U.S. Department of Commerce

Economics and Statistics Administration 

U.S. CENSUS BUREAU 

WP/02-2

Demographic Programs

U.S. Agency for International Development

Bureau for Global Health

Office of HIV/AIDS

The AIDS Pandemic in 

the 21st Century

International Population Reports

Issued March 2004

By 

Karen A. Stanecki
Acknowledgments

The AIDS Pandemic in the 21st report including Jinkie Corbin, cal offices and other national and

Century was prepared in the John Gibson, Lisa Mayberry, international organizations worldInternational Programs Center Brynn Epstein, and Laura Heaton. wide, without whose generous col-

(IPC), Population Division, laboration this kind of report would The discussion in Appendix A of U.S. Census Bureau, under a not be possible. Recognition is due the methodology for incorporating Participating Agency Service to the United Nations Joint AIDS mortality into projections was Agreement with the U.S. Agency Programme on HIV/AIDS. written by Peter O. Way and for International Development. Karen Stanecki. The HIV/AIDS Finally, we wish to express our The report was produced under Surveillance Data Base, which pro- gratitude to colleagues at the the general direction of Peter O. vides the basis for incorporating Way U.S. Agency for International , Chief, and James C. Gibbs, AIDS mortality into population pro- Assistant Chief for Demographic Development for their support jections, is maintained by IPC’s and Economic Studies. throughout the various stages of Health Studies Branch. this project. We acknowledge 

Many of the Center’s staff shared Gregory Carroll, Nelsa D. with thanks the contributions of

in the preparation of the demo- Brown, Barbara Adams, Paul R. De Lay, formerly Senior graphic estimates and projections, Jan Sweeney, and Everett Dove Medical Advisor on HIV/AIDS in as well as other activities upon of the Administrative and Customer USAID, and Chief, HIV/AIDS Division; which this report is based. Services Division, Walter C. Odom, Margaret Neuse, Director, Office of

Chief, provided publications and Peter Johnson, Special Assistant Population and Reproductive Health;

printing management, graphics for International Demographic Scott Radloff, Deputy Director,

design and composition, and Methods, provided guidance in Office of Population and

editorial review for print and determining the methods to use Reproductive Health; and 

electronic media. General direction for evaluating each country’s sta- Ellen Starbird, Chief, Policy, and production management were tistics and reviewed the demo- Evaluation and Communication

provided by James L. Clark, graphic estimates and projections Division. 

Assistant Division Chief of the used in the report. He also coordi- Administrative and Customer Special thanks are due to nated the data capture, aggrega- Services Division and Susan L. David Stanton, Cognizant tion, and retrieval of information Rappa, Chief, Publications Services Technical Officer on this project, from IPC’s International Data Base. Branch. who provided helpful guidance in

Staff of the Health Studies Branch the development and preparation of We are grateful to national AIDS assisted in the preparation of this the report. control programs, national statisti-
The AIDS Pandemic in

the 21st Century

WP/02-2

Issued March 2004

U.S. Agency for 

International Development

Andrew S. Natsios, Administrator

Bureau for Global Health

E. Anne Peterson, 

Assistant Administrator

Linda Morse, Senior Deputy

Assistant Administrator

Office of HIV/AIDS

Constance Carrino, Director

Office of Population and 

Reproductive Health

Margaret Neuse, 

Director

U.S. Department 

of Commerce

Donald L. Evans,

Secretary

Vacant,

Deputy Secretary

Economics and Statistics

Administration

Kathleen B. Cooper, 

Under Secretary for Economic Affairs

U.S. CENSUS BUREAU

Charles Louis Kincannon,

Director
Suggested Citation

U.S. Census Bureau, 

International Population Reports

WP/02-2, 

The AIDS Pandemic in the 21st Century,

U.S. Government Printing Office,

Washington, DC, 

2004.

ECONOMICS

AND STATISTICS

ADMINISTRATION

Economics and Statistics

Administration

Kathleen B. Cooper,

Under Secretary for Economic Affairs

U.S. CENSUS BUREAU

Charles Louis Kincannon,

Director

Hermann Habermann,

Deputy Director and Chief Operating Officer

Vacant,

Principal Associate Director for Programs

Nancy M. Gordon,

Associate Director for Demographic Programs

John F. Long,

Chief, Population Division

Peter O. Way,

Chief, International Programs Center

For sale by the Superintendent of Documents, U.S. Government Printing Office

Internet: bookstore.gpo.gov. Phone: toll-free 866-512-1800; DC area 202-512-1800

Fax: 202-512-2250 Mail: Stop SSOP, Washington, DC 20402-0001.
U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 iii

Contents

Highlights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

The AIDS Pandemic in the 21st Century . . . . . . . . . . . . . . . . . . 11

Appendix A. Population Projections Incorporating AIDS . . . . . A-1

Appendix B. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

Appendix C. Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

FIGURES

1. Adult HIV Prevalence in Africa: December 2001 . . . . . . . . . 11

2. HIV Seroprevalence for Pregnant Women in Selected Urban 

Areas of Africa, Asia, and Latin America: 1985-2001 . . . . 12

3. HIV Seroprevalence by Age and Sex in Rwanda: 1997 . . . . . 13

4. HIV Seroprevalence for Pregnant Women and the General 

Population in Zambia: 1995-1996 . . . . . . . . . . . . . . . . . . 13

5. Death Rates With and Without AIDS by Age and Sex in 

South Africa: 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

6. Population Growth Rates With and Without AIDS for 

Selected Countries: 2002 . . . . . . . . . . . . . . . . . . . . . . . . 14

7. Population Growth Rates With and Without AIDS for 

Selected Countries: 2010 . . . . . . . . . . . . . . . . . . . . . . . . 15

8. Population by Age and Sex With and Without AIDS for 

South Africa: 2002, 2010, and 2020 . . . . . . . . . . . . . . . . 18

9. Population by Age and Sex With and Without AIDS for 

Uganda: 2002, 2010, and 2020 . . . . . . . . . . . . . . . . . . . 19

10. Life Expectancy at Birth With and Without AIDS for 

Selected Countries: 2002 . . . . . . . . . . . . . . . . . . . . . . . . 20

11. Life Expectancy at Birth With and Without AIDS for 

Selected Countries: 2010 . . . . . . . . . . . . . . . . . . . . . . . . 21

12. Crude Death Rates With and Without AIDS for 

Selected Countries: 2002 . . . . . . . . . . . . . . . . . . . . . . . . 22

13. Crude Death Rates With and Without AIDS for 

Selected Countries: 2010 . . . . . . . . . . . . . . . . . . . . . . . . 23

14. Infant Mortality With and Without AIDS for 

Selected Countries: 2002 . . . . . . . . . . . . . . . . . . . . . . . . 24

15. Infant Mortality With and Without AIDS for 

Selected Countries: 2010 . . . . . . . . . . . . . . . . . . . . . . . . 25

16. The Under-5 Mortality Rate With and Without AIDS 

for Selected Countries: 2002 . . . . . . . . . . . . . . . . . . . . . . 26

17. The Under-5 Mortality Rate With and Without AIDS 

for Selected Countries: 2010 . . . . . . . . . . . . . . . . . . . . . . 27

18. Change in Population of Selected African Countries 

With and Without AIDS: 1990-2050 . . . . . . . . . . . . . . . . . 28

A-1. Scenarios and Empirical Trend: Urban Female 

HIV Seroprevalence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-7

A-2. Five Scenarios and Empirical Trend: Total Female 

HIV Seroprevalence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-7

A-3a. Projected HIV Seroprevalence for Selected Countries 

of Africa: 1990-2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . A-8
iv The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

A-3b. Projected HIV Seroprevalence for Selected Countries of Africa: 1990-2010 . . . . . . . . . . . . . . . . . . . . A-8

A-4. Projected HIV Seroprevalence for Uganda: 1990-2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-9

TABLES

1. Demographic Characteristics With and Without AIDS: 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2. Demographic Characteristics With and Without AIDS: 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

A-1. Empirical Seroprevalence Data for Urban and Rural Areas for Selected Countries . . . . . . . . . . . . . . . . . A-5
HIGHLIGHTS
At the beginning of the 21st century, subsequent AIDS mortality con- due to non-AIDS causes will conHuman Immunodeficiency Virus tinue to hold, the AIDS pandem- tinue to decline.

(HIV), which causes the Acquired ic will dictate the size, growth, • Infant mortality rates are now Immune Deficiency Syndrome (AIDS) and age-sex structures of entire higher than they were in 1990 continues to have its greatest populations around the world. in some Sub-Saharan African impact in the developing world. • The U.S. Census Bureau's model- countries. In four Sub-Saharan Although the full demographic ing and projections work indi- African countries, more infants impact is not expected to be felt for cates that severe distortions in are likely to die from AIDS in several more years, and perhaps age-sex structures are likely in 2010 than from all other causes. will not be completely measured at severely affected countries. In the pandemic's epicenter in Sub- • In 26 Sub-Saharan African coun- countries with moderate epiSaharan Africa, the emerging down- demics, AIDS mortality is tries, under-5 mortality rates

ward trends in life expectancy and expected to have less effect on have increased over what they

population growth, the distortions the population structure. would have been without AIDS.

in age structures, and the break- Based on current trends, under-5 • Life expectancies in HIV/AIDS downs in support systems are mortality rates in 2010 are affected countries are projected already being seen in some coun- expected to be much higher to decline, negating gains tries. At the extreme in Southern with AIDS than they would have achieved during the past several Africa, Botswana, South Africa, and been without AIDS. If programs decades. By 2010, many coun- Zimbabwe are thought to be experi- to prevent mother-to-child trans- tries in southern Africa are encing negative population growth mission are dramatically scaled expected to see life expectan- due to AIDS mortality. At the begin- up, then the course of future

ning of the 21st cies falling to near 30 years of century, AIDS is the child mortality rates can be age, levels not seen since the number four cause of death global- beginning of the 20th changed. century. ly but the number one cause of • By 2010, populations in the death in Africa. • The most direct impact of AIDS majority of Sub-Saharan African is expected to be an increase in • If current trends in HIV sero- countries are projected to the number of deaths in popula- prevalence (the proportion of increase, despite the high levels tions affected. In many Sub- population infected with HIV) of mortality. The exceptions are Saharan African countries, crude continue into the near future Botswana, Lesotho, death rates are projected to be and existing relationships Mozambique, South Africa, even higher in 2010 than in between HIV infection rates and Swaziland, and Zimbabwe. 2000, even though mortality

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 3

HIGHLIGHTS
INTRODUCTION
U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 7

INTRODUCTION

The AIDS pandemic in the 21st cen- and demographic events of our methodology and assumptions

tury continues to have devastating time, the worldwide HIV/AIDS pan- used for incorporating AIDS morimpacts on populations, particular- demic, a source of some of the tality into the population estimates

ly in the developing world. Since uncertainty associated with demo- and projections are described in

the beginning of the epidemic two graphic change in the coming Appendix A. This report is also

decades ago, more than 20 million decades. It includes information published as a chapter in the

people have died of AIDS. Twice on the impact of AIDS on mortality Census Bureau's Global

that many-40 million-are now liv- and population. In addition, the Population Profile: 2002.

ing with HIV. Barring some major report reviews the current status of Questions about the demographic breakthrough, most of these peo- the HIV/AIDS epidemics in Africa, impacts of the HIV/AIDS pandemic ple are expected to die during the Asia, and Latin America. presented in this report or about next 10 years or so. In 2001, the This report presents the methodol- the methodology employed in esti- Joint United Nations Programme on ogy and results of incorporating mating those impacts may be AIDS (UNAIDS) estimated that AIDS mortality into the U.S. Census directed to: Chief, Health Studies 5 million people were newly Bureau's population estimates and Branch, International Programs infected with HIV. projections for severely affected Center, U.S. Census Bureau,

This report provides an update on countries of the world. The Washington, DC 20233-8860.

one of the key international health available information and the
8 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

This report is available on the Census Bureau Web

site as a chapter in Global Population Profile:

2002. The Web address is: www.census.gov/ipc

/www/wp02.html. The data presented in this report

draw upon information stored in two databases

maintained and annually updated by the

International Programs Center (IPC) of the U.S.

