A national case-crossover analysis of the short-term effect of PM2.5 on hospitalizations and mortality in subjects with diabetes and neurological disorders

Background: Diabetes and neurological disorders are a growing burden among the elderly, and may also make them more susceptible to particulate air matter with aerodynamic diameter less than 2.5 μg (PM2.5). The same biological responses thought to effect cardiovascular disease through air pollution-mediated systemic oxidative stress, inflammation and cerebrovascular dysfunction could also be relevant for diabetes and neurodegenerative diseases. Methods: We conducted multi-site case-crossover analyses of all-cause deaths and of hospitalizations for diabetes or neurological disorders among Medicare enrollees (>65 years) during the period 1999 to 2010 in 121 US communities. We examined whether 1) short-term exposure to PM2.5 increases the risk of hospitalization for diabetes or neurological disorders, and 2) the association between short-term exposure to PM2.5 and all-cause mortality is modified by having a previous hospitalization of diabetes or neurological disorders. Results: We found that short term exposure to PM2.5 is significantly associated with an increase in hospitalization risks for diabetes (1.14% increase, 95% CI: 0.56, 1.73 for a 10 μg/m3 increase in the 2 days average), and for Parkinson’s disease (3.23%, 1.08, 5.43); we also found an increase in all-cause mortality risks (0.64%, 95% CI: 0.42, 0.85), but we didn’t find that hospitalization for diabetes and neurodegenerative diseases modifies the association between short term exposure to PM2.5 and all-cause mortality. Conclusion: We found that short-term exposure to fine particles increased the risk of hospitalizations for Parkinson’s disease and diabetes, and of all-cause mortality. While the association between short term exposure to PM2.5 and mortality was higher among Medicare enrollees that had a previous admission for diabetes and neurological disorders than among Medicare enrollees that did not had a prior admission for these diseases, the effect modification was not statistically significant. We believe that these results provide useful insights regarding the mechanisms by which particles may affect the brain. A better understanding of the mechanisms will enable the development of new strategies to protect individuals at risk and to reduce detrimental effects of air pollution on the nervous system

Associations of Fine Particulate Matter Species with Mortality in the United States: A Multicity Time-Series Analysis

Background: Epidemiological studies have examined the association between PM2.5 and mortality, but uncertainty remains about the seasonal variations in PM2.5-related effects and the relative importance of species. Objectives: We estimated the effects of PM2.5 species on mortality and how infiltration rates may modify the association. Methods: Using city–season specific Poisson regression, we estimated PM2.5 effects on approximately 4.5 million deaths for all causes, cardiovascular disease (CVD), myocardial infarction (MI), stroke, and respiratory diseases in 75 U.S. cities for 2000–2006. We added interaction terms between PM2.5 and monthly average species-to-PM2.5 proportions of individual species to determine the relative toxicity of each species. We combined results across cities using multivariate meta-regression, and controlled for infiltration. Results: We estimated a 1.18% (95% CI: 0.93, 1.44%) increase in all-cause mortality, a 1.03% (95% CI: 0.65, 1.41%) increase in CVD, a 1.22% (95% CI: 0.62, 1.82%) increase in MI, a 1.76% (95% CI: 1.01, 2.52%) increase in stroke, and a 1.71% (95% CI: 1.06, 2.35%) increase in respiratory deaths in association with a 10-μg/m3 increase in 2-day averaged PM2.5 concentration. The associations were largest in the spring. Silicon, calcium, and sulfur were associated with more all-cause mortality, whereas sulfur was related to more respiratory deaths. County-level smoking and alcohol were associated with larger estimated PM2.5 effects. Conclusions: Our study showed an increased risk of mortality associated with PM2.5, which varied with seasons and species. The results suggest that mass alone might not be sufficient to evaluate the health effects of particles. Citation: Dai L, Zanobetti A, Koutrakis P, Schwartz JD. 2014. Associations of fine particulate matter species with mortality in the United States: a multicity time-series analysis. Environ Health Perspect 122:837–842; http://dx.doi.org/10.1289/ehp.130756

Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia I. Patterns of lagged weather effects reflect biological mechanisms

BACKGROUND: Malaria epidemics due to Plasmodium falciparum are reported frequently in the East African highlands with high case fatality rates. There have been formal attempts to predict epidemics by the use of climatic variables that are predictors of transmission potential. However, little consensus has emerged about the relative importance and predictive value of different factors. Understanding the reasons for variation is crucial to determining specific and important indicators for epidemic prediction. The impact of temperature on the duration of a mosquito's life cycle and the sporogonic phase of the parasite could explain the inconsistent findings. METHODS: Daily average number of cases was modeled using a robust Poisson regression with rainfall, minimum temperature and maximum temperatures as explanatory variables in a polynomial distributed lag model in 10 districts of Ethiopia. To improve reliability and generalizability within similar climatic conditions, we grouped the districts into two climatic zones, hot and cold. RESULTS: In cold districts, rainfall was associated with a delayed increase in malaria cases, while the association in the hot districts occurred at relatively shorter lags. In cold districts, minimum temperature was associated with malaria cases with a delayed effect. In hot districts, the effect of minimum temperature was non-significant at most lags, and much of its contribution was relatively immediate. CONCLUSIONS: The interaction between climatic factors and their biological influence on mosquito and parasite life cycle is a key factor in the association between weather and malaria. These factors should be considered in the development of malaria early warning system

Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions

BACKGROUND: Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy. METHODS: Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4(th)-degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts. RESULTS: The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10–25% more cases at a given sensitivity in cold districts than in hot ones. CONCLUSIONS: The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems

The effect of short-term changes in air pollution on respiratory and cardiovascular morbidity in Nicosia, Cyprus.

Presented at the 6th International Conference on Urban Air Quality, Limassol, March, 2007. Short-paper was submitted for peer-review and appears in proceedings of the conference.This study investigates the effect of daily changes in levels of PM10 on the daily volume of respiratory and cardiovascular admissions in Nicosia, Cyprus during 1995-2004. After controlling for long- (year and month) and short-term (day of the week) patterns as well as the effect of weather in Generalized Additive Poisson models, some positive associations were observed with all-cause and cause-specific admissions. Risk of hospitalization increased stepwise across quartiles of days with increasing levels of PM10 by 1.3% (-0.3, 2.8), 4.9% (3.3, 6.6), 5.6% (3.9, 7.3) as compared to days with the lowest concentrations. For every 10μg/m3 increase in daily average PM10 concentration, there was a 1.2% (-0.1%, 2.4%) increase in cardiovascular admissions. With respects to respiratory admissions, an effect was observed only in the warm season with a 1.8% (-0.22, 3.85) increase in admissions per 10μg/m3 increase in PM10. The effect on respiratory admissions seemed to be much stronger in women and, surprisingly, restricted to people of adult age

Using Forecast and Observed Weather Data to Assess Performance of Forecast Products in Identifying Heat Waves and Estimating Heat Wave Effects on Mortality

Background: Heat wave and health warning systems are activated based on forecasts of health-threatening hot weather. Objective: We estimated heat–mortality associations based on forecast and observed weather data in Detroit, Michigan, and compared the accuracy of forecast products for predicting heat waves. Methods: We derived and compared apparent temperature (AT) and heat wave days (with heat waves defined as ≥ 2 days of daily mean AT ≥ 95th percentile of warm-season average) from weather observations and six different forecast products. We used Poisson regression with and without adjustment for ozone and/or PM10 (particulate matter with aerodynamic diameter ≤ 10 μm) to estimate and compare associations of daily all-cause mortality with observed and predicted AT and heat wave days. Results: The 1-day-ahead forecast of a local operational product, Revised Digital Forecast, had about half the number of false positives compared with all other forecasts. On average, controlling for heat waves, days with observed AT = 25.3°C were associated with 3.5% higher mortality (95% CI: –1.6, 8.8%) than days with AT = 8.5°C. Observed heat wave days were associated with 6.2% higher mortality (95% CI: –0.4, 13.2%) than non–heat wave days. The accuracy of predictions varied, but associations between mortality and forecast heat generally tended to overestimate heat effects, whereas associations with forecast heat waves tended to underestimate heat wave effects, relative to associations based on observed weather metrics. Conclusions: Our findings suggest that incorporating knowledge of local conditions may improve the accuracy of predictions used to activate heat wave and health warning systems. Citation: Zhang K, Chen YH, Schwartz JD, Rood RB, O’Neill MS. 2014. Using forecast and observed weather data to assess performance of forecast products in identifying heat waves and estimating heat wave effects on mortality. Environ Health Perspect 122:912–918; http://dx.doi.org/10.1289/ehp.130685

Diabetes, Obesity, and Hypertension May Enhance Associations between Air Pollution and Markers of Systemic Inflammation

Airborne particulate matter (PM) may lead to increased cardiac risk through an inflammatory pathway. Therefore, we investigated associations between ambient PM and markers of systemic inflammation among repeated measures from 44 senior citizens (≥ 60 years of age) and examined susceptibility by conditions linked to chronic inflammation. Mixed models were used to identify associations between concentrations of fine PM [aerodynamic diameter ≤ 2.5 μm (PM2.5)] averaged over 1–7 days and measures of C-reactive protein (CRP), interleukin-6 (IL-6), and white blood cells (WBCs). Effect modification was investigated for diabetes, obesity, hypertension, and elevated mean inflammatory markers. We found positive associations between longer moving averages of PM2.5 and WBCs across all participants, with a 5.5% [95% confidence interval (CI), 0.10 to 11%] increase per interquartile increase (5.4 μg/m3) of PM2.5 averaged over the previous week. PM2.5 and CRP also exhibited positive associations among all individuals for averages longer than 1 day, with the largest associations for persons with diabetes, obesity, and hypertension. For example, an interquartile increase in the 5-day mean PM2.5 (6.1 μg/m3) was associated with a 14% increase in CRP (95% CI, −5.4 to 37%) for all individuals and an 81% (95% CI, 21 to 172%) increase for persons with diabetes, obesity, and hypertension. Persons with diabetes, obesity, and hypertension also exhibited positive associations between PM2.5 and IL-6. Individuals with elevated mean inflammatory markers exhibited enhanced associations with CRP, IL-6, and WBCs. We found modest positive associations between PM2.5 and indicators of systemic inflammation, with larger associations suggested for individuals with diabetes, obesity, hypertension, and elevated mean inflammatory markers

SAM Domain-Based Protein Oligomerization Observed by Live-Cell Fluorescence Fluctuation Spectroscopy

Sterile-alpha-motif (SAM) domains are common protein interaction motifs observed in organisms as diverse as yeast and human. They play a role in protein homo- and hetero-interactions in processes ranging from signal transduction to RNA binding. In addition, mutations in SAM domain and SAM-mediated oligomers have been linked to several diseases. To date, the observation of heterogeneous SAM-mediated oligomers in vivo has been elusive, which represents a common challenge in dissecting cellular biochemistry in live-cell systems. In this study, we report the oligomerization and binding stoichiometry of high-order, multi-component complexes of (SAM) domain proteins Ste11 and Ste50 in live yeast cells using fluorescence fluctuation methods. Fluorescence cross-correlation spectroscopy (FCCS) and 1-dimensional photon counting histogram (1dPCH) confirm the SAM-mediated interaction and oligomerization of Ste11 and Ste50. Two-dimensional PCH (2dPCH), with endogenously expressed proteins tagged with GFP or mCherry, uniquely indicates that Ste11 and Ste50 form a heterogeneous complex in the yeast cytosol comprised of a dimer of Ste11 and a monomer of Ste50. In addition, Ste50 also exists as a high order oligomer that does not interact with Ste11, and the size of this oligomer decreases in response to signals that activate the MAP kinase cascade. Surprisingly, a SAM domain mutant of Ste50 disrupted not only the Ste50 oligomers but also Ste11 dimerization. These results establish an in vivo model of Ste50 and Ste11 homo- and hetero-oligomerization and highlight the usefulness of 2dPCH for quantitative dissection of complex molecular interactions in genetic model organisms such as yeast