The unforeseen tide: exploring mental health impacts of the 2024 flash flood in Bangladesh

Abstract

Our study examines the mental health impacts of the flood, focusing on depression, anxiety, and stress, utilizing the Depression, Anxiety, and Stress Scale-21 (DASS-21). A total of 451 adult residents were surveyed through face-to-face interviews, selected via convenience and snowball sampling to ethically and effectively reach displaced, hard-to-access individuals affected by the 2024 flash flood in Cumilla District, Bangladesh. The survey collected data on sociodemographic factors, flood-related experiences, and the prevalence of mental health issues. Statistical analyses, including stepwise backward and multiple linear regression, identified key associations between sociodemographic variables (e.g., gender, age, education, chronic illness) and mental health outcomes. The results revealed a high prevalence of mental health disorders: 59.87% of participants experienced extremely severe anxiety, 37.69% experienced extremely severe depression, and 25.72% reported severe stress. Key predictors of mental health challenges included gender, age, education, and chronic illness, with women, older adults, and those with lower educational levels or chronic illnesses being particularly vulnerable. The study highlights the urgent need for targeted mental health interventions and enhanced disaster preparedness in flood-prone regions. Strengthening social networks, improving disaster management education, and addressing the vulnerabilities of specific groups are essential steps in building resilience against future natural disasters.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-24118-9.

Introduction

Natural hazards like floods not only devastate infrastructure and economies but also leave deep psychological scars on affected populations [1–3]. In recent years, the frequency and intensity of such disasters have escalated, primarily driven by climate change and rapid urbanization [4, 5]. Bangladesh remains particularly vulnerable due to its geographical location and socioeconomic conditions. Approximately 60% of the country’s population is exposed to high flood risk, with 45% facing extreme fluvial flood vulnerability—the highest proportion globally. Approximately 60% of the country’s population is exposed to high flood risk, with 45% facing extreme fluvial flood vulnerability—the highest proportion globally. Large low-lying regions, including coastal areas, riverbanks, and the floodplains of the Ganges, Brahmaputra, and Meghna rivers, are highly susceptible to seasonal inundation. These areas, frequently affected by fluvial and coastal flooding due to intense rainfall and rising sea levels, require urgent disaster management interventions [6].

The 2024 flash flood in Burichang Upazila, Cumilla District, was an atypical and abrupt event that caused widespread destruction and displacement, catching many residents unprepared. The disaster resulted in significant loss of life and property, with at least 16 deaths reported in the affected regions [7]. Continuous heavy rainfall and the release of water from upstream dams along the Gomti River led to widespread inundation. Reports indicate that flooding in Cumilla caused extensive damage to crops, livestock, and infrastructure, exacerbating the already precarious livelihoods of the affected population [8]. The economic losses were substantial, estimated at Tk 33.62 billion across the district, with Burichang alone incurring damages worth Tk 5.56 billion [9]. Unlike seasonal flooding, the sudden inundation overwhelmed the region’s limited disaster response capacity and disrupted daily life on an unprecedented scale [10]. The severity of the 2024 flood was attributed to extreme weather events, including unprecedented rainfall, the breach of the Gumti River embankment, and the rapid flow of water from upstream sources. The region’s lack of prior flood experience and preparedness further compounded the disaster’s impact, highlighting the urgent need for enhanced flood resilience and community-based disaster preparedness measures.

Beyond its immediate economic and infrastructural devastation, the 2024 flash flood posed severe challenges to the health and well-being of the affected population. Research on natural hazards in Bangladesh has predominantly focused on physical health consequences, such as waterborne diseases and injuries [11]. However, the psychological effects of such disasters remain largely underexplored [2, 12, 13]. Previous studies indicate that vulnerable groups—including women, children, and older adults—face heightened risks in disaster settings. Women, for example, encounter barriers to preparedness and recovery due to financial constraints and limited decision-making power [14, 15]. Additionally, they often bear the primary caregiving responsibilities, further exacerbating their mental health burdens [16]. Children who cannot fully comprehend trauma may suffer long-term psychological effects, particularly when educational disruptions occur [17]. Older adults, meanwhile, face mobility challenges, social isolation, and pre-existing health conditions that complicate their ability to recover from disasters [18]. Gender-sensitive interventions, child-friendly spaces, and targeted programs for older adults have improved resilience and recovery outcomes in similar contexts [19–21].

Evidence from global flood disasters suggests that mental health impacts are particularly pronounced in resource-constrained settings, where socioeconomic disparities and limited healthcare access exacerbate vulnerabilities. Studies report a high prevalence of mental health disorders among flood survivors, including post-traumatic stress disorder (PTSD), anxiety, and depression [13, 22]. The 2018 Kerala floods in India displaced millions and led to widespread mental health challenges, with PTSD, anxiety, and depression being prevalent among survivors [23]. Similarly, the 2023 glacial lake outburst flood (GLOF) in Sikkim resulted in long-term psychological distress among affected populations [24]. In Pakistan’s Sindh province, agricultural communities faced severe emotional distress following catastrophic flooding, particularly among lower-income groups who lost their livelihoods [25]. Studies in Australia have also shown that flood-displaced populations experience significantly higher rates of depression, anxiety, and PTSD [26]. The disruption of healthcare services during floods further compounds mental health risks, as access to treatment for chronic conditions such as diabetes and hypertension becomes severely restricted, thereby worsening psychological distress [27].

Although research indicates an increase in anxiety and depression symptoms among flood-affected populations [2], these conditions are often the result of complex, interrelated factors, including loss of property, livelihood disruptions, displacement, and the trauma of witnessing or experiencing loss. In Bangladesh, additional stressors—such as increased caregiving responsibilities, gender-based violence, and economic hardship—further exacerbate mental health challenges, particularly among women [2]. Studies indicate that the psychological effects of floods can persist for years, with long-term consequences shaped by socioeconomic, environmental, and cultural factors rather than the flood event itself [28]. A systematic review of research conducted in South and Southeast Asia confirms that floods are consistently linked to adverse mental health outcomes, including PTSD, depression, and anxiety [22]. Findings from Sri Lanka following the 2004 tsunami, for instance, reported a high prevalence of PTSD among affected individuals, while studies in South Africa indicated increased emotional distress and anxiety among flood-affected women [29, 30].

Most studies have focused on physical health and economic loss in Bangladesh, a country that is acutely vulnerable to flooding. However, the mental health consequences—particularly depression, anxiety, and stress—remain underexplored and undervalued in disaster risk reduction strategies. Disaster-affected communities’ socio-cultural and environmental complexities require targeted investigation to develop effective intervention strategies. Understanding the psychological toll of floods—especially in under-researched rural areas—is essential for designing inclusive disaster response strategies. Global evidence links flood exposure to increased rates of PTSD, anxiety, and depression, yet localized data in Bangladesh is sparse. Understanding how sociodemographic and situational factors influence mental health outcomes in post-disaster settings can guide more targeted and effective policy responses.

Our study aims to investigate the mental health impacts—specifically depression, anxiety, and stress—of the 2024 flash flood in Burichang Upazila, Bangladesh, using the Depression, Anxiety, and Stress Scale-21 (DASS-21) as the primary measurement tool [31]. By analyzing how sociodemographic characteristics (e.g., gender, age, education, and chronic illness) and flood-related experiences (e.g., home inundation, access to resources, long-term livelihood impact) correlate with mental health outcomes, the study seeks to identify key predictors of psychological distress among flood-affected individuals. The findings will contribute to the region’s limited but growing body of disaster mental health research and help inform targeted mental health interventions and disaster resilience strategies in similar high-risk communities.

Methods

Research design

Our study employs a quantitative research approach to assess the mental health impact of the 2024 flash flood on adult residents (aged 18 years or older) of Burichang Upazila, Cumilla District, Bangladesh. The unprecedented scale of displacement—affecting approximately 502,501 individuals who sought refuge in 3,403 evacuation centers [32]—suggests a significant likelihood of mental health issues such as depression, anxiety, and stress emerging among the affected population.

Study area

Burichang Upazila, located in Cumilla District (Fig. 1), has a population of 299,605, comprising 144,036 males and 155,569 females [33]. The upazila spans 163.6 square kilometers, yielding a high population density of 1,630 individuals per square kilometer. It is bordered by three rivers—Gomti, Pagli, and Ghunghur—which increase the flood risk. The upazila comprises nine unions, including Bakshimul and Sholanal. The literacy rate is 49.75%, with men at 53.6% and women at 45.64%.

Fig. 1.

Map of study area: Burichang Upazila, Cumilla District, Bangladesh

In the 2024 flash flood, over 13,000 households were severely affected, with 4,143 homes destroyed [34]. All nine unions experienced severe shortages of clean water, along with a lack of food and medical supplies. Damaged roads and infrastructure hampered relief efforts, isolating villages and complicating rescue operations [35]. The Gomti River reached record-high levels, posing a threat to nearby areas. The flood caused significant economic hardship, particularly for vulnerable groups, such as children and older people, who faced overcrowded shelters with limited resources [34, 36].

Survey instrument and data collection

Data collection took place in November 2024 using a structured Bengali-language questionnaire. The DASS-21 [31] was employed to assess mental health outcomes among flood-affected individuals in Burichang Upazila. The DASS-21 consists of 21 items, divided into three subscales: depression, anxiety, and stress, each comprising seven items. Responses are based on a four-point Likert scale: 0 (“Did not apply to me at all”), 1 (“Applied to me to some degree or some of the time”), 2 (“Applied to me to a considerable degree or a good part of the time”), and 3 (“Applied to me very much or most of the time”). Scores are summed and multiplied by two, with a maximum score of 42 per subscale. The DASS-21 categorizes scores into five levels: normal, mild, moderate, severe, and extremely severe (Table 1). While the DASS-21 is rarely used for flash flood mental health studies, it has been effective in disaster-related mental health assessments [37–39].

Table 1.

Cut-off values for the DASS-21’s labels for depression, anxiety, and stress [31]

Severity Label	Depression	Anxiety	Stress
Normal	0–9	0–7	0–14
Mild	10–13	8–9	15–18
Moderate	14–20	10–14	19–25
Severe	21–27	15–19	26–33
Extremely Severe	28+	20+	34+

The questionnaire contained five sections. The first section collected sociodemographic data (e.g., gender, age, education, location, housing type, chronic illnesses, and vulnerable family members). Sociodemographic factors help understand how different groups may experience mental health challenges, especially after disasters. Social satisfaction was measured with a three-option Likert scale: Least Satisfied, Satisfied, and Very Satisfied. Flood-related experiences were assessed in the second section, with questions about prior flood exposure, safety perceptions, and the direct effects of the flood (e.g., house inundation, safe drinking water, and food scarcity). The fourth section addressed flood-related damages, while the final section consisted of the DASS-21.

A validated Bengali version of the DASS-21 was used [40], and face-to-face interviews were conducted to ensure comprehension, particularly among participants with lower educational levels. The research team, experienced in working with flood-affected communities, refined the questionnaire based on feedback from participants in a preliminary survey. Cronbach’s alpha was calculated to assess reliability, with all sections exceeding alpha values of 0.80, indicating good internal consistency [41, 42].

