Using EHR Data to Identify Social Determinants of Health Affecting Disparities in Cancer Survival

Abstract

The aim of this pilot study was to identify social determinants of health (SDH) that affect disparities in cancer survival. A limited dataset was generated by querying electronic medical records (EHR) from an academic medical center in New York City between January 2003 and November 2020. Socio-demographic characteristics that affected survival in 22,096 cancer patients were analyzed using descriptive statistics and logistic regression analyses. Two subsets of adult patients were identified: patients who were deceased less than 1 year after diagnosis and patients who survived over 5 years after diagnosis. Percentage of individuals with short survival in Blacks and Whites was respectively 41.4% and 22.2% for lung cancer, 9.8% and 7.1% for colorectal cancer, 2.9% and 0.7% for breast cancer, 6.8% and 4.0% for multiple myeloma, and 1.4% and 0.8% for prostate cancer. Logistic regression identified SDH factors increasing likelihood of shorter survival that included older age, and being male, Black or Hispanic. We concluded that further analysis of a broader spectrum of SDH factors is warranted.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

“…Beyond infectious outcomes and healthcare utilization, studies also assessed chronic disease control across conditions like diabetes (n = 11) [170][171][172][173][174][175][176][177][178][179][180], hypertension [173,181,182], kidney disease [183,184], and obesity (n = 7) [146,175,[185][186][187][188][189], along with risk factors like elevated blood pressure and cardiovascular events. Some studies focused on cancer (n = 11) screening, diagnoses, treatment disparities and survival outcomes [167,[190][191][192][193][194][195][196][197][198][199], while others addressed mental health (n = 6) indicators [146][147][148][149][150][151] ranging from dementia incidence [200] to suicide (n = 2) risk factors [201,202]. Additional outcomes evaluated included maternal morbidity (n = 2) …”

“…Beyond clinical indicators, several studies incorporated validated SDoH indexes like the Area Deprivation Index [206,207], Social Deprivation Index [208] and CDC's Social Vulnerability Index [209]. In terms of analysis approaches, common methods included multivariate regression models like logistic regression (n = 13) [159,179,180,192,199,[210][211][212][213][214][215][216][217] and Cox proportional hazards models (n = 3) [138,196,212] to assess adjusted outcome associations with SDoH factors. Other advanced techniques leveraged included machine learning algorithms [215], geospatial analysis for clustering [218], and phenome-wide association studies [150] in select studies.…”

Identifying Determinants of Survival Disparities in Multiple Myeloma Patients Using Electronic Health Record Data

“…New York City presents different sociodemographic and economic patterns compared to national levels. Based on our previous studies on identifying survival rate in various cancer types and the success of lung cancer survival analysis [8,9], we aimed in this study to analyze the sociodemographic, economic, and genetic characteristics of long-term and short-term survival of patients with MM using EHR data from a New York City hospital.…”

Social factors and behavioural reactions to radon test outcomes underlie differences in radiation exposure dose, independent of household radon level

“…A majority of public radon-related health strategies focus on getting individuals to test buildings and personally invest in radon mitigation to remove risk. By relying solely on individual action, there is an inherent reliance on individual motivation and capability, influenced by socioeconomic factors and social determinants of health, to act to reduce the health threat of radon exposure 29 , 30 . This can result in inequitable exposure and hence increased risk for lung cancer in those groups less likely to take personal action to mitigate risk.…”

“…Given that the onus is on the individual to act to reduce radon exposure, it is worth noting that typical radon and lung cancer risk communication approaches are not operating in an inclusive manner, with issues in messaging identified on the basis of ethnicity, region, education, age, sex, and profession [24][25][26][27][28] .A majority of public radon-related health strategies focus on getting individuals to test buildings and personally invest in radon mitigation to remove risk. By relying solely on individual action, there is an inherent reliance on individual motivation and capability, influenced by socioeconomic factors and social determinants of health, to act to reduce the health threat of radon exposure 29,30 . This can result in inequitable exposure and hence increased risk for lung cancer in those groups less likely to take personal action to mitigate risk.…”