1
|
Said H, Kachingwe E, Gardee Y, Bhyat Z, Ratabane J, Erasmus L, Lebaka T, van der Meulen M, Gwala T, Omar S, Ismail F, Ismail N. Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa. BMC Public Health 2023; 23:2329. [PMID: 38001453 PMCID: PMC10668341 DOI: 10.1186/s12889-023-17234-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Drug-resistant tuberculosis (DR-TB) epidemic is driven mainly by the effect of ongoing transmission. In high-burden settings such as South Africa (SA), considerable demographic and geographic heterogeneity in DR-TB transmission exists. Thus, a better understanding of risk-factors for clustering can help to prioritise resources to specifically targeted high-risk groups as well as areas that contribute disproportionately to transmission. METHODS The study analyzed potential risk-factors for recent transmission in SA, using data collected from a sentinel molecular surveillance of DR-TB, by comparing demographic, clinical and epidemiologic characteristics with clustering and cluster sizes. A genotypic cluster was defined as two or more patients having identical patterns by the two genotyping methods used. Clustering was used as a proxy for recent transmission. Descriptive statistics and multinomial logistic regression were used. RESULT The study identified 277 clusters, with cluster size ranging between 2 and 259 cases. The majority (81.6%) of the clusters were small (2-5 cases) with few large (11-25 cases) and very large (≥ 26 cases) clusters identified mainly in Western Cape (WC), Eastern Cape (EC) and Mpumalanga (MP). In a multivariable model, patients in clusters including 11-25 and ≥ 26 individuals were more likely to be infected by Beijing family, have XDR-TB, living in Nelson Mandela Metro in EC or Umgungunglovo in Kwa-Zulu Natal (KZN) provinces, and having history of imprisonment. Individuals belonging in a small genotypic cluster were more likely to infected with Rifampicin resistant TB (RR-TB) and more likely to reside in Frances Baard in Northern Cape (NC). CONCLUSION Sociodemographic, clinical and bacterial risk-factors influenced rate of Mycobacterium tuberculosis (M. tuberculosis) genotypic clustering. Hence, high-risk groups and hotspot areas for clustering in EC, WC, KZN and MP should be prioritized for targeted intervention to prevent ongoing DR-TB transmission.
Collapse
Affiliation(s)
- Halima Said
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa.
| | - Elizabeth Kachingwe
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa
| | - Yasmin Gardee
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa
| | - Zaheda Bhyat
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa
| | - John Ratabane
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa
| | - Linda Erasmus
- Centre for Enteric Diseases, National Institute of Communicable Diseases, Sandringham, Johannesburg, South Africa
| | - Tiisetso Lebaka
- Division of Surveillance and Outbreak Response, National Institute of Communicable Diseases, Sandringham, Johannesburg, South Africa
| | - Minty van der Meulen
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa
| | - Thabisile Gwala
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa
| | - Shaheed Omar
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa
| | - Farzana Ismail
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa
| | - Nazir Ismail
- Centre for Tuberculosis, National Institute of Communicable Diseases, Moderfontein Road, Sandringham, Johannesburg, code 2131, South Africa
| |
Collapse
|
2
|
Liu P, Wang Z, Liu N, Peres MA. A scoping review of the clinical application of machine learning in data-driven population segmentation analysis. J Am Med Inform Assoc 2023; 30:1573-1582. [PMID: 37369006 PMCID: PMC10436153 DOI: 10.1093/jamia/ocad111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 06/08/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
OBJECTIVE Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations. MATERIALS AND METHODS MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structured data from January 2000 to October 2022 were included. RESULTS We identified 6077 articles and included 79 for the final analysis. Data-driven population segmentation analysis was employed in various clinical settings. K-means clustering is the most prevalent unsupervised ML paradigm. The most common settings were healthcare institutions. The most common targeted population was the general population. DISCUSSION Although all the studies did internal validation, only 11 papers (13.9%) did external validation, and 23 papers (29.1%) conducted methods comparison. The existing papers discussed little validating the robustness of ML modeling. CONCLUSION Existing ML applications on population segmentation need more evaluations regarding giving tailored, efficient integrated healthcare solutions compared to traditional segmentation analysis. Future ML applications in the field should emphasize methods' comparisons and external validation and investigate approaches to evaluate individual consistency using different methods.
Collapse
Affiliation(s)
- Pinyan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Ziwen Wang
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
| | - Marco Aurélio Peres
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore, Singapore
| |
Collapse
|
3
|
Coinfecção tuberculose/HIV: perfil sociodemográfico e saúde de usuários de um centro especializado. ACTA PAUL ENFERM 2020. [DOI: 10.37689/acta-ape/2020ao00515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
|
4
|
Duarte R, Santos JV, Santos Silva A, Sotgiu G. Epidemiology and socioeconomic determinants of tuberculosis. Tuberculosis (Edinb) 2018. [DOI: 10.1183/2312508x.10020717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
5
|
Shaweno D, Karmakar M, Alene KA, Ragonnet R, Clements AC, Trauer JM, Denholm JT, McBryde ES. Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review. BMC Med 2018; 16:193. [PMID: 30333043 PMCID: PMC6193308 DOI: 10.1186/s12916-018-1178-4] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 09/20/2018] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden. METHODS We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO ( CRD42016036655 ). RESULTS We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined. CONCLUSIONS A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.
Collapse
Affiliation(s)
- Debebe Shaweno
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.
| | - Malancha Karmakar
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - Kefyalew Addis Alene
- Research School of Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
- Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Romain Ragonnet
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Burnet Institute, Melbourne, Australia
| | | | - James M Trauer
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Justin T Denholm
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - Emma S McBryde
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia
| |
Collapse
|
6
|
Gaspar R, Andrade P, Silva M, Peixoto A, Lopes J, Carneiro F, Liberal R, Macedo G. Hepatic granulomas: a 17-year single tertiary centre experience. Histopathology 2018; 73:240-246. [PMID: 29603759 DOI: 10.1111/his.13521] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 03/22/2018] [Indexed: 01/08/2023]
Abstract
AIMS Hepatic granulomas have an estimated prevalence of 5% in liver biopsies, with a wide range of aetiologies globally. Our aim was to assess the clinical relevance, presenting features and underlying aetiology in a non-transplant, tertiary referral centre from a western country. METHODS AND RESULTS This was a retrospective, single-centre review of clinical, laboratory and histological data including all adult patients for whom a liver biopsy was performed from January 1998 to December 2014. A total of 297 cases with hepatic granulomas were found in 9374 biopsies, but 57 were excluded from analysis either because they were lipogranulomas or the biopsy/aetiological work-up had not been performed at our institution. Overall, the most common aetiology was tuberculosis (35.8%), followed by primary biliary cholangitis (PBC) - 15.0%. In 30 patients (12.5%) granulomas were classified as idiopathic. From 1998 to June 2006 there were 147 granulomas in 5304 biopsies (2.8%), a frequency that did not change significantly compared to the period from July 2006 to December 2014 (93 granulomas in 4070 biopsies, 2.3%, P > 0.05). However, for the majority of cases (61.9%) there was a shift in granuloma aetiology during the former time-period that infectious diseases were responsible, whereas in the latter, autoimmune liver diseases (43%) were the main aetiology. In addition, while three cases of drug-induced granulomas were found from 1998 to June 2006, we report two cases in the second time-period. CONCLUSIONS Hepatic granulomas can result from various infectious and non-infectious diseases. During recent years, an epidemiological shift regarding granuloma aetiology was observed, from systemic infectious diseases to non-infectious, mainly immune-mediated primary liver disorders. With an appropriate work-up the aetiology can be identified in the vast majority of cases (~90%), rendering its histological identification and characterisation essential, as disease-specific therapies are becoming available.
