Assessing health disparities in children using a modified housing-related socioeconomic status measure: a cross-sectional study. Issue 7 (22nd July 2016)

• Record Type: Journal Article
• Authors: Ryu, Euijung; Wi, Chung-Il; Crow, Sheri S; Armasu, Sebastian M; Wheeler, Philip H; Sloan, Jeff A; Yawn, Barbara P; Beebe, Timothy J; Williams, Arthur R; Juhn, Young J
• Is Part Of: BMJ open. Volume 6:Issue 7(2016)
• Publication Date: 2016-07-22
• Subjects: socioeconomic status -- HOUSES -- health disparity; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2016-011564
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: Socioeconomic status (SES) is a well-established risk factor for many health outcomes. Recently, we developed an SES measure based on 4 housing-related characteristics (termed HOUSES) and demonstrated its ability to assess health disparities. In this study, we aimed to evaluate whether fewer housing-related characteristics could be used to provide a similar representation of SES. Study setting and participants: We performed a cross-sectional study using parents/guardians of children aged 1–17 years from 2 US Midwestern counties (n=728 in Olmsted County, Minnesota, and n=701 in Jackson County, Missouri). Primary and secondary outcome measures: For each participant, housing-related characteristics used in the formulation of HOUSES (assessed housing value, square footage, number of bedrooms and number of bathrooms) were obtained from the local government assessor's offices, and additional SES measures and health outcomes with known associations to SES (obesity, low birth weight and smoking exposure) were collected from a telephone survey. Housing characteristics with the greatest contribution for predicting the health outcomes were added to formulate a modified HOUSES index. Results: Among the 4 housing characteristics used in the original HOUSES, the strongest contributions for predicting health outcomes were observed from assessed housing value and square footage (combined contribution ranged between 89% and 96%). Based on this observation, these 2 were used to calculate a modified HOUSES index. Correlation between modified HOUSES and other SES measures was comparable to the original HOUSES for both locations. Consistent with the original HOUSES formula, the strongest association with modified HOUSES was observed with smoking exposure (OR=0.24 with 95% CI 0.11 to 0.49 for comparing participants in highest HOUSES vs lowest group; overall p<0.001). Conclusions: The modified HOUSES requires only 2 readily available housing characteristics thereby improving the feasibility of using this index as a proxy for SES in multiple communities, especially in the US Midwestern region.