Adherence to antiretroviral therapy & its determinants amongst HIV patients in India

Indian J Med Res 127, January 2008, pp 28-36 Adherence to antiretroviral therapy & its determinants amongst HIV patients in India A. Sarna, S. Pujari* ++, A.K. Sengar**, R. Garg +, I. Gupta +++ & J. van Dam # Horizons/Population Council, New Delhi, *Ruby Hall Clinic & Grant Medical Foundation, Pune ** Northern Railway Hospital, Delhi, + Employees State Insurance Corporation, Delhi, ++ University of South Florida, Tampa, +++ Institute of Economic Growth, Delhi, India & Population Council, Washigton DC, USA Received January 9, 2007 Background & objectives: Very high levels of adherence are required for ART to be effective. There is limited information available from India on adherence to ART and its predictors. We carried out this study to examine adherence levels and to explore the factors associated with adherence among PLHA receiving ART in India. Methods: Using a cross-sectional study design 310 HIV+ patients receiving ART (252 paying out-ofpocket; 58 free via employee-insurance programme) were interviewed from Pune and Delhi health facilities, using a semi-structural questionnaire. Results: The median age for patients was 36 yr. The median time from diagnosis of HIV-infection was 34.5 months, median time on ART was 16 months and median CD4 cell count at start of ART was 110 cells/ml. 98 per cent of the respondents were using a non protease inhibitor (PI) treatment regimen. Mean 4-day adherence was 93 per cent. Adherence was lower over longer periods of recall: 20 per cent reported missed does over the past 7 days; 33 per cent reported ever missing a full day s medications and 16 per cent had a treatment interruption of more than 7-days at least once. On univariate analysis less than university education, being unemployed, obtaining free treatment, severe depression, baseline CD4 count>200/ml, hospitalization >2 times, having moderate to severe side-effects and taking 4 or more medicines were associated with lower adherence (<90%). However, only obtaining free treatment (adjusted OR, 4.05, 95% CI 1.42-11.54, P=0.009) and severe depression (adjusted OR 4.48, 95% CI 1.64-12.27, P=0.003) were associated with lower adherence in multivariate analysis. Interpretation & Conclusions: Although the overall adherence was high, lower levels of adherence were documented among patients receiving free ART. Provision of free treatment without adequate patient preparation and adherence support may compromise the success of ART scale up programmes. Early diagnosis and management of depression need special focus. Key words Antiretroviral therapy (ART) - adherence - HIV-infected - India - resource limited settings 28

SARNA et al: ADHERENCE TO ART IN INDIA 29 A major concern with scaling up of antiretroviral therapy (ART) in resource-limited settings is the emergence of drug resistant viral strains due to suboptimal adherence and the transmission of these resistant viral strains in the population 1. Very high levels of adherence (> 95%) are required for ART to be effective long term and to prevent the emergence of resistant viral strains 2. There has been a concern about the capability of patients in resource-limited settings to adhere to ART, especially in the African context 3. Although studies from resource-limited settings have documented high levels of adherence amongst these patients 4-6, a recent review 7 highlights the need for an increased focus on adherence in the face of findings from Cote d Ivoire, Cameroon and Botswana that have documented lower adherence levels in ART programmes. Although the Indian national ART Programme was launched in April 2004, antiretroviral (ARV) medications have been widely available in the private sector and through some employer supported health insurance programmes in India since 1998 (personal communication) from ESI and Railways. People living with HIV/AIDS (PLHA) who could access these sources have been on ART for several years. There is limited information regarding levels of adherence and predictors of suboptimal adherence to treatment among PLHA receiving ART in India. Two recent studies from India have documented non-adherence in more than a quarter of the patients interviewed 8,9. Several factors are associated with adherence. Depression and psychiatric illness, active alcohol or drug use, and lack of social support have been found to be associated with lower adherence 2,10. In general, sociodemographic factors do not seem to predict adherence behaviour, although some studies have found that male sex, white ethnicity, older age, higher income and higher education and literacy correlate with better adherence 11. A patient s ability to identify medications and his/her understanding of the relationship between adherence and medication resistance also predict better adherence 10. Health literacy and HIV related knowledge are found to be associated with better adherence 12,13. Disease characteristics such as prior opportunistic infections implying an increased perceived severity of illness appear to motivate patients to adhere better 14. Patient provider relationship and trust in the provider is believed to be a motivating factor for adherence 15. A high pill burden and inability to integrate the treatment regime into patient s daily routine have been reported as barriers to adherence 11. We undertook this study to investigate levels of adherence to treatment among PLHA receiving ART at selected public and private health facilities in India. The specific objectives of the study were to (i) measure current levels of adherence and (ii) explore the factors associated with adherence among PLHA receiving ART. Material & Methods Study population, recruitment and study design: Using a cross-sectional design, a quantitative survey was undertaken. Three hundred and ten PLHA receiving ART were interviewed as they came in for routine follow up visits at outpatient clinics at one private sector and one public sector health facility in Pune, and two public sector health facilities in Delhi between May and August 2004. Clinics were selected if they were providing ART services, had a pool of HIV-infected patients on ART and were willing to participate in the study. Inclusion criteria for a patient to be included in the study were HIV-positive status, 18 yr of age or older, ability to provide consent and having been on ART for at least 30 days. At the time of the study, the national ARV programme had not yet started, and only a few HIVinfected patients were receiving ART at public health facilities through employer supported health insurance programmes. To access patients receiving free ART Employees State Insurance Corporation (ESIC) and Indian Railways clinics were included. As a small number of PLHA were receiving ART, three public sector health facilities were selected (2 in Delhi viz., Jhilmil ESIC Hospital and Northern Railway Hospital and in Chinchowad ESIC Hospital, Pune) and all patients on ART were invited to participate; 47 patients (82%) of a total of 57 receiving ART at these sites were recruited: 6 did not come the clinic for follow up visits during the study period and 4 refused to be interviewed. In the private sector health facility 273 patients (35%) of a total of 800 patients on ART were recruited to participate in the study. The sample size was based on an estimated population proportion of 50 per cent, a confidence level of 90 per cent and alpha of 0.05 using: z 2 1-a/2P (1 P)/d 2. Patients were recruited consecutively till the sample size was reached. Of these 263 patients (96%) completed the interview at the private facility, 3 patients had to leave before completing the interview and 7 refused to participate. Eleven of the 263 patients accessing services at the private sector facility had complete health coverage through an employer provided government

30 INDIAN J MED RES, JANUARY 2008 health insurance scheme (CGHS). In all, 252 patients paying out-of-pocket and 58 patients (47 plus 11) covered by insurance were interviewed. Dedicated adherence counselling was not available at any site; physicians provided adherence related advice as a part of routine clinical care. However, paying patients did receive financial counselling and were assessed for ability to pay before initiating ART. At all sites patients were first approached by the clinic staff who explained the study procedures; patients who agreed to participate were then contacted by the research staff who obtained written informed consent. Patients were paid Rs 100 towards transportation and refreshment costs. Measurement of adherence: A semi-structured questionnaire adapted from the Adult AIDS Clinical Trials Group (AACTG) was used 16. The questionnaire was culturally adapted, translated and back translated by independent persons. Trained external research interviewers, who were not employed by the health facilities, conducted face-to-face interviews in Hindi, Marathi and English as per patient preference. Ethical clearance was obtained from the Ethical Review Board of the Population Council and the managements of participating health facilities. All information collected was based on patient selfreport with the exception of CD4 counts at the start of treatment and confirmation of ARV treatment regimen, which were provided by the treating physician from medical records. The primary adherence measure was based on a 4-day recall. Mean 4-day adherence was calculated by dividing the number of pills actually taken by the number of pills needed to be taken