Tuberculosis remains a public health problem of significance in the developing world, where already strained health service delivery systems continue to impede current control efforts. Globally, it is recognized that limited financial and health resources continue to strain efforts to control infectious diseases like TB, a condition worsened by the advent of HIV/AIDS.
The World Health Organization (WHO) and the International Union against TB and Lung Disease (IUATLD) promote universal adoption of Directly Observed Treatment, Short-Course (DOTS) for TB treatment as a means to improve TB treatment outcomes. The DOTS strategy focuses on five main points of action. These include government commitment to control TB, diagnosis based on sputum-smear microscopy tests done on patients who actively report TB symptoms, direct observation short-course chemotherapy treatments, a definite supply of drugs, and standardized reporting and recording of cases and treatment outcomes. The WHO advises that all TB patients should have at least the first two months of their therapy observed (and preferably the whole of it observed). This means an independent observer is tasked to watching patients swallow their anti-TB therapy. The DOTS treatment regimen is a multidrug combination of Isoniazid, Rifampicin, and Pyrazinamide in the first two months followed by Isoniazid and Rifampicin for the next six months. Because of these and the resource limitations of the health service sector in the developing world, there is a growing recognition of the need to decentralize TB control from hospitals and other primary care facilities to communities. One recommendation is to decentralize services in the communities through the task shifting framework. Task shifting calls for reallocating specific roles to health care workers who have had shorter training or lesser qualifications. In some cases, this includes delegating tasks to newly created cadres that have had specific competency-based training [
13].
To understand the impact of task shifting in DOTS as an effective TB control strategy, a hypothetical TB Susceptible-Infectious-Recovered (SIR) Model is shown in [
14]. This consists of four TB disease states: susceptible or uninfected (S), latently infected (L), infectious (I), and recovered persons (R). For TB, the infectious population is taken to include all individuals with active disease. Latently infected individuals are infected by the bacteria but do not have active disease and are therefore not contagious. Recovered individuals had active disease in the past but were able to contain the infection. Susceptible individuals have not had any infection yet.
In this model, the task shifting with the DOTS program aims to increase the rates of conversion of both infectious and noninfectious TB cases to the recovered group with aims of decreasing treatment failure rates. Treatment failure or inadequately treated TB cases, as discussed in several literatures, can potentially lead to multiple-drug-resistant TB strains [
5]. Emergence of the MDR TB causes severe complications and requires a more intensive and therefore more expensive treatment regimens which causes severe burden on the already resource-limited developing countries.
For diseases such as TB, which require strict adherence to long-term daily therapy, it is particularly important that task shifting is implemented successfully. Evidence supporting showing task shifting or the modified Community-Based Directly Observed Treatment, Short-Course (CB-DOTS) as an effective TB control strategy is supported by several studies conducted in high TB burden.
One of these several studies was a qualitative study conducted by Mafigiri et al. in Kampala, Uganda comparing TB treatment success outcome categories (cured, treatment completion, death, treatment failure, defaulted and transferred out) in modified community-based DOTS versus clinic-based DOTS within the period of May 2005 and September 2006 [
15]. In Uganda, the official CB-DOTS strategy involves the parish development committee, who are a small group chosen by the community to make decisions on social and economic development issues, including health matters. This group in liaison with the sub-county public health worker asks the community to nominate a volunteer who is willing and who is acceptable to the patient to deliver DOTS with overall supervision from the subcounty public health worker. Each participant selected a DOTS supporter from their social support network system. The primary study population consisted of new TB patients presenting to the clinic with their first episode of TB. Eligibility criteria include (1) sputum smear positive or smear negative with clinical signs of disease; (2) within one week of starting therapy; (3) no previous treatment for TB; (4) age 18 years and above; (5) resident in Kawempe Division in Kampala, Uganda. Each participant completed a baseline interview and was followed up on month two, five, and eight, coinciding with the recommended clinic visits for TB patients under DOTS. In total, 107 new smear-positive patients were recruited. Eighty-nine participants (83%) selected the home-based DOTS strategy. Of the 89 patients who chose home-based DOTS, five (6%) died, five (6%) transferred care, eight (9%) defaulted, and nine (10%) were lost to follow up. Sixty-two patients (70%) under home-based DOTS and 16 patients (89%) under clinic-based DOTS had successful outcomes following complete tuberculosis therapy. Analysis of data showed no significant difference in the treatment rate between home-based DOTS and clinic-based DOTS (OR = 0.29; 95% CI: 0.06–1.34).
However, since this study design did not use randomization on treatment assignments, direct comparison between strategies is limited. There is a significant discordance between sample size of the home-based DOTS (n = 89) and that of clinic-based DOTS (n = 18). The sample size of the clinic-based DOTS is not large enough to observe the full range of outcomes as seen in the home-based group. Another limitation is that the patient tracking processes employed for the purpose of the research do not represent the standard clinical practice in this resource-limited setting. It is possible that, while this measure of care may have contributed to the high rate of acceptability and success of the modified CB-DOTS, it may not be sustainable in the current context of the national TB control efforts. Identifying Kawempe, the capital of Uganda and a relatively urban area, limits the generalizability of the results to other parts of the country where resources are more limited. Considering that the study was done in an urban setting, it would also be prudent to stratify the data obtained based on the socioeconomic status and educational level of the enrolled subjects.
To establish consistency of the association presented in Mafigiri et al.'s study in the rural setting, the study conducted by Adatu et al. is reviewed in this paper [
16]. This prospective cohort study was done in Kiboga, a rural district in central Uganda, between 1995 and 1999. Both Kampala and Kiboga districts utilized the parish development committees and the subcounty health care workers for the implementation of the program. In this study design, the exposure is the CB-DOTS program with the TB treatment success being the outcome of interest. Treatment success is further categorized as (1) cured, (2) treatment completed, (3) treatment failure, (4) death, (5) treatment interruption, and (6) transfer. Eligible patients were identified as TB suspects meeting the diagnosis of active TB disease based on (1) positive smear and (2) negative smear but with clinical signs and symptoms and abnormal chest X-ray. Upon diagnosis of TB, patients are given the option to continue treatment with community-based DOTS or clinic-based DOTS. The study population consisted of 540 patients who underwent pre-CB-DOTS from 1995 to 1997 and 450 patients who were introduced to the CB-DOTS from 1998 to 1999. Following the introduction of CB-DOTS, there was a noted increase from 56% to 74% in the proportion of TB cases who were successfully treated (RR = 1.3, 95% CI: 1.2–1.5,
P < 0.001). The authors hypothesize that the rural setting made the selection of community volunteers easier because of the sense of community among its members. In addition, training of staff, volunteers, and the community resulted in increased awareness, decreased stigma, and a more educated population. Many persons became more motivated after seeing the initial success of CB-DOTS and witnessing cures among TB patients, which in turn simplified selection of community volunteers, training, treatment adherence and TB care.
Despite the encouraging results on treatment outcome among TB cases, data from 1997-1998 showed increased TB deaths even after the introduction of CB-DOTS (from 12.5% in 1995 to 14.9 in 1998). Such a high death rate makes it difficult or impossible to achieve the 85% WHO target for treatment success. The factor most likely responsible for this high death rate is HIV coinfection which was not routinely tested for TB patients. Estimates, however, in the neighboring capital district of Kampala, showed that two-thirds of pulmonary TB cases were HIV seropositive.
4.1. Sample Selection Bias
The overall presentation of the results in both studies is fairly clear but the method used for assigning the sample population for either treatment arms increases the chance for sample selection bias. Since randomization was not used in the study design, patients enrolled in both studies were given an option to choose between community-based or clinic-based DOTS. Most of these patients chose to be in the CB-DOTS. This led to an unequal distribution of samples to both treatment arms with the clinic-based DOTS sample group not having enough numbers to show all TB treatment outcomes compared to the CB-DOTS sample group. A bigger sample size for the clinic-based DOTS might report more favorable or superior TB treatment outcomes compared to CB-DOTS than what was originally observed (i.e., a lowered odds ratio in Mafigiri et al.'s study). Thus, the results would have been biased toward the null or show a weaker association than what was actually presented.
4.2. Confounding
There were a few variables associated with TB treatment outcomes that authors in both studies failed to address. These are age, socioeconomic status, neighborhood, BMI, education, history of head and neck cancer, use of immunosuppressive therapy, and most importantly HIV coinfection. To establish HIV coinfection as a potential confounder in both studies, however, is difficult considering that HIV status was not routinely done at baseline for TB diagnosis. We do, however, know that HIV, by causing a weakened immune system, increases the chances of acquiring contagious diseases including TB and indirectly increases the exposure of interest by seeking treatment of diagnosed TB cases with CB-DOTS. Furthermore, weakened immune system secondary to HIV infection renders antimicrobial treatment difficult to completely eradicate the TB bacilli. This can lead to treatment failures and morbidities such as relapse or death. Both of which are the measured treatment outcomes in the two studies we have reviewed. Without any data on the HIV status entering both treatment arms, HIV coinfection cannot be fully established as a potential confounder.
4.3. Assessment of Causality
Evidence of association presented by Mafigiri et al. and Adatu et al.'s studies can be evaluated using Sir Austin Bradford Hill's criteria for causation [
17].
Consistent with the study conducted by Newell et al. in Nepal, community-based DOTS is equally effective compared to clinic-based DOTS in high-burden countries [
18].
In both studies, the sample population was managed through CB-DOTS (our exposure of interest), followed up time, and TB treatment success (categorized as cured, treatment completed, treatment failure, death, treatment interruption, and transfer) was measured as our outcome of interest. Since the measured outcome followed the exposure of interest, the associations presented in the studies meet the criteria for temporality.
The sample selection bias as mentioned previously is a major issue in both studies. Due to the skewed distribution of the sample population, with less subjects choosing the clinic-based DOTS, there is a chance that the measures of association presented may have been overestimated. Strength of association criterion, in this case, cannot be fully met by both studies. A more robust methodology and study design should be utilized in future trials.
4.4. Generalizability
In these two studies, both authors have recognized that a highly organized community structure such as that of the parish development committee and a well-established network of subcounty health care workers are the key factors in the success of task shifting in CB-DOTS. Results of both studies can be generalized only to populations with well-structured community organizations having an available network of trained health care workers.