Effect of air pollution control on life expectancy in the United States: an analysis of 545 U.S. counties for the period from 2000 to 2007.
ABSTRACT: BACKGROUND:In recent years (2000-2007), ambient levels of fine particulate matter (PM2.5) have continued to decline as a result of interventions, but the decline has been at a slower rate than previous years (1980-2000). Whether these more recent and slower declines of PM2.5 levels continue to improve life expectancy and whether they benefit all populations equally is unknown. METHODS:We assembled a data set for 545 U.S. counties consisting of yearly county-specific average PM2.5, yearly county-specific life expectancy, and several potentially confounding variables measuring socioeconomic status, smoking prevalence, and demographic characteristics for the years 2000 and 2007. We used regression models to estimate the association between reductions in PM2.5 and changes in life expectancy for the period from 2000 to 2007. RESULTS:A decrease of 10 ?g/m in the concentration of PM2.5 was associated with an increase in mean life expectancy of 0.35 years (SD = 0.16 years, P = 0.033). This association was stronger in more urban and densely populated counties. CONCLUSIONS:Reductions in PM2.5 were associated with improvements in life expectancy for the period from 2000 to 2007. Air pollution control in the last decade has continued to have a positive impact on public health.
Project description:BACKGROUND: The United States health care debate has focused on the nation's uniquely high rates of lack of insurance and poor health outcomes relative to other high-income countries. Large disparities in health outcomes are well-documented in the US, but the most recent assessment of county disparities in mortality is from 1999. It is critical to tracking progress of health reform legislation to have an up-to-date assessment of disparities in life expectancy across counties. US disparities can be seen more clearly in the context of how progress in each county compares to international trends. METHODS: We use newly released mortality data by age, sex, and county for the US from 2000 to 2007 to compute life tables separately for each sex, for all races combined, for whites, and for blacks. We propose, validate, and apply novel methods to estimate recent life tables for small areas to generate up-to-date estimates. Life expectancy rates and changes in life expectancy for counties are compared to the life expectancies across nations in 2000 and 2007. We calculate the number of calendar years behind each county is in 2000 and 2007 compared to an international life expectancy time series. RESULTS: Across US counties, life expectancy in 2007 ranged from 65.9 to 81.1 years for men and 73.5 to 86.0 years for women. When compared against a time series of life expectancy in the 10 nations with the lowest mortality, US counties range from being 15 calendar years ahead to over 50 calendar years behind for men and 16 calendar years ahead to over 50 calendar years behind for women. County life expectancy for black men ranges from 59.4 to 77.2 years, with counties ranging from seven to over 50 calendar years behind the international frontier; for black women, the range is 69.6 to 82.6 years, with counties ranging from eight to over 50 calendar years behind. Between 2000 and 2007, 80% (men) and 91% (women) of American counties fell in standing against this international life expectancy standard. CONCLUSIONS: The US has extremely large geographic and racial disparities, with some communities having life expectancies already well behind those of the best-performing nations. At the same time, relative performance for most communities continues to drop. Efforts to address these issues will need to tackle the leading preventable causes of death.
Project description:In a previous study, we provided evidence that a decline in fine particulate matter (PM2.5) air pollution during the period between 2000 and 2007 was associated with increased life expectancy in 545 counties in the United States. In this article, we investigated which chemical constituents of PM2.5 were the main drivers of the observed association.We estimated associations between temporal changes in seven major components of PM2.5 (ammonium, sulfate, nitrate, elemental carbon matter, organic carbon matter, sodium, and silicon) and temporal changes in life expectancy in 95 counties between 2002 and 2007. We included US counties that had adequate chemical components of PM2.5 mass data across all seasons. We fitted single pollutant and multiple pollutant linear models, controlling for available socioeconomic, demographic, and smoking variables and stratifying by urban and nonurban counties.In multiple pollutant models, we found that: (1) a reduction in sulfate was associated with an increase in life expectancy; and (2) reductions in ammonium and sodium ion were associated with increases in life expectancy in nonurban counties only.Our findings suggest that recent reductions in long-term exposure to sulfate, ammonium, and sodium ion between 2002 and 2007 are associated with improved public health.
