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Evaluating the Prevalence and Correlates of Major Depressive Disorder Among Residents of Fort McMurray, Canada, One Year After a Devastating Flood

Published online by Cambridge University Press:  20 December 2022

Folajinmi Oluwasina,
Ejemai Eboreime,
Reham Shalaby,
Nnamdi Nkire,
Belinda Agyapong,
Hannah Pazderka,
Gloria Obuobi-DonkorOpen the ORCID record for Gloria Obuobi-Donkor[Opens in a new window],
Medard Adu,
Wanying Mao and
Ernest Owusu
...Show all authors
Folajinmi Oluwasina
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Ejemai Eboreime
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Reham Shalaby
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Nnamdi Nkire
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Belinda Agyapong
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, CanadaGlobal Psychological E-Health Foundation, Edmonton, Canada
Hannah Pazderka
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Gloria Obuobi-Donkor
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Medard Adu
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Wanying Mao
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Ernest Owusu
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, Canada
Vincent I. O. Agyapong*
Affiliation:
Department of Psychiatry, University of Alberta, Edmonton, CanadaDepartment of Psychiatry, Dalhousie University, Halifax, Canada
*
Corresponding author: Vincent Israel Opoku Agyapong, Email: vn602367@dal.ca.
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Abstract

Objective:

This study assessed the prevalence and correlates of depression following the April 2020 flooding in Fort McMurray.

Methodology:

A cross-sectional study design. Questionnaires were self-administered through an anonymous, online survey. Data collected included sociodemographics, flooding-related variables, clinical information, and likely major depressive disorder (MDD) using PHQ-9 scoring. Data were analyzed using descriptive statistics, the chi-square test, and logistic regression at P = < 0.05.

Results:

Of the 186 respondents who completed the survey, 85.5% (159) of the respondents were females, 14.5% (27) were males, 52.7% (98) were above 40 years of age, and 94% (175) were employed. The prevalence of mild to severe depression among the respondents was 53.7% (75). Respondents who reported that they are unemployed are 12 times more likely to have a moderate to severe depression (OR = 12.16; 95% CI: 1.08–136.26). Respondents who had previously received a mental health diagnosis of MDD are five times more likely to have moderate to severe depression (OR = 5.306; 95% CI: 1.84–15.27).

Conclusion:

This study suggests that flooding could impact the psychosocial and mental health of affected people. There is a need to reassess the existing guidelines on emergency planning for flooding to reduce its impacts on mental health and identify where research can support future evidence-based guidelines.

Keywords

diagnosisfloodingmajor depressive disordermental health

Information

Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023, e271
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

Flooding can have a serious effect on people’s health and well-being, both psychological and physical. In common with other types of major disasters, the effects on people’s health, relationships, and welfare can be affected by flooding. Floods are one of the most common types of global natural disasters responsible for almost 53 000 deaths in the last decade. Reference Alderman, Turner and Tong1 Flooding can cause substantial social and welfare problems that may linger over extended periods because of not only being flooded (the primary stressor), but also because of the secondary stressors (those stressors that are indirectly related to the initial extreme event, ie, economic stress associated with re-building) that arise as people try to recover their lives, property, and relationships. Flooding can challenge the psychosocial resilience of the hardiest of people who are affected. Reference Stanke, Murray and Amlot2 Psychosocial impacts of flood events suggest that they can have significant adverse effects on people’s well-being, relationships, and mental health.Reference Fewtrell and Kay 3

Climate change has contributed to an increase in the intensity and frequency of floods. It will also worsen hurricanes, heat waves, droughts, wildfires, dust storms, and tornadoes. Reference Stanke, Murray and Amlot2 This is because climate change entails changes in precipitation, along with temperature, wind patterns, atmospheric pressure, cloudiness, and humidity.

In their 2005 review on the global health impacts of flooding, Ahern et al. Reference Doocy, Daniels, Murray and Kirsch4 reported several epidemiological studies that assessed the effects of flooding on common mental disorders (including anxiety and depression), posttraumatic stress disorder (PTSD), and suicide. Most studies examining the effects of flooding on common mental disorders are from high or middle-income countries, and results revealed significant increases in depression, anxiety, and psychological distress among flood-affected adults. This means that experiencing a flood effect can lead to long-term negative psychological effects, long after the flooding itself has abated. Long-term health problems, in particular, adverse mental health outcomes, have been linked to the experience of flooding, including elevated levels of depression, anxiety, and PTSD in affected populations. Reference Fernandez, Black and Jones5–Reference Gray7 Several factors are known to be associated with the likelihood of experiencing adverse mental health outcomes post-flooding. These include sociodemographic characteristics such as age, gender, socioeconomic and education statuses, and factors related to the nature of flooding (eg, depth and duration). Reference Ahern, Kovats and Wilkinson8 Secondary stressors, including problems with relationships, loss of sentimental items, and difficulties with insurance or compensation, have also been associated with poor psychological outcomes.

Fort McMurray located in Alberta, Canada, has experienced several major natural disasters over the past 5 years. These include wildfires associated with an evacuation order in 2016, flooding in 2020, and also the COVID-19 pandemic in 2020.

These traumatizing experiences are believed to have adversely impacted the mental health and general well-being of survivors. Reference Stanke, Murray and Amlot6,Reference Brown, Agyapong and Greenshaw9 On April 27, 2020, a major ice jam and melting waters caused massive flooding in downtown Fort McMurray. This forced over 15 000 people to be evacuated according to media reports. Reference Snowdon10 At the individual level, experiencing an episode of flooding may trigger memories of previous flooding episodes, intensifying the distress experienced. Reference Brown, Agyapong and Greenshaw9 Multiple factors associated with flooding could trigger psychological distress, including fears and actions taken to protect family or belongings, experience of flooding, and long-term uncertainties concerning insurance. Reference Foudi, Osés-Eraso and Galarraga11–Reference Tapsell, Tunstall and Wilson13 Additionally, it is plausible that the act of evacuation itself would also trigger stressful memories. Those who have been previously affected by flooding may also experience anticipatory anxiety and stress about the prospect of future flooding. Reference Tunstall, Tapsell and Green14,Reference Mao and Agyapong15 This study assessed the clinical correlates of Major Depressive Disorder (MDD) following a flooding incident at Fort McMurray.

Methods

Study Setting

The study was conducted in Fort McMurray, Alberta, Canada. Fort McMurray is the urban service area of the Northern Alberta Regional Municipality of Wood Buffalo, with a diverse population of 111 687 as of the 2018 census, 16 mainly in temporary accommodations (hotels, motels, and boarding house) and in adjoining rural communities. Fort McMurray is a multicultural community, attracting people from all corners of Canada and the world. Generally, moves to Fort McMurray have increased in the last decade. The ethnic groups include 65.8% white, 22.1% Aboriginal, 3.8% South Asian, 2.5% Filipino, 1.9% black, and 1.2% Arab. The total number of dwelling units in the Municipality in 2020 is 28 281. This represents an 8.4% decrease from the total number of dwelling units in 2015. The decrease is largely attributed to dwelling units that were destroyed by the 2016 Horse River Wildfire. Reference Agyapong, Juhas and Omege17 The study site is considered the heart of one of Alberta’s (and Canada’s) hubs of petroleum production, located near the Athabasca oil sands. Besides the oil sands, the economy also relies on natural gas and pipeline transport, forestry, and tourism. People typically move to the Fort McMurray area to work in the oil sands industry. Reference Agyapong, Juhas and Omege17 The major employers of labor in Fort McMurray are oil companies, which made Fort McMurray a thriving industrial center that attracted an influx of oil-industry workers who took up both permanent and temporary residences.

Study Design

This study adopted a descriptive cross-sectional study design. The study was carried out between April 24 and June 2, 2021, through a self-administered questionnaire that was sent to residents of FMM via REDCap. Reference Harris, Taylor and Thielke18

Ethical Consideration

Study approval was granted by the Research Ethics Board at the University of Alberta (Pro00066054). This study was also conducted in accordance with the Declaration of Helsinki (Hong Kong Amendment).

Data Collection

Data collection tools for this study were developed based on published literature and questions from previously validated instruments. The general constructs of interest included relevant demographic information, flood-related questions, and the mental health status of respondents. A standardized measure for likely MDD included in the survey was the Patient Health Questionnaire-9. Reference Kroenke, Spitzer and Williams19

REDCap is a secure web application for building and managing online surveys and databases. It is also used to collect virtually any type of data in any environment. It is specifically geared to support online and offline data capture for research studies and operations. Reference Harris, Taylor and Thielke18 The questionnaire was distributed randomly via email using government, school, occupational, and community platforms. Consent was implied, by completing the survey. Links to self-administered questionnaires were distributed randomly via email using intermediary agencies such as government, schools, and occupational and community platforms.

Statistical Analysis

A data analysis was undertaken using SPSS Statistics for Windows (Version 26; IBM Corp, Armonk, NY). 20 Demographic characteristics, flooding-related variables, as well as responses to questions related to MDD were summarized by absolute numbers and percentages. Only completed responses were reported, with no data imputation. The chi-square test and Fisher’s exact analysis with 2-tailed significance (P ≤ 0.05) were performed to assess the association between the demographic characteristics and responses to questions related to flooding and MDD, as measured using the Patient Health Questionnaire (PHQ-9) index. The PHQ-9 (included in appendix) is a multipurpose instrument used for screening, diagnosing, monitoring, and measuring the severity of depression, and it is a reliable and valid measure of depression severity. Reference Kroenke, Spitzer and Williams19 the PHQ-9 scale is half the length of many other depression measures, has comparable sensitivity and specificity, and consists of 9 criteria upon which the diagnosis of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), fourth edition, depressive disorders is based. The PHQ-9 scoring was done using the standard recommendation with the threshold for likely moderate to high depression being met if 5 of the 9 items were checked and the total score was ≥ 10.

Variables with statistically significant or near-significant association (P ≤ 0.1) for each of the sociodemographic and flooding domains and MDD were entered into a logistic regression model. Before performing the logistic regression analysis, correlational diagnostics were performed to identify any strong intercorrelations (Spearman’s correlation coefficient of 0.7 to 1.0 or -0.7 to -1.0) among predictor variables. Odds ratios from the binary logistic regression analysis were calculated to determine the association between the predictor variables and the presence of MDD controlling for the other variables in each model.

Results

Out of the total of 249 persons who accessed the survey, there were 186 responses received, giving an effective response rate of 74.7% (see Table 1). Of the 186 respondents who completed the survey, 85.5% (159) of the respondents were female while 14.5% (27) were male, 52.7% (98) were above 40 years of age, 40.3% (75) were between the ages of 26 and 40 years, and the lowest proportion (7.0%; 13) was less than 25 years of age. Also, 94.1% (175) were employed and 5.9% (11) were unemployed. The majority (71%; 132) of the respondents were married or partnered, 19.4% (36) were single, and 9.7% (18) were either divorced or separated at the time of this study.

Table 1.

Demographic profile, clinical characteristics, and received support for the study population

A larger percentage of the respondents, 94.6% (176), resided in Fort McMurray during the year 2020 flood while 5.4% (10) of the respondents were temporary residents in Fort McMurray during the flood. The majority, 75.8% (144), own a home, 24.2% (45) were renting a home. Nearly half, 48.4% (90), of the respondents reported that they had not received a mental health diagnosis from a health professional following the flooding. The study also showed that 31.2% (58) of the respondents received a mental health diagnosis for depression, and 41.9% (78) of the participants received a diagnosis for anxiety. However, 1.1% (2) received a mental health diagnosis of bipolar disorder and personality disorder while a few, 9.1% (17), received other diagnoses from a health professional following the flooding.

The respondents reported that they received antidepressants, 31.7% (59); sleeping tablets, 11.3% (21); mood stabilizers, 6.5% (12); antipsychotics, 2.2% (4); benzodiazepines, 2.2% (4); and other medications, 1.6% (3), while 64.5% (120) were not on any medication due to mental health. Most of the respondents, 61.3% (114), did not receive mental health counseling in the past year, while 38.7% (72) received mental health counseling in the past year; 50.7% (98) reported that they would like to receive mental health counseling, while 47.3% (88) declined mental health counseling.

Seventy percent (123) reported that they were fearful of their life and family during the flooding, 29.3% (51) were not fearful; 66.7% (116) watched television images about the devastation caused by the flood on a daily basis; 21.3% (37) and 12.1% (21) did not watch daily and did not watch at all, respectively. Most of the respondents, 88.7% (156), did not suffer any loss of property during the flood, while 11.3% (21) reported that they suffered a property loss. Most, 85% (147), were living in the same household before the floods, 12.7% (22) were living in a different house even though their previous home wasn’t destroyed, and 2.3% (4) reported that they live in a different house because their previous home was destroyed by the flood.

Forty-four percent (74) received absolute support from family and friends, 15.6% (26) had some support, and 10.2% (17) only had limited support from family and friends (absolute support from family and friends implies full support without any restriction or condition while limited support is referred to not receiving full support from their family and friends); 6.4% (11) of the respondents claimed that they received absolute support from the Government of Alberta, 4.1% (7) had some support, 3.5% (6) got limited support, 7.0% (12) did not receive any support, and 78.9% (135) reported they were not impacted by the flood, hence there was no support from the Government of Alberta. The prevalence of mild to severe depression among the respondents was 53.7% (75).

Table 2 shows a summary of the results of univariate analysis of the association between respondents’ demographic profile, flood-related variables, and moderate to severe depression.

Table 2.

Chi-square test of association between demographic profile, clinical, flooding-related variables, and likely depression

The chi-square test showed a significant (P ≤ 0.05) relationship between sociodemography, clinical variables, and moderate to severe depression. Nine variables were statistically significant, including (1) employment status, (2) received mental health diagnosis on depression from a health professional, (3) received mental health diagnosis on anxiety from a health professional, (4) received mental health diagnosis from a health professional, (5) would like to receive mental health counseling, (6) on antidepressant medication, (7) on benzodiazepine medication, (8) on sleeping tablet medication, and (9) on any mental health medication for health concerns.

Table 3 below shows the logistic regression model to determine the likelihood of moderate to severe depression among the study participants in Fort McMurray.

Table 3.

Logistic regression model for respondents’ likelihood to present with likely MDD

*P < 0.05.

The multivariate binary logistics regression model was statistically significant; χ2(df = 11; n = 162) = 59.90, P < 0.001; this indicated that the multivariate binary logistic regression model could differentiate between the respondent who had a moderate to severe depression and respondents who had a mild depression.

The model accounted for 30.9% (Cox and Snell R2) to 41.3% (Nagelkerke R2) of the variance. According to the goodness-of-fit statistic using Hosmer-Lemeshow goodness-of-fit test, the model was adequately fit (χ2 = 5.34; P = 0.72) and correctly classified 75.9% of cases.

According to Table 3, only 3 variables—employment status, received a mental diagnosis of depression, and would like to receive mental health counseling—significantly predicted likely MDD. Study participants who reported that they are unemployed are 12 times more likely to present with MDD symptoms (OR = 12.16; 95% CI: 1.08–136.26).

Study participants who have received a mental health diagnosis of MDD are 5 times more likely to have moderate to severe depression (OR = 5.306; 95% CI: 1.84–15.27). Respondents who would like to receive mental health counseling are 6 times more likely to show MDD symptoms (OR = 6.12; 95% CI: 2.41–15.49).

Discussion

This study provides a strong indication of an association between flooding and probable MDD. Given projections that both the frequency and severity of flooding events will continue to increase in the future, Reference Alfieri, Bisselink and Dottori21–Reference Paul and Moser23 populations living in flood-susceptible areas may be exposed to multiple flooding events, which may cause repeated mental health issues.

