Comparison of Community-Level and Patient-Level Social Risk Data in a Network of Community Health Centers

Issues with Variability in EHR Data About Race and Ethnicity: A Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave (Preprint)

Background

The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations.

Objective

This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database.

Methods

At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as “Declined” were grouped with “Refused,” and “Multiple Race” was grouped with “Two or more races” and “Multiracial.”

Results

“No matching concept” was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category.

Conclusions

Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy.

Lack of Association of Household Income and Acute Gastroenteritis Disease Severity in Young Children: A Cohort Study

OBJECTIVE

To determine if low household income is associated with disease severity following emergency department (ED) discharge in children with acute gastroenteritis (AGE).

METHODS

We conducted a secondary analysis employing data collected in 10 US-based tertiary-care, pediatric EDs between 2014 and 2017. Participants were aged 3 to 48 months and presented for care due to AGE. Income status was defined based on 1) home ZIP Code median annual home income and 2) percentage of home ZIP Code households below the poverty threshold. The primary outcome was moderate-to-severe AGE, defined by a post-ED visit Modified Vesikari Scale (MVS) score ≥9. Secondary outcomes included in-person revisits, revisits with intravenous rehydration, hospitalization, and etiologic pathogens.

RESULTS

About 943 (97%) participants with a median age of 17 months (interquartile range 10, 28) completed follow-up. Post-ED visit MVS scores were lower for the lowest household income group (adjusted: -0.60; 95% confidence interval [CI]: -1.13, -0.07). Odds of experiencing an MVS score ≥9 did not differ between groups (adjusted odds ratio: 0.91; 95% CI: 0.54, 1.52). No difference in the post-ED visit MVS score or the proportion of participants with scores ≥9 was observed using the national poverty threshold definition. For both income definitions, there were no differences in terms of revisits following discharge, hospitalizations, and intravenous rehydration. Bacterial enteropathogens were more commonly identified in the lowest socioeconomic group using both definitions.

CONCLUSIONS

Lower household income was not associated with increased disease severity or resource use. Economic disparities do not appear to result in differences in the disease course of children with AGE seeking ED care.

Food insecurity in Detroit: exploring the relationship between patient-reported food insecurity and proximity to healthful grocery stores

OBJECTIVE

The objective of the current study was to determine if patients of a large health care system in Detroit who self-identify as food insecure live further away from healthy grocery stores compared with food secure patients. Second, we explored whether food insecurity and distance to healthy grocery stores are related to ecological measures of vehicle availability in the area of residence.

DESIGN

A secondary data analysis that uses baseline data from a pilot intervention/feasibility study.

SETTING

Detroit, Michigan, USA.

PARTICIPANTS

Patients of Henry Ford Health System were screened for food insecurity to determine eligibility for a pilot intervention/feasibility study (i.e. Henry's Groceries for Health), conducted through a collaboration with Gleaners Community Foodbank of Southeastern Michigan. Only patients residing in Detroit city limits (including Highland Park and Hamtramck) were included in the secondary analysis. Of the 1,100 patients included in the analysis, 336 (31 %) were food insecure.

RESULTS

After accounting for socio-demographic factors associated with food insecurity, we did not find evidence that food insecure patients lived further away from healthier grocery stores, nor was this modified by ecological measures of vehicle access. However, some neighbourhoods were identified as having a significantly higher risk of food insecurity.

CONCLUSIONS

Food insecure patients in Detroit are perhaps limited by social and political determinants and not their immediate neighbourhood geography or physical access to healthy grocery stores. Future research should explore the complexity in linkages between household socio-economic factors, socio-cultural dynamics and the neighbourhood food environment.

Predicting health-related social needs in Medicaid and Medicare populations using machine learning

Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66–0.70) was achieved by the “any HRSNs” outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.

Can social risks in early life predict children’s health and academic outcomes? An analysis of the Longitudinal Study of Australian Children

Exposure to social risk in early life negatively impacts the health and wellbeing of children. While screening for social determinants of health is recommended, there is little evidence that identifying social risk early in life predicts longer-term poorer outcomes. The purpose of this study is to examine the extent to which assessing social risk using a standardized tool in young children up to age 6 years might predict poor health and academic performance at 10–11 years old. The social risk domains studied were housing instability, food insecurity, financial strain, transport problems, safety, lack of support and unemployment. The predictive validity of these social risk domains measured at 0–5 years was examined using data from the Longitudinal Study of Australian Children. Outcomes at 10–11 years included ongoing diseases and mental health conditions, hospitalization, injury, dental problems, overweight or obesity and academic achievement. Financial strain and inability to access support were the most sensitive measures of poor outcomes. Across all social risks, the positive predictive value was highest for academic outcomes. Across all domains, there was higher sensitivity for children with 2 or more social risks. Items in the social risk screening tool were moderate predictors of academic outcomes, but weak predictors of health outcomes at 10–11 years. This data will be useful for informing screening for social determinants of health.

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Social risks from 0 to 5 y were moderate predictors of 8-year-old school assessments.

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Financial strain and a lack of social support had modest sensitivity.

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Sensitivity is increased by applying a threshold of 2 or more social risks.

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The positive predictive value of social needs was highest for academic outcomes.

Implementing Health Related Social Needs Screening in an Outpatient Clinic

INTRODUCTION/OBJECTIVES

Health-related social needs (HRSN) screening efforts have reported high rates of identified social needs. Little is known if efforts to conduct HRSN screening in resource-constrained federally-qualified health centers (FQHC) successfully captures a representative patient population.

METHODS

This cross-sectional study extracted EMR data from 2016 to 2020 for 4731 screened patients from 7 affiliated clinics of a FQHC. Unscreened patients were pulled as a random sample from the study period. A multivariable logistic regression was used to identify sociodemographic traits, chronic disease diagnoses and burden, and clinic visit type and frequency associated with being screened for HRSN.

RESULTS

BHC screened 4731 unique patients or <1% of the total clinic population. Screened patients had a median of 3.3 (±2.5) unmet HRSN. Medicaid patients had higher odds of being screened (aOR = 1.38, CI 1.19-1.61) relative to Medicare patients. The odds of being screened for social needs increased with more provider visits per year: compared to fewer than 1 visit per year, patients with 1 to 3 provider visits (aOR = 2.06, CI 1.73-2.32), 4 to 6 provider visits (aOR = 3.34, CI 2.89-3.87), and more than 6 provider visits (aOR = 5.16, CI 4.35-6.12) all had higher odds of social needs screening. Patients with a higher comorbid disease burden (>2 conditions, aOR = 2.80, CI 2.07-3.79) had higher odds of screening.

CONCLUSIONS

Our findings demonstrate an increased likelihood to screen patients who visit outpatient services more often and have a higher comorbid disease burden. To meet state-level Medicaid requirements, resource-constrained FQHCs that implement clinic wide HRSN screening may be well served to identify a priori strategies to ensure representative and equitable screening across the patient population.

Racial/ethnic disparities in wait-list outcomes are only partly explained by socioeconomic deprivation among children awaiting liver transplantation

BACKGROUND AND AIMS

Racial/ethnic minority children have worse liver transplant (LT) outcomes. We evaluated whether neighborhood socioeconomic deprivation affected associations between race/ethnicity and wait-list mortality.

APPROACH AND RESULTS

We included children (age <18) listed 2005-2015 in the Scientific Registry of Transplant Recipients. We categorized patients as non-Hispanic White, Black, Hispanic, and other. We matched patient ZIP codes to a neighborhood socioeconomic deprivation index (range, 0-1; higher values indicate worse deprivation). Primary outcomes were wait-list mortality, defined as death/delisting for too sick, and receipt of living donor liver transplant (LDLT). Competing risk analyses modeled the association between race/ethnicity and wait-list mortality, with deceased donor liver transplant (DDLT) and LDLT as competing risks, and race/ethnicity and LDLT, with wait-list mortality and DDLT as competing risks. Of 7716 children, 17% and 24% identified as Black and Hispanic, respectively. Compared to White children, Black and Hispanic children had increased unadjusted hazard of wait-list mortality (subhazard ratio [sHR], 1.44; 95% CI, 1.18, 1.75 and sHR, 1.48; 95% CI, 1.25, 1.76, respectively). After adjusting for neighborhood deprivation, insurance, and listing laboratory Model for End-Stage Liver Disease/Pediatric End-Stage Liver Disease, Black and Hispanic children did not have increased hazard of wait-list mortality (sHR, 1.12; 95% CI, 0.91, 1.39 and sHR, 1.21; 95% CI, 1.00, 1.47, respectively). Similarly, Black and Hispanic children had a decreased likelihood of LDLT (sHR, 0.58; 95% CI, 0.45, 0.75 and sHR, 0.61; 95% CI, 0.49, 0.75, respectively). Adjustment attenuated the effect of Black and Hispanic race/ethnicity on likelihood of LDLT (sHR, 0.79; 95% CI, 0.60, 1.02 and sHR, 0.89; 95% CI, 0.70, 1.11, respectively).

CONCLUSIONS

Household and neighborhood socioeconomic factors and disease severity at wait-list entry help explain racial/ethnic disparities for children awaiting transplant. A nuanced understanding of how social adversity contributes to wait-list outcomes may inform strategies to improve outcomes.

Addressing Social Adversity to Improve Outcomes for Children After Liver Transplant

The social determinants of health, defined as the conditions in which we live, learn, work, and play, undoubtedly impact health outcomes. Social adversity in childhood perpetuates over the life course and has consequences extending into adulthood. This link between social adversity and adverse outcomes extends to children undergoing liver transplant, with children from socioeconomically deprived neighborhoods experiencing a greater burden of morbidity and mortality after transplant. Yet, we lack an in-depth understanding of how to address social adversity for these children. Herein, we lay out a strategy to develop and test interventions to address social adversity for children undergoing liver transplant. To do so, we believe that more granular data on how specific social risk factors (e.g., food insecurity) impact outcomes for children after liver transplant are needed. This will provide the liver transplant community with knowledge on the most pressing problems. Then, using the National Academies of Sciences, Engineering, and Medicine's framework for integrating social needs into medical care, the health system can start to develop and test health system interventions. We believe that attending to our patients' social adversity will realize improved outcomes for children undergoing liver transplant.

The quality of social determinants data in the electronic health record: a systematic review

OBJECTIVE

The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH).

MATERIALS AND METHODS

We conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process.

RESULTS

The most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully.

DISCUSSION

The type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups.

CONCLUSION

Consideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.

Neighbourhood material deprivation and severe maternal morbidity: a population-based cohort study in Ontario, Canada

OBJECTIVES

Rates of age-associated severe maternal morbidity (SMM) have increased in Canada, and an association with neighbourhood income is well established. Our aim was to examine SMM trends according to neighbourhood material deprivation quintile, and to assess whether neighbourhood deprivation effects are moderated by maternal age.

DESIGN, SETTING AND PARTICIPANTS

A population-based retrospective cohort study using linked administrative databases in Ontario, Canada. We included primiparous women with a live birth or stillbirth at ≥20 weeks' gestational age.

PRIMARY OUTCOME

SMM from pregnancy onset to 42 days postpartum. We calculated SMM rate differences (RD) and rate ratios (RR) by neighbourhood material deprivation quintile for each of four 4-year cohorts from 1 April 2002 to 31 March 2018. Log-binomial multivariable regression adjusted for maternal age, demographic and pregnancy-related variables.

RESULTS

There were 1 048 845 primiparous births during the study period. The overall rate of SMM was 18.0 per 1000 births. SMM rates were elevated for women living in areas with high material deprivation. In the final 4-year cohort, the RD between women living in high vs low deprivation neighbourhoods was 3.91 SMM cases per 1000 births (95% CI: 2.12 to 5.70). This was higher than the difference observed during the first 4-year cohort (RD 2.09, 95% CI: 0.62 to 3.56). SMM remained associated with neighbourhood material deprivation following multivariable adjustment in the pooled sample (RR 1.16, 95% CI: 1.11 to 1.21). There was no evidence of interaction with maternal age.

CONCLUSION

SMM rate increases were more pronounced for primiparous women living in neighbourhoods with high material deprivation compared with those living in low deprivation areas. This raises concerns of a widening social gap in maternal health disparities and highlights an opportunity to focus risk reduction efforts toward disadvantaged women during pregnancy and postpartum.

The impact of gender and the social determinants of health on the clinical course of people living with HIV in Myanmar: an observational study

BACKGROUND

There is a growing recognition of the impact of gender and the social determinants of health on the clinical course of people living with HIV (PLHIV). However, the relative contribution of these factors to clinical outcomes of PLHIV is incompletely defined in many countries. This study was performed to gain a greater understanding of the non-clinical determinants of prognosis of PLHIV in Myanmar.

METHODS

Selected demographic, behavioural and socioeconomic characteristics of outpatients at two specialist HIV hospitals and one general hospital in Yangon, Myanmar were correlated with their subsequent clinical course; a poor outcome was defined as death, hospitalisation, loss to follow-up or a detectable viral load at 6 months of follow-up.

RESULTS

221 consecutive individuals with advanced HIV commencing anti-retroviral therapy (ART) were enrolled in the study; their median CD4 T-cell count was 92 (44-158) cells/mm3, 138 (62.4%) were male. Socioeconomic disadvantage was common: the median (interquartile range (IQR) monthly per-capita income in the cohort was US$48 (31-77); 153 (69.9%) had not completed high school. However, in a multivariate analysis that considered demographic, behavioural, clinical factors and social determinants of health, male gender was the only predictor of a poor outcome: odds ratio (95% confidence interval): 2.33 (1.26-4.32, p = 0.007). All eight of the deaths and hospitalisations in the cohort occurred in males (p = 0.03).

CONCLUSIONS

Men starting ART in Myanmar have a poorer prognosis than women. Expanded implementation of gender-specific management strategies is likely to be necessary to improve outcomes.

The Importance of Primary Care Research in Understanding Health Inequities in the United States

Eliminating health and health care inequities is a longstanding goal of multiple United States health agencies, but overwhelming scientific evidence suggests that health and health care inequities persist in the United States, despite decades of research and initiatives to alleviate them. Because of its comprehensiveness, studying health inequities in the context of primary care allows for the use of multiple paradigms and methodologic approaches to understanding almost any state of health, disease, social challenge, or societal circumstance a patient or group of patients might face. We argue in this special communication that the many features/advantages of primary care research have valuable contributions to make in reducing health inequity, and scientists, journals, and funders should increase the incorporation of primary care approaches and findings into their portfolios to better understand and end health inequity.

Patient Recruitment Into a Multicenter Clinical Cohort Linking Electronic Health Records From 5 Health Systems: Cross-sectional Analysis

Background

There is growing interest in identifying and recruiting research participants from health systems using electronic health records (EHRs). However, few studies have described the practical aspects of the recruitment process or compared electronic recruitment methods to in-person recruitment, particularly across health systems.

Objective

The objective of this study was to describe the steps and efficiency of the recruitment process and participant characteristics by recruitment strategy.

Methods

EHR-based eligibility criteria included being an adult patient engaged in outpatient primary or bariatric surgery care at one of 5 health systems in the PaTH Clinical Research Network and having ≥2 weight measurements and 1 height measurement recorded in their EHR within the last 5 years. Recruitment strategies varied by site and included one or more of the following methods: (1) in-person recruitment by study staff from clinical sites, (2) US postal mail recruitment letters, (3) secure email, and (4) direct EHR recruitment through secure patient web portals. We used descriptive statistics to evaluate participant characteristics and proportion of patients recruited (ie, efficiency) by modality.

Results

The total number of eligible patients from the 5 health systems was 5,051,187. Of these, 40,048 (0.8%) were invited to enter an EHR-based cohort study and 1085 were enrolled. Recruitment efficiency was highest for in-person recruitment (33.5%), followed by electronic messaging (2.9%), including email (2.9%) and EHR patient portal messages (2.9%). Overall, 779 (65.7%) patients were enrolled through electronic messaging, which also showed greater rates of recruitment of Black patients compared with the other strategies.

Conclusions

We recruited a total of 1085 patients from primary care and bariatric surgery settings using 4 recruitment strategies. The recruitment efficiency was 2.9% for email and EHR patient portals, with the majority of participants recruited electronically. This study can inform the design of future research studies using EHR-based recruitment.

Eliciting the Social Determinants of Cancer Prevention and Control in the Catchment of an Urban Cancer Center

Objective

The objectives of this study were two-fold: 1) to engage community stakeholders in identifying the top three social determinant of health (SDOH) barriers to the early detection and treatment of cancer in their respective communities; and 2) to develop a tailored plan responsive to the potential social risks identified within the catchment of an urban academic cancer center.

Methods

Stakeholders from four neighborhoods in Brooklyn, New York with disproportionate cancer burden were recruited; the nominal group technique, a semi-quantitative research method, was used to elicit the SDOH barriers. Responses were consolidated into categories and ranked by points received.

Results

112 stakeholders participated in four community-based meetings. The SDOH categories of economic stability, education, and community and social context were identified as the top barriers. The themes of lost wages/employment, competing priorities, and the inability to afford care embodied the responses about economic stability. The domain of education was best described by the themes of low health literacy, targeted health topics to fill gaps in knowledge, and recommendations on the best modalities for improving health knowledge. Lastly, within the category of community and social context, the themes of stigma, bias, and discrimination, eroding support systems, and cultural misconceptions were described.

Conclusions

The implications of our study are three-fold. First, they highlight the strengths of the nominal group technique as a methodology for engaging community stakeholders. Second, our analysis led to identifying a smaller set of social priorities for which tailored screening and practical solutions could be implemented within our health care system. Third, the results provide insight into the actual types of interventions and resources that communities expect from the health care sector.