Abstract
Background:
Understanding the relationship between mental health and COVID-19 prevention practices is crucial but challenging considering COVID-19’s impact on mental well-being. Liberia, a West African country, had well-documented rates of depression and anxiety prior to COVID-19. Liberia responded aggressively to COVID-19 while case counts remained low; thus, it is an ideal setting to study the relationship of mental health and COVID-19 prevention practices.
Methods:
A validated cross-sectional survey was administered to 250 randomly selected residents of Montserrado county, Liberia in June 2021, asking about their mental health and adherence to COVID-19 prevention practices. The survey included the Generalized Anxiety Disorder-7 and the Patient Health Questionnaire-9 to assess for anxiety and depression, respectively. Responses were analyzed using Spearman correlation and regression.
Results:
Scores indicative of depression were present in 43% (95% confidence interval [CI]: 37–49) of participants; scores indicative of anxiety were present in 41% (95% CI: 34–47). Self-reported adherence to COVID-19 prevention practices was middling and varied greatly by behavior. Higher scores for depression and anxiety were significantly associated with lower adherence to COVID-19 prevention practices.
Conclusions:
Results indicate that while the spread of COVID-19 has certainly affected mental health, it is likely that pre-existing mental health conditions affected the spread of COVID-19 through lower adherence to prevention practices. Policymakers should consider investing in mental health services as an important step in managing future epidemics, and the needs of people with poor mental health when designing epidemic responses, particularly in low-income countries where the burdens of adherence are likely to be greater.
Keywords: COVID-19, Liberia, mental health, depression, anxiety disorders, Africa South of the Sahara
The pandemic caused by COVID-19, an infectious respiratory illness, adversely affected mental health globally.1 The prevalence of mental illnesses such as depression and anxiety, as well as suicide risks, increased.2 While the virus itself, which has killed almost 7 million people worldwide,3 has contributed to these changes,1 so too did the COVID-19 prevention practices that were recommended or mandated to prevent its spread.2 Prevention practices included social distancing, lockdowns or curfews, cessation of in-person school for children, mask adherence, avoidance of crowds, and work-from-home arrangements for some workers. These practices contributed to stress, sleep disturbances, social isolation, and loneliness,2 but the precise relationship between these practices and mental health is not well understood.
Depression, Anxiety, and COVID-19 Prevention Practices
Most of the data on the relationship between COVID-19 prevention practices and mental health outcomes come from the United States, Europe, and China.4 However, prior to COVID-19, much of the global burden of depression and anxiety existed in middle- and low-income countries.5 Data on the relationship between depression, anxiety, and COVID-19 prevention practices are crucial to understanding pandemic response in these countries.
Liberia is a low-income country of approximately 5 million people in West Africa. From 1990 to 2019, West Africa had the fastest rising rate of depression globally,6 and one of the fastest rising rates of anxiety.7 A country which is recovering from both an extended period of civil strife and an Ebola epidemic, Liberia has had a previously documented rate of depression of about 45%,8 and anxiety of 34%.8 The mental health needs of the country are understudied, particularly with regard to the general adult population.9
Liberia also has had few COVID-19 cases and deaths relative to much of the world. As of October 2023, 7930 people have been diagnosed with COVID-19, and 294 people have died.3 Liberia’s infection rate of about 1.63/1000 people was far lower than both Africa’s overall (10.1/1000) or the global rate (85.8/1000).10 This was in part due to an aggressive containment strategy spearheaded by the Government of Liberia. Liberia was one of the first countries globally to begin screening people for COVID-19 in airports. Further strategies included a 3-week lockdown in April 2020 which included the suspension of nonessential travel, the closure of schools, and the enacting of successive curfews.11 Throughout the first year of the pandemic, COVID-19 containment measures such as mask adherence, hand hygiene, and social distancing were encouraged via Ministry of Health guidelines throughout the country.12 This combination of an aggressive response with low COVID-19 incidence makes Liberia an ideal country in which to study the relationship of mental health and COVID-19 prevention practices.
Purpose
The purpose of this article was to investigate the relationship between mental health and COVID-19 prevention practices in Liberia’s capital 1 year into the pandemic. We hypothesized that participants who reported depression or anxiety would report lower adherence to COVID-19 prevention practices than those who did not.
Methods
Procedures and Participants
We conducted a cross-sectional survey of people 18 years of age or older living in 1 of 5 randomly selected enumeration areas (EAs) in Montserrado County between June 15 and 25, 2021. The survey was interviewer-administered by experienced enumerators and the participants’ responses recorded on designated coded paper surveys. Enumeration areas were selected with the target of including 3 urban and 2 rural EAs. Within each EA, 4 blocks were selected for inclusion using a random number generator. Enumerators approached 3 to 4 households in each cardinal direction per block. One participant was selected per household; if more than one eligible person was interested in participating, the enumerator rolled a die to choose. Participants had to be able to understand and answers questions in basic culturally appropriate English. Enumerators were trained in all study procedures, including COVID-19 safety, in a 2-day workshop prior to data collection.
Measurement Tools
The survey instrument was developed by 2 researchers both with previous experience conducting research in Liberia. The questionnaire consisted of 113 closed-ended questions addressing participants’ knowledge, attitudes, and practices pertinent to COVID-19, as well as questions on participant demographics and health status (instrument has been included in Supplemental Materials). COVID-related knowledge was assessed using true-false and multiple-choice questions. COVID-related attitudes were assessed using 5-point Likert scale responses ranging from “not at all” to “very.” COVID-related practices were assessed using 5-point Likert scale responses ranging from “never” (1) to “always” (5).
The Patient Health Questionnaire-9 (PHQ-9)13 was selected to assess depression and the Generalized Anxiety Disorder-7 (GAD-7)14 was selected to assess anxiety upon the advice of local experts from an international organization involved with mental health programs and the mental health division of the Ministry of Health in Liberia. These scales, or the closely related PHQ-8, had been used in Liberia previously.8,12 Per Kroenke et al,13 depression was considered present in participants with a score of 5 or greater on the PHQ-9, and per Spitzer et al,14 anxiety was considered present with a score of 5 or greater on the GAD-7. These cut-offs were selected to heighten the rigor of the analysis by including the effects of mild illness.
The completed instrument was reviewed by 4 local experts for face and content validity, with changes made as they suggested. Last, the enumerators piloted the instrument among themselves and in 16 households in 3 EAs that were not included in the study, with a particular focus on wording and clarity, and a final round of edits were made.
Ethical Considerations
All participants provided written informed consent prior to participation. This study was deemed exempt by the Institutional Review Board of the University of Michigan (HUM00191339) and approved by Institutional Review Board of the University of Liberia (Protocol# 20–12-241). Participants were compensated with household goods worth $2 USD. Throughout data collection, enumerators wore personal protective equipment and completed a COVID-19 daily symptom check prior to survey administration.
Analyses
Survey results were loaded manually from the paper surveys into an Excel workbook and then imported into STATA/BE 17.0 (College Station, Texas, United States). Descriptive statistics and group-wise comparisons, including t-tests and chi-squared tests, were used to assess for differences in participants’ demographics according to mental health status. Missing responses were coded as “0” for all scales.
Total PHQ-9 and GAD-7 scores were compiled. A COVID-19 prevention practice scale was created by totaling the Likert-type responses which participants gave to describe the frequency of their adherence to certain COVID-19 prevention practices (1 = never, 5 = always). Spearman correlation coefficients were used to assess for a potential linear relationship between COVID prevention practices and mental health (PHQ-9 and GAD-7 scores). To compare mean adherence score of COVID-19 prevention practices according to mental health status, t-tests were used. Regression was used to assess for the impact of depression and anxiety on adherence overall and to individual prevention practices. To correct for multiple hypothesis testing, we used Bonferroni’s correction (P = 0.0023) for individual COVID-19 prevention practices, but not for the overall score. All statistical analysis procedures were reviewed by a health services researcher with training in data science.
Results
Characteristics of the Study Sample
Two-hundred and fifty people participated in the survey. One-hundred and fifty people lived in urban EAs, 100 in rural EAs. Table 1 lists participant demographics for all participants (N = 250), those who have depression, anxiety, or both (n = 121), and those who have neither (n = 129). There were no statistically significant differences between the geographic locations, gender, education, age, or marital status between those who had neither depression nor anxiety and those who had one or both.
Table 1.
