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.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.

Landscape and forest structure at moose mortality sites on Isle Royale National Park. A LiDAR based assessment

Landscape structure and heterogeneity play a potentially important, but little understood role in predator-prey interactions and behaviourally-mediated habitat selection. For example, habitat complexity may either reduce or enhance the efficiency of a predator's efforts to search, track, capture, kill and consume prey. For prey, structural heterogeneity may affect predator detection, avoidance and defense, escape tactics, and the ability to exploit refuges. This study, investigates whether and how vegetation and topographic structure influence the spatial patterns and distribution of moose (Alces alces) mortality due to predation and malnutrition at the local and landscape levels on Isle Royale National Park. 230 locations where wolves (Canis lupus) killed moose during the winters between 2002 and 2010, and 182 moose starvation death sites for the period 1996-2010, were selected from the extensive Isle Royale Wolf-Moose Project carcass database. A variety of LiDAR-derived metrics were generated and used in an algorithm model (Random Forest) to identify, characterize, and classify three-dimensional variables significant to each of the mortality classes. Furthermore, spatial models to predict and assess the likelihood at the landscape scale of moose mortality were developed. This research found that the patterns of moose mortality by predation and malnutrition across the landscape are non-random, have a high degree of spatial variability, and that both mechanisms operate in contexts of comparable physiographic and vegetation structure. Wolf winter hunting locations on Isle Royale are more likely to be a result of its prey habitat selection, although they seem to prioritize the overall areas with higher moose density in the winter. Furthermore, the findings suggest that the distribution of moose mortality by predation is habitat-specific to moose, and not to wolves. In addition, moose sex, age, and health condition also affect mortality site selection, as revealed by subtle differences between sites in vegetation heights, vegetation density, and topography. Vegetation density in particular appears to differentiate mortality locations for distinct classes of moose. The results also emphasize the significance of fine-scale landscape and habitat features when addressing predator-prey interactions. These finer scale findings would be easily missed if analyses were limited to the broader landscape scale alone.

Evaluating the disparity of female breast cancer mortality among racial groups - a spatiotemporal analysis

Background The literature suggests that the distribution of female breast cancer mortality demonstrates spatial concentration. There remains a lack of studies on how the mortality burden may impact racial groups across space and over time. The present study evaluated the geographic variations in breast cancer mortality in Texas females according to three predominant racial groups (non-Hispanic White, Black, and Hispanic females) over a twelve-year period. It sought to clarify whether the spatiotemporal trend might place an uneven burden on particular racial groups, and whether the excess trend has persisted into the current decade. Methods The Spatial Scan Statistic was employed to examine the geographic excess of breast cancer mortality by race in Texas counties between 1990 and 2001. The statistic was conducted with a scan window of a maximum of 90% of the study period and a spatial cluster size of 50% of the population at risk. The next scan was conducted with a purely spatial option to verify whether the excess mortality persisted further. Spatial queries were performed to locate the regions of excess mortality affecting multiple racial groups. Results The first scan identified 4 regions with breast cancer mortality excess in both non-Hispanic White and Hispanic female populations. The most likely excess mortality with a relative risk of 1.12 (p = 0.001) occurred between 1990 and 1996 for non-Hispanic Whites, including 42 Texas counties along Gulf Coast and Central Texas. For Hispanics, West Texas with a relative risk of 1.18 was the most probable region of excess mortality (p = 0.001). Results of the second scan were identical to the first. This suggested that the excess mortality might not persist to the present decade. Spatial queries found that 3 counties in Southeast and 9 counties in Central Texas had excess mortality involving multiple racial groups. Conclusion Spatiotemporal variations in breast cancer mortality affected racial groups at varying levels. There was neither evidence of hot-spot clusters nor persistent spatiotemporal trends of excess mortality into the present decade. Non-Hispanic Whites in the Gulf Coast and Hispanics in West Texas carried the highest burden of mortality, as evidenced by spatial concentration and temporal persistence.

Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 – 2001

Background Accidental poisoning is one of the leading causes of injury in the United States, second only to motor vehicle accidents. According to the Centers for Disease Control and Prevention, the rates of accidental poisoning mortality have been increasing in the past fourteen years nationally. In Texas, mortality rates from accidental poisoning have mirrored national trends, increasing linearly from 1981 to 2001. The purpose of this study was to determine if there are spatiotemporal clusters of accidental poisoning mortality among Texas counties, and if so, whether there are variations in clustering and risk according to gender and race/ethnicity. The Spatial Scan Statistic in combination with GIS software was used to identify potential clusters between 1980 and 2001 among Texas counties, and Poisson regression was used to evaluate risk differences. Results Several significant (p < 0.05) accidental poisoning mortality clusters were identified in different regions of Texas. The geographic and temporal persistence of clusters was found to vary by racial group, gender, and race/gender combinations, and most of the clusters persisted into the present decade. Poisson regression revealed significant differences in risk according to race and gender. The Black population was found to be at greatest risk of accidental poisoning mortality relative to other race/ethnic groups (Relative Risk (RR) = 1.25, 95% Confidence Interval (CI) = 1.24 – 1.27), and the male population was found to be at elevated risk (RR = 2.47, 95% CI = 2.45 – 2.50) when the female population was used as a reference. Conclusion The findings of the present study provide evidence for the existence of accidental poisoning mortality clusters in Texas, demonstrate the persistence of these clusters into the present decade, and show the spatiotemporal variations in risk and clustering of accidental poisoning deaths by gender and race/ethnicity. By quantifying disparities in accidental poisoning mortality by place, time and person, this study demonstrates the utility of the spatial scan statistic combined with GIS and regression methods in identifying priority areas for public health planning and resource allocation.

A spatial-temporal approach to surveillance of prostate cancer disparities in population subgroups

BACKGROUND: Prostate cancer mortality disparities exist among racial/ethnic groups in the United States, yet few studies have explored the spatiotemporal trend of the disease burden. To better understand mortality disparities by geographic regions over time, the present study analyzed the geographic variations of prostate cancer mortality by three Texas racial/ethnic groups over a 22-year period. METHODS: The Spatial Scan Statistic developed by Kulldorff et al was used. Excess mortality was detected using scan windows of 50% and 90% of the study period and a spatial cluster size of 50% of the population at risk. Time trend was analyzed to examine the potential temporal effects of clustering. Spatial queries were used to identify regions with multiple racial/ethnic groups having excess mortality. RESULTS: The most likely area of excess mortality for blacks occurred in Dallas-Metroplex and upper east Texas areas between 1990 and 1999; for Hispanics, in central Texas between 1992 and 1996: and for non-Hispanic whites, in the upper south and west to central Texas areas between 1990 and 1996. Excess mortality persisted among all racial/ethnic groups in the identified counties. The second scan revealed that three counties in west Texas presented an excess mortality for Hispanics from 1980-2001. Many counties bore an excess mortality burden for multiple groups. There is no time trend decline in prostate cancer mortality for blacks and non-Hispanic whites in Texas. CONCLUSION: Disparities in prostate cancer mortality among racial/ethnic groups existed in Texas. Central Texas counties with excess mortality in multiple subgroups warrant further investigation.

The mood-stabilizer lithium prevents hippocampal apoptosis and improves spatial memory in experimental meningitis.

Pneumococcal meningitis is associated with high morbidity and mortality rates. Brain damage caused by this disease is characterized by apoptosis in the hippocampal dentate gyrus, a morphological correlate of learning deficits in experimental paradigms. The mood stabilizer lithium has previously been found to attenuate brain damage in ischemic and inflammatory diseases of the brain. An infant rat model of pneumococcal meningitis was used to investigate the neuroprotective and neuroregenerative potential of lithium. To assess an effect on the acute disease, LiCl was administered starting five days prior to intracisternal infection with live Streptococcus pneumoniae. Clinical parameters were recorded, cerebrospinal fluid (CSF) was sampled, and the animals were sacrificed 42 hours after infection to harvest the brain and serum. Cryosections of the brains were stained for Nissl substance to quantify brain injury. Hippocampal gene expression of Bcl-2, Bax, p53, and BDNF was analyzed. Lithium concentrations were measured in serum and CSF. The effect of chronic lithium treatment on spatial memory function and cell survival in the dentate gyrus was evaluated in a Morris water maze and by quantification of BrdU incorporation after LiCl treatment during 3 weeks following infection. In the hippocampus, LiCl significantly reduced apoptosis and gene expression of Bax and p53 while it increased expression of Bcl-2. IL-10, MCP-1, and TNF were significantly increased in animals treated with LiCl compared to NaCl. Chronic LiCl treatment improved spatial memory in infected animals. The mood stabilizer lithium may thus be a therapeutic alternative to attenuate neurofunctional deficits as a result of pneumococcal meningitis.

Investigating the potential of reported cattle mortality data in Switzerland for syndromic surveillance

Systems for the identification and registration of cattle have gradually been receiving attention for use in syndromic surveillance, a relatively recent approach for the early detection of infectious disease outbreaks. Real or near real-time monitoring of deaths or stillbirths reported to these systems offer an opportunity to detect temporal or spatial clusters of increased mortality that could be caused by an infectious disease epidemic. In Switzerland, such data are recorded in the "Tierverkehrsdatenbank" (TVD). To investigate the potential of the Swiss TVD for syndromic surveillance, 3 years of data (2009-2011) were assessed in terms of data quality, including timeliness of reporting and completeness of geographic data. Two time-series consisting of reported on-farm deaths and stillbirths were retrospectively analysed to define and quantify the temporal patterns that result from non-health related factors. Geographic data were almost always present in the TVD data; often at different spatial scales. On-farm deaths were reported to the database by farmers in a timely fashion; stillbirths were less timely. Timeliness and geographic coverage are two important features of disease surveillance systems, highlighting the suitability of the TVD for use in a syndromic surveillance system. Both time series exhibited different temporal patterns that were associated with non-health related factors. To avoid false positive signals, these patterns need to be removed from the data or accounted for in some way before applying aberration detection algorithms in real-time. Evaluating mortality data reported to systems for the identification and registration of cattle is of value for comparing national data systems and as a first step towards a European-wide early detection system for emerging and re-emerging cattle diseases.

Recruitment of Ascophyllum Nodosum: Wave Action as a Source of Mortality

The brown alga Ascophyllum nodosum is a dominant rocky intertidal organism throughout much of the North Atlantic Ocean, yet its inability to colonize exposed or denuded shores is well recognized. Our experimental data show that wave action is a major source of mortality to recently settled zygotes. Artificially recruited zygotes consistently exhibited a Type IV survivorship curve in the presence of moving water. As few as 10, but often only 1 relatively low energy wave removed 85 to 99% of recently settled zygotes. Increasing the setting time for attachment of zygotes (prior to disturbance from water movement) had a positive effect on survival. However, survival was significantly lower at high densities, and decreased at long (24 h) setting times, probably as a result of bacteria on the surface of zygotes. Spatial refuges provided significant protection from gentle water movement but relatively little protection from waves.

A geographic analysis of liver cancer mortality and alcohol dependence or abuse in Texas and the U.S., 1980--2003

Background. Liver cancer mortality continues to be a significant factor in deaths worldwide and in the U.S., yet there remains a lack of studies on how mortality burden is impacted by racial groups or by heavy alcohol use. This study evaluated the geographic distribution of liver cancer mortality across population groups in Texas and the U.S. over a 24-year period, as well as determining whether alcohol dependence or abuse correlates with mortality rates. ^ Methods. The Spatial Scan Statistic was used to identify regions of excess liver cancer mortality in Texas counties and the U.S. from 1980 to 2003. The statistic was conducted with a spatial cluster size of 50% of the population at risk, and all analyses used publicly available data. Alcohol abuse data by state and ethnicity were extracted from SAMHSA datasets for the study period 2000–2004. ^ Results. The results of the geographic analysis of liver cancer mortality in both Texas and the U.S. indicate that there were four and seven regions, respectively, that were identified as having statistically significant excess mortality rates with elevated relative risks ranging from 1.38–2.07 and 1.05–1.623 (p = 0.001), respectively. ^ Conclusion. This study revealed seven regions of excess mortality of liver cancer mortality across the U.S. and four regions of excess mortality in Texas between 1980–2003, as well as demonstrated a correlation between elevated liver cancer mortality rates and reporting of alcohol dependence among Hispanics and Other populations. ^

Exploring race/ethnicity as a risk factor for mortality in newborns following congenital heart surgery at a center of excellence

The purpose of this study was to determine if race/ethnicity was a significant risk factor for hospital mortality in children following congenital heart surgery in a contemporary sample of newborns with congenital heart disease. Unlike previous studies that utilized administrative databases, this study utilized clinical data collected at the point of care to examine racial/ethnic outcome differences in the context of the patients' clinical condition and their overall perioperative experience. A retrospective cohort design was used. The study sample consisted of 316 newborns (<31 days of age) who underwent congenital heart surgery between January 2007 through December 2009. A multivariate logistic regression model was used to determine the impact of race/ethnicity, insurance status, presence of a spatial anomaly, prenatal diagnosis, postoperative sepsis, cardiac arrest, respiratory failure, unplanned reoperation, and total length of stay in the intensive care unit on outcomes following congenital heart surgery in newborns. The study findings showed that the strongest predictors of hospital mortality following congenital heart surgery in this cohort were postoperative cardiac arrest, postoperative respiratory failure, having a spatial anomaly, and total ICU LOS. Race/ethnicity and insurance status were not significant risk factors. The institution where this study was conducted is designated as a center of excellence for congenital heart disease. These centers have state-of-the-art facilities, extensive experience in caring for children with congenital heart disease, and superior outcomes. This study suggests that optimal care delivery for newborns requiring congenital heart surgery at a center of excellence portends exceptional outcomes and this benefit is conferred upon the entire patient population despite the race/ethnicity of the patients. From a public health and health services view, this study also contributes to the overall body of knowledge on racial/ethnic disparities in children with congenital heart defects and puts forward the possibility of a relationship between quality of care and racial/ethnic disparities. Further study is required to examine the impact of race/ethnicity on the long-term outcomes of these children as they encounter the disparate components of the health care delivery system. There is also opportunity to study the role of race/ethnicity on the hospital morbidity in these patients considering current expectations for hospital survival are very high, and much of the current focus for quality improvement rests in minimizing the development of patient morbidities.^

Do the spatial characteristics of myocardial scar tissue determine the risk of ventricular arrhythmias?

Sudden cardiac death is one of the main causes of mortality in patients with structural heart disease. Although an implantable cardioverter de?brillator signi?cantly reduces the mortality rate, many patients never receive a shock. Identi?cation of high-risk patients would reduce the costs associated with this therapy and prevent the deleterious effect of inappropriate discharges. As scar tissue is the substrate of ventricular arrhythmias in patients with structural heart disease, scar characterization could allow strati?cation of the risk. The objective of this article is to review the role of scar characteristics in the pathogenesis of ventricular arrhythmias in patients with structural heart disease.

Air quality modeling and mortality impact of fine particles reduction policies in Spain

Background: In recent years, Spain has implemented a number of air quality control measures that are expected to lead to a future reduction in fine particle concentrations and an ensuing positive impact on public health. Objectives: We aimed to assess the impact on mortality attributable to a reduction in fine particle levels in Spain in 2014 in relation to the estimated level for 2007. Methods: To estimate exposure, we constructed fine particle distribution models for Spain for 2007 (reference scenario) and 2014 (projected scenario) with a spatial resolution of 16x16 km2. In a second step, we used the concentration-response functions proposed by cohort studies carried out in Europe (European Study of Cohorts for Air Pollution Effects and Rome longitudinal cohort) and North America (American Cancer Society cohort, Harvard Six Cities study and Canadian national cohort) to calculate the number of attributable annual deaths corresponding to all causes, all non-accidental causes, ischemic heart disease and lung cancer among persons aged over 25 years (2005-2007 mortality rate data). We examined the effect of the Spanish demographic shift in our analysis using 2007 and 2012 population figures. Results: Our model suggested that there would be a mean overall reduction in fine particle levels of 1mg/m3 by 2014. Taking into account 2007 population data, between 8 and 15 all-cause deaths per 100,000 population could be postponed annually by the expected reduction in fine particle levels. For specific subgroups, estimates varied from 10 to 30 deaths for all non-accidental causes, from 1 to 5 for lung cancer, and from 2 to 6 for ischemic heart disease. The expected burden of preventable mortality would be even higher in the future due to the Spanish population growth. Taking into account the population older than 30 years in 2012, the absolute mortality impact estimate would increase approximately by 18%. Conclusions: Effective implementation of air quality measures in Spain, in a scenario with a short-term projection, would amount to an appreciable decline infine particle concentrations, and this, in turn, would lead to notable health-related benefits. Recent European cohort studies strengthen the evidence of an association between long-term exposure to fine particles and health effects, and could enhance the health impact quantification in Europe. Air quality models can contribute to improved assessment of air pollution health impact estimates, particularly in study areas without air pollution monitoring data.

Changes in spatial point patterns of pioneer woody plants across a large tropical landslide

We assessed whether the relative importance of positive and negative interactions in early successional communities varied across a large landslide on Casita Volcano (Nicaragua). We tested several hypotheses concerning the signatures of these processes in the spatial patterns of woody pioneer plants, as well as those of mortality and recruitment events, in several zones of the landslide differing in substrate stability and fertility, over a period of two years (2001 and 2002). We identified all woody individuals with a diameter >1 cm and mapped them in 28 plots measuring 10 × 10-m. On these maps, we performed a spatial point pattern analysis using univariate and bivariate pair-correlation functions; g (r) and g12 (r), and pairwise differences of univariate and bivariate functions. Spatial signatures of positive and negative interactions among woody plants were more prevalent in the most and least stressful zones of the landslide, respectively. Natural and human-induced disturbances such as the occurrence of fire, removal of newly colonizing plants through erosion and clearcutting of pioneer trees were also identified as potentially important pattern-creating processes. These results are in agreement with the stress-gradient hypothesis, which states that the relative importance of facilitation and competition varies inversely across gradients of abiotic stress. Our findings also indicate that the assembly of early successional plant communities in large heterogeneous landslides might be driven by a much larger array of processes than previously thought.

Ethnohistory, genetics, and cancer mortality in Europeans

Geographic variation in cancer rates is thought to be the result of two major factors: environmental agents varying spatially and the attributes, genetic or cultural, of the populations inhabiting the areas studied. These attributes in turn result from the history of the populations in question. We had previously constructed an ethnohistorical database for Europe since 2200 B.C., permitting estimates of the ethnic composition of modern European populations. We were able to show that these estimates correlate with genetic distances. In this study, we wanted to see whether they also correlate with cancer rates. We employed two data sets of cancer mortalities from 42 types of cancer for the European Economic Community and for Central Europe. We subjected spatial differences in cancer mortalities, genetic, ethnohistorical, and geographic distances to matrix permutation tests to determine the magnitude and significance of their association. Our findings are that distances in cancer mortalities are correlated more with ethnohistorical distances than with genetic distances. Possibly the cancer rates may be affected by loci other than the genetic systems available to us, and/or by cultural factors mediated by the ethnohistorical differences. We find it remarkable that patterns of frequently ancient ethnic admixture are still reflected in modern cancer mortalities. Partial correlations with geography suggest that local environmental factors affect the mortalities as well.