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The welfare costs of HIV/AIDS in Eastern Europe: an empirical assessment using the economic value-of-life approach

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Abstract

Based on the aggregation of individual willingness-to-pay for a statistical life, we calibrate an inter-temporal optimisation model to determine the aggregate welfare loss from HIV/AIDS in 25 Eastern European countries. Assuming a discount rate of 3%, we find a total welfare loss for the whole region that exceeds US $800 billion, approximately 10% of the region’s annual GDP between 1995 and 2001. Although prevalence and incidence rates diverge sharply between countries—with central Europe far less affected than major countries in the Commonwealth of Independent States and the Baltics—the epidemic is likely to spread to all countries unless a coherent strategy of prevention and treatment is backed up by substantial increases in healthcare investments. The sheer size of this task and the international nature of the epidemic render this one of the most important current challenges for all of Europe.

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Notes

  1. In this vein, Stover et al. [8] estimate the costs for prevention per infection averted in Eastern Europe at US $ 9,148 and compare this with an estimated net present value of lifetime treatment at US $ 11,203 to infer savings of US $ 2,055 per infection averted during the 2005–2015 period. The value-of-life methodology suggests these cost savings are only a small fraction of the total social value of lower HIV infection rates.

  2. The health economics literature has not found a consensus on the correct discount rate. Viscusi and Moore [9] suggest a discount rate between 1% and 14%, yet newer estimates lie mostly below 10%. Based on contingent valuation, instead of revealed preferences as in wage compensations for occupational risks, Johannesson and Johansson [10] find discount rates for life years between 0 and 3%, Cairns and van der Pol [11] for health effects between 6 and 9%, whereas Ganiats et al. [37] find rates from negative to 116%. See also Frederick et al. [12].

  3. For Albania, the Czech Republic and Slovenia, no original data on ART access are available. To obtain a rough estimate for these countries, we ran an ordinary least squares regression of ART coverage for HIV + persons in the other countries of our sample on real GDP per capita and a constant, yielding (absolute value of t-statistics in parentheses): ART cover = −0.064 (0.55) + 0.073 (4.85)** RGDP based on 22 observations with R2 equal to 51%. The slope coefficient is significant at the 1% level. Using data on real GDP per capita, we obtained predictions for the countries with missing information on ART coverage among HIV + persons.

  4. A descriptive survey of national responses to HIV/AIDS in the Western Balkans and of recommendations for region-wide activities is provided in Godinho et al. [22].

  5. This review is the fifth in a series of six papers that Lancet published in 2008 about HIV prevention, surveying the state-of-the-art in biomedical, behavioural and structural approaches.

  6. The Laspeyres-Index is the geometric mean of the price ratios for products characteristic of a base country, regardless of whether the products are representative or not of the other countries to be compared with. The Laspeyres Index produces a bias because it does not take into account the changes in relative prices, and hence, substitution effects in consumption. That is why in higher income countries, the costs of living still supersede those of countries with lower per-capita income. This could explain why, for countries with a higher income, the calculated life-death indifference parameter α is lower.

  7. Between 1990 and 1995, Hungary exported very much the same products (Hoekman and Djankov [31]), while Romania and Bulgaria changed their composition of exports significantly (especially with regard to the EU). Kazakhstan and Russia’s principal exports are oil (which is why they owe their positive GDP growth to rising international oil and gas prices). Georgia’s main export items are metals, wine and mineral water. The same applies to Moldova and to Ukraine, a metal and steel-exporter. All of these countries are vulnerable to changes in the external economic environment because of their narrow export base.

  8. The country-specific estimates were obtained by the UNAIDS/WHO [2–4] working group in two basic steps. First, point prevalence estimates for 1994 and 1997 were carried out and the starting year of the epidemic was determined for each country. In a second step, these estimates of prevalence over time and the starting date of the epidemic were used to determine the epidemic curve that best described the spread of HIV in each particular country. A simple epidemiological program (EPIMODEL) was used for the calculation of estimates on incidence and mortality from this epidemic curve.

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Authors and Affiliations

  1. Kreditanstalt für Wiederaufbau, Frankfurt, Germany

    Julia Fimpel

  2. Kiel Institute for the World Economy, Duesternbrooker Weg 120, 24105, Kiel, Germany

    Michael Stolpe

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  1. Julia Fimpel
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Correspondence to Michael Stolpe.

Appendix

Appendix

The WHO declared the following countries as belonging to Eastern Europe: Belarus, Bulgaria, Czech Republic, Hungary, Poland, Republic of Moldova, Romania, Russian Federation, Slovakia and Ukraine. We extend this list by adding countries from South Eastern Europe, i.e. Albania, the former Yugoslav Republic of Macedonia, Bosnia and Herzegovina, Serbia and Montenegro, Slovenia, and Croatia. Further, we include Cyprus and Turkey because of their geographic situation, the Baltic states Estonia, Latvia and Lithuania as well as the Caucasian countries Azerbaijan, Armenia and Georgia, which can be considered as Eastern Europe from a historical perspective. While Kazakhstan belongs to Central Asia, a small part of it lies on the outer border of Eastern Europe, which is why we also include this country. This leaves us with 25 countries in the list. The remainder of this appendix reports details of our data sources, how we handled missing observations and issues of data quality.

