This analysis used data from the WHO Study on global AGEing and adult health (SAGE) Waves 2 (2015) and 3 (2019–2020) collected across six Indian states (Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal). The analytic samples comprised 9,116 adults aged 18 years and older in 2015 and 7,885 adults in 2019–2020. The prevalence of multiple long-term conditions (MLTCs) increased from 19.2% in 2015 to 24.4% in 2019–2020.
Prevalence was highest among adults aged 60 years and older, among women, among urban residents, and in the wealthiest socioeconomic quintile. These subgroup patterns indicate demographic and socioeconomic gradients in MLTC burden within the surveyed regions.
The most prevalent two-condition combinations identified were hypertension–cataracts, hypertension–arthritis, and hypertension–diabetes. The analysis reported several high-burden dyads beyond hypertension-predominant pairs; combinations involving mental health and cardiopulmonary disease emerged as particularly consequential for well-being.
The primary outcome was quality of life measured by the 8-item World Health Organization Quality of Life scale (WHOQoL-8, range 0–100). Secondary outcomes were healthcare utilisation measures: the count of outpatient visits and the occurrence of hospitalisations in the preceding 12 months.
Quality of life was examined overall and across domains (including physical and functional domains and a financial domain). The study compared WHOQoL-8 scores for participants with zero, one, two, and three or more chronic conditions.
Associations between MLTC and WHOQoL-8 were estimated using Tobit regression models appropriate for censored outcome data. Associations between MLTC and healthcare utilisation (outpatient visits, hospitalisations) were estimated with Poisson regression. Models adjusted for a range of sociodemographic, socioeconomic, and behavioural covariates. The source reports adjusted declines in WHOQoL-8 scores with increasing MLTC count; some detailed model statistics and exact p-values were not provided in the source summary.
WHOQoL-8 scores declined in a stepwise fashion compared with adults without chronic conditions. In the 2015 wave the reported unadjusted differences were approximately −2.6 points for one condition, −5.1 points for two conditions, and −6.7 points for three or more conditions. In 2019–2020 the reported differences were approximately −3.3, −5.6, and −7.9 points for one, two, and three or more conditions, respectively. These declines reflect a graded relationship between the number of chronic conditions and lower quality of life as measured by WHOQoL-8.
Dyads involving depression and cardiopulmonary or cerebrovascular conditions were associated with the poorest quality of life. Specifically, depression–chronic obstructive pulmonary disease (COPD) and depression–stroke combinations were singled out as having the lowest WHOQoL-8 scores. The analysis found the largest declines across the physical and functional domains with increasing MLTC burden, while the financial domain showed relative stability across MLTC counts.
Higher MLTC burden was associated with greater healthcare use. Both outpatient visits and hospitalisations in the prior 12 months increased with the number of chronic conditions. Poisson regression models adjusted for confounders estimated these associations; the source summary indicates higher utilisation but does not provide full numeric incidence rate ratios in the text provided.
The findings indicate that MLTC are rising in the Indian context and are linked to worse quality of life and increased demand on healthcare services. High-burden and high-impact combinations—particularly depression–COPD, depression–stroke, COPD–stroke, and depression–angina—are highlighted as priorities for targeted interventions aimed at improving quality of life and managing rising healthcare utilisation.
Targeted strategies may need to address integrated management of mental health with chronic physical diseases, strengthened outpatient care pathways to manage multimorbidity, and interventions that focus on maintaining physical and functional capacity.
This report is based on cross-sectional analyses of two survey waves; causal inferences about MLTC leading to lower quality of life or greater utilisation cannot be established from cross-sectional data alone. Some detailed adjusted model estimates and full p-values or confidence intervals were not reported in the source summary provided. Geographic coverage was limited to six Indian states included in WHO SAGE, which may affect generalisability to all Indian states or nationally.
Where specific numerical model outputs or p-values are absent from the source summary, those details were not reported and are therefore not presented here.