The multidimensional clustering of health and its ecological risk factors

The current state of complex systems research on socioeconomic inequalities in health and health behavior-a systematic scoping review

BACKGROUND

Interest in applying a complex systems approach to understanding socioeconomic inequalities in health is growing, but an overview of existing research on this topic is lacking. In this systematic scoping review, we summarize the current state of the literature, identify shared drivers of multiple health and health behavior outcomes, and highlight areas ripe for future research.

METHODS

SCOPUS, Web of Science, and PubMed databases were searched in April 2023 for peer-reviewed, English-language studies in high-income OECD countries containing a conceptual systems model or simulation model of socioeconomic inequalities in health or health behavior in the adult general population. Two independent reviewers screened abstracts and full texts. Data on study aim, type of model, all model elements, and all relationships were extracted. Model elements were categorized based on the Commission on Social Determinants of Health framework, and relationships between grouped elements were visualized in a summary conceptual systems map.

RESULTS

A total of 42 publications were included; 18 only contained a simulation model, 20 only contained a conceptual model, and 4 contained both types of models. General health outcomes (e.g., health status, well-being) were modeled more often than specific outcomes like obesity. Dietary behavior and physical activity were by far the most commonly modeled health behaviors. Intermediary determinants of health (e.g., material circumstances, social cohesion) were included in nearly all models, whereas structural determinants (e.g., policies, societal values) were included in about a third of models. Using the summary conceptual systems map, we identified 15 shared drivers of socioeconomic inequalities in multiple health and health behavior outcomes.

CONCLUSIONS

The interconnectedness of socioeconomic position, multiple health and health behavior outcomes, and determinants of socioeconomic inequalities in health is clear from this review. Factors central to the complex system as it is currently understood in the literature (e.g., financial strain) may be both efficient and effective policy levers, and factors less well represented in the literature (e.g., sleep, structural determinants) may warrant more research. Our systematic, comprehensive synthesis of the literature may serve as a basis for, among other things, a complex systems framework for socioeconomic inequalities in health.

The application of clique percolation method in health disparities research

AIMS

Clique percolation, one of the joint community detection algorithms in network science, is a novel and efficient approach to detecting overlapping communities in real networks. The current study illustrated how clique percolation can help to identify overlapping communities within the complex networks underlying health disparities, particularly highlighting nodes with strong associations with more than one community.

DESIGN

A cross-sectional study.

METHODS

The study used a dataset on Latinx populations (N = 1654; mean age = 43.3 years; 53.1% women) as an example to demonstrate the role of such overlapping nodes in the network of syndemic conditions and their common risk factors. Syndemic conditions in the network included HIV risk, substance abuse (smoking, heavy alcohol consumption and marijuana use) and poor mental health. Moreover, the risk factors encompassed individual (education and income) and sociostructural (adverse childhood experiences [ACEs] and access to services) factors. The network was estimated using the R-package bootnet. Clique percolation was conducted on the estimated network using the R-package CliquePercolation.

RESULTS

A total of three communities were detected, with HIV risk and poor mental health not being assigned to any community. In general, Community 1 was comprised of ACE categories, Community 2 included education, income and access to services and Community 3 included other syndemic conditions. Of note, two nodes were assigned to two communities: 'household dysfunction' to Communities 1 and 2 and 'smoking' to Communities 2 and 3.

CONCLUSION

Household dysfunction might be the key connector, among other ACEs, to individual and structural barriers. Such barriers further exposed Latinx individuals to risky behaviours, especially smoking, which further linked to marijuana use and heavy alcohol consumption.

IMPACT

Clique percolation facilitated our understanding of the complex systems of factors shaping health disparities. The overlapping nodes are promising intervention targets for reducing health disparities in this historically marginalized population.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Conceptualizing the Mechanisms of Social Determinants of Health: A Heuristic Framework to Inform Future Directions for Mitigation

Policy Points A large body of scientific work examines the mechanisms through which social determinants of health (SDOH) shape health inequities. However, the nuances described in the literature are infrequently reflected in the applied frameworks that inform health policy and programming. We synthesize extant SDOH research into a heuristic framework that provides policymakers, practitioners, and researchers with a customizable template for conceptualizing and operationalizing key mechanisms that represent intervention opportunities for mitigating the impact of harmful SDOH. In light of scarce existing SDOH mitigation strategies, the framework addresses an important research-to-practice translation gap and missed opportunity for advancing health equity.

CONTEXT

The reduction of health inequities is a broad and interdisciplinary endeavor with implications for policy, research, and practice. Health inequities are most often understood as associated with the social determinants of health (SDOH). However, policy and programmatic frameworks for mitigation often rely on broad SDOH domains, without sufficient attention to the operating mechanisms, and effective SDOH mitigation strategies remain scarce. To expand the cadre of effective SDOH mitigation strategies, a practical, heuristic framework for policymakers, practitioners, and researchers is needed that serves as a roadmap for conceptualizing and targeting the key mechanisms of SDOH influence.

