MORTALITY IN THE MEDICARE POPULATION AND CHRONIC EXPOSURE TO FINE PARTICULATE AIR POLLUTION

Prospective cohort studies have provided evidence on longer-term mortality risks of fine particulate matter (PM2.5), but due to their complexity and costs, only a few have been conducted. By linking monitoring data to the U.S. Medicare system by county of residence, we developed a retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS), comprising over 20 million enrollees in the 250 largest counties during 2000-2002. We estimated log-linear regression models having as outcome the age-specific mortality rate for each county and as the main predictor, the average level for the study period 2000. Area-level covariates were used to adjust for socio-economic status and smoking. We reported results under several degrees of adjustment for spatial confounding and with stratification into by eastern, central and western counties. We estimated that a 10 µg/m3 increase in PM25 is associated with a 7.6% increase in mortality (95% CI: 4.4 to 10.8%). We found a stronger association in the eastern counties than nationally, with no evidence of an association in western counties. When adjusted for spatial confounding, the estimated log-relative risks drop by 50%. We demonstrated the feasibility of using Medicare data to establish cohorts for follow-up for effects of air pollution. Particulate matter (PM) air pollution is a global public health problem (1). In developing countries, levels of airborne particles still reach concentrations at which serious health consequences are well-documented; in developed countries, recent epidemiologic evidence shows continued adverse effects, even though particle levels have declined in the last two decades (2-6). Increased mortality associated with higher levels of PM air pollution has been of particular concern, giving an imperative for stronger protective regulations (7). Evidence on PM and health comes from studies of acute and chronic adverse effects (6). The London Fog of 1952 provides dramatic evidence of the unacceptable short-term risk of extremely high levels of PM air pollution (8-10); multi-site time-series studies of daily mortality show that far lower levels of particles are still associated with short-term risk (5)(11-13). Cohort studies provide complementary evidence on the longer-term risks of PM air pollution, indicating the extent to which exposure reduces life expectancy. The design of these studies involves follow-up of cohorts for mortality over periods of years to decades and an assessment of mortality risk in association with estimated long-term exposure to air pollution (2-4;14-17). Because of the complexity and costs of such studies, only a small number have been conducted. The most rigorously executed, including the Harvard Six Cities Study and the American Cancer Society’s (ACS) Cancer Prevention Study II, have provided generally consistent evidence for an association of long- term exposure to particulate matter air pollution with increased all-cause and cardio-respiratory mortality (2,4,14,15). Results from these studies have been used in risk assessments conducted for setting the U.S. National Ambient Air Quality Standard (NAAQS) for PM and for estimating the global burden of disease attributable to air pollution (18,19). Additional prospective cohort studies are necessary, however, to confirm associations between long-term exposure to PM and mortality, to broaden the populations studied, and to refine estimates by regions across which particle composition varies. Toward this end, we have used data from the U.S. Medicare system, which covers nearly all persons 65 years of age and older in the United States. We linked Medicare mortality data to (particulate matter less than 2.5 µm in aerodynamic diameter) air pollution monitoring data to create a new retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS), consisting of 20 million persons from 250 counties and representing about 50% of the US population of elderly living in urban settings. In this paper, we report on the relationship between longer-term exposure to PM2.5 and mortality risk over the period 2000 to 2002 in the MCAPS

Hierarchical Bivariate Time Series Models: A Combined Analysis of the Effects of Particulate Matter on Morbidity and Mortality

