Spatial-temporal clustering of notified pulmonary tuberculosis and its predictors in East Gojjam Zone, Northwest Ethiopia

Social protection in areas vulnerable to tuberculosis: a mixed methods study in São Luís, Maranhão

OBJECTIVES

to analyze the risk areas for tuberculosis and the influences of social protection on the development of treatment for the disease in the municipality of São Luís, Maranhão.

METHODS

this is explanatory sequential mixed method research. In the quantitative phase, the data were obtained from the Notifiable Diseases Information System from 2010 to 2019, with georeferencing being carried out to identify areas vulnerable to tuberculosis. In the qualitative phase, semi-structured interviews were carried out with individuals who received social benefits.

RESULTS

7,381 cases were geocoded, and, from the purely spatial scanning analysis, it was possible to identify 13 spatial clusters of risk. As for the interviews, there was a positive relationship between patient improvement and receiving benefits.

CONCLUSIONS

geographic space and social determinants are relevant for reorienting monitoring actions for the conditions that generate the health-disease process.

Factors affecting the incidence of pulmonary tuberculosis based on the GTWR model in China, 2004-2021

Contra-posing panel data on the incidence of pulmonary tuberculosis (PTB) at the provincial level in China through the years of 2004-2021 and introducing a geographically and temporally weighted regression (GTWR) model were used to explore the effect of various factors on the incidence of PTB from the perspective of spatial heterogeneity. The principal component analysis (PCA) was used to extract the main information from twenty-two indexes under six macro-factors. The main influencing factors were determined by the Spearman correlation and multi-collinearity tests. After fitting different models, the GTWR model was used to analyse and obtain the distribution changes of regression coefficients. Six macro-factors and incidence of PTB were both correlated, and there was no collinearity between the variables. The fitting effect of the GTWR model was better than ordinary least-squares (OLS) and geographically weighted regression (GWR) models. The incidence of PTB in China was mainly affected by six macro-factors, namely medicine and health, transportation, environment, economy, disease, and educational quality. The influence degree showed an unbalanced trend in the spatial and temporal distribution.

Spatiotemporal Distribution of Tuberculosis in the Oromia Region of Ethiopia: A Hotspot Analysis

Tuberculosis (TB) is a major public health concern in low- and middle-income countries including Ethiopia. This study aimed to assess the spatiotemporal distribution of TB and identify TB risk factors in Ethiopia's Oromia region. Descriptive and spatiotemporal analyses were conducted. Bayesian spatiotemporal modeling was used to identify covariates that accounted for variability in TB and its spatiotemporal distribution. A total of 206,278 new pulmonary TB cases were reported in the Oromia region between 2018 and 2022, with the lowest annual TB case notification (96.93 per 100,000 population) reported in 2020 (i.e., during the COVID-19 pandemic) and the highest TB case notification (106.19 per 100,000 population) reported in 2019. Substantial spatiotemporal variations in the distribution of notified TB case notifications were observed at zonal and district levels with most of the hotspot areas detected in the northern and southern parts of the region. The spatiotemporal distribution of notified TB incidence was positively associated with different ecological variables including temperature (β = 0.142; 95% credible interval (CrI): 0.070, 0.215), wind speed (β = -0.140; 95% CrI: -0.212, -0.068), health service coverage (β = 0.426; 95% CrI: 0.347, 0.505), and population density (β = 0.491; 95% CrI: 0.390, 0.594). The findings of this study indicated that preventive measures considering socio-demographic and health system factors can be targeted to high-risk areas for effective control of TB in the Oromia region. Further studies are needed to develop effective strategies for reducing the burden of TB in hotspot areas.

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

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.

Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression

Tuberculosis (TB) is a highly infectious disease, representing one of the major causes of death worldwide. Sustainable Development Goal 3.3 implies a serious decrease in the incidence of TB cases. Hence, this study applied a spatial analysis approach to investigate patterns of pulmonary TB cases and its drivers in Bandar Lampung (Indonesia). Our study examined seven variables: the growth rate of pulmonary TB, population, distance to the city center, industrial area, green open space, built area, and slum area using geographically weighted Poisson regression (GWPR). The GWPR model demonstrated excellent results with an R2 and adjusted R2 of 0.96 and 0.94, respectively. In this case, the growth rate of pulmonary TB and population were statistically significant variables. Spatial pattern analysis of sub-districts revealed that those of Panjang and Kedaton were driven by high pulmonary TB growth rate and population, whereas that of Sukabumi was driven by the accumulation of high levels of industrial area, built area, and slums. For these reasons, we suggest that local policymakers implement a variety of infectious disease prevention and control strategies based on the spatial variation of pulmonary TB rate and its influencing factors in each sub-district.

Five-year trend analysis of tuberculosis in Bahir Dar, Northwest Ethiopia, 2015-2019

Background

Tuberculosis (TB) remains a major cause of morbidity and mortality in Ethiopia despite the increased availability of effective treatments. Trend analysis of issues and priorities affecting TB programs across different regions of the country is critical to ensure equitable and sustainable TB outcomes. We aimed to analyze the trends of TB in Bahir Dar, Northwest Ethiopia, over 5 years from 2015 to 2019.

Methods

An institution-based, retrospective cross-sectional study was conducted in Bahir Dar, the capital city of the Amhara Region in Ethiopia. Five-year data and records of individual TB cases were reviewed from all public and private health-care facilities and health bureaus in Bahir Dar. Using a standard checklist adapted from the World Health Organization, data were abstracted relevant to sociodemographic characteristics of the patients, year and type of TB infection, and HIV status. SPSS version 20 software was used for data analysis.

Results

Data of 4275 patients with TB were identified, of which 929 (21.7%) were smear-positive pulmonary TB, 1195 (28%) were smear-negative pulmonary TB, and 2151 (50.3%) were extrapulmonary TB patients. TB was more prevalent in the age group 15-34 years (51.2%), and females (55.5%). In the years from 2015 to 2019, the prevalence of all forms of TB was 922 (21.6%), 812 (19.0%), 843 (19.7%), 876 (20.5%), and 822 (19.2%), respectively, demonstrating a decreasing trend though inconsistent. The variables sex (adjusted odds ratio [AOR]: 1.734, 95% confidence interval [CI] [1.390-2.187]), HIV co-infection (AOR: 1.875, 95% CI [1.553-2.265]), and age <15 years (AOR: 1.372, 95% CI [1.121-1.680]) showed a significant association with TB infection.

Conclusions

The prevalence of TB in Bahir Dar, Northwest Ethiopia, demonstrated a decreasing trend over the years from 2015 to 2019 but with inconsistencies. HIV co-infection significantly increased the risk of developing TB, and productive age groups and females were at the greater prevalence of TB, highlighting the importance of strengthening sustainable TB care and prevention interventions toward these groups of people.