The Impact of Foehn Wind on Mental Distress among Patients in a Swiss Psychiatric Hospital

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https://pmc.ncbi.nlm.nih.gov/articles/PMC9518389/pdf/ijerph-19-10831.pdf

Methods

Methods:

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  • The analysis was based on anonymized individual datasets obtained from routine ANQ records (Swiss National Association for Quality Development in Hospitals and Clinics) on the admission and discharge days of patients from the Private Clinic Meiringen, a psychiatric hospital located in a foehn area in the Swiss Alps. The collected data included the Brief Symptom Checklist (BSCL) questionnaire [45] and sociodemographic parameters (age, sex, and diagnosis). 
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The study used admit and discharge data from a psychiatric hospital in a part of Switzerland where they have foehn winds (a type of hot, dry wind that comes down mountains;  in North America they are sometimes called Chinook or Santa Ana winds).  The data included symptoms on admit and discharge as well as diagnosis, age and sex.


Introduction

Introduction:

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To relate self-reported clinically relevant psychological symptoms to the occurrence of foehn wind, we used the Brief Symptom Checklist (BSCL) in a sample of admissions and discharges from a psychiatric hospital located in a well-known foehn area in Switzerland. Specifically, based on the available literature, we expected foehn to increase ratings of aggressiveness and somatization.

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To examine how people’s experiences of psychological symptoms might be related to foehn wind, we looked at how people scored on a specific symptom checklist when admitted and discharged from a foehn-exposed psychiatric hospital in Switzerland.  Based on previous studies, we thought that being admitted to the hospital during a period of foehn wind might increase levels of aggressiveness and distress-related physical symptoms.



Results

Results:

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When comparing the foehn group with the discharge sample, we found significant results exclusively during negative time lags in obsession-compulsion (tmax=2.03, p=0.048; time lag = day-2), depression (tmax=2.15 p=0.037, time lang = day 2), and psychoticism (tmax = 2.29 p=0.027, time lang = day-2).

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When comparing the foehn-exposed cohort’s data with the non-foehn-exposed group’s discharge data, we found significant time-lagged increases in obsession-compulsion, depression, and psychotic symptoms, meaning these symptoms were higher a day or so after foehn winds.



Discussion

Discussion:

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Moreover, our findings indicate that at the time of admission, foehn episodes might have a negative influence on several specific psychopathological dimensions, namely, obsession–compulsion, interpersonal sensitivity, depression, anxiety, phobic anxiety, and paranoid ideation.

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Based on this set of data, exposure to the hot, dry foehn winds may have a negative influence on several different types of mental health symptoms (obsession-compulsion, interpersonal sensitivity, depression, anxiety, phobia, and paranoid ideation).



Future Directions

Future Directions:

  • What future research should follow up on this work?

While the study is interesting and provocative, the number of total admits was over 10,000 whereas the group admitted on “foehn days” were only 182. Thus, it is an interesting exploratory study, and a next step might be to recruit some similar hospitals also exposed to foehn/Chinook/Santa Ana winds, and see if the other hospitals might be interested in joining forces for a larger study with a common measure of assessment at admit and discharge.



Difficult Material

Difficult Material:

  • What did you not understand about this paper that someone else may be able to help you with? Or, if you understood everything, what did you find most challenging to understand?

I didn’t understand this section:
Moreover, we used generalized linear models (GLM) with conditional quasi-Poisson regression accounting for overdispersion, with the daily number of BSCL entities for each of that day’s patients as the dependent variable.  Each day with events (i.e. Feohn index values >0) was matched with the BSCL values for each patient.  To evaluate the dependency of both variables, we used a ‘finite impulse response’ model within the GLM framework.



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