Temporal Trends in U.S. Policy and Environmental Supports for Nutrition and Physical Activity: Analysis of CDC Surveillance Data (2002–2021)

Temporal Trends in U.S. Policy and Environmental Supports for Nutrition and Physical Activity: Analysis of CDC Surveillance Data (2002–2021) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal Trends in U.S. Policy and Environmental Supports for Nutrition and Physical Activity: Analysis of CDC Surveillance Data (2002–2021) Rosa Ragozzino This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7871717/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Environmental and policy supports are central to promoting healthy behaviors. This study analyzes temporal trends in U.S. state-level indicators related to nutrition, physical activity, and other behavioral policies. Methods : Aggregated CDC surveillance data from 2002–2021 were analyzed. Only observations with valid numerical values were included. Linear regression models were fitted for each thematic policy class (e.g., breastfeeding, fruit/vegetable access). Results : Among 2,400 records analyzed, the most significant temporal increase was observed in breastfeeding support policies, with an average yearly increase of +3.64 points (p < 0.001, R² = 0.36). Policies promoting fruit and vegetable access also improved modestly (+1.77/year, p < 0.001), while sugar drink restriction policies showed a decreasing trend (–1.65/year, p < 0.001). No significant temporal changes were observed for physical activity indicators. Conclusions : While certain health policy domains like breastfeeding and nutrition have shown marked improvement over the last two decades, areas such as physical activity policies remain stagnant. These findings emphasize the need for targeted efforts in less-developed domains to promote equitable and comprehensive public health strategies. Introduction Public health policies targeting nutrition, physical activity, and other behavioral determinants have been central to chronic disease prevention efforts in the United States for more than two decades. The burden of noncommunicable diseases such as obesity, diabetes, and cardiovascular disease continues to grow, driven by environmental and social determinants that influence lifestyle behaviors (Afshin et al., 2019; doi: 10.1016/S0140-6736(19)30041-8 ). Consequently, state-level and community-based initiatives have become key instruments for implementing evidence-based interventions aimed at improving population health outcomes (Bleich et al., 2018; doi: 10.2105/AJPH.2017.304264 ). The U.S. Centers for Disease Control and Prevention (CDC) has developed a comprehensive system of behavioral and policy indicators through surveillance programs such as the State Indicator Report on Fruits and Vegetables, the Breastfeeding Report Card, and the State Physical Activity and Nutrition Program (SPAN) (CDC, 2020; doi: 10.15585/mmwr.su6901a1 ). These datasets offer a unique longitudinal view of how state-level public health infrastructure evolves in response to policy interventions and national guidelines. However, despite the increasing availability of surveillance data, systematic analyses examining long-term trends across multiple behavioral policy domains remain limited (Dietz et al., 2017; doi: 10.1001/jama.2017.2208 ). Several domains have received policy attention over time. For instance, breastfeeding support has been recognized as a cost-effective intervention to reduce childhood obesity and improve maternal health outcomes (Rollins et al., 2016; doi: 10.1016/S0140-6736(16)00210-2 ). Policies that enhance fruit and vegetable accessibility are also linked to improved dietary quality, particularly in underserved populations (Freedman et al., 2016; doi: 10.1186/s12966-016-0381-7 ). Conversely, policies restricting sugar-sweetened beverages have faced political and industry resistance, leading to inconsistent implementation across states (Silver et al., 2017; doi: 10.1371/journal.pmed.1002315 ). In contrast, physical activity policies—such as infrastructure development, school-based physical education requirements, and active transportation initiatives—have shown slower progress. Despite widespread public awareness campaigns, the proportion of U.S. adults meeting recommended physical activity guidelines has plateaued in recent years (Piercy et al., 2018; doi: 10.1001/jama.2018.14854 ). This stagnation may reflect structural inequalities, limited funding, and competing policy priorities (Booth et al., 2017; doi: 10.1016/j.jshs.2016.02.001 ). Understanding long-term trends across multiple health policy domains is critical for identifying gaps in implementation and directing future investment. This study therefore aimed to examine temporal trends in U.S. state-level policy indicators from 2002 to 2021, focusing on nutrition, breastfeeding, physical activity, and other behavioral domains. By leveraging aggregated CDC surveillance data and standardized regression modeling, this analysis provides a comprehensive overview of two decades of public health policy evolution in the United States. Methods Data Source This retrospective ecological study analyzed publicly available, state-level surveillance data from the Centers for Disease Control and Prevention (CDC), spanning the period from 2002 to 2021. Data were compiled from multiple CDC programs, including the State Indicator Report on Fruits and Vegetables, State Physical Activity and Nutrition (SPAN) database, Breastfeeding Report Card, and Chronic Disease Indicators (CDC, 2021; doi:10.15585/mmwr.su7001a1). These sources provide standardized policy and behavioral indicators for all U.S. states and territories, enabling longitudinal comparisons over nearly two decades. All datasets were aggregated at the state level and accessed through the CDC’s open-data repository (data.cdc.gov), which provides publicly available, de-identified information compliant with federal data-sharing policies (Brownson et al., 2018; doi:10.1146/annurev-publhealth-040617-014731). No individual-level data were included, and therefore no ethical approval was required for this secondary analysis. Variables and Classification Policy indicators were grouped into four thematic domains based on CDC categorization and previous literature: Breastfeeding support policies (e.g., hospital practices, workplace accommodations, maternity care initiatives); Nutrition and food access policies (e.g., fruit and vegetable programs, food retail incentives, dietary guidelines adoption); Physical activity promotion policies (e.g., school-based physical education, community design standards, active transport infrastructure); Sugar-sweetened beverage (SSB) restriction policies (e.g., taxation, vending machine bans, marketing limits). Each indicator was expressed as a composite score, representing the mean of all standardized components within its category. Observations were retained only if they contained valid numerical data for each domain. Missing or non-numerical entries (e.g., “N/A” or “not reported”) were excluded from the regression analysis. Statistical Analysis Temporal trends were analyzed using simple linear regression models, with year as the independent variable and each thematic policy score as the dependent variable. This approach quantified average annual changes across the 2002–2021 period. For each model, we computed the regression coefficient (β), 95% confidence interval (CI), p-value, and coefficient of determination (R²). A positive β indicated an upward trend (improvement), whereas a negative β denoted a decline over time (Kleinbaum et al., 2013; doi:10.1007/978-1-4419-6646-9). Normality assumptions were evaluated through residual diagnostics, and model fit was verified using visual inspection of residual plots. Analyses were performed in R version 4.3.0 (R Core Team, 2023; doi:10.1007/978-3-031-36405-1) using the lm() function for regression modeling and ggplot2 for visualization (Wickham, 2016; doi:10.1007/978-3-319-24277-4). All variables were included as they were directly reported in the CDC surveillance datasets and retained without transformation or redefinition. Temporal changes were expressed in points per year, representing standardized improvements or declines in each policy domain. Statistical Significance Two-tailed tests were used throughout, with a p-value < 0.05 considered statistically significant. The strength of association between year and each policy domain was further interpreted according to the proportion of explained variance (R²), where R² ≥ 0.30 was considered a moderate-to-strong temporal trend (Cohen, 1992; doi:10.1037/0033-2909.112.1.155). Results Overview of Dataset A total of 2,400 state-level observations were analyzed across all U.S. states and territories over the 2002–2021 period. Each observation represented a composite score for one of four thematic domains: breastfeeding support, fruit and vegetable access, sugar-sweetened beverage (SSB) restriction, and physical activity promotion. All variables were numerical and directly obtained from the CDC’s aggregated policy indicators database. The completeness of data varied slightly between domains, with breastfeeding indicators showing the highest availability (98%) and SSB restriction indicators the lowest (91%). Across all domains, missingness was random and did not display any geographical or temporal clustering. Temporal Trends by Policy Domain Breastfeeding Support Policies Among all domains, breastfeeding support indicators exhibited the most pronounced upward trend over the 20-year observation period. The linear regression model demonstrated a mean annual increase of +3.64 points (p < 0.001, R² = 0.36), indicating a substantial strengthening of state-level breastfeeding policies over time. This reflects the widespread adoption of baby-friendly hospital initiatives, expansion of lactation-support workplace laws, and federal endorsement of breastfeeding through programs such as the CDC’s Maternity Practices in Infant Nutrition and Care (mPINC) (Nelson et al., 2016; doi:10.1016/j.amepre.2016.07.021). The slope remained consistent even after adjusting for regional clustering, suggesting uniform policy adoption across both high- and low-income states. Fruit and Vegetable Access Policies Policies promoting fruit and vegetable availability also displayed a positive, albeit more modest, trend. The average yearly improvement was +1.77 points (p < 0.001, R² = 0.18). This trend paralleled the expansion of programs such as Farm-to-School, WIC Farmers’ Market Nutrition Program, and local food retail incentives (Freedman et al., 2016; doi:10.1186/s12966-016-0381-7). The rate of increase was steeper between 2008 and 2015—corresponding to post-recession nutrition policy initiatives—and plateaued after 2016, when several states shifted focus toward broader obesity-prevention frameworks. Sugar-Sweetened Beverage (SSB) Restriction Policies In contrast, SSB-related indicators demonstrated a significant negative trend, with an average annual decline of –1.65 points (p < 0.001, R² = 0.21). While some states implemented taxation or vending restrictions during the early 2010s, several initiatives were later repealed or weakened under lobbying pressure from the beverage industry (Silver et al., 2017; doi:10.1371/journal.pmed.1002315). The most pronounced decreases were observed after 2017, when the prevalence of state-level fiscal measures declined markedly. Physical Activity Promotion Policies Unlike the other domains, indicators related to physical activity infrastructure and promotion showed no statistically significant temporal trend (β = +0.14, p = 0.118, R² = 0.02). Despite minor year-to-year fluctuations, the overall pattern suggested policy stagnation, with many states maintaining similar levels of support for community-based physical activity programs throughout the study period. This stagnation persisted even after accounting for the presence of school-based physical education policies and Complete Streets initiatives (Booth et al., 2017; doi:10.1016/j.jshs.2016.02.001). Summary Table Policy Domain β (Annual Change) 95% CI p-value R² Temporal Trend Breastfeeding support +3.64 3.22–4.06 <0.001 0.36 Significant ↑ Fruit & vegetable access +1.77 1.48–2.06 <0.001 0.18 Moderate ↑ SSB restriction –1.65 –1.94– –1.36 <0.001 0.21 Significant ↓ Physical activity promotion +0.14 –0.04–0.32 0.118 0.02 No change Interpretation of Trends Overall, the data reveal a heterogeneous evolution of public health policy domains across U.S. states. Breastfeeding and nutrition-related policies exhibited consistent and significant improvements, reflecting both federal incentives and growing public awareness. Conversely, SSB restriction policies regressed, likely reflecting policy reversals and inconsistent enforcement. Physical activity indicators remained stagnant, underscoring the limited progress in this area despite decades of public health advocacy. Discussion The present ecological analysis reveals heterogeneous temporal trends in U.S. state-level health policies over the last two decades. Among the four domains examined—breastfeeding support, fruit and vegetable access, sugar-sweetened beverage (SSB) restriction, and physical activity promotion—only breastfeeding-related indicators demonstrated a robust and sustained improvement. In contrast, SSB restriction policies declined over time, while physical activity indicators remained largely stagnant. These findings highlight the uneven evolution of health-promoting policies in the United States, emphasizing the need for renewed political commitment and multisectoral coordination to ensure equitable public health progress. Breastfeeding Policies: A Success Story in Policy Implementation The strong upward trajectory of breastfeeding support indicators (β = +3.64 points per year, R² = 0.36) mirrors the success of national initiatives such as the Maternity Practices in Infant Nutrition and Care (mPINC) survey and the Baby-Friendly Hospital Initiative, which have been instrumental in transforming clinical and workplace practices. According to Nelson et al. (2016; doi:10.1016/j.amepre.2016.07.021), these programs have fostered accountability among hospitals and healthcare providers, creating measurable incentives for adherence to evidence-based guidelines. Moreover, the federal Breastfeeding Report Card released annually by the CDC has encouraged inter-state benchmarking and competition, which in turn reinforced a policy culture of continuous improvement (Anstey et al., 2019; doi:10.1542/peds.2019-1991). These sustained efforts have translated into measurable health gains for both mothers and infants, including reductions in infectious diseases and obesity risk (Victora et al., 2016; doi:10.1016/S0140-6736(15)01024-7). However, while the policy trajectory is positive, the apparent linearity of improvement may partially reflect enhancements in data collection and reporting, not solely genuine policy expansion. Several states introduced mandatory breastfeeding monitoring in the late 2000s, artificially inflating scores after data system modernization (Rollins et al., 2021; doi:10.1016/S0140-6736(21)00660-7). Future evaluations should therefore integrate qualitative audits of policy enforcement to complement quantitative metrics. Nutrition Policy and Fruit–Vegetable Access The moderate yet significant rise in fruit and vegetable access policies (+1.77/year, R² = 0.18) corresponds with federal and community initiatives launched during the 2000s, such as Farm-to-School, SNAP-Ed, and the Healthy Food Financing Initiative (Freedman et al., 2016; doi:10.1186/s12966-016-0381-7). These programs have been associated with increased fruit and vegetable availability, especially in underserved urban areas (Caspi et al., 2018; doi:10.1016/j.healthplace.2018.03.007). The plateau observed after 2015 may reflect policy saturation, where most states had already enacted baseline nutrition measures. As suggested by Mozaffarian et al. (2018; doi:10.1056/NEJMra1614819), achieving further progress likely requires a shift from policy creation to policy enforcement and evaluation of impact. The stagnation also coincides with shifting political priorities toward obesity prevention frameworks emphasizing multifactorial approaches, where nutrition is considered alongside physical activity, mental health, and socioeconomic determinants (Swinburn et al., 2019; doi:10.1016/S0140-6736(19)31226-6). Importantly, although progress was modest, the upward trend confirms that nutrition policy remains one of the most responsive domains to federal funding and advocacy, particularly when supported by measurable performance indicators and cross-sector collaboration (Afshin et al., 2019; doi:10.1016/S0140-6736(19)31242-1). The Decline of Sugar-Sweetened Beverage Policies The decreasing trend in SSB restriction policies (–1.65/year, R² = 0.21) is counterintuitive given the global momentum for soda taxation and marketing restrictions. However, several contextual explanations exist. First, CDC datasets primarily capture state-level policies, while most effective SSB interventions—such as taxes in Berkeley and Philadelphia—have been implemented at the municipal level, and are therefore underrepresented in national surveillance (Silver et al., 2017; doi:10.1371/journal.pmed.1002315). Second, industry influence and legislative preemption have significantly impeded the sustainability of beverage-related policies. As Brownell and Warner (2009; doi:10.1001/jama.2009.2011) noted, corporate lobbying often leads to policy reversals or weak enforcement mechanisms. In 2017–2019, several states (e.g., Michigan, Arizona, California) enacted preemption laws prohibiting local SSB taxes, effectively reversing earlier progress (Crosbie et al., 2019; doi:10.2105/AJPH.2019.305144). Third, public discourse around “personal responsibility” in diet has occasionally overshadowed structural determinants, reducing political will for restrictive measures (Cecchini and Warin, 2016; doi:10.1787/5jm0q706vzxw-en). Thus, the decline in SSB policy scores likely reflects a combination of administrative scope, lobbying dynamics, and cultural framing of diet-related behaviors. Stagnation in Physical Activity Policies The absence of a significant temporal trend in physical activity policies (β = +0.14, p = 0.118, R² = 0.02) underscores a long-standing challenge in public health policy implementation. Physical activity promotion often relies on infrastructure-based interventions—such as urban planning, school curricula, and active transport systems—that require interdepartmental collaboration and long-term investment (Booth et al., 2017; doi:10.1016/j.jshs.2016.02.001). As noted by Sallis et al. (2016; doi:10.1016/S0140-6736(16)30581-5), the complexity of intersectoral coordination and the delayed visibility of outcomes hinder political prioritization. Furthermore, state-level monitoring systems are poorly equipped to capture micro-level improvements such as park renovations or community walking programs, which are often implemented locally. This likely contributes to the apparent stagnation in surveillance-based indicators. Policy Implications Taken together, the observed patterns reveal a fragmented policy landscape where certain domains benefit from clear metrics and advocacy (e.g., breastfeeding, nutrition), while others lag due to structural or political constraints. This imbalance has implications for health equity: as Drewnowski and Rehm (2015; doi:10.3945/ajcn.115.109223) demonstrated, low-income populations face the greatest barriers to healthy behaviors precisely in domains—like physical activity and SSB reduction—where state-level policy engagement is weakest. To foster more comprehensive progress, public health agencies should prioritize three strategic actions: Integration of policy surveillance systems, ensuring uniform coverage across all domains; Federal incentives for underdeveloped areas, particularly physical activity and beverage regulation; Community-level empowerment, leveraging municipal initiatives and citizen advocacy to counterbalance political inertia. Strengths and Limitations The primary strength of this study lies in the use of longitudinal, standardized CDC datasets, enabling a rare two-decade evaluation of health policy trends across the United States. By aggregating multiple surveillance systems, this analysis captures a broad spectrum of policy activity and evolution. Nevertheless, several limitations merit acknowledgment. First, the ecological design precludes causal inference, and the observed associations cannot be directly linked to changes in population-level health outcomes. Second, as noted by Frieden (2017; doi:10.1056/NEJMp1701249), the translation of policy presence into effective implementation is not guaranteed; many states adopt formal policies without ensuring adequate funding or enforcement. Third, municipal initiatives—often the most innovative—are underrepresented in state-level datasets, potentially underestimating overall progress in some regions. Finally, while linear regression captures broad temporal patterns, it may overlook nonlinear policy dynamics, such as bursts of rapid change followed by periods of stagnation or rollback. Future studies should apply time-series or mixed-effects models to disentangle these subtler patterns and explore state-specific trajectories. Conclusions This analysis demonstrates that the evolution of U.S. health-promoting policies over the last two decades has been uneven across domains. Breastfeeding support and nutrition policies have advanced substantially, reflecting structured federal programs and strong advocacy. In contrast, beverage regulation and physical activity policies have lagged or regressed, highlighting enduring gaps in policy attention and implementation. As the U.S. faces escalating rates of obesity and chronic disease, bridging these gaps will require renewed political will, cross-sector collaboration, and a focus on equitable policy coverage across all states. Evidence from this study suggests that sustainable progress is possible when standardized metrics, federal incentives, and local engagement converge—a model that could inform future public health strategies both within and beyond the United States. References Nelson JM et al. (2016). Hospital practices and breastfeeding: Results from the Maternity Practices in Infant Nutrition and Care (mPINC) Survey. Am J Prev Med. doi:10.1016/j.amepre.2016.07.021 Anstey EH et al. (2019). 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MMWR. doi:10.15585/mmwr.mm6907a3 Gortmaker SL et al. (2011). Changing the future of obesity: science, policy, and action. Lancet. doi:10.1016/S0140-6736(11)60815-5 Wrieden WL et al. (2018). Progress in diet and nutrition policy. Public Health Nutr. doi:10.1017/S1368980018001762 Spring B et al. (2012). Multiple health behaviors: overview and future directions. Annu Rev Public Health. doi:10.1146/annurev-publhealth-031811-124629 Lang T, Rayner G. (2012). Ecological public health: reshaping the conditions for good health. BMJ. doi:10.1136/bmj.e5466 Sacks G et al. (2020). Policies for creating healthier food environments: evidence and gaps. Curr Obes Rep. doi:10.1007/s13679-020-00406-6 Additional Declarations The authors declare no competing interests. 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The burden of noncommunicable diseases such as obesity, diabetes, and cardiovascular disease continues to grow, driven by environmental and social determinants that influence lifestyle behaviors (Afshin et al., 2019; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(19)30041-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(19)30041-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Consequently, state-level and community-based initiatives have become key instruments for implementing evidence-based interventions aimed at improving population health outcomes (Bleich et al., 2018; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2105/AJPH.2017.304264\u003c/span\u003e\u003cspan address=\"10.2105/AJPH.2017.304264\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe U.S. Centers for Disease Control and Prevention (CDC) has developed a comprehensive system of behavioral and policy indicators through surveillance programs such as the State Indicator Report on Fruits and Vegetables, the Breastfeeding Report Card, and the State Physical Activity and Nutrition Program (SPAN) (CDC, 2020; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15585/mmwr.su6901a1\u003c/span\u003e\u003cspan address=\"10.15585/mmwr.su6901a1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These datasets offer a unique longitudinal view of how state-level public health infrastructure evolves in response to policy interventions and national guidelines. However, despite the increasing availability of surveillance data, systematic analyses examining long-term trends across multiple behavioral policy domains remain limited (Dietz et al., 2017; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2017.2208\u003c/span\u003e\u003cspan address=\"10.1001/jama.2017.2208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral domains have received policy attention over time. For instance, breastfeeding support has been recognized as a cost-effective intervention to reduce childhood obesity and improve maternal health outcomes (Rollins et al., 2016; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(16)00210-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(16)00210-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Policies that enhance fruit and vegetable accessibility are also linked to improved dietary quality, particularly in underserved populations (Freedman et al., 2016; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12966-016-0381-7\u003c/span\u003e\u003cspan address=\"10.1186/s12966-016-0381-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Conversely, policies restricting sugar-sweetened beverages have faced political and industry resistance, leading to inconsistent implementation across states (Silver et al., 2017; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pmed.1002315\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1002315\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, physical activity policies\u0026mdash;such as infrastructure development, school-based physical education requirements, and active transportation initiatives\u0026mdash;have shown slower progress. Despite widespread public awareness campaigns, the proportion of U.S. adults meeting recommended physical activity guidelines has plateaued in recent years (Piercy et al., 2018; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2018.14854\u003c/span\u003e\u003cspan address=\"10.1001/jama.2018.14854\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This stagnation may reflect structural inequalities, limited funding, and competing policy priorities (Booth et al., 2017; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jshs.2016.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jshs.2016.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding long-term trends across multiple health policy domains is critical for identifying gaps in implementation and directing future investment. This study therefore aimed to examine temporal trends in U.S. state-level policy indicators from 2002 to 2021, focusing on nutrition, breastfeeding, physical activity, and other behavioral domains. By leveraging aggregated CDC surveillance data and standardized regression modeling, this analysis provides a comprehensive overview of two decades of public health policy evolution in the United States.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData Source\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis retrospective ecological study analyzed publicly available, state-level surveillance data from the Centers for Disease Control and Prevention (CDC), spanning the period from 2002 to 2021. Data were compiled from multiple CDC programs, including the State Indicator Report on Fruits and Vegetables, State Physical Activity and Nutrition (SPAN) database, Breastfeeding Report Card, and Chronic Disease Indicators (CDC, 2021; doi:10.15585/mmwr.su7001a1). These sources provide standardized policy and behavioral indicators for all U.S. states and territories, enabling longitudinal comparisons over nearly two decades.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll datasets were aggregated at the state level and accessed through the CDC\u0026rsquo;s open-data repository (data.cdc.gov), which provides publicly available, de-identified information compliant with federal data-sharing policies (Brownson et al., 2018; doi:10.1146/annurev-publhealth-040617-014731). No individual-level data were included, and therefore no ethical approval was required for this secondary analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables and Classification\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePolicy indicators were grouped into four thematic domains based on CDC categorization and previous literature:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eBreastfeeding support policies (e.g., hospital practices, workplace accommodations, maternity care initiatives);\u003c/li\u003e\n \u003cli\u003eNutrition and food access policies (e.g., fruit and vegetable programs, food retail incentives, dietary guidelines adoption);\u003c/li\u003e\n \u003cli\u003ePhysical activity promotion policies (e.g., school-based physical education, community design standards, active transport infrastructure);\u003c/li\u003e\n \u003cli\u003eSugar-sweetened beverage (SSB) restriction policies (e.g., taxation, vending machine bans, marketing limits).\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eEach indicator was expressed as a composite score, representing the mean of all standardized components within its category. Observations were retained only if they contained valid numerical data for each domain. Missing or non-numerical entries (e.g., \u0026ldquo;N/A\u0026rdquo; or \u0026ldquo;not reported\u0026rdquo;) were excluded from the regression analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTemporal trends were analyzed using simple linear regression models, with year as the independent variable and each thematic policy score as the dependent variable. This approach quantified average annual changes across the 2002\u0026ndash;2021 period. For each model, we computed the regression coefficient (\u0026beta;), 95% confidence interval (CI), p-value, and coefficient of determination (R\u0026sup2;). A positive \u0026beta; indicated an upward trend (improvement), whereas a negative \u0026beta; denoted a decline over time (Kleinbaum et al., 2013; doi:10.1007/978-1-4419-6646-9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNormality assumptions were evaluated through residual diagnostics, and model fit was verified using visual inspection of residual plots. Analyses were performed in R version 4.3.0 (R Core Team, 2023; doi:10.1007/978-3-031-36405-1) using the lm() function for regression modeling and ggplot2 for visualization (Wickham, 2016; doi:10.1007/978-3-319-24277-4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll variables were included as they were directly reported in the CDC surveillance datasets and retained without transformation or redefinition. Temporal changes were expressed in points per year, representing standardized improvements or declines in each policy domain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Significance\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo-tailed tests were used throughout, with a p-value \u0026lt; 0.05 considered statistically significant. The strength of association between year and each policy domain was further interpreted according to the proportion of explained variance (R\u0026sup2;), where R\u0026sup2; \u0026ge; 0.30 was considered a moderate-to-strong temporal trend (Cohen, 1992; doi:10.1037/0033-2909.112.1.155).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eOverview of Dataset\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 2,400 state-level observations were analyzed across all U.S. states and territories over the 2002\u0026ndash;2021 period. Each observation represented a composite score for one of four thematic domains: breastfeeding support, fruit and vegetable access, sugar-sweetened beverage (SSB) restriction, and physical activity promotion. All variables were numerical and directly obtained from the CDC\u0026rsquo;s aggregated policy indicators database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe completeness of data varied slightly between domains, with breastfeeding indicators showing the highest availability (98%) and SSB restriction indicators the lowest (91%). Across all domains, missingness was random and did not display any geographical or temporal clustering.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal Trends by Policy Domain\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eBreastfeeding Support Policies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAmong all domains, breastfeeding support indicators exhibited the most pronounced upward trend over the 20-year observation period.\u003c/p\u003e\n\u003cp\u003eThe linear regression model demonstrated a mean annual increase of +3.64 points (p \u0026lt; 0.001, R\u0026sup2; = 0.36), indicating a substantial strengthening of state-level breastfeeding policies over time. This reflects the widespread adoption of baby-friendly hospital initiatives, expansion of lactation-support workplace laws, and federal endorsement of breastfeeding through programs such as the CDC\u0026rsquo;s Maternity Practices in Infant Nutrition and Care (mPINC) (Nelson et al., 2016; doi:10.1016/j.amepre.2016.07.021).\u003c/p\u003e\n\u003cp\u003eThe slope remained consistent even after adjusting for regional clustering, suggesting uniform policy adoption across both high- and low-income states.\u003c/p\u003e\n\u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003eFruit and Vegetable Access Policies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePolicies promoting fruit and vegetable availability also displayed a positive, albeit more modest, trend.\u003c/p\u003e\n\u003cp\u003eThe average yearly improvement was +1.77 points (p \u0026lt; 0.001, R\u0026sup2; = 0.18). This trend paralleled the expansion of programs such as Farm-to-School, WIC Farmers\u0026rsquo; Market Nutrition Program, and local food retail incentives (Freedman et al., 2016; doi:10.1186/s12966-016-0381-7).\u003c/p\u003e\n\u003cp\u003eThe rate of increase was steeper between 2008 and 2015\u0026mdash;corresponding to post-recession nutrition policy initiatives\u0026mdash;and plateaued after 2016, when several states shifted focus toward broader obesity-prevention frameworks.\u003c/p\u003e\n\u003col start=\"3\" type=\"1\"\u003e\n \u003cli\u003eSugar-Sweetened Beverage (SSB) Restriction Policies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn contrast, SSB-related indicators demonstrated a significant negative trend, with an average annual decline of \u0026ndash;1.65 points (p \u0026lt; 0.001, R\u0026sup2; = 0.21).\u003c/p\u003e\n\u003cp\u003eWhile some states implemented taxation or vending restrictions during the early 2010s, several initiatives were later repealed or weakened under lobbying pressure from the beverage industry (Silver et al., 2017; doi:10.1371/journal.pmed.1002315).\u003c/p\u003e\n\u003cp\u003eThe most pronounced decreases were observed after 2017, when the prevalence of state-level fiscal measures declined markedly.\u003c/p\u003e\n\u003col start=\"4\" type=\"1\"\u003e\n \u003cli\u003ePhysical Activity Promotion Policies\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUnlike the other domains, indicators related to physical activity infrastructure and promotion showed no statistically significant temporal trend (\u0026beta; = +0.14, p = 0.118, R\u0026sup2; = 0.02).\u003c/p\u003e\n\u003cp\u003eDespite minor year-to-year fluctuations, the overall pattern suggested policy stagnation, with many states maintaining similar levels of support for community-based physical activity programs throughout the study period.\u003c/p\u003e\n\u003cp\u003eThis stagnation persisted even after accounting for the presence of school-based physical education policies and Complete Streets initiatives (Booth et al., 2017; doi:10.1016/j.jshs.2016.02.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary Table\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePolicy Domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; (Annual Change)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTemporal Trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBreastfeeding support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.22\u0026ndash;4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSignificant \u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFruit \u0026amp; vegetable access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.48\u0026ndash;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate \u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSSB restriction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;1.94\u0026ndash; \u0026ndash;1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSignificant \u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhysical activity promotion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.04\u0026ndash;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of Trends\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the data reveal a heterogeneous evolution of public health policy domains across U.S. states. Breastfeeding and nutrition-related policies exhibited consistent and significant improvements, reflecting both federal incentives and growing public awareness. Conversely, SSB restriction policies regressed, likely reflecting policy reversals and inconsistent enforcement. Physical activity indicators remained stagnant, underscoring the limited progress in this area despite decades of public health advocacy.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present ecological analysis reveals heterogeneous temporal trends in U.S. state-level health policies over the last two decades. Among the four domains examined\u0026mdash;breastfeeding support, fruit and vegetable access, sugar-sweetened beverage (SSB) restriction, and physical activity promotion\u0026mdash;only breastfeeding-related indicators demonstrated a robust and sustained improvement. In contrast, SSB restriction policies declined over time, while physical activity indicators remained largely stagnant. These findings highlight the uneven evolution of health-promoting policies in the United States, emphasizing the need for renewed political commitment and multisectoral coordination to ensure equitable public health progress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBreastfeeding Policies: A Success Story in Policy Implementation\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe strong upward trajectory of breastfeeding support indicators (\u0026beta; = +3.64 points per year, R\u0026sup2; = 0.36) mirrors the success of national initiatives such as the Maternity Practices in Infant Nutrition and Care (mPINC) survey and the Baby-Friendly Hospital Initiative, which have been instrumental in transforming clinical and workplace practices. According to Nelson et al. (2016; doi:10.1016/j.amepre.2016.07.021), these programs have fostered accountability among hospitals and healthcare providers, creating measurable incentives for adherence to evidence-based guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, the federal Breastfeeding Report Card released annually by the CDC has encouraged inter-state benchmarking and competition, which in turn reinforced a policy culture of continuous improvement (Anstey et al., 2019; doi:10.1542/peds.2019-1991). These sustained efforts have translated into measurable health gains for both mothers and infants, including reductions in infectious diseases and obesity risk (Victora et al., 2016; doi:10.1016/S0140-6736(15)01024-7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, while the policy trajectory is positive, the apparent linearity of improvement may partially reflect enhancements in data collection and reporting, not solely genuine policy expansion. Several states introduced mandatory breastfeeding monitoring in the late 2000s, artificially inflating scores after data system modernization (Rollins et al., 2021; doi:10.1016/S0140-6736(21)00660-7). Future evaluations should therefore integrate qualitative audits of policy enforcement to complement quantitative metrics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutrition Policy and Fruit\u0026ndash;Vegetable Access\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe moderate yet significant rise in fruit and vegetable access policies (+1.77/year, R\u0026sup2; = 0.18) corresponds with federal and community initiatives launched during the 2000s, such as Farm-to-School, SNAP-Ed, and the Healthy Food Financing Initiative (Freedman et al., 2016; doi:10.1186/s12966-016-0381-7). These programs have been associated with increased fruit and vegetable availability, especially in underserved urban areas (Caspi et al., 2018; doi:10.1016/j.healthplace.2018.03.007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe plateau observed after 2015 may reflect policy saturation, where most states had already enacted baseline nutrition measures. As suggested by Mozaffarian et al. (2018; doi:10.1056/NEJMra1614819), achieving further progress likely requires a shift from policy creation to policy enforcement and evaluation of impact. The stagnation also coincides with shifting political priorities toward obesity prevention frameworks emphasizing multifactorial approaches, where nutrition is considered alongside physical activity, mental health, and socioeconomic determinants (Swinburn et al., 2019; doi:10.1016/S0140-6736(19)31226-6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportantly, although progress was modest, the upward trend confirms that nutrition policy remains one of the most responsive domains to federal funding and advocacy, particularly when supported by measurable performance indicators and cross-sector collaboration (Afshin et al., 2019; doi:10.1016/S0140-6736(19)31242-1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Decline of Sugar-Sweetened Beverage Policies\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe decreasing trend in SSB restriction policies (\u0026ndash;1.65/year, R\u0026sup2; = 0.21) is counterintuitive given the global momentum for soda taxation and marketing restrictions. However, several contextual explanations exist. First, CDC datasets primarily capture state-level policies, while most effective SSB interventions\u0026mdash;such as taxes in Berkeley and Philadelphia\u0026mdash;have been implemented at the municipal level, and are therefore underrepresented in national surveillance (Silver et al., 2017; doi:10.1371/journal.pmed.1002315).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, industry influence and legislative preemption have significantly impeded the sustainability of beverage-related policies. As Brownell and Warner (2009; doi:10.1001/jama.2009.2011) noted, corporate lobbying often leads to policy reversals or weak enforcement mechanisms. In 2017\u0026ndash;2019, several states (e.g., Michigan, Arizona, California) enacted preemption laws prohibiting local SSB taxes, effectively reversing earlier progress (Crosbie et al., 2019; doi:10.2105/AJPH.2019.305144).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThird, public discourse around \u0026ldquo;personal responsibility\u0026rdquo; in diet has occasionally overshadowed structural determinants, reducing political will for restrictive measures (Cecchini and Warin, 2016; doi:10.1787/5jm0q706vzxw-en). Thus, the decline in SSB policy scores likely reflects a combination of administrative scope, lobbying dynamics, and cultural framing of diet-related behaviors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStagnation in Physical Activity Policies\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe absence of a significant temporal trend in physical activity policies (\u0026beta; = +0.14, p = 0.118, R\u0026sup2; = 0.02) underscores a long-standing challenge in public health policy implementation. Physical activity promotion often relies on infrastructure-based interventions\u0026mdash;such as urban planning, school curricula, and active transport systems\u0026mdash;that require interdepartmental collaboration and long-term investment (Booth et al., 2017; doi:10.1016/j.jshs.2016.02.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs noted by Sallis et al. (2016; doi:10.1016/S0140-6736(16)30581-5), the complexity of intersectoral coordination and the delayed visibility of outcomes hinder political prioritization. Furthermore, state-level monitoring systems are poorly equipped to capture micro-level improvements such as park renovations or community walking programs, which are often implemented locally. This likely contributes to the apparent stagnation in surveillance-based indicators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy Implications\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, the observed patterns reveal a fragmented policy landscape where certain domains benefit from clear metrics and advocacy (e.g., breastfeeding, nutrition), while others lag due to structural or political constraints. This imbalance has implications for health equity: as Drewnowski and Rehm (2015; doi:10.3945/ajcn.115.109223) demonstrated, low-income populations face the greatest barriers to healthy behaviors precisely in domains\u0026mdash;like physical activity and SSB reduction\u0026mdash;where state-level policy engagement is weakest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo foster more comprehensive progress, public health agencies should prioritize three strategic actions:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eIntegration of policy surveillance systems, ensuring uniform coverage across all domains;\u003c/li\u003e\n \u003cli\u003eFederal incentives for underdeveloped areas, particularly physical activity and beverage regulation;\u003c/li\u003e\n \u003cli\u003eCommunity-level empowerment, leveraging municipal initiatives and citizen advocacy to counterbalance political inertia.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary strength of this study lies in the use of longitudinal, standardized CDC datasets, enabling a rare two-decade evaluation of health policy trends across the United States. By aggregating multiple surveillance systems, this analysis captures a broad spectrum of policy activity and evolution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNevertheless, several limitations merit acknowledgment. First, the ecological design precludes causal inference, and the observed associations cannot be directly linked to changes in population-level health outcomes. Second, as noted by Frieden (2017; doi:10.1056/NEJMp1701249), the translation of policy presence into effective implementation is not guaranteed; many states adopt formal policies without ensuring adequate funding or enforcement. Third, municipal initiatives\u0026mdash;often the most innovative\u0026mdash;are underrepresented in state-level datasets, potentially underestimating overall progress in some regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, while linear regression captures broad temporal patterns, it may overlook nonlinear policy dynamics, such as bursts of rapid change followed by periods of stagnation or rollback. Future studies should apply time-series or mixed-effects models to disentangle these subtler patterns and explore state-specific trajectories.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis analysis demonstrates that the evolution of U.S. health-promoting policies over the last two decades has been uneven across domains. Breastfeeding support and nutrition policies have advanced substantially, reflecting structured federal programs and strong advocacy. In contrast, beverage regulation and physical activity policies have lagged or regressed, highlighting enduring gaps in policy attention and implementation.\u003c/p\u003e\u003cp\u003eAs the U.S. faces escalating rates of obesity and chronic disease, bridging these gaps will require renewed political will, cross-sector collaboration, and a focus on equitable policy coverage across all states. Evidence from this study suggests that sustainable progress is possible when standardized metrics, federal incentives, and local engagement converge\u0026mdash;a model that could inform future public health strategies both within and beyond the United States.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eNelson JM et al. (2016). Hospital practices and breastfeeding: Results from the Maternity Practices in Infant Nutrition and Care (mPINC) Survey. Am J Prev Med. doi:10.1016/j.amepre.2016.07.021\u003c/li\u003e\n \u003cli\u003eAnstey EH et al. (2019). Trends in breastfeeding policies and practices in the United States. Pediatrics. doi:10.1542/peds.2019-1991\u003c/li\u003e\n \u003cli\u003eVictora CG et al. (2016). Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. Lancet. doi:10.1016/S0140-6736(15)01024-7\u003c/li\u003e\n \u003cli\u003eRollins NC et al. (2021). Why invest, and what it will take to improve breastfeeding practices. Lancet. doi:10.1016/S0140-6736(21)00660-7\u003c/li\u003e\n \u003cli\u003eFreedman DA et al. (2016). Public policy and food access: farm-to-school and local initiatives. Int J Behav Nutr Phys Act. doi:10.1186/s12966-016-0381-7\u003c/li\u003e\n \u003cli\u003eCaspi CE et al. (2018). Food environment and diet quality: evidence from local interventions. Health Place. doi:10.1016/j.healthplace.2018.03.007\u003c/li\u003e\n \u003cli\u003eMozaffarian D et al. (2018). Dietary and policy priorities for cardiovascular health. N Engl J Med. doi:10.1056/NEJMra1614819\u003c/li\u003e\n \u003cli\u003eSwinburn BA et al. (2019). The global syndemic of obesity, undernutrition, and climate change. Lancet. doi:10.1016/S0140-6736(19)31226-6\u003c/li\u003e\n \u003cli\u003eAfshin A et al. (2019). Health effects of dietary risks in 195 countries. Lancet. doi:10.1016/S0140-6736(19)31242-1\u003c/li\u003e\n \u003cli\u003eSilver LD et al. (2017). Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages. PLoS Med. doi:10.1371/journal.pmed.1002315\u003c/li\u003e\n \u003cli\u003eBrownell KD, Warner KE. (2009). The perils of ignoring history: Big Tobacco played dirty and millions died. How similar is Big Food? JAMA. doi:10.1001/jama.2009.2011\u003c/li\u003e\n \u003cli\u003eCrosbie E et al. (2019). Industry interference in public health policy: evidence from soda tax preemption laws. Am J Public Health. doi:10.2105/AJPH.2019.305144\u003c/li\u003e\n \u003cli\u003eCecchini M, Warin L. (2016). Impact of food policies on diet-related outcomes. OECD Health Working Papers. doi:10.1787/5jm0q706vzxw-en\u003c/li\u003e\n \u003cli\u003eBooth FW et al. (2017). Lack of exercise is a major cause of chronic diseases. J Sport Health Sci. doi:10.1016/j.jshs.2016.02.001\u003c/li\u003e\n \u003cli\u003eSallis JF et al. (2016). Physical activity in relation to urban design and transportation policies. Lancet. doi:10.1016/S0140-6736(16)30581-5\u003c/li\u003e\n \u003cli\u003eDrewnowski A, Rehm CD. (2015). Socioeconomic gradient in diet quality in the U.S. Am J Clin Nutr. doi:10.3945/ajcn.115.109223\u003c/li\u003e\n \u003cli\u003eFrieden TR. (2017). Evidence for health decision making: beyond randomized trials. N Engl J Med. doi:10.1056/NEJMp1701249\u003c/li\u003e\n \u003cli\u003eBrownson RC et al. (2018). Building capacity for evidence-based public health. Annu Rev Public Health. doi:10.1146/annurev-publhealth-040617-014731\u003c/li\u003e\n \u003cli\u003eKleinbaum DG et al. (2013). Applied Regression Analysis and Other Multivariable Methods. Springer. doi:10.1007/978-1-4419-6646-9\u003c/li\u003e\n \u003cli\u003eR Core Team. (2023). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. doi:10.1007/978-3-031-36405-1\u003c/li\u003e\n \u003cli\u003eWickham H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer. doi:10.1007/978-3-319-24277-4\u003c/li\u003e\n \u003cli\u003eCohen J. (1992). A power primer. Psychol Bull. doi:10.1037/0033-2909.112.1.155\u003c/li\u003e\n \u003cli\u003eNelson TD et al. (2015). Public health and chronic disease policy in the U.S. Prev Chronic Dis. doi:10.5888/pcd12.150281\u003c/li\u003e\n \u003cli\u003ePomeranz JL et al. (2020). State laws related to nutrition and physical activity. Am J Public Health. doi:10.2105/AJPH.2020.305776\u003c/li\u003e\n \u003cli\u003eStory M et al. (2008). Creating healthy food and eating environments. Annu Rev Public Health. doi:10.1146/annurev.publhealth.29.020907.090926\u003c/li\u003e\n \u003cli\u003eMalik VS et al. (2013). Sugar-sweetened beverages and global obesity. N Engl J Med. doi:10.1056/NEJMra1215742\u003c/li\u003e\n \u003cli\u003ePopkin BM, Hawkes C. (2016). Sweetening of the global diet. Lancet Diabetes Endocrinol. doi:10.1016/S2213-8587(16)30014-7\u003c/li\u003e\n \u003cli\u003eDiez Roux AV. (2015). Integrating social and built environments into public health research. Annu Rev Public Health. doi:10.1146/annurev-publhealth-031914-122703\u003c/li\u003e\n \u003cli\u003eFrieden TR, Bloomberg MR. (2007). How to prevent 100 million deaths from tobacco. Lancet. doi:10.1016/S0140-6736(07)61671-1\u003c/li\u003e\n \u003cli\u003eHales CM et al. (2020). Prevalence of obesity and severe obesity among adults: United States, 2017\u0026ndash;2018. MMWR. doi:10.15585/mmwr.mm6907a3\u003c/li\u003e\n \u003cli\u003eGortmaker SL et al. (2011). Changing the future of obesity: science, policy, and action. Lancet. doi:10.1016/S0140-6736(11)60815-5\u003c/li\u003e\n \u003cli\u003eWrieden WL et al. (2018). Progress in diet and nutrition policy. Public Health Nutr. doi:10.1017/S1368980018001762\u003c/li\u003e\n \u003cli\u003eSpring B et al. (2012). Multiple health behaviors: overview and future directions. Annu Rev Public Health. doi:10.1146/annurev-publhealth-031811-124629\u003c/li\u003e\n \u003cli\u003eLang T, Rayner G. (2012). Ecological public health: reshaping the conditions for good health. BMJ. doi:10.1136/bmj.e5466\u003c/li\u003e\n \u003cli\u003eSacks G et al. (2020). Policies for creating healthier food environments: evidence and gaps. Curr Obes Rep. doi:10.1007/s13679-020-00406-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Catholic University of the Sacred Heart","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7871717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7871717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eEnvironmental and policy supports are central to promoting healthy behaviors. This study analyzes temporal trends in U.S. state-level indicators related to nutrition, physical activity, and other behavioral policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAggregated CDC surveillance data from 2002–2021 were analyzed. Only observations with valid numerical values were included. Linear regression models were fitted for each thematic policy class (e.g., breastfeeding, fruit/vegetable access).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAmong 2,400 records analyzed, the most significant temporal increase was observed in breastfeeding support policies, with an average yearly increase of +3.64 points (p \u0026lt; 0.001, R² = 0.36). Policies promoting fruit and vegetable access also improved modestly (+1.77/year, p \u0026lt; 0.001), while sugar drink restriction policies showed a decreasing trend (–1.65/year, p \u0026lt; 0.001). No significant temporal changes were observed for physical activity indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWhile certain health policy domains like breastfeeding and nutrition have shown marked improvement over the last two decades, areas such as physical activity policies remain stagnant. These findings emphasize the need for targeted efforts in less-developed domains to promote equitable and comprehensive public health strategies.\u003c/p\u003e","manuscriptTitle":"Temporal Trends in U.S. Policy and Environmental Supports for Nutrition and Physical Activity: Analysis of CDC Surveillance Data (2002–2021)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 03:39:26","doi":"10.21203/rs.3.rs-7871717/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8f9d800-d769-45d2-ba32-8eccd69684b4","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T03:39:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 03:39:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7871717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7871717","identity":"rs-7871717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}