The Economics of Sexually Transmitted Infections

RECENT DECADES HAVE REVOLUTIONIZED THE study of sexually transmitted disease (STD) epidemiology and prevention in many ways. Social and behavioral aspects of both epidemiology and prevention have become important components of a multidisciplinary approach; use of mathematical models and policy studies have become widespread; randomized, controlled trials, and even cluster randomized trials with their many strengths and weaknesses, have come to occupy a central place in the field. Increasingly, health economics is becoming an indispensable element in the STD scientific tool kit.

The number of health economics papers published in the STD literature has increased substantially since the 1980s. For example, a literature search using OVID MEDLINE of manuscript titles in Sexually Transmitted Diseases and Sexually Transmitted Infections (including the former names of these journals, Genitourinary Medicine and British Journal of Venereal Diseases) for the terms cost or resource allocation found 7 articles published during the period 1985 to 1994 and 41 articles published during the period 1995 to 2004. The magnitude of the increase in health economics papers is likely understated because this search 1) excluded many journals that publish STD-related research, 2) examined article titles only, not key words or abstracts, and 3) used search terms that focused only on cost and resource allocation, which are just part of the full scope of health economics research.

This increase in the number of health economics articles published in the 2 leading STD journals between 1985 and 2003 is remarkable (Figs. 1 and 2). Moreover, the declining financial resources for health in general and public health in particular suggest that the future will bring even more remarkable increases in the numbers of publications on the health economics of STDs as well as the importance of economic analyses.

Fig. 1

Fig. 2

In this special issue, we include a summary of the theoretical and methodological building blocks of health economics as applied to STD epidemiology and prevention, and a number of cutting edge empiric and modeling articles that exemplify the current state of the art.

Economics and Sexually Transmitted Infection Epidemiology and Prevention

Economics is the science of choice given limited resources and available technology. Healthcare economics applies standard economic concepts, tools, and methods to health-related choices made by healthcare providers, by insurance companies, and by employers as well as to choices made by individuals and policymakers. Although human risk-taking behavior plays a role in determining the prevalence and incidence of most diseases, peoples' risky choices more directly and immediately affect the incidence of sexually transmitted infections (STIs) than for most other diseases. So the economics of risk-taking behavior has particular applicability to the study of STIs.

Economics tools are particularly well adapted to the study of human choices in 2 particular institutional settings: the market and the firm. Markets are relevant not only to the delivery of STI treatment and prevention services, but also to the supply and demand of risky services such as commercial sex and injections (of legal or illegal drugs) that spread STIs. Because most STI treatment services are delivered by healthcare providers who are employees of for-profit, nonprofit, or public healthcare institutions, economic analysis may help understand how to improve the contributions of these different types of providers to public health.

The Economic Perspective

Economic analysis can be applied either to determine how a decision-maker should choose among a set of alternatives or to study how that same decision-maker does in fact choose. Studies of how actors should choose are referred to as normative analyses, whereas studies of how they actually do choose are positive analyses. A frequent application of economics to STIs, exemplified by several articles in the current volume, is to analyze how a government should allocate resources to STI control by comparing the cost-effectiveness of alternative STI prevention or treatment alternatives. Such studies apply a normative approach to analyzing the problem of STI control from the perspective of the government policymaker (rather than that of, e.g., the healthcare provider, the insurer, or the patient). On the other hand, a study of the effect of subsidized condom distribution programs on the individual's choice to use condoms would be primarily a positive analysis of the determinants of condom use. Often positive and normative approaches are mixed in the same article. For example, a normative cost-effectiveness analysis of alternative government policies must incorporate either implicit or explicit positive analysis regarding the choices that healthcare providers or individual risk-takers actually make in response to such policies.

Normative economic analysis typically starts from the assumption that market forces are superior to bureaucratic planning as a mechanism for allocating resources in society. When people have perfect information and their choices affect only their own well-being, economic theorems establish that markets will achieve a version of social optimality that would be difficult or impossible to reach through planned government interventions.

From this description of the discipline, it would be hard to understand why economics has famously been nicknamed the dismal science. After all, choice is inherently an exercise in freedom and thus should perhaps be a liberating, even exhilarating, topic of study. The exercise of free choice supposedly leads to a social optimum. What is dismal about that?

The difficulties in the exercise of choice, and in its study, arise when choices must be constrained by limited resources, technology, and information. Further complications arise when choices affect people other than those who make them. The choices faced by a sex worker who must decide whether to insist on a condom at the expense of losing a client or accepting a lower payment are indeed difficult and depressing-and have consequences for many other people in society. The health policy choices faced by a government with a healthcare budget of less than $10 per capita per year are similarly dismal. The healthcare provider in a rural African village who must choose how to spend his or her limited time between showing up for his or her scheduled clinic hours and working on his or her more remunerative vegetable garden is also less than pleased with his or her options.

Although the conditions under which choices must be made often represent difficulties for the decision-maker, these conditions are what make the application of economics challenging and exciting for the economist and his or her audience. The limitations placed on choices by available financial and human resources and information and by available technologic possibilities transform the study of choice from a sterile exercise in utopian idealism into a concrete and potentially useful contribution to understanding and improving the well-being of humans. Casting light on these difficult choices in the face of limited resources, information, and technology is the essence of the potential contribution of health economics to the study of STIs. What do these actors choose? What costs and information determine their choices? How should policymakers alter information and incentives to help people make choices that will be better for themselves and for each other?

Economics thus has the most to contribute when it is applied in a context in which the available technologic options and the financial and human resource costs of each choice are known or can be determined. The application of health economics to STIs benefits from the substantial existing literature on the epidemiology of STIs and on the psychology and sociology of sexual risk-taking behavior. The epidemiology of STIs provides a quantitative description of the technology of STI transmission and of its prevention without which no quantitative analysis of the cost-effectiveness of public interventions would be possible. The psychology and sociology of risk-taking help the economist to move beyond reductionist, utilitarian models of risk-taking, which are the foundation for most economic models of individual choice to more textured and realistic models of this complex behavior.

Methods

Economists use, and contribute to the development of, a toolkit of quantitative methods, which is common to epidemiology, sociology, operations research, and clinical evaluation research. For the positive analysis of choices that individual risk-takers, healthcare providers, employers, insurance firms, or decision-makers actually make and of the impact of alternative policy options on those choices, economists use a branch of statistics called econometrics, which is closely related to the techniques used by epidemiologists under the name biostatistics.

The Analysis of Nonexperimental Data

Like epidemiologists, economists apply multiple regression analysis to explain the nonexperimental observed variation in either continuous variables (e.g., the frequency of risk behavior or the amount of expenditure on condoms in a given time period) or binary variables (e.g., whether a person becomes infected with a given STI or seeks health care in a given time period). Both disciplines attempt to partition the variance in such dependent variables into a portion that is explained by a set of explanatory variables and a portion that must be attributed to random unexplained error.

Aware that multiple regression analyses require that the explanatory variables be statistically independent of the error term, epidemiologists frequently refer to the estimated relationships as statistical associations and hesitate to assert that they are estimating the causal impacts of explanatory variables on behavior. In contrast, economists are perhaps somewhat more likely than epidemiologists to apply a set of techniques designed to assure that these variables are exogenous to the person or entity making the choice under question so that they can be assumed to be independent variables in the statistical sense so that they can claim that the explanatory variables in such analyses are causes or determinants of the behavior under study. Where statistical independence of an explanatory assumption is clearly an untenable assumption, the economist will typically either omit the offending variable from the list of explanatory variables in the multiple regression or will apply instrumental variable techniques to correct the statistical estimates. However, the distinction between the use of multiple regression in the 2 disciplines is disappearing over time.^1,2

The Analysis of Experimental Data

Economists have benefited over the last 3 decades by adopting and building on the statistical techniques of randomized, controlled trials, which were pioneered by epidemiologists and biostatisticians. The 2 disciplines have jointly and independently applied classic experimental design techniques to analyze the health benefits of policy options more rigorously than would be possible from nonexperimental data. From the econometrician's perspective, a rigorous experimental design provides the perfect instrumental variable to solve the endogeneity problem and allow the analyst to correctly estimate the impact of an explanatory variable on an individual choice or on a health outcome. However, because analysts recognize that the people who are the subjects of experiments sometimes decline to cooperate fully with an experimental design or that ethical considerations constrain the design, the distinction between the simple rigor of the analysis of experimental data and the more complex approaches required for nonexperimental data are disappearing. Both econometricians and biostatisticians have benefited by beginning to incorporate the technique of propensity score matching into their toolkits to better identify the causal impacts of explanatory variables in both experimental and nonexperimental data sets.^3-6

Simulation Modeling as a Tool for Evaluating Sexually Transmitted Infection Interventions

Economists have long used simulation models to analyze the potential impact of changes in various policy instruments on the economic welfare of the population. Simulation modeling also has a long history in epidemiology and particularly in the study of STIs. The first systematic analysis of the dynamics of STI infections using simulation analysis was that of Yorke et al subsequently expanded into book form in Hethcote and Yorke.^7,8 These pioneering authors used their simulation model to identify the importance of heterogeneous sexual behavior for the spread of STI epidemics and argued for targeting interventions to the most sexually active subgroups of the population. By adding cost considerations to the Hethecote and York simulation model and expanding its application beyond gonorrhea to chlamydia, chancroid, syphilis, and HIV, Over and Piot^9,10 showed that interventions that succeeded in reaching and changing the behavior of members of core groups would not only be more effective, but would also be dramatically more cost-effective. However, they caution that their model did not incorporate the costs of actually reaching or of changing the behavior of various groups.¹⁰ Also, it did not include the possibility that individuals would alter their behavior in the absence of government intervention.

It is not an accident that mathematical simulation methods play a larger role in understanding STIs than they do for many other diseases. As Hethcote and Yorke⁸ first showed, the benefits of an STI intervention, and the sensitivity of its benefits to the selected target group for the intervention, can only be understood by incorporating the future benefits to people who do not directly receive the intervention. Furthermore, mathematical simulation models illuminate the complex intertemporal dynamics of STI interventions and the effects of policy interventions on those dynamics. A failure to appreciate the implications of such models is arguably the source of the current contradiction in international health policy, which proposes both the reduction of HIV prevalence and the treatment of patients with AIDS (which raises HIV prevalence) as simultaneous objectives of international development assistance.

Cost-Benefit and Cost-Effectiveness Analysis

The normative branch of economics attempts to advise government policymakers regarding policies that they should choose and implement to improve the well-being of the population. Important economic tools for this purpose include cost-benefit analysis and its less ambitious relative cost-effectiveness analysis. Cost-benefit analysis estimates the value of the expected future stream of social benefits net of the value of the future stream of social costs of a given proposed policy intervention and recommends the implementation of policies for which the net present value of the intervention is greater than zero. Alternatively, analysts sometimes compute the ratio of the benefits to the costs and recommend implementation of the policy if the ratio is greater than one. In either case, the key requirement is to project the future evolution of key cost and benefit variables both with and without the intervention. These 2 future paths for the variables are sometimes referred to as the baseline scenario (without the intervention) and the intervention scenario. When the policy being evaluated is already being implemented, the future without the intervention is referred to as the counterfactual (because it is contrary to fact). The benefits and costs of the intervention are both computed in comparison to the baseline or counterfactual.

On the assumption that government decision-makers should consider all social costs and benefits in their decisions, cost-benefit analysis adopts the perspective of society at large so that it includes benefits that accrue to anyone in the society and also costs borne by anyone with provisions to avoid double counting. Resources used to implement the policy are valued not at their real or market cost, but instead at their shadow price, defined as their value in their most valuable alternative use. The dollar value of benefits to all the members of society might simply be added together or alternatively might be weighted by equity weights whose derivation is problematic. Uncertainty regarding future values of benefits or resources or about future technology can be incorporated through stochastic or Monte Carlo analyses but are dependent on the validity of the assumptions regarding the future joint distribution of the uncertain variables. In principle, cost-benefit analyses could be performed for public investments in all sectors of the economy, and all those investments with positive net present values should be implemented. A theoretical advantage of cost-benefit analysis is that it permits comparison of investments across sectors and thus could potentially be used to advocate reallocation of resources away from other sectors (like road construction or law enforcement) to public health. One of the earliest applications of cost-benefit analysis to public policy was a paper on the economics of syphilis control by Klarman.¹¹ However, cost-benefit analysis has rarely been applied to health policies, because of the controversial issues surrounding the valuation of human health and life.

To address the normative questions of what policies government decision-makers should select, health economists have much more frequently applied the less ambitious technique of cost-effectiveness analysis (CEA) or one of its variants such as cost-utility analysis or cost-risk analysis. CEA computes the costs (i.e., the discounted present value of the future stream of costs) of a policy by comparing the future costs of the policy with the future costs in the baseline or counterfactual scenario. CEA then compares these costs with some measure of the future stream of health benefits from the policy. The result of CEA is thus typically expressed as the estimated dollar cost per unit of health benefit. If the measure of health benefits is sufficiently general, the government could use the results of such analyses on a wide variety of health interventions to construct a league table, which would rank interventions by their cost-effectiveness. Examples of general measures of health benefits include the life-year, the quality-adjusted life-year (which adjusts for the subjectively valued quality of life), and the disability-adjusted life-year (which adjusts for objectively valued disability). For developing countries, the most ambitious attempt at such a league table is embodied in the World Bank's 1993 World Development Report, for which Jamison et al¹² provided background estimates on the cost-effectiveness of interventions by disease group, including Over and Piot⁹ on STIs. This report also originated the concept of the disability-adjusted life-year (DALY) as a practical alternative to the quality-adjusted life-year for comparing the health benefits from interventions in populations that might not attach similar relative qualitative values to alternative health states.

