Estimating long-term care needs in data-scarce settings: a diagnostic model with evidence from MENA.
This paper develops a scalable Long-Term Care Needs Index for eight MENA countries by combining demographic ageing, disability prevalence, and transition probabilities from five major NCDs (including Parkinson's) to estimate and project care dependency through 2030.
What the AI sees
This paper develops a scalable Long-Term Care Needs Index for eight MENA countries by combining demographic ageing, disability prevalence, and transition probabilities from five major NCDs (including Parkinson's) to estimate and project care dependency through 2030.
Research significance
While it offers little mechanistic or therapeutic insight for Parkinson's research, the study is useful for strategic planning by quantifying projected care burden from PD at a population level and highlighting the need for better PD-specific, individual-level data to inform service delivery and…
Source abstract
BACKGROUND: Rapid population ageing, high burdens of non-communicable diseases (NCDs), and limited formal care systems are converging in the Middle East and North Africa (MENA) region, generating an urgent need for evidence-based long-term care (LTC) planning. However, the absence of individual-level data on care dependency hampers assessment and policy design. METHODS: We developed a population-based LTC Needs Index to estimate care dependency in data-scarce contexts. The Index integrates demographic ageing, prevalence of disability, and transition probabilities from five major NCDs (cardiovascular disease, diabetes, cancer, Alzheimer's disease, and Parkinson's disease) using standardized national and international data sources. Cross-country comparability was ensured through normalization and weighting procedures, and the model's robustness was tested using Bayesian, bootstrap, and deterministic sensitivity analyses. RESULTS: The LTC Needs Index reveals substantial heterogeneity in care dependency across eight MENA countries, ranging from approximately 3% of the total population in Oman to 22.8% in Saudi Arabia. Projections for 2024-2030 show a consistent upward trend in LTC needs, primarily driven by demographic ageing. Disability emerged as the dominant factor, accounting for 67-94% of total index values, with diabetes and cardiovascular diseases contributing most strongly in Gulf states. Sensitivity analyses confirmed the index's stability under varying assumptions. CONCLUSIONS: The LTC Needs Index offers a scalable, validated diagnostic model for estimating population-level LTC needs in data-limited settings. It highlights the need for differentiated LTC strategies reflecting the varying contributions of disability and NCDs across countries. To advance equity and precision in planning, countries should invest in nationally representative survey data on ageing, disability, and care dependency to capture intra-country inequalities. The Index provides a transferable framework applicable to other data-scarce regions seeking to strengthen long-term care systems and policy preparedness for population ageing.