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Geographic distribution and predictors of diagnostic delays among possible TB patients in Uganda

BACKGROUND: Understanding the geographic distribution and factors associated with delayed TB diagnosis may help target interventions to reduce delays and improve patient outcomes. METHODS: We conducted a secondary analysis of adults undergoing TB evaluation within a public health demonstration proje...

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Autores principales: Ochom, E., Robsky, K. O., Gupta, A. J., Tamale, A., Kungu, J., Turimumahoro, P., Nakasendwa, S., Rwego, I. B., Muttamba, W., Joloba, M., Ssengooba, W., Davis, J. L., Katamba, A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union Against Tuberculosis and Lung Disease 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10446659/
https://www.ncbi.nlm.nih.gov/pubmed/37736583
http://dx.doi.org/10.5588/pha.23.0010
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author Ochom, E.
Robsky, K. O.
Gupta, A. J.
Tamale, A.
Kungu, J.
Turimumahoro, P.
Nakasendwa, S.
Rwego, I. B.
Muttamba, W.
Joloba, M.
Ssengooba, W.
Davis, J. L.
Katamba, A.
author_facet Ochom, E.
Robsky, K. O.
Gupta, A. J.
Tamale, A.
Kungu, J.
Turimumahoro, P.
Nakasendwa, S.
Rwego, I. B.
Muttamba, W.
Joloba, M.
Ssengooba, W.
Davis, J. L.
Katamba, A.
author_sort Ochom, E.
collection PubMed
description BACKGROUND: Understanding the geographic distribution and factors associated with delayed TB diagnosis may help target interventions to reduce delays and improve patient outcomes. METHODS: We conducted a secondary analysis of adults undergoing TB evaluation within a public health demonstration project in Uganda. Using Global Moran’s I (GMI) and Getis-Ord GI* statistics, we evaluated for residential clustering and hotspots associated with patient-related and health system-related delays. We performed multivariate logistic regression to identify individual predictors of both types of delays. RESULTS: Of 996 adults undergoing TB evaluation (median age: 37 years, IQR 28–49), 333 (33%) experienced patient delays, and 568 (57%) experienced health system delays. Participants were clustered (GMI 0.47–0.64, P ⩽ 0.001) at the sub-county level, but there were no statistically significant hotspots for patient or health system delays. Married individuals were less likely to experience patient delays (OR 0.6, 95% CI 0.48–0.75; P < 0.001). Those aged 38–57 years (OR 1.2, 95% CI 1.07–1.38; P = 0.002) were more likely than those aged ⩾58 years to experience patient delays. Knowledge about TB (OR 0.8, 95% CI 0.63–0.98; P = 0.03) protected against health system delays. CONCLUSIONS: We did not identify geographic hotspots for TB diagnostic delays. Instead, delays were associated with individual factors such as age, marital status and TB knowledge.
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spelling pubmed-104466592023-09-21 Geographic distribution and predictors of diagnostic delays among possible TB patients in Uganda Ochom, E. Robsky, K. O. Gupta, A. J. Tamale, A. Kungu, J. Turimumahoro, P. Nakasendwa, S. Rwego, I. B. Muttamba, W. Joloba, M. Ssengooba, W. Davis, J. L. Katamba, A. Public Health Action Original Articles BACKGROUND: Understanding the geographic distribution and factors associated with delayed TB diagnosis may help target interventions to reduce delays and improve patient outcomes. METHODS: We conducted a secondary analysis of adults undergoing TB evaluation within a public health demonstration project in Uganda. Using Global Moran’s I (GMI) and Getis-Ord GI* statistics, we evaluated for residential clustering and hotspots associated with patient-related and health system-related delays. We performed multivariate logistic regression to identify individual predictors of both types of delays. RESULTS: Of 996 adults undergoing TB evaluation (median age: 37 years, IQR 28–49), 333 (33%) experienced patient delays, and 568 (57%) experienced health system delays. Participants were clustered (GMI 0.47–0.64, P ⩽ 0.001) at the sub-county level, but there were no statistically significant hotspots for patient or health system delays. Married individuals were less likely to experience patient delays (OR 0.6, 95% CI 0.48–0.75; P < 0.001). Those aged 38–57 years (OR 1.2, 95% CI 1.07–1.38; P = 0.002) were more likely than those aged ⩾58 years to experience patient delays. Knowledge about TB (OR 0.8, 95% CI 0.63–0.98; P = 0.03) protected against health system delays. CONCLUSIONS: We did not identify geographic hotspots for TB diagnostic delays. Instead, delays were associated with individual factors such as age, marital status and TB knowledge. International Union Against Tuberculosis and Lung Disease 2023-09-21 2023-09-21 /pmc/articles/PMC10446659/ /pubmed/37736583 http://dx.doi.org/10.5588/pha.23.0010 Text en © 2023 The Union https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/) published by The Union (www.theunion.org (http://www.theunion.org) ).
spellingShingle Original Articles
Ochom, E.
Robsky, K. O.
Gupta, A. J.
Tamale, A.
Kungu, J.
Turimumahoro, P.
Nakasendwa, S.
Rwego, I. B.
Muttamba, W.
Joloba, M.
Ssengooba, W.
Davis, J. L.
Katamba, A.
Geographic distribution and predictors of diagnostic delays among possible TB patients in Uganda
title Geographic distribution and predictors of diagnostic delays among possible TB patients in Uganda
title_full Geographic distribution and predictors of diagnostic delays among possible TB patients in Uganda
title_fullStr Geographic distribution and predictors of diagnostic delays among possible TB patients in Uganda
title_full_unstemmed Geographic distribution and predictors of diagnostic delays among possible TB patients in Uganda
title_short Geographic distribution and predictors of diagnostic delays among possible TB patients in Uganda
title_sort geographic distribution and predictors of diagnostic delays among possible tb patients in uganda
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10446659/
https://www.ncbi.nlm.nih.gov/pubmed/37736583
http://dx.doi.org/10.5588/pha.23.0010
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