Effect of social stressors and social determinants of health (SDOH) on cancer beliefs: Analysis of a cancer center catchment area

Background: Social determinants of health (SDOH) are social barriers that stratify health status. Specifically, socioeconomic status, education level, minority and immigration status correlate with higher risk of onset and severity of chronic disease. We sought to understand how SDOH affect a patient’s belief regarding autonomy over cancer risk and outcomes. Methods: Data from the Sidney Kimmel Cancer Center catchment area including Delaware, Montgomery, and Philadelphia Counties in Pennsylvania; Camden and Burlington Counties in New Jersey were queried and analyzed. The survey included six cancer belief statements. Demographic characteristics of survey participants, as well as data related to cancer risk factors and beliefs were all calculated using unweighted data. Results: 1,557 adults responded to this survey. Survey participants ranged in age from 18 to 88 years old, with 49.6% of participants 40 years old and younger. 64% of respondents identified as female vs 36% male. Poverty classification was based on ASPE 2020 Poverty Guidelines given family size and income information. Based on these parameters, 21.3% of respondents were considered impoverished. Additional demographics included housing security, food security, and health literacy. Results demonstrated, impoverished respondents were more likely to disagree that behavior/lifestyle causes cancer (63.3% vs 53.3%, p<0.001). Housing insecure respondents were more likely to disagree that behavior/lifestyle causes cancer (62.8% vs 54.8% p<0.001). Respondents who are more food insecure were more likely to disagree that behavior/lifestyle causes cancer than those who are food secure (food last: 57.9% vs 54.5%, p<0.001). Respondents who are more food insecure were more likely to agree that everything causes cancer (food last: 67.8% vs 59.2%, p<0.001). Discussion: Adverse SDOH such as poverty, food insecurity, housing insecurity, and health literacy affect cancer beliefs. Overall, results demonstrated that respondents with adverse SDOH were more likely to disagree that behavior/lifestyle can cause cancer and more likely to agree that everything causes cancer. Patients with adverse SDOH may be less likely to actively engage in preventive health measures and screenings, clinical trials, and other factors known to positively impact cancer outcomes. SDOH should be evaluated on patient intake and patients should be provided with appropriate support and targeted education with broad cancer beliefs in mind. Citation Format: Alexandria P. Smith, Ayesha Ali, Ayako Shimada, Brittany C. Smith, Samantha Okere, Kamryn Hines, Amy Leader, Nicole L. Simone. Impact of adverse SDOH on cancer knowledge and beliefs: Analysis of a NCI-designated cancer center’s catchment area survey [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5859.

Background: Absence of adverse cardiovascular (CV) risk factors (RF) or having a favorable CV RF profile during middle-age is associated with better health-related quality of life at older ages. However, the impact of having no adverse CV RFs throughout middle- and older-age remains unknown. Objective: To examine the impact of remaining free of all adverse CV RFs for over 35 years on health-related quality of life among participants of the Chicago Health Aging Study (CHAS). Methods: The CHAS study conducted physical examinations at baseline (1967-1973) and follow-up (2007-2010). Absence of adverse CV RF profile was defined as having all the following: blood pressure <140/<90 mm Hg and not taking antihypertensive medication, serum cholesterol level <240 mg/dL and not taking cholesterol-lowering medication, BMI <30, no diabetes and not currently smoking. Health-related quality of life was measured at follow-up using the SF-36. We compared the SF-36 component scores (range 0-100, higher scores associated with better health-related quality of life) for individuals who continued to remain free of adverse CV RF profile in older age (i.e. from baseline to follow-up) vs. those with elevated levels of CV risk factors at baseline and/or at follow-up using linear regression models adjusted for age, sex, race, and highest educational attainment. Results: Among 1395 CHAS participants 8.8% (123) remained free of adverse CV risk factors for over 35 years from middle to older-age. Individuals who continued to have no adverse CV risk factors were more likely to be white (95.3% vs 87.4%; p<0.01) and have a graduate school education (35% vs 19%; P<0.001). Also, they had higher (better) adjusted physical component scores (54.0 vs 51.7; P=0.005), general health scores (77.0 vs 72.9; P=0.008), and physical functioning scores (99.5 vs 94.1; P=0.003) compared to those with adverse RFs. No differences were observed for the mental component score or the pain index, mental health index, role-emotional, role-physical, social functioning or vitality subscales. Conclusions: Remaining free of adverse cardiovascular risk factors for over 35 years from middle to older-age is associated with better physical quality of life at older ages.

