Faculty Bibliography

BACKGROUND:Impaired functional status attenuates the relationship of systolic blood pressure (SBP) with mortality in older adults but has not been studied in middle-aged populations. METHOD:Among 10,264 stroke-free Atherosclerosis Risk in Communities participants (mean age 62.8 [5.7] years; 6,349 [62%] younger [<65 years]; 5,148 [50%] men; 2,664 [26%] Black), function was defined as good function (GF) for those self-reporting no difficulty performing functional tasks and basic or instrumental tasks of daily living; all others were defined as impaired function (IF). SBP categories were normal (<120 mmHg), prehypertension (120-139 mmHg), and hypertension (≥140 mmHg). Mortality risk associated with SBP was estimated using adjusted Cox proportional hazard models with a triple interaction between age, functional status, and SBP. RESULTS:Mean follow-up was 12.9 years with 2,863 (28%) deaths. Among younger participants, 3,017 (48%) had IF; 2,279 of 3,915 (58%) older participants had IF. Prehypertension (hazard ratio [HR] = 1.48 [1.03, 2.15] p = .04) and hypertension (HR = 1.97 [1.29, 3.03] p = .002) were associated with mortality in younger GF and older (≥65 years) GF participants (prehypertension HR = 1.21 [1.06, 1.37] p = .005; hypertension HR = 1.47 [1.36, 1.59] p < .001). Among IF participants, prehypertension was not associated with mortality in younger participants (HR = 0.99 [0.85, 1.15] p = .93) and was protective in older participants (HR = 0.87 [0.85, 0.90] p < .001). Hypertension was associated with mortality in younger IF participants (HR = 1.54 [1.30, 1.82] p < .001) but not in older IF participants (HR = 0.99 [0.87, 1.14] p = .93). CONCLUSIONS:Compared with younger and well-functioning persons, the additional contribution of blood pressure to mortality is much lower with older age and impaired function, particularly if both are present. Functional status and age could potentially inform optimal blood pressure targets.

Health risks associated with short-term exposure to ambient air pollution are communicated to the public by the US EPA through the Air Quality Index (AQI), but it remains unclear whether the current regulatory-based, single-pollutant AQI fully represents the actual risks of air pollution-related illness. The objective of this study is to quantify cardiovascular hospital admissions attributable to PM2.5 at each AQI category. Based on National Ambient Air Quality Standards (NAAQS), the highest AQI value among criteria pollutants (driver pollutant) is reported daily. We investigated excess cardiovascular hospital admissions attributable to fine particulate matter (PM2.5) exposure from 2000 to 2010 in Bronx, Erie, Queens, and Suffolk counties of New York. Daily total, unscheduled cardiovascular hospital admissions (principal diagnosis) for individuals aged 20-99 years, concentration-response functions for PM2.5, and estimated quarterly effective daily concentrations were used to calculate excess cardiovascular hospital admissions when PM2.5 was reported as the driver pollutant and when PM2.5 was not reported as the driver pollutant at each AQI category. A higher proportion of excess hospital admissions attributable to PM2.5 occurred when PM2.5 was the driver pollutant (i.e., ~70% in Bronx County). The majority of excess hospital admissions (i.e., >90% in Bronx County) occurred when the AQI was <100 ("good" or "moderate" level of health concern) regardless of whether PM2.5 was the driver pollutant. During the warm season (April-September), greater excess admissions in Suffolk County occurred when PM2.5 was not the AQI driver pollutant. These results indicate that a single-pollutant index may inadequately communicate the adverse health risks associated with air pollution.Journal of Exposure Science and Environmental Epidemiology advance online publication, 15 July 2015; doi:10.1038/jes.2015.43.

The American Thoracic Society has previously published statements on what constitutes an adverse effect on health of air pollution in 1985 and 2000. We set out to update and broaden these past statements that focused primarily on effects on the respiratory system. Since then, many studies have documented effects of air pollution on other organ systems, such as on the cardiovascular and central nervous systems. In addition, many new biomarkers of effects have been developed and applied in air pollution studies.This current report seeks to integrate the latest science into a general framework for interpreting the adversity of the human health effects of air pollution. Rather than trying to provide a catalogue of what is and what is not an adverse effect of air pollution, we propose a set of considerations that can be applied in forming judgments of the adversity of not only currently documented, but also emerging and future effects of air pollution on human health. These considerations are illustrated by the inclusion of examples for different types of health effects of air pollution.