METHODS

We conduct a critical review of the extant conceptual and empirical SDOH literature to identify unifying principles of SDOH mechanisms and to synthesize an integrated framework for conceptualizing such mechanisms.

FINDINGS

We highlight eight unifying principles of SDOH mechanisms that emerge from landmark SDOH research. Building on these principles, we introduce and apply a conceptual model that synthesizes key SDOH mechanisms into one organizing, heuristic framework that provides policymakers, practitioners, and researchers with a customizable template for conceptualizing and operationalizing the key SDOH mechanisms that represent intervention opportunities to maximize potential impact for mitigating a given health inequity.

CONCLUSIONS

Our synthesis of the extant SDOH research into a heuristic framework addresses a scarcity of peer-reviewed organizing frameworks of SDOH mechanisms designed to inform practice. The framework represents a practical tool to facilitate the translation of scholarly SDOH work into evidence-based and targeted policy and programming. Such tools designed to close the research-to-practice translation gap for effective SDOH mitigation are sorely needed, given that health inequities in the United States and in many other parts of the world have widened over the past two decades.

Exposure to COVID-19 and aggression: the mediating role of anxiety and the moderating role of rumination

The pandemic of COVID-19 is now posing a worldwide hazard to one's health. Exposure to COVID-19 may cause negative emotions like anxiety, which is one of recognized risk factors for aggressive behaviors. This study looked into the effect of exposure to COVID-19 on aggression and how anxiety may act as a mediating factor, as well as lastly how rumination could moderate a variety of indirect paths during the epidemic of COVID-19. According to the current study's findings, which included a sizable sample of Chinese college students (N = 1,518), being exposed to COVID-19 showed a positive connection with aggression and anxiety, as well as rumination. These findings clarify the role that mediators play in the relationship between anxiety and exposure to COVID-19. The results are also helpful for personalizing treatments and putting preventative measures in place to decrease the aggression brought on by exposure to COVID-19. It is explored how lowering rumination and anxiety may be useful in the context of COVID-19 to lessen the psychopathological effects of the condition.

Community-Level Social Support to Mitigate the Impact of Combined Frailty and Multimorbidity on Psychological Distress Among Rural Chinese Older Adults During the COVID-19 Pandemic: Multilevel Modeling Study

BACKGROUND

Accumulating research provides evidence that the psychological health of older people deteriorated from before to during the COVID-19 pandemic. Unlike robust individuals, coexisting frailty and multimorbidity expose older adults to more complicated and wide-ranging stressors. Community-level social support (CSS) is also an important impetus for age-friendly interventions, and it is 1 of the components of social capital that is seen as an ecological-level property. To date, we have not found research that examines whether CSS buffered the adverse impacts of combined frailty and multimorbidity on psychological distress in a rural setting during COVID-19 in China.

OBJECTIVE

This study explores the combined effect of frailty and multimorbidity on psychological distress in rural Chinese older adults during the COVID-19 pandemic and examines whether CSS would buffer the aforementioned association.

METHODS

Data used in this study were extracted from 2 waves of the Shandong Rural Elderly Health Cohort (SREHC), and the final analytic sample included 2785 respondents who participated in both baseline and follow-up surveys. Multilevel linear mixed effects models were used to quantify the strength of the longitudinal association between frailty and multimorbidity combinations and psychological distress using 2 waves of data for each participant, and then, cross-level interactions between CSS and combined frailty and multimorbidity were included to test whether CSS would buffer the adverse impact of coexisting frailty and multimorbidity on psychological distress.

RESULTS

Frail older adults with multimorbidity reported the most psychological distress compared to individuals with only 1 or none of the conditions (β=.68, 95% CI 0.60-0.77, P<.001), and baseline coexisting frailty and multimorbidity predicted the most psychological distress during the COVID-19 pandemic (β=.32, 95% CI 0.22-0.43, P<.001). Further, CSS moderated the aforementioned association (β=-.16, 95% CI -0.23 to -0.09, P<.001), and increased CSS buffered the adverse effect of coexisting frailty and multimorbidity on psychological distress during the COVID-19 pandemic (β=-.11, 95% CI -0.22 to -0.01, P=.035).

CONCLUSIONS

Our findings suggest that more public health and clinical attention should be paid to psychological distress among multimorbid older adults with frailty when facing public health emergencies. This research also suggests that community-level interventions prioritizing social support mechanisms, specifically improving the average levels of social support within communities, may be an effective approach to alleviate psychological distress for rural older adults who concurrently manifest frailty and multimorbidity.

