Justin Blackburn
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The Economic Burden of Untreated Mental Illness in Indiana: Translating Evidence into Policy
Justin Blackburn, Ph.D., is an Associate Professor of Health Policy and Management at the Richard M. Fairbanks School of Public Health in Indianapolis. He is also the Health Policy Ph.D. Program Director and the Scientific Director Wellbeing Informed by Science and Evidence in Indiana (WISE Indiana), a research partnership between the Indiana Clinical and Translational Science Institute and the Indiana Family and Social Services Administration (FSSA). He earned an MPH in epidemiology from the University of Kentucky College of Public Health and a Ph.D. in epidemiology from the University of Alabama at Birmingham School of Public Health. He has published over 90 peer-reviewed articles, served on 27 dissertation research committees, and generated over $2 million in research funding.
Dr. Blackburn's research primarily involves leveraging large administrative data sets to evaluate public health policy and outcomes at the state, local, and national level. He is a frequent collaborator with state and local public health agencies, and has applied methodologically innovative approaches to evaluate important public health topics including dental health services and outcomes, long-term care policy and outcomes, Medicaid and CHIP coverage, and measuring health care quality. In a new study, Dr. Blackburn and his co-researchers, revealed the economic burden of untreated mental illness in Indiana, which results in $4.2 billion spent annually. Dr. Blackburn's use of data to inform health policies for the benefit of community members is another excellent example of how IUPUI's faculty members are TRANSLATING their RESEARCH INTO PRACTICE.
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Item Age-Related Differences in Health-Related Quality of Life Among Western Canadian Nursing Home Residents(Oxford University Press, 2022) Shieu, Bianca; Schwartz, Todd; Hoben, Matthias; Toles, Mark; Beeber, Anna; Anderson, RuthNursing homes (NHs) typically focus on health-related quality of life (HRQoL) among residents aged 65 and over despite approximately 7% of NH residents are younger (aged 18-64). Research suggests that the needs of younger NH residents are not being met and they may have low HRQoL. However, differences in HRQoL of younger and older NH residents may not be apparent in studies that use HRQoL measures designed for research with older NH residents. We hypothesized that the younger residents would have lower HRQoL mean scores than the older (aged ≥ 65) residents using a HRQoL measure based on the HRQoL score derived from Resident Assessment Instrument – Minimum Data Set 2.0 items. The measure uses items that emphasize physical aspects of quality of life rather than social aspects. In a sample of 21,129 residents from 94 NHs in Western Canada, we performed descriptive analyses, t-test, chi-square test, and an adjusted propensity score (PS) analysis through retrospective cohort study from years 2016 to 2017. The HRQoL index score ranged from -.351 to .996 (Mean= 0.693, SD=0.265). In the PS model, the adjusted mean score for younger was higher than for older adults with a mean difference at 0.061 (95% CI 0.031, 0.091) (p<.001). Other domains such as mental health condition of quality of life must be examined in younger NH residents because it is a crucial factor influencing their daily lives, thereby we can explore a more complete set of HRQoL domains of them and redesign care for their unique needs.Item Age-specific rates of hospital transfers in long-stay nursing home residents(Oxford Academic, 2022-01) Tu, Wanzhu; Li, Ruohong; Stump, Timothy E.; Fowler, Nicole R.; Carnahan, Jennifer L.; Blackburn, Justin; Sachs, Greg A.; Hickman, Susan E.; Unroe, Kathleen T.; Biostatistics, School of Public HealthIntroduction hospital transfers and admissions are critical events in the care of nursing home residents. We sought to determine hospital transfer rates at different ages. Methods a cohort of 1,187 long-stay nursing home residents who had participated in a Centers for Medicare and Medicaid demonstration project. We analysed the number of hospital transfers of the study participants recorded by the Minimum Data Set. Using a modern regression technique, we depicted the annual rate of hospital transfers as a smooth function of age. Results transfer rates declined with age in a nonlinear fashion. Rates were the highest among residents younger than 60 years of age (1.30-2.15 transfers per year), relatively stable between 60 and 80 (1.17-1.30 transfers per year) and lower in those older than 80 (0.77-1.17 transfers per