Study Participant Characteristics.
| Overall (N = 250) n (%) |
Either anxiety or depression (n = 121) n (%) |
Neither anxiety nor depression (n = 129) n (%) |
P | |
|---|---|---|---|---|
|
| ||||
| Urban | .25 | |||
| Urban | 150 (60) | 68 (56.2) | 82 (63.57) | |
| Rural | 100 (40) | 53 (43.8) | 47 (36.4) | |
| Gender | .70 | |||
| Man | 119 (48) | 59 (48.2) | 60 (46.5) | |
| Woman | 130 (52) | 61 (50.8) | 69 (53.5) | |
| Education | .36 | |||
| Grade school | 69 (28) | 38 (31.4) | 31 (24.2) | |
| High school | 78 (31) | 36 (29.8) | 42 (32.8) | |
| College | 56 (22) | 30 (25.8) | 26 (20.3) | |
| Vocational school | 16 (6) | 6 (5.0) | 10 (7.8) | |
| Other | 30 (13) | 11 (9.1) | 19 (14.4) | |
| Marital status | .66 | |||
| Single | 172 (69) | 82 (67.8) | 90 (70.3) | |
| Married | 63 (25) | 32 (26.5) | 31 (24.2) | |
| Divorced | 4 (2) | 3 (2.5) | 1 (0.8) | |
| Widowed | 10 (4) | 4 (3.3) | 6 (4.7) | |
| Employment status | < .001 | |||
| Employed | 77 (31) | 52 (43.7) | 25 (19.4) | |
| Unemployed | 79 (32) | 34 (28.5) | 45 (34.9) | |
| Retired | 4 (2) | 3 (2.5) | 1 (0.8) | |
| Student | 46 (19) | 19 (16.0) | 27 (20.9) | |
| Other | 42 (17) | 11 (9.3) | 31 (24.0) | |
| Mean cohabitants (range) | 9.18 (1–40) | 10.2 (1–40) | 8.2 (1–23) | .01 |
| Mean age in years (range) | 34.3 (18–75) | 35.4 (18–70) | 33.3 (18–75) | .17 |
However, there was a difference between the groups’ employment status and number of cohabitants. Participants without depression or anxiety were less likely to be employed and more likely to be unemployed, students, or other (P < .001). Furthermore, participants without depression or anxiety reported a mean of 8.2 cohabitants, while people with depression and anxiety reported a mean of 10.2 (P = .005). People with depression reported an average of 10.4 people per household (P < .002); people with anxiety reported an average of 10.2 (P < .01).
Forty-one percent of participants scored 5 or higher on the GAD-7 (95% confidence interval [CI]: 34–47), indicating clinically significant levels of anxiety (Figure 1). Only 18% of participants reported being previously diagnosed with anxiety (95% CI: 12–24). The mean GAD-7 scores of participants who had been previously diagnosed with anxiety (9.73) were significantly higher than those who had not (5.09) (P < .001).
Figure 1.
Distribution of GAD-7 scores among study participants.
Abbreviation: GAD-7, Generalized Anxiety Disorder-7.
Forty-three percent of participants scored 5 or higher or the PHQ-9 (95% CI: 37–49), indicating clinically significant levels of depression (Figure 2). Twenty-nine percent of participants reported being previously diagnosed with depression (95% CI: 22–36). The mean PHQ-9 scores of participants who had been previously diagnosed with depression (13.77) were significantly higher than those who had not (3.96) (P < .0001).
Figure 2.
Distribution PHQ-9 scores among study participants.
Abbreviation: PHQ-9, Patient Health Questionnaire-9.
Association Between Mental Health and Adherence to COVID-19 Prevention Practices
Self-reported COVID practices, scored on a 55-point scale (Cronbach’s alpha = 0.72), tended to show middling levels of adherence to COVID prevention measures, with 72% of people scoring between 28 and 43. Higher scores on the PHQ-9 were associated with lower adherence to COVID-19 prevention practices. Spearman’s coefficient between PHQ-9 score and COVID-19 practice score was −0.16 (P = .01) (Figure 3). Comparison by t-test of COVID prevention measures score between depressed and not depressed participants (PHQ-9 score ≥ 5) showed a mean score of 35.25 for people without depression and 32.95 for people with depression (p < .01).
Figure 3.
The relationship of PHQ-9 scores to adherence to COVID-19 prevention practices.
Abbreviation: PHQ-9, Patient Health Questionnaire-9.
