Social determinants of anxiety and mood disorders in a nationally-representative sample – Results from the Saudi National Mental Health Survey (SNMHS)

Abstract

Introduction:

Social determinants of health (SDH) influence and modify the risk for mental health disorders. To our knowledge, no study has explored SDH in the context of mental health in Saudi Arabia (SA) using population-based data. This study investigated the association between several SDH and anxiety and mood disorders in SA.

Methods:

We utilized data from the nationally-representative Saudi National Mental Health Survey (SNMHS) conducted in 2014 to 2016. This study examined associations between personal-level, socioeconomic, physical health, and family environment characteristics and anxiety and mood disorders. Participants were classified as having anxiety-only disorders, mood-only disorders, or comorbidity of both disorders. Multinomial logistic regression models were employed to examine the associations between SDH and anxiety and/or mood disorders, comparing them to participants who had not experienced these disorders.

Results:

A total of 4,004 participants were included in this analysis; the lifetime prevalence of disorders was: anxiety only (18%), mood only (3.8%), and comorbidity of both (5.3%). Regression models indicated that females, young adults (26–35 years), individuals with a higher level of education, and those who were separated or widowed had higher odds of experiencing anxiety and/or mood disorders. Furthermore, there was a significant and direct association between having physical chronic conditions and all three categories of anxiety and mood disorders. Experiencing Adverse Childhood Events (ACEs) was also associated with a significant risk of developing anxiety and/or mood disorders, with the highest risk associated with physical or sexual abuse, followed by violence and neglect.

Conclusion:

This study underscores the correlation between several personal-level, socioeconomic, and environmental SDH and anxiety and mood disorders in SA. These findings provide a foundation for future analyses examining the intricate interplay between upstream and downstream SDH in SA. Such research can enhance local scientific knowledge, aid in planning for social services, and inform policy decisions and treatment strategies.

Keywords

Saudi national health survey (SNMHS)world mental health survey (WMHS)anxiety and mood social determinants mental health

Introduction

Mental health disorders (MHDs), including anxiety and mood disorders, are growing challenges to health and well-being. Globally, anxiety and mood disorders are among the leading causes of years lived with disability (GBD 2019 Mental Disorders Collaborators, 2022; Hidaka, 2012). In Saudi Arabia, data from the nationally representative Saudi National Mental Health Survey (SNMHS) indicated that (34.2%) of the population had experienced at least one mental health disorder during their lifetime, while (20%) experienced a mental health disorder in the past 12 months (Altwaijri, Al-Habeeb, Al-Subaie, et al., 2020; Altwaijri, Al-Subaie, et al., 2020). The most prevalent disorders were anxiety (23.3%), mood (9.3%), and disruptive behavior disorders (11.2%), and one in every four participants experienced two or more MHDs during their lifetime (Altwaijri, Al-Subaie, et al., 2020), while treatment seeking was minimal (Alangari et al., 2020).

In recent decades, the relationship between social determinants of health (SDH) and MHDs, including anxiety and mood disorders, has gained increasing attention (Lund et al., 2018). Social determinants are defined as ‘the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life’ (Blas & Kurup, 2010). Social determinants could encompass elements that are not modifiable (e.g., age or race/ethnicity), while others are amenable to change (e.g., home and neighborhood environments). The whole hierarchy of SDH is linked to MHDs’ etiology, prognosis, and association with seeking mental healthcare. Social determinants such as female gender, age, low socioeconomic status, and unemployment are associated with a higher prevalence of anxiety and depressive disorders (Averina et al., 2005; Cheah et al., 2020; Mohammad et al., 2020; Reile & Sisask, 2021; Silva et al., 2016). Similarly, social factors like poor living conditions and adverse childhood experiences (e.g., violence, neglect, abuse) are associated with MHDs later in life (Dahal et al., 2018; Kessler et al., 2010; Paananen et al., 2013). Contextual social factors play an essential role in MHDs; for example, lack of social support and certain cultural/religious beliefs could ameliorate or exacerbate the risk of MHDs in different contexts (Heim et al., 2017; Maulik et al., 2010; Salsman & Carlson, 2005; Vaingankar et al., 2020). Therefore, examining context-specific SDH and how they correlate with MHDs is paramount for risk stratification, allocation of resources, and provision of care for MHDs.

Evidence for the association between social determinants and mental health comes mainly from Western countries (Patel, 2000). Several studies have demonstrated an association between social determinants and mental disorders (Dahal et al., 2018; Lund et al., 2018; Paananen et al., 2013; Silva et al., 2016). Compared to Western countries, Arab countries have different sociocultural systems; thus, different levels, compositions, and dynamics of SDH. These, in turn, may lead to differences in the major etiologies of common MHDs and should be more thoroughly investigated in the local and regional context.

Saudi Arabia’s research on the association between social determinants and health is still nascent. Some recent efforts have examined the impact of social determinants on general and mental health but were limited to the primary care sector or age-specific individuals (Abou Abbas & AlBuhairan, 2017; Almujadidi et al., 2022; Baghdadi et al., 2021). Moreover, these efforts often explored a limited set of social determinants (e.g., age, gender, marital status, education/employment) in the context of mental health. To our knowledge, no studies have explored the association between social determinants and mental health in Saudi Arabia in a nationally-representative sample. The current study explores the association between various SDH and anxiety and mood disorders in Saudi Arabia.

Methods

Sampling and participants

The Saudi National Mental Health Survey (SNMHS) is a part of the World Health Organization’s (WHO) World Mental Health (WMH) survey initiative (Kessler et al., 2009). The SNMHS is a nationally representative community survey of Arabic-speaking Saudi citizens between the ages of 15 and 65 (Mneimneh et al., 2020). The survey covered 11 out of 13 administrative areas in Saudi Arabia; 2 administrative areas were excluded due to security concerns during the data collection period. SNMHS was a face-to-face household survey conducted through trained interviewers (Altwaijri, Al-Habeeb, Bilal, et al., 2020). Based on the 2010 Saudi population census from the General Authority for Statistics, a multistage clustered probability sampling was conducted between 2011 and 2016. The sampling and weight creation were described in detail elsewhere (Mneimneh et al., 2020). One male and one female participant were invited to participate from selected households within primary sampling units within each of the 11 strata. Participants in (part I) were offered the core sections of the (SNMHS) survey, while a sub-sample (part II) was selected to respond to the non-core sections. Part II participants included two types: those who did not endorse any MHDs in the core sections (chosen with a probability of 0.25) and those who endorsed any MHDs from the core section (selected with certainty into part II). Weights were generated for parts I and II in response to the multistage and individual-weight sampling (Altwaijri, Al-Habeeb, Bilal, et al., 2020; Mneimneh et al., 2020). All analyses in this paper accounted for the multistage sample by applying the relevant weights for each analysis separately (i.e., for part 1 or part 2, according to which disorders were included in each part). Interviewers obtained written informed consent prior to each interview. The study was conducted in accordance with the Declaration of Helsinki and all field and consent procedures were approved by the Institutional Review Board committee at the King Faisal Specialist Hospital & Research Centre, Riyadh (RAC#: 2091093).

Instruments

Like other WMH surveys, the SNMHS utilized the WHO’s Composite International Diagnostic Interview (CIDI) in its third version (Altwaijri, Al-Habeeb, Bilal, et al., 2020). CIDI is a fully structured survey commonly used for community surveys, which employs a stem-branch approach to capture the lifetime and 12-month prevalence of selected MHDs. The CIDI 3.0 was translated, adapted, and pretested in the Saudi population (Shahab et al., 2019). In addition, some sections were added to the Saudi CIDI 3.0 to capture context-specific data (e.g., religiosity, polygamy, and attitude toward alcohol use). The Saudi CIDI 3.0 was administered by lay interviewers using computer-assisted personal interviews (CAPI) except for sections deemed sensitive (e.g., substance and alcohol use), that were administered by Audio Computer-Assisted Self Interview (ACASI). In the SNMHS, the Diagnostic and Statistical Manual for Mental Disorders, fourth edition (DSM-IV) (Altwaijri, Al-Habeeb, Bilal, et al., 2020) were used to generate mental health diagnoses from CIDI 3.0 to facilitate comparing results with WMH results elsewhere.

Variables

SDH variables

We included a variety of SDH variables spanning personal-level, socioeconomic (SES), general health, environmental, and childhood-related domains. For the personal (i.e., demographic) domain, we collected data on gender (binary: male/female); age as a categorical variable (15–25, 26–35, 36–50, and 50+ years) to facilitate comparison with other WMH/SNMHS results and to better capture age-related experiences (e.g., entry to the workforce); for the marital status (Married, Never Married, Separated/Divorced and Widowed) we elected to isolate the widowed individuals into a distinct category as the mechanism of un-coupling is different compared to separated/divorced; ever being in a polygamous marriage (Yes, No, and never-married) was reported for the overall sample and stratified by gender. For the SES domain, we included data on education (low 0–6, low average 7–9, high average 10–15, and high +16 years); employment status (Employed, Unemployed, Homemaker, Student, and Others); ever receiving public assistance (yes, no); and income as a ratio of income per capita to the median of the sample, the income per capita is a composite variable that synthesizes the personal income, spouse’s income, and family’s income. The income was classified as (low if the ratio is below 0.5, low-average if between 0.5 and 1, high-average if between 1.1 to 2, and high if the ratio is bigger than 2).

We included information on physical health as this has an intricate, bidirectional association with SDH; specifically, we asked participants about their body mass index (BMI), smoking habits, and if they have any chronic conditions, including cancer, arthritis or rheumatism, chronic back or neck problems, headaches, any other chronic pain, high blood pressure, heart disease, asthma, any other chronic lung disease, like COPD, tuberculosis or emphysema. Then, participants were categorized if they had not experienced any conditions, 1 to 2, or 3+ conditions. For the religious domain, we included a single item asking participants about the frequency of seeking comfort through religious and/or spiritual practices when faced with problems and classified that into (often/sometimes or rarely/never).

For the environmental domain, we included items on the area where the participants were raised (large cities/suburbs/villages or rural areas) and if they moved neighborhoods or towns during childhood. Also, we included household religiosity during childhood, measured on a 4-point Likert scale and categorized as religious (somewhat religious and very religious) and non-religious (not at all religious/not very religious). Lastly, we included items about several Adverse Childhood events (ACE) like physical or sexual abuse, neglect, family economic adversity, physical illness, violence, parental divorce, loss, or mental illness. We operationalized all variables as dichotomous variables as follows: Yes, if participants gave an affirmative answer to ever being subjected to a specific ACE through a series of questions, and No otherwise. To quantify the frequency of ACE, we created a variable of how many ACEs the participant experienced: none, one to two, and three or more ACEs.

Anxiety and mood disorders

We examined mood and anxiety disorders included in the SNMHS survey and classified participants into four groups: having either anxiety or mood disorders only, having a comorbidity between anxiety and mood disorders, or neither anxiety nor mood disorders. The presence of an anxiety disorder included an affirmative diagnosis of any of the following disorders: general anxiety disorder (GAD), obsessive-compulsive disorder (OCD), adult separation anxiety, panic disorder, agoraphobia, social phobia, and Post-Traumatic Stress Disorder (PTSD). Mood disorders included the presence of either major depressive or bipolar disorders. Lastly, the presence of any MHDs other than anxiety or mood disorders was coded as a binary variable and included in the analyses.

Statistical analysis

The lifetime and 12-month prevalence of anxiety and mood disorders were presented for the sample, with weights for parts I and II used as indicated. The distributions of mood and anxiety disorders by the different SDH variables were presented in raw frequencies and weighted percentages for parts I and II; standard errors (SE) were calculated for prevalence values. The descriptive analyses included the whole sample of SNMHS of 4,004 participants. We stratified the lifetime prevalence by gender to examine potential gender-based differences in anxiety and mood disorders distribution.

To explore the association between the different SDH and anxiety and mood disorders, we used crude and adjusted multinomial regression models, with ‘no anxiety nor mood disorders’ as the reference level. Then, after adjusting for age and gender in each model, we assessed the association between each SDH and the presence of anxiety and mood disorders. We elected not to combine various SDH variables in sequential models as our analysis is exploratory rather than causal, and we expected high collinearity between some variables (e.g., education and income). As a sensitivity analysis, to explore if polygamy is associated differently with anxiety/mood disorders based on gender, we included an interaction term between gender and status of polygamy and anxiety/mood disorders. Results were presented using odds ratios and 95% confidence intervals. As some anxiety disorders were only included in the non-core section of SNMHS, part II participants and weights were included in the regression analyses (n = 1981). All analyses were conducted using SAS software 9.4 (SAS Institute Inc., Cary, NC, USA).

Results

Sample’s characteristics:

Adolescents and young adults (aged 15–25) constituted (39%) of the participants in SNMHS, and the sample was comparably distributed between males and females (Table 1). Around one-third of the sample had at least a bachelor’s degree (36%); close to half of the participants were classified as having low income (43%), with one-tenth (11%) reporting having received public assistance at least once. Half of the participants were married, and (5%) were in polygamous marriages. Most participants described their household during childhood as very religious (95%), and (80%) reported they sometimes or often seek religious/spiritual comfort when faced with problems. More than half of the participants reported BMI above 25, indicating overweight or obesity (54%), had at least one physical condition (69%), and other MHDs in addition to anxiety and/or mood disorders (17.6%). Regarding childhood environment, the most prevalent ACEs reported by participants was physical abuse (14.3%). Around one in every four participants had experienced one to two ACEs during their childhood, while (2.4%) reported exposure to three or more ACEs (Table 1).

Table 1.

Demographic, socioeconomic, physical health, and childhood environment characteristics by categories of anxiety and mood disorders.

