Monitoring delays in diagnosis of pulmonary tuberculosis in eight cities in Colombia - David A. Rodríguez; Kristien Verdonck; Karen Bissell; Juan José Victoria; Mohammed Khogali; Diana Marín; Ernesto Moreno
ABSTRACT Objective To measure time between onset of tuberculosis (TB) symptoms and start of treatment, and to identify factors associated with delay in eight Colombian cities. Methods Operational research with a retrospective analytical cohort design was conducted in 2014 using routinely collected data about new smear-positive pulmonary TB patients from eight cities in Colombia (Barranquilla, Bogotá, Bucaramanga, Cali, Cúcuta, Medellín, Pereira, and Villavicencio). Date of symptom onset was sourced from TB surveillance databases. Data on all other variables came from National TB Program (NTP) registers. Results There were 2 545 new cases of smear-positive pulmonary TB, but a plausible date of symptom onset was available for only 1 456 (57%). Median number of days between symptom onset and treatment start was 51 days (interquartile range: 27-101). A total of 72% of patients had a delay (> 30 days between symptom onset and treatment start), and 28% had a 3+ bacillary load at diagnosis. Based on multiple logistic regression, three factors were significantly associated with delay: being uninsured (odds ratio (OR): 1.30; 95% confidence interval (CI): 1.01-1.68) and having an unknown HIV status (OR: 1.81; CI: 1.04-3.17), which increased risk, and coming from a neighborhood with NTP-employed community health workers, which decreased risk (OR: 0.56; CI: 0.34-0.90). Conclusions Delays still prevent timely TB diagnosis and treatment in Colombia. As the country aims for TB elimination, delays must be reduced, especially in cities and vulnerable neighborhoods, to stop community transmission. The NTP should focus not only on the number of cases detected but also on how long it takes to detect them. To monitor interventions designed to reduce delays, additional dates in the process should be recorded routinely. In addition, reliability and completeness of data are crucial for monitoring.