Strategic and Performance Planning in US Local Health Departments: A Comparative Analysis of Strategic, Community Health Improvement, and Quality Improvement Plans

Abstract

Context:

Local health departments (LHDs) are a critical component of the US public health infrastructure. To guide their work and evaluate progress, LHDs develop and maintain a range of planning documents, including strategic plans (SPs), community health improvement plans (CHIPs), and quality improvement plans (QIPs).

Objective:

The goal of this study was to provide information on current LHD practices related to strategic planning and performance improvement.

Design:

The study used an environmental scan and document analysis of a sample of 93 plans from 31 LHDs to explore commonalities and differences within the strategies, goals, and priorities of SPs, CHIPs, and QIPs. Framework analysis was used, involving initial document review, development of a coding framework, and applying this framework to all documents iteratively. To ensure reliability, 19% of documents were double-coded, with discrepancies resolved through team discussions.

Results:

Of the 31 LHDs included in this analysis, most were urban (89.7%) and medium-sized (64.5%), with a large percentage from the Midwest (41.9%). The analysis revealed that most LHDs used established frameworks to develop plans and identify priority areas. Frequently listed priority areas included behavioral health, social determinants of health, access to care, chronic disease, and nutrition and physical activity. Frequently listed strategies to improve priority areas included building collaborations and partnerships, advocating for policy change, increasing access to services and resources, and increasing awareness of issues. While CHIPs frequently incorporated social determinants of health as part of a guiding framework, QIPs focused on fostering continuous quality improvement. Differences were documented based on jurisdiction size, degree of rurality, and region.

Conclusions:

The study identified a widespread commitment to develop plans using established frameworks, community engagement, and data-driven decision-making, while also highlighting the diverse needs and capacities of LHDs in addressing public health challenges.

Local health departments (LHDs) are essential to the US public health system, with nearly 3300 LHDs guiding the allocation of considerable public resources. The median total annual expenditures were $1.54 million.1 To support their communities, LHDs develop planning documents including strategic plans (SPs), community health improvement plans (CHIPs), and quality improvement plans (QIPs), which align with accreditation requirements of the Public Health Accreditation Board (PHAB).2 SPs are designed to provide LHDs and its stakeholders with a clear plan for the future, what it hopes to achieve, how it will achieve those goals, and how they intend to measure progress.3 SPs are closely aligned with CHIPs which use community health assessment data to develop and implement actionable strategies for achieving health improvement and goals laid out in the SP. Finally, QIPs detail the deliberate and defined process(es) used to improve efficiency, effectiveness, performance, accountability, outcomes, and other indicators of quality.4 None of these plans are intended to be standalone, but rather form a framework to guide strategy, operational implementation of public health initiatives, and ultimately improved population health.

The 2022 National Profile of Local Health Departments (“the NACCHO Profile Study”) reports that 65% of LHDs had participated in a CHIP in the past 5 years and 39% of LHDs with QI activities had a formal QI plan.1 The 2019 NACCHO Profile Study found that 64% of LHDs had participated in an SP within the past 5 years.5 Additionally, organizations such as the Association of State and Territorial Health Officials (ASTHO) and the National Association of City and County Health Departments (NACCHO) provide further guidance and recommendations to ensure comprehensive and effective planning within health departments.6,7

LHD priorities vary widely. Carroll and colleagues found that among 195 CHIPs, all addressed at least 1 social determinant of health (SDoH).8 However, other studies suggest gaps in addressing economic stability, social environment, and/or education.9 Although SDoH are often mentioned, concrete interventions targeting these social factors are lacking.10 Health equity is also a frequent focus, with almost half of the LHDs in the 30 largest US cities prioritizing racial health equity in their formal public health plan.11 Additionally, an analysis of over 4000 objectives from 280 CHIPs found that 85% of accredited LHDs included at least 1 equity-focused objective.12

The literature on QIPs is notably sparse. Data from the 2022 NACCHO Profile Study reports that 55% of LHDs participate in formal QI activities, either in specific programmatic areas or agency-wide.1 A 2008 analysis found that QIP objectives are often focused on health department processes, like human resources, data systems, or management practices, and that less than half of LHDs that report conducting formal QI activities are guided by an established framework.13 These findings contribute to better understanding of QI activities and the extent to which LHD engage in formal QI activities; however they do not provide detailed findings of specific frameworks and/or their practical application.

Few studies have examined how LHD plans differ by region, size of LHD, degree of rurality, and other characteristics. The NACCHO Profile Study found that LHD participation in a recent CHIP varied by size of LHD, with medium and large LHDs being much more likely to have participated.1 Large LHDs were more likely to use the Mobilizing for Action through Planning and Partnerships (MAPP) framework and to include SDoH compared to small or medium-sized ones.1,9 Few regional differences were seen in CHIP content or processes.8 Less is known about differences across SPs and QIPs.

To address these gaps, this study examined SPs, CHIPs, and QIPs from 31 LHDs, totaling 93 documents. Although workforce development plans are also required for accreditation, prior work has already examined trends in these plans.14 The current study sought to answer the following research questions: (1) What goals, priorities, and strategies are included in SPs, CHIPs, and QIPs from LHDs across the country? (2) How are SPs, CHIPs, and QIPs similar and different across LHDs? By examining these documents, this study provides insights into the practical applications of strategic and performance improving planning for public health infrastructure. Findings can be used to inform future planning practices, contributing to the broader evidence base required for addressing complex public health challenges.

Methods

Sampling and data collection

PHAB-accredited LHDs at the time of data request were eligible for the study. In January 2024, a NACCHO staff member contacted all 344 PHAB-accredited LHDs to request their latest SPs, CHIPs, and QIPs, and invited them to contribute to a NACCHO-managed document repository. Participation in this repository was optional. We received responses from 109 unique LHDs (32% response rate) which provided 281 documents in total. Thirty-five LHDs submitted at least 1 document, and 74 submitted all 3. Only LHDs with complete document sets were included in the final analytical sample (22%). All documents were stored securely on Google Drive and an internal shared drive using a standardized naming convention for ease of identification and access.

We selected a purposive sample from the 74 LHDs who submitted complete sets of data to ensure diversity. Each LHD was categorized into urban or rural based on if most individuals living in those jurisdictions were from rural or urban settings, calculated for each census tract.5 The US Census regions (Midwest, Northeast, West, and South) were used to ensure regional diversity.5 Additionally, we collected information on jurisdiction size. Jurisdiction size was separated into 3 categories: small (<50 000 residents), medium (50 000-499 999), and large (≥500 000). The final sample of 31 LHDs was selected from a list of PHAB-accredited LHDs as of November 2023. We sought a diverse sample by degree of rurality, region, and jurisdiction size based on the most recently available data from the 2019 NACCHO Profile Study. While we sought to match proportions across degree of rurality, region, and size, achieving exact alignment was not possible due to the limited availability of responding LHDs that matched the exact combinations of these characteristics. The final sample yielded 93 individual plans for analysis. Plans were from 2016 to 2024 with 57 of 93 documents (61%) being written in 2021 or later (ie, after the onset of the COVID-19 pandemic).

Data analysis

The analysis approach was guided by the framework analysis method, using a team-based approach to coding.15,16 First, 3 study team members (coders) conducted a review of documents from 3 LHDs (total of 9 documents) to gain familiarity with the content and structure of each document type. Next, an initial coding framework was developed by each study team member to systematically categorize and code relevant segments of data, followed by a team meeting to achieve intercoder agreement. Codes that emerged independently across all 3 coders remained in the final coding framework. For codes that appeared in only 1 or 2 coders’ frameworks, the team discussed each to determine whether to merge them with similar existing codes or maintain them as distinct codes. The coders used a hierarchical structure, organizing codes into parent codes (eg, “Methods”), child codes (eg, “Data Collection”) and grandchild codes (eg, “Interviews”), as appropriate. The same coding framework was created for all 3 document types.

The agreed upon framework was applied to the initial 9 documents, and the study team met once again to discuss similarities, discrepancies, and refine the coding framework. Once finalized, the framework was applied to all 93 documents with the addition of analytic memos and document variables.17 Document variables included document type (SP, CHIP, and QIP) and year, as well as LHD degree of rurality, size, and region. Finally, the coded data were systematically analyzed to identify common themes, patterns, and variations. Matrices and tables were generated to organize and summarize findings, facilitating comparisons across documents and LHD characteristics. MAXQDA 24 was used to analyze all data.

