Agitation is a common neuropsychiatric symptom in Alzheimer’s dementia. The Cohen-Mansfield Agitation Inventory (CMAI) assesses the frequency of 29 agitation behaviors in elderly persons. The frequency of each behavior is rated from 1–7 (1=never, 2=less than once a week, 3=once or twice a week, 4=several times a week, 5=once or twice a day, 6=several times a day, 7=several times an hour), typically reported as a single total score. This post hoc analysis explored the efficacy of brexpiprazole on the frequency of individual agitation behaviors.

Post hoc analyses were conducted for two 12-week, randomized, double- blind, placebo-controlled, parallel-arm, fixed-dose trials of brexpiprazole in patients with agitation in Alzheimer’s dementia (NCT01862640, NCT03548584). Data are reported using descriptive statistics for brexpiprazole (2 or 3 mg/day) and placebo, for patients who completed 12 weeks of treatment.

In the first fixed-dose trial (brexpiprazole 2 mg/day, n=120; placebo, n=118), baseline behavior frequency was similar between groups (range 1.12 to 4.92). At baseline, the most frequently observed behavior was “general restlessness” (brexpiprazole, 4.92; placebo, 4.82; approximately “once or twice a day”), and the least frequently observed behaviors were “biting” (brexpiprazole, 1.12) and “making physical sexual advances” (placebo, 1.14). At Week 12, the average reduction in mean frequency was -0.73 (brexpiprazole) and -0.60 (placebo), with a greater numerical reduction for 21/29 behaviors with brexpiprazole versus placebo. In the second fixed-dose trial (brexpiprazole 2 or 3 mg/day, n=192; placebo, n=103), baseline behavior frequency was similar between groups (range 1.12 to 5.22), and higher than in the first trial due to study inclusion criteria. At baseline, the most frequently observed behavior was “general restlessness” (brexpiprazole, 5.22; placebo, 5.09; approximately “once or twice a day”), and the least frequently observed behaviors were “making physical sexual advances” (brexpiprazole, 1.13) and “intentional falling” (placebo, 1.12). At Week 12, the average reduction in mean frequency was -0.78 (brexpiprazole) and -0.54 (placebo), with a greater numerical reduction for 26/29 behaviors with brexpiprazole versus placebo.

In this post hoc analysis, brexpiprazole was associated with numerically greater reduction in the frequency of most individual agitation behaviors versus placebo.

Adherence plays a vital role in the effectiveness of non-pharmacological interventions. The disappearance of interventions’ effects at follow-up was caused by inadequate self-practice beyond intervention period. The purpose of this study is to examine the factors associated with adherence to aerobic exercise and Tai Chi and the impact of adherence on the short- and long-term effectiveness in improving sleep in patients with advanced lung cancer.

This study analyzed data collected in a clinical trial that evaluated the effects of aerobic exercise and Tai Chi in patients with advanced lung cancer. Two types of exercises were maintained at the same intensity but with different dosage. A total of 99 patients with advanced lung cancer who were recruited between 2018 and 2020 were included. Data were collected using self-report questionnaires.

Fifty participants were randomly assigned to aerobic exercise and 49 to Tai Chi intervention. Higher levels of satisfaction and lower levels of depression were significantly associated with higher attendance and compliance in both groups. Low fatigue levels contributed to higher attendance in Tai Chi. Both attendance and compliance were significantly associated with long-term sleep improvement.

Higher levels of satisfaction and lower levels of depression were important characteristics of attendance and compliance with home-based practice in both groups, whereas lower levels of fatigue uniquely contributed to higher attendance in Tai Chi. Better exercise adherence improves long-term effectiveness of sleep in patients with advanced lung cancer. Adopting strategies is imperative to promote exercise adherence in patients with greater levels of depression and fatigue.

U.S. veterans report high rates of traumatic experiences and mental health symptomology [e.g. posttraumatic stress disorder (PTSD)]. The stress sensitization hypothesis posits experiences of adversity sensitize individuals to stress reactions which can lead to greater psychiatric problems. We extend this hypothesis by exploring how multiple adversities such as early childhood adversity, combat-related trauma, and military sexual trauma related to heterogeneity in stress over time and, subsequently, greater risk for PTSD.

1230 veterans were recruited for an observational, longitudinal study. Veterans responded to questionnaires on PTSD, stress, and traumatic experiences five times over an 18-month study period. We used latent transition analysis to understand how heterogeneity in adverse experiences is related to transition into stress trajectory classes. We also explored how transition patterns related to PTSD symptomology.

Across all models, we found support for stress sensitization. In general, combat trauma in combinations with other types of adverse experiences, namely early childhood adversity and military sexual trauma, imposed a greater probability of transitioning into higher risk stress profiles. We also showed differential effects of early childhood and military-specific adversity on PTSD symptomology.

The present study rigorously integrates both military-specific and early life adversity into analysis on stress sensitivity, and is the first to examine how sensitivity might affect trajectories of stress over time. Our study provides a nuanced, and specific, look at who is risk for sensitization to stress based on previous traumatic experiences as well as what transition patterns are associated with greater PTSD symptomology.

The rapid spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) throughout key regions of the United States in early 2020 placed a premium on timely, national surveillance of hospital patient censuses. To meet that need, the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN), the nation’s largest hospital surveillance system, launched a module for collecting hospital coronavirus disease 2019 (COVID-19) data. We present time-series estimates of the critical hospital capacity indicators from April 1 to July 14, 2020.

From March 27 to July 14, 2020, the NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and the availability and/or use of mechanical ventilators. Time series were constructed using multiple imputation and survey weighting to allow near–real-time daily national and state estimates to be computed.

During the pandemic’s April peak in the United States, among an estimated 431,000 total inpatients, 84,000 (19%) had COVID-19. Although the number of inpatients with COVID-19 decreased from April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest regions after stay-at-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July.

The NHSN hospital capacity estimates served as important, near–real-time indicators of the pandemic’s magnitude, spread, and impact, providing quantitative guidance for the public health response. Use of the estimates detected the rise of hospitalizations in specific geographic regions in June after they declined from a peak in April. Patient outcomes appeared to improve from early April to mid-July.

Despite the substantial investment by Australian health authorities to improve the health of rural and remote communities, rural residents continue to experience health care access challenges and poorer health outcomes. Health literacy and community engagement are both considered critical in addressing these health inequities. However, the current focus on health literacy can place undue burdens of responsibility for healthcare on individuals from disadvantaged communities whilst not taking due account of broader community needs and healthcare expectations. This can also marginalize the influence of community solidarity and mobilization in effecting healthcare improvements.

The objective is to present a conceptual framework that describes community literacy, its alignment with health literacy, and its relationship to concepts of community engaged healthcare.

Community literacy aims to integrate community knowledge, skills and resources into the design, delivery and adaptation of healthcare policies, and services at regional and local levels, with the provision of primary, secondary, and tertiary healthcare that aligns to individual community contexts. A set of principles is proposed to support the development of community literacy. Three levels of community literacy education for health personnel have been described that align with those applied to health literacy for consumers. It is proposed that community literacy education can facilitate transformational community engagement. Skills acquired by health personnel from senior executives to frontline clinical staff, can also lead to enhanced opportunities to promote health literacy for individuals.

The integration of health and community literacy provides a holistic framework that has the potential to effectively respond to the diversity of rural and remote Australian communities and their healthcare needs and expectations. Further research is required to develop, validate, and evaluate the three levels of community literacy education and alignment to health policy, prior to promoting its uptake more widely.

Shared patient–clinician decision-making is central to choosing between medical treatments. Decision support tools can have an important role to play in these decisions. We developed a decision support tool for deciding between nonsurgical treatment and surgical total knee replacement for patients with severe knee osteoarthritis. The tool aims to provide likely outcomes of alternative treatments based on predictive models using patient-specific characteristics. To make those models relevant to patients with knee osteoarthritis and their clinicians, we involved patients, family members, patient advocates, clinicians, and researchers as stakeholders in creating the models.

Stakeholders were recruited through local arthritis research, advocacy, and clinical organizations. After being provided with brief methodological education sessions, stakeholder views were solicited through quarterly patient or clinician stakeholder panel meetings and incorporated into all aspects of the project.

Participating in each aspect of the research from determining the outcomes of interest to providing input on the design of the user interface displaying outcome predications, 86% (12/14) of stakeholders remained engaged throughout the project. Stakeholder engagement ensured that the prediction models that form the basis of the Knee Osteoarthritis Mathematical Equipoise Tool and its user interface were relevant for patient–clinician shared decision-making.

Methodological research has the opportunity to benefit from stakeholder engagement by ensuring that the perspectives of those most impacted by the results are involved in study design and conduct. While additional planning and investments in maintaining stakeholder knowledge and trust may be needed, they are offset by the valuable insights gained.

To enhance enrollment into randomized clinical trials (RCTs), we proposed electronic health record-based clinical decision support for patient–clinician shared decision-making about care and RCT enrollment, based on “mathematical equipoise.”

As an example, we created the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) to determine the presence of patient-specific equipoise between treatments for the choice between total knee replacement (TKR) and nonsurgical treatment of advanced knee osteoarthritis.

With input from patients and clinicians about important pain and physical function treatment outcomes, we created a database from non-RCT sources of knee osteoarthritis outcomes. We then developed multivariable linear regression models that predict 1-year individual-patient knee pain and physical function outcomes for TKR and for nonsurgical treatment. These predictions allowed detecting mathematical equipoise between these two options for patients eligible for TKR. Decision support software was developed to graphically illustrate, for a given patient, the degree of overlap of pain and functional outcomes between the treatments and was pilot tested for usability, responsiveness, and as support for shared decision-making.

The KOMET predictive regression model for knee pain had four patient-specific variables, and an r2 value of 0.32, and the model for physical functioning included six patient-specific variables, and an r2 of 0.34. These models were incorporated into prototype KOMET decision support software and pilot tested in clinics, and were generally well received.

Use of predictive models and mathematical equipoise may help discern patient-specific equipoise to support shared decision-making for selecting between alternative treatments and considering enrollment into an RCT.

Farmers’ market interventions are a popular strategy for addressing chronic disease disparities in low-income neighbourhoods. With limited resources, strategic targeting of interventions is critical. The present study used spatial analysis to identify where market interventions have the greatest impact on healthy food access within a geographic region.

All farmers’ markets in a mixed urban/rural county were mapped and those that accepted Supplemental Nutrition Assistance Program (SNAP) electronic benefit transfer (EBT) cards identified. Households were grouped into small neighbourhoods and mapped. The area of ‘reasonable access’ around each market (walking distance (0·8 km; 0·5mile) in urban areas, driving distance (15 min) in rural areas) was calculated using spatial analysis. The percentage of county low-income households within a market’s access area, and the percentage of county SNAP-participating households within an EBT-accepting market’s access area, were calculated. The ten neighbourhoods with the most low-income households and with the most SNAP-participating households were then identified, their access areas calculated and mapped, and those lacking access identified. County-level gains resulting from improving market accessibility in these areas were calculated.

None.

Honolulu County, Hawaii, USA.

Only 44 % of SNAP-participating households had EBT-market access. Six of the ten highest SNAP-participant neighbourhoods lacked access. Improving access for these neighbourhoods increased county-level access by 23 %. Market access for low-income households was 74 %. Adding markets to these low-income neighbourhoods without market access increased county-level access by 4 %.

Geographic identification of market access demographics, and strategic targeting of EBT interventions, could improve regional access to healthy foods.