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 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.

CONTEXTE: Comprendre la distribution géographique et les facteurs associés aux retards de diagnostic de la TB peut aider à cibler les interventions visant à réduire les retards et à améliorer les résultats pour les patients. MÉTHODES: Nous avons effectué une analyse secondaire des adultes soumis à une évaluation de la TB dans le cadre d'un projet de démonstration de santé publique en Ouganda. À l'aide des statistiques Global Moran's I (GMI) et Getis-Ord GI*, nous avons évalué les regroupements résidentiels et les points critiques associés aux retards liés aux patients et au système de santé. Nous avons effectué une régression logistique multivariée pour identifier les prédicteurs individuels des deux types de retards. RÉSULTATS: Sur les 996 adultes soumis à une évaluation de la TB (âge médian : 37 ans, IQR 28­49), 333 (33%) ont subi des retards liés aux patients et 568 (57%) ont subi des retards liés au système de santé. Les participants étaient regroupés (GMI 0,47­0,64 ; P ⩽ 0,001) au niveau du sous-comté, mais il n'y avait pas de points critiques statistiquement significatifs pour les retards des patients ou du système de santé. Les personnes mariées étaient moins susceptibles de subir des retards de la part des patients (OR 0,6 ; 95% CI 0,48­0,75 ; P < 0,001). Les personnes âgées de 38 à 57 ans (OR 1,2 ; 95% CI 1,07­1,38 ; P = 0,002) étaient plus susceptibles que celles âgées de ⩾58 ans de subir des retards. Les connaissances sur la TB (OR 0,8 ; 95% CI 0,63­0,98 ; P = 0,03) protégeaient contre les retards du système de santé. CONCLUSIONS: Nous n'avons pas identifié de points critiques géographiques pour les retards de diagnostic de la TB. Les retards étaient plutôt associés à des facteurs individuels tels que l'âge, la situation matrimoniale et les connaissances sur la TB.

Cost to perform door-to-door universal sputum screening for TB in a high-burden community.

BACKGROUND: Population-based active case-finding (ACF) identifies people with TB in communities but can be costly. METHODS: We conducted an empiric costing study within a door-to-door household ACF campaign in an urban community in Uganda, where all adults, regardless of symptoms, were screened by sputum Xpert Ultra testing. We used a combination of direct observation and self-reported logs to estimate staffing requirements. Study budgets were reviewed to collect costs of overheads, equipment, and consumables. Our primary outcome was the cost per person diagnosed with TB. RESULTS: Over a 28-week period, three teams of two people collected sputum from 11,341 adults, of whom 48 (0.4%) tested positive for TB. Screening 1,000 adults required 258 person-hours of effort at a cost of US$35,000, 70% of which was for GeneXpert cartridges. The estimated cost per person screened was $36 (95% uncertainty range [95% UR] 34­38), and the cost per person diagnosed with Xpert-positive TB was $8,400 (95% UR 8,000­8,900). The prevalence of TB in the underlying community was the primary modifiable determinant of the cost per person diagnosed. CONCLUSION: Door-to-door screening can be feasibly performed at scale, but will require effective triage and identification of high-prevalence populations to be affordable and cost-effective.

The impact of time at home on potential yield of home-based TB contact investigation.

BACKGROUND: The yield of TB contact tracing is often limited by challenges in reaching individuals during the screening process. We investigated the times at which index patients and household contacts were typically at home and the potential effects of expanding the timing of home-based contact investigation.METHODS: Index patients and household contacts in Kampala, Uganda, were asked about their likely availability at different day/time combinations. We calculated the "participant identification gap" (defined as the proportion of participants who reported being home <50% of the time) during business hours only. We then estimated the incremental reduction in the participant identification gap if hours were expanded to include weekday evenings, Saturdays, and Sundays. Statistical significance was assessed using McNemar´s tests.RESULTS: Nearly half of eligible individuals (42% of index patients and 52% of contacts) were not likely to be home during contact investigation conducted only during business hours. Expanding to weekday evenings, Saturdays, and Sundays would reduce this participant identification gap to 15% among index patients and 18% among contacts - while also reducing differences by sex and employment.CONCLUSIONS: Expanding hours for conducting contact investigation or other home-based health interventions could substantially reduce the number of individuals missed and address disparities in access to care.

Number needed to screen for TB in clinical, structural or occupational risk groups.

BACKGROUND: Screening for active TB using active case-finding (ACF) may reduce TB incidence, prevalence, and mortality; however, yield of ACF interventions varies substantially across populations. We systematically reviewed studies reporting on ACF to calculate the number needed to screen (NNS) for groups at high risk for TB.METHODS: We conducted a literature search for studies reporting ACF for adults published between November 2010 and February 2020. We determined active TB prevalence detected through various screening strategies and calculated crude NNS for - TB confirmed using culture or Xpert® MTB/RIF, and weighted mean NNS stratified by screening strategy, risk group, and country-level TB incidence.RESULTS: We screened 27,223 abstracts; 90 studies were included (41 in low/moderate and 49 in medium/high TB incidence settings). High-risk groups included inpatients, outpatients, people living with diabetes (PLWD), migrants, prison inmates, persons experiencing homelessness (PEH), healthcare workers, and miners. Screening strategies included symptom-based screening, chest X-ray and Xpert testing. NNS varied widely across and within incidence settings based on risk groups and screening methods. Screening tools with higher sensitivity (e.g., Xpert, CXR) were associated with lower NNS estimates.CONCLUSIONS: NNS for ACF strategies varies substantially between adult risk groups. Specific interventions should be tailored based on local epidemiology and costs.

A systematic review of the number needed to screen for active TB among people living with HIV.

BACKGROUND: Systematic screening for active TB is recommended for all people living with HIV (PLWH); however, case detection remains poor globally. We investigated the yield of active case finding (ACF) by calculating the number needed to screen (NNS) to detect a case of active TB among PLWH.METHODS: We identified studies reporting ACF for TB among PLWH published from November 2010 to February 2020. We calculated crude NNS for Xpert- or culture-confirmed TB and weighted mean NNS stratified by screening approach, population/risk group, and country TB burden.RESULTS: Of the 27,221 abstracts screened, we identified 58 studies eligible for inclusion, including 5 in low/moderate TB incidence settings and 53 in medium/high incidence settings. Populations screened for TB included inpatients, outpatients not receiving antiretroviral therapy (ART), outpatients receiving ART, those with CD4 < 200 cells/µL, children aged ≤15 years, pregnant PLWH, and PLWH in prisons. Screening tools included symptom-based screening, chest X-ray, C-reactive protein levels, and Xpert. The weighted mean NNS varied across groups but was consistently low, ranging from 4 among inpatients in moderate/high TB burden settings to 137 among pregnant PLWH in moderate/high TB burden settings.CONCLUSIONS: ACF is a high yield intervention among PLWH. Approaches to screening should be tailored to local epidemiological and health-system contexts, and sensitive screening tools such as Xpert should be implemented where feasible.