GOALS OF CAS COMPARED TO INTERVENTION MONITORING AND SUMMATIVE EVALUATION

Community assessment, surveillance, and intervention monitoring and summative evaluation are distinct sets of activities with complementary goals. The goal of community assessments is usually a first-time assessment of status or trends overall. Surveillance provides repeated or continuous assessments of progress over time, whereas intervention monitoring and summative evaluation seeks to establish and share “what works.” The combination of first-time assessment and ongoing surveillance (or CAS) can document, at the local level, associations of the status or trends in obesity prevalence with behaviors, social factors, environments, or interventions. Linking these with the monitoring of implementation of interventions becomes the main sources of data for evaluation. Intervention summative evaluations seek to move beyond association to determine whether observed changes in outcomes can be associated with and, ideally, attributed to the intervention or combination of interventions.

These two purposes—assessing status or progress and evaluating whether interventions are working—require different types or levels of evidence. When assessing status or progress, evidence of current levels or trends in obesity and its determinants (e.g., behaviors, environments, programs, systems, and policies) can be sufficient, without necessarily attributing cause. Causal assumptions will be inevitable because some determinants are found to be above state or national averages, suggesting that interventions need to be developed with those determinants as targets. For implementers of community initiatives, this information can help them to decide whether their approaches are on target or need adjustment. For local efforts that need to show progress to constituents or funders, evidence of progress may be sufficient for accountability. In contrast, when evaluating interventions, the strongest evidence possible is desirable, and this means place-based experiments usually including a comparison or control condition, or the strongest feasible quasi-experiments (see Chapter 8).

CAS and intervention monitoring and summative evaluation interact and share some similarities. Intervention monitoring and summative evaluation can use data generated by CAS and can suggest topics for inclusion in data collection and vice versa. Combining CAS data across communities can contribute data to multi-site assessment designs. Both can incorporate community engagement and participatory research methods.

WHY MEASURE STATUS OR PROGRESS AT THE COMMUNITY LEVEL

Many of the factors that determine obesity rates—and the decisions to change those factors—are local and, therefore, so are many of the APOP strategies (IOM, 2003, 2012a). In communities, for example:

• planning and land use decisions create built environments that support walking and biking and increase access to better food choices and limit exposure to unhealthful foods;
• schools provide better food choices and more opportunities for physical activity;
• organizations provide and support community programs designed to increase physical activity;
• local governments, organizations, and institutions adopt comprehensive strategies to reduce overconsumption of sugar-sweetened beverages and to implement nutritional standards for foods and beverages available at government and public sites;
• health care providers improve practices for prevention, screening, diagnosis, and treatment of overweight and obesity; and
• employers encourage active living and healthful eating at work.

At the local level, people can be more creative and innovative than at the federal or state level. Local communities, in short, can provide direct services, implement policy, change environments, and create systems changes.

LOCALITY-SPECIFIC AND DIVERSE DATA

Not every strategy enumerated by APOP's recommendations (IOM, 2012a) can be expected to be appropriate to the specific circumstances of each community. Because local challenges and assets vary widely across America's communities, selection and adaptation of evidence-based strategies may be most appropriately decided in each community. Local communities across the nation vary widely with respect to population size; cultural, racial and ethnic diversity; and impact of obesity. Local capacities for assessment and surveillance are also highly variable, with a wide range of skills and resources for developing and using health and other data. The Committee tried to account for this heterogeneity by developing a tiered set of guidance suitable to diverse communities and initiatives of varying scales and intensities with differing levels of resources for community assessment and surveillance. Because it is possible for local assessors to connect directly with community organizations, and in some cases residents, the potential for community engagement and use of community-based participatory research (CBPR) methods is greater for CAS than for assessment of progress nationally.

OVERVIEW OF COMMUNITY ASSESSMENT AND SURVEILLANCE

Community assessment² is a process that involves the systematic collection of data over time at the community level for the purposes of describing current health status and determinants of health at points in time and trends over time (Cibula et al., 2003). Community assessment and surveillance may be global assessments of the health of a community or focus specifically on chronic diseases, or more specifically on obesity. An obesity-focused community assessment and surveillance can draw attention to obesity as a priority health concern and include more obesity-related information than a broader CAS. In both cases, they should include indicators that assess progress in obesity prevention, such as obesity prevalence³; obesity-related behaviors such as physical activity and food and beverage consumption; features of the environment that influence behaviors such as accessibility of healthful foods, walkability, or places for physical activity; policies that shape environments and behaviors, nutrition, and physical activity programs; other interventions such as media campaigns or food retail promotion of healthier foods; levels of funding for obesity prevention initiatives; transportation systems; and social assets (e.g., groups with a history of working together to promote health, community leadership and champions, and political will). CAS also may include information on community contextual factors that influence obesity (Fawcett et al., 2011), such as demographics of the community, and social determinants of health leading to differential exposure and vulnerabilities (e.g., education and unemployment, income inequality, racism/discrimination, social norms, social capital, residential proximity to walkable areas, and “food deserts”). Ideally, they also describe policies that shape environments and behaviors such as menu labeling or pedestrian master plans, as well as the interactions of sectors and institutions in addressing obesity from a systems analysis perspective, although data and methods for these domains are just emerging (see Chapter 9). CAS displays and disseminates data through reports, presentations, and websites using a variety of data description and visualization methods (e.g., maps of available community parks and supermarkets).

The general tasks of CAS in the context of this report are to describe the current state of obesity-related and contextual indicators and track them over time. The information gathered from CAS can identify areas that need improvement, monitor the implementation or emergence of policies, programs, or other interventions, and track changes in contextual influences. These provide various forms of data to facilitate planning for future actions and to examine the effects of interventions over time. CAS systems can range from simple reports of generally available indictors easily accessed on the Web to intensive projects that involve a combination of primary and secondary data collection, sophisticated qualitative and quantitative data analysis, and advanced dissemination and visualization techniques. This chapter describes uses of CAS in the context of this report, commonly used indicators and some innovative ones, sources of data for these indicators, methods for conducting CAS, examples of typical and exemplary CAS, gaps in current CAS indicators and methods, and recommendations for obesity-focused CAS. Box 7-1 provides an actionable plan to implement a community obesity assessment. The rest of the chapter provides support and guidance for implementing each step with specific attention to different needs for larger and smaller communities. This support and guidance includes where possible the identification of existing tools, resources, and methods for consideration framed around assessing the environmental and policy strategies recommended in the APOP report (IOM, 2012a).

BOX 7-1

Components of a Community Obesity Assessment and Surveillance Plan. Purpose: To provide accurate and timely knowledge of local obesity-related conditions and relevant changes or trends over time as a result of implementing strategies in the Institute (more...)

DEFINE COMMUNITY BOUNDARIES

The Committee defines community level as activities conducted by local governmental units (e.g., cities, counties), school districts, quasi-governmental bodies (e.g., regional planning authorities, housing authorities) and private-sector organizations (e.g., hospitals, businesses, after school programs). In this context, community is defined as people sharing a common place (e.g., city, neighborhood); they may also share a common experience (e.g., living in a neighborhood with few grocery stores or parks or living in poverty) or interest (e.g., working together to promote better nutrition or active living) (IOM, 2012b). A community may also be defined as a group of people who identify themselves as sharing a common interest or culture, but this interpretation is only applicable here to the extent that such a common-interest community is local.

Geographic community definitions can be based on jurisdictional boundaries (e.g., city, county, school district, hospital district), census-defined boundaries (e.g., census places or metropolitan/micropolitan statistical areas), or customized boundaries (e.g., aggregations of census tracts or ZIP codes). The choice of geographic boundaries often depends on availability of data for the area of interest.

CAS can describe and track health inequities among different groups; for example, those sharing race/ethnicity, gender, sexual orientation, income, and geography. By displaying indicators stratified by demographic and geographic strata, it becomes apparent whether progress in preventing obesity is occurring equitably (see Figures 7-1 and 7-2 for examples). The boundaries of the community must be explicit to help to clarify the conditions of that particular community and to identify the appropriate set of indicators (McIntyre and Ellaway, 2000).

FIGURE 7-1

Example of illustrating community health indicator data—map. SOURCE: Used with permission from Public Health—Seattle & King County (King County, 2013b).

FIGURE 7-2

Examples of illustrating community health indicator data—bar chart. NOTE: AN = Alaskan Native; PI = Pacific Islander. SOURCE: Used with permission from Public Health—Seattle & King County (King County, 2013c).

ENGAGE COMMUNITY MEMBERS AND OTHER KEY STAKEHOLDERS

Collaborative approaches to CAS involving government, community organizations, and private-sector stakeholders have gained recent recognition for addressing the complex set of factors associated with population health. Engaging community members and private-sector stakeholders in planning and sense-making is essential to understanding, implementing, and sustaining community assessments and surveillance and health improvement efforts (IOM, 2003). Interested stakeholders include community organizations and coalitions, hospitals, local public health agencies, human service agencies, schools, business, and community health centers. Meaningful participation extends beyond physical presence of community members to include their active engagement in generating ideas, contributing to decision making, and sharing responsibility for taking action (NIH, 2011). Stakeholders can engage during some or all phases of CAS, including

• Review/revise community definition, participating stakeholders;
• Assess stakeholder priorities for focus/topics, for assessment/surveillance, and to engage in planning the assessment;
• Determine resources and capacities among participants (e.g., staff, technical skills, data, funding, etc.) available for conducting assessment/surveillance;
• Make community participation and involvement easier (i.e., enhance access by arranging meetings at times and places convenient for community members, with language/physical access, transportation, child care, and other necessary accommodations); and
• Include community members in data collection and interpretation of results, and disseminate findings (detailed throughout this chapter).

The extent and type of end-user engagement should be appropriate to the scale and scope of each specific community assessment or development of surveillance capacity.

The principles and methods developed for conducting CBPR are well-suited for promoting community engagement in assessment and surveillance of the assets of the community; identifying local concerns; designing and conducting the assessment/surveillance; interpreting, disseminating, and translating the findings; and sustaining and evaluating partnerships that act on the assessment/surveillance findings (Fawcett et al., 2003; Israel et al., 2013; Minkler and Wallerstein, 2008). CBPR methods can contribute to assuring accurate findings that describe true conditions in the community because they bring diverse perspectives and knowledge bases into the assessment/surveillance process. CBPR contributes to bringing together assessment/surveillance professionals and the community “to establish trust, share power, foster co-learning, enhance strengths and resources, build capacity, and examine and address community-identified needs and health problems” (Israel et al., 2013, p. 14), especially in communities affected by health inequities. Box 7-2 summarizes some key aspects.

BOX 7-2

Increasing Participation in Community Assessment and Surveillance (CAS). Identify those community members and groups, including those experiencing health disparities, that have a stake in community health improvement and conducting a CAS. Invite members (more...)

An example of the application of participatory methods to community obesity assessment and surveillance comes from the work of Faith Leaders for Environmental Justice in New York City (see Box 7-3).

BOX 7-3

Community-Based Participatory Research: The Faith Leaders for Environmental Justice. The Faith Leaders for Environmental Justice (FLEJ), a group of individuals and organizations in New York City with interest in mobilizing communities around environmental (more...)

PLAN ASSESSMENT/SURVEILLANCE

Planning a CAS includes identifying a lead agency responsible for conducting it; clarifying its goals; defining the target audience and what information will move them to action; defining topics to include in the assessment/surveillance; identifying sub-populations and small areas disproportionately affected by obesity; developing approaches to collecting information about them; and selecting local data about context, assets, interventions, barriers, and social determinants.

Valuable resources are available for conducting CAS. The Community Tool Box, Centers for Disease Control and Prevention (CDC), National Association of County and City Health Officials, state health departments, and others offer guidance on methods for conducting CAS. Box 7-4 provides a list of example tools and resources that are available for planning CAS.

BOX 7-4

Example Tools and Resources for Planning Community Assessment/Surveillance. Association for Community Health Improvement: Community Health Assessment Toolkit—http://www.assesstoolkit.org Catholic Health Association: Assessing and Addressing Community (more...)

Define Topics to Include in Assessment/Surveillance

Decisions about topics to include in a CAS are driven by the interests of local stakeholders, availability of data for the community of interest, and the scope and purpose. The Committee recommends including core indicators available at the local level from national data sets (see below), including routinely available local data (e.g., student body mass index [BMI] from schools; health system data; planning department data; crime data; surveys; Selected Metropolitan/Micropolitan Area Risk Trends [SMART]; Behavioral Risk Factor Surveillance System [BRFSS]; local Youth Risk Behavior Surveillance System [YRBSS]; and the Special Supplemental Nutrition Program for Women, Infants, and Children program data), and adding additional indicators of local interest after assessing the feasibility of obtaining local data to describe them. The Committee recommends, when possible, including assessment and surveillance of environments that influence food and physical activity behaviors and of policies and norms and attitudes about causes of obesity and willingness to support strategies to prevent obesity as recommended in the APOP report (IOM, 2012a).

Quantitative Data Sources and Systems Available at the Local Level

Data and indicators are available but options limited. Many indicators have relevance to assessment and surveillance of progress in obesity prevention (see Chapter 4). In practice, local communities use a very small subset of these indicators. An important finding of the Committee is that federal data sources are woefully inadequate for describing progress in obesity prevention at the community level because they do not provide data at the local level, either at all or with sufficient sample sizes, coverage, frequency, and timeliness. Larger communities and states tend to have more data because their populations are large enough that sample sizes in current federal data sources are sufficient to permit statistically appropriate analyses or they have resources to oversample their populations to generate sufficient sample sizes within statewide or national samples.

Table 7-1 provides a summary of data sources that have estimates of obesity prevention–related data available at the community level. These existing federal data systems have significant limitations. For example, BRFSS sample sizes are too small to allow subgroup or stratified analyses (e.g., by race or income) in most communities. In addition, data are not consistently available even for large communities over time. The Metropolitan and Micropolitan Statistical Areas, as defined by the Census Bureau, and counties with data available from SMART BRFSS for a given year will fluctuate because of sample size requirements and because states occasionally face administrative or budgetary requirements to change their sample size and design from one year to another. For low-income preschoolers aged 0–5, BMI data had been available from the Pediatric and Pregnancy Nutrition Surveillance System, but this was discontinued in 2012. Data for other indicators must be collected through special local efforts—and most counties lack resources to do so.

TABLE 7-1

Data Sources for Obesity Prevention Indicator Topics Available at the Local Level.

Table 7-2 shows which of the indicators recommended in the APOP report (IOM, 2012a) for describing and tracking progress in obesity prevention are available for larger and smaller communities to consider for inclusion in a CA or surveillance system. All of the recommended indicators, described in Chapter 4, that are available at the county level are included. In addition, indicators that are available and used in some communities are included, even though the evidence linking them to obesity outcomes is less robust. Green denotes that an indicator is readily available for all communities from online sources, yellow denotes that more effort is required but some communities have capacity to analyze locally available data or existing BRFSS or YRBSS data, and red denotes not available. A “larger” community is operationally defined as one that has either SMART BRFSS or local YRBSS data available.

TABLE 7-2

List of Indicators Available for Use at the Community Level.

The Committee encourages the use of available core indicators so that communities can compare and contrast their progress with their peers and relative to benchmarks and so that data can be aggregated across communities. The Committee also encourages collecting and reporting on indicators that are not part of the core set, including demographics, norms, and attitudes. Table 7-3 provides indicators that may be useful for CAS but currently are not readily available at the local level from available and ongoing data sources that are recommended in Chapter 4 of this report as well as indicators for APOP-recommended strategies that are not readily available (i.e., gaps). As described below, it is possible for communities to obtain or collect data for these indicators by conducting surveys of their own and partnering with others in their community (e.g., academic institutions, hospitals, businesses, organizations). Each community needs to identify priority indicators given its particular needs, resources, and assets.

TABLE 7-3

Indicators Requiring Further Development and/or Implementation at the Community Level, by APOP-Recommended Environment.

Data available in certain communities. In addition to these generally available sources, individual jurisdictions may collect primary data, depending on local resources and interests, or may have unique data available from other sources for secondary analyses. Such data may be quantitative or qualitative. Although the Committee was unable to develop a comprehensive census or representative sample of all such data, Table 7-4 provides some possibilities. Box 7-5 illustrates an example of a local health department collecting primary data through local surveys.

TABLE 7-4

Examples of Data Available in Certain Communities.

BOX 7-5

Public Health–Seattle & King County Supporting Primary Data Collection. Public Health–Seattle & King County supports Communities Count, which provides data on the health and well-being of King County communities, informs (more...)

COLLECT DATA

The Committee recommends that all communities use the data that are readily available online to conduct basic obesity CAS. Some communities may wish to supplement these data through local ad hoc or periodic data collection.

Obtain Existing Data from Web-Based Platforms

Several websites offer county-level data and data visualization tools that map and chart data (see Table 7-5). The most extensive is the Community Commons and its sister site CommunityHealthNeedsAssessment.org (Community Commons, 2013a). Together they include a data warehouse, a mapping tool, and a CA development tool. The Commons provides access to thousands of comprehensive and current geographic information system data layers and tables ranging from national to point level. Through “Make a Map,” users can explore all data sets and make dynamic, multi-layer maps at the state, county, city, or neighborhood (e.g., ZIP code and census tract) levels (Community Commons, 2013b).

TABLE 7-5

Sources of Data from Web-Based Platforms.

Chna.org produces a CA that includes available indicators selected by the user and then produces maps, charts, and tables. CDC provides data at its Diabetes Interactive Atlas website, which allows the user to view data and trends for diagnosed diabetes (new and existing cases), obesity, and leisure-time physical inactivity at national, state, and county levels (CDC, 2013). U.S. Department of Health and Human Services (HHS) offers Community Health Status Indicators for counties, although the latest data are from 2006 (HHS, 2013). USDA hosts the Food Atlas (ERS, 2013), which assembles indicators of three broad categories: the food environment (e.g., food choices including access to and acquisition of healthy, affordable food), health and well-being (e.g., food insecurity, diabetes and obesity rates, and physical activity levels), and community characteristics (e.g., demographics composition, natural amenities, recreation and fitness centers). County Health Rankings compiles county-level data (some of it estimated rather than directly measured in the county) from multiple sources and ranks counties within states (County Health Rankings, 2013; Remington and Booske, 2011). The Census County and ZIP Code Business Patterns provides counts of food and recreation establishments at the county and ZIP code levels (Census Bureau, 2013b). The Census Bureau's American Community Survey provides detailed demographic, housing, commuting, and economic data (Census Bureau, 2013a).

ANALYZE AND INTERPRET THE DATA

Once data are collected, those conducting assessments and surveillance should consider several analytic approaches to maximize their impact. These include

• using estimation methods based on state and/or national data when local data are not available;
• displaying trends over time;
• presenting data separately for children (including stratification by preschool, elementary school, middle school, and high school, when possible) and adults;
• describing health inequities through analysis of variation in indicators across race/ethnic and income groups and across small areas (small areas analysis) as appropriate;
• including comparison to benchmarks such as Healthy People 2020 (HHS, 2010) or state health goals, and to rates in state and peer communities;
• comparing extent of existing policies and programs described by the assessment with those recommended in the APOP report (IOM, 2012a);
• sharing data with community members and other stakeholders for interpretation (i.e., systematic reflection on what is seen, its meaning, and implications for action) to identify gaps in local obesity prevention activities and suggest actions; and
• visualizing the data with effective charts, maps, and infographics.

Because methods for the analysis of CAS data are well described (NACCHO, 2013; Teutsch and Churchill, 2000), the Committee limits the rest of this discussion to key issues and emerging methods for interpreting and analyzing the data that are collected.

Qualitative and Mixed Methods

A variety of methods are used in qualitative assessment. The more common methods include collecting data through individual and group interviews, field observations, focus groups, community meetings, open-ended survey questions, document reviews, pictures and videos (e.g., photovoice and digital storytelling), and case studies (or detailed examinations of individual cases). Rapid assessment methods, initially developed for use in low-resource developing countries without established quantitative public health surveillance systems, have been adapted for use in communities across the United States (Beebe, 2001; Kumar, 1993; Scrimshaw and Gleason, 1992). These methods can produce powerful stories that influence policy development and resource procurement. Applying qualitative methods to evaluate and monitor policy and environmental approaches at a population level has been underutilized in obesity prevention research. However, researchers have offered guidance in its application to environmental and policy interventions (case studies: Mitchell and Bernauer, 1998; surveys: Brownson et al., 2000; general use: Beierle, 1999; Sofaer, 1999; photovoice: Strack et al., 2010). All qualitative methods offer strengths (e.g., insights into context of population of interest, inexpensive) and have limitations (e.g., complexity of analysis, reliability, training, credibility to stakeholders (Issel, 2009; Patton, 1987).

Community assessments often rely on mixed methods—the combination of quantitative and qualitative approaches. As an example, a CA in Douglas County (Kansas) conducted by a partnership among the local health department, community hospital, United Way, and community foundation used a mixed methods approach to help to set priorities for its community health improvement plan (see Table 7-6) (Collie-Akers and Holt, 2012). The evaluators examined data from quantitative sources (e.g., BRFSS data) and qualitative/mixed methods sources (e.g., concerns survey, focus groups) for convergence on priority issues such as lack of access to affordable healthy foods and poverty.

TABLE 7-6

Community Health Issues Identified Using a Mix of Community Assessment Methods (Douglas County, Kansas, 2012).

Describing Health Inequities and Determinants of Health

As described in Chapter 5, a health inequity is difference or disparity in health outcomes or distribution of health determinants exposures that is systematic, avoidable, and unjust (Braveman, 2003; Kawachi, 2002). Thinking about differential exposures, vulnerabilities, and health consequences is a useful starting point. Describing health inequities requires compiling data for the populations or neighborhoods that are affected and then displaying the data to compellingly show the inequities.

Box 7-6 offers guidance that has been developed for ensuring that equity is incorporated into CAS design. Demographic and economic data are readily available from the American Community Survey. Maps at the county, ZIP code, and census tracts levels of some of these data are available from several visualization tool websites (see Table 7-5). Data on environments, behaviors, and obesity are harder to come by, given the limitations of routinely collected data. If quantitative data for the group of interest are not available, then options include collecting supplemental quantitative data (e.g., oversampling in surveys) or using qualitative methods (e.g., community listening sessions, key informant interviews). It may be helpful to develop a health equity value orientation in the CAS process by building awareness and understanding of social determinants of health and upstream (e.g., policy) approaches to health improvement, discussing goals of fairness within the health system and environment and its contribution to health (dis)advantage, health gaps, and social gradients of health outcomes (e.g., related to income inequality). Examples of how communities are assessing health disparities are illustrated in Box 7-7.

BOX 7-6

How to Build Health Equity into Your Assessment/Surveillance. Understand the health equity issues in the project area Identify inequities in health outcomes

BOX 7-7

Assessing Health Inequities and Disparities. Contra Costa Health Services uses small-area analysis to identify health disparities within the Contra Costa county in California. With this information Contra Costa created a 5-year plan to reduce health and (more...)

DISSEMINATE FINDINGS

Community measures of obesity and its determinants are useful to the extent to which they are used to increase awareness of the issue, implement or improve interventions, and track progress (or lack thereof). Therefore, dissemination of findings to end users is an essential component of the assessment and surveillance process. Most commonly, findings are assembled into a report that is posted on a website or distributed to interested parties. Summarizing key findings in an infographic can help users to quickly understand the key messages. A few larger health departments have interactive data analysis or visualization tools on their websites to allow end users to customize their information (Communities Count, 2013; Los Angeles County, 2013; New York City Department of Health and Mental Hygiene, 2013a). Briefings of decision makers and policy makers can increase the likelihood that findings will shape policies, budgets, and programmatic decisions, especially if efforts are made to engage them in interpreting and making sense of the data. Using media advocacy methods to earn media coverage allows the findings to reach a larger audience (APHA, 2000; Wallack et al., 1999). Social media channels can augment coverage and reach more diverse audiences. Hosting community meetings to discuss and make sense of the data can engage residents in devising and implementing interventions and can build support for obesity prevention.

CURRENT PRACTICE IN COMMUNITY ASSESMENT AND SURVEILLANCE

This chapter concludes with an overview of what communities across the United States are doing with respect to obesity-focused CAS. To locate CAS examples, the Committee consulted with experts and organizations that provide technical assistance for conducting CAS and used Internet search engines to identify and review existing CAS. Table 7-7 identifies indicators reported in the sample of CAS reports the Committee was able to identify and does not represent the wide-ranging set of obesity-related indicators measured in CAS across the country. The table aims to illustrate indicators that are reported from CAS in more than one community. Each community reported a number of other obesity-related indicators in its CAS, such as overweight low-income infants and children, use of outdoor recreational areas, safety of cyclists, adults who have been advised by a health care professional to lose weight, and gaining insight into the community's obesity-related norms and attitudes, such as the accessibility to affordable healthy foods and effectiveness of the health care system (see Appendix G).

TABLE 7-7

Examples of Indicators Reported in Broader Community Health Assessment Reports.

SUMMARY

Community assessments are intended to assess the current status, and surveillance systems are intended to assess progress overall in a community. They involve the collection of data at a point in time and over time at the community level for the purpose of describing current health status and determinants of health at points in time and over time. Specific to obesity, these data can describe the current state of obesity-related intended impacts and outcomes (see Figure 3-1) as well as contextual factors that influence obesity (e.g., demographics, social determinants).

Although the chapter identifies several resources available to aid communities across the country, there is no consensus guidance for what indicators to measure or what methodologies to use when conducting obesity-focused CAS. Based on a review of the current infrastructure for conducting obesity-focused CAS, the Committee found

• a lack of data available at the local level for indicators relevant to measuring progress of APOP strategies (IOM, 2012a). Especially needed are data for preschoolers and elementary school students and systematic descriptions of determinants of obesity (e.g., environments, policies, other interventions, norms, and attitudes). Additional sources of data at the local level may exist in multiple sectors, such as health care, planning, and schools; and
• a need to increase sample size of existing surveillance systems, add data on missing indicators, and develop new systems for policy, environmental, and intervention indicators, and for reporting data by race and socioeconomic status to the extent possible and by small areas affected by inequity in larger communities.

Other important findings include the following:

• There is a lack of a common set of indicators to allow cross-community comparisons and aggregation;
• Engaging stakeholders/community in assessment process is valuable;
• Capacity to develop assessments varies widely across communities; and
• Improving the accessibility and dissemination of assessment data through multiple channels will improve their use for decision makers, media, and the public.

The Community Obesity Assessment and Surveillance Plan (see Box 7-1) provides guidance for local communities to identify and use a set of common indicators that measure impacts and outcomes of strategies recommended in the APOP report (IOM, 2012a). It also provides guidance for developing local capacity for these assessments, including common use and understanding of assessment protocols, descriptions of health disparities, community engagement, oversight, and public reporting on progress. The plan was developed not only to accommodate communities with varying resources and assets (i.e., large and small communities), but also to provide a common set of indicators that can be measured, compared, and aggregated across multiple jurisdictions. Given the existing gaps in the current infrastructure for CAS of APOP strategies identified by the Committee, Chapter 10 provides seven recommendations (and a set of potential actions and actors) to support the successful implementation of the components of the Community Obesity Assessment and Surveillance Plan.

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Footnotes

1

This chapter focuses on a plan for conducting community assessments and surveillance for obesity prevention efforts as defined. The Committee deliberately uses community assessments or CAs when referring to this aspect only and uses community assessments and surveillance or CAS when referring collectively to both aspects.

2

Community assessment as defined by this chapter is focused on assessments of obesity prevention efforts. Community health assessment is commonly used in the field as a way to assess overall health of a community, which can include obesity.

3

Incidence data would be preferable, but these are generally not available at the local level.

4

Citizen scientists participate in the systematic data collection and analysis; technology development; natural phenomena testing; and dissemination of activities on an advocational basis (Open Scientist, 2011).

5

Crowd-sourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people, typically through the Internet.

6

See http://healthmap​.org/about (accessed November 11, 2013).

7

See http://properhealth​.com (accessed November 11, 2013).

8

See http://www​.flunearyou.org (accessed November 11, 2013).

9

See http://www​.galaxyzoo.org (accessed November 11, 2013).

10

See http://fold​.it/portal (accessed November 11, 2013).

11

See http://www​.ted.com/talks​/lucien_engelen_crowdsource​_your_health.html (accessed November 11, 2013).

12

See http://curetogether​.com (accessed November 11, 2013).

13

National and state goals can be adjusted for local use using simple methods such as applying the fractional change of these benchmarks to local baseline data. For example, the Healthy People 2020 goal for adult obesity is based on a 10 percent improvement from a 2005–2008 baseline. A local community could apply the same improvement target to its baseline data. A more sophisticated approach could incorporate adjustment for age, gender, race/ethnicity, and income differences between the national and community populations.

14

For examples, see http:​//childhealthdata​.org/browse/rankings; http://www​.cdc.gov/obesity/data/adult​.html; http://www​.communitycommons.org; http://www​.countyhealthrankings.org (accessed November 11, 2013).