Extensive research has highlighted the strong association between chronic stress and negative health outcomes. This relationship is influenced by various factors, including sociobehavioral, environmental, and genetic and epigenomic forces. To comprehensively assess an individual’s stress levels, we propose the development of the Chronic Stress Indicator (CSI), a novel comprehensive multifaceted tool that incorporates key biological, anthropometric, behavioral, and socioeconomic factors. The objective of this study is to assess the effectiveness of the CSI compared to Allostatic Load (AL), a type of chronic stress, in identifying health issues related to stress. The objective of this research is to evaluate the performance of the Chronic Stress Indicator (CSI) versus Allostatic Load (AL) in detecting adverse health outcomes within the U.S. demographic aged 20–49. The information used for this study was sourced from the National Health and Nutrition Examination Survey (NHANES), carried out from 2001 to 2004. Logistic regression modeling was employed to calculate odds ratios and confidence intervals. The Wilcoxon rank-sum test was employed to assess differences in means, whereas the chi-square test, accompanied by Cramer’s V statistic, was used to examine the association among categorical variables. Additionally, the relationship between continuous variables was analyzed using Pearson’s correlation coefficient. Our association tests show that the length of occupation activity and health status were among the strongest associations to CSI risk. Based on our logistic regression models, age and sex were found to be significant factors in determining AL. We also found that age, smoking, and longest occupation activity were significant factors of CSI risk. These findings suggest a need for individuals to limit smoking as it may lead to higher overall stress despite its common use as a coping mechanism for stress. We should also review the level of occupational activity a job has before continuously working on it as this may also lead to higher cumulative stress.
Chronic stress is recognized for its comprehensive impact on various aspects of physical and mental health, including cardiovascular well-being, immune system function, cognitive performance, sleep quality, and overall quality of life. Indeed, the multifaceted impact of stress is such that it can affect mood and have profound consequences on various physiological systems within the human body. One key mechanism through which chronic stress exerts its influence is the activation of the hypothalamic–pituitary–adrenal (HPA) axis, a complex neuroendocrine pathway involved in the stress response.
The HPA axis plays a critical role in regulating the body’s response to stressors, including the release of stress hormones such as cortisol. Prolonged activation of the HPA axis due to chronic stress can disrupt the delicate balance of cortisol production, leading to dysregulation of the stress response system. This dysregulation has been associated with poor health outcomes.
Previous studies have established Allostatic Load (AL) as an indicator of this chronic stress. Allostatic load is a concept that describes the cumulative “wear and tear” on the body as a result of exposure to repeated or chronic stress. Introduced by McEwen and Stellar in 1993, this idea revolves around the body’s physiological systems and their ability to adapt to stressors, albeit with a long-term cost to the body. At the core of allostatic load are biological pathways, as it emphasizes the role of biological markers from various systems, including the cardiovascular, metabolic, and immune systems. This notion of allostatic load is not just about the immediate response to stress but also about the accumulation of stress over time. While this has proved useful, it fails to include the impact of sociodemographic factors and behavioral choices when assessing chronic stress.
There is a need to comprehensively produce an easy-to-use tool which captures the stress response. This is so because there is a need to capture individuals at high risk for stress-related disease processes in order to be able to intervene and mitigate this risk. We use this principle to propose a tool for scoring excessive stress, the Chronic Stress Indicator (CSI), consisting of socioeconomic variables, biomarkers of health, and behavioral and lifestyle variables. Justification for the variables used in the construction of the CSI is based on the literature with variables such as socioeconomic status, physical activity, and critical biomarkers from the cardiometabolic system used to capture the manifestation of the effects of prolonged stress.
Traditional operationalization of AL has focused on biomedical manifestations of stress. The development of a new stress analysis tool that integrates factors such as income, education, physical activity, alcohol, and tobacco use could lead to a more comprehensive and nuanced understanding of stress. This would consider the profound impact of socioeconomic factors on stress levels. For instance, lower income and educational attainment are frequently linked with higher stress, often due to financial instability, job insecurity, and limited access to necessary resources. By including income and education as key components, this would effectively recognize these critical socioeconomic determinants of health.
In addition to socioeconomic factors, this would also consider crucial lifestyle elements. For instance, engaging in physical activity significantly contributes to stress management. Engaging in regular physical activity can help mitigate the adverse effects of stress on the body, while a sedentary lifestyle might worsen chronic stress. Thus, incorporating physical activity into the analysis would underscore the importance of lifestyle in stress management. Furthermore, this would also examine the use of alcohol and tobacco, which are commonly used as coping mechanisms for stress. However, these substances can lead to increased health risks and contribute to worse health outcomes. By including alcohol and tobacco use, this could provide valuable insights into the complex relationship between substance use, stress, and overall health.
