Abstract 218: Challenges and Opportunities in Using Cardiovascular Health Metrics and Social Determinants of Health to Inform Population Health Management

2019 ◽  
Vol 12 (Suppl_1) ◽  
Author(s):  
Caress A Dean ◽  
Brittany Ventline ◽  
Rachita Jagirdar
Keyword(s):  
Social Determinants Of Health ◽  
Population Health ◽  
Social Determinants ◽  
Cardiovascular Health ◽  
Health Management ◽  
Determinants Of Health ◽  
Population Health Management ◽  
Challenges And Opportunities

scholarly journals Integrating Social Determinants of Health to Precision Medicine through Digital Transformation: An Exploratory Roadmap

2021 ◽  
Vol 18 (9) ◽  
pp. 5018
Author(s):  
Ik-Whan G. Kwon ◽  
Sung-Ho Kim ◽  
David Martin
Keyword(s):  
Precision Medicine ◽  
Social Determinants Of Health ◽  
Population Health ◽  
Social Determinants ◽  
Information Technologies ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
Determinants Of Health ◽  
Collaborative Tools ◽  
High Level

The COVID-19 pandemic has altered healthcare delivery platforms from traditional face-to-face formats to online care through digital tools. The healthcare industry saw a rapid adoption of digital collaborative tools to provide care to patients, regardless of where patients or clinicians were located, while mitigating the risk of exposure to the coronavirus. Information technologies now allow healthcare providers to continue a high level of care for their patients through virtual visits, and to collaborate with other providers in the networks. Population health can be improved by social determinants of health and precision medicine working together. However, these two health-enhancing constructs work independently, resulting in suboptimal health results. This paper argues that artificial intelligence can provide clinical–community linkage that enhances overall population health. An exploratory roadmap is proposed.

600 Social Determinants and Their Impact on Burn Surgery Outcomes

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S151-S152
Author(s):  
Luis H Quiroga ◽  
Tomer Lagziel ◽  
Mohammed Asif ◽  
Raymond Fang ◽  
Grace F Rozycki ◽  
...  
Keyword(s):  
Mental Health ◽  
Diabetes Mellitus ◽  
Social Determinants Of Health ◽  
Population Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Hospital Charges ◽  
Burn Patients ◽  
Increased Risk ◽  
History Of

Abstract Introduction To our knowledge, no studies have been conducted assessing the social determinants of health and the impact on the outcomes for burn patients. Such studies are needed considering burn injuries are associated with high costs, severe psychological impact, and a high burden placed on the healthcare systems. The burden is hypothesized to be aggravated by the increasing amount of diabetes and obesity seen in the general population which put patients at increased risk for developing chronic wounds. Studies have shown that several socioeconomic status (SES) factors are associated with increased risk of burns, but none have documented the outcomes of burn patients based on their social determinants of health. In our study, we will be comparing patients in the burn ICU (BICU) to patients in the surgical ICU (SICU). The purpose of this comparison is to evaluate whether the same social determinants of health have similar influences in both groups. Methods We performed a retrospective analysis of population group data from patients admitted to the BICU and SICU from January 1, 2016, to November 18, 2019. The primary outcomes were length-of-stay (LOS), mortality, 30-day-readmission, and hospital charges. Pearson’s chi-square test for categorical variables and t-test for continuous variables were used to compare population health groups. Results We analyzed a total of 487 burn and 510 surgical patients. When comparing BICU and SICU patients, we observed significantly higher mean hospital charges and LOS in burn patients with a history of mental health (mean difference: $42,756.04, p=0.013 and 7.12 days, p=0.0085), ESRD ($57,8124.7, p=0.0047 and 78.62 days, p=0.0104), sepsis ($168,825.19, p=< 0.001 and 20.68 days, p=0.0043), and VTE ($63,9924.1, p=< 0.001 and 72.9 days, p=0.002). Also, higher mortality was observed in burn patients with ESRD, STEMI, sepsis, VTE, and diabetes mellitus. Burn patients with a history of mental health, drug dependence, heart failure, and diabetes mellitus also had greater 30-day-readmissions rates. Conclusions This study sheds new knowledge on the considerable variability that exists between the different population health groups in terms of outcomes for each cohort of critically ill patients. It demonstrates the impacts of population health group on outcomes. These population groups and social determinants have different effects on BICU versus SICU patients and this study provides supporting evidence for the need to identify and develop new strategies to decrease overspending in healthcare. Further research to develop relevant and timely interventions that can improve these outcomes.

scholarly journals The Social Determinants of Health in Disaster Risk Reduction

2019 ◽  
Vol 34 (s1) ◽  
pp. s95-s95
Author(s):  
Joseph Cuthbertson ◽  
Frank Archer ◽  
Jose Rodriguez-llanes ◽  
Andrew Robertson
Keyword(s):  
Social Determinants Of Health ◽  
Population Health ◽  
Social Determinants ◽  
Disaster Risk ◽  
Determinants Of Health ◽  
Health Determinants ◽  
Community Perception ◽  
Individual Interviews ◽  
The Social ◽  
Socially Isolated

