Published on in Vol 5, No 4 (2019): Oct-Dec

Preprints (earlier versions) of this paper are available at, first published .
Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

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

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


1IUPUI Richard M Fairbanks School of Public Health, Indianapolis, IN, United States

2Regenstrief Institute, Inc, Indianapolis, IN, United States

3IUPUI University Library, Indianapolis, IN, United States

*all authors contributed equally

Corresponding Author:

Katie S Allen, BS

Regenstrief Institute, Inc

1101 W 10th Street

Indianapolis, IN,

United States

Phone: 1 317 274 9024


Background: Nonclinical 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.

Objective: This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.

Methods: We 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.

Results: A 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.

Conclusions: A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

JMIR Public Health Surveill 2019;5(4):e12846



Nonclinical Determinants of Health

Nonclinical determinants of health, which refer collectively to the social, behavioral, and environmental factors and contexts that influence patient health outside of health care settings, are of growing importance to health care delivery and health policy. In terms of individual care, unmet needs related to nonclinical determinants of health can influence patient nonadherence to health care recommendations, limit patient-provider communication, exacerbate health conditions, and require significant time and organizational resources to address [1]. Moreover, nonclinical determinants of health needs are common. Estimates suggest that as many as half of primary care patients in the United States have unmet social needs [2,3]. For health care organizations, nonclinical determinants can inform risk stratification or patient segmentation efforts as health systems work to develop and target interventions and outreach appropriately [4,5]. From a health policy perspective, the nonclinical determinants of health illustrate disparities within the current US health system, many of which can only be addressed through policy interventions [6]. As such, health care organizations and policy makers are becoming more attentive to nonclinical determinants of health, as evidenced by initiatives from large, innovative health systems [7,8] and the specific screening and service linkage requirements in the Centers for Medicare and Medicaid Services Accountable Health Communities program [9].

The Potential Utility of Combining Nonclinical Determinants with Clinical Data

Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into the nonclinical determinants of health. Data potentially relevant to nonclinical determinants range from detailed individual-level observations (such as shopping behavior collected through a grocery store’s rewards application) to social networks and area-level measures of climate, built environment, or policy environment [10]. Numerous researchers and commentators see vast potential for indicators derived from these data to improve both the health care system and individual patient care [11-13]. In particular, the greatest gains might be realized using nonclinical determinants of health data in conjunction with clinical data sources, such as electronic health records (EHRs) and clinical registries [10]. These novel combinations of data could provide new insights into patient risk behaviors, factors complicating care delivery, population-level health assessment, health system evaluation, provider decision making, and more [14,15].

However, within this context of increasing availability of nonclinical determinants data, it is not widely understood which nonclinical determinants of health constructs and indicators are supported by the literature as useful for health services and policy research. Therefore, the purpose of this review was to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data. Specifically, we sought guidance on the domains of determinants (eg, socioeconomic status [SES] or built environment), data sources (eg, population registries and US Census data), and specific measures (eg, area median household income) that are necessary to characterize the nonclinical determinants of health for use in combination with clinical patient-level data. Review findings will be used to guide the development of a population health data commons that will link to comprehensive, community-wide clinical information.


We undertook a rapid review [16] of the published literature and relevant policy reports to support our institution’s broader project of developing the data architecture and governance policies necessary to create a data commons for clinical and nonclinical determinants of health information. Rapid reviews are literature reviews that are limited in scope and have a shorter time frame, typically up to 6 months [16]. Our institution’s broader initiative to develop a data architecture was in direct response to high-priority funding focused on the opioid epidemic and needed feedback from the review team within a few months. The information obtained from this rapid review informed the overall architecture of the system and corresponding metadata dictionaries and prioritized data for inclusion.

Search Strategy

For the purpose of this review, we adopted a broad definition of nonclinical determinants of health that included individual-level behaviors, social contexts, physical environments, and health policies [6,17-19]. We operationalized environment to refer to different levels, such as a community, neighborhood, or family [20]. We took this approach to reflect the wide variation in potential use cases and research questions that could benefit from combined nonclinical determinants and clinical data.

Study Eligibility

Articles and reports describing the effect of, the methods for, or the need for combining nonclinical determinants data with clinical data were eligible for inclusion. For this review, we defined clinical data as any patient-level data that were generated by health care encounters (eg, EHR data, claims, discharge records, immunization records, cancer or other disease registries, or genomic data). We did not consider public health surveys (eg, the National Health and Nutrition Examination Survey) to be clinical data. We did not limit study eligibility by study type and allowed for the inclusion of any study design and nonempirical expert commentaries. Only articles from peer-reviewed publications or reports from governmental agencies and grant-making organizations were eligible for inclusion. We limited the literature to English-language studies published in 2010 or after to reflect the widespread clinical information system adoption resulting from the introduction of the Health Information Technology for Economic and Clinical Health Act.

Information Sources and Search Terms

The primary search concepts were nonclinical determinants of health and clinical data. Although there are numerous specific nonclinical determinants of health to make the search and screening manageable in the short amount of time available for a rapid review, we used keywords and Medical Subject Headings terms such as social determinants or factors, socioeconomic factors, behavioral factors, health disparities, environmental exposure, and exposome, a specialized term referring to the measure of all lifetime exposures of an individual and how these exposures relate to health. For the clinical data concept, we used terms such as EHR, electronic or computerized patient and medical data, medical order entry systems, and decision support systems. We selected these concepts based on several key reviews and reports [17,21,22]. Multimedia Appendix 1 provides the full search strategy. We searched 2 databases, MEDLINE (via Ovid) and Web of Science, in April 2018.

All English-language articles from 2010 to April 2018 were exported to EndNote Version 8 citation management software (Clarivate Analytics). In addition, we manually reviewed the articles cited within selected articles, the table of contents from key journals (Multimedia Appendix 1), and the websites of the World Health Organization, Agency for Healthcare Research and Quality, National Institutes of Health, the National Academy of Medicine, and the Robert Wood Johnson Foundation for citations to relevant articles. We elected to search the websites of these governmental and nonprofit organizations in particular because of their focus on nonclinical determinants and population health. If the report summarized or presented previously published findings, we obtained those citations. The initial search yielded 2748 unduplicated records from the database search and 21 records from table of contents screening of key journals and website review (Figure 1).

Figure 1. Diagram of articles reviewed for inclusion and qualitative synthesis.
View this figure

Study Selection

First, we screened the titles and abstracts of all records retrieved from the search. The primary goal of title and abstract screening was to exclude all non-US–based articles and articles with no indication of a focus on nonclinical determinants of health combined with clinical data. A total of 4 members of the research team first conducted a joint screening session on a random selection of citations to establish operational definitions and develop a cohesive screening approach. The team members then independently reviewed the title and abstract for each record to arrive at the included set. Our primary screening based on title and abstract resulted in 617 records for full-text review (Figure 1).

The research team members then independently read the full text of each article and determined its inclusion status on a randomly selected approximately 10% subsample from the 617 records identified from primary review. Agreement on inclusion status for the 10% subsample was kappa=0.70. The research team resolved differences by consensus in a joint reading session and independently reviewed the remainder of the articles. We retained articles for inclusion in the review if the article described the measurement or data source of at least one nonclinical determinant of health, which eliminated articles on technical architectures or database design issues that did not describe actual measurement. Owing to the study focus on nonclinical determinants and clinical data source linkages, we excluded articles in which the only nonclinical determinants of health measures were derived from clinical data (eg, insurance status or smoking history recorded within an EHR). Nonclinical determinants of health measures had to be derived from information systems, repositories, or collection methods apart from a clinical information system, including data from population surveys, epidemiologic registries, and US Census data. Furthermore, because our focus was on the use of these data for research, we limited inclusion to articles that used nonclinical determinants to describe, explore, or relate to a health outcome (eg, disease, condition, health status, and utilization). In addition, we reassessed each full-text article according to the exclusion criteria used for the initial title and abstract screening (ie, non-US–based articles or no focus on nonclinical determinants of health and clinical data). A total of 178 articles met the inclusion criteria after full-text review.

Data Abstraction

An initial codebook was established, and after joint coding and discussion on a subset of articles to ensure consistent and calibrated data collection, the reviewers independently abstracted and coded relevant data elements from the full text using a standardized data collection instrument. We developed and refined the data collection instrument in light of the articles read jointly in the previous steps. We abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported. To organize the diverse set of reported nonclinical determinants into meaningful groups, we created domains based on a combination of existing conceptual frameworks and definitions [22-24]. We did not rely on any single framework to ensure that we captured a breadth of nonclinical determinants of health concepts and not only those of greatest interest to US policy makers and researchers. The domains are summarized in Table 1.

Table 1. Nonclinical determinants of health measurements by domain.
Nonclinical determinants domainExample measures
Socioeconomic status and material conditionsIncome, poverty, access to food, employment, living conditions, race and ethnicity, gender, insurance status
BehaviorsSmoking and tobacco use, diet, illicit substance use, alcohol use, medication adherence, physical activity
Built environmentTransportation, sidewalks, walkability, buildings
Natural environmentAir quality, pollution, climate, greenspace
Public policiesHealth policies, social policies, laws, regulations
Health services and conditionsAccess to health care, utilization, health literacy, disease prevalence
Social circumstancesFamily, social support, caregivers, marital status, civic participation, community stigma

Furthermore, we grouped the reported study populations according to the key defining characteristics for inclusion in the study: geographic location, population focus (eg, Medicare enrollees, females only, and members of a specific racial or ethnic group), health condition of interest, or organization (ie, the study was focused on individuals who were part of the same health system or insurance plan). We also abstracted the study outcome, which we grouped into the broad categories of utilization, disease or health condition status, mortality, behaviors, risk scores, multiple outcomes, and all others. Other data elements that we abstracted include study design, type of clinical data source, use of census measures, and geographic level of measurement (for aggregate measures).

Primary Findings

A total of 178 articles reported combining nonclinical determinants of health with clinical data (Multimedia Appendix 2 [25-145]). The most common source of clinical data was EHRs (62.9%; 112/178), followed by claims or discharge data (20.2%; 36/178) and disease registries (19.1%; 34/178). Approximately one-third of the articles (34.3%; 61/178) focused on utilization outcomes, and more than one-fourth (27.0%; 48/178) treated disease or condition status as the outcome. Among studies in which disease or condition status was the outcome, health status indicators were commonly related to body mass index or obesity, asthma, and diabetes. A common health condition (eg, diabetes and cancer) defined the study population for the majority of articles (53.9%; 96/178). One-fifth of studies included children in the study sample.

