{"paper_id":"6434f79e-0c99-4dee-acdd-d43191b2b876","body_text":"Major depressive disorder in sub-Saharan Africa: findings from the Neuro-GAP-Psychosis study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Major depressive disorder in sub-Saharan Africa: findings from the Neuro-GAP-Psychosis study Allan Kalungi, Wilber Ssembajjwe, Kester Tindi, Emmanuel Mwesiga, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6279456/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Major depressive disorder (MDD) is a significant contributor to the burden of mental disorders in Africa, necessitating an understanding of its prevalence and risk factors across diverse socio-cultural contexts to address health disparities and improve care. This cross-sectional study analyzed 7,073 adult participants from hospital settings in Uganda, Kenya, Ethiopia, and South Africa within the NeuroGAP-Psychosis study. Prevalence estimates, calculated with 95% confidence intervals, revealed overall rates of 0.9% for current MDD and 5.1% for lifetime MDD, with South Africa reporting the highest prevalence (2.1% current, 8.4% lifetime). Multilevel logistic regression identified significant associations between current MDD and negative life events, alcohol use, and Kessler psychological distress scores, while lifetime MDD was linked to age, female sex, chronic pain, frequent headaches, and positive psychosis screening. These findings underscore the need for targeted mental health interventions tailored to the identified risk factors to reduce the burden of MDD in the studied populations. Health sciences Health sciences/Medical research/Epidemiology Major depressive disorder prevalence associated factors Eastern and Southern Africa Introduction Major depressive disorder (MDD) is a common mental disorder characterized by persistent feelings of sadness, hopelessness and lack of interest in normally pleasurable activities. 1 , 2 The global prevalence for MDD has been estimated at 3.4%, accounting for point, 12 month and lifetime prevalence using pooled prevalence ratios 3 . It is estimated that 10–15% of the general population experience clinical depression in their lifetimes with 5% of men and 9% of women experiencing a depressive disorder in a given year. 4 According to the World Health Organisation, MDD was responsible for over 50 million years lived with disability (YLD) in 2015 and can lead to suicide which is responsible for up to 800,000 deaths annually. 5 MDD leads to high levels of morbidity and a 10% increased risk of all-cause mortality. 6 The burden of MDD has been reported to vary per WHO region and the study population. It has been reported to vary from 3.6% in the Western Pacific region to 5.4% in the African region. 5 Africa accounts for over 9% of the global burden of MDD, 5 yet it is a region where very low treatment rates have been reported for MDD. 7 , 8 From existing limited data, in Sub-Saharan Africa (SSA) an age-standardized prevalence of 4.5% has been estimated for MDD. 3 The age-standardized prevalence of MDD has been reported to be higher in SSA than elsewhere globally. 3 In South Africa, a prevalence of 4.6% 9 has been reported among the general population while prevalence rates of 1.9–38% have been reported among small controlled populations. In Uganda, a pooled prevalence of 30.2% has been determined by a systematic review and a meta-analysis among heterogeneous samples 10 while MDD prevalence estimates of 4.2–29.3% have been determined among general populations from various study sites in Uganda. 11 – 15 In Ethiopia, a prevalence of 4.7% has been reported among the general population while prevalence rates of 4.8–57% have been reported among heterogeneous samples. 16 , 17 In Kenya, a prevalence of 4.4% has been reported among the general population while prevalence rates of 6.3–72.9% have been reported among heterogeneous samples. 18 , 19 Despite accumulating data on MDD prevalence across various regions in SSA, a comprehensive understanding of the disorder's epidemiological landscape remains elusive. Current research shows considerable variability in MDD prevalence. This suggests that existing studies may not fully capture the nuances of how depression manifests across SSA’s varied cultural, economic, and demographic contexts. This may also be attributable to the different methods employed I in assessing for MDD and the different populations being sampled. Additionally, while links between MDD and lifestyle diseases such as diabetes and hypertension, as well as infectious diseases like HIV, Ebola, and COVID-19 have been noted, there is a dearth of studies exploring these relationships. The inconsistent use of diagnostic criteria and potential underdiagnosis due to cultural perceptions of mental health highlight the need for more localized and culturally adapted research methodologies. Also, given the heterogeneous nature of MDD, it is important to know the rates and risk factors for MDD in different populations, to inform policies that target reducing the burden of the disorder. This study aimed to investigate the prevalence and specific risk factors (demographic, social and family dynamics, lifestyle and health behaviours, physical health conditions, psychological and geographical/cultural) for MDD among a cross-sectional sample of participants recruited from hospital settings from Eastern (Uganda, Kenya, Ethiopia) and Southern (South Africa) Africa. Results The socio-demographic characteristics and clinical variables of the study participants are shown in Table 1 . Table 1 description of the socio-demographic characteristics and clinical variables of the study participants. SD = standard deviation. Factor Level Frequency (Percentage) n = 7,073 Sex at birth Female 2,259 (31.9%) Male 4,814 (68.1%) Age Mean (SD) 37.1 (11.5) Marital status Married/living together 3,504 (49.6%) Widowed/divorced/separated 822 (11.6%) Single 2,744 (38.8%) Current living arrangement Lives alone 1,077 (15.3%) Lives with family & friends 5,978 (84.7%) Highest level of education Primary or less 1,710 (24.2%) Secondary 3,219 (45.5%) Tertiary 2,141 (30.3%) Study country South Africa 1,805 (25.5%) Kenya 682 (9.6%) Uganda 2,283 (32.3%) Ethiopia 2,303 (32.6%) Current use of tobacco products No 4,803 (67.9%) Yes 2,270 (32.1%) Current use of alcoholic beverages No 2,593 (36.7%) Yes 4,480 (63.3%) Current use of substances No 4,996 (70.6%) Yes 2,077 (29.4%) Blood pressure Normal 5,050 (71.4%) High 2,023 (28.6%) Body mass index Mean (SD) 23.9 (4.9) Prevalence of Major Depressive Disorder As shown in Table 2 , the overall prevalence of current and lifetime MDD from this study was 0.9 and 5.1% respectively. For current MDD at country level, South Africa had the highest prevalence (2.1%) followed by Uganda (0.7%), Ethiopia (0.5%) and Kenya (0.1%). For lifetime MDD at the country level, South Africa still had the highest prevalence (8.4%) followed by Uganda (7.9%), Ethiopia (1.2%) and the lowest being Kenya (0.3%). Table 2 Prevalence of major depressive disorder among the study participants. MDD = major depressive disorder, CI = confidence interval. Study Site Number of participants Number of MDD cases Prevalence (95% CI) a) Current major depressive disorder South Africa 1,805 37 2.1% (1.5, 2.8) Uganda 2,283 16 0.7% (0.4, 1.1) Ethiopia 2,303 12 0.5% (0.3, 0.9) Kenya 682 01 0.1% (0.02, 1.0) Combined in all 4 countries 7,073 66 0.9% (0.7, 1.2) b) Lifetime major depressive disorder South Africa 1,805 151 8.4% (7.2, 9.7) Uganda 2,283 180 7.9% (6.8, 9.1) Ethiopia 2,303 27 1.2% (0.08, 1.7) Kenya 682 2 0.3% (0.07, 1.2) Combined in all 4 countries 7,073 360 5.1% (4.6, 5.6) Factors associated with current major depressive disorder In the multilevel models, male sex at birth was marginally significantly associated with current MDD (aOR = 0.47 [95% CI: 0.22; 1.02]). Level of education did not show strong or consistent associations with MDD, as neither secondary nor tertiary education was significantly linked to MDD in any of the models. Living arrangements and marital status were similarly not associated with significant changes in the odds of MDD across the models. Several behavioural and health-related factors were significantly associated with MDD. Current use of alcohol was strongly associated with MDD (aOR = 6.07 [95% CI: 1.89; 19.46]). On the other hand, current substance use and tobacco consumption were not significantly associated with MDD. Experiencing traumatic life events was significantly associated with MDD (aOR = 2.16 [95% CI: 1.00; 4.66]). Among chronic conditions, frequent headaches were significantly associated with MDD (aOR = 1.89 [95% CI: 1.02; 3.50]), while other chronic conditions such as arthritis and chronic back/neck pain did not show significant associations. Higher scores on the Kessler psychological distress scale (K10) were significantly associated with MDD (aOR = 1.26 [95% CI: 1.21; 1.31]). The geographical location, represented by living in Eastern Africa compared to Southern Africa, was not significantly associated with MDD. The ICC for the fully adjusted model (model 3) was 0.14 (95% CI: 0.09; 0.32), indicating that 14% of the variance in MDD was attributable to differences between countries. These results are summarized in Table 3 a. Table 3 a: Results of fitting multilevel logistic regression model for factors associated with current major depressive disorder. CI = confidence intervals, aOR = adjusted odds ratios, ICC = intraclass correlation coefficient, *p < 0.05. Factor Model 1 aOR (95% CI) Model 2 aOR (95% CI) Model 3 aOR (95% CI) Age 1.00 (0.97; 1.02) 0.98 (0.95; 1.01) 0.98 (0.95; 1.02) Sex at birth Female (reference) 1.00 1.00 1.00 Male 0.55 (0.31; 1.00) 0.44 (0.20; 0.94) 0.47 (0.22; 1.02) Highest level of education Primary or less (reference) 1.00 1.00 1.00 Secondary 0.74 (0.38; 1.44) 0.71 (0.33; 1.52) 0.88 (0.40; 1.96) Tertiary 0.99 (0.47; 2.07) 1.57 (0.66; 3.77) 1.70 (0.71; 4.09) Current living arrangement Lives alone (reference) 1.00 1.00 1.00 Lives with family & friends 1.61 (0.74; 3.54) 2.00 (0.76; 5.22) 2.09 (0.79; 5.50) Marital status Married/Living Together (reference) 1.00 1.00 1.00 Widowed/Divorced/Separated 2.07 (1.03; 4.12) 1.54 (0.66; 3.58) 1.61 (0.69; 3.75) Single 1.13 (0.59; 2.15) 0.88 (0.42; 1.84) 0.97 (0.46; 2.05) Use of alcoholic beverages No (reference) 1.00 1.00 Yes 5.51 (1.76; 17.20) 6.07 (1.89; 19.46)* Substance use No (reference) 1.00 1.00 Yes 0.66 (0.32; 1.36) 0.68 (0.33; 1.40) Use of tobacco products No (reference) 1.00 1.00 Yes 1.75 (0.80; 3.81) 2.19 (0.97; 4.95) Blood pressure Normal (reference) 1.00 1.00 High 1.71 (0.91; 3.21) 1.89 (0.99; 3.61) Body mass index 0.97 (0.92; 1.02) 0.97 (0.93; 1.03) Negative life events No (reference) 1.00 1.00 Yes 1.90 (0.89; 4.03) 2.16 (1.00; 4.66)* Chronic conditions Arthritis 0.88 (0.34; 2.25) 0.91 (0.36; 2.33) Chronic back/neck pain 0.91 (0.45; 1.85) 0.91 (0.45; 1.85) Frequent headaches 1.86 (1.01; 3.43) 1.89 (1.02; 3.50)* Cancer 0.39 (0.04; 3.73) 0.40 (0.04; 3.76) Kessler psychological distress score 1.25 (1.20; 1.30) 1.26 (1.21; 1.31)* Psychosis screening Past year 0.88 (0.31; 2.45) 0.89 (0.32; 2.49) Lifetime 1.91 (0.80; 4.55) 1.97 (0.82; 4.72) Country geographical location Southern Africa (reference) 1.00 Eastern Africa 2.12 (0.88; 5.09) ICC 0.18 (0.03; 0.59) 0.15 (0.09 ; 0.36) 0.14 (0.09 ; 0.32) Factors associated with lifetime major depressive disorder In the multilevel models, age and sex were significant predictors of lifetime MDD. Increased age was negatively associated with MDD (adjusted odds ratio (aOR) of 0.98 [95% CI: 0.97; 0.99]) in the fully adjusted model). Males had significantly lower lifetime MDD compared to females (OR of 0.69 [95% CI: 0.50; 0.94]). Education, living arrangements and marital were not significantly associated with MDD. Several behavioural and health-related factors were also significantly associated with MDD. Alcohol consumption was also associated with MDD (aOR of 1.41 [95% CI: 1.03; 1.93]), while substance and tobacco use were not significantly associated with MDD. Experiencing negative life events was strongly associated with MDD, with those reporting such events having more than twice the odds (aOR = 2.32, [95% CI: 1.72; 3.13]) compared to those who did not. Among chronic conditions, chronic back/neck pain (aOR = 1.53 [95% CI: 1.15; 2.04]) and frequent headaches (aOR = 1.63 [95% CI: 1.25; 2.12]) were significantly associated with MDD, while other chronic conditions like arthritis and cancer were not. Higher scores on the K10 were also asspciated with MDD (aOR = 1.17 [95% CI: 1.14; 1.19]). Additionally, psychosis screening results showed that both past-year psychosis experiences (aOR = 0.49 [95% CI: 0.30; 0.79]), and lifetime psychosis experiences were associated with MDD (aOR = 2.92 [95% CI: 2.11; 4.03]). Geographical location was not a significant factor, as odds of MDD did not statistically differ between individuals from Eastern Africa and those from Southern Africa. The ICC for the fully adjusted model was 0.24 [95% CI: 0.05; 0.64], indicating that 24% of the variance in MDD was attributable to differences between countries. These results are summarized in Table 3 b. Table 3 b: Results of fitting multilevel logistic regression model for factors associated with lifetime major depressive disorder. CI = confidence intervals, aOR = adjusted odds ratios, ICC = intraclass correlation coefficient, *p < 0.05. Factor Model 1 aOR (95% CI) Model 2 aOR (95% CI) Model 3 aOR (95% CI) Age 0.99 (0.98; 1.00) 0.98 (0.97; 0.99) 0.98 (0.97; 0.99)* Sex at birth Female (reference) 1.00 1.00 1.00 Male 0.66 (0.51; 0.55) 0.69 (0.50; 0.95) 0.69 (0.50; 0.94)* Highest Level of Education Primary or less (reference) 1.00 1.00 1.00 Secondary 0.84 (0.63; 1.13) 1.00 (0.73; 1.38) 1.00 (0.72; 1.38) Tertiary 1.17 (0.84; 1.62) 1.63 (1.13; 2.36) 1.63 (1.13; 2.36) Current Living Arrangement Lives alone (reference) 1.00 1.00 1.00 Lives with family or friends 1.09 (0.79; 1.51) 1.02 (0.72; 1.46) 1.02 (0.72; 1.46) Marital Status Married/living together (reference) 1.00 1.00 1.00 Widowed/divorced/separated 1.49 (1.07; 2.08) 1.36 (0.94; 1.97) 1.36 (0.94; 1.97) Single 0.92 (0.69; 1.22) 0.90 (0.66; 1.23) 0.90 (0.66; 1.22) Use of alcoholic beverages No (Ref) 1.00 1.00 Yes 1.41 (1.03; 1.94) 1.41 (1.03; 1.93)* Substance use No (reference) 1.00 1.00 Yes 1.12 (0.81; 1.55) 1.12 (0.80; 1.55) Use of tobacco products No (reference) 1.00 1.00 Yes 1.37 (0.97; 1.94) 1.37 (0.97; 1.93) Blood pressure Normal (reference) 1.00 1.00 High 0.95 (0.72; 1.25) 0.95 (0.72; 1.25) Body mass index 1.01 (0.99; 1.04) 1.01 (0.99; 1.04) Negative life events No (reference) 1.00 1.00 Yes 2.33 (1.73; 3.14) 2.32 (1.72; 3.13)* Chronic conditions Arthritis 0.91 (0.58; 1.43) 0.91 (0.58; 1.43) Chronic back/neck pain 1.53 (1.15; 2.04) 1.53 (1.15; 2.04)* Frequent headaches 1.63 (1.25; 2.12) 1.63 (1.25; 2.12)* Cancer 0.53 (0.16; 1.70) 0.53 (0.16; 1.70) Kessler psychological distress score 1.17 (1.14; 1.19) 1.17 (1.14; 1.19)* Psychosis screening Past year 0.49 (0.30; 0.79) 0.49 (0.30; 0.79)* Lifetime 2.92 (2.11; 4.04) 2.92 (2.11; 4.03)* Country Geographical location Southern Africa (reference) 1.00 Eastern Africa 0.63 (0.05; 7.13) ICC 0.39 (0.12; 0.75) 0.24 (0.05; 0.65) 0.24 (0.05; 0.64) Discussion Prevalence An overall prevalence of 0.9% was observed for current MDD and prevalence of 0.1–2.1% were observed across all the study countries. The observed overall prevalence was lower than the estimated global current prevalence of 3.4% MDD. 3 The overall prevalence of lifetime MDD in this study was 5.1%. This prevalence is lower than the estimated lifetime prevalence of 14.6% and 11.1% which has been reported for MDD in high and low- and middle-income countries respectively. 