This appendix includes information about the domains that the committee reviewed but did not select to be among the candidate set of domains to be included in all electronic health records (EHRs) as part of its Phase 1 task. What follows provides domain descriptions, including examples of each one’s association with health, along with illustrations of useful interventions for individuals or the population and priorities for research. The committee reviewed the evidence for these domains and found that the evidence of the association with health and usefulness was less compelling than that for the candidate domains included in Chapter 3. Table A-1 shows all the domains reviewed by the committee and how the committee voted to rank them. Three stars in the first column indicate a strong relationship between that domain and health, two stars represent a moderate association, and one star indicates weak or insufficient evidence to indicate a relationship. The subsequent columns summarize the usefulness of having information on a given domain in relation to treating individual patients, in relation to managing the health of a population, and for research purposes. Three stars stand for the committee’s judgment that a measure of the domain would be highly useful for a given focus, two stars that it would be moderately helpful, and one star that it would have unproven or minimal value. All of the domains colored in blue became part of the committee’s candidate set of domains for which measures were identified in Chapter 4.
The committee’s work leading to its decision to exclude the indicated domains is described here to help guide future efforts that consider the addition of social and behavioral determinants of health domains in EHRs. The domains are not listed in order of priority but, instead, are organized
TABLE A-1 Applying Committee Criteria to Domains: Strength of Evidence and Usefulness
|Domain||Strength of Evidence of Association with Health||Usefulness|
Country of origin/U.S. born or non-U.S. born
Financial resource strain: Food and housing insecurity
Negative mood and affect: Hostility and anger
Depression and anxiety
Psychological assets: Conscientiousness, patient empowerment/activation, optimism, self-efficacy
Coping, positive affect, life satisfaction
Cognitive function in late life
Abuse of other substances
Tobacco use and exposure
Exposure to firearms
Risk-taking behaviors: Distractive driving and helmet use
|Individual-Level Social Relationships and Living Conditions|
|Social engagement and isolation|
Social connections and social isolation
Social support: Emotional, instrumental, and otherwise
Exposure to violence
History of incarceration
Community and cultural norms: Health decision making
|Domain||Strength of Evidence of Association with Health||Usefulness|
|Neighborhoods and Communities|
Environmental pollutants and other hazards
Availability of nutritious food options
Transportation, parks, and open spaces
Health care and social services
Educational and job opportunities
NOTE: The shaded and bolded text reflects the domains considered to be candidate domains for consideratin to add to all EHRs.
*** = strong relationship or highly useful, ** = moderate association/utlity, * = weak or insufficient relationship/value.
by the committee’s outline in Chapter 2, which ordered domains according to the types of data they represented. Of note, after the committee’s review, the Office of the National Coordinator for Health Information Technology (ONC) requested comments on whether its certification program should require EHRs to be capable of collecting several of the domains that the committee considered (HHS, 2014).
The following domains were considered and are included in the list below in the general context of the domains identified in Table 2-1:
Negative mood and affect: Hostility and anger, hopelessness
Cognitive function in late life
Positive psychological asset: Coping, positive affect, life satisfaction
Abuse of other substances
Exposure to firearms
Risk-taking behaviors: Distractive driving and helmet use
Individual-Level Social Relationships and Living Conditions Domains
Social support: Emotional, instrumental, and other
History of incarceration
Community and cultural norms: Health decision making
Neighborhoods and Communities Contextual Characteristics
Environmental exposures: Air pollution, allergens, other hazardous exposures
Availability of nutritious food options
Transportation, parks, and open spaces
Health care and social services
Educational and job opportunities
Gender identity is a person’s subjective sense of his or her gender, which may or may not be the same as that person’s gender at birth. Shortly after birth, gender is determined on the basis of external genitalia or genetic
tests. However, individuals may not feel that they are truly the gender to which they were assigned or determined to be at birth; this is referred to as “gender dysphoria” (APA, 2013). For example, persons born male may believe themselves to be female, persons born female may feel themselves to be male, and persons may feel that they are neither male nor female. The last group may refer to themselves as being intersex or hermaphrodites.
People who feel themselves to be a gender different from their assigned sex may choose to alter their physical appearance to fit the gender they feel themselves to be. Such individuals may make physical alterations that range from outwardly dressing as the gender they believe themselves to be to taking hormonal treatments and undergoing surgical procedures to physically change their appearance (currently known as “sexual reassignment”). Individuals who are in transition from one gender to another may refer to themselves as “transsexual” or “transgender” (i.e., transgender male to female or transgender female to male).
Evidence of Association with Health
Persons who feel themselves to be a gender different from their genetic sex may experience psychological distress beginning in childhood until such time that they are able to transform their life to fit their self-perceived gender (de Vries et al., 2011). Those who are transitioning from their genetic sex to their perceived gender may seek out health care professionals to receive hormone treatment or sexual reassignment. Persons who do not feel that they can access medical care because of fear of the response to their request for sexual reassignment, discrimination, or a loss of privacy or because of financial strain may use unsafe methods to change their physical appearance. For example, individuals may illegally obtain hormone treatments without proper medical advice or follow up. Hormone treatments have known side effects (e.g., estrogen increases the risk of thromboembolism), and therefore it is important that persons taking hormone treatment be under the care of a physician. Certain transgender persons—specifically, male-to-female transgender individuals—have been shown to be at particularly high risk of HIV infection due to their high-risk sexual behaviors and injection drug use (Clements-Noelle et al., 2001). Data are lacking, however, about whether gender identity is a risk factor for other disorders such that diagnosis or treatment would be informed by knowledge of gender identity.
Understanding the health needs of this population could enable health care providers to provide more culturally appropriate health care and coun-
seling and help patients with nonconforming genders from feeling alienated. Additionally, knowledge of whether a patient is taking hormone treatments allows health care providers to identify adverse side effects and may prevent adverse interactions with other medications. This requires the ability to identify transgender individuals. They are considered one of the least understood populations because of the lack of research on this population, and the committee could find no standard ways to assess transgender status.
The 2011 Institute of Medicine (IOM) Committee on Lesbian, Gay, Bisexual, and Transgender Health Issues and Research Gaps and Opportunities noted the dearth of research on transgender individuals (IOM, 2011). Conducting research on transgender health is difficult in part because it is a statistically rare event and unlikely to be adequately represented in general samples. It is estimated that 0.3 percent of the U.S. adult population are transgender (Gates, 2011). The 2011 IOM report thus recommended inclusion of questions on gender identity as well as sexual orientation in EHRs to facilitate research. They observed that the sensitivity of the issue and the “lack of knowledge by providers of how to elicit this information” are barriers to standard collection in population surveys. That committee, as well as speakers at a related IOM workshop, noted the need to develop valid, reliable measures (IOM, 2011, 2013a).
In sum, although the evidence of the association of gender identity with psychological distress and risk behaviors exists, it is relevant to a very small number of people, reducing its impact on population health. In addition, the same problems of sensitivity and lack of standard assessment that have limited research efforts pose barriers to use in EHRs. The committee realized that this poses a “chicken and egg” dilemma; better research is needed to develop feasible validated measures of gender identity to meet the criteria for inclusion in all EHRs, while inclusion in EHRs could help provide the evidence base for developing such a measure. The committee concluded that including gender identity in every individual’s EHR at this time would not result in sufficient improvements in overall clinical care or population health to justify its inclusion. Instead, the committee concluded that more work needs to be done on the assessment tools. In the meantime, health systems with large numbers of persons who are transgender should include the best available questions to determine gender identity, and clinical care teams and health settings need to be sensitive to gender identity in their interactions with patients.
Hostility and Anger
Anger is considered to be an emotional state that consists of feelings ranging from mild annoyance to extremes of rage (Chida and Steptoe, 2009). In contrast, hostility is a more enduring attitude of mistrust of others that is quite stable across long periods of time, from young adulthood into old age. Aggression is defined as behaviors attempting to inflict verbal or physical harm on others; it is not necessarily accompanied by anger. While anger varies across time and situations, measures of anger frequency are relatively stable. Anger expression is a characteristic style of expressing anger, usually categorized into the outward expression of anger versus the inhibition of anger.
Evidence for Association with Health
Anger and hostility have been studied in the context of the risk for hypertension and coronary heart disease. In a meta-analysis of 25 studies of initially healthy populations and 19 studies of coronary heart disease patients, anger and hostility measures predicted future coronary heart disease in both types of populations (Chida and Steptoe, 2009). There was some suggestion that the effects were stronger in men than in women (see also the work of Low et al., 2010). In subgroup analyses, anger expression styles were not related to heart disease; however, individuals who score high for anger and hostility typically have risky health behaviors, including physical inactivity, cigarette smoking, and obesity.
Knowledge of a patient’s level of hostility and anger could potentially identify patients, especially men, who are at high risk for cardiovascular disease. However, the evidence of usefulness to the individual and population was considered to be insufficient by the committee. This domain was ranked as useful for research purposes.
Anger and hostility predict future heart disease and are associated with risky health behaviors. There is some suggestion that the associations are stronger in men than in women. The committee considered whether it should be measured in men only, but it preferred EHR assessment that is
designed to be universal. Because there are limited evidence-based interventions available to the clinical team if a patient scores high on anger or hostility, the committee elected not to include it as one of its candidate domains.
Hopelessness overlaps conceptually with extremes of pessimism and accompanies severe depression. It refers to an affective-cognitive state in which a person expects bad outcomes in the future, believing that there are few alternatives to make things better, and has a tendency to give up.
Evidence of Association with Health
Several epidemiological analyses have shown that hopelessness is related to mortality from cardiovascular disease and cancer and the incidence of myocardial infarction in men, independent of depression and numerous covariates (Everson et al., 1996). Furthermore, a feeling of hopelessness can be a marker for suicide risk.
It is not clear to the committee that measures of hopelessness are useful for inclusion in the EHR, independent of depression, pessimism, optimism, and coping. The usefulness of inclusion of measures of hopelessness may be in further screening of patients who are depressed, pessimistic, and avoid-ant copers.
Hopelessness overlaps conceptually with extremes of pessimism and is a symptom of depression. There are a number of epidemiological studies showing that hopelessness is associated with cardiovascular and cancer mortality and myocardial infarction in men. Given the conceptual overlap of hopelessness with other negative emotions and attitudes, including epidemiological data on the associations of optimism/pessimism with depression, and stronger and more complete data on the relationship of other variables such as optimism/pessimism and depression with health outcomes, the committee elected not to include hopelessness as one of its candidate domains.
As life expectancy is lengthening, cognitive function in late life is increasingly recognized as a factor that has an important effect on health and health care utilization. Age-related impaired cognitive function is increasingly prevalent as people advance into late life. Conditions like Alzheimer’s disease and other dementias are extremely rare before age 65 years; 5.0 percent of individuals between the ages of 71 and 79 years; 24.2 percent of individuals between the ages of 80 and 89 years; and 37.4 percent of individuals aged 90 years and older have dementia (Plassman et al., 2007).
Evidence of Association with Health
Alzheimer’s disease and related disorders are related to mortality risk, and the effects of such diseases on mortality are greater in younger age groups. In addition, lower levels of cognitive function and the presence of dementia present difficulties for individuals adhering to therapies, reporting symptoms reliably, and seeking appropriate care. Cognitively impaired individuals are more likely to become lost to follow up and to become socially isolated.
At present, there are limited interventions or treatments to correct or effectively treat the cognitive impairment in late life.
Cognitively impaired individuals typically present to health care providers with unrelated problems. It may be useful to their providers in making diagnoses or recommending treatment to take into account possible problems in cognitive functioning. The availability of accurate data about cognitively impaired individuals is also useful from a public health perspective.
Measures of cognitive function are most relevant for older adults and would not be useful to include in all EHRs. Because of this, it does not fully meet the individual patient health management criteria. The committee also notes that the U.S. Preventive Services Task Force (USPSTF) does not recommend screening at this time even in the older population. In addition, the results of brief cognitive screening tests can be affected by education levels, ethnicity, and language—among other personal characteristics—and be misconstrued. Thus, the committee elected not to include cognitive function in late life as one of its candidate domains.
Coping is defined as the processes that people use to manage the demands created by stressful circumstances. These processes are typically aimed initially at changing the stressful circumstances in some way (i.e., problem-focused coping), and, if that is not possible or successful, at managing the emotional sequelae of the stressor (i.e., emotion-focused coping). Anticipatory coping occurs when a person preemptively plans how to handle potentially stressful circumstances, as opposed to coping after the occurrence of the stressor. Coping processes are thought to be specific to the stressful circumstances but may also be generalized across situations. The latter perspective considers coping to be a psychological trait. Scales are available to measure multiple types of coping for both specific situations and in general across situations. These are usually organized into highly specific types of coping (e.g., reappraisal, seeking support) and are then summarized into higher-level concepts.
Evidence of Association with Health
The form of coping used in a given situation is associated with changes in health and adjustment to serious illness. As summarized by Taylor and Stanton (2007), use of problem-focused approach coping methods is associated with better health and positive adjustment, provided that the stressor is potentially controllable, while avoidance coping, especially in relation to long-term stressors, may increase distress and poor adjustment. Avoidance coping may preempt the use of more effective coping methods, can involve risky behaviors, and may foster intrusive thoughts or rumination. The data are more consistent for the negative impact of avoidance coping than for the positive impact of approach coping.
Coping resources and processes affect mental and physical health. Research directed at improving coping processes has not seen adequate translation into strategies for psychosocial intervention. There does appear to be some suggestive evidence that coping resources can be altered with psychosocial intervention. Among individuals undergoing stressful circumstances, interventions that address particular skills and coping deficits may hold more promise than attempts to directly change a person’s disposition. Future research will be guided by increasing understanding of the environ-
mental and genetic inputs to developing coping skills (Taylor and Stanton, 2007).
Coping skills—in particular, use of avoidance coping—are associated with adjustment to illness. However, the health impact of coping skills varies with the nature and duration of the stressor, which makes assessment of coping more complex. Empirical validation of interventions to foster better coping is needed. Therefore, along with the concern about complexity of measuring coping and questions about usefulness given current data, the committee elected not to include coping as a candidate domain.
Positive affect is defined as the extent to which an individual experiences pleasurable feelings, including joy, happiness, and cheerfulness. It can be measured as a general or an immediate affective state. Common measures are obtained through the use of a questionnaire with a list of adjectives. Respondents are asked the extent to which they feel—usually, or at the present time—the affect identified by those adjectives.
Evidence of Association with Health
Chida and Steptoe (2008) conducted a meta-analysis of positive psychological well-being and survival through the use of subgroup analyses specifically examining the role of positive affect. While studies of healthy participants found that positive affect protected individuals from early mortality, studies of patients with serious illnesses did not find a beneficial effect. Similarly, a review conducted by Pressman and Cohen (2005) concluded that positive affect was generally related to a lower risk of mortality and morbidity; the association was less clear among those with severe illness, a conclusion similar to that reached by Chida and Steptoe (2008).
Positive affect appears to be less useful in a clinical context than a measure of negative affect such as depression. Positive affect may be more helpful to assess in response to treatment, but this has not been tested. Measures of happiness and positive feelings are more useful for research purposes.
Positive affect appears to be related to lower risk of mortality and morbidity among those who are initially healthy, but not necessarily among patients with serious illness. Given that this domain is a relatively new focus of investigation and the inverse association of positive affect with negative emotions, the committee believes that evaluating depression is more useful for individual patient health management than assessing an individual’s positive affect. At a later date, there may be additional evidence that monitoring positive affect may be helpful to chart changes in response to treatment. Therefore the committee elected not to include positive affect as a candidate domain.
Life satisfaction refers to the extent to which individuals judge their overall quality of life to be satisfactory. Items indicating life satisfaction can be framed temporally, that is, the quality of life in the past and in the present and the quality of life anticipated in the future. The most common measure of life satisfaction uses a five-item scale of quality of life in the past and in the present, but a more recent version also includes items oriented toward the future.
Evidence of Association with Health
Several prospective studies have reported an association between life satisfaction and reduced heart and cardiovascular disease (Boehm et al., 2011; Shirai et al., 2009) and life satisfaction with mortality in men but not in women (Lacruz et al., 2011). Life satisfaction may predict subsequent major depression and poor mental health (Rissanen et al., 2011). Most of the available literature reviews on positive psychological function combine life satisfaction indicators with other measures of well-being (Lyubomirsky et al., 2005). Thus, it is difficult to isolate the impact of life satisfaction.
The measure of life satisfaction was seen by the committee as moderately useful for research purposes. The domain was seen as less helpful for the individual and the population at this time.
There is evidence indicating that there is an association between life satisfaction and health, but the committee viewed this to be only a moderate association. Most of the evidence combines life satisfaction’s measures with others measures of well-being. Thus, it is difficult to isolate the impact of life satisfaction. This domain predominantly was seen as useful to research criteria. For these reasons, the committee elected not to select life satisfaction as a candidate domain.
Substance abuse includes the abuse of illegal drugs as well as the misuse of household substances and legal substances (e.g., prescription drugs, aerosols, and glue) (HHS, 2013). Substance abuse occurs in individuals over the range of the life span from adolescence to adulthood, and the substances most frequently abused include marijuana, hallucinogens, cocaine, opiates, amphetamines, inhalants, and methamphetamines (NIDA, 2011). When taken as directed and in moderation, prescription drugs are safe and can help manage mental, biological, and physical symptoms. However, regularly taking medication in a way that differs from what a doctor prescribed is referred to as prescription drug abuse. This can happen in several ways, including a patient taking a medication that has not been prescribed for her or him, taking too large of a dose, or taking a medication with the intention of getting high (NIH, 2014).
Evidence of Association with Health
In 2010, there were nearly 40,000 deaths from drug overdose, a majority of which were unintentional (CDC, 2013). Examples of the association of abuse of other substances with health are listed below: pregnant women who use drugs have been found to be more likely to receive little to no prenatal care during pregnancy (Roberts and Pies, 2011); cocaine, methamphetamine, or heroin users who used the substance over the course of their lifetime were found to have higher systolic and diastolic blood pressure (Akkina et al., 2012); driving under the influence of marijuana was found to be associated with a significant increase in fatal motor vehicle accidents (Asbridge et al., 2012); perpetrators of interpersonal violence were found to be significantly more likely to use methamphetamine, alcohol, and cocaine than the victims were (Ernst et al., 2008); and after alcohol and marijuana, prescription and over-the-counter drugs have been found to be the most commonly abused substances in adolescents ages 14 years and older (NIDA, 2012).
In 2008, the USPSTF stated that evidence of the benefits of screening individuals in late childhood and early adolescence about illicit drug use in a clinical setting is insufficient (USPSTF, 2008). However, the comorbidity of drug use/dependence and psychiatric conditions suggests integrated treatment by behavioral health care specialists (Havens et al., 2005). The committee rated usefulness for individual and population health to be moderate and for research to be low, given the difficulty of accurately collecting the information.
There is strong evidence of an association between substance abuse (e.g., illegal drug use, misuse of prescription drugs) and health. This is a growing area of concern within the health system, especially for those individuals who are misusing prescription drugs. As stated, earlier in its deliberation the committee acknowledged the complexity and the sensitivity issues surrounding the collecting of information on illegal substance abuse and legal substance misuse. The U.S. health care industry has yet to resolve the problem of maintaining an accurate medication administration list on patients, which compounds the challenge of exchanging patient drug information among multiple providers and detecting patient prescription drug misuse. In addition, capturing accurate drug use information from individual patients during a clinical encounter is challenging. For these reasons, this domain did not meet the criteria for usefulness as a measure for individual and population health management. Thus, the committee did not select abuse of other substances as a candidate domain.
Sexual practices refer to the specific ways that people have sex with themselves or with others. It is related to the concept of sexual orientation, as certain practices are associated with having a particular sexual orientation (e.g., heterosexual men are likely to engage in vaginal intercourse, whereas men who are exclusively gay are not). However, even when people of the same sexual orientation are considered, tremendous variations in sexual practices exist. Sexual practices also include behaviors that may increase or decrease the health consequences of the behavior, such as condom use, partner selection, and drug use during sex.
Evidence of Association with Health
A large number of diseases referred to as sexually transmitted infections are known to be related to sexual practices. These diseases include, for example, AIDS, syphilis, gonorrhea, human papillomavirus infection, chlamydia infection, pelvic inflammatory disease, trichomoniasis, hepatitis, lymphogranuloma venereum, chancroid, herpes simplex virus, scabies, and pubic lice (CDC, 2010). Condom use is known to markedly decrease the likelihood of transmission of these infectious diseases. If they are untreated, some of those infections (e.g., HIV infection, syphilis) can cause serious morbidity, and if they are left untreated they can cause mortality. The prevalence of those infectious diseases affects not only the health of the individual but also the health of the community through sexual transmission to others. Some of those infections are transmissible to an unborn fetus (e.g., HIV infection, syphilis, hepatitis B and C, gonorrhea). Sexually transmitted infections are known to be more frequent among gay and bisexual men than among heterosexual men and women, are higher among younger persons than older persons, and are higher among African Americans and Latinos than among non-Latino whites (CDC, 2011).
Unprotected vaginal intercourse between a fertile man and a fertile woman may lead to unintended pregnancy. Depending on the circumstances, this may be a desirable or an undesirable outcome, depending on whether the pregnancy is wanted or unwanted. Pregnancy among teenage girls may lead them to drop out of school, compromising their educational and economic potential (NCPTP, 2010). Sexual practices may also be a joyful human expression. A World Health Organization report describes sexual health as “sexual being in ways that are enriching, and that enhance personality, communication, and love” (WHO, 1975, p. 4, 2006a). An inability to engage in sexual activities may reflect physical disease (e.g., diabetes) or mental state (e.g., depression).
Collecting information on sexual practices is a highly sensitive issue. Having knowledge of patient sexual practices could enable health care providers to provide their patients with the appropriate screenings for sexually transmitted infections. For example, rectal cultures for gonorrhea can be done in people who engage in anal intercourse but do not need be done in persons who do not engage in that sexual practice. Guidance on the treatment of sexually transmitted infections from the Centers for Disease Control and Prevention states that as part of the clinical interview, health care providers should routinely and regularly ask their patients about their sexual history to better reduce risk (CDC, 2010, p. 2). However, the com-
mittee rated the usefulness to the individual and population as low, given the likelihood of accurately collecting the information.
Health care providers can provide their patients with appropriate family planning counseling. In particular, adolescents may have incorrect information about the likelihood of pregnancy or sexually transmitted infections involved with certain sexual practices (e.g., transmission of gonorrhea through oral sex). This was viewed by the committee as a good practice by the clinical team but not relevant to inclusion in the EHR.
The evidence of association between sexual practices with health is clear. Health care providers can use this information in developing treatment and intervention plans. However, sexual behavior is for some people a very personal and private topic. Routinely asking all individuals about sexual practices as part of an EHR was also seen as controversial. Further, the committee noted that determining the riskiness of sexual practices for health can require asking many questions about the type of sexual acts, the partners involved, whether protection (e.g., condoms, dental dam) was used, and whether substances concomitantly were used. This would require many questions and complicated skip logic that would not be appropriate for an EHR.
Firearm ownership refers to the personal ownership of a firearm as well as living in a household with a firearm(s) (Hepburn and Hemenway, 2004; IOM and NRC, 2013; NRC, 2004; Wiebe, 2003). The manufacturing, distribution, carrying, transport, selling, acquisition, and use of firearms are regulated by federal, state, and local laws. Interpersonal violence is a related concept. Some interpersonal violence literature addresses the relationship between firearms and coercive behaviors, psychological abuse, and child abuse (physical, sexual) (Howard et al., 2007; Ismail et al., 2007; Shields et al., 2012; Smith and Ford, 2010). Public health injury programs and firearm violence prevention advocacy organizations have worked for several decades to pass legislation aimed at restricting firearm acquisition, imposing waiting periods for acquiring firearms, requiring firearm registration and licensing, creating zero tolerance for the presence of firearms in schools, and preventing child access to firearms. Firearms per se are not a social determinant of health. It is the contexts within which firearms are owned and used that often determine their relationship to injury and death and their being viewed as a social determinant.
Evidence of Association with Health
In 2010, approximately 18 percent of all injury deaths were caused by firearms, accounting for 31,672 firearm deaths, or 10.3 deaths per 100,000 individuals. Suicide (61.2 percent) and homicide (35 percent) were the major components of all deaths from firearm injury (Murphy et al., 2013). Grassel et al. (2003) found an association between handgun purchases and mortality from firearm injury. In general, the research suggests that there is an association between the presence of firearms in the household and homicide, in addition to unintentional injury from a firearm and a higher risk for homicides (Hepburn and Hemenway, 2004; Miller et al., 2006; Wiebe, 2003). Swahn et al. (2002) reported that 25 percent of U.S. adolescents reported that they have easy access to either alcohol or a gun in the home, which suggests that efforts to increase parental awareness of these facts is needed.
The Community Preventive Services Task Force (the Task Force) reviewed evidence examining the effects of selected federal and state firearms laws and their effects on violence-related population health outcomes as well as on other outcomes, such as school expulsion, property crime, and apprehension of criminals (TFCPS, 2005). The Task Force identified population health interventions that are effective at saving lives, increasing the life span, and improving quality of life. To date, the Task Force has found that the scientific evidence on the following types of firearm interventions is insufficient to recommend that community health interventions be implemented to prevent them: bans on specified firearms or ammunition, restrictions on firearm acquisition, waiting periods for firearm acquisition, firearm registration and licensing of firearm owners, laws on carrying concealed weapons, child access prevention laws, zero tolerance of firearms in schools, and combinations of firearms laws. It was unclear to the committee how useful a clinical intervention about firearm ownership could be. Further, it is a sensitive question to ask and use in an EHR, although there could be research benefits from having this knowledge.
The evidence of association between misuse of firearms and adverse health outcomes is apparent. Firearms are associated with unintentional injuries, suicides, and homicides. However, this domain does not fully meet the criteria for usefulness as a measure for individual screening or counseling because of the limited interventions available to the clinical team. The
evidence does not support use as a screening instrument and also does not support ability to screen and counsel in primary care settings. Exposure to firearms is an important indicator for understanding injury statistics, but population-level data can be gathered as readily from other sources (e.g., crime reports) as from EHRs. This domain is also useful for studying relationships between individual characteristics and exposure to firearms. Because of the lack of a research base, the impracticality of screening in primary care settings, and the evidence of appropriate interventions, the committee elected not to select exposure to firearms as a candidate domain.
Risk-taking is defined as engaging in behaviors having at least one uncertain outcome (Fischoff, 1992). Risk-taking has both psychological and behavioral traits. In this report the committee focuses on distractive driving and helmet use. Risk-taking is particularly prevalent among operators of motor vehicles. In the general population, 47 percent of men and women and 34 percent of teens ages 16 or 17 years say that they have sent or read text messages while driving (Madden and Raine, 2010). A study of college students revealed that 74 percent engaged in texting while driving, 52 percent said that they texted while driving on a weekly basis, and 17 percent accessed the Internet while driving (Cook and Jones, 2011). Even under perfect driving conditions, text messaging has been demonstrated to have detrimental effects on such driving behaviors as lane maintenance, speed maintenance, and shifts of attention (McKeever et al., 2013). Individual risk-taking behaviors are often influenced by the risk-taking behaviors of others. Among U.S. teenagers fatally injured between 1995 and 2000, driver seat belt use declined as the number of teenage passengers increased but increased when at least one passenger was older than 30 years of age (McCartt and Northrup, 2004). In the state of Hawaii, passengers were found to be 70 times more likely not to be wearing a seat belt if the driver was also not wearing a seat belt than if the driver was wearing a seat belt (Kim and Kim, 2003).
Head injuries are the principal cause of death and disability among motorcyclists, and injuries often require long-term rehabilitation and specialized medical care. Helmet use while riding a motorcycle or a bicycle is the single most effective way to reduce these fatalities and lessen the severity of injuries. Wearing a helmet has been shown to decrease the risk and severity of injuries among motorcyclists by approximately 70 percent and the likelihood of death by nearly 40 percent (WHO, 2006b).
Evidence of Association with Health
Examples of evidence of the association of distractive driving and helmet use with health include the following: one in six (17 percent) of adults who own a cell phone reported that their talking or texting, a distractive behavior, caused them to physically bump into another person or object (Madden and Raine, 2010); the recent rise in the volume of texting is believed to have contributed to the rise in fatalities because of distracted driving—more than 16,000 additional fatalities on the road from 2001 to 2007 (Wilson and Stimpson, 2010); texting while driving was a factor in 45 percent of motor vehicle fatalities in an autopsy series (Pakula et al., 2013); use of motorcycle helmets was associated with a reduced risk of death and head injury among motorcyclists who crashed (Lui et al., 2008); and wearing a bike helmet reduces the risk of life-threatening head and brain injury by more than 80 percent (OrthoInfo, 2011).
A small study that recruited 14- to 15-year-olds found that screening and a brief counseling intervention in a primary care setting resulted in a significant increase in helmet use (Ozer et al., 2011). Public education and legislation can be effective. From 1994 to 1998, bicycle-related head injuries in children declined by 45 percent in Canadian provinces and territories where legislation requiring helmet use was implemented, whereas the decline was 27 percent in areas without such legislation (Macpherson et al., 2002). For distractive drivers, effective enforcement of legislative bans on texting can deter drivers from engaging in this activity (Wilson and Stimpson, 2010).
The evidence of an association between risk-taking behaviors and health as reviewed was seen to be moderate. Social changes brought about by public health awareness and policy implementations have, in some instances, led to a decrease in the number of individuals engaging in risk-taking behaviors (e.g., more widespread use of helmets). However, further research is still needed to elucidate the best approaches to individuals who engage in distractive driving. The risk-taking behavior domain was rated moderately useful for population health management and research. However, the domain was rated minimally useful for individual patient management given the limited interventions available to the clinical team. Therefore the committee elected not to include risk-taking behaviors as a candidate domain.
Whereas social integration/isolation refers to the presence and quantity of social relationships, social support refers to one aspect of the content of these relationships: actual or perceived support, or benefit from supportive relationships. Analysts often distinguish between instrumental and emotional support and the perceived availability versus actual receipt of such support.
Evidence of Association with Health
Many small-scale studies and some larger population studies have found that social support is positively associated with health indicators. The association may reflect a direct/additive relationship and/or a buffering/interactive effect in which support mitigates or moderates the adverse effects of other risk factors for health, especially acute or chronic stress (Bowen et al., 2013; Cohen and Wills, 1985; Cohen et al., 2007; Dour et al., 2013; Sarason et al., 1990a,b). The evidence mostly supports the health-protective effects of perceived and emotional support, with the effects of other forms of perceived support and its actual receipt being more complex or specific. The receipt of certain types of support under certain circumstances can even have deleterious effects on health (Rook, 1984; Rook et al., 2012).
Although one can briefly get a global sense of a person’s perception of the availability of support, the assessment of social support is generally multifaceted and somewhat complex, posing problems, including the information in EHRs. Social support appears to play a role in the etiology of health problems and even more so in the course of such problems. Interventions seeking to prevent health problems or to facilitate recovery from or adaptation to them often include provision of social support. Information on social support could be valuable in both clinical practice and epidemiological research on the health of populations, but more evidence is needed.
The evidence of association between having social support and health is apparent. This domain, however, was seen as having moderate evidence of the criteria for usefulness as measured for the individual patient and research; it was also ranked low on the committee’s criteria for population
health management. Therefore the committee elected not to include social support as a candidate domain.
Work conditions refer to the existing conditions and environment affecting labor in the workplace, including the amount of time spent at work, a worker’s legal rights, the physical aspects of the work environment, and workers’ responsibilities. The U.S. Congress, for example, defined the purpose of work conditions for the federal Occupational Safety and Health Act to be “to assure so far as possible every working man and woman in the Nation has safe and healthful working conditions.”1
Evidence of Association with Health
Aspects of conditions in the workplace shape health. For example, exposure to physical risks and hazards, stress and poor mental health, and salary level or workplace benefits affect a person’s ability to obtain nutritious foods, achieve adequate physical activity, locate healthy housing, and have access to medical care. Psychosocial aspects of work that influence health include work schedules, commuting conditions, how work is organized, social support at work, and discrimination in the workplace. Members of socially disadvantaged groups tend to have more work-related health risks, fewer health-related benefits, and lower-paying jobs (RWJF, 2011). Work stress has been associated with an increase in asthma (Loerbroks et al., 2010), lower back pain (Lau and Knardahl, 2008), and type 2 diabetes in middle-age women (Norberg, 2007). Numerous studies document health outcomes related to young age, shift work, exposure to toxins, and static or tiring work conditions (Lee and Krause, 2002; Solidaki et al., 2010; Stomberg et al., 2010; Tamosiunas et al., 2005; Van der Windt et al., 2000). Policies in the United States protect individuals from many, but not all, of these exposures. Negative employment experiences may create mental health problems particularly in midlife and suggest the need to consider the role of interventions to better reduce mental health disorders for these individuals (Burgard et al., 2013).
Public policies have improved working conditions; work environments are healthier, but disparities are still widespread in a variety of occupations. The clinical health team could identify groups of patients at risk for expo-
1 29 U.S.C. 651, Section 2.
sures, such as housepainters and construction workers who may benefit from blood lead testing. The usefulness of this domain was seen by the committee as most helpful for research purposes, as the field would benefit from studies that suggest successful interventions that improve outcomes for patients. The committee noted that occupation hazards can be collected when collecting information on employment.
Overall, the evidence of association between work conditions with overall health was viewed by the committee as modest, and the evidence shows modest usefulness in having the information in all EHRs. Further research in this field is needed. The National Institute for Occupational Safety and Health (NIOSH) is currently developing standards that capture a patient’s industry and occupation, including such items as work schedule and external causes related to injury and poisoning (NIOSH, 2014). When available, this may provide useful information. At this time, the committee elected not to include work conditions as a candidate domain.
A history of incarceration refers to prior contact with the correctional system, including prisons and jails. When a person is incarcerated, health care is a responsibility of the correctional system. As described here, this domain refers to the receipt of health care upon release from prison or jail.
Evidence of Association with Health
Currently, more than 2.3 million individuals are incarcerated in the United States. It is estimated that on any given day, one in nine U.S. African American men ages 20 to 34 years is incarcerated, and one in three African American men is expected to be imprisoned at some point in his life, if rates of incarceration stay the same (PEW, 2008). Ninety-five percent of those individuals are ultimately released back into society, but most continue to cycle through the legal system throughout their lives (Wang et al., 2013). Incarceration is related to other social and behavioral factors that place individuals at higher risk for poor health, but it appears to be an independent risk factor. For example, a study conducted by Binswanger et al. (2007) found that former prison inmates were at high risk for death after release from prison. The first leading cause of death among these former inmates was drug overdose, followed by cardiovascular disease, homicide, suicide, cancer, motor vehicle accident, and liver disease, respectively (Binswanger et al., 2007). Interventions could therefore potentially help to
reduce the risk of death after release from prison. A study of Medicare data from 2002 to 2010, which included both time in prison and shorter stays in jail, found that within 1 week of being released, 1 in 70 former inmates was hospitalized for an acute condition, and within 3 months, 1 in 12 former inmates was hospitalized (Wang et al., 2013). These included hospitalization for ambulatory care-sensitive conditions as well as conditions related to mental health and disease of the circulatory system (Wang et al., 2013).
Higher rates of hospitalizations for conditions requiring ambulatory care, such as diabetes mellitus, hypertension, and asthma, among former inmates may reflect higher rates of chronic medical conditions among individuals who have been released from incarceration. A history of incarceration is known to be an independent risk for incident cardiovascular disease (Wang et al., 2009). Alternative explanations include an acute decline in their health status due to barriers in obtaining medications or primary care immediately after release or a poor quality of health care during their incarceration.
Screening for a history of incarceration could potentially lessen preventable hospitalizations and improve access to care for chronic disease and mental health conditions, particularly during the period immediately after release from incarceration, when the individual may be most vulnerable. Several models of care targeting these individuals have focused on this transition (Wang et al., 2012). Collecting incarceration history can be highly sensitive, as individuals may question the value of these data in an EHR and not want to reveal information to a health system connected to their employer. Increased research was seen as a priority.
Incarceration is associated with a variety of social and behavioral factors that place one at risk for poor health, and history of incarceration has been shown to correlate with subsequent poor health outcomes. As the committee deliberated on this domain for consideration as a candidate in all EHRs, it determined it to be relevant for a specific population group (those who have been incarcerated), and it did not find the evidence base to suggest that all EHRs include these data. Therefore the committee did not select history of incarceration as a candidate domain.
For health purposes, military service is a history of service in the armed forces of the United States or other nations, including the length and branch of service, the military occupation, the location and type of duty (e.g., in the United States or abroad with combat, combat support, or noncombat duties), and any ongoing illness, injury, limitation, or disability that began during military service.
Evidence of Association with Health
Military service is a significant risk factor for morbidity (both physical and mental), disability, and mortality (Baker et al., 2012; Foran et al., 2012; Greenberg and Rosenheck, 2009; LeardMann et al., 2013; Stander et al., 2007). The risks of mental disorders and suicide are significantly elevated for members of the military and veterans, even if they sustained no physical injuries or illnesses in the line of duty. Exposure to toxins is one of many risks. Approximately one-third of veterans of the 1991 Gulf War suffer from an array of long-term medically unexplained symptoms known as chronic multisymptom illness (IOM, 2013b).
Other health consequences of military service include traumatic brain injury and posttraumatic stress disorder (PTSD). PTSD is one of the most commonly diagnosed disorders in U.S. military personal. Comorbidity between disorders such as depression, PTSD, and substance abuse disorders is prevalent and poses complex health challenges (IOM, 2014b). At present, the time of military service relevant to this domain is mainly from 1950 to the present (IOM, 2014a). Given that Vietnam veterans are just now entering old age and large numbers of younger adults have served in Iraq and Afghanistan, the health risks of current military service and those of veterans remain substantial. Many veterans’ health care, although not all, is covered through the Veterans Health Administration (VHA).
Given the significant health risks of military service, health care providers could benefit from knowing about their patients’ military service, leading to better diagnoses and better treatment options, including the referral to VHA resources, which are not available to nonveterans. Much of this work is occurring within the U.S. Department of Veterans Affairs (VA) system. Specialty care is often necessary. A previous IOM committee recommends that the VA’s EHR should prompt health care providers to ask patients about symptoms that characterize chronic multisystem illness (IOM, 2013b). A separate IOM committee recently concluded that there is
sufficient evidence that exposure to roadside bomb blasts has contributed to the development of PTSD and concussion-related symptoms, such as persistent headaches. They recommended that the VA develop registries of blast exposures (IOM, 2014a).
There is evidence linking military service with poor health outcomes. However, it is unclear that military service alone is a risk factor; a greater risk is associated with combat experience, which would need to be measured as well. This domain was seen as less useful for individual health or population management because only about 13 percent of U.S. adults overall are veterans (Gallup, 2012); thus, the committee did not identify a strong need to include this domain in every patient’s EHR. Many veterans are covered under the VA’s health plan, and previous employment may be captured under the domain “employment,” one of the selected candidate domains. In addition to employment, several of the measures that are already recommended for inclusion in the parsimonious panel will provide more direct indicators of risk. For example, the recommended screening for depression and stress would likely identify mental health issues that may have their roots in earlier military service and can be addressed without that knowledge. Therefore the committee did not select history of military service as a candidate domain.
Community and cultural norms often shape health-related decision making and behavior. One’s immediate neighborhood and reference group, as well as the norms and values of the larger community, can encourage or dissuade specific behaviors such as diet, substance use, activity level, or health care seeking, and they may also provide support or increase exposure to conflict. Community norms may be particularly powerful in close-knit communities, including ethnic enclaves. In addition to the strength and cohesiveness of community and cultural norms to which patients are exposed, their cultural identities may affect their preferences and behaviors (Kwak and Haley, 2005).
Evidence of Association with Health
Community norms, including peer groups or social networks, have been shown to influence a person’s perception of what he or she thinks is appropriate, correct, or desirable when making health decisions (Karasek
et al., 2012). Social networks have been shown to have an important influence on a person’s tobacco use and drinking patterns (Chen et al., 2001; Christakis and Fowler, 2008) and may operate, in part, through norms and social influence.
In treating an individual patient, health care providers may be able to reinforce health-promoting behaviors that are tied to community norms or be aware of cultural values and norms that may make it difficult for a patient to adhere to a prescribed regimen. Research on issues such as social determinants of smoking cessation could potentially inform interventions that promote health by altering the structural context (e.g., taxation policies) to complement more traditional individual behavior change approaches (Karasek et al., 2012). Knowing environmental norms can assist the health system in adapting policies and interventions that can positively influence their community’s behavior. For example, laws and policies have been implemented in the majority of the United States prohibiting smoking indoors or in public spaces, lessening the likelihood that individuals will smoke.
Community and cultural norms and shared decision making undoubtedly play a role in health, but because there is no standard way to capture these in an EHR, the committee did not prioritize this domain in its review. A narrower focus on participatory decision making in the context of health care is more feasible, but this overlaps with other domains, such as patient engagement. As a result, the committee evaluated this domain as moderately associated to health and usefulness for all three uses identified and did not select community and cultural norms—health decision making—as a candidate domain.
EHR systems collect clinical data about patients and their health problems. To obtain information on environmental factors that influence disease risk and disease outcomes, an EHR can be linked with a community information system (CIS). A CIS includes contextual information such as the geospatial distribution of grocery stores selling healthy food options, transportation resources, open spaces and parks, health care facilities, social services, and job and educational opportunities. In addition to those
factors, a CIS can also contain information on population socioeconomic characteristics—so-called compositional factors—at the county, zip code, and neighborhood levels. Examples of geocodable domains that the committee found particularly compelling are listed in Chapter 3. The sections below provide further examples of the potential that geocodable information holds for communities.
EHR-CIS linkage entails address mapping of a patient’s residence using geocoding software to obtain specific longitude and latitude coordinates. Once this is done, the patient’s county, zip code, and census block of residence can readily be obtained. On average, counties in the United States have 100,000 residents, zip codes have an average population of 7,500 (USA.com, 2014), census tracts have an average of about 4,000 people, census block groups have about 1,500 people, and census blocks have as few as 600 people (STS, 2013)—although great variation in population size exists among all these groupings. Other geographic units not linked to the census bureau definitions can also be formed—such as the health care utilization-defined primary care service areas (each of which has about 17,000 individuals) (Goodman et al., 2003)—depending on the questions of interest.
The addition of environmental factors and community resources to the EHR to enable a more comprehensive understanding of the social and environmental determinants of health and the resources available to patients for implementation of health care treatments does not require any new information to be recorded in the EHR. Patient address is the only field required for the geocoding, and this information is part of every EHR. This makes inclusion of the CIS in the EHR highly feasible from the perspective of health care professionals. Health systems, however, must implement the linking procedures and must develop relationships with community stakeholder organizations that manage CISs. Limitations that make the linkage challenging include the lack of defined standards for reference data or methods for geocoding; the availability in each community of a CIS to which EHR data can be linked; the lack of technical expertise for EHR-CIS linkage; and maintenance of patient privacy during the linkage process.
A few examples of EHR-CIS linkages are available in the literature. For example, Comer and colleagues merged the Indiana Network for Patient Care, a large EHR system that aggregates data across institutions in a health information exchange (HIE), with the SAVI Community Information System (Comer et al., 2011). The EHR/HIE system was established 30 years ago and aggregates more than 200 data sources, including 80 emergency departments, 35 hospitals, more than 100 clinics, health departments, and ancillary data sources. The SAVI CIS collects, geocodes, organizes, and integrates data from more than 30 federal, state, and local providers: for example, departments of human services, welfare, education, housing, and
health; public safety; and community and health facilities (Comer et al., 2011). Such a comprehensive linkage of EHRs and CISs within the same community is uncommon, however.
Social determinants such as education level, poverty, race, ethnicity, housing, social context, social capital, and social connectedness are strongly associated with exposure to environmental hazards. For the purposes of this report, the committee focused on hazards introduced into the environment that cause adverse effects on human health, and concentrated on those hazards and conditions caused by or worsened by exposure that might lend themselves to a clinical intervention. In addition, the committee also concentrated on the clinician’s ability to use information on a patient’s social situation that may be used to improve a patient’s situation. (See, for example, “The Case of Veronica” in Chapter 1, Box 1-1.)
Environmental exposures to hazards, such as pollutants and contaminants, come in many forms: chemical substances, allergens, noise, heat, light, and energy. Some are made by humans, and some are naturally occurring. Exposure to environmental toxicants can occur through one’s occupation, in one’s home, and in one’s daily environment. A vast literature exists on the effects of specific contaminants and their effects on human health. For the most part, that literature is specific to the agent, with research conclusions pointing to the need for additional research, the inadequacy of animal models that limit inferences about the dose–response in humans, or limitations in epidemiological studies addressing threshold limits for exposure effects.
Evidence of Association with Health
Curtis et al. (2006) summarized research on a range of health effects of outdoor air pollution, including particulates, carbon monoxide, sulfur and nitrogen oxides, acid gases, metals, volatile organic compounds, solvents, pesticides, radiation, and bioaerosols. The general finding is that air pollution is associated with increased medical expense, morbidity, and premature mortality. The 2005 World Health Organization (WHO) air quality guidelines represent a widely agreed upon assessment of the health effects of air pollution (WHO, 2005).
Chronic exposure to air pollutants is a risk factor for the development of respiratory and cardiovascular disease. When solid fuel is used indoors, for example, it is the air pollutants produced by the solid fuel that is a risk factor for coronary obstructive pulmonary disease and lung cancer (WHO, 2011). According to the WHO guidelines, current scientific evidence has
not yielded specific thresholds for the elimination of adverse human health effects resulting from particulate matter. Pope and Dockery (2006) reviewed the research literature on particulate matter covering almost a decade (1997 to 2006) and concluded that there is emerging evidence of particulate-matter-related cardiovascular health effects.
The WHO and the U.S. Environmental Protection Agency have set limits for ozone, which at ground level is a major constituent of photochemical smog. Excessive exposure to ozone can trigger breathing problems and asthma and reduce lung function. European studies report that daily mortality increases by 0.3 percent for an increase in ozone exposure of 10 micrograms per cubic meter (µg/m3) (WHO, 2011).
Kampa and Castanas (2008, p. 362) have summarized this literature and conclude that “air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs.” In summarizing the effects of air pollution on children, Schwartz (2004) notes that recent research suggests an association between air pollution and infant mortality and an association between air pollution and the development of asthma. Schwartz (2004) also indicates that the evidence for the overall negative effects of air pollution on children has been growing.
Research evidence also supports a relationship between environmental allergens as a cause for primary care visits. In a study of the association between air pollution and primary care visits and consultations, Hajat et al. (2001) demonstrated that air pollution worsens allergic rhinitis, which leads to increased numbers of visits for medical care. Arbes et al. (2003) estimated the prevalence of dust mite allergens in beds and predictors of dust mite concentrations. That study found that most U.S. homes have detectable limits of dust mite allergens at levels associated with allergic sensitization and asthma. Using cross-sectional data from the 2005 Behavioral Risk Factor Surveillance System, Wen et al. (2009) found that alerts of poor air quality in the news media were significantly related to more changes in outdoor activities among people in whom asthma has been diagnosed than among others.
Cagney and Browning (2004) investigated the relationship between asthma and neighborhood factors associated with breathing problems. Their research indicated that measures of neighborhood context, such as physical and social decay and social trust, may be underlying factors associated with asthma. Research links asthma to social adversity brought on by environmental factors and disparities in population health (Rauh et al., 2008). Canino et al. (2009) noted that disparities in the incidence of asthma result from multiple, complex, and interrelated sources. The authors posit that clinical settings should routinely assess patient beliefs and financial barriers to disease management and that health care providers should receive enhanced cultural competence training to improve their communications
with patients, especially for those whose diseases relate to complex environmental factors.
The committee did not find literature relating to how exposure information may be systematically reflected in medical records through the use of specific standards. No environmental agents per se are a part of Meaningful Use Stage 1 or 2. The International Classification of Disease, 10th Revision, Clinical Modification (WHO, 2013) is currently used to code diagnoses, findings, and so forth in EHRs. It contains major sections that deal with various external causes, such as:
- poisoning by drugs (T36–T50) and nonmedicinal substances, many of which are environmental hazards (T51–T65);
- transportation-related injuries (V00–V99);
- injuries related to falls (W00–W19); and
- injuries from mechanical forces (e.g., struck by falling tree) (W20–W49).
Consequently, diagnostic coding data for environmental hazards can be found in EHRs and can be related to other variables for research purposes. Exposure to select environmental agents, such as lead, is also mandated for reporting to public health agencies and, consequently, is included in electronic laboratory reporting. Laboratory test results can also be found in EHRs.
Health care providers are confronted with a wide spectrum of conditions that may have an environmental element that either causes or exacerbates a patient’s condition. Many of the guidelines for environmental control exist for use at the population level but are not directly applicable to the patient. Much of the environmental research on social factors is not definitive as to cause and effect, nor does the research offer specific recommendations that clinicians may use in advising patients. Residence location may be the single strongest data item that can prove useful for research efforts to relate disease to social factors.
Availability of Nutritious Food Options
Within the context of communities, the availability of nutritious food options refers to the geospatial distribution of grocery stores, food vendors generally located in small stores, and restaurants. It can refer to overall food availability or, more specifically, to access to specific types of food, such as fruits and vegetables, sweetened beverages, calorie-dense foods, and
fast foods. The concept of “food deserts” is a component of the broader construct of nutritious food options and refers to communities that have limited access to affordable and nutritious foods (IOM and NRC, 2009). Section 7527 of the 2008 Farm Bill defined a “food desert” as “an area in the United States with limited access to affordable and nutritious food, particularly such an area composed of predominantly lower-income neighborhoods and communities.”
The availability of nutritious food is just one factor that determines food choices; the relative cost of food options, cultural factors, and taste preferences are additional influences. Although no uniform consensus on the meaning of available nutritious food options exists, most would agree that it includes proximity of food options, price, and travel time—the amount of time required to travel to purchase food.
Evidence on Association with Health
The hypothesized link between food options in the community and health is that the greater availability of nutritious food will increase the intake of healthful foods, such as fruits, vegetables, and whole grains, while lower availability of sweetened beverages will reduce the risk of obesity. The evidence, however, is mixed. Extant studies suggest that just an increase in fruit and vegetable intake without management of total calories is not associated with a reduced risk of obesity. The evidence of a lower risk of obesity and diabetes in association with the decreased intake of sweetened beverages is stronger (Schulze et al., 2004), but linking consumption to availability in neighborhoods has not been established. Ecological studies have found associations between a lack of availability of nutritious food options and obesity and diet-related chronic conditions (IOM and NRC, 2009). At the zip code level, the presence of supermarkets was associated with a lower risk of obesity (see, for example, Lopez-Zetina et al., 2006); particularly in urban areas (Michimi and Wimberly, 2010); whereas high density of small convenience stores was associated with an increased risk of obesity (Gibson, 2011; Wang et al., 2007). However, negative findings have also been reported between the food environment and obesity (Lee, 2012; Sturm and Ruopeng, 2014).
Obesity has become one of the top public health problems affecting the nation. Strategies to lower its incidence and mitigate its impact will need to take a holistic view and involve the entire health system rather than a sector-specific approach that involves only public health, education, behavioral change, or medical care. The distribution of obesity in the U.S.
population is not random, with a much greater risk being seen for lower-income individuals in urban and rural settings. The nutrition environment that individuals are exposed to is one factor in obesity risk.
Building a comprehensive data system that bridges community data with personal health data can provide the type of infrastructure needed for the IOM’s vision of a systems approach to combating the obesity epidemic (IOM, 2012). Because linkage of EHR data with CIS data requires no new data entry for clinicians, the major feasibility issues to consider are costs and technical details of data linkage as well as the availability of information in CISs. The Philadelphia Department of Health, for example, has compiled a rich database on the locations of nutritious food options throughout the city of Philadelphia and has developed programs to reduce the amount of salt in Chinese restaurants and increase the amount of fruit and vegetables sold in corner stores (The Food Trust, 2012; Get Healthy Philly, 2013).