Sampling strategy

Participants were recruited using convenience and snowball sampling due to challenges in accessing post-disaster populations. Initially, a resident helped identify households willing to participate, and respondents were asked to recommend others, expanding the sample. Non-probability sampling was necessary due to logistical constraints in a disaster-affected area, where systematic random sampling was unfeasible. Despite these limitations, the approach ensured the inclusion of hard-to-reach and vulnerable populations.

The sample size was calculated using Morgan’s Table [43] and Yamane’s formula [44], which indicated a minimum sample size of 400 for Burichang Upazila’s population of 349,628 [45]. (Yamane’s formula [44])

where n = sample size, N = population, e = error tolerance.

The final sample of 451 participants exceeded it, ensuring robust analysis. Approximately 500 individuals were approached, and 451 consented to participate. Missing data were handled to minimize bias and ensure valid findings.

Data management and statistical analysis

Data were processed and analyzed using Python (version 2.7) and R [46, 47]. Descriptive statistics were calculated for all variables. The DASS-21 subscales (depression, anxiety, stress) were the dependent variables, while sociodemographic factors, flood-related experiences, preparedness, and damages were independent variables.

Multiple linear regression models were used to assess the relationship between variables and mental health outcomes. Stepwise backward regression identified key predictors by removing less significant variables. Multicollinearity was checked to ensure model robustness. Gender, for instance, was a consistently significant predictor across all outcomes, supporting its inclusion in the final models.

Ethical issue

The research was part of an approved study (Ref. No. KUECC-2022/06/16) by the Ethical Clearance Committee of Khulna University, Khulna, Bangladesh. The study adhered to all ethical guidelines outlined in the Declaration of Helsinki and its subsequent amendments [48]. Informed consent was obtained from all participants and their legal guardian(s) (where required).

Results and discussion

Sample

Table 2 provides an overview of the sociodemographic characteristics of the participants. The study ensured a nearly equal representation from the two unions, with 50.55% of respondents from Sholanal and 49.45% from Bakshimul. The gender distribution was balanced, with 49.89% identifying as male and 50.11% as female.

Table 2.

Sociodemographic characteristics and psychosocial factors

Features	Frequency (Percentage)
1. Gender
Male	225 (49.89)
Female	226 (50.11)
2. Age group (years)
18–25	62 (13.75)
26–35	96 (21.29)
36–45	105 (23.28)
46–55	100 (22.17)
> 55	88 (19.51)
3. Marital status
Married	324 (71.84)
Unmarried	60 (13.30)
Others (Divorced, Separated, Widowed, etc.)	67 (14.86)
4. Education status
No Formal Education	144 (31.93)
Non-Secondary School Certificate (SSC)	130 (28.82)
SSC	97 (21.51)
>SSC	80 (17.74)
5. Occupation
Farmers and Fishers	64 (14.19)
Business	57 (12.64)
Wage Labor	47 (10.42)
Student	42 (9.31)
Employee (government or private)	24 (5.32)
Unemployed	190 (42.13)
Other	27 (5.99)
6. Monthly income (BDT)a
No income	221 (49.00)
Less than 15,000	115 (25.50)
15,000–29,999	89 (19.73)
More than 29,999	26 (5.76)
7. Union
Sholonal	228 (50.55)
Bakshimul	223 (49.45)
8. Are you living with family?
Yes	429 (95.12)
No	22 (4.88)
9.Do you have vulnerable family member (child, pregnant women, old person etc.) in household?
Yes	364 (80.71)
No	87 (19.29)
10. Housing type
Kacha (muddy)	84 (18.63)
Pucca (building)	79 (17.52)
Semi-pucca (half building)	288 (63.86)
11. Do you have any chronic disease?
Maybe	66 (14.63)
No	217 (48.12)
Yes	168 (37.25)
12. Do you have any disability?
No	350 (77.61)
Yes	101 (22.39)
13. How do you perceive your current social life?
Least Satisfied	254 (56.32)
Satisfied	181 (40.13)
Very Satisfied	16 (3.55)

^a1USD Around 120 BDT

Regarding age distribution, the most significant proportion of participants fell within the 36–45 age group (23.28%), followed by those aged 46–55 (22.17%). The younger age groups, 18–25 years and 26–35 years, accounted for 13.75% and 21.29% of the sample, respectively, while 19.51% of respondents were 55 or older.

Most participants were married (71.84%), 13.30% were unmarried, and 14.86% were divorced, separated, or widowed. Regarding education levels, 31.93% of respondents did not have any formal education, 28.82% had some primary education but did not complete a Secondary School Certificate (SSC), 21.51% had attained SSC-level education, and 17.74% had an education level beyond SSC.

Occupationally, a significant portion of participants (42.13%) were unemployed, including homemakers. Other major occupations included farming and fishing (14.19%), business (12.64%), wage labor (10.42%), students (9.31%), and government or private employment (5.32%). An additional 5.99% of respondents worked in other occupations.

Income distribution revealed that nearly half (49.00%) of the participants had no personal income, while 25.50% earned less than 15,000 BDT per month (approximately $123). Another 19.73% reported monthly earnings between 15,000 and 29,999 BDT, while only 5.76% earned more than 29,999 BDT. Given that Bangladesh’s average rural household income is around 32,422 BDT ($270) [49], many respondents faced economic hardship, particularly in the aftermath of the flood. Most participants (95.12%) lived with their families, and a significant proportion (80.71%) reported having at least one vulnerable family member—such as a child, an older person, a pregnant woman, or a person with disabilities—in their household. Housing conditions varied, with the majority (63.86%) residing in semi-pucca houses, while 18.63% lived in kacha (mud-based) houses and 17.52% in pucca (brick or concrete) houses.

