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The main objective of this study was to examine how homelessness and housing instability is captured across data sources in the Veterans Health Administration (VHA). Data from 2021 were extracted from three data repositories, including the Corporate Data Warehouse (CDW), the Homeless Operations Management System (HOMES), and the Homeless Management Information System (HMIS). Using these three data sources, we identified the number of homeless and unstably housed veterans across a variety of indicators. The results showed that the use of diagnostic codes and clinic stop codes identified a large number of homeless and unstably housed veterans, but the use of HOMES and HMIS data identified additional homeless and unstably housed veterans to provide a complete count. A total of 290,431 unique veterans were identified as experiencing homelessness or housing instability in 2021 and there was regional variability in how homelessness and housing stability were captured across data sources, supporting the need for more uniform ways to operationalize these conditions. Together, these findings highlight the and encourage use of all available indicators and data sources to identify homelessness and housing instability in VHA. These methodologies applied to the largest healthcare system in the U.S. demonstrate their utility and possibilities for other healthcare systems. Transparent practices about data sources and indicators used to capture homelessness and housing instability should be shared to increase uniform use.

[This corrects the article DOI: 10.1371/journal.pone.0279973.].

Background Little is known about how the prevalence of dementia in Medicare Advantage (MA) and traditional Medicare (TM) has changed over time. We examine prevalence of dementia in MA and TM overall and by race/ethnicity, the characteristics of these individuals within plans, as well as enrollment and switching rates between MA and TM between 2000 and 2014. Methods Repeated cross-sectional study using eight waves from the Health and Retirement Study (HRS) linked to Medicare enrollment data. Sample includes HRS respondents ≥ 65 years of age ( n  = 18,381) linked to Medicare enrollment data. Measurements used include predicted cognitive function (Langa-Weir classification: dementia, cognitive impairment not dementia, and normal), three race/ethnicity categories (White, Black, and Hispanic), demographic and clinical characteristics from HRS, and Medicare enrollment (MA or TM) per year. Results Among TM enrollees, prevalence of dementia was lower by 4% points in 2014 (9.0%, 95%CI: 7.8%, 9.3%) compared to 2000 (13.0%, 95%CI: 12%, 14%). The prevalence of dementia in MA was higher by 2% points in 2014 (10.0%, 95%CI: 8.5%, 11%) compared to 2000 (8.0%, 95% CI: 7.2%, 9.7%). Prevalence of dementia in MA remained stable for Whites non-Hispanic, was 2% points higher for Blacks non-Hispanic, and 5% points higher for Hispanics in 2014 compared to 2000. MA compared to TM beneficiaries with dementia in 2014 were younger (mean [SE] 81.6 [0.5] vs. 83.5 [0.4]), had fewer activity of daily living limitations (1.9 [0.1] vs. 2.4 [0.1]), instrumental activities of daily living limitations (2.3 [0.1] vs. 2.8 [0.1]), and of chronic conditions (3.2 [0.1] vs. 3.5 [0.1]). By 1-year (2012–2013), 6.3% of MA beneficiaries with dementia switched to TM and 4.3% of TM beneficiaries with dementia switched to MA. Conclusions Between 2000 and 2014, dementia prevalence was lower in TM compared to MA. Evidence suggests that MA beneficiaries with dementia are younger and have fewer functional limitations than their dementia TM counterparts.

