Social determinants of health, adverse childhood experiences, and maternal-infant relationship

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Kapp ◽  
A Frech ◽  
B Hall ◽  
A Kemner
Keyword(s):  
Early Childhood ◽  
Childhood Obesity ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Adverse Childhood Experiences ◽  
Home Visiting ◽  
Obesity Risk ◽  
Childhood Experiences ◽  
Adverse Childhood ◽  
Early Childhood Obesity

Abstract Background Low strength of maternal-infant relationship (MIR) is consistently associated with early childhood obesity risk. Because obesity often persists once it develops, primary prevention is needed early. Home visiting programs support families with social determinants of health (SDH) and adverse childhood experiences (ACEs); SDH and ACEs contribute to health inequities. Addressing SDH and ACEs may facilitate improvements in MIR and ultimately mitigate early childhood obesity risk. Limited to no research has examined the association between ACEs, SDH, and MIR. In the context of a national, evidence-based home visiting program, we asked: are SDH and ACEs associated with low MIR? Methods This sample includes 6,972 children ages 0–<24 months enrolled in the Parents as Teachers home visiting program across the United States from sites using the Life Skills Progression (LSP) instrument through February 2020. Low MIR is dichotomized from a 1-5 scale, with low scores reflecting low nurturing, bonding, and responsiveness. We used the literature, theory, and a stepwise logistic regression model-building process to identify a parsimonious model for MIR. Results Preliminary results reflect 34.2% Hispanic or Latino, 22.7% non-Hispanic Black, 35.3% non-Hispanic Other race; 83.9% low income; 36.9% low education; and 13.4% mothers scoring low for MIR. Notable findings from modeling include: physical ACEs, captured here as child abuse or neglect (OR: 5.01, 95% CI: 4.10-6.11); mental illness ACEs, captured here as a mother/parent with mental illness (OR: 1.31, 95% CI: 1.05-1.63), or the mother/parent treated violently (OR: 1.95, 95% CI: 1.56-2.40). Protective associations include mothers' support of child development and self-esteem scores. Conclusions Understanding the complex interplay of SDH, ACEs, and MIR is critical for developing interventions that address “upstream” family characteristics in order to mitigate early childhood obesity risk. ACEs play a predominant role. Key messages This is the first known study to concurrently examine maternal-infant relationship, social determinants of health, and adverse childhood experiences. Home visiting programs may be critical partners in addressing these needs given their reach.

scholarly journals Adverse Childhood Experiences among Adults in North Carolina, USA: Influences on Risk Factors for Poor Health across the Lifespan and Intergenerational Implications

2020 ◽  
Vol 17 (22) ◽  
pp. 8548
Author(s):  
Adam Hege ◽  
Erin Bouldin ◽  
Manan Roy ◽  
Maggie Bennett ◽  
Peyton Attaway ◽  
...  
Keyword(s):  
Risk Factors ◽  
North Carolina ◽  
Health Risk ◽  
Food Insecurity ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Adverse Childhood Experiences ◽  
Health Risk Factors ◽  
Childhood Experiences ◽  
Adverse Childhood

Adverse childhood experiences (ACEs) are a critical determinant and predictor of health across the lifespan. The Appalachian region of the United States, particularly the central and southern portions, experiences worse health outcomes when compared to the rest of the nation. The current research sought to understand the cross-sectional relationships between ACEs, social determinants of health and other health risk factors in one southcentral Appalachian state. Researchers used the 2012 and 2014 North Carolina Behavioral Risk Factor Surveillance System (BRFSS) for analyses. An indicator variable of Appalachian county (n = 29) was used to make comparisons against non-Appalachian counties (n = 71). Analyses further examined the prevalence of ACEs in households with and without children across Appalachian and non-Appalachian regions, and the effects of experiencing four or more ACEs on health risk factors. There were no statistically significant differences between Appalachian and non-Appalachian counties in the prevalence of ACEs. However, compared with adults in households without children, those with children reported a higher percentage of ACEs. Reporting four or more ACEs was associated with higher prevalence of smoking (prevalence ratio [PR] = 1.56), heavy alcohol consumption (PR = 1.69), overweight/obesity (PR = 1.07), frequent mental distress (PR = 2.45), and food insecurity (PR = 1.58) in adjusted models and with fair or poor health only outside Appalachia (PR = 1.65). Residence in an Appalachian county was independently associated with higher prevalence of food insecurity (PR = 1.13). Developing programs and implementing policies aimed at reducing the impact of ACEs could improve social determinants of health, thereby helping to reduce health disparities.

scholarly journals Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records

2017 ◽  
Vol 25 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Cosmin A Bejan ◽  
John Angiolillo ◽  
Douglas Conway ◽  
Robertson Nash ◽  
Jana K Shirey-Rice ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Adverse Childhood Experiences ◽  
Determinants Of Health ◽  
Homeless Population ◽  
Health Records ◽  
Childhood Experiences ◽  
Adverse Childhood ◽  
Electronic Health

