RESEARCH ARTICLE
Prevalence and Associated Factors of Common Mental Disorders Among Adult Residents in Silte Zone, Southern Ethiopia
Mohammed Muze1, *, Mehbub Denur2, Mubarek Hussein3, Mufta Muzemil1, Mubarek Yesse4, Shemsu Kedir4
Article Information
Identifiers and Pagination:
Year: 2021Volume: 17
First Page: 128
Last Page: 135
Publisher ID: CPEMH-17-128
DOI: 10.2174/1745017902117010128
Article History:
Received Date: 17/4/2021Revision Received Date: 16/8/2021
Acceptance Date: 17/8/2021
Electronic publication date: 15/10/2021
Collection year: 2021
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Introduction:
Mental health problems appear to be increasing in importance in Africa. Mental and substance use disorders were the leading cause of yearly lived with disability in Sub-Saharan Africa. Evidence from previous studies shows considerable variation in the prevalence of these disorders. The most acceptable explanation for this wide variation is likely to be the fact that socio cultural factors are major determinants of mental disorders. Therefore a mental disorder has to be understood in a specific setting to develop effective and tailored interventions.
Objective:
The objective of this study was to determine the prevalence and associated factors of common mental disorders among adult residents in Silte Zone, southern Ethiopia
Methods:
Community based cross-sectional study was conducted in the study area. A total of 1178 adults were selected by using a three-stage systematic sampling technique. The Self-Reporting Questionnaire (SRQ-20) was used to determine the prevalence of common mental disorders. Data were analyzed by using SPSS version 20. Both bivariate and multiple logistic regression analyses were employed to identify factors associated with common mental disorders.
Results:
The prevalence of common mental disorders among adults found to be 39.7%. Increased age (OR = 1.114; 95% CI = 1.095, 1.134), being female (OR = 9.421; 95% CI = 5.947, 14.926), poor social support (OR = 1.987; 95% CI = 1.358, 2.907) and having life threatening experience (OR = 2.162; 95% CI = 1.825, 2.562) were significantly associated with common mental disorders.
Conclusion:
In the study, the magnitude of common mental disorders remains high in the study area. Increased age, being female, poor social support and having life-threatening experience were significantly associated with common mental disorders.
1. INTRODUCTION
Common mental disorders are a group of distress states manifesting with anxiety, depressive and unexplained somatic symptoms that typically come up in the community and primary care setting and also frequently occur together with a shifting combination of symptoms over time indicating emotional or mental abnormality [1, 2] Mental disorders have been recognized as the most public health important globally, around 450 million people currently suffer from such conditions, placing mental disorders among the leading causes of ill-health and disability worldwide [3].
The global prevalence of common mental disorders from 1980–2013 was approximately 1 in 5 people experienced during the 12-month preceding assessment [4]. Adult mental disorders are found to be associated with high role impairment and economic burden [5, 6]. According to the World Economic Forum, mental illness will account for more than half of the economic burden of disease over the next two decades – more than cancer, diabetes and chronic respiratory diseases combined. About 54% of the economic burden of disease falls to low- and middle-income countries [7]. Mental health problems appear to be increasing in importance in Africa. Mental and substance use disorders were the leading cause of YLDs (yearly lived with disability) in Sub-Saharan Africa [8].
Evidence from previous studies shows that mental disorders are common in all countries, there is a considerable variation in the prevalence of these disorders. In developed worlds, the past 12-month prevalence of mental disorders was found 14% in Greece [9] and 44% in a review of Bangladesh, India, and Pakistan [10]. In Africa, the past 12-month prevalence of common mental disorders was found between 10.3%- 33.6% [11-14]. This can be explained by deference in sociocultural factors across countries. Mental problems are influenced by social factors such as gender, social class, race and ethnicity, household patterns, poverty, violence, and other stressful social environments. They are not unique to any part of the globe [15, 16].
Myths and misconceptions about mental illness contribute to the stigma, which leads many people to be ashamed and prevents them from seeking help. Generally, people who have mental disorders are considered lazy, unintelligent, worthless, stupid, unsafe to be with, violent, always in need of supervision, possessed by demons, recipients of divine punishment, unpredictable, unreliable, irresponsible, without conscious, incompetent to marry and raise children, unable to work, affect rich people, increasingly unwell throughout life, and in need of hospitalization [17].
