Multivariate decomposition of gender differentials in cognitive impairment among older adults in India based on Longitudinal Ageing Study in India, 2017–2018

Abstract

Background

Increasing life expectancy and declining fertility rates have increased the ageing population around the world. The literature lacks a consensus regarding the risk of cognitive impairments by gender.

Objective

Our study aims to examine the differences in cognition impairments between male and female older adults in India.

Methodology

We utilized data from the first wave of the Longitudinal Ageing Study in India (LASI) (2017–18), analyzing 31,464 older adults aged 60 years and above (15,098 males and 16,366 females). Cognitive impairment is measured using the Harmonized Cognitive Assessment Protocol (HCAP) which includes five broad domains (memory, orientation, arithmetic function, executive function, and object naming). A multivariate decomposition analysis was performed using STATA 17 software to identify covariates'contributions, which explain the group differences to average predictions.

Findings

The prevalence of cognitive impairment was significantly higher among females (19.8%) than males (6.4%) (p < 0.001). Gender disparities were more pronounced among the oldest-old (41.5% vs. 15.9%), widowed individuals (24.6% vs. 9.8%), those with no education (25.1% vs. 11.8%), and individuals living alone (23.4% vs. 5.0%). Decomposition analysis revealed that 62% of the gender gap in cognitive impairment was attributable to differences in compositional factors, primarily education (42%), marital status (6%), working status (6%), difficulty in instrumental activities of daily living (3%), and physical activity (2%). The remaining 38% of the disparity was due to differences in how these factors impacted men and women.

Conclusion

The findings indicate that cognitive impairments are more pronounced among women. Gender-responsive interventions improving education access among the female gender would bring relevant and desired results.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-025-06811-6.

Background

Improving life expectancy and decreasing fertility rates have increased the ageing population around the world [1]. Aging is a global issue and developed, and developing countries are experiencing an increase in the proportion of the ageing population [2, 3]. The population of older population is expected to rise to 315 million by 2050 [4]. In other words, India’s geriatric population is expected to increase to around 19 percent of India’s total population by the year 2050 [5]. With an ever- increasing proportion of older adults, there are bound to have some repercussions on healthy ageing [6]. Previous studies have also noted a preponderance of depression and loss of cognitive abilities among the older adults [7, 8]. In recent years, research on cognition has developed an understanding of cognitive impairments [9]. However, to date, the notion of cognition has not been defined precisely [10]. In its larger context, cognition is defined as the capability of a subject to attribute mental condition, in terms of beliefs and goals, to one and others [11]. A decline in cognition could result in motor deficits and cognitive impairments [12]. Cognitive impairment defines the context of individuals who are on a conventional path of a wide variety of dementing processes [13]. Manifestation of cognition decline includes changes in verbal ability, decreased numeric activity, slower problem solving, and decreased ability to recall words [14–17]. Ageing is known as a period of high risk of morbidities, including chronic diseases. Therefore it becomes critical to diagnose the cognitive impairments among the older adults at the earliest [18] as Studies have proved that cognitive limitations could hamper adherence to medication which might aggravate illness among the older adults [19].

The ageing process brings significant cognitive challenges [20], which further transforms into poor well-being and might aggravate chronic illness among the older adults and subsequent mortality [21]. However, older people react differently to cognitive impairments, and it is according to certain risk factors they are opened to. Various studies have earmarked certain factors associated with cognitive impairments, including low education level [8, 22], employment status [22], socioeconomic conditions [22], place of residence [22, 23], and gender [24]. Few studies have also linked cognitive impairments to the use of alcohol, tobacco, and smoking [24]. Of all the determinants of cognitive impairments, gender is one determinant for which the evidence is not conclusive and remained scattered [25].

The literature lacks a consensus regarding the risk of cognitive impairments by gender [5]. Some studies have depicted a higher prevalence of cognitive impairments among male older adults [17], whereas some other studies have found a higher prevalence of cognitive impairments among female older adults [24, 26, 27]. However, quite a few studies found no gender differences in cognitive impairment [28]. Furthermore, gender differential in cognitive ability has been widely studied in developed countries than in developing countries [29]. The mixed findings of the association between gender and cognitive impairments prompted us to examine the gender differences in cognition in India by utilizing the latest data source, i.e., Longitudinal Ageing Study in India.

India presents a unique case for studying cognitive impairments among older adults due to its distinct demographic and socioeconomic landscape. The country is experiencing a rapid demographic transition, with an increasing proportion of older adults, coupled with disparities in education, healthcare access, and living conditions across regions and social groups. Additionally, cultural and societal norms in this country have historically shaped gender roles, impacting health outcomes differently for men and women. Factors such as differences in lifetime employment, nutritional status, healthcare utilization and caregiving responsibilities could contribute to gender-based disparities in cognitive health. Understanding these differences within the Indian context is decisive for developing targeted interventions and policies to support healthy ageing and cognitive well-being among the elderly population. Therefore, this study aims to provide an in-depth examination of gender differences in cognition among older adults in India.

Data and methods

Study design and sample selection

The data for this study came from India's Longitudinal Ageing Study's first wave, which was just released (LASI). In 2017–18, LASI undertook large-scale national research to investigate the health, economic, and social drivers and effects of India's population ageing [30]. It is a nationally representative survey of over 72,000 adults aged 45 and over from throughout India's states and union territories. The survey's main objective is to explore the health, social, and economic well-being of older individuals in India. LASI utilized a multistage stratified area probability cluster sampling approach to arrive at the final units of observation, which comprised older people aged 45 and higher, as well as their spouses of any age [30]. The survey employed a three-stage sampling methodology in rural areas and a four-stage sampling methodology in urban areas. In each state/UT, the first stage was to choose Primary Sampling Units (PSUs), which are sub- districts (Tehsils/Talukas), and the second stage was to select villages in rural areas and wards in urban areas within the PSUs. Families were picked in the third phase from a variety of rural regions. In contrast, sampling in urban areas necessitated an extra step. In the third stage, each Census Enumeration Block (CEB) in each urban area was picked at random. In the fourth step, households from this CEB were picked. The whole methodology was included in the survey report, together with detailed information on the survey design and data collecting [30]. The current study is performed on the eligible respondents aged 60 years and above. The total sample size for the present study is 31,464 older adults aged 60 years and above (male- 15,098 and female- 16,366).

Variable description

Outcome variable

The study has used the Harmonized Cognitive Assessment Protocol (HCAP) tool, which includes five broad domains to assess cognitive decline: memory, orientation, arithmetic function, object naming, and executive function (Table 1) [31]. Our study's cognitive impairment is based on a variety of cognitive tests, including, immediate (0–10 points) and delayed word recall (0–10 points); orientation related to time (0–4 points) and place (0–4 points); computation (0–2) and backward counting from 20 (0–2 points); arithmetic ability based on serial 7 s (0–5 points) executive functioning based on paper folding (0–3) and pentagon drawing (0–1); and object naming (0–2). The overall score ranges between 0 and 43, and a higher score value denotes better cognitive functioning. Respondents who received help during the cognitive module were removed from the analysis in our research. Individuals scoring below the 10 th percentile cutoff are classified as"Having cognitive impairment,"while those above this threshold are categorized as"Not having cognitive impairment."[30].

Table 1.

Description of domain-wise cognitive measures in LASI, 2017–2018

Cognitive Domains	Measures	Measurement	Range
Memory	Immediate word recall	Interviewer read out a list of 10 words and respondents were asked to repeat the words	0–10
	Delayed word recall	Respondents were asked to recall the same words read out for immediate recall after some time	0–10
Orientation	Time	Respondents were asked to state today’s date, month and year and day of the week. For each question, the score was 0 or 1. Correct responses received 1point, incorrect responses received 0. The total score for time was 0–4	0–4
	Place	Orientation towards place was captured based on place of interview, name of the village, street number/colony name/landmark/neighbourhood and name of the district. Each correct response scored 1 point. The total score ranged from 0 to 4	0–4
Executive function	Executive (paper folding)	This is a three-stage command task. The respondents were instructed to take a piece of paper from the interviewer, turn it over, fold it in half and give it back to the interviewer. Three points were given if each task was completed successfully	0–3
	Pentagon drawing	Visuo-construction is the ability to coordinate fine motor skills with visuospatial abilities, usually by reproducing geometric figures. Respondents were asked to copy two overlapping pentagons and scored 1 point for a correct drawing	0–1
Object naming	Object naming: 0–2	The interviewer points to a specific object and asks the respondent to name it. Two objects were pointed out and 1 point was given for each correct response	0–2
Arithmetic function	Backward counting	Respondents were asked to count backward as quickly as possible from the number 20. The respondents were asked to stop after correctly counting backward from 20 to 11 or from 19 to 10. Correct counting received 2 points: counts with a mistake received 1 point. Those who could not count received 0 points	0–2
	Serial 7	Respondents were asked to subtract 7 from 100 in the first step and asked to continue subtracting 7 from the previous number in each subsequent step for five times. Each correct response received 1 point	0–5
	Computation	This test involved the mathematical operation of division. Respondents were asked to compute the net sale price of a product after considering a discount sale of half of the original price	0–2
Cognition	Composite cognitive index	Combined scores of memories (total word recall), orientation, arithmetic function, executive function and object naming	0–43

Individual factors

Young old (60–69 years), old-old (70–79 years), and oldest-old (80 + years) were used to classify age. Sex was coded as male and female. Educational status was recoded as no education/primary not completed, primary, secondary and higher. Working status was recoded as currently working, retired, and not working. Marital status was recoded as currently married, widowed, and others. Others included divorced/separated/never married. Living arrangement was recoded as living alone, living with a spouse, living with spouse and children, and living with others. Social participation was categorized as no and yes. Social participation was measured through the question, “Are you a member of any of the organizations, religious groups, clubs, or societies? The response was coded as no and yes. Physical activity status was coded as frequent (every day), rare (more than once a week, once a week, one to three times in a month), and never. The question through which physical activity was assessed was “How often do you take part in sports or vigorous activities, such as running or jogging, swimming, going to a health center or gym, cycling, or digging with a spade or shovel, heavy lifting, chopping, farm work, fast bicycling, cycling with loads”?

The CIDI-SF (Short Form Composite International Diagnostic Interview) score of 3 or higher is used to determine the likelihood of serious depression in older individuals with dysphoria.

symptoms. This scale has been verified in field settings and is extensively used in population- based health surveys to assess a likely psychiatric diagnosis of severe depression. The lowest 10 th percentile is used as a proxy measure for major depression among older adults. Self-rated health was coded as good, including excellent, very good, and good, whereas poor includes fair and poor. ADL (Activities of Daily Living) difficulty was categorized as no or yes. The phrase"activities of daily living"(ADL) refers to everyday self-care activities (such as movement in bed, changing position from sitting to standing, feeding, bathing, dressing, grooming, personal hygiene, etc.). The ability or inability to perform ADLs is used to measure a person’s functional status, especially in people with disabilities and the ones in their older ages. Difficulty in IADL (Instrumental Activities of Daily Living) was coded as no and yes. Activities of daily living that are not necessarily related to the basic functioning of a person, but they let an individual live independently in a community. These tasks are necessary for independent functioning in the community. Respondents were asked if they were having any difficulties that were expected to last more than three months, such as preparing a hot meal, shopping for groceries, making a telephone call, taking medications, doing work around the house or garden, managing money (such as paying bills and keeping track of expenses), and getting around or finding an address in unfamiliar places. Morbidity was coded as no morbidity, 1 and 2 +.

Using household consumption data, the monthly per capita consumption expenditure (MPCE) quintile was determined. The sample households were canvassed using sets of 11 and 29 questions on food and non-food expenses, respectively. Food spending was gathered 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 has been used to standardize food and non-food expenses. The monthly per capita consumption expenditure (MPCE) is calculated and used to summaries.

consumption. The variable was then divided into five quintiles, i.e., from poorest to richest. Religion was coded as Hindu, Muslim, Christian, and Others. Caste was categorized as Scheduled Tribe, Scheduled Caste, Other Backward Class (OBC), and others. As a result of their low caste status in Hindu society, the Scheduled Castes are a group of individuals who are socially isolated and financially/economically disadvantaged. Two of India's most disadvantaged and discriminating socioeconomic groups are the Scheduled Tribes (STs) and Scheduled Castes (SCs). The OBC refers to those who are considered"educationally, economically, and socially backward."The OBCs are considered lower castes in the old caste system, although they are not outcasts. The “other” caste group is seen to have a better social rank. Place of residence was categorized as rural and urban. The regions of India were coded as North, Central, East, Northeast, West, and South.

Statistical analysis

To show the preliminary findings, descriptive analysis and bivariate analysis were used. The gen der differences were evaluated and the significance level was determined using a percentage test [32]. The contributions of variables that explain group differences to average predictors were identified using a multivariate decomposition approach [33].

The goal of the decomposition analysis was to find variables that influenced the difference in cognitive impairment between male and female.

The compositional differences (endowments) ‘E'and the effects of characteristics, which are the difference in the coefficients or behavioural change ‘C'responses for the selected predictor variables, are the two contributing effects in the multivariate decomposition analysis [34]. The observed differences in cognitive impairment thus can be additively decomposed into characteristics (or endowments) components and a coefficient (or effects of characteristics).

component [35]. In the non-linear model, the dependent variable is a function of a linear combination of predictors and regression coefficients:

$$Y=F(X\beta )=logit(Y)=X\beta ,$$

where Y denotes the n* 1 dependent variable vector, X an n*K matrix of independent variables, and β a K* 1 vector of coefficients.

The proportion difference in Y between male A and female B of cognitive impairment can be decomposed as:

$$Y_A-Y_B=F(X_A{\beta }_A)-F(X_B{\beta }_B)$$

For the log odds of cognitive impairment, the proportion of the model is written as

$$\begin{array}{c}\begin{array}{cc}Logit(Y_A)-logit(Y_B)\hfill & =F(X_A{\beta }_A)-F(X_B{\beta }_B)\\ \hfill & =\underset{\mathrm{E}}{\underbrace{F(X_A{\beta }_A)-F(X_B{\beta }_A)}}+\underset{\mathrm{C}}{\underbrace{F(X_B{\beta }_A)-F(X_B{\beta }_B)}}\end{array}\end{array}$$

The component ‘E’ is the difference attributable to endowment change, usually called the explained component. The ‘C’ component is the difference attributable to coefficient (behavioural) change, usually called the unexplained component.

The model structure for the decomposition analysis was:

$Logit(A)-Logit(B)=[{\mathrm{\beta }}_{0\text{A}}-{\mathrm{\beta }}_{0\text{B}}]+\sum {\mathrm{\beta }}_{\text{ijA}}[{\text{X}}_{\text{ijA}}-{\text{X}}_{\text{ijB}}]+\sum {\text{X}}_{\text{ijB}}[{\mathrm{\beta }}_{\text{ijA}}-{\mathrm{\beta }}_{\text{ijB}}],$ where.

• β_0A is the intercept in the regression equation for male

• β_0B is the intercept in the regression equation for female

• β_ijA is the coefficient of the j^tℎ category of the i^tℎ determinant for male

• β_ijB is the coefficient of the j^tℎcategory of the i^tℎ determinant for female

• X_ijA is the proportion of the j^tℎcategory of the i^tℎ determinant for male

• X_ijB is the proportion of the j^tℎcategory of the i^tℎ determinant for male

The command mvdcmp was used to carry out multivariate decomposition analysis in STATA 17 [36].

Results

Background characteristics of older adults

A higher proportion of older adults belonged to the young-old cohort. More than half of the older adults had no education/primary not completed (Supplementary Table 1). However, it was higher among older females than older males. Nearly 44 percent of older males and one-fourth of older females were working, three percent of older males and nine percent of older females were living alone, and about six percent of older males and four percent of older females were engaged in social participation. One-fourth of older males and 12 percent of older females did the frequent physical activity. Moreover, depression was more prevalent among older females compared to the older male. Nearly half of the older adults reported good self-rated health. Difficulty in ADL was more prevalent among older males than older females; however, the prevalence of difficulty in IADL was higher among older females than in older males. About one-fourth of older adults were suffered from 2 + morbidity. The majority of study participants were Hindu and belonged to rural areas.

Cognitive impairment among older males and females in India

Table 2 depicts the gender differences (female-male) in cognitive impairment among older adults. Overall, the prevalence of cognitive impairment was significantly higher among older females than males (p < 0.001). Results show significant gender differences in cognitive impairment among older adults (differences: 13.4; p < 0.001). The highest and significant gender differences in cognitive impairment were observed among oldest-old (difference: 25.6; p < 0.001), living with others (difference: 19.4; p < 0.001), those who suffered from depression (difference: 18.5; p < 0.001), had difficulty in ADL (difference: 18.7; p < 0.001), belonged to scheduled caste (difference: 17.8; p < 0.001) and those who were poorer (difference: 17.1; p < 0.001). However, it was lowest among urban residents (difference: 7.1; p < 0.001), engaged in social participation (difference: 8.4; p < 0.001), living with spouse (difference: 8.9; p < 0.001) and those who were currently married (difference: 9.2; p < 0.001) compared to counterpart who were not in a union. The gender disparity in the incidence of cognitive impairment was relatively higher among those who had no schooling (difference: 13.2; p < 0.001). Older persons who never engaged in physical activities have reported larger difference in cognitive impairment between male and female (difference: 13.4; p < 0.001). The gender gap in cognitive impairment is highest in the East (difference:16.7; p < 0.001) and West (difference:16.3; p < 0.001) regions, followed by the North (difference:15.4; p < 0.001) and Northeast (difference:14.2; p < 0.001). while the South exhibits the smallest gender difference (difference: 7.7; p < 0.001).

Table 2.

Percentage of cognitive impairment among older male and females in India, 2017–18

