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Wennemann S, Mudarshiru B, Zawedde-Muyanja S, Siddharthan T, Jackson PD. The effect of biomass smoke exposure on quality-of-life among Ugandan patients treated for tuberculosis: A cross-sectional analysis. PLOS GLOBAL PUBLIC HEALTH 2024; 4:e0002892. [PMID: 38330053 PMCID: PMC10852290 DOI: 10.1371/journal.pgph.0002892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/17/2024] [Indexed: 02/10/2024]
Abstract
More than half the global population burns biomass fuels for cooking and home heating, especially in low-middle income countries. This practice is a prominent source of indoor air pollution and has been linked to the development of a variety of cardiopulmonary diseases, including Tuberculosis (TB). The purpose of this cross-sectional study was to investigate the association between current biomass smoke exposure and self-reported quality of life scores in a cohort of previous TB patients in Uganda. We reviewed medical records from six TB clinics from 9/2019-9/2020 and conducted phone interviews to obtain information about biomass smoke exposure. A random sample of these patients were asked to complete three validated quality-of-life surveys including the St. Georges Respiratory Questionnaire (SGRQ), the EuroQol 5 Dimension 3 Level system (EQ-5D-3L) which includes the EuroQol Visual Analog Scale (EQ-VAS), and the Patient Health Questionnaire 9 (PHQ-9). The cohort was divided up into 3 levels based on years of smoke exposure-no-reported smoke exposure (0 years), light exposure (1-19 years), and heavy exposure (20+ years), and independent-samples-Kruskal-Wallis testing was performed with post-hoc pairwise comparison and the Bonferroni correction. The results of this testing indicated significant increases in survey scores for patients with current biomass exposure and a heavy smoke exposure history (20+ years) compared to no reported smoke exposure in the SGRQ activity scores (adj. p = 0.018) and EQ-5D-3L usual activity scores (adj. p = 0.002), indicating worse activity related symptoms. There was a decrease in EQ-VAS scores for heavy (adj. p = 0.007) and light (adj. p = 0.017) exposure groups compared to no reported exposure, indicating lower perceptions of overall health. These results may suggest worse outcomes or baseline health for TB patients exposed to biomass smoke at the time of treatment and recovery, however further research is needed to characterize the effect of indoor air pollution on TB treatment outcomes.
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Affiliation(s)
- Sophie Wennemann
- Virginia Commonwealth University School of Medicine, Richmond, Virginia, United States of America
| | | | - Stella Zawedde-Muyanja
- Infectious Disease Institute, Makerere University College of Health Science, Kampala, Uganda
| | - Trishul Siddharthan
- Division of Pulmonary and Critical Care, University of Miami, Miami, Florida, United States of America
| | - Peter D. Jackson
- Division of Pulmonary Critical Care, Virginia Commonwealth University, Richmond, Virginia, United States of America
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Aceng FL, Kabwama SN, Ario AR, Etwom A, Turyahabwe S, Mugabe FR. Spatial distribution and temporal trends of tuberculosis case notifications, Uganda: a ten-year retrospective analysis (2013-2022). BMC Infect Dis 2024; 24:46. [PMID: 38177991 PMCID: PMC10765632 DOI: 10.1186/s12879-023-08951-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 12/25/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Uganda has a high incidence and prevalence of tuberculosis (TB). Analysis of spatial and temporal distribution of TB is an important tool for supporting spatial decision-making, planning, and policy formulations; however, this information is not readily available in Uganda. We determined the spatial distribution and temporal trends of tuberculosis notifications in Uganda, 2013-2022. METHODS We conducted a retrospective analysis of routinely-generated program data reported through the National TB and Leprosy Programme (NTLP) surveillance system. We abstracted data on all TB cases diagnosed from 2013 to 2022 by district and region. We drew choropleth maps for Uganda showing the TB case notification rates (CNR) per 100,000 and calculated the CNR using the cases per district as the numerator and individual district populations as the denominators. Population estimates were obtained from the 2014 National Population and Housing Census, and a national growth rate of 3% was used to estimate the annual population increase. RESULTS Over the entire study period, 568,957 cases of TB were reported in Uganda. There was a 6% annual increase in TB CNR reported from 2013 (134/100,000) to 2022 (213/100,000) (p-value for trend p < 0.00001). Cases were reported from all 12 Ministry of Health regions during the entire period. The distribution of CNR was heterogeneous throughout the country and over time. Moroto, Napak and Kampala districts had consistently high CNR throughout the ten years. Kalangala district had lower CNR from 2013 to 2018 but high CNR from 2019 to 2022. Moroto region, in the northeast, had consistently high CNR while Mbale and Soroti regions in Eastern Uganda had the lowest CNR throughout the ten years. CONCLUSION There was an overall increasing trend in TB CNR from 2013 to 2022. We recommend that the National TB program institutes intensified measures aided by more funding to mitigate and reverse the negative impacts of the COVID-19 pandemic on TB.
