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Int J STD AIDS OnlineFirst, published on March 22, 2015 as doi:10.1177/0956462415578545
Original research article
Adherence to highly active antiretroviral therapy in Hyderabad, India: barriers, facilitators and identification of target groups
International Journal of STD & AIDS 0(0) 1–10 ! The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0956462415578545 std.sagepub.com
Mark S Dworkin1, GW Douglas1, GP Sabitha Rani2 and Apurba Chakraborty1
Abstract We assessed the barriers and facilitators to highly active antiretroviral therapy adherence and determined their prevalence among HIV/AIDS patients in Hyderabad, India. We conducted a cross-sectional study among HIV-infected adults prescribed highly active antiretroviral therapy and receiving care from nine clinics. Depression was screened using Patient Health Questionnaire 9 and facilitators of HIV medication adherence were assessed using an 11-item scale which yielded a total positive attitude to disease score. Prevalence ratios of non-adherence between different categories of potential risk factors were calculated. We compared mean ‘facilitators to adherence’ scores between the adherent and nonadherent population. Multivariable Poisson regression with robust variance was used to identify independent risk factors. Among the 211 respondents, nearly 20% were non-adherent, approximately 8% had either moderately severe or severe depression and mean score for combined facilitators to medication adherence was 33.35 (7.88) out of a possible 44 points. Factors significantly associated with non-adherence included older age, female sex worker, moderate-to-severe depression and the combined facilitators to medication adherence score. These data from a broad range of clinical settings in Hyderabad reveal that key groups to focus on for adherence intervention are female sex workers, older persons and those with depression.
Keywords HIV, adherence, non-adherence, female sex workers, Hyderabad, India, medication, highly active antiretroviral therapy, barriers, facilitators Date received: 21 October 2014; accepted: 24 February 2015
Introduction Medication non-adherence has been associated with greater rates of hospital re-admission, increased cost of health care expenditures, progression of disease, lower quality-of-life, development of co-morbidities1–4 and death5 among HIV/AIDS patients. As a result, adherence to antiretrovirals has emerged as an attractive point of intervention toward promoting better health outcomes in persons infected with HIV.6–8 However, for many highly active antiretroviral therapy (HAART) regimens, high levels of adherence are needed to derive sustained clinical benefit.9 Additionally, poor levels of HAART adherence are associated with rapid development of drug resistance,10 requiring the need for expensive and sometimes lessconvenient second-line therapies.
An estimated 2.1 million people live with HIV/AIDS in India.11 HIV/AIDS is concentrated among high-risk groups with rates as high as 8.82% in transgenders, 7.14% in injection drug users (IDU), 4.43% in men who have sex with men (MSM) and 2.67% in female sex workers (FSWs). These prevalence statistics are in contrast to the antenatal clinic attendee prevalence 1 School of Public Health, University of Illinois at Chicago, Chicago, IL, USA 2 SHARE INDIA, Secunderabad, Andhra Pradesh, India
Corresponding author: Mark S Dworkin, Division of Epidemiology and Biostatistics, University of Illinois at Chicago, School of Public Health M/C 923, 1603W. Taylor Street, Chicago, IL 60612, USA. Email: [email protected]
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(0.40%), considered a proxy for the general population.12 In India, 43% of HIV/AIDS patients with a CD4 þ count 45 years). We calculated the prevalence ratio (95% confidence interval) of nonadherence between different categories of potential risk factors. We used category of the risk factor with lowest prevalence rate as the reference category. To identify statistically significant associations between categorical risk factors and medication adherence status of the respondents we performed Chi square test and Fischer’s exact tests. We categorised household income of the respondents based on the urban income quartiles for India.29 For descriptive analysis, we categorised PHQ-9 scores to five categories of depression (Minimal or no depression [0–4], mild depression [5–9], moderate depression [10–14], moderately severe depression [15–19] and severe depression [20–27]) following the PHQ-9 scoring and interpretation guide.22 During bivariate analysis we dichotomised depression categories into major depression (moderate/moderately severe/severe depression) and no major depression (minimal/mild depression) using the recommended cutoff value of PHQ-9 score of 10 or greater.22 We also dichotomised the employment status of the respondents as ‘Unemployed’ and ‘Student or employed part time or full time in formal or informal job.’ We performed the t test to compare mean ‘facilitators to adherence’ scores between the adherent and non-adherent population. Factors that were significantly associated with non-adherence at p < 0.25 in bivariate analysis were included in a multivariable log binomial regression model to identify the independent risk factors. In addition, demographic factors that were reported in published literature as associated with nonadherence were included into the initial model irrespective of whether those were statistically significant in bivariate analysis. As combined facilitators to adherence score was correlated with depression score, we did not include the ‘facilitators to adherence’ score in the multivariate model. Since the log binomial regression model failed to converge, we used SAS PROC GENMOD’s Poisson regression with the robust variance to estimate multivariate adjusted prevalence
ratios.30 While analysing the reasons for non-adherence we calculated frequencies by category of response for each of the reasons listed.
