Social determinants of health, data science, and decision-making: Forging a transdisciplinary synthesis

Exploring personal aptitudes and personality traits that, together with social determinants, shape health behaviors and conduct: a thematic analysis based on the Capability, Opportunity, Motivation and Behavior (COM-B) change system

Introduction

Effective implementation of strategies to promote health and prevent noncommunicable illnesses requires a profound understanding of the interaction between the individual and society. This study brings to health research the consideration of psychosocial factors that influence the maintenance and change of health behaviors and conduct. From a primary care perspective, it is crucial to propose a biopsychosocial approach for the development of health promotion and self-care programs that embrace personal aptitudes as a relevant individual aspect.

Objectives

To explore experiences related to personal aptitudes and personality traits that influence health behaviors and conduct, taking into account the social determinants of health, through a thematic analysis based on the capability-opportunity-motivation and behavior (COM-B) system.

Methods and analysis

This qualitative research is carried out from a descriptive phenomenological perspective, based on 17 focus groups in which 156 people participated. Inductive and deductive analysis techniques were used following Lincoln and Guba's criteria of methodological rigor. In addition to 7 different triangulations of analysts, 6 main categories were identified based on the COM-B system: psychological capacity, physical capacity, physical opportunity, social opportunity, reflective motivation, and automatic motivation. The importance of considering these factors to promote healthy behaviors was stressed.

Discussion

This study examined how personal experiences related personal aptitudes and personality traits influence health behaviors and conduct in Spain. It was found that personality traits such as health literacy, self-efficacy, activation, and self-determination can influence the adoption of healthy behaviors. Likewise, the need for control, overthinking, and ambivalence made it impossible. Furthermore, social determinants of health and interpersonal relationships also play an important role.

Trial registration

ClinicalTrials.gov, NCT04386135. Registered on April 30, 2020.

Predictive model of suicide risk in Colombian university students: quantitative analysis of associated factors

Introduction

The risk of suicide and completed suicides among young university students presents critical challenges to mental and public health in Colombia and worldwide. Employing a quantifiable approach to comprehend the factors associated with these challenges can aid in visualizing the path towards anticipating and controlling this phenomenon.

Objective

Develop a predictive model for suicidal behavior in university students, utilizing predictive analytics.

Method

We conducted an observational, retrospective, cross-sectional, and analytical research study at the University of Manizales, with a focus on predictive applicability. Data from 2,436 undergraduate students were obtained from the research initiative "Building the Future: World Mental Health Surveys International College Students."

Results

The top ten predictor variables that generated the highest scores (ranking coefficients) for the sum of factors were as follows: history of sexual abuse (13.21), family history of suicide (11.68), medication (8.39), type of student (7.4), origin other than Manizales (5.86), exposure to cannabis (4.27), exposure to alcohol (4.42), history of physical abuse (3.53), religiosity (2.9), and having someone in the family who makes you feel important (3.09).

Discussion

Suicide involves complex factors within psychiatric, medical, and societal contexts. Integrated detection and intervention systems involving individuals, families, and governments are crucial for addressing these factors. Universities also play a role in promoting coping strategies and raising awareness of risks. The predictive accuracy of over 80% in identifying suicide risk underscores its significance.

Conclusion

The risk factors related to suicidal behavior align with the findings in specialized literature and research in the field. Identifying variables with higher predictive value enables us to take appropriate actions for detecting cases and designing and implementing prevention strategies.

Developing Data Sharing Models for Health Research with Real-World Data: A Scoping Review of Patient and Public Preferences

For researchers to realize the benefits of real-world data in healthcare requires broader access to patient data than is currently possible given siloed data systems. To facilitate evidence generation, infrastructure must support integrated data collection and sharing enabled by patient consent. Critical to the success of data sharing is to design secured data sharing platforms around patient preferences and expectations. The objective of this review was to characterize patient and public preferences for secured data sharing platforms and incentives to share real-world data for health research. We conducted a scoping review of the data sharing and health informatics literature capturing patient and public values for data sharing platforms and incentivization. We searched Embase and Medline (OVID) databases for primary data studies. Two reviewers participated in study selection and data abstraction. Findings were summarized according to preference frequency within each major theme. The final search produced 253 articles. After screening, 12 articles were included for data extraction. Two studies discussed preferences for data sharing platforms, 7 discussed incentives preferences, and 3 addressed both. We identified considerable variation of patient and public preferences according to preferred consent mechanisms and level of control, willingness to trade off risks and benefits, and the type of incentivization appropriate to offer for participation. This preference variation informs the conditions under which individuals may be willing to engage with secured data sharing platforms to support research. Our findings indicate that platforms will need to be flexible to meet the diverse preferences of users and facilitate uptake.