The purpose of this study is to then develop and assess the CSI and compare it to a traditional operationalization of chronic stress, such as Allostatic Load (AL). This study will demonstrate if integrating social and economic variables such as income and education, and behavioral variables such as physical activity along with cardiometabolic-related biomarkers better captures stress-related diseases.
The sample for this study came from the 2001–2004 NHANES cohort with individuals aged 20–49 years who had complete data for all indicators for both the AL and CSI indices included. The NHANES 2001–2004 employs a sophisticated, multi-stage, and stratified sampling method to collect data from non-institutionalized individuals in the U.S. For the purposes of this analysis, out of a total of 4161 participants sampled, 1063 were analyzed for AL and 921 for the CSI, contingent upon the availability of data.
In this research, demographic factors were collected with variables measured including age, gender, race/ethnicity, income level, levels of physical activity, educational background, and employment status. Other variables obtained using an in-home interview include cancer, chest pain, shortness of breath on stairs/incline, liver condition, and family history of diabetes, as well as hypertension/stroke. The study also included health-related variables collected using the mobile examination center as follows: cytomegalovirus (CMV) IgM, CMV IgG, Toxoplasma IgG, cancer antigen 125 (CA-125), Cancer antigen 15-3 (CA15-3), current health status, alcohol use, tobacco consumption, and the number of days physical as well as an indicator of mental health status. The laboratory procedures described in the NHANES Laboratory Procedure Manual were performed to assess lab-based variables.
The study focuses on the analysis of primary outcome measures, including AL and the CSI. Investigated variables feature gender, annual family income, ethnicity, age, educational attainment, alcohol intake, smoking status, physical activity level, CMV IgG concentrations, SBP, DBP, TC levels, HDL levels, HBA1C, Albumin, triglycerides, BMI, creatinine clearance (CLCR), C-reactive protein (CRP), Toxoplasma IgG, symptoms such as chest pain and breathlessness on exertion, cancer presence, overall health condition, mental and physical health days, liver disorders, familial diabetes, and history of hypertension/stroke. It also examines current and longest-held job roles, activity levels at work (COA and LOA), along with markers like CA-125 and CA15-3. A categorization of occupations by activity level is provided for reference. In the categorization of occupations based on activity level, low occupational activity roles typically encompass professional, managerial, administrative, and clerical positions such as executives, managers, engineers, secretaries, and technicians. These roles are often characterized by sedentary tasks, intellectual or creative work, and decision-making responsibilities. In contrast, high occupational activity roles are primarily composed of manual labor and skilled trades, including construction workers, machine operators, and agricultural workers. These positions are physically demanding, involving significant on-your-feet tasks, manual skills, and outdoor labor. Additionally, high activity roles include service industry jobs like waiters and cooks, as well as protective service occupations, which require physical exertion and constant alertness. Income was assessed as follows, ($0–$4999), ($5000–$9999), ($10,000–$14,999), ($15,000–$19,999), ($20,000–$24,999), ($25,000–$34,999), ($35,000–$44,999), ($45,000–$54,999), ($55,000–$64,999), ($65,000–$74,999), and (those earning more than $75,000). Participants who preferred not to choose a precise range for this variable were given two alternatives: “More than $20,000” and “Less than $20,000”. Subsequently, this variable was transformed into a binary indicator of poverty status using income data from the period 2001–2004. Education levels included less than 9th grade, 9–11th grade (including 12th grade with no diploma), High school graduate/General Educational Development (GED) or equivalent, some college or AA degree, and college graduate or above. Race/Ethnicity was classified into several groups: non-Hispanic White, non-Hispanic Black, Mexican American, Other Hispanic, and Other/Multiracial. Responses to questions about physical activity were recorded as either “yes” or “no” and “unable to do”. Current smoking status was recorded as “yes” for anyone who had smoked at least one cigarette in the previous month. Alcohol intake was marked as “yes” for individuals who had consumed 12 or more alcoholic beverages over the course of a year, and “no” for those who had not.
To better understand some of the critical factors that influence a person’s risk of disease, we propose the CSI. This scale combines sociodemographic factors and biomarkers, to score individuals from low-risk to high-risk (0–10). Factors for this scale include education level, poverty status, alcohol and tobacco use, physical activity, SBP, DBP, TC, HbA1C, and BMI. The ten facto
