MIMIC-SBDH: A Dataset for Social and Behavioral Determinants of Health.
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ABSTRACT:
SUBMITTER: Ahsan H
PROVIDER: S-EPMC8734043 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The theoretical explanation of human problems is derived from the complex interplay of psychological, social, economic, political, and physical factors.AimsThis study examined the roles of behavioral health (i.e., alcohol abuse and suicidality) and social environment (i.e., family support, school connectedness, and favorable neighborhood) and mental health [i.e., depression, anxiety, and attention deficit hyperactivity disorder (ADHD)] in predicting insomnia in adolescents in an ecological perspective.MethodsApproximately 6445 high school students in Taiwan were administered an anonymous self-report survey. Hierarchical multiple regression was performed to examine how multidimensional social environment, behavioral health, and mental health factors were associated with insomnia in adolescents.ResultsThe prevalence rate of insomnia in the sample was 30%. The results indicated that alcohol abuse (β = 0.04), suicidality (β = 0.06), depression (β = 0.29), anxiety (β = 0.14), and ADHD (β = 0.11) were positively associated with insomnia (p < 0.001), whereas family support (β = -0.06), school connectedness (β = -0.05), and favorable neighborhood (β = -0.10) were negatively associated with insomnia (p < 0.001). Sex did not predict insomnia, but age was positively associated with insomnia (β = 0.09, p < 0.001). Among all predictors of insomnia in the study, mental health factors, especially depression, play a major role on insomnia among adolescents, and is as much important as social environment factors.ConclusionThis study demonstrated how both psychosocial variables (social environment and behavioral health) and psychological symptoms were associated with insomnia in adolescents when the demographic variables (sex and age) were controlled and provided valuable information and evidence for clinicians, social workers, and health professionals who provide support to adolescents with insomnia. Applying an ecological approach in practice can aid in understanding at individual, family, school, and community levels and in identifying the strengths and weaknesses of their interactions with each other.ImplicationsThis perspective enables practitioners in effectively treating problems and addressing the needs of the various levels, including the individual, family, school, and the broader community. Thus, prevention and intervention of insomnia in adolescents should focus on multidimensional risk and protective factors, including mental health, behavioral health, and social environment, in the context of an ecological system.
| S-EPMC6696979 | biostudies-literature
Project description:BackgroundThe context in which a behavioral intervention is delivered is an important source of variability and systematic approaches are needed to identify and quantify contextual factors that may influence intervention efficacy. Machine learning-based phenotyping methods can contribute to a new precision health paradigm by informing personalized behavior interventions. Two primary goals of precision health, identifying population subgroups and highlighting behavioral intervention targets, can be addressed with psychosocial-behavioral phenotypes. We propose a method for psychosocial-behavioral phenotyping that models social determinants of health in addition to individual-level psychological and behavioral factors.PurposeTo demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics.MethodsIn this secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883), we optimized a multichannel mixed membership model (MC3M) using Bayesian inference to identify psychosocial-behavioral phenotypes and used logistic regression to determine which phenotypes were associated with elevated weight status (BMI ≥ 25kg/m2).ResultsWe identified 20 psychosocial-behavioral phenotypes. Phenotypes were conceptually consistent as well as discriminative; most participants had only one active phenotype. Two phenotypes were significantly positively associated with elevated weight status; four phenotypes were significantly negatively associated. Each phenotype suggested different contextual considerations for intervention design.ConclusionsBy depicting the complexity of psychological and social determinants of health while also providing actionable insight about similarities and differences among members of the same community, psychosocial-behavioral phenotypes can identify potential intervention targets in context.
| S-EPMC9672350 | biostudies-literature
Project description:Insufficient sleep is associated with cardiometabolic disease and poor health. However, few studies have assessed its determinants in a nationally representative sample. Data from the 2009 behavioral risk factor surveillance system were used (N = 323,047 adults). Insufficient sleep was assessed as insufficient rest/sleep over 30 days. This was evaluated relative to sociodemographics (age, sex, race/ethnicity, marital status, region), socioeconomics (education, income, employment, insurance), health behaviors (diet, exercise, smoking, alcohol), and health/functioning (emotional support, BMI, mental/physical health). Overall, insufficient sleep was associated with being female, White or Black/African-American, unemployed, without health insurance, and not married; decreased age, income, education, physical activity; worse diet and overall health; and increased household size, alcohol, and smoking. These factors should be considered as risk factors for insufficient sleep.
| S-EPMC4456880 | biostudies-other
Project description:Digital data collection during routine clinical practice is now ubiquitous within hospitals. The data contains valuable information on the care of patients and their response to treatments, offering exciting opportunities for research. Typically, data are stored within archival systems that are not intended to support research. These systems are often inaccessible to researchers and structured for optimal storage, rather than interpretability and analysis. Here we present MIMIC-IV, a publicly available database sourced from the electronic health record of the Beth Israel Deaconess Medical Center. Information available includes patient measurements, orders, diagnoses, procedures, treatments, and deidentified free-text clinical notes. MIMIC-IV is intended to support a wide array of research studies and educational material, helping to reduce barriers to conducting clinical research.
| S-EPMC9810617 | biostudies-literature
Project description:BackgroundSocial and behavioral determinants of health (SBDH) are environmental and behavioral factors that often impede disease management and result in sexually transmitted infections. Despite their importance, SBDH are inconsistently documented in electronic health records (EHRs) and typically collected only in an unstructured format. Evidence suggests that structured data elements present in EHRs can contribute further to identify SBDH in the patient record.ObjectiveExplore the automated inference of both the presence of SBDH documentation and individual SBDH risk factors in patient records. Compare the relative ability of clinical notes and structured EHR data, such as laboratory measurements and diagnoses, to support inference.MethodsWe attempt to infer the presence of SBDH documentation in patient records, as well as patient status of 11 SBDH, including alcohol abuse, homelessness, and sexual orientation. We compare classification performance when considering clinical notes only, structured data only, and notes and structured data together. We perform an error analysis across several SBDH risk factors.ResultsClassification models inferring the presence of SBDH documentation achieved good performance (F1 score: 92.7-78.7; F1 considered as the primary evaluation metric). Performance was variable for models inferring patient SBDH risk status; results ranged from F1?=?82.7 for LGBT (lesbian, gay, bisexual, and transgender) status to F1?=?28.5 for intravenous drug use. Error analysis demonstrated that lexical diversity and documentation of historical SBDH status challenge inference of patient SBDH status. Three of five classifiers inferring topic-specific SBDH documentation and 10 of 11 patient SBDH status classifiers achieved highest performance when trained using both clinical notes and structured data.ConclusionOur findings suggest that combining clinical free-text notes and structured data provide the best approach in classifying patient SBDH status. Inferring patient SBDH status is most challenging among SBDH with low prevalence and high lexical diversity.
| S-EPMC7056402 | biostudies-literature
Project description:IntroductionSocial and behavioral factors are known to affect health but are not routinely assessed in medical practice. To date, no studies have assessed a parsimonious panel of measures of social and behavioral determinants of health (SBDs). This study evaluated the panel of SBD measures recommended by the Institute of Medicine and examined the effect of question order.MethodsAdults, aged ≥18 years, were recruited using ResearchMatch.org for this randomized, parallel design study conducted in 2015 (data analyzed in 2015-2016). Three versions of the SBD measures, sharing the same items but in different orders of presentation (Versions 1-3), were developed. Randomized to six groups, participants completed each version at least 1 week apart (Weeks 1-3). Version order was counterbalanced across each administration and randomization was stratified by gender, race, and age. Main outcomes were effect of question order, completion time, and non-response rates.ResultsOf 781 participants, 624 (80%) completed the Week 1 questionnaire; median completion time for answering all SBD questions was 5 minutes, 583/624 participants answered all items, and no statistically significant differences associated with question order were observed when comparing responses across all versions. No significant differences in responses within assignment groups over time were found, with the exception of the stress measure for Group 5 (p=0.036).ConclusionQuestion order did not significantly impact participant responses. Time to complete the questionnaire was brief, and non-response rate was low. Findings support the feasibility of using the Institute of Medicine-recommended questionnaire to capture SBDs.
