Collateral Impacts of the COVID-19 Pandemic: The New York City Experience.

OBJECTIVE: To adapt an existing surveillance system to monitor the collateral impacts of the COVID-19 pandemic on health outcomes in New York City across 6 domains: access to care, chronic disease, sexual/reproductive health, food/economic insecurity, mental/behavioral health, and environmental health. DESIGN: Epidemiologic assessment. Public health surveillance system. SETTING: New York City. PARTICIPANTS: New York City residents. MAIN OUTCOME MEASURES: We monitored approximately 30 indicators, compiling data from 2006 to 2022. Sources of data include clinic visits, surveillance surveys, vital statistics, emergency department visits, lead and diabetes registries, Medicaid claims, and public benefit enrollment. RESULTS: We observed disruptions across most indicators including more than 50% decrease in emergency department usage early in the pandemic, which rebounded to prepandemic levels by late 2021, changes in reporting levels of probable anxiety and depression, and worsening birth outcomes for mothers who identified as Asian/Pacific Islander or Black. Data are processed in SAS and analyzed using the R Surveillance package to detect possible inflections. Data are updated monthly to an internal Tableau Dashboard and shared with agency leadership. CONCLUSIONS: As the COVID-19 pandemic continues into its third year, public health priorities are returning to addressing non-COVID-19-related diseases and conditions, their collateral impacts, and postpandemic recovery needs. Substantial work is needed to return even to a suboptimal baseline across multiple health topic areas. Our surveillance framework offers a valuable starting place to effectively allocate resources, develop interventions, and issue public communications.

COVID-19 Outbreak - New York City, February 29-June 1, 2020.

New York City (NYC) was an epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States during spring 2020 (1). During March-May 2020, approximately 203,000 laboratory-confirmed COVID-19 cases were reported to the NYC Department of Health and Mental Hygiene (DOHMH). To obtain more complete data, DOHMH used supplementary information sources and relied on direct data importation and matching of patient identifiers for data on hospitalization status, the occurrence of death, race/ethnicity, and presence of underlying medical conditions. The highest rates of cases, hospitalizations, and deaths were concentrated in communities of color, high-poverty areas, and among persons aged ≥75 years or with underlying conditions. The crude fatality rate was 9.2% overall and 32.1% among hospitalized patients. Using these data to prevent additional infections among NYC residents during subsequent waves of the pandemic, particularly among those at highest risk for hospitalization and death, is critical. Mitigating COVID-19 transmission among vulnerable groups at high risk for hospitalization and death is an urgent priority. Similar to NYC, other jurisdictions might find the use of supplementary information sources valuable in their efforts to prevent COVID-19 infections.

A Retrospective Cohort Study of Urgent Care Visits and Revisits for Headache/Migraine.

OBJECTIVE: Increasingly, patients are seeking same-day care at urgent care (UC) facilities. Little is known about the health care utilization patterns of patients who visit UC facilities for headache and migraine. We examined the frequency of headache and migraine visits and revisits at UC facilities. METHODS: We conducted a retrospective cohort study of headache not otherwise specified (NOS) and migraine visits from 67 NYC UC facilities over an eight-month period. We report descriptive analyses, the frequency of headache NOS revisits, and the elapsed time to revisits. RESULTS: There were 10,240 patients who visited UC facilities for headache NOS or migraine within the eight-month period. The majority of patients, 6,994 (68.3%), were female, and the mean age (SD) was 35.1 (15.0) years. Most (93.9%) patients (N = 9,613) lived within 60 miles of NYC; 5.5% (N = 564) had at least one revisit, and among re-visitors, there was an average (SD) of 2.2 (0.7) visits to UC facilities during the study period and an average time to revisit (SD) of 61.3 (55.2) days. CONCLUSIONS: In just eight months, there were >10,000 headache NOS and migraine visits to UC facilities in NYC, with half of revisits occurring within 90 days. Future work should examine headache management in UC facilities.

The associations between daily spring pollen counts, over-the-counter allergy medication sales, and asthma syndrome emergency department visits in New York City, 2002-2012.

