine for life3.

Severe pulmonary haemorrhagic syndrome: haemoptysis, pulmonary and respiratory failure, death.

2. Icteric leptospirosis: Weil’s Syndrome

Renal failure

1. Anicteric leptospirosis - mild flulike illness (two phases)

(i) Septicemic phase – acute: (4-7d): Fever, headache, myalgias (calf) nausea, conjunctival suffusion.

(ii) Immune phase (0- 30d): Antibodies detection recurring fever, meningitis, renal–pulmonary symptoms

Hepatomegaly Jaundice

Data Leptospirosis cases records from 1998 to 2008 were retrieved from the National Surveillance Unit – NSU of the Ministry of health of Trinidad & Tobago (250 confirmed cases ). Within GIS all cases were geo-located using the corresponding addresses and aggregated by community (Figure 1a). Standardized incidence ratio (SIR) in each community (i) was calculated to compare leptospirosis incidence rates among communities using:

The expected number of cases ( ) was used to generate a kernel map and further Geographically Weighted Poisson Regression (GWPR) models.

Figure 1 . Distribution of leptospirosis confirmed cases in Trinidad, 1998-2008: (a) observed number of cases, , for each of 520 communities; (b) Standardized Incidence Ratio ( SIRi) for each community; (c) kernel map of estimated , including pseudo t-values shown as contour lines.

(a) (b) (c)

Kernel mapping 7 (a special case of GWPR with only a local intercept term without any explanatory variables) was used to investigate spatial patterns of observed vs. expected leptospirosis in all communities (Figure 1c).

Geographically Weighted Poisson Regression (GWPR)7 models were used to explore spatial variability of leptospirosis as it relates to environmental factors . GWPR was also used to examine the hydrological and climatic local determinants of leptospirosis in the communities of Trinidad.

Global Poisson Regression (GPR)8 and GWPR model calibrations:

Covariate Identifier Description Data source

RAINMEAN Community average annual rainfall (mm/year)

Isohyetal maps of mean annual rainfall in Trinidad from 1961-1990, 1991, 1999, 2000, 2001 and 2002 (Trinidad & Tobago Meteorological Office)

SOILFREE, SOILIMPD

Proportion of soil with free (SOILFREE) or imperfect/ impeded (SOILIMPD) drainage in each community (% area)

Trinidad 1:25,000 soils vector polygon map and the land capability survey of Trinidad & Tobago10

RIVRDENS Average river density by community (m/ha)

1:25,000 vector line data of rivers in Trinidad

WETINDEX Average wetness index by community Raster Digital Elevation Model (DEM) derived from photogrammetry and processed to remove topographic “sinks”.

Table 1. Summary of covariates and data sources

GIS was used to develop a series of hydrological covariates for each community including rainfall, soils and soil drainage, river density and topographic wetness index (ln (α/tanβ)) 9. See Table 1 for data sources and covariates descriptions, Figures 2 -3 for examples of processing and Figure 4 for final maps.

GIS analysis

Isohyetal rainfall - RAINMEAN Wetness index - ln (a/tanβ)

Figure 4. Covariates developed for each community in Trinidad: (a) mean annual rainfall (RAINMEAN); (b) percentage of free drainage soil (SOILFREE); (c) percentage of imperfect/impeded drainage soil (SOILIMPD); (d) river length by hectare (RIVDENS); and (e) topographic wetness index (WETINDEX).

(e) (b) (d) (c) (a)

Figure 2. Example of RAINMEAN covariate processing.

Dependent (outcome) covariate: observed counts of leptospirosis CC, with expected cases (Ei) used as the offset variable. Independent (explanatory) covariates: for description see table 1. The GPR model was calibrated using: Where refers to the model intercept and refers to the parameters of explanatory variables, in locations . For Geographically Weighted Poisson Regression, parameters were allowed to vary in according to geographical location, , describing location :

Hydrological covariates were standardized (mean zero and standard deviation one). After model calibration, the pseudo t statistic was calculated and used to assess local significance of parameters. The pseudo t-statistic was calculated using: Se is the local standard error of the th parameter estimate, taking in account the variation in the data.

