Geographic predictors of primary multidrug-resistant tuberculosis cases in an endemic area of Lima, Peru

Prevalence of multidrug-resistant tuberculosis in prisons: Systematic review and meta-analysis

BACKGROUND

In the context of prisons, multidrug-resistant tuberculosis (MDR-TB) is a major problem. In this article, we estimate the prevalence of MDR-TB among the population deprived of freedom from countries in South America, Europe, Asia and Africa.

METHODS

The articles were retrieved through systematic search at four databases (EMBASE, CINAHL, LILACS and MEDILINE). The meta-analysis was developed by the random effect model, using the Mantel-Haenszel method, with presentation of the aggregated results through the forest plot. The degree of heterogeneity between the studies was verified using Cochran's Q test and I².

RESULTS

Of the 102 articles analyzed, 21 were included in this systematic review. The analysis showed heterogeneity indicated by the Q test (P ​< ​0.001) and I² statistics (I² ​= ​50.52%). The funnel graph and Egger test (P ​< ​0.830) showed symmetry between investigations. The grouped prevalence of MDR-TB was 0.48% (95% CI: 0.02 to 1.32), advancing to 1.15 (95% CI: 0.15 to 2.73) when culture and sensitivity test were considered by the authors. No specific characteristics were significantly associated with differences in prevalence rates in the population deprived of freedom.

CONCLUSION

The study reaffirms the magnitude of MDR-TB in the population deprived of freedom in the world context. Political and technical-scientific efforts should be mobilized to mitigate TB and MDR-TB in prisons and for successful national and international disease control programs.

Spatio-temporal co-occurrence of hotspots of tuberculosis, poverty and air pollution in Lima, Peru

Growing evidence suggests pollution and other environmental factors have a role in the development of tuberculosis (TB), however, such studies have never been conducted in Peru. Considering the association between air pollution and specific geographic areas, our objective was to determine the spatial distribution and clustering of TB incident cases in Lima and their co-occurrence with clusters of fine particulate matter (PM2.5) and poverty. We found co-occurrences of clusters of elevated concentrations of air pollutants such as PM2.5, high poverty indexes, and high TB incidence in Lima. These findings suggest an interplay of socio-economic and environmental in driving TB incidence.

A Case-Control Study to Identify Community Venues Associated with Genetically-clustered, Multidrug-resistant Tuberculosis Disease in Lima, Peru

BACKGROUND

The majority of tuberculosis transmission occurs in community settings. Our primary aim in this study was to assess the association between exposure to community venues and multidrug-resistant (MDR) tuberculosis. Our secondary aim was to describe the social networks of MDR tuberculosis cases and controls.

METHODS

We recruited laboratory-confirmed MDR tuberculosis cases and community controls that were matched on age and sex. Whole-genome sequencing was used to identify genetically clustered cases. Venue tracing interviews (nonblinded) were conducted to enumerate community venues frequented by participants. Logistic regression was used to assess the association between MDR tuberculosis and person-time spent in community venues. A location-based social network was constructed, with respondents connected if they reported frequenting the same venue, and an exponential random graph model (ERGM) was fitted to model the network.

RESULTS

We enrolled 59 cases and 65 controls. Participants reported 729 unique venues. The mean number of venues reported was similar in both groups (P = .92). Person-time in healthcare venues (adjusted odds ratio [aOR] = 1.67, P = .01), schools (aOR = 1.53, P < .01), and transportation venues (aOR = 1.25, P = .03) was associated with MDR tuberculosis. Healthcare venues, markets, cinemas, and transportation venues were commonly shared among clustered cases. The ERGM indicated significant community segregation between cases and controls. Case networks were more densely connected.

CONCLUSIONS

Exposure to healthcare venues, schools, and transportation venues was associated with MDR tuberculosis. Intervention across the segregated network of case venues may be necessary to effectively stem transmission.

Optimizing community screening for tuberculosis: Spatial analysis of localized case finding from door-to-door screening for TB in an urban district of Ho Chi Minh City, Viet Nam

BACKGROUND

Tuberculosis (TB) is the deadliest infectious disease globally. Current case finding approaches may miss many people with TB or detect them too late.

