Abstract
INTRODUCTION: Diabetes mellitus (DM) and tuberculosis (TB) frequently coexist, with 25-33% of TB patients having DM, underscoring the need for integrated screening. Evaluating additional yield (newly detected cases) and number needed to screen (NNS) can optimize early diagnosis, but risk-specific contributions remain unclear. This study assesses these metrics through household contact tracing to identify high-risk subgroups and guide targeted DM/TB control strategies. OBJECTIVE: The precise contribution of different risk factors and additional yield of subgroups linked to DM and the NNS to identify a new case in TB case households remain elusive. We aimed to document the contribution magnitudes of potential risk factors, additional yields of subgroups, and NNS for detecting a DM case in TB index cases and their household contacts. METHODS: This cross-sectional study enrolled 801 confirmed TB patients and 972 household contacts from 11 randomly selected counties in Guizhou, China, between September 2019 and August 2020. Participants underwent face-to-face interviews assessing sociodemographic, behavioral, and clinical factors, followed by screenings for hypertension, diabetes, and other non-communicable diseases. The quantitative variables, such as age and monthly income, are segmented from continuous exposure variables into new categorical variables, which have important implications for future analysis. A Student's t-test or variance analysis test was applied to compare groups as appropriately summarized using the mean and standard deviation. The 95% confidence interval (95%CI) was used for every relevant factor contributing to screening for DM where appropriate. The Odd Ratios were the values of the positive subgroup divided by the negative subgroup of the related factors contributing to the DM and non-DM cases (DM vs. non-DM) among the TB patients and their household contacts. RESULTS: Among 801 TB index cases and 972 household contacts, DM detection rates were 8.7% and 3.2%, respectively, with additional yields of 20.0% (NNS = 53) and 25.8% (NNS = 119). Multivariate analysis identified advanced age, abnormal BMI, DM family history, and comorbidities (e.g., hypertension) as significant risk factors (P < 0.05). Subgroup analyses revealed higher yields and lower NNS in older individuals (OR = 2.1, 95%CI:1.4-3.2), those with DM family history (OR = 1.8, 95% CI:1.2-2.7), and TB patients with sputum-smear positivity (OR = 1.5, 95% CI:1.1-2.0). CONCLUSION: Integrated DM screening in TB-affected households is clinically valuable, with higher additional yields among TB index cases than contacts. Critical risk factors, such as age, BMI, DM family history, and comorbidities like hypertension, significantly influenced detection rates and screening efficiency.