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 Table of Contents  
REVIEW
Year : 2016  |  Volume : 5  |  Issue : 4  |  Page : 374-378

Passive case finding for tuberculosis is not enough


1 Woolcock Institute of Medical Research, University of Sydney; South Western Sydney Clinical School, University of New South Wales; Centre for Research Excellence in Tuberculosis (TB-CRE) and the Marie Bashir Institute for Infectious Diseases and Biosecurity (MBI), University of Sydney, Sydney, Australia
2 Woolcock Institute of Medical Research, University of Sydney; Centre for Research Excellence in Tuberculosis (TB-CRE) and the Marie Bashir Institute for Infectious Diseases and Biosecurity (MBI), University of Sydney; Sydney Medical School, University of Sydney, Sydney, Australia
3 Centre for Research Excellence in Tuberculosis (TB-CRE) and the Marie Bashir Institute for Infectious Diseases and Biosecurity (MBI), University of Sydney; Sydney Medical School, University of Sydney; The Children's Hospital at Westmead, University of Sydney, Sydney, Australia

Date of Web Publication14-Feb-2017

Correspondence Address:
Jennifer Ho
Woolcock Institute of Medical Research, 431 Glebe Point Road, Glebe, NSW 2037
Australia
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Source of Support: None, Conflict of Interest: None


DOI: 10.1016/j.ijmyco.2016.09.023

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  Abstract 


Current World Health Organisation targets calling for an end to the global tuberculosis (TB) epidemic by 2035 require a dramatic improvement in current case-detection strategies. A reliance on passive case finding (PCF) has resulted consistently, in over three million infectious TB cases per year, being missed by the health system, leading to ongoing transmission of infection within families and communities. Active case finding (ACF) for TB has been recognized as an important complementary strategy to PCF, in order to diagnose and treat patients earlier, reducing the period of infectiousness and therefore transmission. ACF may also achieve substantial population-level TB control. Local TB epidemiology and the resources available in each setting will influence which populations should be screened, and the types of ACF interventions to use for maximal impact. TB control programs should begin with the highest risk groups and broaden their activities as resources allow. Mathematical models can help to predict the population-level effects and the cost-effectiveness of a variety of ACF strategies on different risk populations.

Keywords: Active case finding, Screening, Diagnosis, TB elimination, End TB strategy


How to cite this article:
Ho J, Fox GJ, Marais BJ. Passive case finding for tuberculosis is not enough. Int J Mycobacteriol 2016;5:374-8

How to cite this URL:
Ho J, Fox GJ, Marais BJ. Passive case finding for tuberculosis is not enough. Int J Mycobacteriol [serial online] 2016 [cited 2020 Aug 14];5:374-8. Available from: http://www.ijmyco.org/text.asp?2016/5/4/374/200118




  Introduction Top


In the past two decades, the Directly Observed Treatment Short-Course (DOTS) strategy and the subsequent Stop TB (DOTS expansion) strategy, recommended by the World Health Organisation (WHO), saved more than five million lives [1]. However, total case numbers continue to rise and tuberculosis (TB) remains the leading infectious cause of death worldwide [2]. The WHO launched the End TB Strategy in 2015 with the ambitious goal of ending the global TB epidemic [3]. Targets include a 90% reduction in TB incidence and 95% reduction in TB deaths by 2035, compared to 2015. “Bending the epidemiological curve” of TB incidence and mortality to meet these targets, will require improved case detection to ensure early disease diagnosis, improve individual patient outcomes, and limit ongoing transmission.

In the majority of TB endemic settings worldwide, the status quo for TB case finding is based on “passive case finding” (PCF). This relies upon a patient with active TB experiencing symptoms serious enough to seek health care and a health-care system capable of correctly diagnosing the patient's condition. [4] However, this strategy, as shown consistently in prevalence surveys [5],[6], is grossly inadequate to detect the substantial burden of undiagnosed TB in the community. It is estimated that in 2014, more than 3.5 million people who developed TB (one-third of all cases) were “missed” by the health system [2]. This massive case detection gap culminates in late disease presentation, with poor disease outcomes, and undiagnosed infectious cases continuing to spread infection within families and communities.

The term “active case finding” (ACF) includes any methods for TB identification that does not rely on patients presenting to the healthcare system of their own accord [7]. The objectives of ACF are to diagnose and treat patients earlier, thereby reducing negative treatment outcomes, sequelae, and socioeconomic consequences, as well as reducing the period of infectiousness and therefore transmission [4]. ACF has been increasingly recognised as an important complementary strategy to PCF in high-prevalence settings in order to overcome the gaps in TB detection and treatment. This need has also been recognized by international donors, with initiatives such as TB REACH established to support innovative approaches to increasing TB case detection [8]. Although two WHO guidelines, systematic screening [4] and household contact investigations [9], provide some guidance to ACF in resource limited settings, designing and implementing interventions that target the most appropriate populations, and utilise feasible and cost-effective strategies, may be difficult for national TB control programs (NTP) already struggling to manage the existing burden of known disease.

