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Abstract Background To evaluate institutional nursing care performance in the context of national comparative statistics (benchmarks), approximately one in every three major healthcare institutions (over 1,800 hospitals) across the United States, have joined the National Database for Nursing Quality Indicators® (NDNQI®). With over 18,000 hospital units contributing data for nearly 200 quantitative measures at present, a reliable and efficient input data screening for all quantitative measures for data quality control is critical to the integrity, validity, and on-time delivery of NDNQI reports. Methods With Monte Carlo simulation and quantitative NDNQI indicator examples, we compared two ad-hoc methods using robust scale estimators, Inter Quartile Range (IQR) and Median Absolute Deviation from the Median (MAD), to the classic, theoretically-based Minimum Covariance Determinant (FAST-MCD) approach, for initial univariate outlier detection. Results While the theoretically based FAST-MCD used in one dimension can be sensitive and is better suited for identifying groups of outliers because of its high breakdown point, the ad-hoc IQR and MAD approaches are fast, easy to implement, and could be more robust and efficient, depending on the distributional property of the underlying measure of interest. Conclusion With highly skewed distributions for most NDNQI indicators within a short data screen window, the FAST-MCD approach, when used in one dimensional raw data setting, could overestimate the false alarm rates for potential outliers than the IQR and MAD with the same pre-set of critical value, thus, overburden data quality control at both the data entry and administrative ends in our setting.

Abstract Background Many nursing and health related research studies have continuous outcome measures that are inherently non-normal in distribution. The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data representation and interpretation when the distribution of the dependent variable, or outcome measure, of interest deviates from the normal distribution. The objectives of this study was to contrast the effect of obtaining the Box-Cox power transformation parameter and subsequent analysis of variance with or without a priori knowledge of predictor variables under the classic linear or linear mixed model settings. Methods Simulation data from a 3 × 4 factorial treatments design, along with the Patient Falls and Patient Injury Falls from the National Database of Nursing Quality Indicators (NDNQI®) for the 3rd quarter of 2007 from a convenience sample of over one thousand US hospitals were analyzed. The effect of the nonlinear monotonic transformation was contrasted in two ways: a) estimating the transformation parameter along with factors with potential structural effects, and b) estimating the transformation parameter first and then conducting analysis of variance for the structural effect. Results Linear model ANOVA with Monte Carlo simulation and mixed models with correlated error terms with NDNQI examples showed no substantial differences on statistical tests for structural effects if the factors with structural effects were omitted during the estimation of the transformation parameter. Conclusions The Box-Cox power transformation can still be an effective tool for validating statistical inferences with large observational, cross-sectional, and hierarchical or repeated measure studies under the linear or the mixed model settings without prior knowledge of all the factors with potential structural effects.

Abstract Background Gastric Emptying Scintigraphy (GES) at intervals over 4 hours after a standardized radio-labeled meal is commonly regarded as the gold standard for diagnosing gastroparesis. The objectives of this study were: 1) to investigate the best time point and the best combination of multiple time points for diagnosing gastroparesis with repeated GES measures, and 2) to contrast and cross-validate Fisher's Linear Discriminant Analysis (LDA), a rank based Distribution Free (DF) approach, and the Classification And Regression Tree (CART) model. Methods A total of 320 patients with GES measures at 1, 2, 3, and 4 hour (h) after a standard meal using a standardized method were retrospectively collected. Area under the Receiver Operating Characteristic (ROC) curve and the rate of false classification through jackknife cross-validation were used for model comparison. Results Due to strong correlation and an abnormality in data distribution, no substantial improvement in diagnostic power was found with the best linear combination by LDA approach even with data transformation. With DF method, the linear combination of 4-h and 3-h increased the Area Under the Curve (AUC) and decreased the number of false classifications (0.87; 15.0%) over individual time points (0.83, 0.82; 15.6%, 25.3%, for 4-h and 3-h, respectively) at a higher sensitivity level (sensitivity = 0.9). The CART model using 4 hourly GES measurements along with patient's age was the most accurate diagnostic tool (AUC = 0.88, false classification = 13.8%). Patients having a 4-h gastric retention value >10% were 5 times more likely to have gastroparesis (179/207 = 86.5%) than those with ≤10% (18/113 = 15.9%). Conclusions With a mixed group of patients either referred with suspected gastroparesis or investigated for other reasons, the CART model is more robust than the LDA and DF approaches, capable of accommodating covariate effects and can be generalized for cross institutional applications, but could be unstable if sample size is limited.

Abstract Background Mortality from breast cancer has increased among American Indian/Alaskan Native (AI/AN) women. Despite this alarming reality, AI/AN women have some of the lowest breast cancer screening rates. Only 37% of eligible AI/AN women report a mammogram within the last year and 52% report a mammogram within the last two years compared to 57% and 72% for White women. The experiences and satisfaction surrounding mammography for AI/AN women likely are different from that of women of other racial/ethnic groups, due to cultural differences and limited access to Indian Health Service sponsored mammography units. The overall goals of this study are to identify and understand the mammography experiences and experiential elements that relate to satisfaction or dissatisfaction with mammography services in an AI/AN population and to develop a culturally-tailored AI/AN mammography satisfaction survey. Methods and Design The three project aims that will be used to guide this work are: 1) To compare the mammography experiences and satisfaction with mammography services of Native American/Alaska Native women with that of Non-Hispanic White, Hispanic, and Black women, 2) To develop and validate the psychometric properties of an American Indian Mammography Survey, and 3) To assess variation among AI/AN women's assessments of their mammography experiences and mammography service satisfaction. Evaluations of racial/ethnic differences in mammography patient satisfaction have received little study, particularly among AI/AN women. As such, qualitative study is uniquely suited for an initial examination of their experiences because it will allow for a rich and in-depth identification and exploration of satisfaction elements. Discussion This formative research is an essential step in the development of a validated and culturally tailored AI/AN mammography satisfaction assessment. Results from this project will provide a springboard from which a maximally effective breast cancer screening program to benefit AI/AN population will be developed and tested in an effort to alter the current breast cancer-related morbidity and mortality trajectory among AI/AN women.

