Bacterial zoonoses are established causes of severe febrile illness in East Africa. Within a fever etiology study, we applied a high-throughput 16S rRNA metagenomic assay validated for detecting bacterial zoonotic pathogens. We enrolled febrile patients admitted to 2 referral hospitals in Moshi, Tanzania, during September 2007-April 2009. Among 788 participants, median age was 20 (interquartile range 2-38) years. We performed PCR amplification of V1-V2 variable region 16S rRNA on cell pellet DNA, then metagenomic deep-sequencing and pathogenic taxonomic identification. We detected bacterial zoonotic pathogens in 10 (1.3%) samples: 3 with Rickettsia typhi, 1 R. conorii, 2 Bartonella quintana, 2 pathogenic Leptospira spp., and 1 Coxiella burnetii. One other sample had reads matching a Neoerhlichia spp. previously identified in a patient from South Africa. Our findings indicate that targeted 16S metagenomics can identify bacterial zoonotic pathogens causing severe febrile illness in humans, including potential novel agents.

OBJECTIVES: Leptospira, the spirochaete causing leptospirosis, can be classified into >250 antigenically distinct serovars. Although knowledge of the animal host species and geographic distribution of Leptospira serovars is critical to understand the human and animal epidemiology of leptospirosis, current data are fragmented. We aimed to systematically review, the literature on animal host species and geographic distribution of Leptospira serovars to examine associations between serovars with animal host species and regions and to identify geographic regions in need of study. METHODS: Nine library databases were searched from inception through 9 March 2023 using keywords including Leptospira, animal, and a list of serovars. We sought reports of detection of Leptospira, from any animal, characterised by cross agglutinin absorption test, monoclonal antibody typing, serum factor analysis, or pulsed-field gel electrophoresis to identify the serovar. RESULTS: We included 409 reports, published from 1927 through 2022, yielding data on 154 Leptospira serovars. The reports included data from 66 (26.5%) of 249 countries. Detections were from 144 animal host species including 135 (93.8%) from the class Mammalia, 5 (3.5%) from Amphibia, 3 (2.1%) from Reptilia, and 1 (0.7%) from Arachnida. Across the animal host species, Leptospira serovars that were detected in the largest number of animal species included Grippotyphosa (n = 39), Icterohaemorrhagiae (n = 29), Pomona (n = 28), Australis (n = 25), and Ballum (n = 25). Of serovars, 76 were detected in a single animal host species. We created an online database to identify animal host species for each serovar by country. CONCLUSIONS: We found that many countries have few or no Leptospira serovars detected from animal host species and that many serovars were detected from a single animal species. Our study highlights the importance of efforts to identify animal host species of leptospirosis, especially in places with a high incidence of human leptospirosis. We provide an updated resource for leptospirosis researchers.

BACKGROUND: Leptospirosis is suspected to be a major cause of illness in rural Tanzania associated with close contact with livestock. We sought to determine leptospirosis prevalence, identify infecting Leptospira serogroups, and investigate risk factors for leptospirosis in a rural area of Tanzania where pastoralist animal husbandry practices and sustained livestock contact are common. METHODS: We enrolled participants at Endulen Hospital, Tanzania. Patients with a history of fever within 72 hours, or a tympanic temperature of ≥38.0°C were eligible. Serum samples were collected at presentation and 4-6 weeks later. Sera were tested using microscopic agglutination testing with 20 Leptospira serovars from 17 serogroups. Acute leptospirosis cases were defined by a ≥four-fold rise in antibody titre between acute and convalescent serum samples or a reciprocal titre ≥400 in either sample. Leptospira seropositivity was defined by a single reciprocal antibody titre ≥100 in either sample. We defined the predominant reactive serogroup as that with the highest titre. We explored risk factors for acute leptospirosis and Leptospira seropositivity using logistic regression modelling. RESULTS: Of 229 participants, 99 (43.2%) were male and the median (range) age was 27 (0, 78) years. Participation in at least one animal husbandry practice was reported by 160 (69.9%). We identified 18 (7.9%) cases of acute leptospirosis, with Djasiman 8 (44.4%) and Australis 7 (38.9%) the most common predominant reactive serogroups. Overall, 69 (31.1%) participants were Leptospira seropositive and the most common predominant reactive serogroups were Icterohaemorrhagiae (n = 21, 30.0%), Djasiman (n = 19, 27.1%), and Australis (n = 17, 24.3%). Milking cattle (OR 6.27, 95% CI 2.24-7.52) was a risk factor for acute leptospirosis, and milking goats (OR 2.35, 95% CI 1.07-5.16) was a risk factor for Leptospira seropositivity. CONCLUSIONS: We identified leptospirosis in approximately one in twelve patients attending hospital with fever from this rural community. Interventions that reduce risks associated with milking livestock may reduce human infections.

