INT J TUBERC LUNG DIS 18(3):377–378 © 2014 The Union
CORRESPONDENCE
Correspondence Active case finding for tuberculosis: what is the most informative measure for policy makers? With tuberculosis (TB) case detection trends stagnating, there have been calls for active case finding (ACF) in those not actively seeking care.1 An ACF study in Uganda observed ‘a high yield of undiagnosed TB cases’: 24.4% among those with chronic cough investigated with one spot and one early morning sputum using microscopy and culture, a number needed to screen (NNS) of 5 to detect one case among those with chronic cough, and a NNS of 131 among all participants.2 Using the data of Sekandi et al., one can calculate alternative measures of screening yield: 19.6% (39/199) of all participants with chronic cough, 0.7% (39/5102) of those screened, and 0.5% (39/7391) of those attempted to screen. Which of these are the most informative measures? To judge the opportunity cost of investment in ACF versus other TB control activities, ACF yield measures should: 1) correspond to an ACF intervention that has ‘real-world’ feasibility, 2) identify how ACF complements passive case finding, 3) reflect required resource investment, and 4) be predictive of potential program impact, acknowledging that the impact on incidence and mortality is often too challenging to measure. Below, we highlight how current reporting of ACF studies falls short of these requirements, and suggest alternative approaches. 1 To date, ACF studies often use screening strategies such as induced sputum or culture on all samples that are beyond those used for routine facility-based passive case finding in the same setting, and are thus unlikely to be implemented.2–4 To move beyond proof-of-principle studies, future research should focus on strategies that can be implemented. 2 Screening those who are likely to present for passive case finding will not have an important impact.5 Of the 39 cases detected through home visits, 76.5% had visited a care facility for their cough, suggesting that in this Ugandan urban community improved passive case finding at facility level could be more efficient than door-to-door ACF. Reporting yield and NNS both overall and among those who had not yet visited a facility for their cough could enrich the information conveyed. 3 Yield and NNS do not reflect the human resource effort needed. For example, return visits for spot and morning sputum in the study by Sekandi et al. doubles the human resources but counts as one ‘screen’ in NNS. The NNS also does not reflect the time and resources spent on those visits where
nobody was home (31% in the Uganda study). Reporting a simple yield per one month’s full time worker or performing a full cost effectiveness analysis to measure cost per case detected or cost per disability adjusted life year averted would better communicate the resources needed, and allow for direct comparison of competing uses for the same resources.5 4 A reduction in the pool of infectious individuals can only occur if those diagnosed by ACF start TB treatment at a health facility. While it falls short of measuring population impact, this information is an important next step and could be captured in the ‘number needed to screen to start one individual on TB treatment’. Annelies Van Rie* Colleen Hanrahan† *Department of Epidemiology University of North Carolina at Chapel Hill Chapel Hill, North Carolina † Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland USA e-mail: [email protected] http://dx.doi.org/10.5588/ijtld.13.0924
References 1 Lönnroth K, Corbett E, Golub J, et al. Systematic screening for active tuberculosis: rationale, definitions and key considerations. Int J Tuberc Lung Dis 2013; 17: 289–298. 2 Sekandi J N, List J, Luzze H, et al. Yield of undetected tuberculosis and human inmmunodeficiency virus coinfection from active case finding in urban Uganda. Int J Tuberc Lung Dis 2014; 18: 13–19. 3 Shapiro A E, Variava E, Rakgokong M H, et al. Communitybased targeted case finding for tuberculosis and HIV in household contacts of patients with tuberculosis in South Africa. Am J Respir Crit Care Med 2012; 185: 1110–1116. 4 Thind D, Charalambous S, Tongman A, Churchyard G, Grant A D. An evaluation of ‘Ribolola’: a household tuberculosis contact tracing programme in North West Province, South Africa. Int J Tuberc Lung Dis 2012; 16: 1643–1648. 5 Golub J E, Dowdy D W. Screening for active tuberculosis: methodological challenges in implementation and evaluation. Int J Tuberc Lung Dis 2013; 17: 856–865.