Included articles contained a mix of determinants measured at the aggregate (50.0%; 89/178) and individual (29.2%; 52/178) levels, with many studies using measures at both the aggregate and individual levels (20.7%; 37/178). Among the articles that included any aggregated measures, the geographic level tended toward smaller areas, with 43.6% (55/126) using areas smaller than a ZIP code (eg, a census tract) and 2.3% (3/126) using ZIP code–level measures. Articles with aggregated measures relied heavily on US Census Bureau data (81.7%; 103/126). Individual-level measures typically relied on questionnaires or supplemental screening (eg, studies by Sheppard et al [146] and Hall et al [147]). The literature appeared to be growing over time, as the number of articles meeting our inclusion criteria generally increased annually from ten articles in 2010 to almost forty in 2018.

The articles collectively reported on 744 different nonclinical determinants of health measures (Multimedia Appendix 3). The majority of articles reported using multiple measures as independent variables (Multimedia Appendix 4; however, several articles used existing or created new indices or composite measures (Multimedia Appendix 5). Most indices intended to summarize the SES and material conditions domain using various measures of income, employment, housing conditions, or other material deprivation. Below, we describe specific findings for each domain of nonclinical determinants.

Socioeconomic Status and Material Conditions

Although the literature reflected all 7 of our identified nonclinical determinants of health domains, measures from the SES and material conditions domain dominated the literature, with 89.9% (160/178) of all articles including measures from this area. More than half of articles (57.9%; 103/178) used determinants from only a single domain, and if only 1 domain was reported, it was again largely from the SES and material conditions area. When articles reported on more than 1 domain, the additional domain was also most frequently an SES and material conditions measure.

Income, education, employment, and race and ethnicity-based measures were the most common approaches to representing this domain in the literature. Moreover, measures were highly variable and nuanced. For example, articles reported income as annual household income (eg, a study by Toledo et al [148]), mean household income (eg, a study by Seligman et al [149]), median household income (eg, a study by Grimberg et al [150]), or by various poverty measures (eg, studies by Ye et al [151], Kanzaria et al [152], and Patzer [153]). Similarly, multiple articles used the Gini coefficient to describe income inequality (eg, a study by Wallace [154]). Likewise, articles expressed employment status variously as employed (eg, a study by Shuman et al [155]), unemployment (eg, a study by Tanenbaum et al [156]), seasonal status (eg, a study by Castaneda et al [157]), job class (eg, a study by Eapen et al [158]), hours worked (eg, a study by DeMaria et al [159]), or employment rates by different age groups (eg, studies by Grimberg [150] and Wu [170]).


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].

Built Environment

The built environment domain included articles with measures ranging from a detailed description of neighborhood aesthetics [169] to traffic volume [174] and land use [175]. Measures were predominately at the aggregate level, and compared with articles with other domains, a higher proportion of articles considering built environment factors listed disease or condition status as outcomes.

Natural Environment

Articles with measures related to the natural environment domain measured air pollution and quality [176-179], climate and altitude [179,180], and various hazardous exposures [147,181,182]. This small set of articles linked these measures mostly to EHR and registry data sources.

Public Policies

The search strategy only identified 2 articles that linked public policy to clinical data sources. Achkar et al [183] combined state-level policy dates with prescription drug monitoring system usage data in an interrupted time series. Blosnich et al [184] used multiple measures to determine sociopolitical climate for hate crime protection in relationship to mental health status for transgender US veterans.

Health Services and Conditions

The health services and conditions domain exhibited substantial variation in measures. Aggregate measures of health services and conditions included both the extent of a particular condition within an area (eg, infectious disease incidence rates [185], percent of population reporting a disability [186], or obesity prevalence [151]) and measures of the supply of providers and facilities within an area (eg, studies by Xiao et al [162], Beck et al [187], Roth et al [188], and Newman et al [189]). Measures reported on an individual basis included travel time and distance to health care provider [190,191]. Unique to this domain, variables such as emergency department overcrowding [192] and hospital quality [193,194] were measured at a facility level.

Social Circumstances

Social circumstances was the second most common domain (25%) in the article set, and the most commonly employed measure was an indicator of a patient’s marital status, living arrangements, or family composition (eg, studies by Wu et al [170], Dupre et al [195], and Newgard et al [196]). Some measures moved toward deeper categorization of these arrangements by specifically looking at intimate partner violence or family conflict dynamics (eg, studies by Valentine et al [197] and Schuler et al [198]). Additional social circumstances reflected community stigma [190], social cohesion [169], self-reported social support [199], and structural racism [200].

Summary of Findings

In this review, we sought to describe the extent to which existing research has combined numerous nonclinical determinants of health measures with different clinical datasets to explore a variety of health outcomes and conditions. Using domains derived from several established frameworks, we identified a comprehensive, but unevenly distributed, representation of nonclinical determinants domains across included studies. Measures related to SES and material conditions were most prevalent, followed by the closely related domain of social circumstances, reflecting the existing widespread availability and use of standard demographic measures such as marital status, education, race, ethnicity in public health surveillance. Although used less frequently in included studies, nonclinical determinants of health measures related to the domains of the built environment, the natural environment, and public policies may indicate a small but growing research base connecting these higher-level determinants with clinical data.

Comment on Findings

We do not contend that any domain of nonclinical determinants is the most important; although different determinants arguably have varying importance or relative value, we could not assess the value of every reported measure within the scope of this study. Using the existing literature as a guide, measures reflective of SES and material conditions may be an appropriate initial focus for any work seeking to combine nonclinical determinants of health data with clinical data sources. The frequency with which this domain appears in the literature suggests it is a reasonable starting point for future work in this area that may be applicable to numerous different outcomes. Within this domain, income or lack of income was the most common measure, which is appropriate, given that multiple studies support income as an important determinant of health [201]. In addition, employment and education were common measures in the literature, conceptually distinct from income [202,203], and are supported by expert panels as key nonclinical determinants of health measures [204]. Conversely, a future area of focus may be where the literature is not well-developed (eg, housing and housing stability metrics). In addition, measures derived from race and ethnicity also appeared frequently in the literature. However, patient race and ethnicity are commonly recorded as data elements in routine clinical practice, and many sources of clinical data may already contain these data. Future work might explore the use and role of measures related to race and ethnicity that are less commonly recorded in clinical contexts, such as racism or cultural assimilation, but are significant nonclinical determinants of patient health.

Of potential further value, the SES and material conditions domain also had the largest number of indices and composite measures. Indices may be particularly useful for classification studies [203]. However, indices and composite measures have limitations as well. Indices developed in other countries may not be applicable to US populations, indices that were developed to measure specific constructs may not be applicable to all research studies, and indices and composite measures by design obscure the relationships between individual component measures [202,203].

Nonetheless, the literature in this area may be largely colored by the availability of nonclinical determinants of health data. As noted, most articles used data collected by the US Census Bureau. On the one hand, Census data have the dual advantages of longitudinal data and small area measurement. On the other hand, limitations exist with regard to the accuracy of some Census measures [205], and many measures are probability-based samples with errors around point estimates [206]. Problematically, the extent to which researchers consider the imprecision of measures in their analyses is not always immediately obvious. In addition, although clearly valuable, Census data do not readily, or thoroughly, extend to all nonclinical determinants of health domains. With Census data, nonclinical determinants of health related to SES and material conditions are well described, as are those related to the built environment and social circumstances, albeit to a lesser extent. However, other nonclinical determinants of health are largely unmeasurable using Census data (eg, person-level SES data and housing stability) and require either unique data collection or consideration of sources typically not used by health services and policy researchers. As more data sources become publicly and freely available, the distribution of studies using Census and non-Census sources may become more balanced and, in turn, highlight the growing potential to combine additional nonclinical determinants of health domains with clinical data. Similarly, the use of publicly available nonclinical determinants of health data sources is a reflection on the lack of relevant social determinant and social risk factors data currently captured in clinical information systems. Reliance on Census measures may further decrease as more individual-level measures of social risk are included in clinical datasets through wider implementation of social determinants of health (SDoH) screening tools [207,208] and collection of select social measurers in EHRs [209].

Articles included in this review reported both combining individually measured nonclinical determinants of health with clinical data sources (eg, measures of social support obtained by survey merged with EHR data) as well as combining aggregate, area-level measures with individual-level data sources through common geographical location (eg, applying area-level measures such as median household income to individual patients in an EHR, matched by patient address). In our study, the latter approach was more common. The various individual-level nonclinical determinants of health measures, such as transportation needs or social support, are consistent with the medical concept of a social history and express individual needs or resources [210]. The appropriateness of aggregate, area-level measures applied to individuals in the case of public policy is straightforward, as policy within a geographical jurisdiction is (at least theoretically) universally applied. However, the intention and reasoning for the differing levels of measurement linked to clinical data requires clear articulation for the other nonclinical determinants of health domains because characteristics of the aggregate cannot be attributed to the individual. For example, individuals residing within a high-poverty geographic area may not be themselves living in poverty; in fact, it is possible they are living well above the poverty line. When combined with individual-level data, aggregate or area-level measures are reflective of individuals’ situations and contexts but may not accurately capture individual circumstances [211-213]. As opportunities increase to link both aggregate-level and more comprehensive individual-level nonclinical determinants indicators (eg, derived from social media data, financial records, and surveys), the literature would benefit from a stronger articulation of the theoretical and methodological choices for levels of linkage with clinical data that to best explains the relationships between social determinants and health outcomes. Moreover, this growing data availability increases opportunities to empirically explore the value of combining individual and area-level SDoH data in explaining outcomes relevant at different levels of intervention, such as provider decision making, population health management, and research.

Previous reviews have commented on the frequent lack of theoretical justification for measurement choices and strategies [214,215]. The level of theoretical justification among the articles included in this review was highly variable. At one end of the spectrum, numerous articles provided extensive justification for selected constructs and measurement strategies (eg, studies by Valentine et al [197] and Schuch et al [216]). These articles tended to be those specifically interested in understanding the role of nonclinical determinants in influencing health outcomes. Other articles used nonclinical determinants of health measures as known confounding factors to be controlled and as such provided less explication and interpretation. Although many nonclinical determinants measures are, indeed, important and widely used confounders in analyses of health-related outcomes, it may be fruitful for future research in this area to provide more thoughtfully considered rationale for the selection of the nonclinical determinants used and how they may relate to the health outcome of interest. For example, income is reflective of the money received over a period of time by an individual or family. However, using income as a catch-all control measure may fail to capture the nuances and significant theoretical implications. For example, individuals and families have differing sources of income, such as salary versus investment, overall income fails to account for expenditures, and how income varies from others in society (ie, inequalities) may have stronger relationships with the health outcome of interest [217-219].

