Spatio-temporal analysis of tuberculosis and its correlation with the Living Conditions Index in an elderly population in Brazil

Abstract

The aim of this study was to analyze the spatio-temporal distribution of tuberculosis (TB) in the elderly population in the city of Belém, PA from 2011 to 2015 according to the Living Conditions Index (LCI). This was an epidemiological, descriptive, ecological, and retrospective study involving 1,134 cases. Data were collected through the Information System of Notifiable Diseases (SINAN). For data analysis, we used the incidence coefficient, global and local empirical Bayesian model, Kernel density, and Kernel ratio. The construction of the LCI was based on the United Nations Development Program (UNDP) method. The incidence of TB remained the same over the five years studied. No neighborhood was found to have a high incidence of TB and a high LCI, but most of the cases occurred in the south of the city where the neighborhoods with the most precarious conditions are located. Moreover, the lowest incidence was in neighborhoods that historically had better infrastructure. Spatial analysis tools facilitate studies on the dynamics of disease transmission such as TB. In this study, it was shown that TB is heterogeneously distributed throughout the municipality. Living conditions, especially in slums, influenced TB incidence.

Mapping the threads: Geospatial Exploration of Shared Modifiable Tuberculosis Disease Risks in a clinic-based population

“…Our spatial epidemiologic study of TB cases in Accra, Ghana, strengthens available evidence that shared socio-behavioral factors may drive the geospatial clustering of urban TB (31). While previous studies have documented the spatial heterogeneity of TB cases in different settings ( 32),( 33),( 34), (35), we demonstrated that TB cases in our treatment cohort were spatially clustered in areas of high population density and among patients with shared modi able risk factors, such as those engaged in unskilled labor, those involved in recreational drug use, and those having close contacts with recent cough. The observed signi cance of these shared risk factors in our model aligns with known TB exposure risk in social settings and among close contacts (36),( 37),(38).…”

Spatio-temporal six-year retrospective study on dermatophytosis in Rio de Janeiro, Southeast Brazil: A tropical tourist locality tale

“…Understanding dermatophytosis behavior in an area and its determinants is essential for implementing interventions aimed at reducing fungal transmission through the identification of areas at high vulnerability. In order to investigate the relationships between living conditions, environmental factors and fungal diseases, it is necessary to adopt geoprocessing techniques which include spatiotemporal analysis [ 17 ].…”

Spatial and temporal analysis of tuberculosis incidence in Guinea-Bissau, 2018 to 2020

“…It also highlights the fragility in the management of pharmacies and the availability of medications in the country, verifying serious deficiencies and shortages resulting from a low level of forecasting and mastery of the entire supply chain of medications, shortages in the stock of important drugs and supplies for the diagnosis and treatment of various diseases, with an emphasis on tuberculosis and HIV/AIDS ( 13 - 14 ) .…”

“…One of the main situational and epidemiological diagnostic strategies applied in the context of tuberculosis refers to spatial and temporal analysis tool use, such as geoprocessing and time series, which contribute to the identification and understanding of disease transmission dynamics, providing evidence of priority areas or zones for control and intervention activities ( 14 - 15 ) . In a literature review, used for this research, no studies were found analyzing the situation of tuberculosis in Guinea-Bissau considering spatial and temporal analysis technique use so that the present research will be the first to study the disease in the country using these tools.…”