Faculty Bibliography

BACKGROUND: Opioid overdose deaths have reached epidemic proportions in the United States. This problem stems from both licit and illicit opioid use. Physicians play a role in prescribing opioids, recognizing risky use, and initiating prevention, including Opioid Overdose Prevention Training (OOPT). The American Heart Association (AHA) modified their basic life support (BLS) algorithms to consider naloxone in high risk populations and when a pulse is appreciated; however, the AHA did not provide OOPT. Our intervention filled this training deficiency by teaching medical students opioid overdose resuscitation with a Train-The-Trainer model as part of mandatory BLS training. METHODS: We introduced OOPT, following a Train-The-Trainer model, into the required Basic Life Support (BLS) training for first-year medical students at a single medical school in a large urban area. We administered pre- and post-evaluations to assess the effects of the training on opioid overdose knowledge, self-reported preparedness to respond to opioid overdoses, and attitudes towards patients with SUDS. RESULTS: In the fall 2014, 120 first-year medical students received OOPT. Seventy-three students completed both pre- and post-training evaluations. Improvements in knowledge about and preparedness to respond to opioid overdoses were statistically significant (p <.01) and large (Cohen's D = 2.70 and Cohen's D = 2.10 respectively). There was no statistically significant change in attitude toward patients with SUDs. CONCLUSIONS: We demonstrated the effectiveness of OOPT as an adjunct to BLS in increasing knowledge about and preparedness to respond to opioid overdoses; improving attitudes toward patients with SUDs likely requires additional intervention. We will characterize knowledge and preparedness durability, program sustainability, and long-term changes in attitudes in future evaluations. These results support dissemination of OOPT as a part of BLS training for all medical students, and potentially all BLS providers.

BACKGROUND: National surveys indicate high rates of burnout, particularly among primary care physicians. Despite concerns about the impact of burnout on health outcomes, this relationship is not well studied. HealthyHearts NYC, funded through the EvidenceNOW initiative of the Agency for Healthcare Research and Quality is evaluating the effectiveness of practice facilitation to improve adoption of Million Hearts' evidence-based ABCS guidelines (Aspirin, Blood pressure control (BP), Cholesterol management, and Smoking cessation) in small-to-medium size primary care practices. This study examined the association between site characteristics and physicians' burnout, as well as the relationship with the ABCS outcomes. METHODS: We present data on 99 small practices working with the NYCDOHMH Primary Care Information Project, and 8 Federally Qualified Health Centers (FQHC) from the Community Health Care Association of New York State. Each provider from participating practice sites completed a survey that included a validated question assessing burnout that used a 5-point scale ranging from 1- no symptoms of burnout to 5- completely burned out. For sites with multiple providers, we calculated a site burnout score by extracting the maximum score within each site. The burnout level was dichotomously coded as 1- burned out, and 0- not burned out. Each site completed a practice survey to report site characteristics, including the number of providers (1 vs. 2 or more), Patient Centered Medical Home (PCMH) status (recognized vs. not recognized), average number of patient visits per week, and total number of support staff. We used logistic regression analysis to assess the association of practice site characteristics and burnout. We also calculated a composite measure for patients with multiple CVD risk factors to capture the extent to which targets are met across three of the outcome measures (ABC). We used ttests to assess the difference in ABCS and composite measures by burnout status. RESULTS: Overall, 19% of physicians reported burnout. A significant difference was detected for all ABCS measures by the sites' burnout status. Sites reporting burnout had higher rates of meeting aspirin, smoking, and composite measure targets, but lower rates of meeting BP and cholesterol target measures and were more likely to have achieved PCMH recognition. FQHCs reported higher rates of burnout compared with small practices. CONCLUSIONS: The overall rates of burnout were lower than reported in previous surveys. Lower burnout rates among small independent practices compared with FQHCs may be related to small practice providers' greater level autonomy. We will present additional data exploring organizational factors that may explain variation in burnout across these different practice settings. Finally, previous research has similarly found inconsistent associations between quality and burnout. Further studies are required to determine whether healthier workplaces also result in higher quality care

BACKGROUND: Alcohol and drug use is a leading cause of morbidity and mortality that frequently goes unidentified in medical settings. As part of a multi-phase study to implement the NIDA Common Data Elements for collecting substance use screening information in electronic health records (EHRs), we interviewed key clinical stakeholders with a goal of identifying barriers and facilitators affecting the implementation of substance use screening in primary care clinics. METHODS: Focus groups and individual qualitative interviews were conducted with 67 stakeholders, including primary care patients, medical providers (faculty and resident physicians, nurses), and medical assistants, in two urban academic health systems. Themes were identified, discussed, and revised through an iterative process, and mapped to the Knowledge to Action (KTA) framework (Graham, 2006), which guides the selection and implementation of new clinical practices. RESULTS: Factors affecting implementation based on KTA elements were identified from participant narratives. Identifying the problem: Participants unanimously agreed that having knowledge of a patient's substance use is important because of its impacts on health andmedical care, that substance use is not properly identified in medical settings, and that universal screening is the best approach. Adapting knowledge: The majority of patients and providers stated that the primary care provider should play a key role in substance use screening and interventions. There was discrepancy of opinion regarding the optimal approach to delivering screening. Some felt that patients should self-administer questionnaires, while others thought that patients would be more comfortable having face-to-face discussions with their primary care provider - though not with other members of the care team. Many providers reported that being able to take effective action once unhealthy substance use is identified is crucial. Assessing barriers: Patients expressed concerns about confidentiality, 'denial', and providers' lack of empathy. Barriers identified by providers included individual-level factors such as lack of knowledge and training, and systems-level factors including lack of time, resources, and space, disjointed communication between members of the medical team, and difficulty accessing addiction treatment. CONCLUSIONS: Based on these findings, we designed and are testing an implementation strategy utilizing universal screening, patient self-administered questionnaires, and EHR-integrated clinical decision support to assist providers in conducting brief motivational counseling and linking patients to behavioral health services, to address unhealthy substance use in primary care clinics

