William K. Midodzi

The Impact of Hospital Nursing Characteristics on 30-Day Mortality

BACKGROUND Evidence indicates that hospital nursing characteristics such as staffing contribute to patient outcomes. Less attention has been given to other hospital nursing characteristics central to optimal professional practice, namely nurse education and skill mix, continuity of care, and quality of the work environment. OBJECTIVE To assess the relative effects and importance of nurse education and skill mix, continuity of care, and quality of work environment in predicting 30-day mortality after adjusting for institutional factors and individual patients characteristics. METHOD A cross-sectional analysis of outcome data for 18,142 patients discharged from 49 acute care hospitals in Alberta, Canada, for diagnoses of acute myocardial infarction, congestive heart failure, chronic obstructive pulmonary disease, pneumonia, or stroke between April 1, 1998, and March 31, 1999, was done. Mortality data were linked to patient demographic and comorbidity factors, institutional characteristics, and hospital nursing characteristics derived from a survey of all registered nurses working in acute care hospitals. RESULTS Using multilevel analysis, it was determined that the log-odds for 30-day mortality varied significantly across hospitals (variance = .044, p < .001). Patient comorbidities and age explained 44.2% of the variance in 30-day mortality. After adjustment for patient comorbidities and demographic factors, and the size, teaching, and urban status of the study hospitals in a fixed-effects model, the odds ratios (95% confidence interval) of the significant hospital nursing characteristics that predict 30-day mortality were as follows: 0.81 (0.68-0.96) for higher nurse education level, 0.83 (0.73-0.96) for richer nurse skill mix, 1.26 (1.09-1.47) for higher proportion of casual or temporary positions, and 0.74 (0.60-0.91) for greater nurse-physician relationships. The institutional and hospital nursing characteristics explained an additional 36.9%. DISCUSSION Hospital nursing characteristics are an important consideration in efforts to reduce the risk of 30-day mortality of patients.

Influence of organizational characteristics and context on research utilization.

Background: Despite three decades of empirical investigation into research utilization and a renewed emphasis on evidence-based medicine and evidence-based practice in the past decade, understanding of factors influencing research uptake in nursing remains limited. There is, however, increased awareness that organizational influences are important. Objectives: To develop and test a theoretical model of organizational influences that predict research utilization by nurses and to assess the influence of varying degrees of context, based on the Promoting Action on Research Implementation in Health Services (PARIHS) framework, on research utilization and other variables. Methods: The study sample was drawn from a census of registered nurses working in acute care hospitals in Alberta, Canada, accessed through their professional licensing body (n = 6,526 nurses; 52.8% response rate). Three variables that measured PARIHS dimensions of context (culture, leadership, and evaluation) were used to sort cases into one of four mutually exclusive data sets that reflected less positive to more positive context. Then, a theoretical model of hospital- and unit-level influences on research utilization was developed and tested, using structural equation modeling, and 300 cases were randomly selected from each of the four data sets. Results: Model test results were as follows-low context: &khgr;2 = 124.5, df = 80, p <. 001; partially low: &khgr;2 = 144.2, p <. 001, df = 80; partially high: &khgr;2 = 157.3, df = 80, p <. 001; and partially low: &khgr;2 = 146.0, df = 80, p <. 001. Hospital characteristics that positively influenced research utilization by nurses were staff development, opportunity for nurse-to-nurse collaboration, and staffing and support services. Increased emotional exhaustion led to less reported research utilization and higher rates of patient and nurse adverse events. Nurses working in contexts with more positive culture, leadership, and evaluation also reported significantly more research utilization, staff development, and lower rates of patient and staff adverse events than did nurses working in less positive contexts (i.e., those that lacked positive culture, leadership, or evaluation). Conclusion: The findings highlight the combined importance of culture, leadership, and evaluation to increase research utilization and improve patient safety. The findings may serve to strengthen the PARIHS framework and to suggest that, although it is not fully developed, the framework is an appropriate guide to implement research into practice.

