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Asset index distribution ('1' poorest to '5' wealthiest) of households located within annual hotspots during at least one year (panel A) and outside of the hotspots (panel B), where 'n' is the total number of households. https://doi.org/10.1371/journal.pntd.0006888.g005
Source publication
Background
Despite the overall decrease in visceral leishmaniasis (VL) incidence on the Indian subcontinent, there remain spatiotemporal clusters or ‘hotspots’ of new cases. The characteristics of these hotspots, underlying transmission dynamics, and their importance for shaping control strategies are not yet fully understood and are investigated i...
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Background:
Visceral leishmaniasis (VL) in human immunodeficiency virus (HIV) co-infected patients requires special case management. AmBisome monotherapy at 40 mg/kg is recommended by the World Health Organization. The objective of the study was to assess if a combination of a lower dose of AmBisome with miltefosine would show acceptable efficacy...
Citations
... The data can further be potentially used to predict / forecast VL outbreak or resurgence especially during post elimination. This is important to rule out the possibility that the observed down-trend being accelerated by the "natural" fluctuation of the disease (disease incidence in India is cyclic) [6,7] rather than entirely due to the effect of interventions [8], as well as to prevent potential outbreaks when herd immunity is in weakening phase [9]. ...
... However, the study neither considered any risk factors to identify the clusters nor to forecast incidence [28]. In India, analyses of the association of VL incidence with climatic factors, and vector density have been done but are mostly limited to small geographical areas [9,[29][30][31]. The studies focused towards identifying drivers of hotspots at the village or household level [9,30,31]. ...
... In India, analyses of the association of VL incidence with climatic factors, and vector density have been done but are mostly limited to small geographical areas [9,[29][30][31]. The studies focused towards identifying drivers of hotspots at the village or household level [9,30,31]. Deb et al. [7], applied a negative binomial regression model to the state level annual VL incidence data from Bihar and showed significant negative associations of VL incidence with maximum temperature, and average temperature. Bhunia et al. [29], analyzed district level VL incidence data and observed VL incidence in the Gangetic plain of Bihar is positively associated with environmental (presence of water bodies, woodland and urban, built-up areas, soil type) and climatic (air temperature, relative humidity and annual rainfall) factors. ...
Background
As of 2021, the National Kala-azar Elimination Programme (NKAEP) in India has achieved visceral leishmaniasis (VL) elimination (<1 case / 10,000 population/year per block) in 625 of the 633 endemic blocks (subdistricts) in four states. The programme needs to sustain this achievement and target interventions in the remaining blocks to achieve the WHO 2030 target of VL elimination as a public health problem. An effective tool to analyse programme data and predict/ forecast the spatial and temporal trends of VL incidence, elimination threshold, and risk of resurgence will be of use to the programme management at this juncture.
Methodology/principal findings
We employed spatiotemporal models incorporating environment, climatic and demographic factors as covariates to describe monthly VL cases for 8-years (2013–2020) in 491 and 27 endemic and non-endemic blocks of Bihar and Jharkhand states. We fitted 37 models of spatial, temporal, and spatiotemporal interaction random effects with covariates to monthly VL cases for 6-years (2013–2018, training data) using Bayesian inference via Integrated Nested Laplace Approximation (INLA) approach. The best-fitting model was selected based on deviance information criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) and was validated with monthly cases for 2019–2020 (test data). The model could describe observed spatial and temporal patterns of VL incidence in the two states with widely differing incidence trajectories, with >93% and 99% coverage probability (proportion of observations falling inside 95% Bayesian credible interval for the predicted number of VL cases per month) during the training and testing periods. PIT (probability integral transform) histograms confirmed consistency between prediction and observation for the test period. Forecasting for 2021–2023 showed that the annual VL incidence is likely to exceed elimination threshold in 16–18 blocks in 4 districts of Jharkhand and 33–38 blocks in 10 districts of Bihar. The risk of VL in non-endemic neighbouring blocks of both Bihar and Jharkhand are less than 0.5 during the training and test periods, and for 2021–2023, the probability that the risk greater than 1 is negligible (P<0.1). Fitted model showed that VL occurrence was positively associated with mean temperature, minimum temperature, enhanced vegetation index, precipitation, and isothermality, and negatively with maximum temperature, land surface temperature, soil moisture and population density.