Instead of adopting the social perspective considered as appropriate in orthodox cost-benefit analysis, health economists have often adopted the perspective of the government budget. That is, they have analyzed the additional costs to the government of adopting a certain healthcare intervention (in comparison to a baseline or counterfactual scenario) and considered these costs as the expenditure required to achieve the projected health benefits of the intervention. This approach has the advantage of simplicity and also responds to the observation that government decision-makers may be constrained by their institutional environment to focus only on their own budgetary cost. However, analysis from an exclusively governmental perspective ignores both the true social cost of the healthcare resources used, which may be higher or lower than their budgetary cost and the true costs the policy imposes on patients, providers, and other private individuals. By ignoring the true costs to individuals, this approach ignores the fact that individuals make their own decisions subject to those costs and other incentives. This approach may thus lead to incorrect policy prescriptions.

For example, the true social value of a physician's time is typically much higher than his or her salary. Therefore, adopting a policy with a favorable CEA that is based on the physician's budgetary cost may lead to the kind of manpower problems that are currently appearing in the case of the AIDS treatment initiatives in Africa. A CEA of an intervention that ignores the cost to patients of adopting required behaviors is similarly likely to understate the cost per health benefit that will be achieved. In principle, a social CEA should add the incremental costs of the policy incurred by the patients to the properly shadow-priced incremental costs to the government to arrive at a true or proper cost per unit of health benefit achieved by the proposed intervention. This has rarely been attempted.

Heterogeneity in Costs and Effects and Determinants Thereof

The publication of league tables, which present and compare the cost-effectiveness of each of several interventions, is that they hide a great deal from the reader. No intervention is so well-defined that it has a single cost-effectiveness in every situation and every population. Even within similar populations, each of a set of interventions can vary in cost-effectiveness so much that the ordering of the interventions by cost-effectiveness is unstable.

The variation in the cost per unit of effective treatment across different published studies of the treatment of each of 8 STI syndromes or diagnoses is remarkable (Fig. 3). Note that the cost per effective treatment can vary by a factor of 2 or 3 for the same disease.

Fig. 3

The causes of variation in cost-effectiveness for a single treatment are many. A partial list of the variables that determine the cost and therefore the cost-effectiveness of STI treatment includes the following:

* Public/private delivery

* Economies of scale

* Economies of scope

* Prevalence and incidence

* Epidemic phase

* Transmission efficiency

* Health system parameters

* Population composition and population concentration

* Resource combinations and input prices

* Incentives to providers for high quality and quantity of service delivery

* Demand as a constraint to cost-effective delivery

* Population composition and concentration

* Stigma

* Disutility of the measure (i.e., dislike for condoms); and

* Price, income, and distance elasticities.

Because unit costs can vary so much, an important topic for research is to study the relationship between unit cost and all of its determinants. Such a relationship is called a cost function. By applying the traditional economic tools of accounting and econometrics, economists can shed light on the roles that various determinants of costs and of cost-effectiveness play in various settings.

Contents of the Special Issue

The articles included in the special issue cover a multitude of analyses and answer a considerably varied set of STD/HIV related health economics questions. Gift and colleagues analyze insurance claims data and estimate the direct medical costs of epididymitis and orchitis.¹³ Vickerman and colleagues report on the cost-effectiveness of expanding harm reduction activities for injecting drug users in Odessa Ukraine.¹⁴ In a second paper, Gift and colleagues present the results of their cost-effectiveness analysis of a jail-based chlamydia screening program for men.¹⁵ Terris-Prestholt and colleagues analyze data from the Masaka Intervention Trial, which was conducted in Uganda between 1996 and 1999 and consider the role of community acceptance for costs of HIV and STI prevention interventions.¹⁶ An article by Blandford and colleagues focuses on productivity losses among reproductive-aged women in the United States, which can be attributed to untreated chlamydial infection and associated pelvic inflammatory disease.¹⁷ In a second article, Vickerman and colleagues explore the issue of whether targeted HIV prevention activities are cost-effective in high-prevalence settings, a controversial issue that continues to keep policymakers and prevention scientist engaged in heated debate.¹⁸ Widespread implementation of many efficacious interventions poses problems in all countries as a result of the costs involved in scale-up; in an additional article, Terris-Prestholt and colleagues analyze data from Mwanza, Tanzania, and focus on the costs of an adolescent sexual health program.¹⁹ The effectiveness and cost-effectiveness of gonorrhea prevention efforts in the United States from 1971 to 2003 is the subject of an article by Chesson and colleagues, which explores the impact of changes in prevention funding.²⁰ Finally, an article by Mead Over and colleagues looks at the costs and consequences of policy options involved in the use of antiretroviral therapy in HIV prevention in India.²¹

At this time of increased emphasis on accountability and competition, and declining health resources, the need for evidence-based policy decisions is glaring. Economic analyses of STD interventions support such decision-making. As is apparent in this summary, the collection of articles in this special issue covers many specific sexually transmitted pathogens, many countries, and a wide variety of resource allocation questions.

We hope their publication in this special issue will enhance understanding and appreciation for economics perspectives in the STD field and stimulate future research.