Olaf von dem Knesebeck

Bio: Olaf von dem Knesebeck is an academic researcher from University of Hamburg. The author has contributed to research in topics: Health care & Public health. The author has an hindex of 31, co-authored 152 publications receiving 4233 citations. Previous affiliations of Olaf von dem Knesebeck include University of Düsseldorf & Eppendorf (Germany).

Quality of work, well-being, and intended early retirement of older employees: baseline results from the SHARE Study.

Abstract: Background: Given the challenge of a high proportion of older employees who retire early from work we analyse associations of indicators of a poor psychosocial quality of work with intended premature departure from work in a large sample of older male and female employees in 10 European countries. Methods: Baseline data from the 'Survey of Health, Ageing and Retirement in Europe' (SHARE) were obtained from 3523 men and 3318 women in 10 European countries. Data on intended early retirement, four measures of well-being (self-rated health, depressive symptoms, general symptom load, and quality of life), and quality of work (effort-reward imbalance; low control at work) were obtained from struc- tured interviews and questionnaires. Country-specific and total samples are analysed, using logistic regression analysis. Results: Poor quality of work is significantly associated with intended early retire- ment. After adjustment for well-being odds ratios (OR) of effort-reward imbalance (OR 1.72 (1.43-2.08)) and low control at work (OR 1.51 (1.27-1.80)) on intended early retirement are observed. Poor quality of work and reduced well-being are independently associated with the intention to retire from work. Conclusion: The consistent association of a poor psychosocial quality of work with intended early retire- ment among older employees across all European countries under study calls for improved investments into better quality of work, in particular increased control and an appropriate balance between efforts spent and rewards received at work.

The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. first results from the multicare cohort study

Abstract: Multimorbidity is a phenomenon with high burden and high prevalence in the elderly. Our previous research has shown that multimorbidity can be divided into the multimorbidity patterns of 1) anxiety, depression, somatoform disorders (ADS) and pain, and 2) cardiovascular and metabolic disorders. However, it is not yet known, how these patterns are influenced by patient characteristics. The objective of this paper is to analyze the association of socio-demographic variables, and especially socio-economic status with multimorbidity in general and with each multimorbidity pattern. The MultiCare Cohort Study is a multicentre, prospective, observational cohort study of 3.189 multimorbid patients aged 65+ randomly selected from 158 GP practices. Data were collected in GP interviews and comprehensive patient interviews. Missing values have been imputed by hot deck imputation based on Gower distance in morbidity and other variables. The association of patient characteristics with the number of chronic conditions is analysed by multilevel mixed-effects linear regression analyses. Multimorbidity in general is associated with age (+0.07 chronic conditions per year), gender (-0.27 conditions for female), education (-0.26 conditions for medium and -0.29 conditions for high level vs. low level) and income (-0.27 conditions per logarithmic unit). The pattern of cardiovascular and metabolic disorders shows comparable associations with a higher coefficient for gender (-1.29 conditions for female), while multimorbidity within the pattern of ADS and pain correlates with gender (+0.79 conditions for female), but not with age or socioeconomic status. Our study confirms that the morbidity load of multimorbid patients is associated with age, gender and the socioeconomic status of the patients, but there were no effects of living arrangements and marital status. We could also show that the influence of patient characteristics is dependent on the multimorbidity pattern concerned, i.e. there seem to be at least two types of elderly multimorbid patients. First, there are patients with mainly cardiovascular and metabolic disorders, who are more often male, have an older age and a lower socio-economic status. Second, there are patients mainly with ADS and pain-related morbidity, who are more often female and equally distributed across age and socio-economic groups. ISRCTN89818205

Socioeconomic status in health research: one size does not fit all.

Abstract: Problems with measuring socioeconomic status (SES)—frequently included in clinical and public health studies as a control variable and less frequently as the variable(s) of main interest—could affect research findings and conclusions, with implications for practice and policy.Wecritically examine standard SES measurement approaches, illustrating problems with examples from new analyses and the literature. For example, marked racial/ethnic differences in income at a given educational level and in wealth at a given income level raise questions about the socioeconomic comparability of individuals who are similar on education or income alone. Evidence also shows that conclusions about nonsocioeconomic causes of racial/ethnic differences in health may depend on the measure—eg, income, wealth, education, occupation, neighborhood socioeconomic characteristics, or past socioeconomic experiences—used to “control for SES,” suggesting that findings from studies that have measured limited aspects of SES should be reassessed. We recommend an outcome- and social group–specific approach to SES measurement that involves (1) considering plausible explanatory pathways and mechanisms, (2) measuring as much relevant socioeconomic information as possible, (3) specifying the particular socioeconomic factors measured (rather than SES overall), and (4) systematically considering how potentially important unmeasured socioeconomic factors may affect conclusions. Better SES measures are needed in data sources, but improvements could be made by using existing information more thoughtfully and acknowledging its limitations.

Physician burnout: contributors, consequences and solutions

Abstract: Physician burnout, a work-related syndrome involving emotional exhaustion, depersonalization and a sense of reduced personal accomplishment, is prevalent internationally. Rates of burnout symptoms that have been associated with adverse effects on patients, the healthcare workforce, costs and physician health exceed 50% in studies of both physicians-in-training and practicing physicians. This problem represents a public health crisis with negative impacts on individual physicians, patients and healthcare organizations and systems. Drivers of this epidemic are largely rooted within healthcare organizations and systems and include excessive workloads, inefficient work processes, clerical burdens, work-home conflicts, lack of input or control for physicians with respect to issues affecting their work lives, organizational support structures and leadership culture. Individual physician-level factors also play a role, with higher rates of burnout commonly reported in female and younger physicians. Effective solutions align with these drivers. For example, organizational efforts such as locally developed practice modifications and increased support for clinical work have demonstrated benefits in reducing burnout. Individually focused solutions such as mindfulness-based stress reduction and small-group programmes to promote community, connectedness and meaning have also been shown to be effective. Regardless of the specific approach taken, the problem of physician burnout is best addressed when viewed as a shared responsibility of both healthcare systems and individual physicians. Although our understanding of physician burnout has advanced considerably in recent years, many gaps in our knowledge remain. Longitudinal studies of burnout's effects and the impact of interventions on both burnout and its effects are needed, as are studies of effective solutions implemented in combination. For medicine to fulfil its mission for patients and for public health, all stakeholders in healthcare delivery must work together to develop and implement effective remedies for physician burnout.

Implicit bias in healthcare professionals: a systematic review

Abstract: Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients. PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies. Forty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care. The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics. Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.