High yield of retrospective active case finding for leprosy in Comoros

Burden of leprosy and associated risk factors for disabilities in Tanzania from 2017 to 2020

BACKGROUND

Leprosy is caused by Mycobacterium leprae which affects skin, nerves, eyes, and nasal mucosa. Despite global elimination efforts, Tanzania remains among 13 countries reporting more than 1000 leprosy cases annually. In 2021, Tanzania identified 1,511 new cases, with 10% having grade II disability. Moreover, 14 councils recorded leprosy rates exceeding 10 cases per 100,000 population. This study aimed to assess the burden of leprosy and associated risk factors for disabilities in Tanzania from 2017 to 2020.

METHODOLOGY

A retrospective cross-sectional study was conducted to investigate all registered treated leprosy patients from January 2017 to December 2020. The Leprosy Burden Score (LBS) was used to assess the disease burden, while binary logistic regression was employed to evaluate the risk factors for disability.

RESULT

A total of 6,963 leprosy cases were identified from 2017 to 2020. During this period, the point prevalence of leprosy declined from 0.32 to 0.25 per 10,000 people, and the new case detection rate decreased from 3.1 to 2.4 per 100,000 people; however, these changes were not statistically significant (p > 0.05). Independent risk factors for leprosy-related disabilities included male sex (Adjusted Odds Ratio (AOR) = 1.38, 95% Confidence Interval (CI) 1.22-1.57), age 15 years and above (AOR = 2.42, 95% CI 1.60-3.67), previous treatment history (AOR = 2.18, 95% CI 1.69-2.82), and positive Human Immunodeficiency Virus (HIV) status (AOR = 1.60, 95% CI 1.11-2.30).

CONCLUSION

This study identified male sex, older age, positive HIV status, and prior treatment history as independent risk factors for leprosy-related disabilities. Additionally, despite the observed decline in point prevalence and new case detection rates, these changes were not statistically significant. To address leprosy-related disabilities, it is crucial to implement specific prevention strategies that focus on high-risk groups. This can be accomplished by enhancing screening and contact tracing efforts for early patient identification to prevent delays in intervention. Further research is warranted to analyze the burden of leprosy over a more extended period and to explore additional risk factors not covered in this study.

Exploring Active Case Detection Approaches for Leprosy Diagnosis in Varied Endemic Settings: A Comprehensive Scoping Review

(1) Background: The global burden of leprosy is not shared equally; with the majority of cases being diagnosed in Brazil, India, and Indonesia. Understanding the methods of active case detection (ACD) used in high and low endemic regions is vital for the development of future screening programs. (2) Methods: A systematic search of three databases, PubMed, Embase and Web of Science, was conducted for English language papers, published since the year 2000, which discussed the use of active case detection methods for leprosy screening. The paper utilised the Integrated Screening Action Model (I-SAM) as a tool for the analysis of these methods. (3) Results: 23 papers were identified from 11 different countries. The papers identified 6 different methods of active case detection: Household contact/social contact identification; door-to-door case detection; screening questionnaire distribution; rapid village surveys; school-based screening; and prison-based screening. 15 were located in high endemic regions and 8 of these were located in low endemic regions. (4) Conclusions: For selecting the appropriate methods of active case finding, the leprosy endemicity must be taken into consideration. The findings contribute to policy decision making allowing for more successful future leprosy case detection programs to be designed, ultimately reducing the global burden of the disease, and achieving the WHO's aim of zero leprosy.

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 1^st, 2020, and March 31^st, 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.

India is the highest-burden country in the world where leprosy is known to cluster in hamlets. As no geographical data about hamlets is available, we develop a system to outline them using a high-resolution population density map. Then, using the household coordinates of 169 new leprosy cases enrolled in two hyperendemic blocks of Bihar, we screened household contacts and assessed clustering at hamlet level. The patients interviewed had lived in their current villages for a median of seventeen years at the moment of the survey. We found 11 new cases among 658 contacts examined equivalent to 16.7 per 1,000 population. There were three statistically significant clusters among five at the hamlet level and three including 78 cases in 98,623 population. Our results can be used to guide targeted and more efficient active case finding and post-exposure prophylaxis.