Data-Driven Approach to assess and identify gaps in healthcare set up in South Asia

TL;DR

The paper addresses persistent health inequities in South Asia by proposing a data-driven, Earth-observation–assisted framework to map primary healthcare gaps. It integrates open-source datasets—Accessibility to Healthcare 2019, GPWv4, and VIIRS Nighttime Lights—to compute per-pixel and regional need scores, identifying the top $1\%$ of high-need regions to guide targeted facility placement. The methodology combines travel-time, population density, and nighttime-light signals within a scalable workflow, enabling targeted resource allocation, telemedicine, and cross-sector collaboration, while highlighting regulatory actions as part of a comprehensive strategy. The work aims to enable evidence-based, regionally coordinated policy decisions to extend equitable healthcare access across South Asia, both in normal and crisis contexts, leveraging advances in EO and AI for continual monitoring and planning.

Abstract

Primary healthcare is a crucial strategy for achieving universal health coverage. South Asian countries are working to improve their primary healthcare system through their country specific policies designed in line with WHO health system framework using the six thematic pillars: Health Financing, Health Service delivery, Human Resource for Health, Health Information Systems, Governance, Essential Medicines and Technology, and an addition area of Cross-Sectoral Linkages. Measuring the current accessibility of healthcare facilities and workforce availability is essential for improving healthcare standards and achieving universal health coverage in developing countries. Data-driven surveillance approaches are required that can provide rapid, reliable, and geographically scalable solutions to understand a) which communities and areas are most at risk of inequitable access and when, b) what barriers to health access exist, and c) how they can be overcome in ways tailored to the specific challenges faced by individual communities. We propose to harness current breakthroughs in Earth-observation (EO) technology, which provide the ability to generate accurate, up-to-date, publicly accessible, and reliable data, which is necessary for equitable access planning and resource allocation to ensure that vaccines, and other interventions reach everyone, particularly those in greatest need, during normal and crisis times. This requires collaboration among countries to identify evidence based solutions to shape health policy and interventions, and drive innovations and research in the region.

Figures (7)

• Figure 1: Health Disparities due to a) lack of access to infrastructure, e.g., health facilities, through interventions, e.g., safe medicines and vaccines; b) access to communication infrastructure in remote locations, e.g., poor road networks and communication links; and c) affordability and intersectional determinants of access, such as gender, age, and lifestyle factors.
• Figure 2: Proposed approach for mapping health inequities.
• Figure 3: Datasets: (a): Travel time to the nearest hospital or clinic, (b): Population density using NASA Gridded Population of World version 4 (GPWv4), (c): VIIRS Nighttime Day/Night Annual Band Composites V2.2. Darker areas indicate shorter travel times to the nearest facility, lower population density, and lower nighttime light intensity compared to brighter areas.
• Figure 4: Evaluation Results: (a): Location with too far Points of interest (health facilities is greater than $30$ minutes); (b): Filter those locations where travel time is greater than $30$ minutes & Population density is greater than $50$ persons per $1~{km}^2$; (c): Regional Need score based on population density per $1~{km}^2$ - ${99}^{th}$ percentile.
• Figure 5: Categorization of areas based on Nighttime Light (NTL) intensity: (a) Areas with $0 \leq NTL \leq 10$; (b) Areas with $10 \leq NTL \leq 20$; (c) Areas with $20 \leq NTL \leq 30$.