BACKGROUND: Many types of tree pollen trigger seasonal allergic illness, but their population-level impacts on allergy and asthma morbidity are not well established, likely due to the paucity of long records of daily pollen data that allow analysis of multi-day effects. Our objective in this study was therefore to determine the impacts of individual spring tree pollen types on over-the-counter allergy medication sales and asthma emergency department (ED) visits. METHODS: Nine clinically-relevant spring tree pollen genera (elm, poplar, maple, birch, beech, ash, sycamore/London planetree, oak, and hickory) measured in Armonk, NY, were analyzed for their associations with over-the-counter allergy medication sales and daily asthma syndrome ED visits from patients' chief complaints or diagnosis codes in New York City during March 1st through June 10th, 2002-2012. Multi-day impacts of pollen on the outcomes (0-3 days and 0-7 days for the medication sales and ED visits, respectively) were estimated using a distributed lag Poisson time-series model adjusting for temporal trends, day-of-week, weather, and air pollution. For asthma syndrome ED visits, age groups were also analyzed. Year-to-year variation in the average peak dates and the 10th-to-90th percentile duration between pollen and the outcomes were also examined with Spearman's rank correlation. RESULTS: Mid-spring pollen types (maple, birch, beech, ash, oak, and sycamore/London planetree) showed the strongest significant associations with both outcomes, with cumulative rate ratios up to 2.0 per 0-to-98th percentile pollen increase (e.g., 1.9 [95% CI: 1.7, 2.1] and 1.7 [95% CI: 1.5, 1.9] for the medication sales and ED visits, respectively, for ash). Lagged associations were longer for asthma syndrome ED visits than for the medication sales. Associations were strongest in children (ages 5-17; e.g., a cumulative rate ratio of 2.6 [95% CI: 2.1, 3.1] per 0-to-98th percentile increase in ash). The average peak dates and durations of some of these mid-spring pollen types were also associated with those of the outcomes. CONCLUSIONS: Tree pollen peaking in mid-spring exhibit substantive impacts on allergy, and asthma exacerbations, particularly in children. Given the narrow time window of these pollen peak occurrences, public health and clinical approaches to anticipate and reduce allergy/asthma exacerbation should be developed.

Presyndromic surveillance for improved detection of emerging public health threats.

Existing public health surveillance systems that rely on predefined symptom categories, or syndromes, are effective at monitoring known illnesses, but there is a critical need for innovation in "presyndromic" surveillance that detects biothreats with rare or previously unseen symptomology. We introduce a data-driven, automated machine learning approach for presyndromic surveillance that learns newly emerging syndromes from free-text emergency department chief complaints, identifies localized case clusters among subpopulations, and incorporates practitioner feedback to automatically distinguish between relevant and irrelevant clusters, thus providing personalized, actionable decision support. Blinded evaluations by New York City's Department of Health and Mental Hygiene demonstrate that our approach identifies more events of public health interest and achieves a lower false-positive rate compared to a state-of-the-art baseline.

Epidemiological characteristics of the B.1.526 SARS-CoV-2 variant.

To characterize the epidemiological properties of the B.1.526 SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) variant of interest, here we used nine epidemiological and population datasets and model-inference methods to reconstruct SARS-CoV-2 transmission dynamics in New York City, where B.1.526 emerged. We estimated that B.1.526 had a moderate increase (15 to 25%) in transmissibility, could escape immunity in 0 to 10% of previously infected individuals, and substantially increased the infection fatality risk (IFR) among adults 65 or older by >60% during November 2020 to April 2021, compared to estimates for preexisting variants. Overall, findings suggest that new variants like B.1.526 likely spread in the population weeks before detection and that partial immune escape (e.g., resistance to therapeutic antibodies) could offset prior medical advances and increase IFR. Early preparedness for and close monitoring of SARS-CoV-2 variants, their epidemiological characteristics, and disease severity are thus crucial to COVID-19 (coronavirus disease 2019) response.

A Source Apportionment of U.S. Fine Particulate Matter Air Pollution.

