Simulation Modeling of Health Care Policy

Glied, Sherry; Tilipman, Nicholas

doi:10.1146/annurev.publhealth.012809.103542

Cited by 16 publications

(21 citation statements)

References 64 publications

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“…The use of modeling in policy development is becoming more accepted (25, 26). The USPSTF relied on modeling to determine strategies for breast (27) and colorectal cancer screening (28).…”

Section: Discussionmentioning

confidence: 99%

Comparative Effectiveness of Alternative Prostate-Specific Antigen–Based Prostate Cancer Screening Strategies

Gulati¹,

Gore²,

Etzioni³

2013

Ann Intern Med

149

144

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Background The US Preventive Services Task Force recently concluded that the harms of existing prostate-specific antigen (PSA) screening strategies outweigh benefits. Objective To evaluate comparative effectiveness of alternative PSA screening strategies. Design Microsimulation model of prostate cancer incidence and mortality quantifying harms and lives saved for alternative PSA screening strategies. Data Sources National and trial data on PSA growth, screening and biopsy patterns, incidence, treatment distributions, treatment efficacy, and mortality. Target Population A contemporary cohort of US men. Time Horizon Lifetime. Perspective Societal. Intervention 35 screening strategies that vary by start/stop ages, inter-screening intervals, and thresholds for biopsy referral. Outcome Measurements PSA tests, false positive tests, cancers detected, overdiagnoses, prostate cancer deaths, lives saved, and months of life saved. Results of Base-Case Analysis Without screening, the risk of prostate cancer death is 2.86%. A reference strategy that screens men aged 50–74 annually with a PSA threshold for biopsy referral of 4 μg/L reduces the risk of prostate cancer death to 2.15% with risk of overdiagnosis of 3.3%. A strategy that uses higher PSA thresholds for biopsy referral in older men achieves a similar risk of prostate cancer death (2.23%) but reduces the risk of overdiagnosis to 2.3%. A strategy that screens biennially with longer inter-screen intervals for men with low PSA levels achieves similar risks of prostate cancer death (2.27%) and overdiagnosis (2.4%) but reduces total tests by 59% and false positive tests by 50%. Results of Sensitivity Analysis Varying incidence inputs or reducing the survival improvement due to screening did not change conclusions. Limitations The model is a simplification of prostate cancer natural history, and the survival improvement due to screening is uncertain. Conclusions PSA screening strategies that use higher thresholds for biopsy referral for older men and that screen men with low PSA levels less frequently can reduce harms while preserving lives saved compared to standard screening. Primary Funding Source National Cancer Institute.

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“…The use of modeling in policy development is becoming more accepted (25, 26). The USPSTF relied on modeling to determine strategies for breast (27) and colorectal cancer screening (28).…”

Section: Discussionmentioning

confidence: 99%

Comparative Effectiveness of Alternative Prostate-Specific Antigen–Based Prostate Cancer Screening Strategies

Gulati¹,

Gore²,

Etzioni³

2013

Ann Intern Med

149

144

View full text Add to dashboard Cite

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“…Computer simulation modeling has been instrumental in developing clinical guidelines and policy in general, 14,[23][24][25][26] and for hypertension in particular. 27 Farley and colleagues 27 used mathematical modeling to project the potential mortality benefits of increasing hypertension control, while Turner and Schalkwyk 28 used computer simulation of BP variability to examine spurious identification of hypertension in clinical studies.…”

Section: Discussionmentioning

confidence: 99%

Simulating Strategies for Improving Control of Hypertension Among Patients with Usual Source of Care in the United States: The Blood Pressure Control Model

et al. 2015

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BACKGROUND: Only half of hypertensive adults achieve blood pressure (BP) control in the United States, and it is unclear how BP control rates may be improved most effectively and efficiently at the population level. OBJECTIVE: We sought to compare the potential effects of system-wide isolated improvements in medication adherence, visit frequency, and higher physician prescription rate on achieving BP control at 52 weeks. DESIGN: We developed a Markov microsimulation model of patient-level, physician-level, and system-level processes involved in controlling hypertension with medications. The model is informed by data from national surveys, cohort studies and trials, and was validated against two multicenter clinical trials (ALLHAT and VALUE). SUBJECTS: We studied a simulated, nationally representative cohort of patients with diagnosed but uncontrolled hypertension with a usual source of care. INTERVENTIONS:We simulated a base case and improvements of 10 and 50 %, and an ideal scenario for three modifiable parameters: visit frequency, treatment intensification, and medication adherence. Ideal scenarios were defined as 100 % for treatment intensification and adherence, and return visits occurring within 4 weeks of an elevated office systolic BP. MAIN OUTCOME: BP control at 52 weeks of follow-up was examined. RESULTS: Among 25,000 hypothetical adult patients with uncontrolled hypertension (systolic BP ≥ 140 mmHg), only 18 % achieved BP control after 52 weeks using base-case assumptions. With 10/50 %/idealized enhancements in each isolated parameter, enhanced treatment intensification achieved the greatest BP control (19/23/71 %), compared with enhanced visit frequency (19/21/35 %) and medication adherence (19/23/26 %). When all three processes were idealized, the model predicted a BP control rate of 95 % at 52 weeks.CONCLUSION: Substantial improvements in BP control can only be achieved through major improvements in processes of care. Healthcare systems may achieve greater success by increasing the frequency of clinical encounters and improving physicians' prescribing behavior than by attempting to improve patient adherence to medications.

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“…Micro-simulation modelling has been used to represent systems and processes in health care and to test their functioning for policy purposes (Glied and Tilipman, 2010;Ringel et al, 2010;Rutter et al, 2011;Zucchelli and Rice, 2012). Microsimulation sits within a continuum of social simulation methodologies with more aggregated approaches (for example, system dynamics) on the one hand and more behavioural ones (for example, agent-based modelling) on the other (Gilbert and Troitzsch, 2005).…”

Section: Counterfactual Modellingmentioning

confidence: 99%

Determinants and disparities: A simulation approach to the case of child health care

Lay‐Yee

Milne

Davis

et al. 2015

Social Science & Medicine

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Simulation Modeling of Health Care Policy

Cited by 16 publications

References 64 publications

Comparative Effectiveness of Alternative Prostate-Specific Antigen–Based Prostate Cancer Screening Strategies

Comparative Effectiveness of Alternative Prostate-Specific Antigen–Based Prostate Cancer Screening Strategies

Simulating Strategies for Improving Control of Hypertension Among Patients with Usual Source of Care in the United States: The Blood Pressure Control Model

Determinants and disparities: A simulation approach to the case of child health care

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