Prevalence of depression among elderly women in India-An intersectional analysis of the Longitudinal Ageing Study in India (LASI), 2017-2018

Epidemiological transition in India shows a shift in disease burden from youth to the elderly. As Life Expectancy increases, a greater burden is placed on the state, society, and families in India. Mental health disorders are insidious, debilitating Non-Communicable Diseases (NCDs) that afflict people, their families, and generations down the line. Globally, depression is the leading cause of mental health-related disability. It is estimated that mental illness contributes to 4.7% of Disability Adjusted Life Years (DALYs) in India. It is predicted that by 2026, the elderly's sex ratio will increase to 1,060 feminizing ageing. Research has shown that elderly women in developed countries like the United States are more prone to depression. Chronic morbidities are more common in women than in men, and they may suffer from poor vision, depression, impaired physical performance, and elder abuse. Mostly widowed, economically dependent, lacking proper food and clothing, fearing the future, and lacking proper care, they have difficulty coping with these health problems. There are surprisingly few studies on elderly female depression. Therefore, we want to hypothesize the prevalence of depression among women in different regions and demographic groups in India, and what factors may contribute to these differences. Using intersectional analysis with the data from Wave 1 (2017-2018) of the (Longitudinal Ageing Study in India) LASI (N = 16,737) we were able to explore the intersecting patterns between different variables and how people are positioned simultaneously and position themselves in different multiple categories based on the type of place of residence, age and level of education. Through the study we further aim to determine the prevalence of depression among elderly female in the age group of 60 in different states using the Chloropleth map. The findings of the study highlight the significance of the place of residence in the development of depression among elderly women, with the rural area being associated with a higher prevalence of depression compared to urban area. When compared to people with higher literacy, those with low literacy were significantly associated with depression. State-wise, there is a huge difference between the prevalence of elderly women depression in rural and urban areas. The study highlights the vulnerability of elderly women to depression. It is possible for the government to develop programs that address the needs of elderly women, both in urban and rural areas, to reduce depression. Multi-factor approaches to mental health, which consider age, literacy, and location, are essential. Programs targeting specific populations can be developed to address depression's root causes..


Introduction
People and their families, and generations down the line, suffer greatly from mental health disorders, an insidious, often debilitating form of Non Communicable Diseases(NCDs) 1 . The greatest mental healthrelated burden is attributed to depression, which is a leading cause of disability worldwide 2 . Mental health impairment worsens a number of NCDs risk factors, such as poor lifestyle choices resulting in obesity, inactivity, and tobacco abuse, poor health literacy, and a lack of access to health promotion activities [3][4][5] . As per the WHO Global Burden of Disease Report globally, 4.4% of the population is estimated to be depressed in 2015 6 . The report also says that around the world, 322 million people are suffering from depression 7 . There is a higher incidence of depression among females (5.1%) than among males (3.6%). It is estimated that this number is growing each year. Even though women live longer than men, research shows they are more prone to certain diseases, which can ultimately shorten their lifespan 8 . Strokes, depression 9 , Alzheimer's 10 , and autoimmune diseases 11 such as multiple sclerosis and rheumatoid arthritis 12 are among them. Most of these people live in the South-East Asia Region and the Western Paci c Region, due to the fact that these regions have much larger populations than the others (which include India and China, for example) [13][14][15] . According to estimates from 2015, the number of Years Lived with Disability (YLD) related to depression amounted to more than 50 million around the world 7 . In terms of the prevalence of non-fatal health loss, depression disorders are ranked as one of the most signi cant contributors (7.5% of all YLD) 6 .
As per the WHO report for low-and middle-income countries, depression poses a signi cant public health challenge due to its comorbidity with chronic physical disease 16 . It is estimated that depression has a 2-4-fold higher prevalence in patients with chronic diseases such as cancer, diabetes mellitus, stroke, or cardiovascular disease, and the disease may last longer 16 . Mental disorders such as depression and anxiety contribute to the escalation of non-communicable diseases through non-adherence to treatment 17 . Those with mental disorders may have a harder time accessing healthcare, treatments may require behavioural changes that may be harder for them. Stigma associated with mental disorders is identi ed as a barrier to access the health facilities 3,18 . According to the ndings of a population-based study 19 , it was found that older people with multimorbidity are more likely to experience depressive symptoms in later life 19 . Researchers have identi ed that functional health factors acts as a mediator between multimorbidity and depression, especially when it comes to older women and very few have reported it 20 . Also most of the researches have recommended further longitudinal research to understand functional and behavioural health in multimorbidity-depression relationships [20][21][22] . The WHO says depression is 50% more prevalent in women than in men, and Indians are among the most depressed worldwide 18 As India is the most populous country and the largest democracy, is now emerging as the sixth-largest economy in the world. It is recently seeing a demographic transition with increase in elderly population. In accordance with the United Nations de nition of a "Graying Nation", a country is de ned as a greying country where the percentage of people who are over 60 years of age is at least 7% of its total population 23 . There were almost 7.7% of people in India, at the dawn of the millennium, were old, and this gure increased to 8.6% in 2011, and 9.4%, in 2017 24 . Also most of the researcher have forecasted by seeing the trend that by the year 2050, there will be 20 % of the elderly people ( almost 300 million) [25][26][27] .
From 2011 through 2041, India is forecast to gain a demographic advantage due to a larger proportion of the population in the working age group 25 . And after 2041, when the aging burden shall begin, the older population may contribute to second demographic growth by accumulating capital from their savings accumulated during their working years 28 . But this depends on developing nancial markets, a healthy older population, and social security, all of which seems to be daunting at the moment. Additionally, due to the epidemiological transition, a large portion of the burden of disease has been shifted from the youth to the elderly 29 . Non Communicable Diseases (NCDs) exceeded 50% in the 30-34 age group and were highest at 78.8% in the 65-69 age group 29 . An increasing Life Expectancy can be attributed to increased longevity and growing society, but it can also be attributed to an increased demand for healthcare facilities, placing an increased burden on the state, society, and families in India 19 . Out-of-pocket health expenses account for more than 70% of health expenditures in India, leaving the older population vulnerable to health problems 30 .The Disability Adjusted Life Year (DALY) rate between 1990 and 2016 was the highest for diseases such as diabetes (80.0%), ischaemic heart disease (33.9%), and sense organ diseases (mainly vision and hearing loss disorders 21.7%) 31 .
In India, mental illness is prevalent and pervasive, especially among older adults living in a distressed socioeconomic situation 29 . Researches have reported due to the social stigma of mental illness in older adults and the lack of trained mental health professionals, the prevalence of mental illness among older adults is higher than the reported gures 32,33 .There were 197 million people in India who lived with a mental disorder in 2017. Of those people, 45 million suffered from depression and another 44 million from anxiety 34 . Mental disorders are a major contributor to the total number of DALYs in India, and their share increased from 2·5% in 1990 to 4·7% in 2017 34 . There is a high incidence of depression among the elderly population of India with females being predominant in the group 35 .
As per the census 2011 36 the majority of older Indian adults live in rural areas, over 70% of whom are illiterate, while over half of them do not have a source of income 37 . Quacks, folk healers, or AYUSH practitioners provide health and mental health care to older adults in rural areas, since allopathic doctors and hospitals are far away in urban areas and elderly people often have di culty approaching hospitals 38 .Though there are scarce research publications available related to depression among elderly population in rural area, but those available shows that as a result of different population characteristics, depression prevalence is slightly but signi cantly higher in rural areas than in urban areas 39 . However, these studies are conducted in a small population. In a study in South India it was revealed that the prevalence of depression varies among rural and urban area 35 , 40 .
In India National Mental Health Policy,2014 aims to reduce distress, disability, exclusion morbidity and premature mortality associated with mental health problems across the life-span of a person 41 . Nevertheless, a larger part of the policy is focused on ensuring the mental health of the population as a whole, with little emphasis on mental health of the elderly 42 . The reality is, with the presence of mental health disorders and comorbid conditions, the elderly population is more likely to suffer from mental health problems, contributing to a larger burden of dual disease in the country as a whole 43 . Though mental disorders are studied in different parts of India, including in the National Mental Health Survey there has been very limited resource available which highlights the prevalence of state-wise and genderwise depression its association with disability-adjusted life years (DALYs).
Researches have revealed that female elders are more likely to suffer from physical and mental disabilities that greatly reduce their quality of life 44 . Studies in the United States have shown that elderly women are more susceptible to depression, experience longer and more persistent depression, and have lower mortality rates once depressed 44,45 . There is no doubt that late-life depression poses a signi cant public health problem since it is widespread and expensive, associated with disability, re-hospitalization, and even death among those with chronic diseases 46,47 . According to the literature Compared to elderly men, women are more likely to suffer from chronic morbidity 48 , poor vision 49 , cataracts, high blood pressure, back pain/slipped disk, malnutrition 50 , depression 10 , impaired physical performance 51 , and elder abuse 52 , women have di culty coping with these health problems because they are widowed, economically dependent, lack proper food and clothing 15 , fear the future, lack care, and suffer from progressive health decline 53 . However, existing policies and programmatic capacities are inadequate and lack gender sensitivity to address the socioeconomic and health needs of women. By utilizing secondary data from Longitudinal Ageing Study of India (LASI), this study lls a gap by identifying the prevalence of common mental disorders like depression in elderly females in India by identifying a number of factors related to it, especially depression, in females in India and its trend with respect to age, economic status, place of residence, marital status, alcohol consumption, tobacco consumption, and physical activity. The study also shows its trend in urban as well as rural areas. It will provide valuable insight to policymakers so that they can develop the necessary policy implications to address the rapidly increasing rate of depression among the female elderly population in India.

Aim and Objective:
What is the prevalence of depression among women in different regions and demographic groups in India, and what factors may contribute to these differences?
To determine the extent of depression among women in various regions and demographic groups in India.
To determine the factors associated with depression among the elderly women in India.

Results
Their distribution was calculated using descriptive statistics by gender, age, place of residence, and education status. Binary Logistic Regression was used to estimate the prime factors associated with depression since depression was a binary yes or no variable. Unadjusted odd ratio was obtained through the rst regression model to control other variables. Our second regression model takes into account other variables such as age, place of residence, marital status, education, employment, household income, alcohol, tobacco, physical activity, and yoga to determine the risk factors for depression. Using intersectional analysis, we explored how people are positioned simultaneously according to their place of residence, their age, and their level of education, as well as how they position themselves in different multiple categories. To understand the distribution of depression prevalence among Indian states and Union Territories, we have plotted the Choropleth map using the GeoDa software.
The study collected data on various variables such as age, place of residence, marital status, education, employment status, household income, alcohol consumption, tobacco consumption, physical activity, and depression. Age was categorized into four groups, and place of residence was categorized into rural and urban areas. Marital status was categorized as a nominal variable, and education status was based on the number of years of schooling. Employment status was reported as a nominal variable with Yes for those who were currently working and No for those who were not. Household income was categorized into ve groups. Alcohol and tobacco consumption were captured as nominal variables with Yes for those who consumed them and No for those who did not. Physical activity and yoga were reported as a nominal variable with Yes for those who did them regularly and No for those who did not. The study's outcome variable was depression, which was assessed using the Composite International Diagnostic Interview-Short Form (CIDI-SF) scale, with Yes for those who had depressive symptoms and No for those who did not. The study ensured reliability and validity by training non-clinicians to collect data using the CIDI-SF tool in the local language.  Table 1 The study involved 14553 women who were 60 years old or older, and those below 60 were excluded from the analysis. The percentage of participants in the 60-64 age group was 33.6%, while the proportion decreased as age increased, with women over 75 accounting for 24.4%. This could be due to increased life expectancy and better access to healthcare. The majority of participants (63.8%) were from rural areas, and most (90.2%) were married. Nearly half (46.2%) had some basic education, while 31.6% had completed 10 or more years of education. The majority (60.7%) of the women were employed. Household income was distributed equally across most wealth categories. Only a small percentage of participants consumed alcohol (3.9%), while 19% used tobacco and only 12.2% engaged in regular exercise.    The data provided shows the prevalence of depression across different age groups, literacy statuses, and locations. The prevalence of depression is higher among illiterate individuals compared to literate individuals across all age groups and locations. Depression rates are also generally higher among rural populations compared to urban populations, especially for illiterate individuals.
This information could be useful for healthcare professionals, policymakers, and organizations that aim to address and prevent depression in different populations. For instance, the higher prevalence of depression among illiterate individuals suggests that interventions that focus on improving literacy rates could potentially have a positive impact on reducing depression rates. Additionally, the higher prevalence of depression among rural populations highlights the need for targeted interventions and resources to address mental health in rural areas.
However, it's important to note that the data only provides information on the prevalence of depression and does not provide any information on the causes or risk factors for depression among these populations. Furthermore, the data only includes a limited set of intersectional variables, and other factors such as socioeconomic status, gender, or race could also be important in understanding depression rates in different populations.

Discussion
The study participants consisted of 16,737 women aged 60 and above, with participants under the age of 60 excluded from the analysis. The demographic characteristics of the participants were analysed and several important ndings were observed. The age group of 60-64 years was the largest with 33.6% of the participants, while the proportion of participants decreased as age increased, with 24.4% of the participants aged over 75 years. This increase in the proportion of older participants is likely due to improved access to healthcare and increased life expectancy.  54 , which reported that a majority of women in India reside in rural areas, have low levels of education and employment, and engage in unhealthy behaviours such as tobacco and alcohol consumption.
It is important to note that these ndings provide important insights for policymakers and public health practitioners, highlighting the need for targeted interventions to address the health needs of older women in India, particularly those in rural areas. Further research is needed to understand the impact of demographic and socioeconomic factors on health and well-being in this population, and to develop effective strategies to address the challenges faced by older women in India.
The ndings of the study on depression levels and aging are similar to the Global Adult Tobacco Survey (GATS) 55 , which also found that tobacco use is associated with increased levels of depression. GATS also found that individuals with higher levels of education and those who are employed are less likely to use tobacco and have lower levels of depression. Similarly, the study found that physical activity is associated with lower levels of depression, while GATS found that individuals who are physically active are less likely to use tobacco. However, GATS did not speci cally address the relationship between wealth quintiles and depression levels. The study's nding that depression levels were higher among rural participants compared to urban participants is not addressed in GATS. The ndings of the study are consistent with other reports regarding depression and risk factors 56,57 . The study found that depression levels increased with age from 60 to over 75 years, which is similar to other studies that have shown that older adults are at increased risk for depression 35,58 . Additionally, the study found that depression levels were higher among individuals who consumed alcohol or tobacco, which is also in line with other reports that have linked substance use with increased risk for depression 59,60 .
However, the study also found some unique ndings. For example, the study found that a higher proportion of rural participants reported increased levels of depression compared to urban participants, which is not typically reported in other studies. Additionally, the study found that regularly engaging in physical activity was associated with lower levels of depression, but after adjusting for other variables, physical activity became a risk factor, which is also not a commonly reported nding.
The study's ndings on the associations between depression and education, wealth quintile, and marital status are also consistent with other reports. Education, wealth, and marital status have all been linked with mental health, and the study's ndings provide further support for these relationships 27,61 .
Overall, the study adds to the growing body of literature on depression and its risk factors, particularly in older adults. Further research is needed to better the ndings of this study that rural areas have a higher prevalence of depression compared to urban areas aligns with previous studies and reports on the topic. This disparity could be due to factors such as lack of access to healthcare, lower socio-economic status, and cultural stigma surrounding mental health in rural areas 60,62 . However, it's worth noting that while the study found that depression levels were higher in rural areas, there were also some urban areas like Chandigarh, Delhi and Lakshadweep where depression levels were higher. This indicates that the relationship between urbanization and depression levels is complex and not straightforward. Further research is needed to fully understand the factors contributing to the higher levels of depression in both rural and urban areas. understand the complex relationships between depression, demographics, and risk factors. The ndings of the study highlight the signi cance of the place of residence in the development of depression among elderly women, with the rural area being associated with a higher prevalence of depression compared to the urban area. The results show that the prevalence of depression among illiterate women in the rural area was 20.5% for women aged 65-69, and 18.9% for women over 75 years of age, compared to a prevalence of 13% for women over 75 years of age in the urban area. This shows a reduction of 6 points in the prevalence of depression in the urban area compared to the rural area which is in line with the other studies 23,35 .
Additionally, the study found that the trend of depression prevalence increased with age in the rural area and decreased with age in the urban area. Similarly, the prevalence of depression was found to be higher among literate women over 75 years of age in the rural area (28.6%) compared to urban area (14%) which was just half the prevalence in rural area. The intersectional analysis indicates that the type of place of residence is the major factor for the development of depression, although the underlying cause was not established in the study.
The strength of this study lies in its use of intersectional analysis, which considers the intersection of multiple factors, such as age, literacy, and place of residence, in the development of depression. This provides a more nuanced understanding of the complex relationships between these factors and the development of depression.
However, the weakness of this study is that the underlying cause of the relationship between place of residence and depression was not established. Further research is needed to understand the speci c factors that contribute to the higher prevalence of depression in the rural area and the protective effect of urban residency on depression. Additionally, the study is limited by the small sample size and the lack of data on other possible risk factors for depression.
Overall, the study provides important insights into the relationship between place of residence and depression among elderly women and highlights the importance of considering multiple factors in the analysis of mental health outcomes.
Policy implications and recommendations based on this study would include: 1. Addressing the need for mental health services in rural areas: The study highlights that women living in rural areas are more susceptible to depression as compared to their urban counterparts. Thus, it becomes imperative for the government to focus on providing mental health services in rural areas to help reduce the prevalence of depression.
2. Literacy and mental health awareness programs: The study shows that illiterate women are more prone to depression. Hence, promoting literacy and creating mental health awareness programs can help in reducing the incidence of depression.
3. Focus on elderly women: The study highlights that elderly women are more susceptible to depression. The government can focus on creating programs that cater to the needs of elderly women, both in urban and rural areas, to reduce the incidence of depression.
4. Intersectional approach to mental health: The study highlights the importance of an intersectional approach to mental health, which considers multiple factors such as age, literacy, and place of residence. The government can use this approach to develop programs that target speci c populations and address the root causes of depression.
Limitations of the study: The data from the Longitudinal ageing study in India (LASI) Wave 1 (2017-2018) was used to understand the burden of depression among older women above 60 Years in India and to explore the geographic distribution of depression in India. A public domain LASI dataset was obtained from the Gateway to Ageing Portal once the abstract submission was approved. In light of the fact that the data is secondary data, both national and international forums have approved the use of the data. A number of lters were applied to the data to obtain 16,637 samples from women aged 60 and older. Their distribution was calculated using descriptive statistics by gender, age, place of residence, and education status. The data from the Longitudinal ageing study in India (LASI) Wave 1 (2017-2018) was used to understand the burden of depression among the older women above 60 Y in India and to explore the geographic distribution of the depression in India. LASI is the rst longitudinal dataset in India to provide a reliable basis for designing policies and programmes for the older population's social, health, and economic wellbeing. LASI uses Computer-Assisted Personal Interview (CAPI) technology, internationally Harmonized/ Gold Standard Survey Protocol, Comprehensive Range of Biomarkers. Multistage strati ed area probability cluster sampling design is used for selecting the representative sample in each stage.
The eligibility criteria was older adults aged 45 Years and above (including spouses irrespective of age).The eventual unit of observation of LASI was LASI-eligible household (LEH) with at least onemember age 45 and above. LASI adopted a multistage strati ed area probability cluster sampling design to arrive at the eventual units of observation. All the 30 Indian States and six Union Territories were selected for the survey. The states were further divided in to Districts, Sub districts, Talukas, Tehsils and Blocks. The samples were selected in four stages, where in the rst state was for selection of Primary Sampling Unit (PSU) and second and third stage was for selection of Secondary Sampling Unit (SSU ), and fourth stage was for selection of households. For assessing depression, the tools used are Centre for Epidemiologic Studies Depression (CES-D) to nd the symptoms of depression and Composite International Diagnostic Interview-Short Form (CIDI-SF) scale to diagnose major depression. For more details please refer to LASI India Report 2020 at 29 .
It is a cross sectional study aimed to explore the factors responsible to determine the prevalence among elderly women of 60 and above in India using the intersectional analysis. We were able to explore the intersecting patterns between different variables and how people are positioned simultaneously and position themselves in different multiple categories based on the type of place of residence, age and level of education. Through the study we further aim to determine the prevalence of depression among elderly female in the age group of 60 in different states using the Chloropleth map. Binary Logistic Regression was used to estimate the prime factors associated with depression since depression was a binary yes or no variable. Odds Ratio was calculated using clinical and demographic variables. Unadjusted odd ratio was obtained through the rst regression model to control other variables. Our second regression model takes into account other variables depicted in Fig:4 to determine the risk factors for depression with a con dence interval of .001. Using intersectional analysis, we explored how people are positioned simultaneously according to their place of residence, their age, and their level of education, as well as how they position themselves in different multiple categories and if conditional probability of depression exists in all categories. Our next step was to compute the prevalence of depression at the state level. To understand the distribution of depression prevalence among Indian states and Union Territories, we have plotted the Choropleth map using the GeoDa software.

Conclusion
In conclusion, the study analysed the prevalence of depression among elderly women aged 65 to 69 and more than 75 in rural and urban areas. The results showed that the prevalence of depression among rural elderly women was higher compared to urban elderly women. However a disparity among states was found. The trend of prevalence increased with age among rural women and decreased with age among urban women. Additionally, the intersectional analysis showed that the type of place of residence was the major factor for the development of depression.
The study highlights the need for mental health policies and interventions to address the higher prevalence of depression among elderly rural women. This may involve providing access to mental health services, creating community support systems, and raising awareness about mental health in rural areas. Furthermore, there is a need for further research to understand the underlying causes of depression among rural elderly women.
While the intersectional analysis provides a useful insight into the complex relationship between demographic factors and mental health, it is important to acknowledge its limitations. The study did not establish the cause of depression, and it was beyond the scope of this analysis. Moreover, the study relied on self-reported data, which may be subject to bias.
In summary, the study provides valuable information on the prevalence of depression among elderly women in rural and urban areas and the importance of considering the intersection of demographic factors in understanding mental health. The results of this study can inform policy and practice to improve mental health outcomes among elderly women, especially those living in rural areas.

Declarations Data Availability
The data that support the ndings of this study are available in Gateway to Ageing at https://g2aging.org/downloads, reference number "R01 AG030153". These data were derived from the following resources available in the public domain: https://g2aging.org/login&r=%5Eq%5E https://g2aging.org/?section=login&r=^q^section=downloads https://www.iipsindia.ac.in/content/lasi-wave-i Authors Information  Bar graph showing the prevalence of depression among the women aged 60 and above