Leptospirosis, the most widespread zoonotic disease in the world, is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 266 sampled opportunistically from other California regions and species. In the five target species seroprevalences ranged from 5 to 60%, and infection prevalences ranged from 0.8 to 15.2% in all except fox squirrels (0%). Leptospira phylogenomics and patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas.

Studies of infectious disease ecology would benefit greatly from knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody-level data being one of the most promising sources of information. The use of antibody levels to back-calculate infection time requires the development of a host-pathogen system-specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. We present a way to model antibody dynamics in a Bayesian framework that facilitates the incorporation of all available information about potential infection times and apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans. Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements in infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. When applied to field data we saw reductions up to 83% in the window of possible infection times. The method substantially simplifies the challenge of modelling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology and can even be applied to cross-sectional data once the model is trained. © 2023 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a bacterial infection (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve both our individual-level and population-level understanding of this host-pathogen system. We create a “host-pathogen space” by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to visualize and characterize changes in these values through time. We describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health. This mapping framework enables us to determine an individual’s location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure, including severe acute disease, chronic subclinical infection, and recovery. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and in certain cases we can impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space and using quantitative biomarker data provides more nuanced approaches for understanding and modeling disease dynamics in a system, yielding benefits for the clinician who needs to triage patients and prevent transmission, and for the disease ecologist or epidemiologist wishing to develop appropriate risk management strategies and assess health impacts on a population scale.Author Summary A pathogen can cause a range of disease severity across different host individuals, and these presentations change over the time-course from infection to recovery. These facts complicate the work of epidemiologists and disease ecologists seeking to understand the factors governing disease spread, often working with cross-sectional data. Recognizing these facts also highlights the shortcomings of classical approaches to modeling infectious disease, which typically rely on discrete and well-defined disease states. Here we show that by analyzing multiple biomarkers of health and infection simultaneously, treating these values as quantitative rather than binary indicators, and including a modest amount of longitudinal sampling of hosts, we can create a map of the host-pathogen interaction that shows the full spectrum of disease presentations and opens doors for new insights and predictions. By accounting for individual variation and capturing changes through time since infection, this mapping framework enables more robust interpretation of cross-sectional data; e.g., to detect predictive relationships between biomarkers and key outcomes such as survival, or to assess whether observed disease is associated with the pathogen of interest. This approach can help epidemiologists, ecologists and clinicians to better study and manage the many infectious diseases that exhibit complex relationships with their hosts.

Leptospirosis is the most widespread zoonotic disease in the world, yet it is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), Northern raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 260 sampled opportunistically from other regions and species. In the five target species, seroprevalences ranged from 5-60% and active infection prevalences ranged from 0.8-15.2% in all except fox squirrels (0%). Patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Studies of infectious disease ecology often rely heavily on knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody level data being one of the most promising sources of information. The use of antibody levels to back-calculate infection time requires the development of a host-pathogen system-specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. Here, we present a way to do this in a Bayesian framework that facilitates the incorporation of all available information about potential infection times. We apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans, leading to reductions of 51-92% in the window of possible infection times. Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements of infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. The method substantially simplifies the challenge of modeling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology, and can even be applied to cross-sectional data once the model is trained. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

BACKGROUND: Historically, malaria has been the predominant cause of acute febrile illness (AFI) in sub-Saharan Africa. However, during the last two decades, malaria incidence has declined due to concerted public health control efforts, including the widespread use of rapid diagnostic tests leading to increased recognition of non-malarial AFI etiologies. Our understanding of non-malarial AFI is limited due to lack of laboratory diagnostic capacity. We aimed to determine the etiology of AFI in three distinct regions of Uganda. METHODS: A prospective clinic-based study that enrolled participants from April 2011 to January 2013 using standard diagnostic tests. Participant recruitment was from St. Paul's Health Centre (HC) IV, Ndejje HC IV, and Adumi HC IV in the western, central and northern regions, which differ by climate, environment, and population density. A Pearson's chi-square test was used to evaluate categorical variables, while a two-sample t-test and Krukalis-Wallis test were used for continuous variables. RESULTS: Of the 1281 participants, 450 (35.1%), 382 (29.8%), and 449 (35.1%) were recruited from the western, central, and northern regions, respectively. The median age (range) was 18 (2-93) years; 717 (56%) of the participants were female. At least one AFI pathogen was identified in 1054 (82.3%) participants; one or more non-malarial AFI pathogens were identified in 894 (69.8%) participants. The non-malarial AFI pathogens identified were chikungunya virus, 716 (55.9%); Spotted Fever Group rickettsia (SFGR), 336 (26.2%) and Typhus Group rickettsia (TGR), 97 (7.6%); typhoid fever (TF), 74 (5.8%); West Nile virus, 7 (0.5%); dengue virus, 10 (0.8%) and leptospirosis, 2 (0.2%) cases. No cases of brucellosis were identified. Malaria was diagnosed either concurrently or alone in 404 (31.5%) and 160 (12.5%) participants, respectively. In 227 (17.7%) participants, no cause of infection was identified. There were statistically significant differences in the occurrence and distribution of TF, TGR and SFGR, with TF and TGR observed more frequently in the western region (p = 0.001; p < 0.001) while SFGR in the northern region (p < 0.001). CONCLUSION: Malaria, arboviral infections, and rickettsioses are major causes of AFI in Uganda. Development of a Multiplexed Point-of-Care test would help identify the etiology of non-malarial AFI in regions with high AFI rates.

Because they are difficult to culture, obtaining genomic information from Leptospira spp. is challenging, hindering the overall understanding of leptospirosis. We designed and validated a culture-independent DNA capture and enrichment system for obtaining Leptospira genomic information from complex human and animal samples. It can be utilized with a variety of complex sample types and diverse species as it was designed using the pan-genome of all known pathogenic Leptospira spp. This system significantly increases the proportion of Leptospira DNA contained within DNA extracts obtained from complex samples, oftentimes reaching >95% even when some estimated starting proportions were <1%. Sequencing enriched extracts results in genomic coverage similar to sequenced isolates, thereby enabling enriched complex extracts to be analyzed together with whole genome sequences from isolates, which facilitates robust species identification and high-resolution genotyping. The system is flexible and can be readily updated when new genomic information becomes available. Implementation of this DNA capture and enrichment system will improve efforts to obtain genomic data from unculturable Leptospira-positive human and animal samples. This, in turn, will lead to a better understanding of the overall genomic diversity and gene content of Leptospira spp. that cause leptospirosis, aiding epidemiology and the development of improved diagnostics and vaccines.

A One Health approach to the epidemiology, management, surveillance, and control of leptospirosis relies on accessible and accurate diagnostics that can be applied to humans and companion animals and livestock. Diagnosis should be multifaceted and take into account exposure risk, clinical presentation, and multiple direct and/or indirect diagnostic approaches. Methods of direct detection of Leptospira spp. include culture, histopathology and immunostaining of tissues or clinical specimens, and nucleic acid amplification tests (NAATs). Indirect serologic methods to detect leptospiral antibodies include the microscopic agglutination test (MAT), the enzyme-linked immunosorbent assay (ELISA), and lateral flow methods. Rapid diagnostics that can be applied at the point-of-care; NAAT and lateral flow serologic tests are essential for management of acute infection and control of outbreaks. Culture is essential to an understanding of regional knowledge of circulating strains, and we discuss recent improvements in methods for cultivation, genomic sequencing, and serotyping. We review the limitations of NAATs, MAT, and other diagnostic approaches in the context of our expanding understanding of the diversity of pathogenic Leptospira spp. Novel approaches are needed, such as loop mediated isothermal amplification (LAMP) and clustered regularly interspaced short palindromic repeats (CRISPR)-based approaches to leptospiral nucleic acid detection. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Leptospirosis is a globally widespread spirochetal infection spread from animals to humans. Infections are common in settings of endemicity, primarily in tropical regions of the world. Leptospirosis is typically a self-limited febrile illness but may progress to potentially fatal multiorgan system failure. Patients often present with a nonspecific acute febrile illness that is clinically difficult to distinguish from other similarly presenting infections endemic to tropical regions, including dengue fever, influenza, and malaria. A high index of suspicion is essential to early identification of patients who may benefit from antimicrobial therapy. Diagnostic testing is key to both recognition of early infection and outbreak investigation, typically in the setting of water exposure after heavy rainfall and flooding. This review focuses on the epidemiology, clinical manifestations, and laboratory diagnosis of leptospirosis, including nucleic acid amplification tests, culture, direct detection, and serological approaches. © 2021 Elsevier Inc.

Molecular data are required to improve our understanding of the epidemiology of leptospirosis in Africa and to identify sources of human infection. We applied molecular methods to identify the infecting Leptospira species and genotypes among patients hospitalized with fever in Tanzania and compared these with Leptospira genotypes detected among animals in Tanzania to infer potential sources of human infection. We performed lipL32 real-time PCR to detect the presence of pathogenic Leptospira in acute-phase plasma, serum, and urine samples obtained from study participants with serologically confirmed leptospirosis and participants who had died with febrile illness. Leptospira blood culture was also performed. In positive specimens, we performed species-specific PCR and compared participant Leptospira secY sequences with Leptospira reference sequences and sequences previously obtained from animals in Tanzania. We detected Leptospira DNA in four (3.6%) of 111 participant blood samples. We detected Leptospira borgpetersenii (one participant, 25.0%), Leptospira interrogans (one participant, 25.0%), and Leptospira kirschneri (one participant, 25.0%) (one [25%] undetermined). Phylogenetic comparison of secY sequence from the L. borgpetersenii and L. kirschneri genotypes detected from participants was closely related to but distinct from genotypes detected among local livestock species. Our results indicate that a diverse range of Leptospira species is causing human infection. Although our analysis suggests a close relationship between Leptospira genotypes found in people and livestock, continued efforts are needed to obtain more Leptospira genetic material from human leptospirosis cases to help prioritize Leptospira species and genotypes for control.