Transportation, Parks, and Open Spaces
The design and distribution of the parks, streets, open spaces, homes, schools, other buildings, roads, and walkways in a community constitute its built environment. In broad terms, the built environment is defined “to include land use patterns, the transportation system, and design features that together provide opportunities for travel and physical activity” (TRB and IOM, 2005, p. iii). It can be studied at several geographic levels, from the neighborhood level to the community and county levels. The built environment is a wonderful example of the ingenuity and creativity of humans, but because individuals spend nearly all of their time in it, it has both positive and negative effects on health. For children, the availability of parks and recreational facilities provides opportunities for exercise and prosocial development with friends during unstructured play time (Tester and Baker, 2009). These resources are less available in lower-income neighborhoods, an inequality that contributes to the risk of obesity among the children living there (Gordon-Larsen et al., 2006).
Evidence on Association with Health
Green neighborhoods facilitate physical activity and have also been linked to better physical and mental health (Astell-Burt et al., 2013a,b; Maas et al., 2006, 2009). These connections are just beginning to be understood; understanding the roles that individual, social, and built environmental factors have on physical activity is an emerging area of inquiry. What is now known is that physical activity levels have been decreasing over the past several decades as the amount of work required for activities
of daily living has been minimized. Household chores, jobs, and getting to and from schools and the workplace are less energy intensive today than they were in previous decades. These trends, coupled with increased sedentary behavior during leisure time, have conspired to lower physical activity levels (IOM, 2005; TRB and IOM, 2005). Less clear is how specific changes in the built environment lead to predictable decrements in physical activity, although specific environmental variables such as access to recreation facilities, living in neighborhoods where others exercise, and the presence of enjoyable scenery have been positively associated with physical activity in several studies (Trost et al., 2010). Other attributes of the built environment associated with physical activity are mixed land use, well-connected street networks, more bikeways, and high residential density (Cavill, 2007; HSFC, 2007; Laxer and Janssen, 2013).
Further, people who use public transit get more exercise than drivers (Rissel et al., 2012; Wen and Rissel, 2008). Long commutes decrease physical activity and increase the risk of obesity (Brownson et al., 2005; Lopez-Zetina et al., 2006), and they are associated with less civic engagement (Choi et al., 2013).
If health care providers have information on their patients’ built environment (e.g., urban design, land use), they can potentially use this information to describe treatment options for their patients and develop coordinated care with other health care providers or systems of care. Neighborhood indicators of access to recreational facilities and walking environments can be used to counsel patients on behavioral change and disease management. For example, patient-centered medical homes (PCMHs) are required to link patients with community health care and social service resources as one of their eight core functions (Wagner et al., 2012). A PCMH could leverage community resources by creating a merged EHR-community resources database that can generate a personalized set of recommendations for available local community resources. Research is needed to evaluate the practicability and usefulness of this information.
If the health system has information on its populations’ built environment, it can use the information to tailor and target strategies and interventions. For example, the health system can identify areas where concentrations of the patient populations lack access to open spaces. Policies could be developed to create green spaces allowing for easier access to run or play. The availability of environmental information would also allow monitoring of trends in these factors over time across geographic areas. Building a comprehensive data system that bridges community data with personal health data can provide the type of infrastructure needed for the
IOM’s vision of a systems approach to combating major health problems in the United States, such as the obesity epidemic (IOM, 2012).
If researchers have information on individuals’ environmental attributes, they can perform population research on the causal impacts of changes in these environmental attributes on behaviors and on health outcomes. Longitudinal data on patients derived from their EHRs would be especially valuable in the establishment of causality. The availability of these data would enhance clinical research on determining to what extent environmental factors are useful in improving the outcomes of care for patients with conditions such as hypertension and diabetes.
Health Care and Social Services
The distribution of health care and social service agencies within a community is an important determinant of a population’s access to these services. Assuming that the location of each agency is geocoded in a CIS, the distance between each agency and a patient’s residence can be readily computed. The geographic distribution of health care resources has been termed “geographic accessibility” (Forrest and Starfield, 1998). While related to use in most studies, it is a weaker influence on use than financial accessibility. In virtually all countries without central personnel planning, health care resources are inequitably distributed, with more physicians located in more affluent communities (Matsumoto et al., 2010).
Evidence on Association with Health
For primary health care, the presence of fewer physicians in a locale has been shown to translate into poorer health outcomes (Starfield et al., 2005) at the state and county levels. Very little evidence to date suggests that better integration of community resources in a CIS with patient receipt of care improves outcomes (Stellefson et al., 2013).
PCMHs are required to link patients with community-based health care and social service resources. This is one of the eight core functions of PCMHs (Wagner et al., 2012). One vision of how the PCMH could leverage community resources is through the creation of a merged EHR-community resources database that enables the creation of a personalized set of recommendations of local community resources for each patient that is based on his or her health needs and that can be provided to the patient during a medical encounter.
Educational and Job Opportunities
Education, employment, and income generated from work are important socioeconomic determinants of individual and population health, and these are influenced by the educational and job opportunities available to members of a community. Educational opportunities can be measured by the availability of high-quality schools. Indeed, the quality and location of schools are some of the more important influences on the desirability of a neighborhood to families. Job prospects affect the employment rate and affluence of a community, and community unemployment rates would be a measure of this concept.
Evidence on Association with Health
The biomedical literature is devoid of evidence on how the distribution of educational opportunities relates to health. It is likely that these variables are more prominent in the social science literature, where the outcomes may be socioeconomic variables. Ample evidence suggests, however, that unemployment is associated with health, specifically all-cause mortality (Roelfs et al., 2011), suicide (Milner et al., 2013), and poorer mental health (Dooley et al., 1996; Puig-Barrachina et al., 2011).
Educational opportunities are distal to the causal pathway between socioeconomic status and health. They are better thought of as ecological determinants of a community’s socioeconomic status. As such, they are unlikely to be of immediate interest or use to health care professionals but would be of interest to urban planners and public policy makers. Merely knowing the prevalence of job opportunities in a community is probably of less importance than knowing the unemployment trends and their downstream effects on psychological health and mortality.
Evidence of association between the neighborhood and community (geocodable) domains show that there are associations with health; further research to understand the validity and usefulness of these domains is currently being conducted. In some instances these domains are interrelated with candidate domains (e.g., availability of nutritious food options is interrelated with financial resource strain and dietary food patterns). (See Chapter 3 for more information on these candidate domains.) In other instances, these domains are already being captured in an electronic system (e.g., ICD
codes and public health surveillance systems). Currently, these domains are not routinely available in a standardized format; thus, in its deliberation for this domain, the committee also considered the volume of work required and complexities to capture geocodable domains. For these reasons, the committee elected not to select environmental pollution and neighborhood resources (availability of nutritious food options; transportation, parks, and open spaces; health care and social services; and educational and job opportunities) as candidate domains.
The committee noted that with the collection of a geocodable residential address, a wide variety of exposures can be explored. Some reflect compositional characteristics of the neighborhood, and others reflect contextual characteristics, including hazards and resources in the physical and social environment. The committee opted to focus on aspects of composition and did not identify any contextual domains at this time. If health care providers collect a geocodable address in their EHRs, they may choose to use it to import data relevant to their community and population. The committee hopes that in the future these variables will be routinely standardized and thus able to be linked to all patient records.
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