A longitudinal analysis of the relationship between housing insecurity and physical health among midlife and aging adults in the United States

Bhat, Aarti C.; Almeida, David M.; Fenelon, Andrew; Santos-Lozada, Alexis R.

doi:10.1016/j.ssmph.2022.101128

Cited by 21 publications

(13 citation statements)

References 54 publications

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“…The relationship between housing and chronic pain is an understudied area. Bhat et al (2022) found that experiencing housing insecurity, or a person's increased vulnerability to disruptions in their current housing situation, was associated with poorer self-rated health and a higher number of chronic conditions among middle-aged and older adults. 33 These findings are consistent with our finding that, overall, ongoing housing problems predicted a greater odds of experiencing any chronic pain and high-impact chronic pain.…”

Section: Discussionmentioning

confidence: 99%

“…Bhat et al (2022) found that experiencing housing insecurity, or a person's increased vulnerability to disruptions in their current housing situation, was associated with poorer self-rated health and a higher number of chronic conditions among middle-aged and older adults. 33 These findings are consistent with our finding that, overall, ongoing housing problems predicted a greater odds of experiencing any chronic pain and high-impact chronic pain. However, Bhat et al (2022) also found a significant and positive relationship among increasing housing insecurity and number of chronic health conditions among non-Hispanic Black adults, but not non-Hispanic White adults, which runs counter to our results.…”

Section: Discussionmentioning

confidence: 99%

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More Problems, More Pain: The Role of Chronic Life Stressors and Racial/Ethnic Identity on Chronic Pain Among Middle-Aged and Older Adults in the United States

Spector,

Quinn,

Wang

et al. 2023

Chronic Stress

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There is a high prevalence of chronic pain among middle-aged and older adults in the United States. Chronic life stressors have been shown to have detrimental consequences for myriad health conditions, including chronic pain. However, there is limited evidence on the types of chronic life stressors that affect middle-aged and older adults and how these stressors influence the chronic pain burden in this population. Moreover, the interaction between chronic life stressors and racial/ethnic identity remains poorly understood as it relates to chronic pain. The current analysis used the 2018 Health and Retirement Study to investigate relationships between chronic life stressors and odds to experience any chronic pain and high-impact chronic pain. Chronic life stressors were characterized, overall and by racial/ethnic identity, and the main and interaction effects were calculated to evaluate relationships between chronic life stressors, racial/ethnic identity, and odds of experiencing any chronic pain and high-impact chronic pain. Results indicate that in 2018, the most common chronic life stressor among middle-aged and older adults was dealing with their own health problems (68%), followed by dealing with the physical or emotional issues affecting a spouse or child (46%). Adjusted analyses showed that a higher total of chronic life stressors increased the odds of middle-aged and older adults experiencing any chronic pain and high-impact chronic pain. There were no significant interactions between the overall chronic life stress burden and racial/ethnic identity as a predictor of odds to experience any chronic pain or high-impact chronic pain, but significant interaction effects were found related to specific chronic life stressors. Findings underscore the significant impact of chronic life stressors on the chronic pain burden among middle-aged and older adults in the United States, which cut across racial/ethnic identity.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Extraction and validation of patient housing and food insecurity status in a large electronic health records database using selective prediction and active learning

Swaminathan

Kumar

López

et al. 2022

Preprint

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Objective: Information on patient social determinants of health is frequently recorded in unstructured clinical notes, making it inaccessible for researchers and policymakers. We aimed to extract and validate food and housing insecurity status on a large electronic health record-derived patient cohort by combining selective prediction and active learning. Materials and Methods: Manually labeled charts selected via active learning were used to train L1-regularized logistic regression models to identify the presence of food insecurity (N=372, 42% event rate) and housing insecurity (N=559, 36% event rate) in clinical notes. In addition to validating predictions against labeled data, we further validated predictions on an additional unlabeled dataset through associative studies with demographic, clinical, and environmental variables with known associations with food and housing insecurity. Results: The food insecurity model had AUC=0.83, sensitivity=0.90, PPV=0.90, and undetermined rate=0.59 (n=149); the housing insecurity model had AUC=0.81, sensitivity=0.50, PPV=1, and undetermined rate=0.65 (n=224). Out of 4,337 unlabeled patients, the 395 (9%) patients predicted to have food insecurity were more likely to be Hispanic/Latino (48% vs 24%, p<0.001) and have diabetes (34% vs 12%), hypertension (43% vs 11%), and heart disease (12% vs 0.7%) (p<0.001 for all). Discussion: Selective prediction and active learning can facilitate efficient labeling of social determinants of health from unstructured EHR data to identify vulnerable populations and targets for healthcare system and policy intervention. Conclusion: Machine learning can be used to extract high-fidelity information on patient food and housing insecurity status.

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“…3 There are certain populations that are at increased risk of experiencing housing instability, including young adults, 4 veterans, 5 and members of the Lesbian, Gay, Bisexual, Transgender, Queer, and more community as a result of discrimination, economic instability, and trauma. 6 Individuals with housing instability are at risk for numerous health conditions and are hospitalized at a higher rate than the general population, especially in acute care and psychiatric settings. 7,8 Not only are individuals with housing instability 1.7-2 times more likely to use hospital services than housed patients, 9 this population also experiences higher rates of hospital readmission, 10 longer hospital stays, 11 and, therefore, higher associated hospital spending when compared with housed individuals.…”

mentioning

confidence: 99%

“…Housing instability is a broad term referring to people who move frequently, have trouble paying rent, spend the bulk of their income on housing costs, are experiencing overcrowding, or are unhoused entirely 3 . There are certain populations that are at increased risk of experiencing housing instability, including young adults, 4 veterans, 5 and members of the Lesbian, Gay, Bisexual, Transgender, Queer, and more community as a result of discrimination, economic instability, and trauma 6 …”

mentioning

confidence: 99%

National Overview of Nonprofit Hospitals’ Community Benefit Programs to Address Housing

Celano,

Keselman,

Barley

et al. 2024

Medical Care

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Background: Housing is a critical social determinant of health that can be addressed through hospital-supported community benefit programming. Objectives: To explore the prevalence of hospital-based programs that address housing-related needs, categorize the specific actions taken to address housing, and determine organizational and community-level factors associated with investing in housing. Research Design: This retrospective, cross-sectional study examined a nationally representative dataset of administrative documents from nonprofit hospitals that addressed social determinants of health in their federally mandated community benefit implementation plans. We conducted descriptive statistics and bivariate analyses to examine hospital and community characteristics associated with whether a hospital invested in housing programs. Using an inductive approach, we categorized housing investments into distinct categories. Measures: The main outcome measure was a dichotomous variable representing whether a hospital invested in one or more housing programs in their community. Results: Twenty percent of hospitals invested in one or more housing programs. Hospitals that addressed housing in their implementation strategies were larger on average, less likely to be in rural communities, and more likely to be serving populations with greater housing needs. Housing programs fell into 1 of 7 categories: community partner collaboration (34%), social determinants of health screening (9%), medical respite centers (4%), community social determinants of health liaison (11%), addressing specific needs of homeless populations (16%), financial assistance (21%), and targeting high-risk populations (5%). Conclusions: Currently, a small subset of hospitals nationally are addressing housing. Hospitals may need additional policy support, external partnerships, and technical assistance to address housing in their communities.

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A longitudinal analysis of the relationship between housing insecurity and physical health among midlife and aging adults in the United States

Cited by 21 publications

References 54 publications

More Problems, More Pain: The Role of Chronic Life Stressors and Racial/Ethnic Identity on Chronic Pain Among Middle-Aged and Older Adults in the United States

More Problems, More Pain: The Role of Chronic Life Stressors and Racial/Ethnic Identity on Chronic Pain Among Middle-Aged and Older Adults in the United States

Extraction and validation of patient housing and food insecurity status in a large electronic health records database using selective prediction and active learning

National Overview of Nonprofit Hospitals’ Community Benefit Programs to Address Housing

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