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About the Project

Purpose of the Tool

Our surveyPrev R package and Shiny app were developed to empower hands-on analysis of health and demographic indicators in low- and middle-income countries (LMICs) at the subnational level. The primary goal is to support programming, interventions, and monitoring of progress toward the Sustainable Development Goals (SDGs). Designed to be user-friendly and accessible without prior statistical or programming knowledge, the tools not only facilitate detailed analysis but also embed statistical thinking, enabling users to conduct statistical inference through comprehensive tutorials and creative visualization tools.

Target Audience

This tool is designed for a broad range of users, including:

  • Policymakers: Who need to assess the impact of health and demographic interventions at local levels.
  • Researchers: Engaged in studying health trends and demographic shifts within countries.
  • Public Health Professionals: Working to implement programs and monitor outcomes at subnational levels.

General Aims of the Project

The project is centered around several key objectives:

  • Programming and Interventions: To provide actionable data that can guide health and demographic interventions at the subnational level.
  • Monitoring Progress: To track progress toward achieving the SDGs and other health-related targets, particularly at smaller administrative levels.
  • Handling Various Indicators: To offer tools for analyzing a wide range of prevalence indicators (e.g., neonatal mortality rate, vaccination coverage, stunting).
  • Addressing Within-Country Variation: To account for geographical variation in health and demographic indicators across different areas within a country.

Geographic Levels of Analysis

The tool supports analysis across different levels of geographic hierarchy:

  • Admin-0 (National Level): Provides estimates for the entire country.
  • Admin-1 (First Subnational Level): Offers estimates for large regions or states within the country.
  • Admin-2 (Second Subnational Level) and Finer: Provides estimates for smaller areas, such as districts or municipalities.

Small Area Estimation (SAE)

The core methodology implemented in the tool is Small Area Estimation (SAE). SAE is essential for generating reliable estimates in areas where data may be sparse. It involves estimating a variable of interest within specific geographic areas, with a focus on accuracy despite potentially limited data availability.