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纷纷红紫已成尘·布谷声中夏令新

山西财院78jitong 19781017--19820715

 
 
 

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78jitong.......................................................... 高三李五七弓长,三赵九刘七大王,阎吴谢孙崔氏双,柴米余侯箩万堂, 毛邓陈宋任申杭,曾肖徐翁程董梁,储曲祁解韦国强,男女七十学跟党。

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2016年4月23日  

2016-04-23 11:38:36|  分类: 默认分类 |  标签: |举报 |字号 订阅

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The Association Between Income and Life Expectancy in the United States, 2001-2014

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Importance  The relationship between income and life expectancy is well established but remains poorly understood.

Objectives  To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation.

Design and Setting  Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy.

Exposure  Pretax household earnings as a measure of income.

Main Outcomes and Measures  Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas.

Results  The sample consisted of 1?408?287?218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61?175 per year). There were 4?114?380 deaths among men (mortality rate, 596.3 per 100?000) and 2?694?808 deaths among women (mortality rate, 375.1 per 100?000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% (P?<?.001 for the differences for both sexes). Third, life expectancy for low-income individuals varied substantially across local areas. In the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking (r?=??0.69, P?<?.001), but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low-income individuals was positively correlated with the local area fraction of immigrants (r?=?0.72, P?<?.001), fraction of college graduates (r?=?0.42, P?<?.001), and government expenditures (r?=?0.57, P?<?.001).

Conclusions and Relevance  In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.

Higher incomes are associated with longer life expectancy,19 but several aspects of the relationship between income and longevity remain unclear. First, little is known about the exact shape of the income-longevity gradient. Is there a threshold above which additional income is no longer associated with increased life expectancy or a safety net below which further reductions in income do not harm health?

Second, there is debate about how socioeconomic gaps in longevity are changing over time. Prior work has shown that longevity gaps increased in recent decades. Some studies suggest a reduction in life expectancy for women of low socioeconomic status in recent years, but this conclusion has been questioned.6,1014

Third, most studies have examined the relationship between income and longevity at a national level. To what extent do gaps in longevity vary at the local area level?

Fourth, the sources of the longevity gap remain unclear. The socioeconomic gradient in longevity has been variously attributed to factors such as inequality, economic and social stress, and differences in access to medical care.15 These theories remain debated.

This study addressed these 4 issues by analyzing newly available data on income and mortality for the US population from 1999 through 2014. The following sets of analyses were conducted: (1) characterizing the association between life expectancy at 40 years of age and income in the United States as a whole; (2) estimating the change in life expectancy by income group from 2001 through 2014; (3) mapping geographic variation in life expectancy by income group and over time; and (4) evaluating factors associated with differences in longevity using the variation across areas.

This study was approved by the Office of Tax Analysis of the US Treasury under Internal Revenue Code §6103(h)(1). Institutional review board approval was obtained through the Harvard University Committee on the Use of Human Subjects; participant consent was waived because the analysis used preexisting, deidentified data. The analysis used a deidentified database of federal income tax and Social Security records that includes all individuals with a valid Social Security Number between 1999 and 2014.

Income data were obtained from tax records for every individual for every year from 1999 through 2014. The primary measure of income was pretax household earnings. For those who filed tax returns, household earnings were defined as adjusted gross income plus tax-exempt interest income minus taxable Social Security and disability benefits. For those who did not file a tax return, household earnings were defined as the sum of all wage earnings (reported on form W-2) and unemployment benefits (reported on form 1099-G). When individuals had no tax return and no information returns, household earnings were $0. For nonfilers, earnings did not include the spouse’s income. However, the vast majority of nonfilers who are not receiving Social Security benefits are single.16 Income was adjusted to 2012 dollars using the consumer price index.

Mortality was measured using Social Security Administration (SSA) death records. Total deaths in the SSA data closely match data from the National Center for Health Statistics (NCHS), with correlations exceeding 0.98 across ages and years (part I of the eAppendix, eFigure 1, and eTable 1 in the Supplement). Observations with income of $0 were excluded because the SSA does not fully track deaths of nonresidents, and thus mortality rates for individuals with income of $0 are mismeasured or unavailable. After excluding observations with income of $0, individuals were assigned percentile ranks from 1 to 100 based on their household earnings relative to all other individuals of the same sex and age in the United States during each year.

National Levels of Life Expectancy by Income

The study estimated period life expectancy, which was defined as the expected length of life for a hypothetical individual who experiences mortality rates at each subsequent age that match those in the cross-section during a given year. Period life expectancy conditional on income percentile at 40 years of age (or equivalently, expected age at death, calculated as life expectancy plus 40 years), was constructed by (1) estimating mortality rates for the ages of 40 to 76 years; (2) extrapolating mortality rates beyond the age of 76 years and calculating life expectancy; and (3) adjusting for differences in the proportion of racial and ethnic groups across percentiles. A complete description of these 3 steps appears in part II of the eAppendix in the Supplement. The entire analysis was conducted separately for men and women.

For individuals aged 63 years or younger, mortality rates were calculated based on income percentile 2 years earlier. The 2-year lag helps mitigate reverse causality arising from income changes near death.9 Because of this 2-year lag, mortality rates were available from 2001 through 2014. Mortality rates conditional on income percentile 2 years prior are approximately equivalent to mortality rates conditional on income percentile at the age of 40 years because individuals’ earnings are highly correlated over time between the ages of 40 years and 61 years (eFigure 2 and eTable 2 in the Supplement).

Earnings after the age of 61 years are less highly correlated with earnings at earlier ages because the rate of retirement increases sharply at 62 years of age, the earliest age of eligibility for Social Security benefits.17Therefore, income for individuals aged 63 years or older was measured at 61 years of age. Because 1999 is the earliest year in which income was observed and the mortality data end in 2014, mortality rates were calculated up to 76 years of age.

Beyond the age of 76 years, mortality rates were estimated using Gompertz models, in which mortality rates increase exponentially with age.18,19 In a Gompertz model, the logarithm of the mortality rate is linear in age: log(m(age))?=?α + βage. This log-linear approximation fits NCHS data for mortality rates above 40 years of age with R2 values of greater than 0.99 for both sexes (eFigure 3 in the Supplement). The log-linear approximation also fits mortality rates at specific income percentiles well (for example, R2?>?0.99 at the 5th and 95th percentiles; Figure 1A and eFigure 4).

The Gompertz parameters α (representing the intercept of the Gompertz model) and β (representing the slope) were estimated for each sex, income percentile, and year using maximum likelihood, modeling deaths at each age using a binomial distribution. When pooling all years, mortality rates up to the age of 76 years were used to estimate α and β. When computing year-specific estimates, α and β were estimated using data up to the age of 63 years, so that all years were treated symmetrically. Because the Gompertz model fits less well after the age of 90 years, all survivors at the age of 90 years were assigned sex-specific but income-independent mortality rates based on NCHS and SSA data.2022 The mortality rate estimates were used to construct survival curves for each income percentile (Figure 1B), and life expectancy was calculated as the area under the survival curve.

The life expectancy estimates were adjusted to control for differences in the racial and ethnic composition of income groups in 2 steps. Data from the National Longitudinal Mortality Study (NLMS) were used first to estimate mortality rates by age for black, Hispanic, and Asian individuals, relative to all other groups using Gompertz models controlling for differences in income (eFigure 5 in the Supplement). Log differences in mortality rates across races at a given age were assumed to be constant across income groups and areas, an approximation consistent with the NLMS data (eFigures 6 and 7). US Census data were then used to estimate the share of black, Hispanic, and Asian individuals in each income percentile by sex, age, and year. These data were combined to calculate the mean life expectancy that would prevail if each group had proportions of black, Hispanic, and Asian individuals corresponding to national means at the age of 40 years. In both the NLMS and the US Census, race and ethnicity are reported by individuals based on fixed categories (non-Hispanic black, non-Hispanic Asian, Hispanic or Latino of any race).

National Trends in Life Expectancy by Income

Year-specific estimates of life expectancy were constructed by income quartile and ventile (5 percentile bins) to reduce estimation error. Trends in life expectancy were estimated using linear regressions of race- and ethnicity-adjusted life expectancy in each quartile or ventile on year.

Local Area Variation in Life Expectancy by Income

Individuals’ locations were defined based on the zip code from which they filed tax returns or, for nonfilers, where their W-2 forms were mailed during the year their income was measured. Those individuals who moved after the age of 63 years (ie, after retirement age) were therefore classified as belonging to the location where they lived at the age of 61 years (where they worked).

The level of race- and ethnicity-adjusted life expectancy was estimated by income quartile and ventile for counties, commuting zones, and states, pooling data from 2001 through 2014. Commuting zones are geographic aggregations of counties based on commuting patterns in the 1990 US Census that are widely used as measures of local labor markets. There are 741 commuting zones in the United States compared with more than 3000 counties and more than 40?000 zip codes. The results reported are primarily for commuting zones because these zones constitute broad geographic units analogous to metropolitan statistical areas. However, unlike metropolitan statistical areas, commuting zones provide a complete partition of the country, including rural areas.

The amount of variation in life expectancy across areas was measured as the standard deviation of life expectancy across areas (weighted by population in the 2000 US Census) after subtracting the variance across areas due to sampling error. Trends were estimated by regressions of year-specific race- and ethnicity-adjusted life expectancy estimates on calendar year separately in each area. Trend estimates were constructed by income quartile for the 100 most populated commuting zones (with populations >590?000) and for states.

Correlates of Local Area Variation in Life Expectancy

Theories for differences in life expectancy were evaluated by correlating commuting zone–level estimates of race- and ethnicity-adjusted life expectancy for individuals in the bottom and top income quartiles with local area characteristics. Detailed definitions of these characteristics and sources appear in part III of the eAppendix and in eTable 3 in the Supplement.

Health behaviors were measured by income quartile using the Behavioral Risk Factor Surveillance Surveys from 1996 through 2008. The health behaviors included were rates of current smoking, obesity (defined as body mass index [calculated as weight in kilograms divided by height in meters squared] ≥30), and self-reported exercise during the past month.

Measures of access to medical care included the fraction uninsured, risk-adjusted Medicare spending per enrollee, an index for the quality of inpatient care based on 30-day hospital mortality rates, and an index for the quality of primary and preventive care based on the fraction of people who visited primary care physicians and received routine care, such as mammograms, constructed using Medicare claims data.23

Residential income segregation was measured using the Reardon rank order index; higher numbers indicate greater segregation.24 Income inequality was estimated with the Gini index using tax records; higher numbers indicate a more unequal income distribution. Social cohesion was estimated using a social capital index based on the methods of Putnam25 and the share of the population that is religious. The percentage of black individuals was measured in the 2000 US Census.

The following measures of local labor market conditions were used as proxies for the strength of local economies: the unemployment rate in 2000, population change between 1980 and 2000, and labor force change between 1980 and 2000.

Several other correlates were constructed using US Census data and other sources, for example, population density, the fraction of college graduates, and median home values (a complete list appears in eTable 3 in the Supplement).26

Data Analysis and Availability

The raw data were collapsed into means by sex, age, income, year, and geographic area using SAS version 9.1.3 (SAS Institute Inc). The means by sex, age, income, year, and geographic area were analyzed using Stata version 13 (StataCorp). Tests of statistical significance were based on 2-sided tests with a significance threshold of .05. The 95% confidence intervals for the race- and ethnicity-adjusted life expectancy estimates were calculated using a bootstrap resampling procedure (part II.E of the eAppendix in the Supplement). Correlation coefficients were calculated using Pearson correlation measures, weighted by population. Data sets containing life expectancy estimates by age, sex, year, and income group at the national, state, commuting zone, and county level are available at www.healthinequality.org.

The sample consisted of 1?408?287?218 person-year observations for individuals aged 40 to 76 years from 1999 through 2014. The mean age at which people were analyzed was 53.0 years. Among individuals of working age (38-61 years), the median for household earnings was $61?175 per year and the mean for household earnings was $97?725 per year. There were 4?114?380 deaths from the SSA death files among men (mortality rate of 596.3 per 100?000) and 2?694?808 deaths among women (mortality rate of 375.1 per 100?000).

National Levels of Life Expectancy by Income

Figure 2 shows race- and ethnicity-adjusted expected age at death by household income percentile using pooled data from 2001 through 2014. Higher income was associated with longer life throughout the income distribution. Men in the bottom 1% of the income distribution at the age of 40 years had an expected age of death of 72.7 years. Men in the top 1% of the income distribution had an expected age of death of 87.3 years, which is 14.6 years (95% CI, 14.4-14.8 years) longer than those in the bottom 1%. Women in the bottom 1% of the income distribution at the age of 40 years had an expected age of death of 78.8 years. Women in the top 1% had an expected age of death of 88.9 years, which is 10.1 years (95% CI, 9.9-10.3 years) longer than those in the bottom 1%.

The gap in life expectancy between men and women was narrower at higher income levels. In the bottom 1% of the income distribution, women lived 6.0 years (95% CI, 5.9-6.2 years) longer than men; in the top 1% of the income distribution, women lived only 1.5 years (95% CI, 1.3-1.8 years) longer than men.

The relationship between life expectancy and income percentile was approximately linear above the 2 lowest income percentiles. However, the relationship between life expectancy and dollar income amount was concave (eFigure 8 in the Supplement). That is, an increase in income of a given dollar amount was associated with smaller gains in life expectancy at higher income levels. For example, increases in income from $14?000 to $20?000 (the 10th vs the 15th income percentiles), $161?000 to $224?000 (the 90th vs the 95th income percentiles), and $224?000 to $1.95 million (the 95th vs the 100th income percentiles) were all associated with approximately the same difference in life expectancy (ie, an increase of 0.7-0.9 years, averaging men and women).

Estimates of life expectancy grouping individuals based on individual earnings instead of household earnings were similar, as were estimates that used Gompertz extrapolations up to the age of 100 years instead of the age of 90 years (discussions of these and other sensitivity analyses appear in part IV of the eAppendix and in eFigure 9 in the Supplement).

National Trends in Life Expectancy by Income

The upper panels of Figure 3 show race- and ethnicity-adjusted life expectancy for men and women by income quartile for each year from 2001 through 2014. There was a larger increase in life expectancy for higher income groups during the 2000s. For men, the mean annual increase in life expectancy from 2001 through 2014 was 0.20 years in the highest income quartile compared with only 0.08 years in the lowest income quartile (P?<?.001). For women, the comparable changes were 0.23 years in the highest quartile and 0.10 years in the lowest quartile (P?<?.001). These differences persisted after controlling for the higher growth rate of income for individuals in the top quartile relative to the bottom quartile (eTable 4 in theSupplement).

The lower panels of Figure 3 show the annual increase in race-adjusted life expectancy by income ventiles. The annual increase in longevity was 0.18 years for men (which translates to an increase of 2.34 years from 2001-2014) and 0.22 years for women (an increase of 2.91 years from 2001-2014) in the top 5% of the income distribution. In the bottom 5% of the income distribution, the average annual increase in longevity was 0.02 years (an increase of 0.32 years from 2001-2014) for men and 0.003 years (an increase of 0.04 years from 2001-2014) for women (P?<?.001 for the differences between top and bottom 5% of income distributions for both sexes).

Local Area Variation in Life Expectancy by Income
Levels of Life Expectancy by Commuting Zone

Life expectancy varied significantly across areas within the United States, especially for low-income individuals. Figure 4 shows life expectancy by income ventile for New York, New York; San Francisco, California; Dallas, Texas; and Detroit, Michigan. There was substantial variation across these areas for low-income individuals, but little variation for high-income individuals. Life expectancy ranged from 72.3 years to 78.6 years for men in the lowest income ventile across these 4 cities; the corresponding range for men in the top ventile was 86.5 years to 87.5 years.

The results in Figure 4 are representative of the variation across commuting zones more generally. The SD of life expectancy across all commuting zones (weighted by population) was 1.39 years for men in the bottom income quartile vs 0.70 years in the top income quartile (P?<?.001). Life expectancy varied less across areas for women than men in the bottom income quartile, and the amount of variation across commuting zones also declined with income for women (eTable 5 in the Supplement).

Figure 5 shows maps of expected age at death by commuting zone for men and women in the bottom and top quartiles of the national income distribution (maps for the middle-income quartiles appear in eFigure 10 in the Supplement). For individuals in the bottom income quartile, life expectancy differed by about 5 years for men and 4 years for women between the lowest and highest longevity commuting zones (P?<?.001 for both sexes). A summary of standard errors by commuting zone appears in part V.C of the eAppendix and in eFigure 11.

Nevada, Indiana, and Oklahoma had the lowest life expectancies (<77.9 years) when men and women in the bottom income quartile were averaged. Of the 10 states with the lowest levels of life expectancy for individuals in the bottom income quartile, 8 formed a geographic belt from Michigan to Kansas (Michigan, Ohio, Indiana, Kentucky, Tennessee, Arkansas, Oklahoma, Kansas). The states with the highest life expectancies for individuals in the bottom income quartile (>80.6 years) were California, New York, and Vermont. Life expectancy in the South was similar to the national mean for both sexes (?0.22 years [P?=?.47] for women and ?0.96 years [P?=?.03] for men) in the bottom income quartile. Individuals in the top income quartile had the lowest life expectancies (<85.3 years) in Nevada, Hawaii, and Oklahoma. Individuals in the top income quartile had the highest life expectancies (>87.6 years) in Utah; Washington, DC; and Vermont.

Table 1 lists the top 10 and bottom 10 commuting zones in mean life expectancy (averaging men and women) among the 100 most populated commuting zones for individuals in the bottom and top income quartiles. The expected age at death for the bottom quartile ranged from 74.2 years for men and 80.7 years for women in Gary, Indiana, to 79.5 years for men and 84.0 years for women in New York, New York. The commuting zones with the highest life expectancies were clustered in California (6 of the top 10), whereas the commuting zones with the lowest life expectancies were clustered in the industrial Midwest (5 of the bottom 10). The commuting zones with the highest life expectancies for those in the bottom income quartile also had the smallest gaps in life expectancy between the top and bottom quartiles (r?=??0.82, P?<?.001). The expected age at death for the top income quartile ranged from 82.8 years for men and 85.3 years for women in Las Vegas, Nevada, to 86.6 years for men and 89.0 years for women in Salt Lake City, Utah. The areas with the highest and lowest life expectancies for those in the top income quartile were less clustered geographically; for example, California had commuting zones in both the top 10 and bottom 10 of the list.

The differences in life expectancy across commuting zones were similar in analyses with income measures adjusted for cost of living; with controls for differences across areas in the income distribution within each quartile; and using measures of loss in life years up to the age of 77 years that did not make use of extrapolations beyond observed ages (part IV.C of the eAppendix and eTable 6 in the Supplement). There was also considerable variation in life expectancy across counties within commuting zones (part V of the eAppendix, eFigure 12, and eTable 7).

Trends in Life Expectancy

Similar to levels of life expectancy, temporal trends varied significantly across geographic areas. Figure 6maps the annual change in life expectancy between 2001 and 2014 by state for men and women in the bottom income quartile. Hawaii, Maine, and Massachusetts had the largest gains in life expectancy (gaining >0.19 years annually) when men and women in the bottom income quartile were averaged. The states in which low-income individuals experienced the largest losses in life expectancy (losing >0.09 years annually) were Alaska, Iowa, and Wyoming.



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