Academic literature on the topic 'Multistep bivariate test'

Abstract. Interactions between externally forced and internally generated climate variations on decadal timescales is a major determinant of changing climate risk. Severe testing is applied to observed global and regional surface and satellite temperatures and modelled surface temperatures to determine whether these interactions are independent, as in the traditional signal-to-noise model, or whether they interact, resulting in step-like warming. The multistep bivariate test is used to detect step changes in temperature data. The resulting data are then subject to six tests designed to distinguish between the two statistical hypotheses, hstep and htrend. Test 1: since the mid-20th century, most observed warming has taken place in four events: in 1979/80 and 1997/98 at the global scale, 1988/89 in the Northern Hemisphere and 1968–70 in the Southern Hemisphere. Temperature is more step-like than trend-like on a regional basis. Satellite temperature is more step-like than surface temperature. Warming from internal trends is less than 40 % of the total for four of five global records tested (1880–2013/14). Test 2: correlations between step-change frequency in observations and models (1880–2005) are 0.32 (CMIP3) and 0.34 (CMIP5). For the period 1950–2005, grouping selected events (1963/64, 1968–70, 1976/77, 1979/80, 1987/88 and 1996–98), the correlation increases to 0.78. Test 3: steps and shifts (steps minus internal trends) from a 107-member climate model ensemble (2006–2095) explain total warming and equilibrium climate sensitivity better than internal trends. Test 4: in three regions tested, the change between stationary and non-stationary temperatures is step-like and attributable to external forcing. Test 5: step-like changes are also present in tide gauge observations, rainfall, ocean heat content and related variables. Test 6: across a selection of tests, a simple stepladder model better represents the internal structures of warming than a simple trend, providing strong evidence that the climate system is exhibiting complex system behaviour on decadal timescales. This model indicates that in situ warming of the atmosphere does not occur; instead, a store-and-release mechanism from the ocean to the atmosphere is proposed. It is physically plausible and theoretically sound. The presence of step-like – rather than gradual – warming is important information for characterising and managing future climate risk.

Background Patients in intensive care units are 5 times more likely to have skin integrity issues develop than patients in other units. Identifying the most appropriate assessment tool may be critical to preventing pressure injuries in intensive care patients. Objectives To validate the Cubbin-Jackson skin risk assessment in the critical care setting and to compare the predictive accuracy of the Cubbin-Jackson and Braden scales for the same patients. Methods In 5 intensive care units, the Cubbin-Jackson and Braden assessments were completed by different clinicians within 61 minutes of each other for 4137 patients between October 2017 and March 2018. Bivariate correlations and the Fisher exact test were used to check for associations between the scores. Results The Cubbin-Jackson and Braden scores were significantly and positively correlated (r = 0.80, P < .001). Both tools were significant predictors of skin changes and identified as “at risk” 100% of the patients who had a change in skin integrity occur. The specificity was 18.4% for the Cubbin-Jackson scale and 27.9% for the Braden scale, and the area under the curve was 0.75 (P < .001) for the Cubbin-Jackson scale and 0.76 (P < .001) for the Braden scale. These findings show acceptable construct validity for both scales. Conclusions The predictive validities of the Cubbin-Jackson and Braden scales are similar, but both are sub-optimal because of poor specificity and positive predictive value. Change in practice may not be warranted, because there are no differences between the 2 scales of practical benefit to bedside nurses.

