A novel measure of socioeconomic status using individual housing data to assess the association of SES with rheumatoid arthritis and its mortality: a population-based case–control study

Abstract

ObjectivesTo assess whether HOUSES (HOUsing-based index of socioeconomic status (SES)) is associated with risk of and mortality after rheumatoid arthritis (RA).DesignWe conducted a population-based case–control study which enrolled population-based RA cases and their controls without RA.SettingThe study was performed in Olmsted County, Minnesota.ParticipantsStudy participants were all residents of Olmsted County, Minnesota, with RA identified using the 1987 American College of Rheumatology criteria for RA from 1 January 1988, to 31 December 2007, using the auspices of the Rochester Epidemiology Project. For each patient with RA, one control was randomly selected from Olmsted County residents of similar age and gender without RA.Primary and secondary outcome measureThe disease status was RA cases and their matched controls in relation to HOUSES as an exposure. As a secondary aim, post-RA mortality among only RA cases was an outcome event. The associations of SES measured by HOUSES with the study outcomes were assessed using logistic regression and Cox models. HOUSES, as a composite index, was formulated based on a summed z-score for housing value, square footage and number of bedrooms and bathrooms.ResultsOf the eligible 604 participants, 418 (69%) were female; the mean age was 56±15.6 years. Lower SES, as measured by HOUSES, was associated with the risk of developing RA (0.5±3.8 for controls vs −0.2±3.1 for RA cases, p=0.003), adjusting for age, gender, calendar year of RA index date, smoking status and BMI. The lowest quartile of HOUSES was significantly associated with increased post-RA mortality compared to higher quartiles of HOUSES (HR 1.74; 95% CI 1.10 to 2.74; p=0.017) in multivariate analysis.ConclusionsLower SES, as measured by HOUSES, is associated with increased risk of RA and mortality after RA. HOUSES may be a useful tool for health disparities research concerning rheumatological outcomes when conventional SES measures are unavailable.

Ethnicity, Socioeconomic Status, and Health Disparities in a Mixed Rural-Urban US Community—Olmsted County, Minnesota

“…To overcome this barrier, we applied our recently developed individual housing-based SES index (termed HOUSES) to a population-based cohort including nearly all Olmsted County, Minnesota, residents, instead of relying on self-reported SES measures. 21–26 Olmsted County, Minnesota, is a suitable study setting for conducting a population-based study such as this since the health care environment is self-contained and medical records for nearly all residents are available for clinical research. Using these unusual resources, we examined the degree and nature of health disparities in five common chronic diseases among adults with different SES and ethnicity.…”

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.…”

Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation (Preprint)

“…While other studies have shown that staying in rental housing is associated with an increased risk of frequent admissions [78] and readmission [77], as far as the authors are aware, there has not been studies in the Singapore context that has demonstrated the use of housing type as a proxy for SES. This finding is useful given that most clinical and administrative databases do not collect direct SES information, and other commonly used proxies such as area-based income level from census data [36,37], insurance status [38] or property value [39,40]. Given that postal codes or addresses are routinely collected in most datasets, other studies could also use housing type/size as a proxy of SES in the absence of direct SES data.…”

“…While some studies have used the Charlson Comorbidity Index (CCI) [28,[31][32][33] and drug burden [34,35] to estimate patient complexity, validity of these measures as an estimate for patient complexity has rarely been established in Asia. In the absence of income data, SES is typically derived from area-based income level from census data [36,37], insurance status [38] or property value [39,40]. However, these proxies require additional data as well, which is often not readily available in healthcare administrative datasets or EMRs.…”