Low-and middle-income countries (LMICs) have experienced steady improvements in life expectancy (LE), now averaging around 70–73 years (1, 2). However, these gains have not been paralleled by comparable increases in healthy life expectancy (HLE), which remains 6–10 years lower, primarily due to the rising burden of non-communicable diseases (NCDs), disabilities, and uneven access to healthcare services (3, 4). India, while sharing this broader LMICs trend, presents a distinct and compelling case for focused research. In 2022, India’s LE was approximately 70.8 years, whereas its HLE (or HALE-Healthy Adjusted Life Expectancy) was only 62.6 years, implying that older adults spend over 8 years in ill health (5). Yet, India is not merely following the LMICs trajectory; it is diverging in significant ways (6). Unlike several LMICs, where infectious diseases still dominate, India is undergoing a rapid epidemiological transition marked by a dual burden: persistent infectious diseases alongside a surge in multimorbidity and age-related chronic conditions (7). This is driven by its fast-ageing population, profound lifestyle changes, and entrenched socio-economic and sex inequalities.

Although India has witnessed remarkable improvements in LE over the past decades, reflecting substantial advancements in healthcare, living standards, nutrition, and public health initiatives (8). Since independence in 1947, LE at birth has risen from approximately 32 years to nearly 70.8 years by 2022 (5). This progress has been largely attributed to reductions in infant mortality, improved maternal health, and the successful control of infectious diseases (9, 10). Key programmes, such as the National Health Mission, have played a pivotal role in extending healthcare access, particularly in rural areas (11–13). Although both males and females have benefited from the declining mortality rates and targeted health interventions. However, the sex gap in LE has fluctuated over these periods and currently stands at 2.7 years, favouring females (14). This disparity or inequality (gap) becomes more pronounced in HLE (15), where sociocultural factors, regional disparities shaped the gap between male and female (16), healthcare accessibility (17), and largely by varying burdens of chronic disease such that certain conditions disproportionately affect younger males, while others contribute more significantly to morbidity in older females (18).

Parallelly, since 1990, India has been undergoing the late phase of epidemiological transition and since recent decades, this transition has been coupled with the rapidly ageing population, underscoring the dual challenges of longevity and changing age pattern of mortality (19–21). This aligns with the ongoing “expansion of morbidity” phase, wherein rising LE is accompanied by a growing proportion of life lived with illness, disability, and poor health (6) and further compounded in recent decades by the escalating burden of multimorbidity, defined as the coexistence of two or more chronic conditions in an individual (22). Moreover, as LE increases, India is facing the ongoing challenge of an ageing population (aged 60+), which is growing in size along with the rising burden of NCDs (23, 24).

In concordance with the above patterns, as the escalating burden of multimorbidity is emerging as a significant public health concern in India (25, 26), particularly among the older population, research has increasingly focused on its association with functional decline, reduced quality of life, elevated mortality risk, decreased healthcare utilisation, and escalating healthcare costs (27–29). Also, prior studies in India estimated that the prevalence of multimorbidity among older adult’s ranges from 23 to 42% (25). Hypertension-diabetes and hypertension-obesity were identified as the most common comorbidity dyads (30). Amid this multimorbidity pattern, in the recent study, the gap in prevalence and risk between males and females was sequentially observed in the rapidly ageing demographics (31).

However, this ripple effects of multimorbidity are not merely semantic; complex multimorbidity captures higher clinical severity and systemic burden by accounting for conditions affecting more than two organ systems (32). While the term “multimorbidity” captures disease count, a standard metric in ageing and health research, this binary threshold often fails to capture the clinical heterogeneity and complexity of chronic disease clustering, and it overlooks the heterogeneity and systemic distribution of conditions. By contrast, complex multimorbidity reflects a higher level of physiological burden and clinical management challenges. International literature emphasises that individuals with complex multimorbidity are more vulnerable to functional limitations, care fragmentation, and polypharmacy, and often require more intensive and coordinated healthcare (32). From a public health standpoint, this operational distinction is important for identifying high-need subgroups, improving care targeting, and understanding differential impacts on population health metrics like HLE. Yet, despite its conceptual and clinical importance, complex multimorbidity remains underutilised in Indian ageing research. Therefore, our study adopts this distinction to provide a more nuanced assessment of multimorbidity’s impact on HLE and its inequality (gap). In doing so, it addresses a crucial empirical gap while aligning with emerging global practice.