20 However, this prevalence is comparable to the prevalence of 4.5% which has been reported in SSA for MDD 3 and similar to the prevalence of 5.4% which has been reported for Africa. 5 The low prevalence for MDD in SSA could be due to several factors which often involve a complex interplay of cultural, social and methodological issues. In many cultures across SSA, MDD may not be recognized in the same way MDD presents in other contexts. The diagnostic tools developed primarily in Western contexts may not be sensitive to the cultural expression of depression in African populations leading to misdiagnosis. This can be exacerbated by the immense cultural and linguistic diversity which exist in Africa. There might also exist unique resilience factors and coping mechanisms which could be protective against the development or recognition of depressive symptoms. This is need to understand how this factors interact to influence the development of MDD in Africa. Also, lifetime MDD prevalence are higher than current MDD prevalence due to the broader time frame which captures any instance of MDD throughout a person’s life and the episodic nature of MDD. For South Africa, the observed current and lifetime prevalence of MDD (2.1% and 8.4% respectively) are less than the prevalence of 4.9% and 9.8% which have been reported for point and lifetime MDD respectively in South Africa. 21 , 22 For Uganda, the observed current and lifetime prevalence of MDD (0.7% and 7.9 respectively) are much lower than the pooled prevalence of 20.8% which was reported among general populations in Uganda by a systematic review and meta-analysis. 10 For Ethiopia, the observed current and lifetime prevalence of MDD (0.5% and 1.2% respectively) are much lower than the prevalence of 5.3% and 17.4% which have been reported for current and lifetime MDD respectively. 23 For Kenya, the observed current and lifetime prevalence of MDD (0.5% and 1.2% respectively) are much lower than the prevalence of 4.4% which has been reported among general populations in Kenya. 9 The variations of prevalence by study country is intriguing and could be due to several factors such as socioeconomic factors, cultural differences and stigma and awareness. Higher prevalence for South Africa and Uganda might correlate with socioeconomic stressors such as poverty, unemployment, and social inequality that can contribute to higher rates of depression. For example, South Africa has been reported to have a significant income inequality and high levels of unemployment. 24 Ethiopia and Kenya, with lower rates, might have different socioeconomic dynamics, or there may be other protective factors at play, such as stronger community and family support systems. Cultural norms can influence how individuals express emotions or depression. In some cultures, it's common to express MDD through physical symptoms rather than psychological terms, which could lead to underdiagnosis of MDD. There is need to understand potential ecological factors responsible for variations in the prevalence of MDD across various regions in SSA. Factors associated with major depressive disorder Per year increase in age was associated with lower odds for lifetime MDD. Directions of associatons between age and depression have been inconsistent. A large literature review and meta-analysis has reported depression to be more prevalent among elderly populations 25 while analysis of large national health data from the United States has reported MDD to be more prevalent among young adults aged 18–29 years. 26 There is no clear explanation for the association between increasing age and lower odds for MDD in our study participants. However, several factors such as ocio-cultural differences, socioeconomic, demographic and access to mental health services could be responsible. For example, there may be better economic conditions and pension schemes for older adults, there may be more awareness and access to mental health care for old as compared to young adults. Younger adults in SSA may face more socioeconomic hardships and unemployment. There is need to understand how these factors may influence the risk for MDD across the lifespan in SSA in order to inform targeted mental health interventions. Male sex at birth was associated with lower odds for MDD. This finding is in line with findings from a large systematic analysis of the global burden of MDD in 2014 between 1990–2019, which reported MDD burden to be more among females as compared with males 3 . Additionally, a large systematic review and meta-analysis of the epidemiology of MDD among people living with HIV in SSA, also reported the prevalence of MDD to be much higher among women as compared to men. 27 Although underlying biological mechanisms for sex disparity in MDD are not well known, sex hormones have been postulated to play a role. In addition to the role of genetics and environmental factors like stress, sex hormone status has been postulated to be a third factor which contributes to changes in the epigenome, whose effect may translate into changes in gene expression and brain structure and function, 28 thus resulting in increased vulnerability for MDD in women. Alcohol use was associated with higher odds for MDD. This finding is in line with findings from a large systematic review and meta-analysis of cohort studies which reported an association between alcohol use and MDD. 29 However, it was observed that this association became nonsignificant when confounders were controlled for, suggesting that alcohol use could be leading to MDD through confounders. For example, unemployed individuals are more likely to abuse drugs and be heavy alcohol users and to suffer from MDD. 30 Witnessing or experiencing traumatic life events was associated with higher odds for MDD. This finding is in agreement with findings from previous studies which reported traumatic life events to have a strong relationship with depression 31 including studies in Uganda that found an increased risk of MDD with increasing negative life events. 12 , 32 Previous studies have also found that recent traumatic life events play a key role in the onset of MDD. 33 However, onset of MDD and the timing of the negative life events were not determined by the parent study and so this could not be properly investigated in this study. Chronic neck or back pain was associated with higher odds for MDD. This finding is in line with previous studies which have reported associations between chronic pain and depression and have proposed that this could be mediated through altered neuroplasticity. 34 This relationship has been found to be influenced by anomalies in neurological function that lead to chronic pain preceding depression and depressive symptoms. 34 Back pain in general has been associated with increased risk for MDD among middle aged adults in six countries including Ghana and South Africa. 35 Additionally, a study in southwestern Uganda also found an association between reported back pain and MDD among elderly persons living with HIV/AIDS. 36 Frequent headaches were associated with higher odds for MDD. This finding is in agreement with findings from a systematic review and meta-analysis which reported migraines (frequent headaches) to be a risk factor for incident cases of MDD in both cross-sectional and cohort studies. 37 However, this relationship could be bidirectional as MDD has also been found to be causal to different forms of frequent headaches by a Mendelian randomization study 38 . Frequent headaches have been suggested to lead to or exacerbate MDD through neurotransmitter imbalances, particularly serotonin and norepinephrine which affect both pain perception and mood. 39 Chronic pain from headaches can impact daily life and contribute to the development or worsening of MDD. Per unit increase in K10 score was associated with higher odds for both current and lifetime MDD. This is not surprising as the K10 score measures psychological distress and the K10 tool has items which assess for both depression and anxiety symptoms a person has experienced in the most recent 4 week period. 40 The K10 score could be used as tool which can identify people who are at risk of developing MDD in SSA for early intervention to reduce the overall burden of MDD on individuals and healthcare systems. Lifetime positive screening for psychosis was associated with higher odds for MDD. This could could be due to a fact that some people who suffer from MDD normally report symptoms of psychosis such as delusions and hallucinations. 41 The form of MDD with psychotic features is termed psychotic depression according to the International Classification of Diseases 11th revision. 42 Psychotic depression has been reported to be a risk factor for psychosis in patients whose index depression had psychotic features 43 hence the need for effective preventative treatments. Psychotic depression is hard to treat and has been reported to be unresponsive to an antidepressant alone. 41 Elevated levels of cortisol have been suggested to be responsible for this form of depression. 44 , 45 Paradoxically, a past year positive screening for psychosis was associated with lower odds for MDD. There is no direct explanation for this observation. However, acute management and treatment could mitigate the symptoms of MDD. Indeed, atypical antipsychotics have been reported to have a direct preventive effect on the development of depressive symptoms during management of acute psychosis. 46 The proportion of variance in the prevalence of MDD across the study countries was moderate (14%) for current MDD and moderately large (24%) for lifetime MDD. There is thus a need to understand the specific risk factors for MDD across different groups or communities in Africa. Also, given that moderately large proportion of variance is contributed by country level factors, country level intervention strategies could be effective in mitigating MDD rather than individual level strategies – which would likely be less feasible. In addition, the higher proportion in variance for lifetime MDD potentially suggests that persistent regional-level factors are influential and perhaps reflect long-standing regional-level characteristics, cultural factors or structural factors that affect MDD over the lifespan. Limitations We could not elucidate the causation of MDD given the cross-sectional nature of the study. Also, in assessing life-time MDD, we relied on the respondents’ memory and recall bias which may potentially have resulted in a systematic bias against the recall of temporally distant events. In addition, given the selection criteria for the participants from the hospital settings, our study participants may not truly represent a general population. Additionally, given that participation was volunteer based, we may not have captured people the most severely affected by MDD hence the likelihood of underrepresentation of the most disadvantaged people in the communities studies. Also, since the diagnostic tool used was developed in Western contexts, this could be less sensitive to the cultural expression of MDD in African populations hence leading to misdiagnosis. Conclusions This study investigated several factors associated with MDD in participants present at general medical facilities from a majorly African ancestry population. The main findings from the phenotypic analysis were that among the study participants, being female, using alcohol, experiencing or witnessing negative life events, having higher levels of psychological distress and suffering from chronic conditions of either chronic back or neck pain were associated with higher odds for MDD while per year increase in age was associated with lower odds for depression among the study participants. These results suggest that psychosocial factors as well as psychosomatic complaints and physical comorbidities are important risk factors for MDD among the participants for this study. Methods Study design This study was undertaken using data collected by the NeuroGAP-Psychosis study. Details about the NeuroGAP-Psychosis study, the data variables collected and the tools used have been reported by Stevenson and colleagues. 47 In brief, the NeuroGAP-Psychosis study recruited a total of 42,953 participants (21,715 cases of psychosis, 21,238 psychosis-free controls) from four African countries of Uganda, Kenya, Ethiopia, and South Africa over a 5-year period (2018–2023). The aim of the NeuroGAP-Psychosis study is to investigate the genetic risk for psychosis. Each study participant provided a saliva sample from which DNA was extracted and shipped to the Broad Institute in the United States, from where the DNA was sequenced. Out of the 21,715 NeuroGAP-Psychosis study’s control participants, a total of 7,073 adult participants were assessed for current and lifetime MDD using the Mini-International Neuropsychiatric Interview (MINI, standard version 7.0.2) 48 and are included in this analysis. The NeuroGAP-Psychosis study’s control participants were ascertained from persons who presented at University-affiliated general medical hospitals that draw from similar catchement areas to the psychiatric facilities where the psychosis cases for the NeuroGAP-Psychosis study were recruited. NeuroGAP-Psychosis study control participants were 1) caretakers who had accompanyed someone else to an appointment, 2) students/workers at the hospital/clinic or 3) someone who had visited for a prescription refill or doctor’s appointment. Clinical investigations A questionnaire (module A of the MINI) was administered by trained research assistants onto the study participants at each of the study sites. Assessment of MDD based on presence of at least one of symptoms of either a depressed mood or loss of interest in pleasurable activities and any other four symptoms which may include significant weight change or apetite disturbance, sleep disturbances, psychomotor agitation or retardation, fatigue, feelings of worthlessness or excessive guilt, diminished ability to think or concentrate and recurrent thoughts of death or suicidal ideation. Additionally, these symptoms had to cause significant distress or impairment in social, occupational or other important areas of functing and were not a result of physiological effects of a substance or another medical condition. For current MDD, presence of symptoms in the last two weeks was assessed while for lifetime MDD, the presence of these symptoms in a participant’s lifetime was assessed. The five-item version of the psychosis screening questionnaire (PSQ) 49 was used to screen for psychosis symptoms. The Kessler psychological distress scale (K10) 40 was used to assess for psychological this. This is a ten-item questionnaire based on questions about symptoms for anxiety and depression. The Life events checklist for DSM-5 (LEC-5) 50 was used to assess for negative life events. This checklist assesses exposure to sixteen events known to potentially results in post-traumatic stress disorder or distress. The World Health Organization Alcohol, Smoking and Substance Involvement Screening Test (ASSIST, version3) 51 was used to assess for lifetime use of alcohol, tobacco and other substances. The Composite International Diagnostic Interview (CIDI) screener 52 was used to assess for chronic conditions such as arthritis or rheumatism, chronic back or neck pain, frequent or severe headaches, cancer, among others. Ethical considerations The NeuroGAP-Psychosis study was conducted in compliance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Ethical and scientific clearance for the NeuroGAP-Psychosis study was obtained from each of the study sites as shown in the following sentences; Uganda: The Makerere University School of Medicine Research and Ethics Committee (SOMREC #REC REF 2016-057) and the Uganda National Council for Science and Technology (UNCST #HS14ES); Kenya: Moi University College of Health Sciences/Moi Teaching and Referral Hospital Institutional Research and Ethics Committee (IREC) (#IREC/2016/145, approval number: IREC 1727), Kenya National Council of Science and Technology (#NACOSTI/P/17/56302/19576), KEMRI Centre Scientific Committee (CSC#KEMRI/CGMRC/CSC/070/2016), KEMRI Scientific and Ethics Review Unit (SERU# KEMRI/SERU/CGMR-C/070/3575); Ethiopia: Addis Ababa University College of Health Sciences (#014/17/Psy) and the Ministry of Science and Technology National Research Ethics Review Committee (#3.10/14/2018); South Africa: The University of Cape Town Human Research Ethics Committee (#466/2016); and USA: The Harvard T.H. Chan School of Public Health (#IRB17-0822). Participants provided written informed consent for their genetic and health information to be used in future research. Participants' priorities and experiences were taken into consideration during the design of the NeuroGAP-Psychosis study. Data analysis All statistical analyses were conducted using STATA version 18.0. Descriptive statistics were used to summarize socio-demographic characteristics, including sex, age, marital status, living arrangement, education level, study country, and body mass index, as well as clinical characteristics such as tobacco use, alcohol use, substance use, and blood pressure. Categorical variables were summarized using frequencies and percentages, while continuous variables were reported as medians with interquartile ranges (IQR). The prevalence of both current and lifetime MDD was estimated for each study country, along with corresponding 95% confidence intervals. To examine factors associated with MDD, we employed a multilevel logistic regression model to account for the hierarchical structure of the data, with individuals nested within four different countries. Model selection was guided by the likelihood ratio test (LRT) to ensure that only variables that significantly improved model fit were retained. The modeling process followed a sequential approach. First, model 1 included key socio-demographic variables (age, sex, education level, living arrangement, and marital status), selected a priori based on theoretical and empirical evidence. Model 2 extended model 1 by incorporating individual-level behavioral and clinical characteristics, including alcohol use, substance use, history of traumatic life events, and current physical illness. Variables were retained only if their inclusion significantly improved model fit based on LRT. Finally, model 3 introduced country-level characteristics, such as geographical location, to assess their influence while controlling for individual-level factors. The intraclass correlation coefficient (ICC) was calculated for each model to quantify the proportion of total variance attributable to between-country differences. A variable was considered to be meaningfully associated with MDD only if it remained statistically significant in the final model and contributed to model fit as determined by LRT. To assess the robustness of the findings, a sensitivity analysis was performed by systematically excluding individual countries and re-estimating the models. A two-sided p-value < 0.05 was considered statistically significant. Declarations Data availability The data that support the findings of this study are available via the National Institute of Mental Health Data Archive. Data are also available from the authors upon reasonable request and with permission of the NeuroGAP-Psychosis study consortium. Funding Allan Kalungi is a Wellcome Early Career Fellow [227053/Z/23/Z]. He also received funding for the NARSAD young investigator grant from the Brain and Behavior Research Foundation [Grant number 29610]. The NeuroGAP-Psychosis study was funded by the Stanley Center for Psychiatric Research at the Broad Institute. DA, DS, and ST are supported in part by the United States’ National Institute of Mental Health (NIMH) by grant R01MH120642. SF is supported by both the Wellcome Trust [grant number: 220740/Z/20/Z] and the National Institute of Mental Health [grant number: 1R01MH134468]. Competing interests We declare no competing interests. Author contributions Conceptualization: AK, DHA, EK, SF; Statistical analysis: WS, AK; First draft: AK; Critical review & Final draft: All authors. All authors approved the final manuscript. All authors had full access to all the data and had final responsibility for the decision to submit for publication. RES, SF and DHA accessed and verified the data. Acknowledgements Special gratitude goes out to the NeuroGAP-Psychosis study consortium for granting the access to the data that was used to carry out this study, the Global Initiative for Neuropsychiatric Genetics Education in Research program (GINGER; https://gingerprogram.org) – which provided Biostatistics and Bioinformatics training to the lead author, and the participants who attended the NeuroGAP-Psychosis study. We would also like to acknowledge Dr Edith K Kwobah who led the NeuroGAP-Psychosis study at the Moi Teaching and Referral Hospital in Kenya. Dr Kwobah passed on 22 nd March 2024. We would also like to acknowledge the data managers, clinicians, research assistants, and project managers who have worked on this study from Addis Ababa University, KEMRI-Wellcome Trust, Makerere University, Moi University/Moi Teaching and Referral Hospital, Harvard/Broad Institute, and the University of Cape Town. References American Psychiatric Association D (2013) Diagnostic and statistical manual of mental disorders: DSM-5 . vol 5. American psychiatric association Washington, DC American Psychiatric Association, Association D (2013) AP. Diagnostic and statistical manual of mental disorders: DSM-5 . vol 5. American psychiatric association Washington, DC GBD Mental Disorders Collaborators (2022) Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 9(2):137–150 Lohoff FW (2010) Overview of the genetics of major depressive disorder. Curr psychiatry Rep 12:539–546 World Health Organization. Depression and Other Common mental Disorders, Global Health Estimates. February 14 (2024) 2024. Accessed February 14, 2024, 2024. https://www.afro.who.int/sites/default/files/2017-05/WHO-MSD-MER-2017.2-eng.pdf Otte C, Gold SM, Penninx BW et al (2016) Major depressive disorder. Nat Reviews Disease Primers 09(1):16065. 10.1038/nrdp.2016.65 . /15 2016 Moitra M, Santomauro D, Collins PY et al (2022) The global gap in treatment coverage for major depressive disorder in 84 countries from 2000–2019: A systematic review and Bayesian meta-regression analysis. PLoS Med 19(2):e1003901. 10.1371/journal.pmed.1003901 Patel V, Maj M, Flisher AJ et al (2010) Reducing the treatment gap for mental disorders: a WPA survey. World Psychiatry 9(3):169–176 Gbadamosi IT, Henneh IT, Aluko OM et al (2022) Depression in Sub-Saharan Africa. IBRO Neurosci Rep Jun 12:309–322. 10.1016/j.ibneur.2022.03.005 Kaggwa MM, Najjuka SM, Bongomin F, Mamun MA, Griffiths MD (2022) Prevalence of depression in Uganda: A systematic review and meta-analysis. PLoS ONE 17(10):e0276552 Bolton P, Wilk CM, Ndogoni L (2004) Assessment of depression prevalence in rural Uganda using symptom and function criteria. Soc Psychiatry Psychiatr Epidemiol 39(6):442 Kinyanda E, Woodburn P, Tugumisirize J, Kagugube J, Ndyanabangi S, Patel V (2011) Poverty, life events and the risk for depression in Uganda. Soc Psychiatry Psychiatr Epidemiol 46:35–44 Perkins JM, Nyakato VN, Kakuhikire B et al (2018) Food insecurity, social networks and symptoms of depression among men and women in rural Uganda: a cross-sectional, population-based study. Public Health Nutr 21(5):838–848 Rathod SD, Roberts T, Medhin G et al (2018) Detection and treatment initiation for depression and alcohol use disorders: facility-based cross-sectional studies in five low-income and middle-income country districts. BMJ open 8(10):e023421 Smith ML, Kakuhikire B, Baguma C et al (2019) Relative wealth, subjective social status, and their associations with depression: Cross-sectional, population-based study in rural Uganda. SSM-population health 8:100448 Duko B, Geja E, Zewude M, Mekonen S (2018) Prevalence and associated factors of depression among patients with HIV/AIDS in Hawassa, Ethiopia, cross-sectional study. Ann Gen Psychiatry 17:45. 10.1186/s12991-018-0215-1 Bitew T (2014) Prevalence and risk factors of depression in Ethiopia: a review. Ethiop J Health Sci Apr 24(2):161–169. 10.4314/ejhs.v24i2.9 Cristóbal-Narváez P, Haro JM, Koyanagi A (2020) Perceived stress and depression in 45 low-and middle-income countries. J Affect Disord 274:799–805 Kamau JW, Kuria W, Mathai M, Atwoli L, Kangethe R (2012) Psychiatric morbidity among HIV-infected children and adolescents in a resource-poor Kenyan urban community. AIDS Care 24(7):836–842 Bromet E, Andrade LH, Hwang I et al (2011) Cross-national epidemiology of DSM-IV major depressive episode. BMC Medicine . /07/26 2011;9(1):90. 10.1186/1741-7015-9-90 Cuadros DF, Tomita A, Vandormael A, Slotow R, Burns JK, Tanser F (2019) Spatial structure of depression in South Africa: A longitudinal panel survey of a nationally representative sample of households. Sci Rep 9(1):979 Herman AA, Stein DJ, Seedat S, Heeringa SG, Moomal H, Williams DR (2009) The South African Stress and Health (SASH) study: 12-month and lifetime prevalence of common mental disorders. South Afr Med J. ;99(5) Molla GL, Sebhat HM, Hussen ZN, Mekonen AB, Mersha WF, Yimer TM Depression among Ethiopian adults: cross-sectional study. Psychiatry J. 2016;2016 The World Bank Unemployment, youth total (% of total labor force ages 15–24) (modeled ILO estimate) - Sub-Saharan Africa. 5th April, 2024, 2024. Accessed 5th April, 2024, 2024. https://data.worldbank.org/indicator/SL.UEM.1524.ZS?locations=ZG Zenebe Y, Akele B, Necho MWS (2021) Prevalence and determinants of depression among old age: a systematic review and meta-analysis. Ann Gen Psychiatry Dec 18(1):55. 10.1186/s12991-021-00375-x Villarroel MA, Terlizzi EP (2020) Symptoms of depression among adults: United States, 2019. . NCHS Data Brief . 379. https://www.cdc.gov/nchs/products/databriefs/db379.htm#:~:text=The%20percentage%20of%20adults% 20who%20experienced%20any%20symptoms%20of%20depression,or% 20severe%20symptoms%20of%20depression Bernard C, Dabis F, de Rekeneire N (2017) Prevalence and factors associated with depression in people living with HIV in sub-Saharan Africa: a systematic review and meta-analysis. PLoS ONE 12(8):e0181960 Kundakovic M, Rocks D (2022) Sex hormone fluctuation and increased female risk for depression and anxiety disorders: From clinical evidence to molecular mechanisms. Front Neuroendocrinol Jul 66:101010. 10.1016/j.yfrne.2022.101010 Li J, Wang H, Li M et al (2020) Effect of alcohol use disorders and alcohol intake on the risk of subsequent depressive symptoms: a systematic review and meta-analysis of cohort studies. Addiction 115(7):1224–1243 Compton WM, Gfroerer J, Conway KP, Finger MS (2014) Unemployment and substance outcomes in the United States 2002–2010. Drug Alcohol Depend 142:350–353 Kraaij V, Arensman E, Spinhoven P (2002) Negative life events and depression in elderly persons: a meta-analysis. Journals Gerontol Ser B: Psychol Sci Social Sci 57(1):P87–P94 Mugisha J, Muyinda H, Malamba S, Kinyanda E (2015) Major depressive disorder seven years after the conflict in northern Uganda: burden, risk factors and impact on outcomes (The Wayo-Nero Study). BMC Psychiatry 15:1–12 Stegenga BT, Nazareth I, Grobbee DE et al (2012) Recent life events pose greatest risk for onset of major depressive disorder during mid-life. J Affect Disord 136(3):505–513 Sheng J, Liu S, Wang Y, Cui R, Zhang X (2017) The link between depression and chronic pain: neural mechanisms in the brain. Neural Plast 2017(1):9724371 Selvamani Y, Sangani P, Muhammad T (2022) Association of back pain with major depressive disorder among older adults in six low-and middle-income countries: A cross-sectional study. Exp Gerontol 167:111909 Kinyanda E, Kuteesa M, Scholten F, Mugisha J, Baisley K, Seeley J (2016) Risk of major depressive disorder among older persons living in HIV-endemic central and southwestern Uganda. AIDS Care 28(12):1516–1521 Amiri S, Behnezhad S, Azad E (2019) Sep. Migraine headache and depression in adults: a systematic Review and Meta-analysis. Neuropsychiatr . ;33(3):131–140. Migräne und Depression bei Erwachsenen: Ein systematisches Review und Meta-Analyse. 10.1007/s40211-018-0299-5 Lv X, Xu B, Tang X et al (2023) The relationship between major depression and migraine: A bidirectional two-sample Mendelian randomization study. Front Neurol 14:1143060. 10.3389/fneur.2023.1143060 National Headache Foundation. Depression and Headache. April 1st, 2024 (2024) Accessed April 1st, 2024, 2024. https://headaches.org/depression-and-headache/ Kessler RC, Andrews G, Colpe LJ et al (2002) Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med Aug 32(6):959–976. 10.1017/s0033291702006074 Dubovsky Steven L, Ghosh Biswarup M, Serotte Jordan C, Cranwell V (2021) Psychotic Depression: Diagnosis, Differential Diagnosis, and Treatment. Psychother Psychosom 90(3):160–177. 10.1159/000511348 World Health Organization ICD-11 for Mortality and Mobidity Statistics. 7th April, 2024, 2024. Updated January 2024. Accessed 7th April, 2024, 2024. https://icd.who.int/browse/2024-01/mms/en#104129373 Nelson JC, Bickford D, Delucchi K, Fiedorowicz JG, Coryell WH (2018) Risk of Psychosis in Recurrent Episodes of Psychotic and Nonpsychotic Major Depressive Disorder: A Systematic Review and Meta-Analysis. Am J Psychiatry Sep 1(9):897–904. 10.1176/appi.ajp.2018.17101138 Nelson JC, Davis JM (1997) DST studies in psychotic depression: a meta-analysis. Am J Psychiatry 154(11):1497–1503 Schatzberg AF, Rothschild AJ, Langlais PJ, Bird ED, Cole JO (1985) A corticosteroid/dopamine hypothesis for psychotic depression and related states. J Psychiatr Res 19(1):57–64 Siris SG (2000) Depression in schizophrenia: perspective in the era of atypical antipsychotic agents. Am J Psychiatry 157(9):1379–1389 Stevenson A, Akena D, Stroud RE et al (2019) Neuropsychiatric Genetics of African Populations-Psychosis (NeuroGAP-Psychosis): a case-control study protocol and GWAS in Ethiopia, Kenya, South Africa and Uganda. BMJ Open 9(2):e025469. 10.1136/bmjopen-2018-025469 Sheehan DV, Lecrubier Y, Sheehan KH et al (1998) The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry . ;59 Suppl 20:22–33;quiz 34–57 Bebbington P, Nayani T (1995) The psychosis screening questionnaire. Int J Methods Psychiatr Res Gray MJ, Litz BT, Hsu JL, Lombardo TW (2004) Psychometric properties of the life events checklist. Assessment 11(4):330–341 WHO ASSIST Working Group (2002) The alcohol, smoking and substance involvement screening test (ASSIST): development, reliability and feasibility. Addiction 97(9):1183–1194 Kessler RC, Üstün TB (2004) The world mental health (WMH) survey initiative version of the world health organization (WHO) composite international diagnostic interview (CIDI). Int J Methods Psychiatr Res 13(2):93–121 Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6279456\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":434974196,\"identity\":\"dadf6f9c-3e8d-4335-b967-c8ba92617db3\",\"order_by\":0,\"name\":\"Allan Kalungi\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"https://orcid.org/0000-0002-2890-0145\",\"institution\":\"Medical Research Council, Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Allan\",\"middleName\":\"\",\"lastName\":\"Kalungi\",\"suffix\":\"\"},{\"id\":434974197,\"identity\":\"10bde7b8-6447-4167-8ee9-76d4287f07c4\",\"order_by\":1,\"name\":\"Wilber Ssembajjwe\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"MRC/UVRI and LSHTM Uganda Research Unit\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Wilber\",\"middleName\":\"\",\"lastName\":\"Ssembajjwe\",\"suffix\":\"\"},{\"id\":434974198,\"identity\":\"623bee73-54ac-4f85-bcb5-8bc061a66620\",\"order_by\":2,\"name\":\"Kester Tindi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"School of Psychology and Vision Sciences, University of Leicester, Leicester, UK.