BACKGROUND:Motor vehicle traffic (MVT) crashes kill or seriously injure approximately 4250 people in New York City (NYC) each year. Traditionally, NYC surveillance practices use hospitalization and crash data separately to monitor trends in MVT-related injuries, but key information linking crash circumstances to health outcomes is lost when analyzing these data sources in isolation. Our objective was to match crash reports to hospitalization records to create a traffic injury surveillance dataset that can be used to describe crash circumstances and related injury outcomes. The linkage of the two systems presents a unique challenge since the system tracking crashes and the system tracking hospitalizations and emergency department (ED) visits lack key identifying data such as names and dates of birth. METHODS:NYC Department of Transportation provided electronic records based on reports of motor vehicle crashes submitted to the New York State Department of Motor Vehicles for all crashes occurring in NYC from 2009 to 2013. New York Statewide Planning and Research Cooperative System (SPARCS) ED and hospitalization administrative data from NYC hospitals were used to identify unintentional MVT-related injuries using external cause of injury codes. Since the two systems do not share unique individual identifiers, probabilistic record linkage was conducted using LinkSolv9.0. Sensitivity/specificity calculations and chi-square analyses of linkage rates were conducted to assess linkage results. RESULTS:From 2009-2013, there were 1,054,344 individuals involved in MVT crashes in NYC and 280,340 ED visits and hospitalizations from MVT-related injuries. There were 145,003 linked pairs, giving a linkage rate of 52% of the total MVT-related hospital records. This linkage had a sensitivity of 74% and a specificity of 93%. Linkage rates were comparable by age, sex, crash role, collision type, hospital county, injury location, hospital type, and hospital status, indicating no apparent biases in the match by these variables. CONCLUSIONS:Performing a probabilistic linkage between MVT crash reports and hospitalization records is possible with a limited set of identifying variables. These linked data will inform traffic safety policies by providing new information on how crash circumstances translate to health outcomes.

AIM: The objective of the study was to compare the diagnosis of dysglycemic states by conventional oral glucose tolerance test (OGTT) criteria (fasting and 2-h plasma glucose) with the 1-h post-load plasma glucose level. MATERIAL AND METHODS: 34 individuals (mean age: 55+/-13years; BMI: 27.7+/-6.3kg/m2) at risk for prediabetes were administered a 75g OGTT. Individuals with normal glucose tolerance (NGT) or prediabetes were identified according to fasting and/or 2-h plasma glucose (PG) concentrations. Subsequently, subjects were divided in 2 groups: group 1 (n=21) with a 1-h PG<155mg/dl and group 2 (n=13) with a 1-h PG>/=155mg/dl. HOMA was performed to assess beta-cell function and insulin sensitivity. RESULTS: NGT or prediabetes based on conventional criteria correlated with the 1-h PG/=155mg/dl (p<0.001). Moreover, the 1-h PG>/=155mg/dl was associated with higher HbA1c levels (6.1+/-0.5 vs. 5.5+/-0.3%, p<0.001) and significantly impaired insulin secretion and hyperbolic product (BxS) on HOMA test vs. 1-h PG<155mg/dl. CONCLUSION: The 1-h post-load plasma glucose value >/=155mg/dl is strongly associated with conventional criteria for (pre)diabetes and alterations of beta-cell function.

OBJECTIVE: To use novel geographic methods and large-scale claims data to identify the local distribution of pediatric chronic diseases in New York City. METHODS: Using a 2009 all-payer emergency claims database, we identified the proportion of unique children aged 0 to 17 with diagnosis codes for specific medical and psychiatric conditions. As a proof of concept, we compared these prevalence estimates to traditional health surveys and registry data using the most geographically granular data available. In addition, we used home addresses to map local variation in pediatric disease burden. RESULTS: We identified 549,547 New York City children who visited an emergency department at least once in 2009. Though our sample included more publicly insured and uninsured children, we found moderate to strong correlations of prevalence estimates when compared to health surveys and registry data at prespecified geographic levels. Strongest correlations were found for asthma and mental health conditions by county among younger children (0.88, P = .05 and 0.99, P < .01, respectively). Moderate correlations by neighborhood were identified for obesity and cancer (0.53 and 0.54, P < .01). Among adolescents, correlations by health districts were strong for obesity (0.95, P = .05), and depression estimates had a nonsignificant, but strong negative correlation with suicide attempts (-0.88, P = .12). Using SaTScan, we also identified local hot spots of pediatric chronic disease. CONCLUSIONS: For conditions easily identified in claims data, emergency department surveillance may help estimate pediatric chronic disease prevalence with higher geographic resolution. More studies are needed to investigate limitations of these methods and assess reliability of local disease estimates.

OBJECTIVE: We examined whether the negative relation between television viewing that exceeds the recommendations of the American Academy of Pediatrics (AAP) and school readiness varied by family income. METHODS: Data were collected from 807 children from diverse backgrounds. Parents reported hours of television viewing, as well as family income. Children were assessed using measures of math, knowledge of letters and words, and executive function (EF). RESULTS: Television viewing was negatively associated with math and EF but not with letter and word knowledge. An interaction between television viewing and family income indicated that the effect of television viewing in excess of the AAP recommended maximum had negative associations with math and EF that increased as a linear function of family income. Furthermore, EF partially mediated the relation between television viewing and math. CONCLUSION: Television viewing is negatively associated with children's school readiness skills, and this association increased as family income decreased. Active efforts to reinforce AAP guidelines to limit the amount of television children watch should be made, especially for children from middle- to lower-income families.