The PEP++ study protocol: a cluster-randomised controlled trial on the effectiveness of an enhanced regimen of post-exposure prophylaxis for close contacts of persons affected by leprosy to prevent disease transmission.

BACKGROUND: Leprosy is an infectious disease with a slow decline in global annual caseload in the past two decades. Active case finding and post-exposure prophylaxis (PEP) with a single dose of rifampicin (SDR) are recommended by the World Health Organization as measures for leprosy elimination. However, more potent PEP regimens are needed to increase the effect in groups highest at risk (i.e., household members and blood relatives, especially of multibacillary patients). The PEP++ trial will assess the effectiveness of an enhanced preventive regimen against leprosy in high-endemic districts in India, Brazil, Bangladesh, and Nepal compared with SDR-PEP. METHODS: The PEP++ study is a cluster-randomised controlled trial in selected districts of India, Brazil, Bangladesh, and Nepal. Sub-districts will be allocated randomly to the intervention and control arms. Leprosy patients detected from 2015 - 22 living in the districts will be approached to list their close contacts for enrolment in the study. All consenting participants will be screened for signs and symptoms of leprosy and tuberculosis (TB). In the intervention arm, eligible contacts receive the enhanced PEP++ regimen with three doses of rifampicin (150 - 600 mg) and clarithromycin (150 - 500 mg) administered at four-weekly intervals, whereas those in the control arm receive SDR-PEP. Follow-up screening for leprosy will be done for each individual two years after the final dose is administered. Cox' proportion hazards analysis and Poisson regression will be used to compare the incidence rate ratios between the intervention and control areas as the primary study outcome. DISCUSSION: Past studies have shown that the level of SDR-PEP effectiveness is not uniform across contexts or in relation to leprosy patients. To address this, a number of recent trials are seeking to strengthen PEP regimens either through the use of new medications or by increasing the dosage of the existing ones. However, few studies focus on the impact of multiple doses of chemoprophylaxis using a combination of antibiotics. The PEP++ trial will investigate effectiveness of both an enhanced regimen and use geospatial analysis for PEP administration in the study communities. TRIAL REGISTRATION: NL7022 on the Dutch Trial Register on April 12, 2018. Protocol version 9.0 updated on 18 August 2022 https://www.onderzoekmetmensen.nl/en/trial/23060.

Measuring leprosy case detection delay and associated factors in Indonesia: a community-based study.

BACKGROUND: Leprosy is a public health burden in Indonesia with a high number of new cases every year and a high proportion of disability among new cases. Case detection delay (CDD) can contribute to ongoing transmission and increased disability chances among leprosy patients. This study aimed to establish the CDD of leprosy and the factors associated with detection delay in Indonesia. METHOD: Community-based study with a cross-sectional design. Data were collected through interviews about sociodemographic and behavioral factors, anticipated stigma, and duration of CDD. Leprosy classification and case detection methods were obtained from health service records. A random sample was taken of 126 leprosy patients registered between 1st October 2020 and 31st March 2022 in the Tegal regency in the Central Java Province. Data were analysed by descriptive and analytical statistics using multiple linear regression. RESULTS: The mean CDD, patient delay, and health system delay were 13.0 months, 9.7 months, and 3.2 months, respectively. Factors associated with longer CDD are younger age (below 35 years), male, found through passive case detection, and not having a family member with leprosy. Factors associated with longer patient delay were being younger (below 35 years), being male, not having a family member with leprosy, and anticipated stigma of leprosy. It was not possible to reliably identify factors associated with health system delay. CONCLUSION: CDD in leprosy should be reduced in Indonesia. The Indonesian National Leprosy Control Program (NLCP) is advised to adopt an integrated intervention programme combining active case detection with targeted health education to reduce CDD and thereby preventing disabilities in people affected by leprosy.

Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach.

BACKGROUND: Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type. METHODS: Two sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors. RESULTS: For both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: -1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14-2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08-2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review. CONCLUSIONS: The log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.

Identifying clusters of leprosy patients in India: A comparison of methods.

