What Explains Inequalities in Telehealth Utilization Among North Carolina Medicaid Beneficiaries?

Background: Increased availability of telehealth can improve access to health care. However, there is evidence of persistent disparities in telehealth usage, as well as among people from minoritized racial and ethnic groups and rural residents. The objective of our work was to explore the degree to which disparities in telehealth use for behavioral health (BH) and musculoskeletal (MSK) related services during the COVID-19 pandemic are explained by observed beneficiary- and area-level characteristics. Methods: Using North Carolina Medicaid claims data of Medicaid beneficiaries with BH or MSK conditions, we apply nonlinear regression-based decomposition analysis-based models developed by Kitagawa, Oaxaca, and Blinder to determine which observed variables are associated with racial, ethnic, and rural inequalities in telehealth usage. Results: In the BH cohort, we found statistically significant differences in telehealth usage by race in the adult population, and by race, Hispanic ethnicity, and rurality in the pediatric population. In the MSK cohort, we found significant inequities by Hispanic ethnicity and rurality among adults, and by race and rurality among children. Inequalities in telehealth use between groups were small, ranging from 0.7 percentage points between urban and rural adults with MSK conditions to 3.8 percentage points between white adults and people of color among those with BH conditions. Overall, we found that racial and ethnic inequalities in telehealth use are not well explained by the observed variables in our data. Rural disparities in telehealth use are better explained by observed variables, particularly area-level broadband internet use. Conclusions: For inequalities between rural and urban residents, our analysis provides observational evidence that infrastructure such as broadband internet access is an important driver of differences in telehealth use. For racial and ethnic inequalities, the pathways may be more complex and difficult to measure, particularly when relying on administrative data sources in place of more detailed data on individual-level socioeconomic factors.

Telehealth Utilization Among Adult Medicaid Beneficiaries in North Carolina with Behavioral Health Conditions During the COVID-19 Pandemic.

OBJECTIVE: We examined factors associated with telehealth utilization during COVID-19 among adult Medicaid beneficiaries with behavioral health conditions. DATA SOURCES AND STUDY SETTING: NC Medicaid 2019-2021 beneficiary and claims data. STUDY DESIGN: This retrospective cohort study examined and compared behavioral health service use pre-COVID-19 (03/01/2019 to 02/28/2020) and during COVID-19 (04/01/2020 to 03/31/2021). Telehealth users included those with at least one behavioral health visit via telehealth during COVID-19. Descriptive statistics were calculated for overall sample and by telehealth status. Multilevel modified Poisson generalized estimating equation examined associations between telehealth use and patient- and area-level characteristics. DATA COLLECTION/EXTRACTION METHODS: We identified individuals ages ≥ 21-64, diagnosed with a behavioral health condition, and had at least one behavioral-health specific visit before COVID-19. PRINCIPAL FINDINGS: Almost two-thirds of the cohort received behavioral health services during COVID-19, with half of these beneficiaries using telehealth. Non-telehealth users had steeper declines in service use from pre- to during COVID-19 compared to telehealth users. Beneficiaries identifying as Black, multiracial or other were significantly less likely to use telehealth (ARR = 0.86; 95% CI: (0.83, 0.89)); (ARR = 0.92; 95% CI: (0.87, 0.96)) compared to White beneficiaries. Those eligible for Medicaid through the blind/disabled programs and who qualified for a state-specific specialized behavioral health plan were more likely to use telehealth (17% and 20%, respectively). CONCLUSIONS: During the pandemic, telehealth facilitated continuity of care for beneficiaries with behavioral health conditions. Future research should aim to investigate how to reduce the digital divide and ensure equitable access to telehealth.

Enhanced Primary Care for People With Serious Mental Illness: A Propensity Weighted Cohort Study.

