Dataset Information

Association Between the Medical Home Model of Care and Demographic Characteristics in US Children.

ABSTRACT: Given the medical home model of care's (MHMOC) potential to reduce disparities in health care, this study determined the relationship between MHMOC, MHMOC indicators, and (1) demographic characteristics and (2) health outcomes in US children. Caregivers (n = 50 212) of children 0 to 17 years completed the 2016 National Survey of Children's Health. The MHMOC measure comprised 5 indicators (e.g., having a personal doctor/nurse and receiving family-centered care). Bivariate and multiple logistic regressions assessed the relationships between MHMOC, MHMOC indicators, and (1) demographic characteristics and (2) health outcomes. Approximately 49% of the children were served in a MHMOC. MHMOC and MHMOC indicators were related to race, ethnicity, nativity, caregiver education, federal poverty level, insurance type, and predicted health outcomes. These findings suggest that children most in need of MHMOC are less likely to be served in them. Thus, the current distribution of MHMOC likely contributes to greater disparities in care.

SUBMITTER: Fleary SA

PROVIDER: S-EPMC6642684 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Association Between the Medical Home Model of Care and Demographic Characteristics in US Children.

Fleary Sasha A SA

Clinical pediatrics 20181118 3

Given the medical home model of care's (MHMOC) potential to reduce disparities in health care, this study determined the relationship between MHMOC, MHMOC indicators, and (1) demographic characteristics and (2) health outcomes in US children. Caregivers (n = 50 212) of children 0 to 17 years completed the 2016 National Survey of Children's Health. The MHMOC measure comprised 5 indicators (e.g., having a personal doctor/nurse and receiving family-centered care). Bivariate and multiple logistic re ...[more]

PMID: 30449155

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Project description:ImportanceCareFirst, the largest commercial insurer in the mid-Atlantic Region of the United States, runs a medical home program focusing on financial incentives for primary care practices and care coordination for high-risk patients. From 2013 to 2015, CareFirst extended the program to Medicare fee-for-service (FFS) beneficiaries in participating practices. If the model extension improved quality while reducing spending, the Centers for Medicare and Medicaid Services could expand the program to Medicare beneficiaries broadly.ObjectiveTo test whether extending CareFirst's program to Medicare FFS patients improves care processes and reduces hospitalizations, emergency department visits, and spending.Design, setting, and participantsThis difference-in-differences analysis compared outcomes for roughly 35 000 Medicare FFS patients attributed to 52 intervention practices (grouped by CareFirst into 14 "medical panels") to outcomes for 69 000 Medicare patients attributed to 42 matched comparison panels during a 1-year baseline period and 2.5-year intervention at Maryland primary care practices.Main outcomes and measuresHospitalizations (all-cause and ambulatory-care sensitive), emergency department visits, Medicare Part A and B spending, and 3 quality-of-care process measures: ambulatory care within 14 days of a hospital stay, cholesterol testing for those with ischemic vascular disease, and a composite measure for those with diabetes.InterventionsCareFirst hired nurses who worked with patients' usual primary care practitioners to coordinate care for 3656 high-risk Medicare patients. CareFirst paid panels rewards for meeting cost and quality targets for their Medicare patients and advised panels on how to meet these targets based on analyses of claims data.ResultsOn average, each of the 14 intervention panels had 9.3 primary care practitioners and was attributed 2202 Medicare FFS patients in the baseline period. The panels' attributed Medicare patients were, on average, 73.8 years old, 59.2% female, and 85.1% white. The extension of CareFirst's program to Medicare patients was not statistically associated with improvements in any outcomes, either for the full Medicare population or for a high-risk subgroup in which impacts were expected to be largest. For the full population, the difference-in-differences estimates were 1.4 hospitalizations per 1000 patients per quarter (P = .54; 90% CI, -2.1 to 5.0), -2.5 outpatient ED visits per 1000 patients per quarter (P = .26; 90% CI, -6.2 to 1.1), and -$1 per patient per month in Medicare Part A and B spending (P = .98; 90% CI, -$40 to $39). For hospitalizations and Medicare spending, the 90% CIs did not span CareFirst's expected impacts. Hospitalizations for the intervention group declined by 10% from baseline year to the final 18 months of the intervention, but this was matched by similar declines in the comparison group.Conclusion and relevanceThe extension of CareFirst's program to Medicare did not measurably improve quality-of-care processes or reduce service use or spending for Medicare patients. Further program refinement and testing would be needed to support scaling the program more broadly to Medicare patients.

