Predicting the impact of patient and private provider behavior on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modeling study
Autoři:
Sarang Deo aff001; Simrita Singh aff001; Neha Jha aff001; Nimalan Arinaminpathy aff004; Puneet Dewan aff005
Působiště autorů:
Indian School of Business, Hyderabad, India
aff001; Kellogg School of Management, Northwestern University, Evanston, Illinois, United States of America
aff002; Kenan-Flagler Business School, University of North Carolina, Chapel Hill, North Carolina, United States of America
aff003; School of Public Health, Imperial College London, London, United Kingdom
aff004; Bill & Melinda Gates Foundation, Seattle, Washington, United States of America
aff005
Vyšlo v časopise:
Predicting the impact of patient and private provider behavior on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modeling study. PLoS Med 17(5): e32767. doi:10.1371/journal.pmed.1003039
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003039
Souhrn
Background
Tuberculosis (TB) incidence in India continues to be high due, in large part, to long delays experienced by patients before successful diagnosis and treatment initiation, especially in the private sector. This diagnostic delay is driven by patients’ inclination to switch between different types of providers and providers’ inclination to delay ordering of accurate diagnostic tests relevant to TB. Our objective is to quantify the impact of changes in these behavioral characteristics of providers and patients on diagnostic delay experienced by pulmonary TB patients.
Methods and findings
We developed a discrete event simulation model of patients’ diagnostic pathways that captures key behavioral characteristics of providers (time to order a test) and patients (time to switch to another provider). We used an expectation-maximization algorithm to estimate the parameters underlying these behavioral characteristics, with quantitative data encoded from detailed interviews of 76 and 64 pulmonary TB patients in the 2 Indian cities of Mumbai and Patna, respectively, which were conducted between April and August 2014. We employed the estimated model to simulate different counterfactual scenarios of diagnostic pathways under altered behavioral characteristics of providers and patients to predict their potential impact on the diagnostic delay. Private healthcare providers including chemists were the first point of contact for the majority of TB patients in Mumbai (70%) and Patna (94%). In Mumbai, 45% of TB patients first approached less-than-fully-qualified providers (LTFQs), who take 28.71 days on average for diagnosis. About 61% of these patients switched to other providers without a diagnosis. Our model estimates that immediate testing for TB by LTFQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 35.53 days (95% CI: 34.60, 36.46) to 18.72 days (95% CI: 18.01, 19.43). In Patna, 61% of TB patients first approached fully qualified providers (FQs), who take 9.74 days on average for diagnosis. Similarly, immediate testing by FQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 23.39 days (95% CI: 22.77, 24.02) to 11.16 days (95% CI: 10.52, 11.81). Improving the diagnostic accuracy of providers per se, without reducing the time to testing, was not predicted to lead to any reduction in diagnostic delay. Our study was limited because of its restricted geographic scope, small sample size, and possible recall bias, which are typically associated with studies of patient pathways using patient interviews.
Conclusions
In this study, we found that encouraging private providers to order definitive TB diagnostic tests earlier during patient consultation may have substantial impact on reducing diagnostic delay in these urban Indian settings. These results should be combined with disease transmission models to predict the impact of changes in provider behavior on TB incidence.
Klíčová slova:
Diagnostic medicine – Chemists – India – Patients – Public and occupational health – Simulation and modeling – Tuberculosis – Tuberculosis diagnosis and management
Zdroje
1. World Health Organization. Global tuberculosis report 2018. Geneva: World Health Organization; 2018.
2. Ministry of Health and Family Welfare Central TB Division. TB India 2017: Revised National Tuberculosis Control Programme—annual status report. New Delhi: Ministry of Health and Family Welfare Central TB Division; 2017.
3. World Health Organization. Global tuberculosis report 2013. Report No. 924156539X. Geneva: World Health Organization; 2013.
4. World Health Organization. Gear up to end TB: introducing the end TB strategy. Geneva: World Health Organization; 2015.
5. Pai M. The End TB Strategy: India can blaze the trail. Indian J Med Res. 2015;141(3):259–62. doi: 10.4103/0971-5916.156536 25963483
6. Pai M, Bhaumik S, Bhuyan SS. India’s plan to eliminate tuberculosis by 2025: converting rhetoric into reality. BMJ Global Health. 2017;2:e000326. doi: 10.1136/bmjgh-2017-000326 28589035
7. Arinaminpathy N, Batra D, Khaparde S, Vualnam T, Maheshwari N, Sharma L, et al. The number of privately treated tuberculosis cases in India: an estimation from drug sales data. Lancet Infect Dis. 2016;16(11):1255–60. doi: 10.1016/S1473-3099(16)30259-6 27568356
8. Satyanarayana S, Nair SA, Chadha SS, Shivashankar R, Sharma G, Yadav S, et al. From where are tuberculosis patients accessing treatment in India? Results from a cross-sectional community based survey of 30 districts. PLoS ONE. 2011;6(9):e24160. doi: 10.1371/journal.pone.0024160 21912669
9. Kapoor SK, Raman AV, Sachdeva KS, Satyanarayana S. How did the TB patients reach DOTS services in Delhi? A study of patient treatment seeking behavior. PLoS ONE. 2012;7(8):e42458. doi: 10.1371/journal.pone.0042458 22879990
10. Sreeramareddy CT, Qin ZZ, Satyanarayana S, Subbaraman R, Pai M. Delays in diagnosis and treatment of pulmonary tuberculosis in India: a systematic review. Int J Tuberc Lung Dis. 2014;18(3):255–66. doi: 10.5588/ijtld.13.0585 24670558
11. Satyanarayana S, Subbaraman R, Shete P, Gore G, Das J, Cattamanchi A, et al. Quality of tuberculosis care in India: a systematic review. Int J Tuberc Lung Dis. 2015;19(7):751–63. doi: 10.5588/ijtld.15.0186 26056098
12. Das J, Holla A, Das V, Mohanan M, Tabak D, Chan B. In urban and rural India, a standardized patient study showed low levels of provider training and huge quality gaps. Health Aff (Millwood). 2012;31(12):2774–84.
13. De Costa A, Diwan V. ‘Where is the public health sector?’: public and private sector healthcare provision in Madhya Pradesh, India. Health Policy. 2007;84(2):269–76.
14. Das J, Kwan A, Daniels B, Satyanarayana S, Subbaraman R, Bergkvist S, et al. Use of standardised patients to assess quality of tuberculosis care: a pilot, cross-sectional study. Lancet Infect Dis. 2015;15(11):1305–13. doi: 10.1016/S1473-3099(15)00077-8 26268690
15. Kwan A, Daniels B, Saria V, Satyanarayana S, Subbaraman R, McDowell A, et al. Variations in the quality of tuberculosis care in urban India: a cross-sectional, standardized patient study in two cities. PLoS Med. 2018;15(9):e1002653. doi: 10.1371/journal.pmed.1002653 30252849
16. McDowell A, Pai M. Alternative medicine: an ethnographic study of how practitioners of Indian medical systems manage TB in Mumbai. Trans R Soc Trop Med Hyg. 2016;110(3):192–8. doi: 10.1093/trstmh/trw009 26884500
17. McDowell A, Pai M. Treatment as diagnosis and diagnosis as treatment: empirical management of presumptive tuberculosis in India. Int J Tuberc Lung Dis. 2016;20(4):536–43. doi: 10.5588/ijtld.15.0562 26970165
18. Storla DG, Yimer S, Bjune GA. A systematic review of delay in the diagnosis and treatment of tuberculosis. BMC Public Health. 2008;8(1):15.
19. Tamhane A, Ambe G, Vermund SH, Kohler CL, Karande A, Sathiakumar N. Pulmonary tuberculosis in Mumbai, India: factors responsible for patient and treatment delays. Int J Prev Med. 2012;3(8):569–80. 22973488
20. Mistry N, Rangan S, Dholakia Y, Lobo E, Shah S, Patil A. Durations and delays in care seeking, diagnosis and treatment initiation in uncomplicated pulmonary tuberculosis patients in Mumbai, India. PLoS ONE. 2016;11(3):e0152287. doi: 10.1371/journal.pone.0152287 27018589
21. Mistry N, Lobo E, Shah S, Rangan S, Dholakia Y. Pulmonary tuberculosis in Patna, India: durations, delays, and health care seeking behaviour among patients identified through household surveys. J Epidemiol Glob Health. 2017;7(4):241–8. doi: 10.1016/j.jegh.2017.08.001 29110864
22. Dye C. The potential impact of new diagnostic tests on tuberculosis epidemics. Indian J Med Res. 2012;135(5):737–44. 22771607
23. Dye C, Williams BG. The population dynamics and control of tuberculosis. Science. 2010;328(5980):856–61. doi: 10.1126/science.1185449 20466923
24. Zwerling A, White RG, Vassall A, Cohen T, Dowdy DW, Houben RMGJ. Modeling of novel diagnostic strategies for active tuberculosis—a systematic review: current practices and recommendations. PLoS ONE. 2014;9(10):e110558. doi: 10.1371/journal.pone.0110558 25340701
25. Lin H-H, Dowdy D, Dye C, Murray M, Cohen T. The impact of new tuberculosis diagnostics on transmission: why context matters. Bull World Health Organ. 2012;90:739–47. doi: 10.2471/BLT.11.101436 23109741
26. Lin H-H, Langley I, Mwenda R, Doulla B, Egwaga S, Millington KA, et al. A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools. Int J Tuberc Lung Dis. 2011;15(8):996–1004. doi: 10.5588/ijtld.11.0062 21740663
27. Langley I, Lin H-H, Egwaga S, Doulla B, Ku C-C, Murray M, et al. Assessment of the patient, health system, and population effects of Xpert MTB/RIF and alternative diagnostics for tuberculosis in Tanzania: an integrated modelling approach. Lancet Glob Health. 2014;2(10):e581–91. doi: 10.1016/S2214-109X(14)70291-8 25304634
28. Salje H, Andrews JR, Deo S, Satyanarayana S, Sun AY, Pai M, et al. 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. 2014;11(7):e1001674. doi: 10.1371/journal.pmed.1001674 25025235
29. Sun AY, Pai M, Salje H, Satyanarayana S, Deo S, Dowdy DW. Modeling the impact of alternative strategies for rapid molecular diagnosis of tuberculosis in Southeast Asia. Am J Epidemiol. 2013;178(12):1740–9. doi: 10.1093/aje/kwt210 24100953
30. Wells WA, Uplekar M, Pai M. Achieving systemic and scalable private sector engagement in tuberculosis care and prevention in Asia. PLoS Med. 2015;12(6):e1001842. doi: 10.1371/journal.pmed.1001842 26103555
31. Pai M, Nicol MP, Boehme CC. Tuberculosis diagnostics: state of the art and future directions. Microbiol Spectr. 2016;4(5). doi: 10.1128/microbiolspec.TBTB2-0019-2016 27763258
32. Lawn SD, Mwaba P, Bates M, Piatek A, Alexander H, Marais BJ, et al. Advances in tuberculosis diagnostics: the Xpert MTB/RIF assay and future prospects for a point-of-care test. Lancet Infect Dis. 2013;13(4):349–61. doi: 10.1016/S1473-3099(13)70008-2 23531388
33. Shah D, Vijayan S, Chopra R, Salve J, Gandhi RK, Jondhale V, et al. Map, know dynamics and act; a better way to engage private health sector in TB management. A report from Mumbai, India. Indian J Tuberc. 2020;67(1):65–72. doi: 10.1016/j.ijtb.2019.07.001 32192620
34. Shibu V, Daksha S, Rishabh C, Sunil K, Devesh G, Lal S, et al. Tapping private health sector for public health program? findings of a novel intervention to tackle TB in Mumbai, India. Indian J Tuberc. 2020 Jan 22. doi: 10.1016/j.ijtb.2020.01.007
35. Schmier JK, Halpern MT. Patient recall and recall bias of health state and health status. Expert Rev Pharmacoecon Outcomes Res. 2004;4(2):159–63. doi: 10.1586/14737167.4.2.159 19807519
36. Arinaminpathy N, Deo S, Singh S, Khaparde S, Rao R, Vadera B, et al. Modelling the impact of effective private provider engagement on tuberculosis control in urban India. Sci Rep. 2019;9(1):3810. doi: 10.1038/s41598-019-39799-7 30846709
Článek vyšel v časopise
PLOS Medicine
2020 Číslo 5
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- „Jednohubky“ z klinického výzkumu – 2024/44
- Je libo čepici místo mozkového implantátu?
Nejčtenější v tomto čísle
- National and regional prevalence of posttraumatic stress disorder in sub-Saharan Africa: A systematic review and meta-analysis
- Risk of severe maternal morbidity or death in relation to elevated hemoglobin A1c preconception, and in early pregnancy: A population-based cohort study
- Antibody-based therapies for COVID-19: Can Europe move faster?
- The association of innate and adaptive immunity, subclinical atherosclerosis, and cardiovascular disease in the Rotterdam Study: A prospective cohort study
