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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».