Census Bureau. IPC compiles, evaluates, electronically stores, and analyzes selected demographic and

health data for all countries. IPC's Health Studies

Branch maintains the HIV/AIDS Surveillance Data

Base, a compilation of information on HIV prevalence

from all available studies from Africa, Asia, Latin

America, Eastern Europe and the New Independent

States. The International Data Base (IDB) contains

statistical tables providing demographic and socioeconomic data for all countries of the world. 

• The HIV/AIDS Surveillance Data Base includes all

available epidemiological information on HIV/AIDS

seroprevalence and incidence for countries in

Africa, Asia, Latin America, Eastern Europe, and the

New Independent States taken from the scientific

literature and from unpublished reports prepared

for international conferences and various workshops. The current update of the data base contains over 72,000 individual data records drawn

from over 6,500 publications and presentations.

The HIV/AIDS Surveillance Data Base can be

obtained free of charge on CD-ROM from the

Health Studies Branch or downloaded from the

Internet at: 

www.census.gov/ipc/www/hivaidsn.html

Requests for specific data items or a CD-ROM, or

questions about the HIV/AIDS Surveillance Data

Base should be directed to:

Chief, Health Studies Branch

International Programs Center 

Washington Plaza II, Room 313A

U.S. Census Bureau

Washington, DC 20233-8860 USA

Telephone: 301/763-1433; 

FAX: 301-457-3034

e-mail: ipc-hiv@census.gov

• The International Data Base contains information

derived from censuses, surveys (for example,

population by age and sex, labor force, and contraceptive use), and administrative records (for

example, registered births and deaths) for selected years from 1950 to the present. Some variables are available by urban/rural residence. The

IDB contains the International Programs Center's

current estimates and projections of fertility, mortality, migration, and population on a single-year

basis to the year 2020 and for every fifth year

from 2025 to 2050. IDB estimates and projections may be more recent than those presented in

this report, which are current to October 2002.

Direct access and further information about the

IDB are available through the Internet at:

www/census.gov/ipc/www/idbnew.html

Requests for specific data items from, or questions about, the IDB should be directed to:

Senior Information Specialist for the IDB

International Programs Center

Washington Plaza II, Room 109

U.S. Census Bureau

Washington, DC 20233-8860 USA

Telephone: 301-763-6180; 

FAX: 301-457-1539

e-mail: idb@census.gov
THE AIDS PANDEMIC IN 

THE 21ST CENTURY
The AIDS Pandemic in the 21st

Century Continues to Have Its

Greatest Impact in the

Developing World

Over 90 percent of people infected

with the Human Immunodeficiency

Virus (HIV), which causes AIDS, live

in the developing world. The Joint

United Nations Programme on

HIV/AIDS (UNAIDS) expects that

this “proportion will continue to

rise in countries where poverty,

poor health systems, and limited

resources for prevention and care

fuel the spread of the virus”

(UNAIDS, 1999).

Over 70 percent of the global total

of HIV-positive people, 28.5 million

out of 40 million, live in SubSaharan Africa, even though this

region contains only 11 percent of

the global population. Nine percent

of all adults in Sub-Saharan Africa

are HIV positive, compared to 

0.6 percent of adults in the United

States. Since the beginning of the

epidemic, over 15 million Africans

have died from AIDS; 2.2 million

AIDS deaths occurred there in 2001.

Southern and eastern Africa have

been the most severely affected

regions. According to the latest

UNAIDS/WHO figures, seven countries have an estimated adult (ages

15-49) HIV prevalence of 20 percent or greater: Botswana,

Lesotho, Namibia, South Africa,

Swaziland, Zambia, and Zimbabwe

(UNAIDS/WHO, 2002). In these Republic, Kenya, Malawi, and

countries, all in southern Africa, at Mozambique, have adult HIV

least one adult in five is living with prevalence levels higher than 

HIV. An additional five countries, 10 percent (Figure 1).

Cameroon, Central African

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 11

Percent seropositive

Figure 1.

Adult HIV Prevalence in Africa: December 2001 

In 12 countries, more than one-tenth of the adult

population 15-49 years of age is infected with HIV. 

Source: UNAIDS/WHO (2002).

Under 1

1 to 4

5 to 9

10 to 19

20 and over

Not available
The HIV/AIDS epidemics in southern

Africa started later but they have

been explosive, such as in

Botswana, where HIV prevalence

among pregnant women1 in

Francistown increased from 7 percent in 1991 to 44 percent in 2000. 

The two success stories in SubSaharan Africa continue to be

Uganda and Senegal. HIV prevalence among pregnant women in

Uganda continues to decline in

most sentinel surveillance sites. In

Kampala, HIV prevalence declined

from its peak of 30 percent in

1993 to 11 percent in 2000. In

Dakar, AIDS control programs have

managed to keep HIV prevalence at

very low levels (Figure 2).

In comparison, HIV prevalence levels among pregnant women in Asia

are relatively low. HIV prevalence

exceeds 1 percent in only three

countries: Burma, Cambodia, and

Thailand. However, even these

epidemics differ. In Thailand,

another success story, and

Cambodia, HIV prevalence is

declining in some areas and stabilizing at low levels in other areas.

In Burma, HIV prevalence rates

fluctuated at low levels into the

mid-1990s and show a slight

increase since then.

In Latin America and the

Caribbean, the HIV/AIDS epidemics

vary from those that are concentrated among injecting drug users

(Argentina and Uruguay) and men

who have sex with men (Peru and

Mexico) to epidemics that seem to

be driven by heterosexual transmission. The last include those in

the Bahamas, Haiti, Honduras, and

Guyana, the countries with the

highest HIV prevalence levels

12 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

1 In this report, “pregnant women” refers

to those pregnant women attending antenatal clinics.

Figure 2.

HIV Seroprevalence for Pregnant Women in Selected Urban 

Areas of Africa, Asia, and Latin America: 1985-2001 

HIV/AIDS epidemics in urban areas vary widely 

from region to region with African countries 

typically showing higher urban HIV prevalence 

than countries in Asia and Latin America.

Source: U.S. Census Bureau, International Programs Center, HIV/AIDS Surveillance 

Data Base (2002 release).

Percent

0

10

20

30

40

50

1985 1987 1989 1991 1993 1995 1997 1999 2001

Percent

0

4

8

12

16

20

1985 1987 1989 1991 1993 1995 1997 1999 2001

Georgetown, Guyana

Dakar, Senegal

Nairobi, Kenya 

Francistown, Botswana

Kampala, Uganda

Kwazulu/Natal, 

South Africa 

Bangkok, Thailand Rangoon, Burma

Sao Paulo, 

Brazil

Cite Soleil, Haiti

Phnom Penh, 

Cambodia

See insert below

Insert

San Pedro Sula, 

Honduras
among pregnant women in the

region. In Brazil, already strong

HIV prevention programs were

augmented in recent years by

advances in provision of antiretrovirals to all those HIV positive,

thereby lessening the effects of

AIDS mortality on the population.

In Sub-Saharan Africa, 

More Women Than Men 

Are HIV Positive

At the end of 2001, UNAIDS estimated that 58 percent of all HIV

infections in Sub-Saharan Africa

were among women. Peak HIV

prevalence among women occurs

at a younger age than among men:

around age 25 compared to age

35-40. As Figures 3 and 4 show

for Rwanda and Zambia, younger

women tend to have higher levels

of HIV infection than men of their

same age.

Several studies have shown that

HIV prevalence among pregnant

women attending antenatal clinics

provides a reasonable overall estimate of HIV prevalence in the general adult population, although it

underestimates the rate among all

women while overestimating it

among men. This is shown for

Zambia in Figure 4.

Mortality Patterns Are Driven

by HIV Prevalence Patterns

Median survival time with HIV/AIDS

is estimated to be around 10 years.

In South Africa, by 2020, death

rates for adults at ages 20-45 are

likely to be much higher than they

would have been without AIDS.

Among those under age 60, mortality for women is projected to peak

during the ages of 30-34, earlier

than the peak projected for men:

40-44 years (Figure 5).

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 13

Figure 3.

HIV Seroprevalence by Age and Sex in Rwanda: 1997 

In Sub-Saharan Africa, HIV prevalence tends to be 

much higher among young females than among 

males of their same age.

Source: Rwanda, Ministry of Health (1998).

Percent

0

10

20

30

40

50

12-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50+

Age

Females

Males

0

10

20

30

40

50

15-19 20-24 25-29 30-39

Source: Fylkesnes et al. (1998)

Percent

Figure 4.

HIV Seroprevalence for Pregnant Women and the 

General Population in Zambia: 1995-1996 

Seroprevalence levels for pregnant women are a

useful proxy for the combined adult population of

males and females.

Age

Pregnant women

Females

Males

0

2

4

6

8

10

12

14

16

0 1-4 10-14 20-24 30-34 40-44 50-54 60-64 70-74 80+

Source: U.S. Census Bureau, International Programs Center, International Data Base 

and unpublished tables.

Deaths per 1,000 population

Figure 5.

Death Rates With and Without AIDS by Age and Sex in 

South Africa: 2020 

AIDS mortality increases death rates in those ages 

where mortality due to all other causes is very low. 

Age

Females, including AIDS

Females without AIDS 

Males, including AIDS 

Males without AIDS
At the Beginning of the 21st

Century, the Population Growth

Rate in Botswana Is Now

Negative Due to AIDS Mortality2

Other countries with sharply

reduced growth rates include several additional African countries:

Lesotho, Malawi, Namibia, South

Africa, Swaziland, Zambia, and

Zimbabwe (Figure 6).

The negative population growth

seen in Trinidad and Tobago in

2002 reflects the impact of outmigration and AIDS mortality. The

underlying non-AIDS growth rate

for Trinidad and Tobago is nearly 

-0.6 percent.

In Asia, AIDS mortality has slightly

lowered population growth rates in

Burma, Cambodia, and Thailand.

14 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

2 Refer to Tables 1 and 2 for country-specific indicators.

In Figures 6 through 17, two

series of data are shown for each

of the 51 seriously affected

countries where AIDS is having

an impact on demographic indicators. The first series, "With

AIDS," shows what has happened

and what is projected to happen

in each country because of AIDS

mortality and its demographic

consequences. In this work, fertility is assumed to be unaffected

by HIV/AIDS, though numbers of

births decrease as a result of

mortality-induced reductions in

women of reproductive age.

Second, a hypothetical "Without

AIDS" series shows what the

Census Bureau's modeling work

indicates would have happened

if a country had not been affected by the HIV/AIDS epidemic.

This modeling takes into account

not only lower death rates but

also associated changes to a

country's age-sex structure and,

indirectly, the combined effects

of lower mortality and changing

population composition on

demographic indicators.

Figure 6.

Population Growth Rates With and Without AIDS for 

Selected Countries: 2002 

Botswana is now experiencing negative population 

growth. In South Africa, growth is now less than 

half what it might have been in 2002 

without AIDS. 

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables. 

Percent

With AIDS 

Without AIDS

-3 -2 -1 0 1 2 3 4

Cambodia

Thailand

Burma

Guatemala

Belize

Honduras

Haiti

Dominican Republic

Panama

Bahamas, The

Suriname

Guyana

Barbados

Trinidad & Tobago

Uganda

Malawi

Chad

Sierra Leone

Benin

Congo (Kinshasa)

Burkina Faso

Burundi

Mali

Côte d'Ivoire

Nigeria

Zambia

Togo

Namibia

Niger

Gabon

Senegal

Swaziland

Ethiopia

Rwanda

Djibouti

Cameroon

Zimbabwe

Guinea

Central African Rep.

Kenya

Tanzania

Botswana

Guinea-Bissau

Angola

Congo (Brazzaville)

Lesotho

Liberia

Ghana

Mozambique

Eritrea

South Africa

Sub-Saharan Africa

Asia

Latin America and the Caribbean
By the Year 2010, Five

Countries Are Projected to

Show Negative Population

Growth Because of 

AIDS Mortality

The growth rate for Botswana is

projected to be suppressed and by

2010 it will be -2 percent. In

South Africa it is projected to be 

-1.4 percent and in Swaziland 

-0.4 percent. This negative population growth is due to the high levels of HIV prevalence in these

countries and relatively low fertility. Previously, most HIV/AIDS

experts never expected HIV prevalence rates to reach such high levels for any country. By the end of

2001, adult HIV prevalence had

reached an estimated 39 percent in

Botswana, 20 percent in South

Africa, and 33 percent in Swaziland

(UNAIDS/WHO, 2002). By 2010,

Zimbabwe and Namibia are projected to experience a growth rate

of close to zero. Without AIDS,

these countries would have had a

growth rate of 2 percent or greater

(Figure 7).

