Optimizing Health Policies Malaria and Pneumonia in Sub-Saharan - - PowerPoint PPT Presentation

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Optimizing Health Policies Malaria and Pneumonia in Sub-Saharan - - PowerPoint PPT Presentation

Mar 16, 2023 363 likes •841 views

The webinar will start at: 13:00:00 The current time is: 13:01:13 Central Daylight Time UTC-5 Optimizing Health Policies Malaria and Pneumonia in Sub-Saharan Africa Introduction Your Hosts Today Stefan Conrady

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Malaria and Pneumonia in Sub-Saharan Africa

Optimizing Health Policies

13:01:13

The current time is:

13:00:00

The webinar will start at: Central Daylight Time UTC-5

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Your Hosts Today

  • Stefan Conrady

stefan.conrady@bayesia.us

  • Stacey Blodgett

stacey.blodgett@bayesia.us

Introduction

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Methodological Objective

  • Bayesian networks as a framework for reasoning about health policies

under uncertainty.

Substantive Research Objective

  • Establish optimal policies for malaria and pneumonia diagnosis and

treatment, i.e., “Test & Treat” vs. Presumptive Treatment.

Today’s Objectives

stefan.conrady@bayesia.us

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Motivation & Background

  • Over- and Undertreatment Malaria and Pneumonia
  • Testing Innovations & WHO Guidelines

Methodology

  • Knowledge Modeling & Reasoning with Bayesian Networks

Research Workflow

  • Encode Domain Knowledge in Bayesian Network
  • Elicit Probabilities
  • Assess Costs and Utilities
  • Search for Optimal Policies

Today’s Agenda

stefan.conrady@bayesia.us

30 min. 30 min.

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5 BayesiaLab.com

Teaching Edition Academic Edition BayesiaLab 6 Professional BayesiaLab WebSimulator Bayesia Expert Knowledge Elicitation Environment (BEKEE) Bayesia Engine API for Network Learning Bayesia Engine API for Modeling and Inference Code Export Module Bayesia Market Simulator

Web Application Desktop Software API

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Webinar Slides & Recording Available

stefan.conrady@bayesia.us

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Malaria Testing

Motivation & Background

?

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Caveat

  • The medical subject matter presented in this webinar is

strictly for methodological illustration purposes.

  • Today’s particular problem domain of infectious disease

diagnosis is merely a prototypical example diagnostic inference.

  • Also, the subject matter is presented in a highly

simplified format. This is by no means an exhaustive treatment of the topic.

  • No part of this webinar should be interpreted as medical

research or a health policy recommendation.

Optimizing Health Policies

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Infectious Diseases in Sub-Saharan Africa

  • 4.2 million annual child deaths
  • Over 1 million due to bacterial pneumonia
  • Over 750,000 due to malaria

Source: WHO

Motivation & Background

Similar Symptoms Diagnostic Tool Economical, efficacious treatments are available:

  • Antibiotics for bacterial pneumonia
  • Artimisinin-combination therapies (ACT) for malaria
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Radiography

Pneumonia Diagnosis in the Developed World

Case courtesy of Dr Jeremy Jones, Radiopaedia.org, rID: 20012

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Microscopic Diagnosis

Malaria Diagnosis in the Developed World

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Diagnostic Approach:

  • Fever " Malaria

“Presumptive Treatment”

  • Result:
  • Overtreatment of

malaria

  • Undertreatment of

bacterial infections, e.g. pneumonia

Past Practice in Developing Nations

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  • “…in all settings, clinical suspicion
  • f malaria should be confirmed with

a parasitological diagnosis“

Source: WHO: Guidelines for the treatment of malaria (2e). Geneva: World Health Organisation, 2010.

  • Result:
  • 200,000 Rapid Diagnostic Tests

(RDT) distributed in 2005

  • 50 million in 2010

Rapid Diagnostic Test for Malaria

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  • “The researchers found that after the rapid diagnostic test ruled out malaria,

many patients were prescribed antibiotics — in effect, trading the overuse of anti-malarial drugs for the overuse of antibiotics.”

  • “‘If you’re in a remote area and you don’t have a lot of other options — you’ve

got a sick child or a sick patient in front of you, that person may have come from even up to 20 kilometers away with expensive transport or even walking — you want to do something,’ Hopkins says. ‘And if you don’t feel like you should give an anti-malarial — the test is negative — your alternative, in some cases, is an antibiotic.’” Source: PRI Interview, October 15, 2017, https://www.pri.org/stories/2017-10-15/quick-and-easy- malaria-tests-some-unexpected-drawbacks

Unintended Consequences

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Human reasoning is flawed!

