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

Shah, Lena; Choi, Hojung; Berrang‐Ford, Lea; Henostroza, German; Krapp, Fiorella; Zamudio, Carlos; Heymann, S. Jody; Kaufman, Jay S.; Ciampi, Antonio; Seas, Carlos; Gotuzzo, Eduardo; Brewer, Timothy F.

doi:10.5588/ijtld.14.0011

Cited by 16 publications

(9 citation statements)

References 21 publications

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“…This may not lead to the same estimates as aggregating the results of separate analyses of the different countries as the model that we use is non-linear. National TB programs need to consider particular settings within the country, since transmission dynamics may be altered within “hot-spot” areas, while cultural issues and specific service delivery challenges also require consideration [35, 36]. In future, our model could be adapted to guide local policies, for example through an online tool usable by policy makers who could input parameters from local programmatic data.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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BackgroundGlobally 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.MethodsWe 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.ResultsAt 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.ConclusionsGlobally, 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-2171-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…Segregation may have direct effects on the transmission of TB by concentrating impoverished populations into small geographic areas with poor housing and limited access to healthcare [32]. Spatial analyses of MDR-TB cases have found hotspots of localized transmission in areas of Lima, suggesting that transmission of MDR-TB occurs in small geographic areas that may have formed by socially segregated enclaves [33,34].…”

Section: Page 10mentioning

confidence: 99%

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

Bui

Oren

Roe

et al. 2018

Clinical Infectious Diseases

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“…Studies on spatio-temporal distribution of TB cases have principally addressed their association with demographic and geographical predictors for multidrugresistant TB in Peru [9][10][11], nonetheless, their possible association with environmental factors such as air pollution have yet to be of interest. This descriptive study sought to determine the spatial distribution and clustering of TB cases in Lima, Peru and determine cooccurrence with clusters of PM 2.5 and economic index.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 (PM 2.5 ) and poverty. We found co-occurrences of clusters of elevated concentrations of air pollutants such as PM 2.5 , high poverty indexes, and high TB incidence in Lima. These findings suggest an interplay of socio-economic and environmental in driving TB incidence.

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

Cited by 16 publications

References 21 publications

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

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

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

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

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