This analysis documents the impact of a national program to trace TB patients who interrupted treatment or missed a clinic visit in South Africa. The overall percent quarterly change for all smear positive TB patients in South Africa from Q1 2007 through the end of the TB Tracer Project in Q1 2009 showed a significant decrease in default treatment outcomes and a significant increase in successful treatment outcomes among tracer subdistricts. Changes over time were significantly different between tracer and non-tracer subdistricts for treatment outcomes of default, completed, and failed. Specifically, the decreasing trend in the proportion of patients who defaulted over time was significantly greater among tracer subdistricts than non-tracer subdistricts. The proportion of patients who completed treatment also had a declining trend over the time period for each the tracer and non-tracer subdistricts; however, the slope was significantly less among the tracer subdistricts than the non-tracer subdistricts. These findings demonstrate a significant temporal association between TB tracer teams and TB treatment outcomes.
Our findings are supported by a study conducted in Kenya at clinics operated by Médeicins Sans Frontières (MSF) which demonstrated that the implementation of an active defaulter tracing system for HIV, prevention of mother-to-child transmission, and TB patients resulted in a decrease in TB patients lost to follow up [11
]. Furthermore, the MSF tracing system documented a high resumption of appointments by patients and was able to establish a treatment outcome for almost 85% of patients who missed an appointment [11
In our study, we found that the impact of the TB Tracer Project varied by province. The inconsistency in the results observed between the provinces could be attributable to a variety of factors not assessed in this analysis: differential patient and tracer subdistrict sample sizes between provinces, variability in reporting and recording of TB treatment outcomes, as well as differences in TB burden, HIV prevalence, infrastructure, socioeconomic structure and geography. Previous research has cited the relationship between the health provider and patient and the pattern of health care delivery to be significantly associated with patient default [3
]. The differences in results between provinces may also be due to geographic migration patterns; a study of multidrug resistant TB in South Africa found that being born outside of South Africa and changing residence during treatment were both significantly associated with default from treatment [15
]. Additionally, variations in staffing and in the number of tracer teams operating per health facility and per subdistrict may have affected the efficacy of the TB Tracer Project. While this analysis did not assess these qualitative issues, a parallel study is underway to determine whether the differences in impact of the TB tracer teams may be attributable to some of these factors.
The present study was unique as few other treatment default and adherence studies have been able to assess the issue both nationally and within specific country regions. However, this study is not without limitations. This was an ecological study using a non-randomized selection of tracer and non-tracer subdistricts where in inclusion in the project was based upon one of the outcomes of interest, thereby allowing for differences in case load and for possible bias in our results. The evaluation of the TB Tracer Project was requested and conducted after the completion of the project design and implementation. Many data elements necessary for an epidemiologic evaluation of the impact of this intervention were not available for analysis, including patient level information, details of tracer teams’ duties and actions, and tracer team coverage of subdistricts and/or health facilities. However, by using national programmatic data from the ETR we were able to account for baseline trajectories in modeling with national standardized surveillance data. The subdistrict was utilized as the unit of analysis for this study because it was not possible to reliably account for and categorize the tracer status for all individual health facilities. However, the level of misclassification is likely similar in both groups and therefore would not introduce a systematic bias in the data aggregated at the subdistrict level. This non-directional misclassification would have biased toward a null result of finding no difference in the outcome between tracer and non-tracer sites. Nonetheless, the differences in the proportions of TB treatment outcomes between tracer and non-tracer subdistricts both prior to and during the TB Tracer Project were inherent in the study design [12
]. However, by modeling the proportion of TB treatment outcomes rather than patient counts with a large national sample, we aimed to minimize the effect of this selection bias.
This analysis was restricted to smear positive TB patients registered in the ETR with a treatment outcome recorded and therefore the results may not be representative of all TB patients who defaulted from treatment. However, we were able to capture the majority of patients in the ETR cohorts from Q1 2007 to Q1 2009. The aggregate ETR data available for this analysis limited our ability to produce a quantifiable point estimate to evaluate the effect of the tracer teams on TB treatment outcomes. Yet the data allowed us to examine the impact of the tracer teams over more than a two year period for the entire country of South Africa. Furthermore, the ability to perform a province stratified analysis to assess the effect of the intervention within each South African province allows for a deeper understanding of the underlying processes at work within the NTP in South Africa and allows for greater programmatic improvements.
The programmatic implications of patient tracing extend beyond the focus of this study. The improvements achieved in patient default observed during the TB Tracer Project were statistically significant; however, the current study did not observe a significant difference between tracer and non-tracer subdistricts for overall treatment success. It is likely that other programmatic interventions (i.e., DOTS, effective medication, adequate healthcare staffing, etc.) are necessary to extend beyond decreasing treatment default and to achieve an increase in treatment success. A multi-pronged approach is essential to reach global TB treatment targets, one component of which may be tracing patients to improve adherence in addition to other TB control strategies. While this study focused on default in smear positive TB patients, we did not have information regarding the HIV status of the patients counted in the ETR nor did we have data for smear negative TB patients. Research has found that patients undergoing HIV and TB treatment are more likely to interrupt treatment and the implications of TB treatment default for an HIV positive patient are of particular concern in a high-burden HIV setting [3
]. We chose not to focus on MDR TB patients in this study; however, the repercussions of treatment default for MDR TB patients must be considered when evaluating the importance of a TB tracing program [15