Health-related data indicated that 37.25% of respondents reported having a chronic illness, while 14.63% were uncertain about their health conditions. Additionally, 22.39% of participants identified as having a disability. When asked about their social well-being, 56.32% of respondents reported being “least satisfied” with their social life, 40.13% expressed moderate satisfaction, and only 3.55% reported being “very satisfied.”

These findings highlight the socioeconomic and health-related vulnerabilities of the affected population, emphasizing the need for targeted interventions in disaster recovery efforts. The high prevalence of financial insecurity, chronic illness, and dependent household members underscores the compounded challenges faced by flood-affected individuals in Burichang Upazila.

The table presents the participants’ sociodemographic characteristics, such as age, gender, education level, marital status, and income. Additionally, it includes the psychosocial variable “How do you perceive your current social life?” which reflects participants’ perceptions of their social well-being. While the item is not traditionally classified as a sociodemographic variable, it is included to assess the role of social satisfaction in mental health outcomes, particularly in the context of post-disaster recovery and resilience.

Flood-related information

Tables 3 , 4, and 5 summarize participants’ experiences, preparedness, and the impact of the 2024 flash flood in Burichang Upazila. The data reveal the significant devastation caused by the flood, highlighting the population’s vulnerabilities, lack of preparedness, and the disaster’s consequences on their well-being.

Table 3.

Flood-related information and impact on participants

Features	Frequency (Percentage)
1. I have the previous flood experience before the recent (2024) flood
No	359 (79.60)
Yes	92 (20.40)
2. Flooding in the area is a new phenomenon
Agree	410 (90.91)
Neutral	40 (8.87)
Disagree	1 (0.22)
3. How safe do you think your living place is from flood?
Moderately Safe	220 (48.78)
Unsafe	221 (49.00)
Safe	10 (2.22)
4. Had your house been inundated during the recent flood?
No	6 (1.33)
Yes	445 (98.67)
5. Did you have access to safe drinking water during the flood?
No	97 (21.51)
Yes	354 (78.49)
6. Did you face food scarcity to provide for your family during the recent flood?
No	333 (73.84)
Yes	118 (26.16)
7. Have you been injured or contracted any diseases due to the flood?
Yes	252 (55.88)
No	199 (44.12)
8. Were any family members injured or had disease during the recent flood?
Yes	213 (47.23)
No	238 (52.77)

Table 4.

Flood-related preparedness

Items	Frequency (Percentage)
1. Flood preparedness is important to mitigate the impact of floods.
Agree	427 (94.68)
Neutral	23 (5.10)
Disagree	1 (0.22)
2. I cannot protect myself from my exposure to floods.
Agree	250 (55.43)
Neutral	103 (22.84)
Disagree	98 (21.73)
3. Unsustainable development (filling up water bodies and creating barriers for natural river flow) can increase the exposure of communities to future floods.
Agree	234 (51.88)
Neutral	122 (27.05)
Disagree	95 (21.06)
4. Community people in the locality ensure the proper management of water bodies so that the water capacity of those water bodies becomes maximum during or after the rainy season.
Agree	225 (49.89)
Neutral	98 (21.73)
Disagree	128 (28.38)
5. Community people should take measures to protect themselves along with their properties from floods.
Agree	402 (89.14)
Neutral	45 (9.98)
Disagree	4 (0.89)
6. Did you get social and economic support during the flood?
No	46 (10.20)
Yes	405 (89.80)
7. Did you receive an early warning regarding flood or an early warning for the evacuation?
No	152 (33.70)
Yes	299 (66.30)
8. How would you rate the early warning mechanism for floods in your locality?
Sufficient	106 (23.50)
Insufficient	298 (66.08)
No early warning	47 (10.42)
9. Did you evacuate to the shelter during the flood?
No	220 (48.78)
Yes	231 (51.22)

Table 5.

Flood-related damage and losses

Features	Frequency (Percentage)
1. Has your income or the income of your family been affected due to the recent flood?
Yes	381 (84.48)
No	70 (15.52)
2. Is there any long-term impact on your livelihood due to the recent flood in your locality?
Yes	287 (63.64)
No	164 (36.36)
3. Have you lost any family members due to the recent flood?
Yes	16 (3.55)
No	435 (96.45)
4. Is there any mental health issue or impact on you due to the recent flood in your locality?
Yes	376 (83.37)
No	75 (16.63)

Most respondents (79.60%) reported no prior experience with flooding, while only 20.40% had encountered floods before 2024. It indicates that for most residents, the flood was an unprecedented event, potentially exacerbating their distress and feelings of helplessness. Communities with limited prior exposure to flooding are often more vulnerable to its psychological effects due to a lack of preparedness [50]. Similarly, previous research has shown that individuals without prior flood experience tend to exhibit more severe mental health symptoms, primarily due to inadequate preparedness and the absence of social support systems [51].

Additionally, 90.91% of participants agreed that flooding in their area was new, suggesting that the 2024 flood was exceptional. Research indicates that proactive disaster management and recovery efforts can reduce psychological distress in affected populations [52], emphasizing the importance of community-based preparedness initiatives.

When asked about the safety of their living conditions, nearly half of the participants (49.00%) reported feeling unsafe, while 48.78% considered their living situation to be only moderately safe. A mere 2.22% of respondents felt completely safe. The widespread perception of insecurity likely stems from the unprecedented scale of the 2024 flood and the inadequate flood mitigation measures in the region.

