Prevalence of and factors associated with anxiety, depression and post-traumatic stress disorder among Sudan ebolavirus disease survivors and family members, Uganda, January 2023: a cross-sectional study

Abstract

Background

Communities affected by Ebola disease (EBOD) may face resulting increases in mental health disorders. We evaluated the prevalence of and factors associated with mental health disorders among persons affected by the 2022 Sudan virus disease (SVD) outbreak in Uganda.

Methods

We conducted a cross-sectional study among SVD survivors and family members of survivors and fatal cases from 15–31 January 2023. We included only laboratory-confirmed SVD survivors and family members who lived with or cared for confirmed SVD patients during their illness. The Hospital Anxiety and Depression Scale was used to evaluate anxiety and depression. The post-traumatic stress disorder (PTSD) checklist for the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition was used to evaluate PTSD. Modified Poisson regression was used to determine factors associated with each mental health disorder.

Results

We enrolled 54 survivors and 82 family members; median age was 30 years (range, 15–73) and 54% were female. The prevalence of anxiety (55%) and depression (50%) was higher than PTSD (17%). The prevalence of all mental health disorders was similar between survivors and family members. Household size was associated with both anxiety and PTSD. Number of SVD deaths in the household was associated with depression.

Conclusion

Approximately two-thirds of SVD survivors and family members of patients in the 2022 outbreak in Uganda had ≥ 1 mental health disorders shortly after the outbreak ended. Strengthening mental health services during and after Ebola virus outbreaks for survivors and family members of patients may enhance the quality of outbreak response.

Introduction

Outbreaks, including those from Ebola disease (EBOD), continue to threaten global health security. Large EBOD outbreaks can have far-reaching consequences beyond morbidity and mortality. Previous outbreaks have disrupted health systems, threatening gains made in the treatment and control of other serious diseases like malaria, HIV, tuberculosis and vaccine-preventable diseases [1, 2]. Through reduced agricultural production, loss of employment, reduced tourism, and reduced number of investors, millions of people have been pushed into poverty by the economic disruption caused by EBOD outbreaks [3–5]. EBOD outbreaks are also characterized by social disruption as communities in those areas face social unrest, mistrust of authority, reduced social cohesion, disruption in social life, a breakdown in social norms, and disruptions in social services such as education [6–8].

An often-neglected consequence of EBOD outbreaks is the increased occurrence of mental health disorders during and after the outbreak [9, 10]. Fear of infection and death, the experience of severe illness among survivors, loss of community and family members, isolation, stigmatization, deterioration of socioeconomic conditions, and other risk factors for developing mental health disorders are common during EBOD outbreaks [10–12]. As a result, experiences related to EBOD meet the criteria (e.g. exposure to actual or threatened death and serious injury) to be classified as traumatic events according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)[13]. Indeed, EBOD outbreaks have been linked to an increased prevalence of mental health disorders such as anxiety, depression, and post-traumatic stress disorder among patients, health workers, and affected communities [10, 14–16].

Studies in EBOD-affected communities across Africa have reported prevalence estimates of mental health disorders ranging from 15 to 83% after an outbreak [9, 10]. Most affected are EBOD survivors, family members of patients, health workers, and safe burial teams [17, 18]. However, the larger community could also be affected by mental health disorders arising from the experience of living through EBOD outbreaks [19].

Efforts to improve mental health services related to EBOD outbreaks have been largely unsuccessful across Africa. In 2007, the World Health Organization (WHO), through the Inter-Agency Standing Committee, released guidelines on mental health and psychosocial support in emergency settings [20]. These are meant to guide the planning and implementation of mental health services before, during, and after emergencies. They are useful in coordination, collaboration and advocacy efforts towards improvement of mental health services in emergency settings such as EBOD outbreaks. However, mental health services in countries affected by EBOD typically are of limited scope and duration, face limitations in human resources and essential medications, and do not cater specifically to the social welfare of EBOD-affected communities [21–24].

Uganda has had seven documented EBOD outbreaks between 2000 and 2022 [25, 26]. The most recent, which occurred in late 2022, was caused by Sudan ebolavirus and resulted in 142 confirmed and 22 probable cases in nine districts of Uganda [25–27]. Despite these repeated outbreaks, there are no studies in Uganda documenting the effect of EBOD on mental health. The result has been an under investment in mental health services during EBOD outbreaks. We evaluated the prevalence of, and factors associated with anxiety, depression and PTSD among persons affected by the 2022 Sudan virus disease outbreak in Uganda.

