Hospital Readmissions by Variation in Engagement in the Health Care Hotspotting Trial: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Variability in intervention participation within care management programs can complicate standard analysis strategies. Objective: To evaluate whether care management was associated with reduced hospital readmissions among individuals with higher participation probabilities. Design, Setting, and Participants: A total of 800 hospitalized patients aged 18 years and older were randomized as part of the Health Care Hotspotting randomized clinical trial, which was conducted in Camden, New Jersey, from June 2014 to September 2017. Data were collected through October 2018. In this new analysis performed between April 6, 2022, and April 23, 2023, the distillation method was applied to account for variable intervention participation. A gradient-boosting machine learning model produced predicted probabilities of engaged participation using baseline covariates only. Predicted probabilities were used to trim both intervention and control populations in an equivalent manner, and intervention effects were reevaluated within study population subsets that were increasingly concentrated with patients having higher participation probabilities. Patients had 2 or more hospitalizations in the 6-month preenrollment period and documented evidence of chronic illness and social complexity. Intervention: Multidisciplinary teams provided services to patients in the intervention arm for a mean 120 days after hospital discharge. Patients in the control group received usual postdischarge care. Main Outcomes and Measures: Hospital readmission rates and counts 30, 90, and 180 days postdischarge. Results: Of 800 eligible patients, 782 had complete discharge information and were included in this analysis (mean [SD] age, 56.6 [12.7] years; 395 [50.5%] female). In the intent-to-treat analysis, the unadjusted 180-day readmission rate for treatment and control groups was 60.1% vs 61.7% (adjusted odds ratio, 0.95; 95% CI, 0.71-1.28; P = .73) and the mean (SD) number of 180-day readmissions was 1.45 (1.89) vs 1.48 (1.94) (adjusted incidence rate ratio, 0.99, 95% CI, 0.88-1.12; P = .86). Among the population with the highest participation probabilities, the mean (SD) 180-day readmission count was 1.22 (1.74) vs 1.57 (1.74) and the incidence rate ratio attained statistical significance (adjusted incidence rate ratio, 0.74; 95% CI, 0.56-0.99; P = .045). Adjusted odds ratios and adjusted incidence rate ratios for 30- and 90-day outcomes reached statistical significance after population distillation. Conclusions and Relevance: This secondary analysis of a randomized clinical trial found that care management was associated with reduced readmissions among patients with higher participation probabilities, suggesting that program operation could be improved by addressing barriers to participation and refining inclusion criteria to identify patients most likely to benefit. Trial Registration: ClinicalTrials.gov Identifier: NCT02090426.

Comparing Kaiser Permanente Members to the General Population: Implications for Generalizability of Research.

Introduction Insight into the characteristics of populations from which research samples are drawn is essential to understanding the generalizability of research findings. This study characterizes the membership of Kaiser Permanente and compares members to the population of the communities in which they live. Methods This study is a descriptive comparison of population distributions for Kaiser Permanente members vs the general population within counties in which Kaiser Permanente operates. Kaiser Permanente data on demographics, membership, geographically linked census data, and chronic condition prevalence were compared with community data drawn from the US Census and the Behavioral Risk Factor Surveillance System. Results Overall, Kaiser Permanente members were older (50% aged 40 or older compared to 45.8% of the general population) and more likely to be female (51.8% vs 50.5% of the general population). Distribution by race and ethnicity was similar for all Regions combined but varied somewhat within Regions. Distribution by neighborhood-linked income, education, and social vulnerability was similar between Kaiser Permanente and the community. Prevalence of 6 of 7 chronic conditions was higher in the community than in Kaiser Permanente, with differences ranging from 0.5% for depression to 7.7% for hyperlipidemia. Conclusion The demographic characteristics of Kaiser Permanente members are similar to the general population within each of the Kaiser Permanente Regions. Overall, the size and diversity of the Kaiser Permanente membership offers an effective platform for research. This approach to comparing health system members with the larger community provides valuable context for interpreting real-world evidence, including understanding the generalizability of research and of measures of system performance.

The distillation method: A novel approach for analyzing randomized trials when exposure to the intervention is diluted.

