India is estimated to have 2–3.1 million persons living with HIV, one of the highest in the world [1–3]. A budget of US$2.6 billion has been assigned for India's National AIDS Control Programme (NACP-III) during 2007–2012, which is a four-fold increase in annual funding versus the last year of the NACP-II budget . It is anticipated that the major portion of NACP-III funding would be spent on HIV prevention . Prevention of new HIV infections is central to the control of HIV, which is considered to be deficient at the global level . The emphasis on prevention in the next phase of India's AIDS Control Programme is, therefore, justified. However, the best use of the available resources should be informed by economic analyses of various HIV prevention interventions and the current coverage of these interventions relative to the optimal need [7,8]. Such analyses are not readily available for India . Lack of such data from India and other low-income and middle-income countries has led to the use of average data for regions in recent global economic analysis of HIV prevention . Although very important for a global or regional perspective, these data are not adequate to prioritize HIV prevention interventions at national and subnational levels. The need to enhance the evidence base for an informed and efficient use of resources for HIV control has received major emphasis recently .
India with a population of over a billion has a wide variation in the HIV burden among different states, with the 80 million population of the south Indian state of Andhra Pradesh estimated to have the highest burden . We undertook economic analysis of the entire range of public-funded HIV prevention interventions in Andhra Pradesh to inform the best use of additional available resources to prevent HIV.
Among the 14 types of public-funded HIV prevention interventions implemented in Andhra Pradesh in the 2005–2006 fiscal year, a representative sample of 128 programmes from across the state was selected (Table 1). The sample for each intervention was mostly stratified by the three regions of the state, and also in some cases stratified by subtype of intervention when applicable, to obtain representation proportional to the actual distribution. Data were collected during 2006–2007 by trained investigators using procedures described previously, which included piloting and quality control [11–15].
The 14 types of public-funded HIV prevention interventions are facilitated by the Andhra Pradesh State AIDS Control Society. Four are based in clinical settings, that is, voluntary counselling and testing (VCT) centres, sexually transmitted infection (STI) clinics, prevention of parent to child transmission (PPTCT) centres and blood banks. Eight are implemented by nongovernmental organizations targeting groups at relatively high risk of HIV, that is, women sex worker programmes, men who have sex with men (MSM) programmes, truckers programmes, composite programmes, workplace programmes, migrant labourer programmes, street children programmes and prisoner programmes. Two are state-wide programmes implemented by the Andhra Pradesh State AIDS Control Society, that is, information, education and communication (IEC) programme for the general public (mass media campaigns) and condom promotion programme. Data on the HIV prevention services provided by each programme during the April 2005–March 2006 fiscal year were documented from written records and interviews of programme staff [11–15].
Data on economic costs for implementing the HIV prevention programmes were collected under five categories: personnel, recurrent goods, recurrent services, capital goods and office space rentals [11–15]. These costs included expenditures, resources used and donated inputs. Personnel costs included salaries of programme staff and payments made to peer-workers for some interventions. Common examples of recurrent goods were male condoms, HIV test kits, medications and materials for behavioural change communication; of recurrent services were travel and utilities; and of capital goods were computers, furniture and equipment. Available records were used for costing goods and services, and if records were unavailable, quotes were obtained to estimate cost. If a programme was not paying office space rental due to location in a public sector hospital, economic cost for rent was estimated on the basis of local rates. Similar costing methods were used for all programmes. Generally, personnel and recurrent goods made up the highest proportion of costs for the interventions. Costs in Indian Rupees (INR) were converted into US$ using the average exchange rate of INR 44.27 in the April 2005–March 2006 fiscal year .
The majority of interventions target sex behaviour for which the base scenario of the probability of HIV infection without intervention in a group that would be the beneficiary of the intervention was estimated using the Weinstein formula that is used widely including UNAIDS [17,18].
where Pr is the probability of HIV infection in uninfected, P is the average HIV prevalence among sex partners of the group for which probability is being estimated, R is the risk of HIV acquisition per act of unprotected sex, F is the fraction of sex acts in which condom is used, E is the effectiveness of condoms, N is the average number of sex acts per partner and M is the average number of sex partners. In order to estimate the probability of new HIV infection in an entire group, Pr was multiplied by (1 – I), where I is the proportion in the susceptible group that is already infected with HIV. Probabilities were calculated separately for acquisition of HIV by the group that would receive the prevention intervention and for their sex partners, and the number of new infections from these added to obtain the total new HIV infections. For women sex worker intervention, this also included acquisition of HIV by the other women sex partners of clients of sex workers, and for MSM intervention, this included acquisition of HIV by women sex partners of MSM.
The probabilities of HIV acquisition per act of unprotected sex were adapted from the literature [18–21]. For groups at high risk of HIV, in the absence of STI, we used a probability of 0.0014 for receptive vaginal sex, 0.0007 for insertive vaginal sex, 0.01 for receptive anal sex, 0.001 for insertive anal sex and 0.0004 for receptive oral sex. These probabilities were considered three times higher in the presence of STI [18,21]. For IEC intervention for the general public, 30% lower probabilities of HIV acquisition per act of unprotected sex were used assuming less risky sex as compared with other groups at higher risk . The effectiveness of condom in reducing HIV transmission in vaginal sex was taken as 80%, in anal sex as 70% and in oral sex as 90% [22,23]. The values for HIV prevalence, fraction of sex acts in which condom was used, average number of sex acts per partner and the average number of sex partners were adapted from population-based and other surveys [23–29] and programme data.
To assess the effect of interventions in reducing HIV in the groups that they serve, the estimated impact of interventions on reducing condom nonuse, number of partners and STI nontreatment from a recent global report on low-income and middle-income countries were adapted for use – we mostly used medium values from this source [10,30]. For impact values not available from this source for certain interventions, estimates were adapted from comparable interventions using assumptions informed by understanding of local trends in Andhra Pradesh. On the basis of these most plausible impact values (Table 2), the Weinstein formula was applied to the with-intervention situation with changed values for condom use, number of partners and risk of HIV transmission per act of unprotected sex (because of impact on STI treatment) for an estimate of the number of HIV cases averted per 1000 persons receiving an intervention. These estimates took into account the effect of overlapping target groups by different interventions and of overlapping sex partners within an intervention.
The base scenario and intervention effect for PPTCT were based on data on pregnant women receiving PPTCT and the proportion of HIV positive receiving nevirapine, assuming an HIV transmission rate without treatment as 25% and a reduction in this transmission by 40% with nevirapine treatment . The base scenario and intervention effect for blood banks were based on the proportion of blood units that tested HIV positive and an estimated 92% risk of HIV transmission from infected blood .
The validity of this model to estimate the number of new HIV infections, and from that the infections averted with intervention, was assessed using information available on the magnitude and trend of HIV burden in Andhra Pradesh [2,33]. Published literature, unpublished population-based and programme data and assumptions based on our understanding of local trends were used to estimate the range of plausible values for each variable. Using random values between these plausible ranges, sensitivity analyses for the intervention effects were performed based on the Monte Carlo simulation principle with 100 000 iterations using the @Risk software (Palisade Corporation, Newfield, New York, USA) to obtain the 5th and 95th percentile values of the number of HIV infections averted by each intervention. Using the intervention effect calculations, including the sensitivity analysis results, the number of HIV infections averted by each intervention were calculated based on the services provided by all public-funded HIV prevention programmes in Andhra Pradesh in the 2005–2006 fiscal year.
On the basis of the total target group served and the cost of the sampled programmes, the unit cost for each target person served or unit service by the HIV prevention programmes was calculated for the 2005–2006 fiscal year. Using unit costs and the total public-funded HIV prevention services provided in Andhra Pradesh in the 2005–2006 fiscal year, the total economic cost for each intervention was calculated. The coverage of each intervention in 2005–2006 as compared with the optimal coverage required, defined as the total needed coverage of public-funded interventions, was estimated using the best available data from various sources as explained in the results. The number of HIV infections that could be averted with optimal coverage was estimated applying the intervention effect to that coverage and assuming relative reduction of effect for some interventions at the higher end of the coverage as explained in the results. Sensitivity analysis for this number was done with the @Risk software using the 5th–95th percentile range of intervention effects calculated previously and modified for the assumed reduced effect of some interventions at higher coverage. The total cost of each intervention was then calculated for the optimal coverage, assuming that the unit cost would change for some interventions based on our previous longitudinal assessment  and using assumptions that seemed most plausible to us as explained in the results. From this, the required investment in each intervention was calculated as a percentage of the total investment needed for optimal coverage. We also assessed the number of HIV cases that would be averted through public-funded HIV prevention interventions in Andhra Pradesh if the increased available resources were distributed in the same proportion for the interventions as in 2005–2006 for comparison with preferential resource allocation to certain interventions aimed at an overall maximum reduction of HIV.
The highest number of HIV infections averted per 1000 persons receiving an intervention was for MSM and women sex worker programmes, followed by STI clinics and blood banks, whereas the lowest was for IEC for the general public (Table 2). In the 2005–2006 fiscal year, the public-funded HIV prevention interventions in Andhra Pradesh together were estimated to have averted 9688 HIV infections (Table 3), sensitivity analysis range 6624–16 898. The highest proportion among this was by STI clinics (23.9%), blood banks (18.1%), women sex worker programmes (13.7%) and VCT centres (13.5%) that incurred 13.9, 1.2, 9.5 and 6.3% of the total economic cost of public-funded HIV prevention interventions, respectively. The IEC programme for the general public incurred 38.6% of the total cost and was estimated to have averted 5.1% of the HIV infections (Table 3).
Our transmission model for estimating the number of HIV infections averted, taking into account the interventions in place, estimated that there were 55 396 new infections in Andhra Pradesh in the 2005–2006 fiscal year. This is quite plausible on the following basis. Andhra Pradesh was estimated to have about 550 000 persons living with HIV in 2006, and sentinel surveillance data suggest that HIV prevalence as a whole has plateaued in Andhra Pradesh [2,33]. Assuming a 10-year duration from HIV infection to death without antiretroviral treatment in India , coupled with the fact that most persons with HIV/AIDS were not on antiretroviral treatment in Andhra Pradesh in 2005–2006, an HIV incidence of about 10% of the estimated prevalence could be expected for a stable HIV prevalence, which is close to the incidence estimated by our model. Of the estimated 55 396 new HIV infections in Andhra Pradesh in 2005–2006, the groups at high risk that accounted for the largest proportions were MSM and their sex partners (9608, 17.3%), women sex workers, their clients and the other women sex partners of clients (7659, 13.8%) and migrant labourers (3853, 7%), whereas 27 195 (49.1%) of the new infections occurred in the general population.
The coverage of some public-funded interventions in the 2005–2006 fiscal year was very low as compared with the estimated optimal coverage, for example, VCT (12%), migrant labourer programmes (13%) and MSM programmes (18%), whereas that for some interventions was reasonably high, for example, blood banks (98%), IEC for the general public (96%), trucker programmes (85%) and prisoner programmes (84%) (Table 4) [35–40]. With optimal coverage of all interventions, the number of HIV infections averted could increase 2.4-fold to 22 916 (sensitivity analysis range 14 506–37 318) resulting in an additional 13 228 (sensitivity analysis range 7138 to 27 968) infections averted as compared with 2005–2006. This would be associated with a 2.8 times increase in total economic cost of HIV prevention interventions from US$13.8 million to US$38.8 million annually, which could be accommodated by the recent four-fold rise in public funding for HIV control in India . To achieve optimal increase in the number of HIV infections averted, the proportion of resource allocation to migrant labourer programmes, VCT centres and MSM programmes would have to increase to 20.5, 18.4 and 6.2% of the total economic cost for optimal coverage (Table 4) from the 2005 to 2006 fiscal year allocations of 3.2, 6.3 and 1%, respectively. To accommodate these increases in relative resource allocations, the proportion of allocation to IEC for the general public would have to drop to 14% of total economic cost from the 38.6% allocation in the 2005–2006 fiscal year. With the overall 2.8-fold increase in resources needed for optimal coverage, no intervention would have an absolute reduction in resource allocation even if they had a decrease in the proportional allocation as compared with 2005–2006 (Table 4). If our projected changes in unit costs of interventions at optimal coverage were not applied, and the 2005–2006 unit costs were applied to the optimal coverage, the highest proportional increases in resource allocation would still be needed for VCT, migrant labourer and MSM programmes.
If the proportion of total economic cost allocated for the interventions with 2.8-fold higher resources were to remain similar to that in 2005–2006, 16 858 HIV infections could be averted (Table 5). As compared with the HIV infections averted in 2005–2006, the additional 7170 HIV infections averted with this allocation would be 46% less than the 13 228 additional HIV infections that could be averted with the optimal proportion of resource allocation for interventions.
This analysis suggests that the four-fold recent increase in public resources made available for the AIDS Control Programme in India  should generally be adequate to scale-up interventions to an optimal level in Andhra Pradesh. However, if the proportional allocation of increased resources to the various prevention interventions in Andhra Pradesh were to remain similar to the proportion of resources utilized in the 2005–2006 fiscal year by each intervention, only about half the additional avertable HIV infections would be averted as compared with a preferential higher resource allocation to some interventions, that is, migrant labourer programmes, VCT and MSM programmes. A higher relative and absolute allocation to these programmes at the new higher level of available resources for HIV control would not require any intervention to have an absolute reduction even if its proportional allocation were reduced from the 2005–2006 level to achieve optimal overall coverage.
Over the past 2 years, there has been a move to provide VCT and PPTCT services in the same facility in public hospitals in India referred to as integrated counselling and testing centres . The potential advantages of these combined services would have to be assessed with increasing experience . There has been a major recent proportional increase in the public resources allocated to the combined VCT and PPTCT services in Andhra Pradesh (unpublished data). It is necessary to ensure that this allocation leads to several-fold increase in the number of VCT and is not utilized overwhelmingly for PPTCT alone. A recent large study in Andhra Pradesh has highlighted the high risk of HIV among MSM and their women sex partners [23,28]. The Gates-funded Avahan AIDS Initiative has HIV prevention among MSM as a major focus in Andhra Pradesh . However, allocation of public resources for MSM programmes also needs to be enhanced as indicated by our analysis.
Migrant labourers are recognized to play an important role in the spread of HIV in India [5,42]. HIV prevention programmes for this group are among the least developed in India. Our analysis suggests that major increases in resource allocation are needed for these programmes. If this resource allocation and the evidence base for how prevention in this group could be made effective do not develop rapidly, this may turn out to be the Achilles heel of HIV prevention in Andhra Pradesh.
The IEC mass media campaigns for the general public utilized almost two-fifths of the public HIV prevention resources in 2005–2006. Their relative proportion has come down in the past 2 years in Andhra Pradesh (unpublished data). Although this trend is consistent with our findings, it is important that the pendulum does not swing excessively to the other side with extreme reductions in this allocation. IEC mass media campaigns remain important as they have wide coverage of the general population and groups at high risk, which enhances receptivity to a variety of prevention interventions, thereby contributing broadly to HIV prevention including reduction in stigma. We estimate that one-seventh of the public HIV prevention resources for IEC mass media campaigns with a three-fold increase in total resources from the 2005 to 2006 level would be optimal.
There are limitations of our analysis. This economic analysis focuses mostly on resources at optimal scale with only a passing reference to the quality of HIV prevention services. Our previous longitudinal analysis has shown that unit cost can increase substantially with improved quality as we found for the women sex worker intervention, or the unit cost can decrease with increasing scale if quality remains the same as we found for VCT . We assumed implications of improved quality and higher scale on the unit costs at optimal coverage for several interventions, as longitudinal data are not yet available for all HIV prevention interventions. Also, our analysis included interventions that are currently being implemented. As new interventions are proven to be effective and feasible for Andhra Pradesh, this analysis would have to be updated accordingly.
In the absence of actual data on the effectiveness of HIV interventions in India , we developed a model to estimate the number of HIV infections averted by various interventions using the best available data and assumptions based on a deep understanding of HIV prevention in Andhra Pradesh. The known HIV trend in Andhra Pradesh supported the validity of this model. Although our estimates and model can be refined as more data become available, especially on how the interplay among interventions manifests in reality , this is the most comprehensive understanding of the entire range of currently implemented HIV prevention interventions in any Indian state, which provides practical guidance to policy makers for resource allocation in the immediate timeframe. As the new phase of NACP-III has just started, decisions on resource allocation for optimal HIV prevention should be based on local data and robust analysis.
Our composite economic analysis of the entire range of public-funded HIV prevention interventions in Andhra Pradesh, the state with the highest burden of HIV in India, provides the following policy-relevant messages. First, it points to the acute need to substantially enhance both the resource allocation for and understanding of how to make migrant labourer programmes more effective. Second, it highlights the need for more public resource allocation for MSM programmes. Third, it supports the move in Andhra Pradesh to increase relative resource allocation for integrated counselling and testing centres where VCT and PPTCT services are offered with the caveat that the VCT component needs a particular boost. Fourth, it points to the need for a balanced approach to IEC mass media campaigns for the general public requiring reduction in the proportional allocation of resources from the 2005 to 2006 levels, but not absolute reduction with the recent four-fold increase in total resources available for HIV control. Finally, it suggests that similar economic analysis would be useful in other major HIV states in India and also other low-income and middle-income countries. This effort would contribute to strengthening of the evidence base for HIV/AIDS control, which should aim to not only help make immediate decisions, but also contribute to long-term planning taking into account the evolving knowledge about and options for HIV prevention [3,7,9,42–44].
We thank the Andhra Pradesh State AIDS Control Society and its Technical Support Unit for facilitating this study, the staff of the HIV prevention interventions for participating in this study, our research staff and the participants involved in the various studies from which data were utilized for the analysis presented in this paper. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the organizations that facilitated this study or the institutions with which the authors are affiliated.
L.D. conceived this study, led the design, analysis and interpretation and drafted the manuscript. S.G.P.K., G.A.K. and R.D. contributed to the design, analysis and interpretation. All authors approved the final version of the manuscript.
We declare that we have no conflicts of interest.
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