Abstract
Contact tracing is a key part of tuberculosis prevention and care, aiming to hasten diagnosis and prevent transmission. The proportion of case-contact pairs for which recent transmission occurred and the typical timespans between the index case and their contact accessing care are not known; we aimed to calculate these. We analysed individual-level TB contact tracing data, collected in London from 20/01/2011-31/12/2015, linked to tuberculosis surveillance and MIRU-VNTR 24-locus strain-typing information. Of pairs of index cases and contacts diagnosed with active tuberculosis, 85/314 (27%) had strain typing data available for both. Of these pairs, 79% (67/85) shared indistinguishable isolates, implying probable recent transmission. Of pairs in which both contact and the index case had a social risk factor, 11/11 (100%) shared indistinguishable isolates, compared to 55/75 (75%) of pairs in which neither had a social risk factor (P = 0.06). The median time interval between the index case and their contact accessing care was 42 days (IQR: 16, 96). As over 20% of pairs did probably not involve recent transmission between index case and contact, the effectiveness of contact tracing is not necessarily limited to those circumstances where the index case has transmitted disease to their close contacts.
| Original language | English |
|---|---|
| Article number | 6676 |
| Journal | Scientific Reports |
| Volume | 8 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Dec 2018 |
Bibliographical note
Funding Information:The authors would like to thank Jacqui Carless, Lamya Kanfoudi and others at PHE field Epidemiology London for maintaining the LTBR; all nurses and other healthcare workers involved with collecting the data and presenting it at cohort review; the TB modelling group at LSHTM for advice on presentation and approach; and Lindsay Serene for comments on the manuscript. This work was supported by a joint Public Health England-London School of Hygiene and Tropical Medicine studentship in Infectious Disease Modelling to SMC; the UK Medical Research Council (MRC) and the UK Department for International Development (DFID), under the MRC/DFID Concordat agreement that is also part of the EDCTP2 programme supported by the European Union [MR/J005088/1] to RGW; the Bill and Melinda Gates Foundation [TB Modelling and Analysis Consortium: OPP1084276 to RGW, and SA Modelling for Policy: OPP1110334 to RGW and TS], and UNITAID [4214-LSHTM-Sept15; PO #8477-0-600] to RGW. CA, EV, HM, NM and LT are employed by PHE, a government agency, and received no other source of funding.
Funding Information:
This work was supported by a joint Public Health England- London School of Hygiene and Tropical Medicine studentship in Infectious Disease Modelling to SMC; the UK Medical Research Council (MRC) and the UK Department for International Development (DFID), under the MRC/DFID Concordat agreement that is also part of the EDCTP2 programme supported by the European Union [MR/J005088/1] to RGW; the Bill and Melinda Gates Foundation [TB Modelling and Analysis Consortium: OPP1084276 to RGW, and SA Modelling for Policy: OPP1110334 to RGW and TS], and UNITAID [4214-LSHTM-Sept15; PO #8477-0-600] to RGW. CA, EV, HM, NM and LT are employed by PHE, a government agency, and received no other source of funding
Publisher Copyright:
© 2018 The Author(s).
