Scientific Approaches to Program
Components: Opportunities Challenges and
Impact
Sevgi Aral, PhD
Istanbul, Turkey
March 21, 2011
National Center for HIV/AIDS, Viral Hepatitis, STD , and TB Prevention

Why now?

So many efficacious STI/HIV
interventions – so little impact
Goal: To prevent individual acquisition
To prevent individual transmission
To reduce population incidence
Prevention of individual
acquisition and
transmission
Reduction of
population
incidence
Efficacious Individual
Intervention
Access/
acceptability
Adherence
Disinhibition
(risk compensation)
distribution of technology
dissemination of knowledge
cost
affordability
side-effects
correct use
consistent use
correct timing
correct dose
long-term maintenance of above
changes in risk perception
↑ other risk behaviors
Successful Prevention
Of Individual Acquisition/Transmission
Successful Prevention
of Individual Acquisition/Transmission
Identification
Intervention Mix
Prevention targets
Coverage & scale-up
M&E
Reduction of Population
Incidence
affected subpopulations
sexual structures
social practices; demographic patterns; migration & turnover
organizational structures
epidemic phase/epidemic trajectory
identified need
organizational capacity/financial resources
synergies and antagonisms among interventions
context XX intervention interactions
sexual mixing patterns → who are exposed to pathogen
who are transmitting pathogen
sexual networks → which are central positions and roles
numbers and social location of persons to prevent acquisition/
transmission
pathways for introduction and scale-up of interventions
subpopulations
sexual structures
sexual behaviors
intervention implementation
POPULATION (SUBPOPULATION) INCIDENCE
B
B
B
A
B
A
C
D
B
B
Not only at individual level
bridge populations
men
E
B
Often it seems
Prevention of acquisition and transmission (in
individuals) framework is used when
thinking about reducing population incidence
?? Is the hidden individualistic biomedical
(psychological) model the culprit???
Opportunities
Huge
we are not doing an adequate job of:
identification; determining intervention mix;
determining prevention targets; coverage
and scale-up issues; monitoring and
evaluation (of population impact)
assessing the relationships among these
assessing the interactions
between context and intervention schemes
How well do we assess the context?
Epidemiology
distribution and concentration of infection
emergent clusters
geographic distribution
sexual structures → where the epidemic is going?
epidemic trajectory
epidemic phase
Socio-demographic context
behavior patterns
sexual mixing patterns
migration
turnover in key populations
key populations – powerful men, police, military
Perhaps we do not examine
epidemiology sufficiently
(or we do not respect what we
observe sufficiently)
Paper # 137
Identification of Localized Clusters of High HIV Incidence in a
Widely Disseminated Rural South African Epidemic: A Case for
Targeted Intervention Strategies
Frank Tanser*1, T Bärnighausen1,2, and M-L Newell1,3
1Africa
Ctr for Hlth and Population Studies, Univ of KwaZulu-Natal,
Durban, South Africa; 2Harvard Sch of Publ Hlth, Boston, MA, US;
and 3Inst of Child Hlth, Univ Coll London, UK
Session 38-Oral Abstracts
http://www.retroconference.org/2011/Abstracts/41395.htm
Paper # 137
Identification of Localized Clusters of High HIV Incidence in a
Widely Disseminated Rural South African Epidemic: A Case for
Targeted Intervention Strategies
Conclusions: Targeting efforts at settings where HIV
transmission is most intense is crucial. Our study
provides clear empirical evidence for the localized
clustering of new HIV infections. The results show that
even in a severely affected rural African community,
interventions that specifically target, geographically
defined, high-risk communities could be highly
effective in reducing the overall rate of new infections.
Session 38-Oral Abstracts
http://www.retroconference.org/2011/Abstracts/41395.htm
HIV incidence across the study area with highincidence clusters superimposed
Session 38-Oral Abstracts
http://www.retroconference.org/2011/Abstracts/41395.htm
Distribution of sero-conversions
….and it is not only infections
that cluster geographically….
In the Bagalkot district of
Karnataka in South India
15 % of the villages accounted
for 54% of all rural FSW
Blanchard JF et al. Sex Transm Infect 2007; 83:i30-i36 d oi:10.1136/sti.2006.023572
In the UK…Project SIGMA found
“….Most individuals (60%) who
engage in AI do so only once or twice
a month, but there is a long tail of
those who do it much more. In terms
of the amount of AI acts, one-tenth of
the individuals are performing half of
the acts of AI. The Gini coefficient of