Conclusions/significance
The spatiotemporal model incorporating environmental, bioclimatic, and demographic factors demonstrated that the KAMIS database of the national programmme can be used for block level predictions of long-term spatial and temporal trends in VL incidence and risk of outbreak / resurgence in endemic and non-endemic settings. The database integrated with the modelling framework and a dashboard facility can facilitate such analysis and predictions. This could aid the programme to monitor progress of VL elimination at least one-year ahead, assess risk of resurgence or outbreak in post-elimination settings, and implement timely and targeted interventions or preventive measures so that the NKAEP meet the target of achieving elimination by 2030.
... A similar finding was reported in Nepal 36 . The association between poverty and hotspots reveals that VL is a disease that affects "the poorest of the poor," suggesting a potential role for waning immunity as an underlying driver of hotspots 37 . ...
Abstract Visceral leishmaniasis is a major, life-threatening parasitic disease that still remains a serious public health problem in Ethiopia. Understanding the epidemiological, clinical, and hematological profiles of visceral leishmaniasis patients is important for implementing evidence-based control strategies. It is also important for early treatment and to decrease the mortality rate from the disease. Therefore, this study was aimed at assessing the epidemiological, clinical, and hematological profiles of visceral leishmaniasis among patients visiting Tefera Hailu Memorial Hospital, Northeast Ethiopia. A retrospective study was conducted at Tefera Hailu Memorial Hospital from September 2017 to August 2021. Data were collected from the medical records of suspected patients who were tested by the rK39 rapid diagnostic by strictly following standard operating procedures. The data was summarized using Microsoft Excel and analyzed using SPSS 26 version software. Descriptive statistics were used to describe the epidemiological, clinical, and hematological profiles of visceral leishmaniasis patients. A p-value
... A previous study by Bulstra et al reported that the characteristics of these hotspots, underlying transmission dynamics, and their importance for shaping control strategies are not yet fully understood which is responsible for spatiotemporal heterogeneity in VL incidence at subdistrict level. [7] Priyamvada et al reported on 272 VL patients diagnosed between 2012-2019. They note that hotspots center around disadvantaged communities -scheduled caste / scheduled tribe households and also in households that are economically disadvantaged with lack of sanitary facilities, household crowding, migrant labor etc. [8] There is also a signicant association between household asset index and the likelihood of being in a hot spot, postulating poverty as a driving force. ...
... They note that hotspots center around disadvantaged communities -scheduled caste / scheduled tribe households and also in households that are economically disadvantaged with lack of sanitary facilities, household crowding, migrant labor etc. [8] There is also a signicant association between household asset index and the likelihood of being in a hot spot, postulating poverty as a driving force. [7] In a study from Tamil Nādu, Nandha et al showed that compliance with treatment rounds was less than the 80% mark considered to be optimum and visits from drug delivery personnel to household were also scant in the areas with low compliance. [9] As we have marched in a direction of near future elimination of Visceral Leismaniasis, the role of community also become very important. ...
Visceral Leishmaniasis (VL) is endemic in very few countries and the predominant incidence is in the Indian state of Bihar. We aimed to study the implementation of key elimination strategies in Bihar in the last 5 years. We studied secondary data obtained from the National Vector Borne Disease Control Programme. Variables studied were VL case count, adequacy of drug and diagnostics supply and also of coverage of insecticide residual spray. Our results show a drastic decrease in VL case count in Bihar although some hot spots still remain. Case count does not show any specic trend with drug and diagnostic supply. The continuous drug availability and diagnostic kits along with indoor residual spray(IRS) is key to VL elimination.