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a "host-pathogen space" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.

Many infectious diseases lack robust estimates of incidence from endemic areas, and extrapolating incidence when there are few locations with data remains a major challenge in burden of disease estimation. We sought to combine sentinel surveillance with community behavioural surveillance to estimate leptospirosis incidence. We administered a questionnaire gathering responses on established locally relevant leptospirosis risk factors and recent fever to livestock-owning community members across six districts in northern Tanzania and applied a logistic regression model predicting leptospirosis risk on the basis of behavioural factors that had been previously developed among patients with fever in Moshi Municipal and Moshi Rural Districts. We aggregated probability of leptospirosis by district and estimated incidence in each district by standardizing probabilities to those previously estimated for Moshi Districts. We recruited 286 community participants: Hai District (n = 11), Longido District (59), Monduli District (56), Moshi Municipal District (103), Moshi Rural District (44) and Rombo District (13). The mean predicted probability of leptospirosis by district was Hai 0.029 (0.005, 0.095), Longido 0.071 (0.009, 0.235), Monduli 0.055 (0.009, 0.206), Moshi Rural 0.014 (0.002, 0.049), Moshi Municipal 0.015 (0.004, 0.048) and Rombo 0.031 (0.006, 0.121). We estimated the annual incidence (upper and lower bounds of estimate) per 100,000 people of human leptospirosis among livestock owners by district as Hai 35 (6, 114), Longido 85 (11, 282), Monduli 66 (11, 247), Moshi Rural 17 (2, 59), Moshi Municipal 18 (5, 58) and Rombo 47 (7, 145). Use of community behavioural surveillance may be a useful tool for extrapolating disease incidence beyond sentinel surveillance sites.

OBJECTIVE: Identifying febrile patients requiring antibacterial treatment is challenging, particularly in low-resource settings. In Southeast Asia, C-reactive protein (CRP) has been demonstrated to be highly sensitive and moderately specific in detecting bacterial infections, and to safely reduce unnecessary antibacterial prescriptions in primary care. As evidence is scant in sub-Saharan Africa, we assessed the sensitivity of CRP in identifying serious bacterial infections in Tanzania. METHODS: Samples were obtained from inpatients and outpatients in a prospective febrile illness study at two hospitals in Moshi, Tanzania, 2011-2014. Bacterial bloodstream infections (BSI) were established by blood culture, and bacterial zoonotic infections were defined by >/=4-fold rise in antibody titer between acute and convalescent sera. The sensitivity of CRP in identifying bacterial infections was estimated using thresholds of 10, 20, and 40 mg/L. Specificity was not assessed because determining false positive CRP results was limited by the lack of diagnostic testing to confirm non-bacterial etiologies and because ascertaining true negative cases was limited by the imperfect sensitivity of the diagnostic tests used to identify bacterial infections. RESULTS: Among 235 febrile outpatients and 569 febrile inpatients evaluated, 31 (3.9%) had a bacterial BSI and 61 (7.6%) had a bacterial zoonosis. Median (interquartile range) CRP values were 173 (80-315) mg/L in bacterial BSI, and 108 (31-208) mg/L in bacterial zoonoses. The sensitivity (95% Confidence Intervals) of CRP was 97% (83-99%), 94% (79-98%), 90% (74-97%) for identifying bacterial BSI, and 87% (76-93%), 82% (71-90%), 72% (60-82%) for bacterial zoonoses, using thresholds of 10, 20 and 40mg/L respectively. CONCLUSION: CRP was moderately sensitive for bacterial zoonoses and highly sensitive for identifying BSIs. Based on these results, operational studies are warranted to assess the safety and clinical utility of CRP for the management of non-malaria febrile illness at first-level health facilities in sub-Saharan Africa.