Domains	Characteristics	All (n = 1,981)			Neither anxiety or mood disorder (n = 1,167)				Only anxiety (n = 459)				Only mood (n = 167)				Both anxiety and mood (n = 188)
Domains	Characteristics	N	%	SE	N	%	SE	p-Value	N	%	SE	p-Value	N	%	SE	p-Value	N	%	SE	p-Value
Demographic (personal)	Age (in years)
	15–25	682	38.99	2.00	387	37.03	2.51	.00	175	45.48	3.91	.04	64	46.76	5.19	.09	56	38.48	5.94	.02
	26–35	575	23.54	1.59	322	22.02	1.97		135	25.63	3.16		44	24.59	4.99		74	36.69	5.47
	36–50	547	27.17	1.72	343	29.21	2.21		114	22.17	3.10		49	22.74	3.83		41	19.12	4.42
	51–65	177	10.30	1.37	115	11.74	1.80		35	6.72	1.81		10	5.90	2.32		17	5.72	1.68
	Gender
	Male	833	50.35	2.01	549	53.17	2.53	.01	167	45.62	3.93	.10	57	34.82	4.92	.00	60	38.62	5.93	.05
	Female	1,148	49.65	2.01	618	46.83	2.53	.01	292	54.38	3.93	.10	110	65.18	4.92	.00	128	61.38	5.93	.05
	Marital status
	Married	1,151	50.16	2.01	690	51.61	2.54	.04	249	45.73	3.82	.22	99	45.29	5.05	.70	113	48.73	5.78	.05
	Separated/divorced	75	3.61	0.63	30	2.39	0.62		23	4.94	1.31		5	6.12	4.17		17	14.13	5.47
	Widowed	57	2.68	0.73	35	2.85	0.97		14	2.43	0.95		5	3.45	2.05		3	0.70	0.42
	Never married	698	43.55	2.02	412	43.15	2.55		173	46.90	3.90		58	45.14	5.20		55	36.44	5.66
	Ever in a polygamous marriage
	Yes	142	5.04	0.77	72	4.84	1.00	.76	43	5.63	1.13	.57	13	6.48	2.34	.72	14	4.82	1.63	.45
	No	1,141	51.41	2.01	683	52.00	2.54		243	47.47	3.85		96	48.38	5.20		119	58.73	5.71
	Never married	698	43.55	2.02	412	43.15	2.55		173	46.90	3.90		58	45.14	5.20		55	36.44	5.66
	Ever in a polygamous marriage (male)
	Yes	52	4.83	1.30	32	5.27	1.66	.48	14	3.62	1.24	.64	4	5.43	2.91	.25	2	1.01	0.89	.10
	No	436	47.92	2.90	293	48.72	3.46		79	45.60	6.29		28	33.45	7.25		36	51.24	10.51
	Never married	345	47.25	2.92	224	46.01	3.50		74	50.78	6.34		25	61.12	7.75		22	47.75	10.53
	Ever in a polygamous marriage (female)
	Yes	90	5.26	0.80	40	4.36	0.98	.21	29	7.31	1.77	.22	9	7.03	3.22	.69	12	7.22	2.56	.21
	No	705	54.95	2.77	390	55.73	3.70		164	49.05	4.72		68	56.36	6.35		83	63.45	6.17
	Never married	353	39.79	2.77	188	39.91	3.71		99	43.64	4.80		33	36.60	6.05		33	29.33	5.80
Socioeconomic	Education (in years)
	Low (0–6 years)	311	16.14	1.49	199	18.46	1.97	.00	64	9.51	1.67	.01	23	12.15	3.29	.24	25	9.35	2.26	.08
	Low average (7–9)	278	15.10	1.45	161	15.54	1.84		67	13.53	2.54		22	11.79	3.04		28	16.74	4.53
	High average (10–15)	672	32.68	1.88	380	31.51	2.35		166	37.88	3.81		63	37.25	4.92		63	27.86	5.00
	High (⩾16)	720	36.09	1.93	427	34.49	2.40		162	39.08	3.83		59	38.81	5.26		72	46.05	5.88
	Income (ratio of participants’ to group median income)
	Low (<0.5)	810	43.23	2.03	474	43.87	2.56	.24	192	42.15	3.82	.01	77	45.98	5.18	.15	67	36.15	5.73	.36
	Low average (0.5–1)	193	9.75	1.16	115	10.65	1.52		37	4.48	0.93		12	6.64	2.29		29	17.48	4.65
	High average (1.1–2)	302	16.54	1.46	180	15.62	1.77		68	21.41	3.55		21	10.05	2.55		33	17.31	3.80
	High (>2)	676	30.48	1.75	398	29.86	2.19		162	31.97	3.48		57	37.32	5.22		59	29.07	5.26
	Ever received public assistance
	Yes	217	11.25	1.27	104	9.16	1.49	.01	64	15.88	2.83	.04	24	15.99	3.83	.10	25	20.91	6.20	.13
	No	1,564	88.75	1.27	952	90.84	1.49	.01	338	84.12	2.83	.04	127	84.01	3.83	.10	147	79.09	6.20	.13
		Frequency missing = 200			Frequency missing = 111				Frequency missing = 57				Frequency missing = 16				Frequency missing = 16
	Employment status^a
	Employed	684	36.76	1.93	422	36.95	2.42	.56	142	33.99	3.68	.52	51	27.57	4.81	.38	69	50.04	5.82	.02
	Unemployed	159	6.97	0.85	80	6.75	1.09		40	6.54	1.42		18	11.23	3.08		21	8.45	2.43
	Homemaker	556	22.62	1.67	319	22.77	2.11		133	22.59	3.23		48	22.43	3.90		56	20.75	3.51
	Student	479	26.94	1.86	283	26.12	2.33		122	32.10	3.75		40	30.34	4.88		34	18.32	4.74
	Other	103	6.72	1.01	63	7.42	1.33		22	4.77	1.38		10	8.43	3.26		8	2.44	0.95
General health	Any chronic condition^b
	None	443	30.93	1.99	326	35.56	2.51	<.0001	74	19.74	3.56	.00	21	15.33	4.73	<.0001	22	16.16	5.71	.00
	1–2 conditions	1,178	55.95	2.02	678	54.38	2.54		287	61.81	3.96		102	58.24	5.24		111	56.16	5.95
	3 or more conditions	360	13.12	1.21	163	10.06	1.38		98	18.45	3.14		44	26.43	4.29		55	27.68	5.02
	Presence of other mental disorders^c
	Yes	461	17.60	1.36	146	9.83	1.46	<.0001	173	33.44	3.39	<.0001	45	28.08	4.93	.00	97	63.47	4.90	<.0001
	No	1,520	82.40	1.36	1,021	90.17	1.46	<.0001	286	66.56	3.39	<.0001	122	71.92	4.93	.00	91	36.53	4.90	<.0001
	Body Mass Index (BMI)
	Underweight (<18.5)	162	8.11	0.98	97	8.63	1.28	.57	38	7.34	1.51	.79	13	6.89	2.30	.41	14	4.52	1.49	.17
	Normal (18.5–24.9)	705	37.40	1.92	426	38.04	2.42		161	35.98	3.70		57	34.41	4.77		61	35.57	5.88
	Overweight (25.0–29.9)	577	26.79	1.78	338	26.15	2.27		128	25.35	3.11		49	36.33	5.42		62	33.63	5.33
	Obese (⩾30)	537	27.70	1.87	306	27.19	2.34		132	31.33	3.89		48	22.37	3.79		51	26.27	5.03
	Smoking behavior
	Current smoker	328	16.01	1.39	187	14.84	1.69	.16	77	18.17	3.01	.42	27	16.07	4.57	.80	37	24.73	5.22	.09
	Ex-smoker	150	7.80	1.06	98	8.55	1.35		29	5.96	2.16		12	6.78	2.37		11	4.44	1.62
	Never smoked	1,503	76.19	1.65	882	76.61	2.04		353	75.87	3.44		128	77.15	4.83		140	70.82	5.32
Religious	Frequency of seeking religious comfort
	Often/sometimes	1,632	79.98	1.75	943	78.30	2.22	.05	384	83.35	3.21	.19	143	86.88	3.12	.03	162	86.79	4.45	.13
	Rarely/never	340	20.02	1.75	219	21.70	2.22	.05	72	16.65	3.21	.19	23	13.12	3.12	.03	26	13.21	4.45	.13
Environment and childhood experience	Place where participant was mostly raised
	Large city	1,075	51.43	2.02	627	51.50	2.54	.81	243	45.37	3.74	.57	97	62.62	4.82	–	108	63.05	5.13	–
	Suburbs/small city	351	15.43	1.42	213	15.07	1.74		79	17.65	3.33		28	16.42	3.63		31	12.08	2.89
	Town/village/rural area	541	32.94	1.97	318	33.19	2.48		133	36.84	3.97		41	20.96	3.78		49	24.87	4.39
	Moved around/refused to answer	5	0.20	0.10	4	0.24	0.14		1	0.14	0.14		0	.	.		0	.	.
		Frequency missing = 200			Frequency missing = 111				Frequency missing = 57				Frequency missing = 16				Frequency missing = 16
	Moved neighborhood/towns during childhood
	No	1,117	62.38	1.89	693	64.27	2.38	.05	237	57.83	3.69	.14	96	59.08	5.08	.36	91	54.08	5.62	.12
	Yes	855	37.62	1.89	469	35.73	2.38	.05	219	42.17	3.69	.14	70	40.92	5.08	.36	97	45.92	5.62	.12
		Frequency missing = 9			Frequency missing = 5				Frequency missing = 3				Frequency missing = 1
	Household religiosity during childhood
	Religious	1,890	95.31	1.01	1,123	95.27	1.33	.93	433	96.49	0.86	.44	160	97.67	1.16	.18	174	90.15	4.42	.25
	Not religious	82	4.69	1.01	39	4.73	1.33	.93	23	3.51	0.86	.44	6	2.33	1.16	.18	14	9.85	4.42	.25
		Frequency missing = 9			Frequency missing = 5				Frequency missing = 3				Frequency missing = 1
	Adverse childhood experiences
	No adverse experiences	1,322	71.52	1.62	892	78.89	1.81	<.0001	233	54.35	3.81	<.0001	110	60.55	5.03	.00	87	35.81	4.97	<.0001
	1–2 adverse experiences	583	26.12	1.58	258	20.01	1.77		193	40.79	3.75		52	36.85	4.97		80	52.92	5.68
	3 or more adverse experiences	76	2.37	0.43	17	1.10	0.42		33	4.86	1.26		5	2.60	1.36		21	11.27	3.31
	Physical abuse
	Yes	341	14.31	1.14	129	10.15	1.25	<.0001	123	24.20	2.99	<.0001	29	20.18	4.08	.02	60	33.83	5.41	.00
	No	1,640	85.69	1.14	1,038	89.85	1.25	<.0001	336	75.80	2.99	<.0001	138	79.83	4.08	.02	128	66.17	5.41	.00
	Sexual abuse
	Yes	39	1.84	0.54	10	0.33	0.13	.00	8	3.77	2.38	.16	5	3.80	2.03	.09	16	14.69	4.99	.01
	No	1,942	98.16	0.54	1,157	99.67	0.13	.00	451	96.23	2.38	.16	162	96.20	2.03	.09	172	85.31	4.99	.01
	Neglect
	Yes	61	2.76	0.60	19	1.75	0.65	.01	25	5.83	1.88	.04	4	2.05	1.27	.83	13	6.81	2.72	.13
	No	1,920	97.24	0.60	1,148	98.25	0.65	.01	434	94.17	1.88	.04	163	97.95	1.27	.83	175	93.19	2.72	.13
	Parental death
	Yes	65	2.84	0.59	33	2.60	0.74	.45	21	3.68	1.16	.43	3	2.97	1.84	.85	8	3.04	1.43	.89
	No	1,916	97.16	0.59	1134	97.40	0.74	.45	438	96.32	1.16	.43	164	97.03	1.84	.85	180	96.96	1.43	.89
	Parental divorce
	Yes	49	1.78	0.40	18	1.33	0.49	.05	19	2.80	0.82	.12	5	4.67	2.37	.17	7	2.39	1.05	.56
	No	1,932	98.22	0.40	1,149	98.67	0.49	.05	440	97.20	0.82	.12	162	95.33	2.37	.17	181	97.61	1.05	.56
	Other parental loss
	Yes	62	2.47	0.51	33	2.27	0.57	.53	14	2.90	1.48	.69	5	2.23	1.14	.97	10	3.96	1.67	.37
	No	1,919	97.53	0.51	1,134	97.73	0.57	.53	445	97.10	1.48	.69	162	97.77	1.14	.97	178	96.04	1.67	.37
	Parental mental illness
	Yes	210	7.43	0.79	57	3.72	0.75	<.0001	87	14.94	2.17	<.0001	16	10.95	2.96	.02	50	30.53	5.79	.00
	No	1,771	92.57	0.79	1,110	96.28	0.75	<.0001	372	85.06	2.17	<.0001	151	89.05	2.96	.02	138	69.47	5.79	.00
	Parental substance disorder
	Yes	3	0.04	0.02	1	0.03	0.03	.54	1	0.07	0.07	.57	0	.	.	–	1	0.08	0.08	.59
	No	1,978	99.96	0.02	1,166	99.97	0.03	.54	458	99.93	0.07	.57	167	100.00	0.00	–	187	99.92	0.08	.59
	Family violence
	Yes	128	4.44	0.57	46	3.08	0.63	.00	46	7.38	1.54	.01	17	11.54	3.22	.01	19	8.07	2.34	.10
	No	1,853	95.56	0.57	1,121	96.92	0.63	.00	413	92.62	1.54	.01	150	88.46	3.22	.01	169	91.93	2.34	.10
	Physical illness
	Yes	24	1.38	0.52	10	1.36	0.68	.95	8	0.83	0.35	.49	3	2.11	1.28	.61	3	2.90	2.19	.48
	No	1,957	98.62	0.52	1,157	98.64	0.68	.95	451	99.17	0.35	.49	164	97.89	1.28	.61	185	97.10	2.19	.48
	Family economic adversity
	Yes	27	1.25	0.35	14	1.20	0.43	.81	10	1.78	0.87	.55	2	0.91	0.71	.72	1	0.33	0.33	.06
	No	1,954	98.75	0.35	1,153	98.80	0.43	.81	449	98.22	0.87	.55	165	99.09	0.71	.72	187	99.67	0.33	.06

Note. Part 2 Weights were used (N = 1981).

Employed includes anyone who is employed or self-employed. The Unemployed includes anyone who is currently unemployed, laid-off or looking for work. The Other category includes the following: Maternity leave, Sick leave, Disabled, Retired, Don’t Know, Refused to answer.

Any physical condition includes: Arthritis or rheumatism, chronic back or neck problems, headaches, any other chronic pain, high blood pressure, heart disease, asthma, any other chronic lung disease, like COPD, tuberculosis or emphysema, and cancer.

Other mental disorders include any impulse disorders, substance disorders, or eating disorders.

Anxiety/mood disorders by categories of SDH:

Adolescents and young adults reported a substantial prevalence of anxiety and mood disorders (45.5% and 46.8%, respectively) (Table 1). Two in every five individuals with anxiety only or mood only disorders had at least a bachelor’s degree, while that ratio was almost one in every two participants for having comorbidity between anxiety and mood disorders. In addition, employed or students participants accounted for most cases of anxiety and mood disorders (Table 1). More than two-thirds of the participants with anxiety, mood, or comorbidity had at least one physical chronic condition as well. Among participants who did not have any anxiety or mood disorders, one in ten (9.8%) had another mental health disorder (Table 1).

Participants who sometimes or often experienced physical abuse during childhood represented (24%) of participants with anxiety only disorders and (20%) with mood disorders. Among participants who had anxiety and mood comorbidity, one in every seven was subjected to sexual abuse during childhood (Table 1).

Lifetime prevalence of specific anxiety and mood disorders and differences by gender:

Almost one in every five individuals had an anxiety disorder only during their lifetime (18%), with adult separation anxiety was the most prevalent anxiety disorder (12%). On the other hand, the lifetime prevalence of mood only disorders was (3.8%), and the comorbidity between anxiety and mood disorders 5.3% (Supplemental Material 1).

There were differences in the lifetime prevalence of anxiety/mood disorders by gender (Supplemental Material 1). For instance, females had twice as high lifetime prevalence of mood only disorders compared to males, (5% vs. 2.6%) respectively. The prevalence of different types of anxiety disorders was comparable between females and males with the exception of generalized anxiety disorder, social phobia, and agoraphobia, where females had considerably higher prevalence (Supplemental Material 1).

Association between SDH and anxiety/mood disorders:

In Table 2, the crude multinomial models showed that females had significantly higher odds of having anxiety only OR = 1.4 (95% CI [1.1, 1.7]), mood only 2.1 [1.3, 3.5], and comorbidity between anxiety/mood 1.8 (95% CI [1.2, 2.7]) disorders compared to males. Compared to young adults (26–35 years), participants in older age groups seem to have lower odds of experiencing anxiety and/or mood disorders.

Table 2.

Crude and adjusted multinomial logistic regression models examining demographic, socioeconomic, physical health characteristics with anxiety and mood disorders.

	Crude models						Models adjusted for gender and age
	Both anxiety and mood versus neither anxiety and mood		Only anxiety versus neither anxiety and mood		Only mood versus neither anxiety and mood		Both anxiety and mood versus neither anxiety and mood		Only anxiety versus neither anxiety and mood		Only mood versus neither anxiety and mood
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Age (Ref: 26–35)
15–25 years	0.62	(0.39, 0.99)	1.06	(0.79, 1.41)	1.13	(0.63, 2.02)
36–50	0.39	(0.23, 0.69)	0.65	(0.47, 0.91)	0.70	(0.36, 1.37)
51–60	0.29	(0.12, 0.71)	0.49	(0.30, 0.80)	0.45	(0.16, 1.29)
Wald χ²	34.66**
Gender (ref: male)
Female	1.81	(1.20, 2.71)	1.35	(1.07, 1.71)	2.13	(1.31, 3.46)
Wald χ²	20.41**
Marital status (ref: married)
Separated/divorced	6.26	(3.20, 12.25)	2.33	(1.28, 4.25)	2.92	(1.04, 8.21)	5.07	(2.53, 10.15)	2.03	(1.10, 3.74)	2.22	(0.78, 6.34)
Widowed	0.26	(0.03, 2.67)	0.96	(0.45, 2.05)	1.38	(0.38, 5.09)	0.36	(0.03, 3.86)	1.16	(0.53, 2.53)	1.49	(0.38, 5.83)
Never married	0.89	(0.58, 1.38)	1.23	(0.96, 1.56)	1.19	(0.73, 1.94)	0.49	(0.26, 0.92)	0.72	(0.49, 1.05)	0.61	(0.29, 1.28)
Wald χ²	39.43**						37.03**
Ever in a polygamous marriage (ref: no)
Never married	0.75	(0.49, 1.14)	1.19	(0.94, 1.51)	1.12	(0.70, 1.82)	0.39	(0.21, 0.73)	0.70	(0.48, 1.01)	0.58	(0.28-1.22)
Yes	0.88	(0.34, 2.25)	1.27	(0.75, 2.15)	1.44	(0.54, 3.83)	1.19	(0.45, 3.09)	1.52	(0.89, 2.61)	1.80	(0.66-4.88)
Wald χ²	5.27						16.01*
Education (ref: low)
Low average	2.13	(0.95, 4.74)	1.69	(1.05, 2.72)	1.15	(0.45, 2.96)	1.97	(0.82, 4.72)	1.60	(0.96, 2.68)	1.06	(0.38, 2.94)
High average	1.75	(0.83, 3.65)	2.33	(1.55, 3.50)	1.80	(0.84, 3.86)	1.48	(0.64, 3.43)	2.08	(1.31, 3.31)	1.54	(0.64, 3.72)
High	2.64	(1.31, 5.32)	2.20	(1.47, 3.30)	1.71	(0.80, 3.66)	2.15	(0.95, 4.84)	2.00	(1.25, 3.18)	1.52	(0.63, 3.65)
Wald χ²	28.54**						16.22
Income (ref: low)
Low average	1.99	(1.11, 3.58)	0.44	(0.25, 0.76)	0.60	(0.23, 1.55)	1.96	(1.08, 3.57)	0.45	(0.26, 0.78)	0.64	(0.25, 1.69)
High average	1.34	(0.75, 2.40)	1.43	(1.04, 1.95)	0.61	(0.28, 1.37)	1.41	(0.78, 2.55)	1.51	(1.09, 2.09)	0.70	(0.31, 1.59)
High	1.18	(0.72, 1.93)	1.12	(0.85, 1.46)	1.19	(0.71, 1.99)	1.27	(0.76, 2.12)	1.22	(0.92, 1.62)	1.44	(0.84, 2.46)
Wald χ²	27.90*						29.62**
Ever received public assistance (ref: no)
Yes	2.62	(1.56, 4.42)	1.87	(1.31, 2.67)	1.89	(0.95, 3.74)	2.92	(1.71, 4.98)	2.02	(1.40, 2.90)	2.04	(1.02, 4.09)
Wald χ²	22.29**						25.96**
Employment status (ref: employed)
Unemployed	0.92	(0.44, 1.95)	1.05	(0.64, 1.73)	2.23	(0.97, 5.12)	0.44	(0.20, 0.99)	0.77	(0.46, 1.30)	1.37	(0.56, 3.35)
Homemaker	0.67	(0.4, 1.13)	1.08	(0.79, 1.48)	1.32	(0.68, 2.55)	0.35	(0.19, 0.66)	0.91	(0.61, 1.36)	0.78	(0.36, 1.70)
Student	0.52	(0.3, 0.89)	1.34	(1, 1.78)	1.56	(0.85, 2.87)	0.22	(0.11, 0.45)	0.89	(0.60, 1.33)	0.89	(0.39, 2.00)
Other	0.24	(0.07, 0.87)	0.70	(0.4, 1.21)	1.52	(0.61, 3.81)	0.23	(0.06, 0.85)	0.78	(0.44, 1.39)	1.61	(0.61, 4.25)
Wald χ²	22.56*						28.72*
Frequency of seeking religious comfort (ref: rarely/never)
Often/sometimes	1.82	(1.02, 3.25)	1.39	(1.02, 1.89)	1.84	(0.93, 3.63)	1.94	(1.08, 3.48)	1.51	(1.10, 2.06)	2.03	(1.02, 4.06)
Wald χ²	10.15*						13.50*
Any physical condition (ref: none)
1–2 Conditions	2.27	(1.31, 3.95)	2.05	(1.53, 2.74)	2.48	(1.28, 4.80)	2.20	(1.26, 3.85)	2.08	(1.54, 2.80)	2.32	(1.19, 4.54)
3 or more conditions	6.05	(3.24, 11.33)	3.30	(2.25, 4.85)	6.09	(2.88, 12.90)	8.24	(4.22, 16.06)	4.47	(2.95, 6.77)	7.89	(3.56, 17.49)
Wald χ²	80.00**						94.03**
Presence of other mental disorders (ref: no)
Yes	15.93	(10.33, 24.58)	4.61	(3.48, 6.10)	3.58	(2.10, 6.10)	16.31	(10.41, 25.56)	4.48	(3.36, 5.96)	3.57	(2.08, 6.15)
Wald χ²	221.10**						206.33**
BMI (ref: underweight <18.5)
Normal (18.5–24.9)	1.78	(0.67, 4.73)	1.11	(0.70, 1.77)	1.13	(0.43, 2.97)	1.76	(0.66, 4.69)	1.15	(0.72, 1.84)	1.17	(0.44, 3.09)
Overweight (25.0–29.9)	2.45	(0.92, 6.53)	1.14	(0.70, 1.84)	1.74	(0.66, 4.55)	2.52	(0.93, 6.85)	1.34	(0.82, 2.20)	1.96	(0.73, 5.26)
Obese (⩾30)	1.84	(0.68, 4.98)	1.35	(0.85, 2.17)	1.03	(0.38, 2.82)	2.40	(0.86, 6.70)	1.91	(1.16, 3.14)	1.42	(0.49, 4.07)
Wald χ²	10.53						19.09*
Smoking behavior (ref: never smoked)
Current Smoker	1.80	(1.13, 2.89)	1.24	(0.91, 1.68)	1.08	(0.57, 2.04)	3.45	(1.91, 6.24)	1.70	(1.20, 2.43)	2.19	(1.03, 4.66)
Ex-Smoker	0.56	(0.22, 1.46)	0.70	(0.44, 1.14)	0.79	(0.31, 1.99)	1.21	(0.44, 3.37)	1.05	(0.63, 1.75)	1.72	(0.62, 4.76)
Wald χ²	11.62						26.00**

Note. Part 2 Weights were used.

p < .05. **p <. 01.

In the adjusted multinomial models, compared to participants with lower levels of education, participants with higher levels of education displayed increased odds of anxiety, mood, and comorbidity disorders (Table 3). These associations were less consistent when income per capita was considered.

Table 3.

Crude and adjusted multinomial logistic regression models examining environmental and childhood experience characteristics with anxiety and mood disorders.

	Crude models						Models adjusted for gender and age
	Both anxiety and mood vs. neither anxiety and mood		Only anxiety vs. neither anxiety and mood		Only mood vs. neither anxiety and mood		Both anxiety and mood vs. neither anxiety and mood		Only anxiety vs. neither anxiety and mood		Only mood vs. neither anxiety and mood
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Place where participant was mostly raised (ref: town/village/rural area)
Large city	1.63	[1.02, 2.61]	0.79	[0.61, 1.03]	93	[1.07, 3.45]	1.50	[0.93, 2.40]	0.72	[0.56, 0.94]	1.79	[0.99, 3.23]
Suburbs/small city	1.07	[0.54, 2.13]	1.06	[0.75, 1.48]	1.73	[0.80, 3.71]	1.13	[0.56, 2.26]	1.06	[0.75, 1.49]	1.85	[0.86, 4.00]
Wald χ²	15.84*						16.90*
Household religiosity during childhood (ref: not religious)
Religious	0.45	[0.23, 0.90]	1.37	[0.74, 2.53]	.08	[0.45, 9.55]	0.55	[0.27, 1.12]	1.65	[0.88, 3.08]	2.93	[0.63, 13.58]
Wald χ²	7.81						7.90*
Adverse childhood experiences (ref: none)
1–2 adverse experiences	5.83	[3.78, 8.99]	2.96	[2.30, 3.80]	2.40	[1.47, 3.92]	6.22	[4.01, 9.67]	3.05	[2.37, 3.94]	2.52	[1.54, 4.14]
3 or more adverse experiences	22.67	[9.98, 51.50]	6.43	[3.20, 12.94]	3.10	[0.68, 14.12]	24.77	[10.66, 57.53]	6.58	[3.24, 13.34]	3.39	[0.74, 15.60]
Wald χ²	156.03**						157.88**
Physical abuse (ref: no)
Yes	4.52	[2.92, 7.02]	2.83	[2.10, 3.80]	2.24	[1.24, 4.03]	5.40	[3.40, 8.57]	3.05	[2.24, 4.14]	2.66	[1.45, 4.88]
Wald χ²	76.86**						84.18**
Sexual abuse (ref: no)
Yes	51.55	[18.12, 146.63]	11.73	[4.11, 33.46]	11.81	[2.67, 52.16]	50.25	[17.38, 145.29]	10.70	[3.72, 30.74]	10.41	[2.32, 46.72]
Wald χ²	56.14**						54.08**
Neglect (ref: no)
Yes	4.11	[1.75, 9.67]	3.48	[1.92, 6.30]	1.18	[0.23, 6.11]	4.46	[1.86, 10.66]	3.73	[2.05, 6.80]	1.28	[0.24, 6.67]
Wald χ²	21.66**						23.26**
Parental death (ref: no)
Yes	1.17	[0.37, 3.75]	1.43	[0.75, 2.71]	1.15	[0.29, 4.52]	1.29	[0.40, 4.15]	1.55	[0.81, 2.97]	1.20	[0.30, 4.80]
Wald χ²	1.22**						1.83
Parental divorce (ref: no)
Yes	1.82	[0.48, 6.88]	2.14	[0.99, 4.64]	3.63	[1.13, 11.64]	1.84	[0.48, 7.05]	2.04	[0.93, 4.45]	3.48	[1.08, 11.26]
Wald χ²	7.02**						6.27
Other parental loss (ref: no)
Yes	1.77	[0.63, 5.02]	1.29	[0.63, 2.61]	0.98	[0.20, 4.73]	1.94	[0.67, 5.65]	1.43	[0.70, 2.95]	0.98	[0.20, 4.80]
Wald χ²	1.46**						2.13
Parental mental illness (ref: no)
Yes	11.37	[6.92, 18.69]	4.55	[3.05, 6.77]	3.18	[1.47, 6.91]	10.47	[6.32, 17.35]	4.32	[2.89, 6.45]	2.95	[1.35, 6.42]
Wald χ²	109.30**						99.03**
Family violence (ref: no)
Yes	2.76	[1.29, 5.92]	2.50	[1.52, 4.12]	4.10	[1.90, 8.85]	2.80	[1.29, 6.06]	2.46	[1.49, 4.05]	4.03	[1.85, 8.76]
Wald χ²	23.45**						22.12**
Physical illness (ref: no)
Yes	2.17	[0.64, 7.37]	0.61	[0.18, 2.08]	1.56	[0.30, 8.04]	2.53	[0.73, 8.82]	0.69	[0.20, 2.38]	1.85	[0.35, 9.67]
Wald χ²	2.71**						3.18
Family economic adversity (ref: no)
Yes	0.27	[0.01, 7.97]	1.49	[0.60, 3.73]	0.75	[0.07, 8.59]	0.31	[0.01, 9.36]	1.61	[0.64, 4.05]	0.91	[0.08, 10.44]
Wald χ²	1.50**						1.61

Note. Part 2 Weights were used. Parental substance disorder was not modeled as there were only three participants who gave an affirmative answer.

p < .05. **p < .001.

Participants who were widowed or divorced had significantly higher odds of experiencing anxiety, mood, or anxiety/mood comorbidity compared to married participants. Participants who ever were in polygamous marriages displayed a trend of anxiety and mood only disorders compared to participants who were/are in monogamous marriages (Table 2). When examined in more depth, we found a significant interaction between gender and polygamous marriage status and anxiety or mood disorders – where in polygamous marriages, females had higher odds of experiencing anxiety compared to males (Supplemental Material 2). The frequency of seeking religious/spiritual comfort generally had significant and direct associations with anxiety, mood, and anxiety/mood comorbidity disorders (Table 2).

There were significant dose-response associations between the number of ACEs experienced and all anxiety, mood, and anxiety/mood comorbidity disorders (Table 3). Overall, almost all types of ACEs were associated with increased odds of having anxiety and/or mood disorders. The highest magnitude of association was noted for participants who were subjected to sexual abuse, followed by physical abuse, neglect, and family violence (Table 3).

Discussion

This study presented the first analysis of the relationship between a wide range of social determinants of health (SDH) and anxiety and mood disorders among the Saudi population, utilizing nationally-representative data. Our findings suggested that SDH indicating higher socioeconomic status (e.g., income and education) are associated with increased odds of anxiety disorders. Exposure to adverse childhood experiences (ACEs) was linked with an increased risk of developing anxiety and/or mood disorders. The associations between specific ACEs and the presence of anxiety and/or mood disorders varied in magnitude, and experiencing more ACEs showed increased odds of anxiety and/or mood disorders compared to lower instances of ACEs. Lastly, our results showed that individuals with physical comorbidities have higher odds of experiencing anxiety and/or mood disorders.

This study affirmed previous findings that indicate an increased likelihood of anxiety and/or mood disorders associated with SDH, namely: female gender, being divorced or separated, and younger age (Altwaijri, Al-Subaie, et al., 2020; Lund et al., 2018; Maulik et al., 2010; Tanios et al., 2009). The current analysis revealed that women experienced three times as many mood disorders compared to men, a pattern already observed in literature. This finding may be linked women’s vulnerability to the stress, uncertainty, emotional upheaval, and social changes associated with divorce or separation, which might be more pronounced in the Saudi context where women who are divorced or separated are stigmatized – potentially triggering or exacerbating anxiety or mood disorders (Maulik et al., 2010; Richards et al., 1997). Additionally, our analysis indicated that young adults aged 26 to 35 face higher odds of anxiety and/or mood disorders compared to older age groups. This particular finding is concerning as the Saudi population is among the youngest globally, with a median age of 40 years. Thus, the high prevalence of anxiety and/or mood might continue for years to come. Collectively, these findings highlight the importance of future analyses that explore pathways (that are amenable to interventions) linking these SDH to anxiety and mood disorders among the Saudi population.