Project description:BACKGROUND:Exposure to fine particulate matter pollution (PM2.5) is hazardous to health. Our aim was to directly estimate the health and longevity impacts of current PM2.5 concentrations and the benefits of reductions from 1999 to 2015, nationally and at county level, for the entire contemporary population of the contiguous United States. METHODS AND FINDINGS:We used vital registration and population data with information on sex, age, cause of death, and county of residence. We used four Bayesian spatiotemporal models, with different adjustments for other determinants of mortality, to directly estimate mortality and life expectancy loss due to current PM2.5 pollution and the benefits of reductions since 1999, nationally and by county. The covariates included in the adjusted models were per capita income; percentage of population whose family income is below the poverty threshold, who are of Black or African American race, who have graduated from high school, who live in urban areas, and who are unemployed; cumulative smoking; and mean temperature and relative humidity. In the main model, which adjusted for these covariates and for unobserved county characteristics through the use of county-specific random intercepts, PM2.5 pollution in excess of the lowest observed concentration (2.8 ?g/m3) was responsible for an estimated 15,612 deaths (95% credible interval 13,248-17,945) in females and 14,757 deaths (12,617-16,919) in males. These deaths would lower national life expectancy by an estimated 0.15 years (0.13-0.17) for women and 0.13 years (0.11-0.15) for men. The life expectancy loss due to PM2.5 was largest around Los Angeles and in some southern states such as Arkansas, Oklahoma, and Alabama. At any PM2.5 concentration, life expectancy loss was, on average, larger in counties with lower income and higher poverty rate than in wealthier counties. Reductions in PM2.5 since 1999 have lowered mortality in all but 14 counties where PM2.5 increased slightly. The main limitation of our study, similar to other observational studies, is that it is not guaranteed for the observed associations to be causal. We did not have annual county-level data on other important determinants of mortality, such as healthcare access and quality and diet, but these factors were adjusted for with use of county-specific random intercepts. CONCLUSIONS:According to our estimates, recent reductions in particulate matter pollution in the USA have resulted in public health benefits. Nonetheless, we estimate that current concentrations are associated with mortality impacts and loss of life expectancy, with larger impacts in counties with lower income and higher poverty rate.
Project description:Importance:Despite substantial research, the drivers of the widening gap in life expectancy between rich and poor individuals in the United States-known as the longevity gap-remain unknown. The hypothesis of this study is that social mobility may play an important role in explaining the longevity gap. Objective:To assess whether social mobility is associated with income-related differences in life expectancy in the United States. Design, Setting, and Participants:This cross-sectional, ecological study used data from 1559 counties in the United States to assess the association of social mobility with average life expectancy at age 40 years by sex and income quartile among adult men and women over the period of January 2000 through December 2014. Bayesian generalized linear multilevel regression models were used to estimate the association, with adjustment for a range of socioeconomic, demographic, and health care system characteristics. Exposures:County-level social mobility, here operationalized as the association of the income rank of individuals born during the period of January 1980 through December 1982 (based on tax record data, averaged over the period January 2010 through December 2012) with the income ranks of their parents (averaged over the period January 1996 through December 2000) using the location where the parent first claimed the child as a dependent at age 15 years to identify counties. Main Outcomes and Measures:The main outcome was life expectancy at age 40 years by sex and income quartile. Results:The sample consisted of 1559 counties, which represented 93% of the US population in 2000. Each 1-SD increase in social mobility-equivalent to the difference between a low-mobility state, such as Alabama (ranked 49th on this measure), and a higher-mobility state, such as Massachusetts (ranked 23rd on this measure)-was associated with a 0.38-year (95% credible interval [CrI], 0.29-0.47) and a 0.29-year (95% CrI, 0.21-0.38) increase in county-level life expectancy among men and women, respectively, in the lowest income quartile. Estimates for life expectancies among county residents in the highest income quartile were smaller in magnitude and not robust to covariate adjustment (men: 0.10-year [95% CrI, -0.02 to 0.22] increase; women: 0.08-year [95% CrI, -0.05 to 0.20] increase). Increasing social mobility in all counties to the value of the highest social mobility county was associated with decreases in the life expectancy gap between the highest and lowest income quartiles by 1.4 (95% CrI, 0.7-2.1) years for men and 1.1 (95% CrI, 0.5-1.6) years for women nationally, representing a 20% decrease. Conclusions and Relevance:In this cross-sectional study, higher county-level social mobility was associated with smaller county-level gaps in life expectancy by income. These findings motivate further investigation of causal relationships between policies that shift social mobility and health outcomes.
