HomeAboutusEditorial BoardCurrent issuearchivesSearch articlesInstructions for authorsSubscription detailsAdvertise

  Login  | Users online: 379

   Ahead of print articles    Bookmark this page Print this page Email this page Small font sizeDefault font size Increase font size  


 
 Table of Contents    
ORIGINAL ARTICLE  
Year : 2019  |  Volume : 44  |  Issue : 1  |  Page : 44-47
 

Survival analysis of treatment defaulters among tuberculosis patients in government medical college and hospital, Aurangabad


1 Department of Community Medicine, Government Medical College and Hospital, Aurangabad, Maharashtra, India
2 Department of TB and Chest, Government Medical College and Hospital, Aurangabad, Maharashtra, India

Date of Submission21-Sep-2018
Date of Acceptance31-Jan-2019
Date of Web Publication12-Mar-2019

Correspondence Address:
Dr. Hrishikesh Arvindrao Khadilkar
Department of Community Medicine, Government Medical College, Aurangabad - 431 001, Maharashtra
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijcm.IJCM_292_18

Rights and Permissions

 

   Abstract 


Context: Tuberculosis (TB) patients who do not complete treatment pose a potential public health risk through disease reactivation, increased transmission, and development of drug resistance. Aims: (1) To determine the duration TB patients stay in the treatment before defaulting. (2) Factors associated with defaulters who had been treated in Government Medical College and Hospital (GMCH), Aurangabad. Setting and Design: The study was conducted at TB Unit of GMCH, Aurangabad, and community. This was a retrospective cohort study. Materials and Methods: Based on record review of 440 bacteriological-confirmed TB patients enrolled in the TB Unit of GMCH, Aurangabad, in 2015 from January 1, to December 31, we collected information on potential risk factors of all confirmed cases by primary and secondary data. For survival analysis, outcome of interest was treatment defaulter. Kaplan–Meier curves, log-rank test, and Cox-proportional hazard regression analysis were used to model outcome of interest. Statistical Analysis Used: Statistical analysis is performed using SPSS version 17. Results: Out of total 440 TB patients registered, 13 patients got defaulted in 2015. Overall mean time of default was 279 days, with 276 days for males against 279 days for females. Many patients interrupted treatment during continuation phase. Treatment defaulters had an association with gender, category at the initiation of treatment, HIV status, smoking, and alcohol consumption. Conclusion: Targeted intervention with the goal toward adherence in persons abusing smoking and alcohol is recommended. Necessary actions need to be initiated in the program to strengthen the follow-up of patients and to bring behavioral changes by proper counseling.


Keywords: Cox proportional hazard, log-rank, survival analysis, treatment defaulters


How to cite this article:
Paunikar AP, Khadilkar HA, Doibale MK, Lamb AR. Survival analysis of treatment defaulters among tuberculosis patients in government medical college and hospital, Aurangabad. Indian J Community Med 2019;44:44-7

How to cite this URL:
Paunikar AP, Khadilkar HA, Doibale MK, Lamb AR. Survival analysis of treatment defaulters among tuberculosis patients in government medical college and hospital, Aurangabad. Indian J Community Med [serial online] 2019 [cited 2019 Jul 17];44:44-7. Available from: http://www.ijcm.org.in/text.asp?2019/44/1/44/253915





   Introduction Top


Tuberculosis (TB) is an infectious disease which continues to be the leading cause of death. According to the Global TB Report 2017, released by the WHO, India has topped list of seven countries accounting for 64% of over 10 million new TB cases worldwide in 2016. An estimated 1.7 million people died from TB in 2016.[1]

India accounts for one-fourth of the global TB burden. In 2015, an estimated 28 lakh cases occurred and 4.8 lakh people died due to TB.[2]

TB patients who do not complete treatment pose a potential public health risk. Although DOTS coverage and health workers involvement in TB prevention and control activities is improving, patients still are failing to complete their treatment.[3] Defaulter is defined as a patient whose treatment was interrupted for 2 consecutive months or more.[4]

Many studies have been conducted on TB; however, very few studies that focus specifically on survival analysis of TB patients are present till date. Survival analysis is generally defined as a set of methods for analyzing data where the outcome variable is the time until the occurrence of an event of interest. In survival analysis, subjects are usually followed over a specified time period, and the focus is on the time at which the event of interest occurs. However, the difficulty commonly encountered is only some individuals' experience the event, and subsequently, survival times will be unknown for subset of the study group. This phenomenon is called censoring.[4] For example, a patient has not experienced the relevant outcome by the time of close of the study or patient lost to follow-up during the study or patient experiences different event.

Duration of treatment is important in deciding survival of patients.[5] Further, improving treatment outcomes and designing effective interventions require understanding of factors that prevent people from adhering and those that help in treatment completion.[6],[7]

Hence, the present study was designed to determine the duration TB patients stay in treatment before defaulting and factors associated with defaulters who had been treated in Government Medical College and Hospital (GMCH), Aurangabad.


