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Comparison of emergency department and hospital admissions data for air pollution time-series studies.

ABSTRACT: BACKGROUND: Emergency department (ED) visit and hospital admissions (HA) data have been an indispensible resource for assessing acute morbidity impacts of air pollution. ED visits and HAs are types of health care visits with similarities, but also potentially important differences. Little previous information is available regarding the impact of health care visit type on observed acute air pollution-health associations from studies conducted for the same location, time period, outcome definitions and model specifications. METHODS: As part of a broader study of air pollution and health in St. Louis, individual-level ED and HA data were obtained for a 6.5 year period for acute care hospitals in the eight Missouri counties of the St. Louis metropolitan area. Patient demographic characteristics and diagnostic code distributions were compared for four visit types including ED visits, HAs, HAs that came through the ED, and non-elective HAs. Time-series analyses of the relationship between daily ambient ozone and PM?.? and selected cardiorespiratory outcomes were conducted for each visit type. RESULTS: Our results indicate that, compared with ED patients, HA patients tended to be older, had evidence of greater severity for some outcomes, and had a different mix of specific outcomes. Consideration of 'HA through ED' appeared to more effectively select acute visits than consideration of 'non-elective HA'. While outcomes with the strongest observed temporal associations with air pollutants tended to show strong associations for all visit types, we found some differences in observed associations for ED visits and HAs. For example, risk ratios for the respiratory disease-ozone association were 1.020 for ED visits and 1.004 for 'HA through ED'; risk ratios for the asthma/wheeze-ozone association were 1.069 for ED visits and 1.106 for 'HA through ED'. Several factors (e.g. age) were identified that may be responsible, in part, for the differences in observed associations. CONCLUSIONS: Demographic and diagnostic differences between visit types may lead to preference for one visit type over another for some questions and populations. The strengths of observed associations with air pollutants sometimes varied between different health care visit types, but the relative strengths of association generally were specific to the pollutant-outcome combination.

SUBMITTER: Winquist A

PROVIDER: S-EPMC3511882 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Comparison of emergency department and hospital admissions data for air pollution time-series studies.

Winquist A A Klein M M Tolbert P P Flanders W D WD Hess J J Sarnat S E SE

Environmental health : a global access science source 20120921

<h4>Background</h4>Emergency department (ED) visit and hospital admissions (HA) data have been an indispensible resource for assessing acute morbidity impacts of air pollution. ED visits and HAs are types of health care visits with similarities, but also potentially important differences. Little previous information is available regarding the impact of health care visit type on observed acute air pollution-health associations from studies conducted for the same location, time period, outcome def ...[more]

PMID: 22998927

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Project description:Various temporal metrics of daily pollution levels have been used to examine the relationships between air pollutants and acute health outcomes. However, daily metrics of the same pollutant have rarely been systematically compared within a study. In this analysis, we describe the variability of effect estimates attributable to the use of different temporal metrics of daily pollution levels. We obtained hourly measurements of ambient particulate matter (PM?.?), carbon monoxide (CO), nitrogen dioxide (NO?), and ozone (O?) from air monitoring networks in 20-county Atlanta for the time period 1993-2004. For each pollutant, we created (1) a daily 1-h maximum; (2) a 24-h average; (3) a commute average; (4) a daytime average; (5) a nighttime average; and (6) a daily 8-h maximum (only for O?). Using Poisson generalized linear models, we examined associations between daily counts of respiratory emergency department visits and the previous day's pollutant metrics. Variability was greatest across O? metrics, with the 8-h maximum, 1-h maximum, and daytime metrics yielding strong positive associations and the nighttime O? metric yielding a negative association (likely reflecting confounding by air pollutants oxidized by O?). With the exception of daytime metric, all of the CO and NO? metrics were positively associated with respiratory emergency department visits. Differences in observed associations with respiratory emergency room visits among temporal metrics of the same pollutant were influenced by the diurnal patterns of the pollutant, spatial representativeness of the metrics, and correlation between each metric and copollutant concentrations. Overall, the use of metrics based on the US National Ambient Air Quality Standards (for example, the use of a daily 8-h maximum O? as opposed to a 24-h average metric) was supported by this analysis. Comparative analysis of temporal metrics also provided insight into underlying relationships between specific air pollutants and respiratory health.