Objective: People with serious mental illness (SMI) have high rates of cardiometabolic illness, receive low quality care, and experience poor outcomes. Nevertheless, studies of existing integrated care models have not consistently shown improvements in cardiometabolic health for people with SMI. This study assessed the effect of a novel model of enhanced primary care for people with SMI on cardiometabolic outcomes. Enhanced primary care is a model of integrated care wherein comprehensive primary care delivery is adapted to the needs of people with SMI in coordination with behavioral care.Methods: We conducted a propensity-weighted cohort study comparing 234 patients with SMI receiving enhanced primary care to 4,934 patients with SMI receiving usual primary care using electronic health data from a large academic medical system covering the years 2014-2018. The propensity-weighted models controlled for baseline differences in outcome measures and patient characteristics between groups.Results: Compared to usual primary care, enhanced primary care increased hemoglobin A1c (HbA1c) screening by 18 percentage points (95% confidence interval [CI], 10 to 25), low-density lipoprotein (LDL) screening by 16 percentage points (CI, 8.8 to 24), and blood pressure screening by 7.8 percentage points (CI, 5.8 to 9.9). Enhanced primary care reduced HbA1c by 0.27 percentage points (CI, -0.47 to -0.060) and systolic blood pressure by 3.9 mm Hg (CI, -5.2 to -2.5) compared to usual primary care. We did not find evidence that enhanced primary care consistently affected glucose screening, LDL values, or diastolic blood pressure.Conclusions: Enhanced primary care can achieve clinically meaningful improvements in cardiometabolic health compared to usual primary care.

Association Between Racial Segregation and COVID-19 Vaccination Rates.

OBJECTIVE: To examine the association between county-level Black-White residential segregation and COVID-19 vaccination rates. DESIGN: Observational cross-sectional study using multivariable generalized linear models with state fixed effects to estimate the average marginal effects of segregation on vaccination rates. SETTING: National analysis of county-level vaccination rates. MAIN OUTCOME MEASURE: County-level vaccination rates across the United States. RESULTS: We found an overall positive association between county-level segregation and the proportion population fully vaccinated, with a 6.8, 11.3, and 12.8 percentage point increase in the proportion fully vaccinated by May 3, September 27, and December 6, 2021, respectively. Effects were muted after adjustment for sociodemographic variables. Furthermore, in analyses including an interaction term between the county proportion of Black residents and the county dissimilarity index, the association between segregation and vaccination is positive in counties with a lower proportion of Black residents (ie, 5%) but negative in counties with the highest proportions of Black residents (ie, 70%). CONCLUSIONS: Findings highlight the importance of methodological decisions when modeling disparities in COVID-19 vaccinations. Researchers should consider mediating and moderating factors and examine interaction effects and stratified analyses taking racial group distributions into account. Results can inform policies around the prioritization of vaccine distribution and outreach.

Duration of medical home participation and quality of care for patients with chronic conditions.

OBJECTIVE: To examine whether the length of participation in a patient-centered medical home (PCMH), an evidence-based practice, leads to higher quality care for Medicaid enrollees with multiple co-morbid chronic conditions and major depressive disorder (MDD). DATA SOURCES: This analysis uses a unique data source that links North Carolina Medicaid claims and enrollment data with other administrative data including electronic records of state-funded mental health services, a state psychiatric hospital utilization database, and electronic records from a five-county behavioral health carve-out program. STUDY DESIGN: This retrospective cohort study uses generalized estimating equations (GEEs) on person-year-level observations to examine the association between the duration of PCMH participation and measures of guideline-concordant care, including the receipt of minimally adequate care for MDD, defined as 6 months of antidepressant use or eight psychotherapy visits each year. DATA COLLECTION/EXTRACTION METHODS: Adults with two or more chronic conditions reflected in administrative data, including MDD. PRINCIPAL FINDINGS: We found a 1.7 percentage point increase in the likelihood of receiving guideline-concordant care at 4 months of PCMH participation, as compared to newly enrolled individuals with a single month of participation (p < 0.05). This effect increased with each additional month of PCMH participation; 12 months of participation was associated with a 19.1 percentage point increase in the likelihood of receiving guideline-concordant care over a single month of participation (p < 0.01). CONCLUSIONS: The PCMH model is associated with higher quality of care for patients with multiple chronic conditions and MDD over time, and these benefits increase the longer a patient is enrolled. Providers and policy makers should consider the positive effect of increased contact with PCMHs when designing and evaluating initiatives to improve care for this population.

Effect of Enhanced Primary Care for People with Serious Mental Illness on Service Use and Screening.

