Baseline determinants of adherence for drug-sensitive TB treatment in a South African prospective cohort: a focus on HIV infection and anti-retroviral therapy, clinical care access, and TB stigma

Baseline determinants of adherence for drug-sensitive TB treatment in a South African prospective cohort: a focus on HIV infection and anti-retroviral therapy, clinical care access, and TB stigma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Baseline determinants of adherence for drug-sensitive TB treatment in a South African prospective cohort: a focus on HIV infection and anti-retroviral therapy, clinical care access, and TB stigma Adrian Steulet, Piotr Hippner, Noriah Maraba, Lauren Jennings, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4139836/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Suboptimal adherence to tuberculosis (TB) treatment is common and puts individuals at increased risk of treatment failure. Identifying risk factors for poor adherence may help better target individuals and improve resource allocations. We assessed specific determinants of treatment adherence: HIV status; antiretroviral therapy; time to clinical care access; and perceived stigma, among adults with drug-sensitive TB. Methods This is a secondary analysis of the “TB Mate'' cluster-randomised trial, which implemented a TB treatment adherence intervention in 18 health clinics in South Africa (PACTR201902681157721). Smart pillboxes were used to measure treatment adherence; the recording of the pillbox opening was considered a proxy for dose taken. Adults enrolled in the control arm, using the pillbox in silent mode, were eligible for this analysis. Logistic regression was used to model poor adherence (< 80% doses taken) and negative binomial regression was used to study adherence as a count of doses taken. Directed acyclic graphs guided the selection of confounders in the models. Results Out of 1,213 participants from nine clinics, 51% (614) had adherence of < 80% and the geometric mean of the percentage of doses taken was 59.6%. 63% (769) of participants were living with HIV, of whom 66% (507/769) were taking antiretroviral therapy. The median time to access clinical care was 127 minutes. Ninety-five percent (1151/1213) reported no perceived stigmatisation at the time of starting TB treatment. Living with HIV was identified as a strong determinant of adherence to TB treatment: adjusted odds ratio 1.68 (95% confidence interval [CI] 1.27–2.22) for < 80% adherence and adjusted rate ratio 0.9 (0.83–0.97) for doses taken, compared with being HIV-negative. Being on antiretroviral therapy, time to clinical care access, and perceived stigma were not associated with either adherence measure. Conclusions Very low adherence reported highlights the need for TB treatment support interventions, especially among those living with HIV. Tuberculosis adherence medication monitor HIV South Africa Figures Figure 1 Introduction Before the COVID-19 pandemic, tuberculosis (TB) was the main infectious cause of death at a global scale. South Africa is among the countries with the highest burden of tuberculosis, with an incidence of more than 500/100,000 per year ( 1 ). In 2022, the TB treatment success rate was 77% in South Africa, while it was 85% on average worldwide ( 1 ). Globally, 6.7% of incident TB cases in 2021 were among people living with HIV, with the proportion exceeding 50% in certain regions of Sub-Saharan Africa ( 1 ). TB is a treatable disease with the standard drug regimen for drug-sensitive (DS) TB curing the majority of patients. Adherence is often, however, suboptimal for multiple reasons including the length and posology of TB treatment, side effects of TB drugs, and patient travel costs ( 2 ). For TB treatment, sub-optimal adherence, particularly in the 2-month intensive phase initially, is one of the main reasons for treatment failure and development of drug resistance ( 2 ). Various strategies have been used to improve adherence, such as reducing pill burden through combined pills and developing shorter treatment regimens ( 3 ). Endorsed since 1993 by the World Health Organization (WHO), Directly Observed Therapy (DOT) initially increased treatment adherence, but many trials have since failed to demonstrate improved treatment outcomes compared to unobserved treatment, in addition to being time-consuming and costly to both patients and health care providers ( 4 ). Routine measurement of TB treatment adherence has relied on self-reporting at, often, monthly visits to the clinic. Measuring treatment adherence is complex; recently, digital adherence technologies (DATs) such as smart pillboxes or video-supported therapy have been suggested ( 5 ). The smart pillbox, for example, can be used as a proxy for true adherence, through capturing box opening by the patient, allowing a granular adherence profile to be generated for each patient. Understanding factors associated with TB treatment adherence may help identify and/or refine interventions to improve adherence and treatment outcomes. Systematic reviews report variations in the definition of adherence and in the collection of factors associated with adherence ( 6 ). Living with HIV has been found to decrease adherence to TB treatment ( 6 – 8 ). Among sociodemographic factors, limited access and long distances to health institutions are linked to worse adherence to treatment in Sub-Saharan settings, where HIV prevalence is among the highest globally ( 9 , 10 ). Stigmatisation of disease and lack of social support were also stated as reasons for stopping treatment among lost-to-follow-up patients interviewed in Ethiopia ( 11 ) and South Africa ( 12 ). The number of large-scale prospective cohort studies measuring adherence to TB treatment and its determinants is limited ( 4 ). In this study, we aim to measure the impact of pre-selected baseline determinants on treatment adherence, among adults on a DS-TB regimen, using a large prospective cohort enrolled in cluster-randomised trial of a DAT intervention, conducted in South Africa. Methods This is a secondary analysis of the TB Mate cluster-randomised trial, conducted from 2018 to 2022, which enrolled 2,600 participants being treated for DS-TB ( 13 ). Eighteen clinics from three South African provinces, Gauteng, KwaZulu-Natal, and Western Cape, were randomised to the intervention or control arm. The trial evaluated the effectiveness of a smart pillbox (EvriMED device version 1000) coupled with enhanced care for those with sub-optimal adherence, by measuring treatment adherence and poor treatment outcomes. Participants in the intervention arm used the pillbox to store their medications with a daily audio-visual reminder to take treatment. Real-time data from opening the pillbox (as a proxy for medication intake) were reviewed by study staff and healthcare workers (HCWs), who initiated differentiated care through SMS reminders, calls, and visits for participants. Participants in the control arm were given the pillbox in silent mode and asked to keep their medications in the box with box-opening data only available to the research team (and not HCWs) after the end of the study follow-up. Participants who had a treatment outcome of cured or completed treatment were followed up to 12 months after completing their treatment, with sputum collection to assess for TB recurrence. Baseline data were collected from a medical record abstraction and participant interviews. Medical history, including method of TB diagnosis, HIV status, and whether participants were on antiretroviral therapy (ART) were abstracted from patients’ clinical records. Data on demographics, risk factors for TB, economic situation, and perceived TB stigma were collected from participants by self-report. This secondary analysis is limited to the adult participants (≥ 18 years) in the control arm of the trial ( 13 ). Two adherence outcomes were considered, based on patient engagement (or non-engagement) with the pillbox as a proxy for a dose taken: (i) < 80% of the total doses required over the treatment period; and (ii) a count of doses taken over the total doses required. If the pillbox malfunctioned on a day, based on a lack of a recorded heartbeat, that dose was not considered as part of the total required doses. Similarly, if patients died, the total of required doses was censored at the time of death. Patients lost to follow-up, based on the standard WHO definition, were considered to be non-adherent from the date they were lost to follow-up to the end of the expected 6-month treatment period. Four exposures were considered a priori : HIV status; ART status among participants living with HIV; time to TB care access; and perceived stigma. HIV and ART status were extracted from clinical records at enrolment and at six months to capture HIV testing and ART initiation at the time of the TB diagnosis. Being on ART was defined as evidence ART was started any time prior and up to 60 days after the start of their TB treatment. The time needed to access clinical care was defined as a combination of self-reported travel time from the participant’s home to the health clinic and the waiting time for the appointment once inside the health clinic, collected at the enrolment visit. Perceived stigma was based on a scale with 10 items self-reported by participants at enrolment ( 14 ). A binary stigma variable was generated based on scoring at least one point on the scale versus none. To guide the multivariable analyses and the identification of a sufficient set of confounders, Directed Acyclic Graphs (DAGs) were constructed, summarising the perceived direct and indirect pathways between each exposure of interest and the outcome, using Daggity ( 15 , 16 ). Age and gender were also considered to be a priori confounders, based on the literature search. Socio-economic position (SEP) was identified as a potential confounder for the four exposures. An asset-based wealth score was constructed with polychoric dual-component analysis, a modified version of principal component analysis, using two principal components to increase the proportion of variance explained by the score and reduce urban bias ( 17 ). Data on