This review has several limitations. Although we grounded our categorization of the multiple nonclinical determinants of health in existing frameworks, other authors have grouped determinants in markedly different ways, thereby inhibiting direct comparisons with previous work. Differences may be most pronounced in our domain of SES and material conditions, which tended to be more expansive than in other frameworks. In addition, our search strategy undoubtedly undercounted articles describing the linkage of public policy and clinical data sources. Linking policy data to clinical records is very common in health policy, health services, and health economics research. However, our search strategy did not locate such articles because these disciplines tend to focus on the role of policy change, instead of framing public policy as a social determinant. Similarly, health services and conditions may also be undercounted because these measures may not be presented as measures of nonclinical determinants of health. Regardless, as our objective was focused on identifying measures, our smaller set of articles was still representative of the whole within these 2 domains. Also, our strategy excluded articles that did not merge nonclinical determinants of health data with clinical data. This requirement eliminated both articles that leveraged single information systems (such as EHRs) that already included nonclinical determinants of health measures, as well as articles focused solely on the measurement and impact of the nonclinical determinants of health. Similarly, our approach was focused on data that were linked to clinical information systems; as such, we do not discuss social factors or risks that may be inferred from clinical data (eg, homeless or transportation barriers) or that could be extracted from narrative texts in clinical documents. In addition, we do not comment on the appropriateness of measure choice, level of measurement, or linkage strategy. Finally, our review cannot be generalized to settings outside the United States.


In conclusion, this review represents a comprehensive synthesis of existing attempts to link nonclinical determinants of health indicators with clinical data. Characterizing the work done so far in this area is an important first step in guiding future attempts to harness nonclinical determinants of health data for population health management initiatives. A better understanding of the types of determinants, data sources, and measures used successfully to integrate nonclinical determinants of health indicators with clinical and administrative health services data can help to shed light on feasibility, best practices, and future need in this important, emerging arena of research.


This work was supported by the Trustees of Indiana University (Grant PI: Newhouse, Project PI: Embi). The authors thank Dr Brian Dixon and Mr Daniel Hood of the Regenstrief Institute for their assistance and comments.

Authors' Contributions

JV conceived the study. RH and JV designed the search strategy. RH conducted the literature search. EG, KA, AB, and JV extracted data. EG, KA, AB, RH, and JV drafted the manuscript and revised for critical content. All authors had final approval of the work.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Search strategy.

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Multimedia Appendix 2

Characteristics of literature on nonclinical determinants of health used in combination with clinical patient-level data.

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Multimedia Appendix 3

Social determinant of health measures reported in the literature.

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Multimedia Appendix 4

Combinations of social determinant domains reported by article.

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Multimedia Appendix 5

Composite and index measures by social determinant of health domain.

PDF File (Adobe PDF File)165 KB

  1. Fiscella K, Epstein RM. So much to do, so little time: care for the socially disadvantaged and the 15-minute visit. Arch Intern Med 2008 Sep 22;168(17):1843-1852 [FREE Full text] [CrossRef] [Medline]
  2. Page-Reeves J, Kaufman W, Bleecker M, Norris J, McCalmont K, Ianakieva V, et al. Addressing social determinants of health in a clinic setting: the WellRx pilot in Albuquerque, New Mexico. J Am Board Fam Med 2016;29(3):414-418 [FREE Full text] [CrossRef] [Medline]
  3. Vest JR, Grannis SJ, Haut DP, Halverson PK, Menachemi N. Using structured and unstructured data to identify patients' need for services that address the social determinants of health. Int J Med Inform 2017 Nov;107:101-106. [CrossRef] [Medline]
  4. Kangovi S, Barg FK, Carter T, Long JA, Shannon R, Grande D. Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Aff (Millwood) 2013 Jul;32(7):1196-1203. [CrossRef] [Medline]
  5. Kasthurirathne SN, Vest JR, Menachemi N, Halverson PK, Grannis SJ. Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services. J Am Med Inform Assoc 2018 Jan 1;25(1):47-53. [CrossRef] [Medline]
  6. Solar A, Irwin A. World Health Organization. 2010. A Conceptual Framework for Action on the Social Determinants of Health   URL: [accessed 2019-08-13]
  7. Deloitte US. 2017. Social Determinants of Health: How Are Hospitals and Health Systems Investing in and Addressing Social Needs?   URL: https:/​/www2.​​content/​dam/​Deloitte/​us/​Documents/​life-sciences-health-care/​us-lshc-addressing-social-determinants-of-health.​pdf [accessed 2019-08-13]
  8. Chaiyachati KH, Grande DT, Aysola J. Health systems tackling social determinants of health: promises, pitfalls, and opportunities of current policies. Am J Manag Care 2016 Nov 1;22(11):e393-e394 [FREE Full text] [Medline]
  9. Alley DE, Asomugha CN, Conway PH, Sanghavi DM. Accountable health communities--addressing social needs through medicare and medicaid. N Engl J Med 2016 Jan 7;374(1):8-11. [CrossRef] [Medline]
  10. Weber GM, Mandl KD, Kohane IS. Finding the missing link for big biomedical data. J Am Med Assoc 2014 Jun 25;311(24):2479-2480. [CrossRef] [Medline]
  11. Sadana R, Harper S. Data systems linking social determinants of health with health outcomes: advancing public goods to support research and evidence-based policy and programs. Public Health Rep 2011;126(Suppl 3):6-13 [FREE Full text] [CrossRef] [Medline]
  12. Gottlieb L, Tobey R, Cantor J, Hessler D, Adler NE. Integrating social and medical data to improve population health: opportunities and barriers. Health Aff (Millwood) 2016 Nov 1;35(11):2116-2123. [CrossRef] [Medline]
  13. Murdoch TB, Detsky AS. The inevitable application of big data to health care. J Am Med Assoc 2013 Apr 3;309(13):1351-1352. [CrossRef] [Medline]
  14. Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, Innovative Medicines Initiative 2nd programme‚ Big Data for Better Outcomes‚ BigData@Heart Consortium of 20 academic and industry partners including ESC. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J 2018 Apr 21;39(16):1481-1495 [FREE Full text] [CrossRef] [Medline]
  15. Beam AL, Kohane IS. Big data and machine learning in health care. J Am Med Assoc 2018 Apr 3;319(13):1317-1318. [CrossRef] [Medline]
  16. Haby MM, Chapman E, Clark R, Barreto J, Reveiz L, Lavis JN. What are the best methodologies for rapid reviews of the research evidence for evidence-informed decision making in health policy and practice: a rapid review. Health Res Policy Syst 2016 Nov 25;14(1):83 [FREE Full text] [CrossRef] [Medline]
  17. Vrijheid M. The exposome: a new paradigm to study the impact of environment on health. Thorax 2014 Sep;69(9):876-878. [CrossRef] [Medline]
  18. US Interagency Program Office. 2013. #1 Background & Introduction to Non-Traditional Determinants of Health   URL: [accessed 2019-08-13]
  19. 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 Nov(14 Suppl):S8-17 [FREE Full text] [CrossRef] [Medline]
  20. World Health Organization. In: Currie C, Zanotti C, Morgan A, Currie D, Looze MD, Roberts C, editors. Social Determinants of Health and Well-Being Among Young People: Health Behaviour in School-Aged Children: International Report from the 2009/2010 Survey. Geneva, Switzerland: World Health Organization; 2010.
  21. Braveman P, Egerter S, Williams DR. The social determinants of health: coming of age. Annu Rev Public Health 2011;32:381-398. [CrossRef] [Medline]
  22. Gnadinger T. Health Affairs. 2014. Health Policy Brief: The Relative Contribution Of Multiple Determinants To Health Outcomes   URL: [accessed 2019-08-13]
  23. Healthy People 2020. 2018. Social Determinants of Health   URL: [accessed 2018-05-01]
  24. World Health Organization. 2008. Closing The Gap In a Generation: Health Equity Through Action on The Social Determinants of Health   URL: [accessed 2019-08-13]
  25. Tomayko EJ, Flood TL, Tandias A, Hanrahan LP. Linking electronic health records with community-level data to understand childhood obesity risk. Pediatr Obes 2015 Dec;10(6):436-441 [FREE Full text] [CrossRef] [Medline]
  26. Tannenbaum SL, Hernandez M, Zheng DD, Sussman DA, Lee DJ. Individual- and neighborhood-level predictors of mortality in Florida colorectal cancer patients. PLoS One 2014;9(8):e106322 [FREE Full text] [CrossRef] [Medline]
  27. Small AC, Tsao CK, Moshier EL, Gartrell BA, Wisnivesky JP, Godbold J, et al. Trends and variations in utilization of nephron-sparing procedures for stage I kidney cancer in the United States. World J Urol 2013 Oct;31(5):1211-1217 [FREE Full text] [CrossRef] [Medline]
  28. Sleder A, Tackett S, Cerasale M, Mittal C, Isseh I, Radjef R, et al. Socioeconomic and racial disparities: a case-control study of patients receiving transcatheter aortic valve replacement for severe aortic stenosis. J Racial Ethn Health Disparities 2017 Dec;4(6):1189-1194. [CrossRef] [Medline]
  29. Siminoff L, Thomson M, Dumenci L. Factors associated with delayed patient appraisal of colorectal cancer symptoms. Psychooncology 2014 Sep;23(9):981-988 [FREE Full text] [CrossRef] [Medline]
  30. Sheppard VB, O'Neill SC, Dilawari A, Horton S, Hirpa FA, Isaacs C. Patterns of 21-gene assay testing and chemotherapy use in black and white breast cancer patients. Clin Breast Cancer 2015 Apr;15(2):e83-e92 [FREE Full text] [CrossRef] [Medline]
  31. Sheppard VB, Hurtado-de-Mendoza A, Talley CH, Zhang YH, Cabling ML, Makambi KH. Reducing racial disparities in breast cancer survivors' ratings of quality cancer care: the enduring impact of trust. J Healthc Qual 2016;38(3):143-163. [CrossRef] [Medline]
  32. Sehgal NK, Scallan C, Sullivan C, Cedeño M, Pencak J, Kirkland J, et al. The relationship between verified organ donor designation and patient demographic and medical characteristics. Am J Transplant 2016 Apr;16(4):1294-1297 [FREE Full text] [CrossRef] [Medline]
  33. Sankaranarayanan J, Collier D, Furasek A, Reardon T, Smith LM, McCartan M, et al. Rurality and other factors associated with adherence to immunosuppressant medications in community-dwelling solid-organ transplant recipients. Res Social Adm Pharm 2012;8(3):228-239. [CrossRef] [Medline]
  34. Patzer RE, Perryman JP, Pastan S, Amaral S, Gazmararian JA, Klein M, et al. Impact of a patient education program on disparities in kidney transplant evaluation. Clin J Am Soc Nephrol 2012 Apr;7(4):648-655 [FREE Full text] [CrossRef] [Medline]
  35. Strauchler D, Freeman K, Miller TS. The impact of socioeconomic status and comorbid medical conditions on ionizing radiation exposure from diagnostic medical imaging in adults. J Am Coll Radiol 2012 Jan;9(1):58-63. [CrossRef] [Medline]
  36. Oyana TJ, Podila P, Wesley JM, Lomnicki S, Cormier S. Spatiotemporal patterns of childhood asthma hospitalization and utilization in Memphis metropolitan area from 2005 to 2015. J Asthma 2017 Oct;54(8):842-855 [FREE Full text] [CrossRef] [Medline]
  37. Ng DK, Brotman DJ, Lau B, Young JH. Insurance status, not race, is associated with mortality after an acute cardiovascular event in Maryland. J Gen Intern Med 2012 Oct;27(10):1368-1376 [FREE Full text] [CrossRef] [Medline]
  38. Neuman HB, Weiss JM, Leverson G, O'Connor ES, Greenblatt DY, Loconte NK, et al. Predictors of short-term postoperative survival after elective colectomy in colon cancer patients ≥ 80 years of age. Ann Surg Oncol 2013 May;20(5):1427-1435 [FREE Full text] [CrossRef] [Medline]
  39. Ndao-Brumblay SK, Green CR. Predictors of complementary and alternative medicine use in chronic pain patients. Pain Med 2010 Jan;11(1):16-24. [CrossRef] [Medline]
  40. Movsas A, Ibrahim R, Elshaikh MA, Lamerato L, Lu M, Sitarik A, et al. Do sociodemographic factors influence outcome in prostate cancer patients treated with external beam radiation therapy? Am J Clin Oncol 2016 Dec;39(6):563-567. [CrossRef] [Medline]
  41. Mosen DM, Schatz M, Gold R, Mularski RA, Wong WF, Bellows J. Medication use, emergency hospital care utilization, and quality-of-life outcome disparities by race/ethnicity among adults with asthma. Am J Manag Care 2010 Nov;16(11):821-828 [FREE Full text] [Medline]
  42. Moffet HH, Parker MM, Sarkar U, Schillinger D, Fernandez A, Adler NE, et al. Adherence to laboratory test requests by patients with diabetes: the diabetes study of northern California (DISTANCE). Am J Manag Care 2011 May;17(5):339-344 [FREE Full text] [Medline]
  43. Ratanawongsa N, Karter AJ, Quan J, Parker MM, Handley M, Sarkar U, et al. Reach and validity of an objective medication adherence measure among safety net health plan members with diabetes: a cross-sectional study. J Manag Care Spec Pharm 2015 Aug;21(8):688-698 [FREE Full text] [CrossRef] [Medline]
  44. Mayer V, McDonough K, Seligman H, Mitra N, Long JA. Food insecurity, coping strategies and glucose control in low-income patients with diabetes. Public Health Nutr 2016 Apr;19(6):1103-1111. [CrossRef] [Medline]
  45. Maxson P, Miranda ML. Pregnancy intention, demographic differences, and psychosocial health. J Womens Health (Larchmt) 2011 Aug;20(8):1215-1223. [CrossRef] [Medline]
  46. Li D, Collins B, Velayos FS, Liu L, Lewis JD, Allison JE, et al. Racial and ethnic differences in health care utilization and outcomes among ulcerative colitis patients in an integrated health-care organization. Dig Dis Sci 2014 Feb;59(2):287-294. [CrossRef] [Medline]
  47. Keegan TH, Kurian AW, Gali K, Tao L, Lichtensztajn DY, Hershman DL, et al. Racial/ethnic and socioeconomic differences in short-term breast cancer survival among women in an integrated health system. Am J Public Health 2015 May;105(5):938-946. [CrossRef] [Medline]
  48. Mack JW, Chen K, Boscoe FP, Gesten FC, Roohan PJ, Weeks JC, et al. Underuse of hospice care by medicaid-insured patients with stage IV lung cancer in New York and California. J Clin Oncol 2013 Jul 10;31(20):2569-2579 [FREE Full text] [CrossRef] [Medline]
  49. Mack CD, Carpenter W, Meyer AM, Sanoff H, Stürmer T. Racial disparities in receipt and comparative effectiveness of oxaliplatin for stage III colon cancer in older adults. Cancer 2012 Jun 1;118(11):2925-2934 [FREE Full text] [CrossRef] [Medline]
  50. Goodman SM, Mandl LA, Parks ML, Zhang M, McHugh KR, Lee YY, et al. Disparities in TKA outcomes: census tract data show interactions between race and poverty. Clin Orthop Relat Res 2016 Sep;474(9):1986-1995 [FREE Full text] [CrossRef] [Medline]
  51. Miranda ML, Ferranti J, Strauss B, Neelon B, Califf RM. Geographic health information systems: a platform to support the 'triple aim'. Health Aff (Millwood) 2013 Sep;32(9):1608-1615 [FREE Full text] [CrossRef] [Medline]
  52. 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 [FREE Full text] [CrossRef] [Medline]
  53. Tumin D, Horan J, Shrider E, Smith SA, Tobias JD, Hayes Jr DJ, et al. County socioeconomic characteristics and heart transplant outcomes in the United States. Am Heart J 2017 Aug;190:104-112 [FREE Full text] [CrossRef] [Medline]
  54. Tomayko EJ, Weinert BA, Godfrey L, Adams AK, Hanrahan LP. Using electronic health records to examine disease risk in small populations: obesity among American Indian children, Wisconsin, 2007-2012. Prev Chronic Dis 2016 Feb 25;13:E29 [FREE Full text] [CrossRef] [Medline]
  55. Takahashi PY, Ryu E, Hathcock MA, Olson JE, Bielinski SJ, Cerhan JR, et al. A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population. J Epidemiol Community Health 2016 Mar;70(3):286-291 [FREE Full text] [CrossRef] [Medline]
  56. Sánchez E, Rasmussen A, Riba L, Acevedo-Vasquez E, Kelly JA, Langefeld CD, et al. Impact of genetic ancestry and sociodemographic status on the clinical expression of systemic lupus erythematosus in American Indian-European populations. Arthritis Rheum 2012 Nov;64(11):3687-3694 [FREE Full text] [CrossRef] [Medline]
  57. Nijhawan AE, Clark C, Kaplan R, Moore B, Halm EA, Amarasingham R. An electronic medical record-based model to predict 30-day risk of readmission and death among HIV-infected inpatients. J Acquir Immune Defic Syndr 2012 Nov 1;61(3):349-358. [CrossRef] [Medline]
  58. Nau C, Schwartz BS, Bandeen-Roche K, Liu A, Pollak J, Hirsch A, et al. Community socioeconomic deprivation and obesity trajectories in children using electronic health records. Obesity (Silver Spring) 2015 Jan;23(1):207-212 [FREE Full text] [CrossRef] [Medline]
  59. Martínez ME, Anderson K, Murphy JD, Hurley S, Canchola AJ, Keegan TH, et al. Differences in marital status and mortality by race/ethnicity and nativity among California cancer patients. Cancer 2016 May 15;122(10):1570-1578 [FREE Full text] [CrossRef] [Medline]
  60. Geraghty EM, Balsbaugh T, Nuovo J, Tandon S. Using geographic information systems (GIS) to assess outcome disparities in patients with type 2 diabetes and hyperlipidemia. J Am Board Fam Med 2010;23(1):88-96 [FREE Full text] [CrossRef] [Medline]
  61. Flood TL, Zhao YQ, Tomayko EJ, Tandias A, Carrel AL, Hanrahan LP. Electronic health records and community health surveillance of childhood obesity. Am J Prev Med 2015 Feb;48(2):234-240 [FREE Full text] [CrossRef] [Medline]
  62. Rutten LJ, Wilson PM, Jacobson DJ, Agunwamba AA, Breitkopf CR, Jacobson RM, et al. A population-based study of sociodemographic and geographic variation in HPV vaccination. Cancer Epidemiol Biomarkers Prev 2017 Apr;26(4):533-540 [FREE Full text] [CrossRef] [Medline]
  63. Dalton JE, Perzynski AT, Zidar DA, Rothberg MB, Coulton CJ, Milinovich AT, et al. Accuracy of cardiovascular risk prediction varies by neighborhood socioeconomic position: a retrospective cohort study. Ann Intern Med 2017 Oct 3;167(7):456-464 [FREE Full text] [CrossRef] [Medline]
  64. Collins JE, Katz JN, Donnell-Fink LA, Martin SD, Losina E. Cumulative incidence of ACL reconstruction after ACL injury in adults: role of age, sex, and race. Am J Sports Med 2013 Mar;41(3):544-549 [FREE Full text] [CrossRef] [Medline]
  65. Casey JA, Cosgrove SE, Stewart WF, Pollak J, Schwartz BS. A population-based study of the epidemiology and clinical features of methicillin-resistant Staphylococcus aureus infection in Pennsylvania, 2001-2010. Epidemiol Infect 2013 Jun;141(6):1166-1179 [FREE Full text] [CrossRef] [Medline]
  66. Finks J, Osborne N, Birkmeyer J. Trends in hospital volume and operative mortality for high-risk surgery. N Engl J Med 2011 Jun 2;364(22):2128-2137 [FREE Full text] [CrossRef] [Medline]
  67. Zuckerman SL, Zalneraitis BH, Totten DJ, Rubel KE, Kuhn AQ, Yengo-Kahn AM, et al. Socioeconomic status and outcomes after sport-related concussion: a preliminary investigation. J Neurosurg Pediatr 2017 Jun;19(6):652-661. [CrossRef] [Medline]
  68. Leonard T, Hughes AE, Pruitt SL. Understanding how low-socioeconomic status households cope with health shocks: an analysis of multi-sector linked data. Ann Am Acad Pol Soc Sci 2017 Jan;669(1):125-145 [FREE Full text] [CrossRef] [Medline]
  69. Solem CT, Sun SX, Sudharshan L, Macahilig C, Katyal M, Gao X. Exacerbation-related impairment of quality of life and work productivity in severe and very severe chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2013;8:641-652 [FREE Full text] [CrossRef] [Medline]
  70. Grinspan ZM, Patel AD, Hafeez B, Abramson EL, Kern LM. Predicting frequent emergency department use among children with epilepsy: a retrospective cohort study using electronic health data from 2 centers. Epilepsia 2018 Jan;59(1):155-169 [FREE Full text] [CrossRef] [Medline]
  71. Bristow RE, Powell MA, Al-Hammadi N, Chen L, Miller JP, Roland PY, et al. Disparities in ovarian cancer care quality and survival according to race and socioeconomic status. J Natl Cancer Inst 2013 Jun 5;105(11):823-832 [FREE Full text] [CrossRef] [Medline]
  72. Baldwin MR, Sell JL, Heyden N, Javaid A, Berlin DA, Gonzalez WC, et al. Race, ethnicity, health insurance, and mortality in older survivors of critical illness. Crit Care Med 2017 Jun;45(6):e583-e591 [FREE Full text] [CrossRef] [Medline]
  73. Drewnowski A, Rehm CD, Moudon AV, Arterburn D. The geography of diabetes by census tract in a large sample of insured adults in King County, Washington, 2005-2006. Prev Chronic Dis 2014 Jul 24;11:E125 [FREE Full text] [CrossRef] [Medline]
  74. Fessele KL, Hayat MJ, Mayer DK, Atkins RL. Factors associated with unplanned hospitalizations among patients with nonmetastatic colorectal cancers intended for treatment in the ambulatory setting. Nurs Res 2016;65(1):24-34 [FREE Full text] [CrossRef] [Medline]
  75. Chu DI, Moreira DM, Gerber L, Presti Jr JC, Aronson WJ, Terris MK, et al. Effect of race and socioeconomic status on surgical margins and biochemical outcomes in an equal-access health care setting: results from the shared equal access regional cancer hospital (SEARCH) database. Cancer 2012 Oct 15;118(20):4999-5007 [FREE Full text] [CrossRef] [Medline]
  76. Sills MR, Hall M, Cutler GJ, Colvin JD, Gottlieb LM, Macy ML, et al. Adding social determinant data changes children's hospitals' readmissions performance. J Pediatr 2017 Jul;186:150-7.e1. [CrossRef] [Medline]
  77. Hu J, Gonsahn MD, Nerenz DR. Socioeconomic status and readmissions: evidence from an urban teaching hospital. Health Aff (Millwood) 2014 May;33(5):778-785. [CrossRef] [Medline]
  78. Nagasako EM, Reidhead M, Waterman B, Dunagan WC. Adding socioeconomic data to hospital readmissions calculations may produce more useful results. Health Aff (Millwood) 2014 May;33(5):786-791 [FREE Full text] [CrossRef] [Medline]
  79. Lord JH, Young MT, Gruhn MA, Grey M, Delamater AM, Jaser SS. Effect of race and marital status on mothers' observed parenting and adolescent adjustment in youth with type 1 diabetes. J Pediatr Psychol 2015;40(1):132-143 [FREE Full text] [CrossRef] [Medline]
  80. Salvaggio C, Han SW, Martires K, Robinson E, Madankumar R, Gumaste P, et al. Impact of socioeconomic status and ethnicity on melanoma presentation and recurrence in Caucasian patients. Oncology 2016;90(2):79-87. [CrossRef] [Medline]
  81. Novotny R, Oshiro CE, Wilkens LR. Prevalence of childhood obesity among young multiethnic children from a health maintenance organization in Hawaii. Child Obes 2013 Feb;9(1):35-42 [FREE Full text] [CrossRef] [Medline]
  82. Nguyen OK, Makam AN, Clark C, Zhang S, Xie B, Velasco F, et al. Predicting all-cause readmissions using electronic health record data from the entire hospitalization: model development and comparison. J Hosp Med 2016 Jul;11(7):473-480 [FREE Full text] [CrossRef] [Medline]
  83. Miller AL, Simon D, Roe MT, Kontos MC, Diercks D, Amsterdam E, et al. Comparison of delay times from symptom onset to medical contact in blacks versus whites with acute myocardial infarction. Am J Cardiol 2017 Apr 15;119(8):1127-1134. [CrossRef] [Medline]
  84. Robbins AS, Cox DD, Johnson LB, Ward EM. Persistent disparities in liver transplantation for patients with hepatocellular carcinoma in the United States, 1998 through 2007. Cancer 2011 Oct 1;117(19):4531-4539 [FREE Full text] [CrossRef] [Medline]
  85. McGee SA, Durham DD, Tse CK, Millikan RC. Determinants of breast cancer treatment delay differ for African American and white women. Cancer Epidemiol Biomarkers Prev 2013 Jul;22(7):1227-1238 [FREE Full text] [CrossRef] [Medline]
  86. Lu JC, Lowery R, Yu S, Mahani G, Agarwal PP, Dorfman AL. Predictors of missed appointments in patients referred for congenital or pediatric cardiac magnetic resonance. Pediatr Radiol 2017 Jul;47(8):911-916. [CrossRef] [Medline]
  87. Lipner EM, Knox D, French J, Rudman J, Strong M, Crooks JL. A geospatial epidemiologic analysis of nontuberculous mycobacterial infection: an ecological study in Colorado. Ann Am Thorac Soc 2017 Oct;14(10):1523-1532 [FREE Full text] [CrossRef] [Medline]
  88. Lee SL, Yaghoubian A, Stark R, Shekherdimian S. Equal access to healthcare does not eliminate disparities in the management of adults with appendicitis. J Surg Res 2011 Oct;170(2):209-213. [CrossRef] [Medline]
  89. Le H, Wong S, Iftikar T, Keenan H, King GL, Hsu WC. Characterization of factors affecting attainment of glycemic control in Asian Americans with diabetes in a culturally specific program. Diabetes Educ 2013;39(4):468-477 [FREE Full text] [CrossRef] [Medline]
  90. Lafata JE, Karter AJ, O'Connor PJ, Morris H, Schmittdiel JA, Ratliff S, et al. Medication adherence does not explain black-white differences in cardiometabolic risk factor control among insured patients with diabetes. J Gen Intern Med 2016 Feb;31(2):188-195 [FREE Full text] [CrossRef] [Medline]
  91. Black MH, Zhou H, Takayanagi M, Jacobsen SJ, Koebnick C. Increased asthma risk and asthma-related health care complications associated with childhood obesity. Am J Epidemiol 2013 Oct 1;178(7):1120-1128 [FREE Full text] [CrossRef] [Medline]
  92. Nguyen BC, Alawadi ZM, Roife D, Kao LS, Ko TC, Wray CJ. Do socioeconomic factors and race determine the likelihood of breast-conserving surgery? Clin Breast Cancer 2016 Aug;16(4):e93-e97. [CrossRef] [Medline]
  93. Rubens SL, Patrick KE, Williamson AA, Moore M, Mindell JA. Individual and socio-demographic factors related to presenting problem and diagnostic impressions at a pediatric sleep clinic. Sleep Med 2016 Sep;25:67-72. [CrossRef] [Medline]
  94. Wu JR, Moser DK, DeWalt DA, Rayens MK, Dracup K. Health literacy mediates the relationship between age and health outcomes in patients with heart failure. Circ Heart Fail 2016 Jan;9(1):e002250 [FREE Full text] [CrossRef] [Medline]
  95. Doll KM, Barber EL, Bensen JT, Revilla MC, Snavely AC, Bennett AV, et al. The impact of surgical complications on health-related quality of life in women undergoing gynecologic and gynecologic oncology procedures: a prospective longitudinal cohort study. Am J Obstet Gynecol 2016 Oct;215(4):457.e1-457.13 [FREE Full text] [CrossRef] [Medline]
  96. Roth C, Payne PR, Weier RC, Shoben AB, Fletcher EN, Lai AM, et al. The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study. J Biomed Inform 2016 Apr;60:95-103 [FREE Full text] [CrossRef] [Medline]
  97. Berger RR, Fromkin JB, Stutz H, Makoroff K, Scribano PV, Feldman K, et al. Abusive head trauma during a time of increased unemployment: a multicenter analysis. Pediatrics 2011 Oct;128(4):637-643. [CrossRef] [Medline]
  98. Downing J, Karter A, Rodriguez H, Dow WH, Adler N, Schillinger D, et al. No spillover effect of the foreclosure crisis on weight change: the diabetes study of northern California (DISTANCE). PLoS One 2016;11(3):e0151334 [FREE Full text] [CrossRef] [Medline]
  99. Sharifi M, Sequist TD, Rifas-Shiman SL, Melly SJ, Duncan DT, Horan CM, et al. The role of neighborhood characteristics and the built environment in understanding racial/ethnic disparities in childhood obesity. Prev Med 2016 Oct;91:103-109 [FREE Full text] [CrossRef] [Medline]
  100. Laraia BA, Downing JM, Zhang YT, Dow WH, Kelly M, Blanchard SD, et al. Food environment and weight change: does residential mobility matter?: the diabetes study of northern California (DISTANCE). Am J Epidemiol 2017 May 1;185(9):743-750 [FREE Full text] [CrossRef] [Medline]
  101. Fiechtner L, Sharifi M, Sequist T, Block J, Duncan DT, Melly SJ, et al. Food environments and childhood weight status: effects of neighborhood median income. Child Obes 2015 Jun;11(3):260-268 [FREE Full text] [CrossRef] [Medline]
  102. Zenk SN, Tarlov E, Wing C, Matthews SA, Jones K, Tong H, et al. Geographic accessibility of food outlets not associated with body mass index change among veterans, 2009-14. Health Aff (Millwood) 2017 Aug 1;36(8):1433-1442 [FREE Full text] [CrossRef] [Medline]
  103. Moshkovich O, Lebrun-Harris L, Makaroff L, Chidambaran P, Chung M, Sripipatana A, et al. Challenges and opportunities to improve cervical cancer screening rates in US health centers through patient-centered medical home transformation. Adv Prev Med 2015;2015:182073 [FREE Full text] [CrossRef] [Medline]
  104. Ponce NA, Ko M, Liang SY, Armstrong J, Toscano M, Chanfreau-Coffinier C, et al. Early diffusion of gene expression profiling in breast cancer patients associated with areas of high income inequality. Health Aff (Millwood) 2015 Apr;34(4):609-615. [CrossRef] [Medline]
  105. Pimentel L, Anderson D, Golden B, Wasil E, Barrueto F, Hirshon JM. Impact of health policy changes on emergency medicine in Maryland stratified by socioeconomic status. West J Emerg Med 2017 Apr;18(3):356-365 [FREE Full text] [CrossRef] [Medline]
  106. Noel L, Connors SK, Goodman MS, Gehlert S. Improving breast cancer services for African-American women living in St Louis. Breast Cancer Res Treat 2015 Nov;154(1):5-12 [FREE Full text] [CrossRef] [Medline]
  107. Bernheim SM, Parzynski CS, Horwitz L, Lin Z, Araas MJ, Ross JS, et al. Accounting for patients' socioeconomic status does not change hospital readmission rates. Health Aff (Millwood) 2016 Aug 1;35(8):1461-1470. [CrossRef] [Medline]
  108. Bailey SR, Marino M, Hoopes M, Heintzman J, Gold R, Angier H, et al. Healthcare utilization after a children's health insurance program expansion in Oregon. Matern Child Health J 2016 May;20(5):946-954 [FREE Full text] [CrossRef] [Medline]
  109. Angier H, Marino M, Sumic A, O'Malley J, Likumahuwa-Ackman S, Hoopes M, et al. Innovative methods for parents and clinics to create tools for kids' care (IMPACCT Kids' Care) study protocol. Contemp Clin Trials 2015 Sep;44:159-163 [FREE Full text] [CrossRef] [Medline]
  110. Beck AF, Huang B, Chundur R, Kahn RS. Housing code violation density associated with emergency department and hospital use by children with asthma. Health Aff (Millwood) 2014 Nov;33(11):1993-2002 [FREE Full text] [CrossRef] [Medline]
  111. Ahluwalia SK, Peng RD, Breysse PN, Diette GB, Curtin-Brosnan J, Aloe C, et al. Mouse allergen is the major allergen of public health relevance in Baltimore city. J Allergy Clin Immunol 2013 Oct;132(4):830-5.e1 [FREE Full text] [CrossRef] [Medline]
  112. Pollack CE, Kurd SK, Livshits A, Weiner M, Lynch J. A case-control study of home foreclosure, health conditions, and health care utilization. J Urban Health 2011 Jun;88(3):469-478 [FREE Full text] [CrossRef] [Medline]
  113. Bruden DJ, Singleton R, Hawk CS, Bulkow LR, Bentley S, Anderson LJ, et al. Eighteen years of respiratory Syncytial virus surveillance: changes in seasonality and hospitalization rates in southwestern Alaska native children. Pediatr Infect Dis J 2015 Sep;34(9):945-950. [CrossRef] [Medline]
  114. Omachi TA, Gregorich SE, Eisner MD, Penaloza RA, Tolstykh IV, Yelin EH, et al. Risk adjustment for health care financing in chronic disease: what are we missing by failing to account for disease severity? Med Care 2013 Aug;51(8):740-747 [FREE Full text] [CrossRef] [Medline]
  115. Dhingra L, Dieckmann NF, Knotkova H, Chen J, Riggs A, Breuer B, et al. A high-touch model of community-based specialist palliative care: latent class analysis identifies distinct patient subgroups. J Pain Symptom Manage 2016 Aug;52(2):178-186. [CrossRef] [Medline]
  116. Derakhshan A, Miller J, Lubelski D, Nowacki AS, Wells BJ, Milinovich A, et al. The impact of socioeconomic status on the utilization of spinal imaging. Neurosurgery 2015 Nov;77(5):746-53; discussion 753. [CrossRef] [Medline]
  117. Chan DC, Shrank WH, Cutler D, Jan S, Fischer MA, Liu J, et al. Patient, physician, and payment predictors of statin adherence. Med Care 2010 Mar;48(3):196-202. [CrossRef] [Medline]
  118. Epstein D, Reibel M, Unger JB, Cockburn M, Escobedo LA, Kale DC, et al. The effect of neighborhood and individual characteristics on pediatric critical illness. J Community Health 2014 Aug;39(4):753-759 [FREE Full text] [CrossRef] [Medline]
  119. Roy A, Mehra S, Kelly CP, Tariq S, Pallav K, Dennis M, et al. The association between socioeconomic status and the symptoms at diagnosis of celiac disease: a retrospective cohort study. Therap Adv Gastroenterol 2016 Jul;9(4):495-502 [FREE Full text] [CrossRef] [Medline]
  120. Stahl JE, Drew MA, Kimball AB. Patient-clinician concordance, face-time and access. Int J Health Care Qual Assur 2014;27(8):664-671. [CrossRef] [Medline]
  121. Richardson M, van den Eeden SK, Roberts E, Ferrara A, Paulukonis S, English P. Evaluating the use of electronic health records for type 2 diabetes surveillance in 2 California counties, 2010-2014. Public Health Rep 2017;132(4):463-470 [FREE Full text] [CrossRef] [Medline]
  122. Krivchenia K, Hayes Jr D, Tobias JD, Tumin D. Long-term work participation among cystic fibrosis patients undergoing lung transplantation. J Cyst Fibros 2016 Nov;15(6):846-849 [FREE Full text] [CrossRef] [Medline]
  123. Klobusicky JJ, Aryasomayajula A, Marko N. Evolving patient compliance trends: integrating clinical, insurance, and extrapolated socioeconomic data. AMIA Annu Symp Proc 2015;2015:766-774 [FREE Full text] [Medline]
  124. Johns TS, Estrella MM, Crews DC, Appel LJ, Anderson CA, Ephraim PL, et al. Neighborhood socioeconomic status, race, and mortality in young adult dialysis patients. J Am Soc Nephrol 2014 Nov;25(11):2649-2657 [FREE Full text] [CrossRef] [Medline]
  125. Jhamb M, Cavanaugh KL, Bian A, Chen G, Ikizler TA, Unruh ML, et al. Disparities in electronic health record patient portal use in nephrology clinics. Clin J Am Soc Nephrol 2015 Nov 6;10(11):2013-2022 [FREE Full text] [CrossRef] [Medline]
  126. Holmes L, Vandenberg J, McClarin L, Dabney K. Epidemiologic, racial and healthographic mapping of Delaware pediatric cancer: 2004-2014. Int J Environ Res Public Health 2015 Dec 22;13(1):ijerph13010049 [FREE Full text] [CrossRef] [Medline]
  127. Franco I, Franco J, Harding S, Rosconi D, Cupelli E, Collett-Gardere T. Are seasonal and income variations accountable for bowel and bladder dysfunction symptoms in children? Neurourol Urodyn 2017 Jan;36(1):148-154. [CrossRef] [Medline]
  128. Buu MC, Sanders LM, Mayo JA, Milla CE, Wise PH. Assessing differences in mortality rates and risk factors between Hispanic and non-Hispanic patients with cystic fibrosis in California. Chest 2016 Feb;149(2):380-389 [FREE Full text] [CrossRef] [Medline]
  129. Butler J, Binney Z, Kalogeropoulos A, Owen M, Clevenger C, Gunter D, et al. Advance directives among hospitalized patients with heart failure. JACC Heart Fail 2015 Feb;3(2):112-121 [FREE Full text] [CrossRef] [Medline]
  130. Bourgi K, Brar I, Baker-Genaw K. Health disparities in hepatitis C screening and linkage to care at an integrated health system in southeast Michigan. PLoS One 2016;11(8):e0161241 [FREE Full text] [CrossRef] [Medline]
  131. Aseltine Jr RH, Yan J, Gruss CB, Wagner C, Katz M. Connecticut hospital readmissions related to chest pain and heart failure: differences by race, ethnicity, and payer. Conn Med 2015 Feb;79(2):69-76. [Medline]
  132. Aneja S, Khullar D, Yu JB. The influence of regional health system characteristics on the surgical management and receipt of post operative radiation therapy for glioblastoma multiforme. J Neurooncol 2013 May;112(3):393-401 [FREE Full text] [CrossRef] [Medline]
  133. Feng J, Iser JP, Yang W. Medical encounters for opioid-related intoxications in southern Nevada: sociodemographic and clinical correlates. BMC Health Serv Res 2016 Aug 24;16:438 [FREE Full text] [CrossRef] [Medline]
  134. Eneriz-Wiemer M, Saynina O, Sundaram V, Lee HC, Bhattacharya J, Sanders LM. Parent language: a predictor for neurodevelopmental follow-up care among infants with very low birth weight. Acad Pediatr 2016;16(7):645-652. [CrossRef] [Medline]
  135. Cheng ER, Hawkins SS, Rifas-Shiman SL, Gillman MW, Taveras EM. Association of missing paternal demographics on infant birth certificates with perinatal risk factors for childhood obesity. BMC Public Health 2016 Jul 14;16:453 [FREE Full text] [CrossRef] [Medline]
  136. Freeman K, Strauchler D, Miller TS. Impact of socioeconomic status on ionizing radiation exposure from medical imaging in children. J Am Coll Radiol 2012 Nov;9(11):799-807 [FREE Full text] [CrossRef] [Medline]
  137. Berkowitz SA, Terranova J, Hill C, Ajayi T, Linsky T, Tishler LW, et al. Meal delivery programs reduce the use of costly health care in dually eligible medicare and medicaid beneficiaries. Health Aff (Millwood) 2018 Apr;37(4):535-542 [FREE Full text] [CrossRef] [Medline]
  138. van Vilsteren M, Boot CR, Knol DL, van Schaardenburg D, Voskuyl AE, Steenbeek R, et al. Productivity at work and quality of life in patients with rheumatoid arthritis. BMC Musculoskelet Disord 2015 May 6;16:107 [FREE Full text] [CrossRef] [Medline]
  139. Guerrero WR, Gonzales NR, Sekar P, Kawano-Castillo J, Moomaw CJ, Worrall BB, et al. Variability in the use of platelet transfusion in patients with intracerebral hemorrhage: observations from the ethnic/racial variations of intracerebral hemorrhage study. J Stroke Cerebrovasc Dis 2017 Sep;26(9):1974-1980. [CrossRef] [Medline]
  140. Salow AD, Pool LR, Grobman WA, Kershaw KN. Associations of neighborhood-level racial residential segregation with adverse pregnancy outcomes. Am J Obstet Gynecol 2018 Mar;218(3):351.e1-351.e7. [CrossRef] [Medline]
  141. Bukowski LA, Blosnich J, Shipherd JC, Kauth MR, Brown GR, Gordon AJ. Exploring rural disparities in medical diagnoses among veterans with transgender-related diagnoses utilizing veterans health administration care. Med Care 2017 Sep;55(Suppl 9 Suppl 2):S97-103. [CrossRef] [Medline]
  142. Brown GR, Jones KT. Mental health and medical health disparities in 5135 transgender veterans receiving healthcare in the veterans health administration: a case-control study. LGBT Health 2016 Apr;3(2):122-131. [CrossRef] [Medline]
  143. Cary MP, Baernholdt M, Anderson RA, Merwin EI. Performance-based outcomes of inpatient rehabilitation facilities treating hip fracture patients in the United States. Arch Phys Med Rehabil 2015 May;96(5):790-798 [FREE Full text] [CrossRef] [Medline]
  144. Chien LC, Schootman M, Pruitt SL. The modifying effect of patient location on stage-specific survival following colorectal cancer using geosurvival models. Cancer Causes Control 2013 Mar;24(3):473-484 [FREE Full text] [CrossRef] [Medline]
  145. Blount RJ, Pascopella L, Catanzaro DG, Barry PM, English PB, Segal MR, et al. Traffic-related air pollution and all-cause mortality during tuberculosis treatment in California. Environ Health Perspect 2017 Sep 29;125(9):097026 [FREE Full text] [CrossRef] [Medline]
  146. Sheppard VB, Oppong BA, Hampton R, Snead F, Horton S, Hirpa F, et al. Disparities in breast cancer surgery delay: the lingering effect of race. Ann Surg Oncol 2015 Sep;22(9):2902-2911. [CrossRef] [Medline]
  147. Hall MA, Dudek SM, Goodloe R, Crawford DS, Pendergrass SA, Peissig P, et al. Environment-wide association study (EWAS) for type 2 diabetes in the Marshfield personalized medicine research project biobank. Pac Symp Biocomput 2014:200-211 [FREE Full text] [CrossRef] [Medline]
  148. Toledo P, Eosakul ST, Grobman WA, Feinglass J, Hasnain-Wynia R. Primary spoken language and neuraxial labor analgesia use among Hispanic medicaid recipients. Anesth Analg 2016 Jan;122(1):204-209. [CrossRef] [Medline]
  149. Seligman HK, Bolger AF, Guzman D, López A, Bibbins-Domingo K. Exhaustion of food budgets at month's end and hospital admissions for hypoglycemia. Health Aff (Millwood) 2014 Jan;33(1):116-123 [FREE Full text] [CrossRef] [Medline]
  150. Grimberg A, Feemster KA, Pati S, Ramos M, Grundmeier R, Cucchiara AJ, et al. Medically underserved girls receive less evaluation for short stature. Pediatrics 2011 Apr;127(4):696-702 [FREE Full text] [CrossRef] [Medline]
  151. Ye C, Fu T, Hao S, Zhang Y, Wang O, Jin B, et al. Prediction of incident hypertension within the next year: prospective study using statewide electronic health records and machine learning. J Med Internet Res 2018 Jan 30;20(1):e22 [FREE Full text] [CrossRef] [Medline]
  152. Kanzaria HK, Niedzwiecki MJ, Montoy JC, Raven MC, Hsia RY. Persistent frequent emergency department use: core group exhibits extreme levels of use for more than a decade. Health Aff (Millwood) 2017 Oct 1;36(10):1720-1728. [CrossRef] [Medline]
  153. Patzer RE, Perryman JP, Schrager JD, Pastan S, Amaral S, Gazmararian JA, et al. The role of race and poverty on steps to kidney transplantation in the southeastern United States. Am J Transplant 2012 Feb;12(2):358-368 [FREE Full text] [CrossRef] [Medline]
  154. Wallace ME, Mendola P, Chen Z, Hwang BS, Grantz KL. Preterm birth in the context of increasing income inequality. Matern Child Health J 2016 Jan;20(1):164-171 [FREE Full text] [CrossRef] [Medline]
  155. Shuman AG, Entezami P, Chernin AS, Wallace NE, Taylor JM, Hogikyan ND. Demographics and efficacy of head and neck cancer screening. Otolaryngol Head Neck Surg 2010 Sep;143(3):353-360 [FREE Full text] [CrossRef] [Medline]
  156. Tanenbaum J, Cebul RD, Votruba M, Einstadter D. Association of a regional health improvement collaborative with ambulatory care-sensitive hospitalizations. Health Aff (Millwood) 2018 Feb;37(2):266-274. [CrossRef] [Medline]
  157. Castañeda SF, Rosenbaum RP, Holscher JT, Madanat H, Talavera GA. Cardiovascular disease risk factors among Latino migrant and seasonal farmworkers. J Agromedicine 2015;20(2):95-104 [FREE Full text] [CrossRef] [Medline]
  158. Eapen ZJ, McCoy LA, Fonarow GC, Yancy CW, Miranda ML, Peterson ED, et al. Utility of socioeconomic status in predicting 30-day outcomes after heart failure hospitalization. Circ Heart Fail 2015 May;8(3):473-480 [FREE Full text] [CrossRef] [Medline]
  159. DeMaria AL, Lugo JM, Rahman M, Pyles RB, Berenson A. Association between body mass index, sexually transmitted infections, and contraceptive compliance. J Womens Health (Larchmt) 2013 Dec;22(12):1062-1068 [FREE Full text] [CrossRef] [Medline]
  160. Gebauer S, Salas J, Scherrer JF. Neighborhood socioeconomic status and receipt of opioid medication for new back pain diagnosis. J Am Board Fam Med 2017;30(6):775-783 [FREE Full text] [CrossRef] [Medline]
  161. Zellner BS, Dawson JR, Reichel LM, Schaefer K, Britt J, Hillin C, et al. Prospective nutritional analysis of a diverse trauma population demonstrates substantial hypovitaminosis D. J Orthop Trauma 2014 Sep;28(9):e210-e215. [CrossRef] [Medline]
  162. Xiao H, Tan F, Goovaerts P, Ali A, Adunlin G, Gwede CK, et al. Multilevel factors associated with overall mortality for men diagnosed with prostate cancer in Florida. Am J Mens Health 2014 Jul;8(4):316-326 [FREE Full text] [CrossRef] [Medline]
  163. Morris EJ, Jordan C, Thomas JD, Cooper M, Brown JM, Thorpe H, CLASICC Trialists. Comparison of treatment and outcome information between a clinical trial and the national cancer data repository. Br J Surg 2011 Feb;98(2):299-307. [CrossRef] [Medline]
  164. Kinsky S, Stall R, Hawk M, Markovic N. Risk of the metabolic syndrome in sexual minority women: results from the ESTHER study. J Womens Health (Larchmt) 2016 Aug;25(8):784-790 [FREE Full text] [CrossRef] [Medline]
  165. Fletcher FE, Vidrine DJ, Tami-Maury I, Danysh HE, King RM, Buchberg M, et al. Cervical cancer screening adherence among HIV-positive female smokers from a comprehensive HIV clinic. AIDS Behav 2014 Mar;18(3):544-554 [FREE Full text] [CrossRef] [Medline]
  166. Cantonwine DE, Ferguson KK, Mukherjee B, Chen Y, Smith NA, Robinson JN, et al. Utilizing longitudinal measures of fetal growth to create a standard method to assess the impacts of maternal disease and environmental exposure. PLoS One 2016;11(1):e0146532 [FREE Full text] [CrossRef] [Medline]
  167. Blashill AJ, O'Cleirigh C, Mayer KH, Goshe BM, Safren SA. Body mass index, depression and sexual transmission risk behaviors among HIV-positive MSM. AIDS Behav 2012 Nov;16(8):2251-2256 [FREE Full text] [CrossRef] [Medline]
  168. Henninger ML, Irving SA, Kauffman TL, Kurosky SK, Rompala K, Thompson MG, Pregnancy and Influenza Project Workgroup. Predictors of breastfeeding initiation and maintenance in an integrated healthcare setting. J Hum Lact 2017 May;33(2):256-266. [CrossRef] [Medline]
  169. Smalls BL, Gregory CM, Zoller JS, Egede LE. Assessing the relationship between neighborhood factors and diabetes related health outcomes and self-care behaviors. BMC Health Serv Res 2015 Oct 1;15:445 [FREE Full text] [CrossRef] [Medline]
  170. Wu JR, Lennie TA, Moser DK. A prospective, observational study to explore health disparities in patients with heart failure-ethnicity and financial status. Eur J Cardiovasc Nurs 2017 Jan;16(1):70-78. [CrossRef] [Medline]
  171. Bellin MH, Dicianno BE, Levey E, Dosa N, Roux G, Marben K, et al. Interrelationships of sex, level of lesion, and transition outcomes among young adults with myelomeningocele. Dev Med Child Neurol 2011 Jul;53(7):647-652 [FREE Full text] [CrossRef] [Medline]
  172. Kwan ML, Ergas IJ, Somkin CP, Quesenberry Jr CJ, Neugut AI, Hershman DL, et al. Quality of life among women recently diagnosed with invasive breast cancer: the pathways study. Breast Cancer Res Treat 2010 Sep;123(2):507-524 [FREE Full text] [CrossRef] [Medline]
  173. Chambers EC, Wong BC, Riley RW, Hollingsworth N, Blank AE, Myers C, et al. Combining clinical and population-level data to understand the health of neighborhoods. Am J Public Health 2015 Mar;105(3):510-512 [FREE Full text] [CrossRef] [Medline]
  174. Galler A, Lindau M, Ernert A, Thalemann R, Raile K. Associations between media consumption habits, physical activity, socioeconomic status, and glycemic control in children, adolescents, and young adults with type 1 diabetes. Diabetes Care 2011 Nov;34(11):2356-2359 [FREE Full text] [CrossRef] [Medline]
