Climate Anxiety and Mental Health in Germany
Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, 20246 Hamburg, Germany
*
Author to whom correspondence should be addressed.
Climate 2023, 11(8), 158; https://doi.org/10.3390/cli11080158
Submission received: 14 July 2023
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Revised: 18 July 2023
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Accepted: 19 July 2023
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Published: 25 July 2023
Abstract
:Our aim was to investigate the association between climate anxiety and mental health in a general adult population. Cross-sectional data of the general adult population were used (n = 3091 individuals aged 18 to 74 years; March 2022). The Climate Anxiety Scale was used to assess climate anxiety. Probable depression was quantified using the PHQ-9, and the GAD-7 was used to assess probable anxiety. Adjusted for sex, age, marital status, having children in the household, highest level of school education, employment situation, smoking behavior, alcohol intake, frequency of sports activities, chronic illnesses and self-rated health and coronavirus anxiety, multiple logistic regressions showed that a higher climate anxiety was associated with a higher likelihood of probable depression (OR: 1.37, 95% CI: 1.25–1.50). Moreover, regressions showed that a higher climate anxiety was associated with a higher likelihood of probable anxiety (OR: 1.27, 95% CI: 1.15–1.40). In conclusion, our study demonstrated an association between climate anxiety and mental health in Germany. Further research (e.g., based on longitudinal data) is required to confirm our study’s findings.
1. Introduction
Climate change is happening—and it is also accompanied by natural disasters (e.g., floods and severe storms). Climate change is a key hazard to the overall well-being and survival of mankind [1]. Thus, individuals can develop climate anxiety [2]. Pikhala defined it as “anxiety which is significantly related to anthropogenic climate change” (p. 3) [2]. Additional details regarding the terminology were provided in a recent scoping review [3].
Some studies have already investigated factors associated with climate anxiety [4,5,6]. For example, it has been shown that younger age is associated with higher climate anxiety [7]. Climate anxiety can also lead to loneliness and social isolation [8].
However, to date, only a few studies [5,9,10,11,12,13] have investigated how higher climate anxiety can contribute to lower levels of mental health (in terms of probable depression and probable anxiety). Such cross-sectional studies are mainly based on selective samples focusing on younger adults. For example, Innocenti et al. recruited their 150 Italian adults based on convenience and snowball sampling methods [5]. They found that higher climate anxiety was associated with higher anxiety. A second example: Schwartz et al. [11] used data from 284 undergraduate and graduate students (aged 18 to 35 years) at universities in the United States. For recruitment, they used two channels: a psychology participant pool at a private university, and a broader reach achieved through the utilization of email outreach and social media. Likewise, they found an association between higher climate anxiety and general anxiety symptoms. A further example: Reyes et al. [10] used data from 438 Filipinos aged 18 to 28 years. A convenience sampling technique was applied. Similarly, they revealed an association between higher climate anxiety and lower levels of mental health. In sum, these studies predominantly showed an association between higher climate anxiety and lower levels of mental health.
Clayton [7] assumed that the perception of extreme temperatures, increased air pollution, seasonal haze, earthquakes, rising sea level, or floods can contribute to an increase in mental health problems (e.g., in terms of grief, hopelessness, suicidal ideation, or post-traumatic stress disorder [14,15,16,17]). In light of the limited knowledge at present (in terms of large studies conducted among the general adult population), our aim was to examine the association between climate anxiety and mental health (in terms of probable depression and probable anxiety), based on data from the general German adult population.
Such knowledge is of great importance for public health professionals, clinicians as well as policy makers. Moreover, a lower level of mental health is associated with various adverse factors, such as a higher risk of suicidality [18], risk of unemployment [19], lower well-being [20], and higher healthcare costs [21,22]—which emphasizes the relevance of this topic.
With regard to the theoretical models used to explain the link between climate anxiety and mental health: The terror management theory developed by Greenberg et al. [23] can serve as a theoretical model. Climate anxiety can, among other things, reflect uncertainty and fear of natural disasters such as floods. According to this theory, such discomfort with uncertainty can cause feelings of distress or worry (to overcome death anxiety) [10]. Furthermore, a lack of the ability to regulate such death anxiety can increase the likelihood of anxiety disorders [10].
2. Explanation of the Methods
2.1. Description of the Sample
We used cross-sectional data from a quota-based online survey of 3091 individuals living in Germany. The age range was from 18 to 74 years. Data was gathered between 15 March and 21 March 2022.
In our past work using this online survey, we have identified determinants of higher climate anxiety (e.g., younger age) [4]. Moreover, in two other studies, we found that higher climate anxiety was associated with higher loneliness/social isolation [8] and lower perceived longevity [24], in both cases, particularly among younger adults. In contrast, this study exclusively focuses on the association between climate anxiety and mental health.
In relation to the recruitment of individuals: Participants were invited by the renowned market research company bilendi & respondi, an online sampling service with ISO 26362 certification. To ensure that the age, gender and federal state distribution of the respondents reflected the adult German population as a whole, they were selected from a quota-based online sample. Overall, 11,900 individuals in total were invited to participate. A potential selection bias could not be computed, because an online sample was used. For further details regarding the sample, please see [8].
Each individual gave his or her informed consent. This study was approved by the Psychological Ethics Committee of the University Medical Center Hamburg-Eppendorf (LPEK-0412). The study was conducted in compliance with the Helsinki Declaration and its later amendments.
2.2. Dependent Variables
To measure probable depression, the reliable and valid Patient Health Questionnaire-9 (PHQ-9) [25], which has nine items, was used. We generated a sum score ranging from 0 (absence of depressive symptoms) to 27 (severe depressive symptoms). Both the sensitivity and the specificity were 0.88 for major depressive disorder (based on a PHQ-9 score of ten or above). In accordance with the existing recommendations [26], we used a PHQ-9 score of ten (or higher) as a determination of probable depression. In our study, Cronbach’s alpha was 0.89 and McDonald’s omega was 0.90.
To measure probable anxiety, the Generalized Anxiety Disorder-7 (GAD-7) [27] was used. A sum score was built, one which ranged from 0 to 21 (higher values reflect an increase in anxiety symptoms). The sensitivity was 0.89 and the specificity was 0.82 for detection of generalized anxiety disorder based on a GAD-7 score ≥10; therefore, and in line with the current recommendations [27], we used this cut-off score for probable anxiety. Cronbach’s alpha was 0.91 in our study, and McDonald’s omega was also 0.91.
2.3. Independent Variables
The Climate Anxiety Scale created by Clayton and Karazsia [28] was used to assess climate anxiety. This tool has 13 items (in each case: from 1 (strongly disagree) to 7 (strongly agree/applies completely)), and comprises two factors [28]: cognitive–emotional impairment and functional impairment. We used the German version, which was validated recently [13]. The means of the items were generated to build a total climate anxiety score (from 1 to 7, higher values reflecting a higher level of climate anxiety). Cronbach’s alpha was 0.95 in our study. McDonald’s omega was also 0.95 in our study.
Taking into consideration former research [5,9,10,11,12,13] and theoretical considerations, we selected covariates. More precisely, sociodemographic, lifestyle-related, health-related and psychological covariates were included in our regression analysis (see also: [4]).
With regard to sociodemographic covariates, we included in regression analysis: age, sex, marital status (married, cohabiting with spouse; married, not cohabiting with spouse; single; divorced; widowed), having children in own household (no; yes), highest level of school education (upper secondary school; qualification for applied upper secondary school; polytechnic secondary school; intermediate secondary school; currently in school training/education; without school-leaving qualification/lower secondary school), and labor force participation (employed full-time; retired; other).
With regard to lifestyle-related covariates, we included in regression analysis: alcohol consumption (daily; several times per week; once a week; 1–3 times per month; less often; never), frequency of sports activities (no sports activity; less than one hour per week; regularly, 1–2 h per week; regularly, 2–4 h per week; regularly, more than 4 h per week) and smoking behavior (yes, daily; yes, sometimes; no, not anymore; never a smoker).
With regard to health-related covariates, we included in regression analysis: chronic conditions (absence of chronic diseases; presence of at least one chronic disease), and self-rated health (from 1 = very poor to 5 = very good). With regard to psychological covariates, we included coronavirus anxiety (Coronavirus Anxiety Scale [29], which was translated by Spitzenstätter and Schnell [30]). This tool consists of five items. The calculated sum score ranges from 0 to 20 (with higher scores indicating higher coronavirus anxiety). Prior research has also used this tool [31]. Both Cronbach’s alpha and McDonald’s omega were 0.92 in our study.
