Risk factors associated with default from multi- and extensively drug-resistant tuberculosis treatment, uzbekistan: a retrospective cohort analysis.

The Médecins Sans Frontières project of Uzbekistan has provided multidrug-resistant tuberculosis treatment in the Karakalpakstan region since 2003. Rates of default from treatment have been high, despite psychosocial support, increasing particularly since programme scale-up in 2007. We aimed to determine factors associated with default in multi- and extensively drug-resistant tuberculosis patients who started treatment between 2003 and 2008 and thus had finished approximately 2 years of treatment by the end of 2010

Using routinely reported tuberculosis genotyping and surveillance data to predict tuberculosis outbreaks.

We combined routinely reported tuberculosis (TB) patient characteristics with genotyping data and measures of geospatial concentration to predict which small clusters (i.e., consisting of only 3 TB patients) in the United States were most likely to become outbreaks of at least 6 TB cases. Of 146 clusters analyzed, 16 (11.0%) grew into outbreaks. Clusters most likely to become outbreaks were those in which at least 1 of the first 3 patients reported homelessness or excess alcohol or illicit drug use or was incarcerated at the time of TB diagnosis and in which the cluster grew rapidly (i.e., the third case was diagnosed within 5.3 months of the first case). Of 17 clusters with these characteristics and therefore considered high risk, 9 (53%) became outbreaks. This retrospective cohort analysis of clusters in the United States suggests that routinely reported data may identify small clusters that are likely to become outbreaks and which are therefore candidates for intensified contact investigations

Higher resolution of Figure 1.

<p>Subcohort of 24 clusters of 6 or more cases, without grouping clusters with 10 or more cases into one bar.</p

Patient and cluster characteristics of tuberculosis (TB) genotype clusters and associated risk of the cluster becoming an outbreak within 24 months after diagnosis of the 3^rd patient.

*<p>One or more of 1^st three patients had characteristic.</p>1<p>Patient had acid fast bacilli smear-positive sputum specimens and abnormal chest radiograph results with evidence of cavities.</p>2<p>Patient had isoniazid (INH) and rifampicin (RIF) drug resistance reported in initial susceptibility drug test.</p>3<p>Median values for socioeconomic measures were derived from the 2000 U.S. Census for all zip codes. A cluster was considered “exposed” if the zip code with the most cases had a value above the median.</p

Algorithm based on decision-tree analysis for predicting TB outbreaks.

<p>Algorithm based on data available at time the TB cluster contained 3 cases. Decision-tree analysis categorizes clusters in high-, medium-, or low-risk groups. Clusters in the high-risk group are considered of greatest priority for early interventions, such as intensive contact investigations. Although clusters in medium- and low-risk groups may not be considered highest priority when they have 3 cases, they can be re-evaluated should additional cases occur.</p

Distribution of 148 cohort clusters, by number of cases and outcome.

<p>Cohort composed of incident tuberculosis genotype clusters of 3 or more cases identified by SaTScan from 2006 to 2010, meeting inclusion criteria. Clusters with 10 or more cases are grouped into one bar. Outcome of the cluster could be confirmed as an outbreak, confirmed as not an outbreak, or unable to be confirmed (uncertain).</p

Statistical Method to Detect Tuberculosis Outbreaks among Endemic Clusters in a Low-Incidence Setting

We previously reported use of genotype surveillance data to predict outbreaks among incident tuberculosis clusters. We propose a method to detect possible outbreaks among endemic tuberculosis clusters. We detected 15 possible outbreaks, of which 10 had epidemiologic data or whole-genome sequencing results. Eight outbreaks were corroborated

Baseline proportions and association of factors with default by univariate analysis.

<p>BMI = body-mass index. MDR = multidrug-resistant. Cat I = category I treatment. Cat II = category II treatment. XDR = extensively drug-resistant.</p