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, 2007–11 53
B (1-6): Data Inputs
63
C (1-3): List of Experts and Working Groups
69
Sayan Chatterjee
Secretary & Director General
Department of AIDS Control, NACO, Ministry of Health and Family Welfare, Government of India
30 November, 2012
FOREWORD
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
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.
2012 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 2011. 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
ACRONYMS
AIDS
Acquired Immuno-Deficiency Syndrome
AIIMS
All India Institute of Medical Sciences
AIM
AIDS Impact Model
ANC
Antenatal Care
ART
Antiretroviral Treatment
ASFR
Age Specific Fertility Rate
BSS
Behavioural Surveillance Survey
CDC
Center for Disease Control and Prevention
CMIS
Computerised Management Information System
DAC
Department of AIDS Control
EPP
Estimation and Projection Package
FSW
Female Sex Worker
HIV
Human Immuno-deficiency Virus
HRG
High Risk Group
HSS
HIV Sentinel Surveillance
IBBA
Integrated Biological and Behavioural Assessment
ICMR
Indian Council of Medical Research
IDU
Injecting Drug User
IMR
Infant Mortality Rate
MSM
Men who have Sex with Men
NACO
National AIDS Control Organisation
NACP
National AIDS Control Programme
NFHS
National Family Health Survey
NIHFW
National Institute of Health and Family Welfare
NIMS
National Institute of Medical Statistics
NWG
National Working Group
PLHIV
People Living with HIV
PPTCT
Prevention of Parent to Child Transmission of HIV/AIDS
RGI
Registrar General of India
SACS
State AIDS Control Society
SIMS
Strategic Information Management System
SRS
Sample Registration System
STD
Sexually Transmitted Disease
TFR
Total Fertility Rate
TRG
Technical Resource Group
UNAIDS
Joint United Nations Programme on HIV/AIDS
UTUnion Territory
WHO
World Health Organisation
ix
LIST OF FIGURES and TABLES
FIGURES
Figure 2.1
: Overview of the Process for Generating India HIV Estimates 2010–11
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, 2000-11 with Uncertainty Bounds
Figure 3.2
: Estimated HIV Prevalence among Children (<15 Years) in India 2007-11, with Uncertainty
Bounds
Figure 3.3
: Estimated HIV Prevalence among Young Male Population (15-24 Years) in India, 2007-11,
With Uncertainty Bounds
Figure 3.4
: Estimated HIV Prevalence among Young Female Population (15-24 Years) in India, 2007–
11, With Uncertainty Bounds
Figure 3.5
: Estimated Number of People Living with HIV (All Ages) in India, 2007–2011, With
Uncertainty Bounds
Figure 3.6
: Estimated Number of Children(<15 Years) Living with HIV in India, 2007-2011, With
Uncertainty Bounds
Figure 3.7
: Estimated Number of Adults (15+ Years) Living with HIV in India, 2007-2011, 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, 2000-2011, With Uncertainty
Bounds
Figure 3.10
: Estimated Number of New HIV Infections (All Ages) in India, 2000-2011, Disaggregated by
Sex
xi
Figure 3.11
: Estimated Number of New HIV Infections among Children (<15 Years) in India, 20072011, 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, 2007-2011,
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, 2004-2011
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, 2004-2011
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, 2004-2011
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, 2007–2011
Figure 3.23
: Estimated Adult (15-49 Years) HIV Prevalence in States Showing >50% Increase in
Prevalence, 2007–2011
Figure 3.24 : Estimated Number of New HIV Infections, in States showing >20% Decline in New
Infections, 2007–2011
Figure 3.25 : Estimated Number of New HIV Infections, in States showing >50% Increase in New
Infections, 2007–2011
xii
Figure 3.26
: Estimated Number of Annual AIDS-related Deaths in Major States showing a Significant
Decline in the Number of Deaths, 2007–2011
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
TABLES
Table 1.1
: Regional Working Groups and State Allocation
Table 2.1
: Number of HSS Sites in India, 1998-2011
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, 2007-2011
Table 3.2
: Estimated Number of New Annual HIV Infections in India, Total and by Age and Sex Breakup, 2007–2011
Table 3.3
: Estimated Number of AIDS related death
xiii
EXECUTIVE SUMMARY
India HIV Estimates generated under the 2010-11 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 (1998-2011) 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, 2005-06. 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
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 2011. 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 0.20-0.27%. 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 2011. 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 2011. 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 2011. 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 (0.72–1.99 lakh) annual new HIV infections among adults and
around 14,500 (10,974–19,346) new HIV infections among children in 2011. 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 2011.
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
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 2011. 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 & 2011. 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 2011. Children (<15 yrs) account for 7% (1.45 lakh) of all infections, while 86% are in the age –group
of 15-49 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 2011.
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 (2007-2011). 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 2011. 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
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 2011. 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 2012. 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 2017.
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 2011. The overall number of pregnant
women needing PPTCT has declined in the country from 51 thousand in 2007 to 38 thousand in 2011. 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
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 12th 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 2008-09 round to 1359 in 2010-11 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 2010-11 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 2010-11, 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
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
1.2.1 National Working Group
After the results of HIV Sentinel Surveillance 2010-11 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.
1.2.2 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
States
North
Jammu & Kashmir, Himachal Pradesh, Haryana, Chandigarh, Punjab & Rajasthan
Central
Bihar, Jharkhand, Uttar Pradesh, Uttarakhand & Delhi
West
Maharashtra, Gujarat, Goa, Madhya Pradesh, Daman & Diu, Dadra & Nagar Haveli
South
Tamil Nadu, Andhra Pradesh, Karnataka, Kerala, Puducherry, Andaman & Nicobar
Islands
East & North East
West Bengal, Chhattisgarh, Odisha, Arunachal Pradesh, Assam, Manipur, Meghalaya,
Mizoram, Nagaland, Sikkim & Tripura
2
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
2012. 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.
1.2.4 Interim Workshop on National HIV Estimations & Projections, 28-30 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
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.
1.2.5 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 1981–2017 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.
1.2.6 Final Workshop on HIV Estimations and Projections, 25-26 and 28-29 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
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
2012. 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, 2012.
1.3 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
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 2011. 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
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 software6,7 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 2017. 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
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 15-49 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
2011.
Estimation and projection of population sizes for each year in the projection period (1981-2017) 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 2017. 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.
2.2.1 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.8-10 Hence, the age-specific 1981 census populations for both men and women needed to be
smoothened.
8
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.11
2.2.2 Level
and
Age-Specific Pattern
of
Fertility (1981-2017)
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 45-49 years. Values of TFR and ASFR were available from the reports of the Sample
Registration System (SRS) for all biggerii states from 1981 to 2010 and for smalleriii states from 1990 to 2008.12
For remaining years of the projection period (2009-2017), 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.13,14
2.2.3 Sex Ratio
at
Birth (1981-2017)
For bigger states, sex ratio at birth was available from SRS reports for the period 1981-2010, whereas for smaller
sates, it was available for the period 1990-2008. For smaller states, value of sex ratio for the period 1981-1989
were calculated by applying reverse survival method on census data for 1991. 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 1991. Theoretically, children in
the age-group of 0-4 and 5-9 were the survivors of births that took place during 1987-1991 and 1981-1986
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 quality15, 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
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.
i
9
2.2.4 Level
and
Age-Sex Pattern
of
Mortality (1981-2017)
The level of mortality was measured by the life expectancy at birthiv ), and age-sex pattern of mortality
was measured by age-sex specific mortality rates. For all bigger states, the values of were available from
the SRS reports for the following five year periods: 1981-85, 1986-90, 1991-95, 1996-2000, and 2001-06.
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 2011. For each estimate of IMR, corresponding value of
was
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 elsewhere19,20. Values of 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 11.
2.2.5 Net-Migration (1981-2017)
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 1981-1991 and 1991-2001,
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 2001-2011. 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 reports12. 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 2001-2011. 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 1981-91 and 1991-2001. The
observed age-sex distribution of migrants during 1991-2001 was assumed to be constant for years 2001
onwards.
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.
iv
10
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 1981.
More details about the DemProj module are available in its manuals which are available online at the website
of Future’s Institute (http://www.futuresinstitute.org/spectrum2.aspx).
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
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.
2.3.1 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, 1998-2011
Site Type
STD
ANC
ANC (Rural)
ANC (Youth)
IDU
MSM
FSW
Migrant
Transgender
Truckers
TB
Fisher-Folk/
Seamen
Total
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008–09 2010–11
76
75
98
133
166
163
171
175
251
248
217
184
92
93
111
172
200
266
268
267
470
484
498
506
210
122
124
158
162
162
182
8
8
8
8
5
6
10
10
13
18
24
30
51
52
61
79
3
3
3
9
15
18
31
40
67
96
1
1
2
2
2
32
42
83
138
137
194
261
1
6
3
8
20
1
1
1
1
3
15
7
7
20
2
2
7
4
1
1
176
177
224
320
384
699
649
703 1130 1142
1223
1359
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
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.
2.3.2 Program Statistics on Coverage for Prevention
HIV, and Adults and Children on ART
of
Parent-to-Child Transmission
of
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 2003-2011. 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 2012-2017, 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.
2.3.3 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
eligibility to receive ART for adults was determined by the CD4 cells counts (CD4 counts 200 per mm3 till
2008; CD4 counts 250 per mm3 during 2009-2011, and CD4 counts 350 per mm3 during 2012-2017)
(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:
2004-2009
< 11 months: CD4 count <1500 (or CD4 percent< 25%)
12-35 months: CD4 count < 750 (or CD4 percent< 20%)
36-59 months: CD4 count < 350 (or CD4 percent< 15%)
> 5 yrs: Follow adult guidelines
2010-2012
< 24 months HIV positive: irrespective of clinical/immunological stage, start on ART
25-36 months: clinical stage 3 & 4 &/or CD4 percent< 20%)
36-59 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
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
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
IDU
✔
✔
✔
✔
MSM
✔
✔
✔
✔
✔
✔
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
✔
✔
FSW
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
ANC
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
*Lakshadweep not included due to lack of Surveillance data
15
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 (1981-2017) 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
2.4.2 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
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.
2.4.3 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 15–49 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
The four parameters are: (1) t0 (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)
f0 (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).
Figure 2.7: Parameters Used in the Curve Fitting in AIM Module
t0–The start year of the epidemic;
r–Force of infection, determines the initial growth rate;
f0–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
The SIR algorithm involves sampling randomly a large number of combinations of the four parameters t0,
r, f0, 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 weight28. The epidemic curves as well as their
input parameters were resampledv 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 2.5th and the upper 97.5th 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
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.
2.4.5 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 determined32. 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 2006.
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 0.69. 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
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 15-49.
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 15-19 years olds infected at the time of the
first survey will be 20-24 at the time of the second survey. Any increase in prevalence among 20-24 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 15-49 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 2005-06 (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
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 treatment41
(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 350-499. The transition probabilities λ1, λ2, λ3, λ4, λ5 and λ6 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 μ1, μ2, μ3, μ4, μ5, μ6 and μ7 as well as a chance of death from non-AIDS
causes, μ0 (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
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.
2.7.1 Assumptions
about
Age-Sex Pattern
of
HIV Incidence
Assumptions were made on trend in ratio of female to male HIV incidence among those aged 15-49 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 15-49 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 epidemicsvi.
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 (http://www.retroconference.org/2010/PDFs/840.pdf).
23
Figure 2.11: Sex Ratio Input in Spectrum
2.7.2 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 mm3) as: ≥500, 350-499, 200-349, 100-199, 50-99 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 globe44-48 (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 world49
(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) Consortium6 (Table 2.5).
Table 2.3: Average Number of Years Spent in each CD4 Category by Age and Sex
CD4
Categories
≥500
350–499
250–349
200–249
100–199
50–99
24
15–24
8.02
3.35
2.23
1.12
2.23
1.12
Male
Age-group
25–34
35–44
6.95
3.96
2.43
1.53
1.62
1.02
0.81
0.51
1.62
1.02
0.81
0.51
45–54
2.47
0.94
0.63
0.31
0.63
0.31
15–24
8.02
3.35
2.23
1.12
2.23
1.12
Female
Age-group
25–34
35–44
6.95
3.96
2.43
1.53
1.62
1.02
0.81
0.51
1.62
1.02
0.81
0.51
45–54
2.47
0.94
0.63
0.31
0.63
0.31
Table 2.4: Annual Probability of HIV-related Mortality when not on ART
in each CD4 Category by Age and Sex
CD4
categories
350–499
15–24
0.01
0.01
250–349
200–249
100–199
50–99
< 50
0.01
0.03
0.20
0.30
0.44
≥500
Male
Age-group
25–34
35–44
0.01
0.01
0.01
0.01
0.01
0.03
0.20
0.30
0.44
0.01
0.03
0.20
0.30
0.44
45–54
0.01
0.01
15–24
0.01
0.01
0.01
0.03
0.20
0.30
0.44
0.01
0.03
0.20
0.30
0.44
Female
Age-group
25–34
35–44
0.01
0.01
0.01
0.01
0.01
0.03
0.20
0.30
0.44
0.01
0.03
0.20
0.30
0.44
45–54
0.01
0.01
0.01
0.03
0.20
0.30
0.44
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
and CD4 Categories
at the Initiation of
Treatment
≥500
350-499
250–349
200–249
100–199
50–99
< 50
≥500
350–499
250–349
200–249
100–199
50–99
< 50
≥500
350–499
250–349
200–249
100–199
50–99
< 50
Male
Age-group
15-24
25–34
35–44
45–54
15–24
0-6 Months on Treatment
0.058
0.044
0.039
0.044
0.042
0.105
0.080
0.07
0.079
0.076
0.143
0.108
0.096
0.108
0.103
0.111
0.084
0.074
0.084
0.080
0.128
0.097
0.086
0.096
0.092
0.227
0.172
0.152
0.171
0.164
0.417
0.316
0.279
0.314
0.301
7–12 Months on Treatment
0.018
0.012
0.012
0.014
0.012
0.024
0.016
0.016
0.019
0.016
0.022
0.014
0.015
0.017
0.014
0.017
0.011
0.012
0.014
0.011
0.021
0.014
0.014
0.017
0.014
0.027
0.018
0.018
0.022
0.018
0.034
0.023
0.023
0.028
0.023
Greater than 12 Months on Treatment
0.011
0.007
0.007
0.009
0.007
0.015
0.010
0.010
0.012
0.010
0.013
0.009
0.009
0.011
0.009
0.011
0.007
0.007
0.009
0.007
0.013
0.009
0.009
0.011
0.009
0.017
0.011
0.011
0.013
0.011
0.021
0.014
0.014
0.017
0.014
Female
Age-group
25–34
35–44
45–54
0.032
0.058
0.078
0.061
0.070
0.124
0.228
0.028
0.051
0.069
0.054
0.062
0.110
0.201
0.032
0.057
0.078
0.061
0.070
0.124
0.227
0.008
0.010
0.010
0.008
0.009
0.012
0.015
0.008
0.011
0.010
0.008
0.010
0.012
0.016
0.01
0.013
0.012
0.009
0.011
0.015
0.019
0.005
0.006
0.006
0.005
0.006
0.007
0.009
0.005
0.007
0.006
0.005
0.006
0.008
0.010
0.006
0.008
0.007
0.006
0.007
0.009
0.011
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
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 countries51. 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-up7. 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 Africa52. 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.53
2.7.3 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)
15–19
20–24
25–29
30–34
35–39
40–44
45–49
Ratio
1.2
0.76
0.71
0.65
0.59
0.53
0.47
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
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, 2005-06 (NFHS-3).
Moreover, updated and more comprehensive epidemiological data including surveillance data from 20102011 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 2008-09 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.
3.1. NATIONAL HIV ESTIMATES
National HIV estimates confirm retention of a stable to declining epidemic trends with past five years from
2007 to 2011. 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 2010-11 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
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.
3.1.1 Estimated National Adult (15-49 Years) HIV Prevalence
National adult HIV prevalence is the estimated percent population of the country, aged between 15-49 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, 2000–2011 with Uncertainty Bounds
The current estimates replace those generated under previous round (2008-09 estimates) and a cross
comparison is unadvisable despite a similarity of results.
3.1.1.1 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 15-24 years of age) living with HIV divided by the total
population for these age groups, and multiplied by hundred to determine the percentage.
28
HIV prevalence amongst children (<15 years) has remained stable from 2007 to 2011. 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 2011. (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 2007-11, with Uncertainty Bounds
Figure 3.3: Estimated HIV Prevalence among Young Male Population (15-24 Years) in India,
2007-11, with Uncertainty Bounds
29
Figure 3.4: Estimated HIV Prevalence among Young Female Population (15-24 Years) in India,
2007–11, with Uncertainty Bounds
3.1.2 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 20.88 lakhs (17.20 lakhs–25.30
lakhs), compared to the estimated 22.52 lakhs (19.18 lakhs-25.34 lakhs) PLHIV in the country in 2007. 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 2011.
Figure 3.5: Estimated Number of People Living with HIV (All Ages) in India, 2007–2011,
with Uncertainty Bounds
30
3.1.2.1 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 2011. 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 2011. This was at a slightly lower level than the estimated 1.42 lakhs (1.11 lakhs-1.86
lakhs) children with HIV in 2007.
Number in Lakhs
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 2011. Whilst children accounted
for approximately 6.3% of the total HIV infections in 2007, this proportion increased to approximately
7% in 2011.
Figure 3.6: Estimated Number of Children(<15 Years) Living with HIV in India, 2007-2011,
with Uncertainty Bounds
Number in Lakhs
The number of adults (15+ years) living with HIV in India is declining (Figure 3.7). The estimate for this
indicator in 2011 was 19.43 lakhs (15.92 lakhs-23.72 lakhs) as compared to 21.09 lakhs (17.92 lakhs-23.63
lakhs) in 2007. 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).
Figure 3.7: Estimated Number of Adults (15+ Years) Living with HIV in India, 2007-2011,
with Uncertainty Bounds
31
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, 2007-2011
Year
Total No. of PLHIV
2007
2008
2009
2010
2011
22,52,253
21,92,511
21,41,706
21,06,227
20,88,642
3.1.3 Estimated Number
of
No. of Adult PLHIV
(15+ years)
21,09,601
20,46,578
19,94,190
19,58,962
19,43,196
No. of Child PLHIV
(<15 years)
1,42,652
1,45,933
1,47,516
1,47,265
1,45,446
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.
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.
32
Number in Lakhs
Figure 3.9: Estimated Number of New HIV Infections (All Ages) in India, 2000-2011,
with Uncertainty Bounds
Number in Lakhs
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).
Figure 3.10: Estimated Number of New HIV Infections (All Ages) in India, 2000-2011,
Disaggregated by Sex
3.1.3.1 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 2011. 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 2007. 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
Number in Lakhs
Figure 3.11: Estimated Number of New HIV Infections among Children (<15 Years) in India,
2007-2011, With Uncertainty Bounds
Figure 3.12: Sex-wise Distribution of New HIV Infections among Children (<15 years), 2011
2011 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, 2007–2011
New HIV Infections
Total (Adults + Children)
Male (Total)
Female (Total)
Adults (15+)
Child (<15)
34
2007
1,43,856
87,997
55,859
1,23,890
19,966
2008
1,34,776
82,419
52,358
1,16,731
18,045
2009
1,32,033
80,810
51,223
1,15,285
16,748
2010
1,30,594
79,980
50,614
1,15,051
15,543
2011
1,30,977
80,280
50,697
1,16,455
14,522
Number in Lakhs
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).
Figure 3.13: Estimated Number of New HIV Infections among Adults (15+ Years) in India,
2007-2011, With Uncertainty Bounds
Figure 3.14: Sex-wise Distribution of New HIV Infections among Adults (15+ years), 2011
3.1.4 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
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 2011. 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 2011. Females on the other hand account for an increasing proportion of the total
estimated AIDS related deaths from 2004 to 2011. 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, 2004-2011
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 2004.
36
3.1.4.1 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.
2011 estimates of number of annual AIDS related deaths among children were approximately 10,200 (7,50013,500) 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).
No. of Adults Receiving ART in Lakhs
No. of Annual AIDS Related Deaths among Children (<15 Years) in Lakhs
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).
Figure 3.17: Estimated Number of Annual AIDS Related Deaths among Children (<15 Years) in India
and Number of Children Receiving ART, 2004-2011
Figure 3.18: Sex-wise Distribution of Annual AIDS-Related Deaths
among Children (<15 years), 2011
37
Annual AIDS related deaths among adults have maintained a declining trend from 2004 to 2011. 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 2011.
Figure 3.19: Estimated Number of Annual AIDS Related Deaths among Adults (15+ Years)
and Number of Adults Receiving ART, 2004-2011
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 2011. 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
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/mm3 up to the year 2008 and
at CD4 count < 250 cells/mm3 from 2009 to 2011. National guidelines for initiation of ART in adults and
adolescents were revised at CD4 count < 350 cells/mm3 from 2012. 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 (60,600-0.95 lakhs) in 2011. With the revision
of ART guidelines for treatment initiation at CD4 count < 350 cells/mm3 from 2012 onwards, the projected
need for treatment of children with HIV is estimated at around 0.86 lakhs (70,000--1.08 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 2011.
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/mm3 from 2012 onwards, the projected need for treatment by adult PLHIV
is estimated at around 10.0 lakhs (8.81 lakhs-11.45 lakhs) in 2012.
3.1.6 National Estimated Need
HIV (PPTCT) Services
for
Prevention
of
Mother
to
Child Transmission
of
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 2011. With the scale up of the PPTCT programme, the proportion of
mothers receiving PPTCT increased from 18% in 2007 to approximately 32% by 2011.
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
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.
3.2.1 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 (%), 2011
40
Figure 3.22: Estimated Adult (15-49 Years) HIV Prevalence in States Showing >20% Decline
in Prevalence during 2007–2011
During the period 2007 –2011, 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 2007–2011
41
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 2011. 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 2007-11 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 2007-2011 is also significant in the states of Andhra Pradesh,
Karnataka, Tamil Nadu and West Bengal (Refer to Annex A).
3.2.3 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 2007-2011. (Figure 3.24)
42
Figure 3.24: Estimated Number of New HIV Infections in States showing >20% Decline in New
Infections, 2007–2011
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, 2007–2011
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
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 2007-2011.
Figure 3.26: Estimated Number of Annual AIDS-related Deaths in Major States Showing a Significant
Decline in the Number of Deaths, 2007–2011
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.
3.2.5 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/mm3 and from 2009-2011 at CD4 count < 250 cells/mm3. National guidelines for initiation
of ART in adults and adolescents were revised from December 2011 at CD4 count was < 350 cells/mm3.
44
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
3.2.6. 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
Chapter 4
DISCUSSION
HIV epidemic in India is concentrated in nature and heterogeneous in its distribution. The vulnerabilities that
drive the epidemic are different in different parts of the country. HIV estimations bring out the diversity among
states with respect to levels and trends of HIV epidemic and the stage of epidemic. They bring out the successes
achieved through long-standing prevention and treatment strategies. At the same time, they highlight the areas
where the epidemics are emerging and where programme interventions need to be strengthened and focus
needs to be refined.
Information collected from the HIV positive cases detected at integrated counseling and testing centres across
the country shows that unprotected hetero-sexual contact is the main route of HIV transmission, accounting
for around 88% of all cases. Transmission through contaminated blood and blood products has decreased
significantly to less than 1%. While parent to child transmission accounts for around 5% of HIV infections,
infections through injecting drug use and homosexual route are increasing.
Analysis of epidemic patterns among different risk groups from HIV Sentinel Surveillance data and other
sources helps in explaining the trends of adult prevalence and incidence that are generated through HIV
estimation process and described in earlier sections. Data from HSS shows that declining trends are noted
at national level among general population, Female Sex Worker (FSW) and Men who have Sex with Men
(MSM), while stable trends are recorded among Injecting Drug Users (IDU). Programme has been able to
demonstrate successful declines among FSW, especially in the high prevalence states. These correlate with the
significant declines in the prevalence and incidence of HIV in the high prevalence states, as reflected in the
HIV estimates. In other states, HIV epidemics among FSW are at low levels and portray stable trends.
Injecting drug use was identified as the major driver of the HIV epidemic in the north eastern states. Through
focused prevention interventions for IDUs, north eastern states of Manipur and Nagaland have shown declining
trends among IDU as well as in the overall HIV prevalence and incidence. However, higher levels and rising
trends are noted among IDU in the states of Punjab, Chandigarh, Haryana, Delhi and Mumbai. Emerging
IDU pockets are also noted in Uttar Pradesh, Odisha, Kerala, Madhya Pradesh and Bihar. With a low level of
HIV among FSW and large size of IDU in the states of Punjab, Chandigarh and Haryana, rising trends in IDU
possibly explain the rising trends in adult HIV prevalence and incidence in these states.
High HIV prevalence is also noted among MSM in many states and districts with mixed trends in different parts
of the country. Many more pockets are being identified where MSM have higher HIV prevalence.
Rising trends of adult HIV prevalence and new HIV infections have been observed in low prevalence states of
North India, as highlighted by the results of foregoing HIV estimations. This is also corroborated by evidence
from HIV Sentinel Surveillance and other programme data. Low levels of HIV among high risk groups in these
states, large volume of out-migration from rural areas to high prevalence areas, higher ANC HIV prevalence
in rural than urban population and higher prevalence among pregnant women with migrant spouses indicate
the possible role of migration in fueling HIV epidemics in these states. Evidence on vulnerabilities due to
migration emerging from other behavioural studies and corridor studies among migrants further corroborate
this possibility.
47
One of the key strengths of India’s AIDS Control Programme is its evidence-based approach to addressing
the vulnerabilities. Results of this estimation process should be analysed in greater detail to interpret the
diverse patterns in different epidemiological indicators in different states. They need to be analysed in light
of evidence from other data sources to formulate appropriate recommendations for the programme. Also, in
several states, regional variations exist within the states which usually get masked at the state level. Hence,
HIV estimations and projections need to be undertaken for specific regions or groups of districts within states
to better characterize the epidemics.
India has successfully managed to control its HIV epidemic. Over previous years it has increasingly protected
people from getting infected with HIV, saved many lives from dying due to AIDS-related causes, improved
the quality of life of those who are infected, empowered the marginalized communities by enhancing their
access to health and social services and created an enabling environment where persons and families affected
with HIV are treated with dignity and respect. With changing priorities and epidemic patterns, the programme
has to customize its strategies to effectively address the emerging vulnerabilities and adapt them to suit the
requirements of different geographical regions.
48
REFERENCES
1. National AIDS Control Organization. Technical Report: India HIV Estimates National Institute of
Medical Statistics, ICMR. New Delhi: NACO, DAC, Ministry of Health & Family Welfare, 2010.
2. Pandey A, Sahu D, Bakkali T, Reddy D, Venkatesh S, Kant S, et al. Estimate of HIV prevalence and
number of people living with HIV in India 2008–2009. BMJ Open 2012; 2(5).
3. Brown T, Bao L, Raftery AE, Salomon JA, Baggaley RF, Stover J, et al. Modelling HIV epidemics
in the antiretroviral era: the UNAIDS Estimation and Projection package 2009. Sex Transm Infect
2010; 86 Suppl 2: ii3–10.
4. Brown T, Salomon JA, Alkema L, Raftery AE, Gouws E. Progress and challenges in modelling
country-level HIV/AIDS epidemics: the UNAIDS Estimation and Projection Package 2007. Sex
Transm Infect 2008; 84 Suppl 1: i5–i10.
5. Stover J, Johnson P, Hallett T, Marston M, Becquet R, Timaeus IM. The Spectrum projection
package: improvements in estimating incidence by age and sex, mother-to-child transmission, HIV
progression in children and double orphans. Sex Transm Infect 2010; 86 Suppl 2: ii16–21.
6. UNAIDS. Technical Review of Spectrum: Report of a meeting of the UNAIDS Reference Group on
Estimates, Modelling and Projections. Boston, USA, 2012.
7. Mahy M, Lewden C, Brinkhof MWG, Dabis F, Tassie J-M, Souteyrand Y, et al. Derivation of
parameters used in Spectrum for eligibility for antiretroviral therapy and survival on antiretroviral
therapy. Sex Transm Infect 2010; 86(Suppl 2): ii28–34.
8. Jain SP. Census Single Year Age Returns and Informant Bias. Demography India 1980; 9(1): 286–
296.
9. Saxena PC, Verma KR, Sharma KA. Errors in Age Reporting in India, A Socio-cultural and
Psychological Explanation. Indian Journal of Social Work 1986; 47(2): 127–135.
10. Chandra NK. Adjustment of age data for India’s census population. Demography India 1980; 9(1):
274–285.
11. Registrar General of India. Population Projection for India and States 2001–2026: Report of the
Technical Group on Population Projections. National Commission on Population, Office of the
Registrar General and Census Commissioner, India, New Delhi, 2006.
12. Registrar General of India. Sample Registration System: Statistical Report 2010. Ministry of Home
Affairs, Government of India, New Delhi, 2010.
13. Uddin MM. Fertility estimation for Bangladesh using Gompertz model. Rural Demogr 1983; 10(1–
2): 19–22.
14. Wang Z, Zuidhof MJ. Estimation of growth parameters using a nonlinear mixed Gompertz model.
Poult Sci 2004; 83(6): 847–52.
15. Bhat PNM. Contours of Fertility Decline in India: A District Level Study Based on the 1991 Census.
In: Srinivasan K, editor. Population Policy and Reproductive Health. New Delhi: Hindustan Publishing
Corporation: 96–179, 1996.
16. Kumar S, Sathyanarayana KM. District-Level Estimates of Fertility and Implied Sex Ratio at Birth in
India. Economic & Political Weekly 2012; XLVII(33): 66–72.
17. United Nations. Manual X – Indirect Techniques for Demographic Estimation. Population Studies
No 81, Department of International Economic and Social Affairs, United Nations, New York., 1983.
18. Shryock HS, Siegel JS, Larmon EA. The Methods and Materials of Demography: Volume 1.
Department of Commerce, Bureau of the Census, United States, 1980.
49
19. Coale AJ, Demeny P. Regional Model Life Tables and Stable Populations: Second Edition. New
York, NY: Academic Press, 1983.
20. United Nations. Model Life Tables for Developing Countries. United Nations Publication, 1982.
21. United Nations. Manual VI: Methods of Measuring Internal Migration. Department of International
Economic and Social Affairs, United Nations, New York., 1970.
22. Beers HS. SixTerm Formula for Routine Actuarial Interpolation. The Record of the American Institute
of Actuaries 1945; 34 (Part 1) (69): 59–60.
23. National AIDS Control Organization. Annual Report. New Delhi: Ministry of Health and Family
Welfare, Government of India, 2011.
24. Pandey A, Reddy DC, Ghys PD, Thomas M, Sahu D, Bhattacharya M, et al. Improved estimates of
India’s HIV burden in 2006. Indian J Med Res 2009; 129(1): 50–8.
25. Brown T, Grassly NC, Garnett G, Stanecki K. Improving projections at the country level: the UNAIDS
Estimation and Projection Package 2005. Sex Transm Infect 2006; 82 Suppl 3: iii34–40.
26. Bao L, Salomon JA, Brown T, Raftery AE, Hogan DR. Modelling national HIV/AIDS epidemics:
Revised approach in the UNAIDS Estimation and Projection Package 2011. Sex Transm Infect
2012.
27. Ghys PD, Brown T, Grassly NC, Garnett G, Stanecki KA, Stover J, et al. The UNAIDS Estimation
and Projection Package: A software package to estimate and project national HIV epidemics. Sex
Transm Infect 2004; 80 Suppl 1: i5–9.
28. Alkema L, Raftery AE, Clark SJ. Probabilistic projections of HIV prevalence using bayesian melding.
Ann Appl Stat 2007; 1(1): 229–48.
29. Rubin DB. Comment on ‘‘The calculation of posterior distributions by data augmentation,’’ by M
Tanner and W H Wang. Journal of American Statistical Association 1987; 82: 543–546.
30. Rubin DB. Using the SIR algorithm to simulate posterior distributions. In: JM Bernardo. In: Bernardo
et al, (Eds). Bayesian Statistics 3. Oxford: Clarnedon Press, 1988. p. 395–402.
31. Alkema L, Raftery AE, Brown T. Bayesian melding for estimating uncertainty in national HIV
prevalence estimates. Sex Transm Infect 2008; 84(Suppl I): i11–16.
32. International Institute for Population Sciences and Macro International. National Family Health
Survey (NFHS-3). Mumbai: IIPS, 2007.
33. Bachani D, Sogarwal R, Rao KS. A population based survey on HIV prevalence in Nagaland, India.
SAARC Journal of Tuberculosis and Lung Diseases: HIV/AIDS 2009; 1(1).
34. National AIDS Control Organization. National Behavioral Surveillance Survey, 2006, Men who
have Sex with Men (MSM) and Injecting Drug Users (IDUs). New Delhi: Ministry of Health & Family
Welfare, Government of India. Retrieved from http: //www.nacoonline.org/Quick_Links/Directory_
of_HIV_Data/, 2006.
35. National AIDS Control Organization. National Behavioral Surveillance Survey, 2006, Female
sex workers and clients of female sex workers. New Delhi: Ministry of Health & Family Welfare,
Government of India, 2006b.
36. Solomon SS, Celentano D, Srikrishnan AK, Vasudevan CK, Anand S, Kumar MS, et al. Mortality
among injection drug users in Chennai, India (2005–2008). AIDS 2009; 23(8): 997–1004.
37. L Degenhardt L, W Hall, M Warner-Smith. Using cohort studies to estimate mortality among injecting
drug users that is not attributable to AIDS. Sexually Transmitted Infections 2006; 82(3): iii56–63.
50
38. Solomon SS, Solomon S. HIV serodiscordant relationships in India: Translating science to practice.
Indian J Med Res 2011; 134(6): 904–11.
39. National AIDS Control Organization. National Behavioral Surveillance Survey: General Population.
New Delhi: Ministry of Health and Family Welfare, Government of India. Retrieved from http: //
www.nacoonline.org/Quick_Links/Directory_of_HIV_Data/, 2006.
40. National AIDS Control Organization. NACP-III: To halt and reverse the HIV epidemic in India. New
Delhi Ministry of Health and Family Welfare, Government of India. Retrieved from http: //www.
nacoonline.org/National_AIDS_Control_Program/, 2006.
41. Adam MA, Johnson LF. Estimation of adult antiretroviral treatment coverage in south Africa, SAMT
Sept. 2009: 99 (9): 661-67.
42. Todd J, Glynn JR, Marston M, Lutalo T, Biraro S, Mwita W, et al. Time from HIV seroconversion to
death: a collaborative analysis of eight studies in six low and middle-income countries before highly
active antiretroviral therapy. AIDS 2007; 21 Suppl 6: S55–63.
43. Hallett TB. Estimating the HIV incidence rate: Recent and future developments. Curr Opin HIV
AIDS 2011; 6(2): 102–7.
44. Hallett TB, Zaba B, Todd J, Lopman B, Mwita W, Biraro S, et al. Estimating incidence from prevalence
in generalised HIV epidemics: Methods and validation. PLoS Med 2008; 5(4): e80.
45. Wolbers M, Babiker A, Sabin C, Young J, Dorrucci M, Chene G, et al. Pretreatment CD4 cell slope
and progression to AIDS or death in HIV-infected patients initiating antiretroviral therapy—The
CASCADE collaboration: a collaboration of 23 cohort studies. PLoS Med 2010; 7(2): e1000239.
46. Mellors JW, Margolick JB, Phair JP, Rinaldo CR, Detels R, Jacobson LP, et al. Prognostic value
of HIV-1 RNA, CD4 cell count, and CD4 Cell count slope for progression to AIDS and death in
untreated HIV-1 infection. JAMA 2007; 297(21): 2349–50.
47. Rodriguez B, Sethi AK, Cheruvu VK, Mackay W, Bosch RJ, Kitahata M, et al. Predictive value
of plasma HIV RNA level on rate of CD4 T-cell decline in untreated HIV infection. JAMA 2006;
296(12): 1498–506.
48. Williams BG, Korenromp EL, Gouws E, Schmid GP, Auvert B, Dye C. HIV infection, antiretroviral
therapy, and CD4+ cell count distributions in African populations. J Infect Dis 2006; 194(10):
1450–8.
49. Holmes CB, Wood R, Badri M, Zilber S, Wang B, Maartens G, et al. CD4 decline and incidence of
opportunistic infections in Cape Town, South Africa: Implications for prophylaxis and treatment. J
Acquir Immune Defic Syndr 2006; 42(4): 464–9.
50. Johansson KA, Robberstad B, Norheim Of. Further benefits by early start of HIV treatment in low
income countries: Survival estimates of early versus deferred antiretroviral therapy. AIDS Research
and Therapy 2010; 7(3): 1–9.
51. Coutsoudis A, Dabis F, Fawzi W, Gaillard P, Haverkamp G, Harris DR, et al. Late postnatal
transmission of HIV-1 in breast-fed children: An individual patient data meta-analysis. J Infect Dis
2004; 189(12): 2154–66.
52. UNAIDS, World Health Organization, UNICEF. Consultative meeting on data collection and
estimation methods related to HIV infection in infants and children. New York: UNICEF, 2008.
53. AIDS 2031 Consortium. AIDS Taking a Long Term View, F.T. Press Science, 2010.
54. Becquet R. 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; 2010; San Francisco. Paper#840 http: //www.retroconference.org/2010/PDFs/840.pdf.
55. Marston M, Todd J, Glynn JR, Nelson KE, Rangsin R, Lutalo T, et al. Estimating ‘net’ HIV-related
mortality and the importance of background mortality rates. AIDS 2007; 21 Suppl 6: S65–71.
51
53
Lower
Bound
2007
Estimate
Upper
Bound
Lower
Bound
2008
Estimate
Upper
Bound
Lower
Bound
2009
Estimate
Upper
Bound
Lower
Bound
2010
Estimate
Upper
Bound
Table A.1: National Estimates of Key indicators with Uncertainity Bounds, 2007-2011
Lower
Bound
2011
Estimate
Upper
Bound
Estimated No. of
PLHIV
Total
19,18,567 22,52,253 25,34,517 18,55,140 21,92,511 25,04,379 1,79,616 21,41,706 24,82,790 17,53,109 21,06,227 24,92,840 17,19,325 20,88,642 25,29,136
Adults (15+)
21,09,601
20,46,578
19,94,190
19,58,962
19,43,196
Children (<15)
1,42,652
1,45,933
1,47,516
1,47,265
1,45,446
Male
13,93,391
13,50,104
13,13,513
12,87,193
12,72,663
Female
8,58,862
8,42,407
8,28,193
8,19,034
8,15,979
Estimated Adult
HIV Prevalence (%)
Total
0.29
0.33
0.37
0.27
0.31
0.36
0.25
0.3
0.34
0.24
0.28
0.34
0.22
0.27
0.33
Male
0.40
0.37
0.35
0.34
0.32
Female
0.26
0.25
0.24
0.23
0.22
Estimated No. of
Annual New HIV
Infections
Total
1,06,270 1,43,856 1,97,354
94,745 1,34,776 1,94,380
89,002 1,32,033 1,97,108
84,835 1,30,594 2,04,643
82,714 1,30,978 2,17,406
Adults (15+)
1,23,890
1,16,731
1,15,285
1,15,051
1,16,456
Children (<15)
19,966
18,045
16,748
15,543
14,522
Estimated No.
of Annual AIDSrelated Deaths
Total
1,67,856 2,06,671 2,45,466 1,55,217 1,92,314 2,26,924 1,44,934 1,80,960 2,12,696 1,29,673 1,64,625 1,95,103 1,13,562 1,47,729 1,78,430
Adults (15+)
1,94,669
1,80,796
1,69,869
1,53,948
1,37,516
Children (<15)
12,002
11,518
11,091
10,676
10,213
Estimated ART
Need
Adults (15+)
4,71,786 5,68,498 6,68,377 4,93,294 5,88,092 6,77,126 6,23,794 7,32,291 8,23,354 6,49,379 7,55,589 8,42,556 6,81,085 7,85,117 8,72,999
Children (<15)
35,474
45,974
61,607
37,778
48,570
63,806
40,348
51,150
65,722
59,219
74,443
94,195
60,648
75,481
94,922
Estimated No. of
Mothers needing
PPTCT
42,121
51,375
63,909
38,418
47,122
59,177
35,138
43,788
54,971
31,846
40,767
51,738
29,235
38,202
49,965
Mothers needing
PPTCT
Indicator
ANNEX A
54
Lower
0.86
0.02
0.02
0.18
0.17
0.07
0.27
0.33
0.11
0.06
0.10
0.02
0.58
0.09
0.04
0.53
1.13
0.09
0.50
0.58
0.25
0.11
0.16
0.02
0.32
0.02
0.04
0.10
0.22
0.03
0.01
0.00
0.01
0.08
0.29
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
India
State/UT
0.33
2007
Estimate
0.97
0.06
0.04
0.24
0.22
0.18
0.58
0.40
0.16
0.10
0.14
0.04
0.67
0.14
0.07
0.61
1.51
0.11
0.79
0.82
0.32
0.15
0.21
0.10
0.38
0.16
0.09
0.12
0.29
0.10
0.21
0.09
0.12
0.17
0.37
Upper
1.08
0.17
0.06
0.30
0.33
0.31
1.59
0.48
0.24
0.15
0.19
0.06
0.76
0.21
0.20
0.70
2.13
0.13
1.24
1.16
0.39
0.20
0.25
0.35
0.44
1.30
0.15
0.15
0.37
0.21
0.68
0.52
0.47
0.37
0.27
Lower
0.80
0.02
0.03
0.18
0.17
0.08
0.25
0.31
0.11
0.07
0.11
0.02
0.53
0.09
0.04
0.48
1.07
0.09
0.49
0.57
0.27
0.12
0.16
0.02
0.30
0.02
0.06
0.10
0.21
0.04
0.01
0.00
0.01
0.09
0.31
2008
Estimate
0.91
0.07
0.04
0.23
0.24
0.19
0.53
0.38
0.16
0.11
0.16
0.04
0.62
0.14
0.08
0.55
1.43
0.11
0.77
0.79
0.34
0.16
0.20
0.11
0.35
0.17
0.11
0.12
0.27
0.10
0.22
0.10
0.13
0.17
0.36
Upper
1.01
0.19
0.06
0.30
0.35
0.32
1.46
0.45
0.25
0.16
0.23
0.08
0.71
0.20
0.22
0.63
2.04
0.13
1.21
1.13
0.41
0.21
0.24
0.36
0.41
1.20
0.19
0.14
0.34
0.21
0.77
0.54
0.49
0.36
0.25
Lower
0.75
0.03
0.03
0.17
0.16
0.09
0.23
0.29
0.12
0.07
0.13
0.02
0.50
0.09
0.05
0.43
1.02
0.08
0.49
0.55
0.28
0.13
0.15
0.03
0.27
0.02
0.07
0.09
0.19
0.04
0.02
0.00
0.01
0.09
0.30
2009
Estimate
0.85
0.09
0.05
0.22
0.25
0.20
0.49
0.36
0.16
0.11
0.18
0.05
0.58
0.13
0.09
0.50
1.36
0.10
0.76
0.76
0.36
0.16
0.19
0.12
0.32
0.19
0.14
0.11
0.25
0.09
0.24
0.11
0.14
0.16
0.34
Upper
0.96
0.20
0.07
0.29
0.36
0.34
1.40
0.43
0.26
0.17
0.26
0.10
0.67
0.20
0.25
0.58
1.94
0.12
1.20
1.10
0.44
0.22
0.23
0.38
0.38
1.09
0.24
0.13
0.32
0.19
0.82
0.56
0.50
0.35
0.24
Lower
0.70
0.04
0.04
0.16
0.15
0.10
0.21
0.27
0.12
0.07
0.15
0.03
0.47
0.08
0.06
0.39
0.95
0.08
0.48
0.53
0.29
0.14
0.15
0.03
0.25
0.02
0.08
0.09
0.18
0.04
0.02
0.00
0.01
0.09
0.28
2010
Estimate
0.80
0.11
0.06
0.21
0.26
0.21
0.45
0.34
0.17
0.11
0.21
0.06
0.55
0.13
0.11
0.45
1.29
0.09
0.75
0.74
0.38
0.17
0.18
0.14
0.30
0.22
0.18
0.11
0.24
0.08
0.26
0.12
0.16
0.15
0.34
Upper
0.92
0.23
0.09
0.28
0.41
0.39
1.36
0.42
0.27
0.19
0.31
0.12
0.64
0.20
0.30
0.53
1.85
0.11
1.20
1.07
0.50
0.24
0.23
0.40
0.36
1.06
0.31
0.13
0.31
0.18
0.88
0.66
0.55
0.34
0.22
Lower
0.65
0.05
0.04
0.15
0.15
0.11
0.20
0.26
0.12
0.07
0.16
0.03
0.44
0.08
0.06
0.35
0.89
0.07
0.49
0.51
0.31
0.14
0.14
0.03
0.23
0.02
0.10
0.08
0.16
0.04
0.02
0.00
0.01
0.08
0.27
2011
Estimate
0.75
0.13
0.07
0.20
0.27
0.22
0.43
0.33
0.17
0.11
0.25
0.08
0.52
0.12
0.13
0.42
1.22
0.09
0.74
0.73
0.40
0.18
0.17
0.15
0.28
0.24
0.22
0.10
0.22
0.08
0.28
0.14
0.18
0.15
Table A.2: State-wise Estimated Adult (15-49 Years) HIV Prevalence (%) With Uncertainty Bounds, 2007–2011
0.33
Upper
0.88
0.27
0.11
0.26
0.48
0.42
1.32
0.40
0.29
0.20
0.36
0.15
0.62
0.19
0.32
0.49
1.76
0.11
1.17
1.04
0.57
0.26
0.22
0.48
0.34
1.20
0.40
0.12
0.29
0.18
0.93
0.68
0.60
0.34
55
India
State/UT
Upper
5,51,360
1,292
9,658
1,57,352
44,184
30,577
14,700
1,65,179
9,330
22,235
33,000
4,284
2,74,464
43,244
3,163
4,66,653
37,213
53,001
7,823
13,203
92,598
30,760
91,763
1,266
1,85,446
28,135
7,949
1,54,615
2,00,926
506
4,111
916
591
2,651
2008
Lower Estimate
4,13,656 4,67,994
175
577
4,958
7,444
95,857 1,24,501
24,018
32,878
8,603
19,379
2,322
4,922
1,09,355 1,34,494
4,590
6,418
9,363
15,594
19,988
28,333
1,344
3,029
1,97,949 2,28,552
17,500
27,611
722
1,317
3,28,501 3,79,822
19,740
26,300
35,344
43,126
3,188
5,010
6,641
9,378
63,971
81,756
19,349
25,230
58,977
74,616
79
404
1,29,529 1,52,101
357
3,791
2,979
5,965
1,07,509 1,27,706
1,14,910 1,51,050
86
234
76
1,396
4
187
11
171
640
1,223
Upper
5,26,351
1,437
10,823
1,60,409
47,721
33,366
13,504
1,62,084
9,857
23,676
39,933
5,485
2,61,981
41,652
3,736
4,35,502
36,868
51,834
7,872
13,177
1,00,706
33,332
92,618
1,362
1,76,283
26,714
10,392
1,52,163
1,92,776
490
4,594
1,020
665
2,604
19,18,567 22,52,253 25,34,517 18,55,140 21,92,511 25,04,379
2007
Lower Estimate
Andhra Pradesh
4,31,991 489,063
Arunachal Pradesh
135
458
Assam
4,159
6,225
Bihar
95,205 1,22,770
Chhattisgarh
21,581
30,124
Delhi
7,530
17,656
Goa
2,488
5,312
Gujarat
1,13,285 1,37,468
Himachal Pradesh
4,355
6,113
Haryana
8,269
14,788
Jharkhand
16,929
23,775
Jammu & Kashmir
1,142
2,433
Karnataka
2,08,520 2,39,894
Kerala
17,128
28,187
Meghalaya
612
1,085
Maharashtra
3,55,906 4,09,760
Manipur
19,989
26,569
Madhya Pradesh
36,054
44,256
Mizoram
3,103
4,947
Nagaland
6,571
9,354
Odisha
58,029
74,480
Punjab
18,030
23,401
Rajasthan
58,406
74,410
Sikkim
70
357
Tamil Nadu
1,37,503 1,61,400
Tripura
336
3,318
Uttarakhand
2,276
4,606
Uttar Pradesh
1,09,878 1,29,979
West Bengal
1,20,201 1,56,990
Andaman & Nicobar
73
248
Chandigarh
60
1,271
Dadar & Nagar Haveli
4
161
Daman & Diu
8
148
Puducherry
573
1,245
Upper
5,05,012
1,673
13,007
1,62,627
52,346
36,891
12,856
1,58,249
10,394
25,598
47,811
7,146
2,52,420
40,570
4,514
4,08,477
36,851
50,820
7,958
13,294
1,09,648
36,555
92,749
1,368
1,70,483
24,937
13,155
1,51,003
1,86,046
469
5,081
1,087
704
2,622
2010
Lower Estimate
3,79,083 4,32,643
308
918
7,043
10,675
96,285 1,24,544
24,567
38,327
11,358
23,103
2,065
4,324
1,02,681 1,28,811
5,029
7,011
11,404
17,130
27,605
40,254
1,875
4,688
1,82,291 2,12,612
17,496
25,925
1,023
1,952
2,84,470 3,32,342
19,231
25,696
33,759
41,100
3,396
5,197
6,779
9,529
73,328
96,491
23,060
29,491
58,261
73,910
113
521
1,15,826 1,37,458
406
4,963
4,631
9,974
1,03,375 1,23,695
1,03,481 1,39,364
112
207
108
1,665
6
249
17
230
695
1,226
Upper
4,93,906
1,942
16,157
1,63,407
61,178
41,456
12,494
1,55,733
11,237
28,079
57,382
9,216
2,48,242
40,471
5,462
3,87,633
36,604
49,716
8,161
13,449
1,22,639
40,062
92,355
1,578
1,64,585
24,992
17,487
1,48,117
1,79,741
449
5,630
1,271
777
2,623
2011
Lower Estimate
3,61,450 4,19,180
409
1,156
8,201
12,804
95,317 1,23,875
25,068
40,942
12,785
25,161
2,008
4,126
1,01,382 1,27,092
5,319
7,346
12,054
17,876
31,868
47,976
2,203
5,812
1,77,995 2,09,368
17,189
25,090
1,195
2,381
2,68,304 3,15,849
18,663
25,369
33,103
40,451
3,566
5,346
6,946
9,716
79,358 1,03,862
25,199
31,961
58,197
73,545
125
593
1,10,563 1,32,590
443
5,684
5,633
12,862
1,01,927 1,22,522
1,00,062 1,34,286
106
195
124
1,814
6
289
21
268
736
1,254
Upper
4,88,232
2,368
19,955
1,63,336
75,270
48,919
12,405
1,54,165
11,980
31,261
69,689
11,790
2,46,718
39,451
6,106
3,70,703
36,468
49,120
8,380
13,726
1,42,368
44,064
92,829
1,816
1,61,038
27,286
23,337
1,48,051
1,76,148
436
6,119
1,431
895
2,719
179,616 21,41,706 24,82,790 17,53,109 21,06,227 24,92,840 17,19,325 20,88,642 25,29,136
2009
Lower Estimate
3,95,801 4,49,103
237
729
5,896
8,908
96,528 1,24,908
24,291
35,626
9,995
21,171
2,165
4,587
1,05,963 1,31,380
4,761
6,712
10,544
16,372
23,657
33,772
1,586
3,772
1,89,077 2,18,949
17,570
26,787
857
1,602
3,04,568 3,53,569
19,647
26,024
34,595
42,039
3,305
5,086
6,696
9,425
68,861
89,092
21,029
27,252
58,616
74,349
98
459
1,21,964 1,44,030
380
4,336
3,735
7,721
1,05,941 1,25,500
1,09,774 1,45,066
100
220
91
1,526
5
216
14
199
665
1,217
Table A.3: State-wise Estimated Number of HIV Infections (PLHIV-Adult & Children) With Uncertainty Bounds, 2007–2011
56
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
India
State/UT
<15 Years (%)
6.56
2.72
3.80
7.39
6.06
3.49
8.00
6.58
4.64
5.27
4.12
2.86
6.96
5.24
2.82
9.38
6.19
9.06
4.66
5.80
4.59
3.99
7.51
3.45
4.92
3.61
2.89
9.89
7.16
6.76
3.30
3.60
3.04
5.62
6.99
2010
15-49 Years (%) 50+ Years (%)
87.25
6.18
92.92
4.36
91.38
4.81
87.34
5.26
89.18
4.76
89.43
7.08
85.22
6.78
87.62
5.80
89.69
5.68
89.35
5.38
91.40
4.47
91.96
5.18
86.74
6.30
87.66
7.11
92.67
4.51
84.08
6.54
77.65
16.16
85.21
5.73
89.46
5.89
87.47
6.73
90.73
4.67
90.23
5.78
86.77
5.72
91.57
4.98
86.70
8.38
92.04
4.35
92.73
4.38
84.52
5.59
87.36
5.48
87.92
5.31
91.83
4.86
91.20
5.20
91.74
5.22
85.49
8.88
86.82
6.19
Total PLHIV
432,643
918
10,675
124,544
38,327
23,103
4,324
128,811
7,011
17,130
40,254
4,688
212,612
25,925
1,952
332,342
25,696
41,100
5,197
9,529
96,491
29,491
73,910
521
137,458
4,963
9,974
123,695
139,364
207
1,665
249
230
1,226
2,106,227
<15 Years (%)
6.56
2.60
3.71
7.90
6.16
3.48
8.12
6.77
4.66
5.26
4.02
2.80
6.78
5.61
2.65
9.18
6.12
9.37
4.70
5.63
4.64
3.92
7.77
3.20
4.91
3.52
2.84
10.09
7.27
7.18
3.31
3.46
2.99
5.58
6.96
2011
15-49 Years (%) 50+ Years (%)
86.62
6.81
92.99
4.41
91.25
5.04
86.56
5.54
88.89
4.95
88.79
7.73
84.37
7.51
86.96
6.28
89.21
6.14
88.99
5.76
91.36
4.61
91.84
5.35
86.21
7.01
86.55
7.84
92.65
4.70
83.49
7.34
76.72
17.16
84.56
6.07
88.87
6.43
86.92
7.45
90.56
4.80
89.75
6.33
86.10
6.13
91.57
5.23
85.91
9.18
92.05
4.43
92.64
4.52
83.95
5.96
87.04
5.69
87.69
5.13
91.68
5.02
91.35
5.19
91.42
5.60
84.21
10.21
86.34
6.7
Table A.4: State-wise Distribution of HIV Infections by Age-groups, 2010-11
Total PLHIV
419,180
1,156
12,804
123,875
40,942
25,161
4,126
127,092
7,346
17,876
47,976
5,812
209,368
25,090
2,381
315,849
25,369
40,451
5,346
9,716
103,862
31,961
73,545
593
132,590
5,684
12,862
122,522
134,286
195
1,814
289
268
1,254
2,088,642
57
India
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
State/UT
Upper
33,510
304
1,899
17,842
7,983
4,512
1,010
12,146
1,771
4,105
6,721
1,068
15,460
4,555
616
19,371
3,122
3,368
726
1,013
14,323
5,967
8,167
204
6,378
2,921
2,019
11,458
14,397
35
861
157
87
163
87,956 1,23,890 1,78,463
2007
Lower Estimate
12,209
22,063
13
104
566
1,219
6,104
9,688
1,384
4,130
685
1,902
15
180
4,444
7,677
387
675
931
1,664
3,139
4,621
180
524
7,050
11,126
641
1,499
112
218
2,496
10,431
825
1,730
1,459
2,154
238
394
401
667
7,973
10,815
2,002
2,987
3,728
5,344
1
60
2,395
4,147
51
567
510
1,160
5,679
7,919
4,504
7,916
7
13
20
193
1
27
3
26
12
47
Upper
31,319
357
2,313
15,059
9,791
5,204
1,059
11,837
1,812
4,583
8,079
1,349
14,754
3,895
735
17,014
3,103
3,413
745
964
16,694
6,048
7,138
235
5,485
3,411
2,710
11,354
13,844
36
903
181
109
156
78,441 1,16,731 1,74,770
2008
Lower Estimate
8,899
19,588
19
128
486
1,428
5,688
8,647
1,478
4,249
644
1,986
13
164
4,083
6,995
325
648
862
1,612
3,553
5,441
204
627
6,124
10,299
449
1,161
131
263
1,487
8,510
616
1,594
1,376
2,132
229
389
363
621
8,193
11,340
1,928
2,969
3,330
4,831
1
66
1,617
3,287
52
646
541
1,470
5,460
7,704
4,205
7,620
6
12
21
205
1
31
3
30
4
38
Upper
31,141
396
2,845
12,791
11,817
6,063
1,089
11,530
1,773
5,141
9,562
1,718
14,227
3,186
881
15,933
3,031
3,550
760
936
19,438
6,150
6,741
288
5,466
4,087
3,615
11,419
13,811
35
941
203
131
150
74,904 1,15,285 1,78,991
2009
Lower Estimate
6,904
18,548
22
161
439
1,693
5,475
8,115
1,408
4,370
637
2,080
10
151
3,802
6,750
303
643
803
1,593
3,949
6,461
240
771
5,389
9,695
355
965
151
317
1,251
7,397
574
1,544
1,451
2,238
217
387
345
596
8,308
11,869
1,878
3,020
3,029
4,575
1
75
1,480
3,166
55
736
642
1,886
5,123
7,613
4,120
7,533
6
12
20
220
1
36
4
35
2
33
Upper
30,975
444
3,514
12,075
14,206
7,072
1,079
11,237
1,811
5,985
11,717
2,139
13,862
2,785
1,115
15,106
2,824
3,727
755
899
22,588
6,235
6,652
367
5,173
5,038
4,734
11,610
13,916
36
1,007
278
177
163
72,146 1,15,051 1,86,302
2010
Lower Estimate
5,947
17,465
28
204
425
2,018
5,230
7,866
1,371
4,482
620
2,143
8
140
3,537
6,545
290
637
774
1,588
4,541
7,657
283
958
4,878
9,285
311
863
176
382
1,033
6,570
508
1,433
1,459
2,308
204
380
322
573
8,253
12,306
1,916
3,179
2,889
4,432
1
84
1,306
2,926
55
835
762
2,411
4,999
7,647
3,765
7,375
6
12
20
233
1
41
5
40
2
33
Upper
30,739
537
4,298
11,687
16,981
8,339
1,055
11,005
1,787
6,799
14,185
2,684
13,721
2,585
1,423
14,431
2,736
3,862
748
884
26,403
6,323
6,706
457
4,963
6,018
6,134
11,904
13,992
40
1,057
363
220
174
71,561 1,16,456 1,99,205
2011
Lower Estimate
5,251
16,603
39
257
420
2,408
5,069
7,797
1,363
4,565
621
2,234
7
132
3,336
6,455
276
626
750
1,580
4,848
9,085
329
1,192
4,507
9,024
279
789
204
460
856
5,893
455
1,354
1,457
2,387
194
376
314
560
8,209
12,703
1,983
3,325
2,735
4,364
1
94
1,161
2,738
54
951
870
3,081
4,934
7,745
3,504
7,289
5
12
22
252
1
48
5
47
2
33
Table A.5: State-wise Estimated Number of Annual New HIV Infection among Adults (15+ Years) With Uncertainty Bounds, 2007–2011
58
2009
Lower Estimate
33,209
38,919
5
24
168
304
6,513
9,294
1,189
2,087
152
504
126
426
9,122
11,284
256
402
381
990
963
1,454
25
73
17,281
20,686
799
1,870
37
69
29,232
35,522
1,182
2,059
2,912
3,711
198
363
452
739
3,875
5,360
751
1,196
4,010
5,529
3
21
9,626
12,286
18
226
67
207
8,566
10,653
11,013
14,514
5
23
2
87
0
12
1
11
8
54
Upper
45,333
89
698
12,993
3,624
1,971
1,502
13,878
671
1,720
2,123
191
24,237
3,200
203
41,941
3,497
4,577
660
1,142
7,104
1,820
7,477
111
15,038
2,966
410
13,157
18,550
48
278
83
53
168
2010
Lower Estimate
29,575
35,276
7
32
179
343
6,955
9,660
1,439
2,270
152
445
77
350
8,236
10,489
239
397
413
1,014
1,090
1,677
27
103
13,732
16,927
806
1,825
37
77
23,538
29,350
1,140
1,999
2,772
3,580
157
324
381
666
4,314
5,822
703
1,164
3,947
5,480
2
24
8,053
10,508
11
249
76
260
8,135
10,104
10,560
14,027
5
23
3
93
0
14
1
13
7
39
Upper
41,481
105
691
13,133
3,689
1,851
1,325
12,964
693
1,707
2,469
261
20,413
3,117
242
35,401
3,399
4,411
619
1,072
7,579
1,801
7,291
111
13,043
2,805
544
12,579
18,091
47
300
92
56
142
2011
Lower Estimate
25,669
31,347
11
42
188
388
7,070
9,750
1,642
2,458
161
432
43
281
7,243
9,510
178
355
448
1,025
1,225
1,947
31
146
10,459
13,514
760
1,738
39
88
18,436
23,764
1,060
1,905
2,539
3,324
113
286
299
581
4,739
6,330
629
1,104
3,747
5,276
2
25
6,212
8,582
8
279
90
328
7,618
9,436
9,841
13,310
6
22
3
102
0
17
1
15
5
24
Upper
37,138
123
682
13,031
3,741
1,774
1,136
12,062
681
1,688
2,846
360
16,989
3,067
284
29,497
3,319
4,149
580
985
8,141
1,791
7,028
108
10,992
2,599
693
11,914
17,405
45
335
95
62
113
1,67,856 2,06,671 2,45,466 1,55,217 1,92,314 2,26,924 1,44,934 1,80,960 2,12,696 1,29,673 1,64,625 1,95,103 1,13,562 1,47,729 1,78,430
Upper
49,315
75
680
12,353
3,544
2,005
1,657
14,151
643
1,737
1,811
155
26,940
3,142
164
47,671
3,569
4,598
670
1,171
6,514
1,931
7,358
128
16,496
3,171
314
13,602
18,961
50
250
82
47
198
India
2008
Lower Estimate
36,292
42,337
4
20
156
272
5,987
8,663
1,001
1,935
143
465
168
491
9,273
11,426
244
391
344
970
820
1,256
23
66
19,642
23,136
666
1,792
34
60
33,934
40,734
1,241
2,117
2,871
3,706
220
379
494
778
3,303
4,800
859
1,318
3,791
5,383
3
20
10,843
13,616
22
202
66
168
8,662
10,986
10,933
14,629
4
24
2
78
0
11
1
9
9
74
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
Upper
53,932
65
639
11,611
3,641
1,983
1,837
14,475
658
1,789
1,594
136
29,473
3,130
129
54,952
3,771
4,642
653
1,247
6,036
2,138
7,377
142
19,014
3,233
260
13,824
19,242
51
212
81
41
236
2007
Lower Estimate
40,033
46,427
4
18
157
257
5,290
7,834
902
1,815
130
440
221
564
9,179
11,540
256
412
372
994
733
1,118
30
75
21,877
25,510
609
1,726
31
52
39,912
47,343
1,528
2,372
2,841
3,722
223
382
555
839
2,888
4,347
971
1,483
3,641
5,233
4
19
13,038
15,946
26
181
82
155
8,562
11,074
10,703
14,578
3
24
1
70
0
9
0
8
12
102
State/UT
Table A.6: State-wise Estimated Number of Annual AIDS-related Deaths with Uncertainty Bounds, 2007–2011
59
India
51,375
42,121
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
State/UT
2007
Lower Estimate
7,161
9,185
3
9
110
170
3,275
4,388
545
838
139
339
33
95
2,875
3,949
81
122
228
425
516
746
18
40
3,395
4,377
369
640
16
28
5,868
7,700
365
505
1,239
1,711
60
97
134
196
1,429
1,910
336
490
1,932
2,545
1
7
2,035
2,632
7
65
53
107
3,575
4,592
2,339
3,408
2
5
1
23
0
3
0
3
10
22
63,909
Upper
12,352
32
297
5,849
1,320
621
304
5,408
199
727
1,076
73
5,716
1,030
82
10,081
733
2,310
159
295
2,518
703
3,447
27
3,349
643
189
5,983
4,900
10
84
24
12
51
38,418
47,122
2008
Lower Estimate
6,186
7,969
4
12
130
200
3,326
4,391
603
906
156
366
28
82
2,755
3,734
82
126
257
432
612
886
21
49
3,000
3,813
362
616
18
34
4,922
6,386
337
468
1,175
1,599
62
98
126
187
1,547
2,047
355
523
1,799
2,398
1
8
1,743
2,292
7
74
65
135
3,338
4,263
2,075
2,972
2
4
2
26
0
4
0
3
10
20
59,177
Upper
10,634
34
305
5,798
1,360
666
258
5,114
206
720
1,272
91
4,905
996
98
8,384
679
2,151
159
282
2,662
770
3,263
28
2,960
550
244
5,485
4,297
9
92
24
13
46
35,138
43,788
2009
Lower Estimate
5,508
7,097
5
15
151
235
3,275
4,302
641
981
175
392
23
71
2,583
3,496
84
129
277
443
723
1,051
24
60
2,678
3,454
340
576
21
40
4,128
5,388
312
432
1,095
1,485
62
98
117
176
1,679
2,218
376
545
1,680
2,255
2
9
1,469
1,971
7
84
81
173
3,077
3,943
1,840
2,611
2
4
2
27
0
4
0
4
9
18
54,971
Upper
9,346
37
353
5,723
1,523
696
222
4,760
216
736
1,510
113
4,399
949
115
7,202
621
1,987
160
268
2,853
801
3,109
29
2,606
527
321
5,060
3,709
8
97
27
14
44
31,846
40,767
2010
Lower Estimate
4,710
6,209
6
18
182
281
3,186
4,209
645
1,044
193
417
18
62
2,409
3,254
85
131
286
451
863
1,255
27
72
2,411
3,188
331
531
25
48
3,309
4,489
286
398
1,033
1,381
63
99
110
166
1,780
2,396
399
574
1,552
2,103
2
10
1,235
1,701
8
96
97
218
2,847
3,649
1,572
2,260
2
3
2
29
0
5
0
4
8
16
51,738
Upper
8,068
44
433
5,645
1,772
766
206
4,371
224
791
1,823
142
4,128
882
135
6,079
585
1,835
162
256
3,151
884
2,904
30
2,302
510
400
4,696
3,193
7
105
28
15
41
29,235
38,202
2011
Lower Estimate
4,025
5,465
7
23
206
332
3,048
4,066
608
1,097
214
441
15
55
2,213
3,045
86
133
280
453
999
1,505
32
88
2,175
2,943
301
485
29
57
2,638
3,796
251
358
955
1,277
65
100
106
163
1,848
2,538
426
601
1,409
1,929
2
11
1,030
1,476
8
109
115
270
2,621
3,350
1,345
1,978
1
3
2
32
0
6
0
5
7
14
Table A.7: State-wise Estimated Number of Mothers Needing PPTCT with Uncertainty Bounds, 2007–2011
49,965
Upper
7,311
50
528
5,500
2,124
841
192
4,099
235
882
2,197
181
3,927
815
157
5,327
533
1,696
161
251
3,553
949
2,661
34
2,065
522
505
4,313
2,842
7
118
31
18
39
60
2011
Lower Estimate Upper
152,825 170,005 188,106
50
149
412
1,530
2,079
3,078
27,999
36,014
46,039
6,386
8,912
12,976
4,281
9,584
12,437
1,219
1,895
4,563
38,202
45,309
53,473
1,558
2,059
2,960
2,833
4,819
7,019
5,274
7,238
9,894
621
946
1,503
76,674
86,083
96,732
6,762
10,036
14,395
212
355
984
138,892 154,189 170,313
9,499
11,422
14,531
11,661
14,064
16,510
1,257
1,702
2,571
2,729
3,611
4,834
16,706
21,418
27,165
6,931
8,494
10,616
18,620
23,502
28,622
32
111
399
61,828
69,151
76,602
161
953
8,479
989
1,513
2,511
34,472
40,138
47,421
37,515
48,139
61,029
18
68
141
19
360
1,159
1
50
287
2
46
190
388
701
1,172
4,71,786 5,68,498 6,68,377 4,93,294 5,88,092 6,77,126 6,23,794 7,32,291 8,23,354 6,49,379 7,55,589 8,42,556 6,81,085 7,85,117 8,72,999
2010
Lower Estimate Upper
147,234 164,792 183,772
36
116
372
1,211
1,655
2,889
25,288
33,661
44,356
5,112
7,887
12,682
3,581
8,230
11,184
1,084
1,897
4,914
36,432
43,355
51,188
1,361
1,845
2,711
2,317
4,322
6,639
4,315
5,914
8,207
481
730
1,121
73,389
83,268
94,271
6,015
9,442
13,685
167
281
786
136,126 152,471 170,628
8,954
10,910
14,047
11,330
13,799
16,369
1,017
1,550
2,433
2,490
3,346
4,574
14,273
19,004
24,563
5,620
7,234
9,283
17,372
22,077
27,622
23
94
406
59,440
67,227
75,245
138
819
9,232
745
1,127
1,844
32,982
38,996
46,318
37,648
48,492
61,605
15
71
144
14
320
1,026
1
43
286
2
39
173
311
577
1,035
India
2009
Lower Estimate Upper
142,418 160,436 180,462
26
89
315
932
1,313
2,652
22,232
30,826
41,703
3,992
6,929
11,941
2,591
5,883
8,708
996
1,935
5,378
34,917
41,664
49,412
1,188
1,656
2,476
1,826
3,858
6,297
3,453
4,840
6,764
359
544
774
72,435
82,900
94,095
5,109
8,754
12,967
135
225
618
135,258 153,596 173,409
8,287
10,338
13,814
10,892
13,452
16,028
903
1,434
2,317
2,295
3,147
4,339
11,993
16,828
22,078
4,586
6,142
8,162
15,831
20,546
26,603
17
80
390
57,494
65,787
74,406
102
708
9,739
532
831
1,363
31,648
37,953
45,614
37,878
48,646
61,758
13
74
147
11
286
910
1
37
273
2
33
160
261
522
980
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
2008
Lower Estimate Upper
112,523 128,390 147,589
13
51
191
553
798
1,869
14,204
21,131
30,843
2,319
4,714
9,159
2,106
5,014
7,516
851
1,696
4,811
26,283
31,910
39,213
776
1,181
1,807
1,096
2,728
4,867
2,050
3,023
4,268
262
430
551
60,821
69,864
79,780
3,284
6,352
10,005
82
138
346
115,294 132,545 151,076
6,555
8,352
11,389
8,141
10,362
12,600
661
1,062
1,800
1,756
2,477
3,484
7,624
11,439
15,678
2,881
4,207
5,865
10,714
14,883
20,090
11
53
354
46,974
54,597
62,429
68
474
8,483
301
449
735
23,752
29,816
36,553
29,782
39,243
50,752
9
61
127
6
198
631
1
24
227
1
22
113
186
409
786
2007
Lower Estimate Upper
107,164 124,639 144,674
9
40
157
408
639
1,570
11,375
17,847
28,305
1,938
4,139
9,331
1,661
4,022
6,511
834
1,742
5,150
22,925
29,674
37,919
646
1,070
1,676
929
2,460
4,635
1,629
2,505
3,554
171
231
308
61,108
70,337
81,117
2,521
5,430
9,159
67
113
256
116,458 135,453 156,477
5,936
7,831
10,928
7,154
9,553
12,031
582
969
1,667
1,645
2,363
3,417
6,406
9,993
14,268
2,364
3,770
5,469
9,082
13,173
18,869
9
46
374
45,049
53,059
61,617
63
415
8,321
197
337
533
21,410
28,199
35,377
27,561
37,791
50,039
7
60
130
4
174
534
1
21
219
1
18
97
141
383
750
State/UT
Table A.8: State-wise Estimated Need for ART (Adult 15+) With Uncertainty Bounds, 2007–2011
61
India
45,974
35,474
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
State/UT
2007
Lower Estimate
7,162
8,993
1
5
51
80
1,485
2,096
274
531
304
314
56
120
1,731
2,357
53
87
106
248
225
337
13
23
4,163
5,101
188
356
7
12
9,359
11,812
398
549
742
977
36
61
120
177
709
1,043
201
303
1,042
1,443
1
4
1,893
2,361
6
41
25
43
2,509
3,358
2,162
3,091
1
4
0
13
0
2
0
1
31
32
61,607
Upper
11,625
16
160
3,011
1,027
453
302
3,313
138
428
472
36
6,734
580
29
15,582
778
1,411
120
282
1,484
467
2,001
42
2,988
557
70
4,629
4,571
10
36
15
7
57
37,778
48,570
2008
Lower Estimate
7,589
9,443
1
5
62
95
1,705
2,365
338
600
374
383
61
126
1,909
2,543
64
99
123
267
271
395
24
30
4,327
5,256
235
408
8
14
9,514
11,952
441
595
829
1,075
41
68
129
188
810
1,162
234
339
1,154
1,587
1
5
2,058
2,560
6
46
33
58
2,743
3,615
2,279
3,230
1
5
0
15
0
2
0
2
40
41
63,806
Upper
12,122
18
177
3,335
1,113
523
321
3,515
153
451
556
49
6,690
652
35
15,296
820
1,489
128
285
1,602
509
2,179
39
3,193
588
95
4,908
4,600
11
41
15
8
65
40,348
51,150
2009
Lower Estimate
8,040
9,888
2
7
76
113
1,903
2,631
418
679
513
528
63
129
2,085
2,723
79
113
143
289
324
466
33
42
4,512
5,441
269
450
9
16
9,609
11,987
488
639
903
1,163
47
75
140
199
918
1,293
282
388
1,272
1,725
1
5
2,254
2,770
7
52
45
78
2,953
3,848
2,358
3,339
1
5
1
16
0
2
0
2
47
48
65,722
Upper
12,602
20
192
3,678
1,186
591
334
3,678
172
467
666
67
6,812
709
41
15,086
878
1,574
134
290
1,742
573
2,328
34
3,404
601
128
5,160
4,624
11
47
15
9
71
59,219
74,443
2010
Lower Estimate
11,036
13,636
5
16
166
253
3,834
5,088
887
1,294
617
680
76
165
3,501
4,445
135
188
285
507
736
1,056
49
88
5,884
7,187
468
767
20
37
11,607
14,559
640
855
1,511
1,943
82
128
202
290
1,898
2,539
517
684
2,273
2,968
2
10
2,919
3,596
10
106
100
197
4,981
6,242
3,530
4,812
2
7
1
31
0
5
0
4
58
60
94,195
Upper
17,447
43
379
6,810
1,921
948
467
5,692
290
806
1,520
157
9,066
1,220
102
18,261
1,207
2,493
221
425
3,285
954
3,964
45
4,466
881
345
7,950
6,441
14
98
28
16
100
60,648
75,481
2011
Lower Estimate
10,991
13,495
6
20
200
302
4,034
5,332
1,000
1,409
680
787
83
170
3,562
4,538
160
213
326
538
882
1,257
61
112
5,996
7,300
512
802
22
43
11,344
14,065
673
867
1,538
1,962
95
141
213
300
2,069
2,755
597
771
2,358
3,040
2
12
3,108
3,747
10
119
133
259
5,094
6,304
3,448
4,700
3
7
1
34
0
6
0
5
70
71
Table A.9: State-wise Estimated Need for ART (Children <15 Years) With Uncertainty Bounds, 2007–2011
94,922
Upper
17,164
48
450
7,105
2,093
1,090
469
5,790
320
839
1,809
203
9,052
1,257
118
17,588
1,209
2,502
229
432
3,551
1,064
4,043
44
4,599
826
450
7,901
6,260
14
108
31
18
106
62
ANNEX B
Table B.1: State-wise Number of Adults Alive and on ART, 2004–2011
State/UT
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chandigarh
Chattisgarh
Delhi
Goa
Gujarat
Haryana
Himachal Pradesh
Jammu & Kashmir
Jharkhand
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Manipur
Meghalaya
Mizoram
Nagaland
Odisha
Puducherry
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttar Pradesh
Uttarakhand
West Bengal
India
2004
671
0
0
0
0
0
702
0
0
0
0
0
0
549
0
0
1,533
393
0
0
50
0
0
104
11
0
1,020
0
0
0
0
5,033
2005
2,214
3
33
0
14
0
1902
183
1047
0
22
60
81
2,736
1394
309
7,277
1321
0
0
131
0
48
0
760
0
9,249
0
961
0
709
30,454
2006
2007
2008
2009
2010
2011
7,154
28,745
40903
60,328
79,009
96,111
0
17
25
26
30
25
147
344
537
833
1,175
1,551
403
1,709
2,998
4,970
7,435
9,270
11
17
19
44
53
58
0
556
893
1345
1,791
2,502
2915
3735
4,743
6,655
7,823
9,074
337
498
646
908
1133
1312
1389
5127
6,798
12,765
17,775
23,942
175
701
965
1,425
1,917
2,378
102
368
434
708
1,160
1,317
103
286
417
510
596
648
136
570
828
1435
2,051
2,655
4,933
13,145
19,448
36,220
49,353
63,997
1550
2525
2920
4,070
4,895
5,786
738
1825
2156
3267
4,384
6,893
12,574
31,711
43,924
65,409
85,596 1,03,366
2,354
3,804
4,294
5,236
6,043
6748
0
14
33
75
120
201
37
172
327
635
913
1,282
308
695
921
1,534
2,091
2,848
62
714
779
1,997
2,664
3,930
362
334
267
494
640
746
301
1,756
2,982
4,253
5,950
7514
1,211
2,997
3,966
6,113
8,336
10,531
2
13
18
28
46
60
12,523
23,581
26,930
36,947
45,179
53,099
0
22
44
102
149
193
2,058
4,219
6,710
10,039
13,683
16,579
46
221
318
531
771
913
1,355
2,638
3,315
5,375
7,623
9,500
53,286 1,33,059 1,79,558 2,74,277 3,60,384 4,45,029
63
Table B.2: State-wise Number of Children Alive and on ART, 2004–2011
State/UT
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chandigarh
Chattisgarh
Delhi
Goa
Gujarat
Haryana
Himachal Pradesh
Jammu & Kashmir
Jharkhand
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Manipur
Meghalaya
Mizoram
Nagaland
Odisha
Puducherry
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttar Pradesh
Uttarakhand
West Bengal
India
64
2004
2
0
0
0
0
0
56
0
0
0
0
0
0
21
0
0
1
37
0
0
3
0
0
16
4
0
82
0
0
0
0
222
2005
22
0
0
0
1
0
157
6
45
0
1
0
4
81
30
28
264
99
0
0
9
0
12
0
25
0
389
0
30
0
21
1,224
2006
307
0
4
10
3
0
277
20
59
3
9
0
5
324
114
60
821
180
0
0
15
0
26
17
76
0
920
0
81
3
48
3,382
2007
1,880
0
14
66
1
43
329
26
267
29
47
19
26
1,198
148
140
2,383
355
1
13
45
19
36
102
205
0
1,655
0
192
21
98
9,358
2008
2,646
0
18
124
3
96
417
37
416
51
63
29
38
1,928
171
161
3,357
406
1
24
65
39
43
193
283
1
1,950
1
353
29
136
13,079
2009
3,304
0
30
232
5
129
600
61
669
87
85
39
89
3,003
229
221
5,102
419
2
52
97
81
51
264
404
1
2,439
1
594
53
275
18,618
2010
4050
0
49
371
1
157
621
82
979
119
119
43
128
3640
265
297
6017
491
3
54
99
106
66
348
551
1
2776
2
789
64
382
22,670
2011
4840
1
67
400
5
216
713
96
1433
153
123
46
168
4569
309
459
7094
500
5
93
145
168
73
448
677
0
3066
5
950
75
495
27,392
Table B.3. State-wise Coverage of Mother-Baby Pairs Receiving Single-dose Nevirapine, 2003–2011
State/UT
Andaman & Nicobar
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chandigarh
Chattisgarh
Dadra & Nagar Haveli
Daman & Diu
Delhi
Goa
Gujarat
Haryana
Himachal Pradesh
Jammu & Kashmir
Jharkhand
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Manipur
Meghalaya
Mizoram
Nagaland
Odisha
Puducherry
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttar Pradesh
Uttarakhand
West Bengal
India
2003
0
0
0
0
0
0
0
0
0
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
14
2004
0
0
0
0
2
13
0
0
0
32
4
10
0
0
0
0
0
16
0
97
16
0
0
0
0
0
0
6
0
0
0
0
0
26
222
2005
0
1,337
0
5
3
11
0
0
0
53
16
92
0
0
0
0
242
40
3
486
43
0
1
21
5
1
2
21
0
1,237
0
5
3
44
3,671
2006
0
2,964
0
17
40
31
0
0
0
67
48
199
21
1
0
0
602
65
4
1,035
166
0
16
37
34
11
5
17
1
553
0
19
1
83
6,037
2007
0
3,605
2
22
71
38
29
2
1
81
64
270
19
3
5
10
957
80
19
1,711
187
0
26
41
10
32
19
72
0
1,674
0
107
4
139
9,300
2008
0
3,925
2
33
65
30
17
0
0
141
46
377
28
15
1
55
2,163
113
29
2,673
204
3
57
121
100
0
77
99
6
2,832
1
107
19
135
13,474
2009
0
3,936
2
37
117
35
61
6
2
220
56
506
43
15
13
38
2,200
81
109
2,844
201
11
98
119
96
21
92
191
1
1,537
2
198
17
189
13,094
2010
0
3,397
3
47
192
38
71
3
2
205
27
597
54
9
9
33
2,187
69
154
2,677
186
9
118
97
149
41
129
272
1
1,432
1
242
14
186
12,651
2011
0
3,280
0
67
211
41
76
3
3
230
17
631
84
13
9
29
2,127
62
198
2,421
198
12
131
119
169
44
193
293
3
1,302
3
331
26
257
12,583
65
Table B.4: State-wise Population (All Ages) for Census 1981, 1991, 2001 and 2011
State/UT
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
India*
*Excluding Lakshadweep
66
1981
5,35,49,676
6,31,839
1,88,58,620
5,23,02,664
1,40,10,336
62,20,407
10,07,749
3,40,85,800
42,80,817
1,29,22,620
1,76,12,071
59,87,390
3,71,35,715
2,54,53,678
13,35,821
6,27,84,173
14,20,952
3,81,68,501
4,93,754
7,74,929
2,63,70,271
1,67,88,917
3,42,61,862
3,16,382
4,84,08,077
20,53,060
57,25,969
10,51,36,539
5,45,80,646
1,88,741
4,51,611
1,03,675
78,963
6,04,472
68,26,79,235
1991
6,65,08,006
8,64,563
2,24,14,318
6,69,59,379
1,76,14,923
94,20,653
11,69,794
4,13,09,578
51,70,882
1,64,63,643
1,94,15,088
78,03,721
4,49,77,202
2,90,98,521
17,74,777
7,89,21,148
18,37,147
4,85,66,240
6,89,753
12,09,543
3,16,59,736
2,02,81,969
4,40,05,992
4,06,458
5,58,58,943
27,57,208
71,13,729
13,10,23,818
6,80,77,959
2,80,659
6,42,019
1,38,474
1,01,587
8,07,778
84,33,74,691
2001
7,62,10,003
10,97,965
2,66,55,527
8,29,98,509
2,08,33,797
1,38,50,510
13,47,669
5,06,71,019
60,77,903
2,11,44,568
2,69,45,826
1,01,43,704
5,28,50,555
3,18,41,381
23,18,822
9,68,78,626
21,66,794
6,03,48,029
8,88,575
15,47,665
3,68,04,665
2,43,58,998
5,65,07,187
5,40,850
6,24,05,683
31,99,208
84,89,357
16,61,97,912
8,01,76,202
3,56,154
9,00,640
2,20,490
1,58,208
9,74,342
1,02,54,97,509
2011
8,46,65,530
13,82,609
3,11,69,272
10,38,04,637
2,55,40,195
1,67,53,232
14,57,723
6,03,83,628
68,56,511
2,53,53,083
3,29,66,243
1,25,48,919
6,11,30,701
3,33,87,673
29,64,004
11,23,72,978
27,21,755
7,25,97,568
10,91,013
19,80,603
4,19,47,357
2,77,04,236
6,86,21,007
6,07,696
7,21,38,955
36,71,039
1,01,16,748
19,95,81,478
9,13,47,740
3,79,945
10,54,684
3,42,849
2,42,909
12,44,467
1,20,68,64,133
Table B.5: State-wise Population (Adult 15–49 Years) for Census 1981, 1991, 2001 and 2011
State/UT
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
India*
1981
2,59,24,494
3,16,388
92,89,247
2,36,47,105
66,44,290
33,79,855
5,28,165
1,68,67,712
19,95,154
60,05,045
84,41,713
28,66,706
1,78,78,081
1,29,71,831
6,46,076
3,07,32,335
6,98,485
1,76,33,575
2,48,694
4,00,749
1,26,27,796
82,34,155
1,57,39,743
1,61,405
2,48,35,853
9,88,614
26,62,152
4,77,37,171
2,73,17,260
1,00,317
2,59,564
49,961
35,533
3,04,188
32,74,19,849
1991
3,36,94,416
4,30,964
1,10,40,700
3,10,49,906
86,08,408
51,71,144
6,67,694
2,13,39,631
25,66,512
79,69,828
93,66,775
40,16,372
2,27,72,461
1,57,52,365
8,55,201
3,99,54,059
9,61,285
2,31,64,296
3,51,879
6,22,653
1,58,41,213
1,03,46,012
2,07,68,518
2,05,452
3,00,16,961
13,58,730
35,14,587
6,08,01,750
3,47,64,916
1,53,833
3,72,124
70,961
53,506
4,41,399
41,79,74,688
2001
4,07,24,713
5,46,796
1,36,76,801
3,80,68,531
1,02,97,590
78,39,844
7,86,603
2,72,26,102
32,02,123
1,08,62,131
1,30,53,797
52,27,591
2,83,18,113
1,75,09,074
11,25,693
5,10,71,539
11,75,396
2,94,49,928
4,74,192
8,27,456
1,90,86,011
1,29,42,256
2,69,75,179
2,92,698
3,47,21,984
17,01,629
41,95,014
7,72,75,136
4,25,72,649
2,10,058
5,34,025
1,23,291
98,584
5,58,337
52,12,26,569
2011
4,81,26,325
8,11,931
1,74,25,719
5,35,90,733
1,35,36,214
1,02,52,456
8,14,570
3,37,15,960
38,41,017
1,43,30,718
1,77,87,162
70,40,125
3,44,19,469
1,81,40,751
17,40,759
6,28,08,081
15,98,489
3,84,92,363
6,40,745
11,63,119
2,33,82,927
1,56,28,829
3,62,93,985
3,56,847
4,02,29,867
21,55,925
54,23,704
10,19,47,061
5,22,23,506
2,17,213
5,94,791
1,91,482
1,35,708
6,94,092
65,79,19,357
*Excluding Lakshadweep
67
Table B.6: State-wise Population (Children 0–14 Years ) for Census 1981, 1991, 2001 and 2011
State/UT
Andhra Pradesh
Arunachal Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Goa
Gujarat
Himachal Pradesh
Haryana
Jharkhand
Jammu & Kashmir
Karnataka
Kerala
Meghalaya
Maharashtra
Manipur
Madhya Pradesh
Mizoram
Nagaland
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Andaman & Nicobar
Chandigarh
Dadar & Nagar Haveli
Daman & Diu
Puducherry
India*
*Excluding Lakshadweep
68
1981
2,04,80,427
2,47,316
75,67,487
2,16,34,821
55,51,574
22,08,248
3,46,965
1,31,20,616
16,82,263
53,52,403
71,86,724
24,36,446
1,45,92,560
88,81,105
5,63,006
2,38,73,955
5,56,249
1,57,53,333
1,95,634
2,84,770
1,03,63,203
61,69,684
1,44,06,030
1,24,747
1,68,75,634
8,00,451
22,97,484
4,34,15,602
2,11,01,538
74,439
1,50,663
43,567
34,038
2,19,988
26,85,92,970
1991
2,38,15,780
3,45,710
89,94,258
2,74,60,936
66,23,857
32,79,085
3,35,128
1,47,18,639
18,35,593
64,21,576
77,96,172
27,86,981
1,61,87,086
86,61,499
7,52,362
2,80,46,571
6,52,206
1,91,91,327
2,67,128
4,55,306
1,13,18,326
70,37,713
1,78,47,786
1,60,445
1,72,24,610
10,50,053
25,79,829
5,30,40,011
2,48,95,760
1,01,670
2,04,697
53,769
35,601
2,52,794
31,44,30,264
2001
2,43,41,620
4,39,866
99,10,770
3,45,33,038
76,40,077
44,88,151
3,33,235
1,65,92,265
18,81,043
75,67,583
1,06,17,229
36,12,181
1,67,87,228
83,03,787
9,74,754
3,09,89,376
7,06,367
2,31,06,648
3,13,129
5,66,345
1,21,65,515
76,18,136
2,24,75,978
1,88,866
1,68,34,497
10,69,698
30,77,733
6,75,53,472
2,65,13,535
1,04,676
2,61,611
77,532
43,435
2,63,142
36,19,52,518
2011
2,11,96,187
3,60,370
93,55,536
3,52,58,041
79,91,165
41,01,091
3,87,711
1,63,22,963
17,02,045
72,22,496
1,02,77,328
35,50,755
1,55,18,083
75,75,474
7,72,775
2,98,92,593
7,09,620
2,38,05,538
2,84,437
5,16,266
1,11,32,447
69,11,556
2,24,62,882
1,58,373
1,63,01,633
9,57,018
30,63,808
6,98,65,462
2,30,66,432
95,904
2,63,687
92,920
66,052
2,81,032
35,15,19,680
ANNEX C
Table C1: Technical Resource Group on Surveillance and Estimation, NACO
Name
Shri Sayan Chhaterjee
Dr. V. M. Katoch
Dr. Shiv Lal
Dr. L. M. Nath
Dr. Arvind Pandey
Dr. DCS Reddy
Dr. M. Bhattacharya
Dr. Sanjay Mehendale
Dr. Shashi Kant
Dr. Rajesh Kumar
Mr. Taoufik Bakkali
Dr. Sanjay Dixit
Prof. K. Ramachandran
Prof. Kamala Ganesh
Dr. Peter Ghys
Organisation
Secretary, Department of AIDS Control & Director General, NACO
Secretary, Department of Health Research & Director General, ICMR
Former Spl. DGHS (PH), New Delhi
Former Director, AIIMS, New Delhi
Director, NIMS, ICMR, New Delhi
Independent Expert, Former NPO, WHO—India, New Delhi
Professor & Head, Department of CHA, NIHFW, New Delhi
Director, NIE, Chennai
Professor of Community Medicine, AIIMS, New Delhi
Professor and Head, School of Public Health, PGIMER, Chandigarh
Senior Strategic Information Advisor, UNAIDS India
Head, Department of Community Medicine, MGM Medical College, Indore
Retd. Head, Department of Biostatistics, AIIMS & Consultant, NIE, Chennai
Former Head, Dept. of Obstetrics & Gynaecology, MAMC, New Delhi
Chief of Epidemiology and Analysis Division, UNAIDS Geneva
Dr. Meade Morgan
Prof. Geoffrey P. Garnett
Dr. S. Venkatesh
Dr. Mohammed Shaukat
Dr. Sandhya Kabra
Statistician, CDC, Atlanta
Division of Epidemiology, Imperial College, London
Deputy Director General (M&E), NACO
Assistant Director General, NACO
Assistant Director General, NACO
Special Invitees
Name
Dr. Ambujam Nair Kapoor
Dr. Arun Risbud
Dr. H.K. Kar
Dr. M.K. Saha
Dr Pauline Harvey
Dr. B.B. Rewari
Dr. Raghuram Rao
Organisation
ICMR, New Delhi
NARI, Pune
PGIMER, RMLH, New Delhi
NICED, Kolkata
CDC – DGHA, India
NACO
NACO
69
Table C2: National Working Group on HIV Estimations 2012
Name
Dr. Arvind Pandey
Dr. D.C.S. Reddy
Dr. S. Venkatesh
Organisation
NIMS, New Delhi
Independent Expert
NACO
Dr. Shashi Kant
Dr. M. Bhattacharya
Dr. Damodar Sahu
Mr. Taoufik Bakkali
Dr. Yujwal Raj
Mr. Jitenkumar Singh
Dr. Chinmoyee Das
Mr. Ugra Mohan Jha
Mr. Ananta Basudev Sahu
Dr. Laxmikant Chavan
Dr. Pradeep Kumar
Ms. Nalini Chandra
Dr. Kuru Dindi
Mr. B.K. Gulati
Mr. Sharad Mathur
Ms. Deepika Joshi
Mr. Deepak Bhardwaj
Dr. Dipak Roy Choudhary
AIIMS, New Delhi
NIHFW, New Delhi
NIMS, New Delhi
UNAIDS India
NACO
NIMS, New Delhi
NACO
NACO
NACO
WHO India
NACO
UNAIDS India
NACO
NIMS
NIMS
CDC India
Independent Consultant
Independent Consultant
Special Advisors
70
Dr. John Stover
Futures Institute
Dr. P.M. Kulkarni
Dr. Karan Stanecki
Dr. Wiwat Peerapatinapokin
Dr. Anindya De
Ms. Nalyn Siripong
JNU, New Delhi
UNAIDS, Geneva
East West Centre, Hawaii
CDC, Atlanta
UNC
Table C3: Regional Working Groups on HIV Estimations 2012
Name
NORTH ZONE Dr. PVM Lakshmi
Dr. Atul Sharma
Dr. Titiksha Sirani
Dr. Tarundeep Singh
Ms. Dolly Khurana
Dr. Bindya Jain
Ms. Dalia Sebastian
Mr. Vinay Kumar
CENTRAL ZONE
Dr. Sanjay Rai
Mr. Ram Manohar Mishra
Dr. Preety Pathak
Dr. Sandeep Rai
Mr. Vijay Kumar
WEST ZONE Dr. Sheela Godbole
Dr. A.M. Kadri
Dr. Anil Kumar
Dr. Laxmi Kant Dwivedi
SOUTH ZONE
Dr. Elangovan
Dr. V. Selvaraj
Dr. M. Madan Kumar
Dr. S. Raja Ram
Dr. Vani Srinivas
Dr. J Prabakaran
Dr. Ajay Rajan
Dr. Jaya Krishna
EAST ZONE Dr. Mahesh Goyal
Dr. Susanta Kumar Swain
Dr. Amitav Das
Dr. Lincoln Choudhury
Mr. Raju Tamang
Mr. Kshitiz Dewan
Mr. Ranjanjyoti Deka
Dr. Chiranjeev Bhattacharya
Dr. Tumge Loyi
Mr. Subrata Biswas
Organisation
PGIMER, Chandigarh
PGIMER, Chandigarh
PGIMER, Chandigarh
Punjab SACS & Chandigarh SACS
Punjab SACS
Haryana SACS
Rajasthan SACS TSU
Himachal Pradesh SACS
AIIMS, New Delhi
Population Council, New Delhi
Uttar Pradesh SACS &Uttarakhand SACS
Jharkhand SACS & Bihar SACS
Bihar SACS
NARI, Pune
PDU Medical College, Rajkot, Gujarat
TISS, Mumbai
TISS, Mumbai
NIE, Chennai
NIE, Chennai
NIE, Chennai
KHPT, Bengaluru
Karnataka SACS
Tamil Nadu SACS & Puducherry SACS
Kerala SACS
Andhra Pradesh SACS
KPC Medical College, Kolkata
Odisha SACS TSU
Odisha SACS
NACO-NERO
NACO-NERO
Chhattisgarh SACS
Assam SACS
Assam SACS
Mizoram, Arunachal Pradesh & Tripura SACS
NICED, Kolkata
71