Fighting obesity at the local level? An analysis of predictors of local health departments' policy involvement

Preventive Medicine 133 (2020) 106006

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Fighting obesity at the local level? An analysis of predictors of local health departments' policy involvement

T

Wenhui Fenga, , Erika G. Martinb ⁎

a b

Department of Public Health and Community Medicine, Tufts University School of Medicine, United States of America Rockefeller College of Public Affairs & Policy, University at Albany-State University of New York, United States of America

ARTICLE INFO

ABSTRACT

Keywords: Local health departments Obesity Health policy

Obesity is a critical public health issue in the United States. Local health departments (LHDs) can play a crucial role in public health policy, and are well-positioned to address obesity in their communities. We assess the obesity policy involvement among LHDs across the United States and the factors associated with increased involvement. Data come from 1803 LHDs in the 2016 National Profile of Local Health Department survey, supplemented with county-level obesity prevalence and political ideology. Negative binomial regressions examined LHD and regional characteristics associated with the number of obesity policies with which LHDs were involved. Almost half (46.1%) of LHDs reported no involvement with local obesity policies. Several factors were associated with increased policy involvement: having local boards of health with advisory (IRR = 1.31, p < 0.05) or governance roles (IRR = 1.27, p < 0.01), larger workforces (IRR = 1.34, p < 0.001), accreditation (IRR = 1.40, p < 0.001), higher obesity prevalence (IRR = 1.03, p < 0.01), and being politically more liberal (IRR = 1.01, p < 0.05). Overall, the large number of LHDs with no or limited involvement in obesity policies is a missed opportunity for local action. A better understanding of LHD policy involvement, how organizational and political factors enable or constrain their actions, and how they can leverage their current authority is needed to help LDHs serve local needs.

1. Introduction As a leading cause of diabetes, heart disease, stroke, and other chronic conditions, obesity is a critical public health issue in the United States. Prevalence continues to rise, with almost 40% of adults currently obese (Hales et al., 2017). In addition to morbidity, obese adults have 42% higher medical costs (Finkelstein et al., 2009; Tremmel et al., 2017). Local health departments (LHDs) play a crucial role in public health policy, particularly in the shift towards Public Health 3.0 which expands public health practice to engage with community partners across sectors to generate collective impact and address the social determinants of health (DeSalvo et al., 2017). There are reasons why LHDs are well-positioned to address obesity. First, obesity prevalence varies across local communities. For example, obesity among elementary and middle/high school students in New York school districts ranges from 1.3% to 46.4%, and from 3.8% to 64.3%, respectively; moreover, the relationship between New York's built environment characteristics and student obesity prevalence varies by grade and local region

(Dwicaksono et al., 2018). LHDs use their community needs assessments to tailor interventions to meet local needs. Second, LHDs collaborate with local communities and schools (Slater et al., 2007) to expand their efforts and develop specific policy tools for obesity prevention (Mensah et al., 2004). Third, some LHDs experiment with innovative policy approaches to obesity, serving as “laboratories of democracy.” (Ely, 2009) For example, New York City and some California localities adopted menu-labeling mandates before they were on the national agenda (Pomeranz, 2011). Prior research has examined how different organizational characteristics influence LHDs' functioning and effectiveness. LHDs may be centralized (where they are units of the state government), decentralized (where they are units of local government), or mixed/shared (Public Health Law Center, 2015). Decentralized local control is the most prevalent governance form. Size and the level of governance are key factors in LHDs' policy behaviors (Meyerson and Sayegh, 2016). Higher LHD expenditures are correlated with better health outcomes (Erwin et al., 2011), and accreditation is correlated with better performance (Shah et al., 2018; Ingram et al., 2018; Yeager et al., 2018).

⁎ Corresponding author at: Public Health and Community Medicine, Tufts University School of Medicine, 136 Harrison Ave, M&V 127, Boston, MA 02111, United States of America. E-mail address: [email protected] (W. Feng).

https://doi.org/10.1016/j.ypmed.2020.106006 Received 6 August 2019; Received in revised form 13 December 2019; Accepted 25 January 2020 Available online 30 January 2020 0091-7435/ © 2020 Elsevier Inc. All rights reserved.

Preventive Medicine 133 (2020) 106006

W. Feng and E.G. Martin

Most LHD studies examine specific public health issues such as emergency preparedness (Rutkow et al., 2015; Schoch-Spana et al., 2018), sexually transmitted infections (Rodriguez et al., 2012), and mental health (Chen et al., 2018; Purtle et al., 2016). Past research has examined LHDs' involvement in obesity-related issues, finding that LHDs have limited participation in policy/advocacy related to community physical activity (Goins et al., 2016) and that enforcing laws and regulations for obesity control is not commonly provided as an essential public health services (Luo et al., 2013). LHDs in communities with higher obesity prevalence are not more likely to engage in prevention activities (Stamatakis et al., 2012). Although LHDs may be effective at addressing obesity at a local level (Chen et al., 2013), the governance and policy-related factors associated with obesity policymaking remain understudied. Given the pressing need to address the obesity epidemic and LHDs' unique roles in this process, we build on this literature to assess the level of LHDs' involvement in obesity policies across the US and identify factors associated with varying LHD responses.