for 4 days X 100. Adherence was then dichotomized to define high adherence as >90 per cent and lower adherence as <90 per cent; this measure was used for multivariate analysis. We used a less stringent cut-off of 90 per cent for two reasons (a) in view of emerging evidence that viral load suppression may be achievable at lower adherence levels with non-nucleoside reverse transcriptase inhibitors (NNRTI) treatment regimens 17,18, since the majority of patients were taking a NNRTI based regime, and (b) the 95 per cent cut-off for adherence quoted by Paterson et al 2 refers to protease inhibitor containing treatment regimens. In order to assess adherence over longer periods of time we included the following additional self-reported measures of adherence: (i) the total number of missed doses over the last week (7 days), (ii) the last time the patient missed a full day s medications since starting ART, (iii) the number of times treatment was stopped for more than 1 wk since starting ART, and (iv) how closely did the patient follow the medication dosage schedule as prescribed by the physician over the past 4 days. Patients were asked whether they took fixed dose combinations (FDC) or drugs separately wherein missing doses were asked for each drug in the ART regime. Patients who reported missed doses were asked to provide reasons for missing their medications. Openended responses were collected from patients for missing medications over the past week and from patients reporting stopping medications for periods longer than one week. Multiple responses were permitted and were analyzed qualitatively. Apart from demographic information, responses related to questions on social support from the family, disclosure of HIV-status, alcohol and recreational drug use (alcohol use in last 30 days and drugs in last 6 months) and trust in provider scale (6 items; Cronbach s alpha 0.89) were collected. The 20-item AACTG signs and symptoms questionnaire was used to derive side effects scores 19. Knowledge of treatment regimen and belief and perceptions about ART were assessed. Information on financial mechanisms (paying out-ofpocket or insurance) and expenditure incurred on treatment (ARVs and laboratory monitoring) was analyzed. Economic status (ES) was based on ownership of assets and categorized into ES quartiles. The Beck Depression Inventory II (BDI II), a 21 item validated and widely used tool (Cronbach s alpha 0.86), was used to provide scores for current depression 20. Depression was categorized into minimal, mild, moderate and severe depression scores based on scoring charts provided with BDI II; possible range of scores 0-63. Statistical analysis: Means, medians, and standard deviations were calculated for continuous variables. Logistic regression analysis was undertaken to explore the factors associated with lower adherence (<90%) to provide odds ratios (OR) and 95 per cent confidence intervals (CI). Adherence measures based on the 4-day self report were used in logistic regression analysis. Variables found to be associated with lower adherence on univariate analysis were included in multivariate analysis. A P value of <0.05 was considered significant. Since coverage (paying and non-paying) emerged as an important significant variable, we used chi square tests to assess significance between groups (paying vs.

SARNA et al: ADHERENCE TO ART IN INDIA 31 non paying) for categorical variables and t test and Mann Whitney U tests for continuous variables. To look at the improvement in CD4 counts amongst patients with lower and higher adherence we examined change in CD4 counts among patients on treatment for different periods of time <12, 13-24 and >25 months and applied independent t test to assess significance. Analysis was done using SPSS 11.0 (SPSS, Inc., Chicago, IL. USA). Results Background characteristics: The median age was 36 yr (23-70 yr), median time from diagnosis of HIV infection was 34.5 months (1-144 months), median time on ART was 16 months (1-72 months) and median CD4 cell count at the start of ART was 110 cells/ml (1-388 cells/mm 3 ). Ninety eight per cent (n=304) of the respondents were using a non-protease inhibitor based treatment regimen. Eighty eight per cent of respondents were on a first line nevirapine based regimen [160 on stavudine (D4T)/lamivudine (3TC)/nevirapine (NVP) and 112 on zidovudine (ZDV)/3TC/NVP)], seven per cent on an Efavirenz based regimen (10 on D4T/3TC/ EFV and 12 on AZT/3TC/EFV) and three percent on other NRTI/NNRTI combination regimens with abacavir and didanosine (10 patients). The majority of those on first line NVP based regimens were using fixed dose combinations (n=262). Two percent of respondents were receiving a protease inhibitor based regimen (6 patients). A higher proportion of