Deep learning-assisted concentration gradient generation for the study of 3D cell cultures in hydrogel beads of varying stiffness.

The study of dose-response relationships underpins analytical biosciences. Droplet microfluidics platforms can automate the generation of microreactors encapsulating varying concentrations of an assay component, providing datasets across a large chemical space in a single experiment. A classical method consists in varying the flow rate of multiple solutions co-flowing into a single microchannel (producing different volume fractions) before encapsulating the contents into water-in-oil droplets. This process can be automated through controlling the pumping elements but lacks the ability to adapt to unpredictable experimental scenarios, often requiring constant human supervision. In this paper, we introduce an image-based, closed-loop control system for assessing and adjusting volume fractions, thereby generating unsupervised, uniform concentration gradients. We trained a shallow convolutional neural network to assess the position of the laminar flow interface between two co-flowing fluids and used this model to adjust flow rates in real-time. We apply the method to generate alginate microbeads in which HEK293FT cells could grow in three dimensions. The stiffnesses ranged from 50 Pa to close to 1 kPa in Young modulus and were encoded with a fluorescent marker. We trained deep learning models based on the YOLOv4 object detector to efficiently detect both microbeads and multicellular spheroids from high-content screening images. This allowed us to map relationships between hydrogel stiffness and multicellular spheroid growth.

Congenital brain tumour in a neonate: a therapeutic challenge.

A term neonate with history of ventriculomegaly in the fetal period was diagnosed with a central nervous system tumour after radiological investigations. It was confirmed as an immature teratoma after histopathological examination. He underwent left frontal craniotomy with tumour excision. Intraoperatively, massive haemorrhage (venous bleed) occurred due to the high vascularity of the tumour and led to haemodynamic instability. A massive transfusion protocol was initiated. Despite multiple transfusions and shock management, he succumbed at 2 weeks of life. This case report highlights the importance of antenatal diagnosis and fetal MRI in prognostication and also the possible role of neoadjuvant chemotherapy in reducing tumour vascularity and, hence, bleeding.

A modeling pipeline to relate municipal wastewater surveillance and regional public health data.

As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.

Droplet-based methodology for investigating bacterial population dynamics in response to phage exposure.

An alarming rise in antimicrobial resistance worldwide has spurred efforts into the search for alternatives to antibiotic treatments. The use of bacteriophages, bacterial viruses harmless to humans, represents a promising approach with potential to treat bacterial infections (phage therapy). Recent advances in microscopy-based single-cell techniques have allowed researchers to develop new quantitative methodologies for assessing the interactions between bacteria and phages, especially the ability of phages to eradicate bacterial pathogen populations and to modulate growth of both commensal and pathogen populations. Here we combine droplet microfluidics with fluorescence time-lapse microscopy to characterize the growth and lysis dynamics of the bacterium Escherichia coli confined in droplets when challenged with phage. We investigated phages that promote lysis of infected E. coli cells, specifically, a phage species with DNA genome, T7 (Escherichia virus T7) and two phage species with RNA genomes, MS2 (Emesvirus zinderi) and Qß (Qubevirus durum). Our microfluidic trapping device generated and immobilized picoliter-sized droplets, enabling stable imaging of bacterial growth and lysis in a temperature-controlled setup. Temporal information on bacterial population size was recorded for up to 25 h, allowing us to determine growth rates of bacterial populations and helping us uncover the extent and speed of phage infection. In the long-term, the development of novel microfluidic single-cell and population-level approaches will expedite research towards fundamental understanding of the genetic and molecular basis of rapid phage-induced lysis and eco-evolutionary aspects of bacteria-phage dynamics, and ultimately help identify key factors influencing the success of phage therapy.

Assessing the Patient Outcomes and Performance of a Cardiothoracic and Vascular Surgery (CTVS) Unit During Its First Two Years in a Tier-2 City in India: A Comprehensive Audit and Analysis.