... The spatiotemporal hotspot detection is complex because the number and features, e.g., size, shape and number of objects, of hotspots are unknown. STDM hotspot detection task is utilised for identifying dense conglomeration of events both in space and time in applications such as the outbreaks of diseases (Bulstra et al. 2018;Feng et al. 2015). Kulldorff (1997) proposed a spatial scan statistical (SSS) method for hotspot detection. ...
Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.
... Most research has focused on improving understanding of transmission dynamics for example on a regional scale as for VL in the Indian subcontinent [83], to monitor outbreaks or to assess different transmission modes [84]. More recently, spatio-temporal models have been developed focusing on household or community data [85][86][87] highlighting the heterogeneity of disease incidence and transmission. In contrast, few studies have used epidemiological modeling to predict how vaccines may serve as effective public health measures. ...
Introduction
The leishmaniases represent a group of parasitic diseases caused by infection with one of several species of Leishmania parasites. Disease presentation varies because of differences in parasite and host genetics and may be influenced by additional factors such as host nutritional status or co-infection. Studies in experimental models of Leishmania infection, vaccination of companion animals and human epidemiological data suggest that many forms of leishmaniasis could be prevented by vaccination, but no vaccines are currently available for human use.
Areas covered
We describe some of the existing roadblocks to the development and implementation of an effective leishmaniasis vaccine, based on a review of recent literature found on PubMed, BioRxiv and MedRxiv. In addition to discussing scientific unknowns that hinder vaccine candidate identification and selection, we explore gaps in knowledge regarding the commercial and public health value propositions underpinning vaccine development and provide a route map for future research and advocacy.
Expert opinion
Despite significant progress, leishmaniasis vaccine development remains hindered by significant gaps in understanding that span the vaccine development pipeline. Increased coordination and adoption of a more holistic view to vaccine development will be required to ensure more rapid progress in the years ahead.
... Another study reported that VL incidence (cases/10,000/year) was reduced from 12.3 in the year 2007) to 0.9 in the year 2015. This is just below the target of World Health Organization's threshold as a public health problem [17]. Our study revealed that, there was no uniform pattern of trends for medical college level sites. ...
Background
Visceral leishmaniasis (VL), also known as kala-azar (KA), is a neglected vector-borne disease, targeted for elimination, but several affected blocks of Bihar are posing challenges with the high incidence of cases, and moreover, the disease is spreading in newer areas. High-quality kala-azar surveillance in India, always pose great concern. The complete and accurate patient level data is critical for the current kala-azar management information system (KMIS). On the other side, no accurate data on the burden of post kala-azar dermal leishmaniasis (PKDL) and co-infections are available under the current surveillance system, which might emerge as a serious concern. Additionally, in low case scenario, sentinel surveillance may be useful in addressing post-elimination activities and sustaining kala-azar (KA) elimination. Health facility-based sentinel site surveillance system has been proposed, first time to do a proper accounting of KA, PKDL and co-infection morbidity, mortality, diagnosis, case management, hotspot identification and monitoring the impact of elimination interventions.Methodology/principal findingsKala-azar sentinel site surveillance was established and activated in thirteen health facilities of Bihar, India, using stratified sampling technique during 2011 to 2014. Data were collected through specially designed performa from all patients attending the outpatient departments of sentinel sites. Among 20968 symptomatic cases attended sentinel sites, 2996 cases of KA and 53 cases of PKDL were registered from 889 endemic villages. Symptomatic cases meant a person with fever of more than 15 days, weight loss, fatigue, anemia, and substantial swelling of the liver and spleen (enlargement of spleen and liver).The proportion of new and old cases was 86.1% and 13.9% respectively. A statistically significant difference was observed for reduction in KA incidence from 4.13/10000 in 2011 to 1.75/10000 in 2014 (p
... Hence, data on these patterns can reveal the degree of spatial clustering, assortative (nonhomogeneous) mixing and exposure heterogeneity allowing for improved prediction of village-level incidence and guidelines on spatially targeted interventions [14,15,22,27]. Additionally, for visceral leishmaniasis, data on immune responses and infection combined with presence or absence of symptoms can inform the duration of immunity and identify markers for infection [23,28]. Note that we focus on visceral leishmaniasis in the Indian subcontinent as it is believed to be entirely anthroponotic only there (i.e., humans are the only reservoir of infection) [22]. ...
