FOREWORD. Sayan Chatterjee. Sayan Chatterjee. 30 November, 2012

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3 CONTENTS Foreword v Preface vii Acronyms ix List of Figures and Tables xi Executive Summary xv Chapter 1: Overview of the Process of HIV Estimations 1 Chapter 2: Methodology 7 Chapter 3: Results 27 Chapter 4: Discussion 47 References 49 Annexes A (1-9): Key HIV Estimates with Uncertainty Bounds, B (1-6): Data Inputs 63 C (1-3): List of Experts and Working Groups 69

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5 Sayan Chatterjee Secretary & Director General Department of AIDS Control, NACO, Ministry of Health and Family Welfare, Government of India FOREWORD 30 November, 2012 As India moves into the fourth phase of the National AIDS Control Programme, Government of India reaffirms its commitment to the prevention and control of HIV/ AIDS in the country. Emerging epidemics will be given the highest priority and prevention strategies will be customized to address the key vulnerabilities. Systems to strengthen evidence-based planning and use of data in programmatic decision-making at all levels are given utmost importance. HIV Sentinel Surveillance and HIV Estimations are two critical sources of evidence, based on which the programme priorities are refined from time to time. HIV estimations and projections are generated through epidemiological analysis and modelling taking into account the recent evidences available within India and globally and using tools that allow global comparisons. The 2012 India HIV estimates made use of the most comprehensive sets of epidemiological and demographic data inputs available through the latest round of HIV Sentinel Surveillance, India Census 2011 and other data-sets. Elaborate efforts are made to ensure the accuracy of all the inputs that go into the modelling and projections. The latest tools and methods recommended by the Global Reference Group on Estimations, Projections and Modelling have been adapted and customised adequately to suit the India epidemic and country requirements. HIV Estimations 2012 provide updated information on the current state of HIV epidemic in India. They provide direction on the spread, levels and trends of the epidemic at national and state level and throw light on programme needs for future planning. These estimates confirm the progress made under India s National AIDS Control Programme, while they also bring out evidence on areas where the programme has to focus ahead. I am sure that the results of this exercise will be carefully reviewed and key messages will be internalised into programme implementation at all levels. I commend the efforts made by the members of National Working Group on HIV Estimations, under the leadership of Prof. Arvind Pandey, Director, National Institute of Medical Statistics (ICMR), for bringing out the HIV Estimations 2012 following the highest possible standards. Particular mention also be made of the Regional Working Groups, who brought state level perspectives into the whole process and in turn, got their capacities built in HIV modelling so that they support epidemiological work at the state and district levels. I congratulate all epidemiologists, M&E officers, statisticians, demographers and programme personnel who were part of these teams for their excellent work. The technical support provided by Prof. DCS Reddy, independent expert, and Mr. Taoufik Bakkali, Senior Strategic Information Advisor, UNAIDS, to the entire process is highly valued and appreciated. I would also like to acknowledge the support from CDC India and WHO India for successful completion of this round of HIV Estimations. I commend Dr. S. Venkatesh, Deputy Director General (M&E), NACO, and all the officers of Strategic Information Management Unit at NACO for coordinating the HIV estimation process and disseminating the results. Sayan Chatterjee v

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7 PREFACE The National AIDS Control Organization (NACO), Department of AIDS Control, Ministry of Health and Family Welfare, conducts annual HIV Sentinel Surveillance (HSS) in designated sites all over the country to monitor HIV trends in various risk groups of population in conjunction with the National Institute of Health and Family Welfare (NIHFW), New Delhi, and National Institute of Medical Statistics (NIMS), Indian Council of Medical Research (ICMR), New Delhi. The data generated through HSS is also used for the estimation of disease burden in the population with the National Institute of Medical Statistics as the nodal agency for developing national estimates of HIV prevalence and burden in India. As the data from HIV Sentinel Surveillance is not representative of the general population, certain assumptions are used to generate estimates of prevalence, incidence and mortality for the general population. Over the years, these assumptions have been gradually refined with the help of other available data sources and by customizing the models more and more using inputs based on Indian data. The endeavour receives technical support from the WHO and UNAIDS. National and international epidemiologists, demographers, public health experts and monitoring and evaluation specialists, members of the National Technical Resource Group on Surveillance and Estimations and National Working Group on Estimates are also consulted. Close partnership is retained with the Global Reference Group on Estimations, Modelling and Projections. The latest method recommended by the Global Reference Group on Estimations, Projections and Modelling and updated Spectrum 4.53 Beta19 tool was used for the 2012 HIV Estimates. This version of Spectrum included an inbuilt Estimation and Projection Package and was customised for India considering the Population Projection for the country HIV Estimates provide sound evidence on the current trend of the epidemic. The HIV estimates confirm that the number of annual new HIV infections in India is more than halved from 2000 to The number of AIDS related deaths reduced steadily post 2007 as the Antiretroviral Treatment programme was scaled up under the National AIDS Control Programme. The estimates, however, highlight the diversity of trends at the state level. Cognisance in areas that will yield greater impact of this information is critical to inform planning and in enabling effective and efficient financial investments towards high impact interventions in states. The HIV estimates also highlight programmes and interventions that have yielded impacts and indicate where further focus is required. It is clear that state level responses need to increasingly be tailored according to each state s epidemiological and social-developmental factors for ending the epidemic. In order for India to build on the advancements it has made for HIV/AIDS control under NACP III and eliminate new infections and AIDS related deaths, insight into the current state of the epidemic and programme responses is needed. I encourage all engaged in AIDS response to refer to this Technical Report on 2012 HIV Estimates. This report includes analysis on key HIV indicators. I am sure it will be useful to national and state M&E officers, epidemiologists, programme managers, implementers, researchers and other stakeholders. Prof. Arvind Pandey Director National Institute of Medical Statistics Indian Council of Medical Research vii

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9 ACRONYMS AIDS AIIMS AIM ANC ART ASFR BSS CDC CMIS DAC EPP FSW HIV HRG HSS IBBA ICMR IDU IMR MSM NACO NACP NFHS NIHFW NIMS NWG PLHIV PPTCT RGI SACS SIMS SRS STD TFR TRG UNAIDS UT WHO Acquired Immuno-Deficiency Syndrome All India Institute of Medical Sciences AIDS Impact Model Antenatal Care Antiretroviral Treatment Age Specific Fertility Rate Behavioural Surveillance Survey Center for Disease Control and Prevention Computerised Management Information System Department of AIDS Control Estimation and Projection Package Female Sex Worker Human Immuno-deficiency Virus High Risk Group HIV Sentinel Surveillance Integrated Biological and Behavioural Assessment Indian Council of Medical Research Injecting Drug User Infant Mortality Rate Men who have Sex with Men National AIDS Control Organisation National AIDS Control Programme National Family Health Survey National Institute of Health and Family Welfare National Institute of Medical Statistics National Working Group People Living with HIV Prevention of Parent to Child Transmission of HIV/AIDS Registrar General of India State AIDS Control Society Strategic Information Management System Sample Registration System Sexually Transmitted Disease Total Fertility Rate Technical Resource Group Joint United Nations Programme on HIV/AIDS Union Territory World Health Organisation ix

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11 LIST OF FIGURES and TABLES FIGURES Figure 2.1 : Overview of the Process for Generating India HIV Estimates Figure 2.2 : PTCT Rate Input in Spectrum Figure 2.3 : CD4 Count Threshold for Eligibility for Treatment Inputted in Spectrum Figure 2.4 : CD4 Count Threshold for Eligibility for Treatment for Children Input in Spectrum Figure 2.5 : HRG Population Input in Spectrum Figure 2.6 : HRG Turnover and Reassignments Input in Spectrum Figure 2.7 : Parameters used in the curve fitting in AIM Module Figure 2.8 : Generating HIV Prevalence Curves using EPP Classic in Spectrum Figure 2.9 : Application of Calibration Factor for General Population Figure 2.10 : Model of HIV Infected Population, Eligibility for ART and AIDS Related Mortality Figure 2.11 : Sex ratio input in Spectrum Figure 3.1 : Estimated Adult HIV Prevalence in India, with Uncertainty Bounds Figure 3.2 : Estimated HIV Prevalence among Children (<15 Years) in India , with Uncertainty Bounds Figure 3.3 : Estimated HIV Prevalence among Young Male Population (15-24 Years) in India, , With Uncertainty Bounds Figure 3.4 : Estimated HIV Prevalence among Young Female Population (15-24 Years) in India, , With Uncertainty Bounds Figure 3.5 Figure 3.6 Figure 3.7 : Estimated Number of People Living with HIV (All Ages) in India, , With Uncertainty Bounds : Estimated Number of Children(<15 Years) Living with HIV in India, , With Uncertainty Bounds : Estimated Number of Adults (15+ Years) Living with HIV in India, , With Uncertainty Bounds Figure 3.8 : Estimated Number of Adults (15+ Years) Living with HIV in India, 2011, Disaggregated by sex. Figure 3.9 : Estimated Number of New HIV Infections (All Ages) in India, , With Uncertainty Bounds Figure 3.10 : Estimated Number of New HIV Infections (All Ages) in India, , Disaggregated by Sex xi

12 Figure 3.11 : Estimated Number of New HIV Infections among Children (<15 Years) in India, , With Uncertainty Bounds Figure 3.12 : Sex-wise Distribution of New HIV Infections among Children (<15 years), 2011 Figure 3.13 : Estimated Number of New HIV Infections among Adults (15+ Years) in India, , With Uncertainty Bounds Figure 3.14 : Sex-wise Distribution of New HIV Infections among Adults (15+ years), 2011 Figure 3.15 : Estimated Number of Annual AIDS Related Deaths (All Ages) in India and Number of People (All Ages) Receiving ART, Figure 3.16 : Sex-wise Distribution of Annual AIDS-Related Deaths (All Ages), 2011 Figure 3.17 : Estimated Number of Annual AIDS Related Deaths among Children (<15 Years) in India and Number of Children Receiving ART, Figure 3.18 : Sex-wise Distribution of Annual AIDS-Related Deaths among Children (<15 years), 2011 Figure 3.19 : Estimated Number of Annual AIDS Related Deaths among Adults (15+ Years) and Number of Adults Receiving ART, Figure 3.20 : Sex-wise Distribution of Annual AIDS-Related Deaths among Adults (15+ years), 2011 Figure 3.21 : Estimated Adult HIV Prevalence (15-49 Years) by State, 2011 Figure 3.22 : Estimated Adult (15-49 Years) HIV Prevalence in States Showing >20% Decline in Prevalence, Figure 3.23 : Estimated Adult (15-49 Years) HIV Prevalence in States Showing >50% Increase in Prevalence, Figure 3.24 Figure 3.25 : Estimated Number of New HIV Infections, in States showing >20% Decline in New Infections, : Estimated Number of New HIV Infections, in States showing >50% Increase in New Infections, Figure 3.26 : Estimated Number of Annual AIDS-related Deaths in Major States showing a Significant Decline in the Number of Deaths, Figure 3.27 : Proportional Need for ART among Adults (15+ Years) in major States, 2011 Figure 3.28 : Proportional Need for ART among Children (<15 Years) in major States, 2011 Figure 3.29 : Proportional need for PPTCT in major States, 2011 xii

13 TABLES Table 1.1 : Regional Working Groups and State Allocation Table 2.1 : Number of HSS Sites in India, Table 2.2 : State wise sub-populations Used for HIV Projection Table 2.3 : Average Number of Years Spent in each CD4 Category by Age and Sex Table 2.4 : Annual Probability of HIV-related Mortality when not on ART in each CD4 Category by Age and Sex Table 2.5 : Annual Probability of HIV-related Mortality when Receiving ART in each CD4 Category by Duration on Treatment, Age and Sex Table 2.6 : Levels of Fertility between HIV Infected and Non-Infected Women Table 3.1 : Estimated Number of People Living with HIV in India, Total and by Age, Table 3.2 : Estimated Number of New Annual HIV Infections in India, Total and by Age and Sex Breakup, Table 3.3 : Estimated Number of AIDS related death xiii

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15 EXECUTIVE SUMMARY India HIV Estimates generated under the round is a primary source of updated information on the HIV epidemic at the national and state level. These HIV estimates are an outcome of concerted efforts for over six months by the National Working Group (NWG) on HIV Estimations and the five Regional Working Groups under the leadership of National Institute of Medical Statistics (NIMS) ICMR. The National Working Group comprised experts from the National AIDS Control Organisation (Department of AIDS Control), National Institute of Medical Statistics (Indian Council of Medical Research), National Institute of Health and Family Welfare, All India Institute of Medical Sciences, UNAIDS, WHO and CDC. The Regional Working Groups comprised of epidemiologists, bio-statisticians and Monitoring & Evaluation (M & E) Officers from the State AIDS Control Societies, Regional Institutes for HIV Sentinel Surveillance and other partner organisations. I. TOOLS AND METHODOLOGY As part of the initiative to consistently improve the accuracy of estimates generated, a set of more refined tools and globally recommended methods along with updated data inputs were utilised for HIV Estimations. Spectrum 4.53 Beta19 was used for generating HIV estimates under the current round. This version of Spectrum had an inbuilt Estimation and Projection Package (EPP) for estimating HIV prevalence and incidence so that the entire process could be done using this single tool. Spectrum includes the DemProj module, the AIDS Impact Model (AIM) and the Estimation and Projection Package inbuilt in AIM. The first step for generating the HIV estimates was updating demographic projections based on latest Census data (2011). The DemProj module of Spectrum was utilised for projecting the population for the entire country and for state each by age and sex, based on inputs on fertility, mortality and migration. Through deliberations between the NWG and the National Experts Group on Population census Projections, the values for base year population, migration, mortality, fertility and sex ratio at birth were finalised. Detailed review of demographic projections and necessary adjustments were undertaken to ensure that the results matched with Census 1981, 1991, 2001 and 2011 data. The results were validated with the help of national and international experts. In the AIM module, several programme data and epidemiological data inputs were given. The programmatic inputs included to programme coverage of adult and children on ART and coverage of PPTCT in addition to the eligibility for treatment as per national guidelines. The epidemiological inputs consisted data from twelve rounds of HIV Sentinel Surveillance ( ) among antenatal clinic attendees, Female Sex Workers, Men who have Sex with Men and Injecting Drug Users and Integrated Biological and Behavioural Assessment (IBBA) and size estimates of High Risk Groups (HRG). HIV prevalence curves for 34 States/Union Territories (excluding Lakshadweep) were generated for each of the identified sub-population groups. The curve for the general population for all states was calibrated with data from the National Family Health Survey, State level prevalence and incidence projections produced were used to project consequences of the epidemic in Spectrum. Finally, estimates for adult HIV prevalence, annual new infections, number of people living with HIV, AIDS-related deaths and treatment needs were generated. Results were validated through careful review and comparisons before finalisation. xv

16 II. KEY RESULTS The key results from HIV Estimations 2012 are presented below. Adult HIV Prevalence (15-49) National adult (15-49 years) HIV prevalence is estimated at 0.28% (0.23%-0.33%) in 2010 and 0.27% (0.22%-0.33%) in Adult HIV prevalence among males and females is estimated at 0.33% and 0.23% in 2010 and 0.32% and 0.22% in 2011 respectively. In 2011, among the states, Manipur has shown the highest estimated adult HIV prevalence of 1.22%, followed by Andhra Pradesh (0.75%), Mizoram (0.74%), Nagaland (0.73%), Karnataka (0.52%), Goa (0.43%) and Maharashtra (0.42%). Besides these states, Odisha, Gujarat, Tamil Nadu and Chandigarh have shown estimated adult HIV prevalence greater than the national prevalence (0.27%), while Chhattisgarh, Jharkhand, Tripura, West Bengal, Uttarakhand, Delhi and Bihar have shown estimated adult HIV prevalence in the range of %. All other states/uts have levels of Adult HIV prevalence below 0.2%. The adult HIV prevalence at national level has continued its steady decline from estimated level of 0.41% in 2001 through 0.35% in 2006 to 0.27% in Similar consistent declines are noted among both men and women at national level. Declining trends in adult HIV prevalence are sustained in all the high prevalence states (Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland and Tamil Nadu) and other states such as Mizoram and Goa. HIV Prevalence among Young Population (15-24) HIV prevalence among the young population (15-24) at national level is estimated at 0.11% in Unlike adult (15-49) HIV prevalence where HIV prevalence among males is around 1.5 times that among females, in young (15-24) population, HIV prevalence is equal among men and women at 0.11%. HIV prevalence among the young population (15-24) at national level has also declined from 0.30% in 2000 to 0.11% in There is Stable to declining trend in HIV prevalence among the young population in most of the states. While rising trends are noted in some states including Jharkhand, Odisha, Tripura and Uttarakhand. Annual New HIV Infections India is estimated to have around 1.16 lakh ( lakh) annual new HIV infections among adults and around 14,500 (10,974 19,346) new HIV infections among children in Among states, Andhra Pradesh is estimated to have the highest number of new adult HIV infections in 2011 (16,603) followed by Odisha (12,703), Jharkhand (9,085), Karnataka (9,024), Bihar (7,797), Uttar Pradesh (7,745) and West Bengal (7,289). While the states of Gujarat, Maharashtra, Chhattisgarh, Rajasthan, Punjab and Uttarakhand have new adult HIV infections between 3,000 and 7,000, rest of the states have less than 3,000 new adult HIV infections in Of the 1.16 lakh estimated new infections in 2011 among adults, the six high prevalence states account for only 31%, while the ten low prevalence states of Odisha, Jharkhand, Bihar, Uttar Pradesh, West Bengal, Gujarat, Chhattisgarh, Rajasthan, Punjab & Uttarakhand together account for 57% of new infections. xvi

17 India has demonstrated an overall reduction of 57% in estimated annual new HIV infections (among adult population) during the last decade from 2.74 lakhs in 2000 to 1.16 lakhs in This is one of the most important evidence on the impact of the various interventions under National AIDS Control Programme and scaled-up prevention strategies. Major contribution to this reduction comes from the high prevalence states where a reduction of 76% has been noted during the same period. During the period of NACP-III, the new HIV infections among adults have decreased by 28% in high prevalence states between 2007 & However, rising trends of new infections are noted in the states of Assam, Arunachal Pradesh, Chandigarh, Chhattisgarh, Delhi, Jharkhand, Meghalaya, Odisha, Punjab, Tripura and Uttarakhand. This underscores the need for the programme to focus more on these states with low prevalence, but high vulnerability. People Living with HIV/AIDS (PLHIV) The total number of people living with HIV/AIDS (PLHIV) in India is estimated at 20.9 lakh (17.2 lakh 25.3 lakh) in Children (<15 yrs) account for 7% (1.45 lakh) of all infections, while 86% are in the age group of years. Of all HIV infections, 39% (8.16 lakh) are among women. The four high prevalence states of South India (Andhra Pradesh 4.19 lakh, Karnataka 3.15 lakh, Maharashtra 2.01 lakh, Tamil Nadu 1.32 lakh) account for 53% of all HIV infected population in the country. West Bengal, Gujarat, Bihar, Uttar Pradesh and Odisha are estimated to have more than 1 lakh PLHIV each and together account for another 29% of HIV infections in India. The states of Rajasthan, Jharkhand, Chhattisgarh, Madhya Pradesh, Punjab, Manipur, Delhi and Kerala have estimated HIV infections between 25,000 and 75,000 each and together account for another 15% of HIV infections in the country. The estimated number of people living with HIV in India maintains a steady declining trend from 23.2 lakh in 2006 to 20.9 lakh in AIDS-related Deaths Using globally accepted methodologies and updated evidence on survival to HIV with and without treatment, it is estimated that about 1.48 lakh (1.12 lakhs-1.78 lakhs) people died of AIDS related causes in 2011 in India. Deaths among HIV infected children account for 7% of all AIDS-related deaths Wider access to ART has led to 29% reduction in estimated annual AIDS-related deaths during NACP-III period ( ). Greater declines in estimated annual deaths are noted in states where significant scale up of ART services has been achieved. In high prevalence states, estimated AIDS-related deaths have decreased by around 42% during 2007 to As on September 2012, around 5.8 lakh PLHIV are receiving free ART across the country. Lives Saved Due to ART It is estimated that the scale up of free ART since 2004 has saved over 1.5 lakh lives in the country till 2011 by averting deaths due to AIDS-related causes. With the current scale up of ART services, it is estimated to avert around 50,000 60,000 deaths annually in the next five years. xvii

18 Estimated Programme Needs for ART and PPTCT Based on the assumptions on progression and survival of adults and children infected with HIV, it is estimated that around 8.6 lakh PLHIV needed Anti Retroviral Treatment (ART) in This includes 7.9 lakhs Adults (15+) and 75 thousand children (<15). The four southern high prevalence states account for 58% of country s ART needs. With revision in the national guidelines on eligibility for ART to CD4 count of 350 from 2012, it is estimated that around 11 Lakh PLHIV would need ART by the end of With the strategies for further scale up and expansion of reach through ART and Link ART centres during NACP-IV, India is firmly positioned to reach the targets of universal coverage by Based on the estimated HIV infections among adult females and assumptions on effect of HIV on fertility and mother to child transmission rates, it is estimated that around 38 thousand HIV positive pregnant women needed Prevention of Parent to Child Transmission (PPTCT) services in The overall number of pregnant women needing PPTCT has declined in the country from 51 thousand in 2007 to 38 thousand in The states of Andhra Pradesh, Bihar, Maharashtra, Uttar Pradesh, Gujarat, Karnataka, Odisha and Tamil Nadu account for 71% of all PPTCT needs in the country. III. CONCLUSION India HIV s epidemic is dynamic and heterogeneous. With the increased amount of strategic information made available on the epidemic through HIV Sentinel Surveillance and HIV estimations in addition to other data, there is greater understanding on the levels and trends of infection in specific areas and amongst specific population groups. Appropriate programme response based on this evidence is required for successful control of HIV epidemic in the country. Further analysis has to be undertaken to understand the epidemic at district and regional level within states, so that programme interventions can be tailored according to the local epidemic context. xviii

19 Chapter 1 OVERVIEW OF PROCESS OF HIV ESTIMATIONS Strategic information is one of the critical pillars of the National AIDS Control Programme that enables evidence informed decision making. HIV Sentinel Surveillance (HSS) and HIV estimations are the two most important strategic information activities that generate evidence on the epidemic s patterns. By measuring and analysing the state of HIV epidemic in the country, HIV Sentinel Surveillance data and HIV estimates provide policy makers and programme managers with key markers on the epidemic for use in planning, programming, resource allocation and advocacy efforts at national and decentralised levels. They, thus, have remained a core function of the Department of AIDS Control and continue to play a central role in guiding India s AIDS response. Over the last one and half decade, India has developed a robust system of HIV Sentinel Surveillance to improve tracking of HIV trends and understanding on the epidemic s pattern at national, state and district levels and amongst key population groups. The 12 th round of HSS was conducted during 2010 and 2011 with introduction of key strategies for improving the quality and comprehensiveness of data. The number of sentinel sites increased from 1223 in round to 1359 in round with major expansion in sites for high risk groups (HRG) and bridge population. Improvements were made in methodology, data management as well as laboratory support. Special focus was given to mechanisms for ensuring high quality of data collection, specimen collection and processing. Several key initiatives were undertaken to strengthen the implementation of surveillance and thereby increase the credibility of its outcomes. Although HIV prevalence rates from HSS are a key data input for HIV estimation, discussion on findings from HSS is outside the scope of this report. National AIDS Control Organisation undertakes estimation of HIV burden in the country using the data from all the rounds of HIV Sentinel Surveillance (HSS) among high risk groups and general population. National Institute of Medical Statistics (ICMR), New Delhi is the nodal agency for developing national estimates of HIV prevalence and burden in India. The first HIV estimation in India was done in 1994 based on data from 52 sites. Since then, the process of HIV estimation in the country has evolved to a very great extent. As the data from HIV Sentinel Surveillance is not representative of the general population, certain assumptions are used to generate estimates of prevalence, incidence and mortality for the general population. Over the years, these assumptions have been gradually refined with the help of other available data sources and by customizing the models more and more using inputs based on Indian data. 1.1 OBJECTIVES OF HIV ESTIMATIONS The latest round of HIV Estimations have been undertaken with an overarching aim of generating HIV Estimates for India and states, using updated information from HSS , Census 2011 and other recent global evidence, through a process that adopts high standards of scientific analysis and methodological rigour. The specific objectives of HIV Estimations 2012 are: 1. To generate estimates of number of PLHIV, HIV prevalence, incidence, mortality and programme needs (for the years 2010 & 2011 and back calculate comparable estimates for previous years). 1

20 2. To improve the understanding of epidemic patterns in different states through a critical analysis of key HIV estimates and highlight key areas for programmatic attention 3. To build regional and state level pools of expertise in HIV/AIDS epidemic analysis and modelling through involvement of multi-disciplinary teams from programme units and institutions 1.2 PROCESS OF HIV ESTIMATIONS National Working Group After the results of HIV Sentinel Surveillance became available for ANC and HRG sites, a National Working Group (NWG) for HIV Estimations was constituted under the leadership of National Institute of Medical Statistics (NIMS), ICMR. The NWG comprised epidemiologists, demographers and M&E experts from NACO, NIMS, UNAIDS, WHO, CDC, AIIMS and NIHFW. The group was advised from time to time by international experts from WHO/UNAIDS Global Reference Group on Estimations, Projections and Modelling. NWG coordinated the entire process of HIV Estimations, including identification of experts for Regional Working Groups, planning and conducting training workshops, collecting and organising data inputs, in-depth analysis and refinement of demographic projections, mentoring of Regional Working Groups and consultations with experts from time to time, and finally, compilation, critical review and finalisation of HIV projections for all states and India. After finalisation of the estimates, NWG prepared this technical report for wider dissemination Regional Working Groups As outlined in one of the objectives above, it was identified that there is a need to build capacities of more institutes and officers from State AIDS Control Societies (SACS) in the HIV Estimation process in India. This would ensure that greater technical support is available to SACS for using the data as well as to undertake detailed state and district level analysis. Hence, during this round, Regional Working Groups were constituted comprising of around 50 epidemiologists, demographic statisticians, M&E officers and programme personnel, who were trained and who undertook HIV Estimations for the allotted states. The teams were constituted in such a way that they had personnel from programme as well as leading public health institutions, ensuring multi-disciplinary nature of the teams. The regional working groups were guided through a systematic process of HIV Estimations, starting with understanding the model and reviewing data inputs till generating outputs and their interpretation. The regional teams were mentored by members of the National Working Group throughout the process. The regional teams were responsible for developing the models for the allotted states. The five regional working groups constituted and their state allocation are highlighted in Table 1.1. Table 1.1: Regional Working Groups and State Allocation Regional Working Groups North Central West South East & North East States Jammu & Kashmir, Himachal Pradesh, Haryana, Chandigarh, Punjab & Rajasthan Bihar, Jharkhand, Uttar Pradesh, Uttarakhand & Delhi Maharashtra, Gujarat, Goa, Madhya Pradesh, Daman & Diu, Dadra & Nagar Haveli Tamil Nadu, Andhra Pradesh, Karnataka, Kerala, Puducherry, Andaman & Nicobar Islands West Bengal, Chhattisgarh, Odisha, Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim & Tripura 2

21 1.2.3 Training Workshop on National HIV Estimations & Projections, 1-5 May, 2012 The first national workshop for the regional working groups was conducted at New Delhi from 1-5 May The objective of this workshop was to introduce the regional teams to the process of HIV estimation using Spectrum model, orient them to all the steps involved in the process, take them through a step-by-step practice of working on Spectrum and plan for the follow up work by the regional teams. The training was facilitated by international experts from WHO/UNAIDS Global Reference Group on Estimations, Projections and Modelling, East West Centre, Hawaii and CDC Atlanta, in addition to the members of NWG. Inauguration of Training Workshop on National HIV Estimations & Projections (1-5 May, 2012) The first day focussed on understanding the overall process, get familiar with Spectrum package and its modules, reviewing and entering demographic and programme data into Spectrum. The Second day addressed the steps of configuring the epidemic structure, careful review of epidemiological data on prevalence trends, conducting quality checks and making adjustments, and entering surveillance data into Spectrum. The Third day focussed on understanding the curve fitting, identifying and fixing issues with curve fitting, calibration and examining initial results from curve fitting. The Fourth day covered the advanced options and uncertainty analysis, besides continuing with the hands-on-practice. Last day elaborated the steps in generating and examining the Spectrum outputs and discussions were held to plan the follow-up work by the regional teams. The teams were asked to consult with the respective programme managers at SACS to finalise the programmatic inputs such as ART coverage in adults and children, PPTCT coverage and size of high risk groups Interim Workshop on National HIV Estimations & Projections, May, 2012 The interim workshop was conducted onwards the objectives of finalising the input data (Demographic, ART, PPTCT, HRG), preparing checklists for data inputs and entering the data into Spectrum, and reviewing, finalizing and entering the surveillance data into EPP and working on curve fitting for each state. Regional working groups made presentations on the data inputs that they have collected in consultation onwards programme officers at their respective State AIDS Control Societies. Technical and other practical challenges 3

22 encountered by states/union Territories were identified and steps were suggested to address them. Statespecific scenarios and options to customize the model for each state were discussed. Regional teams were advised to work on their respective state models, refine the curve fits and generate final estimates for discussion and review during the final workshop In-depth Review of Demographic Projections During the discussions in the interim workshop, several limitations were identified in the demographic data as projected by Demproj Module of Spectrum. There were mismatches between the total population, Total Fertility Rate (TFR), Infant Mortality Rate (IMR) and other demographic parameters projected in Demproj and the standard sources in the country such as Census, Sample Registration System (SRS) etc. Migration was also not considered in the projections. These discrepancies were affecting many states demographic projections. Also, areas were identified where global defaults can be replaced with Indian data that is available from authentic sources such as census population projections, NFHS etc. Stemming from this, the National Working Group initiated the process of critically reviewing the state demographic data inputs. It was decided to reprocess the demographic projections in the DemProj module of Spectrum in order to ensure that they matched with Census 1981, 1991, 2001 and 2011 data to an acceptable level. In depth analysis was under taken and Intense deliberations were held between the National Working Group and members of Expert Group on Census Population Projections from June to September, 2012 and until the values for base year population, migration, mortality, fertility and sex ratio at birth until values were finalised. Discussions were held regarding details of the methods and inputs for Demographic projections using Census, SRS and NFHS data. The demographic projections for each state for each decade starting with 1981 were adjusted and smoothened to validate with Census data. Specific demographic parameters such as TFR, ASFR, Sex ratio at birth, IMR, Life expectancy, Net migration rate etc. for each state were closely reviewed. Wherever the information is not available or the available information has limitations, the indicators were estimated and adjusted following standard demographic approaches and the results were validated through expert consultation. Extensive work on estimating net migration rate and age-sex distribution of migration at state level through direct and indirect methods from and their use in Spectrum is a significant addition in this round of estimation process. Finally, it was ensured that the population projections along with the age-sex distribution match with the Census population projections. The results were finally assessed and validated by experts. Details on the methodology utilised for finalising demographic data inputs is included under chapter two Final Workshop on HIV Estimations and Projections, and September, 2012 After finalising the demographic projections, they were shared with the regional teams and they were asked to refine the epidemic projections with the new demographic data. A final workshop was conducted in two batches to finalise the epidemic projections and examine the results for each state. Work done by states was critically reviewed by the National Working Group. The objective was to once again review the epidemiological and demographic data inputted to Spectrum, review initial state results and ensure the validity of any adjustments made by the states. State-specific issues and recommendations were listed down and corrections were carried out by the teams during the next two weeks following the workshop. Projection files worked by the regional teams were shared with the national working group for final review and consolidation. 4

23 1.2.7 Finalising State HIV Estimates and Generating National HIV Estimates An intense process of reviewing, cleaning and finalising state HIV projection files was undertaken by the national working group following receipt of state files from Regional Working Groups during October and November The NWG conducted daily working sessions to verify and revalidate all data inputs, examine correction factors utilised, reprocessed state files and generated trends for various indicators as required. Expert opinion was obtained at various junctures to resolve any uncertainties related to specific state projections to ensure a true reflection of the epidemic in each state. Validity of the results, accuracy of estimation and appropriate portrayal of epidemic patterns in each state were ensured through a critical and multi-faceted analysis of various indicators generated through Spectrum and comparing them with information from other sources. Working Session of the National Working Group for HIV Estimations 2012 The national and state HIV estimates thus generated were presented to the Technical Resource Group on Surveillance and Estimation on November 16, 2012 for final recommendations and approval. Necessary modifications as suggested by the TRG were incorporated with a timeline of two weeks to ensure release of HIV estimates on the eve of World AIDS Day, 01 December, DESCRIPTION OF TECHNICAL REPORT INDIA HIV ESTIMATES 2012 This report provides a technical update on India HIV estimates of number of people living with HIV, HIV prevalence, new HIV infections, number of AIDS related deaths and treatment needs for Antiretroviral Therapy and services for Prevention of Parent to Child Transmission at national and state level. It gives fresh estimates for the years 2010 & 2011 and updated estimates back-calculated for previous years. Estimates are disaggregated by age and sex wherever applicable. 5

24 The report is divided into four chapters. This introductory chapter describes the objectives and process of HIV estimations undertaken through the involvement of experts from across the country. The second chapter describes the methodology of HIV estimation, modules under Spectrum package, analysis and refinement of demographic projections and the assumptions adopted for generating HIV estimates. The results of various HIV indicators for India and States/Union Territories are presented in the third chapter. The fourth chapter presents a discussion on the epidemic trends that need to be considered for policy and programme planning of current and future HIV prevention, treatment and care interventions at national and state level. The documents and articles referred to are included in the Reference section. The report also includes three annexes. Annex A lists the national and state-wise estimates from 2007 to Data inputs along with the final population projections are highlighted in Annex B. The members of the Technical Resource Group on Surveillance and Estimation, National and Regional Working Groups for HIV Estimations 2012 are listed under Annex C. 6

25 Chapter 2 METHODOLOGY This chapter presents the methodology of HIV estimation and projection used in the current round of HIV estimation exercise. The chapter begins with an overview of the process, followed by the detailed descriptions of inputs, methods, and assumptions used in the HIV estimation and projections. For easy replications of the methodology and use of epidemiological tools, the chapter provides all essential details. For further information about the methods, and assumptions the reference list included under the Bibliography can be referred to. 2.1 OVERVIEW OF THE METHODOLOGY Similar to the process of HIV estimation and projection used for the previous round of estimation, the current estimation process also used deterministic modeling techniques to arrive at robust estimates of HIV prevalence, HIV incidence, HIV population by age and sex and other programmatic indicators such as need for Antiretroviral treatment (ART) and need for PPTCT. 1,2 However, while the last round of estimation exercise used two separate epidemiological computer programs, namely Estimation and Projection Package (EPP), 3,4 and Spectrum, 5,6 the current round of HIV estimation used a modified version of Spectrum which included EPP as part of its AIDS Impact Module (AIM) 6. The method of demographic projection, method to estimate ART need, progression to deaths were also improved in the current version of Spectrum software 6,7 according to the latest available evidence. Further details are included in subsequent sections of this chapter. Figure 2.1: Schematic Diagram showing the Structure and Process for Generating HIV Estimates The process of HIV estimation and projection started with the demographic projections using the DemProj module of Spectrum. This module was used to obtain estimates and projected value of single-year age and sex specific populations for the projection period of 1981 to In order to produce projections related to HIV epidemic and calculate its related indicators, Spectrum requires a number of inputs of programme statistics on ART (adult and children) and PPTCT coverage, defining the CD4 threshold used, and a definition 7

26 of the nature of the epidemic along with surveillance data and population size. The in built EPP package in AIM module was used to generate the estimated trend of HIV prevalence and incidence among adults for each population group. Based on these three components (Demographic projections, estimated trend of adult HIV prevalence, and epidemiological assumptions), the AIM module was used to determine the number of people living with HIV/AIDS, HIV incidence, and ART need, by age and sex. The entire process was repeated separately for each State/Union Territory for which estimates have been provided (Figure 2.1). 2.2 DEMOGRAPHIC PROJECTION Demographic data constitute a critical component of the overall process of HIV estimation and projections as it provides accurate measures of the population size to yield accurate HIV estimates related to number of PLHIV by age and sex, number of positive pregnant mothers needing PPTCT, number of people needing ART, and many more indicators. At the start of the process, the Technical Working Group compared the demographic projections used in the last round of estimates with the recently announced results of the 2011 Census for each states/union Territories for determining sex ratio, large age group and also the population totals for all India. It was noted during this review that the old demographic projections used in spectrum were not matching with the census data. There were large differences seen among the reproductive age group of years. These large differences were expected to impact on the epidemiological calculations in the AIM module and especially affect indicators related to PPTCT and children. Differences between the projection generated by the DemProj and Census 2011 for the year 2011 necessitated the demographic projections to be redone. There was need to ensure a complete matching of population by sex and age group between the output of the system and the various census years: 1981, 1991, 2001 and Estimation and projection of population sizes for each year in the projection period ( ) was done separately for each of the States/Union Territories for which HIV estimation was undertaken. This required state-specific inputs on population size by age and sex for the base year 1981, and additional inputs of several demographic parameters to allow for projection of population from the base year till These parameters included level and age-specific pattern of fertility, sex-ratio at birth, level and age and sex-specific pattern of mortality, and volume and age-sex distribution of net-migration. Separate set of inputs on these indicators were derived for each of the States/ Union territories by using several data sources including Census, Sample Registration System (SRS), and other large-scale demographic health surveys in the country. The process of deriving the inputs from various data sources are described in the following sections Population Size by Age and Sex for the Year 1981 The population size by age and sex for the base year was available from the Census 1981 for each of the States/ Union Territories considered for projections. These inputs, however, could not be incorporated directly due to existence of several problems including age under-reporting, age mis-reporting, and undefined ages in raw census data Hence, the age-specific 1981 census populations for both men and women needed to be smoothened. 8

