hiv research in the era of art: changing priorities in tanzania
DESCRIPTION
HIV research in the era of ART: changing priorities in Tanzania. Basia Zaba SOAS 3 rd March 2011. Overview. Introduction – data sources & measurement strategies Monitoring the epidemic at a national level Evaluating prevention and treatment responses - PowerPoint PPT PresentationTRANSCRIPT
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