https://orcid.org/0000-0002-8886-4553
Figures
Abstract
Background
Mental disorders are known to predict poverty, morbidity and mortality. In resource limited settings, low levels of mental health literacy (MHL) and high mental illness stigma (MIS) have been sighted as possible factors that may impede access to mental health care. However, little has been done to examine the association between mental disorders and these factors (MHL and MIS) in sub-Saharan Africa.
Methods
We assessed for the prevalence of major depressive disorders (MDD), substance use disorders (SUD), post-traumatic stress disorder (PTSD), generalized anxiety disorder (GAD), documented MHL and MIS among 814 participants from 24 villages in central Uganda. We conducted regression analyses to examine the association between the prevalence of mental disorders, demographic factors as well as MIS and MHL.
Results
Over two thirds of the participants 581 (70%) were female. The mean age of the participants was 38 years (SD± 13.5). The prevalence of mental disorders ranged from 6.8–32%. Participants who were older were less likely to screen positive for GAD (OR 0.98; 0.96–0.99), female gender was protective against SUD (OR 0.46; 0.3–0.68) and those with MDD had lower education level (OR 0.23; 0.1–0.53). The mean MIS score was 11.3 (SD± 5.4) with a range of 6–30 and the mean MHL score was 21.7 (SD ±3.0) with a range of 10–30. MIS was negatively associated with GAD [β = -1.211 (-2.382 to -0.040)]. There no statistically significant association between MHL and a mental disorder.
Citation: Akena D, Kiguba R, Muwhezi WW, Kwesiga B, Kigozi G, Lukwata H, et al. (2023) The prevalence and factors associated with mental disorders in a community setting in central Uganda. PLoS ONE 18(5): e0285091. https://doi.org/10.1371/journal.pone.0285091
Editor: Daniel Ahorsu, The Hong Kong Polytechnic University, HONG KONG
Received: April 22, 2022; Accepted: April 15, 2023; Published: May 4, 2023
Copyright: © 2023 Akena et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The study received funding from Makerere University Research and Innovations Fund (MAK/DVCFA/113/20). The funder played no role in the design of the study and in the collection, analysis, and interpretation of data as well as in writing the manuscript
Competing interests: The authors declare no conflict of interest
Background
A number of studies including those conducted in low resourced sub-Saharan Africa (SSA) settings have reported a high prevalence of common mental disorders including major depressive disorders (MDD) at 4.5%, generalized anxiety disorder (GAD) at 3.5%, post-traumatic stress disorders (PTSD) at 8% and substance use disorders (SUD) at 1.3% [1–5]. Mental disorders are known to independently predict poor outcomes including but not limited to increased mortality [6–8], poverty [9] and morbidity [10–13].
A number of scholars have recommended that mental disorders are routinely screened for in primary health care settings and treated using psychological therapies (mild to moderate cases) or pharmacotherapy (moderate to severe cases) [14–17]. However, literature shows that there are a number of factors that may hinder adequate identification and treatment of common mental disorders. These factors are worth considering when planning for the integration of mental health care services in SSA.
Health care system barriers such as shortage of mental health providers [18, 19] medication stock-outs [20, 21] and poor funding for mental health [22, 23] have been cited as reasons for the poor access to mental health care services. These barriers generally lead to poor detection of mental illnesses and low uptake of interventions. In a bid to reduce the treatment gap for mental disorders, the World Health Organization devised the mental health gap action program in 2010. Whereas the WHO program was adapted in multiple SSA countries [24–26], a number of individual barriers continue to peg back moves towards the integration of mental health care. These barriers (see below) are worth mentioning.
High levels of mental illness stigma (MIS) [27, 28] and low levels of mental health literacy (MHL) [29, 30] have been documented in SSA. MHL is defined as the ability of an individual to recognize mental illnesses, know about their causes and seek help from both lay and professional sources Jorm (1997) [31]. Individuals with low MHL may not be aware that they are experiencing mental health symptoms for which there are medical remedies. Moreover, individuals who harbor stigma towards mental illness may not report symptoms to health care providers even if they are aware that they are suffering from a mental illness. Similarly, community members who harbor mental illness stigma may be reluctant to refer mentally ill patients to health care facilities or deny them basic services because of their condition—despite the availability of mental health care services within lower level health care (health centers III and IV) facilities found at every parish in the community. Referral of patients for mental health care is likely to happen when patients are grossly disruptive as is the case for people with acute psychotic disorders or bipolar affective disorders in manic phases. Despite the presence of MIS and MHL in SSA, little has been done to examine its association with common mental disorders.