27 For the population where age was not stated, several alternative approaches were considered to assign this population to specific age groups: One approach was to consider fifty per cent of the individuals in the category of undefined ages for age-group 0-4 years, and rest 50% of them to distribute equally in remaining age-groups. Another approach was to consider all individuals in the category of undefined ages to the 0-4 age group. The third approach was to distribute all individuals in the category of undefined ages/ age-group in equal proportion to all ages/ age-groups. However, results obtained from the three methods did not show much variation. Hence the third approach to equally distribute individuals into the category of undefined ages across all age-groups was adopted. Thereafter, the age distribution of the population was smoothened using Strong smoothing technique as suggested by the India s Expert Group for population projection Level and Age-Specific Pattern of Fertility ( ) The level of fertility in the population was measured by total fertility rate (TFR). i and the age pattern of fertility was measured by age-specific fertility rates (ASFR) for the age-groups 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, and years. Values of TFR and ASFR were available from the reports of the Sample Registration System (SRS) for all bigger ii states from 1981 to 2010 and for smaller iii states from 1990 to For remaining years of the projection period ( ), values of TFR and ASFR were projected using the Gompertz model as suggested by the India s Expert Group for population projection. 11 Mathematical details about the Gompertz model and its application can be referred to from specific articles included to the bibliography. 13, Sex Ratio at Birth ( ) For bigger states, sex ratio at birth was available from SRS reports for the period , whereas for smaller sates, it was available for the period For smaller states, value of sex ratio for the period were calculated by applying reverse survival method on census data for This method allowed estimating number of male and female births, in the 5 and 10 year periods prior to the Census of 1991 by using the total count of children aged 0-4 and 5-9 years as found at the time of the Census of Theoretically, children in the age-group of 0-4 and 5-9 were the survivors of births that took place during and respectively. For estimating number of births during these two periods, the number of children counted in the Census of 1991 was reverse-survived, using survival ratios for that time-period. The method was applied separately on both male and female children and then the sex ratio at birth was computed by taking ratio of male to female births. The estimates obtained were kept constant for their respective time-period. The reverse survival method assumes that reporting of age, especially of children, is accurate, that the children s population is not affected by migration; that fertility of migrants and non-migrants do not differ; and, that levels and age patterns of mortality during early childhood are known. The population count of children in census is considered to be of reasonably good quality 15, and the age-pattern of mortality were available from appropriate life-tables (discussed subsequently). More details about the reverse survival method and its application are available elsewhere. 16,17 i TFR represents number of live births a woman would have if she survived to age 50 and had children according to the prevailing age-specific fertility rates. ii Bigger states: Andhra Pradesh, Assam, Bihar, Chhatisgarh, Delhi, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karanataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal. iii Smaller states: Arunachal Pradesh, Goa, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, Tripura, Uttarakhand. United territories: Andaman & Nicobar Island, Chandigarh, D & N Haveli, Daman & Diu, Puducherry, Lakshadweep. 9

28 2.2.4 Level and Age-Sex Pattern of Mortality ( ) The level of mortality was measured by the life expectancy at birth iv was measured by age-sex specific mortality rates. For all bigger states, the values of ), and age-sex pattern of mortality were available from the SRS reports for the following five year periods: , , , , and Values of for these states were obtained from the SRS life tables and also through linear interpolation for the intermediate years. For smaller states where life tables were not available, infant mortality rate (IMR) were estimated from census data using indirect methods based on number of children ever born and number of surviving children. Details about this method can be found in other documents. 17,18 This method provided estimates for the year 1981, 1991, 2001, and For each estimate of IMR, corresponding value of obtained by using the model life tables. For age-sex pattern of mortality, the Coale-Demeny West model life table was considered for all the states excepting Delhi and Meghalaya, where UN South-Asia model life table was considered in view of prevailing level of IMR in the two states. Details about these model life tables, and their applications were available elsewhere 19,20. Values of was for the intermediate years were obtained by linear interpolation. For years beyond 2011, these values were projected following the methodology suggested by the India s Expert Group for population projection Net-Migration ( ) Inputs on net-migration included volume of net-migrants and age distribution of migrants by sex. These inputs were derived by applying different methods on available census volume and age-sex distribution of migration data for various census years starting from 1981, 1991 and 2001 and CBR, CDR data from SRS. Both direct and indirect methods were used to arrive at the required inputs. The direct method involved information on place and duration of residence, and place of remuneration in census. 18,21 In direct method, we used place of origin and place of destination for the duration 9 years in census periods and , calculate state total and age-wise volume of net migrants by sex using volume of in-migrants and out migrants in a particular state by in-migrants minus out-migrants, also distribution of net-migrants by age-sex for all the 34 states. Since migration data is not available for the current census 2011, we have used indirect residual method of estimation to estimate volume of net migrants for the period The residual method of estimation is based on the idea that the population change between any two consecutive censuses is the result of natural growth (births minus deaths) and net migratory movement. It was assumed that the contribution of international migration was negligible compared to population change between the two consecutive censuses, and hence the difference in observed population change and natural growth was considered as the estimate of net intercensal migration for the particular state. The values of births and deaths were obtained by using SRS reports 12. More details of this method can be found in the manuals on methods of estimating populations published by the United Nations. 21 This method was used to estimate the net intercensal migration for both males and females for the period Estimates of net migration for years beyond 2011 were projected following the guidelines from India s Expert Group on population projection. 11 The age-distribution of both male and female migrants was obtained from the census data for the period and The observed age-sex distribution of migrants during was assumed to be constant for years 2001 onwards. 10 iv Life expectancy at birth is the average number of years a newborn can expect to live if he or she experienced the age-specific mortality rates prevalent in a particular year.

29 2.2.6 Methods of Population Projection in Spectrum Population projections were done using the standard cohort component projection method. This method projects the population in a way that duplicates the manner by which populations actually grow or decline. It consist of carrying forward each cohort (individuals in an age-group), in time subject to the age-pattern of mortality to which the cohort has been exposed. These calculations are performed by sex due to observed differentials in mortality pattern among males and females. 18 In addition, the numbers of births that women of childbearing age will have at the assumed birth rates were estimated for each year and were, in turn, subject to infant and child mortality rates. The third and final component of change was considered to be the migration by age and sex over time, measured in terms of net-migration. More details about the cohort-component method can be found in other documents. 17,18 Spectrum contains a demographic projection model that projects the population by age and sex over time on the basis of the starting population by age and sex and annual rates of fertility, mortality and migration. The population by age and sex in that first year (1981) was from the census. Estimates of fertility and mortality were available from annual sample surveys and the Expert Committee projections of population as explained above. These data sources were not necessarily consistent, and as a result the projection of the population from 1981 might not exactly match the census findings for later years. The mismatch might be particularly important for children under 5 years of age. Spectrum could adjust for these discrepancies by comparing the projected population by age and sex in each year with those contained in an external data file. The estimates in the external file were prepared by disaggregating the census population in 1981, 1991, 2001 and 2011 into single year ages using the Beers Interpolation formulas and then interpolating between census years to fill in the intervening years. The Beers procedure uses a series of polynomial equations to divide the population in five-year age groups into single year ages while maintaining the population total and providing a smooth transition from one age to the next. More details about the Beers procedure, and its application are available in separate documents. 18,22 The survival probabilities for single year ages were calculated using the life tables which were provided for five-year age groups. The number of person years lived and number of individuals who survived to the different age-groups were used to calculate single year survival probabilities. Once the single year estimates were derived, the standard cohort-component methodology was adopted to obtain year-wise projected values of males and females population by single year ages. At the end of the calculation cycle for each year, Spectrum calculates the ratio of the projected population to the population in the external file. Separate ratios are calculated for each age and sex. The current projection was adjusted by multiplying the population of that age and sex by the calculated ratio. Thus, small adjustments were made on annual basis to the projected population to ensure that it matches the census data in all years. At the result of this process, the demographic projection in Spectrum s DemProj module were exactly matching the population structure and numbers for each census year since More details about the DemProj module are available in its manuals which are available online at the website of Future s Institute ( 2.3 EPIDEMIOLOGICAL INPUTS The epidemiological inputs for the estimation and projection of HIV epidemic needed to first consider the type of HIV epidemic for the particular State/Union Territory. This involved defining the nature (generalized versus concentrated) of the epidemic, and also defining size of various population subgroups at different levels of risk of HIV infections. 11

30 The subsequent steps included providing inputs on: (1) HIV prevalence in the population subgroups, (2) program statistics on coverage for-prevention of mother-to-child transmission of HIV, adult ART, and child treatment and (3) eligibility criteria for receiving ART. These inputs and steps are described in detail in the following sections Surveillance Data Input HIV Sentinel Surveillance data was used as the primary data source for deriving state-specific prevalence levels among the general population and the high risk groups. The surveillance network has expanded from 176 sites in 1998 to 1,359 sites in 2011 wherein almost all districts are covered under surveillance system (Table 2.1). This increase in number of HSS sites has provided improved representation of data at State level to arrive at better State-specific estimates. 23 Subjects in these sites were selected by consecutive sampling and tested for HIV by unlinked anonymous testing. Similar to the previous rounds of HIV estimation, only valid HSS sites were included to describe the trend in HIV prevalence for a given subpopulation. 1,2,24 Sites with a minimum 75% coverage of the assigned sample were considered as valid and were included in the estimation process. 2,23,24 Thus, for sites which were meant to collect data from women attending the antenatal checkups (assigned sample size: 400) were defined to be valid if the number of such women tested at the site was 300 or more. Similarly, sites which were meant to provide HIV prevalence for higher risk groups (assigned sample: 250), the cut-off point for validity was considered to be 188. These cut-off points were consistent across all States/Union Territories considered for the estimation process. Table 2.1: Number of HSS Sites in India, Site Type STD ANC ANC (Rural) ANC (Youth) IDU MSM FSW Migrant Transgender Truckers TB Fisher-Folk/ Seamen Total It may be noted that, there was a change in the methodology of HRG sentinel surveillance in some states whereby random sampling method was used instead of consecutive sampling method. The resultant data points were considered as new sites thereafter. Also in those states where Integrated Biological and Behavioural Assessment (IBBA) Surveys were conducted for HRG, the data so obtained from the first two rounds were added as independent data points for the sites. This was done to ensure that any change of levels or trends which could be more attributable to change of methodology was not considered as change of trend in the epidemic. The trend was, therefore, determined based on the consistent sites, and the level was adjusted to the latest values where there were more sites of surveillance. 12

31 In addition, the HIV Sentinel Surveillance (both ANC and HRG) data used for HIV estimates were cleaned for outliers, e.g. a data point was deleted if the prevalence of the site for that year was assessed to be too high compared to previous and subsequent years Program Statistics on Coverage for Prevention of Parent-to-Child Transmission of HIV, and Adults and Children on ART Inputs were needed on trend in the: (1) number of mothers receiving single-dose nevirapine, (2) number of adults receiving ART, and (3) number of children receiving ART. Individuals in the age-group (0-14) were considered as children. These data were available for the period of Values of these indicators for the years beyond 2011 were calculated using different methods. The future trend in number of women receiving single-dose Nevirapine to prevent parent-to-child transmission was determined by assuming an increase of 5% every year from 2012 to 2017 (Figure 2.2). For each State/Union Territory, the trend in number of adults and children receiving ART was calculated by keeping the observed state-wise distribution of adults and children receiving ART in 2011 constant for years , while projecting national figures of the total number of adults and children receiving ART according to the targets of fourth phase of the National AIDS Control Program (NACP-IV). State-specific inputs regarding these indicators are provided in Annex B. Figure 2.2: PTCT Rate Input in Spectrum In the PPTCT module, and in order to allow for estimate of probability of HIV transmission during breastfeeding, a specific section requires input of data on the observed duration of breastfeeding whether or not ART is provided. The data for this indicator was derived from NFHS-3 for each state in the country and included in the required fields with the assumption that there was no difference of breastfeeding behaviour whether or not the mother is positive, or she is on ART Eligibility for Receiving ART The trend in eligibility criteria for adults, and children was provided to reflect the changing policy to start providing treatment to those infected from HIV, which had effect on survival of those infected from HIV. The 13

32 eligibility to receive ART for adults was determined by the CD4 cells counts (CD4 counts 200 per mm 3 till 2008; CD4 counts 250 per mm 3 during , and CD4 counts 350 per mm 3 during ) (Figure 2.3). Figure 2.3: CD4 Count Threshold for Eligibility for Treatment for Adults Input in Spectrum For children, the eligibility criteria were defined, as per guidance from the programme, based on their age and CD4 counts. The guidelines being followed in India since 2004 are as follows: < 11 months: CD4 count <1500 (or CD4 percent< 25%) months: CD4 count < 750 (or CD4 percent< 20%) months: CD4 count < 350 (or CD4 percent< 15%) > 5 yrs: Follow adult guidelines < 24 months HIV positive: irrespective of clinical/immunological stage, start on ART months: clinical stage 3 & 4 &/or CD4 percent< 20%) months: clinical stage 3 & 4 &/or CD4 percent< 15%) For children aged 5 years or above, the eligibility criteria were assumed to be same as those specified for adults. This was reflected in the system inputs as shown in the Figure 2.4 below. Figure 2.4: CD4 Count Threshold for Eligibility for Treatment for Children Input in Spectrum 2.4 STEPS OF HIV ESTIMATION AND PROJECTION In order to determine HIV prevalence and incidence trends, the type of epidemic, population size, population turnover and reassignment had to be defined in EPP as detailed below. 14

33 2.4.1 Defining Epidemic and Introducing Population Size For estimation purpose, the HIV epidemic in each State/Union Territories, as well as the epidemic at national level, was considered to be concentrated amongst population subgroups of female sex workers (FSWs), men who have sex with men/ transgender (MSM), and injecting drug users (IDUs). The system requires that the epidemic in any given state / union territory be defined according to the specific categories of population for which surveillance data and population size estimate was available. Furthermore, for being able to fit a prevalence trend for any population, it is required there should be at least one site with 3 data points or at least two sites with at least two data points at different times for this population. On this basis, decision was made for defining the sub-populations to be used for curve fitting in each state. Any other population for which we either did not have surveillance trend or a population size was considered under the category rest of the population and would be represented by ANC surveillance. Table 2.2 highlights the subpopulations analysed separately for each state. Table 2.2: State-wise Sub-populations Used for HIV Projection State/UT IDU MSM FSW ANC Andhra Pradesh Arunachal Pradesh Assam Bihar Chhattisgarh Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Odisha Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal Andaman & Nicobar Chandigarh Dadra Nagar Haveli Daman & Diu Puducherry *Lakshadweep not included due to lack of Surveillance data 15

34 Furthermore, except for the states of Manipur and Nagaland, the epidemic was defined as non-idu concentrated epidemic. The choice about nature of the HIV epidemic in a given State/ Union Territory was based upon the observed epidemiological levels and trends of the epidemic in different subgroups in the particular State/ Union Territory. The total population of each State/Union Territory was divided among (1) the specific populations of FSWs, MSM and IDUs whenever appropriate as per the above using the population size estimates available with the programme, and (2) the rest of the population, referred to as the general population. Estimates of the total population for States/ Union Territories for each year in the period of estimation and projection ( ) were available from the exercise of demographic projections described in previous sections of this chapter. The recent estimates of the population size for each subgroup were taken from mapping exercise and site validation data from targeted intervention programs by National AIDS Control Organization and State AIDS Control Societies (Figure 2.5). The population breakup between these categories was assumed to remain constant over year, and that the population growth in each category is the same as that of the general population Figure 2.5: HRG Population Input in Spectrum Defining Turnover and Reassignments While the population size of each high risk group is assumed to have the same growth as that of the general population, and that the population proportions remain the same; it is also assumed that this population keep renewing itself over time by the fact that there are always those who stop being part of that group, and those who become newly members in it. This is particularly the case for IDUs and FSWs. For MSM, global evidence shows that MSM continue having sexual relationships with other individuals in these subgroups throughout the period when they are sexually active, eventhough their number of sexual encounters may decline over time. However, it is known that IDUs and FSWs do not remain as such throughout their life time. Many of them stop injecting drugs or selling sex after a certain period. Based on the second round of Behaviour Surveillance Survey conducted in 2006, 34,35 it was determined that on average IDUs stop injecting drugs after an average duration of 15 years, less for some and more for some others and hence that they can be considered to be part of the general population due to their reduced risk behaviour after this duration. Similarly, FSWs are assumed to keep involved in sex work for an average duration of about 8 years, and then they stop selling sex and become part of the general population (Figure 2.6). 16

35 Figure 2.6: HRG Turnover and Reassignments Input in Spectrum EPP considers these durations for calculating the reassignments between different categories of population, and assumes that each year, a certain number of IDUs and FSWs carrying the same prevalence of HIV as the population they were in, stop being part of their group and become part of the general population. These are replaced in their respective population by new comers who are young and still not infected with HIV. Further, for IDUs, the non-aids mortality was assumed to be 7% higher compared to non-idus populations. This assumption was made to account for the higher risk of mortality experienced by all IDU regardless of HIV status. 36,37 Intuitively, all FSWs were considered to be female and all MSM were considered to be male. While, for IDUs, it was assumed that 90% of them were male and remaining 10% were females according to several studies made in the country. 34,38-40 The HIV prevalence in the general population was also adjusted for the effects of the movements of FSWs and IDUs into the general population after the pre-specified time period as discussed above. Each year, the proportion of FSWs and IDUs who stop being part of that group and become part of the general population have the same prevalence of the population they were counted in during the previous year. When they move out to the general population, a number of HIV positive individuals are added annually to those already estimated to be HIV positive in the general population (lower risk) category, and as such, these are not considered as new infections; but rather old infections newly considered in that group Curve Fitting, And Estimation of Adult Incidence The curve fitting in the AIM module was earlier known as Estimation and Projection Package (EPP). It uses a deterministic model, in which the population of year old was divided into three groups, a not-at-risk group, an at-risk group and an infected group. Three differential equations described the changes in those groups over time, and thus in prevalence over time. Four parameters determined the shape of the epidemic curve. 17

36 The four parameters are: (1) t 0 (the start year of the HIV epidemic); (2) r (the force of infection a large value of r will cause prevalence to increase rapidly while a small value will cause it to increase slowly); (3) f 0 (the initial fraction of the adult population at risk of infection it determines the peak level of the epidemic curve) and (4) ø (the behaviour adjustment parameter which determines how the proportion of new entrants in the adult population who are at risk of HIV infections changes over time). Following figure shows the four parameters of this model (Figure 2.7). t 0 The start year of the epidemic; Figure 2.7: Parameters Used in the Curve Fitting in AIM Module r Force of infection, determines the initial growth rate; f 0 The initial proportion of the population that is at risk of infection (determines the peak prevalence of the epidemic. ø Adjusts the size of the risk group in response to behavioral changes or interventions. Source: (Brown T et al., 2006) 25 If is negative, people reduce their risk in response to the epidemic and the curve shows a sharper prevalence decline after the peak. If ø is zero, the proportion at risk remains constant and the prevalence declines after the peak as people die. If ø is positive, risk actually increases over time and prevalence falls less quickly or stabilizes at a high level. More details about the parameters and the model equations can be found in available literature. 3,25-27 The AIM module also uses Bayesian melding approach which refers to combining information about the inputs and outputs of a deterministic model to estimate the incidence and their 95% confidence intervals. Bayesian approach starts by quantifying prior beliefs (expert knowledge) about the true value of a quantity of interest-which in our case were the four parameters. Based on the experience, knowledge, and evidences, about HIV epidemic in different States/ Union Territories, a plausible range of these parameters could be specified. For instance, for a given State/ Union Territory, it was possible to define a range of years that during which the epidemic started (say between 1981 and 1986). In Bayesian inference, prior beliefs are represented by probability distributions, e.g., the start year of the epidemic was assumed to have a uniform distribution on the interval between the upper and lower bounds. Similarly, prior distributions were defined for remaining three parameters. The next step was to generate a set of possible epidemic curves. The prior distributions are then updated based on the observed outcomes of comparing these curves with the observed HSS data. Data and information on measurement errors are used to calculate a measure of the so-called likelihood an epidemic curve which is similar to the level and trend in observed prevalence has a high likelihood of representing true prevalence. Combining prior distributions with likelihood (updating prior beliefs) gives the posterior distribution of the quantity of interest. Melding the prior distributions on inputs and output with the likelihood on output gives posterior distributions on inputs as well as outputs. The sample from the posterior distribution of HIV prevalence curves is drawn using the Sampling Importance Resample (SIR) algorithm. 18

37 The SIR algorithm involves sampling randomly a large number of combinations of the four parameters t 0, r, f 0, and ø. For each sampled combination of the four parameters, a curve is generated, and each curve is compared with the HIV prevalence input to the model from HSS data. If the generated epidemic curve is very different from the observed HIV prevalence, that curve is assigned a low or zero weight. If the curve resembles the observed HSS data reasonably well, it is assigned a high weight 28. The epidemic curves as well as their input parameters were resampled v such that the probability of being selected is proportional to the weight that has been assigned to the curves. The result is a sample from the posterior distribution of prevalence, in the form of a set of prevalence curves. Details of the SIR algorithm are available in separate documents. 29,30 Based on the sample of curves from the posterior distribution of HIV prevalence curves, a 95% confidence interval for prevalence in a given year is given by the lower 2.5 th and the upper 97.5 th percentiles of the prevalence for that year within the sample. The best estimates were given by the trajectory that was most likely to represent prevalence over time, given prior distributions and data. This maximum a posteriori trajectory was the one with the highest posterior density, proportional to the product of the prior distributions on the inputs of the curve, the prior distributions on outputs and the likelihood of the data. More details about the process of curve fitting are available elsewhere. 4,31 Following the above methodology, prevalence curves were generated independently for each higher risk group within a given State and subsequently, these curves were cumulated to form a prevalence curve for the State (Figure 2.8). The accumulation was justified by the fact that these populations are separate and are part of the total population of the State. Figure 2.8: Generating HIV Prevalence Curves using EPP Classic in Spectrum The members of the working group discussed the results of each curve fit for each population in each state separately. They assessed the validity and acceptability of the results, and the level to which the produced prevalence trend represents the existing knowledge of the epidemic among that population and that state. Several factors were assessed, among these were the early or late start of the epidemic, the year of peak, and the actual trend as demonstrated by sentinel surveillance. Whenever necessary, advanced options tools were used to set some conditions to eliminate unacceptable results. v A total of 3000 curves were resampled to get a sample from the posterior. 19

38 For example, if in a specific population the trend of HIV prevalence seem to be very high in the early 1980s or peaking at very high level by 1990, the working group sets a reasonable limit for HIV prevalence around 1990 and let the system find the best of the available options using the calculations explained above Calibrating the ANC Prevalence Curves Due to the difference between ANC prevalence observed at HSS sites, and population prevalence measured by population based surveys, calibration of the ANC prevalence curves was required. The key source of information used for calibrating HIV prevalence curves was the 2006 of National Family Health Survey (NFHS-3) where state-specific information on HIV prevalence was determined 32. With use of a calibration factor, the overall curve determined on the basis of ANC HSS trend data was scaled down to the level of the observed prevalence in The calibration factors were derived for individual states in five high prevalence states (Andhra Pradesh, Karnataka, Maharashtra, Manipur and Tamil Nadu) based on calculations from NFHS-3 while for Nagaland the calibration factor was determined from a specific study undertaken by NACO 33. For the remainder of the moderate and low prevalence states, the common constant calibration factor was derived from the NFHS-3 (excluding the aforesaid 6 high prevalence states) for national comparison between general population prevalence and ANC prevalence. This way, for the year 2006, the level of HIV prevalence for each state to be used for calibration was calculated by scaling down the ANC prevalence by a factor by The determined value was then included into EPP for scaling the curve to that level of prevalence in 2006 as shown in Figure 2.9. Figure 2.9: Application of Calibration Factor for General Population 20

39 2.5 METHODOLOGY FOR ESTIMATION OF HIV INCIDENCE FOR ALL AGES As described above, EPP derives smooth trends of prevalence from observed trends of surveillance for each subpopulation. These calculated trends represent the life of the epidemic since its inception for that population in the state up to date. In order to estimate incidence trends for the same sub-populations, EPP uses a simple calculation method where the prevalence for any year is resulting from the number of the new infections of that year added to the existing prevalence of the previous year adjusted by the expected number of AIDS related deaths during that same year. Since the levels of prevalence over years from the start of the epidemic are known, and the number of PLHIV who were supposed to be dying but are kept alive due to treatment are known, EPP determines by this approach, the levels of incidence that are needed to keep the prevalence at the determined levels. These incidence estimates are for the adult population This trend of incidence is then taken as the input into spectrum for further calculation for the age groups below 15 and above 50 years. In spectrum, to estimate incidence by age, including ages older than 49, the model has used data from national surveys (Demographic Health Surveys, AIDS Indicator Surveys, National Family Health Surveys etc.) on prevalence by age from all countries. When there are two such surveys in the same country, the pattern of prevalence by age from the first survey is compared with the second survey to determine incidence. If the two surveys are 5 years apart, then all years olds infected at the time of the first survey will be at the time of the second survey. Any increase in prevalence among at the second survey is due to new infections. The system also does adjustment for mortality. These calculations have been made for a large number of countries, including India, and patterns of incidence by age were prepared and input into the model. The patterns of incidence by age determined this way are used in Spectrum and applied to the incidence calculated for the state by EPP. This allows to split new infections among those into five year age groups and to add the additional new infections that occur to those over age 49. In India, the outputs of this calculation in terms of the pattern of HIV prevalence by age group have been validated with the results of National Family Health Survey (NFHS-3) and these patterns are found to be very well matching. Regarding the infections below age of 15, the basic assumption here is that this population is not sexually active and hence is not exposed to that kind of risk. Hence, all the cases for children age 0-14 years living with HIV are calculated as resulting from Mother to Child transmission. From the ratio of male to female incidence of infections in adults, Spectrum calculates the proportion of women living with HIV at all ages. A specific pattern of fertility rate among HIV positive women is then applied (this had been updated in 2010) to determine the estimated number of HIV positive women. The probability of transmission from mother to child with or without treatment is applied considering also the duration of breastfeeding and the probability of transmission linked to it. The PPTCT programme coverage data has been included into the model to account for extra protection due to the programme, and this will give us annually, from the start of the epidemic, the annual number of children infected through Mother to Child Transmission. Over years, these numbers are added up annually considering the survival of HIV positive children with or without ART or prophylaxis to provide the total number of children living with HIV by single age for each year. Once the final incidence trend is determined, with a breakdown on all age groups, the Spectrum recalculates annual new infections from the beginning of the epidemic and the numbers keep adding up. The adjustment of mortality (with or without treatment) allows then to get the total estimated number of PLHIV each year and for each population group. These estimates of number of PLHIV each year in each population group, when applied over the total population size of the respective groups, give the estimated HIV prevalence. All other parameters are thus recalculated in Spectrum from incidence since the start of the epidemic. 21

40 2.6 METHODOLOGY FOR ESTIMATION OF AIDS-RELATED MORTALITY Over 75% of the annual estimated deaths in India occur at home, and the large majority of these do not have a certified cause. Also, only a small proportion of all AIDS related deaths are identified through the health system as many of those who are living with HIV in the country don t know their status and even many of those who know their status are not registered for care and treatment under the programme. Hence, AIDS related deaths through health facilities are grossly under-reported. The only credible way to have an estimate of AIDS related deaths is to use globally recognized models and methods. It is worth noting that the methods available for estimating AIDS related deaths are the most accurate and reliable as compared to those available for estimating numbers of deaths related to any other causes, including for Malaria, Diarrhea and others. As explained earlier, each person infected with HIV has a probability to follow a specific pattern of survival from the moment of infection to the moment of death depending on whether or not, and when he/she starts treatment. Several studies across the world modeled the progression from new HIV infection to AIDS in the absence of treatment, the progression from infection to need for treatment according to different levels of CD4 counts, and the progression from need for treatment to AIDS related death with or without treatment. Spectrum package includes this globally recognized evidence in its modeling using a Weibull function that describes the proportion dying by time since infection. It uses a simple logic that at the time of infection the CD4 levels are generally high, and with time it keeps decreasing till it reaches a level that causes death. The model tracks the HIV positive population by CD4 count and estimates the need for treatment 41 (see Figure 2.3). It is assumed that the most newly infected people start with CD4 counts above 500, although some portion, p, can start at The transition probabilities λ 1, λ 2, λ 3, λ 4, λ 5 and λ 6 represent the probability of progressing from one CD4 category to the next. In each category, there is some probability of death from HIV-related causes, designated as μ 1, μ 2, μ 3, μ 4, μ 5, μ 6 and μ 7 as well as a chance of death from non-aids causes, μ 0 (not shown in the figure). The probability of HIV-related death increases as CD4 counts decrease. The number of people in the different CD4 count categories represents the HIV-infected population that is not on ART. The number of people eligible for treatment is the number in each CD4 count category that is below the recommended level for initiating ART as per the country guidelines (Figure 2.10). Figure 2.10: Model of HIV Infected Population, Eligibility for ART and AIDS Related Mortality 22

41 By applying this model, the system calculates each year separately, (1) the number of AIDS related deaths among those who have not started any treatment since their infection according to the specific pattern, and (2) the number of AIDS related deaths among those who are on treatment for each CD4 category. To avoid inflating the estimates, the system calculates also the mortality among the HIV population for causes not related to AIDS by applying the same mortality patterns as for the non-hiv population, and subtract it from the total estimated number of AIDS deaths. Similarly, for children, in the absence of ART and cotrimoxazole prophylaxis, children infected through vertical transmission progress over time to AIDS death according to a Weibull pattern. Different progression patterns are used for children infected peri-natally and those infected 0-6 months, 7-12 months and >12 months postpartum through breastfeeding. Analysis of data from sub-saharan Africa shows distinct survival patterns for these four groups. 54,55 Thus, the estimated number of AIDS related deaths among adults and children are calculated for each year for each state and India overall. 2.7 EPIDEMIOLOGICAL ASSUMPTIONS As explained above, certain assumptions about age-sex pattern of HIV incidence and transmission parameters were required to convert the estimated adult incidence to age-and sex-specific incidences. Some of these assumptions were base d on available data from within the country while some were based on experiences in other parts of the world. These assumptions are explained in the following sections Assumptions about Age-Sex Pattern of HIV Incidence Assumptions were made on trend in ratio of female to male HIV incidence among those aged years, and trend in the distribution of HIV incidence by age for both males and females. We assumed the default age-sex pattern of HIV incidence given in the AIM module for concentrated non-idu epidemic (or IDU-driven epidemic, as applicable for different States/ Union Territories). These patterns were derived from the data on HIV prevalence by age and sex from Demographic Health Surveys and AIDS Indicator Surveys in 28 countries across the world. Data from these countries indicated that the ratio of female to male incidence among adults in the age group of years varied from 0.5 to 2.4 with a median of 1.38 in generalized epidemics, 0.84 in low level and concentrated epidemics, and 0.42 in IDU-driven epidemics vi. In India, and in order to ensure consistency with the known distribution of HIV infections between males and females as documented by the programme, and as determined by NFHS-3, the ratio of female to male incidence was considered for all states. The use of this ratio led to exactly matching ratio of PLHIV as per the programme data where 39% of all identified cases are female and 61% are male. Three sets of distribution of HIV incidence by age for both males and females were built-in the AIM module. These patterns were estimated for following three types of epidemics: generalized epidemic, concentrated non-idu epidemic, and concentrated IDU epidemic. vi For all states of India, a concentrated non-idu pattern was used. For Manipur and Nagaland, the Concentrated IDU epidemic pattern was used. More details about this model are available in the provided reference. 42,43 vi Becquet R and UNAIDS Child Survival Working Group. Survival of Children HIV-infected Perinatally or Through Breastfeeding. A Pooled Analysis of Individual Data from Sub-Saharan Africa.The 17th Conference on Retroviruses and Opportunistic Infections, San Francisco, USA, 2010.Paper # 840 ( 23

42 Figure 2.11: Sex Ratio Input in Spectrum Assumptions about Transmission Parameters Assumptions were made on the average time spent by HIV positive individuals by CD4 counts, HIV-related mortality among individuals without ART by CD4 counts, HIV-related mortality among individuals on ART by CD4 counts at the initiation of treatment, the annual increase in CD4 counts when on ART, transmission of HIV from mother-to-child, survival on ART for children, the patterns of progression from infection to death for children, and reduction in fertility rate due to HIV infection. Similar to age-sex patterns of HIV incidence, the inbuilt values in AIM module were assumed for estimation purpose. The assumed values of the average time spent by HIV positive individuals in different CD4 categories, and mortality with and without ART by CD4 counts were used to fit a CD4 compartment model to correctly specify ART need, and progression to deaths due to changing eligibility criteria for treatment over time. In the AIM module, seven CD4 compartments (defined for per mm 3 ) as: 500, , , , and <50, were provided to match current eligibility criteria for treatment, and also the mortality patterns. The default values for the average number of years spent in each CD4 count category were taken from results of various studies across the globe (Table 2.3). The default values on the mortality by CD4 counts among males and females not receiving ART, were taken from a study conducted in six countries of the world 49 (Table 2.4), while inputs on mortality by CD4 counts among males and females receiving ART were available from the International Epidemiologic Database to Evaluate AIDS (IeDEA) Consortium 6 (Table 2.5). Table 2.3: Average Number of Years Spent in each CD4 Category by Age and Sex CD4 Male Female Categories Age-group Age-group

43 Table 2.4: Annual Probability of HIV-related Mortality when not on ART in each CD4 Category by Age and Sex CD4 Male Female categories Age-group Age-group < Table 2.5: Annual Probability of HIV-related Mortality when Receiving ART in each CD4 Category by Duration on Treatment, Age and Sex Duration on Treatment Male Female and CD4 Categories at the Initiation of Age-group Age-group Treatment Months on Treatment < Months on Treatment < Greater than 12 Months on Treatment < Assumptions related to mother-to-child transmission were made to account for the fact that children who are infected in utero, peripatrum and intrapartum, progress fast towards death than children who are infected after birth through breastfeeding. The probability of HIV infection at birth for a child born to an HIV-positive mother was assumed to be 20% in the absence of prophylaxis, and 11% with single-dose Nevirapine. 5 The probability of infection through breast feeding is assumed to be 1.5% per month for mixed feeding during the first 6 months, 0.75% per month for exclusive feeding during the first 6 months, 0.75% per month for months 7 and later, and 0.3% per month when the mother is on triple therapy. 5,50 These rates were applied to 25

44 the expected number of births to HIV positive women to calculate HIV positive children. The number of HIV infected children was linked with the inputs on children s survival and estimate the AIDS-related deaths among children. Data on State-wise percentage distribution of children not breastfeeding by age in months was taken from NFHS-3 for input into the Spectrum. Default inputs in the AIM module on survival on ART for children were taken as 85% in the first year on ART and 93% for subsequent years. These values were arrived at by review of 14 prospective studies from different low and middle income countries 51. These values were arrived under the assumption that an estimated 10% of children were lost to follow-up and among those, 50% were expected to have died within 1 year, and were adjusted for additional mortality among those lost to follow-up 7. The default patterns of progression from infection to death for children were assumed to hold true for Indian context. These values were derived using data from 12 clinical trials and cohort studies in sub-saharan Africa 52. Due to lack of data to inform the survival pattern for children older than 2.5 years, the survival pattern was extended by assuming that children that have survived at least 2.5 years would have survival similar to young adults aged 15-24, with a median survival time of about 20 years Fertility of HIV Positive Women A specific pattern of fertility rate among HIV positive women is then applied to determine the estimated number of HIV positive pregnant women. The difference of levels of fertility between HIV infected and non HIV infected women is reflected in the Table 2.6. Table 2.6: Levels of Fertility between HIV Infected and Non-Infected Women Age-group (in years) Ratio For the age group 15-19, there is a high association between the level of high risk behavior, as reflected by unprotected sex, and the HIV positivity. For this group those who engage in unprotected sex are as highly exposed to pregnancy as they are exposed to HIV infection. This is why the ratio is more than one. In addition, for this group, most of infections are still recent and would have had sufficient time to lead to biological impact for reducing new infections. For the later age groups, it has been observed that HIV positive women have less fertility as compared to other women. 26

45 Chapter 3 RESULTS This chapter presents the national and state level estimates of HIV prevalence, number of people living with HIV in India, new HIV infections, AIDS-related deaths and treatment needs. Age and sex breakup are included for specific indicators as appropriate. The HIV estimates generated under the current round are more accurate as updated tools and methods, which are constantly being refined, were used. Also, the latest demographic data was used as input to the estimation and projection tool Spectrum 4.53 Beta19. The National Working Group on Estimates with the National Expert Group on Population Projection derived separate set of inputs on specific demographic indicators for each of the 34 States/ Union territories from various data sources like Indian Census, Sample Registration System (SRS), and National Family Health Survey, (NFHS-3). Moreover, updated and more comprehensive epidemiological data including surveillance data from HIV Sentinel Surveillance (HSS), conducted at 696 ANC sites and 436 HRG sites, was used. Robust state level programme data sets were used through discussion with SACS and NACO programme divisions. Calibration factors obtained from NFHS corresponding to the year 2006 were utilised. Several measures were undertaken to validate the state and national level results generated. Briefly, this included several rounds of discussion with SACS, M&E officers and epidemiologists on state level epidemic trends, comparative assessment between state estimates generated under the current round vis-à-vis those generated under the round, consultations with national experts including from Regional Institutes, and scrutiny by members of the Technical Resource Group on Surveillance and Estimations. This chapter on results is sub-divided to two broad sections. The first section highlights national level estimates whilst the second section is on state level estimates. Both sections present estimates along with upper and lower uncertainty bounds for the key indicators for national and state levels; the key indicators include adult HIV prevalence, number of people living with HIV, number of new HIV infections, number of AIDS related deaths, treatment needs for Antiretroviral Therapy (ART) and need for Prevention of Parent to Child Treatment (PPTCT). Annexure A presents national summary indicators and individual state estimates for the aforesaid indicators whilst Annexure B includes programme data and population size utilised NATIONAL HIV ESTIMATES National HIV estimates confirm retention of a stable to declining epidemic trends with past five years from 2007 to The declining national epidemic during 2007 to 2011 was primarily attributable to the rollback of the epidemic in the high prevalence categorised states of Andhra Pradesh, Karnataka, Maharashtra, Manipur and Nagaland where much of the load was concentrated during this time period. Post 2010, however, the epidemic is generally stable in these states. On the other hand, the epidemic has either remained stable or is showing a rising trend in the other states. Particular reference is to states such as Assam, Chhattisgarh, Delhi, Haryana, Jharkhand, Odisha, Punjab and Uttarakhand, where the number of annual new infections and number of people living with HIV is estimated to have increased. This trend is corroborated by the HSS data. Whilst the estimated values for states with increasing epidemic may not be significant enough to currently impact on the overall national trend, they nevertheless provide evidence on the changing trend of the epidemic and need for appropriately tailoring the response. 27

46 The sub-sections below deal with the national estimates for HIV prevalence, number of people living with HIV, number of new infections, AIDS-related deaths and treatment need for ART and PPTCT Estimated National Adult (15-49 Years) HIV Prevalence National adult HIV prevalence is the estimated percent population of the country, aged between years, positive for HIV infection within a particular time period. Adult HIV prevalence is a significant indicator for determining the level and spread of the HIV epidemic amongst the total population of the country. It is calculated through the aggregation of the number of people living with HIV in all states divided by the total adult population and multiplied by hundred to determine the percentage. Estimates with uncertainty bounds for HIV prevalence were projected through the Spectrum Version 4.53 Beta19 tool. Adult HIV prevalence was estimated to have peaked in country in 2002 at a level of 0.41% (within bounds 0.35%-0.47%) following which there has been a progressive decline in estimated prevalence in the subsequent years. National adult HIV prevalence in 2011 is estimated at 0.27% (0.22%-0.33%). (Figure 3.1) Figure 3.1: Estimated Adult HIV Prevalence (%) in India, with Uncertainty Bounds The current estimates replace those generated under previous round ( estimates) and a cross comparison is unadvisable despite a similarity of results Estimated HIV Prevalence amongst Children (<15 Years) and Young Male and Female Population (15-24 Years) Children and young males and females positive for HIV were the vulnerable population groups receiving priority attention under the NACP III. HIV prevalence estimated for these populations provide indication on the level of the epidemic s proliferation amongst them. Each indicator is respectively calculated by aggregating the number of people of that age-group (children under 15 years, or young males and females between years of age) living with HIV divided by the total population for these age groups, and multiplied by hundred to determine the percentage. 28

47 HIV prevalence amongst children (<15 years) has remained stable from 2007 to The estimate for this indicator is 0.04% (0.03%-0.05%) during the years 2007 to 2011 (Figure 3.2). HIV prevalence among the young male population (15-24 years) is declining slowly from an estimated 0.15% (0.11%-0.21%) in 2007 to reach an estimated 0.11% (0.07%-0.17%) in (Figure 3.3). Similar to the trend and level estimated among the young male population, HIV prevalence among the young female population (15-24 years) has also slowly declined from an estimated 0.15% (0.11%-0.19%) in 2007 to an estimated 0.11% (0.07%-0.16%) in 2011 (Figure 3.4). Figure 3.2: Estimated HIV Prevalence (%) among Children (<15 Years) in India , with Uncertainty Bounds Figure 3.3: Estimated HIV Prevalence among Young Male Population (15-24 Years) in India, , with Uncertainty Bounds 29

48 Figure 3.4: Estimated HIV Prevalence among Young Female Population (15-24 Years) in India, , with Uncertainty Bounds Estimated number of People Living with HIV The NACP III focused on reversing the HIV epidemic in India. One of its key strategies for achieving this target was prioritising HIV prevention interventions in specific geographical areas where the epidemic was concentrated amongst HRG, the bridge population of long distance truckers and single male migrants in addition to general population. Estimation of the total number of people living with HIV is a useful indicator for assessing the severity of the epidemic at a particular point in time or its trend over duration of time. The estimated number of PLHIV (adults and children) in India in 2011 was lakhs (17.20 lakhs lakhs), compared to the estimated lakhs (19.18 lakhs lakhs) PLHIV in the country in A comparison between 2007 and 2011 estimates reflects an approximate 8% decline in total number of PLHIV in the past five years (Figure 3.5). The decline in number of PLHIVs annually has been at a steady pace by approximately 3% from 2007 to 2008, 2.5% from 2008 to 2009, by about 1.5% from 2009 to 2010 and nearly 1% from 2010 to Figure 3.5: Estimated Number of People Living with HIV (All Ages) in India, , with Uncertainty Bounds 30

49 Estimated Number of People Living with HIV Disaggregated by Age and Sex This section highlights estimates for number of children living with HIV and number of adults over 15 years of age living with HIV in India from 2007 to The estimates for adult population are disaggregated by male and female. Children estimated to be living with HIV increased from 2007 to 2009 before declining from 2009 to 2011 (Figure 3.6). The number of children living with HIV (<15 years) was estimated at about 1.45 lakhs (1.16 lakhs-1.83 lakhs) in This was at a slightly lower level than the estimated 1.42 lakhs (1.11 lakhs-1.86 lakhs) children with HIV in The proportional contribution of the number of children living with HIV out of the total PLHIV population is estimated to have consistently increased at low levels from 2007 up to Whilst children accounted for approximately 6.3% of the total HIV infections in 2007, this proportion increased to approximately 7% in Figure 3.6: Estimated Number of Children(<15 Years) Living with HIV in India, , with Uncertainty Bounds The number of adults (15+ years) living with HIV in India is declining (Figure 3.7). The estimate for this indicator in 2011 was lakhs (15.92 lakhs lakhs) as compared to lakhs (17.92 lakhs lakhs) in The proportional contribution of adults to the total PLHIV population is slightly declining. This group accounted for approximately 94% of total infections in 2007 and 93% of total infections in 2011 (Table 3.1). Number in Lakhs Number in Lakhs Figure 3.7: Estimated Number of Adults (15+ Years) Living with HIV in India, , with Uncertainty Bounds 31

50 Figure 3.8: Estimated Number of Male and Female Adult (15 +years) Population Living with HIV, 2011 Out of the total adult PLHIV population, females are estimated to have accounted for approximately 39% of infections whilst males accounted for approximately 61% of infections in 2011 (Figure 3.8). Table 3.1: Estimated Number of People Living with HIV, Year Total No. of PLHIV No. of Adult PLHIV No. of Child PLHIV (15+ years) (<15 years) ,52,253 21,09,601 1,42, ,92,511 20,46,578 1,45, ,41,706 19,94,190 1,47, ,06,227 19,58,962 1,47, ,88,642 19,43,196 1,45, Estimated Number of Annual New HIV Infections (All Ages) Preventing new HIV infections is a core focus of the NACP III. HIV estimates for the number of annual new HIV infections is a key indicator providing information on the level and spread of new infections. A primary data input for generating this estimate was HSS as stated earlier in the report. The estimated number of new HIV infections has declined steadily over the past decade by about 57% from 2000 to 2011 (Figure 3.9). During 2007, the first year of NACP III, new HIV infections were estimated at 1.43 lakhs (1.04 lakhs-2.03 lakhs). The declining trend at national level was sustained till 2010 when the estimate for this indicator was 1.30 lakh (0.84 lakhs-2.00 lakhs) at rounded off values. Between 2010 and 2011 the number of new HIV infections is estimated to have increased marginally. In 2011 it is estimated that 1.30 lakhs (0.82 lakhs-2.18 lakhs) adults and children were newly infected. 32 The diverse trajectory between states affected the overall national trend for this indicator. The rapidly declining to stabilised trend in the six high prevalence states of Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland and Tamil Nadu from 2000 to 2010 resulted in the overall decline in the estimated number of new infections at national level in the past few years. Owing, however, to certain states in the northern part of the country where an increasing number of annual new infections are estimated in recent years, the estimate for this indicator at national level is stable to marginally increasing between 2010 and 2011 by few hundred cases.

51 Figure 3.9: Estimated Number of New HIV Infections (All Ages) in India, , with Uncertainty Bounds Males account for approximately 61% of total new annual HV infections in 2011 whilst women account for an estimated 39% of total new HIV infections. The disaggregation of total new HIV infections by sex is retained at similar levels of 61% male contribution and 39% female contribution during 2000 to 2011 with slight interyear variations (Figure 3.10 and Table 3.2). Number in Lakhs Number in Lakhs Figure 3.10: Estimated Number of New HIV Infections (All Ages) in India, , Disaggregated by Sex Estimated Number of Annual New HV Infections Disaggregated by Age and Sex For a more micro level perspective on the distribution of total number of new HIV infections by age and sex, this subsection highlights estimated number of new infections among children (<15 years) and adults (15+ years) from 2007 to Estimates for these population groups are also disaggregated by male and female. The estimated number of children newly infected with HIV is slowly declining from approximately 20,000 (16,200-25,000) new infections in 2007 to an estimated 14,500 (11,000-19,500) new infections in 2011 at rounded off values (Figure 3.11). The distribution of new HIV infections among male and female children during 2007 to 2011 was at an annual estimate of 52% and 48% respectively with slight inter-year variation (Figure 3.12). Out of the total number of annual new HIV infections estimated, children accounted for approximately 16% of the total in This proportion declined slowly and by 2011 the proportionate contribution of children to the total number of new infections was approximately 12.5% (Table 3.2). 33

52 Number in Lakhs Figure 3.11: Estimated Number of New HIV Infections among Children (<15 Years) in India, , With Uncertainty Bounds Figure 3.12: Sex-wise Distribution of New HIV Infections among Children (<15 years), estimates for number of annual new HIV infections among adults is 1.16 lakhs (0.71 lakhs-1.99 lakhs) which is a slight decrease from 1.23 lakhs (0.87 lakhs-1.78 lakhs) new infections in 2007 (Figure 3.13). Table 3.2: Sex-wise and Age-wise Estimated Number of New Annual HIV Infections, New HIV Infections Total (Adults + Children) 1,43,856 1,34,776 1,32,033 1,30,594 1,30,977 Male (Total) 87,997 82,419 80,810 79,980 80,280 Female (Total) 55,859 52,358 51,223 50,614 50,697 Adults (15+) 1,23,890 1,16,731 1,15,285 1,15,051 1,16,455 Child (<15) 19,966 18,045 16,748 15,543 14,522 34

53 Males accounted for approximately 62% of the total new HIV infections estimated amongst adults in 2011 and females accounted for approximately 38% of the total new HIV infections (Figure 3.14). The proportional distribution between male and female out of the total estimate for this indicator has remained at similar levels from 2007 to 2010 with slightly inter-year variation (Figure 3.14 and Table 3.2). Number in Lakhs Figure 3.13: Estimated Number of New HIV Infections among Adults (15+ Years) in India, , With Uncertainty Bounds Figure 3.14: Sex-wise Distribution of New HIV Infections among Adults (15+ years), Estimated Number of Annual AIDS Related Deaths (All Ages) in India The national HIV treatment programme was expanded under NACP III to ensure greater access for PLHIV needing ART to improve the quality of life, prevent opportunistic infections and avert AIDS related deaths. By end 2011 approximately 4.45 lakh adults and 0.27 lakh children were alive and on ART. National programme targets were clearly well exceeded in advance of the 2012 timeline. 35

54 Total number of annual AIDS related deaths in India is declining over the past years. The estimate for this indicator is 1.47 lakhs (1.13 lakhs-1.78 lakhs) in In comparison with the 2.06 lakhs (1.67 lakhs-2.45 lakhs) AIDS related deaths estimated in 2007, this marks a near 50,000 reduction in number of AIDS related deaths annually (Figure 3.15). Males accounted for nearly 65% of total estimated AIDS related deaths in 2007 and this proportion decreased gradually to 63% in Females on the other hand account for an increasing proportion of the total estimated AIDS related deaths from 2004 to The increase is from approximately 34.5% in 2004 to 36% in 2007 and 37% in 2011 (Figure 3.16). Figure 3.15: Estimated Number of Annual AIDS Related Deaths (All Ages) in India and Number of People (All Ages) Receiving ART, Figure 3.16: Sex-wise Distribution of Annual AIDS-Related Deaths (All Ages), 2011 It is estimated that cumulatively over 1.50 lakh deaths (all ages) have been averted since the initiation and scale up of the ART services post

55 Estimated Number of Annual AIDS Related Deaths Disaggregated by Age and Sex This section highlights estimates for the total number of annual AIDS related among children (<15 years) and adults (15+ years) from 2004 to 2011 and by male and female breakup. These estimates need to be understood against the national treatment programme target for increasing the number of children and adults alive and on ART. In 2011, the total number of children and adults alive and on ART were increased to 0.27 lakhs and 4.45 lakhs respectively estimates of number of annual AIDS related deaths among children were approximately 10,200 (7,500-13,500) at round off values. Although this reflects slightly over 15% decrease in number of annual AIDS related deaths among children in 2007 which was estimated at 12,000 (9,300-15,700), children account for a slightly increasing proportion of the total estimated annual AIDS related deaths. Whilst children accounted for around 6% of deaths annually from 2004 to 2007, this level increased gradually to around 7% by 2011 (Figure 3.17). Regarding proportional distribution of annual AIDS related deaths among children by male and female, the former accounts for approximately 52% of annual deaths among children while the latter account for 48% from 2007 to 2011 annually with slight inter year variation (Figure 3.18). No. of Annual AIDS Related Deaths among Children (<15 Years) in Lakhs No. of Adults Receiving ART in Lakhs Figure 3.17: Estimated Number of Annual AIDS Related Deaths among Children (<15 Years) in India and Number of Children Receiving ART, Figure 3.18: Sex-wise Distribution of Annual AIDS-Related Deaths among Children (<15 years),

56 Annual AIDS related deaths among adults have maintained a declining trend from 2004 to Approximately 1.37 lakhs (1.05 lakhs-1.66 lakhs) AIDS related deaths were estimated in 2011, in comparison with 1.94 lakhs (1.58 lakhs-2.32 lakhs) AIDS-related deaths estimated in 2004 (Figure 3.19). The proportionate distribution of adults accounting for the total number of AIDS related deaths has also decreased annually from around 94% in 2004 to reach around 93% by Figure 3.19: Estimated Number of Annual AIDS Related Deaths among Adults (15+ Years) and Number of Adults Receiving ART, Figure 3.20: Sex-wise Distribution of Annual AIDS-Related Deaths among Adults (15+ years), 2011 Annual AIDS related deaths among the adult male PLHIV has reduced from approximately 66% in 2004 to 64% in On the other hand, annual AIDS related deaths among the adult female PLHIV has increased from approximately 34% in 2004 to 36% in 2011 (Figure 3.20). 38

57 3.1.5 National Estimated Need for Antiretroviral Therapy (ART) among Children (<15 Years) and amongst Adults (15+ Years) The estimated need for ART is modelled on the revised national guidelines for treatment eligibility based on the CD4 count threshold. Briefly, the threshold was CD4 count < 200 cells/mm 3 up to the year 2008 and at CD4 count < 250 cells/mm 3 from 2009 to National guidelines for initiation of ART in adults and adolescents were revised at CD4 count < 350 cells/mm 3 from The following paragraphs highlight the estimated need for ART among adults and children. The national need for ART amongst children has increased proportionally with the number of estimated HIV positive children alive and in need for treatment every year. The estimated number of children living with HIV increased from 1.42 lakhs (1.11 lakhs-1.86 lakhs) in 2007 to 1.45 lakhs (1.16 lakhs-1.83 lakhs) in 2011 at rounded off values as highlighted previously. The estimated need for treatment accordingly increased from around 46,000 (35,000-61,000) in 2007 to around 75,000 (60, lakhs) in With the revision of ART guidelines for treatment initiation at CD4 count < 350 cells/mm 3 from 2012 onwards, the projected need for treatment of children with HIV is estimated at around 0.86 lakhs (70, lakhs). Whilst the treatment coverage has increased from 2007 to 2011, approximately 34% of the total estimated need for children needing treatment, actually received ART in The national need for ART amongst adults has also increased proportionally every year with the number of estimated HIV positive adults alive and in need for treatment. Of the estimated 5.68 lakhs (4.71 lakhs-6.68 lakhs) adults estimated to be in need of treatment in 2007, approximately 17% were receiving treatment. The treatment coverage increased in 2011, when nearly 52% of the estimated 7.85 lakhs (6.81 lakhs-8.72 lakhs) PLHIV needing treatment were receiving ART. Following the revision of ART guidelines for treatment initiation at CD4 count <350 cells/mm 3 from 2012 onwards, the projected need for treatment by adult PLHIV is estimated at around 10.0 lakhs (8.81 lakhs lakhs) in National Estimated Need for Prevention of Mother to Child Transmission of HIV (PPTCT) Services Parent to Child transmission of HIV is the primary cause of new infections among children. Prevention of Parent to Child Transmission of HIV (PPTCT) services has a triple benefit of saving the life of the woman, protecting her new born and protecting the family against orphan-hood. For this reason the Prevention of Parent to Child Transmission of HIV (PPTCT) programme was a critical component of the NACP III for preventing vertical HIV transmission. As the total number of people living with HIV declined over the previous five years, the total number of HIV positive pregnant mothers in need of PPTCT also reduced from an estimated 0.52 lakhs (0.43 lakhs-0.65 lakhs) in 2007 and 38,000 (30,000-50,000) in With the scale up of the PPTCT programme, the proportion of mothers receiving PPTCT increased from 18% in 2007 to approximately 32% by There is thus scope for improving PPTCT coverage as a large number of pregnant women remain outside the realm of the programme. The initiatives that are underway for strengthening convergence with NRHM may help strengthen target population coverage. 39

58 3.2. STATE HIV ESTIMATES This section highlights state level HIV estimates for the following indicators: Adult HIV prevalence, number of people living with HIV, number of new HIV infections, AIDS-related deaths and treatment need for Antiretroviral Therapy (ART) and Prevention of Parent to Child Transmission. Common trends among specific groups of states are presented based on 2011 estimates. Specific state level estimates, along with upper and lower uncertainty bounds from 2007 to 2011 can be referred to under Annexure A Estimated Adult HIV Prevalence (15-49 Years) State level adult HIV prevalence is the total number of people estimated to be living with HIV in a state calculated as a percent out of the total state population within a particular point in time. These estimates are mainly based on HIV Sentinel Surveillance data for antenatal clinic (ANC) attendees, taken as proxy for the general population, and HRGs of FSW, MSM and IDU and on estimates of the sizes of the populations at high risk and at low risk. In 2011, adult HIV prevalence was estimated at <0.75% in all states excluding Manipur. States with adult HIV prevalence higher than the national average of 0.27% include Andhra Pradesh, Mizoram, Nagaland, Karnataka, Goa, Maharashtra, Odisha, Gujarat, Tamil Nadu and Chandigarh. (Figure 3.21) Figure 3.21: State-wise Estimated Adult (15-49 Years) HIV Prevalence (%),

59 Figure 3.22: Estimated Adult (15-49 Years) HIV Prevalence in States Showing >20% Decline in Prevalence during During the period , the adult HIV prevalence declined in the high prevalence states of Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland and Tamil Nadu. Other states showing a declining trend of HIV prevalence in the same period were Goa, Gujarat, West Bengal, Mizoram, Bihar, Rajasthan, Chhattisgarh, Himachal Pradesh, Kerala, Madhya Pradesh, Uttar Pradesh and Haryana (Figure 3.22). Analysis of estimates of prevalence for the period 2007 to 2011 reflects a rising trend in the states of Arunachal Pradesh, Assam, Chandigarh, Chhattisgarh, Delhi, Jharkhand, Jammu & Kashmir, Odisha, Punjab, Tripura, Punjab and Uttarakhand (Figure 3.23). Figure 3.23: Estimated Adult (15-49) HIV Prevalence in States Showing >50% Increase in Prevalence during

60 3.2.2 Estimated number of People living with HIV (adults and children) Estimating the number of people living with HIV in a state is vital not only to assess the load and level of proliferation of the epidemic, but also to understand the future need for treatment and for motivating testing programs in specific geographical areas. The current round of Estimates of number of people living with HIV is highest in Andhra Pradesh at around 4.20 lakh followed by that in Maharashtra at 3.15 lakh in 2011.The other states with the estimated number of HIV infections more than one lakh in 2011 are Karnataka, Tamil Nadu, West Bengal, Gujarat, Bihar, Uttarakhand and Odisha. Regarding the proportional distribution of estimated number of people living with HIV by states, Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland and Tamil Nadu collectively account for approximately 53% of total infections in Eleven northern and central states of Bihar, Chhattisgarh, Delhi, Gujarat, Jharkhand, Madhya Pradesh, Odisha, Punjab, Rajasthan, Uttar Pradesh and West Bengal on the other hand account for approximately 42% of the total number of people living with HIV. The other states of Assam, Arunachal Pradesh, Haryana, Himachal Pradesh, Jammu and Kashmir, Kerala, Mizoram, Sikkim, Tripura, Uttarakhand and Union Territories account for 5% of the total infections among adults and children. Analysis of estimates for number of people living with HIV indicate a stable to declining trend in the high prevalence states of Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland and Tamil Nadu. These states accounted for 59% of the total infections in 2007.The number of people living with HIV is also estimated to be declining in Gujarat, Kerala, Madhya Pradesh, Rajasthan, Uttar Pradesh and West Bengal at varying rates. On the other hand, the number of people living with HIV is increasing in Odisha, Jharkhand, Punjab, Uttarakhand, Delhi, Assam, Jammu and Kashmir, Tripura, Meghalaya, Bihar, Arunachal Pradesh, Chandigarh, Mizoram, Sikkim and Chhattisgarh. The decline in estimated number of people living with HIV from 2007 to 2011 is more than 20% in Maharashtra and Goa. The decline in estimated PLHIV during is also significant in the states of Andhra Pradesh, Karnataka, Tamil Nadu and West Bengal (Refer to Annex A) Estimated Number of Annual New HIV Infections among Adults (15+) State level estimates of the number of people who got newly infected with HIV within a time period provide a good indication of recent HIV epidemic trend among the population. They also provide direction to the programmes that need to be targeted to meet the needs of those most affected by HIV in specific areas. Analysis of state level estimates is pertinent considering the divergent levels and trends in number of new infections. The high prevalence states of Andhra Pradesh, Karnataka, Maharashtra, Manipur and Tamil Nadu, along with other states of Goa, Kerala and Puducherry have shown a significant decline in the number of new infections during the period (Figure 3.24) 42

61 Figure 3.24: Estimated Number of New HIV Infections in States showing >20% Decline in New Infections, The states which have shown a rising trend in the annual number of new infections are Odisha, Jharkhand, Punjab, Uttarakhand, Delhi, Assam, Jammu and Kashmir, Tripura, Meghalaya, Arunachal Pradesh and Chhattisgarh. (Figure 3.25) Figure 3.25: Estimated Number of New HIV Infections in States showing >50% Increase in New Infections, Regarding the proportional distribution of estimated number of new HIV infections by states, in 2011, twelve northern and central states of Bihar, Delhi, Chhattisgarh, Gujarat, Jharkhand, Madhya Pradesh, Odisha, Punjab, Rajasthan, Uttarakhand, Uttar Pradesh, West Bengal account for approximately 63% of the total number of new HIV infections whilst Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland and Tamil Nadu collectively account for approximately 31%. The rest of the states account for 6% of the total new HIV infections. 43

62 3.2.4 Estimated Annual AIDS Related Deaths Estimating mortality due to AIDS related illnesses at the state level provides indication of survival of people living with HIV after acquiring HIV. They also highlight the role of treatment in improving survival. Although there is a decline in the estimated number of AIDS related deaths at national level from 2007 to 2011, there is state level variance. The estimated number of AIDS related deaths is declining in Andhra Pradesh, Goa, Gujarat, Karnataka, Maharashtra, Mizoram, Manipur, Nagaland, Puducherry, Punjab and Tamil Nadu at higher rates (Figure 3.26). On the other hand, the estimate for this indicator is increasing significantly in states of Arunachal Pradesh, Jammu & Kashmir, Jharkhand, Meghalaya, Tripura and Uttarakhand during Figure 3.26: Estimated Number of Annual AIDS-related Deaths in Major States Showing a Significant Decline in the Number of Deaths, In 2011, the states of Andhra Pradesh and Maharashtra accounted for the highest number of AIDS related deaths at estimated numbers of approximately 31,000 and 24,000 respectively, followed by Karnataka and West Bengal with more than 10,000 annual AIDS-related deaths Proportional Need for ART Resulting from the scale-up of the HIV Care, Support and Treatment programme under NACP-III the programme surpassed its target for the adult population covered under first line treatment. By September 2012, approximately 5.8 lakh adults and children were receiving ART. To capitalise on these gains and ensure the delivery of treatment to persons living with HIV estimates are generated for total number of people needing treatment. These estimates are calculated considering the national guidelines for initiation of ART in adults and adolescents. Up to the year 2008 this was based on CD4 count < 200 cells/mm 3 and from at CD4 count < 250 cells/mm 3. National guidelines for initiation of ART in adults and adolescents were revised from December 2011 at CD4 count was < 350 cells/mm 3. 44

63 Figure 3.27: Proportional Need for ART among Adults (15+ Years) in Major States, 2011 Of the estimated 7.85 lakh adults (>15 years of age) needing ART in India in 2011, the state wise proportional need is highest in Andhra Pradesh and Maharashtra and is estimated at an average of 21% of the total need of ART. This is followed by Karnataka where the proportional need for ART is estimated at around 10% of the total. The other states with ART need <10% and > 4% of the total national need are Tamil Nadu, West Bengal, Gujarat, Uttar Pradesh and Bihar. The rest of states account for an estimated 21% of the total adult ART need (Figure 3.27). Figure 3.28: Proportional Need for ART among Children (< 15 Years) in Major States, 2011 Of the approximately 75 thousand children needing Anti-retroviral treatment in 2011, nine states of Maharashtra, Andhra Pradesh, Karnataka, Uttar Pradesh, Bihar, West Bengal, Gujarat, Tamil Nadu and Rajasthan collectively account for about 83% of total estimated treatment need, whilst the remaining states collectively account for 17% of the total estimated need for ART amongst children in 2011(Figure 3.28). 45

64 Proportional Need for PPTCT by States, 2011 Figure 3.29: Proportional Need for PPTCT in Major States, 2011 Given that around two-thirds of mother to child transformation occurs in utero and at delivery and one-third occurs during breastfeeding, PPTCT programme has been a core component of the NACP III for preventing mother to child transmission. It is estimated that approximately 38,000 pregnant mothers need PPTCT in India. Of this total, eleven states including Andhra Pradesh, Bihar, Maharashtra, Uttar Pradesh, Gujarat, Karnataka, Odisha, Tamil Nadu, West Bengal, Rajasthan and Jharkhand account for approximately 85% of total estimated need whilst the remaining states account for 15%. The highest proportion of PPTCT needed by an individual state is Andhra Pradesh at approximately 14%. The proportional need in Bihar and Maharashtra is estimated at around 10% whilst Uttar Pradesh, Gujarat, Karnataka and Odisha individually account for around 8% of the total country level need (Figure 3.29). Analysis of key trends emerging and their implication on programme planning and design is presented in the subsequent chapter. 46