Accounting for Biases in Survey-Based Estimates of Population Attributable Fractions

Background: This paper discusses best practices for estimating fractions of mortality attributable to health exposures in survey data that are biased by observed confounders and unobserved endogenous selection. Extant research has shown that estimates of population attributable fractions (PAF) from the formula using the proportion of deceased that is exposed (PAFpd) can attend to confounders, whereas the formula using the proportion of the entire sample exposed (PAFpe) is biased by confounders. Research has not explored how PAFpd and PAFpe equations perform when both confounding and selection bias are present. Methods: We review equations for calculating PAF based on either the proportion of deceased (pd) or the proportion of the entire sample (pe) that receives the exposure. We explore how estimates from each equation are affected by confounding bias and selection bias using hypothetical data and real-world survey data from the National Health Interview Survey–Linked Mortality Files, 1987–2011. We examine the association between cigarette smoking and all-cause mortality risk in the US adult population as an example. Results: We show that both PAFpd and PAFpe calculate the true PAF in the presence of confounding bias if one uses the “weighted-sum” approach. We further show that both the PAFpd and PAFpe calculate biased PAFs in the presence of collider bias, but that the bias is more severe in the PAFpd formula. Conclusion: We recommend that researchers use the PAFpe formula with the weighted-sum approach when estimates of the exposure-outcome relationship are biased by endogenous selection

Contributors to Wisconsin’s Persistent Black-White Gap in Life Expectancy

Background Although the black-white gap in life expectancy has narrowed in the U.S., there is considerable variability across states. In Wisconsin, the black-white gap exceeds 6 years, well above the national average. Reducing this disparity is an urgent public health priority, but there is limited understanding of what contributes to Wisconsin’s racial gap in longevity. Our investigation identifies causes of death that contribute most to Wisconsin’s black-white gap in life expectancy among males and females, and highlights specific ages where each cause of death contributes most to the gap. Methods Our study employs 1999–2016 restricted-use mortality data provided by the National Center for Health Statistics. After generating race- and sex-specific life tables for each 3-year period of observation (e.g., 1999–2001), we trace recent trends in the black-white life expectancy gap in Wisconsin. We subsequently conduct a series of analyses to decompose the black-white gap in three time periods into 13 separate causes and 19 different age groups. Results In 2014–16, Wisconsin’s black-white gap in life expectancy was 7.34 years for males (67% larger than the national gap), and 5.61 years for females (115% larger than the national gap). Among males, homicide was the single largest contributor, accounting for 1.56 years of the total gap. Heart disease and cancer followed, contributing 1.43 and 1.42 years, respectively. Among females, heart disease and cancer were the two leading contributors to the gap, accounting for 1.12 and 1.00 years, respectively. Whereas homicide contributed most to the racial gap in male longevity during late adolescence and early adulthood, heart disease and cancer exerted most of their influence between ages 50–70 for both males and females. Other notable contributors were unintentional injuries (males), diabetes and cerebrovascular disease (females), and perinatal conditions (males and females). Conclusions Our study identifies targets for future policy interventions that could substantially reduce Wisconsin’s racial gap in life expectancy. Concerted efforts to eliminate racial disparities in perinatal mortality and homicide early in the life course, and chronic conditions such as cancer and heart disease in later life, promise to help Wisconsin achieve the public health objective of racial parity in longevity

Contributors to Reduced Life Expectancy Among Native Americans in the Four Corners States

To assess trends in life expectancy and the contribution of specific causes of death to Native American-White longevity gaps in the Four Corners states, we used death records from the National Center for Health Statistics and population estimates from the U.S. Census Bureau from 1999–2017 to generate period life tables and decompose racial gaps in life expectancy. Native American-White life expectancy gaps narrowed between 2001 and 2012 but widened thereafter, reaching 4.92 years among males and 2.06 years among females in 2015. The life expectancy disadvantage among Native American males was primarily attributable to motor vehicle accidents (0.96 years), liver disease (1.22 years), and diabetes (0.78 years). These causes of deaths were also primary contributors to the gap among females, forming three successive waves of mortality that occurred in young adulthood, midlife, and late adulthood, respectively, among Native American males and females. Interventions to reduce motor vehicle accidents in early adulthood, alcohol-related mortality in midlife, and diabetes complications at older ages could reduce Native American-White longevity disparities in the Four Corners states

Should age-period-cohort studies return to the methodologies of the 1970s?

Social scientists have recognized the importance of age-period-cohort (APC) models for half a century, but have spent much of this time mired in debates about the feasibility of APC methods. Recently, a new class of APC methods based on modern statistical knowledge has emerged, offering potential solutions. In 2009, Reither, Hauser and Yang used one of these new methods – hierarchical APC (HAPC) modeling – to study how birth cohorts may have contributed to the U.S. obesity epidemic. They found that recent birth cohorts experience higher odds of obesity than their predecessors, but that ubiquitous period-based changes are primarily responsible for the rising prevalence of obesity. Although these findings have been replicated elsewhere, recent commentaries by Bell and Jones call them into question – along with the new class of APC methods. Specifically, Bell and Jones claim that new APC methods do not adequately address model identification and suggest that “solid theory” is often sufficient to remove one of the three temporal dimensions from empirical consideration. They also present a series of simulation models that purportedly show how the HAPC models estimated by Reither et al. (2009) could have produced misleading results. However, these simulation models rest on assumptions that there were no period effects, and associations between period and cohort variables and the outcome were perfectly linear. Those are conditions under which APC models should never be used. Under more tenable assumptions, our own simulations show that HAPC methods perform well, both in recovering the main findings presented by Reither et al. (2009) and the results reported by Bell and Jones. We also respond to critiques about model identification and theoretically-imposed constraints, finding little pragmatic support for such arguments. We conclude by encouraging social scientists to move beyond the debates of the 1970s and toward a deeper appreciation for modern APC methodologies

