Poisson Time Series Research Articles

Case-crossover designs and overdispersion with application to air pollution epidemiology.

Over the last three decades, case-crossover designs have found many applications in health sciences, especially in air pollution epidemiology. They are typically used, in combination with partial likelihood techniques, to define a conditional logistic model for the responses, usually health outcomes, conditional on the exposures. Despite the fact that conditional logistic models have been shown equivalent, in typical air pollution epidemiology setups, to specific instances of the well-known Poisson time series model, it is often claimed that they cannot allow for overdispersion. This paper clarifies the relationship between case-crossover designs, the models that ensue from their use, and overdispersion. In particular, we propose to relax the assumption of independence between individuals traditionally made in case-crossover analyses, in order to explicitly introduce overdispersion in the conditional logistic model. As we show, the resulting overdispersed conditional logistic model coincides with the overdispersed, conditional Poisson model, in the sense that their likelihoods are simple re-expressions of one another. We further provide the technical details of a Bayesian implementation of the proposed case-crossover model, which we use to demonstrate, by means of a large simulation study, that standard case-crossover models can lead to dramatically underestimated coverage probabilities, while the proposed models do not. We also perform an illustrative analysis of the association between air pollution and morbidity in Toronto, Canada, which shows that the proposed models are more robust than standard ones to outliers such as those associated with public holidays.

Assessing Excess Mortality of Baby Boomers from the COVID-19 Pandemic: Taiwan Omicron-naïve Cohort.

Asia's elderly Baby Boomer demographic (born between 1946 and 1964) faced a huge problem during the COVID-19 pandemic due to increased all-cause mortality. We aimed to provide a unique Taiwan situation regarding the impact of Baby Boomers on excess mortalities from all causes relative to non-Baby Boomers throughout distinct times of SARS-CoV-2 mutations during the COVID-19 pandemic. We used the Poisson time series design with a Bayesian directed acyclic graphic approach to build the background mortality prior to the COVID-19 pandemic between 2015 and 2019. It was then used for predicting the expected all-cause deaths compared to the reported figures during the COVID-19 pandemic period based on Taiwan residents, an Omicron-naïve cohort. Baby Boomers experienced a 2% negative excess mortality in 2020 (Wuhan/D614G) and a 4% excess mortality in 2021 (Alpha/Delta) with a rising background mortality trend whereas non-Baby Boomers showed the corresponding figures of 4% negative excess and 1% excess with a stable trend. Baby Boomer and non-Baby Boomer excess mortality soared to 9% (95% CI: 7-10%) and 10% (95% CI: 9-11%), respectively, during the epidemic Omicron period from January to June 2022. Surprisingly, Baby Boomers aged 58-76 experienced the same 9% excess mortality as non-Baby Boomers aged 77 and beyond. Non-COVID-19 deaths were more prevalent among Baby Boomers than non-Baby Boomers (33% vs. 29%). Baby Boomers were more likely to die from COVID-19 in early pandemic and had more non-COVID-19 deaths in late pandemic than older non-Baby Boomers demonstrated in Taiwan Omicron-naïve cohort. For this vulnerable population, adequate access to medical care and medical capacity require more consideration.

A doubly self-exciting Poisson model for describing scoring levels in NBA basketball

Abstract In this article, Poisson time series models are considered to describe the number of field goals made by a basketball team or player at both the game (within-season) and the minute (within-game) level. The model is endowed with a doubly self-exciting structure, following the INGARCH(1,1) specification. To estimate the model at the within-game level, a divide-and-conquer procedure is carried out under a Bayesian framework. Then, we perform a clustering of the players in terms of their similarity according to the corresponding posterior distributions of key model parameters. The model is tested with National Basketball Association (NBA) teams and players from the 2018–2019 season.

Modelling and diagnostic tests for Poisson and negative-binomial count time series

When modelling unbounded counts, their marginals are often assumed to follow either Poisson (Poi) or negative binomial (NB) distributions. To test such null hypotheses, we propose goodness-of-fit (GoF) tests based on statistics relying on certain moment properties. By contrast to most approaches proposed in the count-data literature so far, we do not restrict ourselves to specific low-order moments, but consider a flexible class of functions of generalized moments to construct model-diagnostic tests. These cover GoF-tests based on higher-order factorial moments, which are particularly suitable for the Poi- or NB-distribution where simple closed-form expressions for factorial moments of any order exist, but also GoF-tests relying on the respective Stein’s identity for the Poi- or NB-distribution. In the time-dependent case, under mild mixing conditions, we derive the asymptotic theory for GoF tests based on higher-order factorial moments for a wide family of stationary processes having Poi- or NB-marginals, respectively. This family also includes a type of NB-autoregressive model, where we provide clarification of some confusion caused in the literature. Additionally, for the case of independent and identically distributed counts, we prove asymptotic normality results for GoF-tests relying on a Stein identity, and we briefly discuss how its statistic might be used to define an omnibus GoF-test. The performance of the tests is investigated with simulations for both asymptotic and bootstrap implementations, also considering various alternative scenarios for power analyses. A data example of daily counts of downloads of a TeX editor is used to illustrate the application of the proposed GoF-tests.

Suicide mortality in Spain in 2020: The impact of the COVID-19 pandemic

IntroductionSuicide constitutes a major concern with evident contribution to global mortality worldwide. Evidence on suicide trends is mixed in the COVID-19 pandemic era. Spain may be an at-risk country for increased suicide risk in the time of COVID-19 pandemic due to prolonged restrictions and lockdown, elevated COVID-19 wave recurrence and related mental health impact. This brief report aims to provide some insight into the suicide mortality trends in the first COVID-19 pandemic year in Spain, using national data. Material and methodsData from the National Death Index were used. Annual mortality rate was calculated at a province level under random-effects models for the 2019 and 2020 years. Poisson timeseries regression was used to study the relationship between monthly suicide mortality and COVID-19 pandemic outbreak and lockdown, and second COVID-19 wave peak covariates. Results3671 people died by suicide in 2019 in Spain and 3941 people died by suicide in 2020. The random-effects mortality rate in 2019 was 8.3 (CI95=7.6, 9.0) per 100,000 inhabitants, and mortality rate in 2020 was 8.9 (CI95=8.3, 9.6). No significant differences between mortality rates were found (p=.18). The Poisson regression showed a significant relationship between the COVID-19 outbreak and suicide mortality trend, with OR=1.07 (CI95=1.02, 1.12). ConclusionsAlthough annual mortality rates were not significantly different, an increased suicide risk was found from May, 2020 onwards. Our results claim for action to tackle suicide in the post-pandemic era taking into account the discouraging upcoming scenario.

Cervical cancer prevention program in Mexico disrupted due to COVID-19 pandemic: Challenges and opportunities.

The COVID-19 pandemic disrupted the preventive services for cervical cancer (CC) control programs in Mexico, which will result in increased mortality. This study aims to assess the impact of the pandemic on the interruption of three preventive actions in the CC prevention program in Mexico. This study is a retrospective time series analysis based on administrative records for the uninsured population served by the Mexican Ministry of Health. Patient data were retrieved from the outpatient service information system and the hospital discharge database for the period 2017-2021. Data were aggregated by month, distinguishing a pre-pandemic and a pandemic period, considering April 2020 as the start date of the pandemic. A Poisson time series analysis was used to model seasonal and secular trends. Five process indicators were selected to assess the disruption of the CC program, these were analyzed as monthly data (N=39 pre-pandemic, N=21 during the pandemic). HPV vaccination indicators (number of doses and coverage) and diagnostic characteristics of CC cases were analyzed descriptively. The time elapsed between diagnosis and treatment initiation in CC cases was modeled using restricted cubic splines from robust regression. Annual HPV vaccination coverage declined dramatically after 2019 and was almost null in 2021. The number of positive Papanicolaou smears decreased by 67.8% (90%CI: -72.3, -61.7) in April-December 2020, compared to their expected values without the pandemic. The immediate pandemic shock (April 2020) in the number of first-time and recurrent colposcopies was -80.5% (95%CI:-83.5, -77.0) and -77.9% (95%CI: -81.0, -74.4), respectively. An increasing trend was observed in the proportion of advanced stage and metastatic CC cases. The fraction of CC cases that did not receive medical treatment or surgery increased, as well as CC cases that received late treatment after diagnosis. Our analyses show significant impact of the COVID-19 pandemic with declines at all levels of CC prevention and increasing inequalities. The restarting of the preventive programs against CC in Mexico offers an opportunity to put in place actions to reduce the disparities in the burden of disease between socioeconomic levels.

Healthcare consumption in congenital heart disease: A temporal life-course perspective following pediatric cases to adulthood

BackgroundImprovements in diagnosis, intervention, and care of congenital heart disease (CHD) have led to increased survivability and lifelong dependence on healthcare. This study aims to determine the extensiveness of inpatient care episodes across different life-stages and CHD severity compared to matched controls, and to explore how healthcare utilization among pediatric CHD cases have changed over time. MethodologyNational registry data was used to conduct a 1:9 matching analysis with age and sex matched controls. Then, Poisson timeseries analysis was used to conduct trend analysis for inpatient healthcare utilization among pediatric cases <18 years of age. ResultsMost CHD cases were non-complex (87.3%), with highest hospitalization rates occurring in infancy. Mean number of hospitalizations among complex cases were over twice that of non-complex cases. Also, as age progressed, mean hospitalization for non-complex cases began converging to the control population. In terms of trend analysis within this study period, healthcare utilization increased by 34% among the infant categories, but decreased by 12% and 32% among children between 1-9 years and 10–17 years, respectively. Also, utilization was not trending in one direction substantiating the claim that multiple time periods are required to assess temporal changes within this population. ConclusionInpatient healthcare utilization among the CHD population appears to be decreasing over time in most cases, where non-complex cases transitioning to adult care are increasingly converging to the general population. Additionally, this study validates the need to use multiple time-periods when conducting longitudinal studies across the CHD population.

Association of summertime daily mean temperature and sick leaves among employees of the City of Helsinki, Finland.

BACKGROUND AND AIM Heat affects work productivity and well-being at work. People working outdoors are the most vulnerable to heat-related health issues that may cause absenteeism from work. However, the phenomenon concerns indoor workers without sufficient air conditioning as well. Previous studies concerning occupational heat stress have concentrated on health effects, the incidence of heat-related illness, preventative measures, and productivity losses. Correlation between heat waves and sickness related work absenteeism rates has not been studied. Therefore, this study aims to evaluate the relationship of daily summertime temperatures and heatwaves with sick leaves rates among employees of Helsinki City, Finland. METHODS We used Poisson time series models adjusted for potential confounders, such as time trend, air pollution, pollen, and barometric pressure to investigate the associations of daily temperature with daily rates of new sick leaves. Daily number of sick leaves for May-August 2002-2017 was obtained from the Helsinki City employee register and daily weather information from the Finnish Meteorological Institute. RESULTS Daily mean temperature ranged from 1.5 &amp;#xb0;C to 26.6 &amp;#xb0;C during the warm season. Preliminary results indicate that increased daily mean temperature is associated with decreased risk of sick leaves at lag 0 and lags 2-5. At 1 day lag, increased daily mean temperature is associated with decreased risk of sick leaves until the daily mean temperature exceeds 24 &amp;#xb0;C after which the risk begins to increase. Risk ratio [RR] is 1.003 at 26 &amp;#xb0;C compared to 24 &amp;#xb0;C. CONCLUSIONS Preliminary results indicate that increased daily summertime mean temperature is mostly associated with decreased daily sick leave rate. However, our preliminary analyzes suggest that there may be an increase in daily sick leave rate at 1 day lag when daily mean temperature exceeds a threshold. In further analysis, the effect of prolonged heatwaves will be evaluated. KEYWORDS occupational health, heat

Dust Storms Increase the Risk of Age-Related Macular Degeneration.

The main purpose of this study was to examine the association between dust storms (DSs) and age-related macular degeneration (AMD) using a 5 year representative national dataset with one million participants, according to information on DS, meteorology, and air pollution in Taiwan. There were 18,855 AMD outpatient cases and 1080 AMD inpatient cases during 2008–2012. A Poisson time-series model was used for the analysis. The results show that AMD cases are significantly associated with exposure to dust storm events. Average daily numbers of wet and dry AMD outpatient cases increased from 6.03 and 4.26 on no-event days to 8.25 and 6.67, 2 days after DSs. Average daily numbers of wet and dry AMD inpatient cases increased from 0.26 and 0.33 on no-event days to 0.58 and 0.75, 1 day after DSs. Both genders and different age groups are all affected by the occurrence of DSs, especially 1 and 2 days after DS events. Women are at a higher risk of outpatient visits and hospitalizations for relatively severe wet AMD. Although AMD highly correlated with age, this study also found that dry AMD outpatient visits in people under the age of 50 were also found to be significantly associated with DS events. In order to protect the health of eyes and avoid AMD, one should reduce or avoid outdoor activities when DS events occur.

Impact of weather and pollution on the rate of cerebrovascular events in a large metropolitan area.

Despite mounting evidence, there is uncertainty on the impact of the interplay between weather and pollution features on the risk of acute cerebrovascular events (CVE). We aimed at appraising role of weather and pollution on the daily risk of CVE. Anonymized data from a hub CVE center in a large metropolitan area were collected and analyzed according to weather (temperature, pressure, humidity, and rainfall) and pollution (carbon monoxide [CO], nitrogen dioxide [NO2], nitrogen oxides [NOX], ozone [O3], and particulate matter [PM]) on the same and the preceding days. Poisson regression and time series analyses were used to appraise the association between environmental features and daily CVE, distinguishing also several subtypes of events. We included a total of 2534 days, with 1363 days having ≥1 CVE, from 2012 to 2017. Average daily rate was 1.56 (95% confidence interval: 1.49; 1.63) for CVE, with other event rates ranging between 1.42 for stroke and 0.01 for ruptured intracranial aneurysm. Significant associations were found between CVE and temperature, pressure, CO, NO2, NOX, O3, and PM <10 µm (all P<0.05), whereas less stringent associations were found for humidity, rainfall, and PM <2.5 µm. Time series analysis exploring lag suggested that associations were stronger at same-day analysis (lag 0), but even environmental features predating several days or weeks were significantly associated with events. Multivariable analysis suggested that CO (point estimate 1.362 [1.011; 1.836], P=0.042) and NO2 (1.011 [1.005; 1.016], P<0.001) were the strongest independent predictors of CVE. Environmental features are significantly associated with CVE, even several days before the actual event. Levels of CO and NO2 can be potentially leveraged for population-level interventions to reduce the burden of CVE.

Number of Seclusions in the Netherlands Higher in the 7 Years Since the End of a Nationwide Seclusion-Reduction Program.

Introduction: Between 2006 and 2012 the Dutch government funded a nationwide program for reducing the use of seclusion. Although an initial first trend study showed that the reported number of seclusions declined during the program, the objective of a 10% annual decrease was not met. We wished to establish whether the decline had continued after funding ended in 2012.Method: Using quasi Poisson time series modeling, we retrospectively analyzed the nationally reported numbers of seclusion and involuntary medication between 1998 and 2019, i.e., before, during and after the end of the nationwide program, with and without correction for the number of involuntary admissions.Results: With and without correction for the number of involuntary admissions, there were more seclusions in the seven years after the nationwide program than during the nationwide program. Although the reported number of involuntary medications also increased, the rate of increase was slower after the end of the nationwide program than before.Conclusions: Rather than continuing to decrease after the end of the nationwide program, the number of seclusions rose. This may mean that interventions intended to reduce the use of seclusion within this program are not properly sustained in daily clinical care without an ongoing national program.

A Class of Copula-Based Bivariate Poisson Time Series Models with Applications

A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins. The copula theory was also used again to capture the dependence between the two series using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible dependence structure that allows for positive and negative correlation, as well. In addition, the use of a copula permits applying different margins with a complicated structure such as the ZIP distribution. Likelihood-based inference was used to estimate the models’ parameters with the bivariate integrals of the Gaussian or t-copula functions being evaluated using standard randomized Monte Carlo methods. To evaluate the proposed class of models, a comprehensive simulated study was conducted. Then, two sets of real-life examples were analyzed assuming the Poisson and the ZIP marginals, respectively. The results showed the superiority of the proposed class of models.

