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Predictors of tuberculosis (TB) and antiretroviral (ARV) medication non-adherence in public primary care patients in South Africa: a cross sectional study

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Predictors of tuberculosis (TB) and antiretroviral (ARV) medication non-adherence in public primary care patients in South Africa: a cross sectional study
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  RESEARCH ARTICLE Open Access Predictors of tuberculosis (TB) and antiretroviral(ARV) medication non-adherence in publicprimary care patients in South Africa: a crosssectional study Pamela Naidoo 1,2* , Karl Peltzer 1,3,4 , Julia Louw 1 , Gladys Matseke 1 , Gugu Mchunu 5 and Bomkazi Tutshana 1 Abstract Background:  Despite the downward trend in the absolute number of tuberculosis (TB) cases since 2006 and thefall in the incidence rates since 2001, the burden of disease caused by TB remains a global health challenge. Theco-infection between TB and HIV adds to this disease burden. TB is completely curable through the intake of astrict anti-TB drug treatment regimen which requires an extremely high and consistent level of adherence.The aimof this study was to investigate factors associated with adherence to anti-TB and HIV treatment drugs. Methods:  A cross-sectional survey method was used. Three study districts (14 primary health care facilities in each)were selected on the basis of the highest TB caseload per clinic. All new TB and new TB retreatment patients wereconsecutively screened within one month of anti-tuberculosis treatment. The sample comprised of 3107 TB patientswho had been on treatment for at least three weeks and a sub-sample of the total sample were on both anti-TBtreatment and anti-retro-viral therapy(ART) (N = 757). Data collection tools included: a Socio-DemographicQuestionnaire; a Post-Traumatic-Stress-Disorder (PTSD) Screen; a Psychological Distress Scale; the Alcohol UseDisorder Identification Test (AUDIT); and self-report measures of tobacco use, perceived health status andadherence to anti-TB drugs and ART. Results:  The majority of the participants (N = 3107) were new TB cases with a 55.9% HIV co-infection rate in thisadult male and female sample 18 years and older. Significant predictors of non-adherence common to both anti-TBdrugs and to dual therapy (ART and anti-TB drugs) included poverty, having one or more co-morbid healthcondition, being a high risk for alcohol mis-use and a partner who is HIV positive. An additional predictor fornon-adherence to anti-TB drugs was tobacco use. Conclusions:  A comprehensive treatment programme addressing poverty, alcohol mis-use, tobacco use andpsycho-social counseling is indicated for TB patients (with and without HIV). The treatment care package needs toinvolve not only the health sector but other relevant government sectors, such as social development. Keywords:  Adult TB patients, Bio-Psycho-Social factors, Anti-TB treatment, ART, High burden country, Adherenceto anti-TB treatment and ART  * Correspondence: pnaidoo@hsrc.ac.za 1 Population Health, Health Systems and Innovation (PHHSI)/HIV/STIs and TB(HAST) Research Programmes, Human Sciences Research Council, Pretoria,Durban and Cape Town, South Africa, Private Bag X 9182, Cape Town 8000,South Africa 2 Department of Psychology, University of the Western Cape, Cape Town,South AfricaFull list of author information is available at the end of the article © 2013 Naidoo et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the srcinal work is properly cited. Naidoo  et al. BMC Public Health  2013,  13 :396http://www.biomedcentral.com/1471-2458/13/396  Background Despite the downward trend in the absolute number of TB cases since 2006 and the fall in the incidence ratessince 2001, the burden of tuberculosis (TB) disease re-mains a global health challenge [1]. TB is completely cur-able through the intake of a strict drug treatmentregimen. The Directly Observed Treatments Short-Course Strategy (DOTS) introduced by the World HealthOrganization (WHO) and subsequently the Stop TBStrategy is an inexpensive strategy that could preventmillions of TB cases and deaths. TB/HIV co-infectionand multi-drug resistant (MDR)/ extensive drug-resistant(XDR)TB are given focal attention in the Stop TB Strat-egy and one of its primary TB targets is to reduce by half the TB prevalence rates by 2015 relative to 1990 [1].The HIV and AIDS pandemics have exacerbated thepublic health dimensions of TB due to the fact thatmany individuals are co-infected. TB is the leading causeof death among people who are HIV positive. In theAfrican region, which accounted for 82% of the new TBcases that were also HIV positive, an estimated 900 000(39%) of the 2.3 million people who developed TB wereliving with HIV [1]. Adherence to