Higher scores on the GAD-7 were associated with lower adherence to COVID-19 prevention practices (Figure 4). Spearman’s coefficient between GAD-7 score and COVID-19 practice score was −0.20 (P < .01). Comparison by t-test of COVID prevention measures score between anxious and not anxious participants (GAD-7 score ≥5) showed a mean score of 35.3 for people without anxiety and 32.9 for people with anxiety (P = .01). Eighty-one participants met the criteria for both depression and anxiety. These participants had a mean COVID practice score of 32.6 as opposed to an average score of 35.2 (P = .01) for participants who did not meet both criteria.
Figure 4.
The relationship of GAD-7 scores to adherence to COVID-19 prevention practices.
Abbreviation: GAD-7, Generalized Anxiety Disorder-7.
Tables 2 and 3 explore differences in adherence to COVID-19 prevention practices between people who reported depression (Table 2) or anxiety (Table 3) relative to the entire sample. Comparisons were made between the average self-reported score with Bonferroni’s adjusted alpha level of 0.0023 per test (0.05/22) used for specific behaviors, although not for the overall scale. The COVID prevention measures to which people reported the highest adherence were government-mandated curfew (m = 4.55 [SD = 0.89] on a 1–5 Likert scale), and government-mandated lockdown (m = 4.50 [SD = 1.01] on a 1–5 Likert scale). The COVID prevention measures to which people reported the lowest adherence were keeping children home from school (m = 1.91 [SD = 1.31] on a 1–5 Likert scale) and deferring the purchasing of food (m = 1.55 [SD = 1.12] on a 1–5 Likert scale).
Table 2.
COVID-19 Prevention Practice Adherence Scores Among All Participants and Those With Depression.
| Overall mean [SD] (n = 230) | Depression mean [SD] (n = 96) | Coefficients |
||||
|---|---|---|---|---|---|---|
| Unadjusted β [95% CI] | P valueb | Adjusteda β [95% CI] | P valueb | |||
|
| ||||||
| Overall | 34.29 [7.18] | 32.95 [6.60] | −2.31 [−4.18, −0.43] | .016 | −2.65 [−4.72, −0.59] | .012 |
| Adherence to government curfew | 4.55 [0.89] | 4.33 [0.96] | −0.40 [−0.63, −0.18] | <.001 b | −0.32 [−0.56, −0.09] | .007 |
| Adherence to government lockdown | 4.50 [1.01] | 4.32 [1.05] | −0.31 [−0.56, −0.05] | .018 | −0.22 [−0.49, 0.05] | .11 |
| Wearing a mask | 3.97 [1.23] | 3.47 [1.36] | −0.87 [−1.16, −0.57] | <.001 | −0.85 [−1.17, −0.54] | <.001 |
| Practicing hand hygiene at home | 3.91 [1.26] | 3.48 [1.23] | −0.74 [−1.05, −0.44] | <.001 | −0.78 [−1.11, −0.45] | <.001 |
| Avoiding handshakes | 3.56 [1.42] | 3.16 [1.37] | −0.74 [−1.09, −0.39] | <.001 | −0.71 [−1.09, −0.34] | <.001 |
| Changing general transportation | 3.08 [1.41] | 3.03 [1.27] | −0.10 [−0.45, 0.26] | .592 | −0.20 [−0.58, 0.19] | .313 |
| Changing transportation to/from work | 2.94 [1.32] | 3.15 [1.13] | 0.35 [0.02, 0.68] | .040 | 0.26 [−0.09, 0.62] | .147 |
| Not going to church/mosque | 2.21 [1.50] | 2.02 [1.31] | −0.31 [−0.69, 0.06] | .102 | −0.37 [−0.77, 0.04] | .077 |
| Deferring healthcare | 2.17 [1.50] | 2.63 [1.46] | 0.78 [0.41, 1.15] | <.001 | 0.66 [0.26, 1.05] | .001 |
| Keeping children home from school | 1.91 [1.31] | 2.16 [1.18] | 0.44 [0.11, 0.77] | .009 | 0.32 [−0.04, 0.67] | .081 |
| Deferring food purchasing | 1.55 [1.12] | 1.44 [0.92] | −0.19 [−0.47, 0.10] | .193 | −0.12 [−0.43, 0.18] | .432 |
Abbreviations: SD, standard deviation; CI, confidence interval.
Adjusted for cohabitation and unemployment status.
Significant values bolded; cut-off for significance (Bonferroni’s correction) = .0023 applied to P values for individual behaviors.
Table 3.
COVID-19 Prevention Practice Adherence Scores Among All Participants and Those With Anxiety.
| Overall mean [SD] (n = 230) | Anxiety mean [SD] (n = 94) | Coefficients |
||||
|---|---|---|---|---|---|---|
| Unadjusted β [95% CI] | P valueb | Adjusteda β [95% CI] | P valueb | |||
|
| ||||||
| Overall | 34.29 [7.18] | 32.89 [6.48] | −2.36 [−4.24, −0.49] | .014 | −2.51[−4.52, −0.50] | .015 |
| Adherence to government curfew | 4.55 [0.89] | 4.40 [0.81] | −0.27 [−0.50, −0.04] | .02 | −0.16 [−0.39, 0.08] | .18 |
| Adherence to government lockdown | 4.50 [1.01] | 4.30 [1.01] | −0.33 [−0.58, −0.07] | .012 | −0.25 [−0.52, 0.02] | .073 |
| Wearing a mask | 3.97 [1.23] | 3.41 [1.31] | −0.95 [−1.24, −0.66] | <.001 | −0.93 [−1.24, 0.62] | <.001 |
| Practicing hand hygiene at home | 3.91 [1.26] | 3.41 [1.18] | −0.85 [−1.15, −0.54] | <.001 | −0.84 [−1.16, −0.52] | <.001 |
| Avoiding handshakes | 3.56 [1.42] | 3.16 [1.32] | −0.73 [−1.08, −0.38] | <.001 | −0.64 [−1.01, 0.26] | .001 |
| Changing general transportation | 3.08 [1.41] | 3.01 [1.22] | −0.13 [−0.49, 0.23] | .490 | −0.24 [−0.62, 0.13] | .202 |
| Changing transportation to/from work | 2.94 [1.32] | 3.18 [1.01] | 0.38 [0.05, 0.72] | .025 | 0.28 [−0.07, 0.64] | .117 |
| Not going to church/mosque | 2.21 [1.50] | 1.99 [1.27] | −0.37 [−0.75, 0.01] | .059 | −0.44 [−0.85, −0.04] | .032 |
| Deferring healthcare | 2.17 [1.50] | 2.68 [1.43] | 0.84 [0.47, 1.21] | <.001 | 0.77 [0.38, 1.16] | <.001 |
| Keeping children home from school | 1.91 [1.31] | 2.24 [1.18] | 0.56 [0.23, 0.89] | .001 | 0.44 [0.09, 0.79] | .014 |
| Deferring food purchasing | 1.55 [1.12] | 1.41 [0.88] | −0.24 [−0.52, 0.05] | .104 | −0.20 [−0.51,0.11] | .198 |
Abbreviations: SD, standard deviation; CI, confidence interval.
Adjusted for cohabitation and unemployment status.
Significant values bolded; cut-off for significance (Bonferroni’s correction) = 0.0023 applied to P values for individual behaviors.
Table 2 includes the unadjusted regression coefficient of depression on adherence to COVID-19 prevention practices overall (β = −2.31; CI: −4.18 to −0.43; P = .016) and for each behavior. It also includes the adjusted regression coefficient, adjusted for employment status and number of cohabitants, of depression on adherence to COVID-19 prevention practices overall (β = −2.65; CI: −4.72 to −0.59; P = .012) and for each behavior. Table 3 includes the unadjusted coefficient of anxiety on adherence overall (β = −2.36; CI: −4.24 to −0.49; P = .014) and the adjusted coefficient (β = −2.1; CI: −4.52 to −0.50; P = .015). The COVID-19 prevention practices of wearing a mask, practicing hand hygiene at home, and avoiding handshakes were all adhered to less (lower score = lower adherence) by both participants who reported depression and participants who reported anxiety to a significant degree. This effect was durable to adjusting for employment status and number of cohabitants. Deferring health care was utilized more as a COVID-19 prevention strategy by these 2 groups (P < .001). Adherence to government curfew was significantly lower among depressed participants only with the unadjusted β (P ≤ .001). Keeping children at home from school was higher among anxious participants only with the unadjusted β (P = .001).
Discussion
This study, the first survey of the general population of adults in Liberia during COVID-19, reveals a complex relationship among COVID-19, mental health outcomes, and COVID-19 prevention practices. Overall, our hypothesis that people who reported depression or anxiety were less likely to adhere to COVID-19 prevention practices was supported. This relationship has important implications for future pandemic planning, especially in low-income countries with high rates of depression and/or anxiety.
The rate of depression found in this study, 43% (95% CI: 37–49), was very similar to the rate found in Secor et al,8 who also used the PHQ-9 and found 45% of participants had a score ≥5. The rate of anxiety here, 41% (95% CI: 34–47), was only slightly higher than the one found by Secor et al (34%).
Our analysis indicated that the participants who reported depression, anxiety, or both were largely similar to those who reported neither in locations, age, gender, marital status, and education. However, there were statistically significant differences regarding employment status and the number of cohabitants, with people reporting depression and/or anxiety more likely to live with more people and have employment. This aligns with earlier findings that a higher number of cohabitants increased the likelihood of screening positive for depression.12 We believe this relationship exists because having more cohabitants, as well as a steady income, increases stress by increasing family obligations, which may go well beyond the nuclear family, and thus contribute to depression and anxiety.
The COVID-19 prevention practices that had the highest self-reported adherence were adherence to government curfews and lockdowns, wearing a mask, avoiding handshakes, and practicing hand hygiene at home. These findings suggest these prevention practices are the most feasible and acceptable for Liberians and should be considered by policymakers in similar contexts.
Overall, lower adherence to COVID-19 prevention measures based on our COVID prevention practices scale were associated with higher scores on the PHQ-9 and the GAD-7. This effect was durable when comparing mean adherence to certain COVID prevention practices (mask adherence, handshake avoidance, and hand hygiene) even when adjusting for cohabitation and employment status. We believe that overall, this suggests that the presence of depression or anxiety affects an individual’s ability to adhere to COVID-19 prevention practices, except for deferring medical care, a practice those reporting depression and anxiety were more likely to use.
For other individual prevention practices (e.g., changing transportation, not attending church or mosque), the difference in adherence to individual prevention practices did not meet the cut-off for significance we set using Bonferroni’s correction. However, we believe this supports our contention that these 2 factors meaningfully contribute to mental illness in Liberia. This would be consistent with previous literature from low- and middle-income countries on overcrowding, which has been found to be strongly associated with common mental illnesses, and the literature on unemployment, which has been found to have a much weaker association with common mental illnesses.15 These findings suggest improving housing options to reduce crowding in Montserrado County could potentially be an effective intervention promoting mental health.
These findings have important implications for pandemic planning in Liberia and beyond. While the literature largely discusses the pandemic driving mental health outcomes, we argue these results indicate pre-existing mental health challenges may affect the trajectory of the pandemic. This seems particularly true since some of the prevention practices that had the highest adherence from the sample overall (e.g., masks, hand hygiene) were also the practices to which people with anxiety and/or depression were significantly less likely to adhere.
The burden of mental illness may make it more difficult for people to adhere to common COVID-19 prevention practices, particularly in low-income settings where material conditions already make adherence challenging. We suggest policymakers at all levels routinely consider how to ease the adherence burden on the mentally ill as a part of pandemic response. Furthermore, we believe that continued strengthening of the mental health care systems within low- and middle-income countries will likely positively impact the ability to control future epidemics.
Limitations
First, this study establishes only an association between high PHQ-9 and GAD-7 scores and low adherence to COVID-19 prevention practices. Therefore, we cannot rule out that poor adherence to COVID-19 prevention practices caused poor mental health, although of course we find the reverse more plausible. Second, this survey was conducted in the most urban county of Liberia, a country that is about half urban and half rural,16 which may affect generalizability. However, our sampling strategy was designed to ensure rural participation. There may be some measurement error if participants did not report accurately when asked about their mental health, although one would expect to see underreporting if so, which is not reflected in our data. Last, the approach we took to measuring COVID-19 prevention practices, which involved performing arithmetic functions on ordinal responses to Likert questions, has been flagged as potentially problematic by statisticians, but others believe it is appropriate in cases where multifaceted concepts are being measured.17
Conclusions
Liberia is an important country in which to study the relationship of COVID-19 prevention practices and mental health. These findings suggest that, in this context, the burden of mental illness makes it more difficult for people to adhere to COVID-19 prevention practices. This dynamic should be considered by policymakers planning pandemic responses and should encourage investment in the mental health care systems of countries like Liberia.
Supplementary Material
Supplemental Material
Supplemental material for this article is available online.
Acknowledgments
The authors would like to thank Hannah Berrian, MPH for her contributions to data collection and instrument development. They would like to acknowledge Konah Yeantee, Leo Harris, and Jestina Cole for their contributions to data collection.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute for Nursing Research under Grant [grant no. T32 NR016914. Program Director: Titler]; and a 2020 grant from the African Studies Center of the University of Michigan.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethical Approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed Consent
Informed consent was obtained from all individual participants included in the study.
References
- 1.The Lancet Psychiatry. COVID-19 and mental health. Lancet Psychiatry. 2021;8:87. doi: 10.1016/S2215-0366(21)00005-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Chen PJ, Pusica Y, Sohaei D, Prassas I, Diamandis EP. An overview of mental health during the COVID-19 pandemic. Diagnosis. 2021;8:403–412. doi: 10.1515/dx-2021-0046 [DOI] [PubMed] [Google Scholar]
- 3.World Health Organization. WHO Coronavirus disease (COVID-19) dashboard. Accessed October 10, 2023. https://covid19.who.int/ [Google Scholar]
- 4.Chen Y, Zhang X, Chen S, et al. Bibliometric analysis of mental health during the COVID-19 pandemic. Asian J Psychiatr. 2021;65:102846–102846. doi: 10.1016/j.ajp.2021.102846 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Rehm J, Shield KD. Global Burden of Disease and the impact of mental and addictive disorders. Curr Psychiatry Rep. 2019;21:10. doi: 10.1007/s11920-019-0997-0 [DOI] [PubMed] [Google Scholar]
- 6.Liu Q, He H, Yang J, Feng X, Zhao F, Lyu J. Changes in the global burden of depression from 1990 to 2017: findings from the Global Burden of Disease study. J Psychiatr Res. 2020;126:134–140. doi: 10.1016/j.jpsychires.2019.08.002 [DOI] [PubMed] [Google Scholar]
- 7.Yang X, Fang Y, Chen H, et al. Global, regional and national burden of anxiety disorders from 1990 to 2019: results from the Global Burden of Disease Study 2019. Epidemiol Psychiatr Sci. 2021;30:e36. doi: 10.1017/S2045796021000275 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Secor A, Macauley R, Stan L, et al. Mental health among Ebola survivors in Liberia, Sierra Leone and Guinea: results from a cross-sectional study. BMJ Open. 2020;10:e035217. doi: 10.1136/bmjopen-2019-035217 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hook K, Ando K, Ghebrehiwet S, et al. Current state of the literature on mental health in Liberia: a systematic review. S Afr J Psychiatr. 2020;26:1502–1509. doi: 10.4102/sajpsychiatry.v26i0.1502 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Shitov A COVID-19 world statistics. Accessed October 11, 2023. https://covid.observer/ [Google Scholar]
- 11.Marsh RH, Plyler C, Miller M, et al. Facing COVID-19 in Liberia: adaptations of the resilient and responsive health systems initiative. Ann Glob Health. 2021;87(1):101–107. doi: 10.5334/aogh.3245 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Davis EJ, Amorim G, Dahn B, Moon TD. Perceived ability to comply with national COVID-19 mitigation strategies and their impact on household finances, food security, and mental well-being of medical and pharmacy students in Liberia. PLoS ONE. 2021;16(7):e0254446. doi: 10.1371/journal.pone.0254446 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. doi: 10.1046/j.1525-1497.2001.016009606.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing Generalized Anxiety Disorder: the GAD-7. Arch Intern Med. 2006;166:1092–1097. doi: 10.1001/archinte.166.10.1092 [DOI] [PubMed] [Google Scholar]
- 15.Lund C, Breen A, Flisher AJ, et al. Poverty and common mental disorders in low and middle income countries: a systematic review. Soc Sci Med. 2010;71(3):517–528. doi: 10.1016/j.socscimed.2010.04.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Bank World. Rural Population (% of Total Population)—Liberia. World Bank. Accessed January 2, 2024. https://data.worldbank.org/indicator/SP.RUR.TOTL.ZS?locations=LR [Google Scholar]
- 17.Sullivan GM, Artino AR Jr. Analyzing and interpreting data from Likert-type scales. J Grad Med Educ. 2013;5(4):541–542. doi: 10.4300/JGME-5-4-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.