\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kester\",\"middleName\":\"\",\"lastName\":\"Tindi\",\"suffix\":\"\"},{\"id\":434974199,\"identity\":\"b3755554-df15-4c19-8dcf-e6f517bf743c\",\"order_by\":3,\"name\":\"Emmanuel Mwesiga\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Makerere University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Emmanuel\",\"middleName\":\"\",\"lastName\":\"Mwesiga\",\"suffix\":\"\"},{\"id\":434974200,\"identity\":\"a26e3f4b-3a6a-4f62-8793-ad43aa39e0e8\",\"order_by\":4,\"name\":\"Jared Maina\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Lille\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jared\",\"middleName\":\"\",\"lastName\":\"Maina\",\"suffix\":\"\"},{\"id\":434974201,\"identity\":\"2c2f790a-2d7d-4ef3-aced-18c14e457bdc\",\"order_by\":5,\"name\":\"Fred Kirumira\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine (MRC/UVRI \\u0026 LSHTM) Uganda Research Unit, Entebbe, Uganda.\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fred\",\"middleName\":\"\",\"lastName\":\"Kirumira\",\"suffix\":\"\"},{\"id\":434974202,\"identity\":\"5b7fc501-a3bf-404e-ab62-069e42ce96ce\",\"order_by\":6,\"name\":\"Elizabeth Atkinson\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-6308-776X\",\"institution\":\"Baylor College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Elizabeth\",\"middleName\":\"\",\"lastName\":\"Atkinson\",\"suffix\":\"\"},{\"id\":434974203,\"identity\":\"e1471e14-0b87-43b2-a443-b34139b6c227\",\"order_by\":7,\"name\":\"Lukoye Atwoli\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Mental Health, School of Medicine, Moi University College of Health Sciences, Eldoret, Kenya.\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lukoye\",\"middleName\":\"\",\"lastName\":\"Atwoli\",\"suffix\":\"\"},{\"id\":434974204,\"identity\":\"7f8362f3-6ba7-463d-a02d-42bc7309dd8a\",\"order_by\":8,\"name\":\"Mark Barker\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mark\",\"middleName\":\"\",\"lastName\":\"Barker\",\"suffix\":\"\"},{\"id\":434974205,\"identity\":\"84a1e21d-51f4-4bd9-a931-5056f4a6cf81\",\"order_by\":9,\"name\":\"Lori Chibnik\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0001-6293-806X\",\"institution\":\"Brigham \\u0026 Women's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lori\",\"middleName\":\"\",\"lastName\":\"Chibnik\",\"suffix\":\"\"},{\"id\":434974206,\"identity\":\"bf0f7639-6cf8-4af2-b731-2757533b9d1c\",\"order_by\":10,\"name\":\"Symon Kariuki\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-3934-5132\",\"institution\":\"KEMRI-Wellcome Trust Research Programme\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Symon\",\"middleName\":\"\",\"lastName\":\"Kariuki\",\"suffix\":\"\"},{\"id\":434974207,\"identity\":\"2d2738cb-413c-4f0f-b2ef-6987d3ce324a\",\"order_by\":11,\"name\":\"Charles Newton\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Charles\",\"middleName\":\"\",\"lastName\":\"Newton\",\"suffix\":\"\"},{\"id\":434974208,\"identity\":\"59123adf-58bf-4c8c-9954-a63d869cfb96\",\"order_by\":12,\"name\":\"Rocky Stroud II\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave Room 505F, Boston, MA 02115 USA\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rocky\",\"middleName\":\"Stroud\",\"lastName\":\"II\",\"suffix\":\"\"},{\"id\":434974209,\"identity\":\"8aca77f2-7225-4fb3-bed3-a0a6fe740c8a\",\"order_by\":13,\"name\":\"Solomon Abebe\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Addis Ababa University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Solomon\",\"middleName\":\"\",\"lastName\":\"Abebe\",\"suffix\":\"\"},{\"id\":434974210,\"identity\":\"338c4bc0-9b50-4654-895c-8d0e385375ef\",\"order_by\":14,\"name\":\"Zukiswa Zingela\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Executive Dean's Office, Faculty of Health Sciences, Nelson Mandela University, Port Elizabeth, South Africa.\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zukiswa\",\"middleName\":\"\",\"lastName\":\"Zingela\",\"suffix\":\"\"},{\"id\":434974211,\"identity\":\"4668a055-9a0e-4b70-9834-3dc78e8f667c\",\"order_by\":15,\"name\":\"Alicia Martin\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-0241-3522\",\"institution\":\"Broad Institute of MIT and Harvard\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Alicia\",\"middleName\":\"\",\"lastName\":\"Martin\",\"suffix\":\"\"},{\"id\":434974212,\"identity\":\"33525e06-156a-432b-8605-ff689390b4bf\",\"order_by\":16,\"name\":\"Moffat Nyirenda\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine (MRC/UVRI \\u0026 LSHTM) Uganda Research Unit, Entebbe, Uganda.\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Moffat\",\"middleName\":\"\",\"lastName\":\"Nyirenda\",\"suffix\":\"\"},{\"id\":434974213,\"identity\":\"97ef843f-7a4b-4c6e-ab0b-01887835109f\",\"order_by\":17,\"name\":\"Dan Stein\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0001-7218-7810\",\"institution\":\"University of Cape Town and Neuroscience Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Dan\",\"middleName\":\"\",\"lastName\":\"Stein\",\"suffix\":\"\"},{\"id\":434974214,\"identity\":\"e964245a-14a6-4add-b3b3-8a2fa7f1aab5\",\"order_by\":18,\"name\":\"Eugene Kinyanda\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine (MRC/UVRI \\u0026 LSHTM) Uganda Research Unit, Entebbe, Uganda.\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Eugene\",\"middleName\":\"\",\"lastName\":\"Kinyanda\",\"suffix\":\"\"},{\"id\":434974215,\"identity\":\"3beb0bf2-4fa1-409c-9d37-045c10b4ad4d\",\"order_by\":19,\"name\":\"Karoline Kuchenbaecker\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0001-9726-603X\",\"institution\":\"University College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Karoline\",\"middleName\":\"\",\"lastName\":\"Kuchenbaecker\",\"suffix\":\"\"},{\"id\":434974216,\"identity\":\"3aa90b73-eb9a-478a-bf70-0d7c21614cd2\",\"order_by\":20,\"name\":\"Segun Fatumo\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-4525-3362\",\"institution\":\"MRC/UVRI \\u0026 LSHTM Uganda Research Unit\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Segun\",\"middleName\":\"\",\"lastName\":\"Fatumo\",\"suffix\":\"\"},{\"id\":434974217,\"identity\":\"41f3703e-9514-47bc-b35c-cff5ade2ac16\",\"order_by\":21,\"name\":\"Dickens Akena\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Psychiatry, College of Health Sciences, Makerere University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Dickens\",\"middleName\":\"\",\"lastName\":\"Akena\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-03-21 17:10:30\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6279456/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6279456/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":92865973,\"identity\":\"a0d82936-2bf3-44e8-b2ee-3aa7619d84be\",\"added_by\":\"auto\",\"created_at\":\"2025-10-06 13:08:25\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1548234,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6279456/v1/9fc7579c-da61-4d24-912c-97eb756ad0f6.pdf\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"Major depressive disorder in sub-Saharan Africa: findings from the Neuro-GAP-Psychosis study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eMajor depressive disorder (MDD) is a common mental disorder characterized by persistent feelings of sadness, hopelessness and lack of interest in normally pleasurable activities. \\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe global prevalence for MDD has been estimated at 3.4%, accounting for point, 12 month and lifetime prevalence using pooled prevalence ratios\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. It is estimated that 10\\u0026ndash;15% of the general population experience clinical depression in their lifetimes with 5% of men and 9% of women experiencing a depressive disorder in a given year.\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e According to the World Health Organisation, MDD was responsible for over 50\\u0026nbsp;million years lived with disability (YLD) in 2015 and can lead to suicide which is responsible for up to 800,000 deaths annually.\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e MDD leads to high levels of morbidity and a 10% increased risk of all-cause mortality.\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe burden of MDD has been reported to vary per WHO region and the study population. It has been reported to vary from 3.6% in the Western Pacific region to 5.4% in the African region.\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e Africa accounts for over 9% of the global burden of MDD,\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e yet it is a region where very low treatment rates have been reported for MDD.\\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eFrom existing limited data, in Sub-Saharan Africa (SSA) an age-standardized prevalence of 4.5% has been estimated for MDD.\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e The age-standardized prevalence of MDD has been reported to be higher in SSA than elsewhere globally.\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e In South Africa, a prevalence of 4.6%\\u003csup\\u003e9\\u003c/sup\\u003e has been reported among the general population while prevalence rates of 1.9\\u0026ndash;38% have been reported among small controlled populations. In Uganda, a pooled prevalence of 30.2% has been determined by a systematic review and a meta-analysis among heterogeneous samples\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e while MDD prevalence estimates of 4.2\\u0026ndash;29.3% have been determined among general populations from various study sites in Uganda.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR12 CR13 CR14\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e In Ethiopia, a prevalence of 4.7% has been reported among the general population while prevalence rates of 4.8\\u0026ndash;57% have been reported among heterogeneous samples.\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e In Kenya, a prevalence of 4.4% has been reported among the general population while prevalence rates of 6.3\\u0026ndash;72.9% have been reported among heterogeneous samples.\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eDespite accumulating data on MDD prevalence across various regions in SSA, a comprehensive understanding of the disorder's epidemiological landscape remains elusive. Current research shows considerable variability in MDD prevalence. This suggests that existing studies may not fully capture the nuances of how depression manifests across SSA\\u0026rsquo;s varied cultural, economic, and demographic contexts. This may also be attributable to the different methods employed I in assessing for MDD and the different populations being sampled. Additionally, while links between MDD and lifestyle diseases such as diabetes and hypertension, as well as infectious diseases like HIV, Ebola, and COVID-19 have been noted, there is a dearth of studies exploring these relationships. The inconsistent use of diagnostic criteria and potential underdiagnosis due to cultural perceptions of mental health highlight the need for more localized and culturally adapted research methodologies. Also, given the heterogeneous nature of MDD, it is important to know the rates and risk factors for MDD in different populations, to inform policies that target reducing the burden of the disorder. This study aimed to investigate the prevalence and specific risk factors (demographic, social and family dynamics, lifestyle and health behaviours, physical health conditions, psychological and geographical/cultural) for MDD among a cross-sectional sample of participants recruited from hospital settings from Eastern (Uganda, Kenya, Ethiopia) and Southern (South Africa) Africa.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe socio-demographic characteristics and clinical variables of the study participants are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003edescription of the socio-demographic characteristics and clinical variables of the study participants. SD\\u0026thinsp;=\\u0026thinsp;standard deviation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFactor\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLevel\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFrequency (Percentage)\\u003c/p\\u003e \\u003cp\\u003en\\u0026thinsp;=\\u0026thinsp;7,073\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eSex at birth\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,259 (31.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4,814 (68.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37.1 (11.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMarital status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMarried/living together\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3,504 (49.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWidowed/divorced/separated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e822 (11.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSingle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,744 (38.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCurrent living arrangement\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLives alone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,077 (15.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLives with family \\u0026amp; friends\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5,978 (84.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eHighest level of education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimary or less\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,710 (24.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSecondary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3,219 (45.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTertiary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,141 (30.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eStudy country\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSouth Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,805 (25.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKenya\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e682 (9.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUganda\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,283 (32.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEthiopia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,303 (32.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCurrent use of tobacco products\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4,803 (67.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,270 (32.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCurrent use of alcoholic beverages\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,593 (36.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4,480 (63.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCurrent use of substances\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4,996 (70.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,077 (29.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eBlood pressure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5,050 (71.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,023 (28.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBody mass index\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.9 (4.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePrevalence of Major Depressive Disorder\\u003c/h2\\u003e \\u003cp\\u003eAs shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, the overall prevalence of current and lifetime MDD from this study was 0.9 and 5.1% respectively. For current MDD at country level, South Africa had the highest prevalence (2.1%) followed by Uganda (0.7%), Ethiopia (0.5%) and Kenya (0.1%). For lifetime MDD at the country level, South Africa still had the highest prevalence (8.4%) followed by Uganda (7.9%), Ethiopia (1.2%) and the lowest being Kenya (0.3%).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePrevalence of major depressive disorder among the study participants. MDD\\u0026thinsp;=\\u0026thinsp;major depressive disorder, CI\\u0026thinsp;=\\u0026thinsp;confidence interval.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStudy Site\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber of participants\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNumber of MDD cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePrevalence (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ea) Current major depressive disorder\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1,805\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.1% (1.5, 2.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUganda\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2,283\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.7% (0.4, 1.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEthiopia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2,303\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.5% (0.3, 0.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKenya\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e682\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.1% (0.02, 1.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCombined in all 4 countries\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7,073\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9% (0.7, 1.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eb) \\u003cb\\u003eLifetime major depressive disorder\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1,805\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e151\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.4% (7.2, 9.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUganda\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2,283\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e180\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.9% (6.8, 9.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEthiopia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2,303\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.2% (0.08, 1.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKenya\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e682\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.3% (0.07, 1.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCombined in all 4 countries\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7,073\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e360\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.1% (4.6, 5.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eFactors associated with current major depressive disorder\\u003c/h3\\u003e\\n\\u003cp\\u003eIn the multilevel models, male sex at birth was marginally significantly associated with current MDD (aOR\\u0026thinsp;=\\u0026thinsp;0.47 [95% CI: 0.22; 1.02]). Level of education did not show strong or consistent associations with MDD, as neither secondary nor tertiary education was significantly linked to MDD in any of the models. Living arrangements and marital status were similarly not associated with significant changes in the odds of MDD across the models. Several behavioural and health-related factors were significantly associated with MDD. Current use of alcohol was strongly associated with MDD (aOR\\u0026thinsp;=\\u0026thinsp;6.07 [95% CI: 1.89; 19.46]). On the other hand, current substance use and tobacco consumption were not significantly associated with MDD. Experiencing traumatic life events was significantly associated with MDD (aOR\\u0026thinsp;=\\u0026thinsp;2.16 [95% CI: 1.00; 4.66]). Among chronic conditions, frequent headaches were significantly associated with MDD (aOR\\u0026thinsp;=\\u0026thinsp;1.89 [95% CI: 1.02; 3.50]), while other chronic conditions such as arthritis and chronic back/neck pain did not show significant associations. Higher scores on the Kessler psychological distress scale (K10) were significantly associated with MDD (aOR\\u0026thinsp;=\\u0026thinsp;1.26 [95% CI: 1.21; 1.31]). The geographical location, represented by living in Eastern Africa compared to Southern Africa, was not significantly associated with MDD. The ICC for the fully adjusted model (model 3) was 0.14 (95% CI: 0.09; 0.32), indicating that 14% of the variance in MDD was attributable to differences between countries. These results are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ea: Results of fitting multilevel logistic regression model for factors associated with current major depressive disorder. CI\\u0026thinsp;=\\u0026thinsp;confidence intervals, aOR\\u0026thinsp;=\\u0026thinsp;adjusted odds ratios, ICC\\u0026thinsp;=\\u0026thinsp;intraclass correlation coefficient, *p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFactor\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003cp\\u003eaOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003cp\\u003eaOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003cp\\u003eaOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00 (0.97; 1.02)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.98 (0.95; 1.01)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98 (0.95; 1.02)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex at birth\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.55 (0.31; 1.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.44 (0.20; 0.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.47 (0.22; 1.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHighest level of education\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary or less (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSecondary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.74 (0.38; 1.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.71 (0.33; 1.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.88 (0.40; 1.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTertiary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.99 (0.47; 2.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.57 (0.66; 3.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.70 (0.71; 4.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCurrent living arrangement\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLives alone (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLives with family \\u0026amp; friends\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.61 (0.74; 3.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.00 (0.76; 5.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.09 (0.79; 5.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMarital status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried/Living Together (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWidowed/Divorced/Separated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.07 (1.03; 4.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.54 (0.66; 3.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.61 (0.69; 3.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSingle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.13 (0.59; 2.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.88 (0.42; 1.84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.97 (0.46; 2.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eUse of alcoholic beverages\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.51 (1.76; 17.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e6.07 (1.89; 19.46)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSubstance use\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.66 (0.32; 1.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.68 (0.33; 1.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eUse of tobacco products\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.75 (0.80; 3.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.19 (0.97; 4.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBlood pressure\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNormal (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.71 (0.91; 3.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.89 (0.99; 3.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBody mass index\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.97 (0.92; 1.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.97 (0.93; 1.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNegative life events\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.90 (0.89; 4.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2.16 (1.00; 4.66)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eChronic conditions\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eArthritis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.88 (0.34; 2.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.91 (0.36; 2.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChronic back/neck pain\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.91 (0.45; 1.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.91 (0.45; 1.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFrequent headaches\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.86 (1.01; 3.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.89 (1.02; 3.50)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCancer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.39 (0.04; 3.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.40 (0.04; 3.76)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKessler psychological distress score\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.25 (1.20; 1.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.26 (1.21; 1.31)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePsychosis screening\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePast year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.88 (0.31; 2.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.89 (0.32; 2.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLifetime\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.91 (0.80; 4.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.97 (0.82; 4.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCountry geographical location\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Africa (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.12 (0.88; 5.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eICC\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.18 (0.03; 0.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.15 (0.09 ; 0.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.14 (0.09 ; 0.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eFactors associated with lifetime major depressive disorder\\u003c/h3\\u003e\\n\\u003cp\\u003eIn the multilevel models, age and sex were significant predictors of lifetime MDD. Increased age was negatively associated with MDD (adjusted odds ratio (aOR) of 0.98 [95% CI: 0.97; 0.99]) in the fully adjusted model). Males had significantly lower lifetime MDD compared to females (OR of 0.69 [95% CI: 0.50; 0.94]). Education, living arrangements and marital were not significantly associated with MDD. Several behavioural and health-related factors were also significantly associated with MDD. Alcohol consumption was also associated with MDD (aOR of 1.41 [95% CI: 1.03; 1.93]), while substance and tobacco use were not significantly associated with MDD. Experiencing negative life events was strongly associated with MDD, with those reporting such events having more than twice the odds (aOR\\u0026thinsp;=\\u0026thinsp;2.32, [95% CI: 1.72; 3.13]) compared to those who did not. Among chronic conditions, chronic back/neck pain (aOR\\u0026thinsp;=\\u0026thinsp;1.53 [95% CI: 1.15; 2.04]) and frequent headaches (aOR\\u0026thinsp;=\\u0026thinsp;1.63 [95% CI: 1.25; 2.12]) were significantly associated with MDD, while other chronic conditions like arthritis and cancer were not. Higher scores on the K10 were also asspciated with MDD (aOR\\u0026thinsp;=\\u0026thinsp;1.17 [95% CI: 1.14; 1.19]). Additionally, psychosis screening results showed that both past-year psychosis experiences (aOR\\u0026thinsp;=\\u0026thinsp;0.49 [95% CI: 0.30; 0.79]), and lifetime psychosis experiences were associated with MDD (aOR\\u0026thinsp;=\\u0026thinsp;2.92 [95% CI: 2.11; 4.03]). Geographical location was not a significant factor, as odds of MDD did not statistically differ between individuals from Eastern Africa and those from Southern Africa. The ICC for the fully adjusted model was 0.24 [95% CI: 0.05; 0.64], indicating that 24% of the variance in MDD was attributable to differences between countries. These results are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eb: Results of fitting multilevel logistic regression model for factors associated with lifetime major depressive disorder. CI\\u0026thinsp;=\\u0026thinsp;confidence intervals, aOR\\u0026thinsp;=\\u0026thinsp;adjusted odds ratios, ICC\\u0026thinsp;=\\u0026thinsp;intraclass correlation coefficient, *p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFactor\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003cp\\u003eaOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003cp\\u003eaOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003cp\\u003eaOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.99 (0.98; 1.00)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.98 (0.97; 0.99)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.98 (0.97; 0.99)*\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex at birth\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.66 (0.51; 0.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.69 (0.50; 0.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.69 (0.50; 0.94)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHighest Level of Education\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary or less (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSecondary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.84 (0.63; 1.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00 (0.73; 1.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00 (0.72; 1.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTertiary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.17 (0.84; 1.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.63 (1.13; 2.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.63 (1.13; 2.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCurrent Living Arrangement\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLives alone (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLives with family or friends\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.09 (0.79; 1.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.02 (0.72; 1.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.02 (0.72; 1.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMarital Status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried/living together (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWidowed/divorced/separated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.49 (1.07; 2.