Furthermore, a number of studies have shown that some individuals in SSA harbor non-medical explanatory models of mental illnesses—such persons attribute their ailments to spiritual possession or some evil forces [32, 33] and not a medical illness for which there is treatment. Such individuals may not seek treatment at health facilities. These non-medical explanatory models of mental illness can also be a result of low MHL or high MIS levels.
Studies that examine the association between MIS, MHL and the prevalence of common mental disorders are likely to help in shaping policy and practice regarding the integration of mental health care in primary health care settings in low resourced SSA. Our study objective was to document the prevalence of common mental disorders and examine whether they were associated with MHL and MIS in a community sample of individuals in central Uganda.
Methods
(i) Study design
This cross sectional study was conducted in 24 randomly chosen villages in the districts of Kampala, Mukono, Masaka and Wakiso in the period of November to December 2020. A village is the lowest politico-administrative unit often consisting of between 50 to 70 households and home to anywhere between 250 to 1,000 people [34].
(ii) Sample size calculation
We used the Leslie Kish formular [35] for calculating the sample size for cross sectional studies. The formula states that: n = Z2 p (1-p)/E2. Where n- sample size, E-The standard error (5%), p- Proportion of patients; 50%. The prevalence was estimated at 50% as the conservative estimates for outcomes or effects sizes with limited literature to refer to (in this case, prevalence of mental disorders among community members in Uganda). Z- The standard normal deviation of 1.96 corresponding to 95% confidence interval. Substituting; n = 1.962 X 0.5 X (1–0.5)/0.052 n = 384. We used a community correction effect of 2. Thus, the sample size was determined to be 764. We added 10% to the computed sample size to cater for incomplete data mainly due to failure to complete an interview. Thus, the total sample size was 840 participants
(iii) Eligibility criteria
Individuals were eligible for recruitment if they had been residing in the selected villages for a minimum of 6 months, and were at least 18 years old. All participants were required to provide written informed consent. For individuals who could neither read nor write, the consent form was read to them by a research assistant. Such participants used a thumb print soaked in an ink-pad to provide informed consent. Consent forms and study instruments were translated into Luganda, the local language dialect largely spoken in the study districts.
(iv) Translation
First, we sent the English versions of the consent and study questionnaires for direct translation into the Luganda language by persons who spoke both languages. We then provided the translated versions to mental health specialists who were native Luganda speakers who assessed the instruments for clarity of language; they provided some input and the documents were sent back to the translators to modify the text as recommended. The Luganda version was sent to an independent translator to have it back translated into English; the final text was then compared and a consensus between the forward and back translators in the presence of the mental health experts was reached.
(v) Sampling
We got a list of all the villages in the four districts of Kampala, Mukono, Wakiso and Masaka from the Uganda National Bureau of Statistics website [36]. The four districts have a population of approximately 5,785,600 persons, which is ~12.6% of the total population of Uganda (45,741,000). We then selected the villages using the probability proportional to size approach based on number of villages per district. Thus, our sample consisted of 15 out of 3297 villages from Kampala district, 4 out of 813 villages from Mukono district, 2 out of 436 villages from Masaka district and 3 out of 704 villages from Wakiso.
We employed a multistage sampling approach to select 840 participants from 24 randomly selected villages located in the 362 parishes (5,250 villages) that span the study districts.
(vi) Participant identification
We engaged the village community leaders to help in identification of every 12th household in their community till they accrued 38 participants in each village. This figure was based on an average of 4,710 households per village—thus 4,710/38 which approximately translated into every 12th household.
(vii) Study measures
We used the following instruments during our data collection. All instruments were translated into Luganda. Data collection (November-December 2020) was done by trained research assistants. Research assistants physically administered the questionnaires to each participant in a face to face interview.
- A standardized demographic questionnaire to collect age, gender, education level and place of residence information.
- We used the Mental health Knowledge Schedule (MAKS) [37] to document MHL and MIS. The MAKS comprises 6 stigma-related mental health knowledge areas including help-seeking, recognition, support, employment, treatment, and recovery and 6 items which inquire about knowledge of mental illness conditions including depression, generalized anxiety, substance use disorders, stress, schizophrenia and bipolar disorders. It is scored 1–5 with the first six items determining stigma and the last 6 determining mental health literacy. The MAKS has not been validated for use in Uganda (we reported on the chronbac alpha)
- The Generalized Anxiety Disorder scale (GAD-7) [38] was used to assess for GAD. We used a cut off score of 10, which is indicative of moderate to severe generalized anxiety disorder. A cut off score of ≥10 yields a sensitivity of 89% and a specificity of 82% for GAD.
- The Patient Health Questionnaire-9 (PHQ-9) [39] was used to assess for MDD. The PHQ-9 is a Nine-item instrument based on the Diagnostic and Statistical Manual (DSM). The scale assesses all the 9 DSM symptoms of MDD and is scored 0–3 with higher scores indicative of a possible depression. A cut off score of ≥10 is used to denote possible depression.
- The Post-Traumatic Stress Disorder- Primary Care Screen (PTSD-PC) [40] was used to assess for PTSD. The PTSC-PC is a 5 item instrument with a cut-point of 3 being optimally sensitive to probable PTSD.
- The Alcohol Smoking and Substance Involvement screening Test (ASSIST) [41] was used to assess for SUD. The ASSIST was developed by the World Health Organization to as tool for the early identification of substance use related health risks and substance use disorders in primary health care, general medical care and other settings. It screens for hazardous, harmful and dependent use of 10 substances including alcohol, tobacco, cannabis, cocaine, opioids, amphetamines, inhalants, sedatives, Hallucinogens and other psychoactive drugs. A high scores indicates a likelihood of hazardous use.
(vii) Data analysis
Descriptive statistics were used to present demographic data. The prevalence of MDD, GAD, PTSD and SUD (dependent variables) was calculated using the cut-off scores (above) and reported as percentages. We conducted simple linear regression analyses to determine the factors that were associated with the outcomes of interest and reported β-coefficients, odds ratios and confidence intervals. The independent variables were demographic parameters (age, gender, education level) and address. Variables that showed statistical significance at the bivariate level were then entered into a hierarchical regression model (one), controlling for age and level of education as potential confounders. The level of significance was set to P < .05. All of the statistical analyses were performed using STATA software version 14.
Ethics approval and consent to participate
Ethical approval was obtained from Makerere University School of Medicine Research Ethics Committee (SOMREC reference number 2020–176), and Uganda National Council for Science and Technology (reference number HS1009ES). All participants provided written informed consent. The study was conducted as per guidelines set by the relevant approving entities above. All research assistants were trained to respond appropriately by providing counseling, terminating the interview or contacting the PI for participants who are severely distressed and need urgent care.
Results
We recruited 814 participants, 29% (n = 233) of whom were male. The mean age of the participants was 38 years (SD± 13.5). Only 60% of the participants had attained at least a secondary education.
The prevalence of screen positive cases for a mental disorders ranged from 6–32% (Table 1). There was a high comorbidity between alcohol use disorders and PTSD as well as GAD and depression disorder (Table 1). Individuals who screened positive for depression were more likely to have low levels of education, OR 0.23 (0.1–0.53), p <0.001. Participants who were older were less likely to screen positive for GAD OR 0.98 (0.96–0.99), p = 0.017 while female gender was protective against the use of alcohol OR 0.46 (0.3–0.68) p<0.001 (Table 2). There was no statistically significant association between geographical location and the prevalence of the mental disorders and the associated factors of MIS and MHL.
The mean MIS score was 11.3 (SD± 5.4) with a range of 6–30 and the mean MHL score was 21.7(SD ±3.0) with a range of 10–30. The chronbac alpha for the MIS was 0.73 (item variance of 0.59) and that of the MHL was 0.69 (item variance of 0.58). There was no statistically significant association between MIS and demographic parameters including age, gender and geographical location. However, there was a negative association between MIS and the presence of a GAD (p = 0.043), Table 3. We found statistically significant association between MHL and education level (p = 0.003) but not with a mental disorder or geographical location (Table 4).
Discussion
We report findings from one of the first studies to document the prevalence of mental disorders, MHL and MIS as factors that may impede access to care in a community sample of persons in a low resourced setting.
The prevalence of mental disorders has been shown to vary widely in SSA. For example, the prevalence of PTSD has been shown to range from 4% to 25% in the general population and up to 30% in community samples of persons affected by conflict [4, 42]. Our finding of a 32% prevalence of PTSD is much higher than what has been documented in non-conflict community settings in a meta-analysis by Ng et al (2020) [4]. One possible explanation to the high prevalence documented in our study could be as a result of a surge in exposure to violence during a civil unrest that was witnessed in the country in mid to late 2020—there were multiple reports of high handed responses by security forces against members of the public leading to general elections.
The prevalence of MDD in our study (6.8%) was lower than findings from a community survey that was conducted in Kenya (prevalence 15.6%) [43] as well as those from a meta-analysis of findings from Africa (11.5%) [44–46]. The reasons for the low prevalence of MDD in our study will need further examination. We documented an 11% prevalence of GAD that was equally lower than the 15.7% that was reported in the community survey in Kenya [43], but way higher than figures from the world mental health surveys and global burden of disease findings [47]. The explanation for our findings could be a result in differences in population dynamics as well as survey measures used in data collection. Just like in the case of MDD, the reasons for the low prevalence of GAD will need further scrutiny
We reported a high prevalence of AUD in the study population. Our findings are in keeping with previous studies that have documented high prevalence of consumption of alcohol in Uganda [48–50] with figures as high as 40% in one study [51]. These findings are in keeping with those of a meta-analysis of the prevalence of AUD (40%) in SSA by Olawole et al (2018) [52]. A number of factors including the indiscriminate advertising of alcohol as well as weak laws that are unable to curb production of alcoholic beverages may explain the high prevalence of alcoholic beverage use in Uganda.
The prevalence of PTSD in our study was way higher than findings from a community survey that was conducted in Northern Uganda [53] and almost as high as those reported in conflict areas and refugee populations in the country [54, 55]. We already alluded to a surge in election related violence in some communities in the country in mid to late 2020—this could explain the high prevalence of PTSD in the study population and is in keeping with findings from a meta-analysis that shows a much higher prevalence in conflict areas of SSA [4].
Factors associated with mental disorders
At multi-variable logistic regression, we found that young age was associated with GAD, female gender was protective against AUD and low education was associated with depression. Our findings regarding the GAD have been reported in studies [56, 57] especially during pandemics. A possible reason could be that the younger people were more worried and uncertain about what the future held for them especially at a time when the world was undergoing the COVID-19 pandemic—the pandemic caused significant disruption in academic calendars and job opportunities for the younger generation leading to feelings of despair and uncertainties about the future.
We documented a negative association between AUD and female gender. Studies in SSA have documented low prevalence of AUD among females compared to male [51, 58]. In our situation, the association between a low prevalence of AUD and females gender could be explained by the nature of, and uniqueness of the study setting. In Uganda, like other SSA settings, the consumption of alcohol may not fully embraced, let alone culturally sanctioned among females compared to males. Our findings about the low education being a predictor of depression has been documented in multiple studies [59–62]. We believe that the same factors (higher socioeconomic status among the educated, being more embedded in socially supportive structures, personality characteristics) at play explain this association.
MIS and MHL
Our findings showed high MHL and moderate MIS levels. The high MHL findings could be an indication of progress made by the ministry of health (MOH) of Uganda that set out to deliberately raise awareness about the burden of mental illnesses in communities, especially after the onset of the COVID-19 pandemic. An explanation for the moderate levels of MIS could be a result of deeply ingrained belief systems and explanatory models about the etiology of mental illnesses in the population [32, 33]. Our findings also indicate that persons with low levels of education are likely to have low scores on the MHL. A number of studies have reported low education as being a predictor of poor MHL [63, 64]—our results are in keeping with these findings. We were not able to demonstrate an association between MHL and the presence of mental disorders. The reasons for this finding could be due to the relatively high levels of MHL and low prevalence of mental disorders in the community that we studied. The reverse association between GAD and MIS requires further scrutiny.
Acknowledgments
We would like to acknowledge all the research assistants who collected the data as well as the village health team members.
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