Estimating standard errors for life expectancies based on complex survey data with mortality follow-up: A case study using the National Health Interview Survey Linked Mortality Files.

Life expectancy is an important measure for health research and policymaking. Linking individual survey records to mortality data can overcome limitations in vital statistics data used to examine differential mortality by permitting the construction of death rates based on information collected from respondents at the time of interview and facilitating estimation of life expectancies for subgroups of interest. However, use of complex survey data linked to mortality data can complicate the estimation of standard errors. This paper presents a case study of approaches to variance estimation for life expectancies based on life tables, using the National Health Interview Survey Linked Mortality Files. The approaches considered include application of Chiang's traditional method, which is straightforward but does not account for the complex design features of the data; balanced repeated replication (BRR), which is more complicated but accounts more fully for the design features; and compromise, 'hybrid' approaches, which can be less difficult to implement than BRR but still account partially for the design features. Two tentative conclusions are drawn. First, it is important to account for the effects of the complex sample design, at least within life-table age intervals. Second, accounting for the effects within age intervals but not across age intervals, as is done by the hybrid methods, can yield reasonably accurate estimates of standard errors, especially for subgroups of interest with more homogeneous characteristics among their members.

The economic value of improving the health of disadvantaged Americans.

BACKGROUND: Higher educational attainment is associated with better health status and longer life. PURPOSE: This analysis estimates the annual dollar value of the benefits that would accrue to less-educated American adults if they experienced the lower mortality rates and better health of those with a college education. METHODS: Using estimates of differences in mortality among adults aged ≥ 25 years by educational attainment from the National Longitudinal Mortality Survey and of education-based differentials in health status from published studies based on the Medical Expenditure Panel Survey, combined with existing estimates of the economic value of a healthy life year, the economic value of raising the health of individuals with less than a college education to the health of the college educated is estimated. RESULTS: The annual economic value that would accrue to disadvantaged (less-educated) Americans if their health and longevity improved to that of college-educated Americans is $1.02 trillion. CONCLUSIONS: This modeling exercise does not fully account for the social costs and benefits of particular policies and programs to reduce health disparities; rather, it provides a sense of the magnitude of the economic value lost in health disparities to compare with other social issues vying for attention. The aggregate economic gains from interventions that improve the health of disadvantaged Americans are potentially large.

Comparing relative effects of education and economic resources on infant mortality in developing countries.

Research on the social determinants of health has often considered education and economic resources as separate indicators of socioeconomic status. From a policy perspective, however, it is important to understand the relative strength of the effect of these social factors on health outcomes, particularly in developing countries. It is also important to examine not only the impact of education and economic resources of individuals, but also whether community and country levels of these factors affect health outcomes. This analysis uses multilevel regression models to assess the relative effects of education and economic resources on infant mortality at the family, community, and country level using data from demographic and Health Surveys in 43 low-and lower-middle-income countries. We find strong effects for both per capita gross national income and completed secondary education at the country level, but a greater impact of education within families and communities.

The predicted effects of chronic obesity in middle age on medicare costs and mortality.

BACKGROUND: The prevalence of adult obesity has increased in recent decades. It is important to predict the long-term effect of body weight, and changes in body weight, in middle age on longevity and Medicare costs in older ages. METHODS: The relationships between individuals' characteristics in middle age and subsequent Medicare costs and mortality were estimated from the linkage of the National Health and Nutrition Examination Survey I Epidemiologic Follow-up Study to Medicare administrative records (1991-2000) and mortality information (1971-2000). We predicted longevity and lifetime Medicare costs via simulation for 45-year-old persons by body weight in 1973 and changes in body weight between 1973 and 1983. RESULTS: Obese 45-year-olds had a smaller chance of surviving to age 65 and, if they did, incurred significantly higher average lifetime Medicare costs than normal-weight 45-year-olds ($163,000 compared with $117,000). Those who remained obese between ages 45 and 55 in 1973 to 1983 incurred significantly higher lifetime Medicare costs than those who maintained normal weight. Other weight change categories did not differ significantly from those who maintained normal weight in terms of life expectancy at age 65, but overweight and obese people who lost weight had less chance of surviving to age 65 and the lowest estimated life expectancies thereafter. CONCLUSIONS: Chronic obesity in middle age increases lifetime Medicare costs relative to those who remained normal weight. As the survival of obese persons improves, it is possible that Medicare costs may rise substantially in the future to meet the health care needs of today's obese middle-aged population. Thus, active engagement by both the private and public sectors to prevent and to reduce obesity are critically needed.

