Cost of Care for Newborns With Neonatal Abstinence Syndrome in a State Medicaid Program.

Importance: Neonatal abstinence syndrome (NAS) is a medical condition among neonates experiencing substance withdrawal due to the mother's substance use during pregnancy. While previous studies suggest that the overall incidence and annual costs of NAS are increasing, to date, the long-term costs have yet to be demonstrated in Medicaid populations. Objective: To examine the demographic differences and long-term costs of care for neonates diagnosed with vs not diagnosed with NAS. Design, Setting, and Participants: This cohort study used claims data from the Alabama Medicaid Agency for neonates born to Medicaid-eligible mothers between January 1, 2010, and December 31, 2020. Data were analyzed in June 2022. Exposure: A diagnosis of NAS within 30 days of birth. Main Outcomes and Measures: Rate of NAS by demographic and birth characteristics, long-term costs attributable to NAS status and demographic and birth characteristics, and distribution of this expenditure over the enrollment period. Results: A total of 346 259 neonates with Medicaid eligibility were born during the study period (mean [SD] gestational age, 38.4 [2.2] weeks; 50.5%, male), 4027 (1.2%) of whom had an NAS diagnosis within 30 days of birth. A larger percentage of neonates with an NAS diagnosis were male (52.7%) than in the group without NAS (50.5%). Neonates with NAS also weighed less at birth (mean difference, -212.0 g; 95% CI, -231.1 to -192.8 g) and had older mothers (mean difference, 3.4 years; 95% CI, 2.6-4.2 years). An NAS diagnosis had an estimated additional cost of $17 921 (95% CI, $14 830-$21 012) over the enrollment period, and this cost was not evenly distributed over that period. Conclusions and Relevance: In this cohort study of neonates born into the Alabama Medicaid population, those with an NAS diagnosis had a different demographic profile and a higher cost to state Medicaid agencies than those without NAS. These findings warrant further effort to reduce the occurrence of NAS.

Examining the existing usage gap of electronic health records in the United States: A study of National Health Survey.

Background: Little is known about the extent of geographic variation in online health record usage and related demographic characteristics in the United States. Methods: In order to examine geographical variation in the usage of online e-health records (EHR) patient portals in the US, and the sociodemographic factors effects on the access and use of the EHR patient's portal. This study using data from the 2019 and 2020 Health Information National Trends Survey. Specifically, predictors associated with accessing patients' EHR portal were examined. Furthermore, geographic variation of EHR portal' availability and usage gap were examined and mapped. Results: Respondents had significantly higher likelihood to access EHR portals when they are higher educated, willing to seek health information online, insured and had regular providers (adjusted OR = 2.01, 95% CI: 1.44 - 2.80; adjusted OR = 3.51, 95% CI: 2.49 - 4.94; adjusted OR = 2.38, 95% CI: 1.05 - 5.43; adjusted OR = 2.1, 95% CI: 1.51 - 2.92, respectively). Individuals living in Central-West, South regions or other non-urban areas as well as deprived urban areas are less likely to access their EHR portals (adjusted OR = 0.6, 95% CI: 0.41 - 0.89). Furthermore, we found that people living in the Midwest, Southern regions, and Mountain rural areas are more likely to have greater difficulties to access EHR than other regions. Therefore, populations residing in these underserved (deprived urban, rural or remote) areas tend to face more considerable obstacles to e-healthcare. Conclusions: Improve the disparities, accessibility, and educational initiatives on the usage of eHealth resources and encouragement from both healthcare providers and policymakers should be implemented with a particular focus on targeting non-urban areas and underserved population.

Equitable and effective vaccine access considering vaccine hesitancy and capacity constraints.

The COVID-19 pandemic continues to have an unprecedented impact on people's lives and the economy worldwide. Vaccines are the strongest evidence-based defense against the spread of the disease. The release of COVID-19 vaccines to the general public created policy challenges associated with how to best allocate vaccines among different sub-regions. In the United States, after vaccines became widely available for all eligible adults, policymakers faced objectives such as (i) achieving an equitable allocation to reduce populations' travel times to get vaccinated and (ii) effectively allocating vaccine doses to minimize waste and unmet need. This problem was further exacerbated by the underlying factors of population vaccine hesitancy and sub-regions' varying capacity levels to administer vaccines to eligible and willing populations. Although simple to implement, commonly used pro rata policies do not capture the complexities of this problem. We propose two alternatives to simple pro rata policies. The first alternative is based on a Mixed-Integer Linear Programming Model that minimizes the maximum travel duration of patients and aims to achieve an equitable and effective allocation of vaccines to sub-regions while considering capacity and vaccine hesitancy. A second alternative is a heuristic approach that may be more palatable for policymakers who (i) are not familiar with mathematical modeling, (ii) are reluctant to use black-box models, and (iii) prefer algorithms that are easy to understand and implement. We demonstrate the results of our model through a case study based on real data from the state of Alabama and show that substantial improvements in travel time-based equity are achievable through capacity improvements in a small subset of counties. We perform additional computational experiments that compare the proposed methods in terms of several metrics and demonstrate the promising performance of our model and proposed heuristic. We find that while our mathematical model can achieve equitable and effective vaccine allocation, the proposed heuristic performs better if the goal is to minimize average travel duration. Finally, we explore two model extensions that aim to (i) lower vaccine hesitancy by allocating vaccines, and (ii) prioritize vaccine access for certain high-risk sub-populations.

Association between Body Composition Parameters and Heart Rate in a Sample of Adolescents with Anorexia Nervosa.

OBJECTIVES: Eating disorders affect the physical and mental health of millions of Americans. Body composition trends in relation to heart rate in adolescents with eating disorders remain understudied. The aim of the present study was to determine whether body composition parameters (percent body fat, percent skeletal muscle mass) are correlated with heart rate in a sample of adolescents with anorexia nervosa. METHODS: This study included patients 11 to 19 years old who presented to an outpatient eating disorder clinic (N = 49). Patients underwent bioelectrical impedance analysis to estimate body composition parameters. Descriptive statistics, linear regression, and paired t tests were used to evaluate the data. RESULTS: Heart rate was inversely associated with percent skeletal muscle mass (P < 0.001) and positively associated with percent body fat (P = 0.001). Patients demonstrated significant improvements in weight, body mass index percentile, skeletal muscle mass, percent body fat, and heart rate when comparing results at the first and last visits (P < 0.01). CONCLUSIONS: Overall, there was an inverse relation between percent skeletal muscle mass and heart rate and a positive association between body fat and heart rate. Our study demonstrates the importance of assessing percent body fat and skeletal muscle mass rather than weight or body mass index alone in adolescents with eating disorders.

Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error.

OBJECTIVES: Dietary assessment methods not relying on self-report are needed. The Automatic Ingestion Monitor 2 (AIM-2) combines a wearable camera that captures food images with sensors that detect food intake. We compared energy intake (EI) estimates of meals derived from AIM-2 chewing sensor signals, AIM-2 images, and an internet-based diet diary, with researcher conducted weighed food records (WFR) as the gold standard. SUBJECTS/METHODS: Thirty adults wore the AIM-2 for meals self-selected from a university food court on one day in mixed laboratory and free-living conditions. Daily EI was determined from a sensor regression model, manual image analysis, and a diet diary and compared with that from WFR. A posteriori analysis identified sources of error for image analysis and WFR differences. RESULTS: Sensor-derived EI from regression modeling (R2 = 0.331) showed the closest agreement with EI from WFR, followed by diet diary estimates. EI from image analysis differed significantly from that by WFR. Bland-Altman analysis showed wide limits of agreement for all three test methods with WFR, with the sensor method overestimating at lower and underestimating at higher EI. Nutritionist error in portion size estimation and irreconcilable differences in portion size between food and nutrient databases used for WFR and image analyses were the greatest contributors to image analysis and WFR differences (44.4% and 44.8% of WFR EI, respectively). CONCLUSIONS: Estimation of daily EI from meals using sensor-derived features offers a promising alternative to overcome limitations of self-report. Image analysis may benefit from computerized analytical procedures to reduce identified sources of error.

Lessons learned from developing a statewide research data repository to combat the opioid epidemic in Alabama.

