The researchers defined long patient delay and long health system delay as delays exceeding 14 days, and long diagnostic delay as a total delay exceeding 28 days. Using modified Poisson regression with generalized estimating equations that accounted for township-level clustering, they estimated adjusted prevalence ratios for individual-level correlates of long delays. Older age groups, minority nationality, and household or unemployed status were associated with a higher prevalence of long patient delay, whereas rural residence was associated with a lower prevalence of long health system delay. These are measured associations from regression models, not demonstrated causal effects.

Beyond individual correlates, the central contribution of the study lies in its spatial treatment of the problem at the township level, a scale at which the authors note evidence has been limited. The team applied Global Moran’s I statistics, Getis-Ord Gi* hot spot analysis, Local Indicators of Spatial Association, and global bivariate Moran’s I to test whether long delays cluster geographically. All three delay categories showed significant positive spatial autocorrelation at the township level, with Global Moran’s I values ranging from 0.318 to 0.495 (all P < 0.001). In practical terms, townships with high long-delay prevalence were not randomly scattered but tended to sit beside other high-prevalence townships, and low-prevalence townships likewise clustered together.

After false discovery rate correction, hot spots for long patient delay and long diagnostic delay were mainly concentrated in southern Yichang, particularly in the Yidu–Changyang area, while hot spots for long health system delay were mainly located in Dangyang and the central urban districts. This separation matters because the two components of the recorded diagnostic pathway followed different geographies. The bivariate spatial analysis confirmed the divergence: long patient delay and long health system delay showed a significant negative cross-spatial association, with a global bivariate Moran’s I of −0.192 (P = 0.020). Townships where patients took longest to seek care were not, on the whole, the same townships where the health system took longest to respond.

Because the study relied on routinely reported surveillance records, the delay components reflect what was documented within the Tuberculosis Information Management System, and the authors frame their conclusions accordingly: within the recorded diagnostic pathway, patient delay accounted for the larger recorded component, and the two delay types showed distinct township-level spatial patterns. The analysis covers notified pulmonary tuberculosis cases registered in a single prefecture-level city over five years, so the spatial patterns describe Yichang’s townships during that window rather than national trends or changes over time.

The median figures convey the scale of the challenge. A median total diagnostic delay of 31 days means that half of the 16,099 patients waited more than a month from symptom onset to diagnosis, a period during which delayed diagnosis may prolong transmission before treatment. The interquartile range of 15 to 47 days shows how widely the experience varied, with a quarter of patients waiting more than 47 days. The authors position these results as township-level evidence on where long delays concentrate and which individual characteristics are associated with them, providing a documented pre-pandemic baseline for pulmonary tuberculosis diagnostic delay in Central China without extending their findings to periods or places the data do not cover.

Subject of Research: Township-level spatial patterns of diagnostic delay among pulmonary tuberculosis patients in Yichang City, Central China

Article Title: Township-level spatial heterogeneity and associated factors of diagnostic delay among pulmonary tuberculosis patients in Central China

Article References: Xiong, J., Lin, L., Liu, P., Jiang, W., Yang, X., Wei, X., Xin, L., Zhou, P., Huang, D., Zhang, H., & Liu, J. (2026). Township-level spatial heterogeneity and associated factors of diagnostic delay among pulmonary tuberculosis patients in Central China. BMC Public Health. https://doi.org/10.1186/s12889-026-30059-8

Image Credits: AI Generated

DOI: 10.1186/s12889-026-30059-8

Keywords: pulmonary tuberculosis, diagnostic delay, patient delay, health system delay, spatial epidemiology, hot spot analysis, Global Moran's I, bivariate Moran's I, Yichang, China, tuberculosis surveillance, BMC Public Health