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

Cottrell, Erika; Hendricks, M; Dambrun, Katie; Cowburn, Stuart; Pantell, Matthew S.; Gold, Rachel; Gottlieb, Laura M.

doi:10.1001/jamanetworkopen.2020.16852

Cited by 71 publications

(76 citation statements)

References 32 publications

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“…Ultimately, to facilitate EHR-based research across multiple settings (eg, inpatient and ambulatory), large health systems will need to invest in infrastructure to support and link smaller clinical centers and subsidize their use of a common EHR. An example of a successful academic and community-based network is the OCHIN network, which provides a research infrastructure using EHR data from a national network of smaller community health centers [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

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

Bennett¹,

Bramante²,

Rothenberger³

et al. 2021

J Med Internet Res

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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.

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Section: Discussionmentioning

confidence: 99%

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

Bennett¹,

Bramante²,

Rothenberger³

et al. 2021

J Med Internet Res

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“…In one of the few studies to ask patients about their expectations regarding social risk screening, we find evidence that more than half of patients (64.5%) do not want help addressing their social risk from clinical staff. 20 We additionally see this viewpoint supported in work by Byhoff et al, 21 which reveals that although patients believe social risk screening is necessary and acceptable, they do not expect their health care teams to address the reallife social challenges they face. These findings underscore the importance of health care systems identifying solutions for needs that patients want, will accept and are in practical reach.…”

Section: Discussionmentioning

confidence: 72%

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

et al. 2021

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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.Conclusion: 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.Ethn Dis. 2021;31(1):23-30; doi:10.18865/ ed.31.1.23

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“…Finally, while the contribution of gender and the SDH to outcomes was determined using multivariate analysis, some of the variables’ definitions—for instance, the binary “employed or unemployed”—were crude. More detailed description of these variables and their relationship with health-related behaviours might be expected to result in a more nuanced analysis of their impact on outcomes [ 29 ]. However, at the very least the study provides hypothesis-generating data that might be explored in future studies.…”

Section: Discussionmentioning

confidence: 99%

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

Nyein

Aung

et al. 2021

AIDS Res Ther

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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.

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

Abstract: This cross-sectional study explores the utility of community-level data for accurately identifying patients with social risks by comparing the social deprivation index score for the census tract where a patient lives with patient-level social risk screening data.

Cited by 71 publications

References 32 publications

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

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

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

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

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