Economic analysis at the global level: a resource requirement model for HIV prevention in developing countries

hEAtTHpolicy Health Policy 38 (1996) 45-65

Economic analysis at the global level: a resource requirement model for HIV prevention in developing countries Jonathan Broomberga, Neil S6derlundb, Anne Mills”,* “The

Monitor Company, Johannesburg and the Centre for Health Policy, Department of Community Health, University of the Witwatersrand, PO Box 1038, Johannesburg 2000, South Africa bDepartment of Public Health and Primary Care, University of Oxford, Gibson Building, Radcliffe Infirmary, Oxford OX2 6HE, UK “Health Policy Unit, Department of Public Health and Policy, London School of Hygiene and Tropical Medicine, Keppel St., London WCIE 7HT. UK

Received 10 October 1995; revised 17 April 1996;accepted 17 April 1996

Abstract

Agencies operating at the international level have a need for economicanalysisto help develop global health policiesand determineresourcerequirementsto support their advocacy efforts. This paper presentswork commissionedby the Global Programmeon AIDS to estimate the total resource requirementsof implementinga package of HIV prevention strategiesin developingcountries. The modellingapproachidentified a hypothetical package which shouldbe implementedand developeda set of assumptionsrelating the size, number and coverageof programmesrequired for each strategy to a set of demographicand other characteristicsof individual countries. Costswere attachedto estimatethe total costsof the packagefor individual countries,regionsand the developingworld. Resultsare presentedfor regionsand their implicationsdiscussed.Conclusionsare drawn on the value of this type of modelling approach to estimatingresourcerequirements. Keywords:

HIV; Economic analysis;Resourceallocation; Modelling; Developing countries

* Corresponding author.Tel.: +44 1719272354; fax: + 44 1716375391. 016%8510/96/$15.00 0 1996ElsevierScienceIrelandLtd. All rightsreserved PII SO168-8510(96)00838-X

46

J. Broomberg

et al. / Health

Policy

38 (1996)

4.5-65

Economic analysis has so far been used primarily to feed into management, planning and policy decisions taken at various levels within countries [I]. However, agencies working at the international level also have a need for the advice provided by economic analysis. On the one hand they need to identify which health interventions and strategies are the more cost-effective and should be promoted by them; on the other hand they need to identify the ma~itude of resources required to implement such inte~entions and help mobilise funds. Despite these needs, international agencies, notably WHO, have been slow to employ economic analysis to help them develop and implement global policy, with a few exceptions. The Expanded Programme on Immunization (EPI) has a long history of encouraging programme costing [2] and cost-effectiveness analysis [3]. The Diarrhoeal Disease Control Programme of WHO in the early 1980s commissioned a series of reviews on the effectiveness of alternative approaches to controlling diarrhoeal disease, including a study of their cost-effectiveness 141, in order to help identify which approaches they should be promoting. In the early 1980s there were several attempts to place a price-tag on ‘Health For All by the Year 2000’ and on the provision of a comprehensive package of primary health care [5,6]. However, these represent relatively isolated attempts to use economic analysis in global policy-making. Recently the publication of the World Development Report 1993 ‘Investing in Health’ [7], through its use of cost-eff~tiveness analysis to set priorities for government funding in the health sector and identification of the per capita cost of a minimum package of essential clinical and public health measures, has highlighted the potential contribution (as well as pitfalls) of economic analysis to global health policy-making. In the last few years, there has been a considerable expansion of health economics expertise within WHO, and a greater awareness of the value of economic analysis - if only for advocacy purposes. The work reported here was commissioned by the Global Programme on AIDS in order to help to begin to develop knowledge of the cost-effectiveness of different approaches to HIV prevention and of the total cost of implementing a package of interventions in developing countries. Information on the cost analysis has been published elsewhere [&I; this paper concentrates on the model developed to estimate resource requirements. Ideally, an approach to estimating global resource requirements for HIV prevention should take full account of the cost-eff~tiveness of alternative strategies. In the case of HIV prevention efforts, however, there remains very limited information on the relative costs and effectiveness of the large number of different strategies currently in use. A recent review suggested that almost half of all HIV prevention programmes in industrialised countries, and one-third of those in developing countries, had not been subjected to systematic evaluation [9]. Increasing numbers

J. Broomberg

et al. / Health

Policy

38 (1996)

45-65

41

of programmes have recently begun to collect data on the costs of interventions, which has improved understanding of the relative costs of different strategies, as well as of the dete~inants of those costs [lo,1 l]. Despite this trend, currently available data are not yet su~ciently comprehensive to determine important relationships between such factors as programme scale and cost, or to identify other important determinants of cost. Data on effectiveness of interventions are even more limited, for three main reasons. Firstly, most assessments of effectiveness have relied on process indicators, on the basis of which effectiveness assumptions may have been made [12,13], rather than on measures of outcome. The former suffer from the obvious problem of uncertainty as to the causal links between the measured indicators and reduced HIV transmission [lo]. Secondly, although a small number of recent studies have produced outcome data, showing reductions in HIV incidence levels or serocoversion rates in response to various interventions [9,10,14], these do not yet provide a sufficient data set on which to develop generalisable conclusions as to the absolute or relative cost-effectiveness of different intervention strategies. Moreover, such studies require a considerable time to produce results. Thirdly, despite the fact that several inte~entions are often implemented s~ultaneously, info~ation on interactions between these inte~entions, and on their relative contribution to outcomes, remain scarce. Nonetheless, despite the absence of good cost-effectiveness data, international policy-makers still need to know the resource implications of the policies they promote. Most notably, in the case of the Global Programme on AIDS (GPA), information on the costs of prevention was required in order to help mobilise funds from the donor community. In this paper, we present the results of a model developed for GPA which utilises previous research on costs [8], as well as cost data from some additional sources, to estimate the resources required to implement a hypothetical package of HIV prevention strategies in developing countries. The aims of this exercise were twofold: firstly, to use realistic assumptions about the required scale and number of a group of prevention strategies, and the best available cost data, to estimate the costs of a package of prevention strategies for individuai countries, regions and the whole developing world; and secondly, to assess the affordability of such a package and the effects of budget constraints on its implementation. This approach allows for an assessment of the resource implications of what might be considered ‘feasible best practice’ in HIV prevention, while using best available estimates of actual programme costs obtained under conditions similar to those in which the hypothetical programmes would have to be implemented. Information of the kind produced by this exercise has several uses. It allows for evaluation of current resource allocation patterns, at both national and international levels, and can be used for planning future resource allocation and for the mobilisation of awareness and financial support for HIV prevention efforts. It also provides an example of the kind of detailed planning exercises that individual countries and international agencies will need to undertake in order to allocate HIV

48

J. Broomberg

et al. / Heahh

Policy

38 (1996)

45-65

prevention resources efficiently, and suggests one approach to such planning. It is impo~ant to recognize, however, that this exercise suffers from several Iimitations, largely imposed by data deficiencies. These include the limited generalisability of currently available cost data, the hypothetical basis of the normative formulae developed in the model, and problems in the extrapolation of programme costs to different countries. These problems are discussed in some detail below. In the following sections we present the methodological approach adopted, the results of the model, and some of the major implications of the results.

2. Methods The modelling approach began with the development of a hypothetical package of HIV prevention strategies which ought to be implemented in developing countries. This was followed by the development of a set of assumptions relating the size, number and coverage of programmes required for each strategy to a set of specific demo~aphic and other ~hara~te~sti~s of ~dividual countries. Costs were then attached to these programmes to allow estimates of the total costs of the package for individual countries, regions and the whole developing world. This section outlines our approach to each of these steps in some detail. There is as yet no consensus on what constitutes the essential components of an HIV prevention package, and the dearth of empirical data on the relative effectiveness of most strategies prevents the use of such data in strategy choice. All strategies which have generally been regarded as important components of an HIV prevention package 115-171 were therefore included. These strategies were identified through a review of the literature, and consultation with a wide range of workers and researchers in the field of AIDS prevention. The strategies included in the package are shown in Fig. 1. Normative assumptions as to the size, number and coverage of programmes for each strategy were based on what might be regarded as ‘feasible best practice’, implying an optimal level of programme implementation. What constituted a ‘programme’ was largely determined by the nature of cost data used [8]. Specification of coverage assumptions was also determined by the availability of necessary input data for developing countries. These assumptions were incorporated into a specific formula for each strategy. Table 1 summarises the main assumptions embodied in each of the formulae. In the case of two strategies, condom social marketing (CSM) and treatment for sexually transmitted diseases (STDs), low and high assumptions were used because of the presumed sensitivity of results to the assumptions. Since HIV and AIDS are not uniformly distributed between countries, the formula for the CSM strategy inco~orated an HIV incidence weighting factor, which has the effect of increasing the scale of CSM programmes in countries characterised by current high HIV incidence levels. For the purposes of weighting, countries were grouped into low, medium and high incidence categories (approximately < 0. l%, 0. 1- I%, and > 1% annual incidence, respectively). Incidence rates were not known for most countries, and were estimated on the basis of prevalence

J. Broomberg

et al. 1 Health

Policy

38 (1996)

45-65

49

data [l&19]. HIV incidence weights were not applied to any of the other strategies, since these were regarded as essential and largely affordable, irrespective of current HIV incidence. Formulae were then applied to individual countries, generating estimates of the scale of programmes for each intervention strategy in each country. All countries falling into the low income, low middle income and upper middle income categories in the 1992 World Development Report (WDR 92) [20] were included in the model. Most small island states with populations below 1 million (which constitute a separate category in the WDR 92) were also included, although some Pacific islands were excluded due to lack of data. Countries were grouped into four regions: Africa, Latin America and the Caribbean, Asia (including the Middle East), and Europe (including the former socialist economies and the former USSR). The application of the formulae to individual countries necessitated the use of detailed demographic and economic data, as well as data on the incidence and prevalence of STDs and HIV infection for each country. Demographic data used included population size and structure for urban areas and for the whole country, numbers of males in the sexually active age groups (defined as 15-49 years), and numbers of children attending secondary school. Economic data included Gross National Product (GNP) and national health expenditures. These data were obtained from a number of published sources [20-221. Other sources are given in Table 1. Having determined the scale of programmes for each strategy for individual countries, relevant capital and recurrent programme costs were attached to give the overall resource requirements of implementing the full package of prevention strategies. These costs were derived from previous research by the authors [8] and from some unpublished sources, and are summarised in Table 2. Details of how

1. Promotion

of safer sexual behaviours through mass media programmes

2. Education of prostitutes and their clients, and provision of condoms 3. Provision of combined sex/HIV education in secondary schools 4. Ensuring safety of blood transfusions through screening of donated blood for HIV 5. Provision of STD treatment services 6. Provision of condoms through social marketing programmes 7. Prevention of unsafe drug use behaviours through needle exchange/bleach provision programmes

Fig. 1. Package of essential HIV prevention strategies.

50

J. Broomberg

et al. / Health

Policy

38 (1996)

45-~65

Table I Coverage assumptions used in the model Strategy

Coverage assumptions used

Mass media

Countries with Countries with Countries with per 50 million City population

500 000-10 million population - 1 programme lo-50 million population - 2 programmes r50 million population - additional 1 programme people No of pro~ammes per city:

Prostitute peer education

School education Blood safety STD treatment

Male prost.

0.1-l mill 0 1 l-2 mill 1 2 2 4 2-5 mill > 5 mill 3 6 Alf secondary school students Provision of HIV safety component of blood safety; assumes blood usage at a rate of 10 units of blood/l~ population~year~ % of incident STD cases coveredb Country income level

Condom social marketing

Needle exchange

Female prost.

High assumption

Low assumption ~.-~ Low-income 50% 25% Middle-income 80% 50% High coverage assumption - 30% of males aged 15-49 supphed with 52 condoms each per year Low coverage assumption - 15% of males aged 15-49 supplied with 52 condoms each per year Coverage weighted so that only urban men covered in low incidence countries, urban + half of rural men in intermediate incidence countries, and all men in high incidence countries One programme per city with > 1 million population

“Derived after consultation with WHO blood transfusion experts. bBased on estimated cumulative incidence of conventional STDs for different regions {World Bank. unpublished data collected for 1993 World Development Report), with all countries in the region assumed to have incidence patterns approximating the regional average. The expected number of cases treated was derived by multiplying expected incident cases by a low and high proportion of incident cases assumed to receive treatment.

they were derived are outlined in [23]. Only costs incurred by the providing agency were taken into account, and so are likely to underestimate the true opportunity cost of programmes in most countries. The cost per capita of blood safety takes account of its sensiti~ty to HIV seroprevalence in donor populations, which arises from the need to replace donated blood which tests HIV positive, and hence has to be discarded. Costs were estimated separately for countries of different HIV seroprevalence levels. Levels of I l%, 5% and 10% were used to represent low, medium and high prevalence situations, and all developing countries were grouped into one of these categories

J. Broomberg ef al. /Health Policy 38 (1996) 45-65

51

on the basis of population-based seroprevalence surveys [18,19]. Replacement costs for discarded, infected blood were calculated using the formula: Replacement cost = HIV prev. x (1 + HIV prev.) x pre-transfusion cost [24] The cost per condom sold appeared to depend on programme age (due to low numbers of condoms sold at start-up) and country size (due to size of potential markets). Assuming a programme life of 10 years, an age-adjusted unit cost per condom sold was calculated for each programme for which data were available. Average costs per condom sold were then estimated separately for each of the approximate country sizes and are shown in Table 2. 2.1. Assessmentof affordability and the effect of budget constraints

Two modelling approaches were used to assess the affordability of the hypothetical HIV prevention package, and the effect of budget constraints on the abilities of countries to implement the package. In the first, each country was assumed to allocate the same fixed proportion of its GNP for HIV prevention. This fixed budget was then compared with the estimated total resource requirements for HIV prevention in that country. This approach gives an indication of which countries Table 2 Unit cost assumptions used in the model and sources of information Needle exchange Strategy

HIV prevalence (%I) Cost. condom Unit cost/year (USS 1990)

Mean of 20 CSM programmes in Source of data

Mass media Prostitute education School education

$440 000 per city-wide programme Male programme $33 500 Female programme $72 500 $1.39 per pupil year of education

Blood safety

HIV prevalence (%)

Dominican Republic Rio de Janerio, Brazil Bulawayo, Zimbabwe Mean of two programmes (from Hungary and Zimbabwe) Zimbabwe National Blood transfusion service

STD treatment

1 0.017 5 0.028 10 0.042 $8.50 per episode treated

Condom social marketing

Needle exchange

Country pop. (mill.)

Blood safety cost ($)/cap

Cost. condom sold (S)

110 0.19 10-50 0.13 <50 0.07 $147 000 per city-wide programme

a Unpublished data, AIDSCAP, 1993. b Unpublished data, Population Services International,

Mean of five STD treatment programmes in South Africa, Mozambique and Tanzania” Mean of 20 CSM programmes in Africa, Latin America and Indonesiab

Tacoma-Pierce County Health Dept, Washington 1993.

57

J. Broombqq

et al. i Health

Policy

38 (1996)

45-65

are able to undertake the full package of prevention programmes at different propo~ions of GNP allocated. It assumes that countries are aware of the optimal proportional allocation of funds to strategies for their country and allocate accordingly, so that no strategies are overfunded when others are underfunded. While the use of a proportion of GNP as the basis for modelling the effects of budget constraints on HIV prevention activities is likely to reflect, with reasonable accuracy, the relative abilities of individual countries to sustain such activities, it does have important disadvantages. The substantial differences in per capita GNP between countries mean that the model assumes signi~~antly different levels of fixed resources available to each country for HIV prevention, and thus does not allow for the effects of external assistance for HIV prevention (which may be negatively correlated with per capita GNP). Consequently, a second approach was applied which involved assessing the effect of allocating the same amount per capita in all countries. The resulting HIV prevention budget was then applied to the total resource requirements for HIV prevention, allowing estimation of the number of countries with all programmes covered, and the shortfall to cover all programmes, at each level of per capita expenditure. The high cost assumptions were used in both of the affordability assessment exercises.

Table 3 shows the estimates of the global resource requirements for HIV prevention, under the high and low cost package assumptions outlined above. On a global level, total resource requirements to undertake the full package of HIV prevention programmes of the scale projected in the model for all developing countries are estimated to range from $1.44 billion to $2.16 billion. The table also shows the relative contributions of different regions to these global requirements, with Asia accounting for approximately 53% of the total, Africa for approximately 20% and Latin America for approximately 15%. The use of high or low cost assumptions does not change the relative contributions of each region significantly. The table also shows the proportion of all countries, and of the global developing country population, accounted for by each region. Fig. 2 illustrates the global resource requirements for each of the strategies included in the hypothetical package, ranging from $30 million for needle exchange programmes up to $495 million-~9~ million for CSM programmes. Figs. 3 and 4 show the relative contributions of each strategy to global resource requirements, using the low and high cost sets of assumptions. The figures indicate that the STD and CSM strategies dominate global requirements under the high cost assumptions, together accounting for 67.7% of the total. Under the low

462 333 1145 224

2163

Total

21.4 15.4 52.9 10.3 1443

Amount (millions) ._ 28.5 226 161 164

Amount (millions)

% of total

Low cost assumptions

High cost assumptions

Total resource requirements (USS 1990)

Africa Americas Asia Europe and FSE

Region

Table 3 Global resource requirements for HIV prevention

19.8 15.7 53.2 11.4

% of total 41 27 24 9

% of developing world’s countries in region

1.5 10 66 10

--

% of developing world’s population in region

J. Broomberg

54

et al. / Health

P0lic.v

38 (1996)

o^ 1000 : P, z 800 3 d E F 600 .kal .o = $Z

45-65

i 1990 / 7 I

I

324

400

” MasJbki!! ! SchooiEduc. Prostilute Educ Slwd

1 STClTreat. ! safety Condom

1. I High cost ass. I

NeedieExch SM

Low cost ass.

Fig. 2. Global resource requirements by strategy.

._Mass Media (7.22%) ._ h8ta Education Condom

(1163%)

SM (33.97%)

school

STD Treatment

Fig. 3. Relative contribution

Education

(17.

of strategies to global resource requirements (low cost assumptions).

wIeala ” (4.t16%) .. ---_ ,PMittie Education

(7.64%)

School Education Condom

(22.25%)

(14.99%

SM (45.78%)

D Treatment

Fig. 4. Relative contribution

(21.44%)

of strategies to global resource requirements (high cost assumptions)

J. Broomberg

et al. / Health

Policy

38 (1996)

tAFRICA

’! m

Mass Media Blood Safety Needle exch.

Fig. 5. Contributions

AMERICAS

AEUROPE+FSE

ASIA

ljjm Prostitute Education m STD Treatment

i [:I

55

45-65

TOTAL

School Education Condom SM

~ -i

of strategies to regional resource requirements (high cost assumptions).

cost assumptions, CSM programmes still consume most resources but school education assumes greater importance than STD treatment. Fig. 5 shows considerable variation in the share of each strategy between the different regions, though with CSM, and thereafter, STD programmes, dominating. Table 4 shows per capita costs for each strategy and by region. While these costs show significant variation between strategies for different regions, as well as

Table 4 Per capita costs of HIV prevention strategies (US cents, 1990) Africa Mass media Prostitute education School education Blood safety STD treatment High cost assessment Low cost assessment Condom social marketing High cost assessment Low cost assessment Needle exchange Total (high assessment)

Americas

Asia

Europe and FSE 2.4 8.1

Average

4.8 3.0 5.0 2.4

4.6 7.9 8.7 1.7

1.5 2.7 7.2 1.7

10.0 1.7

2.4 3.8 7.3 1.8

20.5 11.0

15.0 9.4

8.1 4.2

6.5 4.1

10.5 5.7

35.3 17.7 0.7

35.8 17.9 1.2

17.5 8.7 0.5

21.8 10.9 1.1

22.3 11.2 0.7

72

75

39

52

49

56

J. Broomberg et al. /Health Policy 38 (1996) 45-65

/ 823% ’ .?O% 0.50% % of GNP akcabd = AFRICA j b EUROPE+FSE

LiC b TOTAL

-*

ASIA

Fig. 6. Effects of % GNP allocated on percentage of countries with full programme coverage.

between different strategies, these differences do not necessarily occur in predictable directions. The relationships between per capita costs and other determinants of total resource requirements for different strategies and for the package as a whole are explored in more detail in Section 4 below. 3.2. The effects of budget constraints on ~~~~e~entatio~ strategies

of ~r~~rev~ntion

Fig. 6 illustrates the effect of budget constraints on the ability of countries to undertake the full hypothetical package of HIV prevention activities. Table 5 shows the size of populations in countries without full coverage at various levels of budget constraint. The African region has the iowest proportion of countries with complete coverage, at all levels of expenditure. Asia has appro~mately the same proportion of countries covered as Latin America at each level of expenditure, but substantially Table 5 Population in countries without full coverage at various levels of budget constraint Budget constraint

0.01% of GNP 0.03% of GNP 0.1% of GNP 0.5% of GNP

Uncovered population (millions) Africa

LAC

Asia

Europe and FSE

Total

588 480 219 16

305 101 11 0

2511 1852 215 0

213 13 0 0

3683 2441 505 16

J. Broomberg

et al. 1 Health

Policy

38 (1996)

51

45-65

Table 6 Levels of expenditure required to achieve 90% and 100% coverage Region

Africa Latin America Asia Europe and FSE

% national GNP required to cover:

Per capita sum ($) required to cover:

90% countries

100% countries

90% countries

100% countries

0.50 0.22 0.21 0.04

1.oo 0.43 0.64 0.04

1.60 4.13 0.99 0.82

8.50 7.21 2.87 2.06

more people remain uncovered in the Asian region because of the large size of the countries that remain underfunded. Latin American countries not achieving full coverage at 0.1% of GNP tend to be those with small populations, and as a result, the country level analysis underestimates real coverage at the population level. It is important to note the effect of outliers on the country level analysis. Table 6 shows that for all the regions other than Europe, to increase coverage from 90% to 100% of countries would require at least a doubling of the proportion of GNP allocated. Fig. 7 illustrates the use of the second specification of the budget constraint, where the HIV prevention budget is determined by expenditure of a fixed amount per capita in each country (once again using high cost assumptions). In this case again, Europe fares best, reaching full coverage at a level of $2.06 per capita (Table 6). However, the African region fares relatively better than in the previous method of determin~g the budget constraint, reaching 90% coverage at $1.60, while Latin America has only 73% of countries covered at this level of expenditure. This reflects the removal of the influence of the low average GNP of the African region. The poorer coverage obtained by the Latin American region relative to the other

r&

100%

E E 60% z? t 60% 5 'S 40% .-8 3 20% C-3 / s 0% I+.0.10

I i

,,;’

* ,$ :+

0.20

&

0.30

Amount / - AFRICA * E!JROPE+FSE

&‘

:/-I,-.

0.40

-;!... 0.50

0.60

allocated/cap.

LAC -I TOTAL

1.60

do

id0

2.&l

(US$iQ~) * ASIA

Fig. 7. Effects of absolute per capita allocations on programme coverage.

58 Table

J. Broomberg

er al. / Health

Policy

38 (19%)

JS-- 65

7

Comparison of per capita resources required with actual per capita expenditure on HIV prevention by selected countries in 1990 (US$ 1990) Country

Per capita resources required (1990)

Actual per capita expenditure

Act~dl~required ‘%

0.21 0.66 0.19 0.06 0.10 0.13 0.04 0.06 0.65

21 52 19 11 20 12 11 5 8 64

0.60 0.23 0.35 0.02 0.04 0.04

15 17 36 3 5 5

0.01 0.07

5 12

0.02 0.12 0.02

3 20 4

Africa

Cameroon 0.99 Congo 1.28 Cote D’lvoire 1.oo Ethiopia 0.54 Morocco 0.50 Rwanda 0.97 Senegal 1.17 Tanzania 0.75 Uganda 0.77 Zambia 1.02 Latin America/Caribbean Saint Lucia 4.13 Trinidad 1.39 Haiti 0.36 Mexico 0.60 Argentina 0.85 Colombia 0.86 Asia Pakistan 0.22 Thailand 0.56 Europe and FSE Czechoslovakia 0.73 Poland 0.61 USSR 0.46

0.12

regions is explained mainly by the relatively high per capita resource requirements of that region (principally because of large numbers of small countries and consequent inability to exploit economies of scale). As noted earlier, the model is constructed using individual country data, and the results can therefore be explored for individual countries as well. Broomberg et al. [25] provide the full data set. The estimates of per capita resources required for the full prevention package can be compared with estimates of the actual resources mobilised (both local and external) for HIV prevention activities in 1990, for a small list of countries for which the latter data were available [26] (Table 7). This allows some estimation of the resource gap for HIV prevention which existed in these countries in 1990, on the assumption that the model captures resource requirements with reasonable accuracy. No countries for which data are available actually utilised the level of resources estimated as necessary by the model.

J. Broomberg

et al. / Health

Policy

38 (1996)

45-65

59

4. Discwssion Interpretation of these results must take account of several important limitations of an exercise of this kind. One limitation concerns the generalisability of the costs used for the different strategies. Since these data come from case studies of particular programmes selected largely on the basis of data availability, they are unlikely to be representative of the full range of programmes currently being undertaken throughout the developing world, and it is also possible that some of these programmes would not necessarily be appropriate in all countries and regions, While some strategies are likely to be broadly similar across countries {e.g. blood safety and STD treatment progr~mes), others may differ substantially (e.g. mass media campaigns, and person to person education strategies). Even where programmes are likely to be broadly similar, the particular input mix required may differ significantly between countries, as may factor costs. Perhaps more importantly, these particular programmes do not necessarily represent the most cost-effective examples of existing programmes within each strategy. As a result, extrapolation of the unit cost data to all countries may inaccurately reflect true programme costs in many countries. The absence of detailed programme cost data has necessitated this approach at this stage. A second limitation lies in the nature of the formulae used for the dete~ination of the scale of programmes within each strategy. Determination of objective need for most of these programmes is extremely difficult, because of inadequate data both on the underlying problems the programmes are designed to address (e.g. STD incidence in the case of STD treatment programmes), and on the absolute and relative effectiveness of different approaches. The formulae used here should thus not be interpreted as indicators of need, but rather as fairly crude approximations of the nature and size of programmes that might be replicated in all countries. One particular problem concerns the use of *incidence scores’ as the basis for weighting coverage requirements of CSM programmes. These were established on the basis of subjective estimates of different levels of HIV and prevalence of high risk situations/behaviours. In general they are highly correlated with HIV prevalence data. Despite the methodological limitations of such an approach, this weighting was felt to be necessary because of the inordinately high resource requirements of this strategy. It would be politically difficult as well as possibly inef~cient for countries with low current or future HIV risk to spend the same amount as countries at high risk. Likewise, since the strategy is dependent on uptake by the general population, it is unlikely to be successful where individuals’ perceived risk is relatively low. It is arguable, however, that by the time HIV incidence figures are relatively high, the marginal effectiveness of some interventions will be lower than similar interventions in countries at earlier stages of the epidemic. Further specific limitations imposed by the model assumptions are discussed in more detail below. Despite these general limitations, and provided that the results are interpreted with caution, several useful insights for international and national decision~making do emerge from this initial approach to modelling resource requirements. The first of these is some indication of the scale of resources required to undertake HIV

60

J. Broomberg

et al. / Health

Policy

38 (1996)

45 65

prevention programmes in different countries, regions and the whole developing world. A second insight is that estimates of resource requirements for HIV prevention vary widely, both between individual strategies and between different countries and regions. The wide variations in the total and per capita resource requirements between the different strategies are attributable to variations in the cost structures of the types of programme required for different strategies, and to variations in assumptions about the required scale of programmes. The low costs of strategies such as needle exchange and bleach programmes are a function of the low unit costs of these programmes (Table 2), and of the ass~ption that relatively few of these programmes will be required in comparison with other strategies. This assumption is in keeping with the relatively small contribution of these modes of transmission to HIV spread except in some particular urban areas of some regions [15- 171. In the case of mass media programmes, the method of estimating resource requirements implies substantial economies of scale, and hence low per capita costs in most countries, since fixed costs are likely to remain similar irrespective of the number of recipients of such programmes. The high costs of CSM and STD treatment strategies are att~butable to high unit costs (in the case of STD treatment, and less so for CSM) and more importantly to the substantial scale of programmes generated by the model, itself a function of the underlying size of the assumed risk groups. The high unit costs of these strategies are attributable to a combination of labour intensity and costly non labour inputs (in particular, drug costs in STD programmes, and the cost of condoms in CSM programmes [lo]). Labour intensity is in most cases a function of the level and intensity of interaction between individuals required by the programme. Population-based programmes such as STD treatment or CSM require interaction by programme staff (with varying degrees of training) with extremely large numbers of individuals, creating substantial staff requirements. This is in contrast to other population-based programmes which either do not require individual interaction (mass media), or which require interaction with much smaller numbers of individuals (blood safety programmes), or which use existing staff to carry out the prevention activity (school education programmes). This is also in contrast to programmes which are targeted at specific groups (prostitute peer education or needle exchange pro~ammes), where staff numbers may be significantly smaller. The wide variations in total and per capita resource requirements between regions are a function of the programme costs described above, in interaction with the country population and number of countries within each region, these latter factors determining the relative scale of programmes for each strategy assumed in the model. In the case of Asia, for example, the relatively high total resource requirements are primarily att~butable to its substantial share of the total developing world’s population, while this same factor explains its lowest per capita resource requirements overall. The interaction between the number of countries in a region and its total population is well illustrated by Africa and Latin America, both of which account for relatively low proportions of the total developing world’s population, but relatively high proportions of all countries, this latter factor

J. Broomberg

et al. / Health

Policy

38 (1996)

45-65

61

generating a relatively larger number of programmes for most strategies, and hence higher total resource requirements, than would be suggested by the share of total population and HIV risk alone. The relatively low total populations of these regions also explain the relatively high per capita costs for the full package of strategies. As would be expected, the impact of both population size and numbers of countries on regional resource requirements is mediated primarily through the impact of resource requirements for the STD and CSM strategies. Africa’s higher than average per capita resource requirements are essentially attributable to relatively high HIV and STD incidence. These factors are accentuated in the case of the high cost package, since this assumes that a greater proportion of those with STD infections are treated. Latin America’s high per capita requirements are due to a combination of moderately high HIV prevalence and incidence, higher assumed treatment rates of STDs, and lack of scale economies particularly for CSM programmes. Asian countries have the lowest per capita resource requirements for STD treatment because of low input costs and low STD incidence. They also have low per capita costs for CSM programmes because of scale economies. Prostitute education programmes are allocated to urban populations only, in the absence of better info~ation on the size and dist~bution of the target population. The relatively high resource requirements for these programmes in Latin America and Eastern Europe are attributable to their relatively high levels of urbanisation, rather than to any documented differences in observed need for these programmes. The higher costs of blood safety in the African region reflect the higher HIV prevalence in the region (since, as noted earlier, these costs are very sensitive to HIV prevalence). In the case of mass media, the low per capita costs for Asia are a function of the large populations of the countries of that region, particularly India and China, with resulting economies of scale. In reality, however, the ethnic diversity of many of these countries may mean that, in practice, more of such programmes would be required than are allowed for in this model. Similar patterns to those described here can be detected in examining the resource requirements of individual countries, both for individual strategies and for the overall package of interventions [25]. The analysis of the impact of budget constraints on the ability of countries, and regions as a whole, to undertake the hypothetical package of inte~entions reveals several other points of interest. As would be expected, when the simulated budgetary allocation mechanism is based on a fixed proportion of GNP, poorer countries and regions fare worst in their capacity to undertake the package of interventions, particularly where low GNP is correlated with factors which increase resource requirements for HIV prevention. This is illustrated by the case of Africa. However, when allocations are simulated on the basis of a fixed allocation, irrespective of GNP, the situation of poorer countries and regions is improved. This latter simulation provides some illustration of the potential effects of external aid in overcoming domestic constraints on country capacity to undertake the full range of HIV prevention strategies. HIV incidence, and hence estimated resource requirements, are highest in the poorest countries, particularly those of sub-Saharan

Africa. If higher HIV incidence or prevalence is a valid indicator of need for the implementation of prevention strategies, then the results of this model suggest that a strong case can be made for the con~utration of external aid resources on this region. The analysis of individual country data suggests that, with the exceptions noted above, most countries would be capable of undertaking the full package of prevention strategies, at the scale projected by the model to be necessary, for a relatively small percentage of their current health expenditure. Africa is somewhat exceptional in this respect, due to the low levels of health expenditures in most countries. It should be noted, however, that this analysis omits the impact of expenditures on HIV/AIDS related care: the overall HIV/AIDS related expenditures will constitute higher proportions of total health expenditure than this analysis suggests. This analysis also omits the impact of HIV/AIDS on general economic output, which may be reduced to the point where existing health expenditures are more difhcult to sustain [27]. The general affordability of the package of prevention strategies modelled here is borne out by recent calculations in the World Development Report 1993 f77. The report estimates that a minimum package of public health services should cost in the order of $4.20 and $6.80 per capita per year for low and middle income countries respectively at 1990 prices. The average cost of the HIV prevention package modelled here under the high cost assumptions is $0.49 per capita per year, i.e. between 7.2% and 11.7% of the World Bank’s estimate of the minimum public health spending package. If STD treatment is excluded (since the WDR 93 considers this as part of clinical as opposed to public health services), HIV prevention requirements would amount to between 57% and 9.2% of public health spending requirements in developing countries. To co~textuaIise further the model’s estimates of total resource requirements, the estimated high cost total of $2.16 billion would have constituted 1.3% of the $170 billion spent on health care in the developing world in 1990. This exercise therefore suggests that achieving the resource allocation targets projected here would require substantial, but for the most part affordable, proportions of the health care budgets in developing countries. Even modest levels of effectiveness are likely to more-than-offset the considerable social and medical costs that would be associated with caring for AIDS patients. Despite the apparent affordability of the hypothetical package, data from the limited set of countries for which reliable information is available suggest that, in 1990, none were actually spending the required level of resources projected by the model [25]. This comparison between projected and actual resource allocation also indicates that some of the wealthier middle income countries in Europe and Asia perform worse than several of the poorer African countries. This may reflect higher resource requirements and differing domestic resource allocation priorities in the former, and differential aid flows in favour of areas of high perceived incidence. These estimates of the affordability of HIV prevention must, however, be interpreted in the context of some of the major assumptions used in modelling the effects of resource constraints. Critically, the budget constraint part of the model

J. Broomberg

et al. 1 Health

Policy

38 (1996)

45-65

63

calculates a figure based on the assumption that countries allocate a fixed proportion of their GNP to HIV prevention, and that funds are allocated optimally between strategies within individual countries. If the former assumption is changed, and funds are allocated to countries using a fixed, uniform allocation per capita, achievement of full coverage for 90% of all countries in the developing world would require a total of $7.4 billion; or if the high cost estimate of global expenditure of $2.15 billion were distributed equally on a per capita basis, only 17 countries in the developing world (14%) would be able to undertake the full package of prevention strategies.

5. Potential

applications of the modelling

approach

This initial attempt at combining normative assumptions and empirical data to model resource requirements for HIV prevention has several potential uses. As described in this paper, it allows for initial estimates of the resource implications of implementing a package of HIV prevention strategies for regions and for the whole developing world as well as (more tentatively) for individual countries. It therefore provides data which can be used to underpin requests for funding HIV prevention. This approach also allows for an assessment of the relative resource requirements of individual strategies, as well as for assessment of the effects of current (and the planning of future) resource allocation between the range of prevention strategies. Despite the crude nature of the approach, the estimates do allow for comparison with current resource allocation patterns, both to HIV prevention overall, and between different intervention strategies. This in turn allows for the identification of possible resource gaps, and other inefficiencies in resource allocation. As such, these data provide a rough benchmark against which agencies and countries may judge current, and plan future, resource allocation. The value of these benchmark data would be greatly increased if used in conjunction with programme effectiveness data. Indeed, the relative importance of the different strategies in total resource requirements indicates where greatest attention should be focused on determining effectiveness. The modelling of the effects of resource constraints on the capacity of countries to undertake HIV prevention activities may also have important policy implications for donor agencies and countries. From a donor perspective, the identification of regions or specific countries which face resource gaps, or require particularly high resource inputs in order to undertake effective prevention activities, is likely to prove very useful; as would be the ability to identify countries which are well resourced relative to their needs. Countries may be better able to judge the affordability of different programme approaches and the optimal programme mix, and be in a better position to request donor assistance where this is required. All of these potential applications of this modelling approach would be made far more effective by the development of more detailed models, which would require more accurate data on both programme costs and effectiveness, as well as on the specific nature of both the HIV epidemic and its determinants in individual

64

J. Broomberg

et al. /Health

Policy

38 (1996)

45-65

countries. This is likely to be possible in the context of some individual countries in the foreseeable future, and perhaps the greatest value to HIV prevention of this initial attempt is in providing the outline of an approach to these more detailed modelling exercises. An exercise of this kind is also useful in highlighting important gaps in the information required for effective planning and resource allocation for HIV prevention, and hence, in indicating priority areas for future research. Obvious amongst these are the urgent need for further data on the costs and effectiveness of different intervention strategies in different countries, on the determinants of relative costs and effectiveness in different situations, and on the nature and effects of interactions between different intervention strategies. In addition, existing data on current resource allocation by countries to HIV prevention remain inadequate, and planning would be greatly enhanced by improvements in the quality of such data. In more general terms, the model presented here to determine resource requirements is more complex, and hence likely to reflect real resource needs more accurately, than earlier attempts to calculate global resource requirements for specific health policies. In particular, the model used a variety of criteria to determine resource needs on a country by country basis, and drew on a database of unit cost information. In addition, the model was able to explore the implications of two alternative ways of setting a budget constraint. The approach could usefully be further developed to explore the resource requirements of other health policy packages.

