Data from: Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in Western Cape Province, South Africa: a retrospective spatiotemporal analysis

Background: South Africa has the highest tuberculosis incidence globally (781/100,000), with an estimated 4.3% of cases being rifampicin-resistant (RR). 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 Findings: We 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 2008 and June 2013. Since 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 a year 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 to produce heat maps 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, median age was 36 years (IQR 27-44), and 10,255 (4.6%, 95% CI: 4.6-4.7) cases were RR. 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 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. Conclusions: Our 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.