Methods

Study design

We conducted a cross-sectional study from January 15–31, 2023 among SVD survivors and family members of SVD cases. This was approximately 6 weeks after the last SVD case was discharged from the Ebola treatment unit.

A pretested structured questionnaire was used to collect data from SVD survivors/ family members. This was translated to the local language (Luganda) and was interviewer administered. The questionnaire was pretested on five persons from different households neighboring the SVD affected households in Mubende District. These results were not included in the data analysis. The pretest showed that the questions were clear and understandable to the participants.

Study setting

The study was conducted in Mubende and Kassanda Districts in central Uganda. On September 20, 2022, the government of Uganda declared an outbreak of Ebola Disease caused by the Sudan Virus. This outbreak started in Mubende District before spreading to eight other Districts. Mubende and Kassanda were the epicenter of the outbreak contributing 82% of all cases [25].

Study population and sampling procedure

We recruited SVD survivors and family members of SVD cases (both survivors and fatal cases) in Mubende and Kassanda districts, the two most affected districts during the outbreak. These two districts reported 113 confirmed and 21 probable cases [25]. We defined SVD survivors as persons with a confirmed positive reverse-transcriptase polymerase chain reaction test result for Ebola virus who were treated and discharged [28]. Family members were defined as persons residing with an SVD patient at onset of disease or who were involved in the care of the patient.

During the investigation of the 2022 SVD outbreak in Uganda, the Ministry of Health compiled all data into one database named the SVD line list. This file contained vital information such sociodemographic data, disease related data and outcomes. The national SVD line list was used to identify all SVD cases.

We visited the homes of all SVD survivors and fatal cases to enroll study participants. At the homes, SVD survivors were enrolled first, followed by family members. Being an SVD survivor precluded one from being enrolled as a family member. All eligible SVD survivors and family members in each home were enrolled. Homes with SVD survivors gave rise to both SVD survivors and family members while homes of SVD fatal cases gave rise to only family members (Fig. 1).

Fig. 1 [Images not available. See PDF.]

Flow diagram showing enrollment of SVD survivors and family members

Household members who were less than 15 years were excluded because of the complexities of diagnosing anxiety, depression and PTSD in persons below 15 years [29]. Those with prior diagnosis of anxiety, depression or PTSD were also excluded because they already had the outcomes of interest prior to the exposure to SVD. Persons who were not present at home during the time the SVD patient was ill were deemed ineligible and excluded from the study.

Variables

We collected data on these sociodemographic characteristics at the time of the interview: age, sex, level of education (no formal education, primary, post primary), employment status, marital status (single, married/cohabiting, widowed), number of persons currently living in the household. Other EBOD-related variables included: number of SVD cases in the household, number of SVD deaths in the household, respondent’s (if survivor) perception of severity of illness (severe/non-severe), experiencing stigma (yes/no), level of economic disruption due to SVD (none, mild, moderate, serious), social support received from family and friends (none, minimal, satisfactory, very satisfactory). These characteristics were used as the independent variables in bivariate and multivariate analysis.

The three outcome variables were all binary categorical variables, that is, presence of anxiety, depression, PTSD or not.

Presence of anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS). The HADS is a 14-question tool that measures anxiety and depression with seven questions for each. The study participants were asked to respond to a particular response that they thought was closest to how they had been feeling in the past week. Each question was scored on a 4-point Likert scale ranging from zero (no impairment) to three (severe impairment), with a maximum score of 21 for anxiety or depression. To classify a participant as having anxiety and depression, we used the recommended cut off score of ≥ 8. A systematic review demonstrated an optimal balance between sensitivity and specificity being achieved with a cutoff score of ≥ 8 [30–32].

The Post-Traumatic Stress Disorder Checklist from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (PCL-5) was used to identify participants with PTSD. Studies have shown that PCL-5 is a psychometrically sound measure of PTSD symptoms both in the clinical and research setting [33, 34]. The PCL-5 is a self-report measure of the 20 Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) symptoms of post-traumatic stress disorder. The 20 questions assess how much participants were bothered by a PTSD symptom over the past month using a 5-point Likert scale that ranges from 0 (not at all) to 4 (extremely). For example; in the past month, how much were you bothered by suddenly feeling or acting as if the stressful experience were actually happening again (as if you were actually back there reliving it)? The PCL-5 can be used to monitor symptom change, to screen for PTSD, or to make a provisional PTSD diagnosis. A cut-off score of ≥ 38 was used to make a provisional diagnosis of PTSD. This cut-off has been demonstrated to optimize sensitivity (0.78) and specificity (0.98).

