Identifying drug targets for schizophrenia through gene prioritization.

Background: Schizophrenia genome-wide association studies (GWASes) have identified >250 significant loci and prioritized >100 disease-related genes. However, gene prioritization efforts have mostly been restricted to locus-based methods that ignore information from the rest of the genome. Methods: To more accurately characterize genes involved in schizophrenia etiology, we applied a combination of highly-predictive tools to a published GWAS of 67,390 schizophrenia cases and 94,015 controls. We combined both locus-based methods (fine-mapped coding variants, distance to GWAS signals) and genome-wide methods (PoPS, MAGMA, ultra-rare coding variant burden tests). To validate our findings, we compared them with previous prioritization efforts, known neurodevelopmental genes, and results from the PsyOPS tool. Results: We prioritized 62 schizophrenia genes, 41 of which were also highlighted by our validation methods. In addition to DRD2, the principal target of antipsychotics, we prioritized 9 genes that are targeted by approved or investigational drugs. These included drugs targeting glutamatergic receptors (GRIN2A and GRM3), calcium channels (CACNA1C and CACNB2), and GABAB receptor (GABBR2). These also included genes in loci that are shared with an addiction GWAS (e.g. PDE4B and VRK2). Conclusions: We curated a high-quality list of 62 genes that likely play a role in the development of schizophrenia. Developing or repurposing drugs that target these genes may lead to a new generation of schizophrenia therapies. Rodent models of addiction more closely resemble the human disorder than rodent models of schizophrenia. As such, genes prioritized for both disorders could be explored in rodent addiction models, potentially facilitating drug development.

Association of Hospital Incentive Care Management Partnerships for Uninsured Patients With Emergency Department Utilization.

Importance: The South Carolina (SC) Healthy Outcomes Plan (HOP) program aimed to expand access to health care to individuals without insurance; it remains unknown whether there is an association between the SC HOP program and emergency department (ED) use among patients with high health care costs and needs. Objectives: To determine whether participation in the SC HOP was associated with reduced ED utilization among uninsured participants. Design, Setting, and Participants: This retrospective cohort study included 11 684 HOP participants (ages 18-64 years) with at least 18 months of continuous enrollment. Generalized estimating equations and segmented regression of interrupted time-series analyses of ED visits and charges were conducted from October 1, 2012, to March 31, 2020. Exposures: Time intervals related to the HOP were 1 year before and 3 years after participation. Main Outcomes and Measures: ED visits per 100 participants per month and ED charges per participant per month overall and by subcategory. Results: The mean (SD) age of the 11 684 participants in the study was 45.2 (10.9) years; 6293 (54.5%) were women; 5028 (48.4%) were Black participants and 5189 (50.0%) were White participants. Over the study period, the mean (SE) number of ED visits decreased by 44.1%, from 48.1 (5.2) to 26.9 (2.8) per 100 participants per month. The mean (SE) ED charges were reduced to $858 ($46) per participant per month, a decrease from a mean (SE) of $1583 ($88) per participant per month 1 year before HOP implementation. There was an immediate level decrease of 40% (relative risk [RR], 0.61; 99.5% CI, 0.48-0.76; P < .001) from the preenrollment period, with a sustained reduction trend of 8% (RR 0.92; 99.5% CI, 0.89-0.95; P < .001) during the postenrollment period. A level change for ED charges was detected, at a decrease of 40% (RR 0.60; 99.5% CI, 0.47-0.77; P < .001) directly after HOP enrollment with a subsequent downward trend of 10% (RR 0.90; 99.5% CI, 0.86-0.93; P < .001) for the postenrollment period. Conclusions and Relevance: In this retrospective cohort study, proportions and charges of ED visits by uninsured patients saw immediate and sustained decreases after HOP enrollment. Reducing ED charges may have been driven by decreasing the ED as the primary point of patient care, especially for high-frequency users. These findings have implications for other nonexpansion states seeking to maximize uninsured compensation for low-income populations through improved outcomes.

Percutaneous Endoscopic Gastrostomy Placement in Trauma Patients: Early vs Delayed Initiation of Enteral Feeding.

In critically ill trauma patients, adequate nutrition is essential for the body's healing process. Currently, there is no clinical standard for initiating feeds after percutaneous endoscopic gastrostomy (PEG) tube placement. We aimed to demonstrate that early enteral nutrition (EN) is as safe as delayed EN in patients who have undergone PEG tube insertion. We conducted a multi-center, retrospective cohort study of 384 patients from the Prisma Health Trauma Registries who received PEGs. Feeding intolerance was defined as high gastric residuals, nausea, emesis, sustained diarrhea, or ileus. The probability that a patient would experience intolerance was 11.7% in those fed within 6 hours, 5.1% among patients fed between 6 and 12 hours, 6.0% among patients fed between 12 and 24 hours, and 7.6% among patients fed after 24 hours, for which no statistically significant difference was detected. These findings support that early EN after PEG placement is safe in critically ill, trauma patients.

Broadband access within Medically Underserved Areas and its implication for telehealth utilization.

PURPOSE: There is little information as to how America's broadband infrastructure might impact recent efforts to expand access to virtual care for underserved communities. OBJECTIVE: To examine potential and realized access to broadband internet services within Medically Underserved Areas (MUAs) that rely on community health care service providers for primary care. METHODS: This cross-sectional study included 214,946 US Census Block Group estimates from the 2017 and 2019 American Community Survey and the corresponding Federal Communications Commission database. Changes in household broadband subscription rates and Healthy People 2020 access thresholds within MUAs were assessed. FINDINGS: In 2019, 24,304 MUA households (31.9%) met Healthy People 2020 targets for broadband subscription rates, compared to 64.4% of non-MUA households (n = 89,285). On average, 74.7% of MUA households had a broadband internet subscription compared to 85.2% of non-MUA households, whereas 61.1% (n = 46,635) of MUA households had access to broadband speeds of at least 25.0 Mbps, compared to 75.6% (n = 104,696) of non-MUA households. Within urban households, there was a 0.8 to 1.3 to 1.6 annual percentage point convergence in MUA versus non-MUA broadband disparities between across quintiles (P < .05). Rural MUA households showed little improvement in broadband access between 2017 and 2019. CONCLUSIONS: There has been an overall convergence of broadband access disparities between MUA and non-MUA households over time, but less improvements in access among the most rural households. Reimbursement for audio-only telehealth visits by state Medicaid agencies would help drive down barriers to virtual health care options for populations residing in MUAs.

Using medical expenditure panel survey data to explore the relationship between patient-centered medical homes and racial disparities in severe maternal morbidity outcomes.

BACKGROUND: There are persistent racial/ethnic disparities in the occurrence of severe maternal morbidity. Patient-centered medical home care has the potential to address disparities in maternal outcomes. OBJECTIVES: To examine (1) the association between receiving patient-centered medical home care and severe maternal morbidity outcomes and (2) the interaction of race/ethnicity on patient-centered medical home status and severe maternal morbidity. DESIGN/METHODS: Using 2007 to 2016 data from the Medical Expenditures Panel Survey, we conducted a cross-sectional study to estimate the association between receipt of care from a patient-centered medical home and the occurrence of severe maternal morbidity, and racial-specific (White, Black, Asian, Other) relative risks of severe maternal morbidity. Our study used race as a proxy measure for exposure racism. We identified mothers (⩾15 years) who gave birth during the study period. We identified patient-centered medical home qualities using 11 Medical Expenditures Panel Survey questions and severe maternal morbidities using medical claims, and calculated generalized estimating equation models to estimate odds ratios of severe maternal morbidity and 95% confidence intervals. RESULTS: Among all mothers who gave birth (N = 2801; representing 5,362,782 US lives), only 25% received some exposure patient-centered medical home care. Two percent experienced severe maternal morbidity, and this did not differ statistically (p = 0.11) by patient-centered medical home status. However, our findings suggest a 85% decrease in the risk of severe maternal morbidity among mothers who were defined as always attending a patient-centered medical home (odds ratios: 0.15; 95% confidence interval:0.01-1.87; p = 0.14) and no difference in the risk of severe maternal morbidity among mothers who were defined as sometimes attending a patient-centered medical home (odds ratios: 1.00; 95% confidence interval:0.16-6.42; p = 1.00). There was no overall interaction effect in the model between race and patient-centered medical home groups (p = 0.82), or ethnicity and patient-centered medical home groups (p = 0.62) on the severe maternal morbidity outcome. CONCLUSION: While the rate of severe maternal morbidity was similar to US rates, few mothers received care from a patient-centered medical home which may be due to underreporting. Future research should further investigate the potential for patient-centered medical home-based care to reduce odds of severe maternal morbidity across racial/ethnic groups.