   Materials and Methods Top


The retrospective cohort study was carried out at GMCH, Aurangabad, for all the 440 TB cases registered in TB Unit of the hospital from January 1, 2015, to December 31, 2015. This included smear-positive pulmonary TB (PTB) patients, smear-negative PTB patients, and extra-PTB (EPTB) patients. The study protocol was approved by the ethics committee. Informed consent was taken from the participants before inclusion.

Smear-positive pulmonary case was defined as a patient with one or more initial sputum smear examination (acid-fast bacilli [AFB+]) or one sputum examination AFB+ and radiographic abnormalities consistent with active PTB as determined by a clinician. Smear-negative pulmonary case defined as patient with at least two sputum smear examinations negative for AFB and radiographic abnormalities consistent with active PTB with no response to a course of broad-spectrum antibiotics. EPTB case was defined as a patient with TB of organs other than the lungs.

Secondary data were abstracted from TB treatment registers in GMCH, Aurangabad. These included demographic data (age, sex, residence) and treatment data (treatment observer, category, i.e. Category I and Category II, patient types, i.e. new, relapse, failure, treatment after default, transfer status, HIV status, treatment regimen, sputum smear microscopy results, date of start and end of treatment, and treatment outcome).

To obtain primary data, TB patients were interviewed using a questionnaire by visiting their residence with the help of community health workers who had been trained to collect the relevant information from the patient. The information was collected on alcohol, smoking habits, and other relevant information about the patient.

For the survival analysis, the outcome of interest was treatment defaulter. For the purpose of our analysis, only the times for patients who were defaulted were taken as event.

Kaplan–Meier curves, log-rank test, and Cox-proportional hazard regression analysis were used to display the probability of treatment defaulter over time for each risk factor. SPSS version 17 was used for statistically significant.


   Results Top


A total of 440 bacteriological-confirmed TB patients who initiated treatment, 249 (56.59%) were male patients, and 191 (43.41%) were female patients. At the initiation of treatment, 365 (82.95%) were of Category I, 74 (16.82%) of Category II, and 1 (0.23%) patient of Category III. More than half (57.27%) were classified as smear-positive PTB, 12.05% were smear-negative PTB, and 30.68% were EPTB cases. 416 (94.55%) TB patients were from urban area and 24 (5.45%) from rural area. 432 (98.18%) were HIV negative while 8 (1.82%) patients were HIV positive. 135 (30.68%) were smokers and 305 (69.32%) nonsmokers. 147 (33.41%) were consuming alcohol and the rest 293 (66.59%) were nondrinkers [Table 1].
Table 1: Demographic and clinical characteristics of tuberculosis patients initiating tuberculosis treatment at Government Medical College and Hospital, Aurangabad (n=440)

Click here to view


Out of total 440, 13 (2.95%) patients were defaulters. Out of 13 defaulted patients, 12 (92.31%) were males and 1 (7.69%) female. Twelve patients got defaulted during continuation phase. Overall, the mean time of default was 279 days and was 276 days for males against 279 days for females [Table 2] and [Figure 1], with significance on log-rank test (P < 0.05) [Table 3]. Hazard ratio (HR): 0.114 showed that probability of defaulting was less by 89% in females than males at any given time [Table 4].
Table 2: Survival analysis of defaulters according to gender

Click here to view
Figure 1: Kaplan–Meier estimate for survival probability based on gender of patients

Click here to view
Table 3: Descriptive statistics of survival data of defaulters (SPSS output of Kaplan-Meier estimator)

Click here to view
Table 4: SPSS output for covariates with survival time (Cox regression analysis) for gender, HIV status, smoking, and alcohol

Click here to view


Of the total, 5 (38.46%) from Category I and 8 (61.54%) from Category 2 got defaulted and no one defaulted in Category 3. Survival curves showed in [Figure 2]. Log-rank test showed significant difference (P < 0.001) [Table 3]. The value of HR: 276.033 for Category I and HR: 2003.025 for Category II indicated that Category II had far more chance of defaulting than Category I with no default in Category III [Table 5].
Figure 2: Kaplan–Meier estimate for survival probability based on three categories at the initiation of treatment

Click here to view
Table 5: SPSS output for covariates with survival time (Cox regression analysis) in relation to category at start of treatment

Click here to view


Out of 13, 1 (7.69%) HIV-positive and 12 (92.31%) HIV-negative patients defaulted with mean duration of defaulter 238 and 280 days, respectively. Log-rank test (P > 0.05) was not significant [Table 3]. HR: 0.250 indicated that probability of defaulting was 75% less in HIV-negative defaulter patients as compared to HIV positive [Table 4].

Regarding smoking status, 8 (61.54%) were smokers and 5 (38.46%) nonsmokers with a mean survival time of 254 and 283 days, respectively. Log-rank test was significant (P < 0.05) [Table 3]. HR: 0.266 indicates that nonsmokers have 74% less chance of defaulting than smokers with significance at 5% probability level.

In concern with alcohol status, 6 (46.15%) were drinkers and 7 (53.85%) nondrinkers with a mean survival time of 273 and 279 days, respectively [Figure 3]. Log-rank test was not significant (P > 0.05) [Table 3]. HR: 0.602 indicated that nondrinkers have 40% less risk of defaulting as compared to drinkers [Table 4].
Figure 3: Kaplan–Meier estimate for survival probability based on alcohol status

Click here to view



   Discussion Top


Very few studies on survival analysis of defaulter patients among TB have been carried out till recently. In our study, we found that 13 (2.95%) patients got defaulted while the rate of default was 13.5% in Ethiopian study.[3]

In our study, 92.31% of defaulters were documented during continuation phase. This may be due to less vigilant follow-up of patients during continuation phase as compared to intensive phase. This is similar the findings of Ajagbe et al.,[4] which had shown 70% patients interrupted treatment during continuation phase.

In the present study, we found significant difference in survival curves of male and female in contrast to the findings of Ajagbe et al.,[4].in which no significant difference was found.

In this study, there was more chance of defaulting in Category II than Category I with no default in Category III which is similar to the findings of the study in South India which stated that survival probabilities of Category II patients are less than Category I and III.[5]

In our study, although HIV negative defaulted late as compared to HIV positive, difference was not significant on log-rank test (P > 0.05). On the other hand, being HIV-positive appeared to be associated with defaulter in univariate but not in the multivariate analysis in the study of Ethiopia.[3]

In Ajagbe et al.,[4] smokers and alcohol drinkers had hazard of 1.8 and were associated with shorter survival time of defaulters which is similar to the present findings of the study with HR of 0.266 and 0.602 for smoking and alcohol, respectively, indicating that the nonsmokers have 74% less chance of defaulting and nondrinkers have 40% less risk of defaulting.


   Conclusion Top


The present study showed treatment defaulters had an association with gender, category at initiation of treatment, HIV status, smokers, and alcohol consumption. Hence, enhanced patient pretreatment counseling and constant motivation specifically to males, Category II, HIV positive, smokers, and alcoholic TB patients during follow-ups to complete treatment without interruption are needed. Education on behavior change of patients is essential. Targeted intervention aimed at assuring adherence in persons abusing smoking and alcohol is also recommended. Necessary actions need to be initiated in the TB control program to strengthen the follow-up of patients with TB from start to completion of treatment based on the factors associated with defaulters.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
World Health Organization. Global TB Report 2017. Geneva: WHO; 2017.  Back to cited text no. 1
    
2.
TB India 2017, RNTCP Annual Status Report, Central TB Division, Nirman Bhavan, New Delhi: Directorate General Services of Health Services, Ministry of Health and Family Welfare; 2017.  Back to cited text no. 2
    
3.
Akessa GM, Tadesse M, Abebe G. Survival analysis of loss to follow-up treatment among tuberculosis patients at Jimma University Specialized Hospital, Jimma, Southwest Ethiopia. Int J Stat Mech 2015;7:923025. [doi: 10.1155/2015/923025].  Back to cited text no. 3
    
4.
Ajagbe OB, Kabair Z, O'Connor T. Survival analysis of adult tuberculosis disease. PLoS One 2014;9:e112838.  Back to cited text no. 4
    
5.
Vasantha M, Gopi PG, Subramani R. Survival of tuberculosis patients treated under dots in a rural tuberculosis unit (TU), South India. Indian J Tuberc 2008;55:64-9.  Back to cited text no. 5
    
6.
Muture BN, Keraka MN, Kimuu PK, Kabiru EW, Ombeka VO, Oguya F, et al. Factors associated with default from treatment among tuberculosis patients in Nairobi Province, Kenya: A case control study. BMC Public Health 2011;11:696.  Back to cited text no. 6
    
7.
Senbeta A, Weldegerima G, Romha G. Survival analysis and associated risk factors of tuberculosis in-hospital patients' death in Hawassa city and at Yirgalem town health centers. World J Med Sci 2014;11:382-8.  Back to cited text no. 7
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

Top
Print this article  Email this article
           

    

 
   Search
 
  
    Similar in PUBMED
    Search Pubmed for
    Search in Google Scholar for
  Related articles
    Article in PDF (521 KB)
    Citation Manager
    Access Statistics
    Reader Comments
    Email Alert *
    Add to My List *
* Registration required (free)  


    Abstract
   Introduction
    Materials and Me...
   Results
   Discussion
   Conclusion
    References
    Article Figures
    Article Tables

 Article Access Statistics
    Viewed258    
    Printed9    
    Emailed0    
    PDF Downloaded122    
    Comments [Add]    

Recommend this journal

  Sitemap | What's New | Feedback | Copyright and Disclaimer
  2007 - Indian Journal of Community Medicine | Published by Wolters Kluwer - Medknow
  Online since 15th September, 2007