The flood significantly impacted housing, with 98.67% of participants reporting that their homes were inundated. The finding aligns with reports indicating severe damage to residential areas in Burichang Upazila, resulting in mass displacement and loss of shelter [2]. In August 2024, approximately 5.8 million people across 11 districts in Bangladesh were affected by flooding, with extensive damage to homes, infrastructure, and livelihoods [32]. The widespread destruction of homes likely contributed to heightened levels of stress and anxiety among the affected population.

Access to essential resources during the flood was also a significant challenge. While 78.49% of participants reported having access to safe drinking water, 21.51% did not, increasing the risk of waterborne diseases and further exacerbating public health concerns. Additionally, 26.16% of respondents faced food shortages, highlighting the disaster’s economic strain and logistical challenges. Studies have shown that food scarcity following floods can lead to nutritional deficiencies and increased psychological distress among affected populations [53].

Health impacts were severe, with 55.88% of participants reporting that they or their family members suffered injuries or illnesses due to the flood. Research suggests that flooding is associated with increased risks of infectious diseases, respiratory illnesses, and physical injuries, which can lead to long-term psychological distress [27]. Similar findings were reported following the 2017 flash flood in Attica, Greece, where long-term mental health issues, including post-traumatic stress disorder (PTSD), were observed among survivors [54]. Furthermore, a study in China found that survivors diagnosed with PTSD after a flood continued to exhibit symptoms more than a decade later [55, 56], underscoring the long-term consequences of disaster-related trauma. One study emphasizes that floods can lead to skin and soft-tissue infections, gastroenteritis, and other waterborne diseases due to exposure to contaminated environments [57].

These findings highlight the urgent need for improved flood preparedness, infrastructure resilience, and comprehensive disaster response strategies. Addressing the immediate needs of affected populations—such as ensuring access to clean water, food, and medical services—can help mitigate the long-term psychological and physical health impacts of future flooding events.

The table presents flood-related information and its direct impact on participants, such as previous flood experience, perceptions of safety, home inundation, access to drinking water, and food scarcity. The table also includes the frequency and percentage of respondents reporting these flood-related experiences, helping to contextualize the impact of the 2024 flash flood on the mental health and well-being of the affected population.

Table 5 provides a comprehensive overview of the impact of the 2024 flash flood on the local population, highlighting key areas of concern such as income, livelihood disruption, personal loss, and mental health outcomes. The data collected from individuals in the affected areas underscores the disaster’s widespread socioeconomic and psychological consequences.

A substantial majority of respondents (84.48%) reported that the flood had negatively affected either their income or that of their family members, indicating significant economic distress. Many participants relied on agriculture, fishing, or daily wage labor as their primary sources of livelihood, all of which were severely impacted by the flooding. The inundation of farmland, destruction of fishponds, and disruption of local businesses and employment opportunities disproportionately affected those in the agricultural and labor sectors. Given their reliance on climate-sensitive occupations, these groups faced the most severe economic hardships in the aftermath of the disaster. In contrast, only 15.52% of respondents reported that their income remained unaffected, highlighting the flood’s extensive economic repercussions, particularly in vulnerable rural regions [58].

Regarding long-term economic consequences, 63.64% of participants stated that the flood had a lasting impact on their livelihoods, suggesting that the adverse effects of the disaster extended well beyond its immediate aftermath. Conversely, 36.36% of respondents reported no lasting effects on their livelihood. While the overall loss of life was relatively low—only 3.55% of respondents reported losing a family member—the flood’s economic and psychological toll remained significant. Although 96.45% of participants did not experience familial loss, the disruption to income, displacement, and damage to property compounded financial and emotional distress.

Beyond economic hardship, over half of the respondents reported experiencing injuries or illnesses as a direct result of the flood, further exacerbating the disaster’s impact on public health. The findings indicate that the consequences of the flood were not limited to immediate physical damage but extended into long-term livelihood instability and health concerns.

Acknowledging that the absence of widespread fatalities does not minimize the flood’s profound economic and psychological effects is critical. Previous research on post-disaster economic resilience has shown that even in cases of low mortality, communities can suffer long-term financial setbacks, as evidenced by the 2010 floods in Pakistan, where billions in agricultural losses were recorded despite relatively fewer deaths [59]. The psychological toll of such disasters often manifests in heightened anxiety and stress, driven by concerns over property loss, displacement, and the uncertainty of future flooding events. Notably, 83.37% of respondents reported experiencing mental health issues due to the flood, underscoring the urgent need for psychological support and community-based mental health interventions.

These findings highlight the necessity of integrating economic recovery and mental health support into disaster response strategies. Addressing the compounding effects of financial instability and psychological distress is essential to fostering long-term resilience in flood-affected communities.

The table presents the impact of the 2024 flash flood on participants’ livelihoods, including income, long-term effects on livelihoods, loss of family members, and the prevalence of mental health issues. The frequency and percentage of respondents reporting these experiences are displayed, providing insight into the socioeconomic and psychological consequences of the flood. The data highlight the significant economic and emotional toll of the disaster on the affected population, with many reporting a lasting impact on their livelihoods and mental health.

Prevalence of depression, anxiety, and stress

The findings presented in Table 6 reveal a high prevalence of mental health disorders, including depression, anxiety, and stress, among participants affected by the 2024 flash flood. The data indicate that a significant proportion of respondents exhibited symptoms of severe or extremely severe mental health conditions, underscoring the profound psychological impact of the disaster.