policy or advocacy activities in which they are actively involved. Those reporting involvement in obesity and chronic disease were subsequently asked about their approaches taken. LHDs could select up to 11 obesity policy approaches: community-level urban design and land use policies to encourage physical activity, active transportation options, school or child care policies that encourage physical activity, school or child-care policies that reduce availability of unhealthy foods, expanding access to recreational facilities, nutritional labeling, increasing retail availability of fruits and vegetables, fiscal policies to decrease consumption of unhealthy foods or beverages, and policies to promote breastfeeding. Two categories included in the questionnaire (“limit fast food” and “other”) were dropped from the analysis because few LHDs selected these policies (1.3% and 2%, see Appendix Table 1 for more details). In a sensitivity analysis, these two categories were included in the outcome count and yielded similar results (see Appendix Table 2). The main regression analysis used obesity involvement as a count variable. In operationalizing our outcome, we performed a principle component analysis to see if LHDs implemented clusters of policies. We did not use this information to construct the outcome because the only meaningful cluster comprised the two school-oriented approaches (see Appendix Table 3). In addition, for presenting descriptive information, the outcome variable was coded in three levels of obesity policy involvement: “none” for zero approaches taken, “low” for one to four approaches, and “high” for five or more approaches; these cut-points were guided by the distinction from zero to one approach taken, and the mid-point for the remainder (see Fig. 1).

2. Methods 2.1. Analytic overview We used national, cross-sectional 2016 survey data of 1803 LHDs to quantify variation in the number of obesity policy approaches taken by LHDs, and the factors associated with their involvement. To guide the quantitative analysis, we used a two-phase exploratory design (Creswell and Clark, 2006) whereby key informant interviews with 11 LHD experts from multiple backgrounds (practitioners who work at LHDs, state and national public health associations, and accreditation organizations; and academic scholars) elicited factors potentially relevant to LHDs' decision-making process (see Appendix 1 for the interview guide). These informed the selection of explanatory variables and hypothesized mechanisms. Our primary analysis, reported here, relied on the subsequent quantitative analysis of the survey data.

2.4. Independent variables: LHD capacity 2.4.1. Governance category All key informants identified governance as an important feature guiding LHDs' decision-making processes, consistent with a past study (Meyerson and Sayegh, 2016). They predicted that: LHDs structured as units of state health departments would be more likely to implement state-level decisions, LHDs that are part of local governments would have more decision-making power from the local legislature, and LHDs

2.2. Data sources

50

Organizational characteristics and obesity policy outcomes were from the most recent round (2016) of the National Profile of Local Health Department survey (hereinafter “the Profile”). The National Association of County and City Health Officials (NACCHO) conducts the Profile every three years to collect information on LHDs' infrastructure and practice (Newman, 2017). To our knowledge, this is the most informative dataset regarding LHDs. All LHDs received the 2016 round of survey questionnaire and 1930 out of 2533 of LHDs (76%) responded in 48 states and the District of Columbia (Schneider et al., 2012). The Profile does not include Hawaii and Rhode Island, which do not have sub-state units. Our study sample comprises the 1803 LHDs that responded to the obesity policy engagement question and had a corresponding county FIPS code allowing us to merge county-level characteristics from other data sources. Profile data were supplemented with obesity prevalence data from the Centers for Disease Control and Prevention (CDC, 2016). Political ideology are from Dave Leip's Atlas of US Presidential Elections, a commonly source for election results. These were merged with the Profile dataset by county Federal Information Processing Standard (FIPS) code. Where LHDs cover multiple counties, obesity prevalence and election results were computed as a population-weighted averages.

10

20

Percent 30

40

46.1

7.7 6.0

8.4

9.3 6.7

6.7 4.7

3.3

0

1.1

0

1

2 3 4 5 6 7 Number of Obesity Approaches Involved

8

9

Fig. 1. Distribution of US local health departments' involvement in obesity policies, 2016. Notes: Percentage values are based on the weighted sample and represent the characteristics of the study population. Obesity approaches included: community level urban design and land use policies to encourage physical activity, active transportation options, school or child care policies that encourage physical activity, school or child care policies that reduce availability of unhealthy foods, expanding access to recreational facilities, nutritional labeling, increase retail availability of fruits and vegetables, fiscal policies to decrease consumption of unhealthy foods or beverages and policies to promote breastfeeding.

2.3. Outcome variable The dependent variable is the number of obesity policies in which LHDs were involved, based on the Profile's survey questions regarding self-reported obesity policy activities in the past two years from 2014 to 2015 (see Appendix 2 for the questions). LHDs were first asked about 2

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W. Feng and E.G. Martin

in shared jurisdictions would contain both qualities. Governance was coded in three categories: state agency (reference group), local agency, and combined. LHDs structured as local agencies were hypothesized to have more obesity involvement due to their greater discretion.

expectations about its association with the outcome. 2.5.2. Political ideology One reason that obesity prevention policies encounter criticism and political resistance is their perception to be paternalistic (Barnhill and King, 2013). Previous literature has found that soda taxes have been adopted in cities with historically liberal voting patterns (Paarlberg et al., 2017). Ideology was operationalized using the percentage of the county that voted for Hillary Clinton in the 2016 presidential election, and expected to be positively correlated with obesity policy involvement. Because the 2016 presidential candidates were polarizing, a sensitivity analysis used the percentage that voted for Barack Obama in the 2012 presidential election and yielded similar findings.

