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What Lies Behind the Fall in the HIV Population in India?

The availability of multiple data sources and new methods of estimation resulted in a more accurate estimate of the HIV population in India in 2006. A critical review of the data and methods used in the past and current estimation processes is offered in this article.

COMMENTARYEconomic & Political Weekly EPW DECEMBER 27, 200815What Lies Behind the Fall in the HIV Population in India?Arvind Pandey, D C S Reddy, M ThomasThe availability of multiple data sources and new methods of estimation resulted in a more accurate estimate of theHIV population in India in 2006. A critical review of the data and methods used in the past and current estimation processes is offered in this article.The number of HIV-infected adults and children in India in the year 2006 has been estimated as 2.5 (2.0-3.1) million, down from 5.7 (3.4-9.4) millionin 2005 (UNAIDS 2006). The steep downward revision in the estimate is at-tributed to the availability of multiple data sources and a new method of estimation. The revision has generated mixed reac-tions – relief as well as scepticism (Bagla 2007; IANS 2007) and led to a plethora of queries about the process and factors that resulted in the lower estimate. A critical review of data and methods used in the past and current estimation process may provide clarity on these issues.Method and DataThe size of population and prevalence among each category of risk groups are essential data for estimating the number of infections among adults in the population. The major high risk behaviour groups (HRG) are female sex workers (FSW), inject-ing drug users (IDU), clients of FSW and men having sex with men (MSM). The spouses of the aforementioned groups are categorised in the low risk group (LRG), representing the general population. As a majority of HRG are hidden and inaccessi-ble, their size is generally estimated through specific surveys and/or mapping exercises. The HIV prevalence among them is deter-mined by assessing their HIV status under targeted intervention approaches. On the other hand, clients and spouses are dis-persed in the community and form part of the general population. Their size is deter-mined by subtracting the size of HRG from the national and/or sub-national projec-ted population. Only properly designed population-based surveys can provide relia-ble estimates of HIV prevalence in this group.HIV Estimation until 2005India started estimating the number of HIV infections in 1998 under a broadly consultative procedure (Pandey et al 2007). The consultative group in 1998 sug-gested using sexually transmitted disease (STD) prevalence as the basis to estimate the size of the high risk behaviour popula-tion.It was further extrapolated to males and females of urban and rural areas, again based on the consensual assumption for the urban-rural differential inSTD prevalence. The size of HRG was deter-mined by multiplying the STD prevalence with the projected population of the cor-responding year. The size of the general population, male and female in urban and rural areas, was derived after subtracting the said HRG population from the total projected population. The HIV Sentinel Surveillance (HSS) data had been the only source available for estimatingHIV prevalence. It was con-fined to antenatal clinic (ANC) attendees, STD patients and IDUs. HIV prevalence amongst the first two groups was taken as the proxy for the general population and the high risk sexual behaviour groups res-pectively. Since the ANC sites were mainly located in urban areas, consensus assump-tions were made to extrapolate the pre-valence among men and the rural popula-tion. Infections among children were derived from the expected number ofHIV prevalence among pregnant women and probabilities of vertical transmission and survival. The state level estimates were aggregated to arrive at the national esti-mate and the method was called the work-sheet approach. The assumptions were revised in 2003 as more data became available, but they never validated with the community-based population repre-sentative surveys. Over time, the HSS was expanded geographically as well as for other HRGs, FSW andMSM, and state-wise mapping and size estimation of HRG were also un-dertaken. With the availability of data, these groups were also incorporated as independent entities in the estimation process. However, the STD patients were not withdrawn under the assumption that they still represented the clients and that the size estimatesforHRG were in-complete. This led to chances of double counting ofHIV positives in the estimation process. Based on a study in Guntur dis-trict, Dandona et al (2006) observed that there the common practice of referral of Arvind Pandey ( and M Thomas ( are at the National Institute of Medical Statistics, ICMR, New Delhi. D C S Reddy ( is with the World Health Organisation, New Delhi.
COMMENTARYDECEMBER 27, 2008 EPW Economic & Political Weekly16HIV-positive/suspected cases to public hospitals and a preferential use of public hospitals by people in the lower socio- economic strata caused overestimation of the HIV burden in India. HIV Estimation in 2006 The year 2006 presented a landmark when data from multiple sources became available. The third round of the National Family Health Survey (NFHS-3), which in-corporatedHIV testing, enriched the data availability in 2006 for the general popu-lation. The survey providedHIV pre-valence in the general population and female-male and urban-rural ratios for each of the five high prevalence states (Andhra Pradesh, Karnataka, Maharash-tra, Manipur, and Tamil Nadu) individu-ally and for the remaining states together. These data were used to calibrate HIV prevalence rate among ANC attendees and to validate the assumptions. Data generat-ed through the Integrated Biological and Behavioural Assessment (IBBA) survey among HRG and clients in the six high prevalence states was used to validate the HSS results for HRG.Further, the World Health Organisation (WHO)/United Nations AIDS (UNAIDS) work-book (Walker et al 2004) formed the worksheet anchor. Given the same inputs, both the approaches were found to gener-ate the same results. In 2006, the general population and HRG (includingFSW, MSM, IDU) and long distance truckers were in-cluded for estimation. The STD population was dropped, while inclusion of HRG and truckers was considered important to account for missing (mobile/hidden) population in community-based surveys. The HIV prevalence rates among ANC attendees inHSS were adjusted for intra-and inter-state variations by applying mixed-effects logistic regression models, using SAS version 9.1.3 (SAS Institute, Cary, North Carolina). The adjustedHIV prevalence estimates were then calibrated against the same inNFHS-3 before enter-ing into the workbook. In order to obtain the trend estimates with the new meth-od, point estimates were computed for five years starting from 2002. The trend estimates of HIV prevalence among ANC attendees for previous years had also been adjusted for inter-and intra-state variationand then calibrated to the NFHS-3 results. The projection of adult HIV prevalence for the period 1985-2010 was generated by fitting a logistic curve to the five-point estimates. Numeric re-sults of the curve were then entered into the “Spectrum” (Stover et al 2006) to derive the epidemic curve for all ages. For this, additional data such as popula-tion distribution, fertility rates, migra-tion as well as uptake of antiretroviral treatment and prophylaxis for prevention of mother to child transmission were inputs into the model.The number of infections in all ages (adults and children) in 2006 was esti-mated to be 2.5 million.What Caused the Reduction?TheHSS was initiated with the objective of monitoring trends. These data, though, comparable over time, cannot be general-ised even for all women. This limits their use in estimations. For example, over four-fifths of antenatal clinic attendees are in the age range of 20-29 years, sexually more active and have had unprotected sex. TheHIV prevalence observed among them is likely to be high and not repre-sentative ofHIV prevalence among all adult women. Further, low utilisation of antenatal services, particularly in the public sector facilities where theHSS are mostly located, also contribute to poor representation of antenatal clinic data as clearly brought out by Dandona et al (2006) in their study in Guntur district. For this very reason, in 15 out of 20 coun-tries of Africa where demographic and health surveys (DHS) were undertaken, theHIV prevalence in theDHS survey was lower than that was estimated among ANC attendees inHSS (Gouws 2006). The use of such exaggeratedHIV prevalence to all women and men, in turn, inflates the estimate. A community-based study in Cambodia also observed that though HIV prevalence inANC data can be used for estimations, it suffers from the limita-tion of overestimating the infection in younger age groups (Saphonn et al 2002). Despite recognising this limitation, the use ofHIV prevalence among ANC attendees inHSS continued without cor-rection as evidenced by the results of some community-based studies in Tamil Nadu (Thomas et al 2002; Kang et al 2005) which matched with the results of HSS in the state. In retrospect, it was realised that these studies had low power and were conducted either by cluster sam-pling or by campapproach, which probably led to exaggeratedHIV prevalence. Secondly, continued use of STD popula-tion as a risk group even after inclusion of FSW andMSM in the estimation process also pushed the estimates upwards in the past. The assumption that they stood proxy for clients is not tenable because, as mentioned earlier, the HIV prevalence among clients and their spouses is encom-passed in general population prevalence. Inclusion of this group, therefore, led to double counting. Further, the HIV preva-lence rates documented inSTD sites of HSS were also exaggerated because a large proportion of the STD sites were located in tertiary hospitals, which mostly receive referred and chronic patients. A compari-son ofHIV prevalence rates between the STD sites located in medical colleges and those in district hospitals has demonst-rated this point.NFHS-3 covered a sample of over 1,02,000, for an assumed prevalence rate of 0.9%. Now that they have a much smaller prevalence rate, about one-third of the assumed value, many people question the validity of these results. It is common knowledge that an estimate lower than the one assumed for sample size calcula-tions increases the error bounds rather than invalidates the results. These errors are accounted for in the range provided around the estimate. On the other hand, the proportion of the sample from low prevalence states is considerably small. As a result, one calibration factor had to be developed for all the low prevalence states together. This is expected to mask the magnitude of difference in the estimate of HIV prevalence between the states and the range of the estimate will be much wider in these states. In order to facilitate comparison, esti-mates were derived for five years starting from 2002 and it is found that the epide-mic is stable at the national level, although at the state level some high prevalence states showed a decline and some in the low prevalence areas showed an increase in the epidemic. However, the decline was
COMMENTARYEconomic & Political Weekly EPW DECEMBER 27, 200817significant only in Tamil Nadu. Further, in several districts of high and low pre-valence states, HIV prevalence among ANC women was more than 1%. The new emerging areas with highHIV transmis-sion have been identified. TheHIV preva-lence among IDUs remains stable. This abundantly makes it clear that the lowered estimate does not indicate a decline in the epidemic but a correction for some incon-gruities in the data and in the previous method of estimation.ReferencesBagla, Pallava (2007): “Don’t Be Misled on AIDS”, Times of India, New Delhi, 10 July.Dandona, L, V Lakshmi, T Sudha, G A Kumar and R Dandona (2006): “A Population-Based Study of Human Immunodeficiency Virus in South India Reveals Major Differences from Sentinel Surveillance-Based Estimates”,BMC Medicine, 4:31.Gouws, Eleanor (2006): “Comparison of Country Level ANC Prevalence in Household Surveys and ANC in South India”, paper presented in the meeting of WHO/UNAIDS Reference Group on Estimates, Modelling, Projections, held in Prague, Czech Republic, 29 November-1 December.IANS (2007): “Experts Challenge New India HIV Esti-mate”, Indo-Asian New Service, Yahoo! India News.htm, 7 July.Kang, G, R Samuel, T S Vijayakumar et al (2005): “Community Prevalence of Antibodies to Human Immunodeficiency Virus in Rural and Urban Vellore, Tamil Nadu”,National Medical Journal of India,18(1): 15-17.Pandey, Arvind, M Thomas, D C S Reddy, Kant Shashi and M Bhattacharya (2007): Indian Journal of Public Health, January-March.Saphonn, V, L B Hor, S P Ly, S Chhuon, T Saidel, R Detels (2002): “How Well Do Antenatal Clinic (ANC) Attendees Represent the General Popula-tion? A Comparison of HIV Prevalence from ANC Sentinel Surveillance Sites with a Population-Based Survey of Women Aged 15-49 in Cambodia”,Inter-national Journal of Epidemiology, April, 31(2): 449-55. Stover, J, N Walker, N C Grassly and M Marston (2006): “Projecting the Demographic Impact of AIDS and the Number of People in Need of Treat-ment: Updates to the Spectrum Projection Pack-age”, Sexually Transmitted Infections, June, 82 (Supplement 3), iii45-50.Thomas, K, S P Thyagarajan, L Jeyaseelan et al (2002): “Community Prevalence of Sexually Transmitted Diseases and Human Immunodeficiency Virus Infection in Tamil Nadu, India: A Probability Proportional to Size Cluster Survey”,National Medical Journal of India, 15(3):135-40.UNAIDS (2006):Report on the Global AIDS Epidemic, UNAIDS/06.13E, Geneva.Walker, N, J Stover, K Stanecki, A E Zaniewski, N C Grassly, J M Garcia-Calleja, P D Ghys (2004): “The Workbook Approach to Making Estimates and Projecting Future Scenarios of HIV/AIDS in Countries with Low Level and Concentrated Epidemics”,Sexually Transmitted Infections, August, 80 (Supplement 1) i10-13.IIM Review Committee Report: A Critical ExaminationT Krishna KumarThe report of the IIM Review Committee has a very poorly defined purpose and approach. Some of the basic issues have been touched upon only superficially. The report reflects the shortcomings in the constitution of the committee and its inability to take adequate inputs from stakeholders. It has slipped a few crucial terms of reference, demonstrating a casual approach to the whole issue of preparing a report on such an important topic.The Indian Institute of Management (IIM) Review Committee submitted its report, hereafter IIM RCR (GoI 2008). Despite well-known personalities from Indian business being on the com-mittee, my overall impression is that one should not take the report of the committee seriously, and instead one must ignore it. I must give a professional introduction of myself to give my comments the im-portance they deserve. My professional pre-retirement career of 35 years was divided between the United States (US) and India (15 and 20 years respectively). I worked in the US at the Centre for Re-search in Management Science, University of California, Berkeley, Iowa State Uni-versity and Florida State University, I was director of the Institute for Behavioural Research at Florida Atlantic University before I returned to India permanently in 1978 as a professor at IIM Bangalore. I was at iim b for eight years. During that period I was the chairperson of the fellow programme and framed its first set of rules, chairperson of the consultancy review committee, and started the journal, IIMBManagement Review. I resigned from iim b in 1986 as I felt that the first two directors were not academic enough to understand the importance of academic autonomy and of research and consultancy. After resigning from iim b I had written to the then human resource development (HRD) minister and the prime minister on the importance of proper choice of an academically competent director. The prime minister had acknowledged by saying that they would take the neces-sary steps. I am glad to note that all the directors subsequently appointed at iim b have been academically extremely good and competent. After retirement, from 2001 to 2008, I have been a guest faculty at iim b and have taught courses on quantitative methods and managerial economics. 1 IntroductionTheIIMRCR makes a significant contribu-tion to management education, as it is a good illustration of how not to prepare a business report. It also serves as a good case on discovering and understanding how non-stakeholders could prepare such reports. Let me first summarise my rea-sons for these conclusions and substanti-ate some of them subsequently.– Constitution of the committee is essen-tially flawed and prone to bias because it is weighted in favour of the bureaucracy and does not have any management educator on it;T Krishna Kumar ( is with Samkhya Analytica India and is a guest faculty at IIM Bangalore.

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