Using Epidemiology for Data-Driven Decision-Making in ... · Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016 How Can Mathematical Modeling

Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016

How Can Mathematical Modeling Help Us Strategize for TB Prevention and Control

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How can mathematical modeling help

us strategize for TB prevention and control?

Using Epidemiology for Data-Driven Decision-Making in TB Programs

RTMCC pre-meeting training, Denver, CO, February 2016

Emily Kendall, MD

[email protected]

Learning Objectives

• Apply a basic transmission model to a decision

about TB control strategy

• Interpret model results, with consideration of

assumptions and limitations

• Consider how to effectively engage models and

modelers in decision-making

A health department’s dilemma

• You oversee TB control for a local/regional health department.

• Your shrinking budget allows you only to treat diagnosed cases and screen their closest contacts.

• You receive a grant to identify and treat latent TB in your area.

Options include:

1. Expand contact investigation for local cases

2. Screen foreign-born individuals

How will you choose?



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Whom should we screen?

• Assume (for now) that the same resources can screen the same number in either population

120 Foreign born individuals 120 casual contacts of local cases

One simple answer:

Focus on the population with more latent TB

Foreign born population: 1/3 latently infected

Casual contacts of local cases: 1/6 latently infected

Susceptible

Latently infected

… but not all latent TB becomes active TB

from Esmail et al, Philosophical Transactions B 2014,

based on Ferebee Bibl Tuberc 1970

When most local contacts

would be screened and treated

When most foreign-born

individuals would be screened and treated



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Foreign born individuals: infection events are more remote

Casual contacts of local cases: mostly recent infections = greater reactivation risk

Susceptible

Latently infection (recently infected)

Latent infection (remotely infected)

Another simple answer: Find and treat the

individuals at highest risk for reactivation

Casual contacts of local cases: mostly recent infections = greater reactivation risk

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Susceptible

Latently infection (recently infected)

Latent infection (remotely infected)

Foreign born individuals: infection events are more remote

Susceptible

Future active cases

Estimate

individual cumulative lifetime risk

Foreign born: 1/3 latently infection

5% lifetime reactivation risk remaining

= expect 2 individuals to develop active TB

Local contacts: 1/6 latently infected

10% lifetime reactivation risk remaining

= expect 2 individuals to develop active TB

Latently infected, never reactivate



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But that’s still not the whole story.

1. We may care more about shorter-term impact.

2. And, we still haven’t accounted for the people that

these incident cases may infect

… Or if/when secondarily-infected individuals may develop

active TB…

… Or whom else those secondary cases may infect…

A role for modeling

• Epidemics are complex

• In particular, characteristics of TB limit intuition:

– Long time scales

– Complex natural history

– Airborne transmission difficult to pinpoint

– Imperfect treatment efficacy

Susceptible

Early Latent

TB

Active TB

Recovered

Rapid

progression

Reactivation

Late Latent

TB

Treatment

Infection Preventive

therapy

A role for modeling

• Mathematical models help simplify complex systems into something we can explore.

• They translate epi data into a decision-making framework.

– “If your assumptions are correct, these are the implications.”

• They help conceptualize important questions.

– What are the key drivers of impact, cost, etc.?

– If we change our assumptions, how much will our outcomes change?

– What data do we most need to collect?

Inputs

• Data • Assumptions

Outputs

• Implications • Relationships

** Model’s can’t replace data or tell us that our assumptions

are correct



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A role for modeling

A dynamic transmission model can help us choose our target population here.

account for changes

over time

(e.g. short-term vs.

long-term reactivation

risk, plus changing

size of epidemic)

account for secondary

cases

(e.g. preventing one

reactivation may

prevent transmitted

cases too)

Local contacts: Incident active cases are more likely to occur sooner

Foreign born: Future reactivations are spread more evenly over remaining lifetimes

Year 1

First let’s add time (but consider only the latent infections in our screening population)

Susceptible

Latently infected

Active cases

Recovered

Year 3




Susceptible

Latently infected

Active cases

Recovered



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Year

5



Susceptible

Latently infected

Active cases

Recovered


Year

15

Individual risks are equivalent over lifetimes, but not

over a shorter time horizon.

And we still need to add transmission…



Susceptible

Latently infected

Active cases

Recovered


And now also add transmission

• Additional input data/assumptions:

– # secondary infections per active TB case.

– Time-varying risk of progression after infection

• for the individuals we may screen,

• and also for those they may infect if not treated.