Model

GPR AICc

Bandwidth (km)

GWPR AICc Deviance % Deviance

Explained Diff of

Criterion Par Value(-)

1.Global PR 595.52 NA NA 583.36 0.16 NA 2.GWPR (all covariates)* 595.52 42.8 395.77 380.99 0.45 RAINMEAN 3.WETINDEX, SOILIMPD, RAINMEAN 592.68 33.1 392.46 381.78 0.45 RAINMEAN

SOILIMPD -0.053 -0.247

4. WETINDEX, SOILIMPD 637.57 18.4 401.04 384.23 0.46 WETINDEX SOILIMPD

-0.020 -3.592

5. WETINDEX, SOILIMPD, RIVRDENS 639.12 19.0 401.97 380.22 0.45 SOILIMPD -2.223

Table 2. Summary of global (GPR) and local (GWPR) models statistics for model comparisons.

Results and discussion

Acknowledgements We would like to thank Dr Avery Hinds and all the members of the Surveillance Unit of the Min. of Health of T&T for their help and support in the Leptospirosis data collection. PhD research was funded with a scholarship from UWI.

The model with the smallest AICc value (Table 2, model 3), was selected as the model that better fitted the observed data 7. Two parameter estimates showed spatial variability: SOILIMPD (-0.247) and RAINMEAN (-0.053). In order to map the parameter estimates the standard odds ratio were calculated (Figure 5).

In Figure 5c the odds ratio (>1.0), indicate that as wetness index increases, so does leptospirosis. With a strong relationship is in the south. The pseudo t-values showed a strong relationship for all communities.

Figure 6 shows the predicted cases for each community and the residuals. The predicted cases (range 0.006 to 10.45, mean 0.422, variance 0.524) matched well the observed (range 0 to 10, mean 0.422, variance 1.144), with a root mean square error (RMSE) of 0.73 cases per community.

Conclusion Improvement of the local over the global models GWPR indicates spatial heterogeneity in the relationship between human leptospirosis and hydroclimatoligical factors. Throughout the exploration of the data an interesting human leptospirosis cluster was found. Thus, GWPR was used as a spatial analysis technique to reveal important geographical abnormalities 7 in the association of human leptospirosis vs local hydroclimatological dynamics. The data obtained for the predicted cases in each community and the residuals may indicate that the use of spatial analysis for disease modelling could be a valuable tool in the study of the risk of under-reported, misdiagnosed or neglected diseases with a high environmental component, such as leptospirosis. While we acknowledge the multifactorial causes of leptospirosis, here we indicate the important influence of local hydroclimalogical dynamics on human leptospirosis in Trinidad. If the likelihood of severe weather events increases, leptospirosis outbreaks may also increase affecting highly human populations. More detailed and interdisciplinary studies such this one are needed in order to tackle leptospirosis burden in a future climate change scenario.

The Akaike Information Criterion (AICc)8 was the indicator to analyze goodness of fit of the models and bandwidth. The AICc, fixed Gaussian kernel type and golden selection bandwidth were the GWPR indicators used to complete model calibration 7

Model predictions

Communities with a high number of cases such as Sangre Grande and neighboring areas were predicted accurately (residual values close to zero). Predictions of leptospirosis were also made for communities which did not present confirmed cases in the data used for the analysis and, as would be expected, these communities were over-predicted. It is important to note that a lack of observations does not necessarily mean that there is no risk of leptospirosis.

To explore in more detail the underlying relationships, a model that excluded rainfall as a covariate and presented high pseudo t-values in local parameter estimates was examined (model 4, table 2). In Figure 7a, in the north side of the island, is showed a strong positive relationship between the percentage coverage of imperfect/impeded drainage soil and leptospirosis, as indicated by the odds ratios above 1.0. The high pseudo t-values indicate that this relationship may be significant. In Figure 7b the odds ratio for the topographic wetness index presented a positive and strong relationship, particularly towards the south. The pseudo t-values are high particularly in the central south region and remain high in most regions except the north east.

Figure 6. Model predictions by community: (a) predicted cases and (b) residuals.

Figure 7. Model parameter estimates excluding rainfall (model 4, Table 2): standard odds ratio for (a) imperfect/impeded drainage soil (SOILIMPD) and (b) topographic wetness index (WETINDEX). Pseudo –t values are shown as contour lines.

(a) (b)

(b)

Figure 5. Optimum model parameter estimates (model 3, Table 3): standard odds ratio for (a) rainfall (RAINMEAN), (b) imperfect/impeded drainage soil (SOILIMPD), and (c) topographic wetness index (WETINDEX). Pseudo –t values are shown as contour lines.