Background Critical care nurses have a burnout rate among the highest of any nursing field. Nurse burnout may impact care quality. Few studies have considered how temporal patterns may influence outcomes. Objective To test a longitudinal model of burnout clusters and associations with patient and clinician outcomes. Methods An observational study analyzed data from annual employee surveys and administrative data on patient outcomes at 111 Veterans Health Administration intensive care units from 2013 through 2017. Site-level burnout rates among critical care nurses were calculated from survey responses about emotional exhaustion and depersonalization. Latent trajectory analysis was applied to identify clusters of facilities with similar burnout patterns over 5 years. Regression analysis was used to analyze patient and employee outcomes by burnout cluster and organizational context measures. Outcomes of interest included patient outcomes (30-day standardized mortality rate and observed minus expected length of stay) for 2016 and 2017 and clinician outcomes (intention to leave and employee satisfaction) from 2013 through 2017. Results Longitudinal analysis revealed 3 burnout clusters among the 111 sites: low (n = 37), medium (n = 68), and high (n = 6) burnout. Compared with sites in the low-burnout cluster, those in the high-burnout cluster had longer patient stays, higher employee turnover intention, and lower employee satisfaction in bivariate models but not in multivariate models. Conclusions In this multiyear, multisite study, critical care nurse burnout was associated with key clinician and patient outcomes. Efforts to address burnout among nurses may improve patient and employee outcomes.

Background Accessible, safe, and client-centered SARS-CoV-2 testing services are an effective way to halt its transmission. Testing enables infected individuals to isolate or quarantine to prevent further transmission. In countries with limited health systems and laboratory capacity, it can be challenging to provide accessible and safe screening for COVID-19. Self-testing provides a convenient, private, and safe testing option; however, it also raises important concerns about lack of counseling and ensuring timely reporting of self-test results to national surveillance systems. Investigating community members’ views and perceptions regarding SARS-CoV-2 self-testing is crucial to inform the most effective and safe strategies for implementing said testing. Objective We aimed to determine whether SARS-CoV-2 self-testing was useful to diagnose and prevent the spread of SARS-CoV-2 for populations in low-resource settings and under which circumstances it would be acceptable. Methods This multisite, mixed methods, observational study will be conducted in 9 countries—Brazil, India, Indonesia, Kenya, Malawi, Nigeria, Peru, the Philippines, and South Africa—and will consists of 2 components: cross-sectional surveys and interviews (semistructured and group) among 4 respondent groupings: the general population, general population representatives, health care workers, and decision-makers. General population and health care worker survey responses will be analyzed separately from each other, using bivariate and multivariate inferential analysis and descriptive statistics. Semistructured interviews and group interviews will be audiorecorded, transcribed, and coded for thematic comparative analysis. Results As of November 19, 2021, participant enrollment is ongoing; 4364 participants have been enrolled in the general population survey, and 2233 participants have been enrolled in the health care workers survey. In the qualitative inquiry, 298 participants have been enrolled. We plan to complete data collection by December 31, 2021 and publish results in 2022 via publications, presentations at conferences, and dissemination events specifically targeted at local decision-makers, civil society, and patient groups. Conclusions The views and perceptions of local populations are crucial in the discussion of the safest strategies for implementing SARS-CoV-2 self-testing. We intend to identify sociocultural specificities that may hinder or accelerate the widespread utilization of SARS-CoV-2 self-testing. International Registered Report Identifier (IRRID) DERR1-10.2196/33088

Planning for future climate risk tends to incorporate assumptions of smoothly accelerating climate change, around which unchanged variability constitutes the risk boundaries. This constitutes hypothesis H1 – forced warming and natural variability evolve gradually and independently and the climate response is trend-like. Against this, there is evidence for H2 – forced warming interacts with natural variability and the climate response includes abrupt steps. Earlier than expected breaching of risk bounds follows from H2. New automation tools, and post-detection tests find and characterise step-like regime onsets in temperatures. With these tools I show that step-like temperature regime shifts are detectable at all spatial scales at the land and ocean surface, and in the vertical temperature structure of the ocean. Based on published climate models shifts respond to warming by becoming more intense, wider-spread and more frequent. Regimes are regional, differ qualitatively between land and ocean, align with natural variability coincident with known bio-physical shifts. Two, circa 1976 and 1996, align with the Pacific Decadal Oscillation, involving rapid vertical ocean restructuring. One, 1968 in the Southern Hemisphere ocean does not, and 1986 in the Northern Hemisphere reflects atmospheric reorganisation. H2 is strongly supported by the findings. Step-like warming dominates trends, increasingly so at finer scale.