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Objectives We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program. Materials and Methods Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us  and examine associations with undiagnosed hypertension and diabetes. Results Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900. Discussion and Conclusion The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) contain rich clinical information for millions of patients and are increasingly used for public health research. However, non-random inclusion of subjects in EHRs can result in selection bias, with factors such as demographics, socioeconomic status, healthcare referral patterns, and underlying health status playing a role. While this issue has been well documented, little work has been done to develop or apply bias-correction methods, often due to the fact that most of these factors are unavailable in EHRs. To address this gap, we propose a series of Heckman type bias correction methods by incorporating social determinants of health selection covariates to model the EHR non-random sampling probability. Through simulations under various settings, we demonstrate the effectiveness of our proposed method in correcting biases in both the association coefficient and the outcome mean. Our method augments the utility of EHRs for public health inferences, as we show by estimating the prevalence of cardiovascular disease and its correlation with risk factors in the New York City network of EHRs. [ABSTRACT FROM AUTHOR]

Background Although studies to date have broadly shown that cardiovascular disease (CVD) increases cognitive and physical impairment risk, there is still limited understanding of the magnitude of this risk among relevant CVD subtypes or age cohorts. Methods We analyzed longitudinal data from 16 679 U.S. Health and Retirement Study participants who were aged ≥65 years at study entry. Primary endpoints were physical impairment (activities of daily living impairment) or cognitive impairment (Langa–Weir Classification of dementia). We compared these endpoints among participants who developed incident CVD versus those who were CVD free, both in the short term (<2-year postdiagnosis) and long term (>5 years), controlling for sociodemographic and health characteristics. We then analyzed the effects by CVD subtype (atrial fibrillation, congestive heart failure, ischemic heart disease, and stroke) and age-at-diagnosis (65–74, 75–84, and ≥85). Results Over a median follow-up of 10 years, 8 750 participants (52%) developed incident CVD. Incident CVD was associated with significantly higher adjusted odds (aOR) of short-term and long-term physical and cognitive impairment. The oldest (≥85) age-at-diagnosis subgroup had the highest risk of short-term physical (aOR 3.01, 95% confidence interval [CI]: 2.40–3.77) and cognitive impairment (aOR 1.96, 95% CI: 1.55–2.48), as well as long-term impairment. All CVD subtypes were associated with higher odds of physical and cognitive impairment, with the highest risk for patients with incident stroke. Conclusions Incident CVD was associated with an increased risk of physical and cognitive impairment across CVD subtypes. Impairment risk after CVD was highest among the oldest patients (≥85 years) who should therefore remain a target for prevention efforts. [ABSTRACT FROM AUTHOR]

In data collection for predictive modeling, underrepresentation of certain groups, based on gender, race/ethnicity, or age, may yield less accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data‐driven models are increasingly utilized to perform crucial decision‐making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups. These approaches have two major limitations: (i) fairness is often achieved by compromising accuracy for some groups; (ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a joint fairness model (JFM) approach for logistic regression models for binary outcomes that estimates group‐specific classifiers using a joint modeling objective function that incorporates fairness criteria for prediction. We introduce an accelerated smoothing proximal gradient algorithm to solve the convex objective function, and present the key asymptotic properties of the JFM estimates. Through simulations, we demonstrate the efficacy of the JFM in achieving good prediction performance and across‐group parity, in comparison with the single fairness model, group‐separate model, and group‐ignorant model, especially when the minority group's sample size is small. Finally, we demonstrate the utility of the JFM method in a real‐world example to obtain fair risk predictions for underrepresented older patients diagnosed with coronavirus disease 2019 (COVID‐19). [ABSTRACT FROM AUTHOR]

Objectives: Recent investigations have shown strong correlations between cytology and surgical non-small cell lung carcinoma (NSCLC) specimens in programmed death-ligand 1 (PD-L1) immunohistochemical (IHC) evaluations. Our study aims to evaluate the reproducibility of PD-L1 IHC scoring in NSCLC cytology cell blocks (CBs) and to assess the impact of CB cellularity, method of sample collection, and observer subspecialty on scoring agreement.Methods: PD-L1 IHC was performed on 54 NSCLC cytology CBs and was scored independently by seven cytopathologists (three of seven with expertise in pulmonary pathology). Three-tier scoring of negative (<1%), low positive (1%-49%), and high positive (≥50%) and interrater agreement were assessed.Results: Total and majority agreement among cytopathologists was achieved in 48% and 98% of cases, respectively, with κ = 0.608 (substantial agreement; 95% confidence interval, 0.50-0.72). Cytopathologists with pulmonary pathology expertise agreed in 67% of cases (κ = 0.633, substantial agreement), whereas the remaining cytopathologists agreed in 56% of cases (κ = 0.62, substantial agreement). CB cellularity (P = .36) and sample collection type (P = .59) had no statistically significant difference between raters.Conclusions: There is substantial agreement in PD-L1 IHC scoring in cytology CB specimens among cytopathologists. Additional expertise in pulmonary pathology, sample collection type, and CB cellularity have no statistically significant impact on interobserver agreement. [ABSTRACT FROM AUTHOR]

Background: The American Joint Committee on Cancer (AJCC) maintains that the eighth edition of its Staging Manual (AJCC8) has improved accuracy compared with the seventh (AJCC7). However, there are concerns that implementation may disrupt analysis of active clinical trials for stage III patients. We used an independent cohort of melanoma patients to test the extent to which AJCC8 has improved prognostic accuracy compared with AJCC7.Methods: We analyzed a cohort of 1315 prospectively enrolled patients. We assessed primary tumor and nodal classification of stage I-III patients using AJCC7 and AJCC8 to assign disease stages at diagnosis. We compared recurrence-free (RFS) and overall survival (OS) using Kaplan-Meier curves and log-rank tests. We then compared concordance indices of discriminatory prognostic ability and area under the curve of 5-year survival to predict RFS and OS. All statistical tests were two-sided.Results: Stage IIC patients continued to have worse outcomes than stage IIIA patients, with a 5-year RFS of 26.5% (95% confidence interval [CI] = 12.8% to 55.1%) vs 56.0% (95% CI = 37.0% to 84.7%) by AJCC8 (P = .002). For stage I, removing mitotic index as a T classification factor decreased its prognostic value, although not statistically significantly (RFS concordance index [C-index] = 0.63, 95% CI = 0.56 to 0.69; to 0.56, 95% CI = 0.49 to 0.63, P = .07; OS C-index = 0.48, 95% CI = 0.38 to 0.58; to 0.48, 95% CI = 0.41 to 0.56, P = .90). For stage II, prognostication remained constant (RFS C-index = 0.65, 95% CI = 0.57 to 0.72; OS C-index = 0.61, 95% CI = 0.50 to 0.72), and for stage III, AJCC8 yielded statistically significantly enhanced prognostication for RFS (C-index = 0.65, 95% CI = 0.60 to 0.70; to 0.70, 95% CI = 0.66 to 0.75, P = .01).Conclusions: Compared with AJCC7, we demonstrate that AJCC8 enables more accurate prognosis for patients with stage III melanoma. Restaging a large cohort of patients can enhance the analysis of active clinical trials. [ABSTRACT FROM AUTHOR]

Background: Two primary histologic subtypes, superficial spreading melanoma (SSM) and nodular melanoma (NM), comprise the majority of all cutaneous melanomas. NM is associated with worse outcomes, which have been attributed to increased thickness at presentation, and it is widely expected that NM and SSM would exhibit similar behavior once metastasized. Herein, we tested the hypothesis that primary histologic subtype is an independent predictor of survival and may impact response to treatment in the metastatic setting.Methods: We examined the most recent Surveillance, Epidemiology, and End Results (SEER) cohort (n = 118 508) and the New York University (NYU) cohort (n = 1621) with available protocol-driven follow-up. Outcomes specified by primary histology were studied in both the primary and metastatic settings with respect to BRAF-targeted therapy and immunotherapy. We characterized known driver mutations and examined a 140-gene panel in a subset of NM and SSM cases using next-generation sequencing. All statistical tests were two-sided.Results: NM was an independent risk factor for death in both the SEER (hazard ratio [HR] = 1.55, 95% confidence interval [CI] = 1.41 to 1.70, P < .001) and NYU (HR = 1.47, 95% CI = 1.05, 2.07, P = .03) cohorts, controlling for thickness, ulceration, stage, and other variables. In the metastatic setting, NM remained an independent risk factor for death upon treatment with BRAF-targeted therapy (HR = 3.33, 95% CI = 1.06 to 10.47, P = .04) but showed no statistically significant difference with immune checkpoint inhibition. NM was associated with a higher rate of NRAS mutation (P < .001), and high-throughput sequencing revealed NM-specific genomic alterations in NOTCH4, ANK3, and ZNF560, which were independently validated.Conclusions: Our data reveal distinct clinical and biological differences between NM and SSM that support revisiting the prognostic and predictive impact of primary histology subtype in the management of cutaneous melanoma. [ABSTRACT FROM AUTHOR]

Background: Surgical management of primary melanoma is curative for most patients with clinically localized disease at diagnosis; however, a substantial number of patients recur and progress to advanced disease. Understanding molecular alterations that influence differential tumor progression of histopathologically similar lesions may lead to improved prognosis and therapies to slow or prevent metastasis. Methods: We examined microRNA dysregulation by expression profiling of primary melanoma tumors from 92 patients. We screened candidate microRNAs selected by differential expression between recurrent and nonrecurrent tumors or associated with primary tumor thickness (Student's t test, Benjamini-Hochberg False Discovery Rate [FDR] < 0.05), in in vitro invasion assays. We performed in vivo metastasis assays, matrix remodeling experiments, and molecular studies to identify metastasis-regulating microRNAs and their cellular and molecular mechanisms. All statistical tests were two-sided. Results: We identified two microRNAs (hsa-miR-382, hsa-miR-516b) whose expression was lower in aggressive vs nonaggressive primary tumors, which suppressed invasion in vitro and metastasis in vivo (mean metastatic foci: control: 37.9, 95% confidence interval [CI] = 25.6 to 50.2; miR-382:19.5, 95% CI = 12.2 to 26.9, P = .009; miR-516b: 12.5, 95% CI = 7.7 to 17.4, P < .001, Student's t test). Mechanistically, miR-382 overexpression inhibits extracellular matrix degradation by melanoma cells. Moreover, we identified actin regulators CTTN, RAC1, and ARPC2 as direct targets of miR-382. Depletion of CTTN partially recapitulates miR-382 effects on matrix remodeling, invasion, and metastasis. Inhibition of miR-382 in a weakly tumorigenic melanoma cell line increased tumor progression and metastasis in vivo. Conclusions: Aberrant expression of specific microRNAs that can functionally impact progression of primary melanoma occurs as an early event of melanomagenesis. [ABSTRACT FROM AUTHOR]