DATA AND METHODS

This study was a retrospective, spatial analysis of routine TB surveillance and cadastral data in Go Vap district, Ho Chi Minh City. We geocoded TB notifications from 2011 to 2015 and calculated theoretical yields of simulated door-to-door screening in three concentric catchment areas (50m, 100m, 200m) and three notification window scenarios (one, two and four quarters) for each index case. We calculated average yields, compared them to published reference values and fit a GEE (Generalized Estimating Equation) linear regression model onto the data.

RESULTS

The sample included 3,046 TB patients. Adjusted theoretical yields in 50m, 100m and 200m catchment areas were 0.32% (95%CI: 0.27,0.37), 0.21% (95%CI: 0.14,0.29) and 0.17% (95%CI: 0.09,0.25), respectively, in the baseline notification window scenario. Theoretical yields in the 50m-catchment area for all notification window scenarios were significantly higher than a reference yield from literature. Yield was positively associated with treatment failure index cases (beta = 0.12, p = 0.001) and short-term inter-province migrants (beta = 0.06, p = 0.022), while greater distance to the DTU (beta = -0.02, p<0.001) was associated with lower yield.

CONCLUSIONS

This study is an example of inter-departmental collaboration and application of repurposed cadastral data to progress towards the end TB objectives. The results from Go Vap showed that the use of spatial analysis may be able to identify areas where targeted active case finding in Vietnam can help improve TB case detection.

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.

Investigating spillover of multidrug-resistant tuberculosis from a prison: a spatial and molecular epidemiological analysis

BACKGROUND

Congregate settings may serve as institutional amplifiers of tuberculosis (TB) and multidrug-resistant tuberculosis (MDR-TB). We analyze spatial, epidemiological, and pathogen genetic data prospectively collected from neighborhoods surrounding a prison in Lima, Peru, where inmates experience a high risk of MDR-TB, to investigate the risk of spillover into the surrounding community.

METHODS

Using hierarchical Bayesian statistical modeling, we address three questions regarding the MDR-TB risk: (i) Does the excess risk observed among prisoners also extend outside the prison? (ii) If so, what is the magnitude, shape, and spatial range of this spillover effect? (iii) Is there evidence of additional transmission across the region?

RESULTS

The region of spillover risk extends for 5.47 km outside of the prison (95% credible interval: 1.38, 9.63 km). Within this spillover region, we find that nine of the 467 non-inmate patients (35 with MDR-TB) have MDR-TB strains that are genetic matches to strains collected from current inmates with MDR-TB, compared to seven out of 1080 patients (89 with MDR-TB) outside the spillover region (p values: 0.022 and 0.008). We also identify eight spatially aggregated genetic clusters of MDR-TB, four within the spillover region, consistent with local transmission among individuals living close to the prison.

CONCLUSIONS

We demonstrate a clear prison spillover effect in this population, which suggests that interventions in the prison may have benefits that extend to the surrounding community.

Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland

BACKGROUND

In Swaziland, as in many high HIV/TB burden settings, there is not information available regarding the household location of TB cases for identifying areas of increased TB incidence, limiting the development of targeted interventions. Data from "Butimba", a TB REACH active case finding project, was re-analyzed to provide insight into the location of TB cases surrounding Mbabane, Swaziland.

OBJECTIVE

The project aimed to identify geographical areas with high TB burdens to inform active case finding efforts.

METHODS

Butimba implemented household contact tracing; obtaining landmark based, informal directions, to index case homes, defined here as relative locations. The relative locations were matched to census enumeration areas (known location reference areas) using the Microsoft Excel Fuzzy Lookup function. Of 403 relative locations, an enumeration area reference was detected in 388 (96%). TB cases in each census enumeration area and the active case finders in each Tinkhundla, a local governmental region, were mapped using the geographic information system, QGIS 2.16.

RESULTS

Urban Tinkhundla predictably accounted for most cases; however, after adjusting for population, the highest density of cases was found in rural Tinkhundla. There was no correlation between the number of active case finders currently assigned to the 7 Tinkhundla surrounding Mbabane and the total number of TB cases (Spearman rho = -0.57, p = 0.17) or the population adjusted TB cases (Spearman rho = 0.14, p = 0.75) per Tinkhundla.

DISCUSSION

Reducing TB incidence in high-burden settings demands novel analytic approaches to study TB case locations. We demonstrated the feasibility of linking relative locations to more precise geographical areas, enabling data-driven guidance for National Tuberculosis Programs' resource allocation. In collaboration with the Swazi National Tuberculosis Control Program, this analysis highlighted opportunities to better align the active case finding national strategy with the TB disease burden.

A user-friendly mathematical modelling web interface to assist local decision making in the fight against drug-resistant tuberculosis

Multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) represent an important challenge for global tuberculosis (TB) control. The high rates of MDR/RR-TB observed among re-treatment cases can arise from diverse pathways: de novo amplification during initial treatment, inappropriate treatment of undiagnosed MDR/RR-TB, relapse despite appropriate treatment, or reinfection with MDR/RR-TB. Mathematical modelling allows quantification of the contribution made by these pathways in different settings. This information provides valuable insights for TB policy-makers, allowing better contextualised solutions. However, mathematical modelling outputs need to consider local data and be easily accessible to decision makers in order to improve their usefulness. We present a user-friendly web-based modelling interface, which can be used by people without technical knowledge. Users can input their own parameter values and produce estimates for their specific setting. This innovative tool provides easy access to mathematical modelling outputs that are highly relevant to national TB control programs. In future, the same approach could be applied to a variety of modelling applications, enhancing local decision making.

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.

Use of Activity Space in a Tuberculosis Outbreak: Bringing Homeless Persons Into Spatial Analyses

BACKGROUND

Tuberculosis (TB) causes significant morbidity and mortality in US cities, particularly in poor, transient populations. During a TB outbreak in Fulton County, Atlanta, GA, we aimed to determine whether local maps created from multiple locations of personal activity per case would differ significantly from traditional maps created from single residential address.

METHODS

Data were abstracted for patients with TB disease diagnosed in 2008-2014 and receiving care at the Fulton County Health Department. Clinical and activity location data were abstracted from charts. Kernel density methods, activity space analysis, and overlay with homeless shelter locations were used to characterize case spatial distribution when using single versus multiple addresses.

RESULTS

Data were collected for 198 TB cases, with over 30% homeless US-born cases included. Greater spatial dispersion of cases was found when utilizing multiple versus single addresses per case. Activity spaces of homeless and isoniazid (INH)-resistant cases were more spatially congruent with one another than non-homeless and INH-susceptible cases (P < .0001 and P < .0001, respectively).

CONCLUSIONS

Innovative spatial methods allowed us to more comprehensively capture the geography of TB-infected homeless persons, who made up a large portion of the Fulton County outbreak. We demonstrate how activity space analysis, prominent in exposure science and chronic disease, supports that routine capture of multiple location TB data may facilitate spatially different public health interventions than traditional surveillance maps.

High rates of multidrug-resistant and rifampicin-resistant tuberculosis among re-treatment cases: where do they come from?

BACKGROUND

Globally 3.9% of new and 21% of re-treatment tuberculosis (TB) cases are multidrug-resistant or rifampicin-resistant (MDR/RR), which is often interpreted as evidence that drug resistance results mainly from poor treatment adherence. This study aims to assess the respective contributions of the different causal pathways leading to MDR/RR-TB at re-treatment.

METHODS

We use a simple mathematical model to simulate progression between the different stages of disease and treatment for patients diagnosed with TB. The model is parameterised using region and country-specific TB disease burden data reported by the World Health Organization (WHO). The contributions of four separate causal pathways to MDR/RR-TB among re-treatment cases are estimated: I) initial drug-susceptible TB with resistance amplification during treatment; II) initial MDR/RR-TB inappropriately treated as drug-susceptible TB; III) MDR/RR-TB relapse despite appropriate treatment; and IV) re-infection with MDR/RR-TB.

RESULTS

At the global level, Pathways I, II, III and IV contribute 38% (28-49, 95% Simulation Interval), 44% (36-52, 95% SI), 6% (5-7, 95% SI) and 12% (7-19, 95% SI) respectively to the burden of MDR/RR-TB among re-treatment cases. Pathway II is dominant in the Western Pacific (74%; 67-80 95% SI), Eastern Mediterranean (68%; 60-74 95% SI) and European (53%; 48-59 95% SI) regions, while Pathway I makes the greatest contribution in the American (53%; 40-66 95% SI), African (43%; 28-61 95% SI) and South-East Asian (50%; 40-59 95% SI) regions.

CONCLUSIONS

Globally, failure to diagnose MDR/RR-TB at first presentation is the leading cause of the high proportion of MDR/RR-TB among re-treatment cases. These findings highlight the need for contextualised solutions to limit the impact and spread of MDR/RR-TB.

Genotype heterogeneity of Mycobacterium tuberculosis within geospatial hotspots suggests foci of imported infection in Sydney, Australia

In recent years the State of New South Wales (NSW), Australia, has maintained a low tuberculosis incidence rate with little evidence of local transmission. Nearly 90% of notified tuberculosis cases occurred in people born in tuberculosis-endemic countries. We analyzed geographic, epidemiological and genotypic data of all culture-confirmed tuberculosis cases to identify the bacterial and demographic determinants of tuberculosis hotspot areas in NSW. Standard 24-loci mycobacterium interspersed repetitive unit-variable number tandem repeat (MIRU-24) typing was performed on all isolates recovered between 2009 and 2013. In total 1692/1841 (91.9%) cases with confirmed Mycobacterium tuberculosis infection had complete MIRU-24 and demographic data and were included in the study. Despite some year-to-year variability, spatio-temporal analysis identified four tuberculosis hotspots. The incidence rate and the relative risk of tuberculosis in these hotspots were 2- to 10-fold and 4- to 8-fold higher than the state average, respectively. MIRU-24 profiles of M. tuberculosis isolates associated with these hotspots revealed high levels of heterogeneity. This suggests that these spatio-temporal hotspots, within this low incidence setting, can represent areas of predominantly imported infection rather than clusters of cases due to local transmission. These findings provide important epidemiological insight and demonstrate the value of combining tuberculosis genotyping and spatiotemporal data to guide better-targeted public health interventions.

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.

Pulmonary tuberculosis in São Luis, State of Maranhão, Brazil: space and space-time risk clusters for death (2008-2012)

INTRODUCTION

The objective was to identify space and space-time risk clusters for the occurrence of deaths in a priority city for the control of tuberculosis (TB) in the Brazilian Northeast.

METHODS

Ecological research was undertaken in the City of São Luis/Maranhão. Cases were considered that resulted in deaths in the population living in the urban region of the city with pulmonary TB as the basic cause, between 2008 and 2012. To detect space and space-time clusters of deaths due to pulmonary TB in the census sectors, the spatial analysis scan technique was used.

RESULTS

In total, 221 deaths by TB occurred, 193 of which were due to pulmonary TB. Approximately 95% of the cases (n=183) were geocoded. Two significant spatial clusters were identified, the first of which showed a mortality rate of 5.8 deaths per 100,000 inhabitants per year and a high relative risk of 3.87. The second spatial cluster showed a mortality rate of 0.4 deaths per 100,000 inhabitants per year and a low relative risk of 0.10. A significant cluster was observed in the space-time analysis between 11/01/2008 and 04/30/2011, with a mortality rate of 8.10 deaths per 100,000 inhabitants per year and a high relative risk (3.0).

CONCLUSIONS

The knowledge of priority sites for the occurrence of deaths can support public management to reduce inequities in the access to health services and permit an optimization of the resources and teams in the control of pulmonary TB, providing support for specific strategies focused on the most vulnerable populations.