Faculty Bibliography

Longitudinal studies of cognitive performance are sensitive to dropout, as participants experiencing cognitive deficits are less likely to attend study visits, which may bias estimated associations between exposures of interest and cognitive decline. Multiple imputation is a powerful tool for handling missing data, however its use for missing cognitive outcome measures in longitudinal analyses remains limited. We use multiple imputation by chained equations (MICE) to impute cognitive performance scores of participants who did not attend the 2011-2013 exam of the Atherosclerosis Risk in Communities Study. We examined the validity of imputed scores using observed and simulated data under varying assumptions. We examined differences in the estimated association between diabetes at baseline and 20-year cognitive decline with and without imputed values. Lastly, we discuss how different analytic methods (mixed models and models fit using generalized estimate equations) and choice of for whom to impute result in different estimands. Validation using observed data showed MICE produced unbiased imputations. Simulations showed a substantial reduction in the bias of the 20-year association between diabetes and cognitive decline comparing MICE (3-4 % bias) to analyses of available data only (16-23 % bias) in a construct where missingness was strongly informative but realistic. Associations between diabetes and 20-year cognitive decline were substantially stronger with MICE than in available-case analyses. Our study suggests when informative data are available for non-examined participants, MICE can be an effective tool for imputing cognitive performance and improving assessment of cognitive decline, though careful thought should be given to target imputation population and analytic model chosen, as they may yield different estimands.

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.

The period immediately after release from prison or jail carries increased mortality risk. This study sought to better understand postrelease death by matching electronic health records from those incarcerated in New York City jails between 2011 and 2012 with vital statistics records. The in-jail and 6-week postrelease mortality rates were estimated to be 1.39 and 5.89 per 1,000 person-years, respectively. Of 59 deaths occurring within 6 weeks of release from jail, the causes included opioid overdose (37.3%), other drugs (8.5%), chronic disease (25.4%), assaultive trauma (20.3%), and other trauma (8.5%). These data confirm that overdose death accounts for the most frequent cause of postrelease death. Matching between correctional health systems and vital statistics can inform quality improvement efforts in jail health care delivery.

BACKGROUND:Patients with chronic diseases such as Type 2 diabetes mellitus (DM) usually have a relatively poor quality of life (QoL), because the cost of care (living expenses and health) or diet restrictions are heavily felt by these patients, and this is of a public health concern. However, limited data on DM QoL exist in Ghana and Nigeria. This makes it imperative for data to be collated in that regard. MATERIALS AND METHODS/METHODS:We adopted the Strengthening The reporting of observational studies in epidemiology (STROBE) consensus checklist to survey the patients with DM seen at the diabetic clinic at the Department of Medicine of the Korle-Bu Teaching Hospital and University College Hospital, Ibadan, Nigeria. Patients with Type 2 DM aged 40 years and older were recruited by using systematic random sampling method. The World Health Organization Quality of Life-BREF, diabetes empowerment scale, and DM knowledge scale were used to assess QoL, patient empowerment, and knowledge of DM, respectively. The predictors of QoL were determined using multiple linear regression analyses. RESULTS:A total of 198 patients in Ghana and 203 patients in Nigeria completed the survey, with female-to-male ratio being 3:1 and 2:1, respectively. The overall QoL in both countries was relatively low: 56.19 ± 8.23 in Ghana and 64.34 ± 7.34 in Nigeria. In Ghana, significant correlates of higher scores on the QoL scale were medication adherence (P = 0.02) and employment status (P = 0.02). Among patients in Nigeria, employment status (P = 0.02) and DM empowerment (0.03) were significant predictors of QoL in patients with DM. CONCLUSION/CONCLUSIONS:Our study revealed an association between a number of psychosocial factors and QoL among patients with DM in Ghana and Nigeria.

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.