Estimating long-term care needs in data-scarce settings: a diagnostic model with evidence from MENA.

IF 2.5 2区医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Population Health Metrics Pub Date : 2026-04-27 DOI:10.1186/s12963-026-00477-2

Mohamed Ismail, Priyanka D Kanth, Shereen Hussein

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引用次数: 0

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.

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在数据匮乏的环境中估计长期护理需求：一种基于中东和北非证据的诊断模型。

背景：在中东和北非（MENA）地区，人口快速老龄化、非传染性疾病（NCDs）的高负担和有限的正规护理系统正在趋同，迫切需要基于证据的长期护理（LTC）规划。然而，缺乏个人层面的护理依赖数据阻碍了评估和政策设计。方法：我们开发了一个基于人群的LTC需求指数来估计数据稀缺背景下的护理依赖。该指数使用标准化的国家和国际数据源，综合了人口老龄化、残疾流行率和五种主要非传染性疾病（心血管疾病、糖尿病、癌症、阿尔茨海默病和帕金森病）的过渡概率。通过归一化和加权程序确保了跨国可比性，并使用贝叶斯、bootstrap和确定性敏感性分析检验了模型的稳健性。结果：LTC需求指数揭示了八个中东和北非国家护理依赖的巨大异质性，从阿曼约占总人口的3%到沙特阿拉伯的22.8%不等。2024-2030年的预测显示，主要受人口老龄化的推动，长期医疗服务需求呈持续上升趋势。残疾成为主要因素，占总指数值的67-94%，其中糖尿病和心血管疾病在海湾国家的影响最大。敏感性分析证实了该指数在不同假设下的稳定性。结论：LTC需求指数为在数据有限的情况下估计人口水平的LTC需求提供了一个可扩展的、经过验证的诊断模型。报告强调需要制定差异化的长期控制战略，以反映残疾和非传染性疾病在各国的不同贡献。为了促进规划的公平性和准确性，各国应投资于具有全国代表性的老龄化、残疾和护理依赖调查数据，以捕捉国内不平等现象。该指数提供了一个可转移的框架，适用于寻求加强长期护理系统和人口老龄化政策准备的其他数据匮乏地区。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Population Health Metrics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

6.50

自引率

0.00%

发文量

审稿时长

29 weeks

期刊介绍： Population Health Metrics aims to advance the science of population health assessment, and welcomes papers relating to concepts, methods, ethics, applications, and summary measures of population health. The journal provides a unique platform for population health researchers to share their findings with the global community. We seek research that addresses the communication of population health measures and policy implications to stakeholders; this includes papers related to burden estimation and risk assessment, and research addressing population health across the full range of development. Population Health Metrics covers a broad range of topics encompassing health state measurement and valuation, summary measures of population health, descriptive epidemiology at the population level, burden of disease and injury analysis, disease and risk factor modeling for populations, and comparative assessment of risks to health at the population level. The journal is also interested in how to use and communicate indicators of population health to reduce disease burden, and the approaches for translating from indicators of population health to health-advancing actions. As a cross-cutting topic of importance, we are particularly interested in inequalities in population health and their measurement.