Healthcare access and utilisation trends among the elderly in India: Evidence from the LASI Wave-1 survey

Abstract

Background

Population ageing is a global trend, driven by increased life expectancy, which has led to a rise in chronic diseases and greater healthcare needs among older persons. Despite the implementation of national policies and initiatives such as the National Programme for Health Care of the Elderly (NPHCE), Ayushman Bharat and the National Health Policy 2017, older persons, particularly in rural areas, still face barriers to accessing healthcare, including cost, distance, and the quality of care. Socio-economic factors continue to play a key role in healthcare utilisation. This study examines the healthcare access and utilisation patterns and identifies associated determinants among older persons using nationally representative data.

Methods

This study analysed cross-sectional data from the Longitudinal Ageing Study in India (LASI) Wave I Survey (2017–18), which included 31,902 individuals aged 60 years and above. Data from the Individual Schedule was analysed, focusing on healthcare utilisation and related factors. Binary logistic regression was conducted to assess factors influencing outpatient (OPD) and inpatient (IPD) service use. Adjusted odds ratios (aORs) with 95 % confidence intervals (CIs) were reported, and a p value of <0.05 was considered statistically significant.

Findings

Lack of health insurance reduced OPD use (aOR: 0.869, p = 0.006). Females were less likely to use IPD services (aOR: 0.818, p < 0.001), while individuals aged >90 years had higher odds (aOR: 1.470, p < 0.001). Religion, socioeconomic status, and literacy significantly influenced utilisation. Christians and Buddhists had higher IPD use; Muslims and Sikhs had lower odds. Richer groups were less likely to use both services. Limited literacy was linked to reduced OPD use. The North-East reported the highest OPD expenses and travel distances.

Interpretations

There is a pressing need to address healthcare access gaps among older persons through targeted policies and improved outreach. Expanding affordable health insurance can reduce out-of-pocket costs and improve health outcomes in India's ageing population, addressing key Sustainable Development Goals (SDGs), particularly SDG 1,3,10 and 11.

1. Introduction

Population ageing is the absolute increase in the population over 60, both as a percentage of the total population and in comparison to the younger population [1]. This global phenomenon is one of the most significant demographic shifts of the 21st century with profound effects on healthcare systems, social structures, and economies. The United Nations defines an older person as someone over 60 years of age [2]. In 2019, there were approximately 1 billion older persons worldwide, a number projected to rise to 1.4 billion by 2030 and 2.1 billion by 2050, with developing nations accounting for the majority of this growth [2]. India, as a rapidly developing nation, mirrors this trend, with older persons currently constituting over 10 % of the population, equivalent to approximately 104 million individuals. By 2050, an estimated 317 million Indians will be over the age of 60, comprising 19.1 % of the country's total population [3]. According to the World Health Organisation (WHO), rising life expectancy due to overall improvements in population health means that an individual reaching the age of 60 can expect to live an additional 18 years [3,4].

In the past, most deaths were due to acute infections, which left no lasting effects if survived. Today, longer lifespans have shifted mortality toward chronic diseases that develop over the years. Lower mortality in chronic conditions has also increased the number of people living with them [4]. The implementation of various policies and programmes across India has significantly improved the mortality rates, leading to an increase in the older persons' population. However, many of these older persons, particularly in rural areas, face unmet healthcare needs, which continues to contribute to high death rates among them [5]. Older adults account for 23 % of the global disease burden, with nearly half concentrated in high-income countries and approximately one-fifth in low- and middle-income countries [6].

Access to healthcare is largely shaped by the geographic proximity of providers and facilities, indicating how well the medical service can meet the population's needs [7]. Similarly utilisation of healthcare reflects how often and in what ways individuals engage with health services for prevention, treatment, maintaining well-being, or gaining insight into their health status [8]. Together, these concepts are essential to evaluate how effectively healthcare systems serve their populations, especially vulnerable groups like older persons.

A study based on the 75th round of the National Sample Survey (NSS 2017–18) found that only one-third of India's older persons accessed public healthcare services [9]. To improve utilization, the government is implementing the National Programme for the Health Care of Elderly (NPHCE), which provides preventive, curative, promotional, and rehabilitative care through healthcare institutions. The program focuses on the development of a new “architecture” for ageing with the broader goal of active and healthy ageing [10]. However, healthcare access among the older persons depends not only on the availability of providers but also on social, economic, and cultural factors, along with considerations like distance, cost, and quality of care [11]. Launched in 2018, Ayushman Bharat provides affordable healthcare through Health and Wellness Centres (HWCs) and Pradhan Mantri Jan Arogya Yojana (PMJAY) that offer a range of services to integrate geriatric care into primary healthcare. Under this, senior citizens aged 70 years and above, regardless of their economic status, are entitled to an extra cover of ₹5 lakh per year exclusively for their use [12].