women accessed care among respondents receiving free ARVs. ART was initiated at higher CD4 counts among clients receiving free ARVs compared to paying clients (P<0.01, Table I). Clients accessing care in the private sector were almost equally distributed across ES quartiles. Among clients receiving free ART the majority were distributed across the first three ES quartiles; very few made it to the highest ES quartile. Overall, almost half the respondents were classified as having minimal depression scores and nearly a fifth as having severe depression scores, more so among patients receiving free ART (Table I). Three female and one male respondent, all accessing care in the public sector, were unaware of their HIV diagnosis and reported receiving medications for some illness. Clients without coverage were spending, on an average, Rs 2843 (US $ 66) per month out-of-pocket for their treatment (medications and monitoring). Clients with insurance coverage contributed a token amount out of their salaries towards the insurance, no money, for medications or tests, was paid at the time of Table I Background information of study participants (n=310) Pay out-of- Receive Total pocket for ART Free ART n=310 (%) n=252 (%) n=58 (%) Sex* Male 219 (87) 42 (72) 261 (84) Female 33 (13) 16 (28) 49 (16) Age group (yr) < 30 38 (15) 13 (22) 51 (16) 31 45 173 (69) 31 (53) 204 (66) > 45 41 (16) 14 (24) 55 (18) Education* Less than 5 yr of school 9 (4) 21 (36) 30 (10) 6 to 12 yr of school 135 (54) 30 (52) 165 (53) University 108 (43) 7 (12) 115 (37) Employment Employed 212 (84) 43 (74) 255 (82) Unemployed 40 (16) 15 (26) 55 (18) Marital status Currently married 210 (83) 53 (91) 263 (85) Single (never married) 20 (8) 1 (2) 21 (7) Separated/widowed 22 (9) 4 (7) 26 (8) Economic Status* Quartile 1 62 (25) 26 (45) 88 (28) Quartile 2 54 (21) 16 (28) 70 (23) Quartile 3 69 (27) 12 (21) 81 (26) Quartile 4 67 (27) 4 (7) 77 (23) Time since HIV diagnosis (month) < 12 42 (17) 13 (22) 55 (18) 13 24 60 (24) 13 (22) 73 (24) > 24 150 (59) 32 (55) 182 (59) Time since starting ART (month) < 12 106 (42) 22 (38) 128 (41) 13-24 70 (29) 15 (26) 85 (27) > 24 76 (30) 21 (36) 97 (31) CD4 at start of ART* < 100 cells 124 (49) 9 (17) 133 (44) 101-200 cells 98 (40) 16 (30) 114 (37) > 200 cells 30 (12) 29 (54) 59 (20) Current Depression* Minimal 131 (52) 16 (28) 147 (48) Mild 36 (14) 11 (19) 47 (15) Moderate 45 (18) 9 (15) 54 (17) Severe 39 (16) 22 (38) 61 (20) Note: Percentages rounded of the nearest whole. *P<0.01 (chi square for trend) accessing services. For clients with insurance accessing services at the private facility all expenditure incurred was reimbursed to them. Adherence and immunologic improvement: The mean selfreported 4-day adherence for all patients was 93.4 per cent. Eighty four per cent respondents (85% of men and 82% of women) reported higher adherence (>90%); all these respondents reported perfect adherence of 100 per cent.

32 INDIAN J MED RES, JANUARY 2008 Adherence was relatively lower over longer periods of recall. Twenty per cent of the respondents reported missed doses over the past 7 days. Thirty-three per cent reported ever missing a full day s medication; 20 per cent reported doing so in the last 3 months. Sixteen per cent respondents reported having stopped medications (treatment interruption) for a period of more than 7 days at least once. More than a quarter of the respondents reported not having followed the medication schedule over the past 4 days. There were no significant differences in reported adherence between male and female respondents on any measure of adherence. Adherence was significantly lower among patients receiving free ARVs on all measures of adherence: mean 4-day adherence, total number of missed pills over last week (7-days), last time a full day s medication was missed (ever missing a full day s medication), number of times treatment was interrupted for more than 1 wk since initiating ART and following ARV medication schedule over the past 4-days (Table II). Mean 4-day adherence was lower among patients receiving free Table II. Adherence (Self-reported) to the treatment among patients Per cent Out-of-pocket ART Free ART n=252 n=58 Mean 4-day adherence* 96.4 80.6 Mean 4-day cut-off at 90%** Higher adherence (>90%) 89.7 60.3 Lower adherence (<89%) 10.3 39.7 Missed doses over last week (7days)** No doses missed 84.9 56.9 1 to 2 doses missed 11.5 19.0 3 or more doses missed 3.6 24.1 Last time missed a full day s medication** Never 72.2 41.4 In the last 4 wk 8.7 31.0 Between 1 to 3 months 8.7 10.3 More than 3 months back 10.3 17.2 Number of times treatment stopped for more than 1 wk** Never 87.7 65.5 Once 7.9 20.7 2 or more times 4.4 13.8 How closely did respondent follow ARV medication schedule over last 4 days** Never 2.8 