anti-TB treatment and to treatment forHIV/TB co-infection Poor adherence to the prescribed anti-TB treatmentprogrammes, such as those falling under the DOTS strat-egy, is one of the factors associated with low cure rates forTB. In addition, inconsistent adherence to the anti-TB drugregimen may lead to multiple and extensive drug resistance(MDR-TB and XDR-TB respectively) making it difficult toachieve high cure rates. In individuals with TB/HIV dualinfection receiving treatment, correct adherence to anti-TBdrugs and anti-retroviral therapy (ART) are essential forgood treatment outcomes [2]. Poor and inconsistent adher-ence to ART can also lead to drug resistance and evendeath, an outcome which is similar to non-adherence toanti-TB drugs [2,3].The categories of factors influencing adherence todrug treatments for most health-related conditions, in-clude: practitioner ’ s negative assumptions about theirpatients; psychological attributes of the patient; environ-mental, social and cultural factors; treatment character-istics; and the doctor – patient relationship [3,4]. Many quantitative and qualitative studies conducted in SouthAfrica (SA) have identified factors known to influenceadherence to anti-TB treatment [5-7]. Qualitative studiesexploring adult TB patient ’ s adherence to anti-TB treat-ment at public health sites in SA found that the factorsthat influenced patient co-operation included: social andeconomic resources; causal attributions assigned to TB;the social, cultural, economic, disease-related and psy-chological challenges faced as a consequence of havingTB; quality of health care received; use of the traditionalhealing system; and the patient ’ s HIV status [5,7]. Quan-titative studies examining the factors that influenceadherence to anti-TB treatment regimens found the fol-lowing to be important: a good patient – practitioner rela-tionship, ability of the patient to disclose medication useto members of their social network and regular clinic visits [8,9].Numerous studies have also been conducted in Africaand globally on factors influencing adherence to anti-TBtreatment and adherence to the dual treatment approachfor TB and HIV [2,10,11]. A systematic review of quali-tative research, exploring patient adherence to tubercu-losis treatment, conducted by Munro et al found thatthe following four factors interact to affect adherence toTB treatment: structural factors, including poverty andgender discrimination; the social context; health servicefactors and personal factors [5]. An African-based quali-tative study which explored barriers and facilitators of adherence to anti-TB treatment and concomitant TBand HIV treatment in Ethiopia, found that adherence toTB treatment was positively influenced by beliefs in thecurability of TB, beliefs in the severity of TB in the pres-ence of HIV infection and support from family membersand health professionals [11].A systematic review done on studies conducted in theUS and Canada on adherence to treatment for latent tu-berculosis infection found that there was a  “ sub-optimal ” level of adherence indicated across all studies [12]. Thefactors influencing adherence were clinic facilities, treat-ment characteristics and patient factors although the as-sociation between adherence and these factors was foundto be inconsistent [12]. Adherence to ARVs as comparedto anti-TB drugs is, however, reported to be high theworld over and Corless et al based a quantitative study conducted in clinics in Durban, SA, on this premise [10].They found that in fact adherence to ARVs in their study was also high [10]. Amuha et al found that the most im-portant factor associated with non-adherence to anti-TBmedication in individuals co-infected with HIV was beingon a continuous treatment regimen phase as comparedto the intensive treatment phase. Confounding factors in-fluencing the association between the stage of the TBregimen and non-adherence were alcohol consumption,being on ARVs and smoking [13]. The exact nature of these confounding factors and its influence on non-adherence has not been interrogated.Relatively few studies have looked specifically at thesub-group of individuals on anti-TB treatment who alsomeet the criteria for alcohol use disorders in SouthAfrica (SA). This study forms part of a larger study en-titled: Screening and brief interventions for hazardousand harmful alcohol use among patients with active tu-berculosis attending primary public care clinics in SA. Naidoo  et al. BMC Public Health  2013,  13 :396 Page 2 of 10http://www.biomedcentral.com/1471-2458/13/396  There is adequate evidence in the international litera-ture to confirm that TB medication non-adherence isassociated with the use and mis-use of alcohol [14,15].In SA, however, the association between alcohol (mis)use and non-adherence to anti-TB treatment and