In this review, we compare the additional individual and population-level benefits of ACF with those of PCF, consider pragmatic and economic factors relevant to ACF implementation in resource-limited settings, and highlight future research needs and priorities.


  Active case finding versus passive case finding Top


The principle objective of ACF is to find and treat cases of active TB that would otherwise not have been diagnosed at this time, using strategies that are in keeping with available resources. An important distinction between ACF and PCF is that the former is a screening intervention initiated by health services, as opposed to the latter, which is initiated by symptomatic individuals presenting to health-care. In general, ACF activities are additive to PCF. Consequently, the diagnostic algorithms and measures of success used in ACF may differ from those used for PCF. The population targeted for ACF is typically larger, and the prevalence of disease (or pretest probability) is lower. This results in a higher number needed to screen, to diagnose one TB patient, compared to the PCF context.

ACF for TB generally begins with an initial screening step followed by confirmatory testing. Initial screening may comprise of one or a combination of symptom reporting or chest radiography, and if either are positive, a confirmatory microbiological test, such as smear microscopy, or a molecular test e.g. Xpert MTB/RIF [4]. Ideally, the confirmatory test should be rapid, hence Mycobacterium tuberculosis (MTB) culture is a less feasible option, unless the health system in place has sufficient capacity to follow-up screened patients [4].

However, the use of symptoms or chest X-ray as the initial screening step has important limitations. Prevalence surveys have shown consistently that the majority of undiagnosed TB patients in the community lack typical symptoms of TB and a large proportion have no symptoms at all [10],[11],[12]. Furthermore, while chest radiography is more sensitive than using a symptom-based approach alone, this can be logistically difficult in many rural and remote settings. Xpert MTB/RIF used up-front as a primary screening tool (i.e., regardless of symptoms reported or chest X-ray findings), has been shown to be feasible and improve case detection in certain high risk populations, such as people living with HIV (PLHIV) [13],[14], and also in ACF conducted in the general community [15]. While this approach may overcome some limitations of traditional TB screening, the feasibility and cost-effectiveness of this strategy in a programmatic setting is yet to be determined.

One argument against ACF it that it merely detects disease earlier, but does not substantially alter individual patient outcomes. However, diagnosing and treating TB disease earlier is likely to have a substantial impact on TB transmission, decreasing the long term trajectory of TB in a population, and subsequently reducing the cost of TB control overall [16]. It is important however, when evaluating the population-level effects and the cost-effectiveness of ACF, to consider its impact over a longer time frame (e.g., a 20-year time horizon), as short-term assessments can dramatically underestimate longer- term gains of ACF [16]. [Table 1] lists outcome measures that should be considered when evaluating the benefit of ACF, as well as the other key characteristics of ACF compared to PCF for TB.
Table 1: Characteristics of active compared to passive case finding for TB.

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  Selecting a suitable population for screening: high yield target populations versus community-wide interventions Top


Factors that influence the choice of which populations should be targeted for ACF include: TB prevalence, TB risk (e.g., comorbidities and socioeconomic factors), accessibility, acceptability (amongst individuals in the population and health care workers), and the infrastructure and resources available.

In the “systematic screening for active TB” guidelines, the WHO strongly recommend ACF for PLHIV, household contacts of active TB patients, and workers exposed to silica [4]. Conditional recommendations (based on expert opinion and dependent on local TB epidemiology and resource factors), are given for other risk groups such as prisoners, those with untreated fibrotic lesions on chest X-ray; those seeking health care and selected risk groups where the TB prevalence is ≥0.1%; populations where the TB prevalence is ≥1%; or those with poor access to health-care [4]. Clinical groups with a higher risk of developing TB (and poorer treatment outcomes) that may benefit from ACF include those who are underweight, malnourished, diabetic, have alcohol dependence, smoke, have chronic renal failure, or immunosuppression [4],[17]. Homeless people, those living in slums, and other deprived communities generally have poorer access to health-care, a higher risk of TB transmission in some settings, and may also benefit greatly from ACF interventions [18],[19],[20].

Before the 1960 s, mass chest radiography was the primary form of ACF in many industrialized countries [21]. However, in their ninth report in 1974, the WHO expert committee on tuberculosis recommended that “the policy of indiscriminate TB case-finding by mobile mass radiography should now be abandoned” , citing evidence of the inefficiency, low yield, and high cost of mass radiography [22]. Since this recommendation, screening of populations for TB generally has been avoided, other than for selected high-risk groups. Instead, high prevalence countries have been encouraged to focus their resources on optimizing existing diagnostic and treatment services. However, this narrow focus on selected high-risk groups has been unable to reach the majority of undiagnosed cases in the community and, despite substantial investment in PCF, has only achieved a global decline in TB incidence of 2% per year [2]. This figure needs to increase to 10% per year by 2025, to achieve TB elimination targets [3]. Therefore, it is imperative that broader screening strategies that will achieve the desired epidemiological impact are considered.

