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Objectives <p>Delays in tuberculosis (TB) patients are a major obstacle to TB control, patients’ delays during diagnosis and treatment increase the potential of TB transmission and lead to an increased disease burden. The aim of this study was to explore the factors that influence patients’ delay at the ecological level as well as at the individual level.</p> Methods <p>The study utilized tuberculosis patients’ data identified in Shandong Province, China between 2016 and 2020 from China’s National Disease Reporting Information System, including demographic, clinical, and health service information. Ecological factors such as GDP and medical institution density came from the Shandong Statistical Yearbook, while age structure and gender ratio data were sourced from WorldPop. Multiple methods were employed in this study to investigate the determinants of patients’ delay in tuberculosis diagnosis. Geographically weighted regression (GWR), spatial Durbin model (SDM), spatial lag model (SLM), and spatial error model (SEM) were utilized to analyze the ecological factors influencing patients’ delay at the county and district levels. Additionally, logistic regression and variable importance measures (VIM) were employed to identify individual-level factors associated with patients’ delay.</p> Results <p>A total of 134,975 tuberculosis patients were included in the study. Of these patients, 67.9% experienced patients’ delay with a median delay time of 29 days (IQR:10–60). Ecological-level patients’ delay rates were influenced by the age group 60 + and the number of healthcare facilities. Patient delay was also influenced by individual factors such as gender, age, occupation, sputum smear results, census registration, patient source, classification of treatment, and type of tuberculosis. Among them, workers (OR = 0.821), students (OR = 0.747), retired (OR = 0.871) and the unemployed (OR = 1.033) differed from farmers. Direct clinic visits (OR = 5.230), follow-up identified (OR = 6.108), and referrals (OR = 3.945) had significantly higher odds than proactive inspection. The study found that patient source (VIM = 0.479) and occupation (VIM = 0.228) were the most significant factors association with patients’ delay.</p> Conclusion <p>This study demonstrates that tuberculosis patients’ delay is influenced by ecological factors such as the proportion of the elderly population and the number of healthcare facilities, as well as individual factors including gender, age, occupation, and sputum smear results, with patient source and occupation identified as the most significant determinants of delay.</p>

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Tuberculosis patients’ delay in healthcare access and its associated factors in Shandong Province, China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月31日

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  • Yuqi Duan,
  • Minghao Sun,
  • Chuanlong cheng,
  • Shengnan Yu,
  • Sihao Song,
  • Jun Cheng,
  • Xiujun Li

摘要

Objectives

Delays in tuberculosis (TB) patients are a major obstacle to TB control, patients’ delays during diagnosis and treatment increase the potential of TB transmission and lead to an increased disease burden. The aim of this study was to explore the factors that influence patients’ delay at the ecological level as well as at the individual level.

Methods

The study utilized tuberculosis patients’ data identified in Shandong Province, China between 2016 and 2020 from China’s National Disease Reporting Information System, including demographic, clinical, and health service information. Ecological factors such as GDP and medical institution density came from the Shandong Statistical Yearbook, while age structure and gender ratio data were sourced from WorldPop. Multiple methods were employed in this study to investigate the determinants of patients’ delay in tuberculosis diagnosis. Geographically weighted regression (GWR), spatial Durbin model (SDM), spatial lag model (SLM), and spatial error model (SEM) were utilized to analyze the ecological factors influencing patients’ delay at the county and district levels. Additionally, logistic regression and variable importance measures (VIM) were employed to identify individual-level factors associated with patients’ delay.

Results

A total of 134,975 tuberculosis patients were included in the study. Of these patients, 67.9% experienced patients’ delay with a median delay time of 29 days (IQR:10–60). Ecological-level patients’ delay rates were influenced by the age group 60 + and the number of healthcare facilities. Patient delay was also influenced by individual factors such as gender, age, occupation, sputum smear results, census registration, patient source, classification of treatment, and type of tuberculosis. Among them, workers (OR = 0.821), students (OR = 0.747), retired (OR = 0.871) and the unemployed (OR = 1.033) differed from farmers. Direct clinic visits (OR = 5.230), follow-up identified (OR = 6.108), and referrals (OR = 3.945) had significantly higher odds than proactive inspection. The study found that patient source (VIM = 0.479) and occupation (VIM = 0.228) were the most significant factors association with patients’ delay.

Conclusion

This study demonstrates that tuberculosis patients’ delay is influenced by ecological factors such as the proportion of the elderly population and the number of healthcare facilities, as well as individual factors including gender, age, occupation, and sputum smear results, with patient source and occupation identified as the most significant determinants of delay.