|Year : 2017 | Volume
| Issue : 1 | Page : 18-26
Determining the prevalence of and risk factors for depressive symptoms among adults in Nepal: Findings from the Dhulikhel Heart Study
Michelle S Lam1, Annette L Fitzpatrick2, Archana Shrestha3, Biraj M Karmacharya4, Rajendra P Koju5, Deepa Rao6
1 Department of Global Health, University of Washington; Department of Medicine, School of Medicine, University of Washington, Seattle, Washington, USA
2 Department of Global Health; Department of Family Medicine; Department of Epidemiology, University of Washington, Seattle, Washington, USA
3 Department of Epidemiology, University of Washington, Seattle, Washington; Department of Epidemiology, Harvard University, Cambridge, Massachusetts, USA
4 Department of Global Health, University of Washington, Seattle, Washington, USA; Department of Community Medicine; Department of Community Programs, Dhulikhel Hospital - Kathmandu University Hospital, Dhulikhel, Nepal
5 Department of Global Health, University of Washington, Seattle, Washington, USA; Department of Cardiology, Dhulikhel Hospital - Kathmandu University Hospital, Dhulikhel, Nepal
6 Department of Global Health; Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA
|Date of Web Publication||11-Apr-2017|
Michelle S Lam
University of Washington Medical Center, Box 358 250, Seattle, WA 98195
Source of Support: None, Conflict of Interest: None
Context: Nepal is currently experiencing a rapid growth in noncommunicable diseases (NCDs). Depression has previously been associated with NCDs in South Asia; however, data regarding its prevalence and risk factors are lacking in Nepal.
Aims: This study aims to describe the prevalence of and risk factors for depressive symptoms in a suburban population of adults within Nepal.
Setting and Design: We conducted a cross-sectional analysis of baseline data collected from participants enrolled in the Dhulikhel Heart Study, a population-based, longitudinal cohort study investigating cardiovascular risk factors in Dhulikhel, a suburban town outside Kathmandu.
Subjects and Methods: Baseline questionnaire data from 1073 adults age 18 years and older included the Center for Epidemiologic Studies Depression Scale (CES-D). A score of 16 or greater on the CES-D has been shown to indicate major depressive symptomatology.
Statistical Analysis: Using STATA 13, we conducted Pearson's Chi-square tests and multiple logistic regressions to examine associations between the binary CES-D score and gender, age, education, marital status, body mass index, physical activity, and hypertensive status.
Results: The mean CES-D score in the sample was 11.7 (standard deviation: 5.3), with 21.3% scoring 16 or greater. Age over 60 and lack of formal education were associated with increased risk of depressive symptoms. Being physically active was associated with decreased risk of depressive symptoms.
Conclusions: The estimated prevalence of depression among adults in Dhulikhel was 21.3%. Significant risk factors for increased depressive symptoms included lack of formal education, age over 60, and physical inactivity.
Keywords: Adults, depression, mental health, Nepal, South Asia
|How to cite this article:|
Lam MS, Fitzpatrick AL, Shrestha A, Karmacharya BM, Koju RP, Rao D. Determining the prevalence of and risk factors for depressive symptoms among adults in Nepal: Findings from the Dhulikhel Heart Study. Int J Non-Commun Dis 2017;2:18-26
|How to cite this URL:|
Lam MS, Fitzpatrick AL, Shrestha A, Karmacharya BM, Koju RP, Rao D. Determining the prevalence of and risk factors for depressive symptoms among adults in Nepal: Findings from the Dhulikhel Heart Study. Int J Non-Commun Dis [serial online] 2017 [cited 2020 Nov 27];2:18-26. Available from: https://www.ijncd.org/text.asp?2017/2/1/18/204358
| Introduction|| |
Nepal is currently experiencing an epidemiologic transition from infectious diseases to noncommunicable diseases (NCDs). Average life expectancy has increased from 54 years in 1990 to 70 years in 2014, contributing to a double burden of both communicable diseases and NCDs in Nepal. NCDs and depression exhibit a bidirectional relationship, in which chronic diseases increase vulnerability to developing depression due to symptom burden, psychological stress, and functional limitations while the presence of depression increases the risk and severity of these NCDs. This bidirectional relationship results in a perpetuating cycle of poor mental and physical health. Major depression can also adversely impact the ability to self-manage chronic medical illness and can lead to maladaptive behaviors such as smoking, overeating, and sedentary lifestyle. Increasing evidence from large epidemiological studies has shown that depressive symptoms and major depression are associated with increased morbidity and mortality from illnesses such as diabetes and heart disease.
Major depressive disorder is the 2nd leading cause of years lived with disability (YLDs) globally, and within Nepal it is the 5th leading cause of YLDs. Depression has been significantly associated with NCDs in Nepal as cross-sectional data collected from a group of 321 hypertensive patients showed an estimated prevalence of undiagnosed depression of 15%. The studies conducted among type 2 diabetic patients attending tertiary care clinics in Kathmandu, Nepal, showed an estimated prevalence of depression between 40.3 and 54.1%.,, While data regarding risk factors and comorbid depression have been collected among type 2 diabetics and hypertensive patients attending tertiary care clinics in urban Nepal, community-based assessments of the prevalence of and risk factors for depression among the general population are lacking. This analysis aims to characterize the prevalence of and risk factors for depressive symptoms within a cohort of adults living in Dhulikhel, Nepal.
| Subjects and Methods|| |
Study design and setting
We conducted a descriptive, cross-sectional analysis of baseline data collected from participants enrolled in the parent study, an ongoing population-based, longitudinal cohort study investigating risk factors for cardiovascular disease in Dhulikhel, Nepal. Baseline data for participants were recently collected for a random sample of 1073 adults age 18 years or older.
All adults age 18 years and older living in Dhulikhel, Nepal, for at least 6 months were eligible for the parent study. One- third of the households residing in each of the nine administrative divisions of Dhulikhel were selected for collection of baseline data. Exclusion criteria included being pregnant at the time of data collection, temporary residents living in Dhulikhel <6 months or staying in hostels/motels, individuals who are mentally challenged and unable to respond, and individuals who refuse to participate. All participants provided written informed consent or a thumbprint for illiterate participants.
Study protocol for the parent study was reviewed and approved by the Institutional Review Committees of both the hospital in Nepal and the study's originating university. The study protocol for this secondary data analysis was also reviewed and deemed not to be human subjects research by the originating university, as the authors did not participate in collecting individual data from enrolled patients, data were anonymized on collection, and authors did not have access to identifying information.
Data collection and study parameters
The baseline visit included a standardized questionnaire administered through electronic tablet, physical exam, and laboratory testing. All data enumerators were trained in interviewing and measuring variables. Demographic data, including information regarding age, sex, ethnicity, marital status, education, economic status, diet, tobacco use, alcohol consumption, and physical activity were collected. Height, weight, and waist and hip circumference were measured using standardized instruments. Body mass index (BMI) was calculated as kg/m 2 and categorized as underweight (<18.5 kg/m 2), normal (18.5–22.9 kg/m 2), overweight (23–27.4 kg/m 2), and obese (≥27.5 kg/m 2), according to the World Health Organization (WHO) expert consultation guidelines for South Asia. Using a standard digital blood pressure machine (Microlife, Switzerland), blood pressure was measured three times in a seated position on the right arm over loose clothing. The mean of the three measurements was calculated and used for analysis. Hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg or receiving medication for hypertension.
Assessment of physical activity
The Global Physical Activity Questionnaire developed by the WHO was used to assess the several components of physical activity, including intensity, duration, and frequency in three domains of occupational physical activity, transport-related physical activity, and physical activity during leisure time. The WHO recommends that during 1 week, adults should do at least 600 MET-minutes of physical activity, including activity for work, during transport, and leisure time. Thus, the physical activity variable was dichotomized with a cutoff of 600 MET-minutes of physical activity per week for analysis.
Assessment of depressive symptoms
The Center for Epidemiologic Studies Depression Scale (CES-D) was used to assess depressive symptoms. The CES-D is a 20-item measure that asks participants to rate how often over the past week they have experienced symptoms associated with depression such as restless sleep, poor appetite, sadness, and hopelessness. There are four response options – rarely (<1 day), some (1–2 days), occasionally (3–4 days), and most (5–7 days). Items are scored 0–3 with an instrument range of 0–60. The CES-D represents an accurate and valid measurement of depression with high internal consistency and is frequently used in clinical and research settings to determine depression symptomatology in the general population., Prior studies in Nepal have used the CES-D questionnaire to examine depressive symptoms among leprosy-affected patients and female sex workers., Higher scores indicate greater frequency of depressive symptoms and correlate with an increased risk of clinical depression. Depression scores were dichotomized with a cutoff score of 16 or greater on the CES-D for analysis.
STATA 13 (StataCorp LP. College Station. TX) was used for data analysis. Descriptive statistics were presented in frequency and percentages to identify the distribution of risk factors for depressive symptoms among sociodemographic characteristics. Baseline characteristics were compared between high and low depressive symptoms groups using Pearson's Chi-squared tests for categorical variables and t-tests with unequal variance for continuous variables. All tests were two-tailed, and P ≤ 0.05 was considered statistically significant. Multiple logistic regression was used to examine associations between the binary CES-D depressive symptom score variable by gender, age, education level, marital status, BMI, and hypertensive status. Potential confounders including gender, age, ethnicity, religion, education, and marital status were adjusted for in the regression analysis.
| Results|| |
Sociodemographic characteristics of participants by gender
[Table 1] shows the sociodemographic characteristics of participants by gender. Among 1073 participants, the mean age was 40.3 years (standard deviation [SD]: 16.4), and 58.4% were female. 49.2% of the participants were of Newar ethnicity, and 84.8% self-reported their religion as Hindu. Women were much more likely to have no formal education (42%) and to be separated or widowed (9.4%) compared to men (17.3% and 1.1%, respectively). Women were also more likely to be obese (20.1%) compared to men (13.7%). More males were current smokers (35.7%) compared to females (14.2%). Men were more likely to be heavier drinkers, with 29.6% reporting having >3 drinks/week compared to 7.8% for women. Men were also more likely to be hypertensive (39.5%) compared to women (23.1%).
|Table 1: Sociodemographic, lifestyle, and clinical characteristics of participants by gender|
Click here to view
[Table 2] shows the sociodemographic characteristics of participants by depression status. The mean CES-D score in the sample was 11.7 (SD: 5.3), and the estimated prevalence of depressive symptoms was 21.3%. Women were more likely to have depressive symptoms (24.1%) than men (17.3%). Increasing age, especially age above 60 years old, those with no formal education, and those who were separated or widowed also represented significant risk factors for depressive symptoms. Indeed, when examining the age distribution of participants with depressive symptoms, the prevalence of depressive symptoms remains stable at approximately 20% for adults until about age 60, when it begins to steadily increase to almost 50% at age 90 [Figure 1]. Thus, increasing age above 60 years old was associated with a higher prevalence of depressive symptoms. Having no formal education was also associated with higher risk of depressive symptoms as 28.8% of those with no formal education had depressive symptoms, compared with only 17.2% among those who had less than high school, and 18.8% who had high school level education or more. Being separated or widowed was also significantly associated with a higher risk of depressive symptoms, as 37.5% of those who were separated or widowed had depressive symptoms, compared to only 19.3% among those who were currently married.
|Table 2: Sociodemographic and basic clinical characteristics by depression status|
Click here to view
Major risk factors for depressive symptoms
The results of the multiple logistic regression analysis to determine the association between sociodemographic and lifestyle factors and depression symptoms are shown in [Table 3]. In unadjusted models, a statistically significant increased risk of depressive symptoms was associated with female gender (P = 0.007), increasing age above 60 years (P = 0.002), having no formal education (P < 0.001), being separated/widowed (P = 0.002), and physical inactivity (P < 0.001).
|Table 3: Multiple logistic regression analysis for determining the association of sociodemographic characteristics and depression symptoms among parent study participants|
Click here to view
In the adjusted model, increasing age above 60 years (P = 0.01), having no formal education (P = 0.01), and physical inactivity (P < 0.001) were associated with a statistically significant increased risk of depressive symptoms. In the adjusted model, for every 1-year increase above age 60, odds of having depressive symptoms were higher by 5%. Having no formal education was associated with 2.2 times higher odds of depressive symptoms when compared to those with at least a high school education. Physical activity as a risk factor remained relatively unchanged in the adjusted model as recommended levels of physical activity were associated with 63% lower odds of depressive symptoms. Risk factors that were found to have no statistically significant association with depressive symptoms after adjustment for demographic variables included gender, ethnicity, religion, tobacco use, alcohol use, BMI, and hypertensive status.
| Discussion|| |
This analysis of adults age 18 and older in Dhulikhel found an overall prevalence of depressive symptoms of 21.3%. Statistically significant risk factors for depressive symptoms in the adjusted model included having no formal education, increasing age above 60 years old, and physical inactivity. Being obese was associated with decreased risk of depressive symptoms. Marital status, gender, ethnicity, religion, tobacco and alcohol use, and hypertension status were not statistically significantly associated with depressive symptoms in the adjusted model.
Interpretation and implications
The prevalence of depressive symptoms of 21.3% found in this sample is relatively consistent with previously reported studies, which range from 11.7% prevalence of depression among 2100 Nepalese adults age 18–65, to 15% among 321 hypertensive patients in Kathmandu, to 27.5% among 720 adults in five districts in Nepal. This prevalence of depressive symptoms is also 4 times greater than the estimated global prevalence of major depressive disorder of 4.7%, indicating significant mental health burden in this population.
Risk factors for depressive symptoms
Low level of education and illiteracy have been described as risk factors for depression in prior studies among type 2 diabetics and hypertensive patients, respectively, attending tertiary care centers in Kathmandu, Nepal., Older age has also been shown in prior studies to be a risk factor for depressive symptoms in Nepal. A study among 321 patients with hypertension attending a tertiary care center in Kathmandu, Nepal found increasing depression scores with each additional decade of age, with 29% of those age above 64 years having depressive symptoms, compared to only 11% of those who were age 24–44 years and 45–64 years. Moreover, Joshi et al. found that among 379 patients with type 2 diabetes attending three treatment centers in Kathmandu, there was a positive association between depression and those age 61–70 years. Kohrt et al. also found a similar trend in a postconflict setting in northwestern Nepal, with increasing depression scores associated with increasing decades of age, with the highest risk group being those age 52–80. A community-based assessment among 165 adults age 60 and above living in the Kathmandu Valley found a prevalence of depression of 29.7%.
Physical inactivity has also been shown to be a risk factor for depression and is known to mediate the relationship between depression and mortality among community-dwelling adults in a multicenter study in the US among adults age 65 and older. Prior studies show that gender may modify the relationship between physical activity and depression, as Gautam et al. showed that among 489 community-dwelling adults age 60 and above in Nepal, physical activity was associated with decreased depressive symptoms in men but not in women.
We also found that being obese (BMI ≥27.5) was associated with lower odds of depressive symptoms by 36% when compared with participants of normal weight. This represents a new finding that has not been reported in previous data from Nepal. Results from prior studies globally have been mixed as those conducted in the US and Europe have found a positive association between obesity and depression., Nevertheless, data from Asian countries such as Japan, Taiwan, and China have shown a negative association between obesity and depression.,, Still others, such as a study in Korea, found a U-shaped distribution, where the highest level of depressive symptoms was found among the underweight (BMI <18.5), followed by the severely obese (BMI ≥30), and then the obese (BMI 25–30). Our data showed that compared to participants with normal BMI, the highest risk of depressive symptoms was found among the underweight (BMI <18.5), followed by overweight (BMI 23–27.4), and then obese (BMI ≥27.5).
One potential explanation for this finding is that being obese may be a sign of higher socioeconomic status (SES) within the caste system. Data from India has shown that high caste is positively associated with obesity, mainly through increased wealth., Moreover, a prior study examining the relationship between BMI and SES in 37 lower- and middle-income countries found that increased BMI was associated with increased wealth and higher SES. In this way, being obese may signify having higher income and greater access to food, thus less likelihood to have depressive symptoms.
Another explanation could be due to differing perceptions of body weight. Studies of body weight among women in South India have shown that women tend to have discrepancies in self-perceived body weight and actual body weight. In this way, women who are overweight or obese perceive themselves as normal weight. This finding is an important consideration, as obesity is a risk factor for many NCDs including cardiovascular disease, hypertension, and diabetes. Our data, however, show that those who are underweight are most at risk for depressive symptoms and being obese was associated with a decreased risk of depressive symptoms.
While female gender has been associated with depressive symptoms in Nepal in the previous studies,, another study conducted in Nepal showed no association between gender and depressive symptoms among a cohort of 385 patients with type 2 diabetes attending tertiary care centers in Kathmandu, Nepal. This analysis found that although female gender was associated with depressive symptoms in unadjusted models, after adjustment for other demographic variables, it was no longer statistically significant (odds ratio: 1.22, 95% confidence interval: 0.87–1.71). This was likely due to education confounding the association between female gender and depressive symptoms given that 42% of females had no formal education and having no formal education was strongly associated with depressive symptoms.
Our study did not show a significant association of alcohol consumption and tobacco use with depressive symptoms. Although some studies among type 2 diabetics in Nepal have shown an association between tobacco use and depressive symptoms, other studies conducted among type 2 diabetics and hypertensive patients in Kathmandu, Nepal have not shown an association between either tobacco use or alcohol use and depressive symptoms., In this analysis, the lack of a statistically significant association may be reflective of the fact that both tobacco use and alcohol use were self-reported measures and not many individuals with depressive symptoms reported heavy smoking or drinking, as almost 70% of those with depressive symptoms had never smoked, and 81% had <1 alcoholic drink per week.
Hypertension has also been associated with depressive symptoms in the previous studies in Nepal. However, our analysis did not show an association between hypertensive status and depressive symptoms. This could be due to the fact that our definition of hypertension in the study included individuals taking anti-hypertensive medications. Taking anti-hypertensive medications has been associated with lower risk of depression. Thus, the fact that some hypertensive individuals were receiving treatment for their hypertension could have confounded the relationship between hypertension and depressive symptoms.
Our study also did not show a significant association between ethnicity and depressive symptoms. While some prior studies that have shown an association between low-caste ethnicity groups and higher prevalence of depression,, others have not found a significant association between ethnicity and depressive symptoms.,, Our finding is likely due to the fact that the majority of our sample belonged to the Brahmins and Newars, which are the higher-income ethnic groups. Thus, with a higher SES, they may have been more likely to have access to healthcare facilities to start treatment for chronic conditions and less likely to develop associated depressive symptoms.
| Conclusion|| |
Strengths and limitations
This study has several limitations. The cross-sectional design of this study prevents the ability to establish causal associations or directionality between risk factors and development of depressive symptoms. Since subjective self-reported behaviors were reported in the questionnaire data, participants may have not disclosed the full extent of their depressive symptoms due to stigma. In addition, the questionnaire format may not have captured other unmeasured variables that could be contributing to the development of depressive symptoms, and the estimated prevalence from this analysis likely underestimates the true prevalence of depression in the community. This analysis is also specific to community members in Dhulikhel, a suburban town with a tertiary healthcare center, and thus these results may not be generalizable to other populations in Nepal due to high levels of ethnic and geographic variation within the country.
To the best of our knowledge, this represents one of the first suburban, community-based assessments of depressive symptoms among a general population of adults age 18 and older in a nonconflict zone of Nepal. The random sampling technique, large sample size, and extensive collection of information on demographic and clinical risk factors represent strengths of this study in determining the prevalence and risk factors for depressive symptoms in this cohort.
Future research directions
Major depressive symptoms are highly prevalent in Nepal, and a significant treatment gap exists between the population's mental health needs and appropriate accessible care. Integrated programs specifically targeting the uneducated, the elderly, and the physically inactive should be explored to address this high burden of depressive symptoms.
The authors would like to acknowledge Dr. Ram for his invaluable contributions to the study. The authors would especially like to thank the participants who participated in the Dhulikhel Heart Study.
Financial support and sponsorship
This study was supported by Kathmandu University School of Medical Sciences.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Whooley MA, Wong JM. Depression and cardiovascular disorders. Annu Rev Clin Psychol 2013;9:327-54.
Katon WJ. Clinical and health services relationships between major depression, depressive symptoms, and general medical illness. Biol Psychiatry 2003;54:216-26.
Global Burden of Disease Study Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015;386:743-800.
Neupane D, Panthi B, McLachlan CS, Mishra SR, Kohrt BA, Kallestrup P. Prevalence of undiagnosed depression among persons with hypertension and associated risk factors: A cross-sectional study in urban Nepal. PLoS One 2015;10:e0117329.
Niraula K, Kohrt BA, Flora MS, Thapa N, Mumu SJ, Pathak R, et al.
Prevalence of depression and associated risk factors among persons with type-2 diabetes mellitus without a prior psychiatric history: A cross-sectional study in clinical settings in urban Nepal. BMC Psychiatry 2013;13:309.
Joshi S, Dhungana RR, Subba UK. Illness perception and depressive symptoms among persons with type 2 diabetes mellitus: An analytical cross-sectional study in clinical settings in Nepal. J Diabetes Res 2015;2015:908374.
Mishra SR, Sharma A, Bhandari PM, Bhochhibhoya S, Thapa K. Depression and health-related quality of life among patients with type 2 diabetes mellitus: A cross-sectional study in Nepal. PLoS One 2015;10:e0141385.
Shrestha A, Koju RP, Beresford SA, Chan KC, Karmacharya BM, Fitzpatrick AL. Food patterns measured by principal component analysis and obesity in the Nepalese adult. BMJ Heart Asia 2016;8:46-53.
WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157-63.
Van Dam NT, Earleywine M. Validation of the Center for Epidemiologic Studies Depression Scale – Revised (CESD-R): Pragmatic depression assessment in the general population. Psychiatry Res 2011;186:128-32.
Radloff L. The CES-D scale: A self report depression scale for research in the general population. Appl Psychol Meas 1977;1:385-401.
Yamaguchi N, Poudel KC, Jimba M. Health-related quality of life, depression, and self-esteem in adolescents with leprosy-affected parents: Results of a cross-sectional study in Nepal. BMC Public Health 2013;13:22.
Sagtani RA, Bhattarai S, Adhikari BR, Baral D, Yadav DK, Pokharel PK. Violence, HIV risk behaviour and depression among female sex workers of eastern Nepal. BMJ Open 2013;3. pii: E002763.
Risal A, Manandhar K, Steiner TJ, Holen A, Koju R, Linde M. Estimating prevalence and burden of major disorders of the brain in Nepal: Cultural, geographic, logistic and philosophical issues of methodology. J Headache Pain 2014;15:51.
Luitel NP, Jordans MJ, Sapkota RP, Tol WA, Kohrt BA, Thapa SB, et al.
Conflict and mental health: A cross-sectional epidemiological study in Nepal. Soc Psychiatry Psychiatr Epidemiol 2013;48:183-93.
Ferrari AJ, Somerville AJ, Baxter AJ, Norman R, Patten SB, Vos T, et al.
Global variation in the prevalence and incidence of major depressive disorder: A systematic review of the epidemiological literature. Psychol Med 2013;43:471-81.
Kohrt BA, Hruschka DJ, Worthman CM, Kunz RD, Baldwin JL, Upadhaya N, et al.
Political violence and mental health in Nepal: Prospective study. Br J Psychiatry 2012;201:268-75.
Chalise HN, Rai SL. Prevalence and correlates of depression among Nepalese Rai older adults. J Gerontol Geriatr Res 2013;2:1-5.
Win S, Parakh K, Eze-Nliam CM, Gottdiener JS, Kop WJ, Ziegelstein RC. Depressive symptoms, physical inactivity and risk of cardiovascular mortality in older adults: The Cardiovascular Health Study. Heart 2011;97:500-5.
Gautam R, Saito T, Kai I. Leisure and religious activity participation and mental health: Gender analysis of older adults in Nepal. BMC Public Health 2007;7:299.
Xiang X, An R. Obesity and onset of depression among U.S. middle-aged and older adults. J Psychosom Res 2015;78:242-8.
de Wit L, Luppino F, van Straten A, Penninx B, Zitman F, Cuijpers P. Depression and obesity: A meta-analysis of community-based studies. Psychiatry Res 2010;178:230-5.
Zhi T, Wang Q, Liu Z, Zhu Y, Wang Y, Shi R, et al.
Body mass index, waist circumference and waist-hip ratio are associated with depressive symptoms in older Chinese women: Results from the Rugao Longevity and Ageing Study (RuLAS). Aging Ment Health 2015;19:1-6.
Kuriyama S, Koizumi Y, Matsuda-Ohmori K, Seki T, Shimazu T, Hozawa A, et al.
Obesity and depressive symptoms in elderly Japanese: The Tsurugaya Project. J Psychosom Res 2006;60:229-35.
Kuo SY, Lin KM, Chen CY, Chuang YL, Chen WJ. Depression trajectories and obesity among the elderly in Taiwan. Psychol Med 2011;41:1665-76.
Noh JW, Kwon YD, Park J, Kim J. Body mass index and depressive symptoms in middle aged and older adults. BMC Public Health 2015;15:310.
Samal S, Panigrahi P, Dutta A. Social epidemiology of excess weight and central adiposity in older Indians: Analysis of Study on global AGEing and adult health (SAGE). BMJ Open 2015;5:e008608.
Little M, Humphries S, Patel K, Dewey C. Factors associated with BMI, underweight, overweight, and obesity among adults in a population of rural south India: A cross-sectional study. BMC Obes 2016;3:12.
Neuman M, Finlay JE, Davey Smith G, Subramanian SV. The poor stay thinner: Stable socioeconomic gradients in BMI among women in lower- and middle-income countries. Am J Clin Nutr 2011;94:1348-57.
Agrawal P, Gupta K, Mishra V, Agrawal S. A study on body-weight perception, future intention and weight-management behaviour among normal-weight, overweight and obese women in India. Public Health Nutr 2014;17:884-95.
Kohrt BA, Speckman RA, Kunz RD, Baldwin JL, Upadhaya N, Acharya NR, et al.
Culture in psychiatric epidemiology: Using ethnography and multiple mediator models to assess the relationship of caste with depression and anxiety in Nepal. Ann Hum Biol 2009;36:261-80.
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||Domestic Violence, Social Support, Coping and Depressive Symptomatology among South Asian Women in Hong Kong
| ||Kareen N. Tonsing,Jenny C. Tonsing,Terri Orbuch |
| ||Journal of Loss and Trauma. 2020; : 1 |
|[Pubmed] | [DOI]|
||Parental depression and nutrition: findings from a cross-sectional household survey in Nepal
| ||Ramesh Prasad Adhikari,Ryoko Williamson,Thalia M Sparling,Elaine Ferguson,Kenda Cunningham |
| ||Public Health Nutrition. 2020; : 1 |
|[Pubmed] | [DOI]|
||The Association between Marital Transitions and the Onset of Major Depressive Disorder in a South Asian General Population
| ||William G. Axinn,Yang Zhang,Dirgha J. Ghimire,Stephanie A. Chardoul,Kate M. Scott,Ronny Bruffaerts |
| ||Journal of Affective Disorders. 2020; |
|[Pubmed] | [DOI]|
||The health risks of informal waste workers in the Kathmandu Valley: a cross-sectional survey
| ||M. Black,J. Karki,A.C.K. Lee,P. Makai,Y.R. Baral,E.I. Kritsotakis,A. Bernier,A. Fossier Heckmann |
| ||Public Health. 2019; 166: 10 |
|[Pubmed] | [DOI]|
||Depression among inmates in a regional prison of eastern Nepal: a cross-sectional study
| ||Gambhir Shrestha,Deepak Kumar Yadav,Nidesh Sapkota,Dharanidhar Baral,Birendra Kumar Yadav,Avaniendra Chakravartty,Paras Kumar Pokharel |
| ||BMC Psychiatry. 2017; 17(1) |
|[Pubmed] | [DOI]|