Using daily fine particulate matter (PM2.5) composition data from the 2000-2005 U.S. EPA Chemical Speciation Network (CSN) for over 200 sites, we applied multivariate methods to identify and quantify the major fine particulate matter (PM2.5) source components in the U.S. Novel aspects of this work were: (1) the application of factor analysis (FA) to multi-city daily data, drawing upon both spatial and temporal variations of chemical species; and, (2) the exclusion of secondary components (sulfates, nitrates and organic carbon) from the source identification FA to more clearly discern and apportion the PM2.5 mass to primary emission source categories. For the quantification of source-related mass, we considered two approaches based upon the FA results: 1) using single key tracers for sources identified by FA in a mass regression; and, 2) applying Absolute Principal Component Analysis (APCA). In each case, we followed a two-stage mass regression approach, in which secondary components were first apportioned among the identified sources, and then mass was apportioned to the sources and to other secondary mass not explained by the individual sources. The major U.S. PM2.5 source categories identified via FA (and their key elements) were: Metals Industry (Pb, Zn); Crustal/Soil Particles (Ca, Si); Motor Vehicle Traffic (EC, NO2); Steel Industry (Fe, Mn); Coal Combustion (As, Se); Oil Combustion (V, Ni); Salt Particles (Na, Cl) and Biomass Burning (K). Nationwide spatial plots of the source-related PM2.5 impacts were confirmatory of the factor interpretations: ubiquitous sources, such as Traffic and Soil, were found to be spread across the nation, more unique sources (such as Steel and Metals Processing) being highest in select industrialized cities, Biomass Burning was highest in the U.S. Northwest, while Residual Oil combustion was highest in cities in the Northeastern U.S. and in cities with major seaports. The sum of these source contributions and the secondary PM2.5 components agreed well with the U.S. PM2.5 observed during the study period (mean=14.3 ug/m3; R2= 0.91). Apportionment regression analyses using single-element tracers for each source category gave results consistent with the APCA estimates. Comparisons of nearby sites indicated that the PM2.5 mass and the secondary aerosols were most homogenous spatially, while traffic PM2.5 and its tracer, EC, were among the most spatially representative of the source-related components. Comparison of apportionment results to a previous analysis of the 1979-1982 IP Network revealed similar and correlated major U.S. source category factors, albeit at lower levels than in the earlier period, suggesting a consistency in the U.S. spatial patterns of these source-related exposures over time, as well. These results indicate that applying source apportionment methods to the nationwide CSN can be an informative avenue for identifying and quantifying source components for the subsequent estimation of source-specific health effects, potentially contributing to more efficient regulation of PM2.5.

Alteration of cardiac function in ApoE-/- mice by subchronic urban and regional inhalation exposure to concentrated ambient PM2.5.

Ambient PM(2.5) (particulate matter with an aerodynamic diameters of less than 2.5 mum) is associated with alterations in the autonomic nervous system and cardiac function, but there are significant response variations. The authors simultaneously studied the effects of concentrated PM(2.5) (CAPs) in Sterling Forest (SF; dominated by long-range transported PM) and at the Mount Sinai School of Medicine (MS; rich in Ni and elemental/organic carbon [EC/OC]) in Manhattan, NY. ApoE(-/-) mice (n = 8/group) were exposed to filtered air or CAPs (average 133 and 123 microg/m(3) in SF and MS, respectively) for 6 h/day, 5 days/week for 6 months. Electrocardiogram (ECG) tracings were monitored using telemetry. At MS, current day CAPs mass was negatively associated with short-term changes in heart rate (HR), and positively with HR variability (HRV). At SF, CAPs mass was positively associated with HR, and negatively with HRV. At MS, HR and HRV changes were associated with PM(2.5) components associated with residual oil combustion > long-range transport > traffic > FeMn > incineration > soil, and fireworks had no associations. At SF, HR and HRV were associated with long-range transport > Ni refinery > soil > residual oil combustion/traffic. At both sites, there were cardiac function associations with PM(2.5), but not EC. At MS, there were associations with Ni and P, whereas at SF, they were with a mixture of long-range transported PM, crustal material, and combustion products. Thus subchronic CAPs exposures at locations with different particle compositions produced different effects on cardiac function in ApoE(-/-) mice.

Novel use of syndromic surveillance to monitor the impact of synthetic cannabinoid control measures on morbidity.

