Main description:

A systematic review of the most current decision models and techniques for disease prevention and treatment

Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment. With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operations research tools and techniques in advancing medical decision making.

This vital resource features the most recent and effective approaches to the quickly growing field of healthcare decision analytics, which involves cost-effectiveness analysis, stochastic modeling, and computer simulation. Throughout the book, the contributors discuss clinical applications of modeling and optimization techniques to assist medical decision making within complex environments. Accessible and authoritative, Decision Analytics and Optimization in Disease Prevention and Treatment:

Presents summaries of the state-of-the-art research that has successfully utilized both decision analytics and optimization tools within healthcare operations research
Highlights the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology
Includes contributions by well-known experts from operations researchers to clinical researchers, and from data scientists to public health administrators
Offers clarification on common misunderstandings and misnomers while shedding light on new approaches in this growing area

Designed for use by academics, practitioners, and researchers, Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource for accessing the power of decision analytics and optimization tools within healthcare operations research.

Contents:

CONTRIBUTORS xiii

PREFACE xvii

PART 1 INFECTIOUS DISEASE CONTROL AND MANAGEMENT 1

1 Optimization in Infectious Disease Control and Prevention: Tuberculosis Modeling Using Microsimulation 3
SzeA -chuan Suen

1.1 Tuberculosis Epidemiology and Background 4

1.1.1 TB in India 5

1.2 Microsimulations for Disease Control 6

1.3 A Microsimulation for Tuberculosis Control in India 8

1.3.1 Population Dynamics 9

1.3.2 Dynamics of TB in India 9

1.3.3 Activation 10

1.3.4 TB Treatment 11

1.3.5 Probability Conversions 13

1.3.6 Calibration and Validation 14

1.3.7 Intervention Policies and Analysis 16

1.3.8 Time Horizons and Discounting 18

1.3.9 Incremental CostA -Effectiveness Ratios and Net Monetary Benefits 19

1.3.10 Sensitivity Analysis 22

1.4 Conclusion 22

References 23

2 Saving Lives with Operations Research: Models to Improve HIV Resource Allocation 25
Sabina S. Alistar and Margaret L. Brandeau

2.1 Introduction 25

2.1.1 Background 25

2.1.2 Modeling Approaches 27

2.1.3 Chapter Overview 31

2.2 HIV Resource Allocation: Theoretical Analyses 31

2.2.1 Defining the Resource Allocation Problem 31

2.2.2 Production Functions for Prevention and Treatment Programs 35

2.2.3 Allocating Resources among Prevention and Treatment Programs 37

2.3 HIV Resource Allocation: Portfolio Analyses 39

2.3.1 Portfolio Analysis 39

2.3.2 O piate Substitution Therapy and ART in Ukraine 40

2.3.3 PreA -exposure Prophylaxis and ART 42

2.4 HIV Resource Allocation: A Tool for Decision Makers 44

2.4.1 REACH Model Overview 44

2.4.2 Example Analysis: Brazil 45

2.4.3 Example Analysis: Thailand 48

2.5 Discussion and Further Research 50

Acknowledgment 53

References 53

3 Adaptive DecisionA -Making During Epidemics 59
Reza Yaesoubi and Ted Cohen

3.1 Introduction 59

3.2 Problem Formulation 61

3.3 Methods 63

3.3.1 The 1918 Influenza Pandemic in San Francisco, CA 63

3.3.2 Stochastic Transmission Dynamic Models 64

3.3.3 Calibration 66

3.3.4 O ptimizing Dynamic Health Policies 69

3.4 Numerical Results 73

3.5 Conclusion 75

Acknowledgments 76

References 76

4 Assessing RegisterA -Based Chlamydia Infection Screening Strategies: A CostA -Effectiveness Analysis on Screening Start/End Age and Frequency 81
Yu Teng, Nan Kong, and Wanzhu Tu

4.1 Introduction 81

4.2 Background Literature Review 83

4.2.1 Clinical Background on CT Infection and Control 83

4.2.2 CT Screening Programs 85

4.2.3 Computational Modeling on CT Transmission and Control 85

4.3 Mathematical Modeling 89

4.3.1 An AgeA -Structured Compartmental Model 89

4.3.2 Model Parameterization and Validation 93

4.4 Strategy Assessment 98

4.4.1 BaseA -Case Assessment 98

4.4.2 Sensitivity Analysis 100

4.5 Conclusions and Future Research 101

References 102

5 Optimal Selection of Assays for Detecting Infectious Agents in Donated Blood 109
Ebru K. Bish, Hadi ElA -Amine, Douglas R. Bish, Susan L. Stramer, and Anthony D. Slonim

5.1 Introduction and Challenges 109

5.1.1 Introduction 109

5.1.2 The Challenges 111

5.2 The Notation and Decision Problem 113

5.2.1 Notation 114

5.2.2 Measures of Interest 115

5.2.3 Model Formulation 117

5.2.4 Relationship of the Proposed Mathematical Models to CostA -Effectiveness Analysis 118

5.3 The Case Study of the SubA -Saharan Africa Region and the United States 119

5.3.1 Uncertainty in Prevalence Rates 122

5.4 Contributions and Future Research Directions 123

Acknowledgments 123

References 124

6 Modeling Chronic Hepatitis C During Rapid Therapeutic Advance: CostA -Effective Screening, Monitoring, and Treatment Strategies 129
Shan Liu

6.1 Introduction 129

6.2 Method 131

6.2.1 Modeling Disease Natural History and Intervention 132

6.2.2 Estimating Parameters for Disease Progression and Death 134

6.3 Four Research Areas in Designing Effective HCV Interventions 139

6.3.1 CostA -Effective Screening and Treatment Strategies 139

6.3.2 CostA -Effective Monitoring Guidelines 141

6.3.3 O ptimal Treatment Adoption Decisions 141

6.3.4 O ptimal Treatment Delivery in Integrated Healthcare Systems 145

6.4 Concluding Remarks 148

References 148

PART 2 NONCOMMUNICABLE DISEASE PREVENTION 153

7 Modeling Disease Progression and RiskA -Differentiated Screening for Cervical Cancer Prevention 155
Adriana LeyA -Chavez and Julia L. Higle

7.1 Introduction 155

7.2 Literature Review 157

7.3 Modeling Cervical Cancer Screening 159

7.3.1 Model Components 160

7.3.2 Parameter Selection 166

7.3.3 Implementation 169

7.4 ModelA -Based Analyses 171

7.4.1 CostA -Effectiveness Analysis 171

7.4.2 Sensitivity Analysis 172

7.5 Concluding Remarks 174

References 175

8 Using FiniteA -Horizon Markov Decision Processes for Optimizing PostA -Mammography Diagnostic Decisions 183
Sait Tunc, Oguzhan Alagoz, Jagpreet Chhatwal, and Elizabeth S. Burnside

8.1 Introduction 183

8.2 Model Formulations 185

8.3 Structural Properties 188

8.4 Numerical Results 193

8.5 Summary 196

Acknowledgments 196

References 197

9 Partially Observable Markov Decision Processes for Prostate Cancer Screening, Surveillance, and Treatment: A Budgeted Sampling Approximation Method 201
Jingyu Zhang and Brian T. Denton

9.1 Introduction 201

9.2 Review of POMDP Models and Benchmark Algorithms 204

9.3 A POMDP Model for Prostate Cancer Screening, Surveillance, and Treatment 206

9.4 Budgeted Sampling Approximation 209

9.4.1 Lower and Upper Bounds 209

9.4.2 Summary of the Algorithm 211

9.5 Computational Experiments 213

9.5.1 FiniteA -Horizon Test Instances 213

9.5.2 Computational Experiments 214

9.6 Conclusions 217

References 219

10 CostA -Effectiveness Analysis of Breast Cancer Mammography Screening Policies Considering Uncertainty in Women's Adherence 223
Mahboubeh Madadi and Shengfan Zhang

10.1 Introduction 223

10.2 Model Formulation 225

10.3 Numerical Studies 231

10.4 Results 233

10.4.1 Perfect Adherence Case 233

10.4.2 General Population Adherence Case 234

10.5 Summary 236

References 237

11 An AgentA -Based Model for Ideal Cardiovascular Health 241
Yan Li, Nan Kong, Mark A. Lawley, and Jose A. Pagan

11.1 Introduction 241

11.2 Methodology 243

11.2.1 AgentA -Based Modeling 243

11.2.2 Model Structure 244

11.2.3 Parameter Estimation 246

11.2.4 User Interface 248

11.2.5 Model Validation 249

11.3 Results 250

11.3.1 Simulating American Adults 250

11.4 Simulating the MedicareA -Age Population and the DiseaseA -Specific Subpopulations 252

11.5 Future Research 254

11.6 Summary 255

References 255

PART 3 TREATMENT TECHNOLOGY AND SYSTEM 259

12 Biological Planning Optimization for HighA -DoseA -Rate Brachytherapy and its Application to Cervical Cancer Treatment 261
Eva K. Lee, Fan Yuan, Alistair Templeton, Rui Yao, Krystyna Kiel, and James C.H. Chu

12.1 Introduction 261

12.2 Challenges and Objectives 263

12.3 Materials and Methods 265

12.3.1 HighA -DoseA -Rate Brachytherapy 265

12.3.2 PET Image 266

12.3.3 Novel ORA -Based TreatmentA -Planning Model 266

12.3.4 Computational Challenges and Solution Strategies 271

12.4 Validation and Results 273

12.5 Findings, Implementation, and Challenges 276

12.6 Impact and Significance 279

12.6.1 Quality of Care and Quality of Life for Patients 279

12.6.2 Advancing the Cancer Treatment Frontier 279

12.6.3 Advances in Operations Research Methodologies 280

Acknowledgment 281

References 281

13 Fluence Map Optimization in IntensityA -Modulated Radiation Therapy Treatment Planning 285
Dionne M. Aleman

13.1 Introduction 285

13.2 Treatment Plan Evaluation 288

13.2.1 Physical Dose Measures 289

13.2.2 Biological Dose Measures 291

13.3 FMO Optimization Models 292

13.3.1 O bjective Functions 293

13.3.2 Constraints 295

13.3.3 Robust Formulation 297

13.4 O ptimization Approaches 299

13.5 Conclusions 300

References 301

14 Sliding Window IMRT and VMAT Optimization 307
David Craft and Tarek Halabi

14.1 Introduction 307

14.2 TwoA -Step IMRT Planning 309

14.3 O neA -Step IMRT Planning 310

14.3.1 O neA -Step Sliding Window Optimization 310

14.4 Volumetric Modulated ARC Therapy 313

14.5 Future Work for Radiotherapy Optimization 315

14.5.1 Custom Solver for Radiotherapy 315

14.5.2 Incorporating Additional Hardware Considerations into Sliding Window VMAT Planning 315

14.5.3 TradeA -Off between Delivery Time and Plan Quality 316

14.5.4 What Do We Optimize? 316

14.6 Concluding Thoughts 317

References 318

15 Modeling the Cardiovascular Disease Prevention-Treatment TradeA -Off 323
George Miller

15.1 Introduction 323

15.2 Methods 325

15.2.1 Model Overview 325

15.2.2 Model Structure 327

15.2.3 Model Inputs 331

15.3 Results 334

15.3.1 Base Case 334

15.3.2 Interaction between Prevention and Treatment Spending 335

15.3.3 Impact of Discount Rate on CostA -Effectiveness 336

15.3.4 O ptimal Spending Mix 337

15.3.5 Impact of Prevention Lag on Optimal Mix 338

15.3.6 Impact of Discount Rate on Optimal Mix 340

15.3.7 Impact of Time Horizon on Optimal Mix 340

15.3.8 Impacts of Research 341

15.4 Discussion 344

Acknowledgment 346

References 346

16 Treatment Optimization for Patients with Type 2 Diabetes 349
Jennifer Mason Lobo

16.1 Introduction 349

16.2 Literature Review 350

16.3 Model Formulation 353

16.3.1 Decision Epochs 354

16.3.2 States 354

16.3.3 Actions 355

16.3.4 Probabilities 355

16.3.5 Rewards 356

16.3.6 Value Function 356

16.4 Numerical Results 357

16.4.1 Model Inputs 357

16.4.2 Optimal Treatment Policies to Reduce Polypharmacy 358

16.5 Conclusions 362

References 363

17 Machine Learning for Early Detection and Treatment Outcome Prediction 367
Eva K. Lee

17.1 Introduction 367

17.2 Background 369

17.3 Machine Learning with Discrete Support Vector Machine Predictive Models 372

17.3.1 Modeling of ReservedA -Judgment Region for General Groups 373

17.3.2 Discriminant Analysis via MixedA -Integer Programming 374

17.3.3 Model Variations 376

17.3.4 Theoretical Properties and Computational Strategies 379

17.4 Applying Damip to RealA -World Applications 380

17.4.1 Validation of Model and Computational Effort 381

17.4.2 Applications to Biological and Medical Problems 381

17.4.3 Applying DAMIP to UCI Repository of Machine Learning Databases 389

17.5 Summary and Conclusion 393

Acknowledgment 394

References 394

INDEX 401