Managing the onset of a new disease in older age: Housing wealth, mortgage borrowing, and medication adherence

Moulton, Stephanie; Rhodes, Alec P.; Haurin, Donald R.; Loibl, Cäzilia

doi:10.1016/j.socscimed.2022.115437

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…Concurrently, Pool et al., 57 utilizing HRS data, unveiled a correlation between late‐ and middle‐age negative wealth shocks and an elevated risk of depressive symptoms, albeit without impacting CRN. In a similar vein, Moulton and colleagues 58 examined the effect of borrowing against the equity in one's home on healthcare spending among individuals aged 65 or older who have experienced one of six expensive diseases. The researchers assessed for CRN and they found that for every $10,000 more mortgage loaned out, the CRN was reduced by 1.6 percentage points.…”

Section: Leveraging Public Databases For Enhanced Medication Adherenc...mentioning

confidence: 99%

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Xu,

Zheng,

et al. 2023

J Evidence Based Medicine

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BackgroundIncreasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data‐rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.MethodsWe conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.ResultsOur study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision‐making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.ConclusionAdvanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

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Section: Leveraging Public Databases For Enhanced Medication Adherenc...mentioning

confidence: 99%

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Xu,

Zheng,

et al. 2023

J Evidence Based Medicine

View full text Add to dashboard Cite

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Mortgage Borrowing and Chronic Disease Outcomes in Older Age: Evidence From Biomarker Data in the Health and Retirement Study

Rhodes,

Moulton,

Loibl

et al. 2024

The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences

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Objective The medical diagnosis of a disease is common in older age and can carry significant financial costs. For many older adults, equity in a home is their primary component of wealth; however, housing wealth is illiquid. We analyze the relationship between the liquidation of housing wealth through mortgage borrowing on older homeowners’ ability to successfully control a disease. Methods We use data on homeowners age 65 and older from the 1998-2016 waves of the Health and Retirement Study (N=3,457). We use biomarkers and physical health indicators to measure disease control following a medical diagnosis of diabetes, heart condition, high blood pressure, lung disease, or cancer. Random effects linear probability and instrumental variable regressions estimate the associations of housing wealth, new mortgage borrowing, and disease control. Results Descriptively, 28% of older homeowners who borrow against home equity are not controlled on their disease, compared to 33% of non-borrowers. Panel data instrumental variable regressions show that each $10,000 borrowed from home equity after diagnosis is associated with a 17 percentage-point reduction in the probability of the disease not being controlled. Discussion Many older adults are not able or willing to liquidate housing wealth, and the ability to borrow also depends on changes in home values. Thus, housing wealth is not a uniform social determinant of health but is shaped by older adults’ participation in financial markets.

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Managing the onset of a new disease in older age: Housing wealth, mortgage borrowing, and medication adherence

Cited by 2 publications

References 48 publications

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Mortgage Borrowing and Chronic Disease Outcomes in Older Age: Evidence From Biomarker Data in the Health and Retirement Study

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