# India Tuberculosis Tracker Tuberculosis (TB) is the 7th leading cause of death in India and the World Health Organization (WHO) estimates that 445,000 people in India died of Tuberculosis in 2019. Through a collaborative partnership with the Central Tuberculosis Division of the Government of India, we map a variety of TB metrics for parliamentary and assembly constituencies of India to enable policymakers to monitor the live status of TB in thier constituency. This can allow users to examine the geographic variation of TB metrics in India. - [Geographic Visualization](#dashboard) - [Methodology](#method) - [Publications](#publications) --- ## Geographic Visualization ### India Tuberculosis Tracker [ View Visualization arrow\_circle\_right ](https://app.powerbi.com/view?r=eyJrIjoiMWFkYzg5NTQtMzM3ZS00ZTc4LTljOWYtMDlhZDU0NDg2NmM4IiwidCI6IjFlZTNiZjllLWRlNjktNDczOC1hZTZiLTAzZTNiMmM2MTVlMiJ9&pageName=ReportSection) ![graph of India](/sites/g/files/omnuum10546/files/styles/hwp_1_1__480x480/public/2025-05/India-Tuberculosis-Tracker.png?itok=jF1Dz8oA) ## Methodology ### Data Sources expand\_more We extracted 2017 district-wise TB data sets from the 2018 annual TB report linked here: . ### Crosswalk Methodology (Districts to Parliamentary Constituencies) expand\_more Using district-level information for vaccines, we generated a crosswalk between districts and parliamentary constituencies. We adopted a dasymetric mapping technique that allowed for weighting of the population in each PC assuming the indicators were distributed uniformly across the population in one district. See the workflow below for more details: 1. Calculate district area (dist\_area): perform 'Calculate Geometry' function on the district shapefile, using the Kalianpur 1975/lndia Zone lla (EPSG: 24379) coordinate system 2. Calculate district population (dist\_pop) in 2015: perform 'zonal statistics' command using district shapefile as the 'zone' and the WoldPop2015 as the data raster 3. Link each segment contained both district ID and PC ID of the overlapping over: perform 'intersect' command to district and PC shapefile, create a new shapefile called Dist\_PC\_intersect. In total, there are 3,828 segments 4. Calculate segment area (seg\_area) and segment population in 2015 (seg\_pop) following step 1 and 2 5. Calculate percentage of district population (Pct\_pop) in each segment: divide segment population by district population in 2015, this is the estimate for percentage of population in 2017. 6. Calculate percentage of district area (Pct\_ area) in each segment by segment area dividing district area. Remove segments that was less than a hundredth of a percent of the original district area. Those extremely small area are created possibly because the slightly boundary inaccuracies between district and PC shapefiles. A hundredth is chosen as a conservative threshold. There remains 3,377 in the intersected shapefile 7. Calculate number of total patients notified in each segment: multiply total patients notified in the district by percentage of district population (2015) in the given segment 8. Calculate segment population in 2017: multiply district population in 2017 (from TB report ) by percentage of population (Pct\_pop, from WorldPop2015) 9. Calculate PC level estimates for total patients notified and population: aggregated segment level total patients notified , and population by PC ID 10. Calculate PC level total patients notified per 100,000 population ## Related Publications Download 1 citation download- [BibTeX](/bibcite/export?pager_style=standard_pager&number_of_items=3&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_domain1%5D&taxonomy_filters%5Bfield_hwp_c_country1234567%5D&taxonomy_filters%5Bfield_hwp_c_projects12345678910%5D%5B0%5D%5Btarget_id%5D=123240&&&format=bibtex) - [EndNote X3 XML](/bibcite/export?pager_style=standard_pager&number_of_items=3&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_domain1%5D&taxonomy_filters%5Bfield_hwp_c_country1234567%5D&taxonomy_filters%5Bfield_hwp_c_projects12345678910%5D%5B0%5D%5Btarget_id%5D=123240&&&format=endnote8) - [EndNote 7 XML](/bibcite/export?pager_style=standard_pager&number_of_items=3&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_domain1%5D&taxonomy_filters%5Bfield_hwp_c_country1234567%5D&taxonomy_filters%5Bfield_hwp_c_projects12345678910%5D%5B0%5D%5Btarget_id%5D=123240&&&format=endnote7) - [Endnote tagged](/bibcite/export?pager_style=standard_pager&number_of_items=3&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_domain1%5D&taxonomy_filters%5Bfield_hwp_c_country1234567%5D&taxonomy_filters%5Bfield_hwp_c_projects12345678910%5D%5B0%5D%5Btarget_id%5D=123240&&&format=tagged) - [Marc](/bibcite/export?pager_style=standard_pager&number_of_items=3&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_domain1%5D&taxonomy_filters%5Bfield_hwp_c_country1234567%5D&taxonomy_filters%5Bfield_hwp_c_projects12345678910%5D%5B0%5D%5Btarget_id%5D=123240&&&format=marc) - [PubMedId](/bibcite/export?pager_style=standard_pager&number_of_items=3&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_domain1%5D&taxonomy_filters%5Bfield_hwp_c_country1234567%5D&taxonomy_filters%5Bfield_hwp_c_projects12345678910%5D%5B0%5D%5Btarget_id%5D=123240&&&format=pubmed_id) - [RIS](/bibcite/export?pager_style=standard_pager&number_of_items=3&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_domain1%5D&taxonomy_filters%5Bfield_hwp_c_country1234567%5D&taxonomy_filters%5Bfield_hwp_c_projects12345678910%5D%5B0%5D%5Btarget_id%5D=123240&&&format=ris) ### 2021 Pardeshi G, Wang W, Kim J, Blossom J, Kim R, Subramanian. [TB notification rates across parliamentary constituencies in India: a step towards data-driven political engagement](https://doi.org/10.1111/tmi.13574). *Tropical Medicine & International Health*. 2021;26(7):730-742. Pardeshi G, Wang W, Kim J, Blossom J, Kim R, Subramanian. [TB notification rates across parliamentary constituencies in India: a step towards data-driven political engagement](https://doi.org/10.1111/tmi.13574). *Tropical Medicine & International Health*. 2021;26(7):730-742. - add\_circle do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://doi.org/10.1111/tmi.13574) Objective National averages obscure geographic variation in program performance. We determined Parliamentary Constituency (PC)-wise estimates of TB notification to guide political engagement. Methods We extracted district-level TB notification data from... - [ descriptionPublisher's Version](https://doi.org/10.1111/tmi.13574) [ More Publications arrow\_circle\_right ](/publications)