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

Particulate air pollution and survival in a COPD cohort

<p>Abstract</p> <p>Background</p> <p>Several studies have shown cross-sectional associations between long term exposure to particulate air pollution and survival in general population or convenience cohorts. Less is known about susceptibility, or year to year changes in exposure. We investigated whether particles were associated with survival in a cohort of persons with COPD in 34 US cities, eliminating the usual cross-sectional exposure and treating PM₁₀as a within city time varying exposure.</p> <p>Methods</p> <p>Using hospital discharge data, we constructed a cohort of persons discharged alive with chronic obstructive pulmonary disease using Medicare data between 1985 and 1999. 12-month averages of PM₁₀were merged to the individual annual follow up in each city. We applied Cox's proportional hazard regression model in each city, with adjustment for individual risk factors.</p> <p>Results</p> <p>We found significant associations in the survival analyses for single year and multiple lag exposures, with a hazard ratio for mortality for an increase of 10 μg/m³PM₁₀over the previous 4 years of 1.22 (95% CI: 1.17–1.27).</p> <p>Conclusion</p> <p>Persons discharged alive for COPD have substantial mortality risks associated with exposure to particles. The risk is evident for exposure in the previous year, and higher in a 4 year distributed lag model. These risks are significantly greater than seen in time series analyses.</p

Acute and Chronic Effects of Particles on Hospital Admissions in New-England

Background: Many studies have reported significant associations between exposure to $PM_{2.5}$ and hospital admissions, but all have focused on the effects of short-term exposure. In addition all these studies have relied on a limited number of $PM_{2.5}$ monitors in their study regions, which introduces exposure error, and excludes rural and suburban populations from locations in which monitors are not available, reducing generalizability and potentially creating selection bias. Methods Using our novel prediction models for exposure combining land use regression with physical measurements (satellite aerosol optical depth) we investigated both the long and short term effects of $PM_{2.5}$ exposures on hospital admissions across New-England for all residents aged 65 and older. We performed separate Poisson regression analysis for each admission type: all respiratory, cardiovascular disease (CVD), stroke and diabetes. Daily admission counts in each zip code were regressed against long and short-term $PM_{2.5}$ exposure, temperature, socio-economic data and a spline of time to control for seasonal trends in baseline risk. Results: We observed associations between both short-term and long-term exposure to $PM_{2.5}$ and hospitalization for all of the outcomes examined. In example, for respiratory diseases, for every10-µg/m$^3$ increase in short-term $PM_{2.5}$ exposure there is a 0.70 percent increase in admissions (CI = 0.35 to 0.52) while concurrently for every10-µg/m$^3$ increase in long-term $PM_{2.5}$ exposure there is a 4.22 percent increase in admissions (CI = 1.06 to 4.75). Conclusions: As with mortality studies, chronic exposure to particles is associated with substantially larger increases in hospital admissions than acute exposure and both can be detected simultaneously using our exposure models

Is there adaptation in the ozone mortality relationship: A multi-city case-crossover analysis

<p>Abstract</p> <p>Background</p> <p>Ozone has been associated with daily mortality, mainly in the summer period. Despite the ample literature on adaptation of inflammatory and pulmonary responses to ozone, and the link, in cohort studies, between lung function and mortality risk there has been little done to date to examine the question of adaptation in the acute mortality risk associated with ambient ozone.</p> <p>Methods</p> <p>We applied a case-crossover design in 48 US cities to examine the ozone effect by season, by month and by age groups, particularly focusing on whether there was an adaptation effect.</p> <p>Results</p> <p>We found that the same day ozone effect was highest in summer with a 0.5% (95% CI: 0.38, 0.62) increase in total mortality for 10 ppb increase in 8-hr ozone, whilst the effect decrease to null in autumn and winter. We found higher effects in the months May- July with a 0.46% (95% CI: 0.24, 0.68) increase in total mortality for 10 ppb increase in ozone in June, and a 0.65% (95% CI: 0.47, 0.82) increase in mortality during July. The effect decreased in August and became null in September. We found similar effects from the age group 51–60 up to age 80 and a lower effect in 80 years and older.</p> <p>Conclusion</p> <p>The mortality effects of ozone appear diminished later in the ozone season, reaching the null effect previously reported in winter by September. More work should address this issue and examine the biological mechanism of adaptation.</p

Extreme Temperatures and Mortality: Assessing Effect Modification by Personal Characteristics and Specific Cause of Death in a Multi-City Case-Only Analysis

BACKGROUND: Extremes of temperature are associated with short-term increases in daily mortality. OBJECTIVES: We set out to identify subpopulations and mortality causes with increased susceptibility to temperature extremes. METHODS: We conducted a case-only analysis using daily mortality and hourly weather data from 50 U.S. cities for the period 1989–2000, covering a total of 7,789,655 deaths. We used distributions of daily minimum and maximum temperature in each city to define extremely hot days (≥ 99th percentile) and extremely cold days (≤ 1st percentile), respectively. For each (hypothesized) effect modifier, a city-specific logistic regression model was fitted and an overall estimate calculated in a subsequent meta-analysis. RESULTS: Older subjects [odds ratio (OR) = 1.020; 95% confidence interval (CI), 1.005–1.034], diabetics (OR = 1.035; 95% CI, 1.010–1.062), blacks (OR = 1.037; 95% CI, 1.016–1.059), and those dying outside a hospital (OR = 1.066; 95% CI, 1.036–1.098) were more susceptible to extreme heat, with some differences observed between those dying from a cardiovascular disease and other decedents. Cardiovascular deaths (OR = 1.053; 95% CI, 1.036–1.070), and especially cardiac arrest deaths (OR =1.137; 95% CI, 1.051–1.230), showed a greater relative increase on extremely cold days, whereas the increase in heat-related mortality was marginally higher for those with coexisting atrial fibrillation (OR = 1.059; 95% CI, 0.996–1.125). CONCLUSIONS: In this study we identified several subpopulations and mortality causes particularly susceptible to temperature extremes. This knowledge may contribute to establishing health programs that would better protect the vulnerable

A Multi-Country Analysis on Potential Adaptive Mechanisms to Cold and Heat in a Changing Climate

Background: Temporal variation of temperature-health associations depends on the combination of two pathways: pure adaptation to increasingly warmer temperatures due to climate change, and other attenuation mechanisms due to non-climate factors such as infrastructural changes and improved health care. Disentangling these pathways is critical for assessing climate change impacts and for planning public health and climate policies. We present evidence on this topic by assessing temporal trends in cold- and heat-attributable mortality risks in a multi-country investigation. Methods: Trends in country-specific attributable mortality fractions (AFs) for cold and heat (defined as below/ above minimum mortality temperature, respectively) in 305 locations within 10 countries (1985–2012) were estimated using a two-stage time-series design with time-varying distributed lag non-linear models. To separate the contribution of pure adaptation to increasing temperatures and active changes in susceptibility (non-climate driven mechanisms) to heat and cold, we compared observed yearly-AFs with those predicted in two counterfactual scenarios: trends driven by either (1) changes in exposure-response function (assuming a constant temperature distribution), (2) or changes in temperature distribution (assuming constant exposure-response relationships). This comparison provides insights about the potential mechanisms and pace of adaptation in each population. Results: Heat-related AFs decreased in all countries (ranging from 0.45–1.66% to 0.15–0.93%, in the first and last 5-year periods, respectively) except in Australia, Ireland and UK. Different patterns were found for cold (where AFs ranged from 5.57–15.43% to 2.16–8.91%), showing either decreasing (Brazil, Japan, Spain, Australia and Ireland), increasing (USA), or stable trends (Canada, South Korea and UK). Heat-AF trends were mostly driven by changes in exposure-response associations due to modified susceptibility to temperature, whereas no clear patterns were observed for cold. Conclusions: Our findings suggest a decrease in heat-mortality impacts over the past decades, well beyond those expected from a pure adaptation to changes in temperature due to the observed warming. This indicates that there is scope for the development of public health strategies to mitigate heat-related climate change impacts. In contrast, no clear conclusions were found for cold. Further investigations should focus on identification of factors defining these changes in susceptibility

Modifiers of short-term effects of ozone on mortality in eastern Massachusetts - A case-crossover analysis at individual level

<p>Abstract</p> <p>Background</p> <p>Substantial epidemiological studies demonstrate associations between exposure to ambient ozone and mortality. A few studies simply examine the modification of this ozone effect by individual characteristics and socioeconomic status, but socioeconomic status was usually coded at the city level.</p> <p>Methods</p> <p>This study used a case-crossover design to examine whether impacts of ozone on mortality were modified by socioeconomic status coded at the tract or characteristics at an individual level in eastern Massachusetts, US for a period May-September, 1995-2002, with a total of 157,197 non-accident deaths aging 35 years or older. We used moving averages of maximal 8-hour concentrations of ozone monitored at 8 stationary stations as personal exposure.</p> <p>Results</p> <p>A 10 ppb increase in the four-day moving average of maximal 8-hour ozone was associated with 1.68% (95% confidence interval (CI): 0.51%, 2.87%), 1.96% (95% CI: -1.83%, 5.90%), 8.28% (95% CI: 0.66%, 16.48%), 0.44% (95% CI: -1.45%, 2.37%), -0.83% (95% CI: -2.94%, 1.32%), -1.09% (95% CI: -4.27%, 2.19%) and 6.5% (95% CI: 1.74%, 11.49%) changes in all natural deaths, respiratory disorders, diabetes, cardiovascular diseases, heart diseases, acute myocardial infarction and stroke, respectively. We did not find any evidence that the associations were significantly modified by socioeconomic status or individual characteristics although small differences of estimates across subpopulations were demonstrated.</p> <p>Conclusions</p> <p>Exposure to ozone was associated with specific cause mortality in Eastern Massachusetts during May-September, 1995-2002. There was no evidence that effects of ozone on mortality were significantly modified by socioeconomic status and individual characteristics.</p