Spatiotemporal analysis of drug-resistant TB patients registered in selected districts of Karnataka, South India: a cross-sectional study

Abstract

Background: Tuberculosis (TB) depicts heterogeneous spatial patterns with geographical aggregation of TB cases due to either ongoing person-to-person transmission or reactivation of latent infection in a community sharing risk factor. In this regard, we aimed to assess the spatiotemporal aggregation of drug-resistant TB (DR-TB) patients notified to the national TB program (NTP) from 2015 to 2018 in selected districts of Karnataka, South India. Methods: This was a cross-sectional study among DR-TB patients notified from Dakshina Kannada, Udupi, and Chikamagalur districts of the state of Karnataka. Clinico-demographic details were extracted from treatment cards. The registered addresses of the patients were geocoded (latitude and longitude) using Google Earth. Using the QGIS software, spot map, heat maps and grid maps 25 km 2 with more than the expected count of DR-TB patients were constructed. Results: Of the total 507 patients studied, 376 (74%) were males and the mean (standard deviation) age of the study participants was 41.4 (13.9) years. From 2015 to 2018, the number of patients increased from 85 to 209 per year, the area of aggregation in square kilometers increased from 113.6 to 205.7, and the number of rectangular grids with more than the expected DR-TB patients (> 1) increased from 12 to 47. Conclusions: The increase in the number of DR-TB patients, area of aggregation, and grids with more than the expected count is a cause for concern. The NTP can use routine programmatic data to develop maps to identify areas of aggregation of disease for targeted TB control activities.

Neighbourhood risk factors of recurrent tuberculosis in Cape Town: a cohort study using geocoded notification data

“…Issues with geocoding have also been reported in other studies on TB in Viet Nam26 and India 27. Another study in Cape Town, focusing on neighbourhood characteristics of hospitalised childhood burn injury using hospital register data, reported that 18.4% of addresses could not be geocoded,28 compared with 11.1% in our study.…”

Epidemic investigations within an arm’s reach – role of google maps during an epidemic outbreak

“…In addition, these were pre-decided locations such as home, work, etc. Unlike this study, they did not use the dynamic daily timeline to track the exact visited places [17,45,46].…”