There is a dearth of validated tools for measuring anxiety, depression and PTSD in Uganda. However, both the HADS and the PCL-5 have been validated in several African countries and found to be reliable [35–38]. Given the social, cultural and economic similarities of these populations with ours, we adopted the two tools.

Data analysis

Data was analyzed using Stata v14. Frequencies and percentages were used to describe categorical variables while continuous variables were expressed as means with standard deviation (SD) or median and range where appropriate.

Modified Poisson regression analysis (Poisson regression with a robust variance estimation) using generalized linear models was used to determine factors associated with anxiety, depression and PTSD. In cross-sectional studies like ours, were outcome of interest is binary and not rare, Modified Poisson regression approximates the risk ratios or relative ratios better than binary logistic regression [39]. Assumptions (linearity and homoscedasticity assumptions) for modified Poisson regression were tested using residual plots.

For each of the three outcome variables (anxiety, depression, PTSD), we conducted separate bivariable and multivariable modified Poisson regression analyses to determine their relationship with the independent variables (predictors).

All variables that had a p-value < 0.2 [40, 41] at bivariable analyses were considered candidates for the multivariable analysis models. First, we tested these variables for collinearity (r > 0.40). Were two variables were correlated, only one of them was included in the model. The remaining variables were then added to the multivariable model all at once. Backward elimination technique was applied to remove predictors that were not significant one at a time until we developed a parsimonious model. Goodness of fit was tested using the Pearson goodness of fit test for each model. Associations were tested at a 95% confidence interval and P-values at p ≤ 0.05 were considered statistically significant. Prevalence ratios, their 95% confidence intervals and p-values were reported and presented in tables.

Results

We enrolled 136 participants into the study. Of these, 54 (40%) were SVD survivors and 82 (60%) were family members. The median age was 30 years (range, 15–73). Among all participants, 80 (58%) had attained primary level education, 78 (57%) were married, and 77 (57%) were working as farmers. The median household size was 5 members (range, 1–16). Of the 54 survivors, 9 (17%) lived alone. Most of the participants (60%) reported at least one SVD-related death in the household (Table 1).

Table 1. Comparison of sociodemographic and SVD related characteristics between SVD survivors and family members after the 2022 SVD outbreak in Uganda

Characteristic	Survivors (n = 54)		Family members (n = 82)		p-value
	n	(%)	n	(%)
Age
15–24	13	(24)	23	(28)	0.13
25–35	24	(44)	23	(28)
> 35	17	(31)	36	(44)
Sex
Female	20	(37)	53	(65)	0.002
Male	34	(63)	29	(35)
Education level
None	7	(13)	14	(17)	0.11
Primary	30	(56)	50	(61)
Post-primary	17	(31)	18	(22)
Marital status after SVD outbreak
Single	13	(24)	24	(29)	0.20
Married	35	(65)	43	(52)
Widowed	6	(11)	15	(18)
Employment status after the outbreak
Employed	41	(76)	72	(88)	0.10
Unemployed	6	(11)	7	(8)
NA (school going)	7	(13)	3	(4)
Economic disruption due to SVD outbreak
None/Mild	6	(11)	13	(16)	0.42
Moderate	19	(35)	25	(30)
Severe	29	(54)	44	(54)
Social support from family and friends
None/minimal	23	(43)	31	(38)	0.03
Some	17	(31)	42	(51)
A lot	14	(26)	9	(11)
Household size
1	9	(17)	2	(3)	0.002
2–4	21	(39)	24	(29)
> 4	24	(44)	56	(68)
SVD deaths in the household
None	43	(80)	12	(22)	< 0.001
1 death	10	(18)	59	(72)
> 1 death	1	(2)	11	(6)
Experienced stigma	44	(81)	70	(85)	0.55

NA Not applicable, SVD Sudan Virus Disease

Survivors and family members were similar across age, education level, marital status, employment status, experience of stigma and level of economic disruption due to SVD outbreak. However, more family members than survivors were female (65% vs. 35%, p = 0.002). More survivors lived alone compared to family members (17% vs. 3%, p = 0.002). More family members reported an SVD death compared to SVD survivors (78% vs. 20%, p =  < 0.001). SVD survivors were more likely than family members to report having received ‘a lot’ of social support (26% vs. 11%, p = 0.03). (Table 1).

