Abstract

Social determinants of health significantly impact morbidity and mortality; however, physicians lack ready access to this information in patient care and population management. Just as traditional vital signs give providers a biometric assessment of any patient, “community vital signs” (Community VS) can provide an aggregated overview of the social and environmental factors impacting patient health. Knowing Community VS could inform clinical recommendations for individual patients, facilitate referrals to community services, and expand understanding of factors impacting treatment adherence and health outcomes. This information could also help care teams target disease prevention initiatives and other health improvement efforts for clinic panels and populations. Given the proliferation of big data, geospatial technologies, and democratization of data, the time has come to integrate Community VS into the electronic health record (EHR). Here, the authors describe (i) historical precedent for this concept, (ii) opportunities to expand upon these historical foundations, and (iii) a novel approach to EHR integration.

INTRODUCTION

Social determinants of health (SDH)—the milieu of social, economic, occupational, and environmental factors—influence morbidity and mortality more than traditional medical care. 1–13 However, healthcare providers rarely have tools to incorporate information about patients’ SDH into healthcare decision-making. Providers begin each patient encounter with “vital signs”—biometric markers essential to clinical assessment. It is time for providers to also have meaningful information on “community vital signs” (Community VS) at the point of care to inform panel management efforts. Community VS, defined as one or aggregated measures of SDH, are constructed from community-level geocoded data from publicly available sources, such as the US Census Bureau.

The Institute of Medicine’s (IOM) recent call to enhance meaningful use of electronic health records (EHRs) envisions patient report as the principal means of adding SDH data to EHRs. 14,15 Though this approach is useful, as population data availability increases, so does capacity to augment EHR records with community-level data through geospatial integration. Community VS can now be imported into EHRs to enable healthcare providers to offer context-informed care —care that meaningfully accounts for neighborhood factors that affect patients’ health .16 We describe historical precedents for integrating data in clinical settings to facilitate context-informed and community-oriented care, contemporary opportunities to expand on these foundations, and our specific efforts to build and test integration of Community VS into EHRs.

Pioneers in Community-oriented Care

In the 1940s, Sidney and Emily Kark pioneered the collection and integration of community data into the delivery of effective primary care, calling it Community Oriented Primary Care (COPC). 17,18 The Karks’ conducted demographic and epidemiologic assessments to define and characterize the South African communities they served. In a pre-EHR era, these pioneers integrated community and patient data to understand population risk, prioritize health problems, and develop interventions tailored to their particular community context. 19

These principles took root in countries around the world. 20 Notably, after spending time with the Karks, H. Jack Geiger brought COPC methods to the United States in the 1960s, partnering with epidemiologist John Hatch to create the nation’s first two neighborhood health centers. 21 By understanding the communities in which patients live, and addressing a broad spectrum of SDH, Geiger sought to create healthcare systems that improved the health of the community, broadening the focus beyond traditional medical care. The Tufts-Delta Health Center, the first US federally qualified health center (FQHC), not only provided health services, but also surveyed and assessed local needs, conducted outreach and health education, and implemented interventions to address community issues such as housing, water supply, sanitation. 19 The legacy of Geiger’s early centers is reflected in the nation’s 8000+ FQHCs, which are governed by community-based boards and provide services according to local need. 21

Advancing the idea of providing context-informed care within a practice, Farley and Froom 22 organized patient charts by family and neighborhood as a way to better understand the community factors that influence patient health. Curtis Hames used aerial photography and frequent assessments and observations of the community surrounding his clinic to inform individual patient care, while contributing to scientific understanding of community influences on heart disease. 23,24

In the 1960s and 1970s, Larry Weed and Jan Schultz introduced the Problem-Oriented Medical Information System, one of the first point-of-care EHRs. Dr Weed advocated for including SDH, such as patient-reported financial and housing stressors, in the problem list. 25 He believed that all data, including SDH, could be acquired, stored in the EHR as structured data, and used to support clinical decision-making.

