Faculty Bibliography

BACKGROUND:Current guidelines recommend the resection of main duct- (MD) and mixed-type (MT) intraductal papillary mucinous neoplasms (IPMN) based on specific risk criteria to prevent or treat pancreatic cancer in selected patients. This paradigm follows high rates of malignancy observed in published surgical series. The aim of this systematic review and meta-analysis was to provide robust, pooled rates of invasive carcinoma (IC) and high-grade dysplasia (HGD) in resected MD- and MT-IPMNs of the pancreas. METHODS:The PubMed, Embase, Scopus, Web of Science, and Cochrane CENTRAL databases were systematically searched. Studies that reported rates of IC or HGD, diagnosed by histopathology of surgical specimens, in MD- or MT-IPMNs were included. Pooled prevalence with 95 % confidence interval (95 % CI) was calculated using a random effects model. Galbraith plots were used to evaluate heterogeneity. Risk of bias was assessed using the National Institutes of Health Quality Assessment Tool. RESULTS:Based on 51 studies, 59 % (95 % CI: 54 %, 64 %) of resected MD- and MT-IPMN had IC or HGD, with IC in up to 39 % (95 % CI: 33 %, 44 %) of lesions and HGD in 20 % (95 % CI: 16 %, 25 %). Most studies were deemed to be of good quality and Galbraith plots demonstrated high concordance. CONCLUSIONS:These results confirm the rates of IC and HGD in resected MD/MT-IPMNs. However, a significant proportion of patients have benign lesions, and future research is needed to develop precise diagnostics to distinguish between patients with and without high-risk or cancerous disease.

BACKGROUND:Smoking among adults in substance use disorder (SUD) treatment programs is common with limited success in quitting. Given e-cigarettes' potential for smoking harm reduction, it is important to examine e-cigarette use among people in SUD treatment. METHODS:We analyzed data from adults who have received SUD treatment in the past year, from the 2020-2021 National Survey on Drug Use and Health (N = 1,246). We delineated correlates of lifetime and current (past-month) use e-cigarette use. RESULTS:Among adults receiving SUD treatment, an estimated 39.4 % (95 % CI: 34.1, 45.1) have used e-cigarettes in their lifetime and 19.5 % (95 % CI: 16.1, 23.6) of those currently vape. Among those reporting current vaping, an estimated 57.3 % (95 % CI: 44.9-68.8) currently smoke cigarettes and half (54.2 % [95 % CI: 41.1-66.7]) currently use cannabis. Compared to those who only received treatment for alcohol use disorder, those receiving treatment for drug use (aPR = 1.47, 95 % CI: 1.09-1.99) and alcohol and drug use (aPR = 1.60, 95 % CI: 1.16-2.22) had higher prevalence of lifetime e-cigarette use, and those reporting treatment for drug use only (aPR = 2.60, 95 % CI: 1.52-4.46) and alcohol and drug use (aPR = 2.82, 95 % CI: 1.63-4.87) also had higher prevalence of current e-cigarette use. CONCLUSIONS:The prevalence of e-cigarette use was higher among those in treatment for both drug or alcohol and drugs only than those receiving treatment for only alcohol use. Smoking harm reduction interventions can potentially include e-cigarette among those receiving drug treatment. There is also a need to address challenges of dual e-cigarette and cigarette use, as well as dual e-cigarette use with cannabis.

BACKGROUND:Results from national surveys indicate that many older adults reported delayed medical care during the acute phase of the COVID-19 pandemic, yet few studies have used objective data to characterize healthcare utilization among vulnerable older adults in that period. In this study, we characterized healthcare utilization during the acute pandemic phase (March 7-October 6, 2020) and examined risk factors for total disruption of care among older adults with multiple chronic conditions (MCC) in New York City. METHODS:This retrospective cohort study used electronic health record data from NYC patients aged ≥ 50 years with a diagnosis of either hypertension or diabetes and at least one other chronic condition seen within six months prior to pandemic onset and after the acute pandemic period at one of several major academic medical centers contributing to the NYC INSIGHT clinical research network (n=276,383). We characterized patients by baseline (pre-pandemic) health status using cutoffs of systolic blood pressure (SBP) < 140mmHg and hemoglobin A1C (HbA1c) < 8.0% as: controlled (below both cutoffs), moderately uncontrolled (below one), or poorly controlled (above both, SBP > 160, HbA1C > 9.0%). Patients were then assessed for total disruption versus some care during shutdown using recommended care schedules per baseline health status. We identified independent predictors for total disruption using logistic regression, including age, sex, race/ethnicity, baseline health status, neighborhood poverty, COVID infection, number of chronic conditions, and quartile of prior healthcare visits. RESULTS:Among patients, 52.9% were categorized as controlled at baseline, 31.4% moderately uncontrolled, and 15.7% poorly controlled. Patients with poor baseline control were more likely to be older, female, non-white and from higher poverty neighborhoods than controlled patients (P < 0.001). Having fewer pre-pandemic healthcare visits was associated with total disruption during the acute pandemic period (adjusted odds ratio [aOR], 8.61, 95% Confidence Interval [CI], 8.30-8.93, comparing lowest to highest quartile). Other predictors of total disruption included self-reported Asian race, and older age. CONCLUSIONS:This study identified patient groups at elevated risk for care disruption. Targeted outreach strategies during crises using prior healthcare utilization patterns and disease management measures from disease registries may improve care continuity.

IMPORTANCE/UNASSIGNED:The US health care sector accounts for about 8.5% of national greenhouse gas (GHG) emissions. Reliable estimates of emissions associated with health care-related travel are essential for informing policy changes. OBJECTIVE/UNASSIGNED:To generate a comprehensive national estimate of carbon emissions due to patient health care-related travel in the US. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This cross-sectional study used data from the 2022 National Household Travel Survey (NHTS), conducted from January 2022 to January 2023. Participants were selected using an address-based sample from the US Postal Service Delivery Sequence File. Participating households reported all trips taken within 24 hours by all household members aged 5 years or older. Approximate emissions per mile were obtained from typical vehicle emissions data provided by US government institutions. Data were analyzed between March 11 and May 29, 2024. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Estimated annual CO2 equivalent (CO2e) emissions from patient health care-related travel per year, per patient, per trip, and per mile. A survey-weighted λ regression analysis was used to identify factors associated with higher CO2e emissions per trip. An alternative scenario analysis estimated reductions if 30% or 50% of private vehicle users switched to electric vehicles. RESULTS/UNASSIGNED:The sample included 16 997 participants with a weighted total of 3 506 325 536 US health care trips. Of these trips, 52.0% were reported by female travelers, 80.1% were made in urban areas, and 19.9% were made in rural areas. These trips accounted for 84 057 963 340 miles, resulting in weighted annual estimated emissions of 35.7 megatons (Mt) (95% CI, 27.5-43.9 Mt) CO2e. Each mile traveled generated an estimated 424 g (95% CI, 418-428 g) CO2e. Emissions per trip were higher (exponentiated coefficient [exp(β)], 2.19; 95% CI, 1.51-2.86; P < .001) for rural patients compared with urban patients. However, 69.3% of emissions were attributable to urban patients and 30.7% to rural patients. Patients with annual median household incomes of $50 000 to $99 999 generated higher trip emissions (exp[β], 1.92; 95% CI, 1.09-2.76; P = .003) compared with those with incomes of $25 000 or less. A 30% shift to electric vehicles was estimated to reduce health care-related carbon emissions to 27.6 Mt (95% CI, 20.7-34.6 Mt) CO2e, and a 50% shift was estimated to lower emissions to 22.3 Mt (95% CI, 16.0-28.6 Mt) CO2e. CONCLUSIONS AND RELEVANCE/UNASSIGNED:This cross-sectional study estimated that annual patient health care-related travel in the US generated 35.7 Mt CO2e, which accounts for a small but important proportion of total health care-related emissions in the US. These findings are essential for informing health care policy decisions and suggest that strategies such as telehealth and the adoption of electric vehicles may contribute to a small but significant reduction in health care-related GHG emissions.

Little is known about ecstasy/MDMA use among Asian American and Pacific Islander populations. Research is important because AAPIs face unique cultural factors that may influence use. We estimated the prevalence and correlates of lifetime ecstasy/MDMA use based on a representative sample of US AAPI adults aged ≥18 from the 2015-2020 National Survey on Drug Use and Health. An estimated 5.1% of AAPI adults used ecstasy in their lifetime. Compared to males, females had higher odds of use (aOR = 1.45, 95% CI: 1.08-1.98). Compared to those aged 18-25, those aged 26-34 were at increased odds for use (aOR = 1.99, 95% CI: 1.30-3.06), while those aged ≥50 were at lower odds for use. Lifetime use of other substances including cannabis (aOR = 28.4, 95% CI: 17.1-47.2), ketamine (aOR = 10.9, 95% CI: 1.63-73.4), LSD (aOR = 3.82, 95% CI: 1.98-7.37), cocaine (aOR = 3.77, 95% CI: 2.54-5.59), psilocybin (aOR = 3.29, 95% CI: 1.75-6.16), prescription opioids (aOR = 2.43, 95% CI: 1.44-4.09), and prescription stimulants (aOR = 1.96, 95% CI: 1.29-2.99) were associated with increased odds of ecstasy/MDMA use. We estimated that over 1 in 20 AAPI adults have ever used ecstasy/MDMA. Variations by age, sex, family income, substance type, and mental health service utilization emphasize the need for targeted public health strategies.