Of 136 participants, 87 (64%) had at least one mental health disorder. Anxiety was present in 75 (55%), depression in 68 (50%), and PTSD in 23 (17%). In bivariate analysis, family members had a higher prevalence of depression than survivors (57% vs. 39%, p = 0.04).

Living in households with 2–4 members was associated with decreased prevalence (PR = 0.54, 95%CI 0.31–0.92) of anxiety when compared to living alone. Having one (PR = 1.6, 95%CI 1.1–2.6) or more than one SVD deaths (PR = 1.9, 95%CI 1.1–3.5) in the household was associated with increased prevalence of anxiety compared to having no deaths in the household (Table 2).

Table 2. Factors associated with anxiety among SVD survivors and family members (n = 136) after the 2022 SVD outbreak in Uganda

Characteristic	Anxiety n(%)		cPR	(95% CI)	p-value	aPR	(95% CI)	p-value
Household size
1	7	(64)	Ref			Ref
2–4	39	(38)	0.59	(0.33–1.1)	0.08	0.54	(0.31–0.92)	0.02
> 4	51	(64)	1.0	(0.62–1.63)	0.99	0.87	(0.56–1.38)	0.56
Economic disruption due to SVD outbreak
None/Mild	9	(47)	Ref			Ref
Moderate	23	(52)	1.1	(0.65–1.9)	0.72	1.2	(0.74–2.1)	0.42
Serious	43	(59)	1.2	(0.73–2.1)	0.43	1.3	(0.75–2.1)	0.37
SVD deaths in household
None	22	(40)	Ref			Ref
One death	44	(64)	1.6	(1.1–2.4)	0.02	1.6	(1.1–2.6)	0.03
> 1 death	9	(75)	1.9	(1.1–3.2)	0.02	1.9	(1.1–3.5)	0.04
Participant type
SVD survivor	25	(46)	Ref			Ref
Family member	50	(61)	1.3	(0.9–1.9)	0.12	0.93	(0.63–1.38)	0.74

cPR Crude prevalence ratio, aPR Adjusted prevalence ratio, CI Confidence interval, Ref Reference category

Having more than one SVD death in the household was associated with increased prevalence (aPR = 1.8, 95%CI 1.1–3.3) of depression compared to having no SVD deaths in the household (Table 3).

Table 3. Factors associated with depression among SVD survivors and family members (n = 136) after the 2022 SVD outbreak in Uganda

Characteristic	Depression n (%)		cPR	(95%CI)	p-value	aPR	(95%CI)	p-value
Economic disruption due to SVD outbreak
None/Mild	7	(37)	Ref			Ref
Moderate	24	(55)	1.5	(0.84–2.6)	0.18	1.6	(1.0–2.7)	0.07
Severe	37	(51)	1.4	(0.80–2.4)	0.25	1.4	(0.88–2.3)	0.15
SVD deaths in household
None	20	(36)	Ref			Ref
One death	39	(57)	1.6	(1.0–2.5)	0.07	1.4	(0.8–2.5)	0.21
> 1 death	9	(75)	2.1	(1.2–3.6)	0.01	1.8	(1.1–3.3)	0.04
Sex
Female	42	(58)	Ref			Ref
Male	26	(41)	0.72	(0.51–1.0)	0.06	0.77	(0.54–1.1)	0.15
Participant type
SVD survivor	21	(39)	Ref			Ref
Family member	47	(57)	1.5	(1.0–2.2)	0.06	1.1	(0.68–1.7)	0.72

cPR Crude prevalence ratio, aPR Adjusted prevalence ratio, CI Confidence interval, Ref Reference category

Living in households with 2–4 members (PR = 0.24, 95%CI 0.09–0.66) and > 4 members (PR = 0.32, 95%CI.0.13–0.78) members was associated with decreased prevalence of PTSD when compared to living alone (Table 4).