Relationship Between Neighborhood Disadvantage and Mild Traumatic Brain Injury Symptoms

OBJECTIVE

To test the hypotheses that (1) higher neighborhood disadvantage is associated with greater injury-related symptom severity in civilians with mild traumatic brain injury (mTBI) and (2) neighborhood disadvantage remains predictive after controlling for other established predictors.

SETTING

Level 1 trauma center and affiliated academic medical center.

PARTICIPANTS

N = 171 individuals with mTBI.

DESIGN

Prospective cohort study.

MAIN MEASURES

Rivermead Post Concussion Symptoms Questionnaire (RPQ) total score assessed less than 24 hours and at 2 weeks, 3 months, and 6 months postinjury. Linear mixed-effects models were used to assess the relationship between predictor variables and mTBI-related symptom burden (RPQ score). Neighborhood disadvantage was quantified by the Area Deprivation Index (ADI), a composite of 17 markers of socioeconomic position (SEP) scored at the census block group level.

RESULTS

Individuals in the upper ADI quartile of the national distribution displayed higher RPQ symptoms than those in the lower 3 quartiles ( P < .001), with a nonsignificant ADI × visit interaction ( P = .903). In a multivariable model, the effect of ADI remained significant ( P = .034) after adjusting for demographics, individual SEP, and injury factors. Other unique predictors in the multivariable model were gender (gender × visit P = .035), health insurance type ( P = .017), and injury-related litigation ( P = .012).

CONCLUSION

Neighborhood disadvantage as quantified by the ADI is robustly associated with greater mTBI-related symptom burden throughout the first 6 months postinjury. That the effect of ADI remained after controlling for demographics, individual SEP, and injury characteristics implies that neighborhood disadvantage is an important, understudied factor contributing to clinical recovery from mTBI.

Environmental inequality in the neighborhood networks of urban mobility in US cities

Exposure to air pollution within one’s residential neighborhood has detrimental consequences on health and well-being. Yet, this effect may be mitigated or exacerbated because individuals spend much of their time outside of their residential neighborhood to travel to neighborhoods across a city for work, errands, and leisure. Using mobile phone data to track neighborhood mobility in large US cities, I find that residents from minority and poor neighborhoods travel to neighborhoods that have greater air pollution levels than the neighborhoods that residents from White and nonpoor neighborhoods visit. These results reveal that minority and poor residents face environmental inequalities at three geographic scales: the neighborhoods they live in, their bordering neighborhoods, and the neighborhoods they visit.

Research has made clear that neighborhoods impact the health and well-being of their residents. A related strand of research shows that neighborhood disadvantage is geographically clustered. Because the neighborhoods of low-income and minority populations tend to be more disadvantaged, neighborhood conditions help explain racial and socioeconomic inequalities. These strands of research restrict processes of neighborhood influence to operate only within and between geographically contiguous neighbors. However, we are underestimating the role of neighborhood conditions in explaining inequality if disadvantage extends beyond the residential and extralocal environments into a network of neighborhoods spanning the urban landscape based on where residents move within a city. I use anonymized mobile phone data to measure exposure to air pollution among residents of poor and minority neighborhoods in 88 of the most populous US cities. I find that residents from minority and poor neighborhoods travel to neighborhoods that have greater air pollution levels than the neighborhoods that residents from White and nonpoor neighborhoods visit. Hispanic neighborhoods exhibit the greatest overall pollution burden, Black/White and Asian/White disparities are greatest at the network than residential scale, and the socioeconomic advantage of lower risk exposure is highest for residents from White neighborhoods. These inequalities are notable given recent declines in segregation and air pollution levels in American cities.

Trends in psychological distress and COVID-19 incidence across 15 U.S. metropolitan statistical areas in 2020

The United States experienced three surges of COVID-19 community infection since the World Health Organization declared the pandemic on March 11, 2020. The prevalence of psychological distress among U.S. adults increased from 11 % in 2019 to 35.9 % in April 2020 when New York City become the epicenter of the COVID-19 outbreak. Analyzing 21 waves of the Household Pulse Survey data collected between April 2020 and December 2020, this study aimed to examine the distress level in the 15 most populated metropolitan areas in the U.S. Our study found that, as the pandemic swept from East to South and soared in the West, 39.9%–52.3 % U.S. adults living in these 15 metropolitan areas reported symptoms of psychological distress. The highest distress levels were found within the Western areas including Riverside-San Bernardino-Ontario (52.3 % in July 2020, 95 % CI: 44.9%–59.6 %) and Los Angeles-Long Beach-Anaheim (49.9 % in December 2020, 95 % CI: 44.5%–55.4 %). The lowest distress level was observed in Washington-Arlington-Alexandria ranging from 29.1 % in May 2020 to 39.9 % in November 2020. COVID-19 and its complex ecology of social and economic stressors have engaged high levels of sustained psychological distress. Our findings will support the efforts of local, state and national leadership to plan interventions by addressing not only the medical, but also the economic and social conditions associated with the pandemic.