Psychological impact of the Spring 2019 flood among adult population of Iran

Setting, study design, and population

In the spring of 2019, heavy rains followed by flash floods occurred in different parts of the country. Major floods started in northeastern regions after heavy rains on March 17 to 22. Subsequent floods occurred in the periods of March 24 to 26 and between March 31 and April 2 after heavy rains and overflowing of dams, which had the greatest impact on the western and southwestern regions of the country (Aminyavari et al., 2019).

To prepare the National Report on the Psychosocial Consequences of the 2019 Flood, a household survey with face-to-face interviews was carried out on a random sample of the flood-affected population, 5 to 6 months after the event. The survey in the social section included the study and evaluation of various social, cultural and media aspects of the flood. The psychological section included the evaluation of mental health services for flood-affected people and the prevalence and determinants of mental disorders including psychological distress, depression and PTSD in adults, and quality of life and PTSD in children and adolescents. In the present study, we used the survey data to investigate the prevalence and determinants of psychological distress and depression in flood-affected adults.

The study population was adults aged 16 years and older affected by flood in three provinces of Golestan (Aq Qala, Gomishan), Lorestan (Pol Dokhtar, Mamulan), and Khuzestan (Dasht-e-Azadegan, west Ahvaz, Hamidiyeh), located in the northeast, west, and southwest of Iran, respectively.

Participants

In this study, a three-stage random sampling was used and samples were randomly selected in the form of 210 clusters each containing eight adult people. In the first stage, 70 clusters were randomly selected in each of the three provinces. To select clusters in each province, first, information of population affected by floods in the province of interest, by city and village, was provided. Then, according to the ratio of flood victims in each city and village to the total flood victims of the province, the number of clusters in that area was determined from a total of 70 clusters in the province. To select clusters in affected cities and villages, first, the areas affected by the flood were identified on a geographical map and then based on the definition of the block as an area that leads to the street from four sides, the total number of blocks was determined and numbered. The desired number of blocks was randomly selected from the total identified blocks and a cluster of each them was entered into the study.

In the second stage, to select the households in each of the clusters, one of the plaques was randomly selected and systematically eight households were included every second plaque (k = 2). In the third stage, for the entry of eight adults from eight households into the study, the details of all members of each household were entered in the form and among the eligible individuals, a person whose day and month of birth were closer to the interview day, was randomly selected. Exclusion criteria included age less than 16 years, not understanding the Persian language, physical or cognitive disorders preventing from conducting interviews, and absence from the household until the end of data collection.

Measures and variables

The data were collected using a checklist including demographic and flood related variables and tools including General Health Questionnaire (GHQ-28) and Patient Health Questionnaire (PHQ-9). The demographic variables included age, gender, marital status, education, employment status, place of residence, the history of mental disorders diagnosed by a psychiatrist, and the ownership of assets. The height of the incoming flood in centimeters to the home, amount of flood damage to assets, compensation for flood damage, temporary living in camp after the flood, and access to health care services after the flood were flood-related variables. We estimated the wealth index for each participant and categorized them into tertiles, using the principal component analysis (PCA) of assets including ownership of the house, agricultural land and garden, heavy livestock, private car, motorcycle, light-emitting diodes (LED) television, freezer, washing machine, dishwasher, furniture, desktop computer and laptop, microwave, bedstead, and handmade carpet (Filmer & Pritchett, 2001). Also, the amount of flood damage to the mentioned assets was converted to tertiles using the PCA.

General Health Questionnaire (GHQ-28)

This questionnaire includes four subscales of somatic complaints, anxiety and sleep disorders, social functioning, and depressive symptoms (Goldberg & Hillier, 1979). The Persian version of the GHQ-28 was validated in a study and showed acceptable reliability and validity for epidemiological studies (Noorbala et al., 2009). This questionnaire includes 4-point Likert questions. The range of scores that can be obtained is between zero and 84, and a higher score indicates more unfavorable psychological distress. Based on the study of Noorbala et al. (2009), the best cut-off score was 23 with a sensitivity of 70.5 and a specificity of 92.3%. Therefore, in this study, anyone with 24 scores or more was defined as having psychological distress.

Patient Health Questionnaire-9 (PHQ-9)

The PHQ-9 is a tool to assess the severity of depression in the past 2 weeks (Kroenke et al., 2001). This questionnaire has 9 items and each item scores from zero to three by asking about the symptoms of depression (one of them is about dysfunction). The range of scores that can be obtained from this questionnaire is between zero and 27, and a higher score indicates a more unfavorable depressive state. In this study, anyone with 10 scores or more was defined as having depression, with a sensitivity of 90 and a specificity of 93% (Mohamadian et al., 2022). Dadfar et al. (2018) reported the validity and reliability of the Persian version of this questionnaire as desirable.