In Latin America and the

Caribbean, the Bahamas and

Guyana are projected to see the

greatest relative impact, with

growth rates reduced from 1 percent to 0.5 percent. Trinidad and

Tobago’s already negative population growth, due to out-migration,

is projected to decline further due

to AIDS mortality.

In Asia, growth rates are projected

to be slightly lower in Burma,

Thailand, and Cambodia due to

HIV/AIDS.

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 15

Figure 7.

Population Growth Rates With and Without AIDS for 

Selected Countries: 2010 

By 2010, Botswana, Mozambique, Lesotho, Swaziland, and 

South Africa, are all expected to experience negative 

population growth due to AIDS.

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables. 

Percent

With AIDS 

Without AIDS

Sub-Saharan Africa

Asia

Latin America and the Caribbean

-3 -2 -1 0 1 2 3 4

Cambodia

Thailand

Burma

Guatemala

Haiti

Belize

Honduras

Dominican Republic

Panama

Guyana

Bahamas, The

Barbados

Suriname

Trinidad & Tobago

Uganda

Burundi

Congo (Kinshasa)

Malawi

Chad

Burkina Faso

Benin

Mali

Gabon

Guinea

Eritrea

Liberia

Côte d'Ivoire

Nigeria

Niger

Rwanda

Namibia

Zambia

Tanzania

Ethiopia

Sierra Leone

Djibouti

Senegal

Cameroon

Togo

Central African Rep.

Zimbabwe

Angola

Guinea-Bissau

Swaziland

Botswana

Kenya

Congo (Brazzaville)

Lesotho

Mozambique

Ghana

South Africa
Table 1.

Demographic Characteristics With and Without AIDS: 2002

Growth rate

Life expectancy

at birth Crude death rate Infant mortality rate

Under-5

mortality rate

Total

fertility

rate

Country With- Net

With out deAIDS AIDS crease

With- Net

With out deAIDS AIDS crease

With- Net

With out inAIDS AIDS crease

With- Net

With out inAIDS AIDS crease

With- Net

With out inAIDS AIDS crease

Angola ...........

Benin ............

Botswana ........

Burkina Faso .....

Burundi ..........

Cameroon ........

Central African

Republic ........

Chad ............

Congo (Brazzaville) .............

Congo (Kinshasa) .

Côte d’Ivoire . . . . . .

Djibouti...........

Eritrea ...........

Ethiopia . . . . . . . . . .

Gabon ...........

Ghana ...........

Guinea. . . . . . . . . . .

Guinea-Bissau ....

Kenya............

Lesotho . . . . . . . . . .

Liberia ...........

Malawi . . . . . . . . . . .

Mali. . . . . . . . . . . . . .

Mozambique......

Namibia ..........

Nigeria . . . . . . . . . . .

Niger . . . . . . . . . . . .

Rwanda ..........

Senegal ..........

Sierra Leone......

South Africa ......

Swaziland ........

Tanzania . . . . . . . . .

Togo . . . . . . . . . . . . .

Uganda ..........

Zambia...........

Zimbabwe ........

2.0

3.0

-0.2

2.6

2.2

2.1

1.7

3.1

1.6

2.8

2.2

2.2

1.3

2.0

2.6

1.6

2.4

2.0

1.4

0.2

1.7

2.3

2.9

1.0

1.7

2.6

2.8

1.8

2.6

3.0

0.2

1.1

1.8

2.5

2.9

1.6

1.0

2.2

3.1

2.3

3.1

3.0

2.6

2.6

3.3

2.0

3.1

2.9

2.7

1.5

2.7

2.8

1.8

2.6

2.2

2.3

2.0

1.9

3.3

3.0

1.8

2.9

2.9

2.8

2.7

2.7

3.2

1.2

2.7

2.3

2.9

3.5

2.9

2.6

0.2

0.1

2.5

0.5

0.8

0.5

0.9

0.1

0.4

0.3

0.8

0.5

0.2

0.7

0.3

0.3

0.1

0.2

1.0

1.7

0.2

1.0

0.1

0.8

1.1

0.3

0.1

0.8

0.1

0.2

1.0

1.6

0.6

0.4

0.6

1.3

1.6

37.1

51.3

33.9

44.7

43.0

48.1

42.1

48.7

50.5

48.7

42.8

43.1

53.6

41.6

57.7

56.8

49.4

47.0

45.5

37.1

48.3

38.5

45.6

32.1

45.0

51.5

42.2

39.5

56.2

43.0

48.8

41.4

44.7

53.8

44.5

35.3

40.2

38.9

53.8

72.4

52.3

57.6

58.8

57.5

51.4

58.1

55.2

55.6

51.6

56.6

53.1

63.7

62.4

51.4

49.8

65.6

64.4

51.8

56.3

47.4

40.0

65.8

57.8

43.5

51.5

59.0

46.0

66.3

72.6

54.3

62.6

56.0

55.4

69.0

1.8

2.5

38.5

7.6

14.6

10.6

15.4

2.7

7.6

6.5

12.7

8.5

3.0

11.5

6.0

5.7

2.0

2.9

20.1

27.3

3.5

17.8

1.9

7.9

20.9

6.3

1.3

12.1

2.9

3.0

17.5

31.1

9.6

8.9

11.5

20.1

28.8

25.8

13.6

28.6

18.8

18.0

15.3

19.5

16.4

14.0

15.1

18.4

19.5

13.1

20.0

10.9

10.4

15.9

16.7

15.7

24.4

17.9

22.3

19.3

29.3

17.6

13.6

21.9

21.6

11.0

20.7

16.6

19.3

17.3

11.4

17.3

24.3

20.8

24.3

12.4

4.8

14.7

10.4

10.0

10.8

15.0

10.1

11.9

11.2

14.4

11.7

13.1

8.4

7.9

14.8

15.0

6.2

8.2

16.0

12.0

18.3

21.9

7.0

10.5

21.1

14.0

9.8

18.8

7.3

4.1

12.1

7.7

11.6

11.5

5.4

1.5

1.2

23.8

4.1

7.6

5.3

8.7

1.4

3.9

3.2

7.1

5.1

1.4

6.9

2.5

2.5

1.1

1.7

9.5

16.3

1.8

10.3

1.0

7.4

10.6

3.1

0.8

7.6

1.2

1.9

9.3

15.2

5.3

3.7

5.7

12.8

15.4

195.2

87.7

64.8

100.9

72.7

71.1

94.5

96.7

96.8

98.5

99.6

108.4

77.1

104.3

55.8

53.8

94.8

111.9

64.1

87.2

133.9

106.1

120.4

199.7

67.3

72.2

124.6

103.6

58.6

148.5

59.5

66.5

105.3

69.8

89.7

100.2

65.9

191.6

84.4

20.0

93.5

59.1

61.7

78.6

93.5

88.8

92.4

86.9

99.6

73.5

92.1

50.2

49.2

92.8

108.4

46.9

59.1

130.1

87.2

119.5

186.1

42.0

65.8

122.7

90.4

56.2

144.3

38.9

34.2

96.0

62.6

79.2

76.5

34.7

3.6

3.3

44.8

7.5

13.6

9.4

15.9

3.3

8.0

6.0

12.6

8.8

3.5

12.2

5.6

4.7

2.0

3.4

17.2

28.2

3.8

18.9

0.9

13.6

25.3

6.5

1.9

13.2

2.4

4.2

20.6

32.3

9.3

7.2

10.5

23.7

31.2

287.4

149.6

107.1

199.8

133.8

124.1

144.7

171.1

148.2

142.5

152.6

175.3

130.2

169.9

82.8

92.9

168.8

180.3

95.0

127.7

203.2

184.7

218.1

304.1

104.1

136.2

269.8

185.9

110.7

225.6

97.3

103.5

156.5

119.1

145.2

170.8

100.7

281.8

143.8

30.6

187.9

111.5

108.4

118.2

165.4

134.1

132.4

131.6

161.0

123.7

150.0

72.6

84.4

165.3

174.6

65.7

80.6

196.8

155.0

215.4

280.9

59.2

124.9

267.1

164.9

106.4

218.9

61.4

48.1

140.9

106.6

127.9

132.7

47.2

5.6

5.9

76.5

11.8

22.3

15.7

26.5

5.8

14.1

10.1

20.9

14.3

6.5

20.0

10.2

8.5

3.5

5.7

29.2

47.1

6.4

29.7

2.7

23.1

44.9

11.4

2.7

21.0

4.4

6.8

35.9

55.4

15.6

12.5

17.3

38.1

53.4

6.4

6.1

3.4

6.4

6.1

4.7

4.8

6.5

3.8

6.8

5.6

5.6

5.8

5.7

4.9

3.5

5.9

5.1

3.6

3.6

6.3

6.2

6.7

5.0

4.8

5.5

7.0

5.7

5.0

5.9

2.3

4.0

5.3

5.1

6.8

5.4

3.7

Bahamas, The ....

Barbados.........

Belize. . . . . . . . . . . .

Dominican

Republic ........

Guatemala .......

Guyana ..........

Haiti . . . . . . . . . . . . .

Honduras.........

Panama..........

Suriname.........

Trinidad and

Tobago..........

0.8

0.4

2.5

1.4

2.7

0.3

1.6

2.4

1.4

0.4

-0.6

1.2

0.5

2.6

1.6

2.8

0.5

2.0

2.6

1.5

0.5

-0.6

0.3

0.1

0.1

0.2

0.1

0.2

0.4

0.2

0.1

0.1

0.1

65.8

72.0

67.3

68.3

65.3

63.7

51.4

67.1

72.5

69.3

69.9

74.0

74.5

71.3

74.2

67.8

69.0

58.7

71.5

76.1

71.9

71.7

8.2

2.5

4.1

5.9

2.5

5.3

7.2

4.4

3.6

2.6

1.8

8.6

9.0

6.1

6.7

6.8

8.9

13.5

6.3

6.1

6.7

8.4

5.2

7.9

4.7

4.5

5.9

6.6

10.3

4.8

4.9

5.7

7.7

3.4

1.1

1.4

2.2

0.9

2.3

3.3

1.5

1.3

1.0

0.7

26.7

12.8

27.8

35.1

39.0

37.9

77.7

30.3

21.9

25.4

25.3

21.4

10.9

25.0

31.3

36.9

33.3

71.5

26.8

19.6

23.4

23.6

5.4

1.9

2.8

3.8

2.0

4.6

6.2

3.5

2.3

2.0

1.7

35.8

15.6

36.0

45.3

51.2

52.3

117.1

41.4

27.9

32.3

30.0

26.3

12.2

30.8

38.2

47.5

43.8

106.6

34.9

23.7

28.8

27.1

9.5

3.4

5.2

7.1

3.7

8.5

10.5

6.5

4.1

3.5

2.9

2.3

1.6

4.0

2.9

4.7

2.1

5.0

4.2

2.6

2.4

1.8

Burma . . . . . . . . . . .

Cambodia ........

Thailand. . . . . . . . . .

0.6

1.8

1.0

0.7

2.0

1.1

0.2

0.2

0.1

55.6

57.4

71.1

58.5

61.9

72.7

3.0

4.5

1.6

12.2

9.4

6.8

10.6

7.5

6.1

1.6

1.9

0.7

72.0

78.3

22.5

69.5

74.5

22.2

2.4

3.8

0.3

101.0

103.0

30.0

96.4

96.0

28.8

4.6

7.0

1.2

2.2

3.6

1.9

Note: Growth rate, life expectancy at birth (e0), crude death rate, infant mortality, and under-5 mortality (5q0) are for both sexes combined.

Source: U.S. Census Bureau, International Data Base and unpublished tables.

16 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau
Table 2.

Demographic Characteristics With and Without AIDS: 2010

Growth rate

Life expectancy

at birth Crude death rate Infant mortality rate

Under-5

mortality rate

Total

fertility

rate

Country With- Net

With out deAIDS AIDS crease

With- Net

With out deAIDS AIDS crease

With- Net

With out inAIDS AIDS crease

With- Net

With out inAIDS AIDS crease

With- Net

With out inAIDS AIDS crease

Angola ...........

Benin ............

Botswana ........

Burkina Faso .....

Burundi ..........

Cameroon ........

Central African

Republic .........

Chad ............

Congo (Brazzaville) .............

Congo (Kinshasa) .

Côte d’Ivoire . . . . . .

Djibouti...........

Eritrea ...........

Ethiopia . . . . . . . . . .

Gabon ...........

Ghana ...........

Guinea. . . . . . . . . . .

Guinea-Bissau ....

Kenya............

Lesotho . . . . . . . . . .

Liberia . . . . . . . . . . .

Malawi . . . . . . . . . . .

Mali. . . . . . . . . . . . . .

Mozambique......

Namibia ..........

Nigeria ...........

Niger . . . . . . . . . . . .

Rwanda ..........

Senegal ..........

Sierra Leone......

South Africa ......

Swaziland ........

Tanzania . . . . . . . . .

Togo . . . . . . . . . . . . .

Uganda ..........

Zambia...........

Zimbabwe ........

1.7

2.4

-2.1

2.4

2.7

1.7

1.3

2.7

0.9

2.9

1.8

1.9

2.2

1.5

2.2

1.0

2.7

1.9

0.5

-0.2

2.3

1.9

2.5

-0.2

0.2

2.0

2.4

1.6

2.3

2.0

-1.4

-0.4

1.9

1.7

3.0

1.0

0.0

2.3

2.9

1.9

3.0

3.4

2.4

2.3

3.1

1.7

3.2

2.7

2.5

2.8

2.5

2.9

1.4

2.8

2.3

1.8

1.7

2.7

3.2

2.9

1.6

2.7

2.7

2.7

2.7

2.5

2.5

1.0

2.3

2.6

2.3

3.5

2.6

2.3

0.6

0.5

4.0

0.7

0.7

0.7

1.0

0.5

0.8

0.3

0.9

0.6

0.6

1.0

0.6

0.5

0.2

0.3

1.3

1.9

0.5

1.3

0.4

1.8

2.4

0.7

0.3

1.0

0.2

0.5

2.4

2.6

0.7

0.7

0.5

1.5

2.3

35.0

47.9

26.7

43.6

44.6

47.9

41.0

46.2

47.0

50.5

41.7

43.4

48.9

40.1

52.9

55.6

51.3

47.3

43.7

36.5

46.3

36.9

44.3

27.1

33.8

47.3

41.4

38.7

58.2

41.9

36.5

33.0

44.6

50.7

46.8

34.4

34.6

41.3

57.0

74.4

55.4

60.7

61.9

60.6

54.6

61.2

58.4

58.7

54.8

59.8

56.3

66.5

65.4

54.6

52.9

68.3

67.2

55.0

59.4

50.5

42.5

68.5

60.9

46.3

54.7

62.1

48.9

68.4

74.6

57.5

65.5

59.2

58.6

71.4

6.2

9.0

47.7

11.9

16.2

14.0

19.6

8.4

14.3

7.9

17.0

11.4

10.8

16.3

13.6

9.8

3.2

5.6

24.7

30.6

8.7

22.6

6.2

15.4

34.8

13.6

4.9

16.0

3.9

7.1

31.9

41.6

12.9

14.8

12.3

24.3

36.9

26.8

14.8

42.8

18.6

16.7

15.5

20.2

16.8

16.5

13.4

19.0

19.1

15.0

20.7

12.9

11.5

14.3

16.2

18.3

26.0

17.9

23.1

18.8

36.2

28.1

15.7

21.0

22.3

9.8

20.5

30.1

28.8

17.1

12.9

15.2

25.4

27.4

21.6

10.2

4.2

12.2

8.5

8.5

9.2

12.5

8.5

9.8

9.4

12.6

9.8

10.9

7.0

7.0

12.7

13.0

5.4

7.1

13.5

9.9

15.3

19.4

5.7

8.7

17.9

12.0

8.2

16.1

7.2

3.8

10.0

6.3

9.5

9.5

4.8

5.2

4.6

38.6

6.4

8.1

7.0

11.0

4.3

8.0

3.6

9.6

6.5

5.2

9.8

5.9

4.5

1.6

3.2

12.9

18.8

4.4

13.2

3.4

16.9

22.4

7.0

3.1

10.3

1.6

4.4

22.9

25.1

7.1

6.6

5.7

15.9

22.6

183.6

80.1

74.6

92.1

63.3

62.7

83.9

88.6

85.7

83.2

88.4

95.9

70.9

94.9

50.5

47.6

83.1

99.4

58.0

78.1

120.5

97.9

110.1

194.1

73.4

65.8

117.1

94.5

50.4

135.2

65.1

70.8

92.3

61.8

76.6

92.3

69.0

174.7

71.3

15.8

80.7

49.4

51.4

65.2

80.8

72.8

76.7

72.5

85.3

61.7

77.9

39.7

40.2

80.1

93.8

37.2

46.7

112.7

73.2

105.1

169.2

32.8

54.6

110.9

78.1

47.1

127.2

31.5

25.6

80.1

50.6

66.5

64.7

27.0

8.9

8.7

58.8

11.4

13.9

11.3

18.7

7.8

12.9

6.6

16.0

10.6

9.1

16.9

10.8

7.4

2.9

5.6

20.9

31.5

7.8

24.7

5.0

25.0

40.6

11.2

6.2

16.4

3.3

8.0

33.7

45.3

12.2

11.2

10.1

27.6

42.0

268.9

132.2

122.9

173.9

111.4

105.5

127.1

151.6

128.8

118.9

133.7

152.0

115.9

150.8

74.4

78.6

143.6

157.0

86.3

115.9

180.7

165.1

193.6

292.8

113.9

117.3

244.8

164.4

90.3

203.3

104.3

111.7

135.5

101.2

121.1

153.4

107.6

255.2

117.5

22.8

156.0

88.3

86.5

95.6

138.6

107.1

107.8

107.1

134.6

100.3

123.3

55.5

65.7

138.5

147.6

50.6

62.3

167.8

125.2

184.8

253.5

44.8

98.3

235.0

138.0

84.5

190.5

47.3

34.6

115.3

82.0

104.1

108.3

35.8

13.7

14.7

100.1

18.0

23.1

19.0

31.5

13.0

21.7

11.2

26.6

17.4

15.6

27.5

18.9

12.9

5.1

9.3

35.7

53.7

12.9

39.9

8.8

39.3

69.1

19.0

9.8

26.4

5.8

12.9

57.0

77.1

20.3

19.2

17.0

45.2

71.9

5.9

5.4

2.7

5.9

5.3

4.1

4.1

6.0

3.0

6.1

4.8

5.0

5.2

4.8

4.6

2.5

5.6

4.6

2.6

3.0

5.7

5.7

6.1

4.3

4.3

4.8

6.3

5.2

4.3

5.3

2.0

3.2

4.6

3.8

6.1

4.5

3.3

Bahamas, The ....

Barbados.........

Belize. . . . . . . . . . . .

Dominican

Republic ........

Guatemala .......

Guyana ..........

Haiti . . . . . . . . . . . . .

Honduras.........

Panama..........

Suriname.........

Trinidad and

Tobago..........

0.5

0.2

2.1

1.2

2.4

0.4

2.0

1.7

1.1

0.0

-1.0

0.9

0.4

2.3

1.5

2.6

1.1

2.3

2.1

1.3

0.3

-0.6

0.4

0.2

0.2

0.3

0.2

0.7

0.4

0.4

0.2

0.2

0.4

65.8

71.2

68.3

66.7

65.9

57.1

53.3

62.2

72.1

69.1

64.5

75.8

76.3

73.5

76.0

70.3

71.4

61.7

73.6

77.6

74.0

73.8

10.0

5.1

5.2

9.3

4.5

14.3

8.5

11.4

5.5

4.9

9.3

9.4

9.5

5.9

8.1

6.6

13.6

12.3

8.4

6.9

7.8

12.5

5.4

7.4

4.1

4.6

5.0

6.4

8.6

4.3

5.0

5.8

8.5

4.0

2.1

1.8

3.5

1.6

7.1

3.8

4.1

2.0

2.0

4.0

22.7

11.9

22.7

28.6

31.4

35.8

64.8

28.0

18.5

20.9

25.2

16.3

8.7

19.0

22.9

28.2

25.7

58.2

20.7

15.2

17.8

17.9

6.4

3.2

3.7

5.8

3.2

10.1

6.6

7.4

3.4

3.2

7.3

30.8

15.6

29.4

37.7

41.4

50.7

95.7

39.3

24.2

27.3

34.6

19.7

9.7

23.0

27.5

35.6

32.9

84.4

26.2

18.1

21.5

20.5

11.1

5.9

6.4

10.2

5.8

17.7

11.3

13.1

6.1

5.8

14.1

2.1

1.7

3.3

2.7

4.2

2.0

4.1

3.4

2.3

2.2

1.7

Burma . . . . . . . . . . .

Cambodia ........

Thailand. . . . . . . . . .

0.3

1.8

0.7

0.5

2.0

0.8

0.2

0.2

0.1

57.7

60.6

73.0

61.6

64.9

74.7

3.9

4.2

1.6

12.0

8.5

7.3

9.8

6.7

6.5

2.2

1.8

0.7

59.7

61.9

17.4

56.4

58.5

17.2

3.3

3.4

0.2

82.5

80.2

22.5

76.6

74.3

21.7

5.8

6.0

0.8

1.9

3.1

1.8

Note: Growth rate, life expectancy at birth (e0), crude death rate, infant mortality, and under-5 mortality (5q0) are for both sexes combined.

Source: U.S. Census Bureau, International Data Base and unpublished tables.

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 17
AIDS Mortality Is Likely to

Produce Population Pyramids

That Have Never Been 

Seen Before

In countries with projected negative population growth—Botswana,

Lesotho, Mozambique, South

Africa, and Swaziland—population

pyramids will have a new shape,

“the population chimney.” The

implications of this new population

structure are not clear. By 2020,

men between the ages of 15 and

44 are likely to out number women

in each of the 5-year age cohorts.

This may influence men to seek

sexual relationships with younger

and younger women. This factor

in turn may increase HIV infection

rates among younger women.

Current evidence (Glynn et al.,

2001) indicates that, indeed, older

men are infecting younger women.

As these women marry, their partners are then at increased risk of

HIV infection. This vicious cycle

could result in even higher HIV

infection levels (Figure 8).

18 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

12 10 8 6 4 2 0 2 4 6 8 10 12

 0- 4

 5- 9

 10-14

 15-19

 20-24

 25-29

 30-34

 35-39

 40-44

 45-49

 50-54

 55-59

 60-64

 65-69

 70-74

 75-79

 80+

12 10 8 6 4 2 0 2 4 6 8 10 12

 0- 4

 5- 9

 10-14

 15-19

 20-24

 25-29

 30-34

 35-39

 40-44

 45-49

 50-54

 55-59

 60-64

 65-69

 70-74

 75-79

 80+

12 10 8 6 4 2 0 2 4 6 8 10 12

 0- 4

 5- 9

 10-14

 15-19

 20-24

 25-29

 30-34

 35-39

 40-44

 45-49

 50-54

 55-59

 60-64

 65-69

 70-74

 75-79

 80+

Male 2002 Female Age

2010

2020

Percent

Figure 8.

Population by Age and Sex With and Without AIDS for 

South Africa: 2002, 2010, and 2020

Population structures of badly affected 

countries are likely to be radically 

altered by HIV.

Source: U.S. Census Bureau, International Programs Center, International Data 

Base and unpublished tables. 

With AIDS 

Without AIDS
In Countries With Moderate

Epidemics, AIDS Mortality Is

Likely to Have Less Effect on

the Population Structure

For example, in Uganda, the greatest relative differences in future

population size by cohort are evident in the youngest age groups

and among people 30-50 years of

age in 2002 and 2010. However,

the population pyramid maintains

its traditional shape in 2020

(Figure 9).