| S-EPMC5253326 | biostudies-literature
Project description:This work is an implementation science study that examines different aspects of implementing a single intervention. The intervention consists of asking community health centers to implement an outreach strategy to screen patients for colorectal cancer and for social determinants of health in community health centers at the same contact point. These are both clinical targets that the CHCs feel that their patients need and want to offer at a higher rate. The intervention consists of outreach to patients in need of colorectal cancer screening (CRC) to offer fecal immunochemical test (FIT) screening and screening for social determinants of health (SDOH). In this implementation science study, the intervention is an evidence-based intervention being implemented in real-world clinical practice. The intervention is the outreach to offer FIT and SDOH, conducted by clinic staff. Both evidence-based screening activities-FIT and SDOH screening-are used in the practices included in the study but pairing them is intended to increase efficiency and patient-centeredness by addressing health related social needs that may impact patients’ ability to engage in cancer screening. The study aims to test the effect of implementing the intervention on clinical and process outcomes. Clinical outcomes are CRC screening and SDOH screening. Analysis of process outcomes includes measuring what organizational factors influence implementation.
| 2354406 | ecrin-mdr-crc
Project description:Maternal race, ethnicity and socio-economic position are known to be associated with increased risk for a range of poor pregnancy outcomes, including maternal morbidity and mortality. Previously, researchers seeking to identify the contributing factors focused on maternal behaviors and pregnancy complications. Less understood is the contribution of the social determinants of health (SDoH) in observed differences by race/ethnicity in these key outcomes. In this secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) dataset, latent mixture modeling was used to construct groups of healthy, nulliparous participants with a non-anomalous fetus in a cephalic presentation having a trial of labor (N = 5763) based on SDoH variables. The primary outcome was a composite score of postpartum maternal morbidity. A postpartum maternal morbidity event was experienced by 350 individuals (6.1%). Latent class analysis using SDoH variables revealed six groups of participants, with postpartum maternal morbidity rates ranging from 8.7% to 4.5% across groups (p < 0.001). Two SDoH groups had the highest odds for maternal morbidity. These higher-risk groups were comprised of participants with the lowest income and highest stress and those who had lived in the USA for the shortest periods of time. SDoH phenotype predicted MM outcomes and identified two important, yet distinct groups of pregnant people who were the most likely have a maternal morbidity event.
| S-EPMC9288477 | biostudies-literature
Project description:Previous studies indicate multiple influences on the overall health of HIV-infected persons; however, few assess and rank longitudinal changes in social and structural barriers that are disproportionately found in impoverished populations. We empirically ranked factors that longitudinally impact the overall health status of HIV-infected homeless and unstably housed men.Between 2002 and 2008, a cohort of 288 HIV+ homeless and unstably housed men was recruited and followed over time. The population was 60% non-Caucasian and the median age was 41 years; 67% of study participants reported recent drug use and 20% reported recent homelessness. At baseline, the median CD4 cell count was 349 cells/µl and 18% of eligible persons (CD4<350) took antiretroviral therapy (ART). Marginal structural models were used to estimate the population-level effects of behavioral, social, and structural factors on overall physical and mental health status (measured by the SF-36), and targeted variable importance (tVIM) was used to empirically rank factors by their influence. After adjusting for confounding, and in order of their influence, the three factors with the strongest negative effects on physical health were unmet subsistence needs, Caucasian race, and no reported source of instrumental support. The three factors with the strongest negative effects on mental health were unmet subsistence needs, not having a close friend/confidant, and drug use. ART adherence >90% ranked 5th for its positive influence on mental health, and viral load ranked 4th for its negative influence on physical health.The inability to meet food, hygiene, and housing needs was the most powerful predictor of poor physical and mental health among homeless and unstably housed HIV-infected men in an urban setting. Impoverished persons will not fully benefit from progress in HIV medicine until these barriers are overcome, a situation that is likely to continue fueling the US HIV epidemic.
| S-EPMC3338834 | biostudies-other
Project description:As the articles in this Supplement demonstrate, the social determinants of health are a major focus for Kaiser Permanente and the broader US health care system. Mounting evidence of the impact of social determinants on people’s health has stimulated a surge of activity among policymakers, health systems, and a growing number of social entrepreneurs to integrate health and social services and to find novel ways to finance those efforts. The question is no longer whether there is an appropriate role for the US health care system in addressing the social determinants of health, but what that role is, how to create the right policy context for innovation and how health care can partner more effectively with providers of social services to meet patients’ most pressing needs given the fragmented, typically underresourced nature of the social sector.
| S-EPMC6207436 | biostudies-other