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Local contacts Foreign born

Year 1 – no intervention

Susceptible

Latently infected

Active cases

Recovered




Susceptible

Latently infected

Active cases

Recovered




Susceptible

Latently infected

Active cases

Recovered

`




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Susceptible

Latently infected

Active cases

Recovered




Susceptible

Latently infected

Active cases

Recovered


Interpretation of model projections

• Our no-transmission analysis underestimated TB

incidence (not a surprise)

• Timing also mattered in transmission dynamics

Cases in local contacts occur earlier

Transmission occurs sooner

Epidemic grows faster



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Now, simulate preventive interventions

• Underlying epidemiologic data and assumptions, again:

– LTBI prevalence

– Reactivation rates

– Transmission intensity

– [No] overlap of target populations

• Additional assumptions about the interventions:

– Our ability to reach and diagnose each target population

– Expected effectiveness of LTBI treatment

– Timing/duration of intervention – For now, assume one-time intervention at year 0

Say, 50% effectively

diagnosed + treated

Year 5 outcome:

Screening of foreign-born Total Year 5 prevalence falls from 4 to 3, And cumulative incidence from 11 to 10

Susceptible

Latently infected

Active cases

Recovered

Treated for LTBI

Year 5 outcome:

Expanded local contact tracing Total Year 5 prevalence falls from 4 to 2, And cumulative incidence from 11 to 4

Susceptible

Latently infected

Active cases

Recovered

Treated for LTBI



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Example results - projected impact

For this single run:

Uncertainty analysis:

• Parameter/data uncertainty

• Random effects

Sensitivity analysis:

• What estimates drive the uncertainty in our predictions?

– Small change in parameter x large change in outcome?

• Which assumptions most influence our outcomes?

– Different model structure vastly different result?

We may want to refine these inputs, e.g. through more data collection

Parameter uncertainty in this model:

• LTBI prevalences (in each group)

• Recentness of infection (in each group)

• Factors affecting reactivation (e.g. age, diabetes)

• # of secondary cases (disease burden, treatment delay, mixing)

• Effectiveness of preventive therapy

• Resource requirements

– For example, might we engage private providers in foreign-born

screening to reach more people with the same $$?



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A published example – Household contact tracing One-way sensitivity analysis: Compares influence of each model parameter on a

single outcome (here, TB incidence at 5 years) to identify critical data

Kasaie et al, AJRCCM 2014 189(7):845

Another published example -- Impact of a TB strain typing service via improved contact tracing and reduced

diagnostic delay

J Mears et al. Thorax doi:10.1136/thoraxjnl-2014-206480

Copyright © BMJ Publishing Group Ltd & British Thoracic Society. All rights reserved.

Structural uncertainty in this model

• Epidemiological assumptions – What if we included heterogeneity of …

• infection risk (age, HIV status) ?

• transmission risk (disease burden, time to treatment initiation)?

– What if we specified contact structure (households, social networks)?

• Specifics of intervention – Timing (duration or frequency) of intervention

– Different amounts or mixes of resource allocation

– Other types of interventions

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Aside about cost-effectiveness models

• Considers added benefit of an intervention per added cost, versus an alternative (often standard of care)

• Can stand alone…

• …or can be added to a transmission model

– Captures transmission-related cost savings

via changes in epidemic size

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# people affected (e.g.

current incidence)

Costs with

intervention

e.g. the

intervention

itself

Costs without

intervention

e.g. treating

cases it might

prevent

Individual

well-being

with

intervention

Individual

well-being

without

intervention

Costs with

intervention

Costs without

intervention

Total well-being with Total well-being without

Using models (and modelers!)

in decision-making

• Define the question.

– Models must simplify. The best ones tailor assumptions to a specific situation.

– What factors will guide decisions? Short vs long-term impact, cost, etc.?

• Identify relevant data

– Including their degree of uncertainty.

• Engage modelers – early when possible.

– Model development process may identify knowledge gaps and clarify thinking.

Summary

• Models simplify complex systems

• They are useful for seeing relationships: If this, then that

• Outputs are only as accurate as the data and assumptions

that go in

• Using models can aid decision making

– Set priorities, clarify assumptions, identify data gaps

Documents

Using Epidemiology for Data-Driven Decision-Making in ... · Using Epidemiology for Data-Driven Decision-Making in Tuberculosis Programs February 24, 2016 How Can Mathematical Modeling