Population Segmentation for COVID-19 Vaccine Outreach: A Clustering Analysis and Implementation in Missouri : Journal of Public Health Management and Practice

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Population Segmentation for COVID-19 Vaccine Outreach: A Clustering Analysis and Implementation in Missouri

Chessen, Eleanor G. MPP; Ganser, Madelyn E. MS; Paulish, Colin A. MBA; Malik, Aamia MBDS; Wishner, Allison G. MBDS; Turabelidze, George MD; Glenn, Jeffrey J. PhD

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Journal of Public Health Management and Practice 29(4):p 563-571, July/August 2023. | DOI: 10.1097/PHH.0000000000001740



The purpose of this work was to segment the Missouri population into unique groups related to COVID-19 vaccine acceptance using data science and behavioral science methods to develop tailored vaccine outreach strategies.


Cluster analysis techniques were applied to a large data set that aggregated vaccination data with behavioral and demographic data from the American Community Survey and Deloitte's HealthPrism™ data set. Outreach recommendations were developed for each cluster, specific to each group's practical and motivational barriers to vaccination.


Following selection procedures, 10 clusters—or segments—of census tracts across Missouri were identified on the basis of k-means clustering analysis of 18 different variables. Each cluster exhibited unique geographic, demographic, socioeconomic, and behavioral patterns, and outreach strategies were developed on the basis of each cluster's practical and motivational barriers.


The segmentation analysis served as the foundation for “working groups” comprising the 115 local public health agencies (LPHAs) across the state. LPHAs with similar community segments in their service area were grouped together to discuss their communities' specific challenges, share lessons learned, and brainstorm new approaches. The working groups provided a novel way for public health to organize and collaborate across the state. Widening the aperture beyond Missouri, population segmentation via cluster analysis is a promising approach for public health practitioners interested in developing a richer understanding of the types of populations they serve. By pairing segmentation with behavioral science, practitioners can develop outreach programs and communications campaigns that are personalized to the specific behavioral barriers and needs of the population in focus. While our work focused on COVID-19, this approach has broad applicability to enhance the way public health practitioners understand the populations they serve to deliver more tailored services.

Increasing vaccine hesitancy has presented a unique public health challenge over recent years. The most substantial evidence of this trend has been the hesitation—and, in some areas, resistance—associated with the COVID-19 vaccine. While the initial vaccine release was met with excitement by much of the American population, the rate at which first doses were administered nationally began to slow down by the late summer of 2021 and uptake of subsequent boosters has been even lower than that of the primary 2-dose series.1 Vaccination rates have also been variable across different communities. For instance, uptake was higher for older than for younger people, with 95% of the individuals 65 years and older having received at least one dose compared with only 82% of individuals aged 18 to 24 years.1 In addition, communities of color were, and continue to be, disproportionately affected by both the virus2,3 and low vaccine uptake.4,5 At the root of these disparate levels of vaccine acceptance has been not just vaccine access issues but also a variety of types of vaccine hesitancy—from feelings of invincibility in the young,6 mistrust of the health care system often among Black or African Americans due to historical and institutional racism,7,8 to resistance linked with different political affiliations.9

In response to the pressing need to encourage vaccination, public health entities at the local, state, and national levels crafted numerous innovative outreach strategies. For example, Alabama ran a statewide competition on popular social media platform TikTok, asking contestants to share why they chose to get vaccinated.10 However, such tactics were often deployed broadly, effectively treating the public as one homogeneous group. Evidence suggests that tailored COVID-19 interventions designed for specific cohorts are more effective than broad outreach efforts. Targeted social media outreach, such as engaging with geographically dispersed Spanish-speaking agriculture workers on Facebook, was shown to be more effective than generalized door-to-door outreach for sharing COVID-19–related information.11 Findings such as these suggest the need for data-driven methods to better understand local populations' barriers to vaccination and customize more nuanced and equitable interventions for COVID-19 and other public health priorities.

One technique to identify subgroups within a population is cluster analysis, a machine learning technique that classifies data points into groups based on shared features.12 Clustering has been commonly used in the private sector to segment customers, informing more targeted advertising and marketing. Although this approach has existed for some time, its application in public health—both in research and in practice—has been somewhat limited. A review of existing literature suggests that clustering has been used to identify groups based on lifestyle, risk, care access, and care-seeking behaviors among subsets of the population.13–16 However, few studies have applied clustering to larger, heterogeneous populations and most do not leverage the latest data science methods. Similarly, while many public health jurisdictions ran targeted COVID-19 vaccine campaigns to reach specific subsets of their population, few appear to have leveraged population-level cluster analyses. Moreover, while there are several examples that recommend intervention strategies based upon population segment identification,16–18 to our knowledge, few have leveraged behavioral science principles on motivation, decision-making, and behavior change to develop specific outreach strategies for each identified segment.