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.36 (0.94; 1.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.36 (0.94; 1.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSingle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.92 (0.69; 1.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.90 (0.66; 1.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.90 (0.66; 1.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eUse of alcoholic beverages\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo (Ref)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.41 (1.03; 1.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.41 (1.03; 1.93)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSubstance use\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.12 (0.81; 1.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.12 (0.80; 1.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eUse of tobacco products\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.37 (0.97; 1.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.37 (0.97; 1.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBlood pressure\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNormal (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.95 (0.72; 1.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.95 (0.72; 1.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBody mass index\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.01 (0.99; 1.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.01 (0.99; 1.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNegative life events\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.33 (1.73; 3.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2.32 (1.72; 3.13)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eChronic conditions\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eArthritis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.91 (0.58; 1.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.91 (0.58; 1.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChronic back/neck pain\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.53 (1.15; 2.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.53 (1.15; 2.04)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFrequent headaches\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.63 (1.25; 2.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.63 (1.25; 2.12)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCancer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.53 (0.16; 1.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.53 (0.16; 1.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKessler psychological distress score\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.17 (1.14; 1.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.17 (1.14; 1.19)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePsychosis screening\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePast year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.49 (0.30; 0.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.49 (0.30; 0.79)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLifetime\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.92 (2.11; 4.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2.92 (2.11; 4.03)*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCountry Geographical location\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Africa (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.63 (0.05; 7.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eICC\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.39 (0.12; 0.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.24 (0.05; 0.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.24 (0.05; 0.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePrevalence\\u003c/h2\\u003e \\u003cp\\u003eAn overall prevalence of 0.9% was observed for current MDD and prevalence of 0.1\\u0026ndash;2.1% were observed across all the study countries. The observed overall prevalence was lower than the estimated global current prevalence of 3.4% MDD.\\u003csup\\u003e3\\u003c/sup\\u003e The overall prevalence of lifetime MDD in this study was 5.1%. This prevalence is lower than the estimated lifetime prevalence of 14.6% and 11.1% which has been reported for MDD in high and low- and middle-income countries respectively.\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e However, this prevalence is comparable to the prevalence of 4.5% which has been reported in SSA for MDD\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e and similar to the prevalence of 5.4% which has been reported for Africa.\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e The low prevalence for MDD in SSA could be due to several factors which often involve a complex interplay of cultural, social and methodological issues. In many cultures across SSA, MDD may not be recognized in the same way MDD presents in other contexts. The diagnostic tools developed primarily in Western contexts may not be sensitive to the cultural expression of depression in African populations leading to misdiagnosis. This can be exacerbated by the immense cultural and linguistic diversity which exist in Africa. There might also exist unique resilience factors and coping mechanisms which could be protective against the development or recognition of depressive symptoms. This is need to understand how this factors interact to influence the development of MDD in Africa. Also, lifetime MDD prevalence are higher than current MDD prevalence due to the broader time frame which captures any instance of MDD throughout a person\\u0026rsquo;s life and the episodic nature of MDD.\\u003c/p\\u003e \\u003cp\\u003eFor South Africa, the observed current and lifetime prevalence of MDD (2.1% and 8.4% respectively) are less than the prevalence of 4.9% and 9.8% which have been reported for point and lifetime MDD respectively in South Africa.\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e For Uganda, the observed current and lifetime prevalence of MDD (0.7% and 7.9 respectively) are much lower than the pooled prevalence of 20.8% which was reported among general populations in Uganda by a systematic review and meta-analysis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e For Ethiopia, the observed current and lifetime prevalence of MDD (0.5% and 1.2% respectively) are much lower than the prevalence of 5.3% and 17.4% which have been reported for current and lifetime MDD respectively.\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e For Kenya, the observed current and lifetime prevalence of MDD (0.5% and 1.2% respectively) are much lower than the prevalence of 4.4% which has been reported among general populations in Kenya.\\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe variations of prevalence by study country is intriguing and could be due to several factors such as socioeconomic factors, cultural differences and stigma and awareness. Higher prevalence for South Africa and Uganda might correlate with socioeconomic stressors such as poverty, unemployment, and social inequality that can contribute to higher rates of depression. For example, South Africa has been reported to have a significant income inequality and high levels of unemployment.\\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e Ethiopia and Kenya, with lower rates, might have different socioeconomic dynamics, or there may be other protective factors at play, such as stronger community and family support systems. Cultural norms can influence how individuals express emotions or depression. In some cultures, it's common to express MDD through physical symptoms rather than psychological terms, which could lead to underdiagnosis of MDD. There is need to understand potential ecological factors responsible for variations in the prevalence of MDD across various regions in SSA.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFactors associated with major depressive disorder\\u003c/h2\\u003e \\u003cp\\u003ePer year increase in age was associated with lower odds for lifetime MDD. Directions of associatons between age and depression have been inconsistent. A large literature review and meta-analysis has reported depression to be more prevalent among elderly populations \\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e while analysis of large national health data from the United States has reported MDD to be more prevalent among young adults aged 18\\u0026ndash;29 years.\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e There is no clear explanation for the association between increasing age and lower odds for MDD in our study participants. However, several factors such as ocio-cultural differences, socioeconomic, demographic and access to mental health services could be responsible. For example, there may be better economic conditions and pension schemes for older adults, there may be more awareness and access to mental health care for old as compared to young adults. Younger adults in SSA may face more socioeconomic hardships and unemployment. There is need to understand how these factors may influence the risk for MDD across the lifespan in SSA in order to inform targeted mental health interventions.\\u003c/p\\u003e \\u003cp\\u003eMale sex at birth was associated with lower odds for MDD. This finding is in line with findings from a large systematic analysis of the global burden of MDD in 2014 between 1990\\u0026ndash;2019, which reported MDD burden to be more among females as compared with males \\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. Additionally, a large systematic review and meta-analysis of the epidemiology of MDD among people living with HIV in SSA, also reported the prevalence of MDD to be much higher among women as compared to men.\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e Although underlying biological mechanisms for sex disparity in MDD are not well known, sex hormones have been postulated to play a role. In addition to the role of genetics and environmental factors like stress, sex hormone status has been postulated to be a third factor which contributes to changes in the epigenome, whose effect may translate into changes in gene expression and brain structure and function,\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e thus resulting in increased vulnerability for MDD in women.\\u003c/p\\u003e \\u003cp\\u003eAlcohol use was associated with higher odds for MDD. This finding is in line with findings from a large systematic review and meta-analysis of cohort studies which reported an association between alcohol use and MDD.\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e However, it was observed that this association became nonsignificant when confounders were controlled for, suggesting that alcohol use could be leading to MDD through confounders. For example, unemployed individuals are more likely to abuse drugs and be heavy alcohol users and to suffer from MDD.\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eWitnessing or experiencing traumatic life events was associated with higher odds for MDD. This finding is in agreement with findings from previous studies which reported traumatic life events to have a strong relationship with depression\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e including studies in Uganda that found an increased risk of MDD with increasing negative life events.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e Previous studies have also found that recent traumatic life events play a key role in the onset of MDD.\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e However, onset of MDD and the timing of the negative life events were not determined by the parent study and so this could not be properly investigated in this study.\\u003c/p\\u003e \\u003cp\\u003eChronic neck or back pain was associated with higher odds for MDD. This finding is in line with previous studies which have reported associations between chronic pain and depression and have proposed that this could be mediated through altered neuroplasticity.\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e This relationship has been found to be influenced by anomalies in neurological function that lead to chronic pain preceding depression and depressive symptoms.\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e Back pain in general has been associated with increased risk for MDD among middle aged adults in six countries including Ghana and South Africa.\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e Additionally, a study in southwestern Uganda also found an association between reported back pain and MDD among elderly persons living with HIV/AIDS.\\u003csup\\u003e36\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eFrequent headaches were associated with higher odds for MDD. This finding is in agreement with findings from a systematic review and meta-analysis which reported migraines (frequent headaches) to be a risk factor for incident cases of MDD in both cross-sectional and cohort studies.\\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e However, this relationship could be bidirectional as MDD has also been found to be causal to different forms of frequent headaches by a Mendelian randomization study \\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e. Frequent headaches have been suggested to lead to or exacerbate MDD through neurotransmitter imbalances, particularly serotonin and norepinephrine which affect both pain perception and mood.\\u003csup\\u003e\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u003c/sup\\u003e Chronic pain from headaches can impact daily life and contribute to the development or worsening of MDD.\\u003c/p\\u003e \\u003cp\\u003ePer unit increase in K10 score was associated with higher odds for both current and lifetime MDD. This is not surprising as the K10 score measures psychological distress and the K10 tool has items which assess for both depression and anxiety symptoms a person has experienced in the most recent 4 week period.\\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e The K10 score could be used as tool which can identify people who are at risk of developing MDD in SSA for early intervention to reduce the overall burden of MDD on individuals and healthcare systems.\\u003c/p\\u003e \\u003cp\\u003eLifetime positive screening for psychosis was associated with higher odds for MDD. This could could be due to a fact that some people who suffer from MDD normally report symptoms of psychosis such as delusions and hallucinations.\\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e The form of MDD with psychotic features is termed psychotic depression according to the International Classification of Diseases 11th revision.\\u003csup\\u003e\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e Psychotic depression has been reported to be a risk factor for psychosis in patients whose index depression had psychotic features \\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e hence the need for effective preventative treatments. Psychotic depression is hard to treat and has been reported to be unresponsive to an antidepressant alone.\\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e Elevated levels of cortisol have been suggested to be responsible for this form of depression.\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e Paradoxically, a past year positive screening for psychosis was associated with lower odds for MDD. There is no direct explanation for this observation. However, acute management and treatment could mitigate the symptoms of MDD. Indeed, atypical antipsychotics have been reported to have a direct preventive effect on the development of depressive symptoms during management of acute psychosis.\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe proportion of variance in the prevalence of MDD across the study countries was moderate (14%) for current MDD and moderately large (24%) for lifetime MDD. There is thus a need to understand the specific risk factors for MDD across different groups or communities in Africa. Also, given that moderately large proportion of variance is contributed by country level factors, country level intervention strategies could be effective in mitigating MDD rather than individual level strategies \\u0026ndash; which would likely be less feasible. In addition, the higher proportion in variance for lifetime MDD potentially suggests that persistent regional-level factors are influential and perhaps reflect long-standing regional-level characteristics, cultural factors or structural factors that affect MDD over the lifespan.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eLimitations\\u003c/h3\\u003e\\n\\u003cp\\u003eWe could not elucidate the causation of MDD given the cross-sectional nature of the study. Also, in assessing life-time MDD, we relied on the respondents\\u0026rsquo; memory and recall bias which may potentially have resulted in a systematic bias against the recall of temporally distant events. In addition, given the selection criteria for the participants from the hospital settings, our study participants may not truly represent a general population. Additionally, given that participation was volunteer based, we may not have captured people the most severely affected by MDD hence the likelihood of underrepresentation of the most disadvantaged people in the communities studies. Also, since the diagnostic tool used was developed in Western contexts, this could be less sensitive to the cultural expression of MDD in African populations hence leading to misdiagnosis.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study investigated several factors associated with MDD in participants present at general medical facilities from a majorly African ancestry population. The main findings from the phenotypic analysis were that among the study participants, being female, using alcohol, experiencing or witnessing negative life events, having higher levels of psychological distress and suffering from chronic conditions of either chronic back or neck pain were associated with higher odds for MDD while per year increase in age was associated with lower odds for depression among the study participants. These results suggest that psychosocial factors as well as psychosomatic complaints and physical comorbidities are important risk factors for MDD among the participants for this study.\\u003c/p\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003ch2\\u003eStudy design\\u003c/h2\\u003e\\u003cp\\u003eThis study was undertaken using data collected by the NeuroGAP-Psychosis study. Details about the NeuroGAP-Psychosis study, the data variables collected and the tools used have been reported by Stevenson and colleagues.\\u003csup\\u003e\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e In brief, the NeuroGAP-Psychosis study recruited a total of 42,953 participants (21,715 cases of psychosis, 21,238 psychosis-free controls) from four African countries of Uganda, Kenya, Ethiopia, and South Africa over a 5-year period (2018–2023). The aim of the NeuroGAP-Psychosis study is to investigate the genetic risk for psychosis. Each study participant provided a saliva sample from which DNA was extracted and shipped to the Broad Institute in the United States, from where the DNA was sequenced. Out of the 21,715 NeuroGAP-Psychosis study’s control participants, a total of 7,073 adult participants were assessed for current and lifetime MDD using the Mini-International Neuropsychiatric Interview (MINI, standard version 7.0.2)\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e and are included in this analysis. The NeuroGAP-Psychosis study’s control participants were ascertained from persons who presented at University-affiliated general medical hospitals that draw from similar catchement areas to the psychiatric facilities where the psychosis cases for the NeuroGAP-Psychosis study were recruited. NeuroGAP-Psychosis study control participants were 1) caretakers who had accompanyed someone else to an appointment, 2) students/workers at the hospital/clinic or 3) someone who had visited for a prescription refill or doctor’s appointment.\\u003c/p\\u003e\\u003ch2\\u003eClinical investigations\\u003c/h2\\u003e\\u003cp\\u003eA questionnaire (module A of the MINI) was administered by trained research assistants onto the study participants at each of the study sites. Assessment of MDD based on presence of at least one of symptoms of either a depressed mood or loss of interest in pleasurable activities and any other four symptoms which may include significant weight change or apetite disturbance, sleep disturbances, psychomotor agitation or retardation, fatigue, feelings of worthlessness or excessive guilt, diminished ability to think or concentrate and recurrent thoughts of death or suicidal ideation. Additionally, these symptoms had to cause significant distress or impairment in social, occupational or other important areas of functing and were not a result of physiological effects of a substance or another medical condition. For current MDD, presence of symptoms in the last two weeks was assessed while for lifetime MDD, the presence of these symptoms in a participant’s lifetime was assessed. The five-item version of the psychosis screening questionnaire (PSQ)\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e\\u003c/sup\\u003e was used to screen for psychosis symptoms. The Kessler psychological distress scale (K10)\\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e was used to assess for psychological this. This is a ten-item questionnaire based on questions about symptoms for anxiety and depression. The Life events checklist for DSM-5 (LEC-5)\\u003csup\\u003e\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e was used to assess for negative life events. This checklist assesses exposure to sixteen events known to potentially results in post-traumatic stress disorder or distress. The World Health Organization Alcohol, Smoking and Substance Involvement Screening Test (ASSIST, version3)\\u003csup\\u003e\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e was used to assess for lifetime use of alcohol, tobacco and other substances. The Composite International Diagnostic Interview (CIDI) screener\\u003csup\\u003e\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u003c/sup\\u003e was used to assess for chronic conditions such as arthritis or rheumatism, chronic back or neck pain, frequent or severe headaches, cancer, among others.\\u003c/p\\u003e\\u003ch2\\u003eEthical considerations\\u003c/h2\\u003e\\u003cp\\u003e The NeuroGAP-Psychosis study was conducted in compliance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Ethical and scientific clearance for the NeuroGAP-Psychosis study was obtained from each of the study sites as shown in the following sentences; Uganda: The Makerere University School of Medicine Research and Ethics Committee (SOMREC #REC REF 2016-057) and the Uganda National Council for Science and Technology (UNCST #HS14ES); Kenya: Moi University College of Health Sciences/Moi Teaching and Referral Hospital Institutional Research and Ethics Committee (IREC) (#IREC/2016/145, approval number: IREC 1727), Kenya National Council of Science and Technology (#NACOSTI/P/17/56302/19576), KEMRI Centre Scientific Committee (CSC#KEMRI/CGMRC/CSC/070/2016), KEMRI Scientific and Ethics Review Unit (SERU# KEMRI/SERU/CGMR-C/070/3575); Ethiopia: Addis Ababa University College of Health Sciences (#014/17/Psy) and the Ministry of Science and Technology National Research Ethics Review Committee (#3.10/14/2018); South Africa: The University of Cape Town Human Research Ethics Committee (#466/2016); and USA: The Harvard T.H. Chan School of Public Health (#IRB17-0822). Participants provided written informed consent for their genetic and health information to be used in future research. Participants' priorities and experiences were taken into consideration during the design of the NeuroGAP-Psychosis study.\\u003c/p\\u003e\\u003ch2\\u003eData analysis\\u003c/h2\\u003e\\u003cp\\u003eAll statistical analyses were conducted using STATA version 18.0. Descriptive statistics were used to summarize socio-demographic characteristics, including sex, age, marital status, living arrangement, education level, study country, and body mass index, as well as clinical characteristics such as tobacco use, alcohol use, substance use, and blood pressure. Categorical variables were summarized using frequencies and percentages, while continuous variables were reported as medians with interquartile ranges (IQR). The prevalence of both current and lifetime MDD was estimated for each study country, along with corresponding 95% confidence intervals.\\u003c/p\\u003e\\u003cp\\u003eTo examine factors associated with MDD, we employed a multilevel logistic regression model to account for the hierarchical structure of the data, with individuals nested within four different countries. Model selection was guided by the likelihood ratio test (LRT) to ensure that only variables that significantly improved model fit were retained. The modeling process followed a sequential approach. First, model 1 included key socio-demographic variables (age, sex, education level, living arrangement, and marital status), selected a priori based on theoretical and empirical evidence. Model 2 extended model 1 by incorporating individual-level behavioral and clinical characteristics, including alcohol use, substance use, history of traumatic life events, and current physical illness. Variables were retained only if their inclusion significantly improved model fit based on LRT. Finally, model 3 introduced country-level characteristics, such as geographical location, to assess their influence while controlling for individual-level factors.\\u003c/p\\u003e\\u003cp\\u003eThe intraclass correlation coefficient (ICC) was calculated for each model to quantify the proportion of total variance attributable to between-country differences. A variable was considered to be meaningfully associated with MDD only if it remained statistically significant in the final model and contributed to model fit as determined by LRT. To assess the robustness of the findings, a sensitivity analysis was performed by systematically excluding individual countries and re-estimating the models. A two-sided p-value \\u0026lt; 0.05 was considered statistically significant.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data that support the findings of this study are available via the National Institute of Mental Health Data Archive. Data are also available from the authors upon reasonable request and with permission of the NeuroGAP-Psychosis study consortium.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAllan Kalungi is a Wellcome Early Career Fellow [227053/Z/23/Z]. He also received funding for the NARSAD young investigator grant from the Brain and Behavior Research Foundation [Grant number 29610]. The NeuroGAP-Psychosis study was funded by the Stanley Center for Psychiatric Research at the Broad Institute. DA, DS, and ST are supported in part by the United States\\u0026rsquo; National Institute of Mental Health (NIMH) by grant R01MH120642. \\u0026nbsp;SF is supported by both the Wellcome Trust [grant number: 220740/Z/20/Z] and the National Institute of Mental Health [grant number: 1R01MH134468].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConceptualization: AK, DHA, EK, SF; Statistical analysis: WS, AK; First draft: AK; Critical review \\u0026amp; Final draft: All authors. All authors approved the final manuscript. All authors had full access to all the data and had final responsibility for the decision to submit for publication. RES, SF and DHA accessed and verified the data.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSpecial gratitude goes out to the NeuroGAP-Psychosis study consortium for granting the access to the data that was used to carry out this study, the Global Initiative for Neuropsychiatric Genetics Education in Research program (GINGER; https://gingerprogram.org) \\u0026ndash; which provided Biostatistics and Bioinformatics training to the lead author, and the participants who attended the NeuroGAP-Psychosis study. \\u0026nbsp;We would also like to acknowledge Dr Edith K Kwobah who led the NeuroGAP-Psychosis study at the Moi Teaching and Referral Hospital in Kenya. Dr Kwobah passed on 22\\u003csup\\u003end\\u003c/sup\\u003e March 2024. We would also like to acknowledge the data managers, clinicians, research assistants, and project managers who have worked on this study from Addis Ababa University, KEMRI-Wellcome Trust, Makerere University, Moi University/Moi Teaching and Referral Hospital, Harvard/Broad Institute, and the University of Cape Town.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAmerican Psychiatric Association D (2013) \\u003cem\\u003eDiagnostic and statistical manual of mental disorders: DSM-5\\u003c/em\\u003e. vol 5. American psychiatric association Washington, DC\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAmerican Psychiatric Association, Association D (2013) AP. \\u003cem\\u003eDiagnostic and statistical manual of mental disorders: DSM-5\\u003c/em\\u003e. vol 5. American psychiatric association Washington, DC\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGBD Mental Disorders Collaborators (2022) Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 9(2):137\\u0026ndash;150\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLohoff FW (2010) Overview of the genetics of major depressive disorder. Curr psychiatry Rep 12:539\\u0026ndash;546\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWorld Health Organization. Depression and Other Common mental Disorders, Global Health Estimates. February 14 (2024) 2024. Accessed February 14, 2024, 2024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.afro.who.int/sites/default/files/2017-05/WHO-MSD-MER-2017.2-eng.pdf\\u003c/span\\u003e\\u003cspan address=\\\"https://www.afro.who.int/sites/default/files/2017-05/WHO-MSD-MER-2017.2-eng.pdf\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOtte C, Gold SM, Penninx BW et al (2016) Major depressive disorder. Nat Reviews Disease Primers 09(1):16065. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/nrdp.2016.65\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nrdp.2016.65\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. /15 2016\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMoitra M, Santomauro D, Collins PY et al (2022) The global gap in treatment coverage for major depressive disorder in 84 countries from 2000\\u0026ndash;2019: A systematic review and Bayesian meta-regression analysis. PLoS Med 19(2):e1003901. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1371/journal.pmed.1003901\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pmed.1003901\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePatel V, Maj M, Flisher AJ et al (2010) Reducing the treatment gap for mental disorders: a WPA survey. World Psychiatry 9(3):169\\u0026ndash;176\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGbadamosi IT, Henneh IT, Aluko OM et al (2022) Depression in Sub-Saharan Africa. IBRO Neurosci Rep Jun 12:309\\u0026ndash;322. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ibneur.2022.03.005\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ibneur.2022.03.005\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKaggwa MM, Najjuka SM, Bongomin F, Mamun MA, Griffiths MD (2022) Prevalence of depression in Uganda: A systematic review and meta-analysis. PLoS ONE 17(10):e0276552\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBolton P, Wilk CM, Ndogoni L (2004) Assessment of depression prevalence in rural Uganda using symptom and function criteria. Soc Psychiatry Psychiatr Epidemiol 39(6):442\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKinyanda E, Woodburn P, Tugumisirize J, Kagugube J, Ndyanabangi S, Patel V (2011) Poverty, life events and the risk for depression in Uganda. Soc Psychiatry Psychiatr Epidemiol 46:35\\u0026ndash;44\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePerkins JM, Nyakato VN, Kakuhikire B et al (2018) Food insecurity, social networks and symptoms of depression among men and women in rural Uganda: a cross-sectional, population-based study. Public Health Nutr 21(5):838\\u0026ndash;848\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRathod SD, Roberts T, Medhin G et al (2018) Detection and treatment initiation for depression and alcohol use disorders: facility-based cross-sectional studies in five low-income and middle-income country districts. BMJ open 8(10):e023421\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSmith ML, Kakuhikire B, Baguma C et al (2019) Relative wealth, subjective social status, and their associations with depression: Cross-sectional, population-based study in rural Uganda. SSM-population health 8:100448\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDuko B, Geja E, Zewude M, Mekonen S (2018) Prevalence and associated factors of depression among patients with HIV/AIDS in Hawassa, Ethiopia, cross-sectional study. Ann Gen Psychiatry 17:45. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12991-018-0215-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12991-018-0215-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBitew T (2014) Prevalence and risk factors of depression in Ethiopia: a review. Ethiop J Health Sci Apr 24(2):161\\u0026ndash;169. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.4314/ejhs.v24i2.9\\u003c/span\\u003e\\u003cspan address=\\\"10.4314/ejhs.v24i2.9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCrist\\u0026oacute;bal-Narv\\u0026aacute;ez P, Haro JM, Koyanagi A (2020) Perceived stress and depression in 45 low-and middle-income countries. J Affect Disord 274:799\\u0026ndash;805\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKamau JW, Kuria W, Mathai M, Atwoli L, Kangethe R (2012) Psychiatric morbidity among HIV-infected children and adolescents in a resource-poor Kenyan urban community. AIDS Care 24(7):836\\u0026ndash;842\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBromet E, Andrade LH, Hwang I et al (2011) Cross-national epidemiology of DSM-IV major depressive episode. \\u003cem\\u003eBMC Medicine\\u003c/em\\u003e. /07/26 2011;9(1):90. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/1741-7015-9-90\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/1741-7015-9-90\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCuadros DF, Tomita A, Vandormael A, Slotow R, Burns JK, Tanser F (2019) Spatial structure of depression in South Africa: A longitudinal panel survey of a nationally representative sample of households. Sci Rep 9(1):979\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHerman AA, Stein DJ, Seedat S, Heeringa SG, Moomal H, Williams DR (2009) The South African Stress and Health (SASH) study: 12-month and lifetime prevalence of common mental disorders. South Afr Med J. ;99(5)\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMolla GL, Sebhat HM, Hussen ZN, Mekonen AB, Mersha WF, Yimer TM Depression among Ethiopian adults: cross-sectional study. Psychiatry J. 2016;2016\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eThe World Bank Unemployment, youth total (% of total labor force ages 15\\u0026ndash;24) (modeled ILO estimate) - Sub-Saharan Africa. 5th April, 2024, 2024. Accessed 5th April, 2024, 2024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://data.worldbank.org/indicator/SL.UEM.1524.ZS?locations=ZG\\u003c/span\\u003e\\u003cspan address=\\\"https://data.worldbank.org/indicator/SL.UEM.1524.ZS?locations=ZG\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZenebe Y, Akele B, Necho MWS (2021) Prevalence and determinants of depression among old age: a systematic review and meta-analysis. Ann Gen Psychiatry Dec 18(1):55. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12991-021-00375-x\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12991-021-00375-x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVillarroel MA, Terlizzi EP (2020) \\u003cem\\u003eSymptoms of depression among adults: United States, 2019.\\u003c/em\\u003e. \\u003cem\\u003eNCHS Data Brief\\u003c/em\\u003e. 379. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.cdc.gov/nchs/products/databriefs/db379.htm#:~:text=The%20percentage%20of%20adults% 20who%20experienced%20any%20symptoms%20of%20depression,or% 20severe%20symptoms%20of%20depression\\u003c/span\\u003e\\u003cspan address=\\\"https://www.cdc.gov/nchs/products/databriefs/db379.htm#:~:text=The%20percentage%20of%20adults% 20who%20experienced%20any%20symptoms%20of%20depression,or% 20severe%20symptoms%20of%20depression\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBernard C, Dabis F, de Rekeneire N (2017) Prevalence and factors associated with depression in people living with HIV in sub-Saharan Africa: a systematic review and meta-analysis. PLoS ONE 12(8):e0181960\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKundakovic M, Rocks D (2022) Sex hormone fluctuation and increased female risk for depression and anxiety disorders: From clinical evidence to molecular mechanisms. Front Neuroendocrinol Jul 66:101010. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.yfrne.2022.101010\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.yfrne.2022.101010\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi J, Wang H, Li M et al (2020) Effect of alcohol use disorders and alcohol intake on the risk of subsequent depressive symptoms: a systematic review and meta-analysis of cohort studies. Addiction 115(7):1224\\u0026ndash;1243\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCompton WM, Gfroerer J, Conway KP, Finger MS (2014) Unemployment and substance outcomes in the United States 2002\\u0026ndash;2010. Drug Alcohol Depend 142:350\\u0026ndash;353\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKraaij V, Arensman E, Spinhoven P (2002) Negative life events and depression in elderly persons: a meta-analysis. Journals Gerontol Ser B: Psychol Sci Social Sci 57(1):P87\\u0026ndash;P94\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMugisha J, Muyinda H, Malamba S, Kinyanda E (2015) Major depressive disorder seven years after the conflict in northern Uganda: burden, risk factors and impact on outcomes (The Wayo-Nero Study). BMC Psychiatry 15:1\\u0026ndash;12\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStegenga BT, Nazareth I, Grobbee DE et al (2012) Recent life events pose greatest risk for onset of major depressive disorder during mid-life. J Affect Disord 136(3):505\\u0026ndash;513\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSheng J, Liu S, Wang Y, Cui R, Zhang X (2017) The link between depression and chronic pain: neural mechanisms in the brain. Neural Plast 2017(1):9724371\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSelvamani Y, Sangani P, Muhammad T (2022) Association of back pain with major depressive disorder among older adults in six low-and middle-income countries: A cross-sectional study. Exp Gerontol 167:111909\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKinyanda E, Kuteesa M, Scholten F, Mugisha J, Baisley K, Seeley J (2016) Risk of major depressive disorder among older persons living in HIV-endemic central and southwestern Uganda. AIDS Care 28(12):1516\\u0026ndash;1521\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAmiri S, Behnezhad S, Azad E (2019) Sep. Migraine headache and depression in adults: a systematic Review and Meta-analysis. \\u003cem\\u003eNeuropsychiatr\\u003c/em\\u003e. ;33(3):131\\u0026ndash;140. Migr\\u0026auml;ne und Depression bei Erwachsenen: Ein systematisches Review und Meta-Analyse. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s40211-018-0299-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s40211-018-0299-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLv X, Xu B, Tang X et al (2023) The relationship between major depression and migraine: A bidirectional two-sample Mendelian randomization study. Front Neurol 14:1143060. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fneur.2023.1143060\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fneur.2023.1143060\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNational Headache Foundation. Depression and Headache. April 1st, 2024 (2024) Accessed April 1st, 2024, 2024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://headaches.org/depression-and-headache/\\u003c/span\\u003e\\u003cspan address=\\\"https://headaches.org/depression-and-headache/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKessler RC, Andrews G, Colpe LJ et al (2002) Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med Aug 32(6):959\\u0026ndash;976. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1017/s0033291702006074\\u003c/span\\u003e\\u003cspan address=\\\"10.1017/s0033291702006074\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDubovsky Steven L, Ghosh Biswarup M, Serotte Jordan C, Cranwell V (2021) Psychotic Depression: Diagnosis, Differential Diagnosis, and Treatment. Psychother Psychosom 90(3):160\\u0026ndash;177. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1159/000511348\\u003c/span\\u003e\\u003cspan address=\\\"10.1159/000511348\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWorld Health Organization ICD-11 for Mortality and Mobidity Statistics. 7th April, 2024, 2024. Updated January 2024. Accessed 7th April, 2024, 2024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://icd.who.int/browse/2024-01/mms/en#104129373\\u003c/span\\u003e\\u003cspan address=\\\"https://icd.who.int/browse/2024-01/mms/en#104129373\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNelson JC, Bickford D, Delucchi K, Fiedorowicz JG, Coryell WH (2018) Risk of Psychosis in Recurrent Episodes of Psychotic and Nonpsychotic Major Depressive Disorder: A Systematic Review and Meta-Analysis. Am J Psychiatry Sep 1(9):897\\u0026ndash;904. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1176/appi.ajp.2018.17101138\\u003c/span\\u003e\\u003cspan address=\\\"10.1176/appi.ajp.2018.17101138\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNelson JC, Davis JM (1997) DST studies in psychotic depression: a meta-analysis. Am J Psychiatry 154(11):1497\\u0026ndash;1503\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSchatzberg AF, Rothschild AJ, Langlais PJ, Bird ED, Cole JO (1985) A corticosteroid/dopamine hypothesis for psychotic depression and related states. J Psychiatr Res 19(1):57\\u0026ndash;64\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSiris SG (2000) Depression in schizophrenia: perspective in the era of atypical antipsychotic agents. Am J Psychiatry 157(9):1379\\u0026ndash;1389\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStevenson A, Akena D, Stroud RE et al (2019) Neuropsychiatric Genetics of African Populations-Psychosis (NeuroGAP-Psychosis): a case-control study protocol and GWAS in Ethiopia, Kenya, South Africa and Uganda. BMJ Open 9(2):e025469. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1136/bmjopen-2018-025469\\u003c/span\\u003e\\u003cspan address=\\\"10.1136/bmjopen-2018-025469\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSheehan DV, Lecrubier Y, Sheehan KH et al (1998) The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. \\u003cem\\u003eJ Clin Psychiatry\\u003c/em\\u003e. ;59 Suppl 20:22\\u0026ndash;33;quiz 34\\u0026ndash;57\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBebbington P, Nayani T (1995) The psychosis screening questionnaire. Int J Methods Psychiatr Res\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGray MJ, Litz BT, Hsu JL, Lombardo TW (2004) Psychometric properties of the life events checklist. Assessment 11(4):330\\u0026ndash;341\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWHO ASSIST Working Group (2002) The alcohol, smoking and substance involvement screening test (ASSIST): development, reliability and feasibility. Addiction 97(9):1183\\u0026ndash;1194\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKessler RC, \\u0026Uuml;st\\u0026uuml;n TB (2004) The world mental health (WMH) survey initiative version of the world health organization (WHO) composite international diagnostic interview (CIDI). Int J Methods Psychiatr Res 13(2):93\\u0026ndash;121\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-portfolio\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Nature Portfolio\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Major depressive disorder, prevalence, associated factors, Eastern and Southern Africa\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6279456/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6279456/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eMajor depressive disorder (MDD) is a significant contributor to the burden of mental disorders in Africa, necessitating an understanding of its prevalence and risk factors across diverse socio-cultural contexts to address health disparities and improve care. This cross-sectional study analyzed 7,073 adult participants from hospital settings in Uganda, Kenya, Ethiopia, and South Africa within the NeuroGAP-Psychosis study. Prevalence estimates, calculated with 95% confidence intervals, revealed overall rates of 0.9% for current MDD and 5.1% for lifetime MDD, with South Africa reporting the highest prevalence (2.1% current, 8.4% lifetime). Multilevel logistic regression identified significant associations between current MDD and negative life events, alcohol use, and Kessler psychological distress scores, while lifetime MDD was linked to age, female sex, chronic pain, frequent headaches, and positive psychosis screening. These findings underscore the need for targeted mental health interventions tailored to the identified risk factors to reduce the burden of MDD in the studied populations.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Major depressive disorder in sub-Saharan Africa: findings from the Neuro-GAP-Psychosis study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-03-28 07:54:07\",\"doi\":\"10.21203/rs.3.rs-6279456/v1\",\"editorialEvents\":[],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-communications\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"NCOMMS\",\"sideBox\":\"Learn more about [Nature Communications](http://www.nature.com/ncomms/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://mts-ncomms.nature.com/\",\"title\":\"Nature Communications\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature Communications\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"68e63ac3-750d-4078-98a9-a5736eda1739\",\"owner\":[],\"postedDate\":\"March 28th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":46318277,\"name\":\"Health sciences\"},{\"id\":46318278,\"name\":\"Health sciences/Medical research/Epidemiology\"}],\"tags\":[],\"updatedAt\":\"2026-02-12T16:26:01+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-03-28 07:54:07\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6279456\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6279456\",\"identity\":\"rs-6279456\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}