Understanding Social Determinants of Brain Health During Development.

The cumulative effects of adverse social factors on the diabetes risk remains to be clarified. Cross-sectional analysis of the US National Health and Nutrition Examination Survey (NHANES) 1999-2006. We included 10,276 adults aged ≥20 years. Diabetes mellitus was defined by physician diagnosis or fasting plasma glucose (≥126 mg/dl) or glycated hemoglobin (≥6.5%). Social risk factors (low family income, low education level, minority racial/ethnic group status, and single-living status) and health-related behaviors (physical activity and dietary intake) were self-reported. Social risk factors were combined in a cumulative social risk index (range 0 to ≥3) and logistic regression used to assess the association of cumulative social risk and diabetes, taking into account complex survey design and sampling weights. Of 10,276 participants, 1515 (weighted proportion - 10%) had diabetes, 3295 (32.3%) and 1830 (9.0%) were exposed to ≥1 adverse social risk factor and ≥3 social risk factors, respectively. Diabetes was associated with increasing cumulative social risk in a graded manner (p for trend <0.001). Compared with a cumulative social risk score of 0, the age- and sex-adjusted diabetes odds for a cumulative social risk score of ≥3 was 2.84 (95% confidence interval: 2.23-3.62), and 2.72 (95% confidence interval: 2.05-3.60) after further adjustment for family history of diabetes, body mass index, smoking, dietary intake and leisure time physical activity. Health behaviors and adiposity only partially influenced the cumulative social risk and diabetes relationship. Simultaneous exposure to several adverse social risk factors significantly influences the odds of diabetes. Better prevention and control of diabetes needs accounting for all aspects of social disadvantage.

Ethnic inequalities are often associated with social determinants of health. This study seeks to identify the latest scientific evidence on inequalities in the health of people of African descent in the Americas. For this, a systematic review of the literature on health and people of African descent in the Americas was carried out in the LILACS, PubMed, MEDLINE, and IBECS databases. Institutional and academic repositories were also consulted. Evidence was obtained on the presence and persistence of health inequalities in the population of African descent in the Americas from the identification of five types of quantitative and qualitative evidence: (1) ethnic/racial concept and variables; (2) relations with other social determinants; (3) health risks; (4) barriers and inequalities in health services; and, (5) morbi-mortality from chronic diseases. Studies with qualitative methods revealed invisibility, stereotypes, and rejection or exclusion as main factors of inequality. This review evidenced the existence of health inequalities, its interconnection with other adverse social determinants and risk factors, and its generation and perpetuation by discrimination, marginalization, and social disadvantage. These conditions make people of African descent a priority population group for action on equity, as demanded by the 2030 Agenda for Sustainable Development.

Adverse social factors and all-cause mortality among male and female patients receiving care in the Veterans Health Administration

