Geographic and racial variation in premature mortality in the U.S.: analyzing the disparities

Regional variations in vaccination against COVID-19 in Germany

Vaccination willingness against COVID-19 is generally perceived as low. Moreover, there is large heterogeneity across and within countries. As a whole, Germany has average vaccination rates compared to other industrialized countries. However, vaccination rates in the 16 different German federal states differ by more than 20 percentage points. We describe variation in vaccination rates on the level of the 400 German counties using data on all vaccinations carried out until December 2022. Around 52-72% of that variation can be explained by regional differences in demographic characteristics, housing, education and political party preferences. We find indications that the remaining part may be due to differences in soft factors such as risk aversion, trust in the German government, trust in science, and beliefs in conspiracy theories regarding the origins of the Corona virus. We conclude that improving the trust in science and the fight against conspiracy theories may possibly be effective tools to improve vaccination rates and effectively fight pandemics.

The effect of Medicaid expansion on the take-up of disability benefits by race and ethnicity

Public disability programs provide financial support to 12 million working-age individuals per year, though not all eligible individuals take up these programs. Mixed evidence exists regarding the impact of Medicaid eligibility expansion on program take-up, and even less is known about the relationship between Medicaid expansion and racial and ethnic disparities in take-up. Using 2009-2020 Current Population Survey data, we compare changes in Supplemental Security Income (SSI) and Social Security Disability Insurance (SSDI) take-up among respondents with disabilities living in Medicaid expansion states to respondents with disabilities living in non-expansion states, before and after Medicaid expansion. We further explore heterogeneity by race/ethnicity. We find that Medicaid expansion reduced SSI take-up by 10% overall, particularly among White and Hispanic respondents (10% and 21%, respectively). Medicaid expansion increased SSDI take-up by 8% overall, particularly among White and Black respondents (9% and 11%, respectively). Moreover, we find that Medicaid expansion reduced the probability that respondents with disabilities had employer-sponsored health insurance by approximately 8%, suggesting that expansion may have reduced job-lock among the SSDI-eligible, contributing to the observed increase in SSDI take-up.

COVID-19 and All-Cause Mortality by Race, Ethnicity, and Age Across Five Periods of the Pandemic in the United States

Racial/ethnic and age disparities in COVID-19 and all-cause mortality during 2020 are well documented, but less is known about their evolution over time. We examine changes in age-specific mortality across five pandemic periods in the United States from March 2020 to December 2022 among four racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian) for ages 35+. We fit Gompertz models to all-cause and COVID-19 death rates by 5-year age groups and construct age-specific racial/ethnic mortality ratios across an Initial peak (Mar-Aug 2020), Winter peak (Nov 2020-Feb 2021), Delta peak (Aug-Oct 2021), Omicron peak (Nov 2021-Feb 2022), and Endemic period (Mar-Dec 2022). We then compare to all-cause patterns observed in 2019. The steep age gradients in COVID-19 mortality in the Initial and Winter peak shifted during the Delta peak, with substantial increases in mortality at working ages, before gradually returning to an older age pattern in the subsequent periods. We find a disproportionate COVID-19 mortality burden on racial and ethnic minority populations early in the pandemic, which led to an increase in all-cause mortality disparities and a temporary elimination of the Hispanic mortality advantage at certain age groups. Mortality disparities narrowed over time, with racial/ethnic all-cause inequalities during the Endemic period generally returning to pre-pandemic levels. Black and Hispanic populations, however, faced a younger age gradient in all-cause mortality in the Endemic period relative to 2019, with younger Hispanic and Black adults in a slightly disadvantageous position and older Black adults in a slightly advantageous position, relative to before the pandemic.

Systematic population-wide ecological analysis of regional variability in disease prevalence

The prevalence of diseases often varies substantially from region to region. Besides basic demographic properties, the factors that drive the variability of each prevalence are to a large extent unknown. Here we show how regional prevalence variations in 115 different diseases relate to demographic, socio-economic, environmental factors and migratory background, as well as access to different types of health services such as primary, specialized and hospital healthcare. We have collected regional data for these risk factors at different levels of resolution; from large regions of care (Versorgungsregion) down to a 250 by 250 m square grid. Using multivariate regression analysis, we quantify the explanatory power of each independent variable in relation to the regional variation of the disease prevalence. We find that for certain diseases, such as acute heart conditions, diseases of the inner ear, mental and behavioral disorders due to substance abuse, up to 80% of the variance can be explained with these risk factors. For other diagnostic blocks, such as blood related diseases, injuries and poisoning however, the explanatory power is close to zero. We find that the time needed to travel from the inhabited center to the relevant hospital ward often contributes significantly to the disease risk, in particular for diabetes mellitus. Our results show that variations in disease burden across different regions can for many diseases be related to variations in demographic and socio-economic factors. Furthermore, our results highlight the relative importance of access to health care facilities in the treatment of chronic diseases like diabetes.

Life Expectancy for White, Black, and Hispanic Race/Ethnicity in U.S. States: Trends and Disparities, 1990 to 2019

BACKGROUND

Life expectancy (LE) differences within and between states by race/ethnicity have not been examined.

OBJECTIVE

To estimate LE for selected race/ethnicity groups in states from 1990 to 2019.

DESIGN

Cross-sectional time-series analysis.

SETTING

United States.

PARTICIPANTS

Deidentified death records and Census data were used to construct regression models with smoothed time series of mortality from 1990 to 2019.

MEASUREMENTS

LE at birth, by sex and year, for subgroups of people reporting Hispanic, non-Hispanic Black, or non-Hispanic White race/ethnicity.

RESULTS

Disparities in LE across states were 8.0 years for females and 12.2 years for males in 1990 and 7.9 years for females and 7.8 years for males in 2019. When race/ethnicity groups were accounted for, disparities across states were 20.7 years for females and 24.5 years for males in 1990, decreasing to 18.5 years for females and 23.7 years for males in 2019. Disparities across states increased within each race/ethnicity group between 1990 and 2019, with the largest increase for non-Hispanic White males and the smallest for Hispanic females. The disparity between race/ethnicity groups within states decreased for most of the 23 states with estimates for all 3 groups but increased for females in 7 states and males in 5 states.

LIMITATION

Because of small sample size, LE was not estimated for 37 of 153 state-race/ethnicity groups.

CONCLUSION

Disparity in LE across states was greater when race/ethnicity groups were considered. Disparities across all state-race/ethnicity groups in general have decreased over the past 3 decades. Within each race/ethnicity group, disparities across states have increased. Although racial/ethnic disparities decreased in most of the 23 states for which LE was estimated for all 3 groups, they increased for females in 7 states and males in 5 states.

PRIMARY FUNDING SOURCE

National Heart, Lung, and Blood Institute.

Life expectancy by county, race, and ethnicity in the USA, 2000-19: a systematic analysis of health disparities

BACKGROUND

There are large and persistent disparities in life expectancy among racial-ethnic groups in the USA, but the extent to which these patterns vary geographically on a local scale is not well understood. This analysis estimated life expectancy for five racial-ethnic groups, in 3110 US counties over 20 years, to describe spatial-temporal variations in life expectancy and disparities between racial-ethnic groups.

METHODS

We applied novel small-area estimation models to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual sex-specific and age-specific mortality rates stratified by county and racial-ethnic group (non-Latino and non-Hispanic White [White], non-Latino and non-Hispanic Black [Black], non-Latino and non-Hispanic American Indian or Alaska Native [AIAN], non-Latino and non-Hispanic Asian or Pacific Islander [API], and Latino or Hispanic [Latino]) from 2000 to 2019. We adjusted these mortality rates to correct for misreporting of race and ethnicity on death certificates and then constructed abridged life tables to estimate life expectancy at birth.

FINDINGS

Between 2000 and 2019, trends in life expectancy differed among racial-ethnic groups and among counties. Nationally, there was an increase in life expectancy for people who were Black (change 3·9 years [95% uncertainty interval 3·8 to 4·0]; life expectancy in 2019 75·3 years [75·2 to 75·4]), API (2·9 years [2·7 to 3·0]; 85·7 years [85·3 to 86·0]), Latino (2·7 years [2·6 to 2·8]; 82·2 years [82·0 to 82·5]), and White (1·7 years [1·6 to 1·7]; 78·9 years [78·9 to 79·0]), but remained the same for the AIAN population (0·0 years [-0·3 to 0·4]; 73·1 years [71·5 to 74·8]). At the national level, the negative difference in life expectancy for the Black population compared with the White population decreased during this period, whereas the negative difference for the AIAN population compared with the White population increased; in both cases, these patterns were widespread among counties. The positive difference in life expectancy for the API and Latino populations compared with the White population increased at the national level from 2000 to 2019; however, this difference declined in a sizeable minority of counties (615 [42·0%] of 1465 counties) for the Latino population and in most counties (401 [60·2%] of 666 counties) for the API population. For all racial-ethnic groups, improvements in life expectancy were more widespread across counties and larger from 2000 to 2010 than from 2010 to 2019.

INTERPRETATION

Disparities in life expectancy among racial-ethnic groups are widespread and enduring. Local-level data are crucial to address the root causes of poor health and early death among disadvantaged groups in the USA, eliminate health disparities, and increase longevity for all.

FUNDING

National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Science Research, US National Institutes of Health.

From convergence to divergence: Lifespan variation in US states, 1959-2017

BACKGROUND

Large disparities in life expectancy exist across US states and the gaps have been widening in recent decades. Less is known about the lifespan variability - a measure that can provide important insights into mortality inequalities both between and within states.

METHOD

Using yearly lifetables from the United States Mortality Database, we explore geographic and temporal patterns in lifespan variation (unconditional and conditional on survival to age 10, 35 and 65) across US states between 1959 and 2017. We also examine the contribution of state differences in life expectancy to overall lifespan variation using standard decomposition techniques.

RESULTS

Despite overall convergence in lifespan variation across states over the last six decades, in more recent years there has been notable divergence. Gender-specific analyses show that lifespan variation was generally greater among males than among females; but this pattern reverses for mortality past age 65. Much of the state disparities in lifespan variation, unconditional and conditional on survival to age 10 and 35, were due to mortality differences under the age 65. Decomposition analysis shows that while within-state variability remains the primary driver of overall lifespan variation, the contribution of cross-state differences in life expectancy is growing.

CONCLUSIONS

Variation in longevity is greater within US States than between them, yet cross-states disparities in mortality are increasing. This likely reflects the long-term consequences of rising social, economic, and political stratification for health inequalities both within and across states.

Progress of Inequality in Age at Death in India: Role of Adult Mortality

India has seen a reduction in infant and child mortality rates for both the sexes since the early 1980s. However, a decline in mortality at adult ages is marked by significant differences in the subgroups of sex and regions. This study assesses the progress of inequality in age at death with the advances in mortality transition during 36 years period between 1981-1985 and 2012-2016 in India, using the Gini coefficients at the age of zero (G ₀ ). The Gini coefficients show that in the mid-2000s, women outpaced men in G ₀ . The reduction in inequality in age at death is a manifestation of the process of homogeneity in mortality. The low G ₀ is concomitant of high life expectancy at birth (e ₀ ) in India. The results show the dominance of adult mortality over child mortality in the medium-mortality and low-mortality regimes. Varying adult mortality in the subgroups of sex and variance in the mortality levels of regions are the predominant factors for the variation in inequality in age at death. By lowering of the mortality rates in the age group of 15-29 years, India can achieve a high e ₀ that appears at high demographic development and the narrow sex differentials in e ₀ and G ₀ in a short time. Men in the age group of 15-29 years are the most vulnerable subgroup with respect to mortality. There is an immediate need for health policies in India to prioritise the aversion of premature deaths in men aged 15-29 years.

Spatial and temporal inequalities in mortality in the USA, 1968-2016

Previous UK and European research has highlighted important variations in mortality between populations after adjustment for key determinants such as poverty and deprivation. The aim here was to establish whether similar populations could be identified in the US, and to examine changes over time. We employed Poisson regression models to compare county-level mortality with national rates between 1968 and 2016, adjusting for poverty, education, race (a proxy for exposure to racism), population change and deindustrialisation. Results are presented by means of population-weighted cartograms, and highlight widening spatial inequalities in mortality over time, including an urban to rural, and south-westward, shift in areas with the highest levels of such unexplained 'excess' mortality. There is a need to understand the causes of the excess in affected communities, given that it persists after adjustment for such a broad range of important health determinants.

Quantifying and explaining variation in life expectancy at census tract, county, and state levels in the United States

Studies on geographic inequalities in life expectancy in the United States have exclusively focused on single-level analyses of aggregated data at state or county level. This study develops a multilevel perspective to understanding variation in life expectancy by simultaneously modeling the geographic variation at the levels of census tracts (CTs), counties, and states. We analyzed data from 65,662 CTs, nested within 3,020 counties and 48 states (plus District of Columbia). The dependent variable was age-specific life expectancy observed in each of the CTs. We also considered the following CT-level socioeconomic and demographic characteristics as independent variables: population density; proportions of population who are black, who are single parents, who are below the federal poverty line, and who are aged 25 or older who have a bachelor's degree or higher; and median household income. Of the total geographic variation in life expectancy at birth, 70.4% of the variation was attributed to CTs, followed by 19.0% for states and 10.7% for counties. The relative importance of CTs was greater for life expectancy at older ages (70.4 to 96.8%). The CT-level independent variables explained 5 to 76.6% of between-state variation, 11.1 to 58.6% of between-county variation, and 0.7 to 44.9% of between-CT variation in life expectancy across different age groups. Our findings indicate that population inequalities in longevity in the United States are primarily a local phenomenon. There is a need for greater precision and targeting of local geographies in public policy discourse aimed at reducing health inequalities in the United States.

Risk Factors for Cancer-specific Mortality and Cardiovascular Mortality in Patients With Diffuse Large B-cell Lymphoma

BACKGROUND

The purpose of this study was to assess the risk factors for cancer-specific mortality and cardiovascular mortality in patients with diffuse large B-cell lymphoma (DLBCL).

PATIENTS AND METHODS

A retrospective cohort study involving patients with DLBCL who were registered in the Surveillance, Epidemiology, and End Results (SEER) database was performed. The risk factors for cancer-specific mortality and cardiovascular mortality were analyzed using the competing risk regression model.

RESULTS

A total of 62,950 patients with DLBCL were enrolled, of which 23,302 (37.50%) died of cancer and 2940 (4.70%) died of cardiovascular disease. The competing risk multivariate analysis displayed that age at diagnosis (hazard ratio [HR], 1.033; 95% confidence interval [CI], 1.032-1.034), marriedstatus (HR, 1.293; 95% CI, 1.241-1.347), black race (HR, 1.079; 95% CI, 1.021-1.139), and tumor stage (II: HR, 1.143; 95%CI, 1.095-1.192; III: HR, 1.459; 95% CI, 1.395-1.526; IV: HR, 1.961; 95% CI. 1.889-2.035) were the risk factors for cancer-specific mortality, but not female gender (HR, 0.938; 95% CI, 0.913,0.965) or treatment modalities (chemotherapy: HR, 0.522; 95% CI, 0.505-0.540; radiotherapy: HR, 0.782; 95% CI, 0.728-0.839; chemotherapy + radiotherapy: HR, 0.422; 95% CI, 0.403-0.441). Age at diagnosis (HR, 1.059; 95% CI, 1.055-1.062) and black race (HR, 1.246; 95% CI, 1.067-1.456) were the risk factors for cardiovascular mortality rather than female gender (HR, 0.803; 95% CI, 0.743-0.867) and married status (HR, 0.841; 95% CI, 0.745-0.950).

CONCLUSIONS

Age at diagnosis, married status, black race, and higher tumor stage are associated with an increased risk of cancer-specific mortality in patients with DLBCL, whereas age at diagnosis and black race are associated with a higher risk of cardiovascular mortality.

Area deprivation amplifies racial inequities in premature mortality: Analysis of 242,667 deaths in Washington State, USA 2011-15

Racial and socioeconomic inequalities in health are consistently reported, but less is known about the interplay between racial and deprivation-related inequities. We used geographically-localized data on all deaths recorded in Washington state 2011 to 2015 (n = 242,667 decedents) and multi-level regression models to examine premature (<65 years) mortality by race and neighborhood deprivation separately and in combination. White versus non-white inequities in premature mortality did not vary substantially with increasing levels of deprivation. However, most non-white races from deprived neighborhoods had odds of premature mortality between three and eight times that of more-affluent whites. These findings may reflect the compounding of disadvantage stemming from social and environmental risk factors.

Chronic Disease, the Built Environment, and Unequal Health Risks in the 500 Largest U.S. Cities

Health is increasingly subject to the complex interplay between the built environment, population composition, and the structured inequity in access to health-related resources across communities. The primary objective of this paper was to examine cardiometabolic disease (diabetes, cardiovascular diseases, stroke) markers and their prevalence across relatively small geographic units in the 500 largest cities in the United States. Using data from the American Community Survey and the 500 Cities Project, the current study examined cardiometabolic diseases across 27,000+ census tracts in the 500 largest cities in the United States. Earlier works clearly show cardiometabolic diseases are not randomly distributed across the geography of the U.S., but rather concentrated primarily in Southern and Eastern regions of the U.S. Our results confirm that chronic disease is correlated with social and built environment factors. Specifically, racial concentration (%, Black), age concentration (% 65+), housing stock age, median home value, structural inequality (Gini index), and weight status (% overweight/obese) were consistent correlates (p < 0.01) of cardiometabolic diseases in the sample of census tracts. The paper examines policy-related features of the built and social environment and how they might play a role in shaping the health and well-being of America’s metropolises.

Reflection on modern methods: years of life lost due to premature mortality-a versatile and comprehensive measure for monitoring non-communicable disease mortality

The analysis of causes impacting on premature mortality is an essential function of public health surveillance. Diverse methods have been used for accurately assessing and reporting the level and trends of premature mortality; however, many have important limitations, particularly in capturing actual early deaths. We argue that the framework of years of life lost (YLL), as conceptualized in disability-adjusted life-years (DALYs), is a robust and comprehensive measure of premature mortality. Global Burden of Disease study is systematically providing estimates of YLL; however, it is not widely adopted at country level, among other reasons because its conceptual and methodological bases seem to be not sufficiently known and understood. In this paper, we provide the concepts and the methodology of the YLL framework, including the selection of the loss of function that defines the time lost due to premature deaths, and detailed methods for calculating YLL metrics. We also illustrate how to use YLL to quantify the level and trends of premature non-communicable disease (NCD) mortality in the Americas. The tutorial style of the illustrative example is intended to educate the public health community and stimulate the use of YLL in disease prevention and control programmes at different levels.

Structural racism, economic opportunity and racial health disparities: Evidence from U.S. counties

In this study, we introduce the 'racial opportunity gap' as a place-based measure of structural racism for use in population health research. We first detail constructing the opportunity gap using race-sex specific estimates of intergenerational economic mobility outcomes for a recent cohort. We then illustrate its utility in examining spatial variation in the racial mortality gap. First we demonstrate a correlation between the racial opportunity gap and the racial mortality gap across U.S. counties; where the gap in the adult earnings of black and white children born to families at the same income level is greater so, too, is the gap in mortality. Second, we show in a multivariable framework that the racial opportunity gap is associated with the racial mortality gap net of differences in the socioeconomic composition of the two groups. In so doing, we aim to provide population health researchers with a new empirical tool and analytic framework for examining the role of structural racism in generating racial health disparities.

Structural racism, economic opportunity and racial health disparities: Evidence from U.S. counties

In this study, we introduce the ‘racial opportunity gap’ as a place-based measure of structural racism for use in population health research. We first detail constructing the opportunity gap using race-sex specific estimates of intergenerational economic mobility outcomes for a recent cohort. We then illustrate its utility in examining spatial variation in the racial mortality gap. First we demonstrate a correlation between the racial opportunity gap and the racial mortality gap across U.S. counties; where the gap in the adult earnings of black and white children born to families at the same income level is greater so, too, is the gap in mortality. Second, we show in a multivariable framework that the racial opportunity gap is associated with the racial mortality gap net of differences in the socioeconomic composition of the two groups. In so doing, we aim to provide population health researchers with a new empirical tool and analytic framework for examining the role of structural racism in generating racial health disparities.

Provision of health care services and regional diversity in Germany: insights from a Bayesian health frontier analysis with spatial dependencies

The German health care system is among the most patient-oriented systems in Europe. Nevertheless, distinct utilisation patterns, access barriers due to socio-economic profiles, and potentials of misallocation of medical resources lead to disparities in the provision of health care services. We analyse how a possible over- and undersupply of services and the utilisation of and the access to the health care system relate to regional variations in the population's well-being. For this purpose, we employ a recent Bayesian stochastic frontier approach that allows for spatial dependence structures. Our results indicate that patient migration plays an important role in contributing to regional differences in the utilisation of the medical infrastructure. As a consequence, policy should take spatial patterns of health care utilisation into account to improve the allocation of medical resources.

Decomposition of the US black/white inequality in premature mortality, 2010-2015: an observational study

OBJECTIVE

Decompose the US black/white inequality in premature mortality into shared and group-specific risks to better inform health policy.

SETTING

All 50 US states and the District of Columbia, 2010 to 2015.

PARTICIPANTS

A total of 2.85 million non-Hispanic white and 762 639 non-Hispanic black US-resident decedents.

PRIMARY AND SECONDARY OUTCOME MEASURES

The race-specific county-level relative risks for US blacks and whites, separately, and the risk ratio between groups.