2.4. Statistical Analysis
Sample characteristics are displayed for the total sample. Effect sizes (Cohen’s d) were also calculated. Additionally, Pearson correlations (with Bonferroni-adjusted significance levels) were displayed for the continuous variables to achieve a better understanding of our data. In a further step, multiple logistic regressions were conducted to investigate the associations between climate anxiety and probable depression and probable anxiety. The multiple logistic regressions were adjusted for sociodemographic, lifestyle-related and health-related factors. It should be noted that the average variance inflation factor (VIF) was 1.75 (ranging from 1.01 to 3.95; details are shown in Supplementary Table S1). Thus, one may conclude that multicollinearity is not a concern. Further diagnostics of the logistic regression models are shown in Supplementary Tables S2 and S3. The Stata-tool “omegacoef” developed by Shaw was used to calculate McDonald’s omega [32].
The significance level was set at p < 0.05. Stata 16.1 (Stata Corp., College Station, TX, USA) was used for statistical analyses in this current study.
3. Results of the Analyses
3.1. Sample Characteristics and Prevalence Rates
In total, 49.5% of the individuals were female, and the average age equaled 46.5 years (SD: 15.3 years), ranging from 18 to 74 years. The average climate anxiety score was 2.0 (SD: 1.2). In sum, 23.1% of the individuals had probable depression, and 16.0% of the individuals had probable anxiety. Furthermore, 69.8% of the individuals did not have children in their own household. Moreover, 59.0% of the individuals were married, and living together with their spouse; additionally, 59.0% of the individuals had completed a upper secondary school course. In total, 44.2% of the individuals were employed full-time. In sum, 23.4% of the individuals reported themselves to be daily smokers, 6.4% of the individuals reported drinking alcohol daily and 27.1% of the individuals reported that they did not perform sports activities. All in all, 54.1% of the individuals reported an absence of chronic diseases. The average self-rated health (ranging from 1 = very bad to 5 = very good) was 3.6 (SD: 0.9), and the average coronavirus anxiety (from 0 to 20, with higher values reflecting higher coronavirus anxiety) was 1.4 (SD: 3.1). More details are shown in Table 1.
Pearson correlations for continuous variables are shown in Table 2 (with Bonferroni-adjusted significance levels). For example, the association between climate anxiety and age was r = −0.18 (p < 0.001). Moreover, the association between climate anxiety and self-rated health was r = −0.02 (p = 1.00), and the association between climate anxiety and coronavirus anxiety was r = 0.44 (p < 0.001). Additional details are shown in Table 2.
Among individuals without probable depression, average climate anxiety was 1.9 (SD: 1.1), whereas it was 2.6 (SD: 1.4) among individuals with probable depression. Cohen’s d for this difference was 0.68. Moreover, average climate anxiety was 1.9 (SD: 1.1) among individuals without probable anxiety, whereas it was 2.7 (SD: 1.4) among individuals with probable anxiety. Cohen’s d was 0.67 for this difference.
3.2. Regression Analysis
The results of the multiple logistic regressions are shown in Table 3 (for reasons of readability, without potential confounders in Table 3; regression tables with potential confounders are given in Table 4). Pseudo R2 was 0.30 (with probable depression as outcome) and 0.26 (with probable anxiety as outcome). In both cases, the sample size equaled 3091 individuals.
In regression analysis, it was adjusted for sex, age, marital status, having children in their own household, highest level of school education, employment situation, smoking behavior, alcohol intake, frequency of sports activities, chronic illnesses, and self-rated health and coronavirus anxiety. Regressions showed that a higher climate anxiety was associated with a higher likelihood of probable depression (OR: 1.37, 95% CI: 1.25–1.50). Moreover, regressions showed that a higher climate anxiety was associated with a higher likelihood of probable anxiety (OR: 1.27, 95% CI: 1.15–1.40).
It should be noted: In logistic regressions, the explanatory variables have to be linearly related to the log-odds of the outcome. Since the link tests are significant (in both cases: p < 0.001), we additionally estimated a model with log climate anxiety due to the right-skewed distribution of climate anxiety. In these models (where we pass the link test: p = 0.53 with probable depression as outcome; p 0.48 with probable anxiety as outcome), the association between log climate anxiety and probable depression was OR: 2.30, 95% CI: 1.85–2.87, and the association between log climate anxiety and probable anxiety was OR: 1.93, 95% CI: 1.52–2.45.
4. Discussion
Our aim was to investigate, based on data from the general adult population in Germany, the association between climate anxiety and mental health. Bivariate analysis showed an association between higher climate anxiety and lower levels of mental health (medium to large effect size). Adjusting for several sociodemographic, lifestyle-related and health-related factors, multiple logistic regressions showed that a higher climate anxiety was associated with a higher likelihood of probable depression and that a higher climate anxiety was associated with a higher likelihood of probable anxiety. Using data from the general German adult population (individuals aged 18 to 74 years living in Germany), our current study extends our current understanding regarding the association between climate anxiety and mental health, which, to this point, has mainly been based on rather small and specific samples (e.g., younger adults or student samples [10,11]). Thus, it can contribute to our current knowledge in this research area regarding climate anxiety and mental health.
Higher climate anxiety may partly reflect the more anxious nature of individuals—which can particularly explain the association between higher climate anxiety and a higher likelihood of probable anxiety. Moreover, the association between higher climate anxiety and a higher likelihood of probable depression is plausible, because the former factors can contribute to insomnia symptoms [33]—insomnia being common among individuals with depression [34]. Moreover, climate anxiety can manifest itself via expectations regarding future life threats caused by climate change. For example, severe floods took place in some European countries, including some regions in Germany, in July 2021. In Germany, not less than 184 individuals died because of the consequences of these floods—making the flooding the deadliest natural disaster which has taken place in Germany since the early 1960s. Overall, our findings align with the terror management theory described in Section 1. The uneasiness with uncertainty associated with potential future natural disasters can cause feelings of stress and worry [10] (to cope with death anxiety). Moreover, and in line with the terror management theory, the inability to regulate death anxiety (due to impending natural disasters) can foster mood disorders and anxiety disorders [35].
We would like to note some strengths and limitations of our present study. A quota-based sample (age, sex and state) of the German general adult population (18 to 74 years) was used in this study. Additionally, several covariates were included in multiple regressions. Established and valid tools were used to quantify climate anxiety and mental health (depressive symptoms and anxiety symptoms). Our study is limited by its cross-sectional design—which makes it difficult to clarify the directionality. For example, it may be the case that initial depressive or anxiety symptoms may lead to subsequent changes in climate anxiety. Thus, longitudinal studies in this area are clearly needed. Moreover, an online bias cannot be ruled out—this can increase the likelihood that certain subgroups are underrepresented in this online survey.
5. Conclusions
Our study demonstrated an association between climate anxiety and mental health levels in Germany. Further representative studies examining the association between climate anxiety and mental health (e.g., in other areas of the world) are clearly needed. Moreover, future research based on representative samples in other age groups (such as children, adolescents and individuals aged 75 years and over) is needed. For example, it may be of interest to examine these findings relative to older individuals living in institutionalized settings.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cli11080158/s1, Supplementary Table S1. Collinearity Diagnostics; Supplementary Table S2. Diagnostics for logistic regression (with probable depression as outcome); Supplementary Table S3. Diagnostics for logistic regression (with probable anxiety as outcome).
Author Contributions
Conceptualization, A.H. and H.-H.K.; visualization, A.H. and H.-H.K.; review and editing of original draft, A.H. and H.-H.K.; data curation, A.H.; methodology, A.H.; formal analysis, A.H.; project administration, A.H.; writing of original draft, A.H.; supervision, H.-H.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The research was carried out in line with the Helsinki Declaration. The Local Psychological Ethics Committee of the Center for Psychosocial Medicine of the University Medical Center Hamburg-Eppendorf approved the study (number: LPEK-0412). Our research adheres to the ethical norms outlined in the Helsinki Declaration of 1964 and its subsequent modifications.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to legal restrictions, but are available from the corresponding author on reasonable request.
Conflicts of Interest
The authors declare no conflict of interest.
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Table 1.
Sample characteristics for the total sample (n = 3091).
| Variables | Characteristics of the Variables | Total Sample |
|---|---|---|
| Gender | Male | 1554 (50.3) |
| Female | 1531 (49.5) | |
| Diverse | 6 (0.2) | |
| Age (in years) | 46.5 (15.3) | |
| Children in own household | No | 2158 (69.8) |
| Yes | 933 (30.2) | |
| Marital status | Single/Divorced/Widowed/Married, not living together with spouse | 1266 (41.0) |
| Married, living together with spouse | 1825 (59.0) | |
| Education | Upper secondary school | 1234 (39.9) |
| Qualification for applied upper secondary school | 356 (11.5) | |
| Polytechnic secondary school | 196 (6.3) | |
| Intermediate secondary school | 956 (30.9) | |
| Lower secondary school/Without school-leaving qualification | 333 (10.8) | |
| Currently in school training/education | 16 (0.5) | |
| Employment status | Full-time employment | 1365 (44.2) |
| Retired | 646 (20.9) | |
| Other | 1080 (34.9) | |
| Smoking status | Yes, daily | 722 (23.4) |
| Yes, sometimes | 238 (7.7) | |
| No, not anymore | 943 (30.5) | |
| Never a smoker | 1188 (38.4) | |
| Alcohol consumption | Daily | 199 (6.4) |
| Several times per week | 544 (17.6) | |
| Once per week | 466 (15.1) | |
| 1–3 times per month | 545 (17.6) | |
| Less often | 746 (24.1) | |
| Never | 591 (19.1) | |
| Sports activities | No sports activity | 838 (27.1) |
| Less than one hour per week | 575 (18.6) | |
| Regularly, 1–2 h per week | 771 (24.9) | |
| Regularly, 2–4 h per week | 490 (15.9) | |
| Regularly, more than 4 h per week | 417 (13.5) | |
| Chronic diseases | Absence of at least one chronic disease | 1673 (54.1) |
| Presence of at least one chronic disease | 1418 (45.9) | |
| Self-rated health (from 1 = very bad to 5 = very good) | 3.6 (0.9) | |
| Coronavirus anxiety scale (from 0 to 20, with higher values reflecting higher coronavirus anxiety) | 1.4 (3.1) | |
| Climate anxiety score (from 1 to 7, with higher values reflecting higher climate anxiety) | 2.0 (1.2) | |
| Probable depression | Absence of probable depression | 2377 (76.9) |
| Presence of probable depression | 714 (23.1) | |
| Probable anxiety | Absence of probable anxiety | 2595 (84.0) |
| Presence of probable anxiety | 496 (16.0) |
Table 2.
Correlation matrix for continuous variables among the total sample (n = 3091).
| Depressive Symptoms | Anxiety Symptoms | Climate Anxiety | Age | Self-Rated Health | Coronavirus Anxiety | |
|---|---|---|---|---|---|---|
| Depressive symptoms | 1.00 | |||||
| Anxiety symptoms | 0.82 *** | 1.00 | ||||
| Climate anxiety | 0.30 *** | 0.31 *** | 1.00 | |||
| Age | −0.24 *** | −0.23 *** | −0.18 *** | 1.00 | ||
| Self-rated health | −0.41 *** | −0.34 *** | −0.02 | −0.27 *** | 1.00 | |
| Coronavirus anxiety | 0.44 *** | 0.45 *** | 0.44 *** | −0.15 *** | −0.12 *** | 1.00 |
Notes: Pearson correlations are shown; *** p < 0.001 (Bonferroni-adjusted significance levels were calculated).
Table 3.
Climate anxiety and mental health. Results of multiple logistic regressions.
| Independent Variables | Outcome: Probable Depression | Outcome: Probable Anxiety |
|---|---|---|
| Climate anxiety | 1.37 *** | 1.27 *** |
| (1.25–1.50) | (1.15–1.40) | |
| Potential confounders | ✓ | ✓ |
| Pseudo-R2 | 0.30 | 0.26 |
| Observations | 3091 | 3091 |
Odds ratios are reported; 95% CI in parentheses; *** p < 0.001; potential confounders cover sex, age, marital status, having children in own household; highest level of school education, employment situation, smoking behavior, alcohol intake, frequency of sports activities, chronic illnesses, and self-rated health and coronavirus anxiety.
Table 4.
Climate anxiety and mental health. Results of multiple logistic regressions (all potential confounders are displayed).
Table 4.