Background: The American Heart Association recognizes the pre-pregnancy period as a window to optimize cardiovascular health and mitigate the risk of adverse pregnancy outcomes, particularly in those with cardiovascular disease (CVD) risk factors (RF). However, disparities in access to and utilization of preventive care are well-established outside of pregnancy. Understanding the social factors associated with lower pre-pregnancy care can inform interventions to improve equity in maternal health. Methods: We used data from the Pregnancy Risk Assessment Monitoring System, a nationally representative survey of women with a recent live birth. We included women age ≥18 years with self-reported data on pre-pregnancy CVD RF (obesity, diabetes, hypertension, or tobacco use) and visit attendance. The primary outcome was attendance at a clinic visit (i.e., “regular checkup”) with an OB/GYN or primary care doctor in the year prior to pregnancy. We calculated age-adjusted odds of attendance in those with 0, 1 or &gt; 2 of the following adverse social factors: poor transportation access, food insecurity, unstable housing, lack of health insurance, difficulty paying bills, history of domestic abuse, and lack of a high school education. Next, we conducted subgroup analyses by presence of 0, 1 or &gt; 2 CVD RF. Analyses were weighted accounting for survey design. Results: Of 233,815 women (weighted N=11,424,885), the mean age was 29.5 years, 32.6% had 1 CVD RF, and 10.2% had &gt; 2 CVD RF. Adverse social factors were more often reported by those with CVD RF: 17.8% of those with no CVD RF, 25.7% with 1 CVD RF, and 28.3% with &gt; 2 CVD RF (p=&lt;0.01). Overall frequency of a pre-pregnancy clinic visit was 55.9% with a lower frequency in those with higher burden of adverse social factors ( TABLE ). For example, odds of a pre-pregnancy clinic visit was lower in those with &gt; 2 compared with no adverse social factors in those with 0 CVD RF (63.8% VS 19.2%; aOR 0.15; 95% CI: 0.13-0.17), 1 CVD RF (60.3% VS 27.9%; aOR: 0.26; 95% CI: 0.23-0.30), and &gt; 2 CVD RF (60.9% VS 33.4%; aOR: 0.35; 95% CI: 0.29-0.42). Conclusion: Adverse social factors are associated with a lower frequency of pre-pregnancy care utilization even among those with CVD RF. Among those with ≥2 adverse social factors, less than one-third of women attended a pre-pregnancy visit. Public health efforts should address upstream drivers of health to improve pre-pregnancy preventive care and optimize cardiovascular health.

Cancer beliefs and perceptions of cancer risk affect the cancer continuum. Identifying underlying factors associated with these beliefs and perceptions in Texas can help inform and target prevention efforts. We developed a cancer-focused questionnaire and administered it online to a nonprobability sample of the Texas population. Weighted multivariable logistic regression analysis identified key factors associated with perceptions and beliefs about cancer. The study population comprised 2,034 respondents (median age, 44.4 years) of diverse ethnicity: 45.5% were non-Hispanic white, 10.6% non-Hispanic black, and 35.7% Hispanic. Self-reported depression was significantly associated with cancer risk perceptions and cancer beliefs. Those indicating frequent and infrequent depression versus no depression were more likely to believe that: (i) compared to other people their age, they were more likely to get cancer in their lifetime [OR, 2.92; 95% confidence interval (CI), 1.95-4.39 and OR, 1.79; 95% CI, 1.17-2.74, respectively]; and (ii) when they think about cancer, they automatically think about death (OR, 2.05; 95% CI, 1.56-2.69 and OR, 1.46; 95% CI, 1.11-1.92, respectively). Frequent depression versus no depression was also associated with agreement that (i) it seems like everything causes cancer (OR, 1.67; 95% CI, 1.26-2.22) and (ii) there is not much one can do to lower one's chance of getting cancer (OR, 1.44; 95% CI, 1.09-1.89). Other predictors for perceived cancer risk and/or cancer beliefs were sex, age, ethnicity/race, being born in the United States, marital status, income, body mass index, and smoking. Depression and other predictors are associated with cancer risk perceptions and beliefs in Texas. Increased attention to reducing depression may improve cancer risk perceptions and beliefs.

Disparities in Vision Health and Eye Care: Where Do We Go from Here?

