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1. The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research

Author

Machkour, Jasin, Muma, Michael, and Palomar, Daniel P.

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

Modern genomics research relies on genome-wide association studies (GWAS) to identify the few genetic variants among potentially millions that are associated with diseases of interest. Only reproducible discoveries of groups of associations improve our understanding of complex polygenic diseases and enable the development of new drugs and personalized medicine. Thus, fast multivariate variable selection methods that have a high true positive rate (TPR) while controlling the false discovery rate (FDR) are crucial. Recently, the T-Rex+GVS selector, a version of the T-Rex selector that uses the elastic net (EN) as a base selector to perform grouped variable election, was proposed. Although it significantly increased the TPR in simulated GWAS compared to the original T-Rex, its comparably high computational cost limits scalability. Therefore, we propose the informed elastic net (IEN), a new base selector that significantly reduces computation time while retaining the grouped variable selection property. We quantify its grouping effect and derive its formulation as a Lasso-type optimization problem, which is solved efficiently within the T-Rex framework by the terminated LARS algorithm. Numerical simulations and a GWAS study demonstrate that the proposed T-Rex+GVS (IEN) exhibits the desired grouping effect, reduces computation time, and achieves the same TPR as T-Rex+GVS (EN) but with lower FDR, which makes it a promising method for large-scale GWAS., Comment: Published in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 10-13 December 2023, Los Sue\~nos, Costa Rica

Published
2024

2. False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks

Author

Machkour, Jasin, Muma, Michael, and Palomar, Daniel P.

Subjects
Statistics - Methodology
Abstract

Genomics biobanks are information treasure troves with thousands of phenotypes (e.g., diseases, traits) and millions of single nucleotide polymorphisms (SNPs). The development of methodologies that provide reproducible discoveries is essential for the understanding of complex diseases and precision drug development. Without statistical reproducibility guarantees, valuable efforts are spent on researching false positives. Therefore, scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods are urgently needed, especially, for complex polygenic diseases and traits. In this work, we propose the Screen-T-Rex selector, a fast FDR-controlling method based on the recently developed T-Rex selector. The method is tailored to screening large-scale biobanks and it does not require choosing additional parameters (sparsity parameter, target FDR level, etc). Numerical simulations and a real-world HIV-1 drug resistance example demonstrate that the performance of the Screen-T-Rex selector is superior, and its computation time is multiple orders of magnitude lower compared to current benchmark knockoff methods., Comment: Published in IEEE Statistical Signal Processing Workshop (SSP), 2-5 July 2023, Hanoi, Vietnam

Published
2024

3. Solving FDR-Controlled Sparse Regression Problems with Five Million Variables on a Laptop

Author

Scheidt, Fabian, Machkour, Jasin, and Muma, Michael

Subjects
Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Currently, there is an urgent demand for scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods to ensure the repro-ducibility of discoveries. However, among existing methods, only the recently proposed Terminating-Random Experiments (T-Rex) selector scales to problems with millions of variables, as encountered in, e.g., genomics research. The T-Rex selector is a new learning framework based on early terminated random experiments with computer-generated dummy variables. In this work, we propose the Big T-Rex, a new implementation of T-Rex that drastically reduces its Random Access Memory (RAM) consumption to enable solving FDR-controlled sparse regression problems with millions of variables on a laptop. We incorporate advanced memory-mapping techniques to work with matrices that reside on solid-state drive and two new dummy generation strategies based on permutations of a reference matrix. Our nu-merical experiments demonstrate a drastic reduction in memory demand and computation time. We showcase that the Big T-Rex can efficiently solve FDR-controlled Lasso-type problems with five million variables on a laptop in thirty minutes. Our work empowers researchers without access to high-performance clusters to make reproducible discoveries in large-scale high-dimensional data., Comment: Conference article (IEEE CAMSAP 2023), 5 pages, 7 figures

Published
2024
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5. High-Dimensional False Discovery Rate Control for Dependent Variables

Author

Machkour, Jasin, Muma, Michael, and Palomar, Daniel P.

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

Algorithms that ensure reproducible findings from large-scale, high-dimensional data are pivotal in numerous signal processing applications. In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples. However, these methods often fail to reliably control the FDR in the presence of highly dependent variable groups, a common characteristic in fields such as genomics and finance. To tackle this critical issue, we introduce a novel framework that accounts for general dependency structures. Our proposed dependency-aware T-Rex selector integrates hierarchical graphical models within the T-Rex framework to effectively harness the dependency structure among variables. Leveraging martingale theory, we prove that our variable penalization mechanism ensures FDR control. We further generalize the FDR-controlling framework by stating and proving a clear condition necessary for designing both graphical and non-graphical models that capture dependencies. Additionally, we formulate a fully integrated optimal calibration algorithm that concurrently determines the parameters of the graphical model and the T-Rex framework, such that the FDR is controlled while maximizing the number of selected variables. Numerical experiments and a breast cancer survival analysis use-case demonstrate that the proposed method is the only one among the state-of-the-art benchmark methods that controls the FDR and reliably detects genes that have been previously identified to be related to breast cancer. An open-source implementation is available within the R package TRexSelector on CRAN.

Published
2024

6. FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking

Author

Machkour, Jasin, Palomar, Daniel P., and Muma, Michael

Subjects
Quantitative Finance - Portfolio Management, Computer Science - Machine Learning, Statistics - Methodology, Statistics - Machine Learning
Abstract

In high-dimensional data analysis, such as financial index tracking or biomedical applications, it is crucial to select the few relevant variables while maintaining control over the false discovery rate (FDR). In these applications, strong dependencies often exist among the variables (e.g., stock returns), which can undermine the FDR control property of existing methods like the model-X knockoff method or the T-Rex selector. To address this issue, we have expanded the T-Rex framework to accommodate overlapping groups of highly correlated variables. This is achieved by integrating a nearest neighbors penalization mechanism into the framework, which provably controls the FDR at the user-defined target level. A real-world example of sparse index tracking demonstrates the proposed method's ability to accurately track the S&P 500 index over the past 20 years based on a small number of stocks. An open-source implementation is provided within the R package TRexSelector on CRAN.

Published
2024

7. Fast and Robust Sparsity-Aware Block Diagonal Representation

Author

Tastan, Aylin, Muma, Michael, and Zoubir, Abdelhak M.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients that are concentrated in blocks. However, recovering a block diagonal affinity matrix is challenging in real-world applications, in which the data may be subject to outliers and heavy-tailed noise that obscure the hidden cluster structure. To address this issue, we first analyze the effect of different fundamental outlier types in graph-based cluster analysis. A key idea that simplifies the analysis is to introduce a vector that represents a block diagonal matrix as a piece-wise linear function of the similarity coefficients that form the affinity matrix. We reformulate the problem as a robust piece-wise linear fitting problem and propose a Fast and Robust Sparsity-Aware Block Diagonal Representation (FRS-BDR) method, which jointly estimates cluster memberships and the number of blocks. Comprehensive experiments on a variety of real-world applications demonstrate the effectiveness of FRS-BDR in terms of clustering accuracy, robustness against corrupted features, computation time and cluster enumeration performance., Comment: 16 pages article, 4 pages supplementary, 51 pages accompanying material

Published
2023

14. Identifying the Complete Correlation Structure in Large-Scale High-Dimensional Data Sets with Local False Discovery Rates

Author

Gölz, Martin, Hasija, Tanuj, Muma, Michael, and Zoubir, Abdelhak M.

Subjects
Statistics - Methodology, Electrical Engineering and Systems Science - Signal Processing
Abstract

The identification of the dependent components in multiple data sets is a fundamental problem in many practical applications. The challenge in these applications is that often the data sets are high-dimensional with few observations or available samples and contain latent components with unknown probability distributions. A novel mathematical formulation of this problem is proposed, which enables the inference of the underlying correlation structure with strict false positive control. In particular, the false discovery rate is controlled at a pre-defined threshold on two levels simultaneously. The deployed test statistics originate in the sample coherence matrix. The required probability models are learned from the data using the bootstrap. Local false discovery rates are used to solve the multiple hypothesis testing problem. Compared to the existing techniques in the literature, the developed technique does not assume an a priori correlation structure and work well when the number of data sets is large while the number of observations is small. In addition, it can handle the presence of distributional uncertainties, heavy-tailed noise, and outliers.

Published
2023

15. Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar

Author

Schroth, Christian A., Eckrich, Christian, Kakouche, Ibrahim, Fabian, Stefan, von Stryk, Oskar, Zoubir, Abdelhak M., and Muma, Michael

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Robotics
Abstract

The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This article presents a complete signal processing chain for radar-based multi-person detection, 2D-MUSIC localization and breathing frequency estimation. The proposed method shows promising results on a challenging emergency response dataset that we collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included. The full emergency response benchmark data set as well as all codes to reproduce our results, are publicly available at https://doi.org/10.21227/4bzd-jm32., Comment: Dataset availabe at https://doi.org/10.21227/4bzd-jm32, code available at https://github.com/schrchr/radar-vitals-estimation

Published
2023
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16. Telemedicine integration into the eye health ecosystem in scaling of effective refractive error coverage in Kenya

Author

Shadrack Muma, Kovin Shunmugam Naidoo, and Rekha Hansraj

Subjects
Task shifting, Telemedicine, Scaling, Integration, Eye health, Refractive error, Medicine, Science
Abstract

Abstract This study explored the potential of telemedicine integration into the eye health ecosystem with an aim of scaling effective refractive error coverage in Kenya. This exploratory study was conducted telephonically and through online interviews with key opinion leaders, ophthalmologists, optometrists, ophthalmic clinical officers, optical technicians and beneficiaries of the optical technician’s services in rural areas. A telemedicine workflow was developed and validated based on the comments from the key opinion leaders using the Delphi technique. Quantitative and qualitative data were analysed using SPSS and NVivo Software respectively. All of the key opinion leaders agreed that telemedicine is relevant in the eye health ecosystem and recognition of primary vision technicians is critical for effective telemedicine integration. The reasons for the need of telemedicine integration were categorized into; good relationship, organized refractive error service delivery, convenience and availability and cost reduction. The possible factors influencing integration of telemedicine identified were categorized into cost, unwillingness, dominance, perception, lack of technical team, policies and network coverage. The limited human resources in the eye health ecosystem in developing countries cannot effectively deliver refractive error services to the growing population. Hence, integration of telemedicine and establishment of policies recognizing telemedicine are desirable to strengthen task shifting and scale effective refractive error coverage.

Published
2024
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17. An in-depth analysis of factors influencing small-scale cattle farmers’ participation in livestock markets in Western Province of Zambia: navigating challenges and barriers

Author

Chisoni Mumba, Bertha Kasanga, Chisanga T. Mwamba, Timothy Sichilima, Enock Siankwilimba, Doreen C. Sitali, Joshua Munkombwe, Reuben Banda, and John Bwalya Muma

Subjects
Livestock markets, socioeconomic factors, cultural factors, smallholder-cattle-farmer, Zambia, Veterinary medicine, SF600-1100
Abstract

IntroductionsThe low participation of small-scale farmers in livestock markets in sub-Saharan Africa, including Zambia, is a major inhibitor to private sector investment in the livestock subsector. Despite the immense potential of the livestock industry to bolster the economic development of countries in this region, several socioeconomic, environmental, and cultural factors contribute to this hindrance.MethodsThis study was conducted in the Western Province of Zambia and adopted a qualitative research approach to understand the challenges and barriers affecting livestock markets among smallholder cattle farmers. In-depth interviews were conducted with 23 key informants and focus group discussions involving key actors in the dairy and beef value chains. Thematic analysis was employed to analyze the gathered data.ResultsFour themes, including socioeconomic, cultural, market dynamics, and policy and regulatory factors, emerged from this analysis. The sub-themes for socioeconomic factors included access to capital, infrastructure challenges, limited technical knowledge, and inadequate veterinary services. Cultural factors included traditional practices, social norms, and perceptions of livestock, and subthemes for market dynamics included price volatility, lack of market information, and middlemen exploitation. The sub-themes for policy and regulatory factors included policy inconsistencies, land tenure issues, and inadequate government support.ConclusionBy identifying and understanding these factors, policymakers and stakeholders can develop effective and sustainable targeted interventions and policies to encourage smallholder cattle farmers’ participation in the livestock sector.

Published
2024
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18. Shuffled Multi-Channel Sparse Signal Recovery

Author

Koka, Taulant, Tsakiris, Manolis C., Muma, Michael, and Haro, Benjamín Béjar

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

Mismatches between samples and their respective channel or target commonly arise in several real-world applications. For instance, whole-brain calcium imaging of freely moving organisms, multiple-target tracking or multi-person contactless vital sign monitoring may be severely affected by mismatched sample-channel assignments. To systematically address this fundamental problem, we pose it as a signal reconstruction problem where we have lost correspondences between the samples and their respective channels. Assuming that we have a sensing matrix for the underlying signals, we show that the problem is equivalent to a structured unlabeled sensing problem, and establish sufficient conditions for unique recovery. To the best of our knowledge, a sampling result for the reconstruction of shuffled multi-channel signals has not been considered in the literature and existing methods for unlabeled sensing cannot be directly applied. We extend our results to the case where the signals admit a sparse representation in an overcomplete dictionary (i.e., the sensing matrix is not precisely known), and derive sufficient conditions for the reconstruction of shuffled sparse signals. We propose a robust reconstruction method that combines sparse signal recovery with robust linear regression for the two-channel case. The performance and robustness of the proposed approach is illustrated in an application related to whole-brain calcium imaging. The proposed methodology can be generalized to sparse signal representations other than the ones considered in this work to be applied in a variety of real-world problems with imprecise measurement or channel assignment., Comment: Submitted to TSP

Published
2022

19. Situation analysis on the integration of refractive error services provided by optometrists into the national health services in Kenya

Author

Shadrack Muma, Kovin Shunmugam Naidoo, and Rekha Hansraj

Subjects
Refractive error, Refractive services, Kenya, Eye care, National health service, Public aspects of medicine, RA1-1270
Abstract

Abstract Introduction Even though the burden of uncorrected refractive error could potentially be addressed through innovative and cost-effective approaches, integration of the services into the National Health Services (NHS) is desirable. However, minimal information exists on the current situation warranting the need for evidence about the integration of refractive error service provided by optometrists into the national health services in Kenya. Methods A situation analysis of the Kenyan refractive error services provided by optometrists within the NHS was undertaken based on access to service delivery, service coverage, and human resource. A strengths, weaknesses, opportunities, and threats analysis was undertaken based on the existent evidence to identify the core factors that could potentially facilitate or hinder the integration of refractive error services provided by optometrists within the National Health Services. The proportion of optometrists to be integrated in the NHS was estimated based on the minimum ratios recommended by the World Health Organization. Results A section of tertiary and secondary healthcare facilities in Kenya have specific services to address refractive errors within the NHS with most facilities lacking such services. Treatment of refractive error occurs at the level of eye care general services. There are 11,547 health facilities offering primary care services in Kenya. However, none of them offers refractive error services and only a section of facilities offering county health referral services provides eye care services which is limited to refraction without provision of spectacles. The existing workforce comprises of ophthalmologists, optometrists and ophthalmic clinical officers, together with nurses and other general paramedical assistants. Optometrists, ophthalmologists and ophthalmic clinical officers are allowed to undertake refraction. However, optometrists majorly practices in the private sector. Centralization of eye care services in urban areas, weak referral systems, and a shortage in the workforce per population was observed. Conclusions The Kenyan NHS should advocate for primary care and reorient the current hospital-based delivery approach for refractive error services. This is attributed to the fact that provision of refractive error services at primary care remains effective and efficient and could translate to early detection of other ocular conditions. The existing human resources in the eye health ecosystem in Kenya should maximize their efforts towards addressing uncorrected refractive error and optometrists should be integrated into the NHS.

Published
2024
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20. Retinoblastoma with and without Extraocular Tumor Extension

Author

Swathi Kaliki, MD, Vijitha S. Vempuluru, MD, Ido Didi Fabian, MD, Elhassan Abdallah, MD, Shehu U. Abdullahi, MD, Rula A. Abdulqader, MD, Aminatu A. Abdulrahaman, MD, Sherif Abouelnaga, MD, Dupe S. Ademola-Popoola, FMCOph, FWACS, Adedayo Adio, FWACS, Mahmoud A. Afifi, MD, Armin R. Afshar, MD, Priyanka Aggarwal, MD, Ada E. Aghaji, FMCOph MSc, Alia Ahmad, MRCPCH UK, Marliyanti N.R. Akib, MD, Adeseye M. Akinsete, MBBS, Lamis Al Harby, MD, Saleh A. Al Mesfer, MD, Mouroge H. Al Ani, MD, Silvia Alarcón Portabella, MD, Safaa A.F. Al-Badri, MD, Ana Patricia A. Alcasabas, MD, Saad A. Al-Dahmash, MD, Amanda Alejos, MD, Ernesto Alemany-Rubio, MD, Amadou I. Alfa Bio, MD, Yvania Alfonso Carreras, MD, Christiane E. Al-Haddad, MD, Hamoud H.Y. Al-Hussaini, MD, MSc, Amany M. Ali, MD, Donjeta B. Alia, MD, Mazin F. Al-Jadiry, MD, Usama Al-Jumaly, MD, Hind M. Alkatan, MD, Charlotta All-Eriksson, MD, PhD, Ali A.R.M. Al-Mafrachi, FIBMS, Argentino A. Almeida, MD, Khalifa M. Alsawidi, MD, Athar A.S.M. Al-Shaheen, MD, Entissar H. Al-Shammary, MD, Doreen Amankwaa-Frempong, MBChB, Primawita O. Amiruddin, MD, Inggar Armytasari, MD, Nicholas J. Astbury, FRCS, FRCOphth, Hatice T. Atalay, MD, Eda Ataseven, MD, La-ongsri Atchaneeyasakul, MD, Rose Atsiaya, OCO, Rudolf Autrata, MD, PhD, Julia Balaguer, MD, PhD, Ruhengiz Balayeva, PhD, Honorio Barranco, MD, PhD, Paulina Bartoszek, MD, Katarina Bartuma, MD, PhD, Covadonga Bascaran, MD, MSc, Nikolaos E. Bechrakis, MD, Maja Beck Popovic, MD, Ainura S. Begimkulova, MD, Sarra Benmiloud, MD, Rokia C. Berete, MD, PhD, Jesse L. Berry, MD, Anirban Bhaduri, MD, Sunil Bhat, MBBS, MD, Arpita Bhattacharyya, MD, Eva M. Biewald, MD, Elaine Binkley, MD, Sharon Blum, MD, Nadia Bobrova, MD, H. Culver Boldt, MD, Maria Teresa B.C. Bonanomi, MD, PhD, Gabrielle C. Bouda, MD, Hédi Bouguila, MD, PhD, Rachel C. Brennan, MD, Bénédicte G. Brichard, MD, PhD, Jassada Buaboonnam, MD, Aléine Budiongo, MD, Matthew Burton, FRCOphth, Patricia Calderón-Sotelo, MD, Doris A. Calle Jara, MD, Jayne E. Camuglia, FRANZCO, Miriam R. Cano, MD, MSc, Michael Capra, FRCPI, Shani Caspi, MD, Nathalie Cassoux, MD, PhD, Guilherme Castela, MD, Luis Castillo, MD, Jaume Català-Mora, MD, PhD, Isabel Caviedes, MD, Arthika Chandramohan, MD, Guillermo L. Chantada, MD, PhD, Shabana Chaudhry, MD, Bhavna Chawla, MD, Wensi Chen, MD, Faraja S. Chiwanga, MSc, Tsengelmaa Chuluunbat, MD, PhD, Krzysztof Cieslik, MD, Antony Clark, FRANZCO, Ruellyn L. Cockcroft, MB ChB , M Med Paed, Codruta Comsa, MD, Maria G. Correa Llano, MD, Timothy W. Corson, PhD, Line Couitchere, MD, Kristin E. Cowan-Lyn, MD, MBBS, Monika Csóka, MD, PhD, Wantanee Dangboon, MD, Anirban Das, MD, Pranab Das, MD, Sima Das, MS, Jacquelyn M. Davanzo, BSN, BSPH, Alan Davidson, MBChB, MPhil, Sonia De Francesco, MD, Patrick De Potter, MD, PhD, Karina Q. Delgado, MD, PhD, Hakan Demirci, MD, Laurence Desjardins, MD, Rosdali Y. Diaz Coronado, MD, Helen Dimaras, PhD, Andrew J. Dodgshun, M Phil, Carla R. Donato Macedo, MD, Monica D. Dragomir, MD, PhD, Yi Du, MD, Magritha Du Bruyn, MD, Johannes P. Du Plessis, MMed (Paed), Gagan Dudeja, MBBS, MS, Katrin Eerme, MD, I Wayan Eka Sutyawan, MD, Asmaa El Kettani, MD, Amal M. Elbahi, MD, James E. Elder, MBBS, Alaa M. Elhaddad, MD, PhD, Moawia M.A. Elhassan, MD, Mahmoud M. Elzembely, MD, Connor Ericksen, MD, Vera A. Essuman, FWACS, Ted Grimbert A. Evina, MD, Ifeoma R. Ezegwui, FMCOph, FWACS, FAEH, Zehra Fadoo, MBBS, Adriana C. Fandiño, MD, Mohammad Faranoush, MD, Oluyemi Fasina, FWACS, Delia D.P.G. Fernández, MSc, Ana Fernández-Teijeiro, MD, PhD, Allen Foster, FRCOphth, Shahar Frenkel, MD, PhD, Ligia D. Fu, MD, Soad L. Fuentes-Alabi, MD, MPH, Juan L. Garcia, MSc, David García Aldana, MD, Henry N. Garcia Pacheco, MD, Jennifer A. Geel, MBChB, MMed, Fariba Ghassemi, MD, Ana V. Girón, MD, Marco A. Goenz, MD, Aaron S. Gold, OD, Hila Golberg, MD, Glen A. Gole, MD, FRANZCO, Nir Gomel, MD, Efren Gonzalez, MD, Graciela Gonzalez Perez, MD, Liudmira González-Rodríguez, MD, Malka Gorfine, PhD, Jaime Graells, MD, Pernille A. Gregersen, MD, Nathalia D.A.K. Grigorovski, MD, Koffi M. Guedenon, MD, D Sanjeeva Gunasekera, MD, Ahmet K. Gündüz, MD, Himika Gupta, MD, Sanjiv Gupta, MS, Vineeta Gupta, MD, Theodora Hadjistilianou, MD, Patrick Hamel, MD, Syed A. Hamid, FCPS, Norhafizah Hamzah, MSc, Eric D. Hansen, MD, J William Harbour, MD, M. Elizabeth Hartnett, MD, Murat Hasanreisoglu, MD, Sadiq Hassan, MD, FWACS, Shadab Hassan, FRCS, FCPS, Wojciech Hautz, MD, Huda A. Haydar, CHD, Stanislava Hederova, MD, Laila Hessissen, MD, Hoby Lalaina, MD, Suradej Hongeng, MD, Diriba F. Hordofa, MD, G. Baker Hubbard, MD, Marlies Hummlen, MD, Kristina Husakova, MD, Allawi N. Hussein Al-Janabi, MD, Affiong A. Ibanga, MB.BCh, FMCOph, Russo Ida, MD, Vesna R. Ilic, MD, Ziyavuddin Islamov, MD, Vivekaraj Jairaj, DNB, Teyyeb A. Janjua, MD, FCPS, FRCSEd, Irfan Jeeva, FRCOphth, Xunda Ji, MD, Dong Hyun Jo, MD, PhD, Michael M. Jones, MD, PhD, FRANZCO, Theophile B. Amani Kabesha, MD, PhD, Rolande L. Kabore, MD, Abubakar Kalinaki, MD, Pius Kamsang, MD, Mehmet Kantar, MD, Noa Kapelushnik, MD, Tamar Kardava, PhD, Rejin Kebudi, MD, Jonny Keomisy, MD, Tomas Kepak, MD, Petra Ketteler, MD, Zohora J. Khan, MD, Hussain A. Khaqan, MD, Vikas Khetan, FRCS, FACS, Alireza Khodabande, MD, Zaza Khotenashvili, MD, Jonathan W. Kim, MD, Jeong Hun Kim, MD, PhD, Hayyam Kiratli, MD, Tero T. Kivelä, MD, Artur Klett, MD, PhD, Irem Koç, MD, Jess Elio Kosh Komba Palet, MD, Dalia Krivaitiene, MD, PhD, Mariana Kruger, Mmed Paed, PhD, Kittisak Kulvichit, MD, Mayasari W. Kuntorini, MD, Alice Kyara, BA, Geoffrey C. Lam, FRANZCO, Scott A. Larson, MD, Slobodanka Latinović, MD, PhD, Kelly D. Laurenti, MD, Yotam Lavi, MD, PhD, Alenka Lavric Groznik, MD, Amy A. Leverant, MD, Cairui Li, MD, Kaijun Li, MD, Ben Limbu, MD, Chun-Hsiu Liu, MD, Quah Boon Long, FRCS (Ed), MMed ( Ophth), FAMS, Juan P. López, MD, Robert M. Lukamba, MD, Sandra Luna-Fineman, MD, Delfitri Lutfi, MD, Lesia Lysytsia, MD, Shiran Madgar, MD, George N. Magrath, MD, Amita Mahajan, MD, Puja Maitra, MD, Erika Maka, MD, Emil K. Makimbetov, MD, Azza M.Y. Maktabi, MD, Carlos Maldonado, MD, Ashwin Mallipatna, MD, Rebecca Manudhane, MD, Lyazat Manzhuova, MD, Nieves Martín Begue, MD, PhD, Sidra Masud, MBBS, Ibrahim O. Matende, MD, M. Med (Oph), Clarissa C.D.S. Mattosinho, MD, Marchelo Matua, BAPH, Ismail Mayet, MD, Freddy B. Mbumba, MD, MMed Paed, John D. McKenzie, MD, Azim Mehrvar, MD, Aemero A. Mengesha, MD, Vikas Menon, MD, Gary John V.D.D. Mercado, MD, Marilyn B. Mets, MD, Edoardo Midena, MD, PhD, Audra Miller, MD, Divyansh K.C. Mishra, DNB, Furahini G. Mndeme, MD, Ahmed A. Mohamedani, FRCPath, Mona T. Mohammad, MD, FRCS, Annette C. Moll, MD, PhD, Margarita M. Montero, MD, Claude Moreira, MD, PhD, Prithvi Mruthyunjaya, MD, MHS, Mchikirwa S. Msina, MMed Ophth, Gerald Msukwa, MMed Ophth, Sangeeta S. Mudaliar, DNB Pediatric, Hassan Muhammad, MD, Kangwa I. Muma, MMed Ophth, FCOphth, Francis L. Munier, MD, Timothy G. Murray, MD, MBA, Kareem O. Musa, FWACS, FMCOphth, FICO, Asma Mushtaq, MD, Anne A. Musika, MD, Hamzah Mustak, MD, Tajudeen Mustapha, MBBS, FWACS, Okwen M. Muyen, MD, Khumo H. Myezo, Msc, Gita Naidu, MMed Paed, PhD, Natasha Naidu, MBCHB, FCS Ophthalmol, Akshay Gopinathan Nair, MD, Sundaram Natarajan, FRCS, Larisa Naumenko, MD, PhD, Paule Aïda Ndoye Roth, MD PhD, Yetty M. Nency, MD, Vladimir Neroev, MD, PhD, Yvonne Ng, MBChB ( Auckland) , FRANZCO, Marina Nikitovic, MD, PhD, Elizabeth D. Nkanga, FMCOph, Henry E. Nkumbe, MD, Marcel N. Numbi, MD, Kalle Nummi, MD, Murtuza Nuruddin, FRCS, Mutale Nyaywa, MD, MMed Ophth, FCOphth, Chinsisi Nyirenda, MD, Ghislaine Obono-Obiang, MD, Scott C.N. Oliver, MD, Joaquin Ooporto, MD, Miriam Ortega-Hernández, MD, Alexander Oscar, MD, Diego Ossandon, MD, Halimah Pagarra, MD, PhD, Vivian Paintsil, FWACP, Luisa Paiva, MD, Mahesh Shanmugam Palanivelu, FRCSED, Ruzanna Papyan, MD, Raffaele Parrozzani, MD, PhD, Claudia R. Pascual Morales, MD, Katherine E. Paton, MD, FRCSC, Jacob Pe'er, MD, Jesús Peralta Calvo, MD, Sanja Perić, MD, PhD, Chau T.M. Pham, MD, Remezo Philbert, MD, David A. Plager, MD, Pavel Pochop, MD, PhD, Rodrigo A. Polania, MD, Vladimir Polyakov, MD, Jimena Ponce, MD, Ali O. Qadir, MD, Seema Qayyum, FCPS, Jiang Qian, MD, Ardizal Rahman, MD, Purnima Rajkarnikar, MD, Rajesh Ramanjulu, MD, Aparna Ramasubramanian, MD, Marco A. Ramirez-Ortiz, MD, MPH, Jasmeen K. Randhawa, BA, Léa Raobela, MD, Riffat Rashid, MS, M. Ashwin Reddy, FRCOphth, Lorna A. Renner, FRCPCH (UK), David Reynders, MD, Dahiru Ribadu, FMCOph, Petra Ritter-Sovinz, MD, Anna Rogowska, MD, Duangnate Rojanaporn, MD, Livia Romero, MD, Soma R. Roy, DCO, Raya H. Saab, MD, Svetlana Saakyan, MD, PhD, Ahmed H. Sabhan, MD, Mandeep S. Sagoo, FRCS (Ed), Azza M.A. Said, MD, Rohit Saiju, MD, Beatriz Salas, MD, Sonsoles San Román Pacheco, MD, Gissela L. Sánchez, MD, Alma Janeth Sanchez Orozco, MD, Phayvanh Sayalith, MD, Trish A. Scanlan, MRCPI, MSc, Christoph Schwab, MD, Ahad Sedaghat, MD, Rachna Seth, DNB MNAMS, Mariana Sgroi, MD, Ankoor S. Shah, MD, PhD, Shawkat A. Shakoor, MS, Manoj K. Sharma, MD, Sadik T. Sherief, MD, Carol L. Shields, MD, David Sia, MB ChB, FRANZCO, Sorath Noorani Siddiqui, MD, Sidi Sidi cheikh, MD, PhD, Sónia Silva, MD, Arun D. Singh, MD, Usha Singh, MS, Penny Singha, MD, Rita S. Sitorus, MD, PhD, Alison H. Skalet, MD, PhD, Hendrian D. Soebagjo, MD, PhD, Tetyana Sorochynska, MD, PhD, Grace Ssali, MD, Andrew W. Stacey, MD, Sandra E. Staffieri, PhD, Erin D. Stahl, MD, David M. Steinberg, PhD, David K. Stones, MBChB, FCPaed, Caron Strahlendorf, MD, Maria Estela Coleoni Suarez, MD, Sadia Sultana, FCPS, Xiantao Sun, MD, Rosanne Superstein, MD, Eddy Supriyadi, MD, PhD, Supawan Surukrattanaskul, MD, Shigenobu Suzuki, MD, PhD, Karel Svojgr, MD, PhD, Fatoumata Sylla, MD, Gevorg Tamamyan, MD, PhD, Deborah Tan, MBBS, Alketa Tandili, MD, PhD, Jing Tang, MD, Fanny F. Tarrillo Leiva, MD, Maryam Tashvighi, MD, Bekim Tateshi, MD, PhD, Kok Hoi Teh, MD, Edi S. Tehuteru, MD, Luiz F. Teixeira, MD, Manca Tekavcic Pompe, MD, PhD, Abdullah Dahan M. Thawaba, MD, Tuyisabe Theophile, MSc, Helen Toledano, MBChB, Doan L. Trang, MD, Fousseyni Traoré, MD, Devjyoti Tripathy, MD, Samuray Tuncer, MD, Harba Tyau-Tyau, MD, Ali B. Umar, MD, FMCPath, Emel Unal, MD, Ogul E. Uner, BA, Steen F. Urbak, MD, PhD, Tatiana L. Ushakova, MD, Rustam H. Usmanov, MD, Sandra Valeina, MD, Paola Valente, MD, Milo van Hoefen Wijsard, MD, Jacqueline Karina Vasquez Anchaya, MD, Leon O. Vaughan, FRCS (Ed), Nevyana V. Veleva-Krasteva, MD, PhD, Nishant Verma, MD, Andi A. Victor, MD, PhD, Maris Viksnins, MD, Edwin G. Villacís Chafla, MD, Victor M. Villegas, MD, Victoria Vishnevskia-Dai, MD, Keith Waddell, DM, FRCP, FRCS, FRCOphth, Amina H. Wali, MD, FMCOph Nigeria, Yi-Zhuo Wang, MD, Nutsuchar Wangtiraumnuay, MD, FICO, Julie A. Wetter, MMed Rad Onc, FCRad Onc, Widiarti P. Riono, MD, Matthew W. Wilson, MD, Amelia D.C. Wime, MD, Atchareeya Wiwatwongwana, MD, Damrong Wiwatwongwana, MD, Charlotte Wolley Dod, MD, Emily S. Wong, FCOphth HK, FHKAM, Phanthipha Wongwai, MD, PhD, Si-qi Wu, MSc, Daoman Xiang, MD, PhD, Yishuang Xiao, MSc, Bing Xu, MD, Kang Xue, MD, Antonio Yaghy, MD, Jason C. Yam, FRCSEd, Huasheng Yang, MD, Jenny M. Yanga, MD, Muhammad A. Yaqub, MD, FCPS, FRCSEd, Vera A. Yarovaya, MD, Andrey A. Yarovoy, MD, PhD, Huijing Ye, MD, Roberto I. Yee, MD, Yacoub A. Yousef, MD, Putu Yuliawati, MD, Arturo M. López, MD, Ekhtelbenina Zein, MD, Yi Zhang, MD, PhD, Katsiaryna Zhilyaeva, MD, Nida Zia, MBBS, MCPS, Othman A.O. Ziko, MD, PhD, Marcia Zondervan, MBA, Sabrina Schlüter, MD, and Richard Bowman, FRCOphth