Abstract Background American Indians (AI) have the highest smoking rates of any ethnic group in the US (40.8%), followed most closely by African Americans (24.3%) and European Americans (23.6%). AI smokers also have more difficulty quitting smoking compared to other ethnic groups, evidenced by their significantly lower quit ratios, and are among the least successful in maintaining long term abstinence. While health disparities like these have existed for years among AI, the epidemiology of smoking and nicotine dependence has not been optimally described among this underserved population. Our overarching hypothesis is that the susceptibility of AI to cigarette smoking and nicotine dependence and its consequences has both an underlying nicotine metabolism component as well as psychosocial, cultural, and environment causes. We are well-positioned to explore this issue for the first time in this population. Our objective is to establish a cohort of AI tribal college/university students to determine the predictors of smoking initiation (non-use to experimentation), progression (experimentation to established use), and cessation (established use to cessation). Much of what is known about the process of smoking initiation and progression comes from quantitative studies with non-Native populations. Information related to smoking use among AI tribal college/university (TCU) students is entirely unknown and critically needs further investigation. This study will be the first of its kind among AI college students who are at the highest risk among all ethnic groups for tobacco dependence. Methods/design First year students at Haskell Indian Nations University in Kansas will be recruited over four consecutive years and will be surveyed annually and repeatedly through year 5 of the study. We will use both longitudinal quantitative surveys and qualitative focus group methods to examine key measures and determinants of initiation and use among this high risk group.

Abstract Background The National Database for Nursing Quality Indicators® (NDNQI®) was established in 1998 to assist hospitals in monitoring indicators of nursing quality (eg, falls and pressure ulcers). Hospitals participating in NDNQI transmit data from nursing units to an NDNQI data repository. Data are summarized and published in reports that allow participating facilities to compare the results for their units with those from other units across the nation. A disadvantage of this reporting scheme is that the sampling variability is not explicit. For example, suppose a small nursing unit that has 2 out of 10 (rate of 20%) patients with pressure ulcers. Should the nursing unit immediately undertake a quality improvement plan because of the rate difference from the national average (7%)? Methods In this paper, we propose approximating 95% credible intervals (CrIs) for unit-level data using statistical models that account for the variability in unit rates for report cards. Results Bayesian CrIs communicate the level of uncertainty of estimates more clearly to decision makers than other significance tests. Conclusion A benefit of this approach is that nursing units would be better able to distinguish problematic or beneficial trends from fluctuations likely due to chance.

Three reports from the Institute of Medicine found that errors in hospital care were more common than previously thought; that health care delivery should be reorganized to improve the quality of care; and that, operationally, nurses have a critical role in securing patient safety. Now the contribution of nursing to the reduction of adverse events must be established empirically, so that nursing-sensitive indicators can be incorporated in such health care-improvement strategies as public reporting of hospital quality and performance-based payment systems. This article reviews what is known from previous nursing outcomes research and identifies gaps in the current state of knowledge. It then describes the contribution to research that can be made through the National Database of Nursing Quality Indicators TM (NDNQI®). Next it reports an NDNQI study that found three nursing workforce characteristics to be related significantly to patient outcomes: total nursing hours per patient day, percentage of hours supplied by RNs, and years of experience in nursing, and concludes with a discussion of the implications of these findings for both for nursing administrators and outcomes-based, quality-improvement initiatives. © 2007 OJIN: The Online Journal of Issues in Nursing Article published September 30, 2007 [ABSTRACT FROM AUTHOR]

Background: Excessive gestational weight gain (GWG) is related to increased offspring fat accrual, and increased fat mass (FM) is related to obesity development. Prenatal DHA supplementation has been linked to lower levels of offspring FM; however, conflicting data exist., Objectives: This study aimed to determine if there is a protective effect of prenatal DHA supplementation on offspring fat accrual and adipose tissue deposition at 24 mo in offspring born to females who gain excessive weight compared with nonexcessive weight during pregnancy. We also explored if the effect of DHA dose on FM differed by offspring sex., Methods: Infants born to females who participated in the Assessment of DHA on Reducing Early Preterm Birth randomized controlled trial (ADORE) were recruited. In ADORE, females were randomly assigned to either a high or low prenatal DHA supplement. Offspring body composition and adipose tissue distribution were measured using dual-energy x-ray absorptiometry (DXA). GWG was categorized as excessive or not excessive based on clinical guidelines., Results: For total FM, there was a significant main effect for the DHA dose ( P = 0.03); however, the dose by GWG status was nonsignificant ( P = 0.44). Therefore, a higher prenatal DHA dose was related to greater offspring FM (622.9 g greater) and unrelated to GWG status. When investigating a DHA dose by sex effect, a significant main effect for DHA dose ( P = 0.01) was detected for central FM. However, no interaction was detected ( P = 0.98), meaning that both boys and girls had greater central FM if their mother was assigned to the higher DHA dose., Conclusions: Greater prenatal DHA supplementation was associated with greater offspring FM and adipose tissue distribution at 24 mo. It will be important to understand if these effects persist into childhood.This trial was registered at clinicaltrials.gov as NCT03310983., (© 2024 The Author(s).)

Introduction: The Symbol Digit Modalities Test (SDMT) is a highly sensitive neuropsychological tool used for the assessment of information processing speed (IPS) in various neurological disorders., State of the Art: In this review, we have focused on the current knowledge regarding the use of SDMT selectively in the evaluation of progressive multiple sclerosis (PMS) patients. A literature review was performed regarding the application of SDMT in PMS, with a focus on the primary progressive and secondary progressive subtypes. Relationships of diverse disease-associated factors with SDMT have been described, including disease course, imaging findings, molecular biomarkers, treatment and others., Clinical Implications: SDMT is a very useful and easily applicable instrument in the diagnostic armamentarium of neurologists and neuropsychologists. It is especially valuable in the evaluation of PMS patients, in whom the prevalence of IPS deficits is higher than in relapsing-remitting multiple sclerosis subjects or in healthy individuals., Future Directions: An emphasis should be laid on larger study groups and differentiating between individual PMS subtypes and their separate analysis in the context of cognitive assessment.

Background: Lung cancer is the leading cause of cancer related deaths. In Kansas, where coal-fired power plants account for 34% of power, we investigated whether hosting counties had higher age-adjusted lung cancer incidence rates. We also examined demographics, poverty levels, percentage of smokers, and environmental conditions using spatial analysis., Methods: Data from the Kansas Health Matters, and the Behavioral Risk Factor Surveillance System (2010-2014) for 105 counties in Kansas were analyzed. Multiple Linear Regression (MLR) assessed associations between potential risk factors and age-adjusted lung cancer incidence rates while Geographically Weighted Regression (GWR) examined regional risk factors., Results: Moran's I test confirmed spatial autocorrelation in age-adjusted lung cancer incidence rates (p<0.0003). MLR identified percentage of smokers, population size, and proportion of elderly population as significant predictors of age-adjusted lung cancer incidence rates (p<0.05). GWR showed positive associations between percentage of smokers and age-adjusted lung cancer incidence rates in over 50% of counties., Conclusion: Contrary to our hypothesis, proximity to a coal-fired power plant was not a significant predictor of age-adjusted lung cancer incidence rates. Instead, percentage of smokers emerged as a consistent global and regional risk factor. Regional lung cancer outcomes in Kansas are influenced by wind patterns and elderly population., Competing Interests: Competing interests: The authors report there are no competing interests to declare.