Table 6.

Depression, anxiety, and stress labels among respondents

Severity Label	Depression (n (%))	Anxiety (n (%))	Stress (n (%))
Normal	51 (11.31)	34 (7.54)	88 (19.51)
Mild	22 (4.88)	16 (3.55)	56 (12.42)
Moderate	128 (28.38)	78 (17.29)	80 (17.74)
Severe	80 (17.74)	53 (11.75)	116 (25.72)
Extremely Severe	170 (37.69)	270 (59.87)	111 (24.61)

Depression

Among participants, 37.69% were classified as experiencing extremely severe depression, while 28.38% exhibited moderate depressive symptoms. An additional 17.74% reported severe depression, while only 11.31% fell within the normal range. The high prevalence of depression highlights the profound psychological impact of the flash flood on individuals, with a notable portion of the population experiencing severe emotional distress. The finding is consistent with previous research showing that depression is a typical psychological response to disaster situations, exacerbated by displacement and loss of livelihood [60].

Anxiety

Anxiety levels were alarmingly high, with 59.87% of respondents experiencing extremely severe anxiety, followed by 17.29% with moderate anxiety and 11.75% with severe symptoms. In contrast, only 7.54% of participants reported normal levels of anxiety. The disproportionately high prevalence of anxiety suggests that the acute nature of the flash flood may have contributed to heightened fear, uncertainty, and distress among affected individuals. Previous studies have indicated that anxiety disorders are a typical immediate psychological response to natural hazards, often preceding longer-term symptoms of depression and stress as individuals cope with ongoing displacement and financial instability [60].

Stress

Stress levels, though comparatively lower, were still significant, with 25.72% experiencing severe stress and 24.61% classified as extremely severe. It highlights the substantial psychological burden of the disaster, with nearly 50% of respondents reporting significant stress. Stress, as observed in the study, is commonly associated with the immediate aftermath of disasters, where individuals face uncertainty about the future and struggle to adapt to changed circumstances.

The severity of mental health symptoms observed in the study is consistent with findings from other disaster-stricken regions. For example, research on the mental health impact of flooding in Bangladesh has shown that post-disaster psychological distress remains a significant public health concern, particularly among vulnerable populations such as women, older adults, and those with pre-existing health conditions [2, 13]. Similarly, studies conducted following the 2018 Kerala floods in India and the 2023 GLOF in Sikkim reported high rates of PTSD, anxiety, and depression among survivors, with socioeconomic status and loss of livelihood being key determinants of mental health outcomes [23, 24].

The findings emphasize the urgent need for mental health interventions in post-disaster settings. Given that more than 80% of participants reported some form of psychological distress, targeted psychosocial support programs, community-based mental health services, and long-term coping mechanisms must be prioritized in disaster response and recovery efforts. Without timely intervention, these mental health issues could persist, affecting overall well-being, productivity, and social cohesion within affected communities.

Associated factors with depression, anxiety, and stress

The section builds directly on the study’s objective to identify key predictors of mental health symptoms following the 2024 flash flood in Burichang Upazila by examining the influence of sociodemographic and flood-related factors using multiple regression models (Table 7). The findings indicate that gender, age, education, chronic illness, disability, and direct mental health impacts from the flood were all statistically significant predictors of depression, anxiety, and stress. Below, we provide a detailed explanation of these associations.

Table 7.

Factors affecting depression, anxiety, and stress: multiple regression analysis

Features	Model I Depression (β# (SE))	Model II Anxiety (β# (SEa))	Model III Stress (β# (SE))
Gender
Male	−10.40*** (1.18)	−11.73*** (0.86)	−12.89*** (0.87)
Female	Reference	Reference	Reference
Age group (years)
18–25			Reference
26–35			0.04 (1.26)
36–45			0.06 (1.35)
46–55			3.83* (1.53)
> 55			2.48 (1.72)
Marital status
Married	Reference
Unmarried	−2.13 (1.35)
Others (Divorced, Separated, Widowed etc.)	2.24 (1.23)
Education Status
No Formal Education	Reference	Reference	Reference
Non-Secondary School Certificate (SSC)	−2.75* (1.14)	−3.81*** (1.09)	−1.52 (1.19)
SSC	−1.96 (1.31)	−3.55** (1.22)	−0.13 (1.45)
>SSC	−0.76 (1.52)	−2.12 (1.43)	2.22 (1.73)
Are you living with family?
Yes		0.13 (1.76)
No		Reference
Monthly income (BDTb)
No income	2.12 (1.42)
Less than 15,000	1.76 (1.18)
15,000–29,999	Reference
More than 29,999	−1.32 (1.77)
Do you have any chronic disease?
Maybe	Reference	Reference	Reference
No	1.12 (1.22)	0.78 (1.19)	−0.27 (1.19)
Yes	3.05* (1.29)	3.26* (1.28)	1.68 (1.27)
Do you have any disability?
No	Reference	Reference	Reference
Yes	3.73** (1.36)	3.84** (1.31)	3.79** (1.32)
How do you perceive your current social life?
Least Satisfied		Reference
Satisfied		3.88*** (0.94)
Very Satisfied		3.41** (2.09)
Flooding in the area is a new phenomenon
Agree	Reference	Reference	Reference
Neutral	0.59 (1.51)	0.21 (1.48)	1.77 (1.48)
Disagree	9.97 (7.85)	0.74 (7.76)	4.40 (7.65)
Had your house been inundated during the recent flood?
No		Reference
Yes		1.39 (3.13)
Did you have access to safe drinking water during the flood?
No		Reference
Yes		2.45* (0.96)
I do not have the capacity to protect myself from my exposure to floods.
Agree	Reference	Reference	Reference
Neutral	0.48 (0.91)	0.07 (0.89)	0.62 (0.90)
Disagree	0.01 (1.15)	0.44 (1.11)	0.10 (1.11)
Unsustainable development (filling up water bodies, creating barriers for natural river flow) can increase the exposure of communities to future floods.
Agree	Reference	Reference	Reference
Neutral	−0.37 (0.94)	−1.76 (0.92)	−0.47 (0.90)
Disagree	1.24 (1.60)	−0.45 (1.35)	−0.28 (1.58)
Community people in the locality ensure the proper management of water bodies so that the water capacity of those water bodies becomes maximum during or after the rainy season.
Agree	Reference		Reference
Neutral	−2.01 (1.04)		−3.41*** (1.01)
Disagree	−2.22 (1.28)		−3.08* (1.26)
How would you rate the early warning mechanism for floods in your locality?
Sufficient		0.05 (0.91)
Insufficient		Reference
No early warning		0.93 (1.44)
Did you evacuate to the shelter during the flood?
No	Reference	Reference	Reference
Yes	0.10 (0.83)	0.33 (0.80)	0.11 (0.81)
Is there any long-term impact on your livelihood due to the recent flood in your locality?
Yes	0.05 (0.90)	2.49** (0.94)	0.51 (0.88)
No	Reference	Reference	Reference
Is there any mental health issue on you due to the recent flood in your locality?
Yes	5.00*** (1.08)	7.25*** (1.09)	5.08*** (1.06)
No	Reference	Reference	Reference

