Frikkie Booysen Department of Economics and Centre for Health Systems Research & Development University of the Free State

Transcription

1 The heart in ART: socio-economic status as determinant of treatment and prevention outcomes in the public sector antiretroviral treatment programme in the Free State province of South Africa 1 Frikkie Booysen Department of Economics and Centre for Health Systems Research & Development University of the Free State NOTE: This is a first draft. As the findings presented here and the conclusions drawn from the evidence are based on preliminary data analysis, which will be revisited for reasons noted in the paper, readers should please NOT cite the paper without prior approval from the author(s). Paper to International Union for the Scientific Study of Population (IUSSP) seminar on Interactions between Poverty and HIV/AIDS, University of Cape Town, December 2004, Cape Town, South Africa Abstract Econometric analysis conducted using data from a representative sample of public sector patients eligible for antiretroviral treatment in the Free State province suggest that a number of treatment and prevention outcomes related to the public sector ART programme are in some way closely linked to socio-economic status. In essence, the evidence emphasises the fact that even individuals living with HIV/AIDS can, as the poor have also been shown to do, experience worthwhile, fulfilling and quality lives. Yet, socio-economic status do in some ways constrain access to treatment and may also indirectly affect health-related quality of life and quality of life negatively, given that the poor have been shown to experience higher levels of perceived stigma and stigma in turn has been shown to impact negatively on health status and quality of life. Policies that make VCT services accessible to the poor and encourage them to get to know their HIV status are therefore required, as are policies that encourage the poor to then access treatment if eligible for ART and interventions aimed at ensuring equitable access. Furthermore, social protection policies that enhance or protect the socio-economic status of individuals receiving treatment may also be important in enhancing the general quality of their lives. 1 The generous material and moral support of CIDA, DCI, DfID, IDRC, JEAPP, USAID, AUSAID and the NRF is gratefully acknowledged. In addition, those patients in the ART programme who were willing to act as respondents in this study are sincerely thanked for sacrificing their time and energy, and for frankly sharing their views and experiences with the researchers. In similar vain, the management and staff of the Free State Department of Health and of several local municipalities are gratefully acknowledged for facilitating the research. In addition, a special word of thanks is due Shaun Anderson and Goedele Louwagie who have provided me with valuable assistance in coding some of the variables employed in these analyses.

2 1. Background In South Africa, which currently faces one of the highest HIV prevalence rates in the world (Dorrington et al., 2004), until recently only some people enjoyed access to anti-retroviral treatment (ART). By 2001, for example, almost three quarters of options offered by private medical schemes in South Africa provided access to ART, which covers 92% of beneficiaries of medical schemes (Stein et al., 2002). Yet, only 16% of the population have access to medical aid (Goudge et al., 2001). Since there has been a shift towards providing universal access to ART. On the one hand, ART in 2005 was added to the list of prescribed minimum benefits that all public hospitals and other designated service providers are by law required to provide according to specified clinical protocols and criteria. These benefits must be covered by all benefit options offered by private medical schemes, while the use of monetary limits, levies and co-payments are prohibited for coverage of these minimum benefits (Pillay et al., 2002; Forman et al., 2004). On the other hand, access to ART also became a reality in the public health care sector in 2003/04, when the Department of Health embarked on its Operational Plan for the Comprehensive HIV and AIDS Care, Management and Treatment, which includes the roll-out of free anti-retroviral treatment in public health care facilities over a five-year period (National Department of Health, 2003). Anti-retroviral treatment (ART) stands to benefit society and the economy at large, given the resultant increase in life expectancy (Dorrington et al., 2004) and gains in productivity resulting not only from the treatment of infected individuals, but also from changes in time utilisation by caregivers (Cleary et al., 2004; Sendi et al., 2004). There, however, is still relatively limited information on experiences with ART in developing countries such as South Africa. It is necessary therefore to monitor and to evaluate the ART programme in South Africa on a continuous basis, particularly to identify problematic areas that prohibit the delivery of ART in an effective, efficient, and equitable manner and to put forward policy recommendations aimed at addressing these problems. This paper attempts to do exactly this, using for this purpose baseline data from a longitudinal study of patients enrolled in the public sector anti-retroviral programme in the Free State province of South Africa. The paper is structured as follows. The primary and secondary research objectives addressed in this paper are put forward in section 2. In section 3, the data are described, while section 4 elaborates on the econometric methods employed in the analysis. The results of the econometric analysis are presented and discussed in section 5. Section 6 concludes. 2. Primary and secondary research objectives The primary objective of this paper is to assess the role of socio-economic status (measured here among others by income, education and employment status) in explaining differences in selected outcomes of the public sector ART programme in the Free State province of South Africa. The choice of outcomes was informed by a number of policy documents, including the Operational Plan for the Comprehensive HIV and AIDS Care, Management and Treatment, the official Monitoring and Evaluation Framework for the Operational Plan, and the Treatment Guidelines for ART adopted by the National Department of Health (2003, 2004a/b), as well as availability of data (see section on data for more detail). As the policy puts an equal 2

