HIV research in the era of ART: changing priorities in Tanzania

Basia Zaba

SOAS3rd March 2011

α - networkAnalysingLongitudinalPopulation-basedHIV/AIDSdata onAfrica

Overview• Introduction – data sources & measurement strategies

– Monitoring the epidemic at a national level– Evaluating prevention and treatment responses– What difference does ART availability make?

• Results from observational studies of HIV in Tanzania– Ante-natal Clinic Surveillance: historical trends– Nationally representative X-section sample surveys– Clinical cohorts for studying ART patients– Community-based cohort studies– Combining data from different sources

• Tanzania compared to other African countries

Monitoring the epidemic at a national level

• Need for a representative population sample – men and women, all ages– rural and urban– not just sick people

• Convenience and cost– accessibility of health facilities– build on routine record keeping– locate in interested institution

Nationally representative household survey with HIV testing (e.g. DHS)

Ante-Natal clinic surveillance

Evaluating prevention and treatment response

• Prevention: need individual follow-up data to measure– rate of new infections– prior behavioural risks– influence of campaigns

• Treatment questions:– do people know they are infected?– what proportion accesses treatment?– how do those on treatment fare?

Community cohort studies (e.g. Kisesa)

Clinical cohort studies

Referral studies

Household surveys e.g. DHS

HIV observational study designs

Cross-sectional Longitudinal

Facility based ANC surveillance Clinic cohorts

Community based DHS surveys Community cohorts

Cross-sectional studies may be repeated several times to get overall trends, they are only called longitudinal if individuals are linked from round to round

simple & cheap

complex & costly

What difference does ART availability make?

• Greater willingness to learn HIV status– HIV is no longer a death sentence– stigma is still a big issue

• Ethical obligation of researchers to encourage people to learn status, and if necessary access treatment – study design allows for diagnosis as well as measurement– protocols must include “realistic” referral procedures

• Facility data analysis has to account for possible biases due to treatment seeking or test avoidance

• Need to link individual’s clinic and community records to study certain impacts – e.g. treatment drop out, partner infections

Ante-natal clinic surveillance• Testing of pregnant women coming to ANC is still main

source of national estimates of HIV trends world wide – Before ~2005 based on unlinked anonymous tests of residue

of blood sample used for syphilis testing (no feedback)– Since ~2005, usually based on results of PMTCT testing

(mothers get test feedback)

• Representative samples of Tanzanian clinics began to be selected after 2000, prior trend estimates must take account of changing clinic selection

• Clinic samples may be biased if women who think they are infected seek out clinics that do PMTCT testing

HIV prevalence by sentinel site

0

5

10

15

20

25

30

35

1986 1988 1990 1992 1994 1996 1998

Dar es Salaam (1)Dar es Salaam (2)Dar es Salaam (3)Mwanza (urban)Mwanza (rural)Mwanza (Mkula)Mbeya (Itete)Mbeya(urban)Mbeya(rural)Mbeya (Kyela)Mbeya (Isoko)Mbeya (Mwanbani)Mbeya (Chimala)Mbeya (Meta)Mbeya (Kiwanjampaka)Mbeya (Mbozi)Mbeya (Mwanjelwa)BukobaKilamanjaro(Moshi)Kilimanjaro (Umbe)Mara (Nyasio)Mara (Musoma)ArushaDodomaSingidaTangaLindiIringa (Mafinga)ShinyangaMtwara (Nanguruwe)Rukwa (Sumbawange)Rukwa (Namanyere)Ruvuma (Songea)Ruvuma (Namtumbo)ZanzibarPemba Island

TanzaniaTanzania: HIV prevalence data from ANC surveillance sites: 1988-1998

Deriving prevalence trends when reporting clinics vary over time

Method developed by UNAIDS• Only use data from clinics that report more

than once• Do a separate trend analysis for urban and

rural clinics, then weight results by size of urban and rural populations

• Use median clinic prevalence rather than mean to give less weight to extremes

Fitting UNAIDS model to median prevalence in ANC clinics

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

1980 1985 1990 1995 2000 2005

year

HIV

pre

vale

nce

Tanzania ANC prevalence

model prevalence

Peak in early 90’s

Projected to level out at under 10%

Demographic & Health Surveys

• DHS: nationally representative sample surveys with an international standard questionnaire