Building on Historical Foundations: EHRs, Geospatial Technology, and Accessible Data Present Big Opportunities

The pioneers described above could only dream of today’s opportunities for collecting and organizing data. We now have the unique ability to build on this foundation by using community-level data and geospatial technologies to incorporate Community VS into EHRs. Most healthcare providers now utilize EHRs, which offer tremendous potential to aggregate, analyze, and integrate individual- and community-level data across settings and over time. 16 Though EHRs provide crucial information to providers treating individual patients, they currently do not capture and integrate the wealth of publicly available population health data on SDH. Community-level SDH data has been used to develop public health and policy interventions, 26,27 but providers at the point of care lack both access and education regarding the potential use of SDH in clinical decision-making and management. 28 Until Community VS are encoded in the EHR using a structured terminology (e.g., Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT)); geocoded community-level SDH data will remain isolated from existing clinical workflows.

National initiatives are taking first steps toward incorporating SDH data into EHRs. Most notably, the IOM’s Committee on Recommended Social and Behavioral Domains and Measures for EHRs convened to “identify [SDH] domains [to inform] recommendations for stage three Meaningful Use of EHRs.” 14,15 This committee’s 2014 report, “Capturing Social and Behavioral Domains in EHRs: Phase 2,” presents a set of 11 candidate SDH data domains recommended for inclusion in all EHRs ( Table 1 ). 15 These domains (i.e., data elements) were identified based on evidence of their association with health; the potential usefulness of information about that domain in treating patients, developing interventions or policies, and conducting research; and the availability of valid measures.

Table 1.

Summary of IOM Recommended Social and Behavioral Domains for Inclusion in all EHRs

Domains 
Individual-level (patient-reported) 
Race/ethnicity a 
Education 
Financial resource strain 
Stress 
Depression a 
Physical activity 
Tobacco use and exposure a 
Alcohol use a 
Social connections and social isolation 
Exposure to violence: intimate partner violence 
Community-level (geocodable) 
Neighborhood and community compositional characteristics (residential address a ; census tract-median income)  
Domains 
Individual-level (patient-reported) 
Race/ethnicity a 
Education 
Financial resource strain 
Stress 
Depression a 
Physical activity 
Tobacco use and exposure a 
Alcohol use a 
Social connections and social isolation 
Exposure to violence: intimate partner violence 
Community-level (geocodable) 
Neighborhood and community compositional characteristics (residential address a ; census tract-median income)  

Source: IOM. Recommended Social and Behavioral Domains and Measures for EHRs: Phase 2.

a Denotes domains which are already recommended for collection as structured data in the EHR at the patient level for various purposes, including Meaningful Use.

Most of the IOM’s candidate domains address individual-level SDH which must be collected directly from each patient then entered into the EHR by clinic staff or patients (e.g., through the patient portal); only 1 of the candidate domains would utilize geocoded community-level data. 15 Of the 10 individual-level patient-reported domains, 4 are already recommended for collection in most EHRs as structured patient-level data. However, few healthcare settings are currently equipped with workflows, staff resources, and technological tools to systematically collect and record data about the remaining individual-level SDH domains. With limited EHR functionality supporting collection and use of these data, and Meaningful Use Stage 3 requirements delayed until 2017, it could take years to operationalize these domains and develop care systems’ capacity to integrate the collection of individual-level SDH into the EHR.

While enabling clinics’ collection of diverse individual-level patient-reported data may take time, the capacity to integrate community-level geocoded data exists now. An increasing wealth of public data at the small area level (e.g., census tract or city block) on median income (recommended by the IOM) and many other potentially relevant community-level factors are available now and could be used to populate discrete fields in the EHR. 29 Other countries routinely use community-level data to inform and strategically resource clinics. 30,31 These deprivation indices have been modeled in the United States using existing data sources and tested against disease prevalence and outcomes at the Primary Care Service Area level. 32 We have an opportunity to bring these data, or derived indices, into EHRs as structured content. The SNOMED-CT terminology (a standardized, clinical terminology used by physicians and other healthcare providers for the electronic exchange of clinical health information) already includes a hierarchy for social context, including ethnicity, occupation, and economic status at the person-level. Moreover, SNOMED-CT has a hierarchy for environment and geographic locations which could be expanded to include concepts not already mapped. 33

Advances in geospatial technology and access to contextual information in the form of publicly available large datasets make it possible to automate processes for embedding Community VS into every patient’s chart. With georeferenced and geocodable information readily available, and technology that enables integrating these data into the EHR, all healthcare professionals could see a patient knowing not just her blood pressure, pulse, respiratory rate, and temperature, but also whether she lives in the presence of poverty, healthy food and water sources, walkable streets and parks, and has social capital—or how these add up to predict increased risk of morbidity, early mortality, or other adverse health outcomes.

Our Approach: Incorporating Community-Level Data into EHRs

Through the A ccelerating D ata V alue A cross a N ational C ommunity Health Center N e twork ( ADVANCE ) Clinical Data Research Network (CDRN) 34 (funded by the Patient-Centered Outcomes Research Institute) the Robert Graham Center (RGC), OCHIN (originally called the Oregon Community Health Information Network, renamed OCHIN as other states joined), and HealthLandscape are partnering with FQHCs to integrate geocoded information from neighborhood geospatial maps into every patient’s EHR. This work builds on similar efforts that used indices derived from US census data linked to a given patient’s home address, such as the Neighborhood SocioEconomic Status index, 35–40 the Neighborhood Deprivation Index, 41–44 and the Social Deprivation Index. 32 HealthLandscape 45 and the RGC 46 have systematically acquired social, behavioral, economic, and health data from multiple national, state, and local sources for almost a decade. HealthLandscape’s comprehensive data library includes nearly 10 000 national, regional, county, and small area measures ranging from health economics, healthcare workforce, population estimates, education, vital statistics, criminal justice, migration, healthcare quality indicators, demographics, poverty, social and physical environment, mental health, and substance abuse and prevention. 47

We drew upon recommendations from the IOM 14,15 and ADVANCE community stakeholders to select a pilot set of Community VS potentially useful to healthcare teams. Elements selected are described in Table 2 . Elements that were considered but not selected include: alternate measures of residential segregation, occupational dissimilarity, and access to public transportation (the latter was omitted due to the lack of a single comprehensive data source). Using HealthLandscape’s geospatial technology and extensive data library, we created a Community VS Geocoding Application Programming Interface (API) designed to accept bulk or single address data requests, assign detailed geographic identifiers, and append Community VS to the EHR.

Table 2:

Indicators selected for ADVANCE Pilot by Community VS Type

Community VS Indicators Data Source 
Built environment Fast food restaurants per 100 000 population; liquor stores per 100 000 population; population density American Community Survey 
US Census Bureau, county business patterns 
US Census Bureau, ZIP code business patterns 
Environmental exposures Median housing structure age; number of person-days with maximum 8-h average ozone concentration over the National Ambient Air Quality Standard (monitored and modeled data); number of person-days with PM2.5 over the National Ambient Air Quality Standard (monitored and modeled data); percent of occupied housing units without complete plumbing facilities; percent of population potentially exposed to water exceeding a violation limit during the past year American Community Survey 
Center for Disease Control and Prevention (CDC), Environmental Public Health Tracking Network 
Environmental Protection Agency, Safe Drinking Water Information System 
Neighborhood economic conditions Dependency ratio (old-age); estimated percent of foreclosure starts over the past 18 months through June 2008; estimated percent of vacant addresses in June 2008 (90-day vacancy rate); Gini coefficient– inequality; overall percentile ranking for the CDC Social Vulnerability Index Agency for Toxic Substances and Disease Registry 
American Community Survey 
Department of Housing and Urban Development, Neighborhood Stabilization Program 
Neighborhood race/ethnic composition Count and percent by race; residential segregation (dissimilarity and exposure) American Community Survey 
Neighborhood resources Low access tract at 1 mile and at ½ mile for urban areas or 10 miles for rural areas; metro/non-metro classification codes; Modified Retail Food Environment Index (no. of healthy food stores divided by all food stores); percent of people in a county living more than 1 mile from a supermarket or large grocery store if in an urban area, or more than 10 miles if in a rural area; percentage of population living within ½ mile of a park; recreation facilities per 100 000 population; Urban Classification Code—rural, urban cluster (>10 000 population, <50 000 population), urban area (>50 000 population) Center for Disease Control and Prevention, Environmental Public Health Tracking Network 
US Census Bureau, county business patterns 
US Census Bureau, ZIP code business patterns 
USDA Food Access Research Atlas 
USDA, Economic Research Service 
Neighborhood socioeconomic composition Number with Bachelor's Degree or higher; median household income; number and percent of persons in managerial, professional, or executive occupations; percent below 100% of Federal Poverty Level (FPL); percent below 200% of FPL; unemployment rate American Community Survey 
Social Deprivation Index A composite measure of social deprivation validated to be more strongly associated with poor access to healthcare and poor health outcomes than a measure of poverty alone.  Robert Graham Center 32 
Community VS Indicators Data Source 
Built environment Fast food restaurants per 100 000 population; liquor stores per 100 000 population; population density American Community Survey 
US Census Bureau, county business patterns 
US Census Bureau, ZIP code business patterns 
Environmental exposures Median housing structure age; number of person-days with maximum 8-h average ozone concentration over the National Ambient Air Quality Standard (monitored and modeled data); number of person-days with PM2.5 over the National Ambient Air Quality Standard (monitored and modeled data); percent of occupied housing units without complete plumbing facilities; percent of population potentially exposed to water exceeding a violation limit during the past year American Community Survey 
Center for Disease Control and Prevention (CDC), Environmental Public Health Tracking Network 
Environmental Protection Agency, Safe Drinking Water Information System 
Neighborhood economic conditions Dependency ratio (old-age); estimated percent of foreclosure starts over the past 18 months through June 2008; estimated percent of vacant addresses in June 2008 (90-day vacancy rate); Gini coefficient– inequality; overall percentile ranking for the CDC Social Vulnerability Index Agency for Toxic Substances and Disease Registry 
American Community Survey 
Department of Housing and Urban Development, Neighborhood Stabilization Program 
Neighborhood race/ethnic composition Count and percent by race; residential segregation (dissimilarity and exposure) American Community Survey 
Neighborhood resources Low access tract at 1 mile and at ½ mile for urban areas or 10 miles for rural areas; metro/non-metro classification codes; Modified Retail Food Environment Index (no. of healthy food stores divided by all food stores); percent of people in a county living more than 1 mile from a supermarket or large grocery store if in an urban area, or more than 10 miles if in a rural area; percentage of population living within ½ mile of a park; recreation facilities per 100 000 population; Urban Classification Code—rural, urban cluster (>10 000 population, <50 000 population), urban area (>50 000 population) Center for Disease Control and Prevention, Environmental Public Health Tracking Network 
US Census Bureau, county business patterns 
US Census Bureau, ZIP code business patterns 
USDA Food Access Research Atlas 
USDA, Economic Research Service 
Neighborhood socioeconomic composition Number with Bachelor's Degree or higher; median household income; number and percent of persons in managerial, professional, or executive occupations; percent below 100% of Federal Poverty Level (FPL); percent below 200% of FPL; unemployment rate American Community Survey 
Social Deprivation Index A composite measure of social deprivation validated to be more strongly associated with poor access to healthcare and poor health outcomes than a measure of poverty alone.  Robert Graham Center 32 

The API performs two broad tasks. First, using data from the originating system (in this case, the ADVANCE data network) geographic identifiers (e.g., county, census tract) are assigned to each address using latitude and longitude coordinates interpolated from street address and zip code. Second, using the assigned geographic identifiers, a core set of Community VS are joined to each address record. Next, the API returns the geographic identifiers and Community VS to the originating system. The result is a set of Community VS for any patient with a valid address, which can be made available to care team members at the point of care from within the EHR.

The Community VS Geocoding API transports data requests and results through a secure, platform-independent system to geospatial data partner HealthLandscape, operating under a Business Associate Agreement to maintain Health Insurance Portability and Accountability Act compliance. Transfers are conducted with full data encryption and strict authentication to ensure that only authorized entities have access to the API, and that only the originating system receives the geocoding and data results. No identifiers other than address and a unique, anonymous identifier are transmitted to the API. The API produces a geocoding quality score, which provides an indication of the quality of street-level interpolation, and can be used to determine whether the quality of the appended community characteristics is sufficiently rigorous for the data to be included in the ADVANCE system, helping to ensure that clinicians are not working with poor quality Community VS data.

Next Steps for Incorporating Community VS into the EHR

The API is developed and is being deployed in a testing environment. However, incorporating these data into the EHR is only a first step. For Community VS to indeed be vital and worthy of inclusion in EHRs, further development and integration is essential. To that end, we are engaging stakeholders in decisions regarding how to aggregate these community-level data elements into one or more Community VS and how to provide them to clinicians within the EHR. In addition, research is needed to expand our knowledge of (i) which community data elements or combination of elements best predict health outcomes, (ii) how providers and patients adapt varying definitions of “community” to the available geospatial boundaries, (iii) how best to make this information available and useful in clinical settings, and (iv) which interventions providers can employ in response to Community VS.

Beyond simply displaying a set of community vitals in the EHR, similar to how traditional vital signs currently appear in most EHRs, Community VS could also be incorporated into clinical decision support (CDS) and Population Management tools. Researchers should partner with clinicians, clinical teams, and EHR developers to identify best practices for how these data should be displayed in the EHR, which member of the healthcare team should act on the data, and when in the clinical workflow the data will be most useful. EHR developers should also engage in efforts to link directories of social and community services into EHR systems and CDS tools so that care teams have information about the resources available to patients in a given community.

In addition to CDS tools at the point of care, similar tools could be built for population health and panel management. For example, the EHR could be programmed to identify all patients on a provider’s panel who live in an area with a high proportion of fast food restaurants and send them information about where they can purchase fresh produce along with recipes for quick, healthy meals. Using data from the Environmental Protection Agency’s Safe Drinking Water Information System, one of the data sources selected for ADVANCE Community VS, care teams could be automatically notified of water contamination and equip patients in the affected area with information about how to create safe drinking water (e.g., boiling instructions) or where to find alternative water sources. Another CDS tool could alert a care team member during an office visit that a patient may benefit from depression screening based on a high rate of unemployment or other community predictor in the patient’s neighborhood.

The lack of current evidence for potential Community VS should not delay development, rigorous testing, and implementation of these concepts and associated CDS tools. There is an urgent need to expand upon early efforts that link increasingly available and community-sourced spatial data into patient records 48 and to investigate their effective use via practice-based research, pragmatic trials, and comparative effectiveness research studies. As this work evolves, it is likely that various definitions of community will need to be considered, using the most granular data available (e.g., census tract, block group) as building blocks. Traditional and geopolitical boundaries—including legislative boundaries, census tracts or other Census Bureau geographies, ZIP codes or even a city or county—are regularly used to define community. However, there are alternative interpretations of community and community identity that are not encompassed by these boundaries. Future efforts should include further validation and refinement of geographic constructs through measured, ground-level interactions of providers and patients, and others facilitating practice change (e.g., practice facilitators, primary care extension program agents). 49

Research must also assess changes in providers’ knowledge, attitudes, and skills related to SDH; patients’ perceptions on the utility of this information in the clinical encounter; and the health outcomes associated with integrating Community VS into the EHR. Finally, the IOM’s report highlights the need to integrate additional individual-level, patient-reported SDH data to improve granularity and impact. While Community VS can be added to EHRs now, they should be developed in such a way that they can accommodate and integrate additional patient-reported data over time. This could be accomplished by adding Community VS into the Office of the National Coordinator for Health Information Technology Standards and Interoperability (S&I) Framework. 50 Thus, allowing researchers to access Community VS from multiple EHR systems using distributed query methods as proposed in the S&I Data Access Framework. 51 Furthermore, standardized, EHR-embedded Community VS should be incorporated into the S&I Clinical Quality Framework, an emerging national standard for clinical quality measures (CQMs), to allow benchmarking of population-level CQMs related to Community VS. 52

CONCLUSION

Given the impact social, demographic, and physical factors have on health and recent increases in access to big data and geospatial technologies, the time has arrived to integrate community-level SDH data at the point of care. Incorporating Community VS into every patient’s EHR will give patients and healthcare providers information that better enables context-informed and community-oriented care. This paper provides a roadmap for integrating geocoded community-level data into patient-level EHRs being piloted in a national network of FQHCs. Future steps include developing CDS tools to integrate Community VS into the clinical workflow, conducting practice-based and comparative effectiveness research to understand how care teams use and act on Community VS, and assessing the impact of Community VS and related CDS tools on the ability to provide context-informed care.

Competing Interests

The authors have no competing interests to report.

Funding

This work was financially supported by the Patient-Centered Outcomes Research Institute grant number CDRN-1306–04716.

Contributors

A.B. helped conceive of the work, wrote the first draft, and gave final approval of the manuscript.

E.C. contributed substantial edits, and gave final approval of the manuscript.

R.G. helped conceive of the work, contributed substantial edits, and gave final approval of the manuscript.

L.H. contributed substantial edits, and gave final approval of the manuscript.

R.P. contributed substantial edits, and gave final approval of the manuscript.

H.A. contributed substantial edits, and gave final approval of the manuscript.

T.B. contributed substantial edits, and gave final approval of the manuscript.

M.C. provided the specifications for the indicators, contributed substantial edits, and gave final approval of the manuscript.

J.E.DeV. helped conceive of the work, secured the funding, contributed substantial edits, and gave final approval of the manuscript.

We would like to acknowledge the federally qualified health center providers and patients for their time and insight that have shaped this work and the efforts of everyone working on the A ccelerating D ata V alue A cross a N ational C ommunity Health Center N e twork project.

REFERENCES

1
Chandola
T
Ferrie
J
Sacker
A
Marmot
M
.
Social inequalities in self reported health in early old age: follow-up of prospective cohort study
.
BMJ.
 
2007
;
334
(
7601
):
990
.
2
Hammig
O
Bauer
GF
.
The social gradient in work and health: a cross-sectional study exploring the relationship between working conditions and health inequalities
.
BMC Public Health.
 
2013
;
13
:
1170
.
3
Lahiri
S
Moure-Eraso
R
Flum
M
Tilly
C
Karasek
R
Massawe
E
.
Employment conditions as social determinants of health
.
Part I: the external domain. New Solut.
 
2006
;
16
(
3
):
267
288
.
4
Moure-Eraso
R
Flum
M
Lahiri
S
Tilly
C
Massawe
E
.
A review of employment conditions as social determinants of health part II: the workplace
.
New Solutions.
 
2006
;
16
(
4
):
429
448
.
5
Lahelma
E
Laaksonen
M
Aittomaki
A
.
Occupational class inequalities in health across employment sectors: the contribution of working conditions
.
IntArch Occup Environ Health.
 
2009
;
82
(
2
):
185
190
.
6
Galobardes
B
Davey Smith
G
Jeffreys
M
McCarron
P
.
Childhood socioeconomic circumstances predict specific causes of death in adulthood: the Glasgow student cohort study
.
J Epidemiol Commun Health.
 
2006
;
60
(
6
):
527
529
.
7
Ferrie
JE
Shipley
MJ
Davey Smith
G
Stansfeld
SA
Marmot
MG
.
Change in health inequalities among British civil servants: the Whitehall II study
.
J Epidemiol Commun Health.
 
2002
;
56
(
12
):
922
926
.
8
van Lenthe
FJ
Borrell
LN
Costa
G
et al
.
Neighbourhood unemployment and all cause mortality: a comparison of six countries
.
J Epidemiol Commun Health.
 
2005
;
59
(
3
):
231
237
.
9
Barnett
E
Casper
M
.
A definition of “social environment''
.
Am J Public Health.
 
2001
;
91
(
3
):
465
.
10
Soto
K
Petit
S
Hadler
JL
.
Changing disparities in invasive pneumococcal disease by socioeconomic status and race/ ethnicity in Connecticut, 1998-2008
.
Public Health Rep.
 
2011
;
126
(
Suppl 3
):
81
88
.
11
Marmot
MG
Shipley
MJ
.
Do socioeconomic differences in mortality persist after retirement? 25 year follow up of civil servants from the first Whitehall study
.
BMJ.
 
1996
;
313
(
7066
):
1177
1180
.
12
Kawachi
I
Berkman
LF
.
Neighborhoods and Health
  .
Oxford
:
Oxford University Press
;
2003
.
13
Mokdad
AH
Marks
JS
Stroup
DF
Gerberding
JL
.
Actual causes of death in the United States, 2000
.
JAMA.
 
2004
;
291
(
10R
)
:
1238
1245
.
14
Institute of Medicine. Capturing Social and Behavioral Domains in Electronic Health Records: Phase 1. Washington, DC: The National Academies Press; 2014.
15
Institute of Medicine. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. Washington, DC: The National Academies Press; 2014.
16
Bazemore
A
Phillips
RL
Miyoshi
T
.
Harnessing Geographic Information Systems (GIS) to enable community-oriented primary care
.
J Am Board Fam Med.
 
2010
;
23
(
1
):
22
31
.
17
Nutting
PA
.
Community-Oriented Primary Care: From Principle to Practice
  .
Albuquerque, NM
:
University of New Mexico Press
;
1990
.
18
Kark
JD
Abramson
JH
.
Sidney Kark's contributions to epidemiology and community medicine
.
Int J Epidemiol.
 
2003
;
32
(
5
):
882
884
.
19
Geiger
J
.
Community-oriented primary care: a path to community development
.
Am J Public Health.
 
2002
;
92
(
11
):
1713
1716
.
20
Gofin
J
Gofin
R
.
Community-oriented primary care and primary health care
.
Am J Public Health.
 
2005
;
95
(
5
):
757; author reply 757
.
21
Adashi
EY
Geiger
HJ
Fine
MD
.
Health care reform and primary care–the growing importance of the community health center
.
New Engl J Med.
 
2010
;
362
(
22
):
2047
2050
.
22
Farley
ES
Jr
Boisseau
V
Froom
J
.
An integrated medical record and data system for primary care. Part 5: Implications of filing family folders by area of residence
.
J Fam Pract.
 
1977
;
5
(
3
):
427
432
.
23
Johnson
JL
Heineman
EF
Heiss
G
Hames
CG
Tyroler
HA
.
Cardiovascular disease risk factors and mortality among black women and white women aged 40-64 years in Evans County, Georgia
.
Am J Epidemiol.
 
1986
;
123
(
2
):
209
220
.
24
Andrews
JW
Hames
CG
Metts
JC
Jr
Waters
L
Davis
JM
Carpenter
R
.
Relationships between selenium and other parameters in drinking water and blood of subjects from high and low cardiovascular disease rate areas of Georgia
.
J Environ Pathol Toxicol.
 
1980
;
4
(
2–3
):
313
318
.
25
Weed
LL
.
Medical Records, Medical Education and Patient Care
  .
Cleveland, OH
:
Case Western Reserve Press
;
1969
.
26
Frieden
TR
.
A framework for public health action: the health impact pyramid
.
Am J Public Health.
 
2010
;
100
(
4
):
590
595
.
27
U. S. Department of Health and Human Services. Healthy People 2010, 2nd edn. Washington, DC: US Government Printing Office; 2000.
28
Williams
DR
Costa
MV
Odunlami
AO
Mohammed
SA
.
Moving upstream: how interventions that address the social determinants of health can improve health and reduce disparities
.
J Public Health Manag Pract.
 
2008
;
14
(
Suppl
):
S8
S17
.
29
Comer
KF
Grannis
S
Dixon
BE
Bodenhamer
DJ
Wiehe
SE
.
Incorporating geospatial capacity within clinical data systems to address social determinants of health
.
Public Health Rep.
 
2011
;
126
(
Suppl 3
):
54
61
.
30
UK Department for Communities and Local Government. The English Indices of Deprivation 2010 Neighbourhoods Statistical Release 2011; https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/6871/1871208.pdf . Accessed February 27, 2015.
31
University of Otago W. Socioeconomic Deprivation Indexes: NZDep and NZiDep, Department of Public Health. 2013. http://www.otago.ac.nz/wellington/otago020233.pdf . Accessed February 27, 2015.
32
Butler
DC
Petterson
S
Phillips
RL
Bazemore
AW
.
Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery
.
Health Services Res.
 
