Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in the 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). 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 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 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. 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.

Extensions to Bayesian generalized linear mixed effects models for household tuberculosis transmission.

Household contact studies, a mainstay of tuberculosis transmission research, often assume that tuberculosis-infected household contacts of an index case were infected within the household. However, strain genotyping has provided evidence against this assumption. Understanding the household versus community infection dynamic is essential for designing interventions. The misattribution of infection sources can also bias household transmission predictor estimates. We present a household-community transmission model that estimates the probability of community infection, that is, the probability that a household contact of an index case was actually infected from a source outside the home and simultaneously estimates transmission predictors. We show through simulation that our method accurately predicts the probability of community infection in several scenarios and that not accounting for community-acquired infection in household contact studies can bias risk factor estimates. Applying the model to data from Vitória, Brazil, produced household risk factor estimates similar to two other standard methods for age and sex. However, our model gave different estimates for sleeping proximity to index case and disease severity score. These results show that estimating both the probability of community infection and household transmission predictors is feasible and that standard tuberculosis transmission models likely underestimate the risk for two important transmission predictors. Copyright © 2017 John Wiley & Sons, Ltd.

Incident Mycobacterium tuberculosis infection in household contacts of infectious tuberculosis patients in Brazil.

BACKGROUND: In household contact investigations of tuberculosis (TB), a second tuberculin skin test (TST) obtained several weeks after a first negative result consistently identifies individuals that undergo TST conversion. It remains unclear whether this delay in M. tuberculosis infection is related to differences in the infectious exposure, TST boosting, partial host resistance, or some other factor. METHODS: We conducted a household contact study Vitória, Brazil. Between 2008 and 2013, we identified culture-positive pulmonary TB patients and evaluated their household contacts with both a TST and interferon gamma release assay (IGRA), and identified TST converters at 8-12 weeks post study enrollment. Contacts were classified as TST-positive (≥10 mm) at baseline, TST converters, or persistently TST-negative. We compared TST converters to TST-positive and to TST-negative contacts separately, using generalized estimating equations. RESULTS: We enrolled 160 index patients and 838 contacts; 523 (62.4%) were TST+, 62 (7.4%) TST converters, and 253 (30.2%) TST-. TST converters were frequently IGRA- at 8-12 weeks. In adjusted analyses, characteristics distinguishing TST converters from TST+ contacts (no contact with another TB patient and residence ownership) were different than those differentiating them from TST- contacts (stronger cough in index patient and contact BCG scar). CONCLUSIONS: The individual risk and timing of M. tuberculosis infection within households is variable and dependent on index patient, contact and environmental factors within the household, and the surrounding community. Our findings suggest a threshold effect in the risk of infection in humans.

Partitioning the risk of tuberculosis transmission in household contact studies.

Household contact studies of tuberculosis (TB) are a common way to study disease transmission dynamics. However these studies lack a mechanism for accounting for community transmission, which is known to be significant, particularly in high burden settings. We illustrate a statistical approach for estimating both the correlates with transmission of TB in a household setting and the probability of community transmission using a modified Bayesian mixed-effects model. This is applied to two household contact studies in Vitória, Brazil from 2008-2013 and Kampala, Uganda from 1995-2004 that enrolled households with an individual that was recently diagnosed with pulmonary TB. We estimate the probability of community transmission to be higher in Uganda (ranging from 0.21 to 0.69, depending on HHC age and HIV status of the index case) than in Brazil (ranging from 0.13 for young children to 0.50 in adults). These estimates are consistent with a higher overall burden of disease in Uganda compared to Brazil. Our method also estimates an increasing risk of community-acquired TB with age of the household contact, consistent with existing literature. This approach is a useful way to integrate the role of the community in understanding TB disease transmission dynamics in household contact studies.

Familial Risk for Exceptional Longevity.

One of the most glaring deficiencies in the current assessment of mortality risk is the lack of information concerning the impact of familial longevity. In this work, we update estimates of sibling relative risk of living to extreme ages using data from more than 1,700 sibships, and we begin to examine the trend for heritability for different birth-year cohorts. We also build a network model that can be used to compute the increased chance for exceptional longevity of a subject, conditional on his family history of longevity. The network includes familial longevity from three generations and can be used to understand the effects of paternal and maternal longevity on an individual's chance to live to an extreme age.