Publications

INTRODUCTION

Incomplete understanding of TB transmission dynamics in high HIV prevalence settings remains an obstacle for prevention. Understanding where transmission occurs could provide a platform for case finding and interrupting transmission.

METHODS

From 2012-2015, we sought to recruit all adults starting TB treatment in a Ugandan community. Participants underwent household (HH) contact investigation, and provided names of social contacts, sites of work, healthcare and socializing, and two sputum samples. Mycobacterium tuberculosis culture-positive specimens underwent 24-loci MIRU-VNTR and spoligotyping. We sought to identify epidemiologic links between genotype-matched cases by analyzing social networks and mapping locations where cases reported spending ≥12 hours over the one-month pre-treatment. Sites of spatial overlap (≤100m) between genotype-matched cases were considered potential transmission sites. We analyzed social networks stratified by genotype clustering status, with cases linked by shared locations, and compared network density by location type between clustered vs. non-clustered cases.

RESULTS

Of 173 adults with TB, 131 (76%) were enrolled, 108 provided sputum, and 84/131 (78%) were MTB culture-positive: 52% (66/131) tested HIV-positive. Of 118 adult HH contacts, 105 (89%) were screened and 3 (2.5%) diagnosed with active TB. Overall, 33 TB cases (39%) belonged to 15 distinct MTB genotype-matched clusters. Within each cluster, no cases shared a HH or reported shared non-HH contacts. In 6/15 (40%) clusters, potential epidemiologic links were identified by spatial overlap at specific locations: 5/6 involved health care settings. Genotype-clustered TB social networks had significantly greater network density based on shared clinics (p<0.001) and decreased density based on shared marketplaces (p<0.001), compared to non-clustered networks.

CONCLUSIONS

In this molecular epidemiologic study, links between MTB genotype-matched cases were only identifiable via shared locations, healthcare locations in particular, rather than named contacts. This suggests most transmission is occurring between casual contacts, and emphasizes the need for improved infection control in healthcare settings in rural Africa.

INTRODUCTION

Electronic cigarettes (e-cigarettes) are often promoted to assist with cigarette smoking cessation. In 2016-2017, the relationship between e-cigarette use and having stopped smoking among ever (current and former) smokers was assessed in the European Union and Great Britain by itself.

METHODS

Cross-sectional logistic regression of the association between being a former smoker and e-cigarette use was applied to the 2014 Eurobarometer survey of 28 European Union countries controlling for demographics.

RESULTS

Among all ever smokers, any regular ever use of nicotine e-cigarettes was associated with lower odds of being a former smoker (unadjusted OR=0.34, 95% CI=0.26, 0.43, AOR=0.43, 95% CI=0.32, 0.58) compared with smokers who had never used e-cigarettes. In unadjusted models, daily use (OR=0.42, 95% CI=0.31, 0.56); occasional use (OR=0.25, 95% CI=0.18, 0.35); and experimentation (OR=0.24, 95% CI=0.19, 0.30) of nicotine e-cigarettes were associated with lower odds of being a former smoker compared with having never used nicotine-containing e-cigarettes. Comparable results were found in adjusted models. Results were similar in Great Britain alone. Among current smokers, daily cigarette consumption was 15.6 cigarettes/day (95% CI=14.5, 16.7) among those who also used e-cigarettes versus 14.4 cigarettes/day (95% CI=13.4, 15.4) for those who did not use them (p<0.05).

CONCLUSIONS

These results suggest that e-cigarettes are associated with inhibiting rather than assisting in smoking cessation. On the population level, the net effect of the entry of e-cigarettes into the European Union (and Great Britain) is associated with depressed smoking cessation of conventional cigarettes.

AIMS

Methods to identify patients at risk for incident HF would be welcome as such patients might benefit from earlier interventions.

METHODS AND RESULTS

From a registry of 1251 patients referred for coronary and/or peripheral angiography, we sought to identify independent predictors of incident HF during follow-up and develop a clinical and biomarker strategy to predict this outcome. There were 991 patients free of prevalent HF at baseline. Cox proportional hazard models were developed to predict adjudicated diagnosis of incident HF. Model discrimination and reclassification were evaluated. At follow-up, 177 (18%) developed new-onset HF. Independent predictors of new-onset HF included five clinical variables (age, male sex, heart rate, history of atrial fibrillation/flutter, and history of hypertension) and two biomarkers (amino-terminal pro-B type natriuretic peptide and ST2). The c-statistic for the model without biomarkers was 0.69; including biomarkers increased the c-statistic to 0.76 (P < 0.001). A score was developed from the model. Patients in the highest score quintile had shortest time to incident HF compared with lower quintiles (log-rank P < 0.001). Following 100 bootstrap iterations, internal validation was confirmed with Harrell's c-statistic of 0.77. Use of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and beta-blockers at enrollment was associated with substantial attenuation of predictive value of the risk score.

CONCLUSIONS

Patients undergoing coronary/peripheral angiographic procedures are a population at high risk for incident HF. We describe an accurate clinical and biomarker strategy for predicting incident HF and possibly intervening in such patients (NCT00842868).