Assessing spatiotemporal patterns of multidrug-resistant and drug-sensitive tuberculosis in a South American setting

Nano vs Resistant Tuberculosis: Taking the Lung Route

Tuberculosis (TB) is among the top 10 infectious diseases worldwide. It is categorized among the leading killer diseases that are the reason for the death of millions of people globally. Although a standardized treatment regimen is available, non-adherence to treatment has increased multi-drug resistance (MDR) and extensive drug-resistant (XDR) TB development. Another challenge is targeting the death of TB reservoirs in the alveoli via conventional treatment. TB Drug resistance may emerge as a futuristic restraint of TB with the scarcity of effective Anti-tubercular drugs. The paradigm change towards nano-targeted drug delivery systems is mostly due to the absence of effective therapy and increased TB infection recurrent episodes with MDR. The emerging field of nanotechnology gave an admirable opportunity to combat MDR and XDR via accurate diagnosis with effective treatment. The new strategies targeting the lung via the pulmonary route may overcome the new incidence of MDR and enhance patient compliance. Therefore, this review highlights the importance and recent research on pulmonary drug delivery with nanotechnology along with prevalence, the need for the development of nanotechnology, beneficial aspects of nanomedicine, safety concerns of nanocarriers, and clinical studies.

Missed phenotypic drug resistance in pediatric tuberculosis: A cause of concern in a resource-limited setting

BACKGROUND

Multi-drug resistance (MDR) in pediatric tuberculosis (TB) is a growing global threat. Unavailability of conventional or molecular drug susceptibility test (DST) in resource-limited settings often impede the determination of the extent of first line anti-tubercular drugs deployed in national programs.

MATERIALS AND METHOD

Pulmonary and extra pulmonary specimens were collected from clinically suspected pediatric TB cases, who were microbiologically confirmed. Resistance to first-line anti-TB was detected by 1% proportion method. KatG315 and inhA-15 genes were amplified by PCR and detection of mutations were done by sequencing. Genotypic resistance for rifampicin was detected by Xpert MTB/RIF assay (Cepheid Inc., Sunnyvale, California).

RESULTS

Fifty-one cases of pediatric tuberculosis were confirmed microbiologically. Resistance to isoniazid, streptomycin, rifampicin and ethambutol were 5 (14%), 4 (11%), 2 (5.5%) and 2 (5.5%) respectively by 1% proportion method. Genotypic Rifampicin and isoniazid resistance was found in 2 (5.5%) and 7 (14%) samples respectively.

CONCLUSION

Existing genotypic methods, detect targeted mutations conferring rifampicin resistance, however isoniazid (INH) resistance often go undetected. Since the resistance to pivotal anti-TB drugs are often encoded by multiple genes which may not be targeted by widely available molecular tests, discrepancies in molecular and culture-based DST reports should be interpreted with caution.

Spatial heterogeneity of extensively drug resistant-tuberculosis in Western Cape Province, South Africa

Tuberculosis (TB) remains a leading infectious disease killer globally. Treatment outcomes are especially poor among people with extensively drug-resistant (XDR) TB, until recently defined as rifampicin-resistant (RR) TB with resistance to an aminoglycoside (amikacin) and a fluoroquinolone (ofloxacin). We used laboratory TB test results from Western Cape province, South Africa between 2012 and 2015 to identify XDR-TB and pre-XDR-TB (RR-TB with resistance to one second-line drug) spatial hotspots. We mapped the percentage and count of individuals with RR-TB that had XDR-TB and pre-XDR-TB across the province and in Cape Town, as well as amikacin-resistant and ofloxacin-resistant TB. We found the percentage of pre-XDR-TB and the count of XDR-TB/pre-XDR-TB highly heterogeneous with geographic hotspots within RR-TB high burden areas, and found hotspots in both percentage and count of amikacin-resistant and ofloxacin-resistant TB. The spatial distribution of percentage ofloxacin-resistant TB hotspots was similar to XDR-TB hotspots, suggesting that fluoroquinolone-resistace is often the first step to additional resistance. Our work shows that interventions used to reduce XDR-TB incidence may need to be targeted within spatial locations of RR-TB, and further research is required to understand underlying drivers of XDR-TB transmission in these locations.

Strategies to Combat Multi-Drug Resistance in Tuberculosis

"Drug resistance is an unavoidable consequence of the use of drugs; however, the emergence of multi-drug resistance can be managed by accurate diagnosis and tailor-made regimens."Antimicrobial resistance (AMR), is one of the most paramount health perils that has emerged in the 21st century. The global increase in drug-resistant strains of various bacterial pathogens prompted the World Health Organization (WHO) to develop a priority list of AMR pathogens. Mycobacterium tuberculosis (Mtb), an acid-fast bacillus that causes tuberculosis (TB), merits being one of the highest priority pathogens on this list since drug-resistant TB (DR-TB) accounts for ∼29% of deaths attributable to AMR. In recent years, funded collaborative efforts of researchers from academia, not-for-profit virtual R&D organizations and industry have resulted in the continuous growth of the TB drug discovery and development pipeline. This has so far led to the accelerated regulatory approval of bedaquiline and delamanid for the treatment of DR-TB. However, despite the availability of drug regimes, the current cure rate for multi-drug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) treatment regimens is 50% and 30%, respectively. It is to be noted that these regimens are administered over a long duration and have a serious side effect profile. Coupled with poor patient adherence, this has led to further acquisition of drug resistance and treatment failure. There is therefore an urgent need to develop new TB drugs with novel mechanism of actions (MoAs) and associated regimens.This Account recapitulates drug resistance in TB, existing challenges in addressing DR-TB, new drugs and regimens in development, and potential ways to treat DR-TB. We highlight our research aimed at identifying novel small molecule leads and associated targets against TB toward contributing to the global TB drug discovery and development pipeline. Our work mainly involves screening of various small molecule chemical libraries in phenotypic whole-cell based assays to identify hits for medicinal chemistry optimization, with attendant deconvolution of the MoA. We discuss the identification of small molecule chemotypes active against Mtb and subsequent structure-activity relationships (SAR) and MoA deconvolution studies. This is followed by a discussion on a chemical series identified by whole-cell cross-screening against Mtb, for which MoA deconvolution studies revealed a pathway that explained the lack of in vivo efficacy in a mouse model of TB and reiterated the importance of selecting an appropriate growth medium during phenotypic screening. We also discuss our efforts on drug repositioning toward addressing DR-TB. In the concluding section, we preview some promising future directions and the challenges inherent in advancing the drug pipeline to address DR-TB.

Children as sentinels of tuberculosis transmission: disease mapping of programmatic data

BACKGROUND

Identifying hotspots of tuberculosis transmission can inform spatially targeted active case-finding interventions. While national tuberculosis programs maintain notification registers which represent a potential source of data to investigate transmission patterns, high local tuberculosis incidence may not provide a reliable signal for transmission because the population distribution of covariates affecting susceptibility and disease progression may confound the relationship between tuberculosis incidence and transmission. Child cases of tuberculosis and other endemic infectious disease have been observed to provide a signal of their transmission intensity. We assessed whether local overrepresentation of child cases in tuberculosis notification data corresponds to areas where recent transmission events are concentrated.

METHODS

We visualized spatial clustering of children < 5 years old notified to Peru's National Tuberculosis Program from two districts of Lima, Peru, from 2005 to 2007 using a log-Gaussian Cox process to model the intensity of the point-referenced child cases. To identify where clustering of child cases was more extreme than expected by chance alone, we mapped all cases from the notification data onto a grid and used a hierarchical Bayesian spatial model to identify grid cells where the proportion of cases among children < 5 years old is greater than expected. Modeling the proportion of child cases allowed us to use the spatial distribution of adult cases to control for unobserved factors that may explain the spatial variability in the distribution of child cases. We compare where young children are overrepresented in case notification data to areas identified as transmission hotspots using molecular epidemiological methods during a prospective study of tuberculosis transmission conducted from 2009 to 2012 in the same setting.