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 19

12 10 8 6 4 2 0

 0- 4

 5- 9

 10-14

 15-19

 20-24

 25-29

 30-34

 35-39

 40-44

 45-49

 50-54

 55-59

 60-64

 65-69

 70-74

 75-79

 80+

0 2 4 6 8 10 12

Male 2002 Female Age

12 10 8 6 4 2 0

 0- 4

 5- 9

 10-14

 15-19

 20-24

 25-29

 30-34

 35-39

 40-44

 45-49

 50-54

 55-59

 60-64

 65-69

 70-74

 75-79

 80+

0 2 4 6 8 10 12

2010

12 10 8 6 4 2 0

 0- 4

 5- 9

 10-14

 15-19

 20-24

 25-29

 30-34

 35-39

 40-44

 45-49

 50-54

 55-59

 60-64

 65-69

 70-74

 75-79

 80+

0 2 4 6 8 10 12

2020

Percent

Figure 9.

Population by Age and Sex With and Without AIDS 

for Uganda: 2002, 2010, and 2020 

The population structure of Uganda is probably 

only slightly altered by HIV. 

Source: U.S. Census Bureau, International Programs Center, International Data 

Base and unpublished tables. 

With AIDS 

Without AIDS
AIDS Mortality Is Causing

Falling Life Expectancies 

at Birth

Already, life expectancies in SubSaharan Africa have fallen dramatically from levels they likely would

have reached without AIDS. In

Botswana, life expectancy is now 

34 years instead of 72. In

Zimbabwe, life expectancy is 40

years instead of 69. In fact, seven

countries in Sub-Saharan Africa

(Angola, Botswana, Lesotho,

Malawi, Mozambique, Rwanda, and

Zambia) have life expectancies

below 40 years. Each of the countries, except for Angola and

Mozambique, would have had an

estimated life expectancy of 50

years or more without AIDS 

(Figure 10).

In Latin America and the

Caribbean, the impact on life

expectancy is not as great as in

Sub-Saharan Africa because of

lower HIV prevalence levels.

However, life expectancy is still

lower than it would have been

without AIDS. In the Bahamas, life

expectancy is now 66 years

instead of 74; in Haiti, it is 51

instead of 59.

Thailand, Cambodia, and Burma

have lost 2 to 5 years of life

expectancy.

20 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

Figure 10.

Life Expectancy at Birth With and Without AIDS for 

Selected Countries: 2002

In Botswana, nearly 40 years of life expectancy 

have been lost due to AIDS.

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables.

Life expectancy at birth (years)

With AIDS 

Without AIDS

0 10 20 30 40 50 60 70 80 90

Burma

Cambodia

Thailand

Haiti

Guatemala

Guyana

Belize

Honduras

Trinidad & Tobago

Suriname

Bahamas, The

Dominican Republic

Barbados

Panama

Angola

Mozambique

Niger

Sierra Leone

Mali

Guinea-Bissau

Chad

Guinea

Rwanda

Djibouti

Liberia

Burkina Faso

Ethiopia

Benin

Tanzania

Congo (Kinshasa)

Zambia

Côte d'Ivoire

Uganda

Malawi

Eritrea

Central African Rep.

Burundi

Nigeria

Congo (Brazzaville)

Cameroon

Senegal

Ghana

Togo

Gabon

Lesotho

Kenya

Namibia

South Africa

Zimbabwe

Botswana

Swaziland

Sub-Saharan Africa

Asia

Latin America and the Caribbean
In Less Than 10 Years, Some

Countries Are Projected to See

Life Expectancies Fall to Near

30 Years of Age, Levels Not

Seen Since the Beginning of

the 20th Century

Among countries in Southern

Africa that would have approached

or exceeded life expectancies of

70 years of age by 2010 in the

absence of AIDS, several are likely

to see life expectancies fall to

around 30:

• Botswana–27 years

• Namibia–34 years

• Swaziland–33 years

Other countries are likely to see

life expectancies fall to 30-40

years instead of 50-60 years

(Figure 11).

By 2010, AIDS mortality is projected to continue to result in lower

life expectancies in Latin America,

the Caribbean, and Asia. Life

expectancies are projected to be

10-14 years lower in Honduras, the

Bahamas, and Guyana than they

would have been without AIDS.

They are likely to be 2 years lower

in Thailand and 4 years lower in

Cambodia and Burma.

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 21

Figure 11.

Life Expectancy at Birth With and Without AIDS for 

Selected Countries: 2010 

By 2010, Botswana and Swaziland are likely to see 

life expectancy reduced by over 40 years. 

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables. 

Life expectancy at birth (years)

With AIDS 

Without AIDS

0 10 20 30 40 50 60 70 80 90

Burma

Cambodia

Thailand

Haiti

Guatemala

Guyana

Belize

Honduras

Trinidad & Tobago

Suriname

Bahamas, The

Dominican Republic

Barbados

Panama

Angola

Mozambique

Niger

Sierra Leone

Mali

Guinea-Bissau

Chad

Guinea

Rwanda

Djibouti

Liberia

Burkina Faso

Ethiopia

Benin

Tanzania

Congo (Kinshasa)

Zambia

Côte d'Ivoire

Uganda

Malawi

Eritrea

Central African Rep.

Burundi

Nigeria

Congo (Brazzaville)

Cameroon

Senegal

Ghana

Togo

Gabon

Lesotho

Kenya

South Africa

Namibia

Zimbabwe

Botswana

Swaziland

Sub-Saharan Africa

Asia

Latin America and the Caribbean
The Most Direct Impact 

of AIDS Is the Increase in 

the Number of Deaths in

Affected Populations 

Crude death rates, the number of

people dying per 1,000 population,

have already been affected by AIDS.

In Africa, HIV epidemics have had

their greatest impact in the eastern

and the southern regions. Adult

HIV prevalence is 20 percent or

higher in seven countries and 

10 percent to 20 percent in an

additional five countries. In many

of these countries, reports indicate

the presence of the HIV virus since

the early 1980s.

As a result of these high levels of

HIV infection over several years,

estimated crude death rates including AIDS mortality are greater by

50 percent to 500 percent in eastern and southern Africa over what

they would have been without

AIDS. For example, in Kenya, with

an adult HIV prevalence of 15 percent at the end of 2001, the crude

death rate in 2002 was estimated

to be more than two and a half

times as high (16 deaths per 1,000

population) as it would have been

without AIDS (6 deaths per 1,000

population). In South Africa, with

an estimated 20 percent adult HIV

prevalence at the end of 2001, the

crude death rate in 2002 was also

over twice as high as it would have

been without AIDS (17 deaths per

1,000 population compared with 7,

as shown in Figure 12).

In Asia and Latin America, estimated crude death rates in 2002 were

also higher than they would have

been without AIDS, especially in

Haiti and the Bahamas.

22 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

Figure 12.

Crude Death Rates With and Without AIDS for 

Selected Countries: 2002 

Crude death rates are four times as high in 

Zimbabwe as they would have been without AIDS. 

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables. 

Deaths per 1,000 population

Without AIDS

With AIDS 

0 5 10 15 20 25 30 35 40 45

Burma

Cambodia

Thailand

Haiti

Barbados

Trinidad & Tobago

Guyana

Guatemala

Suriname

Bahamas, The

Panama

Honduras

Belize

Dominican Republic

Angola

Mozambique

Niger

Sierra Leone

Mali

Liberia

Guinea-Bissau

Chad

Guinea

Burkina Faso

Djibouti

Rwanda

Ethiopia

Benin

Tanzania

Malawi

Congo (Kinshasa)

Eritrea

Uganda

Zambia

Côte d'Ivoire

Central African Rep.

Nigeria

Burundi

Congo (Brazzaville)

Cameroon

Senegal

Gabon

Lesotho

Ghana

Togo

South Africa

Namibia

Kenya

Zimbabwe

Botswana

Swaziland

Sub-Saharan Africa

Asia

Latin America and the Caribbean
In Many Sub-Saharan African

Countries, Crude Death Rates

Are Projected To Be Higher 

in 2010 Than in 2002, 

Even Though Mortality Due 

to Non-AIDS Causes Is Likely

to Decline

In Botswana, the crude death rate

is likely to increase from 29 deaths

per 1,000 population in 2002 to

43 in 2010 (Tables 1 and 2). In

South Africa, the crude death rate

is likely to increase from 17 deaths

per 1,000 population to 30; in

Zimbabwe, from just under 21 to

over 27. In the absence of the

AIDS pandemic, crude death rates

in 2010 for these three countries

that are now projected to range

from 27 deaths per 1,000 population to 43 would have ranged,

instead, from 4 to 7 (Figure 13).

In Latin America and the

Caribbean, Honduras and Guyana

are likely to see crude death rates

in 2010 twice as high as they

would have been without AIDS.

In Asia, crude death rates in 2010

are projected to be somewhat

higher with AIDS than they would

have been without AIDS. In

Thailand, the crude death rate with

AIDS is likely to be just over 7

deaths per 1,000 population, or

about 12 percent higher than the

level without AIDS. In Cambodia,

the crude death rate is expected to

be between 8 and 9 deaths per

1,000 population, a level 26 percent higher than the projected

level without AIDS.

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 23

Figure 13.

Crude Death Rates With and Without AIDS for 

Selected Countries: 2010 

By 2010, crude death rates are projected to be ten times 

as high in Botswana and seven times as high in Swaziland 

as they would have been without AIDS.

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables. 

Deaths per 1,000 population

Without AIDS

With AIDS 

0 5 10 15 20 25 30 35 40 45

Burma

Cambodia

Thailand

Haiti

Trinidad & Tobago

Barbados

Guyana

Suriname

Bahamas, The

Guatemala

Panama

Dominican Republic

Honduras

Belize

Angola

Mozambique

Niger

Sierra Leone

Mali

Liberia

Guinea-Bissau

Guinea

Djibouti

Chad

Burkina Faso

Rwanda

Ethiopia

Benin

Tanzania

Malawi

Congo (Kinshasa)

Eritrea

Uganda

Zambia

Côte d'Ivoire

Central African Rep.

Nigeria

Burundi

Congo (Brazzaville)

Cameroon

Senegal

South Africa

Lesotho

Gabon

Ghana

Togo

Namibia

Kenya

Zimbabwe

Botswana

Swaziland

Sub-Saharan Africa

Asia

Latin America and the Caribbean
In Some Sub-Saharan African

Countries, Infant Mortality

Rates Are Now Higher Than

They Were in 19903

AIDS mortality has reversed the

declines in infant mortality rates

that occurred during the 1980s

and early 1990s. Over 30 percent

of all children born to HIV-infected

mothers in Sub-Saharan Africa are

likely to be HIV positive, either

through the birth process or due to

breastfeeding. The relative impact

of AIDS on infant mortality is likely

to depend on both the levels of

HIV prevalence in the population

and the infant mortality rate from

other causes. In 1990,4 the infant

mortality rate in Zimbabwe was 52

infant deaths per 1,000 live births;

in 2002 it is 66. In South Africa,

the infant mortality rate in 1990

was 51 infant deaths per 1,000; in

2002 it is 60. Without AIDS, infant

mortality in Zimbabwe and South

Africa would likely have been 35

infant deaths per 1,000 and 39,

respectively (Figure 14).

In western and central Africa,

where epidemics are generally less

severe, infant mortality rates are

still higher than they would have

been without AIDS. The increase

ranges from less than 1 percent in

Mali to about 13 percent in Côte

d’Ivoire and Rwanda.

In countries most affected by AIDS

in Latin America, the Caribbean,

and Asia, infant mortality rates are

also higher than they would have

been without AIDS. In Latin

America and the Caribbean, infant

mortality rates are 2 percent to 

6 percent higher. In Asia, infant

mortality is less than 1 percent

higher in Thailand and 4 percent

higher in Cambodia.

24 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

3 U.S. Census Bureau, International

Programs Center, International Data Base and

unpublished tables. 4 Figures for 1990 also include AIDS mortality.

Figure 14.