BACKGROUND: Strategies are needed to better address the physical health needs of people with serious mental illness (SMI). Enhanced primary care for people with SMI has the potential to improve care of people with SMI, but evidence is lacking. OBJECTIVE: To examine the effect of a novel enhanced primary care model for people with SMI on service use and screening. DESIGN: Using North Carolina Medicaid claims data, we performed a retrospective cohort analysis comparing healthcare use and screening receipt of people with SMI newly receiving enhanced primary care to people with SMI newly receiving usual primary care. We used inverse probability of treatment weighting to estimate average differences in outcomes between the treatment and comparison groups adjusting for observed baseline characteristics. PARTICIPANTS: People with SMI newly receiving primary care in North Carolina. INTERVENTIONS: Enhanced primary care that includes features tailored for individuals with SMI. MAIN MEASURES: Outcome measures included outpatient visits, emergency department (ED) visits, inpatient stays and days, and recommended screenings 18 months after the initial primary care visit. KEY RESULTS: Compared to usual primary care, enhanced primary care was associated with an increase of 1.2 primary care visits (95% confidence interval [CI]: 0.31 to 2.1) in the 18 months after the initial visit and decreases of 0.33 non-psychiatric inpatient stays (CI: - 0.49 to - 0.16) and 3.0 non-psychiatric inpatient days (CI: - 5.3 to - 0.60). Enhanced primary care had no significant effect on psychiatric service and ED use. Enhanced primary care increased the probability of glucose and HIV screening, decreased the probability of lipid screening, and had no effect on hemoglobin A1c and colorectal cancer screening. CONCLUSIONS: Enhanced primary care for people with SMI can increase receipt of some preventive screening and decrease use of non-psychiatric inpatient care compared to usual primary care.

Medical home effects on enrollees with mental and physical illness.

OBJECTIVES: To assess the effect of medical home enrollment on acute care use and healthcare spending among Medicaid beneficiaries with mental and physical illness. STUDY DESIGN: Retrospective cohort analysis of administrative data. METHODS: We used 2007-2010 Medicaid claims and state psychiatric hospital data from a sample of 83,819 individuals diagnosed with schizophrenia or depression and at least 1 comorbid physical condition. We performed fixed-effects regression analysis at the person-month level to examine the effect of medical home enrollment on the probabilities of emergency department (ED) use, inpatient admission, and outpatient care use and on amount of Medicaid spending. RESULTS: Medical home enrollment had no effect on ED use in either cohort and was associated with a lower probability of inpatient admission in the depression cohort (P <.05). Medical home enrollees in both cohorts experienced an increase in the probability of having any outpatient visits (P <.05). Medical home enrollment was associated with an increase in mean monthly spending among those with schizophrenia ($65.8; P <.05) and a decrease among those with depression (-$66.4; P <.05). CONCLUSIONS: Among Medicaid beneficiaries with comorbid mental and physical illness, medical home enrollment appears to increase outpatient healthcare use and has mixed effects on acute care use. For individuals in this population who previously had no engagement with the healthcare system, use of the medical home model may represent an investment in providing improved access to needed outpatient services with cost savings potential for beneficiaries with depression.

Effect of the Patient-Centered Medical Home on Racial Disparities in Quality of Care.

BACKGROUND: Research demonstrates that the patient-centered medical home (PCMH) is associated with improved clinical outcomes and quality of care, and the populations that can most benefit from this model require long-term management, e.g., persons with chronic illness and behavioral health conditions. However, different populations may not benefit equally from the PCMH, and empirical evidence about the effects of this model on racial disparities is limited. OBJECTIVE: Estimate the association between enrollment in National Committee for Quality Assurance (NCQA)-recognized PCMHs and racial disparities in quality of care for adults with major depressive disorder (MDD) and comorbid medical conditions. DESIGN: Applying a quasi-experimental instrumental variable design to account for differential selection into the PCMH, we used generalized estimating equations to determine the probability of receiving eight disease-specific quality measures. SUBJECTS: Medicaid enrollees in three states not dually enrolled in Medicare, ages 18-64 with MDD and > 1 other chronic condition. A subgroup analysis was conducted for enrollees with comorbid diabetes. INTERVENTIONS: Enrollment in an NCQA-recognized PCMH. MAIN MEASURES: Disease-specific quality indicators for MDD (e.g., antidepressant use, receipt of psychotherapy), and for diabetes, (e.g. A1c testing, LDL-C testing, retinal exams, and medical attention for nephropathy). KEY RESULTS: PCMH enrollment was associated with an increase in the overall likelihood of receiving six of eight recommended services and a decrease in the likelihood of receiving any psychotherapy (4.94 percentage points, p < 0.01) and retinal exams (5.51 percentage points, p < 0.05). Although both groups improved, PCMH enrollment was associated with an exacerbation of the Black-white disparity in adequate antidepressant use by 4.20 percentage points (p < 0.01). CONCLUSIONS: While PCMH enrollment may improve the overall quality of care, the effect is inconsistent across racial groups and not always associated with reductions in racial disparities in quality.