RESULTS

Areas in which childhood tuberculosis cases are overrepresented align with areas of spatial concentration of transmission revealed by molecular epidemiologic methods.

CONCLUSIONS

Age-disaggregated notification data can be used to identify hotspots of tuberculosis transmission and suggest local force of infection, providing an easily accessible source of data to target active case-finding intervention.

Drug-resistance in Mycobacterium tuberculosis: where we stand

Tuberculosis (TB), an infectious disease caused by the bacterium Mycobacterium tuberculosis (Mtb), has burdened vulnerable populations in modern day societies for decades. Recently, this global health threat has been heightened by the emergence and propagation of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mtb that are resistant to current treatment regimens. The End-TB strategy, launched by the World Health Organization (WHO), aims to reduce TB-related deaths by 90%. This program encourages universal access to drug susceptibility testing, which is not widely available owing to the lack of laboratory capacity or resources in certain under-resourced areas. Clinical assays are further complicated by the slow growth of Mtb, resulting in the long turn-around time of tests which severely limits their application in guiding a patient's treatment regimen. This review provides a comprehensive overview of current TB treatments, mechanisms of resistance to anti-tubercular drugs and their diagnosis and the current pipeline of drugs targeting drug-resistant TB (DR-TB) with particular attention paid to ways in which drug-resistance is combated.

Spatial clustering of drug-resistant tuberculosis in Hlabisa subdistrict, KwaZulu-Natal, 2011-2015

SETTING:

Incidence rates of tuberculosis (TB) in South Africa are among the highest in the world, and drug resistance is a major concern. Understanding geographic variations in disease may guide targeted interventions.

OBJECTIVE:

To characterise the spatial distribution of drug-resistant TB (DR-TB) in a rural area of KwaZulu-Natal, South Africa, and to test for clustering.

DESIGN:

This was a cross-sectional analysis of DR-TB patients managed at a rural district hospital from 2011 to 2015. We mapped all patients in hospital data to local areas, and then linked to a population-based demographic surveillance system to map the patients to individual homesteads. We used kernel density estimation to visualise the distribution of disease and tested for clustering using spatial scan statistics.

RESULTS:

There were 489 patients with DR-TB in the subdistrict; 111 lived in the smaller demographic surveillance area. Spatial clustering analysis identified a high-risk cluster (relative risk of DR-TB inside vs. outside cluster 3.0, P <0.001) in the south-east, a region characterised by high population density and a high prevalence of human immunodeficiency virus infection.

CONCLUSION:

We have demonstrated evidence of a geographic high-risk cluster of DR-TB. This suggests that targeting interventions to spatial areas of highest risk, where transmission may be ongoing, could be effective.

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

BACKGROUND

Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden.

METHODS

We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO ( CRD42016036655 ).

RESULTS

We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined.

CONCLUSIONS

A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.

Spatial patterns of multidrug resistant tuberculosis and relationships to socio-economic, demographic and household factors in northwest Ethiopia

Background

Understanding the geographical distribution of multidrug-resistant tuberculosis (MDR-TB) in high TB burden countries such as Ethiopia is crucial for effective control of TB epidemics in these countries, and thus globally. We present the first spatial analysis of multidrug resistant tuberculosis, and its relationship to socio-economic, demographic and household factors in northwest Ethiopia.

Methods

An ecological study was conducted using data on patients diagnosed with MDR-TB at the University of Gondar Hospital MDR-TB treatment centre, for the period 2010 to 2015. District level population data were extracted from the Ethiopia National and Regional Census Report. Spatial autocorrelation was explored using Moran’s I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate Poisson regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian Markov chain Monte Carlo (MCMC) simulation approach with Gibbs sampling, in WinBUGS.

