Faster Increases in Human Life Expectancy Could Lead to Slower Population Aging

Counterintuitively, faster increases in human life expectancy could lead to slower population aging. The conventional view that faster increases in human life expectancy would lead to faster population aging is based on the assumption that people become old at a fixed chronological age. A preferable alternative is to base measures of aging on people's time left to death, because this is more closely related to the characteristics that are associated with old age. Using this alternative interpretation, we show that faster increases in life expectancy would lead to slower population aging. Among other things, this finding affects the assessment of the speed at which countries will age

Measuring the Speed of Aging across Population Subgroups

People in different subgroups age at different rates. Surveys containing biomarkers can be used to assess these subgroup differences. We illustrate this using hand-grip strength to produce an easily interpretable, physical-based measure that allows us to compare characteristic-based ages across educational subgroups in the United States. Hand-grip strength has been shown to be a good predictor of future mortality and morbidity, and therefore a useful indicator of population aging. Data from the Health and Retirement Survey (HRS) were used. Two education subgroups were distinguished, those with less than a high school diploma and those with more education. Regressions on hand-grip strength were run for each sex and race using age and education, their interactions and other covariates as independent variables. Ages of identical mean hand-grip strength across education groups were compared for people in the age range 60 to 80. The hand-grip strength of 65 year old white males with less education was the equivalent to that of 69.6 (68.2, 70.9) year old white men with more education, indicating that the more educated men had aged more slowly. This is a constant characteristic age, as defined in the Sanderson and Scherbov article "The characteristics approach to the measurement of population aging" published 2013 in Population and Development Review. Sixty-five year old white females with less education had the same average hand-grip strength as 69.4 (68.2, 70.7) year old white women with more education. African-American women at ages 60 and 65 with more education also aged more slowly than their less educated counterparts. African American men with more education aged at about the same rate as those with less education. This paper expands the toolkit of those interested in population aging by showing how survey data can be used to measure the differential extent of aging across subpopulations

Socioeconomic status and central adiposity as determinants of stress-related biological responses relevant to cardiovascular disease risk

Stress-related processes have been implicated in the associations between lower socioeconomic status (SES), central adiposity, and cardiovascular disease risk. This study analysed the impact of SES and central adiposity on cardiovascular, inflammatory and neuroendocrine stress responses, and associations with cytomegalovirus (CMV) infection in a sample of 537 men and women aged 53-76 years (mean 62.89 years). SES was defined by grade of employment (higher, intermediate, and lower categories), and central adiposity was indexed by waist-hip ratio (WHR). Cardiovascular, inflammatory and cortisol responses were monitored during administration of a standardized mental stress testing protocol and salivary cortisol was measured repeatedly over the day. Lower SES was associated with raised systolic and diastolic blood pressure (BP), plasma interleukin (IL-6), fibrinogen, C-reactive protein, and salivary cortisol, and a large WHR accentuated SES differences in fibrinogen, C-reactive protein, and likelihood of CMV seropositivity, independently of general adiposity indexed by body mass index. During mental stress testing, return to resting levels (recovery) following behavioural challenge in systolic and diastolic BP and heart rate was impaired among lower SES participants, particularly those with large WHR. Lower SES participants had greater cortisol concentrations across the day, but this pattern did not vary with WHR. These findings extend the evidence relating lower SES to stress-related biological risk factors for cardiovascular disease, and indicate that central adiposity may augment these effects

Seroprevalence of Epstein-Barr Virus Infection in U.S. Children Ages 6-19, 2003-2010

