Economies of diversification and stochastic dominance analysis in French mixed sheep farms

Farm diversification is mainly driven by risk mitigation effects and economic gains related to complementarities between production activities. By combining these two aspects, we investigate diversification economies in a sample of French mixed sheep farming systems and rank these systems using stochastic dominance criteria. Partially diversified systems (Sheep-Grass, Sheep-Crop, Sheep-Landless) and fully diversified systems (Sheep-Grass-Crop-Landless) were evaluated. We find a high degree of diversification diseconomies in the sheep farming systems considered. The results also indicate that the fully diversified system is driven by its risk-reducing effects (including downside risk exposure) and that Sheep-Crop is the dominant system in terms of risk-adjusted returns.

Assessing Materials Management Practices Effect on Firm’s Performance in Ghana Using Dominance Analysis: Evidence from a Listed Company

The objective of this study is to empirically examine the effect of materials management on a firm’s performance by exploring employee views and utilizing a firm’s financial statement data within the Ghanaian context, focusing on Fan Milk Limited, a listed manufacturing firm in Ghana. A cross-sectional research design was espoused for the study, utilizing questionnaires completed by 240 respondents selected via simple random sampling. SPSS version 25.0 was utilized for data analysis, which included descriptive statistics, principal component analysis, exploratory factor analysis with varimax rotation, reliability/validity analysis, and multivariate regression aided with relative importance analysis via dominance analysis. The study found significant positive relationships between all eight materials management practices and a firm’s performance. Dominance analysis further revealed that materials requirement planning and control was the most important predictor of a firm’s performance, while ergonomics was the least important predictor of performance. Managerial recommendations established should be viewed critically while managing manufacturing firms.

Using Dominance Analysis to Identify the Most Important Dimensions of Safety Culture for Predicting Patient Safety

Studies have demonstrated associations between safety culture and patient safety based on the perceptions of healthcare professionals, but limited attention has been given to the perceptions of nurses. Moreover, most studies have used regression modeling, an approach that limits researchers’ ability to identify the most important predictors of patient safety due to intercorrelations among predictors in the model. Therefore, the purpose of this study was to examine the effects of seven dimensions of safety culture on nurse-rated patient safety and identify the relative importance of these dimensions for predicting patient safety. This correlational study used data from the Agency for Healthcare Research and Quality’s 2018 Hospital Survey on Patient Safety Culture. Data from 13,031 nurses working in surgical areas of 443 hospitals in the United States were examined using logistic regression and dominance analysis. Staffing adequacy was the strongest predictor of patient safety, followed by hospital management support for patient safety and organizational learning/continuous improvement. However, dominance analysis showed that hospital management support for patient safety was the most important predictor rather than staffing adequacy. Nurse managers and hospital administrators should role model a culture of safety and demonstrate their valuing of patient safety by providing sufficient resources, listening to and valuing staff suggestions regarding patient safety, and providing feedback about organizational changes to improve patient safety.

Determining relative importance in Stata using dominance analysis: domin and domme

Dominance analysis is a common method applied to statistical models to determine the importance of independent variables. In this article, I describe two community-contributed commands, domin and domme, that can be used to dominance-analyze both independent variables and parameter estimates in Stata estimation commands. I discuss how to compute dominance statistics, provide multiple examples of each command applied to data, and outline how to interpret the results from each data-analytic example. I conclude with computational considerations for users applying larger models.

Future Risk of Bovine Tuberculosis (Mycobacterium bovis) Breakdown in Cattle Herds 2013–2018: A Dominance Analysis Approach

Bovine tuberculosis (bTB) remains a significant endemic pathogen of cattle herds, despite multi-decadal control programmes being in place in several countries. Understanding the risks of future bTB breakdown (BD) and the associated characteristics of herds and index breakdowns could help inform risk categorisation. Such risk categories could then contribute to tailored management and policies. Here, we estimated the future risk of herd BD for the cohort of herds that were derestricted during 2013 in Ireland using multivariable logit regression models, with a dominance analysis approach. One third of herds that were derestricted in 2013 experienced a breakdown during the follow-up five year period (1469/4459; 33%). BD length was a significant predictor of future risk, primarily driven by long BDs > 230 days relative to short BDs < 130 days (OR 95%CI: 1.157–1.851), as was having had a previous BD (OR 95%CI: 1.012–1.366). Herd-size was the dominant predictor of future risk (accounted for 46% of predicted variance), suggesting significant increase in risk of future breakdown with increasing (log) herd-size (OR 95%CI: 1.378–1.609). There was significant spatial variation in future risk across counties, and it was the second most dominant predictor of future risk (25% of predicted variance). The size of index breakdowns was not a strong predictor of future risk over a 5-year period. These findings can inform a risk-based policy development.

Risk Factors for Development of Acute Kidney Injury in COVID-19 Patients: A Retrospective Observational Cohort Study

<b><i>Introduction:</i></b> Acute kidney injury (AKI) in coronavirus infection disease (COVID-19) is associated with disease severity. We aimed to evaluate risk factors associated with AKI beyond COVID-19 severity. <b><i>Methods:</i></b> A retrospective observational study of COVID-19 patients admitted to a tertiary hospital in Singapore. Logistic regression was used to evaluate associations between risk factors and AKI (based on Kidney Disease Improving Global Outcomes criteria). Dominance analysis was performed to evaluate the relative importance of individual factors. <b><i>Results:</i></b> Seven hundred seven patients were included. Median age was 46 years (interquartile range [IQR]: 29–57) and 57% were male with few comorbidities (93%, Charlson Comorbidity Index [CCI] &#x3c;1). AKI occurred in 57 patients (8.1%); 39 were in AKI stage 1 (68%), 9 in stage 2 (16%), and 9 in stage 3 (16%). Older age (adjusted odds ratio [aOR] 1.04; 95% confidence interval [CI]: 1.01–1.07), baseline use of angiotensin-converting enzyme inhibitor (ACE-I) or angiotensin receptor blocker (ARB) (aOR 2.86; 95% CI: 1.20–6.83), exposure to vancomycin (aOR 5.84; 95% CI: 2.10–16.19), use of nonsteroidal anti-inflammatory drugs (NSAIDs) (aOR 3.04; 95% CI: 1.15–8.05), and severe COVID-19 with hypoxia (aOR 13.94; 95% CI: 6.07–31.98) were associated with AKI in the multivariable logistic regression model. The 3 highest ranked predictors were severe COVID-19 with hypoxia, vancomycin exposure, and age, accounting for 79.6% of the predicted variance (41.6, 23.1, and 14.9%, respectively) on dominance analysis. <b><i>Conclusion:</i></b> Severe COVID-19 is independently associated with increased risk of AKI beyond premorbid conditions and age. Appropriate avoidance of vancomycin and NSAIDs are potentially modifiable means to prevent AKI in patients with COVID-19.