Identification of Cross-sector Service Utilization Patterns Among Urban Medicaid Expansion Enrollees

Abstract

Background-The expansion of Medicaid as part of the Affordable Care Act opened new opportunities to provide health coverage to low-income adults who may be involved in other public sectors. Objective-The main objective of this study was to describe cross-sector utilization patterns among urban Medicaid expansion enrollees. Research Design-We merged data from 4 public sectors (health care, human services, housing, and criminal justice) for 98,282 Medicaid expansion enrollees in Hennepin County, MN. We fit a latent class model to indicators of cross-sector involvement. Measures-Indicator variables described involvement levels within each sector from March 2011 through December 2014. Demographic and chronic condition indicators were included post hoc to characterize classes. Results-We found 6 archetypes of cross-sector involvement: The "Low Contact" class (33.9%) had little involvement in any public sector; "Primary Care" (26.3%) had moderate, stable health care utilization; "Health and Human Services" (15.3%) had high rates of health care and cash assistance utilization; "Minimal Criminal History" (11.0%) had less serious criminal justice involvement; "Cross-sector" (7.8%) had elevated emergency department use, involvement in all 4 sectors, and the highest prevalence of behavioral health conditions; "Extensive Criminal History" (5.7%) had serious criminal justice involvement. The 3 most expensive classes (Health and Human Services, Cross-sector, and Extensive Criminal History) had the highest rates of behavioral health conditions. Together, they comprised 29% of enrollees and 70% of total public costs.

Aligning healthcare, public health and social services: A scoping review of the role of purpose, governance, finance and data

“…Recommended practices include linking data to an evaluation plan and tying data to collaborative goals (Bodurtha et al., 2017 ; Connolly et al., 2016 ; Koo et al., 2016 ; Public Health Leadership Forum, 2018 ; Scally et al., 2017 ; Siegel et al., 2015 ). Ultimately, data linked in this way can be used to implement feedback loops, where needs, efforts and outcomes are tied together cyclically to assess progress (Beers et al., 2018 ; Browne et al., 2016 ; Center for Health Care Strategies & Nonprofit Finance Fund, 2018 ; Center for Sharing Public Health Services & Public Health National Center for Innovations, 2019 , 2011 ; Chandran et al., 2019 , 2011 ; Corbin et al., 2018 ; Costenbader et al., 2018 ; Davis & Tsao, 2015 ; Fastring et al., 2018 ; Fuller et al., 2015 ; Gottlieb et al., 2019 ; Jones, 2018 ; Prevention Institute, 2018 ; Public Health Leadership Forum, 2018 ; Spencer & Freda, 2016 ).…”

“…For example, data being used in a feedback loop can be used to demonstrate impact to outside parties (Mattessich & Rausch, 2014 ). Collaboratives also can create online dashboards to help orient partners (Nemours, 2012 ), share data with policy advocacy partners (Bull et al., 2015 ; de Leeuw, 2017 ; Rodriguez et al., 2015 ; Tsai & Petrescu‐Prahova, 2016 ) or work with researchers on theoretical research, providing a firmer basis for practice across the entire field (Bodurtha et al., 2017 ; Liljas et al., 2019 ; Liljegren, 2013 ; Maxwell et al., 2014 ; Rasanathan et al., 2018 ; Spencer & Freda, 2016 ).…”

Trends in Pediatric Viral Symptoms, Influenza Testing, and SARS-CoV-2 Testing From a Statewide Electronic Health Record Consortium, January 2017 to July 2021

“…9,20,21 Key strengths of our statewide study include the ability to simultaneously surveil viral symptoms and corresponding test results, the integration of data from all encounter locations and levels of care across nine large health systems, and the demonstrated feasibility of a rapidly scaled multisystem partnership in addressing a pressing public health surveillance need. Moreover, in reflecting upon the Consortium's broader work throughout the pandemic and looking towards the post-COVID-19 future, our successful linkage of EHR data across multiple health systems has become a powerful data source for surveillance of other public health priorities, 13,15 an opportunity for cross-sector linkages with social services, housing, and criminal justice agencies, 22 and a mechanism to tie disease outcomes and population health metrics with placebased vulnerability indices. 14,23 Our study also has limitations.…”

Differential Effects of an Emergency Department-to-Home Care Transitions Intervention in an Older Adult Population

“…LCA has been widely used to segment patient populations, including patients deemed to have a high risk of ED readmission. [19][20][21][22][23][24] Studies looking at high use, high cost, and medically complex patients have found distinct classes that may benefit from different interventions. 21,22,25 This study aims to identify latent classes within an older adult population from a randomized controlled trial testing the effectiveness of an ED-to-home transitional care program.…”

“…Latent class analysis (LCA) is a model-based method for breaking down a heterogeneous population into more homogenous classes with respect to a set of measured variables. LCA has been widely used to segment patient populations, including patients deemed to have a high risk of ED readmission 19–24. Studies looking at high use, high cost, and medically complex patients have found distinct classes that may benefit from different interventions 21,22,25…”