OBJECTIVE: To introduce a novel analytical approach for randomized controlled trials that are underpowered because of low participant enrollment or engagement. DATA SOURCES: Reanalysis of data for 805 patients randomized as part of a pilot complex care intervention in 2015-2016 in a large delivery system. In the pilot randomized trial, only 64.6% of patients assigned to the intervention group participated. STUDY DESIGN: A case study and simulation. The "Distillation Method" capitalizes on the frequently observed correlation between the probability of subjects' participation or engagement in the intervention and the magnitude of benefit they experience. The novel method involves three stages: first, it uses baseline covariates to generate predicted probabilities of participation. Next, these are used to produce nested subsamples of the randomized intervention and control groups that are more concentrated with subjects who were likely to participate/engage. Finally, for the outcomes of interest, standard statistical methods are used to re-evaluate intervention effectiveness in these concentrated subsets. DATA EXTRACTION METHODS: We assembled secondary data on patients who were randomized to the pilot intervention for one year prior to randomization and two follow-up years. Data included program enrollment status, membership data, demographics, utilization, costs, and clinical data. PRINCIPAL FINDINGS: Using baseline covariates only, Generalized Boosted Regression Models predicting program enrollment performed well (AUC 0.884). We then distilled the full randomized sample to increasing levels of concentration and reanalyzed program outcomes. We found statistically significant differences in outpatient utilization and emergency department utilization (both follow-up years), and in total costs (follow-up year two only) at select levels of population concentration. CONCLUSIONS: By offering an internally valid analytic framework, the Distillation Method can increase the power to detect effects by redefining the estimand to subpopulations with higher enrollment probabilities and stronger average treatment effects while maintaining the original randomization.

Defining Complex Patient Populations: Implications for Population Size, Composition, Utilization, and Costs.

BACKGROUND: Interventions to support patients with complex needs have proliferated in recent years, but the question of how to identify patients with complex needs has received relatively little attention. There are innumerable ways to structure inclusion and exclusion criteria for complex care interventions, and little is known about the implications of choices made in designing patient selection criteria. OBJECTIVE: To provide insights into the design of patient selection criteria for interventions, by implementing criteria sets within a large health plan member population and comparing the characteristics of the resulting complex patient cohorts. DESIGN: Retrospective observational descriptive study. SUBJECTS: Patients identified as having complex needs, within the membership population of Kaiser Permanente Southern California, a large, population-based health plan with more than 4 million members. We characterize five commonly used archetypes of complex needs: high-cost patients, patients with multiple chronic conditions, frail elders, emergency department high-utilizers, and inpatient high-utilizers. MEASURES: We selected an initial set of criteria for identifying patients in each of the archetypical complex populations, based on available administrative data. We then tested multiple variants of each definition. We compared the resulting patient cohorts using univariate and bivariate descriptive statistics. KEY RESULTS: Overall, 32.7% of the 3,112,797 adults in our population-based sample were selected by at least one of the 25 definitions of complexity we tested. Across definitions the total number of patients identified as complex ranged from 622,560 to 1583 and complex patient cohorts varied widely in demographic characteristics, chronic conditions, healthcare utilization, spending, and survival. CONCLUSIONS: Choice of patient population is critical to the design of complex care programs. Exploratory analyses of population criteria can provide useful information for program planning in the setting of limited resources for interventions. Data such as these should be generated as a key step in program design.

The Effectiveness of Neuromuscular Warmups for Lower Extremity Injury Prevention in Basketball: A Systematic Review.