Background Pragmatic randomized, controlled trials (PCTs) test the effectiveness of interventions implemented in routine clinical practice. Because PCT findings are generalizable, this approach is gaining momentum among interventionists and funding agencies seeking to accelerate the testing and adoption of evidence-based strategies to improve care and outcomes. Particular attention is being paid to non-pharmacological interventions, which are often complex and may be difficult to uniformly implement across multiple sites. While many such non-pharmacological interventions have proven efficacious in small trials, most have not been widely adopted. PCTs could accelerate effectiveness testing and adoption, yet there are no established criteria to identify interventions ready for testing in a PCT. Methods We convened 30 interventionists and healthcare leaders to identify criteria to assess the readiness of non-pharmacological interventions for PCTs. Based on this discussion, we created a model with multiple domains, qualitative scoring guidelines for each domain, and a graphical summary of readiness assessments. All workshop participants had an opportunity to review and comment on the resulting model; three piloted it with their own interventions. Several other experts also provided input. Results The Readiness Assessment for Pragmatic Trials (RAPT) model enables interventionists to assess an intervention’s readiness for PCTs. RAPT includes nine domains: implementation protocol, evidence, risk, feasibility, measurement, cost, acceptability, alignment, and impact. Domains reflect a range of considerations regarding the feasibility of successfully employing PCT methods and the prospect of an intervention’s widespread adoption, if proven effective. Individuals evaluating an intervention are asked to qualitatively assess each domain from low to high readiness. In this report, we provide assessment guidelines and examples of scored interventions. Conclusions RAPT is the first model to help interventionists and funders assess the extent to which interventions are ready for PCTs. Scoring efficacious interventions using RAPT can inform research team discussions regarding whether or not to advance an intervention to effectiveness testing using a PCT and how to design that PCT.

Background Persons with Alzheimer’s disease and related dementias (ADRD) receive care from family/friends, but how care changes from the onset of dementia remains less understood. Methods We used the Health and Retirement Study (2002–2012) to identify community-dwelling individuals predicted to have incident ADRD. We investigated the amount of caregiving received for activities of daily living in the 8-years after disease onset. Results At incidence ( n  = 1158), persons with ADRD received 151 h (SD = 231) of caregiving a month, 25 (SD = 26) caregiving days a month and had 1.3 (SD = 1.4) caregivers a month. By 8-years post incidence, 187 (16%) individuals transitioned to a nursing home and 662 (57%) died in the community. Community-dwelling persons with ADRD at 8-years post incidence ( n  = 30) received 283 h (SD = 257) of caregiving, 38 (SD = 24) caregiving days, and had 2.2 (SD = 1.3) caregivers. Conclusions Community-dwelling persons with ADRD receive a substantial amount of caregiving over the first 8-years after disease onset.

Background Inequities in health access and outcomes exist between Indigenous and non-Indigenous populations. Embedded pragmatic randomized, controlled trials (ePCTs) can test the real-world effectiveness of health care interventions. Assessing readiness for ePCT, with tools such as the Readiness Assessment for Pragmatic Trials (RAPT) model, is an important component. Although equity must be explicitly incorporated in the design, testing, and widespread implementation of any health care intervention to achieve equity, RAPT does not explicitly consider equity. This study aimed to identify adaptions necessary for the application of the ‘Readiness Assessment for Pragmatic Trials’ (RAPT) tool in embedded pragmatic randomized, controlled trials (ePCTs) with Indigenous communities. Methods We surveyed and interviewed participants (researchers with experience in research involving Indigenous communities) over three phases (July-December 2022) in this mixed-methods study to explore the appropriateness and recommended adaptions of current RAPT domains and to identify new domains that would be appropriate to include. We thematically analyzed responses and used an iterative process to modify RAPT. Results The 21 participants identified that RAPT needed to be modified to strengthen readiness assessment in Indigenous research. In addition, five new domains were proposed to support Indigenous communities’ power within the research processes: Indigenous Data Sovereignty; Acceptability – Indigenous Communities; Risk of Research; Research Team Experience; Established Partnership). We propose a modified tool, RAPT-Indigenous (RAPT-I) for use in research with Indigenous communities to increase the robustness and cultural appropriateness of readiness assessment for ePCT. In addition to producing a tool for use, it outlines a methodological approach to adopting research tools for use in and with Indigenous communities by drawing on the experience of researchers who are part of, and/or working with, Indigenous communities to undertake interventional research, as well as those with expertise in health equity, implementation science, and public health. Conclusion RAPT-I has the potential to provide a useful framework for readiness assessment prior to ePCT in Indigenous communities. RAPT-I also has potential use by bodies charged with critically reviewing proposed pragmatic research including funding and ethics review boards.

