Abstract

BackgroundGlobally, almost 40% of tuberculosis (TB) patients remain undiagnosed, and those that are diagnosed often experience prolonged delays before initiating correct treatment, leading to ongoing transmission. While there is a push for active case finding (ACF) to improve early detection and treatment of TB, there is extremely limited evidence about the relative cost-effectiveness of different ACF implementation models. Cambodia presents a unique opportunity for addressing this gap in evidence as ACF has been implemented using different models, but no comparisons have been conducted. The objective of our study is to contribute to knowledge and methodology on comparing cost-effectiveness of alternative ACF implementation models from the health service perspective, using programmatic data, in order to inform national policy and practice.MethodsWe retrospectively compared three distinct ACF implementation models - door to door symptom screening in urban slums, checking contacts of TB patients, and door to door symptom screening focusing on rural populations aged above 55 - in terms of the number of new bacteriologically-positive pulmonary TB cases diagnosed and the cost of implementation assuming activities are conducted by the national TB program of Cambodia. We calculated the cost per additional case detected using the alternative ACF models.ResultsOur analysis, which is the first of its kind for TB, revealed that the ACF model based on door to door screening in poor urban areas of Phnom Penh was the most cost-effective (249 USD per case detected, 737 cases diagnosed), followed by the model based on testing contacts of TB patients (308 USD per case detected, 807 cases diagnosed), and symptomatic screening of older rural populations (316 USD per case detected, 397 cases diagnosed).ConclusionsOur study provides new evidence on the relative effectiveness and economics of three implementation models for enhanced TB case finding, in line with calls for data from ‘routine conditions’ to be included in disease control program strategic planning. Such cost-effectiveness comparisons are essential to inform resource allocation decisions of national policy makers in resource constraint settings. We applied a novel, pragmatic methodological approach, which was designed to provide results that are directly relevant to policy makers, costing the interventions from Cambodia’s national TB program’s perspective and using case finding data from implementation activities, rather than experimental settings.

Highlights

  • Almost 40% of tuberculosis (TB) patients remain undiagnosed, and those that are diagnosed often experience prolonged delays before initiating correct treatment, leading to ongoing transmission

  • Selection of active case finding (ACF) models for comparison To initiate the cost-effectiveness comparison, we first compiled a list of all ACF interventions implemented in Cambodia over the past 5 years, through discussions with officials at CENAT and the Cambodia World Health Organisation (WHO) office which had held a meeting of all ACF implementing agencies, supplemented with searches of peer reviewed literature, grey literature and organizational websites

  • We identified nine different approaches to delivering ACF services across the country by CENAT and various non-governmental organisations (NGOs): symptom screening in prisons; testing contacts of TB patients; testing patients with HIV; door to door symptom screening in urban slums; symptom screening focusing on elderly members of the community; and using community based volunteers for identification of symptomatic patients in rural areas

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Summary

Introduction

Almost 40% of tuberculosis (TB) patients remain undiagnosed, and those that are diagnosed often experience prolonged delays before initiating correct treatment, leading to ongoing transmission. While there is a push for active case finding (ACF) to improve early detection and treatment of TB, there is extremely limited evidence about the relative cost-effectiveness of different ACF implementation models. Tuberculosis (TB) control remains a critical global challenge, becoming the leading cause of death from an infectious disease in 2014. An estimated 37% of TB patients globally remain undiagnosed or are not reported [1], and prolonged delays to diagnosis are reported among some patients who do eventually access DOTS services. The WHO has raised concerns that the impact of current interventions to improve early detection of TB may have been saturated [3]

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