In this article, we provide an overview and results of an approach that marries statistical and behavioral science methods to develop tailored outreach strategies that encourage COVID-19 primary dose vaccine uptake in the state of Missouri. We combined statewide individual- and population-level data to group Missouri's census tracts into manageable, distinct groups based on shared likeness across social determinants of health and behavioral data points. The resulting clusters, or “community segments,” provided a more nuanced view of typologies within Missouri for the state's public health department to better tailor outreach.

Our team of public health and behavioral science practitioners used the vaccination model originated by Brewer and colleagues19 and adopted by the World Health Organization as its guiding framework, which outlines the vaccine decision-making process and highlights factors that are relevant for vaccine attitudes and behavior. Brewer and colleagues19 propose that vaccination behavior is influenced by a combination of motivational and practical factors, including individual factors (ie, perceived risk, trust, safety concerns), social factors (ie, social norms, information sharing, rumors), and practical issues (ie, convenience, cost). Using this model, we hypothesized primary barriers to vaccination—both motivational and practical—for each segment and developed corresponding intervention recommendations to address each segment's barriers and promote behavior change.


Data sources

We obtained data from 3 sources: ShowMeVax Immunization Information System from the state of Missouri20; the American Community Survey (ACS)21; and HealthPrism.22 ShowMeVax contains weekly vaccination rates by census tract for the study period between August 29 and November 20, 2021. ACS is an ongoing, representative survey that collects demographic, economic, social, and housing data on the US population from a sample of approximately 3.5 million addresses in all 50 states, the District of Columbia, and Puerto Rico. At the time of analysis, the latest ACS data available were from the 2019 release and grouped at the census tract level. HealthPrism is a proprietary Deloitte data source that includes individual-level demographic, behavioral, geographic, and social determinants of health data on 240 million US adults—and nearly all Missourian adults—over the past 7 years, updated monthly.* Analyses were conducted using Python in Google Cloud Platform (GCP). Geospatial mapping and cluster evaluation was performed using Tableau.

Step 1: Variable preparation and selection

We pulled and aggregated variables from our data sources at the census tract level. Given the number of candidate variables for a model was quite large, variable or feature selection was used to remove variables that were either unreliable and weakly associated with a target variable or collinear with other variables. Variable selection for inclusion in cluster models was performed through a literature review of factors likely to influence vaccination and 2 statistical approaches: ordinary least squares (OLS) linear regression and geographically weighted regression (GWR). Regression techniques were chosen in lieu of other data science techniques because of their explanatory power and geographic sensitivity.

We then performed a series of regressions using vaccination as the dependent variable starting with baseline OLS models. Variables most strongly associated with vaccination (ie, significant at 60% level or with a negative or positive relationship of 90%) were retained in the GWR. Conducting the GWR enabled a more nuanced approach by identifying variables that were driving spatial variations within the 9 emergency management regions across Missouri and pinpointing variables that were spatially fixed or significantly associated with vaccination across all 9 regions. The GWR similarly used vaccination as the dependent variable at both the statewide and regional level, with resulting P values outputted for each model.

A bivariate correlation matrix was outputted for those variables with significant explanatory power to eliminate multicollinearity effects, reducing the variable pool to 14 variables. The resulting list was then appended with 4 variables that have been historically linked to vaccine uptake per the literature review (Table 1).

TABLE 1 - Summary of Clustering Variables and Their Variable Selection Source(s)
Variable Source
Vaccination status Literature
Health behavior Literature
Religion Literature
Age Literature
Religion Literature
Uninsured rate OLS
Disability OLS
Census tract density GWR
Veteran status GWR
Households receiving transfer payments GWR
Substandard housing GWR
Housing: Mobile home GWR, OLS
Foreign born GWR, OLS
Census tract mean hours worked GWR, OLS
Occupation GWR, OLS
Percent Black and Latinx GWR, OLS
Households with children GWR, OLS
Education (bachelor's degree) OLS, GWR
Income OLS, GWR
Abbreviations: GWR, geographically weighted regression; OLS, ordinary least squares.

Step 2: Clustering of census tracts

A combination of dimensionality reduction and clustering models was performed at the census tract level across Missouri using the 18 selected variables as inputs. To test which models fit our data distribution best, 2 dimensionality reduction algorithms (UMAP, t-SNE) and 3 clustering models (GMM, k-means, hierarchical) were deployed. Dimensionality reduction is a common practice when analyzing data sets with very large numbers of variables (ie, dimensions) and involves transforming data from a high- to a low-dimensional space. Cluster analysis refers to a series of quantitative approaches to grouping objects (eg, census tracts) based on likeness or similarity. The specific techniques were chosen to empirically test a mix of established (eg, t-SNE) and emerging methods (eg, UMAP) within the data science field. These models produced 32 potential combinations of statistical clusters, with potential cluster totals ranging from k = 3 to k = 15.

The 32 potential combinations were then evaluated through a 2-step process. First, quantitative methods, including assessing silhouette scores23 and 2-dimensional cluster maps, were applied to determine which potential combinations yielded relatively weaker model performance—resulting in all GMM options being removed.

Next, the remaining 16 combinations were reviewed to determine the combination that produced distinct clusters that both fit with existing COVID-19 patterns (eg, higher-income groups often being more vaccinated) and were manageable enough for a public health leader to feasibly act upon. A team of 6 individuals with backgrounds in public health, data science, and behavioral science reviewed crosstabs of the model combinations across more than 100 demographic and behavioral characteristics. For each combination, each cluster was compared with the rest of the clusters in the model output to determine whether it was distinct enough to benefit from unique public health outreach. Geographic dispersion was also reviewed for each potential combination to determine which clusters tracked most closely with local understanding of population patterns. The final dimensional reduction technique and cluster combination was selected because of its geographic diversity, the distinctiveness in clusters across demographic and behavioral variables, and an actionable number of resulting segments from a public health intervention standpoint (see Supplemental Digital Content Table S1, available at https://links.lww.com/JPHMP/B158, for a summary of our evaluation steps, methods, and summary description).

Step 3: Post–cluster development of community segments

To equip local public health officials with more action-oriented data on their local communities, each census tract cluster from the final model was developed into a “community segment.” First, segments were mapped geospatially to understand the location of each segment across the state. These insights provided context into each segment's environment, as vaccine attitudes, social influences, and practical barriers vary greatly by geography.

Next, a detailed profile was created for each segment using descriptive statistics. In line with the Brewer model, we selected variables that provided more insight into practical issues and motivational factors to describe each segment (see Supplemental Digital Content Table S2, available at https://links.lww.com/JPHMP/B159). For example, some of the most prominent barriers that influenced vaccination behavior included practical issues (eg, access to a car), individual factors (eg, likelihood to manage health), and social factors (eg, political or religious affiliation). In total, each community segment was described in detail using more than 25 motivational, practical, and demographic variables.

Finally, customized messaging and outreach strategies were developed on the basis of each segment's practical barriers and motivational influences, leveraging best practices from the behavioral science and public health literature.24,25 Using a health equity lens, practical barriers were examined first. On the basis of available data, the primary practical barriers considered for each segment included income, transportation access, language spoken at home, broadband access, health insurance status, and disability status. For segments facing many practical barriers, recommended outreach strategies focused on boosting access, such as establishing regular vaccine clinics with weekend and evening hours, providing transportation to vaccine sites, and using local channels to spread the word that the vaccine is free regardless of insurance status. When segments lacked discernable practical barriers to vaccination, motivational barriers (eg, trust and safety concerns, anti-vaccine attitudes, social norms) became the focal point for intervention. Recommendations for these segments emphasized leveraging trusted messengers to serve as ambassadors, hosting listening sessions, and tailoring messaging to vaccine concerns most relevant for the community.


Ten community segments, UMAP k-means = 10, were selected on the basis of cluster analyses of 18 variables, and each was assigned a letter label A through J (Table 2). Geospatial patterns emerged when viewing the segments across the state. For instance, segments B, C, and G were concentrated in the most urban parts of the state while segments A and E were predominately rural. Areas with greater population density (eg, Kansas City, Saint Louis, and Springfield) also had a more diverse distribution of clusters than the less-populated, rural parts of the state (Figure).