The Impact of Obesity on US Mortality Levels: The Importance of Age and Cohort Factors in Population Estimates

To estimate the percentage of excess death for US Black and White men and women associated with high body mass, we examined the combined effects of age variation in the obesity–mortality relationship and cohort variation in age-specific obesity prevalence

A procedure to correct proxy-reported weight in the National Health Interview Survey, 1976–2002

<p>Abstract</p> <p>Background</p> <p>Data from the National Health Interview Survey (NHIS) show a larger-than-expected increase in mean BMI between 1996 and 1997. Proxy-reports of height and weight were discontinued as part of the 1997 NHIS redesign, suggesting that the sharp increase between 1996 and 1997 may be artifactual.</p> <p>Methods</p> <p>We merged NHIS data from 1976–2002 into a single database consisting of approximately 1.7 million adults aged 18 and over. The analysis consisted of two parts: First, we estimated the magnitude of BMI differences by reporting status (i.e., self-reported versus proxy-reported height and weight). Second, we developed a procedure to correct biases in BMI introduced by reporting status.</p> <p>Results</p> <p>Our analyses confirmed that proxy-reports of weight tended to be biased downward, with the degree of bias varying by race, sex, and other characteristics. We developed a correction procedure to minimize BMI underestimation associated with proxy-reporting, substantially reducing the larger-than-expected increase found in NHIS data between 1996 and 1997.</p> <p>Conclusion</p> <p>It is imperative that researchers who use reported estimates of height and weight think carefully about flaws in their data and how existing correction procedures might fail to account for them. The development of this particular correction procedure represents an important step toward improving the quality of BMI estimates in a widely used source of epidemiologic data.</p

Clarifying hierarchical age–period–cohort models: A rejoinder to Bell and Jones

Previously, Reither et al. (2015) demonstrated that hierarchical age–period–cohort (HAPC) models perform well when basic assumptions are satisfied. To contest this finding, Bell and Jones (2015) invent a data generating process (DGP) that borrows age, period and cohort effects from different equations in Reither et al. (2015). When HAPC models applied to data simulated from this DGP fail to recover the patterning of APC effects, B&J reiterate their view that these models provide “misleading evidence dressed up as science.” Despite such strong words, B&J show no curiosity about their own simulated data—and therefore once again misapply HAPC models to data that violate important assumptions. In this response, we illustrate how a careful analyst could have used simple descriptive plots and model selection statistics to verify that (a) period effects are not present in these data, and (b) age and cohort effects are conflated. By accounting for the characteristics of B&J's artificial data structure, we successfully recover the “true” DGP through an appropriately specified model. We conclude that B&Js main contribution to science is to remind analysts that APC models will fail in the presence of exact algebraic effects (i.e., effects with no random/stochastic components), and when collinear temporal dimensions are included without taking special care in the modeling process. The expanded list of coauthors on this commentary represents an emerging consensus among APC scholars that B&J's essential strategy—testing HAPC models with data simulated from contrived DGPs that violate important assumptions—is not a productive way to advance the discussion about innovative APC methods in epidemiology and the social sciences

Allostatic Load

The Encyclopedia of the Life Course and Human Development examines three key life stages from a sociological perspective, exploring how enduring experiences, as well as transitions and events such as childcare, education, stress, marriage, career, addiction, friendship, parenthood, disease, spirituality, and retirement influence the individual?s life course. The nearly 400 entries in this three-volume set are organized by life stage: Childhood and Adolescence; Adulthood; and Later Life. Included in each is an overview essay that features a detailed discussion of that stage of human development, followed by signed entries that apply sociological as well as economic, biological, psychological, and educational perspectives to a range of topics. Also covered are sociological theories and their significance to life course study; the impact of social and government policies; and racial, gender, and geographic patterns of many life course phenomena. Entries are heavily illustrated with photos, graphs, charts, and tables. Also included is coverage of research methods and key data sources, which enhance and reinforce the topical entries, as well as a glossary, thematic outline, annotated bibliography, and cumulative index

Availability, entitlements and conflict : elements of an integrated explanation of world hunger

Includes bibliographical references (pages [70]-74)M.A. (Master of Arts

Educational Status and HIV Disparities in Cameroon: Are Uneducated Women at Reduced Risk of HIV Infection?

The socioeconomic gradient in health and mortality is a persistent finding in social epidemiology. Indicators of socioeconomic status (SES) such as wealth and education are routinely found to be strongly and inversely related to various health outcomes. However, data from the 2004 Cameroon Demographic and Health Survey (DHS) show that educational status is positively associated with HIV prevalence, particularly among women. In this investigation, we analyzed data from 5,287 women in the 2004 Cameroon DHS to explore possible demographic, socioeconomic and behavioral mechanisms that could account for this association. After controlling for variables such as age, marital status, region of residence, and partner’s educational attainment, the association between education and HIV was not merely attenuated, but essentially eliminated. This research contributes to a growing body of literature on SES and HIV in sub-Saharan Africa, which has the potential to improve our collective understanding and refine current social policies