OBJECTIVE: The purpose of this study was to measure the number of repeat computed tomography (CT) scans performed across an established health information exchange (HIE) in New York City. The long-term objective is to build an HIE-based duplicate CT alerting system to reduce potentially avoidable duplicate CTs. METHODS: This retrospective cohort analysis was based on HIE CT study records performed between March 2009 and July 2012. The number of CTs performed, the total number of patients receiving CTs, and the hospital locations where CTs were performed for each unique patient were calculated. Using a previously described process established by one of the authors, hospital-specific proprietary CT codes were mapped to the Logical Observation Identifiers Names and Codes (LOINC(R)) standard terminology for inter-site comparison. The number of locations where there was a repeated CT performed with the same LOINC code was then calculated for each unique patient. RESULTS: There were 717 231 CTs performed on 349 321 patients. Of these patients, 339 821 had all of their imaging studies performed at a single location, accounting for 668 938 CTs. Of these, 9500 patients had 48 293 CTs performed at more than one location. Of these, 6284 patients had 24 978 CTs with the same LOINC code performed at multiple locations. The median time between studies with the same LOINC code was 232 days (range of 0 to 1227); however, 1327 were performed within 7 days and 5000 within 30 days. CONCLUSIONS: A small proportion (3%) of our cohort had CTs performed at more than one location, however this represents a large number of scans (48 293). A noteworthy portion of these CTs (51.7%) shared the same LOINC code and may represent potentially avoidable studies, especially those done within a short time frame. This represents an addressable issue, and future HIE-based alerts could be utilized to reduce potentially avoidable CT scans.

PURPOSE: To examine if there was spatial misclassification in exposure to neighborhood noise complaints among a sample of low-income housing residents in New York City, comparing home-based spatial buffers and Global Positioning System (GPS) daily path buffers. METHODS: Data came from the community-based NYC Low-Income Housing, Neighborhoods and Health Study, where GPS tracking of the sample was conducted for a week (analytic n = 102). We created a GPS daily path buffer (a buffering zone drawn around GPS tracks) of 200 m and 400 m. We also used home-based buffers of 200 m and 400 m. Using these "neighborhoods" (or exposure areas), we calculated neighborhood exposure to noisy events from 311 complaints data (analytic n = 143,967). Friedman tests (to compare overall differences in neighborhood definitions) were applied. RESULTS: There were differences in neighborhood noise complaints according to the selected neighborhood definitions (P < .05). For example, the mean neighborhood noise complaint count was 1196 per square kilometer for the 400-m home-based and 812 per square kilometer for the 400-m activity space buffer, illustrating how neighborhood definition influences the estimates of exposure to neighborhood noise complaints. CONCLUSIONS: These analyses suggest that, whenever appropriate, GPS neighborhood definitions can be used in spatial epidemiology research in spatially mobile populations to understand people's lived experience.

Purpose . Interest and funding continue to grow for bringing supermarkets to underserved areas, yet little is known about their impact. Design . A quasi-experimental study was used to determine the impact of a new supermarket opening as a result of tax and zoning incentives. Setting . The study took place in the South Bronx, New York City, New York. SUBJECTS: Studied were residents of two South Bronx neighborhoods deemed high need. Measures . Food purchasing and consumption were examined via surveys and 24-hour dietary recalls before and at two points after the supermarket opened (1-5, 13-17 months). Analysis . Data were analyzed using difference-in-difference models controlling for gender, race and ethnicity, age, education, marital status, and self-reported income. Ordinary least squares and logistic regression models were estimated for continuous and binary outcomes, respectively. Results . At baseline, 94% to 97% of consumers shopped at a supermarket. There was a 2% increase in this behavior in the intervention community (p < .05) not seen in the comparison community. One year later there was a 7% net increase in eating at home (p < .1) and a 20% decrease in drinking sugary beverages (p < .05), but no appreciable change in fruit/vegetable consumption or overall dietary quality. Conclusion . The new supermarket did not result in substantial or broad changes in purchasing patterns or nutritional quality of food consumed, though smaller, positive changes were observed over a 1-year period. Future work should examine different contexts and a broader set of outcomes, including economic development.

BACKGROUND: Public schools provide students with opportunities to participate in many discretionary, unmandated wellness programs. Little is known about the number of these programs, their distribution across schools, and the kinds of students served. We provide evidence on these questions for New York City (NYC) public schools. METHODS: Data on wellness programs were collected from program websites, NYC's Office of School Food and Wellness, and direct contact with program sponsors for 2013. Programs were grouped into categories, nutrition, fitness, and comprehensive, and were combined with data on school characteristics available from NYC's Department of Education. Numbers of programs and provision of programs were analyzed for relationships with demographic and school structural characteristics, using descriptive statistics and multiple regression. RESULTS: Discretionary wellness programs are numerous, at 18 programs. Little evidence supports inequity according to student race/ethnicity, income, or nativity, but high schools, new schools, co-located schools, small schools, and schools with larger proportions of inexperienced teachers are less likely to provide wellness programs. CONCLUSIONS: Opportunities exist to further the reach of wellness programs in public schools by modifying them for high school adoption and building capacity in schools less likely to have the administrative support to house them.

BACKGROUND/OBJECTIVES: Maternal obesity may influence neonatal and childhood morbidities through increased inflammation and/or altered immune response. Less is known about paternal obesity. We hypothesized that excessive parental weight contributes to elevated inflammation and altered immunoglobulin (Ig) profiles in neonates. SUBJECTS/METHODS: In the Upstate KIDS Study maternal pre-pregnancy body mass index (BMI) was obtained from vital records and paternal BMI from maternal report. Biomarkers were measured from newborn dried blood spots (DBS) among neonates whose parents provided consent. Inflammatory scores were calculated by assigning one point for each of five pro-inflammatory biomarkers above the median and one point for an anti-inflammatory cytokine below the median. Linear regression models and generalized estimating equations were used to estimate mean differences (beta) and 95% confidence intervals (CI) in the inflammatory score and Ig levels by parental overweight/obesity status compared with normal weight. RESULTS: Among 2974 pregnancies, 51% were complicated by excessive maternal weight (BMI>25), 73% by excessive paternal weight and 28% by excessive gestational weight gain. Maternal BMI categories of overweight (BMI 25.0-29.9) and obese class II/III (BMI>/=35) were associated with increased neonatal inflammation scores (beta=0.12, 95% CI: 0.02, 0.21; P=0.02 and beta=0.13, CI: -0.002, 0.26; P=0.05, respectively) but no increase was observed in the obese class I group (BMI 30-34.9). Mothers with class I and class II/III obesity had newborns with increased IgM levels (beta=0.11, CI: 0.04, 0.17; P=0.001 and beta=0.12, CI: 0.05, 0.19); P<0.001, respectively). Paternal groups of overweight, obese class I and obese class II/III had decreased neonatal IgM levels (beta=-0.08, CI: -0.13,-0.03, P=0.001; beta=-0.07, CI: -0.13, -0.01, P=0.029 and beta=-0.11, CI:-0.19,-0.04, P=0.003, respectively). CONCLUSIONS: Excessive maternal weight was generally associated with increased inflammation and IgM supporting previous observations of maternal obesity and immune dysregulation in offspring. The role of paternal obesity requires further study.

BACKGROUND: While primary care work conditions are associated with adverse clinician outcomes, little is known about the effect of work condition interventions on quality or safety. DESIGN: A cluster randomized controlled trial of 34 clinics in the upper Midwest and New York City. PARTICIPANTS: Primary care clinicians and their diabetic and hypertensive patients. INTERVENTIONS: Quality improvement projects to improve communication between providers, workflow design, and chronic disease management. Intervention clinics received brief summaries of their clinician and patient outcome data at baseline. MAIN MEASURES: We measured work conditions and clinician and patient outcomes both at baseline and 6-12 months post-intervention. Multilevel regression analyses assessed the impact of work condition changes on outcomes. Subgroup analyses assessed impact by intervention category. KEY RESULTS: There were no significant differences in error reduction (19 % vs. 11 %, OR of improvement 1.84, 95 % CI 0.70, 4.82, p = 0.21) or quality of care improvement (19 % improved vs. 44 %, OR 0.62, 95 % CI 0.58, 1.21, p = 0.42) between intervention and control clinics. The conceptual model linking work conditions, provider outcomes, and error reduction showed significant relationships between work conditions and provider outcomes (p