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Developing a method to derive alcoholattributable fractions for HIV/AIDS mortality based on alcohol's impact on adherence to antiretroviral medication
 Gerrit Gmel^{1, 2},
 Kevin D Shield^{1, 3} and
 Jürgen Rehm^{1, 3, 4}Email author
https://doi.org/10.1186/1478795495
© Gmel et al; licensee BioMed Central Ltd. 2011
Received: 14 September 2010
Accepted: 14 February 2011
Published: 14 February 2011
Abstract
Background
Alcohol consumption is causally linked to nonadherence to antiretroviral treatment that in turn causes an increase in HIV/AIDS mortality. This article presents a method to calculate the percentage of HIV/AIDS deaths attributable to alcohol consumption and the associated uncertainty.
Methods
By combining information on risk relations from a number of published sources, we estimated alcoholattributable fractions (AAFs) of HIV/AIDS in a stepwise procedure. First, we estimated the effect of alcohol consumption on adherence to antiretroviral treatment, and then we combined this estimate with the impact of nonadherence on death. The 95% uncertainty intervals were computed by estimating the variance of the AAFs using Taylor series expansions of one and multiple variables. AAFs were determined for each of the five Global Burden of Disease regions of Africa, based on countryspecific treatment and alcohol consumption data from 2005.
Results
The effects of alcohol on HIV/AIDS in the African Global Burden of Disease regions range from 0.03% to 0.34% for men and from 0% to 0.17% for women, depending on region and age category. The detrimental effect of alcohol consumption was statistically significant in every region and age category except for the North Africa/Middle East region.
Conclusions
Although the method has its limitations, it was shown to be feasible and provided estimates of the impact of alcohol use on the mortality outcome of HIV/AIDS.
Background
Alcohol has been identified as a major risk factor for mortality and burden of disease in past comparative risk assessments within Global Burden of Disease studies [1, 2]. In past iterations of comparative risk assessments, infectious diseases have not been included. However, evidence has been accumulating that alcohol has a causal impact on infectious disease categories [3, 4].
Evidence indicates a strong association between alcohol and HIV/AIDS [5, 6], but personality variables such as risktaking or impulsive behavior cannot be excluded as potential alternative explanations [6]. There is sufficient evidence, however, that alcohol worsens the course of the disease, especially by impacting adherence to antiretroviral treatment. Globally, HIV/AIDS led to about 2 million deaths and 58.5 million lost disabilityadjusted life years (DALYs) in 2004 [7]. Failing to estimate the alcoholattributable HIV/AIDS burden could lead to substantial underestimation of the burden of disease and mortality attributable to alcohol.
Antiretroviral therapy has led to a change in the natural history of HIV [8–10]. Lima and colleagues have shown that nonadherence to antiretroviral therapy by as little as 5% has a significant effect on the mortality of HIVinfected people in a highincome country [11]. Studies from low to middleincome countries have shown similarly elevated risks [12]. Therefore, adherence is a key to the success of antiretroviral therapy, and adherence levels of at least 90% to 95% are generally necessary to maximize treatment benefits [13, 14]. Poor adherence to antiretroviral therapy is associated with an increased likelihood of hospitalization [15], as well as increased mortality [3, 11, 12, 16].
Adherence is impacted by multiple variables such as injection drug use, forgetfulness, suspicions about treatment, complicated dosing regimens, number of pills required, decreased quality of life, and work and family responsibilities [17]. Alcohol use, and especially occasions of heavy drinking, has been shown to have a marked impact on adherence by interfering with one's capacity to plan for or remember dosing requirements. In addition, alcohol users might have decreased access to antiretroviral therapy or may use alcohol to reduce or avoid HIVrelated negative mood states [18]. A recent metaanalysis [18] indicated that, compared to abstainers, drinkers have an odds ratio of 0.604 (95% confidence interval [CI]: 0.531, 0.687) of adhering at least 95% of the time to their treatment, meaning that those who used alcohol were about 0.60 times as likely to be classified as adherent to treatment as nonusers. Overall, the effect of alcohol use on antiretroviral treatment has been found to be causal [3, 19].
Methods
We conducted an analysis using data from the five Global Burden of Disease (GBD) regions of Africa. These regions were chosen for their high variation in both prevalence of HIV/AIDS and adult per capita estimates of alcohol consumption. These regions are defined as follows:

North Africa/Middle East: Algeria, Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Occupied Palestinian Territory, Oman, Qatar, Saudi Arabia, Syria, Tunisia, Turkey, United Arab Emirates, Western Sahara, Yemen

SubSaharan Africa, Central: Angola, Central African Republic, Congo, the Democratic Republic of the Congo, Equatorial Guinea, Gabon

SubSaharan Africa, East: Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mayotte, Mozambique, Rwanda, Somalia, Sudan, Tanzania, Uganda, Zambia

SubSaharan Africa, South: Botswana, Lesotho, Namibia, South Africa, Swaziland, Zimbabwe

SubSaharan Africa, West: Benin, Burkina Faso, Cameroon, Cape Verde, Chad, Côte d'Ivoire, Gambia, Ghana, Guinea, GuineaBissau, Liberia, Mali, Mauritania, Niger, Nigeria, Saint Helena, Sao Tome and Principe, Senegal, Sierra Leone, Togo
Data sources
Proportion of people in need of antiretroviral therapy receiving antiretroviral therapy.
Region  Proportion  

lower bound  point estimate  upper bound  
North Africa/Middle East  15.01%  17.36%  19.71% 
SubSaharan Africa, Central  3.00%  4.25%  5.50% 
SubSaharan Africa, East  12.07%  14.24%  16.41% 
SubSaharan Africa, South  19.11%  21.67%  24.22% 
SubSaharan Africa, West  11.36%  13.47%  15.59% 
Parameters used in calculating the AAF for people who die of AIDS due to nonadherence to antiretroviral therapy.
Parameter  Symbol  Value  95% Confidence Interval  Source 

Proportion of people on antiretroviral therapy who are not adhering to medication regimen  P_{na}  40.1%  (36.9% to 43.3%)  Lima and colleagues [22] 
Risk ratio of nonadherence to antiretroviral therapy, comparing current drinkers to abstainers  RR_{drink}  1.82  (1.63 to 2.04)  Hendershot and colleagues[18] 
Mortality risk ratio for nonadherence to antiretroviral therapy compared to those who adhere to antiretroviral therapy  RR_{na}  3.13  (1.95 to 5.05)  Lima and colleagues[11] 
Mortality hazard ratio for people not on antiretroviral therapy treatment compared to those who are  HR_{nontreat}  2.63  (1.92 to 3.57)  Murphy and colleagues[23] 
1 = risk of people in antiretroviral treatment adhering on an arbitrary scale
3.13 = risk of people in antiretroviral treatment not adhering to treatment
1.85 = risk of people in antiretroviral treatment overall (assuming 0.401 nonadherent)
4.88 = risk of people not in treatment
Multiple sources were used to characterize the relationship between alcohol consumption and nonadherence to antiretroviral therapy and the effect of nonadherence on HIV/AIDSrelated mortality. To characterize the effect of alcohol consumption on antiretroviral adherence, we used the odds ratio of 0.60 (95% CI: 0.53, 0.69) from the metaanalysis performed by Hendershot and colleagues [18, 22] for adherence by global drinkers (defined as alcohol consumption within the past month) compared to abstainers. Since there have been no metaanalyses of nonadherence on mortality, we used a hazard ratio of 3.13 (95% CI: 1.95, 5.05) for deaths of people not adhering to treatment (defined as less than 95% of the time) compared to people adhering to treatment, as reported by Lima and colleagues [11]. This estimate is similar to but slightly lower than the hazard ratios of 3.2 and 3.9 for adherence rates of <80% and <90%, respectively, as described in other studies [12, 16]. For the rate ratio of HIV/AIDSrelated deaths of people on antiretroviral treatment compared to people not on treatment, we used a rate ratio reported by Murphy and colleagues [23] that gives a point estimate of 0.38 (95% CI: 0.28, 0.52). These values were combined to obtain the AAF for HIV/AIDS for a given population. The following sections will first present the derivation of the AAF for HIV/AIDS mortality from nonadherence to antiretroviral therapy and then describe the derivation of the 95% uncertainty intervals associated with these AAFs. Estimations of uncertainty for the calculated AAFs are of critical importance for the estimated burden of disease attributable to alcohol and for policy recommendations aimed at reducing this burden. Table 2 outlines the measures of association used to compute the AAFs for the five African GBD regions and the prevalence of nonadherence to antiretroviral therapy for those who are currently receiving antiretroviral therapy.
Derivation of AAFs for HIV/AIDS
Continuous modeling of the effects of alcohol use on adherence [24, 25] was impossible given the data available to us, and thus we limited our analysis to two broad categories: drinkers versus nondrinkers, and antiretroviral therapy adherence (at least 95% of the time) versus nonadherence.
where P represents prevalence of exposure and RR represents the relative risk for exposure compared to no exposure. Depending on the design of the original epidemiological study, the RR information could be derived from hazard ratios, risk ratios, or odds ratios.
However, for the AAF of mortality due to not adhering to antiretroviral therapy for people with HIV/AIDS, the lack of research and limited information available on the effects of alcohol on HIV/AIDS forced us to calculate the AAFs for HIV/AIDSrelated mortality in several steps (for the definition and calculation of AAF in general, see [26, 27]).
where P_{adher} is the proportion of people adhering to treatment more than 95% of the time, P_{na} is the proportion of people adhering to treatment less than 95% of the time, and RR_{na} is the relative risk of mortality for those who are not adhering compared to those who are.
Prevalence of current drinkers by region.
Current Drinkers  

Region  Men (age in years)  Women (age in years)  
15  34  35  54  55+  15  34  35  54  55+  
North Africa/Middle East  7.2%  12.0%  5.5%  3.5%  1.1%  0.4% 
SubSaharan Africa, Central  51.0%  51.9%  20.5%  32.4%  28.5%  11.9% 
SubSaharan Africa, East  24.6%  38.8%  37.2%  16.1%  24.9%  21.4% 
SubSaharan Africa, South  38.0%  38.0%  29.5%  12.6%  15.7%  9.8% 
SubSaharan Africa, West  36.2%  50.9%  40.5%  20.8%  31.2%  27.1% 
Estimation of the variance of AAFs
It is possible to derive a mathematical expression of the variance of the AAFs when the variances of each parameter making up the final expression are known. Calculating the variance of the AAF of HIV/AIDSrelated mortality requires the combination of several steps. Because these steps involve Taylor series expansions, they yield only an approximation of the real variance. We calculated the variance of the AAFs by calculating the variance of the simplest combinations first, which were then used in more complex functions. The derivations of the variance of two distributions and the derivation of the variances using Taylor series expansions can be found in 'additional file 1 appendix 1'.
In the literature, the uncertainty intervals for AFs due to risk factors based on metaanalyses are usually computed using a Monte Carlotype method (for alcohol, see [3]). The Monte Carlo method uses repeated random samples of an AF to estimate its variance [28].
The AAF samples are obtained by randomly generating each parameter using the information we have on its distribution. The main advantage of this method is its broad range of applications. The method can be applied to virtually any field and any function and is especially useful in cases in which an algebraic solution to a problem is impossible to derive. Our AAF, however, is a relatively simple function of several independent variables that makes the use of Monte Carlo samples unnecessary. Indeed, an algebraic solution, when possible to obtain, should always be preferred to a simulation, as the results are more precise and require fewer resources. In fact, the results obtained with this method are generated instantaneously on any computer and do not require the use of servers or the handling of large datasets, as is usually the case with a Monte Carlo analysis. In addition, a decomposition of sources of variation and their relative contribution to overall variance can be obtained more readily. Even though it is possible to derive only an approximation of the variance of the original function using second order Taylor series expansions, the assumption that the spread of the function around its mean value is relatively small guarantees an accurate result. The variations around the point estimate of each AF function depend only on the variance of the parameters used; therefore, knowledge of these parameters is important. It should be noted that for large variances of each parameter, the error in the variance of the final function will increase as the assumption of small variations is not satisfied. This is not specific to the analytic expression derived here, but would also affect a Monte Carlo method for which a convergence would not be ensured unless a very large number of data points are collected [29]. Finally, the use of a second order approximation has the advantage of yielding very simple expressions that combine only the derivative of the function and the variance of its parameters; this means that they can be computed nearly instantaneously in R using only a few command lines. It should be noted that in many fields, such as electrical engineering, quantum mechanics, and protein engineering, Taylor series of order two or fewer are used daily and yield excellent results.
Variance of AdAF and NAAAF
Using the variances of P_{na} and RR_{na} described in the previous section, we derived a mathematical expression for the variance of AdAF and NAAAF using Taylor series expansions. The complete derivations can be found in 'additional file 1, appendix1', and the final expression of the variance of these terms is as follows:
Variance of PDT
In this manner, the variances of all three components of the final AAF function are determined. Using the variances for AdAF, NAAAF, and PDT, the variance of the AAF can be determined by the variance of (AdAF·NAAAF) PDT (see 'additional file 1, appendix 1').
Computation of variances for hazard ratios and relative risks
Little information on the relative risks and hazard ratios mentioned above is available in the literature. Accordingly, previously published raw data had to be transformed to be useful for our analysis. The following paragraphs describe how these data from various sources yielded values for the relative risks and hazard ratios.
Computation of variance for the hazard ratio for people under treatment
Computation of variance for the relative risk of death for nonadherence
where RR_{a} is the relative risk of mortality for people who adhere to antiretroviral therapy.
Computation of variance for relative risk of nonadherence due to alcohol
The odds ratio for adherence related to alcohol use provided by Hendershot and colleagues [18] can be transformed into a relative risk function following two simple steps:

Take the inverse of the odds ratio to obtain the odds ratio of nonadherence for drinkers compared to nondrinkers.

Compute the relative risk for nonadherence for drinkers versus nondrinkers.
where OR is the odds ratio of nonadherence for drinkers compared to nondrinkers, and P is the proportion of nonadherence among drinkers. Lacking the necessary information about this proportion, we took the proportion of nonadherence of the entire population as a conservative estimate of this value.
The above relations define the relative risks, hazard ratio, and their respective variances, and allow us to compute the AAF of HIV/AIDS deaths.
Results
Overall, in Africa, men have higher AAFs than women, with AAFs generally highest in people under 55 years of age; however, not all of these differences are statistically significant. The highest AAFs are in subSaharan Africa, East, where the AAF was 0.34 (95% CI: 0.12, 0.56) for men aged 15 to 34 years, and 0.27 (95% CI: 0.09, 0.45) for men aged 35 to 54 years. In women, the highest AAFs are observed in the subSaharan Africa regions; however, unlike the men, the AAFs are very similar for the East, South, and West regions.
Discussion
This method allows a systematic computation of partial AAFs for HIV/AIDS in any given population, including the 95% uncertainty intervals. Despite the limited information available, the results show a clear negative impact of alcohol on adherence to an antiretroviral treatment regime and consequently on the outcome of the disease, especially in the subSaharan regions of Africa. Although the AAFs are relatively low, HIV/AIDS was responsible for 58.5 million DALYs in 2004 [7], indicating that the burden of alcoholattributable nonadherence to HIV/AIDS treatment is substantial.
The very broad categories used to describe alcohol consumption and adherence to antiretroviral therapy do not allow us to distinguish between moderate and heavy drinking or different percentages of nonadherence, and future research will be necessary to estimate a more accurate AAF for HIV/AIDS mortality. Ideally, AAFs would be estimated by using a continuous distribution of alcohol consumption and the risk relations for adherence to antiretroviral treatment associated with each level of consumption, as well as by specific patterns of drinking, such as heavy occasional drinking [30]. Modeling alcohol consumption with these two dimensions continuously and triangulating data obtained from surveys with adult per capita consumption information [31, 32] are especially important for countries with high variation in patterns of drinking among drinkers.
These results may not be relevant for all parts of Africa. Many Africans completely abstain from alcohol, while many of those who drink tend to do so heavily, resulting in comparatively small variations in drinking patterns among drinkers http://apps.who.int/globalatlas/default.asp. Since we expect alcohol consumed as part of heavy drinking patterns to interfere more with antiretroviral treatment than moderate patterns of drinking [18, 33], our estimates of AAFs based on the dichotomous variable of alcohol consumption (yes/no) constitute conservative estimates of the alcoholattributable HIV/AIDS disease burden.
It is important to note that both the proportion of drinkers as well the proportion of people on antiretroviral therapy influence the AAF. Indeed, even though the proportion of drinkers in the subSaharan Africa, South region, is lower than in the subSaharan Africa, West region, the proportion of people in treatment is larger in the South region, which yields a larger overall AAF for this region. It is very likely that alcohol also has an effect on the progress of the disease in people without treatment that we did not estimate, but today's research does not allow us to deduce causality or quantify such an effect [6]. Improving this situation by conducting research to ascertain and quantify the effect of alcohol use on the disease progression of HIV/AIDSrelated morbidity and mortality without antiretroviral treatment should be a priority.
Conclusions
Although the presented method could be improved by better definition and quantification of exposure, it provides an evidencebased conservative estimate of the impact of alcohol use on HIV/AIDS mortality and highlights the importance of further research in this field. It also points to the need to integrate alcohol prevention measures [34] into the protocol for antiretroviral treatment.
Declarations
Acknowledgements
Financial support for this study was provided to the last author listed above by the National Institute for Alcohol Abuse and Alcoholism (NIAAA) with contract # HHSN267200700041C to conduct the study titled "Alcohol and Drugattributable Burden of Disease and Injury in the US." In addition, the last author received a salary and infrastructure support from the Ontario Ministry of Health and LongTerm Care. We would like to thank Gerhard Gmel, Paul Shuper, and Ben Taylor for helpful comments on earlier versions of this manuscript.
Authors’ Affiliations
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