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Patients should be asked annually about falls in the past year to identify those at high risk for future falls. The risk of falling can be reduced by exercise programs focused on balance and strength training and, among persons at high risk, by assessing a standard set of risk factors for falls and addressing modifiable ones.

Background Organizational readiness assessments have a history of being developed as important support tools for successful implementation. However, it remains unclear how best to operationalize readiness across varied projects or settings. We conducted a synthesis and content analysis of published readiness instruments to compare how investigators have operationalized the concept of organizational readiness for change. Methods We identified readiness assessments using a systematic review and update search. We mapped individual assessment items to the Consolidated Framework for Implementation Research (CFIR), which identifies five domains affecting implementation (outer setting, inner setting, intervention characteristics, characteristics of individuals, and implementation process) and multiple constructs within each domain. Results Of 1370 survey items, 897 (68%) mapped to the CFIR domain of inner setting, most commonly related to constructs of readiness for implementation ( n  = 220); networks and communication ( n  = 207); implementation climate ( n  = 204); structural characteristics ( n  = 139); and culture ( n  = 93). Two hundred forty-two items (18%) mapped to characteristics of individuals (mainly other personal attributes [ n  = 157] and self-efficacy [ n  = 52]); 80 (6%) mapped to outer setting; 51 (4%) mapped to implementation process; 40 (3%) mapped to intervention characteristics; and 60 (4%) did not map to CFIR constructs. Instruments were typically tailored to specific interventions or contexts. Discussion Available readiness instruments predominantly focus on contextual factors within the organization and characteristics of individuals, but the specificity of most assessment items suggests a need to tailor items to the specific scenario in which an assessment is fielded. Readiness assessments must bridge the gap between measuring a theoretical construct and factors of importance to a particular implementation.

One third of older adults fall each year, and falls are costly to both the patient in terms of morbidity and mortality and to the health system. Given that falls are a preventable cause of injury, our objective was to understand the characteristics and trends of emergency department (ED) fall-related visits among older adults. We hypothesize that falls among older adults are increasing and examine potential factors associated with this rise, such as race, ethnicity, gender, insurance and geography. We conducted a secondary analysis of data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) to determine fall trends over time by examining changes in ED visit rates for falls in the United States between 2003 and 2010, detailing differences by gender, sociodemographic characteristics and geographic region. Between 2003 and 2010, the visit rate for falls and fall-related injuries among people age ≥ 65 increased from 60.4 (95% confidence interval [CI][51.9-68.8]) to 68.8 (95% CI [57.8-79.8]) per 1,000 population (p=0.03 for annual trend). Among subgroups, visits by patients aged 75-84 years increased from 56.2 to 82.1 per 1,000 (P <.01), visits by women increased from 67.4 to 81.3 (p = 0.04), visits by non-Hispanic Whites increased from 63.1 to 73.4 (p < 0.01), and visits in the South increased from 54.4 to 71.1 (p=0.03). ED visit rates for falls are increasing over time. There is a national movement to increase falls awareness and prevention. EDs are in a unique position to engage patients on future fall prevention and should consider ways they can also partake in such initiatives in a manner that is feasible and appropriate for the ED setting.

Future disruptions to fire activity will threaten ecosystems and human well-being throughout the world, yet there are few fire projections at global scales and almost none from a broad range of global climate models (GCMs). Here we integrate global fire datasets and environmental covariates to build spatial statistical models of fire probability at a 0.5° resolution and examine environmental controls on fire activity. Fire models are driven by climate norms from 16 GCMs (A2 emissions scenario) to assess the magnitude and direction of change over two time periods, 2010-2039 and 2070-2099. From the ensemble results, we identify areas of consensus for increases or decreases in fire activity, as well as areas where GCMs disagree. Although certain biomes are sensitive to constraints on biomass productivity and others to atmospheric conditions promoting combustion, substantial and rapid shifts are projected for future fire activity across vast portions of the globe. In the near term, the most consistent increases in fire activity occur in biomes with already somewhat warm climates; decreases are less pronounced and concentrated primarily in a few tropical and subtropical biomes. However, models do not agree on the direction of near-term changes across more than 50% of terrestrial lands, highlighting major uncertainties in the next few decades. By the end of the century, the magnitude and the agreement in direction of change are projected to increase substantially. Most far-term model agreement on increasing fire probabilities (∼62%) occurs at mid- to high-latitudes, while agreement on decreasing probabilities (∼20%) is mainly in the tropics. Although our global models demonstrate that long-term environmental norms are very successful at capturing chronic fire probability patterns, future work is necessary to assess how much more explanatory power would be added through interannual variation in climate variables. This study provides a first examination of global disruptions to fire activity using an empirically based statistical framework and a multi-model ensemble of GCM projections, an important step toward assessing fire-related vulnerabilities to humans and the ecosystems upon which they depend.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements, download and use without any other setup. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

The advent of initiatives to reduce emissions from deforestation and degradation and enhance forest carbon stocks (REDD+) in developing countries has raised much concern regarding impacts on local communities. To inform this debate, we analyze the initial outcomes of those REDD+ projects that systematically report on their socio-economic dimensions. To categorize and compare projects, we develop a participation and benefits framework that considers REDD+’s effects on local populations’ opportunities (jobs, income), security (of tenure and ecosystem services), and empowerment (participation in land use and development decisions). We find material benefits, in terms of jobs and income, to be, thus far, modest. On the other hand, we find that many projects are helping populations gain tenure rights. A majority of projects are obtaining local populations’ free, prior, and informed consent (FPIC). However, for those projects interacting with multiple populations, extent of participation and effects on forest access are often uneven. Our participation and benefits framework can be a useful tool for identifying the multi-faceted socio-economic impacts of REDD+, which are realized under different timescales. The framework and initial trends reported here can be used to build hypotheses for future REDD+ impact evaluations and contribute to evolving theories of incentive-based environmental policy.

Health services made many changes quickly in response to the SARS-CoV-2 pandemic. Many more are being made. Some changes were already evaluated, and there are rigorous research methods and frameworks for evaluating their local implementation and effectiveness. But how useful are these methods for evaluating changes where evidence of effectiveness is uncertain, or which need adaptation in a rapidly changing situation? Has implementation science provided implementers with tools for effective implementation of changes that need to be made quickly in response to the demands of the pandemic? This perspectives article describes how parts of the research and practitioner communities can use and develop a combination of implementation and improvement to enable faster and more effective change in the future, especially where evidence of local effectiveness is limited. We draw on previous reviews about the advantages and disadvantages of combining these two domains of knowledge and practice. We describe a generic digitally assisted rapid cycle testing (DA-RCT) approach that combines elements of each in order to better describe a change, monitor outcomes, and make adjustments to the change when implemented in a dynamic environment.

Objective We aimed to explore the construct of “high need” and identify common need domains among high-need patients, their care professionals, and healthcare organizations; and to describe the interventions that health care systems use to address these needs, including exploring the potential unintended consequences of interventions. Methods We conducted a modified Delphi panel informed by an environmental scan. Expert stakeholders included patients, interdisciplinary healthcare practitioners (physicians, social workers, peer navigators), implementation scientists, and policy makers. The environmental scan used a rapid literature review and semi-structured interviews with key informants who provide healthcare for high-need patients. We convened a day-long virtual panel meeting, preceded and followed by online surveys to establish consensus. Results The environmental scan identified 46 systematic reviews on high-need patients, 19 empirical studies documenting needs, 14 intervention taxonomies, and 9 studies providing construct validity for the concept “high need.” Panelists explored the construct and terminology and established that individual patients’ needs are unique, but areas of commonality exist across all high-need patients. Panelists agreed on 11 domains describing patient (e.g., social circumstances), 5 care professional (e.g., communication), and 8 organizational (e.g., staffing arrangements) needs. Panelists developed a taxonomy of interventions with 15 categories (e.g., care navigation, care coordination, identification and monitoring) directed at patients, care professionals, or the organization. The project identified potentially unintended consequences of interventions for high-need patients, including high costs incurred for patients, increased time and effort for care professionals, and identification of needs without resources to respond appropriately. Conclusions Care for high-need patients requires a thoughtful approach; differentiating need domains provides multiple entry points for interventions directed at patients, care professionals, and organizations. Implementation efforts should consider outlined intended and unintended downstream effects on patients, care professionals, and organizations.

Routinely collected data (RCD) from healthcare claims and encounters are increasingly used for outcomes in randomized trials; however, methods for estimating the validity and relative precision of RCD-derived outcomes compared to those from conventional outcome ascertainment are limited. We developed an approach to measuring validity and relative precision of RCD and quantifying uncertainty. We reanalyzed data from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) cluster-randomized, controlled trial. Eighty-six primary care practices in 10 US healthcare systems were randomized to either a multifactorial intervention delivered by nurse falls care managers, or enhanced usual care, with 5451 persons age ≥ 70 at increased fall injury risk enrolled in the study. We estimated the hazard ratio (HR) and confidence interval (CI) for STRIDE's primary outcome (time to first serious fall injury) using original study data and RCD. The ratio of the RCD HR to original HR (“ratio of HRs”) measured validity. The confidence limit ratio (CLR; upper divided by lower confidence limits of CI) measured precision, with the ratio of the CLR with RCD to the CLR from the original study data (“ratio of CLRs”), measuring relative precision. We estimated uncertainty around the ratio of HRs and ratio of CLRs using bootstrapped 95% CIs and performed sensitivity analyses to assess the effects of adaptations needed to use RCD. Among the original sample of 5451 study participants, 5036 (92%) were linked to RCD. The intervention to control HR was 0.91 (95% CI: 0.78–1.07) in RCD, compared to 0.92 (95% CI: 0.80–1.06) in the original data. Using all RCD through STRIDE's administrative end date, the ratio of HRs was 1.00 (95% CI: 0.89–1.11) and ratio of CLRs was 1.03 (95% CI: 0.96–1.06). The CI around ratio of HRs was about three-fold wider for RCD than for the original STRIDE data in individuals who linked to RCD. Relative precision of RCD improved with increased length of follow-up. Relying solely on RCD to ascertain the primary outcome in STRIDE would have resulted in similar point estimates and confidence limits for the treatment effect as in the original data. However, there was meaningful uncertainty around the estimate of validity. Efforts to validate RCD-derived outcomes for use as clinical trial endpoints should include measurement of uncertainty around validity estimates. •Routinely collected data (RCD) accurately reproduced the primary outcome of a trial.•RCD generated valid results, but with greater uncertainty.•Trials using RCD should account for uncertainty in validity.