Background and Objectives : The aim of this study was to assess the relationship between habitual physical activity, body composition, serum myokine concentration, and all-cause mortality in chronic hemodialysis patients. Materials and Methods : A prospective cohort study with a 7-year follow-up was conducted in a group of 38 patients (24 men, 14 women, mean age 65.6 ± 13.9 years, dialysis vintage 1.17 ± 1.25 years). Baseline serum concentrations of myokines-follistatin and myostatin-were assessed along with a measurement of physical activity with multidimensional accelerometery, body composition, and the force of forearm muscle contraction. Survival analysis was performed using the Kaplan-Meier method for tertiles of follistatin, serum myostatin, body composition, and physical activity expressed in metabolic equivalents (MET). Results : The mean physical activity among patients was 81 min/24 h (median 38.5 min), and the mean weekly 3MET activity was 493 min (median 218 min). The probability of survival of patients was significantly lower in the subgroup with 3MET/24 h less than 26 min/24 h and 3METt less than 148 min per week compared to the other subgroup ( p = 0.006 and p = 0.006, respectively). During the 70-month follow-up, the subgroup with the lowest baseline follistatin concentration showed a significantly lower risk of death ( p = 0.02). Baseline myostatin levels were not significant risk factors for mortality, nor were BMI or lean and fat tissue index categories. Conclusions : Physical activity and low plasma follistatin, but not body composition indexes or plasma myostatin, could serve as predictors of all-cause mortality in hemodialysis patients.

Background: The study startup process for interventional clinical trials is a complex process that involves the efforts of many different teams. Each team is responsible for their startup checklist in which they verify that the necessary tasks are done before a study can move on to the next team. This regulatory process provides quality assurance and is vital for ensuring patient safety [10]. However, without having this startup process centralized and optimized, study approval can take longer than necessary as time is lost when it passes through many different hands., Objective: This manuscript highlights the process and the systems that were developed at The University of Kansas Comprehensive Cancer Center regarding the study startup process. To facilitate this process the regulatory management, site development, cancer center administration, and the Biostatistics & Informatics Shared Resources (BISR) teams came together to build a platform aimed at streamlining the startup process and providing a transparent view of where a study is in the startup process., Process: Ensuring the guidelines are clearly articulated for the review criteria of each of the three review boards, i.e., Disease Working Group (DWG), Executive Resourcing Committee (ERC), and Protocol Review and Monitoring Committee (PRMC) along with a system that can track every step and its history throughout the review process., Results: Well-defined processes and tracking methodologies have allowed the operations teams to track each study closely and ensure the 90-day and 120-day deadlines are met, this allows the operational team to dynamically prioritize their work daily. It also provides Principal investigators a transparent view of where their study stands within the study startup process and allows them to prepare for the next steps accordingly., Conclusion/future Work: The current process and technology deployment has been a significant improvement to expedite the review process and minimize study startup delays. There are still a few opportunities to fine-tune the study startup process; an example of which includes automatically informing the operational managers or the study teams to act upon deadlines regarding study review rather than the current manual communication process which involves them looking it up in the system which can add delays., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Smoking during pregnancy and postpartum remains an important public health problem. No known prior study has prospectively examined mutual changes in risk factors and women's smoking trajectory across pregnancy and postpartum. The objective of this study was to report methods used to implement a prospective cohort (Msgs4Moms), present participant baseline characteristics, and compare our sample characteristics to pregnant women from national birth record data. The cohort study was designed to investigate smoking patterns, variables related to tobacco use and abstinence, and tobacco treatment quality across pregnancy through 1-year postpartum. Current smokers or recent quitters were recruited from obstetrics clinics. Analyses included Chi-square and independent sample t-tests using Cohen's d . A total of 62 participants (41 smokers and 21 quitters) were enrolled. Participants were Black (45.2%), White (35.5%), and multiracial (19.3%); 46.8% had post-secondary education; and most were Medicaid-insured (64.5%). Compared with quitters, fewer smokers were employed (65.9 vs 90.5%, Cohen's d = 0.88) and more reported financial strain (61.1% vs 28.6%; Cohen's d = 0.75). Women who continue to smoke during pregnancy cope with multiple social determinants of health. Longitudinal data from this cohort provide intensive data to identify treatment gaps, critical time points, and potential psychosocial variables warranting intervention.

Background: Pediatric population presents several barriers for clinical trial design and analysis, including ethical constraints on the sample size and slow accrual rate. Bayesian adaptive design methods could be considered to address these challenges in pediatric clinical trials., Methods: We developed an innovative Bayesian adaptive design method and demonstrated the approach as a re-design of a published phase III pediatric trial. The innovative design used early success criteria based on skeptical prior and early futility criteria based on enthusiastic prior extrapolated from a historical adult trial, and the early and late stopping boundaries were calibrated to ensure a one-sided type I error of 2.5%. We also constructed several alternative designs which incorporated only one type of prior belief and the same stopping boundaries. To identify a preferred design, we compared operating characteristics including power, expected trial size and trial duration for all the candidate adaptive designs via simulation when performing an increasing number of equally spaced interim analyses., Results: When performing an increasing number of equally spaced interim analyses, the innovative Bayesian adaptive trial design incorporating both skeptical and enthusiastic priors at both interim and final analyses outperforms alternative designs which only consider one type of prior belief, because it allows more reduction in sample size and trial duration while still offering good trial design properties including controlled type I error rate and sufficient power., Conclusions: Designing a Bayesian adaptive pediatric trial with both skeptical and enthusiastic priors can be an efficient and robust approach for early trial stopping, thus potentially saving time and money for trial conduction., (© 2022. The Author(s).)