In this paper we develop a hierarchical bivariate time series model to characterize the relationship between particulate matter less than 10 microns in aerodynamic diameter (PM10) and both mortality and hospital admissions for cardiovascular diseases. The model is applied to time series data on mortality and morbidity for 10 metropolitan areas in the United States from 1986 to 1993. We postulate that these time series should be related through a shared relationship with PM10. At the first stage of the hierarchy, we fit two seemingly unrelated Poisson regression models to produce city-specific estimates of the log relative rates of mortality and morbidity associated with exposure to PM10 within each location. The sample covariance matrix of the estimated log relative rates is obtained using a novel generalized estimating equation approach that takes into account the correlation between the mortality and morbidity time series. At the second stage, we combine information across locations to estimate overall log relative rates of mortality and morbidity and variation of the rates across cities. Using the combined information across the 10 locations we find that a 10 mu g/m3 increase in average PM10 at the current day and previous day is associated with a 0:26% increase in mortality (95% posterior interval -0:37; 0:65), and a 0:71% increase in hospital admissions (95% posterior interval 0:35; 0:99). The log relative rates of mortality and morbidity have a similar degree of heterogeneity across cities: the posterior means of the between-city standard deviations of the mortality and morbidity air pollution effects are 0:42 (95% interval 0:05; 1:18), and 0:31 (95% interval 0:10; 0:89), respectively. The city-specific log relative rates of mortality and morbidity are estimated to have very low correlation, but the uncertainty in the correlation is very substantial (posterior mean = 0:20; 95% interval -0:89; 0:98). With the parameter estimates from the model, we can predict the hospitalization log relative rate for a new city for which hospitalization data are unavailable, using that city\u27s estimated mortality relative rate. We illustrate this prediction using New York as an example

Seasonal Analyses of Air Pollution and Mortality in 100 U.S. Cities

Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors develop Bayesian semi-parametric hierarchical models for estimating time-varying effects of pollution on mortality in multi-site time series studies. The methods are applied to the updated National Morbidity and Mortality Air Pollution Study database for the period 1987--2000, which includes data for 100 U.S. cities. At the national level, a 10 micro-gram/m3 increase in PM(10) at lag 1 is associated with a 0.15 (95% posterior interval: -0.08, 0.39),0.14 (-0.14, 0.42), 0.36 (0.11, 0.61), and 0.14 (-0.06, 0.34) percent increase in mortality for winter, spring, summer, and fall, respectively. An analysis by geographical regions finds a strong seasonal pattern in the northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data

Quantitative Methods for Tracking Cognitive Change 3 Years After Coronary Artery Bypass Surgery

Background: The analysis and interpretation of change in cognitive function test scores after Coronary Artery Bypass Grafting (CABG). Longitudinal studies with multiple outcomes present considerable statistical challenges. Application of hierarchical linear statistical models can estimate the effects of a surgical intervention on the time course of multiple biomarkers. Methods: We use an analyze then summarize approach whereby we estimate the intervention effects separately for each cognitive test and then pool them, taking appropriate account of their statistical correlations. The model accounts for dropouts at follow-up, the chance of which may be related to past cognitive score, by implicitly imputing the missing data from individuals’ past scores and group patterns. We apply this approach to a study of the effects of CABG on the time course of cognitive function as measured by 16 separate neuropsychological test scores, clustered into 8 cognitive domains. The study includes measurements on 140 CABG patients and 92 nonsurgical controls at baseline, and 3, 12, and 36 months. Including a nonsurgical control group allows comparison of changes in cognition over time between the surgery group and patients with similar risk factors, controlling for potential effects of aging and vascular disease. Results: We find that CABG patients have very longitudinal changes from baseline in cognitive function similar to those observed for nonsurgical controls. Any small differences tend to favor greater improvement in CABG patients than in the nonsurgical controls. Conclusions: The methods used have application to a wide range of intervention studies in which multiple biomarkers are followed over time to quantify health effects. Software to implement the methods in commonly used statistical packages is available from the authors at http://www.biostat.jhsph.edu/research/software.shtml

Humidity and gravimetric equivalency adjustments for nephelometer-based particulate matter measurements of emissions from solid biomass fuel in cookstoves

Great uncertainty exists around indoor biomass burning exposure-disease relationships due to lack of detailed exposure data in large health outcome studies. Passive nephelometers can be used to estimate high particulate matter (PM) concentrations during cooking in low resource environments. Since passive nephelometers do not have a collection filter they are not subject to sampler overload. Nephelometric concentration readings can be biased due to particle growth in high humid environments and differences in compositional and size dependent aerosol characteristics. This paper explores relative humidity (RH) and gravimetric equivalency adjustment approaches to be used for the pDR-1000 used to assess indoor PM concentrations for a cookstove intervention trial in Nepal. Three approaches to humidity adjustment performed equivalently (similar root mean squared error). For gravimetric conversion, the new linear regression equation with log-transformed variables performed better than the traditional linear equation. In addition, gravimetric conversion equations utilizing a spline or quadratic term were examined. We propose a humidity adjustment equation encompassing the entire RH range instead of adjusting for RH above an arbitrary 60% threshold. Furthermore, we propose new integrated RH and gravimetric conversion methods because they have one response variable (gravimetric PM2.5 concentration), do not contain an RH threshold, and is straightforward

Theoretical Study of Cubic Structures Based on Fullerene Carbon Clusters: C28_{28}C and (C28)2_{28})_{2}

We study a new hypothetical form of solid carbon \csc, with a unit cell which is composed of the \cs \ fullerene cluster and an additional single carbon atom arranged in the zincblende structure. Using {\it ab initio} calculations, we show that this new form of solid carbon has lower energy than hyperdiamond, the recently proposed form composed of \cs \ units in the diamond structure. To understand the bonding character of of these cluster-based solids, we analyze the electronic structure of \csc \ and of hyperdiamond and compare them to the electronic states of crystalline cubic diamond.Comment: 15 pages, latex, no figure

Effect of an improved biomass stove on acute lower respiratory infections in young children in rural Nepal: a cluster-randomised, step-wedge trial

Background Acute lower respiratory infections (ALRI) are an important cause of death in young children in low income countries. High concentrations of fine particulate matter (PM2.5) indoors caused by open burning of biomass are associated with risk of ALRI. However, improved biomass stoves reduce emissions and might reduce the incidence of lower respiratory illness. A cluster-randomised, step-wedge, community-based trial was conducted to estimate the eff ect that a change from open burning of biomass to improved biomass stoves could have on rates of ALRI in children younger than 36 months in a rural area of southern Nepal. Methods Households were enrolled in Sarlahi district that had at least one child aged younger than 36 months or a married woman aged 15–30 years. Respiratory morbidity data were collected for 6 months prior to the introduction of improved biomass stoves between March, 2010, and December, 2010. Mothers were asked about respiratory signs and symptoms (cough, difficult or rapid breathing, wheeze, ear discharge, fever) in their participating children in the past 7 days during weekly visits from local study staff. A 12-month stepped-wedge introduction of an improved biomass stove with chimney to participating households followed the 6-month run-in period (Envirofit Corp. Colorado Springs, CO, USA). Weekly morbidity assessments continued during the step-wedge period (from January, 2011, to February, 2012) and for 6 months after stove introduction (from March, 2012, to December, 2012). Children were discharged at age 36 months. The primary outcome was ALRI, defined as a maternal report of 2 or more consecutive days of fast or difficult breathing accompanied by fever. Episodes were separated by a minimum of 7 symptom-free days. An environmental assessment was done in households once before and once after the improved stove was installed. The trial is registered at clinicaltrials.gov (NCT00786877). Findings 5254 children from 3376 households were enrolled either at baseline or during the trial period. Mean 20-h kitchen concentration of PM2.5 was reduced from 1386 μg/m3 to 930 μg/m3 There was a strong secular decline in the incidence of ALRI over the period of the study. The intervention was associated with a 13% decline in the incidence of ALRI but the strength of evidence was weak (0·87, 95% CI 0·67–1·13). There were statistically significant reductions in persistent cough (0·91, 0·85–0·97), wheeze (0·87, 0·78–0·97) and burn injury (0·68, 0·48–0·95) but not for fever, severe ALRI, or ear discharge. Interpretation There was weak evidence for a modest decline in the incidence of ALRI. Post-installation PM2.5 concentrations remained well above current indoor air standards of 25 μg/m3 . Better performing biomass stoves or cleaner fuels such as liquid petroleum gas or ethanol are needed to reduce concentrations enough to estimate the impact on ALRI incidence