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Regional differences of leptospirosis in Sri Lanka: observations from a flood-associated outbreak in 2011.

ABSTRACT: Leptospirosis is known to be an important cause of weather disaster-related infectious disease epidemics. In 2011, an outbreak of leptospirosis occurred in the relatively dry district of Anuradhapura, Sri Lanka where diagnosis was resisted by local practitioners because leptospirosis was not known in the area and the clinical presentation was considered atypical. To identify the causative Leptospira associated with this outbreak, we carried out a cross-sectional study. Consecutive clinically suspected cases in this district were studied during a two-and-a-half-month period. Of 96 clinically suspected cases, 32 (33.3%) were confirmed by qPCR, of which the etiological cause in 26 cases was identified using 16S rDNA sequencing to the species level. Median bacterial load was 4.1 × 10(2)/mL (inter-quartile range 3.1-6.1 × 10(2)/mL). In contrast to a 2008 Sri Lankan leptospirosis outbreak in the districts of Kegalle, Kandy, and Matale, in which a predominance of Leptospira interrogans serovars Lai and Geyaweera was found, most cases in the 2011 outbreak were caused by Leptospira kirschneri. Seven (21.9%) confirmed cases had acute renal failure; five (15.6%) had myocarditis; severe thrombocytopenia (<20,000/uL) was seen in five (15.6%) cases. This outbreak of leptospirosis in the relatively dry zone of Sri Lanka due primarily to L. kirschneri was characterized by markedly different clinical presentations and low leptospiremia. These observations and data demonstrate the public health relevance of molecular diagnostics in such settings, possibly related to the microgeographic variations of different Leptospira species, but of particular value to public health intervention in what appears to have been a regionally neglected tropical disease.

SUBMITTER: Agampodi SB

PROVIDER: S-EPMC3894175 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Regional differences of leptospirosis in Sri Lanka: observations from a flood-associated outbreak in 2011.

Agampodi Suneth B SB Dahanayaka Niroshan J NJ Bandaranayaka Anoma K AK Perera Manoj M Priyankara Sumudu S Weerawansa Prasanna P Matthias Michael A MA Vinetz Joseph M JM

PLoS neglected tropical diseases 20140116 1

Leptospirosis is known to be an important cause of weather disaster-related infectious disease epidemics. In 2011, an outbreak of leptospirosis occurred in the relatively dry district of Anuradhapura, Sri Lanka where diagnosis was resisted by local practitioners because leptospirosis was not known in the area and the clinical presentation was considered atypical. To identify the causative Leptospira associated with this outbreak, we carried out a cross-sectional study. Consecutive clinically sus ...[more]

PMID: 24454971

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Project description:Leptospirosis is considered a neglected tropical disease despite its considerable mortality and morbidity. Lack of prediction remains a major reason for underestimating the disease. Although many models have been developed, most of them focused on the districts situated in the wet zone due to higher case numbers in that region. However, leptospirosis remains a major disease even in the dry zone of Sri Lanka. The objective of this study is to develop a time series model to predict leptospirosis in the Anuradhapura district situated in the dry zone of Sri Lanka. Time series data on monthly leptospirosis incidences from January 2008 to December 2018 and monthly rainfall, rainy days, temperature, and relative humidity were considered in model fitting. The first 72 months (55%) were used to fit the model, and the subsequent 60 months(45%) were used to validate the model. The log-transformed dependent variable was employed for fitting the Univariate seasonal ARIMA model. Based on the stationarity of the mean of the five variables, the ARDL model was selected as the multivariate time series technique. Residuals analysis was performed on normality, heteroskedasticity, and serial correlation to validate the model. The lowest AIC and MAPE were used to select the best model. Univariate models could not be fitted without adjusting the outliers. Adjusting seasonal outliers yielded better results than the models without adjustments. Best fitted Univariate model was ARIMA(1,0,0)(0,1,1)12,(AIC-1.08, MAPE-19.8). Best fitted ARDL model was ARDL(1, 3, 2, 1, 0),(AIC-2.04,MAPE-30.4). The number of patients reported in the previous month, rainfall, rainy days, and temperature showed a positive association, while relative humidity was negatively associated with leptospirosis. Multivariate models fitted better than univariate models for the original data. Best-fitted models indicate the necessity of including other explanatory variables such as patient, host, and epidemiological factors to yield better results.

Project description:BackgroundLeptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI.MethodsWe acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected.ResultsWavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9-11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2-5-month lag periods.ConclusionsResults of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

Project description:BackgroundNeurological manifestations of SARS-CoV-2 infection have been reported from many countries around the world, including the South Asian region. This surveillance study aimed to describe the spectrum of neurological disorders associated with COVID-19 in Sri Lanka.MethodsCOVID-19 patients manifesting neurological disorders one week prior and up to six weeks after infection were recruited from all the neurology centres of the government hospitals in Sri Lanka from May 2021 - May 2022. Data was collected using a structured data form that was electronically transmitted to a central repository. All patients were evaluated and managed by a neurologist. Data were analysed using simple descriptive analysis to characterise demographic and disease related variables, and simple comparisons and logistic regression were performed to analyse outcomes and their associations.ResultsOne hundred and eighty-four patients with neurological manifestations associated with COVID-19 were recruited from all nine provinces in Sri Lanka. Ischaemic stroke (31%) was the commonest neurological manifestation followed by encephalopathy (13.6%), Guillain-Barre syndrome (GBS) (9.2%) and encephalitis (7.6%). Ischaemic stroke, encephalitis and encephalopathy presented within 6 days of onset of COVID-19 symptoms, whereas GBS and myelitis presented up to 10 days post onset while epilepsy and Bell palsy presented up to 20 - 40 days post onset. Haemorrhagic stroke presented either just prior to or at onset, or 10 - 25 days post onset of COVID-19 symptomatic infection. An increased frequency of children presenting with encephalitis and encephalopathy was observed during the Omicron variant predominant period. A poor outcome (no recovery or death) was associated with supplemental oxygen requirement during admission (Odds Ratio: 12.94; p = 0.046).ConclusionsThe spectrum and frequencies of COVID-19 associated neurological disorders in Sri Lanka were similar to that reported from other countries, with strokes and encephalopathy being the commonest. Requiring supplemental oxygen during hospitalisation was associated with a poor outcome.

Project description:BackgroundLeptospirosis is one of the most significant zoonoses across the world not only because of its impact on human and animal health but also because of the economic and social impact on agrarian communities. Leptospirosis is endemic in Sri Lanka where paddy farming activities, the use of draught animals in agriculture, and peridomestic animals in urban and rural areas play important roles in maintaining the infection cycle of pathogenic Leptospira, especially concerning animals as a potential reservoir. In this study, an environmental DNA (eDNA) metabarcoding methodology was applied in two different agro-ecological regions of Sri Lanka to understand the eco-epidemiology of leptospirosis.Methodology/principal findingsIrrigation water samples were collected in Kandy District (wet zone mid-country region 2) and Girandurukotte, Badulla District (intermediate zone low-country region 2); and analysed for the presence of pathogenic Leptospira, associated microbiome and the potential reservoir animals. Briefly, we generated PCR products for high-throughput sequencing of multiple amplicons through next-generation sequencing. The analysis of eDNA showed different environmental microbiomes in both regions and a higher diversity of Leptospira species circulating in Kandy than in Girandurukotte. Moreover, the number of sequence reads of pathogenic Leptospira species associated with clinical cases such as L. interrogans was higher in Kandy than in Girandurukotte. Kandy also showed more animal species associated with pathogenic bacterial species than Girandurukotte. Finally, several pathogenic bacterial species including Arcobacter cryaerophilus, responsible for abortion in animals, was shown to be associated with pathogenic Leptospira.Conclusions/significanceLeptospirosis has been considered to be endemic in wet regions, consistently, leptospiral sequences were detected strongly in Kandy. The great Leptospira species diversity in Kandy observed in this study shows that the etiological agents of leptospirosis in Sri Lanka might be underestimated. Furthermore, our eDNA metabarcoding can be used to discriminate bacterial and animal species diversity in different regions and to explore environmental microbiomes to identify other associated bacterial pathogens in the environment.

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Regional differences of leptospirosis in Sri Lanka: observations from a flood-associated outbreak in 2011.

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Regional differences of leptospirosis in Sri Lanka: observations from a flood-associated outbreak in 2011.

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