Under the bidirectional screening policy, impact of diabetes mellitus on patient and diagnosis delays of pulmonary tuberculosis patients in Ningxia, China: a propensity score analysis
Bibliographic record
Abstract
BACKGROUND: Although the policy of screening tuberculosis (TB) in individuals with diabetes mellitus (DM) was suggested, the coexistence of TB and DM is rapidly increasing in China, hindering the country's TB elimination efforts. The delay in TB consultation and diagnosis poses a challenge for DM-TB patients. However, the impact of DM on TB is not fully understood. Our study aimed to evaluate patient and diagnosis delays in pulmonary tuberculosis (PTB) patients in Ningxia, China, investigate the relationship between DM comorbidity and delays, and provide recommendations for improving the implementation of TB prevention and control strategies. METHODS: We examined all PTB patients with DM registered in the Chinese TB Information Management System (TBIMS) in the Ningxia region from 2016 to 2019. Patient delay was defined as > 20 days from symptom onset to first medical consultation, and diagnosis delay as > 1 day from first consultation to diagnosis, both measured in days. In our study, possible covariates were screened based on relevant literatures, then mediating variables were excluded, and finally covariates were screened by univariate analysis. Propensity score matching (PSM) was conducted (1:1 nearest matching with a caliper of 0.25 standard deviation (SD) of the propensity score (PS), without replacement) using covariates including sex, age, ethnicity, household registration, occupation, type of hospital for initial PTB diagnosis, level of hospital for initial PTB diagnosis, X-ray, the presence of other types of TB, and patient source. Logistic regression was then employed to assess the impact of DM on patient and diagnosis delays. Additionally, we conducted sensitivity analyses, exploring different ratios and caliper sizes, and mahalanobis distance matching. To address the potential for residual confounding by age, we conducted a further sensitivity analysis using age as a continuous variable in the PS model. RESULTS: Out of 9,625 PTB patients registered in TBIMS, 260 (2.7%) were also diagnosed with DM. In the unadjusted analysis, there was no difference in the median duration of patient delay between NDM-PTB and DM-PTB patients (20 days vs. 22 days, P > 0.05), while the median diagnosis delay was significantly longer in DM-PTB patients (1 day vs. 3 days, P < 0.05). However, after PSM to control for confounders, regression analysis indicated that DM was associated with a higher likelihood of experiencing patient delay [odds ratio (OR) = 1.85; 95% confidence interval (CI) = 1.30-2.64], but not with diagnosis delay (OR = 1.29; 95% CI = 0.89-1.87). In conclusion, after balancing baseline characteristics, DM was a risk factor for patient delay in PTB patients, but not for diagnosis delay. CONCLUSIONS: Our study revealed that DM only exacerbated patient delay and ultimately had no significant effect on diagnosis delay. This suggests that screening for TB in DM patients can reduce diagnosis delay. Therefore, emphasizing community TB health education in DM patients is crucial. Additionally, there is a need to promote the integrated model of TB management and enhance the awareness of primary medical institutions' doctors regarding the comorbidity of DM-PTB, differential diagnosis, and diagnostic capabilities to effectively prevent and control TB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".