Faculty Bibliography

BACKGROUND:Hepatocelluar cancer (HCC) is the leading cause of death among people with hepatitis C virus (HCV)-related cirrhosis. Our aim was to determine the optimal surveillance frequency for patients with HCV-related compensated cirrhosis. METHODS:We developed a decision analytic Markov model and validated it against data from the Veterans Outcomes and Costs Associated with Liver Disease (VOCAL) study group and published epidemiologic studies. Four strategies of different surveillance intervals were compared: no surveillance and ultrasound surveillance every 12, 6, and 3 months. We estimated lifetime survival, life expectancy, quality adjusted life years (QALY), total costs associated with each strategy, and incremental cost effectiveness ratios. We applied a willingness to pay threshold of $100,000. Analysis was conducted for two scenarios: a scenario reflecting current HCV and HCC surveillance compliance rates and treatment use and an aspirational scenario. RESULTS:In the current scenario the preferred strategy was 3-month surveillance with an incremental cost-effectiveness ratio (ICER) of $7,159/QALY. In the aspirational scenario, 6-month surveillance was preferred with an ICER of $82,807/QALY because treating more people with HCV led to a lower incidence of HCC. Sensitivity analyses suggested that surveillance every 12 months would suffice in the particular circumstance when patients are very likely to return regularly for testing and when appropriate HCV and HCC treatment is readily available. Compared with the current scenario, the aspirational scenario resulted in a 1.87 year gain in life expectancy for the cohort because of large reductions in decompensated cirrhosis and HCC incidence. CONCLUSIONS:HCC surveillance has good value for money for patients with HCV-related compensated cirrhosis. Investments to improve adherence to surveillance should be made when rates are suboptimal. Surveillance every 12 months will suffice when patients are very likely to return regularly for testing and when appropriate HCV and HCC treatment is readily available.

BACKGROUND:A study of a comprehensive HIV Care Coordination Program (CCP) showed effectiveness in increasing viral load suppression (VLS) among PLWH in New York City (NYC). We evaluated the cost-effectiveness of a scale-up of the CCP in NYC. METHODS:We incorporated observed effects and costs of the CCP into a computer simulation of HIV in NYC, comparing strategy scale-up with no implementation. The simulation combined a deterministic compartmental model of HIV transmission with a stochastic microsimulation of HIV progression, and was calibrated to NYC HIV epidemiological data from 1997 to 2009. We assessed incremental cost-effectiveness from a health sector perspective using 2017 $US, a 20-year time horizon, and a 3% annual discount rate. We explored two scenarios: (1) two-year average enrollment and (2) continuous enrollment. RESULTS:In scenario 1, scale-up resulted in a cost-per-infection-averted of $898,104 and a cost-per-QALY-gained of $423,721. In sensitivity analyses, scale-up achieved cost-effectiveness if effectiveness increased from RR1.11 to RR1.37 or costs decreased by 41.7%. Limiting the intervention to persons with unsuppressed viral load prior to enrollment (RR1.32) attenuated the cost reduction necessary to 11.5%. In scenario 2, scale-up resulted in a cost-per-infection-averted of $705,171 and cost-per-QALY-gained of $720,970. In sensitivity analyses, scale-up achieved cost-effectiveness if effectiveness increased from RR1.11 to RR1.46 or program costs decreased by 71.3%. Limiting the intervention to persons with unsuppressed viral load attenuated the cost reduction necessary to 38.7%. CONCLUSION/CONCLUSIONS:Cost-effective CCP scale-up would require reduced costs and/or focused enrollment within NYC, but may be more readily achieved in cities with lower background VLS levels.