North American lower-extremity revascularization and amputation during COVID-19: Observations from the Vascular Quality Initiative.

The coronavirus disease 2019 (COVID-19) pandemic's impact on vascular procedural volumes and outcomes has not been fully characterized. Volume and outcome data before (1/2019 - 2/2020), during (3/2020 - 4/2020), and following (5/2020 - 6/2020) the initial pandemic surge were obtained from the Vascular Quality Initiative (VQI). Volume changes were determined using interrupted Poisson time series regression. Adjusted mortality was estimated using multivariable logistic regression. The final cohort comprised 57,181 patients from 147 US and Canadian sites. Overall procedure volumes fell 35.2% (95% CI 31.9%, 38.4%, p < 0.001) during and 19.8% (95% CI 16.8%, 22.9%, p < 0.001) following the surge, compared with presurge months. Procedure volumes fell 71.1% for claudication (95% CI 55.6%, 86.4%, p < 0.001) and 15.9% for chronic limb-threatening ischemia (CLTI) (95% CI 11.9%, 19.8%, p < 0.001) but remained unchanged for acute limb ischemia (ALI) when comparing surge to presurge months. Adjusted mortality was significantly higher among those with claudication (0.5% vs 0.1%; OR 4.38 [95% CI 1.42, 13.5], p = 0.01) and ALI (6.4% vs 4.4%; OR 2.63 [95% CI 1.39, 4.98], p = 0.003) when comparing postsurge with presurge periods. The first North American COVID-19 pandemic surge was associated with a significant and sustained decline in both elective and nonelective lower-extremity vascular procedural volumes. When compared with presurge patients, in-hospital mortality increased for those with claudication and ALI following the surge.

Bayesian modeling of multivariate time series of counts

AbstractIn this article, we present an overview of recent advances in Bayesian modeling and analysis of multivariate time series of counts. We discuss basic modeling strategies including integer valued autoregressive processes, multivariate Poisson time series and dynamic latent factor models. In so doing, we make a connection with univariate modeling frameworks such as dynamic generalized models, Poisson state space models with gamma evolution and present Bayesian approaches that extend these frameworks to multivariate setting. During our development, recent Bayesian approaches to the analysis of integer valued autoregressive processes and multivariate Poisson models are highlighted and concepts such as “decouple/recouple” and “common random environment” are presented. The role that these concepts play in Bayesian modeling and analysis of multivariate time series are discussed. Computational issues associated with Bayesian inference and forecasting from these models are also considered.This article is categorized under: Statistical and Graphical Methods of Data Analysis &gt; Bayesian Methods and Theory Statistical Models &gt; Time Series Models

The effect of nitrogen dioxide and atmospheric pressure on hospitalization risk for chronic obstructive pulmonary disease in Guangzhou, China

BackgroundThe relationship between air pollution and meteorological factors on diseases has become a research hotspot recently. Nevertheless, few studies have touched the inferences of nitrogen dioxide (NO2) and atmospheric pressure (AP) on hospitalization risk for chronic obstructive pulmonary disease (COPD). ObjectivesTo investigate the short-term impact of particulate air pollutants and meteorology factors on hospitalizations for COPD and quantify the corresponding risk burden of hospital admission. MethodsIn our study, COPD cases were collected from Guangzhou Panyu Central Hospital (n = 11,979) from Dec of 2013 to Jun 2019. The 24-h average temperature, relative humidity (RH), wind speed (V), AP and other meteorological data were obtained from Guangzhou Meteorological Bureau. Air pollution data were collected from Guangzhou Air Monitoring Station. The influence of different NO2 and AP values on COPD risk was quantified by a distributed lag nonlinear model (DLNM) combined with Poisson Regression and Time Series analysis. ResultsWe found that NO2 had a non-linear relationship with the incidence of COPD, with an approximate “M" type, appearing at the peaks of 126 μg/m³ (RR = 1.32, 95%CI, 1.07 to 1.64) and 168 μg/m³ (RR = 1.21, 95%CI, 0.94 to 1.55), respectively. And the association between AP and COPD incidence exhibited an approximate J-shape with a peak occurring at 1035 hPa (RR = 1.16, 95% CI, 1.02 to 1.31). ConclusionsThe nonlinear relationship of NO2 and AP on COPD admission risk in different periods of lag can be used to establish an early warning system for diseases and reduce the possible outbreaks and burdens of COPD in a sensitive population.

Short-Term Effects of Particulate Matter and Its Constituents on Emergency Room Visits for Chronic Obstructive Pulmonary Disease: A Time-Stratified Case-Crossover Study in an Urban Area.

Background: PM2.5 exposure is associated with pulmonary and airway inflammation, and the health impact might vary by PM2.5 constitutes. This study evaluated the effects of increased short-term exposure to PM2.5 constituents on chronic obstructive pulmonary disease (COPD)-related emergency department (ED) visits and determined the susceptible groups. Methods: This retrospective observational study performed in a medical center from 2007 to 2010, and enrolled non-trauma patients aged >20 years who visited the emergency department (ED) and were diagnosed as COPD. Concentrations of PM2.5, PM10, and the four PM2.5 components, including organic carbon (OC), elemental carbon (EC), nitrate (NO3−), and sulfate (SO42−), were collected by three PM supersites in Kaohsiung City. We used an alternative design of the Poisson time series regression models called a time-stratified and case-crossover design to analyze the data. Results: Per interquartile range (IQR) increment in PM2.5 level on lag 2 were associated with increments of 6.6% (95% confidence interval (CI), 0.5–13.0%) in risk of COPD exacerbation. An IQR increase in elemental carbon (EC) was significantly associated with an increment of 3.0% (95% CI, 0.1–5.9%) in risk of COPD exacerbation on lag 0. Meanwhile, an IQR increase in sulfate, nitrate, and OC levels was not significantly associated with COPD. Patients were more sensitive to the harmful effects of EC on COPD during the warm season (interaction p = 0.019). The risk of COPD exacerbation after exposure to PM2.5 was higher in individuals who are currently smoking, with malignancy, or during cold season, but the differences did not achieve statistical significance. Conclusion: PM2.5 and EC may play an important role in COPD events in Kaohsiung, Taiwan. Patients were more susceptible to the adverse effects of EC on COPD on warm days.

Tracheitis hospital admissions are associated with Asia dust storm

ABSTRACT The risks of tracheitis have been widely studied, but no investigation has yet to assess the impact of air pollutants on tracheitis hospital admissions. This research explores the relationship between Asia dust storm (ADS) and tracheitis hospital admissions, by using a Poisson time-series model on the 2000–2012 National Health Insurance Research Database (NHIRB) from Taiwan and linking air pollutants and temperature data. From a total of 126,013 tracheitis hospital admissions, the average number of daily tracheitis hospital admissions is 26.53 and increases 10% notably one day after ADS. The empirical result shows that ADS does significantly affect tracheitis hospital admissions 3 and 5 days after an event for the overall sample and 2–4 days after it for females. For the age group <45, the number significantly increases 3–4 days after ADS, revealing that ADS has a prolonged effect on tracheitis hospital admissions.

Short-Term Exposure to Fine Particulate Air Pollution and Emergency Department Visits for Renal Diseases in the Atlanta Metropolitan Area