^aSE Standard Error; ^b1USD Around 120 BDT

^#β Beta Coefficient

*p<0.05; **p<0.01; ***p<0.001

Gender was a consistent and significant predictor across all mental health outcomes. Males had significantly lower scores on depression (β = −10.40), anxiety (β = −11.73), and stress (β = −12.89) compared to females (p < 0.001 for all). It supports existing literature highlighting women’s greater vulnerability in post-disaster mental health due to caregiving roles, reduced access to resources, and social norms limiting agency during crises. A previous study reported the mental health impact on women due to the 2022 flood in Bangladesh [2]. The gender disparity is consistent with prior research indicating that women are generally more vulnerable to mental health issues following natural hazards, likely due to socio-cultural, economic, and caregiving roles [61]. One study indicated that women often bear a disproportionate burden due to existing inequalities, caregiving responsibilities, and limited access to resources [62]. Research has consistently shown that natural hazards disproportionately affect women, not only because of their caregiving roles within families and communities but also due to societal norms that restrict their access to essential resources and limit their decision-making power during recovery efforts. These gendered vulnerabilities may be further exacerbated in low-income settings, where women face compounded challenges in the aftermath of disasters, including increased mental health risks such as anxiety, depression, and PTSD. Such challenges stem from both socio-cultural expectations and economic inequalities, making women’s recovery more difficult and prolonging their vulnerability to future hazards [63]. For example, during disasters, women may face increased risks of violence and social isolation, which can further contribute to mental health challenges [62]. Biological differences may also play a role in the observed gender disparities in depression. Hormonal fluctuations and neurodevelopmental changes during adolescence have been linked to increased vulnerability to depression in females [64–66]. Women exposed to natural hazards exhibit higher rates of anxiety and depression compared to their male counterparts [67].

Age also emerged as a relevant factor, particularly for stress. Respondents aged 46–55 reported higher stress levels (β = 3.83, p < 0.05), consistent with prior studies that show older individuals may face heightened stress due to physical frailty and chronic conditions. Research indicates that older adults often face unique challenges during disasters, including physical impairments and chronic health conditions that can exacerbate stress levels. A study on the vulnerability of elderly populations in disasters highlights that older adults typically have less physiological reserve and are more prone to severe injuries during such events [68]. While some studies suggest that older adults may possess more mature coping styles and resilience due to life experience, others indicate that age-related factors can diminish their coping capacity. For instance, a study found that older adults might struggle with anticipatory coping mechanisms compared to younger adults, leading to increased stress when faced with environmental challenges [69].

Additionally, education played a key role in anxiety levels, with individuals having at least SSC and non-SSC education reporting lower anxiety levels (β = −3.81, p < 0.001 for non-secondary education and β = −3.55, p < 0.01) compared with the individuals without any formal education. Education is a well-documented factor in mental health resilience, where lower educational attainment is often associated with poorer coping mechanisms in the face of stress and trauma. One study found that individuals with low educational attainment were significantly more likely to experience anxiety and depressive symptoms throughout their lives, suggesting that education serves as a protective factor against mental health issues [70]. Research indicates that education equips individuals with the knowledge and resources to navigate stressful situations more effectively. For instance, the study conducted in Kerala, India, found that individuals with lower educational levels were more susceptible to PTSD, depression, and anxiety following floods, highlighting how education can serve as a buffer against mental health issues [23]. Higher education levels are often associated with stronger social networks and support systems. The review on the psychosocial impacts of flooding emphasizes that social support is vital for mental health recovery after disasters [60].