3 emphasis on both treatment and prevention (ART is considered an important means to enhance prevention efforts, while successful prevention efforts at the same time are required for the success of treatment), the selected outcomes include both treatmentand prevention-related outcomes. The outcomes, listed here in the approximate order in which they manifest in the programme, include the following: Delays in HIV testing Delays between HIV testing and commencing ART Problems experienced in accessing ART facilities Access to ART Disclosure of HIV status prior to and during ART Health-related quality of life of patients in the ART programme General well-being or quality of life of patients in the ART programme Improvements in physical health and emotional well-being of ART patients Stigmatisation of patients in the ART programme Sexual behaviour and access to ART Barriers to adherence to ART These outcomes are assessed for patients eligible to start ART, as well as for patients who have actually commenced ART, or in other words are actually taking ARVs. 2 The specific measures employed in assessing each of the above outcomes, are described in more detail in the section on methods (refer section 4). 3. Data The study was conducted using quantitative research methods. Semi-structured faceto-face interviews, guided by an interview schedule and conducted by trained fieldworkers, were conducted with a total of 374 patients enrolled in the public sector anti-retroviral treatment programme in the Free State province. The Ethics committee of the Faculty of Humanities (UFS) approved the study protocol. Written, informed consent was obtained from all study participants, including consent to access respondents clinic files and to link data from the health care system to the data from the patient survey. Informed consent was obtained from respondents, first by the nursing staff at the respective clinics who interact with the client, and again by the fieldworker prior to the interview. Table 1: Interviewed patients by treatment status, by district Non-treatment Treatment cases cases Total District N % N % N % Lejweleputswa Motheo Fezile Dabi Thabo Mofutsanyana Xhariep Total Note: Figures may not add up due to rounding. 2 As the sample only include patients that have been assessed as ready to commence ART (refer discussion on sampling design in the next section of this paper), thus excluding patients who for clinical or other reasons cannot yet commence with ART (e.g. patients with tuberculosis first have to complete six months of their TB treatment and are then assessed again prior to being declared as ready to commence with treatment), the results cannot be generalised to patients eligible for ART, but rather to patients that are ready to start ART following assessment. 3

4 Interviewed patients were sampled randomly from a list of patients eligible to start with ART in each district in the Free State province by a date two months after the first patient had commenced treatment in the particular district, i.e. patients who when assessed had CD4 counts below 200 and/or were in WHO stage 4. The sample was stratified by treatment status. So-called treatment patients represent patients who by the date the sample was drawn had actually commenced treatment according to information obtained from the health care system. So-called non-treatment patients in turn represent patients who had been certified as ready to commence treatment following a medical examination, but had not by the time the sample was drawn actually started taking ARVs. 3 For each district, eighty patients were sampled randomly from the sampling frame and proportional to the number of treatment and non-treatment patients at each health care facility in the particular district. In Xhariep district, where the sampling frame included fewer than 80 patients, a census of all treatment and non-treatment cases was conducted. Table 1 reports the number of treatment and non-treatment cases 4 for which interviews were completed at baseline. The sample population can be described as follows (refer Tables I and II in the appendices for more detailed descriptive statistics of all variables): Almost two thirds of respondents were female. The average age was 37.6 years, while the households to which these persons belonged on average included four household members. Interestingly, while almost half of respondents were married, a relatively large number of these respondents (76 of 186) indicated that they are not living with their spouse. The remainder of respondents indicated that they are single (50.3% or 188 persons). As one would expect in a public sector health care programme, the respondents can in certain respects be described as relatively poor. Only 14.4% (54) of respondents were employed at the time, of which 42.6% (23) were employed in the informal sector. Yet, 55.6% of respondents (208) reported that they were the main breadwinner in their households. The unemployment rate (broad definition) in the sample population amounted to a staggering 66.9%. However, less than half of respondents participated in the labour market (labour force participation rate = 43.6%). As many as 171 respondents (45.7%) indicated that they were not able to work or look for work due to illness/disability. Only 2.9% (11) had access to medical aid. Just more than 80% (306) of respondents indicated their highest educational qualification as being grade 11 or lower, while 3.7% (14) only had obtained some form of tertiary education. 3 The motivation for this distinction in the sample between so-called treatment and non-treatment cases was the need to over time assess the impact of treatment on select health, social, behavioural and economic outcomes in this group of patients, which necessitated the inclusion in the sample of patients awaiting treatment. 4 A total of seven additional interviews were conducted during the actual survey compared to the number of respondents sampled at the outset of the study (n=267). These observations are all included in the analysis as it should not necessarily bias the results given the random nature of the sampling method. Given the lapse of time moreover between when the sample was drawn and when interviews were conducted (mostly due to the time necessary to make contact with sampled respondents and to set up an interview), a number of patients sampled as non-treatment cases had actually started taking ARVs by the time the interview was conducted. This resulted in a different distribution of observations between the so-called treatment and non-treatment groups in the actual dataset, or to be more specific, an overrepresentation of treatment cases compared to non-treatment cases. While 173 non-treatment cases were sampled, interviews were conducted with 147 only, this compared to the 194 sampled treatment cases and the 227 interviews conducted with treatment cases. 4

