Dataset Information

Data-driven model for the assessment of Mycobacterium tuberculosis transmission in evolving demographic structures.

ABSTRACT: In the case of tuberculosis (TB), the capabilities of epidemic models to produce quantitatively robust forecasts are limited by multiple hindrances. Among these, understanding the complex relationship between disease epidemiology and populations' age structure has been highlighted as one of the most relevant. TB dynamics depends on age in multiple ways, some of which are traditionally simplified in the literature. That is the case of the heterogeneities in contact intensity among different age strata that are common to all airborne diseases, but still typically neglected in the TB case. Furthermore, while demographic structures of many countries are rapidly aging, demographic dynamics are pervasively ignored when modeling TB spreading. In this work, we present a TB transmission model that incorporates country-specific demographic prospects and empirical contact data around a data-driven description of TB dynamics. Using our model, we find that the inclusion of demographic dynamics is followed by an increase in the burden levels predicted for the next decades in the areas of the world that are most hit by the disease today. Similarly, we show that considering realistic patterns of contacts among individuals in different age strata reshapes the transmission patterns reproduced by the models, a result with potential implications for the design of age-focused epidemiological interventions.

SUBMITTER: Arregui S

PROVIDER: S-EPMC5889657 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundPatients born outside the UK have contributed to a 20% rise in the UK's tuberculosis incidence since 2000, but their effect on domestic transmission is not known. Here we use whole-genome sequencing to investigate the epidemiology of tuberculosis transmission in an unselected population over 6 years.MethodsWe identified all residents with Oxfordshire postcodes with a Mycobacterium tuberculosis culture or a clinical diagnosis of tuberculosis between Jan 1, 2007, and Dec 31, 2012, using local databases and checking against the national Enhanced Tuberculosis Surveillance database. We used Illumina technology to sequence all available M tuberculosis cultures from identified cases. Sequences were clustered by genetic relatedness and compared retrospectively with contact investigations. The first patient diagnosed in each cluster was defined as the index case, with links to subsequent cases assigned first by use of any epidemiological linkage, then by genetic distance, and then by timing of diagnosis.FindingsAlthough we identified 384 patients with a diagnosis of tuberculosis, country of birth was known for 380 and we sequenced isolates from 247 of 269 cases with culture-confirmed disease. 39 cases were genomically linked within 13 clusters, implying 26 local transmission events. Only 11 of 26 possible transmissions had been previously identified through contact tracing. Of seven genomically confirmed household clusters, five contained additional genomic links to epidemiologically unidentified non-household members. 255 (67%) patients were born in a country with high tuberculosis incidence, conferring a local incidence of 109 cases per 100,000 population per year in Oxfordshire, compared with 3·5 cases per 100,000 per year for those born in low-incidence countries. However, patients born in the low-incidence countries, predominantly UK, were more likely to have pulmonary disease (adjusted odds ratio 1·8 [95% CI 1·2-2·9]; p=0·009), social risk factors (4·4 [2·0-9·4]; p<0·0001), and be part of a local transmission cluster (4·8 [1·6-14·8]; p=0·006).InterpretationAlthough inward migration has contributed to the overall tuberculosis incidence, our findings suggest that most patients born in high-incidence countries reactivate latent infection acquired abroad and are not involved in local onward transmission. Systematic screening of new entrants could further improve tuberculosis control, but it is important that health care remains accessible to all individuals, especially high-risk groups, if tuberculosis control is not to be jeopardised.FundingUK Clinical Research Collaboration (Wellcome Trust, Medical Research Council, National Institute for Health Research [NIHR]), and NIHR Oxford Biomedical Research Centre.

Dataset Information

Data-driven model for the assessment of Mycobacterium tuberculosis transmission in evolving demographic structures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets