Prevalence and potential determinants of subthreshold and major depression in the general population of Qatar

Journal of Affective Disorders 252 (2019) 382–393

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Research paper

Prevalence and potential determinants of subthreshold and major depression in the general population of Qatar

T

Salma M. Khaled Social and Economic Survey Research Institute, Qatar University, Doha, Qatar

ABSTRACT

Background: There is a paucity of epidemiological studies of depression in warfree Arab countries. This study estimated the prevalence and potential determinants of Subthreshold (SUBDE) and Major Depressive Episode (MDE) in migrants and non-migrants typical of Qatar and neighboring Gulf countries. Methods: A telephone survey of a probability-based sample of 2,424 participants was conducted in February 2017. The sample was divided based on nationality and income: Low-Income Migrants (LIMs), High Income Migrants (HIMs), and non-migrants or Qatari Nationals (QNs). Participants completed the nine-item Physician Health Questionnaire (PHQ-9). Ethnicity, sociodemographics, health- and work-related information was collected. Bivariate and multinomial logistic regression analyses were used. Results: Overall prevalence of any depression ranged between 4.2% (95% CI: 3.3–5.3) and 6.6% (95% CI: 5.4–7.9) for a cut-off of 12 and 10, respectively. The diagnostic algorithm for SUBDE and MDE resulted in estimates of 5.5% (95% CI: 4.4–6.8) and 3.6% (95% CI: 2.8–4.5), respectively. SUBDE, but not MDE rates, were significantly increased in LIMs (OR=2.96, p = 0.004) and HIMs (OR = 2.00, p = 0.014) compared with non-migrants. Arab ethnicity was significantly associated with SUBDE: relative to South Asians (OR = 3.77, p < 0.001) and other ethnicities (OR = 3.61, p = 0.029). Arab ethnicity was significantly associated with MDE: relative to South Asians (OR = 10.42, p < 0.001) and South East Asians (OR = 3.54, p = 0.007). Limitations: Clinical diagnostic interviews for depression were not included. Conclusion: Using the PHQ-9, depression prevalence in Qatar was comparable to general population estimates from Western countries. Migrant status and ethnicity were associated with SUBDE and MDE with implications for early screening and community intervention.

1. Introduction Worldwide, depression is a burdensome mental disorder; people who suffer from depression experience greater functional impairment, poorer health, quality of life and financial outcomes (Barge-Schaapveld et al., 1999; Murray and Lopez, 1996; Simon et al., 2000); they are also at significantly increased risk of death by suicide (Hawton et al., 2013). Among known risk factors for depression include family history of depression, childhood trauma, stressful life events, and substance dependence (de Graaf et al., 2002). There are few general population studies on the prevalence of common mental disorders in Arab countries (Karam et al., 2006). Stigma due to severe social consequences of mental illness remains the most overriding concern for seeking treatment from health professionals in this part of the world (Bener and Ghuloum, 2010; Ciftci, 2012; Zolezzi et al., 2017). Therefore, data from non-clinical populations are necessary to better understand and address treatment needs and to establish a robust baseline for monitoring and planning purposes for the entire population. Most of the published epidemiological evidence to date comes from war-afflicted countries (Alhasnawi et al., 2009; Karam et al., 2008) with

a paucity of data from war-free Arab countries. Qatar is a small, stable, and economically prosperous Muslim country in the Arabian Peninsula, which has witnessed rapid urbanization and modernization over the past three decades. In addition to the unprecedented growth in its economy, winning the right to host the 2022 FIFA World Cup has increased the country's reliance on migrants to build the necessary infrastructure. Today, Qatar has the highest proportion of migrants on short-term contracts in the world (Kamrava and Babar, 2012), making up approximately 90% of Qatar's 2.4 million population (Planning and Statistics Authority, 2018). Far from being a homogenous group, there are broadly speaking, two types of migrants in Qatar. Clearly differentiated by the type of work they do: low-income migrants (LIMs) make up the largest segment of the country's population. They are mostly labourers from South Asia (India, Pakistan, Bangladesh, Sri Lanka, and Nepal) and South East Asia (the Philippines) (De Bel-Air, 2014) often possessing little or no formal training, who are employed in the construction sector or oil-and-gas industry. In contrast, high-income migrants (HIMs) engage in higher paid professional, managerial, or administrative jobs that require formal training or education. HIMs in Qatar are mostly nationals from other Arab or Asian countries. Nationals from North America, Australia,

E-mail address: [email protected]. https://doi.org/10.1016/j.jad.2019.04.056 Received 5 January 2019; Received in revised form 28 February 2019; Accepted 8 April 2019 Available online 09 April 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.

Journal of Affective Disorders 252 (2019) 382–393

S.M. Khaled

and Europe, mostly HIMs, make up only a relatively small proportion of the migrant population in Qatar. Unlike LIMs who mostly come to Qatar alone, HIMs often have their families living with them. Both LIMs and HIMs are sponsored under a set of migration rules, called the Kafala, which require that all migrants have an in-country sponsor – normally their employer – who is responsible for their legal status in the country. To date, no general population study of common mental disorders has been conducted in Qatar. There are a few studies that reported on the prevalence of depression in the primary care setting focusing only on an indigenous segment of the population (Bener et al., 2012; Ghuloum et al., 2011). Generalizability from these studies is limited primarily because non-migrants or Qatari nationals (QNs) comprise the minority of the population. The ethnic diversity of the majority of the population in Qatar and neighboring countries of the Gulf General Council (GCC) – United Arab Emirates, Kuwait, Bahrain, Saudi Arabia, and Oman – poses methodological challenges that prohibits the use of lengthy diagnostic interviews like the Composite International Diagnostic Interview (Kessler et al., 2009) because of the extensive crosscultural adaptation and validation required for successful implementation. The use of brief screening tools becomes the most reasonable first stage method for assessment of current symptoms and treatment needs for the general population in this context. There are also other major barriers to collecting representative epidemiological data in this part of the world. LIMs are a hard to access population. Most live in camps owned by their employers. LIMs may also be wary of participating in health research because of fear of deportation if any underlying physical or mental disorder is identified. No published studies examining how living and working in Qatar or other GCC countries impacts depressive symptoms of migrants and nonmigrants were found. The aim of this study was to estimate the prevalence and potential determinants of subthreshold (SUBDE) and Major Depressive Episode (MDE) in Qatar's general population using a relatively brief and well-validated screening measure of depression. The current study also provides a rare opportunity to explore potential associations of ethnicity and migration with depressive symptomology in multicultural setting typical of Qatar and its neighboring countries.

population groups at the 5% significance level, the target sample of completed interviews was estimated to be about 750 per group (LIMs, HIMs, and QNs). 2.3. Strategies to minimize bias To improve survey response and reduce selection bias, selected phone numbers were released in batches to help ensure that standard call procedures were followed for all cellular (mobile) phone numbers. Seven attempts were made to contact each potential participant. Phone calls were made at different times of the day and on different days of the week to reduce non-response. 2.4. Data collection The study was reviewed and approved by Qatar University's Institutional Review Board (Reference number: 264-E/13). The Computer Assisted Telephone Interview system was used to conduct the interview with participants (Kelly, 2008). Researchers entered responses directly into Blaise survey management software as they interviewed participants over the phone (Blaise, Statistics Netherlands, n.d.) 2.5. Depression symptoms The nine-item Physician Health Questionnaire (PHQ-9) is a relatively brief and well-validated screening measure of depression used globally in both clinical and general population samples (Alonso et al., 2004; Gelaye et al., 2014; Hyphantis et al., 2011; Kiely and Butterworth, 2015; Kocalevent et al., 2013; Kroenke et al., 2010; McGuire et al., 2013; Mitchell et al., 2016; Navines et al., 2012). The PHQ-9 (dependent variable) captures the frequency of nine symptom criteria for diagnoses of depressive disorders in the DSM-5 (American Psychiatric Association, 2013) within the past 2 weeks with 4-point response options for each symptom: 0 = “not at all,” 1 = “several days,” 2 = “more than half the days,” and 3 = “nearly every day.” Total scores can range from 0 to 27. The PHQ-9 has been previously validated for screening purposes in primary care (Kroenke and Spitzer, 2002; Spitzer et al., 1999), demonstrating good sensitivity and specificity against a gold-standard diagnostic interview for MDE (Gilbody et al., 2007; Kroenke et al., 2001) and other brief screening instruments (Henkel et al., 2003; Lowe et al., 2004). In the general population, several studies have established the validity of the PHQ-9 or shorter versions (Kroenke et al., 2009; Lowe et al., 2010, 2005; Martin et al., 2006; Rancans et al., 2018). According to a recent study conducted in Arabic-speaking outpatient population, the sensitivity and specificity of the PHQ-9 using a cutoff of 10 were 77% and 46%, respectively (Sawaya et al., 2016). In this study, the prevalence of any depressive disorder was estimated using a composite scoring method applying two different composite score cut-offs for the PHQ-9 including score ≥10 and score ≥12 (Kroenke et al., 2001). While the PHQ-9 is not a diagnostic instrument, the algorithm identifies a syndrome that at the level of face validity resembles MDE; therefore, the PHQ-9 diagnostic algorithm was used to estimate the prevalence of MDE, SUBDE (including both dysthymia and minor depression), and no depression. Participants were categorized as MDE positive if five or more of the nine symptoms have been present at least “more than half the days” in the past 2 weeks, and one of the symptoms is depressed mood or anhedonia. Participants were considered SUBDE positive if two, three, or four depressive symptoms have been present at least “more than half the days” in the past 2 weeks, and one of the symptoms is depressed mood or anhedonia (Kroenke et al., 2001).

2. Methods A telephone survey conducted in February of 2017 of people who were 18 years or older living in Qatar by the Social and Economic Research Institute at Qatar University. The survey covered a broad range of sociopolitical issues. Up to ten minutes in the survey was allocated for questions relating to mental health. 2.1. Sampling strategy The sample was selected from a frame generated from information obtained from the two main cell phone network providers in Qatar. As most people in Qatar do not have a landline phone, but do own a cell phone from one of these two providers, the sample frame covered the majority of the population (98.0% coverage rate). A probability based sampling approach was used to select a representative sample from this frame using a list-based dialing technique (Casady and Lepkowski, 1993). Systematic stratified sampling was carried out separately for non-migrants and migrants. The former were over sampled, as they are a minority group in the population. Weights were constructed to account for sampling disproportionality and nonresponse. 2.2. Sample size determination The target sample size for this survey was estimated at 2252 (see Appendix I for details) and was calculated based on standard sample size formula for complex survey design (Cornfield, 1951; Kish, 1965). To allow for statistically adequate comparisons between the three 383

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2.6. Health rating and status

Quartiles of net household income were further generated for the entire sample based on these different cut-offs and the quartile distribution for income in each group.

Health status rating was ascertained using the following question” How do you rate your overall health?” The following response options were provided including: poor, fair, good, and excellent. Participants were also asked if they ever been diagnosed with or told by a health professional that they have any of the following health conditions: 1) Diabetes 2) High Blood Pressure or high cholesterol; 3) Asthma; 4) Heart Disease; 5) Mental or Psychological Problems such as Anxiety, Sleep Problems or Depression; 6) Cancer or Cancerous Tumors; 7) Disability (physical, visual, hearing) or any other condition (please specify); and 8) Never been diagnosed with any health condition.