Socioeconomic disparities in health in the United States: what the patterns tell us.

OBJECTIVES: We aimed to describe socioeconomic disparities in the United States across multiple health indicators and socioeconomic groups. METHODS: Using recent national data on 5 child (infant mortality, health status, activity limitation, healthy eating, sedentary adolescents) and 6 adult (life expectancy, health status, activity limitation, heart disease, diabetes, obesity) health indicators, we examined indicator rates across multiple income or education categories, overall and within racial/ethnic groups. RESULTS: Those with the lowest income and who were least educated were consistently least healthy, but for most indicators, even groups with intermediate income and education levels were less healthy than the wealthiest and most educated. Gradient patterns were seen often among non-Hispanic Blacks and Whites but less consistently among Hispanics. CONCLUSIONS: Health in the United States is often, though not invariably, patterned strongly along both socioeconomic and racial/ethnic lines, suggesting links between hierarchies of social advantage and health. Worse health among the most socially disadvantaged argues for policies prioritizing those groups, but pervasive gradient patterns also indicate a need to address a wider socioeconomic spectrum-which may help garner political support. Routine health reporting should examine socioeconomic and racial/ethnic disparity patterns, jointly and separately.

Causes of death responsible for the widening gap in mortality among educational groups in Austria between 1981 and 1991.

OBJECTIVE: In Austria, educational differentials in all-cause mortality increased in the decade between 1981/82 and 1991/92. The aim of this study was to identify which causes of death contributed most to this increase. METHODS: Census records for the Austrian population for the years 1981 and 1991 were linked with death register records for a follow-up period of one year. Education-related disparities in mortality were measured using regression-based indices to determine both absolute and relative levels of inequality at each timepoint and also the changes over the decade. RESULTS: Among men, increasing education-related disparity in deaths from ischemic heart disease was the major contributor to the change in disparity in overall mortality. Without this increase, mortality disparity would have declined, because modest increases in disparity among deaths from colorectal and lung cancers, and digestive and alcohol-associated diseases, were more than offset by reduction of disparity for cerebrovascular and other circulatory diseases, respiratory diseases and external causes. In women, increasing education-related disparity in deaths from ischemic heart disease also contributed most to the slight increase in absolute inequality in overall mortality, but diabetes and colorectal cancer also contributed significantly. In relative terms, there were striking increases in disparity for deaths from colorectal cancer and digestive diseases among men, and for diabetes deaths among women. CONCLUSION: The increase in mortality disparity for ischemic heart disease among men shows how rapidly social gradients in mortality can change. Public health measures concentrating on reversing increasing disparities would not only reduce the mortality gap between social classes but would have a very positive effect on average health status.

Methodological issues in measuring health disparities

This report discusses issues that affect the measurement of disparities in health between groups in a population, published on National Center for Health Statistics. Vital Health Stat 2(141). 2005. Document on PDF format, Acrobat Reader required.

Methodological issues in measuring health disparities.

OBJECTIVES: This report discusses six issues that affect the measurement of disparities in health between groups in a population: Selecting a reference point from which to measure disparity. Measuring disparity in absolute or in relative terms. Measuring in terms of favorable or adverse events. Measuring in pair-wise or in summary fashion. Choosing whether to weight groups according to group size. Deciding whether to consider any inherent ordering of the groups. These issues represent choices that are made when disparities are measured. METHODS: Examples are used to highlight how these choices affect specific measures of disparity. RESULTS: These choices can affect the size and direction of disparities measured at a point in time and conclusions about the size and direction of changes in disparity over time. Eleven guidelines for measuring disparities are presented. CONCLUSIONS: Choices concerning the measurement of disparity should be made deliberately, recognizing that each choice will affect the results. When results are presented, the choices on which the measurements are based should be described clearly and justified appropriately.

Contextual effect of income inequality on birth outcomes.

BACKGROUND: Though associations between income inequality and birth outcome have been suggested, mechanisms underlying this relationship are not known. In this analysis, we examined the relationship between income inequality and preterm birth (PTB) and post-neonatal mortality (PNM) to explore two potential mechanisms-the proposed psychosocial stress and neo-material pathways. METHODS: Data on singleton births from 1998 to 2000 were obtained from the CDC's National Center for Health Statistics' Linked Birth and Infant Death files. The Gini Index was utilized to measure income inequality and was divided into tertiles representing high, medium, and low county-level inequality. To determine the association between the birth outcomes and county income inequality and to account for clustering within counties, we employed generalized estimating equation (GEE) modelling. RESULTS: PTB increased from 8.3% in counties with low income inequality to 10.0% in counties with high inequality. The Gini Index remained modestly associated with PTB after adjusting for individual level variables and mean county-level per capita income within the total population (AOR: 1.06; 95% CI 1.03-1.09) as well as within most of the racial/ethnic groups. PNM increased from 1.15 deaths per 1000 live births in low inequality counties to 1.32 in high-inequality counties. However, after adjustment, income inequality was only associated with PNM within the non-Hispanic black population (AOR: 1.20; 95% CI 1.03-1.39). CONCLUSIONS: These findings may provide some support for the association between income inequality and PTB. Further research is required to elucidate the biological mechanisms of income inequality.

The importance of place of residence: examining health in rural and nonrural areas.

We examined differences in health measures among rural, suburban, and urban residents and factors that contribute to these differences. Whereas differences between rural and urban residents were observed for some health measures, a consistent rural-to-urban gradient was not always found. Often, the most rural and the most urban areas were found to be disadvantaged compared with suburban areas. If health disparities are to be successfully addressed, the relationship between place of residence and health must be understood.

Estimating deaths attributable to obesity in the United States.

Estimates of deaths attributable to obesity in the United States rely on estimates from epidemiological cohorts of the relative risk of mortality associated with obesity. However, these relative risk estimates are not necessarily appropriate for the total US population, in part because of exclusions to control for baseline health status and exclusion or underrepresentation of older adults. Most deaths occur among older adults; estimates of deaths attributable to obesity can vary widely depending on the assumptions about the relative risks of mortality associated with obesity among the elderly. Thus, it may be difficult to estimate deaths attributable to obesity with adequate accuracy and precision. We urge efforts to improve the data and methods for estimating this statistic.

Achieving national health objectives: the impact on life expectancy and on healthy life expectancy.

Our study quantifies the impact of achieving specific Healthy People 2010 targets and of eliminating racial/ethnic health disparities on summary measures of health. We used life table methods to calculate gains in life expectancy and healthy life expectancy that would result from achievement of Healthy People 2010 objectives or of current mortality rates in the Asian/Pacific Islander (API) population. Attainment of Healthy People 2010 mortality targets would increase life expectancy by 2.8 years, and reduction of population wide mortality rates to current API rates would add 4.1 years. Healthy life expectancy would increase by 5.8 years if Healthy People 2010 mortality and assumed morbidity targets were attained and by 8.1 years if API mortality and activity limitation rates were attained. Achievement of specific Healthy People 2010 targets would produce significant increases in longevity and health, and elimination of racial/ethnic health disparities could result in even larger gains.