Community determinants of COPD exacerbations in elderly patients in Lodz province, Poland: a retrospective observational Big Data cohort study

OBJECTIVES

To evaluate the prevalence and identify demographic, economic and environmental local community determinants of chronic obstructive pulmonary disease (COPD) exacerbations in elderly in primary care using Big Data approach.

DESIGN

Retrospective observational case-control study based on Big Data from the National Health Found, Tax Office and National Statistics Center databases in 2016.

SETTING

Primary care clinics in the Lodz province in Poland.

PARTICIPANTS

472 314 patients aged 65 and older in primary care, including 17 240 patients with COPD and 1784 with exacerbations (including deaths).

OUTCOME MEASURES

Exacerbations with demographic, economic and environmental local community determinants were retrieved. Conditional logistic regression for matched pairs was used to evaluate the local community determinants of COPD exacerbations among patients with COPD.

RESULTS

The overall prevalence of COPD in the population of elderly patients registered in primary healthcare clinic clinics in Lodz province in 2016 was 3.65%, 95% CI (3.60% to 3.70%) and the prevalence of exacerbations was 10.35%, 95% CI (9.89% to 10.80%). The high number of consultations in primary care clinics was associated with higher risk of COPD exacerbations (p=0.0687).High-income patients were less likely to have exacerbations than low-income patients (high vs low OR 0.601, 95% CI (0.385 to 0.939)). The specialisation of the primary care physician did not have an effect on exacerbations (OR 1.076, 95% CI (0.920 to 1.257)). Neither the forest cover per gmina (high vs low OR 0.897, 95% CI (0.605 to 1.331); medium vs low OR 0.925, 95% CI (0.648 to 1.322)), nor location of gmina (urban vs urban-rural OR 1.044; 95% CI (0.673 to 1.620)), (rural vs urban-rural OR 0.897, 95% CI (0.630 to 1.277)) appears to influence COPD exacerbations.

CONCLUSIONS

Big Data statistical analysis facilitated the evaluation of the prevalence and determinants of COPD exacerbation in the elderly residents of Lodz province, Poland.Modification of identified local community determinants may potentially decrease the number of exacerbations in elderly patients with COPD.

Towards better Data Science to address racial bias and health equity

Data Science can be used to address racial health inequities. However, a wealth of scholarship has shown that there are many ethical challenges with using Data Science to address social problems. To develop a Data Science focused on racial health equity, we need the data, methods, application, and communication approaches to be antiracist and focused on serving minoritized groups that have long-standing worse health indicators than majority groups. In this perspective, we propose eight tenets that could shape a Data Science for Racial Health Equity research framework.

The Social Data Foundation model: Facilitating health and social care transformation through datatrust services

Turning the wealth of health and social data into insights to promote better public health, while enabling more effective personalized care, is critically important for society. In particular, social determinants of health have a significant impact on individual health, well-being, and inequalities in health. However, concerns around accessing and processing such sensitive data, and linking different datasets, involve significant challenges, not least to demonstrate trustworthiness to all stakeholders. Emerging datatrust services provide an opportunity to address key barriers to health and social care data linkage schemes, specifically a loss of control experienced by data providers, including the difficulty to maintain a remote reidentification risk over time, and the challenge of establishing and maintaining a social license. Datatrust services are a sociotechnical evolution that advances databases and data management systems, and brings together stakeholder-sensitive data governance mechanisms with data services to create a trusted research environment. In this article, we explore the requirements for datatrust services, a proposed implementation—the Social Data Foundation, and an illustrative test case. Moving forward, such an approach would help incentivize, accelerate, and join up the sharing of regulated data, and the use of generated outputs safely amongst stakeholders, including healthcare providers, social care providers, researchers, public health authorities, and citizens.

