Evaluating the impact of an Xpert® MTB/RIF- based TB diagnostic algorithm in a routine operational setting in Cape Town

[摘要] ENGLISH SUMMARY: Decades of reliance on slow, inaccurate diagnostic tests have contributed to poor case detection and impeded tuberculosis (TB) control efforts globally. The development of an accurate, rapid molecular diagnostic test, Xpert® MTB/RIF (Cepheid, Sunnyvale, CA, USA) (Xpert), offers the prospect of identifying more cases, detecting them rapidly and enabling quicker treatment initiation. Xpert is a nucleic acid amplification test that simultaneously detects genetic sequences for Mycobacterium tuberculosis complex and the presence of mutations conferring resistance to rifampicin. Xpert sensitivity is substantially higher than smear microscopy (88% compared to 53.8% for a single smear) and provides a test result within a day (compared to 8-16 days for liquid culture). Whilst laboratory and demonstration studies suggest that Xpert has the technical capacity to address the limitations of conventional smear and culture tests, very little is known about how this translates into patient and public health benefits in routine operational conditions.The overall aim of this thesis was to undertake rigorous scientific research into the impact of an Xpert® MTB/RIF-based TB diagnostic algorithm in a routine operational setting in Cape Town. This entailed a pragmatic comparison between the existing smear/culture-based TB diagnostic algorithm and the newly introduced Xpert-based algorithm. The magnitude and range of benefits for laboratory confirmed cases of TB and MDR-TB were assessed.Impact analysis was guided by the Impact Assessment Framework which ensured a systematic and comprehensive approach to the evaluation of the new diagnostic algorithm. This framework addresses five aspects of impact: Effectiveness Analysis assesses the impact on the numbers of cases diagnosed and appropriately started on treatment as well as the timeliness of results and of treatment initiation. Equity Analysis assesses whether marginalised groups who may be more affected benefit from the new test – poor people, women and HIV-infected specifically. Health Systems Analysis assesses the human resource, laboratory infrastructure, procurement and quality assurance implications. Scale-up Analysis assesses the economic costs and benefits of scaling up the new technology from both a provider and a patient perspective. Horizon Scanning assesses what other similar technologies are available or likely to become available and how these compare in their projected performance.The stepped-wedge analysis of TB yield (Chapter 2) in five sub-districts between 2010 and 2013 showed that among the 54,393 presumptive cases tested, the proportion with a bacteriological diagnosis of TB was not increased in the Xpert-based algorithm. We found a decline in TB yield over time, possibly attributable to a declining TB prevalence. When the time-effect was taken into consideration, there was no difference TB yield – yield was 19.3% (95% CI 17.7% to 20.9%) in the Xpert-based algorithm compared to 19.1% (95% CI 17.6% to 20.5%) in the smear/culture-based algorithm with a risk difference of 0.3% (95% CI -1.8% to 2.3%, p=0.796). Inconsistent implementation of the Xpert-based algorithm and the frequent use of culture tests in the smear/culture-based algorithm may have contributed to the yield parity.The multidrug-resistant (MDR)-TB yield study (Chapter 3) found that amongst the 10,284 TB cases identified in the five sub-districts, the Xpert-based algorithm was more effective in identifying MDR-TB than the smear/culture-based algorithm. Pre-treatment, there was a higher probability of having drug susceptibility tests undertaken (RR=1.82, p<0.001) and of being diagnosed with MDR-TB (RR=1.42, p<0.001) in the Xpert-based algorithm than in the smear/culture-based algorithm. Overall 8.5% of TB cases were detected with MDR-TB in the Xpert-based algorithm compared to 6% in the smear/culture-based algorithm, translating to approximately 375 additional MDR-TB cases diagnosed in Cape Town annually.The study on TB treatment initiation and treatment success undertaken in five sub-districts in October – December 2011 (Chapter 4) found that a higher proportion of cases initiated TB treatment in the Xpert group (84%, 508/603) than in the smear/culture group (71%, 493/693, p<0.001). The adjusted odds ratio for treatment initiation in the Xpert group was 1.98 (p<0.001). Cases >44 years in age (AOR=0.49, p<0.001) and previously treated cases (AOR=0.64, p=0.020) were less likely to initiate treatment. Laboratory delay was associated with non-initiation (AOR=0.96 per day, p<0.001). The reduction in TB treatment delay from a median of 15 days in the smear/culture group to 7 days in the Xpert group did not translate into improved TB treatment outcomes and treatment success rates were 80% in both groups (AOR=0.95, p=0.764).The MDR-TB treatment commencement study (Chapter 5) undertaken in 10 high TB burden facilities found that the time from test taken to treatment initiation was reduced from 43 days in the smear/culture-based algorithm (n=375) to 17 days in the Xpert-based algorithm (n=120) with a mean reduction of 25 days (p<0.001). Median laboratory turnaround time from test taken to result available in the laboratory was reduced from 24 days to <1 day with a mean reduction of 20 days (P<0.001) between algorithms. The qualitative study on MDR-TB patient pathways (Chapter 6) showed that patients experienced substantial delays before