India faces significant challenges in addressing the needs of its ageing population, including long-term care, managing noncommunicable diseases and mental health conditions, and ensuring healthcare services are accessible to all older individuals. Limited facilities and gaps in policy implementation continue to hinder healthcare access [13]. Furthermore, research on older persons' healthcare utilisation in India remains scarce. The NSS analysis highlights that public healthcare is not the preferred choice for inpatient care in most states, with socio-demographic and need-based factors, along with financial constraints, playing a crucial role in healthcare decisions [14]. Healthcare utilisation among older persons in India can be influenced by factors such as age, gender, religion, socioeconomic status, insurance coverage, and literacy.

These disparities highlight the need to understand the complex interplay of demographic and socio-economic determinants in shaping access to and use of healthcare services among older adults. The pattern of health care utilisation among this age group is directly related to Goal 3 of the Sustainable Development Goal (SDG), which underpins healthy lives and promotes wellbeing for all age groups, thus indicating equity in access and utilisation of healthcare services by older persons.

The current study assesses the availability, accessibility and utilisation of healthcare services among the older persons in India using LASI Wave I data (2017–18), and also identifies the key demographic and socio-economic factors influencing healthcare access and use, in order to inform targeted, region-specific interventions that promote equitable and inclusive healthcare for the ageing population [15]. Identifying these gaps can help develop targeted, region-specific interventions to address inequalities, strengthen healthcare systems, and ensure equitable access and enhance the quality of life among them.

2. Methods

2.1. Data and sample

We analysed cross-sectional data from the Longitudinal Ageing Study in India (LASI) Wave I Survey, carried out between 2017 and 18, by the International Institute for Population Sciences, Mumbai, India. LASI is a full-scale, national survey of scientific investigation of the health, economic, and social determinants and consequences of population ageing in India. It collects data on individuals aged 45 years and older from all states and union territories of India. The Wave I survey included 73,396 participants, with data collection taking place between April 2017 and December 2018, except in Sikkim, where it was conducted in 2020–21. The current study included individuals aged 60 years and older. Among a total of 73,396 participants, 31,902 were aged 60 years and above.

2.2. Sampling design

The LASI adopted a multistage stratified area probability cluster sampling design to arrive at the eventual units of observation: older adults aged 45 and above and their spouses, irrespective of age. Within each state, LASI Wave 1 adopted a three-stage sampling design in rural areas and a four-stage sampling design in urban areas. In each state/UT, the first stage involved the selection of Primary Sampling Units (PSUs), that is, sub-districts (Tehsils/Talukas), and the second stage involved the selection of villages in rural areas and wards in urban areas in the selected PSUs. In rural areas, households were selected from selected villages in the third stage. However, sampling in urban areas involved an additional stage. Specifically, in the third stage, one Census Enumeration Block (CEB) was randomly selected in each urban area. In the fourth stage, households were selected from this CEB. For the purpose of this study, the focus was on older persons aged 60 years and above.

2.3. Study tools

The LASI survey consisted of four main components: the Household Schedule, Individual Schedule, Biomarker Survey, and Community Schedule. For this study, data were specifically taken from the Individual Schedule, which includes detailed information on healthcare use, social factors, and economic conditions.

In this study, the dependent variables included healthcare utilisation, specifically the use of outpatient department (OPD) and inpatient department (IPD) services among the older persons, measured as binary outcomes (Yes/No) to simplify modelling and interpretation in logistic regression analysis [16,17]. The independent variables include demographic factors such as age, gender, and religion; socioeconomic factors such as education level, monthly per capita expenditure (MPCE) quintiles (a proxy for economic status), and health insurance coverage; and healthcare-related factors such as the distance from health facilities, out-of-pocket expenses for OPD and IPD services for public as well as private facilities [18]. Health insurance (HI) was considered by combining individuals covered by any health insurance plan that includes coverage for surgery, diagnostic tests, doctor visits, medications, dental care, in-home care, hospitalisation costs, and other related expenses. Geographic variables such as urban or rural residence and state/union territory were also considered to examine regional differences. Additionally, factors such as the time required to access healthcare services were included as control variables. Together, these variables were analysed to assess their influence on healthcare access and utilisation among older persons in India. Access to the LASI data set is granted for academic research purposes by the International Institute of Population Sciences (IIPS), Mumbai, India (https://www.iipsindia.ac.in/content/LASI-data) [15].

2.4. Ethics review

The study used publicly available de-identified data for secondary data analysis from the Longitudinal Ageing Study in India (LASI) Wave I Survey. Therefore, it was granted ethical waiver vide letter number AIMS/IEC-HR/2024/80, dated 23rd December 2024.

2.5. Data analysis

The analysis focused on the older persons population, aged 60 years and above, resulting in a sample size of 31,902 participants. Descriptive statistics was conducted, and categorical data are presented as numbers (n) and percentages (%) with a 95 % confidence interval.

To assess the normality of qualitative and quantitative variables, the Kolmogorov-Smirnov test was used. Tests like the Chi-Square test, Mann-Whitney U test, and Hosmer and Lemeshow test were applied for further analysis. Missing values were excluded from the data analysis. Binary logistic regression analysis was conducted to estimate the likelihood of OPD and IPD utilisation based on predictor variables. All selected independent variables were included in the model simultaneously, without applying variable selection criteria. Adjusted Odds Ratios (aORs) and 95 % Confidence Intervals (CIs) were calculated after applying sampling weights. Statistical significance was considered at p < 0.05. Data analysis was conducted using IBM SPSS version 20.0.

3. Results

3.1. Healthcare utilisation patterns

The analysis of LASI Wave 1 (2017–18) revealed significant patterns in healthcare utilisation (Table 1) among older adults (aged 60+). Age was a key factor, with the 60–69 age group accounting for the majority of IPD (59.8 % rural, 60.5 % urban) and OPD users (61.3 % rural, 60.6 % urban) (p < 0.01). Health insurance significantly increased utilization, with 22.5 % of rural and 19.7 % of urban IPD users, and 22.4 % of rural and 18.9 % of urban OPD users being insured (p < 0.001). Literacy was also significant, as older adults who could read and write accessed more healthcare facilities in urban areas (IPD: 30.3 %, OPD: 29.5 %) than in rural areas (IPD: 16.7 %, OPD: 16.6 %) (p < 0.001). These findings highlight the impact of age, gender, religion, health insurance, and literacy on healthcare access, with rural-urban differences.

Table 1.

Patterns of utilisation of IPD and OPD facilities by background characteristics of older adults (aged 60 years and above) in urban and rural India, LASI wave 1, 2017–18.

Variables (N = 31,902)	IPD			OPD
	Rural	Urban	p-value	Rural	Urban	p-value
Age group
60 to 69 years (19,211, 60.2 %)	5007 (59.8 %)	2435 (60.5 %)	0.005	11,066 (61.3 %)	5684 (60.6 %	<0.001
70 to 79 years (9250, 29.0 %)	2374 (28.3 %)	1169 (29.1 %)		5237 (28.6 %)	2801 (29.8 %)
80 to 89 years (2862, 9.0 %)	782 (9.3 %)	353 (8.8 %)		1676 (9.1 %)	773 (8.2 %)
More than 90 years (579, 1.8 %)	216 (2.6 %)	65 (1.6 %)		363 (2.0 %)	127 (1.4 %)
Sex
Male (15,340, 48.1 %)	4281 (51.1 %)	2009 (50.0 %)	0.234	8930 (48.7 %)	4358 (46.4 %)	<0.001
Female (16,562, 51.9 %)	4098 (48.9 %)	2013 (51.0 %)		9412 (51.3 %)	5027 (53.6 %)
Religion
Hindu (23,292, 73.0 %)	6008 (71.7 %)	2742 (68.2 %)	<0.001	13,569 (74.0 %)	6667 (71.0 %)	<0.001
Muslim (3731, 11.7 %)	544 (6.5 %)	603 (15.0 %)		1752 (9.6 %)	1490 (15.9 %)
Christian (3194, 10.0 %)	1416 (16.9 %)	519 (12.9 %)		1988 (10.8 %)	798 (8.5 %)
Sikh (979, 3.1 %)	138 (1.6 %)	86 (2.1 %)		626 (3.4 %)	250 (2.7 %)
Buddhist (347, 1.1 %)	138 (1.6 %)	33 (0.8 %)		198 (1.1 %)	75 (0.8 %)
Others (358, 1.1 %)	136 (1.6 %)	39 (1.0 %)		208 (1.1 %)	105 (1.1 %)
MPCE Quintile
Poorest (6850, 20.6 %)	1934 (23.1 %)	930 (23.1 %)	0.539	3826 (20.9 %)	1989 (21.2 %)	0.847
Poorer (6573, 20.6 %)	1697 (20.3 %)	824 (20.5 %)		3813 (20.8 %)	1956 (20.8 %)
Middle (6502, 20.4 %)	1715 (20.5 %)	774 (19.2 %)		3765 (20.5 %)	1873, (20.0 %)
Richer (6259, 19.6 %)	1509 (18.0 %)	757 (18.8 %)		3573 (19.5 %)	1839 (19.6 %)
Richest (5988, 18.8 %)	1524 (18.2 %)	737 (18.3 %)		3365 (18.3 %)	1728 (18.4 %)
Covered by Health Insurance
Yes (6590, 20.7 %)	1854 (22.5 %)	763 (19.7 %)	<0.001	4070 (22.4 %)	1749 (18.9 %)	<0.001
No (25,006, 78.4 %)	6385 (77.5 %)	33,119 (80.3 %)		14,114 (77.6 %)	7491 (81.1 %)
Read or write
Can read only (414, 1.3 %)	94 (1.4 %)	69 (3.1 %)	<0.001	203 (1.4 %)	163 (3.2 %)	<0.001
Can write only (540, 2.3 %)	155 (2.3 %)	48 (2.2 %)		308 (2.1 %)	145 (2.8 %)
Can read and write (4601,19.9 %)	1138 (16.7 %)	665 (30.3 %)		2488 (16.6 %)	1517 (29.5 %)
Cannot read and write (17,538, 75.9 %)	5447 (79.7 %)	1414 (64.4 %)		11,959 (80.0 %)	3314 (64.5 %)

