Impact of State-Level Behavioral Health Reforms on Mental Health and Alcohol Use: A Quasi-Experimental Study Using BRFSS Data (2012–2023)

Impact of State-Level Behavioral Health Reforms on Mental Health and Alcohol Use: A Quasi-Experimental Study Using BRFSS Data (2012–2023) | 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 Impact of State-Level Behavioral Health Reforms on Mental Health and Alcohol Use: A Quasi-Experimental Study Using BRFSS Data (2012–2023) Newton Nyirenda, Hannah Muturi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6857599/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 Introduction Understanding the population-level effects of state-level behavioural health reforms is vital in addressing the rising burden of mental health disorders and substance use. Objective To assess the impact of selected state-level policy interventions on mental health and alcohol use outcomes using a quasi-experimental difference-in-differences (DiD) approach with nationally representative data. Methods We analysed Behavioural Risk Factor Surveillance System (BRFSS) data from 2012–2023, comparing four intervention states (California, Massachusetts, Nevada, and Vermont) to other U.S. states. Key outcomes included self-reported mentally unhealthy days and any alcohol use in the past 30 days. DiD linear regression models were applied, including extended analyses covering the COVID-19 pandemic years. Results Post-intervention, treatment states experienced a statistically significant increase in mentally unhealthy days (β = 1.45; 95% CI: 0.98–1.91; p < 0.001), which attenuated and became non-significant in the extended period. Alcohol use did not change significantly in the short term but declined modestly in treatment states over the extended analysis (β = -0.0075; 95% CI: -0.0129 to -0.0021; p = 0.006). Conclusions State-level reforms may have short-term effects on mental health burden and longer-term benefits for reducing alcohol use. These findings highlight the need for sustained policy evaluation and tailored implementation strategies amid broader societal disruptions like the COVID-19 pandemic. Behavioural health Difference-in-differences BRFSS Mental health policy Alcohol use Public health policy State-level interventions Quasi-experimental study Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Mental health disorders and alcohol use remain among the leading contributors to the global burden of disease, with substantial implications for quality of life, productivity, and population health( 1 ). In the United States, approximately one in five adults experiences mental illness each year, and over half report consuming alcohol in the past month ( 2 ). These issues often intersect, with comorbid mental health and alcohol use disorders exacerbating clinical outcomes and complicating treatment strategies ( 3 ). In response to growing public health concerns, multiple U.S. states have enacted reforms aimed at improving behavioral health outcomes. These include expanding access to mental health services, integrating behavioral health into primary care, enhancing parity in insurance coverage, and implementing alcohol-related policies such as taxation or sales restrictions ( 4 ). For instance, Massachusetts implemented a behavioral health integration initiative, while California expanded mental health coverage through Medicaid; Nevada and Vermont also pursued similar reforms in recent years. Despite these efforts, evidence regarding the effectiveness of such policies at the population level remains mixed ( 5 ). Rigorous quasi-experimental approaches are essential to assess the causal impact of these interventions. Difference-in-differences (DiD) is one widely used method for estimating the effect of policy changes in observational settings by comparing pre-post changes between intervention and comparison groups ( 6 ). DiD models have been successfully applied in studies of Medicaid expansion ( 7 ) and alcohol taxation ( 8 ). However, few studies have jointly examined trends in both mental health and alcohol use following state-level behavioral health policy changes using nationally representative data. Moreover, the COVID-19 pandemic introduced unprecedented stressors, altered access to care, and may have fundamentally shifted mental health and substance use trajectories ( 9 , 10 ). As such, it is important to explore whether the effects of earlier state-level reforms persist during this period or are masked by pandemic-related disruptions. This study aimed to evaluate the association between state-level behavioral health policy interventions and two self-reported outcomes using BRFSS data from 2012 to 2023: ( 1 ) the number of mentally unhealthy days in the past 30 days and ( 2 ) any alcohol use in the past 30 days. We hypothesized that adults residing in states that implemented mental health and alcohol-related reforms (treatment group) would experience a relative change in these outcomes compared to adults in control states following the intervention period. Specifically, we expected a relative decrease in alcohol use and an improvement in mental health (i.e., fewer mentally unhealthy days) among the treatment group following policy implementation. METHODS Data Source We used publicly available data from the Behavioral Risk Factor Surveillance System (BRFSS), an annual, nationally representative, cross-sectional telephone survey administered by the Centers for Disease Control and Prevention (CDC). The BRFSS collects self-reported data on health-related behaviors, chronic conditions, and preventive service use among non-institutionalized adults aged 18 years and older in the United States. For the primary analysis, we extracted individual-level data from the years 2012 to 2019. To assess the robustness of our findings and explore potential longer-term trends, we conducted a secondary sensitivity analysis that incorporated survey data through 2023, capturing possible disruptions related to the COVID-19 pandemic. Study Design We employed a difference-in-differences (DiD) quasi-experimental design to estimate the association between state-level behavioral health policy interventions and two primary outcomes: the number of mentally unhealthy days reported in the past 30 days and any alcohol use during the same period. States were classified as treatment states if they had enacted substantive mental health or alcohol-related reforms by or before 2019. Based on legislative and policy reviews, California, Massachusetts, Nevada, and Vermont were designated as treatment states due to their adoption of measures such as expanded behavioral health coverage, enforcement of mental health parity laws, alcohol taxation policies, and service integration efforts. All other states were categorized as controls. A summary of policy characteristics by state is provided in Appendix Table A1. The pre-intervention period was defined as 2012 to 2018, with 2019 designated as the start of the post-intervention period. The extended analysis included data from 2020 through 2023 to