Results

A total of 264 MDR-TB patients were included in the analysis. The overall crude incidence rate of MDR-TB for the six-year period was 3.0 cases per 100,000 population. The highest incidence rate was observed in Metema (21 cases per 100,000 population) and Humera (18 cases per 100,000 population) districts; whereas nine districts had zero cases. Spatial clustering of MDR-TB was observed in districts located in the Ethiopia-Sudan and Ethiopia-Eritrea border regions, where large numbers of seasonal migrants live. Spatial clustering of MDR-TB was positively associated with urbanization (RR: 1.02; 95%CI: 1.01, 1.04) and the percentage of men (RR: 1.58; 95% CI: 1.26, 1.99) in the districts; after accounting for these factors there was no residual spatial clustering.

Conclusion

Spatial clustering of MDR-TB, fully explained by demographic factors (urbanization and percent male), was detected in the border regions of northwest Ethiopia, in locations where seasonal migrants live and work. Cross-border initiatives including options for mobile TB treatment and follow up are important for the effective control of MDR-TB in the region.

Estimating infectious disease transmission distances using the overall distribution of cases

The average spatial distance between transmission-linked cases is a fundamental property of infectious disease dispersal. However, the distance between a case and their infector is rarely measurable. Contact-tracing investigations are resource intensive or even impossible, particularly when only a subset of cases are detected. Here, we developed an approach that uses onset dates, the generation time distribution and location information to estimate the mean transmission distance. We tested our method using outbreak simulations. We then applied it to the 2001 foot-and-mouth outbreak in Cumbria, UK, and compared our results to contact-tracing activities. In simulations with a true mean distance of 106m, the average mean distance estimated was 109m when cases were fully observed (95% range of 71-142). Estimates remained consistent with the true mean distance when only five percent of cases were observed, (average estimate of 128m, 95% range 87-165). Estimates were robust to spatial heterogeneity in the underlying population. We estimated that both the mean and the standard deviation of the transmission distance during the 2001 foot-and-mouth outbreak was 8.9km (95% CI: 8.4km-9.7km). Contact-tracing activities found similar values of 6.3km (5.2km-7.4km) and 11.2km (9.5km-12.8km), respectively. We were also able to capture the drop in mean transmission distance over the course of the outbreak. Our approach is applicable across diseases, robust to under-reporting and can inform interventions and surveillance.

Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: an example from Peru

PURPOSE

To map the geographical distribution and spatial clustering of depressive symptoms cases in an area of Lima, Peru.

METHODS

Presence of depressive symptoms suggesting a major depressive episode was assessed using a short version of the Center for Epidemiologic Studies Depression Scale. Data were obtained from a census conducted in 2010. One participant per selected household (aged 18 years and above, living more than 6 months in the area) was included. Residence latitude, longitude, and elevation were captured using a GPS device. The prevalence of depressive symptoms was estimated, and relative risks (RRs) were calculated to identify areas of significantly higher and lower geographical concentrations of depressive symptoms.

RESULTS

Data from 7946 participants, 28.3% male, mean age 39.4 (SD, 13.9) years, were analyzed. The prevalence of depressive symptoms was 17.0% (95% confidence interval = 16.2%-17.8%). Three clusters with high prevalence of depressive symptoms (primary cluster: RR = 1.82; P = .003 and secondary: RR = 2.83; P = .004 and RR = 5.92; P = .01), and two clusters with significantly low prevalence (primary: RR = 0.23; P = .016 and secondary: RR = 0; P = .035), were identified. Further adjustment by potential confounders confirmed the high prevalence clusters but also identified newer ones.

CONCLUSIONS

Screening strategies for depression, in combination with mapping techniques, may be useful tools to target interventions in resource-limited areas.

Burden of transmitted multidrug resistance in epidemics of tuberculosis: a transmission modelling analysis

Background

Multidrug-resistant tuberculosis (MDR-TB) can be acquired through de novo mutation during TB treatment or through transmission from other individuals with active MDR-TB. Understanding the balance between these two mechanisms is essential when allocating resources for MDR-TB.

Methods

We constructed a dynamic transmission model of an MDR-TB epidemic, allowing for both treatment-related acquisition and person-to-person transmission of resistance. We used national TB notification data to inform Bayesian estimates of the fraction of each country’s 2013 MDR-TB incidence that resulted from MDR transmission rather than treatment-related MDR acquisition.

Findings

Global estimates of 3·5% MDR-TB prevalence among new TB notifications and 20·5% among retreatment notifications translate into an estimate that resistance transmission rather than acquisition accounts for a median 96% (95% UR: 68–100%) of all incident MDR-TB, and 61% (16–95%) of incident MDR-TB in previously-treated individuals. The estimated percentage of MDR-TB resulting from transmission varied substantially with different countries’ notification data; for example, we estimated this percentage at 48% (30–75%) of MDR-TB in Bangladesh, versus 99% (91–100%) in Uzbekistan. Estimates were most sensitive to estimates of the transmissibility of MDR strains, the probability of acquiring MDR during tuberculosis treatment, and the responsiveness of MDR TB to first-line treatment.

Interpretation

Notifications of MDR prevalence from most high-burden settings are most consistent with the vast majority of incident MDR-TB resulting from transmission rather than new treatment-related acquisition of resistance. Merely improving the treatment of drug-susceptible TB is unlikely to greatly reduce future MDR-TB incidence. Improved diagnosis and treatment of MDR-TB – including new tests and drug regimens – should be highly prioritized.

Identifying Hotspots of Multidrug-Resistant Tuberculosis Transmission Using Spatial and Molecular Genetic Data

BACKGROUND

We aimed to identify and determine the etiology of "hotspots" of concentrated multidrug-resistant tuberculosis (MDR-tuberculosis) risk in Lima, Peru.

METHODS

From 2009 to 2012, we conducted a prospective cohort study among households of tuberculosis cases from 106 health center (HC) areas in Lima, Peru. All notified tuberculosis cases and their household contacts were followed for 1 year. Symptomatic individuals were screened by microscopy and culture; positive cultures were tested for drug susceptibility (DST) and genotyped by 24-loci mycobacterial interspersed repetitive units-variable-number tandem repeats (MIRU-VNTR).

RESULTS

3286 individuals with culture-confirmed disease, DST, and 24-loci MIRU-VNTR were included in our analysis. Our analysis reveals: (1) heterogeneity in annual per-capita incidence of tuberculosis and MDR-tuberculosis by HC, with a rate of MDR-tuberculosis 89 times greater (95% confidence interval [CI], 54,185) in the most-affected versus the least-affected HC; (2) high risk for MDR-tuberculosis in a region spanning several HCs (odds ratio = 3.19, 95% CI, 2.33, 4.36); and (3) spatial aggregation of MDR-tuberculosis genotypes, suggesting localized transmission.

CONCLUSIONS

These findings reveal that localized transmission is an important driver of the epidemic of MDR-tuberculosis in Lima. Efforts to interrupt transmission may be most effective if targeted to this area of the city.

Multidrug-Resistant Tuberculosis and Extensively Drug-Resistant Tuberculosis

The continuing spread of drug-resistant tuberculosis (TB) is one of the most urgent and difficult challenges facing global TB control. Patients who are infected with strains resistant to isoniazid and rifampicin, called multidrug-resistant (MDR) TB, are practically incurable by standard first-line treatment. In 2012, there were approximately 450,000 new cases and 170,000 deaths because of MDR-TB. Extensively drug-resistant (XDR) TB refers to MDR-TB strains that are resistant to fluoroquinolones and second-line injectable drugs. The main causes of the spread of resistant TB are weak medical systems, amplification of resistance patterns through incorrect treatment, and transmission in communities and facilities. Although patients harboring MDR and XDR strains present a formidable challenge for treatment, cure is often possible with early identification of resistance and use of a properly designed regimen. Community-based programs can improve treatment outcomes by allowing patients to be treated in their homes and addressing socioeconomic barriers to adherence.

Spatial scales of interactions among bacteria and between bacteria and the leaf surface

Microbial life on plant leaves is characterized by a multitude of interactions between leaf colonizers and their environment. While the existence of many of these interactions has been confirmed, their spatial scale or reach often remained unknown. In this study, we applied spatial point pattern analysis to 244 distribution patterns of Pantoea agglomerans and Pseudomonas syringae on bean leaves. The results showed that bacterial colonizers of leaves interact with their environment at different spatial scales. Interactions among bacteria were often confined to small spatial scales up to 5-20 μm, compared to interactions between bacteria and leaf surface structures such as trichomes which could be observed in excess of 100 μm. Spatial point-pattern analyses prove a comprehensive tool to determine the different spatial scales of bacterial interactions on plant leaves and will help microbiologists to better understand the interplay between these interactions.

Geographical heterogeneity of multidrug-resistant tuberculosis in Georgia, January 2009 to June 2011

In 2011, Georgia, in the Caucasus, reported that 11% of new and 32% of previously treated tuberculosis (TB) cases nationally had multidrug-resistant TB (MDR-TB). To help understand the mechanisms driving these high risks of drug-resistance and plan for targeted interventions, we identified geographical variability in the MDR-TB burden in Georgia and patient-level MDR-TB risk factors. We used routinely collected surveillance data on notified TB cases to estimate the MDR-TB incidence/100,000 people and the percentage of TB cases with MDR-TB for each of 65 districts and regression modelling to identify patient-level MDR-TB risk factors. 1,795 MDR-TB cases were reported (January 2009–June 2011); the nationwide notified MDR-TB incidence was 16.2/100,000 but far higher (837/100,000) in the penitentiary system. We found substantial geographical heterogeneity between districts in the average annual MDR-TB incidence/100,000 (range: 0.0–5.0 among new and 0.0–18.9 among previously treated TB cases) and the percentage of TB cases with MDR-TB (range: 0.0%–33.3% among new and 0.0%–75.0% among previously treated TB cases). Among treatment-naïve individuals, those in cities had greater MDR-TB risk than those in rural areas (increased odds: 43%; 95% confidence interval: 20%–72%). These results suggest that interventions for interrupting MDR-TB transmission are urgently needed in prisons and urban areas.

Use of spatial information to predict multidrug resistance in tuberculosis patients, Peru

Knowing whether a patient has multidrug-resistant tuberculosis is crucial for prescribing the best treatment. The challenge is choosing the most effective drug with the fewest side effects while saving the “big guns” for the most resistant infections. The best way to find out whether a patient has this type of infection is to conduct drug-susceptibility testing. Unfortunately, this testing requires laboratory capabilities that are in short supply, so often only patients at high risk are tested. But who is at high risk? A recent study found an association between patients’ locations (health center at which they were seen) and likelihood of multidrug-resistant infection. Added to other known risk factors (young age, previous TB treatment, or contact with someone with similar infection), this information can further pinpoint who should be tested, which will ultimately lead to faster diagnoses, better treatments and less spread of multidrug-resistant TB.

To determine whether spatiotemporal information could help predict multidrug resistance at the time of tuberculosis diagnosis, we investigated tuberculosis patients who underwent drug susceptibility testing in Lima, Peru, during 2005–2007. We found that crude representation of spatial location at the level of the health center improved prediction of multidrug resistance.

Linking surveillance with action against drug-resistant tuberculosis

The speed at which most countries with high burdens of multidrug-resistant tuberculosis (MDRTB) have scaled up their capacity to diagnose and treat individuals with these forms of TB has failed to keep pace with the problem. Limited availability of drug susceptibility testing, high costs and inefficiencies in the supply of second-line drugs, and inadequate capacity for the management of patients with MDRTB have contributed to the wide gap between the estimated need for and the delivery of MDRTB treatment. The most recent global estimates indicate that only about 1 in 20 individuals with incident MDRTB will be properly diagnosed; fewer still receive quality-assured treatment. As policy makers confront the threat of growing levels of drug-resistant TB, there is a clear role for improved surveillance methods that can facilitate more effective public health responses. In countries that cannot yet test all incident cases for drug resistance, analysis of programmatic data and use of periodic, efficient surveys can provide information to help prioritize the use of limited resources to geographic areas or population subgroups of greatest concern. We describe methods for the analysis of routinely collected data and alternative surveys that can help tighten the link between surveillance activities and interventions.