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. 2021 Mar 31;15(3):e0009279.
doi: 10.1371/journal.pntd.0009279. eCollection 2021 Mar.

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

David J Blok  1 , Peter Steinmann  2   3 , Anuj Tiwari  1 , Tanja Barth-Jaeggi  2   3 , Mohammad A Arif  4 , Nand Lal Banstola  5 , Rabindra Baskota  6 , David Blaney  7 , Marc Bonenberger  8 , Teky Budiawan  9 , Arielle Cavaliero  10 , Zaahira Gani  10 , Helena Greter  2   3 , Eliane Ignotti  11 , Deusdedit V Kamara  12 , Christa Kasang  13 , Pratap R Manglani  4 , Liesbeth Mieras  14 , Blasdus F Njako  15 , Tiara Pakasi  16 , Unnati R Saha  1 , Paul Saunderson  17 , W Cairns S Smith  18 , René Stäheli  8 , Nayani D Suriyarachchi  19 , Aye Tin Maung  20 , Tin Shwe  20 , Jan van Berkel  14 , Wim H van Brakel  14 , Bart Vander Plaetse  8 , Marcos Virmond  21 , Millawage S D Wijesinghe  22 , Ann Aerts  10 , Jan Hendrik Richardus  1
Affiliations

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

David J Blok et al. PLoS Negl Trop Dis. .

Abstract

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.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: Novartis Foundation and International Federation of Anti-Leprosy Association partners provided technical input in the design phase of the LPEP program and ensured overall programme coordination. All authors are either staff of the Novartis Foundation (Arielle Cavaliero, Zaahira Gani, and Ann Aerts), work as paid consultants for the programme described here (David J Blok, Peter Steinmann, Anuj Tiwari, Tanja Barth-Jaeggi, Marc Bonenberger, Helena Grete, Christa Kasang, Liesbeth Mieras, Unnati R Saha, Wim H van Brakel, Bart Vander Plaetse, and Jan Hendrik Richardus), act as national programme coordinators (Mohammad A Arif, Nand Lal Banstola, Rabindra Baskota, Teky Budiawan, Eliane Ignotti, Deusdedit V Kamara, Pratap R Manglani, Blasdus F Njako, Tiara Pakasi, Nayani D Suriyarachchi, Aye Tin Maung, Tin Shwe, and Millawage S D Wijesinghe) or serve on the Steering Committee of the programme (David Blaney, Paul Saunderson, W Cairns S Smith, René Stäheli, Jan van Berkel, Marcos Virmond). The funder had no role in the interpretation of findings or decision to publish this manuscript. Author Aye Tin Maung was unavailable to confirm his authorship contributions. On his behalf, the corresponding author has reported his contributions to the best of his knowledge.

Figures

Fig 1
Fig 1. Comparison of modelled trends with the observed leprosy new case detection rates in LPEP areas across states and districts in seven countries.
Each panel represents the trends in subnational states or districts (arranged in descending order of baseline endemicity level): India (Union Territory Dadra and Nagar Haveli); Brazil (Alta Floresta city and region and Rondonópolis city in Mato Grosso, Araguaína and Colinas do Tocantins in Tocantins, Petrolina city and region in Pernambuco); Indonesia (Sumenep district); Tanzania (Kilombero, Liwale and Nanyumbu); Nepal (Jhapa, Morang and Parsa districts); Sri Lanka (Kalutara and Puttalam districts); Myanmar (Nyaung Oo, Myingyan and Tharyarwaddy Townships). Data are represented by black dots and crosses. The solid line represents the mean model estimate and the shaded area the 95% prediction interval.
Fig 2
Fig 2. Predicted long term trends of the leprosy new case detection rate under the routine programme, and combined with the LPEP program, stratified by LPEP area.
Each panel represents the trends in subnational states or districts (arranged in descending order of baseline endemicity level): India (Union Territory Dadra and Nagar Haveli); Brazil (Alta Floresta city and region and Rondonópolis city in Mato Grosso, Araguaína and Colinas do Tocantins in Tocantins, Petrolina city and region in Pernambuco); Indonesia (Sumenep district); Tanzania (Kilombero, Liwale and Nanyumbu); Nepal (Jhapa, Morang and Parsa districts); Sri Lanka (Kalutara and Puttalam districts); Myanmar (Nyaung Oo, Myingyan and Tharyarwaddy Townships). Model predictions are represented by means of 1,000 repeats (solid line). The blue line represents the LPEP program and the black line the routine programme.
Fig 3
Fig 3. Number of cumulative new leprosy cases prevented due to the LPEP program activities as compared to the routine programme alone.
Each panel represents the trends in subnational states or districts (arranged in descending order of baseline endemicity level): India (Union Territory Dadra and Nagar Haveli); Brazil (Alta Floresta city and region and Rondonópolis city in Mato Grosso, Araguaína and Colinas do Tocantins in Tocantins, Petrolina city and region in Pernambuco); Indonesia (Sumenep district); Tanzania (Kilombero, Liwale and Nanyumbu); Nepal (Jhapa, Morang and Parsa districts); Sri Lanka (Kalutara and Puttalam districts); Myanmar (Nyaung Oo, Myingyan and Tharyarwaddy Townships). Model predictions are represented by means of 1,000 repeats (solid line). The blue line represents the LPEP program. The shaded area is the 95% prediction interval.
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