Mapping local hot spots with routine tuberculosis data: A pragmatic approach to identify spatial variability.
Autor: | Brooks MB; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America., Millones AK; Socios En Salud Sucursal Perú, Lima, Peru., Puma D; Socios En Salud Sucursal Perú, Lima, Peru., Contreras C; Socios En Salud Sucursal Perú, Lima, Peru., Jimenez J; Socios En Salud Sucursal Perú, Lima, Peru., Tzelios C; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.; Socios En Salud Sucursal Perú, Lima, Peru., Jenkins HE; Boston University School of Public Health, Department of Biostatistics, Boston, Massachusetts, United States of America., Yuen CM; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.; Brigham and Women's Hospital, Division of Global Health Equity, Boston, Massachusetts, United States of America., Keshavjee S; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.; Socios En Salud Sucursal Perú, Lima, Peru.; Brigham and Women's Hospital, Division of Global Health Equity, Boston, Massachusetts, United States of America., Lecca L; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.; Socios En Salud Sucursal Perú, Lima, Peru., Becerra MC; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.; Socios En Salud Sucursal Perú, Lima, Peru. |
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Jazyk: | angličtina |
Zdroj: | PloS one [PLoS One] 2022 Mar 24; Vol. 17 (3), pp. e0265826. Date of Electronic Publication: 2022 Mar 24 (Print Publication: 2022). |
DOI: | 10.1371/journal.pone.0265826 |
Abstrakt: | Objective: To use routinely collected data, with the addition of geographic information and census data, to identify local hot spots of rates of reported tuberculosis cases. Design: Residential locations of tuberculosis cases identified from eight public health facilities in Lima, Peru (2013-2018) were linked to census data to calculate neighborhood-level annual case rates. Heat maps of tuberculosis case rates by neighborhood were created. Local indicators of spatial autocorrelation, Moran's I, were used to identify where in the study area spatial clusters and outliers of tuberculosis case rates were occurring. Age- and sex-stratified case rates were also assessed. Results: We identified reports of 1,295 TB cases across 74 neighborhoods during the five-year study period, for an average annual rate of 124.2 reported TB cases per 100,000 population. In evaluating case rates by individual neighborhood, we identified a median rate of reported cases of 123.6 and a range from 0 to 800 cases per 100,000 population. Individuals aged 15-44 years old and men had higher case rates than other age groups and women. Locations of both hot and cold spots overlapped across age- and gender-specific maps. Conclusions: There is significant geographic heterogeneity in rates of reported TB cases and evident hot and cold spots within the study area. Characterization of the spatial distribution of these rates and local hot spots may be one practical tool to inform the work of local coalitions to target TB interventions in their zones. Competing Interests: The authors have declared that no competing interests exist. |
Databáze: | MEDLINE |
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