Chronic illness and disability were also significant. Individuals with chronic diseases (β = 3.05 for depression, β = 3.26 for anxiety, p < 0.05) and disabilities (β ≈ 3.7–3.8 across all DASS domains, p < 0.01) showed elevated distress. These findings emphasize the compounding burden of physical and psychological vulnerability during disasters. In addition, these findings indicate that pre-existing health conditions can exacerbate mental health symptoms in the aftermath of disasters. Individuals with chronic diseases are at a higher risk of developing depressive symptoms due to the ongoing psychological and physical stress associated with managing their illnesses [71]. It reinforces the idea that pre-existing health conditions can exacerbate mental health symptoms in the aftermath of disasters, as individuals already struggling with chronic illnesses may find it even more challenging to cope with additional stressors brought on by such events [72]. A study examining various types of disabilities in South Korea found significant correlations between disability status and depressive symptoms, indicating that those with disabilities are particularly vulnerable to mental health issues [73]. The study also suggests that socioeconomic factors play a crucial role in how chronic diseases and disabilities affect mental health. Individuals from lower socioeconomic backgrounds often face additional stressors related to financial instability and limited access to healthcare, which can further exacerbate depressive symptoms [74]. Although financial instability is widely acknowledged as a contributing factor to mental health distress, our findings did not indicate a statistically significant association between monthly income and depression, anxiety, or stress (see Table 7). It may be due to the relative economic homogeneity of the study population, where a large proportion of participants reported low or no income. The limited variation in income levels could have reduced the ability to detect significant effects. Future studies with a more diverse income distribution and larger sample size may provide further insights into the role of financial instability in shaping post-disaster mental health outcomes.

Interestingly, higher social satisfaction correlated with increased anxiety levels (β = 3.88 for “satisfied”, β = 3.41 for “very satisfied”, p < 0.001 and p < 0.01, respectively). The counterintuitive result may indicate social obligations or the psychological toll of witnessing peers’ suffering and warrants further qualitative investigation. It contrasts with previous research, highlighting social networks’ protective role in mitigating the mental health effects of natural hazards. Typically, individuals with strong social ties experience lower levels of anxiety and depression during stressful events, including natural hazards [75]. Social networks are often seen as a key source of resilience, helping individuals use adaptive coping strategies to mitigate stress. However, our findings suggest that, in the context, increased social satisfaction may be associated with heightened anxiety, potentially due to underlying social dynamics or expectations. Individuals with greater social satisfaction in the population may face different stressors or pressures, which exacerbate anxiety levels. Moreover, the literature consistently links social isolation with increased vulnerability to anxiety and depression [76, 77]. Still, our findings indicate a complex relationship between social satisfaction and mental health that warrants further investigation.

Flood-specific experiences were also influential. Reporting mental health issues post-flood was the strongest predictor of all three DASS outcomes (β ≈ 5.00–7.25, p < 0.001), affirming the flood’s profound psychological impact. Additionally, long-term livelihood impact was a significant predictor of anxiety (β = 2.49, p < 0.01), linking economic instability with heightened emotional distress. A study found that almost all households surveyed experienced a reduced income due to flooding, emphasizing that the consequences are temporary and can affect livelihoods for years [78]. Research indicates that financial worries significantly contribute to psychological distress among affected populations [79].

These results confirm the study’s hypothesis and fulfill its objective: that specific sociodemographic and disaster-related factors significantly predict depression, anxiety, and stress following the 2024 flood. They underscore the urgent need for targeted mental health interventions tailored to vulnerable populations, especially women, the chronically ill, and those with limited education.

The table presents the results of multiple linear regression analyses, showing the relationship between sociodemographic and flood-related factors and the levels of depression, anxiety, and stress among participants. The table includes the beta coefficients (β) and standard errors (SE) for each variable, indicating the strength and direction of the associations. Statistical significance is noted, with variables such as gender, age, chronic illness, and disability found to significantly affect mental health outcomes, as measured by the Depression, Anxiety, and Stress Scale (DASS-21).

Conclusion and recommendation

Our study underscores the significant psychological impact of the 2024 floods in Burichang Upazila, with high levels of depression, anxiety, and stress observed among the affected individuals. The findings highlight the community’s vulnerability, exacerbated by factors such as inadequate infrastructure, limited education, pre-existing health conditions, and gender disparities. Additionally, the population faced intensified challenges due to insufficient social support systems. However, the study has several limitations, including the use of non-probability sampling, which may affect the generalizability of the results and introduce potential bias. Stratified analyses on vulnerabilities such as age, sex, and income were not possible due to sample constraints. Furthermore, the cross-sectional design restricts the ability to assess long-term mental health outcomes. Based on these findings, it is recommended that future flood preparedness efforts focus on enhancing community-based training, improving flood mitigation infrastructure, and establishing mobile mental health units to provide accessible care. Additionally, gender-sensitive disaster response strategies should be prioritized, along with support for vulnerable groups such as older people and those with chronic illnesses. Strengthening social support systems through community networks and integrating disaster risk reduction into policy frameworks is essential. Ultimately, future research should focus on the long-term mental health effects of disasters, refine sampling methods, and investigate the role of social support in mitigating distress, thereby contributing to more effective disaster recovery strategies and mental health interventions.

Abbreviations

DASS

depression, anxiety, and stress

Authors’ contributions

MMR and IAS contributed to the work’s conception, design, and drafting. MMR, IAS, SA, AMS, SK, MTH, and EA contributed to the literature review and manuscript revision for important intellectual content. All authors approved the final version of the manuscript and agreed on all aspects of the work.

Funding

No external funding was received.

Data availability

Data can be available upon request to the corresponding author.

Declarations

Ethics approval and consent to participate

The research was part of an approved study (Ref. No. KUECC-2022/06/16) by the Ethical Clearance Committee of Khulna University, Khulna, Bangladesh. The study adhered to all ethical guidelines outlined in the Declaration of Helsinki and its subsequent amendments [48]. Informed consent was obtained from all participants and their legal guardian(s) (where required).

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.