Ethics statement The study design and methods were approved by University of Illinois at Chicago Institutional Review Board (Protocol number: 2012-0505). Informed consent in the language of their choice (Telugu, Hindi or English) was obtained from all participants in the study. A research assistant read out the consent statement to the participants who could not read. Respondents consented either providing their signature or their thumb impression (if they could not sign) on the consent form.
Results A total of 211 respondents completed the interview, 22 (10.4%) from GYD Diagnostics & Reference laboratories (P) Ltd., 9 (4.3%) from MIMS, 12 (5.7%) from CHAI, 5 (2.4%) from CMM, 17 (8.1%) from Darpan Foundation, 22 (10.4%) from HOPES, 24 (11.4%) from King Koti Hospital, 63 (29.9%) from Sivananda Rehabilitation Home and 37 (17.5%) from the Network of HIV Positive People clinics. Among the 211 respondents, nearly 54% were men and the median age was 34 years (range 19 years to 62 years) (Table 1). The educational level was generally low with more than one-third having either never attended or not reached higher than primary school (Table 1). Approximately 80% had some form of employment including 58% employed full-time. Although more than half had some level of depression, approximately 8% had either moderately severe or severe depression. Of the 211 respondents, 42 (20%) were non-adherent to HAART. The mean score for combined facilitators to medication adherence was 33.35 (7.88) (Table 2). Low scores were observed for having once-daily dosing (only 14% had such a regimen) and using reminder tools. More than 80% of the patients owned a cell phone. Phone ownership was not associated with adherence. Out of 175 respondents who answered to the question regarding use of their phone’s alarm function, 35 (25%) of 141 adherents used the alarm function for any purpose compared to 6 (18%) of 34 non-adherents (p ¼ 0.38). In bivariate analysis, factors significantly associated with non-adherence included older age, being an FSW, moderate-to-severe depression and the combined facilitators to medication adherence score (Table 3). Adjusting for other covariates in the multivariable regression analysis, those aged 46 years or more had
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Table 1. Demographic characteristics of the HIV-infected persons receiving highly active antiretroviral therapy (HAART) from nine facilities in Hyderabad, Andhra Pradesh, India (N ¼ 211). Variable
Categories
Number (%)
Age categories
30 years or less 31–45 years 46 or more Male Female Never attended school Primary school Secondary school High school College Graduate or professional school Female sex worker MSM No high-risk sexual behavior Urban Rural GYD Diagnostics & Reference laboratories (P) Ltd. Mediciti Institute of Medical Sciences Catholic Health Association of India Chaithanya Mahila Mandali Darpan Foundation HIV of Positive People Efficiency Society King Koti Hospital Sivananda Rehabilitation Home Network of HIV Positive People Hinduism Christianity Islam Decline to answer Married Widowed Unmarried Separated Employed full-time Employed part-time Working in informal jobs
70 (33.18) 123 (58.29) 18 (8.53) 113 (53.81) 97 (46.19) 52 (24.76) 29 (13.81) 19 (9.05) 64 (30.48) 18 (8.57) 28 (13.33)
Gendera Education
b
Risk group
Place of residenceb Study site
Religionb
Marital status
Employment statusb
18 (8.53) 21 (9.95) 172 (81.52) 149 (70.95) 61 (29.05) 22 (10.4)
9 (4.3) 12 (5.7) 5 (2.4) 17 (8.1) 22 (10.4) 24 (11.4) 63 (29.9) 37 (17.5) 178 (84.36) 17 (8.06) 13 (6.16) 2 (0.95) 129 (61.14) 52 (24.64) 29 (13.74) 1 (0.47) 121 (57.62) 37 (17.62) 5 (2.38) (continued)
Table 1. Continued. Variable
Income by national quartiles (Urban)
Depression category
Cellphone ownership Adherence
Categories
Number (%)
Student Unemployed Less than 25th percentile 25th–50th percentile 50th–75th percentile More than 75th percentile Minimal Mild Moderate Moderately severe Severe Yes
5 (2.38) 42 (20.00) 41 (19.43)
Non-adherent
43 (23.38) 86 (36.02) 51 (24.17) 99 64 30 12
(46.92) (30.33) (14.22) (5.69)
6 (2.84) 174 (82.46) 42 (19.91)
a
1 transgender. 1 missing.
b