2013
;
48
(
2 Pt 1
):
539
559
.
33
International Health Terminology Standards Organisation. SNOMED CT® User Guide. 2013. http://ihtsdo.org/fileadmin/user_upload/doc/download/doc_UserGuide_Current-en-US_INT_20130731.pdf . Accessed October 20, 2014.
34
DeVoe
JE
Gold
R
Cottrell
E
et al
.
The ADVANCE network: accelerating data value across a national community health center network
.
JAMIA.
 
2014
;
21
(
4
):
591
595
.
35
Diez Roux
AV
.
Investigating neighborhood and area effects on health
.
Am J Public Health.
 
2001
;
91
(
11
):
1783
1789
.
36
Diez Roux
AV
Merkin
SS
Arnett
D
et al
.
Neighborhood of residence and incidence of coronary heart disease
.
New Engl J Med.
 
2001
;
345
(
2
):
99
106
.
37
Diez-Roux
AV
Kiefe
CI
Jacobs
DR
Jr
et al
.
Area characteristics and individual-level socioeconomic position indicators in three population-based epidemiologic studies
.
Ann Epidemiol.
 
2001
;
11
(
6
):
395
405
.
38
Nordstrom
CK
Diez Roux
AV
Jackson
SA
Gardin
JM
.
The association of personal and neighborhood socioeconomic indicators with subclinical cardiovascular disease in an elderly cohort. The cardiovascular health study
.
Soc Sci Med.
 
2004
;
59
(
10
):
2139
2147
.
39
Brown
AF
Liang
LJ
Vassar
SD
et al
.
Neighborhood socioeconomic disadvantage and mortality after stroke
.
Neurology.
 
2013
;
80
(
6
):
520
527
.
40
Roblin
DW
.
Validation of a neighborhood SES index in a managed care organization
.
Medical Care.
 
2013
;
51
(
1
):
e1
e8
.
41
Stoddard
PJ
Laraia
BA
Warton
EM
et al
.
Neighborhood deprivation and change in BMI among adults with type 2 diabetes: the Diabetes Study of Northern California (DISTANCE)
.
Diabetes Care.
 
2013
;
36
(
5
):
1200
1208
.
42
Laraia
BA
Karter
AJ
Warton
EM
Schillinger
D
Moffet
HH
Adler
N
.
Place matters: neighborhood deprivation and cardiometabolic risk factors in the Diabetes Study of Northern California (DISTANCE)
.
Soc Sci Med.
 
2012
;
74
(
7
):
1082
1090
.
43
Zeigler-Johnson
CM
Tierney
A
Rebbeck
TR
Rundle
A
.
Prostate cancer severity associations with neighborhood deprivation
.
Prostate Cancer.
 
2011
;
2011:846263
.
44
Messer
LC
Laraia
BA
Kaufman
JS
et al
.
The development of a standardized neighborhood deprivation index
.
J Urban Health.
 
2006
;
83
(
6
):
1041
1062
.
45
HealthLandscape. 2014. http://healthlandscape.org/ . Accessed December 19, 2014.
46
The Robert Graham Center. About Us. 2012. http://www.graham-center.org/online/graham/home.html . Accessed December 19, 2014.
47
HealthLandscape. Community Vital Signs Core Community Characteristics, Data Definitions. http://www.healthlandscape.org/geocodeapi_listofindicators_V1.pdf . Accessed May 5, 2015.
48
Simpson
CL
Novak
LL
.
Place matters: the problems and possibilities of spatial data in electronic health records
.
AMIA Ann Symp Proc.
 
2013
;
2013:1303-1311
.
49
Phillips
RL
Jr
Kaufman
A
Mold
JW
et al
.
The primary care extension program: a catalyst for change
.
Ann Fam Med.
 
2013
;
11
(
2
):
173
178
.
50
The Office of the National Coordinator for Health Information Technology. Standards and Interoperability (S&I) Framework. http://www.siframework.org/framework.html . Accessed May 31, 2015.
51
The Office of the National Coordinator for Health Information Technology. Data Access Framework Homepage. 2015. http://wiki.siframework.org/Data+Access+Framework+Homepage . Accessed May 31, 2015.
52
The Office of the National Coordinator for Health Information Technology. Clinical Quality Framework Initiative. 2015. http://wiki.siframework.org/Clinical+Quality+Framework+Initiative . Accessed May 31, 2015.

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