dualtherapy in those individuals co-infected with HIV hasbeen not been adequately studied. In addition, the way in which individual mental health risk factors, such asdistress and post-traumatic stress, mediate TB and TB/HIV health outcomes for individuals mis-using alcohol,has been neglected.The value of this study is that it used a different meth-odology to most other TB treatment adherence studies.The study uses different questionnaires (such as themeasures of distress and PTSD) and different outcomemeasures (namely, adherence to anti-TB treatment, andadherence to ART and anti TB treatment (dual therapy)as compared to other adherence studies. Consequently,the primary aim of this study, using base-line data wasto investigate the factors that are associated with non-adherence to anti-TB drugs for those with active TB andto ART and anti-TB drugs taken by individuals who havedual infection. The secondary aim, which also addressesa gap in research, was to specifically investigate the sig-nificance of alcohol misuse as a predictive factor fornon-adherence to anti-TB drugs and to dual therapy (namely, anti-TB drugs and ART). Methods Study design, sample and procedure This study is a cross-sectional survey. Fourteen (14)public primary health care clinics in only one districtin each of three provinces, namely, Northern Cape,Eastern Cape and Kwa-Zulu Natal in SA with thehighest TB caseload were included in the study. Allnew TB and new retreatment patients were consecu-tively screened within one month of anti-TB treat-ment. The public primary health care clinics thatwere utilized in this study followed the SA Depart-ment of Health ’ s (DoH) Guidelines for TB treatmentwho in turn are guided by the WHOs TB treatmentguidelines framed by the Stop TB strategy whichstrongly recommends the DOT programme [16]. TheDOT programme requires intensive involvement andmonitoring by the clinic staff but the treatment suc-cess is also dependent on the patient adhering strictly to the treatment guidelines. In this study a total of 3129 potential participants were screened and 22(0.7%) refused to participate. It was not necessary toperform an attrition analysis due to the good responserate. In the data analysis the total sample size usedwas 3107 participants (N = 3107). All the participants ’ in this study were taking anti-TB treatment for atleast three weeks, which fell into the intensive phaseof the DOTS programme which required them to at-tend the clinic during the week and take their anti-TBdrugs dose at their homes over the week-end.Within the larger sample there was a sub-sample of patients who were HIV positive (N =1729) and a pro-portion of the HIV positive patients (44%) who were onART (N =757).A screening interview was conducted by trained re-search assistants over a period of six (6) months in 2011.A health care provider who identified a new TB treat-ment or retreatment patient 18 years and above informedthe patient about the study and referred the patient forparticipation if they were willing. A consenting procedurewas adopted prior to the start of the screening interview.Ethical approval was received from the Human SciencesResearch Council Research Ethics Committee (ProtocolREC No.1/16/02/11) and the Department of Health(DoH) in SA. Data collection tools The data collection tools specified in this section wasadministered to all the participants in this study. Socio-demographic questionnaire A researcher-designed questionnaire was used to recordinformation on participants ’  age, gender, educationallevel, marital status, income, employment status, dwell-ing characteristics and residential status. Using a previ-ously designed questionnaire, which is also extensively used in the Human Sciences Research Council (HSRC_SA)national health surveys,  poverty   was assessed by five (5)pertinent items on the questionnaire by asking about theavailability or non-availability of shelter, fuel or electricity,clean water, food and cash income in the past week[17]. Response options ranged from 1= “ Not one day  ” to 4= “ Every day of the week ” . Participants ranked high onpoverty if they had higher scores on non-availability of es-sential items. The total poverty score ranged from 5 to 20with the following categories: 5 indicating low poverty; 6 to12 indicating a medium level of poverty; and 13 to 20 indi-cating high levels of poverty. The Cronbach alpha for thepoverty index in this study was fairly good (0.89). The Kessler Psychological Distress Scale (K-10) The Kessler Psychological Distress Scale (K-10) wasused to measure global psychological distress [18,19].The K-10 measures the following symptoms over thepreceding 30 days: nervousness, hopelessness, restless-ness, depression, worthlessness and tiredness. The fre-quency with which each of these items was experiencedwas recorded using