housing structure and assets owned were used to develop the wealth score which was grouped into quintiles for analysis. The DAG for the causal relationship between HIV status and TB treatment adherence is illustrated in Fig. 1 . The following sufficient set of confounders was identified to measure the total causal effect of HIV status: gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, tobacco, alcohol, and illicit drug consumption, time to access clinical care, and socio-economic position. See Figure S1 for the DAGs for the other a priori exposures. The binary adherence outcome was analysed using logistic regression. The count outcome was analysed using negative binomial regression, as evidence of overdispersion was found, with the total number of days on treatment used as an offset. For both models fixed-effects to account for the clustering at the clinic level were adopted rather than a random effect, given there were only nine clinics. No variables were considered to be a priori effect modifiers, based on a literature approach. For multivariable analyses, separate regression models were developed for each exposure variable, adjusting for the full set of confounders based on the DAG. Due to the minimal proportion of missing values for variables included in the multivariable analyses (< 0.5%), a complete records analysis was conducted. Sensitivity analyses were conducted, with binary poor adherence defined as taking less than 70% and less than 90% of doses. Sensitivity analyses were also conducted with the exclusion of participants lost to follow-up, to observe the potential impact of the assumption that they had stopped taking their treatment. All analyses were conducted using Stata version 17.0. The TB Mate trial received ethics approval from the Universities of Witwatersrand (Ref 180705), Cape Town (Ref 452/2018), and the London School of Hygiene and Tropical Medicine (Ref 16107). In addition, ethics approval was obtained from the MSc Research Ethics Committee at the London School of Hygiene and Tropical Medicine (Ref 27519) to conduct this secondary data analysis. Results From May 2019 to December 2020, 1,213 adults (range 18-91 years) in the control arm of the TB Mate trial were recruited and followed up. Of these, 742 (61.2%) were male and the median age was 36 years (interquartile range [IQR]: 29-46). The majority, 787 (65.0%), had been diagnosed with TB using GeneXpert MTB/RIF, and 350 (28.9%) had received a clinical diagnosis based on symptoms and clinical signs. 913 (75.3%) were single, not married, at enrolment, and 912 (75.2%) were living with family members, friends, or both. The cost of travelling to the clinic on the day of enrolment was free for 379 individuals (31.2%) and less than 30 South African Rands (ZAR), or 2 US Dollars (1 ZAR = 0.067 USD, 01.01.2022), for an additional 787 (64.9%). 769 participants (63.3%) were living with HIV, or whom 507 were taking ART (65.9%). The median time to access clinical care was 127 minutes (IQR: 67-188 min). 1,151 participants (96.5%) reported no perceived stigmatisation at the time of starting TB treatment. 76 participants (6.3%) were lost to follow-up during the treatment period. Overall, 50.6% (614/1213) of participants had adherence of <80% and the geometric mean of the percentage of doses taken was 59.6% (95%CI: 57.2-62.0). There was strong evidence that living with HIV was associated with increased odds of poor adherence to TB treatment (odds ratio [OR] 1.49, 95% confidence interval [CI] 1.17-1.90, p=0.001), though taking ART versus not, was not associated with poor adherence (OR 0.90, 95%CI 0.65-1.26, p=0.55). For the other a priori exposures of perceived stigma or time to access clinical care, there was no evidence of an association with poor adherence (table 1). Being of younger age was associated with worse adherence to TB treatment with strong evidence of a linear trend: each additional year was associated with a 2% reduction in the odds of poor adherence (p<0.001). Results for the count adherence outcome were broadly similar (supplement Table S1). Table 1 Participants’ characteristics and Crude odds ratio for poor adherence (<80%) estimated using logistic regression (n=1213) Number of participants (%)* 1 No. with adherence <80% (%)* 2 Crude OR (95%CI) p-value* 3 HIV status Negative Positive 440 769 (36.4) (63.6) 196 415 (44.6) (54.0) 1 1.49 (1.17-1.90) 0.001 Antiretroviral therapy HIV+ not on ART HIV+ on ART 262 507 (34.1) (65.9) 138 277 (52.7) (54.6) 1 0.90 (0.65-1.26) 0.55 Time to access care [min] =240 229 346 299 166 173 (18.9) (28.5) (24.7) (13.7) (14.3) 104 172 156 96 86 (45.4) (49.7) (52.2) (57.8) (49.7) 1 0.76 0.76 1.04 0.84 (0.45-1.27) (0.44-1.30) (0.58-1.86) (0.46-1.52) 0.45 Perceived stigma No stigma reported >=1 point of stigma 1,151 58 (95.2) (4.8) 580 31 (50.4) (53.5) 1 1.05 (0.61-1.82) 0.85 Age [years] 18-25 26-30 31-35 36-40 41-45 46-50 51-60 >=61 199 168 211 191 137 123 115 69 (16.4) (13.9) (17.4) (15.8) (11.3) (10.1) (9.5) (5.7) 114 96 112 103 62 53 52 22 (57.3) (57.1) (53.1) (53.9) (45.3) (43.1) (45.2) (31.9) 1 0.97 0.84 0.88 0.64 0.56 0.61 0.38 (0.64-1.48) (0.56-1.24) (0.59-1.32) (0.41-1.00) (0.35-0.89) (0.38-0.98) 0.21-0.69) 0.008 Gender Male Female 742 471 (61.2) (38.8) 371 243 (50.0) (51.6) 1 1.06 (0.84-1.34) 0.61 Previous TB episode No Yes 880 333 (72.6) (27.5) 426 188 (48.4) (56.5) 1 1.36 (1.05-1.77) 0.02 Mode of TB diagnosis Bact. positive Clinical diagnosis 861 350 (71.1) (28.9) 437 162 (50.8) (46.3) 1 1.24 (0.95-1.61) 0.11 Province Gauteng Kwa-Zulu Natal Western Cape 306 434 473 (25.2) (35.8) (39.0) 138 216 260 (45.1) (49.8) (55.0) 1 1.13 1.70 (0.68-1.88) (1.01-2.84) 0.09 Household cohabitants Alone Partner/spouse only Family and/or friends 155 146 912 (12.8) (12.0) (75.2) 96 64 454 (61.9) (43.8) (49.8) 1 0.51 0.61 (0.32-0.81) (0.42-0.86) 0.008 Cost for trip to clinic [Rd] Free of charge 1-10 11-20 21-30 >=31 379 15 487 285 47 (31.2) (1.2) (40.2) (23.5) (3.9) 188 19 269 130 17 (49.6) (66.7) (55.2) (45.6) (36.2) 1 2.79 1.44 1.22 0.75 (0.90-8.67) (1.03-2.01) (0.79-1.88) (0.37-1.51) 0.04 * 1 Missing values not included: HIV status (4), Antiretroviral therapy (4), Perceived stigma (4), and Previous TB episode (2) // % are column percentages * 2 Number of participants experiencing poor adherence and percentage from the total number of participants with this exposure’s category * 3 P-values calculated using the Likelihood Ratio Test Abbreviations : OR=Odds ratio; CI=Confidence interval; TB=tuberculosis; min=minutes; Rd=South African Rand In multivariable analyses, there was strong evidence that living with HIV was associated with increased odds of poor adherence (adjusted OR [aOR] 1.68, 95%CI=1.27-2.22, p<0.001). Similarly to the crude analyses, there was however no evidence that antiretroviral therapy, time to access care, or perceived stigma were associated with poor adherence (table 2). For adherence modelled as a count of doses taken, there was strong evidence that living with HIV was associated with a 10% lower rate of dose intake, compared to being HIV-negative (adjusted RR [aRR] 0.90, 95%CI=0.83-0.97, p=0.005). There was no evidence that receiving ART was associated with better or worse adherence (aRR 0.95, 95%CI=0.86-1.04, p=0.26). Similarly, there was no evidence that perceived stigma was associated with adherence count (aRR 0.97, 95%CI=0.84-1.14, p=0.74), or that longer time needed to access care was associated with adherence count. Table 2 – Adjusted odds ratios of binary poor adherence and adjusted rate ratios of quantitative adherence to TB Adjusted ORs (95%CI)* 1 p-value* 2 Adjusted RRs (95%CI)* 1 p-value* 2 HIV status* 3 Negative Positive Antiretroviral therapy* 4 HIV+ not on ART HIV+ on ART Time to access care [min]* 5 =240 Perceived stigma [over 10 pts]* 6 No stigma reported >=1 point of stigma 1 1.68 1 1.09 1 0.83 0.84 1.08 0.89 1 1.02 (1.27-2.22) (0.75-1.57) (0.48-1.42) (0.47-1.48) (0.59-1.98) (0.48-1.68) (0.58-1.82) <0.001 0.65 0.73 0.94 1 0.90 1 0.95 1 1.02 1.00 1.00 1.07 1 0.97 (0.83-0.97) (0.86-1.04) (0.88-1.18) (0.86-1.16) (0.85-1.18) (0.90-1.27) (0.84-1.14) 0.005 0.26 0.87 0.74 * 1 Results of four separate models for each exposure, adjusted for the confounders identified with the DAGs * 2 P-values calculated using the Likelihood Ratio Test * 3 Model with 1,209 observations; adjusted for gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, tobacco, alcohol and illicit drug consumption, time to access clinical care, and socio-economic position. * 4 Model with 769 observations; adjusted for gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, previous TB episode, tobacco, alcohol and illicit drug consumption, time to access clinical care, perceived stigma, and socio-economic position. * 5 Model with 1,213 observations; adjusted for gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, previous TB episode, HIV status, diabetes, tobacco, alcohol and illicit drug consumption, and socio-economic position. * 6 Model with 1,209 observations; adjusted for gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, previous TB episode, tobacco, alcohol and illicit drug consumption, time to access clinical care, HIV status, and socio-economic position CI confidence interval; OR odds ratio; ART=antiretroviral therapy; RR rate ratio The sensitivity analysis varying the definition of poor adherence to <70% (40.8%, 495/1213) and <90% (64.8%, 786/1213) showed similar results: notably, the strong evidence of worse adherence among participants living with HIV persisted (aOR 70% 1.50, 95%CI=1.13-2.00; aOR 90% 1.77, 95%CI=1.32-2.36) (Table 3). The observed associations remained consistent when excluding the 76 participants (6.3%) who were lost to follow-up on treatment; participants living with HIV had increased odds of poor adherence to TB treatment (OR 1.62, 95%CI 1.21-2.17, p=0.001). The sensitivity analyses for the count outcome gave similar results (data not shown). Table 3 (sensitivity analyses): Adjusted odds ratios of poor adherence to TB treatment defined as <70%, and <90% *1 <70% adherence <90% adherence aOR (95%CI) aOR (95%CI) HIV status Negative Positive Antiretroviral therapy HIV+ not on ART HIV+ on ART Time to access care [min] =240 Perceived stigma [over 10 pts] No stigma reported >=1 point of stigma 1 1.50 1 1.21 1 0.85 0.88 1.08 0.95 1 1.05 (1.13-2.00) (0.84-1.75) (0.50-1.47) (0.50-1.56) (0.59-1.99) (0.50-1.79) (0.59-1.86) 1 1.77 1 1.13 1 1.12 1.06 1.34 1.04 1 1.07 (1.32-2.36) (0.77-1.67) (0.64-1.96) (0.59-1.91) (0.71-2.54) (0.54-1.99) (0.58-1.98) *1 Separate logistic regression models for each exposure, adjusted for the same set of confounders and with the same number of observations as regression models in table 2 CI confidence interval; aOR adjusted odds ratio; ART antiretroviral therapy Discussion In this large South African cohort of 1,213 adults receiving treatment for DS-TB, the geometric mean of the percentage of doses taken was 59.6% (95%CI: 57.2-62.0), as measured by engagement with the pillbox. We found strong evidence that participants living with HIV had increased odds of poor adherence, and a 10% lower rate of dose intake, compared to those HIV-negative. No evidence of an association was found for ART status, time to clinic, and stigma being associated with adherence. Results also showed that younger age was associated with higher odds of poor adherence. The absence of association between our exposures and patient engagement with the pillbox, except for HIV status, was not consistent with that previously reported (10–12). This may be due to the differences in design between this cohort study and previous studies conducted on adherence to TB treatment, which were mostly cross-sectional or retrospective by design, and mostly used other means of measuring adherence, such as DOT (18). Additionally, adherence to TB treatment is linked to complex health behaviours, as well as beliefs linked to the disease, which is strongly context-dependent, and prone to change with time. This may explain that, even within South Africa, determinants of adherence may vary depending on geographical areas and time. Taking antiretroviral therapy among participants living with HIV was not associated with worse TB treatment adherence, though people living with HIV had lower adherence than those HIV-negative. ART status was determined by ascertaining whether participants living with HIV were prescribed ART within the first two months of TB treatment: adherence to ART itself was not recorded and might have been a more sensible exposure variable. As we relied on abstraction from clinic records, ART status may have also been misclassified if ART records were not found. There was no evidence suggesting that perceived stigma was associated with the outcome either. One issue with this exposure variable was the small number of participants, 58 (4.8%), that reported any stigma at baseline associated with their TB diagnosis. This lowered the statistical power accessible to observe a possible association. In addition, participants were enrolled shortly after their TB diagnosis: stigma may have appeared after a certain delay, and therefore be missed during baseline data collection. Finally, there was no evidence showing that the time needed to access care was linked to treatment adherence, although limited access to healthcare is recognised as a risk factor of poor adherence (9–11). We hypothesised that participants requiring a longer time to access their clinic would be at increased risk of missing follow-up visits, due to the associated direct and indirect costs, and their disengagement from healthcare would then increase their risk of poor daily adherence. Most participants lived in urban areas relatively close to the clinics (<8km away) and needed less than 4 hours in total to access care. It may be possible that only a few participants included in this study truly experienced limited access to healthcare, at such a level observed in other studies. This study’s sample size, as well as the large number of outcomes recorded, provided good power to measure associations and conduct multivariable regression models.Since there lacks a universal definition of poor adherence to DS-TB treatment, we conducted sensitivity analyses to ensure that our findings remained consistent with different chosen cut-offs for binary adherence. There are, however, some important limitations. Misclassification of the outcome may have occurred: the electronic monitoring device recorded the engagement of participants with the smart pillbox technology and was only a proxy for adherence itself. Voluntary non-adherence from participants, i.e. opening the box without ingesting the medication, would not have been detected. A study from India showed suboptimal accuracy of DAT engagement (99DOTS) with results from urine isoniazid testing, a direct measure of drug intake (19). In our study participants in the control arm had no personal benefit from adherence monitoring by the pillbox and so may have opted not to use the pillbox at all. Our study is restricted to individuals who have initiated TB treatment. Clinic attendees diagnosed with TB but who never started treatment, that is, lost to follow-up before taking their first dose, were excluded. These individuals represent a group with zero adherence, that is extreme non-adherence, and understanding their factors is important. This limits the generalisability of our results, therefore, to a population that actively engages with the local healthcare system, at least once post-diagnosis. Sufficient adherence to TB treatment is important to avoid treatment failure or development of new drug resistance, and increased TB burden globally (20). To best allocate health resources aimed at improving treatment adherence, it is necessary to identify subpopulations at risk of poor adherence. Younger individuals and those living with HIV, comprising a large proportion of this cohort, were identified as having worse DAT engagement, a proxy for adherence to DS-TB treatment. These results highlight the need to bring reinforced differentiated care to this specific population of TB patients in South Africa to improve adherence and ultimately success of TB treatment. Further, qualitative research should also be conducted to understand treatment adherence issues in such groups to determine how best to support their care and treatment. Abbreviations ART : antiretroviral therapy CI : confidence interval DAG : directed acyclic graph DAT : digital adherence technology DOT : Directly Observed Therapy DS : drug-sensitive HCW : healthcare workers HIV : human immunodeficiency virus IQR : interquartile range OR : odds ratio aOR : adjusted odds ratio RR : rate ratio aRR : adjusted rate ratio SEP : socio-economic position TB : tuberculosis USD : US Dollar WHO : World Health Organization ZAR : South African Rand Declarations Ethics approval and consent to participate The parent study (trial) and associated analyses obtained approval from the Wits Human Research Committee (Ref 180705), the University of Cape Town Human Ethics Research Committee (Ref 452/2018) and the London School of Hygiene and Tropical Medicine (Ref 16107). In addition, ethics approval was obtained from the MSc Research Ethics Committee at the London School of Hygiene and Tropical Medicine (Ref 27519) to conduct this secondary data analysis. The study also obtained approval from the three district health departments: eThekwini, Ekurhuleni and the City of Cape Town. The trial was registered with the Pan African Trial Registry PACTR201902681157721. Written informed consent was obtained from all study participants prior to study initiation. Consent for publication Written informed consent was obtained from all study participants prior to study enrolment. Availability of data and materials The datasets used and/or analysed during the current study are available upon submission of a request to the Aurum Data Governance Committee, on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: The study is funded by the (1) Bill and Melinda Gates Foundation (OPP1205388), (2) TB REACH Wave 6 project of Stop TB Partnership (STBP/TBREACH/GSA/W6-34), and (3) South African Medical Research Council through the Strategic Health Innovation Partnerships. Funders did not play a part in the design of the study or the decision to submit the manuscript for publication. Authors contributions: AS, KLF, SC and CO conceptualized the study AS, KLF, SC, PH, CO, NM, and KV interpreted the study findings. PH, LJ, IR, RM, NX, LM contributed to study implementation and collection of data. AS analysed the data. AS and KF wrote the original draft and revised subsequent versions of the manuscript. PH, NM, LJ, IR, RM, NX, LM, KV, CO and SC reviewed and edited previous versions. All the authors read and approved the final manuscript. Acknowledgments We would like to thank the following: Ekurhuleni, City of Cape Town, eThekwini districts and the Ekurhuleni Health District Research Committee (EHDRC) for allowing us to conduct the study in their districts. The study coordinators from Gauteng, Western Cape and KwaZulu-Natal in South Africa for their assistance with data collection, namely Vuyelwa Mehlomakulu, Vumile Gumede and Bongani Zondi, and their field-based teams of research assistants and interns. References Global Tuberculosis Report. 2022 [Internet]. [cited 2023 Jul 29]. Available from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022 . Official American Thoracic Society/Centers for Disease Control and Prevention/Infectious Diseases Society of America Clinical Practice Guidelines. Treatment of Drug-Susceptible Tuberculosis | Read by QxMD [Internet]. [cited 2022 Aug 19]. Available from: https://read.qxmd.com/read/27516382/official-american-thoracic-society-centers-for-disease-control-and-prevention-infectious-diseases-society-of-america-clinical-practice-guidelines-treatment-of-drug-susceptible-tuberculosis . TREATMENT OF TUBERCULOSIS. Guidelines for treatment of drug-susceptible tuberculosis and patient care. Karumbi J, Garner P. Directly observed therapy for treating tuberculosis. Cochrane Database of Systematic Reviews [Internet]. 2015 May 29 [cited 2022 Aug 19];2015(5). Available from: https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD003343.pub4/full . Vernon A, Fielding K, Savic R, Dodd L, Nahid P. The importance of adherence in tuberculosis treatment clinical trials and its relevance in explanatory and pragmatic trials. PLoS Med [Internet]. 