31.0 Some of the time 2.8 12.1 Most of the time 13.9 17.2 Always 80.6 39.7 *t-test and Mann-Whitney U. P<0.01 **chi square test P< 0.01 ARVs at all sites, even among the eleven patients with government insurance coverage who were receiving care at the private facility. On stratified analysis mean 4-day adherence was found to be lower among patients receiving free ARVs on the first three economic status quartiles (Q 1: paying patients 96.1 per cent vs. free ART 83.3 per cent, P<0.01; Q 2: paying patients 95.8 per cent vs. free ART 70.7 per cent, P<0.01; Q 3: paying patients 96.2 per cent vs. free ART 81.2 per cent, P<0.01). Analysis was not done on the highest economic status quartile due to a small case pool. Factors associated with adherence: Table III depicts variables studied for predicting lower adherence. ART regimen (PI vs. non PI) and active drug use were not included in the analysis due to a very small number of cases; only 4 respondents each reported active drug use and were on a PI based treatment regimen. On univariate analysis less than university education, being unemployed, obtaining free treatment, severe depression, baseline CD4 count>200/ml, hospitalization >2 times, and taking 4 or more medicines were associated with lower adherence. Age, sex, social support, economic status, time since diagnosis of HIV disease, time on ART, knowledge of medication and alcohol use in past 30 days were not found to be associated with lower adherence (Table III). On multivariate analysis patients receiving free ARVs were 4.4 times more likely to report lower adherence than patients paying out-of-pocket for ART. Patients with severe depression were 4 times more likely to report lower adherence than patients with minimal depression. Figure demonstrates mean change in CD4 counts amongst patients with lower and higher adherence on ART for <12, 13-24 and >25 months. The increase in CD4 counts was higher in patients with higher adherence taking ART for 13-24 months (P=0.02) and >25 months (not significant). Reasons for missed doses: Sixty three respondents reported missing medications in the past one week. The most frequently cited reasons were being busy with other things and forgetting, being away from home and running out of pills. Of those reporting running out of pills (n=16), 6 respondents were receiving free ARVs and reported dispensary stock-outs (dispensary running out of medication stocks) and 10 respondents in the private sector were unable to procure medications.

SARNA et al: ADHERENCE TO ART IN INDIA 33 Table III. Factors associated with lower adherence (<90% adherence) Adherence <90% Unadjusted Odds P value Adjusted Odds P value n (%) (95% CI) (95%CI) Sex* Female (n=49) 9 (18.4) 1.0 Male (n=261) 40 (15.3) 0.80 (0.36-1.78) 0.593 Age group* Less than 30 yr (n=51) 15 (29.4) 2.13 (0.84-5.42) 0.113 31-45 yr (n=204) 25 (12.3) 0.71 (0.31-1.63) 0.425 More than 45 yr (n=55) 9 (16.4) 1.0 Education Less than 5 yr of school (n=30) 8 (15.6) 4.28 (1.49-12.33) 0.007 0.3 (0.07 1.73) 0.195 6 to 12 yr of school (n=165) 32 (15.6) 2.83 (1.29-6.19) 0.009 0.91 (0.80-4.58) 0.144 University (n=115) 9 (16.3) 1.0 Employment Employed (n=255) 34 (13.3) 1.0 Unemployed (n=55) 15 (27.3) 2.35 (1.22 4.88) 0.012 2.16 (0.92 5.04) 0.078 Coverage of ARV medications Obtained free (n=58) 23 (39.7) 5.71 (2.94 11.10) 0.000 4.05 (1.42 11.54) 0.009 Paid out-of-pocket (n=252) 26 (10.3) 1.0 Depression Minimal (n=147) 11 (7.5) 1.0 Mild (n=47) 8 (18.1) 2.53 (0.95 6.74) 0.062 2.18 (0.72 6.57) 0.313 Moderate (n=54) 9 (16.7) 2.47 (0.96 6.35) 0.060 1.91 (0.66 5.51) 0.395 Severe (n =61) 21 (34.4) 6.49 (2.89 14.59) 0.000 4.48 (1.64 12.27) 0.003 CD4 count at start of ART Less than 100 cells/ ml (n=133) 14 (10.5) 1.0 101 to 200 cells/mm 3 (n=114) 16 (14.0) 1.39 (0.64 2.98) 0.401 1.22 (0.52 2.85) 0.651 More than 200 cells/mm 3 (n=59) 19(32.2) 4.04(1.85 8.79) 0.000 2.53 (0.90 7.10) 0.076 Time since diagnosis of HIV disease* (month) < 12 (n=55) 9 (16.4) 1.0 13-24 (n=73) 12 (16.4) 1.00 (0.39-2.59) 0.991 > 24 (n=182) 28 (15.4) 0.93 (0.41-2.11) 0.861 Time on ART* (month) < 12 (n=128) 23 (18.0) 1.0 13-24 (n=85) 14 (16.5) 0.90 (0.43-1.87) 0.778 > 24 (n=97) 12 (12.4) 0.64 (0.30-1.37) 0.254 Number of hospitalizations due to HIV related illness Never (n=235) 35 (15.2) 1.0 Once (n=58) 7 (12.1) 0.24 (0.33 1.87) 0.583 0.67 (0.25 1.83) 0.641 Two or more times (n=17) 7 (43.7) 1.38 (1.43 11.21) 0.008 3.45 (0.87 13.77) 0.079 Medication burden (Number of all medications) One medication (n=118) 14 (12.0) 1.0 2-3 medications (n=136) 17 (12.8) 1.05 (0.49 2.24) 0.897 0.92 (0.39 2.30) 0.917 4 or more medications (n=56) 17 (30.4) 3.21 (1.44 7.12) 0.004 2.57 (0.92 6.72) 0.071 Side effects None to mild side effects (n=278) 35 (12.6) 1.0 Moderate to severe side effects (n=32) 14 (43.8) 5.40 (2.47-11.81) 0.000 0.90 (0.28-2.88) 0.857 Knowledge of medications* Knows medications (n=166) 20 (12.0) 1.0 Does not know medications (n=144) 29 (20.1) 1.84 (0.99-3.42) 0.054 Economic status* Quartile 1 (n=88) 17 (19.3) 1.0 Quartile 2 (n=70) 12 (17.1) 1.88 (0.76-4.67) 0.170 Quartile 3 (n=81) 12 (14.8) 1.63 (0.66-4.27) 0.320 Quartile 4 (n=71) 8 (11.3) 1.37 (0.55-3.57) 0.520 Family support* Yes (n=285) 44 (15.4) 1.0 No (n=25) 5 (20.0) 1.37 (0.49-3.84) 0.550 Alcohol use in last 30 days* No drinking (n=255) 37 (14.5) 1.0 Low drinking (n=47) 12 (25.5) 2.02 (0.96-4.24) 0.063 * Not included in multivariate analysis

