A Novel Socioeconomic Measure Using Individual Housing Data in Cardiovascular Outcome Research

Abstract

Background: To assess whether the individual housing-based socioeconomic status (SES) measure termed HOUSES was associated with post-myocardial infarction (MI) mortality. Methods: The study was designed as a population-based cohort study, which compared post-MI mortality among Olmsted County, Minnesota, USA, residents with different SES as measured by HOUSES using Cox proportional hazards models. Subjects’ addresses at index date of MI were geocoded to real property data to formulate HOUSES (a z-score for housing value, square footage, and numbers of bedrooms and bathrooms). Educational levels were used as a comparison for the HOUSES index. Results: 637 of the 696 eligible patients with MI (92%) were successfully geocoded to real property data. Post-MI survival rates were 60% (50–72), 78% (71–85), 72% (60–87), and 87% (81–93) at 2 years for patients in the first (the lowest SES), second, third, and fourth quartiles of HOUSES, respectively (p < 0.001). HOUSES was associated with post-MI all-cause mortality, controlling for all variables except age and comorbidity (p = 0.036) but was not significant after adjusting for age and comorbidity (p = 0.24). Conclusions: Although HOUSES is associated with post-MI mortality, the differential mortality rates by HOUSES were primarily accounted for by age and comorbid conditions. HOUSES may be useful for health disparities research concerning cardiovascular outcomes, especially in overcoming the paucity of conventional SES measures in commonly used datasets.

A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population

“…The increased risk of being hospitalised in 1 year was greater than 50%, after adjustment for age, sex and MMT, for patients in the lowest HOUSES quartile compared with the highest quartile. In a previous study of the HOUSES index for 637 patients with myocardial infarction, a lower HOUSES score predicted death; however, it was non-significant after adjustment for age and comorbidity 20. In a systematic review of 27 risk prediction models for hospitalisation, 8 models included a measure of SES as a predictor of hospitalisation, with most accounting for living alone or the presence of a caregiver 30.…”

“…Development and initial testing of the HOUSES index were completed in Olmsted County and in Jackson County, Missouri, USA. After development, HOUSES was applied to a study setting in Sioux Falls, South Dakota,19 20 24–26 which suggested the potential generalisability of HOUSES to other study settings. The details of HOUSES have been described elsewhere 19.…”

“…19 This SES index was formulated by geocoding address information with individual property data (ie, individual-level SES measure) at the time of a health event of interest. Although HOUSES has been shown to be associated with paediatric health outcomes19 and postmyocardial infarction mortality in adults,20 whether HOUSES can predict future all-cause hospitalisation and MCC is unknown. We hypothesised that a lower SES, as measured by HOUSES, is associated with higher risk of hospitalisation and MCC.…”

Assessing health disparities in children using a modified housing-related socioeconomic status measure: a cross-sectional study

“…Furthermore, these same health outcomes, defined by physician diagnosis or predetermined criteria, were also significantly associated with our original HOUSES index. [7][8][9][10][11][12][13] The performance of modified HOUSES for objective measure-based health outcomes can be expected to be similar to these findings. HOUSES, which is developed based on real property data for US taxation purposes, may not work well in other countries where housing data are not routinely collected or made publicly available in databases, or even in communities within the USA where housing assessments are infrequent or of poor quality.…”

“…The HOUSES index predicts health outcomes in adults and children that have previously been identified to be associated with SES (low birth weight, obesity, smoking exposure, asthma control status, pneumococcal diseases, postmyocardial infarction mortality, rheumatoid arthritis (RA) and post-RA mortality). [7][8][9][10][11][12][13] One of the challenges in calculating the original HOUSES index is the need for complex assessor's real property data generated for US taxation purposes. However, these data often does not include key variables of interest such as the number of bedrooms and bathrooms.…”