Many states are continuing to struggle with opioids and other commonly abused drugs. Alabama, being the highest opioid prescription per-capita state since 2012, has pulled together state agencies, private companies, academia, and community organizations to form a data council and repository to provide unified insights and information to the public and partner stakeholders. The lessons learned in constructing this data environment are documented so that other states and organizations can benefit from the challenges and success that Alabama has experienced. The centralized data repository consists of almost a dozen data streams from public agencies and private companies. The data are transformed and linked within the repository to provide geo-temporal linkages between data sources. The data are stored in a secure multi-tiered environment in a Microsoft SQL Server database, de-identified, aggregated, and then published to a public web portal for open consumption. The public-facing website from the project successfully integrates multiple disparate data sources into a common platform for streamlined and cohesive data communications. Drug addiction cannot be easily quantified, viewed, or otherwise examined when only looking at a portion of society. By bringing together multiple data sources and linking them, a more clear picture of trends, influences, and metrics can be obtained. A statewide drug use data partnership between public and private entities is both possible as well as beneficial to all parties involved. Items like legal contracts can inhibit data sharing, but certain best practices can help scale and streamline multiple agreements.

Pandemic-Triggered Adoption of Telehealth in Underserved Communities: Descriptive Study of Pre- and Postshutdown Trends.

BACKGROUND: The adoption of telehealth services has been a challenge in rural communities. The reasons for the slow adoption of such technology-driven services have been attributed to social norms, health care policies, and a lack of infrastructure to support the delivery of services. However, the COVID-19 pandemic-related shutdown of in-person health care services resulted in the usage of telehealth services as a necessity rather than a choice. The pandemic also fast-tracked some needed legislation to allow medical cost reimbursement for remote examination and health care services. As services return to normalcy, it is important to examine whether the usage of telehealth services during the period of a shutdown has changed any of the trends in the acceptance of telehealth as a reliable alternative to traditional in-person health care services. OBJECTIVE: Our aim was to explore whether the temporary shift to telehealth services has changed the attitudes toward the usage of technology-enabled health services in rural communities. METHODS: We examined the Medicaid reimbursement data for the state of Alabama from March 2019 through June 2021. Selecting the telehealth service codes, we explored the adoption rates in 3 phases of the COVID-19 shutdown: prepandemic, pandemic before the rollout of mass vaccination, and pandemic after the rollout of mass vaccination. RESULTS: The trend in telemedicine claims had an opposite pattern to that in nontelemedicine claims across the 3 periods. The distribution of various characteristics of patients who used telemedicine (age group, gender, race, level of rurality, and service provider type) was different across the 3 periods. Claims related to behavior and mental health had the highest rates of telemedicine usage after the onset of the pandemic. The rate of telemedicine usage remained at a high level after the rollout of mass vaccination. CONCLUSIONS: The current trends indicate that adoption of telehealth services is likely to increase postpandemic and that the consumers (patients), service providers, health care establishments, insurance companies, and state and local policies have changed their attitudes toward telehealth. An increase in the use of telehealth could help local and federal governments address the shortage of health care facilities and service providers in underserved communities, and patients can get the much-needed care in a timely and effective manner.

Geographic disparities in access to Medication for Opioid Use Disorder across US census tracts based on treatment utilization behavior.

Drug overdose is the leading cause of accidental death in the U.S. with deaths from opioid overdose occurring at a higher rate in rural areas. The gaps in the provision of healthcare services have been exacerbated by the opioid crisis leaving vulnerable populations without access to preventative care and education, harm reduction, both chronic and acute treatment of the symptoms of opioid use disorder (OUD), and long-term psychological support for those with OUD and their families. There has been a call in the literature -and a federal mandate-for increased access to opioid treatment facilities, but to date this access has not been operationalized using best practices in geography. Medication for Opioid Use Disorder (MOUD) with FDA-approved methadone or buprenorphine has been shown to increase treatment retention, reduce opioid use and associated health and societal harms, and reduce opioid related overdose, and as such is considered the most effective treatment for OUD. The objective of this study is to examine U.S. adults' spatial access to MOUD - specifically locations of certified Opioid Treatment Programs (OTPs) and DATA-waived Buprenorphine providers. A gravity-based variant of the enhanced two-step floating catchment area model is employed, where friction of distance is based on previously published willingness to travel distances for patients visiting OTPs, to assess how opioid agonist treatment accessibility varies across the nation. Findings suggest that there are extensive 'treatment deserts' where there is little to no physical access to MOUD, especially in rural areas. The significance of this work lies in the incorporation of treatment utilization behavior in the access metric, and the continued confirmation of gaps in access to OUD services despite federal efforts to improve accessibility.

Disparity and Factors Associated With Internet Health Information Seeking Among US Adults Living With Diabetes Mellitus: Cross-sectional Study.

BACKGROUND: Many patients with chronic medical conditions search the internet to obtain medical advice and health information to improve their health condition and quality of life. Diabetes is a common chronic disease that disproportionately affects different race and ethnicity groups in the United States. In the existing literature on the popularity of internet health information seeking among persons with a chronic medical condition, there are limited data on US adults living with diabetes. OBJECTIVE: This study aims to examine the factors associated with internet health information seeking among US adults living with diabetes and whether there is a disparity in internet health information seeking stratified by race and ethnicity. METHODS: We conducted a cross-sectional study using the Health Information National Trends Survey data from 2017 to 2020. We selected our study sample based on respondents' reports on whether they were told they had diabetes, and our primary outcome was internet health information-seeking behavior. We used 2 multivariable logistic regression models to examine the effects of sociodemographic factors and other covariates on the internet health information-seeking behavior of adults with diabetes. Jackknife replicate weights were used to provide bias-corrected variance estimates. RESULTS: Our study sample included 2903 adults who self-reported that they had diabetes. In total, 60.08% (1744/2903) were non-Hispanic White individuals, 46.88% (1336/2850) were men, and 64% (1812/2831) had some college or graduate education. The prevalence of internet health information seeking in this population was 64.49% (1872/2903), and the main factors associated with internet health information seeking included education level (some college vs less than high school: odds ratio [OR] 1.42, 95% CI 1.44-1.88; and college graduate or higher vs less than high school: OR 2.50, 95% CI 1.79-3.50), age (age group ≥65 years vs age group 18-44 years: OR 0.46, 95% CI 0.34-0.63), and household income level (P<.001). In addition, we found significant differences in the effects of predictors stratified by race. CONCLUSIONS: The findings from this study suggest that internet health information seeking is common among US adults living with diabetes. Internet health information could influence the relationship between health care providers and adults living with diabetes and improve their self-management and quality of life.

Shifting of opioid prescription rate reporting impacts both rural and urban counties.

BACKGROUND: CDC annual reporting of opioid prescriptions per capita are key data used by systems for tracking opioid utilization and deaths. These data go back to 2006 and are measured at county-level resolution. Researchers and policy makers regularly use these data as inputs to models for tracking opioid trends geographically. METHODS: Recent changes in reporting of these data from the dispenser to provider cause a longitudinal break in the ability for data users to evaluate data points pre and post-change. This report examines the geographical impact of the data change in the context of both rural and urban counties. Data were segmented by county and tied to corresponding USDA rural-urban continuum codes for comparison. RESULTS: Opioid prescription data provides a distinct view into the opioid epidemic that allows all states and counties to view the trends of opioid utilization within their areas compared to themselves longitudinally as well as others. The reporting format change causes a break in the ability for communities to connect data before and after the change, especially in rural counties. CONCLUSION: Researchers and policy makers need to be aware of the changes in opioid reporting metrics to avoid drawing false conclusions. In most cases, data before 2019 cannot be compared to later data points. The policy change in reporting significantly alters the ability to longitudinally analyze and connect information when examining county-level data. State-level trends are not impacted by this reporting change.

Changes in Cardiometabolic Risk Among Older Adults with Obesity: An Ancillary Analysis of a Randomized Controlled Trial Investigating Exercise Plus Weight Maintenance and Exercise Plus Intentional Weight Loss by Caloric Restriction.

BACKGROUND: Obesity imposes risk to cardiometabolic health; however, intentional weight loss among older adults with obesity remains controversial. OBJECTIVE: To explore the influence of exercise plus weight maintenance and exercise plus intentional weight loss by caloric restriction on changes in cardiometabolic risk among older adults with obesity assessed by four risk-scoring tools. DESIGN: Using longitudinal data from the Calorie Restriction and Changes in Body Composition, Disease, Function, and Quality of Life in Older Adults study (CROSSROADS) (ClinicalTrials.gov identifier: NCT00955903; May 2009 to October 2014), scores were calculated using baseline and 12-month data according to criteria from the International Diabetes Federation, National Cholesterol Education Program's Adult Treatment Panel, Framingham Risk Score, and Cardiometabolic Disease Staging. PARTICIPANTS AND SETTING: Participants (39% men, 23% African American, aged 70.2 ± 4.7 years) were randomized to exercise (n = 48), exercise plus nutrient-dense weight maintenance diet (n = 44), or exercise plus weight loss by moderate caloric restriction (n = 42). MAIN OUTCOME MEASURES: To evaluate effects of exercise plus weight maintenance and exercise plus intentional weight loss on changes in cardiometabolic risk. STATISTICAL ANALYSES PERFORMED: Generalized estimating equations were used to assess changes in risk with ethnicity, biological sex, and age as covariates. RESULTS: Group-time interaction was only significant for Framingham and Cardiometabolic Disease Staging (P = 0.005 and 0.041, respectively). Upon post hoc analysis, significant within-group improvements in Framingham scores were observed for exercise plus weight maintenance (P < 0.001; r = -1.682) and exercise plus weight loss (P = 0.020; r = -0.881). In analysis of between-group differences in Framingham scores, significant decreases were observed in the exercise plus weight maintenance group (P = 0.001; r = -1.723) compared with the exercise group. For Cardiometabolic Disease Staging, the exercise plus weight loss group had significant within-group improvements (P = 0.023; r = -0.102). For between-group differences in Cardiometabolic Disease Staging, the exercise plus weight loss group showed significant risk reduction (P = 0.012; r = -0.142) compared with the exercise group. CONCLUSIONS: Among risk scores evaluated, Framingham and Cardiometabolic Disease Staging showed significantly greater sensitivity to change in cardiometabolic risk. Older adults with obesity can significantly lower cardiometabolic risk through exercise plus weight maintenance or exercise plus weight loss by moderate caloric restriction.

Health Care Utilization and Medicaid Spending in Children with Type 1 Diabetes in the Alabama Medicaid Program.

There are limited data on health service use and cost in low-income children with type 1 diabetes. This study examined the pattern of use and cost of health care services among low-income children diagnosed with type 1 diabetes in the state of Alabama Medicaid program. The authors performed descriptive analysis and examined factors that influence cost and health service utilization. Results showed that 5638 children with type 1 diabetes were enrolled in the Medicaid program over 7 years. Direct medical costs for patients with type 1 diabetes increased at a rate substantially higher than total Medicaid spending. White children with type 1 diabetes were found to have significantly higher Medicaid spending and service utilization than Black children with type 1 diabetes, while Hispanic children had the lowest costs. Further, older children with type 1 diabetes were found to have significantly higher Medicaid spending and service utilization than younger children with type 1 diabetes.

Patterns and Influencing Factors of eHealth Tools Adoption Among Medicaid and Non-Medicaid Populations From the Health Information National Trends Survey (HINTS) 2017-2019: Questionnaire Study.

BACKGROUND: Evidence suggests that eHealth tools adoption is associated with better health outcomes among various populations. The patterns and factors influencing eHealth adoption among the US Medicaid population remain obscure. OBJECTIVE: The objective of this study is to explore patterns of eHealth tools adoption among the Medicaid population and examine factors associated with eHealth adoption. METHODS: Data from the Health Information National Trends Survey from 2017 to 2019 were used to estimate the patterns of eHealth tools adoption among Medicaid and non-Medicaid populations. The effects of Medicaid insurance status and other influencing factors were assessed with logistic regression models. RESULTS: Compared with the non-Medicaid population, the Medicaid beneficiaries had significantly lower eHealth tools adoption rates for health information management (11.2% to 17.5% less) and mobile health for self-regulation (0.8% to 9.7% less). Conversely, the Medicaid population had significantly higher adoption rates for using social media for health information than their counterpart (8% higher in 2018, P=.01; 10.1% higher in 2019, P=.01). Internet access diversity, education, and cardiovascular diseases were positively associated with health information management and mobile health for self-regulation among the Medicaid population. Internet access diversity is the only factor significantly associated with social media adoption for acquisition of health information (OR 1.98, 95% CI 1.26-3.11). CONCLUSIONS: Our results suggest digital disparities in eHealth tools adoption between the Medicaid and non-Medicaid populations. Future research should investigate behavioral correlates and develop interventions to improve eHealth adoption and use among underserved communities.

Interdisciplinary speed dating augments diabetes self-management education and support to improve health outcomes.