Project description:BACKGROUND:PM2.5 precursor emissions have declined over the course of several decades, following the implementation of local, state, and federal air quality policies. Estimating the corresponding change in population exposure and PM2.5-attributable risk of death prior to the year 2000 is made difficult by the lack of PM2.5 monitoring data. OBJECTIVES:We used a new technique to estimate historical PM2.5 concentrations, and estimated the effects of changes in PM2.5 population exposures on mortality in adults (age ?30y), and on life expectancy at birth, in the contiguous United States during 1980-2010. METHODS:We estimated annual mean county-level PM2.5 concentrations in 1980, 1990, 2000, and 2010 using universal kriging incorporating geographic variables. County-level death rates and national life tables for each year were obtained from the U.S. Census and Centers for Disease Control and Prevention. We used log-linear and nonlinear concentration-response coefficients from previous studies to estimate changes in the numbers of deaths and in life years and life expectancy at birth, attributable to changes in PM2.5. RESULTS:Between 1980 and 2010, population-weighted PM2.5 exposures fell by about half, and the estimated number of excess deaths declined by about a third. The States of California, Virginia, New Jersey, and Georgia had some of the largest estimated reductions in PM2.5-attributable deaths. Relative to a counterfactual population with exposures held constant at 1980 levels, we estimated that people born in 2050 would experience an ?1-y increase in life expectancy at birth, and that there would be a cumulative gain of 4.4 million life years among adults ?30y of age. CONCLUSIONS:Our estimates suggest that declines in PM2.5 exposures between 1980 and 2010 have benefitted public health. https://doi.org/10.1289/EHP507.
Project description:BACKGROUND: The United States spends more than any other country on health care. The poor relative performance of the US compared to other high-income countries has attracted attention and raised questions about the performance of the US health system. An important dimension to poor national performance is the large disparities in life expectancy. METHODS: We applied a mixed effects Poisson statistical model and Gaussian Process Regression to estimate age-specific mortality rates for US counties from 1985 to 2010. We generated uncertainty distributions for life expectancy at each age using standard simulation methods. RESULTS: Female life expectancy in the United States increased from 78.0 years in 1985 to 80.9 years in 2010, while male life expectancy increased from 71.0 years in 1985 to 76.3 years in 2010. The gap between female and male life expectancy in the United States was 7.0 years in 1985, narrowing to 4.6 years in 2010. For males at the county level, the highest life expectancy steadily increased from 75.5 in 1985 to 81.7 in 2010, while the lowest life expectancy remained under 65. For females at the county level, the highest life expectancy increased from 81.1 to 85.0, and the lowest life expectancy remained around 73. For male life expectancy at the county level, there have been three phases in the evolution of inequality: a period of rising inequality from 1985 to 1993, a period of stable inequality from 1993 to 2002, and rising inequality from 2002 to 2010. For females, in contrast, inequality has steadily increased during the 25-year period. Compared to only 154 counties where male life expectancy remained stagnant or declined, 1,405 out of 3,143 counties (45%) have seen no significant change or a significant decline in female life expectancy from 1985 to 2010. In all time periods, the lowest county-level life expectancies are seen in the South, the Mississippi basin, West Virginia, Kentucky, and selected counties with large Native American populations. CONCLUSIONS: The reduction in the number of counties where female life expectancy at birth is declining in the most recent period is welcome news. However, the widening disparities between counties and the slow rate of increase compared to other countries should be viewed as a call for action. An increased focus on factors affecting health outcomes, morbidity, and mortality such as socioeconomic factors, difficulty of access to and poor quality of health care, and behavioral, environmental, and metabolic risk factors is urgently required.
Project description:Counties are the smallest unit for which mortality data are routinely available, allowing consistent and comparable long-term analysis of trends in health disparities. Average life expectancy has steadily increased in the United States but there is limited information on long-term mortality trends in the US counties This study aimed to investigate trends in county mortality and cross-county mortality disparities, including the contributions of specific diseases to county level mortality trends.We used mortality statistics (from the National Center for Health Statistics [NCHS]) and population (from the US Census) to estimate sex-specific life expectancy for US counties for every year between 1961 and 1999. Data for analyses in subsequent years were not provided to us by the NCHS. We calculated different metrics of cross-county mortality disparity, and also grouped counties on the basis of whether their mortality changed favorably or unfavorably relative to the national average. We estimated the probability of death from specific diseases for counties with above- or below-average mortality performance. We simulated the effect of cross-county migration on each county's life expectancy using a time-based simulation model. Between 1961 and 1999, the standard deviation (SD) of life expectancy across US counties was at its lowest in 1983, at 1.9 and 1.4 y for men and women, respectively. Cross-county life expectancy SD increased to 2.3 and 1.7 y in 1999. Between 1961 and 1983 no counties had a statistically significant increase in mortality; the major cause of mortality decline for both sexes was reduction in cardiovascular mortality. From 1983 to 1999, life expectancy declined significantly in 11 counties for men (by 1.3 y) and in 180 counties for women (by 1.3 y); another 48 (men) and 783 (women) counties had nonsignificant life expectancy decline. Life expectancy decline in both sexes was caused by increased mortality from lung cancer, chronic obstructive pulmonary disease (COPD), diabetes, and a range of other noncommunicable diseases, which were no longer compensated for by the decline in cardiovascular mortality. Higher HIV/AIDS and homicide deaths also contributed substantially to life expectancy decline for men, but not for women. Alternative specifications of the effects of migration showed that the rise in cross-county life expectancy SD was unlikely to be caused by migration.There was a steady increase in mortality inequality across the US counties between 1983 and 1999, resulting from stagnation or increase in mortality among the worst-off segment of the population. Female mortality increased in a large number of counties, primarily because of chronic diseases related to smoking, overweight and obesity, and high blood pressure.