2019 [cited 2023 Aug 9];16(12). Available from: /pmc/articles/PMC6903706/ . McNabb KC, Bergman A, Farley JE. Risk factors for poor engagement in drug-resistant TB care in South Africa: a systematic review. Public Health Action [Internet]. 2021 Sep 23 [cited 2022 Aug 19];11(3):139–45. Available from: https://pubmed.ncbi.nlm.nih.gov/34567990/ . Masini EO, Mansour O, Speer CE, Addona V, Hanson CL, Sitienei JK et al. Using Survival Analysis to Identify Risk Factors for Treatment Interruption among New and Retreatment Tuberculosis Patients in Kenya. PLoS One [Internet]. 2016 Oct 1 [cited 2022 Aug 19];11(10). Available from: https://pubmed.ncbi.nlm.nih.gov/27706230/ . Tadesse AW, Cusinato M, Weldemichael GT, Abdurhman T, Assefa D, Yazew H et al. Risk factors for poor engagement with a smart pillbox adherence intervention among persons on tuberculosis treatment in Ethiopia. BMC Public Health [Internet]. 2023 Dec 1 [cited 2024 Mar 19];23(1):1–12. Available from: https://bmcpublichealth.biomedcentral.com/articles/ 10.1186/s12889-023-16905-z . Castelnuovo B. A review of compliance to anti tuberculosis treatment and risk factors for defaulting treatment in Sub Saharan Africa. Afr Health Sci [Internet]. 2010 [cited 2022 Aug 28];10(4):320. Available from: /pmc/articles/PMC3052808/ . Woimo TT, Yimer WK, Bati T, Gesesew HA. The prevalence and factors associated for anti-tuberculosis treatment non-adherence among pulmonary tuberculosis patients in public health care facilities in South Ethiopia: a cross-sectional study. BMC Public Health [Internet]. 2017 Mar 20 [cited 2022 Aug 19];17(1). Available from: https://pubmed.ncbi.nlm.nih.gov/28320351/ . Moodley N, Saimen A, Zakhura N, Motau D, Setswe G, Charalambous S et al. ‘They are inconveniencing us’ - exploring how gaps in patient education and patient centred approaches interfere with TB treatment adherence: perspectives from patients and clinicians in the Free State Province, South Africa. BMC Public Health [Internet]. 2020 Apr 6 [cited 2022 Mar 29];20(1). Available from: /pmc/articles/PMC7137430/. Finlay A, Lancaster J, Holtz TH, Weyer K, Miranda A, Van Der Walt M. Patient- and provider-level risk factors associated with default from tuberculosis treatment, South Africa, 2002: A case-control study. BMC Public Health [Internet]. 2012 Jan 20 [cited 2022 Aug 28];12(1):1–12. Available from: https://bmcpublichealth.biomedcentral.com/articles/ 10.1186/1471-2458-12-56 . Maraba N, Orrell C, Chetty-Makkan CM, Velen K, Mukora R, Page-Shipp L, et al. Evaluation of adherence monitoring system using evriMED with a differentiated response compared to standard of care among drug-sensitive TB patients in three provinces in South Africa: a protocol for a cluster randomised control trial. Trials. 2021;22(1):1–9. Bond V, Floyd S, Fenty J, Schaap A, Godfrey-Faussett P, Claassens M et al. Secondary analysis of tuberculosis stigma data from a cluster randomised trial in Zambia and South Africa (ZAMSTAR). Int J Tuberc Lung Dis [Internet]. 2017 Nov 1 [cited 2022 Aug 29];21(11):S49–59. Available from: https://pubmed.ncbi.nlm.nih.gov/29025485/ . Tennant PWG, Murray EJ, Arnold KF, Berrie L, Fox MP, Gadd SC, et al. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International journal of epidemiology. Volume 50. NLM (Medline); 2021. pp. 620–32. Textor J, van der Zander B, Gilthorpe MS, Liśkiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: The R package dagitty. Int J Epidemiol. 2016;45(6):1887–94. Martel P, Mbofana F, Cousens S. The polychoric dual-componentm wealth index as an alternative to the DHS index: Addressing the urban bias. J Glob Health. 2021;11:1–19. Munro SA, Lewin SA, Smith HJ, Engel ME, Fretheim A, Volmink J. Patient Adherence to Tuberculosis Treatment: A Systematic. Rev Qualitative Res. 2007;4(7). Thomas BE, Kumar JV, Chiranjeevi M, Shah D, Khandewale A, Thiruvengadam K et al. Evaluation of the Accuracy of 99DOTS, a Novel Cellphone-based Strategy for Monitoring Adherence to Tuberculosis Medications: Comparison of DigitalAdherence Data With Urine Isoniazid Testing. Clin Infect Dis [Internet]. 2020 Nov 1 [cited 2023 Aug 9];71(9):E513–6. Available from: https://pubmed.ncbi.nlm.nih.gov/32221550/ . Seung KJ, Keshavjee S, Rich ML. Multidrug-Resistant Tuberculosis and Extensively Drug-Resistant Tuberculosis. Cold Spring Harb Perspect Med [Internet]. 2015 Sep 1 [cited 2022 Aug 28];5(9). Available from: /pmc/articles/PMC4561400/ . Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Vol. 10, Epidemiology. 1999. p. 37–48. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4139836","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282155638,"identity":"975f7188-5290-4062-aee2-875ef8382ca4","order_by":0,"name":"Adrian Steulet","email":"","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Adrian","middleName":"","lastName":"Steulet","suffix":""},{"id":282155639,"identity":"4b84b362-d1a6-4710-b7b2-2c0c8f556a13","order_by":1,"name":"Piotr Hippner","email":"","orcid":"","institution":"The Aurum 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Fielding","suffix":""}],"badges":[],"createdAt":"2024-03-21 00:14:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4139836/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4139836/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53418771,"identity":"f78ea3c0-6b20-4388-b1e0-3af44dc7825e","added_by":"auto","created_at":"2024-03-25 18:09:53","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159354,"visible":true,"origin":"","legend":"\u003cp\u003eDAG illustrating the causal association between HIV status and the level of adherence to TB treatment.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4139836/v1/0d62c07445573898f13f4f0f.jpeg"},{"id":53420748,"identity":"cefc9277-75f0-44f1-8316-1fafe0279784","added_by":"auto","created_at":"2024-03-25 18:25:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":464407,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4139836/v1/a34d82a8-59cc-4596-b15c-0e8590178ac9.pdf"},{"id":53418772,"identity":"7ed4a18b-7752-43a4-9c82-65c4c766a3df","added_by":"auto","created_at":"2024-03-25 18:09:53","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":559087,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4139836/v1/64aa458452b0d8e622aadfa1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Baseline determinants of adherence for drug-sensitive TB treatment in a South African prospective cohort: a focus on HIV infection and anti-retroviral therapy, clinical care access, and TB stigma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBefore the COVID-19 pandemic, tuberculosis (TB) was the main infectious cause of death at a global scale. South Africa is among the countries with the highest burden of tuberculosis, with an incidence of more than 500/100,000 per year (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2022, the TB treatment success rate was 77% in South Africa, while it was 85% on average worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Globally, 6.7% of incident TB cases in 2021 were among people living with HIV, with the proportion exceeding 50% in certain regions of Sub-Saharan Africa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTB is a treatable disease with the standard drug regimen for drug-sensitive (DS) TB curing the majority of patients. Adherence is often, however, suboptimal for multiple reasons including the length and posology of TB treatment, side effects of TB drugs, and patient travel costs (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For TB treatment, sub-optimal adherence, particularly in the 2-month intensive phase initially, is one of the main reasons for treatment failure and development of drug resistance (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Various strategies have been used to improve adherence, such as reducing pill burden through combined pills and developing shorter treatment regimens (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Endorsed since 1993 by the World Health Organization (WHO), Directly Observed Therapy (DOT) initially increased treatment adherence, but many trials have since failed to demonstrate improved treatment outcomes compared to unobserved treatment, in addition to being time-consuming and costly to both patients and health care providers (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRoutine measurement of TB treatment adherence has relied on self-reporting at, often, monthly visits to the clinic. Measuring treatment adherence is complex; recently, digital adherence technologies (DATs) such as smart pillboxes or video-supported therapy have been suggested (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The smart pillbox, for example, can be used as a proxy for true adherence, through capturing box opening by the patient, allowing a granular adherence profile to be generated for each patient.\u003c/p\u003e \u003cp\u003eUnderstanding factors associated with TB treatment adherence may help identify and/or refine interventions to improve adherence and treatment outcomes. Systematic reviews report variations in the definition of adherence and in the collection of factors associated with adherence (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Living with HIV has been found to decrease adherence to TB treatment (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Among sociodemographic factors, limited access and long distances to health institutions are linked to worse adherence to treatment in Sub-Saharan settings, where HIV prevalence is among the highest globally (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Stigmatisation of disease and lack of social support were also stated as reasons for stopping treatment among lost-to-follow-up patients interviewed in Ethiopia (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and South Africa (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe number of large-scale prospective cohort studies measuring adherence to TB treatment and its determinants is limited (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In this study, we aim to measure the impact of pre-selected baseline determinants on treatment adherence, among adults on a DS-TB regimen, using a large prospective cohort enrolled in cluster-randomised trial of a DAT intervention, conducted in South Africa.