Faculty Bibliography

BACKGROUND: The U.S. Preventive Services Task Force (USPSTF) makes recommendations for 60 distinct clinical services, but clinicians rarely have time to fully evaluate and implement the recommendations. OBJECTIVE: To complete a proof of concept for prioritization and personalization of USPSTF recommendations, using patient-specific clinical characteristics. DESIGN: Mathematical model. DATA SOURCES: USPSTF recommendations and supporting evidence and National Vital Statistics Reports. TARGET POPULATION: Nonpregnant adults. TIME HORIZON: Lifetime. PERSPECTIVE: Individual. INTERVENTION: USPSTF grade A and B recommendations. OUTCOME MEASURES: Personalized gain in life expectancy associated with each recommendation. RESULTS OF BASE-CASE ANALYSIS: Increases in life expectancy varied more than 100-fold across USPSTF recommendations, and the rank order of benefits varied considerably among patients. For an obese man aged 62 years who smoked and had hypercholesterolemia, hypertension, and a family history of colorectal cancer, the model's top 3 recommendations (from most to least gain in life expectancy) were tobacco cessation (adding 2.8 life-years), weight loss (adding 1.6 life-years), and blood pressure control (adding 0.8 life-year). Lower-ranked recommendations were a healthier diet, aspirin use, cholesterol reduction, colonoscopy, screening for abdominal aortic aneurysm, and HIV testing (each adding 0.1 to 0.3 life-years). For a person with the same characteristics plus uncontrolled type 2 diabetes mellitus, the model's top 3 recommendations were diabetes control, tobacco cessation, and weight loss (each adding 1.4 to 1.8 life-years). RESULTS OF SENSITIVITY ANALYSIS: Robust to variation of model inputs and satisfied face validity criteria. LIMITATION: Expected adherence rates and quality of life were not considered. CONCLUSION: Models of personalized preventive care may illustrate how magnitude and rank order of benefit associated with preventive guidelines vary across recommendations and patients. These predictions may help clinicians to prioritize USPSTF recommendations at the patient level. PRIMARY FUNDING SOURCE: New York University School of Medicine.Chinese translation.

BACKGROUND: New York City (NYC) remains an epicenter of the HIV epidemic in the United States. Given the variety of evidence-based HIV prevention strategies available and the significant resources required to implement each of them, comparative studies are needed to identify how to maximize the number of HIV cases prevented most economically. METHODS: A new model of HIV disease transmission was developed integrating information from a previously validated micro-simulation HIV disease progression model. Specification and parameterization of the model and its inputs, including the intervention portfolio, intervention effects and costs were conducted through a collaborative process between the academic modeling team and the NYC Department of Health and Mental Hygiene. The model projects the impact of different prevention strategies, or portfolios of prevention strategies, on the HIV epidemic in NYC. RESULTS: Ten unique interventions were able to provide a prevention benefit at an annual program cost of less than $360,000, the threshold for consideration as a cost-saving intervention (because of offsets by future HIV treatment costs averted). An optimized portfolio of these specific interventions could result in up to a 34% reduction in new HIV infections over the next 20 years. The cost-per-infection averted of the portfolio was estimated to be $106,378; the total cost was in excess of $2 billion (over the 20 year period, or approximately $100 million per year, on average). The cost-savings of prevented infections was estimated at more than $5 billion (or approximately $250 million per year, on average). CONCLUSIONS: Optimal implementation of a portfolio of evidence-based interventions can have a substantial, favorable impact on the ongoing HIV epidemic in NYC and provide future cost-saving despite significant initial costs.

