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      Work in nursing homes and occupational exposure to endotoxin and bacterial and fungal species

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          Abstract

          Indoor microbial exposure may cause negative health effects. Only little is known about the occupational microbial exposure in nursing homes and the factors that influence the exposure. The exposure in nursing homes may be increased due to close contact with elderly persons who may carry infectious or antimicrobial-resistant microorganisms and due to handling of laundry, such as used clothing and bed linen. We investigated the microbial exposure in 5 nursing homes in Denmark, by use of personal bioaerosol samples from different groups of staff members taken during a typical working day, stationary bioaerosol measurements taken during various work tasks, sedimented dust samples, environmental surface swabs, and swabs from staff members’ hands. From the samples, we explored bacterial and fungal concentrations and species composition, endotoxin levels, and antimicrobial resistance in Aspergillus fumigatus isolates. Microbial concentrations from personal exposure samples differed among professions, and geometric means (GM) were 2,159 cfu/m 3 (84 to 1.5 × 10 5) for bacteria incubated on nutrient agar, 1,745 cfu/m 3 (82 to 2.0 × 10 4) for bacteria cultivated on a Staphylococcus selective agar, and 16 cfu/m 3 air for potential pathogenic fungi incubated at 37 °C (below detection limit to 257). Bacterial exposures were elevated during bed making. On surfaces, the highest bacterial concentrations were found on bed railings. The majority of bacterial species found were related to the human skin microflora, such as different Staphylococcus and Corynebacterium species. Endotoxin levels ranged from 0.02 to 59.0 EU/m 3, with a GM of 1.5 EU/m 3. Of 40 tested A. fumigatus isolates, we found one multiresistant isolate, which was resistant towards both itraconazole and voriconazole, and one isolate resistant towards amphotericin B. In conclusion, we give an overview of the general microbial exposure in nursing homes and show that microbial exposures are higher for staff with more care and nursing tasks compared with administrative staff.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Fitting Linear Mixed-Effects Models Using lme4

            Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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              Simultaneous inference in general parametric models.

              Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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                Author and article information

                Contributors
                Journal
                Ann Work Expo Health
                Ann Work Expo Health
                annhyg
                Annals of Work Exposures and Health
                Oxford University Press (UK )
                2398-7308
                2398-7316
                August 2023
                10 June 2023
                10 June 2023
                : 67
                : 7
                : 831-846
                Affiliations
                National Research Centre for the Working Environment , Lersø Parkallé 105, 2100 Copenhagen Ø, Denmark
                National Research Centre for the Working Environment , Lersø Parkallé 105, 2100 Copenhagen Ø, Denmark
                National Research Centre for the Working Environment , Lersø Parkallé 105, 2100 Copenhagen Ø, Denmark
                National Research Centre for the Working Environment , Lersø Parkallé 105, 2100 Copenhagen Ø, Denmark
                Author notes
                Corresponding author: National Research Centre for the Working Environment, Lersø Parkallé 105, 2100 Copenhagen Ø, Denmark. Email: pur@ 123456nfa.dk
                Author information
                https://orcid.org/0000-0003-0607-4230
                https://orcid.org/0000-0001-9118-8448
                https://orcid.org/0000-0001-7184-1171
                https://orcid.org/0000-0002-5895-7521
                Article
                wxad032
                10.1093/annweh/wxad032
                10410494
                37300561
                02cffc66-972f-4674-ae8a-bf016533777d
                © The Author(s) 2023. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 October 2022
                : 23 May 2023
                Page count
                Pages: 16
                Funding
                Funded by: Helsefonden, DOI 10.13039/501100005860;
                Award ID: 16-B-0239
                Funded by: Danish Government;
                Funded by: National Research Centre for the Working Environment;
                Funded by: Danish Environmental Protection Agency;
                Funded by: Miljøstyrelsen, DOI 10.13039/501100007036;
                Award ID: 2020-67648
                Categories
                Original Articles
                AcademicSubjects/MED00640

                antibiotic resistance,azole,bioaerosols,healthcare worker (hcw),long-term care facility (ltcf),occupational health

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