Longitudinal Study of the Variation in Patient Turnover and Patient-to-Nurse Ratio: Descriptive Analysis of a Swiss University Hospital

Variations in patient demand increase the challenge of balancing high-quality nursing skill mixes against budgetary constraints. Developing staffing guidelines that allow high-quality care at minimal cost requires first exploring the dynamic changes in nursing workload over the course of a day.; Accordingly, this longitudinal study analyzed nursing care supply and demand in 30-minute increments over a period of 3 years. We assessed 5 care factors: patient count (care demand), nurse count (care supply), the patient-to-nurse ratio for each nurse group, extreme supply-demand mismatches, and patient turnover (ie, number of admissions, discharges, and transfers).; Our retrospective analysis of data from the Inselspital University Hospital Bern, Switzerland included all inpatients and nurses working in their units from January 1, 2015 to December 31, 2017. Two data sources were used. The nurse staffing system (tacs) provided information about nurses and all the care they provided to patients, their working time, and admission, discharge, and transfer dates and times. The medical discharge data included patient demographics, further admission and discharge details, and diagnoses. Based on several identifiers, these two data sources were linked.; Our final dataset included more than 58 million data points for 128,484 patients and 4633 nurses across 70 units. Compared with patient turnover, fluctuations in the number of nurses were less pronounced. The differences mainly coincided with shifts (night, morning, evening). While the percentage of shifts with extreme staffing fluctuations ranged from fewer than 3% (mornings) to 30% (evenings and nights), the percentage within "normal" ranges ranged from fewer than 50% to more than 80%. Patient turnover occurred throughout the measurement period but was lowest at night.; Based on measurements of patient-to-nurse ratio and patient turnover at 30-minute intervals, our findings indicate that the patient count, which varies considerably throughout the day, is the key driver of changes in the patient-to-nurse ratio. This demand-side variability challenges the supply-side mandate to provide safe and reliable care. Detecting and describing patterns in variability such as these are key to appropriate staffing planning. This descriptive analysis was a first step towards identifying time-related variables to be considered for a predictive nurse staffing model

Developing a reflection and analysis tool (We-ReAlyse) for readmissions to the intensive care unit: A quality improvement project

Readmissions to the intensive care unit are associated with poorer patient outcomes and health prognoses, alongside increased lengths of stay and mortality risk. To improve quality of care and patients' safety, it is essential to understand influencing factors relevant to specific patient populations and settings. A standardized tool for systematic retrospective analysis of readmissions would help healthcare professionals understand risks and reasons affecting readmissions; however, no such tool exists.; This study's purpose was to develop a tool (We-ReAlyse) to analyze readmissions to the intensive care unit from general units by reflecting on affected patients' pathways from intensive care discharge to readmission. The results will highlight case-specific causes of readmission and potential areas for departmental- and institutional-level improvements.; A root cause analysis approach guided this quality improvement project. The tool's iterative development process included a literature search, a clinical expert panel, and a testing in January and February 2021.; The We-ReAlyse tool guides healthcare professionals to identify areas for quality improvement by reflecting the patient's pathway from the initial intensive care stay to readmission. Ten readmissions were analyzed by using the We-ReAlyse tool, resulting in key insights about possible root causes like the handover process, patient's care needs, the resources on the general unit and the use of different electronic healthcare record systems.; The We-ReAlyse tool provides a visualization/objectification of issues related to intensive care readmissions, gathering data upon which to base quality improvement interventions. Based on the information on how multi-level risk profiles and knowledge deficits contribute to readmission rates, nurses can target specific quality improvements to reduce those rates.; With the We-ReAlyse tool, we have the opportunity to collect detailed information about ICU readmissions for an in-depth analysis. This will allow health professionals in all involved departments to discuss and either correct or cope with the identified issues. In the long term, this will allow continuous, concerted efforts to reduce and prevent ICU readmissions. To obtain more data for analysis and to further refine and simplify the tool, it may be applied to larger samples of ICU readmissions. Furthermore, to test its generalizability, the tool should be applied to patients from other departments and other hospitals. Adapting it to an electronic version would facilitate the timely and comprehensive collection of necessary information. Finally, the tool's emphasis comprises reflecting on and analyzing ICU readmissions, allowing clinicians to develop interventions targeting the identified problems. Therefore, future research in this area will require the development and evaluation of potential interventions

Variation in detected adverse events using trigger tools: A systematic review and meta-analysis

Adverse event (AE) detection is a major patient safety priority. However, despite extensive research on AEs, reported incidence rates vary widely.; This study aimed: (1) to synthesize available evidence on AE incidence in acute care inpatient settings using Trigger Tool methodology; and (2) to explore whether study characteristics and study quality explain variations in reported AE incidence.; Systematic review and meta-analysis.; To identify relevant studies, we queried PubMed, EMBASE, CINAHL, Cochrane Library and three journals in the patient safety field (last update search 25.05.2022). Eligible publications fulfilled the following criteria: adult inpatient samples; acute care hospital settings; Trigger Tool methodology; focus on specialty of internal medicine, surgery or oncology; published in English, French, German, Italian or Spanish. Systematic reviews and studies addressing adverse drug events or exclusively deceased patients were excluded. Risk of bias was assessed using an adapted version of the Quality Assessment Tool for Diagnostic Accuracy Studies 2. Our main outcome of interest was AEs per 100 admissions. We assessed nine study characteristics plus study quality as potential sources of variation using random regression models. We received no funding and did not register this review.; Screening 6,685 publications yielded 54 eligible studies covering 194,470 admissions. The cumulative AE incidence was 30.0 per 100 admissions (95% CI 23.9-37.5; I2 = 99.7%) and between study heterogeneity was high with a prediction interval of 5.4-164.7. Overall studies' risk of bias and applicability-related concerns were rated as low. Eight out of nine methodological study characteristics did explain some variation of reported AE rates, such as patient age and type of hospital. Also, study quality did explain variation.; Estimates of AE studies using trigger tool methodology vary while explaining variation is seriously hampered by the low standards of reporting such as the timeframe of AE detection. Specific reporting guidelines for studies using retrospective medical record review methodology are necessary to strengthen the current evidence base and to help explain between study variation

Incidence and characteristics of adverse events in paediatric inpatient care: a systematic review and meta-analysis

Background: Adverse events (AEs) cause suffering for hospitalised children, a fragile patient group where the delivery of adequate timely care is of great importance. Objective: To report the incidence and characteristics of AEs, in paediatric inpatient care, as detected with the Global Trigger Tool (GTT), the Trigger Tool (TT) or the Harvard Medical Practice Study (HMPS) method. Method: MEDLINE, Embase, Web of Science and Google Scholar were searched from inception to June 2021, without language restrictions. Studies using manual record review were included if paediatric data were reported separately. We excluded studies reporting: AEs for a specific disease/diagnosis/treatment/procedure, or deceased patients; study protocols with no AE outcomes; conference abstracts, editorials and systematic reviews; clinical incident reports as the primary data source; and studies focusing on specific AEs only. Methodological risk of bias was assessed using a tool based on the Quality Assessment Tool for Diagnostic Accuracy Studies 2. Primary outcome was the percentage of admissions with ≥1 AEs. All statistical analyses were stratified by record review methodology (GTT/TT or HMPS) and by type of population. Meta-analyses, applying random-effects models, were carried out. The variability of the pooled estimates was characterised by 95% prediction intervals (PIs). Results: We included 32 studies from 44 publications, conducted in 15 countries totalling 33 873 paediatric admissions. The total number of AEs identified was 8577. The most common types of AEs were nosocomial infections (range, 6.8%-59.6%) for the general care population and pulmonary-related (10.5%-36.7%) for intensive care. The reported incidence rates were highly heterogeneous. The PIs for the primary outcome were 3.8%-53.8% and 6.9%-91.6% for GTT/TT studies (general and intensive care population). The equivalent PI was 0.3%-33.7% for HMPS studies (general care). The PIs for preventable AEs were 7.4%-96.2% and 4.5%-98.9% for GTT/TT studies (general and intensive care population) and 10.4%-91.8% for HMPS studies (general care). The quality assessment indicated several methodological concerns regarding the included studies. Conclusion: The reported incidence of AEs is highly variable in paediatric inpatient care research, and it is not possible to estimate a reliable single rate. Poor reporting standards and methodological differences hinder the comparison of study results

Describing adverse events in medical inpatients using the Global Trigger Tool

AIMS: The purpose of the study was to describe the type, prevalence, severity and preventability of adverse events (AEs) that affected hospitalised medical patients. We used the previously developed and validated Global Trigger Tool from the Institute for Healthcare Improvement.METHODS: Using an adapted version of the Global Trigger Tool, we conducted a retrospective chart review of adult patients hospitalised in five medical wards at a university hospital in Switzerland. We reviewed a random sample of 20 patients' charts for a total study period of 12 months (September 2016 to August 2017). Two trained nurses searched independently for triggers and possible AEs. All AEs were further validated by a senior physician. The number of triggers and AEs detected, as well as the severity and preventability of each, was assessed and analysed using descriptive statistics.RESULTS: From a sample of 240 patient charts, we identified 1371 triggers and 336 AEs in 144 (60%) inpatients. This translates to an AE rate of 95.7 AEs per 1000 patient days. Most AEs (86.1%) caused temporary harm to the patient and required an intervention and/or prolonged hospitalisation. The estimated preventability of the in-hospital AEs was 29%. Healthcare-associated infections (25.8%) and neurological reactions (22.9%) were the most frequent AE types.CONCLUSION: We found that about two thirds of patients suffered from AEs with harm during hospitalisation. It is common knowledge that AEs occur in hospitals and that they have potentially harmful consequences for patients, as well as a strong economic impact. However, to adequately prioritise patient safety interventions, it is essential to explore the nature, prevalence, severity and preventability of AEs. This is not only beneficial for the patients, but also cost effective in terms of shorter hospital stays

Incidence and characteristics of adverse events in paediatric inpatient care: a systematic review and meta-analysis.

BACKGROUND Adverse events (AEs) cause suffering for hospitalised children, a fragile patient group where the delivery of adequate timely care is of great importance. OBJECTIVE To report the incidence and characteristics of AEs, in paediatric inpatient care, as detected with the Global Trigger Tool (GTT), the Trigger Tool (TT) or the Harvard Medical Practice Study (HMPS) method. METHOD MEDLINE, Embase, Web of Science and Google Scholar were searched from inception to June 2021, without language restrictions. Studies using manual record review were included if paediatric data were reported separately. We excluded studies reporting: AEs for a specific disease/diagnosis/treatment/procedure, or deceased patients; study protocols with no AE outcomes; conference abstracts, editorials and systematic reviews; clinical incident reports as the primary data source; and studies focusing on specific AEs only. Methodological risk of bias was assessed using a tool based on the Quality Assessment Tool for Diagnostic Accuracy Studies 2. Primary outcome was the percentage of admissions with ≥1 AEs. All statistical analyses were stratified by record review methodology (GTT/TT or HMPS) and by type of population. Meta-analyses, applying random-effects models, were carried out. The variability of the pooled estimates was characterised by 95% prediction intervals (PIs). RESULTS We included 32 studies from 44 publications, conducted in 15 countries totalling 33 873 paediatric admissions. The total number of AEs identified was 8577. The most common types of AEs were nosocomial infections (range, 6.8%-59.6%) for the general care population and pulmonary-related (10.5%-36.7%) for intensive care. The reported incidence rates were highly heterogeneous. The PIs for the primary outcome were 3.8%-53.8% and 6.9%-91.6% for GTT/TT studies (general and intensive care population). The equivalent PI was 0.3%-33.7% for HMPS studies (general care). The PIs for preventable AEs were 7.4%-96.2% and 4.5%-98.9% for GTT/TT studies (general and intensive care population) and 10.4%-91.8% for HMPS studies (general care). The quality assessment indicated several methodological concerns regarding the included studies. CONCLUSION The reported incidence of AEs is highly variable in paediatric inpatient care research, and it is not possible to estimate a reliable single rate. Poor reporting standards and methodological differences hinder the comparison of study results

Trigger Tool-Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review.

BACKGROUND Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. OBJECTIVE The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies' designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard. METHODS PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. RESULTS A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0% to 17.9%, with a median of 0.8%. The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8% to 60%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20% to 91%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9% to 83.3%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0% to 60%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4%, 10.5%, 71.1%, and 68.4% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. CONCLUSIONS We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies

Developing a predictive model for shift-level nurse staffing using routine data - WER@INSEL project (Workforce Effectiveness Research at the Inselspital)

One substantial challenge facing healthcare systems worldwide is the aging population. Here in Switzerland, the 2017 census showed that almost 20% of the population was aged 65 or older. Accompanying rates of chronic and comorbid diseases are leading to increased health care demand. In terms of quality of care, the Swiss healthcare system is consistently ranked in the top five of over 195 countries; however, criticism is often raised concerning its costs (ranked behind only the United States). As elsewhere, then, cost containment measures have been implemented. Switzerland’s acute care hospitals currently employ around 100,000 nursing staff (42% of all healthcare providers). Depending on their qualifications, these workers belong to three groups: 1) registered nurses (70.6%); 2) licensed practical nurses, (18.8%); and 3) unlicensed personnel (10.6%). As their large number makes them the most costly group of healthcare employees, nurses have become a popular target for cost-containment measures. These include staff cuts, or the replacement of registered nurses with licensed practical nurses, who are themselves replaced by unlicensed aides. For over three decades, researchers have investigated the complex relationship between nurse staffing, i.e., the quantity of nursing care available, and quality of care received by patients. Appropriate (i.e., safe, sustainable) nurse staffing levels, are associated not only with high quality of care, but also with greater patient satisfaction and lower patient morbidity rates. Nurse managers and administrators still struggle to determine nurse numbers and skill mixes that will safely and reliably achieve optimal patient outcomes. Considering the pressure exerted on those same groups by ongoing cost-containment measures, they urgently need detailed data on this topic. Part of the reason for the current information shortfall is that research on nurse staffing and its links to patient outcomes has long been constrained by one or more of four limiting factors: 1) aggregated data; 2) cross-sectional design; 3) comparison of high- vs low-performing hospitals; and 4) a lack of relevant details, e.g., shifts, time information, or patient turnover. To our knowledge, to date very few longitudinal studies – and none in Switzerland – have used detailed analyses to examine the association between nurse staffing and patient outcomes. Replication of these studies is needed to confirm both their results and the transferability of their methods. Overall, this dissertation is structured in six chapters. Chapter 1 provides an overview of the Swiss healthcare context with associated costs and cost-containment measures. A description and overview of the literature on nurse staffing is presented in terms of nurse education, experience, and skill mix (along with measures to calculate it). An overview of patient outcomes, specifically adverse ones, is then provided concerning incidence, costs, tools for measuring them, as well as the links between some of them and nurse staffing. Finally, short descriptions of nurse outcomes and the Swiss context for nurse staffing are provided. The chapter ends by summarizing the current state of research, including the gaps in the literature, alongside this dissertation‘s contribution to bridging those gaps. This dissertation’s main aim was to examine the association of nurse staffing with adverse patient outcomes on the shift, unit, and patient levels with a longitudinal design. Chapter 2 gives a detailed description of this aim, which was divided between three studies. In order to achieve this aim, two routine administrative data sources from one Switzerland’s five university hospitals were used for a three-year period (2015 to 2017). The studies’ findings are reported in Chapters 3 to 5. In Chapter 3, the aim was to describe current study methods and challenges regarding the use of automatic trigger tool-like detection methods via a systematic review. A total of 11 studies met all criteria. The results showed broad variation in applied methods, selection and definition of triggers, estimates of adverse event prevalence, and positive predictive values. Across all 11 studies, adverse event prevalence ranged from 0% to 17.9% (median: 0.8%). The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. No clear evidence was found supporting the development of either a semi- or a fully-automated method of detecting adverse events in electronic health records using trigger tools. I.e., we found no evidence on how to apply or adapt any level of automation to detect adverse events in routine administrative data. For this reason, we chose to use mortality as an adverse patient outcome for Chapter 5. Before exploring the relationship between nurse staffing and the selected patient outcome (i.e., mortality), a descriptive analysis was conducted with the two merged routine data sources (in Chapter 4). The aim was to longitudinally analyze fluctuations in patient count, nurse count, patient-to-nurse ratio, extreme patient-to-nurse ratio, and patient turnover at 30-minute intervals over a three-year period with a descriptive approach. Our final dataset included more than 58 million data points on 128,484 patients and 4,633 nurses across 70 units. The number of patients showed the highest variability, whereas the number of nurses varied mainly between shifts. Variations occurred between departments, but also within department (i.e., between units), with Intensive Care showing the most stable levels of both nurses and patients. Observing raw numbers of patients and nurses as well as patient-to-nurse ratios and patient turnover figures allowed us to detect the source of that stability. A stable number of patients on a unit does not mean that no patient turnover occurred, but that entries are equal to exits. The same can be said for a stable patient-to-nurse ratio. Additionally, the percentages of shifts subject to extreme staffing situations varied widely across individual departments, quite effectively showing differences in their approaches to maintaining desirable patient-to-nurse ratios. Extreme staffing fluctuations ranged from fewer than 3% (mornings) to 30% (evenings and nights), with percentages falling within “normal” ranges ranging from fewer than 50% to more than 80%. This study provided us with the range of detailed variables on unit-, shift-, and patient-level fluctuations used for the modelling approaches in Chapter 5. Our final aim, presented in Chapter 5, was to explore the relationship between nurse staffing and patient outcomes. As shown in Chapter 3, as we could not extract any evidence to develop our own semi- or fully-automated adverse event detection methods, we selected mortality as a patient outcome present in our routine administrative data. From our logistic mortality model, we found consistent results for registered nurses: shifts with higher staffing had lower odds of mortality all nights (weekday nights: -17.8%; weekend nights: -13.5%) and weekend mornings (-15.5%), while shifts with lower staffing had higher odds of mortality on weekday nights (+3.9%) and evenings (+30.6%). Findings for licensed practical nurses and Others (including unlicensed and administrative personnel) were not consistent. Unusually high licensed practical nurse staffing was associated with lower odds of mortality for weekday evenings (-5.2%), unusually low staffing with lower mortality nights (-26.4%). For Others, staffing yielded similar results to registered nurses for weekday mornings, where high staffing correlated with lower mortality odds (-3.7%), low staffing with higher odds (+7%). For weekend evenings, though, the association was inverted, with high Other staffing linked to higher mortality odds (+20.3%) and low staffing to lower odds (-6.9%). To return to the results regarding the registered nurse sample, this high-granularity longitudinal approach supports the hypothesized association between high registered nurse staffing levels and improvement regarding the selected patient outcome. Additionally, the results provide details regarding shifts and other periods during which nurse staffing levels impact patient mortality. As these results eliminate various alternative explanations for the association between nurse staffing and patient outcomes, they also leave a strong likelihood of a causal link. The sixth and final chapter (Chapter 6) both synthesizes these three individual studies’ major findings and discusses the methodological strengths and limitations of the dissertation as a whole. Moreover, based on the findings and their implications, it includes recommendations for further research, clinical practice and policy. Overall, via its longitudinal design and unit-, shift-, and patient-level data, this dissertation contributes to the understanding of the association between nurse staffing and patient mortality. Its rich granularity attests to the value of the few studies in nurse staffing field to have used this design and methodology. More importantly, it provides insights that apply specifically to nurse staffing in the Swiss context. The high granularity of our descriptive overview showed variations mainly in numbers of patients – through patient turnover – but also in those of nurses between shifts. Additionally, our exploration of extremes revealed that, since extreme situations occurred quite frequently, even in the apparently well-staffed context of this analysis, many departments struggled to maintain appropriate patient-to-nurse ratios. This was also the case when looking at unusually low- and high-staffed shifts. Unusually low-registered nurse shifts (for weekday nights and all evenings) were associated with an increase in mortality risk, and unusually high registered nurse staffing (for all nights and weekend mornings) with lower risk. This suggests a possible causal relationship between RN staffing levels and mortality. Based on data drawn from the same sources, licensed practical nurses’ and Others’ contributions to patient safety are nebulous – a result that does not support substitution of either of these groups for registered nurses. Clarification of this and other relationships will require direct examinations – preferably using longitudinal design and shift-, unit-, and patient-level analyses – both of the independent effects of nursing support staffing and of additional adverse patient outcomes more directly linked to nursing care (e.g., pressure ulcers)

The association between nurse staffing and inpatient mortality: A shift-level retrospective longitudinal study.

BACKGROUND Worldwide, hospitals face pressure to reduce costs. Some respond by working with a reduced number of nurses or less qualified nursing staff. OBJECTIVE This study aims at examining the relationship between mortality and patient exposure to shifts with low or high nurse staffing. METHODS This longitudinal study used routine shift-, unit-, and patient-level data for three years (2015-2017) from one Swiss university hospital. Data from 55 units, 79,893 adult inpatients and 3646 nurses (2670 registered nurses, 438 licensed practical nurses, and 538 unlicensed and administrative personnel) were analyzed. After developing a staffing model to identify high- and low-staffed shifts, we fitted logistic regression models to explore associations between nurse staffing and mortality. RESULTS Exposure to shifts with high levels of registered nurses had lower odds of mortality by 8.7% [odds ratio 0.91 95% CI 0.89-0.93]. Conversely, low staffing was associated with higher odds of mortality by 10% [odds ratio 1.10 95% CI 1.07-1.13]. The associations between mortality and staffing by other groups was less clear. For example, both high and low staffing of unlicensed and administrative personnel were associated with higher mortality, respectively 1.03 [95% CI 1.01-1.04] and 1.04 [95% CI 1.03-1.06]. DISCUSSION AND IMPLICATIONS This patient-level longitudinal study suggests a relationship between registered nurses staffing levels and mortality. Higher levels of registered nurses positively impact patient outcome (i.e. lower odds of mortality) and lower levels negatively (i.e. higher odds of mortality). Contributions of the three other groups to patient safety is unclear from these results. Therefore, substitution of either group for registered nurses is not recommended

Bone Volume Fraction and Fabric Anisotropy Are Better Determinants of Trabecular Bone Stiffness Than Other Morphological Variables

As our population ages, more individuals suffer from osteoporosis. This disease leads to impaired trabecular architecture and increased fracture risk. It is essential to understand how morphological and mechanical properties of the cancellous bone are related. Morphologyelasticity relationships based on bone volume fraction (BV/TV) and fabric anisotropy explain up to 98% of the variation in elastic properties. Yet, other morphological variables such as individual trabeculae segmentation (ITS) and trabecular bone score (TBS) could improve the stiffness predictions. A total of 743 micro-computed tomography reconstructions of cubic trabecular bone samples extracted from femur, radius, vertebrae and iliac crest were analysed. Their morphology was assessed via 25 variables and their stiffness tensor (inline image) was computed from six independent load cases using micro finite element analyses. Variance inflation factors were calculated to evaluate collinearity between morphological variables and decide upon their inclusion in morphology-elasticity relationships. The statistically admissible morphological variables were included in a multi-linear regression modelling the dependent variable inline image. The contribution of each independent variable was evaluated (ANOVA). Our results show that BV/TV is the best determinant of inline image (inline image=0.889), especially in combination with fabric (inline image=0.968). Including the other independent predictors hardly affected the amount of variance explained by the model (inline image=0.975). Across all anatomical sites, BV/TV explained 87% of the variance of the bone elastic properties. Fabric further described 10% of the bone stiffness, but the improvement in variance explanation by adding other independent factors was marginal (<1%). These findings confirm that BV/TV and fabric are the best determinants of trabecular bone stiffness and show, against common belief, that other morphological variables do not bring any further contribution. These overall conclusions remain to be confirmed for specific bone diseases and post-elastic properties