Interviewer training and procedures

The data were collected through face-to-face interviews with trained interviewers. The mean questioning time was about 45 minutes. The interviewers of the study were 18 mental health experts (six interviewers in each province). The educational level of the interviewers was a bachelor’s or master’s degree in psychology and related majors. These interviewers were trained and practiced in 2 days of the in-person workshop for a total of 16 hours regarding the executive protocol and data collection tools. During the study, in each province, an observer checked the questionnaires and forms related to the clusters every day to ensure the correct selection of the sample and accurate questioning. To observe ethical considerations, while explaining the objectives of the study and emphasizing the confidentiality of personal information, informed consent was obtained from the participants. This study has been approved by the ethics committee of the Iran University of Medical Sciences (#IR.IUMS.REC.1398.718).

Statistical analysis

The data were analyzed through STATA version 11 (Stata Corporation, College Station, TX, USA). The mean and standard deviation were used to describe continuous variables and frequency and percentage were used to describe categorical variables. To consider the complex sampling method in the analysis, the ‘svyset’ command was used and the provinces and clusters were introduced to the data. Sampling weights were calculated so that each respondent can be inflated to represent other individuals in the study provinces. The consolidated weights (w) were the joint product of inverse probability of unit selection into the sample (w1), and post-stratification weights (w2) (w = w1 × w2). To calculate the post-stratification weights (w), the proportion of subjects in each stratum in the National Census 2016 was divided by the proportion of the same group in the sample. Based on five age groups, gender, and six counties of study provinces, 60 post-stratification weights were generated. We used the Rogan-Gladen estimator to estimate the true prevalence and population in need of (Rogan & Gladen, 1978):

True Prevalence = Apparent Prevalence + Specificity − 1 Specificity + Sensitivity − 1

The relationship between variables was analyzed using statistical tests including Chi-square, t-test, one-way analysis of variance with Tukey post hoc and logistic regression analysis. In logistic regression analysis, considering the Hosmer-Lemeshow method for modeling (Hosmer et al., 2013), the variables associated with outcomes with a p-value smaller than .2 in univariable models, were entered into the multiple model and the odds ratios were reported with 95% confidence interval. In addition to checking possible multicollinearity and interactions, models fitness was checked with the Hosmer-Lemeshow goodness of fit test for complex survey data (Archer & Lemeshow, 2006). A p-value less than .05 was considered significant.

Prevalence of psychological distress and depression

Psychological distress in flood victims in the last month was assessed by the GHQ-28 questionnaire. Accordingly, the total prevalence of psychological distress was 33.6% and that is about one third of the flood victims. Also, 24.6, 42.3, and 29.4% of flood-affected adults of Golestan, Lorestan, and Khuzestan provinces were diagnosed with psychological distress, respectively. The study by Noorbala et al. (2017) using the same tool showed that these frequencies in 2015 in the provinces of Golestan, Lorestan and Khuzestan were 12.8, 36.3, and 21.8%, respectively. It seems that this increase could be due to the flood event. Compared to other studies, Reacher et al. (2004) reported that 9 months after the Lewes flood in England, 48% of flood victims had scores above the cut-off of the GHQ-12, while this value was 25% in the study of Tunstall et al. (2006), some years after flooding.

The prevalence of depression in flood victims in the last 2 weeks was assessed by the PHQ-9 questionnaire. Accordingly, 23.0% of the subjects had depression. According to the Iranian Mental Health Survey in 2011, the 1-year prevalence of major depressive disorder in Iranians aged 15 to 64 years was 12.7% (Sharifi et al., 2015). However, the 23.0% 1-month prevalence of depression in flood victims is plainly higher than its prevalence in the general population and it can be considered as one of the negative effects of flood on mental health. The background of higher prevalence, occurrence of severe sudden floods in the region and the subsequent severe damages can be the reason for the higher prevalence of disorders in the Lorestan province.

In different studies in the world, in examining the mental health status of flood victims after the flood, the prevalence of depression has been different. In the study by Paranjothy et al. (2011) 3 to 6 months after the 2007 floods in England using the same tool, the prevalence of depression varied between 7 and 43% according to the severity of the exposure. In a study 6 months after the 2017 floods in Australia, Fitzgerald et al. (2020) reported a 17% prevalence of depression based on the PHQ-2.

The psychological effects of the flood depend on various factors such as the severity of the flood and the shock caused by it, the vulnerability of the victims and various environmental, cultural, and social factors. Different study methods can also cause differences in the reported frequencies due to the time interval from the event, sampling methods, outcomes, and questionnaires (Ishikawa et al., 2013; Lowe et al., 2013). Therefore, simply comparing the prevalence of mental disorders between different studies may not be an adequate way to compare the psychological effects of floods. What is clear is the higher prevalence of mental disorders in the flood-affected population than in the general population.

Many people experience psychological distress after disasters in a transitory period, usually 1 month, but anyway, remaining in this condition 6 months after the disaster can be an indication of people suffering from mental disorders and an important warning for intervention measures (National Collaborating Centre for Mental Health, 2005).

In the present study, after considering the sensitivity and specificity of the tools on the apparent prevalence, we estimated the true prevalence and the number of people in need of mental health services. With this method, it is possible to estimate mental health services more accurately by modifying the estimates of screening tools.

Determinants of psychological distress and depression

The results showed that a history of mental disorders had the strongest adjusted association with psychological distress in the flooded victims so participants with a history of mental disorders had 4.67 times more than others odds of developing psychological distress. A history of mental disorder with an adjusted odds ratio of 4.07 was also one of the main determinants of depression. This was consistent with the results of other studies that reported a higher prevalence of post-flood mental disorders in individuals with a prior history (Hetherington et al., 2018; Mao et al., 2022). The history of mental disorders before the disaster by increasing the possibility to confront traumatic experiences and reducing resilience can make a person more vulnerable to post-disaster disorders (Bonanno & Gupta, 2009).

Based on the results, unemployment had the strongest adjusted association with depression. Odds of developing depression in the unemployed participants was 5.29 times more than others. Similarly, previous studies have reported a higher prevalence of depression in unemployed individuals and emphasized the need to pay special attention to them in post-disaster care (Bosmans & Van der Velden, 2018; Fitzgerald et al., 2020; Musa et al., 2014). Independent of a natural disaster, a recent systematic review and meta-analysis has confirmed the relationship between unemployment and depression (Amiri, 2022). It seems that factors such as decreasing psychosocial well-being and life satisfaction and unhealthy behaviors or lifestyles in unemployed people play a role in this case (Paul & Moser, 2009).

As the results showed, it is not far from the mind that more exposure to flood and the damage caused by it can have more negative effects on the mental health of flood-affected people. Previous studies have used different variables to determine the severity of exposure to floods and their damages, however, their results have confirmed the relationship between exposure to more severe and damaging floods with a higher prevalence of psychological morbidity (Bandla et al., 2019; Paranjothy et al., 2011; Waite et al., 2017). This can be a useful indicator to identify, help and support to people at risk for mental disorders and shows that trying to limit the entry of water into the homes of flood victims can reduce the effects of floods on people's mental health (Makwana, 2019; Waite et al., 2017).

Based on the results of the study, not receiving compensation for flood damage was one of the important determinants of psychological distress and depression in the flood-affected population. Similarly, previous studies have also listed not receiving compensation after a disaster as one of the risk factors for mental illness (Daniel & Michaela, 2021; Sodeyama et al., 2022). Timely and appropriate compensation for flood victims based on a realistic assessment, by removing economic concerns, reducing stressors, and helping people recover and return to normal, can be effective in preventing further psychological consequences (Abbas et al., 2018). The measures are suggested for universal access of people to insurance services in flood-prone areas, for quick recovery and compensation of flood victims (Foudi et al., 2017).

It is important to note that the cross-sectional nature of our study limits our ability to establish causality between variables, and therefore, meaning that the findings should be interpreted cautiously.

The results indicate the need for organized management to reduce the psychosocial problems of flood-affected population. Public health officials and policymakers should consider these risk factors in disaster health programs (Paranjothy et al., 2011). In upcoming disasters, that may be more common and extreme, to provide psychiatric and mental health services to flood victims and prevent mental disorders, high-risk groups such as people with a previous history of mental disorders and people who face more severe and damaging floods should be prioritized (López-Cepero et al., 2022; Tunstall et al., 2006).

Strengths and limitations

To the best of our knowledge, this is the first household survey aimed to investigate various aspects of mental disorders in the flood-affected people of Iran with suitable sample size, random sampling, adjusted, and representative results.

Regarding limitations, self-report measures in our study may introduce biases that could impact the accuracy of our findings regarding psychological distress and depression prevalence and severity. However, we minimized this concern by using reliable and widely accepted standardized questionnaires. Additionally, we ensured participant anonymity and confidentiality while clearly explaining the study’s purpose, reducing potential biases.

The study, conducted 6 months after the flood event, may not capture the immediate or long-term effects on mental health. However, it can offer valuable insights into the medium-term effects, which have important implications for post-disaster intervention and support. It is crucial to interpret the findings within the context of the study’s timeframe.

The requirement for understanding Persian language in our study may have underestimated the prevalence of psychological distress and depression among flood-affected populations. Considering the potential impact on the generalizability of the findings is crucial. Furthermore the displacement of individuals following the flood and their exclusion from our sample may have led to an underestimation of those potentially experiencing mental disorders.

Future research also should focus on including a wider range of variables, such as social support and coping strategies, to enhance our understanding of these issues.