concentration is high (0.55).”
Coxon PM and McManus TS. The Journal of Sex Research 2000
In the U.S.
20% of women
account for
60% of vaginal sex acts in past 4 weeks
and
24% of men
account for
61% of vaginal sex acts in past 4 weeks
Leichliter JS et al. Sex Transm Infect December 2010; 86(Suppl 3):
In the U.S.
20% of women
account for
47% of opposite sex partners in past year
and
20% of men
account for
57% of opposite sex partners in the past year
Leichliter JS et al. Sex Transm Infect December 2010; 86(Suppl 3)
In the U.S. (county level analysis)
20% of the population
accounts for
39% of Chlamydia
52% of Gonorrhea
64% of Primary and Secondary Syphilis
Chesson HW et al. Sex Transm Infect December 2010; 86(Suppl 3)
Perhaps we do not assess
“context” sufficiently
(or we do not respect the context
we observe adequately)
The concurrency debate
Aral SO. Partner concurrency and the STD/HIV Epidemic. Curr Infect Dis Rep 2010; 12(2):134-139.
Concurrency is more complex than it seems
Mirjam Kretzschmar,1,2 Richard G. White,3 and Michel Caraël4
1Centre
for Infectious Disease Control, RIVM, Bilthoven, The Netherlands
2Julius Centre for Health Sciences and Primary Care, University Medical
Centre Utrecht, The Netherlands
3Infectious Disease Epidemiology Unit, Department of Epidemiology and
Population Health and Centre for the Mathematical Modelling of Infectious
Diseases, London School of Hygiene and Tropical Medicine, London, UK
4Department of Social Sciences, Free University of Brussels, Belgium
Corresponding Author: Dr Mirjam Kretzschmar, Corresponding Author’s
Institution: University of Bielefeld
Keywords: Polygyny, concurrency, HIV transmission
Published in final edited form as: AIDS. 2010 January 16; 24(2): 313–315.
doi: 10.1097/QAD.0b013e328333eb9d.
Concurrency is more complex than it seems
Mirjam Kretzschmar,1,2 Richard G. White,3 and Michel Caraël4
However, the empirical basis proving that concurrency actually is the
driving force behind the continuing high prevalence of HIV in sub
Saharan Africa has been lacking. While some studies investigated the
impact of concurrent partnerships on the prevalence of HIV in various
sub-Saharan Africa populations [7-9], they were not able to identify
concurrency as a strong explanatory factor. Also, epidemiological
observations like the decrease of HIV prevalence in Uganda following
the advocacy of the “zero grazing” strategy for HIV prevention [10, 11]
is not conclusive evidence for the impact of concurrent partnerships on
HIV transmission, because of the possibility of ecological inference
fallacy. Now Reniers and Watkins demonstrate in an ecological study of
HIV prevalence in 34 sub-Saharan Africa countries that concurrency in
the traditional form of polygyny can even be negatively correlated with
HIV prevalence [12].
Published in final edited form as: AIDS. 2010 January 16; 24(2): 313–315.
doi: 10.1097/QAD.0b013e328333eb9d.
Concurrency is more complex than it seems
Mirjam Kretzschmar,1,2 Richard G. White,3 and Michel Caraël4
It clearly shows the need to view concurrent partnerships in
their social and cultural context. It matters what the
motivation is for establishing concurrent partnerships, how
they are distributed among men and women, and in how far
they are anchored in the culture of a society.
Published in final edited form as: AIDS. 2010 January 16; 24(2): 313–315.
doi: 10.1097/QAD.0b013e328333eb9d.
It turns out that it is “mutual non-monogamy”
or “symmetric concurrency” that drives
STI/HIV spread….
Polygyny and symmetric concurrency: comparing long-duration
sexually transmitted infection prevalence using simulated sexual
networks
Shalini Santhakumaran, Katie O'Brien, Roel Bakker, Toby Ealden, Leigh Anne Shafer,
Rhian M Daniel, Ruth Chapman, Richard J Hayes, Richard G White
Sex Transm Infect 2010;86:553-558 doi:10.1136/sti.2009.041780
Non-monogamy: risk factor for STI transmission and acquisition
and determinant of STI spread in populations
Sevgi O. Aral, Jami S. Leichliter
Sex Transm Infect 2010;86:iii29-iii36 Published Online First: 5 October 2010
doi:10.1136/sti.2010.044149
….. “The role of STI in HIV spread” debate
Differences among
Mwanza
Rakai
Masaka
….
Perhaps we do not respect
sexual structures adequately
(or we do not integrate what we
know about sexual structures into
program design sufficiently)
Infections reflect where the
epidemic is
Sexual structures and mixing
patterns reflect where the
epidemic is going
St. Petersburg;
Saratov;
?? Tallin
Take home message:
Sexual structures can be assessed
through systematic rigorous
rapid assessments
migration and population
movements of all kinds
where the infection is going
need to be considered in
program planning and design