Adverse Family Social Circumstances and Outcome in Pediatric Cardiac Transplant Recipients at a UK Center

INTRODUCTIONSocioeconomic factors are powerful determinants of health. Current discourse abounds with references to addressing social determinants of health to improve the health and well-being of people and populations. Certainly, from a worksite health promotion perspective, social determinants of health play an important part in the health, well-being, and functional status of employees. In fact, in early 2010, we wrote a column in ACSM’s Health & Fitness Journal® on exactly this topic (1). We considered a “causal chain” of determinants of premature deaths and highlighted how social determinants of health relate to health behaviors, biological markers, diagnosed disease, and premature death (see Figure 1). Biological markers included the early indicators (or signal events) for subsequent disease diagnoses, the topic of our earlier column on the 2018 Physical Activity Guidelines for Americans (2). This time, we consider the relationship between physical activity and the upstream social determinants of health themselves among members of the workforce.Figure 1: A causal chain of determinants of premature death and its agents. Reprinted with permission from Pronk and Kottke (1).Social Determinants of Health In the 1970s, British epidemiologist Sir Michael Marmot studied British civil servants and found that people in high-status jobs tended to be healthier than those in low-status jobs (3). Since then, awareness has been heightened regarding the notion that socioeconomic factors are powerful determinants of health. Furthermore, Marmot and colleagues (4) continued their research and uncovered a “Top 10” most important group of social determinants of health: low social status, relentless stress, social exclusion, work, unemployment, social support, addiction, food, and transport (see Table 1).TABLE 1: Top 10 Social Determinants of HealthSome of these Top 10 factors are more closely linked to the workplace than others. In addition, measurement of the social determinants is a complex endeavor. Clearly, the metrics need to be related to topics that reflect both an organizational (company) context as well as an individual (worker) context. As such, measurement strategies need to be of appropriate interest for both of these stakeholders while also actionable. In Table 2, we have outlined a set of 10 metrics that reflect social determinants of health (2,4,5). The metrics we have outlined include life satisfaction, happiness, happiness of friends and relatives, social support, financial management, savings, donation practices, job satisfaction, neighborhood safety, and volunteerism. Although these factors may not be fully or wholly representative of each of the Top 10 social determinants as outlined by Marmot and colleagues (4), they are considered to be directionally aligned.TABLE 2: Scoring of Social Determinants of Health MetricsPhysical Activity Physical activity benefits the health and well-being of individuals of all ages and has been associated with many health factors and reduced risk of illness. These include improvements in quality of life, sleep, bone health, physical and emotional function, blood pressure, cancer risk, dementia risk, weight loss and risk of weight regain, among others (1,6,7). It also affects outcomes of importance and related to the workplace setting, including work performance and family income. Furthermore, these benefits are applicable to virtually everyone, from men and women to younger children, older adults, pregnant women, people with chronic disease, people with disabilities, those who wish to prevent disease, employees, and many other categories, regardless of race or ethnicity (1,6,7). Is Physical Activity Linked to Social Determinants of Health? It may be that higher levels of physical activity behavior represent a surrogate measure for higher levels of individual attention to and engagement in taking care of oneself. It also may be true that lower levels of physical activity behavior are linked to more challenges related to socioeconomic barriers to health. Regardless, if a relationship exists, it may provide an opportunity to create innovative solutions that support employees in achieving higher levels of health and well-being that subsequently may be tested for their effectiveness. It may be that higher levels of physical activity behavior represent a surrogate measure for higher levels of individual attention to and engagement in taking care of oneself. It also may be true that lower levels of physical activity behavior are linked to more challenges related to socioeconomic barriers to health. A CASE STUDY We considered data from 7342 HealthPartners employees, including 1998 spouses, who completed a health assessment (HA). The HealthPartners “Be Well” program is offered to health plan members, covered spouses of health plan members, and nonmembers who are HealthPartners employees. All three categories of HA completers are included. Assessments were completed between 1/15/2018 and 12/31/2018 with an overall HA response rate of 72%. The level of physical activity in which respondents engaged was defined as the total number of reported minutes of physical activity in a week. This is a sum of reported moderate and vigorous minutes without any adjustment for vigorous activity, i.e., the amount of vigorous minutes is not doubled to total minutes