A System Dynamics-Based Evaluation 

of the Mandatory Offer of HIV Testing 

in New York State

Health Economics Resource Center Cyber-Seminar

March 14, 2013

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Erika Martin, PhD MPH

Rockefeller College of Public Affairs & Policy, University at Albany

Nelson A. Rockefeller Institute of Government

State University of New York
Acknowledgments

• SUNY-Albany: 

– Roderick MacDonald, PhD

• New York State Department of Health AIDS Institute:

– Daniel O’Connell, MA MLS

– Lou Smith, MD MPH

– Daniel Gordon, PhD

– James Tesoriero, PhD

– Franklin Laufer, PhD

– John Leung, MS

• Centers for Disease Control and Prevention HIV prevention 

projects cooperative agreement

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Roadmap

• Motivation for system dynamics policy evaluation

• Research methods

– System dynamics modeling approach

– HIVSIM model of New York HIV testing and treatment system

• Results: baseline projections, in absence of the law

• Results: potential effects of the law

• Key model insights

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New York State HIV testing law

• Effective September 1, 2010

• Aims to increase HIV testing and subsequent entry into care 

and treatment

• Key features:

– HIV testing required in routine medical care (ages 13-64)

– Simplified informed consent and pre-test counseling

– Providers/facilities offering HIV tests must arrange follow-up care 

appointments

• Statutory requirement for Commissioner of Health to evaluate 

the number of HIV tests and number who access care and 

treatment; due September 1, 2012

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Rationale for system dynamics 

modeling study

• Quantitative data (surveys, administrative data, surveillance 

system) may not address complexities in the system of HIV 

testing and care

• Qualitative research can examine nuances, but cannot 

generate quantitative predictions

• Empirical data from other evaluation studies limited to short 

time horizon and measurable outcomes

• Law implemented in context of multiple concurrent policies 

that may affect outcomes

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Features of system dynamics models

• Analyze problems in complex social, managerial, economic, or 

ecological systems, at the population level

• Work closely with stakeholders and experts to develop system 

structure and incorporate data

• Holistic view of the interaction of organizations and processes 

in system producing system-wide results

• Feedback loops to model dynamic system processes

• Nonlinearities in relationships among variables

• Dynamic implications of policies, why and how outcome will 

change, potential unintended consequences, areas where 

implementation may not lead to intended outcomes

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Methods overview

• Computer simulation model of HIV testing and care in New 

York State (HIVSIM)

• Data sources used for HIVSIM parameters and calibration

– HIV surveillance system (NYSDOH)

– Medicaid claims (NYSDOH)

– Incidence estimates (NYSDOH, using CDC methodology)

– Expert opinion

– Published literature

• Counterfactual analyses of short- and long-term outcomes 

under alternate implementation scenarios

• Results presented as graphs over time; % changes over time 

and across scenarios (“differences in differences”)

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HIVSIM stock and flow diagram

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HIVSIM stock and flow diagram

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Stock and flow diagram: zoomed in

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Key features of HIVSIM

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Disease progression (acute to late stage)
Key features of HIVSIM

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Slower disease progression for individuals in treatment
Key features of HIVSIM

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Diagnosis and engagement in care
Dynamics of different transmission rates

• Unaware individuals transmit 3.5 times more infections than 

diagnosed individuals (risk behaviors, viral load)

• 75% of New Yorkers engaged in care have a transmission rate 

of zero (complete viral suppression)

• 25% of new infections are attributable to acutely infected 

individuals

• Assumption that individuals in stages 1-3 (early, mid, late) 

have identical transmission rates 

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Different transmission rates in HIVSIM

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Higher transmission rates for individuals who 

are unaware and in acute stage disease
Different transmission rates in HIVSIM

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Very low transmission rates for individuals who are 

engaged in care and have achieved viral suppression
HIVSIM stock and flow diagram: HIV 

testing structure

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Mapping between the diagrams

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Population eligible for testing

Population that previously tested positive
Mapping between the diagrams

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Population that 

previously tested 

positive

Population 

eligible for 

testing 

(uninfected and 

unaware)
HIVSIM stock and flow diagram: HIV 

testing structure

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“Not Recently” and 

“Recently” tested 

refers to offers in 

routine care 

(incremental 

testing)
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Time delay between 

test offers in routine 

care (specified by 

modelers)

HIVSIM stock and flow diagram: HIV 

testing structure
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Anyone can be 

diagnosed as part of 

background testing 

(even if ineligible 

for another test in 

routine care)

HIVSIM stock and flow diagram: HIV 

testing structure
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Individuals eligible 

for a test in routine 

care may also be 

diagnosed through 

incremental testing

HIVSIM stock and flow diagram: HIV 

testing structure
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Uninfected 

individuals may 

subsequently 

become infected 

and diagnosed 

through background 

or incremental 

testing

HIVSIM stock and flow diagram: HIV 

testing structure
Recap of dynamic model features

• HIVSIM aggregates individual trajectories (disease 

progression, engagement in care) at the population level

• Dynamic feedback: 

– Existing cases generate new infections

– Infectiousness and health outcomes (survival, mortality) change 

depending on disease stage and level of engagement in care

• Nonlinear feedback: 

– Continued testing of the whole population will result in a lower yield

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Policy scenarios

• No law

• Level of implementation (perfect, high, and low)

• Frequency of repeat testing in routine care (annual, five-year, 

and one-time)

• Perfect viral suppression among individuals in care

• Range of implementation times (18 months to five years)

• All scenarios represent implementation of incremental testing 

in routine care settings, and assume New Yorkers also 

continue to be diagnosed as part of background testing 

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Outcome variables

• Increase in HIV tests*

• New infections

• Newly diagnosed HIV cases; newly diagnosed AIDS cases; 

fraction of newly diagnosed cases with concurrent AIDS

• Diagnosed HIV cases newly linked to care*; diagnosed HIV 

cases ever linked to care; diagnosed HIV cases currently 

engaged in care

• People living with diagnosed HIV infection; people living with 

HIV (diagnosed and undiagnosed)

• Fraction of HIV cases who are undiagnosed

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* Law requires that the NYSDOH evaluate impact of statute with respect to 

“number of persons tested for HIV infection” and “number of persons who access 

care and treatment”
Baseline projections, in absence of law

• Continuing decline in annual new infections, annual new 

diagnoses, and fraction of undiagnosed cases

• Slight increase in people living with diagnosed HIV infection 

and diagnosed HIV cases currently in care

• Explanations: current HIV prevention efforts, system delays, 

survival and transmission benefits of antiretroviral therapy

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Potential impact of law, if 

implemented as designed

• Reductions in annual new infections and fraction of 

undiagnosed cases

• Initial surge then decline in newly diagnosed HIV cases per 

year

• Steady decline in newly diagnosed AIDS cases per year

• Explanations: rapid identification of unaware individuals, 

individuals diagnosed earlier before progressing to late stage 

disease

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If implemented as designed, the HIV testing law will 

lead to fewer new infections.

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Results for scenarios of annual repeat testing in routine care (in addition to 

continued targeted risk-based testing), and three levels of implementation

No law

Perfect 

implementation
If implemented as designed, the HIV testing law will lead 

to fewer newly diagnosed AIDS cases.

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Results for scenarios of annual repeat testing in routine care (in addition to 

continued targeted risk-based testing), and three levels of implementation

No law

Perfect 

implementation
If implemented as designed, the HIV testing law will 

reduce the fraction of undiagnosed cases. 

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Results for scenarios of annual repeat testing in routine care (in addition to 

continued targeted risk-based testing), and three levels of implementation

No law

Perfect 

implementation
If implemented as designed, the HIV testing law will lead to 

an initial surge then decline in newly diagnosed HIV cases.

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Results for scenarios of annual repeat testing in routine care (in addition to 

continued targeted risk-based testing), and three levels of implementation

No law

Perfect 

implementation
Potential impact of law, if 

implemented as designed

• No surge in individuals newly linked to care annually (relative 

increase, not absolute increase)

• Minimal changes in people living with diagnosed HIV infection 

and number of cases in care

• Number of annual new infections and fraction of undiagnosed 

cases do not approach zero

• Explanations: declining trend in individuals newly linked to 

care, people stay in care for long time due to system delays, 

ongoing transmissions from individuals unaware and 

diagnosed cases not virally suppressed

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Even under perfect implementation, there will not be a 

large surge in diagnosed cases newly linked to care.

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Results for scenarios of annual repeat testing in routine care (in addition to 

continued targeted risk-based testing), and three levels of implementation

No law

Perfect 

implementation
Even under perfect implementation, there will be minimal 

differences in people living with diagnosed HIV infection.

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Results for scenarios of annual repeat testing in routine care (in addition to 

continued targeted risk-based testing), and three levels of implementation
Comparison of level of 

implementation vs. testing frequency

• Frequency of testing in routine care (annual, five-year, and 

one-time)

– Overall minimal differences in outcomes

– Largest difference is number of tests performed per year

• Level of implementation (perfect, high, low)

– Increasing level of implementation improves new infections, newly 

diagnosed cases, fraction of newly diagnosed cases with concurrent 

AIDS, and fraction of undiagnosed cases

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Sensitivity analysis on time to 

implementation

• No substantial changes if implementation time is varied

• Surges in new diagnoses and individuals newly linked to care 

appear larger, but outcomes are similar by the end of the 

period

• Explanations: unaware individuals are identified more quickly; 

because the unaware population relatively small, diagnosing 

them a few years earlier does not have dramatic changes on 

new infections

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Perfect viral suppression

• Perfect viral suppression among individuals in care yields 

similar improvements in annual new infections, compared to 

perfect implementation

• Largest impact on new infections is from perfect viral 

suppression among individuals in care and perfect 

implementation of the testing law

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Perfect viral suppression among individuals in care 

yields similar reductions to new infections.

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Perfect 

implementation

VL suppression
Limitations

• All models are imperfect representations of reality

• No empirical data for some parameters

• True “level of implementation” unknown

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Key model insights

• Continue to invest resources in programs that provide HIV 

medical care, improve retention in care, encourage reductions 

in risky behaviors

• Temporary increases in new HIV diagnoses under the law will 

be offset by an anticipated decline in new infections and new 

diagnoses under baseline projections

• Continue to use broad policy approach with wide range of HIV 

prevention interventions, in addition to HIV testing law

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Key model insights

• One-time testing in routine care (in addition to continued 

targeted testing) is most efficient use of resources

• Useful indicators of the law’s success are newly diagnosed HIV 

cases and newly diagnosed AIDS cases per year

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Thank you!

• Feel free to contact me at:

Erika Martin

email: emartin@albany.edu

phone: 518-442-5243

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