being diagnosed – these delays may not have been reflected using the data from the laboratory and clinics. Avoidable health system delays resulted from providers not testing for TB at initial health contact, non-adherence to testing algorithms, results not being available and failure to promptly recall patients with positive results. Negative perceptions of the public sector (as over-burdened, with long waiting times, negative staff attitudes and lack of privacy) were prevalent and contributed to deferred health-seeking, interruptions to the diagnostic process and to patient's preferential use of the private sector, contributing to delays in both algorithms.The MDR-TB patient costing study (Chapter 7) assessed direct (out-of-pocket expenses) and indirect costs (lost productivity costs for patient's time) incurred. The median patient cost from initial health visit to treatment initiation was reduced from $68.1 in the smear/culture-based algorithm to $38.3 (p=0.004) in the Xpert-based algorithm. Median direct costs were low at $6.7 and $4.4 (p=0.321) respectively. The difference in costs was attributable to time costs as the median number of visits to MDR-TB treatment was reduced from 20 in the smear/culture-based algorithm to 7 in the Xpert-based algorithm (p<0.001). Further details are provided below in the section on equity.From a laboratory costing perspective (Chapter 8) we found a 43% increase in overall PTB laboratory costs at the central laboratory, from $440,967 in the smear/culture-based algorithm to $632,262 in the Xpert-based algorithm for 3-month periods. The cost per TB case diagnosed increased by 157% from $48.77 in the smear/culture-based algorithm to $125.32 in the Xpert-based algorithm. The mean total cost per MDR-TB case diagnosed was similar at $190.14 in the smear/culture-based algorithm compared to $183.86 in the Xpert-based algorithm.From an effectiveness perspective, the Xpert-based algorithm did not result in an increase in the number TB cases diagnosed or improve treatment outcomes amongst those initiating treatment. It did however significantly reduce treatment delay and increased the proportion of TB cases initiating treatment. The Xpert-based algorithm resulted in a higher proportion of MDR-TB cases being diagnosed and reduced MDR-TB treatment commencement time. From an equity perspective the Xpert-based algorithm helped reduce health inequities through improving effectiveness as described above. However, these benefits did not shield patients from economic losses. The proportion unemployed increased (from symptom onset to the time of the interview) in both groups: from 39% to 73% in the smear/culture group (p<0.001) and from 53% to 89% in the Xpert group (p<0.001). From symptom onset to the time of the interview there was a 16% decrease in median household income in the smear/culture group and 13% decrease in the Xpert group and 'catastrophic costs were experienced by 38% and 27% (p=0.165) in respective groups who lost >10% of monthly household income.Health system failures at several levels from poor initial planning for Xpert implementation to human resource and IT infrastructure deficits, to poor accountability and inefficient service delivery as well as low community preparedness are likely to have diminished the full potential impact of the Xpert-based algorithm. Urgent attention needs to be paid to these issues to optimise the benefit of Xpert.From a scale-up perspective the increase in laboratory costs in our study are offset to some extent by the cost-saving to MDR-TB patients. As part of broader work we have developed a discrete event simulation model and validated it using the results from the studies presented in this thesis. This model will be used to evaluate more cost-effective diagnostic options and the benefits of a more sensitive test such as Xpert Ultra, which our horizon scanning suggests is the most likely current replacement for Xpert.These studies have limitations. It was difficult to control for bias - for example the non-random allocation of facilities to different study arms was ouside our control. Generalisability to other settings, especially rural settings, is limited as these studies were undertaken within a well-resourced, urban setting, with relatively good health and laboratory infrastructure. It was possible to address temporal trends in some studies (for example the stepped-wedge analysis of TB yield) but not in others (for example the MDR-TB treatment commencement study where decentralization of services may have contributed to the findings).The studies presented in this thesis have several novel aspects: they were undertaken at the level of the Xpert-based diagnostic algorithm and not the individual test, reflecting how tests were used in clinical practice. They reflect the patient, provider and health system factors that influenced outcomes and that are essential to understanding the impact of the new diagnostic algorithm in routine programmatic conditions. In addition, the use of Impact Assessment Framework provided a comprehensive view of the benefits and limitations of Xpert.These studies highlight the effect of the early introduction of new tools into under-prepared and inefficient health systems and provide insights into some of the health system weaknesses that could be addressed to optimise the impact of Xpert. Unless concerted efforts are made to address these weaknesses, the investment in this expensive new technology will not provide the full range of benefits possible.

[发布机构] Stellenbosch University