Chronic Medication Burden After Cardiac Surgery for Pediatric Medicaid Beneficiaries.

BACKGROUND: Congenital heart defects are the most common and resource-intensive birth defects. As children with congenital heart defects increasingly survive beyond early childhood, it is imperative to understand longitudinal disease burden. OBJECTIVES: The purpose of this study was to examine chronic outpatient prescription medication use and expenditures for New York State pediatric Medicaid enrollees, comparing children who undergo cardiac surgery (cardiac enrollees) and the general pediatric population. METHODS: This was a retrospective cohort study of all Medicaid enrollees age <18 years using the New York State Congenital Heart Surgery Collaborative for Longitudinal Outcomes and Utilization of Resources database (2006-2019). Primary outcomes were total chronic medications per person-year, enrollees per 100 person-years using ≥1 and ≥3 medications, and medication expenditures per person-year. We described and compared outcomes between cardiac enrollees and the general pediatric population. Among cardiac enrollees, multivariable regression examined associations between outcomes and clinical characteristics. RESULTS: We included 5,459 unique children (32,131 person-years) who underwent cardiac surgery and 4.5 million children (22 million person-years) who did not. More than 4 in 10 children who underwent cardiac surgery used ≥1 chronic medication compared with approximately 1 in 10 children who did not have cardiac surgery. Medication expenditures were 10 times higher per person-year for cardiac compared with noncardiac enrollees. Among cardiac enrollees, disease severity was associated with chronic medication use; use was highest among infants; however, nearly one-half of adolescents used ≥1 chronic medication. CONCLUSIONS: Children who undergo cardiac surgery experience high medication burden that persists throughout childhood. Understanding chronic medication use can inform clinicians (both pediatricians and subspecialists) and policymakers, and ultimately the value of care for this medically complex population.

Sensitivity of Medicaid Claims Data for Identifying Opioid Use Disorder in Patients Admitted to 6 New York City Public Hospitals.

OBJECTIVES: Behavioral health diagnoses are frequently underreported in administrative health data. For a pragmatic trial of a hospital addiction consult program, we sought to determine the sensitivity of Medicaid claims data for identifying patients with opioid use disorder (OUD). METHODS: A structured review of electronic health record (EHR) data was conducted to identify patients with OUD in 6 New York City public hospitals. Cases selected for review were adults admitted to medical/surgical inpatient units who received methadone or sublingual buprenorphine in the hospital. For cases with OUD based on EHR review, we searched for the hospitalization in Medicaid claims data and examined International Classification of Diseases, Tenth Revision discharge diagnosis codes to identify opioid diagnoses (OUD, opioid poisoning, or opioid-related adverse events). Sensitivity of Medicaid claims data for capturing OUD hospitalizations was calculated using EHR review findings as the reference standard measure. RESULTS: Among 552 cases with OUD based on EHR review, 465 (84.2%) were found in the Medicaid claims data, of which 418 (89.9%) had an opioid discharge diagnosis. Opioid diagnoses were the primary diagnosis in 49 cases (11.7%), whereas in the remainder, they were secondary diagnoses. CONCLUSION: In this sample of hospitalized patients receiving OUD medications, Medicaid claims seem to have good sensitivity for capturing opioid diagnoses. Although the sensitivity of claims data may vary, it can potentially be a valuable source of information about OUD patients.

Long-Term Health Care Utilization After Cardiac Surgery in Children Covered Under Medicaid.