BACKGROUND: Using data from syndromic surveillance, the New York City Department of Health and Mental Hygiene (DOHMH) identified an increase in the number of emergency department (ED) visits related to synthetic cannabinoids. Syndromic surveillance data were used to target community-level interventions and assess the real-time impact of control measures in reducing synthetic cannabinoid ("K2")-related morbidity. METHODS: From April 2015 through September 2015, DOHMH implemented 3 separate interventions to reduce K2-related morbidity by limiting the availability of K2 products. Difference-in-difference analyses compared pre- and post-intervention differences in cannabinoid-related ED visit rates between neighborhoods and controls for Interventions A and B. City-wide count data were used to compare K2-related ED visits before and after Intervention C. RESULTS: Syndromic data showed a reduction in K2-related ED visits following the 3 interventions. Respective decreases in rates of synthetic cannabinoid-related ED visits of 33 and 38% were detected at the neighborhood-level due to Interventions A and B, respectively. A decrease of 29% was calculated at the city level following Intervention C. CONCLUSIONS: In addition to identifying emerging public health concerns, syndromic data can provide valuable real-time evidence on the effectiveness of public health interventions.

Developing Syndromic Surveillance to Monitor and Respond to Adverse Health Events Related to Psychoactive Substance Use: Methods and Applications.

INTRODUCTION: Recent increases in drug overdose deaths, both in New York City and nationally, highlight the need for timely data on psychoactive drug-related morbidity. We developed drug syndrome definitions for syndromic surveillance to monitor drug-related emergency department (ED) visits in real time. MATERIALS AND METHODS: We used 2012 archived syndromic surveillance data from New York City hospitals to develop definitions for psychoactive drug-related syndromes. The dataset contained ED visit-level information that included patients' chief complaints, dates of visits, ZIP codes of residence, discharge diagnoses, and dispositions. After manually reviewing chief complaints, we developed a classification scheme comprising 3 categories (overdose, drug mention, and drug abuse/misuse), which we used to define 25 psychoactive drug syndromes. From July 2013 through December 2015, the New York City Department of Health and Mental Hygiene performed daily syndromic surveillance of psychoactive drug-related ED visits using the 25 syndrome definitions. RESULTS: Syndromic surveillance triggered 4 public health investigations, supported 8 other public health investigations that had been triggered by other mechanisms, and resulted in the identification of 5 psychoactive drug-related outbreaks. Syndromic surveillance also identified a substantial increase in synthetic cannabinoid-related visits (from an average of 3 per week in January 2014 to >300 per week in July 2015) and an increase in heroin overdose visits (from 80 to 171 in the first 3 quarters of 2012 and 2014, respectively) in a single neighborhood. PRACTICE IMPLICATIONS: Syndromic surveillance using these novel definitions enabled monitoring of trends in psychoactive drug-related morbidity, initiation and support of public health investigations, and targeting of interventions. Health departments can refine these definitions for their jurisdictions using the described methods and integrate them into existing syndromic surveillance systems.

Advancing the Use of Emergency Department Syndromic Surveillance Data, New York City, 2012-2016.

INTRODUCTION: The use of syndromic surveillance has expanded from its initial purpose of bioterrorism detection. We present 6 use cases from New York City that demonstrate the value of syndromic surveillance for public health response and decision making across a broad range of health outcomes: synthetic cannabinoid drug use, heat-related illness, suspected meningococcal disease, medical needs after severe weather, asthma exacerbation after a building collapse, and Ebola-like illness in travelers returning from West Africa. MATERIALS AND METHODS: The New York City syndromic surveillance system receives data on patient visits from all emergency departments (EDs) in the city. The data are used to assign syndrome categories based on the chief complaint and discharge diagnosis, and analytic methods are used to monitor geographic and temporal trends and detect clusters. RESULTS: For all 6 use cases, syndromic surveillance using ED data provided actionable information. Syndromic surveillance helped detect a rise in synthetic cannabinoid-related ED visits, prompting a public health investigation and action. Surveillance of heat-related illness indicated increasing health effects of severe weather and led to more urgent public health messaging. Surveillance of meningitis-related ED visits helped identify unreported cases of culture-negative meningococcal disease. Syndromic surveillance also proved useful for assessing a surge of methadone-related ED visits after Superstorm Sandy, provided reassurance of no localized increases in asthma after a building collapse, and augmented traditional disease reporting during the West African Ebola outbreak. PRACTICE IMPLICATIONS: Sharing syndromic surveillance use cases can foster new ideas and build capacity for public health preparedness and response.

Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system.