Geographic Distribution of the 10 Segments Across the State of MissouriThis figure is available in color online (www.JPHMP.com).
TABLE 2 - Sample of Descriptive Statistics, Barriers, and Interventions Across the 10 Segments
Segment % Missouri Population # People First-Dose Vaccination Rate Example Barrier to Vaccination Example Intervention or Messaging
A 12% 602 000 44% Social norms against vaccination Make it discreet: Appeal to residents concerned about social backlash by offering flexible and discrete vaccination options (eg, drive-through, at-home).
B 6% 315 000 74% Low perceived COVID-19 risk Emphasize positive norms to apply social pressure: Convey that getting vaccinated is widely accepted and practiced in their community (“Most people in your neighborhood have gotten vaccinated so far”).
C 4% 215 000 57% Non–English-speaking Help navigate the system: Utilize ambassadors who speak the language (eg, promotoras, who are lay health workers who work with Spanish-speaking communities) to help navigate the system via home visits, help signing up for appointments, and serving as navigators to connect to social support services).
D 16% 824 000 68% Friction Make it easy: Engage PCPs and pediatricians to provide vaccinations at routine appointments. Encourage clinics to craft appointment messages to patients ahead of routine checkups to instill ownership (eg, “A COVID-19 vaccine has been reserved for you at your upcoming appointment”).
E 13% 652 000 49% Perceived loss of control/personal freedom Provide a sense of control: Combat perceived loss of control by framing vaccination as a personal choice and giving individuals ample options surrounding vaccination, eg, providers can encourage patients to choose their vaccine type and use commitment devices such as “vaccinate later.”
F 9% 430 000 62% Lack of trust in health care system Rebuild trust: Recruit health care professionals of color to address historical mistrust head-on and discuss reasons to trust the vaccine (eg, improved racial representation in the clinical trials, their personal experience with getting vaccinated).
G 6% 310 000 53% Lack of health insurance Make it accessible: Include vaccine scheduling with clinical and social services (eg, upon hospital admission, at the emergency department, annual visits, food banks, unemployment centers, WIC locations).
H 14% 687 000 56% Distrust of government and the pharmaceutical industry Keep it local with trusted messengers: Collaborate with and train trusted community leaders (eg, veterans, active-duty members, pediatricians) to serve as ambassadors, having one-to-one conversations using motivational interviewinga to appeal to local values, hosting listening sessions, and emphasizing safety.
I 10% 492 000 52% Health literacy Educate with empathy: Create easy-to-understand materials that are tailored to the literacy level and culture of the community, avoiding overly technical language.
J 10% 528 000 62% Inertia Bundle it: Work with local businesses and schools to host joint flu and COVID-19 vaccine drives on-site.
Abbreviations: PCP, primary care physician; WIC, Special Supplemental Nutrition for Women, Infants, and Children.
aMotivational interviewing is a human-centric interview process designed to help individuals reduce ambivalence and elicit the internal motivation needed for behavior change. See https://www.cdc.gov/vaccines/covid-19/hcp/engaging-patients.html for more information.

In addition to the geographic patterns, trends emerged in associations between vaccine uptake and demographic, socioeconomic, and behavioral characteristics. Segments B, D, J, and F had the highest vaccination rates, ranging from 62% to 74%. Relative to the state average and other segments with lower vaccination rates, these clusters typically had higher educational attainment and income levels, slightly more diversity across racial groups, and low unemployment, with residents generally more likely to work in white-collar professions, suggesting residents in these communities may be experiencing fewer practical barriers to vaccination. However, certain metrics across demographics and social values illustrate their distinctness. For example, segments F and D had above-average first-dose vaccination rates (62% and 68%, respectively) but differed in their racial distribution and religiosity: 17% of segment F was Black compared with only 1% of segment D, and 30% of segment F highly prioritized religion, twice that of segment D (Table 3).

TABLE 3 - Sample of Key Preferences, Values, and Access Factors Across the 10 Segmentsa
Segment Age 65+ y Less Than High School Education Black Latinx Born Outside US Veteran Household Income <$40 000 Disability Uninsured No Car Access Non-Native English Speaker Lack Broadband Access High Religiosity Personal Health
A 28% 56% 0% 1% 1% 11% 48% 20% 16% 5% 1% 25% 31% 7%
B 23% 28% 7% 3% 10% 6% 27% 12% 12% 8% 7% 11% 19% 5%
C 20% 55% 8% 8% 9% 8% 72% 18% 21% 13% 9% 22% 34% 5%
D 25% 19% 1% 2% 6% 7% 7% 9% 9% 3% 5% 6% 14% 4%
E 25% 48% 0% 1% 1% 10% 26% 14% 14% 3% 1% 18% 25% 6%
F 22% 38% 17% 3% 7% 7% 45% 14% 16% 9% 4% 14% 30% 5%
G 21% 49% 73% 2% 4% 7% 78% 18% 22% 21% 3% 26% 42% 5%
H 23% 40% 1% 2% 3% 10% 30% 14% 14% 5% 3% 13% 23% 5%
I 25% 55% 2% 2% 3% 9% 57% 20% 18% 8% 2% 23% 31% 6%
J 23% 30% 2% 2% 4% 9% 13% 11% 11% 3% 3% 8% 19% 5%
aFor additional questions about data, contact the corresponding author.

Segments C, G, H, and I had average to slightly below-average vaccination rates, ranging from 52% to 57%, but experienced some of the largest increases in vaccine uptake in the 3 months preceding this analysis. These segments had a mix of income that skewed lower, had higher proportions of racial and ethnic minority populations, and were more likely to be uninsured and utilize community health clinics, hospitals, or emergency departments for health care. Unlike the first group of segments, residents in these segments appeared to face both practical and motivational barriers. For instance, 78% of segment G's population had an income below $40 000 per year and 21% lacked access to a car, signaling potential practical obstacles in accessing the vaccine. Also, 73% were Black and 22% were uninsured, potentially deterred by mistrust of the health care system and historical, institutional racism.8 In contrast, while also having an above-average uninsured and low-income population, segment C had the highest percentages of Hispanic or Latinx (8%) and non-native English speakers (9%), suggesting barriers associated with lack of inclusive language services, health literacy, and care navigation as reasons for lower vaccination rates.26

The remaining segments—segments A and E—had the lowest overall vaccination rates (44% and 49%, respectively) and showed minimal recent uptake over the preceding 3 months. These segments' residents had relatively lower levels of education, were more likely to live in rural areas, and were more likely to support religious causes. Residents experienced several practical barriers, such as lack of reliable broadband access; however, their primary barriers to vaccination were hypothesized to be motivational, given trends of vaccine resistance observed in rural parts of the state and among populations with lower educational attainment. Still, these segments varied slightly across a few metrics, such as income: 48% of segment A's population had an income below $40 000 per year compared with 26% of segment E.

Recommended interventions to boost vaccine uptake were developed according to these practical and motivational obstacles paired with each segment (Table 2), in addition to outreach and messaging best practices in the behavioral science and public health literature. For instance, given segment C had several practical barriers including transportation and lack of linguistic inclusion, paired with their steady recent vaccine uptake, recommendations focused not only on making vaccination as accessible as possible for those interested in getting vaccinated but also on building trust for those still on the fence. Strategies included establishing local mobile clinics to mitigate transportation and/or mobility barriers and recruiting linguistic peer ambassadors to help navigate the system (eg, help signing up for appointments and navigating social support services).

On the other hand, given segment E's below-average vaccination rate, fewer apparent practical barriers, and a plateau of recent vaccine uptake, recommendations focused on fostering vaccine motivation through collaborating with and training trusted community leaders to serve as vaccine ambassadors and combatting perceived loss of control by providing ample choice and options around vaccination (eg, timing, type of vaccination). For segments with above-average vaccine uptake, such as segment B, strategies focused more on leveraging the power of social norms—highlighting that most of these communities were vaccinated. Additional detailed results can be found in the Community Segments Playbook available on the Missouri DHSS Web site.


Despite the significant potential impact, few public health efforts currently harness the latest data science and behavioral science tools that afford a nuanced understanding of and concrete approaches to addressing the health needs faced by different communities. In this applied research, we combined robust, statewide individual- and population-level data to group Missouri's census tracts into 10 manageable, distinct groups, based on shared likeness across an array of social determinants of health and behavioral data points. The resulting community segments provided a view into typologies within Missouri to better tailor outreach for the COVID-19 primary vaccine. For each segment, we hypothesized primary barriers to vaccination—both practical and motivational—and developed sets of recommended public health interventions to address these barriers and promote behavior change.

While corresponding outreach recommendations that accounted for statewide heterogeneity were developed for each segment, the more detailed understanding of the community types also offered public health leaders a means of identifying which areas may be the ripest for intervention. Behavioral science research suggests that lessening the intention-action gap can lead to positive health behavior change27 and that solving practical barriers for those who are either on the fence or want to get vaccinated would likely be less challenging than changing their ideologies.24,28–30 Based on this evidence, segments with higher-than-average practical barriers (eg, segments C and G) were recommended as primary focus areas for the state. In turn, this analysis aided in geographically identifying the remaining “moveable middle” in Missouri—the residents whose primary barriers were posited as practical (eg, getting time off from work, coordinating transportation to a vaccination site) as opposed to motivational.

This approach has the potential to not only inform hyper-local outreach but also strengthen statewide collaboration between local public health leaders at the county and city levels. An immediate application of the current analysis involved using the distribution of community segments across the jurisdictions of each of Missouri's 114 local public health agencies (LPHAs) to serve as the organizing factor in creating strategy “working groups.” Public health leaders from LPHAs with similar community segments in their county were grouped together and met virtually to share lessons learned throughout the pandemic and brainstorm innovative ways to better engage their populations—and resulting community segments—in getting vaccinated. In a survey following working group implementation, 92% of local public health leaders said they had either already started, or planned to, enact a new outreach strategy based on something they had learned from the sessions. Programs by LPHAs included offering vaccine education and administration at a local bingo event and enhancing a mobile vaccine clinic's location strategy. Ultimately, these working groups provided a novel venue for public health practitioners to collaborate in data-driven ways and strategize how to boost vaccination rates in their communities. A timeline delineating steps of the behavioral segmentation analysis, from variable preparation to working group implementation, can be found in Supplemental Digital Content Table S3 (available at https://links.lww.com/JPHMP/B160).

Overall, this research and its application demonstrate the potential impact of combining data science and behavioral science methods to provide local health leaders with actionable insights into the barriers to vaccination their residents may be facing. By identifying such barriers, pinpointing the geographies in which they were most prevalent, and leveraging behavioral science best practices, the analysis enabled a shift away from a one-size-fits-all approach to enacting new outreach strategies tailored to the specific needs of local communities.

Methodological Limitations

This work has several potential limitations. Given clustering was performed using data at the census tract level as opposed to the individual level, the characteristics of each segment are estimates of communities, not metrics representative of each resident in a census tract. Individuals in each community segment may vary from the average in any of these groups and for any of the sociodemographic and behavioral variables used. However, clustering at the census tract level allows for an actionable public health approach and enables agencies to prioritize specific communities within their service area.

In addition, our data sources introduced limitations due to the nature of the variables and the timing at which they were developed. Several of the variables from HealthPrism are based on predictive models. There was some overlap in variables used to build the predictive variables and the raw variables used for clustering (eg, race), but multicollinearity concerns were mitigated through correlation analysis during variable selection. Other HealthPrism variables are reliant on survey data and credit card expense data, which introduce potential selection and response biases. Finally, ACS variables were from 2019 and it is possible the dynamics of these metrics may have changed in the communities surveyed during the time since data capture. Such considerations notwithstanding, the benefit of clustering and community segmentation on “big data” is the ability to detect meaningful patterns among communities in the absence of complete data precision.

Finally, given the dynamic nature of the COVID-19 pandemic, vaccine attitudes and intentions may change over time. This analysis was conducted at a point in time; however, as the pandemic evolves and the nature of vaccine hesitancy shifts, the process should be reconducted: selecting variables that are associated with current vaccine attitudes, completing cluster analysis on recent data, and confirming the stability of the segmentation or adjusting as needed.

Implications for Policy & Practice

  • Enabling practitioners to understand subgroups within populations and develop tailored interventions to meet the needs of each group connects to precision public health, a concept coined by Martin Khoury, to develop evidence-based approaches that deliver “the right intervention to the right population at the right time.”31(p1),32 This incorporation of data science and behavioral science could be used not just for COVID-19 but also for myriad health priorities. For example, replacing the vaccine uptake variables with factors related to a chronic disease (eg, type 2 diabetes) could provide new insight into socioeconomic and behavioral characteristics in communities with the highest morbidity rates. These insights could inform interventions to encourage medical and lifestyle changes to better prevent, manage, and reverse chronic disease risk.
  • Formation of working groups based on the distribution of community segments in each jurisdiction could also be applied to other priorities. The groups enabled collaboration with practitioners outside the state's regions—fostering idea sharing in novel settings. Grouping leaders together not based on geography, but based on typologies within jurisdictions, could generate more coordination and innovation across state and local public health. We recommend identifying local and state “champions” to shepherd the process forward and garner engagement.


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*Given intellectual property agreements with HealthPrism data vendors, data are not publicly available. Additional questions about data sources may be directed to the corresponding author.



behavioral science; cluster analysis; COVID-19 vaccine hesitancy; population health analytics

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