Background Epstein-Barr virus (EBV) is a common herpesvirus linked to infectious mononucleosis and multiple cancers. There are no national estimates of EBV seroprevalence in the United States. Our objective was to estimate the overall prevalence and sociodemographic predictors of EBV among U.S. children and adolescents aged 6–19. Methods We calculated prevalence estimates and prevalence ratios for EBV seroprevalence using data from the 2003–2010 U.S. National Health and Nutrition Examination Survey (NHANES) for children aged 6–19 (n = 8417). Poisson regression was used to calculate multivariable-adjusted prevalence ratios across subgroup categories (sex, race/ethnicity, parental education, household income, household size, foreign-born, BMI, and household smoking). Findings Overall EBV seroprevalence was 66.5% (95% CI 64.3%–68.7%.). Seroprevalence increased with age, ranging from 54.1% (95% CI 50.2%–57.9%) for 6–8 year olds to 82.9% (95% CI 80.0%–85.9%) for 18–19 year olds. Females had slightly higher seroprevalence (68.9%, 95% CI 66.3%–71.6%) compared to males (64.2%, 95% CI 61.7%–66.8%). Seroprevalence was substantially higher for Mexican-Americans (85.4%, 95% CI 83.1%–87.8%) and Non-Hispanic Blacks (83.1%, 95% CI 81.1%–85.1%) than Non-Hispanic Whites (56.9%, 95% CI 54.1%–59.8%). Large differences were also seen by family income, with children in the lowest income quartile having 81.0% (95% CI 77.6%–84.5%) seroprevalence compared to 53.9% (95% CI 50.5%–57.3%) in the highest income quartile, with similar results for parental education level. These results were not explained by household size, BMI, or parental smoking. Among those who were seropositive, EBV antibody titers were significantly higher for females, Non-Hispanic Blacks and Mexican-Americans, with no association found for socioeconomic factors. Conclusions In the first nationally representative U.S. estimates, we found substantial socioeconomic and race/ethnic differences in the seroprevalence of EBV across all ages for U.S. children and adolescents. These estimates can help researchers and clinicians identify groups most at risk, inform research on EBV-cancer etiology, and motivate potential vaccine development

Demographic science aids in understanding the spread and fatality rates of COVID-19

Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the coronavirus disease 2019 (COVID-19) pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly, how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider the age composition of local and national contexts as well as intergenerational interactions. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs

The Impact of Bisphenol A and Triclosan on Immune Parameters in the U.S. Population, NHANES 2003–2006

Background: Exposure to environmental toxicants is associated with numerous disease outcomes, many of which involve underlying immune and inflammatory dysfunction. Objectives: To address the gap between environmental exposures and immune dysfunction, we investigated the association of two endocrine-disrupting compounds (EDCs) with markers of immune function. Methods: Using data from the 2003–2006 National Health and Nutrition Examination Survey, we compared urinary bisphenol A (BPA) and triclosan levels with serum cytomegalovirus (CMV) antibody levels and diagnosis of allergies or hay fever in U.S. adults and children ≥ 6 years of age. We used multivariate ordinary least squares linear regression models to examine the association of BPA and triclosan with CMV antibody titers, and multivariate logistic regression models to investigate the association of these chemicals with allergy or hay fever diagnosis. Statistical models were stratified by age (\u3c 18 years and ≥ 18 years). Results: In analyses adjusted for age, sex, race, body mass index, creatinine levels, family income, and educational attainment, in the ≥ 18-year age group, higher urinary BPA levels were associated with higher CMV antibody titers (p \u3c 0.001). In the \u3c 18-year age group, lower levels of BPA were associated with higher CMV antibody titers (p \u3c 0.05). However, triclosan, but not BPA, showed a positive association with allergy or hay fever diagnosis. In the \u3c 18-year age group, higher levels of triclosan were associated with greater odds of having been diagnosed with allergies or hay fever (p \u3c 0.01). Conclusions: EDCs such as BPA and triclosan may negatively affect human immune function as measured by CMV antibody levels and allergy or hay fever diagnosis, respectively, with differential consequences based on age. Additional studies should be done to investigate these findings

What Were the Information Voids? A Qualitative Analysis of Questions Asked by Dear Pandemic Readers between August 2020-August 2021

In the current infodemic, how individuals receive information (channel), who it is coming from (source), and how it is framed can have an important effect on COVID-19 related mitigation behaviors. In light of these challenges presented by the infodemic, Dear Pandemic (DP) was created to directly address persistent questions related to COVID-19 and other health topics in the online environment. This is a qualitative analysis of 3806 questions that were submitted by DP readers to a question box on the Dear Pandemic website between August 30, 2020 and August 29, 2021. Analyses resulted in four themes: the need for clarification of other sources; lack of trust in information; recognition of possible misinformation; and questions on personal decision-making. Each theme reflects an unmet informational need of Dear Pandemic readers, which may be reflective of the broader informational gaps in our science communication efforts. This study highlights the role of an ad hoc risk communication platform in the current environment and uses questions submitted to the Dear Pandemic question box to identify informational needs of DP readers over the course of the COVID-19 pandemic. These findings may help clarify how organizations addressing health misinformation in the digital space can contribute to timely, responsive science communication and improve future communication efforts

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

© 2015 Luo et al. For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease