Luwis Diya

The relationship between inpatient cardiac surgery mortality and nurse numbers and educational level: Analysis of administrative data

BACKGROUND In most multicenter studies that examine the relationship between nurse staffing and patient safety, nurse-staffing levels are measured per hospital. This can obscure relationships between staffing and outcomes at the unit level and lead to invalid inferences. OBJECTIVE In the present study, we examined the association between nurse-staffing levels in nursing units that treat postoperative cardiac surgery patients and the in-hospital mortality of these patients. DESIGN-SETTING-PARTICIPANTS: We illustrated our approach by using administrative databases (Year 2003) representing all Belgian cardiac centers (n=28), which included data from 58 intensive care and 75 general nursing units and 9054 patients. METHODS We used multilevel logistic regression models and controlled for differences in patient characteristics, nursing care intensity, and cardiac procedural volume. RESULTS Increased nurse staffing in postoperative general nursing units was significantly associated with decreased mortality. Nurse staffing in postoperative intensive care units was not significantly associated with in-hospital mortality possibly due to lack of variation in ICU staffing across hospitals. CONCLUSION This study, together with the international body of evidence, suggests that nurse staffing is one of several variables influencing patient safety. These findings further suggest the need to study the impact of nurse-staffing levels on in-hospital mortality using nursing-unit-level specific data.

Nurse staffing and patient outcomes in Belgian acute hospitals: Cross-sectional analysis of administrative data

BACKGROUND Studies have linked nurse staffing levels (number and skill mix) to several nurse-sensitive patient outcomes. However, evidence from European countries has been limited. OBJECTIVES This study examines the association between nurse staffing levels (i.e. acuity-adjusted Nursing Hours per Patient Day, the proportion of registered nurses with a Bachelors degree) and 10 different patient outcomes potentially sensitive to nursing care. DESIGN-SETTING-PARTICIPANTS: Cross-sectional analyses of linked data from the Belgian Nursing Minimum Dataset (general acute care and intensive care nursing units: n=1403) and Belgian Hospital Discharge Dataset (general, orthopedic and vascular surgery patients: n=260,923) of the year 2003 from all acute hospitals (n=115). METHODS Logistic regression analyses, estimated by using a Generalized Estimation Equation Model, were used to study the association between nurse staffing and patient outcomes. RESULTS The mean acuity-adjusted Nursing Hours per Patient Day in Belgian hospitals was 2.62 (S.D.=0.29). The variability in patient outcome rates between hospitals is considerable. The inter-quartile ranges for the 10 patient outcomes go from 0.35 for Deep Venous Thrombosis to 3.77 for failure-to-rescue. No significant association was found between the acuity-adjusted Nursing Hours per Patient Day, proportion of registered nurses with a Bachelors degree and the selected patient outcomes. CONCLUSION The absence of associations between hospital-level nurse staffing measures and patient outcomes should not be inferred as implying that nurse staffing does not have an impact on patient outcomes in Belgian hospitals. To better understand the dynamics of the nurse staffing and patient outcomes relationship in acute hospitals, further analyses (i.e. nursing unit level analyses) of these and other outcomes are recommended, in addition to inclusion of other study variables, including data about nursing practice environments in hospitals.

Effective strategies for nurse retention in acute hospitals: A mixed method study

BACKGROUND The realization of an organizational context that succeeds to retain nurses within their job is one of the most effective strategies of dealing with nursing shortages. OBJECTIVES First, to examine the impact of nursing practice environments, nurse staffing and nurse education on nurse reported intention to leave the hospital. Second, to provide understanding of which best practices in the organization of nursing care are being implemented to provide sound practice environments and to retain nurses. METHODS 3186 bedside nurses of 272 randomly selected nursing units in 56 Belgian acute hospitals were surveyed. A GEE logistic regression analysis was used to estimate the impact of organization of nursing care on nurse reported intention to leave controlling for differences in region (Walloon, Flanders, and Brussels), hospital characteristics (technology level, teaching status, and size) and nurse characteristics (experience, gender, and age). For the second objective, in-depth semi-structured interviews with the chief nursing officers of the three high and three low performing hospitals on reported intention to leave were held. RESULTS 29.5% of Belgian nurses have an intention-to-leave the hospital. Patient-to-nurse staffing ratios and nurse work environments are significantly (p<0.05) associated with intention-to-leave. Interviews with Chief Nurse Officers revealed that high performing hospitals showing low nurse retention were--in contrast to the low performing hospitals--characterized by a flat organization structure with a participative management style, structured education programs and career opportunities for nurses. CONCLUSION This study, together with the international body of evidence, suggests that investing in improved nursing work environments is a key strategy to retain nurses.

The relationship between in-hospital mortality, readmission into the intensive care nursing unit and/or operating theatre and nurse staffing levels

AIM   The aim of this article was to assess the relationship between (1) in-hospital mortality and/or (2) unplanned readmission to intensive care units or operating theatre and nurse staffing variables. BACKGROUND   Adverse events are used as surrogates for patient safety in nurse staffing and patient safety research. A single adverse event cannot adequately capture the multi-dimensional attributes of patient safety; hence, there is a need to consider composite measures. Unplanned readmission into the postoperative Intensive Care nursing unit and/or operating Theatre and in-hospital mortality can be viewed as measures that incorporate the effects of several adverse events. METHODS   We conducted a Bayesian multilevel analysis on a subset of the 2003 Belgian Hospital Discharge and Nursing Minimum Data sets. The sample included 9054 patients who underwent coronary artery bypass surgery or heart valve procedures from 28 Belgian acute hospitals. Two proxies of patient safety were considered, namely postoperative in-hospital mortality in the first postoperative intensive care unit and unplanned readmission into the intensive care and/or operating theatre (including mortality beyond the first postoperative intensive care unit) after the first-operative intensive care nursing unit. RESULTS   There is an association between in-hospital mortality and/or unplanned readmissions and nurse staffing levels, but the relationship is moderated by volume and severity of illness respectively. In addition, the relationship differs between the two endpoints. CONCLUSION   Higher nurse staffing levels on postoperative general nursing cardiac surgery units protected patients from unplanned readmission to intensive care units or operating theatre and in-hospital mortality.

Does the organization of care processes affect outcomes in patients undergoing total joint replacement

BACKGROUND Surgeons realize that safe and efficient care processes for total joint replacement requires more than just well-performed operations. Orthopaedic teams are reorganizing care process to improve efficacy and shorten length of stay. Little is known on the impact of organizational changes on patient outcome. This paper studies the relation between the organization of care processes and patient outcomes in hip and knee. Clinical pathways are used as one of the methods to structure the care process. Although evidence is available on the effect of pathways in total joint replacement, their impact with the organization of the care process has not been studied previously. METHODS A cross-sectional multicentre study was performed on 39 care processes and 737 consecutive patients. Regression models were used to analyse the relation between the organization of the care process and risk-adjusted patient outcomes. The use of pathways and the organization of the care process, measured by the Care Process Self Evaluation Tool (CPSET), were measured at organizational level. Length of stay, pain, mobility and elapsed time to discharge were measured at patient level. RESULTS The use of pathways had a positive effect on four out of five subscales and the overall CPSET score. Using pathways decreased length of stay (P = 0.014), pain (P = 0.052) and elapsed time to discharge (P = 0.003). The CPSET subscale communication was related with three risk adjusted outcomes. Multivariate analysis demonstrated a significant effect by three different variables on the length of stay; (1) use of pathways; (2) coordination of care processes; and (3) communication with patients and family. Both the use of pathways and coordination of the care process were determinants for the elapsed time to discharge. A significant interaction effect was found between use of pathways and coordination of the care process. CONCLUSION This large multicentre study revealed the relation between the use of pathways, organization of the care process and patient outcomes. This information is important for both clinicians and managers to understand and further improve the organization of orthopaedic care. LEVEL OF EVIDENCE Level I prognostic study.

Impact of self-esteem on the relationship between orthodontic treatment need and oral health-related quality of life in 11- to 16-year-old children

The interest in the psychological aspects of orthodontic treatment increases, but a drawback of many studies is that the psychological characteristics of the children themselves are often ignored. One of these psychological attributes is self-esteem (SE), which is a relatively stable personal resource that might moderate the effects of conditions or events. The aim of this study was to investigate whether there is a relationship between orthodontic treatment need and oral health-related quality of life (OHRQoL) and whether this relationship is influenced by SE. This cross-sectional study comprised 223 children (113 boys and 110 girls) between 11 and 16 years of age (mean age 13.2 years), seeking orthodontic treatment. The OHRQoL was scored by the use of the Child Perception Questionnaire (CPQ(11-14)). The Dutch adaptation of the Harters Self-Perception Profile was used to assess SE, and the Index of Orthodontic Treatment Need defined the need for treatment. Spearman correlations, Mann-Whitney U-tests, and regression models were used to analyze the data. There was a significant relationship between orthodontic treatment need and OHRQoL, and between SE and OHRQoL. No evidence was found that SE moderates the relationship between OHRQoL and treatment need.

Benchmarking nurse staffing levels: the development of a nationwide feedback tool

AIM This paper is a report of a study to develop a methodology that corrects nurse staffing for nursing care intensity in a way that allows nationwide benchmarking of nurse staffing data. BACKGROUND Although nurse workload measurement systems are recognized to be informative in nurse staffing decisions, they are rarely used. When these systems are used, however, it is only possible to compare units within hospitals, because currently available instruments are not standardized for comparisons beyond hospital boundaries. The Belgian Nursing Minimum Dataset (B-NMDS) contains uniformly measured data about the intensity of nursing care and nurse staffing levels for all hospitals in Belgium. METHOD We conducted a retrospective multilevel analysis of the B-NMDS for the year 2003. The sample included 690,258 inpatient days for 298,691 patients, recorded from 1637 acute care nursing units in 115 hospitals. We corrected the number of nursing staff by using different covariates available in the B-NMDS: intensity of nursing care, type of day (week vs. weekend), service type (general vs. intensive) and hospital type (academic vs. general). FINDINGS The multilevel approach allowed us to explain about 70% of the variability in the number of nursing staff per nursing unit using hospital type (P = 0.0053); intensity of nursing care (P < 0.0001) and service type (P < 0.0001) as the only covariates. CONCLUSION The feedback tool we developed can inform nurse managers and policymakers about nursing intensity-adjusted nurse staffing levels according to different benchmarks. Our study demonstrates that investing in large nursing datasets is appropriate for the international nursing community.

Factors that influence data quality in caries experience detection: a multilevel modeling approach.

Caries experience detection is prone to misclassification. For this reason, calibration exercises which aim at assessing and improving the scoring behavior of dental raters are organized. During a calibration exercise, a sample of children is examined by the benchmark scorer and the dental examiners. This produces a 2 × 2 contingency table with the true and possibly misclassified responses. The entries in this misclassification table allow to estimate the sensitivity and the specificity of the raters. However, in many dental studies, the uncertainty with which sensitivity and specificity are estimated is not expressed. Further, caries experience data have a hierarchical structure since the data are recorded for the surfaces nested in the teeth within the mouth. Therefore, it is important to report the uncertainty using confidence intervals and to take the clustering into account. Here we apply a Bayesian logistic multilevel model for estimating the sensitivity and specificity. The main goal of this research is to find the factors that influence the true scoring of caries experience accounting for the hierarchical structure in the data. In our analysis, we show that the dentition type and tooth or surface type affect the quality of caries experience detection.

The Use of "Lives Saved" Measures in Nurse Staffing and Patient Safety Research: Statistical Considerations.

Background:Lives saved predictions are used to quantify the impact of certain remedial measures in nurse staffing and patient safety research, giving an indication of the potential gain in patient safety. Data collected in nurse staffing and patient safety are often multilevel in structure, requiring statistical techniques to account for clustering in the data. Objective:The purpose of this study was to assess the impact of model specifications on lives saved estimates and inferences in a multilevel context. Methods:A simulation study was carried out to assess the impact of model assumptions on lives saved predictions. Scenarios considered were omitting an important covariate, taking different link functions, neglecting the correlations coming from the multilevel data structure, and neglecting a level in a multilevel model. Finally, using a cardiac surgery data set, predicted lives saved from the random intercept logistic model and the clustered discrete time logistic model were compared. Results:Omitting an important covariate, neglecting the association between patients within the same hospital, and the complexity of the model affect the prediction of lives saved estimates and the inferences thereafter. On the other hand, a change in the link function led to the same predicted lives saved estimates and standard deviations. Finally, the lives saved estimates from the two-level random intercept model were similar to those of the clustered discrete time logistic model, but the standard deviations differed greatly. Conclusions:The results stress the importance of verifying model assumptions. It is recommended that researchers use sensitivity analyses to investigate the stability of lives saved results using different statistical models or different data sets.