Chikungunya, a mosquito-borne viral, acute febrile illness (AFI) is associated with polyarthralgia and polyarthritis. Differentiation from other AFI is difficult due to the non-specific presentation and limited availability of diagnostics. This 3-year study identified independent clinical predictors by day post-illness onset (DPO) at presentation and age-group that distinguish chikungunya cases from two groups: other AFI and dengue. Specimens collected from participants with fever </=7 days were tested for chikungunya, dengue viruses 1-4, and 20 other pathogens. Of 8,996 participants, 18.2% had chikungunya, and 10.8% had dengue. Chikungunya cases were more likely than other groups to be older, report a chronic condition, and present <3 DPO. Regardless of timing of presentation, significant positive predictors for chikungunya versus other AFI were: joint pain, muscle, bone or back pain, skin rash, and red conjunctiva; with dengue as the comparator, red swollen joints (arthritis), joint pain, skin rash, any bleeding, and irritability were predictors. Chikungunya cases were less likely than AFI and dengue to present with thrombocytopenia, signs of poor circulation, diarrhea, headache, and cough. Among participants presenting <3 DPO, predictors for chikungunya versus other AFI included: joint pain, skin rash, and muscle, bone or back pain, and absence of thrombocytopenia, poor circulation and respiratory or gastrointestinal symptoms; when the comparator was dengue, joint pain and arthritis, and absence of thrombocytopenia, leukopenia, and nausea were early predictors. Among all groups presenting 3-5 DPO, pruritic skin became a predictor for chikungunya, joint, muscle, bone or back pain were no longer predictive, while arthritis became predictive in all age-groups. Absence of thrombocytopenia was a significant predictor regardless of DPO or comparison group. This study identified robust clinical indicators such as joint pain, skin rash and absence of thrombocytopenia that can allow early identification of and accurate differentiation between patients with chikungunya and other common causes of AFI.

A chronic kidney disease of unknown etiology (CKDu) has been killing workers in Central America. Occupational heat stress is thought to play an important role. Leptospirosis and hantavirus have been suggested as additional possible risk factors. In a case-control study in a Nicaraguan mining community, a structured survey was administered to adults, and biological measurements and specimens were taken. Serum was analyzed for antibodies to Leptospira and hantavirus. Before statistical analysis, a board-certified nephrologist determined final case and control status based on serum creatinine and other laboratory values. Multivariable analysis was by logistic regression. In sensitivity analyses, cases were restricted to those diagnosed with CKDu in the previous 3 years. Of 320 eligible participants, 112 were classified as presumptive cases, 176 as controls and 32 as indeterminant. The risk of CKDu in those ever having worked in mining or construction was 4.4 times higher than in other participants (odds ratio = 4.44, 95% CI: 1.96-10.0, P = 0.0003). Eighty-three (26%) of the 320 participants were seropositive for at least one tested strain of Leptospira. No evidence of a causal link between leptospirosis or hantavirus and CKDu was found. The sensitivity analyses provide some evidence against the hypotheses that leptospirosis or hantavirus leads to CKDu within a few years. A major limitation was the impossibility of determining the absolute or relative timing of infection and CKDu onset. A prospective cohort design, with repeated collection of specimens over several years, could yield clearer answers about infections as potential etiologic agents in CKDu.

INTRODUCTION: Leptospirosis is a major cause of febrile illness in Africa but little is known about risk factors for human infection. We conducted a cross-sectional study to investigate risk factors for acute leptospirosis and Leptospira seropositivity among patients with fever attending referral hospitals in northern Tanzania. METHODS: We enrolled patients with fever from two referral hospitals in Moshi, Tanzania, 2012-2014, and performed Leptospira microscopic agglutination testing on acute and convalescent serum. Cases of acute leptospirosis were participants with a four-fold rise in antibody titers, or a single reciprocal titer >/=800. Seropositive participants required a single titer >/=100, and controls had titers <100 in both acute and convalescent samples. We administered a questionnaire to assess risk behaviors over the preceding 30 days. We created cumulative scales of exposure to livestock urine, rodents, and surface water, and calculated odds ratios (OR) for individual behaviors and for cumulative exposure variables. RESULTS: We identified 24 acute cases, 252 seropositive participants, and 592 controls. Rice farming (OR 14.6), cleaning cattle waste (OR 4.3), feeding cattle (OR 3.9), farm work (OR 3.3), and an increasing cattle urine exposure score (OR 1.2 per point) were associated with acute leptospirosis. CONCLUSIONS: In our population, exposure to cattle and rice farming were risk factors for acute leptospirosis. Although further data is needed, these results suggest that cattle may be an important source of human leptospirosis. Further investigation is needed to explore the potential for control of livestock Leptospira infection to reduce human disease.

Leptospirosis is an emerging bacterial zoonosis that is endemic but underrecognized throughout the tropics. Through prospective surveillance for acute febrile illness (AFI) among patients who presented to the emergency department of a hospital located in an urban region of Puerto Rico, four patients with laboratory-confirmed leptospirosis were identified. All patients had signs and symptoms of AFI, including fever, headache, and dehydration. Three patients had leukocytosis with thrombocytopenia and were admitted to the hospital. One hospitalized patient presented with jaundice, icteric sclera, and hematuria and developed rhabdomyolysis, whereas another patient with pulmonary edema was admitted to the intensive care unit. Microscopic agglutination titers among the four patients were highest against serogroups Icterohaemorrhagiae (serovar Mankarso), Australis (serovar Bratislava), Bataviae (serovar Bataviae), and Canicola (serovar Canicola). These case reports demonstrate that infection with these apparently uncommon serogroups can result in illness ranging from mild to life-threatening.