Infant Mortality With and Without AIDS for 

Selected Countries: 2002 

AIDS doubles infant mortality in some 

Sub-Saharan African Countries. 

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables. 

Infant deaths per 1,000 live births

Without AIDS

With AIDS 

0 50 100 150 200 250

Cambodia

Burma

Thailand

Haiti

Guatemala

Guyana

Dominican Republic

Honduras

Belize

Trinidad & Tobago

Suriname

Bahamas, The

Panama

Barbados

Angola

Mozambique

Sierra Leone

Liberia

Niger

Mali

Guinea-Bissau

Djibouti

Tanzania

Chad

Burkina Faso

Guinea

Congo (Kinshasa)

Ethiopia

Rwanda

Congo (Brazzaville)

Malawi

Côte d'Ivoire

Benin

Uganda

Central African Rep.

Zambia

Eritrea

Nigeria

Togo

Cameroon

Burundi

Lesotho

Senegal

Gabon

Ghana

Kenya

Namibia

South Africa

Zimbabwe

Swaziland

Botswana

Sub-Saharan Africa

Asia

Latin America and the Caribbean
In Five Countries of 

Sub-Saharan Africa, More

Infants Are Likely to Die From

AIDS in 2010 Than From 

All Other Causes

In Botswana, Swaziland, and

Zimbabwe, twice as many infants

are likely to die from AIDS in 2010

as from all other causes; in South

Africa and Namibia, more infants

are likely to die from AIDS than

from all other causes. In 46 of the

51 countries examined, overall

infant mortality rates are projected

to decline between 2002 and

2010. However, in 43 of these 46

countries, infant mortality due to

AIDS is projected to increase over

the same period, offsetting the

greater drop that would otherwise

have been achieved. Moreover, in

the five countries with projected

overall increases, the entire change

can be attributed to increases in

AIDS mortality among infants.

Without the effect of AIDS, infant

mortality would have been projected to decline in these countries

(Figures 14 and 15).

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 25

Figure 15.

Infant Mortality With and Without AIDS for 

Selected Countries: 2010

By 2010, nearly 60 infants out of every 1,000 live 

births are expected to die in Botswana from AIDS.

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables. 

Infant deaths per 1,000 live births

Without AIDS

With AIDS 

0 50 100 150 200 250

Cambodia

Burma

Thailand

Haiti

Guatemala

Guyana

Dominican Republic

Honduras

Belize

Trinidad & Tobago

Suriname

Bahamas, The

Panama

Barbados

Angola

Mozambique

Sierra Leone

Liberia

Niger

Mali

Guinea-Bissau

Djibouti

Chad

Burkina Faso

Guinea

Tanzania

Rwanda

Ethiopia

Congo (Kinshasa)

Malawi

Congo (Brazzaville)

Côte d'Ivoire

Benin

Uganda

Central African Rep.

Zambia

Eritrea

Nigeria

Cameroon

Togo

Burundi

Senegal

Lesotho

Ghana

Gabon

Kenya

Namibia

South Africa

Zimbabwe

Swaziland

Botswana

Sub-Saharan Africa

Asia

Latin America and the Caribbean
In 37 Sub-Saharan African

Countries, Under-5 Mortality

Rates in 2002 Were Higher

Than They Would Have Been

Without AIDS

The impact of HIV/AIDS on under-5

mortality is highest among countries that had substantially reduced

under-5 mortality due to other

causes and where HIV prevalence

is high. Many HIV-infected children

survive their first birthdays, only

to die before the age of 5. In

Botswana, more than 70 percent of

under-5 mortality is due to AIDS. In

Zimbabwe and Swaziland, over half

of all deaths among children under

5 are due to AIDS (Table 1 and

Figure 16). 

The impact of HIV/AIDS in Latin

America and the Caribbean has

been generally less severe than in

Sub-Saharan Africa. For the 11

seriously-affected countries in this

region (shown in Table 1), AIDS

contributed between 3 child deaths

per 1,000 births and 11 per 1,000

in 2002. AIDS accounted for 

7 percent to 27 percent of under 

5-deaths occurring in these

countries.

For Burma, Cambodia, and

Thailand, AIDS accounted for 

4 percent to 7 percent of under-5

deaths in 2002. 

26 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

Figure 16.

The Under-5 Mortality Rate With and Without AIDS 

for Selected Countries: 2002 

AIDS deaths among children under 5 years of age 

are resulting in higher mortality rates.

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables. 

Deaths under age 5 per 1,000 live births

Without AIDS

With AIDS 

0 50 100 150 200 250 300 350

Burma

Cambodia

Thailand

Haiti

Guatemala

Guyana

Dominican Republic

Honduras

Belize

Suriname

Trinidad & Tobago

Bahamas, The

Panama

Barbados

Angola

Mozambique

Niger

Sierra Leone

Mali

Liberia

Burkina Faso

Guinea-Bissau

Chad

Guinea

Rwanda

Djibouti

Malawi

Ethiopia

Benin

Tanzania

Congo (Brazzaville)

Zambia

Congo (Kinshasa)

Côte d'Ivoire

Uganda

Nigeria

Eritrea

Central African Rep.

Burundi

Cameroon

Togo

Senegal

Ghana

Lesotho

Gabon

Kenya

South Africa

Namibia

Swaziland

Zimbabwe

Botswana

Sub-Saharan Africa

Asia

Latin America and the Caribbean
In the Absence of 

Prevention of Mother-to-Child

Transmission, Under-5

Mortality Rates in 2010 Are

Projected to Be Much Higher

With AIDS Than They Would

Have Been Without AIDS

In Botswana, where under-5 mortality rates in 2010 may have been

below 30 deaths per 1,000 live

births without AIDS, over 120 children per 1,000 live births born are

likely to die before their fifth birthday in 2010. Of that total, over 80

percent are likely to be due to

AIDS. In many of the countries in

southern Africa, over 50 percent of

under-5 deaths are likely to be due

to AIDS. In Malawi and Zambia,

where under-5 mortality rates due

to other causes are already high,

AIDS mortality is likely to increase

those rates by 30 percent or more

(Figure 17).

In Trinidad and Tobago, 40 percent

of under-5 deaths are likely to be

due to AIDS. In a number of other

countries in Latin America and the

Caribbean, one-third of under-5

deaths are likely to be due to AIDS.

In Burma, Cambodia, and Thailand,

under-5 mortality rates are likely to

be 1 percent to 6 percent higher

with AIDS mortality than they

would have been without AIDS.

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 27

Figure 17.

The Under-5 Mortality Rate With and Without AIDS 

for Selected Countries: 2010 

Over 80 percent of deaths among children 

under 5 years of age in Botswana in 2010 

are likely to be due to AIDS. 

Source: U.S. Census Bureau, International Programs Center, International Data Base and 

unpublished tables. 

Deaths under age 5 per 1,000 live births

Without AIDS

With AIDS 

0 50 100 150 200 250 300

Burma

Cambodia

Thailand

Haiti

Guatemala

Guyana

Dominican Republic

Honduras

Belize

Suriname

Trinidad & Tobago

Bahamas, The

Panama

Barbados

Angola

Mozambique

Niger

Sierra Leone

Mali

Liberia

Burkina Faso

Guinea-Bissau

Chad

Guinea

Rwanda

Djibouti

Malawi

Ethiopia

Benin

Tanzania

Zambia

Congo (Kinshasa)

Côte d'Ivoire

Congo (Brazzaville)

Uganda

Eritrea

Nigeria

Central African Rep.

Burundi

Cameroon

Senegal

Togo

Ghana

Lesotho

Gabon

Kenya

South Africa

Namibia

Zimbabwe

Swaziland

Botswana

Sub-Saharan Africa

Asia

Latin America and the Caribbean
28 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

Populations in Most 

Sub-Saharan African Countries

Are Projected to Increase, 

in Spite of the High Levels 

of Mortality. The Exceptions

Are Botswana, Lesotho,

Mozambique, South Africa,

and Swaziland

Although AIDS mortality has resulted in lower growth rates, fertility i

still high and population growth is

still positive in most countries

affected by AIDS. Such is the case

for Uganda. However, the population in the most severely affected

countries, such as Botswana and

South Africa, is projected to

decline over time, in that the popu

lation, by 2050, is likely to be

lower than it was in 1990, even if

current AIDS control programs

result in lowering future HIV incidence and prevalence (Figure 18).

s

-

Figure 18.

Change in Population of Selected African Countries 

With and Without AIDS: 1990-2050 

The populations of Botswana and South Africa are 

projected to decline over the next few decades 

whereas in Uganda, population growth 

is projected to continue.

Source: U.S. Census Bureau, International Programs Center, International Data Base 

and unpublished tables.

Botswana

Percent change relative to 1990 population

-100

0

100

200

300

400

500

600

1990 2000 2010 2020 2030 2040 2050

South Africa

Percent change relative to 1990 population

-100

0

100

200

300

400

500

600

1990 2000 2010 2020 2030 2040 2050

Uganda

Percent change relative to 1990 population

-100

0

100

200

300

400

500

600

1990 2000 2010 2020 2030 2040 2050

With AIDS 

Without AIDS
U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 29

At the Beginning of the 21st

Century, AIDS Is the Number

One Cause of Death in Africa

and Is Number Four Globally5

Just 20 years ago when AIDS first

appeared, few would have predicted the current state of the pandemic, particularly in Sub-Saharan

Africa. That over 30 percent of

adults would be living with

HIV/AIDS in any country was

unthinkable. Yet, this is the current situation in four countries. In

seven Sub-Saharan African countries, at least one out of five adults

is living with HIV/AIDS and in an

5 See WHO (1999).

additional five Sub-Saharan African impact of these high levels of HIV

countries, one out of ten adults is prevalence.

HIV positive (UNAIDS/WHO, 2002). Thailand, Senegal, and Uganda are

Many individuals and governments notable success stories. In

have difficulty grasping the reality Thailand and Uganda, concerted

of these high prevalence levels, efforts at all levels of civil society

and the resulting AIDS mortality is have turned around increasing HIV

difficult to comprehend. The mag- prevalence rates. In Senegal, pronitude of the current epidemic in grams put into place early in the

HIV infection and the low likeli- epidemic have kept HIV prevalence

hood of an effective vaccine or rates low. These successes can be

even widespread availability of repeated but doing so would take

therapeutic medication strongly time. Hence, the current burden of

suggest that many more millions disease, death, and orphanhood is

of individuals are likely to die of likely to be a problem in many

AIDS over the next decade than countries of Sub-Saharan Africa for

have over the past two decades. the foreseeable future.

Many of the southern African countries are only beginning to see the
APPENDIX A.

POPULATION PROJECTIONS 

INCORPORATING AIDS
Background

Although it has been clear for a

number of years that mortality estimates and projections for many

countries would have to be revised

due to AIDS mortality, the lack of

accurate empirical data on AIDS

deaths, the paucity of data on HIV

infection among the general population, and the absence of tools to

project the impact of AIDS epidemics

into the future have all hampered

these efforts. Currently, although

the accuracy of data on AIDS deaths

has not substantially improved,

knowledge of HIV infection has

expanded and modeling tools have

become available to project current

epidemics into the future. 

The methodology used to project

AIDS mortality into the future for this

report follows generally the method

adopted for World Population

Profile: 1994, World Population

Profile: 1996, and World

Population Profile: 1998 with continuing modifications. The method

consists of the following steps:

1. Establishing criteria for selecting

countries for which AIDS mortality will be incorporated into the

projections.

2. For each selected country, determining the empirical epidemic

trend and a point estimate of

national HIV prevalence.

3. Modeling the spread of HIV

infection and the development

of AIDS in the population, generating alternative scenarios

ranging from super high to low

AIDS epidemics, and producing

the seroprevalence rates and

AIDS-related, age-specific mortality rates which correspond to

each epidemic.

4. Using the empirical levels and

trends (from step 2) to establish a

factor representing each country's

position on a continuum between

super high and low epidemics

(from step 3), and the derived factor to generate a unique interpolated epidemic curve.

5. Using weighted country total

adult seroprevalence to determine the appropriate location on

the interpolated total country epidemic curve from step 4. This

curve establishes the likely beginning date of the epidemic in the

country in question, the progression of the epidemic up to the

date of the last empirical data

point, and the projection of HIV

seroprevalence into the future.

