Use of and Adherence to Antiretroviral Therapy in a Large U.S. Sample of HIV-infected Adults in Care, 2007-2008



Antiretroviral therapy (ART) is the cornerstone of HIV clinical care and is increasingly recognized as a key component of HIV prevention. However, the benefits of ART can be realized only if HIV-infected persons maintain high levels of adherence.


We present interview data (collected from June 2007 through September 2008) from a national HIV surveillance system in the United States—the Medical Monitoring Project (MMP)—to describe persons taking ART. We used multivariate logistic regression to assess behavioral, sociodemographic, and medication regimen factors associated with three measures that capture different dimensions of nonadherence to ART: dose, schedule, and instruction.


The use of ART among HIV-infected adults in care was high (85%), but adherence to ART was suboptimal and varied across the three measures of nonadherence. Of MMP participants currently taking ART, the following reported nonadherence during the past 48 hours: 13% to dose, 27% to schedule, and 30% to instruction. The determinants of the three measures also varied, although younger age and binge drinking were associated with all aspects of nonadherence.


Our results support the measurement of multiple dimensions of medication-taking behavior in order to avoid overestimating adherence to ART.

Keywords:: HIV, medication adherence, antiretroviral therapy, Centers for Disease Control and Prevention (U.S.)..


A high level of adherence to antiretroviral therapy (ART) is necessary to maintain viral suppression and achieve optimal clinical outcomes for HIV-infected persons [1, 2], though there is little consensus on the minimum threshold of adherence needed for virologic suppression [3, 4]. Medication adherence often accompanies other healthy behaviors such as diet and health care utilization [5]. ART may also improve population health, reduce the number of premature deaths, and lower health care costs, as studies indicate that access and adherence to ART may play an important role in reducing HIV transmission by suppressing HIV viral load (VL) [6, 7]. Successful ART requires clinicians to assess the risks of nonadherence before deciding on a regimen and to accurately monitor and support adherence throughout therapy [8, 9].

Clinical outcomes have been better predicted by measuring multiple dimensions of medication-taking behaviors than by measuring missed doses [10]. Gill and colleagues found the timing of doses to be important for VL suppression [11]. Despite these findings, many studies of ART adherence have been focused on missed doses, not on adherence to medication schedules and special instructions such as dietary restrictions. Failure to inquire about adherence to schedule and instruction may lead to inaccurate estimates of a patient’s level of adherence and may thus result in missed opportunities to educate or failure to identify nonadherence as a cause of treatment failure. The few studies that have compared different dimensions of nonadherence have found that their predictors vary [12, 13]. Because multiple measures of nonadherence have proved useful in predicting clinical outcomes and our knowledge of their predictors (and how they may vary across measures) is limited, examination of multiple measures is warranted. Better knowledge of these factors may inform the development of interventions to improve all aspects of adherence.

The primary objective of our analysis was to describe the use of ART and to assess the various measures of ART nonadherence in a national sample of adult HIV patients. A secondary objective was to explore the factors associated with the various dimensions of medication-taking behavior, which have been incompletely described to date.


We performed a cross-sectional analysis of data from the Medical Monitoring Project (MMP), a supplemental surveillance system for collecting clinical and behavioral data on HIV-infected adults receiving care. The methods have been described in detail elsewhere [14, 15]. MMP uses a three-stage sampling design to obtain annual cross-sectional probability samples of HIV-infected adults in care. In the first stage, states are selected to participate, then HIV care facilities in these states are sampled, and finally HIV-infected adults in care at participating facilities are sampled. Face-to-face interviews are conducted to collect information on demographics, adherence to HIV medication regimens, and behavioral risk factors. The data were collected in 19 states and Puerto Rico from June 2007 through September 2008.1 The Centers for Disease Control and Prevention has determined that MMP is not human subjects research because it is a routine disease surveillance activity. However, participating project areas obtain local Institutional Review Board (IRB) approval to conduct MMP as required locally. We asked participants about their medication-taking behaviors during the 48 hours before interview. Dose nonadherence refers to failure to take a prescribed dose or set of pills/spoonfuls/injections of antiretroviral (ARV) medications. Schedule nonadherence refers to failure to follow the prescribed schedule for ARV medications, such as “2 times a day” or “every 8 hours.” Instruction nonadherence refers to failure to follow special instructions accompanying ARV medication prescriptions, such as “take with food” or “take on an empty stomach.” Instruction nonadherence was asked of only the participants who reported having received special instructions for their ARV medications. Although self-reported measures have generally been found to indicate lower nonadherence than other measures (such as pill counts, Medication Event Monitoring System [MEMS] devices, or pharmacy refill records), no standard exists for measuring nonadherence [16] and self-reported measures have been correlated with clinical outcomes such as VL [17]. We defined nonadherence as failure to take ARV medication exactly as prescribed (i.e., 100% of doses taken for dose adherence, 100% of doses on the prescribed schedule for schedule adherence, and 100% instructions followed for instruction adherence) given that 100% adherence is a common threshold for self-reported measures [18].

After determining the frequencies of selected characteristics to describe participants currently taking ART and those not taking ART, we tested for differences in these characteristics by using chi-square tests. To assess whether our measures of nonadherence are related to clinical outcomes, we then tested for bivariate associations between each of the three measures of nonadherence and self-reported undetectable VL, using the Mantel-Haenszel chi-square test and binomial logistic regression. In addition, we used these methods to examine the relationship between the total number of reported nonadherence measures (measured as 0 = 100% adherent on all three measures, 1 = nonadherent on one measure, 2 = nonadherent on two measures, 3 = nonadherent on all three measures) and self-reported undetectable VL.

Finally, to explore differences in the factors associated with the measures of nonadherence, we tested for bivariate relationships between dose, schedule, and instruction nonadherence and the factors associated with nonadherence in the scientific literature. Factors associated with each measure of nonadherence (significant at the p < 0.10 level) were included in three multivariate regression models. Final models were determined by using backward stepwise regression with a p < 0.05 retention criterion. Model fit was assessed by using the Hosmer and Lemeshow goodness-of-fit test.

All independent variables refer to the past 12 months except where otherwise noted. The following independent variables were examined for each of the three models:

  • Sociodemographic: gender, age at interview, race/ethnicity, educational attainment (<high school, >high school or equivalent), receipt of public assistance, homelessness (defined as living on the street, in a shelter, a single-room-occupancy hotel, temporarily staying with friends/family, or living in a car), lack of continuous health insurance coverage
  • Alcohol and drug use: crack use, amphetamine use, binge drinking in the past 30 days (>4 drinks per day for men, >3 drinks per day for women)
  • Mental health: feeling downhearted and depressed in past 4 weeks (5-point Likert scale ranging from 1 = “none of the time” to 5 = “all of the time”)
  • HIV-related: not knowing most recent VL test result, number of daily ARV doses (defined as a set of pills/spoonfuls/injections of antiretroviral (ARV) medications), years since HIV diagnosis, having a discussion with a provider about ARV drug resistance


All sampled states and territories agreed to participate in MMP. During January-April 2007, a sample of 10,503 patients was selected from 582 participating HIV care facilities in the 26 participating project areas. The overall median facility participation rate was 91.4% (range across project areas, 65% to 100%). Of the sample, 311 were ineligible, resulting in a total of 10,192 patients. Reasons for ineligibility included: 29 duplicates, 18 less than 18 years of age, 63 HIV-negative, 114 no care during the sample selection period, and 87 ineligible for other reasons. The median patient participation rate was 40% (range across project areas, 3% to 76%). A total of 3,944 standard interviews were conducted from June 2007 through September 2008. For this analysis, the data of 43 participants were excluded because interviewers were not confident of the validity of the responses (for example, cases where the participant was incoherent or unresponsive to interview questions).

Antiretroviral Therapy Use

Of 3886 participants who provided information about ART, 3307 (85%) were currently taking ART. Roughly equal proportions had never taken ART (8%) or had previously taken ART (7%). Compared to those not currently taking ART, those who were currently taking ART were significantly more likely to be male, older, non-Hispanic white (hereafter referred to as white) or Hispanic, HIV-diagnosed for a longer period of time, and not homeless; to have continuous healthcare coverage for the past year and a lower self-reported nadir CD4+ T-lymphocyte (CD4) count; to report a lower VL and feeling downhearted and depressed (hereafter referred to as depressed) less often; to know the result of their most recent VL test; and not to receive public assistance, not to use crack or amphetamines, and not to binge drink (Table 1).

Table 1.

Factors associated with current use of ART–United States, Medical Monitoring Project, 2007-2008

Taking ART (n = 3307)
Not Currently Taking ART (n = 579) Chi-Square Test for Differences
(p Value)

Characteristic No.    % of Total No.     % of Total


   2431      74

   373      64

Female    830       25    197      34 (<.0001)
Transgender    45        1     8       1

Age (years)

   45        1

   33       6

25-34    280        8    101      17 (<.0001)
35-44    1039       31    214      37
45-54    1311       40    180      31
≤55    632       19    51       9

Black, non-Hispanic

   1254      38

   288      50

Hispanic    663       20    95      16 (<.0001)
White, non-Hispanic    1173      35    156      27
Other    212       6    39       7

< High School

    729      22

   149      26

≥ High School or equivalent    2576      78    430      74 (0.05)

Public assistance, past 12 months

   1639      50

   322      56

Yes    1667      50    256      44 (0.01)

Lapse in health coverage, past 12 months

   2491      75

   379      65

Yes    808       24    197      34 (<.0001)

Homeless, past 12 months

   3080      93

   500      86

Yes    227       7    79      14 (<.0001)

Years since HIV diagnosis
≤ 1

   18        1

   5       1

2-5    616       19    189      33 (<.0001)
6-10    764       23    127      22
11-25    1711      52    226      39

Knew most recent HIV viral load test result

   639       19

   162      28

Yes    2587      78    383      66 (<.0001)

Most recent HIV viral load test result

   2056      62

   110      19

Detectable but <5000    307       9    93      16 (<.0001)
≥5000-<100000    166       5    142      25
≥100000     58        2    38       7
Missing     720      22       196      34

CD4 count (self-reported nadir)

   1511      46

   90      16

200-349    642       19      128      22 (<.0001)
350-499    261       8      112      19
500+    140       4      102      18
Missing    753       23      147      25

Feeling downhearted and depressed, past 4 weeks
All of the time

   140       4

   39      7

Most of the time    315      10       77      13 (0.00)
Some of the time    992      30      155      27
A little of the time    799      24      153      26
None of the time    1054     32      154      27

Crack use, past 12 months

   3128     95

   521      90

Yes    167      5       56      10 (<.0001)

Amphetamine use, past 12 months

   3142     95

   537      93

Yes    152      5       40       7 (0.02)

Binge drinking, past 30 days

   2864      87

   459      79

Yes     426     13       116      20 (<.0001)

Note: ART, antiretroviral therapy; values may not sum to total due to missing data; missing values shown when >10% of total.

Table 2.

Nonadherence to ART During the Past 48 Hours–United States, Medical Monitoring Project, 2007-2008

Currently Taking ART Dosea Scheduleb Instructionc
Nonadherence Nonadherence Nonadherence
n = 3307 n = 444
Chi-Square Test for Differences (p Value)
n = 893
Chi-Square Test for Differences (p Value)
n = 638
Chi-Square Test for Differences (p Value)

Characteristic  No.     %   No.     %   No.     %   No.     %

    Male  2431     74  293     66    15.6  2591    66    34.68  462    72    1.60
   Female  830     25  145     33    (0.00)  289   32   (<.0001)  167   26   (0.45)
    Transgender  45      1  6       1  13    1  9    1

 Age (years)
    18-24  45      1  18      4     11.33  21    2    12.48  17    3    25.85
    25-34  280      8  43      10    (0.00)  83    9    (0.00)  68    11    (<.0001)
    35-44  1039     31  135     30  300    34  226    35
    45-54  1311     40  182     41  340    38  245    38
    ≤55  632     19  66     15  149    17  82    13

    Black, non-Hispanic  1254     38  208    47    33.14  400    45    64.78  243    38    0.86
    Hispanic  663     20  102    23    (<.0001)  219    25    (<.0001)  146    23    (0.83)
    White, non-Hispanic  1173    36  105    24  230    26  207    32
    Other  212     6  29     7  44    5  42    7

    < High School  729     22  111    25    2.68  250    28    25.41  149    23    0.88
    ≥ High School or equivalent  2576     78  332    75    (0.10)  643    72    (<.0001)  488    77    (0.35)

 Public assistance, past 12 months
    No  1639    50  184    41    13.58  380    43    23.85  323    51    0.03
    Yes  1667    50  260    59    (0.00)  513    57    (<.0001)  315    49    (0.86)

 Lapse in health coverage, past 12 months
    No  2491    76  327    74    0.64  671    75    0.04  453    71    0.94
    Yes  808     24  115    26    (0.42)  220    25    (0.84)  185    29    (0.33)

 Homeless, past 12 months
    No  3080    93  398    90    9.81  808    90    13.63  581    91    3.47
    Yes  227     7  46    10    (0.00)  85    10    (0.00)  57    9    (0.06)

 Years since HIV diagnosis
    ≤ 1  18      1  <5     <1    6.29  <5     <1    4.00  <5     1    1.03
    2-5  616     20  69    17    (0.01)  140    17    (0.05)  131    22    (0.31)
    6-10  764     25  93    22  219    26  147    25
    11-25  1711     55  254    61  467    56  315    53

 Knew most recent HIV viral load test result
    No  639     20  108    25    8.14  238    27    44.22  104    17    2.25
    Yes  2587     80  326    75    (0.00)  629    73    (<.0001)  513    83    (0.13)

 Most recent HIV viral load test result
    Undetectable  2056    62  218    49    44.11  438    49    49.47  384    60    4.48
    Detectable but <5000  307     9  54    12    (<.0001)  104    12    (<.0001)  73    11    (0.03)
     ≥5000-<100000  166     5  36    8  61    7  41    6
     ≥100000  58      2  18    4  26    3  15    2
    Missing  720     22  118    27  264    30  125    20

  Feeling downhearted and depressed, past 4 weeks
    All of the time  140     4  36    8    29.39  39    4    12.11  32    5    14.15
    Most of the time  315    10  56    13    (<.0001)  104    12    (0.00)  67    11    (0.00)
    Some of the time  992    30  143    32  287    32  212    33
    A little of the time  799    24  97    22  215    24  165    26
     None of the time   1054   32   110     25   245     28   158     25

 Crack use, past 12 months
    No  3128    95  403    91    18.36  829    93    8.00  597    94    4.69
    Yes  167    5  41    9    (<.0001)  61    7    (0.00)  39    6    (0.03)

  Amphetamine use, past 12 months
    No  3142    95  418    94    1.72  842    95    0.74  601    95    3.30
   Yes   152    5   26     6     (0.19)   46     5     (0.39)   34     5     (0.07)

 Binge drinking, past 30 days
    No  2864    87  359    81    16.42  745    84    11.41  519    82    29.95
    Yes  426     13  84    19    (<.0001)  144    16    (0.00)  115    18    (<.0001)

  Discussed resistance with provider, past 12 months
    No  1174    36  128    29    10.12  310    35    0.24  197    31    0.33
    Yes   2079    64   309     71     (0.00)   567     65     (0.62)   431     69     (0.56)

Note: ART, antiretroviral therapy; OR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval; Ref, reference category; NS, not significant.

Dose nonadherence refers to not taking a dose or set of pills/spoonfuls/injections of ARV medications.

Schedule nonadherence refers to not following a specific schedule for ARV medication, such as “2 times a day” or “every 8 hours.”

Instruction nonadherence refers to not following special instructions for ARV medication, such as “take with food” or “take on an empty stomach.” Dietary instruction adherence is only calculated for those participants reporting having special instructions for their ARV medications.

Table 3.

Logistic Regression Models of Factors Associated with Nonadherence to ART During the Past 48 Hours–United States, Medical Monitoring Project, 2007-2008

Dosea Nonadherence
(N = 3307)
Scheduleb Nonadherence
(N = 3298)
Instructionc Nonadherence
(N = 2101)

Characteristic OR
(95% CI) aOR (95% CI)
OR (95% CI) aOR (95% CI) OR (95% CI) aOR (95% CI)

Gender   NS
 Male Ref           Ref Ref           Ref
 Female 1.55 (1.24-1.92)   1.36 (1.07-1.74) 1.66 (1.40-1.97)   1.46 (1.20-1.76)
 Transgender 1.12 (0.47-2.68)   0.62 (0.21-1.80) 1.26 (0.66-2.42)   0.99 (0.49-2.01)

Age (years) 0.98 (0.97-0.99)   0.98 (0.96-0.99) 0.99 (0.98-0.99)   0.98 (0.98-0.99) 0.97 (0.96-0.98)   0.98 (0.97-0.99)

Race/ethnicity   NS
 Black, non-Hispanic 2.02 (1.58-2.60)  1.96 (1.48-2.59) 1.93 (1.60-2.32)  1.56 (1.27-1.93)
 Hispanic 1.85 (1.38-2.47)   1.63 (1.18-2.24) 2.03 (1.63-2.52)   1.59 (1.25-2.02)
 White, non-Hispanic Ref          Ref Ref           Ref
 Other 1.61 (1.04-2.50)   1.55 (0.96-2.51) 1.07 (0.75-1.54)  0.97 (0.66-1.41)

Education   NS   NS
 < high school Ref           Ref
 ≥ high school or equivalent 0.64 (0.53-0.76)   0.78 (0.64-0.95)

Homeless, past 12 months
 No Ref           Ref Ref           Ref Ref           Ref
 Yes 1.71 (1.22-2.41)   - 1.69 (1.28-2.24)   1.43 (1.05-1.95) 1.38 (0.98-1.94)   -

Public assistance, past 12 months   NS
 No Ref           Ref Ref           Ref
 Yes 1.46 (1.19-1.79)   1.31 (1.05-1.65) 1.47 (1.26-1.72)   1.35 (1.14-1.60)

Feeling downhearted and depressed, past 4 weeks
 All of the time 2.97 (1.94-4.56)   2.63 (1.64-4.22) 1.27 (0.86-1.89)   - 1.81 (1.13-2.89)   1.61 (0.98-2.64)
 Most of the time 1.86 (1.31-2.63)   1.81 (1.23-2.66) 1.63 (1.24-2.14)   - 1.63 (1.15-2.30)   1.54 (1.07-2.22)
 Some of the time 1.45 (1.11-1.89)   1.37 (1.02-1.83) 1.35 (1.11-1.65)   - 1.56 (1.22-1.99)   1.51 (1.17-1.95)
 A little of the time 1.19 (0.89-1.59)   1.20 (0.88-1.64) 1.22 (0.99-1.51)   - 1.50 (1.16-1.94)   1.51 (1.15-1.98)
 None of the time Ref           Ref Ref           Ref Ref           Ref

Crack use, past 12 months
 No Ref           Ref Ref           Ref Ref           Ref
 Yes 2.19 (1.52-3.17)  1.66 (1.09-2.52) 1.59 (1.15-2.20)   - 1.58 (1.04-2.40)   -

Amphetamine use, past 12 months   NS   NS
 No Ref           Ref
 Yes 1.50 (0.97-2.33)   -

Binge drinking, past 30 days
 No Ref           Ref Ref           Ref Ref           Ref
 Yes 1.71 (1.32-2.23)   1.63 (1.21-2.20) 1.45 (1.17-1.81)   1.55 (1.23-1.96) 2.08 (1.60-2.72)   1.88 (1.41-2.49)

Years since HIV diagnosis 1.02 (1.00-1.03)   1.02 (1.00-1.04) NS 0.98 (0.97-1.00)   -

Number of ARV doses per day 1.16 (1.10-1.23)   1.18 (1.10-1.25) 1.13 (1.08-1.19)   1.16 (1.11-1.22) NS

Knew most recent HIV viral load test result   NS
 No 1.41 (1.11-1.79)  - 1.86 (1.55-2.23)  1.48 (1.21-1.82)
 Yes Ref           Ref Ref           Ref

Discussed resistance with provider, past 12 months   NS   NS
 No Ref           Ref
 Yes 1.43 (1.15-1.78)   -

Note: ART, antiretroviral therapy; OR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval; Ref, reference category; NS, not significant.

Dose nonadherence refers to not taking a dose or set of pills/spoonfuls/injections of ARV medications.

Schedule nonadherence refers to not following a specific schedule for ARV medication, such as “2 times a day” or “every 8 hours.”

Instruction nonadherence refers to not following special instructions for ARV medication, such as “take with food” or “take on an empty stomach.” Dietary instruction adherence is only calculated for those participants reporting having special instructions for their ARV medications.

Nonadherence to Antiretroviral Therapy

Table 2 presents descriptive statistics for participants who reported nonadherence during the past 48 hours. Of MMP participants currently taking ART, 13% reported nonadherence to dose, 27% to schedule, and 30% to instruction. Considering all measures, nonadherence was reported by 38% of participants: 22% reported nonadherence to one measure, 11% to two measures, and 5% to all three measures.

Association between Self-reported Detectable Viral Load and Nonadherence

Participants were asked about the results of their most recent VL test (see Table 2). Tests of association revealed statistically significant positive relationships between nonadherence and detectable VL test result (p < 0.0001 for dose and schedule nonadherence and p = 0.03 for instruction nonadherence). In addition, there was a significant and positive association between the number of nonadherence measures reported and detectable VL (p < 0.0001). Logistic regression modeling results indicated that the odds of a detectable VL more than doubled for those reporting dose nonadherence (odds ratio [OR], 2.2; 95% confidence interval [CI], 1.7-2.8). Similar analyses yielded an OR of 2.1 for schedule nonadherence (CI, 1.7-2.6) and 1.3 for instruction nonadherence (CI, 1.0-1.7). In addition, nonadherence to multiple measures was associated with increased odds of a detectable VL. In a comparison of participants who reported nonadherence and participants who did not, the odds of detectable VL was 1.3 times as high (CI, 1.0-1.7) for those who reported nonadherence to one measure, 2.1 times as high (CI, 1.6-2.8) for those who reported nonadherence to two measures, and 2.9 times as high (CI, 2.0-4.1) for those who reported nonadherence to all three measures.

Factors Associated with Each Measure of Nonadherence

The factors independently associated with dose nonadherence were female gender; per year decrease in age; non-Hispanic black (hereafter referred to as black) and &#xB;

Hispanic race/ethnicity; receipt of public assistance; feeling depressed all, most, or some of the time; crack use; binge drinking; higher number of years since HIV diagnosis; and higher number of daily ARV doses (Table 3). The factors independently associated with schedule nonadherence were female gender, per year decrease in age, black and Hispanic race/ethnicity, less than high school education, homelessness, receipt of public assistance, binge drinking, higher number of daily ARV doses, and not knowing one’s most recent VL test result. The factors independently associated with instruction nonadherence were per year decrease in age; feeling depressed most, some, or a little of the time; and binge drinking.


Although studies on nonadherence to ART are common, one strength of this analysis is the use of probability sampling methods, which resulted in coverage of patients from 582 medical facilities in 19 U.S. states and Puerto Rico. The use of ART was high among adults in care for HIV. The persons not receiving ART were more likely to be female, to be black, and to report a lapse in health coverage, substance use, higher VL results, higher CD4 count as the nadir, and more frequent feelings of depression. Our findings agree with those in other reports [19-25]. Understanding factors associated with use of ART may inform efforts to increase appropriate ART among HIV-infected persons.

Nonadherence to ART was substantial. Reported nonadherence during the past 48 hours ranged from 13% to 30%, depending on which measure was examined; 38% were nonadherent to at least one measure. We found a direct relationship between a detectable VL and each of the three measures of nonadherence. The association increased with the number of measures to which a patient was nonadherent, indicating the clinical importance of assessing multiple dimensions of nonadherence. Our findings are consistent with those reported previously [10, 11], but there have been few studies comparing dose, schedule, and instruction nonadherence, so further examination of the impact of different measures of nonadherence on clinical outcomes is warranted.

Although predictors of the three measures of nonadherence varied, younger age and binge drinking were consistently associated with poorer adherence. Like other researchers [26], we found associations between younger age and multiple measures of nonadherence. Lifestyle differences, decreased awareness of mortality and thus motivation to care for one’s health, and a survivor effect have been proposed as reasons for poorer adherence among younger HIV-infected persons [27]. Measured in various ways, alcohol use has consistently been associated with nonadherence [28, 29]. We found that binge drinking was strongly associated with all three measures of nonadherence. Others have found that the quantity of alcohol ingested was more important in predicting nonadherence than was the frequency of use [29, 30]. Taken together, these findings suggest that HIV care providers should ask patients about both the frequency and the quantity of alcohol consumed. When a patient acknowledges binge drinking, the provider should explain how binge drinking affects ART adherence and other health behaviors. Such conversations could help HIV care providers identify patients who need more intensive counseling about adherence and substance use. Few effective interventions have addressed problem drinking among HIV-infected persons [31], and the effects of the small number of trials on adherence among problem drinkers have not been sustained [32, 33]. The limited evidence suggests that adapting adherence interventions to persons with alcohol problems may be challenging [31]. Our results underscore the need to develop effective adherence interventions for problem drinkers, which potentially could have positive effects on all aspects of adherence.

Black and Hispanic race/ethnicity, female gender, receipt of public assistance, feeling depressed, and number of daily ARV doses were also independently associated with poorer adherence for at least two of the three measures of nonadherence. Several recent studies have found that race/ethnicity is independently associated with nonadherence [17, 34, 35]. Race/ethnicity may also be a proxy for factors associated with nonadherence, such as differences in trust of physicians [36] or HIV conspiracy beliefs (e.g., the belief that HIV was created by the U.S. government to perpetrate genocide against blacks) [37]. Recent studies indicate that disparities in health literacy and numeracy skills, which we were not able to measure, may also be important mediators of the relationship between race and medication management [38, 39].

Similar to findings regarding adherence to treatment for chronic diseases such as diabetes [40], published descriptions of the effect of gender on ART adherence in the scientific literature have been mixed; some studies find women more likely to be nonadherent [41, 42], and others find no difference by gender [43]. Our finding suggests gender may influence some, but not all, dimensions of medication-taking behavior (dose and schedule nonadherence, but not instruction nonadherence). Another possible explanation for the observed gender differences is that other factors affecting nonadherence, such as alcohol or drug use, may differ for men and women [44]. Additional factors we were not able to measure, such as numeracy skills [45], may also mediate the relationship between gender and nonadherence.

Our finding that the frequency of feelings of depression was associated with dose and instruction nonadherence to ART is consistent with other studies [46, 47]. This association reinforces the importance of active screening for depression among HIV-infected persons [48]. Studies have suggested that training in stress management [49] and prescription of antidepressant medications [50] may improve adherence in this population.

Our finding that nonadherence increases as the daily ARV dose increases agrees with published results [51, 52]. Airoldi and colleagues found that switching to a single-pill regimen improved adherence [53]. Although some ART regimens are less susceptible to nonadherence than others [9], our findings suggest that simplifying ART regimens by decreasing dosing frequency would improve adherence.


Although MMP uses population-based sampling methods, due to low patient response rates across data collection sites MMP data are not weighted and thus are not representative of HIV-infected patients in care in the United States. However, probability sampling is advantageous even with low response rates because of the value of unbiased sampling from well-defined populations [54]. States with the lowest patient response rates were not substantially different by region or HIV epidemiology from those with higher response rates. Examination of the patient sampling frame indicates that respondents and nonrespondents did not differ by gender. Participants were slightly older than nonparticipants, and African Americans were somewhat underrepresented. Because we found that younger age and black race are associated with higher nonadherence, as have other studies [17, 26, 34, 35], these relationships may in fact be stronger than those found for our sample. Despite low patient response, the response rates at the project area and facility stages of sampling were high, and the size and breadth of our facility and patient samples are notable.

Because we studied self-reported measures, our findings are subject to the limitations of self-reported information, such as recall and social desirability biases. Because self-reported measures of adherence have underestimated the extent of nonadherence [16, 18], our estimates of nonadherence may in fact be lower than actual levels of nonadherence. In addition, we were not able to verify through medical record review self-reported clinical information such as receipt of ART. However, Wood and colleagues found relatively high correspondence between self-reported and medical record documentation of current use of ART [55], leading us to have confidence in our results. Also, our analysis was restricted to self-reported VL, and the number of missing responses was high because of difficulties with recall. However, for undetectable VL results, the reported agreement between self-report and the medical record has been relatively high [56, 57]. Also, as our data showed a positive association between dose and schedule nonadherence and not knowing one’s most recent VL, it is unlikely that missing data would account for the relationship found between higher VL and the number of nonadherence measures reported.

Our analysis of use of ART is limited due to lack of comprehensive clinical information that would allow us to determine need for therapy among those not on ART. Use of self-reported CD4 values to indicate need for ART is insufficient because other clinical factors are also relevant to treatment decisions (e.g. Hepatitis B coinfection, HIV-associated nephropathy). In addition, because recent treatment guidelines [8] suggest that ART should be an option for all patients regardless of CD4, we felt it was important to describe the difference between those on and not on ART for the entire sample.

Our logistic regression models regarding nonadherence to instruction included only the participants who had received special instructions. However, limiting our analysis of the dose and schedule nonadherence models to those who received special instructions would have introduced bias by restricting the population to those whose regimens require special instructions. To ensure that our findings were not due to differences in the participants included in the models, we ran the dose and schedule models, restricting them to participants who have received special instructions: we did not find substantive differences compared with results that included all respondents.


Recent guidelines recommend initiation early in the course of infection (CD4 cell count ≤500 cells/µL), but caution that patients should be willing to commit to lifelong treatment and should understand the importance of adherence [8]. In this large sample of HIV-infected patients in the United States, the use of ART was high, but adherence to ART was suboptimal. The determinants of nonadherence to dose, schedule, and instruction varied, although younger age and binge drinking were associated with all three measures. Our results support the need to measure multiple dimensions of medication-taking behavior, because focusing on nonadherence to dose may overestimate adherence to ART. Comprehensive assessment of ART nonadherence should include measures of dose, schedule, and instruction, because accurate information is crucial for evaluating both the effectiveness of ART regimens and interventions designed to increase adherence.


The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.


The authors have no institutional or commercial affiliations that might pose a conflict of interest regarding the publication of this manuscript.


The 26 participating project areas: California, Chicago (Illinois), Delaware, Florida, Georgia, Houston (Texas), Illinois, Indiana, Los Angeles County (California), Maryland, Massachusetts, Michigan, Mississippi, New Jersey, New York, New York City (New York), North Carolina, Oregon, Pennsylvania, Philadelphia (Pennsylvania), Puerto Rico, San Francisco (California), South Carolina, Texas, Virginia, and Washington.


We thank participating MMP facilities and patients. We also thank MMP staff in the 26 health jurisdictions and the Clinical Outcomes Team at CDC.


Conway B. The role of adherence to antiretroviral therapy in the management of HIV infection J Acquir Immune Defic Syndr 2007; 45(Suppl 1): S14-8.
Gulick RM. Antiretroviral treatment 2010: progress and controversies J Acquir Immune Defic Syndr 2010; 55(Suppl 1): S43-8.
Bangsberg DR. Less than 95% adherence to nonnucleoside reverse-transcriptase inhibitor therapy can lead to viral suppression Clin Infect Dis 2006; 43(7): 939-41.
Martin M, Del Cacho E, Codina C, et al. Relationship between adherence level, type of the antiretroviral regimen, and plasma HIV type 1 RNA viral load: a prospective cohort study AIDS Res Hum Retroviruses 2008; 24(10): 1263-8.
Simpson SH, Eurich DT, Majumdar SR, et al. A meta-analysis of the association between adherence to drug therapy and mortality BMJ 2006; 333(7557): 15.
Granich R, Crowley S, Vitoria M, et al. Highly active antiretroviral treatment for the prevention of HIV transmission J Int AIDS Soc 2010; 13: 1.
Graham SX, Masese L, Gitau R, et al. Antiretroviral adherence and development of drug resistance are the strongest predictors of genital HIV-1 shedding among women initiating treatment J Infect Dis 2010; 202: 1538-42.
Panel on Antiretroviral Guidelines for Adults and Adolescents. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents Department of Health and Human Services 2009; 1-161.
Pham PA. Antiretroviral adherence and pharmacokinetics: review of their roles in sustained virologic suppression AIDS Patient Care STDS 2009; 23(10): 803-7.
Reynolds NR, Sun J, Nagaraja HN, Gifford AL, Wu AW, Chesney MA. Optimizing measurement of self-reported adherence with the ACTG Adherence Questionnaire: a cross-protocol analysis J Acquir Immune Defic Syndr 2007; 46(4): 402-9.
Gill CJ, Sabin LL, Hamer DH, et al. Importance of dose timing to achieving undetectable viral loads AIDS Behav 2010; 14(4): 785-93.
Nilsson Schonnesson L, Williams ML, Ross MW, Bratt G, Keel B. Factors associated with suboptimal antiretroviral therapy adherence to dose, schedule, and dietary instructions AIDS Behav 2007; 11(2): 175-83.
Schonnesson LN, Williams ML, Ross MW, Diamond PM, Keel B. Three types of adherence to HIV antiretroviral therapy and their association with AIDS diagnosis, medication side-effects, beliefs about antiretroviral therapy, and beliefs about HIV disease Int J STD AIDS 2007; 18(6): 369-73.
McNaghten AD, Wolfe MI, Onorato I, et al. Improving the representativeness of behavioral and clinical surveillance for persons with HIV in the United States: the rationale for developing a population-based approach PLoS One 2007; 2(6): e550.
Centers for Disease Control and Prevention. Clinical and Behavioral Characteristics of Adults Receiving Medical Care for HIV Infection: Medical Monitoring Project, 2005 Pilot Data Collection Cycle HIV Special Surveillance Report 2010; 6
Chesney MA. The elusive gold standard. Future perspectives for HIV adherence assessment and intervention J Acquir Immune Defic Syndr 2006; 43(Suppl 1): S149-55.
Sullivan PS, Campsmith ML, Nakamura GV, Begley EB, Schulden J, Nakashima AK. Patient and regimen characteristics associated with self-reported nonadherence to antiretroviral therapy PLoS One 2007; 2(6): e552.
Simoni JM, Kurth AE, Pearson CR, Pantalone DW, Merrill JO, Frick PA. Self-report measures of antiretroviral therapy adherence: A review with recommendations for HIV research and clinical management AIDS Behav 2006; 10(3): 227-45.
Cook JA, Cohen MH, Grey D, et al. Use of highly active antiretroviral therapy in a cohort of HIV-seropositive women Am J Public Health 2002; 92(1): 82-7.
Cook JA, Grey D, Burke-Miller J, et al. Effects of treated and untreated depressive symptoms on highly active antiretroviral therapy use in a US multi-site cohort of HIV-positive women AIDS Care 2006; 18(2): 93-100.
Giordano TP, White AC Jr, Sajja P, et al. Factors associated with the use of highly active antiretroviral therapy in patients newly entering care in an urban clinic J Acquir Immune Defic Syndr 2003; 32(4): 399-405.
Kremer H, Ironson G, Schneiderman N, Hautzinger M. To take or not to take: decision-making about antiretroviral treatment in people living with HIV/AIDS AIDS Patient Care STDS 2006; 20(5): 335-49.
Lemly DC, Shepherd BE, Hulgan T, et al. Race and sex differences in antiretroviral therapy use and mortality among HIV-infected persons in care J Infect Dis 2009; 199(7): 991-8.
Lillie-Blanton M, Stone VE, Snow Jones A, et al. Association of race, substance abuse, and health insurance coverage with use of highly active antiretroviral therapy among HIV-infected women, 2005 Am J Public Health 2010; 100(8): 1493-9.
Palacio H, Kahn JG, Richards TA, Morin SF. Effect of race and/or ethnicity in use of antiretrovirals and prophylaxis for opportunistic infection: a review of the literature Public Health Rep 2002; 117(3): 233-51. discussion 1-2
Sherr L, Lampe FC, Clucas C, et al. Self-reported non-adherence to ART and virological outcome in a multiclinic UK study AIDS Care 2010; 22(8): 939-45.
Barclay TR, Hinkin CH, Castellon SA, et al. Age-associated predictors of medication adherence in HIV-positive adults: health beliefs, self-efficacy, and neurocognitive status Health Psychol 2007; 26(1): 40-9.
Azar MM, Springer SA, Meyer JP, Altice FL. A systematic review of the impact of alcohol use disorders on HIV treatment outcomes, adherence to antiretroviral therapy and health care utilization Drug Alcohol Depend 2010; 112: 178-93.
Hendershot CS, Stoner SA, Pantalone DW, Simoni JM. Alcohol use and antiretroviral adherence: review and meta-analysis J Acquir Immune Defic Syndr 2009; 52(2): 180-202.
Braithwaite RS, Conigliaro J, Roberts MS, et al. Estimating the impact of alcohol consumption on survival for HIV+ individuals AIDS Care 2007; 19(4): 459-66.
Samet J, Walley A. Interventions Targeting HIV-Infected Risky Drinkers: Drops in the Bottle Alcohol Res Health 2010; 33(3): 267-79.
Parsons JT, Golub SA, Rosof E, Holder C. Motivational interviewing and cognitive-behavioral intervention to improve HIV medication adherence among hazardous drinkers: a randomized controlled trial J Acquir Immune Defic Syndr 2007; 46(4): 443-50.
Samet JH, Horton NJ, Meli S, et al. A randomized controlled trial to enhance antiretroviral therapy adherence in patients with a history of alcohol problems Antivir Ther 2005; 10(1): 83-93.
Oh DL, Sarafian F, Silvestre A, et al. Evaluation of adherence and factors affecting adherence to combination antiretroviral therapy among White, Hispanic, and Black men in the MACS Cohort J Acquir Immune Defic Syndr 2009; 52(2): 290-3.
Silverberg MJ, Leyden W, Quesenberry CP Jr, Horberg MA. Race/ethnicity and risk of AIDS and death among HIV-infected patients with access to care J Gen Intern Med 2009; 24(9): 1065-72.
Saha S, Jacobs EA, Moore RD, Beach MC. Trust in physicians and racial disparities in HIV care AIDS Patient Care STDS 2010; 24(7): 415-20.
Bogart LM, Wagner G, Galvan FH, Banks D. Conspiracy beliefs about HIV are related to antiretroviral treatment nonadherence among african american men with HIV J Acquir Immune Defic Syndr 2010; 53(5): 648-55.
Osborn CY, Paasche-Orlow MK, Davis TC, Wolf MS. Health literacy: an overlooked factor in understanding HIV health disparities Am J Prev Med 2007; 33(5): 374-8.
Waldrop-Valverde D, Osborn CY, Rodriguez A, Rothman RL, Kumar M, Jones DL. Numeracy skills explain racial differences in HIV medication management AIDS Behav 2010; 14(4): 799-806.
Geisel-Marbaise S, Stummer H. Diabetes adherence—does gender matter? J Public Health 2010; 18(3): 219-6.
Kuyper LM, Wood E, Montaner JS, Yip B, O'Connell J M, Hogg RS. Gender differences in HIV-1 RNA rebound attributed to incomplete antiretroviral adherence among HIV-Infected patients in a population-based cohort J Acquir Immune Defic Syndr 2004; 37(4): 1470-6.
Turner BJ, Laine C, Cosler L, Hauck WW. Relationship of gender, depression, and health care delivery with antiretroviral adherence in HIV-infected drug users J Gen Intern Med 2003; 18(4): 248-57.
Applebaum AJ, Richardson MA, Brady SM, Brief DJ, Keane TM. Gender and other psychosocial factors as predictors of adherence to highly active antiretroviral therapy (HAART) in adults with comorbid HIV/AIDS, psychiatric and substance-related disorder AIDS Behav 2009; 13(1): 60-5.
Berg KM, Demas PA, Howard AA, Schoenbaum EE, Gourevitch MN, Arnsten JH. Gender differences in factors associated with adherence to antiretroviral therapy J Gen Intern Med 2004; 19(11): 1111-7.
Waldrop-Valverde D, Jones DL, Jayaweera D, Gonzalez P, Romero J, Ownby RL. Gender Differences in Medication Management Capacity in HIV infection: the role of health literacy and numeracy AIDS Behav 2009; 13(1): 46-52.
Ammassari A, Antinori A, Aloisi MS, et al. Depressive symptoms, neurocognitive impairment, and adherence to highly active antiretroviral therapy among HIV-infected persons Psychosomatics 2004; 45(5): 394-402.
Diiorio C, McCarty F, Depadilla L, et al. Adherence to antiretroviral medication regimens: a test of a psychosocial model AIDS Behav 2009; 13(1): 10-22.
Shacham E, Nurutdinova D, Onen N, Stamm K, Overton ET. The interplay of sociodemographic factors on virologic suppression among a U.S. outpatient HIV clinic population AIDS Patient Care STDS 2010; 24(4): 229-35.
Bottonari KA, Safren SA, McQuaid JR, Hsiao CB, Roberts JE. A longitudinal investigation of the impact of life stress on HIV treatment adherence J Behav Med 2010; 33(6): 486-95.
Dalessandro M, Conti CM, Gambi F, et al. Antidepressant therapy can improve adherence to antiretroviral regimens among HIV-infected and depressed patients J Clin Psychopharmacol 2007; 27(1): 58-61.
Kleeberger CA, Phair JP, Strathdee SA, Detels R, Kingsley L, Jacobson LP. Determinants of heterogeneous adherence to HIV-antiretroviral therapies in the Multicenter AIDS Cohort Study J Acquir Immune Defic Syndr 2001; 26(1): 82-92.
Howard AA, Arnsten JH, Lo Y, et al. A prospective study of adherence and viral load in a large multi-center cohort of HIV-infected women AIDS 2002; 16(16): 2175-82.
Airoldi M, Zaccarelli M, Bisi L, et al. One-pill once-a-day HAART: a simplification strategy that improves adherence and quality of life of HIV-infected subjects Patient Prefer Adherence 2010; 4: 115-25.
Groves RM. Nonresponse rates and nonresponse bias in household surveys Public Opin Q 2006; 70(5): 646-74.
Wood E, Kerr T, Hogg RS, Zhang R, Tyndall MW, Montaner JS. Validity of self-reported antiretroviral therapy use among injection drug users J Acquir Immune Defic Syndr 2006; 41(4): 530-31.
Kalichman SC, Rompa D, Cage M. Reliability and validity of self-reported CD4 lymphocyte count and viral load test results in people living with HIV/AIDS Int J STD AIDS 2000; 11(9): 579-85.
Kinsler J, Cunningham W, Mohanty S, Wong M. Are medical records a more reliable and valid source of CD4 count, viral load, and outpatient visit data than self-reports? A comparison with electronic medical records as the gold standard Health Serv Outcomes Res Methodol 2008; 8(4): 187-200.