Impact of Risk Stratification on Referrals and Uptake of Wraparound Services That Address Social Determinants: A Stepped Wedged Trial

Vest, Joshua R.; Menachemi, Nir; Grannis, Shaun J.; Ferrell, Jennifer L.; Kasthurirathne, Suranga Nath; Zhang, Ying; Tong, Yan; Halverson, Paul K.

doi:10.1016/j.amepre.2018.11.009

Cited by 42 publications

(32 citation statements)

References 43 publications

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“…Prior studies have identified increased referral to and use of wraparound services including clinical team members or behavioral health when such pathways are implemented. [54,55] Addressing SDoH in risk stratification models and care pathways is an important step toward improving the equity of outcomes from spine surgery.…”

Section: Plos Onementioning

confidence: 99%

Quantifying the collective influence of social determinants of health using conditional and cluster modeling

et al. 2020

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Objectives Our objective was to analyze the collective effect of social determinants of health (SDoH) on lumbar spine surgery outcomes utilizing two different statistical methods of combining variables. Methods This observational study analyzed data from the Quality Outcomes Database, a nationwide United States spine registry. Race/ethnicity, educational attainment, employment status, insurance payer, and gender were predictors of interest. We built two models to assess the collective influence of SDoH on outcomes following lumbar spine surgery—a stepwise model using each number of SDoH conditions present (0 of 5, 1 of 5, 2 of 5, etc) and a clustered subgroup model. Logistic regression analyses adjusted for age, multimorbidity, surgical indication, type of lumbar spine surgery, and surgical approach were performed to identify the odds of failing to demonstrate clinically meaningful improvements in disability, back pain, leg pain, quality of life, and patient satisfaction at 3- and 12-months following lumbar spine surgery. Results Stepwise modeling outperformed individual SDoH when 4 of 5 SDoH were present. Cluster modeling revealed 4 distinct subgroups. Disparities between the younger, minority, lower socioeconomic status and the younger, white, higher socioeconomic status subgroups were substantially wider compared to individual SDoH. Discussion Collective and cluster modeling of SDoH better predicted failure to demonstrate clinically meaningful improvements than individual SDoH in this cohort. Viewing social factors in aggregate rather than individually may offer more precise estimates of the impact of SDoH on outcomes.

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Section: Plos Onementioning

confidence: 99%

Quantifying the collective influence of social determinants of health using conditional and cluster modeling

et al. 2020

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“…Many pediatric scientific medical societies actively recommend universal screening for social determinants of health, including FI, in order to identify people at risk at a social level. Unfortunately, although tools to efficiently assess social determinants exist, they are still lacking comprehensiveness in providing a concise view of health in socially deprived populations [101][102][103][104][105]. Therefore, public health programs to evaluate the increased frequency of micronutrient deficiencies and diseases in these populations need to be promoted.…”

Section: Discussionmentioning

confidence: 99%

Food Insecurity and Major Diet-Related Morbidities in Migrating Children: A Systematic Review

Dondi

Piccinno²,

Morigi

et al. 2020

Nutrients

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Children of migrant families are known to be at a higher risk of diet-related morbidities due to complex variables including food insecurity, cultural and religious beliefs, and sociodemographic factors like ethnicity, socioeconomic status, and education. Several studies have assessed the presence of specific diseases related to dietary issues in migrant children. This systematic review aims to highlight the existing body of work on nutritional deficiencies in the specific vulnerable pediatric population of immigrants. Refugees were intentionally excluded because of fundamental differences between the two groups including the reasons for migration and health status at the time of arrival. A total of 29 papers were included and assessed for quality. Most of them described a strong correlation between obesity and migration. A high prevalence of stunting, early childhood caries, iron and vitamin D deficiency was also reported, but the studies were few and heterogeneous. Food insecurity and acculturation were found important social factors (nevertheless with inconclusive results) influencing dietary habits and contributing to the development of morbidities such as obesity and other metabolic disorders, which can cause progressive unsustainability of health systems. Public health screening for diet-related diseases in migrant children may be implemented. Educational programs to improve children’s diet and promote healthy-living behaviors as a form of socioeconomic investment for the health of the new generations may also be considered.

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“…We have integrated the updated decision models into the existing platform for all scheduled and walk-in appointments. Model results are presented to end users using a customized interface within the electronic health record with metadata on which features drove the extreme gradient boosting decision-making process, and with predicted probabilities categorized as low, rising, or high risk [ 12 ] ( Figure 2 ). This study’s methodological work sets the foundation for our future evaluations of our intervention’s impact on patient outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…We integrated these models into nine federally qualified health center sites operated by Eskenazi Health, a county-owned safety-net provider located in Indianapolis, Indiana. A subsequent trial identified increased rates of referral when predicted-need scores were shared with primary care end users [ 12 ]. Nevertheless, there were several limitations in our previous study such as limited patient-level measures, a level of aggregated data that was too coarse, poor optimization, lack of consideration of data temporality, and limited generalizability.…”

Section: Introductionmentioning

confidence: 99%

Precision Health–Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation

Kasthurirathne¹,

Grannis²,

Halverson³

et al. 2020

JMIR Med Inform

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Background Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited. Objective The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services. Methods We used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models. Results Decision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score P<.001, AUROC P=.01; social work: F1 score P<.001, AUROC P=.03; dietitian: F1 score P=.001, AUROC P=.001; other: F1 score P=.01, AUROC P=.02); however, inclusion of additional population-level social determinants of health did not contribute to any performance improvements (behavioural health: F1 score P=.08, AUROC P=.09; social work: F1 score P=.16, AUROC P=.09; dietitian: F1 score P=.08, AUROC P=.14; other: F1 score P=.33, AUROC P=.21) in predicting the need for referral in our population of vulnerable patients seeking care at a safety-net provider. Conclusions Precision health–enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance.

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Impact of Risk Stratification on Referrals and Uptake of Wraparound Services That Address Social Determinants: A Stepped Wedged Trial

Cited by 42 publications

References 43 publications

Quantifying the collective influence of social determinants of health using conditional and cluster modeling

Quantifying the collective influence of social determinants of health using conditional and cluster modeling

Food Insecurity and Major Diet-Related Morbidities in Migrating Children: A Systematic Review

Precision Health–Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation

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