Open Access Research article

Prioritising between direct observation of therapy and case-finding interventions for tuberculosis: use of population impact measures

Richard F Heller1*, Islay Gemmell1, Richard Edwards2, Iain Buchan1, Shally Awasthi3 and James A Volmink4

Author Affiliations

1 Evidence for Population Health Unit, University of Manchester, Oxford Road, Manchester M13 9PT, UK

2 University of Otago, New Zealand

3 King George's Medical College, Lucknow, India

4 Faculty of Health Sciences, University of Stellenbosch, South Africa

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BMC Medicine 2006, 4:35  doi:10.1186/1741-7015-4-35

Published: 20 December 2006



Population impact measures (PIMs) have been developed as tools to help policy-makers with locally relevant decisions over health risks and benefits. This involves estimating and prioritising potential benefits of interventions in specific populations. Using tuberculosis (TB) in India as an example, we examined the population impact of two interventions: direct observation of therapy and increasing case-finding.


PIMs were calculated using published literature and national data for India, and applied to a notional population of 100 000 people. Data included the incidence or prevalence of smear-positive TB and the relative risk reduction from increasing case finding and the use of direct observation of therapy (applied to the baseline risks over the next year), and the incremental proportion of the population eligible for the proposed interventions.


In a population of 100 000 people in India, the directly observed component of the Directly Observed Treatment, Short-course (DOTS) programme may prevent 0.188 deaths from TB in the next year compared with 1.79 deaths by increasing TB case finding. The costs of direct observation are (in international dollars) I$5960 and of case finding are I$4839 or I$31702 and I$2703 per life saved respectively.


Increasing case-finding for TB will save nearly 10 times more lives than will the use of the directly observed component of DOTS in India, at a smaller cost per life saved. The demonstration of the population impact, using simple and explicit numbers, may be of value to policy-makers as they prioritise interventions for their populations.