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Open Access Research article

How to optimize tuberculosis case finding: explorations for Indonesia with a health system model

Riris A Ahmad12*, Yodi Mahendradhata13, Jane Cunningham4, Adi Utarini1 and Sake J de Vlas2

Author Affiliations

1 Department of Public Health, Faculty of Medicine, Gadjah Mada University, Jogjakarta, Indonesia

2 Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands

3 Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium

4 WHO/TDR, Geneva, Switzerland

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BMC Infectious Diseases 2009, 9:87  doi:10.1186/1471-2334-9-87

Published: 8 June 2009

Abstract

Background

A mathematical model was designed to explore the impact of three strategies for better tuberculosis case finding. Strategies included: (1) reducing the number of tuberculosis patients who do not seek care; (2) reducing diagnostic delay; and (3) engaging non-DOTS providers in the referral of tuberculosis suspects to DOTS services in the Indonesian health system context. The impact of these strategies on tuberculosis mortality and treatment outcome was estimated using a mathematical model of the Indonesian health system.

Methods

The model consists of multiple compartments representing logical movement of a respiratory symptomatic (tuberculosis suspect) through the health system, including patient- and health system delays. Main outputs of the model are tuberculosis death rate and treatment outcome (i.e. full or partial cure). We quantified the model parameters for the Jogjakarta province context, using a two round Delphi survey with five Indonesian tuberculosis experts.

Results

The model validation shows that four critical model outputs (average duration of symptom onset to treatment, detection rate, cure rate, and death rate) were reasonably close to existing available data, erring towards more optimistic outcomes than are actually reported. The model predicted that an intervention to reduce the proportion of tuberculosis patients who never seek care would have the biggest impact on tuberculosis death prevention, while an intervention resulting in more referrals of tuberculosis suspects to DOTS facilities would yield higher cure rates. This finding is similar for situations where the alternative sector is a more important health resource, such as in most other parts of Indonesia.

Conclusion

We used mathematical modeling to explore the impact of Indonesian health system interventions on tuberculosis treatment outcome and deaths. Because detailed data were not available regarding the current Indonesian population, we relied on expert opinion to quantify the parameters. The fact that the model output showed similar results to epidemiological data suggests that the experts had an accurate understanding of this subject, thereby reassuring the quality of our predictions. The model highlighted the potential effectiveness of active case finding of tuberculosis patients with limited access to DOTS facilities in the developing country setting.