a 2.92 (95% confidence interval [CI]: 1.28–6.68) times higher prevalence of non-adherence compared to those aged 31–45 years. Younger age had an elevated prevalence ratio and approached statistical significance. FSWs had a 2.83 times (95% CI: 1.44–5.57) higher prevalence of non-adherence compared to those with no high-risk sexual behavior and those with moderate-to-severe depression had a 2.29 time (95% CI: 1.31–4.00) higher prevalence of non-adherence compared to those with minimal or mild depression. Among the facilitators to medication adherence, having lack of a strong sense of self-worth, low acceptance of their HIV diagnosis and having less of an understanding of the need to adhere to their HIV medication were associated with non-adherence (Table 2). Among non-adherent respondents, the most frequent reason for missing doses was that they forgot or were traveling away from home (Table 4). Among the other reasons that accounted for at least 10% total for often or sometimes responses were being busy doing other things, transportation problems and running out of pills. The most frequent reasons varied somewhat by group. For FSWs, they were travel (often for 3 [17%], forgot (often for 3 [17%]) and avoid side effects (often for 1 [5.6%]). For persons who were older age, they were busy (Sometimes for 3 [22%]), to avoid side effect (Often for 2 [11%]) and travel (Often for 2 [11%]). For those with moderate-to-severe depression (n ¼ 47), they were forgot (Often for 5 [10%] and
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Table 2. Mean scores for the facilitators to medication adherence among HIV-infected persons receiving highly active antiretroviral therapy (HAART) from nine facilities in Hyderabad, Andhra Pradesh, India (N ¼ 211). I have . . .a strong sense of self-worth . . .seen and felt positive effects from my HIV medication . . .accepted my HIV diagnosis . . .understood the need to adhere to my HIV medication . . .made use of reminder tools such as pill organisers for my medicine . . .an HIV medicine regimen that ‘fits’ into my daily schedule . . .once daily dosing of HIV medication . . .motivation and readiness to deal with my HIV . . .strong support from family and friends . . .gotten to know others like me who are living with HIV . . .regular health check-ups
Mean score (SD)
Non-adherent Mean (SD)
Adherent Mean (SD)
t Score, p value
3.43 (1.01) 3.22 (1.19)
3.00 (1.43) 2.90 (1.42)
3.54 (0.85) 3.31 (1.11)
2.33, 0.02 1.92, 0.06
3.63 (0.93) 3.46 (1.11)
3.24 (1.39) 2.66 (1.62)
3.73 (0.75) 3.66 (0.83)
2.19, 0.03 3.84,
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Int J STD AIDS OnlineFirst, published on March 22, 2015 as doi:10.1177/0956462415578545
Original research article
Adherence to highly active antiretroviral therapy in Hyderabad, India: barriers, facilitators and identification of target groups
International Journal of STD & AIDS 0(0) 1–10 ! The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0956462415578545 std.sagepub.com
Mark S Dworkin1, GW Douglas1, GP Sabitha Rani2 and Apurba Chakraborty1
Abstract We assessed the barriers and facilitators to highly active antiretroviral therapy adherence and determined their prevalence among HIV/AIDS patients in Hyderabad, India. We conducted a cross-sectional study among HIV-infected adults prescribed highly active antiretroviral therapy and receiving care from nine clinics. Depression was screened using Patient Health Questionnaire 9 and facilitators of HIV medication adherence were assessed using an 11-item scale which yielded a total positive attitude to disease score. Prevalence ratios of non-adherence between different categories of potential risk factors were calculated. We compared mean ‘facilitators to adherence’ scores between the adherent and nonadherent population. Multivariable Poisson regression with robust variance was used to identify independent risk factors. Among the 211 respondents, nearly 20% were non-adherent, approximately 8% had either moderately severe or severe depression and mean score for combined facilitators to medication adherence was 33.35 (7.88) out of a possible 44 points. Factors significantly associated with non-adherence included older age, female sex worker, moderate-to-severe depression and the combined facilitators to medication adherence score. These data from a broad range of clinical settings in Hyderabad reveal that key groups to focus on for adherence intervention are female sex workers, older persons and those with depression.
