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Epidimiology & Social

Using cost-effectiveness league tables to compare interventions to prevent sexual transmission of HIV

Pinkerton, Steven D.a,b; Johnson-Masotti, Ana P.a; Holtgrave, David R.b; Farnham, Paul G.b,c

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From the aCenter for AIDS Intervention Research, Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, the bDivision of HIV/AIDS Prevention: Intervention Research & Support, National Center for HIV, STD, and TB Prevention, Centers for Disease Control and Prevention, and cDepartment of Economics, Georgia State University, Atlanta, Georgia, USA.

Correspondence to Steven D. Pinkerton, PhD, Center for AIDS Intervention Research, Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, 2071 North Summit Avenue, Milwaukee, WI 53202, USA. Tel: +1 414 456 7762; fax: +1 414 287 4206; e-mail:

Received: 26 September 2000;

revised: 19 January 2001; accepted: 25 January 2001.

Sponsorship: This research was supported by grants R01-MH55440, K02-MH01919, and P30-MH52776 from the National Institute of Mental Health, and by Intergovernmental Personnel Agreements awarded to S.D.P. and to P.G.F. by the Centers for Disease Control and Prevention.

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Cost-effectiveness information is needed to help public health decision makers choose between competing HIV prevention programs. One way to organize this information is in a ‘league table’ that lists cost-effectiveness ratios for different interventions and which facilitates comparisons across interventions. Herein we propose a common outcome measure for use in HIV prevention league tables and present a preliminary league table of interventions to reduce sexual transmission of HIV in the US. Fifteen studies encompassing 29 intervention for different population groups are included in the table. Approximately half of the interventions are cost-saving (i.e. save society money, in the long run), and three-quarters are cost-effective by conventional standards. We discuss the utility of such a table for informing the HIV prevention resource allocation process and delineate some of the difficulties associated with the league table approach, especially as applied to HIV prevention cost-effectiveness analysis.

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As resources to fund HIV prevention are limited, public health decision makers – including state and local health departments, HIV prevention community planning groups, and the federal government – need accurate information about the cost-effectiveness of various HIV prevention strategies. Health department officials who need to prioritize spending on HIV prevention are especially interested in the relative efficiency of different prevention strategies: they need to know which strategies are the most cost-effective, and which are less so.

There is now a small but expanding body of evidence regarding the cost-effectiveness of different HIV prevention interventions. These individual studies demonstrate that various HIV prevention strategies – from counseling and antibody testing programs, to distributing free condoms to economically disadvantaged persons – represent highly cost-effective uses of limited health care resources. In short, spending on HIV prevention can be a sound ‘investment'.

However, the cost-effectiveness of different prevention strategies varies widely. As noted above, in order to allocate resources efficiently, public health decision makers need to know which HIV prevention interventions are the most cost-effective. This question of the comparative efficiency of different interventions cannot be answered by individual cost-effectiveness studies. Although several qualitative reviews of the HIV prevention cost-effectiveness literature have appeared recently [1–4], none provides explicit quantitative comparisons among interventions.

The goal of such comparisons is to facilitate construction of a ‘league table’ that rank-orders interventions by their cost-effectiveness ratios. The information contained in such a table could help inform resource allocation decision making for HIV prevention by indicating the relative cost-effectiveness of different interventions.

The paradigmatic use of league tables in resource allocation decision making entails dividing all interventions into groups of comparable, mutually exclusive interventions and listing the interventions from each group in a separate league table, sorted in order of increasing effectiveness. Presumably, costs will increase as one goes from the least effective intervention (at the top of the list) to the most effective (at the bottom of the list). If, at any point in the list, the cost decreases when going from a less effective to a more effective intervention, then the less effective intervention should be eliminated (it is ‘dominated’ by the more effective intervention). Similarly, in some instances a combination of programs may be more economically efficient than a single program; if so, that program should be excluded by ‘extended dominance’ [5,6]. Once all dominated programs have been eliminated from the league table, incremental cost-effectiveness ratios (described below) can be computed for adjacent interventions. These pairwise incremental ratios specify how much it costs for each additional infection averted by the more effective intervention, relative to the less effective intervention. Because the interventions in a particular league table are mutually exclusive, only one intervention from each table should be funded. A resource allocation algorithm can be used to determine which intervention from each league table should be funded in order to maximize the number of infections averted as a result of this spending [6].

However, the construction of such a table is difficult because it requires substantial standardization across studies, with respect to both methods and underlying assumptions [7–9]. It is especially difficult for HIV prevention cost-effectiveness studies due to the heavy reliance, in most such studies, on mathematical models of HIV transmission to estimate the epidemiological impact of these interventions.

In this article we describe these difficulties in detail and propose a common outcome measure of economic efficiency that can be used to compare disparate HIV prevention interventions. We argue that comparisons properly should be restricted to interventions that serve the same population and that address the same or closely related risk behaviors (e.g. condom promotion programs for sexually active adolescents should not compete with drug injection-related programs for this population or with condom promotion interventions for adults). Finally, we present a league table of interventions to reduce sexual transmission of HIV in the US, and discuss the utility of such a table for informing the HIV prevention resource allocation process.

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Cross-study comparisons

There are several well-recognized general difficulties in attempts to compare cost-effectiveness studies, as well as additional difficulties that are specific to HIV prevention. The general difficulties include cross-study differences in methodologies and in the specific parameters used to establish intervention costs and effectiveness; differences in study populations and settings; and questions about appropriate comparator interventions [7,8].

Several methodological issues arise when determining intervention costs [10]. The choice of which costs to include in an analysis can differ from one study to the next. The perspective of the analysis helps determine which costs are included [11,12]. Many analyses adopt a societal perspective that includes all costs, regardless of who pays; other analyses may use a less inclusive perspective, such as a hospital's perspective or an AIDS service provider's perspective, which may omit important costs (such as those borne by the client). Furthermore, costs may differ in the base year for which they were calculated; therefore, all costs should be converted to a common base year before comparison.

The choice of effectiveness outcome measures is a fundamental aspect of the study. Two obvious choices for HIV prevention studies are the cost per HIV infection averted and the cost per quality-adjusted life year (QALY) saved. When the latter outcome is used, the study sometimes is referred to as a cost–utility analysis, whereas in the former instance it is called a cost-effectiveness analysis. Determining the number of QALYs saved when an HIV infection is prevented is difficult, and despite efforts at standardization [13,14], a wide range of disparate estimates have been used in extant cost–utility analyses.

Health care and health promotion interventions necessarily serve particular populations, and therefore the cost-effectiveness of the intervention is in part a function of the specific characteristics and epidemiological circumstances of those populations. Two HIV prevention programs might have the same impact on participants’ risk behaviors, but nonetheless might have very different epidemiological consequences, hence cost-effectiveness, due to differences in prevailing epidemiological conditions (e.g. HIV prevalence, existing levels of risk behavior). Attempts to standardize across populations, though tempting, usually are best avoided because populations react to their environment, and thus effectiveness measures are often confounded with population characteristics [15].

Finally, the choice of an appropriate comparator can greatly affect the outcome of a cost-effectiveness analysis. Often, the question addressed in a cost-effectiveness analysis is whether the greater effectiveness of Program B is worth the additional cost, relative to some baseline Program A. Program A might be the status quo (`standard practice') or it might be a ‘no program’ option. In the former case especially, an incremental cost-effectiveness ratio is computed, R = (CBCA)/(EBEA), where C and E denote the net costs and effects of the respective programs. If the comparison is to a ‘no program’ option, then an average cost-effectiveness ratio is calculated, R = CB/ EB. Most HIV prevention cost-effectiveness studies have compared the target intervention to a ‘no program’ option.

Several additional difficulties that arise when comparing HIV prevention cost-effectiveness studies result from the need, in most cases, to model the effects of these interventions, in terms of HIV infections averted or QALYs saved. Very few intervention outcome studies have the statistical power needed to detect small changes in HIV seroincidence – this is especially true for studies conducted in the US. Therefore, mathematical models typically are used to convert self-reported behavioral changes (e.g. increases in condom use or decreases in needle sharing) resulting from the intervention into an estimate of the number of infections averted [15]. The main concern here is not that relying on self-reported behavior changes and mathematical models introduces additional error into HIV prevention cost-effectiveness analyses (it almost certainly does), but rather that a number of features of this process can differ between studies, thereby limiting comparability.

For example, different studies may utilize behavioral data collected using surveys with different recall periods, and may extrapolate these data to very different time frames. Some analyses look at the impact of the intervention for as long as 10 years into the future [16], whereas others are concerned only with effects in the few months following the intervention [17]. Studies also differ in how extensively they model the spread of interventions effects. Simple Bernoullian models [18–20] typically include the effect of the intervention on participants and their partners only; more complex epidemiological models can be used to estimate the community-wide impact of an intervention, although this seldom has been done.

Furthermore, there is little standardization of the parameter values used in these models, which reduces the generalizability and comparability of studies. One of the most important of these parameters reflects the prevalence of HIV infection in the target population. As suggested earlier, too much standardization here could be undesirable. Prevalence is a characteristic of the population that is likely to influence intervention effectiveness, both directly (by affecting how risky particular behaviors are) and indirectly (by affecting individual behavior) [21]. Thus, standardized but population-specific values should be used when available [22].

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Comparisons of HIV prevention interventions

How, then, should HIV prevention interventions be compared? First, we assume that the goal of such comparisons is to determine the most cost-effective intervention option or options; this does not mean that only the most cost-effective interventions should be implemented – only that the goal of the comparison process is to identify these interventions so that decision makers can make better informed choices.

Because the goal is to directly compare HIV prevention interventions, rather than comparing HIV prevention to some other area of health care or health promotion, we propose that the best measure of economic efficiency is the cost-effectiveness ratio, defined simply as the ratio of program costs (C) to the number of infections averted by the intervention (A). This ratio, C/ A, can be contrasted with the more complex cost–utility ratio (cost per QALY saved), EQUATION where Q is the number of QALYs saved, and T is the savings in HIV-related medical care, each time an infection is averted [23]. As mentioned above, it is difficult to calculate standardized values for T and Q. This is true, in part, because HIV/AIDS therapeutics continue to progress, so that T and Q must be continually revised. More effective and costly therapies make already cost-effective interventions more so, and inefficient programs less cost-effective [24]. It is, of course, possible to update existing cost-effectiveness estimates using common values of T and Q. Nevertheless, it seems simpler to compare interventions on the basis of the cost-effectiveness ratio, C/ A.

Equation U1
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A second reason to favor the cost-effectiveness ratio over the cost–utility ratio arises from the dependence of the latter on the medical care received by persons who become infected (this affects T and Q), as well as the age of these persons (which affects Q but usually not T). Simple arithmetic shows that, if two interventions for the same population are being compared (so that T and Q are the same for both population), the one will have a smaller cost-effectiveness ratio if and only if it also has a smaller cost–utility ratio [23].

But what if the interventions target different populations? Studies have shown that HIV-infected persons from economically disadvantaged populations and HIV-infected injection drug users (IDU) utilize fewer AIDS care resources than do white, non-IDU patients [25–27]. Thus, T is larger for white, non-IDU patients, which tends to make interventions for this group appear to be more cost-effective than interventions for other groups. In this instance, the difference between populations reflects existing inequities in health care utilization. It would be unethical to base cost-effectiveness resource allocation decisions on inequities such as these.

Moreover, one may question whether pitting interventions for one group against interventions for another group is a reasonable and a politically-feasible approach to community-based HIV prevention. Many HIV prevention community planning groups explicitly reject such comparisons, preferring instead to first define populations in need of prevention services and then prioritize interventions separately for each of these groups [28]. Because the goal of the present project is to provide useful comparisons for community planning groups and other decision makers, we too shall follow this model of the prioritization process.

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Economic efficiency studies

To identify cost-effectiveness studies of interventions to prevent the sexual transmission of HIV in the US, we searched several large electronic databases, including AIDSLINE, Medline, PsycINFO, and HealthSTAR using combinations of the following keywords: costs, cost-effectiveness, cost–benefit, HIV prevention, AIDS prevention, and HIV infection. We only included articles from peer-reviewed journals because conference abstracts do not provide sufficient information to fully characterize a study [29], and we restricted our attention to the results of US studies because of the substantial epidemiological, social, and health system differences between the US and other countries.

We identified 14 published cost-effectiveness studies that met the inclusion criteria [16,17,30–41]. Two additional studies provided effectiveness information (in the form of a ‘cost-threshold analysis') [42] and cost data for a closely-related intevention. [43]; these studies were combined into a single cost-effectiveness analysis for the present purposes. Some studies included multiple interventions (e.g. an ‘experimental’ intervention and a status quo or ‘control’ intervention). Some interventions were narrowly targeted toward a particular population group, such as young men who have sex with men, whereas others were intended for the general population. These programs are described only briefly here. Detailed descriptions of these interventions and the associated economic analyses can be found in two recent review articles [1,3].

The most common type of intervention utilized social-cognitive principles of behavior modification to help intervention participants reduce their risk of acquiring HIV. These interventions consisted of between one and as many as nine sessions, which typically were conducted in a small group setting, led by a trained facilitator. Small-group interventions have been implemented for men who have sex with men [30,31], at risk women residing in urban areas [32], men and women attending inner-city STD and other health clinics [33], and among men and women with mental illnesses [17,34]. A similar format was utilized in a one-day workshop for adolescent African American males that used role-playing exercises, games, and other activities to emphasize the importance of abstinence and condom use [35]. There was also a small-group or individual-level counseling component to several other interventions, which additionally included such elements as outreach services, antibody testing, and the provision of condoms [16,42,43].

A peer-educator or peer advocate component was included in several interventions [16,17,36]. In these interventions, a small subset of intervention participants were taught to act as risk reduction ‘leaders’ for their peers. The goal of this community-level approach to HIV prevention is to use social network structures and social influence to help individuals modify their own behaviors and to gradually change the social norms surrounding sexual behavior. In the most well-known example of this type of intervention, Kelly and his colleagues [44] enrolled popular, socially respected and influential members (`peer leaders') of the gay community in a series of brief educational sessions. During these sessions the peer leaders were instructed in effective communication techniques for conversing with peers about safer sex. The leaders were encouraged to actively engage friends and other bar patrons in conversations about sexual risk reduction, with the hope that new safer sex norms would diffuse throughout the community as more and more men took steps to reduce their own HIV risk. Two independent cost-effectiveness analyses of this intervention have been conducted [36,45,46]. Because they produced qualitatively similar conclusions, only the more comprehensive analysis is included here [36,46].

One of the studies included here focused on condom distribution. In this intervention, free condoms were made available through public health clinics, community mental health centers, substance abuse treatment centers, and businesses in a community with high rates of sexually transmitted diseases and HIV [37]. The aim of this program was to reduce financial and logistic barriers (e.g. availability, embarrassment) to condom use.

Two studies developed mathematical models to investigate the cost-effectiveness of post-exposure prophylaxis (PEP) after a risky sexual encounter [38,39]. Both analyses assumed that the patient receives 4 weeks of antiretroviral therapy with two reverse transcriptase inhibitors, together with necessary laboratory work. PEP was assumed to be about 79% effective in preventing infection in exposed individuals, based on a case–control study of occupationally-exposed health care workers. The analyses differed in several assumptions regarding the cost of the therapy and in the scenarios that they examined (exposure route and whether or not the patient's sex partner was known to be HIV-positive).

Although there are numerous studies of the cost-effectiveness of HIV counseling and testing programs [47], few of them focus on sexual behavior per se. We included two counseling and testing studies. The first presents an analysis of the national counseling, testing, partner referral and notification program [40]. The authors assumed that some, but not all, people will modify their behavior after learning that they are HIV-positive. The authors of the second study [41], which focuses on publicly-funded counseling and testing at sexually transmitted disease clinics, also assumed that some clients would reduce their HIV risk as a consequence of the counseling and testing experience, regardless of whether they were HIV-positive or HIV-negative (the extent of change was assumed to be greater for HIV-positive persons).

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Cost-effectiveness league table

The ‘league table’ of cost-effectiveness ratios (C/ A) is displayed in Tables 1–4. We have divided it into four sub-tables by target population: men who have sex with men, at-risk women, at-risk men, and counseling and testing programs for the general population. Notably, this grouping hides important differences between subgroups. For instance, although most of these interventions are intended to serve adult men and women, several explicitly target adolescents and young adults. Thus, the placement of different interventions in the same sub-table does not necessarily indicate that they are strictly comparable. This important comparability issue is revisited in the discussion.

Table 1
Table 1
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Table 2
Table 2
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Table 3
Table 3
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Table 4
Table 4
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The league table lists several cost and effectiveness parameters and outcome variables for each intervention. The per-client intervention costs were obtained from the original publications and inflated to 1999 dollars. In the one instance [16] in which the original article did not specify costs on a per-client basis, an estimate of the total size of the target population was used to convert total program costs to per-client costs. As intervention effectiveness also is quantified as the number of infections averted per-client, the number of clients is essentially arbitrary. This formulation encodes a strong assumption that effects and costs increase in the same proportion as programs are scaled up or down in size.

We did not attempt to standardize the effectiveness estimates of the original studies. Instead, we trust that the original analysts have done their best to accurately estimate the epidemiological impact of the intervention. Because of this, the effectiveness parameters are largely incommensurate. Some analyses assumed that the interventions effects are very short lasting (e.g. 3 months), whereas others assumed that effects persist as long as 10 years after the conclusion of the intervention. Most analyses included the impact of the intervention on the participants, and possibly their sex partners. In only one analysis were the community-wide effects of the intervention modeled. The studies included in the league table span a broad range of prevalence values; from less than one in a hundred, to several in ten. Values of other key parameters, such as condom effectiveness and the per-contact probability of transmission, also differ across studies (values not shown in Tables 1–4).

The main effectiveness outcome variable, the number of infections averted by the intervention (on a per-client basis), A, is listed in the league table, together with the program cost, C. The economic efficiency of the intervention is measured by the cost-effectiveness ratio, C/ A, which specifies the cost per HIV infection averted. As shown in the league table, the cost-effectiveness ratios for HIV prevention range from a few thousand to many million dollars per infection averted.

The (discounted) lifetime cost of medical care for HIV infection and AIDS is approximately US$200 000 [13]. Therefore, interventions that can prevent someone from becoming infected for less than this amount are likely to save society money in the long run. As illustrated in Figure 1, approximately half of the intervention strategies listed in the league table are ‘cost-saving’ in this sense.

Fig. 1
Fig. 1
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Moreover, a health-related intervention need not be cost-saving to be a sound economic investment. Most health care programs and preventive interventions do not save society money [48]. Although there is no universally-accepted threshold that distinguishes cost-effective interventions from inefficient ones, health-related programs and interventions that can save a year of life or a quality-adjusted year of life (QALY) for less than about US$50 000 are usually considered cost-effective [49–51]. Preventing a 26-year-old from becoming infected with HIV saves approximately 11 (discounted) QALYs [13]. This suggests that HIV prevention interventions that can avert an infection for less than about US$750 000 (11 × US$50 000 + US$200 000) should be considered cost-effective [52]. Approximately three-quarters of the intervention strategies reviewed here are ‘cost-effective’ in this sense (see Fig. 1).

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The league table indicates that many different HIV prevention strategies are cost-effective, or even cost-saving. Among the most economically efficient interventions were several of the small-group workshops (not all were cost-effective, however), the peer leader intervention for men who have sex with men, and the free condom distribution program. Counseling, testing, and partner notification programs also appear to be reasonably cost-effective. PEP was cost-effective only under highly circumscribed conditions. Nevertheless, the question remains, which of these interventions should be given the highest priority when limited HIV prevention funds are available?

We have argued that it is inappropriate to compare across population groups, because decisions about whether or not HIV prevention programs should be implemented for particular populations are (and should be made) on more than economic grounds. One may therefore question whether the broad population categories used to organize the league table presented here (`men who have sex with men', ‘at-risk women and men') are refined enough. One of the studied interventions addressed the HIV prevention needs of African American adolescent males, and another provided prevention services to young males who have sex with other males. Should these interventions compete for funds against interventions for adults? Likewise, should adults with mental illnesses be considered a distinct population for the purposes of HIV prevention programming? We believe that interventions for each distinct subgroup should be considered independently, in separate league tables [53].

Likewise, it seems clear that potentially synergistic (i.e. non-exclusive) HIV prevention interventions should not compete against one another in the same league table. Thus, interventions to prevent mother-to-child transmission or to address drug injection-related transmission should not compete with sexual risk reduction interventions. Similarly, community-level, peer leader interventions and small-group interventions represent complementary approaches to preventing the sexual transmission of HIV. Well-balanced prevention portfolios should include interventions on multiple levels: individual, small-group, and community-wide [54]. Direct comparisons across intervention levels can be misleading if they suggest, for example, that condom distribution programs obviate the need for intensive intervention strategies. Some people might need one or the other, and some people might need both.

There are several clear-cut instances in which direct comparisons are appropriate. Some of the studies included in the present review directly compared two or more related intervention strategies. Thus, for example, Pinkerton and colleagues [33] compared a seven-session small-group risk reduction intervention for at-risk men and women with a single-session, video-based educational intervention. As indicated in Tables 2 and 3, both were highly cost-effective when compared with a no-program option. As the seven-session intervention was both more costly and more effective than the single-session intervention, Pinkerton and colleagues [33] conducted an incremental analysis to determine whether the greater effectiveness of the seven-session intervention was sufficient to offset its greater cost; their results indicate that it was. Similarly, Pinkerton et al. [31] showed that adding a 60- to 90-minute safer sex skills training session to a basic 80-minute educational intervention for gay men was cost-saving, and Johnson-Masotti and colleagues [17] found that a more intensive intervention was incrementally cost-effective in comparison with a briefer intervention for men with mental illnesses, but not for women.

Moreover, it may be possible to directly compare the results of some disparate studies. The other (nine-session, small-group) intervention for women with mental illnesses [34] produced smaller epidemiological effects, but at greater cost, than the single-session intervention that Johnson-Masotti and colleagues [17] found to be most cost-effective. Therefore, the more expensive, nine-session intervention is ‘dominated’ by the less expensive single-session program, and can be eliminated from further consideration [55].

Thus, there are at present only a few interventions that are directly comparable. However, if one must choose between interventions at different levels – between a small-group intervention and a peer leader program, for example – then the information in the league table presented here can facilitate the comparison process. However, choosing to prioritize across levels is largely a political decision, not a strictly economic matter. League tables at best provide an aid to decision makers faced with difficult resource allocation choices. Political, ethical, or social issues often take precedence over economic ones [56–58].

HIV prevention research is still a relatively young field. The aim of much of the research conducted to-date has been to establish the effectiveness of basic classes of intervention approaches (such as small-group programs based on established theories of behavioral change, or condom distribution programs) and their appropriateness for different target groups. Less effort has been directed at refining and comparing the effectiveness of these strategies. Thus, although there is now a wealth of evidence that various prevention strategies do work, less is known about what works best. This shortage of comparative results is reflected in the cost-effectiveness literature. Several different prevention strategies have been shown to be cost-effective, but whether a one-, two-, three-, four-, or five-session intervention would be the most cost-effective approach for a particular population is less clear.

The generalizability of HIV prevention cost-effectiveness results is limited by the role that local epidemiological conditions play in determining intervention effectiveness. Unlike a surgical procedure, which is likely to be as effective in Cleveland as it is in Dallas, the epidemiological impact of HIV prevention interventions (i.e. the number of infections averted) depends on the prevalence of infection in the community. Thus, cost-effectiveness results obtained in one location should only be generalized to similar locations.

In conclusion, few direct cross-study comparisons of HIV prevention interventions are possible at this time. Nevertheless, we believe that it is instructive and informative to array in a league table the available knowledge of HIV prevention cost-effectiveness [7]. This information can be used to guide the HIV prevention resource allocation process, in a qualitative if not a strictly quantitative manner. Because HIV prevention is an emotionally- and politically-charged subject, it is unlikely that funding decisions will ever be based on purely economic considerations. However, whenever possible and to whatever extent possible, economic concerns should be incorporated into the decision making process, in order to make HIV prevention more efficient, and thereby to save more lives.

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HIV; cost-effectiveness; prevention; economic analysis

© 2001 Lippincott Williams & Wilkins, Inc.


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