Background To determine the net cost of non-drug interventions that maintain or improve a person with dementia’s physical function and/or reduce challenging behaviors. Cost data are needed to inform the adoption of non-drug interventions in health systems and the development of policies to incentivize their use. Methods We modified a person-level microsimulation to model the cost of four non-drug interventions relative to usual care: Collaborative Care, Care of Persons with Dementia in their Environments (COPE), Tailored Activity Program (TAP), and Skills2Care. We also conducted a value of information analysis to quantify the optimal sample size of conducting a new randomized trial that would reduce uncertainty on the cost savings of each intervention from a societal perspective. Finally, we conducted sensitivity analyses. Results Collaborative Care, TAP and COPE were cost savings compared to usual care (-$572, -$1,816, and -$5,262, respectively). Skills2Care results in a $89 net increase in cost compared to usual care. The value of information analysis identified the optimal sample size of a potential future study: Skills2Care (optimal n  = 8,560), TAP (optimal n  = 5,650), COPE (optimal n  = 3,910) and Collaborative Care (optimal n  = 3,630). In one-way sensitivity analyses, when we applied a pessimistic assumption for the treatment effect, COPE and TAP were still cost saving, while Collaborative Care cost more than usual care. Conclusions did not materially change in sensitivity analyses that varied treatment cost. Conclusions Non-drug dementia care interventions that maintain or improve a person with dementia’s function and/or reduce challenging behaviors present a viable clinical / economic model of care for health systems. Key points Three (Collaborative Care, Care of Persons with Dementia in their Environments, and Tailored Activity Program) of four (Skills2Care) non-drug dementia interventions that maintain or improve a person with dementia’s physical function and/or reduce challenging behaviors are cost savings compared to usual care. The National Institutes of Health is investing in pragmatic trials to test the effectiveness of dementia care interventions. Using a value of information analysis, we found that large sample sizes (between 3,630 and 8,560 people) are needed to reduce uncertainty as to whether non-drug dementia care interventions are cost savings. This finding reinforces the need for pragmatic studies and also highlights the value of microsimulation methodologies. Findings demonstrate the financial benefit of selected evidence-based caregiver support programs for health systems engaged in the GUIDE model or operating in a capitated payment system.

While the positive effects of digital technology on cognitive function are established, the specific impacts of different types of technology activities on distinct cognitive domains remain underexplored. This study aimed to examine the associations between transitions into and out of various technology activities and trajectories of cognitive domains among community-dwelling older adults without dementia. Data were drawn from 5566 community-dwelling older adults without dementia who participated in the National Health and Aging Trends Study from 2015 to 2022. Technology activities assessed included online shopping, banking, medication refills, social media use, and checking health conditions online. The cognitive domains measured were episodic memory, executive function, and orientation. Asymmetric effects models were used to analyze the associations between technology activity transitions and cognitive outcomes, adjusting for demographic, socioeconomic, and health-related covariates. Lagged models were applied for sensitivity analysis. In the asymmetric effects models, the onset of online shopping (β=.046, P=.02), medication refills (β=.073, P<.001), and social media use (β=.065, P=.01) was associated with improved episodic memory. The cessation of online shopping was associated with faster episodic memory decline (β=-.023, P=.047). In contrast, the cessation of online banking (β=-.078, P=.01) and social media use (β=-.066, P=.003) was associated with decreased episodic memory. The initiation of instrumental, social, and health-related technology activities was associated with slower cognitive decline in orientation. The lagged models further emphasized the effects of stopping online banking and starting online medication refills in relation to episodic memory, as well as the positive associations between online shopping and social media use and orientation. All significant effects were of small magnitude. Combining findings from the main and sensitivity analyses, results suggest that interventions designed to support episodic memory in older adults should emphasize promoting the use of online medication refill services and sustaining engagement with online banking, particularly among those who have already established these habits. To support orientation, strategies should focus on facilitating adoption of online shopping and social media use, helping older adults become comfortable navigating these platforms. Future trials are needed to assess the clinical relevance of targeted interventions for specific cognitive domains, to promote the initiation and maintenance of digital activities to help mitigate domain-specific cognitive decline in aging populations.