Background: The National Clinical Trials Network program conducts phase 2 or phase 3 treatment trials across all National Cancer Institute's designated cancer centers. Participant accrual across these clinical trials is a critical factor in deciding their success. Cancer centers that cater to rural populations, such as The University of Kansas Cancer Center, have an additional responsibility to ensure rural residents have access and are well represented across these studies., Objective: There are scant data available regarding the factors that act as barriers to the accrual of rural residents in these clinical trials. This study aims to use electronic screening logs that were used to gather patient data at several participating sites in The Kansas University of Cancer Center's Catchment area., Methods: Screening log data were used to assess what clinical trial participation barriers are faced by these patients. Additionally, the differences in clinical trial participation barriers were compared between rural and urban participating sites., Results: Analysis revealed that the hospital location rural urban category, defined as whether the hospital was in an urban or rural setting, had a medium effect on enrolment of patients in breast cancer and lung cancer trials (Cohen d=0.7). Additionally, the hospital location category had a medium effect on the proportion of recurrent lung cancer cases at the time of screening (d=0.6)., Conclusions: In consideration of the financially hostile nature of cancer treatment as well as geographical and transportation barriers, clinical trials extended to rural communities are uniquely positioned to alleviate the burden of nonmedical costs in trial participation. However, these options can be far less feasible for patients in rural settings. Since the number of patients with cancer who are eligible for a clinical trial is already limited by the stringent eligibility criteria required of such a complex disease, improving accessibility for rural patients should be a greater focus in health policy., (©Dinesh Pal Mudaranthakam, Byron Gajewski, Hope Krebill, James Coulter, Michelle Springer, Elizabeth Calhoun, Dorothy Hughes, Matthew Mayo, Gary Doolittle. Originally published in JMIR Cancer (https://cancer.jmir.org), 21.04.2022.)

Objective: In this secondary analysis of the Rural Engagement in Primary Care for Optimizing Weight Reduction (RE-POWER) randomized trial, the authors determined the effectiveness of weight-loss interventions in people with diabetes compared with those without diabetes living in rural areas., Methods: The RE-POWER study was a randomized trial designed to determine the effectiveness of nonpharmacological behavioral weight-loss interventions in rural participants with obesity, comparing the individual in-clinic visit model to in-person group sessions and phone group sessions over 24 months. In this secondary analysis, weight loss was compared in participants with and without diabetes. The effects of factors such as medications, insulin, and behavioral factors were compared., Results: Participants with diabetes were less likely to lose weight during the study compared with those without diabetes up to 18 months (4.12% vs. 5.31%; net difference = 1.46%; 95% CI: 0.63%-2.28%). Participants with diabetes on insulin lost less weight than patients with diabetes not on insulin at 6 months (4.52% vs. 6.88%; net difference = 2.35%; 95% CI: 0.55%-4.16%). The group with diabetes had significantly lower changes in blood pressure and lipid parameters versus the group without diabetes., Conclusions: Patients with diabetes in rural areas were less likely to lose weight, and metabolic parameters were less responsive to weight loss, compared with patients without diabetes., (© 2022 The Obesity Society (TOS). This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

Objective: Participant recruitment is a challenge for any clinical trial but is especially complex in cancer specifically due to the need to initiate treatment urgently. Most participants enrolled in oncology clinical trials are identified as potential participants by the oncologist or other referring provider. Optimal clinical care for patients with cancer includes consideration of participation in a clinical trial. However, the process of finding a clinical trial that is appropriate the patient can be cumbersome and time consuming., Material and Methods: The University of Kansas Cancer Center has developed a mobile application (app) which streamlines the clinical trial search process for physicians, patients, and caregivers by cohesively integrating all clinical trials currently recruiting in the center and making them easy to browse., Results: Key aspects of the app include simple filtering options, the ability to search for trials by name, easily accessible assistance, and in-app referral by phone or email. Initial feedback on the app has been very positive, with several suggestions already being implemented in future development. The app was designed to be used both by physicians to find trials, as well as patients in collaboration with their physicians., Conclusion: While long-term results will be crucial to understanding how the app can best serve our patient population, our initial results suggest that health system specific clinical trial apps can address a currently unmet need in the clinical trial recruitment process., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Background: As the cost of clinical trials continues to rise, novel approaches are required to ensure ethical allocation of resources. Multisite trials have been increasingly utilized in phase 1 trials for rare diseases and in phase 2 and 3 trials to meet accrual needs. The benefits of multisite trials include easier patient recruitment, expanded generalizability, and more robust statistical analyses. However, there are several problems more likely to arise in multisite trials, including accrual inequality, protocol nonadherence, data entry mistakes, and data integration difficulties., Objective: The Biostatistics & Data Science department at the University of Kansas Medical Center developed a clinical trial management system (comprehensive research information system [CRIS]) specifically designed to streamline multisite clinical trial management., Methods: A National Institute of Child Health and Human Development-funded phase 3 trial, the ADORE (assessment of docosahexaenoic acid [DHA] on reducing early preterm birth) trial fully utilized CRIS to provide automated accrual reports, centralize data capture, automate trial completion reports, and streamline data harmonization., Results: Using the ADORE trial as an example, we describe the utility of CRIS in database design, regulatory compliance, training standardization, study management, and automated reporting. Our goal is to continue to build a CRIS through use in subsequent multisite trials. Reports generated to suit the needs of future studies will be available as templates., Conclusions: The implementation of similar tools and systems could provide significant cost-saving and operational benefit to multisite trials., Trial Registration: ClinicalTrials.gov NCT02626299; https://tinyurl.com/j6erphcj., (©Dinesh Pal Mudaranthakam, Alexandra Brown, Elizabeth Kerling, Susan E Carlson, Christina J Valentine, Byron Gajewski. Originally published in JMIR Formative Research (https://formative.jmir.org), 22.12.2021.)

Objective: The aim of this study was to report the results of five weight-loss interventions in primary care settings in underserved patients and to compare the level of pragmatism across the interventions using the Pragmatic Explanatory Continuum Indicator Summary (PRECIS-2) tool., Methods: Data from 54 primary care clinics (2,210 patients) were pooled from the Promoting Successful Weight Loss in Primary Care in Louisiana (PROPEL) and Rural Engagement in Primary Care for Optimizing Weight Reduction (REPOWER) cluster-randomized trials. Clinics were randomized to one of five comparators: PROPEL usual care, PROPEL combination of in-clinic and telephone visits, REPOWER in-clinic individual visits, REPOWER in-clinic group visits, or REPOWER telephone group visits., Results: At 24 months, weight loss (kilograms) was -0.50 (95% CI: -1.77 to 0.76), -3.05 (-4.10 to -2.01), -4.30 (-5.35 to -3.26), -4.79 (-5.83 to -3.75), and -4.80 (-5.96 to -3.64) in the PROPEL usual care, REPOWER in-clinic individual visits, REPOWER telephone group visits, REPOWER in-clinic group visits, and PROPEL in-clinic and telephone visits arms, respectively. At 24 months, percentage of weight loss was -0.360 (-1.60 to 0.88), -3.00 (-4.02 to -1.98), -4.23 (-5.25 to -3.20), -4.67 (-5.69 to -3.65), and -4.69 (-5.82 to -3.56), respectively, in the five arms. The REPOWER in-clinic individual visits intervention was the most pragmatic and reflects the current Centers for Medicare and Medicaid Services funding model, although this intervention produced the least weight loss., Conclusions: Clinically significant weight loss over 6 months in primary care settings is achievable using a variety of lifestyle-based treatment approaches. Longer-term weight-loss maintenance is more difficult to achieve., (© 2021 The Obesity Society.)

