Objective. —To identify and quantify the major external (nongenetic) factors that contribute to death in the United States. Data Sources. —Articles published between 1977 and 1993 were identified through MEDLINE searches, reference citations, and expert consultation. Government reports and compilations of vital statistics and surveillance data were also obtained. Study Selection. —Sources selected were those that were often cited and those that indicated a quantitative assessment of the relative contributions of various factors to mortality and morbidity. Data Extraction. —Data used were those for which specific methodological assumptions were stated. A table quantifying the contributions of leading factors was constructed using actual counts, generally accepted estimates, and calculated estimates that were developed by summing various individual estimates and correcting to avoid double counting. For the factors of greatest complexity and uncertainty (diet and activity patterns and toxic agents), a conservative approach was taken by choosing the lower boundaries of the various estimates. Data Synthesis. —The most prominent contributors to mortality in the United States in 1990 were tobacco (an estimated 400000 deaths), diet and activity patterns (300 000), alcohol (100 000), microbial agents (90 000), toxic agents (60 000), firearms (35 000), sexual behavior (30 000), motor vehicles (25 000), and illicit use of drugs (20 000). Socioeconomic status and access to medical care are also important contributors, but difficult to quantify independent of the other factors cited. Because the studies reviewed used different approaches to derive estimates, the stated numbers should be viewed as first approximations. Conclusions. —Approximately half of all deaths that occurred in 1990 could be attributed to the factors identified. Although no attempt was made to further quantify the impact of these factors on morbidity and quality of life, the public health burden they impose is considerable and offers guidance for shaping health policy priorities. (JAMA. 1993;270:2207-2212)

https://jamanetwork.com/journals/JAMA/articlepdf/409171/jama_270_18_038.pdf

Actual causes of death in the United States.

Problems with measuring socioeconomic status (SES)—frequently included in clinical and public health studies as a control variable and less frequently as the variable(s) of main interest—could affect research findings and conclusions, with implications for practice and policy.Wecritically examine standard SES measurement approaches, illustrating problems with examples from new analyses and the literature. For example, marked racial/ethnic differences in income at a given educational level and in wealth at a given income level raise questions about the socioeconomic comparability of individuals who are similar on education or income alone. Evidence also shows that conclusions about nonsocioeconomic causes of racial/ethnic differences in health may depend on the measure—eg, income, wealth, education, occupation, neighborhood socioeconomic characteristics, or past socioeconomic experiences—used to “control for SES,” suggesting that findings from studies that have measured limited aspects of SES should be reassessed. We recommend an outcome- and social group–specific approach to SES measurement that involves (1) considering plausible explanatory pathways and mechanisms, (2) measuring as much relevant socioeconomic information as possible, (3) specifying the particular socioeconomic factors measured (rather than SES overall), and (4) systematically considering how potentially important unmeasured socioeconomic factors may affect conclusions. Better SES measures are needed in data sources, but improvements could be made by using existing information more thoughtfully and acknowledging its limitations.

https://jamanetwork.com/journals/jama/articlepdf/202015/jsc50021.pdf

Socioeconomic status in health research: one size does not fit all.

Socioeconomic status (SES) underlies three major determinants of health: health care, environmental exposure, and health behavior. In addition, chronic stress associated with lower SES may also increase morbidity and mortality. Reducing SES disparities in health will require policy initiatives addressing the components of socioeconomic status (income, education, and occupation) as well as the pathways by which these affect health. Lessons for U.S. policy approaches are taken from the Acheson Commission in England, which was charged with reducing health disparities in that country.