Examining Neighborhood Socioeconomic Status as a Mediator of Racial/Ethnic Disparities in Hypertension Control Across Two San Francisco Health Systems

BACKGROUND

A contextual understanding of hypertension control can inform population health management strategies to mitigate cardiovascular disease events. This retrospective cohort study links neighborhood-level data with patients' health records to describe racial/ethnic differences in uncontrolled hypertension and determine if and to what extent these differences are mediated by neighborhood socioeconomic status (nSES).

METHODS

We conducted a mediation analysis using a sample of patients with hypertension from 2 health care delivery systems in San Francisco over 2 years (n=47 031). We used generalized structural equation modeling, adjusted for age, sex, and health care system, to estimate the contribution of nSES to disparities in uncontrolled hypertension between White patients and Black, Hispanic/Latino, and Asian patients, respectively. Sensitivity analysis removed adjustment for health care system.

RESULTS

Over half the cohort (62%) experienced uncontrolled hypertension during the study period. Racial/ethnic groups showed substantial differences in prevalence of uncontrolled hypertension and distribution of nSES quintiles. Compared with White patients, Black, and Hispanic/Latino patients had higher adjusted odds of uncontrolled hypertension: odds ratio, 1.79 [95% CI, 1.67-1.91] and odds ratio, 1.38 [95% CI, 1.29-1.47], respectively and nSES accounted for 7% of the disparity in both comparisons. Asian patients had slightly lower adjusted odds of uncontrolled hypertension when compared with White patients: odds ratio, 0.95 [95% CI, 0.89-0.99] and the mediating effect of nSES did not change the direction of the relationship. Sensitivity analysis increased the proportion mediated by nSES to 11% between Black and White patients and 13% between Hispanic/Latino and White patients, but did not influence differences between Asian and White patients.

CONCLUSIONS

Among patients with hypertension in this study, nSES mediated a small proportion of racial/ethnic disparities in uncontrolled hypertension. Population health management strategies may be most effective by focusing on additional structural and interpersonal pathways such as racism and discrimination in health care settings.

Recyclable waste pickers: life and work in light of the social determinants of health

OBJECTIVES

to know how the social determinants of health relate to the context of life and work of recyclable waste pickers.

METHODS

a qualitative study, derived from Convergent-Care Research, conducted with waste pickers from two recycling associations in the South of Brazil. We used systematic participant observation, semi-structured interviews, and convergence groups. The analysis followed the steps of Seizure, Synthesis, Theorization, and Transfer.

RESULTS

advanced age, precarious self-care, gender inequalities, violence, and family conflicts have shown to be elements linked to the individual, behavioral, and social network determinants. Determinants connected to living and working conditions were related to poor access to education and formal work, as well as to the daily occupational risks in recycling. The lack of labor rights and public policies represented macro-determinants. Final.

CONSIDERATIONS

social and economic deficiencies are potentiated in the context of life and work of waste pickers, strongly related to their determinants.

Integrating Social Determinants in Decision-Making Processes for Health: Insights from Conceptual Frameworks-the 3-D Commission

The inclusion of social determinants of health offers a more comprehensive lens to fully appreciate and effectively address health. However, decision-makers across sectors still struggle to appropriately recognise and act upon these determinants, as illustrated by the ongoing COVID-19 pandemic. Consequently, improving the health of populations remains challenging. This paper seeks to draw insights from the literature to better understand decision-making processes affecting health and the potential to integrate data on social determinants. We summarised commonly cited conceptual approaches across all stages of the policy process, from agenda-setting to evaluation. Nine conceptual approaches were identified, including two frameworks, two models and five theories. From across the selected literature, it became clear that the context, the actors and the type of the health issue are critical variables in decision-making for health, a process that by nature is a dynamic and adaptable one. The majority of these conceptual approaches implicitly suggest a possible role for data on social determinants of health in decision-making. We suggest two main avenues to make the link more explicit: the use of data in giving health problems the appropriate visibility and credibility they require and the use of social determinants of health as a broader framing to more effectively attract the attention of a diverse group of decision-makers with the power to allocate resources. Social determinants of health present opportunities for decision-making, which can target modifiable factors influencing health-i.e. interventions to improve or reduce risks to population health. Future work is needed to build on this review and propose an improved, people-centred and evidence-informed decision-making tool that strongly and explicitly integrates data on social determinants of health.