OBJECTIVE: To determine if a novel interdisciplinary "speed-dating" clinic augments Diabetes Self-Management Education and Support (DSMES). METHODS: Adult patients with diabetes attended a DSMES class. Two weeks later patients attended an interdisciplinary clinic utilizing a "speed-dating" format during which they progressed through 5 stations hosted by different healthcare disciplines at 30-minute increments: physician, pharmacist, nurse/dietitian, case manager, and psychologist. Shared decision-making was utilized to identify mutually agreeable recommendations. Change in clinical outcomes were compared for DSMES-only attenders versus Dual-attendees; utilization of emergency department and hospital services were measured 12 months before and after attending the Speed Dating clinic. This analysis represents patients attending the program during 2016. RESULTS: Sixty-nine attended the DSMES class, 40 of whom followed-up in the "speed-dating" clinic (58% return rate). Attending the Speed Dating clinic improved A1C (p = 0.003) and LDL-C (p = 0.003) compared to the DSMES class alone. Comparatively, after attending the speed-dating clinic, patients had fewer emergency department (p = 0.366) and hospital admissions (p = 0.036), and shorter lengths of hospital stay (p = 0.030). CONCLUSIONS: The interdisciplinary "speed-dating" approach improved diabetes outcomes beyond DSMES alone and reduced utilization of hospital services. PRACTICE IMPLICATIONS: Patients should attend DSMES but also participate in an Interdisciplinary Speed Dating follow-up to further improve outcomes.

Effect of Patient-Physician Relationship on Withholding Information Behavior: Analysis of Health Information National Trends Survey (2011-2018) Data.

BACKGROUND: Patients' withholding information from doctors can undermine medical treatment, create barriers for appropriate diagnoses, and increase systemic cost in health care systems. To date, there is limited literature detailing the association between trends of patients withholding information behavior (WIB) and the patient-physician relationship (PPR). OBJECTIVE: The aim of this study was to explore the prevalence trend of WIB after 2011 and examine the effects of PPR on WIB and its time trend. METHODS: A total of 5 iterations of data from the Health Information National Trends Survey (years: 2011-2018; n=11,954) were used to explore curvilinear trends of WIB among the US population. Multiple logistic regression models were used to examine curvilinear time trends of WIB, effects of PPR on WIB, and moderation effects of PPR on the WIB time trend. RESULTS: The WIB prevalence has an increasing trend before 2014, which has the highest rate of 13.57%, and then it decreases after 2014 to 8.65%. The trend of WIB is curvilinear as the quadratic term in logistic regression model was statistically significant (P=.04; beta=-.022; SE=0.011; odds ratio [OR] 0.978, 95% CI 0.957-0.999). PPR is reversely associated with WIB (P<.001; beta=-.462; SE=0.097; OR 0.630, 95% CI 0.518-0.766) and has a significant moderation effect on time trends (P=.02; beta=-.06; SE=0.025; OR 0.941, 95% CI 0.896-0.989). In general, poor quality of PPR not only significantly increased the WIB probability but also postponed the change of point for WIB curvilinear trend. CONCLUSIONS: Findings suggest that the time trend of WIB between 2011 and 2018 is curvilinear and moderated by the quality of the PPR. Given these results, providers may reduce WIB by improving PPR. More research is needed to confirm these findings.

Classification of obesity, cardiometabolic risk, and metabolic syndrome in adults with spinal cord injury.

Objective: To describe and compare (1) classification of obesity using clinical proxies of body composition that are easily accessible in the outpatient clinic setting, (2) cardiometabolic risk using existing screening tools and staging systems, and (3) the presence of metabolic syndrome (MetS) using four commonly-used definitions in adults with spinal cord injury (SCI). Design: Retrospective chart review Setting: Outpatient Veterans Affairs (VA) SCI Annual Evaluation Clinic Participants: Patients who attended an annual evaluation appointment with demographic, anthropometric, and biochemical data documented in their medical records as part of routine medical care. Outcome measures: Obesity classification (body mass index, waist circumference, ideal body weight percentage), cardiometabolic risk scores (Framingham Risk Score, Cardiometabolic Disease Staging System, Edmonton Obesity Staging System), and MetS classification (using four commonly-used definitions) were described and compared. Results: Of the 155 veterans included in this analysis, 93% were considered "at risk" by at least one of the measurements studied. However, there was considerable variation between the different screening tools. The κ-agreement between various definitions of MetS ranged from fair to moderate. Conclusion: Screening tools that were developed for the non-SCI population produced variable assessments of risk when applied to veterans with SCI. Due to the fair to moderate inter-rater agreement between MetS definitions, it is unknown which definition is superior to identify MetS in the SCI population. An SCI-specific screening tool is needed to accurately classify obesity, cardiometabolic risk, and MetS in order to provide timely education and intervention.