Health system leaders sometimes adopt quality metrics without robust supporting evidence of improvements in quality and/or quantity of life, which may impair rather than facilitate improved health outcomes. In brief, there is now no easy way to measure how much "health" is conferred by a health system. However, we argue that this goal is achievable. Health-weighted composite quality metrics have the potential to measure "health" by synthesizing individual evidence-based quality metrics into a summary measure, utilizing relative weightings that reflect the relative amount of health benefit conferred by each constituent quality metric. Previously, it has been challenging to create health-weighted composite quality metrics because of methodological and data limitations. However, advances in health information technology and mathematical modeling of disease progression promise to help mitigate these challenges by making patient-level data (eg, from the electronic health record and mobile health (mHealth) more accessible and more actionable for use. Accordingly, it may now be possible to use health information technology to calculate and track a health-weighted composite quality metric for each patient that reflects the health benefit conferred to that patient by the health system. These health-weighted composite quality metrics can be employed for a multitude of important aims that improve health outcomes, including quality evaluation, population health maximization, health disparity attenuation, panel management, resource allocation, and personalization of care. We describe the necessary attributes, the possible uses, and the likely limitations and challenges of health-weighted composite quality metrics using patient-level health data.

ABSTRACT: BACKGROUND: Hospitalization may be a particularly important time to promote smoking cessation, especially in the immediate post-discharge period. However, there are few studies to date that shed light on the most effective or cost-effective methods to provide post-discharge cessation treatment, especially among low-income populations and those with a heavy burden of mental illness and substance use disorders. METHODS/DESIGN: This randomized trial will compare the effectiveness and cost-effectiveness of two approaches to smoking cessation treatment among patients discharged from two urban public hospitals in New York City. During hospitalization, staff will be prompted to ask about smoking and to offer nicotine replacement therapy (NRT) on admission and at discharge. Subjects will be randomized on discharge to one of two arms: one arm will be proactive multi-session telephone counseling with motivational enhancement delivered by study staff, and the other will be a faxed or online referral to the New York State Quitline. The primary outcome is 30-day point-prevalence abstinence from smoking at 6-month follow-up post-discharge. We will also examine cost-effectiveness from a societal and a payer perspective, as well as explore subgroup analyses related to patient location of hospitalization, race/ethnicity, immigrant status, and inpatient diagnosis. DISCUSSION: This study will explore issues of implementation feasibility in a post-hospitalization patient population, as well as add information about the effectiveness and cost-effectiveness of different strategies for designing smoking cessation programs for hospitalized patients. TRIAL REGISTRATION: Clinicaltrials.gov ID# NCT01363245.

In this perspective piece, the authors consider what has been learned and is being studied about aging with HIV in resource-rich settings. The authors argue that although there is much that will be different about aging with HIV in other parts of the globe, there are common themes and approaches to care. These include the observation that most patients have more than one health condition, and the need to assess individual risk, prioritize care, and consider the total burden of disease when considering further testing and treatment.

OBJECTIVE: To estimate the survival and quality-adjusted life-years (QALYs) of Full Code versus Do Not Intubate (DNI) advance directives in patients with severe chronic obstructive pulmonary disease and to evaluate how patient preferences and place of residence influence these outcomes. METHODS: A Markov decision model using published data for COPD exacerbation outcomes. The advance directives that were modeled were as follows: DNI, allowing only noninvasive mechanical ventilation, or Full Code, allowing all forms of mechanical ventilation including invasive mechanical ventilation with endotracheal tube (ETT) insertion. RESULTS: In community-dwellers, Full Code resulted in a greater likelihood of survival and higher QALYs (4-year survival: 23% Full Code, 18% DNI; QALYs: 1.34 Full Code, 1.24 DNI). When considering patient preferences regarding complications, however, if patients were willing to give up >3 months of life expectancy to avoid ETT complications, or >1 month of life expectancy to avoid long-term institutionalization, DNI resulted in higher QALYs. For patients in long-term institutions, DNI resulted in a greater likelihood of survival and higher QALYs (4-year survival: 2% DNI, 1% Full Code; QALYs: 0.29 DNI, 0.24 Full Code). In sensitivity analyses, the model was sensitive to the probabilities of ETT complication and noninvasive mechanical ventilation failure and to patient preferences about ETT complications and long-term institutionalization. CONCLUSION: Our model demonstrates that patient preferences regarding ETT complications and long-term institutionalization, as well as baseline place of residence, affect the advance directive recommendation when considered in terms of both survival and QALYs. Decision modeling can demonstrate the potential trade-off between survival and quality of life, using patient preferences and disease-specific data, to inform the shared advance directive decision.