Locally tailored interventions for neglected tropical diseases (NTDs) are becoming increasingly important for ensuring that the World Health Organization (WHO) goals for control and elimination are reached. Mathematical models, such as those developed by the NTD Modelling Consortium, are able to offer recommendations on interventions but remain constrained by the data currently available. Data collection for NTDs needs to be strengthened as better data are required to indirectly inform transmission in an area. Addressing specific data needs will improve our modelling recommendations, enabling more accurate tailoring of interventions and assessment of their progress. In this collection, we discuss the data needs for several NTDs, specifically gambiense human African trypanosomiasis, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminths (STH), trachoma, and visceral leishmaniasis. Similarities in the data needs for these NTDs highlight the potential for integration across these diseases and where possible, a wider spectrum of diseases.
... Spatial and spatiotemporal data analyses using geographical information system (GIS) have been widely employed in the evaluation of the occurrence of human and canine VL in Brazil (Almeida and Werneck, 2014;Ursine et al., 2016;Campos et al., 2017) and abroad (Abdullah et al., 2017;Bulstra et al., 2018;Moradi-Asl et al., 2019;Agayev et al., 2020). These analyses contribute to a better understanding of VL epidemiology, and allow the identification of distribution patterns and priority risk areas (Marchi et al., 2019). ...
In this ecological study, we investigated spatial patterns of human visceral leishmaniasis (VL) incidence, its correlation with socioeconomic aspects, environmental indices (obtained through remote sensing) and canine VL during 2011-2016 in the municipality of Rondonópolis, a relevant endemic area for VL in Central-Western Brazil. Human VL cases were georeferenced and point patterns were analyzed by univariate Ripley's K function and Kernel density estimation (KDE). Poisson-based scan statistics were used to investigate spatial and spatiotemporal clusters of human VL incidence at the neighborhood level. Socioeconomic and environmental characteristics were compared between neighborhoods within and outside spatial human VL clusters. Also, we assessed the correlation between smoothed human VL incidence and canine VL seropositivity rates within and between neighborhoods. Human VL cases were clustered up to 2000 meters; four hotspots were identified by KDE in peripheral areas. Spatial and spatiotemporal low-risk clusters for human VL were identified in central and southern areas. Neighborhoods within spatial low-risk cluster presented higher mean income, literacy rate, sanitary sewage service coverage and lower altitude, compared to the rest of the municipality. A positive correlation was found between the occurrence of human and canine VL. On the northern outskirts, high human VL incidence was spatially correlated with high canine VL seropositivity in surrounding neighborhoods. In conclusion, human VL demonstrated a heterogeneous, aggregated and peripheral spatial pattern. This distribution was correlated with intra-urban socioeconomic differences and canine VL seropositivity at the neighborhood level.
... In contrast to the so-called index-case approach (Singh et al., 2011;Huda et al., 2012), we did not base the search on a fixed distance around the identified VL case, but sought other cases through the social networks of the known case, key informants active in health issues in the community, and through private practitioners, both formal and informal. In India, social links and caste connections have strong predictive value for where subsequent VL cases occur (Pascual Martinez et al., 2012;Bulstra et al., 2018), and ongoing links with the same key informants over years facilitate collaboration. Raising awareness in affected communities and improving the knowledge base of community health workers can also enhance both care-seeking and ACD efforts (Malaviya et al., 2013;Khatun et al., 2014). ...