2.10. Employment status, socio-demographics and other variables The current employment status of migrants and nationals was also ascertained. The following demographic information was also elicited: age, gender, education, and marital status. 2.11. Statistical analyses Initial bivariate analyses were conducted using the Chi-square test of proportions to compare the distribution of socio-demographics, work-related and health-related characteristics across different cutoffs and diagnostic algorithms of depression. To correct for survey design effects on the sampling variances of these proportions, the F-transformed version of the Pearson Chi-square statistic was used (Heeringa et al., 2011). Multinomial logistic regression models were fit to the data; corresponding odds ratios (OR) and standard errors (SE) were estimated simultaneously for each nominal level of the dependent variable – SUBDE and MDE - relative to no depression as a function of each independent variable entered into the model. In the univariable models, each variable was entered alone and the unadjusted odds ratio was estimated for each level of the dependent variable relative to the baseline (no depression). In the fully adjusted model, all potential explanatory variables were included: gender, age (continuous), marital status, migrant status, ethnicity, employment status, education, net household income, and chronic health condition status. The contribution of each variable to the final or reduced model was assessed using a variety of fit statistics, including the F-adjusted Wald test and the F-adjusted mean residual goodness of fit test. The indirect and direct contributions of ethnicity to the association between migrant status and depression (SUBDE and MDE) or basic model were further examined using the percentage change method (Maldonado and Greenland, 1993). This method computes percentage change in the estimate of the association from basic model with estimates of the same association as result of separately fitting the following models: 1) a model that simultaneously adjusts for all variables including ethnicity; 2) a model that simultaneously adjusts for all variables except ethnicity. The difference in the% change from 1 to 2 produces direct estimate of % change as a result of controlling for ethnicity on the association between migrant status and depression. Post-hoc statistical interactions were tested by fitting main effects of all variables in the fully adjusted model and two-way interaction terms between select variables. The design-adjusted Wald test was used to assess the goodness of fit by comparing models with and without these interaction terms. To aid in the interpretation of the two-way interaction between the selected variables, we estimated and plotted the average marginal effects from these models (Long and Freese, 2014). Data diagnostics assessed outliers, influential observations, and violations of model assumptions including the Suest-based Huasmen test for independence of irrelevant alternative assumption (Hausman and McFadden, 1984). Two-tailed p-values < 0.05 were considered statistically significant. All statistical analyses were weighted to adjust for non-response as well as unequal probability of selection resulting from the complex sample design. All analyses were carried out in STATA version 13 (StataCorp, 2013).

2.7. Language and translation The survey questionnaire was professionally translated from English to seven other languages including Arabic, Hindi, Malayalam, Tagalog, Tamil, Urdu, and Nepali. Officially translated versions of the PHQ-9 in most of these languages (English, Arabic, Hindi, Malayalam, Tagalog, Tamil, Urdu) were obtained freely from the PHQ website (www. phqscreeners.com) and were developed by the MAPI research institute using internationally accepted translation methodology (Acquadro et al., 2012). The Nepali version of the PHQ-9 was not available from the same source and was adapted from a recent study (Kohrt et al., 2016). All linguistic versions of the PHQ-9 and the rest of the questionnaire were further verified by two independent reviewers who were fluent in each of these languages and English. Disagreements with the readily translated versions of the PHQ-9 and professional translation of the questionnaire were further discussed and resolved by consensus between two independent reviewers for each language. All survey questions were pre-tested on a sample of 50 respondents for clarity and comprehension before fielding. 2.8. Classification of participants Participants were classified into one of three migration status groups based on their responses to a series of questions related to nationality and income. Participants who stated they were Qatari Citizens were classified as non-migrants. Migrants were asked a series of questions about income. Participants were defined as LIMs if they had a combined household income of less than $1100 per month. Respondents with earnings above this threshold were classified as HIMs. This threshold represents a viable income cut-off, which reliably delineates between LIMs and HIMs in Qatar (Gardner et al., 2013). Ethnicity was defined based on country of origin, which were then coded based on geographical regions into the following: Arab, South Asian, South East Asian, and other (including participants from East Asia, Asia other, Africa, Latin America, Europe, UK, Russia, US, Canada, Australia). The language chosen by the respondent to complete the interview was also used to confirm the ethnic background of most participants. 2.9. Monthly income To reduce non-response for income, both non-migrants and migrants were asked a series of broad questions about their total monthly income. However, as non-migrants generally have higher income than migrants in Qatar, different lower and upper income brackets were asked for each group. For non-migrants or QNs, the following income categories were generated based on income questions asked of these participants (Qatari Riyal converted to US Dollars): less than $2746; $2747 to $9259; $9260 to $19,228 and greater than $19,228. For HIMs, the following income categories were generated: less than $2746; $2747 to $4120; $4121 to $4745, and more than $4745. For LIMs, the following income categories were generated: less than $275; $276 to $413; $413 to $550, and greater than $550, but less than $1100).

3. Results 3.1. Sample characteristics A total of 2424 respondents completed the phone interview. These were participants who were administered the entire questionnaire over 384

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Table 1 Characteristics of general population sample. Variables

Variable levels

n/N

%

95% CI

Gender

Female Male 18–24 25–34 35–44 45+ Unmarried§ Married Non-migrant Migrant Lower-income expatriate Higher-income expatriate Arab South Asian East Asian Other Level 1 (None) Level 2 (Grades 1 thru 12) Level 3 (Diploma) Level 4 (Post-Secondary) Unemployed Employed First quartile Second quartile Third quartile Fourth quartile Yes No Poor Fair Good Excellent

(718/2424) (1706/2424) (294/2419) (874/2419) (665/2419) (586/2419) (701/2419) (1718/2419) (744/2424) (1680/2424) (936/1680) (744/1680) (1205/2394) (884/2394) (209/2394) (96/2394) (91/2411) (505/2411) (737/2411) (1078/2411) (432/2423) (1991/2432) (487/2292) (469/2292) (540/2292) (796/2292) (609/2424) (1815/2424) (50/2418) (448/2418) (1154/2418) (766/2418)

19.3 80.7 7.4 37.6 30.4 24.6 22.2 77.8 8.0 92.0 70.7 29.3 25.2 59.8 10.0 5.0 6.2 26.8 31.3 35.7 9.0 91.0 19.5 20.1 23.6 36.8 21.6 78.4 3.0 19.9 48.6 28.5

17.5–21.3 78.6–82.5 6.2–8.7 34.9–40.4 27.8–33.0 22.4–27.1 20.1–24.3 75.6–79.9 7.2–8.7 91.2–92.8 68.3 –72.9 27.0–31.7 23.3–27.2 57.2–62.4 8.5–11.7 4.0–6.3 4.8–8.0 24.2–29.6 28.8–34.0 33.2–38.1 7.9–10.2 89.8–92.0 17.4–21.8 17.8–22.5 21.2–26.1 34.1–39.6 19.5–23.9 76.1–80.5 2.1–4.3 17.8–22.2 45.8–51.4 26.1–31.1

Age (years)

Current marital status Migrant status Type of migrant Ethnicity

Education

Current employment status Household income categories

Ever diagnosed with a chronic health condition Health rating

Note. All percentages are based on weighted proportions calculated using survey weights and therefore differ from the raw percentages. § Currently unmarried refers to those who were previously married and those who were never married.