Acknowledgements

The research reported here was supported by the WHO Global Programme on AIDS, and was done when Broomberg and Sijderlund were attached to the Health Economics and Financing Programme at the London School of Hygiene and Tropical Medicine. The Health Economics and Financing Programme is supported by the UK Overseas Development Administration. Comments on this research by Drs Seth Berkeley, Stefano Bertozzi, Thierry Mertens, Mead Over, Peter Piot, and Doris Schopper, are gratefully acknowledged.

References [l] Social Science and Medicine, Special Issue: Economic Appraisal and Health Technology Policy, Social Science and Medicine, 38 (1994). [2] WHO, Expanded Programme on Immunisation, Costing Guidelines, EPI/GEN/79/5, WHO, Geneva, 1979. [3] Mills, A. and Thomas, M., Economic Evaluation of Health Programmes in Developing Countries, EPC Publication No. 3, London School of Hygiene and Tropical Medicine, 1984. [4] Phillips, M., Setting global priorities for strategies to control diarrhoeal disease: the contribution of cost-effectiveness, In A. Mills and K. Lee (Eds.), Health Economics Research in Developing Countries, Oxford University Press, London, 1993, pp. 214-219.

1. Broomberg

et af. / Health

Policy

38 (1996)

45-65

65

[5] Cumper, G., The costs of primary health care, Tropical Doctor, 14 (1984) 19-22; Cumper, G., The resource requirements for basic health care in developing countries, with special reference to Africa, Journal of Tropical Paediatrics, 33 Supplement 1 (1987) 18-Z. [6] Patel, M., An economic evaluation of health for all, Health Policy and Planning, 1 (1986) 37-47. [7] World Bank, World Development Report 1993, World Bank. Washington, DC, 1393. [S] Soderlund. N., Lavis. J., Broomberg, J. and Mills, A., The costs of HIV prevention strategies in developing countries, Bulletin of the WHO, 71 (1993) 5955604. [9] O’Malley, J., Mann, J. and Tarantola, D., Vulnerabiiity and HIV prevention, Hygiene, 12 (1993) 12--14. [lo] WHO, Global Programme on AIDS, Effective Approaches to AIDS Prevention. Report of a meeting, World Health Organization, Geneva, 1992. [II] World Bank, Tanzania AIDS Assessment and Planning Study, World Bank, Washington. DC. 1992.

[12] University of California, School of Public Health (Berkeley) and Institute for Health Policy Studies (San Francisco), The Public Health Impact of Needle Exchange Programs in the United States and Abroad, University of California, Berkeley and San Francisco, California, 1993, [13] Moses, SAP. and Ngugi, EN., Controlling HIV in Africa: effectivenessand cost of an intervention in a high-frequency STD transmitter core group, AIDS, 5 (1991) 407-411. [14] Roper, W.L., Peterson, H.B. and Curran, J.W., Commentary: condoms and HlVjSTD prevention - clarifying the message, American Journal of Public Health, 83 (1993) 501-503. [15] Schopper. D., Research on AIDS inte~ent~ons in developing countries: state of the art, Sociai Science and Medicine, 30 (1990) 1265- 1272. [16] Fransen. L., Van Dam, C.J. and Piot, P., Health policies for controlling AIDS and STDs in developing countries, Health Policy and Planning, 6 (1991) 148-I 56. [17] Hunter, D.J., AIDS in sub-Saharan Africa: the epidemiology of heterosexual transmission and the prospects for prevention, Epidemiology, 4 (1993) 63-72. [18] US Bureau of the Census Centre for International Research, Trends and Patterns of HIV/AlDS Infection in Selected Developing Countries, Research Note No. 5, US Bureau of the Census, Washington, DC, 1992. [19] US Bureau of the Census Centre for International Research, Recent HIV Seroprevalence Levels by Country. Research Note No. 6, US Bureau of the Census, W~hi~gton. DC, 1992. ]20] World Bank, World Development Report 1992, World Bank, Washington, DC, 1992. [21] United Nations. 1990 Demographic Yearbook, United Nations Department of International Economic and Social Affairs Statistical Office, New York, 1992. [22] United Nations Development Programme, Human Development Report, UNDP, New York. 1992. [23] Mills, A., Broomberg, J., Lavis, J. and Siiderlund, N., The Costs of HIV/AIDS Prevention Strategies in Developing Countries. Global Programme on AIDS, GPA/DIR/93.2, WHO, Geneva, 1992. 1241 Beal. R.W., Brunger, E., Delaporte, L., Deville, L. and Emmanuel, J.C., Safe Blood in Developing Countries - A Report of the EEC’s Expert Meeting, Brussels, EEC AIDS Task Force, 1992. [25] Broomberg, J., Siiderlund, N. and Mills, A., Modelling the Costs of HIV Prevention: Results of a Resource Requirement Model for Developing Countries, Global Programme on AIDS, WHO, Geneva, 1995. 1261 Mann, J. and Tarantola, D., AIDS in the World, Global AIDS Policy Coalition. Harvard University Press, Boston, 1992. [27] Ainsworth, M. and Over, M., The Economic Impact of AIDS: Shocks, Responses and Outcomes, Technical Working Paper No. 1, World Bank, Africa Technical Department, Washington, DC, 1992.