34 INDIAN J MED RES, JANUARY 2008 Fig. Mean change in CD4 counts over time in patients on ART. Fifty one respondents reported treatment interruptions of more than 7 days. The most frequently cited reason for longer breaks in treatment were financial difficulty, travel and side effects. The majority of respondents (85%) who reported financial difficulties were paying out-of-pocket for ARV services. Patients covered by insurance reported pharmacy stock-outs. Discussion Our results showed high levels of adherence in a large proportion of HIV infected adults receiving ART at a private and three employer-supported insurance based public sector facilities in a resource-limited setting. These rates of adherence were similar to those reported from India 9 and other developing countries like Senegal 21 and South Africa 5,6. We found adherence to be strongly associated with coverage and depression. Respondents receiving free ARVs were found to have lower adherence; differences in mean 4-day adherence were seen even after controlling for economic status. Importantly, lower adherence was seen on all measures of adherence. Our findings are different from those reported in a metaanalysis of ART programmes in resource-poor settings that provision of medications free of charge was associated with a higher probability of achieving higher adherence and undetectable viral loads than where patients were required to pay for ART 22, and also from those reported from Chennai 8,23. Possible explanations for the differences between the two groups in our study include: (i) patients in the private sector were assessed for ability to afford treatment and treatment readiness prior to initiating ART while patients receiving free ARVs received minimal counselling; thus there were differences in patient preparation prior to initiating ART, (ii) the disease stage at which ART was initiated may have had an influence on adherence levels; the majority of patients who paid for their treatment initiated ART when they had advanced disease and may have experienced serious and life threatening opportunistic infections influencing their perceptions about disease severity compared to those who received free ARVs and initiated treatment comparatively earlier, (iii) there may be differences in the way the two groups value their treatment. Patients in the private sector were paying significantly higher amounts of money as compared to patients in the public sector who were paying a small percentage of their salary towards the insurance coverage; and (iv) stock-outs in public sector dispensaries may have contributed to lower adherence among this group. Running out of pills including stockouts were the third most commonly cited reason for missing doses over the past week. Paying money for antiretroviral therapy may work in two ways. While on one hand it may be a strong motivation for taking treatment correctly, on the other hand the financial burden may be the very reason for discontinuing treatment. The most commonly cited reason for longer breaks in treatment in our study was due to financial reasons; and of the patients who gave this reason, 85 per cent were paying for treatment. These findings were similar to those reported from Chennai that the most common form of non-adherence was due to planned or unplanned breaks in treatment (drug holidays) due to inability to pay for treatment 23. The implications of our research findings for the National ART programme, as it scales up, is the need to ensure quality care and services: adequate preparation of patients through quality adherence counselling prior to initiating treatment; provision of ongoing adherence support and ensuring uninterrupted supply of medications. An assessment of adherence amongst patients receiving ART at public sector sites in the national ART programme is recommended. Severe depression was found to be strongly associated with lower adherence in our study. Depression has been shown to be an independent predictor of poor adherence in other studies 2-24. Depression is also common among HIV infected persons 25,26. Providers need to be alert to the presence of depression among their patients.