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Affiliation(s)
- Freda Loy Aceng
- Uganda Public Health Fellowship Program, P.O. Box 7072, Kampala, Uganda.
| | | | | | - Alfred Etwom
- National Tuberculosis and Leprosy Program, Ministry of Health, Kampala, Uganda
| | - Stavia Turyahabwe
- National Tuberculosis and Leprosy Program, Ministry of Health, Kampala, Uganda
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Ledesma JR, Basting A, Chu HT, Ma J, Zhang M, Vongpradith A, Novotney A, Dalos J, Zheng P, Murray CJL, Kyu HH. Global-, Regional-, and National-Level Impacts of the COVID-19 Pandemic on Tuberculosis Diagnoses, 2020-2021. Microorganisms 2023; 11:2191. [PMID: 37764035 PMCID: PMC10536333 DOI: 10.3390/microorganisms11092191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Evaluating cross-country variability on the impact of the COVID-19 pandemic on tuberculosis (TB) may provide urgent inputs to control programs as countries recover from the pandemic. We compared expected TB notifications, modeled using trends in annual TB notifications from 2013-2019, with observed TB notifications to compute the observed to expected (OE) ratios for 170 countries. We applied the least absolute shrinkage and selection operator (LASSO) method to identify the covariates, out of 27 pandemic- and tuberculosis-relevant variables, that had the strongest explanatory power for log OE ratios. The COVID-19 pandemic was associated with a 1.55 million (95% CI: 1.26-1.85, 21.0% [17.5-24.6%]) decrease in TB diagnoses in 2020 and a 1.28 million (0.90-1.76, 16.6% [12.1-21.2%]) decrease in 2021 at a global level. India, Indonesia, the Philippines, and China contributed the most to the global declines for both years, while sub-Saharan Africa achieved pre-pandemic levels by 2021 (OE ratio = 1.02 [0.99-1.05]). Age-stratified analyses revealed that the ≥ 65-year-old age group experienced greater relative declines in TB diagnoses compared with the under 65-year-old age group in 2020 (RR = 0.88 [0.81-0.96]) and 2021 (RR = 0.88 [0.79-0.98]) globally. Covariates found to be associated with all-age OE ratios in 2020 were age-standardized smoking prevalence in 2019 (β = 0.973 [0.957-990]), school closures (β = 0.988 [0.977-0.998]), stay-at-home orders (β = 0.993 [0.985-1.00]), SARS-CoV-2 infection rate (β = 0.991 [0.987-0.996]), and proportion of population ≥65 years (β = 0.971 [0.944-0.999]). Further research is needed to clarify the extent to which the observed declines in TB diagnoses were attributable to disruptions in health services, decreases in TB transmission, and COVID-19 mortality among TB patients.
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Affiliation(s)
- Jorge R. Ledesma
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Epidemiology, Brown University School of Public Health, 121 S Main St, Providence, RI 02912, USA
| | - Ann Basting
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Huong T. Chu
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Health Metrics Sciences, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA
| | - Jianing Ma
- Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH 43210, USA;
| | - Meixin Zhang
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Avina Vongpradith
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Amanda Novotney
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Jeremy Dalos
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Peng Zheng
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Health Metrics Sciences, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA
| | - Christopher J. L. Murray
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Health Metrics Sciences, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA
| | - Hmwe H. Kyu
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Health Metrics Sciences, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA
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