Post kala azar dermal leishmaniasis and leprosy prevalence and distribution in the Muzaffarpur health and demographic surveillance site

Hasker, Epco; Malaviya, Paritosh; Scholar, Vivek Kumar; Koning, Pieter de; Singh, Om Prakash; Kansal, Sangeeta; Cloots, Kristien; Boelaert, Marleen

doi:10.1371/journal.pntd.0007798

Cited by 13 publications

(13 citation statements)

References 15 publications

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“…1•3-10•2) respectively when compared to those living at more than 100 meters. (Hasker et al, 2019) But here too, the majority of leprosy cases (75%) were at more than 25 meters from the nearest index case.…”

Section: Discussionmentioning

confidence: 86%

Exploring clustering of leprosy in the Comoros and Madagascar: A geospatial analysis

Ortuño-Gutiérrez¹,

Mzembaba²,

Ramboarina³

et al. 2021

International Journal of Infectious Diseases

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Section: Discussionmentioning

confidence: 86%

Exploring clustering of leprosy in the Comoros and Madagascar: A geospatial analysis

Ortuño-Gutiérrez¹,

Mzembaba²,

Ramboarina³

et al. 2021

International Journal of Infectious Diseases

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“…[ 22 ] However, focusing only on household contacts will miss an important number of new leprosy cases as the increased risk of leprosy extends beyond households. [ 11 ] Using a GIS-based approach we were able to identify larger high-risk clusters beyond household level that would potentially benefit from measures such as active screening and post-exposure prophylaxis.…”

Section: Discussionmentioning

confidence: 99%

“…Leprosy is known to be distributed unevenly and to cluster [ 10 , 11 ], focusing on the lowest geographical unit at high risk is key in view of the limited resources available. [ 10 ]…”

Section: Introductionmentioning

confidence: 99%

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Less is more: Developing an approach for assessing clustering at the lower administrative boundaries that increases the yield of active screening for leprosy in Bihar, India

Ortuño-Gutiérrez¹,

Shih

Wagh³

et al. 2022

PLoS Negl Trop Dis

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Background In India, leprosy clusters at hamlet level but detailed information is lacking. We aim to identify high-incidence hamlets to be targeted for active screening and post-exposure prophylaxis. Methodology We paid home visits to a cohort of leprosy patients registered between April 1st, 2020, and March 31st, 2022. Patients were interviewed and household members were screened for leprosy. We used an open-source app(ODK) to collect data on patients’ mobility, screening results of household members, and geographic coordinates of their households. Clustering was analysed with Kulldorff’s spatial scan statistic(SaTScan). Outlines of hamlets and population estimates were obtained through an open-source high-resolution population density map(https://data.humdata.org), using kernel density estimation in QGIS, an open-source software. Results We enrolled 169 patients and screened 1,044 household contacts in Bisfi and Benipatti blocks of Bihar. Median number of years of residing in the village was 17, interquartile range(IQR)12-30. There were 11 new leprosy cases among 658 household contacts examined(167 per 10,000), of which seven had paucibacillary leprosy, one was a child under 14 years, and none had visible disabilities. We identified 739 hamlets with a total population of 802,788(median 163, IQR 65–774). There were five high incidence clusters including 12% of the population and 46%(78/169) of the leprosy cases. One highly significant cluster with a relative risk (RR) of 4.7(p<0.0001) included 32 hamlets and 27 cases in 33,609 population. A second highly significant cluster included 32 hamlets and 24 cases in 33,809 population with a RR of 4.1(p<0.001). The third highly significant cluster included 16 hamlets and 17 cases in 19,659 population with a RR of 4.8(p<0.001). High-risk clusters still need to be screened door-to-door. Conclusions We found a high yield of active household contact screening. Our tools for identifying high-incidence hamlets appear effective. Focusing labour-intensive interventions such as door-to-door screening on such hamlets could increase efficiency.

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“…[ 28 ] From the same region of Muzaffarpur, the prevalence was found to have declined to 1.1/10,000 population in 2017. [ 29 ] The decline was attributed to greater time lapse between the peak of VL-epidemic in that region, which occurred in 2007–2008.…”

Section: Magnitudementioning

confidence: 99%

Epidemiology of Post-Kala-azar Dermal Leishmaniasis

Ghosh

Roy²,

Chaudhuri

et al. 2021

Indian Journal of Dermatology

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Post-kala-azar dermal leishmaniasis (PKDL) is a cutaneous sequel of visceral leishmaniasis (VL) or kala-azar and has become an entity of epidemiological significance by virtue of its ability to maintain the disease in circulation during inter-epidemic periods. PKDL has been identified as one of the epidemiological marker of “kala-azar elimination programme.” Data obtained in 2018 showed PKDL distribution primarily concentrated in 6 countries, which includes India, Sudan, south Sudan, Bangladesh, Ethiopia, and Nepal in decreasing order of case-burden. In India, PKDL cases are mainly found in 54 districts, of which 33 are in Bihar, 11 in West Bengal, 4 in Jharkhand, and 6 in Uttar Pradesh. In West Bengal the districts reporting cases of PKDL cases include Darjeeling, Uttar Dinajpur, Dakshin Dinajpur, Malda, and Murshidabad. The vulnerability on the young age is documented in various studies. The studies also highlights a male predominance of the disease but recent active surveillance suggested that macular form of PKDL shows female-predominance. It is recommended that along with passive case detection, active survey helps in early identification of cases, thus reducing disease transmission in the community. The Accelerated plan for Kala-azar elimination in 2017 introduced by Government of India with the goal to eliminate Kala-azar as a public health problem, targets to reduceing annual incidence <1/10,000. Leishmania donovani is the established causative agent, but others like L. tropica or L. infantum may occasionally lead to the disease, especially with HIV-co-infection. Dermal tropism of the parasite has been attributed to overexpression of parasite surface receptors (like gp 63, gp46). Various host factors are also identified to contribute to the development of the disease, including high pretreatment IL 10 and parasite level, inadequate dose and duration of treatment, malnutrition, immuno-suppression, decreased interferon-gamma receptor 1 gene, etc. PKDL is mostly concentrated in the plains below an altitude of 600 mts which is attributed to the environment conducive for the vector sand fly ( Phlebotumus ). Risk factors are also linked to the habitat of the sand fly. Keeping these things in mind “Integrated vector control” is adopted under National vector borne disease control programme as one of the strategies to bring down the disease burden.

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Post kala azar dermal leishmaniasis and leprosy prevalence and distribution in the Muzaffarpur health and demographic surveillance site

Cited by 13 publications

References 15 publications

Exploring clustering of leprosy in the Comoros and Madagascar: A geospatial analysis

Exploring clustering of leprosy in the Comoros and Madagascar: A geospatial analysis

Less is more: Developing an approach for assessing clustering at the lower administrative boundaries that increases the yield of active screening for leprosy in Bihar, India

Epidemiology of Post-Kala-azar Dermal Leishmaniasis

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