SARNA et al: ADHERENCE TO ART IN INDIA 35 Early identification and management of severe depression and interventions to provide adherence support would be required to maintain high levels of adherence. Our study did not show any other factors 2,11-15 to be associated with adherence. There is concern that patients on ART would become less adherent once they feel better physically. Our study did not find any evidence supporting this view. There was no difference in adherence levels related to duration of time patients had been receiving ART. We used self-report to assess adherence. A metaanalysis of several studies demonstrated a good correlation between self-reported medication adherence and virologic outcomes and that self-reported lower/ non-adherence to be more reliable than self-reported higher adherence 27,28. The adherence information was collected by the non-clinic research staff so there is less reason to over-report adherence. We were able to demonstrate an association between self-reported adherence and improvement in CD4 counts. Lower improvement in CD4 counts during the initial period may be attributed to the delay between virologic and immunologic failure. Our study had some limitations. All quantitative data were based on this convenience sample of selected health facilities. Our sample had a small number of participants receiving free ARVs through an employer supported insurance-based programme, most of who were employed. The study was unable to comment on adherence among HIV infected individuals who might have stopped treatment or dropped out of the programme. Finally, the cross sectional study design had its own limitations. In conclusion, overall adherence to ART was found to be high. Adequate preparation of patients prior to initiating ART and incorporating adherence support measures, particularly those addressing management of depression are crucial for all patients receiving ART. Further with a larger number of patients and a wider range of public and private service delivery sites are needed to study the impact of payment for ART on adherence. Acknowledgment Authors acknowledge the financial support received from the President s Emergency Plan for AIDS Relief through the US Agency for International Development and thank Dr R.R. Gangakhedkar, National AIDS Research Institute (NARI), Pune, for his careful review and expert inputs into the data analysis and interpretation. References 1. Blower S, Ma L, Farmer P, Koenig S. Predicting the impact of antiretrovirals in resource-poor settings: prevention of HIV infection whilst controlling drug resistance. Curr Drug Targets Infect Disord 2003; 3 : 345-53. 2. Paterson DL, Swindells S, Mohr J, Brester M, Vergis E, Squire C, et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med 2000; 133 : 21-30. 3. Harries AD, Nyangulu DS, Hargreaves NJ, Kaluwa O, Salaniponi FM. Preventing antiretroviral anarchy in Africa. Lancet 2001, 358 : 410-4. 4. Nemes MI, Carvalho HB, Souza MF. Antiretroviral therapy adherence in Brazil. AIDS 2004; 18 (Suppl 1) : S15-20. 5. Orwell C, Bangsberg D, Badri M, Wood R. Adherence is not a barrier to successful antiretroviral therapy in South Africa. AIDS 2003; 17 : 1369-75. 6. Nachega JB, Stein DM, Lehman DA, Hlatshwayo D, Mothopeng R, Chaisson RE, et al. Adherence to antiretroviral therapy in HIV-infected adults in Soweto, South Africa. AIDS Res Hum Retroviruses 2004; 20 : 1053-6. 7. Gill CJ, Hamer DH, Simon JL, Thea DM, Sabin LA. No room for complacency about adherence to antiretroviral therapy in sub-saharan Africa. AIDS 2005; 19 : 1243-9. 8. Safren S, Kumarasamy N, James R, Raminani S, Solomon S, Mayer KH. ART adherence, demographic variables and CD4 outcome among HIV-positive patients on antiretroviral therapy in Chennai, India. AIDS Care 2005; 17 : 853-62. 9. Wanchu A, Kaur R, Bamberry P, Singh S. 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