2.4.2. Local board of health Local boards of health have the authority to make enforceable policy decisions; these entities have an advisory or governance role (Schneider et al., 2012). Key informants hypothesized that consistent with past research (Shah et al., 2018), having a local board of health with a governance role would be associated with higher obesity policy involvement. This measure has three categories: no local board of health (reference group), local board of health serving in an advisory role, and local board of health serving in a governance role (Ingram et al., 2018).

2.5.3. State fixed effects A vector of state fixed effects controlled for additional unobservable characteristics that may have influenced LHDs' decisions.

2.4.3. Accreditation The national Public Health Accreditation Board's (PHAB) voluntary accreditation program aims to improve LHDs' service quality (Jacob, 2017), and past research has found that accredited LHDs have higher engagement in obesity-related policies (Goins et al., 2016; Luo et al., 2013). Accreditation was operationalized as not accredited (reference group), with accredited LHDs hypothesized to have more obesity policy involvement. In the Profile, LHDs are identified as accredited if they are accredited by the Public Health Accreditation Board or part of a PHABaccredited centralized state integrated local public health department system.

2.6. Analytic methods Descriptive analyses reported the mean number of approaches taken by LHDs and regional variation. Variation in the level of involvement was mapped using county FIPS codes and Stata's geographic template of US counties (year 2014, maptile function). Bivariate analyses tested unadjusted associations between LHD characteristics and their levels of obesity policy involvement (none, low, or high). The main multivariable analysis encompassed a negative binomial regression with state fixed effects and all independent variables excluding those with multicollinearity, and yielded adjusted incidence rate ratios. As a sensitivity analysis, ordered logistic regression models assessed the relationship between the predictor variables and LHDs' levels of involvement; results were consistent with those from the negative binomial regressions (see Appendix Table 5). Among the 1803 LHDs in our sample, there were 3 missing values for ideology, 33 missing values for local board of health, and about 150 missing values for workforce and LHD top executive characteristics. We adjusted for missingness among independent variables with 20 rounds of multiple imputation using the predictive mean matching method, drawing from the five closest observations. The Profile's sample weights were applied to adjust for non-responses among surveyed LHDs. All analyses were performed in Stata 15.1.

2.4.4. Workforce Key informants identified personnel cost as a major expense for LHDs, consistent with evidence that LHDs with larger workforce size have higher engagement in policy activities (Hyde and Shortell, 2012). The workforce variable, operationalized as the number of full-time equivalent staff, was used as a proxy for LHDs' resources. Although budget size and spending were considered, they were omitted due to multi-collinearity and high numbers of missing values. The workforce variable was log-transformed due to the skewed distribution. In a supplemental model, we present the NACCHO categories for comparison to other studies (see Appendix Table 4). 2.4.5. Population served LHD size was measured by the population served, which was logtransformed to address skewness. We hypothesized that LHDs covering larger populations have higher obesity policy involvement. In a supplemental model, we present the NACCHO categories for comparison to other studies (see Appendix Table 4).

3. Results 3.1. Descriptive statistics

2.4.6. Tenure of LHDs' top executive Key informants suggested that LHD executives with longer tenures would have more experience and connections to enable more policy actions. This variable was measured as years of service, and expected to be positively correlated with the policy outcome.

LHDs had variable obesity policy involvement (Fig. 1). Nearly one half (46.1%) adopted no obesity prevention policies. Almost a third (31.4%) had low involvement (one to four obesity approaches), and only 22.5% were involved in five or more approaches. The most common policy approaches were breastfeeding promotion (reported by 40.2% of LHDs), encouraging physical activity in schools (36.1%), and reducing unhealthy food in schools (32.8%, see Appendix Table 1). A geographic distribution of LHDs' level of obesity policy involvement is depicted in Fig. 2. The Northeast has the highest percentage of non-involvement (72.2.%), followed by LHDs in the Midwest (47.3% not involved), Southwest (47.1%), West (42.5%), South (42.3%), and Mid-Atlantic (35.5%). The Mid-Atlantic and the West have the highest portion of LHDs with high involvement (25.8% and 25.1%, respectively), while the Northeast and Southwest have the fewest LHDs with high involvement (10.8% and 15.9%, respectively). See Appendix Table 6 for a detailed break-down of regional obesity policy involvement. Table 1 reports the LHD characteristics (see “All LHDs” column). Most LHDs (72.3%) belonged to local governments. One third (32.5%)

2.4.7. LHD top executives' education background Higher formal education of top executives was hypothesized to be associated with increased obesity policy involvement. This was operationalized as the highest degree earned: less than a master's degree (reference group), master's degree, or doctoral degree. 2.5. Independent variables: local context 2.5.1. Obesity prevalence If LHDs respond to local health needs, LHDs in regions with a higher obesity prevalence should have higher obesity policy involvement although a previous study found no such association (Stamatakis et al., 2012). We included county-level obesity prevalence, with weak 3

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High Involvement Low Involvement None No data Fig. 2. Geographic distribution of US local health departments' level of involvement in obesity policies, 2016. Notes: Low involvement is defined as involved in one to four types of policies, and high involvement is defined as involved in five or more types of policies. Level of involvement is mapped by using the county Federal Information Processing Standard code provided by NACCHO and applying geographic template of US counties in 2014 through maptile function in Stata.