In reply In selecting the measure of active case finding (ACF) yield in the Kampala, Uganda, study,1 we carefully considered most of the points raised by Drs Van Rie and Hanrahan. The authors pose a valid question that we need to think about with regard to policy decision
CORRESPONDENCE
Correspondence Active case finding for tuberculosis: what is the most informative measure for policy makers? With tuberculosis (TB) case detection trends stagnating, there have been calls for active case finding (ACF) in those not actively seeking care.1 An ACF study in Uganda observed ‘a high yield of undiagnosed TB cases’: 24.4% among those with chronic cough investigated with one spot and one early morning sputum using microscopy and culture, a number needed to screen (NNS) of 5 to detect one case among those with chronic cough, and a NNS of 131 among all participants.2 Using the data of Sekandi et al., one can calculate alternative measures of screening yield: 19.6% (39/199) of all participants with chronic cough, 0.7% (39/5102) of those screened, and 0.5% (39/7391) of those attempted to screen. Which of these are the most informative measures? To judge the opportunity cost of investment in ACF versus other TB control activities, ACF yield measures should: 1) correspond to an ACF intervention that has ‘real-world’ feasibility, 2) identify how ACF complements passive case finding, 3) reflect required resource investment, and 4) be predictive of potential program impact, acknowledging that the impact on incidence and mortality is often too challenging to measure. Below, we highlight how current reporting of ACF studies falls short of these requirements, and suggest alternative approaches. 1 To date, ACF studies often use screening strategies such as induced sputum or culture on all samples that are beyond those used for routine facility-based passive case finding in the same setting, and are thus unlikely to be implemented.2–4 To move beyond proof-of-principle studies, future research should focus on strategies that can be implemented. 2 Screening those who are likely to present for passive case finding will not have an important impact.5 Of the 39 cases detected through home visits, 76.5% had visited a care facility for their cough, suggesting that in this Ugandan urban community improved passive case finding at facility level could be more efficient than door-to-door ACF. Reporting yield and NNS both overall and among those who had not yet visited a facility for their cough could enrich the information conveyed. 3 Yield and NNS do not reflect the human resource effort needed. For example, return visits for spot and morning sputum in the study by Sekandi et al. doubles the human resources but counts as one ‘screen’ in NNS. The NNS also does not reflect the time and resources spent on those visits where
nobody was home (31% in the Uganda study). Reporting a simple yield per one month’s full time worker or performing a full cost effectiveness analysis to measure cost per case detected or cost per disability adjusted life year averted would better communicate the resources needed, and allow for direct comparison of competing uses for the same resources.5 4 A reduction in the pool of infectious individuals can only occur if those diagnosed by ACF start TB treatment at a health facility. While it falls short of measuring population impact, this information is an important next step and could be captured in the ‘number needed to screen to start one individual on TB treatment’. Annelies Van Rie* Colleen Hanrahan† *Department of Epidemiology University of North Carolina at Chapel Hill Chapel Hill, North Carolina † Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland USA e-mail: [email protected] http://dx.doi.org/10.5588/ijtld.13.0924
References 1 Lönnroth K, Corbett E, Golub J, et al. Systematic screening for active tuberculosis: rationale, definitions and key considerations. Int J Tuberc Lung Dis 2013; 17: 289–298. 2 Sekandi J N, List J, Luzze H, et al. Yield of undetected tuberculosis and human inmmunodeficiency virus coinfection from active case finding in urban Uganda. Int J Tuberc Lung Dis 2014; 18: 13–19. 3 Shapiro A E, Variava E, Rakgokong M H, et al. Communitybased targeted case finding for tuberculosis and HIV in household contacts of patients with tuberculosis in South Africa. Am J Respir Crit Care Med 2012; 185: 1110–1116. 4 Thind D, Charalambous S, Tongman A, Churchyard G, Grant A D. An evaluation of ‘Ribolola’: a household tuberculosis contact tracing programme in North West Province, South Africa. Int J Tuberc Lung Dis 2012; 16: 1643–1648. 5 Golub J E, Dowdy D W. Screening for active tuberculosis: methodological challenges in implementation and evaluation. Int J Tuberc Lung Dis 2013; 17: 856–865.
In reply In selecting the measure of active case finding (ACF) yield in the Kampala, Uganda, study,1 we carefully considered most of the points raised by Drs Van Rie and Hanrahan. The authors pose a valid question that we need to think about with regard to policy decision