Table 4. Factors associated with PTSD among SVD survivors and family members (n = 136) after the 2022 SVD outbreak in Uganda

Characteristic	PTSD		cPR	(95% CI)	p-value	aPR	(95% CI)	p-value
	n (%)
Household size
1	5	(45)	Ref			Ref
2–4	5	(11)	0.24	(0.08–0.71)	0.01	0.24	(0.09–0.66)	0.01
> 4	13	(16)	0.36	(0.15–0.86)	0.02	0.32	(0.13–0.78)	0.01
Sex
Female	16	(22)	Ref			Ref
Male	7	(11)	0.51	(0.23–1.11)	0.09	0.49	(0.23–1.0)	0.06
SVD cases in household
1 case	13	(13)	Ref			Ref
> 1 case	10	(28)	2.1	(1.0–4.6)	0.05	2.1	(1.0–4.3)	0.05
Participant type
SVD survivor	11	(20)	Ref			Ref
Family member	12	(15)	0.72	(0.32–1.63)	0.43	0.77	(0.34–1.76)	0.53

cPR Crude prevalence ratio, aPR Adjusted prevalence ratio, CI Confidence interval, Ref Reference category

Participant type (SVD survivor versus family member) was not independently associated with any mental health disorder on multivariable analysis.

Discussion

We found a high prevalence (64%) of at least one mental health disorder among SVD survivors and family members of patients affected by the SVD outbreak in Uganda in late 2022. Approximately half of persons interviewed experienced depression and anxiety; fewer than 1 in 5 experienced PTSD. Participants who lived alone had increased prevalence of anxiety and PTSD. Participants with SVD deaths in the household had increased prevalence of anxiety and depression. The prevalence of all mental health disorders was similar between survivors and family members.

Depression is the most common mental health disorder related to EBOD, with studies reporting post-outbreak prevalence of depression from 15 to 65% [9, 10, 42, 43]. Post-epidemic depression can slow the social and economic recovery of affected persons and communities, particularly through loss of productivity, direct costs associated with depression treatment, and relationship challenges leading to family breakdown [44–46]. Half of SVD survivors and family members of SVD patients in our study experienced depression. Variations between studies could be related to differences in screening tools used, mental health services available, outbreak management, cultural differences between affected communities, and timing of data collection (how long after the end of the outbreak the data were collected). A study from the Democratic Republic of Congo that used the same tools used in our study showed a post-epidemic prevalence of depression of 24% [43]. However, this study did not indicate how much time after discharge from the Ebola treatment unit had passed before the survivors were interviewed.

Anxiety often accompanies depression, and the two may feed each other [47–49]. In low-resource settings, anxiety is often a neglected mental health disorder [50]. In our study, more than half (55%) of SVD survivors and family members of SVD patients had anxiety. This is higher than the prevalence found in most studies done among EBOD-affected communities (13–37%) [9, 42, 43, 51, 52]. A meta-analysis of five studies on mental health disorders in EBOD survivors in West Africa showed a prevalence of anxiety of 14%, also noted to be lower than the prevalence found in most studies [10]. Additional studies may be needed to understand why the prevalence in Uganda was higher than in other locations, to implement effective interventions.

PTSD is a serious psychiatric disorder characterized by a failure to recover after experiencing or witnessing a traumatic event. The experience of EBOD survivors and family members involved in caring for EBOD patients meets the criteria of a traumatic event as defined in the DSM-5 [13, 53]. Consistent with other studies done after EBOD outbreaks, we found that PTSD was the least prevalent mental health disorder among survivors and family members. Still, the prevalence of PTSD reported in our study (17%) was consistent with most studies among post Ebola virus outbreak communities; a study done 3 to 4 weeks post-discharge for survivors in Sierra Leone revealed a PTSD prevalence of 21% [54]. A study in Guinea that assessed PTSD among survivors 8 months post-discharge reported a prevalence of 9%, which was lower than that in our study [55]. PTSD, as well as other mental health disorders, may decrease with time after the outbreak [56–58].

Persons living with others had reduced prevalence of anxiety and PTSD, compared to persons who lived alone. Households with only one person may represent either survivors who lived alone before the outbreak or persons who did not live alone, but who lost their household members during the outbreak. This group may be qualitatively different from persons who did not lose their household members. Studies have established an association between living alone and increased risk of mental health disorders [59, 60], and it is possible that the combination of factors facing this group—either recovering from SVD or managing the loss of other household members—presents unique challenges that increase the risk for these disorders.

EBOD outbreaks, including SVD outbreaks, are characterized by a high mortality rate [61]. Consistent with existing literature, our study showed that having a death in the household increased the risk of depression and anxiety [62]. This association persisted even after controlling for participant type (being a survivor versus being a family member). Increased numbers of household deaths were associated with increased prevalence of anxiety among family members. This could be due to several factors, such as the ensuing economic disruption caused by the death of a breadwinner, fear of stigmatization, or other factors that may affect families that lose members during EBOD outbreaks [63]. Ensuring that households that lose family members receive appropriate mental health care during and after the outbreak is important in addressing this issue.

Few studies have reported on mental health disorders among family members of EBOD patients. We found no significant difference in having a mental health disorder if one was a survivor or an SVD patient family member. This suggests a need to expand mental health services during and after an EBOD outbreak to include family members of patients. While temporary mental health clinics embedded in EBOD survivors’ rehabilitation programs are often available [64], these services usually exclude family members of EBOD patients [21, 22]. Ensuring the inclusion of family members of both survivors and patients who die may facilitate improved recovery of communities affected by such outbreaks.

Limitations

Our study had some limitations. First, we utilized tools that were not validated in Uganda and relied on cutoff points stipulated in literature from other African countries. It is not known how these tools perform in the Ugandan population. Second, our sample size was small. This could have limited our ability to find significant associations between some factors that may have been associated with mental health disorders. Third, approximately 1/3 of household members were not home at the time of our visit and were not able to participate in the study; a small number of persons also declined. It is not known whether those who were unavailable were more or less likely to be experiencing mental health disorders; thus, the effect of this on our study cannot be determined.

Conclusion

Approximately two-thirds of SVD survivors and family members of patients in the 2022 outbreak in Uganda had ≥ 1 mental health disorder shortly after the outbreak ended. Strengthening mental health services during and after EBOD outbreaks for survivors and family members of patients may enhance the quality of outbreak response. This may require expanding the scope and duration of mental health services during and after EBOD outbreaks, incorporating mental health services in outbreak preparedness, evaluation and documentation of mental health services provided in emergency settings to identify gaps and areas for improvement.

Acknowledgements

The authors thank the Uganda Ministry of health, Mubende and Kassanda District health authorities and the community health workers that facilitated the identification of study participants and collection of data during this study.

Author contributions

BA and JH conceived and designed the study. HNN, MWW, JFZ, BS, RZ, PCK, MGZ, SMM collected data and contributed to manuscript writing. BA, RM and BS analyzed and interpreted data. BA led the writing of the manuscript. DK, LB, RM, AR and JH critically reviewed the manuscript for intellectual content. All co-authors read and approved the final manuscript. BA is the guarantor of the paper.

Funding

We received funding from the US CDC to conduct this project.

Data availability

The data collected and utilized in this project belongs to the Uganda Ministry of Health. For confidentiality reasons, the data are not publicly available. However, the data can be availed upon reasonable request from the corresponding author and with permission from the Ministry of Health.

Declarations

Ethics approval and consent to participate

This study was in response to a public health emergency. Ministry of Health Uganda granted us permission to conduct this study in response to the need to improve the mental health and psychosocial support pillar of the outbreak response during Ebola disease outbreaks. In-addition, this activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. §§See e.g., 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq.

We sought verbal informed consent from the respondents who were at least 18 years old as well as those that were below 18 years of age and emancipated. The authors also sought assent from children below 18 years of age who were not emancipated and informed verbal consent from their parents or guardians. Verbal consent was preferred over written consent to reduce a possible risk of spread of the Ebola virus. The data was collected with no identifying information. Data was stored on a password protected computer and only accessible to the study team.

Consent for publication

Not applicable.

Competing interest

The authors declare no competing of interest.

Abbreviations

aPR

Adjusted prevalence ratio

CI

Confidence Interval

DSM-5

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

EBOD

Ebola disease

HADS

Hospital Anxiety and Depression Scale

PCL-5

Post-traumatic stress disorder checklist for the DSM-5

PTSD

Post-traumatic stress disorder

SVD

Sudan virus disease

US CDC

United States Center for Disease Control and Prevention

WHO

World Health Organization

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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