Impact of Hospital Strain on Excess Deaths During the COVID-19 Pandemic - United States, July 2020-July 2021.

Surges in COVID-19 cases have stressed hospital systems, negatively affected health care and public health infrastructures, and degraded national critical functions (1,2). Resource limitations, such as available hospital space, staffing, and supplies led some facilities to adopt crisis standards of care, the most extreme operating condition for hospitals, in which the focus of medical decision-making shifted from achieving the best outcomes for individual patients to addressing the immediate care needs of larger groups of patients (3). When hospitals deviated from conventional standards of care, many preventive and elective procedures were suspended, leading to the progression of serious conditions among some persons who would have benefitted from earlier diagnosis and intervention (4). During March-May 2020, U.S. emergency department visits declined by 23% for heart attacks, 20% for strokes, and 10% for diabetic emergencies (5). The Cybersecurity & Infrastructure Security Agency (CISA) COVID Task Force* examined the relationship between hospital strain and excess deaths during July 4, 2020-July 10, 2021, to assess the impact of COVID-19 surges on hospital system operations and potential effects on other critical infrastructure sectors and national critical functions. The study period included the months during which the highly transmissible SARS-CoV-2 B.1.617.2 (Delta) variant became predominant in the United States.† The negative binomial regression model used to calculate estimated deaths predicted that, if intensive care unit (ICU) bed use nationwide reached 75% capacity an estimated 12,000 additional excess deaths would occur nationally over the next 2 weeks. As hospitals exceed 100% ICU bed capacity, 80,000 excess deaths would be expected in the following 2 weeks. This analysis indicates the importance of controlling case growth and subsequent hospitalizations before severe strain. State, local, tribal, and territorial leaders could evaluate ways to reduce strain on public health and health care infrastructures, including implementing interventions to reduce overall disease prevalence such as vaccination and other prevention strategies, as well as ways to expand or enhance capacity during times of high disease prevalence.

Life-course trajectories of body mass index from adolescence to old age: Racial and educational disparities.

No research exists on how body mass index (BMI) changes with age over the full life span and social disparities therein. This study aims to fill the gap using an innovative life-course research design and analytic methods to model BMI trajectories from early adolescence to old age across 20th-century birth cohorts and test sociodemographic variation in such trajectories. We conducted the pooled integrative data analysis (IDA) to combine data from four national population-based NIH longitudinal cohort studies that collectively cover multiple stages of the life course (Add Health, MIDUS, ACL, and HRS) and estimate mixed-effects models of age trajectories of BMI for men and women. We examined associations of BMI trajectories with birth cohort, race/ethnicity, parental education, and adult educational attainment. We found higher mean levels of and larger increases in BMI with age across more recent birth cohorts as compared with earlier-born cohorts. Black and Hispanic excesses in BMI compared with Whites were present early in life and persisted at all ages, and, in the case of Black-White disparities, were of larger magnitude for more recent cohorts. Higher parental and adulthood educational attainment were associated with lower levels of BMI at all ages. Women with college-educated parents also experienced less cohort increase in mean BMI. Both race and education disparities in BMI trajectories were larger for women compared with men.

Socioeconomic Status and Biological Risks for Health and Illness Across the Life Course.

OBJECTIVES: We assess the temporal properties and biosocial mechanisms underlying the associations between early-life socioeconomic status (SES) and later health. Using a life-course design spanning adolescence to older adulthood, we assess how early life and various dimensions of adult SES are associated with immune and metabolic function in different life stages and examine possible bio-behavioral and psychosocial mechanisms underlying these associations. METHOD: Data for this study come from 3 national studies that collectively cover multiple stages of the life course (Add Health, MIDUS, and HRS). We estimated generalized linear models to examine the prospective associations between early-life SES, adult SES, and biomarkers of chronic inflammation and metabolic disorder assessed at follow-up. We further conducted formal tests of mediation to assess the role of adult SES in linking early SES to biological functions. RESULTS: We found that early-life SES exerted consistent protective effects for metabolic disorder across the life span, but waned with time for CRP. The protective effect of respondent education remained persistent for CRP but declined with age for metabolic disorder. Adult income and assets primarily protected respondents against physiological dysregulation in middle and old ages, but not in early adulthood. DISCUSSION: These findings are the first to elucidate the life-course patterns of SES that matter for underlying physiological functioning during the aging process to produce social gradients in health.

Young Adult Risk Factors for Cancer: Obesity, Inflammation, and Sociobehavioral Mechanisms.

INTRODUCTION: The paper assesses social disparities in the burdens of metabolic and inflammatory risks for cancer in the U.S. young adult population and examines psychosocial and behavioral mechanisms in such disparities. METHODS: Using data of 7,889 individuals aged 12-32 years from the National Longitudinal Study of Adolescent to Adult Health from 1994 to 2009, generalized linear models were used to assess the sex, race/ethnicity, and SES differences in the risks of obesity and inflammation, measured by C-reactive protein. Further tests examined the extent to which social isolation, smoking, physical inactivity, alcohol abuse, and illicit drug use explain social differentials in each biomarker outcome. RESULTS: Women, blacks, Hispanics, and socioeconomically disadvantaged groups had higher risks of obesity and elevated C-reactive protein, with the SES gradients being more pronounced in female participants. Health-related behaviors showed large variation across sex, race, and SES strata. After adjusting for these behavioral variables, sex, and race disparities in obesity and excess inflammation in blacks diminished, whereas the adolescent SES disparity in obesity remained. The associations of adolescent and young adult SES disadvantage and inflammation were also explained by behavioral mechanisms. Behavioral factors associated with higher risks of obesity and inflammation differed, with the exception of fast food consumption, a risk factor for both. CONCLUSIONS: This study provides new knowledge of social distribution of early life exposures to physiologic precedents to cancer development later in life with implications for prevention and early intervention of modifiable risky behaviors in adolescents and young adults.