Project description:BACKGROUND:Evidence of the short-term effects of ambient air pollution on the risk of ischemic stroke in low- and middle-income countries is limited and inconsistent. We aimed to examine the associations between air pollution and daily hospital admissions for ischemic stroke in China. METHODS AND FINDINGS:We identified hospital admissions for ischemic stroke in 2014-2016 from the national database covering up to 0.28 billion people who received Urban Employee Basic Medical Insurance (UEBMI) in China. We examined the associations between air pollution and daily ischemic stroke admission using a two-stage method. Poisson time-series regression models were firstly fitted to estimate the effects of air pollution in each city. Random-effects meta-analyses were then conducted to combine the estimates. Meta-regression models were applied to explore potential effect modifiers. More than 2 million hospital admissions for ischemic stroke were identified in 172 cities in China. In single-pollutant models, increases of 10 ?g/m3 in particulate matter with aerodynamic diameter <2.5 ?m (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) and 1 mg/m3 in carbon monoxide (CO) concentrations were associated with 0.34% (95% confidence interval [CI], 0.20%-0.48%), 1.37% (1.05%-1.70%), 1.82% (1.45%-2.19%), 0.01% (-0.14%-0.16%), and 3.24% (2.05%-4.43%) increases in hospital admissions for ischemic stroke on the same day, respectively. SO2 and NO2 associations remained significant in two-pollutant models, but not PM2.5 and CO associations. The effect estimates were greater in cities with lower air pollutant levels and higher air temperatures, as well as in elderly subgroups. The main limitation of the present study was the unavailability of data on individual exposure to ambient air pollution. CONCLUSIONS:As the first national study in China to systematically examine the associations between short-term exposure to ambient air pollution and ischemic stroke, our findings indicate that transient increase in air pollution levels may increase the risk of ischemic stroke, which may have significant public health implications for the reduction of ischemic stroke burden in China.

Project description:BackgroundRelatively few studies have been conducted of the association between air pollution and emergency department (ED) visits, and most of these have been based on a small number of visits, for a limited number of health conditions and pollutants, and only daily measures of exposure and response.MethodsA time-series analysis was conducted on nearly 400,000 ED visits to 14 hospitals in seven Canadian cities during the 1990 s and early 2000s. Associations were examined between carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and particulate matter (PM 10 and PM2.5), and visits for angina/myocardial infarction, heart failure, dysrhythmia/conduction disturbance, asthma, chronic obstructive pulmonary disease (COPD), and respiratory infections. Daily and 3-hourly visit counts were modeled as quasi-Poisson and analyses controlled for effects of temporal cycles, weather, day of week and holidays.Results24-hour average concentrations of CO and NO2 lag 0 days exhibited the most consistent associations with cardiac conditions (2.1% (95% CI, 0.0-4.2%) and 2.6% (95% CI, 0.2-5.0%) increase in visits for myocardial infarction/angina per 0.7 ppm CO and 18.4 ppb NO2 respectively; 3.8% (95% CI, 0.7-6.9%) and 4.7% (95% CI, 1.2-8.4%) increase in visits for heart failure). Ozone (lag 2 days) was most consistently associated with respiratory visits (3.2% (95% CI, 0.3-6.2%), and 3.7% (95% CI, -0.5-7.9%) increases in asthma and COPD visits respectively per 18.4 ppb). Associations tended to be of greater magnitude during the warm season (April - September). In particular, the associations of PM 10 and PM2.5 with asthma visits were respectively nearly three- and over fourfold larger vs. all year analyses (14.4% increase in visits, 95% CI, 0.2-30.7, per 20.6 microg/m3 PM 10 and 7.6% increase in visits, 95% CI, 5.1-10.1, per 8.2 microg/m3 PM2.5). No consistent associations were observed between three hour average pollutant concentrations and same-day three hour averages of ED visits.ConclusionIn this large multicenter analysis, daily average concentrations of CO and NO2 exhibited the most consistent associations with ED visits for cardiac conditions, while ozone exhibited the most consistent associations with visits for respiratory conditions. PM 10 and PM2.5 were strongly associated with asthma visits during the warm season.

Project description:Although regulatory improvements for air quality in the European Union have been made, air pollution is still a pressing problem and, its impact on health, both mortality and morbidity, is a topic of intense research nowadays. The main goal of this work is to assess the impact of the exposure to air pollutants on the number of daily hospital admissions due to respiratory causes in 58 spatial locations of Portugal mainland, during the period 2005-2017. To this end, INteger Generalised AutoRegressive Conditional Heteroskedastic (INGARCH)-based models are extensively used. This family of models has proven to be very useful in the analysis of serially dependent count data. Such models include information on the past history of the time series, as well as the effect of external covariates. In particular, daily hospitalisation counts, air quality and temperature data are endowed within INGARCH models of optimal orders, where the automatic inclusion of the most significant covariates is carried out through a new block-forward procedure. The INGARCH approach is adequate to model the outcome variable (respiratory hospital admissions) and the covariates, which advocates for the use of count time series approaches in this setting. Results show that the past history of the count process carries very relevant information and that temperature is the most determinant covariate, among the analysed, for daily hospital respiratory admissions. It is important to stress that, despite the small variability explained by air quality, all models include on average, approximately two air pollutants covariates besides temperature. Further analysis shows that the one-step-ahead forecasts distributions are well separated into two clusters: one cluster includes locations exclusively in the Lisbon area (exhibiting higher number of one-step-ahead hospital admissions forecasts), while the other contains the remaining locations. This results highlights that special attention must be given to air quality in Lisbon metropolitan area in order to decrease the number of hospital admissions.

Dataset Information

Comparison of emergency department and hospital admissions data for air pollution time-series studies.

Publications

Comparison of emergency department and hospital admissions data for air pollution time-series studies.

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