Population-based reference values for kidney function and kidney function decline in 25- to 95-year-old Germans without and with diabetes.

Understanding normal aging of kidney function is pivotal to help distinguish individuals at particular risk for chronic kidney disease. Glomerular filtration rate (GFR) is typically estimated via serum creatinine (eGFRcrea) or cystatin C (eGFRcys). Since population-based age-group-specific reference values for eGFR and eGFR-decline are scarce, we aimed to provide such reference values from population-based data of a wide age range. In four German population-based cohorts (KORA-3, KORA-4, AugUR, DIACORE), participants underwent medical exams, interview, and blood draw up to five times within up to 25 years. We analyzed eGFRcrea and eGFRcys cross-sectionally and longitudinally (12,000 individuals, age 25-95 years). Cross-sectionally, we found age-group-specific eGFRcrea to decrease approximately linearly across the full age range, for eGFRcys up to the age of 60 years. Within age-groups, there was little difference by sex or diabetes status. Longitudinally, linear mixed models estimated an annual eGFRcrea decline of -0.80 [95% confidence interval -0.82, -0.77], -0.79 [-0.83, -0.76], and -1.20 mL/min/1.73m2 [-1.33, -1.08] for the general population, "healthy" individuals, or individuals with diabetes, respectively. Reference values for eGFR using cross-sectional data were shown as percentile curves for "healthy" individuals and for individuals with diabetes. Reference values for eGFR-decline using longitudinal data were presented as 95% prediction intervals for "healthy" individuals and for individuals with diabetes, obesity, and/or albuminuria. Thus, our results can help clinicians to judge eGFR values in individuals seen in clinical practice according to their age and to understand the expected range of annual eGFR-decline based on their risk profile.

Relevance of Protein Intake for Weaning in the Mechanically Ventilated Critically Ill: Analysis of a Large International Database.

OBJECTIVES: The association between protein intake and the need for mechanical ventilation (MV) is controversial. We aimed to investigate the associations between protein intake and outcomes in ventilated critically ill patients. DESIGN: Analysis of a subset of a large international point prevalence survey of nutritional practice in ICUs. SETTING: A total of 785 international ICUs. PATIENTS: A total of 12,930 patients had been in the ICU for at least 96 hours and required MV by the fourth day after ICU admission at the latest. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We modeled associations between the adjusted hazard rate (aHR) of death in patients requiring MV and successful weaning (competing risks), and three categories of protein intake (low: < 0.8 g/kg/d, standard: 0.8-1.2 g/kg/d, high: > 1.2 g/kg/d). We compared five different hypothetical protein diets (an exclusively low protein intake, a standard protein intake given early (days 1-4) or late (days 5-11) after ICU admission, and an early or late high protein intake). There was no evidence that the level of protein intake was associated with time to weaning. However, compared with an exclusively low protein intake, a standard protein intake was associated with a lower hazard of death in MV: minimum aHR 0.60 (95% CI, 0.45-0.80). With an early high intake, there was a trend to a higher risk of death in patients requiring MV: maximum aHR 1.35 (95% CI, 0.99-1.85) compared with a standard diet. CONCLUSIONS: The duration of MV does not appear to depend on protein intake, whereas mortality in patients requiring MV may be improved by a standard protein intake. Adverse effects of a high protein intake cannot be excluded.

Collaborative nowcasting of COVID-19 hospitalization incidences in Germany.

Real-time surveillance is a crucial element in the response to infectious disease outbreaks. However, the interpretation of incidence data is often hampered by delays occurring at various stages of data gathering and reporting. As a result, recent values are biased downward, which obscures current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhancing situational awareness. In this paper, we present a preregistered real-time assessment of eight nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences during the COVID-19 pandemic. This indicator played an important role in the management of the outbreak in Germany and was linked to levels of non-pharmaceutical interventions via certain thresholds. Due to its definition, in which hospitalization counts are aggregated by the date of case report rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this study, all methods were applied from 22 November 2021 to 29 April 2022, with probabilistic nowcasts produced each day for the current and 28 preceding days. Nowcasts at the national, state, and age-group levels were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall, the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. The accompanying prediction intervals were also too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. For the most recent days, which are often considered the most relevant in practice, a mean ensemble of the submitted nowcasts performed best. We conclude by providing some lessons learned on the definition of nowcasting targets and practical challenges.

Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models.

BACKGROUND: Proteins are an essential part of medical nutrition therapy in critically ill patients. Guidelines almost universally recommend a high protein intake without robust evidence supporting its use. METHODS: Using a large international database, we modelled associations between the hazard rate of in-hospital death and live hospital discharge (competing risks) and three categories of protein intake (low: < 0.8 g/kg per day, standard: 0.8-1.2 g/kg per day, high: > 1.2 g/kg per day) during the first 11 days after ICU admission (acute phase). Time-varying cause-specific hazard ratios (HR) were calculated from piece-wise exponential additive mixed models. We used the estimated model to compare five different hypothetical protein diets (an exclusively low protein diet, a standard protein diet administered early (day 1 to 4) or late (day 5 to 11) after ICU admission, and an early or late high protein diet). RESULTS: Of 21,100 critically ill patients in the database, 16,489 fulfilled inclusion criteria for the analysis. By day 60, 11,360 (68.9%) patients had been discharged from hospital, 4,192 patients (25.4%) had died in hospital, and 937 patients (5.7%) were still hospitalized. Median daily low protein intake was 0.49 g/kg [IQR 0.27-0.66], standard intake 0.99 g/kg [IQR 0.89- 1.09], and high intake 1.41 g/kg [IQR 1.29-1.60]. In comparison with an exclusively low protein diet, a late standard protein diet was associated with a lower hazard of in-hospital death: minimum 0.75 (95% CI 0.64, 0.87), and a higher hazard of live hospital discharge: maximum HR 1.98 (95% CI 1.72, 2.28). Results on hospital discharge, however, were qualitatively changed by a sensitivity analysis. There was no evidence that an early standard or a high protein intake during the acute phase was associated with a further improvement of outcome. CONCLUSIONS: Provision of a standard protein intake during the late acute phase may improve outcome compared to an exclusively low protein diet. In unselected critically ill patients, clinical outcome may not be improved by a high protein intake during the acute phase. Study registration ID number ISRCTN17829198.

Changes in healthcare seeking and lifestyle in old aged individuals during COVID-19 lockdown in Germany: the population-based AugUR study.

BACKGROUND: Containment measures in the COVID-19 pandemic protected individuals at high risk, particularly individuals at old age, but little is known about how these measures affected health-related behavior of old aged individuals. We aimed to investigate the impact of the spring 2020 lockdown in Germany on healthcare-seeking and health-related lifestyle in the old aged and to identify susceptible subgroups. METHODS: We conducted a follow-up survey among the pre-pandemically well-characterized participants of our AugUR cohort study, residents in/around Regensburg aged 70+ years and relatively mobile. A self-completion questionnaire on current behavior, perceived changes, and SARS-Cov-2 infection was mailed in May 2020, shortly before contact restrictions ended. Pre-pandemic lifestyle and medical conditions were derived from previous study center visits. RESULTS: Among 1850 survey participants (73-98 years; net-response 89%), 74% were at increased risk for severe COVID-19 according to medical conditions; four participants reported SARS-CoV-2 infection (0.2%). Participants reported changes in behavior: 29% refrained from medical appointments, 14% increased TV consumption, 26% reported less physical activity, but no systematic increase of smoking or alcohol consumption. When comparing during- and pre-lockdown reports of lifestyle within participant, we found the same pattern as for the reported perceived changes. Women and the more educated were more susceptible to changes. Worse QOL was perceived by 38%. CONCLUSIONS: Our data suggest that the spring 2020 lockdown did not affect the lifestyle of a majority of the mobile old aged individuals, but the substantial proportions with decreased physical activity and healthcare-seeking are markers of collateral damage.