• May include additional modules on special topics such as malaria prevention

• Recent studies have added collection of bio-markers, including anonymous HIV tests

• Tanzania has done more DHS surveys than any other country, including two with HIV testing (2004, 2007)

Tanzania HIV prevalence: DHS 2004

Adjusting UNAIDS model to observed DHS prevalence

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

1980 1985 1990 1995 2000 2005

year

HIV

pre

vale

nce

ANC prevalenceFitted modelDHS prevalenceAdjusted model

Projected to level out at under 9%

0

2

4

6

8

10

12

14

16

HIV

prev

alen

ce, %

HIV prevalence by region, Tanzania 2004-07

2004

2007

Putting together results of two DHS surveys

Most regions experienced a significant prevalence decline between 2004-07

The UNAIDS prevalence trend model needs re-adjusting

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

1980 1985 1990 1995 2000 2005

year

HIV

pre

vale

nce

ANC prevalenceFitted modelDHS prevalenceAdjusted model

2010

Decline has been much steeper than UNAIDS prediction

Treatment and care: interpreting data from different sources

• Community-based data on HIV diagnostic testing (but no direct questions about results or treatment)

• Referral data: what do HIV+ people do after learning they are infected

• Care and Treatment Clinic (CTC) follow-up data: what happens to people referred to clinics for care (monitoring) and treatment

Access to ARTAccess to ART

HIV negative

HIV positive – no ART need

HIV positive - needs ART

UNDERGO VCT

AGREE TO UNDERGO REFERRAL

ELIGIBLE FOR ART

INITIATE ART

"KISES A COM M UNITY"

ATTEND ART CLINIC

ART ACCESS PRO CESS

whole community

Attend VCT

Referred ART

Attend ART

eligible ART

start ART

clinic data only tell us this part of the story

Trend in knowledge of HIV status, %

05

101520253035404550

2004 2007

HIV positive persons by HIV test historymen ever had VCT (national)

men had VCT last year (national)

men had VCT at sero-survey (Kisesa)

women ever had VCT (national)

women had VCT last year (national)

women had VCT at sero-survey (Kisesa)

Know their HIV status

Estimated % of HIV infected in care (ART or pre-treatment monitoring), by region, 2010.

• Varies by region from 14% to 55% of HIV infected in care• Overall estimate: 255,000 to 367,000 HIV +ve in care in

Tanzania = 21%-30% of those infected (aim is 100%)

Access to HIV preventative treatment for mothers and newborn children in Magu district, 2009

0%

20%

40%

60%

80%

100%

Urban Roadside settlement

Remote village

Cum

ulat

ed %

tre

ated

Residence area of mother

Percent HIV positive mothers accessing PMTCT treatment by residence

only child treated

only mother treated

mother and baby treated

n=84 n=31 n=53

−110 (66%) did not receive any PMTCT drug treatment at all

−2 (1%) reported obtaining drugs only for the child;

−15 (9%) only received drugs for herself;

−41 (24%) received full PMTCT drug treatment for self and her child

Of the 168 HIV-positive women who had a live birth:

HIV Care & Treatment Clinic (CTC) record follow-up

• Studies done as part of monitoring and evaluation of national Anti Retroviral Treatment (ART) programme

• In Tanzania, 666 out of 909 CTC facilities reported current and/or new numbers of patients receiving care

• 101 facilities have computerised patient record databases, can even trace patients moving between facilities (unique patient IDs)

• Can use the computerised data to construct a clinic cohort to study patient welfare and clinic attendance

Tracking enrolment, attendance & drop-out: Kisesa 2005-07

0102030No. of Patients

12/200711/2007

10/20079/2007

8/20077/2007

6/20075/2007

4/20073/2007

2/20071/200712/2006

11/200610/2006

9/20068/2006

7/20066/2006

5/20064/2006

3/20062/2006

1/200612/2005

11/200510/2005

9/20058/2005

7/20056/2005

5/20054/2005

3/20052/2005

1/2005

MaleMale currently on ART

Male ever on ART

Male currently enrolled

Male ever enrolled

0 10 20 30No. of Patients

FemaleFemale currently on ART

Female ever on ART

Female currently enrolled

Female ever enrolled

Mon

thTreatment pyramid

Death rates following ART initiation0

.05

.1H

azar

d ra

te p

er p

erso

n ye

ar

0 1 2 3 4 5Treatment Period on ART (years)