This detailed article presents a comprehensive overview of the initial two-year experience in establishing a new cardiothoracic vascular surgery (CTVS) facility in a tier-2 city in India. The article discusses various aspects of setting up and operating a specialized healthcare facility. The first two years of developing the CTVS facility were included in the study period. The manpower included one cardiothoracic vascular surgeon, one cardiac anesthesiologist, two perfusionists, and two physician assistants, along with four other ancillary staff to assist in the smooth functioning of the operation theater. The CTVS recovery staff included 15 nursing officers. There was only one modular operation theater reserved for cardiothoracic vascular surgeries, along with a five-bed recovery room (CTVS intensive care unit). One-hundred-seventy-two procedures were done, including 122 open heart surgeries, 36 vascular procedures, and 14 thoracic procedures. The majority of patients were discharged by the seventh day postoperatively. Overall complication and mortality rates were 8% and 4.6%, respectively. This article also discusses relevant hospital policy, challenges faced, and future recommendations for similar endeavors. The findings highlight the successful implementation of the facility and its impact on providing specialized cardiac care to the local population.

Artificial intelligence and remote patient monitoring in US healthcare market: a literature review.

BACKGROUND: Artificial intelligence (AI) enables remote patient monitoring (RPM) which reduces costs by triaging patients to optimize hospitalization and avoid complications. The FDA regulates AI in medical devices and aims to ensure patient safety, effectiveness, and transparent AI solutions. OBJECTIVES: Identify and summarize FDA approved RPM devices to provide information for the US medical device industry based on previous approvals and the markets' needs. METHODS: We searched publicly available databases on FDA-approved RPM devices. Selection criteria were established to classify a solution as AI. Technical information was analyzed on pre-identified 16 parameters for the qualified solutions. RESULTS: A total of 47 RPM devices were reviewed, among which 12.8% were classified as a De Novo product and the remaining devices fell under the 510(K) FDA category. The cardiovascular (74%) AI RPM solutions dominated the US market, followed by ECG-based arrhythmia detection algorithms (59.4%), and Hemodynamics and Vital Sign monitoring algorithms (21.9%). The trend observed in the FDA rejected devices was their inability to be classified into clinically relevant categories (Criteria 2 and 3). CONCLUSION: The market needs more innovative RPM solutions under the De Novo category, as there are very few. The transparency is low on the technical aspect of AI algorithms. The market needs AI algorithms that can effectively classify patients rather than merely improve device functionality.

Interplay of lipid head group and packing defects in driving amyloid-beta-mediated myelin-like model membrane deformation.

Accumulating evidence suggests that amyloid plaque-associated myelin lipid loss as a result of elevated amyloid burden might also contribute to Alzheimer's disease. The amyloid fibrils are closely associated with lipids under physiological conditions; however, the progression of membrane remodeling events leading to lipid-fibril assembly remains unknown. Here we first reconstitute the interaction of amyloid Beta 40 (Aß-40) with myelin-like model membrane and show that the binding of Aß-40 induces extensive tubulation. To look into the mechanism of membrane tubulation, we chose a set of membrane conditions varying in lipid packing density and net charge that allows us to dissect the contribution of lipid specificity of Aß-40 binding, aggregation kinetics, and subsequent changes in membrane parameters such as fluidity, diffusion, and compressibility modulus. We show that the binding of Aß-40 depends predominantly on the lipid packing defect densities and electrostatic interactions and results in rigidification of the myelin-like model membrane during the early phase of amyloid aggregation. Furthermore, elongation of Aß-40 into higher oligomeric and fibrillar species leads to eventual fluidization of the model membrane followed by extensive lipid membrane tubulation observed in the late phase. Taken together, our results capture mechanistic insights into snapshots of temporal dynamics of Aß-40-myelin-like model membrane interaction and demonstrate how short timescale, local phenomena of binding, and fibril-mediated load generation results in the consequent association of lipids with growing amyloid fibrils.

Low-distortion information propagation with noise suppression in swarm networks.

A method for low-distortion (low-dissipation, low-dispersion) information propagation in swarm-type networks with suppression of high-frequency noise is presented. Information propagation in current neighbor-based networks, where each agent seeks to achieve a consensus with its neighbors, is diffusion-like, dissipative, and dispersive and does not reflect the wave-like (superfluidic) behavior seen in nature. However, pure wave-like neighbor-based networks have two challenges: i) It requires additional communication for sharing information about time derivatives and ii) it can lead to information decoherence through noise at high frequencies. The main contribution of this work is to show that delayed self-reinforcement (DSR) by the agents using prior information (e.g., using short-term memory) can lead to the wave-like information propagation at low-frequencies as seen in nature without the need for additional information sharing between the agents. Moreover, it is shown that the DSR can be designed to enable suppression of high-frequency noise transmission while limiting the dissipation and dispersion of (lower-frequency) information content leading to similar (cohesive) behavior of agents. In addition to explaining noise-suppressed wave-like information transfer in natural systems, the result impacts the design of noise-suppressing cohesive algorithms for engineered networks.