Changes in access to community health services among rural areas affected and unaffected by hospital closures between 2006 and 2018: A comparative interrupted time series study.

PURPOSE: Recent studies suggest that Federally Qualified Health Centers (FQHC) may be expanding their provision of primary care in rural communities that experience a hospital loss. Whether these trends are different from rural areas not being affected by rural hospital closures is unknown. METHODS: Data included Centers for Medicare and Medicaid Services Provider of Services files, the Cecil G. Sheps hospital closure database, and American Community Survey estimates. Changes in straight-line distances to the nearest FQHC and rural health clinic (RHC) were compared between areas affected and unaffected by a rural hospital closure in a matched case control study design using an interrupted time series model. FINDINGS: There was no instantaneous percentage point increase in FQHC (2.41, 95% CI -0.79 to 5.60, P .140) or RHC (3.27, 95% CI -1.12 to 7.67, P .144) access following hospital closures compared to changes in access occurring in other rural areas. On average, rural ZIP codes affected by hospital closures exhibited a 0.84 percentage point increase in FQHC access over time (95% CI 0.40-1.28, P .000), but similar trends were also found within unaffected ZIP codes classified as small rural areas. CONCLUSIONS: Rural areas impacted by hospital closures did not experience an increase in proximity to FQHCs or RHCs relative to changes in access occurring in other rural areas. Over time, most rural areas are seeing an increase in access to FQHCs and RHCs. Policies are needed to incentivize primary care providers to target geographic areas experiencing a hospital closure.

Geographic proximity to primary care providers as a risk-assessment criterion for quality performance measures.

IMPORTANCE: Previous studies have found a mixed association between Patient-Centered Medical Home (PCMH) designation and improvements in primary care quality indicators, including avoidable pediatric emergency department (ED) encounters. Whether these associations persist after accounting for the geographic locations of providers relative to where patients reside is unknown. OBJECTIVE: To examine the association between geographic proximity to primary care providers versus hospitals and risk of avoidable and potentially avoidable ED visits among children with pre-existing diagnosis of attention-deficit/hyperactivity disorder or asthma. METHODS: Retrospective cohort study of a panel of pediatric Medicaid claims data from the South Carolina from 2016-2018 for 2,959 beneficiaries having a pre-existing diagnosis of attention-deficit/hyperactivity disorder (ADD, ages 6-12) and 6,390 beneficiaries with asthma (MMA, ages 5-18), as defined using Healthcare Effectiveness Data and Information Set (HEDIS) performance measures. We calculated differences in avoidable and potentially avoidable ED visits by the beneficiary's PCMH attribution type and in relation to differences in proximity to their primary care providers versus hospitals. Outcomes were defined using the New York University Emergency Department Algorithm (NYU-EDA). Differences in ED visit risk were assessed using generalized estimation equations and compared using marginal effects models. RESULTS: The 2.4 percentage point reduction in risk of avoidable ED visits among children in the ADD cohort who attended a PCMH versus those who did not increased to 3.9 to 7.2 percentage points as relative proximity to primary care providers versus hospitals improved (p < 0.01). Children in the ADD and MMA cohorts that were enrolled in a medical home, but did not attend one for primary care services exhibited a 5.4 and 3.0 percentage point increase in avoidable ED visit compared to children who were unenrolled and did not attend medical homes (p < 0.05), but these differences were only observed when geographic proximity to hospitals was more convenient than primary care providers. Mixed findings were observed for potentially avoidable visits. CONCLUSIONS: In several health care performance evaluations, patient-centered medical homes have not been found to reduce differences in hospital utilization for conditions that are treatable in primary care settings among children with chronic illnesses. Analytical approaches that also consider geographic proximity to health care services can identify performance benefits of medical homes. Expanding risk-adjustment models to also include geographic data would benefit ongoing quality improvement initiatives.