BACKGROUND: Preventive interventions with post-exposure prophylaxis (PEP) are needed in leprosy high-endemic areas to interrupt the transmission of Mycobacterium leprae. Program managers intend to use Geographic Information Systems (GIS) to target preventive interventions considering efficient use of public health resources. Statistical GIS analyses are commonly used to identify clusters of disease without accounting for the local context. Therefore, we propose a contextualized spatial approach that includes expert consultation to identify clusters and compare it with a standard statistical approach. METHODOLOGY/PRINCIPAL FINDINGS: We included all leprosy patients registered from 2014 to 2020 at the Health Centers in Fatehpur and Chandauli districts, Uttar Pradesh State, India (n = 3,855). Our contextualized spatial approach included expert consultation determining criteria and definition for the identification of clusters using Density Based Spatial Clustering Algorithm with Noise, followed by creating cluster maps considering natural boundaries and the local context. We compared this approach with the commonly used Anselin Local Moran's I statistic to identify high-risk villages. In the contextualized approach, 374 clusters were identified in Chandauli and 512 in Fatehpur. In total, 75% and 57% of all cases were captured by the identified clusters in Chandauli and Fatehpur, respectively. If 100 individuals per case were targeted for PEP, 33% and 11% of the total cluster population would receive PEP, respectively. In the statistical approach, more clusters in Chandauli and fewer clusters in Fatehpur (508 and 193) and lower proportions of cases in clusters (66% and 43%) were identified, and lower proportions of population targeted for PEP was calculated compared to the contextualized approach (11% and 11%). CONCLUSION: A contextualized spatial approach could identify clusters in high-endemic districts more precisely than a standard statistical approach. Therefore, it can be a useful alternative to detect preventive intervention targets in high-endemic areas.

Delayed detection of leprosy cases: A systematic review of healthcare-related factors.

BACKGROUND: In new leprosy cases, grade 2 disability (G2D) is still a public health burden worldwide. It is often associated with the delayed leprosy diagnoses that healthcare systems should play a crucial role in preventing. The aim of this systematic review was to identify healthcare factors related to delays in case detection in leprosy. METHODS: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) was used as a guideline in this research. The study protocol was registered in the PROSPERO (International Prospective Register of Systematic Reviews) with reference code CRD42020189274. Data was collected from five electronic databases: Embase, Medline All Ovid, Web of Science, Cochrane CENTRAL, and the WHO Global Health Library. RESULTS: After applying the selection criteria for original empirical studies, and after removing duplicates, we included 20 papers from 4313 records. They had been conducted in ten countries and published between January 1, 2000, and January 31, 2021. We identified three categories of healthcare factors related to delayed case. 1) Structural factors, such as i) financial and logistic issues, and geographical circumstances (which we classified as barriers); ii) Health service organization and management including the level of decentralization (classified as facilitators). 2) Health service factors, such as problems or shortages involving referral centers, healthcare personnel, and case-detection methods. 3) Intermediate factors, such as misdiagnosis, higher numbers of consultations before diagnosis, and inappropriate healthcare services visited by people with leprosy. CONCLUSIONS: Delays in leprosy case detection are due mainly to misdiagnosis. It is crucial to improve the training and capacity of healthcare staff. To avoid misdiagnosis and reduce detection delays, national leprosy control programs should ensure the sustainability of leprosy control within integrated health services.

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.

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.

Leprosy and cutaneous leishmaniasis affecting the same individuals: A retrospective cohort analysis in a hyperendemic area in Brazil.

BACKGROUND: Leprosy and cutaneous leishmaniasis (CL) are neglected tropical diseases (NTDs) affecting the skin. Their control is challenging but the integration of skin NTDs control programs is recommended to improve timely detection and treatment. However, little is known about the occurrence of leprosy and CL in the same individuals, and what are the characteristics of such patients. This study aimed to identify and characterize patients diagnosed with both leprosy and CL (i.e., outcome) in the hyperendemic state of Mato Grosso, Brazil. Also, we investigated the demographic risk factors associated with the period between the diagnosis of both diseases. METHODOLOGY/PRINCIPAL FINDINGS: A retrospective cohort study was conducted with patients diagnosed between 2008 and 2017. From the leprosy (n = 28,204) and CL (n = 24,771) databases of the national reporting system, 414 (0.8%; 414/52,561) patients presenting both diseases were identified through a probabilistic linkage procedure. This observed number was much higher than the number of patients that would be expected by chance alone (n = 22). The spatial distribution of patients presenting the outcome was concentrated in the North and Northeast mesoregions of the state. Through survival analysis, we detected that the probability of a patient developing both diseases increased over time from 0.2% in the first year to 1.0% within seven years. Further, using a Cox model we identified male sex (HR: 2.3; 95% CI: 1.7-2.9) and low schooling level (HR: 1.5; 95% CI: 1.2-1.9) as positively associated with the outcome. Furthermore, the hazard of developing the outcome was higher among individuals aged 40-55 years. CONCLUSIONS/SIGNIFICANCE: Leprosy and CL are affecting the same individuals in the area. Integration of control policies for both diseases will help to efficiently cover such patients. Measures should be focused on timely diagnosis by following-up patients diagnosed with CL, active case detection, and training of health professionals.

BCG-induced immunity profiles in household contacts of leprosy patients differentiate between protection and disease.

Leprosy is an infectious disease caused by Mycobacterium leprae leading to irreversible disabilities along with social exclusion. Leprosy is a spectral disease for which the clinical outcome after M. leprae infection is determined by host factors. The spectrum spans from anti-inflammatory T helper-2 (Th2) immunity concomitant with large numbers of bacteria as well as antibodies against M. leprae antigens in multibacillary (MB) leprosy, to paucibacillary (PB) leprosy characterised by strong pro-inflammatory, Th1 as well as Th17 immunity. Despite decades of availability of adequate antibiotic treatment, transmission of M. leprae is unabated. Since individuals with close and frequent contact with untreated leprosy patients are particularly at risk to develop the disease themselves, prophylactic strategies currently focus on household contacts of newly diagnosed patients. It has been shown that BCG (re)vaccination can reduce the risk of leprosy. However, BCG immunoprophylaxis in contacts of leprosy patients has also been reported to induce PB leprosy, indicating that BCG (re)vaccination may tip the balance between protective immunity and overactivation immunity causing skin/nerve tissue damage. In order to identify who is at risk of developing PB leprosy after BCG vaccination, amongst individuals who are chronically exposed to M. leprae, we analyzed innate and adaptive immune markers in whole blood of household contacts of newly diagnosed leprosy patients in Bangladesh, some of which received BCG vaccination. As controls, individuals from the same area without known contact with leprosy patients were similarly assessed. Our data show the added effect of BCG vaccination on immune markers on top of the effect already induced by M. leprae exposure. Moreover, we identified BCG-induced markers that differentiate between protective and disease prone immunity in those contacts.

Measuring endemicity and burden of leprosy across countries and regions: A systematic review and Delphi survey.

BACKGROUND: Leprosy is a chronic infectious disease caused by Mycobacterium leprae, the annual new case detection in 2019 was 202,189 globally. Measuring endemicity levels and burden in leprosy lacks a uniform approach. As a result, the assessment of leprosy endemicity or burden are not comparable over time and across countries and regions. This can make program planning and evaluation difficult. This study aims to identify relevant metrics and methods for measuring and classifying leprosy endemicity and burden at (sub)national level. METHODS: We used a mixed-method approach combining findings from a systematic literature review and a Delphi survey. The literature search was conducted in seven databases, searching for endemicity, burden and leprosy. We reviewed the available evidence on the usage of indicators, classification levels, and scoring methods to measure and classify endemicity and burden. A two round Delphi survey was conducted to ask experts to rank and weigh indicators, classification levels, and scoring methods. RESULTS: The literature review showed variation of indicators, levels, and cut-off values to measure leprosy endemicity and/or burden. The most used indicators for endemicity include new case detection rate (NCDR), new cases among children and new cases with grade 2 disability. For burden these include NCDR, MB cases, and prevalence. The classification levels 'high' and 'low' were most important. It was considered most relevant to use separate scoring methods for endemicity and burden. The scores would be derived by use of multiple indicators. CONCLUSION: There is great variation in the existing method for measuring endemicity and burden across countries and regions. Our findings contribute to establishing a standardized uniform approach to measure and classify leprosy endemicity and burden at (sub)national level, which would allow effective communication and planning of intervention strategies.

PEP4LEP study protocol: integrated skin screening and SDR-PEP administration for leprosy prevention: comparing the effectiveness and feasibility of a community-based intervention to a health centre-based intervention in Ethiopia, Mozambique and Tanzania.

