Association of a housing based individual socioeconomic status measure with diabetic control in primary care practices

Greenwood, Jason; Zurek, Kaitlyn I.; Grimm, Jade M.; Wi, Chung Il; Vogel, John T; Garrison, Gregory M.

doi:10.1016/j.pcd.2021.10.001

Cited by 3 publications

(4 citation statements)

References 34 publications

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Section: Methodsmentioning

confidence: 99%

“…Based upon prior experience with HOUSES data, the first quartile often has substantially different disease outcomes, thus we converted the HOUSES score to a quartile rank for analysis. 28 , 37 …”

Section: Methodsmentioning

confidence: 99%

“…HOUSES Q1, the poorest quartile, was used as the reference category because it tends to be distinct from Q2, Q3, and Q4 in terms of outcomes. 49 , 50 Because the number of previous hospitalizations in 12 months and the number of previous ED visits over 6 months are highly correlated in our data (Pearson Correlation Coefficient = 0.66), we chose to include only previous hospitalizations in our multivariate analysis to eliminate issues with multicollinearity. 51 95% Confidence Intervals which did not cross unity were considered significant (Pr(>|z|) < 0.05).…”

Section: Methodsmentioning

confidence: 99%

“…27 The HOUSES score also demonstrates construct validity by predicting (often better than other SES measures) a broad range of health outcomes which are known to be inversely associated with SES such as acute myocardial infarction, coronary artery disease, diabetes, rheumatoid arthritis, asthma, pneumococcal pneumonia, mood disorders, hypertension, transplantation failure, and overall mortality. 28,[31][32][33][34][35][36][37] The patient's current address at initial admission was used to formulate their HOUSES score. HOUSES scores are available for owner occupied properties as well as rental properties, including multi-unit dwellings which take into account each individual unit's characteristics and proportional valuation.…”

Section: Houses Scorementioning

confidence: 99%

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Association of Early and Late Hospital Readmissions with a Novel Housing-Based Socioeconomic Measure

Zurek

Boswell

Miller

et al. 2022

Health Services Research and Managerial Epidemiology

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Background While socioeconomic status has been linked to hospital readmissions for several conditions, reliable measures of individual socioeconomic status are often not available. HOUSES, a new measure of individual socioeconomic status based upon objective public data about one's housing unit, is inversely associated with overall hospitalization rate but it has not been studied with respect to readmissions. Purpose To determine if patients in the lowest HOUSES quartile are more likely to be readmitted within 30 days (short-term) and 180 days (long-term). Methods A retrospective cohort study of 11 993 patients having 21 633 admissions was conducted using generalized linear mixed-effects models. Results HOUSES quartile did not show any significant association with early readmission. However, when compared to the lowest HOUSES quartile, the second quartile (OR = 0.90, 95%CI 0.83-0.98) and the third quartile (OR = 0.91, 95%CI 0.83-0.99) were associated with lower odds of late readmission while the highest quartile (OR = 0.91, 95%CI 0.82-1.01) was not statistically different. Conclusion HOUSES was associated with late readmission, but not early readmission. This may be because early readmissions are influenced by medical conditions and hospital care while late readmissions are influenced by ambulatory care and home-based factors. Since HOUSES relies on public county assessor data, it is generally available and may be used to focus interventions on those at highest risk for late readmission.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Houses Scorementioning

confidence: 99%

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Association of Early and Late Hospital Readmissions with a Novel Housing-Based Socioeconomic Measure

Zurek

Boswell

Miller

et al. 2022

Health Services Research and Managerial Epidemiology

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The Role of Individual-Level Socioeconomic Status on Nursing Home Placement Accounting for Neighborhood Characteristics

Ryu

Wheeler

et al. 2023

Journal of the American Medical Directors Association

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Self-scheduling Medical Visits in a Multispecialty, Multisite Medical Practice: Complexity, Challenges, and Successes

North,

Buss,

Nelson

et al. 2024

Health Services Research and Managerial Epidemiology

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Background Self-scheduling of medical visits is becoming more common but the complexity of applying multiple requirements for self-scheduling has hampered implementation. Mayo Clinic implemented self-scheduling in 2019 and has been increasing its portfolio of self-schedulable visits since then. Our aim was to show measures quantifying the complexity associated with medical visit scheduling and to describe how opportunities and challenges of scheduling complexity apply in self-scheduling. Methods We examined scheduled visits from January 1, 2022, through August 24, 2023. For seven visit categories, we counted all unique visit types that were scheduled, for both staff-scheduled and self-scheduled. We examined counts of self-scheduled visit types to identify those with highest uptake during the study period. Results There were 9555 unique visit types associated with 20.8 M (million) completed visits. Self-scheduled visit types accounted for 4.0% (838,592/20,769,699) of the completed total visits. Of seven visit categories, self-scheduled established patient visits, testing visits, and procedure visits accounted for 93.5% (784,375/838,592) of all self-scheduled visits. Established patient visits in primary care (10 visit types) accounted for 273,007 (32.6%) of all self-scheduled visits. Testing visits (blood and urine testing, 2 visit types) accounted for 183,870 (21.9%) of all self-scheduled visits. Procedure visits for screening mammograms, bone mineral density, and immunizations (8 visit types) accounted for 147,358 (17.6%) of all self-scheduled visits. Conclusion Large numbers of unique visit types comprise a major challenge for self-scheduling. Some visit types are more suitable for self-scheduling. Guideline-based procedure visits such as screening mammograms, bone mineral density exams, and immunizations are examples of visits that have high volumes and can be standardized for self-scheduling. Established patient visits and laboratory testing visits also can be standardized for self-scheduling. Despite the successes, there remain thousands of specific visit types that may need some staff-scheduler intervention to properly schedule.

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Association of a housing based individual socioeconomic status measure with diabetic control in primary care practices

Cited by 3 publications

References 34 publications

Association of Early and Late Hospital Readmissions with a Novel Housing-Based Socioeconomic Measure

Association of Early and Late Hospital Readmissions with a Novel Housing-Based Socioeconomic Measure

The Role of Individual-Level Socioeconomic Status on Nursing Home Placement Accounting for Neighborhood Characteristics

Self-scheduling Medical Visits in a Multispecialty, Multisite Medical Practice: Complexity, Challenges, and Successes

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