2024-10-02
088_ Atmospheric variables and their impact on the quality of life in patients within the psychotic to affective spectrum
Research Question and Aims
One of the most significant challenges of the 21st century is the impact of climate change and the intensifying fluctuations in
atmospheric variables (Romanello et al. 2023). Mental health is highly relevant in the context of climate change and is recently becoming more recognized in the discourse (Lawrence et al. 2022). Mental illnesses like schizophrenia and bipolar disorder and their
hospitalization are correlated with changes in atmospheric variables (e.g., temperature, air pressure, cloud cover/cloudiness,
precipitation, humidity, solar radiation, air quality) (Asimakopoulos et al. 2021, Christensen et al 2008, Hu et al. 2023, Tupinier Martin
et al. 2024). The observed increase in hospitalization rates suggests that an exacerbation/intensification of the disease/symptoms may
be correlated with changes in atmospheric variables, but analyses are still limited. Even lesser information is available on how
atmospheric variables, weather conditions and anomalies impact the quality of life of individuals which are diagnosed with a psychotic
or affective disorder.
Therefore, the objective is to analyse how atmospheric variables influence the quality of life of individuals with psychotic and/or
affective disorder.
The main research question is: Are there differences in the quality of life between individuals diagnosed with psychotic or affective
disorders (patients) and individuals without diagnosed mental disorders (control group) which are affected by weather conditions and
anomalies and does the polygenic risk score for resilience (PRS) modify this relationship?
H1: People diagnosed with mental diseases like psychotic or affective disorders have a reduced quality of life during weather anomalies
and fluctuating weather conditions (temperature, heat waves, cold spells, wet bulb globe temperature (WBGT), precipitation, wind,
air pressure, radiation, air quality index) compared to people without a diagnosis.
H2: The effects of weather conditions on the quality of life are moderated by the PRS for resilience. Therefore, the quality of life in
people with lower PRS for resilience is lower during weather conditions and anomalies compared to people with a higher PRS for
resilience.
H3: Changes of the atmospheric variables have a higher impact on the quality of life of females than on males.
Analytic Plan
The analysis will be conducted as a case-control study design, comparing the results of the models of the case group to those of the
control group.
Data:
The primary data source of the atmospheric data will be retained from the gridded ECMWF ERA5 and CAMS reanalyses. Additionally,
station data from the German Weather Service as well as from the provincial environmental agencies are available. Besides using the raw atmospheric variables, human biometeorological indices like the WBGT will be calculated as well as air quality indices like the
European Air Quality Index.
The phenotypic and genetic data will be used from the PsyCourse study.
Dependent variable: WHO quality of life score (WHOQOL-BREF): global dimension
Independent variables: atmospheric variables (temperature-related, air pressure, precipitation, hours of sunshine, radiation, wind,
air quality index)
Modification/Covariate variables: age, sex, PRS for resilience, ancestry principal components, visit number, clinical/control status,
diagnostic group, Global assessment of functioning (GAF)
Methods:
For hypothesis 1-3 first exploratory analyses will be conducted. Based on basic statistical methods such as the Spearman correlation
to compile a correlation matrix in order to analyse if there are correlations between the predictors themselves and between the
predictors and the quality of life scores.
Generalized linear (GLM) and generalized additive (GAM) regression models with, depending on the data structure, a normal
distribution or (overdispersed) Poisson distribution will be implemented for each hypothesis. Starting with baseline models using only
each single atmospheric variable as independent variable, gradually more complex models will be generated which include different
atmospheric variables as independent variables as well as age, sex, PRS for resilience, ancestry principal components, clinical/control
status, and GAF as modifiers and/or confounder. As an additional modifier the type of disease (affective, psychotic disorder) will be
regarded. Each patient visit is regarded as an independent case. However, the four study visits of an individual in the overall 18-months
study period, will be included as a potential modifier.
Within the exposure-outcome relationships the influence of two specific time periods will be considered- the direct impact of weather
represented as the mean weather and its anomalies in the two-week period matching the WHO quality of life score survey horizon as
well as the longer-term weather conditions represented by the conditions in the preceding four weeks to the study visit. For each
individual the atmospheric information will be retrieved for the area of the individual’s study centre location.
