You are here

  • Home
  • Archive
  • Volume 9, Issue 11
  • Prevalence of anxiety and depressive symptoms and their association with multimorbidity and demographic factors: a community-based, cross-sectional survey in Karachi, Pakistan

Article Text

Article menu

Download PDFPDF

XML
Epidemiology
Original research
Prevalence of anxiety and depressive symptoms and their association with multimorbidity and demographic factors: a community-based, cross-sectional survey in Karachi, Pakistan
  1. Salima Farooq1,
  2. Tahir Khan2,
  3. Sidra Zaheer2,
  4. Kashif Shafique2,3
  1. 1 Institute of Nursing, Aga Khan University Faculty of Health Sciences, Karachi, Pakistan
  2. 2 School of Public Health, Dow University of Health Sciences, Karachi, Pakistan
  3. 3 Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
  1. Correspondence to Dr Tahir Khan; tahir.khan{at}duhs.edu.pk

Abstract

Objective This study aimed to estimate the prevalence of anxiety and depressive symptoms and their association with multimorbidity and the demographic characteristics of adults aged 30 years and above in Karachi, Pakistan.

Design Observational cross-sectional study.

Study setting and participants The study was carried out among a general population aged 30 years and above residing in the Gulshan-e-Iqbal town of Karachi, Pakistan. The study participants were recruited using the systematic random sampling approach based on the inclusion criteria. The data collected from 2867 participants were analysed.

Main outcome measures The primary outcome of the study was the prevalence of anxiety and depressive symptoms, measured with the Aga Khan University Anxiety Depression Scale. The secondary outcome was the association of anxiety and depressive symptoms with multimorbidity as well as with the sociodemographic characteristics of the participants, using binary logistic regression.

Results Out of 2867 individuals, 27.4% reported having anxiety and depressive symptoms. The multivariate logistic regression model revealed that the presence of multimorbidity (adjusted OR (AOR)=1.33, 95% CI 1.11 to 1.58), female gender (AOR=2.40, 95% CI 2.01 to 2.87), illiteracy (AOR=1.51, 95% CI 1.09 to 2.07), more children (AOR=0.74, 95% CI 0.59 to 0.93) and visiting a faith healer (AOR=2.29, 95% CI 1.82 to 2.88) were the significant factors associated with anxiety and depressive symptoms.

Conclusion This study revealed a moderately high prevalence of anxiety and depressive symptoms among adults aged 30 years and above in Karachi, Pakistan. The key variables associated with anxiety and depressive symptoms were multimorbidity, female gender, lower level of education, more children and visiting faith healers.

  • multi-morbidity
  • anxiety and depression
  • adult health

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2019-029315

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Strengths and limitations of this study

    • The study sample was a large, community-based, ethnically and socioeconomically diverse group of population.

    • A validated screening tool (Aga Khan University Anxiety Depression Scale) assessed the presence of anxiety and depressive symptoms, which enhanced the internal validity of the research.

    • Inferences could not be drawn on causality of associations due to the nature of the design.

    • The study data were self-reported; hence, the results should be interpreted cautiously.

    Introduction

    Mental illness, anxiety and depressive symptoms are major public health issues. Globally, 14% of people suffer from mental illnesses.1 More than three-quarters of these people belong to low-income and middle-income countries.2 The magnitude of anxiety and depression is on the rise among the general population. Additionally, individuals with chronic diseases are at even higher risk of developing anxiety and depressive symptoms.3–6 In fact, depression has been ranked as the fourth leading cause of disease burden.7 Moreover, it is predicted that by the year 2020, it will be the second leading cause of disability and death, after cardiovascular disorders.7–10

    Multimorbidity is the co-occurrence of two or more chronic diseases, such as cancer, diabetes, hypertension, stroke, asthma, heart disease, arthritis or osteoporosis, within an individual.9 11 12 Research studies suggest that one in four adults in developed countries has multimorbidity.13 14 ,15 One study claims that 52% of people, between the ages of 21 and 84 years, are suffering from multimorbidity,16 whereas a study conducted by Garin et al 17 in 2014 found a burden of 67% multimorbidity among elderly Spanish women. Furthermore, among developing countries, in Bangladesh, the prevalence of multimorbidity is 53.8% among older adults,11 and in India the burden of multimorbidity is at 22%,18 while among the Pakistani population aged 18 years and older it was reported at 4.9%.19 Thus, the prevalence of multimorbidity varies from 4.9% to 67% in various countries.19–21

    The reasons for the variation in prevalence could be due to differences in the definition of multimorbidity, number of chronic conditions included in the studies, diagnostic criteria for medical conditions, as well as the study’s sample size and population characteristics. Literature suggests that the prevalence of multimorbidity increases with age,12 22 and this burden is reported to be more common in women. Additionally, individuals living in underprivileged areas are at a higher risk of developing multimorbidity.23

    Healthcare providers often face challenges when dealing with clients who have multimorbidity, along with anxiety and depressive symptoms.24 25 As multimorbidity conditions such as diabetes, hypertension, stroke, asthma, arthritis and osteoporosis have synergetic effects with anxiety and depression, they increase the probability of disabilities among patients.3 Moreover, individuals with multimorbidity need to deal with multiple treatment recommendations.6 In addition, the presence of anxiety and depression with multimorbidity leads to increased utilisation of healthcare facilities. This consequently leads to financial healthcare burden and poorer health outcomes.2 13 26 27 Thus, anxiety and depression, with multimorbidity, are linked with a 50%–100% increase in the utilisation of health services and cost.28

    Anxiety and depression are extremely common and alarmingly underdiagnosed healthcare problems especially in low-income and middle-income countries. In addition, they are associated with social stigma and self-stigma, which act as a barrier to the mental help-seeking behaviour of individuals suffering from mental health issues, which leads to an even greater public health concern. Furthermore, anxiety and depression are specifically reported to be common in individuals with multimorbidity.3 6 However, the studies that report these associations have mostly been conducted in high-income countries. To the best of our knowledge, no such population-based study has been reported from Pakistan as yet. In a resource-limited setting like Pakistan, underdiagnosis of mental illnesses can cause potential delay in the initiation of treatment. Hence, it is of utmost importance that anxiety and depression be screened in adults suffering from multimorbidity in order to reduce the burden of complications and suboptimal adherence to the treatment of multimorbidity conditions. Moreover, due to scarcity of literature from Pakistan, quantification of the association between anxiety and depression and multimorbidity within the Pakistani population is an urgent need. In this study, it was hypothesised that there is an association between anxiety and depressive symptoms and multimorbidity among the Pakistani population. Therefore, the purposes of this study were to estimate the prevalence of anxiety and depressive symptoms and to determine their association with multimorbidity and demographic factors among adults aged 30 years and above in Karachi, Pakistan.

    Methodology

    This study was part of a larger community-based research project titled ‘The Burden of Multimorbidity, Its Patterns and Consequences on Health and Well-Being: The IMPACT Study’, conducted in 2015–2016.

    Study design

    An observational cross-sectional study was conducted to estimate the burden of anxiety and depressive symptoms, and to identify their associated factors.

    Study population

    The study population comprised all adults aged 30 years and above living in the Gulshan-e-Iqbal town of Karachi, Pakistan. This particular age group was selected as chronic medical conditions are more prevalent in this group.

    Inclusion criteria

    The study included all persons aged 30 years and above who were residents of the Gulshan-e-Iqbal town of Karachi, Pakistan, and who signed the consent form to take part in the study.

    Sample size calculation

    To meet the objective of this study, the sample size was calculated using an online version of the OpenEpi calculator. The prevalence of anxiety and depression is reported to be 30%–50% among adults in Pakistan.29 30 With a 2% margin of error and 95% confidence level, the required sample size was 2401 participants. Similarly, to attain the secondary objective (association of anxiety and depressive symptoms with multimorbidity), keeping CI at 95%, with 90% power, 1:1 ratio of non-depressed and depressed individuals, OR of 1.55, and with proportion of multimorbidity among non-depressed taken as 10%,31 the required sample size was 2148 individuals. In order to account for missing values, the higher sample size (2401) was chosen. Ultimately, 3250 individuals were recruited for data collection.

    Sampling technique

    The present study was conducted in the Gulshan-e-Iqbal town of Karachi, Pakistan. A sample of 3250 participants were recruited using the systematic random sampling technique. This sample was achieved using proportionate estimates of the number of individuals in 13 union councils (UCs) in the selected town. The proportionate allocation method was used in order to allocate appropriate weightage to each UC. Within each UC, blocks of houses were selected with a random start, where every 109th (kth sampling interval) household was selected. Individuals within these households who met the eligibility criteria were included in the study.

    Data collection

    Data were collected from participants by interviews using a structured questionnaire; the questionnaire was developed after a thorough literature review. The questionnaire was designed to collect data related to sociodemographic information including age, gender, marital status, educational status, employment status, mother tongue, number of family members and information about housing, presence of any chronic disease, and living conditions of each participant. Participants were interviewed by trained data collectors. Anthropometric measurements as well as blood pressure were measured by trained health professionals, which mainly included doctors and nurses, who were involved in data collection.

    Dependent variable

    The Aga Khan University Anxiety Depression Scale (AKUADS) was used to assess the anxiety and depressive symptoms of the participants. This study used the Urdu version of the AKUADS, which had been validated in a previous research.32 The value of the reliability coefficient tool in the study population was 0.93. This tool had a total of 25 questions, and for each of the questions the participants could choose an answer from never (scored as 0), sometimes (scored as 1), often (scored as 2) and always (scored as 3). A cumulative cut-off score of 20 or greater on this scale was used to label anxiety and depression. At a cut-off score of 20, the AKUADS had a sensitivity of 66% and a specificity of 79%.32 33

    Independent variables

    The presence of disease was assessed by a combination of self-reported diagnosis, medication use and laboratory findings. Self-reported disease was determined by the question ‘Have you ever been told by a doctor, nurse, or other health professional that you have hypertension, obesity, dyslipidemia, diabetes, heart diseases (Myocardial Infarction and/or chronic ischemic heart diseases), stroke, migraines, asthma and chronic obstructive pulmonary disease [COPD], anemia, thyroid disease, diseases of bones and joints, dyspepsia/peptic ulcer, hepatitis B or C, chronic kidney diseases including stones, cancer, and/or disability?’

    Individuals with two or more chronic diseases (excluding anxiety and depression) were labelled as having ‘multimorbidity’, and others who reported one or no chronic medical disease were considered as having ‘no multimorbidity’. Set criteria were used to define chronic diseases, such as hypertension, obesity, diabetes and dyspepsia. A person was said to be hypertensive if they had a high systolic blood pressure (>140 mm Hg) or a high diastolic blood pressure (>90 mm Hg). A person was considered obese if they had a calculated body mass index ≥30.0 kg/m2. A person was said to be diabetic if blood glucose level was ≥200 mg/dL. A person was considered to have dyspepsia if they had four or more symptoms of dyspepsia for at least 6 months, based on the Leeds Dyspepsia Questionnaire.

    Besides disease-related variables, other sociodemographic variables were also measured, such as household income, occupation and education. This variable was initially taken as continuous variable, such as what is your monthly income (in Pakistani rupees). Then it was further classified into quintiles, that is, <16 000, 16 001–24 999, 25 000–39 999, 40 000–54 999 and 55 000 rupees and above. The occupation of all the participants was also assessed as a categorical variable and categorised as unemployed, unskilled worker, skilled worker, labour worker, non-manual worker and professional office job. Data on education were collected as the total number of years of full education, which were then categorised into five categories: no education (0 years or those who never attended school), primary (1–5 years), secondary (6–10 years), higher education (11–14 years) and postgraduate (≥15 years).

    Statistical analysis

    Variables were precoded and data were entered and analysed in SPSS V.19.0. Descriptive statistics were calculated in the form of frequencies and percentages for categorical variables. Descriptive statistics were calculated in order to estimate the prevalence of anxiety and depressive symptoms. Univariate and multivariate binary logistic regressions were used to assess the associations between anxiety and depressive symptoms and multimorbidity. ORs and their 95% CIs were also calculated. The multivariate model was adjusted only for variables with p values <0.250 in univariate analysis, using a stepwise logistic regression method, following the Hosmer and Lemeshow protocol.34 Moreover, this manuscript followed the cross-sectional reporting guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology.35

    Patient and public involvement

    In the current study, patients were not involved in the research question, study design, outcome measures and recruitment.

    Results

    A total of 3250 participants were interviewed. However, the data were analysed using information from 2867 study participants. Of the total, 383 participants were excluded due to missing information on the following variables: anxiety and depression symptoms (n=06), presence of multimorbidity (n=09), respondents’ age (n=06), gender (n=08), education (n=48), income (n=81), occupation (n=72), marital status (n=05), number of children (n=67), ethnicity (n=09), home status (n=14), smoking status (n=66) and visiting faith healer (n=93).

    The mean age of the 2867 study participants was 45.5 years (SD ±11.7 years). The male to female ratio was approximately equal, 51.5% (n=1477) male and 48.5% (n=1390) female. Regarding the educational level of the participants, 14% (n=404) had no formal education, 38.3% (n=1098) had higher education and 20.4% (n=585) had postgraduate education. In addition, 22.3% (n=638) reported their monthly household income to be less than 16 000 rupees, while 18.2% (n=523) claimed their monthly income to be more than 55 000 rupees. The majority of participants (42%, n=1205) had professional jobs, 14% (n=389) were labourers and 17% (n=484) were unemployed. Anxiety and depressive symptoms and multimorbidity were reported by 27.4% (n=786) and 50.2% (n=1440) of the study participants, respectively. The sociodemographic and other characteristics of the participants are shown in table 1.

    View this table:
    Table 1

    Sociodemographic and other characteristics of participants (N=2867)

    The adjusted model revealed that participants who had multimorbidity had 33% increased odds of being anxious and depressed (adjusted OR (AOR)=1.33, 95% CI 1.11 to 1.58), as compared with those who did not report multimorbidity. Moreover, the odds of having anxiety and depressive symptoms were 2.40 times higher among female participants, as compared with male participants (AOR=2.40, 95% CI 2.01 to 2.87), and the odds of having anxiety and depressive symptoms were 51% higher among participants who had no formal education (AOR=1.51, 95% CI 1.09 to 2.07), as compared with participants who had postgraduate education. Furthermore, the odds of being anxious and depressed were 1.34 times higher among participants who did labour work as compared with participants who were doing professional office jobs (AOR=1.34, 95% CI 1.00 to 1.79). Also, study participants who had one to two children were at 32% reduced odds (AOR=0.68, 95% CI 0.56, 0.84) and those who had more than two children were at 26% reduced odds (AOR=0.74, 95% CI 0.59 to 0.93) of having anxiety and depressive symptoms, as compared with participants who had no children. Furthermore, subjects belonging to the Punjabi ethnicity (AOR=1.66, 95% CI 1.16 to 2.37) had 66% increased odds of being anxious and depressed as compared with Sindhi subjects. In addition, in subjects who were visiting a faith healer, the odds of exhibiting anxiety and depressive symptoms were 2.29 times (AOR=2.29, 95% CI 1.82 to 2.88), as compared with participants who did not visit a faith healer (table 2).

    View this table:
    Table 2

    Univariate and multivariate analyses of factors associated with anxiety and depressive symptoms among adults (N=2867)

    Table 3 shows the estimated association of anxiety and depressive symptoms and the number of chronic diseases. The anxiety and depressive symptoms were significantly associated with increasing number of chronic diseases. The odds of anxiety and depressive symptoms were highest among individuals with four chronic diseases (AOR=1.92, 95% CI 1.33 to 2.78) and more than four chronic diseases (AOR=2.62, 95% CI 1.66 to 4.13), as compared with healthy individuals, when adjusted for other variables (table 3).

    View this table:
    Table 3

    Univariate and multivariate analyses of anxiety and depressive symptom with number of chronic diseases among adults (N=2867)

    Discussion

    This study showed that nearly a quarter of the study participants had symptoms of anxiety and depression. Moreover, it illustrated that the presence of multimorbidity, female gender, lower level of education, more children, ethnicity and visiting a faith healer were significantly associated with anxiety and depressive symptoms.

    The current study used a screening tool (AKUADS) to assess the presence of anxiety and depressive symptoms. According to the findings, the overall prevalence of anxiety and depressive symptoms was 27.4%. This has previously been reported to be around 10% and 44% in developed and developing countries, respectively.36 37 Islam et al,38 in their study conducted in 2003, found a 28% prevalence of depression among the Bangladesh population. In the USA, the prevalence of anxiety and depression was reported to be 16.2%.39 Papadopoulos et al 40 reported that the prevalence of depression in Greece was 27%, whereas in India the prevalence of depression was found to be 28%.41 However, in Pakistan, the prevalence of depression and anxiety has been found to be around 30%–50%.29 36 37 42 A plausible reason for the difference in the prevalence of anxiety and depression could be different inclusion criteria, sample sizes, recruitment methods, various screening tools and cut-off points that were employed in these diverse studies.21

    In the past decade, several research studies have established a strong relationship between anxiety and depression and multimorbidity.4 6 9 31 The findings of the present study were in concurrence with the previous studies showing a significant association between anxiety and depression and multimorbidity.6 9 43–46 A probable reason could be that mental illness is linked with unhealthy habits, such as smoking, poor self-care, lack of physical activities and poor medication compliance.44 47

    In the present study, anxiety and depressive symptoms were found to be strongly associated with women. The prevalence of anxiety and depressive symptoms was found to be 2.5 times higher in women than in men. Likewise, previous studies have also revealed a positive association between anxiety, depression and gender. A research conducted by Mirza and Jenkins42 found that the prevalence of anxiety and depressive symptoms was 29%–66% among women, as compared with 10%–33% in men. Plausible reasons for the higher prevalence of anxiety and depression among women could be biological factors, socioeconomic disadvantages, deprivation of social status, maladaptive coping styles and lack of a support system for women in our country.48

    The current study also found illiteracy as one of the potential risk factors among participants. Similarly, previous studies have found a strong association between low levels of education and anxiety and depressive symptoms.49 50Among the literate population, education could be one of the factors that facilitate adaptive coping skills, and enables them to deal with distress situations and in improving their self-confidence, self-efficacy, social skills building and developing a sense of control over the environment.51

    According to the findings of the current study, participants who bore more children were less likely to develop anxiety and depressive symptoms. The plausible reason could be that parents tend to be positively engaged with their children and to be bound with their lives by giving them continuous love, time, support and commitment, which can all be coping strategies against distress. Having more children can also act as a buffer, considering the cultural dimension of the South Asian population where older children provide and ensure financial security for the family.52 Moreover, they also perform the role of a caretaker and/or may help with family household chores. This provides positive hope to the family that their future financial and social security are guaranteed and thus decreases anxiety and depression. In contrast, other studies have found that having more children is a risk factor for anxiety and depression among women.49 53

    Our study also revealed that ethnicity of a participant is linked with anxiety and depressive symptoms. Subjects who were Punjabi were more likely to have anxiety and depressive symptoms. Consistent findings have been reported by previous studies that ethnicity was a significant risk factor for depression.54 55 However, another study reported no association between these variables.53

    A notable finding of the present study was that visiting a faith healer was positively associated with anxiety and depression in both genders. In the Pakistani culture, distressed individuals generally seek help from traditional healers or faith healers prior to visiting a doctor or a psychiatrist for their illness. A few probable reasons why people visit faith healers could be easy accessibility, availability and affordability, cultural beliefs, and the predominant stigma attached to the notion of mental illness.53 56 Moreover, in Pakistan, many people believe that mental illnesses are due to wrongdoings, being possessed, witchcraft and evil spirits; hence, the affected population consults these faith healers rather than doctors. Our result is consistent with a past study where it was reported that 16% of the Pakistani population first approach faith healers for their illness.57

    Strengths and limitations

    This is probably the first research that has determined an association between anxiety and depressive symptoms and multimorbidity among Pakistani population. Due to the cross-sectional nature of the study, inferences could not be determined on the causality of the relationship. Potential risk factors such as family history of psychiatric disorders, marital dissatisfaction, domestic violence, maladaptive coping, current life stressors and death of a loved one could not be addressed in the current study. Moreover, this study was based on self-reported information about multimorbidity and anxiety and depression.

    Conclusion

    This study suggests that almost a quarter of the study population had anxiety and depressive symptoms. Among women, anxiety and depressive symptoms are twice as common as compared with men. Early prevention through screening is the key measure for preventing the rising burden of mental disorders and their association with chronic physical conditions. Therefore, there is a dire need to create awareness regarding identification of symptoms of mental illness and to develop a proper referral system for essential treatment at the early stage of the disease, to avoid delayed presentation when the disease is more advanced.

    Acknowledgments

    We would like to acknowledge the contributions of Ms Yasmin Parpio and Ms Ambreen Gowani in this research work findings of which have been published as an absract.

