<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Ayala S</submitter><funding>ANID FONDECYT Regular</funding><funding>ANID's National Doctoral Fellowship</funding><pagination>6319</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12905264</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>16(1)</volume><pubmed_abstract>In recent years, global concern about tuberculosis (TB) has grown due to the slow progress in achieving control goals, with a record number of cases reported worldwide in 2022. This study analyzed the spatial distribution of all pulmonary TB cases reported between 2016 and 2020 in the Gran Santiago (n = 3348), Chile's largest urban area. Cases were geocoded using an automated cascade method (Bing Maps, Google Maps, and manual verification) and aggregated at the neighborhood level. To detect spatial clusters, we estimated gender-adjusted rates, assessed spatial autocorrelation using Moran's I, and identified spatial clusters with Flexible Scan Statistics. Bivariate analyses were used to explore associations with demographic and residential variables. We observed higher incidence rates in ma</pubmed_abstract><journal>Scientific reports</journal><pubmed_title>Spatial neighborhood patterns of pulmonary tuberculosis in a large urban area: the case of Santiago, Chile.</pubmed_title><pmcid>PMC12905264</pmcid><funding_grant_id>21191111</funding_grant_id><funding_grant_id>1251995</funding_grant_id><pubmed_authors>Chiaravalloti-Neto F</pubmed_authors><pubmed_authors>Ayala S</pubmed_authors><pubmed_authors>Escobar N</pubmed_authors><pubmed_authors>Vizeu Barrozo L</pubmed_authors><pubmed_authors>Canals M</pubmed_authors></additional><is_claimable>false</is_claimable><name>Spatial neighborhood patterns of pulmonary tuberculosis in a large urban area: the case of Santiago, Chile.</name><description>In recent years, global concern about tuberculosis (TB) has grown due to the slow progress in achieving control goals, with a record number of cases reported worldwide in 2022. This study analyzed the spatial distribution of all pulmonary TB cases reported between 2016 and 2020 in the Gran Santiago (n = 3348), Chile's largest urban area. Cases were geocoded using an automated cascade method (Bing Maps, Google Maps, and manual verification) and aggregated at the neighborhood level. To detect spatial clusters, we estimated gender-adjusted rates, assessed spatial autocorrelation using Moran's I, and identified spatial clusters with Flexible Scan Statistics. Bivariate analyses were used to explore associations with demographic and residential variables. We observed higher incidence rates in ma</description><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Jan</publication><modification>2026-07-15T16:29:37.103Z</modification><creation>2026-07-07T03:08:15.211Z</creation></dates><accession>S-EPMC12905264</accession><cross_references><pubmed>41588111</pubmed><doi>10.1038/s41598-026-36462-w</doi></cross_references></HashMap>