A spatial analysis of TB cases and abnormal X-rays detected through active case-finding in Karachi, Pakistan

Abstract

Tuberculosis (TB) is the leading cause of avoidable deaths from an infectious disease globally and a large of number of people who develop TB each year remain undiagnosed. Active case-finding has been recommended by the World Health Organization to bridge the case-detection gap for TB in high burden countries. However, concerns remain regarding their yield and cost-effectiveness. Data from mobile chest X-ray (CXR) supported active case-finding community camps conducted in Karachi, Pakistan from July 2018 to March 2020 was retrospectively analyzed. Frequency analysis was carried out at the camp-level and outcomes of interest for the spatial analyses were mycobacterium TB positivity (MTB+) and X-ray abnormality rates. The Global Moran's I statistic was used to test for spatial autocorrelation for MTB+ and abnormal X-rays within Union Councils (UCs) in Karachi. A total of 1161 (78.1%) camps yielded no MTB+ cases, 246 (16.5%) camps yielded 1 MTB+, 52 (3.5%) camps yielded 2 MTB+ and 27 (1.8%) yielded 3 or more MTB+. A total of 79 (5.3%) camps accounted for 193 (44.0%) of MTB+ cases detected. Statistically significant clustering for MTB positivity (Global Moran's I: 0.09) and abnormal chest X-rays (Global Moran's I: 0.36) rates was identified within UCs in Karachi. Clustering of UCs with high MTB positivity were identified in Karachi West district. Statistically significant spatial variation was identified in yield of bacteriologically positive TB cases and in abnormal CXR through active case-finding in Karachi. Cost-effectiveness of active case-finding programs can be improved by identifying and focusing interventions in hotspots and avoiding locations with no known TB cases reported through routine surveillance.

Figure 1

Overview of TB case-detection cascade for mobile chest X-ray supported active case-finding camps in Karachi, Pakistan (July 2018–March 2020).

Figure 2

Global Moran’s I statistic for MTB positivity and X-ray abnormality rates from mobile chest X-ray supported active case-finding camps in Karachi, Pakistan (July 2018–March 2020).

Figure 3

(a) Local Indicators of Spatial Association (LISA) analysis for MTB positivity rates in Union Councils (UCs) from mobile chest X-ray supported active case-finding camps in Karachi, Pakistan (July 2018–March 2020). A High-high result indicates potential clustering of UCs with high MTB positivity. A High-low result indicates a UC with a potentially high MTB positivity surrounded by UCs with low positivity. A low–low result indicates clustering of UCs with potentially low MTB positivity. A low–high result indicates a UC with potentially low MTB positivity surrounded by UCs with high positivity. QGIS Geographic Information System v3.26.3. QGIS.org, 2022. QGIS Association. http://www.qgis.org. (b) Local Indicators of Spatial Association (LISA) analysis for abnormal X-ray rates in Union Councils (UCs) from mobile chest X-ray supported active case-finding camps in Karachi, Pakistan (July 2018–March 2020). A High-high result indicates clustering of potential UCs with high abnormal X-ray rates. A High-low result indicates a UC with potentially high abnormal X-ray ratio surrounded by UCs with low rates. A low–low result indicates clustering of UCs with low abnormal X-ray rates. A low–high result indicates a UC with potentially low MTB positivity surrounded by UCs with high positivity. QGIS Geographic Information System v3.26.3. QGIS.org, 2022. QGIS Association. http://www.qgis.org.

Figure 4

(a) GI* analysis (distance-based method) for MTB positivity rates from locations of mobile chest X-ray supported active case-finding camps in Karachi, Pakistan (July 2018–March 2020). A high–high result indicates clustering of GPS locations of camps with high MTB positivity. A low–low result indicates clustering of GPS locations of camps with low MTB positivity. QGIS Geographic Information System v3.26.3. QGIS.org, 2022. QGIS Association. http://www.qgis.org. (b) GI* analysis (distance-based method) for abnormal X-rays rates from locations of mobile chest X-ray supported active case-finding camps in Karachi, Pakistan (July 2018–March 2020). A high–high result indicates clustering of GPS locations of camps with high abnormal X-ray rates. A low–low result indicates clustering of GPS locations of camps with low abnormal X-ray rates. QGIS Geographic Information System v3.26.3. QGIS.org, 2022. QGIS Association. http://www.qgis.org.