Several strategies were employed to ensure the reliability and validity of the analysis. First, as described above, the coding framework was refined iteratively based on ongoing analyses and team discussions. Second, we double-coded 18 of 93 documents (19%) to ensure consistency and reliability in the coding process, exceeding the recommended minimum of 10%.18,19 During study team meetings, coders discussed example segments and resolved disagreements, iteratively refining the coding framework until we were satisfied with the level of agreement as recommended by Campbell et al.18 Finally, the full coding framework was applied to all documents with regular meetings between coders to discuss questions about the data, reflections, and emerging themes. This process ensured that the codebook was comprehensive. Finally, regular team meetings between study staff and NACCHO were held to discuss progress, resolve any issues, and ensure consistency in the analysis.

Results

This document analysis of 93 LHD planning documents yielded important insights into the goals, priorities, and strategies employed by LHDs across the United States. Table 1 presents LHD sample characteristics. Most LHDs were urban (n = 28) and medium size (n = 20) and/or located in the Midwest (n = 13).

TABLE 2.

Frameworks in SPs, CHIPs, and QIPs

Frameworka	SP (n)	CHIP (n)	QIP (n)
10 Essential Public Health Services20	4	3	2
Collective Impact21	1	1	-
Community Health Assessment Framework22		1
Health Impact Pyramid23	2	4	-
Kaizen24	-	-	1
Mobilizing for Action through Planning and Partnerships25	-	14	-
Plan-Do-Study-Act/Plan-Do-Check-Act (PDSA/PDCA)26	3	-	22
SMARTIE27	-	-	1
Strengths, Opportunities, Aspirations, Results	1	1	-
Strategic Doing28	1	1	-
Turning Point Performance Management29	-	-	9

^aLocal health departments may have used more than one framework in their planning documents.

Frameworks and methodologies

Nearly all (97%) LHDs utilized at least 1 established framework in any of their planning documents (ie, CHIP, SP, or QIP) (Table 2). The most prevalent frameworks used were Plan-Do-Study-Act or Plan-Do-Check-Act (PDCA)26 (referenced by 81% of LHDs), MAPP25 (referenced by 14% of LHDs), and the 10 Essential Public Health Services20 (referenced by 23% of LHDs).

TABLE 1.

Sample Characteristics

	N	%
Degree of rurality
Rural	3	9.7
Urban	28	90.3
Size
Small	2	6.5
Medium	20	64.5
Large	9	29.0
Region
Midwest	13	41.9
South	10	32.3
West	5	16.1
Northeast	3	9.7

LHDs used various methods to collect, analyze, and synthesize data for their plans. Many relied on surveys (collecting primary data and analyzing secondary data), interviews or focus groups, and reviewing meeting notes to inform their goals and priorities (Table 3). When emphasized, community engagement and data-driven decision-making were included as part of plan development. Community engagement was a focus for more than half of LHDs in at least one of their plans (n =6 SPs, n =17 CHIPs, and n =4 QIPs), with LHDs employing diverse approaches such as resident surveys and the inclusion of community members in committees and workgroups. Many LHDs actively involved community partners, advisory councils, and local stakeholders in shaping and implementing SPs. These partnerships help identify relevant needs, provide feedback, and ensure plans reflect community priorities.

TABLE 3.

Data Collection Method to Inform SPs, CHIPs, and QIPs

Data Collection Method	SP (n)	CHIP (n)	QIP (n)
Document review	9	-	1
Focus groups	8	8	1
Interviews	6	7	1
Meetings	10	17	5
Surveillance	-	5	-
Surveys	14	14	13

Social determinants of health and health equity

Societal factors affecting health outcomes were referenced in LHD planning documents, often described as “social determinants of health” (SDoH) but also discussed alongside “structural determinants of health” and “health equity.” SDoH encompasses nonmedical factors influencing health, including economic stability, education, health care access and quality, neighborhood and build environment and social/community context.30 Structural determinants of health are broader, including the economic and political mechanisms that shape health.31 And finally, health equity has been described as “the assurance of the condition of optimal health for all people.”32 When mentioned, SDoH were recognized as key health predictors, either broadly or with special attention to transportation, housing, employment, education, economic stability, community and social context, access to health care, food security, or neighborhood and physical environment.

SDoH appeared more frequently in CHIPs (n = 26) compared to SPs (n = 20), and QIPs (n = 5). Many CHIPs and SPs utilized SDoH as a guiding lens, or framework, with some CHIPs further integrating SDoH into data collection, strategy selection, and monitoring. Only 3 QIPs acknowledged how SDoH could be integrated into LHD policies, procedures, and quality improvement activities, but lacked details or context on how this could be operationalized. Plans that embraced a health equity lens (n =15 CHIPs; n =14 SPs) included historical context, community-engaged methods for identifying problems and strategies, and relied on data to identify priority populations. These LHDs identified inequities and proposed solutions to address social needs, indicating a deeper level of commitment to promoting health equity.

Priorities and strategies

Across the 31 LHDs, 15 priority areas were specified: access to care, aging, behavioral health, chronic disease, environmental health, infectious disease, LHD operations, maternal and infant health, nutrition and physical activity, prevention, public health preparedness, SDoH, social wellness and community cohesion, violence, youth, and adolescent health (Supplemental Table, available at http://links.lww.com/JPHMP/B521). Thirteen strategies were identified to improve health outcomes within priority areas: resource mapping, providing education, optimize technology, gather data, improve cost-effectiveness, integrate care, grow the workforce, build collaborations and partnerships, improve communication, increase quantity and type of services and resources, increase awareness, improve public trust, and make structural changes.

Strategic plans)

SPs emphasized organizational growth, focusing on enhancing internal processes and workforce development to support CHIP implementation. All 31 SPs prioritized aspects of LHD operations, such as workforce development aimed at enhancing staff satisfaction, advancement, and success including training, professional development, and creating a culture of continuous learning (Figure 1). Another focus was improving system efficiency and reducing redundancies to improve the engagement and satisfaction of their staff.

FIGURE 1.

Strategic plan priority areas, N = 31. This figure is available in color online (www.JPHMP.com).

Although less common, SDoH ranked second-most referenced priority (n = 12). LHDs recognized that addressing SDoH, such as socioeconomic status, poverty, and housing, is critical for improving complex health issues. SPs also discussed the importance of training the LHD workforce to recognize the significance of SDoH and to effectively work alongside communities to address social risks. SDoH was a priority area among 2 of 5 LHDs in the West, 2 of 3 in the Northeast, 5 of 10 in the South, and 3 of 13 in the Midwest. Similarly, SDoH was a priority area among 1 in 2 small jurisdictions, 7 in 20 medium jurisdictions, and 4 in 9 large jurisdictions. There were no meaningful differences by degree of rurality.

LHDs proposed 22 unique strategies, including improving technological capacity, reducing costs, promoting budget transparency, and diversifying the public health workforce. The most frequently cited strategy, present in 26 SPs, was developing collaboration and partnerships. LHDs mentioned the importance of leveraging internal partners’ expertise to address the challenges they face and the value of developing external working relationships with other city or county departments.

Community health improvement plans

CHIPs outlined LHDs’ key health priorities for improving community health (Figure 2). Each CHIP outlined 2 to 7 priorities, with behavioral health (n = 30), SDoH (n = 14), access to care (n = 13), chronic disease (n = 13), and nutrition and physical activity (n = 12) as some of the most common. Six of the 14 CHIPs that prioritized SDoH aimed to improve housing stability and/or reduce poverty, recognizing SDoH as essential for broader health improvements. For example, 1 large, urban, Midwest LHD aimed to reduce youth pregnancy rates by addressing upstream factors like equitable education pathways and reproductive health care access.

FIGURE 2.

Community Health Improvement Plans Priority Areas, N = 31. This figure is available in color online (www.JPHMP.com).

SDoH was prioritized in 4 of 5 LHDs in the West, 1 of 3 in the Northeast, 2 of 10 in the South, and 7 of 13 in the Midwest. Similarly, SDoH was a priority area among 1 in 2 small jurisdictions, 9 in 20 medium jurisdictions, and 4 in 9 large jurisdictions. The third most common priority area was access to care which was prioritized among 3 of 5 LHDs in the West, 2 of 3 in the Northeast, 6 of 10 in the South, and 2 of 13 in the Midwest. Similarly, access to care was a priority area among 6 in 20 medium jurisdictions and 7 in 9 large jurisdictions, but neither of the 2 small jurisdictions.

The analysis of CHIPs identified 12 distinct strategies to address health priorities. The most common strategies included resource and service enhancements and expansion, educational campaigns, collaborations and partnerships, and policy advocacy. Most CHIPs (n = 28) identified strategies to create new or enhance existing resources and services, such as community classes on nutrition, behavioral health crisis intervention programs, and online resources. LHDs varied in who they chose to partner with and how. Collaborations included but were not limited to partnerships with community-based organizations, other LHDs within the state, academic institutions (eg, school districts, community colleges, universities), health care systems and providers, health professional associations, and community residents. Collaborations most often involved individuals representing those institutions as members of work groups or strategic planning committees. In rare instances, academic institutions (n = 1) conducted a community health needs assessment or provided training (n = 1).

When analyzing the sample by jurisdiction size and region, a small number of differences were found. CHIPs from all small and large jurisdictions focused on increasing the number of services or resources, but only 11 out of 20 CHIPs from medium jurisdictions included this strategy. Gathering additional data to inform future actions was a strategy among approximately half of all CHIPs by region: 7 of 13 in the Midwest, 2 of 3 in the Northeast, 6 of 10 in the South, and 2 of 5 in the West. There were no meaningful differences by degree of rurality.

Quality improvement plans

QIPs functioned as a space for LHDs to outline their vision for how process improvement would happen across their activities by highlighting frameworks to guide their work, methods for collecting and monitoring data, and systems for disseminating results and implementing insights from their QI initiatives. During plan development, QIPs used tools like NACCHO’s Organizational Culture of Quality Self-Assessment to evaluate their current QI efforts and identify their QI phase.33 Twenty-three LHDs referenced NACCHO’s Roadmap to a Culture of Quality as a method to guide their progress, and 26 QIPs detailed how they identify, manage, monitor, and communicate findings from QI projects.

The selection process for QI projects generally involved several key steps designed to ensure alignment with strategic goals and performance measures. This included reviewing project checklists, completing project aim worksheets, and presenting proposals to divisional team representatives for review. Priority was given to projects that aligned with SP goals and performance measures. Additionally, several plans emphasized project tracking mechanisms, incorporating improved documentation and simplified submission forms to guide teams through the process. Input from various stakeholders, including staff, community partners, and service recipients, was sought to identify areas for improvement.

Monitoring and evaluation

Monitoring and evaluation activities were referenced most frequently in QIPs (n = 29) compared to SPs (n = 20) and CHIPs (n = 22). Both SPs and CHIPs described specific indicators, measurement approaches, timelines, tracking mechanisms, and reporting. Specific indicators were often aligned with priority areas, measurable, and characterized as short- or long-term indicators. Because SPs focused more on LHD operations, several of the indicators were related to data infrastructure, audits, staff training and development, accreditation, and other operations-related measures. However, CHIPs included indicators that were more specific to community-level changes. The process of identify indicators and a measurement plan was similar among SPs and CHIPs, using work groups to drive collaboration. Some plans discussed specific platforms and software to track progress, whereas others indicated progress would be documented in publicly available reports. Many LHDs, across both plan types, noted that plans evolve, and that updates would be made to ensure transparency.

Twenty-nine LHDs documented evaluation and monitoring as one of the most important steps in their QI process. Several used a Performance Management System as their chosen method for evaluating progress toward their established QI goals and informing a process of continuous QI. Twenty-five LHDs included in the sample emphasized the importance of communication with internal and external partners for achieving a culture of quality. Fundamental to fostering a culture of quality, robust communication plans that share QI progress and learnings helped LHD staff stay engaged and maintain a sense of ownership over activities. Communication plans often included the intended audience, details about the communication product, frequency, medium, and responsible party. Examples of communication strategies include sharing wins in employee newsletters, websites, or social media to reach both internal and external audiences and disseminating results more broadly at webinars, conferences, and workshops.

Discussion

This study provides insights into the strategic frameworks, priorities, and methodologies employed by 31 US LHDs. Like previous studies, LHDs included in this analysis adopted frameworks aligned with the goal and structure of each plan, which underscores a strong commitment to structured and systematic public health planning.34 While past research suggests LHDs primarily use frameworks from NACCHO and ASTHO, our findings show they incorporate quality improvement and knowledge transfer frameworks to promote continuous improvement and comprehensive public health activities.4,13,34 Plans that emphasize community engagement and data-driven decision-making demonstrate investment in inclusivity and evidence-based approaches in public health planning.

Consistent with previous research, this study reveals varied integration of SDoH and/or health equity within LHD plans.8,9,12 Addressing upstream factors, such as social inequities, environmental and neighborhood conditions, cultural factors, and institutional injustices is essential for sustained health improvements.35,36 While many LHDs acknowledged SDoH and health equity, few offered detailed action plans to address these factors broadly. One explanation may be the emphasis of SDoH and health equity within the planning process, but a lack of understanding on how to operationalize relevant approaches within an action plan.

Our analysis identified several priority areas including behavioral health, chronic disease, nutrition and physical activity, SDoH, and access to care. As discussed above, there were notable differences by region and jurisdiction size which may be a result of regional health disparities and different resource availability. Such differences underscore the importance of context-specific planning (ie, considering population demographics, geographic and community infrastructure) and place-based interventions to improve public health.37

LHDs proposed several strategies such as building new partnerships, resource mapping, and increasing access to services and resources. Yet many of these strategies focused on addressing downstream factors—outcomes directly influenced by social determinants—rather than the root causes that give rise to health disparities such as systemic inequities in housing, education, and economic opportunities. Research suggests that while downstream interventions can provide immediate support, their long-term impact on public health is limited if the underlying drivers of health disparities are not addressed.35 Public health frameworks, like the Health Impact Pyramid, can provide a model for designing interventions policy change, community development, and economic reform which focus on upstream determinants to improve systemic inequities.23,38

Applying an equity lens to public health work can include addressing systemic barriers, prioritizing historically marginalized populations, centering community voice, and using a data-driven strategy to identify gaps, strategies included by half of LHDs included in this analysis.39 These LHDs demonstrated a deeper commitment to promoting health equity by identifying inequities and proposing actionable solutions that target social needs. This approach highlights the importance of operationalizing health equity strategies within public health planning to address both immediate needs and long-term structural inequities.

There are limitations to this study worth noting. Although our study included plans from 31 LHDs that were selected from a sample of PHAB-accredited LHDs, the findings cannot be generalized to all LHDs nationwide or all PHAB-accredited LHDs, particularly those that have not pursued or achieved accreditation. Second, nonresponse bias may have impacted our results, as LHDs who are less engaged in the public health network or smaller or rural LHDs with less resources, may have been less likely to respond to document requests and therefore unrepresented in our sample. Third, coding priority areas presented some challenges due to the interconnectedness of public health issues. For instance, an LHD might identify chronic disease as a priority area but discuss how SDoH may be related to chronic disease trends. In this scenario, the segment would be coded as “chronic disease” not as “social determinants of health.” This overlap complicates the analysis and may lead to potential misclassification of priority areas. Finally, formal plans reviewed may not fully capture LHD goals, priorities, and strategies, potentially leading to incomplete understanding by the research team. Additionally, frequent changes in LHD priorities, plans, and budgets, driven by internal and external factors, may not be reflected in this analysis.

There were many strengths to this study that enhance its contribution to the field. First, we had an acceptable response rate (32%) which allowed for a large pool of LHDs from which to sample planning documents. Second, this is among the first studies to comprehensively examine commonalities and differences not only in the priority areas and collaborating entities listed by each LHD, but also in the strategies linked to these priority areas. Most importantly, by analyzing 3 document types and integrating findings across plans, we provide a more holistic view of how LHDs approach public health challenges to achieve their broader vision for their jurisdictions.

Conclusions

This analysis provides a comprehensive look at the frameworks, priority areas, and strategies across 31 US LHDs. LHDs often employ existing frameworks to design and implement strategic, community health improvement, and QIPs, demonstrating a commitment to systematic planning. Key priority areas included behavioral health, chronic disease, nutrition and physical activity, and SDoH. This study highlights the importance of tailored strategies that consider local contexts and needs, as well as the diverse capacities of LHDs, in addressing public health challenges. The findings offer valuable insights for public health professionals and LHDs in enhancing their planning processes and improving community health outcomes. Further research is needed to understand development and implementation of plans among rural and small LHDs.

Implications for Policy & Practice

• The findings from this study underscore the essential role of established frameworks and methodologies in guiding LHDs across the US in developing comprehensive planning documents.

• The emphasis on SDoH and health equity within SPs and CHIPs indicates a growing awareness of the broader factors that can influence health outcomes. LHDs should prioritize and allocate resources toward improving SDoH which has the potential for lasting impacts on population health.

• Community engagement and data-driven strategies for public health planning can ensure plans more accurately reflect the unique needs of residents and build trust between residents and institutions. LHDs should continue to embed and/or expand community engagement and data-driven strategies into their practice.

• The integration of QIPs to cultivate a culture of continuous quality improvement within LHDs highlights the need for ongoing investment in workforce development and building internal health department capacity. Policies that support the establishment of performance management systems and quality improvement training for public health staff are essential for sustaining quality improvement over time.