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The Importance of Implementation Strategy in Scaling Up Xpert MTB/RIF for Diagnosis of Tuberculosis in the Indian Health-Care System: A Transmission Model


Background:
India has announced a goal of universal access to quality tuberculosis (TB) diagnosis and treatment. A number of novel diagnostics could help meet this important goal. The rollout of one such diagnostic, Xpert MTB/RIF (Xpert) is being considered, but if Xpert is used mainly for people with HIV or high risk of multidrug-resistant TB (MDR-TB) in the public sector, population-level impact may be limited.

Methods and Findings:
We developed a model of TB transmission, care-seeking behavior, and diagnostic/treatment practices in India and explored the impact of six different rollout strategies. Providing Xpert to 40% of public-sector patients with HIV or prior TB treatment (similar to current national strategy) reduced TB incidence by 0.2% (95% uncertainty range [UR]: −1.4%, 1.7%) and MDR-TB incidence by 2.4% (95% UR: −5.2%, 9.1%) relative to existing practice but required 2,500 additional MDR-TB treatments and 60 four-module GeneXpert systems at maximum capacity. Further including 20% of unselected symptomatic individuals in the public sector required 700 systems and reduced incidence by 2.1% (95% UR: 0.5%, 3.9%); a similar approach involving qualified private providers (providers who have received at least some training in allopathic or non-allopathic medicine) reduced incidence by 6.0% (95% UR: 3.9%, 7.9%) with similar resource outlay, but only if high treatment success was assured. Engaging 20% of all private-sector providers (qualified and informal [providers with no formal medical training]) had the greatest impact (14.1% reduction, 95% UR: 10.6%, 16.9%), but required >2,200 systems and reliable treatment referral. Improving referrals from informal providers for smear-based diagnosis in the public sector (without Xpert rollout) had substantially greater impact (6.3% reduction) than Xpert scale-up within the public sector. These findings are subject to substantial uncertainty regarding private-sector treatment patterns, patient care-seeking behavior, symptoms, and infectiousness over time; these uncertainties should be addressed by future research.

Conclusions:
The impact of new diagnostics for TB control in India depends on implementation within the complex, fragmented health-care system. Transformative strategies will require private/informal-sector engagement, adequate referral systems, improved treatment quality, and substantial resources.

Please see later in the article for the Editors' Summary


Vyšlo v časopise: The Importance of Implementation Strategy in Scaling Up Xpert MTB/RIF for Diagnosis of Tuberculosis in the Indian Health-Care System: A Transmission Model. PLoS Med 11(7): e32767. doi:10.1371/journal.pmed.1001674
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1001674

Souhrn

Background:
India has announced a goal of universal access to quality tuberculosis (TB) diagnosis and treatment. A number of novel diagnostics could help meet this important goal. The rollout of one such diagnostic, Xpert MTB/RIF (Xpert) is being considered, but if Xpert is used mainly for people with HIV or high risk of multidrug-resistant TB (MDR-TB) in the public sector, population-level impact may be limited.

Methods and Findings:
We developed a model of TB transmission, care-seeking behavior, and diagnostic/treatment practices in India and explored the impact of six different rollout strategies. Providing Xpert to 40% of public-sector patients with HIV or prior TB treatment (similar to current national strategy) reduced TB incidence by 0.2% (95% uncertainty range [UR]: −1.4%, 1.7%) and MDR-TB incidence by 2.4% (95% UR: −5.2%, 9.1%) relative to existing practice but required 2,500 additional MDR-TB treatments and 60 four-module GeneXpert systems at maximum capacity. Further including 20% of unselected symptomatic individuals in the public sector required 700 systems and reduced incidence by 2.1% (95% UR: 0.5%, 3.9%); a similar approach involving qualified private providers (providers who have received at least some training in allopathic or non-allopathic medicine) reduced incidence by 6.0% (95% UR: 3.9%, 7.9%) with similar resource outlay, but only if high treatment success was assured. Engaging 20% of all private-sector providers (qualified and informal [providers with no formal medical training]) had the greatest impact (14.1% reduction, 95% UR: 10.6%, 16.9%), but required >2,200 systems and reliable treatment referral. Improving referrals from informal providers for smear-based diagnosis in the public sector (without Xpert rollout) had substantially greater impact (6.3% reduction) than Xpert scale-up within the public sector. These findings are subject to substantial uncertainty regarding private-sector treatment patterns, patient care-seeking behavior, symptoms, and infectiousness over time; these uncertainties should be addressed by future research.

Conclusions:
The impact of new diagnostics for TB control in India depends on implementation within the complex, fragmented health-care system. Transformative strategies will require private/informal-sector engagement, adequate referral systems, improved treatment quality, and substantial resources.

Please see later in the article for the Editors' Summary


Zdroje

1. World Health Organization (2012) Global tuberculosis report 2012. Geneva: World Health Organization.

2. UdwadiaZF, AmaleRA, AjbaniKK, RodriguesC (2012) Totally drug-resistant tuberculosis in India. Clin Infect Dis 54: 579–581.

3. UdwadiaZF, PintoLM, UplekarMW (2010) Tuberculosis management by private practitioners in Mumbai, India: has anything changed in two decades? PLoS ONE 5: e12023.

4. UplekarM, JuvekarS, MorankarS, RanganS, NunnP (1998) Tuberculosis patients and practitioners in private clinics in India. Int J Tuberc Lung Dis 2: 324–329.

5. DowdyDW, SteingartKR, PaiM (2011) Serological testing versus other strategies for diagnosis of active tuberculosis in India: a cost-effectiveness analysis. PLoS Med 8: e1001074.

6. JarosławskiS, PaiM (2012) Why are inaccurate tuberculosis serological tests widely used in the Indian private healthcare sector? A root-cause analysis. J Epidemiol Glob Health 2: 39–50.

7. WellsWA, GeCF, PatelN, OhT, GardinerE, et al. (2011) Size and usage patterns of private TB drug markets in the high burden countries. PLoS ONE 6: e18964.

8. SatyanarayanaS, NairSA, ChadhaSS, ShivashankarR, SharmaG, et al. (2011) From where are tuberculosis patients accessing treatment in India? Results from a cross-sectional community based survey of 30 districts. PLoS ONE 6: e24160.

9. HazarikaI (2011) Role of private sector in providing tuberculosis care: evidence from a population-based survey in India. J Glob Infect Dis 3: 19–24.

10. DyeC (2012) The potential impact of new diagnostic tests on tuberculosis epidemics. Indian J Med Res 135: 737–744.

11. DasJ, HollaA, DasV, MohananM, TabakD, et al. (2012) In urban and rural India, a standardized patient study showed low levels of provider training and huge quality gaps. Health Aff (Millwood) 31: 2774–2784.

12. KapoorSK, RamanAV, SachdevaKS, SatyanarayanaS (2012) How did the TB patients reach DOTS services in Delhi? A study of patient treatment seeking behavior. PLoS ONE 7: e42458.

13. SudhaG, NirupaC, RajasakthivelM, SivasusbramanianS, SundaramV, et al. (2003) Factors influencing the care-seeking behaviour of chest symptomatics: a community-based study involving rural and urban population in Tamil Nadu, South India. Trop Med Int Health 8: 336–341.

14. SreeramareddyCT, QinZZ, SatyanarayanaS, SubbaramanR, PaiM (2014) Delays in diagnosis and treatment of pulmonary tuberculosis in India: a systematic review. Int J Tuberc Lung Dis 18: 255–266.

15. World Health Organization Country Office for India (2014) Standards for TB care in India. New Delhi: World Health Organization. Available: http://www.tbcindia.nic.in/pdfs/STCI%20Book_Final%20%20060514.pdf.

16. SachdevaKSK, KumarAA, DewanPP, KumarAA, SatyanarayanaSS (2012) New vision for Revised National Tuberculosis Control Programme (RNTCP): universal access—“reaching the un-reached”. Indian J Med Res 135: 690–694.

17. SteingartKR, HenryM, NgV, HopewellPC, RamsayA, et al. (2006) Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review. Lancet Infect Dis 6: 570–581.

18. StallN, RubinT, MichaelJS, MathaiD, AbrahamOC, et al. (2011) Does solid culture for tuberculosis influence clinical decision making in India? Int J Tuberc Lung Dis 15: 641–646.

19. World Health Organization (2011) Commercial serodiagnostic tests for diagnosis of tuberculosis: policy statement. Geneva: World Health Organization.

20. PaiM (2013) Promoting affordable and quality tuberculosis testing in India. J Lab Physicians 5: 1–4.

21. World Health Organization (2011) MDR-TB & XDR-TB 2011 progress report. Geneva: World Health Organization.

22. SteingartKR, SohnH, SchillerI, KlodaLA, BoehmeCC, et al. (2013) Xpert® MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst Rev 1: CD009593.

23. BoehmeCC, NabetaP, HillemannD, NicolMP, ShenaiS, et al. (2010) Rapid molecular detection of tuberculosis and rifampin resistance. N Engl J Med 363: 1005–1015.

24. BoehmeCC, NicolMP, NabetaP, MichaelJS, GotuzzoE, et al. (2011) Feasibility, diagnostic accuracy, and effectiveness of decentralised use of the Xpert MTB/RIF test for diagnosis of tuberculosis and multidrug resistance: a multicentre implementation study. Lancet 377: 1495–1505.

25. RaizadaN, SachdevaKS, SreenivasA, VaderaB, GuptaRS, et al. (2014) Feasibility of decentralised deployment of Xpert MTB/RIF test at lower level of health system in India. PLoS ONE 9: e89301.

26. MenziesNAN, CohenTT, LinH-HH, MurrayMM, SalomonJAJ (2012) Population health impact and cost-effectiveness of tuberculosis diagnosis with Xpert MTB/RIF: a dynamic simulation and economic evaluation. PLoS Med 9: e1001347.

27. Central TB Division Directorate General of Health Services (2014) TB India 2014. Revised National TB Control Programme. New Delhi: Central TB Division Directorate General of Health Services.

28. DyeC, GarnettGP, SleemanK, WilliamsBG (1998) Prospects for worldwide tuberculosis control under the WHO DOTS strategy. Directly observed short-course therapy. Lancet 352: 1886–1891.

29. DowdyDW, BasuS, AndrewsJR (2013) Is passive diagnosis enough?: the impact of subclinical disease on diagnostic strategies for tuberculosis. Am J Respir Crit Care Med 187: 543–551.

30. DrobniewskiFAF, PozniakALA (1996) Molecular diagnosis, detection of drug resistance and epidemiology of tuberculosis. Br J Hosp Med (Lond) 56: 204–208.

31. PaiM, DasJ (2013) Management of tuberculosis in India: time for a deeper dive into quality? Natl Med J India 377: 1495–1505.

32. Montagu DD, Anglemyer A, Tiwari M, Drasser K, Rutherford GW, et al.. (2011) Private versus public strategies for health service provision for improving health outcomes in resource-limited settings. San Francisco: Global Health Sciences, University of California, San Francisco.

33. AchantaS, JajuJ, KumarAMV, NagarajaSB, ShamraoSRM, et al. (2012) Tuberculosis management practices by private practitioners in Andhra Pradesh, India. PLoS ONE 8: e71119.

34. EspinalMA, KimSJ, SuarezPG, KamKM, KhomenkoAG, et al. (2000) Standard short-course chemotherapy for drug-resistant tuberculosistreatment outcomes in 6 countries. JAMA 283: 2537–2545.

35. BroydenCG (1970) The convergence of a class of double-rank minimization algorithms 1. General considerations. IMA J Appl Math 6: 76–90.

36. KhanMS, KhanS, Godfrey-FaussettP (2009) Default during TB diagnosis: quantifying the problem. Trop Med Int Health 14: 1437–1441.