as is the case when assessing physical activity risk. When we considered the criteria for determining physical activity risk as a function of meeting guidelines, we did account for vigorous physical activity levels by multiplying those minutes by 2 to determine overall activity levels. The absence of risk levels for the social determinants metrics used in this analysis was based on a 0 to 10 score (rating of 9 or 10) or according to two or more categories (see Table 2). Table 3 presents the general population demographics. On average, this population may be characterized as mostly female, middle-aged, white, and well-educated. Also included in the table is the percent not “at risk” for each of the 10 social determinants, as well as the average number of social determinants risk factors reported for the population studied. Figure 2 shows the number of participants assigned a category of risk between 0 and 10 corresponding with the number of reported social determinant risks. This graph is overlaid with the number of physical activity minutes for the participants in each category. Figure 2 visualizes the aggregated data.TABLE 3: Descriptive Characteristics of the Population (N = 7,342)Figure 2: Social determinants of health aggregate score and physical activity levels.Major differences in weekly minutes of physical activity were noted between those workers at risk for each social determinant and those not at risk. In Figure 3, these data are presented according to presence or absence of risk among the 10 social determinants and weekly activity levels. All “no risk” social determinants, except for the financial savings determinant, are associated with significantly higher levels of physical activity.Figure 3: Physical activity (minutes/week) by social determinants of health risks.The magnitude of the differences should be noted. There are major differences between physical activity levels within each single social determinant risk factor groups. However, the magnitude of physical activity difference among those who have or do not have the social determinant risk factor is relatively small, although statistically significant (except for financial savings). For example, the difference between life satisfaction risk and no risk is 7 minutes of physical activity per week among those who do not meet the PA guideline. For every 10-minute increment of physical activity, the likelihood of not having the life satisfaction risk is reduced by 0.6% (at a P < 0.000). This observation holds true for all social determinants studied. However, it also is important to note that we observed increasingly lower levels of physical activity with the accumulation of social determinant risk factors (or socioeconomic risk) (see Figure 2). This observation is consistent with the studies of Marmot mentioned earlier. Those who are exposed to more socioeconomic challenges tend to engage in less healthy behaviors, experience more biological risk factors (or “signal events”), and, as a result, more disease diagnoses and premature deaths (2,3). Indeed, employees in this population were experiencing more than one social determinant risk factor at the time of the HA. As noted in Table 2, employees reported, on average, 4.2 of the 10 social determinants. INSIGHTS AND CONCLUSIONS Physical activity appears to be related to various social determinants of health. However, at the level of each single, individual socioeconomic risk factor we looked at, the magnitude of the differences in physical activity levels alone may not persuade worksite health practitioners to prioritize programs aimed at improving social determinants. This case study does not provide clarity regarding the precise nature of the relationship between social determinants and physical activity. Pathways other than those represented by the factors considered in this case study should be considered. Physical activity appears to be related to various social determinants of health. However, at the level of each single, individual socioeconomic risk factor we looked at, the magnitude of the differences in physical activity levels alone may not persuade worksite health practitioners to prioritize programs aimed at improving social determinants. On the other hand, those who experience higher numbers of socioeconomic risks simultaneously may benefit greatly from additional support to overcome such challenges to be able to engage in healthy behaviors such as physical activity. At the practitioner level, knowledge of the cumulative effect of multiple socioeconomic risk factors on physical activity may help in working one-on-one with employees to help address social and economic barriers to living a healthier lifestyle. Employers too may want to think about the potential to engage the workforce in beneficial socioeconomic programs through company policy approaches to support employees in coping with social and economic challenges. For example, using opt-out strategies to increase engagement in a company-sponsored 401K program instead of relying on opt-in approaches will inevitably engage more workers and prepare them for retirement financially. Similarly, employers may consider promoting community programs aimed at increasing social support and decreasing loneliness. As a result, stress may be reduced over time and job satisfaction increased. Perhaps this may even lead to enjoyment of physically active breaks at the workplace and family bike rides on the weekends.