Amyloids on Membrane Interfaces: Implications for Neurodegeneration.

Membrane interfaces are vital for various cellular processes, and their involvement in neurodegenerative disorders such as Alzheimer's and Parkinson's disease has taken precedence in recent years. The amyloidogenic proteins associated with neurodegenerative diseases interact with the neuronal membrane through various means, which has implications for both the onset and progression of the disease. The parameters that regulate the interaction between the membrane and the amyloids remain poorly understood. The review focuses on the various aspects of membrane interactions of amyloids, particularly amyloid-ß (Aß) peptides and Tau involved in Alzheimer's and α-synuclein involved in Parkinson's disease. The genetic, cell biological, biochemical, and biophysical studies that form the basis for our current understanding of the membrane interactions of Aß peptides, Tau, and α-synuclein are discussed.

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.

Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India.

Due to ongoing climate change, water mass redistribution and related hazards are getting stronger and frequent. Therefore, predicting extreme hydrological events and related hazards is one of the highest priorities in geosciences. Machine Learning (ML) methods have shown promising prospects in this venture. Every ML method requires training where we know both the output (extreme event) and input (relevant physical parameters and variables). This step is critical to the efficacy of the ML method. The usual approach is to include a wide variety of hydro-meteorological observations and physical parameters, but recent advances in ML indicate that the efficacy of ML may not improve by increasing the number of input parameters. In fact, including unimportant parameters decreases the efficacy of ML algorithms. Therefore, it is imperative that the most relevant parameters are identified prior to training. In this study, we demonstrate this concept by predicting avalanche susceptibility in Leh-Manali highway (one of the most severely affected regions in India) with and without Parameter Importance Assessment (PIA). The avalanche locations were randomly divided into two groups: 70% for training and 30% for testing. Then, based on temporal and spatial sensor data, eleven avalanche influencing parameters were considered. The Boruta algorithm, an extension of Random Forest (RF) ML method that utilizes the importance measure to rank predictors, was used and it found nine out of eleven parameters to be important. Support Vector Machine (SVM) based ML technique is used for avalanche prediction, and to be comprehensive, four different kernel functions were employed (linear, polynomial, sigmoid, and radial basis function (RBF)). The prediction accuracy for linear, polynomial, sigmoid, and RBF kernels, with all the eleven parameters were found to be 80.4%, 81.7%, 39.2%, and 85.7%, respectively. While, when using selected parameters, the prediction accuracy for linear, polynomial, sigmoid, and RBF kernels were 84.1%, 86.6%, 43.0%, and 87.8%, respectively. We also identified locations where occurrences of avalanches are most likely. We conclude that parameter selection should be considered when applying ML methods in geosciences.

Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI).

COVID-19 is now one of the most leading causes of death in the United States (US). Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of vulnerable communities. This study reports a COVID-19 Vulnerability Index (C19VI) for identifying and mapping vulnerable counties. We proposed a Random Forest machine learning-based vulnerability model using CDC's sociodemographic and COVID-19-specific themes. An innovative 'COVID-19 Impact Assessment' algorithm was also developed for evaluating severity of the pandemic and to train the vulnerability model. Developed C19VI was statistically validated and compared with the CDC COVID-19 Community Vulnerability Index (CCVI). Finally, using C19VI and the census data, we explored racial inequalities and economic disparities in COVID-19 health outcomes. Our index indicates that 575 counties (45 million people) fall into the 'very high' vulnerability class, 765 counties (66 million people) in the 'high' vulnerability class, and 1435 counties (204 million people) in the 'moderate' or 'low' vulnerability class. Only 367 counties (20 million people) were found as 'very low' vulnerable areas. Furthermore, C19VI reveals that 524 counties with a racial minority population higher than 13% and 420 counties with poverty higher than 20% are in the 'very high' or 'high' vulnerability classes. The C19VI aims at helping public health officials and disaster management agencies to develop effective mitigation strategies especially for the disproportionately impacted communities.