In Figure 5a, the odds ratio for rainfall (RAINMEAN) are >1.0, indicating that as the amount of rainfall in a community increases, leptospirosis cases also tend to increase. At the local level, the pseudo t-values were high, suggesting that the relationship between leptospirosis and rainfall is strong, particularly in the north-west region.

(b) (c) (a)

1. Lau, C. L., Smythe, L. D., Craig, S. B., & Weinstein, Philip. (2010). Climate change, flooding, urbanisation and leptospirosis: fuelling the fire? Transactions of the Royal Soci. of Tropical Medicine and Hygiene, 104(10), 631-8. Royal Society of Tropical Medicine and Hygiene 2. Bharti, A, R, E Nally, J, N Ricaldi, J, A Matthias, M, M Diaz, M, A Lovett, M, N Levett, P, et al. 2003. Leptospirosis : a zoonotic disease of global importance. The Lancet Inf Dis 3: 757-771. 3. Vijayachari, P., A.P. Sugunan and A.N. Shriram, 2008. Leptospirosis: An emerging global public health problem. J. Biosci., 33: 557-569. 4. Gaynor K, Katz AR, Park SY, Nakata M, Clark TA, Effler PV: Leptospirosis on Oahu: an outbreak associated with flooding of a university campus. Am J Trop Med Hyg 2007, 76:882–885. 5. Adler B, de la Peña Monctezuma A: Leptospira and leptospirosis. Vet microbiol 2010, 140: 287-96. 6. Mohan, Andrea R M, Anton Cumberbatch, A a Adesiyun, and Dave D Chadee. 2009. Epidemiology of human leptospirosis in Trinidad and Tobago, 1996-2007:a retrospective study. Acta tropica 112, no.3: 260-5. 7. Nakaya T, Fotheringham AS, Brunsdon C, Charlton M: Geographically weighted Poisson regression for disease association mapping. Stat Med 2005, 24:2695–2717. 8. Fotheringham A, Brunsdon C, Charlton M: Geographically weighted regression: the analysis of the spatially varying relationships. Chichester: John Wiley & Sons; 2002. 9. Beven KJ, Kirby MJ: A physically based, variable contributing area model of basin hydrology. Hydrolog Sci B. 1979, 24:43-69. 10. Brown CB, Bally GS: Land Capability Survey of Trinidad and Tobago No. 4: Soils of Central Trinidad. Edited by De Verteuil LL, McConaghy RK, Haworth F, Hardy F. Trinidad: The Government of Trinidad and Tobago, The University of the West Indies, Texaco Oil Company, Shell Trinidad Limited, Tate and Lyle (Caroni Limited), British Petroleum (Trinidad) Limited; 1970a :1-142.

References

The Standardized Incidence Ratio for each community (Figure 1b) shows a general trend for higher numbers of leptospirosis cases in the north-east, but the overall geographical pattern is not clear. The kernel map in Figure 1c facilitates the visualization of patterns across the communities.

Figure 3. Example of wetness index 9 covariate processing.

Incidence: 10–100/100 000 (Underestimated). Trinidad & Tobago: 1.84/ 100.000pop6.

Leptospirosis is a waterborne disease of public health importance, currently reported as the most widespread bacterial zoonosis, and closely related to environmental and climate factors1. Outbreaks frequently happen after episodes of heavy rainfall and floods, where leptospirosis can be transmitted to humans by direct or indirect contact with contaminated water, soil or infected urine, blood or tissue of carrier animals2. Human leptospirosis is often misdiagnosed with dengue, viral hepatitis and influenza 3, but, as the infection progresses, internal organs can be compromised, potentially causing death 4. In less developed countries, leptospirosis is often poorly recognized. In these regions a main problem for the study of the role of environmental factors on disease dynamics is the lack of accurate data since, in many cases, data are unavailable or do not exist 5.

Introduction Epidemiology

(a)

Research presented in this poster has been published in the following open-access paper: Vega-Corredor, M.C. and Opadeyi, J. (2014) Hydrology and public health: linking human leptospirosis and local hydrological dynamics in Trinidad, West Indies, Earth Perspectives 1:3. doi:10.1186/2194-6434-1-3 PDF available from: http://www.earth-perspectives.com/content/pdf/2194-6434-1-3.pdf