HIV Surveillance and data availability

MTT Winter School, Durban August 2004

Dr Anthony Kinghorn

Controversy and HIV/AIDS

Antenatal Survey

reported that 24%

of pregnant

women are HIV

positive

The HSRC study

reported that

11.4% of people

are HIV positive.

Who is right?

Prevalence of HIV Infection in the Under 20 Year Age Group of Antenatal Clinic Attendees in SA

Year

Prevalence

(%)

95% Confidence

Interval

1996 12.78 11.33-14.23 1997 12.7 11.3-14.2 1998 21.0 18.4-23.8 1999 16.5 14.9-18.1 2000 16.1 14.5-17.7 2001 15.4 13.8-16.9 2002 14.8 13.4-16.1

Source: DOH. National HIV Surveys of Women Attending Public ANC Clinics in SA

HIV INFECTION LEVELS - 15 - 19 year olds

0.00

5.00

10.00

15.00

20.00

25.00

1994 1995 1996 1997 1998 1999 2000 2001 2002

%

Source: National Surveys of Women Attending Antenatal Clinics

Outline of Presentation

– Why measure?

– What can we measure?

• HIV Prevalence

• HIV Incidence

• AIDS Prevalence and incidence

• Mortality

– Using models to understand the epidemic

and it’s impacts

– Second Generation surveillance

Why measure?

– Identify trends in infections and impact

– Identify levels of infection and impact

– Predict future trends and levels of impact

Advocacy and planning

Evaluate interventions for staff and

learners

The Prevalence (Rate)

Definition:Definition:The proportion of a population at risk The proportion of a population at risk

affected by a disease at a specific point in affected by a disease at a specific point in timetime

Prevalence = Prevalence =

No. of people with the disease orNo. of people with the disease orcondition at a specific time condition at a specific time

No. of people at risk in theNo. of people at risk in the population at the specified timepopulation at the specified time

Population at risk for Cancer of the Cervix

All Men All Women

0-25 years

26-69 years

70+ years

Factors influencing the prevalence:

Increased by:•Longer duration of

disease

•Prolonging life but no

cure

•Increase in incidence

•In-migration of cases

•Out-migration of

healthy

•Improved diagnosis/

reporting

Decreased by:•Shorter duration of

disease

•High death rate

•Decrease in incidence

•Out-migration of cases

•In-migration of healthy

•Increased cure rate

The Incidence Rate

This is the rate at which new events This is the rate at which new events occur in a occur in a populationpopulation

= = No. of new cases of a disease in No. of new cases of a disease in aa

specified timespecified time Total number of people at riskTotal number of people at risk

HIV Prevalence

– The main source of HIV Prevalence data is National Surveys of Pregnant Women at Antenatal Clinics

– Other sources include:• Hospital admissions• TB patients• STD clinic attendees• Blood donors• Pre-insurance testing • Workplace and population surveys

– What are the limitations of these sources?

– What are they useful for?

Antenatal HIV Seroprevalence Survey

Source: DOH. National HIV Surveys of Women Attending Public ANC Clinics in SA

0%

5%

10%

15%

20%

25%

30%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Limitations of the Antenatal Data

• Usually designed to track trends not national levels • Rising ANC prevalence usually reflects rise in general

population

• May overestimate HIV female and male adult prevalence • Reflects sexually active women, reproductive years, not

using condoms• Over estimates prevalence in teens and high age groups

• But may also underestimate HIV• Excludes women on contraceptives• HIV positive women have a decreased fertility

• Some studies suggest that ANC HIV prevalence is a

reasonable proxy for community adult rate

• Other sampling biases • Rural populations often under-sampled? Other?