BACKGROUND:Off-label prescribing of ketamine to treat psychiatric disorders has been increasing, as has recreational use and availability of illicit ketamine. It is important to examine trends in ketamine-related poisonings to inform public health efforts. METHODS:We examined data from poisonings (exposures) involving ketamine reported to Poison Centers in the US between 2019 and 2023 (n = 1519). Annual trends were examined for number of exposures and characteristics of exposures, and we delineated correlates of major (life-threatening) adverse effects and death compared to less severe outcomes. RESULTS:The number of reported ketamine exposures increased from 205 in 2019 to 414 in 2023. Most cases involved ketamine ingestion (57.2 %), and a plurality involved misuse or "abuse" (36.2 %) and moderate effects (44.8 %). The percentage of cases involving suspected suicide attempts doubled from 12.7 % in 2019 to 25.9 % (a 103.9 % increase; P < 0.001); ingestion use increased from 46.0 % to 65.2 % (a 41.7 % increase; P < 0.001), and cases involving ketamine in liquid form decreased from 65.1 % to 41.2 % (a 36.7 % decrease; P < 0.001). Cases with major effects or death decreased from 23.4 % in 2019 to 15.6 % in 2023 (a 33.2 % decrease; P = 0.039). A major event or death was experienced by 18.6 % of cases, but prevalence was lower for those who inhaled ketamine (aPR=0.49, 95 % CI:0.29-0.85) compared to those who did not inhale. DISCUSSION/CONCLUSIONS:Poisonings involving ketamine are at their highest in reporting history. Both medical and recreational ketamine use and related adverse events need to be monitored, especially as off-label prescribing of take-home oral formulations appears to be increasing.

OBJECTIVE:To characterize the prevalence of continuous glucose monitoring (CGM)-defined glucose abnormalities in a large, community-based population of very old adults (>75 years). RESEARCH DESIGN AND METHODS/METHODS:A cross-sectional analysis of 1,150 older adults with and without diabetes who attended the Atherosclerosis Risk in Communities Study (2021-2022). Diabetes was based on a self-reported diagnosis of diabetes by a health care provider, use of diabetes medication, or a hemoglobin A1c (HbA1c) ≥6.5%. Prediabetes was defined as an HbA1c of 5.7% to <6.5% and normoglycemia as an HbA1c of <5.7%. We analyzed CGM metrics, including mean glucose, measures of hyperglycemia, and the coefficient of variation, by diabetes status. RESULTS:Of the 1,150 participants (mean age 83 years, 59% women, 26% who are Black), 35.1% had normoglycemia, 34.5% had prediabetes, and 30.4% had diabetes. The summary 24-h ambulatory glucose profile for participants with prediabetes was very similar to those with normoglycemia. No participants with normoglycemia or prediabetes had a CGM mean glucose >140 mg/dL, while 32.7% of participants with diabetes had a CGM mean glucose >140 mg/dL. CONCLUSIONS:In very old adults with normal or prediabetes HbA1c, hyperglycemia detected by CGM was rare. This suggests that HbA1c adequately captures the burden of hyperglycemia for most people in this population.

BACKGROUND:Visual symptoms are common after concussion. Rapid automatized naming (RAN) tasks are simple performance measures that demonstrate worse time scores in the setting of acute or more remote injury. METHODS:We evaluated the capacity for the Mobile Universal Lexicon Evaluation System (MULES) and Staggered Uneven Number (SUN) testing to be feasibly administered during preseason testing in a cohort of youth ice hockey athletes using a novel computerized app, the Mobile Integrated Cognitive Kit (MICK). Participants from a youth hockey league underwent preseason testing. RESULTS:Among 60 participants, the median age was 13 years (range 6-17). The median best time for the MULES was 49.8 seconds (range = 34.2-141.0) and the median best time for the SUN was 70.1 (range = 36.6-200.0). As is characteristic of timed performance measures, there were learning effects between the first and second trials for both the MULES (median improvement = 10.6 seconds, range = -32.3 to 92.0, P < 0.001, Wilcoxon signed-rank test) and SUN (median improvement = 2.4 seconds, range= -8.0 to 15.1, P = 0.001, Wilcoxon signed-rank test). Age was a predictor of best baseline times, with longer (worse) times for younger participants for MULES (P < 0.001, rs = -0.67) and SUN (P < 0.001, rs = -0.54 Spearman rank correlation). Degrees of learning effect did not vary with age (P > 0.05, rs = -0.2). CONCLUSIONS:Vision-based RAN tasks, such as the MULES and SUN, can be feasibly administered using the MICK app during preseason baseline testing in youth sports teams. The results suggest that more frequent baseline tests are necessary for preadolescent athletes because of the relation of RAN task performance to age.