Revised estimates of leprosy disability weights for assessing the global burden of disease: A systematic review and individual patient data meta-analysis.

BACKGROUND: Leprosy is a chronic bacterial infection caused by Mycobacterium leprae, which may lead to physical disability, stigma, and discrimination. The chronicity of the disease and disabilities are the prime contributors to the disease burden of leprosy. The current figures of the disease burden in the 2017 global burden of disease study, however, are considered to be under-estimated. In this study, we aimed to systematically review the literature and perform individual patient data meta-analysis to estimate new disability weights for leprosy, using Health-Related Quality of Life (HRQOL) data. METHODOLOGY/PRINCIPAL FINDINGS: The search strategy included all major databases with no restriction on language, setting, study design, or year of publication. Studies on human populations that have been affected by leprosy and recorded the HRQOL with the Short form tool, were included. A consortium was formed with authors who could share the anonymous individual-level data of their study. Mean disability weight estimates, sorted by the grade of leprosy disability as defined by WHO, were estimated for individual participant data and pooled using multivariate random-effects meta-analysis. Eight out of 14 studies from the review were included in the meta-analysis due to the availability of individual-level data (667 individuals). The overall estimated disability weight for grade 2 disability was 0.26 (95%CI: 0.18-0.34). For grade 1 disability the estimated weight was 0.19 (95%CI: 0.13-0.26) and for grade 0 disability it was 0.13 (95%CI: 0.06-0.19). The revised disability weight for grade 2 leprosy disability is four times higher than the published GBD 2017 weights for leprosy and the grade 1 disability weight is nearly twenty times higher. CONCLUSIONS/SIGNIFICANCE: The global burden of leprosy is grossly underestimated. Revision of the current disability weights and inclusion of disability caused in individuals with grade 0 leprosy disability will contribute towards a more precise estimation of the global burden of leprosy.

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.

Leprosy post-exposure prophylaxis with single-dose rifampicin (LPEP): an international feasibility programme.

BACKGROUND: Innovative approaches are required for leprosy control to reduce cases and curb transmission of Mycobacterium leprae. Early case detection, contact screening, and chemoprophylaxis are the most promising tools. We aimed to generate evidence on the feasibility of integrating contact tracing and administration of single-dose rifampicin (SDR) into routine leprosy control activities. METHODS: The leprosy post-exposure prophylaxis (LPEP) programme was an international, multicentre feasibility study implemented within the leprosy control programmes of Brazil, India, Indonesia, Myanmar, Nepal, Sri Lanka, and Tanzania. LPEP explored the feasibility of combining three key interventions: systematically tracing contacts of individuals newly diagnosed with leprosy; screening the traced contacts for leprosy; and administering SDR to eligible contacts. Outcomes were assessed in terms of number of contacts traced, screened, and SDR administration rates. FINDINGS: Between Jan 1, 2015, and Aug 1, 2019, LPEP enrolled 9170 index patients and listed 179 769 contacts, of whom 174 782 (97·2%) were successfully traced and screened. Of those screened, 22 854 (13·1%) were excluded from SDR mainly because of health reasons and age. Among those excluded, 810 were confirmed as new patients (46 per 10 000 contacts screened). Among the eligible screened contacts, 1182 (0·7%) refused prophylactic treatment with SDR. Overall, SDR was administered to 151 928 (86·9%) screened contacts. No serious adverse events were reported. INTERPRETATION: Post-exposure prophylaxis with SDR is safe; can be integrated into different leprosy control programmes with minimal additional efforts once contact tracing has been established; and is generally well accepted by index patients, their contacts, and health-care workers. The programme has also invigorated local leprosy control through the availability of a prophylactic intervention; therefore, we recommend rolling out SDR in all settings where contact tracing and screening have been established. FUNDING: Novartis Foundation.

