PurposeThis study evaluated current fertility care forCKD patients by assessing the perspectives of nephrolo-gists and nurses in the dialysis department.MethodsTwo different surveys were distributed forthis cross-sectional study among Dutch nephrologists(N=312) and dialysis nurses (N=1211). ResultsResponse rates were 50.9% (nephrologists) and45.4% (nurses). Guidelines on fertility care were presentin the departments of 9.0% of the nephrologists and 15.6%of the nurses. 61.7% of the nephrologists and 23.6% ofthe nurses informed ≥50% of their patients on potentialchanges in fertility due to a decline in renal function.Fertility subjects discussed by nephrologists included “wishto have children” (91.2%), “risk of pregnancy for patients’health” (85.8%), and “inheritance of the disease” (81.4%).Barriers withholding nurses from discussing FD werebased on “the age of the patient” (62.6%), “insufficienttraining” (55.2%), and “language and ethnicity” (51.6%).29.2% of the nurses felt competent in discussing fertility,8.3% had sufficient knowledge about fertility, and 75.7%needed to expand their knowledge. More knowledge andcompetence were associated with providing fertility healthcare (p< 0.01). ConclusionsIn most nephrology departments, the guide-lines to appoint which care provider should provide fertil-ity care to CKD patients are absent. Fertility counselingis routinely provided by most nephrologists, nurses oftenskip this part of care mainly due to insufficiencies in self-imposed competence and knowledge and barriers based oncultural diversity. The outcomes identified a need for fer-tility guidelines in the nephrology department and trainingand education for nurses on providing fertility care. CC BY 4.0https://creativecommons.org/licenses/by/4.0/
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ObjectiveTo compare estimates of effect and variability resulting from standard linear regression analysis and hierarchical multilevel analysis with cross-classified multilevel analysis under various scenarios.Study design and settingWe performed a simulation study based on a data structure from an observational study in clinical mental health care. We used a Markov chain Monte Carlo approach to simulate 18 scenarios, varying sample sizes, cluster sizes, effect sizes and between group variances. For each scenario, we performed standard linear regression, multilevel regression with random intercept on patient level, multilevel regression with random intercept on nursing team level and cross-classified multilevel analysis.ResultsApplying cross-classified multilevel analyses had negligible influence on the effect estimates. However, ignoring cross-classification led to underestimation of the standard errors of the covariates at the two cross-classified levels and to invalidly narrow confidence intervals. This may lead to incorrect statistical inference. Varying sample size, cluster size, effect size and variance had no meaningful influence on these findings.ConclusionIn case of cross-classified data structures, the use of a cross-classified multilevel model helps estimating valid precision of effects, and thereby, support correct inferences.
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