Predicting research use in nursing organizations : a multilevel analysis

Background: No empirical literature was found that explained how organizational context (operationalized as a composite of leadership, culture, and evaluation) influences research utilization. Similarly, no work was found on the interaction of individuals and contextual factors, or the relative importance or contribution of forces at different organizational levels to either such proposed interactions or, ultimately, to research utilization. Objective: To determine independent factors that predict research utilization among nurses, taking into account influences at individual nurse, specialty, and hospital levels. Design: Cross-sectional survey data for 4,421 registered nurses in Alberta, Canada were used in a series of multilevel (three levels) modeling analyses to predict research utilization. Methods: A multilevel model was developed in MLwiN version 2.0 and used to: (a) estimate simultaneous effects of several predictors and (b) quantify the amount of explained variance in research utilization that could be apportioned to individual, specialty, and hospital levels. Findings: There was significant variation in research utilization (p <.05). Factors (remaining in the final model at statistically significant levels) found to predict more research utilization at the three levels of analysis were as follows. At the individual nurse level (Level 1): time spent on the Internet and lower levels of emotional exhaustion. At the specialty level (Level 2): facilitation, nurse-to-nurse collaboration, a higher context (i.e., of nursing culture, leadership, and evaluation), and perceived ability to control policy. At the hospital level (Level 3): only hospital size was significant in the final model. The total variance in research utilization was 1.04, and the intraclass correlations (the percent contribution by contextual factors) were 4% (variance = 0.04, p <.01) at the hospital level and 8% (variance = 0.09, p <.05) at the specialty level. The contribution attributable to individual factors alone was 87% (variance = 0.91, p <.01). Conclusions: Variation in research utilization was explained mainly by differences in individual characteristics, with specialty- and organizational-level factors contributing relatively little by comparison. Among hospital-level factors, hospital size was the only significant determinant of research utilization. Although organizational determinants explained less variance in the model, they were still statistically significant when analyzed alone. These findings suggest that investigations into mechanisms that influence research utilization must address influences at multiple levels of the organization. Such investigations will require careful attention to both methodological and interpretative challenges present when dealing with multiple units of analysis.

Development and validation of a derived measure of research utilization by nurses.

Background: Theoretical models are needed to guide strategies for the implementation of research into clinical practice. To develop and test such models, including analyses of complex theoretical constructs and causal relationships, rich datasets are needed. Working with existing datasets may mean that important variables are lacking. Objective: The aim of this study was to derive a nursing research utilization variable and validate it using the Promoting Action on Research Implementation in Health Services (PARIHS) conceptual framework on research implementation. Methods: This study was based on data from two surveys of registered nurses. The first survey (1996; N = 600) contained robust research utilization variables but few organizational variables. The second (1998; N = 6,526) was rich in organizational variables but contained no research utilization variables. A linear regression model with predictors common to both datasets was used to derive a research utilization variable in the 1998 dataset. To validate these scores, four separate procedures based on the hypothesis of a positive relationship between context and research utilization were completed. Mutually exclusive groups reflecting various levels of context were created to accomplish these procedures. Results: The derived research utilization variable was successfully mapped onto the cases in the 1998 dataset. The derived scores ranged from 0.21 to 21.40, with a mean of 10.85 (SD = 3.23). The mean score per subgroup ranged from 8.28 for the lowest context group to 12.75 for the highest context group. One of the validation procedures showed that significant differences in mean research utilization existed only among four conceptually unique context groups (p < .001). These groups showed a positive incremental relationship in research utilization (p < .001; the better the context, the higher the research utilization score). The validity of the derived variable was supported by using the three remaining validation procedures. Discussion: The successful creation and validation of a derived research utilization variable will enable advanced modeling of the relationships between research utilization and individual and organizational characteristics. The findings also support the construct validity of the context element of the PARIHS theoretical framework.

The Contribution of Hospital Nursing Leadership Styles to 30-day Patient Mortality

Background:Nursing work environment characteristics, in particular nurse and physician staffing, have been linked to patient outcomes (adverse events and patient mortality). Researchers have stressed the need for nursing leadership to advance change in healthcare organizations to create safer practice environments for patients. The relationship between styles of nursing leadership in hospitals and patient outcomes has not been well examined. Objective:The purpose of this study was to examine the contribution of hospital nursing leadership styles to 30-day mortality after controlling for patient demographics, comorbidities, and hospital factors. Methods:Ninety acute care hospitals in Alberta, Canada, were categorized into five styles of nursing leadership: high resonant, moderately resonant, mixed, moderately dissonant, and high dissonant. In the secondary analysis, existing data from three sources (nurses, patients, and institutions) were used to test a hypothesis that the styles of nursing leadership at the hospital level contribute to patient mortality rates. Results:Thirty-day mortality was 7.8% in the study sample of 21,570 medical patients; rates varied across hospital categories: high resonant (5.2%), moderately resonant (7.4%), mixed (8.1%), moderately dissonant (8.8%), and high dissonant (4.3%). After controlling for patient demographics, comorbidities, and institutional and hospital nursing characteristics, nursing leadership styles explained 5.1% of 72.2% of total variance in mortality across hospitals, and high-resonant leadership was related significantly to lower mortality. Conclusions:Hospital nursing leadership styles may contribute to 30-day mortality of patients. This relationship may be moderated by homogeneity of leadership styles, clarity of communication among leaders and healthcare providers, and work environment characteristics.

Assessment of variation in the alberta context tool: the contribution of unit level contextual factors and specialty in Canadian pediatric acute care settings

BackgroundThere are few validated measures of organizational context and none that we located are parsimonious and address modifiable characteristics of context. The Alberta Context Tool (ACT) was developed to meet this need. The instrument assesses 8 dimensions of context, which comprise 10 concepts. The purpose of this paper is to report evidence to further the validity argument for ACT. The specific objectives of this paper are to: (1) examine the extent to which the 10 ACT concepts discriminate between patient care units and (2) identify variables that significantly contribute to between-unit variation for each of the 10 concepts.Methods859 professional nurses (844 valid responses) working in medical, surgical and critical care units of 8 Canadian pediatric hospitals completed the ACT. A random intercept, fixed effects hierarchical linear modeling (HLM) strategy was used to quantify and explain variance in the 10 ACT concepts to establish the ACTs ability to discriminate between units. We ran 40 models (a series of 4 models for each of the 10 concepts) in which we systematically assessed the unique contribution (i.e., error variance reduction) of different variables to between-unit variation. First, we constructed a null model in which we quantified the variance overall, in each of the concepts. Then we controlled for the contribution of individual level variables (Model 1). In Model 2, we assessed the contribution of practice specialty (medical, surgical, critical care) to variation since it was central to construction of the sampling frame for the study. Finally, we assessed the contribution of additional unit level variables (Model 3).ResultsThe null model (unadjusted baseline HLM model) established that there was significant variation between units in each of the 10 ACT concepts (i.e., discrimination between units). When we controlled for individual characteristics, significant variation in the 10 concepts remained. Assessment of the contribution of specialty to between-unit variation enabled us to explain more variance (1.19% to 16.73%) in 6 of the 10 ACT concepts. Finally, when we assessed the unique contribution of the unit level variables available to us, we were able to explain additional variance (15.91% to 73.25%) in 7 of the 10 ACT concepts.ConclusionThe findings reported here represent the third published argument for validity of the ACT and adds to the evidence supporting its use to discriminate patient care units by all 10 contextual factors. We found evidence of relationships between a variety of individual and unit-level variables that explained much of this between-unit variation for each of the 10 ACT concepts. Future research will include examination of the relationships between the ACTs contextual factors and research utilization by nurses and ultimately the relationships between context, research utilization, and outcomes for patients.

Understanding hierarchical linear models: applications in nursing research.

Nurses practice within hierarchical organizations and occupational structures. Hence, data emanating from nursing environments are structured, often inherently, hierarchically. From the perspective of ordinary regression, such structuring constitutes a statistical problem because this violates the assumption that we have observed independent and identical cases. A preferable approach is to employ analytical methods that mesh with the kinds of natural aggregations present in nursing environments. Consequently, there has been increasing interest in applying hierarchical, or multilevel, linear models to nursing contexts because this powerful analytical tool recognizes and accommodates naturally hierarchical data structures. The purpose of this article is to foster an understanding of both the strengths and limitations of hierarchical models. A hypothetical nursing example is progressively extended from the most basic hierarchical linear model toward a full two-level model. The structural similarities between two-level and three-level models are pointed out while focusing on the hierarchical nature of models rather than statistical technicalities. The limitations of hierarchical models are discussed also.

Safety and Tolerability of North American Ginseng Extract in the Treatment of Pediatric Upper Respiratory Tract Infection: A Phase II Randomized, Controlled Trial of 2 Dosing Schedules

BACKGROUND. Upper respiratory tract infections are the most common childhood illness. Panax quinquefolius (American ginseng root extract) standardized to contain 80% poly-furanosyl-pyranosyl-saccharides is purported to be effective in adult upper respiratory tract infection but has not been evaluated yet in a pediatric population. OBJECTIVES. Our primary objective was to document the safety and tolerability of 2 weight-based dosing schedules (standard dose versus low dose versus placebo) in children. We also used the Canadian Acute Respiratory Infection Flu Scale, a quantitative scoring sheet for measuring the severity and duration of upper respiratory symptoms, to establish the SD of the treatment effect to allow sample-size calculations for future clinical trials. METHODS. We conducted a randomized, double-blind dose-finding 3-arm trial (2 dosing schedules of American ginseng extract with 1 placebo control) during the winter months (November 2005 to March 2006) in children 3 to 12 years of age. RESULTS. Seventy-five subjects were prerecruited from the general population in Edmonton. Of these, 46 subjects developed an upper respiratory tract infection and were randomly assigned (15 standard dose, 16 low dose, and 15 placebo), with 1 subject withdrawing from the low-dose arm before beginning the intervention. No serious adverse events were reported. The frequency, severity, and degree of association between the intervention and reported adverse events were not significantly different among each of the 3 treatment arms. CONCLUSIONS. Standard doses of ginseng were well tolerated and merit additional evaluation with regard to treatment of pediatric upper respiratory tract infection.

The impact of hospital nursing characteristics on 30-day mortality.

BackgroundEvidence indicates that hospital nursing characteristics such as staffing contribute to patient outcomes. Less attention has been given to other hospital nursing characteristics central to optimal professional practice, namely nurse education and skill mix, continuity of care, and quality of the work environment. ObjectiveTo assess the relative effects and importance of nurse education and skill mix, continuity of care, and quality of work environment in predicting 30-day mortality after adjusting for institutional factors and individual patients characteristics. MethodA cross-sectional analysis of outcome data for 18,142 patients discharged from 49 acute care hospitals in Alberta, Canada, for diagnoses of acute myocardial infarction, congestive heart failure, chronic obstructive pulmonary disease, pneumonia, or stroke between April 1, 1998, and March 31, 1999, was done. Mortality data were linked to patient demographic and comorbidity factors, institutional characteristics, and hospital nursing characteristics derived from a survey of all registered nurses working in acute care hospitals. ResultsUsing multilevel analysis, it was determined that the log-odds for 30-day mortality varied significantly across hospitals (variance = .044, p < .001). Patient comorbidities and age explained 44.2% of the variance in 30-day mortality. After adjustment for patient comorbidities and demographic factors, and the size, teaching, and urban status of the study hospitals in a fixed-effects model, the odds ratios (95% confidence interval) of the significant hospital nursing characteristics that predict 30-day mortality were as follows: 0.81 (0.68–0.96) for higher nurse education level, 0.83 (0.73–0.96) for richer nurse skill mix, 1.26 (1.09–1.47) for higher proportion of casual or temporary positions, and 0.74 (0.60–0.91) for greater nurse-physician relationships. The institutional and hospital nursing characteristics explained an additional 36.9%. DiscussionHospital nursing characteristics are an important consideration in efforts to reduce the risk of 30-day mortality of patients.

Association of insulin dosage with mortality or major adverse cardiovascular events: a retrospective cohort study

BACKGROUND Existing studies have shown conflicting evidence regarding the safety of exogenous insulin therapy in patients with type 2 diabetes. In particular, observational studies have reported an increased risk of death and cardiovascular disease among users of higher versus lower doses of insulin. We aimed to quantify the association between increasing dosage of insulin exposure and death and cardiovascular events, while taking into account time-dependent confounding and mediation that might have biased previous studies. METHODS We did a cohort study using primary care records from the UK-based Clinical Practice Research Datalink (CPRD). New users of metformin monotherapy were identified in the period between Jan 1, 2001, and Dec 31, 2012. We then identified those in this group with a new prescription for insulin. Insulin exposure was categorised into groups according to the mean dose (units) per day within 180-day time segments throughout each patients follow-up. Relative differences in mortality and major adverse cardiovascular events (non-fatal myocardial infarction, non-fatal stroke, cardiovascular-related mortality) were assessed using conventional multivariable Cox proportional hazards models. Marginal structural models were then applied to reduce bias introduced by the time-dependent confounders affected by previous treatment. FINDINGS We identified 165 308 adults with type 2 diabetes in the CPRD database. After applying our exclusion criteria, 6072 (mean age 60 years [SD 12·5], 3281 [54%] men, mean HbA1c 8·5% [SD 1·75], and median follow-up 3·1 years [IQR 1·7-5·3) were new add-on insulin users and were included in the study cohort; 3599 were new add-on insulin users and were included in the subcohort linked to hospital records and death certificate information. Crude mortality rates were comparable between insulin dose groups; <25 units per day (46 per 1000 person-years), 25 to <50 units per day (39 per 1000 person-years), 50 to <75 units per day (27 per 1000 person-years), 75 to <100 units per day (34 per 1000 person-years), and at least 100 units per day (32 per 1000 person-years; p>0·05 for all; mean rate of 31 deaths per 1000 person-years [95% CI 29-33]). With adjustment for baseline covariates, mortality rates were higher for increasing insulin doses: less than 25 units per day [reference group]; 25 to <50 units per day, hazard ratio (HR) 1·41 [95% CI 1·12-1·78]; 50 to <75 units per day, 1·37 [1·04-1·80]; 75 to <100 units per day, 1·85 [1·35-2·53]; and at least 100 units per day, 2·16 [1·58-2·93]. After applying marginal structural models, insulin dose was not associated with mortality in any group (p>0·1 for all). INTERPRETATION In conventional multivariable regression analysis, higher insulin doses are associated with increased mortality after adjustment for baseline covariates. However, this effect seems to be confounded by time-dependent factors such as insulin exposure, glycaemic control, bodyweight gain, and the occurrence of cardiovascular and hypoglycaemic events. This study provides reassurance of the overall safety of insulin use in the treatment of type 2 diabetes and contributes to our understanding of the contrasting conclusions from non-randomised and randomised studies regarding dose-dependent effects of insulin on cardiovascular events and mortality. FUNDING Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada, and the Newfoundland and Labrador Research and Development Corporation.