DOI: https://doi.org/10.21203/rs.3.rs-1902295/v1
Depression is a major public health concern in India contributing significantly to morbidity, dysfunction, reduced quality of life, and economic loss. Evidence from previous studies suggest that risk to developing mental illness begins in the early years of life. Thus, we aim to examine the effect of early life conditions-childhood health status and childhood socioeconomic status on depression later in life and the mediating role of other risk factors along life-course- education, current economic status, chronic physical condition(s), and health behaviours - in the effect of early life conditions on depression later in life.
We used data from Longitudinal Aging Study in India (2018–2019) to find the association between the possible risk factors, including early life conditions, and depression later in life using logistic regression. The factors that were found to be significantly associated with depression later in life were used in the second step of mediation analysing using Karlson-Holm-Breen method, to examine the role of mediators in the effect of early life conditions on depression later in life.
Childhood health, childhood socioeconomic status, education, current economic status, chronic physical condition(s), and tobacco use were found to be associated with depression later in life. Childhood health directly influences the occurrence of depression later in life and its effect is not mediated by risk factors along life course. On the contrary, the effect of childhood socioeconomic status on depression later in life is at least partially mediated by risk factors along life course- education, current economic status, chronic physical condition(s), experience of depression and tobacco use. The greatest contribution to this mediation is by discrimination, followed by tobacco use and education. Chronic physical condition is also a major contributor but it acts as a suppressor. Furthermore, the childhood health also mediates the influence of early life socioeconomic status on depression later in life.
Our findings demonstrate the pertinence of childhood health in reducing the burden of depression later in life. It also underscores the importance of focussing on risk factors along life course- discrimination, tobacco use, and education, health behaviours- for individuals who belonged to families having low socioeconomic status during childhood, and initiatives to prevent chronic physical conditions particularly among those who did not have adverse socio economic status during childhood in order to reduce the burden of depression among middle-aged and elderly population in India.
Mental disorders, being the second highest contributor to years lived with disability (YLDs) and sixth highest contributor to disability-adjusted life-years (DALYs) globally, is a significant public health concern around the world [1]. The burden of mental disorders is particularly high in low-income and middle-income countries [2]. Around 197 million people in India were estimated to have mental disorder in 2017. Mental disorders, including depression, anxiety, schizophrenia, bipolar disorders, are one of the leading causes of years lived with disability (YLD). Depression contributes the highest to DALYs due to mental disorders in India [1]. Depression has been found to be associated with overall reduced quality of life, impaired normal functioning, greater risk of cardiovascular diseases, as well as higher morbidity and mortality [3, 4]. Estimates suggest that over 45 million people in India alone are suffering with depression [1].
Given the large burden of depression and its adverse outcomes, efforts for prevention of the disorder are of paramount importance. A growing body of literature has underscored that risk to developing mental illness begins in the early years of life [5–9]. There exists more than one mechanism via which early-life conditions influence the health later stages in life [6]. The ‘critical period model’, also known as ‘latency model’ advocates that exposure to a risk factor during certain stage of life may have permanent adverse or protective effects on the body by altering the anatomical structures and metabolic systems, and later experiences may not be able to modify these 0effects. The model primarily focusses on the concept of the ‘biological programming’ [10]. Another contrasting model, known as the ‘pathway model’ highlights the importance of conditions along the life-course that are influenced by early life conditions and in turn affect health at later stages in life, acting as modifiable mediators between the two [6]. Yet the majority of existing life course studies have focused only on the direct influence of childhood conditions on mental health in later life [11].
A strand of literature underscores the association of early life conditions with factors like education, economic status, chronic physical conditions, discrimination, and health behaviours along the life course [12–16]. Another strand of literature highlights the association of these factors along the life course with depression at older age [17–21]. Thus largely the literature is fragmented with regards to the possible role of these conditions along life-course as mediators linking early-life conditions and depression later in life. To address this gap, study aims to contribute to the existing literature by assessing if and to what extent the effect of early life conditions (childhood health and socioeconomic status) on depression later in life are mediated by factors during life-course. Further to examine the role of childhood health as a mediator in the effect of childhood socioeconomic status on depression later in life. The findings of this paper have policy implications for prioritizing investment towards improvement of childhood conditions in order to reduce the burden of depression later in life in India, and it also highlights key areas for policy initiatives for reducing the risk for depression later in life among adults who have been exposed to adverse conditions during their childhood.
Conceptual model (Fig. 1) shows childhood socio-economic and health conditions that through factors along life-course pathways can lead to depression later in life.
Data source and study population
The data from first wave of the Longitudinal Ageing Study of India (LASI, 2017–18) was used for this study which is a survey of national representative sample of 72,250 adults aged 45 years and above (including spouses irrespective of their age) from all states and union territories in India, except Sikkim. Only older adults aged 45 years and above were included in the present study making the effective sample size 65,562. Sampling design adopted by LASI was a multistage stratified area probability cluster sampling design. More in-depth information on methodology, the survey design and data collection, was published in the survey report [22].
Measures
Dependent variable
Probable psychiatric diagnosis of major depression is the dependent variable of the study. LASI has adopted the Short Form Composite International Diagnostic Interview (CIDI-SF) is used for this purpose. The scale has been validated in field settings and is commonly used in population-based surveys. A score greater than or equal to three is used as an indicator of probable major depression [22].
Early life conditions
Childhood socioeconomic status. Socioeconomic status during childhood was assessed in LASI by asking: “Compared to other families in your community, would you say your family during that time was pretty well off financially, about average, or poor”. The possible responses were: “pretty well off financially”, “about average”, “poor”, and “varied”. The variable was dichotomized by collapsing “pretty well off financially”, “about average”, and “varied” responses into the one category, the other category being “poor” childhood SES.
Childhood health. Health during childhood was assessed by asking: “In general, would you say your childhood health was very good, good, fair, poor or very poor on the basis of what you remember, or what you heard or perceived from your parents?” The variable was dichotomized by grouping those reporting ‘very good’, ‘good’ or fair into one category and ‘poor’ or ‘very poor’ into the other.
Life-course factors
Education. The variable representing the highest educational qualification of the respondents was recoded into the following categories: No formal education, primary school, secondary school, and higher education. For mediation analysis, the responses were dichotomized into ‘no formal education’ and ‘any level of formal education’.
Current economic status. The monthly per capita consumption expenditure (MPCE) as used as a measure of current economic status. Respondents were asked sets of 11 and 29 questions on food and non-food expenses, respectively. Food expenditure was collected over a seven-day reference period, whereas non-food expenditure was collected over 30-day and 365-day reference periods. The 30-day reference period was used to standardise the two types of expenditures. MPCE is computed as a summary measure of consumption and household were categorised into five quintiles based on it. These quintiles were used as categories in logistic regression. For mediation analysis, dichotomization was done by using poorest as one category and collapsing the rest into other category.
Chronic physical morbidity. Status of physical comorbidity was assessed with the question “Has any health professional ever diagnosed you with the following chronic conditions or diseases?” Responses included yes/no for the following chronic physical conditions: ” Hypertension or high blood pressure”, “Diabetes or high blood sugar”, “Cancer or a malignant tumor”, “Chronic lung disease such as asthma ,chronic obstructive pulmonary disease/Chronic bronchitis or other chronic lung problems”, “Chronic heart diseases such as Coronary heart disease (heart attack or Myocardial Infarction), congestive heart failure, or other chronic heart problems”, “Stroke”, “Arthritis or rheumatism, Osteoporosis or other bone/joint diseases” , “High cholesterol”. The variable used to assess the status of physical comorbidity was dichotomous. Those who responded “yes” one or more of the options mentioned in the above mentioned questioned were grouped together, and those responded “no’ to all the options were grouped into other category.
Discrimination. Discrimination was assessed using the six-item Everyday Discrimination Scale (EDS) (Short version). Responses ranged from 1 = “never” to 6 = “almost every day” for 31 the following items "You are treated with less courtesy or respect than other people", "You receive poorer service than other people at restaurants or stores", "People act as if they think you are not smart", "People act as if they are afraid of you", "You are threatened or harassed", "You receive poorer service or treatment than other people from doctors or hospitals ". For this study, the response for each of the six were first dichotomized to ‘never’ = 0 and ‘ever’ (for which responses ‘less than once a year’ to 'almost every day’ were collapsed into one category) = 1. The scores (0 or 1) were summed. The sum of total score ranged from 0 to 6. For logistic regression, these scores were then categories into no discrimination (sum of score=0), moderate discrimination (sum of scores 1-2), and high discrimination (sum of scores 3-6) [23]. For mediation analysis, the sum of scores was dichotomized into no discrimination (sum of score=0), and any level of discrimination experienced (sum of scores 1-6).
Health behaviours. Two health behaviours- tobacco use and alcohol consumption were included in the analysis. In the survey, the respondents were asked whether they have ever used tobacco (smoking or smokeless). It was a yes/no question. The same categories were used in this paper for the variable to assess use of tobacco. Alcohol consumption status was also assessed with a yes/no question.
Confounders
To adjust for the effect of other sociodemographic variables on the analysis, all the models were controlled for individual and household sociodemographic factors. Factors at individual level included were age (45–54, 55–64, 65–74, or 75+ years) and gender (male, female). Factors controlled for at household level were caste (scheduled tribe, scheduled caste, other backward class, or other), religion (Hindu, Muslim, Christian, Sikh, or other). At community level place of residence (rural or urban), and region (North, Central, East, Northeast, West, and South) was controlled for. The models were also adjusted for living arrangement (living alone, living with spouse and/or others, living with spouse and children, living with children and others, living with others only) which can be a potential confounder [24,25].
Statistical Analysis
The statistical analysis was done in three steps. In the first step, bivariate analysis using simple logistic regression was performed to examine association between depression and each of the independent variables in the early life as well as during life-course (childhood SES, childhood health, education, employment, tobacco use, alcohol consumption, chronic physical morbidity) to find the odds ratio (OR). In the second step, the models for testing the association between each of the independent variables with depression was adjusted for age, gender, caste, religion, region, place of residence, and living arrangement, to test the associations with adjusted odds ratio (AOR). The 95 percent confidence interval (CI) for both OR and AOR were calculated. The independent variables which were found to be significantly associated with depression were included in the mediation analysis, which is the third step of our empirical analysis. Mediation analysis was performed using KHB method [26,27]. KHB method allows to decompose the total effect of independent variables on dependent variables into direct and indirect components. The indirect component corresponds to extent of mediation, and the direct component represents the proportion of effect of independent variable that was not mediated. It allows to assess the extent to which each of the mediator mediates the relationship. For mediation analysis also alpha was set at 0.05. Analysis were performed after assigning survey weight which was given in LASI. Statistical analysis was performed using statistical software STATA, version 14.0 (StataCorp, Texas).
Descriptive Analysis
Table 1 presents the descriptive statistics of the study sample. Weighted estimate shows that prevalence of depression was 7.8 % among the adult population in India. While fewer than 2 percent of the respondents reported to have poor health conditions during childhood, over 40 % of them belonged to poor socioeconomic status in early life. Over 45 % were diagnosed with one or more chronic physical conditions. Around 11 percent experienced moderate discrimination and seven percent experienced high discrimination. Fifteen percent of them reported to have consumed alcohol, and over one-third had used tobacco product. Around two-third of the respondents were below 65 years of age. While over half of them were living with their spouse and children, approximately 17 % live with spouse and others, and 19% with children and others. Rest of the respondents i.e. about eight percent lived either alone or with others only. Majority of them were Hindus and around 11 % were Muslims. Forty-five percent belonged to Other backward classes, one-fifth to scheduled caste, over eight percent to scheduled tribe and around one-fourth to others caste . Around a third of them were from urban areas. Majority of the respondents were either from south, east, or central region of India.
Table 1 Descriptive statistics for a national sample of older adults age 45 and above, LASI Wave 1, 2017-18
| % (n) | |
| Major Depression | 7.8 (4,056) |
| Childhood health status | |
| Poor | 1.6 (976) |
| Childhood socioeconomic status | |
| Poor | 42.2 (25,323) |
| Education | |
| No education | 50.6 (30,818) |
| Primary | 23.0 (16,096) |
| Secondary | 16.3 (12,126) |
| Higher | 10.1 (6,519) |
| Current economic status | |
| Richest | 18.0 (13,058) |
| Richer | 19.4 (13,210) |
| Middle | 20.5 (13,163) |
| Poorer | 21.2 (13,190) |
| Poorest | 20.9 (12,941) |
| Chronic physical condition(s) | |
| Present | 45.8 (30,066) |
| Discrimination | |
| No discrimination | 82.5 (54,291) |
| Moderate discrimination | 10.8 (5,617) |
| High discrimination | 6.7 (3,901) |
| Tobacco use | |
| Ever used | 37.2 (23,842) |
| Alcohol consumption | |
| Ever consumed | 15.1 (11,718) |
| Age in completed years | |
| 45-54 | 35.0 (24, 094) |
| 55-64 | 29.8 (20,136) |
| 65-74 | 23.8 (14,583) |
| 75+ | 11.4 (6,749) |
| Gender | |
| Female | 54.1 (35,083) |
| Living arrangement | |
| Living alone | 3.7 (2,313) |
| Living with spouse and children | 55.6 (37,519) |
| Living with spouse and/or others | 16.7 (10,358) |
| Living with children and others | 19.4 (12,441) |
| Living with others only | 4.6 (2,931) |
| Caste | |
| Scheduled caste | 19.2 (10,959) |
| Schedule Tribe | 8.6 (11,365) |
| Other backward classes | 45.5 (24,629) |
| Others | 26.7 (18,507) |
| Religion | |
| Hindu | 82.0 (48,099) |
| Muslim | 11.5 (7,803) |
| Christian | 3.0 (6,536) |
| Sikh | 1.8 (1,866) |
| Others | 1.7 (1,254) |
| Place of residence | |
| Urban | 31.5 (23,138) |
| Region | |
| North | 12.3 (11,966) |
| Central | 20.6 (8,907) |
| East | 23.2 (11,580) |
| Northeast | 3.4 (8,513) |
| West | 16.5 (8,894) |
| South | 24.0 (15,702) |
Table 2 presents the weighted prevalence of depression for the sample by background characteristics. The prevalence of depression was around three times among the respondents who had poor health status during childhood as compared to those among those who responded to have good or fair health status. Prevalence was also higher among the respondents who belonged to poor socioeconomic group during childhood (9.6 %) than those who had good or varied conditions during their early life (6.5%). The prevalence was the greatest among illiterates (9.1 %) compared to other categories of educational status. Among categories of current economic status, prevalence of depression was the highest among the richest category (9.9 %). Prevalence was higher among those who experienced moderate (14.3 %) or high discrimination (7.8 %) than those who did not experience it (6.4%). While prevalence was higher among those had used tobacco, or those who had a chronic physical condition, but it was lower among those who had consumed alcohol. Prevalence was slightly higher among female (8.5 %) than male (7.0 %). Furthermore, the prevalence was also the highest among the age group 75 and above (9.4 %), living alone (12.7 %), Muslims (9.3 %), Scheduled caste (8.8 %), rural areas (8.7%), and central region (13 %).
Table 2 Prevalence (%) of major depression among older adults age 45 and above by background characteristics, LASI Wave 1, 2017-18
| Major depression based on CIDI-SF |
|
| % (n) | |
| Childhood health status | |
| Good/ fair | 7.6 (3,930) |
| Poor | 20.1 (124) |
| Childhood socioeconomic status | |
| Good/ varied | 6.5 (2,040) |
| Poor | 9.6 (2,014) |
| Education | |
| No education | 9.1 (2,204) |
| Primary | 7.8 (996) |
| Secondary | 5.8 (591) |
| Higher | 4.8 (265) |
| Current economic status | |
| Richest | 9.9 (954) |
| Richer | 7.4 (848) |
| Middle | 7.7 (739) |
| Poorer | 6.9 (760) |
| Poorest | 7.5 (755) |
| Chronic physical condition(s) | |
| No chronic physical condition | 6.3 (1,766) |
| One or more chronic condition present | 9.7 (2,286) |
| Discrimination | |
| No discrimination | 6.4 (2,806) |
| Moderate discrimination | 14.3 (673) |
| High discrimination | 7.8 (526) |
| Tobacco use | |
| Never used | 7.3 (2,397) |
| Used in past/ currently using | 8.6 (1,654) |
| Alcohol consumption | |
| Never consumed | 8.0 (3,381) |
| Consumed in past/ currently consuming | 7.1 (674) |
| Age | |
| 45-54 | 7.3 (1,388) |
| 55-64 | 7.9 (1,273) |
| 65-74 | 7.7 (927) |
| 75+ | 9.4 (468) |
| Gender | |
| Male | 7.0 (1,617) |
| Female | 8.51 (2,439) |
| Living arrangement | |
| Living alone | 12.7 (225) |
| Living with spouse and children | 7 (2,028) |
| Living with spouse and/or others | 7.3 (596) |
| Living with children and others | 9.2 (996) |
| Living with others only | 9.5 (211) |
| Caste | |
| Scheduled caste | 8.8 (805) |
| Schedule Tribe | 4.6 (342) |
| Other backward classes | 8.2 (1,757) |
| Others | 7.5 (1,145) |
| Religion | |
| Hindu | 7.7 (3,174) |
| Muslim | 9.3 (497) |
| Christian | 4.3 (165) |
| Sikh | 8.7 (155) |
| Others | 9 (65) |
| Place of residence | |
| Rural | 8.7 (2,958) |
| Urban | 5.9 (1,098) |
| Region | |
| North | 6.5 (765) |
| Central | 13 (1,024) |
| East | 7.6 (809) |
| Northeast | 4.7 (226) |
| West | 6.9 (530) |
| South | 5.4 (702) |
Logistic regression models
Unadjusted and Adjusted odds ratio of depression and its 95% confidence interval using simple and multivariable logistic regression analysis is given in Table 3. In the unadjusted model, we found strong evidence of association between the childhood poor health status and depression later in life (OR=3.04, CI=2.08-4.46). The association was significant even after adjusting for sociodemographic factors at individual, household, and community level (AOR=2.69, CI=1.88-3.86). We also found evidence for association of poor childhood socioeconomic status and depression later in life in unadjusted (OR=1.52, CI=1.32-1.75) as well as in adjusted (AOR=1.38, CI=1.21-1,57) models. The odds of depression later in life, as compared to its odds among uneducated category, reduced with any level of education attained and the findings were significant for all categories. After adjusting for sociodemographic factors, the relationship between no more significant for primary education (AOR=0.70, CI=0.76-1.20), but it remained significant for other educational categories- secondary (AOR=0.75, CI=0.57-0.99), and higher (AOR=0.63, CI=0.43-0.91). In the unadjusted model, odds of depression significantly decreased for each of the categories of current economic status- richer (OR=0.73, CI=0.56-0.94), middle (OR=0.75, CI=0.57-0.99), poorer (OR=0.67, CI=0.52-0.87), poorest (OR=0.73, CI=0.57-0.95)- taking richest category as the reference. Even after adjusted for the confounders, current economic status remained negatively associated with depression for all the categories of current economic status- richer (AOR=0.67, CI=0.52-0.88), middle (AOR=0.68, CI=0.52-0.89), poorer (AOR=0.58, CI=0.44-0.75), poorest (AOR=0.63, CI=0.48-0.83). Having one or more chronic condition also increased the risk of depression in both unadjusted (OR=1.61, CI=1.41-1.83), as well as in the adjusted model (AOR=1.86, CI=1.64-2.10). As compared to the “no discrimination” category, the odds of depression were significantly higher among those who face moderate depression (OR=2.46, CI=1.85-3.27), or high discrimination (OR=2.69, CI=2.25-3.19) in the unadjusted model. In the adjusted model as well, risk of depression was significantly higher among both categories of discrimination-moderate (AOR=2.32, CI=1.73-3.11) and high (AOR=2.45, CI=2.03-2.95). Finally, for the health behaviours or tobacco use and alcohol consumption, we found evidence of significant association between tobacco use and depression in unadjusted model (OR=1.19, CI=1.05-1.36), the association remained significant after controlling for sociodemographic factors (AOR=1.25, CI=1.05-1.48). While consumption of alcohol reduced the risk of depression as per the unadjusted model, this finding was insignificant (OR=0.89, CI=0.76-1.05). On the other hand, the adjusted model shows that there is a higher risk of depression among those who consumed alcohol, though effect of alcohol consumption in this model as well remained insignificant (OR=1.14, CI=0.93-1.41).
Since the all the predictors other than alcohol consumption were found to be associated with depression later in life after controlling for sociodemographic characteristics, we included all predictors other than alcohol consumption in the mediation analysis. The conceptual framework of this analysis is given in Figure 1.
Table 3 Logistic regression results for major depression among older adults, India, LASI, 2017–18.
| Unadjusted model | Adjusted model | |||
| OR | p-value (95% CI) | AOR | p-value (95% CI) | |
| Childhood health status | ||||
| Good/ fair® | ||||
| Poor | 3.04 | 0.00(2.08-4.46) | 2.69 | 0.00(1.88-3.86) |
| Childhood socioeconomic status | ||||
| Good/ varied® | ||||
| Poor | 1.52 | 0.00(1.32-1.75) | 1.38 | 0.00(1.21-1.57) |
| Education | ||||
| No education® | ||||
| Primary | 0.85 | 0.02(0.73-0.98) | 0.96 | 0.70(0.76-1.20) |
| Secondary | 0.62 | 0.00(0.53-0.73) | 0.75 | 0.04(0.57-0.99) |
| Higher | 0.50 | 0.00(0.40-0.62) | 0.63 | 0.01(0.43-0.91) |
| Current economic status | ||||
| Richest® | ||||
| Richer | 0.73 | 0.20(0.56-0.94) | 0.67 | 0.00(0.52-0.88) |
| Middle | 0.75 | 0.04(0.57-0.99) | 0.68 | 0.01(0.52-0.89) |
| Poorer | 0.67 | 0.00(0.52-0.87) | 0.58 | 0.00(0.44-0.75) |
| Poorest | 0.73 | 0.02(0.57-0.95) | 0.63 | 0.00(0.48-0.83) |
| Chronic physical condition(s) | ||||
| No chronic physical condition® | ||||
| One or more chronic condition present | 1.61 | 0.00(0.57-0.95) | 1.86 | 0.00(1.64-2.10) |
| Discrimination | ||||
| No discrimination® | ||||
| Moderate discrimination | 2.46 | 0.00(1.85-3.27) | 2.32 | 0.00(1.73-3.11) |
| High discrimination | 2.69 | 0.00(2.25-3.19) | 2.45 | 0.00(2.03-2.95) |
| Tobacco use | ||||
| Never used® | ||||
| Used in past/ currently using | 1.19 | 0.01(1.05-1.36) | 1.25 | 0.01(1.05-1.48) |
| Alcohol consumption | ||||
| Never consumed® | ||||
| Consumed in past/ currently consuming | 0.89 | 0.16(0.76-1.05) | 1.14 | 0.22(0.93-1.41) |
® Reference category.
Mediation Analysis
The findings of this analysis are presented in table 4. The total effect, which represents the effect of early life conditions without adjusting for the role of mediators along life-course, both poor health, as well as poor socioeconomic status were found to be significantly associated with depression. Upon decomposing this total effect, we found that although both of the early life conditions still had a significant direct effect on depression, the indirect effect (mediated by risk factors along life course) was significant only in case of poor childhood socioeconomic status. This underscores that effect of childhood health on depression later in life follows critical period model.