Background: Emerging toxicological evidence has shown that inhaled fine particulate matter (PM2.5) may directly or indirectly affect distant organs including kidneys. However, there is limited epidemiologic evidence about the impacts of short-term exposure to PM2.5 on kidneys.Objectives: We investigated the associations between short-term exposure to PM2.5, major PM2.5 components, and criteria gases and emergency department (ED) visits for renal disease outcomes during 2002-2008 in Atlanta, Georgia.Methods: Poisson time-series models with unconstrained distributed lags were run to estimate the acute effects of air pollutants on counts of ED visits for renal disease outcomes (all renal diseases and acute renal failure [ARF]), controlling for meteorology (air temperature and dew-point temperature) and time (season, day of week, holidays, and long term trend).Results: For all renal diseases, significant associations were observed for 8-day (lag 0-7) exposure to CO (rate ratio [RR] = 1.025, [95% CI: 1.009, 1.042]), NO2 (1.021, [1.002, 1.040]), NOx (1.019, [1.002, 1.035]), organic carbon (OC) (1.019, [1.006, 1.033]), and elemental carbon (EC) (1.014, [1.000, 1.029]) per interquartile-range (IQR) increase. For ARF, significant associations were shown for 8-day exposure to O3 (1.081, [1.012, 1.156]), PM2.5 (1.035 [1.007, 1.063]), nitrate (1.039, [1.006, 1.074]), OC (1.035, [1.009, 1.063]), and EC (1.032, [1.004, 1.061]) per IQR increase. The estimated associations were stronger for ARF than all renal diseases. Single-day exposures showed a specific lag pattern, where lags 2 and 3 had the strongest associations for all renal diseases and lag 7 was an additional peak for ARF.Conclusions: Robust associations between short-term exposure to fine particulate air pollution and renal disease outcomes, particularly ARF, were observed. This study adds to the growing epidemiologic evidence that fine particles may impact distant organs. In particular, the air pollution-related impacts on renal disease may be experienced over the short term.

Age-specific seasonal associations between acute exposure to PM2.5 sources and cardiorespiratory hospital admissions in California

Numerous studies have explored the relationships between short-term exposure to fine particulate matter (PM2.5) and morbidity. However, few studies have investigated which PM2.5 sources and constituents contribute to the health associations, and even fewer studies are available which explored age or seasonal effect modification for the associations between PM2.5 sources and health. We explored age-specific associations between short-term exposure to PM2.5 chemical constituents and its sources, and hospital admissions in California. We linked hospital admission data (n = 1,679,094) with PM2.5 chemical constituents and source apportionment data for eight sites in California for the period of 2002–2009. Site-specific source apportionment was conducted using Positive Matrix Factorization, and five PM2.5 sources were commonly identified in most sites (biomass burning, soil, secondary ammonium nitrate, secondary ammonium sulfate, and vehicular emissions). Age-stratified Poisson time-series regression was conducted for each site, and the health risk estimates were combined to generate overall age-specific associations with cardiovascular- and respiratory-related hospital admissions. We further conducted seasonal interaction models to assess seasonal effect modification. An interquartile range increase in PM2.5 vehicular emissions was associated with increased risk of cardiovascular-related hospital admission at lag 0 (1.32% [95% confidence interval (CI): 0.16, 2.49]) for elderly people (≥65 years old). Exposure to PM2.5 vehicular emissions increased the risk of respiratory-related hospitalizations at lag 2 (3.58% [95% CI: 0.90, 6.33]) for children (0–18 years old). Risk estimates of PM2.5 total mass, vehicular emissions, and its related constituents (e.g., iron) for respiratory admissions were higher in the warm season among children. Heterogeneous seasonal estimates were not observed for other age groups. Our results suggest that short-term exposures to several PM2.5 sources and their related constituents are more harmful than exposures to other pollutants, particularly for children in summer. Identifying toxic sources is important for developing effective interventions and protecting susceptible populations.

Use of Emergency Departments by Frail Elderly Patients: Temporal Patterns and Case Complexity.

Emergency department (ED) care for frail elderly patients is associated with an increased use of resources due to their complex medical needs and frequently difficult psycho-social situation. To better target their needs with specially trained staff, it is vital to determine the times during which these particular patients present to the ED. Recent research was inconclusive regarding this question and the applied methods were limited to coarse time windows. Moreover, there is little research on time variation of frail ED patients' case complexity. This study examines differences in arrival rates for frail vs. non-frail patients in detail and compares case complexity in frail patients within vs. outside of regular GP working hours. Arrival times and case variables (admission rate, ED length of stay [LOS], triage level and comorbidities) were extracted from the EHR of an ED in an urban German teaching hospital. We employed Poisson time series regression to determine patterns in hourly arrival rates over the week. Frail elderly patients presented more likely to the ED during already high frequented hours, especially at midday and in the afternoon. Case complexity for frail patients was significantly higher compared to non-frail patients, but varied marginally in time only with respect to triage level and ED LOS. The results suggest that frailty-attuned emergency care should be available in EDs during the busiest hours. Based on EHR data, hospitals thus can tailor their staff needs.