Background: Rural residence is commonly thought to be a risk factor for poor cancer outcomes. However, a number of studies have reported seemingly conflicting information regarding cancer outcome disparities with respect to rural residence, with some suggesting that the disparity is not present and others providing inconsistent evidence that either urban or rural residence is associated with poorer outcomes. We suggest a simple explanation for these seeming contradictions: namely that rural cancer outcome disparities are related to factors that occur differentially at a local level, such as environmental exposures, lack of access to care or screening, and socioeconomic factors, which differ by type of cancer., Methods: We conducted a retrospective cohort study examining ten cancers treated at the University of Kansas Medical Center from 2011 to 2018, with individuals from either rural or urban residences. We defined urban residences as those in a county with a U.S. Department of Agriculture Urban Influence Code (UIC) of 1 or 2, with all other residences defines a rural. Inverse probability of treatment weighting was used to create a pseudo-sample balanced for covariates deemed likely to affect the outcomes modeled with cumulative link and weighted Cox-proportional hazards models., Results: We found that rural residence is not a simple risk factor but rather appears to play a complex role in cancer outcome disparities. Specifically, rural residence is associated with higher stage at diagnosis and increased survival hazards for colon cancer but decreased risk for lung cancer compared to urban residence., Conclusion: Many cancers are affected by unique social and environmental factors that may vary between rural and urban residents, such as access to care, diet, and lifestyle. Our results show that rurality can increase or decrease risk, depending on cancer site, which suggests the need to consider the factors connected to rurality that influence this complex pattern. Thus, we argue that such disparities must be studied at the local level to identify and design appropriate interventions to improve cancer outcomes., (© 2021. The Author(s).)

Patient reported outcomes are gaining more attention in patient-centered health outcomes research and quality of life studies as important indicators of clinical outcomes, especially for patients with chronic diseases. Factor analysis is ideal for measuring patient reported outcomes. If there is heterogeneity in the patient population and when sample size is small, differential item functioning and convergence issues are challenges for applying factor models. Bayesian hierarchical factor analysis can assess health disparity by assessing for differential item functioning, while avoiding convergence problems. We conducted a simulation study and used an empirical example with American Indian minorities to show that fitting a Bayesian hierarchical factor model is an optimal solution regardless of heterogeneity of population and sample size.

Breast cancer screening guidelines serve as crucial evidence-based recommendations in deciding when to begin regular screenings. However, due to developments in breast cancer research and differences in research interpretation, screening guidelines can vary between organizations and within organizations over time. This leads to significant lapses in adopting updated guidelines, variable decision making between physicians, and unnecessary screening for low to moderate risk patients (Jacobson and Kadiyala, 2017; Corbelli et al., 2014). For analysis, risk factors were assessed for patient screening behaviors and results. The outcome variable for the first analysis was whether the patient had undergone screening. The risk factors considered were age, marital status, education level, rural versus urban residence, and family history of breast cancer. The outcome variable for the second analysis was whether patients who had undergone breast cancer screening presented abnormal results. The risk factors considered were age, Body Mass Index, family history, smoking and alcohol status, hormonal contraceptive use, Hormone Replacement Therapy use, age of first pregnancy, number of pregnancies (parity), age of first menses, rural versus urban residence, and whether or not patients had at least one child. Logistic regression analysis displayed strong associations for both outcome variables. Risk of screening nonattendance was negatively associated with age as a continuous variable, age as a dichotomous variable, being married, any college education, and family history. Risk of one or more abnormal mammogram findings was positively associated with family history, and hormonal contraceptive use. This procedure will be further developed to incorporate additional risk factors and refine the analysis of currently implemented risk factors., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Anticoagulants prevent thrombosis and death in patients with atrial fibrillation and venous thromboembolism (VTE) but also increase bleeding risk. The benefit/risk ratio favors anticoagulation in most of these patients. However, some will have a bleeding complication, such as the common trip-and-fall brain injury in elderly patients that results in traumatic intracranial hemorrhage. Clinicians must then make the difficult decision about when to restart the anticoagulant. Restarting too early risks making the bleeding worse. Restarting too late risks thrombotic events such as ischemic stroke and VTE, the indications for anticoagulation in the first place. There are more data on restarting patients with spontaneous intracranial hemorrhage, which is very different than traumatic intracranial hemorrhage. Spontaneous intracranial hemorrhage increases the risk of rebleeding because intrinsic vascular changes are widespread and irreversible. In contrast, traumatic cases are caused by a blow to the head, usually an isolated event portending less future risk. Clinicians generally agree that anticoagulation should be restarted but disagree about when. This uncertainty leads to long restart delays causing a large, potentially preventable burden of strokes and VTE, which has been unaddressed because of the absence of high quality evidence. Restart Traumatic Intracranial Hemorrhage (the "r" distinguished intracranial from intracerebral) (TICrH) is a prospective randomized open label blinded end-point response-adaptive clinical trial that will evaluate the impact of delays to restarting direct oral anticoagulation (1, 2, or 4 weeks) on the composite of thrombotic events and bleeding in patients presenting after traumatic intracranial hemorrhage.

The primary goal for any clinical trial after it receives a funding notification is to receive regulatory approval and initiate the trial for recruitment. Every trial must go through documentation and regulatory process before it can start recruiting participants and collecting data; this initial process of review and approval is known as the study start-up process (SSU). We evaluated the average time taken for studies to receive approvals. Using data from clinical trials conducted at the University of Kansas Medical Center, various times to reach the start of the study were calculated based on the dates of individual study. The results of this analysis showed that chart review studies and investigator-initiated trials had a shorter time to activation than other types of studies. Additionally, single-center studies had a shorter activation time than multi-center studies. The analysis also demonstrated that the overall processing time consistently had been reduced over time., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors. Published by Elsevier Inc.)