The percentage of indirect effect, that is the effect of mediators, out of total effect of childhood SES on depression later in life was found to be approximately 10 percent. Upon further examining the contribution of each of the mediators to the effect of poor childhood SES on depression in older age we found that, the highest contribution was due to discrimination (65.33%). Contribution of education was found was around 50 percent and that of tobacco use was approximately 64 percent. Both chronic physical conditions and current financial status worked as suppressors and contributed around -61 percent and -19 percent respectively, out of total effect of mediators.
Table 4 Decomposing the effects of early-life conditions on depression later in life
| Poor childhood health | Poor childhood SES | ||||
| Logistic regression coefficient |
p-value (95% CI) | Logistic regression coefficient |
p-value (95% CI) |
||
| Total effect | 1.07 | 0.00(0.65-1.48) | 0.35 | 0.00(0.22-0.48) | |
| Direct effect | 1.04 | 0.00(0.59-1.42) | 0.31 | 0.00(0.17-0.45) | |
| Indirect effect | 0.06 | 0.06(-0.003-0.12) | 0.04 | 0.04(0.002-0.07) | |
| Decomposition (%): | |||||
| Education | 50.21 | ||||
| Current economic status | -18.62 | ||||
| Chronic physical condition | -61.24 | ||||
| Discrimination | 65.33 | ||||
| Health behaviour (tobacco use) | 64.33 | ||||
Table 5 presents our findings from the analysis to examine the mediating role of childhood health in the effect of childhood socioeconomic status on depression later in life. Total effect of childhood socioeconomic status was found to be significant, similar to our aforementioned findings, as expected. We also found that the direct effect was still significant. Furthermore, though the contribution of childhood health as a mediator here was small, at less than 3 %, it was still found to be a significant mediator between childhood socioeconomic conditions and depression later in life.
Table 5 Examining the role of childhood health in mediating the effect of childhood SES on depression later in life
| Poor childhood SES | ||||
| Logistic regression coefficient | p>z |
p-value (95% CI) | ||
| Total effect | 0.34 | 0.00 | 0.00(0.22-0.47) | |
| Direct effect | 0.33 | 0.00 | 0.00(0.21-0.46) | |
| Indirect effect | 0.01 | 0.00 | 0.00(0.004-0.014) | |
| Contribution of childhood health (%) | ||||
| 2.7 | ||||
The association of early life conditions on depression later in life and the possible role of mediators along the life-course has been examined mostly in Western society [28], but not in other cultures like India, where the results might vary. We examined whether the effects of early life conditions- childhood health and childhood socioeconomic status- on depression later in life were mediated by factors along life-course using data from the first wave of LASI (2017-2018). We first performed logistic regression to examine the association of various early life conditions, as well as factors along life-course, with depression later in life. The variables which were found significant were then used in mediation analysis using KHB method, to assess the role of mediators [26,27]. We also examined if the effect of childhood socio-economic status was mediated by health status during childhood.
Our findings support that significant direct effect of both childhood health and childhood socioeconomic status on depression later in life. We also found that the role of mediators, depicted by indirect effect, was insignificant in case of association between childhood health and depression later in life. This finding supports the critical period model for explaining the impact of childhood health on depression at older age. Thus, it underscores the importance of prioritizing improvement of health status of children in India. Their health conditions during this critical period of the life may lead ‘biological programming’ which may have a lasting effect on their mental health. Similar findings from a study conducted in Europe lend support for this model to explain the effect of childhood health on health in later stages of life, particularly depression [6].
By contrast, the ‘pathway model’, which emphases on the importance of factors along life-course to reduce the risk of depression at older ages, is suitable for at least partially explaining the effect of childhood socioeconomic conditions on depression later in life. The role of mediators, i.e. indirect effect, were found to be significant in this case. This finding is also supported by studies conducted elsewhere in Europe [6] and Mexico [29] . Experience of discrimination and tobacco use had the highest contribution in the relationship between childhood SES and depression later in life. Education was also found to be a mediator of paramount importance.
Interestingly, though chronic physical condition contributed a major proportion to the total mediation, the percentage was negative, implying that it was a suppressor. This may be because those who had poor childhood socioeconomic status may have poor SES later in life [30, 31] resulting in reduced risk of chronic physical conditions later in life [32]. Chronic physical condition has been found to increase the risk of depression in this study. Thus, poor SES in adulthood reduces the risk of depression when the effect of chronic physical conditions is not controlled. Similarly, economic status also played the role of a suppressor.
In the next step of analysis, we examined the role of childhood health as a mediator between childhood socioeconomic status and depression at later stages in life. We found that although childhood health status could explain only a meagre proportion (2.7%) of the total effect of early life socio-economic conditions, its role as a mediator was significant. Thus in addition to childhood health status not having any significant mediator in its the effect of on depression later in life, childhood health was itself found to be a mediator in effect of childhood socioeconomic status on depression. This further underscores the pertinence of childhood health.
This study has several limitations that future research may address. First, the findings are based on the cross-sectional data collected in the first wave of the survey. Thus, inferences on causal relationship were beyond the scope of this study. Though LASI is a panel study and data will be collected prospectively in later waves of the survey, data regarding the early-life conditions will still remain retrospective in nature as the target population for this survey is older adults of age 45 years and above. This bring us to our second limitation i.e. recall bias in reporting the early life conditions. Moreover, depression may have led to reporting bias of stressful event during childhood. Furthermore, the findings regarding childhood socioeconomic status were in relation to their relative socioeconomic status during that time, as using a scale to assess their absolute socio-economic status in early-life was also beyond the scope of this study. Another limitation was that temporality between health behaviours and depression could not be demonstrated due to the study design.
Despite having certain limitations, the findings of this study highlight the importance of adopting a life-course perspective to reduce the growing burden of depression among older adults in India. This can be used as a basis for further research on prevention of mental disorders from a life-course perspective. The results would also inform policy decisions for reducing risk of depression later in life. This paper emphasises on the importance of focus on improving childhood health conditions, as not only the role of mediators is insignificant in the effect of childhood health on depression later in life, childhood health also mediates the effect childhood socioeconomic conditions on depression. As the results also suggest that effect of poor childhood socioeconomic status is mediated by discrimination, tobacco use, and education, thus interventions along the life-course are of utmost importance to reduce the occurrence of depression among those who belonged to poor SES during childhood. Since chronic physical conditions acted as major suppressor, it highlights the pertinence of emphasizing on interventions to prevent chronic physical conditions in order to reduce the burden of depression, as chronic physical conditions may increase the risk of depression among those who did not even have adverse SES conditions during childhood.
Longitudinal Ageing Study of India
Years Lived with Disability
Disability-adjusted Life-years
Socioeconomic Status
Short Form Composite International Diagnostic Interview
Odds Ratio
Adjusted Odds Ratio
Confidence Interval
Karlson-Holm-Breen
(a) Ethics Approval and Consent to Participate: The field survey for the data collection was done after obtaining prior informed consent (written and verbal) from all the participants. The Indian Council of Medical Research (ICMR) and partner institutions provided guidance and ethical approval for conducting the LASI survey. All methods were carried out in accordance with relevant guidelines and regulations by the Indian Council of Medical Research (ICMR).
(b) Consent for Publication: Not applicable
(c) Availability of data and materials: The data used in this study was obtained from International Institute of Population Science which can be accessed upon request on their website (https://www.iipsindia.ac.in/content/LASI-data.).
(d) Competing interests: The authors declare that they have no competing interests
(e) Funding: Not applicable
(f) Acknowledgement: Not applicable
(g) Author's Contribution:
Dr. Farheen: Conceptualization, Methodology, Formal Analysis, Writing-original draft
Dr. Priyanka Dixit: Supervision, Validation, Writing-review & editing