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

Author:

Ochom E.1,Robsky K. O.2,Gupta A. J.3,Tamale A.4,Kungu J.5,Turimumahoro P.1,Nakasendwa S.1,Rwego I. B.6,Muttamba W.7,Joloba M.8,Ssengooba W.8,Davis J. L.9,Katamba A.10

Affiliation:

1. Uganda Tuberculosis Implementation Research Consortium, Kampala, Uganda;

2. Uganda Tuberculosis Implementation Research Consortium, Kampala, Uganda;, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD

3. Uganda Tuberculosis Implementation Research Consortium, Kampala, Uganda;, Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

4. Departments of Veterinary Medicine and Animal Resources

5. Biotechnical and Biolab Sciences, and

6. Biosecurity, Ecosystem and Veterinary Public Health, College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda

7. Lung Institute, and

8. Department of Medical Microbiology, College of Health Sciences, Makerere University, Kampala, Uganda;

9. Uganda Tuberculosis Implementation Research Consortium, Kampala, Uganda;, Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, Pulmonary, Critical Care and Sleep Medicine Section, Yale School of Medicine, New Haven, CT, USA;

10. Uganda Tuberculosis Implementation Research Consortium, Kampala, Uganda;, Clinical Epidemiology Unit, Makerere University, College of Health Sciences, Kampala, Uganda

Abstract

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.

Publisher

International Union Against Tuberculosis and Lung Disease

Subject

Public Health, Environmental and Occupational Health,Health Policy

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Rural–Urban Inequities in Tuberculosis-Related Practices in Equatorial Guinea;Journal of Epidemiology and Global Health;2023-10-23

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