The New York City Department of Health and Mental Hygiene has operated an emergency department syndromic surveillance system since 2001, using temporal and spatial scan statistics run on a daily basis for cluster detection. Since the system was originally implemented, a number of new methods have been proposed for use in cluster detection. We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. We also evaluated each method's implementation, programming time, run time, and the ease of use. Among the temporal methods, at a set specificity of 95%, a Holt-Winters exponential smoother performed the best, detecting 19% of the simulated injects across all shapes and sizes, followed by an autoregressive moving average model (16%), a generalized linear model (15%), a modified version of the Early Aberration Reporting System's C2 algorithm (13%), a temporal scan statistic (11%), and a cumulative sum control chart (<2%). Of the spatial/spatio-temporal methods we tested, a spatial scan statistic detected 3% of all injects, a Bayes regression found 2%, and a generalized linear mixed model and a space-time permutation scan statistic detected none at a specificity of 95%. Positive predictive value was low (<7%) for all methods. Overall, the detection methods we tested did not perform well in identifying the temporal and spatial clusters of cases in the inject dataset. The spatial scan statistic, our current method for spatial cluster detection, performed slightly better than the other tested methods across different inject magnitudes and types. Furthermore, we found the scan statistics, as applied in the SaTScan software package, to be the easiest to program and implement for daily data analysis.

Ischemic Heart Disease Mortality and Long-Term Exposure to Source-Related Components of U.S. Fine Particle Air Pollution.

BACKGROUND: Fine particulate matter (PM2.5) air pollution exposure has been identified as a global health threat. However, the types and sources of particles most responsible are not yet known. OBJECTIVES: We sought to identify the causal characteristics and sources of air pollution underlying past associations between long-term PM2.5 exposure and ischemic heart disease (IHD) mortality, as established in the American Cancer Society's Cancer Prevention Study-II cohort. METHODS: Individual risk factor data were evaluated for 445,860 adults in 100 U.S. metropolitan areas followed from 1982 through 2004 for vital status and cause of death. Using Cox proportional hazard models, we estimated IHD mortality hazard ratios (HRs) for PM2.5, trace constituents, and pollution source-associated PM2.5, as derived from air monitoring at central stations throughout the nation during 2000-2005. RESULTS: Associations with IHD mortality varied by PM2.5 mass constituent and source. A coal combustion PM2.5 IHD HR = 1.05 (95% CI: 1.02, 1.08) per microgram/cubic meter, versus an IHD HR = 1.01 (95% CI: 1.00, 1.02) per microgram/cubic meter PM2.5 mass, indicated a risk roughly five times higher for coal combustion PM2.5 than for PM2.5 mass in general, on a per microgram/cubic meter PM2.5 basis. Diesel traffic-related elemental carbon (EC) soot was also associated with IHD mortality (HR = 1.03; 95% CI: 1.00, 1.06 per 0.26-µg/m3 EC increase). However, PM2.5 from both wind-blown soil and biomass combustion was not associated with IHD mortality. CONCLUSIONS: Long-term PM2.5 exposures from fossil fuel combustion, especially coal burning but also from diesel traffic, were associated with increases in IHD mortality in this nationwide population. Results suggest that PM2.5-mortality associations can vary greatly by source, and that the largest IHD health benefits per microgram/cubic meter from PM2.5 air pollution control may be achieved via reductions of fossil fuel combustion exposures, especially from coal-burning sources. CITATION: Thurston GD, Burnett RT, Turner MC, Shi Y, Krewski D, Lall R, Ito K, Jerrett M, Gapstur SM, Diver WR, Pope CA III. 2016. Ischemic heart disease mortality and long-term exposure to source-related components of U.S. fine particle air pollution. Environ Health Perspect 124:785-794; http://dx.doi.org/10.1289/ehp.1509777.

Workgroup report: workshop on source apportionment of particulate matter health effects--intercomparison of results and implications.

Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 microm (PM2.5) and human mortality is well established, the most responsible particle types/sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency's Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of PM Health Effects. The goal was to evaluate the consistency of the various source apportionment methods in assessing source contributions to daily PM2.5 mass-mortality associations. Seven research institutions, using varying methods, participated in the estimation of source apportionments of PM2.5 mass samples collected in Washington, DC, and Phoenix, Arizona, USA. Apportionments were evaluated for their respective associations with mortality using Poisson regressions, allowing a comparative assessment of the extent to which variations in the apportionments contributed to variability in the source-specific mortality results. The various research groups generally identified the same major source types, each with similar elemental makeups. Intergroup correlation analyses indicated that soil-, sulfate-, residual oil-, and salt-associated mass were most unambiguously identified by various methods, whereas vegetative burning and traffic were less consistent. Aggregate source-specific mortality relative risk (RR) estimate confidence intervals overlapped each other, but the sulfate-related PM2.5 component was most consistently significant across analyses in these cities. Analyses indicated that source types were a significant predictor of RR, whereas apportionment group differences were not. Variations in the source apportionments added only some 15% to the mortality regression uncertainties. These results provide supportive evidence that existing PM2.5 source apportionment methods can be used to derive reliable insights into the source components that contribute to PM2.5 health effects.

Utility of a near real-time emergency department syndromic surveillance system to track injuries in New York City.

BACKGROUND: The New York City emergency department (ED) syndromic surveillance (SS) system provides near real-time data on the majority of ED visits. The utility of ED SS for injury surveillance has not been thoroughly evaluated. We created injury syndromes based on ED chief complaint information and evaluated their utility compared to administrative billing data. METHODS: Six injury syndromes were developed: traffic-related injuries to pedal cyclists, pedestrians, and motor vehicle occupants; fall-related injuries; firearm-related injuries; and assault-related stabbings. Daily injury counts were compared for ED SS and the administrative billing data for years 2008-2010. We examined characteristics of injury trends and patterns between the two systems, calculating descriptive statistics for temporal patterns and Pearson correlation coefficients (r) for temporal trends. We also calculated proportions of demographic and geospatial patterns for both systems. RESULTS: Although daily volume of the injuries varied between the two systems, the temporal patterns were similar (all r values for daily volume exceeded 0.65). Comparisons of injuries by time of day, day of week, and quarter of year demonstrated high agreement between the two systems-the majority had an absolute percentage point difference of 2.0 or less. Distributions of injury by sex and age group also aligned well. Distribution of injury by neighborhood of residence showed mixed results-some neighborhood comparisons showed a high level of agreement between systems, while others were less successful. CONCLUSIONS: As evidenced by the strong positive correlation coefficients and the small absolute percentage point differences in our comparisons, we conclude that ED SS captures temporal trends and patterns of injury-related ED visits effectively. The system could be used to identify changes in injury patterns, allowing for situational awareness during emergencies, timely response, and public messaging.

Distributed lag analyses of daily hospital admissions and source-apportioned fine particle air pollution.

BACKGROUND: Past time-series studies of the health effects of fine particulate matter [aerodynamic diameter ≤ 2.5 µm (PM2.5)] have used chemically nonspecific PM2.5 mass. However, PM2.5 is known to vary in chemical composition with source, and health impacts may vary accordingly. OBJECTIVE: We tested the association between source-specific daily PM2.5 mass and hospital admissions in a time-series investigation that considered both single-lag and distributed-lag models. METHODS: Daily PM2.5 speciation measurements collected in midtown Manhattan were analyzed via positive matrix factorization source apportionment. Daily and distributed-lag generalized linear models of Medicare respiratory and cardiovascular hospital admissions during 2001-2002 considered PM2.5 mass and PM2.5 from five sources: transported sulfate, residual oil, traffic, steel metal works, and soil. RESULTS: Source-related PM2.5 (specifically steel and traffic) was significantly associated with hospital admissions but not with total PM2.5 mass. Steel metal works-related PM2.5 was associated with respiratory admissions for multiple-lag days, especially during the cleanup efforts at the World Trade Center. Traffic-related PM2.5 was consistently associated with same-day cardiovascular admissions across disease-specific subcategories. PM2.5 constituents associated with each source (e.g., elemental carbon with traffic) were likewise associated with admissions in a consistent manner. Mean effects of distributed-lag models were significantly greater than were maximum single-day effect models for both steel- and traffic-related PM2.5. CONCLUSIONS: Past analyses that have considered only PM2.5 mass or only maximum single-day lag effects have likely underestimated PM2.5 health effects by not considering source-specific and distributed-lag effects. Differing lag structures and disease specificity observed for steel-related versus traffic-related PM2.5 raise the possibility of distinct mechanistic pathways of health effects for particles of differing chemical composition.

Time-series analysis of mortality effects of fine particulate matter components in Detroit and Seattle.