37. World Health Organization (2014) WHO monitoring of Xpert MTB/RIF roll-out. Available: http://who.int/tb/laboratory/mtbrifrollout. Accessed 9 Jun 2014.

38. SudhinarasetM, IngramM, LofthouseHK, MontaguD (2013) What is the role of informal healthcare providers in developing countries? A systematic review. PLoS ONE 8: e54978.

39. CaoY, GillespieDT, PetzoldLR (2007) Adaptive explicit-implicit tau-leaping method with automatic tau selection. J Chem Phys 126: 224101.

40. BlowerSM, DowlatabadiH (1994) Sensitivity and uncertainty analysis of complex models of disease transmission: an HIV model, as an example. Int Stat Rev 62: 229–243.

41. R Development Core Team (2010) R: a language and environment for statistical computing, version 2.12. Vienna: R Foundation for Statistical Computing.

42. KayM (2013) Private firms form initiative to offer accurate and affordable TB tests. BMJ 346: f2161.

43. SunAY, PaiM, SaljeH, SatyanarayanaS, DeoS, et al. (2013) Modeling the impact of alternative strategies for rapid molecular diagnosis of tuberculosis in southeast Asia. Am J Epidemiol 178: 1740–1749.

44. PaiM, YadavP, AnupindiR (2014) Tuberculosis control needs a complete and patient-centric solution. Lancet Glob Health 2: e189–e190.

45. World Bank (2013) Data: India. Available: http://data.worldbank.org/country/india. Accessed 5 February 2013.

46. Joint United Nations Programme on HIV/AIDS (2013) UNAIDS report on the global AIDS epidemic. Geneva: Joint United Nations Programme on HIV/AIDS.

47. TiemersmaEW, van der WerfMJ, BorgdorffMW, WilliamsBG, NagelkerkeNJD (2011) Natural history of tuberculosis: duration and fatality of untreated pulmonary tuberculosis in HIV negative patients: a systematic review. PLoS ONE 6: e17601.

48. HillemannD, Ruesch-GerdesS, BoehmeC, RichterE (2011) Rapid molecular detection of extrapulmonary tuberculosis by the automated GeneXpert MTB/RIF system. J Clin Microbiol 49: 1202–1205.

49. CohenT, SommersB, MurrayM (2003) The effect of drug resistance on the fitness of Mycobacterium tuberculosis. Lancet Infect Dis 3: 13–21.

50. CorbettEL, WattCJ, WalkerN, MaherD, WilliamsBG, et al. (2003) The growing burden of tuberculosis: global trends and interactions with the HIV epidemic. Arch Intern Med 163: 1009–1021.

51. GetahunH, HarringtonM, O'BrienR, NunnP (2007) Diagnosis of smear-negative pulmonary tuberculosis in people with HIV infection or AIDS in resource-constrained settings: informing urgent policy changes. Lancet 369: 2042–2049.

52. VynnyckyE, FinePE (1997) The natural history of tuberculosis: the implications of age-dependent risks of disease and the role of reinfection. Epidemiol Infect 119: 183–201.

53. DaleyCL, SmallPM, SchecterGF, SchoolnikGK, McAdamRA, et al. (1992) An outbreak of tuberculosis with accelerated progression among persons infected with the human immunodeficiency virus. An analysis using restriction-fragment-length polymorphisms. N Engl J Med 326: 231–235.

54. BehrMA, WarrenSA, SalamonH, HopewellPC, de LeonAP, et al. (1999) Transmission of Mycobacterium tuberculosis from patients smear-negative for acid-fast bacilli. Lancet 353: 444–449.

55. LewW, PaiM, OxladeO, MartinD, MenziesD (2008) Initial drug resistance and tuberculosis treatment outcomes: systematic review and meta-analysis. Ann Intern Med 149: 123–134.

56. HorsburghCR, O'DonnellM, ChambleeS, MorelandJL, JohnsonJ, et al. (2010) Revisiting rates of reactivation tuberculosis: a population-based approach. Am J Respir Crit Care Med 182: 420–425.

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