The role of data in health care disparities in medicaid managed care

BACKGROUND: The Affordable Care Act includes provisions to standardize the collection of data on health care quality that can be used to measure disparities. We conducted a qualitative study among leaders of Medicaid managed care plans, that currently have access to standardized quality data stratified by race and ethnicity, to learn how they use it to address disparities. METHODS: We conducted semi-structured interviews with 21 health plan leaders across 9 Medicaid managed care plans in California. We used purposive sampling to maximize heterogeneity in geography and plan type (e.g., non-profit, commercial). We performed a thematic analysis based on iterative coding by two investigators. RESULTS: We found 4 major themes. Improving overall quality was tightly linked to a focus on standardized metrics that are integral to meeting regulatory or financial incentives. However, reducing disparities was not driven by standardized data, but by a mix of factors. Data were frequently only examined by race and ethnicity when overall performance was low. Disparities were attributed to either individual choices or cultural and linguistic factors, with plans focusing interventions on recently immigrated groups. CONCLUSIONS: While plans' efforts to address overall quality were often informed by standardized data, actions to reduce disparities were not, at least partly because there were few regulatory or financial incentives driving meaningful use of data on disparities. Standardized data, as envisaged by the Affordable Care Act, could become more useful for addressing disparities if they are combined with policies and regulations that promote health care equity

A comparison of field-delineated wetlands to the New Jersey freshwater wetland maps

A comparison of the New Jersey Freshwater Wetland maps to field-delineated wetlands was undertaken to assess the accuracy of the Freshwater Wetland maps. The evaluation revealed substantial differences in the amount of wetland acreage on the Freshwater Wetland maps compared to field-verified wetlands. Forty study sites comprising 21,877 acres (8,854h) were evaluated. Of these, twenty-seven were overmapped (more wetland acreage than was field-verified), and thirteen were undermapped. Forty-three percent had mapping discrepancies at or above fifty percent; when modified land designations were included as wetlands, the number of sites at this threshold increased to fifty percent. Paper copies of the Freshwater Wetland maps have been distributed to each municipality in the state and have also been digitized for Geographic Information System (GIS) applications. The maps are now a standard component of the state\u27s GIS database. There is growing evidence that the maps are being used for wider applications than the accuracy of the mapping can justify. The data sources about the purpose and limitations of the maps are confusing and often contradictory. The analysis conducted as part of this study suggests that the Freshwater Wetland maps should be used for only the most general land-use and planning purposes. They are generally unsuitable for regulatory and land transaction decisions

Standing of Future Residents in Exclusionary Zoning Cases

The purpose of this article is to explore the standing of future residents to bring suit in exclusionary-zoning cases. Exclusionary zoning may be defined as zoning and land-use control practices that have the effect of precluding construction of dwelling units that could house low-income and moderate-income persons either by direct exclusion or by raising the price of access.1 An example of direct exclusion would be the imposition of restrictions upon the number of bedrooms in apartment units, which would have the direct effect of excluding large families. An example of indirect exclusion would be the effect of zoning upon land prices, which would have the effect of raising the price of land, thereby raising the ultimate cost of the home built upon that land or the rent charged for apartments constructed thereon

Automatically Defined Templates for Improved Prediction of Non-stationary, Nonlinear Time Series in Genetic Programming

Soft methods of artificial intelligence are often used in the prediction of non-deterministic time series that cannot be modeled using standard econometric methods. These series, such as occur in finance, often undergo changes to their underlying data generation process resulting in inaccurate approximations or requiring additional human judgment and input in the process, hindering the potential for automated solutions. Genetic programming (GP) is a class of nature-inspired algorithms that aims to evolve a population of computer programs to solve a target problem. GP has been applied to time series prediction in finance and other domains. However, most GP-based approaches to these prediction problems do not consider regime change. This paper introduces two new genetic programming modularity techniques, collectively referred to as automatically defined templates, which better enable prediction of time series involving regime change. These methods, based on earlier established GP modularity techniques, take inspiration from software design patterns and are more closely modeled after the way humans actually develop software. Specifically, a regime detection branch is incorporated into the GP paradigm. Regime specific behavior evolves in a separate program branch, implementing the template method pattern. A system was developed to test, validate, and compare the proposed approach with earlier approaches to GP modularity. Prediction experiments were performed on synthetic time series and on the S&P 500 index. The performance of the proposed approach was evaluated by comparing prediction accuracy with existing methods. One of the two techniques proposed is shown to significantly improve performance of time series prediction in series undergoing regime change. The second proposed technique did not show any improvement and performed generally worse than existing methods or the canonical approaches. The difference in relative performance was shown to be due to a decoupling of reusable modules from the evolving main program population. This observation also explains earlier results regarding the inferior performance of genetic programming techniques using a similar, decoupled approach. Applied to financial time series prediction, the proposed approach beat a buy and hold return on the S&P 500 index as well as the return achieved by other regime aware genetic programming methodologies. No approach tested beat the benchmark return when factoring in transaction costs

Is Primary Care Providers’ Trust in Socially Marginalized Patients Affected by Race?

Interpersonal trust plays an important role in the clinic visit. Clinician trust in the patient may be especially important when prescribing opioid analgesics because of concerns about misuse. Previous studies have found that non-white patients are perceived negatively by clinicians.To examine whether clinicians' trust in patients differed by patients' race/ethnicity in a socially marginalized cohort.Cross-sectional study of patient-clinician dyads.169 HIV infected indigent patients recruited from the community and their 61 primary care providers (PCPs.)The Physician Trust in Patients Scale (PTPS), a validated scale that measures PCPs' trust in patients.The mean PTPS score was 43.2 (SD 10.8) out of a possible 60. Reported current illicit drug use and prescription opioid misuse were similar across patients' race or ethnicity. However, both patient illicit drug use and patient non-white race/ethnicity were associated with lower PTPS scores. In a multivariate model, non-white race/ethnicity was independently associated with PTPS scores 6.3 points lower than whites (95% CI: -9.9, -2.7). Current illicit drug use was associated with PTSP scores 5.5 lower than no drug use (95% CI -8.5, -2.5).In a socially marginalized cohort, non-white patients were trusted less than white patients by their PCPs, despite similar rates of illicit drug use and opioid analgesic misuse. The effect was independent of illicit drug use. This finding may reflect unconscious stereotypes by PCPs and may underlie disparities in chronic pain management

Factors associated with post-arrest withdrawal of life-sustaining therapy.

INTRODUCTION: Most successfully resuscitated cardiac arrest patients do not survive to hospital discharge. Many have withdrawal of life sustaining therapy (WLST) as a result of the perception of poor neurologic prognosis. The characteristics of these patients and differences in their post-arrest care are largely unknown. METHODS: Utilizing the Penn Alliance for Therapeutic Hypothermia Registry, we identified a cohort of 1311 post-arrest patients from 26 hospitals from 2010 to 2014 who remained comatose after return of spontaneous circulation. We stratified patients by whether they had WLST post-arrest and analyzed demographic, arrest, and post-arrest variables. RESULTS: In our cohort, 565 (43%) patients had WLST. In multivariate regression, patients who had WLST were less likely to go to the cardiac catheterization lab (OR 0.40; 95% CI: 0.26-0.62) and had shorter hospital stays (OR 0.93; 95% CI: 0.91-0.95). When multivariate regression was limited to patient demographics and arrest characteristics, patients with WLST were older (OR 1.18; 95% CI: 1.07-1.31 by decade), had a longer arrest duration (OR 1.14; 95% CI: 1.05-1.25 per 10min), more likely to be female (OR: 1.41; 95% CI: 1.01-1.96), and less likely to have a witnessed arrest (OR 0.65; 95% CI: 0.42-0.98). CONCLUSION: Patients with WLST differ in terms of demographic, arrest, and post-arrest characteristics and treatments from those who did not have WLST. Failure to account for this variability could affect both clinical practice and the interpretation of research

Moving from evidence-based medicine to evidence-based health.

While evidence-based medicine (EBM) has advanced medical practice, the health care system has been inconsistent in translating EBM into improvements in health. Disparities in health and health care play out through patients' limited ability to incorporate the advances of EBM into their daily lives. Assisting patients to self-manage their chronic conditions and paying attention to unhealthy community factors could be added to EBM to create a broader paradigm of evidence-based health. A perspective of evidence-based health may encourage physicians to consider their role in upstream efforts to combat socially patterned chronic disease

Rabbit Knee Joint Biomechanics: Motion Analysis and Modeling of Forces during Hopping

Although the rabbit hindlimb has been commonly used as an experimental animal model for studies of osteoarthritis, bone growth and fracture healing, the in vivo biomechanics of the rabbit knee joint have not been quantified. The purpose of this study was to investigate the kinematic and kinetic patterns during hopping of the adult rabbit, and to develop a model to estimate the joint contact force distribution between the tibial plateaus. Force platform data and three-dimensional motion analysis using infrared markers mounted on intracortical bone pins were combined to calculate the knee and ankle joint intersegmental forces and moments. A statically determinate model was developed to predict muscle, ligament and tibiofemoral joint contact forces during the stance phase of hopping. Variations in hindlimb kinematics permitted the identification of two landing patterns, that could be distinguished by variations in the magnitude of the external knee abduction moment. During hopping, the prevalence of an external abduction moment led to the prediction of higher joint contact forces passing through the lateral compartment as compared to the medial compartment of the knee joint. These results represent critical data on the in vivo biomechanics of the rabbit knee joint, which allow for comparisons to both other experimental animal models and the human knee, and may provide further insight into the relationships between mechanical loading, osteoarthritis, bone growth, and fracture healing