In this study, unemployment showed a relationship with likely MDD among the study participants. Climate change and economic downturns have become a global problem that has been influencing many of the key social determinants of health, especially through unemployment and changes to living conditions. A natural disaster such as flooding is characterized by a rise in unemployment, reductions in income, as well as a reduction in total well-being. Unemployment is associated with an increase in distress and psychosomatic symptoms, anxiety, depression, melancholy, feeling of dependency, inability to solve routine problems, self-dissatisfaction, and lower self-esteem. Reference Warr, Drenth, Thierry and Willems24,Reference Cao, Wang and Cao25 While this study has shown an association between unemployment and depression in this vulnerable population exposed to flooding, it has not ascertained causality.

In our study, the prevalence of mild to severe depression among the respondents was 53.7% (75); this is in line with a prevalence of 54% among adults in a study on depression following natural disaster. Reference Tang, Liu and Liu26 Our study also showed that respondents who have received a mental health diagnosis for depression are more likely to have moderate to severe depression. Those who were already diagnosed with MDD seem to be having MDD symptoms, meaning that they were either not controlled on medications or therapies they received or the experienced trauma was out of control. In a community sample, Cao et al. Reference Cao, Wang and Cao25 identified 4 response patterns in the outcome of a natural disaster: (1) minimal symptoms, (2) predominant depression, (3) predominant PTSD, and (4) comorbid PTSD and depression. Of note, persons with a predominately depressed presentation represented approximately one-fifth of the community sample (and 40% of those presenting with symptoms) and reported more somatic complaints and a high level of interpersonal problems compared to those considered to be PTSD symptoms.

Experiences during the natural disaster (eg, flooding) may be more indicative of PTSD following a natural disaster compared to depression. Meanwhile, a collection of studies shows that further processes, specifically disaster-related stressors and individual-level factors, may lead to post-disaster depression. Reference Brewin, Andrews and Valentine27–Reference Stander, Thomsen and Highfill-McRoy29 Furthermore, past symptoms of depression may also serve as a strong indicator of post-disaster depression. Stander et al. Reference Stander, Thomsen and Highfill-McRoy29 proposed a causal relation between PTSD and depression, in which pre-military trauma PTSD forecasted depressive symptoms succeeding exposure to war, signifying a strong relation between lifetime PTSD and post-disaster depression can have important implications in disaster-preparedness protocols within mental health services. Reference Tracy, Norris and Galea30

The study population has experienced multiple natural disasters in the past 5 years, and in view of climate change, this may become the norm in other places, making it an ideal population. However, because they have experienced other natural disasters may act as a confounding factor either by increasing resilience or increasing vulnerability to developing mental health problems like depression.

MDD has a substantive negative impact on quality of life. Several studies suggest that depression is underdiagnosed and undertreated amongst older people in Western nations. Reference Mitchell, Rao and Vaze31–Reference Briggs, Kirchhoff and Hammes34

Respondents who would like to receive mental health counseling were more likely to express moderate to severe depression. Seeking counseling from a health care professional with a self-care focus contributes greatly to the treatment of any illness. Recent studies have shown that counseling encourages patients to actively contribute in treatment programs and promotes their positive outcomes. Reference Höglund, Winblad, Arnetz and Arnetz35,Reference Macdonald, Rogers, Blakeman and Bower36 Counseling helps the patients control their feelings, adhere to the therapy, clearly understand the treatment rationale, improve quality of life, decrease stress, reduce anxiety, feel more secured, and increase life satisfaction. Reference Macdonald, Rogers, Blakeman and Bower36

One of the factors that influences people’s perception of flood disaster preparedness is an early warning system. Early warning systems are an important part of community preparedness mechanism. Reference Marfai, Sekaranom and Ward37 Shelter becomes a disaster preparedness factor that cannot be excluded in flood disaster management. People affected by floods or vulnerable to disasters are usually advised to evacuate. The existence of shelter is also one of the factors influencing community preparedness.

Studies on disaster victims found that changes in substance abuse vary from mild to moderate increases among diverse populations. Reference David, Mellman and Mendoza38–Reference Knudsen, Roman, Johnson and Ducharme40 For example, a study following Hurricane Andrew of August 1992 found only 2% of area residents reporting alcohol dependence. Reference David, Mellman and Mendoza38

Study Limitations

Ethnicity and income may be potential confounders given that some studies have found correlations between these variables and mental health. Reference Phelan, Link and Diez-Roux41,Reference Link, Northridge, Phelan and Ganz42 Unfortunately, this study did not collect data on these. As part of an online survey limitation, the population to which gave responses cannot be described, and respondents with biases may select themselves into the sample.

Additionally, there may be a strong indication that the results of this study would have been different if the study were conducted prior to the flooding. Also, flooding at Fort McMurray occurred during the COVID-19 pandemic, so considering that some people may have been exposed to COVID-19 during that time, it is our assumption that this may have affected our results. We mainly focused on flooding variables and their correlation with Major Depression Disorder, through a cross-sectional study design that captured the data at the time.

Conclusions

This study makes a good contribution to the growing literature on the mental health effects of flooding, and its large scale and natural disaster also represent a unique opportunity to increase the understanding of the impacts of flooding on population health and well-being. It is important that the impact of flooding on people’s health and well-being is carefully understood. This will help inform public health actions to mitigate adverse health consequences of future flooding events.

Acknowledgments

Support with survey link distribution was received from the Fort McMurray Public and Catholic School Boards, Keyano College, and the Canadian Mental Health Association.

Author contributions

The study was conceived and designed by VIOA. FO drafted the initial manuscript, and RS conducted the data analysis. EE contributed to the data collection. All authors contributed to the study design, reviewing, and revising the initial draft manuscript and approved the final draft before its submission.

Funding statement

This study was supported by grants from the Mental Health Foundation and the Douglas Harden Trust Fund.

The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the results for publication.

Conflict(s) of interest

None.

References

Alderman, K, Turner, LR, Tong, S. Floods and human health: a systematic review. Environ Int. 2012;47:37-47. doi: 10.1016/j.envint.2012.06.003 Google ScholarPubMed
Stanke, C, Murray, V, Amlot, R, et al. The effects of flooding on mental health: outcomes and recommendations from a review of the literature. PLoS Curr. 2012;4:  e4f9f1fa9c3cae Google ScholarPubMed
Fewtrell, L, Kay, D. An attempt to quantify the health impacts of flooding in the UK using an urban case study. Public Health. 2008;122(5):446-451. doi: 10.1016/j.puhe.2007.09.010 CrossRefGoogle ScholarPubMed
Doocy, S, Daniels, A, Murray, S, Kirsch, TD. The human impact of floods: a historical review of events 1980–2009 and systematic literature review. PLoS Curr. 2013;5:ecurrents.dis.f4deb457904936b07c09daa98ee8171a.Google ScholarPubMed
Fernandez, A, Black, J, Jones, M, et al. Flooding and mental health: a systematic mapping review. PLoS One. 2015;10:e0119929. doi: 10.1371/journal.pone.0119929 CrossRefGoogle ScholarPubMed
Stanke, C, Murray, V, Amlot, R, et al. The effects of flooding on mental health: outcomes and recommendations from a review of the literature. PLoS Curr. 2012;4:e4f9f1fa9c3cae. doi: 10.1371/4f9f1fa9c3cae Google ScholarPubMed
Gray, S. Long-term health effects of flooding. J Public Health. 2008;30:353-354. doi: 10.1093/pubmed/fdn092 Google ScholarPubMed
Ahern, M, Kovats, RS, Wilkinson, P, et al. Global health impacts of floods: epidemiologic evidence. Epidemiol Rev. 2005;27:36-46.Google ScholarPubMed
Brown, MRG, Agyapong, V, Greenshaw, AJ, et al. Significant PTSD and other mental health effects present 18 months after the Fort McMurray wildfire: findings from 3,070 grades 7–12 students. Front Psychiatry. 2019;10(623)CrossRefGoogle ScholarPubMed
Snowdon, W. Warmer weather only possible weapon against northern Alberta ice-jam flooding. CBC News. 2020. Accessed October 9, 2021. https://www.cbc.ca/news/canada/edmonton/fort-mcmurray-wood-buffalo-flooding-1.5547546 Google Scholar
Foudi, S, Osés-Eraso, N, Galarraga, I. The effect of flooding on mental health: lessons learned for building resilience. Water Resour Res. 2017;53: 5831–5844. http://onlinelibrary.wiley.com/doi/10.1002/2017WR0 CrossRefGoogle Scholar
McEwen, L, Jones, O. Building local/lay flood knowledges into community flood resilience planning after the July 2007 floods, Gloucestershire, UK. Hydrol Res. 2012;43(5):675-688. http://eprints.uwe.ac.uk/20322 CrossRefGoogle Scholar
Tapsell, SM, Tunstall, SM, Wilson, T. Banbury and Kidlington Four years after the flood: an examination of the long-term health effects of flooding. Report to the environment agency, Thames Region. Flood Hazard Research Centre, Middlesex University, Enfield; 2003.Google Scholar
Tunstall, S, Tapsell, S, Green, C, et al. The health effects of flooding: social research results from England and Wales. J Water Health. 2006;4:365-380. doi: 10.2166/wh.2006.03 Google ScholarPubMed
Mao, W, Agyapong, VIO. The role of social determinants in mental health and resilience after disasters: implications for public health policy and practice. Front Public Health. 2021;9:658528.CrossRefGoogle ScholarPubMed
Census 2018. Municipal Census Report. Published 2018. Accessed October 11, 2021. https://www.rmwb.ca/en/permits-and-development/resources/Documents/Latest-Census-Report-2018.pdf Google Scholar
Agyapong, VIO, Juhas, M, Omege, J, et al. Prevalence rates and correlates of likely post-traumatic stress disorder in residents of Fort McMurray 6 months after a wildfire. Int J Ment Health Addict. 2019;19(3):632-650.Google Scholar
Harris, PA, Taylor, R, Thielke, R et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381.CrossRefGoogle ScholarPubMed
Kroenke, K, Spitzer, RL, Williams, JBW. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606-613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x Google ScholarPubMed
IBM Support. Release Notes – IBM® SPSS® Statistics 25.0. IBM. Accessed December 8, 2020. https://www.ibm.com/support/pages/release-notes-ibm%C2%AE-spss%C2%AE-statistics-250 Google Scholar
Alfieri, L, Bisselink, B, Dottori, F, et al. Global projections of river flood risk in a warmer world. Earth’s Future. 2017;5:171-182. doi: 10.1002/2016EF000485 CrossRefGoogle Scholar
Sayers, PB, Horritt, M, Penning-Rowsell, E, et al. Climate change risk assessment 2017: projections of future flood risk in the UK. London, UK: Committee on Climate Change. Published 2017. Accessed October 11, 2021. https://www.theccc.org.uk/wp-content/uploads/2015/10/CCRA-Future-Flooding-Main-Report-Final-06Oct2015.pdf Google Scholar
Paul, KI, Moser, K. Unemployment impairs mental health: meta-analysis. J Vocat Behav. 2009;74(3):264-282.CrossRefGoogle Scholar
Warr, P, Drenth, PJD, Thierry, H, Willems, PJ. Handbook of work and organizational psychology. Work and unemployment [Google Scholar]. Wiley; 1983.Google Scholar
Cao, X, Wang, L, Cao, C, et al. Patterns of DSM-5 posttraumatic stress disorder and depression symptoms in an epidemiological sample of Chinese earthquake survivors: a latent profile analysis. J Affect Disord. 2015;186:5-65. doi: 10.1016/j.jad.2015.06.058 CrossRefGoogle Scholar
Tang, B, Liu, X, Liu, Y, et al. A meta-analysis of risk factors for depression in adults and children after natural disasters. BMC Public Health. 2014;14:623. doi: 10.1186/1471-2458-14-623.Google ScholarPubMed
Brewin, CR, Andrews, B, Valentine, JD. Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults. J Consult Clin Psychol. 2000;68(5):748. doi: 10.1037/0022–006X.68.5.748 CrossRefGoogle ScholarPubMed
La Greca, AM, Silverman, WK, Lai, B, Jaccard, J. Hurricane-related exposure experiences and stressors, other life events, and social support: concurrent and prospective impact on children’s persistent posttraumatic stress symptoms. J Consult Clin Psychol. 2010;78(6):794. doi: 10.1037/a0020775 CrossRefGoogle ScholarPubMed
Stander, VA, Thomsen, CJ, Highfill-McRoy, RM. Etiology of depression comorbidity in combat-related PTSD: a review of the literature. Clin Psychol Rev. 2014;34:87-98. doi: 10.1016/j.cpr.2013.12.002 CrossRefGoogle ScholarPubMed
Tracy, MS, Norris, FH, Galea, S. Differences in the determinants of posttraumatic stress disorder and depression in the aftermath of a mass traumatic event. Depress Anxiety. 2011;28:666-675. doi: 10.1002/da.20838 CrossRefGoogle Scholar
Mitchell, AJ, Rao, S, Vaze, A. Can general practitioners identify people with distress and mild depression? A meta-analysis of clinical accuracy. J Affect Disord. 2011;130(1-2):26-36.Google ScholarPubMed
Borus, JF, Howes, MJ, Devins, NP, et al. Primary health care providers’ recognition and diagnosis of mental disorders in their patients. Gen Hosp Psychiatry. 1988;10(5):317-321. doi: 10.1016/0163-8343(88)90002-3 CrossRefGoogle ScholarPubMed
Kamphuis, MH, Stegenga, BT, Zuithoff, NP, et al. Recognition of depression in primary care: does it affect outcome? The Predict-Nl Study. Fam Pract. Published online August 24, 2011. Google Scholar
Briggs, LA, Kirchhoff, KT, Hammes, BJ, et al. Patient-centered advance care planning in special patient populations: a pilot study. J Prof Nurs. 2004;20(1):47-58.Google ScholarPubMed
Höglund, AT, Winblad, U, Arnetz, B, Arnetz, JE. Patient participation during hospitalization for myocardial infarction: perceptions among patients and personnel. Scand J Caring Sci. 2010;24(3):482-489.CrossRefGoogle ScholarPubMed
Macdonald, W, Rogers, A, Blakeman, T, Bower, P. Practice nurses and the facilitation of self-management in primary care. J Adv Nurs. 2008;62(2):191-199.CrossRefGoogle ScholarPubMed
Marfai, MA, Sekaranom, AB, Ward, P. Community responses and adaptation strategies toward flood hazard in Jakarta, Indonesia. Nat Hazards. 2015;75:1127-1144.CrossRefGoogle Scholar
David, D, Mellman, TA, Mendoza, LM, et al. Psychiatric morbidity following Hurricane Andrew. J Traum Stress. 1996;9(3):607-612.CrossRefGoogle ScholarPubMed
Bravo, M, Rubio-Stipec, M, Canino, GJ, et al. The psychological sequelae of disaster stress prospectively and retrospectively evaluated. Am J Commun Psychol. 1990;18(5):661-680.CrossRefGoogle ScholarPubMed
Knudsen, HK, Roman, PM, Johnson, JA, Ducharme, LJ. A changed America? The effects of September 11th on depressive symptoms and alcohol consumption. J Health Soc Behav. 2005;46(3):260-273.CrossRefGoogle ScholarPubMed
Phelan, JC, Link, BG, Diez-Roux, A, et al. “Fundamental causes” of social inequalities in mortality: a test of the theory. J Health Soc Behav. 2004;45:265-285. doi: 10.1177/002214650404500303. [PubMed] [CrossRef] [Google Scholar] [Ref list]CrossRefGoogle ScholarPubMed
Link, BG, Northridge, ME, Phelan, JC, Ganz, ML. Social epidemiology and the fundamental cause concept: on the structuring of effective cancer screens by socioeconomic status. Milbank Q. 1998;76:375-402. doi: 10.1111/1468-0009.00096.CrossRefGoogle ScholarPubMed
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Table 1.Demographic profile, clinical characteristics, and received support for the study population

Figure 1

Table 2.Chi-square test of association between demographic profile, clinical, flooding-related variables, and likely depression

Figure 2

Table 3.Logistic regression model for respondents’ likelihood to present with likely MDD