Unlocking insights from complex data: Leveraging heat maps for decision-making in LMIC

Area of Science:

• Public Health
• Health Informatics
• Geographic Information Systems (GIS)

Background:

• Health outcomes in low- and middle-income countries (LMICs) are hampered by poor resource allocation and limited access to essential services like contraception and immunization.
• Spatial heat maps are explored as a tool to analyze stock availability and usage patterns of contraceptives and vaccines in Pakistan.

Purpose of the Study:

• To assess contraceptive and vaccine stock levels, dispensation/vaccination, and wastage across districts in Pakistan using spatial heat maps.
• To identify regional disparities in health service delivery and understand the impact of inputs on utilization.

Main Methods:

• Utilized data from national contraceptive (cLMIS) and vaccine logistics management information systems (vLMIS).
• Applied univariate, bivariate, and trivariate spatial heat maps to analyze stock levels, dispensation/vaccination rates, and wastage.
• Standardized contraceptive stocks per 100,000 married women of reproductive age (MWRA) and focused on specific vaccine indicators like Pentavalent-3 (Penta3) and Bacillus Calmette-Guérin (BCG).

Main Results:

• Identified significant regional disparities, with developed regions like Punjab showing better stock availability compared to underserved areas like Balochistan.
• Observed higher dispensation rates and stockouts in underserved regions, indicating unequal access to services.
• Demonstrated the influence of supplies and outreach efforts on the dispensation and utilization of contraceptives and vaccines, respectively.

Conclusions:

• Integrating spatial data visualization with health logistics data effectively identifies critical gaps in health service supply and demand.
• This approach provides policymakers with actionable insights for resource allocation, stock management, and service outreach in LMICs.
• The scalable and automated nature of this method makes it suitable for systems with limited analytical resources, supporting efficient health system strengthening.