a five-point Likert scale ranging from “ none of the time ”  to  “ all the time ” . Increasing totalscores reflect an increasing degree of psychological dis-tress. The K-10 has been shown to capture variability  Naidoo  et al. BMC Public Health  2013,  13 :396 Page 3 of 10http://www.biomedcentral.com/1471-2458/13/396  related to non-specific depression, anxiety and substanceabuse [19]. This scale serves to identify individuals whoare likely to meet formal definitions for anxiety and/ordepressive disorders, as well as to identify individualswith sub-clinical illness who may not meet formal defini-tions for a specific disorder [18]. This scale is increasingly used in population based mental health research and hasbeen validated in multiple settings [20,21] including HIV positive individuals in SA [22]. The internal reliability co-efficient for the K-10 in this study was alpha =0.92 whichis fairly high.  Alcohol consumption The 10-item Alcohol Use Disorder Identification Test(AUDIT) [23] assesses alcohol consumption level, symp-toms of alcohol dependence and problems associated withalcohol use. The AUDIT largely measures heavy episodicdrinking with only one (1) item measuring binge drinking.Heavy episodic drinking is defined as the consumption of six standard drinks or more on a single occasion. A stand-ard drink in this instance is 10 g of alcohol. In SA a stand-ard drink is 12 g of alcohol and not 10 g. The AUDIT isreported to be less sensitive at identifying risk drinking inwomen [24] so it was recommended that the cut-off pointfor the binge drinking measure is reduced by one (1) unitamong women in SA and the overall heavy episodic drink-ing measure is also reduced due to the fact that a standarddrink is 12 g of alcohol (and not 10 g as stipulated by theAUDIT). Responses to items on the AUDIT are rated on afour-point-Likert scale from 0 to 4, for a maximum scoreof 40 points. A cut off score of 8 indicates a tendency toproblematic drinking. The AUDIT was developed by theWHO as an effective screening instrument for alcoholuse problems among patients seeking primary care forother medical problems in international settings includingAfrican countries (Kenya and Zimbabwe) [24-27]. TheAUDIT has been validated in HIV patients in SA showingexcellent sensitivity and specificity in detecting alcohol de-pendence and alcohol abuse as defined on the Mini-International Neuropsychiatric Interview (MINI) [26]. TheMINI is an internationally recognized diagnostic tool inthe form of a psychiatric interview.The MINI used in a study of TB and HIV patients in pri-mary care in Zambia also demonstrated good discrimin-atory ability in detecting MINI-defined current AlcoholUse Disorders (AUDs) (AUDIT=0.98 for women and 0.75for men) [27]. Cronbach alpha for the AUDIT in this sam-ple was 0.92, indicating excellent reliability.  Additional self-report measures (a) Tobacco use: Two researcher generated questionswere asked about the use of tobacco products. Thefirst question asked about current tobacco use andthe second question asked about the frequency of use over the past month.(b) Non-adherence to anti-TB treatment, non-adherenceto ART, and HIV testing: Adherence was assessed by self-report. Whether a participant tested for HIV infection was also assessed by self-report. Anti-TBmedication adherence was assessed with the followingquestion:  “ In your tuberculosis treatment in the past3 – 4 weeks how many percent (%) of your anti-tuberculosis medication did you take? ”  TB medicationnon-adherence was defined as having taken less than90% of the anti-TB drugs. Self-reporting adherencebehaviour over the past 3 – 4 week period for anti-TBdrugs was asked due to the fact that the participantsin this study were recruited only if they had startedtheir TB treatment at least three weeks before beingenrolled into the study.ART adherence was assessed with the question: “ How many percent of your HIV medication did you take in the past 4 weeks? ”  ART non-adherencewas defined as having taken less than 90% of ART.In each instance, that is for both ART and anti-TBtreatment non-adherence, participants wererequired to mark the percentage adherence on anillustrated scale indicating progression from 0% to100% adherence.