Introduction:The rationale for undertaking this study was to investigate how characteristics of population health relate to and impact disaster risk, resilience, vulnerability, impact, and recovery. The multi-disciplinary environment that contextualizes disaster practice can influence determinants of health. Robust health determinants, or lack thereof, may influence the outcomes of disaster events affecting an individual or a community.Aim:To investigate how the social determinants of health inform community perceptions of disaster risk.Methods:Community perception of disaster risk in reference to the social determinants of health was assessed in this study. Individual interviews with participants from a community were conducted, all of whom were permanent community residents. Thematic analysis was conducted using narrative inquiry to gather firsthand insights on their perceptions of how characteristics of population health relate to and impact an individual’s disaster risk.Results:Analysis demonstrated commonality between interviewees in perceptions of the influence of the social determinants of health on individual disaster risk by determinant type. Interviewees sensed a strong correlation between low community connection and disaster risk vulnerability. Specific populations thought to have low community connection were perceived to be socially isolated, resulting in low knowledge or awareness of the surrounding disaster risks, or how to prepare and respond to disasters. In addition, they had reduced access to communication and support in time of need.Discussion:The importance of a strong social community connection was a feature of this research. Further research on how health determinants can enable disaster risk awareness and disaster risk communication is warranted.

scholarly journals Enhancing understandings of social determinants of health in China: Linkage and analysis of a national multilevel population health surveillance with routinely collected mortality records for 98 058 people in 31 provinces of mainland China

2019 ◽  
Author(s):  
Yunning Liu ◽  
Thomas Astell-Burt ◽  
Xiaoqi Feng ◽  
Fan Mao ◽  
Ruiming Liang ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Social Determinants Of Health ◽  
Population Health ◽  
Social Determinants ◽  
Communicable Disease ◽  
Mainland China ◽  
Health Surveillance ◽  
Determinants Of Health ◽  
County Level ◽  
Injury Mortality

Abstract Background: The aim of this study was to enhance capability in research on social determinants of health in China by linking and analyzing routinely-collected death records over 5 years with national population health surveillance.Methods: Linkage of 98 058 participants in the 2010 China Chronic Disease and Risk Factor Surveillance (CCDRFS) to records in the national death surveillance data from 2011 to 2015 was conducted through a matching program involving identification numbers, name, gender and residential address, followed by a structured checking process. Multilevel regressions were used to investigate five-year odds of all-cause, non-communicable disease (NCD), infectious disease and injury mortality in relation to person- and county-level factors.Results: A total of 3,365 deaths were observed in the linked mortality and population health surveillance. Cross-checks and comparisons with national mortality distributions provided assurance that the linkage was reasonable. Geographic variation in mortality was observed via age and gender adjusted median odds ratios for all-cause mortality (>1.30), infectious disease (>2.01), NCD (>1.24) and injury (>1.12). Increased odds of all-cause and all three cause-specific mortality outcomes were higher with age and among men. Low educational attainment was a predictor of all-cause, NCD and injury mortality. Longer mean years of education at the county-level was only associated with lower injury mortality. Divorcees had a higher odd of all-cause and NCD mortality than singletons. Rurality was a predictor of all-cause and NCD mortality.Conclusion: The results of this study provide utility for future investigations of social determinants of health and mortality using linked data in China.

Epidemiological Transition and Population Health: Understanding Social Determinants of Health in China

2016 ◽  
Vol 26 (3) ◽  
pp. 223-246
Author(s):  
Soma Hewa ◽  
Bo Liu
Keyword(s):  
Health Promotion ◽  
Social Determinants Of Health ◽  
Chronic Diseases ◽  
Population Health ◽  
Social Determinants ◽  
Social Epidemiology ◽  
Determinants Of Health ◽  
Effective Population ◽  
Future Health ◽  
Mortality And Morbidity

This article has twin objectives: First, the article briefly examines major theoretical interpretations of disease causations in Western medicine, their limitations in understanding social epidemiology, and the gradual development of the population health approach to health promotion and disease prevention in the context of chronic diseases in Western industrialized societies. Second, the article examines the current epidemiological trends in China and the relevance of population health perspectives and strategies to promote health. While analyzing some recent findings on social determinants of health in China, the article argues that effective population health strategies for health promotion must be based on a social epidemiology that provides information necessary to promote health. Although infectious diseases still make a significant contribution to China’s mortality and morbidity figures, the incidence of chronic diseases such as malignancies, heart disease, respiratory disease, and cerebrovascular disease is steadily increasing. Finally, in view of the current epidemiological trend, and the need to tackle the multiple health challenges, this discursive analysis proposes a number of key research areas within the broader context of social epidemiology that may facilitate future health policies in China.

scholarly journals Modeling the Impact of Social Determinants of Health on HIV

2021 ◽  
Author(s):  
Joseph W. Hogan ◽  
Noya Galai ◽  
Wendy W. Davis
Keyword(s):  
Social Determinants Of Health ◽  
Population Health ◽  
Social Services ◽  
Social Determinants ◽  
Critical Role ◽  
Individual Characteristics ◽  
Determinants Of Health ◽  
Modeling Approaches ◽  
The Impact

AbstractThere is growing evidence for the key role of social determinants of health (SDOH) in understanding morbidity and mortality outcomes globally. Factors such as stigma, racism, poverty or access to health and social services represent complex constructs that affect population health via intricate relationships to individual characteristics, behaviors and disease prevention and treatment outcomes. Modeling the role of SDOH is both critically important and inherently complex. Here we describe different modeling approaches and their use in assessing the impact of SDOH on HIV/AIDS. The discussion is thematically divided into mechanistic models and statistical models, while recognizing the overlap between them. To illustrate mechanistic approaches, we use examples of compartmental models and agent-based models; to illustrate statistical approaches, we use regression and statistical causal models. We describe model structure, data sources required, and the scope of possible inferences, highlighting similarities and differences in formulation, implementation, and interpretation of different modeling approaches. We also indicate further needed research on representing and quantifying the effect of SDOH in the context of models for HIV and other health outcomes in recognition of the critical role of SDOH in achieving the goal of ending the HIV epidemic and improving overall population health.

scholarly journals An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness, Response, and Management: Design and Development Study (Preprint)

2021 ◽  
Author(s):  
Whitney S Brakefield ◽  
Nariman Ammar ◽  
Olufunto A Olusanya ◽  
Arash Shaban-Nejad
Keyword(s):  
Public Health ◽  
Social Determinants Of Health ◽  
Population Health ◽  
Social Determinants ◽  
Urban Population ◽  
Data Science ◽  
Determinants Of Health ◽  
Case Scenario ◽  
Effective Interventions ◽  
Novel Approach

BACKGROUND COVID-19 is impacting people worldwide and is currently a leading cause of death in many countries. Underlying factors, including Social Determinants of Health (SDoH), could contribute to these statistics. Our prior work has explored associations between SDoH and several adverse health outcomes (eg, asthma and obesity). Our findings reinforce the emerging consensus that SDoH factors should be considered when implementing intelligent public health surveillance solutions to inform public health policies and interventions. OBJECTIVE This study sought to redefine the Healthy People 2030’s SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we aim to provide a blueprint and implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data. METHODS The process of building the UPHO involves collecting and integrating data from several sources, classifying the collected data into drivers and outcomes, incorporating data science techniques for calculating measurable indicators from the raw variables, and studying the extent to which interventions are identified or developed to mitigate drivers that lead to the undesired outcomes. RESULTS We generated and classified the indicators of social determinants of health, which are linked to COVID-19. To display the functionalities of the UPHO platform, we presented a prototype design to demonstrate its features. We provided a use case scenario for 4 different users. CONCLUSIONS UPHO serves as an apparatus for implementing effective interventions and can be adopted as a global platform for chronic and infectious diseases. The UPHO surveillance platform provides a novel approach and novel insights into immediate and long-term health policy responses to the COVID-19 pandemic and other future public health crises. The UPHO assists public health organizations and policymakers in their efforts in reducing health disparities, achieving health equity, and improving urban population health.

scholarly journals Social determinants of health in the Big Data mode of population health risk calculation

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110628
Author(s):  
Rachel Rowe
Keyword(s):  
Public Health ◽  
Big Data ◽  
Social Determinants Of Health ◽  
Population Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Individual Risk ◽  
Risk Calculation ◽  
Health And Illness ◽  
The Usa

Amidst the climate of crisis surrounding the rise in opioid-related overdose in the USA, early in 2019, Google and Deloitte launched ‘Opioid360’. Here came a platform combining browser histories, credit, insurance, social media, and traditional survey data to sell the service of risk calculation in population health. Opioid360's approach to automating risk calculation not only promised to identify persons ‘at risk’ of opioid dependence, but also paved the way for broader applications anticipating common chronic diseases and coordinating logistical operations involved in pandemic response. Beginning with this experimental platform, this paper develops an analysis of the Big Data mode of risk calculation - an epistemological and political shift that involves technology companies, investors, insurers, governments, and public health institutions. The analysis focuses on the re-emergence of ‘social determinants of health’ (SDOH) in the rhetoric accompanying novel analytic platforms that estimate, calculate, and compute individual health risks. While the treatment of SDOH has always been a site of political contestation within the discipline of public health, powerful interests are crystallising around the concept and instrumentalising it in platforms that sell algorithmic prediction. Silicon Valley's breed of asset-oriented technoscience appears not only to be amplifying the behaviouralist elements of public health. Among the stakes of the Big Data mode is the paradoxical retreat from changing social conditions that contribute to the prevalence of health and illness in populations; and instead, the promotion of an apparatus for pricing and exchanging individual risk or excluding from services those who bear risk most acutely.

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