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis is a secondary analysis of the TB Mate cluster-randomised trial, conducted from 2018 to 2022, which enrolled 2,600 participants being treated for DS-TB (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Eighteen clinics from three South African provinces, Gauteng, KwaZulu-Natal, and Western Cape, were randomised to the intervention or control arm. The trial evaluated the effectiveness of a smart pillbox (EvriMED device version 1000) coupled with enhanced care for those with sub-optimal adherence, by measuring treatment adherence and poor treatment outcomes. Participants in the intervention arm used the pillbox to store their medications with a daily audio-visual reminder to take treatment. Real-time data from opening the pillbox (as a proxy for medication intake) were reviewed by study staff and healthcare workers (HCWs), who initiated differentiated care through SMS reminders, calls, and visits for participants. Participants in the control arm were given the pillbox in silent mode and asked to keep their medications in the box with box-opening data only available to the research team (and not HCWs) after the end of the study follow-up. Participants who had a treatment outcome of cured or completed treatment were followed up to 12 months after completing their treatment, with sputum collection to assess for TB recurrence.\u003c/p\u003e \u003cp\u003eBaseline data were collected from a medical record abstraction and participant interviews. Medical history, including method of TB diagnosis, HIV status, and whether participants were on antiretroviral therapy (ART) were abstracted from patients\u0026rsquo; clinical records. Data on demographics, risk factors for TB, economic situation, and perceived TB stigma were collected from participants by self-report.\u003c/p\u003e \u003cp\u003eThis secondary analysis is limited to the adult participants (\u0026ge;\u0026thinsp;18 years) in the control arm of the trial (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Two adherence outcomes were considered, based on patient engagement (or non-engagement) with the pillbox as a proxy for a dose taken: (i)\u0026thinsp;\u0026lt;\u0026thinsp;80% of the total doses required over the treatment period; and (ii) a count of doses taken over the total doses required. If the pillbox malfunctioned on a day, based on a lack of a recorded heartbeat, that dose was not considered as part of the total required doses. Similarly, if patients died, the total of required doses was censored at the time of death. Patients lost to follow-up, based on the standard WHO definition, were considered to be non-adherent from the date they were lost to follow-up to the end of the expected 6-month treatment period.\u003c/p\u003e \u003cp\u003eFour exposures were considered \u003cem\u003ea priori\u003c/em\u003e: HIV status; ART status among participants living with HIV; time to TB care access; and perceived stigma. HIV and ART status were extracted from clinical records at enrolment and at six months to capture HIV testing and ART initiation at the time of the TB diagnosis. Being on ART was defined as evidence ART was started any time prior and up to 60 days after the start of their TB treatment. The time needed to access clinical care was defined as a combination of self-reported travel time from the participant\u0026rsquo;s home to the health clinic and the waiting time for the appointment once inside the health clinic, collected at the enrolment visit. Perceived stigma was based on a scale with 10 items self-reported by participants at enrolment (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). A binary stigma variable was generated based on scoring at least one point on the scale versus none.\u003c/p\u003e \u003cp\u003eTo guide the multivariable analyses and the identification of a sufficient set of confounders, Directed Acyclic Graphs (DAGs) were constructed, summarising the perceived direct and indirect pathways between each exposure of interest and the outcome, using Daggity (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Age and gender were also considered to be a priori confounders, based on the literature search. Socio-economic position (SEP) was identified as a potential confounder for the four exposures. An asset-based wealth score was constructed with polychoric dual-component analysis, a modified version of principal component analysis, using two principal components to increase the proportion of variance explained by the score and reduce urban bias (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Data on housing structure and assets owned were used to develop the wealth score which was grouped into quintiles for analysis.\u003c/p\u003e \u003cp\u003eThe DAG for the causal relationship between HIV status and TB treatment adherence is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The following sufficient set of confounders was identified to measure the total causal effect of HIV status: gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, tobacco, alcohol, and illicit drug consumption, time to access clinical care, and socio-economic position. See Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for the DAGs for the other \u003cem\u003ea priori\u003c/em\u003e exposures.\u003c/p\u003e \u003cp\u003eThe binary adherence outcome was analysed using logistic regression. The count outcome was analysed using negative binomial regression, as evidence of overdispersion was found, with the total number of days on treatment used as an offset. For both models fixed-effects to account for the\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eclustering at the clinic level were adopted rather than a random effect, given there were only nine clinics. No variables were considered to be a priori effect modifiers, based on a literature approach. For multivariable analyses, separate regression models were developed for each exposure variable, adjusting for the full set of confounders based on the DAG. Due to the minimal proportion of missing values for variables included in the multivariable analyses (\u0026lt;\u0026thinsp;0.5%), a complete records analysis was conducted. Sensitivity analyses were conducted, with binary poor adherence defined as taking less than 70% and less than 90% of doses. Sensitivity analyses were also conducted with the exclusion of participants lost to follow-up, to observe the potential impact of the assumption that they had stopped taking their treatment.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using Stata version 17.0.\u003c/p\u003e \u003cp\u003e The TB Mate trial received ethics approval from the Universities of Witwatersrand (Ref 180705), Cape Town (Ref 452/2018), and the London School of Hygiene and Tropical Medicine (Ref 16107). In addition, ethics approval was obtained from the MSc Research Ethics Committee at the London School of Hygiene and Tropical Medicine (Ref 27519) to conduct this secondary data analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFrom May 2019 to December 2020, 1,213 adults (range 18-91 years) in the control arm of the TB Mate trial were recruited and followed up. Of these, 742 (61.2%) were male and the median age was 36 years (interquartile range [IQR]: 29-46). The majority, 787 (65.0%), had been diagnosed with TB using GeneXpert MTB/RIF, and 350 (28.9%) had received a clinical diagnosis based on symptoms and clinical signs. 913 (75.3%) were single, not married, at enrolment, and 912 (75.2%) were living with family members, friends, or both. The cost of travelling to the clinic on the day of enrolment was free for 379 individuals (31.2%) and less than 30 South African Rands (ZAR), or 2 US Dollars (1 ZAR = 0.067 USD, 01.01.2022), for an additional 787 (64.9%). 769 participants (63.3%) were living with HIV, or whom 507 were taking ART (65.9%). The median time to access clinical care was 127 minutes (IQR: 67-188 min). 1,151 participants (96.5%) reported no perceived stigmatisation at the time of starting TB treatment. 76 participants (6.3%) were lost to follow-up during the treatment period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, 50.6% (614/1213) of participants had adherence of \u0026lt;80% and the geometric mean of the percentage of doses taken was 59.6% (95%CI: 57.2-62.0). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was strong evidence that living with HIV was associated with increased odds of poor adherence to TB treatment (odds ratio [OR] 1.49, 95% confidence interval [CI] 1.17-1.90, p=0.001), though taking ART versus not, was not associated with poor adherence (OR 0.90, 95%CI 0.65-1.26, p=0.55). For the other \u003cem\u003ea priori\u003c/em\u003e exposures of perceived stigma or time to access clinical care, there was no evidence of an association with poor adherence (table 1). Being of younger age was associated with worse adherence to TB treatment with strong evidence of a linear trend: each additional year was associated with a 2% reduction in the odds of poor adherence (p\u0026lt;0.001). Results for the count adherence outcome were broadly similar (supplement Table S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 \u0026nbsp;Participants\u0026rsquo; characteristics and Crude odds ratio for poor adherence (\u0026lt;80%) estimated using logistic regression (n=1213)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.174183514774494%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of participants (%)*\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.08398133748056%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. with adherence \u0026lt;80% (%)*\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30637636080871%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude OR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value*\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003cp\u003e769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(36.4)\u003c/p\u003e\n \u003cp\u003e(63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003cp\u003e415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(44.6)\u003c/p\u003e\n \u003cp\u003e(54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.17-1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntiretroviral therapy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHIV+ not on ART\u003c/p\u003e\n \u003cp\u003eHIV+ on ART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(34.1)\u003c/p\u003e\n \u003cp\u003e(65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(52.7)\u003c/p\u003e\n \u003cp\u003e(54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.65-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime to access care [min]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003cp\u003e60-119\u003c/p\u003e\n \u003cp\u003e120-179\u003c/p\u003e\n \u003cp\u003e180-239\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;=240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(18.9)\u003c/p\u003e\n \u003cp\u003e(28.5)\u003c/p\u003e\n \u003cp\u003e(24.7)\u003c/p\u003e\n \u003cp\u003e(13.7)\u003c/p\u003e\n \u003cp\u003e(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(45.4)\u003c/p\u003e\n \u003cp\u003e(49.7)\u003c/p\u003e\n \u003cp\u003e(52.2)\u003c/p\u003e\n \u003cp\u003e(57.8)\u003c/p\u003e\n \u003cp\u003e(49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.45-1.27)\u003c/p\u003e\n \u003cp\u003e(0.44-1.30)\u003c/p\u003e\n \u003cp\u003e(0.58-1.86)\u003c/p\u003e\n \u003cp\u003e(0.46-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived stigma\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo stigma reported\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026gt;=1 point of stigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1,151\u003c/p\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95.2)\u003c/p\u003e\n \u003cp\u003e(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(50.4)\u003c/p\u003e\n \u003cp\u003e(53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.61-1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge [years]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e18-25\u003c/p\u003e\n \u003cp\u003e26-30\u003c/p\u003e\n \u003cp\u003e31-35\u003c/p\u003e\n \u003cp\u003e36-40\u003c/p\u003e\n \u003cp\u003e41-45\u003c/p\u003e\n \u003cp\u003e46-50\u003c/p\u003e\n \u003cp\u003e51-60\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026gt;=61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(16.4)\u003c/p\u003e\n \u003cp\u003e(13.9)\u003c/p\u003e\n \u003cp\u003e(17.4)\u003c/p\u003e\n \u003cp\u003e(15.8)\u003c/p\u003e\n \u003cp\u003e(11.3)\u003c/p\u003e\n \u003cp\u003e(10.1)\u003c/p\u003e\n \u003cp\u003e(9.5)\u003c/p\u003e\n \u003cp\u003e(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e114\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e112\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(57.3)\u003c/p\u003e\n \u003cp\u003e(57.1)\u003c/p\u003e\n \u003cp\u003e(53.1)\u003c/p\u003e\n \u003cp\u003e(53.9)\u003c/p\u003e\n \u003cp\u003e(45.3)\u003c/p\u003e\n \u003cp\u003e(43.1)\u003c/p\u003e\n \u003cp\u003e(45.2)\u003c/p\u003e\n \u003cp\u003e(31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.64-1.48)\u003c/p\u003e\n \u003cp\u003e(0.56-1.24)\u003c/p\u003e\n \u003cp\u003e(0.59-1.32)\u003c/p\u003e\n \u003cp\u003e(0.41-1.00)\u003c/p\u003e\n \u003cp\u003e(0.35-0.89)\u003c/p\u003e\n \u003cp\u003e(0.38-0.98)\u003c/p\u003e\n \u003cp\u003e0.21-0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e742\u003c/p\u003e\n \u003cp\u003e471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(61.2)\u003c/p\u003e\n \u003cp\u003e(38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e371\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e243\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(50.0)\u003c/p\u003e\n \u003cp\u003e(51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.84-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious TB episode\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e880\u003c/p\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(72.6)\u003c/p\u003e\n \u003cp\u003e(27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e426\u003c/p\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(48.4)\u003c/p\u003e\n \u003cp\u003e(56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.05-1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMode of TB diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBact. positive\u003c/p\u003e\n \u003cp\u003eClinical diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e861\u003c/p\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(71.1)\u003c/p\u003e\n \u003cp\u003e(28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(50.8)\u003c/p\u003e\n \u003cp\u003e(46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.95-1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvince\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eGauteng\u003c/p\u003e\n \u003cp\u003eKwa-Zulu Natal\u003c/p\u003e\n \u003cp\u003eWestern Cape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003cp\u003e473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(25.2)\u003c/p\u003e\n \u003cp\u003e(35.8)\u003c/p\u003e\n \u003cp\u003e(39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(45.1)\u003c/p\u003e\n \u003cp\u003e(49.8)\u003c/p\u003e\n \u003cp\u003e(55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.68-1.88)\u003c/p\u003e\n \u003cp\u003e(1.01-2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold cohabitants\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAlone\u003c/p\u003e\n \u003cp\u003ePartner/spouse only\u003c/p\u003e\n \u003cp\u003eFamily and/or friends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003cp\u003e912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(12.8)\u003c/p\u003e\n \u003cp\u003e(12.0)\u003c/p\u003e\n \u003cp\u003e(75.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(61.9)\u003c/p\u003e\n \u003cp\u003e(43.8)\u003c/p\u003e\n \u003cp\u003e(49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.32-0.81)\u003c/p\u003e\n \u003cp\u003e(0.42-0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.993779160186627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost for trip to clinic [Rd]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFree of charge\u003c/p\u003e\n \u003cp\u003e1-10\u003c/p\u003e\n \u003cp\u003e11-20\u003c/p\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003cp\u003e\u0026gt;=31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.909797822706065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e487\u003c/p\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(31.2)\u003c/p\u003e\n \u003cp\u003e(1.2)\u003c/p\u003e\n \u003cp\u003e(40.2)\u003c/p\u003e\n \u003cp\u003e(23.5)\u003c/p\u003e\n \u003cp\u003e(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.219284603421462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(49.6)\u003c/p\u003e\n \u003cp\u003e(66.7)\u003c/p\u003e\n \u003cp\u003e(55.2)\u003c/p\u003e\n \u003cp\u003e(45.6)\u003c/p\u003e\n \u003cp\u003e(36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.90-8.67)\u003c/p\u003e\n \u003cp\u003e(1.03-2.01)\u003c/p\u003e\n \u003cp\u003e(0.79-1.88)\u003c/p\u003e\n \u003cp\u003e(0.37-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.441679626749611%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*\u003csup\u003e1\u003c/sup\u003e Missing values not included: HIV status (4), Antiretroviral therapy (4), Perceived stigma (4), and Previous TB episode (2) \u0026nbsp;// \u0026nbsp;% are column percentages \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*\u003csup\u003e2\u003c/sup\u003e Number of participants experiencing poor adherence and percentage from the total number of participants with this exposure\u0026rsquo;s category\u003c/p\u003e\n\u003cp\u003e*\u003csup\u003e3\u003c/sup\u003e P-values calculated using the Likelihood Ratio Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: OR=Odds ratio; CI=Confidence interval; TB=tuberculosis; min=minutes; Rd=South African Rand\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn multivariable analyses, there was strong evidence that living with HIV was associated with increased odds of poor adherence (adjusted OR [aOR] 1.68, 95%CI=1.27-2.22, p\u0026lt;0.001). Similarly to the crude analyses, there was however no evidence that antiretroviral therapy, time to access care, or perceived stigma were associated with poor adherence (table 2). For adherence modelled as a count of doses taken, there was strong evidence that living with HIV was associated with a 10% lower rate of dose intake, compared to being HIV-negative (adjusted RR [aRR] 0.90, 95%CI=0.83-0.97, p=0.005). There was no evidence that receiving ART was associated with better or worse adherence (aRR 0.95, 95%CI=0.86-1.04, p=0.26). Similarly, there was no evidence that perceived stigma was associated with adherence count (aRR 0.97, 95%CI=0.84-1.14, p=0.74), or that longer time needed to access care was associated with adherence count.\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026ndash; Adjusted odds ratios of binary poor adherence and adjusted rate ratios of quantitative adherence to TB\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"659\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.528072837632777%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.127465857359635%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted ORs\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(95%CI)*\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.532625189681335%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value*\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.279210925644918%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted RRs (95%CI)*\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.532625189681335%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value*\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.528072837632777%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV status*\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAntiretroviral therapy*\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHIV+ not on ART\u003c/p\u003e\n \u003cp\u003eHIV+ on ART\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTime to access care [min]*\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003cp\u003e60-119\u003c/p\u003e\n \u003cp\u003e120-179\u003c/p\u003e\n \u003cp\u003e180-239\u003c/p\u003e\n \u003cp\u003e\u0026gt;=240\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived stigma\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[over 10 pts]*\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e6\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo stigma reported\u003c/p\u003e\n \u003cp\u003e\u0026gt;=1 point of stigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.559939301972685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56752655538695%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.27-2.22)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.75-1.57)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.48-1.42)\u003c/p\u003e\n \u003cp\u003e(0.47-1.48)\u003c/p\u003e\n \u003cp\u003e(0.59-1.98)\u003c/p\u003e\n \u003cp\u003e(0.48-1.68)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.58-1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.532625189681335%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.408194233687405%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.87101669195751%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.83-0.97)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.86-1.04)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.88-1.18)\u003c/p\u003e\n \u003cp\u003e(0.86-1.16)\u003c/p\u003e\n \u003cp\u003e(0.85-1.18)\u003c/p\u003e\n \u003cp\u003e(0.90-1.27)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.84-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.532625189681335%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eResults of four separate models for each exposure, adjusted for the confounders identified with the DAGs\u003c/p\u003e\n\u003cp\u003e*\u003csup\u003e2\u003c/sup\u003e P-values calculated using the Likelihood Ratio Test\u003c/p\u003e\n\u003cp\u003e*\u003csup\u003e3\u003c/sup\u003e Model with 1,209 observations; adjusted for gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, tobacco, alcohol and illicit drug consumption, time to access clinical care, and socio-economic position.\u003c/p\u003e\n\u003cp\u003e*\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eModel with 769 observations; adjusted for\u0026nbsp;gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, previous TB episode, tobacco, alcohol and illicit drug consumption, time to access clinical care, perceived stigma, and socio-economic position.\u003c/p\u003e\n\u003cp\u003e*\u003csup\u003e5\u0026nbsp;\u003c/sup\u003eModel with 1,213 observations; adjusted for\u0026nbsp;gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, previous TB episode, HIV status, diabetes, tobacco, alcohol and illicit drug consumption, and socio-economic position.\u003c/p\u003e\n\u003cp\u003e*\u003csup\u003e6\u003c/sup\u003e Model with 1,209 observations; adjusted for gender, age, clinic, country of origin, ethnicity, education level, occupation, marital status, household status, previous TB episode, tobacco, alcohol and illicit drug consumption, time to access clinical care, HIV status, and socio-economic position\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI confidence interval; OR odds ratio; ART=antiretroviral therapy; RR rate ratio\u003c/p\u003e\n\u003cp\u003eThe sensitivity analysis varying the definition of poor adherence to \u0026lt;70% (40.8%, 495/1213) and \u0026lt;90% (64.8%, 786/1213) showed similar results: notably, the strong evidence of worse adherence among participants living with HIV persisted (aOR\u003csub\u003e70%\u003c/sub\u003e 1.50, 95%CI=1.13-2.00; aOR\u003csub\u003e90%\u003c/sub\u003e 1.77, 95%CI=1.32-2.36) (Table 3). \u0026nbsp;The observed associations remained consistent when excluding the 76 participants (6.3%) who were lost to follow-up on treatment; participants living with HIV had increased odds of poor adherence to TB treatment (OR 1.62, 95%CI 1.21-2.17, p=0.001). The sensitivity analyses for the count outcome gave similar results (data not shown).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 (sensitivity analyses): Adjusted odds ratios of poor adherence to TB treatment defined as \u0026lt;70%, and \u0026lt;90%\u003csup\u003e*1\u003c/sup\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.595015576323988%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;70% adherence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.60436137071651%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;90% adherence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.320872274143302%\" valign=\"top\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.274143302180686%\" valign=\"top\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"top\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.725856697819314%\" valign=\"top\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Positive\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAntiretroviral therapy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHIV+ not on ART\u003c/p\u003e\n \u003cp\u003eHIV+ on ART\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTime to access care [min]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003cp\u003e60-119\u003c/p\u003e\n \u003cp\u003e120-179\u003c/p\u003e\n \u003cp\u003e180-239\u003c/p\u003e\n \u003cp\u003e\u0026gt;=240\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived stigma\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[over 10 pts]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo stigma reported\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;=1 point of stigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.320872274143302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.274143302180686%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.13-2.00)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.84-1.75)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.50-1.47)\u003c/p\u003e\n \u003cp\u003e(0.50-1.56)\u003c/p\u003e\n \u003cp\u003e(0.59-1.99)\u003c/p\u003e\n \u003cp\u003e(0.50-1.79)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.59-1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.725856697819314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.32-2.36)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.77-1.67)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.64-1.96)\u003c/p\u003e\n \u003cp\u003e(0.59-1.91)\u003c/p\u003e\n \u003cp\u003e(0.71-2.54)\u003c/p\u003e\n \u003cp\u003e(0.54-1.99)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.58-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e*1\u003c/sup\u003e\u003c/strong\u003e Separate logistic regression models for each exposure, adjusted for the same set of confounders and with the same number of observations as regression models in table 2\u003c/p\u003e\n\u003cp\u003eCI confidence interval; aOR adjusted odds ratio; ART antiretroviral therapy\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large South African cohort of 1,213 adults receiving treatment for DS-TB, the geometric mean of the percentage of doses taken was 59.6% (95%CI: 57.2-62.0), as measured by engagement with the pillbox. We found strong evidence that participants living with HIV had increased odds of poor adherence, and a 10% lower rate of dose intake, compared to those HIV-negative. No evidence of an association was found for ART status, time to clinic, and stigma being associated with adherence. Results also showed that younger age was associated with higher odds of poor adherence.\u003c/p\u003e\n\u003cp\u003eThe absence of association between our exposures and patient engagement with the pillbox, except for HIV status, was not consistent with that previously reported (10\u0026ndash;12). This may be due to the differences in design between this cohort study and previous studies conducted on adherence to TB treatment, which were mostly cross-sectional or retrospective by design, and mostly used other means of measuring adherence, such as DOT\u0026nbsp;(18). Additionally, adherence to TB treatment is linked to complex health behaviours, as well as beliefs linked to the disease, which is strongly context-dependent, and prone to change with time. This may explain that, even within South Africa, determinants of adherence may vary depending on geographical areas and time.\u003c/p\u003e\n\u003cp\u003eTaking antiretroviral therapy among participants living with HIV was not associated with worse TB treatment adherence, though people living with HIV had lower adherence than those HIV-negative. ART status was determined by ascertaining whether participants living with HIV were prescribed ART within the first two months of TB treatment: adherence to ART itself was not recorded and might have been a more sensible exposure variable. As we relied on abstraction from clinic records, ART status may have also been misclassified if ART records were not found. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was no evidence suggesting that perceived stigma was associated with the outcome either. One issue with this exposure variable was the small number of participants, 58 (4.8%), that reported any stigma at baseline associated with their TB diagnosis. This lowered the statistical power accessible to observe a possible association. In addition, participants were enrolled shortly after their TB diagnosis: stigma may have appeared after a certain delay, and therefore be missed during baseline data collection.\u003c/p\u003e\n\u003cp\u003eFinally, there was no evidence showing that the time needed to access care was linked to treatment adherence, although limited access to healthcare is recognised as a risk factor of poor adherence (9\u0026ndash;11). We hypothesised that participants requiring a longer time to access their clinic would be at increased risk of missing follow-up visits, due to the associated direct and indirect costs, and their disengagement from healthcare would then increase their risk of poor daily adherence. Most participants lived in urban areas relatively close to the clinics (\u0026lt;8km away) and needed less than 4 hours in total to access care. It may be possible that only a few participants included in this study truly experienced limited access to healthcare, at such a level observed in other studies.\u003c/p\u003e\n\u003cp\u003eThis study\u0026rsquo;s sample size, as well as the large number of outcomes recorded, provided good power to measure associations and conduct multivariable regression models.Since there lacks a universal definition of poor adherence to DS-TB treatment, we conducted sensitivity analyses to ensure that our findings remained consistent with different chosen cut-offs for binary adherence. There are, however, some important limitations. Misclassification of the outcome may have occurred: the electronic monitoring device recorded the engagement of participants with the smart pillbox technology and was only a proxy for adherence itself. Voluntary non-adherence from participants, i.e. opening the box without ingesting the medication, would not have been detected. A study from India showed suboptimal accuracy of DAT engagement (99DOTS) with results from urine isoniazid testing, a direct measure of drug intake (19). In our study participants in the control arm had no personal benefit from adherence monitoring by the pillbox and so may have opted not to use the pillbox at all. Our study is restricted to individuals who have initiated TB treatment. Clinic attendees diagnosed with TB but who never started treatment, that is, lost to follow-up before taking their first dose, were excluded. These individuals represent a group with zero adherence, that is extreme non-adherence, and understanding their factors is important. This limits the generalisability of our results, therefore, to a population that actively engages with the local healthcare system, at least once post-diagnosis.\u003c/p\u003e\n\u003cp\u003eSufficient adherence to TB treatment is important to avoid treatment failure or development of new drug resistance, and increased TB burden globally (20). To best allocate health resources aimed at improving treatment adherence, it is necessary to identify subpopulations at risk of poor adherence. Younger individuals and those living with HIV, comprising a large proportion of this cohort, were identified as having worse DAT engagement, a proxy for adherence to DS-TB treatment. These results highlight the need to bring reinforced differentiated care to this specific population of TB patients in South Africa to improve adherence and ultimately success of TB treatment. Further, qualitative research should also be conducted to understand treatment adherence issues in such groups to determine how best to support their care and treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eART : antiretroviral therapy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI : confidence interval\u003c/p\u003e\n\u003cp\u003eDAG : directed acyclic graph\u003c/p\u003e\n\u003cp\u003eDAT : digital adherence technology\u003c/p\u003e\n\u003cp\u003eDOT : Directly Observed Therapy\u003c/p\u003e\n\u003cp\u003eDS : drug-sensitive\u003c/p\u003e\n\u003cp\u003eHCW : healthcare workers\u003c/p\u003e\n\u003cp\u003eHIV : human immunodeficiency virus\u003c/p\u003e\n\u003cp\u003eIQR : interquartile range\u003c/p\u003e\n\u003cp\u003eOR : odds ratio\u003c/p\u003e\n\u003cp\u003eaOR : adjusted odds ratio\u003c/p\u003e\n\u003cp\u003eRR : rate ratio\u003c/p\u003e\n\u003cp\u003eaRR : adjusted rate ratio\u003c/p\u003e\n\u003cp\u003eSEP : socio-economic position\u003c/p\u003e\n\u003cp\u003eTB\u0026nbsp;: tuberculosis\u003c/p\u003e\n\u003cp\u003eUSD : US Dollar\u003c/p\u003e\n\u003cp\u003eWHO : World Health Organization\u003c/p\u003e\n\u003cp\u003eZAR : South African Rand\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe parent study (trial) and associated analyses obtained approval from the Wits Human Research Committee (Ref 180705), the University of Cape Town Human Ethics Research Committee (Ref 452/2018) and the London School of Hygiene and Tropical Medicine (Ref 16107). In addition, ethics approval was obtained from the MSc Research Ethics Committee at the London School of Hygiene and Tropical Medicine (Ref 27519) to conduct this secondary data analysis. The study also obtained approval from the three district health departments: eThekwini, Ekurhuleni and the City of Cape Town. The trial was registered with the Pan African Trial Registry PACTR201902681157721. Written informed consent was obtained from all study participants prior to study initiation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all study participants prior to study enrolment.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available upon submission of a request to the Aurum Data Governance Committee, on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study is funded by the (1) Bill and Melinda Gates Foundation (OPP1205388), (2) TB REACH Wave 6 project of Stop TB Partnership (STBP/TBREACH/GSA/W6-34), and (3) South African Medical Research Council through the Strategic Health Innovation Partnerships. Funders did not play a part in the design of the study or the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors contributions:\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAS, KLF, SC and CO conceptualized the study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAS, KLF, SC, PH, CO, NM, and KV interpreted the study findings.\u003c/p\u003e\n\u003cp\u003ePH, LJ, IR, RM, NX, LM contributed to study implementation and collection of data.\u003c/p\u003e\n\u003cp\u003eAS analysed the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAS and KF wrote the original draft and revised subsequent versions of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePH, NM, LJ, IR, RM, NX, LM, KV, CO and SC reviewed and edited previous versions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the following: Ekurhuleni, City of Cape Town, eThekwini districts and the Ekurhuleni Health District Research Committee (EHDRC) for allowing us to conduct the study in their districts. The study coordinators from Gauteng, Western Cape and KwaZulu-Natal in South Africa for their assistance with data collection, namely Vuyelwa Mehlomakulu, Vumile Gumede and Bongani Zondi, and their field-based teams of research assistants and interns.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal Tuberculosis Report. 2022 [Internet]. [cited 2023 Jul 29]. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/32221550/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/32221550/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeung KJ, Keshavjee S, Rich ML. Multidrug-Resistant Tuberculosis and Extensively Drug-Resistant Tuberculosis. Cold Spring Harb Perspect Med [Internet]. 2015 Sep 1 [cited 2022 Aug 28];5(9). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC4561400/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC4561400/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Vol. 10, Epidemiology. 1999. p. 37\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, adherence, medication monitor, HIV, South Africa","lastPublishedDoi":"10.21203/rs.3.rs-4139836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4139836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSuboptimal adherence to tuberculosis (TB) treatment is common and puts individuals at increased risk of treatment failure. Identifying risk factors for poor adherence may help better target individuals and improve resource allocations. We assessed specific determinants of treatment adherence: HIV status; antiretroviral therapy; time to clinical care access; and perceived stigma, among adults with drug-sensitive TB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a secondary analysis of the “TB Mate'' cluster-randomised trial, which implemented a TB treatment adherence intervention in 18 health clinics in South Africa (PACTR201902681157721). Smart pillboxes were used to measure treatment adherence; the recording of the pillbox opening was considered a proxy for dose taken. Adults enrolled in the control arm, using the pillbox in silent mode, were eligible for this analysis. Logistic regression was used to model poor adherence (\u0026lt; 80% doses taken) and negative binomial regression was used to study adherence as a count of doses taken. Directed acyclic graphs guided the selection of confounders in the models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 1,213 participants from nine clinics, 51% (614) had adherence of \u0026lt; 80% and the geometric mean of the percentage of doses taken was 59.6%. 63% (769) of participants were living with HIV, of whom 66% (507/769) were taking antiretroviral therapy. The median time to access clinical care was 127 minutes. Ninety-five percent (1151/1213) reported no perceived stigmatisation at the time of starting TB treatment. Living with HIV was identified as a strong determinant of adherence to TB treatment: adjusted odds ratio 1.68 (95% confidence interval [CI] 1.27–2.22) for \u0026lt; 80% adherence and adjusted rate ratio 0.9 (0.83–0.97) for doses taken, compared with being HIV-negative. Being on antiretroviral therapy, time to clinical care access, and perceived stigma were not associated with either adherence measure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVery low adherence reported highlights the need for TB treatment support interventions, especially among those living with HIV.\u003c/p\u003e","manuscriptTitle":"Baseline determinants of adherence for drug-sensitive TB treatment in a South African prospective cohort: a focus on HIV infection and anti-retroviral therapy, clinical care access, and TB stigma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 18:09:48","doi":"10.21203/rs.3.rs-4139836/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef8b9dd7-274a-4541-b16a-77528d89b8bd","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-03T03:47:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-25 18:09:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4139836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4139836","identity":"rs-4139836","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}