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Making HIV Prevalence and AIDS Estimates

UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance

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HIV/AIDS: Data Needs

What are the levels and trends in HIV infection?

Who is getting infected? Who is more at risk for or vulnerable to HIV

infection? Impact assessment (need for care, planning) Is the response effective?

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Courtesy of Thomas Rehle, Family Health International

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UNAIDS/WHO Classification of epidemic states

LOW LEVEL: HIV prevalence has not consistently exceeded five percent

in any defined sub-population

CONCENTRATED HIV prevalence consistently over five percent in at least one

defined sub-population but below one percent in pregnant women in urban areas.

GENERALISED HIV prevalence consistently over one percent in pregnant

women nation-wide

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Three Steps in Making Estimates

Calculating HIV Prevalence

Curve Fitting

Generating other variables (e.g.,

mortality, incidence)

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Calculating HIV prevalence:Industrialized Countries

AIDS Back-calculation• Statistical method that allows estimating

the past HIV incidence required to provide the present level of AIDS cases, corrected by underreporting

• Curve fitting of past incidence

HIV Incidence estimates• HIV case reporting• Incidence studies using “detuned” ELISA

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Two different sets of procedures for:

low-level and concentrated epidemics• HIV is concentrated mainly in sub-populations

which may vary from country to country (e.g., IV drug users, CSW, MSM)

generalized epidemics• HIV has spread widely in the adult population• primary mode of transmission is heterosexual

Calculating HIV prevalence:Developing Countries

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Calculating HIV prevalence: Concentrated Epidemics

Estimates are made by adding together: the number of individuals assumed to

be infected in each identifiable sub-population at risk.

a minimum estimate of HIV infection in the general population

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Identify risk groupsEstimate size of groupsEstimate HIV prevalence in risk

groupsEstimate HIV prevalence in the

general populationSum of all groups

Calculating HIV prevalence:concentrated and low-level epidemics

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Estimating Prevalence in a Concentrated Epidemic

Group

Population -

Low estimate

Population -

High estimate

Population

prevalence,

low estimate

Population

prevalence,

high estimate

People

infected,

low-low

People infected,

low-high

People infected,

high-low

People infected,

high-high

Average, adults

with HIV

MSM 21,362 42,724 0.1 0.2 2,136 4,272 4,272 8,545 4,806 General population 2,093,489 2,050,764 0.001 0.003 2,093 6,280 2,051 6,152 4,144 CSW - - - STI attenders 21,362 42,724 0.033 0.053 705 1,132 1,410 2,264 1,378

2,136,213 2,136,213 0.13 0.26 4,935 11,685 7,733 16,962 10,329

Assumptions:

MSM population: Used 2% of adult men for low and 4% for highMSM Prevalence Rate Used 1995 prevalence data (15%), with 10% for low and 20% for high STI Population Used 1% of adult as low, 2% as high. Assume higher rates among menSTI Prevalence Rate 96-97 sentinel surveillance = 4.3%, used 3.3 as low and 5.3 as high General Population: Used 2000 UN population numbers for 15-49 minus the risk groupsGeneral population prevalence Based on anc sentinel data (0.55%). Used adjustment for rural, then had 0.1% for low, and 0.3% for high

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Estimating Prevalence in a Concentrated Epidemic

Population

Population Size

High

Population Size

Low

Percent Infected

High

Percent Infected

Low

Low-Low

Estimate

Low-High

Estimate

High-Low

Estimate

High-High

Estimate

IVDU 700,000 244,400 0.6 0.078 19,063 146,640 54,600 420,000

CSW Promiscuous People 502,260 125,565 0.01 0.003 377 377 377 5,023

People with STI 565,043 376,695 0.0084 0.0028 1,055 1,055 1,055 4,746

General (15-49) 23,345,698 24,366,340 0.0012 0.0007 17,056 17,056 17,056 28,015

Total 25,113,000 25,113,000 37,551 165,128 73,088 457,784

Assumptions:

IVDU Population Size Used high estimate from Karl's paper (and close to 6% of males) and 2% of males as low (see table below)IVDU Prevalence Rate Used High from Lev's study in Odessa, and a low from Karl's registered IVDU'sCSW * P.P. Population Used 2% of population for CSW/Promiscuous and .5% for low estimateCSW Prevalence Rate Used CSW prevalence rate from Karl's paper as high, half of that (overlap with IVDU) as lowSTI Population Used 15 times reported syphilis rate as high, and 10 times reported rate as lowSTI Prevalence Rate Used 50% higher than reported (Karl's paper) as high prevalence, 50% lower, as low General Population: Used 98 UN population numbers for 15-49 minus the risk groupsGeneral population prevalenceUsed anc rates for high and blood donors for low estimate

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Calculating HIV prevalence: Generalized Epidemics

Prevalence estimates are based primarily on surveillance data collected from women attending antenatal clinics.

Two groups of clinics based on their location• major urban areas• outside major urban areas

median prevalence rates are calculated separately for the two groups

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Calculating Prevalence

1.Determine median of urban and outside major urban sites

2. Adjust medians based on representativeness of sites

3. Apply adjusted rates to female urban and outside urban populations (15-49)

4.Use M/F ratio to determine number of men infected

5. Combine males and females to get adult rate

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Critical Issues in Estimating Prevalence

Representativeness of ANC sitesEffects of HIV infection on fertilityMale-to-female ratioUrban to rural prevalence

differential

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Representativeness of ANC:Other Factors

Age-related fertility reduction in HIV positive women

Changes in risk behaviors (condom use, contraception)

“Active aging” (risk beyond reproductive ages)

Selection and participation bias (users fees, availability/access to ANC services)

These factors have been identified for further study but are not considered in the present methodology

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Representativeness of ANC for total Population: Male/Female Differentials

0

10

20

30

40

50

60

15-19 20-24 25-29 30-39 40-49

% H

IV se

ro-p

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Men

Women

Population-based HIV prevalence in men and women - Lusaka, Zambia, 1995

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Assessing Urban/Rural Differentials: Surveillance Data for Kenya

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Comparison of HIV Prevalence Among Pregnant Women and All Adults 15-49

0

5

10

15

20

25

30

35

Lusaka Mposhi Mwanza Rakai-90 Rakai-91 Rakai-92 Kisumu

ANCPop

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Three Steps in Making Estimates

Calculating HIV Prevalence

Curve Fitting

Generating other variables (e.g.,

mortality, incidence)

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Curve Fitting

Epimodel was used to fit a curve to yearly estimates of adult prevalence. Not designed to make HIV projections. Not designed to fit prevalence curves Limited to the gamma curve May not suitable for slowly progressing

epidemics (Asia) New curve-fitting software is being

developed

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EPIModel

Developed by WHO/GPA Tool for making short-term projections

of AIDS cases, AIDS mortality, paediatric AIDS and AIDS orphans.

NOT DESIGNED to make estimates or projections of prevalence of HIV infections

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Curve Fitting

Point prevalence estimates (at least one, the more the better!)

Year of initial spreadAssumptions about peak of the

epidemicPost-peak curve assumptions

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0.0

10.0

20.0

30.0

40.0

50.0

1985 1987.5 1990 1992.5 1995 1997.5 2000

Year

URBAN OUTSIDE MED-URBAN

MED-OUTSIDE UNAIDS/WHO adult prevalence curve

HIV Prevalence for Pregnant WomenMajor Urban and Outside Major Urban Areas

Zambia

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Three Steps in Making Estimates

Calculating HIV Prevalence

Curve Fitting

Generating other variables (e.g.,

mortality, incidence, orphans)

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Generating Other Variables

Epimodel (or the new model) is used to generate additional information on

Incidence Vertical transmission Mortality (adult and child) Orphans

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Generating Other Variables:information needed

Population size (15-49) Progression rates from infection to

death Age specific fertility rates Fertility reduction for HIV Male-to-female ratio Mother-to-child transmission rate

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Age-specific information about population and fertility

United Nations Population Division estimates: age-specific fertility rates population figures

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Generating Other Variables Progression Rates

Infection to death for adults Median 9 years in countries with poor health care Median 11 years in countries with better health care

Infection to death for children Median 2 years in countries with poor health care Median 4 years in countries with better health care

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Progression Rates

Child Survival Rates

0

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20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Years since infection

Per

cen

t su

rviv

ing

SlowFast

Adult Survival Rates

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Years since infection

Per

cen

t su

rviv

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FastSlow

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Estimating Vertical Transmission

Male to female ratioFertility rateFertility reduction for HIV+Vertical transmission rate

(25% 10%) Impact of ARV prophylaxis on MTCT

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Mortality estimates

Due to the limitations in vital registration and case reporting, AIDS deaths are derived from the estimated HIV prevalence curve and the progression rate from infection to AIDS and death

Pre-AIDS mortality (deaths in HIV infected adults and children due to other causes unrelated to HIV) is deducted from the total mortality estimate

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Projections

He who predicts the future lies, even if he is telling the truth!

Predictions are very difficult, particularly when the future is concerned.