Background More than 4 million older adults in the United States use publicly funded home-and community-based services (HCBS) which were disrupted during the COVID-19 pandemic. There is paucity of empirical evidence of how service disruptions influenced consumer needs in different types of HCBS. Therefore, we evaluate changes in service use and consumer-reported unmet service needs in HCBS during the COVID-19 pandemic (2021–2022) versus pre-pandemic (2018–2019), to inform future public health emergency (PHE) preparedness. Methods We analyzed repeated cross-sectional survey data from the National Core Indicators- Aging and Disability Adult Consumer Survey in two survey waves, 2018–2019 and 2021–2022. We included community-dwelling, older HCBS consumers (age ≥ 65 years; n  = 7143) from 11 states that participated in both survey waves. We measured service use and consumer-reported unmet needs as outcomes for six commonly used HCBS including personal care, homemaker, meal delivery, adult day, transportation, and caregiver respite/support. Using logistic regression, we calculated adjusted odds ratios (aOR) and 95% confidence interval (CI) to evaluate changes in outcomes during versus pre-pandemic, adjusting for demographics, health-related variables, and self- versus proxy-response, with random intercepts for each state. Results Compared to 2018–2019, during 2021–2022, odds of service use increased for personal care (aOR, 1.24; 95% CI, 1.09, 1.40) and caregiver respite/support (aOR, 1.28; 95% CI, 1.00, 1.63) but decreased for homemaker services (OR, 0.69; 95% CI, 0.60, 0.79) and meal delivery (aOR, 0.81; 95% CI, 0.70, 0.93). During the PHE, odds of unmet service needs increased for personal care (aOR, 1.23; 95% CI, 1.03, 1.46) and meal delivery (aOR, 1.26; 95% CI, 1.01, 1.56), and decreased for caregiver respite/support (aOR, 0.49; 95% CI, 0.35, 0.70). Conclusions During the PHE, simultaneous increase in services use and unmet service needs for some HCBS (e.g. personal care) suggests that temporary PHE measures taken were insufficient to offset the demand for those services. For caregiver respite/support, increased service use and decreased unmet service needs suggests that the temporary PHE measures for caregiver support may have offset a rise in service demand. These findings can inform evaluations of temporary PHE policies for HCBS and disaster preparedness efforts for future PHEs. Clinical trial number Not applicable.

Background Data harmonization is a powerful method to equilibrate items in measures that evaluate the same underlying construct. There are multiple measures to evaluate dementia related behavioral symptoms. Pre-statistical harmonization of behavioral instruments in dementia research is the first step to develop a statistical crosswalk between measures. Studies that conduct pre-statistical harmonization of behavioral instruments rarely document their methods in a structured, reproducible manner. This is a crucial step which entails careful review, documentation and scrutiny of source data to ensure sufficient comparability between items prior to data pooling. Here, we document the pre-statistical harmonization of items measuring behavioral and psychological symptoms among people with dementia. We provide a box of recommended procedure for future studies. Methods We identified behavioral instruments that are used in clinical practice, a national survey, and randomized trials of dementia care interventions. We rigorously reviewed question content and scoring procedures to establish sufficient comparability across items as well as item quality prior to data pooling. Additionally, we standardized coding to Stata-readable format, which allowed us to automate approaches to identify potential cross-study differences in items and low-quality items. To ensure reasonable model fit for statistical co-calibration, we estimated two-parameter logistic Item Response Theory models within each of the eight studies. Results We identified 59 items from 11 behavioral instruments across the eight datasets. We found considerable cross-study heterogeneity in administration and coding procedures for items that measure the same attribute. Discrepancies existed in terms of directionality and quantification of behavioral symptoms for even seemingly comparable items. We resolved item response heterogeneity, missingness and skewness, conditional dependency prior to estimation of item response theory models for statistical co-calibration. We used several rigorous data transformation procedures to address these issues, including re-coding and truncation. Conclusions This study highlights the importance of each aspect involved in the pre-statistical harmonization process of behavioral instruments. We provide guidelines and recommendations for how future research may detect and account for similar issues in pooling behavioral and related instruments.