year). Factors associated with increased risk of transfers included prior diagnoses of hip fracture (annual incidence rate ratio or IRR: 2.057, 95% confidence interval (CI): [1.240, 3.412]), dialysis (IRR: 1.717, 95% CI: [1.313, 2.246]), urinary tract infection (IRR: 1.755, 95% CI: [1.361, 2.264]), pneumonia (IRR: 1.501, 95% CI: [1.072, 2.104]), daily pain (IRR: 1.297, 95% CI: [1.055,1.594]), anaemia (IRR: 1.229, 95% CI [1.068, 1.414]) and chronic obstructive pulmonary disease (IRR: 1.168, 95% CI: [1.010,1.352]). Transfer rates were lower in residents who had orders reflecting preferences for comfort care (IRR: 0.79, 95% CI: [0.665, 0.936]). Discussion younger nursing home residents may require specialised interventions to reduce hospital transfers; declining transfer rates with the oldest age groups may reflect preferences for comfort-focused care.Item Assessing the Quality Measure for Follow-up Care After Children’s Psychiatric Hospitalizations(AAP, 2019-11) Blackburn, Justin; Sharma, Pradeep; Corvey, Kathryn; Morrisey, Michael A.; Menachemi, Nir; Sen, Bisakha; Caldwell, Cathy; Becker, David; Health Policy and Management, School of Public HealthOBJECTIVES: Medicaid and Children’s Health Insurance Program plans publicly report quality measures, including follow-up care after psychiatric hospitalization. We aimed to understand failure to meet this measure, including measurement definitions and enrollee characteristics, while investigating how follow-up affects subsequent psychiatric hospitalizations and emergency department (ED) visits. METHODS: Administrative data representing Alabama’s Children’s Health Insurance Program from 2013 to 2016 were used to identify qualifying psychiatric hospitalizations and follow-up care with a mental health provider within 7 to 30 days of discharge. Using relaxed measure definitions, follow-up care was extended to include visits at 45 to 60 days and visits to a primary care provider. Logit regressions estimated enrollee characteristics associated with follow-up care and, separately, the likelihood of subsequent psychiatric hospitalizations and/or ED visits within 30, 60, and 120 days. RESULTS: We observed 1072 psychiatric hospitalizations during the study period. Of these, 356 (33.2%) received follow-up within 7 days and 566 (52.8%) received it within 30 days. Relaxed measure definitions captured minimal additional follow-up visits. The likelihood of follow-up was lower for both 7 days (−18 percentage points; 95% confidence interval [CI] −26 to −10 percentage points) and 30 days (−26 percentage points; 95% CI −35 to −17 percentage points) regarding hospitalization stays of ≥8 days. Meeting the measure reduced the likelihood of subsequent psychiatric hospitalizations within 60 days by 3 percentage points (95% CI −6 to −1 percentage point). CONCLUSIONS: Among children, receipt of timely follow-up care after a psychiatric hospitalization is low and not sensitive to measurement definitions. Follow-up care may reduce the need for future psychiatric hospitalizations and/or ED visits.Item Attitudes and Experiences of Frontline Nursing Home Staff Towards Coronavirus Testing(2020) Hofschulte-Beck, Spencer L.; Hickman, Susan E.; Blackburn, Justin L.; Mack, Laramie M.; Unroe, Kathleen; Medicine, School of MedicineItem Challenges in Translating National and State Reopening Plans Into Local Reopening Policies During the COVID-19 Pandemic(Sage, 2021-03) Vest, Joshua R.; Blackburn, Justin; Yeager, Valerie A.; Health Policy and Management, School of Public HealthPandemic events, such as coronavirus disease 2019 (COVID-19), affect health and economics at national and international scales, but in the United States, health care delivery and public health practice occur at the local level. Transmission control and eventual economic recovery require detailed guidance for communities, cities, metropolitan areas, and states. Our recent experience as consultants on the control and reopening plans for the city of Indianapolis and Marion County, Indiana, illustrated challenges with national plans, highlighted fundamental tensions in identifying the best course for policy, and emphasized gaps in the evidence base and our public health resources.Item Characterizing Avoidability of Nursing Home Residents: Comparing the Claims-Based Algorithm and Nurse Assessment(Oxford University Press, 2022-12-20) Blackburn, Justin; Carnahan, Jennifer; Hickman, Susan; Sachs, Greg; Unroe, Kathleen; Health Policy and Management, School of Public HealthThe elevated risks associated with transferring nursing home residents to the hospital are problematic, but identifying which transfers can be avoided is complex. Using billing claims to determine “avoidability” based on hospital discharge diagnostic codes ignores resource constraints, clinical comorbidities, and asymmetrical information between nursing home staff making the transfer decision at the onset of clinical changes and hospital billing departments following treatment and diagnostic procedures. Conversely, relying on clinical staff assessments at the time of transfer may be an impractical and resource-intensive strategy to drive payment reform and improve quality. Using Medicare claims data representing emergency department and hospitalization transfers from 38 nursing facilities in Indiana from 2016-2020, we compared classification of transfers using a claims-based algorithm and trained nurse assessments of avoidability. Among 960 transfers, nurses judged 48.4% were potentially avoidable while 30.8% were classified as such using claims data. Of concordant assessments, 15.3% were avoidable and 36.0% as not avoidable. Of discordant assessments, 33.1% were judged avoidable by nurses only and 15.5% via the claims-based algorithm (Kappa=0.0153). Discordance was most frequent among transfers with heart failure (64%, n=42), psychosis (74.5%, n=34), acute renal disease (50%, n=28); and lowest among urinary tract infections (31.3%, n=64). No resident demographic or clinical characteristics were associated with discordance (age, race, sex, cognitive function scale, activities of daily living, or CHESS scale). High discordance in determining avoidability may be driven by presentation of symptoms or other condition-specific factors. Policies to reduce avoidable hospitalizations must not rely on overly simplistic approaches for identification.Item Characterizing Home and Community-Based Service Enrollees in Minnesota’s Medicaid Waiver Program(Oxford University Press, 2022-12-20) Blackburn, Justin; Baggett, Sharon; Burton, Ellen; Snyder, Yonda; Health Policy and Management, School of Public HealthMedicaid waivers allow states’ provision of home and community-based services (HCBS), leading to variations in design and delivery. States monitor expenditures, but cannot easily anticipate growth. Understanding the needs of this population can aid in early identification and improve service delivery. We conducted a Partitioning Around Medioids cluster analysis of first-time enrollees in Minnesota’s Elderly Waiver HCBS program during 2019 to identify trajectories of entry and characterize activities of daily living (ADL) and instrumental activities of daily living (IADL) needs at enrollment. Administrative data collected via long term care consultation assessments provided enrollment, ADL/IADL needs, living arrangements, and other clinically relevant information that was linked to other sources including Minnesota health care program enrollment and utilization, Minimum Data Set skilled nursing facility (SNF) assessments, and calls to the Senior LinkAge Line (SLL)—a free long-term care counseling service. Of 5,284 first-time enrollees, most had prior engagement with state programs—nearly two-thirds had called the SLL, 36% had a SNF stay, and 56% had prior Medicaid enrollment. We identified six clusters representing three levels of living arrangements and two levels of need: 1) lived alone, low needs (32%), 2) lived with others, moderate needs (20%), 3) congregate living, moderate needs (14%), 4) congregate living, high needs (14%), 5) lived with others, high needs (11%), and 6) lived alone, high needs (9%). Lacking caregivers and prior Medicaid were possible exacerbating reasons for HCBS enrollment. The magnitude of the differences between clusters highlights the constellation of factors leading to enrollment in HCBS.Item Community COVID-19 activity level and nursing home staff testing for active SARS-CoV-2 infection in Indiana(Elsevier, 2020) Blackburn, Justin; Weaver, Lindsay; Cohen, Liza; Menachemi, Nir; Rusyniak, Dan; Unroe, Kathleen T.; Health Policy and Management, School of Public HealthObjectives: To assess whether using coronavirus disease 2019 (COVID-19) community activity level can accurately inform strategies for routine testing of facility staff for active severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Design: Cross-sectional study. Setting and Participants: In total, 59,930 nursing home staff tested for active SARS-CoV-2 infection in Indiana. Measures: Receiver operator characteristic curves and the area under the curve to compare the sensitivity and specificity of identifying positive cases of staff within facilities based on community COVID-19 activity level including county positivity rate and county cases per 10,000. Results: The detection of any infected staff within a facility using county cases per 10,000 population or county positivity rate resulted