BACKGROUND:Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. The ability to predict obesity before age five could be a useful tool, allowing prevention strategies to focus on high risk children. The few existing prediction models for obesity in childhood have primarily employed data from longitudinal cohort studies, relying on difficult to collect data that are not readily available to all practitioners. Instead, we utilized real-world unaugmented electronic health record (EHR) data from the first two years of life to predict obesity status at age five, an approach not yet taken in pediatric obesity research. METHODS AND FINDINGS/RESULTS:We trained a variety of machine learning algorithms to perform both binary classification and regression. Following previous studies demonstrating different obesity determinants for boys and girls, we similarly developed separate models for both groups. In each of the separate models for boys and girls we found that weight for length z-score, BMI between 19 and 24 months, and the last BMI measure recorded before age two were the most important features for prediction. The best performing models were able to predict obesity with an Area Under the Receiver Operator Characteristic Curve (AUC) of 81.7% for girls and 76.1% for boys. CONCLUSIONS:We were able to predict obesity at age five using EHR data with an AUC comparable to cohort-based studies, reducing the need for investment in additional data collection. Our results suggest that machine learning approaches for predicting future childhood obesity using EHR data could improve the ability of clinicians and researchers to drive future policy, intervention design, and the decision-making process in a clinical setting.

Background/UNASSIGNED:Tobacco spending may exacerbate financial hardship in low-income populations by using funds that could go toward essentials. This study examined post-quit spending plans among low-income smokers and whether financial hardship was positively associated with motivation to quit in the sample. Methods/UNASSIGNED:= 410). Linear regression was used to examine the relationship between financial distress, food insecurity, smoking-induced deprivation (SID) and motivation to quit (measured on a 0-10 scale). We performed summative content analyses of open-ended survey questions to identify the most common plans among participants with and without SID for how to use their tobacco money after quitting. Results/UNASSIGNED:The top three spending plans among participants with and without SID were travel, clothing and savings. There were three needs-based spending plans unique to a small number of participants with SID: housing, health care and education. Conclusions/UNASSIGNED:Financial distress and food insecurity did not enhance overall motivation to quit, while smokers with SID were less motivated to quit. Most low-income smokers, including those with SID, did not plan to use their tobacco money on household essentials after quitting.

Background/UNASSIGNED:Smoking remains a major public health burden among persons with opioid and/or alcohol use disorder. Methods/UNASSIGNED:A 49-item semi-structured survey was conducted among urban, inpatient detoxification program patients eliciting demographic and clinical characteristics, smoking profile, technology use patterns, and preferences for adopting technology-based smoking cessation interventions. Multivariate logistic regression models further evaluated the association between participant demographic and clinical characteristics and technology preferences. Results/UNASSIGNED:Participants were mostly male (91%), and admitted for detoxification for alcohol (47%), heroin (31%), or both alcohol and heroin (22%). Past 30-day smoking was reported by 78% of the sample. Mobile phone ownership was common (89%); with an average past-year turnover of 3 mobile phones and 3 phone numbers. Computer ownership was low (28%) and one third reported daily internet use (34%). Telephone (41%) and text message-based interventions (40%) were the most popular platforms to facilitate smoking cessation. Conclusions/UNASSIGNED:Despite concurrent AUD-OUD, most respondents had attempted to quit smoking in the last year and preferred telephone- and text message-based interventions to facilitate smoking cessation. High turnover of mobile phones, phone numbers, and limited access to computers pose barriers to dissemination of technology-based smoking cessation interventions in this vulnerable population.

Over the last decades, psychophysical and electrophysiological studies in patients and animal models of Parkinson's disease (PD), have consistently revealed a number of visual abnormalities. In particular, specific alterations of contrast sensitivity curves, electroretinogram (ERG), and visual-evoked potentials (VEP), have been attributed to dopaminergic retinal depletion. However, fundamental mechanisms of cortical visual processing, such as normalization or "gain control" computations, have not yet been examined in PD patients. Here, we measured electrophysiological indices of gain control in both space (surround suppression) and time (sensory adaptation) in PD patients based on steady-state VEP (ssVEP). Compared with controls, patients exhibited a significantly higher initial ssVEP amplitude that quickly decayed over time, and greater relative suppression of ssVEP amplitude as a function of surrounding stimulus contrast. Meanwhile, EEG frequency spectra were broadly elevated in patients relative to controls. Thus, contrary to what might be expected given the reduced contrast sensitivity often reported in PD, visual neural responses are not weaker; rather, they are initially larger but undergo an exaggerated degree of spatial and temporal gain control and are embedded within a greater background noise level. These differences may reflect cortical mechanisms that compensate for dysfunctional center-surround interactions at the retinal level.