image: This week in PLOS Medicine, Karen Jacobson, from the Boston University School of Medicine and Boston Medical Center, and colleagues, describe a new technique for linking samples submitted for tuberculosis testing to the individuals who provided the samples and the location from where they were submitted, in a way that can provide the continuous national surveillance necessary for eradicating tuberculosis and drug-resistant tuberculosis. view more
Credit: fernandozhiminaicela, Pixabay
Global tuberculosis control and elimination will require detailed real-time information on the location of individuals with the disease, the presence of drug resistance, and the patterns of transmission. The surveys currently used are only conducted periodically and are not sufficient to effectively control tuberculosis, which causes more than 4,500 deaths daily. This week in PLOS Medicine, Karen Jacobson, from the Boston University School of Medicine and Boston Medical Center, and colleagues, describe a new technique for linking samples submitted for tuberculosis testing to the individuals who provided the samples and the location from where they were submitted, in a way that can provide the continuous national surveillance necessary for eradicating tuberculosis and drug-resistant tuberculosis.
Using a person-matching algorithm to link repeat and longitudinal specimens to the same individual and to the same episode of disease, the researchers linked 2,219,891 samples from the Western Cape National Health Laboratory Service in South Africa, submitted for tuberculosis testing between 2008 and 2013, to 799,779 individuals who were mappable to clinic locations. Of these individuals, 222,735 (27.8%) had microbiologically confirmed tuberculosis, and of these, 10,255 (4.6%, 95% CI: 4.6-4.7) had documented resistance to the drug rifampicin. The researchers found that the percentage of rifampicin-resistant (RR-) tuberculosis cases was spatially heterogeneous, ranging from 0% to 25% across the province, and that the percentages of RR-tuberculosis fluctuated from year to year at several locations.
The researchers note that because the South African database lacks unique identifiers, these figures are approximations that are reliant on the person-matching algorithm, and that the analysis does not include data from non-clinic locations or private clinics. Still, this method for leveraging routinely collected laboratory data is a promising tool for understanding and eliminating tuberculosis.
As the authors note: "In the future, this framework could allow public health providers to have near real-time surveillance of drug resistance burden, evaluate programmatic interventions, and monitor progress towards national and global tuberculosis reduction goals."
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Research Article
Funding:
AIM was supported by the National Institute of General Medical Sciences Interdisciplinary Training Grant for Biostatisticians (NIH T32 GM74905). KRJ was supported by the Fogarty International Center (US NIH 1K01TW009213) and by the Burroughs Wellcome Fund/American Society of Tropical Medicine and Hygiene. HEJ was supported by the U.S. National Institutes of Health (US NIH K01AI102944). LFW was supported by the U.S. National Institutes of Health (NIH R01GM122876). PDvH and RMW were supported by the South African Medical Research Council. JS and TD were supported by the South African National Health Laboratory Service. EMS was supported by the South African National Research Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests:
MBM is a member of the Editorial Board of PLOS Medicine. The authors declare no other competing interests exist.
Citation:
McIntosh AI, Jenkins HE, White LF, Barnard M, Thomson DR, Dolby T, et al. (2018) Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in the Western Cape Province, South Africa: A retrospective spatiotemporal analysis. PLoS Med 15(8): e1002638. https://doi.org/10.1371/journal.pmed.1002638
Image Credit: fernandozhiminaicela, Pixabay
Author Affiliations:
Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
National Health Laboratory Service, Cape Town, South Africa
Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, United States of America
In your coverage please use this URL to provide access to the freely available paper: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002638
Journal
PLoS Medicine