in an area under the curve of 0.648 (95% confidence interval 0.601‒0.696) and 0.649 (95% confidence interval 0.601‒0.696), respectively. Of staff tested, 28.0% were certified nursing assistants, yet accounted for 36.9% of all staff testing positive. Similarly, licensed practical nurses were 1.4% of staff, but 4.7% of positive cases. Conclusions and Implications: We failed to observe a meaningful threshold of community COVID-19 activity for the purpose of predicting nursing homes with any positive staff. Guidance issued by the Centers for Medicare and Medicaid Services in August 2020 sets the minimum frequency of routine testing for nursing home staff based on county positivity rates. Using the recommended 5% county positivity rate to require weekly testing may miss asymptomatic infections among nursing home staff. Further data on results of all-staff testing efforts, particularly with the implementation of new widespread strategies such as point-of-care testing, is needed to guide policy to protect high-risk nursing home residents and staff. If the goal is to identify all asymptomatic SARS-Cov-2 infected nursing home staff, comprehensive repeat testing may be needed regardless of community level activity.Item Decreasing deceased donor transplant rates among children (≤6 years) under the new kidney allocation system(Elsevier, 2018) Shelton, Brittany A.; Sawinski, Deirdre; Ray, Christopher; Reed, Rhiannon D.; MacLennan, Paul A.; Blackburn, Justin; Young, Carlton J.; Gray, Stephen; Yanik, Megan; Massie, Allan; Segev, Dorry L.; Locke, Jayme E.; Health Policy and Management, School of Public HealthThe Kidney Allocation System (KAS) was implemented in December 2014 with unknown impact on the pediatric waitlist. To understand the effect of KAS on pediatric registrants, deceased donor kidney transplant (DDKT) rate was assessed using interrupted time series analysis and time-to-event analysis. Two allocation eras were defined with an intermediary washout period: Era 1 (01/01/2013-09/01/2014), Era 2 (09/01/2014-03/01/2015), and Era 3(03/01/2015-03/01/2017). When using Cox proportional hazards, there was no significant association between allocation era and DDKT likelihood as compared to Era 1 (Era 3: aHR: 1.07, 95% CI: 0.97-1.18, P = .17). However, this was not consistent across all subgroups. Specifically, while highly sensitized pediatric registrants were consistently less likely to be transplanted than their less sensitized counterparts, this disparity was attenuated in Era 3 (Era 1 aHR: 0.04, 95%CI: 0.01-0.14, P < .001; Era 3 aHR: 0.33, 95% CI: 0.21-0.53, P < .001) whereas the youngest registrants aged 0-6 experienced a 21% decrease in DDKT likelihood in Era 3 as compared to Era 1 (aHR: 0.79, 95% CI: 0.64-0.98, P = .03). Thus, while overall DDKT likelihood remained stable with the introduction of KAS, registrants ≤ 6 years of age were disadvantaged, warranting further study to ensure equitable access to transplantation.Item Differential Impact of Hospital and Community Factors on Medicare Readmission Penalties(Wiley, 2018) Aswani, Monica S.; Kilgore, Meredith L.; Becker, David J.; Redden, David T.; Sen, Bisakha; Blackburn, Justin; Health Policy and Management, School of Public HealthObjective: To identify hospital/county characteristics and sources of regional heterogeneity associated with readmission penalties. Data sources/study setting: Acute care hospitals under the Hospital Readmissions Reduction Program from fiscal years 2013 to 2018 were linked to data from the Annual Hospital Association, Centers for Medicare and Medicaid Services, Medicare claims, Hospital Compare, Nursing Home Compare, Area Resource File, Health Inequity Project, and Long-term Care Focus. The final sample contained 3,156 hospitals in 1,504 counties. Data collection/extraction methods: Data sources were combined using Medicare hospital identifiers or Federal Information Processing Standard codes. Study design: A two-level hierarchical model with correlated random effects, also known as the Mundlak correction, was employed with hospitals nested within counties. Principal findings: Over a third of the variation in readmission penalties was attributed to the county level. Patient sociodemographics and the surrounding access to and quality of care were significantly associated with penalties. Hospital measures of Medicare volume, percentage dual-eligible and Black patients, and patient experience were correlated with unobserved area-level factors that also impact penalties. Conclusions: As the readmission risk adjustment does not include any community-level characteristics or geographic controls, the resulting endogeneity bias has the potential to disparately penalize certain hospitals.