95% CI Smoothed hazard function

high death rates at start of treatment due to late initiation and drug toxicity

Median CD4 count following ART

threshold for treatment initiation

most people initiate treatment too late

CD4 counts improve due to drugs and because of deaths of those with very low initial counts

HIV community cohort studies• Whole communities are followed over long periods of

time, with frequent (at least yearly) household censuses (demographic surveillance)

• Adults in the communities have HIV status measured at regular intervals (at least once every 3 years) and HIV status is individually linked to demographic data

• Also do periodic surveys of known HIV risk factors (e.g. sexual partnerships, condom use, blood transfusions) and possible consequences (e.g. infant mortality) and people’s knowledge and attitudes

Kisesa cohort study components

0

ART clinic

VCT service

ANC surveillance

HIV Serology

Demography

1994 1997 2000 2003 2006 2009sero 1 sero 2 sero 3 sero 4 sero 5 sero 6

- - - -

- - -

- - - + + +

- - - - -

+ + + D

- - - - -

+ + D

- - -

+ + + +

- - - - - -

HIV status life-histories collected in cohort study

new infectio

n

at risk of infectio

n

at risk of death

HIV+ death

Describing incidence (rate of new infections)

• Crude measures (and trends):

• Specific patterns – incidence classified by: – age and sex – place of residence – marital status

• Comparing different populations: life time risk

period time in risk to exposed personsperiod time in infectionsnew rate incidence

Incidence trends by age, sex and residence

0

5

10

15

20

25

1994-96 1997-99 2000-02 2003-05

Inci

denc

e ra

te p

er 1

000

Sero-survey interval

Rural area, men15-2425-3435-4445-54all ages

0

5

10

15

20

25

1994-96 1997-99 2000-02 2003-05

Inci

denc

e ra

te p

er 1

000

Sero-survey interval

Roadside area, men

0

5

10

15

20

25

1994-96 1997-99 2000-02 2003-05

Inci

denc

e ra

te p

er 1

000

Sero-survey interval

Rural area, women

0

5

10

15

20

25

1994-96 1997-99 2000-02 2003-05

Inci

denc

e ra

te p

er 1

000

Sero-survey interval

Roadsaide area, women

Incidence age pattern, Kisesa 1994-20040

.005

.01

.015

.02

15 20 25 30 35 40 45 50 55 60 65analysis time

95% CI Smoothed hazard function

males

0.0

05.0

1.0

15.0

2

15 20 25 30 35 40 45 50 55 60 65analysis time

95% CI Smoothed hazard function

females

smoothed hazards with confidence limits by sex

Mode 30 yrsPeak 1.5 %

Mode 27 yrsPeak 1.2 %

Incidence LEVEL measure: life time risk = cumulated risk to age 65

0.1

.2.3

.4.5

15 20 25 30 35 40 45 50 55 60 65age

95% CI Failure function

Kaplan-Meier failure estimate

Kisesa, 1994-2004

Life time risk of HIV infection = 40%

Kisesa, 1994-2004

Average HIV prevalence = 9.3%

Mortality and survival after HIV infection• Most common way of comparing severity of HIV mortality

across sites is to look at how long infected people survive without treatment

• Not ethical to try to measure this in the era of ART treatment, but community cohort studies like Kisesa have survival data collected over many years before treatment was available

• In Kisesa as in other sites we found that people infected at older ages have much worse survival patterns – this is not just because older people have higher mortality

• We can also study age-specific mortality patterns and compare infected and uninfected, and mortality among HIV infected persons before and after ART became available

Proportion surviving following HIV infection, Kisesa 1994-2005

0.00

0.25

0.50

0.75

1.00

Pro

port

ion

surv

ivin

g

0 2 4 6 8 10 12

Years since sero-conversion

Survivor functionNet survivor function

0.00

0.25

0.50

0.75

1.00

0 2 4 6 8 10 12

Years since sero-conversion

Survivor functionNet survivor function

Infected aged < 30 Infected aged 30+

0.0

5.1

.15

.2.2

5m

orta

lity

rate

15 20 25 30 35 40 45 50 55age

negative positive unknown

pooled data from ALPHA cohorts prior to ART availabilityAge-specific mortality rate by HIV status

