Research Article: Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in the Western Cape Province, South Africa: A retrospective spatiotemporal analysis

Date Published: August 21, 2018

Publisher: Public Library of Science

Author(s): Avery I. McIntosh, Helen E. Jenkins, Laura F. White, Marinus Barnard, Dana R. Thomson, Tania Dolby, John Simpson, Elizabeth M. Streicher, Mary B. Kleinman, Elizabeth J. Ragan, Paul D. van Helden, Megan B. Murray, Robin M. Warren, Karen R. Jacobson, Amitabh Bipin Suthar

Abstract: BackgroundSouth Africa has the highest tuberculosis incidence globally (781/100,000), with an estimated 4.3% of cases being rifampicin resistant (RR). Control and elimination strategies will require detailed spatial information to understand where drug-resistant tuberculosis exists and why it persists in those communities. We demonstrate a method to enable drug-resistant tuberculosis monitoring by identifying high-burden communities in the Western Cape Province using routinely collected laboratory data.Methods and findingsWe retrospectively identified cases of microbiologically confirmed tuberculosis and RR-tuberculosis from all biological samples submitted for tuberculosis testing (n = 2,219,891) to the Western Cape National Health Laboratory Services (NHLS) between January 1, 2008, and June 30, 2013. Because the NHLS database lacks unique patient identifiers, we performed a series of record-linking processes to match specimen records to individual patients. We counted an individual as having a single disease episode if their positive samples came from within two years of each other. Cases were aggregated by clinic location (n = 302) to estimate the percentage of tuberculosis cases with rifampicin resistance per clinic. We used inverse distance weighting (IDW) to produce heatmaps of the RR-tuberculosis percentage across the province. Regression was used to estimate annual changes in the RR-tuberculosis percentage by clinic, and estimated average size and direction of change was mapped. We identified 799,779 individuals who had specimens submitted from mappable clinics for testing, of whom 222,735 (27.8%) had microbiologically confirmed tuberculosis. The study population was 43% female, the median age was 36 years (IQR 27–44), and 10,255 (4.6%, 95% CI: 4.6–4.7) cases had documented rifampicin resistance. Among individuals with microbiologically confirmed tuberculosis, 8,947 (4.0%) had more than one disease episode during the study period. The percentage of tuberculosis cases with rifampicin resistance documented among these individuals was 11.4% (95% CI: 10.7–12.0). Overall, the percentage of tuberculosis cases that were RR-tuberculosis was spatially heterogeneous, ranging from 0% to 25% across the province. Our maps reveal significant yearly fluctuations in RR-tuberculosis percentages at several locations. Additionally, the directions of change over time in RR-tuberculosis percentage were not uniform. The main limitation of this study is the lack of unique patient identifiers in the NHLS database, rendering findings to be estimates reliant on the accuracy of the person-matching algorithm.ConclusionsOur maps reveal striking spatial and temporal heterogeneity in RR-tuberculosis percentages across this province. We demonstrate the potential to monitor RR-tuberculosis spatially and temporally with routinely collected laboratory data, enabling improved resource targeting and more rapid locally appropriate interventions.

Partial Text: In 2015, tuberculosis became the leading infectious disease killer globally, responsible for more than 4,500 deaths daily [1]. The World Health Organization (WHO) End Tuberculosis Strategy aims to reduce tuberculosis deaths by 95% and disease incidence by 90% before 2035 [2]. Given the current 1.9% annual decline in tuberculosis incidence [1,2], dramatic changes to the tuberculosis control strategy are required to meet WHO targets [2]. Multidrug-resistant (MDR) tuberculosis, defined as resistance to both rifampicin and isoniazid, is a significant barrier to successful tuberculosis control because of diagnostic delays, high treatment failure rates, and the cost burden on health systems [3,4]. Control and elimination strategies for other diseases, such as polio and smallpox, have only succeeded by using detailed spatial information to understand where and why pathogens persist and then tailoring interventions accordingly [5–9]. To make tuberculosis elimination affordable, sustainable, and effective, the global control program needs surveillance systems to monitor drug resistance and identify high burden communities from regularly collected, routine data [10].

Between January 1, 2008, and June 30, 2013, 2,219,891 biological specimens, of which 94% were sputum and bronchial, were submitted for tuberculosis testing to the NHLS; these specimens had a person-name and location from which they were submitted. We removed 47,147 samples (2.1%) because of lack of person-name or location. After applying the person-matching algorithm, we counted 942,358 unique individuals in the database (Fig 1). Of these individuals, 799,779 (85%) were mappable to a clinic location: 748,691 (80%) had specimens submitted for testing solely from a clinic and 51,088 (5%) had tests submitted from a clinic and at least one additional location. The remaining 142,579 (15%) individuals had tests submitted exclusively from non-clinic locations and were therefore excluded from further analyses (S1 Table details the non-clinic locations from which samples were submitted). The percentage of individuals with microbiologically confirmed tuberculosis did not differ between clinic and non-clinic locations (27.8% versus 28.0%, p = 0.16). The percentage of microbiologically confirmed RR-tuberculosis was lower for individuals with at least one specimen submitted from a clinic compared to individuals with specimens submitted only from non-clinic locations (4.6% versus 6.7%, p < 0.0001). Our data aggregation, cleaning, linkage, and mapping method detected substantial heterogeneity in the RR-tuberculosis case percentages across the Western Cape Province, with the percentage of tuberculosis cases found to be RR at clinics ranging from 0% to 25%. This wide variability in drug resistance among clinics indicates an urgent need for more granular surveillance to locate emerging and chronic hot spots. Through this project, we have developed a road map for organizing routinely collected laboratory data from the South African NHLS, showing that molecular diagnostics can be transformed into a meaningful surveillance tool. Source:


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