The Role of Public Health Informatics in the Coordination of Consistent Messaging from Local Health Departments and Public Health Partners During COVID-19

Abstract

Introduction: Efficient communication and collaboration among local health departments (LHDs), healthcare organizations, governmental entities, and other community stakeholders are critical for public health preparedness and response. This study evaluates (1) the impact of informatics on LHDs’ frequency and collaboration in creating consistent COVID-19 messaging; (2) the influence of informatics on targeted messaging for vulnerable populations; and (3) LHD characteristics linked to their consistent and/or targeted messaging engagement. Methods: This study analyzed the 2020 National Association of County and City Health Officials (NACCHO) Forces of Change (FOC) survey, the COVID-19 Edition. Of the 2390 LHDs invited to complete the core questionnaire, 905 were asked to fill out the module questionnaire as well. The response rate for the core was 24% with 587 responses, while the module received 237 responses, achieving a 26% response rate. Descriptive analyses and six logistic regression models were utilized. Results: Over 80% (183) of LHDs collaborated regularly with public health partners, and 95% (222) used information management applications for COVID-19. Most interacted with local and state agencies, but only half with federal ones. LHDs that exchanged data with local non-health agencies, engaged with local non-health agencies, and communicated weekly to daily with the public about long-term/assisted care had higher odds of creating consistent messages for the public, and about the use and reuse of masks had lower odds of collaborating with public health partners to develop consistent messages for the public. Conclusion: Our findings underscore the centrality of informatics infrastructure and collaboration in ensuring equitable public health messaging. Strengthening public health agencies and investing in targeted training are crucial for effective communication across the communities served by these agencies.

1. Introduction

Local health departments (LHDs) are the face of public health, serving as the front line of public health services. Effective communication and coordination between LHDs, healthcare organizations, governmental agencies, and other community stakeholders are imperative for public health preparedness and response [1]. COVID-19 and its global impact made everyone aware of the communication gaps that led to many challenges in response to the pandemic, particularly among disadvantaged populations in the United States [2]. As a result, developed guidance facilitated horizontal communication between local and state health departments, as well as vertical communication among local health departments, healthcare organizations, governmental agencies, and other community stakeholders [2]. The transferring of information occurs frequently between these entities, but it must be accurate and timely in conveyance [3]. LHDs continually identify and work towards addressing communication gaps both internally and externally; however, these gaps continue to exacerbate health disparities [3].

To address health disparities and some aspects of the social determinants of health, including public education and awareness, social and community context, health care access, and neighborhood environment, LHDs and community-based organizations, collaborate in defining what Public Health 3.0 means in practice and strategically refine the leadership roles within LHDs to serve the community better [4]. LHDs will require robust infrastructure, standardized and high-quality service delivery, and a robust workforce capacity to implement these changes. Public Health 3.0 is an approach that redefines the role of public health, extending beyond traditional services. Public Health 3.0 recommends prioritizing cross-sector collaboration and systems-level action to address the social determinants of health. Public Health 3.0 envisions public health executives as “chief health strategists” who collaborate across sectors to enhance population health by implementing equity-focused strategies, modernizing data, and implementing policies. This method necessitates the development of strategic partnerships, infrastructure, and leadership to facilitate sustainable, community-level transformation [4]. Public Health Accreditation Board (PHAB) accreditation is associated with improved cross-sector partnerships and relationships, which could increase LHD’s ability to interact with stakeholders outside of the health sector, including non-health sector organizations such as schools, businesses, and faith-based groups—by adhering to standards for risk communication, health information exchange, and community engagement [5]. One specific partnership challenge we examine in this study is how health information communicated by those external, non-health entities aligns with LHD messaging and public health goals.

Public health partnerships are the foundation of successful health communication initiatives, including public health messaging [5]. Thus, LHD leaders tend to strategically identify partners and community engagement strategies in the development of health information technology tools to increase diverse stakeholder engagement and promote their use [6,7,8,9]. Engaging with partnerships across various sectors expands on the innovative efforts that will lead to equitable outcomes in health communication. Some communication gaps that impact health have emerged from infodemics and the dissemination of inaccurate information on the internet and social media.

Current research on the utilization of social media for health communication suggests that there is a need to better understand both short-term and long-term use by engaging in robust evaluations [10]. To improve public health messaging and communication during the COVID-19 pandemic, studies have explored current attitudes towards public health messaging. A study in Alberta, Canada, revealed that public health messaging regarding COVID-19 has been conflicting and unclear, primarily due to inconsistent information, which has fostered mistrust in public health agencies and the communities they serve [11,12,13,14]. Positive framing and tailored messages are imperative in reaching target populations [11,12]. Research indicated that the main reason participants adhered to public health messaging was to protect family and friends from COVID-19 [13]. Furthermore, research has revealed that people’s identification with a message source (via social media) improves the effect of health communication outcomes; in fact, people who received health information through social media from a generic organization had lower health knowledge retention [15]. Establishing effective messaging mechanisms is crucial for addressing the health information needs of diverse populations and earning the public’s trust.

Health communication has been one of the Healthy People goals since 2010. Based on 10 years of data from the Health Information National Trends Survey (HINTS), internet penetration has exceeded the target of 75%; however, it is apparent that differences exist in age and education. Older Americans and those with less than a college education fell short [16]. Public health organizations have worked diligently to establish real-time response, population-centered, evidence-based public health information, and public health workforce training; however, obstacles, including interoperability, data standardization, privacy, and technology transfer, persist [17].

The dissemination of public health messaging is a critical component of public health informatics, which is defined as the application of information technology and computer science to public health practice, research, and education. The COVID-19 pandemic has taught us that reporting policies and technology systems must improve before the next public health pandemic or outbreak [18]. Public health surveillance and response, facilitated by effective data visualization, enable the delivery of successful public health messaging. An applied example is Dixon et al.’s development and implementation of population-level dashboards, which gathered information on COVID-19 infections and surveillance in 2020, in partnership with state and local public health agencies [19]. These dashboards are still in use today. Given the importance of accurate and timely health information exchange, collaborations will facilitate necessary support in public health responses [19]. While various healthcare and public health organizations utilize informatics and Big Data tools to share information, leaders must bridge current informatics efforts with environmental and social factors relevant to community health [20]. This shifts the focus to the population, aiming to lead to better health outcomes. Studies on messaging from LHDs and public health partners during the COVID-19 pandemic in the United States are scarce or nonexistent altogether.

While consumer health informatics has advanced health communication strategies, significant gaps remain in theoretically-informed approaches to aligning communication with users’ needs [21]. This study employs two complementary social science frameworks to examine how LHDs and their partners develop and disseminate COVID-19 health messaging. Wilson’s Model of Information Behavior provides the foundation for understanding how LHDs assess community information needs, motivations, and contextual factors that shape message development. The Information-Motivation-Behavioral Skills (IMB) Model then extends this understanding by examining how LHDs ensure their messages lead to action through building recipients’ knowledge, motivation, and behavioral capacity. Together, these frameworks directly support the objectives of the present study [22,23].

This study has three primary aims: (1) to analyze the impact of the informatics’ use on the frequency of LHDs and their public health partners’ collaboration to create consistent COVID-19 messaging for the public; (2) to evaluate any influence that informatics may have on the use of targeted messaging for vulnerable populations; and (3) to examine LHDs’ characteristics associated with their engagement in consistent and/or targeted messaging to their population. This focus is motivated by documented communication inequalities that exacerbate COVID-19-related disparities. However, scant empirical work has assessed how public health informatics tools shape LHD collaboration in tailoring outreach to these groups. Key gaps include the absence of standardized vulnerability definitions in messaging research, limited metrics for partnership-driven informatics workflows, and a dearth of studies evaluating how data-driven coordination influences message consistency. In the wake of pervasive infodemics and the urgent WHO mandate for infodemic management, this investigation is both timely and necessary to establish evidence-based, equity-centered guidelines for LHD–partner messaging strategies.

2. Materials and Methods

2.1. Data Source

This study utilized the 2020 National Association of County and City Health Officials (NACCHO) Forces of Change (FOC) survey, the COVID-19 Edition, obtained directly from NACCHO. To measure changes in local health department (LHD) budgets, staff, programs, and their impacts, NACCHO has been conducting the FOC surveys periodically. To select a sample of 904 LHDs from a list of 2390 LHDs nationwide, the FOC used a stratified sampling design with stratification by LHDs’ population size and state. The stratification by jurisdiction population consisted of three groups—below 50,000, 50,000 to 499,999, and 500,000 or higher, with oversampling for the highest population group to ensure sufficient numbers in that stratum. NACCHO excluded two states: Rhode Island, because the state did not have any LHD, and Florida. After all, the state health department asked to be excluded from this wave of the FoC. The 2020 FOC consisted of two components: the core, based on the census design, and a module, based on the sampling design. All 2390 LHDs were requested to respond to the core set of questions, while LHDs in the representative sample were asked to complete an additional set of questions in the module. The survey closed with 587 responses for the core component, yielding a response rate of 24%, and 237 responses for the module component, resulting in a response rate of 26%. LHD’s leadership and key staff completed responses. Detailed survey information can be found on NACCHO’s FOC webpage [24].

2.2. Measures

The dependent variable, the frequency at which LHDs collaborated with public health partners in creating consistent messages for the public, was operationalized through the question: “How often does your LHD coordinate with public health partners around the following issues?” Among issues covered by the question, the issue captured by the dependent variable for this study was creating consistent messages for the public. The answer choices were “not at all, occasionally, monthly, weekly, or daily”. To avoid small cell sizes, the dependent variable became dichotomous after it was re-coded as not-at-all or occasionally “0” and Monthly, Weekly, or Daily “1”.

This study had 35 independent variables separated into six groups: (1) the utilization of information management applications for collecting, managing, or sharing information, especially for COVID-19, (2) the LHDs’ interaction in exchanging information relevant to public health with local/state/federal agencies, (3) the LHDs’ interaction in exchanging data relevant to public health with local/state/federal agencies, (4) the LHDs’ interaction in coordinating messages to the public with local/state/federal agencies, (5) the frequency of LHD communicated with the public around issues about COVID-19 pandemic, and (6) the frequency of LHD faced significant COVID-19 public communication challenges regarding some activities. To avoid the issue of small cell sizes, the variables were recoded as dichotomous ones.

The first independent variable was evaluated through the survey item (Q32): “which of the following information management applications your LHD used for collecting, managing, or sharing health information, specifically for COVID-19.” The listed applications included Outpatient Influenza-Like Illness Surveillance Network (ILINet), National Syndromic Surveillance Program—BioSense platform, COVID-19-Associated Hospitalization Surveillance Network (COVID-NET), ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics), MTX or other text messaging-based system, Other digital contact tracing tools, State disease surveillance system, Locally disease surveillance system, Geographic Information System (GIS), Statistical analysis software (R, Stata, SAS, SPSS, etc.), Microsoft Excel, and others. LHDs’ selections were combined and recoded as dichotomous: did not use any information management application “0” and used information management applications “1”.

The following three groups of independent variables (a total of 12 variables) were assessed through survey item Q40: “During the COVID-19 crisis, identify which type of interactions you had with the following sectors.” Among the interactions listed, three types are deemed to be relevant to our study: (1) exchanging information relevant to public health, (2) exchanging data relevant to public health, and (3) coordinating messages to the public. The listed agencies are grouped into four categories: (i) local non-health agencies (Local Energy/Electric Utility, Local Public Drinking Water System, Local Public Sewer System, Local Communications, and Local Food and Agriculture). Local Waste Management, Local Emergency Management, Local Public Safety, K-12 Schools, and Social Services), (ii) local health agencies (other local public health departments, Federally qualified health centers, Hospitals, Long-term care/skilled nursing/nursing facilities, and Pharmacies), (iii) State agencies (State drinking water primacy agency, and State Level Public Health Agency), and (iv) Federal agencies (US Environmental Protection Agency, and Centers for Disease Control and Prevention). The variable categories were no interaction “0” and interaction “1”.

The survey item Q48 was utilized to formulate the independent variable group #5: “During the COVID-19 outbreak, how frequently has your LHD communicated with the public about the following issues?” The issues listed were Symptoms, When and how to seek medical advice, Numbers of cases/deaths, Availability/procedures for testing, The need for social distancing, The need for handwashing, Water shut-offs, Requirements for shelter in place, Updates on use/reuse of mask, Long-term care or assisted care issues, Contagion/disease trends, and Disease comorbidities. This group comprised 13 dichotomous variables of Not-at-all and Occasionally “0” and Weekly and Daily “1”.

The last group of independent variables used survey item Q49, which asked: “How frequently does your local health department (LHD) face significant COVID-19 public communication challenges regarding the following activities?” The list included: creating scientifically accurate messages, creating open and transparent messages, creating clear messages, tailoring messages to specific audiences, creating consistent messages, creating sufficient messages, offering actionable messages, communicating in a timely manner, and disseminating messages through public health partners. This group had nine dichotomous variables as never, very rarely, rarely, and occasionally “0” and frequently and very frequently “1”.

This study used the survey-stratified factors as control variables, LHD’s jurisdictional size (small, fewer than 50,000 people “0”, medium, 50,000–499,999 people “1”, and large, 500,000 people or more “2”) and LHD’s type of governance (local governance “0”, state governance “1”, and shared governance “2”).

2.3. Statistical Analysis

To contextualize our multivariable analysis, descriptive analyses were conducted for all variables included in this study. Due to the presence of substantial correlations between the groups of independent variables, six separate logistic regression models were computed—one for each group—to avoid multicollinearity. The size of LHDs’ jurisdictional population and the type of governance (relative to the state health agency authority) were used as control variables in each of the logistic regression models. The estimation weights developed by NACCHO were applied to account for two factors: oversampling for LHDs in the largest population group and differential response rates by state and population size. Additional information about the survey methods is available in the 2020 Forces of Change technical documentation [25]. All analyses were conducted using STATA version 18. The significant threshold was set at a 95% confidence level. Augusta University’s institutional review board had previously determined that using NACCHO’s 2020 FOC data did not meet the definition of human subject research. Thus, no further review was needed.

3. Results

Figure 1 illustrates the dependent variable, showing the frequency of LHD coordination with public health partners in creating consistent messages for the public.

Table 1. Descriptive statistics for the variables in this study (N = 237).

Variables		Categories	Unweighted (n)	Weighted (%)
The frequency at which LHD collaborated with public health partners in creating consistent messages for the public.		Not-at-all or Occasionally	44	19.38
		Monthly to Daily	183	80.62
Utilization of information management applications for collecting, managing, or sharing information, especially for COVID-19.		No	10	4.31
		Yes	222	95.69
Interaction in exchanging information relevant to public health with	Local non-health agencies	No	12	5.41
		Yes	183	94.59
	Local health agencies	No	33	15.28
		Yes	183	84.72
	State agencies	No	27	18.75
		Yes	117	81.25
	Federal agencies	No	31	36.90
		Yes	53	63.10
Interaction in exchanging data relevant to public health with	Local non-health agencies	No	47	21.17
		Yes	175	78.83
	Local health agencies	No	65	30.09
		Yes	151	69.91
	State agencies	No	41	28.47
		Yes	103	71.53
	Federal agencies	No	43	51.19
		Yes	41	48.81
Interaction in coordinating messages to the public with	Local non-health agencies	No	34	15.32
		Yes	188	84.68
	Local health agencies	No	68	31.48
		Yes	148	68.52
	State agencies	No	41	28.47
		Yes	103	71.53
	Federal agencies	No	43	51.19
		Yes	41	48.81
The frequency of LHD communicating with the public around the issue of	Symptoms	Not at all or Occasionally	33	15.21
		Weekly to Daily	184	84.79
	When and how to seek medical advice	Not at all or Occasionally	37	16.89
		Weekly to Daily	182	83.11
	Number of cases/deaths	Not at all or Occasionally	26	11.93
		Weekly to Daily	192	88.07
	Availability/procedures for testing	Not at all or Occasionally	32	14.61
		Weekly to Daily	187	85.39
	The need for social distancing	Not at all or Occasionally	25	11.42
		Weekly to Daily	194	88.58
	The need for hand washing	Not at all or Occasionally	28	12.90
		Weekly to Daily	189	87.10
	Water shut off	Not at all or Occasionally	188	87.44
		Weekly to Daily	27	12.56
	Requirements for shelter-in-place	Not at all or Occasionally	134	62.91
		Weekly to Daily	79	37.09
	Updates on the use/reuse of masks	Not at all or Occasionally	104	48.15
		Weekly to Daily	112	51.85
	Long-term care or assisted care issues	Not at all or Occasionally	101	46.98
		Weekly to Daily	114	53.02
	Contagion/disease trends	Not at all or Occasionally	83	38.25
		Weekly to Daily	134	61.75
	Disease comorbidities	Not at all or Occasionally	110	50.93
		Weekly to Daily	106	49.07
	Rumor management	Not at all or Occasionally	82	38.14
		Weekly to Daily	133	61.86
The frequency of LHD faced significant COVID-19 public communication challenges regarding the activity of	Creating significantly accurate messages	Never to Occasionally	143	66.82
		Frequently to Very Frequently	71	33.18
	Creating open and transparent messages	Never to Occasionally	148	69.48
		Frequently to Very Frequently	65	30.52
	Creating clear messages	Never to Occasionally	136	63.85
		Frequently to Very Frequently	77	36.15
	Tailoring messages to specific audiences	Never to Occasionally	137	64.62
		Frequently to Very Frequently	75	35.38
	Creating consistent messages	Never to Occasionally	140	66.04
		Frequently to Very Frequently	72	33.96
	Creating sufficient messages	Never to Occasionally	129	61.14
		Frequently to Very Frequently	82	38.86
	Offering actionable messages	Never to Occasionally	138	64.79
		Frequently to Very Frequently	75	35.21
	Communicating in a timely manner	Never to Occasionally	133	62.74
		Frequently to Very Frequently	79	37.26
	Disseminating messages through public health partners	Never to Occasionally	143	68.42
		Frequently to Very Frequently	66	31.58
Population Jurisdiction Size		Small	361	61.92
		Medium	199	34.13
		Large	23	3.95
Governance Type		Local	420	72.04
		State	134	22.98
		Shared	29	4.97

details the description of the study variables. Notably, more than 80% of LHDs collaborated with public health partners on a monthly, weekly, or daily basis to create consistent messages for the public. In comparison, less than 20% of LHDs either never or occasionally did so. Ninety-five percent (95%) of LHDs utilized information management applications for collecting, managing, and sharing information about COVID-19. While high proportions of LHDs (nearly 70% to 90%) interacted with local non-health agencies, other LHDs, and state agencies, approximately half of the LHDs also interacted with federal agencies. During the COVID-19 pandemic, half to 88% of LHDs communicated with the public weekly to daily about some issues related to the disease, except the topic of water, with 12% doing so. Although LHDs faced numerous public communication challenges during the COVID-19 pandemic, approximately 30–35% of them often (frequently or very frequently) encountered these issues. Moreover, small LHDs accounted for 61.9% of all LHDs, medium LHDs accounted for 34.1%, and large LHDs accounted for 3.9%. The most significant proportion (72.0%) of LHDs had local governance, 23.0% had state governance, and 5.0% had shared governance.

Table 2. Logistic regression of LHDs’ collaboration with public health partners in creating consistent messages for the public.

Variables	Categories	AOR	p-Value	95% CI
MODEL 1: LHDs’ utilization of information management applications for COVID-19’s collecting, managing, or sharing information.
LHDs use information management applications.	No	--	--	--
	Yes	1.870	0.432	0.393–8.900
MODEL 2: LHDs’ interaction in exchanging information relevant to public health with governmental agencies
Local non-health agencies	No	--	--	--
	Yes	6.713	0.004	1.862–24.194
Local health agencies	No	--	--	--
	Yes	0.142	0.204	0.007–2.890
State agencies	No	--	--	--
	Yes	8.326	0.131	0.532–130.188
Federal agencies	No	--	--	--
	Yes	10.968	0.010	1.763–68.248
MODEL 3: LHDs’ interaction in exchanging data relevant to public health with governmental agencies
Local non-health agencies	No	--	--	--
	Yes	10.439	0.044	1.060–102.795
Local health agencies	No	--	--	--
	Yes	0.649	0.827	0.134–31.343
State agencies	No	--	--	--
	Yes	0.567	0.731	0.022–14.399
Federal agencies	No	--	--	--
	Yes	5.940	0.118	0.635–55.550
MODEL 4: LHDs’ interaction in coordinating messages to the public with governmental agencies
Local non-health agencies	No	--	--	--
	Yes	8.208	0.049	1.005–67.023
Local health agencies	No	--	--	--
	Yes	4.284	0.270	0.324–56.690
State agencies	No	--	--	--
	Yes	0.581	0.686	0.042–8.081
Local health agencies	No	--	--	--
	Yes	2.220	0.353	0.412–11.958
MODEL 5: The frequency of LHD communicating with the public around the issue of concern
Symptoms	Not at all or Occasionally	--	--	--
	Weekly to Daily	1.045	0.955	0.221–4.935
When and how to seek medical advice	Not at all or Occasionally	--	--	--
	Weekly to Daily	2.392	0.325	0.421–13.591
Number of cases/deaths	Not at all or Occasionally	--	--	--
	Weekly to Daily	4.088	0.108	0.733–22.807
Availability/procedures for testing	Not at all or Occasionally	--	--	--
	Weekly to Daily	0.042	0.170	0.001–3.892
The need for social distancing	Not at all or Occasionally	--	--	--
	Weekly to Daily	4.678	0.237	0.362–60.458
The need for hand washing	Not at all or Occasionally	--	--	--
	Weekly to Daily	4.960	0.154	0.483–100.259
Water shut off	Not at all or Occasionally	--	--	--
	Weekly to Daily	1.867	0.548	0.306–9.287
Requirements for shelter-in-place	Not at all or Occasionally	--	--	--
	Weekly to Daily	1.005	0.993	0.329–3.074
Updates on the use/reuse of masks	Not at all or Occasionally	--	--	--
	Weekly to Daily	0.239	0.019	0.072–0.794
Long-term care or assisted care issues	Not at all or Occasionally	--	--	--
	Weekly to Daily	6.507	0.003	1.923–22.016
Contagion/disease trends	Not at all or Occasionally	--	--	--
	Weekly to Daily	1.317	0.648	0.404–4.289
Disease comorbidities	Not at all or Occasionally	--	--	--
	Weekly to Daily	0.707	0.560	0.221–2.266
Rumor management	Not at all or Occasionally	--	--	--
	Weekly to Daily	1.084	0.869	0.413–2.845
MODEL 6: The frequency of LHD faced significant COVID-19 public communication challenges regarding activities messaging
Creating significantly accurate messages	Never to Occasionally	--	--	--
	Frequently to Very Frequently	0.527	0.342	0.141–1.975
Creating open and transparent messages	Never to Occasionally	--	--	--
	Frequently to Very Frequently	0.833	0.870	0.093–7.429
Creating clear messages	Never to Occasionally	--	--	--
	Frequently to Very Frequently	0.614	0.498	0.149–2.518
Tailoring messages to specific audiences	Never to Occasionally	--	--	--
	Frequently to Very Frequently	2.183	0.231	0.609–7.830
Creating consistent messages	Never to Occasionally	--	--	--
	Frequently to Very Frequently	2.265	0.271	0.527–9.722
Creating sufficient messages	Never to Occasionally	--	--	--
	Frequently to Very Frequently	1.560	0.616	0.275–8.864
Offering actionable messages	Never to Occasionally	--	--	--
	Frequently to Very Frequently	3.022	0.137	0.704–12.966
Communicating in a timely manner	Never to Occasionally	--	--	--
	Frequently to Very Frequently	0.412	0.135	0.130–1.315
Disseminating messages through public health partners	Never to Occasionally	--	--	--
	Frequently to Very Frequently	1.310	0.684	0.357–4.801
Control variables in each logistic model
Population Jurisdiction Size	Small	--	--	--
	Medium	1.505	0.409	0.559–4.095
	Large	0.490	0.221	0.156–1.536
Governance Type	Local	--	--	--
	State	0.582	0.148	0.279–1.212
	Shared	0.564	0.494	0.109–2.906

LHD, Local Health Department; AOR, adjusted odds ratio.

presents the results from our logistic regression models.

According to the logistic regression model 1, the utilization of information management applications by LHDs for collecting, managing, or sharing information, especially for COVID-19, was not statistically significant in association with the frequency of LHDs collaborating with public health partners to create consistent messages for the public.

Among independent variables in group 2 (logistic regression model 2), LHDs that interacted with local non-health agencies and federal agencies to exchange pandemic information relevant to public health had higher odds of collaborating with public health partners to create consistent public messages than those that did not (adjusted odds ratio (AOR) = 6.713, p < 0.01; AOR = 10.968; p = 0.01, respectively). In contrast, the interaction of LHDs with local health agencies and state agencies in exchanging information showed no significant association with their collaboration with public health partners in creating consistent public messages regarding COVID-19.

Logistic regression model 3 reveals the significant association between LHDs’ interaction with local non-health agencies in exchanging COVID-19 data relevant to public health and their interaction with public health partners in creating consistent public messages. LHDs that interacted with local non-health agencies to exchange COVID-19 data relevant to public health had higher odds of collaborating with public health partners to create consistent public messages than those that did not (AOR = 10.439, p = 0.04). LHDs that interacted with local, state, or federal agencies in exchanging COVID-19 data relevant to public health showed no significant association with the odds of their interaction with public health partners in creating consistent public messages.

Among the interactions between LHDs and public health agencies in coordinating messages to the public (logistic regression model 4), LHDs that interacted with local non-health agencies had higher odds of collaborating with public health partners to create consistent public messages than those that did not (AOR = 8.208, p < 0.05). In contrast, LHDs that interacted with local, state, or federal agencies in coordinating messages to the public showed no association with the odds of their interaction with public health partners in creating consistent public messages.

Logistic model 5 examined the association between the frequency of LHDs communicating with the public around several issues of concern and the frequency of LHDs collaborating with public health partners to create consistent messages for the public. LHDs’ updating on the use and reuse of masks and conversations about long-term and assisted care were significantly associated with the frequency of LHDs collaborating with public health partners in creating consistent messages for the public. LHDs that communicated weekly to daily with the public about long-term and assisted care had higher odds of collaborating with public health partners in creating consistent messages for the public than those that did not (AOR = 6.507, p < 0.01). In contrast, LHDs that communicated with the public weekly to daily about the use and reuse of masks had lower odds of collaborating with public health partners in creating consistent messages for the public than those that did not (AOR = 0.239, p = 0.02).

Logistic model 6 revealed that although LHDs faced various challenges in communicating with the public about COVID-19, none of the examined factors were statistically associated with the frequency of LHDs collaborating with public health partners to create consistent messages for the public.

Control variables, the served population size and the governance type, were added to each logistic model and showed no significant association with the frequency of LHDs collaborating with public health partners to create consistent messages for the public.

4. Discussion

This study sought to assess how LHDs leveraged informatics tools and interagency collaboration to promote consistent and targeted COVID-19 messaging, particularly for vulnerable populations. Consistent with the aims of Public Health 3.0, our findings reveal that the widespread adoption of digital information management systems (95.7%) among LHDs has played a central role in facilitating communication efforts. Overall, 80.6% of LHDs collaborated frequently (monthly to daily) with public health partners to coordinate consistent messaging. Cross-sector alignment in health communication efforts is important. Academic and community partnerships strengthened LHD capacity during the pandemic response [26]. However, the variation in engagement with federal partners—only 63.1% of LHDs exchanged information regularly—reflects enduring fragmentation in the vertical coordination of public health messaging. This observation aligns with prior literature documenting decentralized and inconsistent communication strategies during the early stages of the COVID-19 Pandemic [27].

Pre-pandemic research on health information exchange by LHDs found that it did not significantly improve population health outcomes [28]. However, studies during the COVID-19 pandemic demonstrated that reliable data visualization, exchange, and consolidation were paramount in supporting surveillance and enhancing the response to the pandemic [19,29]. LHDs with robust informatics capabilities were better equipped to manage case surveillance, resource allocation, and public communication during the pandemic [30,31]. These departments could more effectively track disease spread, predict service needs, and coordinate with healthcare providers to improve public health services delivery and decision-making. However, significant disparities in informatics capacity persist across jurisdictions, particularly between well-resourced urban departments and their rural counterparts [32].

This study’s findings highlight actionable implications. Engagement with local non-health agencies to exchange information and data related to public health and coordinate messages was strongly associated with consistent messaging efforts (AOR = 6.713–10.439). This reinforces the role of whole-of-government approaches, as these partnerships often function as trusted channels for community engagement [30,33,34]. Additionally, federal partnerships (AOR = 10.968) were positively associated with frequent collaboration, indicating that federal guidance can facilitate harmonized messaging across jurisdictions. The strong association between collaboration with long-term care facilities and messaging consistency (AOR = 6.507) further reflects the prioritization of vulnerable populations within LHD communication strategies. Vulnerable or high-risk populations addressed in the FOC 2020 survey include older adults; nursing-home residents; individuals with chronic conditions; low-income groups; those with limited English proficiency; Hispanics and other racial or ethnic minorities; children; people experiencing homelessness; persons with disabilities; individuals with mental health or substance use disorders; pregnant people; undocumented immigrants; and members of the lesbian, gay, bisexual, transgender, and queer communities. Nonetheless, over one-third of respondents (35.4%) reported frequent difficulty tailoring messages to specific populations, reflecting broader challenges in health literacy, cultural competency, and the equitable distribution of communication-related resources. Moreover, 51.9% of LHDs reported communicating mask-use updates on a weekly to daily basis. The observed negative association between mask-update messaging and collaboration (AOR = 0.239) may indicate messaging fatigue resulting from frequent and prolonged interaction with repetitive preventive health messages, as documented in prior studies [35].

The findings from this study are consistent with prior research, suggesting that infodemics—defined by the WHO as an overabundance of information, some of which is accurate and some of which is not—can erode public trust and hinder effective public health response [36]. Local informatics capacity, when paired with community partnerships, appears essential in countering misinformation and tailoring messages to specific populations. In this context, the role of faith-based and other community-based organizations remains critical, as documented in global case studies where the absence of clear government guidance led to misinformation and public confusion.

This current study demonstrates that informatics tools are not only instrumental in tracking public health trends but also in shaping equitable communication. Data-based digital health solutions possess transformative potential for healthcare systems [37]. However, to fully realize this potential, LHD informatics staff require continuous training to keep pace with evolving technologies, data governance standards, and community-specific communication strategies. Training initiatives that emphasize cultural tailoring, digital fluency, and adaptive technical skills can help ensure that messages are accessible and relevant to all populations. Such ongoing professional development is critical, as gaps in informatics competencies—identified in the 2023 Forces of Change Survey [38]—may otherwise hinder the effective translation of data for vulnerable groups. Policymakers must also consider the structural determinants of communication equity, ensuring that funding mechanisms prioritize both sustained workforce training and the needs of LHDs with historically limited resources.

Although our study focuses on the COVID-19 response five years ago, its lessons remain highly relevant for contemporary public health practice. The rapid scale-up of informatics infrastructure and cross-sector partnerships during the pandemic established a blueprint for agile communication strategies that can now be applied to emerging threats such as mpox, influenza, and future novel pathogens. Today’s LHDs should institutionalize these best practices—maintaining dynamic dashboards, layered stakeholder networks, and culturally tailored messaging pipelines—to ensure that real-time, equitable public health communications are not only reactive but also proactive in safeguarding community health.

The findings mentioned above hold significant implications for practice. Federal and state public health authorities must formalize communication pathways with LHDs, ensuring bi-directional, timely data exchange. Additionally, targeted funding is needed to build informatics infrastructure in under-resourced jurisdictions. Investments in training to enhance the cultural and linguistic tailoring of messages may help bridge persistent gaps in health communication equity. The intersection of health informatics and cross-sector collaboration must be leveraged to ensure resilient, community-informed public health messaging strategies that are effective and impactful.

Several limitations must be acknowledged. The cross-sectional design precludes causal inference, and the use of self-reported survey data introduces the potential for recall and social desirability biases. Additionally, dichotomizing outcome variables may obscure gradations in informatics use and communication practices. Lastly, this study analyzed the 2020 FOC, acknowledging the inherent limitations of the survey as a result [25]. Nonetheless, the breadth of the sample and the focus on critical tools and partnerships enhance the relevance of these findings for ongoing public health preparedness. Finally, future research should directly assess variability in LHD informatics infrastructure, using objective measures of hardware, software, and network capacity, and evaluate how these differences impact messaging consistency and overall performance.

5. Conclusions

In summary, this study underscores the critical importance of a robust informatics infrastructure and effective cross-sector collaboration in delivering consistent and equitable public health messaging. The integration of advanced health informatics tools, paired with trusted community partnerships, can significantly enhance communication strategies, particularly for vulnerable populations. Given the persistent disparities in informatics capacity across various jurisdictions, it is essential to prioritize targeted investments in technological resources and culturally responsive training programs for healthcare professionals. These initiatives should focus on equipping LHDs with the necessary skills and tools to analyze and disseminate health-related information effectively.

Moreover, strengthening the data collection, analysis, and communication capabilities of LHDs must remain a top priority. This proactive approach will not only prepare communities for future health crises but also ensure that equity is at the forefront of public health responses, thereby preventing the further marginalization of already vulnerable populations. By fostering a culture of collaboration and continuous improvement, public health messaging can be enhanced, ultimately leading to improved health outcomes for all individuals.