BACKGROUND: Recent toxicological and epidemiological studies have shown associations between particulate matter (PM) and adverse health effects, but which PM components are most influential is less well known. OBJECTIVES: In this study, we used time-series analyses to determine the associations between daily fine PM [PM ≤ 2.5 µm in aerodynamic diameter (PM2.5)] concentrations and daily mortality in two U.S. cities-Seattle, Washington, and Detroit, Michigan. METHODS: We obtained daily PM2.5 filters for the years of 2002-2004 and analyzed trace elements using X-ray fluorescence and black carbon using light reflectance as a surrogate measure of elemental carbon. We used Poisson regression and distributed lag models to estimate excess deaths for all causes and for cardiovascular and respiratory diseases adjusting for time-varying covariates. We computed the excess risks for interquartile range increases of each pollutant at lags of 0 through 3 days for both warm and cold seasons. RESULTS: The cardiovascular and respiratory mortality series exhibited different source and seasonal patterns in each city. The PM2.5 components and gaseous pollutants associated with mortality in Detroit were most associated with warm season secondary aerosols and traffic markers. In Seattle, the component species most closely associated with mortality included those for cold season traffic and other combustion sources, such as residual oil and wood burning. CONCLUSIONS: The effects of PM2.5 on daily mortality vary with source, season, and locale, consistent with the hypothesis that PM composition has an appreciable influence on the health effects attributable to PM.

Personal exposures to traffic-related air pollution and acute respiratory health among Bronx schoolchildren with asthma.

BACKGROUND: Previous studies have reported relationships between adverse respiratory health outcomes and residential proximity to traffic pollution, but have not shown this at a personal exposure level. OBJECTIVE: We compared, among inner-city children with asthma, the associations of adverse asthma outcome incidences with increased personal exposure to particulate matter mass ≤ 2.5 µm in aerodynamic diameter (PM(2.5)) air pollution versus the diesel-related carbonaceous fraction of PM2.5. METHODS: Daily 24-hr personal samples of PM(2.5), including the elemental carbon (EC) fraction, were collected for 40 fifth-grade children with asthma at four South Bronx schools (10 children per school) during approximately 1 month each. Spirometry and symptom scores were recorded several times daily during weekdays. RESULTS: We found elevated same-day relative risks of wheeze [1.45; 95% confidence interval (CI), 1.03-2.04)], shortness of breath (1.41; 95% CI, 1.01-1.99), and total symptoms (1.30; 95% CI, 1.04-1.62) with an increase in personal EC, but not with personal PM(2.5) mass. We found increased risk of cough, wheeze, and total symptoms with increased 1-day lag and 2-day average personal and school-site EC. We found no significant associations with school-site PM(2.5) mass or sulfur. The EC effect estimate was robust to addition of gaseous pollutants. CONCLUSION: Adverse health associations were strongest with personal measures of EC exposure, suggesting that the diesel "soot" fraction of PM(2.5) is most responsible for pollution-related asthma exacerbations among children living near roadways. Studies that rely on exposure to PM mass may underestimate PM health impacts.

Residual oil combustion: a major source of airborne nickel in New York City.

On the basis of previous observations that: (1) both the nickel (Ni) concentration in ambient air fine particulate matter (PM(2.5)) and daily mortality rates in New York City (NYC) were much higher than in any other US city; and (2) that peaks in Ni concentration was strongly associated with cardiac function in a mouse model of atherosclerosis, we initiated a study of the spatial and seasonal distributions of Ni in NYC and vicinity to determine the feasibility of productive human population-based studies of the extent to which ambient fine particle Ni may account for cardiovascular health effects. Using available speciation data from previous studies at The New York University, Environmental Protection Agency's Speciation Trends Network; and the Interagency Monitoring of Protected Visual Environments network, we determined that Ni in NYC is on average 2.5 times higher in winter than in summer. This apparent seasonal gradient is absent, or much less pronounced, at NJ and CT speciation sites. Ni concentrations at a site on the east side of Manhattan and at two sites in the western portion of the Bronx were a factor of two higher than at a site on the west side of Manhattan, or at one at Queens College in eastern Queens County, indicating a strong spatial gradient within NYC. We conclude that the winter peaks of fine particle Ni indicate that space heating, which involves the widespread reliance on residual oil combustion in many older residential and commercial buildings in NYC, is a major source of ambient air Ni. Epidemiologic studies based on data generated by a network of speciation sites throughout NYC could effectively test the hypothesis that Ni could account for a significant portion of the excess mortality and morbidity that have been associated with elevated mass concentrations of PM(2.5).