Resources needed
v1_id
v1_stat
v1_center
v1_interv_date
v1_sex
v1_age
v1_scid_dsm_dx
v1_scid_dsm_dx_cat
v1_gaf
v1_whoqol_itm1
v1_whoqol_itm2
v1_whoqol_itm3
v1_whoqol_itm4
v1_whoqol_itm5
v1_whoqol_itm6
v1_whoqol_itm7
v1_whoqol_itm8
v1_whoqol_itm9
v1_whoqol_itm10
v1_whoqol_itm11
v1_whoqol_itm12
v1_whoqol_itm13
v1_whoqol_itm14
v1_whoqol_itm15
v1_whoqol_itm16
v1_whoqol_itm17
v1_whoqol_itm18
v1_whoqol_itm19
v1_whoqol_itm20
v1_whoqol_itm21
v1_whoqol_itm22
v1_whoqol_itm23
v1_whoqol_itm24
v1_whoqol_itm25
v1_whoqol_itm26
v1_whoqol_dom_glob
v1_whoqol_dom_phys
v1_whoqol_dom_psy
v1_whoqol_dom_soc
v1_whoqol_dom_env
v2_age
v2_interv_date
v2_gaf
v2_whoqol_itm1
v2_whoqol_itm2
v2_whoqol_itm3
v2_whoqol_itm4
v2_whoqol_itm5
v2_whoqol_itm6
v2_whoqol_itm7
v2_whoqol_itm8
v2_whoqol_itm9
v2_whoqol_itm10
v2_whoqol_itm11
v2_whoqol_itm12
v2_whoqol_itm13
v2_whoqol_itm14
v2_whoqol_itm15
v2_whoqol_itm16
v2_whoqol_itm17
v2_whoqol_itm18
v2_whoqol_itm19
v2_whoqol_itm20
v2_whoqol_itm21
v2_whoqol_itm22
v2_whoqol_itm23
v2_whoqol_itm24
v2_whoqol_itm25
v2_whoqol_itm26
v2_whoqol_dom_glob
v2_whoqol_dom_phys
v2_whoqol_dom_psy
v2_whoqol_dom_soc
v2_whoqol_dom_env
v3_age
v3_interv_date
v3_gaf
v3_whoqol_itm1
v3_whoqol_itm2
v3_whoqol_itm3
v3_whoqol_itm4
v3_whoqol_itm5
v3_whoqol_itm6
v3_whoqol_itm7
v3_whoqol_itm8
v3_whoqol_itm9
v3_whoqol_itm10
v3_whoqol_itm11
v3_whoqol_itm12
v3_whoqol_itm13
v3_whoqol_itm14
v3_whoqol_itm15
v3_whoqol_itm16
v3_whoqol_itm17
v3_whoqol_itm18
v3_whoqol_itm19
v3_whoqol_itm20
v3_whoqol_itm21
v3_whoqol_itm22
v3_whoqol_itm23
v3_whoqol_itm24
v3_whoqol_itm25
v3_whoqol_itm26
v3_whoqol_dom_glob
v3_whoqol_dom_phys
v3_whoqol_dom_psy
v3_whoqol_dom_soc
v3_whoqol_dom_env
v4_age
v4_interv_date
v4_gaf
v4_whoqol_itm1
v4_whoqol_itm2
v4_whoqol_itm3
v4_whoqol_itm4
v4_whoqol_itm5
v4_whoqol_itm6
v4_whoqol_itm7
v4_whoqol_itm8
v4_whoqol_itm9
v4_whoqol_itm10
v4_whoqol_itm11
v4_whoqol_itm12
v4_whoqol_itm13
v4_whoqol_itm14
v4_whoqol_itm15
v4_whoqol_itm16
v4_whoqol_itm17
v4_whoqol_itm18
v4_whoqol_itm19
v4_whoqol_itm20
v4_whoqol_itm21
v4_whoqol_itm22
v4_whoqol_itm23
v4_whoqol_itm24
v4_whoqol_itm25
v4_whoqol_itm26
v4_whoqol_dom_glob
v4_whoqol_dom_phys
v4_whoqol_dom_psy
v4_whoqol_dom_soc
v4_whoqol_dom_env
gsa_id