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The Get Healthy Idaho Index (GHI Index) is a comprehensive assessment tool that measures and ranks neighborhoods' health and well-being condition. It considers factors like access to healthy food, parks, clean air, and healthcare services to provide a holistic view of community health. The index aims to guide policymakers and investors to improve the overall health of Idaho. By identifying such areas, policymakers and community leaders can allocate more resources and implement policies and programs that aim to enhance health outcomes in these communities. The data for the GHI was gathered from eight public websites. Half of the individual indicators were from the American Community Survey (ACS), which was conducted from 2020 to 2024. Indicators were checked for missing data and their correlation with life expectancy at birth (LEB).

Indices' data were obtained from application programming interfaces (APIs) or as downloaded CSV files from the public websites of the organizations that developed or processed data from primary sources. The online public sources used are as follows:U. S. Census Bureau's American Community Survey (ACS), 2020-2024US Department of Housing and Urban Development (HUD) or Comprehensive Housing Affordability Strategy (CHAS), 2018-2022Agency for Toxic Substances and Disease Registry (ATSDAR), 2023Idaho State Police (ISP) Crime in Idaho Data Dashboard, 2024Agency for Healthcare Research and Quality (AHRQ) Community-Level Health Database, 2023U.S. Department of Agriculture (USDA) Economic Research, 2020University of Wisconsin Population health Institute, County health Rankings and Roadmaps, 2025Agency for Toxic Substances and Disease Registry (ASTDAR) Environmental Justice Index, 2024 To calculate indicators, Excel was employed to extract numerator, denominator, outcomes, and margin of error from the data sources. The construction of indicators was based on the source files, with specific information available in the Data Dictionary in the Appendix. Data quality checks involved examining distributions, missing data, outliers, and establishing simple statistical correlations with life expectancy at birth (LEB) using Python programming. A Python program was utilized to validate and scale the data, perform data analysis, generate statistics, compute domain averages, and recalculate the GHI score considering domain weights.

To identify health outcomes inequities, the GHI Index is an in-depth tool that assesses and evaluates the healthiness of community circumstances at the census tract level in Idaho. By identifying such communities, policy decisionmakers and community leaders can allocate more resources and implement policies to enhance health outcomes in these communities. The GHI Index collected 30 individual indicators that are organized into seven domains, including Economy, Education, Healthcare Access, Housing, Neighborhoods, Clean Environment, and Transportation. Also, more than 154 community data indicators for health outcomes, climate change exposures, and social vulnerabilities were added. For indicators in the GHI Index with a significant amount of missing data, the median and KNN methods were used to impute missing values. While KNN showed slightly better result, the median method was ultimately chosen due to its simplicity and ease of understanding. The GHI indicators were scaled by Z-Scale and MinMax Scaling (range between 0 to 100) methods, with results compared to determine the better approach. While Z-Scale showed slightly better results, MinMax Scaling was ultimately chosen due to its ease of understanding and implementation. To calculate the weight domains for the GHI score, the California Healthy Places Index (HPI) was used as a reference point. The California HPI domains were developed using a multi-step approach that included input from public health subject matter experts and statistical analysis to validate a statistically significant correlation between the indicator estimate(s) and life expectancy at birth (LEB) at the census tract level. So, domain weights were computed using Weighted Quantile Sum Regression to optimize the correlation of GHI with LEB. The Division of Public Health in Idaho used a similar methodology, along with three additional methods, to compute weight domains for the GHI score. These methods included unweighted, WQS weighting like California, Analytic Hierarchy Process (AHP) weighting for multi-criteria decision making, and an average of WQS and AHP weighting methods. The average of the WQS and AHP weighting methods was ultimately chosen due to a better result. Thus, The GHI score was calculated by adding the weighted domain averages, with weights estimated to maximize the correlation and variance of the GHI and LEB.

The indicators in domains in the GHI Index reproduce recognized social determinants of health. Indicators were selected based on criteria such as accessibility of public data, relevance to life expectancy, non-collinearity, and alignment with policy actions. Some indicators were retired or modified based on data accessibility.

A key component of the GHI Index are domains and domain indicators. Indicators in the GHI are grouped into domains, which are thematic groups that represent various social determinants of health-related policy action areas. An organized framework for evaluating several sides of community health and well-being is provided by the domains. Each domain consists of a set of indicators that capture relevant information within that theme topic. These indicators are carefully selected based on factors such as data availability, relevance to community health, and alignment with policy actions. We compute individual indices by referencing their corresponding descriptions and subsequently calculate the standard deviation utilizing the methodology outlined in the American Community Survey. The GHI considers multiple domains to ensure a comprehensive understanding of the strengths and challenges related to community health and informs policy decisions aimed at improving overall well-being.