Low emission zones reduced PM10 but not NO2 concentrations in Berlin and Munich, Germany.

Low emission zones (LEZs) aiming at improving the air quality in urban areas have been implemented in many European cities. However, studies are limited in evaluating the effects of LEZ, and most of which used simple methods. In this study, a general additive mixed model was utilized to account for confounders in the atmosphere and validate the effects of LEZ on PM10 and NO2 concentrations in two German cities. In addition, the effects of LEZ on elemental carbon (EC) and total carbon (TC) in Berlin were also evaluated. The LEZ effects were estimated after taking into account air pollutant concentrations at a reference site located in the regional background, and adjusting for hour of the week, public holidays, season, and wind direction. The LEZ in Berlin, and the LEZ in combination with the heavy-duty vehicle (HDV) transit ban in Munich significantly reduced the PM10 concentrations, at both traffic sites (TS) and urban background sites (UB). The effects were greater in LEZ stage 3 than in LEZ stages 2 and 1. Moreover, compared with PM10, LEZ was more efficient in reducing EC, a component that is considered more toxic than PM10 mass. In contrast, the LEZ had no consistent effect on NO2 levels: no effects were observed in Berlin; in Munich, the combination of the LEZ and the HDV transit ban reduced NO2 at UB site in LEZ stage 1, but without further reductions in subsequent stages of the LEZ. Overall, our study indicated that LEZs, which target the major primary air pollution source in the highly populated city center could be an effective way to improve urban air quality such as PM mass concentration and EC level.

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points.

We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

Nowcasting the COVID-19 pandemic in Bavaria.

To assess the current dynamics of an epidemic, it is central to collect information on the daily number of newly diseased cases. This is especially important in real-time surveillance, where the aim is to gain situational awareness, for example, if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamics of an epidemic close to now when looking at the number of daily reported cases only. Nowcasting can be used to adjust daily case counts for occurred-but-not-yet-reported events. Here, we present a novel application of nowcasting to data on the current COVID-19 pandemic in Bavaria. It is based on a hierarchical Bayesian model that considers changes in the reporting delay distribution over time and associated with the weekday of reporting. Furthermore, we present a way to estimate the effective time-varying case reproduction number Re(t) based on predictions of the nowcast. The approaches are based on previously published work, that we considerably extended and adapted to the current task of nowcasting COVID-19 cases. We provide methodological details of the developed approach, illustrate results based on data of the current pandemic, and evaluate the model based on synthetic and retrospective data on COVID-19 in Bavaria. Results of our nowcasting are reported to the Bavarian health authority and published on a webpage on a daily basis (https://corona.stat.uni-muenchen.de/). Code and synthetic data for the analysis are available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaption of our approach to different data.

Penalized estimation of complex, non-linear exposure-lag-response associations.

We propose a novel approach for the flexible modeling of complex exposure-lag-response associations in time-to-event data, where multiple past exposures within a defined time window are cumulatively associated with the hazard. Our method allows for the estimation of a wide variety of effects, including potentially smooth and smoothly time-varying effects as well as cumulative effects with leads and lags, taking advantage of the inference methods that have recently been developed for generalized additive mixed models. We apply our method to data from a large observational study of intensive care patients in order to analyze the association of both the timing and the amount of artificial nutrition with the short term survival of critically ill patients. We evaluate the properties of the proposed method by performing extensive simulation studies and provide a systematic comparison with related approaches.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics.

We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

Risk of lung adenocarcinoma from smoking and radiation arises in distinct molecular pathways.

KRAS mutations of lung adenocarcinoma (LADC) are associated with smoking but little is known on other exposure-oncogene associations. Hypothesizing that different inciting agents may cause different driver mutations, we aimed to identify distinct molecular pathways to LADC, applying two entirely different approaches. First, we examined clinicopathologic features and genomic signatures of environmental exposures in the large LADC Campbell data set. Second, we designed a molecular mechanistic risk model of LADC (M3LADC) that links environmental exposure to incidence risk by mathematically emulating the disease process. This model was applied to incidence data of Japanese atom-bomb survivors which contains information on radiation and smoking exposure. Grouping the clinical data by driver mutations revealed two main distinct molecular pathways to LADC: one unique to transmembrane receptor-mutant patients that displayed robust signatures of radiation exposure and one shared between submembrane transducer-mutant patients and patients with no evident driver mutation that carried the signature of smoking. Consistently, best fit of the incidence data was achieved with a M3LADC with two pathways: in one LADC risk increased with radiation exposure and in the other with cigarette consumption. We conclude there are two main molecular pathways to LADC associated with different environmental exposures. Future molecular measurements in lung cancer tissue of atom-bomb survivors may allow to further test quantitatively the M3LADC-predicted link of radiation to transmembrane receptor mutations. Moreover, the developed molecular mechanistic model showed that for low doses, as relevant e.g. for medical imaging, smokers have the same radiation risk compared with never smokers.

Association between donor age and risk of graft failure after liver transplantation: an analysis of the Eurotransplant database.

Grafts from elderly donors are increasingly used for liver transplantation. As of yet there is no published systematic data to guide the use of specific age cutoffs the effect of elderly donors on patient outcomes must be clarified. This study analyzed the Eurotransplant database (01/01/2000-31/07/2014; N = 26 294) out of whom 8341 liver transplantations were filtered to identify for this analysis. 2162 of the grafts came from donors >60 including 203 from octogenarians ≥80 years. Primary outcome was the risk of graft failure according to donor age using a confounder adjusted Cox-Regression model with frailty terms (or random effects). The proportion of elderly grafts increased during the study period [i.e., octogenarians 0.1% (n = 1) in 2000 to 3.4% (n = 45) in 2013]. Kaplan-Meier and Cox-analyses revealed a reduced survival and a higher risk for graft failure with increasing donor age. Although the age effect was allowed to vary non-linearly, a linear association hazard ratio (HR = 1.1 for a 10 year increase in donor age) was evident. The linearity of the association suggests that there is no particular age at which the effect increases more rapidly, providing no evidence for a cutoff age. In clinical practice, the combination of high donor age with HU-transplantations, hepatitis C, high MELD-scores and long cold ischemic time should be avoided.

Outcome of Psychoanalytic and Cognitive-Behavioural Long-Term Therapy with Chronically Depressed Patients: A Controlled Trial with Preferential and Randomized Allocation.

OBJECTIVE: For chronic depression, the effectiveness of brief psychotherapy has been limited. This study is the first comparing the effectiveness of long-term cognitive-behavioural therapy (CBT) and long-term psychoanalytic therapy (PAT) of chronically depressed patients and the effects of preferential or randomized allocation. METHODS: A total of 252 adults met the inclusion criteria (aged 21-60 years, major depression, dysthymia, double depression for at least 24 months, Quick Inventory of Depressive Symptoms [QIDS] >9, Beck Depression Inventory II [BDI] >17, informed consent, not meeting exclusion criteria). Main outcome measures were depression self-rating (BDI) and rating (clinician-rated QIDS [QIDS-C]) by independent, treatment-blinded clinicians. Full remission rates (BDI ≤12, QIDS-C ≤5) were calculated. An independent center for data management and biostatistics analyzed the treatment effects and differences using linear mixed models (multilevel models and hierarchical models). RESULTS: The average BDI declined from 32.1 points by 12.1 points over the first year and 17.2 points over 3 years. BDI overall mean effect sizes increased from d = 1.17 after 1 year to d = 1.83 after 3 years. BDI remission rates increased from 34% after 1 year to 45% after 3 years. QIDS-C overall effect sizes increased from d = 1.56 to d = 2.08, and remission rates rose from 39% after 1 year to 61% after 3 years. We found no significant differences between PAT and CBT or between preferential and randomized allocation. CONCLUSIONS: Psychoanalytic as well as cognitive-behavioural long-term treatments lead to significant and sustained improvements of depressive symptoms of chronically depressed patients exceeding effect sizes of other international outcome studies.

Exposure-lag-response associations between lung cancer mortality and radon exposure in German uranium miners.

Exposure-lag-response associations shed light on the duration of pathogenesis for radiation-induced diseases. To investigate such relations for lung cancer mortality in the German uranium miners of the Wismut company, we apply distributed lag non-linear models (DLNMs) which offer a flexible description of the lagged risk response to protracted radon exposure. Exposure-lag functions are implemented with B-Splines in Cox models of proportional hazards. The DLNM approach yielded good agreement of exposure-lag-response surfaces for the German cohort and for the previously studied cohort of American Colorado miners. For both cohorts, a minimum lag of about 2 year for the onset of risk after first exposure explained the data well, but possibly with large uncertainty. Risk estimates from DLNMs were directly compared with estimates from both standard radio-epidemiological models and biologically based mechanistic models. For age > 45 year, all models predict decreasing estimates of the Excess Relative Risk (ERR). However, at younger age, marked differences appear as DLNMs exhibit ERR peaks, which are not detected by the other models. After comparing exposure-responses for biological processes in mechanistic risk models with exposure-responses for hazard ratios in DLNMs, we propose a typical period of 15 year for radon-related lung carcinogenesis. The period covers the onset of radiation-induced inflammation of lung tissue until cancer death. The DLNM framework provides a view on age-risk patterns supplemental to the standard radio-epidemiological approach and to biologically based modeling.

Response misclassification in studies on bilateral diseases.

Misclassification in binary outcomes can severely bias effect estimates of regression models when the models are naively applied to error-prone data. Here, we discuss response misclassification in studies on the special class of bilateral diseases. Such diseases can affect neither, one, or both entities of a paired organ, for example, the eyes or ears. If measurements are available on both organ entities, disease occurrence in a person is often defined as disease occurrence in at least one entity. In this setting, there are two reasons for response misclassification: (a) ignorance of missing disease assessment in one of the two entities and (b) error-prone disease assessment in the single entities. We investigate the consequences of ignoring both types of response misclassification and present an approach to adjust the bias from misclassification by optimizing an adequate likelihood function. The inherent modelling assumptions and problems in case of entity-specific misclassification are discussed. This work was motivated by studies on age-related macular degeneration (AMD), a disease that can occur separately in each eye of a person. We illustrate and discuss the proposed analysis approach based on real-world data of a study on AMD and simulated data.

Nonparametric change point estimation for survival distributions with a partially constant hazard rate.

We present a new method for estimating a change point in the hazard function of a survival distribution assuming a constant hazard rate after the change point and a decreasing hazard rate before the change point. Our method is based on fitting a stump regression to p values for testing hazard rates in small time intervals. We present three real data examples describing survival patterns of severely ill patients, whose excess mortality rates are known to persist far beyond hospital discharge. For designing survival studies in these patients and for the definition of hospital performance metrics (e.g. mortality), it is essential to define adequate and objective end points. The reliable estimation of a change point will help researchers to identify such end points. By precisely knowing this change point, clinicians can distinguish between the acute phase with high hazard (time elapsed after admission and before the change point was reached), and the chronic phase (time elapsed after the change point) in which hazard is fairly constant. We show in an extensive simulation study that maximum likelihood estimation is not robust in this setting, and we evaluate our new estimation strategy including bootstrap confidence intervals and finite sample bias correction.

Perinatal mortality after the Fukushima accident: a spatiotemporal analysis.

OBJECTIVE: This study investigates the trend of perinatal mortality rates in Fukushima Prefecture and four neighboring prefectures (Miyagi, Gunma, Tochigi, and Ibaraki) after the disaster at the Fukushima Daiichi nuclear power plant in March 2011. MATERIAL AND METHODS: Japanese monthly perinatal mortality data on a prefecture level are available on a website of the Japanese government. A combined regression of perinatal mortality rates from the study region and the rest of Japan (the control region) is conducted. The regression model allows for an asymptotic lower limit and a level change of perinatal mortality rates in 2012-2017 in the study region relative to the predicted trend. RESULTS: In 2012-2017, perinatal mortality in the study region shows a significant 10.6% increase relative to the trend in preceding years (p = 0.006). The excess mortality translates to 195 (95% CI: 28, 462) excess perinatal deaths. The increase is three times greater in Fukushima Prefecture than in the four neighboring prefectures and the difference in excess rates is statistically significant (p = 0.010). Periodic peaks of perinatal mortality are found in 2012-2017 with maxima around April. CONCLUSION: We find an increase in perinatal mortality in Fukushima and four neighboring prefectures after the Fukushima nuclear accident. The results agree with similar observations in Germany and Ukraine after the Chernobyl disaster. Due to its ecological design, the study cannot prove a causal link between radiation exposure and perinatal mortality. Continued observation of the trend of perinatal mortality in contaminated regions of Japan is recommended.

Mixtures of Berkson and classical covariate measurement error in the linear mixed model: Bias analysis and application to a study on ultrafine particles.

The ultrafine particle measurements in the Augsburger Umweltstudie, a panel study conducted in Augsburg, Germany, exhibit measurement error from various sources. Measurements of mobile devices show classical possibly individual-specific measurement error; Berkson-type error, which may also vary individually, occurs, if measurements of fixed monitoring stations are used. The combination of fixed site and individual exposure measurements results in a mixture of the two error types. We extended existing bias analysis approaches to linear mixed models with a complex error structure including individual-specific error components, autocorrelated errors, and a mixture of classical and Berkson error. Theoretical considerations and simulation results show, that autocorrelation may severely change the attenuation of the effect estimations. Furthermore, unbalanced designs and the inclusion of confounding variables influence the degree of attenuation. Bias correction with the method of moments using data with mixture measurement error partially yielded better results compared to the usage of incomplete data with classical error. Confidence intervals (CIs) based on the delta method achieved better coverage probabilities than those based on Bootstrap samples. Moreover, we present the application of these new methods to heart rate measurements within the Augsburger Umweltstudie: the corrected effect estimates were slightly higher than their naive equivalents. The substantial measurement error of ultrafine particle measurements has little impact on the results. The developed methodology is generally applicable to longitudinal data with measurement error.

Nonlinear Analysis to Detect if Excellent Nursing Work Environments Have Highest Well-Being.

PURPOSE: To detect potentially nonlinear associations between nurses' work environment and nurse staffing on the one hand and nurse burnout on the other hand. DESIGN: A cross-sectional multicountry study for which data collection using a survey of 33,731 registered nurses in 12 European countries took place during 2009 to 2010. METHODS: A semiparametric latent variable model that describes both linear and potentially nonlinear associations between burnout (Maslach Burnout Inventory: emotional exhaustion, depersonalization, personal accomplishment) and work environment (Practice Environment Scale of the Nursing Work Index: managerial support for nursing, doctor-nurse collegial relations, promotion of care quality) and staffing (patient-to-nurse ratio). FINDINGS: Similar conclusions are reached from linear and nonlinear models estimating the association between work environment and burnout. For staffing, an increase in the patient-to-nurse ratio is associated with an increase in emotional exhaustion. At about 15 patients per nurse, no further increase in emotional exhaustion is seen. CONCLUSIONS: Absence of evidence for diminishing returns of improving work environments suggests that continuous improvement and achieving excellence in nurse work environments pays off strongly in terms of lower nurse-reported burnout rates. Nurse staffing policy would benefit from a larger number of studies that identify specific minimum as well as maximum thresholds at which inputs affect nurse and patient outcomes. CLINICAL RELEVANCE: Nurse burnout is omnipresent and has previously been shown to be related to worse patient outcomes. Additional increments in characteristics of excellent work environments, up to the highest possible standard, correspond to lower nurse burnout.