Identifying etiologies of acute febrile illnesses (AFI) is challenging due to non-specific presentation and limited availability of diagnostics. Prospective AFI studies provide a methodology to describe the syndrome by age and etiology, findings that can be used to develop case definitions and multiplexed diagnostics to optimize management. We conducted a 3-year prospective AFI study in Puerto Rico. Patients with fever ≤7 days were offered enrollment, and clinical data and specimens were collected at enrollment and upon discharge or follow-up. Blood and oro-nasopharyngeal specimens were tested by RT-PCR and immunodiagnostic methods for infection with dengue viruses (DENV) 1-4, chikungunya virus (CHIKV), influenza A and B viruses (FLU A/B), 12 other respiratory viruses (ORV), enterovirus, Leptospira spp., and Burkholderia pseudomallei. Clinical presentation and laboratory findings of participants infected with DENV were compared to those infected with CHIKV, FLU A/B, and ORV. Clinical predictors of laboratory-positive dengue compared to all other AFI etiologies were determined by age and day post-illness onset (DPO) at presentation. Of 8,996 participants enrolled from May 7, 2012 through May 6, 2015, more than half (54.8%, 4,930) had a pathogen detected. Pathogens most frequently detected were CHIKV (1,635, 18.2%), FLU A/B (1,074, 11.9%), DENV 1-4 (970, 10.8%), and ORV (904, 10.3%). Participants with DENV infection presented later and a higher proportion were hospitalized than those with other diagnoses (46.7% versus 27.3% with ORV, 18.8% with FLU A/B, and 11.2% with CHIKV). Predictors of dengue in participants presenting <3 DPO included leukopenia, thrombocytopenia, headache, eye pain, nausea, and dizziness, while negative predictors were irritability and rhinorrhea. Predictors of dengue in participants presenting 3-5 DPO were leukopenia, thrombocytopenia, facial/neck erythema, nausea, eye pain, signs of poor circulation, and diarrhea; presence of rhinorrhea, cough, and red conjunctiva predicted non-dengue AFI. By enrolling febrile patients at clinical presentation, we identified unbiased predictors of laboratory-positive dengue as compared to other common causes of AFI. These findings can be used to assist in early identification of dengue patients, as well as direct anticipatory guidance and timely initiation of correct clinical management.

Coinfection with pathogens that cause acute febrile illness (AFI) can complicate patient diagnosis and management. This report describes a fatal case of Leptospira spp./Zika virus (ZIKV) coinfection in Puerto Rico. The patient presented with a 5-day history of AFI; reported behavioral risk factors for leptospirosis; was diagnosed with possible leptospirosis, dengue, chikungunya, or ZIKV disease; and received appropriate treatment of leptospirosis and dengue. Following a 3-day hospitalization, the patient died due to acute gastrointestinal hemorrhage, and kidney and liver failure. Serologic diagnostic testing for leptospirosis and ZIKV disease was both negative; however, molecular diagnostic testing performed postmortem was positive for detection of Leptospira spp. and ZIKV nucleic acid. This case demonstrates the need for continued clinical awareness of leptospirosis in areas experiencing outbreaks of pathogens that cause AFI and the need for evaluation of coinfection with AFI-causing pathogens as a risk factor for increased severity of disease.

BACKGROUND: Leptospirosis is postulated as a possible cause of Mesoamerican Nephropathy (MeN) in Central American workers. OBJECTIVES: Investigate job-specific Leptospira seroprevalence and its association with kidney disease biomarkers. METHODS: In 282 sugarcane workers, 47 sugarcane applicants and 160 workers in other industries, we measured anti-leptospiral antibodies, serum creatinine, and urinary injury biomarkers, including neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), and N-acetyl-D-glucosaminidase (NAG). RESULTS: Leptospira seroprevalence differed among job categories and was highest among sugarcane cutters (59%). Seropositive sugarcane workers had higher NGAL concentrations (relative mean: 1.28; 95% CI: 0.94-1.75) compared to those who were seronegative, with similar findings among field and non-field workers. CONCLUSIONS: Leptospira seroprevalence varied by job category. There was some indication that seropositivity was associated with elevated biomarker levels, but results were inconsistent. Additional studies may help establish whether Leptospira infection plays any role in MeN among Central American workers.