Socioeconomic Disparities In Health: Pathways And Policies

University of Illinois, Urbana The positive association between education and health is well established, but explanations for this association are not. Our explanations fall into three categories: (1) work and economic conditions, (2) social-psychological resources, and (3) health lifestyle. We replicate analyses with two samples, cross-sectionally and over time, using two health measures (self-reported health and physical functioning). The first data set comes from a national probability sample of U.S. households in which respondents were interviewed by telephone in 1990 (2,031 respondents, ages 18 to 90). The second data set comes from a national probability sample of U.S. households in which respondents ages 20 to 64 were interviewed by telephone first in 1979 (3,025 respondents), and then again in 1980 (2,436 respondents). Results demonstrate a positive association between education and health and help explain why the association exists. (1) Compared to the poorly educated, well educated respondents are less likely to be unemployed, are more likely to work full-time, to have fulfilling, subjectively rewarding jobs, high incomes, and low economic hardship. Full-time work, fulfilling work, high income, and low economic hardship in turn significantly improve health in all analyses. (2) The well educated report a greater sense of control over their lives and their health, and they have higher levels of social support. The sense of control, and to a lesser extent support, are associated with good health. (3) The well educated are less likely to smoke, are more likely to exercise, to get health check-ups, and to drink moderately, all of which, except check-ups, are associated with good health. We conclude that high educational attainment improves health directly, and it improves health indirectly through work and economic conditions, social-psychological resources, and health lifestyle. he positive association between education and health is well established, but explanations for this association are not. Well educated people experience better health than the poorly educated, as indicated by high levels of self-reported health and physical functioning and low levels of morbidity, mortality, and disability. In contrast, low educational attainment is associated with high rates of infectious disease, many chronic noninfectious diseases, self-reported poor health, shorter survival when sick, and shorter life expectancy (Feldman, Makuc, Kleinman, and Cornoni-Huntley 1989; Guralnik, Land, Fillenbaum, and Branch 1993; Gutzwiller, LaVecchia, Levi, Negri, and Wietlisbach 1989; Kaplan, Haan, and Syme 1987; Kitagawa and Hauser 1973; Liu, Cedres, and Stamler 1982; Morris 1990; Pappas, Queen,

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The links between education and health.

CONTEXT. Environmental factors are suggested to play a major role in physical activity (PA) and other obesity-related behaviors, yet there is no national research on the relationship between disparity in access to recreational facilities and additional impact on PA and overweight patterns in US adolescents. 

OBJECTIVE. In a nationally representative cohort, we sought to assess the geographic and social distribution of PA facilities and how disparity in access might underlie population-level PA and overweight patterns. 

DESIGN, SETTING, AND PARTICIPANTS. Residential locations of US adolescents in wave I (1994–1995) of the National Longitudinal Study of Adolescent Health (N = 20745) were geocoded, and a 8.05-km buffer around each residence was drawn (N = 42857 census-block groups [19% of US block groups]). PA facilities, measured by national databases and satellite data, were linked with Geographic Information Systems technology to each respondent. Logistic-regression analyses tested the relationship of PA-related facilities with block-group socioeconomic status (SES) (at the community level) and the subsequent association of facilities with overweight and PA (at the individual level), controlling for population density. 

MAIN OUTCOME MEASURES. Outcome measures were overweight (BMI ≥ 95th percentile of the Centers for Disease Control and Prevention/National Center for Health Statistics growth curves) and achievement of ≥5 bouts per week of moderate-vigorous PA. 

RESULTS. Higher-SES block groups had a significantly greater relative odds of having 1 or more facilities. Low-SES and high-minority block groups were less likely to have facilities. Relative to zero facilities per block group, an increasing number of facilities was associated with decreased overweight and increased relative odds of achieving ≥5 bouts per week of moderate-vigorous PA. 

CONCLUSIONS. Lower-SES and high-minority block groups had reduced access to facilities, which in turn was associated with decreased PA and increased overweight. Inequality in availability of PA facilities may contribute to ethnic and SES disparities in PA and overweight patterns.

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Erica Frank

Papers

Improvements in cholesterol-related knowledge and behavior and plasma cholesterol levels in youths during the 1980s.

Multidisciplinary findings on socioeconomic status and health. author's reply

Winkleby and Colleagues Respond