Cloud-Based HOUSES Index for Individual SES Measurement
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Solution Overview
Problem
The absence of individual-level socioeconomic status (SES) measures in commonly used datasets hinders the assessment and addressing of SES impact in clinical care and research, with aggregate measures like zip code or Census geographical unit-based measures suffering from significant misclassification bias.
Innovation Solution
A method for generating housing-based socioeconomic status (HOUSES) index scores using real property data such as the number of bedrooms, bathrooms, square footage, and estimated building value, accessible through a cloud-based system that computes and delivers individual-level SES data, thereby overcoming the limitations of existing aggregate-level measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If aggregate measures like zip code or Census geographical unit-based measures are used, then data availability is improved, but measurement precision deteriorates due to significant misclassification bias
Solution Approach 1:
The patent segments the population from aggregate-level groupings into individual-level units by creating SES measures based on specific housing characteristics (number of bedrooms, bathrooms, square footage, building value) for each individual's residence. This segmentation allows precise assignment of SES measures to individual patients while maintaining data availability through the use of existing housing data elements.
2Measurement precision
If individual-level SES measures are created using detailed housing characteristics, then measurement precision is improved, but device complexity increases due to multiple data elements required
Solution Approach 1:
The patent creates a universal SES measurement system that uses multiple housing data elements (bedrooms, bathrooms, square footage, building value) that can be applied across different populations and research contexts. The standardized methodology allows the same approach to be used universally for creating individual-level SES measures, reducing long-term complexity despite the multi-element nature of the measure.
Solution Approach 2:
The patent transforms housing characteristics into standardized SES measures through parameter changes - converting physical housing attributes (number of rooms, square footage) into normalized SES scores that can be directly used in health research. This parameter transformation simplifies the integration of housing data into health outcomes analysis.
3Measurement precision
If individual-level housing data are collected and processed, then measurement precision is improved, but loss of information increases due to data privacy concerns
Solution Approach 1:
The patent extracts only the necessary housing characteristics (number of bedrooms, bathrooms, square footage, building value) that are needed to create SES measures, while excluding personally identifiable information. This extraction approach maintains individual-level measurement precision while protecting data privacy by removing unnecessary sensitive information from the analysis dataset.
Data Source
AI summary
Housing-based socioeconomic status (“HOUSES”) index scores as an individual-level socioeconomic status (“SES”) measure are formulated and managed using a secure cloud-based interface that maintains data privacy for individuals. The cloud-based environment enables a scalable solution for generating and managing HOUSES index data by enabling access to publicly available real property data used when formulating a HOUSES index score. The cloud-based environment provides a reproducible, agile, and scalable algorithm deployment enabling the generation and management of de-identified HOUSES index data.