As India moves toward the elimination of visceral leishmaniasis (VL) as a public health problem, comprehensive timely case detection has become increasingly important, in order to reduce the period of infectivity and control outbreaks. During the 2000s, localized research studies suggested that a large percentage of VL cases were never reported in government data. However, assessments conducted from 2013 to 2015 indicated that 85% or more of confirmed cases were eventually captured and reported in surveillance data, albeit with significant delays before diagnosis. Based on methods developed during these assessments, the CARE India team evolved new strategies for active case detection (ACD), applicable at large scale while being sufficiently effective in reducing time to diagnosis. Active case searches are triggered by the report of a confirmed VL case, and comprise two major search mechanisms: 1) case identification based on the index case’s knowledge of other known VL cases and searches in nearby houses (snowballing); and 2) sustained contact over time with a range of private providers, both formal and informal. Simultaneously, house-to-house searches were conducted in 142 villages of 47 blocks during this period. We analyzed data from 5030 VL patients reported in Bihar from January 2018 through July 2019. Of these 3033 were detected passively and 1997 via ACD (15 (0.8%) via house-to-house and 1982 (99.2%) by light touch ACD methods). We constructed multinomial logistic regression models comparing time intervals to diagnosis (30-59, 60-89 and ≥90 days with <30 days as the referent). ACD and younger age were associated with shorter time to diagnosis, while male sex and HIV infection were associated with longer illness durations. The advantage of ACD over PCD was more marked for longer illness durations: the adjusted odds ratios for having illness durations of 30-59, 60-89 and >=90 days compared to the referent of <30 days for ACD vs PCD were 0.88, 0.56 and 0.42 respectively. These ACD strategies not only reduce time to diagnosis, and thus risk of transmission, but also ensure that there is a double check on the proportion of cases actually getting captured. Such a process can supplement passive case detection efforts that must go on, possibly perpetually, even after elimination as a public health problem is achieved.
... The well-documented clustering of VL cases at the village and sub-village levels provides the basis for developing ACD targeting strategies (Bulstra et al., 2018;Chapman et al., 2020;Priyamvada et al., 2021). It is not possible to cover all villages in the affected districts of the affected states using any ACD method, particularly the more intensive ones such as house-to-house searches, since Bihar alone has more than 43,000 villages in 33 endemic districts. ...
... A targeting strategy based on reliable predictions of villages with future cases is therefore essential. Because VL cases cluster in time and space, villages with cases in recent years are known to be at risk for more cases in the near future (Courtenay et al., 2017;Bulstra et al., 2018;Chapman et al., 2020). However, development of specific targeting strategies requires a quantitative expression of this clustering. ...
Background
India has made major progress in improving control of visceral leishmaniasis (VL) in recent years, in part through shortening the time infectious patients remain untreated. Active case detection decreases the time from VL onset to diagnosis and treatment, but requires substantial human resources. Targeting approaches are therefore essential to feasibility.
Methods
We analyzed data from the Kala-azar Management Information System (KAMIS), using village-level VL cases over specific time intervals to predict risk in subsequent years. We also graphed the time between cases in villages and examined how these patterns track with village-level risk of additional cases across the range of cumulative village case-loads. Finally, we assessed the trade-off between ACD effort and yield.
Results
In 2013, only 9.3% of all villages reported VL cases; this proportion shrank to 3.9% in 2019. Newly affected villages as a percentage of all affected villages decreased from 54.3% in 2014 to 23.5% in 2019, as more surveillance data accumulated and overall VL incidence declined. The risk of additional cases in a village increased with increasing cumulative incidence, reaching approximately 90% in villages with 12 cases and 100% in villages with 45 cases, but the vast majority of villages had small cumulative case numbers. The time-to-next-case decreased with increasing case-load. Using a 3-year window (2016–2018), a threshold of seven VL cases at the village level selects 329 villages and yields 23% of cases reported in 2019, while a threshold of three cases selects 1,241 villages and yields 46% of cases reported in 2019. Using a 6-year window increases both effort and yield.
Conclusion
Decisions on targeting must consider the trade-off between number of villages targeted and yield and will depend upon the operational efficiencies of existing programs and the feasibility of specific ACD approaches. The maintenance of a sensitive, comprehensive VL surveillance system will be crucial to preventing future VL resurgence.