The sum of frequencies or percentages in each variable category may not be equal to the corresponding total in that category, due to missing values in that category.

p value < 0.05 shall be considered as significant.

3.2. Patterns of service utilization by background characteristics

The comparison of IPD and OPD service utilization and expenditures by place of residence (LASI Wave 1, 2017–18) revealed significant rural-urban differences (Table 2). For IPD services, urban residents had a significantly higher MPCE (Rs 4077.76 vs. Rs 2832.72, p < 0.001) and average expenditure on the last four IPD visits (Rs 54,959.5 vs. Rs 37,233.57, p < 0.001). Urban residents also spent more nights in the hospital during the last year (7.43 vs. 6.38, p = 0.007) and travelled shorter distances (37.67 km vs. 51.4 km, p < 0.001) with less travel time (105.18 min vs. 141.76 min, p < 0.001). Rural residents experienced higher working days lost in accompanying partners (4.47 vs. 3.53, p < 0.001).

Table 2.

Comparison of IPD and OPD Service Utilisation and Expenditures by Place of Residence, LASI wave 1 survey, 2017–18.

IPD Services	Place of residence	N	Mean	Mean Rank	Sum of Ranks	p-value
MPCE amount in rupees	Rural	8379	2832.72	5561.18	46,597,088.50	<0.001
	Urban	4022	4077.76	7533.94	30,301,512.50
The average expenditure of last four IPD visits	Rural	422	37,233.57	326.50	137,781.00	<0.001
	Urban	274	54,959.5	382.39	104,775.00
Number of times admitted to Hospital during last 12 months	Rural	1635	1.3	1276.88	2,087,700.50	0.765
	Urban	922	1.32	1282.76	1,182,702.50
Number of nights spent during hospitalization in last 12 months	Rural	1632	6.38	1248.29	2,037,203.50	0.007
	Urban	922	7.43	1329.21	1,225,531.50
Person working days lost during last hospitalisation accompanying partner	Rural	1424	4.47	1169.78	1,665,771.50	<0.001
	Urban	809	3.53	1024.09	828,489.50
Person working days lost during last hospitalisation respondent	Rural	323	13.16	227.11	73,355.00	0.279
	Urban	139	14.24	241.71	33,598.00
Distance of health care facility from residence (in kilometres)	Rural	1601	51.4	1423.64	2,279,246.00	<0.001
	Urban	894	37.67	933.46	834,514.00
Travel time for IPD service from home (in minutes)	Rural	1621	141.76	1412.64	2,289,894.00	<0.001
	Urban	911	105.18	1006.46	916,884.00
OPD services
MPCE amount in rupees (Rs)	Rural	18,342	2873.96	12,333.95	226,229,337.50	<0.001
	Urban	9385	4141.35	16,854.32	158,177,790.50
Total amount that you or your household spent in last outpatient visit (Rs)	Rural	1454	2405.75	1033.56	1,502,797.00	<0.001
	Urban	712	3984.74	1185.48	844,064.00
Total amount spent on all out-patient visits (Rs)	Rural	11,362	5993.76	8494.05	96,509,437.00	<0.001
	Urban	6121	8803.91	9202.25	56,326,949.00
Number of times visited as an outpatient (including home visits)	Rural	11,425	4.8	8585.38	98,087,967.00	<0.001
	Urban	6160	5.08	9178.07	56,536,938.00
Person working hours lost during last out-patient visit accompanying person	Rural	11,416	2.04	8902.52	101,631,114.00	<0.001
	Urban	6154	4.26	8568.43	52,730,121.00
Person working hours lost during last out-patient visit respondent	Rural	11,417	1.72	8928.20	101,933,225.00	<0.001
	Urban	6155	1.3	8523.66	52,463,153.00
Distance to healthcare facility from residence outpatient visit (in Kilometres)	Rural	10,851	17.29	9161.98	99,416,692.00	<0.001
	Urban	6028	14.53	7140.36	43,042,068.00
Travel time for OPD service from home (in minutes)	Rural	10,940	71.14	9187.13	100,507,207.00	<0.001
	Urban	6072	56.52	7280.20	44,205,371.00

The above scale variable did not follow a normal distribution; therefore, the Mann-Whitney U test was applied.

p value < 0.05 shall be considered as significant.