BACKGROUND: Neuromuscular warmups have gained increasing attention as a means of preventing sports-related injuries, but data on effectiveness in basketball are sparse. The objective of this systematic review was to evaluate evidence of the effectiveness of neuromuscular warmup-based strategies for preventing lower extremity injuries among basketball athletes. METHODS: PubMed and Cochrane Library databases were searched in February 2019. Studies were included if they were English-language randomized controlled, non-randomized comparative, or prospective cohort trials, tested neuromuscular and/or balance-focused warmup interventions among basketball players, and assessed at least one type of lower extremity injury as a primary outcome. Criteria developed by the USPSTF were used to appraise study quality, and GRADE was used to appraise the body of evidence for each outcome. Due to heterogeneity in the included studies, meta-analyses could not be performed. RESULTS: In total, 825 titles and abstracts were identified. Of the 13 studies which met inclusion criteria for this review, five were balance interventions (3 randomized controlled trials) and eight were multicomponent interventions involving multiple categories of dynamic neuromuscular warmup (5 randomized controlled trials). Authors of four of the studies were contacted to obtain outcome data specific to basketball athletes. Basketball specific results from the studies suggest significant protective effects for the following lower extremity injuries: ankle injuries (significant in 4 out of the 9 studies that assessed this outcome); ACL injuries (2 of 4 studies); knee injuries generally (1 of 5 studies); and overall lower extremity injuries (5 of 7 studies). All but one of the non-significant results were directionally favorable. Evidence was moderate for the effect of multicomponent interventions on lower extremity injuries generally. For all other outcomes, and for balance-based interventions, the quality of evidence was rated as low. CONCLUSION: Overall, the evidence is supportive of neuromuscular warmups for lower extremity injury prevention among basketball players. However, most studies are underpowered, some used lower-quality research study designs, and outcome and exposure definitions varied. Due to the nature of the study designs, effects could not be attributed to specific intervention components. More research is needed to identify the most effective bundle of warmup activities.

Association Between Removal of a Warning Against Cephalosporin Use in Patients With Penicillin Allergy and Antibiotic Prescribing.

Importance: Electronic health records (EHRs) often include default alerts that can influence physician selection of antibiotics, which in turn may be associated with a suboptimal choice of agents and increased antibiotic resistance. Objective: To examine whether removal of a default alert in the EHR to avoid cephalosporin use in patients with penicillin allergies is associated with changes in cephalosporin dispensing or administration in these patients. Design, Setting, and Participants: This retrospective cohort study of a natural experiment included data on patients who had received antibiotic treatment in the hospital or outpatient setting in 2 regions of a large, integrated health system in California from January 1, 2017, to December 31, 2018. Of 4 398 792 patients, 4 206 480 met the eligibility criteria: enrollment in the health system during antibiotic use, availability of complete demographic data, and use of antibiotics outside of the washout period. Interventions or Exposures: Oral or parenteral antibiotics dispensed or administered after removal of an EHR alert to avoid cephalosporin use in patients with a recorded penicillin allergy. Main Outcomes and Measures: Probability that an antibiotic course was a cephalosporin. A multinomial logistic regression model was used to examine the change in rates of cephalosporin use before and after an EHR penicillin allergy alert was removed in 1 of the study regions. Temporal changes in use rates were controlled for by comparing changes in cephalosporin use among patients with or without a penicillin allergy at the site that removed the warning and among patients at a comparison site that retained the warning. Regression models were used to examine adverse events. Results: Of the 4 206 480 patients who met all inclusion criteria, 2 465 849 (58.6%) were women; the mean (SD) age was 40.5 (23.2) years. A total of 10 652 014 antibiotic courses were administered or dispensed, divided approximately evenly between the period before and after removal of the warning. Before removal of an alert in the electronic health record system to avoid prescribing of cephalosporins to patients with a penicillin allergy at 1 of the 2 sites, 58 228 courses of cephalosporins (accounting for 17.9% of all antibiotic use at the site) were used among patients with a penicillin allergy; after removal of the alert, administration or dispensing of cephalosporins increased by 47% compared with cephalosporin administration or dispensing among patients without a penicillin allergy at the same site and patients at the comparison site that retained the warning (ratio of ratios of odds ratios [RROR], 1.47; 95% CI, 1.38-1.56) . No significant differences in anaphylaxis (9 total cases), new allergies (RROR, 1.02; 95% CI, 0.93-1.12), or treatment failures (RROR, 1.02; 95% CI, 0.99-1.05) were found at the course level. No significant differences were found in all-cause mortality (ratio of ratios of rate ratios [RRRR], 1.03; 95% CI, 0.94-1.13), hospital days (RRRR, 1.04; 95% CI, 0.99-1.10), and new infections (Clostridioides difficile: RRRR, 1.02; 95% CI, 0.84-1.22; methicillin-resistant Staphylococcus aureus: RRRR, 0.87; 95% CI, 0.75-1.00; and vancomycin-resistant Enterococcus: RRRR, 0.82; 95% CI, 0.55-1.22) at the patient level. Conclusions and Relevance: In this cohort study, removal of a warning in the electronic health record to avoid cephalosporin use in patients with penicillin allergies was associated with increased administration and dispensing of cephalosporin. This simple and rapidly implementable system-level intervention may be useful for improvement in antibiotic stewardship.

Identifying Populations with Complex Needs: Variation in Approaches Used to Select Complex Patient Populations.

Interventions to support patients with complex needs are proliferating. However, little attention has been paid to methods for identifying complex patients. This study aims to summarize approaches used to define populations with complex needs in practice, by cataloging specific population criteria and organizing them into a taxonomy. The authors conducted a pragmatic review of literature published January 2000-December 2018 using PubMed. Search results were limited to English-language studies of adults that specified a set of objective criteria to identify a population with complex needs. The authors abstracted data from each article on population parameters, and conducted thematic analysis guided by deductive coding. The review identified 70 studies reflecting 90 unique complex population definitions. Complex populations criteria reflected 3 approaches: stratification, segmentation, and targeting. Six domains of population criteria were found within, including age-based criteria (59 populations); income (12); health care costs (45); health care utilization (39); health conditions (35); and subjective criteria (15). Criteria from multiple domains were frequently used in combination, and exact specifications were highly variable within each domain. Overall, 83% of the 90 population definitions included at least 1 cost- or utilization-based criterion. Nearly every study in the review presented a unique approach to identifying patients with complex needs but a limited number of "schools of thought" were found. Variability in definitions and inconsistent terminology are potential sources of ambiguity between stakeholders. Greater specificity and transparency in complex population definition would be a substantial contribution to the emerging field of complex care.

Age-Dependent Hemoglobin A1c Therapeutic Targets Reduce Diabetic Medication Changes in the Elderly.

OBJECTIVE: To assess whether implementation of age-dependent therapeutic targets for high hemoglobin A1c (HbA1c) changed clinicians' ordering of diabetes medications for older adults. BACKGROUND: In 2016, Kaiser Permanente Southern California (KPSC) changed the therapeutic targets for alerting clinicians about high HbA1c results in the electronic health record, KP HealthConnect (KPHC). Previously, all HbA1c results ≥7.0 percent were flagged as high in adult patients with diabetes. Starting in 2016, HbA1c therapeutic targets were relaxed to <7.5 percent for patients age 65 to 75, and to <8.0 percent for patients over age 75 to reduce treatment intensity and adverse events. METHODS: This retrospective analysis used logistic regression models to calculate the change in odds of a medication change following an HbA1c result after age-dependent HbA1c flags were introduced. RESULTS: The odds of medication change decreased among patients whose HbA1c targets were relaxed: Odds Ratio (OR) 0.72 (95 percent CI 0.67-0.76) for patients age 65-75 and HbA1c 7.0 percent-7.5 percent; OR 0.72 (95 percent CI 0.65-0.80) for patients over age 75 and HbA1c 7.0 percent-7.5 percent; and OR 0.67 (95 percent CI 0.61-0.75) for patients over age 75 and HbA1c 7.5 percent-8.0 percent. In the age and HbA1c ranges for which the alerts did not change, the odds of medication change generally increased or stayed the same. There was little evidence of medication de-intensification in any group. CONCLUSIONS: These findings suggest that the change in therapeutic targets was associated with a reduction in medication intensification among older adults with diabetes.

Timely Outpatient Follow-up Is Associated with Fewer Hospital Readmissions among Patients with Behavioral Health Conditions.