    https://stti.confex.com/stti/bc17/webprogram/Paper86091.html

    References

    1. 1.↵
      2. Yatham S ,
      3. Sivathasan S ,
      4. Yoon R , et al
      . Depression, anxiety, and post-traumatic stress disorder among youth in low and middle income countries: a review of prevalence and treatment interventions. Asian J Psychiatr 2018;38:78–91.doi:10.1016/j.ajp.2017.10.029
      OpenUrl
    2. 2.↵
      2. Jordans MJD ,
      3. Kaufman A ,
      4. Brenman NF , et al
      . Suicide in South Asia: a scoping review. BMC Psychiatry 2014;14:358.doi:10.1186/s12888-014-0358-9
    3. 3.↵
      2. Deschênes SS ,
      3. Burns RJ ,
      4. Schmitz N
      . Associations between depression, chronic physical health conditions, and disability in a community sample: a focus on the persistence of depression. J Affect Disord 2015;179:6–13.doi:10.1016/j.jad.2015.03.020
      OpenUrlCrossRefPubMed
    4. 4.↵
      2. Winkler P ,
      3. Horáček J ,
      4. Weissová A , et al
      . Physical comorbidities in depression co-occurring with anxiety: a cross sectional study in the Czech primary care system. Int J Environ Res Public Health 2015;12:15728–38.doi:10.3390/ijerph121215015
      OpenUrl
    5. 5.↵
      2. Levinson D ,
      3. Karger CJ ,
      4. Haklai Z
      . Chronic physical conditions and use of health services among persons with mental disorders: results from the Israel National health survey. Gen Hosp Psychiatry 2008;30:226–32.doi:10.1016/j.genhosppsych.2008.02.007
      OpenUrlCrossRefPubMedWeb of Science
    6. 6.↵
      2. Gunn JM ,
      3. Ayton DR ,
      4. Densley K , et al
      . The association between chronic illness, multimorbidity and depressive symptoms in an Australian primary care cohort. Soc Psychiatry Psychiatr Epidemiol 2012;47:175–84.doi:10.1007/s00127-010-0330-z
      OpenUrlCrossRefPubMed
    7. 7.↵
      2. Moussavi S ,
      3. Chatterji S ,
      4. Verdes E , et al
      . Depression, chronic diseases, and decrements in health: results from the world health surveys. Lancet 2007;370:851–8.doi:10.1016/S0140-6736(07)61415-9
      OpenUrlCrossRefPubMedWeb of Science
    8. 8.↵
      2. Mathers C ,
      3. Fat DM ,
      4. Boerma JT
      . The global burden of disease: 2004 update. World Health Organization, 2008.
    9. 9.↵
      2. O’Connor K ,
      3. Vizcaino M ,
      4. Ibarra JM , et al
      . Multimorbidity in a Mexican community: secondary analysis of chronic illness and depression outcomes. Int J Nurs (N Y) 2015;2:35–47.doi:10.15640/ijn.v2n1a4
      OpenUrl
    10. 10.↵
      1. World Health Organization
      . Depression and other common mental disorders: global health estimates, 2017.
    11. 11.↵
      2. Khanam MA ,
      3. Streatfield PK ,
      4. Kabir ZN , et al
      . Prevalence and patterns of multimorbidity among elderly people in rural Bangladesh: a cross-sectional study. J Health Popul Nutr 2011;29:406.doi:10.3329/jhpn.v29i4.8458
    12. 12.↵
      2. Liddy C ,
      3. Blazkho V ,
      4. Mill K
      . Challenges of self-management when living with multiple chronic conditions: systematic review of the qualitative literature. Can Fam Physician 2014;60:1123–33.
      OpenUrlAbstract/FREE Full Text
    13. 13.↵
      2. Ahmadi B ,
      3. Alimohammadian M ,
      4. Yaseri M , et al
      . Multimorbidity: epidemiology and risk factors in the Golestan cohort study, Iran: a cross-sectional analysis. Medicine 2016;95:e2756.doi:10.1097/MD.0000000000002756
    14. 14.↵
      2. Alaba O ,
      3. Chola L
      . The social determinants of multimorbidity in South Africa. Int J Equity Health 2013;12:63–10.doi:10.1186/1475-9276-12-63
      OpenUrl
    15. 15.↵
      2. Chatterji S ,
      3. Byles J ,
      4. Cutler D , et al
      . Health, functioning, and disability in older adults—present status and future implications. Lancet 2015;385:563–75.doi:10.1016/S0140-6736(14)61462-8
      OpenUrlCrossRefPubMed
    16. 16.↵
      2. Aarts S ,
      3. den Akker Mvan ,
      4. Bosma H , et al
      . The effect of multimorbidity on health related functioning: temporary or persistent? results from a longitudinal cohort study. J Psychosom Res 2012;73:211–7.doi:10.1016/j.jpsychores.2012.05.014
      OpenUrlCrossRefPubMed
    17. 17.↵
      2. Garin N ,
      3. Olaya B ,
      4. Perales J , et al
      . Multimorbidity patterns in a national representative sample of the Spanish adult population. PLoS One 2014;9:e84794.doi:10.1371/journal.pone.0084794
    18. 18.↵
      2. Akinsulore A ,
      3. Esimai O ,
      4. Afolabi O , et al
      . Prevalence and factors associated with depressive symptoms among Yoruba adults in a semi-urban community in south-western Nigeria. J Behav Health 2015;4:81–6.doi:10.5455/jbh.20150407103425
      OpenUrl
    19. 19.↵
      2. Afshar S ,
      3. Roderick PJ ,
      4. Kowal P , et al
      . Multimorbidity and the inequalities of global ageing: a cross-sectional study of 28 countries using the world health surveys. BMC Public Health 2015;15:1.doi:10.1186/s12889-015-2008-7
    20. 20.↵
      2. Roberts KC ,
      3. Rao DP ,
      4. Bennett TL , et al
      . Prevalence and patterns of chronic disease multimorbidity and associated determinants in Canada. Health Promot Chronic Dis Prev Can 2015;35:87–94.doi:10.24095/hpcdp.35.6.01
      OpenUrl
    21. 21.↵
      2. Taylor AW ,
      3. Price K ,
      4. Gill TK , et al
      . Multimorbidity - not just an older person's issue. Results from an Australian biomedical study. BMC Public Health 2010;10:1.doi:10.1186/1471-2458-10-718
    22. 22.↵
      2. Marengoni A ,
      3. Angleman S ,
      4. Melis R , et al
      . Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev 2011;10:430–9.doi:10.1016/j.arr.2011.03.003
      OpenUrlCrossRefPubMedWeb of Science
    23. 23.↵
      2. Orueta JF ,
      3. Nuño-Solinís R ,
      4. García-Alvarez A , et al
      . Prevalence of multimorbidity according to the deprivation level among the elderly in the Basque country. BMC Public Health 2013;13:918.doi:10.1186/1471-2458-13-918
    24. 24.↵
      2. Roca M ,
      3. Gili M ,
      4. Garcia-Garcia M , et al
      . Prevalence and comorbidity of common mental disorders in primary care. J Affect Disord 2009;119:52–8.doi:10.1016/j.jad.2009.03.014
      OpenUrlCrossRefPubMed
    25. 25.↵
      2. Naqvi H ,
      3. Khan MM
      . Depression in primary care: difficulties and paradoxes. J Pak Med Assoc 2005;55:393–8.
      OpenUrlPubMed
    26. 26.↵
      2. Uijen AA ,
      3. van de Lisdonk EH
      . Multimorbidity in primary care: prevalence and trend over the last 20 years. Eur J Gen Pract 2008;14 Suppl 1:28–32.doi:10.1080/13814780802436093
      OpenUrl
    27. 27.↵
      2. Lockett H ,
      3. Lai J ,
      4. Tuason C , et al
      . Primary healthcare utilisation among adults with mood and anxiety disorders: an analysis of the New Zealand health survey. J Prim Health Care 2018;10:68–75.doi:10.1071/HC17077
      OpenUrl
    28. 28.↵
      2. Simon GE ,
      3. VonKorff M ,
      4. Barlow W
      . Health care costs of primary care patients with recognized depression. Arch Gen Psychiatry 1995;52:850–6.doi:10.1001/archpsyc.1995.03950220060012
      OpenUrlCrossRefPubMedWeb of Science
    29. 29.↵
      2. Ali B ,
      3. Amanullah S
      . Prevalence of anxiety and depression in an urban squatter settlement of karachi. J Coll Physicians Surg Pak 2000;10:4–6.
      OpenUrl
    30. 30.↵
      2. Waheed A ,
      3. Hameed K ,
      4. Khan AM , et al
      . The burden of anxiety and depression among patients with chronic rheumatologic disorders at a tertiary care hospital clinic in Karachi, Pakistan. J Pak Med Assoc 2006;56:243–7.
      OpenUrlPubMed
    31. 31.↵
      2. Smith DJ ,
      3. Court H ,
      4. McLean G , et al
      . Depression and multimorbidity: a cross-sectional study of 1,751,841 patients in primary care. J Clin Psychiatry 2014;75:1202–8.doi:10.4088/JCP.14m09147
      OpenUrlPubMed
    32. 32.↵
      2. Ali BS
      . Validation of an Indigenous screening questionnaire for anxiety and depression in an urban squatter settlement of karachi. J Coll Physicians Surg Pak 1998;8:207–11.
      OpenUrl
    33. 33.↵
      2. Ali BS ,
      3. Reza H ,
      4. Khan MM , et al
      . Development of an Indigenous screening instrument in Pakistan: the Aga Khan university anxiety and depression scale. J Pak Med Assoc 1998;48:261–5.
      OpenUrlPubMed
    34. 34.↵
      2. Hosmer Jr DW ,
      3. Lemeshow S ,
      4. Sturdivant RX
      . Applied logistic regression. John Wiley & Sons, 2013.
    35. 35.↵
      2. von Elm E ,
      3. Altman DG ,
      4. Egger M , et al
      . The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med 2007;4:e296.doi:10.1371/journal.pmed.0040296
    36. 36.↵
      2. Luni FK ,
      3. Ansari B ,
      4. Jawad A , et al
      . Prevalence of depression and anxiety in a village in Sindh. J Ayub Med Coll Abbottabad 2009;21:68–72.
      OpenUrlPubMed
    37. 37.↵
      2. Muhammad Gadit AA ,
      3. Mugford G
      . Prevalence of depression among households in three capital cities of Pakistan: need to revise the mental health policy. PLoS One 2007;2:e209.doi:10.1371/journal.pone.0000209
    38. 38.↵
      2. Islam MM ,
      3. Ali M ,
      4. Ferroni P , et al
      . Prevalence of psychiatric disorders in an urban community in Bangladesh. Gen Hosp Psychiatry 2003;25:353–7.doi:10.1016/S0163-8343(03)00067-7
      OpenUrlPubMed
    39. 39.↵
      2. Kessler RC ,
      3. Berglund P ,
      4. Demler O , et al
      . The epidemiology of major depressive disorder: results from the National comorbidity survey replication (NCS-R). JAMA 2003;289:3095–105.doi:10.1001/jama.289.23.3095
      OpenUrlCrossRefPubMedWeb of Science
    40. 40.↵
      2. Papadopoulos FC ,
      3. Petridou E ,
      4. Argyropoulou S , et al
      . Prevalence and correlates of depression in late life: a population based study from a rural Greek town. Int J Geriatr Psychiatry 2005;20:350–7.doi:10.1002/gps.1288
      OpenUrlCrossRefPubMedWeb of Science
    41. 41.↵
      2. Patel V ,
      3. Prince M
      . Ageing and mental health in a developing country: who cares? qualitative studies from Goa, India. Psychol Med 2001;31:29–38.doi:10.1017/S0033291799003098
      OpenUrlCrossRefPubMedWeb of Science
    42. 42.↵
      2. Mirza I ,
      3. Jenkins R
      . Risk factors, prevalence, and treatment of anxiety and depressive disorders in Pakistan: systematic review. BMJ 2004;328.doi:10.1136/bmj.328.7443.794
    43. 43.↵
      2. Patten SB
      . Long-Term medical conditions and major depression in a Canadian population study at waves 1 and 2. J Affect Disord 2001;63:35–41.doi:10.1016/S0165-0327(00)00186-5
      OpenUrlCrossRefPubMedWeb of Science
    44. 44.↵
      2. Patten SB
      . An analysis of data from two general health surveys found that increased incidence and duration contributed to elevated prevalence of major depression in persons with chronic medical conditions. J Clin Epidemiol 2005;58:184–9.doi:10.1016/j.jclinepi.2004.06.006
      OpenUrlCrossRefPubMed
    45. 45.↵
      2. Patten SB ,
      3. Wang JL ,
      4. Williams JVA , et al
      . Descriptive epidemiology of major depression in Canada. Can J Psychiatry 2006;51:84–90.doi:10.1177/070674370605100204
      OpenUrlCrossRefPubMedWeb of Science
    46. 46.↵
      2. Spangenberg L ,
      3. Forkmann T ,
      4. Brähler E , et al
      . The association of depression and multimorbidity in the elderly: implications for the assessment of depression. Psychogeriatrics 2011;11:227–34.doi:10.1111/j.1479-8301.2011.00375.x
      OpenUrlPubMed
    47. 47.↵
      2. Katon WJ
      . Clinical and health services relationships between major depression, depressive symptoms, and general medical illness. Biol Psychiatry 2003;54:216–26.doi:10.1016/S0006-3223(03)00273-7
      OpenUrlCrossRefPubMedWeb of Science
    48. 48.↵
      2. Zahidie A ,
      3. Jamali T
      . An overview of the predictors of depression among adult Pakistani women. J Coll Physicians Surg Pak 2013;23.
    49. 49.↵
      2. Husain N ,
      3. Creed F ,
      4. Tomenson B
      . Depression and social stress in Pakistan. Psychol Med 2000;30:395–402.doi:10.1017/S0033291700001707
      OpenUrlCrossRefPubMedWeb of Science
    50. 50.↵
      2. Lovisi GM ,
      3. López JRRA ,
      4. Coutinho ESF , et al
      . Poverty, violence and depression during pregnancy: a survey of mothers attending a public hospital in Brazil. Psychol Med 2005;35:1485–92.doi:10.1017/S0033291705005362
      OpenUrlCrossRefPubMedWeb of Science
    51. 51.↵
      2. Harpham T
      . Urbanization and mental health in developing countries: a research role for social scientists, public health professionals and social psychiatrists. Soc Sci Med 1994;39:233–45.doi:10.1016/0277-9536(94)90332-8
      OpenUrlCrossRefPubMed
    52. 52.↵
      2. Hussain M ,
      3. Saud A
      . Socio-Economic determinants of working children: evidence from Capital Territory of Islamabad, Pakistan. Pak Adm Rev 2017;1:145–58.
      OpenUrl
    53. 53.↵
      2. Nisar N ,
      3. Billoo N ,
      4. Gadit AA
      . Prevalence of depression and the associated risks factors among adult women in a fishing community. J Pak Med Assoc 2004;54:519–25.
      OpenUrlPubMed
    54. 54.↵
      2. Costello DM ,
      3. Swendsen J ,
      4. Rose JS , et al
      . Risk and protective factors associated with trajectories of depressed mood from adolescence to early adulthood. J Consult Clin Psychol 2008;76:173–83.doi:10.1037/0022-006X.76.2.173
      OpenUrlCrossRefPubMedWeb of Science
    55. 55.↵
      2. Lankarani MM ,
      3. Assari S
      . Association between number of comorbid medical conditions and depression among individuals with diabetes; race and ethnic variations. J Diabetes Metab Disord 2015;14:1.doi:10.1186/s40200-015-0171-0
    56. 56.↵
      2. Mishra N ,
      3. Nagpal SS ,
      4. Chadda RK , et al
      . Help-Seeking behavior of patients with mental health problems visiting a tertiary care center in North India. Indian J Psychiatry 2011;53:234.doi:10.4103/0019-5545.86814
    57. 57.↵
      2. Mubbashar MH ,
      3. Saeed K
      . Development of mental health services in Pakistan. East Mediterr Health J 2001;7:392–6.
      OpenUrlPubMed

    Footnotes

    • Contributors KS and SF conceived the research question and the design of this project. SF conducted all the literature reviews and manuscript drafting and review, and took part in data collection. TK and SZ helped in the data analysis and preparation of tables, and reviewed the manuscript. KS supervised the research project and conducted the final editing and review of the manuscript. The final version of this manuscript was seen and approved by all authors.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Ethics approval The study’s proposal was approved by the Institutional Review Board (IRB) of the Dow University of Health Sciences (reference number: IRB-554/DUHS/Approval/2015/04). Only individuals who provided written informed consent were interviewed. A referral plan for counselling was made for participants who scored 20 and above on the AKUADS.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available upon reasonable request by the corresponding author.