Although HLE is a crucial indicator of healthy longevity and obscures substantial variations in healthy lifespan inequality (HLI). HLI reflects the uncertainty surrounding the timing and onset of morbidity, which profoundly influences life-course decisions (33). From a public health perspective, a higher HLI suggests an increase in societal vulnerability and the inefficacy of policies to mitigate life-course risks (34). Despite its importance, evidence on addressing stark inequalities in HLE between older males and females in India, especially in the context of multimorbidity and complex multimorbidity, is limited. While previous studies have explored sex disparities in LE by cause of death (17, 35, 36) research on age-specific HLE inequalities due to multimorbidity is scarce. Thus, it is among the few countries where rapid ageing, uneven healthcare access, and rising multimorbidity intersect so sharply, making it an important case to investigate how multimorbidity influences healthy lifespan and the inequalities.

This study aims to bridge these critical knowledge gaps by providing the first comprehensive analysis of HLE patterns and inequalities related to multimorbidity among India’s older adult population. Our research objectives are threefold: (1) to estimate age-specific HLE for individual morbidities, multimorbidity, and complex multimorbidity among older adults in India; (2) to quantify the causes and magnitude of age-specific gaps in HLE and mortality between males and females for different morbidity patterns; and (3) to assess and quantify the age-specific contribution of complex multimorbidity disparities to overall HLE inequalities (gap) by sex. By focusing on these analytical objectives, this study contributes to the theoretical understanding of health inequality in ageing populations and offers empirical evidence for designing gender-responsive and equity-oriented health interventions in India and similar transitioning LMICs.

This is the first study that comprehensively estimated the HLE for multimorbidity, complex multimorbidity and the extensive number of morbidities for the older adult population in India. Additionally, we quantified the inequality in HLE between male–female by multimorbidity/complex multimorbidity and morbidities, attributing these disparities to prevalence rates and mortality rates. This study finally deepened the analysis by focusing on the disparity caused by complex multimorbidity. This structured discussion provides a concise synthesis of the results and highlights their broader implications for understanding sex and age-specific longevity inequalities, incorporating new insights from both mortality and morbidity factors

First, females consistently exhibited higher LE than males across all age groups and maintained slightly higher MFLE across most age groups. At 45–49, females had 25.50 multimorbidity-free years compared to 23.30 years for males and this aligns with global patterns of female longevity advantages (39, 40). At first glance of statistical view this was plausibly due to higher age-specific LE and lower multimorbidity prevalence among females (19.63%) compared to males (19.98%), which supports the idea that females not only live longer but also have relatively better health status in midlife. However, the decline in the sex gap at older ages suggests that males who survive to advanced ages may be a select, healthier subgroup (41).

Although age-specific disparities in CMFLE between male and female appear relatively small but females tend to spend significantly more years living with complex multimorbidity, and the widest differential was observed in the 45–49 age group (see Table 1; Figure 2). However, as individuals age, this sex gap gradually diminishes, indicating parity in later years. By ages 85+, the MFLE gap becomes negligible (−0.06 years), while the CMFLE gap reduces to 0.18 years. This suggests parity in health outcomes between sexes as they age for both multimorbidity and complex multimorbidity. Consistent with previous study evidence, this pattern aligns with the hypothesis that as LE increases, males and females may experience a more compressed period of morbidity, leading to a reduced sex gap in health expectancy measures (42). This persistent but reducing sex differences highlight the importance of considering age-specific and sex-specific factors in understanding the burden of multimorbidity in the mid-year of life course. This pattern persists across age groups, with females experiencing a prolonged burden of chronic conditions, while males display a sharper decline in years lived with complex multimorbidity (YLCM) as age advances.

However, a widely documented paradoxical pattern of healthy longevity by females in ageing and epidemiological studies was also observed in this study, such that females consistently spend more years as unhealthy years as complex multimorbidity but had higher age-specific LE than males (39, 43). At ages 45–49, females spend 6.09 years with multimorbidity compared to 5.85 years for males and 10.77 years with complex multimorbidity compared to 8.93 years. Subsequently, the male population also experience a sharper decline in YLCM with age, while females endure a prolonged period of life with chronic health conditions. However, this gap in YLCM between male–female showed parity in the oldest of the old age groups.

Several factors may underlie this paradox. Biologically females have a greater resilience to fatal conditions, often surviving longer despite poorer health (44). Behaviourally, females are more likely to seek healthcare and report illnesses, leading to higher diagnosis rates (45). Socially, cumulative disadvantages in nutrition, labour, and access to healthcare may exacerbate females’ late-life health burdens, yet social support networks may bolster their survival (46). In contrast, higher early-life mortality in males, often due to riskier behaviours and greater exposure to fatal diseases, results in survivor bias, where only healthier males live to older ages (47). Consequently, while females experience an expansion of morbidity, males may undergo a compression of morbidity near the end of life (19). Interestingly, the sex gap in YLCM tends to show parity in the oldest-old groups, likely due to selective survival and the equalising effects of advanced biological ageing. However, in the Indian context, long-standing sex disparities in access to education, nutrition, and health services further deepen this paradox, particularly in rural and socioeconomically disadvantaged populations (48). Additionally, patriarchal norms often deprioritise females’ health across the life course, contributing to the observed higher burden of complex multimorbidity among older Indian females. Interestingly, the sex gap in YLCM tends to also diminish in the oldest-old groups, likely due to selective survival and the equalising effects of advanced biological ageing.

Second, the decomposition analysis provides a nuanced understanding of how the age pattern of mortality and multimorbidity/complex multimorbidity contribute to HLE gaps, particularly highlighting the increasing role of complex multimorbidity over an ageing population (49–51). As outlined in research by a recent study, mortality differences largely govern disparities in HLE between sexes, with their findings revealing that such differences predominantly hinge on specific health conditions (52). However, as this study analysis articulates, the impact of complex multimorbidity becomes increasingly significant, accounting for a notable reduction in HLE gaps, specifically 0.87 years from complex multimorbidity compared to 1.47 years sourced from mortality. This shift suggests a critical balancing act in understanding health disparities, especially in older adults, where the quality of life deteriorates significantly due to the compounded effect of multiple health conditions, thus emphasising that focusing solely on mortality may undercut a comprehensive grasp of the health landscape (53).

Nonetheless, the findings underscore a critical limitation in relying predominantly on mortality-driven interpretations of health disparities. While males experience greater premature mortality, resulting in a larger HLE gap at younger ages, the role of complex multimorbidity in shaping quality of life and functional independence becomes more pronounced with ageing. For instance, in this study, mortality differences between males and females contribute 1.47 years to the healthy life expectancy gap, while complex multimorbidity burden reduces this advantage by 0.87 years, yielding a net difference of 0.60 years. This quantification demonstrates that focusing exclusively on mortality-based health metrics substantially underestimates the full scope of sex-based health inequalities in ageing populations. This aligns with prior research highlighting that females’ extended lifespan often coincides with a prolonged period of morbidity and disability, making multimorbidity a crucial determinant of later-life well-being (54). Thus, policies aimed at healthy ageing must move beyond mortality reduction and prioritise interventions that mitigate the functional burden of multimorbidity, ensuring that increased longevity translates into better health-adjusted life years rather than extended periods of ill health.

Third, chronic morbidities such as chronic bone diseases and CVDs showed the least gap in HLE between male–female, but these morbidities showed the highest inequality explained by each morbidity by −1.991 years and −1.994 years than mortality rates, because the differential of prevalence for these diseases was much significant than mortality. A recent study on sex differentials in chronic diseases among older adults in India also concluded the significant differential between males and females in the majority of chronic diseases (55). On the contrary severe disease like chronic lung diseases and cancer showed distinct pattern, such that cancer had the lowest prevalence for both male (0.48%) and female (0.77%) but showed the significant high inequality of 2.35 years in HLE out of which 2.424 years were explained by mortality and reduction of −0.073 years by the cancer prevalence. The gap in HLE was highest in chronic lung diseases (Female: 29.46, Male: 26.93, Difference: 2.53), where 2.220 years were explained by mortality and 0.65 years by morbidity. This underscores cancer’s high fatality rate, which disproportionately affects HLE disparities (56).

The results revealed nuanced findings across all 14 morbidities, showing that although women experience higher prevalence of cardiovascular, musculoskeletal, endocrine, and blood diseases, they generally enjoy longer DFLE than men, particularly at younger ages. This advantage diminishes with age, with near convergence in conditions such as cardiovascular and musculoskeletal diseases. Men, in contrast, face higher prevalence of respiratory and infectious diseases in midlife and overall shorter DFLE, reflecting faster health decline. These patterns underscore the importance of adopting sex- and age-specific health strategies, chronic disease management for women and premature morbidity prevention for men, to foster healthy ageing. One plausible explanation for the observed pattern of declining life expectancy in older age groups in India is the high mortality patterns due to severe morbidities, which can mask the true mortality pattern (57). Specifically, the presence of severe health conditions, such as those associated with multimorbidity, may be contributing to the shortening of life expectancy in older age groups rather than any underlying trend in mortality. This is particularly evident in India, where the burden of non-communicable diseases (NCDs) and other health issues is significant, especially among older adults (58). As a result, the actual mortality pattern may be obscured by the impact of these severe morbidities, leading to an apparent decline in life expectancy in older age groups. Disease-specific analyses emphasise the importance of addressing preventable and manageable conditions (e.g., CVDs, chronic bone diseases). However, the findings also highlight the need for a holistic approach that considers comorbidities and the broader determinants of health.