5 Yet, the data also suggests that the respondents can not by any means be made out to represent the poorest of the poor. Almost 70% (258) of respondents lived in dwellings where they had access to a flush or chemical toilet, either in the dwelling or the yard. As many as 90% (336) of respondents lived in dwellings with access to piped water: either on site or in the dwelling. Almost three quarters of respondents (275) lived in formal dwellings as opposed to 20.3% (76) and 6.2% (23) living in informal and traditional dwellings respectively. Mean personal income, moreover, amounted to R634 (one would perhaps expect income to be even lower). This is the result of half of respondents (187/374) receiving a social grant, in particular disability grants, which amounted to either R740 or R780 per month, depending on when the interview was conducted. These data for this sample population, for which the key socio-demographic and socio-economic characteristics described above in part already hint at the inequities in access to ART, are employed in the analysis in this paper Methods The following variables were employed to assess each of the nine main outcomes of the ART programme assessed in this paper: Delays in HIV testing represent the negative value of the number of days that has elapsed since the person first learnt that they were HIV-positive and the interview date. As not all respondents could remember the exact date, two variables were employed in the analysis, the one coded based on only those cases where the specific date was known (testdelay1) and the other coded based on imputed dates (testdelay3). In the latter case, the delay was estimated by using 30 June in cases where patients only reported the year in which they first learnt that they were HIV-positive. 6 The delay between HIV testing and the commencement of ART represents the negative value of the number of days that has elapsed since the patient first learnt that they were HIV-positive status and the date when they started taking ARVs. Treatment start dates, which were obtained from patient files at the assessment sites (clinics), were not available for all patients, while in some cases their was no exact date available. As with delays in HIV testing, two variables were employed in the analysis, the one coded based on only those cases where the specific date was known (treatdelay1) and the other coded based on imputed dates (treatdelay3). A similar 5 The study has a longitudinal design, with these patients being followed up at six-monthly intervals over a period of three to four years. As the data from the first follow-up interviews are not as yet available (all but a few first round follow-up interviews have been completed, while in three of the five districts the second round of follow-up interviews has commenced), the focus in this paper is on an interrogation of the baseline data. Although these panel data will be much more suitable to investigate many of the issues re the impact of treatment on selection outcomes that we investigate in this paper, the nature of the sampling design and the retrospective manner in which some of these outcomes were assessed (refer methods section) the analysis of these cross-sectional data do allow for some conclusions to be drawn in regards to selected aspects of the public sector ART programme. 6 Admittedly, a more conservative approach would have been to employ December 31 st of this year to estimate these delays, thus underestimating the time that elapsed between the patient first learning they are HIV status and the date of the interview. However, using the middle of the year also means that the estimate represents an average of the amount of time that may have elapsed, given that the date could vary between January 1 st and December 31 st of the reported year. 5

6 approach to that followed in imputing delays in HIV testing was employed in imputing delays between HIV testing and the uptake of treatment. 7 During the interview, respondents were asked whether they had ever left the [facility] without being helped during a visit for your ARV-related treatment and care, as well as whether they had experience[d] any difficulty to visit the [facility] for your ARV treatment as expected according to your schedule. These two questions were asked separately with reference to visits to assessment and treatment sites. Based on patients responses, three binary variables (yes=1, no=0) were coded to denote when patients had experienced problems when accessing facilities, including assessment sites (accessas), treatment sites (accessts), and either an assessment or treatment site (access). Two variables were employed in order to investigate the factors associated with access to ART. A binary variable (yes=1, no=0) was employed to distinguish between patients that have commenced with ART as opposed to patients who have not yet commenced ART, but have been assessed as ready to start treatment (treatment2). The classification of patients as treatment and non-treatment cases as represent in Table 1 above for two reasons was not employed for this purpose. On the one hand, the treatment and non-treatment status of patients changed between sampling and data collection (refer discussion on sampling design). On the other hand, a comparison between this classification, patients self-reported treatment status, and the information on treatment start dates exhibited a number of discrepancies. Hence, this variable was coded by comparing the interview dates, reported treatment start dates and self-reported treatment status of patients to classify patients as treatment and nontreatment cases. In addition, the number of days that patients have actually been receiving ARVs (treattime) was employed as a linear variable to assess factors associated with access to treatment. 8 Ultimately, the analysis here asks questions about (i) the manner in which patients that have commenced treatment differ significantly from those patients who have not as yet started taking ARVs, and (ii) the factors associated with having being on treatment for a longer period of time when already having started taking ARVs. Factors associated with disclosure were assessed by means of a binary variable (yes=1, no=0) indicating whether the patient had disclosed their HIV-positive status to someone other than a health care worker since joining the ART programme 7 Theoretically, the time elapsed represent two distinct delays, the one between learning you are HIVpositive and first presenting at the assessment site in order to be assessed for eligibility for ART and the other between first presenting at the assessment site and starting to take ARVs. Different factors may be associated with these two types of delays in the health care system, thus meaning that these factors should be investigated using separate econometric models that employ two distinct estimated of these delays in accessing ART. This was not possible here, but as the clinic records of patients most probably contains the date the person first presented at the assessment site and as we are in a process of negotiating access to these patient level data to be able to link it to the patient survey data, this avenue of analysis will be explored once the necessary data are available to estimate the length of these two separate delays in the treatment continuum. 8 According to these data, 110 patients were classified as non-treatment cases and 233 as treatment cases, this compared to the 147 and 227 non-treatment and treatment cases interviewed respectively (refer Table 1). The reason why not all 374 patients were classified in this way was because either of the dates necessary for such classification was missing from the data. As the outstanding treatment start dates for patients become available, this classification will be revisited, as will the econometric analysis and the discussion of the regression results. 6

7 (newdisclosed), as well as the number of different types of persons/parties they had disclosed to since joining the programme (newdisclosures). In addition, a categorical variable indicating the disclosure status of patients prior to and after joining the ART programme were employed to assess the factors associated with decisions regarding disclosure (disccat), with a distinction being made between persons who have not as yet disclosed their status to someone other than a health care worker (=1), persons who had disclosed to someone prior to joining the ART programme (=2), and persons who only disclosed their HIV status for the first time after having joined the programme (=3). 9 The health-related quality of life of patients on ART was assessed with the aid of an internationally standardised measures: EQ-5D, a simple descriptive 5-domain index of health status (euroqol) 10 Since health can however be defined not only as a physical disease or medical condition, but as a state of complete physical, mental and social well-being (Michalos, 2004), we also in this paper assess factors associated not with health-related quality of life per se, but with the general well-being or quality of life of patients in the ART programme. For this purpose, we employed measures of overall life satisfaction and global happiness adopted from the South Africa Quality of Life Trends project (Moller and Schlemmer, 1983/89; Moller, 1989/99; Moller and Dickow, 2002). We employed both the standard five-point scales (life, happy) as well as binary variables (yes=1, no=0) coded from these scales and which distinguish between persons who were satisfied or very satisfied (lifesat), or happy or very happy (happiness) with life in general. The dataset also include two other variables that can be employed to assess physical and general quality of life. Patients were asked, Since you started taking ARV medication has the following changed: 1) your physical health, 2) your emotional well-being. The response scale included four options: improved, remained the same, deteriorated, fluctuated. These information were employed to code two binary variables (yes=1, no=0) that distinguish between patients whose physical health (physwell) and emotional well-being (emotwell) had improved, as opposed to those patients whose health and well-being have not improved and either remained unchanged, fluctuated or declined. In addition, a categorical variable (physemot) were coded based on these two binary variables, thus distinguishing between patients whose physical and emotional well-being both improved (=1), those who experienced an improvement in either their physical or emotional well-being (=2), and those for whom neither physical nor emotional well-being improved (=3). 9 It would be possible to estimate the factors associated with disclosure to each of the different types of persons or parties identified in the survey instrument, using separate models. This may in fact be required, because the factors associated with disclosure may differ depending on the characteristics of the patient and the specific person or party involved. Due to constraints of space, we did not attempt such analysis for the purposes of this particular paper. However, it does represent an important avenue for further analysis, albeit that the small number of disclosures to certain types of persons or parties (e.g. colleagues at work or the public) may constrain such analysis in the case of this dataset. 10 The EQ-5D index assesses health status in five domains, namely mobility, self care, usual activities, pain/discomfort, and anxiety/depression. Each domain is answerable by 3 statements indicating no problem (1), some/moderate problems (2) or extreme problems (3). The index can range from to 1 with negative values referring to health states valued as worse than death. The instrument has been widely tested and validated, also in South Africa and more particularly among others in the Free State province (Louwagie et al., 2005). 7

8 The survey instrument included a number of items on the perceived stigmatisation experienced by respondents. These items were aggregated into three simple, equally weighted, summary indices of stigmatisation, which are all expressed in percentage terms relative to the minimum and maximum possible scores on each summary index. These indices were then employed to assess factors associated with stigma. 11 The indices include pstigma (seven indirectly phrased, five-point response scale items), stigma13 (thirteen five-point response scale items), and stigma (responses to twentytwo items, including both categorical and binary variables). 12 Respondents were asked as to what best described their sexual life prior to and since joining the ART programme. The responses categories to these two questions included: abstain, always protected sex with one person, always protected sex with more than one person, and unprotected sex with one person, and unprotected sex with more than one person. This information were employed to code three binary variables to be employed in assessing risky sexual behaviour and its correlates in the sample population of patients enrolled in the ART programme. The variables were coded as follows: sex1: 1 = no condoms used before joining the ART programme 0 = used condoms or abstained before joining the ART programme sex5: 1 = no condoms used before joining the ART programme 0 = used condoms or abstained before and after joining the ART programme sexsex: 1 = no condoms used after joining the ART programme 0 = used condoms or abstained after joining the ART programme sex6: 1 = no condoms used before or after joining the ART programme 0 = used condoms or abstained before and after joining the ART programme Self-reported adherence was measured with the aid of a binary variable (yes=1, no=0) that distinguishes between patients who reported that they during the past week had taken their ARVs as prescribed, as opposed to patients who reported missing at least one dose during the past week (adherence). 13 Evidently, a number of the above outcomes are assessed with the aid of self-reported data collected directly from patients. The reliability of certain measures therefore may admittedly be queried, in particular those related to outcomes such as sexual 11 As noted here and in other places in the text, the various indices that are employed in this paper to among others measure not only perceived stigmatization, but also knowledge of ART and quality of care, are relatively simply ones. Thus, there remains great scope to employ data reduction techniques such as principal components or multiple correspondence analyses to develop alternative indices of these constructs, which can then be employed in the econometric models estimated here. 12 While the pstigma index was employed in the majority of the econometric models where stigmatisation was also employed as independent variable, either alone and/or interacted with disclosure, it would be possible with a view to assessing the robustness of the results reported here to re-estimate the same econometric models using these or other alternative stigmatisation indices as independent variables. 13 Such self-reported measures admittedly are not ideal measures of adherence, but in the absence of more objective, clinical measures of adherence to ART this represents the only variable in the dataset that can be employed to assess adherence, barring access to the clinical records of patients. 8

9 behaviour, disclosure and adherence, where respondents either know very well what they are expected to answer and/or where the issue is a very sensitive one. However, one cannot do much else here than caution readers against the possible problems with the subjective nature of certain measures, the nature of which may of course have implications for the results reported in this paper. The data analysis, for which we employed Stata9, was approached as follows: Firstly, the appropriate tools for bivariate quantitative analysis were employed for two purposes. On the one hand, we assessed whether these outcomes differ significantly by treatment status (treatment2) and time on treatment (treattime). On the other hand, we determined whether these same outcomes differ significantly across three common measures of socio-economic status, i.e. income quintiles, type of education, and employment status. The tools employed in the statistical analysis include t-, chi2- and Fischer exact-tests as well as one-way analysis of variance (ANOVA), the choice of which depends on the nature of the variables employed in each particular two-way comparison (refer Tables III and IV in the appendices for the detailed results). Secondly, econometric analysis was employed in exploring the factors associated with each of the above outcomes. These models all took the following general form: I i = a IC i + b HC i + c TE i + d SHC i + e OS i + f D i + ε i, where I i represents the reported value of outcome I for individual i; IC i a vector of individual socio-demographic and socio-economic characteristics of individual i; HC i a vector of household-level characteristics pertaining to individual i; TE i a vector of variables representing the treatment- and related HIV/AIDS experiences of individual i; SHC i a vector of variables representing support available within the health care sector (both directly related to the ART programme, but also to the broader health care sector) to individual i; OS i a vector of variables representing various types of support available to individual i from outside the health care sector (including informal support from household members and other service organizations); D i a categorical variable indicating the health district where the patient accessed treatment (which captures differences in the outcome explained by unobserved differences between districts) 14, and ε i the error term (a to e are vectors of parameters for each of the respective vectors of independent variables). 14 Similarly, dummy variables can be included in the econometric models for each of the sixteen facilities (or in other words the so-called assessment and treatment sites) that these patients visited to either have their HIV status determined and their CD counts assessed ( assessment sites) or where they underwent a full medical examination and a physician assessed their eligibility and readiness to commence ART ( treatment sites). However, this would constrain the degrees of freedom considerably. Furthermore, the sample, although stratified by facility (refer discussion on sampling design), do not include adequate numbers to allow such comparisons between facilities (comparisons can only be made at the district level with any degree of certainty). Hence, these facilities dummies were not included in the regression models. Yet, there remains an alternative avenue for exploring the manner in which facility-level factors may impact on the selected outcomes. The study of which the patient survey described in this paper form part, also include a longitudinal facility survey of these sixteen facilities. (The facility-level data in most cases were collected approximately one to three months prior to the patient survey. This raises the question as to whether these facility-level outcomes may have changed by the time the patients were interviewed, thus ruling this option out as a way of using these data to assess the impact of the performance of health care systems on the selected outcomes.) As such it is possible to at the facility level measure certain institutional factors (e.g. human resource constraints, infrastructure and equipment) that impact on the selected outcomes and to include 9

10 Model specifications were informed by a literature review (refer bibliography) as well as a careful consideration of other factors that may influence the selected outcome and which can be measured using data from the patient survey. 15 In cases where estimates of the length of time that people has known their HIV status and/or the length of time they have been on treatment for are employed in the econometric model as independent variable(s), two variants of the relevant model was estimated, one using each of these estimates (refer discussion above). The choice of econometric technique was informed by the nature of the particular dependent variable employed in each regression. These techniques mainly consist of linear OLS models, as well as probit and ordered probit models. In all cases, the models were estimated with robust standard errors. The data included very few nonadherent cases (adherence) and persons who reportedly had engaged in risky sexual behaviour since joining the ART programme (sexsex), i.e. n=11. Hence, it was not possible to estimate statistically significant probit models to investigate factors associated with risky sexual behaviour and self-reported adherence using these two indicators. All the econometric models performed adequately in terms of overall fit (p<0.10). The detailed econometric results are reported in Tables V-XXII in the appendices. Due to constraints of space, the focus in the subsequent pages is exclusively on (i) robust statistically significant results from the data analysis (p<0.10) and (ii) the further exposition of specific associations of socio-economic status (measured using income quintiles) with selected outcomes and its relation with other significant determinants of the above outcomes. Furthermore, a word of caution is necessary insofar as there are a number of cases in which endogeneity and multicollinearity may be particularly problematic in these econometric models. We did not at present consider the use of instrumental variables to adjust the results for endogeneity (assuming of course that we can only do so if the data include variables that represent suitable instruments of these outcomes), nor did we fully reflect on how problems with multicollinearity needs to be dealt with in the econometric analysis. Therefore, as noted elsewhere, the results presented in this paper should be interpreted with due care until such time as we have dealt with these two econometric issues in a satisfactory manner. 5. Results The results section of the paper is structured as follows: Firstly, significant differences in outcomes by treatment status and time on treatment are briefly reported, followed by significant differences in outcomes by income, education and employment status. these variables in the econometric models. In order to embark on such analysis though, the required information from the facility survey needs to be first captured electronically, a process that we have started with but which is not yet completed. 15 As noted elsewhere, the possibility remains for expanding the list of independent variables included in these econometric models by adding to these models relevant facility-level characteristics available from the facility survey. In addition, it may be possible to employ routine data available from the health care services in these models, including not only clinical individual-level data from patient files, but also facility-level data available from the information system of the Free State Department of Health. 10

11 The focus then shifts to the results of the multivariate/econometric analysis employed in exploring the determinants of the selected outcomes. 5.1 Differences in outcomes by treatment status and time on treatment A total of seven outcome variables differed significantly by both treatment status and treatment duration (Table III), while either treatment status or duration differed significantly by a further eight outcome variables (p<0.10). Most robust were the differences in disclosure, health-related quality of life, and overall quality of life, which varied significantly across the variables used to access to treatment. Persons that had commenced taking ARVs and those who had been on treatment for longer were significantly more likely to have disclosed since joining the ART programme and had also disclosed to a significantly larger number of different parties or persons since commencing ART. Health-related quality of life was significantly higher among patients who had starting taking ARVs and was positively associated with treatment duration. Patients in the socalled treatment group were also significantly more like to have reported that their physical health and emotional well-being have improved, while those who did report such improvement in physical health and emotional well-being had been on treatment for a significantly longer period of time. Given these results, those on treatment were significantly more likely to have reported that both their physical health and emotional well-being had improved since joining the ART programme. Such persons also had been on treatment for a significantly longer period of time. In addition, patients on treatment reported a significantly higher level of overall life satisfaction. Differences in treatment duration across the response categories in the global life satisfaction and happiness scores, although significant, do not exhibit a clear-cut gradient. Patients that had commenced ART reported significantly lower levels of stigma compared to patients who had not done so, whereas they also reported fewer problems in accessing assessment sites. Interestingly, patients who reported risky sexual behaviour, although not being significantly more like to be receiving ARVs, had however been on treatment for significantly longer periods of time. This was the case for all four measures of risky sexual behaviour employed in the analysis. Finally, treatment duration did differ significantly by self-reported adherence status, with adherent patients having been on treatment for longer compared to those patients whom reported not taking their pills as prescribed in the past seven days. 5.2 Differences in outcomes by income, education and employment status More educated individuals were significantly more likely to have known their HIV status for longer, but had also delayed commencing treatment for a longer period of time (Table IV). Persons in higher income quintiles were significantly more likely to already have commenced taking ARVs, this compared to persons in lower quintiles. In addition, more affluent persons (i.e. those higher in the income distribution) had been on treatment for longer compared to poorer individuals, as had employed persons. 11

12 Health-related quality of life differed significantly by income and employment status. Employed persons reported significantly higher levels of health-related health compared to the unemployed. Although individuals in the highest income quintile reported significantly higher levels of health-related quality of life, self-reported health status did not decline consistently as one moved down the income distribution. In fact, those in the bottom income quintile reported higher health-related quality of life compared to persons in the second and third quintiles. Two measures of quality of life also differed significantly by both income and employment status. Employed persons were significantly more likely to have reported improvements in emotional well-being and to have been happy or very happy with life in general. As with health-related quality of life, however, global happiness and improvements in emotional well-being did not exhibit a clear income gradient, although persons in the top income quintile did fair best on these two quality of life indicators compared to other persons. The global happiness scale also exhibited significant differences by income quintile, but again the income gradient was not that clear-cut. The binary indicator of overall life satisfaction, though, did exhibit a more clear-cut socio-economic gradient, with more educated persons being more likely to be satisfied or very satisfied with life compared to less educated persons. Employed persons also were significantly more likely to have reported improvements in both physical health and emotional well-being compared to persons that were not employed at the time. Finally, there was a relatively clear-cut socio-economic gradient in risky sexual behaviour. With the exception of persons with grade 12, more educated individuals were significantly less like to have reported risky sexual behaviour compared to less educated individuals. 5.3 Factors associated with delays in HIV testing Seven factors were significant in explaining differences in delays in HIV testing (Table V). Patients whom reported loosing greater amounts of income when accessing assessment sites (a proxy measure of barriers to access) delayed testing for a significantly shorter period. For every one percentage point increase in lost income, for example, the delay in testing on average declined by 16 to 20 days. Delays in HIV testing furthermore increased as income declined. The delay in testing on average increased by as much as 105 to 140 days for every one percentage point decline in income. Figures 1(a) and 1(b), which report the predicted delays in HIV testing by income quintiles, show persons in the upper quintiles to have delayed testing for significantly longer compared with persons in lower quintiles. Delays in HIV testing were also significantly longer for patients from Motheo district when compared with patients whom accessed treatment in Lejweleputswa district. These three relationships were statistically significant in both the regression models. 12

13 Figure 1(a): Predicted delay in HIV testing (testdelay1 ) by income quintiles (days) Figure 1(b): Predicted delay in HIV testing (testdelay3 ) by income quintiles (days) Predicted delay between first HIV+ test and date of interview (days) Predicted delay between first HIV+ test and date of interview (days) Average Average Income quintile Income quintile In the model using the more narrowly defined measure of delays in HIV testing, persons with formal education were shown to have delayed their treatment for a longer period of time (by between one and two years) compared to persons with no formal education. Patients whom resided a greater distance from assessment sites, however, had delayed HIV testing for shorter (another proxy measure of barriers to access). When we employed imputed delays in HIV testing as dependent variable, males were significantly more like compared to females to have delayed testing. 5.4 Factors associated with delays between first testing HIV+ and commencing ART Given that males have been shown to delay HIV testing for longer, the delay between first testing HIV-positive and starting to take ARVs was significantly shorter for men (around one year)(table VI). The opposite was true for patients who had received ART prior to joining the public sector ART programme, whom experienced significantly longer delays (around three years). As with delays in HIV testing, moreover, patients whom reported loosing greater amounts of income when accessing assessment sites (a proxy measure of barriers to access) experienced shorter delays between HIV testing and commencement of ART. For every one percentage point increase in lost income, for example, the delay on average declined by 17 to 21 days. Once again, delays differed significantly across districts, with patients from Xhariep district having experienced shorter delays between HIV testing and commencement of ART compared to patients from Lejweleputswa district. These four associations were statistically significant in both the regression models. In the model using the more narrowly defined measure of delays between HIV testing and commencement of treatment, individuals with some primary or secondary education interestingly experienced significantly longer delays between HIV testing and commencement of ART compared to persons with no formal education. 5.5 Factors associated with problems experienced when accessing ART facilities Numerous socio-demographic and socio-economic characteristics of patients are associated with reported difficulties in accessing ART facilities (Table VII). Patients whom incurred greater transport, accommodation and subsistence costs when visiting ART facilities in all cases were more likely to have had difficulties with access. Males were significantly more likely to have reported problems in accessing treatment sites, as were persons living in informal dwellings as opposed to formal dwellings, and persons that reported lower levels of health-related quality of life. Married persons whom lived with their spouses were less likely to have had problems 13

14 accessing assessment sites compared to single persons (one model only). The same was true for individuals from smaller households, individuals from households including more dependents, individuals residing in more distant locations, and individuals with either grade 12 or tertiary education as compared to persons with no formal education (one model only). Interestingly, persons whom had disclosed their HIV status whom perceived higher levels of stigma were also more likely to have had problems when visiting assessment sites. Barriers to accessing assessment sites, measured here by income loss and distance from facilities, according to the above evidence were associated with shorter delays in HIV testing and between HIV testing and commencement of ART. One would however have expected the opposite to be true. We suspect that these positive associations between delays and barriers to access captures can be attributed to endogeneity. 16 However, different factors were associated with problems experienced by ART patients when visiting treatment sites. In both regression models individuals with formal education were more likely to have sited problems accessing treatment sites. In addition, persons with lower levels of health-related quality of life and those whom had known their HIV+ status for longer were more likely to have reported having problems accessing treatment sites. The latter two associations were however only significant in one of the two regression models, as was the association with marital status: married persons whom lived with their spouses were less likely to have had problems accessing treatment sites compared to single persons. Persons in formal employment likewise were significantly less likely to have had problems accessing treatment sites when compared with unemployed individuals, but again in one regression model only. In the joint analysis of factors associated with problems accessing both assessment and treatment sites, only four variables (five if we include the cost variable) were statistically significant in both regression models. In this case, males were significantly more likely to have reported difficulties when accessing ART facilities, as were persons from households that included more dependents. In turn, persons in formal employment were significantly less likely to have had problems accessing ART facilities compared to unemployed individuals. The same was true for persons who reported lower levels of health-related quality of life. Persons whom had disclosed their HIV status and whom perceived higher levels of stigma also were more likely to have had problems when visiting ART facilities, but this result were statistically significant in one regression model only. 5.6 Factors associated with access to treatment Socio-economic status, in particular income and education, were significant in determining who commenced with treatment and who not, but was not significant however in explaining differences in treatment duration among those that have commenced ART (Table VIII). The probability of patients eligible for ART having commenced treatment increased as income increased. The probability of having started to take ARVs increased by 9.2 and by 6.9 percentage points respectively for a one percentage point increase in personal monthly income. Figures 2(a) and 2(b), 16 Those whom reported losing income when accessing assessment sites were mainly in informal employment, had some formal education and were in the upper half of the income distribution. 14

15 which report the predicted probability of having commenced treatment by income quintiles, also clearly show persons in the two upper quintiles of the income distribution to be significantly more likely to have started taking ARVs compared to individuals in the bottom three quintiles of the income distribution. Figure 2(a): Predicted probability of having commenced ART (treatment2 ) by income quintiles (days) Figure 2(b): Predicted probability of having commenced ART (treatment2 ) by income quintiles (days) Predicted probability of having commenced ART Predicted probability of having commenced ART Average Average Income quintile Income quintile Furthermore, individuals with grade 12 or with tertiary education were significantly more likely to have commenced treatment compared to persons with no formal education. However, the difference between persons with tertiary education and no education was only statistically significant in one of the two regression models. Given, moreover, that the ART programme requires patients to have identified a socalled treatment buddy prior to commencing actual treatment, those patients who had a treatment buddy were significantly more likely to have commenced treatment compared to persons without treatment buddies. Finally, the probability of patients having commenced treatment also differed significantly across districts. Patients whom accessed treatment in the Fezile Dabi and Thabo Mofutsanyane districts were significantly more likely to have commenced treatment compared to patients in Lejweleputswa districts. The opposite was true for Xhariep district, where patients were significantly less likely to have commenced with ART compared to patients from Lejweleputwa district. What these results imply, therefore, is that uptake in the former two districts, as measured by the variable treatment2, had been relatively lower than in Lejweleputswa district (i.e. a greater percentage of eligible patients had commenced treatment), but relatively higher in the latter district (i.e. a smaller percentage of patients had commenced treatment). There was no statistically significant difference in uptake between the Lejweleputswa and Motheo districts. Note, however, that these results need to be interpreted with caution, given that this indicator, for reasons explained elsewhere, will be recoded once the treatment start dates for all patients are available. As noted above, it is socio-demographic characteristics of patients and programmatic issues pertaining to the ART treatment programme rather than socio-economic factors that explain differences in treatment duration (Table IX). Patients whom had disclosed for the first time since joining the ART programme were significantly more like to have been on treatment for longer compared to patients who have not yet disclosed. (The same result holds if one employs the binary disclosure variable in the regression models as opposed to the categorical variable currently employed in the models. The latter result, however, suggest that there may be some rationing taking place.) This makes perfect sense insofar as the programme requires patients to have disclosed to someone prior to starting to take ARVs. For the same reason noted above, patients with treatment buddies were also significantly more likely to have been on treatment 15