Project description:This study aims at quantifying the level and changes over time of inequality in age-specific mortality and life expectancy between the 19 Norwegian counties from 1980 to 2014.Data on population and mortality by county was obtained from Statistics Norway for 1980-2014. Life expectancy and age-specific mortality rates (0-4, 5-49 and 50-69 age groups) were estimated by year and county. Geographic inequality was described by the absolute Gini index annually.Life expectancy in Norway has increased from 75.6 to 82.0 years, and the risk of death before the age of 70 has decreased from 26 to 14% from 1980 to 2014. The absolute Gini index decreased over the period 1980 to 2014 from 0.43 to 0.32 for life expectancy, from 0.012 to 0.0057 for the age group 50-69 years, from 0.0038 to 0.0022 for the age group 5-49 years, and from 0.0009 to 0.0006 for the age group 0-4 years. It will take between 2 and 32 years (national average 7 years) until the counties catch up with the life expectancy in the best performing county if their annual rates of increase remain unchanged.Using the absolute Gini index as a metric for monitoring changes in geographic inequality over time may be a valuable tool for informing public health policies. The absolute inequality in mortality and life expectancy between Norwegian counties has decreased from 1980 to 2014.
Project description:Examining life expectancy by county allows for tracking geographic disparities over time and assessing factors related to these disparities. This information is potentially useful for policy makers, clinicians, and researchers seeking to reduce disparities and increase longevity.To estimate annual life tables by county from 1980 to 2014; describe trends in geographic inequalities in life expectancy and age-specific risk of death; and assess the proportion of variation in life expectancy explained by variation in socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors.Annual county-level life tables were constructed using small area estimation methods from deidentified death records from the National Center for Health Statistics (NCHS), and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Measures of geographic inequality in life expectancy and age-specific mortality risk were calculated. Principal component analysis and ordinary least squares regression were used to examine the county-level association between life expectancy and socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors.County of residence.Life expectancy at birth and age-specific mortality risk.Counties were combined as needed to create stable units of analysis over the period 1980 to 2014, reducing the number of areas analyzed from 3142 to 3110. In 2014, life expectancy at birth for both sexes combined was 79.1 (95% uncertainty interval [UI], 79.0-79.1) years overall, but differed by 20.1 (95% UI, 19.1-21.3) years between the counties with the lowest and highest life expectancy. Absolute geographic inequality in life expectancy increased between 1980 and 2014. Over the same period, absolute geographic inequality in the risk of death decreased among children and adolescents, but increased among older adults. Socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors explained 60%, 74%, and 27% of county-level variation in life expectancy, respectively. Combined, these factors explained 74% of this variation. Most of the association between socioeconomic and race/ethnicity factors and life expectancy was mediated through behavioral and metabolic risk factors.Geographic disparities in life expectancy among US counties are large and increasing. Much of the variation in life expectancy among counties can be explained by a combination of socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors. Policy action targeting socioeconomic factors and behavioral and metabolic risk factors may help reverse the trend of increasing disparities in life expectancy in the United States.
Project description:This article contains data on county-level socioeconomic status for 2132 US counties and each county's average annual cardiovascular mortality rate (CMR) and fine particulate matter (PM2.5) concentration for 21 years (1990-2010). County CMR, PM2.5, and socioeconomic data were obtained from the US National Center for Health Statistics, US Environmental Protection Agency's Community Multiscale Air Quality modeling system, and the US Census, respectively. Annual socioeconomic indices were created using seven county-level measures from the 1990, 2000, and 2010 US Census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This national data set contains data for annual PM2.5 and CMR changes over a time-period when there was a significant reduction in US air pollutants (following the enactment of the 1970 Clean Air Act). These data are associated with the article "The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time" . Data are stored in a comma separated value format and can be downloaded from the USEPA ScienceHub data repository (https://doi.org/10.23719/1506014).