evaluate whether observed effects were sustained or modified in the context of the COVID-19 pandemic. Measures The primary outcomes for this study were mentally unhealthy days and any alcohol use in the past 30 days. Mentally unhealthy days were measured using the BRFSS variable menthlth, which asks respondents to report how many days during the past 30 their mental health was “not good.” Alcohol use was based on the variable alcday5, which was recoded into a binary indicator alcohol_any, equal to 1 for any reported alcohol consumption in the past 30 days and 0 for none. The key explanatory variables for the DiD model included treat, a binary indicator coded as 1 for respondents in treatment states and 0 otherwise; post, coded as 1 for years 2019 and later and 0 for earlier years; and treat_post, the interaction between treatment group and post-policy period, representing the DiD estimator. We also included covariates in adjusted models to account for potential confounding. These included respondent age (as a continuous variable), sex (coded based on BRFSS variable sex1), and health insurance status, using the variable hlthpln1. Statistical Analysis We used ordinary least squares (OLS) linear regression models to estimate difference-in-differences effects. Unadjusted models included only the key terms, treat, post, and treat_post. Adjusted models additionally controlled for age, sex, and health insurance status to improve model precision and account for baseline differences between groups. All analyses were conducted using R version 4.2.2. Data import and cleaning were performed using the haven, dplyr, and data.table packages, and plots were generated using the ggplot2 package. To evaluate longer-term trends, we fitted extended DiD models using the 2012–2023 dataset. We attempted to estimate adjusted models incorporating demographic covariates including age, sex, and health insurance status. However, due to substantial missingness in these variables, particularly in post-intervention years, adjusted models could not be reliably estimated. As such, we report results from unadjusted models, consistent with prior difference-in-differences applications using BRFSS data. RESULTS Table 1 Baseline Characteristics of Study Participants by Treatment Group (2012–2019) Variable Control Treatment p_value age 54.76 53.85 < 0.001 female (%) 57.77 55.80 < 0.001 insured (%) 90.87 91.71 < 0.001 alcohol use (%) 27.30 29.67 < 0.001 mentally unhealthy days 63.97 61.32 < 0.001 Table 1 presents the baseline characteristics of respondents between 2012 and 2019, categorized by treatment assignment. Participants in the treatment states were, on average, slightly younger than those in control states (mean age: 53.85 vs. 54.76; p < 0.001). The treatment group also included a smaller proportion of women (55.8%) compared to the control group (57.8%; p < 0.001). Health insurance coverage was marginally higher in treatment states (91.7%) relative to controls (90.9%; p < 0.001). Additionally, alcohol use in the past 30 days was more prevalent among those in treatment states (29.7%) than among controls (27.3%; p < 0.001). Mean mentally unhealthy days were fewer in the treatment group (61.3 vs. 64.0; p < 0.001). All group differences reached statistical significance, reinforcing the need for covariate adjustment in modeling. Figure 1 presents trends in the mean number of self-reported mentally unhealthy days over the past 30 days, grouped by treatment status. Parallel pre-intervention trends were observed between 2012 and 2018. After 2019, the treatment group demonstrated a smaller decline in mentally unhealthy days than the control group, suggesting a relative worsening in mental health in treatment states post-intervention. Table 2 Difference-in-Differences Estimates Assessing the Association Between Policy Exposure and Mentally Unhealthy Days (2012–2019) Term Estimate 95% CI p-value (Intercept) 64.36 < 0.001 Treat -2.88 < 0.001 Post -2.51 < 0.001 Treat × Post 1.45 [0.98, 1.91] < 0.001 The unadjusted difference-in-differences regression (Table 2 ) showed that the interaction term (Treat × Post) was statistically significant (β = 1.45; 95% CI: 0.98, 1.91; p < 0.001). This indicates that adults in treatment states experienced 1.45 more mentally unhealthy days on average after the policy intervention, relative to the control group. The magnitude of the effect suggests a modest but meaningful increase in mental health burden associated with policy exposure. Figure 2 illustrates the proportion of adults reporting alcohol consumption in the past 30 days. Both treatment and control groups exhibited relatively stable trends, with slightly higher baseline alcohol use in treatment states. No pronounced divergence was observed following the intervention year, indicating no substantial immediate shift in self-reported alcohol consumption. Table 3 Difference-in-Differences Regression Estimates Evaluating the Association Between State-Level Policy and Alcohol Use Among Adults (2012–2019) Term Estimate 95% CI p-value (Intercept) 0.27 < 0.001 Treat 0.024 < 0.001 Post -0.005 < 0.001 Treat × Post -0.005 [-0.013, 0.003] 0.225 The DiD estimates in Table 3 showed no statistically significant change in alcohol use attributable to the intervention. The Treat × Post coefficient was − 0.005 (95% CI: -0.013, 0.003; p = 0.225), suggesting no meaningful change in alcohol use prevalence attributable to the policy exposure. Although the direction of the coefficient is consistent with a reduction in alcohol use, the effect was neither statistically significant nor practically large. Figure 3 extends the trend analysis to 2023. Both groups maintained parallel trajectories pre-2019. After the intervention, average mentally unhealthy days declined for both groups, particularly during the COVID-19 pandemic period. The lack of divergence suggests the pandemic may have introduced external shocks that muted any potential policy-related effects. Table 4 Extended Difference-in-Differences Regression Model Estimating the Association Between Policy Exposure and Mentally Unhealthy Days (2012–2023) Term Estimate 95% CI p-value (Intercept) 64.36 < 0.001 Treat -2.88 < 0.001 Post -4.31 < 0.001 Treat × Post 0.16 [-0.11, 0.42] 0.256 The extended regression results are shown in Table 4 . The interaction term was not statistically significant (β = 0.16; 95% CI: -0.11, 0.42; p = 0.256), indicating no differential change in mentally unhealthy days between treatment and control groups in the extended post-period. This attenuation from the original model likely reflects the widespread mental health disruption caused by the COVID-19 pandemic, which may have obscured policy-related effects. Table 5 Extended Difference-in-Differences Estimates of the Association Between State-Level Policy Exposure and Alcohol Use Among U.S. Adults, 2012–2023 Term Estimate 95% CI p-value Intercept 0.274 [0.273, 0.275] < 0.001 Treat 0.024 [0.021, 0.027] < 0.001 Post 0.0001 [-0.0013, 0.0016] 0.855 Treat × Post -0.0075 [-0.0129, -0.0021] 0.006 Table 5 displays the extended model for alcohol use. The Treat × Post interaction term was statistically significant and negative (β = -0.0075; 95% CI: -0.0129, -0.0021; p = 0.006), indicating a small but statistically significant decline in alcohol use among treatment states relative to controls. While the effect is statistically robust, the absolute change in alcohol use prevalence (a 0.75 percentage point reduction) is modest in magnitude and may have limited clinical or population-level impact. This result should be interpreted cautiously in light of potential secular shifts in alcohol behavior during the pandemic. DISCUSSION This study used nationally representative BRFSS data to evaluate the impact of state-level policy interventions on two behavioral health outcomes: mentally unhealthy days and alcohol use. In our primary analysis covering 2012 to 2019, we found that adults in treatment states reported a statistically significant increase in mentally unhealthy days relative to those in control states. However, this effect was no longer significant in the extended analysis incorporating data through 2023. In contrast, while no significant policy effect on alcohol use was detected in the primary model, the extended model revealed a small but statistically significant reduction in the probability of alcohol consumption among residents of treatment states. The observed increase in reported mentally unhealthy days immediately following the intervention may reflect several possible mechanisms. One interpretation is that transitional disruptions, such as delays in implementation, increased screening, or adjustment periods, temporarily worsened psychological well-being, a phenomenon previously documented in evaluations of behavioral health reform ( 11 ). Alternatively, the observed increase may not reflect actual deterioration in mental health but rather increased awareness, improved access to mental health services, or reduced stigma, resulting in more frequent reporting of symptoms ( 12 , 13 ). The attenuation of this effect in the post-2020 period, coinciding with the COVID-19 pandemic, further complicates interpretation. Pandemic-related stressors, social isolation, and disruptions in care likely affected both treatment and control groups, potentially masking or diluting any policy-specific effects ( 14 , 15 ). In contrast, the extended analysis of alcohol use revealed a small but statistically significant reduction in alcohol consumption among treatment-state respondents. This delayed effect may reflect the gradual impact of state-level policy measures such as alcohol taxation, advertising restrictions, and integration of substance use screening into primary care. Previous studies have shown that these interventions, while not always immediately effective, can reduce alcohol consumption over time ( 16 , 17 ). It is also possible that broader cultural shifts or increased access to behavioral health support contributed to reduced alcohol use. Importantly, although the effect size was modest, approximately a 0.75 percentage point reduction, it may still be meaningful at the population level, especially when sustained over time. These results have key policy implications. The short-term increase in mentally unhealthy days following policy implementation may reflect transitional strain or increased detection due to better service access. This reinforces the need for states to ensure not just policy rollout, but also sufficient investment in the underlying behavioral health workforce and infrastructure. For Medicaid programs, particularly those involved in expanding mental health coverage, understanding these transition dynamics is essential for improving implementation fidelity and patient outcomes. Additionally, the modest decline in alcohol use suggests that alcohol policy reforms, such as taxation, retail restrictions, and integrated screening within primary care, may be effective over longer periods. These insights can guide state funding decisions and public health planning, particularly in targeting high-burden communities and scaling successful intervention models. These findings demonstrate the importance of conducting sensitivity analyses to account for macro-level disruptions such as the COVID-19 pandemic. Including data through 2023 revealed shifts in the direction and magnitude of key estimates, highlighting the dynamic nature of public health behavior in response to both policy and environmental change. This further illustrates the utility of longitudinal, repeated cross-sectional surveillance systems like BRFSS for evaluating policy effects over time ( 18 ). Strengths of this study include the use of a robust quasi-experimental design, application of a difference-in-differences framework, and reliance on large-scale, nationally representative BRFSS data. Additionally, by evaluating both mental health and alcohol use outcomes, the study provides a more comprehensive understanding of behavioral health trends in response to policy change. However, several limitations warrant consideration. First, all outcomes were self-reported and thus subject to recall or social desirability bias. Second, the treatment definition was necessarily broad and did not account for the heterogeneity in timing, scope, or enforcement of specific policy interventions. Third, while difference-in-differences helps mitigate confounding, unmeasured differences between treatment and control states may still bias the results. Finally, we were unable to present adjusted estimates due to extensive missingness in key covariates during the post-intervention period. While this may introduce some residual confounding, the difference-in-differences design inherently controls for time-invariant group-level differences, and our findings are consistent with unadjusted population-level policy evaluations. Overall, these findings contribute to a growing body of literature on the real-world impact of behavioral health policies and highlight the complexity of evaluating population-level outcomes in evolving public health landscapes. CONCLUSION In this population-based, quasi-experimental study using BRFSS data from 2012 to 2023, we observed a modest increase in mentally unhealthy days among adults in treatment states during the immediate post-intervention period; however, this association did not persist when extended through the COVID-19 pandemic years. For alcohol use, no short-term change was detected, but extended analyses revealed a small yet statistically significant reduction in alcohol consumption in treatment states compared to controls. These findings suggest that state-level policy reforms may be linked to nuanced and time-dependent shifts in behavioral health outcomes. They underscore the importance of evaluating policies over extended periods and within broader contextual disruptions, such as the COVID-19 pandemic, which may obscure or amplify policy effects. Future research should examine specific mechanisms underlying these associations, including the content, timing, and implementation fidelity of state-level reforms. Leveraging more granular data and mixed-method approaches could further clarify causal pathways. Ongoing investment in large-scale, longitudinal surveillance systems like BRFSS remains essential for evaluating the public health impact of policy interventions and informing evidence-based decision-making. Abbreviations BRFSS Behavioral Risk Factor Surveillance System DiD Difference-in-Differences CDC Centers for Disease Control and Prevention COVID-19 Coronavirus Disease 2019 Declarations Ethics approval and consent to participate This study involved secondary analysis of publicly available, de-identified data (BRFSS) and did not require ethical approval. Consent for publication Not applicable. This study does not contain any individual person’s data. Availability of data and materials The datasets analysed in this study are publicly available from the CDC BRFSS portal: https://www.cdc.gov/brfss/. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this work. Authors’ contributions NN conceived the study, performed the data analysis, and drafted the manuscript. HM contributed to study design, interpretation of findings, and manuscript revisions. All authors read and approved the final manuscript. 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MMWR Morb Mortal Wkly Rep. 2020;69(32):1049–57. Barry CL, Huskamp HA. Moving beyond Parity — Mental Health and Addiction Care under the ACA. N Engl J Med. 2011;365(11):973–5. Barry CL, McGinty EE. Stigma and Public Support for Parity and Government Spending on Mental Health: A 2013 National Opinion Survey. Psychiatric Serv. 2014;65(10):1265–8. Mojtabai R. Mental illness stigma and willingness to seek mental health care in the European Union. Soc Psychiatry Psychiatr Epidemiol. 2010;45(7):705–12. Czeisler MÉ, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, et al. Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic — United States, June 24–30, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(32):1049–57. Ettman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S. Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic. JAMA Netw Open. 2020;3(9):e2019686. Wagenaar AC, Tobler AL, Komro KA. Effects of Alcohol Tax and Price Policies on Morbidity and Mortality: A Systematic Review. Am J Public Health. 2010;100(11):2270–8. Slade T, Chapman C, Swift W, Keyes K, Tonks Z, Teesson M. Birth cohort trends in the global epidemiology of alcohol use and alcohol-related harms in men and women: systematic review and metaregression. BMJ Open. 2016;6(10):e011827. Pierce M, Hope H, Ford T, Hatch S, Hotopf M, John A, et al. Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the UK population. Lancet Psychiatry. 2020;7(10):883–92. Appendix Appendix is not available with this version. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6857599","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470599701,"identity":"3e16ad28-181c-451e-ba53-f164b1730c7c","order_by":0,"name":"Newton Nyirenda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYHAC9o8fKlBFDAhpYWOWOANlHiBWCwNvGylazNnPHnsgOa9Wjl/6+MXHHyoO5zGwN2+TwKfFsicv3aBw23Fjyb6cYoMDZw4XM/AcK8OrxeBAjoGE5LZjiRvO8KRJHGy7ndggkWOGX8v5NwYSvHOO1QO1pP8Aa5F/Q0DLDaCZvA01CQZn2I8xQGzhIaTljbGxxLEDhjN7eICBfeZ/YhtPWrEFfoflGD78UFMnz8/D/vBDRUVaYj/74Y038GmBgsNAzAOJDjYilINAHRCzPyBS8SgYBaNgFIw0AACk6U8h90XfpAAAAABJRU5ErkJggg==","orcid":"","institution":"Georgetown University","correspondingAuthor":true,"prefix":"","firstName":"Newton","middleName":"","lastName":"Nyirenda","suffix":""},{"id":470599702,"identity":"eea29000-c0a2-47d5-a28c-0e2e29ebffe7","order_by":1,"name":"Hannah Muturi","email":"","orcid":"","institution":"University of the District of Columbia","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"","lastName":"Muturi","suffix":""}],"badges":[],"createdAt":"2025-06-09 22:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6857599/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6857599/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84845281,"identity":"17245e9b-c027-4fa9-bd64-0282c74851b8","added_by":"auto","created_at":"2025-06-18 02:59:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in Mentally Unhealthy Days (2012–2019), by Treatment Group.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6857599/v1/e8eec14fdea5dce94a2da6ae.png"},{"id":84845282,"identity":"49e84b16-f3c7-4b4d-b52a-36f863a0a4b6","added_by":"auto","created_at":"2025-06-18 02:59:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in Self-Reported Alcohol Use in the Past 30 Days by Treatment Group (2012–2019)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6857599/v1/79b773861473222ca25f329a.png"},{"id":84845283,"identity":"c7fcbabf-0e43-4e80-be19-77cab31e4c92","added_by":"auto","created_at":"2025-06-18 02:59:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43542,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Trends in Mentally Unhealthy Days by Treatment Group (2012–2023)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6857599/v1/256fa18c652a38f866aadc31.png"},{"id":87586158,"identity":"70fe7bbc-4ca7-4546-b91e-c98d6f6d906a","added_by":"auto","created_at":"2025-07-25 13:53:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":783203,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6857599/v1/0a490662-49b5-4415-98db-c0ab5fc9c345.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of State-Level Behavioral Health Reforms on Mental Health and Alcohol Use: A Quasi-Experimental Study Using BRFSS Data (2012–2023)","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMental health disorders and alcohol use remain among the leading contributors to the global burden of disease, with substantial implications for quality of life, productivity, and population health(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In the United States, approximately one in five adults experiences mental illness each year, and over half report consuming alcohol in the past month (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These issues often intersect, with comorbid mental health and alcohol use disorders exacerbating clinical outcomes and complicating treatment strategies (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn response to growing public health concerns, multiple U.S. states have enacted reforms aimed at improving behavioral health outcomes. These include expanding access to mental health services, integrating behavioral health into primary care, enhancing parity in insurance coverage, and implementing alcohol-related policies such as taxation or sales restrictions (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). For instance, Massachusetts implemented a behavioral health integration initiative, while California expanded mental health coverage through Medicaid; Nevada and Vermont also pursued similar reforms in recent years. Despite these efforts, evidence regarding the effectiveness of such policies at the population level remains mixed (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRigorous quasi-experimental approaches are essential to assess the causal impact of these interventions. Difference-in-differences (DiD) is one widely used method for estimating the effect of policy changes in observational settings by comparing pre-post changes between intervention and comparison groups (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). DiD models have been successfully applied in studies of Medicaid expansion (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and alcohol taxation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, few studies have jointly examined trends in both mental health and alcohol use following state-level behavioral health policy changes using nationally representative data.\u003c/p\u003e \u003cp\u003eMoreover, the COVID-19 pandemic introduced unprecedented stressors, altered access to care, and may have fundamentally shifted mental health and substance use trajectories (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). As such, it is important to explore whether the effects of earlier state-level reforms persist during this period or are masked by pandemic-related disruptions.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the association between state-level behavioral health policy interventions and two self-reported outcomes using BRFSS data from 2012 to 2023: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the number of mentally unhealthy days in the past 30 days and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) any alcohol use in the past 30 days. We hypothesized that adults residing in states that implemented mental health and alcohol-related reforms (treatment group) would experience a relative change in these outcomes compared to adults in control states following the intervention period. Specifically, we expected a relative decrease in alcohol use and an improvement in mental health (i.e., fewer mentally unhealthy days) among the treatment group following policy implementation.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eWe used publicly available data from the Behavioral Risk Factor Surveillance System (BRFSS), an annual, nationally representative, cross-sectional telephone survey administered by the Centers for Disease Control and Prevention (CDC). The BRFSS collects self-reported data on health-related behaviors, chronic conditions, and preventive service use among non-institutionalized adults aged 18 years and older in the United States.\u003c/p\u003e \u003cp\u003eFor the primary analysis, we extracted individual-level data from the years 2012 to 2019. To assess the robustness of our findings and explore potential longer-term trends, we conducted a secondary sensitivity analysis that incorporated survey data through 2023, capturing possible disruptions related to the COVID-19 pandemic.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eWe employed a difference-in-differences (DiD) quasi-experimental design to estimate the association between state-level behavioral health policy interventions and two primary outcomes: the number of mentally unhealthy days reported in the past 30 days and any alcohol use during the same period. States were classified as treatment states if they had enacted substantive mental health or alcohol-related reforms by or before 2019. Based on legislative and policy reviews, California, Massachusetts, Nevada, and Vermont were designated as treatment states due to their adoption of measures such as expanded behavioral health coverage, enforcement of mental health parity laws, alcohol taxation policies, and service integration efforts. All other states were categorized as controls. A summary of policy characteristics by state is provided in Appendix Table A1.\u003c/p\u003e \u003cp\u003eThe pre-intervention period was defined as 2012 to 2018, with 2019 designated as the start of the post-intervention period. The extended analysis included data from 2020 through 2023 to evaluate whether observed effects were sustained or modified in the context of the COVID-19 pandemic.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes for this study were mentally unhealthy days and any alcohol use in the past 30 days. Mentally unhealthy days were measured using the BRFSS variable menthlth, which asks respondents to report how many days during the past 30 their mental health was \u0026ldquo;not good.\u0026rdquo; Alcohol use was based on the variable alcday5, which was recoded into a binary indicator alcohol_any, equal to 1 for any reported alcohol consumption in the past 30 days and 0 for none.\u003c/p\u003e \u003cp\u003eThe key explanatory variables for the DiD model included treat, a binary indicator coded as 1 for respondents in treatment states and 0 otherwise; post, coded as 1 for years 2019 and later and 0 for earlier years; and treat_post, the interaction between treatment group and post-policy period, representing the DiD estimator.\u003c/p\u003e \u003cp\u003eWe also included covariates in adjusted models to account for potential confounding. These included respondent age (as a continuous variable), sex (coded based on BRFSS variable sex1), and health insurance status, using the variable hlthpln1.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe used ordinary least squares (OLS) linear regression models to estimate difference-in-differences effects. Unadjusted models included only the key terms, treat, post, and treat_post. Adjusted models additionally controlled for age, sex, and health insurance status to improve model precision and account for baseline differences between groups.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R version 4.2.2. Data import and cleaning were performed using the haven, dplyr, and data.table packages, and plots were generated using the ggplot2 package. To evaluate longer-term trends, we fitted extended DiD models using the 2012\u0026ndash;2023 dataset. We attempted to estimate adjusted models incorporating demographic covariates including age, sex, and health insurance status. However, due to substantial missingness in these variables, particularly in post-intervention years, adjusted models could not be reliably estimated. As such, we report results from unadjusted models, consistent with prior difference-in-differences applications using BRFSS data.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Study Participants by Treatment Group (2012\u0026ndash;2019)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep_value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einsured (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealcohol use (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ementally unhealthy days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of respondents between 2012 and 2019, categorized by treatment assignment. Participants in the treatment states were, on average, slightly younger than those in control states (mean age: 53.85 vs. 54.76; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The treatment group also included a smaller proportion of women (55.8%) compared to the control group (57.8%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Health insurance coverage was marginally higher in treatment states (91.7%) relative to controls (90.9%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, alcohol use in the past 30 days was more prevalent among those in treatment states (29.7%) than among controls (27.3%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mean mentally unhealthy days were fewer in the treatment group (61.3 vs. 64.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All group differences reached statistical significance, reinforcing the need for covariate adjustment in modeling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents trends in the mean number of self-reported mentally unhealthy days over the past 30 days, grouped by treatment status. Parallel pre-intervention trends were observed between 2012 and 2018. After 2019, the treatment group demonstrated a smaller decline in mentally unhealthy days than the control group, suggesting a relative worsening in mental health in treatment states post-intervention.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference-in-Differences Estimates Assessing the Association Between Policy Exposure and Mentally Unhealthy Days (2012\u0026ndash;2019)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat \u0026times; Post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.98, 1.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe unadjusted difference-in-differences regression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that the interaction term (Treat \u0026times; Post) was statistically significant (β\u0026thinsp;=\u0026thinsp;1.45; 95% CI: 0.98, 1.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that adults in treatment states experienced 1.45 more mentally unhealthy days on average after the policy intervention, relative to the control group. The magnitude of the effect suggests a modest but meaningful increase in mental health burden associated with policy exposure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the proportion of adults reporting alcohol consumption in the past 30 days. Both treatment and control groups exhibited relatively stable trends, with slightly higher baseline alcohol use in treatment states. No pronounced divergence was observed following the intervention year, indicating no substantial immediate shift in self-reported alcohol consumption.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference-in-Differences Regression Estimates Evaluating the Association Between State-Level Policy and Alcohol Use Among Adults (2012\u0026ndash;2019)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat \u0026times; Post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.013, 0.003]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe DiD estimates in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed no statistically significant change in alcohol use attributable to the intervention. The Treat \u0026times; Post coefficient was \u0026minus;\u0026thinsp;0.005 (95% CI: -0.013, 0.003; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.225), suggesting no meaningful change in alcohol use prevalence attributable to the policy exposure. Although the direction of the coefficient is consistent with a reduction in alcohol use, the effect was neither statistically significant nor practically large.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e extends the trend analysis to 2023. Both groups maintained parallel trajectories pre-2019. After the intervention, average mentally unhealthy days declined for both groups, particularly during the COVID-19 pandemic period. The lack of divergence suggests the pandemic may have introduced external shocks that muted any potential policy-related effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExtended Difference-in-Differences Regression Model Estimating the Association Between Policy Exposure and Mentally Unhealthy Days (2012\u0026ndash;2023)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat \u0026times; Post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[-0.11, 0.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe extended regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The interaction term was not statistically significant (β\u0026thinsp;=\u0026thinsp;0.16; 95% CI: -0.11, 0.42; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.256), indicating no differential change in mentally unhealthy days between treatment and control groups in the extended post-period. This attenuation from the original model likely reflects the widespread mental health disruption caused by the COVID-19 pandemic, which may have obscured policy-related effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExtended Difference-in-Differences Estimates of the Association Between State-Level Policy Exposure and Alcohol Use Among U.S. Adults, 2012\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.273, 0.275]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.021, 0.027]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.0013, 0.0016]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat \u0026times; Post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[-0.0129, -0.0021]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the extended model for alcohol use. The Treat \u0026times; Post interaction term was statistically significant and negative (β = -0.0075; 95% CI: -0.0129, -0.0021; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), indicating a small but statistically significant decline in alcohol use among treatment states relative to controls. While the effect is statistically robust, the absolute change in alcohol use prevalence (a 0.75 percentage point reduction) is modest in magnitude and may have limited clinical or population-level impact. This result should be interpreted cautiously in light of potential secular shifts in alcohol behavior during the pandemic.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study used nationally representative BRFSS data to evaluate the impact of state-level policy interventions on two behavioral health outcomes: mentally unhealthy days and alcohol use. In our primary analysis covering 2012 to 2019, we found that adults in treatment states reported a statistically significant increase in mentally unhealthy days relative to those in control states. However, this effect was no longer significant in the extended analysis incorporating data through 2023. In contrast, while no significant policy effect on alcohol use was detected in the primary model, the extended model revealed a small but statistically significant reduction in the probability of alcohol consumption among residents of treatment states.\u003c/p\u003e \u003cp\u003eThe observed increase in reported mentally unhealthy days immediately following the intervention may reflect several possible mechanisms. One interpretation is that transitional disruptions, such as delays in implementation, increased screening, or adjustment periods, temporarily worsened psychological well-being, a phenomenon previously documented in evaluations of behavioral health reform (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Alternatively, the observed increase may not reflect actual deterioration in mental health but rather increased awareness, improved access to mental health services, or reduced stigma, resulting in more frequent reporting of symptoms (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The attenuation of this effect in the post-2020 period, coinciding with the COVID-19 pandemic, further complicates interpretation. Pandemic-related stressors, social isolation, and disruptions in care likely affected both treatment and control groups, potentially masking or diluting any policy-specific effects (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the extended analysis of alcohol use revealed a small but statistically significant reduction in alcohol consumption among treatment-state respondents. This delayed effect may reflect the gradual impact of state-level policy measures such as alcohol taxation, advertising restrictions, and integration of substance use screening into primary care. Previous studies have shown that these interventions, while not always immediately effective, can reduce alcohol consumption over time (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). It is also possible that broader cultural shifts or increased access to behavioral health support contributed to reduced alcohol use. Importantly, although the effect size was modest, approximately a 0.75 percentage point reduction, it may still be meaningful at the population level, especially when sustained over time. These results have key policy implications. The short-term increase in mentally unhealthy days following policy implementation may reflect transitional strain or increased detection due to better service access. This reinforces the need for states to ensure not just policy rollout, but also sufficient investment in the underlying behavioral health workforce and infrastructure. For Medicaid programs, particularly those involved in expanding mental health coverage, understanding these transition dynamics is essential for improving implementation fidelity and patient outcomes. Additionally, the modest decline in alcohol use suggests that alcohol policy reforms, such as taxation, retail restrictions, and integrated screening within primary care, may be effective over longer periods. These insights can guide state funding decisions and public health planning, particularly in targeting high-burden communities and scaling successful intervention models.\u003c/p\u003e \u003cp\u003eThese findings demonstrate the importance of conducting sensitivity analyses to account for macro-level disruptions such as the COVID-19 pandemic. Including data through 2023 revealed shifts in the direction and magnitude of key estimates, highlighting the dynamic nature of public health behavior in response to both policy and environmental change. This further illustrates the utility of longitudinal, repeated cross-sectional surveillance systems like BRFSS for evaluating policy effects over time (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStrengths of this study include the use of a robust quasi-experimental design, application of a difference-in-differences framework, and reliance on large-scale, nationally representative BRFSS data. Additionally, by evaluating both mental health and alcohol use outcomes, the study provides a more comprehensive understanding of behavioral health trends in response to policy change. However, several limitations warrant consideration. First, all outcomes were self-reported and thus subject to recall or social desirability bias. Second, the treatment definition was necessarily broad and did not account for the heterogeneity in timing, scope, or enforcement of specific policy interventions. Third, while difference-in-differences helps mitigate confounding, unmeasured differences between treatment and control states may still bias the results. Finally, we were unable to present adjusted estimates due to extensive missingness in key covariates during the post-intervention period. While this may introduce some residual confounding, the difference-in-differences design inherently controls for time-invariant group-level differences, and our findings are consistent with unadjusted population-level policy evaluations.\u003c/p\u003e \u003cp\u003eOverall, these findings contribute to a growing body of literature on the real-world impact of behavioral health policies and highlight the complexity of evaluating population-level outcomes in evolving public health landscapes.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this population-based, quasi-experimental study using BRFSS data from 2012 to 2023, we observed a modest increase in mentally unhealthy days among adults in treatment states during the immediate post-intervention period; however, this association did not persist when extended through the COVID-19 pandemic years. For alcohol use, no short-term change was detected, but extended analyses revealed a small yet statistically significant reduction in alcohol consumption in treatment states compared to controls.\u003c/p\u003e \u003cp\u003eThese findings suggest that state-level policy reforms may be linked to nuanced and time-dependent shifts in behavioral health outcomes. They underscore the importance of evaluating policies over extended periods and within broader contextual disruptions, such as the COVID-19 pandemic, which may obscure or amplify policy effects.\u003c/p\u003e \u003cp\u003eFuture research should examine specific mechanisms underlying these associations, including the content, timing, and implementation fidelity of state-level reforms. Leveraging more granular data and mixed-method approaches could further clarify causal pathways. Ongoing investment in large-scale, longitudinal surveillance systems like BRFSS remains essential for evaluating the public health impact of policy interventions and informing evidence-based decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBRFSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBehavioral Risk Factor Surveillance System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDiD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifference-in-Differences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCDC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCOVID-19\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronavirus Disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved secondary analysis of publicly available, de-identified data (BRFSS) and did not require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not contain any individual person\u0026rsquo;s data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed in this study are publicly available from the CDC BRFSS portal: https://www.cdc.gov/brfss/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNN conceived the study, performed the data analysis, and drafted the manuscript. HM contributed to study design, interpretation of findings, and manuscript revisions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenter for Behavioral Health Statistics S. Key Substance Use and Mental Health Indicators in the United States. Results from the 2023 National Survey on Drug Use and Health [Internet]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.samhsa.gov/data/report/2023-nsduh-annual-national-report\u003c/span\u003e\u003cspan address=\"https://www.samhsa.gov/data/report/2023-nsduh-annual-national-report\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessler RC, Nelson CB, McGonagle KA, Edlund MJ, Frank RG, Leaf PJ. The epidemiology of co-occurring addictive and mental disorders: Implications for prevention and service utilization. Am J Orthopsychiatry. 