Movement networks and disease transmission
Networks of movements as
explanation for spatio temporal
spread of infections
Matt Keeling et al. PNAS, May 2010
A study of geographic profile of partnerships

Proportionately more long distance partnerships among
gc infected compared to those with chlamydia

Proportionately more long distance partnerships among
co-infected compared to those infected with gc or
chlamydia

Proportionately more long distance partnerships among
chlamydia repeaters compared to non-repeaters.

? Implications for effectiveness of PN
Hippe and Jolly – In preparation
Jolly – personal conveersation
How well do we plan
and design prevention programs?
Do we determine prevention
targets based on a good
understanding of sexual
structures?
Do we determine the intervention mix
based on a good understanding of:
prevention targets
affordability
sustainability
interactions with context
synergies and antagonisms among interventions
cost-effectiveness?
In program planning and design – do
we consider the required coverage
(for population impact)?
Do we plan adequately for scale-up?
Do our scale-up plans consider
populations and health systems to be
CAS’s?
Do we have the correct monitoring and
evaluation plans in place?
Do we know what needs monitoring?
Do we know what can be effectively
evaluated?
Questions
Effect of an intervention?
or
Effect of the interaction between
context and intervention?
Questions (con’t)
Is it possible to tease out
the effects of a particular intervention
on population incidence?
Questions (con’t)
Do Community Randomized Trials
provide the best evidence for
population impact of community
interventions?
Choices as we delve into our knowledge base
interventions
or
programs
scale-up
or
resource allocation
generalities
or
specificity / heterogeneity
randomization
or
context appropriate specificity
Choices ….. (con’t)
individual behaviors
or
subpopulation behaviors
mixing patterns
averages (means, medians)
or
shapes of distributions
concentration patterns
Biostatistics
or
mathematical modeling
standardized intervention
packages
or
custom built intervention mix
Population health as complex adaptive system
• Location
• Life course perspective/ path dependence (chains of consequences)
• Mutual determination
feedback loops (feedback – feed forward)
• Dynamic aspects
• Spatial aspects
• Multilevel aspects
• Interactions between levels
Population health as complex adaptive system (con’t)
• Interactions between determinants
• There is heterogeneity and heterogeneity counts
• Variance is important – it is the distribution (not central tendency)
and tail of distribution that plays a real big role
• Adaptation to feedback
• Emergence; emergent properties
Need for agent-based modeling
“The reason to look at
epidemiology from a complex
systems approach is that it does
not make sense to try any other
approach”
Carl Simon
“Everything should be as simple
as it is, but not simpler”
Albert Einstein
We do have tools
• Systematic rigorous rapid assessments of context
• Venue mapping – the “place” method
• Mapping/monitoring of members of key populations
• Geographic Information Systems (GIS)
• Distribution analysis – Gini Coefficients and Lorenz Curves
• Resource allocation decision making tools
• Cost, cost-benefit, cost effectiveness analyses
• Mathematical modeling – for targeting and coverage issues
(• Agent based modeling for emergent properties)
• CAS based approaches to scale-up
• New approaches to M&E being developed
What we need
is
not a paradigm shift
but a major paradigm enhancement
An expanded approach to bring “population health”
and “complexity science” approaches into STI/HIV
prevention.
Thank you!