.05

.1.1

5.2

.25

mor

talit

y ra

te

15 20 25 30 35 40 45 50 55age

before ART after ART

pooled data from ALPHA cohortsMortality rate of HIV positive by study site and ART availability

HIV mortality and ART need• In CTC clinics, individual ART need is assessed using CD4 count

and clinical staging of HIV disease• For the population as a whole, we can define the need for ART

in an age group as the proportion who would die within the next 3 years if they didn’t get treatment

• Cohort data on age-specific HIV mortality in the pre-treatment era allow us to estimate proportions of HIV infected persons by age who would be expected to die within 3 years – this is the base-year treatment need for an ART program at start-up

• We can also determine the build up of treatment need in a successful program, with suitable assumptions about mortality of those on treatment

Theoretical build up of treatment need by program year

020

4060

80

15 20 25 30 35 40 45 50 55 60 65 70 75 15 20 25 30 35 40 45 50 55 60 65 70 75

Male Female

need ART need care

Num

ber o

f HIV

infe

cted

Graphs by sex

Kisesa 2005ART need by sex and five year age group

Initial ART need in 2005, KisesaTotal need, both sexes: 123Total on treatment: 27

Cumulated ART need by 2008, Kisesa0

2040

60

15 20 25 30 35 40 45 50 55 60 65 70 75 15 20 25 30 35 40 45 50 55 60 65 70 75

Male Female

need ART need care

Num

ber o

f HIV

infe

cted

Graphs by sex

Kisesa 2008Maximum ART need by sex and five year age group

Total need, both sexes: 193Total on treatment: 207

Tanzania compared to other African countries

(data from other cohort studies in the ALPHA network)

Results: Incidence level & pattern comparison across sitesStudy sex LEVEL:

Life time risk % PATTERN: modal age

peak incidence rate %

50 x peak incidence

at age 65

95% CI smooth hazard

95% CI smooth hazard

95% CI relative to level

Masaka 1990 - 05

males females

23.2 20.5

20.1 – 26.7 18.0 – 23.3

29 26

27 – 31 24 – 28

0.7 0.7

0.6 – 0.9 0.6 – 0.8

1.5 1.7

Rakai 1994 - 06

males females

38.1 38.0

33.4 – 43.2 33.3 – 43.1

29 26

27 – 31 23 - 28

1.5 1.6

1.3 – 1.8 1.3 – 1.8

2.0 2.1

Kisesa 1994 - 04

males females

41.8 35.1

34.7 – 49.7 29.4 – 41.4

31 28

28 – 33 15 – 33

1.5 1.2

1.2 – 1.7 1.0 – 1.9

1.8 1.7

Manicaland 1998 - 03

males females

54.7 41.3

45.8 – 64.1 35.4 – 47.7

33 26

30 - 36 23 - 29

2.2 2.4

1.8 – 3.4 1.7 – 2.8

2.0 2.9

Hlabisa 2001 - 06 (LTR at 55)

males females

78.3 75.3

65.8 – 88.6 39.8 – 82.7

32 27

28 – 35 25 – 30

7.1 6.7

4.6 - 11.1 5.0 – 9.0

3.6 * 3.6 *

Males have a higher life time risk of HIV infection ...

… an older age distribution of risk …

… peak rates are broadly similar … … pattern is

slightly less concentrated

* 40 x peak incidence for Hlabisa

Graphical results: smoothed age-specific incidence rates by sex and study site

Males

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

15 25 35 45 55 65 75

age

Ann

ual i

ncid

ence

rate

MasakaRakaiKisesaManicalandHlabisa

Females

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

15 25 35 45 55 65 75

age

Ann

ual i

ncid

ence

rate

MasakaRakaiKisesaManicalandHlabisa

To compare incidence patterns in the South African cohort with the others demands some re-scaling

0.00

0.25

0.50

0.75

1.00

Pro

porti

on s

urvi

ving

0 1 2 3 4 5 6 7 8 9 10 11 12Years since seroconversion

Kisesa Masaka RakaiS.African minersRwanda ANCThai Blood donorsThai militaryHaiti STD clinic

Overall survival

Non-African

studies