How Do Social Needs Cluster Among Low-Income Individuals?

Kreuter, Matthew W.; Garg, Rachel; Li, Linda; McNulty, Laura; Thompson, Tess; McQueen, Amy; Luke, Alina A.

doi:10.1089/pop.2020.0107

Cited by 25 publications

(28 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also compared our findings with Medicaid insurance status as a baseline assumption and potential proxy for HRSN status. Previous studies have found strong associations between Medicaid coverage, social needs, and healthcare utilization and outcomes 64 . Finally, we used state of the art Gradient Boosting Decision Tree ML approaches whose hyperparameters where automatically tuned with Bayesian optimization without using a non-overlapping test set.…”

Section: Discussionmentioning

confidence: 90%

Predicting health-related social needs in Medicaid and Medicare populations using machine learning

Holcomb

Oliveira

Highfield

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66–0.70) was achieved by the “any HRSNs” outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.

show abstract

Section: Discussionmentioning

confidence: 90%

Predicting health-related social needs in Medicaid and Medicare populations using machine learning

Holcomb

Oliveira

Highfield

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…34 However, these patients also have well-documented difficulties accessing healthcare resources. 29,35 Prospective studies of HRSN screening in non-healthcaresystem-based populations are needed to gain a more accurate picture of the health risks associated increased need and the values of screening for those needs for population health. Interestingly, the AHC Model evaluation report found substantially higher rates of social needs in patients screened versus available community-level data (e.g., County Health Rankings).…”

Section: Discussionmentioning

confidence: 99%

Association of Social Needs and Healthcare Utilization Among Medicare and Medicaid Beneficiaries in the Accountable Health Communities Model

et al. 2022

View full text Add to dashboard Cite

Background Integration of health-related social needs (HRSNs) data into clinical care is recognized as a driver for improving healthcare. However, few published studies on HRSNs and their impact are available. CMS sought to fill this gap through the Accountable Health Communities (AHC) Model, a national RCT of HRSN screening, referral, and navigation. Data from the AHC Model could significantly advance the field of HRSN screening and intervention in the USA. Objective To present data from the Greater Houston AHC (GH-AHC) Model site on HRSN frequency and the association between HRSNs, sociodemographic factors, and self-reported ED utilization using a cross-sectional design. Analyses included descriptive statistics and multinomial logistic regression. Participants (or Patients or Subjects) All community-dwelling Medicare, Medicaid, or dually covered beneficiaries at participating GH-AHC clinical delivery sites were eligible. Main Measures Self-reported ED utilization in the previous 12 months served as the outcome; demographic characteristics including race, ethnicity, age, sex, income, education level, number of people living in the household, and insurance type were treated as covariates. HRSNs included food insecurity, housing instability, transportation, difficulty paying utility bills, and interpersonal safety. Clinical delivery site type was used as the clustering variable. Key Results Food insecurity was the most common HRSN identified (38.7%) followed by housing instability (29.0%), transportation (28.0%), and difficulty paying utility bills (26.7%). Interpersonal safety was excluded due to low prevalence. More than half of the beneficiaries (56.9%) reported at least one of the four HRSNs. After controlling for covariates, having multiple co-occurring HRSNs was strongly associated with increased risk of two or more ED visits (OR 1.8–9.47 for two to four needs, respectively; p < 0.001). Beneficiaries with four needs were at almost 10 times higher risk of frequent ED utilization (p < 0.001). Conclusions To our knowledge, this is only the second published study to report screening data from the AHC Model. Future research focused on the impact of multiple co-occurring needs on health outcomes is warranted.

show abstract

“…Because participants were identified when calling 2-1-1, they likely have at least one acute social need they are trying to address. In one study comparing 2-1-1 callers to other low-income populations (e.g., Medicaid plan members), 2-1-1 callers reported about twice as many unmet social needs [ 44 ]. Thus, it is not clear how representative this sample is relative to all low-income smokers in the U.S.…”

Section: Discussionmentioning

confidence: 99%

“…Social needs were assessed using 10 items based on Segal's Personal Empowerment Scale [ 42 ], studies by Blazer and colleagues [ 43 ], and used in prior trials and large studies [ 44 ]. Items assessed the likelihood that, in the next month, the respondent would: (1) have a place to stay; (2) be able to pay their current electric, gas or water bill in full; (3) have enough food to feed themselves and others in their home; (4) have reliable transportation to get to appointments, meetings, work, and getting the things they need for daily living; (5) have enough money for necessities like food, shelter and clothing; (6) have enough money to deal with unexpected expenses; (7) be threatened physically by another person; and (8) have trouble finding or paying for childcare.…”

Section: Methodsmentioning

confidence: 99%

Stress, depression, sleep problems and unmet social needs: Baseline characteristics of low-income smokers in a randomized cessation trial

Garg

McQueen

Roberts

et al. 2021

Contemporary Clinical Trials Communications

Self Cite

View full text Add to dashboard Cite

How Do Social Needs Cluster Among Low-Income Individuals?

Cited by 25 publications

References 30 publications

Predicting health-related social needs in Medicaid and Medicare populations using machine learning

Predicting health-related social needs in Medicaid and Medicare populations using machine learning

Association of Social Needs and Healthcare Utilization Among Medicare and Medicaid Beneficiaries in the Accountable Health Communities Model

Stress, depression, sleep problems and unmet social needs: Baseline characteristics of low-income smokers in a randomized cessation trial

Contact Info

Product

Resources

About