The focus on older females’ disproportionate burden of complex multimorbidity (see Supplementary Table S1) is critical for designing equitable healthcare policies. Sex differences in disease progression highlight the dual challenges of longevity and quality of life. Tailored interventions that address the unique needs of ageing females, including support for caregiving roles and improved access to specialised care, are essential. The findings underscore the need for targeted interventions to reduce the burden of complex multimorbidity among older females, as they experience compounded effects in later life. The study effectively captures how male–female ageing trajectories influence health outcomes, with females living longer but enduring greater multimorbidity burdens.

Finally, the data highlights the necessity and importance of prioritising chronic bone diseases, CVDs, and multimorbidity management to improve health outcomes, particularly among females. Promoting healthy ageing requires strategies aimed at reducing the prevalence and complexity of multimorbidity, especially in younger age groups, which may provide long-term benefits for females with chronic conditions.

Moreover, our study highlights significant policy implications and the need for adaptive healthcare system responses as India faces the challenges of rapid population ageing. The demonstration that mortality reduction alone provides an incomplete picture of health inequality suggests that policy frameworks must incorporate quality-of-life metrics alongside traditional mortality-based indicators. The Healthy India 2025 initiative and similar programmes should emphasise multimorbidity prevention and management as core components of healthy ageing strategies. The sex-specific patterns identified suggest the need for differentiated approaches to males’ and females’ health in ageing populations. For males, interventions should continue emphasising mortality reduction through cardiovascular disease prevention, tobacco control, and accident prevention. For females, priority should be placed on managing chronic conditions that may not be immediately life-threatening but significantly impact quality of life over extended periods. Healthcare delivery systems must be redesigned to address complex multimorbidity effectively. This includes developing integrated care models that coordinate management across multiple conditions, training healthcare providers in geriatric medicine principles, and establishing comprehensive geriatric assessment programmes. The finding that complex multimorbidity impacts are most pronounced with five or more conditions suggests that early intervention targeting individuals with 2–3 conditions may prevent progression to more complex disease patterns.

A key strength of this study lies in its use of nationally representative data from the Longitudinal Ageing Study in India (LASI), which allows for robust, population-level insights into sex differences in life expectancy and healthy ageing. By incorporating measures of both multimorbidity-free and complex multimorbidity-free life expectancy, the analysis moves beyond simple mortality metrics to offer a more nuanced understanding of health inequalities. The study’s decomposition approach further strengthens its contribution by quantifying the relative roles of mortality and disease burden in shaping the sex gap in healthy life expectancy. Additionally, by highlighting the narrowing sex gap in advanced ages and the specific influence of chronic conditions such as cardiovascular and bone diseases, the study sheds light on critical stages in the life course where policy interventions can be most effective.

This study acknowledges several important limitations. While it attributes observed differences mainly to multimorbidity, it does not adequately examine the influence of biological, lifestyle, or socioeconomic factors that may shape sex disparities. The analysis treats sex differences in isolation, overlooking intersections with socioeconomic status, education, healthcare access, and regional variation, all of which could confound the relationship between multimorbidity and mortality. Methodologically, the use of stepwise replacement decomposition fails to account for competing risks and interactions between coexisting morbidities, potentially biasing estimates of healthy life expectancy. The disease-specific focus also neglects the mitigating role of preventive healthcare, while inconsistent contributions of conditions such as vision and hearing loss point to unexamined social and environmental determinants. Data limitations further constrain the findings. Reliance on self-reported diagnoses introduces risks of recall bias, underreporting, and misclassification, especially among older adults with low health literacy or limited disease awareness. These issues may compromise morbidity prevalence estimates and, by extension, the accuracy of healthy life expectancy calculations. Finally, the observed higher morbidity burden among women may partly reflect survival bias, since their longer life expectancy increases exposure to chronic conditions, complicating interpretations of morbidity gaps.

This study underscores the complex relationship between longevity and morbidity in shaping sex disparities in healthy ageing in India. While males experience higher mortality rates, females face a greater burden of years spent in complex multimorbidity, which impacts their healthy lifespan. The findings emphasise the need for public health interventions that extend beyond solely focusing on increasing life expectancy and prioritise improving the quality of life by addressing the burden of multimorbidity, especially among older females. Future research should further explore the underlying biological, socioeconomic, and lifestyle factors that contribute to these sex-based health inequalities in the ageing population of India.