BACKGROUND: We conducted a study to identify Rickettsia, Coxiella, Leptospira, Bartonella, and Chikungunya virus infections among febrile patients presenting at hospitals in Bangladesh. METHODS: We collected blood samples from patients at six tertiary hospitals from December 2008 to November 2009 and performed laboratory tests at the United States Centers for Disease Control and Prevention (CDC). RESULTS: Out of 720 enrolled patients, 263 (37%) were infected with Rickettsia; 132 patients had immunofluorescence antibody titer >64 against spotted fever, 63 patients against scrub typhus fever and 10 patients against typhus fever. Ten patients were identified with Coxiella. We isolated Leptospira from two patients and Bartonella from one patient. Ten patients had antibodies against Chikungunya virus. The proportion of patients who died was higher with rickettsial fever (5%) compared to those without a diagnosis of rickettsial infection (2%). None of the patients were initially diagnosed with rickettsial fever. CONCLUSIONS: Rickettsial infections are frequent yet under-recognized cause of febrile illness in Bangladesh. Clinical guidelines should be revised so that local clinicians can diagnose rickettsial infections and provide appropriate drug treatment.

BACKGROUND: The sole report of annual leptospirosis incidence in continental Africa of 75-102 cases per 100,000 population is from a study performed in August 2007 through September 2008 in the Kilimanjaro Region of Tanzania. To evaluate the stability of this estimate over time, we estimated the incidence of acute leptospirosis in Kilimanjaro Region, northern Tanzania for the time period 2012-2014. METHODOLOGY AND PRINCIPAL FINDINGS: Leptospirosis cases were identified among febrile patients at two sentinel hospitals in the Kilimanjaro Region. Leptospirosis was diagnosed by serum microscopic agglutination testing using a panel of 20 Leptospira serovars belonging to 17 separate serogroups. Serum was taken at enrolment and patients were asked to return 4-6 weeks later to provide convalescent serum. Confirmed cases required a 4-fold rise in titre and probable cases required a single titre of ≥800. Findings from a healthcare utilisation survey were used to estimate multipliers to adjust for cases not seen at sentinel hospitals. We identified 19 (1.7%) confirmed or probable cases among 1,115 patients who presented with a febrile illness. Of cases, the predominant reactive serogroups were Australis 8 (42.1%), Sejroe 3 (15.8%), Grippotyphosa 2 (10.5%), Icterohaemorrhagiae 2 (10.5%), Pyrogenes 2 (10.5%), Djasiman 1 (5.3%), Tarassovi 1 (5.3%). We estimated that the annual incidence of leptospirosis was 11-18 cases per 100,000 population. This was a significantly lower incidence than 2007-08 (p<0.001). CONCLUSIONS: We estimated a much lower incidence of acute leptospirosis than previously, with a notable absence of cases due to the previously predominant serogroup Mini. Our findings indicate a dynamic epidemiology of leptospirosis in this area and highlight the value of multi-year surveillance to understand leptospirosis epidemiology.

BACKGROUND: In 2014, we investigated a cluster of Guillain-Barre syndrome (GBS) in Fiji that occurred during a dengue epidemic. We designed a case-control study to determine the etiology. METHODS: Cases were patients meeting Brighton Collaboration criteria for GBS with onset from February 2014 to May 2014. Controls were persons without symptoms of GBS who were matched by age group and location. We collected information on demographics and potential exposures. Serum samples were tested for evidence of recent arboviral or Leptospira spp. infections. RESULTS: Nine cases of GBS were identified for an incidence of five cases per 100,000 population/year. Median age of cases was 27years (range: 0.8-52); five (56%) were male. Six (67%) reported an acute illness prior to GBS onset. Among the 9 cases and 28 controls enrolled, odds ratios for reported exposures or antibodies against various arboviruses or Leptospira spp. were not statistically significant. CONCLUSIONS: No clear etiologies were identified for this unusual GBS cluster. There was a temporal association between the GBS cluster and a dengue epidemic, but we were unable to substantiate an epidemiologic or laboratory association. Further study is needed to explore potential associations between arboviral infections and GBS.

The history and epidemiology of human leptospirosis in Malaysia from 1925 to 2012 are described. Previous studies have demonstrated that leptospirosis is an endemic disease in Malaysia occurring in both urban and rural locations. The number of cases has risen dramatically since the Ministry of Health Malaysia highlighted leptospirosis as a notifiable disease in 2010, with reported cases increasing from 248 cases in 2004 to 3604 in 2012. The incidence of infection among the population suggests that occupation, sex, age, ethnic background, water recreational activities, and sporting events are risk factors. A robust surveillance system is now in place to monitor temporal and spatial changes in the incidence and prevalence of infection and to identify risk areas and disease behavior. Despite extensive studies over the past decade, there is a still a need to describe local serovars in host carriers and the human population, with the view to develop an effective vaccine against leptospirosis.

Serum specimens from free-ranging but nonnative patas monkeys (Erythrocebus patas) and rhesus macaques (Macaca mulatta) in southwestern Puerto Rico (PR) were tested for antibodies to infection with dengue viruses (DENVs), West Nile virus (WNV), Leptospira species, and Burkholderia pseudomallei by microneutralization, plaque reduction neutralization, microscopic agglutination, and indirect hemagglutination, respectively. Of 23 animals (21 E. patas and two M. mulatta) tested, all had evidence of prior DENV infection, and of 17 animals tested for WNV, nine (53%) had evidence of prior infection. Of 24 (22 E. patas, two M. mulatta) tested for Leptospira spp., 10 (42%) had evidence of prior exposure, and one patas monkey had antibodies against B. pseudomallei. The acquisition of pathogens endemic among humans in PR by resident nonhuman primates merits further study to define modes of acquisition.

BACKGROUND: Leptospirosis is an emerging infectious disease of global significance, and is endemic in tropical countries, including Malaysia. Over the last decade, a dramatic increase of human cases was reported; however, information on the primary vector, the rat, and the Leptospira serovars circulating among the rat population is limited. Therefore, the present study was undertaken to isolate Leptospira and characterise the serovars circulating in the urban rat populations from selected main cities in Peninsular Malaysia. METHODS: Rat trappings were carried out between October 2011 to February 2014 in five urban cities which were chosen as study sites to represent different geographical locations in Peninsular Malaysia. Microscopic agglutination test (MAT) and PCR were carried out to identify the Leptospiral serogroup and determine the pathogenic status of the isolates, respectively while pulsed-field gel electrophoresis (PFGE) and random amplified polymorphic DNA (RAPD)-PCR were used to characterize the isolates. RESULTS: Three rat species were identified from the three hundred and fifty seven rats captured with Rattus rattus, being the dominant rat species (285, 80 %) followed by Rattus norgevicus (53, 15 %) and Rattus exulans (19, 5 %). Only 39 samples (11.0 %) were positive by culture and further confirmed as pathogenic Leptospira by PCR. Significant associations were shown between host infection with locality, season, host-age and species. Based on MAT, two serogroups were identified in the population namely; L. borgpetersenii serogroup Javanica (n = 16) and L. interrogans serogroup Bataviae (n = 23). Pulsed-field gel electrophoresis (PFGE) distinguished the two serovars in the urban rat populations: L. borgpetersenii serovar Javanica (41 %), and L. interrogans serovar Bataviae (59 %). RAPD-PCR yielded 14 distinct patterns and was found to be more discriminative than PFGE. CONCLUSIONS: This study confirms two Leptospira serovars circulating among the urban rats population in Peninsular Malaysia namely; L. borgpetersenii serovar Javanica and L. interrogans serovars Bataviae. Despite the low number of isolates obtained from the rat population, this study suggests that rodent control programs and disease surveillance may help to reduce the possible risk of disease transmission.

BACKGROUND: Leptospirosis is a potentially fatal bacterial zoonosis that is endemic throughout the tropics and may be misdiagnosed as dengue. Delayed hospital admission of leptospirosis patients is associated with increased mortality. METHODOLOGY/PRINCIPAL FINDINGS: During a concurrent dengue/leptospirosis epidemic in Puerto Rico in 2010, suspected dengue patients that tested dengue-negative were tested for leptospirosis. Fatal and non-fatal hospitalized leptospirosis patients were matched 1:1-3 by age. Records from all medical visits were evaluated for factors associated with fatal outcome. Among 175 leptospirosis patients identified (4.7 per 100,000 residents), 26 (15%) were fatal. Most patients were older males and had illness onset during the rainy season. Fatal case patients first sought medical care earlier than non-fatal control patients (2.5 vs. 5 days post-illness onset [DPO], p < 0.01), but less frequently first sought care at a hospital (52.4% vs. 92.2%, p < 0.01). Although fatal cases were more often diagnosed with leptospirosis at first medical visit (43.9% vs. 9.6%, p = 0.01), they were admitted to the hospital no earlier than non-fatal controls (4.5 vs. 6 DPO, p = 0.31). Cases less often developed fever (p = 0.03), but more often developed jaundice, edema, leg pain, hemoptysis, and had a seizure (p ≤ 0.03). Multivariable analysis of laboratory values from first medical visit associated with fatal outcome included increased white blood cell (WBC) count with increased creatinine (p = 0.001), and decreased bicarbonate with either increased WBC count, increased creatinine, or decreased platelet count (p < 0.001). CONCLUSIONS/SIGNIFICANCE: Patients with fatal leptospirosis sought care earlier, but were not admitted for care any earlier than non-fatal patients. Combinations of routine laboratory values predictive of fatal outcome should be considered in admission decision-making for patients with suspected leptospirosis.