For OPD services, urban residents had a significantly higher MPCE (Rs 4141.35 vs. Rs 2873.96, p < 0.001) and spent more on the last outpatient visit (Rs 3984.74 vs. Rs 2405.75, p < 0.001). Total expenditures on all OPD visits were also higher in urban areas (Rs 8803.91 vs. Rs 5993.76, p < 0.001). However, rural residents travelled longer distances (17.29 km vs. 14.53 km, p < 0.001) and took more travel time for OPD services (71.14 min vs. 56.52 min, p < 0.001). Rural respondents lost fewer working hours during outpatient visits than their urban counterparts (p < 0.001).

3.3. Determinants of healthcare facility utilization

The binary logistic regression analysis revealed significant findings for healthcare utilization (Table 3). For IPD services, individuals aged over 90 years had significantly higher odds of utilization (aOR: 1.470, p < 0.001), while females were less likely to utilize IPD services compared to males (aOR: 0.818, p < 0.001). Christians, Buddhists, and other religions showed higher odds of IPD utilization compared to Hindus (aORs: 2.846, 1.875, and 1.941, respectively, p < 0.001), while Muslims and Sikhs had lower odds (p < 0.001). Higher socioeconomic status, as indicated by MPCE quintiles, was associated with reduced odds of IPD utilization, with the richest quintile having an aOR of 0.798 (p < 0.001). For OPD services, individuals aged 80–89 years had slightly lower odds of utilization (aOR: 0.880, p = 0.049). Higher socioeconomic status also reduced OPD utilisation, with the richest quintile having the lowest odds (aOR: 0.696, p < 0.001). Additionally, lack of health insurance significantly reduced OPD utilization (aOR: 0.869, p = 0.006), and individuals who could only write had lower odds of OPD utilization (aOR: 0.682, p = 0.049). These findings highlight the influence of age, gender, religion, socioeconomic status, health insurance, and literacy on healthcare utilisation.

Table 3.

Determinants of healthcare facility utilisation for IPD and OPD services.

Variables	IPD		OPD
	aOR (95 % CI)	p-value	aOR (95 % CI)	p-value
Age
60 to 69 years	Reference		Reference
70 to 79 years	0.984 (0.926–1.047)	0.614	0.973 (0.892–1.062)	0.541
80 to 89 years	1.025 (0.934–1.124)	0.603	0.880 (0.775–1.000)	0.049
More than 90 years	1.470 (1.231–1.756)	<0.001	0.843 (0.661–1.076)	0.171
Gender
Male	Reference		Reference
Female	0.818 (0.772–0.866)	<0.001	1.039 (0.958–1.128)	0.356
Residence
Rural	Reference		Reference
Urban	0.948 (0.888–1.012)	0.108	0.968 (0.884–1.061)	0.489
Religion
Hindu	Reference		Reference
Muslim	0.726 (0.667–0.792)	<0.001	0.972 (0.865–1.091)	0.627
Christian	2.846 (2.594–3.121)	<0.001	1.087 (0.949–1.244)	0.229
Sikh	0.424 (0.349–0.514)	<0.001	1.633 (1.255–2.124)	<0.001
Buddhist	1.875 (1.471–2.390)	<0.001	0.595 (0.441–0.802)	0.001
Others	1.941 (1.515–2.486)	<0.001	1.107 (0.762–1.608)	0.594
MPCE Quintile
Poorest	Reference		Reference
Poorer	0.807 (0.746–0.874)	<0.001	0.970 (0.862–1.092)	0.616
Middle	0.813 (0.749–0.882)	<0.001	0.856 (0.761–0.964)	0.010
Richer	0.721 (0.662–0.784)	<0.001	0.821 (0.727–0.926)	0.001
Richest	0.798 (0.729–0.874)	<0.001	0.696 (0.614–0.789)	<0.001
Covered by Health Insurance
Yes	Reference		Reference
No	0.996 (0.931–1.065)	0.900	0.869 (0.787–0.960)	0.006
Read or Write
Can read-only	Reference		Reference
Can write only	0.901 (0.687–1.182)	0.452	0.682 (0.467–0.998)	0.049
Can read and write	0.930 (0.753–1.149)	0.502	0.873 (0.638–1.195)	0.397
Cannot read and write	1.018 (0.828–1.251)	0.866	0.859 (0.633–1.167)	0.332

Hosmer and Lemeshow Test: P value >0.05 for IPD (0.367) and OPD (0.419) (non-significant, hence indicating a good fit); Binary logistic regression was performed; values are presented as adjusted Odds ratio aOR (95 % Confidence Interval, CI).

p value < 0.05 shall be considered as significant.