So we need to use models to estimate levels of HIV infection in the population and sub-populations

Trends in HIV infection levels in pregnant women

0%

5%

10%

15%

20%

25%

30%Urban

Rural

Adjusted allurban

All pregwomen 15-49

Source: Rwanda HIV Sentinel Sero Surveys and adjustments from population surveys

Limitations of the Antenatal Data

• Increasing difficulties of interpreting ANC data in mature epidemics– Deaths off-setting new infections– Prolonged life due to ARVs– Plateaux due to saturation or behaviour

change?– Etc

Community Prevalence Studies

Community studies more representative of all settings, ages, both sexes

Can link with behavioural surveillance/KAPB/ other data

Big differences from ANC prevalence in the young and old – due to sample bias

Can refine assumptions about community infections used in interpreting ANC data

Results can be surprising or easy to misinterpret

eg. HSRC/NMF study in South Africa HIV prevalence of 11.4% in all > 2 years old 32% prevalence in women aged 25-29

HIV prevalence in Zambia DHS vs Antenatal

0

5

10

15

20

25

30

urban ♀ rural ♀ transitional rural♀

total*

HIV

pre

vale

nce

% (

ages

15-

49)

ANC DHS

* DHS Total = men and women)

ANC vs ZDHS (cont)

• ZDHS: 15.6% prevalence all adults

• ANC 2002: 19% prevalence adult ♀ (15-44yrs)

• ZDHS: 18% prevalence adult ♀ (15-49yrs)

– Similar estimates indicate epidemic still

severe

– Overall, ANC estimates fairly robust?

KDHS versus ANC (2003)

• Adult prevalence – DHS 2003 (women & men): 6.7%

– ANC 2003: 9.4%

→previous over-estimation?

• However, for women 15-49:– ANC 2003: prevalence estimated 9.4%

– DHS 2003: prevalence estimated 8.7%

Age profile of HIV infection levels – Men vs Women

(Zambia DHS 2001)

0

5

10

15

20

25

30

35

15-19 20-24 25-29 30-34 35-39 40-44 45-49

Age group

Prev

alen

ce (%

)

Women

Men

Source: Zambia DHS 2001, Preliminary Report

Age profile of HIV infection levels – Men vs Women(Kenya DHS 2003)

0

2

4

6

8

10

12

14

15-19 20-24 25-29 30-34 35-39 40-44 45-49

Age group

Prev

alen

ce (%

)

Women

Men

Source: Kenya DHS 2003, Preliminary Report

Community Prevalence Studies

Limitations Sample sizes

Especially for sub-groups Biases

Non-Response Other

Expense and complexity Time to establish new time series and trend data Frequency

Probably only repeat every 3-5 years if initial results in line with ANC and expectations

Biological surveillance - workplace sero-prevalence

surveys• Blood or saliva tests for HIV; (STD rates)

• Unlinked anonymous surveys

– VCT usually inadequate for workforce levels

• Advantages

– Accurate refection of risk, including for

employee sub-categories

– Plausible

– Inform projections (still required)

– Track changes and monitor success

Biological surveillance cont.

Challenges• Clear objectives and use of data, including

strategy to communicate results• Limited accuracy if low participation• Employee buy-in

– Credible confidentiality, non-discrimination, programme and response options needed

• Ethics– VCT availability and promotion– Informed consent, anonymity – Ethics committee approval

• Technical and analytical issues – eg. sampling; response rates; stats analysis; lab

• Cost• Limited trend data

HIV prevalence in a service sector workforce (South Africa)

0%

5%

10%

15%

20%

25%

30%

Projected HIV + 2002 National Antenatal Survey Employee HIV salivasurvey

Which data source gave most information and value for money?

HIV Incidence

– Very few sources of data on HIV Incidence

– Usually from large HIV prevention studies

– Main measure of vaccine effectiveness

AIDS Prevalence / Incidence

• Very difficult to measure without

notification

• Only tells us about HIV infections from 5-

10 years ago

• Critical to use a consistent and

recognized classification system!

Other Sources of Data

• Death Registration

– Can be a very useful way to track AIDS trends, as

age related mortality from AIDS is unique

• EMIS; HR and payroll databases; pension funds

• Measuring incidence of opportunistic diseases,

especially TB, is very important for health service

planning

DEATHS by age band 1998 DHS vs Projected(Botswana)

(For previous 24 months)

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64

1998 DHS

d1

Behavioural surveillance - KAPB

• Standardised questionnaires generate indicators

of Knowledge, Attitudes, Practices, Behaviour eg. – Basic knowledge – Risk e.g. number of non-regular partners; condom use– Views on HIV/AIDS programme

• Can link to blood or saliva tests

• Objectives– Identify target knowledge gaps, behaviour, groups– Identify sources of e.g. information, services– Assess manager and supervisor preparedness– Track levels and trends: baseline and follow-up – Advocacy

KAPB cont.

• Challenges– Usually outsourced for expertise and neutrality

• Employee and union buy-in

• Sample size or biases, incl. % responding;

truthfulness

• Survey administration skills

• Ethics

– Cost

– Interpreting, using and communicating results

– Simple or complex questionnaires/ surveys?

– Interfering programmes and influences on KAPB?

– May miss unexpected issues and suggestions

PERCENTAGE OF CHILDREN IN AGE GROUPS WHO WILL BE ORPHANED BY AIDS

0%

5%

10%

15%

20%

25%

30%

35%

0-4'5-9'10-1415-19

Source: Kinghorn et al (2001). The impact of HIV/AIDS on Education in Namibia

What is a Model?

– A model is a hypothesis or theory that tries to explain the real

world

• It gives a framework for design of tools to give answers to questions

about the 'model world'

– A model is only as good as:

• Its underlying assumptions

• Quality of input data

Some use of modeling is probably inescapable to make sense of any

empirical data

Models - Examples

– ASSA 2000/ Doyle/ Metropolitan

• Mix of macro- and micro-model features

• Includes risk groups and geographic differences

– AIM

– US Bureau of Census

– Epimodel

– Other

Projection methodology

Antenatal data – levels and trends in infection

General population projections: age, gender, geographic region

Cross mapping of e.g. educators by age, gender, location, origin

Scenarios; validation/calibration using prevalence, mortality data

Analysis and action

Extrapolation to all women and men

Modifiers

•Mortality data

•(HIV prevalence data)

•(Risk behaviour data)

ASSA 2000 Output

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

5000019

8519

8719

8919

9119

9319

9519

9719

9920

0120

0320

0520

0720

09

To

tal p

op

ula

tio

n

0

1000

2000

3000

4000

5000

6000

7000

Nu

mb

ers

HIV

, AID

S s

ick

an

d H

IV d

eat

hs Total

population

Total HIV

TotalnumberAIDS sick

CumulativeHIV deaths

*Source: Prof R Dorrington, ASSA

Projections - challenges

1. Limitations of all models

2. Demographic data limitations• Population and personnel

• Migration

• Fertility

3. Epidemiological data limitations, particularly• Extrapolation from ANC to general population• Survival time • Fertility impacts - multiple determinants• Epidemic curves for urban/rural, local areas, sub-

groups

Projections - challenges(2)

4. Other enrollment or attrition influences • Policy, other factors – often dominate AIDS

5. Key techniques• Validation – quality of data?• Sensitivity testing • Intervention modeling - Behaviour change; ARVS• Qualitative data

6. Experienced modelers7. Shorter term and more aggregated projections

probably more accurate

Severity of limitations depends on the planning question to be answered

Second Generation Surveillance

• Continue with ante-natal surveys

• Behavioural Surveillance

• Focus on young people

• High-risk sub-groups

• Morbidity and mortality surveillance

BUT – every country is different – needs it’s own

research agenda

HIV prevalence in a large company workforce (South Africa)

Category % HIV+ (95%

CI)

Sexual behaviours

Sex with non-regular partner (last 3

months)

16.8 (14.5 – 19)

No non-regular partner (last 3 months) 6.9 (6.1 – 7.7)

Condom use

Used condom with last non regular partner 14.4 (12 – 16.8)

No condom with last non-regular partner 11.8 (10 – 13.5)

No non-regular partners 4.5 (3.7 – 5.3)

Source: Colvin M Gouws E Kleinschmidt I Dlamini M. The prevalence of HIV in a South African working population. AIDS 2000 Conference poster, Durban 2000

Summary

• Data maybe limited, and the models may be inaccurate, but the main messages in terms of levels and trends are usually clear

• But the epidemic is complex and needs customised responses

• “What is occurring is a collection of epidemics in different stages of increase, stability, and decline” (Sentinel Surveillance of HIV/Syphilis in Zambia, 2003)

• Averages hide variation – much worse or less affected communities

• Don’t contribute to confusion through lack of understanding of HIV/AIDS statistics OR enthusiasm for technical debate