Rapid population ageing and a rising burden of non-communicable diseases (NCDs) are creating an urgent demand for evidence-based planning regarding long-term care needs India and other developing regions. However, many countries lack individual-level data on care dependency. This data gap often hampers the design of effective health policies. A recent study has introduced a diagnostic model called the LTC Needs Index to bridge this gap in data-scarce contexts.

The LTC Needs Index Methodology

Researchers developed the population-based LTC Needs Index to estimate dependency by integrating multiple national and international data sources. This model specifically evaluates demographic ageing, disability prevalence, and transition probabilities from five major NCDs. These diseases include cardiovascular disease, diabetes, cancer, Alzheimer's disease, and Parkinson's disease. To ensure cross-country comparability, the team utilized normalization and weighting procedures. Furthermore, they validated the model's robustness through Bayesian, bootstrap, and deterministic sensitivity analyses.

How to Estimate Long-Term Care Needs India?

While the initial study focused on the MENA region, the framework is highly transferable to the Indian healthcare landscape. In India, nearly 75% of the elderly population suffers from at least one chronic disease. Moreover, 24% of seniors report limitations in activities of daily living. By applying this index, policymakers can better quantify long-term care needs India even when granular survey data is missing. The model emphasizes that disability is a dominant factor, often accounting for over 60% of total care needs. Therefore, focusing on disability prevention is as crucial as managing clinical NCD symptoms.

Key Findings and Future Projections

The study revealed substantial heterogeneity in care dependency across different countries. For instance, dependency rates ranged from 3% to nearly 23% of the total population. Projections for 2024-2030 indicate a consistent upward trend in care requirements. This rise is primarily driven by the demographic shift toward an older population. Notably, in regions with high metabolic risks, diabetes and cardiovascular diseases contribute significantly to the overall index values. Consequently, the researchers advocate for differentiated strategies that reflect the specific disease contributions of each country.

Strategic Recommendations for Clinicians

The LTC Needs Index offers a scalable tool for estimating population-level needs. However, the authors stress that countries should still invest in nationally representative surveys. These surveys should capture intra-country inequalities to advance equity in planning. For clinicians, this means recognizing the growing importance of geriatric assessment in daily practice. Strengthening care dependency documentation at the primary care level will eventually provide the precision needed for better national planning.

FAQs

What are the primary drivers of long-term care needs according to this model?

The model identifies demographic ageing and the prevalence of disability as the primary drivers. Disability alone accounted for 67% to 94% of the index values in the study.

Why is this model useful for data-scarce regions?

It integrates standardized international data with national NCD prevalence rates. This allows for a validated estimate of care needs even when individual-level dependency surveys are unavailable.

How do NCDs impact the LTC Needs Index?

Specific NCDs like diabetes and cardiovascular diseases significantly increase the index values. They act as major contributors to transition probabilities toward care dependency.

Disclaimer: This content is for informational and educational purposes only. It does not constitute medical advice or a professional healthcare recommendation. Refer to the latest local and national guidelines for clinical practice.

References

Ismail M et al. Estimating long-term care needs in data-scarce settings: a diagnostic model with evidence from MENA. Popul Health Metr. 2026 Apr 27. doi: 10.1186/s12963-026-00477-2. PMID: 42045931.

NITI Aayog. Senior Care Reforms in India – Reimaging the senior care paradigm. February 2024.

International Institute for Population Sciences (IIPS). Longitudinal Ageing Study in India (LASI) Wave 1, 2017-18, India Report. 2020.