Data, Social Determinants, and Better Decision-making for Health: the 3-D Commission

More than a decade after the World Health Organization Commission on the Social Determinants of Health (SDoH), it is becoming widely accepted that social and economic factors, including but not limited to education, energy, income, race, ethnicity, and housing, are important drivers of health in populations. Despite this understanding, in most contexts, social determinants are not central to local, national, or global decision-making. Greater clarity in conceptualizing social determinants, and more specificity in measuring them, can move us forward towards better incorporating social determinants in decision-making for health. In this paper, first, we summarize the evolution of the social framing of health. Second, we describe how the social determinants are conceptualized and contextualized differently at the global, national, and local levels. With this, we seek to demonstrate the importance of analyzing and understanding SDoH relative to the contexts in which they are experienced. Third, we problematize the gap in data across contexts on different dimensions of social determinants and describe data that could be curated to better understand the influence of social determinants at the local and national levels. Fourth, we describe the necessity of using data to understand social determinants and inform decision-making to improve health. Our overall goal is to provide a path for our collective understanding of the foundational causes of health, facilitated by advances in data access and quality, and realized through improved decision-making.

Energy, Data, and Decision-Making: a Scoping Review-the 3D Commission

Access to energy is an important social determinant of health, and expanding the availability of affordable, clean energy is one of the Sustainable Development Goals. It has been argued that climate mitigation policies can, if well-designed in response to contextual factors, also achieve environmental, economic, and social progress, but otherwise pose risks to economic inequity generally and health inequity specifically. Decisions around such policies are hampered by data gaps, particularly in low- and middle-income countries (LMICs) and among vulnerable populations in high-income countries (HICs). The rise of "big data" offers the potential to address some of these gaps. This scoping review sought to explore the literature linking energy, big data, health, and decision-making.Literature searches in PubMed, Embase, and Web of Science were conducted. English language articles up to April 1, 2020, were included. Pre-agreed study characteristics including geographic location, data collected, and study design were extracted and presented descriptively, and a qualitative thematic analysis was performed on the articles using NVivo.Thirty-nine articles fulfilled eligibility criteria. These included a combination of review articles and research articles using primary or secondary data sources. The articles described health and economic effects of a wide range of energy types and uses, and attempted to model effects of a range of technological and policy innovations, in a variety of geographic contexts. Key themes identified in our analysis included the link between energy consumption and economic development, the role of inequality in understanding and predicting harms and benefits associated with energy production and use, the lack of available data on LMICs in general, and on the local contexts within them in particular. Examples of using "big data," and areas in which the articles themselves described challenges with data limitations, were identified.The findings of this scoping review demonstrate the challenges decision-makers face in achieving energy efficiency gains and reducing emissions, while avoiding the exacerbation of existing inequities. Understanding how to maximize gains in energy efficiency and uptake of new technologies requires a deeper understanding of how work and life is shaped by socioeconomic inequalities between and within countries. This is particularly the case for LMICs and in local contexts where few data are currently available, and for whom existing evidence may not be directly applicable. Big data approaches may offer some value in tracking the uptake of new approaches, provide greater data granularity, and help compensate for evidence gaps in low resource settings.

Toward a New Strategic Public Health Science for Policy, Practice, Impact, and Health Equity

The COVID-19 pandemic and its social and health impact have underscored the need for a new strategic science agenda for public health. To optimize public health impact, high-quality strategic science addresses scientific gaps that inform policy and guide practice. At least 6 scientific gaps emerge from the US experience with COVID-19: health equity science, data science and modernization, communication science, policy analysis and translation, scientific collaboration, and climate science. Addressing these areas within a strategic public health science agenda will accelerate achievement of public health goals. Public health leadership and scientists have an unprecedented opportunity to use strategic science to guide a new era of improved and equitable public health.