the phone without necessarily answering all questions. As representative of the general population of Qatar, males outnumbered females by 4 to 1, while migrants outnumbered non-migrants by 11 to 1 (Table 1). Approximately two-thirds of participants were in their midthirties to mid-forties and three-quarters were currently married (Table 1). South Asians constituted approximately 60% of the sample, followed by 25% Arabs, 10% South East Asians, and only 5% of participants were of other ethnicities. Just over two-thirds of the respondents had completed post-secondary education and the proportion not working was low (9.0%). Approximately similar proportions of the sample were distributed in the first (19.5%), second (20.1%), and third (23.6%) quartiles of combined household income, respectively. The largest proportion of participants (36.8%) was in the highest quartile of income. The majority of migrants (70.7%) were considered lower-income expatriates with combined household income of less than 1100 USD per month. Approximately, 22% of the sample reported having ever been diagnosed with chronic health condition with over threequarters of respondents rated their current health as either good or excellent (Table 1). Only 54 respondents did not answer all nine questions on the PHQ. These respondents were excluded from the rest of the analysis.

3.3. Subthreshold depressive episode (SUBDE) prevalence and bivariate associations with explanatory variables Table 2 presents prevalence estimates of depression by socio-demographics and other characteristics. The point prevalence of SUBDE was significantly higher in females than males (8.2% versus 4.9%, p = 0.045), yet similar in non-migrants and migrants (5.9% versus 5.5%, p = 0.743). Generally, SUBDE prevalence estimates significantly varied with ethnicity (p < 0.001) and were highest in Arabs (9.3%) and South East Asians (8.6%). Overall, the prevalence of SUBDE increased with higher education (p < 0.001) and income (p < 0.001). 3.4. Major depressive episode (MDE) prevalence and bivariate associations with explanatory variables Also shown in Table 2, the prevalence of MDE was significantly higher in females than males (7.5% versus 2.6%, p < 0.001) and nonmigrants than migrants (8.8% versus 3.1%, p < 0.001). MDE was more prevalent in higher-income rather than lower-income migrants (5.5% versus 2.1%, p = 0.002). Generally, MDE prevalence estimates significantly varied with ethnicity and were lowest in participants from South Asia (1.2%) and highest in Arabs (8.9%). Within Arab ethnicity (not shown in Table 2), both migrants and non-migrants had similar prevalence of MDE at 9.1% and 8.8%, respectively. Overall, the prevalence of MDE increased with higher education (p = 0.0007), higher income (p < 0.001), and poorer health rating (p = 0.007).

3.2. Overall prevalence estimates of depression The prevalence of any depression for scored PHQ9 was 4.2% (95% CI: 3.3–5.3) and 6.6% (95% CI: 5.4 –7.9) for a cut-off of 12 and 10, respectively. The prevalence of SUBDE and MDE was 5.5% (95% CI: 4.4–6.8) and 3.6% (95% CI: 2.8–4.5), respectively.

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Table 2 Prevalence of depression in the past two weeks by socio-demographics, work-related and health-related characteristics.

Gender Age categories (Years)

Current marital status Migrant status Type of migrant Ethnicity

Female Male 18–24 25–34 35–44 45+ Unmarried§ Married Non-migrant Migrant Higher-Income Lower-Income Arab South Asian South East Asian Other

Education

Current employment status Net household income

Ever diagnosed with chronic health condition Health rating

PHQ9 cut-off 10+ % 95% CI p value

PHQ9 cut-off 12+ % 95% CI p value

Subthreshold depressive episode % 95% CI p value

Major depressive episode % 95% CI p value

13.0 5.0 8.3 7.6 6.6 4.5 8.4 6.0 14.0 5.9 10.4 4.1 14.5 2.8 8.2 9.2

7.8 3.3 4.3 4.7 4.9 2.4 4.5 4.1 9.0 3.7 6.0 2.8 9.0 1.8 5.1 6.5

8.2 4.9 5.7 6.3 4.7 5.2 5.2 5.6 5.9 5.5 6.8 4.9 9.3 3.6 8.6 3.0

7.5 2.6 3.3 4.6 3.5 2.2 4.8 3.2 8.8 3.1 5.5 2.1 8.9 1.2 3.8 4.4

10.0–16.7 3.9–6.4 5.2–13.2 5.6–10.2 4.6–9.3 3.0–6.7 6.3−11.2 4.5–7.6 11.3–17.2 4.7–7.4 8.1–13.2 2.8–5.9 12.1–17.3 1.7–4.6 4.5 –14.6 3.8–21.0

<0.001 0.178

0.076 <0.001 <0.001 <0.001

5.4–11.1 2.4–4.5 2.1–8.6 3.3–6.8 3.1–7.5 1.4–4.3 2.9–6.8 3.0–5.4 6.9–11.7 2.8–5.0 4.3–8.3 1.8–4.4 7.0–11.4 1.0–3.2 2.2–11.4 2.0–19.4

<0.001 0.233

0.729 <0.001 0.009 <0.001

5.6–11.7 3.7–6.3 3.1–10.4 4.5–8.8 3.1–7.1 3.2–8. 4 3.5–7.8 4.3 –7.2 4.2–8.1 4.3–6.9 5.1–9.1 3.5–6.8 7.2–12.0 2.4–5.4 4.7–15.2 1.0–8.4

0.045 0.731

0.786 0.743 0.152 <0.001

5.4–10.3 1.9–3.7 1.9–5.6 3.2–7.0 2.3–5.3 1.2–4.0 3.4–7.0 2.4–4.4 6.7–11.5 2.3–4.2 3.9 –7.7 1.3–3.4 7.0–11.3 0.6–2.3 1.7–7.9 0.8–20.0

<0.001 0.164

0.115 <0.001 0.002 <0.001

PHQ9 cut-off 10+ % 95% CI p value

PHQ9 cut-off 12+ % 95% CI p value

Subthreshold depressive episode % 95% CI p value

Major depressive episode % 95% CI p value

Level 1 Level 2 Level 3 Level 4 Unemployed Employed 1st Quartile 2nd Quartile 3rd Quartile 4th Quartile Yes

1.4 7.1 5.2 8.3 11.5 6.1 9.2 5.1 6.2 5.8 8.3

0.6–3.3 4.7–10.5 3.6–7.6 6.4–10.5 8.5–15.3 4.9 –7.5 6.5–13.0 3.4–7.6 3.9–9.9 4.2–7.9 5.9–11.4

0.8 4.1 3.4 5.4 6.3 3.9 5.4 2.6 5.2 3.5 5.4

0.2–2.6 2.3–7.2 2.2–5.3 3.9–7.4 4.3–9.2 3.0–5.2 3.5–8.3 1.5–4.4 3.0–8.8 2.3–5.4 3.7 −7.9

1.2 4.6 6.1 6.4 7.8 5.3 5.0 4.0 4.8 7.0 7.7

0.5–2.9 3.0–7.1 4.0–9.2 4.8–8.7 4.9–11.9 4.2–6.7 3.2–7.5 2.2–7.1 3.0–7.5 5.0–9.7 5.2 −11.4

1.0 3.3 2.7 5.1 5.3 3.4 4.9 2.6 3.8 3.1 5.3

0.4–3.0 1.8–5.9 1.7–4.0 3.7–6.9 3.6–7.9 2.6–4.4 3.1–7.6 1.5–4.4 2.1–6.9 2.1– 4.5 3.6–7.7

No Excellent Good Fair Poor

6.1 3.7 6.4 9.1 19.2

4.8–7.6 2.4–5.5 4.8–8.4 6.5–12.8 8.9 −36.6

3.8 2.3 3.8 6.4 11.6

2.8 −5.1 1.4–4.0 2.6–5.5 4.0–9.9 4.8 –25.5

4.9 5.7 4.4 6.3 13.0

3.8–6.3 4.0–8.1 3.0–6.3 3.9–9.9 5.8–26.6

3.1 1.9 3.3 5.7 10.8

2.3–4.2 1.1–3.2 2.3–4.8 3.7–8.5 4.1–24.3

0.024

<0.001 0.1491

0.136 <0.001

0.021

0.0467 0.1904

0.145 0.003

<0.001

0.182 0.3253

0.095 0.247

<0.001

0.102 0.3535

0.053 0.007

The PHQ9 diagnostic algorithm defined subthreshold and major depressive Episodes. CI is confidence intervals. All estimates were weighted. P-value was based on the F-transformed version of the Pearson Chi-square statistic with significance level defined at 0.05 for a two-tailed test. Currently unmarried refers to those who were previously married and those who were never married.

3.5. Statistical models

The contribution of ethnicity to the association between migrant status and SUBDE were further investigated by fitting a model that simultaneously adjusted for all variables except ethnicity (see Supplementary Table 1), which produced estimates for HIMs (OR = 1.42, p = 0.210) and LIMs (OR = 1.05, p = 0.867) that were positively associated with SUBDE, respectively. Furthermore, HIMs (OR = 2.02, p = 0.020) and LIMs (OR=2.55, p = 0.018) were positively associated with MDE in a model that simultaneously adjusted for all variables including ethnicity (see Supplementary Table 1). In fact, controlling for ethnicity resulted in a 461.5% increase in the odds ratio for HIMs and SUBDE relative to the unadjusted estimate of this association. Similarly, controlling for ethnicity resulted in a 652.2% increase in the odds ratio for LIMs and SUBD relative to the unadjusted estimate of this association. For more information, please see Supplementary Table 1.

Univariable and multivariable multinomial logistic regression models with SUBDE and MDE relative to no depression are shown in Table 3. Results from fully adjusted and final (reduced) adjusted models were largely consistent for both SUBDE and MDE. Therefore, unless otherwise stated, the results described below are from the final models. 3.6. Univariable and multivariable results for subthreshold depressive episode (SUBDE) Female gender was positively and significantly associated with SUBDE before simultaneous adjustment for other variables (OR = 1.84, p = 0.013). However, this association was reduced and rendered nonstatistically significant in the final model (OR = 1.27, p = 0.396). Both types of migrants were not significantly associated with SUBDE before adjustment for other variables. However, both higher-income (OR = 2.00, p = 0.014) and lower-income (OR = 2.96, p = 0.004) migrants relative to non-migrants were positively associated with SUBDE in the final model (Table 3). The only other variables that were significantly associated with SUBDE in all models were ethnicity and chronic health condition. The latter variable was positively associated with SUBDE (OR = 2.04, p = 0.015). As shown in Table 3, Arab ethnicity was positively associated with SUBDE relative to South Asian (OR = 3.77, p < 0.001) or other ethnicities (OR = 3.61, p = 0.029).

3.7. Univariable and multivariable results for major depressive episode (MDE) Female gender was strongly associated with MDE in the univariable model (OR = 3.15, p < 0.001), but this association was reduced in the final multivariable model (OR = 2.00, p = 0.019). Relative to non-migrants, HIMs (OR = 0.61, p = 0.039) and LIMs (OR = 0.22, p < 0.001) were negatively associated with MDE in the univariable model. Upon simultaneous adjustment for select other 386

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variables, these associations were positive (OR = 1.40 and OR = 1.30), but were not statistically significant in the final model (Table 3). The contribution of ethnicity to the association between migrant status and MDE were further investigated by fitting a model that simultaneously adjusted for all variables except ethnicity (Supplementary Table 1). This model produced estimates for HIMs (OR = 0.71, p = 0.173) and LIMs (OR = 0.25, p<0.001) that were negatively associated with MDE, respectively. However, HIM (OR = 1.21, p = 0.466) and LIMs (OR = 1.33,p = 0.356) were positively associated with MDE in model that simultaneously adjusted for all variables including ethnicity (see Supplementary Table 1). Therefore, controlling for ethnicity resulted in a 128.2% increase in the odds ratio for HIMs and MDE relative to the unadjusted estimate of this association. Similarly, controlling for ethnicity resulted in a 138.5% increase in the odds ratio for LIMs and MDE relative to the unadjusted estimate of this association. For more information, please see Supplementary Table 1. Young age, having ever been diagnosed with chronic health condition, and Arab ethnicity were all positively associated with MDE in all the models presented in Table 3. Arab ethnicity was strongly associated with MDE compared with South Asian (OR = 10.42, p < 0.001) or South East Asian (OR = 3.54, p = 0.007). Unemployment was positively associated with MDE in the univariable model (OR=1.65, p = 0.054), but was negatively associated with MDE in the final model (OR=0.37, p = 0.004).

estimate of 6.6% was obtained using the same cut-off in our study. An earlier population-based study in Germany reported estimates of SUBDE and MDE of 5.4% and 3.8%, respectively (Martin et al., 2006), which are comparable to our respective estimates of 5.5% and 3.6%. Although not directly comparable due to differences in methodology, our MDE estimate of 3.6% (95% CI: 2.8–4.5) was more conservative than the Qatar-specific estimate of 5.1% reported by the WHO based on data from the 2015 Global Burden of Disease study (World Health Organization, 2017). Our estimate for MDE was slightly higher than the 2.6% estimate reported by the World Mental Health Survey Initiative for developing (low-to middle-income) countries (Bromet et al., 2011), but on par with the prevalence estimate of 4.5% for the entire Eastern Mediterranean Region reported by the 2015 Global Burden of Disease study (Vos et al., 2016). While current literature is far from clear on the association between migration and depression (Bhugra, 2003; Bhugra and Jones, 2001; Bhugra and Minas, 2007; Lindert et al., 2009), findings from this study supports a positive association between migrant status and depression independent of ethnicity and other known risk factors for depression. Relative to non-migrants, both LIMs and HIMs experienced higher rates of SUBDE and MDE. However, these associations were by far stronger and only statistically significant for SUBDE, but not for MDE, relative to non-depressed state. Migrants of Qatar appear to suffer from a higher burden of depressive symptoms that may not meet clinical criteria for a MDE diagnosis. Reports of subthreshold symptoms of depression were substantially higher in LIMs relative to non-migrants (OR = 2.96, p = 0.004). Different psychosocial factors may be implicated in the development of these symptoms in LIMs than HIMs of Qatar. LIMs may be more susceptible to these symptoms due to factors related to their work and living arrangements, which include long working hours, physical demands of their employment, and long-term separation from their spouses and families (Bener, 2017; Kronfol et al., 2014). A recent study of conveniently sampled LIMs of Saudi Arabia reported a much higher prevalence of depressive symptoms (20.0%) using the Center for Epidemiological Studies on Depression scale in this segment of the population (Nadim et al., 2016). Our study is the first in the region to provide evidence in support of the subthreshold nature of these symptoms in a representative sample of this segment of the population. Existing evidence from the literature supports SUBDE as an important variant of depressive disorder (Kessler et al., 1997) associated with significant functional impairment (Spijker et al., 2004) and a risk factor for transition to MDE (Meeks et al., 2011). Future prospective studies are needed to further elucidate the long-term outcomes of subthreshold symptoms in the context of LIMs and HIMs typical of Qatar and neighboring GCC countries. Specifically, whether screening for subthreshold depressive symptoms in migrants of Qatar needs to be considered for early targeted preventative intervention and treatment. This is an important consideration for public health and health professionals in Qatar, where community-based and outreach programs for mental health are still in their infancy and screening for depression in migrants is not routinely conducted. Another important finding relates to the positive association between Arab ethnicity and depression relative to other ethnicities. A large systematic review reported statistically significant global variation in prevalence estimates of MDE by world region (Ferrari et al., 2013). In particular, the prevalence estimates of MDE from South America, South Asia, and Africa/ Middle East were statistically higher than estimates from Western Europe (Ferrari et al., 2013). The authors speculated that elevated estimates from Africa/Middle East were due to higher trauma exposure in these conflict settings (Ferrari et al., 2013). Our findings further extend this literature by showing that Arab ethnicity is associated with both SUBDE and MDE independent of migrant status or migrant type. This is surprising given that Arabs living and working in Qatar and those indigenous to Qatar do not share ethnic minority status or conflict-related traumatizing experiences that often characterize Middle Eastern groups in Western settings. Therefore,

3.8. Interactive effects of employment status on associations between gender and depression We found no evidence that the association between unemployment and SUBDE differed by males and females (OR = 2.98, p = 0.174). However, there was evidence for a statistically significant interaction between employment status and gender on association with MDE (OR = 0.25, p = 0.025). To aid in the interpretation of the latter twoway interaction, Fig. 1 presents a plot of the population-averaged predicted probability for MDE as function of employment status among males and females. Although the predicted probability of MDE for females, on average, was significantly higher than males for those who are currently employed, the estimates were almost identical for unemployed males and females. For females, there was a trend of decreasing predicted probability of MDE as function of unemployment contrasted with a slight increase in predicted probability of MDE among males. The average marginal effect of a shift to unemployment for females compared to males was a statistically significant decrease in the predicted probability of MDE by 7.8% (p = 0.004). 4. Discussion To our knowledge, this is the first study to report on the prevalence of depression in a representative general population sample of a nonwar afflicted country in the Middle East using the PHQ-9. The prevalence of any depression for scored PHQ9 was 4.2% (95% CI: 3.3–5.3) and 6.6% (95% CI: 5.4 –7.9) for a cut-off of 12 and 10, respectively. Using a PHQ-9 cut-point of 12 and the algorithm for MDE resulted in similar prevalence estimates of 4.2% (95% CI: 3.3–5.3) and 3.6% (95% CI: 2.8–4.5), respectively. Using the PHQ-9 diagnostic algorithm for other depression, the prevalence of SUBDE in our sample was estimated at 5.5% (95% CI: 4.4–6.8). Qatar has witnessed rapid economic growth, migration, and urbanization in the past three decades. All these factors may contribute to a high prevalence of depression relative to other developed Western countries. However, prevalence of depression estimates here are comparable to estimates previously reported in the Western literature. For example, in a similar study conducted in Germany, where depression was assessed using the PHQ-9 with the cutoff score ≥ 10, the prevalence of depression was 5.6% (Kocalevent et al., 2013). A comparable 387

388 1.03 –3.44 0.93–0.99 0.59–1.85 0.72 –2.05 0.66 –2.69 5.18 –32.65 0.36 –11.59 0.18–0.75 0.36–1.80 0.37–1.53 0.51–2.45 0.45–1.61 1.64–4.40

1.88 0.96 1.04 1.21 1.33 13.00 2.04 0.37 0.80 0.75 1.12 0.85 2.69

0.040 0.010 0.890 0.466 0.356 <0.001 0.012 0.422 0.006 0.601 0.433 0.776 0.620 <0.001

p value

0.86–6.31 0.90–2.65 0.99–2.71 0.36–1.65 0.49–1.83 0.79–2.51 1.00–2.78

1.75–4.78 0.55–2.28

0.47–1.26

1.13–3.00 0.97–1.00 0.64–1.73 0.71–1.80

2.13 1.65 1.42 0.52 0.78 0.63 1.82

8.78 2.55

0.22

3.15 0.97 0.65 0.61

1.27 0.98 — 2.00 2.96 3.77 1.69 3.61 0.95 — — — — 2.04

0.73 –2.23 0.96 –1.00 — 1.15–3.46 1.43 –6.14 2.10–6.79 0.76 –3.77 1.14–11.39 0.51 –1.77 — — — — 1.15 –3.60

Final adjusted (reduced) model¶ Odds ratios for SUBDE Odds ratio 95% CI

0.096 0.116 0.056 0.508 0.884 0.239 0.048

0.000 0.752

0.298

0.013 0.490 0.844 0.614

p value

0.396 0.107 — 0.014 0.004 <0.001 0.200 0.029 0.877 — — — — 0.015

p value

0.39 –11.78 0.99–2.73 0.75–2.68 0.25–1.07 0.36–1.71 0.34–1.17 1.1–3.03

4.25 18.12 1.11–5.86

0.12–0.39

1.93 −5.16 0.95 –1.00 0.40–1.07 0.38–0.97

Odds ratios for MDE Odds ratio 95% CI

2.00 0.96 — 1.40 1.30 10.42 3.54 2.13 0.37 — — — — 2.57

3.59 1.00 1.14 0.84 1.05 1.42 1.97

3.57 1.50

2.55

1.32 0.98 1.19 2.02

1.12–3.60 0.93–0.98 — 0.83–2.35 0.64–2.62 4.33 –25.06 1.42–8.83 0.40 –11.41 0.19–0.73 — — — — 1.56–4.22

Odds ratios for MDE Odds ratio 95% CI

0.385 0.054 0.275 0.076 0.539 0.142 0.023

<0.001 0.028

<0.001

<0.001 0.035 0.095 0.039

p value

0.019 0.001 — 0.211 0.471 <0.001 0.007 0.376 0.004 — — — — <0.001

p value

Fully adjusted model§ Odds ratios for SUBDE Odds ratio

SUBDE and MDE are subthreshold and major depressive episodes as defined by the PHQ9 diagnostic algorithm. Ref. is reference category. CI is confidence intervals. δ Currently unmarried refers to those who were previously married and those who were never married All estimates were based on weighted models. § Model was based on adjustment for all variables and n = 2111. ¶Model adjusted was based on n = 2232 and adjustment for all variables with exception of marital status, education and income.

Ever diagnosed with chronic health condition

Current employment status Education Net household income

Ethnicity

0.339 0.131 0.495 0.020 0.018 <0.001 0.330 0.029 0.990 0.806 0.612 0.879 0.264 0.023

0.75–2.33 0.95 –1.01 0.65 –2.20 1.12–3.65 1.18–5.52 1.95–6.52 0.66–3.42 1.14 –11.26 0.53–1.88 0.62–2.11 0.38–1.84 0.54–2.06 0.77–2.64 1.10–3.54

Gender Age Current marital status Migrant types/status

Odds ratios for MDE Odds ratio 95% CI

2.33 1.54 1.64 0.77 0.95 1.41 1.67

2.89 1.12

0.77

Qatari South Asian South East Asian Other Employed Less/None Quartile 1 Quartile 1 Quartile 1 No

1.84 0.99 1.05 1.13

Unadjusted model Odds ratios for SUBDE Odds ratio 95% CI

Male — Married Qatari

Fully adjusted model§ Odds ratios for SUBDE 95% CI p value

Arab Unemployed Diploma Quartile 2 Quartile 3 Quartile 4 Yes

Female Years Unmarriedδ Higherincome Lowerincome Arab Arab

Ref.

Variables

Ever diagnosed with chronic health condition

Current employment status Education Net household income

Ethnicity

Gender Age Current marital status Migrant types/status

Variables

Table 3 Models of the association between depression and socio-demographics, work– and health-related characteristics.

S.M. Khaled

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part of the world to depression and other mental illness (Douki et al., 2007). In fact, despite the larger than ever role that females play in the labor force of the Middle East today, the system does not recognize conflicting demands on working mothers, for example part time positions or job sharing are not allowed, and there are essentially no social policies that address the mental health needs of working females and their children in Qatar and neighboring Arab countries. In Qatar specifically and the Middle East more generally, mental illness is a taboo subject. Outreach mental health services are essentially non-existent and there is limited epidemiological data on the prevalence of common mental disorders in the general population. Our study was undertaken in a large representative sample of the population of Qatar and the first of its kind in the Arabian Peninsula to include migrants, who make up a large proportion of the populations in these countries, but are typically excluded from psychiatric epidemiological studies. Participants were interviewed over the phone by lay interviewers working in a call center at Qatar University. Our response rate was good for a phone survey and we had few missing data. However, the absence of visual cues on the phone might compromise rapport and probing (Novick, 2008). However, the reliability of PHQ-9′s administration over the phone has been previously demonstrated (Pinto-Meza et al., 2005) The decision to use brief instrument like the PHQ-9 to assess depression symptomology was based on urgency; the measure needed to be administered over the phone, in multiple languages and within the short amount of time allocated. As far as can be determined, the PHQ-9 has not been previously used in ethnically diverse populations within one study in this part of the world. This study has thus demonstrated the feasibility of using the PHQ-9 as first stage screening tool in a heterogeneously diverse population in a non-Western setting.

Fig. 1. Predicted probability of major depressive episode by employment status and gender.

different socio-behavioral mechanisms may be implicated in explaining ethnic differences in depression symptomology observed in this context. Furthermore, South Asians and South East Asians paradoxically experienced significantly lower rates of MDE relative to Arabs even though they may be subject to higher psychosocial adversity and cultural distance than Arab migrants of Qatar. Previous studies conducted in Europe have also reported on low prevalence of affective disorders in migrants from Asia in general (Hahm et al., 2015) and India, Pakistan, and Bangladesh in particular compared to other migrants and non-migrants (Cochrane, 1977; Nazroo, 1997). These findings have been attributed to selective migration, fatalism of control (lack of control due to beliefs in fate or predestination), and adjustment to culture (Bhugra, 2003; Cochrane, 1983; Furnham and Bochner, 1986). In the bivariate analysis, depression was positively associated with non-migrants status and Arab ethnicity. However, the results of multivariable analysis showed both high- and low-income migrants were more likely to have depression than non-migrants. This was largely due to ethnicity, which was also strongly associated with both depression and with migrant status. Therefore, findings from this study further supported the importance of simultaneously controlling for the effects of ethnicity and migrant status in population-based studies of depression. We also replicated some established associations between depression and other variables including gender and age (Blazer et al., 1994; Kessler, 2003). Female gender was associated with higher and statistically significant prevalence of MDE, but the association was weaker and not statistically significant for SUBDE. In contrast to consistent reports of significant associations between MDE and lower levels of income and/or education in the literature, both household income and education were not significantly associated with depression in our sample. Many sociocultural reasons may account for these null associations in Qatar's context including the importance of social capital and exclusive non-monetary benefits of Qatari citizenship, which are not well captured by standard monthly household income questions used in this survey. Furthermore, higher educational attainment in sending countries does not necessarily translate to white-collar or higher paying jobs for all migrants in Qatar. Our results also provide evidence for gender as a potential moderator of the association between employment status and MDE. Relative to the same change in employment status among males, the probability of MDE among unemployed females was, on average, 8% lower than the probability of MDE among employed females. Ambiguity in the literature still exists about whether employment status is a protective or a risk factor for depression in females (Kasen et al., 2005, 2003). Dual responsibilities of work and family may well predispose females in this

4.1. Limitations Previous history of depressive symptoms was not assessed. The study also did not assess current and past history of other mental illness including anxiety. Limited information on functional impairment was collected. Clinical diagnostic interviews for depression were not included. Given the very high false positive rate previously reported in Arabic speaking outpatient sample (Sawaya et al., 2016), future population-based studies should further delineate the specificity of the PHQ-9 in follow-up clinical interviews. Although survey questionnaires were thoroughly translated and administered in the mother tongue of most respondents, we cannot rule out other sources of response bias. Although the PHQ-9 was previously shown to measure the same concept in ethnically diverse groups (Huang et al., 2006) and associated with the same level of functional impairment (Huang et al., 2006), we did not verify these assumptions in our sample. 5. Conclusion Qatar has the highest proportion of migrant workers in the world. Up to this point, little was known about their mental health status relative to non-migrants. This is the first study to report on the prevalence rates of depression in a representative sample of migrants and nonmigrants typical of Qatar and other countries in the Arabian Peninsula. The overall prevalence proportions of subthreshold and major depressive episodes were comparable to what has been reported in Western developed countries. Findings from this study supported independent associations between migrant status, ethnicity, and depression. Prevalence for subthreshold depression were significantly higher for migrants in general and substantially higher for LIMs compared to the non-migrant population suggesting that the realities of life in the host country are likely an important determining factor. Arab ethnicity was also strongly associated with higher prevalence of SUBDE and MDE relative to other ethnicities. These observations warrant further investigation and focused and tailored interventions for the different 389

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segments of the population with careful consideration of the best way to outreach to hard to access groups of individuals in Qatar's general population.

CRediT authorship contribution statement Salma M. Khaled: Conceptualization, Supervision, Data curation, Formal analysis, Writing - original draft.

Declaration of interest The author has no conflicts of interest to report.

Acknowledgment

Role of the funding source

The Social and Economic Survey Research Institute at Qatar University made this study possible. The statements made herein are solely the responsibility of the author.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.04.056. Appendix I Sample size The sample size (n) for the study is calculated from the following formula:

n = z2

p (1 p) deff e2

Where: z is the value from the standard normal distribution which is determined by the significance level set at 5.0% significance level. Thus, z = 1.96. p is the estimate of the proportion. This proportion would vary from one question to the other. Following the convention, we set p at 50.0% to identify the largest sample size requirement. deff is the design effect which reflects the relative efficiency of a statistical estimate based on a complex sample design compared with a sample of the same size selected by simple random sampling. The design effect usually comes from the stratification, weighting, and clustering. The average design effect is estimated at 1.8 based on previous phone surveys with the same sample design. e is the desired sampling error. This is set at 2.7%, which is a reasonable level of sampling error compared to previous studies on this topic. With the above formula, the target number of completed interviews was estimated at 2252. As the response rate in previous phone surveys conducted in Qatar is about 48.0%, we needed to draw a minimum sample of approximately 4692 units from the frame to have 2252 participants complete the phone survey interview. Response rate calculation For approximately 10,000 cellphone numbers that were drawn from the frame and exhausted (seven attempts to complete an interview were made for every number) in the course of the survey, the following table shows the disposition of all dialed phone numbers. On the basis of Table A, response rates were calculated following AAPOR standards.1 We report two response rates in the last two rows of the C table. First, the raw response rate is the ratio between the number of completes and total sample sizes after excluding ineligibles: RR1 = C + E + UE where C is the number of completes, E is the number of eligible responses, and UE is the number of unknown eligibility Second, the adjusted response C C+E rate is RR2 = C + E + eUE where e is the estimated proportion of eligibilities which is given by this expression e = C + E + IE where IE is the number of ineligibles With the numbers of completes presented in Table A, the maximum sampling error for a percentage is +/– 2.7 percentage points. The calculation of this sampling error takes into account the design effects (i.e. the effects from weighting and stratification). One possible interpretation of sampling Table A Calling dispositions. Disposition

Frequency

Completed* Not completed Eligible Ineligible Unknown eligibility Raw response rate (RR1) Adjusted response rate (RR2)

2495 7555 1513 4677 1365 46•4% 53•8%

⁎

1

This includes some partially completed interviews.

http://wwwaapororg/AAPORKentico/AAPOR_Main/media/publications/Standard-Definitions2015_8theditionwithchanges_April2015_logopdf 390

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