RESULTS

There is substantial geographic variation in premature mortality for both groups and the risk ratio between groups. After adjusting for median household income, county-level relative risks ranged from 0.46 to 2.04 (median: 1.03) for whites and from 0.31 to 3.28 (median: 1.15) for blacks. County-level risk ratios (black/white) ranged from 0.33 to 4.56 (median: 1.09). Half of the geographic variation in white premature mortality was shared with blacks, while only 15% of the geographic variation in black premature mortality was shared with whites. Non-Hispanic blacks experience substantial geographic variation in premature mortality that is not shared with whites. Moreover, black-specific geographic variation was not accounted for by median household income.

CONCLUSION

Understanding geographic variation in mortality is crucial to informing health policy; however, estimating mortality is difficult at small spatial scales or for small subpopulations. Bayesian joint spatial models ameliorate many of these issues and can provide a nuanced decomposition of risk. Using premature mortality as an example application, we show that Bayesian joint spatial models are a powerful tool as researchers grapple with disentangling neighbourhood contextual effects and sociodemographic compositional effects of an area when evaluating health outcomes. Further research is necessary in fully understanding when and how these models can be applied in an epidemiological setting.

Local Area Variation in Morbidity Among Low-Income, Older Adults in the United States: A Cross-sectional Study

Background

Recent studies have reported that low-income adults living in more affluent areas of the United States have longer life expectancies. Less is known about the relationship between the affluence of a geographic area and morbidity of the low-income population.

Objective

To evaluate the association between the prevalence of chronic conditions among low-income, older adults and the economic affluence of a local area.

Design

Cross-sectional association study.

Setting

Medicare in 2015.

Participants

6 363 097 Medicare beneficiaries aged 66 to 100 years with a history of low-income support under Medicare Part D.

Measurements

Adjusted prevalence of 48 chronic conditions was computed for 736 commuting zones (CZs). Factor analysis was used to assess spatial covariation of condition prevalence and to construct a composite condition prevalence index for each CZ. The association between morbidity and area affluence was measured by comparing the average of condition prevalence index across deciles of median CZ house values.

Results

The mean age of study participants was 77.7 years (SD, 8.2); 67% were women, and 61% were white. The crude prevalence of 48 chronic conditions ranged from 72.5 per 100 for hypertension to 0.6 per 100 for posttraumatic stress disorder. The prevalence of these 48 chronic conditions was highly spatially correlated. Composite condition prevalence was on average substantially lower in more affluent CZs.

Limitation

Low-income status measured on the basis of receipt of Medicare Part D low-income subsidies and not capturing persons not enrolled in Medicare Part D.

Conclusion

Low-income, older adults living in more affluent areas of the country are healthier, and areas with poor health in the low-income, older adult population tend to have a high prevalence of most chronic conditions.

Primary Funding Source

National Institute on Aging.

Using Census Data to Understand County-Level Differences in Overall Drug Mortality and Opioid-Related Mortality by Opioid Type

Objectives. To examine associations of county-level demographic, socioeconomic, and labor market characteristics on overall drug mortality rates and specific classes of opioid mortality. Methods. We used National Vital Statistics System mortality data (2002-2004 and 2014-2016) and county-level US Census data. We examined associations between several census variables and drug deaths for 2014 to 2016. We then identified specific classes of counties characterized by different levels and rates of growth in mortality from specific opioid types between 2002 to 2004 and 2014 to 2016. We ran multivariate and multivariable regression models to predict probabilities of membership in each "opioid mortality class" on the basis of county-level census measures. Results. Drug mortality rates overall are higher in counties characterized by more economic disadvantage, more blue-collar and service employment, and higher opioid-prescribing rates. High rates of prescription opioid overdoses and overdoses involving both prescription and synthetic opioids cluster in more economically disadvantaged counties with larger concentrations of service industry workers. High heroin and "syndemic" opioid mortality counties (high rates across all major opioid types) are more urban, have larger concentrations of professional workers, and are less economically disadvantaged. Syndemic opioid counties also have greater concentrations of blue-collar workers. Conclusions. Census data are essential tools for understanding the importance of place-level characteristics on opioid mortality. Public Health Implications. National opioid policy strategies cannot be assumed universally applicable. In addition to national policies to combat the opioid and larger drug crises, emphasis should be on developing locally and regionally tailored interventions, with attention to place-based structural economic and social characteristics.

Do Employees From Less-Healthy Communities Use More Care and Cost More? Seeking to Establish a Business Case for Investment in Community Health

INTRODUCTION

Few studies have examined the impact of community health on employers. We explored whether employed adults and their adult dependents living in less-healthy communities in the greater Philadelphia region used more care and incurred higher costs to employers than employees from healthier communities.

METHODS

We used a multi-employer database to identify adult employees and dependents with continuous employment and mapped them to 31 zip code regions. We calculated community health scores at the regional level, by using metrics similar to the Robert Wood Johnson Foundation (RWJF) County Health Rankings but with local data. We used descriptive analyses and multilevel linear modeling to explore relationships between community health and 3 outcome variables: emergency department (ED) use, hospital use, and paid claims. Business leaders reviewed findings and offered insights on preparedness to invest in community health improvement.

RESULTS

Poorer community health was associated with high use of ED services, after controlling for age and sex. After including a summary measure of racial composition at the zip code region level, the relationship between community health and ED use became nonsignificant. No significant relationships between community health and hospitalizations or paid claims were identified. Business leaders expressed interest in further understanding health needs of communities where their employees live.

CONCLUSION

The health of communities in which adult employees and dependents live was associated with ED use, but similar relationships were not seen for hospitalizations or paid claims. This finding suggests a need for more primary care access. Despite limited quantitative evidence, business leaders expressed interest in guidance on investing in community health improvement.

Identifying County-Level All-Cause Mortality Rate Trajectories and Their Spatial Distribution Across the United States

INTRODUCTION

All-cause mortality in the United States declined from 1935 through 2014, with a recent uptick in 2015. This national trend is composed of disparate local trends. We identified distinct groups of all-cause mortality rate trajectories by grouping US counties with similar temporal trajectories.

METHODS

We used all-cause mortality rates in all US counties for 1999 through 2016 and estimated discrete mixture models by using county level mortality rates. Proc Traj in SAS was used to detect how county trajectories clustered into groups on the basis of similar intercepts, slopes, and higher order terms. Models with increasing numbers of groups were assessed on the basis of model fit. We created county-level maps of mortality trajectory groups by using ArcGIS.

RESULTS

Eight unique trajectory groups were detected among 3,091 counties. The average mortality rate in the most favorable trajectory group declined 29.4%, from 592.3 deaths per 100,000 in 1999 to 418.2 in 2016. The least favorable mortality trajectory group declined 3.4% over the period, from 1,280.3 deaths per 100,000 to 1,236.9. We saw significant differences in the demographic and socioeconomic profiles and geographic patterns across the trajectory categories, with favorable mortality trajectories in the Northeast, Midwest, and on the West Coast and unfavorable trajectories concentrated in the Southeast.

CONCLUSIONS

County-level disparities in all-cause mortality rates widened over the past 18 years. Further investigation of the determinants of the trajectory groupings and the geographic outliers identified by our research could inform interventions to achieve equitable distribution of county mortality rates.

Socioeconomic Differences in the Epidemiologic Transition From Heart Disease to Cancer as the Leading Cause of Death in the United States, 2003 to 2015: An Observational Study

BACKGROUND

Recent data suggest that the United States is in the midst of an epidemiologic transition in the leading cause of death.

OBJECTIVE

To examine county-level sociodemographic differences in the transition from heart disease to cancer as the leading cause of death in the United States.

DESIGN

Observational study.

SETTING

U.S. death records, 2003 to 2015.

PARTICIPANTS

Decedents aged 25 years or older, classified by racial/ethnic group.

MEASUREMENTS

All-cause, heart disease, and cancer mortality stratified by quintiles of county median household income. Age- and sex-adjusted mortality rates and average annual percentage of change were calculated.

RESULTS

Heart disease was the leading cause of death in 79% of counties in 2003 and 59% in 2015. Cancer was the leading cause of death in 21% of counties in 2003 and 41% in 2015. The shift to cancer as the leading cause of death was greatest in the highest-income counties. Overall, heart disease mortality rates decreased by 28% (30% in high-income counties vs. 22% in low-income counties) from 2003 to 2015, and cancer mortality rates decreased by 16% (18% in high-income counties vs. 11% in low-income counties). In the lowest-income counties, heart disease remained the leading cause of death among all racial/ethnic groups, and improvements were smaller for both heart disease and cancer.

LIMITATION

Use of county median household income as a proxy for socioeconomic status.

CONCLUSION

Data show that heart disease is more likely to be the leading cause of death in low-income counties. Low-income counties have not experienced the same decrease in mortality rates as high-income counties, which suggests a later transition to cancer as the leading cause of death in low-income counties.

PRIMARY FUNDING SOURCE

National Institute on Minority Health and Health Disparities.

Self-Identified Social Determinants of Health during Transitions of Care in the Medically Underserved: a Narrative Review

BACKGROUND

Medically underserved or low socioeconomic status (SES) patients face significant vulnerability and a high risk of adverse events following hospital discharge. The environmental, social, and economic factors, otherwise known as social determinants, that compound this risk have been ineffectually described in this population. As the underserved comprise 30% of patients discharged from the hospital, improving transitional care and preventing readmission in this group has profound quality of care and financial implications.

METHOD

EMBASE and MEDLINE searches were conducted to examine specific barriers to care transitions in underserved patients following an episode of acute care. Articles were reviewed for barriers and categorized within the context of five general themes.

RESULTS

This review yielded 17 peer-reviewed articles. Common factors affecting care transitions were cost of medications, access to care, housing instability, and transportation. When categorized within themes, social fragility and access failures, as well as therapeutic misalignment, disease behavior, and issues with accountability were noted.

DISCUSSION

Providers and health systems caring for medically underserved patients may benefit through dedicating increased resources and broadening collaboration with community partners in order to expand health care access and enhance coordination of social services within this population. Future studies are needed to identify potential interventions targeting underserved patients to improve their post-hospital care.

Health care service provision in Europe and regional diversity: a stochastic metafrontier approach

■■■: In the last decades, demographic change coupled with new and expensive medical innovations have put most health care systems in developed countries under financial pressure. Therefore, ensuring efficient service provision is essential for a sustainable health care system. This paper investigates the performance of regional health care services in six West European countries between 2005 and 2014. We apply a stochastic metafrontier model to capture the different conditions in the health care systems in the countries within the European Union. By means of this approach, it is possible to detect performance differences in the European health care systems subject to different conditions and technologies relative to the potential technology available. The results indicate that regional deprivation plays a key role for the efficiency of health care provision. Furthermore, a pooled model which assumes a similar technology for all countries cannot sufficiently account for differences between countries. Surprisingly, the Scandinavian regions lag behind other regions with respect to the metafrontier.

JEL CLASSIFICATION

C23, D61, I12, I18, R10.

Decomposing Mortality Disparities in Urban and Rural U.S. Counties

OBJECTIVE

To understand the role of county characteristics in the growing divide between rural and urban mortality from 1980 to 2010.

DATA SOURCE

Age-adjusted mortality rates for all U.S. counties from 1980 to 2010 were obtained from the CDC Compressed Mortality File and combined with county characteristics from the U.S. Census Bureau, the Area Health Resources File, and the Inter-University Consortium for Political and Social research.

STUDY DESIGN

We used Oaxaca-Blinder decomposition to assess the extent to which rural-urban mortality disparities are explained by observed county characteristics at each decade.

PRINCIPAL FINDINGS

Decomposition shows that, at each decade, differences in rural/urban characteristics are sufficient to explain differences in mortality. Furthermore, starting in 1990, rural counties have significantly lower predicted mortality than urban counties when given identical county characteristics. We find changes in the effect of characteristics on mortality, not the characteristics themselves, drive the growing mortality divide.

CONCLUSIONS

Differences in economic and demographic characteristics between rural and urban counties largely explain the differences in age-adjusted mortality in any given year. Over time, the role these characteristics play in improving mortality has increased differentially for urban counties. As characteristics continue changing in importance as determinants of health, this divide may continue to widen.

Social, Psychological, And Physical Aspects Of The Work Environment Could Contribute To Hypertension Prevalence

Studies on the physical and social characteristics of the workplace have begun to provide evidence for the role of specific workplace factors on health. However, the overall contribution of the workplace to health has not been considered. Estimates of the influences on health across domains of the work environment are a critical first step toward understanding what level of priority the workplace should take as the target for public policies to improve health. The influences or contribution of these domains on health in the work environment are particularly useful to study since they are potentially modifiable through changes in policies and environment. Our analysis used detailed data from blue-collar industrial workers at two dozen Alcoa plants. It includes work environmental measures of psychological hazards, physical hazards, and the workplace social environment, to estimate the overall importance of the workplace environment for hypertension. Our findings suggest that social, psychological, and physical aspects of the work environment could contribute to a substantial proportion of hypertension prevalence. These attributes of the workplace could thus be a useful target for improving workforce health.

Increasing Opportunities for Health in a Southeast Michigan Community Through Local Policy Change

Disparities in health outcomes are closely linked with social, economic, and environmental conditions. The burden of these disparities are most often experienced by racial and ethnic individuals of color, those with low income, and those who live in vulnerable communities. Local policy and systems change efforts provide a means to address health inequities and create sustainable change at the community level. The Inkster Partnership for a Healthier Community was formed in 2010 to create sustainable opportunities for health, with a special focus on diabetes prevention and management. Policy and infrastructure change efforts were documented and tracked over time and qualitative data were collected to create deeper understanding of the change efforts. Eleven policy outcomes have created sustainable change around access to health resources and services, access to healthy foods, opportunities for physical activity, diabetes prevention and management education, and increased capacity for ongoing community change.

A Multiple-Imputation "Forward Bridging" Approach to Address Changes in the Classification of Asian Race/Ethnicity on the US Death Certificate

The incomparability of old and new classification systems for describing the same data can be seen as a missing-data problem, and, under certain assumptions, multiple imputation may be used to "bridge" 2 classification systems. One example of such a change is the introduction of detailed Asian-American race/ethnicity classifications on the 2003 version of the US national death certificate, which was adopted for use by 38 states between 2003 and 2011. Using county- and decedent-level data from 3 different national sources for pre- and postadoption years, we fitted within-state multiple-imputation models to impute ethnicities for decedents classified as "other Asian" during preadoption years. We present mortality rates derived using 3 different methods of calculation: 1) including all states but ignoring the gradual adoption of the new death certificate over time, 2) including only the 7 states with complete reporting of all ethnicities, and 3) including all states and applying multiple imputation. Estimates from our imputation model were consistently in the middle of the other 2 estimates, and trend results demonstrated that the year-by-year estimates of the imputation model were more similar to those of the 7-state model. This work demonstrates how multiple imputation can provide a "forward bridging" approach to make more accurate estimates over time in newly categorized populations.

Calculating census tract-based life expectancy in New York state: a generalizable approach

BACKGROUND

Life expectancy at birth (LE) has been calculated for states and counties. LE estimates at these levels mask health disparities in local communities. There are no nationwide estimates at the sub-county level. We present a stepwise approach for calculating LE using census tracts in New York state to identify health disparities.

METHODS

Our study included 2751 census tracts in New York state, but excluded New York City. We used population data from the 2010 United States Census and 2008-2010 mortality data from the state health department. Tracts were assigned to 99.97% of the deaths. We removed tracts which had a majority of people living in group quarters. Deaths in these tracts are often recorded elsewhere. Of the remaining 2679 tracts, 6.6% of the tracts had standard errors ≥ 2 years. A geographic aggregation tool was used to aggregate tracts with fewer than 60 deaths, and then aggregate areas that had standard errors of ≥ 2 years.

RESULTS

Aggregation resulted in a 9.9% reduction in the number of areas. Tracts with < 2% of population living below the poverty level had a LE of 82.8 years, while tracts with a poverty level ≥ 25% had a LE of 75.5. We observed differences in LE in border areas, of up to 10.4 years, when excluding or including deaths of study area residents that occurred outside the study area. The range and standard deviation at the county level (77.5-82.8, SD = 1.2 years) were smaller than our final sub-county areas (64.7-92.0, SD = 3.3 years). The correlation between LE and poverty were similar and statistically significant (p < 0.0001) at the county (r = - 0.58) and sub-county level (r = - 0.58). The correlations between LE and percent African-American at the county level were (r = 0.11, p = 0.43) and at the sub-county level (r = - 0.25, p < 0.0001).

CONCLUSION

The proposed approach for geocoding and aggregation of mortality and population data provides a solution for health departments to produce stable empirically-derived LE estimates using data coded to the tract. Reliable estimates within sub-county areas are needed to aid public health officials in focusing preventive health programs in areas where health disparities would be masked by county level estimates.

Active Life Expectancy In The Older US Population, 1982-2011: Differences Between Blacks And Whites Persisted

Understanding long-range trends in longevity and disability is useful for projecting the likely impact of the baby-boom generation on long-term care utilization and spending. We examine changes in active life expectancy in the United States from 1982 to 2011 for white and black adults ages sixty-five and older. For whites, longevity increased, disability was postponed to older ages, the locus of care shifted from nursing facilities to community settings, and the proportion of life at older ages spent without disability increased. In contrast, for blacks, longevity increases were accompanied by smaller postponements in disability, and the percentage of remaining life spent active remained stable and well below that of whites. Older black women were especially disadvantaged in 2011 in terms of the proportion of years expected to be lived without disability. Public health measures directed at older black adults-particularly women-are needed to offset impending pressures on the long-term care delivery system as the result of population aging.

Sub-County Life Expectancy: A Tool to Improve Community Health and Advance Health Equity

Compared with people in other developed countries, Americans live shorter lives, have more disease and disability, and lag on most population health measures. Recent research suggests that this poor comparative performance is primarily driven by profound local place-based disparities. Several initiatives successfully used sub-county life expectancy estimates to identify geographic disparities, generate widespread interest, and catalyze multisector actions. To explore the feasibility of scaling these efforts, the Centers for Disease Control and Prevention and the Council of State and Territorial Epidemiologists initiated a multiphase project - the Sub-County Assessment of Life Expectancy. Phase I participants reviewed the literature, assessed and identified appropriate tools, calculated locally relevant estimates, and developed methodological guidance. Phase I results suggest that most state and local health departments will be able to calculate actionable sub-county life expectancy estimates despite varying resources, expertise, and population sizes, densities, and geographies. To accelerate widespread scaling, we describe several successful case examples, identify user-friendly validated tools, and provide practical tips that resulted from lessons learned.

Social Determinants of LGBT Cancer Health Inequities

OBJECTIVES

To describe the extant literature on social determinants of health as they relate to the cancer disparities and to highlight the research findings relating to lesbian, gay, bisexual, and transgender (LGBT) populations.

DATA SOURCES

Published scientific literature and clinical literature, and published reports from the World Health Organization and US Department of Health and Human Services.

CONCLUSION

The larger literature on health inequities is moving beyond individual-level predictors of risk to evaluate the influence of social determinants of health on the persistent health inequalities in a population. As it has for other groups, additional research into social determinants of health for LGBT persons of color may play an important role in identifying and reducing cancer inequities for this group.

IMPLICATIONS FOR NURSING PRACTICE

Increased awareness of the factors that contribute to health inequities for the LGBT population may provide insight into improving patient-provider relationships with LGBT patients. A large body of experiential and clinical knowledge positions nurses to conduct meaningful research to expand the current understanding of the social determinants of LGBT cancer health inequities.

Improving health care service provision by adapting to regional diversity: An efficiency analysis for the case of Germany

The provision of health care in Germany exhibits sizeable geographic variation with a heterogeneous allocation of medical services in rural and urban areas. Furthermore, distinct utilisation patterns and access barriers due to the socio-economic environment might cause inefficiencies in the provision of health care services. Accordingly, an improved understanding of factors governing inefficiencies in health care provision is likely to benefit an efficient spatial allocation of health care infrastructure. We analyse how socio-economic factors influence the regional distribution of (in)efficiencies in the provision of health care services by means of a stochastic frontier analysis. Our results highlight that regional deprivation relates to inefficient provision of health care services. As a consequence, policies should also consider socio-economic conditions to improve the allocation of medical services and overall health.

Inequalities in Life Expectancy Among US Counties, 1980 to 2014: Temporal Trends and Key Drivers

IMPORTANCE

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.

OBJECTIVE

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.

DESIGN, SETTING, AND PARTICIPANTS

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.

EXPOSURES

County of residence.

MAIN OUTCOMES AND MEASURES

Life expectancy at birth and age-specific mortality risk.

RESULTS

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.

CONCLUSIONS AND RELEVANCE

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.

Real-time measures of context to improve fall-detection models

Real-time fall detection has been a challenging area of research and even more challenging as a viable commercial service, given the need for near perfect classification algorithms. True fall events are rare is monitored data sets, whereas confounding events for automated algorithms are quite frequent. In this paper we describe a decision theoretic approach to classification and alerting that incorporates context, such as location and activities, to improve probability and utility estimates for new classes, including near falls and known confounding events. We describe how to use monitored context to provide real-time assessment of true patient state to improve training data sets, as well as the use of context in improving classification, detection and alerting.

Disparities in kidney transplantation across the United States: Does residential segregation play a role?

BACKGROUND

Although residential segregation has been implicated in various negative health outcomes, its association with kidney transplantation has not been examined.

METHODS

Age- and sex-standardized kidney transplantation rates were calculated from the Scientific Registry of Transplant Recipients, 2000-2013. Population characteristics including segregation indices were derived from the 2010 U.S. Census data and the U.S. Renal Data System. Separate multivariable Poisson regression models were constructed to identify factors independently associated with kidney transplantation among Blacks and Whites.

RESULTS

Median age- and sex-standardized kidney transplantation rates were 114 per 100,000 for Blacks and 38 per 100,000 for Whites. 16.1% of the U.S. population lived in counties with high segregation. There was no difference in the kidney transplantation rates across the levels of segregation among Blacks and Whites.

CONCLUSION

Factors other than residential segregation may play roles in kidney transplantation disparities. Continued efforts to identify these factors may be beneficial in reducing transplantation disparities across the U.S.

SUMMARY

Using the Scientific Registry of Transplant Recipients and U.S. census data, we aimed to determine whether residential segregation was associated with kidney transplantation rates. We found that there was no association between residential segregation and kidney transplantation rates.

The weaker sex? Vulnerable men and women's resilience to socio-economic disadvantage

Sex differences in mortality vary over time and place as a function of social, health, and medical circumstances. The magnitude of these variations, and their response to large socioeconomic changes, suggest that biological differences cannot fully account for sex differences in survival. Drawing on a wide swath of mortality data across countries and over time, we develop a set of empiric observations with which any theory about excess male mortality and its correlates will have to contend. We show that as societies develop, M/F survival first declines and then increases, a "sex difference in mortality transition" embedded within the demographic and epidemiologic transitions. After the onset of this transition, cross-sectional variation in excess male mortality exhibits a consistent pattern of greater female resilience to mortality under socio-economic adversity. The causal mechanisms underlying these associations merit further research.

Predicting later life health status and mortality using state-level socioeconomic characteristics in early life

Studies extending across multiple life stages promote an understanding of factors influencing health across the life span. Existing work has largely focused on individual-level rather than area-level early life determinants of health. In this study, we linked multiple data sets to examine whether early life state-level characteristics were predictive of health and mortality decades later. The sample included 143,755 U.S. employees, for whom work life claims and administrative data were linked with early life state-of-residence and mortality. We first created a "state health risk score" (SHRS) and "state mortality risk score" (SMRS) by modeling state-level contextual characteristics with health status and mortality in a randomly selected 30% of the sample (the "training set"). We then examined the association of these scores with objective health status and mortality in later life in the remaining 70% of the sample (the "test set") using multivariate linear and Cox regressions, respectively. The association between the SHRS and adult health status was β=0.14 (95%CI: 0.084, 0.20), while the hazard ratio for the SMRS was 0.96 (95%CI: 0.93, 1.00). The association between the SHRS and health was not statistically significant in older age groups at a p-level of 0.05, and there was a statistically significantly different association for health status among movers compared to stayers. This study uses a life course perspective and supports the idea of "sensitive periods" in early life that have enduring impacts on health. It adds to the literature examining populations in the U.S. where large linked data sets are infrequently available.

US Mortality: Influence of Race, Geography and Cardiovascular Risk Among Participants in the Population-Based REGARDS Cohort

OBJECTIVES

We evaluated whether differences in cardiovascular risk factors, as assessed by the Framingham risk scores for stroke and cardiovascular disease (FSRS and FCRS), contributed to disparities in all-cause mortality across race and regional strata of USA.

DESIGN

Race-region-specific FSRS and FCRS scores were computed for 30,086 REGARDS participants who were recruited between January 2003 and October 2007. They were divided across six regions of the "Eight Americas" and then compared after adjusting for race and sex. Kaplan-Meier curves and hazard ratios for all-cause mortality were estimated between regions, first adjusted for age and sex, and then for the risk scores.

RESULTS

After adjustment for age, sex, FCRS, and FSRS, there was no difference in mortality among Middle-America Whites versus Low-Income White. However, mortality was lower among Middle-America Blacks (-23 %; p = 0.06) and High-Risk Urban Blacks (-24 %; p = 0.01) compared to Southern Low-Income Rural Blacks. Compared to Middle-American Whites, mortality was higher among Middle-America Blacks (+39 %; p < 0.001), High-Risk Urban Blacks (+35 %; p < 0.001) and Southern Low-Income Rural Blacks (+85 %; p < 0.001).

CONCLUSION

Accounting for cardiovascular risk unmasked a greater disparity in mortality between Blacks and Whites and among Southern Rural Blacks compared to Middle-America Blacks and High-Risk Urban Blacks.

Plasma Lycopene Is Associated with Pizza and Pasta Consumption in Middle-Aged and Older African American and White Adults in the Southeastern USA in a Cross-Sectional Study

BACKGROUND

The role of dietary lycopene in chronic disease prevention is not well known.

METHODS

This study examined intake of lycopene and other antioxidants from lycopene-rich foods (e.g., pizza and pasta) simultaneously with plasma levels of lycopene and other antioxidants in a representative cross-sectional sample (187 Blacks, 182 Whites, 40-79 years old) from the Southern Community Cohort Study (SCCS). The SCCS is an ongoing study conducted in populations at high risk for chronic diseases living in Southeastern United States. Dietary intake was assessed using a validated food frequency questionnaire (FFQ), and plasma levels of lycopene and other antioxidants were measured at baseline (2002-2005). The participants were classified into tertiles according to consumption of pizza and pasta food groups.

RESULTS

Lycopene dietary intake and plasma lycopene concentrations were significantly higher in the highest (tertile 3) compared to tertiles 1 and 2 (both P < 0.01). Total energy intake ranged from 1964.3 ± 117.1 kcal/day (tertile 1) to 3277.7 ± 115.8 kcal/day (tertile 3) (P<0.0001). After adjusting for age and energy intake, total dietary fat, saturated fatty acids, trans-fatty acids, and sodium intakes were significantly higher in tertile 3 than tertiles 2 and 1 (all P <0.01). Vitamin C intake was significantly lower in tertile 3 than tertiles 1 and 2 (P = 0.003). Except for γ-tocopherol being higher in tertile 3 than tertiles 1 and 2 (P = 0.015), the plasma concentrations of antioxidants were lower in tertile 3 than tertiles 1 and 2 (β-carotene, α-carotene, lutein and zeaxanthin, all P<0.05).

CONCLUSIONS

In the SCCS population, pizza and pasta were the main sources of dietary lycopene and their intake was associated with plasma lycopene concentration. Diets with frequent pizza and pasta consumption were high in energy, saturated fatty acids, trans-fatty acids, sodium and low in other antioxidants. Future studies of lycopene as a protective dietary factor against chronic disease should consider the overall nutritional quality of lycopene-containing foods.

The Geographic Distribution of Genetic Risk as Compared to Social Risk for Chronic Diseases in the United States

There is an association between chronic disease and geography, and there is evidence that the environment plays a critical role in this relationship. Yet at the same time, there is known to be substantial geographic variation by ancestry across the United States. Resulting geographic genetic variation-that is, the extent to which single nucleotide polymorphisms (SNPs) related to chronic disease vary spatially-could thus drive some part of the association between geography and disease. We describe the variation in chronic disease genetic risk by state of birth by taking risk SNPs from genome-wide association study meta-analyses for coronary artery disease, diabetes, and ischemic stroke and creating polygenic risk scores. We compare the amount of variability across state of birth in these polygenic scores to the variability in parental education, own education, earnings, and wealth. Our primary finding is that the polygenic risk scores are only weakly differentially distributed across U.S. states. The magnitude of the differences in geographic distribution is very small in comparison to the distribution of social and economic factors and thus is not likely sufficient to have a meaningful effect on geographic disease differences by U.S. state.

Environmental Predictors of US County Mortality Patterns on a National Basis

A growing body of evidence has found that mortality rates are positively correlated with social inequalities, air pollution, elevated ambient temperature, availability of medical care and other factors. This study develops a model to predict the mortality rates for different diseases by county across the US. The model is applied to predict changes in mortality caused by changing environmental factors. A total of 3,110 counties in the US, excluding Alaska and Hawaii, were studied. A subset of 519 counties from the 3,110 counties was chosen by using systematic random sampling and these samples were used to validate the model. Step-wise and linear regression analyses were used to estimate the ability of environmental pollutants, socio-economic factors and other factors to explain variations in county-specific mortality rates for cardiovascular diseases, cancers, chronic obstructive pulmonary disease (COPD), all causes combined and lifespan across five population density groups. The estimated models fit adequately for all mortality outcomes for all population density groups and, adequately predicted risks for the 519 validation counties. This study suggests that, at local county levels, average ozone (0.07 ppm) is the most important environmental predictor of mortality. The analysis also illustrates the complex inter-relationships of multiple factors that influence mortality and lifespan, and suggests the need for a better understanding of the pathways through which these factors, mortality, and lifespan are related at the community level.

Future trends and inequalities in premature coronary deaths in England: Modelling study

BACKGROUND

Coronary heart disease (CHD) is a major cause of premature mortality, particularly in deprived groups. Might recent declines in overall mortality obscure different rates of decline among social strata, creating potentially misleading views on inequalities?

METHODS

We used a Bayesian analysis of an age-period-cohort model for the English population. We projected age-specific premature CHD mortality (ages 35-74) by gender and area-based deprivation status for the period 2007-2035, using 1982-2006 as the input. Deprivation status was measured by Index of Multiple Deprivation quintiles, which aggregate seven types of deprivation, including health and income. We analysed inequality in premature CHD mortality. We investigated the annual changes in inequality and the contributions of changes in each IMDQ to the overall annual changes, using both absolute (probability) and relative (logit) scales. We quantified inequality using the statistical variance in the probability of premature death among deprivation quintiles.

RESULTS

The overall premature CHD mortality trends conceal marked heterogeneities. Our models predict more rapid declines in premature CHD mortality for the most affluent quintiles than for the most deprived (annualized rate of decline 2006-2025, 7.5% [95% Credible Interval 4.3-10.5%] versus 5.4% [2.2-8.7%] for men, and 6.3% [3.0-9.9%] versus 5.9% [1.5-10.8%] for women). For men, the posterior probability that the rate of decline is greater for the most affluent was 82%. Variance in premature CHD mortality across deprivation quintiles was projected to decrease by approximately 81% [28-95%] among men and by 89% [30-99%] among women. This decrease was particularly driven by the most deprived groups due to their higher premature death rates. However, relative inequality was projected to rise by 93% among men [81-125%] and rise by 13% [-25-58%] among women. These increases are also mostly influenced by the most deprived, reflecting their slower declines in premature deaths.

CONCLUSIONS

Overall, premature coronary death rates in England continue to decline steeply. Absolute inequalities are decreasing, reflecting declines in the high premature mortality in deprived groups. However, relative inequalities are projected to widen further, reflecting slower mortality declines in the most deprived groups. More aggressive and progressive prevention policies are urgently needed.

Paths to health equity: Local area variation in progress toward eliminating breast cancer mortality disparities, 1990-2009

BACKGROUND

US breast cancer deaths have been declining since 1989, but African American women are still more likely than white women to die of breast cancer. Black/white disparities in breast cancer mortality rate ratios have actually been increasing.

METHODS

Across 762 US counties with enough deaths to generate reliable rates, county-level, age-adjusted breast cancer mortality rates were examined for women who were 35 to 74 years old during the period of 1989-2010. Twenty-two years of mortality data generated twenty 3-year rolling average data points, each centered on a specific year from 1990 to 2009. Mixed linear models were used to group each county into 1 of 4 mutually exclusive trend patterns. The most recent 3-year average black breast cancer mortality rate for each county was also categorized as being worse or not worse than the breast cancer mortality rate for the total US population.

RESULTS

More than half of the counties (54%) showed persistent, unchanging disparities. Roughly 1 in 4 (24%) had a divergent pattern of worsening black/white disparities. However, 10.5% of the counties sustained racial equality over the 20-year period, and 11.7% of the counties actually showed a converging pattern from high disparities to greater equality. Twenty-three counties had 2008-2010 black mortality rates better than the US average mortality rate.

CONCLUSIONS

Disparities are not inevitable. Four US counties have sustained both optimal and equitable black outcomes as measured by both absolute (better than the US average) and relative benchmarks (equality in the local black/white rate ratio) for decades, and 6 counties have shown a path from disparities to health equity.

The Association of Geographic Coordinates with Mortality in People with Lower and Higher Education and with Mortality Inequalities in Spain

Objective

Geographic patterns in total mortality and in mortality by cause of death are widely known to exist in many countries. However, the geographic pattern of inequalities in mortality within these countries is unknown. This study shows mathematically and graphically the geographic pattern of mortality inequalities by education in Spain.

Methods

Data are from a nation-wide prospective study covering all persons living in Spain's 50 provinces in 2001. Individuals were classified in a cohort of subjects with low education and in another cohort of subjects with high education. Age- and sex-adjusted mortality rate from all causes and from leading causes of death in each cohort and mortality rate ratios in the low versus high education cohort were estimated by geographic coordinates and province.

Results

Latitude but not longitude was related to mortality. In subjects with low education, latitude had a U-shaped relation to mortality. In those with high education, mortality from all causes, and from cardiovascular, respiratory and digestive diseases decreased with increasing latitude, whereas cancer mortality increased. The mortality-rate ratio for all-cause death was 1.27 in the southern latitudes, 1.14 in the intermediate latitudes, and 1.20 in the northern latitudes. The mortality rate ratios for the leading causes of death were also higher in the lower and upper latitudes than in the intermediate latitudes. The geographic pattern of the mortality rate ratios is similar to that of the mortality rate in the low-education cohort: the highest magnitude is observed in the southern provinces, intermediate magnitudes in the provinces of the north and those of the Mediterranean east coast, and the lowest magnitude in the central provinces and those in the south of the Western Pyrenees.

Conclusion

Mortality inequalities by education in Spain are higher in the south and north of the country and lower in the large region making up the central plateau. This geographic pattern is similar to that observed in mortality in the low-education cohort.

Early-Life State-of-Residence Characteristics and Later Life Hypertension, Diabetes, and Ischemic Heart Disease

OBJECTIVES

We examined how state characteristics in early life are associated with individual chronic disease later in life.

METHODS

We assessed early-life state of residence using the first 3 digits of social security numbers from blue- and white-collar workers from a US manufacturing company. Longitudinal data were available from 1997 to 2012, with 305 936 person-years of observation. Disease was assessed using medical claims. We modeled associations using pooled logistic regression with inverse probability of censoring weights.

RESULTS

We found small but statistically significant associations between early-state-of-residence characteristics and later life hypertension, diabetes, and ischemic heart disease. The most consistent associations were with income inequality, percentage non-White, and education. These associations were similar after statistically controlling for individual socioeconomic and demographic characteristics and current state characteristics.

CONCLUSIONS

Characteristics of the state in which an individual lives early in life are associated with prevalence of chronic disease later in life, with a strength of association equivalent to genetic associations found for these same health outcomes.