Income variables

We use the real gross domestic income adjusted for changes in the terms of trade (RGDPTT) from the Penn World Table 6.1 [30] as an indicator for per capita income, taking averages for the years from 1995 to 2000, and, in the case of missing data, the average from the available years. The RGDPTT measures domestic absorption in international price value for 1996 for a given country and year, but allows for current export and import prices in valuing the net foreign balance. It takes into account a country’s changing ability to use its exports to buy imports, as its terms of trade change over time. This is particularly important for developing countries, which rely on a limited range of products for their overall export earnings. The “real” stands for purchasing power parity (PPP)-converted GDPL (gross domestic income after Laspeyres) with the impact of inflation already taken into account. This is done by using a weighted basket of goods and services according to the Laspeyres Index.Footnote 6

Many Eastern European countries still have a narrow export base, i.e. only few primary export products, such as metal, steel, oil, or fruit.Footnote 7 Furthermore, some countries are very small, making them even more dependent on their exports and imports. The adjustment for changes in the terms of trade includes the impact of international price changes on the gross domestic income due to imports and exports, and therefore should be used for our purposes. For two countries (Bosnia and Herzegovina, and Serbia and Montenegro), the RGDPTT could not be obtained from the Penn World Tables 6.1. [30]. Therefore, we additionally use GDP in PPPs from the International Monetary Fund (IMF) economic outlook database [32]. The IMF-figures are higher for the Balkan region because these countries are all very import-intensive. For the purpose of our calculations, we carried out a regression for the Southern region to predicted RDGPTT for Bosnia and Herzegovina and Serbia Montenegro.

Number of AIDS deaths

The number of deaths is usually a reliable figure, but the number of reported AIDS-deaths is still very low. This is why our result is likely to underestimate the true welfare losses from HIV/AIDS. The annual number of reported AIDS deaths is obtained from the EuroHIV—HIV Surveillance report for Europe in 2004 [33]. We assume these deaths to be distributed proportionally to the population in each age group and then calculated counterfactual survival probabilities if no AIDS existed, calculated as described in the section “Data and empirical methods”.

The estimated number of people living with HIV

Today, an HIV-infected person can survive for a long time and so his/her life will not become worthless after an infection. But this person still suffers utility reductions in having to protect others from being infected, loss of reputation or friends due to fear of potential infection etc. This utility reduction is difficult to determine empirically, but it can be incorporated if we assume that at a certain stage these HIV-infected people succumb to AIDS, and finally, to death.

The share of a population currently living with HIV is expressed by a prevalence rate. An HIV-incidence rate, on the other hand, counts only newly diagnosed cases of HIV per specified population in 1 year. The incidence rate is a measure of the speed at which the epidemic is spreading while the prevalence measures the overall burden at a given time. UNAIDS provides estimatesFootnote 8 for the HIV-prevalence in adults between 15 and 49 years of age.

Age distribution of the total number of deaths

The distribution of actual deaths is available from the WHO life tables (see below, [34]) for the years 2000 and 2001 stating the total number of deaths per age-group.

Population

The population distribution for the years 2000 and 2001 is from the WHO life tables [34]. These state the actual population size for each country in the year 2000 and 2001 within different age groups of 5-year intervals. For Serbia and Montenegro, this data is not available, so that for this case, we found that the figures from the UN Population Division [35], providing the percentage of the total population in different age groups (0–4, 5–14, 15–24, 60+, 65+ and 80+) as well as the median age for 1995, 2000 and 2005, are very similar to the population distribution of Bosnia and Herzegovina. Hence, we assume the same distribution for both countries.

Incidence rates

The incidence rate is the rate of new HIV-infections in each year, while the prevalence rate is based on the number of people living with HIV. This means that a person infected with HIV in 1 year will be included in the prevalence rate for all following years until his/her death, while the incidence rate counts each HIV-infected individual only once. The incidence rate is calculated as the annual number of new infections divided by the population at risk in this period. The incidence rates for 1994–2001 were obtained from the EuroHIV HIV Surveillance report for Europe. For estimating the distribution of HIV-incidence across the population, we do not assign HIV/AIDS-mortality proportionally to each age group (as Philipson and Soares [1] did for Africa), because the HIV-prevalence in children under 15 is nearly zero in Eastern Europe. In Africa, due to a high infection rate in children, HIV-mortality can be assumed to show a more continuous trend than in Europe. For our study, we assume that the HIV/AIDS-prevalence in children is equal to zero and that there are no new infections beyond the age of 49, simply because there are no figures available for those over 50 years old.

Human capital/education

The level of educational attainment is one available indicator to approximate the amount of human capital that is already present in a country. This indicator gives the percentage of the population with a completed university degree and was obtained from the UN Economic Commission for Europe [36].

We also use an index of the number of people enrolled in a certain level of education to quantify the amount of human capital that is currently being built up in the country. Percentages of children in school are represented by GER. The GER is the number of pupils enrolled in a given level of education regardless of age expressed as a percentage of the population in the theoretical age group for that level of education [20].

Treatment

The survival of an HIV-infected individual depends on access to, and on the quality of, medical treatment. A low rate of HIV-treatment could pose a strong incentive for HIV-infected people to emigrate to countries providing better anti-retroviral treatment, leading to a lower prevalence rate in the affected countries. While in Western Europe basically every individual has access to ART, in Eastern Europe this medical treatment is still very limited. On the other hand, it could be argued that people who can afford to emigrate financially can also afford to access ART in their own country. Furthermore, the prices for ART are rapidly falling, from an initial price for a three-drug-ART regimen of US $10,000, to currently US $300 in sub-Saharan Africa (in Europe, the costs are still higher) with a falling trend. In Eastern Europe, access to treatment in a country is significantly correlated to the real GDP per capita. This implies higher survival probabilities for HIV-positive individuals in the richer countries in our sample.

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Fimpel, J., Stolpe, M. The welfare costs of HIV/AIDS in Eastern Europe: an empirical assessment using the economic value-of-life approach. Eur J Health Econ 11, 305–322 (2010). https://doi.org/10.1007/s10198-009-0177-y

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