Rationale of family medicine physicians in effectively identifying patients with chronic hyperglycemia through point-of-care hemoglobin A1C screenings.

PURPOSE: Many patients are unknowingly living with chronic hyperglycemia, possibly due to low screening rates. We aimed to correlate detection of unidentified chronic hyperglycemia to practitioner reported rationale for conducting diabetes screening. METHODS: Physicians screened patients via a point-of-care A1C tests and recorded corresponding rationales. Elevated outcomes (A1C ≥ 5.7%) were correlated to recorded rationales, frequency of repeat screenings, documented diagnoses, and therapeutic actions taken as a result of elevated A1C. RESULTS: Nearly one-half (45%) of selected patients were unknowingly living with chronic hyperglycemia, having an average A1C of 7.92% for outcomes ≥6.5%. Most commonly recorded rationales were overweight status (71%), high-risk ethnicity (58%), and age > 45 years (48%); previously recorded A1C result of ≥5.7% (χ2 16.02, p < 0.001) and hypertension diagnosis (χ2 10.37, p = 0.0013) showed statistically significant correlation with elevated A1C outcomes. A1C results ≥6.5% versus 5.7-6.5% more frequently prompted repeat screenings (77% vs 20%), ICD-10 code documentation (91% vs 28%), lifestyle modification recommendations (78% vs 35%), and drug therapy initiation (78% vs 9%). CONCLUSIONS: Reported rationales were largely impacted by visual inspections of age, race, and weight, and prediabetic A1C values garnered less attention compared to higher values. Utilization of POC A1C screening followed by conformational repeat testing is a practical approach to improve diagnostic rates and initiation of care for diabetes.

Group Means and Intersubject and Intrasubject Variability Estimates for Absolute and Relative (Categorical) Loudness Judgments of Typical Young Adult Listeners.

Purpose This brief research note is motivated by an ever-increasing need for typical repeated-measures loudness judgments and variability estimates of the kind necessary to conduct evidence-based treatment studies and clinical trials. Method These judgments and variability data, originally collected but not reported by Formby, Payne, Yang, Wu, and Parton (2017) , are presented here for relative (categorical) and absolute loudness judgments for typical young adult listeners with normal auditory function. Results As shown in this research note, these data may differ appreciably between young and older adult listeners with audiometric pure-tone thresholds within the clinically normal range. Conclusion In general, these findings highlight the need for good age-based, repeated-measures data for planning and powering evidence-based treatment studies and, specifically, for clinical trials that rely on categorical loudness judgments (i.e., as measured with the Contour Test of loudness; Cox, Alexander, Taylor, & Gray, 1997 ) as primary and secondary outcome measures.

Multimodal Neurological Enhancement Intervention for Self-regulation in Premature Infants.

BACKGROUND: The neonatal intensive care unit is often a noisy, overstimulating environment that disrupts infants' regulation of physiological and behavioral states and interrupts caregiver bonding; however, infants benefit from early intervention, including the use of multimodal neurological enhancement (MMNE) intervention to provide appropriate neurodevelopmental stimulation. No one has investigated whether it assists infants in self-regulation. PURPOSE: The purpose of this retrospective longitudinal analysis was to examine the effect of a music therapy intervention, MMNE, on self-regulation of premature infants as measured by changes in heart rate (HR). METHODS: A convenience sample of 60 premature infants received 486 MMNE sessions provided by a board-certified music therapist (MT-BC). Documentation, taken during routine clinical services, involved recording infant's HRs from the standard monitor for 3 minutes at baseline, during, and after a 20-minute MMNE intervention. RESULTS: Infants' mean HRs were decreased during and post-MMNE sessions compared with baseline (P < .004 and P < .001, respectively). Furthermore, infants with a baseline HR above 170 had significant decreases both during and after the MMNE session (P < .001 for both time periods). IMPLICATIONS FOR PRACTICE: Results of this study support the existing body of evidence showing the benefits of MMNE with premature infants. Based on our results, MMNE may help infants develop and demonstrate self-regulation as indicated by maintained HRs during and after the intervention as well as a lowered HR for infants who had high HRs prior to MMNE. IMPLICATIONS FOR RESEARCH: Further research needs to be done regarding how infants process MMNE and its potential to aid sensory processing.