6. Interpolate AIDS-related mortality

rates, by age and sex, associated

with the estimated speed and

level of HIV from epidemic results

for the period 1990 to 2010.

In the sections that follow, each of

these steps is described, and the

method is illustrated.

Country Selection Criteria

The International Programs Center,

U.S. Census Bureau, maintains the

HIV/AIDS Surveillance Data Base.

This data base is a compilation of

aggregate data from HIV seroprevalence and incidence studies

in developing countries. Currently,

it contains over 72,000 data items

drawn from over 6,500 publications and presentations. As a part

of the biannual updating of the

data base, new data are reviewed

for inclusion into a summary table

which, for each country, lists the

most recent and best study of

seroprevalence levels for high- and

low-risk populations in urban and

6 rural areas.

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 A-3

6 High risk includes samples of prostitutes and their clients, sexually-transmitted

disease patients, or other persons with

known risk factors. Low risk includes

samples of pregnant women, volunteer

blood donors, or others with no known risk

factors. For a more complete description of

the selection criteria, see U.S. Census Bureau

(2002).

A review of the data in the summary table suggested that a reasonable cut-off point for selection

would be countries which had

reached 5 percent HIV prevalence

among their low-risk urban populations, or, based on recent trends,

appeared to be likely to reach this

level in the near future. In addition, countries were selected that

had national HIV prevalence above

1 percent, as estimated by UNAIDS

for year-end 1999.

A total of 51 countries met these

criteria for the incorporation of

AIDS mortality in the projections.

Thirty-seven of these countries

were in Africa. The African countries are as follows (newly added

countries in italics):

Angola

Benin

Botswana

Burkina Faso

Burundi

Cameroon

Chad

Central African Republic

Côte d’Ivoire

Congo (Brazzaville)

Congo (Kinshasa)

Djibouti

Eritrea

Ethiopia

Gabon

Ghana

Guinea

Guinea-Bissau

Kenya

Lesotho

Liberia

Malawi

Mali

Mozambique

Namibia

Niger

Nigeria

Rwanda

Senegal

Sierra Leone

South Africa

Swaziland
A-4 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

Tanzania corresponding to low, medium,

Togo high, higher, and super high AIDS

Uganda epidemics. The highest scenarios

Zambia were added this round to incorpoZimbabwe rate the very explosive HIV epidemics in southern Africa, and Outside of Africa, the following those epidemics where there is little countries met the criteria: difference between the urban and

The Bahamas rural HIV prevalence levels. These

Barbados scenarios were developed using

Belize iwgAIDS, which is a complex deterBurma ministic model of the spread of HIV

Cambodia infection and the development of

Dominican Republic AIDS in a population. This model

Guatemala was developed under the sponsorGuyana ship of the Interagency Working

Haiti Group (iwg) on AIDS Models and

Honduras Methods of the U.S. Department of

Panama State (Stanley et al., 1991). 

Trinidad and Tobago All five of these epidemic scenarios Suriname incorporate increasing levels of Thailand behavior change in the form of

increased condom use. This Empirical Epidemic Trends assumption corresponds to actual

For 50 of the countries meeting changes in behavior that are now

the selection criteria, staff mem- beginning to occur in some counbers reviewed the HIV seropreva- tries. In addition, all five epilence information available in the demics exhibit plateauing and subHIV/AIDS Surveillance Data Base to sequent declines in prevalence in

establish urban seroprevalence the later stages of the epidemic,

trends over time (Table A-1, col. particularly in urban areas. 

1-4) and to establish the estimated

prevalence for the whole country Interpolation of a 

Unique Epidemic (Table A-1, col. 5). The two data

points judged to be most represen- The empirical urban trend from

tative for the urban low-risk popu- each country was used to interpolation were identified and used to late among the five epidemic scecalculate the annual change narios to derive an epidemic trend

between the dates of the two stud- line matching the observed HIV

ies. National prevalence figures seroprevalence increase between

were based on year-end 1999 esti- the two points. Thus, both the

mates prepared by the World level and the rate of increase of

Health Organization and the United the urban epidemic were matched

Nations Joint Programme on through this procedure and resultHIV/AIDS. Table A-1, column 6 ed in an interpolation factor used

contains the corresponding esti- in subsequent steps (Figure A-1). 

mate for year-end 2001.

Projected Total

Alternative Scenarios Seroprevalence

To project the impact in the select- At this point in the estimation proed countries, five alternative epi- cedure, no direct linkage has been

made to the total country preva- demic scenarios were developed,

lence or to a particular calendar

year in this country’s epidemic.

The next step accomplishes these

tasks. The total-country adult

prevalence estimate (Table A-1, 

col. 5) was matched with the one

implied using the interpolation

factor. From this comparison, an

“offset” figure was calculated, corresponding to the number of years

of difference between the start of

the epidemics in the five scenarios,

and the empirical epidemic at the

reference date (Figure A-2). The

resulting projected epidemics for

the 1990 to 2010 period for selected countries in Africa are shown in

Figure A-3. 

AIDS-Related Mortality Rates

Based on the “interpolation factor”

and the “offset” described above,

AIDS-related age-sex-specific mortality rates ( m values) at 5-year n x

intervals from 1990 to 2010 were

interpolated and added to nonAIDS m values for the same n x

period.7 Population projections

were prepared with the combined

m values as input, using the n x

Rural-Urban Projection (RUP) program of the U.S. Census Bureau.

The future course of the AIDS pandemic is uncertain, but the projections require that some assumptions be made. It was assumed

that the epidemics would peak in

2010, with no further growth in

HIV infection after that year. AIDS

mortality was assumed to decline

from the level reached in 2010 to

nil by 2070, thus implying a return

to “normal” mortality levels in the

latter year. To implement the projection process, life tables for

2070, assuming no AIDS mortality,

were used. 

7 Non-AIDS m values were derived by n x making standard assumptions concerning

the improvement in mortality conditions.
Table A-1.

Empirical Seroprevalence Data for Urban and Rural Areas for Selected Countries

Country

Urban trend, pregnant women Estimated percent

seropositive, total country

Date4

Percent

seropositive Date

Percent

seropositive

December December

31, 1999 31, 2001

Angola .......................

Benin ........................

Botswana ....................

Burkina Faso .................

Burundi ......................

Cameroon ....................

Chad ........................

Central African Republic........

Congo (Brazzaville)............

Congo (Kinshasa) .............

Côte d’Ivoire . . . . . . . . . . . . . . . . .

Djibouti3......................

Eritrea .......................

Ethiopia . . . . . . . . . . . . . . . . . . . . . .

Gabon .......................

Ghana .......................

Guinea. . . . . . . . . . . . . . . . . . . . . . .

Guinea-Bissau ................

Kenya........................

Lesotho ......................

Liberia . . . . . . . . . . . . . . . . . . . . . . .

Malawi . . . . . . . . . . . . . . . . . . . . . . .

Mali. . . . . . . . . . . . . . . . . . . . . . . . . .

Mozambique..................

Namibia ......................

Niger . . . . . . . . . . . . . . . . . . . . . . . .

Nigeria . . . . . . . . . . . . . . . . . . . . . . .

Rwanda ......................

Senegal ......................

Sierra Leone..................

South Africa ..................

Swaziland ....................

Tanzania . . . . . . . . . . . . . . . . . . . . .

Togo . . . . . . . . . . . . . . . . . . . . . . . . .

Uganda—High1 ...............

Uganda—Low Stable1 .........

Zambia.......................

Zimbabwe ....................

1995.00

1994.50

1994.50

1991.00

1986.00

1992.60

1995.00

1986.50

1987.50

1985.50

1989.50

1993.00

(NA)

1991.00

1998.50

1992.50

1990.00

1990.00

1992.50

1991.50

1992.00

1991.50

1988.00

1994.90

1991.50

1988.00

1992.00

1989.00

(NA)

1990.00

1994.90

1993.50

1986.50

1995.50

1987.50

1996.50

1990.00

1990.00

1.2

1.1

27.8

7.8

14.7

4.0

2.4

4.7

3.1

6.9

6.0

4.0

(NA)

10.7

0.5

1.2

1.1

0.9

14.4

5.5

3.7

22.0

1.3

10.7

4.2

0.5

2.9

26.8

(NA)

0.8

6.4

21.9

3.7

6.0

24.0

15.3

24.5

23.8

1999.00

1998.50

1997.30

1996.75

1998.90

1994.60

1999.00

1996.50

1993.50

1991.50

1997.00

1995.50

1994.00

1996.50

1994.50

1996.50

1996.00

1997.00

1995.50

1996.50

1993.00

1995.50

1994.00

1998.90

1996.60

1993.00

1994.00

1992.00

1991.00

1992.00

1997.90

1998.50

1996.50

1997.50

1992.00

1997.50

1994.75

1995.00

3.4

2.5

34.0

10.0

19.1

5.7

6.2

11.7

7.2

9.2

15.9

6.1

3.0

17.9

1.7

2.2

2.1

2.5

18.5

20.6

4.0

27.6

4.4

17.0

16.0

1.3

5.4

28.9

0.3

2.0

16.1

31.6

13.7

6.8

29.5

14.7

27.5

30.0

2.8

2.5

35.8

6.4

11.3

7.7

2.7

13.8

6.4

5.1

10.8

6.1

2.9

10.6

4.2

3.6

1.5

2.5

14.0

23.6

2.8

16.0

2.0

13.2

19.5

1.4

5.1

11.2

1.8

3.0

19.9

25.5

8.1

6.0

12.0

8.3

20.0

25.1

5.5

3.6

38.8

6.5

8.3

11.8

3.6

12.9

7.2

4.9

9.7

(NA)

2.8

6.4

(NA)

3.0

(NA)

2.8

15.0

31.0

(NA)

15.0

1.7

13.0

22.5

(NA)

5.8

8.9

0.5

7.0

20.1

33.4

7.8

6.0

(NA)

5.0

21.5

33.7

Bahamas, The ................

Barbados.....................

Belize. . . . . . . . . . . . . . . . . . . . . . . .

Dominican Republic ...........

Guatemala ...................

Guyana ......................

Haiti . . . . . . . . . . . . . . . . . . . . . . . . .

Honduras.....................

Panama......................

Suriname.....................

1990.50

1991.00

1993.50

1995.50

1991.50

1990.50

1989.80

1992.35

1993.50

1991.50

3.0

1.3

0.2

1.2

0.0

1.5

7.1

2.0

0.8

0.8

1993.50

1996.00

1995.50

1999.50

1998.50

1991.50

1993.50

1995.50

1995.50

(NA)

3.6

1.1

2.3

1.7

0.9

1.9

8.4

4.1

0.9

(NA)

4.1

1.2

2.0

2.8

1.4

3.0

5.2

1.9

1.5

1.3

3.5

1.2

2.0

2.5

1.0

2.7

6.1

1.6

1.5

1.2

Trinidad and Tobago ...........

Burma . . . . . . . . . . . . . . . . . . . . . . .

Cambodia ....................

Thailand1.....................

1991.50

1992.50

1995.75

(NA)

0.2

0.5

3.0

(NA)

1999.50

1997.50

1998.75

(NA)

3.4

2

1.4

4.9

(NA)

1.1

2.0

4.0

2.2

2.5

(NA)

2.7

1.8

NA Data not available.

1

Country-specific ‘‘modeling’’ was undertaken for Thailand and Uganda.

2

Burma military recruit data.

3

Estimated percentage shown in column 5 for Djibouti is for 1995.

4

The decimal part of dates shown refers to the timing of seroprevalence estimates within calendar years. For example, 1995.00 is January 1, 1995; 1994.50 is

June 30, 1994 (midyear 1994).

Source: Urban and rural data are from U.S. Census Bureau, International Programs Center, HIV/AIDS Surveillance Data Base, January 2000. Estimated seropositive

percentages at the national level are from UNAIDS (2000), UNAIDS/WHO (2000), and Burton and Mertens (1998).