Previous studies comparing the social and behavioural determinants of health in Europe have largely focused on individual countries or combined data from various national surveys. In this article, we present the findings from the new rotating module on social determinants of health in the European Social Survey (ESS) (2014) to obtain the first comprehensive comparison of estimates on the prevalence of the following social and behavioural determinants of health: working conditions, access to healthcare, housing quality, unpaid care, childhood conditions and health behaviours. We used the 7th round of the ESS. We present separate results for men and women. All estimates were age-standardized in each separate country using a consistent metric. We show country-specific results as well as pooled estimates for the combined cross-national sample. We found that social and behavioural factors that have a clear impact on physical and mental health, such as lack of healthcare access, risk behaviour and poor working conditions, are reported by substantial numbers of people in most European countries. Furthermore, our results highlight considerable cross-national variation in social and behavioural determinants of health across European countries. Substantial numbers of Europeans are exposed to social and behavioural determinants of health problems. Moreover, the extent to which people experience these social and behavioural factors varies cross-nationally. Future research should examine in more detail how these factors are associated with physical and mental health outcomes, and how these associations vary across countries.

AimsTo ascertain whether social risk factors have a significant association with complex ADHD and assess the relative role played by biological risk factors and coexisting neurodevelopmental disorders. This will enable...

Social determinants of health (SDoHs) are increasingly recognized as important contributors to the development, course, and outcomes of psychiatric disorders. However, their integration into clinical psychiatry and mechanistic models remains limited. This overview synthesizes emerging evidence on the biopsychosocial mechanisms through which SDoHs influence mental health. There is a need to distinguish between individual-level, clinically actionable health-related social needs and family-, community-, and society-level structural SDoHs, and to consider both adverse and protective social factors. Converging research demonstrates that social experiences are biologically embedded through interacting pathways, including exposomics, epigenetics, allostatic load, accelerated inflammaging, immune dysregulation, and gut-brain-microbiome signaling. These mechanisms influence neural circuitry underlying stress regulation, reward processing, and social cognition. Psychological processes-including individual differences in resilience, wisdom, compassion, and purpose in life-shape responses to SDoHs and are supported by identifiable neurobiological substrates. Social connection has emerged as a central, potentially modifiable SDoH that is strongly associated with whole health and longevity. Loneliness and social isolation have become major global public health challenges. The authors propose a biopsychosocial framework that integrates social exposures, biological mechanisms, neural systems, and psychological processes to better understand the risk, course, and prevention of mental illnesses. Clinical and public health implications include the need for routine assessment of SDoHs, incorporation of protective factors at individual and societal levels, and development of pragmatic, multidomain interventions. Finally, rapidly evolving digital technologies, including artificial intelligence, offer new opportunities but also require careful governance. Advancing toward human-centered "artificial wisdom" may enhance the capacity of technology to promote whole health in individuals with mental illnesses globally.

Simple SummaryThe role of the social determinants of health (i.e., physical, social and economic factors affecting human health) and their impact on companion animal welfare have not been fully explored. Through a social determinants lens, it is possible to improve the understanding of companion animal guardian challenges in managing their companion animal’s welfare needs. Considering the five domains of animal welfare in conjunction with the social determinants enables us to explore the impact of the social determinants of human health on animal welfare. This highlights the importance of multidisciplinary collaboration to achieve positive health outcomes for guardians and positive welfare outcomes for their companion animals.The social determinants of health (SDH) focus on the social, physical and economic factors that impact human health. Studies have revealed that animal guardians face a range of challenges in attaining positive welfare outcomes for their companion animals, which can be influenced by socioeconomic and environmental factors. Despite this, there is a lack of research specifically exploring the relationship between SDH and animal welfare outcomes. Given that the SDH impact on humans, which in turn directly impacts on their companion animal, it is important to adapt an SDH framework for companion animal welfare by characterising the impact of the SDH on companion animal guardians in their attempts to care for their animals and, by extension, the associated welfare outcomes. This paper explores how these human health determinants may impact animal welfare and the possible challenges that may arise for the guardian when attempting to meet their companion animal’s welfare needs. By integrating the SDH with other key frameworks, including the five domains model of animal welfare, through multidisciplinary collaboration, this framework can be used to inform future programs aiming to improve animal welfare.