AI Article Synopsis
- Urban slums like those in Hyderabad face increased TB transmission due to overcrowding and poor infection control, making these areas "hot spots" for the disease.
- An investigation was conducted to use local postmen and GPS technology to accurately map TB hotspots, utilizing data from a 12-year period to pinpoint infectious locations.
- The findings suggested that mapping helped visualize clusters of TB cases, identified risk factors like crowding and poor sanitation, and facilitated early intervention to combat TB.
Article Abstract
Setting: Mahavir DOT Centre, Hyderabad, Telangana, India INTRODUCTION: Urban slums are characterized by crowding, poverty. In such setting due to lack of infection control the transmission of tuberculosis is known to rise, thereby creating a "Hot" spot. Distribution of residences in such areas does not necessarily follow postal codes, making it difficult for health workers to locate TB patients unless accompanied by the STLS.
Objective: To investigate the utility of integrating the help of local postman and geographic positioning system (GPS) to identify and create map of hot spots in an area under a regional DOT centre.
Materials & Methods: Retrospective and prospective demographic data of TB patients enrolled during 12 years (1999-2011) was analysed from the TB register at a ward where number of cases continued to increase despite active implementation of DOTS strategy. Non-Spatial data was generated with the local postman identifying individual house addresses. The corresponding co-ordinates were recorded with GPS and uploaded in Google Earth to identify the locations. Area map was created by software (AutoCAD, Map R3, MapInfo Pro 7.5 Trial Version and MS office Tools). Residences of Index patients were marked in different colours year wise on the map.
Results: Maps displayed in the DOT centre area helped in identifying HOT SPOT and visualization of the clustering of TB cases in the area. Time interval between subsequent infections (3 months-5 years) could be calculated in the locality, within household, neighbourhood and random contacts. Average distances (<1 m) between houses indicated the probable source of infection. Risk factors included crowding, poor ventilation and sanitation contributed to TB transmission in HOT spot area.
Conclusion: Integrating local postman and information technology to identify HOT SPOT in RNTCP, will help in early intervention by health personnel to arrest TB transmission.
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| http://dx.doi.org/10.1016/j.ijtb.2019.02.008 | DOI Listing |
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