Tendência temporal da incidência de tuberculose e sua distribuição espacial em Macapá-AP

Giacomet, Clóvis Luciano; Santos, Márcio Souza dos; Berra, Thaís Zamboni; Alves, Yan Mathias; Alves, Luana Seles; Costa, Fernanda Bruzadelli Paulino da; Ramos, Antônio Carlos Vieira; Crispim, Juliane de Almeida; Monroe, Aline Aparecida; Pinto, Ione Carvalho; Fiorati, Regina Célia; Arcoverde, Marcos Augusto Moraes; Gomes, Dulce; Freitas, Giselle Lima de; Yamamura, Mellina; Arcêncio, Ricardo Alexandre

doi:10.11606/s1518-8787.2021055003431

Cited by 6 publications

References 11 publications

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A distributional regression approach to modeling the impact of structural and intermediary social determinants on communities burdened by tuberculosis in Eastern Amazonia – Brazil

Giacomet

Ramos

Moura

et al. 2022

Preprint

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Background: TB is a disease affected by social determinants of health; however, it is unclear what its structural and intermediary determinants are in Eastern Amazonia. The region contains many natural resources, yet it suffers drastically from poverty, inequality, and neglected diseases. Here, we aimed to employ mathematical modeling to evaluate the influence of structural and intermediary determinants of health on TB in Eastern Amazonia – Brazil. Methods: We conducted an ecological study. We considered cases diagnosed of TB and collected data by census tract to measure the social determinants. We applied the generalized additive model for location, scale, and shape (GAMLSS) framework to identify the effect of social determinants on communities with a high prevalence of TB. The Double Poisson distribution (DPO) was selected and we tested the inclusion of quadratic effects. Results: 1,730 people were selected. The majority were female (59.3%), aged 31 to 59 years (47.6%), blacks (67.9%), schooling level of 5 to 8 years (18.7%). Prevalence of alcoholism was 8.6% and mental illness 0.7%. The GAMLSS analyses showed that the risk of community incidence of TB is associated with the proportion of the population without basic sanitation and also with the age groups 16-31 years and > 61 years. Conclusions: The study revealed that GAMLSS is an strategic tool to identify territories at greatest risk for TB. Models should have broader scope to include social determinants so as to better inform policy to reduce inequality and achieving the goal of the End TB strategy.

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A distributional regression approach to modeling the impact of structural and intermediary social determinants on communities burdened by tuberculosis in Eastern Amazonia – Brazil

Giacomet

Ramos

Moura

et al. 2022

Preprint

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Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil

et al. 2022

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Objectives To identify risk-prone areas for the spread of tuberculosis, analyze spatial variation and temporal trends of the disease in these areas and identify their determinants in a high burden city. Methods An ecological study was carried out in Ribeirão Preto, São Paulo, Brazil. The population was composed of pulmonary tuberculosis cases reported in the Tuberculosis Patient Control System between 2006 and 2017. Seasonal Trend Decomposition using the Loess decomposition method was used. Spatial and spatiotemporal scanning statistics were applied to identify risk areas. Spatial Variation in Temporal Trends (SVTT) was used to detect risk-prone territories with changes in the temporal trend. Finally, Pearson's Chi-square test was performed to identify factors associated with the epidemiological situation in the municipality. Results Between 2006 and 2017, 1760 cases of pulmonary tuberculosis were reported in the municipality. With spatial scanning, four groups of clusters were identified with relative risks (RR) from 0.19 to 0.52, 1.73, 2.07, and 2.68 to 2.72. With the space–time scan, four clusters were also identified with RR of 0.13 (2008–2013), 1.94 (2010–2015), 2.34 (2006 to 2011), and 2.84 (2014–2017). With the SVTT, a cluster was identified with RR 0.11, an internal time trend of growth (+ 0.09%/year), and an external time trend of decrease (− 0.06%/year). Finally, three risk factors and three protective factors that are associated with the epidemiological situation in the municipality were identified, being: race/brown color (OR: 1.26), without education (OR: 1.71), retired (OR: 1.35), 15 years or more of study (OR: 0.73), not having HIV (OR: 0.55) and not having diabetes (OR: 0.35). Conclusion The importance of using spatial analysis tools in identifying areas that should be prioritized for TB control is highlighted, and greater attention is necessary for individuals who fit the profile indicated as “at risk” for the disease.

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Geo-spatial high-risk clusters of Tuberculosis in the global general population: a systematic review

Teibo,

Andrade,

Rosa

et al. 2023

BMC Public Health

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Introduction The objective of this systematic review is to identify tuberculosis (TB) high-risk among the general population globally. The review was conducted using the following steps: elaboration of the research question, search for relevant publications, selection of studies found, data extraction, analysis, and evidence synthesis. Methods The studies included were those published in English, from original research, presented findings relevant to tuberculosis high-risk across the globe, published between 2017 and 2023, and were based on geospatial analysis of TB. Two reviewers independently selected the articles and were blinded to each other`s comments. The resultant disagreement was resolved by a third blinded reviewer. For bibliographic search, controlled and free vocabularies that address the question to be investigated were used. The searches were carried out on PubMed, LILACS, EMBASE, Scopus, and Web of Science. and Google Scholar. Results A total of 79 published articles with a 40-year study period between 1982 and 2022 were evaluated. Based on the 79 studies, more than 40% of all countries that have carried out geospatial analysis of TB were from Asia, followed by South America with 23%, Africa had about 15%, and others with 2% and 1%. Various maps were used in the various studies and the most used is the thematic map (32%), rate map (26%), map of temporal tendency (20%), and others like the kernel density map (6%). The characteristics of the high-risk and the factors that affect the hotspot’s location are evident through studies related to poor socioeconomic conditions constituting (39%), followed by high population density (17%), climate-related clustering (15%), high-risk spread to neighbouring cities (13%), unstable and non-random cluster (11%). Conclusion There exist specific high-risk for TB which are areas that are related to low socioeconomic conditions and spectacular weather conditions, these areas when well-known will be easy targets for intervention by policymakers. We recommend that more studies making use of spatial, temporal, and spatiotemporal analysis be carried out to point out territories and populations that are vulnerable to TB.

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Tendência temporal da incidência de tuberculose e sua distribuição espacial em Macapá-AP

Cited by 6 publications

References 11 publications

A distributional regression approach to modeling the impact of structural and intermediary social determinants on communities burdened by tuberculosis in Eastern Amazonia – Brazil

A distributional regression approach to modeling the impact of structural and intermediary social determinants on communities burdened by tuberculosis in Eastern Amazonia – Brazil

Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil

Geo-spatial high-risk clusters of Tuberculosis in the global general population: a systematic review

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