Project description:Questionnaires are among the most basic and widespread tools to assess the mental health of a population in epidemiological and public health studies. Their most obvious advantage (firsthand self-report) is also the source of their main problems: the raw data requires interpretation, and are a snapshot of the specific sample's status at a given time. Efforts to deal with both issues created a bi-dimensional space defined by two orthogonal axes, in which most of the quantitative mental health research can be located. Methods aimed to assure that mental health diagnoses are solidly grounded on existing raw data are part of the individual validity axis. Tools allowing the generalization of the results across the entire population compose the collective validity axis. This paper raises a different question. Since one goal of mental health assessments is to obtain results that can be generalized to some extent, an important question is how robust is a questionnaire result when applied to a different population or to the same population at a different time. In this case, there is deep uncertainty, without any a priori probabilistic information. The main claim of this paper is that this task requires the development of a new robustness to deep uncertainty axis, defining a three-dimensional research space. We demonstrate the analysis of deep uncertainty using the concept of robustness in info-gap decision theory. Based on data from questionnaires collected before and during the Covid-19 pandemic, we first locate a mental health assessment in the space defined by the individual validity axis and the collective validity axis. Then we develop a model of info-gap robustness to uncertainty in mental health assessment, showing how the robustness to deep uncertainty axis interacts with the other two axes, highlighting the contributions and the limitations of this approach. The ability to measure robustness to deep uncertainty in the mental health realm is important particularly in troubled and changing times. In this paper, we provide the basic methodological building blocks of the suggested approach using the outbreak of Covid-19 as a recent example.

Project description:ImportanceInterventions to transform primary care practices into medical homes are increasingly common, but their effectiveness in improving quality and containing costs is unclear.ObjectiveTo measure associations between participation in the Southeastern Pennsylvania Chronic Care Initiative, one of the earliest and largest multipayer medical home pilots conducted in the United States, and changes in the quality, utilization, and costs of care.Design, setting, and participantsThirty-two volunteering primary care practices participated in the pilot (conducted from June 1, 2008, to May 31, 2011). We surveyed pilot practices to compare their structural capabilities at the pilot's beginning and end. Using claims data from 4 participating health plans, we compared changes (in each year, relative to before the intervention) in the quality, utilization, and costs of care delivered to 64,243 patients who were attributed to pilot practices and 55,959 patients attributed to 29 comparison practices (selected for size, specialty, and location similar to pilot practices) using a difference-in-differences design.ExposuresPilot practices received disease registries and technical assistance and could earn bonus payments for achieving patient-centered medical home recognition by the National Committee for Quality Assurance (NCQA).Main outcomes and measuresPractice structural capabilities; performance on 11 quality measures for diabetes, asthma, and preventive care; utilization of hospital, emergency department, and ambulatory care; standardized costs of care.ResultsPilot practices successfully achieved NCQA recognition and adopted new structural capabilities such as registries to identify patients overdue for chronic disease services. Pilot participation was associated with statistically significantly greater performance improvement, relative to comparison practices, on 1 of 11 investigated quality measures: nephropathy screening in diabetes (adjusted performance of 82.7% vs 71.7% by year 3, P < .001). Pilot participation was not associated with statistically significant changes in utilization or costs of care. Pilot practices accumulated average bonuses of $92,000 per primary care physician during the 3-year intervention.Conclusions and relevanceA multipayer medical home pilot, in which participating practices adopted new structural capabilities and received NCQA certification, was associated with limited improvements in quality and was not associated with reductions in utilization of hospital, emergency department, or ambulatory care services or total costs over 3 years. These findings suggest that medical home interventions may need further refinement.

Dataset Information

Association Between the Medical Home Model of Care and Demographic Characteristics in US Children.

Publications

Association Between the Medical Home Model of Care and Demographic Characteristics in US Children.

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