Table 1 Characteristics of US local health departments by their level of involvement in obesity policies, 2016. All LHDs

Governance type, N (weighted %) State Local Shared Local board of health, N (weighted %) None Advisory role Governance role Accreditation, N (weighted %) Not accredited Accredited LHD top executive's highest education, N (weighted %) Less than Masters degree Masters degree Doctoral degree LHD workforce in FTE, median (IQR) Population served in thousands, median (IQR) LHD top executive's tenure in years, median (IQR) % Obesity, median (IQR) % Voted for Clinton in 2016, median (IQR) Total number of LHDs

LHD's obesity policy involvement

p-Value

None

Low

High

345 (19.2%) 1299 (72.3%) 159 (8.5%)

168 (19.9%) 619 (74.9%) 44 (5.2%)

120 (21.1%) 382 (67.7%) 66 (11.2%)

57 (14.6%) 298 (73.3%) 49 (12.1%)

578 (32.5%) 83 (4.5%) 1109 (63.1%)

290 (35.6%) 30 (3.5%) 495 (61.0%)

166 (29.2%) 31 (5.4%) 361 (65.4%)

122 (30.3%) 22 (5.4%) 253 (64.3%)

1650 (92.2%) 153 (7.8%)

802 (96.8%) 29 (3.2%)

518 (91.8%) 50 (8.2%)

330 (82.4%) 74 (17.6%)

612 (37.8%) 813 (46.5%) 287 (15.8%) 17.0 (7.0–46.0) 40.7 (17.3–109.0) 5.0 (2.0–11.0) 30.4 (27.0–33.2) 33.3 (23.1–48.1) 1803

325 (43.5%) 339 (42.6%) 113 (13.9%) 11.0 (5.5–25.5) 30.4 (13.9–69.3) 5.0 (2.0–12.0) 30.3 (26.7–33.4) 31.3 (21.9–49.5) 831

186 (36.4%) 280 (50.2%) 77 (13.4%) 17.4 (7.5–47.5) 39.5 (17.6–100.1) 4.0 (2.0–11.0) 31.0 (27.8–33.4) 31.3 (22.0–44.6) 568

101 (27.5%) 194 (49.2%) 97 (23.3%) 40.3 (17.0–111.5) 86.3 (34.6–267.0) 5.0 (2.0–9.0) 29.8 (26.3–32.7) 39.3 (28.5–51.2) 404

< 0.001

0.041

< 0.001 < 0.001

< 0.001 < 0.001 0.251 0.009 < 0.001

Notes: Descriptive summary values are based on the weighted sample and represent the characteristics of the study population. The p-values reflect results from Pearson F tests (for categorical variables) and ANOVA tests (for continuous variables), based on the weighted sample. Bivariate tests for population served and workforce used logged form of the variables because of skewness. Low involvement is defined as involved in one to four types of policies and high involvement is defined as involved in five or more types of policies. LHDs are identified as accredited if they are accredited by the Public Health Accreditation Board (PHAB) or part of a PHAB-accredited centralized state integrated local public health department system. FTE = full time equivalent; IQR = interquartile range. 4

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4. Discussion

Table 2 Negative binomial regression of the factors predicting the number of Obesity policy involvement among US local health departments, 2016. Variable Local board of health (reference: none) Serving advisory role Serving governance role Accredited Population served (log) Workforce (log) LHD top executive's tenure LHD top executive's education level (reference: less than Masters degree) Masters degree Doctoral degree % Obesity % Voted for Clinton in 2016 Constant Number of observations

We analyzed obesity policy involvement among LHDs across the US, finding that almost one half (46.1%) of LHDs were not involved in any obesity policy activities. With obesity identified as a priority area for public health, it was surprising that many LHDs were not involved in obesity policy practices. Many of the CDC guidelines for overweight and obesity prevention strategies and guidelines call for localized strategies (CDC, 2018). Health foundations have also emphasized the need for a localized approach, such as the Robert Wood Johnson Foundation's priority area to build healthy communities (RWJF, n.d.). However, we found that there are opportunities for LHDs to be more involved in obesity policy, especially among regions with high obesity prevalence. In addition, given that no or low involvement in obesity policy is concentrated in certain states, state health departments can consider promoting more state-level obesity policies or providing more guidance to their LHDs. Higher obesity prevalence was associated with more involvement, which suggests that LHDs' actions reflect community needs. However, the magnitude of this association was small: every percentage point increase in the obesity prevalence was associated with an increase in LHD obesity policy involvement by a factor of 1.03. This finding was consistent with previous research that also found that counties with high obesity prevalence were not substantially more likely to have obesity policies (Stamatakis et al., 2012). In addition, previous research found that LHDs with a higher burden of diabetes were more likely to conduct screening but not obesity prevention (Stamatakis et al., 2012). This may be because obesity policies address the health of the entire population, or the ideological nature of obesity interventions: counties with higher obesity prevalence also had more conservative ideologies. Several organizational characteristics were associated with higher obesity policy involvement, demonstrating the importance of LHDs' organizational capacities. Having a local board of health with either advisory or governance role was associated with higher obesity policy involvement. LHDs can only exercise authority within the roles delegated to them by states, and thus local boards of health with rule making privileges enable LHDs to more actively engage in policymaking. For instance, New York City's mandatory menu calorie labeling, trans-fat ban, and proposal to limit the size of sugar-sweetened beverages were enabled by the authority granted to its local board of health to set rules without city council approval (Farley et al., 2009; City of New York, 2006; Gostin et al., 2014). Additionally, LHDs with larger workforces and accreditation status, markers of higher overall capacity, were more likely to have greater obesity prevention policy involvement. To promote more LHD policy involvement, NACCHO and other professional associations could consider increased technical assistance and peer training to support LHDs with lower capacity. We found that ideology was modestly associated with LHDs' obesity policy involvement, signaling that LHD policy variation may partly reflect their residents' political ideology. Some obesity prevention approaches, especially restricting fast food outlets other limits on individual choice, may not be politically feasible in some regions. A parallel example of how local ideology constrains LHD decision making is syringe exchange programs, whereby there is wide national variation in state laws and their availability at the local level (MMWR, 2010; Green et al., 2012). Whereas some local decision makers have chosen to establish syringe exchange programs to prevent the spread of human immunodeficiency virus and viral hepatitis, others have not established programs due to factors such as concerns about encouraging illegal substance use (Rich and Adashi, 2015) or because they are not legal (Burris, n.d.). To address obesity, LHDs face choices between actively combatting obesity versus not overtly intervening in individual and industry choices. Further research on the politics of health policy making (Bambra et al., 2005) is needed to understand how political constraints affect LHD decision-making and how LHDs can effectively address local needs within these constraints.

IRR (95% CI) 1.31 1.27 1.40 1.02 1.34 1.00

(1.02, (1.08, (1.14, (0.93, (1.21, (1.00,

1.67) 1.50) 1.72) 1.12) 1.49) 1.01)

0.99 (0.85, 1.10 (0.88, 1.03 (1.01, 1.01 (1.00, 0.01 (0.00, 1803

1.15) 1.36) 1.05) 1.01) 0.08)

Notes: The model applied multiple imputation and sample weight to be representative of the population. Obesity policy involvement is defined as number of types of policies involved and ranges from zero to nine. State fixed effects are included as control variables. IRR = incidence rate ratio; CI = confidence interval.

had no board of health, 4.5% had local boards of health with an advisory role (4.5%), and 63.1% had boards of health with a governance role. Most LHDs (92.2%) were unaccredited. Three-fifths (62.3%) of top executives had at least a master's degree. 3.2. Bivariate analysis In Table 1, the None, Low, and High columns stratify the descriptive findings by level of obesity policy involvement. In bivariate analyses, there were positive relationship between level of policy involvement and: being accredited (p < 0.001), workforce size (p < 0.001), population served (p < 0.001), obesity prevalence (p = 0.009), percentage of citizens who voted for Clinton in 2016 (p < 0.001), and the top executive's education (p < 0.001). Governance type was associated with significant differences in LHDs' obesity policy involvement (p < 0.001). Having a local board of health and the board's role were not significantly associated with LHDs' levels of obesity policy involvement. 3.3. Multivariable analysis Table 2 displays the regression results, presented as incidence rate ratios (IRR) of each additional unit of LHD obesity policy involvement. The governance category variable was excluded due to multicollinearity. The LHD top executive's tenure was no longer significant after adjusting for other covariates. Higher obesity policy involvement was associated with: having a local board of health with an advisory (IRR = 1.31, p = 0.032) or governance (IRR = 1.27, p = 0.005), being accredited (IRR = 1.40, p = 0.001), having a larger workforce (IRR = 1.34, p < 0.001), higher obesity prevalence (IRR = 1.03, p = 0.002), and the percentage of residents who voted for Clinton (IRR = 1.01, p = 0.017). Although the type of governance was excluded due to multicollinearity, a sensitivity analysis was performed with governance level instead of state fixed effects to predict LHD obesity policy involvement. The sensitivity analysis found no significant association between governance level and policy involvement. This was significant in the bivariate analysis, and this null finding may be attributable to the inclusion of the accreditation measure as some states applied for accreditation as an integrated system including LHDs. 5

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In Arkansas, Maine, and Mississippi, most LHDs have no or low policy involvement despite their high obesity prevalence. This is counter to the finding that overall there is a positive relationship between obesity prevalence and LHD's involvement. The low involvement in these regions might be explained by the governance level. These LHDs are units of their state health departments and likely have low levels of decision-making power. Our study has several limitations that future studies can address. The outcome measure, number of obesity approaches taken, provides an overall picture of the LHDs' policy activities. However, it cannot address how much effort each LHD devoted to each policy approach, or the diversity of approaches taken. Some states have preemption rules that prohibit certain LHD policymaking abilities (Dewey, 2018). We were unable to determine whether LHDs had low levels of policy involvement because state efforts were already in place, or because obesity policy interventions were not prioritized. Future studies can investigate the role of preemption in LHD decision-making. The crosssectional design limits causal interpretations; although additional Profile years are available, a repeated cross-sectional design could not be used because the relevant questions were not consistent across surveys. The survey may have measurement error if LHDs did not understand the way the questions were asked, although to our knowledge the NACCHO Profile is the best source of LHD information for this type of national sample. Finally, the independent variables may be associated with be observable or unobservable community characteristics that are also associated with LHDs' obesity policy involvement. The selection of independent variables was guided by the key informant interviews and a literature review of LHD decision-making, but there may be additional factors that warrant further study.

Overall, LHDs' involvement in forming obesity policy is low, which is a missed opportunity for local action. Capacity is a strong predictor of LHDs' involvement. The findings reinforce a fundamental tension in the devolution of authority to local governments: while local control allows for tailored policy approaches, variation in capacity may exacerbate inequities. A better understanding of LHD policy involvement and how to leverage their current authority is needed to help LHDs serve local needs. Author contribution Wenhui Feng served as the lead writer. Both authors contributed to the study design, analysis of data, interpretation of data, drafting the article, and preparation of this article. Both authors approved the final version. Declaration of competing interest This work was partially supported by a grant from the University at Albany Rockefeller College Departmental Dissertation Award towards Wenhui Feng. The authors declare no conflicts of interest. Acknowledgments We are grateful for the valuable input from Ashley Fox, Lucy Sorensen, and Patricia Strach at University at Albany, who provided feedback throughout the development of this paper. We are also grateful to the multiple experts that agreed to be interviewed to share their expertise in local health department issues.

Appendix 1. Key Informant Interview Guide Grand tour questions Before I dive into the main questions, I want to start by asking you some quick questions about your professional involvement with Local Health Departments (1) What is your position title and can you briefly describe your main duties? (2) As part of your job, how do you interact with Local Health Departments? - For people working in a LHD, what is the basic information of your agency? Probes: number of employees, educational/professional backgrounds. - For people not working in a LHD, how do you usually interact with LHDs? (3) As part of your job, how does your work relate to obesity interventions? - Do you make decisions, work as consultants, or carry out other's decisions? General overview of LHDs First, I want to hear about the general information to get the big picture of LHDs

• I understand there are about 2000 LHDs across the country. From your perspective, what are the most important ways in which they differ from each other? Probes: size, jurisdiction, fund.

LHD's decision making We talked about important differences between LHDs. Now I am hoping to understand their level of autonomy and ability to make decisions on local health policies

• How much autonomy do you think the LHDs have in their decision-making process? • What agencies, organizations or groups influence the decisions LHDs?

Probes: State Health Department, CDC, local officials (mayor, city manager, county manager etc.), founders, other LHDs. LHD and obesity policies We have talked about how LHDs differ and where they can make decisions. I'm hoping we can now talk about obesity policies in particular.

• How much autonomy do the LHDs have in making these decisions about obesity? • To what extent are LHDs prioritizing obesity as an important area? Probe: LHDs have many priorities; how does obesity rank compared to other priorities like responding to outbreaks, HIV, or tobacco control? • What are some common approaches they take to reduce the burden of obesity? • How do LHDs select which policies to adopt? 6

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Specific factors relevant to LHD's decision making This background has been really helpful to get a better understanding of how LHDs work, important differences, and how they approach obesity. I will use the National Profile of Local Health Departments datasets to look at LHDs' adoption of nine specific obesity policies. I have identified a tentative list of organizational factors. Building off our conversation, I would like your input on how each characteristic could potentially influence policy adoption.

• Whether the LHD is a state and/or local government unit • Whether it has local boards of health • Whether it is a part of Health and Human Services (HHS) agency • Number of employees in the LHD • The LHD's total revenue • Region's average socioeconomic status • Am I missing anything important? I learned that the background of the Local Health Department director/commissioner is important to the decision making of that department, and I would like to seek suggestions of how to better specify this. Which distinction do you think make more sense? Distinctions in terms of educational attainment (doctoral versus master's degree), or The orientation of the background (in terms of medical/clinical versus population health) NACCHO Specific Question For this research project, I am using the National Profile of Local Health Departments data. I really appreciate your organization's efforts in compiling and publishing this series of datasets. Is there someone you would suggest me to reach out to, to get a better understanding of how the data are collected and any limitations that I should be thinking about? Closing questions (1) Before we finish our conversation, do you have any other comments about LHDs' decision making about obesity interventions that we have not yet discussed? (2) Based on our conversation today, is there anyone else you recommend me speak to? (3) I hope that I can get back to you with additional questions as they arise. Is that okay with you? o If specific documents/resources were mentioned in the discussion, ask them to share, e.g. organizational charts, mission statements, implementation plans, project reports o Thank respondent for his/her time Appendix 2. Profile questions related to the outcome variable 49. Indicate areas where your LHD has been actively involved in policy or advocacy activities in the past two years. (Select all that apply) (Variable values: unchecked = 0, checked = 1) □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □

Affordable housing (c12q260a) Animal control or rabies (c12q260l) Body art (c12q260m) Criminal justice system (c12q260b) Education (c12q260c) Emergency preparedness and response (c12q260n) Food safety (c12q260o) Funding for access to healthcare (c12q260e) Infectious disease (e.g., vaccination) (c12q260w) Injury or violence prevention (c12q260p) Labor (c12q260g) Land use (c12q260f) Mental health (c12q260q) Obesity/chronic disease (c12q260r) → (If checked, answer question 51) Occupational health and safety (c12q260h) Oral health (c12q260s) Safe and healthy housing (c12q260t) Tobacco, alcohol, or other drugs (c12q260u) → (If checked, answer question 50) Waste, water, or sanitation (c12q260v) Other (please specify): (c12q260j) ___(c12q260text)____ None (c12q260k)

51. For respondents who selected “Obesity/chronic disease” in question 49, answer question 51. Indicate areas where your LHD has been actively involved in policy or advocacy activities focused on obesity or chronic disease in the past two years. (Select all that apply) (Variable values: unchecked = 0, checked = 1). □ Community level urban design and land use policies to encourage physical activity (c12q402a) 7

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W. Feng and E.G. Martin

□ □ □ □ □ □ □ □ □ □

Active transportation options (c12q402b) School or child care policies that encourage physical activity (c12q402c) School or child care policies that reduce availability of unhealthy foods (c12q402d) Expanding access to recreational facilities (c12q402e) Nutritional labeling (c12q402f) Increasing retail availability of fruits and vegetables (c12q402g) Limiting fast food outlets (c12q402h) Fiscal policies to decrease consumption of unhealthy foods or beverages (c12q402j) Policies to promote breastfeeding (c12q402k) Other (please specify): (c12q402i) ___(c12q402itext)_________ Appendix Table 1

Obesity policy approaches taken by local health departments. Approach

Percent taken

Community level urban design and land use policies to encourage physical activity Active transportation options School or child care policies that encourage physical activity School or child care policies that reduce availability of unhealthy foods Expanding access to recreational facilities Nutritional labeling Increasing retail availability of fruits and vegetables Limiting fast food outlets Fiscal policies to decrease consumption of unhealthy foods or beverages Policies to promote breastfeeding Other (please specify) Observations

27.2% 18.9% 36.1% 32.8% 26.1% 9.3% 29.1% 1.3% 6.7% 40.2% 2.1% 1803

Appendix Table 2

Sensitivity analysis of including “Limiting Fast Food Outlets” and “Other” into the count of total approaches taken. Variable Local Board of Health (reference: none) Serving advisory role Serving governance role Accredited Population served (log) Workforce (log) LHD top executive's tenure LHD top executive's education level (reference: less than Masters degree) Masters degree Doctoral degree % Obesity % Voted for Clinton in 2016 Constant Number of observations

Main model

Alternative model

1.31 1.27 1.40 1.02 1.34 1.00

(1.02, (1.08, (1.14, (0.93, (1.21, (1.00,

1.67) 1.50) 1.72) 1.12) 1.49) 1.01)

1.33 1.26 1.41 1.02 1.33 1.00

(1.04, (1.06, (1.15, (0.92, (1.19, (0.99,

1.71) 1.48) 1.74) 1.12) 1.49) 1.01)

0.99 (0.85, 1.10 (0.88, 1.03 (1.01, 1.01 (1.00, 0.01 (0.00, 1803

1.15) 1.36) 1.05) 1.01) 0.08)

0.99 (0.84, 1.10 (0.89, 1.02 (1.00, 1.01 (1.00, 0.01 (0.00, 1803

1.16) 1.36) 1.05) 1.01) 0.08)

Notes: The main model excludes the “limiting fast food outlets” and “other” categories into the count of total approaches taken, because < 2% of LHDs selected those options. The alternative model includes those items in the count. The model applied multiple imputation and sample weight to be representative of the population. Obesity policy involvement is defined as number of types of policies involved and ranges from zero to nine. State fixed effects are included as control variables. IRR = incidence rate ratio; CI = confidence interval.

Appendix Table 3

Principal component analysis - promax rotation findings. Variable

Comp1

Community level urban design and land use policies to encourage physical activity Active transportation options School or child care policies that encourage physical activity School or child care policies that reduce availability of unhealthy foods Expanding access to recreational facilities Nutritional labeling Increasing retail availability of fruits and vegetables Limiting fast food outlets Fiscal policies to decrease consumption of unhealthy foods or beverages Policies to promote breastfeeding Other (please specify)

Comp2

Comp3

Unexplained

0.9774

0.4212 0.5344 0.3362 0.337 0.4906 0.6197 0.4681 0.3362 0.337 0.4247 0.03384

0.4227 0.4144

0.7422 0.5334

Note: Blanks represent loadings < 0.4.

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Appendix Table 4

Model comparison, continuous and categorical factors predicting the level of obesity policy involvement: United States, 2016.

Variable Local Board of Health (reference: none) Serving advisory role Serving governance role Accredited Population served (log) Population served (reference: < 25,000) 25,000–49,999 50,000–99,999 100,000–249,999 250,000–499,999 500,000–999,999 1,000,000+ Workforce (log) Workforce (reference: < 5) 5–9.9 10–24.9 25–49.9 50–99.9 100–199.9 200+ LHD top executive's tenure LHD top executive's education level (reference: less than Masters degree) Masters degree Doctoral degree % Obesity % Voted for Clinton in 2016 Constant Number of observations

Model 1

Model 2

Model 3

Model 4

IRR (95% CI)

IRR (95% CI)

IRR (95% CI)

IRR (95% CI)

1.31 1.27 1.40 1.02

1.29 (1.00, 1.65) 1.25 (1.05, 1.47) 1.42 (1.16, 1.73)

1.34 1.29 1.42 1.11

1.32 (1.02, 1.71) 1.28 (1.08, 1.52) 1.43 (1.17, 1.75)

(1.02, (1.08, (1.14, (0.93,

1.67) 1.50) 1.72) 1.12)

1.34 (1.21, 1.49)

0.95 1.00 0.98 0.77 0.71 0.75 1.41

(0.79, (0.80, (0.75, (0.54, (0.47, (0.47, (1.28,

(1.04, (1.10, (1.16, (1.01,

1.15) 1.24) 1.27) 1.10) 1.07) 1.20) 1.56)

1.00 (1.00, 1.01)

1.00 (0.99, 1.01)

1.43 1.68 1.93 2.19 2.04 2.59 1.00

0.99 (0.85, 1.10 (0.88, 1.03 (1.01, 1.01 (1.00, 0.01 (0.00, 1803

0.98 (0.84, 1.13 (0.91, 1.02 (1.01, 1.01 (1.00, 0.02 (0.00, 1803

0.98 (0.84, 1.11 (0.90, 1.03 (1.01, 1.01 (1.00, 0.01 (0.00, 1803

1.15) 1.36) 1.05) 1.01) 0.08)

1.72) 1.53) 1.73) 1.21)

1.15) 1.40) 1.04) 1.01) 0.08)

0.98 1.09 1.15 1.03 0.99 1.19

(0.81, (0.87, (0.87, (0.71, (0.66, (0.76,

1.19) 1.37) 1.51) 1.48) 1.49) 1.86)

(1.06, (1.25, (1.36, (1.48, (1.32, (1.58, (0.99,

1.92) 2.28) 2.73) 3.24) 3.17) 4.24) 1.01)

1.53 1.86 2.18 2.57 2.64 3.63 1.00

(1.14, (1.38, (1.55, (1.75, (1.71, (2.26, (0.99,

2.06) 2.51) 3.08) 3.77) 4.09) 5.82) 1.01)

1.15) 1.37) 1.04) 1.01) 0.05)

1.00 (0.85, 1.14 (0.92, 1.02 (1.01, 1.01 (1.00, 0.02 (0.00, 1803

1.17) 1.41) 1.04) 1.02) 0.10)

Notes: The model applied multiple imputation and sample weight to be representative of the population. Obesity policy involvement is defined as number of types of policies involved and ranges from zero to nine. State fixed effects are included as control variables. IRR = incidence rate ratio. CI = confidence interval. The main model uses log-transformed continuous measures of population served and workforce. In the supplemental models, we present the NACCHO categories for comparison to other studies. Model fit statistics indicate a similar goodness of fit.

Appendix Table 5

Model comparison, negative binomial and ordinary least square regression of the factors predicting the level of obesity policy involvement: United States, 2016.

Variable Local Board of Health (reference: none) Serving advisory role Serving governance role Accredited Population served (log) Workforce (log) LHD top executive's tenure LHD top executive's education level (reference: less than Masters degree) Masters degree Doctoral degree % Obesity % Voted for Clinton in 2016 Constant Number of observations

Negative binomial model

Ordinary least square model

IRR (95% CI)

Coefficient (95% CI)

1.31 (1.02, 1.67) 1.27 (1.08, 1.50) 1.40 (1.14, 1.72) 1.02 (0.93, 1.12) 1.34 (1.21, 1.49) 1.00 (1.00, 1.01)

0.59 (0.07, 1.11) 0.34 (0.08, 0.59) 1.16 (0.51, 1.80) 0.09 (−0.06, 0.24) 0.49 (0.33, 0.65) 0.00 (−0.01, 0.02)

0.99 (0.85, 1.15) 1.10 (0.88, 1.36) 1.03 (1.01, 1.05) 1.01 (1.00, 1.01) 0.01 (0.00, 0.08) 1803

−0.09 (−0.35, 0.17) 0.42 (0.05, 0.80) 0.04 (0.01, 0.07) 0.02 (0.01, 0.03) −4.30 (−6.12, −2.48) 1803

Notes: The model applied multiple imputation and sample weight to be representative of the population. Obesity policy involvement is defined as number of types of policies involved and ranges from zero to nine. State fixed effects are included as control variables. IRR = incidence rate ratio. CI = confidence interval.

Appendix Table 6

Obesity policy involvement by region.

None Low High

Mid-Atlantic

Midwest

Northeast

South

Southwest

West

35.5% 38.7% 25.8%

47.3% 30.9% 21.8%

72.2% 17.0% 10.8%

42.3% 34.7% 23.1%

47.1% 37.0% 15.9%

42.5% 32.4% 25.1%

Notes: Descriptive summary values are based on the weighted sample and represent the characteristics of the study population. Low involvement is defined as involved in one to four types of policies and high involvement is defined as involved in five or more types of policies.

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