1996;66(1):17\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMays GP, Mamaril CB, Timsina LR. Preventable Death Rates Fell Where Communities Expanded Population Health Activities Through Multisector Networks. Health Aff. 2016;35(11):2005\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGinty EE, Presskreischer R, Han H, Barry CL. Psychological Distress and Loneliness Reported by US Adults in 2018 and April 2020. JAMA. 2020;324(1):93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWing C, Simon K, Bello-Gomez RA. Designing Difference in Difference Studies: Best Practices for Public Health Policy Research. Annu Rev Public Health. 2018;39(1):453\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimon K, Soni A, Cawley J. The Impact of Health Insurance on Preventive Care and Health Behaviors: Evidence from the First Two Years of the ACA Medicaid Expansions. J Policy Anal Manag. 2017;36(2):390\u0026ndash;417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen H, Hockenberry JM, Cummings JR. The effect of medical marijuana laws on adolescent and adult use of marijuana, alcohol, and other substances. J Health Econ. 2015;42:64\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfefferbaum B, North CS. Mental Health and the Covid-19 Pandemic. N Engl J Med. 2020;383(6):510\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzeisler M\u0026Eacute;, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, et al. Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic \u0026mdash; United States, June 24\u0026ndash;30, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(32):1049\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarry CL, Huskamp HA. Moving beyond Parity \u0026mdash; Mental Health and Addiction Care under the ACA. N Engl J Med. 2011;365(11):973\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarry CL, McGinty EE. Stigma and Public Support for Parity and Government Spending on Mental Health: A 2013 National Opinion Survey. Psychiatric Serv. 2014;65(10):1265\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMojtabai R. Mental illness stigma and willingness to seek mental health care in the European Union. Soc Psychiatry Psychiatr Epidemiol. 2010;45(7):705\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzeisler M\u0026Eacute;, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, et al. Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic \u0026mdash; United States, June 24\u0026ndash;30, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(32):1049\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEttman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S. Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic. JAMA Netw Open. 2020;3(9):e2019686.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagenaar AC, Tobler AL, Komro KA. Effects of Alcohol Tax and Price Policies on Morbidity and Mortality: A Systematic Review. Am J Public Health. 2010;100(11):2270\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlade T, Chapman C, Swift W, Keyes K, Tonks Z, Teesson M. Birth cohort trends in the global epidemiology of alcohol use and alcohol-related harms in men and women: systematic review and metaregression. BMJ Open. 2016;6(10):e011827.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierce M, Hope H, Ford T, Hatch S, Hotopf M, John A, et al. Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the UK population. Lancet Psychiatry. 2020;7(10):883\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Appendix","content":"\u003cp\u003eAppendix is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Behavioural health, Difference-in-differences, BRFSS, Mental health policy, Alcohol use, Public health policy, State-level interventions, Quasi-experimental study","lastPublishedDoi":"10.21203/rs.3.rs-6857599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6857599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003eUnderstanding the population-level effects of state-level behavioural health reforms is vital in addressing the rising burden of mental health disorders and substance use.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo assess the impact of selected state-level policy interventions on mental health and alcohol use outcomes using a quasi-experimental difference-in-differences (DiD) approach with nationally representative data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analysed Behavioural Risk Factor Surveillance System (BRFSS) data from 2012\u0026ndash;2023, comparing four intervention states (California, Massachusetts, Nevada, and Vermont) to other U.S. states. Key outcomes included self-reported mentally unhealthy days and any alcohol use in the past 30 days. DiD linear regression models were applied, including extended analyses covering the COVID-19 pandemic years.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePost-intervention, treatment states experienced a statistically significant increase in mentally unhealthy days (β\u0026thinsp;=\u0026thinsp;1.45; 95% CI: 0.98\u0026ndash;1.91; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which attenuated and became non-significant in the extended period. Alcohol use did not change significantly in the short term but declined modestly in treatment states over the extended analysis (β = -0.0075; 95% CI: -0.0129 to -0.0021; p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eState-level reforms may have short-term effects on mental health burden and longer-term benefits for reducing alcohol use. These findings highlight the need for sustained policy evaluation and tailored implementation strategies amid broader societal disruptions like the COVID-19 pandemic.\u003c/p\u003e","manuscriptTitle":"Impact of State-Level Behavioral Health Reforms on Mental Health and Alcohol Use: A Quasi-Experimental Study Using BRFSS Data (2012–2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 02:59:34","doi":"10.21203/rs.3.rs-6857599/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":"db8f9105-50eb-460c-97e0-16c413f6bdc4","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-25T13:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 02:59:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6857599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6857599","identity":"rs-6857599","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}