Subjects
External beam radiotherapy, Extraocular extension, Multimodal treatment, Retinoblastoma, Tumor, Ophthalmology, RE1-994
Abstract

Purpose: To study the treatment and outcomes of children with retinoblastoma (RB) with extraocular tumor extension (RB-EOE) and compare them with RB without extraocular tumor extension (RB-w/o-EOE). Design: Multicenter intercontinental collaborative prospective study from 2017 to 2020. RB-EOE cases included those with overt orbital tumor extension in treatment-naive patients. Cases with microscopic orbital extension detected postenucleation were excluded from the study. Participants: A total of 319 children with RB-EOE and 3116 children with RB-w/o-EOE. Intervention: Chemotherapy, enucleation, exenteration, radiotherapy. Main Outcome Measures: Systemic metastasis and death. Results: Of the 3435 RB patients included in this study, 309 (9%) were from low-income countries (LIC), 1448 (42%) from lower-middle income, 1012 (29%) from upper-middle income, and 666 (19%) patients from high-income countries. There was an inverse relationship between the percentage of RB-EOE and national income level, with 96 (31%) patients from LIC, 197 (6%) lower-middle income, 20 (2%) upper-middle income, and 6 (1%) patients from high-income countries (P = 0.0001). The outcomes were statistically significant for RB-EOE compared with RB-w/o-EOE: systemic metastasis (32% vs. 4% respectively; P = 0.0001) and metastasis-related death (63% vs. 6% respectively; P = 0.0001). Multimodal treatment was the most common form of treatment (n = 177; 54%) for RB-EOE, with most cases undergoing a combination of intravenous chemotherapy and enucleation (n = 97; 30%). Adjuvant external beam radiotherapy (EBRT) after surgery (enucleation/orbital exenteration) was given in only 68 (21%) cases. Kaplan–Meier analysis for systemic metastasis and metastasis-related death in RB-EOE was 28% and 57% at 1 year, 29% and 60% at 2 years, and 29% and 61% at 3 years, respectively. Cox regression analysis revealed that the risk of death from RB-EOE was greater in patients aged >4 years than