Probabilistic Reasoning

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Human Cognitive Limitations and Biases Under Uncertainty

BayesiaLab.com

Probabilistic Reasoning

Human Reasoning ≈ Normative Reasoning Human Reasoning ≠ Normative Reasoning

Disease Test

Remember?

FALLACIES

Webinar on March 2

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Reasoning about an individual case:

Probabilistic Reasoning

= True = Negative “Explaining Away” “Don’t treat for malaria” “Treat for pneumonia”

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Why Not?

  • The probability of pneumonia

given a negative malaria test result depends on:

  • Prevalence of pneumonia
  • Sensitivity & Specificity
  • f RDT
  • Prevalence of malaria

Probabilistic Reasoning

50% Probability of Pneumonia

Prevalence of Malaria Malaria Test = Negative

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Testing for Malaria and Pneumonia?

New Health Policy?

C-reactive protein (CRP) Test for Pneumonia Rapid Diagnosis Test for Malaria

&

Hypothetical Example

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Policy Questions

  • For Malaria
  • Test & Treat or Presumptive Treatment
  • For Bacterial Pneumonia
  • Test & Treat or Presumptive Treatment

New Health Policy?

?

Hypothetical Example

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Additional Considerations for New Guidelines

  • Cost of tests
  • Cost of treatments
  • Limited resources
  • Efficacy of treatments
  • Unintended consequences of overtreatment,

e.g., antibiotics resistance

  • Adherence to protocols
  • Prevalence of diseases
  • Comorbidities
  • Regional factors, e.g., climate, seasonality

New Health Policy?

If it is difficult to reason about a single case, how can we establish a policy for the world? Hypothetical Example

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Big Data? No Data!

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Optimization Attribution Simulation Explanation Prediction Description

Model Purpose Model Source

Association/Correlation Causation Theory Data

Map of Analytic Modeling & Reasoning

BayesiaLab.com

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Optimization Attribution Simulation Explanation Prediction Description

Model Purpose Model Source

Association/Correlation Causation Theory Data

Map of Analytic Modeling & Reasoning

BayesiaLab.com

Knowledge Modeling & Reasoning on Theoretical Grounds

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Optimization Attribution Simulation Explanation Prediction Description

Model Purpose Model Source

Association/Correlation Causation Theory Data

Map of Analytic Modeling & Reasoning

BayesiaLab.com

Knowledge Modeling & Reasoning on Theoretical Grounds

YELLOW CAB CO.

WHITE CAB COMPANY

?

Webinar on March 2, 2018

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Proposed Policy Development Approach

  • Domain Knowledge Encoding
  • Probability Elicitation
  • Cost/Utility Assessment
  • Optimization

New Health Policy?

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Building a Bayesian Network Model

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  • 1. Brainstorming & Model Construction

stefan.conrady@bayesia.us

Qualitative Network

Facilitator Experts

  • Variables of interest
  • Causal relationships
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Qualitative Bayesian Network Structure from Brainstorming

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  • 2. Knowledge Elicitation

stefan.conrady@bayesia.us BEKEE Server BAYESIALAB

Quantitative Elicitation

? ? ? ?

Facilitator Experts

Web Client

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“Parameters” from BEKEE

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  • 3. Inference, Analysis, and Optimization

stefan.conrady@bayesia.us BAYESIALAB

Final Network

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Inference & Optimization

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Sounds Familiar?

  • It’s all about encoding a qualitative structure and

eliciting probabilities.

Previous Examples

YELLOW CAB CO.

WHITE CAB COMPANY

?

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VR

In Conclusion…

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User Forum: bayesia.com/community

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bayesia.com/pricing-2018

BayesiaLab.com

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Try BayesiaLab Today!

  • Download Demo Version:

www.bayesialab.com/trial-download

  • Apply for Unrestricted Evaluation Version:

www.bayesialab.com/evaluation

BayesiaLab Trial

BayesiaLab.com

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Upcoming Webinars:

  • March 23

Quantifying Product Cannibalization — A Case Study in Marketing Science

  • March 30

t.b.d.

Register here: bayesia.com/events

Webinar Series: Friday at 1 p.m. (Central)

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BayesiaLab Courses Around the World in 2018

Learn More & Register: bayesia.com/events stefan.conrady@bayesia.us

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San Francisco

Introductory BayesiaLab Course in San Francisco, California July 23–25, 2018

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Chicago

6th Annual BayesiaLab Conference in Chicago November 1–2, 2018

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Thank You!

BayesiaLab.com

stefan.conrady@bayesia.us linkedin.com/in/stefanconrady facebook.com/bayesia BayesianNetwork