Inconsistency in Self-Reporting the Use of Substances over Time

Broman, Michael J.; Bista, Shikha; Broman, Clifford L.

doi:10.1080/10826084.2022.2083168

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…For example, response rates are hard to control, and the data produced can lack many important details about the topic under investigation (Kelley et al, 2003). When used longitudinally (e.g., cohort studies), survey research suffers from other limitations due to the potential for significant changes in individual's responsiveness over time, as well as in the consistency of respondents in their replies to public health-relevant questions when asked again at a later time (ethnic origin: Johnson 1974; patterns of abuse: Abramsky et al, 2022;Loxton et al, 2019;smoking: Kaestle, 2015; substance use: Broman et al, 2022;suicidal attempts: Hart et al, 2013). In general, there may be systematic differences in the ability of individuals to self-evaluate their own health across time (Black et al, 2017;Vuolo et al, 2014).…”

Section: Challenge 3: Recognizing the Limitations Of Survey Datamentioning

confidence: 99%

Advancing methods for the biodemography of aging within social contexts

Hernández‐Pacheco¹,

Steiner²,

Rosati³

et al. 2023

Preprint

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Several social traits including status, integration, early-life adversity, and their interactions across the life course can predict health, reproduction, and mortality in humans. Accordingly, individual sociality plays a fundamental role in the emergence of phenotypes driving the evolution of aging. Recent work placing human social gradients on a biological continuum with other species provides a useful evolutionary context for aging questions, but there is still a need for a unified evolutionary framework for sociality, health, and aging. Here, we first summarize current challenges to disentangle the effects of the social environment on human life courses. Next, we review recent advances in comparative biodemography and propose a biodemographic perspective to address socially driven health phenotype distributions and their evolutionary consequences using a nonhuman primate population. This new comparative approach uses evolutionary demography to address the joint dynamics of populations, sociality, phenotypes, and life history parameters. The long-term goal is to advance our understanding of the link between individual sociality, population-level outcomes, and the evolution of aging.

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