Published
2025
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28. Retinoblastoma with and without Extraocular Tumor Extension: A Global Comparative Study of 3435 Patients

Author

Fabian, Ido Didi, Abdallah, Elhassan, Abdullahi, Shehu U., Abdulqader, Rula A., Abdulrahaman, Aminatu A., Abouelnaga, Sherif, Ademola-Popoola, Dupe S., Adio, Adedayo, Afifi, Mahmoud A., Afshar, Armin R., Aggarwal, Priyanka, Aghaji, Ada E., Ahmad, Alia, Akib, Marliyanti N.R., Akinsete, Adeseye M., Al Harby, Lamis, Al Mesfer, Saleh A., Al Ani, Mouroge H., Portabella, Silvia Alarcón, Al-Badri, Safaa A.F., Alcasabas, Ana Patricia A., Al-Dahmash, Saad A., Alejos, Amanda, Alemany-Rubio, Ernesto, Alfa Bio, Amadou I., Carreras, Yvania Alfonso, Al-Haddad, Christiane E., Al-Hussaini, Hamoud H.Y., Ali, Amany M., Alia, Donjeta B., Al-Jadiry, Mazin F., Al-Jumaly, Usama, Alkatan, Hind M., All-Eriksson, Charlotta, Al-Mafrachi, Ali A.R.M., Almeida, Argentino A., Alsawidi, Khalifa M., Al-Shaheen, Athar A.S.M., Al-Shammary, Entissar H., Amankwaa-Frempong, Doreen, Amiruddin, Primawita O., Armytasari, Inggar, Astbury, Nicholas J., Atalay, Hatice T., Ataseven, Eda, Atchaneeyasakul, La-ongsri, Atsiaya, Rose, Autrata, Rudolf, Balaguer, Julia, Balayeva, Ruhengiz, Barranco, Honorio, Bartoszek, Paulina, Bartuma, Katarina, Bascaran, Covadonga, Bechrakis, Nikolaos E., Popovic, Maja Beck, Begimkulova, Ainura S., Benmiloud, Sarra, Berete, Rokia C., Berry, Jesse L., Bhaduri, Anirban, Bhat, Sunil, Bhattacharyya, Arpita, Biewald, Eva M., Binkley, Elaine, Blum, Sharon, Bobrova, Nadia, Boldt, H. Culver, Bonanomi, Maria Teresa B.C., Bouda, Gabrielle C., Bouguila, Hédi, Brennan, Rachel C., Brichard, Bénédicte G., Buaboonnam, Jassada, Budiongo, Aléine, Burton, Matthew, Calderón-Sotelo, Patricia, Calle Jara, Doris A., Camuglia, Jayne E., Cano, Miriam R., Capra, Michael, Caspi, Shani, Cassoux, Nathalie, Castela, Guilherme, Castillo, Luis, Català-Mora, Jaume, Caviedes, Isabel, Chandramohan, Arthika, Chantada, Guillermo L., Chaudhry, Shabana, Chawla, Bhavna, Chen, Wensi, Chiwanga, Faraja S., Chuluunbat, Tsengelmaa, Cieslik, Krzysztof, Clark, Antony, Cockcroft, Ruellyn L., Comsa, Codruta, Correa Llano, Maria G., Corson, Timothy W., Couitchere, Line, Cowan-Lyn, Kristin E., Csóka, Monika, Dangboon, Wantanee, Das, Anirban, Das, Pranab, Das, Sima, Davanzo, Jacquelyn M., Davidson, Alan, De Francesco, Sonia, De Potter, Patrick, Delgado, Karina Q., Demirci, Hakan, Desjardins, Laurence, Diaz Coronado, Rosdali Y., Dimaras, Helen, Dodgshun, Andrew J., Donato Macedo, Carla R., Dragomir, Monica D., Du, Yi, Du Bruyn, Magritha, Du Plessis, Johannes P., Dudeja, Gagan, Eerme, Katrin, Eka Sutyawan, I Wayan, El Kettani, Asmaa, Elbahi, Amal M., Elder, James E., Elhaddad, Alaa M., Elhassan, Moawia M.A., Elzembely, Mahmoud M., Ericksen, Connor, Essuman, Vera A., Evina, Ted Grimbert A., Ezegwui, Ifeoma R., Fadoo, Zehra, Fandiño, Adriana C., Faranoush, Mohammad, Fasina, Oluyemi, Fernández, Delia D.P.G., Fernández-Teijeiro, Ana, Foster, Allen, Frenkel, Shahar, Fu, Ligia D., Fuentes-Alabi, Soad L., Garcia, Juan L., Aldana, David García, Garcia Pacheco, Henry N., Geel, Jennifer A., Ghassemi, Fariba, Girón, Ana V., Goenz, Marco A., Gold, Aaron S., Golberg, Hila, Gole, Glen A., Gomel, Nir, Gonzalez, Efren, Perez, Graciela Gonzalez, González-Rodríguez, Liudmira, Gorfine, Malka, Graells, Jaime, Gregersen, Pernille A., Grigorovski, Nathalia D.A.K., Guedenon, Koffi M., Gunasekera, D Sanjeeva, Gündüz, Ahmet K., Gupta, Himika, Gupta, Sanjiv, Gupta, Vineeta, Hadjistilianou, Theodora, Hamel, Patrick, Hamid, Syed A., Hamzah, Norhafizah, Hansen, Eric D., Harbour, J William, Hartnett, M. Elizabeth, Hasanreisoglu, Murat, Hassan, Sadiq, Hassan, Shadab, Hautz, Wojciech, Haydar, Huda A., Hederova, Stanislava, Hessissen, Laila, Lalaina, Hoby, Hongeng, Suradej, Hordofa, Diriba F., Hubbard, G. Baker, Hummlen, Marlies, Husakova, Kristina, Hussein Al-Janabi, Allawi N., Ibanga, Affiong A., Ida, Russo, Ilic, Vesna R., Islamov, Ziyavuddin, Jairaj, Vivekaraj, Janjua, Teyyeb A., Jeeva, Irfan, Ji, Xunda, Jo, Dong Hyun, Jones, Michael M., Amani Kabesha, Theophile B., Kabore, Rolande L., Kaliki, Swathi, Kalinaki, Abubakar, Kamsang, Pius, Kantar, Mehmet, Kapelushnik, Noa, Kardava, Tamar, Kebudi, Rejin, Keomisy, Jonny, Kepak, Tomas, Ketteler, Petra, Khan, Zohora J., Khaqan, Hussain A., Khetan, Vikas, Khodabande, Alireza, Khotenashvili, Zaza, Kim, Jonathan W., Kim, Jeong Hun, Kiratli, Hayyam, Kivelä, Tero T., Klett, Artur, Koç, Irem, Kosh Komba Palet, Jess Elio, Krivaitiene, Dalia, Kruger, Mariana, Kulvichit, Kittisak, Kuntorini, Mayasari W., Kyara, Alice, Lam, Geoffrey C., Larson, Scott A., Latinović, Slobodanka, Laurenti, Kelly D., Lavi, Yotam, Groznik, Alenka Lavric, Leverant, Amy A., Li, Cairui, Li, Kaijun, Limbu, Ben, Liu, Chun-Hsiu, Long, Quah Boon, López, Juan P., Lukamba, Robert M., Luna-Fineman, Sandra, Lutfi, Delfitri, Lysytsia, Lesia, Madgar, Shiran, Magrath, George N., Mahajan, Amita, Maitra, Puja, Maka, Erika, Makimbetov, Emil K., Maktabi, Azza M.Y., Maldonado, Carlos, Mallipatna, Ashwin, Manudhane, Rebecca, Manzhuova, Lyazat, Begue, Nieves Martín, Masud, Sidra, Matende, Ibrahim O., Mattosinho, Clarissa C.D.S., Matua, Marchelo, Mayet, Ismail, Mbumba, Freddy B., McKenzie, John D., Mehrvar, Azim, Mengesha, Aemero A., Menon, Vikas, Mercado, Gary John V.D.D., Mets, Marilyn B., Midena, Edoardo, Miller, Audra, Mishra, Divyansh K.C., Mndeme, Furahini G., Mohamedani, Ahmed A., Mohammad, Mona T., Moll, Annette C., Montero, Margarita M., Moreira, Claude, Mruthyunjaya, Prithvi, Msina, Mchikirwa S., Msukwa, Gerald, Mudaliar, Sangeeta S., Muhammad, Hassan, Muma, Kangwa I., Munier, Francis L., Murray, Timothy G., Musa, Kareem O., Mushtaq, Asma, Musika, Anne A., Mustak, Hamzah, Mustapha, Tajudeen, Muyen, Okwen M., Myezo, Khumo H., Naidu, Gita, Naidu, Natasha, Nair, Akshay Gopinathan, Natarajan, Sundaram, Naumenko, Larisa, Ndoye Roth, Paule Aïda, Nency, Yetty M., Neroev, Vladimir, Ng, Yvonne, Nikitovic, Marina, Nkanga, Elizabeth D., Nkumbe, Henry E., Numbi, Marcel N., Nummi, Kalle, Nuruddin, Murtuza, Nyaywa, Mutale, Nyirenda, Chinsisi, Obono-Obiang, Ghislaine, Oliver, Scott C.N., Ooporto, Joaquin, Ortega-Hernández, Miriam, Oscar, Alexander, Ossandon, Diego, Pagarra, Halimah, Paintsil, Vivian, Paiva, Luisa, Palanivelu, Mahesh Shanmugam, Papyan, Ruzanna, Parrozzani, Raffaele, Pascual Morales, Claudia R., Paton, Katherine E., Pe'er, Jacob, Calvo, Jesús Peralta, Perić, Sanja, Pham, Chau T.M., Philbert, Remezo, Plager, David A., Pochop, Pavel, Polania, Rodrigo A., Polyakov, Vladimir, Ponce, Jimena, Qadir, Ali O., Qayyum, Seema, Qian, Jiang, Rahman, Ardizal, Rajkarnikar, Purnima, Ramanjulu, Rajesh, Ramasubramanian, Aparna, Ramirez-Ortiz, Marco A., Randhawa, Jasmeen K., Raobela, Léa, Rashid, Riffat, Reddy, M. Ashwin, Renner, Lorna A., Reynders, David, Ribadu, Dahiru, Ritter-Sovinz, Petra, Rogowska, Anna, Rojanaporn, Duangnate, Romero, Livia, Roy, Soma R., Saab, Raya H., Saakyan, Svetlana, Sabhan, Ahmed H., Sagoo, Mandeep S., Said, Azza M.A., Saiju, Rohit, Salas, Beatriz, San Román Pacheco, Sonsoles, Sánchez, Gissela L., Sanchez Orozco, Alma Janeth, Sayalith, Phayvanh, Scanlan, Trish A., Schwab, Christoph, Sedaghat, Ahad, Seth, Rachna, Sgroi, Mariana, Shah, Ankoor S., Shakoor, Shawkat A., Sharma, Manoj K., Sherief, Sadik T., Shields, Carol L., Sia, David, Noorani Siddiqui, Sorath, Sidi cheikh, Sidi, Silva, Sónia, Singh, Arun D., Singh, Usha, Singha, Penny, Sitorus, Rita S., Skalet, Alison H., Soebagjo, Hendrian D., Sorochynska, Tetyana, Ssali, Grace, Stacey, Andrew W., Staffieri, Sandra E., Stahl, Erin D., Steinberg, David M., Stones, David K., Strahlendorf, Caron, Coleoni Suarez, Maria Estela, Sultana, Sadia, Sun, Xiantao, Superstein, Rosanne, Supriyadi, Eddy, Surukrattanaskul, Supawan, Suzuki, Shigenobu, Svojgr, Karel, Sylla, Fatoumata, Tamamyan, Gevorg, Tan, Deborah, Tandili, Alketa, Tang, Jing, Tarrillo Leiva, Fanny F., Tashvighi, Maryam, Tateshi, Bekim, Teh, Kok Hoi, Tehuteru, Edi S., Teixeira, Luiz F., Pompe, Manca Tekavcic, Thawaba, Abdullah Dahan M., Theophile, Tuyisabe, Toledano, Helen, Trang, Doan L., Traoré, Fousseyni, Tripathy, Devjyoti, Tuncer, Samuray, Tyau-Tyau, Harba, Umar, Ali B., Unal, Emel, Uner, Ogul E., Urbak, Steen F., Ushakova, Tatiana L., Usmanov, Rustam H., Valeina, Sandra, Valente, Paola, van Hoefen Wijsard, Milo, Vasquez Anchaya, Jacqueline Karina, Vaughan, Leon O., Veleva-Krasteva, Nevyana V., Verma, Nishant, Victor, Andi A., Viksnins, Maris, Villacís Chafla, Edwin G., Villegas, Victor M., Vishnevskia-Dai, Victoria, Waddell, Keith, Wali, Amina H., Wang, Yi-Zhuo, Wangtiraumnuay, Nutsuchar, Wetter, Julie A., Riono, Widiarti P., Wilson, Matthew W., Wime, Amelia D.C., Wiwatwongwana, Atchareeya, Wiwatwongwana, Damrong, Dod, Charlotte Wolley, Wong, Emily S., Wongwai, Phanthipha, Wu, Si-qi, Xiang, Daoman, Xiao, Yishuang, Xu, Bing, Xue, Kang, Yaghy, Antonio, Yam, Jason C., Yang, Huasheng, Yanga, Jenny M., Yaqub, Muhammad A., Yarovaya, Vera A., Yarovoy, Andrey A., Ye, Huijing, Yee, Roberto I., Yousef, Yacoub A., Yuliawati, Putu, López, Arturo M., Zein, Ekhtelbenina, Zhang, Yi, Zhilyaeva, Katsiaryna, Zia, Nida, Ziko, Othman A.O., Zondervan, Marcia, Schlüter, Sabrina, Bowman, Richard, and Vempuluru, Vijitha S.

Published
2025
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29. SWOT analysis of the models used by social enterprises in scaling effective refractive error coverage to achieve the 2030 in SIGHT in Kenya

Author

Shadrack Muma, Kovin Shunmugam Naidoo, and Rekha Hansraj

Subjects
Telemedicine, Social enterprises, Refractive error, Uncorrected refractive error, Effective refractive error coverage, 2030 IN SIGHT, Medicine, Science
Abstract

Abstract Uncorrected refractive error has predominantly been delivered through commercial entrepreneurship in Kenya. However, to achieve the 2030 IN SIGHT, integration of other forms of entrepreneurship such as the social entrepreneurship is desirable to supplement the efforts of the dominant commercial entrepreneurship. Therefore, this study intended to undertake a SWOT analysis of the current models used by social enterprises in scaling effective refractive error coverage to achieve the 2030 IN SIGHT in Kenya. A review of the seven national strategic plans for eye health in Kenya was undertaken to get a glimpse on the efforts directed towards uncorrected refractive error in achieving the 2030 IN SIGHT. The review was inclined towards assessing the efforts directed by the strategic plans towards scaling human resource, spectacle provision and refraction points. A SWOT analysis was undertaken based on the financial, impact and the approach report for each model. A key informant interview was conducted with a representative and three to five members of the social enterprise about the model. Thereafter, the modified SWOT analysis based on the review and the interview was presented to the representatives of the social enterprises. Purposive sampling was used to identify seven models used by social enterprises in the delivery of refractive error services in Kenya. Finally, the recommendations were presented to key opinion leaders for an input through a Delphi technique. Out of the seven national strategic plans for eye health reviewed, only the strategic plan 2020–2025 intends to establish optical units within 15 different counties in Kenya. Of the seven models currently utilized by social enterprises, only the Kenya Society for the Blind has integrated the telemedicine concept. On application of mHealth, all of the social enterprises models tend to embrace the approach for screening activities. None of the models has a strengthened referral pathway utilizing telereferral and telemedicine. Out of all the models, only Operation Eyesight Universal, Fred Hollow Foundation and Peek Acuity do not depend on sales of subsidized spectacles for sustainability. Every model has the capacity to propel the delivery of refractive error services depending on its comprehensiveness. However, for the 2030 IN SIGHT to be achieved, models prioritizing human resource through telemedicine integration, service provision across all sectors, awareness creation and enhancing cost efficiency are desirable.

Published
2024
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30. Robust and Efficient Aggregation for Distributed Learning

Author

Vlaski, Stefan, Schroth, Christian, Muma, Michael, and Zoubir, Abdelhak M.

Subjects
Computer Science - Machine Learning, Computer Science - Multiagent Systems, Statistics - Machine Learning
Abstract

Distributed learning paradigms, such as federated and decentralized learning, allow for the coordination of models across a collection of agents, and without the need to exchange raw data. Instead, agents compute model updates locally based on their available data, and subsequently share the update model with a parameter server or their peers. This is followed by an aggregation step, which traditionally takes the form of a (weighted) average. Distributed learning schemes based on averaging are known to be susceptible to outliers. A single malicious agent is able to drive an averaging-based distributed learning algorithm to an arbitrarily poor model. This has motivated the development of robust aggregation schemes, which are based on variations of the median and trimmed mean. While such procedures ensure robustness to outliers and malicious behavior, they come at the cost of significantly reduced sample efficiency. This means that current robust aggregation schemes require significantly higher agent participation rates to achieve a given level of performance than their mean-based counterparts in non-contaminated settings. In this work we remedy this drawback by developing statistically efficient and robust aggregation schemes for distributed learning.

Published
2022

32. Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study

Author

Ina Matzke, Sophie Huhn, Mara Koch, Martina Anna Maggioni, Stephen Munga, Julius Okoth Muma, Collins Ochieng Odhiambo, Daniel Kwaro, David Obor, Till Bärnighausen, Peter Dambach, and Sandra Barteit

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundClimate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time. ObjectiveThe aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures. MethodsWe conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes. ResultsOur cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness. ConclusionsOur study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study’s application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Published
2024
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33. The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control

Author

Machkour, Jasin, Muma, Michael, and Palomar, Daniel P.

Subjects
Statistics - Methodology, Electrical Engineering and Systems Science - Signal Processing, Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract

We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the original predictors and multiple sets of randomly generated dummy predictors. A finite sample proof based on martingale theory for the FDR control property is provided. Numerical simulations confirm that the FDR is controlled at the target level while allowing for high power. We prove that the dummies can be sampled from any univariate probability distribution with finite expectation and variance. The computational complexity of the proposed method is linear in the number of variables. The T-Rex selector outperforms state-of-the-art methods for FDR control in numerical experiments and on a simulated genome-wide association study (GWAS), while its sequential computation time is more than two orders of magnitude lower than that of the strongest benchmark methods. The open source R package TRexSelector containing the implementation of the T-Rex selector is available on CRAN., Comment: R packages 'TRexSelector' and 'tlars' on CRAN, 33 pages, 21 figures, 2 tables

Published
2021

34. Robust Regularized Locality Preserving Indexing for Fiedler Vector Estimation

Author

Aylin Tastan, Michael Muma, and Abdelhak M. Zoubir

Subjects
Dimension reduction, eigen-decomposition, eigenvectors, Fiedler vector, locality preserving indexing, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The Fiedler vector is the eigenvector associated with the algebraic connectivity of the graph Laplacian. It is central to graph analysis as it provides substantial information to learn the latent structure of a graph. In real-world applications, however, the data may be subject to heavy-tailed noise and outliers which deteriorate the structure of the Fiedler vector estimate and lead to a breakdown of popular methods. Thus, we propose a Robust Regularized Locality Preserving Indexing (RRLPI) Fiedler vector estimation method that approximates the nonlinear manifold structure of the Laplace Beltrami operator while minimizing the impact of outliers. To achieve this aim, an analysis of the effects of two fundamental outlier types on the eigen-decomposition of block affinity matrices is conducted. Then, an error model is formulated based on which the RRLPI method is developed. It includes an unsupervised regularization parameter selection algorithm that leverages the geometric structure of the projection space. The performance is benchmarked against existing methods in terms of detection probability, partitioning quality, image segmentation capability, robustness and computation time using a large variety of synthetic and real data experiments.

Published
2024
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35. Proposed task shifting integrated with telemedicine to address uncorrected refractive error in Kenya: Delphi study

Author

Shadrack Muma, Kovin Shunmugam Naidoo, and Rekha Hansraj

Subjects
Task shifting, Human resource, Refractive error, Vision corridors, Telemedicine, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Developing countries such as Kenya still experience challenges around human resource to deliver refractive error services. However, given the burden of uncorrected refractive error, adoption of innovative and cost effective approaches is desirable. Hence this study intended to develop a task shifting framework integrated with telemedicine to potentially scale refractive error services. Methods This was an exploratory study conducted in four phases as follows: a scoping review of the scope of practice for ophthalmic workers in Kenya, an interview with key opinion leaders on the need for integration of public health approaches such as the vision corridors within the eye health ecosystem in Kenya and their knowledge on task shifting, and finally development and validation of a proposed task shifting framework through a Delphi technique. Purposive sampling was used to recruit key opinion leaders and data was collected via telephonic interviews. The qualitative data was analyzed thematically using NVivo Software, Version 11. Results The scoping review showed that only optometrists, ophthalmologists and ophthalmic clinical officers are allowed to undertake refraction in Kenya. All of the key opinion leaders (100%) were aware of task shifting and agreed that it is suitable for adoption within the eye health ecosystem in Kenya. All of the key opinion leaders (100%) agreed that skills development for healthcare workers without prior training on eye health supervised by optometrists through telemedicine is desirable. Notwithstanding, all of the key opinion leaders (100%) agreed that integration of public health approaches such as the vision corridors across all levels of healthcare delivery channels and development of a self-assessment visual acuity tool is desirable. Finally all of the key opinion leaders (100%) agreed that task shifting is relevant for adoption within the eye health ecosystem in Kenya. The developed framework prioritized partnership, advocacy, skills development, establishment and equipping of refraction points. The proposed framework advocated for a telemedicine between professionals with conventional training and those with skills development. Conclusion Task shifting integrated with telemedicine could cost effectively scale refractive error service delivery. However, internal and external factors may hinder the success warranting the need for a multi-faceted interventions and a connection between planning and training to scale the uptake.

Published
2024
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36. Population pharmacokinetic analysis of enrofloxacin and its active metabolite ciprofloxacin after intravenous injection to cats with reduced kidney function

Author

Jonathan D. Foster, Mahmoud Abouraya, Mark G. Papich, and Nancy A. Muma

Subjects
drug prescribing, feline, fluroquinolone, renal failure, Veterinary medicine, SF600-1100
Abstract

Abstract Background It is unknown if enrofloxacin accumulates in plasma of cats with reduced kidney function. Hypothesis To determine if enrofloxacin and its active metabolite ciprofloxacin have reduced clearance in azotemic cats. Animals Thirty‐four cats hospitalized for clinical illness with variable degree of kidney function. Methods Prospective study. After enrofloxacin (dose 5 mg/kg) administration to cats, sparse blood sampling was used to obtain 2 compartment population pharmacokinetic results using nonlinear mixed‐effects modeling. Plasma enrofloxacin and ciprofloxacin concentrations were measured and summed to obtain the total fluoroquinolone concentration. A model of ciprofloxacin metabolism from enrofloxacin was created and evaluated for covariate effects on clearance, volume of distribution, and the metabolic rate of ciprofloxacin generation from enrofloxacin. Results Body weight was the only covariate found to affect total fluoroquinolone volume of distribution (effect 1.63, SE 0.19, P

Published
2023
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37. Robust Regularized Locality Preserving Indexing for Fiedler Vector Estimation

Author

Tastan, Aylin, Muma, Michael, and Zoubir, Abdelhak M.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

The Fiedler vector of a connected graph is the eigenvector associated with the algebraic connectivity of the graph Laplacian and it provides substantial information to learn the latent structure of a graph. In real-world applications, however, the data may be subject to heavy-tailed noise and outliers which results in deteriorations in the structure of the Fiedler vector estimate. We design a Robust Regularized Locality Preserving Indexing (RRLPI) method for Fiedler vector estimation that aims to approximate the nonlinear manifold structure of the Laplace Beltrami operator while minimizing the negative impact of outliers. First, an analysis of the effects of two fundamental outlier types on the eigen-decomposition for block affinity matrices which are essential in cluster analysis is conducted. Then, an error model is formulated and a robust Fiedler vector estimation algorithm is developed. An unsupervised penalty parameter selection algorithm is proposed that leverages the geometric structure of the projection space to perform robust regularized Fiedler estimation. The performance of RRLPI is benchmarked against existing competitors in terms of detection probability, partitioning quality, image segmentation capability, robustness and computation time using a large variety of synthetic and real data experiments.

Published
2021

38. A Situation Analysis of the Capacity of Laboratories in Faith-Based Hospitals in Zambia to Conduct Surveillance of Antimicrobial Resistance: Opportunities to Improve Diagnostic Stewardship

Author

Doreen Mainza Shempela, Steward Mudenda, Maisa Kasanga, Victor Daka, Mundia Hendrix Kangongwe, Mapeesho Kamayani, Jay Sikalima, Baron Yankonde, Cynthia Banda Kasonde, Ruth Nakazwe, Andrew Mwandila, Fatim Cham, Michael Njuguna, Bertha Simwaka, Linden Morrison, Joseph Yamweka Chizimu, John Bwalya Muma, Roma Chilengi, and Karen Sichinga

Subjects
antimicrobial resistance, bacteriology, laboratory capacity, surveillance, Zambia, Biology (General), QH301-705.5
Abstract

Antimicrobial resistance (AMR) is a public health problem exacerbated by the overuse and misuse of antibiotics and the inadequate capacity of laboratories to conduct AMR surveillance. This study assessed the capacity of laboratories in seven faith-based hospitals to conduct AMR testing and surveillance in Zambia. This multi-facility, cross-sectional exploratory study was conducted from February 2024 to April 2024. We collected and analysed data using the self-scoring Laboratory Assessment of Antibiotic Resistance Testing Capacity (LAARC) tool. This study found an average score of 39%, indicating a low capacity of laboratories to conduct AMR surveillance. The highest capacity score was 47%, while the lowest was 25%. Only one hospital had a full capacity (100%) to utilise a laboratory information system (LIS). Three hospitals had a satisfactory capacity to perform data management with scores of 83%, 85%, and 95%. Only one hospital had a full capacity (100%) to process specimens, and only one hospital had good safety requirements for a microbiology laboratory, with a score of 89%. This study demonstrates that all the assessed hospitals had a low capacity to conduct AMR surveillance, which could affect diagnostic stewardship. Therefore, there is an urgent need to strengthen the microbiology capacity of laboratories to enhance AMR surveillance in Zambia.

Published
2024
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39. Drivers of antimicrobial resistance in layer poultry farming: Evidence from high prevalence of multidrug-resistant Escherichia coli and enterococci in Zambia

Author

Steward Mudenda, Flavien Nsoni Bumbangi, Kaunda Yamba, Musso Munyeme, Sydney Malama, Moses Mukosha, Mwendalubi Albert Hadunka, Victor Daka, Scott Kaba Matafwali, Godfrey Siluchali, Geoffrey Mainda, Mercy Mukuma, Bernard Mudenda Hang’ombe, and John Bwalya Muma

Subjects
antimicrobial resistance, drivers, escherichia coli, poultry, risk factors, zambia, Animal culture, SF1-1100, Veterinary medicine, SF600-1100
Abstract

Background and Aim: Inappropriate use of antimicrobials exacerbates antimicrobial resistance (AMR) in the poultry sector. Information on factors driving AMR in the layer poultry sector is scarce in Zambia. This study examined the drivers of AMR in the layer poultry sector in the Lusaka and Copperbelt Provinces of Zambia. Materials and Methods: This cross-sectional study employed a structured questionnaire in 77 layer poultry farms in the provinces of Lusaka and Copperbelt, Zambia, from September 2020 to April 2021. Data analysis was conducted using Stata version 16.1. Antimicrobial resistance was defined as the presence of multidrug resistance (MDR) isolates. Multivariable regression analysis was used to identify drivers of AMR. Results: In total, 365 samples were collected, from which 339 (92.9%) Escherichia coli and 308 (84.4%) Enterococcus spp. were isolated. Multidrug resistance was identified in 39% of the E. coli and 86% of the Enterococcus spp. The overall prevalence of AMR in layer poultry farms was 51.7% (95% confidence interval [CI]: 40.3%–63.5%). Large-scale farmers (Adjusted odds ratio [AOR] = 0.20, 95% CI: 0.04%–0.99%) than small-scale and farmers who were aware of AMR than those who were unaware (AOR = 0.26, 95% CI: 0.08%–0.86%) were less likely to experience AMR problems. Conclusion: This study found a high prevalence of AMR in layer poultry farming linked to the type of farm management practices and lack of AMR awareness. Evidence of high MDR in our study is of public health concern and requires urgent attention. Educational interventions must increase AMR awareness, especially among small- and medium-scale poultry farmers.

Published
2023
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40. Sparsity-aware Robust Community Detection(SPARCODE)

Author

Tastan, Aylin, Muma, Michael, and Zoubir, Abdelhak M.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Community detection refers to finding densely connected groups of nodes in graphs. In important applications, such as cluster analysis and network modelling, the graph is sparse but outliers and heavy-tailed noise may obscure its structure. We propose a new method for Sparsity-aware Robust Community Detection (SPARCODE). Starting from a densely connected and outlier-corrupted graph, we first extract a preliminary sparsity improved graph model where we optimize the level of sparsity by mapping the coordinates from different clusters such that the distance of their embedding is maximal. Then, undesired edges are removed and the graph is constructed robustly by detecting the outliers using the connectivity of nodes in the improved graph model. Finally, fast spectral partitioning is performed on the resulting robust sparse graph model. The number of communities is estimated using modularity optimization on the partitioning results. We compare the performance to popular graph and cluster-based community detection approaches on a variety of benchmark network and cluster analysis data sets. Comprehensive experiments demonstrate that our method consistently finds the correct number of communities and outperforms existing methods in terms of detection performance, robustness and modularity score while requiring a reasonable computation time.

Published
2020

41. Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis

Author

Schroth, Christian A. and Muma, Michael

Subjects
Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

This article proposes a new class of Real Elliptically Skewed (RESK) distributions and associated clustering algorithms that allow for integrating robustness and skewness into a single unified cluster analysis framework. Non-symmetrically distributed and heavy-tailed data clusters have been reported in a variety of real-world applications. Robustness is essential because a few outlying observations can severely obscure the cluster structure. The RESK distributions are a generalization of the Real Elliptically Symmetric (RES) distributions. To estimate the cluster parameters and memberships, we derive an expectation maximization (EM) algorithm for arbitrary RESK distributions. Special attention is given to a new robust skew-Huber M-estimator, which is also the maximum likelihood estimator (MLE) for the skew-Huber distribution that belongs to the RESK class. Numerical experiments on simulated and real-world data confirm the usefulness of the proposed methods for skewed and heavy-tailed data sets.

Published
2020
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42. Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions

Author

Schroth, Christian A. and Muma, Michael

Subjects
Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications. Cluster enumeration becomes challenging when the true underlying structure in the observed data is corrupted by heavy-tailed noise and outliers. Recently, Bayesian cluster enumeration criteria have been derived by formulating cluster enumeration as maximization of the posterior probability of candidate models. This article generalizes robust Bayesian cluster enumeration so that it can be used with any arbitrary Real Elliptically Symmetric (RES) distributed mixture model. Our framework also covers the case of M-estimators that allow for mixture models, which are decoupled from a specific probability distribution. Examples of Huber's and Tukey's M-estimators are discussed. We derive a robust criterion for data sets with finite sample size, and also provide an asymptotic approximation to reduce the computational cost at large sample sizes. The algorithms are applied to simulated and real-world data sets, including radar-based person identification, and show a significant robustness improvement in comparison to existing methods.

Published
2020
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48. The data-driven supply chain raises new governance and security issues: With every business now a data business, good data stewardship is a must

Author

Muma, Shawn, Kappos, David, and Webb, Carys

Subjects
Privacy, Global economy, Logistics, Privacy issue, Business, general, Business
Abstract

Over a century ago, companies transitioned to making ubiquitous use of electricity, helping to usher in the Second Industrial Revolution with innovations that made it easier to mass produce goods [...]

Published
2023

50. Rabies Realities: Navigating Barriers to Rabies Control in Rural Zambia—A Case Study of Manyinga and Mwansabombwe Districts

Author

Muma Chipo Misapa, Eugene C. Bwalya, Ladslav Moonga, Josiah Zimba, Emmanuel S. Kabwali, Mwenya Silombe, Edgar Chilanzi Mulwanda, Christopher Mulenga, Martin C. Simuunza, Hirofumi Sawa, Bernard Hang’ombe, and Walter Muleya

Subjects
rabies, prevention, control, vaccination, barriers, determinants, Medicine
Abstract

Rabies persists as a longstanding issue in Zambia, despite being preventable. The current control measures, including dog vaccination, population control, and movement restriction, guided by ‘The Control of Dogs Act Chapter 247 of the Laws of Zambia’, have not yielded the desired impact in many areas of the country including Manyinga and Mwansabombwe districts. These two districts continue to report low dog vaccination rates, unrestricted dog movements, and escalating cases of animal and human rabies, along with dog bites. Aligned with global aspirations to achieve zero human rabies cases by 2030, this study scrutinizes the determinants and obstacles hampering the execution of rabies control initiatives in Manyinga and Mwansabombwe. Spanning approximately 11 months, this cross-sectional study gathered pre- and post-vaccination data from 301 households in Manyinga and 100 households in Mwansabombwe. Questionnaires probed knowledge, attitudes, and practices related to rabies prevention and control. A transect survey, key informant interviews, and assessment of rabies vaccination and dog bite records complemented the data collection. Findings revealed that 88.0% of respondents from both districts possessed knowledge about rabies, confirming affected species and transmission. Moreover, 76.8% in Manyinga and 88.6% in Mwansabombwe were acquainted with rabies prevention and control methods. Concerning dog owners, 89.0% were aware of rabies, 66.0% understood its prevention and control, and the majority identified bites as the primary mode of transmission. Despite the high level of knowledge recorded during the survey, the implementation of preventive measures was low, which was attributed to low levels of law enforcement by the local government authority, inadequate staffing in the veterinary department, unwillingness to pay for dog vaccinations, and unavailability of rabies vaccine at the veterinary office in both districts. Vaccination coverage stood at 64.0% in Manyinga and 21.0% in Mwansabombwe. Notably, education and occupation exhibited a positive significant association with rabies knowledge. In terms of dog bite cases, Manyinga recorded 538 dog bite cases from 2017 to June 2022, while Mwansabombwe recorded 81 dog bite and 23 jackal bite cases from 2021 to June 2022. The study underscores critical knowledge gaps in rural areas and emphasizes the imperative for enhanced public education and awareness programs, improved rabies surveillance, free mass vaccination campaigns, and community engagement to augment vaccination coverage and knowledge about rabies.

Published
2024
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