Keywords HIV, adherence, non-adherence, female sex workers, Hyderabad, India, medication, highly active antiretroviral therapy, barriers, facilitators Date received: 21 October 2014; accepted: 24 February 2015
Introduction Medication non-adherence has been associated with greater rates of hospital re-admission, increased cost of health care expenditures, progression of disease, lower quality-of-life, development of co-morbidities1–4 and death5 among HIV/AIDS patients. As a result, adherence to antiretrovirals has emerged as an attractive point of intervention toward promoting better health outcomes in persons infected with HIV.6–8 However, for many highly active antiretroviral therapy (HAART) regimens, high levels of adherence are needed to derive sustained clinical benefit.9 Additionally, poor levels of HAART adherence are associated with rapid development of drug resistance,10 requiring the need for expensive and sometimes lessconvenient second-line therapies.
An estimated 2.1 million people live with HIV/AIDS in India.11 HIV/AIDS is concentrated among high-risk groups with rates as high as 8.82% in transgenders, 7.14% in injection drug users (IDU), 4.43% in men who have sex with men (MSM) and 2.67% in female sex workers (FSWs). These prevalence statistics are in contrast to the antenatal clinic attendee prevalence 1 School of Public Health, University of Illinois at Chicago, Chicago, IL, USA 2 SHARE INDIA, Secunderabad, Andhra Pradesh, India
Corresponding author: Mark S Dworkin, Division of Epidemiology and Biostatistics, University of Illinois at Chicago, School of Public Health M/C 923, 1603W. Taylor Street, Chicago, IL 60612, USA. Email: [email protected]
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(0.40%), considered a proxy for the general population.12 In India, 43% of HIV/AIDS patients with a CD4 þ count 45 years). We calculated the prevalence ratio (95% confidence interval) of nonadherence between different categories of potential risk factors. We used category of the risk factor with lowest prevalence rate as the reference category. To identify statistically significant associations between categorical risk factors and medication adherence status of the respondents we performed Chi square test and Fischer’s exact tests. We categorised household income of the respondents based on the urban income quartiles for India.29 For descriptive analysis, we categorised PHQ-9 scores to five categories of depression (Minimal or no depression [0–4], mild depression [5–9], moderate depression [10–14], moderately severe depression [15–19] and severe depression [20–27]) following the PHQ-9 scoring and interpretation guide.22 During bivariate analysis we dichotomised depression categories into major depression (moderate/moderately severe/severe depression) and no major depression (minimal/mild depression) using the recommended cutoff value of PHQ-9 score of 10 or greater.22 We also dichotomised the employment status of the respondents as ‘Unemployed’ and ‘Student or employed part time or full time in formal or informal job.’ We performed the t test to compare mean ‘facilitators to adherence’ scores between the adherent and non-adherent population. Factors that were significantly associated with non-adherence at p < 0.25 in bivariate analysis were included in a multivariable log binomial regression model to identify the independent risk factors. In addition, demographic factors that were reported in published literature as associated with nonadherence were included into the initial model irrespective of whether those were statistically significant in bivariate analysis. As combined facilitators to adherence score was correlated with depression score, we did not include the ‘facilitators to adherence’ score in the multivariate model. Since the log binomial regression model failed to converge, we used SAS PROC GENMOD’s Poisson regression with the robust variance to estimate multivariate adjusted prevalence
ratios.30 While analysing the reasons for non-adherence we calculated frequencies by category of response for each of the reasons listed.
Ethics statement The study design and methods were approved by University of Illinois at Chicago Institutional Review Board (Protocol number: 2012-0505). Informed consent in the language of their choice (Telugu, Hindi or English) was obtained from all participants in the study. A research assistant read out the consent statement to the participants who could not read. Respondents consented either providing their signature or their thumb impression (if they could not sign) on the consent form.
Results A total of 211 respondents completed the interview, 22 (10.4%) from GYD Diagnostics & Reference laboratories (P) Ltd., 9 (4.3%) from MIMS, 12 (5.7%) from CHAI, 5 (2.4%) from CMM, 17 (8.1%) from Darpan Foundation, 22 (10.4%) from HOPES, 24 (11.4%) from King Koti Hospital, 63 (29.9%) from Sivananda Rehabilitation Home and 37 (17.5%) from the Network of HIV Positive People clinics. Among the 211 respondents, nearly 54% were men and the median age was 34 years (range 19 years to 62 years) (Table 1). The educational level was generally low with more than one-third having either never attended or not reached higher than primary school (Table 1). Approximately 80% had some form of employment including 58% employed full-time. Although more than half had some level of depression, approximately 8% had either moderately severe or severe depression. Of the 211 respondents, 42 (20%) were non-adherent to HAART. The mean score for combined facilitators to medication adherence was 33.35 (7.88) (Table 2). Low scores were observed for having once-daily dosing (only 14% had such a regimen) and using reminder tools. More than 80% of the patients owned a cell phone. Phone ownership was not associated with adherence. Out of 175 respondents who answered to the question regarding use of their phone’s alarm function, 35 (25%) of 141 adherents used the alarm function for any purpose compared to 6 (18%) of 34 non-adherents (p ¼ 0.38). In bivariate analysis, factors significantly associated with non-adherence included older age, being an FSW, moderate-to-severe depression and the combined facilitators to medication adherence score (Table 3). Adjusting for other covariates in the multivariable regression analysis, those aged 46 years or more had
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Table 1. Demographic characteristics of the HIV-infected persons receiving highly active antiretroviral therapy (HAART) from nine facilities in Hyderabad, Andhra Pradesh, India (N ¼ 211). Variable
Categories
Number (%)
Age categories
30 years or less 31–45 years 46 or more Male Female Never attended school Primary school Secondary school High school College Graduate or professional school Female sex worker MSM No high-risk sexual behavior Urban Rural GYD Diagnostics & Reference laboratories (P) Ltd. Mediciti Institute of Medical Sciences Catholic Health Association of India Chaithanya Mahila Mandali Darpan Foundation HIV of Positive People Efficiency Society King Koti Hospital Sivananda Rehabilitation Home Network of HIV Positive People Hinduism Christianity Islam Decline to answer Married Widowed Unmarried Separated Employed full-time Employed part-time Working in informal jobs
70 (33.18) 123 (58.29) 18 (8.53) 113 (53.81) 97 (46.19) 52 (24.76) 29 (13.81) 19 (9.05) 64 (30.48) 18 (8.57) 28 (13.33)
Gendera Education
b
Risk group
Place of residenceb Study site
Religionb
Marital status
Employment statusb
18 (8.53) 21 (9.95) 172 (81.52) 149 (70.95) 61 (29.05) 22 (10.4)
9 (4.3) 12 (5.7) 5 (2.4) 17 (8.1) 22 (10.4) 24 (11.4) 63 (29.9) 37 (17.5) 178 (84.36) 17 (8.06) 13 (6.16) 2 (0.95) 129 (61.14) 52 (24.64) 29 (13.74) 1 (0.47) 121 (57.62) 37 (17.62) 5 (2.38) (continued)
Table 1. Continued. Variable
Income by national quartiles (Urban)
Depression category
Cellphone ownership Adherence
Categories
Number (%)
Student Unemployed Less than 25th percentile 25th–50th percentile 50th–75th percentile More than 75th percentile Minimal Mild Moderate Moderately severe Severe Yes
5 (2.38) 42 (20.00) 41 (19.43)
Non-adherent
43 (23.38) 86 (36.02) 51 (24.17) 99 64 30 12
(46.92) (30.33) (14.22) (5.69)
6 (2.84) 174 (82.46) 42 (19.91)
a
1 transgender. 1 missing.
b
a 2.92 (95% confidence interval [CI]: 1.28–6.68) times higher prevalence of non-adherence compared to those aged 31–45 years. Younger age had an elevated prevalence ratio and approached statistical significance. FSWs had a 2.83 times (95% CI: 1.44–5.57) higher prevalence of non-adherence compared to those with no high-risk sexual behavior and those with moderate-to-severe depression had a 2.29 time (95% CI: 1.31–4.00) higher prevalence of non-adherence compared to those with minimal or mild depression. Among the facilitators to medication adherence, having lack of a strong sense of self-worth, low acceptance of their HIV diagnosis and having less of an understanding of the need to adhere to their HIV medication were associated with non-adherence (Table 2). Among non-adherent respondents, the most frequent reason for missing doses was that they forgot or were traveling away from home (Table 4). Among the other reasons that accounted for at least 10% total for often or sometimes responses were being busy doing other things, transportation problems and running out of pills. The most frequent reasons varied somewhat by group. For FSWs, they were travel (often for 3 [17%], forgot (often for 3 [17%]) and avoid side effects (often for 1 [5.6%]). For persons who were older age, they were busy (Sometimes for 3 [22%]), to avoid side effect (Often for 2 [11%]) and travel (Often for 2 [11%]). For those with moderate-to-severe depression (n ¼ 47), they were forgot (Often for 5 [10%] and
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Table 2. Mean scores for the facilitators to medication adherence among HIV-infected persons receiving highly active antiretroviral therapy (HAART) from nine facilities in Hyderabad, Andhra Pradesh, India (N ¼ 211). I have . . .a strong sense of self-worth . . .seen and felt positive effects from my HIV medication . . .accepted my HIV diagnosis . . .understood the need to adhere to my HIV medication . . .made use of reminder tools such as pill organisers for my medicine . . .an HIV medicine regimen that ‘fits’ into my daily schedule . . .once daily dosing of HIV medication . . .motivation and readiness to deal with my HIV . . .strong support from family and friends . . .gotten to know others like me who are living with HIV . . .regular health check-ups
Mean score (SD)
Non-adherent Mean (SD)
Adherent Mean (SD)
t Score, p value
3.43 (1.01) 3.22 (1.19)
3.00 (1.43) 2.90 (1.42)
3.54 (0.85) 3.31 (1.11)
2.33, 0.02 1.92, 0.06
3.63 (0.93) 3.46 (1.11)
3.24 (1.39) 2.66 (1.62)
3.73 (0.75) 3.66 (0.83)
2.19, 0.03 3.84,