Background characteristics	Male (%)	Female (%)	Difference (%)	p-value
Age
Young-old	4.2	14.1	9.9	0.001
Old-old	7.7	25.4	17.7	0.001
Oldest-old	15.9	41.5	25.6	0.001
Education
Not educated/primary not completed	11.8	25.1	13.2	0.001
Primary	2.2	2.5	0.4	0.064
Secondary	0.6	0.8	0.3	0.859
Higher	0.4	0.5	0.1	0.770
Working status
Working	5.2	17.0	11.8	0.001
Retired	7.5	25.7	18.2	0.001
Not working	7.1	17.0	9.9	0.001
Marital status
Currently married	5.7	14.8	9.2	0.001
Widowed	9.8	24.6	14.8	0.001
Others	10.3	17.9	7.6	0.045
Living arrangement
Living alone	5.0	23.4	18.4	0.001
Living with spouse	7.7	16.6	8.9	0.001
Living with children and spouse	5.8	19.2	13.4	0.001
Living with others	10.6	30.0	19.4	0.001
Social participation
No	6.7	20.2	13.5	0.001
Yes	1.6	10.0	8.4	0.001
Physical activity
Frequent	4.7	15.6	10.9	0.001
Rarely	4.8	16.3	11.5	0.001
Never	7.7	21.0	13.4	0.001
Depression
No	6.4	19.2	12.8	0.001
Yes	7.1	25.6	18.5	0.001
Self-rated health
Good	5.0	15.9	10.9	0.001
Poor	8.2	23.9	15.7	0.001
Difficulty in ADL				0.001
No	5.4	16.6	11.3	0.001
Yes	11.0	29.7	18.7
Difficulty in IADL
No	4.1	13.1	8.9	0.001
Yes	10.5	25.3	14.8	0.001
Morbidity
No morbidity	7.6	22.3	14.7	0.001
1	5.1	19.7	14.5	0.001
2 +	5.6	15.9	10.3	0.001
Wealth index
Poorest	11.5	27.9	16.3	0.001
Poorer	10.1	27.2	17.1	0.001
Middle	4.8	17.8	13.0	0.001
Richer	3.2	14.8	11.6	0.001
Richest	1.2	8.0	6.9	0.001
Religion
Hindu	6.2	19.3	13.2	0.001
Muslim	7.1	22.7	15.5	0.001
Christian	10.7	23.3	12.6	0.001
Others	7.6	19.7	12.1	0.001
Caste
Scheduled Caste	8.6	26.4	17.8	0.001
Scheduled Tribe	14.8	29.3	14.5	0.001
Other Backward Class	5.4	17.9	12.5	0.001
Others	4.8	16.4	11.7	0.001
Place of residence
Rural	8.1	24.8	16.7	0.001
Urban	2.3	9.4	7.1	0.001
Region
North	5.6	21.1	15.4	0.001
Central	7.4	19.9	12.5	0.001
East	6.7	23.4	16.7	0.001
Northeast	8.2	22.4	14.2	0.001
West	6.0	22.3	16.3	0.001
South	5.6	13.2	7.7	0.001
Total	6.4	19.8	13.4	0.001

Differences: Female-Male; ADL Activities of daily living, IADL Instrumental activities of daily living; p-value based on proportion test

Multivariate decomposition logistic regression analysis (Table 3)

Table 3.

Multivariate logistic regression decomposition estimates for gender differentials in cognitive impairment among older adults in India, 2017–18

Background characteristics			Due to difference in characteristics					Due to difference in coefficients
		Coef	Standard error	p -value	Percent		Coef	Standard error	p -value	Percent
Age
Young-old
Old-old		− 0.001	0.000	0.000	− 0.6		0.005	0.003	0.180	3.3
Oldest-old		0.000	0.000	0.000	0.1	− 0.53	0.000	0.001	0.822	− 0.2	3.1
Education
Not educated/primary not completed
Primary		0.072	0.012	0.000	51.7		0.013	0.042	0.757	9.5
Secondary		− 0.007	0.003	0.022	− 5.0		0.001	0.013	0.933	0.8
Higher		− 0.006	0.008	0.439	− 4.3	42.3	− 0.005	0.018	0.799	− 3.4	6.9
Working status
Working
Retired		− 0.004	0.001	0.003	− 2.7		0.008	0.007	0.260	5.6
Not working		0.012	0.003	0.000	9.0	6.3	0.000	0.001	0.615	0.3	5.9
Marital status
Currently married
Widowed		0.009	0.002	0.000	6.4		0.001	0.002	0.720	0.5
Others		0.000	0.000	0.136	0.0	6.4	− 0.001	0.001	0.381	− 0.5	0.0
Living arrangement
Living alone
Living with spouse		− 0.001	0.001	0.132	− 0.9		− 0.009	0.007	0.186	− 6.8
Living with children and spouse		0.000	0.000	0.056	0.0		− 0.034	0.019	0.078	− 24.8
Living with others		0.001	0.000	0.001	0.8	− 0.1	− 0.001	0.001	0.540	− 0.5	− 32.0
Social participation
No
Yes		0.001	0.000	0.020	0.6	0.6	− 0.025	0.023	0.265	− 18.3	− 18.3
Physical activity
Frequent
Rarely		0.000	0.000	0.290	− 0.3		0.002	0.003	0.510	1.3
Never		0.004	0.001	0.001	2.6	2.3	0.000	0.008	0.971	0.2	1.5
Depression
No
Yes		0.000	0.000	0.105	0.1	0.1	0.002	0.001	0.158	1.2	1.2
Self-rated health
Good
Poor	0.001		0.000	0.000	0.8	0.8	0.003	0.004	0.527	2.0	2.0
Difficulty in ADL
No						1.0					− 1.3
Yes	0.001		0.000	0.000	1.0		− 0.002	0.002	0.356	− 1.3
Difficulty in IADL
No
Yes	0.004		0.001	0.000	2.6	2.6	− 0.002	0.004	0.493	− 1.7	− 1.7
Morbidity
No morbidity
1	0.000		0.000	0.002	− 0.2		0.000	0.003	0.962	0.1
2 +	− 0.001		0.000	0.000	− 0.5	− 0.7	− 0.004	0.003	0.221	− 2.8	− 2.7
Wealth index
Poorest
Poorer	0.000		0.000	0.003	0.0		0.001	0.002	0.503	1.0
Middle	0.000		0.000	0.000	0.0		0.004	0.003	0.168	2.6
Richer	0.000		0.000	0.000	0.2		0.008	0.004	0.024	5.9
Richest	0.001		0.000	0.000	1.0	1.2	0.004	0.005	0.385	2.9	12.4
Religion
Hindu
Muslim	0.000		0.000	0.033	0.0		0.007	0.002	0.002	5.4
Christian	0.000		0.000	0.731	0.0		− 0.001	0.002	0.536	− 0.8
Others	0.000		0.000	0.393	0.0	0.0	0.000	0.001	0.796	0.2	4.7
Caste
Scheduled Caste
Scheduled Tribe	0.000		0.000	0.000	0.1		− 0.002	0.003	0.370	− 1.7
Other Backward Class	0.000		0.000	0.002	0.1		− 0.002	0.005	0.628	− 1.7
Others	0.000		0.000	0.000	− 0.1	0.2	− 0.017	0.006	0.003	− 11.9	− 15.4
Place of residence
Rural
Urban	− 0.001		0.000	0.000	− 0.4	− 0.4	− 0.007	0.005	0.120	− 5.1	− 5.1
Region
North
Central	0.000		0.000	0.000	0.3		− 0.001	0.002	0.735	− 0.6
East	0.000		0.000	0.000	0.2		0.000	0.003	0.919	− 0.2
Northeast	0.000		0.000	0.027	0.0		− 0.003	0.002	0.167	− 2.4
West	0.000		0.000	0.158	0.1		0.001	0.002	0.581	0.9
South	0.000		0.000	0.001	− 0.2	0.4	− 0.006	0.004	0.135	− 4.3	− 6.6
Constant							0.115	0.072	0.112	83.1	83.1
Overall	0.086		0.003	0.000	62.4		0.052	0.004	0.000	37.6

ADL Activities of daily living, IADL Instrumental activities of daily living

About 62 percent of the overall gender inequalities in the cognitive impairment were explained by the differences in compositional characteristics (Endowments) between males and females. In contrast, the remaining 38 percent was due to the difference in the effect of characteristics (Coefficient). Among the compositional change factors, education, marital status, working status, difficulty in IADL, and physical activity had a statistically significant effect on the change contribution. The gender gap is mostly explained by the education level of older adults, marital status, working status, difficulty in IADL, and physical activity. The gender inequalities in cognitive impairment would be eliminated if males and females had equal access to education and work. For example, if males and females had the same distribution of educational attainment and work status, the gender inequality in cognitive impairment would be reduced by 42 percent and.

six percent, respectively. The study also found that the marital status of older adults accounts for nearly 6 percent of the explained gap in gender differential. Similarly, if males and females had the same distribution in the prevalence of difficulty in IADL and had the same distribution of physical activity, the gender gap in cognitive impairment would be reduced by 3 percent and 2 percent, respectively.

Discussion

Cognitive impairment is one of the most significant problems in the study of geriatric healthcare. The older adults with cognitive impairments require obligatory care and time of family members to maintain their quality of life [20]. There is minimal evidence on prevalence and gender differences in cognitive impairments among the older adults in India. Few studies have examined gender differences in cognitive impairments; however, all of those studies were community- specific [7, 8]. In light of the above-stated problems, our study findings provide several interesting results that might significantly reduce gender differential in cognitive impairments among the older adults in India. The present study examined the gender differences in cognitive impairments among the older adults in India. Furthermore, the study attempted to identify various individuals’ characteristics that could reduce possible gender differences in cognitive impairments among the older adults.

Similar to various previous studies, the study found a higher prevalence of cognitive impairments among older women than in older men [7, 24]. Around 6 percent of the older men were reported cognitive impairments, and almost one-fifth (19.8%) of the older women had cognitive impairments. A study in an urban area of Kerala, India, has reported the prevalence of mild cognitive impairments to be around 26 percent [7]. A study examining gender differences noted a higher prevalence of cognitive difference among female older adults (34.7% vs. 23.4%) than in.

male older adults in South India. Although, there are multiple hypotheses available that explain gender differences, they are indecisive. Women’s survival to longer ages may be one explanation for a higher incidence of cognitive decline among them compared to men, possibly. Other explanations may be etiological differences, selective attrition of men due to early mortality attributable to cardiovascular risk factors with a competing risk of death or dementia, and lower thresholds of disease pathology required to produce symptoms [37]. Based on our analysis, the findings of this study were that socioeconomic factors related to cognitive reserve (education and work status) account for gender differences in cognitive abilities in India. Lower educational attainment in women, compared to that in men, contributed to higher cognitive impairment among women. Furthermore, the larger proportion of those solely engaged in domestic work among women also accounted for women’s worse cognitive outcomes [38]. In India gender roles remained static for long, restricting women’s educational and occupational opportunities [39], which might have contributed to the reduced cognitive functionability among women.

Furthermore, a study examining gender differences in cognitive impairments among the overweight and obese population noted a higher prevalence of cognitive impairments among male (14.8% vs. 8.3%) than in females, however the study consisted adults and not the older adults’ population [40]. Interestingly, global studies have demonstrated similar gender disparities in cognitive impairments, with older women generally showing a higher prevalence of cognitive decline than men which in line with the present study. For instance, a study in United States reported higher risk of dementia and mild cognitive impairment among women than men, potentially attributed to longer life expectancy and biological differences [41]. Similarly, European studies, such as those conducted in United Kingdom and Norway, have found that older women are more likely to experience cognitive impairments than men [42, 43]. However, the patterns vary in some regions. For example, research from China and Japan has suggested narrower gender gaps in cognitive impairment prevalence, which may be influenced by differing sociocultural factors and health behaviors [38, 44].

The results from this study found that the prevalence of cognitive impairments was lower among those with higher education than those with lesser education. Also, it was noticed that providing equalizing access to education would reduce gender gaps in cognitive impairments by almost 42 percent. This finding is in agreement with the previous studies [24]. As found in this study, the inverse relationship between educational status and cognitive impairment has found allied support from the literature available in developed [45] and developing countries [46]. However, some research contradicts the above findings, suggesting that higher education does not improve cognitive efficiency [47, 48].

Previous researches suggest that higher education enhances cognitive ability in the older population [49, 50]. The association between education and cognitive impairments could be affected by various pathways. The brain reserve capacity theory specifies that well-educated people probably have greater brain reserve capacity than their counterparts which might be associated with better cognition ability in educated people [51]. Furthermore, educated people nurture the need to seek emotional support over less educated people, leading to positive changes in brain structure and function, improving their cognitive abilities [52]. Besides, education could also mediate assuage one’s behaviour to a certain degree and can improve general health and cognitive function in particular [46]. Highly educated people may follow healthy lifestyles linked to better cognition [46].

Furthermore, education can increase cultural competencies, improving reading, math, reasoning skills, and test-taking abilities, which could further enhance cognitive abilities [53]. Marital status is another important factor predicting cognitive impairment in this study. Those currently in the marital union were less likely to report cognitive impairments than widowed. Almost 6 percent of the inequality in cognitive impairments among the older adults was associated with marital status. The above finding concordant with previously available literature [54, 55]. However, Selvamani & Singh (2018) examined cognition combined with the issue of underweight among older adults in India [54]. The adverse effect of widowhood on various health outcomes have been documented in cognitive loss [56–58]. However inevitable, the spouse's death is one of the most stressful events in life, imposing economic, psychosocial, and emotional breakdown [59], further leading to a decline in cognitive abilities. Spousal bereavement worsens stress from the grief process and the significant loss of emotional and social support, leading to reduced cognitive abilities during old age [60].

This study found that those working had a lower prevalence of cognitive impairments than those who were not working. Considering previous studies, the finding herein agrees with other research that has found that work status was associated with improved cognitive functions [24]. Education attainment in early life is also likely to affect late-life cognitive outcomes through socioeconomic status and social behaviour pathways. Education is associated with adult occupation and lifestyle, and higher education in early life might result in greater mental activity as part of occupation [61–63]. Therefore, education attainment may directly influence cognitive function in early life and education has the potential to play an indirect role in maintaining cognitive function later in life through its association with adult socioeconomic status and social behaviours.

Physical activity was a noticeable predictor of cognitive impairment among the older adults in this study. Previous several researches have noticed the same finding [64–66]. Physical activity may slow cognitive decline directly through intensified blood flow or indirectly by minimizing other risk factors, namely cardiovascular disease, obesity, and diabetes [67]. People who are engaged in physical activity can minimize their cortisol levels that help prevent stress, which could have a positive effect on cognitive functions [68]. Another study noted that physical activity helps protect cerebrovascular integrity, meaning that physical activity sustains flow of blood to the brain and oxygen supply and other nutrients leading to better cognitive functioning [69, 70].

The study further noted several other factors associated with cognitive impairments among older adults including; increasing age, absence of social participation, presence of depression, poor self- rated health, difficulty in ADL and IADL, and morbidities in rural areas and belonging to poor households. All these factors have previously been noticed as important factors in the study of cognitive impairments among older adults in various settings [71–76]. Social participation posits a rich opportunity for older adults to experience a dynamic and engaging environment that helps maintain better cognitive functioning [72]. Furthermore, social participation reduces stress, resulting in neuronal changes improving cognitive functioning [77]. Depression may influence the hippocampus and other neural systems that regulate the stress axis, leading to increased vulnerability towards cognitive impairments [78]. Rural areas have a dearth of health professionals who could administer screening and diagnose cognitive functioning, leading to higher cognitive impairments [75].

To address cognitive impairments among older adults, several targeted interventions are essential. Promoting lifelong education, particularly for women, and expanding global physical activity programs, such as those focused on reducing cognitive decline, are crucial. Strengthening community-based mental health programs and integrating cognitive screenings with routine healthcare will ensure early detection and support, especially in underserved and rural areas. Encouraging social participation through culturally appropriate activities, such as intergenerational programs, provides vital cognitive stimulation. Reducing educational disparities by implementing targeted programs for women and girls can yield long-term benefits for cognitive health.

Additionally, developing support systems for widows that offer emotional, social, and financial assistance can alleviate stress, reducing cognitive decline. Accessible physical activity initiatives, such as yoga or walking clubs, alongside campaigns promoting social engagement, will improve overall cognitive function. Expanding healthcare access to address comorbidities, including depression and cardiovascular diseases, particularly in rural regions, is key to mitigating cognitive impairments. These multifaceted interventions can significantly improve cognitive health and reduce gender disparities in cognitive impairments among older adults.

Limitations and strengths of the study

This study has certain limitations. Firstly, this study utilized cross-sectional data, which could only determine association and not causation and effect. A few of the important predictors were self-

reported, such as self-rated health. Self-reporting of the data could have been affected by recall bias or report bias [79] and missing responses may affect the accuracy of some variables included in the study. Despite a few limitations, the study has some considerable strengths too. The study's strengths included the use of a large population-based dataset that was released in the year 2021.

Conclusion

This study contributes to a better understanding of cognitive impairments among older adults in India. The results found that equalizing access to education and work would reduce gender gaps in cognitive impairments by almost 49 percent. The findings highlight a higher prevalence of cognitive impairments among older women in India. Therefore, addressing gender-related inequalities in education and work status shall be promoted. Gender- responsive interventions improving education access among the female gender would bring relevant and desired results. There is a need to develop interventions focusing on building an informal support group at the community level for widows. Additionally, community-based support for widows specially women and initiatives promoting physical activity and social engagement could play a crucial role in mitigating cognitive deterioration. These insights provide valuable directions for policymakers in fostering healthy aging and reducing cognitive impairments in later life.

Abbreviations

ADL

Activities of Daily Living

IADL

Instrumental Activities of Daily Living

LASI

Longitudinal Ageing Study in India

MPCE

Monthly Per Capita Consumption Expenditure

OBC

Other Backward Classes

SC

Scheduled Caste

ST

Scheduled Tribe

Authors’ contributions

MS: Conceptualized and designed the study, developed the methodology, conducted data analysis and interpretation, and wrote the original draft of the manuscript. Additionally, MS reviewed and edited subsequent drafts of the paper. IG : Assisted in refining the study design and methodology, contributed to data interpretation, and provided critical review and feedback on the manuscript.

Funding

This study received no funding.

Data availability

This research utilized publicly available secondary datasets. The data used in this study can be accessed through The Gateway to Global Aging Data platform, website: (https://g2aging.org/?section = overviews&study = lasi). Data access requests can be made via this portal, which contains information on the Longitudinal Ageing Study in India (LASI).

Declarations

Ethics approval and consent to participate

The LASI survey, involving human participants, underwent review and received ethical clearance from Indian Council of Medical Research (ICMR). The ICMR provided essential guidelines for the study's execution and granted the necessary ethical clearance. All study participants gave their informed consent prior to involvement, with options for both written and verbal consent formats. The study rigorously adhered to relevant guidelines and regulations, aligning with the ethical principles set forth in the World Medical Association's (WMA) Declaration of Helsinki. This comprehensive approach ensured ethical integrity of the study and participants'rights were protected throughout the research process.

Competing interests

The authors declare no competing interests.