Climate anxiety and mental health. Results of multiple logistic regressions (all potential confounders are displayed).
| Independent Variables | Probable Depression | Probable Anxiety |
|---|---|---|
| Climate anxiety | 1.37 *** | 1.27 *** |
| (1.25–1.50) | (1.15–1.40) | |
| Sex: - Female (Ref.: Men) | 1.56 *** | 1.79 *** |
| (1.23–1.98) | (1.38–2.33) | |
| - Diverse | 0.86 | 0.54 |
| (0.10–7.42) | (0.05–5.82) | |
| Age (in years) | 0.95 *** | 0.96 *** |
| (0.94–0.96) | (0.95–0.97) | |
| Children in own household: - Yes (Ref.: No) | 0.87 | 0.84 |
| (0.68–1.12) | (0.64–1.10) | |
| Marital status: - Living together: married or in a partnership (Ref.: widowed; divorced; single; living separately: married or in partnership) | 0.81 + | 0.89 |
| (0.65–1.01) | (0.70–1.14) | |
| Highest educational degree: - Qualification for applied upper secondary school (Ref.: upper secondary school) | 0.95 | 0.74 |
| (0.67–1.35) | (0.50–1.10) | |
| - Polytechnic secondary school | 0.87 | 0.84 |
| (0.54–1.41) | (0.49–1.43) | |
| - Intermediate secondary school | 0.85 | 0.91 |
| (0.65–1.10) | (0.69–1.21) | |
| - Lower secondary school/Without school-leaving qualification | 0.81 | 0.85 |
| (0.55–1.19) | (0.56–1.30) | |
| - Currently in school training/education | 0.63 | 0.54 |
| (0.15–2.64) | (0.13–2.27) | |
| Employment status: - Retired (Ref.: Full-time employment) | 1.01 | 0.96 |
| (0.71–1.44) | (0.65–1.44) | |
| - Other | 0.90 | 1.22 |
| (0.70–1.15) | (0.94–1.59) | |
| Smoking status: - Yes, daily (Ref.: Never a smoker) | 1.23 | 1.07 |
| (0.92–1.64) | (0.78–1.46) | |
| - Yes, sometimes | 1.54 * | 1.42 |
| (1.03–2.28) | (0.93–2.18) | |
| - No, not anymore | 1.25 + | 1.12 |
| (0.96–1.63) | (0.84–1.50) | |
| Alcohol consumption: - Daily (Ref.: Never) | 1.21 | 1.30 |
| (0.75–1.97) | (0.77–2.18) | |
| - Several times per week | 0.93 | 0.99 |
| (0.65–1.34) | (0.67–1.46) | |
| - Once per week | 0.88 | 0.86 |
| (0.61–1.27) | (0.57–1.29) | |
| - 1–3 times per month | 0.92 | 0.89 |
| (0.65–1.29) | (0.61–1.29) | |
| - Less often | 0.91 | 0.75 + |
| (0.67–1.24) | (0.53–1.05) | |
| Sports activities: - Less than one hour a week (Ref.: No sports activity) | 0.76 + | 1.05 |
| (0.56–1.03) | (0.75–1.46) | |
| - Regularly, 1–2 h per week | 0.75 * | 0.95 |
| (0.56–1.00) | (0.69–1.30) | |
| - Regularly, 2–4 h per week | 0.64 * | 0.71 + |
| (0.45–0.91) | (0.48–1.07) | |
| - Regularly, more than 4 h per week | 0.67 * | 0.88 |
| (0.46–0.97) | (0.59–1.34) | |
| Chronic diseases: - Presence of at least one chronic disease (Ref.: Absence of chronic diseases) | 1.40 ** | 1.10 |
| (1.10–1.79) | (0.84–1.44) | |
| Self-rated health (from 1 = very bad to 5 = very good) | 0.30 *** | 0.35 *** |
| (0.26–0.35) | (0.30–0.41) | |
| Coronavirus anxiety (from 0 to 20, with higher values reflecting higher coronavirus anxiety) | 1.23 *** | 1.20 *** |
| (1.19–1.27) | (1.16–1.24) | |
| Constant | 64.00 *** | 12.29 *** |
| (27.88–146.93) | (5.16–29.30) | |
| Pseudo-R2 | 0.30 | 0.26 |
| Observations | 3091 | 3091 |
Odds ratios are reported; 95% CI in parentheses; *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.10.
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Hajek, A.; König, H.-H. Climate Anxiety and Mental Health in Germany. Climate 2023, 11, 158. https://doi.org/10.3390/cli11080158
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Hajek A, König H-H. Climate Anxiety and Mental Health in Germany. Climate. 2023; 11(8):158. https://doi.org/10.3390/cli11080158
Chicago/Turabian StyleHajek, André, and Hans-Helmut König. 2023. "Climate Anxiety and Mental Health in Germany" Climate 11, no. 8: 158. https://doi.org/10.3390/cli11080158
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