Combining Clinical and Population-Level Data to Understand the Health of Neighborhoods

Chambers, Earle C.; Wong, Barbara C.; Riley, Rachael Weiss; Hollingsworth, Nicole; Blank, Arthur E.; Myers, Christa; Bedell, Jane; Selwyn, Peter A.

doi:10.2105/ajph.2014.302326

Cited by 9 publications

(5 citation statements)

References 2 publications

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“…Six CATCH-affiliated FQHCs were selected as the pilot locations for this YDPP intervention. These FQHCs are located throughout the Bronx, servicing an area in which more than 70% of the population and clinic patients are black or Hispanic ( 19 , 20 ). For patients to be eligible for YDPP, they must meet the criteria set by YDPP, which include being aged 18 or older, being without a previous diagnosis of diabetes, and having a hemoglobin A1c value between 5.7% and 6.4% (fasting plasma glucose of 100–125 mg/dL or 2-hour plasma glucose of 140–199 mg/dL can also be used).…”

Section: Methodsmentioning

confidence: 99%

Increasing Referrals to a YMCA-Based Diabetes Prevention Program: Effects of Electronic Referral System Modification and Provider Education in Federally Qualified Health Centers

Chambers¹,

Wylie‐Rosett

Blank

et al. 2015

Prev. Chronic Dis.

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IntroductionThe Diabetes Prevention Program has been translated to community settings with varying success. Although primary care referrals are used for identifying and enrolling eligible patients in the Diabetes Prevention Program, little is known about the effects of strategies to facilitate and sustain eligible patient referrals using electronic health record systems.MethodsTo facilitate and sustain patient referrals, a modification to the electronic health record system was made and combined with provider education in 6 federally qualified health centers in the Bronx, New York. Referral data from April 2012 through November 2014 were analyzed using segmented regression analysis.ResultsPatient referrals increased significantly after the modification of the electronic health record system and implementation of the provider education intervention. Before the electronic system modification, 0 to 2 patients were referred per month. During the following year (September 2013 through August 2014), which included the provider education intervention, referrals increased to 1 to 9 per month and continued to increase to 5 to 11 per month from September through November 2014.ConclusionsModification of an electronic health record system coupled with a provider education intervention shows promise as a strategy to identify and refer eligible patients to community-based Diabetes Prevention Programs. Further refinement of the electronic system for facilitating referrals and follow-up of eligible patients should be explored.

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Section: Methodsmentioning

confidence: 99%

Increasing Referrals to a YMCA-Based Diabetes Prevention Program: Effects of Electronic Referral System Modification and Provider Education in Federally Qualified Health Centers

Chambers¹,

Wylie‐Rosett

Blank

et al. 2015

Prev. Chronic Dis.

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“…Primary care providers (PCPs) can reduce the number of preventable cardiovascular-related deaths by discussing health-related lifestyle behaviors and promoting behavior change with their patients (16). Correspondingly, primary care centers that incorporate patient-reported health behaviors such as SSB consumption into their electronic health record (EHR) systems can share this information with public health agencies and help them coordinate health promotion among patients and other populations (17,18). In addition, the data may be used to examine patient outcomes in the context of population trends (19).…”

Section: Introductionmentioning

confidence: 99%

Factors Associated With Daily Consumption of Sugar-Sweetened Beverages Among Adult Patients at Four Federally Qualified Health Centers, Bronx, New York, 2013

Kristal¹,

Blank²,

Wylie‐Rosett³

et al. 2014

Prev. Chronic Dis.

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IntroductionConsumption of sugar-sweetened beverages (SSBs) is associated with cardiovascular disease risk factors. This study examined the relationships between SSB consumption and demographic, health behavior, health service, and health condition characteristics of adult patients of a network of federally qualified health centers (FQHCs) in a low-income, urban setting. MethodsValidated, standardized self-reported health behavior questions were incorporated into the electronic health record (EHR) and asked of patients yearly, at 4 FQHCs. We conducted cross-sectional analysis of EHR data collected in 2013 from 12,214 adult patients by using logistic regression. ResultsForty percent of adult patients consumed 1 or more SSBs daily. The adjusted odds ratios indicated that patients who consumed more than 1 SSB daily were more likely to be aged 18 to 29 years versus age 70 or older, current smokers versus never smoking, eating no servings of fruits and/or vegetables daily or 1 to 4 servings daily versus 5 or more servings daily, and not walking or biking more than 10 blocks in the past 30 days. Patients consuming 1 or more servings of SSBs daily were less likely to speak Spanish than English, be women than men, be diagnosed with type 2 diabetes versus no diabetes, and be diagnosed with hypertension versus no hypertension. ConclusionSSB consumption differed by certain demographic characteristics, health behaviors, and health conditions. Recording SSB intake and other health behaviors data in the EHR could help clinicians in identifying and counseling patients to promote health behavior changes. Future studies should investigate how EHR data on patient health behavior can be used to improve the health of patients and communities.

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“…All identified studies combining behavioral data with clinical data sources involved individual-level measurement (Multimedia Appendix 2), and nearly all (90%) in combination with EHR data. Current or historical substance, alcohol, or tobacco use [147,155,161-168]; self-care behaviors [169-171]; and self-reported physical activity levels and nutrition were commonly reported measures in the behaviors domain [147,172,173].…”

Section: Resultsmentioning

confidence: 99%

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Golembiewski¹,

Allen²,

Blackmon³

et al. 2019

JMIR Public Health Surveill

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BackgroundNonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health.ObjectiveThis review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.MethodsWe conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported.ResultsA total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location.ConclusionsA variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

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Combining Clinical and Population-Level Data to Understand the Health of Neighborhoods

Cited by 9 publications

References 2 publications

Increasing Referrals to a YMCA-Based Diabetes Prevention Program: Effects of Electronic Referral System Modification and Provider Education in Federally Qualified Health Centers

Increasing Referrals to a YMCA-Based Diabetes Prevention Program: Effects of Electronic Referral System Modification and Provider Education in Federally Qualified Health Centers

Factors Associated With Daily Consumption of Sugar-Sweetened Beverages Among Adult Patients at Four Federally Qualified Health Centers, Bronx, New York, 2013

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

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