Do Medical Homes Improve Quality of Care for Persons with Multiple Chronic Conditions?

OBJECTIVE: To examine the association between medical home enrollment and receipt of recommended care for Medicaid beneficiaries with multiple chronic conditions (MCC). DATA SOURCES/STUDY SETTING: Secondary claims data from fiscal years 2008-2010. The sample included nonelderly Medicaid beneficiaries with at least two of eight target conditions (asthma, chronic obstructive pulmonary disease, diabetes, hypertension, hyperlipidemia, seizure disorder, major depressive disorder, and schizophrenia). STUDY DESIGN: We used linear probability models with person- and year-level fixed effects to examine the association between patient-centered medical home (PCMH) enrollment and nine disease-specific quality-of-care metrics, controlling for selection bias and time-invariant differences between enrollees. DATA COLLECTION METHODS: This study uses a dataset that links Medicaid claims with other administrative data sources. PRINCIPAL FINDINGS: Patient-centered medical home enrollment was associated with an increased likelihood of receiving eight recommended mental and physical health services, including A1C testing for persons with diabetes, lipid profiles for persons with diabetes and/or hyperlipidemia, and psychotherapy for persons with major depression and persons with schizophrenia. PCMH enrollment was associated with overuse of short-acting ß-agonists among beneficiaries with asthma. CONCLUSIONS: The PCMH model can improve quality of care for patients with multiple chronic conditions.

Building and strengthening infrastructure for data exchange: lessons from the beacon communities.

INTRODUCTION: The Beacon Community Cooperative Agreement Program supports interventions, including care-delivery innovations, provider performance measurement and feedback initiatives, and tools for providers and consumers to enhance care. Using a learning health system framework, we examine the Beacon Communities' processes in building and strengthening health IT (HIT) infrastructures, specifically successes and challenges in sharing patient information to improve clinical care. BACKGROUND: In 2010, the Office of the National Coordinator for Health Information Technology (ONC) launched the three-year program, which provided $250 million to 17 Beacon Communities to invest in HIT and health information exchange (HIE) infrastructure. Beacon Communities used this funding to develop and disseminate HIT-enabled quality improvement practices found effective in particular community and practice environments. METHODS: NORC conducted 7 site visits, November 2012-March 2013, selecting Communities to represent diverse program features. From August-October 2013, NORC held discussions with the remaining 10 Communities. Following each visit or discussion, NORC summarized the information gathered, including transcripts, team observations, and other documents the Community provided, to facilitate a within-Community analysis of context and stakeholders, intervention strategies, enabling factors, and challenges. RESULTS: Although each Community designed and implemented data-sharing strategies in a unique environment, similar challenges and enabling factors emerged across the Beacons. From a learning health system perspective, their strategies to build and strengthen data-sharing infrastructures address the following crosscutting priorities: promoting technical advances and innovations by helping providers adapt EHRs for data exchange and performance measurement with customizable IT and offering technical support to smaller, independent providers; engaging key stakeholders; and fostering transparent governance and stewardship of the infrastructure with neutral conveners. CONCLUSION: While all the Communities developed or strengthened data-exchange infrastructure, each did this in a unique environment of existing health care market and legal factors. The Communities, however, encountered similar challenges and enabling factors. Organizations undertaking collaborative data sharing, performance measurement and clinical transformation can learn from the Beacon Communities' experience.