Understanding High-Utilizing Patients Based on Social Risk Profiles: a Latent Class Analysis Within an Integrated Health System

Rogers, Artair; Hu, Yi R; Schickedanz, Adam; Gottlieb, Laura M.; Sharp, Adam L.

doi:10.1007/s11606-019-05510-9

Cited by 11 publications

(14 citation statements)

References 6 publications

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“…Unmet HRSNs result in higher rates of chronic conditions, emergency department (ED) use, hospital readmissions, and no-show appointments. [7][8][9][10][11] Few studies have examined multiple HRSNs through a standardized screening tool, large-scale randomized controlled trials (RCT), diverse healthcare settings, geographies, and populations. [12][13][14] We analyzed data from a representative sample of community-dwelling Medicare and Medicaid beneficiaries through the national CMS Accountable Health Communities (AHC) Model.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Studies involving latent class analysis combined the patientreported social risks to ascertain social risk classes and categorized the participants in these classes. In the study of Blalock et al, 8 between the studies and included mail, email, and telephone surveys 8,10,12,14,15 or in-person interviews. 9,11,13…”

Section: Resultsmentioning

confidence: 99%

“…[5][6][7] In recent years, several studies have demonstrated the substantial impact of social determinants of health (SDOH) on hospitalization rates. [8][9][10][11][12][13][14][15] The World Health Organization (WHO) 16 defines the SDOH as "the conditions in which people are born, grown, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life." These conditions are influenced by a broader set of factors, including living conditions, economy, social policies, politics, and commercial determinants of health.…”

Section: Introductionmentioning

confidence: 99%

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The association of patient‐reported social determinants of health and hospitalization rate: A scoping review

Ardekani

Fereidooni

Heydari

et al. 2023

Health Science Reports

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Introduction The interplay between social determinants of health (SDOH) and hospitalization is significant as targeted interventions can improve the social status of the individuals. This interrelation has been historically overlooked in health care. In the present study, we reviewed studies in which the association between patient‐reported social risks and hospitalization rate was assessed. Method We performed a scoping literature review of articles published until September 1, 2022 without time limit. We searched PubMed, Embase, Web of Science, Scopus, and Google Scholar to find relevant studies using terms representing “social determinants of health” and “hospitalization.” Forward and backward reference checking was done for the included studies. All studies that used patient‐reported data as a proxy of social risks to determine the association between social risks and hospitalization rates were included. The screening and data extraction processes were done independently by two authors. In case of disagreement, senior authors were consulted. Results Our search process retrieved a total of 14,852 records. After the duplicate removal and screening process, eight studies met the eligibility criteria, all of which were published from 2020 to 2022. The sample size of the studies ranged from 226 to 56,155 participants. All eight studies investigated the impact of food security on hospitalization, and six investigated economic status. In three studies, latent class analysis was applied to divide participants based on their social risks. Seven studies found a statistically significant association between social risks and hospitalization rates. Conclusion Individuals with social risk factors are more susceptible to hospitalization. There is a need for a paradigm shift to meet these needs and reduce the number of preventable hospitalizations.

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“…However, given the high AUC in our overall sample and subgroups, even a modest reduction in discrimination is likely to be useful. Additionally, the COVAS score does not include social risks that may predict adverse outcomes among at-risk populations, such as those with housing instability or food insecurity [ 33 ]. Our study population uses only the initial ED patient encounter for risk assessment, therefore, patients with a return visit may have slightly different risks for an adverse event.…”

Section: Discussionmentioning

confidence: 99%

Identifying patients with symptoms suspicious for COVID-19 at elevated risk of adverse events: The COVAS score

Sharp

Huang

Broder

et al. 2021

The American Journal of Emergency Medicine

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Objective Develop and validate a risk score using variables available during an Emergency Department (ED) encounter to predict adverse events among patients with suspected COVID-19. Methods A retrospective cohort study of adult visits for suspected COVID-19 between March 1 – April 30, 2020 at 15 EDs in Southern California. The primary outcomes were death or respiratory decompensation within 7-days. We used least absolute shrinkage and selection operator (LASSO) models and logistic regression to derive a risk score. We report metrics for derivation and validation cohorts, and subgroups with pneumonia or COVID-19 diagnoses. Results 26,600 ED encounters were included and 1079 experienced an adverse event. Five categories (comorbidities, obesity/BMI ≥ 40, vital signs, age and sex) were included in the final score. The area under the curve (AUC) in the derivation cohort was 0.891 (95% CI, 0.880–0.901); similar performance was observed in the validation cohort (AUC = 0.895, 95% CI, 0.874–0.916). Sensitivity ranging from 100% (Score 0) to 41.7% (Score of ≥15) and specificity from 13.9% (score 0) to 96.8% (score ≥ 15). In the subgroups with pneumonia ( n = 3252) the AUCs were 0.780 (derivation, 95% CI 0.759–0.801) and 0.832 (validation, 95% CI 0.794–0.870), while for COVID-19 diagnoses ( n = 2059) the AUCs were 0.867 (95% CI 0.843–0.892) and 0.837 (95% CI 0.774–0.899) respectively. Conclusion Physicians evaluating ED patients with pneumonia, COVID-19, or symptoms suspicious for COVID-19 can apply the COVAS score to assist with decisions to hospitalize or discharge patients during the SARS CoV-2 pandemic.

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Understanding High-Utilizing Patients Based on Social Risk Profiles: a Latent Class Analysis Within an Integrated Health System

Cited by 11 publications

References 6 publications

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

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

The association of patient‐reported social determinants of health and hospitalization rate: A scoping review

Identifying patients with symptoms suspicious for COVID-19 at elevated risk of adverse events: The COVAS score

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