  175. Schwartz BS, Stewart WF, Godby S, Pollak J, Dewalle J, Larson S, et al. Body mass index and the built and social environments in children and adolescents using electronic health records. Am J Prev Med 2011 Oct;41(4):e17-e28. [CrossRef] [Medline]
  176. Di Q, Wang Y, Zanobetti A, Wang Y, Koutrakis P, Choirat C, et al. Air pollution and mortality in the medicare population. N Engl J Med 2017 Jun 29;376(26):2513-2522 [FREE Full text] [CrossRef] [Medline]
  177. Sheffield P, Roy A, Wong K, Trasande L. Fine particulate matter pollution linked to respiratory illness in infants and increased hospital costs. Health Aff (Millwood) 2011 May;30(5):871-878 [FREE Full text] [CrossRef] [Medline]
  178. Kranjac AW, Kimbro RT, Denney JT, Osiecki KM, Moffett BS, Lopez KN. Comprehensive neighborhood portraits and child asthma disparities. Matern Child Health J 2017 Jul;21(7):1552-1562. [CrossRef] [Medline]
  179. Boland MR, Parhi P, Li L, Miotto R, Carroll R, Iqbal U, et al. Uncovering exposures responsible for birth season - disease effects: a global study. J Am Med Inform Assoc 2017 Sep 28 (epub ahead of print). [CrossRef] [Medline]
  180. Wang VJ, Cavagnaro CS, Clark S, Camargo Jr CA, Mansbach JM. Altitude and environmental climate effects on bronchiolitis severity among children presenting to the emergency department. J Environ Health 2012 Oct;75(3):8-15; quiz 54. [Medline]
  181. Saligan LN, Levy-Clarke G, Wu T, Faia LJ, Wroblewski K, Yeh S, et al. Quality of life in sarcoidosis: comparing the impact of ocular and non-ocular involvement of the disease. Ophthalmic Epidemiol 2010 Aug;17(4):217-224 [FREE Full text] [CrossRef] [Medline]
  182. Davis MA, Higgins J, Li Z, Gilbert-Diamond D, Baker ER, Das A, et al. Preliminary analysis of in utero low-level arsenic exposure and fetal growth using biometric measurements extracted from fetal ultrasound reports. Environ Health 2015 Mar 30;14:12 [FREE Full text] [CrossRef] [Medline]
  183. Al Achkar M, Grannis S, Revere D, MacKie P, Howard M, Gupta S. The effects of state rules on opioid prescribing in Indiana. BMC Health Serv Res 2018 Jan 18;18(1):29 [FREE Full text] [CrossRef] [Medline]
  184. Blosnich JR, Marsiglio MC, Gao S, Gordon AJ, Shipherd JC, Kauth M, et al. Mental health of transgender veterans in US states with and without discrimination and hate crime legal protection. Am J Public Health 2016 Mar;106(3):534-540. [CrossRef] [Medline]
  185. Fine AM, Reis BY, Nigrovic LE, Goldmann DA, Laporte TN, Olson KL, et al. Use of population health data to refine diagnostic decision-making for pertussis. J Am Med Inform Assoc 2010;17(1):85-90 [FREE Full text] [CrossRef] [Medline]
  186. Leukhardt WH, Golob JF, McCoy AM, Fadlalla AM, Malangoni MA, Claridge JA. Follow-up disparities after trauma: a real problem for outcomes research. Am J Surg 2010 Mar;199(3):348-52; discussion 353. [CrossRef] [Medline]
  187. Beck AF, Sandel MT, Ryan PH, Kahn RS. Mapping neighborhood health geomarkers to clinical care decisions to promote equity in child health. Health Aff (Millwood) 2017 Jun 1;36(6):999-1005 [FREE Full text] [CrossRef] [Medline]
  188. Roth C, Foraker RE, Payne PR, Embi PJ. Community-level determinants of obesity: harnessing the power of electronic health records for retrospective data analysis. BMC Med Inform Decis Mak 2014 May 8;14:36 [FREE Full text] [CrossRef] [Medline]
  189. Newman KL, Lynch RJ, Adams AB, Zhang R, Pastan SO, Patzer RE. Hospitalization among individuals waitlisted for kidney transplant. Transplantation 2017 Dec;101(12):2913-2923 [FREE Full text] [CrossRef] [Medline]
  190. Evon DM, Simpson KM, Esserman D, Verma A, Smith S, Fried MW. Barriers to accessing care in patients with chronic hepatitis C: the impact of depression. Aliment Pharmacol Ther 2010 Nov;32(9):1163-1173 [FREE Full text] [CrossRef] [Medline]
  191. Pendleton C, Cristofalo EA, Biondo GN, Jallo GI, Quiñones-Hinojosa A, Ahn ES. Posthemorrhagic hydrocephalus in preterm neonates: socioeconomic characteristics in a single-institution experience. Pediatr Neurosurg 2012;48(2):80-85. [CrossRef] [Medline]
  192. Zook HG, Kharbanda AB, Flood A, Harmon B, Puumala SE, Payne NR. Racial differences in pediatric emergency department triage scores. J Emerg Med 2016 May;50(5):720-727 [FREE Full text] [CrossRef] [Medline]
  193. SooHoo NF, Farng E, Zingmond DS. Disparities in the utilization of high-volume hospitals for total hip replacement. J Natl Med Assoc 2011 Jan;103(1):31-35. [CrossRef] [Medline]
  194. Huntington SF, Weiss BM, Vogl DT, Cohen AD, Garfall AL, Mangan PA, et al. Financial toxicity in insured patients with multiple myeloma: a cross-sectional pilot study. Lancet Haematol 2015 Oct;2(10):e408-e416. [CrossRef] [Medline]
  195. Dupre ME, Nelson A, Lynch SM, Granger BB, Xu H, Churchill E, et al. Socioeconomic, psychosocial and behavioral characteristics of patients hospitalized with cardiovascular disease. Am J Med Sci 2017 Dec;354(6):565-572 [FREE Full text] [CrossRef] [Medline]
  196. Newgard CD, Schmicker RH, Sopko G, Andrusiek D, Bialkowski W, Minei JP, Resuscitation Outcomes Consortium Investigators. Trauma in the neighborhood: a geospatial analysis and assessment of social determinants of major injury in North America. Am J Public Health 2011 Apr;101(4):669-677. [CrossRef] [Medline]
  197. Valentine SE, Elsesser S, Grasso C, Safren SA, Bradford JB, Mereish E, et al. The predictive syndemic effect of multiple psychosocial problems on health care costs and utilization among sexual minority women. J Urban Health 2015 Dec;92(6):1092-1104 [FREE Full text] [CrossRef] [Medline]
  198. Schuler TA, Zaider TI, Li Y, Hichenberg S, Masterson M, Kissane DW. Typology of perceived family functioning in an American sample of patients with advanced cancer. J Pain Symptom Manage 2014 Aug;48(2):281-288 [FREE Full text] [CrossRef] [Medline]
  199. Parker MM, Moffet HH, Schillinger D, Adler N, Fernandez A, Ciechanowski P, et al. Ethnic differences in appointment-keeping and implications for the patient-centered medical home--findings from the Diabetes Study of Northern California (DISTANCE). Health Serv Res 2012 Apr;47(2):572-593 [FREE Full text] [CrossRef] [Medline]
  200. Wallace ME, Mendola P, Liu D, Grantz KL. Joint effects of structural racism and income inequality on small-for-gestational-age birth. Am J Public Health 2015 Aug;105(8):1681-1688. [CrossRef] [Medline]
  201. Chetty R, Stepner M, Abraham S, Lin S, Scuderi B, Turner N, et al. The association between income and life expectancy in the United States, 2001-2014. J Am Med Assoc 2016 Apr 26;315(16):1750-1766 [FREE Full text] [CrossRef] [Medline]
  202. Shavers VL. Measurement of socioeconomic status in health disparities research. J Natl Med Assoc 2007 Sep;99(9):1013-1023. [Medline]
  203. Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, et al. Socioeconomic status in health research: one size does not fit all. J Am Med Assoc 2005 Dec 14;294(22):2879-2888. [CrossRef] [Medline]
  204. Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records, Board on Population Health and Public Health Practice, Institute of Medicine. Capturing Social and Behavioral Domains in Electronic Health Records: Phase 1. Washington, DC: National Academies Press; 2014.
  205. Boudreaux MH, Call KT, Turner J, Fried B, O'Hara B. Measurement error in public health insurance reporting in the American community survey: evidence from record linkage. Health Serv Res 2015 Dec;50(6):1973-1995 [FREE Full text] [CrossRef] [Medline]
  206. Macdonald H. The American community survey: warmer (more current), but fuzzier (less precise) than the decennial census. J Am Plan Assoc 2008 Mar 5;72(4):491-503. [CrossRef]
  207. Center for Medicare & Medicaid Innovation - CMS. 2019. The Accountable Health Communities Health-Related Social Needs Screening Tool   URL: [accessed 2019-08-13]
  208. National Association of Community Health Centers. 2019. About the PRAPARE Assessment Tool   URL: [accessed 2019-08-13]
  209. HealthIT. 2019. Vocabulary/Code Set/Terminology Standards and Implementation Specifications   URL: https:/​/www.​​isa/​section-i-vocabularycode-setterminology-standards-and-implementation-specifications [accessed 2018-05-01]
  210. Behforouz HL, Drain PK, Rhatigan JJ. Rethinking the social history. N Engl J Med 2014 Oct 2;371(14):1277-1279. [CrossRef] [Medline]
  211. Marra CA, Lynd LD, Harvard SS, Grubisic M. Agreement between aggregate and individual-level measures of income and education: a comparison across three patient groups. BMC Health Serv Res 2011 Mar 31;11:69 [FREE Full text] [CrossRef] [Medline]
  212. Soobader MJ, LeClere FB, Hadden W, Maury B. Using aggregate geographic data to proxy individual socioeconomic status: does size matter? Am J Public Health 2001 Apr;91(4):632-636. [CrossRef] [Medline]
  213. Geronimus AT, Bound J. Use of census-based aggregate variables to proxy for socioeconomic group: evidence from national samples. Am J Epidemiol 1998 Sep 1;148(5):475-486. [CrossRef] [Medline]
  214. Rajaratnam JK, Burke JG, O'Campo P. Maternal and child health and neighborhood context: the selection and construction of area-level variables. Health Place 2006 Dec;12(4):547-556. [CrossRef] [Medline]
  215. O'Campo P, O'Brien CM. Measures of residential community contexts. In: Oakes JM, Kaufman JS, editors. Methods in Social Epidemiology. San Francisco, CA: Jossey-Bass; 2017.
  216. Schuch L, Curtis A, Davidson J. Reducing lead exposure risk to vulnerable populations: a proactive geographic solution. Ann Am Assoc Geogr 2017 Feb 13;107(3):606-624. [CrossRef]
  217. Marmot M. The influence of income on health: views of an epidemiologist. Health Aff (Millwood) 2002;21(2):31-46. [CrossRef] [Medline]
  218. Cauthen NF, Fass S. National Center for Clinical in Poverty. 2008. Measuring Poverty in the United States   URL: [accessed 2019-08-13]
  219. Betson DW, Warlick JL. Measuring poverty. In: Oakes JM, Kaufman JS, editors. Methods in Social Epidemiology. Second Edition. San Francisco: Jossey-Bass; 2017.

EHR: electronic health record
SDoH: social determinants of health
SES: socioeconomic status

Edited by G Eysenbach; submitted 16.11.18; peer-reviewed by C Weijs, B Xie, E Weitzman, R Pankomera; comments to author 31.03.19; revised version received 23.05.19; accepted 19.07.19; published 07.10.19


©Elizabeth Golembiewski, Katie S Allen, Amber M Blackmon, Rachel J Hinrichs, Joshua R Vest. Originally published in JMIR Public Health and Surveillance (, 07.10.2019

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