Abstract Objective Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository. Materials and Methods We first constructed lexicons to capture homelessness and ACE phenotypic profiles. We employed word2vec and lexical associations to mine homelessness-related words. Next, using relevance feedback, we refined the 2 profiles with iterative searches over 100 million notes from the Vanderbilt EHR. Seven assessors manually reviewed the top-ranked results of 2544 patient visits relevant for homelessness and 1000 patients relevant for ACE. Results word2vec yielded better performance (area under the precision-recall curve [AUPRC] of 0.94) than lexical associations (AUPRC = 0.83) for extracting homelessness-related words. A comparative study of searches for the 2 phenotypes revealed a higher performance achieved for homelessness (AUPRC = 0.95) than ACE (AUPRC = 0.79). A temporal analysis of the homeless population showed that the majority experienced chronic homelessness. Most ACE patients suffered sexual (70%) and/or physical (50.6%) abuse, with the top-ranked abuser keywords being “father” (21.8%) and “mother” (15.4%). Top prevalent associated conditions for homeless patients were lack of housing (62.8%) and tobacco use disorder (61.5%), while for ACE patients it was mental disorders (36.6%–47.6%). Conclusion We provide an efficient solution for mining homelessness and ACE information from EHRs, which can facilitate large clinical and genetic studies of these social determinants of health.

Advocacy and Ac­tion in Appalachia Aimed at Adverse Childhood Experiences

2020 ◽  
pp. 125-148
Author(s):  
Adam Hege ◽  
Denise Presnell ◽  
Kellie B. Reed-Ashcraft ◽  
Karen Caldwell ◽  
Damiana Gibbons Pyles ◽  
...  
Keyword(s):  
Social Determinants Of Health ◽  
Social Determinants ◽  
Adverse Childhood Experiences ◽  
Academic Research ◽  
Social Determinant ◽  
Determinants Of Health ◽  
Childhood Experiences ◽  
Adverse Childhood ◽  
Community Initiative ◽  
Rural Appalachia

Rural Appalachia faces extreme poverty-linked hardships, often referred to as ‘social determinants of health'. One social determinant of health often linked with rural poverty is adverse childhood experiences (ACEs). ACEs refer to numerous experiences (e.g., forms of abuse and maltreatment, a dysfunctional household, mental illness in the household, etc.) that one encounters before the age of 18. Local leaders of a county in the heart of Appalachia in northwestern North Carolina (Watauga) embarked on a community level initiative (Watauga Compassionate Community Initiative) aimed at preventing and treating the effects of ACEs and building resiliency. This chapter delves into the academic research related to ACEs and poverty and details the background and ongoing story of the Watauga Compassionate Community Initiative, concluding with lessons learned and insights into what other rural communities can focus on when addressing ACEs, poverty, and other social determinants of health.

scholarly journals Assessing the Social Determinants of Health and Adverse Childhood Experiences in Patients Attending a Children's Hospital Cleft Palate-Craniofacial Program

2021 ◽  
pp. 105566562110487
Author(s):  
Ethan Ponton ◽  
Rebecca Courtemanche ◽  
Tanjot K. Singh ◽  
Damian Duffy ◽  
Douglas J. Courtemanche ◽  
...  
Keyword(s):  
Cleft Palate ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Adverse Childhood Experiences ◽  
Determinants Of Health ◽  
Childhood Experiences ◽  
Children's Hospital ◽  
Adverse Childhood ◽  
The Social ◽  
Children’S Hospital

This study aimed to describe the social determinants of health (SDoH) for patients receiving multidisciplinary team care in a Cleft Palate-Craniofacial program, develop responsive and consistent processes to include trauma-informed psychosocial histories, promote discussions about additional “non-medical” factors influencing health and surgical outcomes, and demonstrate that these activities are feasible in the context of multidisciplinary patient-provider interactions. Single-site, cross-sectional study using a questionnaire. Participants were recruited from a provincial quaternary care Cleft Palate-Craniofacial program at British Columbia Children's Hospital in Vancouver, BC, Canada. 290 families completed the questionnaire. 34% of families experience significant barriers to accessing primary health care, 51% struggle financially, and 11% scored four or more on the Adverse Childhood Experiences scale. Furthermore, 47% reported not having adequate social support in their lives, and 5% reported not feeling resilient at the time of the survey. Patients with cleft and craniofacial anomalies have complex needs that extend beyond the surgical and medical care they receive. It is critical that all Cleft and Craniofacial teams incorporate social histories into their clinic workflow and be responsive to these additional needs. Discussions surrounding SDoH and adversity are welcomed by families; being involved in the care and decision-making plans is highly valued. Healthcare providers can and should ask about SDoH and advocate for universal access to responsive, site-based, social work support for their patients.

Childhood obesity: how long should we wait to predict weight?

2018 ◽  
Vol 31 (5) ◽  
pp. 497-501 ◽  
Author(s):  
Éadaoin M. Butler ◽  
José G.B. Derraik ◽  
Rachael W. Taylor ◽  
Wayne S. Cutfield
Keyword(s):  
Early Childhood ◽  
Childhood Obesity ◽  
Prediction Models ◽  
Population Based ◽  
Future Research ◽  
Clinical Prediction ◽  
Obesity Risk ◽  
Clinical Prediction Models ◽  
Predictive Capacity ◽  
Early Childhood Obesity

AbstractObesity is highly prevalent in children under the age of 5 years, although its identification in infants under 2 years remains difficult. Several clinical prediction models have been developed for obesity risk in early childhood, using a number of different predictors. The predictive capacity (sensitivity and specificity) of these models varies greatly, and there is no agreed risk threshold for the prediction of early childhood obesity. Of the existing models, only two have been practically utilized, but neither have been particularly successful. This commentary suggests how future research may successfully utilize existing early childhood obesity prediction models for intervention. We also consider the need for such models, and how targeted obesity intervention may be more effective than population-based intervention.

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