Leprosy post-exposure prophylaxis with single-dose rifampicin (LPEP): an international feasibility programme

Background: Innovative approaches are required for leprosy control to reduce cases and curb transmission of Mycobacterium leprae. Early case detection, contact screening, and chemoprophylaxis are the most promising tools. We aimed to generate evidence on the feasibility of integrating contact tracing and administration of single-dose rifampicin (SDR) into routine leprosy control activities. Methods The leprosy post-exposure prophylaxis (LPEP) programme was an international, multicentre feasibility study implemented within the leprosy control programmes of Brazil, India, Indonesia, Myanmar, Nepal, Sri Lanka, and Tanzania. LPEP explored the feasibility of combining three key interventions: systematically tracing contacts of individuals newly diagnosed with leprosy; screening the traced contacts for leprosy; and administering SDR to eligible contacts. Outcomes were assessed in terms of number of contacts traced, screened, and SDR administration rates. Findings Between Jan 1, 2015, and Aug 1, 2019, LPEP enrolled 9170 index patients and listed 179 769 contacts, of whom 174782 (97·2%) were successfully traced and screened. Of those screened, 22 854 (13·1%) were excluded from SDR mainly because of health reasons and age. Among those excluded, 810 were confirmed as new patients (46 per 10 000 contacts screened). Among the eligible screened contacts, 1182 (0·7%) refused prophylactic treatment with SDR. Overall, SDR was administered to 151 928 (86·9%) screened contacts. No serious adverse events were reported. Interpretation Post-exposure prophylaxis with SDR is safe; can be integrated into different leprosy control programmes with minimal additional efforts once contact tracing has been established; and is generally well accepted by index patients, their contacts, and health-care workers. The programme has also invigorated local leprosy control through the availability of a prophylactic intervention; therefore, we recommend rolling out SDR in all settings where contact tracing and screening have been established(AU).

Revised estimates of leprosy disability weights for assessing the global burden of disease: a systematic review and individual patient data meta-analysis

Leprosy is a chronic, disabling disease that causes various kinds of disability in the affected person. This includes physical impairment, activity limitation, and participation restriction. However, the published global burden of disease estimates for leprosy is considered to be a gross under-estimation. Disability weights form an integral component in the calculation of the burden estimates. But the methodology for calculation of the weights focuses only on physical impairment and lacks the perspective of the patient. In this study, we systematically reviewed the literature and performed an individual patient data meta analysis for revising the disability weights for leprosy using domain scores from health related quality of life instruments. The domains of these instruments cover all aspects of disability from a patient's perspective. We found that the revised weights were considerably higher than the current weights, and were more reflective of the actual disability caused by leprosy. We also found that for individuals without any severe disability due to leprosy (grade 0), they still experience comparable suffering. Revision of the current disability weights and inclusion of the disability caused in grade 0 individuals will contribute towards better estimation of the global burden of leprosy.

The long-term impact of the leprosy post-exposure prophylaxis (LPEP) program on leprosy incidence: a modelling study

The Leprosy Post-Exposure Prophylaxis (LPEP) program explored the feasibility and impact of contact tracing and the provision of SDR to eligible contacts of newly diagnosed leprosy patients in states or districts of Brazil, India, Indonesia, Myanmar, Nepal, Sri Lanka and Tanzania. This study investigated the long-term impact of the LPEP program on the leprosy new case detection rate (NCDR). Our results show that LPEP could reduce the NCDR beyond the impact of the routine leprosy control programme and that many new cases could be prevented. The benefit of LPEP increases gradually over time. LPEP could accelerate the time of reaching predicted NCDR levels of 2040 under routine program by up to six years. Furthermore, we highlighted how the impact varies between countries due to differences in the number of contacts per index patient screened and differences in leprosy epidemiology and national control programme. Generally, including both household contacts and neighbours (> 20 contacts per index patient) would yield the highest impact.

Leprosy post-exposure prophylaxis in the Indian health system: A cost-effectiveness analysis.

India has the highest burden of leprosy in the world. Following a recent WHO guideline, the Indian National Leprosy Programme is introducing post-exposure prophylaxis with single-dose rifampicin (SDR-PEP) in all high-endemic districts of the country. The aim of this study is to estimate the long-term cost-effectiveness of SDR-PEP in different leprosy disability burden situations. We used a stochastic individual-based model (SIMCOLEP) to simulate the leprosy new case detection rate trend and the impact of implementing contact screening and SDR-PEP from 2016 to 2040 (25 years) in the Union Territory of Dadra Nagar Haveli (DNH) in India. Effects of the intervention were expressed as disability adjusted life years (DALY) averted under three assumption of disability prevention: 1) all grade 1 disability (G1D) cases prevented; 2) G1D cases prevented in PB cases only; 3) no disability prevented. Costs were US$ 2.9 per contact. Costs and effects were discounted at 3%. The incremental cost per DALY averted by SDR-PEP was US$ 210, US$ 447, and US$ 5,673 in the 25th year under assumption 1, 2, and 3, respectively. If prevention of G1D was assumed, the probability of cost-effectiveness was 1.0 at the threshold of US$ 2,000, which is equivalent to the GDP per capita of India. The probability of cost-effectiveness was 0.6, if no disability prevention was assumed. The cost per new leprosy case averted was US$ 2,873. Contact listing, screening and the provision of SDR-PEP is a cost-effective strategy in leprosy control in both the short (5 years) and long term (25 years). The cost-effectiveness depends on the extent to which disability can be prevented. As the intervention becomes increasingly cost-effective in the long term, we recommend a long-term commitment for its implementation.

Prolaxis post-exposición para lepra con dosis única de rifampicina: manual para su implementación / No disponible

OBJETIVO: La profilaxis post-exposición de la lepra con dosis única de rifampicina (SDR-PEP) ha demostrado ser efectiva y aplicable y está recomendada por la OMS desde 2018. Esta caja de herramientas SDR-PEP se desarrolló a través de la experiencia de la profilaxis lepra post-eliminación (LPEP). Se ha diseñado para facilitar y estandarizar la implementación del seguimiento de contactos y la administración SDR-PEP en regiones y países que iniciaron la intervención. RESULTADOS: Se desarrollaron cuatro instrumentos, incorporando la evidencia existente actual para SDR-PEP y los métodos y enseñanzas del proyecto LPEP en ocho países. (1) El conjunto de diapositivas Powerpoint política/apoyo que ayudarán a los programadores sobre la evidencia, practicabilidad y recursos necesarios para SDR-PEP, (2) La colección de diapositivas PowerPoint sobre formación e implementación en el campo para formar al personal implicado en el seguimiento de contactos y PEP con SDR, (3) manual genérico de campo SDR-PEP que puede ser usado para formar un protocolo específico de campo para el seguimiento de contactos y SDR-PEP como referencia para el personal directamente implicado. Finalmente, (4) el manual director SDR-PEP, que resume los distintos componentes de la caja de herramientas y contiene las instrucciones para su uso. CONCLUSIÓN: En respuesta al interés manifestado por varios países de implementar el seguimiento de contactos de lepra con PEP con SDR, con las recomendaciones OMS sobre SDR-PEP, esta caja de herramientas basada en la evidencia concreta pero flexible, ha sido diseñada para servir a los directores de programas nacionales de lepra con un medio práctico para trasladar los planteamientos a la práctica. Está disponible gratuitamente en la página de Infolep y actualizada constantemente: https://www.leprosy-information.org/keytopic/leprosy-post-exposure-prophylaxis-lpep-programme

OBJECTIVE: Leprosy post-exposure prophylaxis with single-dose rifampicin (SDRPEP) has proven effective and feasible, and is recommended by WHO since 2018. This SDR-PEP toolkit was developed through the experience of the leprosy post-exposure prophylaxis (LPEP) programme. It has been designed to facilitate and standardise the implementation of contact tracing and SDR-PEP administration in regions and countries that start the intervention. RESULTS: Four tools were developed, incorporating the current evidence for SDRPEP and the methods and learnings from the LPEP project in eight countries. (1) the SDR-PEP policy/advocacy PowerPoint slide deck which will help to inform policy makers about the evidence, practicalities and resources needed for SDR-PEP, (2) the SDR-PEP field implementation training PowerPoint slide deck to be used to train front line staff to implement contact tracing and PEP with SDR, (3) the SDR-PEP generic field guide which can be used as a basis to create a location specific field protocol for contact tracing and SDR-PEP serving as a reference for frontline field staff. Finally, (4) the SDR-PEP toolkit guide, summarising the different components of the toolkit and providing instructions on its optimal use. CONCLUSION: In response to interest expressed by countries to implement contact tracing and leprosy PEP with SDR in the light of the WHO recommendation of SDRPEP, this evidence-based, concrete yet flexible toolkit has been designed to serve national leprosy programme managers and support them with the practical means to translate policy into practice. The toolkit is freely accessible on the Infolep homepages and updated as required: https://www.leprosy-information.org/keytopic/leprosy-postexposure-prophylaxis-lpep-programme