Jornal de Doenças Infecciosas e Medicina Preventiva

Jornal de Doenças Infecciosas e Medicina Preventiva
Acesso livre

ISSN: 2329-8731

Abstrato

A Study of Latent Tuberculosis Infection and Its Influencing Factors in Diabetic Patients in Nanshan District, Shenzhen, China

Tao Zhong*, Shengyuan Liu, Qi Ye, Shengbin Li, Xujun Guo

Objective: Diabetes meLlitus (DM) confers a higher risk for active Tuberculosis (TB). However, there is a paucity of information about the prevalence and risk factors of Latent Tuberculosis Infection (LTBI) in DM patients. Therefore, we aimed to analyze the prevalence and infection risk factors of LTBI in DM patients in Nanshan District, Shenzhen, China.

Methods: We conducted a cross-sectional study to screen for TB in a random sample of DM patients included in basic public health management from 2019 to 2020 in two regional community health centers in the Nanshan District of Shenzhen, China. Questionnaires, Interferon-Gamma Release Assay (IGRA), and glycosylated Hemoglobin test (HbA1c) were performed on DM patients who met the criteria for inclusion. Univariate analysis and multiple logistic regression analysis were used to analyze risk factors for LTBI in DM patients.

Results: The prevalence of LTBI among DM patients was 40.47% (189/467). By univariate analysis, factors significantly associated with LTBI in DM patients were age, educational level, a previous history of tuberculosis, and recent suspected tuberculosis symptoms (P<0.05). Multiple logistic regression analysis showed that infection risk factors for LTBI in DM patients were a low educational level (OR=1.689, 95% CI:1.111-2.568; P=0.014) and a previous history of TB (OR=4.264,95% CI:1.258-14.447; P=0.020), while having recent suspected TB symptoms (OR=0.316, 95% CI:0.118-0.850; P=0.023) was protective.

Conclusion: There is a high prevalence of LTBI in DM patients in the Nanshan District of Shenzhen. A low educational level was the most prominent infection risk factor.

Isenção de responsabilidade: Este resumo foi traduzido com recurso a ferramentas de inteligência artificial e ainda não foi revisto ou verificado.
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