BACKGROUND: Understanding the longitudinal burden of health care expenditures and utilization after pediatric cardiac surgery is needed to counsel families, improve care, and reduce outcome inequities. OBJECTIVES: The purpose of this study was to describe and identify predictors of health care expenditures and utilization for Medicaid-insured pediatric cardiac surgical patients. METHODS: All Medicaid enrolled children age <18 years undergoing cardiac surgery in the New York State CHS-COLOUR database, from 2006 to 2019, were followed in Medicaid claims data through 2019. A matched cohort of children without cardiac surgical disease was identified as comparators. Expenditures and inpatient, primary care, subspecialist, and emergency department utilization were modeled using log-linear and Poisson regression models to assess associations between patient characteristics and outcomes. RESULTS: In 5,241 New York Medicaid-enrolled children, longitudinal health care expenditures and utilization for cardiac surgical patients exceeded noncardiac surgical comparators (cardiac surgical children: $15,500 ± $62,000 per month in year 1 and $1,600 ± $9,100 per month in year 5 vs noncardiac surgical children: $700 ± $6,600 per month in year 1 and $300 ± $2,200 per month in year 5). Children after cardiac surgery spent 52.9 days in hospitals and doctors' offices in the first postoperative year and 90.5 days over 5 years. Being Hispanic, compared with non-Hispanic White, was associated with having more emergency department visits, inpatient admissions, and subspecialist visits in years 2 to 5, but fewer primary care visits and greater 5-year mortality. CONCLUSIONS: Children after cardiac surgery have significant longitudinal health care needs, even among those with less severe cardiac disease. Health care utilization differed by race/ethnicity, although mechanisms driving disparities should be investigated further.

Association of Insurance Mix and Diagnostic Coding Practices in New York State Hospitals.

Importance: Given higher reimbursement rates, hospitals primarily serving privately insured patients may invest more in intensive coding than hospitals serving publicly insured patients. This may lead these hospitals to code more diagnoses for all patients. Objective: To estimate whether, for the same Medicaid enrollee with multiple hospitalizations, a hospital's share of privately insured patients is associated with the number of diagnoses on claims. Design, Setting, and Participants: This cross-sectional study used patient-level fixed effects regression models on inpatient Medicaid claims from Medicaid enrollees with at least 2 admissions in at least 2 different hospitals in New York State between 2010 and 2017. Analyses were conducted from 2019 to 2021. Exposures: The annual share of privately insured patients at the admitting hospital. Main Outcomes and Measures: Number of diagnostic codes per admission. Probability of diagnoses being from a list of conditions shown to be intensely coded in response to payment incentives. Results: This analysis included 1 614 630 hospitalizations for Medicaid-insured patients (mean [SD] age, 48.2 [20.1] years; 829 684 [51.4%] women and 784 946 [48.6%] men). Overall, 74 998 were Asian (4.6%), 462 259 Black (28.6%), 375 591 Hispanic (23.3%), 486 313 White (30.1%), 128 896 unknown (8.0%), and 86 573 other (5.4%). When the same patient was seen in a hospital with a higher share of privately insured patients, more diagnoses were recorded (0.03 diagnoses per percentage point [pp] increase in share of privately insured; 95% CI, 0.02-0.05; P < .001). Patients discharged from hospitals in the bottom quartile of privately insured patient share received 1.37 more diagnoses when they were subsequently discharged from hospitals in the top quartile, relative to patients whose admissions were both in the bottom quartile (95% CI, 1.21-1.53; P < .001). Those going from hospitals in the top quartile to the bottom had 1.67 fewer diagnoses (95% CI, -1.84 to -1.50; P < .001). Diagnoses in hospitals with a higher private payer share were more likely to be for conditions sensitive to payment incentives (0.08 pp increase for each pp increase in private share; 95% CI, 0.06-0.10; P < .001). These findings were replicated in 2016 to 2017 data. Conclusions and Relevance: In this cross-sectional study of Medicaid enrollees, admission to a hospital with a higher private payer share was associated with more diagnoses on Medicaid claims. This suggests payment policy may drive differential investments in infrastructure to document diagnoses. This may create a feedback loop that exacerbates resource inequity.

Improving Longitudinal Outcomes, Efficiency, and Equity in the Care of Patients With Congenital Heart Disease.

BACKGROUND: Longitudinal follow-up, resource utilization, and health disparities are top congenital heart research and care priorities. Medicaid claims include longitudinal data on inpatient, outpatient, emergency, pharmacy, rehabilitation, home health utilization, and social determinants of health-including mother-infant pairs. OBJECTIVES: The New York Congenital Heart Surgeons Collaborative for Longitudinal Outcomes and Utilization of Resources linked robust clinical details from locally held state and national registries from 10 of 11 New York congenital heart centers to Medicaid claims, building a novel, statewide mechanism for longitudinal assessment of outcomes, expenditures, and health inequities. METHODS: The authors included all children <18 years of age undergoing cardiac surgery in The Society of Thoracic Surgeons Congenital Heart Surgery Database or the New York State Pediatric Congenital Cardiac Surgery Registry from 10 of 11 New York centers, 2006 to 2019. Data were linked via iterative, ranked deterministic matching on direct identifiers. Match rates were calculated and compared. Proportions of the linked cohort trackable over 3, 5, and 10 years were described. RESULTS: Of 14,097 registry cases, 59% (n = 8,322) reported Medicaid use. Of these, 7,414 were linked to New York claims, at an 89% match rate. Of matched cases, the authors tracked 79%, 74%, and 65% of children over 3, 5, and 10 years when requiring near-continuous Medicaid enrollment. Allowing more lenient enrollment criteria, the authors tracked 86%, 82%, and 76%, respectively. Mortality over this time was 7.7%, 8.4%, and 10.0%, respectively. Manual validation revealed ∼100% true matches. CONCLUSIONS: This establishes a novel statewide data resource for assessment of longitudinal outcome, health expenditure, and disparities for children with congenital heart disease.

Impact of a Primary Care Provider Tele-Mentoring and Community Health Worker Intervention on Utilization in Medicaid Patients with Diabetes.

OBJECTIVE: The Endocrinology ECHO intervention utilized a tele-mentoring model that connects primary care providers (PCPs) and community health workers (CHWs) with specialists for training in diabetes care. We evaluated the impact of the Endo ECHO intervention on healthcare utilization and care for Medicaid patients with diabetes in New Mexico. METHODS: Between January 2015 and April 2017, patients with complex diabetes from 10 health centers in NM were recruited to receive diabetes care from a PCP and CHW upskilled through Endo ECHO. We matched intervention patients in the NM Medicaid claims database to comparison Medicaid beneficiaries using 5:1 propensity matching. We used a difference-in-difference (DID) approach to compare utilization and processes of care between intervention and comparison patients. RESULTS: Of 541 Medicaid patients enrolled in Endo ECHO, 305 met inclusion criteria and were successfully matched. Outpatient visits increased with Endo ECHO for intervention patients as compared to comparison patients (rate ratio, 1.57; 95% confidence interval [CI], 1.43 to 1.72). The intervention was associated with an increase in emergency department (ED) visits (rate ratio, 1.30; 95% CI, 1.04 to 1.63) but no change in hospitalizations (rate ratio, 1.47; 95% CI, 0.95 to 2.23). Among intervention patients, utilization of metformin increased from 57.1% to 60.7%, with a DID between groups of 8.8% (95% CI, 4.0% to 13.6%). We found similar increases in use of statins (DID, 8.5%; 95% CI, 3.2% to 13.8%), angiotensin-converting enzyme inhibitors (DID, 9.5%; 95% CI, 3.5% to 15.4%), or antidepressant therapies (DID, 9.4%; 95% CI, 1.1% to 18.1%). CONCLUSION: Patient enrollment in Endo ECHO was associated with increased outpatient and ED utilization and increased uptake of prescription-related quality measures. No impact was observed on hospitalization.

Dispelling an urban legend: frequent emergency department users have substantial burden of disease.

Urban legend has often characterized frequent emergency department (ED) patients as mentally ill substance users who are a costly drain on the health care system and who contribute to ED overcrowding because of unnecessary visits for conditions that could be treated more efficiently elsewhere. This study of Medicaid ED users in New York City shows that behavioral health conditions are responsible for a small share of ED visits by frequent users, and that ED use accounts for a small portion of these patients' total Medicaid costs. Frequent ED users have a substantial burden of disease, and they have high rates of primary and specialty care use. They also have linkages to outpatient care that are comparable to those of other ED patients. It is possible to use predictive modeling to identify who will become a repeat ED user and thus to help target interventions. However, policy makers should view reducing frequent ED use as only one element of more-comprehensive intervention strategies for frequent health system users.

Effect of telecare on use of health and social care services: findings from the Whole Systems Demonstrator cluster randomised trial.

OBJECTIVE: to assess the impact of telecare on the use of social and health care. Part of the evaluation of the Whole Systems Demonstrator trial. PARTICIPANTS AND SETTING: a total of 2,600 people with social care needs were recruited from 217 general practices in three areas in England. DESIGN: a cluster randomised trial comparing telecare with usual care, general practice being the unit of randomisation. Participants were followed up for 12 months and analyses were conducted as intention-to-treat. DATA SOURCES: trial data were linked at the person level to administrative data sets on care funded at least in part by local authorities or the National Health Service. MAIN OUTCOME MEASURES: the proportion of people admitted to hospital within 12 months. Secondary endpoints included mortality, rates of secondary care use (seven different metrics), contacts with general practitioners and practice nurses, proportion of people admitted to permanent residential or nursing care, weeks in domiciliary social care and notional costs. RESULTS: 46.8% of intervention participants were admitted to hospital, compared with 49.2% of controls. Unadjusted differences were not statistically significant (odds ratio: 0.90, 95% CI: 0.75-1.07, P = 0.211). They reached statistical significance after adjusting for baseline covariates, but this was not replicated when adjusting for the predictive risk score. Secondary metrics including impacts on social care use were not statistically significant. CONCLUSIONS: telecare as implemented in the Whole Systems Demonstrator trial did not lead to significant reductions in service use, at least in terms of results assessed over 12 months.

The role of matched controls in building an evidence base for hospital-avoidance schemes: a retrospective evaluation.

OBJECTIVE: To test whether two hospital-avoidance interventions altered rates of hospital use: "intermediate care" and "integrated care teams." DATA SOURCES/STUDY SETTING: Linked administrative data for England covering the period 2004 to 2009. STUDY DESIGN: This study was commissioned after the interventions had been in place for several years. We developed a method based on retrospective analysis of person-level data comparing health care use of participants with that of prognostically matched controls. DATA COLLECTION/EXTRACTION METHODS: Individuals were linked to administrative datasets through a trusted intermediary and a unique patient identifier. PRINCIPAL FINDINGS: Participants who received the intermediate care intervention showed higher rates of unscheduled hospital admission than matched controls, whereas recipients of the integrated care team intervention showed no difference. Both intervention groups showed higher rates of mortality than did their matched controls. CONCLUSIONS: These are potentially powerful techniques for assessing impacts on hospital activity. Neither intervention reduced admission rates. Although our analysis of hospital utilization controlled for a wide range of observable characteristics, the difference in mortality rates suggests that some residual confounding is likely. Evaluation is constrained when performed retrospectively, and careful interpretation is needed.

A person based formula for allocating commissioning funds to general practices in England: development of a statistical model.

OBJECTIVES: To develop a formula for allocating resources for commissioning hospital care to all general practices in England based on the health needs of the people registered in each practice DESIGN: Multivariate prospective statistical models were developed in which routinely collected electronic information from 2005-6 and 2006-7 on individuals and the areas in which they lived was used to predict their costs of hospital care in the next year, 2007-8. Data on individuals included all diagnoses recorded at any inpatient admission. Models were developed on a random sample of 5 million people and validated on a second random sample of 5 million people and a third sample of 5 million people drawn from a random sample of practices. SETTING: All general practices in England as of 1 April 2007. All NHS inpatient admissions and outpatient attendances for individuals registered with a general practice on that date. SUBJECTS: All individuals registered with a general practice in England at 1 April 2007. MAIN OUTCOME MEASURES: Power of the statistical models to predict the costs of the individual patient or each practice's registered population for 2007-8 tested with a range of metrics (R(2) reported here). Comparisons of predicted costs in 2007-8 with actual costs incurred in the same year were calculated by individual and by practice. RESULTS: Models including person level information (age, sex, and ICD-10 codes diagnostic recorded) and a range of area level information (such as socioeconomic deprivation and supply of health facilities) were most predictive of costs. After accounting for person level variables, area level variables added little explanatory power. The best models for resource allocation could predict upwards of 77% of the variation in costs at practice level, and about 12% at the person level. With these models, the predicted costs of about a third of practices would exceed or undershoot the actual costs by 10% or more. Smaller practices were more likely to be in these groups. CONCLUSIONS: A model was developed that performed well by international standards, and could be used for allocations to practices for commissioning. The best formulas, however, could predict only about 12% of the variation in next year's costs of most inpatient and outpatient NHS care for each individual. Person-based diagnostic data significantly added to the predictive power of the models.

Do 'virtual wards' reduce rates of unplanned hospital admissions, and at what cost? A research protocol using propensity matched controls.

BACKGROUND: This retrospective study will assess the extent to which multidisciplinary case management in the form of virtual wards (VWs) leads to changes in the use of health care and social care by patients at high risk of future unplanned hospital admission. VWs use the staffing, systems and daily routines of a hospital ward to deliver coordinated care to patients in their own homes. Admission to a VW is offered to patients identified by a predictive risk model as being at high risk of unplanned hospital admission in the coming 12 months. STUDY DESIGN AND DATA COLLECTION METHODS: We will compare the health care and social care use of VW patients to that of matched controls. Controls will be drawn from (a) national, and (b) local, individual-level pseudonymous routine data. The costs of setting up and running a VW will be determined from the perspectives of both health and social care organizations using a combination of administrative data, interviews and diaries. METHODS OF ANALYSIS: Using propensity score matching and prognostic matching, we will create matched comparator groups to estimate the effect size of virtual wards in reducing unplanned hospital admissions. CONCLUSIONS: THIS STUDY WILL ALLOW US TO DETERMINE RELATIVE TO MATCHED COMPARATOR GROUPS: whether VWs reduce the use of emergency hospital care; the impact, if any, of VWs on the uptake of primary care, community health services and council-funded social care; and the potential costs and savings of VWs from the perspectives of the national health service (NHS) and local authorities.

Predicting who will use intensive social care: case finding tools based on linked health and social care data.

BACKGROUND: the costs of delivering health and social care services are rising as the population ages and more people live with chronic diseases. OBJECTIVES: to determine whether predictive risk models can be built that use routine health and social care data to predict which older people will begin receiving intensive social care. DESIGN: analysis of pseudonymous, person-level, data extracted from the administrative data systems of local health and social care organisations. SETTING: five primary care trust areas in England and their associated councils with social services responsibilities. SUBJECTS: people aged 75 or older registered continuously with a general practitioner in five selected areas of England (n = 155,905). METHODS: multivariate statistical analysis using a split sample of data. RESULTS: it was possible to construct models that predicted which people would begin receiving intensive social care in the coming 12 months. The performance of the models was improved by selecting a dependent variable based on a lower cost threshold as one of the definitions of commencing intensive social care. CONCLUSIONS: predictive models can be constructed that use linked, routine health and social care data for case finding in social care settings.

Medicaid patients at high risk for frequent hospital admission: real-time identification and remediable risks.

Patients with frequent hospitalizations generate a disproportionate share of hospital visits and costs. Accurate determination of patients who might benefit from interventions is challenging: most patients with frequent admissions in 1 year would not continue to have them in the next. Our objective was to employ a validated regression algorithm to case-find Medicaid patients at high-risk for hospitalization in the next 12 months and identify intervention-amenable characteristics to reduce hospitalization risk. We obtained encounter data for 36,457 Medicaid patients with any visit to an urban public hospital from 2001 to 2006 and generated an algorithm-based score for hospitalization risk in the subsequent 12 months for each patient (0 = lowest, 100 = highest). To determine medical and social contributors to the current admission, we conducted in-depth interviews with high-risk hospitalized patients (scores >50) and analyzed associated Medicaid claims data. An algorithm-based risk score >50 was attained in 2,618 (7.2%) patients. The algorithm's positive predictive value was equal to 0.67. During the study period, 139 high-risk patients were admitted: 60 met inclusion criteria and 50 were interviewed. Fifty-six percent cited the Emergency Department as their usual source of care or had none. Sixty-eight percent had >1 chronic medical conditions, and 42% were admitted for conditions related to substance use. Sixty percent were homeless or precariously housed. Mean Medicaid expenditures for the interviewed patients were $39,188 and $84,040 per patient for the years immediately prior to and following study participation, respectively. Findings including high rates of substance use, homelessness, social isolation, and lack of a medical home will inform the design of interventions to improve community-based care and reduce hospitalizations and associated costs.

Improving the management of care for high-cost Medicaid patients.

Increased policy attention is being focused on the management of high-cost cases in Medicaid. In this paper we present an algorithm that identifies patients at high risk of future hospitalizations and offer a business-case analysis with a range of assumptions about the rate of reduction in future hospitalization and the cost of the intervention. The characteristics of the patients identified by the algorithm are described, and the implications of these findings for policymakers, payers, and providers interested in responding more effectively to the needs of these patients are discussed, including the challenges likely to be encountered in implementing an intervention initiative.

Some reflections on a few of the pitfalls in the world of foundation grant making.

This paper offers some reflections on the grant-making process from a former foundation executive. Some of the opportunities, challenges, and pitfalls inherent in the foundation world are described, and one approach to grant making, the "call for proposals," is examined as an example of the need for greater attention to and investment in the science of grant making itself, to maximize the potential return from philanthropy.

The role of faith-based institutions in addressing health disparities: a case study of an initiative in the southwest Bronx.

Although many public health initiatives have been implemented through collaborations with faith-based institutions, little is known about best practices for developing such programs. Using a community-based participatory approach, this case study examines the implementation of an initiative in the Bronx, New York, that is designed to educate community members about health promotion and disease management and to mobilize church members to seek equal access to health care services. The study used qualitative methods, including the collaborative development of a logic model for the initiative, focus groups, interviews, analysis of program reports, and participant observation. The paper examines three key aspects of the initiative's implementation: (1) the engagement of the church leadership; (2) the use of church structures as venues for education and intervention; and (3) changes in church policies. Key findings include the importance of pre-existing relationships within the community and the prominent agenda-setting role played by key pastors, and the strength of the Coalition's dual focus on health behaviors and health disparities. Given the churches' demonstrated ability to pull people together, to motivate and to inspire, there is great potential for faith-based interventions, and models developed through such interventions, to address health disparities.

Fostering organizational change through a community-based initiative.

Program funders and managers are increasingly interested in fostering changes in the policies, practices, and procedures of organizations participating in community-based initiatives. But little is known about what factors contribute to the institutionalization of change. In this study, the authors assess whether the organizational members of the Bronx Health REACH Coalition have begun to change their functioning and role with regard to their clients and their staff and in the broader community, apart from their implementation of the funded programs for which they are responsible. The study identifies factors that seemed to contribute to or hinder such institutional change and suggests several strategies for coalitions and funders that are seeking to promote and sustain organizational change.

Racial and ethnic disparities in health: a view from the South Bronx.

This study seeks to understand the perspective of Black and Hispanic/Latino residents of the South Bronx, New York, on the causes of persistent racial and ethnic disparities in health outcomes. In particular, it focuses on how people who live in this community perceive and interact with the health care system. Findings from 9 focus groups with 110 participants revealed a deep and pervasive distrust of the health care system and a sense of being disrespected, exacerbated by difficulties that patients experience in communicating with their providers. The paper suggests how health care institutions might respond to these perceptions.