Sensitivity and Specificity of Real-World Social Factor Screening Approaches

Vest, Joshua R.; Wu, Wei; Mendonça, Eneida A.

doi:10.1007/s10916-021-01788-7

Cited by 21 publications

(15 citation statements)

References 45 publications

(57 reference statements)

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“…This study relied on 5 imperfect and underutilized ICD-10 Z59-codes that, according to the literature, are likely more specific than sensitive indicators of housing instability. 41 , 42 , 43 , 44 , 45 The Z59-codes likely capture the most extreme experience of housing instability: homelessness. Studies 26 , 41 , 43 , 46 , 47 , 48 , 49 have also shown that these and other SDOH-related Z-codes (Z55-65) inconsistently map to SDOH screening instruments.…”

Section: Discussionmentioning

confidence: 99%

“… 41 , 42 , 43 , 44 , 45 The Z59-codes likely capture the most extreme experience of housing instability: homelessness. Studies 26 , 41 , 43 , 46 , 47 , 48 , 49 have also shown that these and other SDOH-related Z-codes (Z55-65) inconsistently map to SDOH screening instruments. Moreover, study results were consistent with prior work 25 , 42 , 43 , 44 that demonstrated that Z-codes Z55-65 are only found in 1% to 2% of inpatient medical records.…”

Section: Discussionmentioning

confidence: 99%

“…[41][42][43][44][45] The Z59-codes likely capture the most extreme experience of housing instability: homelessness. Studies 26,41,43,[46][47][48][49] have also shown that these and other SDOH-related Z-codes (Z55-65) inconsistently map to SDOH screening instruments.…”

Section: Limitationsmentioning

confidence: 99%

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Association of Coded Housing Instability and Hospitalization in the US

et al. 2022

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ImportanceHousing instability and other social determinants of health are increasingly being documented by clinicians. The most common reasons for hospitalization among patients with coded housing instability, however, are not well understood.ObjectiveTo compare the most common reasons for hospitalization among patients with and without coded housing instability.Design, Setting, and ParticipantsThis cross-sectional, retrospective study identified hospitalizations of patients between age 18 and 99 years using the 2017 to 2019 National Inpatient Sample. Data were analyzed from May to September 2022.ExposuresHousing instability was operationalized using 5 International Classification of Diseases, 10th Revision, Social Determinants of Health Z-Codes addressing problems related to housing: homelessness; inadequate housing; discord with neighbors, lodgers, and landlords; residential institution problems; and other related problems.Main Outcomes and MeasuresThe primary outcome of interest was reason for inpatient admission. Bivariate comparisons of patient characteristics, primary diagnoses, length of stay, and hospitalization costs among patients with and without coded housing instability were performed.ResultsAmong the 87 348 604 hospitalizations analyzed, the mean (SD) age was 58 (20) years and patients were more likely to be women (50 174 117 [57.4%]) and White (58 763 014 [67.3%]). Housing instability was coded for 945 090 hospitalizations. Hospitalized patients with housing instability, compared with those without instability, were more likely to be men (668 255 patients with coded instability [70.7%] vs 36 506 229 patients without [42.3%]; P &lt; .001), younger (mean [SD] age 45.5 [14.0] vs 58.4 [20.2] years), Black (235 355 patients [24.9%] vs 12 929 158 patients [15.0%]), Medicaid beneficiaries (521 555 patients [55.2%] vs 15 541 175 patients [18.0%]), uninsured (117 375 patients [12.4%] vs 3 476 841 patients [4.0%]), and discharged against medical advice (28 890 patients [8.4%] vs 451 855 patients [1.6%]). The most common reason for hospitalization among patients with coded housing instability was mental, behavioral, and neurodevelopmental disorders (475 575 patients [50.3%]), which cost a total of $3.5 billion. Other common reasons included injury (69 270 patients [7.3%]) and circulatory system diseases (64 700 patients [6.8%]). Coded housing instability was also significantly associated with longer mean (SD) hospital stays (6.7 [.06] vs 4.8 [.01] days) and a cost of $9.3 billion. Hospitalized patients with housing instability had 18.6 times greater odds of having a primary diagnosis of mental, behavioral, and neurodevelopmental disorders (475 575 patients [50.3%] vs 4 470 675 patients [5.2%]; odds ratio, 18.56; 95% CI, 17.86 to 19.29).Conclusions and RelevanceIn this cross-sectional study, hospitalizations among patients with coded housing instability had higher admission rates for mental, behavioral, and neurodevelopmental disorders, longer stays, and increased costs. Findings suggest that efforts to improve housing instability, mental and behavioral health, and inpatient hospital utilization across multiple sectors may find areas for synergistic collaboration.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Association of Coded Housing Instability and Hospitalization in the US

et al. 2022

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“…Homeless (15) Marginally housed (5) Housing unsuited to needs (13) Subsidized housing (14) lives in facility (8) Stably housed (432)…”

Section: Supplementary Figuresmentioning

confidence: 99%

“…Much of the current research exploring disparities in cLBP care has been limited to stratifying analyses by socioeconomic status and race, as social risk data are not readily available in electronic health records (EHRs). While some social risk information exists in structured data fields, such as patient demographics and problem lists, these data are known to be under identified by clinical teams, under reported by patients, and under documented in structured fields [15][16][17][18][19][20][21][22] . When clinical teams screen for and identify social risks, that information is more often documented within free-text fields, or unstructured data 18,21 .…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Social Determinants of Health from Medical Notes of Chronic Lower Back Pain Patients

Lituiev

Lacar

Pak

et al. 2022

Preprint

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Background. Adverse social determinants of health (SDoH), or social risk factors, such as food insecurity and housing instability, are known to contribute to poor health outcomes and inequities. Our ability to study these linkages is limited because SDoH information is more frequently documented in free-text clinical notes than structured data fields. To overcome this challenge, there is a growing push to develop techniques for automated extraction of SDoH. In this study, we explored natural language processing (NLP) and inference (NLI) methods to extract SDoH information from clinical notes of patients with chronic low back pain (cLBP), to enhance future analyses of the associations between SDoH and low back pain outcomes and disparities. Methods. Clinical notes (n=1,576) for patients with cLBP (n=386) were annotated for seven SDoH domains: housing, food, transportation, finances, insurance coverage, marital and partnership status, and other social support, resulting in 626 notes with at least one annotated entity for 364 patients. We additionally labelled pain scores, depression, and anxiety. We used a two-tier taxonomy with these 10 first-level ontological classes and 68 second-level ontological classes. We developed and validated extraction systems based on both rule-based and machine learning approaches. As a rule-based approach, we iteratively configured a clinical Text Analysis and Knowledge Extraction System (cTAKES) system. We trained two machine learning models (based on convolutional neural network (CNN) and RoBERTa transformer), and a hybrid system combining pattern matching and bag-of-words models. Additionally, we evaluated a RoBERTa based entailment model as an alternative technique of SDoH detection in clinical texts. We used a model previously trained on general domain data without additional training on our dataset. Results. Four annotators achieved high agreement (average kappa=95%, F1=91.20%). Annotation frequency varied significantly dependent on note type. By tuning cTAKES, we achieved a performance of F1=47.11% for first-level classes. For most classes, the machine learning RoBERTa-based NER model performed better (first-level F1=84.35%) than other models within the internal test dataset. The hybrid system on average performed slightly worse than the RoBERTa NER model (first-level F1=80.27%), matching or outperforming the former in terms of recall. Using an out-of-the-box entailment model, we detected many but not all challenging wordings missed by other models, reaching an average F1 of 76.04%, while matching and outperforming the tested NER models in several classes. Still, the entailment model may be sensitive to hypothesis wording and may require further fine tuning. Conclusion. This study developed a corpus of annotated clinical notes covering a broad spectrum of SDoH classes. This corpus provides a basis for training machine learning models and serves as a benchmark for predictive models for named entity recognition for SDoH and knowledge extraction from clinical texts.

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Neighborhood social deprivation and healthcare utilization, disability, and comorbidities among young adults with congenital heart defects: Congenital heart survey to recognize outcomes, needs, and well‐being 2016–2019

Judge

Kramer

Downing

et al. 2023

Birth Defects Research

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BackgroundResearch on the association between neighborhood social deprivation and health among adults with congenital heart defects (CHD) is sparse.MethodsWe evaluated the associations between neighborhood social deprivation and health care utilization, disability, and comorbidities using the population‐based 2016–2019 Congenital Heart Survey To Recognize Outcomes, Needs, and well‐beinG (CH STRONG) of young adults. Participants were identified from active birth defect surveillance systems in three U.S. sites and born with CHD between 1980 and 1997. We linked census tract‐level 2017 American Community Survey information on median household income, percent of ≥25‐year‐old with greater than a high school degree, percent of ≥16‐year‐olds who are unemployed, and percent of families with children <18 years old living in poverty to survey data and used these variables to calculate a summary neighborhood social deprivation z‐score, divided into tertiles. Adjusted prevalence ratios (aPR) and 95% confidence intervals (CI) derived from a log‐linear regression model with a Poisson distribution estimated the association between tertile of neighborhood social deprivation and healthcare utilization in previous year (no encounters, 1 and ≥2 emergency room [ER] visits, and hospital admission), ≥1 disability, and ≥1 comorbidities. We accounted for age, place of birth, sex at birth, presence of chromosomal anomalies, and CHD severity in all models, and, additionally educational attainment and work status in all models except disability.ResultsOf the 1435 adults with CHD, 43.8% were 19–24 years old, 54.4% were female, 69.8% were non‐Hispanic White, and 33.7% had a severe CHD. Compared to the least deprived tertile, respondents in the most deprived tertile were more likely to have no healthcare visit (aPR: 1.5 [95% CI: 1.1, 2.1]), ≥2 ER visits (1.6 [1.1, 2.3]), or hospitalization (1.6 [1.1, 2.3]) in the previous 12 months, a disability (1.2 [1.0, 1.5]), and ≥1 cardiac comorbidities (1.8 [1.2, 2.7]).ConclusionsNeighborhood social deprivation may be a useful metric to identify patients needing additional resources and referrals.

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Sensitivity and Specificity of Real-World Social Factor Screening Approaches

Cited by 21 publications

References 45 publications

Association of Coded Housing Instability and Hospitalization in the US

Association of Coded Housing Instability and Hospitalization in the US

Automatic Extraction of Social Determinants of Health from Medical Notes of Chronic Lower Back Pain Patients

Neighborhood social deprivation and healthcare utilization, disability, and comorbidities among young adults with congenital heart defects: Congenital heart survey to recognize outcomes, needs, and well‐being 2016–2019

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