INTRODUCTION: Leprosy, or Hansen's disease, remains a cause of preventable disability. Early detection, treatment and prevention are key to reducing transmission. Post-exposure prophylaxis with single-dose rifampicin (SDR-PEP) reduces the risk of developing leprosy when administered to screened contacts of patients. This has been adopted in the WHO leprosy guidelines. The PEP4LEP study aims to determine the most effective and feasible method of screening people at risk of developing leprosy and administering chemoprophylaxis to contribute to interrupting transmission. METHODS AND ANALYSIS: PEP4LEP is a cluster-randomised implementation trial comparing two interventions of integrated skin screening combined with SDR-PEP distribution to contacts of patients with leprosy in Ethiopia, Mozambique and Tanzania. One intervention is community-based, using skin camps to screen approximately 100 community contacts per leprosy patient, and to administer SDR-PEP when eligible. The other intervention is health centre-based, inviting household contacts of leprosy patients to be screened in a local health centre and subsequently receive SDR-PEP when eligible. The mobile health (mHealth) tool SkinApp will support health workers' capacity in integrated skin screening. The effectiveness of both interventions will be compared by assessing the rate of patients with leprosy detected and case detection delay in months, as well as feasibility in terms of cost-effectiveness and acceptability. ETHICS AND DISSEMINATION: Ethical approval was obtained from the national ethical committees of Ethiopia (MoSHE), Mozambique (CNBS) and Tanzania (NIMR/MoHCDEC). Study results will be published open access in peer-reviewed journals, providing evidence for the implementation of innovative leprosy screening methods and chemoprophylaxis to policymakers. TRIAL REGISTRATION NUMBER: NL7294 (NTR7503).

Changing perception and improving knowledge of leprosy: An intervention study in Uttar Pradesh, India.

INTRODUCTION: Since ancient times leprosy has had a negative perception, resulting in stigmatization. To improve the lives of persons affected by leprosy, these negative perceptions need to change. The aim of this study is to evaluate interventions to change perceptions and improve knowledge of leprosy. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a pre-post intervention study in Fatehpur and Chandauli districts, Uttar Pradesh, India. Based on six steps of quality intervention development (6SQuID) two interventions were designed: (1) posters that provided information about leprosy and challenged misconceptions, and (2) meetings with persons affected by leprosy, community members and influential people in the community. The effect of the interventions was evaluated in a mixed-methods design; in-depth interviews, focus group discussions, and questionnaires containing a knowledge measure (KAP), two perception measures (EMIC-CSS, SDS) and an intervention evaluation tool. 1067 participants were included in Survey 1 and 843 in Survey 2. The interventions were effective in increasing knowledge of all participant groups, and in changing community and personal attitudes of close contacts and community members (changes of 19%, 24% and 13% on the maximum KAP, EMIC-CSS and SDS scores respectively, p<0.05). In Survey 1, 13% of participants had adequate knowledge of leprosy versus 53% in Survey 2. Responses showed stigmatizing community attitudes in 86% (Survey 1) and 61% (Survey 2) of participants and negative personal attitudes in 37% (Survey 1) and 19% (Survey 2). The number of posters seen was associated with KAP, EMIC-CSS and SDS scores in Survey 2 (p<0.001). In addition, during eight post-intervention focus group discussions and 48 interviews many participants indicated that the perception of leprosy in the community had changed. CONCLUSIONS/SIGNIFICANCE: Contextualized posters and community meetings were effective in changing the perception of leprosy and in increasing leprosy-related knowledge. We recommend studying the long-term effect of the interventions, also on behavior.

Individual and community factors determining delayed leprosy case detection: A systematic review.

BACKGROUND: The number of new leprosy cases is declining globally, but the disability caused by leprosy remains an important disease burden. The chance of disability is increased by delayed case detection. This review focusses on the individual and community determinants of delayed leprosy case detection. METHODS: This study was conducted according to the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). The study protocol is registered in PROSPERO (code: CRD42020189274). To identify determinants of delayed detection, data was collected from five electronic databases: Embase.com, Medline All Ovid, Web of Science, Cochrane CENTRAL, and the WHO Global Health Library. RESULTS: We included 27 papers from 4315 records assessed. They originated in twelve countries, had been published between January 1, 2000, and January 31, 2021, and described the factors related to delayed leprosy case detection, the duration of the delayed case, and the percentage of Grade 2 Disability (G2D). The median delay in detection ranged from 12 to 36 months, the mean delay ranged from 11.5 to 64.1 months, and the percentage of G2D ranged from 5.6 to 43.2%. Health-service-seeking behavior was the most common factor associated with delayed detection. The most common individual factors were older age, being male, having a lower disease-symptom perception, having multibacillary leprosy, and lack of knowledge. The most common socioeconomic factors were living in a rural area, performing agricultural labor, and being unemployed. Stigma was the most common social and community factor. CONCLUSIONS: Delayed leprosy case detection is clearly correlated with increased disability and should therefore be a priority of leprosy programs. Interventions should focus on determinants of delayed case detection such as health-service-seeking behavior, and should consider relevant individual, socioeconomic, and community factors, including stigmatization. Further study is required of the health service-related factors contributing to delay.

Blood RNA signature RISK4LEP predicts leprosy years before clinical onset.

BACKGROUND: Leprosy, a chronic infectious disease caused by Mycobacterium leprae, is often late- or misdiagnosed leading to irreversible disabilities. Blood transcriptomic biomarkers that prospectively predict those who progress to leprosy (progressors) would allow early diagnosis, better treatment outcomes and facilitate interventions aimed at stopping bacterial transmission. To identify potential risk signatures of leprosy, we collected whole blood of household contacts (HC, n=5,352) of leprosy patients, including individuals who were diagnosed with leprosy 4-61 months after sample collection. METHODS: We investigated differential gene expression (DGE) by RNA-Seq between progressors before presence of symptoms (n=40) and HC (n=40), as well as longitudinal DGE within each progressor. A prospective leprosy signature was identified using a machine learning approach (Random Forest) and validated using reverse transcription quantitative PCR (RT-qPCR). FINDINGS: Although no significant intra-individual longitudinal variation within leprosy progressors was identified, 1,613 genes were differentially expressed in progressors before diagnosis compared to HC. We identified a 13-gene prospective risk signature with an Area Under the Curve (AUC) of 95.2%. Validation of this RNA-Seq signature in an additional set of progressors (n=43) and HC (n=43) by RT-qPCR, resulted in a final 4-gene signature, designated RISK4LEP (MT-ND2, REX1BD, TPGS1, UBC) (AUC=86.4%). INTERPRETATION: This study identifies for the first time a prospective transcriptional risk signature in blood predicting development of leprosy 4 to 61 months before clinical diagnosis. Assessment of this signature in contacts of leprosy patients can function as an adjunct diagnostic tool to target implementation of interventions to restrain leprosy development. FUNDING: This study was supported by R2STOP Research grant, the Order of Malta-Grants-for-Leprosy-Research, the Q.M. Gastmann-Wichers Foundation and the Leprosy Research Initiative (LRI) together with the Turing Foundation (ILEP# 702.02.73 and # 703.15.07).

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

OBJECTIVES: To identify patterns of spatial clustering of leprosy. DESIGN: We performed a baseline survey for a trial on post-exposure prophylaxis for leprosy in Comoros and Madagascar. We screened 64 villages, door-to-door, and recorded results of screening, demographic data and geographic coordinates. To identify clusters, we fitted a purely spatial Poisson model using Kulldorff's spatial scan statistic. We used a regular Poisson model to assess the risk of contracting leprosy at the individual level as a function of distance to the nearest known leprosy patient. RESULTS: We identified 455 leprosy patients; 200 (44.0%) belonged to 2735 households included in a cluster. Thirty-eight percent of leprosy patients versus 10% of the total population live ≤25 m from another leprosy patient. Risk ratios for being diagnosed with leprosy were 7.3, 2.4, 1.8, 1.4 and 1.7, for those at the same household, at 1-<25 m, 25-<50 m, 50-<75 m and 75-<100 m as/from a leprosy patient, respectively, compared to those living at ≥100 m. CONCLUSIONS: We documented significant clustering of leprosy beyond household level, although 56% of cases were not part of a cluster. Control measures need to be extended beyond the household, and social networks should be further explored.

Mycobacterium leprae transmission characteristics during the declining stages of leprosy incidence: A systematic review.

BACKGROUND: Leprosy is an infectious disease caused by Mycobacterium leprae. As incidence begins to decline, the characteristics of new cases shifts away from those observed in highly endemic areas, revealing potentially important insights into possible ongoing sources of transmission. We aimed to investigate whether transmission is driven mainly by undiagnosed and untreated new leprosy cases in the community, or by incompletely treated or relapsing cases. METHODOLOGY/PRINCIPAL FINDINGS: A literature search of major electronic databases was conducted in January, 2020 with 134 articles retained out of a total 4318 records identified (PROSPERO ID: CRD42020178923). We presented quantitative data from leprosy case records with supporting evidence describing the decline in incidence across several contexts. BCG vaccination, active case finding, adherence to multidrug therapy and continued surveillance following treatment were the main strategies shared by countries who achieved a substantial reduction in incidence. From 3950 leprosy case records collected across 22 low endemic countries, 48.3% were suspected to be imported, originating from transmission outside of the country. Most cases were multibacillary (64.4%) and regularly confirmed through skin biopsy, with 122 cases of suspected relapse from previous leprosy treatment. Family history was reported in 18.7% of cases, while other suspected sources included travel to high endemic areas and direct contact with armadillos. None of the countries included in the analysis reported a distinct increase in leprosy incidence in recent years. CONCLUSIONS/SIGNIFICANCE: Together with socioeconomic improvement over time, several successful leprosy control programmes have been implemented in recent decades that led to a substantial decline in incidence. Most cases described in these contexts were multibacillary and numerous cases of suspected relapse were reported. Despite these observations, there was no indication that these cases led to a rise in new secondary cases, suggesting that they do not represent a large ongoing source of human-to-human transmission.

The long-term impact of the Leprosy Post-Exposure Prophylaxis (LPEP) program on leprosy incidence: A modelling study.

BACKGROUND: The Leprosy Post-Exposure Prophylaxis (LPEP) program explored the feasibility and impact of contact tracing and the provision of single dose rifampicin (SDR) to eligible contacts of newly diagnosed leprosy patients in Brazil, India, Indonesia, Myanmar, Nepal, Sri Lanka and Tanzania. As the impact of the programme is difficult to establish in the short term, we apply mathematical modelling to predict its long-term impact on the leprosy incidence. METHODOLOGY: The individual-based model SIMCOLEP was calibrated and validated to the historic leprosy incidence data in the study areas. For each area, we assessed two scenarios: 1) continuation of existing routine activities as in 2014; and 2) routine activities combined with LPEP starting in 2015. The number of contacts per index patient screened varied from 1 to 36 between areas. Projections were made until 2040. PRINCIPAL FINDINGS: In all areas, the LPEP program increased the number of detected cases in the first year(s) of the programme as compared to the routine programme, followed by a faster reduction afterwards with increasing benefit over time. LPEP could accelerate the reduction of the leprosy incidence by up to six years as compared to the routine programme. The impact of LPEP varied by area due to differences in the number of contacts per index patient included and differences in leprosy epidemiology and routine control programme. CONCLUSIONS: The LPEP program contributes significantly to the reduction of the leprosy incidence and could potentially accelerate the interruption of transmission. It would be advisable to include contact tracing/screening and SDR in routine leprosy programmes.

Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study.

BACKGROUND: Leprosy is known to be unevenly distributed between and within countries. High risk areas or 'hotspots' are potential targets for preventive interventions, but the underlying epidemiologic mechanisms that enable hotspots to emerge, are not yet fully understood. In this study, we identified and characterized leprosy hotspots in Bangladesh, a country with one of the highest leprosy endemicity levels globally. METHODS: We used data from four high-endemic districts in northwest Bangladesh including 20 623 registered cases between January 2000 and April 2019 (among ~ 7 million population). Incidences per union (smallest administrative unit) were calculated using geospatial population density estimates. A geospatial Poisson model was used to detect incidence hotspots over three (overlapping) 10-year timeframes: 2000-2009, 2005-2014 and 2010-2019. Ordinal regression models were used to assess whether patient characteristics were significantly different for cases outside hotspots, as compared to cases within weak (i.e., relative risk (RR) of one to two), medium (i.e., RR of two to three), and strong (i.e., RR higher than three) hotspots. RESULTS: New case detection rates dropped from 44/100 000 in 2000 to 10/100 000 in 2019. Statistically significant hotspots were identified during all timeframes and were often located at areas with high population densities. The RR for leprosy was up to 12 times higher for inhabitants of hotspots than for people living outside hotspots. Within strong hotspots (1930 cases among less than 1% of the population), significantly more child cases (i.e., below 15 years of age) were detected, indicating recent transmission. Cases in hotspots were not significantly more likely to be detected actively. CONCLUSIONS: Leprosy showed a heterogeneous distribution with clear hotspots in northwest Bangladesh throughout a 20-year period of decreasing incidence. Findings confirm that leprosy hotspots represent areas of higher transmission activity and are not solely the result of active case finding strategies.