3.4. Regional disparities in healthcare expenditures and accessibility

Fig. 1 indicates that older adults in the North Eastern states of India incur the highest expenses on OPD services. The states of Lakshadweep, Sikkim, and Arunachal Pradesh reported the highest average OPD expenses per visit, exceeding Rs 12,000 per visit. In contrast, older adults in Punjab had the lowest OPD expenses among all states, averaging just ₹856 per visit.

Fig. 1.

OPD and IPD (average of last 4 IPD visits) expenses in Rupees (₹) across States/UTs among older adults LASI wave 1, 2017–18.

Telangana and Haryana offer the costliest IPD services for older adults, with costs exceeding Rs 1,00,000 per visit. Meanwhile, Meghalaya provides the most affordable IPD services, with an average cost of Rs 4500 per visit for older adults.

Fig. 2A & B highlights significant disparities in access to healthcare services. Older adults in the Northeastern states face the greatest challenges in accessing OPD services. The older persons in Nagaland require an average of 6.5 h to access these services. Similarly, Lakshadweep residents face limited access to both OPD and IPD services, requiring 3.1 h and 6.4 h, respectively. The most inaccessible IPD services were reported in Arunachal Pradesh, where older adults need more than 15 h to access care.

Fig. 2.

Time and travel distance required to avail OPD and IPD services by the elderly in India.

(A: Time taken to reach OPD: 1- Within 1 h; 2–1 to 1.5 h; 3–1.5 to 2 h; 4- More than 2 h);

(B: Time taken to reach IPD: 1- Within 1 h; 2–1 to 1.5 h; 3–1.5 to 2 h; 4–2 to 2.5 h; 5–2.5 to 3 h; 6- More than 3 h)

(C: Travel distance to OPD: 1- Less than 5 km; 2–5 to 10 km; 3–10 to 15 km; 4–15 to 20 km; 5- More than 20 km);

(D: Travel distance to IPD: 1- Less than 10 km; 2–10 to 20 km; 3–20 to 30 km; 4–30 to 40 km; 5- More than 40 km)

Regarding the distance between home and IPD services (Fig. 2C & D), the greatest distances were reported in Nagaland (137 km), Lakshadweep (110 km), Jharkhand (83 km), and Jammu & Kashmir (83 km). In contrast, Delhi (8.8 km), Chandigarh (12 km), and Punjab (18 km) had the shortest distances, with access to IPD services being less than 20 km.

For OPD services, regional disparities were evident, with the longest distances observed in Nagaland (70 km) and Arunachal Pradesh (58 km), followed by Lakshadweep. On the other hand, Delhi, Chandigarh, Dadra & Nagar Haveli, Kerala, Puducherry, Tamil Nadu, and Haryana reported distances of less than 10 km to access OPD services.

4. Discussion

Healthcare access is essential for older persons, as ageing increases the risk of chronic conditions requiring regular medical care. Healthcare utilisation depends on multiple factors, including the need for care, awareness of this need, willingness to seek care, and the ability to access it. Studies show variations in healthcare utilisation across age groups, with older Indians reporting higher outpatient and inpatient visits [19]. As life expectancy increases, it is crucial not only to extend lifespan but also to improve the quality of life of older persons. This requires addressing key factors such as affordability, availability, and accessibility of healthcare services.

Our analysis from LASI Wave-1 primarily reflects immediate healthcare access and utilization patterns among older adults in India, without evaluating long-term retention of health-seeking behaviours or subsequent health outcomes. Our study found that health insurance coverage was linked to higher healthcare utilisation. Among inpatients, 22.5 % in rural areas and 19.7 % in urban areas were insured, while among outpatients, 22.4 % in rural and 18.9 % in urban areas had insurance (Table 2). Additionally, higher literacy levels contributed to increased healthcare use, particularly in urban areas, where 30.3 % of inpatients and 29.5 % of outpatients were literate. Earlier studies reported that 35 % of hospitalisations among older persons took place in public health facilities, and enrolment in publicly funded health insurance schemes increased the likelihood of seeking care at these facilities [20,21]. Research also suggests that older adults with higher education and health literacy are better at navigating healthcare systems and utilising services effectively [22].

Health insurance programs like the Pradhan Mantri Jan Arogya Yojana (PMJAY), launched in 2018, play a crucial role in reducing financial barriers [23]. Since the Longitudinal Ageing Study in India (LASI) was conducted between 2017 and 2019, PMJAY may not have been fully implemented at that time. Even now, efforts are needed to improve enrolment and ensure that older adults are aware of their entitlements. While PMJAY mainly focuses on inpatient care, many older persons, especially in rural areas, require primary healthcare services. Expanding the scheme to cover preventive and outpatient care would help improve access and reduce out-of-pocket expenses. Enhancing financial protection, simplifying claim procedures, and expanding coverage for long-term care are essential measures to prevent catastrophic medical costs for older persons.

A study by the United Nations Population Fund (UNFPA) reported that 65 % of India's older population suffers from chronic morbidities, leading to high out-of-pocket expenditures and financial hardship, given the lack of a strong social security system [24]. With an ageing population, the demand for healthcare services is expected to rise. Public healthcare utilisation by older individuals remains low, as shown by the 75th round of the National Sample Survey (2017–18), which found that only one-third of older individuals used public healthcare services (IPD: 39.8 %, outpatient care: 33.6 %) [9]. Managing chronic conditions requires ongoing treatment, including consultations, medication, and tests. However, both public and private healthcare systems in India lack continuity of care, leading to suboptimal treatment for older persons [25]. Rehabilitative services are limited in public facilities, while private options in urban areas remain costly for many low- and middle-income older individuals [26]. Strengthening outpatient care at the primary level is essential to ensuring longevity is accompanied by a better quality of life.

Our study also identified significant regional disparities in healthcare access and utilisation among older persons. Older adults in the Northeastern states, particularly in Nagaland, face considerable barriers to outpatient services, with an average travel time of 6.5 h (Figs. 2A & 2B). Similar findings by Swargiary and Lhungdim show that 54 % of individuals in the North East prefer private healthcare over public facilities (47 %), citing long travel times, extended waiting hours, and unavailable services as major challenges [27].

To address these issues, the Government of India introduced the National Programme for the Health Care of the Elderly (NPHCE), which aims to provide preventive, curative, and rehabilitative services through public healthcare institutions [10]. However, healthcare utilisation is influenced by more than just availability. Social, economic, and cultural factors, as well as distance, cost, and quality of care, also play a role in healthcare-seeking behaviour [11].

Our study further revealed that the elderly rural residents struggle to access healthcare primarily due to long travel distances, whereas urban residents have better access but face higher financial burdens (Table 2). Previous studies also confirm that transportation is a major barrier for older individuals in rural areas, where healthcare facilities are often distant, public transport is inadequate, and travelling alone is difficult, especially during odd hours or in adverse conditions [28]. Strengthening rural healthcare infrastructure by establishing telemedicine centres and mobile health clinics could help reduce travel time and associated costs [29].

In our study, women had lower odds of seeking inpatient (IPD) care (aOR: 0.818, p < 0.001), which may be attributed to entrenched gender disparities, lower autonomy, and sociocultural norms that deprioritise women's health needs in later life. This aligns with prior literature that has consistently documented gender-based inequities in healthcare utilisation among older adults in India [30,31].

A similar trend was observed in another study, where a higher proportion of males were hospitalized compared to females, reinforcing the gender gap in inpatient care. Women were more likely to seek outpatient treatment, which could suggest either a lower severity of illness or financial and social constraints limiting their access to inpatient care. The high cost of hospitalization may be a significant barrier, preventing women from receiving necessary inpatient treatment despite their healthcare needs [32] The findings of our study have direct implications for achieving the United Nations Sustainable Development Goal 3 (SDG 3), which aims to “ensure healthy lives and promote well-being for all at all ages.” In particular, targets 3.4 (reducing premature mortality from non-communicable diseases), 3.8 (achieving universal health coverage, including financial risk protection, access to quality essential healthcare services, and access to safe, effective, quality and affordable essential medicines and vaccines), and 3.c (increasing health financing and recruitment, development, training and retention of the health workforce) are highly relevant. The findings from our study underscore the critical role of expanding insurance coverage, reducing out-of-pocket expenditure, addressing gender and rural-urban disparities, and strengthening primary healthcare systems for the older population. Without focused attention on equitable healthcare access and financial protection for the ageing population, progress toward SDG 3 will remain incomplete. Promoting inclusive health systems that cater to the unique needs of older adults is fundamental to ensuring that no one is left behind in the pursuit of health-related SDGs [33].

This study aligns closely with the United Nations Sustainable Development Goals (SDGs), particularly SDG 3, which emphasizes ensuring healthy lives and promoting well-being for all at all ages, including achieving universal health coverage for older adults. By identifying patterns and disparities in healthcare access and utilization, the study also reflects SDG 1, highlighting the role of financial vulnerability in healthcare access, and SDG 10, which calls for reducing inequalities, including those based on age and socioeconomic status. Additionally, by examining urban–rural variations, the findings indirectly support SDG 11, advocating for age-friendly, inclusive, and accessible health services within communities [33]. This study also supports the World Health Organisation's Decade of Healthy Ageing (2021–2030) goals, especially promoting integrated care, enhancing access to services, and providing financial protection for older persons. It also aligns with the WHO's aim of achieving Universal Health Coverage (UHC) by addressing disparities in healthcare access and utilisation among the elderly [34,35].

These findings underscore the urgent need for evidence-based policies to close gaps in healthcare access and reduce out-of-pocket expenditures for older persons. Policymakers must focus on raising awareness, expanding equitable insurance coverage, and ensuring the availability of comprehensive healthcare services to improve the overall well-being of India's ageing population. The findings from LASI Wave-1 primarily reflect healthcare access and utilization trends among older adults in India and are broadly transferable to older populations with similar demographic and healthcare contexts in low- and middle-income countries. While this study uses nationally representative data, transferability to specific underserved or highly marginalised subpopulations (e.g., tribal communities, remote rural settlements, or those with severe disabilities) may be limited. Nevertheless, the findings remain highly generalisable to India's older adult population, given the LASI survey's design. Future research focusing on these specific subgroups can help tailor targeted interventions to further reduce disparities.

4.1. Strengths and limitations

This study offers a comprehensive and empirically grounded analysis of healthcare utilization among older persons in India, highlighting the influence of demographic, socioeconomic, and geographical factors. A key strength lies in its use of data from a large, nationally representative survey, enhancing the robustness, reliability, and generalisability of the findings to the broader older adult population in India. The methodological rigor of stratified subgroup analyses provides nuanced insights into differential healthcare access, utilization patterns, and underlying inequities.

Geographically, the study captures considerable regional and rural–urban variation, offering one of the few analyses that systematically examine disparities across states, including underserved regions like the North East. By identifying spatial inequalities and barriers such as travel time, infrastructure gaps, and financial constraints, the study contributes valuable evidence to support geographically targeted policy responses and interventions.

Empirically, this study extends existing literature by quantifying the associations between health insurance coverage, literacy levels, and gender with both inpatient and outpatient healthcare use, key indicators relevant for monitoring progress toward Universal Health Coverage (UHC) and SDG 1,3,10 and 11. The findings provide a robust empirical base to advocate for equity-focused reforms in insurance coverage, infrastructure development, and gender-sensitive health service delivery.

This study's cross-sectional design limits causal inference because it only assesses immediate healthcare utilisation patterns at a single point in time rather than over a period. Additionally, reliance on self-reported data may have introduced recall or reporting bias. The study did not evaluate retention, changes over time, or long-term health outcomes related to healthcare utilisation, which could offer deeper insights into the sustained impact of healthcare access interventions. Future research should explore these aspects using longitudinal data from subsequent LASI waves, complemented by mixed-methods approaches to capture the lived experiences of older adults. Such work could also examine how healthcare utilisation translates into improved health and quality of life over time.

4.2. Conclusion

This study reveals significant disparities in healthcare utilisation among older adults in India, particularly in rural areas with low health insurance coverage (20.7 %) and longer travel distances to healthcare facilities. Gender-based inequities with women less likely to access inpatient care highlight the need for gender-sensitive healthcare strategies. The inverse relationship between wealth and healthcare access, especially in wealthier quintiles, calls for policy reforms to enhance equity. Expanding health insurance to cover inpatient services and strengthening rural healthcare infrastructure, including telemedicine and mobile clinics, are crucial. Future efforts should prioritise evaluating these interventions to improve healthcare accessibility, affordability, and equity, particularly in underserved regions like the North East and Lakshadweep.

Data sharing statement

LASI data is publicly available and can be accessed through the DHS Program.

CRediT authorship contribution statement

Dhruvendra Lal: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. Amrit Virk: Writing – review & editing, Writing – original draft, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. Ashish Goel: Writing – review & editing, Visualization, Validation, Methodology. Sonu Goel: Writing – review & editing, Visualization, Validation, Supervision. Kavisha Kapoor Lal: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision. Suneela Garg: Writing – review & editing, Visualization, Validation, Supervision. Bhavneet Bharti: Writing – review & editing, Visualization, Validation, Supervision, Methodology.

Funding

There was no source of funding for the present study.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary material