Successful community-level health initiatives require implementing an effective portfolio of strategies and understanding their impact on population health. These factors are complicated by the heterogeneity of overlapping multicomponent strategies and availability of population-level data that align with the initiatives. To address these complexities, the population dose methodology was developed for planning and evaluating multicomponent community initiatives. Building on the population dose methodology previously developed, this paper operationalizes dose estimates of one initiative targeting youth physical activity as part of the Kaiser Permanente Community Health Initiative, a multicomponent community-level obesity prevention initiative. The technical details needed to operationalize the population dose method are explained, and the use of population dose as an interim proxy for population-level survey data is introduced. The alignment of the estimated impact from strategy-level data analysis using the dose methodology and the data from the population-level survey suggest that dose is useful for conducting real-time evaluation of multiple heterogeneous strategies, and as a viable proxy for existing population-level surveys when robust strategy-level evaluation data are collected.
Supplement information
This article is part of a supplement entitled Building Thriving Communities Through Comprehensive Community Health Initiatives, which is sponsored by Kaiser Permanente, Community Health.
Introduction
Communities are increasingly addressing health issues through place-based initiatives aligned with the Social Ecologic Model at multiple levels (e.g., individual, family, community) across multiple sectors (e.g., school, worksite, neighborhood).
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This approach recognizes that health is driven by multiple behaviors, with complex cultural, economic, social, and environmental influences that are difficult to comprehensively understand and address.3
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, - Kumanyika S.K.
- Obarzanek E.
- Stettler N.
- et al.
Population-based prevention of obesity: the need for comprehensive promotion of healthful eating, physical activity, and energy balance: a scientific statement from American Heart Association Council on Epidemiology and Prevention, Interdisciplinary Committee for Prevention (formerly the Expert Panel on Population and Prevention Science).
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Key imperatives for addressing multicomponent community health initiatives (CHIs) include (1) selecting, prioritizing, and implementing the most effective portfolio of strategies in alignment with available resources; and (2) evaluating the impact of multiple overlapping strategies on population-level health. Addressing these imperatives is complicated by the heterogeneity of the strategies being implemented, from intensive programs targeting relatively few people to more broad-based policy and environmental changes.Data are essential to assess trends, monitor the effectiveness of health interventions, and identify opportunities for improvement. Rather than attempt to evaluate individual strategies, evaluations of large-scale multisectoral initiatives typically use national and state health surveys. Although these surveys provide important information on population-level health indicators and health risks, they may not be sufficient to yield meaningful data at a local level. National- or state-level public health surveillance data may not align with the geography of a local initiative (e.g., neighborhood, census tract, ZIP code), making it difficult to sufficiently describe or make statistical inferences about the target population. They may also lack survey items that address local health concerns, and have a significant lag time between data collection and data availability.
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Additionally, national and state health survey questions and methodology may change over time, impacting the validity of data trends. These factors can impede real-time understanding of the initiative’s progress and data-driven decisions targeting improvement.For example, in 2011 there were changes in data collection and processing for the Behavioral Risk Factor Surveillance System and the Colorado Child Health Survey (CHS): The wording of the physical activity (PA) questions was modified, and the survey methodology was changed to include cell-phone numbers and an advanced data weighting method. Because of these changes, Behavioral Risk Factor Surveillance System and CHS data collected since 2011 cannot be accurately compared with Behavioral Risk Factor Surveillance System and CHS data collected prior to 2011. Similarly, for the rural county discussed in this paper, the survey sample size of the CHS was too small to provide estimates with acceptable levels of statistical reliability for the years of interest. Therefore, the data for children aged 5–14 years that met PA guidelines was unavailable even with the data being combined for years 2011–2013 and 2013–2015.
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, Shupe A. Colorado Department of Public Health and Environment. Behavioral Risk Factor Surveillance System (BRFSS) Dataset Details. www.chd.dphe.state.co.us/cohid/brfssdata.html. Accessed December 15, 2017.
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Given these limitations, national, state, or county data are often inadequate to inform work being implemented at a community level.Colorado Department of Public Health and Environment. VISION: Visual Information System for Identifying Opportunities and Needs website. www.colorado.gov/pacific/cdphe/vision-data-tool. Accessed June 14, 2017.
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To address these challenges, the population dose methodology was developed and has become fundamental to the work of Kaiser Permanente’s CHI, a multisector place-based initiative designed to promote obesity prevention, policy, and environmental change in communities.
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Although the term “dose” is being used more frequently in public health literature, there is no widely accepted definition of dose or method for dose measurement.16
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This paper focuses on a dose–response relationship found through measurement of a Kaiser Permanente CHI youth PA initiative, building on the population dose methodology previously described by Cheadle et al.19
Using one CHI community as an example, the technical details needed to operationalize the population dose method will be explored, as well as the potential use of population dose estimates as an interim proxy for population-level survey data. The objective of the population dose methodology is to quantify community interventions to estimate their impact at the population level, particularly in the absence of timely population-level data aligned with the behavior change being targeted. The dose method relies on real-time understanding of how community work is implemented, including its reach (number of people exposed to an intervention and assumed to be influenced by it) and strength (relative change in behavior for each person exposed). More background and details regarding operationalizing dose are available in an online toolkit.20
Center for Community Health and Evaluation. Healthy dose: a toolkit for boosting the impact of community health strategies, v.1.1. https://share.kaiserpermanente.org/article/dose-creating-measuring-impact/.
Methods
Dose Defined
Population dose uses elements of the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) method of combining reach and effectiveness to estimate the likely impact of a community change strategy on population-level behavior.
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Dose is the product of reach (number of people affected by a strategy divided by target population size) and strength (the effect size or relative change in behavior for each person exposed to the strategy). For example, if 50% of youth enrolled in a school district participate in a Safe Routes to School (SRTS) program that increases minutes of daily PA by an average of 2%, the population dose is 50% (reach) × 2% (strength) = 1%. Conceptually, population dose estimates the average effect size of a strategy across the whole target population.Estimating Reach
Reach is defined as the number of people affected by a strategy divided by target population size (the average effect across the population). For programmatic strategies, the numerator for reach is the number of participants in the program. For environmental and policy strategies, there are four options to calculate the numerator for reach:
- 1observational data collected through a validated tool, such as System for Observing Play and Leisure Activity in Youth22;
- 2number of people who regularly encounter an improved environment, and are likely affected by it (e.g., number of residents living within 1/4 mile of a newly renovated park);
- 3population residing within geographic extent of adopted policy (e.g., city-level complete streets policy); and
- 4literature standards referencing reach of evidence-based strategy.23
Estimating Strength
Three approaches to rating strategy strength were developed depending on the data that are accessible. The first and preferred approach is to conduct strategy-level behavior change evaluations. For example, an SRTS strategy might collect pre and post data on students’ active transport habits that include distance to and from school and the number of times students actively transport throughout the school year. From this information, strength and dose can be estimated directly.
When strategy-level results are not available, a second approach is to assign a default strength rating to the strategy based on general characteristics of the strategy as typically implemented, similar to the interventions and associated ratings outlined in the Guide to Community Preventive Services (The Community Guide).
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Minimal- and low-strength strategies include estimated low-frequency and low-intensity programs, promotion, and environmental changes. Medium-strength strategies have greater estimated intensity or frequency of exposure. High strength ratings are only given to evidence-based strategies with demonstrated effect sizes of ≥10%. The following default effect sizes are assigned to the corresponding category: high 10%, medium 5%, low 2%, and minimal 0.5%. The strength rating guide in the dose toolkit has further details on default ratings of specific strategies.Task Force on Community Preventive Services. Guide to Community Preventive Services. Atlanta, GA: CDC. www.thecommunityguide.org/topic/physical-activity. Accessed December 15, 2017.
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Center for Community Health and Evaluation. Healthy Dose Toolkit Strength Rating Guide. https://share.kaiserpermanente.org/wp-content/uploads/2015/08/StrengthRatingGuide.pdf. Published March 2016. Accessed December 15, 2017.
The third approach for estimating strength is applied when strategy-level evaluation data are lacking, but detailed information about the frequency and intensity of strategy implementation is available. This information allows the refinement of default strength ratings to reflect actual implementation in the community. For example, SRTS strategies have a default low rating, but for a walking school bus that occurs daily, the default could be increased to medium. Conversely, SRTS strategies might be rated minimal if the strategy is primarily infrequent media and promotion. Decisions on strength ratings are based on these factors:
- 1Frequency of exposure—the frequency at which the environment or program is encountered; for example, a walking school bus 1 day a week versus 5 days a week.
- 2Intensity of exposure—the magnitude of environmental changes or the intensity of a given program. For example, children in a walking school bus program walk 1/4 mile to and from school versus 1/2 mile to and from school.
- 3Degree to which the healthy choice is the only choice—the completeness of the exposure. For example, all school buses drop off children 1/4 mile from school versus just one school bus.
Calculating Dose
Dose is calculated by multiplying reach times strength. To compute dose, the authors assign strategy effect sizes of high, medium, low, and minimal strength when they do not have strategy-level evaluation data available. Effect sizes are the percentage change in behaviors resulting from exposure to the interventions. For example, a 10% change is interpreted as a 10% increase in a behavior over baseline. High strength strategies have an effect size of ≥10%; medium strength 5%, low strength 2%, and minimal strength 0.5%.
In alignment with a similar approach of summing individual domain scores to obtain an intensity score,
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dose of multiple strategies targeting similar outcomes and target populations are summed to form a dose cluster (e.g., minutes of PA among youth). In the case of PA, summing dose clusters assumes that there is not a ceiling above which additional activity has no effect. Neither synergy (whole greater than the sum of the parts) nor saturation (lower than expected impact of adding additional strategies) is assumed. Dose clusters allow approximation of the collective impact of multiple overlapping policy, programmatic, and environmental strategies that comprise most community initiatives.Example: LiveWell Colorado Initiative in Routt County, Colorado
Routt County, a rural area of roughly 22,500 people, received LiveWell Colorado funding from 2009 to 2016. Four elementary schools in Routt County collectively serve ≅1,250 students. The Routt County coalition selected coordinated school health as a focus, overlapping policy, environmental, and programmatic strategies designed to increase PA of students. Their efforts included incorporating active living language into school wellness policies (e.g., recommend teachers provide 3–5-minute PA boosts to students during and between classroom times, review physical education curriculum to ensure that programs are consistent with current standards and best instructional practices); adoption of evidence-based physical education curriculum; action-based learning in classrooms (e.g., one 5-minute PA break during core classes); after-school PA programs; SRTS (e.g., signage, flashing lights at crosswalk, new sidewalk); and promotional efforts to raise awareness and educate students around active living.
Dose Computations, Population Surveys
Dose was computed for five individual strategies targeting youth PA that were fully implemented by the third year of the initiative in all elementary schools in Routt County. Table 1 lists the reach and strength calculations, and the dose estimates for each strategy. A detailed example for action-based learning may be helpful for understanding the approach. Teacher surveys indicated that students received an additional 4.1 minutes of PA each day in classrooms. The strength calculation included these elements:
- 1Amount of behavior change—4.1 additional minutes of daily PA (over the baseline national average of 85 daily PA minutes for elementary aged youth).28
- 2Frequency—elementary students engaged in action-based learning 5 of 7 days per week.
- 3Duration—elementary students are in school 8 of 12 months per year.
Table 1Strategy-Level Dose Calculations for Physical Activity Behavior Change
| Strategy | Year 3 strategy-level calculations | Data source |
|---|---|---|
| Safe routes to school | Teacher tallies | |
| After-school PA programs | Attendance, class description | |
| Action-based learning | Teacher survey | |
| School wellness policy |
| Literature review and school policy review |
| Evidence-based PE curriculum | Class descriptions, PE teacher KII | |
| Total dose |
| Sum of individual PA strategies’ dose |
a For dose estimate calculations, it was assumed that school PA minutes engaged in as a result of the community interventions were in addition to established baseline minutes of PA.
b When strategy baseline data were not available, the national average daily minutes of PA as established through accelerometer measures for elementary and secondary school–aged youth was used.
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c Existence of a school wellness policy does not necessarily mean that it is implemented, but updating policy language to incorporate and encourage active living indicates a shifting cultural norm for schools, so this strategy was assigned a minimal strength.KII, key informant interview; PA, physical activity; PE, physical education.
As shown in Table 1, reach = 100% (all students); strength = (4.1/85) × (5/7) × (8/12) = 2.3%; and dose = 100% × 2.3% = 2.3%.
In addition to the strategy-level data, all elementary schools in Routt County collected population-level data from a yearly “5210” parent survey of first- through fifth-grade students for one baseline year and 3 years’ follow-up. The 5210 initiative promotes evidence-informed recommendations about fruit and vegetable consumption, screen time, PA, and sugar-sweetened beverages.
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, Let’s Go! Annual Report 2016. https://mainehealth.org/-/media/lets-go/files/reports/lets-go-annual-report-year-10.pdf. Accessed December 15, 2017.
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A survey was implemented in Routt County schools during the students’ annual registration and consisted of four questions, one for each of the 5210 behaviors. The survey question for PA asked how many minutes per day the student was physically active outside of school time (walking, running, biking, swimming, playing outside, dancing). Percentage change in students’ PA minutes from baseline was calculated for each of the three follow-up survey years. Average minutes of PA reported at baseline were compared to follow-up time periods using t-tests.5210 Gulf Coast. Healthcare resources. www.letsgogulfcoast.org/healthcare-resources/. Accessed December 15, 2017.
Alignment Between Estimated Dose and Measured Population Change
The estimated population-level dose was 3% after 1 year, which included three strategies: SRTS, after-school PA programs, and action-based learning. In Year 2, population dose was estimated at 3.5% based on the continuing Year 1 strategies plus the strategy to update the school wellness policies. In Year 3, the population dose of the fully implemented strategies (SRTS, after-school PA programs, action-based learning, school wellness policy updates, evidence-based physical education curriculum) was estimated to be 7.1% (Table 2).
Table 2Dose Estimates and 5210 Survey Results for Physical Activity Behavior Change
| Strategy | Year 1 dose, % | Year 2 dose, % | Year 3 dose, % |
|---|---|---|---|
| Safe routes to school | 0.8 | 1.1 | 1.8 |
| After-school PA programs | 0.2 | 0.4 | 0.5 |
| Action-based learning | 2.0 | 1.5 | 2.3 |
| School wellness policy | N/A | 0.5 | 0.5 |
| Evidence-based PE curriculum | N/A | N/A | 2 |
| Total dose | 3.0 | 3.5 | 7.1 |
| 5210 Population-level Survey | 4.1 | 2.6 | 5.3 |
| Change in PA minutes from baseline, p-value | 0.014 | 0.133 | 0.002 |
a Percent change in PA from baseline calculated using dose formulas,
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with examples for Year 3 presented in Table 1.N/A, not applicable; PA, physical activity; PE, physical education.Center for Community Health and Evaluation. Healthy Dose Toolkit Strength Rating Guide. https://share.kaiserpermanente.org/wp-content/uploads/2015/08/StrengthRatingGuide.pdf. Published March 2016. Accessed December 15, 2017.
The 5210 parent survey was conducted in Fall 2011 (n=598 of 1,214 enrolled first- through fifth-grade students); Fall 2012 (n=536 of 1,214 enrolled first- through fifth-grade students); Fall 2013 (n=495 of 1,214 enrolled first- through fifth-grade students); and Fall 2014 (n=546 of 1,257 enrolled first- through fifth-grade students), with an overall average response rate of 44%. At baseline, survey results indicated an average of 54.7 minutes PA per day. After 3 years, there was a statistically significant 5.3% increase in reported PA compared with baseline levels (p<0.05).
Comparing the dose estimate with the population-level survey results shows relatively close alignment. The 7% dose estimate is considered higher dose (i.e., a level where the authors might expect to see a statistically significant change in a population-level survey), and the change in PA activity from baseline to Year 3 shown in the population-level 5210 survey was statistically significant (p<0.05). Therefore, the population change was consistent and aligned with the implementation of high-dose strategies in this LiveWell community.
Discussion
Routt County was unique among CHI communities in that they collected both detailed strategy-level data for multiple strategies to estimate dose and annual population-level data to assess initiative impact. This breadth of data allowed the authors to illustrate the utility of dose for conducting real-time evaluation of multiple heterogeneous strategies, allowing for process evaluation and course corrections throughout the initiative. For example, the action-based learning teacher survey results were reviewed each year with stakeholders to assess progress and refine strategies as needed to increase impact. In the second year, the surveys showed a slight drop in classroom PA. After sharing the dose estimates from both years with school district superintendents, they reviewed the action-based learning process and barriers for use in the classroom, then implemented supportive actions to encourage its use (e.g., action-based learning training for teachers, incorporation of action-based learning into school wellness policy). The Year 3 teacher surveys showed an increase in classroom PA. Yearly review of population dose estimates and strategy implementation facilitate the understanding of progress, allowing for course corrections and strategic allocation of resources to ensure sustainability of successful strategies.
Having strategy reach, strength, and population-level behavior change data allowed the estimated population dose to be used for logic model confirmation. The measured population-level changes in PA aligned with the high-dose PA strategies implemented by Routt County. There was less likelihood of false positive results because findings were corroborated with data from high-dose scores that indicated significant work was happening around PA. Therefore, attributing favorable population-level changes to the initiative was reasonable.
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Finally, given how well-aligned dose evaluation was with 5210 population-level survey results, dose was a viable proxy for the survey. Population-level surveys often do not meet community evaluation needs because of resource constraints, geographic scale, survey questions, timing, or suppressed data. In the case of Routt County, relevant PA data from the annual Colorado Child Health Survey for children aged 5–14 years meeting PA guidelines was suppressed even with the data being combined for years 2011–2013 and 2013–2015. Because of LiveWell funding, the school was able to conduct an annual 5210 survey for all elementary school–aged youth in Routt county. Although the population-level surveys were not sustainable long term, the strategy-level evaluations were, allowing Routt County to continue monitoring PA strategies.
Limitations
The most important limitation of using dose for evaluation is that the components, reach, and strength of strategies and population-level change are difficult to measure with sufficient precision to draw firm conclusions about attribution or impact. Additionally, quality of data collection can affect accuracy and generalizability of information for other programs. Given the study design, the authors were unable to control for the variable 1–3 years of exposure that youth may have experienced. Descriptions of strategy implementation required for reach and strength estimates rely largely on progress reporting from the community implementers and other institutions involved (e.g., schools). These self-reported accomplishments may have been biased in favor of making changes appear to be more comprehensive and sustainable than was true in practice. Where possible, progress reporting was supplemented with secondary data for verification, and strategy-level evaluations involving direct observation and environmental assessments. The ratings of the strength component of population dose are often subjective given the lack of information from the scientific literature or strategy-level evaluations, particularly for environmental and policy strategies. Additionally, although duration and amount of PA were considered, this method does not analyze by breadth of PA (e.g., moderate PA, vigorous PA) but rather counts all PA types equally. Multiple independent raters were used to standardize ratings as much as possible. In addition, several strategy-level evaluations were conducted to estimate effect sizes and used to further refine and validate the ratings.
Quantifying the cumulative dose of strategies targeting the same outcomes requires assumptions about how overlapping strategies interact within a population. For example, is synergy occurring, whereby a combined effect of action-based learning, SRTS, and after-school PA programming results in a greater overall increase in PA than would be expected from the effect of the individual strategies alone? Simply adding up dose may not be accounting for a potential larger impact as strategies reinforce each other. The authors feel that this conservative approach is justified given the lack of empirical evidence to support the synergy hypothesis.
Conclusions
Despite limitations, the dose methodology provides a useful lens for interpreting population-level results and determining attribution to the initiative. In particular, the approach emphasizes real-time evaluations while implementing an initiative, and rules out chance findings in population-level changes at initiative end by only attributing changes where accompanied by high-dose strategies. Beyond schools, population dose has utility in workplaces, hospital systems, towns, or other defined populations. Beyond active living, dose has potential utility where multiple interventions are being employed to change the same behavior.
Acknowledgments
We would like to acknowledge the contributions of the LiveWell Colorado organization, the LiveWell Northwest Colorado coalition, and community residents for their dedication to healthy eating and active living efforts in Routt County. Cheadle, Kuo, Harner, Rauzon, Schwartz, and Solomon contributed to development of the dose method. Harner, Kuo, and Cheadle wrote the manuscript, with input from Kelly, Rauzon, Schwartz, and Solomon. Harner, Kuo, and Parnell, conducted the design, data collection, and analysis of the evaluation in Routt county used as an example of the dose method. This project was funded by Kaiser Permanente and the Colorado Health Foundation. The authors acknowledge and thank Kaiser Permanente for financial support.
No financial disclosures were reported by the authors of this paper.
Supplement Note
This article is part of a supplement entitled Building Thriving Communities Through Comprehensive Community Health Initiatives: Evaluations from 10 Years of Kaiser Permanente's Community Health Initiative to Promote Healthy Eating and Active Living, which is sponsored by Kaiser Permanente, Community Health.
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