Few randomized trials that have assessed the effectiveness of ACF in a community-wide setting. Four randomized controlled trials (RCTs) have been performed in Africa, with mixed results. A cluster RCT conducted in Zimbabwe (the DETECTB study) [23] evaluated an intervention of six rounds of six monthly screening for adults with ≥2 weeks of cough, to over 100,000 participants using either mobile vans or door-to-door visits. The study found a decline in disease prevalence from 6.5 to 3.7 per 1000 adults (risk ratio 0.59, 95% CI 0.40–0.89, p = 0.0112) at the end of, compared to before, the intervention. In contrast, a large community randomized trial of active case-finding among almost 45,000 participants in Zambia and the Western Cape of South Africa (the ZAMSTAR study), did not demonstrate a population-wide benefit [24]. The study compared two enhanced case finding interventions: community mobilization and promotion of sputum smear examination; and combined TB-HIV household-level activities. Neither intervention led to a reduction in the community prevalence or incidence of TB [24]. While these two trials yielded different effects on disease prevalence, the actual interventions used in these two studies differed considerably.

Two additional RCTs of community-wide ACF studies in rural southern Ethiopia [25],[26] implemented periodic education about TB symptoms, by community workers in peripheral health facilities, and encouraged symptomatic individuals to seek health-care. One study found the intervention improved case detection rates, compared to passive case finding [25], whereas the other found a reduction in time to diagnosis, but not the extent of case finding for smear positive TB [26].


  What active case finding approach is optimal? Top


ACF for TB is likely to be feasible to some degree in all settings; however, determining the most appropriate strategy for each setting requires careful planning and consideration. The most suitable approach will depend on the resources available and the local TB epidemiology. NTPs should start with a narrow focus (e.g., highest risk groups) and broaden as resources allow. Baseline information required includes high quality surveillance data such as prevalence rates, incidence rates, and transmission ‘hot spots’. Mathematical modelling can help predict the population level impact, cost-effectiveness and optimal duration and frequency for specific ACF interventions [16],[27],[28],[29]. A web-based modeling tool, developed by Nishikori [30], can be used to compare cost-effectiveness parameters for different diagnostic algorithms when applied to different risk populations [30].

Novel strategies involving community workers, volunteers, mobile phone technology, or the private sector to implement ACF activities [25],[31], may improve the efficiency and feasibility of ACF on a broader scale. Integrating ACF into existing health-care services is also important to optimize resources and achieve sustainability [32]. Adequate diagnostic and treatment facilities, with the capacity for scale-up, are crucial to avoid treatment delays and loss to follow-up. Lastly, additional treatment support should be considered for patients diagnosed via ACF, given that they may be minimally symptomatic and their failure to initiate health care contact may indicate reduced “readiness” for treatment. These patients may also be burdened with socioeconomic factors that make them more likely to fail to start, or be noncompliant with treatment [33],[34].


  Research gaps and priorities Top


While there is consensus on the effectiveness of ACF in certain high-risk groups such as PLHIV and household contacts [4], major research gaps remain. Whether ACF leads to better treatment outcomes, substantially reduces transmission, or has a beneficial impact on the longer-term epidemiology of TB, remains unknown. We also do not know what strategies are most effective in different settings, or, how frequently and for how long ACF interventions need to be performed. As the prevalence of disease decreases, ACF is likely to appear less cost-effective (per case diagnosed) but will be important to sustain long-term declines in incidence to achieve TB elimination targets.

Further operational and public health research with appropriate impact evaluation, together with mathematical and economic modeling, will be greatly beneficial in answering some of these questions. Careful monitoring and evaluation of all current and future ACF activities is also essential. Better screening tools will also facilitate up-scaling of ACF strategies. A sensitive, portable, low-cost, point-of-care test will revolutionize TB screening, improving the efficiency, feasibility, and cost-effectiveness of all screening interventions.


  Conclusion Top


Current passive TB case finding approaches are insufficient to meet ambitious TB elimination targets, especially in endemic areas where the bulk of the “missing 3.5 million cases” reside [2]. Until new breakthroughs in disease prevention occur, significant improvements in case detection will be essential if the global TB epidemic is to be overcome. ACF interventions are likely to be feasible in all settings, but the scale and focus of these interventions will need to be contextualized and will inevitably be limited by available resources. When designing an ACF strategy, TB control programs should begin with easily identifiable high-risk target groups and then widen their scope of activities as resources allow. Further research is imperative to determine the most feasible and cost-effective ACF approaches in different settings.


  Conflicts of interest Top


None to declare.



 
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    Tables

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