J. Šaltytė Benth

Decreased lung function among employees at Norwegian smelters

BACKGROUND In the smelting industry airborne pollutants are emitted into the workplace atmosphere during the production process. Our aim in this study was to investigate the relationship between production and lung function among employees at Norwegian smelters. METHODS Spirometry was performed on 3,924 employees, who also completed a standardized questionnaire. The employees were classified by job functions: (i) line operators were employed full-time on the production line, (ii) non-exposed employees did not work in production, and (iii) the remainder of the employees were classified as non-line operators. RESULTS The mean age of the participants was 38.6 (range 20.0-55.0) years, 88.5% were males. The multivariate analyses showed that, compared to the forced expiratory volume in one second (FEV(1)) in non-exposed employees, the FEV(1) (95% confidence interval) was 87 (33-141) ml and 65 (12-118) ml lower in line and non-line operators, respectively. The prevalence of airflow limitation (FEV(1)/forced vital capacity (FVC) below the 5th percentile of the predicted value) was 4.7% in non-exposed employees, 7.5% in non-line operators and 8.3% in line operators. CONCLUSION Compared with non-exposed employees, impairment of lung function among employees at Norwegian smelters was significantly related to the job categories of line operator and non-line operator.

Quantitative and Qualitative Assessment of Exposure among Employees in Norwegian Smelters

OBJECTIVES To generate a job exposure matrix (JEM) for dust exposure in Norwegian smelters to be used in an epidemiologic study of respiratory diseases and to identify determinants of exposure. METHODS The arithmetic mean and geometric mean (GM) of 2619 personal dust exposure measurements were applied in constructing the JEM, which was assigned to 2620 employees participating in a respiratory survey including yearly spirometry and a respiratory questionnaire. A qualitative exposure classification was constructed: (i) line operators were those employed full time in the production line, (ii) non-exposed employees were those who did not work in production and (iii) the remainder were classified as non-line operators. RESULTS In the ferrosilicon alloy and silicon metal production group (FeSi/Si-metal), the median GM of dust exposure was 2.3 mg m(-3) (0.04-5.6) (10-90% percentiles) compared with 1.6 mg m(-3) (0.02-2.3) in the silicomanganese, ferromanganese and ferrochromium production group (SiMn/FeMn/FeCr). Multivariate analyses showed that dust exposure concentration levels decreased significantly with increasing age (FeSi/Si-metal), was significantly lower in females than in males and was significantly higher in current smokers than in never-smokers. Dust exposure concentration levels were also higher in employees reporting previous exposure to dust, fumes and gases than in employees without such previous exposure, though, significant only in the FeSi/Si-metal production group. CONCLUSION The dust exposure levels of the employees were higher in the FeSi/Si-metal production group than in the SiMn/FeMn/FeCr production group. Age, gender, smoking status and previous exposure were significant determinants of dust exposure and should be evaluated in future analyses of the relationship between health outcomes and dust exposure in this industry.

Neurobiological correlates of depressive symptoms in people with subjective and mild cognitive impairment.

To test the hypothesis that depressive symptoms correlate with Alzheimers disease (AD) type changes in CSF and structural and functional imaging including hippocampus volume, cortical thickness, white matter lesions, Diffusion tensor imaging (DTI), and fluoro‐deoxy‐glucose positron emission tomography (FDG‐PET) in patient with subjective (SCI) and mild (MCI) cognitive impairment.

Intake of a protein-enriched milk and effects on muscle mass and strength. A 12-week randomized placebo controlled trial among community-dwelling older adults

ObjectivesTo investigate the effect of 20 g protein with breakfast and evening meal on muscle mass, muscle strength and functional performance in older adults.DesignA double-blinded randomized controlled study.SettingOslo and Akershus University College of Applied Sciences, Norway.ParticipantsHealthy community-dwelling men and women (≥ 70 years) with reduced physical strength and/or performance.InterventionSubjects were randomly assigned to receive either protein-enriched milk (2 x 0.4 L/d; protein group) or an isocaloric carbohydrate drink (2 x 0.4 L/d; control group) with breakfast and evening meal for 12 weeks.MeasurementsThe primary endpoints were muscle mass measured by dual X-ray absorptiometry, and tests of muscle strength (one repetition maximum test of chest press and leg press) and functional performance (handgrip strength, stair climb and repeated chair rise).ResultsIn total, 438 subjects were screened, 50 subjects were randomized and 36 completed the study. Chest press improved significantly in the protein (1.3 kg (0.1-2.5), p=0.03) and the control group (1.5 kg (0.0-3.0), p=0.048), but with no difference between the groups (p=0.85). No significant change in leg press (p=0.93) or muscle mass (p=0.54) were observed between the protein and the control group. Nor did we observe any significant differences in the functional performance tests (p>0.05 for all tests) between the groups.ConclusionIncreased protein intake (2 x 20 g/d) did not significantly improve muscle mass, muscle strength or functional performance in healthy older weight stable adults. Whether intake of > 20 g protein to each meal is necessary for preservation of muscle mass and strength in older adults should be further investigated in a larger study. This underscores the need for well-designed studies that can differentiate between the effect of protein intake and increased energy. This trial was registered at Clinicaltrials.gov (ID no. NCT02218333).

Can linear regression model give reliable conclusions about the postoperative recovery? Bootstrap for case study in Mitic et al. 2007.

in the overall data and suggest that appropriate surgical technique among other factors can in particular minimise severe haemorrhage requiring return to theatre. Unfortunately at least one of the hospitals described in the letter was not able to be considered by the NPTA in the separate analysis of hospitals with good data quality. The data is therefore likely to be representative of other trusts that took part where data quality was flagged up as an issue during the Audit. Any included complications from underreporting units are extremely unlikely to have affected trends identified between tonsillectomy techniques or other risk factors and haemorrhage. The NPTA never set out to pinpoint the complication rates to the decimal point and I agree that quoting such rates is of limited value to clinicians and meaningless to most patients. In particular the NPTA results underline the multifactorial nature of the aetiology of haemorrhage after tonsillectomy and help us understand the previous increases in haemorrhage of which we were already fully aware through analysis of HES data.

Response to Burton and Perera, and Henney

postoperative day (1–10) for both treatment groups. Transformation of the data into a linear regression model is the obvious choice when a patient characteristic has only two values (treatment A or B), but it is not necessarily the most appropriate when a characteristic varies widely (e.g. pain scores). Other possibilities include the logarithmic model (logistic regression) and the power model (Cox regression). In a randomised controlled trial, such as this, randomisation aims to ensure that confounding variables are equally distributed among the treatment groups. Regression analysis tests correlations rather than causal relationships. In testing the data from clinical trials, we are mainly interested in causal relationships. When statistically analysed through correlation analyses, we should probably be less convinced of causation. Regression analysis could mean uncertainties are added to the data in the form of subjective judgments and uncertainty about the appropriate transformation of the data, as well as doubtful claims about causal relationships. Conflict of interest