Disentangling Genetic Risks for Metabolic Syndrome.

A quarter of the world's population is estimated to meet the criteria for metabolic syndrome (MetS), a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type 2 diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with MetS components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations of fasting glucose, HDL cholesterol, systolic blood pressure, triglycerides, and waist circumference was used, which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on MetS to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more MetS components, indicating that MetS is a complex, heterogeneous disorder. Associated loci harbor genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the MetS factor GWAS predicts 5.9% of the variance in MetS. These results provide mechanistic insights into the genetics of MetS and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple MetS components.

Post-Hospital Availability of Instrumental Support May Influence Patients' Readiness for Discharge.

PURPOSE OF STUDY: Evaluate the relationship between unplanned acute care utilization after discharge from an index hospital admission and registered nurse and patient perceptions of available instrumental support the patient would have after discharge. PRIMARY PRACTICE SETTING: Three hospitals in a large regional hospital system in the southeastern United States. METHODOLOGY AND SAMPLE: Retrospective, secondary quantitative analysis of 13,361 patient records (mean age 58.4 years; 51% female) from index hospitalizations evaluating patient and nurse responses to 2 questions that specifically address instrumental support on both the patient and nurse versions of the Readiness for Hospital Discharge Survey (RHDS) and subsequent unexpected care received (emergency department [ED] visit, observation stay, hospital readmission) in the acute care setting within 60 days of discharge. Logistic regression was used to evaluate the relationship between RHDS scores and unplanned care received. RESULTS: Patients who required hospital-based acute care within 60 days after discharge had lower average RN-RHDS scores than those who did not require hospital-based acute care. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Including a nursing assessment of potential postdischarge expected instrumental support may be helpful in identifying patients who are at a higher risk of experiencing postdischarge acute care utilization. Monitoring ED visits and observation stays in addition to readmissions will facilitate capturing significantly more points of care received after discharge and provide additional information regarding postdischarge care utilization.

Scoping review: Social support impacts hospital readmission rates.

AIMS AND OBJECTIVES: To review and synthesise the current literature on social support and hospital readmission rates. BACKGROUND: Hospital readmission rates have not declined significantly since 2010 despite efforts to identify and implement strategies to reduce readmissions. After discharge, patients often report the need for help at home with personal care, medical care and/or transportation. Social factors can positively or negatively affect the transition from hospital to home and the extended recovery period experienced by patients. METHODS: Published primary studies in peer-reviewed journals, written in English, assessing the adult medical/surgical population and discussing social support and hospital readmission rates were included. A Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was completed for this scoping review. RESULTS: The search resulted in 2919 articles. After removing duplicates and reviewing content for the inclusion and exclusion criteria, 23 articles were selected for review. Social support is provided by those within one's social circle. There are several types of social support and depending on the needs to the patient, the type of social required and provided is different. CONCLUSIONS: The most common form of social support needed at home for people recovering after a hospitalisation was instrumental support, tangible care in the form of assistance with daily personal and medical care, and transportation. Patients who lacked adequate social support after discharge were at an increased risk of hospital readmission. RELEVANCE TO CLINICAL PRACTICE: Identifying factors, such as social support, that may impact hospital readmission rates is important for quality hospital to home care transitions. Assessing patients' needs and available social support to meet those needs may be an essential part of the discharge planning process to decrease the risk of hospital readmission.

Geographic, racial/ethnic, and socioeconomic inequities in broadband access.

INTRODUCTION: Broadband access is a "super determinant of health." Understanding the spatial distribution and predictors of access may help target government programs and telehealth applications. Our aim was to examine broadband access across geography and sociodemographic characteristics using American Community Survey (ACS) data. METHODS: We used 5-year ACS estimates from 2014 to 2018 to evaluate broadband access across contiguous US census tracts. Rural-Urban Commuting Area (RUCA) codes were categorized as metropolitan, micropolitan, small town, and isolated rural. We performed bivariate analyses to determine differences by RUCA categories and meeting the Healthy People 2020 (HP2020) objective (83.2% broadband access) or not. We conducted spatial statistics and spatial regression analyses to identify clusters of broadband access and sociodemographic factors associated with broadband access. RESULTS: No RUCA grouping met the HP2020 objective; 80.6% of households had broadband access, including 82.0% of metropolitan, 73.9% of micropolitan, 70.7% of small town, and 70.0% of isolated rural households. Areas with high percentages of Black residents had lower broadband access, particularly in isolated rural tracts (54.9%). Low access was spatially clustered in the Southeast, Southwest, and northern plains. In spatial regression models, poverty and education were most strongly associated with broadband access, while the proportion of American Indian/Alaska Native population was the strongest racial/ethnic factor. CONCLUSIONS: Rural areas had less broadband access with the greatest disparities experienced among geographically isolated areas with larger Black and American Indian/Alaska Native populations, more poverty, and lower educational attainment, following well-known social gradients in health. Resources and initiatives should target these areas of greatest need.

Evaluating a symptom-triggered gastric residual volume policy in a surgical trauma intensive care unit: Simple and safe.

BACKGROUND: Routine checking of gastric residual volumes (GRVs) during enteral feeding within surgical trauma intensive care units (STICUs) is a common practice. However, data on the necessity of this practice and its impact on nutrient delivery are limited. We aim to study the association between the replacement of a routine GRV (rGRV) policy with a triggered GRV (tGRV) policy and the safe achievement of daily nutrition goals. METHODS: We prospectively collected data on patients after we instituted a tGRV policy and compared them with a historical cohort of patients who had rGRV assessments in our STICU at a level 1 trauma center. The primary end point was achieving 80% of prescribed nutrient goals. Secondary end points included aspiration pneumonia, witnessed emesis, and glycemic control. RESULTS: A total of 145 patients accounting for 1405 STICU days were treated under the tGRV policy, and 156 patients accounting for 1694 STICU days were treated under the rGRV policy. There were no statistically significant differences between the tGRV and rGRV groups with regard to the proportion of days meeting or exceeding protein (56.7% vs 56.2%) or calorie (56.4% vs 56.0%) goals. After adjusting for in-hospital deaths, injury severity score, complications, and STICU time, the predictive margins for meeting caloric and protein goals were higher among the tGRV patients (57% vs 56%), but these differences were not statistically significant. CONCLUSION: A tGRV policy did not change protein or calorie delivery among patients or increase the risk of emesis compared with traditional monitoring methods.

The need for GIScience in mapping COVID-19.

Since first being tracked in China in late 2019, the effects of the COVID-19 coronavirus have shaped global patterns of morbidity and mortality, as well as exposed the strengths and limitations of health care systems and social safety nets. Without question, reporting of its impact has been bolstered in large part through near real-time daily mapping of cases and fatalities. Though these maps serve as an effective political and social tool in communicating disease impact, most visualizations largely over-emphasize their usefulness for tracking disease progression and appropriate responses. Messy and inconsistent health data are a big part of this problem, as is a paucity of high-resolution spatial data to monitor health outcomes. Another issue is that the ease of producing out-of-the box products largely out paces the response to the core challenges inherent in the poor quality of most geo-referenced data. Adopting a GIScience approach, and in particular, making use of location-based intelligence tools, can improve the shortcomings in data reporting and more accurately reveal how COVID-19 will have a long-term impact on global health.

Community social determinants and health outcomes drive availability of patient-centered medical homes.

The collaborative design of America's patient-centered medical homes places these practices at the forefront of emerging efforts to address longstanding inequities in the quality of primary care experienced among socially and economically marginalized populations. We assessed the geographic distribution of the country's medical homes and assessed whether they are appearing within communities that face greater burdens of disease and social vulnerability. We assessed overlapping spatial clusters of mental and physical health surveys; health behaviors, including alcohol-impaired driving deaths and drug overdose deaths; as well as premature mortality with clusters of medical home saturation and community socioeconomic characteristics. Overlapping spatial clusters were assessed using odds ratios and marginal effects models, producing four different scenarios of resource need and resource availability. All analyses were conducted using county-level data for the contiguous US states. Counties having lower uninsured rates and lower poverty rates were the most consistent indicators of medical home availability. Overall, the analyses indicated that medical homes are more likely to emerge within communities that have more favorable health and socioeconomic conditions to begin with. These findings suggest that intersecting the spatial footprints of medical homes in relation to health and socioeconomic data can provide crucial information for policy makers and payers invested in narrowing the gaps between clinic availability and the communities that experience the brunt of health and social inequalities.

Association of Patient-Centered Medical Home designation and quality indicators within HRSA-funded community health center delivery sites.

BACKGROUND: Patient-Centered Medical Home (PCMH) adoption is an important strategy to help improve primary care quality within Health Resources and Service Administration (HRSA) community health centers (CHC), but evidence of its effect thus far remains mixed. A limitation of previous evaluations has been the inability to account for the proportion of CHC delivery sites that are designated medical homes. METHODS: Retrospective cross-sectional study using HRSA Uniform Data System (UDS) and certification files from the National Committee for Quality Assurance (NCQA) and the Joint Commission (JC). Datasets were linked through geocoding and an approximate string-matching algorithm. Predicted probability scores were regressed onto 11 clinical performance measures using 10% increments in site-level designation using beta logistic regression. RESULTS: The geocoding and approximate string-matching algorithm identified 2615 of the 6851 (41.8%) delivery sites included in the analyses as having been designated through the NCQA and/or JC. In total, 74.7% (n = 777) of the 1039 CHCs that met the inclusion criteria for the analysis managed at least one NCQA- and/or JC-designated site. A proportional increase in site-level designation showed a positive association with adherence scores for the majority of all indicators, but primarily among CHCs that designated at least 50% of its delivery sites. Once this threshold was achieved, there was a stepwise percentage point increase in adherence scores, ranging from 1.9 to 11.8% improvement, depending on the measure. CONCLUSION: Geocoding and approximate string-matching techniques offer a more reliable and nuanced approach for monitoring the association between site-level PCMH designation and clinical performance within HRSA's CHC delivery sites. Our findings suggest that transformation does in fact matter, but that it may not appear until half of the delivery sites become designated. There also appears to be a continued stepwise increase in adherence scores once this threshold is achieved.

Reliability of the American Community Survey Estimates of Risk-Adjusted Readmission Rankings for Hospitals Before and After Peer Group Stratification.

Importance: Since the transition to the American Community Survey, data uncertainty has complicated its use for policy making and research, despite the ongoing need to identify disparities in health care outcomes. The US Centers for Medicare & Medicaid Services' new, stratified payment adjustment method for its Hospital Readmissions Reduction Program may be able to reduce the reliance on data linkages to socioeconomic survey estimates. Objective: To determine whether there are differences in the reliability of socioeconomically risk-adjusted hospital readmission rates among hospitals that serve a disproportionate share of low-income populations after stratifying hospitals into peer group-based classification groups. Design, Setting, and Participants: This cross-sectional study uses data from the 2014 New York State Health Cost and Utilization Project State Inpatient Database for 96 278 hospital admissions for acute myocardial infarction, pneumonia, and congestive heart failure. The analysis included patients aged 18 years and older who were not transferred to another hospital, who were discharged alive, who did not leave the hospital against medical advice, and who were discharged before December 2014. Main Outcomes and Measures: The main outcomes were 30-day hospital readmissions after acute myocardial infarction, pneumonia, and congestive heart failure assessed using hierarchical logistic regression. Results: The mean (SD) age of the patients was 69.6 (16.0) years for the safety-net hospitals and 74.9 (14.7) years for the non-safety-net hospitals; 9382 (48.8%) and 7003 (48.5%) patients, respectively, were female. For safety net designations, 20% (3 of 15) of all evaluations concealed and distorted differences in risk, with factors such as poverty failing to identify similar risk of acute myocardial infarction readmission until unreliable estimates were excluded from the analysis (OR, 1.23 [95% CI, 1.00-1.52], P = .02; vs OR, 1.17 [95% CI, 0.94-1.46], P = .15). By comparison, 2 of the 60 models (3%) for the peer group-based classification altered the association between socioeconomic status and readmission risk, concealing similarities in congestive heart failure readmission when adjusted using high school completion rates (OR, 1.27 [95% CI 1.02-1.58], P = .04; vs OR, 1.23 [95% CI, 0.98-1.53], P = .06) and distorting similarities in pneumonia readmissions when accounting for the proportion of lone-parent families (OR, 1.27 [95% CI, 0.98-1.66], P = .07; vs OR, 1.35 [95% CI, 1.02-1.80], P = .04) between the lowest and highest socioeconomic status hospitals in quartile 1. Conclusions and Relevance: There was greater precision in socioeconomic adjusted readmission estimates when hospitals were stratified into the new payment adjustment criteria compared with safety net designations. A contributing factor for improved reliability of American Community Survey estimates under the new payment criteria was the merging of patients from low-income neighborhoods with greater homogeneity in survey estimates into groupings similar to those for higher-income patients, whose neighborhoods often exhibit greater estimate variability. Additional efforts are needed to explore the effect of measurement error on American Community Survey-adjusted readmissions using the new peer group-based classification methods.

Incidental Cancer Diagnoses in Trauma Patients: A Case-Control Study Evaluating Long-term Outcomes.

BACKGROUND: This study evaluates whether trauma patients who incidentally learned about a malignancy have similar long-term outcomes as patients who organically learned about their malignancy. MATERIALS AND METHODS: Incidental findings (IF) patients were matched to noninjured cancer controls on age group, sex, cancer site, stage, and year of diagnosis. Unadjusted covariates included race, insurance type, rural residence, and time from diagnosis to first cancer intervention. Cox proportional hazard regression models were used to measure adjusted all-cause and cancer-specific mortality risk. RESULTS: Adjusted long-term mortality risk among IF cases was 1.42 (95% confidence interval [1.11-1.81]) compared with noninjured cancer controls. There was no statistically significant difference in all-cause mortality among IF cases who survived at least 30 d (1.24 [0.88-1.74]). IF cases had no increased risk of cancer-related mortality compared with controls (1.26 [0.96-1.64]). CONCLUSIONS: Long-term mortality risks among trauma patients with incidental cancer diagnoses are no different than the cancer population as a whole among patients who survive at least 30 d after injury. IF trauma patients are not more susceptible to cancer-related causes of death as a result of a physiological stress response due to injury.

Precision of provider licensure data for mapping member accessibility to Medicaid managed care provider networks.

BACKGROUND: In July 2018, the Centers for Medicare and Medicaid Services (CMS) updated its Medicaid Managed Care (MMC) regulations that govern network and access standards for enrollees. There have been few published studies of whether there is accurate geographic information on primary care providers to monitor network adequacy. METHODS: We analyzed a sample of nurse practitioner (NP) and physician address data registered in the state labor, licensing, and regulation (LLR) boards and the National Provider Index (NPI) using employment location data contained in the patient-centered medical home (PCMH) data file. Our main outcome measures were address discordance (%) at the clinic-level, city, ZIP code, and county spatial extent and the distance, in miles, between employment location and the LLR/NPI address on file. RESULTS: Based on LLR records, address information provided by NPs corresponded to their place of employment in 5% of all cases. NP address information registered in the NPI corresponded to their place of employment in 64% of all cases. Among physicians, the address information provided in the LLR and NPI corresponded to the place of employment in 64 and 72% of all instances. For NPs, the average distance between the PCMH and the LLR address was 21.5 miles. Using the NPI, the distance decreased to 7.4 miles. For physicians, the average distance between the PCMH and the LLR and NPI addresses was 7.2 and 4.3 miles. CONCLUSIONS: Publicly available data to forecast state-wide distributions of the NP workforce for MMC members may not be reliable if done using state licensure board data. Meaningful improvements to correspond with MMC policy changes require collecting and releasing information on place of employment.

The reliability of the American Community Survey for injury surveillance.

BACKGROUND: To evaluate the reliability and predictability of 49 socioeconomic indicators constructed from the annual and multiyear American Community Survey (ACS) data cycles for monitoring injury inequalities across the USA. METHODS: Cross-sectional analysis of the 2006-2013 annual and multiyear county-level ACS data cycles. Indicator reliability was assessed using the margin of error and coefficient of variation (CV). Overlapping multiyear data cycles were assessed for statistical dependence in the estimates. Negative binomial regression models were constructed from a selection of the most reliable indicators over time and across all data cycles using all-cause unintentional and homicide-related mortality records from the National Center for Health Statistics (NCHS). RESULTS: Fewer than half of all indicators for each data cycle generated 'high reliability' CV estimates for at least 95% of all census counties. Indicator reliability did not linearly improve with increasing sample size afforded from the multiyear surveys. On average, changes in socioeconomic conditions for the same geographic areas were statistically significantly different (p<0.05) in 14% (rage 0-99%) to 16% (rage 0-93%) of all overlapping multiyear data cycles. ACS indicators that were among the most reliable across data cycles corroborated variable relationships derived using estimates from the 2000 decennial census and corresponding NCHS records for that year. CONCLUSIONS: Few of the socioeconomic indicators previously used to measure injury disparities are consistently reliable across all ACS data cycles. Researchers should be judicious when selecting consecutive multiyear data cycles to approximate changes in annual socioeconomic conditions. Among the indicators that are reliable, it is advisable to use estimates from the annual ACS data cycle as a crude barometer of injury inequalities and the multiyear files to confirm and add precedence to national trends every three and five years.

Data capture and communication during transfers to definitive care in an inclusive trauma system.

INTRODUCTION: Background trauma survivors in rural areas transferred to urban centers have higher mortality than trauma patients admitted directly to urban centers. Transfer data in trauma registries is important for injury control. Prehospital and early physiologic data may reflect processes of pre-hospital care. British Columbia currently has no standardized process for trauma patient data transfer. PATIENTS AND METHODS: We performed a retrospective data analysis for major trauma patients (ISS>15) transferred to a Level I trauma center over a 1year period (n=243). Completion rates of paramedic form and ATLS primary survey variables were extracted. Nominal and interval descriptives were calculated. Documentation rates were considered deficient at <80% and severely deficient <60%. Odds ratios were calculated for primary facility data based on ISS ≥30 vs ISS <30, with 2-sided p-values for confidence intervals RESULTS: Two hundred forty-three patients met inclusion criteria with a mean ISS of 26. Most injured patients were male (79%), the predominant mechanism was blunt (93%) and the average age at injury was 51 years old. Two hundred eighteen patients arrived by Emergency Health Services, and 140 (64%) of EHS pre-hospital forms were transferred with the patient chart. Pre-hospital airway, physiologic data, and GCS completion rates were severely deficient (43-49%). Primary facility data was adequately completed for airway management, systolic blood pressure, and heart rate in (80-83%). Completion rates were deficient for respiratory rate, GCS and temperature (60-77%). An ISS score ≥30 was significantly associated with a lower completion rate for GCS. DISCUSSION AND CONCLUSION: Overall, documentation for inter-hospital transfer of major trauma patients in BC has significant deficiencies. Physiologic and basic ATLS variables are often omitted in transferred charts. The potential for adverse events is high but performance improvement is achievable. We recommend education, training and a standardized trauma transfer protocol to improve system-wide information transfer.