Does completion of sputum smear monitoring have an effect on treatment success and cure rate among adult tuberculosis patients in rural Eastern Uganda? A propensity score-matched analysis
Autoři:
Jonathan Izudi aff001; Imelda K. Tamwesigire aff001; Francis Bajunirwe aff001
Působiště autorů:
Department of Community Health, Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda
aff001
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0226919
Souhrn
Background
Tuberculosis is a global public health problem. Bacteriologically confirmed pulmonary tuberculosis (BC-PTB) patients require three sputum smear monitoring (SSM) tests to establish cure or treatment success, but few studies have assessed the relationship. We evaluated the effect of completing SSM on treatment success rate (TSR) among adult BC-PTB patients in rural eastern Uganda.
Methods
We conducted a propensity score-matched (PSM) analysis of a retrospective observational cohort data. Participants who completed SSM were matched to those who had not, through nearest neighbor 1:1 caliper matching. Balance of baseline characteristics between the groups was compared before and after PSM using standardized mean differences. Logistic regression analysis was performed in matched and unmatched samples, reported as odds ratio (OR) with 95% confidence intervals (CI). Robustness of the results to hidden bias was checked through sensitivity analysis. The primary outcome was TSR (treatment completion or cure), while the secondary was cure rate, measured as an individual outcome.
Results
Before PSM, 591 (72.3%) of the 817 participants had incomplete SSM, with statistically significant differences in baseline covariates between completers and non-completers. After PSM, there were 185 participants in either group, balanced on baseline covariates. Before PSM, SSM completion was not associated with TSR, with unadjusted (OR, 0.92; 95%CI, 0.32–2.63) and adjusted analysis (Adjusted OR, 1.32; 95%CI, 0.41–4.22). For cure rate, there was a statistically significant effect before (OR, 93.34; 95%CI, 29.53–295.99) and after adjusted analysis (Adjusted OR, 86.24; 95%CI, 27.05–274.94), although imprecise. In PSM analysis, SSM completion was associated with increased odds of cure (OR, 87.00; 95%CI, 12.12–624.59) but not TSR (OR, 1.67; 95%CI, 0.40–6.97).
Conclusions
Completing SSM increases cure but has no effect on TSR among adult BC-PTB patients in eastern Uganda. Implementation of SSM should be encouraged to ensure improvement in cure rates among tuberculosis patients in rural areas.
Klíčová slova:
Age groups – Balance and falls – Drug therapy – Observational studies – Regression analysis – Sputum – Tuberculosis – Uganda
Zdroje
1. World Health Organization. Global Tuberculosis Report 2019. Geneva, Switzerland: 2019.
2. World Health Organization. Joint Initiative "FIND. TREAT. ALL. #ENDTB" Geneva, Switzerland2019 [cited 2019 02 Nov]. https://www.who.int/tb/joint-initiative/en/.
3. World Health Organization. Global Tuberculosis report 2018. Geneva, Switzerland: 2018.
4. TB CARE. International Standards for Tuberculosis Care. The Hague: 2014.
5. World Health Organization. Use of high burden country lists for TB by WHO in the post-2015 era. Geneva, Switzerland: 2015.
6. Republic of Uganda. Annual Health Sector Peformance Report 2017/2018. Kampala, Uganda: Ministry of Health, 2019.
7. Republic of Uganda. National Tuberculosis and Leprosy Control Programme: Revised National Strategic Plan 2015/2016-2019/2020. Kampala, Uganda: Ministry of Health, 2017.
8. Musaazi J, Kiragga A, Castelnuovo B, Kambugu A, Bradley J, Rehman A. Tuberculosis treatment success among rural and urban Ugandans living with HIV: a retrospective study. Public health action. 2017;7(2):100–9. doi: 10.5588/pha.16.0115 28695082
9. Nakaggwa P, Odeke R, Kirenga BJ, Bloss E. Incomplete sputum smear microscopy monitoring among smear-positive tuberculosis patients in Uganda. The international journal of tuberculosis and lung disease: the official journal of the International Union against Tuberculosis and Lung Disease. 2016;20(5):594–9.
10. Zenebe T, Tefera E. Tuberculosis treatment outcome and associated factors among smear-positive pulmonary tuberculosis patients in Afar, Eastern Ethiopia: a retrospective study. The Brazilian journal of infectious diseases: an official publication of the Brazilian Society of Infectious Diseases. 2016;20(6):635–6.
11. Wu C-H, Chen L-S, Yen M-F, Chiu Y-H, Fann C-Y, Chen H-H, et al. Does non-central nervous system tuberculosis increase the risk of ischemic stroke? A population-based propensity score-matched follow-up study. PloS one. 2014;9(7):e98158-e.
12. Guo L, Qu P, Zhang R, Zhao D, Wang H, Liu R, et al. Propensity Score-Matched Analysis on the Association Between Pregnancy Infections and Adverse Birth Outcomes in Rural Northwestern China. Sci Rep. 2018;8(1):5154-. doi: 10.1038/s41598-018-23306-5 29581446
13. Yao XI, Wang X, Speicher PJ, Hwang ES, Cheng P, Harpole DH, et al. Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies. Journal of the National Cancer Institute. 2017;109(8).
