Tuberculosis inequalities and socio-economic deprivation in Portugal

Apolinário, Dulce; Ribeiro, Ana Isabel; Krainski, Elias Teixeira; Sousa, Pedro; Abranches, M; Duarte, Raquel

doi:10.5588/ijtld.16.0907

Cited by 30 publications

(31 citation statements)

References 23 publications

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“…The structural components are related to macro-policies, cultural and societal values, while the intermediaries are linked to material circumstances such as living conditions, work, sanitation, income, education and psychosocial, behavioural and cultural factors. 7 Although studies have evidenced the association of social inequities with TB, 8,9 few studies have been developed that consider spatial and temporal dependence, 10 as well as the interaction between space and time. Bayesian hierarchical models have become extremely helpful in the analysis of spatial and spatiotemporal data, mainly to identify variations on smaller geographical scales in Brazilian cities.…”

Section: Introductionmentioning

confidence: 99%

Bayesian spatio-temporal models for mapping TB mortality risk and its relationship with social inequities in a region from Brazilian Legal Amazon

Alves

Neto

Arroyo

et al. 2020

Transactions of the Royal Society of Tropical Medicine and Hygiene

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Background Reducing TB mortality is a great challenge in Brazil due to its territorial extension, cultural variations and economic and political crises, which impact the health system. This study aimed to estimate in space and time the risk of TB mortality and test its relationship with social inequities. Methods This was an ecological study that included deaths from TB between 2006 and 2016 in Cuiabá, Brazilian Legal Amazon. Bayesian models based on the integrated nested Laplace approximation approach were used to estimate spatio-temporal RRs. RRs for TB mortality were obtained according to the covariables representative of social inequities. Results The risk of TB mortality was stable between 2006 and 2016 and high-risk areas were identified throughout the municipality studied. Regarding social inequities, income was an important factor associated with TB mortality risk, as an increase of 1 SD in income resulted in a 35.4% (RR 0.646; CI 95% 0.476 to 0.837) decrease in risk. Conclusions The results provided evidence of areas with higher TB mortality risks that have persisted over time and are related to social inequities. Advancing social policies and protections in these areas will contribute to achieving the WHO's End TB strategy.

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Section: Introductionmentioning

confidence: 99%

Bayesian spatio-temporal models for mapping TB mortality risk and its relationship with social inequities in a region from Brazilian Legal Amazon

Alves

Neto

Arroyo

et al. 2020

Transactions of the Royal Society of Tropical Medicine and Hygiene

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“…The u i effect was scaled in order to render the model more intuitive and interpretable 16 , so that ϕ expresses the proportion of the spatial effect due to the structured part and 1/τ is the marginal variance of s i . Additionally, we used the function 'excursions' to delimitate high risk and low risk areas 8,17,18 . High-risk areas are those whose standardized notification ratio is significantly above 1 (i.e., above the country's average) and low risk areas are those whose standardized notification ratio is significantly below 1 (i.e., below the country's average).…”

Section: Discussionmentioning

confidence: 99%

Using Bayesian spatial models to map and to identify geographical hotspots of multidrug-resistant tuberculosis in Portugal between 2000 and 2016

Oliveira

Ribeiro

Krainski

et al. 2020

Sci Rep

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Multidrug-resistant tuberculosis (MDR-TB) is a major threat to the eradication of tuberculosis. TB control strategies need to be adapted to the necessities of different countries and adjusted in high-risk areas. In this study, we analysed the spatial distribution of the MDR- and non-MDR-TB cases across municipalities in Continental Portugal between 2000 and 2016. We used Bayesian spatial models to estimate age-standardized notification rates and standardized notification ratios in each area, and to delimitate high- and low-risk areas, those whose standardized notification ratio is significantly above or below the country’s average, respectively. The spatial distribution of MDR- and non-MDR-TB was not homogeneous across the country. Age-standardized notification rates of MDR-TB ranged from 0.08 to 1.20 and of non-MDR-TB ranged from 7.73 to 83.03 notifications per 100,000 population across the municipalities. We identified 36 high-risk areas for non-MDR-TB and 8 high-risk areas for MDR-TB, which were simultaneously high-risk areas for non-MDR-TB. We found a moderate correlation (ρ = 0.653; 95% CI 0.457–0.728) between MDR- and non-MDR-TB standardized notification ratios. We found heterogeneity in the spatial distribution of MDR-TB across municipalities and we identified priority areas for intervention against TB. We recommend including geographical criteria in the application of molecular drug resistance to provide early MDR-TB diagnosis, in high-risk areas.

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“…Epidemiologic and sociodemographic factors collectively define Mexico as a high-priority country for TB control in the Americas [17][18][19]. In this study, some factors related to the development of TB [20][21], as well as the diversity of infection localization [22][23], were studied at the town level in Jalisco. Moreover, no previous studies performed in Mexico have analysed the spatial distribution of cases at the individual level, as well as the sociodemographic characteristics of tuberculosis patients.…”

Section: Discussionmentioning

confidence: 99%

The First exploratory spatial distribution analysis of tuberculosis and associated factors in Tonala, Mexico

Escobar‐Gutiérrez

Martínez-Guarneros²,

Mora-Aguilera

et al. 2020

J Infect Dev Ctries

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Introduction: The US-Mexico region is at high risk of elevated tuberculosis (TB) incidence due to mobility and migration. Knowledge of how socio-demographic factors varies geographically, provides clues to understanding the determinants of tuberculosis and may provide guidance for regional prevention and control strategies to improve public health in Mexico. The aim of the present study was to describe the epidemiologic characteristics and spatial patterns of the incidence of tuberculosis in Tonala, Jalisco (Mexico) from 2013-2015. Methodology: The Surveillance System Database from the Health Department, complemented by information from the National Institute of Statistics and Geography, was used to obtain data for a spatial-temporal analysis of TB cases. For the geographical analysis map creation and geoinformation storing, ArcGIS software was used. Results: This study sought to characterize problem areas and jurisdictional locations of TB via a spatial approach based on analyses of case distributions and individual patient variables. The study found that tuberculosis cases were dispersed throughout Tonala County and were mainly concentrated on the Guadalajara city border. The TB cases were mainly individuals between 31 and 45 years old. Most of the cases reported during the observation period were male patients, and most cases primarily had lung involvement; however, there were quite a few cases with lymph node and intestinal disease. Conclusion: Our findings show that TB cases are essentially located in areas close to the city of Guadalajara and that most TB cases were pulmonary cases spread throughout the whole jurisdiction.

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Tuberculosis inequalities and socio-economic deprivation in Portugal

Abstract: Wide inequalities in TB notification rates were observed, and some areas continued to exhibit high TB notification rates. We found significant associations between TB and some socio-economic factors of the EDI.

Cited by 30 publications

References 23 publications

Bayesian spatio-temporal models for mapping TB mortality risk and its relationship with social inequities in a region from Brazilian Legal Amazon

Bayesian spatio-temporal models for mapping TB mortality risk and its relationship with social inequities in a region from Brazilian Legal Amazon

Using Bayesian spatial models to map and to identify geographical hotspots of multidrug-resistant tuberculosis in Portugal between 2000 and 2016

The First exploratory spatial distribution analysis of tuberculosis and associated factors in Tonala, Mexico

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