Our findings regarding the relationship between indicators of higher socioeconomic status (such as income, education, and employment) and higher odds of anxiety-only disorders were unexpected and contrary to most existing studies in the literature. Previous research indicates that lower levels of education and/or income are associated with a greater risk of anxiety disorders (Bjelland et al., 2008; Kronfol et al., 2018; Lund et al., 2018; Paananen et al., 2013; Tanios et al., 2009). The association between education, employment, and anxiety/mood disorders in our analysis may be partially attributed to the stress experienced in the workforce, such as job-security concerns and a competitive labor market, which could increase the risk of anxiety disorders (Chang-Quan et al., 2010). In our analysis, the income variable was a composite measure (income per capita) that accounts for family, spouse, and individual income and may not necessarily reflect personal income or financial independence. This situation is further influenced by some cultural norms among some communities within Saudi Arabia that might assign financial responsibility to males as heads of households, resulting in less financial autonomy for other household members, or view females’ contribution as unworthy (Alhawsawi & Jawhar, 2023; Syed et al., 2018). Furthermore, other analyses of SNMHS data indicated that women and men had differential associations between relative income and MHDs. These results and potential explanations emphasize that socioeconomic variables are interrelated, and the impact of each component is context-specific within the social settings individuals inhabit (Alegria et al., 2018; Phelan et al., 2010). This conclusion aligns with observations that anxiety disorders demonstrate differential distribution across socioeconomic indicators when stratified by race/ethnicity or country-level income (Nunes et al., 2022; Ruscio et al., 2017).

Our findings provided evidence supporting a significant association between physical comorbidities and anxiety and mood disorders. Specifically, participants with a higher number of physical conditions exhibited greater odds of experiencing anxiety and/or mood disorders. Our results agreed with the existing literature that offers insights into various mechanisms through which physical and mental health mutually influence each other, including pain, worry, functional limitations, and lifestyle modifications (Baghdadi et al., 2021; Gabilondo et al., 2012; Jurisson et al., 2021; Scott et al., 2016; Tsang et al., 2008). These findings hold particular significance within the Saudi context for two main reasons. Firstly, in Saudi Arabia chronic diseases are highly prevalent, indicating a higher potential risk of comorbidity with anxiety and/or mood disorders (Alzahrani et al., 2023; Tyrovolas et al., 2020). Secondly, this association should be taken into consideration when designing healthcare for individuals with physical comorbidities. An interdisciplinary approach that addresses the potential psychosomatic repercussions of physical conditions may prove more effective in delivering comprehensive and effective healthcare (Ford et al., 2004; Jones et al., 2023; Thurston et al., 2023).

Global evidence suggested that exposure to ACEs increases the risk of developing anxiety disorders during both adolescence and adulthood (Kaminer et al., 2022; Sahle et al., 2022; Zarse et al., 2019). Our analysis showed ACEs had a significant, direct association with the presence of anxiety disorders and the comorbidity of anxiety and mood disorders, similar to results from Saudi Arabia and elsewhere (AlHemyari et al., 2022; Alhowaymel & Alenezi, 2022; Almuneef et al., 2017; Kessler et al., 2010; McCutchen et al., 2022; Zarse et al., 2019). However, the association was stronger for certain types of ACEs (e.g., physical or sexual abuse), while others had a weaker association (e.g., family violence) or did not reach statistical significance (e.g., family economic adversity). Moreover, different ACEs, in general, had weaker associations with mood only disorders (compared to anxiety or comorbidity disorder) than other studies (Kessler et al., 2010; McCutchen et al., 2022; Spinhoven et al., 2010). One potential explanation is our choice of model (multinomial logistic) and reference category (those with no anxiety nor mood), which intentionally controlled for anxiety disorders. For example, in other analyses, the comparison could be individuals with mood vs. individuals with no mood disorders but can have anxiety disorders, as such, measures of association could be inflated by including other disorders. Moreover, results of stronger associations between ACEs and anxiety only disorders might reflect that we looked at the collective presence of any of the seven anxiety disorders at once rather than focusing on selected disorders as noted in previous research. Nevertheless, these results invite future research into the specificity of ACEs in the risk of particular anxiety and mood disorders, which the SNMHS intends to explore soon.

Lastly, one of the interesting results was the association between seeking comfort through religious/spiritual practices and anxiety and/or mood disorders. As the prompt for this question was a single item, it is not comprehensive enough to measure religiosity as a construct. Thus, it is hard to infer a relationship between religiosity and anxiety and/or mood disorders among our population; however, the observed association deserves deeper exploration. Specifically, our observation hints that religiosity/spirituality could be leveraged as a complementary approach to mental health care, as evidence has shown its effectiveness in other contexts, especially in anxiety disorders (Goncalves et al., 2015; Razali et al., 1998; Saged et al., 2022).

Recently in Saudi Arabia, there have been new national priorities and policies aiming to enhance mental health and access to mental health care. Some of these efforts include the establishment of electronic mental health services platforms, a national hotline to support domestic violence victims, and initiatives to promote gender equality and women empowerment (Human Resources and Social Development, 2022; National Center for Mental Health, 2022). Therefore, results from the SNMHS serve as a baseline for the extent and burden of MHDs in Saudi Arabia. The current analysis is much needed to assess the specific association of SDH with mental health, especially considering the rapid economic transformation happening in Saudi Arabia. Future efforts can utilize our results, along with others from SNMHS, to help in planning, evaluating, and prioritizing national interventions that pertain to SDH and mental health in Saudi Arabia and the region.

Strengths and limitations

This study presented findings from Saudi Arabia’s largest national survey on mental health, which holds particular significance in revealing the prevalence of undiagnosed mental health disorders within the general, non-institutionalized Saudi population. It is the first population-based analysis to concentrate on a broad range of SDH in Saudi Arabia in the context of mental health. Additionally, the study employed a validated and standardized methodology (CIDI 3.0) to enable comparisons with results from the WMH initiative. The modeling strategy we used (multinomial logistic regression) allowed for simultaneous exploration and comparison of the specific associations between SDH and anxiety only, the mood only, or the comorbidity of both disorders, using a unified reference category. Furthermore, this study investigated culture-specific SDH (such as polygamy) that are seldom examined in the context of mental health and well-being. However, we could not explore the specific mechanisms through which socioeconomic status (SES) and environmental variables interact and influence anxiety and/or mood disorders. Additionally, our analysis centered on anxiety and mood disorders, primarily due to their high prevalence, so the findings regarding SDH may not apply to other mental health disorders (e.g., eating disorders or substance abuse) that may have differential associations with SDH.

Conclusion

This study utilized nationally representative data from the Saudi National Mental Health Survey, a part of the World Mental Health Initiative, to investigate the link between selected social determinants of health and anxiety and mood disorders. We specifically examined personal-level, socioeconomic, and family environment indicators in relation to the presence of anxiety disorders, mood disorders, and the comorbidity of both. Our findings demonstrated that higher socioeconomic status indicators, such as income and education, were associated with experiencing anxiety only disorders. Furthermore, exposure to adverse childhood events was strongly associated with developing anxiety disorders in isolation or combination with mood disorders. Different ACEs increased the odds of anxiety and/or mood disorders differentially.

To advance the field, future research should delve into the context-specific dynamics of socioeconomic indicators within Saudi Arabia. Additionally, a comprehensive examination of religiosity as a construct that influences and/or moderates anxiety and mood disorders might be valuable in the context of providing mental health care. Lastly, exploring the specificity of adverse childhood experiences (ACEs) in relation to mental health burden warrants further investigation.

Supplemental Material

sj-docx-1-isp-10.1177_00207640231197944 – Supplemental material for Social determinants of anxiety and mood disorders in a nationally-representative sample – Results from the Saudi National Mental Health Survey (SNMHS)

Supplemental material, sj-docx-1-isp-10.1177_00207640231197944 for Social determinants of anxiety and mood disorders in a nationally-representative sample – Results from the Saudi National Mental Health Survey (SNMHS) by Lubna A Alnasser, Maria Francesca Moro, Mohammad Talal Naseem, Lisa Bilal, Marya Akkad, Reema Almeghim, Abdulhameed Al-Habeeb, Abdullah S. Al-Subaie and Yasmin A Altwaijri in International Journal of Social Psychiatry

Footnotes

Author contributions

Conceptualization: LAA, MFM, YA, LB; Project administration and investigation: YA, LB, AAS, AAH; Supervision: LAA, MFM, YA, LB; Methodology: LAA, MFM, RA, YA, LB, MTN; Formal analysis: MTN, LAA, MFM; Writing – original draft preparation: LAA, MFM, RA, LB; Writing – review and editing: LAA, MFM, YA, LB, MA; Funding acquisition and resources: YA, LB, AAS, AAH.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The SNMHS is funded by the King Salman Center for Disability Research, Grant Number: KSRG-2022-048. The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors reviewed the manuscript and were responsible for the final decision to submit it for publication.

ORCID iDs

Reema Almeghim

Yasmin A Altwaijri

Supplemental material

Supplemental material for this article is available online.

References

Abou Abbas

AlBuhairan

. (2017). Predictors of adolescents’ mental health problems in Saudi Arabia: Findings from the Jeeluna((R)) national study. Child Adolesc Psychiatry Ment Health, 11, 52. https://doi.org/10.1186/s13034-017-0188-x

Alangari

A. S.

Knox

S. S.

Kristjansson

A. L.

Wen

Innes

K. E.

Bilal

Alhabeeb

Al-Subaie

A. S.

Altwaijri

Y. A.

(2020). Barriers to mental health treatment in the Saudi National Mental Health Survey. International Journal of Environmental Research and Public Health, 17(11), 3877. https://doi.org/10.3390/ijerph17113877

Alegria

NeMoyer

Falgas Bague

Wang

Alvarez

(2018). Social determinants of mental health: Where we are and where we need to go. Current Psychiatry Reports, 20(11), 95. https://doi.org/10.1007/s11920-018-0969-9

Alhawsawi

Jawhar

S. S.

(2023). Education, employment, and empowerment among Saudi women. Gender and Education, 35(4), 401–419.

AlHemyari

A. H.

Al-Zamil

N. A.

Shaikh

A. Y.

Al-Eidi

D. A.

Al-Dahlan

H. W.

Al-Shamekh

S. S.

(2022). Prevalence of adverse childhood experiences and their relationship to mental and physical illnesses in the Eastern Region of Saudi Arabia. Brain and Behavior, 12(7), e2668. https://doi.org/10.1002/brb3.2668

Alhowaymel

F. M.

Alenezi

(2022). Adverse childhood experiences and health in rural areas of riyadh province in Saudi Arabia: A cross-sectional study. Healthcare, 10(12), MDPI. https://doi.org/10.3390/healthcare10122502

Almujadidi

Adams

Alquaiz

Van Gurp

Schuster

Andermann

(2022). Exploring social determinants of health in a Saudi Arabian primary health care setting: The need for a multidisciplinary approach. International Journal for Equity in Health, 21(1), 24. https://doi.org/10.1186/s12939-022-01627-2

Almuneef

ElChoueiry

Saleheen

H. N.

Al-Eissa

(2017). Gender-based disparities in the impact of adverse childhood experiences on adult health: Findings from a national study in the Kingdom of Saudi Arabia. International Journal for Equity in Health, 16(1), 90. https://doi.org/10.1186/s12939-017-0588-9

Altwaijri

Y. A.

Al-Habeeb

Al-Subaie

A. S.

Bilal

Al-Desouki

Shahab

M. K.

Hyder

Sampson

N. A.

King

A. J.

Kessler

R. C.

(2020). Twelve-month prevalence and severity of mental disorders in the Saudi National Mental Health Survey. International Journal of Methods in Psychiatric Research, 29(3), e1831.

10.

Altwaijri

Y. A.

Al-Habeeb

Bilal

Shahab

M. K.

Pennell

B. E.

Mneimneh

Bin Mummar

Baig

Talal Naseem

Hyder

Akkad

Al-Subaie

A. S.

(2020). The Saudi National Mental Health Survey: Survey instrument and field procedures. International Journal of Methods in Psychiatric Research, 29(3), e1830.

11.

Altwaijri

Y. A.

Al-Subaie

A. S.

Al-Habeeb

Bilal

Al-Desouki

Aradati

King

A. J.

Sampson

N. A.

Kessler

R. C.

(2020). Lifetime prevalence and age-of-onset distributions of mental disorders in the Saudi National Mental Health Survey. International Journal of Methods in Psychiatric Research, 29(3), e1836.

12.

Alzahrani

M. S.

Alharthi

Y. S.

Aljamal

J. K.

Alarfaj

A. A.

Vennu

Noweir

M. D.

(2023). National and regional rates of chronic diseases and all-cause mortality in Saudi Arabia-analysis of the 2018 household health survey data. International Journal of Environmental Research and Public Health, 20(7), 5254. https://doi.org/10.3390/ijerph20075254

13.

Averina

Nilssen

Brenn

Brox

Arkhipovsky

V. L.

Kalinin

A. G.

(2005). Social and lifestyle determinants of depression, anxiety, sleeping disorders and self-evaluated quality of life in Russia. A population-based study in Arkhangelsk. Social Psychiatry and Psychiatric Epidemiology, 40(7), 511–518. https://doi.org/10.1007/s00127-005-0918-x

14.

Baghdadi

L. R.

Alhassan

M. K.

Alotaibi

F. H.

AlSelaim

K. B.

Alzahrani

A. A.

AlMusaeed

F. F.

(2021). Anxiety, depression, and common chronic diseases, and their association with social determinants in saudi primary care. Journal of Primary Care & Community Health, 12, 5–9. https://doi.org/10.1177/21501327211054987

15.

Bjelland

Krokstad

Mykletun

Dahl

A. A.

Tell

G. S.

Tambs

(2008, Mar). Does a higher educational level protect against anxiety and depression? The HUNT study. Social Science & Medicine, 66(6), 1334–1345. https://doi.org/10.1016/j.socscimed.2007.12.019

16.

Blas

Kurup

A. S.

(2010). Equity, social determinants and public health programmes. World Health Organization.

17.

Chang-Quan

Zheng-Rong

Yong-Hong

Yi-Zhou

Qing-Xiu

(2010). Education and risk for late life depression: A meta-analysis of published literature. The International Journal of Psychiatry in Medicine, 40(1), 109–124. https://doi.org/10.2190/PM.40.1.i

18.

Cheah

Y. K.

Azahadi

Phang

S. N.

Abd Manaf

N. H.

(2020). Sociodemographic, lifestyle, and health factors associated with depression and generalized anxiety disorder among Malaysian adults. Journal of Primary Care and Community Health, 11, 4–6. https://doi.org/10.1177/2150132720921738

19.

Dahal

Swahn

M. H.

Hayat

M. J.

(2018). Association between Neighborhood Conditions and Mental Disorders among Children in the US: Evidence from the National Survey of Children’s Health 2011/12. Psychiatry Journal, 2018, 4–5. https://doi.org/10.1155/2018/5914315

20.

Human Resources and Social Development. (2022). Family protection department. https://www.hrsd.gov.sa/en/ministry/aboutministry/about-us/ministry-sectors/767521/767581

21.

Ford

J. D.

Trestman

R. L.

Steinberg

Tennen

Allen

(2004). Prospective association of anxiety, depressive, and addictive disorders with high utilization of primary, specialty and emergency medical care. Social Science & Medicine, 58(11), 2145–2148.

22.

Gabilondo

Vilagut

Pinto-Meza

Haro

J. M.

Alonso

(2012). Comorbidity of major depressive episode and chronic physical conditions in Spain, a country with low prevalence of depression. General Hospital Psychiatry, 34(5), 510–517. https://doi.org/10.1016/j.genhosppsych.2012.05.005

23.

GBD 2019 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. (2022). The Lancet Psychiatry, 9(2), 137–150. https://doi.org/10.1016/S2215-0366(21)00395-3

24.

Goncalves

J. P.

Lucchetti

Menezes

P. R.

Vallada

(2015). Religious and spiritual interventions in mental health care: A systematic review and meta-analysis of randomized controlled clinical trials. Psychological Medicine, 45(14), 2937–2949. https://doi.org/10.1017/S0033291715001166

25.

Heim

Wegmann

Maercker

(2017). Cultural values and the prevalence of mental disorders in 25 countries: A secondary data analysis. Social Science and Medicine, 189, 96–104. https://doi.org/10.1016/j.socscimed.2017.07.024

26.

Hidaka

B. H

. (2012). Depression as a disease of modernity: Explanations for increasing prevalence. Journal of Affective Disorders, 140(3), 205–214.

27.

Jones

A. L.

Kelley

A. T.

Suo

Baylis

J. D.

Codell

N. K.

West

N. A.

Gordon

A. J.

(2023). Trends in health service utilization after enrollment in an interdisciplinary primary care clinic for veterans with addiction, social determinants of health, or other vulnerabilities. Journal of General Internal Medicine, 38(1), 12–20.

28.

Jurisson

Pisarev

Uuskula

Lang

Oona

Elm

Kalda

(2021). Physical-mental health comorbidity: A population-based cross-sectional study. PLoS ONE, 16(12), e0260464. https://doi.org/10.1371/journal.pone.0260464

29.

Kaminer

Bravo

A. J.

Mezquita

Pilatti

(2022). Adverse childhood experiences and adulthood mental health: A cross-cultural examination among university students in seven countries. Current Psychology. Advance online publication. https://doi.org/10.1007/s12144-022-02978-3

30.

Kessler

R. C.

Aguilar-Gaxiola

Alonso

Chatterji

Lee

Ustun

T. B.

(2009). The WHO World Mental Health (WMH) Surveys. Psychiatrie, 6(1), 5–9. https://www.ncbi.nlm.nih.gov/pubmed/21132091

31.

Kessler

R. C.

McLaughlin

K. A.

Green

J. G.

Gruber

M. J.

Sampson

N. A.

Zaslavsky

A. M.

Aguilar-Gaxiola

Alhamzawi

A. O.

Alonso

Angermeyer

Benjet

Bromet

Chatterji

De Girolamo

Demyttenaere

Fayyad

Florescu

Gal

Gureje

, . . . Williams

D. R.

(2010). Childhood adversities and adult psychopathology in the WHO world mental health surveys. British Journal of Psychiatry, 197(5), 378–385. https://doi.org/10.1192/bjp.bp.110.080499

32.

Kronfol

Khalifa

Khoury

Omar

Daouk

deWitt

J. P.

ElAzab

Eisenberg

(2018). Selected psychiatric problems among college students in two Arab countries: Comparison with the USA. BMC Psychiatry, 18(1), 147. https://doi.org/10.1186/s12888-018-1718-7

33.

Lund

Brooke-Sumner

Baingana

Baron

E. C.

Breuer

Chandra

Haushofer

Herrman

Jordans

Kieling

Medina-Mora

M. E.

Morgan

Omigbodun

Tol

Patel

Saxena

(2018). Social determinants of mental disorders and the Sustainable Development Goals: A systematic review of reviews. Lancet Psychiatry, 5(4), 357–369. https://doi.org/10.1016/S2215-0366(18)30060-9

34.

Maulik

P. K.

Eaton

W. W.

Bradshaw

C. P.

(2010). The effect of social networks and social support on common mental disorders following specific life events. Acta Psychiatrica Scandinavica, 122(2), 378–385. https://doi.org/10.1111/j.1600-0447.2009.1511.x

35.

McCutchen

Hyland

Shevlin

Cloitre

(2022). The occurrence and co-occurrence of ACEs and their relationship to mental health in the United States and Ireland. Child Abuse & Neglect, 129, 105681. https://doi.org/10.1016/j.chiabu.2022.105681

36.

Mneimneh

Z. N.

Heeringa

S. G.

Lin

Y. C.

Altwaijri

Y. A.

Nishimura

(2020). The Saudi National Mental Health Survey: Sample design and weight development. International Journal of Methods in Psychiatric Research, 29(3), e1829.

37.

Mohammad

Almakran

I. W.

Al-Montashri

A. S.

Mursi

A. M.

Alharbi

S. M.

Pasha

T. S.

Khalid

(2020). Depression, anxiety and stress and their associated social determinants in the Saudi college students. Health Psychology Research, 8(3), 196–197. https://doi.org/10.4081/HPR.2020.9263

38.

National Center for Mental Health. (2022). Women mental health empowering. https://ncmh.org.sa/

39.

Nunes

J. C.

Carroll

M. K.

Mahaffey

K. W.

Califf

R. M.

Doraiswamy

P. M.

Short

Shah

S. H.

Swope

Williams

Hernandez

A. F.

Hong

D. S.

(2022). General Anxiety Disorder-7 Questionnaire as a marker of low socioeconomic status and inequity. Journal of Affective Disorders, 317, 287–297. https://doi.org/10.1016/j.jad.2022.08.085

40.

Paananen

Ristikari

Merikukka

Gissler

(2013). Social determinants of mental health: A finnish nationwide follow-up study on mental disorders. Journal of Epidemiology and Community Health, 67(12), 1025–1031. https://doi.org/10.1136/jech-2013-202768

41.

Patel

(2000). Poverty, inequality, and mental health in developing countries. In Leon

D. W. G.

(Ed.), Poverty, Inequality, and Health: An International Perspective (pp. 274–262). Oxford Academic. https://doi.org/10.1093/acprof:oso/9780192631961.003.0012

42.

Phelan

J. C.

Link

B. G.

Tehranifar

(2010). Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. Journal of Health and Social Behavior, 51(Suppl. 1), S28–S40. https://doi.org/10.1177/0022146510383498

43.

Razali

S. M.

Hasanah

C. I.

Aminah

Subramaniam

(1998). Religious—Sociocultural psychotherapy in patients with anxiety and depression. Australian and New Zealand Journal of Psychiatry, 32(6), 867–872. https://doi.org/10.3109/00048679809073877

44.

Reile

Sisask

(2021). Socio-economic and demographic patterns of mental health complaints among the employed adults in Estonia. PLoS ONE, 16(10 October). https://doi.org/10.1371/journal.pone.0258827

45.

Richards

Hardy

Wadsworth

(1997, Sep). The effects of divorce and separation on mental health in a national UK birth cohort. Psychological Medicine, 27(5), 1121–1128. https://doi.org/10.1017/s003329179700559x

46.

Ruscio

A. M.

Hallion

L. S.

Lim

C. C. W.

Aguilar-Gaxiola

Al-Hamzawi

Alonso

Andrade

L. H.

Borges

Bromet

E. J.

Bunting

Caldas de Almeida

J. M.

Demyttenaere

Florescu

de Girolamo

Gureje

Haro

J. M.

Hinkov

, . . .Scott

K. M.

(2017, May 1). Cross-sectional comparison of the epidemiology of DSM-5 generalized anxiety disorder across the globe. JAMA Psychiatry, 74(5), 465–475. https://doi.org/10.1001/jamapsychiatry.2017.0056

47.

Saged

A. A. G.

Sa’ari

C. Z.

Abdullah

M. B.

Al-Rahmi

W. M.

Ismail

W. M.

Zain

M. I. A.

alShehri

N. B. A.B. M

. (2022). The effect of an islamic-based intervention on depression and anxiety in Malaysia. Journal of Religion and Health, 61(1), 79–92. https://doi.org/10.1007/s10943-021-01484-3

48.

Sahle

B. W.

Reavley

N. J.

Morgan

A. J.

Yap

M. B. H.

Reupert

Jorm

A. F.

(2022). The association between adverse childhood experiences and common mental disorders and suicidality: An umbrella review of systematic reviews and meta-analyses. European Child & Adolescent Psychiatry, 31(10), 1489–1499. https://doi.org/10.1007/s00787-021-01745-2

49.

Salsman

J. M.

Carlson

C. R.

(2005).Religious orientation, mature faith, and psychological distress: Elements of positive and negative associations. Journal for the Scientific Study of Religion, 44(2), 201–209. https://doi.org/10.1111/j.1468-5906.2005.00276.x

50.

Scott

K. M.

Lim

Al-Hamzawi

Alonso

Bruffaerts

Caldas-De-Almeida

J. M.

Florescu

De Girolamo

De Jonge

Kawakami

Medina-Mora

M. E.

Moskalewicz

Navarro-Mateu

O’Neill

Piazza

Posada-Villa

Torres

Kessler

R. C.

(2016). Association of mental disorders with subsequent chronic physical conditions: World mental health surveys from 17 countries. JAMA Psychiatry, 73(2), 201–209. https://doi.org/10.1001/jamapsychiatry.2015.2688

51.

Shahab

Al-Tuwaijri

Kattan

Bilal

Hyder

Mneimneh

Lin

Y. C.

Al-Habeeb

Al-Subaie

Binmuammar

Altwaijri

(2019). Implementing the TRAPD model for the Saudi adaptation of the World Mental Health Composite International Diagnostic Interview 3.0. International Journal of Mental Health Systems, 13, 12. https://doi.org/10.1186/s13033-019-0267-x

52.

Silva

Loureiro

Cardoso

. (2016). Social determinants of mental health: A review of the evidence. The European Journal of Psychiatry, 30(4), 259–292.

53.

Spinhoven

Elzinga

B. M.

Hovens

J. G. F.M.

Roelofs

Zitman

F. G.

Van Oppen

Penninx

B. W. J.H

. (2010). The specificity of childhood adversities and negative life events across the life span to anxiety and depressive disorders. Journal of Affective Disorders, 126(1-2), 103–112. https://doi.org/10.1016/j.jad.2010.02.132

54.

Syed

Ali

Hennekam

(2018). Gender equality in employment in Saudi Arabia: A relational perspective. Career Development International, 23(2), 163–177.

55.

Tanios

C. Y.

Abou-Saleh

M. T.

Karam

A. N.

Salamoun

M. M.

Mneimneh

Z. N.

Karam

E. G.

(2009). The epidemiology of anxiety disorders in the Arab world: A review. Journal of Anxiety Disorders, 23(4), 409–419. https://doi.org/10.1016/j.janxdis.2008.10.009

56.

Thurston

I. B.

Alegría

Hood

K. B.

Miller

G. E.

Wilton

Holden

(2023). How psychologists can help achieve equity in health care—Advancing innovative partnerships and models of care delivery: Introduction to the special issue. American Psychologist, 78(2), 73.

57.

Tsang

Von Korff

Lee

Alonso

Karam

Angermeyer

M. C.

Borges

G. L.

Bromet

E. J.

Demytteneare

de Girolamo

de Graaf

Gureje

Lepine

J. P.

Haro

J. M.

Levinson

Oakley Browne

M. A.

Posada-Villa

Seedat

Watanabe

(2008). Common chronic pain conditions in developed and developing countries: Gender and age differences and comorbidity with depression-anxiety disorders. The Journal of Pain, 9(10), 883–891. https://doi.org/10.1016/j.jpain.2008.05.005

58.

Tyrovolas

El Bcheraoui

Alghnam

S. A.

Alhabib

K. F.

Almadi

M. A. H.

Al-Raddadi

R. M.

Bedi

El Tantawi

Krish

V. S.

Memish

Z. A.

Mohammad

(2020). The burden of disease in Saudi Arabia 1990–2017: Results from the Global Burden of Disease Study 2017. Lancet Planet Health, 4(5), e195–e208. https://doi.org/10.1016/S2542-5196(20)30075-9

59.

Vaingankar

J. A.

Abdin

Chong

S. A.

Shafie

Sambasivam

Zhang

Y. J.

Chang

Chua

B. Y.

Shahwan

Jeyagurunathan

Kwok

K. W.

Subramaniam

(2020). The association of mental disorders with perceived social support, and the role of marital status: Results from a national cross-sectional survey. Archives of Public Health, 78(1), 1–11. https://doi.org/10.1186/s13690-020-00476-1

60.

Zarse

E. M.

Mallory

N. R.

Yoder

Hulvershorn

Chambers

J. E.

Chambers

A. R.

(2019). The adverse childhood experiences questionnaire: Two decades of research on childhood trauma as a primary cause of adult mental illness, addiction, and medical diseases. Cogent Medicine, 6(1),1581447.

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