Opportunistic random blood glucose screening among professional drivers in northeastern Bangladesh: Assessing undiagnosed diabetes and health awareness

Abstract

Diabetes remains a silent epidemic in underrepresented high-risk groups like professional drivers, highlighting the urgent need for informed health policies and targeted interventions. This study aimed to assess the prevalence of undiagnosed diabetes and related health awareness among professional drivers in northeastern Bangladesh using opportunistic random blood glucose (RBG) testing to address knowledge gaps and inform health policy. A cross-sectional study was conducted on 1,454 participants enrolled between February 5, 2024 and July 27, 2024, using a consent-based questionnaire, anthropometric measurements, and RBG testing with a glucometer. Diabetes awareness was assessed using pre-tested questionnaires, while the prevalence of diabetes and associated factors were evaluated using Mann-Whitney U tests, Welch ANOVA, and Spearman correlation analysis. A total of 2.20% of the driver population were found to have undiagnosed diabetes. RBG levels differed significantly across regions. Middle age (7.63%) and overweight (3.77%) groups exhibited the highest prevalence of undiagnosed diabetes. Confounding variables such as BMI (r = 0.22, p < 0.0001), age (r = 0.19, p < 0.0001), and sleep duration (r = -0.05, p = 0.04) were significantly associated with glucose levels, indicating potential risk factors for diabetes. The obese group (AOR: 3.04, 95% CI: 0.81-11.46) and overweight group (AOR: 1.81, 95% CI: 0.83-3.99) were 3.04 and 1.81 times more likely, respectively, to develop diabetes compared to the healthy weight group. Participants with less than 7 hours of sleep (AOR: 1.13, 95% CI: 0.46-2.75) were also at greater risk. Co-morbidities and a family history of diabetes were also significantly associated with elevated RBG levels. Overall, this study highlights the regional and behavioral disparities influencing the development of diabetes risk among professional drivers, a population often neglected in health policy. It underscores the need for health education and large-scale RBG testing to improve awareness and alert policymakers in formulating effective health guidelines.

Fig 1. Flowchart depicting the study design, sampling, and exclusion criteria.

Out of 1,516 participants who provided consent, 62 were excluded for those pre-diagnosed with diabetes or using medication affecting blood glucose levels.

Fig 2. Distribution of the studied driver population into different subcategories.

(A) Distribution based on the RBG levels: without diabetes (<11.1 mmol/L) and undiagnosed diabetes (≥11.1 mmol/L). (B) Regional distribution of the studied driver population across Sylhet (n = 918), Moulvibazar (n = 404), Habiganj (n = 31), and Sunamganj districts (n = 68) and other areas (n = 33) outside of Sylhet division (C) Comparison of mean RBG levels across different regional groups. (D) Proportion of participants based on their vehicle type: Car (n = 476), Microbus (n = 274), Bike (n = 26), Bus (n = 50), CNG (n = 305), Truck (n = 142), and Other (n = 181). (E) Comparative analysis of mean RBG levels across different vehicle driver groups.

Fig 3. Relationship between Random Blood Glucose (RBG) levels, age, BMI, and sleep duration categories.

(A) Distribution of RBG levels across age groups: young adult (n = 324), mature adult (n = 992), middle age (n = 131), and old (n = 7). (B) ANOVA comparison of mean RBG levels among age categories. (C) Frequency of participants, within different BMI groups: underweight (n = 157), healthy weight (n = 844), overweight (n = 398), and obese (n = 55). (D) ANOVA comparison of mean RBG levels among BMI categories.

Fig 4. Correlation of RBG level with confounding variables BMI, age, and sleep duration.

(A) Spearman’s correlation analysis shows a positive association between BMI and age with RBG levels and a negative correlation between RBG levels and sleep duration. (B) Multiple regression model assessing the interaction effect of BMI and RBG levels concerning age and sleep duration. R² denotes the proportion of variance in RBG levels explained by the independent variables (BMI, age, and sleep duration).

Fig 5. Association of RBG levels with various lifestyle parameters.

Statistical analyses were conducted using the Mann-Whitney test and Welch ANOVA, with significance denoted as * (p < 0.05) and ** (p < 0.005); “ns” denotes not significant.