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[This corrects the article DOI: 10.1371/journal.pone.0316699.].

Financial health, understood as one's ability to manage expenses, prepare for and recover from financial shocks, have minimal debt, and ability to build wealth, underlies all facets of daily living such as securing food and paying for housing, yet there is inconsistency in measurement and definition of this critical concept. Most social determinants research and interventions focus on siloed solutions (housing, food, utilities) rather than on a root solution such as financial health. In light of the paucity of public health research on financial health, particularly among low-income populations, this study seeks to: 1) introduce the construct of financial health into the domain of public health as a useful root term that underlies other individual measures of economic hardship and 2) demonstrate through outcomes on financial, physical and mental health among low-income caregivers of young children that the construct of financial health belongs in the canon of social determinants of health. In order to extract features of financial health relevant to overall well-being, principal components analysis were used to assess survey data on banking and personal finances among caregivers of young children who participate in public assistance. Then, a series of logistic regressions were utilized to examine the relationship between components of financial health, depression and self-rated health. Components aligned with other measures of financial health in the literature, and there were strong associations between financial health and health outcomes. Financial health can be conceived of and measured as a key social determinant of health.

Background Disparities in non-medical health factors, such as social determinants of health (SDoH), are associated with increased risk of negative health outcomes. Leveraging contextual (or area-based) measures of SDoH is essential for uncovering broader factors influencing disparities in critical care-related outcomes. Our objective is to review evidence analyzing the association between contextual SDoH obtained from publicly available databases and critical care-related outcomes in the United States (US). Methods We conducted searches in the Web of Science, PubMed, Cochrane, and Embase electronic database to obtain clinical studies utilizing SDoH datasets from publicly available data sources and analyzed these studies for associations between critical care-related outcomes and SDoH (search date June 8th, 2025). We excluded non-English articles, reviews, editorial commentaries, letters to editors, studies without intensive care unit (ICU) patients or SDoH variables, studies based on countries outside of the US and studies that lacked full text or contained only the abstract. We extracted cohort characteristics, SDoH measures and domains, SDOH database characteristics, ICU admissions and outcomes, analytical method used for determining the association between SDoH and ICU variables, and significant SDoH variables. Results We identified 87 publications (44 with pediatric patients, 40 with adult patients, and 3 with a mixture of both) and study population characteristics (e.g., surgical or specific disease-diagnosed patients). Child Opportunity Index and American Community Survey were the top platforms utilized for acquiring SDoH in pediatric and adult cohorts, respectively, followed by Area Deprivation Index and Social Vulnerability Index in both cohort types. Area-level granularity included boundaries determined by counties, ZIP codes, census block groups and census tracts. Conclusions Among five SDoH domains, economic stability was found to be the top investigated SDoH category for critical care-related outcomes. Contextual SDoH variables, indicating more vulnerable and adverse conditions, were associated with higher ICU admissions, greater need for ICU resource utilization, longer ICU duration, higher likelihood of developing critical illnesses, worsened life quality following ICU discharge, and higher mortality. Social determinants of health offer a broad area for modifiable intervention targets. Public databases serve as facilitators towards SDoH integration into electronic health records, promoting value-based care and mitigating health inequities.

Pulmonary tuberculosis (TB) with positive Acid-Fast Bacilli (AFB+) remains one of the most transmissible infectious diseases worldwide. This disease poses a significant public health challenge in many countries. This study aimed to develop a risk-factor-based intervention model to reduce the incidence of Pulmonary TB (AFB+). A case-control approach was employed, with the case group comprising people diagnosed with Pulmonary TB (AFB+), and the control group consisting of non-TB individuals from the same neighborhoods. Binary logistic regression was used for bivariate analysis, and multivariate analysis utilized logistic regression. This study found that the social determinants model accounted for 34.9% of the variance in the incidence of Pulmonary TB (AFB+) (R² = 0.349). The biological determinants model showed an R² of 0.127, indicating that this model explains 12.7% of the variance in the disease. The third model, which focused on behavioral determinants, had an R² of 0.312, meaning that behavioral factors accounted for 31.2% of the variance. The fourth model, examining the physical condition of housing, showed an R² of 0.425, indicating that 42.5% of the variance in Pulmonary TB (AFB+) is explained by variables related to housing conditions. In conclusion, the physical condition of housing emerged as the strongest predictor of Pulmonary TB (AFB+). These findings suggest that improving housing conditions should be a key component of public health strategies to reduce the incidence of Pulmonary TB (AFB+). Targeted interventions to improve the household environment are crucial for reducing the risk and transmission of Pulmonary TB (AFB+).

Introduces a vision for the future of health equity and explains practical policy measures for how to achieve it. Health inequity is one of the defining problems of our time. But current efforts to address the problem focus on mitigating the harms of injustice rather than confronting injustice itself. In Equal Care, Seth A. Berkowitz, MD, MPH, offers an innovative vision for the future of health equity by examining the social mechanisms that link injustice to poor health. He also presents practical policies designed to create a system of social relations that ensures equal care for everyone. As Berkowitz illustrates, the project of social democracy works to improve health by bringing relationships of equality to the sites of human cooperation: in civil society, in political processes, and in economic activities. This book synthesizes three elements necessary for such a project—normative justification, mechanistic knowledge, and technical proficiency—into a practical vision of how to create health equity. Drawing from the fields of medicine, social epidemiology, sociology, economics, political science, philosophy, and more, Berkowitz makes clear that health inequity is social failure embodied, and the only true cures are political. Equal Care is essential reading for anyone concerned with the future of health equity.

Social prescribing is growing rapidly globally as a way to tackle social determinants of health. However, whom it is reaching and how effectively it is being implemented remains unclear. To gain a comprehensive picture of social prescribing in the UK, from referral routes, reasons, to contacts with link workers and prescribed interventions. This study undertook the first analyses of a large database of administrative data from over 160 000 individuals referred to social prescribing across the UK. Data were analysed using descriptive analyses and regression modelling, including logistic regression for binary outcomes and negative binomial regression for count variables. Mental health was the most common referral reason and mental health interventions were the most common interventions prescribed. Between 72% and 85% of social prescribing referrals were from medical routes (primary or secondary healthcare). Although these referrals demonstrated equality in reaching across sociodemographic groups, individuals from more deprived areas, younger adults, men, and ethnic minority groups were reached more equitably via non-medical routes (e.g. self-referral, school, charity). Despite 90% of referrals leading to contact with a link worker, only 38% resulted in any intervention being received. A shortage of provision of community activities - especially ones relevant to mental health, practical support and social relationships - was evident. There was also substantial heterogeneity in how social prescribing is implemented across UK nations. Mental health is the leading reason for social prescribing referrals, demonstrating its relevance to psychiatrists. But there are inequalities in referrals. Non-medical referral routes could play an important role in addressing inequality in accessing social prescribing and therefore should be prioritised. Additionally, more financial and infrastructural resource and strategic planning are needed to address low intervention rates. Further investment into large-scale data platforms and staff training are needed to continue monitoring the development and distribution of social prescribing.

The opportunities for healthy choices in homes, neighborhoods, schools, and workplaces can have decisive impacts on health. We review scientific evidence from promising interventions focused on the social determinants of health and discuss how such interventions can improve population health and reduce health disparities. We found sufficient evidence of successful outcomes to support disparity-reducing policy interventions targeted at education and early childhood; urban planning and community development; housing; income enhancements and supplements; and employment. Cost-effectiveness evaluations show that these interventions lead to long-term societal savings, but the interventions require more routine attention to cost considerations. We discuss challenges to implementation, including the need for long-term financing to scale up effective interventions for implementation at the local, state, and national levels.

This paper discusses the upper bound analysis for the determinant of a matrix constructed using the numbers 1 to n2. We propose four different methods to approximate the upper bound, including using the AM-GM inequality, Hadamard’s inequality, maximizing diagonal elements, and the Perron-Frobenius theorem. Our results provide important insights into the structure and properties of matrices that maximize their determinants.