Collapse
Affiliation(s)
- Rui Gaspar
- Gastroenterology Department, Centro Hospitalar São João, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patricia Andrade
- Gastroenterology Department, Centro Hospitalar São João, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Marco Silva
- Gastroenterology Department, Centro Hospitalar São João, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Armando Peixoto
- Gastroenterology Department, Centro Hospitalar São João, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Joanne Lopes
- Pathology Department, Centro Hospitalar São João, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Fatima Carneiro
- Pathology Department, Centro Hospitalar São João, Faculty of Medicine of the University of Porto, Porto, Portugal.,Instituto de Patologia e Imunologia Molecular da Universidade do Porto (Ipatimup, and i3S-Instituto de Investigação e Inovação em Saúde), Universidade do Porto, Porto, Portugal
| | - Rodrigo Liberal
- Gastroenterology Department, Centro Hospitalar São João, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Guilherme Macedo
- Gastroenterology Department, Centro Hospitalar São João, Faculty of Medicine of the University of Porto, Porto, Portugal
| |
Collapse
|
7
|
Duarte R, Migliori G, Zumla A, Cordeiro C. Strengthening tuberculosis control to advance towards elimination: The 2018 Rev. Port. Pneumol. (RPP) TB Series. Pulmonology 2018. [DOI: 10.1016/j.pulmoe.2018.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
|
8
|
Dias A, Gaio R, Sousa P, Gomes M, Oliveira O, Duarte R. Migration Flow and Its Impact on Tuberculosis Notification in Portugal. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.arbr.2017.07.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
9
|
Dias A, Gaio R, Sousa P, Gomes M, Oliveira O, Duarte R. Migration Flow and Its Impact on Tuberculosis Notification in Portugal. Arch Bronconeumol 2018; 54:18-23. [DOI: 10.1016/j.arbres.2017.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 07/25/2017] [Accepted: 07/26/2017] [Indexed: 10/18/2022]
|
10
|
Duarte R, Lönnroth K, Carvalho C, Lima F, Carvalho ACC, Muñoz-Torrico M, Centis R. Tuberculosis, social determinants and co-morbidities (including HIV). Pulmonology 2017; 24:115-119. [PMID: 29275968 DOI: 10.1016/j.rppnen.2017.11.003] [Citation(s) in RCA: 126] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2017] [Accepted: 11/12/2017] [Indexed: 11/26/2022] Open
Abstract
The risk of exposure, progression to active tuberculosis (TB) and then to cure is a process affected by several risk factors. Along with well known risk factors such as human immunodeficiency virus (HIV), use of immunosuppressive drugs and being of young age, emerging risk factors such socio-economic and behavioral aspects play a significant role in increasing the susceptibility to infection, and unsuccessful treatment outcomes. This paper summarizes the effects of these socio-economic determinants and co-morbidities (including HIV) on TB infection and disease.
Collapse
Affiliation(s)
- R Duarte
- Centro Hospitalar Vila Nova de Gaia/Espinho EPE, Departamento de Pneumologia, Vila Nova de Gaia, Portugal; ISPUP-EPIUnit, Universidade do Porto, Porto, Portugal; Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.
| | - K Lönnroth
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden; Centre for Epidemiology and Community Medicine, Stockholm County, Sweden
| | - C Carvalho
- Serviço de Doenças Infecciosas, Centro Hospitalar São João, Porto, Portugal
| | - F Lima
- Centro Hospitalar Vila Nova de Gaia/Espinho EPE, Departamento de Pneumologia, Vila Nova de Gaia, Portugal
| | - A C C Carvalho
- Laboratory of Innovations in Therapies, Education and Bioproducts (LITEB), Oswaldo Cruz Institute (IOC), FioCruz, Rio de Janeiro, Brazil
| | - M Muñoz-Torrico
- Clínica de Tuberculosis, Instituto Nacional de Enfermedades Respiratorias, Mexico
| | - R Centis
- WHO Collaborating Centre for TB and Lung Diseases, Maugeri Institute, IRCCS Tradate, Italy
| |
Collapse
|