Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States

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} div.type-section h2 { font-size: 20px; line-height: 26px; font-weight: 300; } div.type-section h3 { margin-left: 15px; margin-bottom: 0px; font-weight: 300; } .journal-tabs .tab-title.active a { } </style> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/slick.css?f38b2db10e01b157?1732615622"> <meta name="title" content="Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States"> <meta name="description" content="Previous studies indicate that increased healthcare access through Medicaid expansion and alleviation of socioeconomic stressors via higher minimum wages improved health outcomes. This study investigates the spatial relationships between the Medicaid expansion, minimum wage policy, and Alzheimer’s Disease (AD) dementia prevalence across the US. We used county-level AD dementia prevalence adjusted for age, sex, race/ethnicity, and education. Social Vulnerability Index (SVI) data, Medicaid expansion status, and state minimum wage law status were incorporated from CDC, Kaiser Family Foundation, and US Department of Labor sources, respectively. We employed the Getis-Ord Gi* statistic to identify hotspots and cold spots of AD dementia prevalence at the county level. We compared these locations with the overall SVI scores using univariate analyses. We also assessed the proportion of hot and cold spots at the state level based on Medicaid expansion and minimum wage status using the logistic regression model. The most vulnerable SVI quartile (Q4) had the highest number of hotspots (n = 311, 64.8%), while the least vulnerable quartile (Q1) had the fewest hotspots (n = 22, 4.6%) (χ2 = 307.41, p < 0.01). States that adopted Medicaid expansion had a significantly lower proportion of hotspots compared to non-adopting states (p < 0.05), and the non-adopting states had significantly higher odds of having hotspots than adopting states (OR = 2.58, 95% CI: 2.04–3.26, p < 0.001). Conversely, the non-adopting states had significantly lower odds of having cold spots compared to the adopting states (OR = 0.24, 95% CI: 0.19–0.32, p < 0.01). States with minimum wage levels at or below the federal level showed significantly higher odds of having hotspots than states with a minimum wage above the federal level (OR = 1.94, 95% CI: 1.51–2.49, p < 0.01). Our findings suggest significant disparities in AD dementia prevalence related to socioeconomic and policy factors and lay the groundwork for future causal analyses." > <link rel="image_src" href="https://pub.mdpi-res.com/img/journals/information-logo.png?8600e93ff98dbf14" > <meta name="dc.title" content="Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States"> <meta name="dc.creator" content="Abolfazl Mollalo"> <meta name="dc.creator" content="Sara Knox"> <meta name="dc.creator" content="Jessica Meng"> <meta name="dc.creator" content="Andreana Benitez"> <meta name="dc.creator" content="Leslie A. Lenert"> <meta name="dc.creator" content="Alexander V. Alekseyenko"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="Information 2024, Vol. 15, Page 688"> <meta name="dc.date" content="2024-11-01"> <meta name ="dc.identifier" content="10.3390/info15110688"> <meta name="dc.publisher" content="Multidisciplinary Digital Publishing Institute"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf" > <meta name="dc.language" content="en" > <meta name="dc.description" content="Previous studies indicate that increased healthcare access through Medicaid expansion and alleviation of socioeconomic stressors via higher minimum wages improved health outcomes. This study investigates the spatial relationships between the Medicaid expansion, minimum wage policy, and Alzheimer’s Disease (AD) dementia prevalence across the US. We used county-level AD dementia prevalence adjusted for age, sex, race/ethnicity, and education. Social Vulnerability Index (SVI) data, Medicaid expansion status, and state minimum wage law status were incorporated from CDC, Kaiser Family Foundation, and US Department of Labor sources, respectively. We employed the Getis-Ord Gi* statistic to identify hotspots and cold spots of AD dementia prevalence at the county level. We compared these locations with the overall SVI scores using univariate analyses. We also assessed the proportion of hot and cold spots at the state level based on Medicaid expansion and minimum wage status using the logistic regression model. The most vulnerable SVI quartile (Q4) had the highest number of hotspots (n = 311, 64.8%), while the least vulnerable quartile (Q1) had the fewest hotspots (n = 22, 4.6%) (χ2 = 307.41, p < 0.01). States that adopted Medicaid expansion had a significantly lower proportion of hotspots compared to non-adopting states (p < 0.05), and the non-adopting states had significantly higher odds of having hotspots than adopting states (OR = 2.58, 95% CI: 2.04–3.26, p < 0.001). Conversely, the non-adopting states had significantly lower odds of having cold spots compared to the adopting states (OR = 0.24, 95% CI: 0.19–0.32, p < 0.01). States with minimum wage levels at or below the federal level showed significantly higher odds of having hotspots than states with a minimum wage above the federal level (OR = 1.94, 95% CI: 1.51–2.49, p < 0.01). Our findings suggest significant disparities in AD dementia prevalence related to socioeconomic and policy factors and lay the groundwork for future causal analyses." > <meta name="dc.subject" content="Alzheimer’s disease dementia" > <meta name="dc.subject" content="Medicaid expansion" > <meta name="dc.subject" content="minimum wage policy" > <meta name="dc.subject" content="social vulnerability index" > <meta name="dc.subject" content="spatial analysis" > <meta name ="prism.issn" content="2078-2489"> <meta name ="prism.publicationName" content="Information"> <meta name ="prism.publicationDate" content="2024-11-01"> <meta name ="prism.volume" content="15"> <meta name ="prism.number" content="11"> <meta name ="prism.section" content="Article" > <meta name ="prism.startingPage" content="688" > <meta name="citation_issn" content="2078-2489"> <meta name="citation_journal_title" content="Information"> <meta name="citation_publisher" content="Multidisciplinary Digital Publishing Institute"> <meta name="citation_title" content="Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States"> <meta name="citation_publication_date" content="2024/11"> <meta name="citation_online_date" content="2024/11/01"> <meta name="citation_volume" content="15"> <meta name="citation_issue" content="11"> <meta name="citation_firstpage" content="688"> <meta name="citation_author" content="Mollalo, Abolfazl"> <meta name="citation_author" content="Knox, Sara"> <meta name="citation_author" content="Meng, Jessica"> <meta name="citation_author" content="Benitez, Andreana"> <meta name="citation_author" content="Lenert, Leslie A."> <meta name="citation_author" content="Alekseyenko, Alexander V."> <meta name="citation_doi" content="10.3390/info15110688"> <meta name="citation_id" content="mdpi-info15110688"> <meta name="citation_abstract_html_url" content="https://www.mdpi.com/2078-2489/15/11/688"> <meta name="citation_pdf_url" content="https://www.mdpi.com/2078-2489/15/11/688/pdf?version=1730473986"> <link rel="alternate" type="application/pdf" title="PDF Full-Text" href="https://www.mdpi.com/2078-2489/15/11/688/pdf?version=1730473986"> <meta name="fulltext_pdf" content="https://www.mdpi.com/2078-2489/15/11/688/pdf?version=1730473986"> <meta name="citation_fulltext_html_url" content="https://www.mdpi.com/2078-2489/15/11/688/htm"> <link rel="alternate" type="text/html" title="HTML Full-Text" href="https://www.mdpi.com/2078-2489/15/11/688/htm"> <meta name="fulltext_html" content="https://www.mdpi.com/2078-2489/15/11/688/htm"> <link rel="alternate" type="text/xml" title="XML Full-Text" href="https://www.mdpi.com/2078-2489/15/11/688/xml"> <meta name="fulltext_xml" content="https://www.mdpi.com/2078-2489/15/11/688/xml"> <meta name="citation_xml_url" content="https://www.mdpi.com/2078-2489/15/11/688/xml"> <meta name="twitter:card" content="summary" /> <meta name="twitter:site" content="@MDPIOpenAccess" /> <meta name="twitter:image" content="https://pub.mdpi-res.com/img/journals/information-logo-social.png?8600e93ff98dbf14" /> <meta property="fb:app_id" content="131189377574"/> <meta property="og:site_name" content="MDPI"/> <meta property="og:type" content="article"/> <meta property="og:url" content="https://www.mdpi.com/2078-2489/15/11/688" /> <meta property="og:title" content="Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States" /> <meta property="og:description" content="Previous studies indicate that increased healthcare access through Medicaid expansion and alleviation of socioeconomic stressors via higher minimum wages improved health outcomes. This study investigates the spatial relationships between the Medicaid expansion, minimum wage policy, and Alzheimer’s Disease (AD) dementia prevalence across the US. We used county-level AD dementia prevalence adjusted for age, sex, race/ethnicity, and education. Social Vulnerability Index (SVI) data, Medicaid expansion status, and state minimum wage law status were incorporated from CDC, Kaiser Family Foundation, and US Department of Labor sources, respectively. We employed the Getis-Ord Gi* statistic to identify hotspots and cold spots of AD dementia prevalence at the county level. We compared these locations with the overall SVI scores using univariate analyses. We also assessed the proportion of hot and cold spots at the state level based on Medicaid expansion and minimum wage status using the logistic regression model. The most vulnerable SVI quartile (Q4) had the highest number of hotspots (n = 311, 64.8%), while the least vulnerable quartile (Q1) had the fewest hotspots (n = 22, 4.6%) (χ2 = 307.41, p < 0.01). States that adopted Medicaid expansion had a significantly lower proportion of hotspots compared to non-adopting states (p < 0.05), and the non-adopting states had significantly higher odds of having hotspots than adopting states (OR = 2.58, 95% CI: 2.04–3.26, p < 0.001). Conversely, the non-adopting states had significantly lower odds of having cold spots compared to the adopting states (OR = 0.24, 95% CI: 0.19–0.32, p < 0.01). States with minimum wage levels at or below the federal level showed significantly higher odds of having hotspots than states with a minimum wage above the federal level (OR = 1.94, 95% CI: 1.51–2.49, p < 0.01). Our findings suggest significant disparities in AD dementia prevalence related to socioeconomic and policy factors and lay the groundwork for future causal analyses." /> <meta property="og:image" content="https://pub.mdpi-res.com/information/information-15-00688/article_deploy/html/images/information-15-00688-g001-550.jpg?1730474133" /> <link rel="alternate" type="application/rss+xml" title="MDPI Publishing - Latest articles" href="https://www.mdpi.com/rss"> <meta name="google-site-verification" content="PxTlsg7z2S00aHroktQd57fxygEjMiNHydKn3txhvwY"> <meta name="facebook-domain-verification" content="mcoq8dtq6sb2hf7z29j8w515jjoof7" /> <script id="Cookiebot" data-cfasync="false" src="https://consent.cookiebot.com/uc.js" data-cbid="51491ddd-fe7a-4425-ab39-69c78c55829f" type="text/javascript" async></script>  <script type="text/plain" data-cookieconsent="statistics"> (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); 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</div> </div> </div> </div> <article ><div class='html-article-content'> <span itemprop="publisher" content="Multidisciplinary Digital Publishing Institute"></span><span itemprop="url" content="https://www.mdpi.com/2078-2489/15/11/688"></span> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <h1 class="title hypothesis_container" itemprop="name"> Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States </h1> <div class="art-authors hypothesis_container"> by <span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop13349815' data-options='is_hover:true, hover_timeout:5000'> Abolfazl Mollalo</div><div id="profile-card-drop13349815" data-dropdown-content class="f-dropdown content profile-card-content" 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button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 1,*</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="13349815" href="/cdn-cgi/l/email-protection#c8e7aba6ace5abafa1e7a4e7ada5a9a1a4e5b8baa7bcadabbca1a7a6ebf8f8f8fafeacf8f9f8f9f8abf8f9f8fafaacf8f8f9f0f9adf8adfcfbf8f0f8f1f9f0"><sup><i class="fa fa-envelope-o"></i></sup></a><a href="https://orcid.org/0000-0001-5092-0698" target="_blank" rel="noopener noreferrer"><img src="https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1732615622" title="ORCID" style="position: relative; width: 13px; margin-left: 3px; max-width: 13px !important; height: auto; top: -5px;"></a>, </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop13349816' data-options='is_hover:true, hover_timeout:5000'> Sara Knox</div><div id="profile-card-drop13349816" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Sara Knox</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/3836963?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Sara%20Knox" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Sara%20Knox&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Sara%20Knox" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div></div><sup> 2</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="13349816" href="/cdn-cgi/l/email-protection#fad599949ed7999d93d596d59f979b9396d78a88958e9f998e939594d9cacacacfcc98cacecbc9cbc2ca9bc898cacccb9fcbc2cac2cecfca9fca9ccb9f"><sup><i class="fa fa-envelope-o"></i></sup></a>, </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop13349817' data-options='is_hover:true, hover_timeout:5000'> Jessica Meng</div><div id="profile-card-drop13349817" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img 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Alekseyenko</div><div id="profile-card-drop13349820" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"><div class="profile-card__title"><div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name">Alexander V. Alekseyenko</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/616014?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Alexander%20V.%20Alekseyenko" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Alexander%20V.%20Alekseyenko&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Alexander%20V.%20Alekseyenko" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div><div class="profile-card__biography"><div class="js-profile-card__biography-item js-default-open"> Dr. Alexander V. Alekseyenko is a Professor at the Department of Public Health Sciences, Medical of [...] </div><div class="js-profile-card__biography-item js-default-closed" style="display: none;"> Dr. Alexander V. Alekseyenko is a Professor at the Department of Public Health Sciences, Medical University of South Carolina. He is recognized for his visionary research and education leadership achievements in Biomedical Data Science. He has distinguished himself by a comprehensive portfolio of original quantitative methodology and impactful collaborative applied research on the role of microbes in human health and disease. Dr. Alekseyenko received his PhD training in Biomathematics at the University of California Los Angeles in 2008. A year later, following two short-term postdoctoral experiences in the European Bioinformatics Institute, a branch of European Microbiology Labs, and at Stanford University Department of Statistics, Dr. Alekseyenko has been appointed as an Assistant Professor at the Center for Health Informatics and Bioinformatics at New York University School of Medicine. Recognizing the need to develop new quantitative techniques to support the field, he founded the Microbiome Informatics Lab at NYU. His scientific value was recognized by his promotion to Associate Professor in the tenure-track in 2014. He was recruited by MUSC Biomedical Informatics Center in 2015 and was appointed the Founding Director of the Program for Human Microbiome Research. In 2017, he was appointed the inaugural Graduate Program Director for the Joint Clemson-MUSC PhD training program in Biomedical Data Science and Informatics. </div><div><a href="#" class='js-profile-card__biography-item js-default-open'>Read more</a><a href="#" class='js-profile-card__biography-item js-default-closed' style="display: none;">Read less</a></div></div></div><sup> 1</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="13349820" href="/cdn-cgi/l/email-protection#507f333e347d3337397f3c7f353d31393c7d20223f24353324393f3e73606060346661606460316162606461686064626160336164616260626436606460656164"><sup><i class="fa fa-envelope-o"></i></sup></a><a href="https://orcid.org/0000-0002-5748-2085" target="_blank" rel="noopener noreferrer"><img src="https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1732615622" title="ORCID" style="position: relative; width: 13px; margin-left: 3px; max-width: 13px !important; height: auto; top: -5px;"></a></span> </div> <div class="nrm"></div> <span style="display:block; height:6px;"></span> <div></div> <div style="margin: 5px 0 15px 0;" class="hypothesis_container"> <div class="art-affiliations"> <div class="affiliation "> <div class="affiliation-item"><sup>1</sup></div> <div class="affiliation-name ">Biomedical Informatics Center, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>2</sup></div> <div class="affiliation-name ">Department of Health Sciences and Research, Medical University of South Carolina, Charleston, SC 29425, USA</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>3</sup></div> <div class="affiliation-name ">College of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA</div> </div> <div class="affiliation "> <div class="affiliation-item"><sup>4</sup></div> <div class="affiliation-name ">Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA</div> </div> <div class="affiliation"> <div class="affiliation-item"><sup>*</sup></div> <div class="affiliation-name ">Author to whom correspondence should be addressed. </div> </div> </div> </div> <div class="bib-identity" style="margin-bottom: 10px;"> <em>Information</em> <b>2024</b>, <em>15</em>(11), 688; <a href="https://doi.org/10.3390/info15110688">https://doi.org/10.3390/info15110688</a> </div> <div class="pubhistory" style="font-weight: bold; padding-bottom: 10px;"> <span style="display: inline-block">Submission received: 9 September 2024</span> / <span style="display: inline-block">Revised: 18 October 2024</span> / <span style="display: inline-block">Accepted: 30 October 2024</span> / <span style="display: inline-block">Published: 1 November 2024</span> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/information/special_issues/59Q4KUH379 ">Feature Papers in <em>Information</em> in 2024–2025</a>)<br/> </div> <div class="highlight-box1"> <div class="download"> <a class="button button--color-inversed button--drop-down" data-dropdown="drop-download-1512588" aria-controls="drop-supplementary-1512588" aria-expanded="false"> Download <i class="material-icons">keyboard_arrow_down</i> </a> <div id="drop-download-1512588" class="f-dropdown label__btn__dropdown label__btn__dropdown--button" data-dropdown-content aria-hidden="true" tabindex="-1"> <a class="UD_ArticlePDF" href="/2078-2489/15/11/688/pdf?version=1730473986" data-name="Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States" data-journal="information">Download PDF</a> <br/> <a id="js-pdf-with-cover-access-captcha" href="#" data-target="/2078-2489/15/11/688/pdf-with-cover" class="accessCaptcha">Download PDF with Cover</a> <br/> <a id="js-xml-access-captcha" href="#" data-target="/2078-2489/15/11/688/xml" class="accessCaptcha">Download XML</a> <br/> <a href="/2078-2489/15/11/688/epub" id="epub_link">Download Epub</a> <br/> </div> <div class="js-browse-figures" style="display: inline-block;"> <a href="#" class="button button--color-inversed margin-bottom-10 openpopupgallery UI_BrowseArticleFigures" data-target='article-popup' data-counterslink = "https://www.mdpi.com/2078-2489/15/11/688/browse" >Browse Figures</a> </div> <div id="article-popup" class="popupgallery" style="display: inline; line-height: 200%"> <a href="https://pub.mdpi-res.com/information/information-15-00688/article_deploy/html/images/information-15-00688-g001.png?1730474132" title=" <strong>Figure 1</strong><br/> <p>Geospatial distribution of (<b>A</b>) hot spots, cold spots, and non-significant areas of AD dementia prevalence identified by Getis-Ord Gi* across the US at various confidence levels, (<b>B</b>) the minimum wage policy at or below federal level, and (<b>C</b>) Medicaid expansion adoption.</p> "> </a> <a href="https://pub.mdpi-res.com/information/information-15-00688/article_deploy/html/images/information-15-00688-g002.png?1730474133" title=" <strong>Figure 2</strong><br/> <p>The frequency of hot spot and cold spot counties of AD dementia prevalence in the US, classified by SVI quartiles.</p> "> </a> <a href="https://pub.mdpi-res.com/information/information-15-00688/article_deploy/html/images/information-15-00688-g003.png?1730474134" title=" <strong>Figure 3</strong><br/> <p>Boxplots showing the proportion of (<b>A</b>) hot spot and (<b>B</b>) cold spot counties per state, categorized by Medicaid expansion status.</p> "> </a> <a href="https://pub.mdpi-res.com/information/information-15-00688/article_deploy/html/images/information-15-00688-g004.png?1730474134" title=" <strong>Figure 4</strong><br/> <p>Boxplots showing the proportion of (<b>A</b>) hot spot and (<b>B</b>) cold spot counties per state, categorized by the minimum wage policy.</p> "> </a> </div> <a class="button button--color-inversed" href="/2078-2489/15/11/688/notes">Versions Notes</a> </div> </div> <div class="responsive-moving-container small hidden" data-id="article-counters" style="margin-top: 15px;"></div> <div class="html-dynamic"> <section> <div class="art-abstract art-abstract-new in-tab hypothesis_container"> <p> <div><section class="html-abstract" id="html-abstract"> <h2 id="html-abstract-title">Abstract</h2><b>:</b> <div class="html-p">Previous studies indicate that increased healthcare access through Medicaid expansion and alleviation of socioeconomic stressors via higher minimum wages improved health outcomes. This study investigates the spatial relationships between the Medicaid expansion, minimum wage policy, and Alzheimer’s Disease (AD) dementia prevalence across the US. We used county-level AD dementia prevalence adjusted for age, sex, race/ethnicity, and education. Social Vulnerability Index (SVI) data, Medicaid expansion status, and state minimum wage law status were incorporated from CDC, Kaiser Family Foundation, and US Department of Labor sources, respectively. We employed the Getis-Ord Gi* statistic to identify hotspots and cold spots of AD dementia prevalence at the county level. We compared these locations with the overall SVI scores using univariate analyses. We also assessed the proportion of hot and cold spots at the state level based on Medicaid expansion and minimum wage status using the logistic regression model. The most vulnerable SVI quartile (Q4) had the highest number of hotspots (n = 311, 64.8%), while the least vulnerable quartile (Q1) had the fewest hotspots (n = 22, 4.6%) (<math display="inline"><semantics> <mrow> <mi>χ</mi> </mrow> </semantics></math><sup>2</sup> = 307.41, <span class="html-italic">p</span> < 0.01). States that adopted Medicaid expansion had a significantly lower proportion of hotspots compared to non-adopting states (<span class="html-italic">p</span> < 0.05), and the non-adopting states had significantly higher odds of having hotspots than adopting states (OR = 2.58, 95% CI: 2.04–3.26, <span class="html-italic">p</span> < 0.001). Conversely, the non-adopting states had significantly lower odds of having cold spots compared to the adopting states (OR = 0.24, 95% CI: 0.19–0.32, <span class="html-italic">p</span> < 0.01). States with minimum wage levels at or below the federal level showed significantly higher odds of having hotspots than states with a minimum wage above the federal level (OR = 1.94, 95% CI: 1.51–2.49, <span class="html-italic">p</span> < 0.01). Our findings suggest significant disparities in AD dementia prevalence related to socioeconomic and policy factors and lay the groundwork for future causal analyses.</div> </section> <div id="html-keywords"> <div class="html-gwd-group"><div id="html-keywords-title">Keywords: </div><a href="/search?q=Alzheimer%E2%80%99s+disease+dementia">Alzheimer’s disease dementia</a>; <a href="/search?q=Medicaid+expansion">Medicaid expansion</a>; <a href="/search?q=minimum+wage+policy">minimum wage policy</a>; <a href="/search?q=social+vulnerability+index">social vulnerability index</a>; <a href="/search?q=spatial+analysis">spatial analysis</a></div> <div> </div> </div> </div> </p> </div> </section> </div> <div class="hypothesis_container"> <ul class="menu html-nav" data-prev-node="#html-quick-links-title"> </ul> <div class="html-body"> <section id='sec1-information-15-00688' type='intro'><h2 data-nested='1'> 1. Introduction</h2><div class='html-p'>Alzheimer’s disease (AD) dementia, an escalating public health concern, affected approximately 5.6 million US residents in 2019 [<a href="#B1-information-15-00688" class="html-bibr">1</a>]. With the aging of the baby boomer generation, this number is projected to increase significantly to 13.8 million by 2050 [<a href="#B2-information-15-00688" class="html-bibr">2</a>], highlighting the urgent need to improve healthcare access and address disparities. In response, various policies have been enacted to improve health coverage and care for underserved populations. Two such policies in the US are Medicaid and Medicare programs. Medicaid, a program jointly funded by federal and state governments, provides healthcare coverage to individuals and families with limited income or resources. Medicare, funded by the federal government, primarily offers health coverage to individuals aged 65 and older as well as certain younger people with disabilities, regardless of income [<a href="#B3-information-15-00688" class="html-bibr">3</a>].</div><div class='html-p'>The Affordable Care Act (ACA) expanded Medicaid eligibility to include all US residents with incomes up to 138% of the federal poverty line; however, each state can choose whether to adopt expansion [<a href="#B4-information-15-00688" class="html-bibr">4</a>]. As of 14 July 2023, 40 states and Washington DC have implemented this expansion [<a href="#B5-information-15-00688" class="html-bibr">5</a>]. Following this implementation, numerous studies investigated the impacts of Medicaid expansion on healthcare access and clinical outcomes. Findings consistently indicate that the expansion has led to lower uninsurance rates [<a href="#B6-information-15-00688" class="html-bibr">6</a>,<a href="#B7-information-15-00688" class="html-bibr">7</a>], increased utilization of primary care and preventive services [<a href="#B8-information-15-00688" class="html-bibr">8</a>], decreased inpatient hospitalization rates [<a href="#B9-information-15-00688" class="html-bibr">9</a>], and reduced all-cause mortality [<a href="#B10-information-15-00688" class="html-bibr">10</a>].</div><div class='html-p'>Socioeconomic deprivation and neighborhood disadvantage are associated with an increased risk of dementia [<a href="#B11-information-15-00688" class="html-bibr">11</a>,<a href="#B12-information-15-00688" class="html-bibr">12</a>]. Thus, it is essential to consider how US governmental policies that are linked to socioeconomics, such as the minimum wage, may influence the risk of dementia. Since 2009, the federal minimum wage has been set at USD 7.25 per hour. However, as of July 2024, 34 states have enacted higher minimum wages, with some, such as California, reaching up to USD 16 per hour [<a href="#B13-information-15-00688" class="html-bibr">13</a>]. Research on the impacts of minimum wage policies on health outcomes is limited and varies in results. For instance, increases in minimum wage have been associated with modest decreases in heart disease mortality [<a href="#B14-information-15-00688" class="html-bibr">14</a>] and improved healthcare access among White men, Black women, and Latina women [<a href="#B15-information-15-00688" class="html-bibr">15</a>]. Additionally, while minimum wage increases have been found to improve women’s health, they may adversely affect men’s health [<a href="#B16-information-15-00688" class="html-bibr">16</a>]. States with stagnant minimum wages experienced some of the poorest health outcomes, including higher rates of obesity and low birth rates [<a href="#B17-information-15-00688" class="html-bibr">17</a>]. Notably, prior research has shown that cumulative exposure to low wages in midlife is associated with faster memory decline in later life [<a href="#B18-information-15-00688" class="html-bibr">18</a>]. These findings underscore the critical need to consider socioeconomic factors in health policies. In this context, the Social Vulnerability Index (SVI) can be a valuable tool for assessing the level of disadvantage within neighborhoods [<a href="#B19-information-15-00688" class="html-bibr">19</a>].</div><div class='html-p'>Geospatial analysis has provided a robust framework for investigating the spatial relationship of Medicaid adoption or minimum wage policies with various health outcomes, such as cancer [<a href="#B20-information-15-00688" class="html-bibr">20</a>], sexually transmitted infections [<a href="#B21-information-15-00688" class="html-bibr">21</a>], and access to healthcare [<a href="#B22-information-15-00688" class="html-bibr">22</a>,<a href="#B23-information-15-00688" class="html-bibr">23</a>]. Jemal et al. (2017) examined changes in uninsured rates and early-stage cancer diagnoses among nonelderly patients following the ACA implementation. Their results indicated a notable decrease in uninsured rates among newly diagnosed cancer patients across all income brackets in Medicaid expansion and non-expansion states. However, the most significant decrease was observed among low-income patients in expansion states compared to non-expansion states [<a href="#B24-information-15-00688" class="html-bibr">24</a>]. In a recent study, Li et al. (2024) utilized inpatient claims data to explore spatiotemporal patterns of hospitalizations involving comorbid cancer and dementia. They identified 22 hotspots and found that the likelihood of a county being a hotspot significantly increased with the percentage of dual Medicare/Medicaid beneficiaries while decreasing with higher rurality [<a href="#B25-information-15-00688" class="html-bibr">25</a>]. The impact of minimum wage on health studies remains a relatively unexplored area of research [<a href="#B26-information-15-00688" class="html-bibr">26</a>] and is rarely assessed from a geospatial perspective. Rath et al. (2022) examined the effects of implementing a minimum wage in Hong Kong on suicide rates and found that the introduction of the minimum wage led to an immediate 13.0% reduction in monthly suicide rates (<span class='html-italic'>p</span> < 0.05). Notably, there was a 15.8% immediate decline in suicide rates among older working-aged men (<span class='html-italic'>p</span> < 0.05) [<a href="#B27-information-15-00688" class="html-bibr">27</a>].</div><div class='html-p'>In spatial epidemiology, hotspot analysis refers to methods that pinpoint significant clusters of high or low health outcome rates by assessing each area in relation to its neighbors and the overall area of study. An area is classified as a statistically significant hot spot of a disease if it shows a high disease rate and is surrounded by other high-rate areas. Conversely, statistically significant cold spots of a disease are regions with significantly lower disease rates compared to their surroundings. Identifying both hot and cold spots is crucial for understanding spatial patterns for public health interventions and effective service enhancements [<a href="#B28-information-15-00688" class="html-bibr">28</a>]. Spatial hotspot analysis was utilized in relation to various health policy interventions. For example, Dong et al. (2022) examined the variations of clusters of advanced-stage breast cancer diagnoses in Ohio before and after Medicaid expansion using space-time scan statistics [<a href="#B20-information-15-00688" class="html-bibr">20</a>]. Dawit et al. (2022) used Getis-Ord Gi* to analyze spatial variations in pre-exposure prophylaxis prescription rates across the Ending the HIV Epidemic (EHE) initiative and non-EHE counties in the US [<a href="#B29-information-15-00688" class="html-bibr">29</a>]. Feng et al. (2023) used Getis-Ord Gi* and local indicator of spatial autocorrelation methods to investigate the factors influencing the adoption of tobacco policy clusters across 351 municipalities in Massachusetts [<a href="#B30-information-15-00688" class="html-bibr">30</a>].</div><div class='html-p'>Our research is motivated by the hypothesis that state-level socioeconomic policies, such as Medicaid expansion and minimum wage, are significantly associated with the distribution of AD dementia. In this study, we identify hot spots and cold spots of AD dementia prevalence across the US and overlay them with state-level Medicaid expansion and minimum wage policy status. Our main objectives are: (1) to assess the association between the counties identified as hot spots and cold spots and the state’s Medicaid expansion status, (2) to evaluate the association between the counties identified as hot spots and cold spots and the state’s minimum wage policy, and (3) to determine whether there are significant differences in the counts of hotspots and cold spots across SVI quartiles. The findings can provide valuable insights for policymakers regarding financial policies, as well as community resilience and vulnerabilities in addressing disparities in AD dementia in the US.</div></section><section id='sec2-information-15-00688' type=''><h2 data-nested='1'> 2. Materials and Methods</h2><section id='sec2dot1-information-15-00688' type=''><h4 class='html-italic' data-nested='2'> 2.1. Data</h4><div class='html-p'>The AD dementia prevalence data were obtained from the Dhana et al. (2023) article, which provides first-ever county-level estimates across the US for adults aged 65 and older [<a href="#B31-information-15-00688" class="html-bibr">31</a>]. The estimated prevalence was adjusted for age, sex, race/ethnicity, and years of formal schooling [<a href="#B31-information-15-00688" class="html-bibr">31</a>]. County-level overall SVI data were sourced from the State Cancer Profiles, which draw upon data provided by the Centers for Disease Control and Prevention (CDC). This index measures the resilience of communities when confronted by external stresses on human health [<a href="#B32-information-15-00688" class="html-bibr">32</a>]. SVI values, a composite score derived from 16 unique census variables, ranged from 0 to 1, with lower values representing lower vulnerability and higher values indicating higher vulnerability [<a href="#B19-information-15-00688" class="html-bibr">19</a>]. Information on state Medicaid expansion status under the ACA as of April 2019 was obtained from the Kaiser Family Foundation [<a href="#B5-information-15-00688" class="html-bibr">5</a>]. Data on state minimum wage laws were retrieved from the US Department of Labor [<a href="#B33-information-15-00688" class="html-bibr">33</a>] and the Economic Policy Institute [<a href="#B34-information-15-00688" class="html-bibr">34</a>]. Both minimum wage and Medicaid expansion policies were operationalized as binary variables in our analysis. Minimum wage was coded as 1 for states with a minimum wage above the federal level and 0 for those at or below that level. Similarly, Medicaid expansion was coded as 1 for states that expanded Medicaid under the ACA and 0 for those that did not.</div></section><section id='sec2dot2-information-15-00688' type=''><h4 class='html-italic' data-nested='2'> 2.2. Spatial Analysis</h4><div class='html-p'>We assessed the global spatial pattern of AD dementia prevalence across the US using the Getis-Ord General G statistic under the null hypothesis of complete spatial randomness [<a href="#B35-information-15-00688" class="html-bibr">35</a>]. The alternative hypothesis states the distribution is clustered in some areas. A higher-than-expected General G with a positive Z-score indicates a clustering of high values. In contrast, a lower-than-expected General G with a negative Z-score indicates an overall tendency to cluster low values [<a href="#B35-information-15-00688" class="html-bibr">35</a>]. The General G statistic is defined as:<div class='html-disp-formula-info' id='FD1-information-15-00688'> <div class='f'> <math display='block'><semantics> <mrow> <mi>G</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mrow> <msubsup> <mo stretchy="false">∑</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msubsup> <mrow> <mrow> <msubsup> <mo stretchy="false">∑</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msubsup> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </mrow> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>j</mi> </mrow> </msub> </mrow> </mrow> </mrow> <mrow> <mrow> <msubsup> <mo stretchy="false">∑</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msubsup> <mrow> <mrow> <msubsup> <mo stretchy="false">∑</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msubsup> <mrow> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </mrow> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>j</mi> </mrow> </msub> </mrow> </mrow> </mrow> </mfrac> </mstyle> <mo>,</mo> <mo> </mo> <mo> </mo> <mo>∀</mo> <mi>j</mi> <mo>≠</mo> <mi>i</mi> </mrow> </semantics></math> </div> <div class='l'> <label >(1)</label> </div> </div> where <math display='inline'><semantics> <mrow> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> and <math display='inline'><semantics> <mrow> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>j</mi> </mrow> </msub> </mrow> </semantics></math> represent AD dementia prevalence rates for counties <math display='inline'><semantics> <mrow> <mi>i</mi> </mrow> </semantics></math> and <math display='inline'><semantics> <mrow> <mi>j</mi> </mrow> </semantics></math>, respectively; <math display='inline'><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </semantics></math> is the spatial weight between counties <math display='inline'><semantics> <mrow> <mi>i</mi> </mrow> </semantics></math> and <math display='inline'><semantics> <mrow> <mi>j</mi> </mrow> </semantics></math> based on the first-order Queen’s contiguity weight matrix (i.e., neighbors if they share a border or vertex); <math display='inline'><semantics> <mrow> <mi>n</mi> </mrow> </semantics></math> is the total number of counties; and <math display='inline'><semantics> <mrow> <mo>∀</mo> <mi>j</mi> <mo>≠</mo> <mi>i</mi> </mrow> </semantics></math> ensures the statistic does not consider self-pairs.</div><div class='html-p'>The Getis-Ord General G is a global measure of spatial autocorrelation and cannot identify the location of hot or cold spots [<a href="#B36-information-15-00688" class="html-bibr">36</a>]. Thus, we employed Getis-Ord Gi* statistics to identify local clustering of high (hot spots) or low (cold spots) values of AD dementia prevalence across the US [<a href="#B35-information-15-00688" class="html-bibr">35</a>]. Using the same notations as Equation (1), the Getis-Ord Gi* statistic for each county is calculated as:<div class='html-disp-formula-info' id='FD2-information-15-00688'> <div class='f'> <math display='block'><semantics> <mrow> <msubsup> <mrow> <mi>G</mi> </mrow> <mrow> <mi>i</mi> </mrow> <mrow> <mo>*</mo> </mrow> </msubsup> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mrow> <msubsup> <mo stretchy="false">∑</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msubsup> <mrow> <msub> <mrow> <mi>w</mi> </mrow> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>j</mi> </mrow> </msub> <mo>−</mo> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo stretchy="false">¯</mo> </mover> <mrow> <msubsup> <mo stretchy="false">∑</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msubsup> <mrow> <msub> <mrow> <mi>w</mi> </mrow> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </mrow> </mrow> </mrow> </mrow> <mrow> <mi>S</mi> <msqrt> <mfrac> <mrow> <mo stretchy="false">[</mo> <mi>n</mi> <mrow> <msubsup> <mo stretchy="false">∑</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msubsup> <mrow> <msubsup> <mrow> <mi>w</mi> </mrow> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msubsup> <mo>−</mo> <msup> <mrow> <mfenced separators="|"> <mrow> <mrow> <msubsup> <mo stretchy="false">∑</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msubsup> <mrow> <msub> <mrow> <mi>w</mi> </mrow> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </mrow> </mrow> </mfenced> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo stretchy="false">]</mo> </mrow> </mrow> </mrow> <mrow> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </mfrac> </msqrt> </mrow> </mfrac> </mstyle> </mrow> </semantics></math> </div> <div class='l'> <label >(2)</label> </div> </div> where <math display='inline'><semantics> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo stretchy="false">¯</mo> </mover> </mrow> </semantics></math> and <math display='inline'><semantics> <mrow> <mi>S</mi> </mrow> </semantics></math> represent the mean and standard deviation of AD dementia prevalence across the US, respectively. A significant positive Gi* value above the expected indicates a hot spot, while a negative significant value below the expected indicates a cold spot. We conducted the Getis-Ord analyses using ArcGIS Pro 3.3 (ESRI, Redlands, CA, USA) and mapped the outputs with the same software.</div></section><section id='sec2dot3-information-15-00688' type=''><h4 class='html-italic' data-nested='2'> 2.3. Univariate Analysis</h4><div class='html-p'>Based on the Getis-Ord Gi* statistics results, we classified US counties into hot spots, cold spots, and non-significant areas of AD dementia prevalence. The SVI scores were also categorized into quartiles from least vulnerable (Q1) to most vulnerable (Q4). We counted and plotted the number of hot and cold spots that fall into each SVI quartile. Subsequently, we conducted a Chi-square test to assess whether significant differences exist in the counts of hot and cold spots across SVI quartiles. The null hypothesis is that the counts of hot and cold spots are independent of SVI quartile classification, while the alternative hypothesis is that they vary across SVI quartiles. Since the Medicaid adoption expansion is decided at the state level [<a href="#B37-information-15-00688" class="html-bibr">37</a>], we initially calculated the proportion of counties identified as hot and cold spots per state. Subsequently, we used boxplots to visually compare these proportions categorized by the state’s Medicaid adoption expansion status (adopted vs. not adopted) across the US. We repeated this process for the minimum wage policy and compared these proportions, which were classified by whether the state had a higher minimum wage than the federal level. Mann–Whitney U and Levene’s tests were employed to statistically compare the mean differences and assess the homogeneity of variance between the groups. The null hypothesis states that the median proportion of counties identified as hot and cold spots does not differ by Medicaid expansion or minimum wage status, with differences implied under the alternative hypothesis.</div></section><section id='sec2dot4-information-15-00688' type=''><h4 class='html-italic' data-nested='2'> 2.4. Multivariate Analysis</h4><div class='html-p'>We conducted logistic regression analysis on the proportion of hot spot or cold spot counties in each state as the dependent variable. The independent variables were two categorical variables: (1) Medicaid expansion status (adopted vs. non-adopted) and (2) Minimum wage status (above federal level or not). The null hypothesis assumes no association between these policies and the likelihood of counties being hot or cold spots, while the alternative suggests an association exists. All statistical analyses were performed in RStudio 2023.06.0 (R Foundation for Statistical Computing, Vienna, Austria).</div></section></section><section id='sec3-information-15-00688' type='results'><h2 data-nested='1'> 3. Results</h2><div class='html-p'>The results of the Getis-Ord General G test suggest that the spatial distribution of AD dementia prevalence in the US is more clustered than expected under random spatial processes. The observed General G (0.0322) exceeded the expected General G (0.0319), with a significant <span class='html-italic'>Z</span>-score (15.77) and a small <span class='html-italic'>p</span>-value (<span class='html-italic'>p</span> < 0.001), indicating a robust spatial autocorrelation and significant clustering of high values. The Getis-Ord Gi* statistics further identified hot spots and cold spots of AD dementia prevalence at 90%, 95%, and 99% confidence levels. The hot spots were primarily located in the Deep South states, with approximately 15.5% of US counties (n = 480 out of 3105) (<span class='html-italic'>p</span> < 0.05). In contrast, the cold spots were mainly found in western and northeastern states, with additional counties in Kentucky, Alaska, northern Michigan, and northern Wisconsin. Approximately 14.8% of US counties (n = 460 out of 3105) were identified as cold spots (<span class='html-italic'>p</span> < 0.05). However, most counties in central and northern states were neither hot spots nor cold spots. <a href="#information-15-00688-f001" class="html-fig">Figure 1</a> illustrates the spatial distribution of hot spots, cold spots, and non-significant areas of AD dementia prevalence in the US.</div><div class='html-p'>A comparison of SVI quartiles with the location of hot spot counties revealed that the most vulnerable SVI quartile (Q4) had the highest number of hot spots (n = 311, 64.8% of all hot spots), followed by Q3 (n = 101, 21.0% of all hot spots), and Q2 (n = 46, 9.6% of all hot spots). The least vulnerable SVI quartile (Q1) had the fewest hot spots (n = 22, 4.6% of all hot spots). Conversely, the comparison of SVI quartiles with the location of cold spots indicated that the most vulnerable SVI quartile (Q4) had the fewest cold spots (n = 54, 11.7% of all cold spots). The highest number of cold spots were in Q2 (n = 150, 32.6%), followed by Q3 (n = 138, 30.0%) and Q1 (n = 118, 25.7%). Moreover, the difference was statistically significant (<math display='inline'><semantics> <mrow> <mi>χ</mi> </mrow> </semantics></math><sup>2</sup> = 307.41, df = 3, <span class='html-italic'>p</span> < 0.01). <a href="#information-15-00688-f002" class="html-fig">Figure 2</a> illustrates the frequency of hot spots and cold spots of AD dementia prevalence in the US, classified by SVI quartiles.</div><div class='html-p'>The comparison of hot spot proportion per state with Medicaid expansion status showed that the states that adopted the Medicaid expansion had a lower proportion of hot spots than non-adopting states. Mann–Whitney U test showed that this difference was significant (<span class='html-italic'>p</span> < 0.05). Additionally, the adopting states exhibited smaller variability (more consistent pattern) in the proportion of hot spots than non-adopting (<a href="#information-15-00688-f003" class="html-fig">Figure 3</a>A). Levene’s test indicated unequal variances between these two groups (<span class='html-italic'>F</span> = 5.6, <span class='html-italic'>p</span> < 0.05). In contrast, when comparing the proportion of cold spots per state, adopting states had a higher proportion of cold spots than those that did not; however, the difference was not statistically significant (<span class='html-italic'>p</span> > 0.05). The adopting states demonstrated greater variability in the proportion of cold spots (<a href="#information-15-00688-f003" class="html-fig">Figure 3</a>B), and the difference was significant at a 90% confidence level (<span class='html-italic'>F</span> = 3.0, <span class='html-italic'>p</span> < 0.1). <a href="#information-15-00688-t001" class="html-table">Table 1</a> summarizes the statistics on the proportion of hot and cold spots per state according to Medicaid expansion status.</div><div class='html-p'>The comparison of hot spots proportion per state with the minimum wage policy suggests that the states with a higher minimum wage than the federal level had a lower proportion of hot spots than those at or below minimum wage than the federal level. However, the Mann–Whitney U test showed that this difference was insignificant (<span class='html-italic'>p</span> > 0.05). Additionally, the higher minimum wage states exhibited smaller variability in the proportion of hot spots (<a href="#information-15-00688-f004" class="html-fig">Figure 4</a>A). Levene’s test indicated unequal variances between these two groups at a 90% confidence level (<span class='html-italic'>F</span> = 3.2, <span class='html-italic'>p</span> < 0.1). In contrast, when comparing the proportion of cold spots per state, higher minimum wage states showed almost a similar proportion of cold spots than at or below the minimum wage states. The mean and variance differences were not significantly different (<span class='html-italic'>p</span> > 0.05). <a href="#information-15-00688-t002" class="html-table">Table 2</a> summarizes the statistics on the proportion of hot and cold spots per state according to the minimum wage policy.</div><div class='html-p'>Logistic regression was performed to examine the association between the count of hot spots vs. non-hot spots counties per state, considering Medicaid adoption status and minimum wage policy as predictors. <a href="#information-15-00688-t003" class="html-table">Table 3</a> presents the results of the logistic regression model. The non-adopting states had significantly higher odds of having hot spots compared to the adopting states (OR = 2.58, 95% CI: 2.04–3.26, <span class='html-italic'>p</span> < 0.001). Additionally, the lower minimum wage states showed significantly higher odds of having hot spots of AD dementia prevalence than higher minimum wage states (OR = 1.94, 95% CI: 1.51–2.49, <span class='html-italic'>p</span> < 0.01). Similarly, a logistic regression model was fitted to examine the relationship between the count of cold spots vs. non-cold spots counties per state, considering Medicaid adoption status and minimum wage policy as predictors. The non-adopting states had significantly lower odds of having cold spots than the adopting states (OR = 0.24, 95% CI: 0.19–0.32, <span class='html-italic'>p</span> < 0.01). However, the lower minimum wage states exhibited significantly higher odds of cold spots than higher minimum wage states (OR = 1.45, 95% CI: 1.16–1.81, <span class='html-italic'>p</span> < 0.05).</div><div class='html-p'><a href="#information-15-00688-t004" class="html-table">Table 4</a> presents various metrics used to evaluate the goodness-of-fit of logistic regression models for hot spots and cold spots. Hot spots have a significantly lower residual deviance compared to cold spots, suggesting the model for hot spots fits the data better. The hot spots model has a much lower Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), indicating it provides a more parsimonious fit than the cold spots model. Moreover, the hot spots model has a higher log-likelihood, reinforcing that it fits the data better. Nagelkerke R<sup>2</sup> adjusts McFadden’s Pseudo R<sup>2</sup> to provide a value between 0 and 1. Both models have high Nagelkerke R<sup>2</sup> values, indicating that they explain a substantial amount of the variance in the response variable. However, the hot spots model has a slightly higher value, suggesting it better explains the variability in hot spots compared to cold spots.</div></section><section id='sec4-information-15-00688' type='discussion'><h2 data-nested='1'> 4. Discussion</h2><div class='html-p'>To support the National Plan to Address Alzheimer’s Disease goals [<a href="#B38-information-15-00688" class="html-bibr">38</a>], our study aimed to provide geospatial insights into the nationwide distribution of AD dementia prevalence in relation to Medicaid expansion, minimum wage policies, and overall SVI scores. Based on our findings, a higher proportion of hotspots was identified in states that did not adopt Medicaid expansion, had minimum wage policies at or below the federal level, were in the Deep South, and were in the most vulnerable SVI quartiles. Conversely, cold spots were predominantly found in states that adopted Medicaid expansion, had minimum wage policies above the federal level, were in Western and Northeastern regions, and fell into the least vulnerable SVI quartiles. These findings provide geospatial evidence into the relationship between governmental policies and social determinants of health with the prevalence of AD dementia in the US.</div><div class='html-p'>Previous geospatial studies of AD across the US have identified different hotspot locations. For instance, Xu et al. (2018) used individual death certificate data and space–time scan statistics to identify hotspots of AD and all-cause dementia mortality from 2000 to 2010, finding that hotspots were mainly located in the Carolinas, the Ohio River Valley, and the Pacific Northwest [<a href="#B39-information-15-00688" class="html-bibr">39</a>]. In another study, Amin et al. (2018) used the CDC Wonder Multiple Cause of Death database and spatial scan statistics, identifying age-adjusted AD mortality hotspots from 2008 to 2012 with hotspots primarily in Washington, Iowa, and the Dakotas [<a href="#B40-information-15-00688" class="html-bibr">40</a>]. More recently, Li et al. (2024) used inpatient claims data for Medicare fee-for-service beneficiaries and space–time scan statistics to identify the hotspots of comorbid cancer and dementia from 2013 to 2018, locating them in the Middle and South Atlantic regions and the Midwestern US [<a href="#B25-information-15-00688" class="html-bibr">25</a>]. The variations in hotspot locations across these studies may be attributed to differences in data sources, the type of data (prevalence vs. mortality), the method used, and the study periods. However, the data sources in our study utilized the first-ever county-level estimates of AD dementia prevalence in the US. The data providers emphasized the distinct advantages of their estimates over conventional methods, which often rely on medical reports, insurance claims, and national surveys. These traditional methods frequently underestimate prevalence due to data discrepancies and systemic diagnostic biases, particularly affecting racial and ethnic minority groups that was mainly addressed in the data used in our study [<a href="#B31-information-15-00688" class="html-bibr">31</a>].</div><div class='html-p'>When considering governmental policies that address care coverage, the Medicaid expansion program is critically important for people living with AD dementia. As AD dementia progresses, individuals increasingly rely on paid care assistance for long-term support services, which are not covered by Medicare. This reliance results in high out-of-pocket expenses that low-income individuals cannot afford [<a href="#B41-information-15-00688" class="html-bibr">41</a>]. However, Medicaid does cover long-term support services [<a href="#B42-information-15-00688" class="html-bibr">42</a>], making it a crucial resource for those in need. People living with AD dementia frequently qualify for Medicaid based on income and disability, and some states have extended eligibility through pathways targeting people with disabilities or long-term support service needs. Despite these provisions, not all low-income people living with AD dementia qualify for Medicaid, leaving many struggling to secure the necessary assistance. This gap in coverage makes access to adequate long-term support services more essential, as these services help to prevent adverse outcomes related to safety (falls, wandering, harm), nutrition (forgetting to eat, aspiration), self-care (poor hygiene leading to secondary infections), and pain (decreased mobility, contractures, pressure sores) [<a href="#B43-information-15-00688" class="html-bibr">43</a>]. Conversely, areas with better access to care often benefit from enhanced preventative and early intervention services that can manage or delay the progression of symptoms, thereby reducing the number of AD diagnoses. Comprehensive care settings might result in diagnosing other conditions with similar symptoms, potentially influencing the diagnostic rates.</div><div class='html-p'>The relationship between higher minimum wages and increased AD prevalence may be linked to underlying socioeconomic factors rather than the wage level itself. In states that have not adopted the Medicaid expansion, a significant proportion of low-income individuals living with AD dementia likely do not qualify for Medicaid [<a href="#B44-information-15-00688" class="html-bibr">44</a>]. Our findings suggest that AD dementia prevalence is prevalent in areas that have limited access to Medicaid, lower minimum wages, and higher social vulnerability. This suggests that a significant percentage of low-income individuals with AD dementia in these areas may not have access to the long-term support services they need. States may have programs outside of Medicaid to support the long-term care needs of people living with AD dementia. Future studies should explore these alternative programs and assess the extent of service access issues in these states.</div><div class='html-p'>Unlike the recent Medicaid expansion program, the government has established a minimum wage as a long-standing policy. Prior research has established that prolonged exposure to low minimum wage [<a href="#B18-information-15-00688" class="html-bibr">18</a>] and lower household income [<a href="#B45-information-15-00688" class="html-bibr">45</a>,<a href="#B46-information-15-00688" class="html-bibr">46</a>] is associated with an increased risk of AD dementia; our geospatial analysis supports this relationship. However, the causal relationship between these factors is still unclear. The relationship is likely mediated by several factors. Low-wage jobs are associated with factors known to increase the risk for AD dementia, including toxic exposures, risk of injuries and trauma [<a href="#B47-information-15-00688" class="html-bibr">47</a>], obesity [<a href="#B48-information-15-00688" class="html-bibr">48</a>], and factors associated with low cognitive reserve (e.g., depression, low cognitive stimulation, limited diversity in lived experiences, and high stress) [<a href="#B49-information-15-00688" class="html-bibr">49</a>,<a href="#B50-information-15-00688" class="html-bibr">50</a>]. It is also noteworthy that minimum wage laws often correlate with the cost of living; higher living costs typically prompt states or localities to set higher minimum wages. Further causal analyses are needed to explore the direct impact of these factors on the geospatial pattern observed for AD dementia prevalence.</div><div class='html-p'>We observed several insignificant findings, particularly concerning the prevalence of cold spots in Medicaid expansion states or those with minimum wages at or below the federal threshold. These results bear key implications for understanding the complex relationship between these policies and AD dementia prevalence. Insignificant findings suggest that there may not be a straightforward or uniform relationship between these policies and the geospatial clustering of AD dementia prevalence. This raises the possibility that other factors—such as environmental conditions, healthcare accessibility, local health policies, or cultural attitudes—might also play a substantial role in certain regions. Such observations prompt us to delve deeper into the underlying mechanisms that might elucidate these discrepancies. For instance, some areas may have varying levels of healthcare service utilization, different demographic profiles, or unique social determinants of health that are not fully captured in our analysis. Additionally, these findings may highlight the potential for disparities in the implementation and effectiveness of these policies across different states. Future studies should also conduct causal analysis to uncover the underlying mechanisms through which the policies might exert their impact.</div><div class='html-p'>This study utilized the first-ever county-level estimates of AD dementia prevalence in the US obtained from [<a href="#B31-information-15-00688" class="html-bibr">31</a>]. The data providers highlighted the advantages of their estimates over conventional methods, which often rely on medical reports, insurance claims, and national surveys that may underestimate prevalence due to data discrepancies and systemic diagnostic biases, especially among racial and ethnic minority groups. To address these issues, they employed a multi-step approach, incorporating demographic and cognitive data from a population-based study while adjusting for key variables such as age, sex, race/ethnicity, and education. However, their estimates primarily focus on major racial/ethnic groups (Black/African American, Hispanic, and White) and assumed similar risks for Asian American and American Indian/Alaska Native individuals, which may not accurately reflect their unique risk profiles [<a href="#B31-information-15-00688" class="html-bibr">31</a>]. Additionally, the potential for underreporting and bias persists, particularly in rural or underserved counties. Variability in data collection methods and reporting practices across states could also adversely impact the reliability of county-level estimates, further complicating the identification of true hotspots and cold spots.</div><div class='html-p'>Several limitations should be noted. The cross-sectional nature of this study and the lack of longitudinal data preclude establishing causality. Further, there are lags between policy adoption and implementation in certain states, which can bias the attribution of observed patterns to those policies. Moreover, other influential contextual factors, such as healthcare access and the cultural and political leanings of the states, were not explored, potentially confounding or modifying the observed relationships. Additionally, different specifications of hot/cold spot detection techniques may yield varying results, highlighting the need for conducting further robust sensitivity analyses.</div><div class='html-p'>Based on our findings, several policy implications emerge. Expanding Medicaid coverage in states that have not yet adopted this program could improve healthcare access for vulnerable populations by enabling earlier diagnosis and management. Additionally, raising the minimum wage above the federal level could alleviate financial stress, improve access to nutritious food, and foster healthier living conditions. Our findings also underscore the necessity for strategic resource deployment to counties identified as hot spots, where healthcare providers and community organizations should establish outreach initiatives to address transportation barriers and limited healthcare access. These efforts can be further supported through increased funding for community health programs to promote public awareness and early intervention.</div></section><section id='sec5-information-15-00688' type='conclusions'><h2 data-nested='1'> 5. Conclusions</h2><div class='html-p'>In summary, this nationwide study offers foundational geospatial insights into disparities of AD dementia prevalence across the US in relation to socioeconomic policies. The hotspots were more prevalent in states that did not adopt Medicaid expansion, had minimum wage policies at or below the federal level, were situated in the Deep South, and fell within the most vulnerable SVI quartiles. In contrast, cold spots were primarily found in states that adopted Medicaid expansion, had minimum wage policies above the federal level, were in the Western and Northeastern regions, and fell within the least vulnerable SVI quartiles. Based on our findings, we recommend that state policymakers prioritize socioeconomic policies to enhance access to care for vulnerable populations. For instance, healthcare providers and community organizations can allocate resources to hotspot regions and establish outreach programs to address the unique socioeconomic challenges, such as transportation barriers and limited healthcare access to mitigate the disparities. While our findings are centered on the US, the methodologies and insights derived from this study can serve as a baseline for other countries facing significant socioeconomic disparities in the prevalence of AD dementia. Future research should explore the causal relationships between socioeconomic policies and AD dementia to strengthen evidence for policy advocacy. Additionally, the incorporation of other socioeconomic variables such as housing quality, food security, and environmental variables in causal models could provide a more comprehensive understanding of the pathways linking policy to AD dementia prevalence.</div></section> </div> <div class="html-back"> <section class='html-notes'><h2 >Author Contributions</h2><div class='html-p'>Conceptualization, A.M., S.K., L.A.L. and A.V.A.; methodology, A.M.; software, A.M.; validation, A.M.; formal analysis, A.M.; investigation, A.M., L.A.L. and A.V.A.; resources, A.M. and A.V.A.; data curation, A.M.; writing—original draft preparation, A.M., S.K. and J.M.; writing—review and editing, A.M., S.K., J.M., A.B., L.A.L. and A.V.A.; visualization, A.M. and A.B.; supervision, A.V.A.; project administration, A.M.; funding acquisition, A.M. and A.V.A. All authors have read and agreed to the published version of the manuscript.</div></section><section class='html-notes'><h2>Funding</h2><div class='html-p'>A.M. and A.V.A. are supported by the South Carolina SmartState Endowed Center for Environmental and Biomedical Panomics (CEABP); A.V.A. is supported by the South Carolina Cancer Disparities Research Center (SC CADRE) from NIH/NCI U54 CA210962.</div></section><section class='html-notes'><h2 >Institutional Review Board Statement</h2><div class='html-p'>Not applicable.</div></section><section class='html-notes'><h2 >Informed Consent Statement</h2><div class='html-p'>Not applicable.</div></section><section class='html-notes'><h2 >Data Availability Statement</h2><div class='html-p'>The data used in this study can be accessed at the following GitHub repository (<a href='https://github.com/DrMollalo/Alzheimer-s-Disease' target='_blank' rel="noopener noreferrer">https://github.com/DrMollalo/Alzheimer-s-Disease</a>) accessed on 1 October 2024.</div></section><section class='html-notes'><h2 >Conflicts of Interest</h2><div class='html-p'>The authors declare no conflicts of interest.</div></section><section id='html-references_list'><h2>References</h2><ol class='html-xx'><li id='B1-information-15-00688' class='html-x' data-content='1.'>Gustavsson, A.; Norton, N.; Fast, T.; Frölich, L.; Georges, J.; Holzapfel, D.; Kirabali, T.; Krolak-Salmon, P.; Rossini, P.M.; Ferretti, M.T. 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data-large="/information/information-15-00688/article_deploy/html/images/information-15-00688-g001.png" data-original="/information/information-15-00688/article_deploy/html/images/information-15-00688-g001.png" alt="Information 15 00688 g001" data-lsrc="/information/information-15-00688/article_deploy/html/images/information-15-00688-g001-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#fig_body_display_information-15-00688-f001"></a> </div> </div> <div class="html-fig_description"> <b>Figure 1.</b> Geospatial distribution of (<b>A</b>) hot spots, cold spots, and non-significant areas of AD dementia prevalence identified by Getis-Ord Gi* across the US at various confidence levels, (<b>B</b>) the minimum wage policy at or below federal level, and (<b>C</b>) Medicaid expansion adoption.  </div> </div> <div class="html-fig_show mfp-hide" id="fig_body_display_information-15-00688-f001"> <div class="html-caption"> <b>Figure 1.</b> Geospatial distribution of (<b>A</b>) hot spots, cold spots, and non-significant areas of AD dementia prevalence identified by Getis-Ord Gi* across the US at various confidence levels, (<b>B</b>) the minimum wage policy at or below federal level, and (<b>C</b>) Medicaid expansion adoption.</div> <div class="html-img"><img data-large="/information/information-15-00688/article_deploy/html/images/information-15-00688-g001.png" data-original="/information/information-15-00688/article_deploy/html/images/information-15-00688-g001.png" alt="Information 15 00688 g001" data-lsrc="/information/information-15-00688/article_deploy/html/images/information-15-00688-g001.png" /></div> </div> <div class="html-fig-wrap" id="information-15-00688-f002"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#fig_body_display_information-15-00688-f002"> <img data-large="/information/information-15-00688/article_deploy/html/images/information-15-00688-g002.png" data-original="/information/information-15-00688/article_deploy/html/images/information-15-00688-g002.png" alt="Information 15 00688 g002" data-lsrc="/information/information-15-00688/article_deploy/html/images/information-15-00688-g002-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#fig_body_display_information-15-00688-f002"></a> </div> </div> <div class="html-fig_description"> <b>Figure 2.</b> The frequency of hot spot and cold spot counties of AD dementia prevalence in the US, classified by SVI quartiles.  </div> </div> <div class="html-fig_show mfp-hide" id="fig_body_display_information-15-00688-f002"> <div class="html-caption"> <b>Figure 2.</b> The frequency of hot spot and cold spot counties of AD dementia prevalence in the US, classified by SVI quartiles.</div> <div class="html-img"><img data-large="/information/information-15-00688/article_deploy/html/images/information-15-00688-g002.png" data-original="/information/information-15-00688/article_deploy/html/images/information-15-00688-g002.png" alt="Information 15 00688 g002" data-lsrc="/information/information-15-00688/article_deploy/html/images/information-15-00688-g002.png" /></div> </div> <div class="html-fig-wrap" id="information-15-00688-f003"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#fig_body_display_information-15-00688-f003"> <img data-large="/information/information-15-00688/article_deploy/html/images/information-15-00688-g003.png" data-original="/information/information-15-00688/article_deploy/html/images/information-15-00688-g003.png" alt="Information 15 00688 g003" data-lsrc="/information/information-15-00688/article_deploy/html/images/information-15-00688-g003-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#fig_body_display_information-15-00688-f003"></a> </div> </div> <div class="html-fig_description"> <b>Figure 3.</b> Boxplots showing the proportion of (<b>A</b>) hot spot and (<b>B</b>) cold spot counties per state, categorized by Medicaid expansion status.  </div> </div> <div class="html-fig_show mfp-hide" id="fig_body_display_information-15-00688-f003"> <div class="html-caption"> <b>Figure 3.</b> Boxplots showing the proportion of (<b>A</b>) hot spot and (<b>B</b>) cold spot counties per state, categorized by Medicaid expansion status.</div> <div class="html-img"><img data-large="/information/information-15-00688/article_deploy/html/images/information-15-00688-g003.png" data-original="/information/information-15-00688/article_deploy/html/images/information-15-00688-g003.png" alt="Information 15 00688 g003" data-lsrc="/information/information-15-00688/article_deploy/html/images/information-15-00688-g003.png" /></div> </div> <div class="html-fig-wrap" id="information-15-00688-f004"> <div class='html-fig_img'> <div class="html-figpopup html-figpopup-link" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#fig_body_display_information-15-00688-f004"> <img data-large="/information/information-15-00688/article_deploy/html/images/information-15-00688-g004.png" data-original="/information/information-15-00688/article_deploy/html/images/information-15-00688-g004.png" alt="Information 15 00688 g004" data-lsrc="/information/information-15-00688/article_deploy/html/images/information-15-00688-g004-550.jpg" /> <a class="html-expand html-figpopup" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#fig_body_display_information-15-00688-f004"></a> </div> </div> <div class="html-fig_description"> <b>Figure 4.</b> Boxplots showing the proportion of (<b>A</b>) hot spot and (<b>B</b>) cold spot counties per state, categorized by the minimum wage policy.  </div> </div> <div class="html-fig_show mfp-hide" id="fig_body_display_information-15-00688-f004"> <div class="html-caption"> <b>Figure 4.</b> Boxplots showing the proportion of (<b>A</b>) hot spot and (<b>B</b>) cold spot counties per state, categorized by the minimum wage policy.</div> <div class="html-img"><img data-large="/information/information-15-00688/article_deploy/html/images/information-15-00688-g004.png" data-original="/information/information-15-00688/article_deploy/html/images/information-15-00688-g004.png" alt="Information 15 00688 g004" data-lsrc="/information/information-15-00688/article_deploy/html/images/information-15-00688-g004.png" /></div> </div> <div class="html-table-wrap" id="information-15-00688-t001"> <div class="html-table_wrap_td"> <div class="html-tablepopup html-tablepopup-link" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href='#table_body_display_information-15-00688-t001'> <img data-lsrc="https://pub.mdpi-res.com/img/table.png" /> <a class="html-expand html-tablepopup" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#table_body_display_information-15-00688-t001"></a> </div> </div> <div class="html-table_wrap_discription"> <b>Table 1.</b> Summary statistics of the proportion of hot and cold spot counties per state classified by the Medicaid expansion status. </div> </div> <div class="html-table_show mfp-hide " id="table_body_display_information-15-00688-t001"> <div class="html-caption"><b>Table 1.</b> Summary statistics of the proportion of hot and cold spot counties per state classified by the Medicaid expansion status.</div> <table > <thead ><tr ><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' > </th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Medicaid <br>Expansion</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Mean </th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Median</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Std</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >IQR</th><th colspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Mann–Whitney U</th><th colspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Levene’s Test</th></tr><tr ><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >W </th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><span class='html-italic'>p</span>-Value</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >F</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><span class='html-italic'>p</span>-Value</th></tr></thead><tbody ><tr ><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Proportion of Hot spots</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Adopted</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.09</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.00</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.16</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.08</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >140</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><0.05</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >5.6</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><0.05</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Non-adopted</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.23</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.09</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.26</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.41</td></tr><tr ><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Proportion of Cold spots</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Adopted</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.24</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.07</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.30</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.48</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >301</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >>0.05</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >3.0</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><0.10</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Non-adopted</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.11</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.02</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.19</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.06</td></tr></tbody> </table> <div class='html-table_foot html-p'><div class='html-p' style='text-indent:0em;'><span class='html-fn-content'>IQR: Interquartile Range; Std: Standard deviation.</span></div><div style='clear:both;'></div></div> </div> <div class="html-table-wrap" id="information-15-00688-t002"> <div class="html-table_wrap_td"> <div class="html-tablepopup html-tablepopup-link" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href='#table_body_display_information-15-00688-t002'> <img data-lsrc="https://pub.mdpi-res.com/img/table.png" /> <a class="html-expand html-tablepopup" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#table_body_display_information-15-00688-t002"></a> </div> </div> <div class="html-table_wrap_discription"> <b>Table 2.</b> Summary statistics of the proportion of hot and cold spots per state by minimum wage policy. </div> </div> <div class="html-table_show mfp-hide " id="table_body_display_information-15-00688-t002"> <div class="html-caption"><b>Table 2.</b> Summary statistics of the proportion of hot and cold spots per state by minimum wage policy.</div> <table > <thead ><tr ><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' > </th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Minimum Wage</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Mean </th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Median</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Std</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >IQR</th><th colspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Mann–Whitney U</th><th colspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Levene’s Test</th></tr><tr ><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >W</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><span class='html-italic'>p</span>-Value</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >F</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><span class='html-italic'>p</span>-Value</th></tr></thead><tbody ><tr ><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Proportion of Hot spots</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Above</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.09</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.00</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.14</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.13</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >246</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >>0.05</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >3.2</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><0.10</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >At or below</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.18</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.02</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.26</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.37</td></tr><tr ><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Proportion of Cold spots</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Above</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.20</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.04</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.28</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.39</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >312</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >>0.05</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.01</td><td rowspan='2' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >>0.05</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >At or below</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.21</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.03</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.28</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.37</td></tr></tbody> </table> <div class='html-table_foot html-p'><div class='html-p' style='text-indent:0em;'><span class='html-fn-content'>IQR: Interquartile Range; Std: Standard deviation.</span></div><div style='clear:both;'></div></div> </div> <div class="html-table-wrap" id="information-15-00688-t003"> <div class="html-table_wrap_td"> <div class="html-tablepopup html-tablepopup-link" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href='#table_body_display_information-15-00688-t003'> <img data-lsrc="https://pub.mdpi-res.com/img/table.png" /> <a class="html-expand html-tablepopup" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#table_body_display_information-15-00688-t003"></a> </div> </div> <div class="html-table_wrap_discription"> <b>Table 3.</b> Results of logistic regression model. </div> </div> <div class="html-table_show mfp-hide " id="table_body_display_information-15-00688-t003"> <div class="html-caption"><b>Table 3.</b> Results of logistic regression model.</div> <table > <thead ><tr ><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Variable</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Reference Category</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Estimate</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Std. Error</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >OR</th><th colspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >95% CI for OR</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Z-Value</th><th rowspan='2' align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' ><span class='html-italic'>p</span>-Value</th></tr><tr ><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Lower</th><th align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Upper</th></tr></thead><tbody ><tr ><td colspan='9' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Hot spots</b></td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Intercept</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >--</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >−2.63</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.10</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.07</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.06</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.09</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >−25.88</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><0.001 **</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Non-Adopted Medicaid expansion</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Adopted Medicaid expansion</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.95</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.12</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >2.58</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >2.04</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >3.26</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >7.997</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><0.001 **</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Minimum Wage (At or Below Federal)</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Minimum Wage (Above Federal)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.66</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.13</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.94</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.51</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >2.49</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >5.265</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><0.001 **</td></tr><tr ><td colspan='9' align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Cold spots</b></td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Intercept</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >--</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >−1.49</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.07</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.23</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.20</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.26</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >−20.81</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><0.001 **</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Non-Adopted Medicaid expansion</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Adopted Medicaid expansion</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >−1.42</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.13</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.24</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.19</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.32</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >−10.49</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><0.001 **</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Minimum Wage (At or Below Federal)</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >Minimum Wage (Above Federal)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.37</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.11</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.45</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.16</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1.81</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >3.29</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.001 **</td></tr></tbody> </table> <div class='html-table_foot html-p'><div class='html-p' style='text-indent:0em;'><span class='html-fn-content'>OR: Odds Ratio; CI: Confidence Interval. ** <span class='html-italic'>p</span> < 0.01.</span></div><div style='clear:both;'></div></div> </div> <div class="html-table-wrap" id="information-15-00688-t004"> <div class="html-table_wrap_td"> <div class="html-tablepopup html-tablepopup-link" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href='#table_body_display_information-15-00688-t004'> <img data-lsrc="https://pub.mdpi-res.com/img/table.png" /> <a class="html-expand html-tablepopup" data-counterslinkmanual = "https://www.mdpi.com/2078-2489/15/11/688/display" href="#table_body_display_information-15-00688-t004"></a> </div> </div> <div class="html-table_wrap_discription"> <b>Table 4.</b> Model fit statistics. </div> </div> <div class="html-table_show mfp-hide " id="table_body_display_information-15-00688-t004"> <div class="html-caption"><b>Table 4.</b> Model fit statistics.</div> <table > <thead ><tr ><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Metric</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Model for Hot Spots</th><th align='center' valign='middle' style='border-top:solid thin;border-bottom:solid thin' class='html-align-center' >Model for Cold Spots</th></tr></thead><tbody ><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Null Deviance</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1048.37 (49 df)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1107.35 (49 df)</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Residual Deviance</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >868.41 (47 df)</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >977.72 (47 df)</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>AIC</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >963.78</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1088.60</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>BIC</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >969.51</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >1094.34</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Log Likelihood</b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >−478.89</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >−541.30</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>McFadden’s Pseudo R<sup>2</sup></b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.158</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.107</td></tr><tr ><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' ><b>Nagelkerke R<sup>2</sup></b></td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.973</td><td align='center' valign='middle' style='border-bottom:solid thin' class='html-align-center' >0.925</td></tr></tbody> </table> <div class='html-table_foot html-p'><div class='html-p' style='text-indent:0em;'><span class='html-fn-content'>AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; df: Degrees of Freedom.</span></div><div style='clear:both;'></div></div> </div> </section><section class='html-fn_group'><table><tr id=''><td></td><td><div class='html-p'><b>Disclaimer/Publisher’s Note:</b> The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). 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Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States. <em>Information</em> <b>2024</b>, <em>15</em>, 688. https://doi.org/10.3390/info15110688 </p> <div style="display: block"> <b>AMA Style</b><br> <p> Mollalo A, Knox S, Meng J, Benitez A, Lenert LA, Alekseyenko AV. Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States. <em>Information</em>. 2024; 15(11):688. https://doi.org/10.3390/info15110688 </p> <b>Chicago/Turabian Style</b><br> <p> Mollalo, Abolfazl, Sara Knox, Jessica Meng, Andreana Benitez, Leslie A. Lenert, and Alexander V. Alekseyenko. 2024. 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Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States. <em>Information</em> <b>2024</b>, <em>15</em>, 688. https://doi.org/10.3390/info15110688 </p> <div style="display: block"> <b>AMA Style</b><br> <p> Mollalo A, Knox S, Meng J, Benitez A, Lenert LA, Alekseyenko AV. Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States. <em>Information</em>. 2024; 15(11):688. https://doi.org/10.3390/info15110688 </p> <b>Chicago/Turabian Style</b><br> <p> Mollalo, Abolfazl, Sara Knox, Jessica Meng, Andreana Benitez, Leslie A. Lenert, and Alexander V. Alekseyenko. 2024. "Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States" <em>Information</em> 15, no. 11: 688. https://doi.org/10.3390/info15110688 </p> <b>APA Style</b><br> <p> Mollalo, A., Knox, S., Meng, J., Benitez, A., Lenert, L. A., & Alekseyenko, A. V. (2024). Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States. <em>Information</em>, <em>15</em>(11), 688. https://doi.org/10.3390/info15110688 </p> </div> </div> <div class="info-box no-margin"> Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. 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src="https://pub.mdpi-res.com/assets/js/foundation-5.5.3.min.js?6b2ec41c18b29054?1732615622"></script> <script src="https://pub.mdpi-res.com/assets/js/foundation-5.5.3.equalizer.min.js?0f6c549b75ec554c?1732615622"></script> <script src="https://pub.mdpi-res.com/assets/js/jquery.multiselect.js?0edd3998731d1091?1732615622"></script> <script src="https://pub.mdpi-res.com/assets/js/jquery.cycle2.min.js?63413052928f97ee?1732615622"></script> <script> // old browser fix - this way the console log rows won't throw (silent) errors in browsers not supporting console log if (!window.console) window.console = {}; if (!window.console.log) window.console.log = function () { }; var currentJournalNameSystem = "information"; $(document).ready(function() { $('select.foundation-select').multiselect({ search: true, minHeight: 130, maxHeight: 130, }); $(document).foundation({ orbit: { timer_speed: 4000, }, reveal: { animation: 'fadeAndPop', animation_speed: 100, } }); 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Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer&rsquo;s Disease Dementia Prevalence in the United States

Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States