14. Republic of Uganda. Uganda National Tuberculosis and Leprosy Control Program: Manual for management and control of Tuberculosis and Leprosy. Kampala: Ministry of Health, 2017 Aug 2017. Report No.
15. West SG, Cham H, Thoemmes F. Propensity score analysis. The Encyclopedia of Clinical Psychology. 2015.
16. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American statistical Association. 1984;79(387):516–24.
17. Gertler PJ, Martinez S, Premand P, Rawlings LB, Vermeersch CM. Impact evaluation in practice: World Bank Publications; 2016.
18. Garrido MM, Kelley AS, Paris J, Roza K, Meier DE, Morrison RS, et al. Methods for constructing and assessing propensity scores. Health services research. 2014;49(5):1701–20. doi: 10.1111/1475-6773.12182 24779867
19. Okoli G, Sanders R, Myles P. Demystifying propensity scores. British journal of anaesthesia. 2014;112(1):13–5. doi: 10.1093/bja/aet290 24318697
20. Thavaneswaran A. Propensity score matching in observational studies. Manitoba Center for Health Policy Retrieved from: http://www.umanitoba.ca/faculties/health_sciences/medicine/units/community_health_sciences/departmental_units/mchp/protocol/media/propensity_score_matching.pdf. 2008.
21. Austin PC. A tutorial and case study in propensity score analysis: an application to estimating the effect of in-hospital smoking cessation counseling on mortality. Multivariate behavioral research. 2011;46(1):119–51. doi: 10.1080/00273171.2011.540480 22287812
22. Starks H, Garrido MM, editors. Observational & Quasi-experimental Research Methods. 8th annual Kathleen Foley palliative care retreat method workshop Google Scholar; 2004.
23. Grilli L, Rampichini C, editors. Propensity scores for the estimation of average treatment effects in observational studies. Training Sessions on Causal Inference; 2011; Bristol—June 28–29, 20112011.
24. StataCorp. Stata statistical software: Release 15. College Station, TX: StataCorp LLC. 2017;10:733.
25. Staffa SJ, Zurakowski D. Five steps to successfully implement and evaluate propensity score matching in clinical research studies. Anesthesia & Analgesia. 2018;127(4):1066–73.
26. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research. 2011;46(3):399–424. doi: 10.1080/00273171.2011.568786 21818162
27. Caliendo M, Kopeinig S. Some practical guidance for the implementation of propensity score matching. Journal of economic surveys. 2008;22(1):31–72.
28. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in medicine. 2009;28(25):3083–107. doi: 10.1002/sim.3697 19757444
29. Becker SO, Caliendo M. Sensitivity analysis for average treatment effects. The Stata Journal. 2007;7(1):71–83.
30. Olmos A, Govindasamy P. Propensity Scores: A Practical Introduction Using R. Journal of MultiDisciplinary Evaluation. 2015;11(25):68–88.
31. Satyanarayana S, Nagaraja S, Kelamane S, Jaju J, Chadha S, Chander K, et al. Did successfully treated pulmonary tuberculosis patients undergo all follow-up sputum smear examinations? Public health action. 2011;1(2):27–9. doi: 10.5588/pha.11.0013 26392932
32. Chaimani A, Mavridis D, Salanti G. A hands-on practical tutorial on performing meta-analysis with Stata. Royal College of Psychiatrists; 2014.
33. Harries A, Gausi F, Salaniponi F. When are follow-up sputum smears actually examined in patients treated for new smear-positive pulmonary tuberculosis? The international journal of tuberculosis and lung disease. 2004;8(4):440–4. 15141736
34. Streiner DL, Norman GR. The pros and cons of propensity scores. Chest. 2012;142(6):1380–2. doi: 10.1378/chest.12-1920 23208333
35. Tumlinson SE, Sass DA, Cano SM. The search for causal inferences: using propensity scores post hoc to reduce estimation error with nonexperimental research. Journal of pediatric psychology. 2014;39(2):246–57. doi: 10.1093/jpepsy/jst143 24464252
36. Mason C, Sabariego C, Thắng ĐM, Weber J. Can propensity score matching be applied to cross-sectional data to evaluate Community-Based Rehabilitation? Results of a survey implementing the WHO’s Community-Based Rehabilitation indicators in Vietnam. BMJ Open. 2019:9:e022544. doi: 10.1136/bmjopen-2018-022544 30782679
37. Malhotra S, Zodpey SP, Chandra S, Vashist RP, Satyanaryana S, Zachariah R, et al. Should Sputum Smear Examination Be Carried Out at the End of the Intensive Phase and End of Treatment in Sputum Smear Negative Pulmonary TB Patients? PloS one. 2012;7(11):e49238 doi: 10.1371/journal.pone.0049238 23152880
Článek vyšel v časopise
PLOS One
2019 Číslo 12
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy