Measuring contextual characteristics for community health.
Publication: Health Services Research
Date: Monday, December 1 2003
Much has been accomplished to improve health and reduce disparities through understanding and intervening on individual-level risk factors for major causes of morbidity and mortality (U.S. Preventive Services Task Force 1996; Ketola, Sipila, and Makela 2000). However, far less attention has been
Just as risk factor profiles have been developed based upon our knowledge of how individual-level determinants affect health outcomes--it may also be important to develop profiles using information about the contextual characteristics of communities. In collaboration with the Centers for Disease Control and Prevention, we have undertaken a project to address the conceptualization and assessment of community contextual characteristics that are believed to impact population health and disparities in population health. Our goals include (1) providing a conceptualization of which contextual characteristics could plausibly affect patterns of population health; and (2) developing an extensive data library of geocoded datasets containing data to measure community contextual characteristics.
The main purpose of this article is to share ideas about how to conceptualize the contextual characteristics of communities that may affect health, and to present this work as a resource for public health research and advocacy. We describe the consultative process we followed to identify 12 overarching dimensions of a community contextual health profile with specific subcomponents and indicators within each dimension. We then present the rationale supporting each dimension and provide examples of the data sources assembled in our data library. We conclude with a discussion of three important research approaches for addressing the impact of community contextual characteristics on population health and health disparities.
BACKGROUND ON CONTEXTUAL CHARACTERISTICS AND HEALTH
In recent years, there has been growing research interest in examining associations between characteristics of places and a wide range of health outcomes including all-cause mortality (Lynch et al. 1998; Waitzman and Smith 1998a, 1998b; Bosma et al. 2001; Ross et al. 2000), cardiovascular disease and mortality (Diez-Roux et al. 2001; Casper et al. 1999; Armstrong et al. 1998; LeClere et al. 1998), tuberculosis (Barr et al. 2001; Acevedo-Garcia 2001), injury (Cubbin, LeClere, and Smith 2000; Reading et al. 1999), suicide (Kaplan and Geling 1999; Whitley et al. 1999), AIDS (Zierler et al. 2000), self-rated health (Subramanian, Kawachi, and Kennedy 2001; Malmstrom, Sundquist, and Johansson 1999), mental illness (Ross 2000; Aneshensel and Sucoff 1996), and adverse birth outcomes (Gorman 1999; Matteson, Burr, and Marshall 1998; Collins and David 1997). There has also been increasing awareness that health risks traditionally seen as arising from individual lifestyle choices are strongly influenced by the characteristics of the environments in which individuals grow and live out their daily lives (Lynch and Kaplan 2000; Lynch, Kaplan, and Salonen 1997; Macintyre, Ellaway, and Cummins 2002). For example, unhealthy dietary patterns and obesity (Diez-Roux et al. 1999; Ellaway, Anderson, and Macintyre 1997), smoking (Duncan, Jones, and Moon 1999; Kleinschmidt, Hills, and Elliot 1995), reduced physical activity levels (Yen and Kaplan 1998; Ellaway and Macintyre 1996), and greater alcohol consumption (Diehr et al. 1993; Hart, Ecob, and Smith 1997) have each been shown to be related to aspects of the communities in which people live, over and above the characteristics of the individuals living there.
Although there are now a large number of empirical studies documenting relationships between particular aspects of residential areas and selected health outcomes, critics have noted a relative paucity of attention to conceptualizing and testing the particular pathways underlying the observed associations (Pickett and Pearl 2001; Macintyre, Ellaway, and Cummins 2002; Diez-Roux 1998; Yen and Syme 1999). In the absence of conceptual frameworks, selection of contextual variables for study has been somewhat ad hoc and constrained within the range of those commonly used or readily available through routinely collected data (Mitchell et al. 2000). To some extent contextual effects research on health remains mired in a "poverty paradigm" (Rowley et al. 1993), focusing mostly on the association between census-based indicators of community socioeconomic position and individual health outcomes, with a heavy emphasis on the deleterious effects of concentrated poverty and other forms of disadvantage. The main thrust of such studies has been to show that poorer places are associated with worse health outcomes, above and beyond the characteristics of the individuals who live there (Robert 1999). This emphasis on disadvantage, to the exclusion of other facets of contextual environments, is party the result of a lack of appropriate data, but it also reflects a paucity of alternative models to inform questions about how community contextual characteristics may plausibly affect specific types of health outcomes.
For instance, studies have found smoking to be significantly associated with neighborhood deprivation level after accounting for individual characteristics (Duncan, Jones, and Moon 1999; Kleinschmidt, Hills, and Elliot 1995). The area-level variables generally included in these analyses, however, consist largely of census-based indicators of economic status such as mean household income, unemployment rate, proportion of the population living in overcrowded housing, proportion without access to a car, and proportion of household heads with lower social class occupations, considered either separately or in an aggregate index. While living in deprived communities is found to be associated with higher smoking levels in the analyses, it may be instructive to examine a wider set of characteristics of poor areas that are theoretically relevant to smoking rates, including the cost and availability of cigarettes, targeted advertising, the quality and quantity of preventive and smoking cessation services available, and smoking policies in schools and other public areas.
In the sections that follow we describe the process of developing a set of community characteristics that can affect health and we provide details of the specific factors within each dimension.
EXPERT CONSULTATION PROCESS
Drawing upon relevant theoretical and empirical literature on community contextual characteristics and their relationship to health status, a preliminary set of contextual dimensions was developed. This framework served as a basis for a structured workshop for invited consultants, which included prominent investigators from the United States and around the world engaged in cutting-edge research on community contextual characteristics and their relation to health, in a wide range of disciplines including epidemiology, sociology, geography, medicine, demography, economics, developmental psychology, education, and toxicology. Others with interests and expertise in the conceptual design and practical implementation of the indices were also invited, including government experts on data sources and geographic information systems, public health practitioners, and experts on community consultation and processes. The names and affiliations of those attending the workshop are available from the authors.
The initial sessions were full group discussions, in which participants addressed fundamental issues including:
* Is it worthwhile to create indices measuring community contextual health characteristics?
* Would such an effort be feasible?
* For which audiences would the indices be useful?
* Is the preliminary set of dimensions reasonable, and what others should be included?
In the course of the discussion general consensus was reached on a core set of 12 contextual dimensions plausibly linked to health status. These dimensions are diagrammed in Figure 1. There was widespread agreement among the participants that the construction of these dimensions and the identification of traditional and nontraditional data sources to measure these dimensions would potentially be of significant benefit to both the research community and to public health practitioners and advocates.
[FIGURE 1 OMITTED]
In the next phase of the workshop, participants formed smaller working groups to brainstorm ideas for sets of components within each dimension that would most optimally quantify that dimension of contextual health. Each working group generated detailed lists of these components, along with suggestions for possible data sources and specific variables that might be used to measure the components of each dimension. This content was then presented to the flail group for further input and revision. The workshop concluded with a discussion of issues related to measurement design and index construction, with participants sharing their own experiences and recommendations concerning assessment of contextual factors and their relationship to health outcomes.
After the workshop, we evaluated and further refined the lists of components and data indicators for each dimension, taking into consideration both conceptual relevance and availability of appropriate data at the local level. Table 1 presents the resulting set of contextual dimensions, components, and indicator variables. In the next section we discuss the rationale for inclusion of the components within each dimension, based on input from meeting participants as well as reviews of relevant literature.
CONTEXTUAL DIMENSIONS AND RELATED COMPONENTS
It is important to note that although all the proposed indicators offer further plausible conceptual differentiation of the various dimensions of context, it remains to be seen whether their effects on health can be empirically distinguished from more traditional indicators such as poverty (Krieger et al. 2002, 2003). Thus we make no claims about their empirical veracity; rather our goal is to take the first step in providing a broader conceptualization of the specific contextual factors that may affect health.
Economic Dimension
The association between higher levels of economic resources and more optimal health is one of the most well-documented relationships in public health research (for reviews see Susser, Watson, and Hopper 1985; Krieger et al. 1993; Lynch and Kaplan 2000), and economic aspects of local areas have been among the most frequently analyzed contextual factors with regard to mortality and other outcomes. Significant associations have been shown between health status and community economic characteristics including income (Anderson et al. 1997; Diez-Roux et al. 1997) and inequality in income distribution (Lynch et al. 1998; Kennedy et al. 1998), wealth (Diez-Roux et al. 1997; O'Campo et al. 1997), and poverty (Yen and Kaplan 1999; Shaw et al. 2000) and the geographic concentration of poverty (Waitzman and Smith 1998a, 1998b).
The fact that data for most of these economic indicators are readily available for small areas in census data is undoubtedly an important factor accounting for their widespread use (Mitchell et al. 2000). Our consultants encouraged a broadened perspective to more fully assess the economic status of communities. On one hand, this involved identifying a more diversified set of indicators for commonly studied components, such as considering various types of income (earnings, investments, and transfers) in addition to the overall mean or median income in an area. On the other hand, a number of additional components of economic well-being were also suggested for inclusion. For example, the opportunities for community residents to obtain financial resources would be influenced by characteristics of economic development in an area such as productivity, industrial mix, amount of area business lending, as well as exchanges of goods and services through the informal economy. The availability of financial services including banks and other sources of credit were considered important, as were local costs of living, patterns of redistribution through taxes and transfers, and the fiscal capacity of the area. One other seldom-considered aspect of the economic milieu concerns the degree to which segments of the community are differentially exploited, and thereby constrained in their access to monetary resources. Indicators of exploitation include the ratio of wages to corporate profits, as well as issues related to location of jobs, such as length of commute and commuter taxation.
Employment Dimension
Aspects of employment in residential areas have also been among the more frequently considered factors in research on context and health. Adverse outcomes have generally been found to be positively associated with higher community levels of unemployment (Guest, Almgren, and Hussey 1998; LeClere, Rogers, and Peters 1998), as well as with larger proportions of employed residents working at lower social class occupations (Armstrong et al. 1998; Cubbin, LeClere, and Smith 2000). Unemployment rates or occupational status measures are also frequently combined with other indicators of areal deprivation including median income, car ownership, education level, and overcrowded housing to form summary measures that are associated with poorer health (e.g. Townsend, Phillimore, and Beattie 1988; Carstairs and Morris 1989).
In addition to the usual employment indicators, we include a number of other measures. Looking in detail at characteristics of the workforce, for example, along with the area business capacity and the geography of job growth, would facilitate assessment of job access, as well as the degree of spatial "mismatch" which may adversely affect the employment opportunities of central city residents (Holzer 1991; Mouw 2000). Racial, gender-based, and antigay discrimination also limit access to employment, as well as having possible stress-related consequences for health (Williams 1999; Krieger and Sidney 1997; Yen et al. 1999). The degree to which occupational safety regulations and policies are in place and enforced is likely to influence the frequency and severity of work-related injuries (McQuiston, Zakocs, and Loomis 1998), while aspects of job quality including wage equity, family-friendly policies and demand/control characteristics of jobs can reduce or exacerbate job-related stress and its sequellae (Cheng et al. 2000; de Jonge et al. 2000; Saltzstein, Ting, and Saltzstein 2001). The presence of labor unions is also associated with more optimal working conditions and employee compensation (Hirsch and Macpherson 2001).
Education Dimension
Researchers studying educational context and health have generally used percentage of the adult population not completing high school as an indicator, finding positive associations with all-cause mortality (Guest, Almgren, and Hussey 1998; Bosma et al. 2001), homicide (Cubbin, LeClere, and Smith 2000), motor vehicle deaths (Cubbin, LeClere, and Smith 2000), coronary heart disease prevalence (Diez-Roux et al. 1997), neural tube defects (Wasserman et al. 1998), smoking (Diez-Roux et al. 1997), severe pediatric injury (Durkin et al. 1994), and elevated serum cholesterol (Diez-Roux et al. 1997). High school noncompletion rate and median educational level have also been used in combination with other areal economic and employment measures to form aggregate socioeconomic scores that are correlated with adverse health outcomes (Diez-Roux et al. 2001; Roberts 1997).
In these studies the contextual educational variable tends to be treated as a marker for a more generalized concept of community socioeconomic status and resources, rather than being considered in its own right. Our consultants suggested that a focused assessment of aspects of education that are likely to vary among communities is warranted. Multiple measures of the population's educational attainment and functioning are included in the recommended indicators. Moreover, the levels of funding, characteristics of school systems and curricula, and learning-related aspects of community life such as prevalence of television viewing and numbers of library books per capita, can provide insights into the priority placed on education and corresponding investment within an area, which itself may be related to health outcome.
Specific aspects of the curriculum also have implications for the health of children at school ages and throughout their lives. For instance, bullying and violence is a serious problem among children and adolescents (Nansel et al. 2001), and the presence of violence prevention programs has been found to be effective in decreasing physically aggressive behavior (Twemlow et al. 2001; Grossman et al. 1997). Similarly, obesity in children is approaching epidemic proportions, and is related to adult obesity and levels of lipids, cholesterol, triglycerides, insulin, blood pressure, and to risk of coronary heart disease (Styne 2001). Incorporation of nutrition modification programs and optimal physical education curricula in schools can be effective in modifying these risks (Stone et al. 1998; Snyder et al. 1999).
Political Dimension
Aspects of community political participation have been found to be associated with population health status. Davey Smith and Dorling (1996), for example, showed that in England and Wales mortality rates in electoral constituencies were negatively correlated with Conservative voting patterns and positively correlated with Labour voting. Area deprivation was also negatively associated with Conservative voting while positively related to Labour voting. The authors concluded that in areas with better material circumstances and more optimal health, voters were more likely to support leadership that favors reducing public assistance programs. In the United States, Blakely and colleagues (2001) studied disparities among states in voting across socioeconomic status groups. Individuals living in states with the highest voting inequality were shown to have increased odds of fair or poor self-rated health relative to those in other states. They reasoned that disproportionate political participation by the more economically well-off skews subsequent policymaking toward their interests, a conclusion supported in the political science literature (Hill and Leighley 1992).
More broadly, political participation has been of recent interest as an indicator of embeddedness in the institutions of civil society. As such, it is considered to be a reflection of social capital within a community (Kawachi 1999). Social capital, measured in several different ways, has been associated with positive health outcomes (Subramanian, Kawachi, and Kennedy 2001; Kawachi, Kennedy, and Glass 1999; Kawachi et al. 1997) (see the Psychosocial Dimension section below for further discussion of the social capital concept).
Within the political contextual dimension, we include aspects of political participation such as voting and political party membership, as well as donations to parties and candidates, which are known to influence public policy (Ferguson 1995). Likewise the degree to which elected officials are representative of their areas in terms of gender and race/ethnicity may be an important factor in their responsiveness to constituents' needs (Whitby 1997; Bratton and Haynie 1999). The percent of the local budget devoted to public health investments can be considered an indication of the priority placed on health by the community as well as a measure of available fiscal resources. We also include the number and influence of various politically active community groups.
Environmental Dimension
The environmental dimension includes physical and chemical components that have known associations with adverse health outcomes: air pollutants (American Lung Association 2001; Pope, Baitz, and Raizenne 1995), water pollutants (Griffith et al. 1989), and environmental hazards including hazardous waste (Johnson 1999; Schell 1991), heavy metals (Goldman, Shannon, and the American Academy of Pediatrics 2001; Mendelsohn et al. 1999), pesticides (Blindauer, Jackson, and McGeehin 1999; Landrigan et al. 1999), climatic extremes (Greenough et al. 2001; Patz, McGeehin, and Bernard 2001), and excessive noise (Passchier-Vermeer and Passchier 2000; Schell 1991). These exposures are known to vary by area and to be disproportionately concentrated among disadvantaged populations (American Lung Association 2001; Brown 1995).
In addition, this contextual dimension encompasses structural features of communities such as physical design of streets, sidewalks, and safety structures that are associated with level of injury risk (Navin, Zein, and Felipe 2000; Agran et al. 1996). Aspects of land usage are also considered, such as public spaces and parks that may facilitate greater physical activity levels (French, Story, and Jeffery 2001), as are services related to environmental quality like waste disposal and recycling programs.
Housing Dimension
Associations between housing and health have been studied from several perspectives. Most concretely, physical characteristics of housing have been linked to adverse outcomes. For example, the presence of dampness and mold lead to increased risk of respiratory and other illnesses (Platt et al. 1989; Packer, Stewart-Brown, and Fowle 1994). Dilapidated and abandoned housing in the local area increases the risk of accidental injury among residents (Gielen et al. 1995), is associated with increased emotional stress (Ellaway and Macintyre 2000), and may provide situational opportunities for high-risk behaviors (Cohen et al. 2000). Population density and overcrowding have also been associated with increased chances of contracting infections and sustaining injury (Agran et al. 1998; Acevedo-Garcia 2000).
Home ownership has been associated with reduced morbidity and mortality risk (Filakti and Fox 1995; O'Campo et al. 1997). In most cases this housing variable is regarded as a marker for general material well-being. It has been suggested, however, that long-term exposure to specific health-promoting or health-damaging characteristics of housing itself is likely to account for some of the observed health effects (Macintyre et al. 1998; Ellaway and Macintyre 1998).
There is also some evidence that poor housing conditions during childhood can adversely affect health in later life. For example, Barker and colleagues (1990) found an association between domestic crowding during childhood and later stomach cancer mortality rates, suggesting that overcrowding may promote the transmission of causative organisms among children that exert negative health effects later in life. Similarly, Dedman et al. (2001) noted aspects of poorer childhood housing conditions were associated with increased mortality risk from common diseases in adulthood.
Our consultants suggested that we include characteristics of housing discussed above in our framework, as well as other aspects of residential patterns within communities. Homelessness, for example, has known associations with differentially poorer health (Barrow et al. 1999; Hwang 2001). Segregation by race has been associated with adverse health outcomes among blacks (Williams and Collins 2001; Jackson et al. 2000), as well as among whites in some cases (Collins and Williams 1999). Similarly, concentration of poverty has been found to be associated with elevated mortality risk (Waitzman and Smith 1998a).
We also include two other components within the housing dimension. Regulations such as zoning and industrial/residential segregation can affect the degree to which residential areas are exposed to industrial pollution and other health threats such as increased traffic. Financial issues specific to housing are also considered, such as housing costs, the availability and characteristics of low-income housing, mortgage lending practices, and community reinvestment initiatives.
Medical Dimension
Health care services are generally considered to be an important determinant of health status (Andrulis 1998; Frenk 1998), although the degree to which medical care impacts health status over and above social and economic conditions has been the subject of considerable controversy (Pincus et al. 1998; McKeown 1979). The medical contextual dimension encompasses a range of health care services, including primary care, specialty care, emergency services, home health care, emergency services, mental health services, long-term care, oral health care, and alternative care.
We also look specifically at aspects of access to health care services, which is related to health status and known to vary among population groups (U.S. Department of Health and Human Services 2000). Some of the factors included, such as insurance coverage and the availability of indigent care, are well-known determinants of access (Baker, Shapiro, and Schur 2000; Newacheck et al. 1998; Nelson et al. 1999). The racial/ethnic makeup of medical staff in relation to the patient population and the cultural competence of providers and institutions may also he important in encouraging utilization of health care resources that are present in an area (Flores et al. 1998; Langer 1999). In addition to traditional measures of access, we also include rates of hospitalization for ambulatory care sensitive conditions. These are conditions considered to be manageable on an outpatient basis given access to high quality primary care, and therefore higher hospitalization rates can be used as an indicator of poorer access to appropriate care (Institute of Medicine 1993).
Governmental Dimension
Within this dimension we consider characteristics and functioning of local area governments. Levels of funding are assessed, as well as the relative contributions from various revenue sources. Policies and legislation that have potential health effects are included, such as obstacles to unionization, living wage or minimum wage ordinances, and employer requirements for provision of health benefits. The nature and quality of local governmental services are also considered.
Another aspect of local governance with potential health relevance concerns the degree to which there is municipal fragmentation. This term refers to a situation in which large numbers of smaller governmental entities exist within a metropolitan area. It has been argued that in cases where there are high levels of municipal fragmentation and no single government empowered to act for the good of the entire region, a host of problems result, including resource and public service imbalances within the area and the protection of privilege (Mitchell-Weaver, Miller, and Deal 2000; Ross and Leone 1996).
Public Health Dimension
This dimension includes assessment of the implementation of core public health functions of assessment, policy development, and assurance at the local level (Turnock and Handler 1997; Institute of Medicine 1988). We focus on three primary areas of interest. First, there are a variety of programs aimed at prevention, early detection, and optimal management of a range of health problems. Local public health departments may provide these services directly, or oversee their implementation by other organizations, both governmental and private sector. The second category concerns development, regulation, and enforcement of standards, which has become a salient issue as provision of traditional public health services is increasingly being privatized (Beauchamp 1997). Third, funding issues, including budget allocations and financial arrangements for service provision are of interest in gauging the priority given to public health issues in the community.
Psychosocial Dimension
There has been longstanding scientific interest in the organization of social Fife, and the implications of interpersonal and group interactions for emotional and physical health status (for reviews see House, Landis, and Umberson 1988; Yen and Syme 1999). Research in the 1970s on social support suggested a health-enhancing role for social relationships in buffering the iii effects of stress (Cassel 1976), and subsequent studies confirmed an inverse relationship between social relationships and mortality risk (House, Robbins, and Metzner 1982; Schoenbach et al. 1986).
More recently, aspects of social interactions and relationships have been increasingly conceptualized as forms of social capital, although there is widespread disagreement about the meaning of the term and the level of aggregation at which it operates (Lynch, Due et al. 2000; Woolcock 2001). Portes (1998, p. 6) defines social capital as "the ability of actors to secure benefits by virtue of social membership in other networks or social structures." Coleman (1988, p. 598) sees social capital as a resource for organizations as well as individuals: "Social capital is defined by its function. It is not a single entity but a variety of different entities, with two elements in common: they all consist of some aspect of social structures, and they facilitate certain actions of actors--whether persons or corporate actors--within the structure. Like other forms of capital, social capital is productive, making possible the achievement of certain ends that in its absence would not be possible." Putnam et al. (1993, p. 167) consider social capital broadly as "features of social organization, such as trust, norms, and networks that can improve the efficiency of society by facilitating coordinated actions."
Social capital has been operationalized in different ways in health-related empirical research. Per capita membership in groups and associations has been used to assess civic engagement (Kawachi et al. 1997; Kawachi, Kennedy, and Glass 1999), as has political participation (Blakely, Kennedy, and Kawachi 2001). Several studies have considered greater mistrust to be indicative of lower levels of social capital (Kawachi et al. 1997; Kawachi, Kennedy, and Glass 1999; Subramanian, Kawachi, and Kennedy 2001). Mistrust is generally defined as the percentage in an area who agree with the second part of the statement: "Generally speaking, would you say that most people can be trusted or that you can't be too careful in dealing with people". A related indicator is perceived lack of fairness, indexed by percent agreeing that "most people would try to take advantage of you if they got the chance" (Kawachi et al. 1997). Perceived helpfulness/reciprocity has also been used as a gauge of social capital, based on answers to the question "Would you say that most of the time people try to be helpful, or are they mostly looking out for themselves?" (Kawachi et al. 1997). It has also been hypothesized that crime level is an indicator of collective well-being that is influenced by the degree of cohesiveness in social relations or social capital (Sampson, Raudenbush, and Earls 1997; Kawachi, Kennedy, and Wilkinson 1999).
Within the psychosocial dimension we include theorized aspects of social capital such as civic engagement via political participation, membership in voluntary organizations and unions, and charitable giving. Crime as a marker for social cohesion is assessed through expenditures on jails and incarceration rates. Collection of information on lawsuits and the presence and utilization of protective services was also suggested as an indicator of the level of trust in communities.
Behavioral Dimension
As mentioned earlier, there has been increasing recognition that aspects of social, physical, and cultural context can affect health status in a community by facilitating or inhibiting behaviors that impact well-being (Macintyre, Ellaway, and Cummins 2002). We focus on behavior areas identified as being among the nation's leading health indicators and which have been repeatedly cited as major determinants of premature morbidity and mortality (U.S. Department of Health and Human Services 2000; McGinnis and Foege 1998; Wilson 1994): tobacco use, physical activity, diet/obesity, alcohol and illicit drug use, and violence.
For each of the behaviors, we examine specific characteristics of communities that might influence the degree to which they will be adopted by residents. In the case of tobacco use, these characteristics include current smoking rates, the presence of cessation and preventive education programs, workplace smoking restrictions, the cost and accessibility of cigarettes, and targeted advertising. In the area of physical activity, we include reported activity levels, physical education requirements in schools, participation in local sports and recreational activities, as well as availability of exercise facilities in the workplace and in the area more generally. We also consider indicators of sedentary activities such as television viewing patterns and video game sales and use. Regarding diet and obesity, we look at consumption patterns of healthy foods such as fruit and vegetables as well as high-fat and high-sugar foods. The quality, availability, and cost of a range of different foods is of interest, as is the availability of generally less-nutritious "fast food" as indexed by the number of such establishments in the area. We also include aspects of nutrition in the schools, including the prevalence of subcontracting to vendors of nonnutritious items and the presence of nutrition education programs. In the area of alcohol and illicit drug use, we consider availability as indicated by number of liquor stores and marketing laws as well as the nature of public advertising. We also include drug and alcohol treatment service availability, and the presence of syringe laws and exchange programs. Violence in the community is indicated by factors such as the availability of guns and the level of exposure to violence perceived by residents.
Transport Dimension
The transportation system in place in communities has multiple implications for the health of residents. Most directly, motor vehicles are the leading cause of injury in the United States, and are responsible for about one-third of all injury deaths (Fingerhut and Warner 1997). The nature of the transportation modes and the volume of use also influence the types and magnitude of pollution introduced into the environment (Sharpe 1999). A third consideration is the degree to which employment patterns and, therefore, economic well-being are determined by the accessibility of jobs through adequate and affordable transportation systems (Pugh 1998).
In our framework we include measures within each of these health-related aspects of transportation. Vehicle occupant and pedestrian safety factors are included, as well as characteristics of the infrastructure of roads, sidewalks, and bike lanes. We examine characteristics and numbers of vehicles, and aspects of the public transportation system. Finally, we include economic issues such as government transportation spending priorities, funding for public transportation, and personal insurance rates.
NEXT STEPS
With this conceptual "road map" as a framework, the next phase was to attempt to find out what measures of these recommended dimensions actually existed. Thus, we proceeded with building a data library with datasets containing information for Metropolitan Statistical Areas (MSAs) relevant to each dimensional component. We chose to focus on MSAs as our unit of analysis in quantifying contextual factors because they are the smallest census-defined geographic unit for which a broad range of data is routinely collected and geocoded. We recognize, however, that there is no single ideal level at which to measure context in terms of its relationship to health-damaging and health-promoting factors. In fact different characteristics may operate at different levels. An argument can be made that using more localized units, such as county, zip code, census tract, and census block, increases the likelihood that certain aspects of the social and physical environment actually experienced by individuals are being measured. Conversely, there are considerably more richly detailed contextual data sources available for larger units such as states and MSAs. Given the inevitable tradeoffs between data availability and proximity to lived experience, we find the MSA to be the most satisfactory alternative for our purposes.
An important aspect of our project is the exploration of data sources not traditionally used by public health researchers but which can provide valuable information about the health enhancing aspects of the local environment. Tables 2-13 provide lists of such data sources related to each of the 12 dimensions. The majority of these sources provide data for MSAs or smaller geographic units such as counties; a few sources at the state level have been included when more localized data did not exist.
Some sources are familiar to many researchers and practitioners, such as the Census Bureau decennial data files and the State and Metropolitan Area Data Book, a resource that is compiled by the Bureau and incorporates a wide variety of data from governmental agencies and the private sector. Others may not be so well known, such as aggregate databases assembled by research, governmental, or private sector organizations with particular interests in MSA--or county-level information. One example is Demographics USA, a commercially available database containing measures of disposable income, income capacity, consumer expenditures, and information on area retail trade establishments and retail sales. Another is the State of the Nation's Cities, which was compiled by the Center for Urban Policy Research in collaboration with the U.S. Department of Housing and Urban Development. This database has measures of area income inequality, gross metropolitan product, and fiscal health. A third such source is the Contextual Data Archive, containing indices of economic segregation. We have also obtained economic indicator data from organizations including the Brookings Institution, the Federal Financial Institutions Examination Council, the American Chamber of Commerce Researchers Association, and the Economic Policy Institute. Further information about accessing these and other data sources listed in Tables 2-13 is available from the authors.
Having provided details of potential characteristics of communities that may affect health and the process through which they were generated, we will conclude this paper by describing three general principles that we think can help guide the development of future research and advocacy on the importance of community contextual characteristics for health.
HOW MIGHT COMMUNITY CONTEXTUAL CHARACTERISTICS AFFECT HEALTH DISPARITIES? THREE GUIDING PRINCIPLES TO CONSIDER
A key question underlying much of the literature on contextual characteristics and health is whether local variations in outcomes are attributable to the composition of the population in an area, or whether aspects of the areas themselves influence health status. While authors of several earlier studies concluded that area effects were not significant once individual factors had been taken into account (Duncan, Jones, and Moon 1993; Sloggett and Joshi 1994), reviews of more recent literature incorporating more complex multilevel modeling have concluded that there is fairly consistent evidence for the presence of so-called "area effects" for a range of health outcomes (Pickett and Pearl 2001; Macintyre, Ellaway, and Cummins 2002).
We propose three related guiding principles that we think are useful for understanding how aspects of contextual characteristics can affect health and disparities in health.
Community Contextual Characteristics and the Specificity of Health Outcomes
The first principle involves the conceptualization of "health." It is obvious that health is multidimensional and cannot be captured in a single outcome. In considering how community contextual characteristics may affect health, we are concerned with a broad range of health outcomes, such as heart disease, diabetes, stroke, cancer, suicide, and asthma. In addition, we are concerned with mental health, aspects of physical, social, emotional, and cognitive functioning, and health-related behaviors such as exercise, diet, and medication and health care utilization. This list is not intended to be exhaustive or exclusive. Rather, it serves to illustrate our first principle.
Greater understanding of how community contextual characteristics may affect health will require thinking about mechanisms linking contextual characteristics to specific health outcomes. The contextual characteristics associated with falls among the elderly are not necessarily the same characteristics implicated in respiratory disease in children, or in suicide. Likewise, the uneven or broken pavements implicated in falls among the elderly do not cause respiratory disease, even though we may be forced to pragmatically use a count of uneven or broken pavements as a surrogate for the causal exposures for respiratory disease. Indeed, tallies of dilapidated buildings or broken windows have been used as surrogate measures of contextual environment linked to certain health outcomes (Cohen et al. 2000). Nevertheless, it is important to be clear that these may not be the causal exposures relevant to different types of health outcomes.
The point is that unless we attempt to study rather specific health outcomes, it will be difficult to know which contextual characteristics are important for which health outcomes. A study by Merlo and colleagues (in press) is an example of showing how a specific aspect of contextual environment--levels of social participation and the communication networks it fostered--is plausibly related to use of hormone replacement therapy, but is not related to anti-hypertensive medication use, in spite of the fact that these are very similar outcomes.
This specific outcome/mechanism approach is, in our opinion, one way forward and needs to be applied more rigorously across a range of potential health outcomes relevant to contextual influences on health disparities. We do not wish to appear overly reductionist and would hold open the possibility that many contextual characteristics are likely to be empirically implicated in a range of health outcomes (e.g., poverty). Moreover, we should also be aware of the potential for clusters of exposures to interact in producing disease, so that it is the spatial aggregation of a series of negative contextual forces that act synergistically to magnify the total exposure load or increase susceptibility to certain outcomes (Diderichsen, Evans, and Whitehead 2001). However, the general principle of moving toward a search for greater conceptual and mechanistic specificity (both social and biological) of contextual influences on health is important.
Contextual Characteristics and the Natural History of Different Health Outcomes The second and related principle to consider is the "natural history" of different types of health outcomes. This means thinking about which stage in the natural history of the outcome is of concern. For instance, it is possible that contextual characteristics may play little simultaneous role in the pathogenesis of stroke, but currently existing contextual conditions may be very important in the quality of life and access to resources of people who have survived stroke and are living with some functional limitations. For many health outcomes, especially chronic disease outcomes, there is a natural history that may involve early exposure to certain risk factors such as a high fat diet or cigarette smoking, metabolic or hemostatic changes, and the appearance of subclinical manifestations that increase susceptibility, such as asymptomatic atherosclerosis or elevated blood pressure. At a later stage there may be triggering mechanisms that lead to a clinically defined event (such as a heart attack), and assuming survival, the consequences of the disease where afflicted individuals learn to live on a day-to-day basis with any physical, social, behavioral, and psychological limitations imposed by their condition. It seems likely that different aspects of community contextual characteristics could affect these processes differently (Morenoff and Lynch 2002).
Our point here is to suggest that there is no single pathway to health and therefore there is not a unique set of contextual characteristics that will be universally important for all health outcomes or at all stages of the outcomes. The two principles described so far are important in guiding the next phase of research on health-related contextual effects and this recognition is motivated by the overwhelming body of knowledge that suggests that there are indeed different pathways to different types and stages of health outcomes.
Contextual Characteristics and the Lifecourse
The third principle involves the consideration of lifecourse processes (Kuh and Ben-Shlomo 1997). If research on contextual health effects is to fulfill its promise for better understanding health and disparities in health, then it can be misleading to look for "explanations" based solely on contemporaneous contextual exposures. As we have discussed above, chronic diseases have latency periods--that implies that exposures from across the lifecourse may be important in the pathogenesis and progression of these diseases (Davey Smith and Lynch in press; Barker 1998; Davey Smith et al. 1997, 1998, 2000; Davey Smith, Harding, and Rosato 2000; Hertzman et al. 2001; Leon 1998).
This does not mean that there are not important proximal contextual characteristics related to the triggering of events. However, we should conceptualize how measured exposures to contextual characteristics match up with the temporal logic of the stage of the outcome. This means we should start thinking about the potential effects of community contextual characteristics through a lifecourse perspective. It is possible that exposure to current environmental conditions per se has little to do with the current distribution of disparities in the incidence of chronic disease outcomes like rates of heart disease or cancer. Rather, they may more strongly reflect both contextual characteristics and individual influences from earlier in the lifecourse. This recognition means that both individual and contextual exposures over the lifecourse are of interest in better understanding contemporary levels and disparities in population health outcomes such as heart disease, cancer, chronic lung disease, and diabetes. Thus, it is important to remember that exposures in early life can have both direct and indirect long-term effects on health. Biological and social exposures co-evolve over the lifecourse so that contextual exposures at one point may be literally "embodied" to the extent they become an "individual characteristic" later in the lifecourse. (Kuh et al. in press; Aboderin et al. 2002; Davey Smith et al. 2000; Davey Smith, Harding, and Rosato 2000; Harding 2001; Ben-Shlomo and Kuh 2002; Leon 2000).
CONCLUSION
In this paper, we have documented a project designed to elucidate those aspects of community contextual characteristics that are plausibly linked to health. We hope the information presented here is useful to public health researchers and advocates in terms of broadening the conceptualization of potential contextual influences on health and understanding the health-promoting and health-endangering characteristics within which decisions about the provision of health care services are made. In addition to this very practical contribution, we have also attempted to provide some thoughts on how the research agenda on contextual health effects might move most profitably forward. We believe that it is important to adopt a lifecourse approach to more fully understand the effects of contextual characteristics on health and health disparities.
Table 1: Contextual Dimensions, Components, and Indicators Potentially
Related to Health
ECONOMIC DIMENSION
1. Income
A. Summary income measures
B. Income components
C. Disposable income
D. Income distribution
E. Geographic concentration of income
F. Economic segregation
2. Wealth
A. Geographic concentration of wealth
B. Debt levels
C. Savings rates
D. Real estate ownership/values
3. Poverty
A. Geographic concentration of poverty
B. Deprivation associated with poverty-level income
4. Economic Development
A. Productivity
B. Industrial mix
C. Business lending indicators
D. Informal economy
5. Financial Services
A. Availability of credit
B. Availability of banking and check-cashing services
6. Cost of Living
A. Local cost of living indexes
B. Spending/consumption patterns
C. Income to spending ratios
7. Redistribution
A. Taxes
B. Transfers
8. Fiscal Capacity
A. Property values
B. Sales levels
C. Income capacity
9. Exploitation
A. Ratio of wages to corporate profits
B. Commuter taxes
C. Commuting patterns
EMPLOYMENT DIMENSION
1. Employment-Unemployment Rates
A. Job security
B. Labor market turnover
2. Workforce Characteristics
A. Racial/ethnic diversity
B. Skill level
C. Unionization
D. Migrant workers
3. Area Business Capacity
A. Tax breaks offered
B. Number and size of businesses
C. Business space available
4. Job Access
A. Geography of job growth
B. Discrimination/affirmative action policies
C. Distance traveled to work
D. Transportation system
E. Training/retraining
5. Occupational Safety
A. Laws, regulations, and company-specific policies
B. Enforcement/number of violations
6. Job Quality
A. Compensation
B. Ratio of CEO to worker earnings
C. Family-friendly policies
D. Demand/control characteristics
7. Job Characteristics
A. Unionized employers/size and power of unions
B. Skills needed by employers
C. Full vs. part-time employment
EDUCATION DIMENSION
1. Educational Attainment
A. Graduation rates
B. Dropout rates
C. Literacy rates
D. Test scores
E. Rates of progression to post-secondary education
2. Funding
A. Teacher salaries
B. Facilities
C. Teacher training/support
D. Fiscal capacity of school district
E. Proportion of funds by source
F. Corporate presence in schools
3. Private Schools
A. Number
B. Enrollment
4. School Characteristics
A. Size of schools/classes
B. Student/teacher ratios
C. Teacher turnover
D. Parental attitude/involvement in schools
E. School segregation
1. Race/ethnicity
2. Economic status
F. Curriculum quality
1. Physical education requirements
2. Health education
3. Nutrition education
4. Sex education
5. Language requirements
6. Vocational education
7. Enrichment programs
G. Preschool/Kindergarten/Early Intervention
H. School-based clinics
I. Physical environment of school/safety
J. Disciplinary climate
1. Violence prevention programs
2. Police involvement
K. School-based nutrition programs
5. Community Climate
A. Television viewing
B. Radio stations
C. Reading/reading to children
D. Libraries
E. Corporate-sponsored educational programs
POLITICAL DIMENSION
1. Civic Participation
A. Voting
1. Registration and voting rates
2. Ease of registration
3. Racial/ethnic representativeness of registered voters
B. Political party membership
C. Donations to parties and candidates
2. Political Structure
A. Gender/racial/ethnic representation in elected office
B. Percent of local budget for public health investments
3. Power Groups
A. Community organizations
B. Unions
C. Advocacy Groups
ENVIRONMENTAL DIMENSION
1. Air Quality
A. Outdoor (exhaust, ozone, pollutants, particulate)
B. Indoor (tobacco, insect, mold, dust)
2. Water Quality (PCBs, dioxin)
3. Environmental Hazards
A. Hazardous waste
B. Heavy metals
C. Pesticides
D. Climate extremes
E. Noise
4. Physical Safety
A. Traffic
B. Street repair
C. Sidewalk availability/quality
D. Street signs/lights
E. Safety structures (e.g. guard rails)
5. Land Use
A. Public recreational space/number of parks
B. Waste disposal/dumping/sanitation services
C. Curbside recycling programs
HOUSING DIMENSION
1. Housing Stock
A. Age
B. Scarcity
C. Value
D. Characteristics
E. Gentrification/gatedness
F. Rental vs. owner occupied
G. Safety violations
2. Residential Patterns
A. Homelessness
B. Number of institutional facilities
C. Segregation
1. Racial/ethnic
2. Economic
D. Vacancy rates
E. Crowded housing
F. Population density
G. Abandoned housing
H. Social isolation
3. Regulation
A. Zoning policies
B. Industrial/residential segregation
C. Housing policies (e.g. Section 8)
4. Financial Issues
A. Housing costs
B. Low-income housing
1. Percent of total housing
2. Ratio of low-income units to low-income workers
3. Elderly housing
C. Mortgage lending practices by race/ethnicity
D. Community reinvestment initiatives
MEDICAL DIMENSION
1. Primary Care
A. Number of providers
B. Provider training/competence/certification
C. Rates of ambulatory care sensitive hospitalizations
D. Medicaid reimbursement levels
2. Specialty Care
A. Number of providers
B. Provider training/competence/certification
3. Emergency Services
4. Home Health Care Services
5. Mental Health Care
6. Long-Term Care
7. Oral Health Care
8. Access to/Utilization of Care
A. Insurance coverage
B. Race/ethnic staff to population ratios
C. Provision of indigent care
D. Costs of care
E. Rates of ambulatory care sensitive hospitalizations
F. Cultural competence among providers and institutions
9. Alternative Care
GOVERNMENTAL DIMENSION
1. Funding
A. Revenue
1. Intergovernmental
2. Taxes
3. Lottery
B. Debt
2. Policy/Legislation
A. Obstacles to unionization
B. Living wage/minimum wage ordinances
C. Employer requirements to provide health benefits
3. Services
A. Privatization
B. Local safety net resources
4. Municipal Fragmentation (number of subunit governments within
a metro area)
PUBLIC HEALTH DIMENSION
1. Programs
A. Screening
B. Nutrition
C. Family planning
D. Chronic disease control
E. Home visiting
F. Outreach
G. School-based clinics/education
H. Substance abuse prevention
I. Domestic violence prevention
J. Mental health services
K. Immunization
2. Regulation-Enforcement
A. Sanitation
B. Health/food inspection
C. Health violations
D. Legislative efforts
4. Funding
A. Budget allocations
B. Private sector provision of public health services
PSYCHOSOCIAL DIMENSION
1. Political
A. Contributions to parties, candidates
B. Women in elected office
C. Number of registered voters
D. Voter registration procedures
2. Volunteer Organizations
A. Types/functions
B. Number of members
3. Union Participation
4. Charitable Giving
5. Jails
A. Expenditures
B. Incarceration rates
C. Crime
6. Lawsuits
A. Civil lawsuits
B. Small claims court cases
C. Lawsuits against businesses
7. Protective Services
A. Government services
B. Household systems
BEHAVIORAL DIMENSION
1. Tobacco Use
A. Smoking rates
B. Cessation programs
C. Smoking prevention
D. Workplace/public space smoking restrictions
E. Cost/accessibility of cigarettes
F. Advertising
2. Physical Activity
A. Physical activity levels
B. Physical education requirements in schools
C. Public and private recreational facilities
D. Television viewing patterns
E. Workplace exercise programs/facilities
F. Participation in local sports/recreational activities
G. Video game sales/use
3. Diet/Obesity
A. Fresh fruit and vegetable consumption
B. High fat, high sugar food consumption
C. Food quality/availability/cost
D. Number of fast food establishments
E. School nutrition
1. Regulation of subcontracting to vendors
2. Nutrition education
3. Breakfast/lunch programs
4. Alcohol and Illicit Drug Use
A. Number of liquor stores
B. Drug and alcohol treatment services
C. Syringe laws/exchange programs
D. Liquor marketing laws
E. Advertising
5. Violence
A. Guns
1. Availability
2. Gun Shows
3. Licensing
B. Exposure to violence
C. Police protection
D. Gang activity
TRANSPORT DIMENSION
1. Safety
A. Seat belts/child restraints
B. Helmets
C. Age curfews/graduated driver's license program
D. Driving while intoxicated laws/enforcement
E. Speed restriction/enforcement
2. Infrastructure
A. Roads
1. Quantity
2. Quality
3. Speed bumps
4. Buffers from pedestrians
B. Sidewalks
C. Bike lanes
1. Quantity
2. Mandating legislation
3. Traffic Patterns
A. Spatial location of jobs
B. Traffic volume
C. Car pooling
4. Vehicles
A. Number of heavy/diesel vehicles
B. Cars per capita
C. Types and ages of vehicles
5. Public Transportation
A. Availability/density/efficiency
B. Types of public transportation available
C. Cohesiveness/integration
D. Reliability/quality
E. Geographic equity
F. Environmental soundness
G. Transportation for special needs groups
H. Employer promotion of public transportation
6. Economic Issues
A. Expenditures
B. Spending on local roads vs. alternative transportation
C. Percent of transit revenue from fares
D. Insurance rates
E. Commuter taxes
Table 2: Selected Data Sources Relevant to the Economic Dimension
INDICATORS DATA SOURCES/NOTES
1. Income
A. Summary income measures
1. Median and per capita annual Census Bureau
income (http://www.census.gov)
B. Income components
1. Mean hourly and annual wage Bureau of Labor Statistics
(http://www.stat.bls.gov/
oes/home.htm). Data by
occupation available in
downloadable Excel files
2. Hourly wage union and nonunion Union Membership and
workers Earnings Data Book
(http://www.bna.com/
bnaplus/labor/
laborrpts.html). Separate
tables for public and
private sector workers and
for manufacturing and
nonmanufacturing workers;
customized reports
available for any or all
years since 1983
3. Per capita personal income Bureau of Economic Analysis
(http://www.bea.doc.gov/
bea/regional/reis).
Downloadable compressed
comma-separated-value
files
C. Disposible income
1. Median and per capita Effective Demographics USA
Buying Index (http://www.trade-
dimensions.com/
p_demographics.html).
Effective Buying Index
represents money income
minus taxes Data available
on CD-ROM
D. Income distribution
1. Gini coefficient of income Census Bureau
inequality; 90 percentile/10 (http://www.census.gov)
percentile ratio
E. Geographic concentration of income
1. Concentration of poverty Jargowsky, P. A. 2003.
Stunning Progress, Hidden
Problems: The Dramatic
Decline of Concentrated
Poverty in the 1990s
(http://www.brookings.edu/
dybdocroot/es/urban/
publications/
jargowskypoverty.pdf).
Percentage of the poor
residing in high poverty
neighborhoods; total and
race-specific rates
F. Economic segregation
1. Dissimilarity index (d), Sociometrics Contextual Data
poor/nonpoor segregation; Archive
Contact index (xpy*), (http://www.socio.com).
poor/nonpoor segregation Downloadable compressed
data files for PC and
UNIX, including raw data
and SPSS and SAS program
files
2. Wealth
A. Geographic concentration of wealth
1. Mean and median net worth ESRI Business Information
Solutions
(http://www.esribis.com).
Data tables can be
integrated into AreGIS
B. Debt levels
1. Bankruptcy filings Economy.com
(http://www.econoray.com/
research). Personal and
business bankruptcy
filings and rates per
thousand households, by
type
C. Savings rates
1. Dollar amount of deposits in Federal Deposit Insurance
savings institutions and banks Corporation
D. Real estate ownership/values (http://www3.fdic.gov/sod/
index.asp). From webpage
choose "Summary Tables"
then MSA or county tables
1. Median value owner-occupied Census Bureau
housing units (http://www.census.gov/)
3. Poverty
A. Geographic concentration of poverty
1. Poverty rate Census Bureau
(http://www.census.gov/)
2. Concentration of poverty See Jargowsky 2003 above
B. Deprivation associated with
poverty-level income
1. Percent of families with Census Bureau
incomes <half of the poverty (http://www.census.gov/)
line
4. Economic Development
A. Productivity
1. Gross metropolitan product U.S. Metro Economies
(GMP) and GMP growth rate (http://www.usmayors.org/
usom/home.asp)
Table 3: Selected Data Sources Relevant to the Employment Dimension
INDICATORS DATA SOURCE
1. Employment/Unemployment Rates
A. Job security
1. Employment volatility State of the Nation's Cities
Database (http://policy.
rutgers.edu/cupr/sonc/sonc.htm).
Variable calculated by Center
for Urban Policy Research for
this database indicating
employment volatility relative
to volatility in the U.S. as a
whole. Database available in 4
PC formats (tab-delimited
ASCII; SPSS portable file; Excel
file; SAS formatted file) and
MAC
B. Labor market turnover
1. Unemployment rates, 1. Bureau of Labor Statistics,
total; by race/ Local Area Series (http://
ethnicity and sex; by www.stats.bls.gov/lau/). From
occupation and by webpage select monthly or annual
industry average tables of total
unemployment rates for
metropolitan areas; tables
available as pdf files
2. Bureau of Labor Statistics,
Geographic Profile Series
(http://www.stats.bls.gov/opub/
gp/laugp.htm). From webpage
select "Estimates for
Metropolitan Areas and Cities";
tables available as pdf files
2. Labor force Bureau of Labor Statistics,
participation rates, Geographic Profile Series
total, by race/ (http://www.stats.bls.gov/opub/
ethnicity, by sex gp/laugp.htm). From webpage
select "Estimates for
Metropolitan Areas and Cities"
as above
2. Workforce Characteristics
A. Racial/ethnic/gender
diversity
1. Percent distribution of Bureau of Labor Statistics,
employed persons by Geographic Profile Series
sex, race/ethnicity, (http://www.stats.bls.gov/opub/
and occupation gp/laugp.htm). From webpage
select "Estimates for
Metropolitan Areas and Cities"
as above
2. Percent of workers who Union Membership and Earnings Data
are female Book (http://www.bna.com/
bnaplus/labor/laborrpts.html).
Total, private, public sector,
and private manufacturing
workers; customized reports
available for any or all years
since 1983
B. Skill level
1. Percent distribution of Bureau of Labor Statistics,
employed persons as Geographic Profile Series
above (http://www.stats.bls.gov/opub/
gp/laugp.htm) as above
C. Unionization
1. Percent of workforce Union Membership and Earnings Data
unionized; percent of Book (http://www.bna.com/
workers covered by bnaplus/labor/laborrpts.html).
union contract Total, private, public sector,
and private manufacturing
workers; customized reports
available for any or all years
since 1983
2. Collective bargaining Dilts, D. A., C. R. Deitsch, and
protection laws A. Rassuli. 1992. Labor
covering state and Relations Law in State and Local
local employees; laws Government. Westport CT: Quorum
protecting public Books.
employees' right to
strike
3. Area Business Capacity
A. Tax breaks offered
1. Corporate income tax Tax Foundation (http://
rate www.taxfoundation.org). From
webpage select "State Finance";
select "Corporate Income Tax
Rates"
B. Number and size of businesses
1. Number establishments County Business Patterns (http://
by employment size www.census.gov/pub/epcd/cbp/
(1-4/5-9/10-19/20-49/ download/cbpdownload.html).
50-99/100-249/250-499/ Downloadable comma-delimited
500-999/1000+) data files and record layout
documentation
C. Business space available
1. Commercial office space Society of Industrial and Office
(sq ft) in and outside Realtors (http://www.sior.com).
central business From webpage select
district "Publications" on-line data from
Comparative Statistics of
Industrial and Office Real
Estate Markets available for
purchase
2. Commercial office space Society of Industrial and Office
vacancy rate in and Realtors as above
outside central
business district
4. Job Access
A. Geography of job growth
1. Central city and Brookings Institution (http://
suburban: employment www.brook.edu/es/urban/
growth rate; number and hillfa.pdf). Brennan J. Hill E.
% change in number of W. 1999. Where Are The Jobs?
jobs; share and % change Cities, Suburbs, and the
in share of private Competition for Employment.
employment
2. Number and increase in Blue Chip Job Growth Update:
nonagricultural jobs Arizona State University
(http://www.cob.asu.edu/seid/
eoc/pubs/JGUsample). From
webpage select "Ranking of MSAs"
B. Discrimination/affirmative
action policies
1. Employment-population Bureau of Labor Statistics,
ratio by race, by sex Geographic Profile Series
(http://www.stats.bls.gov/opub/
gp/laugp.htm). From webpage
select "Estimates for
Metropolitan Areas and Cities";
tables available as pdf files
C. Distance traveled to work
1. Share of metro Brookings Institution
employment > 10 mi from (http://www.brook.edu/es/urban/
central business publications/
district glaeserjobsprawlexsum.htm).
Glaeser E. L., Kahn M., Chu C.
2001. Job Sprawl: Employment
Location in U.S. Metropolitan
Areas. Downloadable pdf
D. Transportation system
1. Percent of workers 16+ Census Bureau
using various means of (http://www.census.gov)
transportation to work
2. Percent of residents American Housing Survey
without satisfactory (http://www.census.gov/hhes/www/
public transportation ahs.html). Data for each of 47
available in selected metropolitan areas are
neighborhood collected about every four
years, with an average of 12
areas included each year.
Downloadable data in SAS and
ASCII
5. Occupational Safety
A. Laws, regulations, and
company-specific policies
1. Directory of states Occupational Safety and Health
with approved Administration
occupational safety and (http://www.osha.gov/oshdir/
health plans states.html)
B. Enforcement/number of
violations
1. OSHA workplace Occupational Safety and Health
inspections and Administration Workplace Safety
penalties for Data (http://www.nicar.org/data/
violations osha/). Businesses classified by
city; data since 1972 available
for purchase
6. Job Quality
A. Compensation See Economic Dimension, Income
B. Ratio of CEO to worker
earnings
1. Ratio of mean annual Can be calculated from 1999
wages, chief executives Occupational Employment
to production workers Statistics data (http://
www.bls.gov/oes/ocs_data.htm)
7. Job Characteristics
A. Unionized employers/size and See Unionization above
power of unions
B. Skills needed by employers
1. Percent of total Bureau of Economic Analysis
employment in various (http://www.bea.doc.gov/bea/
industries regional/reis/). Can be
C. Full- vs. part-time calculated from data in
employment downloadable compressed
comma-separated-value files
1. Percent of workers who Census Bureau
work part time (http://www.census.gov)
Table 4: Selected Data Sources Relevant to the Education Dimension
INDICATORS DATA SOURCE
1. Educational Attainment
A. Graduation rates
1. Educational attainment Census Bureau
among persons 25+ (http://www.census.gov/)
2. Number of diploma National Center for Education
recipients; number of Statistics Common Core of Data
other high school (CCD). Downloadable
completers comma-separated-value and excel
data tables for MSAs, counties,
districts, schools can be
created with "Build a Table"
tool (http://www.nces.ed.gov/
ccd/bat). Source CCD datasets
also downloadable in ASCII
format (http://www.nces.ed.gov/
ccd/ccddata.asp)
3. High school graduation U.S. Department of Education, No
rates Child Left Behind (http://
www.nochildleftbehind.gov/
index.html). Starting with
2002-3 school districts will
publicly report graduation rates
B. Dropout rates
1. Percent of 16-19 years Census Bureau
not enrolled, not high (http://www.census.gov)
school grads
2. Dropout rates for 7-12 CCD Local Education Agency (School
and 9-12 grades District) Universe Dropout Data
(http://www.nces.ed.gov/ccd/
drpagency.asp). Downloadable in
ASCII format
3. High school dropout U.S. Department of Education, No
rates Child Left Behind (http://
www.nochildleftbehind.gov/
index.html). Starting with
2002-3 school districts will
publicly report dropout rates
C. Literacy rates
1. Reading assessment U.S. Department of Education, No
results Child Left Behind (http://
www.nochildleftbehind.gov/
index.html). Starting with
2002-3 school districts will
publicly report test results
D. Test scores
1. Reading, math, and U.S. Department of Education,
science assessment No Child Left Behind (http://
results www.nochildleftbehind.gov/
index.html). Starting with
2002-3 school districts will
publicly report test results
2. Average SAT scores The College Board (http://
E. Rates of progression to www.collegeboard.com). Releases
post-secondary education data to states/districts
1. Post-secondary Census Bureau
enrollment (http://www.census.gov)
2. Funding
A. Teacher salaries
1. Mean annual wage, Bureau of Labor Statistics
preschool, elementary, (http://www.stat.bls.gov/oes/
middle school, and home.htm). Downloadable excel
secondary teachers files
B. Facilities
1. Percent of schools with National Education Association
at least 1 inadequate (http://www.nea.org/lac/modern/
building feature modchart.html). State-level data
C. Teacher training/support
1. Professional U.S. Department of Education, No
qualifications of Child Left Behind (http://
teachers www.nochildleftbehind.gov/
index.html). Starting with
2002-3 school districts will
publicly report this information
2. Percent of expenditures School District Data Book
on instructional staff (http://www.census.gov/mp/www/
support rom/msrom6i.html). Data on
CD-ROM available for purchase
D. Fiscal capacity of school
district
1. Expenditures per pupil School District Data Book
(http://www.census.gov/mp/www/
rom/msrom6i.html) as above
2. Long-term debt School District Data Book
outstanding (http://www.census.gov/mp/www/
rom/msrom6i.html) as above
E. Proportion of funds by School District Data Book
source (http://www.census.gov/mp/www/
rom/msrom6i.html) as above
1. Revenues by source for
public schools
2. Local government Census of Governments
expenditures on (http://www.census.gov/govs/www)
education
4. Consumer expenditures See Economic Dimension, Cost of
on education Living
3. Private Schools
A. Number
1. Number of private National Private Schools
schools Association Group (http://
www.npsag.com/dalabase.html).
Commercially available database
on CD-ROM or diskette
B. Enrollment
1. Percent enrolled School District Data Book (http://
students not enrolled www.census.gov/mp/www/rom/
in public school msrom6i.html) as above
2. Enrollment in private National Private Schools
schools Association Group (http://
www.npsag.com/database.html) as
above
4. School Characteristics
A. Size of schools/classes
1. Public school School District Data Book (http://
enrollment www.census.gov/mp/www/rom/
msrom6i.html) as above. CCD data
(http://nces.ed.gov/ccd/
ccddata.asp) as above
2. Mean number students in Characteristics of the 100 Largest
primary, middle, and Public Elementary and Secondary
high school School Districts in th U.S.
(http://www.nces.ed.gov/
pubs200l/100_largest/index.asp)
B. Student/teacher ratios
1. Pupils per teacher National Center for Education
Statistics Common Core of Data
(CCD). Downloadable
comma-separated-value and excel
data tables for MSAs, counties,
districts, schools can be
created with "Build a Table"
tool (http://www.nces.ed.gov/
ccd/bat)
C. Teacher turnover
1. Rates of teacher Schools and Staffing Survey and
turnover Teacher Follow-up Survey
(http://www.nces.ed.gov)
D. Parental attitude/
involvement in schools
1. Percent of households American Housing Survey (http://
with kids 0-13 years www.census.gov/hhes/www/
old reporting ahs.htm). Data for each of 47
unsatisfactory public selected metropolitan areas are
schools in their collected about every four
neighborhood years, with an average of 12
areas included each year.
Downloadable data in SAS and
ASCII
E. School segregation
1. Race/ethnicity
A. Enrollment by National Center for Education
race/ethnicity Statistics Common Core of Data
(CCD). Downloadable
comma-separated-value and excel
data tables for MSAs, counties,
districts, schools can be
created with "Build a Table"
tool (http://www.nces.ed.gov/
ccd/bat)
B. Exposure of minority Frankenberg, E., C. Lee, and G.
students to white Orfield. 2003. A Multi-Racial
students Society with Segregated Schools:
2. Economic status Are We Losing the Dream?
(http://www.civilrightsproject.
harvard.edu/research/reseg03/
reseg03_full.php)
A. Percent of students Calculated from data in School
eligible for free lunch District Data Book (http://
F. Curriculum quality www.census.gov/mp/www/rom/
1. Physical education msrom6i.html) as above
requirements
A. Mandated requirements School Health Policies and
for physical education Programs Study (http://
2. Health education www.cdc.gov/nccdphp/dash/shpps/
index.htm). Data available in
ASCII, SAN and SPSS formats
A. Health education School Health Policies and
coordinator in place; Programs Study (http://
health education www.cdc.gov/nccdphp/dash/shpps/
standards required; index.htm). Data available in
curriculum required for ASCII, SAS, and SPSS formats
accident/injury
prevention, alcohol/
drug use prevention,
consumer health, CPR,
death and dying, dental
and oral health,
emotional and mental
health, first aid,
growth and development,
HIV prevention,
immunizations, personal
hygiene, suicide
prevention, sun safety
or skin cancer
prevention, tobacco
use, violence
prevention
3. Nutrition education
A. Nutrition and dietary School Health Policies and
behavior curriculum Programs Study (http://
required www.cdc.gov/nccdphp/dash/shpps/
index.htm). Data available in
ASCII, SAS, and SPSS formats
4. Sex education
A. Required human School Health Policies and
sexuality curriculum, Programs Study (http://
pregnancy prevention www.cdc.gov/nccdphp/dash/shpps/
curriculum, STD index.htm). Data available in
prevention curriculum ASCII, SAS, and SPSS formats
G. Preschool/Kindergarden/Early
Intervention
1. Nursery school, Census Bureau
preschool enrollment (http://www.census.gov)
H. School-based clinics
1. Number of school-based Center for Health and Health Care
health centers in Schools (http://
www.healthinschools.org/
home.asp)
I. Physical environment of
school/safety
1. Availability of drugs National Education Goals Panel
on school property; on (http://www.negp.gov)
school property %
students threatened/
injured with weapon,
involved in physical
fights, carrying a
weapon; % students who
do not feel safe on
school property; %
teachers victimized
5. Community Climate
A. Television viewing
1. Hours per week of TV Nielson Media Research
viewing, by age (http://www.nielsonmedia.com)
B. Radio stations
1. Number of radio Gale Directory of Publications and
stations Broadcast Media (http://
www.galenet.gale.com/a/acp/db/
gdpbm)
C. Reading/reading to children
1. Proportion of SRDS Corporation
households receiving (http://www.srds.com)
daily newspapers
2. Number of local Gale Directory of Publications and
newspapers Broadcast Media (http://
www.galenet.gale.com/a/acp/db/
gdpbm)
D. Libraries
1. Number of libraries; Public Libraries Survey
number of library books (http://www.nces.ed.gov/surveys/
and serial volumes libraries)
Table 5: Selected Data Sources Relevant to the Political Dimension
INDICATORS DATA SOURCE
1. Civic Participation
A. Voting
1. Voting and registration
rates
A. Votes cast for U.S.A. Counties (http://www.census.
president, by party gov/statab/www/county.html). Data
available on CD-ROM; online data
for single counties downloadable as
text or comma-separated-value file
B. Percent of persons Census Bureau (http://www.census.gov/
registered to vote and prod/3/98pubs/p20-504u.pdf). State
voting by race/ data in pdf file
ethnicity
2. Ease of registration
A. Voter registration by Moving and Relocation Sourcebook and
mail allowed; Directory (http://www.omnigraphics.
registration deadline com). Hardcover book available for
prior to election purchase. See Voting and
registration rates above
3. Racial/ethnic
representativeness of
registered voters
B. Census participation
1. Census response rates Census Bureau (http://www.census.
gov). See Voting and registration
rates above
C. Political party
membership
D. Donations to parties and
candidates
1. Donations to Republican Center for Responsive Politics (http:
and Democratic //www.opensecrets.org).
candidates, parties, Contributions for selected
and political action metropolitan areas and zip codes,
committees and for states
2. Political Structure
A. Gender/racial/ethnic
representation in elected
office
1. Women in governing body Carpenter, A. 1996. Facts about the
Cities. New York: H.W. Wilson
2. Elected officials in Census of Governments (http://www.
local governments by census.gov/govs/www). From webpage
sex and race and state select Census of Governments for
1992; select Vol. 1, Nov. 2,
"Popularly Elected Officials." Pdf
file
3. Percent women in Center for American Women and
statewide elective Politics (http://www.rci.rutgers.
office edu/-cawp/)
4. Blacks in elected Joint Center for Political and
office Economic Studies' DataBank (http:
//www.jointcenter.org/DB/index.htm)
B. Percent of local budget
for public health
investments
1. Expenditures for health Census of Governments (http://www.
and welfare census.gov/govs/www). From webpage
select year of interest; select
Vol. 4, No. 3 or 4, "Finances of
County or Municipal and Township
Governments" or downloadable
spreadsheet or comma-separated-
value files
3. Power Groups
A. Community organizations
1. Number and size of County Business Patterns (http://www.
organizations: census.gov/epcd/cbp/view/cbp/view/
religious, political cbpview.html). From webpage select
civic and social, "County, State, U.S., ZIP or MSA
social advocacy, human Database on a NAICS Basis," select
rights, environmental area of interest; in "Number of
and wildlife, business, Establishments" table select detail
labor, grant making and for Industry Code 81, Other
giving Services. Data downloadable as text
or comma-separated-value tables;
CD-ROM also available
B. Unions See Employment Dimension, Workforce
Characteristics
Table 6: Selected Data Sources Relevant to the Environmental Dimension
INDICATORS DATA SOURCE
1. Air Qualify
A. Outdoor
1. Peak air concentration Environmental Protection Agency
carbon monoxide, lead, (http://www.epa.gov/airtrends).
nitrogen dioxide, From webpage select "Metropolitan
ozone, sulfur dioxide; Area Trends"; choose Table A-15 for
particulate matter air peak concentrations, Table A-17 for
concentration; days Air Air Quality Index. Pdf files
Quality Index > 100
2. Total pounds air Environmental Protection Agency,
chemicals emitted by Toxics Release Inventory (http://
industry, by chemical www.epa.gov/tri). From webpage
select "Get TRI Data"; select "TRI
Explorer"; under "Chemical
Released" choose "Select a Chemical
Group," then "Hazardous Air
Pollutants"; select geographic area
of interest, then generate
downloadable report
B. Indoor
1. Percent of households American Housing Survey (http://www.
reporting neighborhood census.gov/hhes/www/abs.htm). Data
odor to be a problem or for each of 47 selected
bothersome metropolitan areas are collected
about every four years, with an
average of 12 areas included each
year. Downloadable data in SAS and
ASCII
2. Water Quality
A. Number of violations per Environmental Protection Agency
year for federally (http://www.epa.gov/safewater/data/
regulated drinking water pivottables.html#summdetails).
contaminants Downloadable compressed Excel files
B. Total pounds surface Environmental Protection Agency,
water chemicals Toxics Release Inventory (http://
discharged by industry, www.epa.gov/tri). From webpage
by chemical select "Get TRI Data"; select "TRI
Explorer"; select report by
industry or chemical, choose
geographic area of interest, then
generate downloadable report
3. Environmental Hazards
A. Hazardous waste
1. Total pounds of Environmental Protection Agency,
chemical waste released Toxics Release Inventory (http://
by industry, by www.epa.gov/tri). From webpage
chemical select "Get TRI Data"; select "TRI
Explorer"; select report by
industry or chemical, choose
geographic area of interest, then
generate downloadable report
B. Heavy metals
1. Total pounds of Environmental Protection Agency,
selected heavy metals Toxics Release Inventory (http://
released by industry www.epa.gov/tri). From webpage
select "Get TRI Data"; select "TRI
Explorer"; under "Chemical
Released" choose "Select a Chemical
Group," then "Metals and Metal
Compounds"; select geographic area
of interest, then generate
downloadable report
C. Pesticides
1. Total pounds of Environmental Protection Agency,
pesticide chemicals Toxics Release Inventory (http://
www.epa.gov/tri). From webpage
select "Get TRI Data"; select "TRI
Explorer"; select report by
industry or chemical, choose
geographic area of interest, then
generate downloadable report
D. Climate extremes
1. Maximum and minimum Statistical Abstract of the United
temperatures States (http://www.census.gov/
statab/www). From webpage select
desired year; select "Geography and
Environment"
E. Noise
1. Percent of households American Housing Survey (http://www.
reporting noise to be a census.gov/hhes/www/ahs.htm). Data
problem or bothersome for each of 47 selected
metropolitan areas are collected
about every four years, with an
average of 12 areas included each
year. Downloadable data in SAS and
ASCII
4. Physical Safety
A. Traffic
1. Total miles of local Federal Highway Administration (http:
roads; total vehicle //www.fhwa.dot.gov/policy/ohpi/hss/
miles of local road hsspubs.htm). From webpage select
travel daily "Highway Statistics" for desired
year; select "Roadway Extent,
Characteristics, and Performance."
Pdf and Excel files
2. Percent of households American Housing Survey (http://www.
perceiving traffic as a census.gov/hhes/www/ahs.htm). Data
problem or bothersome for each of 47 selected
metropolitan areas are collected
about every four years, with an
average of 12 areas included each
years. Downloadable data in SAS and
ASCII
B. Street repair
1. Percent of households American Housing Survey (http://www.
reporting major street census.gov/hhes/www/ahs.htm). Data
repair needed in their for each of 47 selected
neighborhood metropolitan areas are collected
about every four years, with an
average of 12 areas included each
year. Downloadable data in SAS and
ASCII
5. Land Use
A. Public recreational
space/number of parks
1. Expenditures on natural Census of Governments (http://www.
resources, parks, and census.gov/goys/www). From website
recreation select desired year; select Vol. 4,
No. 3, 4, "Finances of County or
Municipal and Township
Governments," or downloadable state
and local government finance data
B. Waste disposal/dumping/
sanitation services
1. Pounds of waste managed Environmental Protection Agency,
Toxics Release Inventory (http://
www.epa.gov/tri). From webpage
select "Get TRI Data"; select "TRI
Explorer"; select report by
industry or chemical, choose
geographic area of interest; choose
waste quantity reports; generate
downloadable report
2. Percent households American Housing Survey (http://www.
reporting major trash, census.gov/hhes/www/ahs.htm). Data
litter, or junk on for each of 47 selected
streets near their metropolitan areas are collected
home; reporting about every four years, with an
neighborhood litter/ average of 12 areas included each
deterioration to be a year. Downloadable data in SAS and
problem or bothersome; ASCII
reporting poor city or
county services in
neighborhood
C. Curbside recycling
programs
1. Pounds of waste Environmental Protection Agency,
transferred to Toxics Release Inventory (http://
recycling www.epa.gov/tri). From webpage
select "Get TRI Data"; select "TRI
Explorer"; select report by
industry or chemical, choose
geographic area of interest; choose
waste quantity reports; generate
downloadable report
Table 7: Selected Data Sources Relevant to the Housing Dimension
INDICATORS DATA SOURCE
1. Housing Stock
A. Age
1. Median age of housing Census Bureau (http://www.census.gov)
units
2. New private housing 1. State and Metropolitan Area Data
units authorized by Book (http://www.census.gov/statab/
building permits as a www/smadb.html)
percent of housing 2. State of the Nation's Housing
stock (http://www.jchs.harvard.edu). From
webpage choose publications, then
most recent edition
B. Scarcity
1. Percent housing units Census Bureau (http://www.census.gov)
vacant
C. Value
1. Median value, owner Census Bureau (http://www.census.gov)
occupied housing units
2. Median sales price of State of the Nation's Housing
existing homes (http://www.jchs.harvard.edu). From
webpage choose publications, then
most recent edition
3. Valuation of See Economic Dimension, Fiscal
residential Capacity
construction
D. Characteristics
1. Percent of housing Census Bureau (http://www.census.gov)
units lacking complete
kitchen facilities,
complete plumbing
facilities, and/or
telephone
2. Percent of households American Housing Survey (http://www.
reporting 1 or more census.gov/hhes/www/ahs.htm). Data
vandalized buildings in for each of 47 selected metropoli-
neighborhood; percent tan areas are collected about every
of households with four years, with an average of 12
best/worst opinion of areas included in each year.
their neighborhood (10 Downloadable data in SAS and ASCII
point scale)
E. Gentrification/gatedness
1. Percent of home loans State of the Nation's Housing (http:
to high-income //www.jchs.harvard.edu). From web-
borrowers made in page choose publications, then most
low-income areas of recent edition
central cities
F. Rental vs. owner occupied
1. Percent of occupied Census Bureau (http://www.census.gov)
housing units that are
owner occupied
2. Residential Patterns
A. Homelessness
1. Estimated homeless U.S. Conference of Mayors (http://
population www.usmayors.org/uscm/home.asp).
From webpage select "Hunger and
Homelessness" from "Reports and
Publications"
B. Number of institutional
facilities
1. Number of homeless U.S. Conference of Mayors (http://
shelter beds; number of www.usmayors.org/uscm/home.asp) as
months wait for public above
housing and Section 8
vouchers
C. Segregation
1. Racial/ethnic
A. Indices of Iceland, J., D.H. Weinberg, and E.
dissimilarity, Steinmetz Racial and Ethnic Resi-
isolation, delta, dential Segregation in the United
absolute centraliza- States: 1980-2000. Census 2000.
tion, and spatial Special Report (http://www.
proximity landview.census.gov/hhes/www/
housing/resseg/pdftoc.html)
2. Economic See Economic Dimension, Income
D. Vacancy rates
1. Percent housing units Census Bureau (http://www.census.gov)
vacant
E. Crowded housing
1. Mean number of persons Census Bureau (http://www.census.gov)
per room
F. Population density
1. Persons per square mile Census Bureau (http://www.census.gov)
3. Regulation
A. Zoning policies
1. Percent of households American Housing Survey (http://www.
perceiving undesirable census.gov/hhes/www/abs.htm). Data
commercial, institutio- for each of 47 selected metropoli-
nal, or industrial use tan areas are collected about every
as a problem or four years, with an average of 12
bothersome areas included each year. Downloa-
dable data in SAS and ASCII
B. Industrial/residential
segregation
1. Segregation indices for Anderton, D. L. and K. L. Egan 2002
blacks, whites, and Industrial and Residential
Hispanics from high- Segregation: Employment Opportuni-
employment and hazar- ties and Environmental Burdens in
dous manufacturing Metropolitan Areas. (http://www.
industries umass.edu/sadri/papers/wp20002.
pdf).
4. Financial issues
A. Housing costs
1. Cost of living index, American Chamber of Commerce Resear-
housing and utilities; chers Association (http://www.
average 950 sq. ft. accra.org). Quarterly and annual
apartment rent, 2,400 average data may be purchased as
sq. ft. new home price, downloadable spreads