Researchers found that Khat use is directly linked to common mental disorders [18]. Khat is a flowering plant that contains the alkaloid cathinone, which is said to cause excitement [19], which is common in Horne of Africa in including study area. According to the 2016 Ethiopia Demographic and Health Survey report, 12% of women and 27% of men reported the use of Khat [20]. The presence of khat in the study area may increase the burden of common mental disorders.
The majority of the global burden of mental disorders is located in low and middle-income countries [21]. Therefore, integration of mental health in primary health care in resource-limited settings is more important for two reasons. The first reason is mental health resources are unlikely to be adequate to address the burden of mental disorders in low and middle-income countries. The second reason is most patients with mental disorders, particularly less severe disorders, prefer to seek care from their own family or primary health care settings [22]. Evidence from previous studies shows that mental disorders are common in all countries, there is a considerable variation in the prevalence of these disorders. Apart from methodological factors, the most acceptable explanation for this wide variation is likely to be the fact that socio-cultural factors are major determinants of mental disorders. Therefore a mental disorder has to be understood in a specific setting to develop effective and tailored interventions. Hence, this community-based study was intended to determine the prevalence and associated factors of common mental disorders among adult residents in the study area.
2. METHODS
2.1. Study Design, Period and Area
Community-based cross-sectional study was conducted from January to April/2019 in Silte Zone. Silte zone is located 172km south of Addis Ababa, the capital of Ethiopia. The zone is divided into 10 Weredas (Districts) and 3 administrative towns. Wereda and administrative town are a cluster of kebeles which are the smallest unit administration. Ten Weredas (Districts) and 3 administrative towns consist of a total of 204 kebeles, and 211,012 households. According to the 2007 central statistical agency of Ethiopia, the estimated population size of the zone is 1,033,954, and 584,184 were adults.
2.2. Study Subjects
The source population of this study was all adults. Individuals whose age >=18 yrs and who have lived in the study area for at least 6 months were included. Individuals who had a hearing problem, cognitive and memory impairment were excluded.
2.3. Sample Size Determination
The sample size was determined by using single population proportion formula: with an assumption of prevalence rate of 33.6% (11) for common mental disorders from a previous study in Jimma southwest of Ethiopia, which gave the maximum sample size than prevalence rates found elsewhere in the country, margin of error(d) 4%, design effect of 2, Z=1.96 at 95% confidence level. Using the above information, the calculated sample size was 1071, and after adding a 10% potential non-response rate, the final sample size was 1179.
2.4. Sampling Technique
A three-stage sampling technique was employed for the study. The study unit was households with the assumption that each household would have a study subject. Using a simple random sampling technique, in the first stage, 4 Weredas were selected from the 13 Weredas of the zone. In the second stage, 36 kebeles were randomly selected out of 121 kebeles of four Weredas. The calculated sample size was proportionally allocated to selected kebeles based on the number of households found in the kebeles. In the third stage, 1179 households were selected by using a systematic random sampling technique. In the selected kebeles, there were 34187 households. When more than one study subject were found in one household, a lottery method was used to select a participant.
2.5. Measurements and Data Collection Tools
Twelve data collectors collected the data by using paper-based structured interviewer-administered questionnaire. The questionnaire was adapted from articles published in peer-reviewed journals. A Self-reporting questionnaire (SRQ-20) was used to classify whether a common mental disorder was present or not. Each of the 20 items scored 0 or 1. A score of 1 indicates the presence of symptoms, and 0 indicates the absence of symptoms during the last month. The maximum score is, therefore, 20. It has been validated in Ethiopia [23]. A score above the cut-off point indicates the existence of a probable mental disorder. In neighboring country Eritrea, a study found a good sensitivity and specificity at a lower cut of value (5/6) for SRQ-20 [24]. Therefore, in this study, 6 was used as a cut-off value to determine the presence or absence of CMDs. Another study in another part of Ethiopia also used 6 as a cut-off value for SRQ-20 [25]. The Oslo-3 Social Support Scale (OSS-3) was used to collect social support characteristics. The scale consists of three items. The sum score ranges from 3 to 14. By adding the three items, those with 3–8 values were considered to have poor social support, while those with 9–11 and 12–14 values were considered to have intermediate and strong social support, respectively [26]. OSS-3 has acceptable internal consistency and construct validity in the general population [27]. Finally, stressful life events were assessed by using an adapted version of the List of threatening experiences (LTE) questionnaire. It is a 12 items instrument measuring common life events that tend to be threatening [28].
2.6. Data Quality Management
Trained data collectors collected the data. A locally validated and translated tool was used to collect the data. The instrument was pretested in 5% of the sample size. It was conducted on individuals’ study population who were not a part of the actual study. Based on the pretest results, the instrument was modified. Variables such as IV drug use, ethnicity, religion were modified in the final tool. The principal investigators and supervisors checked out the collected data for completeness, accuracy, consistency and clarity every day and made amendments before the next data collection measure. Principal investigators carefully cleaned and entered collected data into a computer.
2.7. Data Processing and Analysis
Data were checked for completeness and consistencies, then edited, coded and entered by using Epi-Info version 7 and exported to SPSS version 20. Both bivariate and multiple logistic regression analyses were employed to identify factors associated with CMDs. Then all variables significantly associated with bivariate analysis were a candidate to multivariable logistic regression analysis. Multivariable analysis was performed to see an association between dependent and independent variables. To estimate the strength of association, an adjusted odds ratio (AOR) with a 95% confidence interval was reported between study variables. A P-value less than 0.05 is considered statistically significant.
3. RESULTS
3.1. Socio-demographic Characteristics of Respondents
Table 1 summarizes the socio-demographic characteristics of the study participants. Of the total 1179 initially planned for the study, 1168 participated in the study, with a response rate of 99%. The majority (63.7%) of the respondents were female. The majority of the participants were in the age group 45-54 years with a mean and standard deviation of 45±15 years. Most (91.8%) of the respondents were rural dwellers. Nearly two-third (74.7%) of study subjects had no formal education, and more than half (60%) of them were housewives. The average household size was 2±1.
| Variables | Categories | Frequency | Percent |
|---|---|---|---|
| Age of respondent | 18-24 | 172 | 14.7 |
| 25-34 | 180 | 15.4 | |
| 35-44 | 136 | 11.6 | |
| 45-54 | 338 | 28.9 | |
| 55-64 | 235 | 20.1 | |
| >=65 | 107 | 9.2 | |
| Total | 1168 | 100.0 | |
| Sex | Male | 424 | 36.3 |
| Female | 744 | 63.7 | |
| Total | 1168 | 100.0 | |
| Residency | rural | 1072 | 91.8 |
| urban | 96 | 8.2 | |
| Total | 1168 | 100.0 | |
| Marital status | married | 736 | 63.0 |
| single | 120 | 10.3 | |
| separated | 22 | 1.9 | |
| widowed | 290 | 24.8 | |
| Total | 1168 | 100.0 | |
| Educational status | Not read and write | 873 | 74.7 |
| Primary | 236 | 20.2 | |
| Secondary | 59 | 5.1 | |
| Total | 1168 | 100.0 | |
| Occupation | No job | 59 | 5.1 |
| Housewife | 702 | 60.1 | |
| Student | 119 | 10.2 | |
| Farmer | 288 | 24.7 | |
| Total | 1168 | 100.0 | |
| Family size | 2-4 | 700 | 59.9 |
| 5-7 | 374 | 32.0 | |
| >=8 | 94 | 8.0 | |
| Total | 1168 | 100.0 | |
| Monthly income in Ethiopian birr | <400 | 189 | 16.2 |
| 401-800 | 451 | 38.6 | |
| 801-1500 | 417 | 35.7 | |
| >1501 | 111 | 9.5 | |
| Total | 1168 | 100.0 |
3.2. Psycho-social and Lifestyle Factors
Among study participants, 530 (45.4%) reported no stressful life events, 463 (39.6%) reported one up to two stressful life events and the remaining 175 (15%) reported three and above stressful life events in the last four weeks. The majority (70.1%) of the participants had poor social support, 29.9% of the participants had intermediate social support and none of the study participants had strong social support. Out of the total 1168 study subjects, 876 (75%) never have a history of khat use, 235 (20%) reported the use of khat sometimes, and the rest 57 (5%) reported khat use usually. About 40% of the respondents had a family history of khat use. All of the respondents reported they are not current alcohol drinkers. Most (94.5%) of the respondents were non smokers.
3.3. Clinical Related Factors
About 582 (49.8%) of the respondents had a family history of mental illness, and 177 (15%) had a personal history of mental illness. Out of the total, 57 (4.9%) of the respondents had diabetes mellitus, 85 (7.5%) had hypertension and 68 (5.8%) had Asthma.
3.4. Prevalence of Common Mental Disorders
The prevalence of common mental disorders among adults was found to be 39.7% (N = 464/1168). When we look closely at the prevalence of specific symptoms of CMD (Table 2), the following symptoms were found to be highly prevalent: headaches (44.8%), uncomfortable feelings in the stomach (35.1%), easily tired (44.7%), sleep badly (29.8%) and feel nervous, tense or worried (29.6%), whereas symptoms like difficult of enjoying in daily activities (4.9%), difficult in decision making in day to day life (4.9%), unable to play a useful part in life (4.9%), lost interest in things (9.8%), feel that you are a worthless person (9.9%) and trouble thinking (10%) were relatively less common (Table 2).
| Variables | Response | Frequency | Percent |
|---|---|---|---|
| Do you often have a headache? | no | 645 | 55.2 |
| yes | 523 | 44.8 | |
| Total | 1168 | 100.0 | |
| Is your appetite poor? | no | 876 | 75.0 |
| yes | 292 | 25.0 | |
| Total | 1168 | 100.0 | |
| Do you sleep badly? | no | 820 | 70.2 |
| yes | 348 | 29.8 | |
| Total | 1168 | 100.0 | |
| Are you easily frightened? | no | 993 | 85.0 |
| yes | 175 | 15.0 | |
| Total | 1168 | 100.0 | |
| Do your hands shake? | no | 993 | 85.0 |
| yes | 175 | 15.0 | |
| Total | 1168 | 100.0 | |
| Do you feel nervous, tense or worried? | no | 822 | 70.4 |
| yes | 346 | 29.6 | |
| Total | 1168 | 100.0 | |
| Is your digestion poor? | no | 879 | 75.3 |
| yes | 289 | 24.7 | |
| Total | 1168 | 100.0 | |
| Do you have trouble thinking clearly? | no | 1050 | 89.9 |
| yes | 118 | 10.1 | |
| Total | 1168 | 100.0 | |
| Are you unhappy? | no | 994 | 85.1 |
| yes | 174 | 14.9 | |
| Total | 1168 | 100.0 | |
| Do you cry more than usual? | no | 1168 | 100.0 |
| Do you find it difficult to enjoy your daily activities? | no | 1111 | 95.1 |
| yes | 57 | 4.9 | |
| Total | 1168 | 100.0 | |
| Do you find it difficult in decision making in day-to-day life? | no | 1111 | 95.1 |
| yes | 57 | 4.9 | |
| Total | 1168 | 100.0 | |
| Is your daily work suffering? | no | 995 | 85.2 |
| yes | 173 | 14.8 | |
| Total | 1168 | 100.0 | |
| Are you unable to play a useful part in life? | no | 1111 | 95.1 |
| yes | 57 | 4.9 | |
| Total | 1168 | 100.0 | |
| Have you lost interest in things? | no | 1053 | 90.2 |
| yes | 115 | 9.8 | |
| Total | 1168 | 100.0 | |
| Do you feel that you are a worthless person? | no | 1052 | 90.1 |
| yes | 116 | 9.9 | |
| Total | 1168 | 100.0 | |
| Has the thought of ending your life been on your mind? | no | 995 | 85.2 |
| yes | 173 | 14.8 | |
| Total | 1168 | 100.0 | |
| Do you feel tired all the time? | no | 879 | 75.3 |
| yes | 289 | 24.7 | |
| Total | 1168 | 100.0 | |
| Do you have uncomfortable feelings in your stomach? | no | 758 | 64.9 |
| yes | 410 | 35.1 | |
| Total | 1168 | 100.0 | |
| Are you easily tired? | no | 646 | 55.3 |
| yes | 522 | 44.7 | |
| Total | 1168 | 100.0 |
3.5. Factors Associated with Common Mental Disorders
Both bivariable and multivariable logistic regression analyses were done. Initially, all variables included in the study were analyzed using bivariable logistic regression. Then variables with p-value <0.25 were included in the multivariable analysis (Table 3). In the bivariable model, increased age, being female, low educational status, poor social support and having a life-threatening experience were positively associated with CMD; and all these associations were statistically significant. In the multivariable model older age (OR = 1.114; 95% CI = 1.095, 1.134), being female (OR = 9.421; 95% CI = 5.947, 14.926), poor social support (OR = 1.987; 95% CI = 1.358, 2.907) and having a life-threatening experience (OR = 2.162; 95% CI = 1.825, 2.562) were significantly associated with CMDs (Table 3).
| Variables | Categories | CMDs | Crude odds ratio (95% C.I) | Adjusted odds ratio (95% C.I) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| No | Yes | P value | COR | Lower | Upper | P value | COR | Lower | Upper | ||
| Sex | Male | 297 | 127 | - | - | - | - | - | - | - | - |
| Female | 407 | 337 | .000 | 1.936 | 1.503 | 2.494 | .000 | 9.421 | 5.947 | 14.926 | |
| Education | Illiterate | 468 | 405 | - | - | - | - | - | - | - | - |
| Literate | 236 | 59 | .000 | .289 | .211 | .396 | .160 | 2.121 | 0.148 | 3.916 | |
| Social support | Good | 234 | 115 | - | - | - | - | - | - | - | - |
| Poor | 470 | 349 | .002 | 1.511 | 1.162 | 1.965 | .000 | 1.98 | 1.358 | 2.907 | |
| LTE | As increased by one | .000 | 1.573 | 1.430 | 1.730 | .000 | 2.16 | 1.825 | 2.562 | ||
| Age | As increased by one | .000 | 1.056 | 1.047 | 1.066 | .000 | 1.11 | 1.095 | 1.134 | ||
4. DISCUSSION
The aim of this study was to assess the prevalence of common mental disorders and their associated factors. The results showed that 39.7% of the adults had common mental disorders. Our result is comparable with a study carried out in another area of Ethiopia and a review done in three countries of Europe (Bangladesh, India, and Pakistan), where it was estimated at 33.6% [11] and 44% [10], respectively. On the other hand, our finding is higher than those of two area studies of Ethiopia, namely in Harari 14.9% (25), a review in Ethiopia 21.58% [29]. Our finding is also higher compared to two studies conducted in Kenya, and a study in Greece reported 10.3% [13], 10.8% [12] and 14% [9], respectively. The variation might be due to distinctions in the measurement tools, and the socio-culture distinctions between Ethiopia and the other counties.
Regarding factors, we found a highly increased prevalence in women compared to men; CMDs were 9 times higher among female respondents compared to males. This finding is supported by other studies [11, 30]. It may be attributed to gender-specific risk factors for common mental disorders that disproportionately affect women including gender-based violence, socioeconomic disadvantage, low income and income inequality, low or subordinate social status and rank and unremitting responsibility for the care of others [31]
Older age is also positively associated with CMDs, older age increases the likelihood of CMDs. This result is consistent with that of previous studies done in Ethiopia and Kenya [11, 25, 32]. There may be multiple risk factors for mental health problems at any point in life. Older people may experience life stressors common to all people; stressors may be more common in later life, like a significant ongoing loss in capacities and a decline in functional ability lead to common mental disorders.
Adults who had poor social support were 2 times more likely to develop CMDs compared to those who had good social support. Evidence showed that the impact of social support on mental health can occur through two mechanisms: either as a main effect influence in which social support has a beneficial effect on mental health regardless of whether or not the individuals are under stress, or social support improves the wellbeing of those under stress by acting as a buffer or moderator of that stress [33].
In the current study, a strong association between stressful life events and common mental disorders was observed. Stressful life event increases the likelihood of having common mental disorders. This could be explained by hormones, neuroendocrine mediators, peptides, and neurotransmitters involved in the body's response to stress. Many disorders originate from severe and prolonged stress that leads to mental disorders [34, 35]
4.1. Strengths and Limitations of the Study
The strengths of the study are the random sample of households, large sample size and the high response rate. Limitations of the study are the use of cross-sectional study design; we cannot permit conclusions about some variables, for example, about deciding whether common mental disorder symptoms are at risk or a consequence. The measurement used to diagnose common mental disorders was self-reported, which is subjected to recall and reporting bias. Further research should be conducted on risk factors for CMDs to strengthen and broaden our results.
CONCLUSION
In the study, the magnitude of the common mental disorder remains high in the study area. Older age, being female, increased family size, poor social support, and stressful life event were significantly associated with common mental disorders. Based on our findings, we would like to forward the following recommendation; mental health screening and counseling should be strengthened in health facilities; due attention should be given to the mental health aspect of those older adults, women, adults who have poor social support. Future research could beneficially include consideration of the severity and impairment of activities of daily living caused by CMD and would contribute to an understanding of the impact of CMDs on the lives of this population.
ETHICS APPROVAL AND CONSENT TO PARTICIPATE
Ethical clearance was obtained from Werabe University.
HUMAN AND ANIMAL RIGHTS
Not applicable.
CONSENT FOR PUBLICATION
Informed consent was taken from the participants.
STANDARDS OF REPORTING
STROBE guidelines and methodology were followed.
AVAILABILITY OF DATA AND MATERIALS
The data supporting the finding of the article is available in the Zenodo Repository at zenodo.org, reference number https://zenodo.org/record/5536246#.YVRi7JpBzIU and DOI is 10.5281/zenodo.5536246.
CONFLICT OF INTEREST
The authors declare no conflict of interest, financial or otherwise.
FUNDING
The study was funded by the Werabe University, and the funder was only involved by giving the funding for the design of a study, data collection, analysis, and interpretation.
ACKNOWLEDGEMENTS
We would like to thank the Werabe university for supporting this work. We wish to express our deep appreciation to all staff and data collectors for their contribution to the overall success of this study and all respondents for their cooperation, time and genuine response.
REFERENCES
| [1] | Goldberg D. A bio-social model for common mental disorders. Acta Psychiatr Scand Suppl 1994; 385: 66-70. |
| [2] | Goldberg D. A dimensional model for common mental disorders. Br J Psychiatry Suppl 1996; (30): 44-9. |
| [3] | WHO Mental disorders affect one in four people. WHO 2020; 2013 |
| [4] | Steel Z, Marnane C, Iranpour C, et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980-2013. Int J Epidemiol 2014; 43(2): 476-93. |
| [5] | Kessler RC, Aguilar-Gaxiola S, Alonso J, et al. The global burden of mental disorders: an update from the WHO World Mental Health (WMH) surveys. Epidemiol Psichiatr Soc 2009; 18(1): 23-33. |
| [6] | Alonso J, Vilagut G, Mortier P, et al. The role impairment associated with mental disorder risk profiles in the WHO World Mental Health International College Student Initiative. Int J Methods Psychiatr Res 2019; 28(2): e1750. |
| [7] | MHPSS worldwide: facts and figures | Mental health and psychosocial support in crisis situations | Government.nl. 2019. |
| [8] | Charlson FJ, Diminic S, Lund C, Degenhardt L, Whiteford HA. Mental and substance use disorders in Sub-Saharan Africa: predictions of epidemiological changes and mental health workforce requirements for the next 40 years. PLoS One 2014; 9(10): e110208. |
| [9] | Skapinakis P, Bellos S, Koupidis S, Grammatikopoulos I, Theodorakis PN, Mavreas V. Prevalence and sociodemographic associations of common mental disorders in a nationally representative sample of the general population of Greece. BMC Psychiatry 2013; 13(1): 163. |
| [10] | Eleonora PU, Louise N, Ian W, et al. A systematic review and meta-analysis of the prevalence of common mental disorders in people with non-communicable diseases in Bangladesh, India, and Pakistan. J Glob Health 2019; 9(2) |
| [11] | Kerebih HSM. Prevalence of common mental disorders and associated factors among residents of Jimma Town, Southwest Ethiopia. J Psychiatry 2016; 19: 373. |
| [12] | Jenkins R, Njenga F, Okonji M, et al. Prevalence of common mental disorders in a rural district of Kenya, and socio-demographic risk factors. Int J Environ Res Public Health 2012; 9(5): 1810-9. |
| [13] | Jenkins R, Othieno C, Ongeri L, et al. Common mental disorder in Nyanza province, Kenya in 2013 and its associated risk factors--an assessment of change since 2004, using a repeat household survey in a demographic surveillance site. BMC Psychiatry 2015; 15(1): 309. |
| [14] | Husain N, Mukherjee I, Notiar A, et al. Prevalence of Common Mental Disorders and its Association with Life Events and Social Support in Mothers Attending a Well-Child Clinic:Findings from Mombasa, Kenya. SAGE Open 2016; 6(4): 2158244016677324. |
| [15] | Jafar Hassanzadeh MA-L, Haleh Ghaem, et al. The association of poor mental health status and sociocultural factors in men: A population-based study in Tehran, Iran. Am J Men Health 2016; 12(1): 96-103. |
| [16] | Mwape L, McGuinness TM, Dixey R, Johnson SE. Socio-cultural factors surrounding mental distress during the perinatal period in Zambia: a qualitative investigation. Int J Ment Health Syst 2012; 6(1): 12. |
| [17] | Kishore J, Gupta A, Jiloha RC, Bantman P. Myths, beliefs and perceptions about mental disorders and health-seeking behavior in Delhi, India. Indian J Psychiatry 2011; 53(4): 324-9. |
| [18] | Gebrie A, Alebel A, Zegeye A, Tesfaye B. Prevalence and predictors of khat chewing among Ethiopian university students: A systematic review and meta-analysis. PLoS One 2018; 13(4): e0195718. |
| [19] | Al-Mugahed L. Khat chewing in Yemen: turning over a new leaf. Bull World Health Organ 2008; 86(10): 741-2. |
| [20] | Central Statistical Agency - CSA/Ethiopia. ICF Ethiopia Demographic and Health Survey 2016 2017. |
| [21] | Organization WH. Mental Health Atlas 2005. |
| [22] | Patel V. Mental health in low- and middle-income countries. Br Med Bull 2007; 81-82(1): 81-96. |
| [23] | Hanlon C, Medhin G, Alem A, et al. Detecting perinatal common mental disorders in Ethiopia: validation of the self-reporting questionnaire and Edinburgh Postnatal Depression Scale. J Affect Disord 2008; 108(3): 251-62. |
| [24] | Netsereab TB, Kifle MM, Tesfagiorgis RB, Habteab SG, Weldeabzgi YK, Tesfamariam OZ. Validation of the WHO self-reporting questionnaire-20 (SRQ-20) item in primary health care settings in Eritrea. Int J Ment Health Syst 2018; 12(1): 61. |
| [25] | Gari Hunduma MG. Tesfaye Digaffe, Fitsum Weldegebreal, Assefa Tola, Prevalence and determinants of common mental illness among adultresidents of Harari Regional State, Eastern Ethiopia. Pan Afr Med J 2017; 28(262) |
| [26] | Bøen H, Dalgard OS, Bjertness E. The importance of social support in the associations between psychological distress and somatic health problems and socio-economic factors among older adults living at home: a cross sectional study. BMC Geriatr 2012; 12: 27. |
| [27] | Kocalevent R-D, Berg L, Beutel ME, et al. Social support in the general population: standardization of the Oslo social support scale (OSSS-3). BMC Psychol 2018; 6(1): 31. |
| [28] | Brugha T, Bebbington P, Tennant C, Hurry J. The list of threatening experiences: A subset of 12 life event categories with considerable long-term contextual threat. Psychol Med 1985; 15(1): 189-94. |
| [29] | Getachew M, Kassaa A, Alemu A. Prevalence of common mental illnesses in Ethiopia: A systematic review and meta-analysis |
| [30] | Yimam K KY, Azale T. Prevalence of common mental disorders and associated factors among adults in Kombolcha Town, Northeast Ethiopia. J Depress Anxiety 2014; 007. |
| [31] | World health organization. Gender and women’s mental health 2020. Available from: https://www.who.int/mental_health/prevention/genderwomen/en/ |
| [32] | Jenkins R, Njenga F, Okonji M, et al. Prevalence of common mental disorders in a rural district of Kenya, and socio-demographic risk factors. Int J Environ Res Public Health 2012; 9(5): 1810-9. |
| [33] | Tafari S, Aboud FE, Larson CP. Determinants of mental illness in a rural Ethiopian adult population. Soc Sci Med 1991; 32(2): 197-201. |
| [34] | Yaribeygi H, Panahi Y, Sahraei H, Johnston TP, Sahebkar A. The impact of stress on body function: A review. EXCLI J 2017; 16: 1057-72. |
| [35] | Lupien SJ, Juster R-P, Raymond C, Marin M-F. The effects of chronic stress on the human brain: From neurotoxicity, to vulnerability, to opportunity. Front Neuroendocrinol 2018; 49: 91-105. |
