Neighborhood Socioeconomic Status and Identification of Patients With CKD Using Electronic Health Records

Lama Ghazi; J. Michael Oakes; Richard F. MacLehose; Russell V. Luepker; Theresa L. Osypuk; Paul E. Drawz

doi:10.1053/j.ajkd.2020.10.019

Neighborhood Socioeconomic Status and Identification of Patients With CKD Using Electronic Health Records

Lama Ghazi, J. Michael Oakes, Richard F. MacLehose, Russell V. Luepker, Theresa L. Osypuk, Paul E. Drawz

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

RATIONALE & OBJECTIVE: Screening for chronic kidney disease (CKD) is recommended for patients with diabetes and hypertension as stated by the respective professional societies. However, CKD, a silent disease usually detected at later stages, is associated with low socioeconomic status (SES). We assessed whether adding census tract SES status to the standard screening approach improves our ability to identify patients with CKD.

STUDY DESIGN: Screening test analysis.

SETTINGS & PARTICIPANTS: Electronic health records (EHR) of 256,162 patients seen at a health care system in the 7-county Minneapolis/St. Paul area and linked census tract data.

EXPOSURE: The first quartile of census tract SES (median value of owner-occupied housing units <$165,200; average household income <$35,935; percentage of residents >25 years of age with a bachelor's degree or higher <20.4%), hypertension, and diabetes.

OUTCOMES: CKD (eGFR <60 mL/min/1.73 m 2, or urinary albumin-creatinine ratio >30mg/g, or urinary protein-creatinine ratio >150mg/g, or urinary analysis [albuminuria] >30 mg/d).

ANALYTICAL APPROACH: Sensitivity, specificity, and number needed to screen (NNS) to detect CKD if we screened patients who had hypertension and/or diabetes and/or who lived in low-SES tracts (belonging to the first quartile of any of the 3 measures of tract SES) versus the standard approach.

RESULTS: CKD was prevalent in 13% of our cohort. Sensitivity, specificity, and NNS of detecting CKD after adding tract SES to the screening approach were 67% (95% CI, 66.2%-67.2%), 61% (95% CI, 61.1%-61.5%), and 5, respectively. With the standard approach, sensitivity of detecting CKD was 60% (95% CI, 59.4%-60.4%), specificity was 73% (95% CI, 72.4%-72.7%), and NNS was 4.

LIMITATIONS: One health care system and selection bias.

CONCLUSIONS: Leveraging patients' addresses from the EHR and adding tract-level SES to the standard screening approach modestly increases the sensitivity of detecting patients with CKD at a cost of decreased specificity. Identifying further factors that improve CKD detection at an early stage are needed to slow the progression of CKD and prevent cardiovascular complications.

Original language	English (US)
Pages (from-to)	57-65.e1
Journal	American Journal of Kidney Diseases
Volume	78
Issue number	1
DOIs	https://doi.org/10.1053/j.ajkd.2020.10.019
State	Published - Jul 2021

Bibliographical note

Funding Information:
This research was supported by US National Institutes of Health / National Center for Advancing Translational Sciences grant UL1TR002494 and University of Minnesota doctoral dissertation fellowship to Dr Ghazi. Dr Osypuk was supported by National Institute of Child Health and Human Development grant R01HD090014 . Dr MacLehose was supported by the US National Library of Medicine grant R01LM013049 . None of the funders had any role in the study design, data collection, analysis, reporting or decision to submit the manuscript for publication.

Funding Information:
Lama Ghazi, MD, PhD, J. Michael Oakes, PhD, Richard F. MacLehose, PhD, Russell V. Luepker, MD, MS, Theresa L. Osypuk, SD, and Paul E. Drawz, MD, MS. Research idea and study design: LG, JMO, RFM, RVL, TLO, PED; data acquisition: LG, PED; data analysis and interpretation: LG, JMO, RFM, RVL, TLO, PED; statistical analysis: LG, PED. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual's own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate. This research was supported by US National Institutes of Health/National Center for Advancing Translational Sciences grant UL1TR002494 and University of Minnesota doctoral dissertation fellowship to Dr Ghazi. Dr Osypuk was supported by National Institute of Child Health and Human Development grant R01HD090014. Dr MacLehose was supported by the US National Library of Medicine grant R01LM013049. None of the funders had any role in the study design, data collection, analysis, reporting or decision to submit the manuscript for publication. The authors declare that they have no relevant financial interests. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health's National Center for Advancing Translational Sciences. Received February 6, 2020. Evaluated by 2 external peer reviewers, with direct editorial input from a Statistics/Methods Editor and an Associate Editor, who served as Acting Editor-in-Chief. Accepted in revised form October 24, 2020. The involvement of an Acting Editor-in-Chief was to comply with AJKD's procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.

Publisher Copyright:
© 2020 National Kidney Foundation, Inc.

Keywords

CKD detection
chronic kidney disease (CKD)
diabetes
education
electronic health record (EHR)
health disparities
hypertension
income
number needed to screen (NNS)
provider awareness
social determinants of health (SDOH)
social environment measurements
socioeconomic status (SES)
targeted screening
Residence Characteristics
Humans
Middle Aged
Male
Renal Insufficiency, Chronic/diagnosis
Adult
Female
Minnesota/epidemiology
Social Class
Mass Screening
Aged
Electronic Health Records

PubMed: MeSH publication types

Journal Article
Research Support, N.I.H., Extramural

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1053/j.ajkd.2020.10.019

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title = "Neighborhood Socioeconomic Status and Identification of Patients With CKD Using Electronic Health Records",

abstract = "RATIONALE & OBJECTIVE: Screening for chronic kidney disease (CKD) is recommended for patients with diabetes and hypertension as stated by the respective professional societies. However, CKD, a silent disease usually detected at later stages, is associated with low socioeconomic status (SES). We assessed whether adding census tract SES status to the standard screening approach improves our ability to identify patients with CKD.STUDY DESIGN: Screening test analysis.SETTINGS & PARTICIPANTS: Electronic health records (EHR) of 256,162 patients seen at a health care system in the 7-county Minneapolis/St. Paul area and linked census tract data.EXPOSURE: The first quartile of census tract SES (median value of owner-occupied housing units <$165,200; average household income <$35,935; percentage of residents >25 years of age with a bachelor's degree or higher <20.4%), hypertension, and diabetes.OUTCOMES: CKD (eGFR <60 mL/min/1.73 m 2, or urinary albumin-creatinine ratio >30mg/g, or urinary protein-creatinine ratio >150mg/g, or urinary analysis [albuminuria] >30 mg/d). ANALYTICAL APPROACH: Sensitivity, specificity, and number needed to screen (NNS) to detect CKD if we screened patients who had hypertension and/or diabetes and/or who lived in low-SES tracts (belonging to the first quartile of any of the 3 measures of tract SES) versus the standard approach.RESULTS: CKD was prevalent in 13% of our cohort. Sensitivity, specificity, and NNS of detecting CKD after adding tract SES to the screening approach were 67% (95% CI, 66.2%-67.2%), 61% (95% CI, 61.1%-61.5%), and 5, respectively. With the standard approach, sensitivity of detecting CKD was 60% (95% CI, 59.4%-60.4%), specificity was 73% (95% CI, 72.4%-72.7%), and NNS was 4.LIMITATIONS: One health care system and selection bias.CONCLUSIONS: Leveraging patients' addresses from the EHR and adding tract-level SES to the standard screening approach modestly increases the sensitivity of detecting patients with CKD at a cost of decreased specificity. Identifying further factors that improve CKD detection at an early stage are needed to slow the progression of CKD and prevent cardiovascular complications.",

keywords = "CKD detection, chronic kidney disease (CKD), diabetes, education, electronic health record (EHR), health disparities, hypertension, income, number needed to screen (NNS), provider awareness, social determinants of health (SDOH), social environment measurements, socioeconomic status (SES), targeted screening, Residence Characteristics, Humans, Middle Aged, Male, Renal Insufficiency, Chronic/diagnosis, Adult, Female, Minnesota/epidemiology, Social Class, Mass Screening, Aged, Electronic Health Records",

author = "Lama Ghazi and Oakes, {J. Michael} and MacLehose, {Richard F.} and Luepker, {Russell V.} and Osypuk, {Theresa L.} and Drawz, {Paul E.}",

note = "Funding Information: This research was supported by US National Institutes of Health / National Center for Advancing Translational Sciences grant UL1TR002494 and University of Minnesota doctoral dissertation fellowship to Dr Ghazi. Dr Osypuk was supported by National Institute of Child Health and Human Development grant R01HD090014 . Dr MacLehose was supported by the US National Library of Medicine grant R01LM013049 . None of the funders had any role in the study design, data collection, analysis, reporting or decision to submit the manuscript for publication. Funding Information: Lama Ghazi, MD, PhD, J. Michael Oakes, PhD, Richard F. MacLehose, PhD, Russell V. Luepker, MD, MS, Theresa L. Osypuk, SD, and Paul E. Drawz, MD, MS. Research idea and study design: LG, JMO, RFM, RVL, TLO, PED; data acquisition: LG, PED; data analysis and interpretation: LG, JMO, RFM, RVL, TLO, PED; statistical analysis: LG, PED. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual's own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate. This research was supported by US National Institutes of Health/National Center for Advancing Translational Sciences grant UL1TR002494 and University of Minnesota doctoral dissertation fellowship to Dr Ghazi. Dr Osypuk was supported by National Institute of Child Health and Human Development grant R01HD090014. Dr MacLehose was supported by the US National Library of Medicine grant R01LM013049. None of the funders had any role in the study design, data collection, analysis, reporting or decision to submit the manuscript for publication. The authors declare that they have no relevant financial interests. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health's National Center for Advancing Translational Sciences. Received February 6, 2020. Evaluated by 2 external peer reviewers, with direct editorial input from a Statistics/Methods Editor and an Associate Editor, who served as Acting Editor-in-Chief. Accepted in revised form October 24, 2020. The involvement of an Acting Editor-in-Chief was to comply with AJKD's procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies. Publisher Copyright: {\textcopyright} 2020 National Kidney Foundation, Inc.",

year = "2021",

month = jul,

doi = "10.1053/j.ajkd.2020.10.019",

language = "English (US)",

volume = "78",

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T1 - Neighborhood Socioeconomic Status and Identification of Patients With CKD Using Electronic Health Records

AU - Ghazi, Lama

AU - Oakes, J. Michael

AU - MacLehose, Richard F.

AU - Luepker, Russell V.

AU - Osypuk, Theresa L.

AU - Drawz, Paul E.

N1 - Funding Information: This research was supported by US National Institutes of Health / National Center for Advancing Translational Sciences grant UL1TR002494 and University of Minnesota doctoral dissertation fellowship to Dr Ghazi. Dr Osypuk was supported by National Institute of Child Health and Human Development grant R01HD090014 . Dr MacLehose was supported by the US National Library of Medicine grant R01LM013049 . None of the funders had any role in the study design, data collection, analysis, reporting or decision to submit the manuscript for publication. Funding Information: Lama Ghazi, MD, PhD, J. Michael Oakes, PhD, Richard F. MacLehose, PhD, Russell V. Luepker, MD, MS, Theresa L. Osypuk, SD, and Paul E. Drawz, MD, MS. Research idea and study design: LG, JMO, RFM, RVL, TLO, PED; data acquisition: LG, PED; data analysis and interpretation: LG, JMO, RFM, RVL, TLO, PED; statistical analysis: LG, PED. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual's own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate. This research was supported by US National Institutes of Health/National Center for Advancing Translational Sciences grant UL1TR002494 and University of Minnesota doctoral dissertation fellowship to Dr Ghazi. Dr Osypuk was supported by National Institute of Child Health and Human Development grant R01HD090014. Dr MacLehose was supported by the US National Library of Medicine grant R01LM013049. None of the funders had any role in the study design, data collection, analysis, reporting or decision to submit the manuscript for publication. The authors declare that they have no relevant financial interests. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health's National Center for Advancing Translational Sciences. Received February 6, 2020. Evaluated by 2 external peer reviewers, with direct editorial input from a Statistics/Methods Editor and an Associate Editor, who served as Acting Editor-in-Chief. Accepted in revised form October 24, 2020. The involvement of an Acting Editor-in-Chief was to comply with AJKD's procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies. Publisher Copyright: © 2020 National Kidney Foundation, Inc.

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N2 - RATIONALE & OBJECTIVE: Screening for chronic kidney disease (CKD) is recommended for patients with diabetes and hypertension as stated by the respective professional societies. However, CKD, a silent disease usually detected at later stages, is associated with low socioeconomic status (SES). We assessed whether adding census tract SES status to the standard screening approach improves our ability to identify patients with CKD.STUDY DESIGN: Screening test analysis.SETTINGS & PARTICIPANTS: Electronic health records (EHR) of 256,162 patients seen at a health care system in the 7-county Minneapolis/St. Paul area and linked census tract data.EXPOSURE: The first quartile of census tract SES (median value of owner-occupied housing units <$165,200; average household income <$35,935; percentage of residents >25 years of age with a bachelor's degree or higher <20.4%), hypertension, and diabetes.OUTCOMES: CKD (eGFR <60 mL/min/1.73 m 2, or urinary albumin-creatinine ratio >30mg/g, or urinary protein-creatinine ratio >150mg/g, or urinary analysis [albuminuria] >30 mg/d). ANALYTICAL APPROACH: Sensitivity, specificity, and number needed to screen (NNS) to detect CKD if we screened patients who had hypertension and/or diabetes and/or who lived in low-SES tracts (belonging to the first quartile of any of the 3 measures of tract SES) versus the standard approach.RESULTS: CKD was prevalent in 13% of our cohort. Sensitivity, specificity, and NNS of detecting CKD after adding tract SES to the screening approach were 67% (95% CI, 66.2%-67.2%), 61% (95% CI, 61.1%-61.5%), and 5, respectively. With the standard approach, sensitivity of detecting CKD was 60% (95% CI, 59.4%-60.4%), specificity was 73% (95% CI, 72.4%-72.7%), and NNS was 4.LIMITATIONS: One health care system and selection bias.CONCLUSIONS: Leveraging patients' addresses from the EHR and adding tract-level SES to the standard screening approach modestly increases the sensitivity of detecting patients with CKD at a cost of decreased specificity. Identifying further factors that improve CKD detection at an early stage are needed to slow the progression of CKD and prevent cardiovascular complications.

AB - RATIONALE & OBJECTIVE: Screening for chronic kidney disease (CKD) is recommended for patients with diabetes and hypertension as stated by the respective professional societies. However, CKD, a silent disease usually detected at later stages, is associated with low socioeconomic status (SES). We assessed whether adding census tract SES status to the standard screening approach improves our ability to identify patients with CKD.STUDY DESIGN: Screening test analysis.SETTINGS & PARTICIPANTS: Electronic health records (EHR) of 256,162 patients seen at a health care system in the 7-county Minneapolis/St. Paul area and linked census tract data.EXPOSURE: The first quartile of census tract SES (median value of owner-occupied housing units <$165,200; average household income <$35,935; percentage of residents >25 years of age with a bachelor's degree or higher <20.4%), hypertension, and diabetes.OUTCOMES: CKD (eGFR <60 mL/min/1.73 m 2, or urinary albumin-creatinine ratio >30mg/g, or urinary protein-creatinine ratio >150mg/g, or urinary analysis [albuminuria] >30 mg/d). ANALYTICAL APPROACH: Sensitivity, specificity, and number needed to screen (NNS) to detect CKD if we screened patients who had hypertension and/or diabetes and/or who lived in low-SES tracts (belonging to the first quartile of any of the 3 measures of tract SES) versus the standard approach.RESULTS: CKD was prevalent in 13% of our cohort. Sensitivity, specificity, and NNS of detecting CKD after adding tract SES to the screening approach were 67% (95% CI, 66.2%-67.2%), 61% (95% CI, 61.1%-61.5%), and 5, respectively. With the standard approach, sensitivity of detecting CKD was 60% (95% CI, 59.4%-60.4%), specificity was 73% (95% CI, 72.4%-72.7%), and NNS was 4.LIMITATIONS: One health care system and selection bias.CONCLUSIONS: Leveraging patients' addresses from the EHR and adding tract-level SES to the standard screening approach modestly increases the sensitivity of detecting patients with CKD at a cost of decreased specificity. Identifying further factors that improve CKD detection at an early stage are needed to slow the progression of CKD and prevent cardiovascular complications.

KW - CKD detection

KW - chronic kidney disease (CKD)

KW - diabetes

KW - education

KW - electronic health record (EHR)

KW - health disparities

KW - hypertension

KW - income

KW - number needed to screen (NNS)

KW - provider awareness

KW - social determinants of health (SDOH)

KW - social environment measurements

KW - socioeconomic status (SES)

KW - targeted screening

KW - Residence Characteristics

KW - Humans

KW - Middle Aged

KW - Male

KW - Renal Insufficiency, Chronic/diagnosis

KW - Adult

KW - Female

KW - Minnesota/epidemiology

KW - Social Class

KW - Mass Screening

KW - Aged

KW - Electronic Health Records

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Neighborhood Socioeconomic Status and Identification of Patients With CKD Using Electronic Health Records

Abstract

Bibliographical note

Keywords

PubMed: MeSH publication types

UN SDGs

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