Anticoagulant-associated traumatic intracranial hemorrhage (tICrH) is a devastating injury with high morbidity and mortality. For survivors, treating clinicians face the dilemma of restarting oral anticoagulation with scarce evidence to guide them. Thromboembolic risk is high from the bleeding event, patients' high baseline risks, that is, the pre-existing indication for anticoagulation, and the risk of immobility after the bleeding episode. This must be balanced with potentially devastating hematoma expansion or new hemorrhagic lesions. Retrospective evidence and expert opinion support restarting oral anticoagulants in most patients with tICrH, but timing is uncertain. Researchers have failed to make clear distinctions between tICrH and spontaneous intracranial hemorrhage (sICrH), which have differing natural histories. While both appear to benefit from restarting, sICrH has a higher rebleeding risk and similar or lower thrombotic risk. Clinical equipoise on restarting is also divergent. In sICrH, equipoise is centered on whether to restart. In tICrH, it is centered on when. Several prospective randomized clinical trials are ongoing or about to start to examine the risk-benefit of restarting. Most of them are restricted to patients with sICrH, with antiplatelet control groups. Most are also restricted to direct oral anticoagulants (DOACs), as they are associated with a lower overall risk of ICrH. There is some overlap with tICrH via subdural hematoma, and one trial is specific to restart timing with DOACs in only traumatic cases. This is a narrative review of the current evidence for restarting anticoagulation and restart timing after tICrH along with a summary of the ongoing and planned clinical trials., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Poor recruitment of patients is the predominant reason for early termination of randomized clinical trials (RCTs). Systematic empirical investigations and validation studies of existing recruitment models, however, are lacking. We aim to provide evidence-based guidance on how to predict and monitor recruitment of patients into RCTs. Our specific objectives are the following: (1) to establish a large sample of RCTs (target n = 300) with individual patient recruitment data from a large variety of RCTs, (2) to investigate participant recruitment patterns and study site recruitment patterns and their association with the overall recruitment process, (3) to investigate the validity of a freely available recruitment model, and (4) to develop a user-friendly tool to assist trial investigators in the planning and monitoring of the recruitment process., Methods: Eligible RCTs need to have completed the recruitment process, used a parallel group design, and investigated any healthcare intervention where participants had the free choice to participate. To establish the planned sample of RCTs, we will use our contacts to national and international RCT networks, clinical trial units, and individual trial investigators. From included RCTs, we will collect patient-level information (date of randomization), site-level information (date of trial site activation), and trial-level information (target sample size). We will examine recruitment patterns using recruitment trajectories and stratifications by RCT characteristics. We will investigate associations of early recruitment patterns with overall recruitment by correlation and multivariable regression. To examine the validity of a freely available Bayesian prediction model, we will compare model predictions to collected empirical data of included RCTs. Finally, we will user-test any promising tool using qualitative methods for further tool improvement., Discussion: This research will contribute to a better understanding of participant recruitment to RCTs, which could enhance efficiency and reduce the waste of resources in clinical research with a comprehensive, concerted, international effort.

Background: Monitoring and reporting of drug safety during a clinical trial is essential to its success. More recent attention to drug safety has encouraged statistical methods development for monitoring and detecting potential safety signals. This paper investigates the potential impact of the process of the blinded investigator identifying a potential safety signal, which should be further investigated by the Data and Safety Monitoring Board with an unblinded safety data analysis., Methods: In this paper, two-stage Bayesian hierarchical models are proposed for safety signal detection following a pre-specified set of interim analyses that are applied to efficacy. At stage 1, a hierarchical blinded model uses blinded safety data to detect a potential safety signal and at stage 2, a hierarchical logistic model is applied to confirm the signal with unblinded safety data., Results: Any interim safety monitoring analysis is usually scheduled via negotiation between the trial sponsor and the Data and Safety Monitoring Board. The proposed safety monitoring process starts once 53 subjects have been enrolled into an eight-arm phase II clinical trial for the first interim analysis. Operating characteristics describing the performance of this proposed workflow are investigated using simulations based on the different scenarios., Conclusions: The two-stage Bayesian safety procedure in this paper provides a statistical view to monitor safety during the clinical trials. The proposed two-stage monitoring model has an excellent accuracy of detecting and flagging a potential safety signal at stage 1, and with the most important feature that further action at stage 2 could confirm the safety issue.

Background: Interventions to prevent excessive gestational weight gain (GWG) have had limited success This pilot study examined the effectiveness of a single goal (SG) high dietary fiber intervention to prevent excessive GWG., Methods: Twelve weekly lessons focused on consuming a high fiber diet (≥30 g/day). Snacks containing 10-12 g of dietary fiber were given for the first 6 weeks only. Body composition was measured at baseline and at the end of the intervention. At one-year postpartum, body weight retention and dietary practices were assessed. A p-value is reported for the primary analysis only. For all other comparisons, Cohen's d is reported to indicate effect size., Results: The SG group increased fiber intake during the study (32 g/day at 6 weeks, 27 g/day at 12 weeks), whereas the UC group did not (~ 17 g/day). No differences were found for the proportion of women classified as excessive gainers (p = 0.13). During the intervention, the SG group gained less body weight (- 4.1 kg) and less fat mass (- 2.8 kg) (d = 1.3). At 1 year postpartum, the SG group retained less weight (0.35 vs. 4.4 kg, respectively, d = 1.8), and reported trying to currently eat high fiber foods., Conclusion: The SG intervention resulted in less weight gain, fat accrual, and weight retention at 1 year postpartum. A residual intervention effect was detected postpartum with the participants reporting continued efforts to consume a high fiber diet., Trial Registration: NCT03984630; Trial registered June 13, 2019 (retrospectively registered).

To fully support their role in translational and personalized medicine, biorepositories and biobanks must continue to advance the annotation of their biospecimens with robust clinical and laboratory data. Translational research and personalized medicine require well-documented and up-to-date information, but the infrastructure used to support biorepositories and biobanks can easily be out of sync with the host institution. To assist researchers and provide them with accurate pathological, epidemiological, and bio-molecular data, the Biospecimen Repository Core Facility (BRCF) at the University of Kansas Medical Center (KUMC) merges data from medical records, the tumor registry, and pathology reports using the Curated Cancer Clinical Outcomes Database (C3OD). In this report, we describe the utilization of C3OD to optimally retrieve and dispense biospecimen samples using these 3 data sources and demonstrate how C3OD greatly increases the efficiency of obtaining biospecimen samples for the researchers., Competing Interests: Declaration of conflicting interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2019.)

Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural language processing (NLP) can structure and learn from text, but NLP algorithms were not designed for the unique characteristics of EHR. Here, we propose Relevant Word Order Vectorization (RWOV) to aid with structuring. RWOV is based on finding the positional relationship between the most relevant words to predicting the class of a text. This facilitates machine learning algorithms to use the interaction of not just keywords but positional dependencies (e.g. a relevant word occurs 5 relevant words before some term of interest). As a proof-of-concept, we attempted to classify the hormone receptor status of breast cancer patients treated at the University of Kansas Medical Center, comparing RWOV to other methods using the F1 score and AUC. RWOV performed as well as, or better than other methods in all but one case. For F1 score, RWOV had a clear edge on most tasks. AUC tended to be closer, but for HER2, RWOV was significantly better for most comparisons. These results suggest RWOV should be further developed for EHR-related NLP.

When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II - Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with R Shiny to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize patient allocation in each stage to maximize power. We assume that endpoints are distributed normally with unknown but common variance between treatments. We recommend this software as an easy way to engage statisticians and researchers in two-stage designs as well as to actively investigate the power of two-stage designs relative to more traditional approaches. The software is freely available at https://github.com/stefangraw/Allocation-Power-Optimizer.

Parental nutrition literacy (PNL) correlates positively with child diet quality, but interventions for improving PNL are lacking. "Nutricity" is a novel bilingual (English/Spanish) mobile tool designed by the research team to engage parents and young children to interact with nutrition information to make nutrition decisions. The purpose of this study was to inform a future intervention through (1) assessing parental likability of Nutricity, and (2) collecting perceptions of pediatric clinic personnel on the feasibility of introducing Nutricity in pediatric clinics. PNL scores and feedback about Nutricity were collected using mixed methods from 15 English-speaking and 15 Spanish-speaking parents of 1-5 year-old children. Three parents from each language group provided additional feedback via semi-structured interviews. Interviews with 11 pediatric clinic personnel were also conducted to anticipate barriers and formulate strategies for implementing Nutricity as a clinic-based intervention. Nutricity was liked by both language groups and across all PNL levels, with a mean rating of 4.6 on a 5-point scale. Clinic personnel interviews affirmed need for and feasibility of offering Nutricity in clinics.

Data used to determine patient eligibility for cancer clinical trials often come from disparate sources that are typically maintained by different groups within an institution, use differing technologies, and are stored in different formats. Collecting data and resolving inconsistencies across sources increase the time it takes to screen eligible patients, potentially delaying study completion. To address these challenges, the Biostatistics and Informatics Shared Resource at The University of Kansas Cancer Center developed the Curated Cancer Clinical Outcomes Database (C3OD). C3OD merges data from the electronic medical record, tumor registry, bio-specimen and data registry, and allows querying through a single unified platform. By centralizing access and maintaining appropriate controls, C3OD allows researchers to more rapidly obtain detailed information about each patient in order to accelerate eligibility screening. This case report describes the design of this informatics platform as well as initial assessments of its reliability and usability.

Background: Despite the widely recognized association between the severity of early preterm birth (ePTB) and its related severe diseases, little is known about the potential risk factors of ePTB and the sub-population with high risk of ePTB. Moreover, motivated by a future confirmatory clinical trial to identify whether supplementing pregnant women with docosahexaenoic acid (DHA) has a different effect on the risk subgroup population or not in terms of ePTB prevalence, this study aims to identify potential risk subgroups and risk factors for ePTB, defined as babies born less than 34 weeks of gestation., Methods: The analysis data (N = 3,994,872) were obtained from CDC and NCHS' 2014 Natality public data file. The sample was split into independent training and validation cohorts for model generation and model assessment, respectively. Logistic regression and CART models were used to examine potential ePTB risk predictors and their interactions, including mothers' age, nativity, race, Hispanic origin, marital status, education, pre-pregnancy smoking status, pre-pregnancy BMI, pre-pregnancy diabetes status, pre-pregnancy hypertension status, previous preterm birth status, infertility treatment usage status, fertility enhancing drug usage status, and delivery payment source., Results: Both logistic regression models with either 14 or 10 ePTB risk factors produced the same C-index (0.646) based on the training cohort. The C-index of the logistic regression model based on 10 predictors was 0.645 for the validation cohort. Both C-indexes indicated a good discrimination and acceptable model fit. The CART model identified preterm birth history and race as the most important risk factors, and revealed that the subgroup with a preterm birth history and a race designation as Black had the highest risk for ePTB. The c-index and misclassification rate were 0.579 and 0.034 for the training cohort, and 0.578 and 0.034 for the validation cohort, respectively., Conclusions: This study revealed 14 maternal characteristic variables that reliably identified risk for ePTB through either logistic regression model and/or a CART model. Moreover, both models efficiently identify risk subgroups for further enrichment clinical trial design.

Cognitive training may be beneficial for individuals with Alzheimer's disease (AD); however, the effects are modest with little evidence of carryover. Prior studies included limited hours and low intensity of training. The purpose of this study was to test the feasibility and efficacy of many hours of intensive cognitive training with adults in the early stages of AD. Twenty-one adults with very mild or mild AD participated in cognitive training for 10 days over 2 weeks with 4 to 5 hours of training each day. Participants significantly improved in practiced computer-based tasks including those involving working memory, sustained attention, and switching attention. Outcome measures that improved included the Mini-Mental State Examination, letter fluency, and 3 of 5 Trail-Making Tests. Gains in outcome measures were maintained at 2- and 4-month follow-up. Adults in early-stage AD can participate in intensive cognitive training and make modest gains in both practiced and unpracticed cognitive tasks.

Studies evaluating the impact of modifiable lifestyle factors on cognition offer potential insights into sources of cognitive aging variability. Recently, we reported an association between extent of musical instrumental practice throughout the life span (greater than 10 years) on preserved cognitive functioning in advanced age. These findings raise the question of whether there are training-induced brain changes in musicians that can transfer to non-musical cognitive abilities to allow for compensation of age-related cognitive declines. However, because of the relationship between engagement in general lifestyle activities and preserved cognition, it remains unclear whether these findings are specifically driven by musical training or the types of individuals likely to engage in greater activities in general. The current study controlled for general activity level in evaluating cognition between musicians and nomusicians. Also, the timing of engagement (age of acquisition, past versus recent) was assessed in predictive models of successful cognitive aging. Seventy age and education matched older musicians (>10 years) and non-musicians (ages 59-80) were evaluated on neuropsychological tests and general lifestyle activities. Musicians scored higher on tests of phonemic fluency, verbal working memory, verbal immediate recall, visuospatial judgment, and motor dexterity, but did not differ in other general leisure activities. Partition analyses were conducted on significant cognitive measures to determine aspects of musical training predictive of enhanced cognition. The first partition analysis revealed education best predicted visuospatial functions in musicians, followed by recent musical engagement which offset low education. In the second partition analysis, early age of musical acquisition (<9 years) predicted enhanced verbal working memory in musicians, while analyses for other measures were not predictive. Recent and past musical activity, but not general lifestyle activities, predicted variability across both verbal and visuospatial domains in aging. These findings are suggestive of different use-dependent adaptation periods depending on cognitive domain. Furthermore, they imply that early age of musical acquisition, sustained and maintained during advanced age, may enhance cognitive functions and buffer age and education influences.

Context: Efforts to improve care for nursing home residents stand to be enhanced by measures to assess the degree to which staff provide palliative care. As the incidence of death in nursing homes increases with the aging population, the gap in measurement must be addressed. To that end, we report the development and psychometric testing of a nursing home palliative care survey., Objectives: The purpose of this study was to evaluate the psychometric properties of the Palliative Care Survey (PCS) for use in nursing homes., Methods: Psychometric evaluation of the instrument was completed in two phases. Phase 1 focused on individual item analyses and subsequent revision or deletion of items, and Phase 2 evaluated evidence for reliability and validity. Phase 1 included 26 nursing homes and staff (n=717), and Phase 2 included 85 nursing homes and staff (n=2779). Data were analyzed using item-total correlations, Cronbach's alpha, confirmatory factor analysis, and analysis of variance., Results: Support was obtained for a 51-item PCS made up of two constructs, Palliative Care Practice and Palliative Care Knowledge., Conclusion: The PCS measures the extent to which the nursing home staff engage in palliative care practices and have knowledge consistent with good end-of-life care. Both practice and knowledge are an essential foundation to providing good end-of-life care to nursing home residents. Efforts to improve care for the dying in nursing homes have been slowed by an absence of measurement tools that capture care processes, a gap that the PCS reported here helps fill., (Copyright © 2011 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved.)

Background: American Indians (AI) have the highest smoking rates of any ethnic group in the US (40.8%), followed most closely by African Americans (24.3%) and European Americans (23.6%). AI smokers also have more difficulty quitting smoking compared to other ethnic groups, evidenced by their significantly lower quit ratios, and are among the least successful in maintaining long term abstinence. While health disparities like these have existed for years among AI, the epidemiology of smoking and nicotine dependence has not been optimally described among this underserved population. Our overarching hypothesis is that the susceptibility of AI to cigarette smoking and nicotine dependence and its consequences has both an underlying nicotine metabolism component as well as psychosocial, cultural, and environment causes. We are well-positioned to explore this issue for the first time in this population. Our objective is to establish a cohort of AI tribal college/university students to determine the predictors of smoking initiation (non-use to experimentation), progression (experimentation to established use), and cessation (established use to cessation). Much of what is known about the process of smoking initiation and progression comes from quantitative studies with non-Native populations. Information related to smoking use among AI tribal college/university (TCU) students is entirely unknown and critically needs further investigation. This study will be the first of its kind among AI college students who are at the highest risk among all ethnic groups for tobacco dependence., Methods/design: First year students at Haskell Indian Nations University in Kansas will be recruited over four consecutive years and will be surveyed annually and repeatedly through year 5 of the study. We will use both longitudinal quantitative surveys and qualitative focus group methods to examine key measures and determinants of initiation and use among this high risk group.

Resistiveness to care is common in older adults with dementia. Resistiveness to care disrupts nursing care, increasing costs of care by 30%. Elderspeak (infantilizing communication used by nursing staff) may trigger resistiveness to care in individuals with dementia. Videotaped care episodes (n = 80) of nursing home residents with dementia (n = 20) were coded for type of staff communication (normal talk and elderspeak) and subsequent resident behavior (cooperative or resistive to care). Bayesian statistical analysis tested relationships between staff communication and subsequent resident resistiveness to care. The probability of resistiveness to care varied significantly with communication (Bayes P = .0082). An increased probability of resistiveness to care occurred with elderspeak (.55, 95% CrI, .44-.66), compared with normal talk (.26, 95% CrI, .12-.44). Communication training has been shown to reduce elderspeak and may reduce resistiveness to care in future research.

Objective: To examine the efficiency of the care planning process in nursing homes., Methods: We collected detailed primary data about the care planning process for a stratified random sample of 107 nursing homes from Kansas and Missouri. We used these data to calculate the average direct cost per care plan and used data on selected deficiencies from the Online Survey Certification and Reporting System to measure the quality of care planning. We then analyzed the efficiency of the assessment process using corrected ordinary least squares (COLS) and data envelopment analysis (DEA)., Results: Both approaches suggested that there was considerable inefficiency in the care planning process. The average COLS score was 0.43; the average DEA score was 0.48. The correlation between the two sets of scores was quite high, and there was no indication that lower costs resulted in lower quality. For-profit facilities were significantly more efficient than not-for-profit facilities., Conclusions: Multiple studies of nursing homes have found evidence of inefficiency, but virtually all have had measurement problems that raise questions about the results. This analysis, which focuses on a process with much simpler measurement issues, finds evidence of inefficiency that is largely consistent with earlier studies. Making nursing homes more efficient merits closer attention as a strategy for improving care. Increasing efficiency by adopting well-designed, reliable processes can simultaneously reduce costs and improve quality.

Background: Body system impairments following stroke have a complex relationship with functional activities. Although gait and balance deficits are well-documented in people after stroke, the overlapping influence of body impairments makes it difficult to prioritize interventions., Objective: This study examined the relationship between prospectively selected measures of body function and structure (body mass index, muscle strength, sensation, and cognition) and activity (gait speed, gait endurance, and functional balance) in people with chronic stroke., Design: This was a cross-sectional, observational study., Methods: Twenty-six individuals with mean (SD) age of 57.6 (11) years and time after stroke of 45.4 (43) months participated. Four variables (body mass index, muscle strength difference between the lower extremities, sensation difference between the lower extremities, and Mini-Mental Status Exam score) were entered into linear regression models for gait speed, Six-Minute Walk Test distance, and Berg Balance Scale score., Results: Lower-extremity strength difference was a significant individual predictor for gait speed, gait endurance, and functional balance. Cognition significantly predicted only gait speed., Limitations: The authors did not include all possible factors in the model that may have influenced gait and balance in these individuals., Conclusions: Strength deficits in the hemiparetic lower extremity should be an important target for clinical interventions to improve function in people with chronic stroke.