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 A-5
A-6 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

The Special Case of Uganda by linear interpolation between the grow, future behavior changes and

two epidemics. interventions being implemented Prevalence levels for pregnant in countries around the world may women in major urban areas in The Special Case of Thailand alter that course. Uganda appear to have peaked in

the early 1990s, with rather dra- Modeling activities have also been What if AIDS epidemics do not

matic declines subsequently. undertaken for Thailand with the peak in 2010 as assumed? Will

Infection levels of nearly 30 per- support of the Interagency Working entire populations become infected

cent were detected in 1992; by Group. The AIDS epidemic in with HIV and eventually die from

1996, HIV prevalence rates had Thailand has substantial injecting AIDS? The simulations used for

declined by nearly 50 percent drug use components, while those this report and available epidemio-

(Table A-1). Although discussion in Africa do not (WHO/GPA, 1993). logical and behavioral evidence

of the causes of these declines is For Thailand, AIDS-related mortality suggest that this will not happen

still underway, it appears clear that rates from recent epidemiological in any population. Variations in

a substantial change has occurred. and demographic projections (TNES- sexual behavior help to ensure that

Consequently, the approach DB, 1994) were added to the non- the majority of the population in

described above needed to be AIDS m values for the 1990 to n x countries around the world is not

modified to conform to the empiri- 2010 period. at high risk of HIV infection. And

cal evidence of declining HIV when substantial proportions of Caveats and Limitations prevalence rates. the population are at lower risk of

In developing the methodology for infection, a plateau in HIV sero- To handle this epidemiological pat- these projections, the International prevalence after an initial rise is tern in Uganda, the 1990-2010 Programs Center has attempted to likely. Indeed, some of the coun- period was divided into a rising maximize the use of both the tries with high HIV seroprevalence epidemic period (1990-1995), a empirical data and the modeling levels are beginning to show evi- transition period (1995-2005), and tools available. However, much is dence of this plateau effect. a period of a relatively low and sta- unknown about the dynamics of However, as evidenced in our pro- ble epidemic (2005-2010). This AIDS epidemics in countries jections, population declines are classification is represented in around the world, and the method- possible in countries with a sus- Figure A-4. Mortality rates corre- ology is necessarily imprecise. tained widespread epidemic, par- sponding to the rising epidemic The actual path of AIDS epidemics ticularly in the presence of low fer- and the stable epidemic were sepa- in the countries that were selected tility levels. rately derived, and the transition will undoubtedly differ from the between the two was accomplished course projected. As epidemics
U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 A-7

Figure A-1.

Scenarios and Empirical Trend: Urban Female HIV Seroprevalence

Note: For assumptions, see text of Appendix A.

Source: U.S. Census Bureau, International Programs Center, unpublished tables. 

Years from onset of epidemic

0

10

20

30

40

50

0 5 10 15 20 25 30 35 40 45 50

HIV seroprevalence (percent)

Super high scenario

Low scenario

Medium scenario

High scenario

Interpolated epidemic

Higher scenario

Observed points

Figure A-2.

Five Scenarios and Empirical Trend: Total Female HIV Seroprevalence 

Note: For assumptions, see text of Appendix A.

Source: U.S. Census Bureau, International Programs Center, unpublished tables. 

Years from onset of epidemic

0

10

20

30

40

50

0 5 10 15 20 25 30 35 40 45 50

HIV seroprevalence (percent)

Low scenario 

Medium scenario

Higher scenario

High scenario

Super high scenario

Interpolated epidemic

1990

2010
A-8 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

Figure A-3a.

Projected HIV Seroprevalence for Selected Countries of Africa: 1990-2010 

Note: For assumptions, see text of Appendix A.

Source: U.S. Census Bureau, International Programs Center, unpublished tables. 

0

10

20

30

40

50

1990 1995 2000 2005 2010

HIV seroprevalence (percent)

Zambia 

Botswana

South Africa

Mozambique

Zimbabwe

Malawi

Figure A-3b.

Projected HIV Seroprevalence for Selected Countries of Africa: 1990-2010 

Note: For assumptions, see text of Appendix A.

Source: U.S. Census Bureau, International Programs Center, unpublished tables. 

0

10

20

30

40

50

1990 1995 2000 2005 2010

HIV seroprevalence (percent)

Congo (Kinshasa)

Kenya

Côte d'Ivoire

Cameroon

Nigeria

Burundi
U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 A-9

Figure A-4.

Projected HIV Seroprevalence for Uganda: 1990-2010 

Note: For assumptions, see text of Appendix A.

Source: U.S. Census Bureau, International Programs Center, unpublished tables. 

0

5

10

15

20

25

30

35

40

45

50

1990 1995 2000 2005 2010

HIV seroprevalence (percent)

Low epidemic

High epidemic Interpolated epidemic
APPENDIX B.

REFERENCES
Arriaga, Eduardo E. and Associates. International Programs Center Analysis, Population Division.

1994. Population Analysis with Staff Paper No. 86. ST/ESA/SER.A/145. New York.

Microcomputers. U.S. Bureau of Stanley, E. Ann, Steven T. Seitz, ______. 2001. World Population the Census, International Pro- Peter O. Way, Peter D. Johnson Prospects: The 2000 Revision. grams Center. Washington, DC. and Thomas F. Curry. 1991. Department for Economic and

Burton, A.H., and T.E. Mertens. 1998. “The iwgAIDS Model for the Social Information and Policy

“Provisional country estimates of Heterosexual Spread of HIV and Analysis, Population Division. 

prevalent adult human immunode- the Demographic Impacts of the New York. 

ficiency virus infections as of end AIDS Epidemic,” in United ______. 2002. World Urbanization 1994: a description of methods.” Nations, The AIDS Epidemic and Prospects: The 2001 Revision: Journal of Epidemiology, vol. 27, Its Demographic Consequences, Data tables and highlights. pp 101-107. ST/ESA/SER.A/119. New York. Department for Economic and

Fylkesnes, Knut, Zacchaeu Stover, John. 1996. “The Future Social Information and Policy

Ndhlovu, Kelvin Kasumba, Demographic Impact of AIDS: Analysis, Population Division.

Rosemary Musonda and Moses What Do We Know?” Paper pre- New York.

Sichone. 1998. “Studying pared for the conference on UNAIDS. 1999. “AIDS Epidemic dynamics of the HIV epidemic: AIDS in Development: The Role update: December 1999.” population-based data compared of Government, Chateau de Geneva: Joint United Nations with sentinel surveillance in Limelette, 17-19 June. Programme on HIV/AIDS. Zambia.” AIDS, vol. 12, no. 10, Thailand, National Economic and pp 1227-1234. ______. 2000. Report on the Social Development Board Global HIV/AIDS Epidemic June Glynn, J.R, M. Carael, B. Auvert, M. (TNESDB). 1994. “Projections for 2000. Geneva: Joint United Kahindo, J. Chege, R. Musonda, HIV/AIDS in Thailand: 1987- Nations Programme on F. Kaona, A. Buvé, and the Study 2020.” Bangkok: Thai Red Cross HIV/AIDS. Group on the Heterogeneity of Society Program on AIDS.

HIV Epidemics in African Cities. UNAIDS/WHO. 2002. Report on United Nations. 1989. The United 2001. “Why do young women the Global HIV/AIDS Epidemic Nations Population Projection have a much higher prevalence July 2002. Geneva: Joint United Computer Program. A User’s of HIV than young men? A study Nations Programme on HIV/AIDS Manual. Department for in Kisumu, Kenya and Ndola, and the World Health Economic and Social Information Zambia.” AIDS, vol. 15, Organization. and Policy Analysis, Population Supplement 4, August. Division. ST/ESA/SER.R.92. U.S. Census Bureau, International

Kenya, National AIDS Control New York. Programs Center. 1994. World

Program (KNACP). 1994. “AIDS Population Profile: 1994. By ______. 1994. “World Contraceptive in Kenya: Background Ellen Jamison and Frank Hobbs. Use 1994.” Wallchart. Projections Impacts and Report WP/94. Washington, DC. Department for Economic and Interventions.” July. Social Information and Policy ______. 1996. World Population

Rwanda, Ministry of Health. 1998. Analysis, Population Division. Profile: 1996. By Thomas M.

“1997 Population Based ST/ESA/SER.A/143. New York. McDevitt. Report WP/96.

Serosurvery.” Ministry of Health, Washington, DC. ______. 1995a. Population and Programme National de Lutte Development. Volume 1. ______. 1999. World Population Control le SIDA, Republic of Program of Action adopted at Profile: 1998. By Thomas M. Rwanda. January. the International Conference on McDevitt. Report WP/98.

Stanecki, Karen A., and Peter O. Population and Development, Washington, DC.

Way. 1997. “The Demographic Cairo, 5-13 September 1994.

______, Health Studies Branch. Impacts of HIV/AIDS, ST/ESA/SER.A/149. New York.

1994. The Impact of HIV/AIDS Perspectives from the World ______. 1995b. World Population on World Population. By Peter Population Profile: 1996.” U.S. Prospects: The 1994 Revision. O. Way and Karen A. Stanecki. Bureau of the Census, Department for Economic and Washington, DC. 

Social Information and Policy

U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 B-3
B-4 The AIDS Pandemic in the 21st Century: 2002 U.S. Census Bureau

______. 1998. HIV/AIDS in the Studies Branch Research Note World Health Organization/Global

Developing World. Report No. 29. Washington, DC. Programme on AIDS (WHO/GPA).

WP/98-2. Washington, DC. 1993. “The Current Global World Health Organization (WHO). Situation of the HIV/AIDS ______. 2002. “Recent HIV 1999. The World Health Report Pandemic.” WHO/GPA/CNP Seroprevalence Levels by 1999: Making a Difference. /EVA/93.1. January 4. Country: July 2002.” Health Geneva: World Health

Organization.
APPENDIX C.

GLOSSARY
U.S. Census Bureau The AIDS Pandemic in the 21st Century: 2002 C-3

Age structure. The distribution Infant mortality rate. The num- Seroprevalence. The percentage

of a population according to age, ber of deaths of infants under 1 of a population testing positive for

usually by 5-year age groups. year of age from a cohort of 1,000 infection in a blood test. In the

live births. Denoted q or IMR, it 1 0 context of this report, the percent- Age-specific fertility rate. The is the probability of dying between age testing positive for antibodies number of births during a year to birth and exact age 1. to HIV. women in a particular age group,

usually per 1,000 women in a iwgAIDS. Interagency Working Total fertility rate. The average

5-year age group at midyear. Group on AIDS. number of children that would be

born per woman if all women lived AIDS. Acquired immune deficien- Life expectancy at birth. The

to the end of their childbearing cy syndrome. average number of years a group

of people born in the same year years and bore children according

Birth rate. The average annual can be expected to live if mortality to a given set of age-specific

number of births during a year per at each age remains constant in fertility rates.

1,000 population at midyear. Also the future. Under-5 mortality. Number of known as the crude birth rate.

Natural increase. The difference deaths of children under 5 years of

Crude birth rate. See birth rate. between the number of births and age from a cohort of 1,000 live

the number of deaths. births. Denoted q , it is the Crude death rate. See death 5 0

probability of dying between birth rate. Pandemic. A global epidemic. and exact age 5.

Death rate. The average annual Projections. Data on population UNAIDS. United National Joint number of deaths during a year per and vital rates derived for future

1,000 population at midyear. Also Programme on HIV/AIDS. years based on statistics from popknown as the crude death rate. ulation censuses, vital registration Vital events. Births and deaths.

systems, or sample surveys per- Growth rate. The average annual taining to the recent past, and on Vital rates. Birth rates and death

percent change in the population, assumptions about future trends. rates. resulting from a surplus (or deficit)

of births over deaths and the bal- Rate of natural increase. The WHO. World Health Organization.

ance of migrants entering and difference between the crude birth

WHO/GPA. World Health leaving a country. The rate may be rate and the crude death rate.

Organization/Global Programme positive or negative. Also known Sentinel surveillance. on AIDS. as population growth rate or aver- Surveillance conducted through age annual rate of growth. “watchpost” sites that provide

HIV. Human immunodeficiency access to populations that are of

virus. The virus that causes AIDS. particular interest or represent a

larger population. 
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IDS Pandemic in the 21 U S C E N S U S B U R E A U st Century International Population Reports