Health Education Research, Vol. 15, No. 1, 109-116,
February 2000
© 2000 Oxford University Press
Short Communication |
Promising community-level indicators for evaluating cardiovascular health-promotion programs
Department of Health Services, University of Washington, Seattle, WA 98195,
1 Division of Chronic Disease Control and Community Intervention, Centers for Disease Control, Atlanta, GA 30341, and
2 Work Group on Health Promotion and Community Development, University of Kansas, Lawrence, KS 66045, USA
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Abstract |
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Rigorous evaluation of community-based programs can be costly, particularly when a representative sample of all members of the community are surveyed in order to assess the impact of a program on individual health behavior. Community-level indicators (CLIs), which are based on observations of aspects of the community other than those associated with individuals, may serve to supplement individual-level measures in the evaluation of community-based programs or in some cases provide a lower-cost alternative to individual-level measures. Because they are often based on observations of the community environment, CLIs also provide a way of measuring environmental changes—often an intermediate goal of community-based programs. The Centers for Disease Control and Prevention convened a panel of experts knowledgeable about community-based program evaluation and cardiovascular disease (CVD) prevention to develop a list of CLIs, and rate their feasibility, reliability and validity. The indicators developed by the panel covered tobacco use, physical activity, diet and a fourth group that were considered `cross-cutting' because they related to all three behaviors. The indicators were subdivided into policy and regulation, information, environmental change, and behavioral outcome. For example, policy and regulation indicators included laws and ordinances on tobacco use, policies on physical education, and guidelines for menu and food preparation. These indicators provide a good starting point for communities interested in tracking CVD-related outcomes at the community level.
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Introduction |
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Community-based approaches to health promotion and disease prevention have become increasingly popular and are believed to offer an effective strategy for addressing many health problems, including cardiovascular disease (CVD) and cancer (Farquhar et al., 1977
, 1990; Maccoby et al., 1977; Rose, 1981; Blackburn, 1983; Kottke et al., 1985; Puska et al., 1985; Mittelmark et al., 1986; Ramirez and McAlister, 1988; Shea and Basch, 1990; COMMIT Research Group, 1995a; Glasgow et al., 1995; Lando et al., 1995). Changing risk factors in the community as a whole rather than focusing on specific high-risk individuals can be a cost-effective approach to disease prevention (Mittelmark et al., 1986). Also, community-based approaches offer a way of changing overall community attitudes about health problems, which may be a prerequisite for sustaining individual-level improvements in health behavior (Curry et al., 1993).
Rigorous evaluation of community-based programs can be costly, particularly when a representative sample of all members of the community are surveyed in order to assess the impact of a program on individual health behavior. In multi-community trials, survey costs can approach or even exceed the intervention budget. We have previously suggested that `environmental' or `community-level' indicators (CLIs) might serve to supplement individual-level measures in the evaluation of community-based programs or in some cases provide a lower-cost alternative to individual-level measures (Cheadle et al., 1992; Fawcett et al., 1999). Because they are often based on observations of the community environment, CLIs also provide a way of measuring environmental changes—often an intermediate goal of community-based programs (Glanz et al., 1995; Stokols et al., 1996). In this context the term `environment' is broadly defined to include aspects of the legal, social, political as well as the physical environment.
CLIs as we have conceptualized them are most easily defined `negatively': they consist of all community measures that are not derived from individual-level information. Examples of individual-level data sources used in constructing community measures include mail and telephone surveys, health insurance claims information, census data, vital records, and disease registries. Examples of CLIs include grocery store shelf-space measures (e.g. percent of shelf space that is low-fat milk) (Cheadle et al., 1990, 1991, 1993, 1995; Fisher and Strogatz, 1999) and characteristics of restaurant no-smoking areas (e.g. percent of seating that set aside as non-smoking) (Cheadle et al., 1994). Given this negative, open-ended definition, there are a potentially very large number of CLIs—the only limiting factor is that they are feasible to collect and yield meaningful (i.e. valid and reliable) measures of an important dimension of community health. In practice, however, since most community-based programs focus on the health and well-being of individuals, most community measures developed to date have been aggregates of individual-level information.
CLIs as we define them are not a new idea—they have been used in a variety of disciplines and specifically in evaluations of other community-based health programs. These previous efforts will be reviewed in more detail in the Discussion below. However, the literature on alternatives to individual-level measures is limited enough that the Centers for Disease Control and Prevention (CDC) deemed it useful to convene a working group with the express purpose of generating indicators that were not based on individual-level characteristics. This paper presents examples of CLIs generated by the CDC working group.
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Methods |
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The Community Level Indicators of Cardiovascular Health project was part of an effort to build capacity for evaluation of community-based initiatives to reduce risks for CVD (Elder et al., 1993
; Schwartz et al., 1993; Mittelmark, 1993; Fawcett et al., 1995). A modified Delphi technique (Worthen and Sanders, 1987) was used to identify CLIs of cardiovascular health. The Delphi technique is a survey procedure that is designed to develop consensus among experts with diverse backgrounds. The experts generally respond independently of each other and the process is iterative, typically consisting of three or more rounds of data refinement. Conference organizers from CDC identified nationally known experts in program evaluation and CVD prevention based on their professional reputations, and invited them to participate as panelists for the Delphi process. Twenty experts representing state public health departments, academic institutions and CDC participated in three rounds of the Delphi survey. The first survey asked panelists to generate potential indicators for the three major behavioral risk factors of CVD: diet, physical inactivity and tobacco use. To guide the selection process, respondents were asked to consider the sectors that would be involved in a community effort (e.g. work sites, schools, religious institutions, health care agencies, public and private agencies) and the strategies that could be employed to promote changes in a community (e.g. information, skill building, policy and regulation, environmental change, and barrier modification).
For the second round, a subset of 15 panelists (nine representing academia and state public health departments; six from CDC) plus about 12 additional participants from CDC attended a 2-day meeting in Atlanta. During this meeting participants reviewed and refined the lists of indicators, added important or missing indicators, and deleted indicators considered unimportant or clearly not feasible. The resulting lists of indicators were then sent to all panelists after the meeting, who were asked to rate the indicators (round three) according to their quality (accuracy, sensitivity, reliability, validity) and feasibility (cost, ease of data collection), as well as to give each indicator an overall, global rating (not necessarily an average of quality and feasibility). The results were then tabulated and a final list, including mean ratings, distributed to all panelists for final comments.
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Results |
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In all, 141 separate indicators were generated over the course of the Delphi process and subsequently rated by panelists. Tables I–IV
present selected indicators from this overall list. Indicators were not shown if they were not `true' CLIs, i.e. if they were based on observations that could be linked directly to individuals (e.g. smoking prevalence). [Note that there are gray areas—measures derived from sales data were included as CLIs even though conceptually they can be linked to individuals. In practice, sales data are almost always reported at higher levels of aggregation (store, city, county)]. Other indicators were not shown because they were somewhat redundant or were rated by panelists as having low feasibility or validity.
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The indicators are organized by risk behavior: tobacco use (Table I
), physical activity (Table II), diet (Table III) and `cross-cutting'—those that applied to all three behaviors (Table IV). Within each risk behavior, the indicators are further subdivided by whether they refer primarily to policy and regulation, information, environmental change, and behavioral outcomes. The mean ratings assigned by the panelists are also shown in the tables: the overall rating plus separate ratings for quality (accuracy, sensitivity, reliability, validity) and feasibility (availability, accessibility, cost).
We now summarize the indicators shown in Tables I–IV. Policy and regulation indicators primarily included laws and ordinances for tobacco use, policies related to physical education (PE) for physical activity, and guidelines for menus and food preparation for diet. No cross-cutting indicators were identified for policy and regulation. Under information indicators, all three health behaviors included `point of purchase' information (in a variety of media), as well as measures of how much information is provided by various health-related professionals. Environmental change indicators for tobacco included limitations on access to tobacco products and availability of no-smoking areas in a variety of settings. Measures for physical activity focused on the availability of facilities; and dietary measures focused on the availability of healthy products. Cross-cutting environmental measures included program availability, screenings and the number of agencies sponsoring CVD-related activities. Behavioral outcome indicators included sales data and observations in stores (e.g. proportion of milk that is of the low-fat variety).
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Discussion |
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The CDC assembled a working panel of experts in community-based health promotion evaluation and CVD prevention to generate promising CLIs for use in evaluating community-based CVD programs. CLIs are based on observations of aspects of the community other than those associated with individuals. This paper presented examples of candidate indicators generated by the working group for tobacco use, physical activity, and diet and nutrition. The indicators were grouped in four broad categories: policy and regulation, information, environmental change, and behavioral outcome. Panelists gave overall ratings of the potential usefulness of the indicators as well as separate ratings of their quality and feasibility.
Two features of CLIs make them useful for evaluations of community-based health interventions. First, by avoiding individually based measures, CLIs may be cheaper to collect, e.g. visiting 10 large workplaces or grocery stores rather than surveying 1000 people. In a previous study, we showed that grocery store shelf-space measures could detect community-level changes in dietary indicators (e.g. percent drinking low-fat milk) with roughly the same relative power as individual-level surveys at less than one-tenth of the cost (Cheadle et al., 1990). Second, CLIs are often derived from aspects of the community environment, which is a target of many community-based interventions and therefore important to measure as an intermediate outcome.
It is useful to place the CLIs in the context of the existing literature. There is a vast literature on social indicators that can be applied to communities, but most of the measurements are made at the individual level and then aggregated to larger geographic units (e.g. cities, counties). Individual-level measures include census information, employment surveys, disease registries, mortality data and health surveys. There are some examples of non-individually based measures, e.g. measures of health system capacity (such as number of hospital beds) (Fitzsimmons and Lavey, 1975; Carley, 1981).
Another important strand in the literature related to CLIs are the unobtrusive or non-reactive measures collected and categorized by Webb and Sechrest [see Webb et al., 1966)]. A measure is unobtrusive if the object of interest is unaware that they are being observed. Non-reactive measures do not suffer from the problem of reactivity bias, i.e. the `true' response is not altered by the process of measurement (Sechrest and Phillips, 1979). All unobtrusive measures are non-reactive, but some non-reactive measures may be highly obtrusive (e.g. blood tests). Unobtrusive measures are used frequently in social psychology because of the high probability of reactivity bias, e.g. in studies of attitudes toward race (Bochner, 1979). A number of measures reported in the literature are based on characteristics of the community environment [e.g. graffiti (Sechrest and Belew 1983)] and can therefore meet our definition of CLIs.
Some of the indicators generated by the CDC working group have already been applied in other studies, particularly in the area of environmental and regulatory interventions related to tobacco use. One line of research examined their utility with community coalitions for reducing risks for CVD (Paine-Andrews et al., 1997). Several studies have profiled the stringency of tobacco-related laws in a state or county (Forster et al., 1992, 1996; Kolpien and Lippert, 1995; Cismoski et al., 1997; McDermott et al., 1998). Others have used the stringency of tobacco-related laws to evaluate the effectiveness of environmental interventions related to tobacco (Feighery et al., 1991; Rogers et al., 1995; DiFranza et al., 1996; Elder et al., 1996). Finally, there have been studies looking at changes in the availability of worksite programs as an indicator of the effectiveness of worksite smoking interventions (Fielding, 1990; Weisbrod et al., 1991).
An important limitation of the measures presented in the paper is that they are somewhat sketchy, since the goal of the process was to brainstorm and generate as many indicators as possible, not to try and refine them into operational measures. For example, in Table I the first indicator listed under `policy and regulation' is `clean air laws for public buildings, restaurants, work sites, etc.'. To create operational measures, the characteristics of clean air laws would need to be further specified, e.g.. indicators for restaurants might include presence of a clean air law related to restaurants (yes/no), stringency of the law (percent of seating required to be set aside), resources devoted to enforcement, number of violators cited and fines collected. Because the actual procedures for making the measures operational were not specified, the ratings of feasibility shown in the tables should be viewed with some skepticism.
The capability of these indicators to usefully track community-level changes can only be assessed by using them in actual program evaluations. The next step in the CDC-organized process is to collaborate with coalitions and other community-based initiatives to implement some of the suggested indicators. A web page has been set up to provide examples of CLIs and assistance for groups interested in using CLIs to track changes in their own communities (http://faculty.washington.edu/cheadle/cli/), as well as a more general resource for community evaluation: the Community Tool Box (http://ctb.lsi.ukans.edu/). CDC staff have been working with a variety of people outside traditional public health disciplines to identify potential CLIs for physical activity. Representatives from transportation, urban design, architecture, environmental protection and other disciplines identified a number of candidate CLIs for walking and bicycling. For instance, housing density, number of sidewalks, street connectivity (pattern of intersection and proximity of trip destinations), access to transit and proximity to walking/bike paths may be useful indicators of the level of walking and biking within a community. Further research is being done to validate these measures and determine their usefulness for program planning and evaluation.
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Acknowledgments |
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This work was supported in part by a grant from the National Cancer Institute (90-2118-06).
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Received on April 2, 1998; accepted on March 23, 1999
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