(c) Co-morbidity with other chronic medical conditionsincluding hypertension, diabetes, depression, stomachulcer, migraine headache, cancer, arthritis, asthma,diabetes, cholesterol were also ascertained. Data analysis Data were analyzed using the Statistical Package for the So-cial Sciences (SPSS-version 19). Frequencies, means, andstandard deviations, were calculated to describe the sam-ple. Data were checked for normality distribution and out-liers. For non-normal distribution non-parametric testswere used. Associations between TB medication and ARTnon-adherence were identified using logistics regressionanalyses. Following each univariate regression, multivari-able logistic regression models were constructed. A total of 574 participants were used in the multivariate analysis. In-dependent variables from the univariate analyses were en-tered into the multivariable model if significant at P<0.05level. For each model, the R 2 are presented to describethe amount of variance explained by the multivariablemodel. Probability below 0.05 was regarded as statistically significant. Results Characteristics of the final sample Table 1 indicates that the sample included 671 (21.8%;N =3107) retreatment cases, 2408 (78.2%; N = 3107)new TB cases and 55.6% HIV infected cases. A little Naidoo  et al. BMC Public Health  2013,  13 :396 Page 4 of 10http://www.biomedcentral.com/1471-2458/13/396  below half the sample was 35 years and older. Half thesample reported medium levels of poverty, about a third(35.3%) good perceived health status with more partici-pants (46.1%) reporting poor perceived health status.Other characteristics of significance include the follow-ing: 776 (25%) met the criteria for severe psychologicaldistress and 16.4% for the category of medium risk foralcohol misuse on the AUDIT. Finally, 26.1% were non-adherent to the anti-TB treatment (min =0, max=100,mean = 77.0, median = 90.0, SD =43.9).A sub-sample (N =757) of the total sample was ondual anti-TB treatment and ART. The characteristics of this group were as follows: the majority of participantswere between the ages of 25 and 44 years, half the sam-ple reported medium levels of poverty, two-fifths (40.5%)reported poor perceived health status, 15.9% were at themedium risk category for alcohol misuse on the AUDIT,38.2% had a partner who was HIV positive, and 42.4%were non-adherent to ART (min =0, max= 100, mean =64.8, median = 100.0, SD =43.9). Of a total of 268 partic-ipants 83.8% were not adherent to both anti-TB andART medication (r =0.71). Predictors of non-adherence to TB treatment Univariate analysis (see Table 2) shows that the follow-ing factors were more likely to be associated withnon-adherence to TB treatment: being male [OR: 1.26(1.07 – 1.48), p<0.05], medium poverty [OR: 1.97 (1.62 – 2.41), p<0.001], high levels of poverty [OR: 4.01 (3.12 – 5.10), p<0.001], having one (1) chronic condition [OR:1.54 (1.23 – 1.93), p < 0.001], having two (2) chronicconditions [OR: 1.84 (1.34 – 2.51), p < 0.001], havingthree or more chronic conditions [OR: 1.98 (1.34 – 2.92), p < 0.001], severe psychological distress [OR: 1.31(1.09 – 1.57), p < 0.01], medium risk for alcohol misuse[OR: 1.74 (1.42 – 2.19), p < 0.001], high risk for alcoholmisuse [OR: 2.42 (1.76 – 3.32), p < 0.001], tobacco use[OR: 2.09 (1.76 – 2.49), p < 0.001] and having a partnerwho is HIV positive [OR: 1.43 (1.10 – 1.84), p < 0.01].Univariate analysis (see Table 2) shows that the follow-ing factors were more likely to be associated with adher-ence to TB treatment: being in the 25 – 34 year age group Table 1 Characteristics of the total sample of TB patientsand sub-sample of TB patients on ART Socio-demographics Total TB patients(N=3107)Total TB-ARTpatients (N=757)N (%) N (%) Age 18 – 24 416 (13.5) 65 (8.7)Age 25 – 34 1160 (37.7) 283 (37.8)Age 35 – 44 861 (28.0) 252 (33.7)Age 45 and older 639 (20.8) 148 (19.8)Missing N 31 9Female 1427 (46.5) 387 (51.7)Male 1639 (53.5) 362 (48.3)Missing N 41 8Grade 7 or less 774 (25.2) 234 (31.5)Grade 8 – 11 1415 (16.1) 330 (44.4)Grade 12 or more 881 (28.7) 179 (24.1)Missing N 37 14Poverty low 1052 (35.5) 173 (24.4)Poverty medium 1484 (50.1) 377 (53.1)Poverty high 427 (14.4) 160 (22.5)Missing N 144 47Health variablesPerceived health statusExcellent/Very good 573 (18.6) 217 (28.8)Good 1089 (35.3) 231 (30.2)Fair/Poor 1423 (46.1) 305 (40.5)Unknown N 22 4 TB retreatment patient 671 (21.8) 224 (30.0)New TB patient 2408 (78.2) 523 (70.0)Unknown N 28 10HIV positive 1729 (55.9)HIV negative 1073 (34.5)HIV unknown status 305 (9.8)Chronic conditionsZero 1983 (72.5) 422 (65.7)One 443 (16.2) 112 (17.4) Two 194 (7.1) 60 (9.3) Three or more 117 (4.3) 48 (7.5)Unknown N 370 115Severe psychological distress(K  ≥ 30)776 (25.0) 208 (27.5)Alcohol: low risk (AUDIT 0 – 7)2392 (78.0) 599 (80.0)Medium (AUDIT 8 – 19) 504 (16.4) 119 (15.9)High (AUDIT 20 – 40) 172 (5.6) 31 (4.1)Unknown N 39 8Current tobacco use 799 (26.2) 203 (27.4) Table 1 Characteristics of the total sample of TB patientsand sub-sample of TB patients on ART  (Continued) Partner HIV negative/ unknown2061 (71.6) 436 (61.8)Partner HIV positive 818 (28.4) 269 (38.2)Unknown/missing N 228 (7.3) 52 (6.9)Sex partner on ART 300 (11.8) 355 (48.8) TB non-adherence 812 (26.1) 315 (41.6)ART non adherence 321 (42.4) Naidoo  et al. BMC Public Health  2013,  13 :396 Page 5 of 10http://www.biomedcentral.com/1471-2458/13/396
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