Housing data-based socioeconomic index and risk of invasive pneumococcal disease: an exploratory study

Johnson, Michael D.; Urm, Sang‐Hwa; Jung, Jiyoun; Yun, Huifeng; Munitz, G. E.; Tsigrelis, Constantine; Baddour, L.M.; Juhn, Young J.

doi:10.1017/s0950268812001252

Cited by 39 publications

(32 citation statements)

References 33 publications

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“…A high HOUSES score correlates with high SES. Previous research has been used to document the successful application of HOUSES as an effective SES measure in clinical research, [55][56][57][58][59][60][61][62] and HOUSES was recently found to be a useful surrogate for SES to assess risk stratification and hospitalization in adults with multiple chronic conditions. 63 Children with missing address information were excluded from the HOUSES analyses.…”

Section: Sociodemographic Variablesmentioning

confidence: 99%

Epidemiology of Children With Multiple Complex Chronic Conditions in a Mixed Urban-Rural US Community

Bjur

Ryu

et al. 2019

Hospital Pediatrics

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A B S T R A C TOBJECTIVES: Children with multiple complex chronic conditions (MCCs) represent a small fraction of our communities but a disproportionate amount of health care cost and mortality. Because the temporal trends of children with MCCs within a geographically well-defined US pediatric population has not been previously assessed, health care planning and policy for this vulnerable population is limited. METHODS:In this population-based, repeated cross-sectional study, we identified and enrolled all eligible children residing in Olmsted County, Minnesota, through the Rochester Epidemiology Project, a medical record linkage system of Olmsted County residents. The pediatric complex chronic conditions classification system version 2 was used to identify children with MCCs. Five-year period prevalence and incidence rates were calculated during the study period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) and characterized by age, sex, ethnicity, and socioeconomic status (SES) by using the housing-based index of socioeconomic status, a validated individual housing-based SES index. Age-, sex-, and ethnicity-adjusted prevalence and incidence rates were calculated, adjusting to the 2010 US total pediatric population. RESULTS:Five-year prevalence and incidence rates of children with MCCs in Olmsted County increased from 1200 to 1938 per 100 000 persons and from 256 to 335 per 100 000 person-years, respectively, during the study period. MCCs tend to be slightly more prevalent among children with a lower SES and with a racial minority background. CONCLUSIONS:Both 5-year prevalence and incidence rates of children with MCCs have significantly increased over time, and health disparities are present among these children. The clinical and financial outcomes of children with MCCs need to be assessed for formulating suitable health care planning given limited resources.

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Section: Sociodemographic Variablesmentioning

confidence: 99%

Epidemiology of Children With Multiple Complex Chronic Conditions in a Mixed Urban-Rural US Community

Bjur

Ryu

et al. 2019

Hospital Pediatrics

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“…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.…”

Section: Discussionmentioning

confidence: 82%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Ryu

Crow

et al. 2016

BMJ Open

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ObjectivesSocioeconomic 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 participantsWe 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 measuresFor 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.ResultsAmong 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).ConclusionsThe 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.

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“…Since its original validation, it has been widely used for clinical and epidemiological studies concerning a broad range of health outcomes and behaviors as well as health care delivery in children and adults. 6,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] Identifying Areas of Observed and Expected Case Density for AEs…”

Section: Houses As An Individual-level Ses Measurementioning

confidence: 99%

Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis

Patel

Wheeler

et al. 2020

J. Clin. Trans. Sci.

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Background: Given the significant health effects, we assessed geospatial patterns of adverse events (AEs), defined as physical or sexual abuse and accidents or poisonings at home, among children in a mixed rural–urban community. Methods: We conducted a population-based cohort study of children (<18 years) living in Olmsted County, Minnesota, to assess geographic patterns of AEs between April 2004 and March 2009 using International Classification of Diseases, Ninth Revision codes. We identified hotspots by calculating the relative difference between observed and expected case densities accounting for population characteristics ( $$Relative\;Difference = {\rm{ }}{{Observed\;Case\;Density - Expected\;Case\;Density} \over {Expected\;Case\;Density}}$$ ; hotspot ≥ 0.33) using kernel density methods. A Bayesian geospatial logistic regression model was used to test for association of subject characteristics (including residential features) with AEs, adjusting for age, sex, and socioeconomic status (SES). Results: Of the 30,227 eligible children (<18 years), 974 (3.2%) experienced at least one AE. Of the nine total hotspots identified, five were mobile home communities (MHCs). Among non-Hispanic White children (85% of total children), those living in MHCs had higher AE prevalence compared to those outside MHCs, independent of SES (mean posterior odds ratio: 1.80; 95% credible interval: 1.22–2.54). MHC residency in minority children was not associated with higher prevalence of AEs. Of addresses requiring manual correction, 85.5% belonged to mobile homes. Conclusions: MHC residence is a significant unrecognized risk factor for AEs among non-Hispanic, White children in a mixed rural–urban community. Given plausible outreach difficulty due to address discrepancies, MHC residents might be a geographically underserved population for clinical care and research.

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Housing data-based socioeconomic index and risk of invasive pneumococcal disease: an exploratory study

Cited by 39 publications

References 33 publications

Epidemiology of Children With Multiple Complex Chronic Conditions in a Mixed Urban-Rural US Community

Epidemiology of Children With Multiple Complex Chronic Conditions in a Mixed Urban-Rural US Community

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

Mobile home residence as a risk factor for adverse events among children in a mixed rural–urban community: A case for geospatial analysis

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