BACKGROUND: Hospital readmissions contribute to high health care costs and are an indicator of poor performance. Reducing readmissions through reconnecting patients to primary care after hospitalization is a solution that is particularly relevant to complex patients with behavioral health conditions. We therefore aimed to examine the rate of follow-up visits among patients with behavioral health conditions and to assess the impact of this visit on the subsequent rate of readmission. METHODS: In this retrospective, observational study, we analyzed data from low-income uninsured adults with behavioral health conditions (n = 1905) enrolled in a health care coverage program implemented by a California County from 2012 to 2013. We used administrative encounter and eligibility data and 2 logistic regression models to predict the (1) likelihood of a timely follow-up outpatient visit and (2) likelihood of a readmission given a timely outpatient visit. Our outcomes were to calculate the marginal effects of an outpatient visit within 15 days and a readmission within 30 days of the index admission. RESULTS: The 15-day follow-up visit rate was 42% and readmission rate was 13%. Higher severity of illness (2.5%; P = .004; 95% CI, 0.01-0.04) and prior visits to providers (5.8%; P = .000; 95% CI, 0.04-0.08) increased the probability of a follow-up visit within 15 days. Follow-up visits (-2.5%; P = .021; 95% CI, -0.05-0.00) and a shorter index admission (0.5%; P = .039; 95% CI, 0.00-0.01) also reduced the risk of 30-day readmissions. CONCLUSION: The findings provide evidence that timely linking of behavioral health patients to outpatient care after hospitalization is an effective care transition strategy, as it is likely to reduce readmission rates.

Segmentation of High-Cost Adults in an Integrated Healthcare System Based on Empirical Clustering of Acute and Chronic Conditions.

BACKGROUND: High-cost patients are a frequent focus of improvement projects based on primary care and other settings. Efforts to characterize high-cost, high-need patients are needed to inform care planning, but such efforts often rely on a priori assumptions, masking underlying complexities of a heterogenous population. OBJECTIVE: To define recognizable subgroups of patients among high-cost adults based on clinical conditions, and describe their survival and future spending. DESIGN: Retrospective observational cohort study. PARTICIPANTS: Within a large integrated delivery system with 2.7 million adult members, we selected the top 1% of continuously enrolled adults with respect to total healthcare expenditures during 2010. MAIN MEASURES: We used latent class analysis to identify clusters of alike patients based on 53 hierarchical condition categories. Prognosis as measured by healthcare spending and survival was assessed through 2014 for the resulting classes of patients. RESULTS: Among 21,183 high-cost adults, seven clinically distinctive subgroups of patients emerged. Classes included end-stage renal disease (12% of high-cost population), cardiopulmonary conditions (17%), diabetes with multiple comorbidities (8%), acute illness superimposed on chronic conditions (11%), conditions requiring highly specialized care (14%), neurologic and catastrophic conditions (5%), and patients with few comorbidities (the largest class, 33%). Over 4 years of follow-up, 6566 (31%) patients died, and survival in the classes ranged from 43 to 88%. Spending regressed to the mean in all classes except the ESRD and diabetes with multiple comorbidities groups. CONCLUSIONS: Data-driven characterization of high-cost adults yielded clinically intuitive classes that were associated with survival and reflected markedly different healthcare needs. Relatively few high-cost patients remain persistently high cost over 4 years. Our results suggest that high-cost patients, while not a monolithic group, can be segmented into few subgroups. These subgroups may be the focus of future work to understand appropriateness of care and design interventions accordingly.

In California, Primary Care Continuity Was Associated With Reduced Emergency Department Use And Fewer Hospitalizations.

The expansion of health insurance to millions of Americans through the Affordable Care Act has given rise to concerns about increased use of emergency department (ED) and hospital services by previously uninsured populations. Prior research has demonstrated that continuity with a regular source of primary care is associated with lower use of these services and with greater patient satisfaction. We assessed the impact of a policy to increase patients' adherence to an individual primary care provider or clinic on subsequent use of ED and hospital services in a California coverage program for previously uninsured adults called the Health Care Coverage Initiative. We found that the policy was associated with a 42 percent greater probability of adhering to primary care providers. Furthermore, patients who were always adherent had a higher probability of having no ED visits (change in probability: 2.1 percent) and no hospitalizations (change in probability: 1.7 percent), compared to those who were never adherent. Adherence to a primary care provider can reduce the use of costly care because it allows patients' care needs to be managed within the less costly primary care setting.

Disparities in CD4+ T-Lymphocyte Monitoring Among Human Immunodeficiency Virus-Positive Medicaid Beneficiaries: Evidence of Differential Treatment at the Point of Care.

BACKGROUND: Monitoring of immune function, measured by CD4 cell count, is an essential service for people with Human Immunodeficiency Virus (HIV). Prescription of antiretroviral (ARV) medications is contingent on CD4 cell count; patients without regular CD4 monitoring are unlikely to receive ARVs when indicated. This study assesses disparities in CD4 monitoring among HIV-positive Medicaid beneficiaries. METHODS: In this retrospective observational study, we examined 24 months of administrative data on 2,250 HIV-positive, continuously-enrolled fee-for-service Medicaid beneficiaries with at least two outpatient healthcare encounters. We used logistic regression to evaluate the association of patient demographics (age, gender, race/ethnicity, and language) with receipt of at least one CD4 test per year, controlling for other potentially confounding variables. RESULTS: Having a history of ARV therapy was positively associated with receipt of CD4 tests. We found racial/ethnic, gender, and age disparities in CD4 testing. Among individuals with a history of ARV use, all racial/ethnic groups were significantly less likely to have CD4 tests than White non-Latinos (African Americans, OR = 0.35, p<0.0001; Asian/Pacific Islanders, OR = 0.31, p=0.0047; and, Latinos, OR = 0.42, p<0.0001). CONCLUSIONS: Disparities in receipt of CD4 tests elucidate one potential pathway for previously reported disparities in ARV treatment. Further qualitative and quantitative research is needed to identify the specific factors that account for these disparities, so that appropriate interventions can be implemented.

Geographic clustering of diabetic lower-extremity amputations in low-income regions of California.

For patients suffering from diabetes and other chronic conditions, a large body of work demonstrates income-related disparities in access to coordinated preventive care. Much less is known about associations between poverty and consequential negative health outcomes. Few studies have assessed geographic patterns that link household incomes to major preventable complications of chronic diseases. Using statewide facility discharge data for California in 2009, we identified 7,973 lower-extremity amputations in 6,828 adults with diabetes. We mapped amputations based on residential ZIP codes and used data from the Census Bureau to produce corresponding maps of poverty rates. Comparisons of the maps show amputation "hot spots" in lower-income urban and rural regions of California. Prevalence-adjusted amputation rates varied tenfold between high-income and low-income regions. Our analysis does not support detailed causal inferences. However, our method for mapping complication hot spots using public data sources may help target interventions to the communities most in need.

Disproportionate-share hospital payment reductions may threaten the financial stability of safety-net hospitals.

Safety-net hospitals rely on disproportionate-share hospital (DSH) payments to help cover uncompensated care costs and underpayments by Medicaid (known as Medicaid shortfalls). The Affordable Care Act (ACA) anticipates that insurance expansion will increase safety-net hospitals' revenues and will reduce DSH payments accordingly. We examined the impact of the ACA's Medicaid DSH reductions on California public hospitals' financial stability by estimating how total DSH costs (uncompensated care costs and Medicaid shortfalls) will change as a result of insurance expansion and the offsetting DSH reductions. Decreases in uncompensated care costs resulting from the ACA insurance expansion may not match the act's DSH reductions because of the high number of people who will remain uninsured, low Medicaid reimbursement rates, and medical cost inflation. Taking these three factors into account, we estimate that California public hospitals' total DSH costs will increase from $2.044 billion in 2010 to $2.363-$2.503 billion in 2019, with unmet DSH costs of $1.381-$1.537 billion.

Diabetes tied to a third of California hospital stays, driving health care costs higher.

Increasing diabetes prevalence has been found to be a primary driver of increased health care costs in the United States. This policy brief examines the impact of diabetes on hospitalizations and related hospitalization costs in California. Using 2011 hospital patient discharge data and annual financial data from the Office of Statewide Health Planning and Development (OSHPD), this study found that patients with diabetes represented 31 percent of hospitalizations in California in 2011 among patients 35 years or older, including 39 percent of African-American and Asian-American patients and 43 percent of Latino patients. Moreover, these hospitalizations cost nearly $2,200 more per hospitalization than those for patients without diabetes, regardless of the primary reason for the hospitalization. Given that approximately 90-95 percent of diagnosed diabetes among adults is type 2 diabetes and is therefore preventable, public health measures can and should be taken to relieve the burden of type 2 diabetes. Such measures include promoting a healthy diet and regular physical activity and providing adequate access to primary and specialty care.

In ten California counties, notable progress in system integration within the safety net, although challenges remain.

When fully implemented, the Affordable Care Act will expand insurance coverage to the currently uninsured, and experiments in delivery and payment under the law's auspices could produce greater efficiencies in how care is delivered. Both factors may accelerate the development of one viable model to streamline care, integrated delivery systems--coordinated care networks that deliver all needed health services to a defined population. Through interviews and surveys, we examined ten California counties that participated in two federally and locally funded initiatives to redesign how care is delivered to predominantly poor and uninsured populations. We found substantial progress in assessing and managing access to specialists, monitoring and promoting quality, and offering disease management and care coordination training in a majority of counties. However, efforts to coordinate care, electronically disseminate patient information, and align financial incentives were less successful or more difficult to assess. We posit that integrated delivery systems could improve care efficiency and quality and make countywide safety-net systems a desirable source of care for newly insured patients under health reform.

Power of grammatical evolution neural networks to detect gene-gene interactions in the presence of error.

BACKGROUND: With the advent of increasingly efficient means to obtain genetic information, a great insurgence of data has resulted, leading to the need for methods for analyzing this data beyond that of traditional parametric statistical approaches. Recently we introduced Grammatical Evolution Neural Network (GENN), a machine-learning approach to detect gene-gene or gene-environment interactions, also known as epistasis, in high dimensional genetic epidemiological data. GENN has been shown to be highly successful in a range of simulated data, but the impact of error common to real data is unknown. In the current study, we examine the power of GENN to detect interesting interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic heterogeneity. Additionally, we compare the performance of GENN to that of another computational method - Multifactor Dimensionality Reduction (MDR). FINDINGS: GENN is extremely robust to missing data and genotyping error. Phenocopy in a dataset reduces the power of both GENN and MDR. GENN is reasonably robust to genetic heterogeneity and find that in some cases GENN has substantially higher power than MDR to detect functional loci in the presence of genetic heterogeneity. CONCLUSION: GENN is a promising method to detect gene-gene interaction, even in the presence of common types of error found in real data.

Linkage Disequilibrium in Genetic Association Studies Improves the Performance of Grammatical Evolution Neural Networks.

One of the most important goals in genetic epidemiology is the identification of genetic factors/features that predict complex diseases. The ubiquitous nature of gene-gene interactions in the underlying etiology of common diseases creates an important analytical challenge, spurring the introduction of novel, computational approaches. One such method is a grammatical evolution neural network (GENN) approach. GENN has been shown to have high power to detect such interactions in simulation studies, but previous studies have ignored an important feature of most genetic data: linkage disequilibrium (LD). LD describes the non-random association of alleles not necessarily on the same chromosome. This results in strong correlation between variables in a dataset, which can complicate analysis. In the current study, data simulations with a range of LD patterns are used to assess the impact of such correlated variables on the performance of GENN. Our results show that not only do patterns of strong LD not decrease the power of GENN to detect genetic associations, they actually increase its power.

Xtn3 is a developmentally expressed cardiac and skeletal muscle-specific novex-3 titin isoform.

Titin proteins play an essential role in maintaining muscle function and structure. Recent work has implicated the involvement of the novex-3 titin isoform in sarcomere restructuring and disease. Unlike avian and mammalian systems, Xenopus laevis myogenesis is characterized by a wave of primary myogenesis followed by apoptosis of the primary muscles and formation of new muscles by secondary myogenesis. We show here that the Xenopus laevis novex-3 titin isoform (Xtn3) is developmentally expressed throughout the somites, heart, and primary muscles of the developing embryo. Downregulation of Xtn3 expression at tadpole stages appears to coincide with the change in myofiber composition from solely embryonic "fast" fiber types to myofibers containing both "fast" and "slow" fiber types. We demonstrate that Xtn3 is expressed early in the presomitic mesoderm and remains expressed in the somites, ventral myoblasts, and developing jaw muscles through late tailbud stage. Furthermore, we show that Xtn3 is expressed in the cardiac primordia prior to linear heart tube formation and remains expressed in the heart until tadpole stage, at which point it is downregulated in the heart except in discrete patches of cardiac cells. Finally, we demonstrate that Xtn3 transcripts are detectable in adult heart and muscle tissues.