Submitted by Sara Ribeiro (sara.ribeiro@ucb.br) on 2017-06-12T13:59:52Z No. of bitstreams: 1 J??lioCesarDaCunhaLopesDissertacao2016.pdf: 5954507 bytes, checksum: 3d54d8db00b8c7c9adb70d31efdca84d (MD5) / Approved for entry into archive by Sara Ribeiro (sara.ribeiro@ucb.br) on 2017-06-12T14:00:28Z (GMT) No. of bitstreams: 1 J??lioCesarDaCunhaLopesDissertacao2016.pdf: 5954507 bytes, checksum: 3d54d8db00b8c7c9adb70d31efdca84d (MD5) / Made available in DSpace on 2017-06-12T14:00:28Z (GMT). No. of bitstreams: 1 J??lioCesarDaCunhaLopesDissertacao2016.pdf: 5954507 bytes, checksum: 3d54d8db00b8c7c9adb70d31efdca84d (MD5) Previous issue date: 2016-03-10 / Although there is a wide variety of measures to measure the levels of agglomeration and location of firms, the areal unit problem modifiable - MAUP - is always remembered in literature as a problem in regional science. The purpose of the research is to apply the newly developed tracing measures that surpass largely the MAUP problem. With unprecedented use in Brazil, these measures assess the level of sectoral location of manufacturing industry in the Greater S??o Paulo and the results show that the fabrication industries of automotive vehicles, trailers and bodies and clothing and accessories are more localized . On the other hand, the non-metallic minerals and pulp and paper segments have a higher dispersion in relation to the other. / Embora exista uma extensa variedade de medidas para se mensurar os n??veis de aglomera????o e localiza????o das firmas, o modifiable areal unit problem ??? MAUP ??? sempre ?? lembrado na literatura como um problema na ci??ncia regional. O prop??sito da pesquisa ?? aplicar as medidas de localiza????o recentemente desenvolvidas que superam, em boa parte, o problema do MAUP. Com utiliza????o in??dita no Brasil, tais medidas avaliam o n??vel de localiza????o setorial da ind??stria da transforma????o na Regi??o Metropolitana de S??o Paulo e os resultados mostram que as ind??strias de fabrica????o de ve??culos automotores, reboques e carrocerias e de artigos de vestu??rio e acess??rios s??o mais localizadas. Por outro lado, os segmentos de minerais n??o met??licos e celulose e papel apresentam maior dispers??o em rela????o aos demais.

There are a myriad of studies of animal habitat use that employ the notion of “home range”. Aggregated information on animal locations provide insight into a geographically discrete units that represents the use of space by an animal. Among various methods to delineate home range is the commonly used Kernel Density Estimation (KDE). The KDE method delineates home ranges based on an animal’s Utilization Distribution (UD). Specifically, a UD estimates a three-dimensional surface representing the probability or intensity of habitat use by an animal based on known locations. The choice of bandwidth (i.e., kernel radius) in KDE determines the level of smoothing and thus, ultimately circumscribes the size and shape of an animal’s home range. The bounds of interest in a home range can then be delineated using different volume contours of the UD (e.g., 95% or 50%). Habitat variables can then be assessed within the chosen UD contour(s) to ascertain selection for certain habitat characteristics. Home range analyses that utilize the KDE method, and indeed all methods of home range delineation, are subject to the Modifiable Areal Unit Problem (MAUP) whereby the changes in the scale at which data (e.g., habitat variables) are analysed can alter the outcome of statistical analyses and resulting ecological inferences. There are two components to MAUP, the scale and zoning effects. The scale effect refers to changes to the data and, consequently the outcome of analyses as a result of aggregating data to coarser spatial units of analysis. The aggregation of data can result in a loss of fine-scale detail as well as change the observed spatial patterns. The zone effect refers to how, when holding scale constant, the delineation of areal units in space can alter data values and ultimately the results of analyses. For example, habitat features captured within 1km2 gridded sampling units may change if instead 1km2 hexagon units are used. This thesis holds there are three “modifiable” factors in home range analyses that render it subject to the MAUP. The first two relate specifically to the use of the KDE method namely, the choice of bandwidth and UD contour. The third is the grain (e.g., resolution) by which habitat variables are aggregated, which applies to KDE but also more broadly to other quantitative methods of home range delineation In the following chapters we examine the changes in values of elevation and slope that result from changes to KDE bandwidth (Chapter 2) UD contour (Chapter 3) and DEM resolution (Chapter 4). In each chapter we also examine how the observed effects of altering each individual parameter of scale (e.g., bandwidth) changes when different scales of the other two parameters are considered (e.g., contour and resolution). We expected that the scale of each parameter examined would change the observed effect of other parameters. For example, that the homogenization of data at coarser resolutions would reduce the degree of difference in variable values between UD contours of each home range. To explore the potential effects of MAUP on home range analyses we used as model population 13 northern woodland caribou (Rangifer tarandus). We created seasonal home ranges (winter, calving, summer, rut and fall) for each caribou using three different KDE bandwidths. Within each home range we delineated four contours based on differing levels of an animal’s UD. We then calculated values of elevation and slope (mean, standard deviation and coefficient of variation) using a Digital Elevation Model (DEM) aggregated to four different resolutions within the contours of each seasonal home range. We found that each parameter of scale significantly changed the values of elevation and slope within the home ranges of the model caribou population. The magnitude as well as direction of change in slope and elevation often varied depending the specific contour or season. There was a greater decrease in the variability of elevation within the fall and winter seasons at smaller KDE bandwidths. The topographic variables were significantly different between all contours of caribou home ranges and the difference between contours were in general, significantly higher in fall and winter (elevation) or calving and summer (slope). The mean and SD of slope decreased at coarser resolutions in all caribou home ranges, whereas there was no change in elevation. We also found interactive effects of all three parameters of scale, although these were not always as direct as initially anticipated. Each parameter examined (bandwidth, contour and resolution) may potentially alter the outcome of northern woodland caribou habitat analyses. We conclude that home range analyses that utilize the KDE method may be subject to MAUP by virtue the ability to modify the spatial dimensions of the units of analysis. As such, in habitat analyses using the KDE careful consideration should be given to the choice of bandwidth, UD contour and habitat variable resolution. / Graduate / 0366 / 0329 / spicym@uvic.ca

I den här uppsatsen undersöks fem olika boendesegregationsindex som används för att beräkna och skildra graden av boendesegregation i samhället. Syftet är att studera hur index påverkas av slumpmässigt genererade befolkningssammansättningar av inrikes och utrikes födda, samt hur tre olika geografiska indelningar av ett område påverkar utfallet av utvalda index. Uppsatsen utförs med kvantitativ metod och deduktiv slutledning. Det data som samtliga index beräknas på är syntetiskt framtagen baserad på information om hur andelarna inrikes och utrikes födda ser ut i Sveriges tre största kommuner. Mätområdet utgörs av en hypotetisk stad som delas in på tre olika sätt varpå samtliga index testas. Beräkningar och analyser görs på stickprov för respektive index och geografisk indelning. Stickproven består alla av ett hundra element och är framtagna genom ett obundet slumpmässigt urval ur en teoretisk oändlig population simulerade befolkningssammansättningar. Den geografiska effekten undersöks genom MAUPs två delproblem, Scale Effect och Zoning Effect. Utifrån studiens resultat har följande slutsatser dragits. (1) Den slumpmässiga sammansättningen av befolkningen i den hypotetiska staden påverkade generellt index väldigt lite och låg spridning av resulterade indexvärden uppmättes för många av stickproven. De undantag som påvisades var för The Entropy Index och The Dissimilarity Index. (2) Scale Effect har stor påverkan på hur samtliga index uttrycker sig samtidigt som Zoning Effect uteblir för respektive index som testas. (3) Lägre grad av segregation uppvisas då upplösningen av den geografiska informationen är lägre.

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Det som ofta faller bort i den offentliga debatten är att segregationens innebörd anspelar på åtskillnader av olika grupper, inte enbart de ”utsatta” utan även de socioekonomiskt starkare grupperna. Denna studie kommer att undersöka hur segregationen ser ut inom Sundsvalls tätortsområden och vilka faktorerna är som har störst påverkan till de skillnader som finns. Studien baseras på att urskilja den relativa segregationen, d.v.s. fördelningen av segregationspåverkande faktorer, i positiv och negativ riktning. Syftet i studien är sedan att jämföra två olika geografiska indelningssystem. Det ena är det kommunala nyckelkodssystemet (NYKO) och det andra är Statistiska centralbyråns regionala indelningssystem, demografiska statistikområden (DeSO). I metoden har två olika index använts för att mäta fördelningen av segregationsfaktorer genom en multikriterieanalys (MKA). Den första mätningen har skett genom en nyutvecklad segregationsindex och den andra mätningen genom index of dissimilarity. Studien har visat att det finns svårigheter kring att använda ett indelningssystem som kan verka funktionellt i alla avseenden. Beroende på vad studien syftar till att mäta så spelar olika zon- och skalindelningar en stor roll i hur resultatet framställs. Resultatet visar att segregationen utspelar sig inom både de socioekonomiskt svaga och starka områdena. Det finns däremot svårigheter med att bedöma vilken områdesindelning som är mest användbar då de verkar på olika grunder. Genom att jämföra DeSO och NYKO har resultaten av studien visat att befolkningsantalet och storleken på den geografiska områdesindelningen har en stor betydelse för hur pålitlig en studie kan bli. / What often falls away in the public debate is that the meaning of the segregation alludes to the separation of different groups, not just the "vulnerable" but also the socio-economically stronger groups. This study will investigate how the segregation plays out within Sundsvall's urban areas and which factors have the greatest impact on the differences that exist. The study is based on distinguishing the relative segregation, i. e. the distribution of factors affecting segregation, in a positive and negative direction. The purpose of the study is then to compare two different geographical area systems. One is the municipal key code system (NYKO) and the other is the state regional area system, demographic statistics areas (DeSO). In the method, two different indexes have been used to measure the distribution of segregation factors through a multi-criteria decision analysis (MKA). The first measurement has been made by a newly developed segregation index and the second measurement by the index of dissimilarity. The study has shown that there are difficulties in finding an area system that can function efficiently for all purposes. Depending on what the study aims to measure, the different zone and scale configurations play a major role in how the result is produced. The result of the study shows that the segregation takes place in both the socio-economically weak and strong areas. There are, on the other hand, difficulties in assessing which of the two area systems that is the most practical since they both operate on different grounds. By comparing DeSO and NYKO, the results of the study have shown that the population and size of the geographical area unit are of great importance for how reliable a study can be.

The Modifiable Areal Unit Phenomenon (MAUP) has traditionally been regarded as a problem in the analysis of spatial data organised in areal units. However, the approach adopted here is that the MAUP provides an opportunity to gain information about the data under investigation. Crucially, attempts to remove the MAUP from spatial data are regarded as an attempt to remove the geography. Therefore, the work seeks to provide an insight to the causes of, and information behind, the MAUP. The data used is from the 1991 Census of Great Britain. This was chosen over 2001 data due to the availability of individual level data. These data are of key importance to the methods employed. The methods seek to provide evidence of the magnitude of the MAUP, and more specifically the scale effect in the GB Census. This evidence is built on using correlation analysis to demonstrate the statistical significance of the MAUP. Having established the relevance of the MAUP in the context of current geographical research, the factors that contribute to the incidence of the MAUP are considered, and it is noted that a wide range of influences are important. These include the population size and density of an area, along with proportion of a variable. This discussion also recognises the importance of homogeneity as an influential factor, something that is referenced throughout the work. Finally, a search is made for spatial processes. This uses spatial autocorrelation and multilevel modelling to investigate the impact spatial processes have in a range of SAR Districts, like Glasgow, Reigate and Huntingdonshire, on the scale effect. The research is brought together, not to solve the MAUP but to provide an insight into the factors that cause the MAUP, and demonstrate the usefulness of the MAUP as a concept rather than a problem.

The spatial distribution of future population and employment has become a focus of recent academic enquiry and planning policy concerns. This is largely driven by the rapid urban expansion in major Australian cities and the need to plan ahead for new housing growth and demand for urban infrastructure and services. At a national level forecasts for population and employment are produced by the government and research institutions; however there is a further need to break these forecasts down to a disaggregate geographic scale for growth management within regions. Appropriate planning for the urban growth needs forecasts for fine-grained spatial units. This thesis has developed methodologies to predict the future settlement of the population, employment and urban form by applying a spatial disaggregation approach. The methodology uses the existing regional forecasts reported at regional geographic units and applies a novel spatially-based technique to step-down the regional forecasts to smaller geographical units. South East Queensland (SEQ) is the experimental context for the methodologies developed in the thesis, being one of the fastest-growing metropolitan regions in Australia. The research examines whether spatial disaggregation methodologies that can be used to enhance the forecasts for urban planning purposes and to derive a deeper understanding of the urban spatial structure under growth conditions. The first part of this thesis develops a method by which the SEQ population forecasts can be spatially disaggregated. This is related to a classical problem in geographical analysis called to modifiable area unit problem, where spatial data disaggregation may give inaccurate results due to spatial heterogeneity in the explanatory variables. Several statistical regression and dasymetric techniques are evaluated to spatially disaggregate population forecasts over the study area and to assess their relative accuracies. An important contribution arising from this research is that: i) it extends the dasymetric method beyond its current simple form to techniques that incorporate more complex density assumptions to disaggregate the data and, ii) it selects a method based on balancing the costs and errors of the disaggregation for a study area. The outputs of the method are spatially disaggregated population forecasts across the smaller areas that can be directly used for urban form analysis and are also directly available for subsequent employment disaggregation. The second part in this thesis develops a method to spatially disaggregate the employment forecasts and examine their impact on the urban form. A new method for spatially disaggregating the employment data is evaluated; it analyses the trend and spatial pattern of historic regional employment patterns based on employment determinants (for example, the local population and the proximity of an area to a shopping centre). The method we apply, namely geographically weighted regression (GWR), accounts for spatial effects of data autocorrelation and heterogeneity. Autocorrelation is where certain variables for employment determinants are related in space, and hence violate traditional statistical independence assumptions, and heterogeneity is where the associations between variables change across space. The method uses a locally-fitted relationship to estimate employment in the smaller geography whilst being constrained by the regional forecast. Results show that, by accounting for spatial heterogeneity in the local dependency of employment, the GWR method generates superior estimates over a global regression model. The spatially disaggregate projections developed in this thesis can be used to better understand questions on urban form. From a planning perspective, the results of spatial disaggregation indicate that the future growth of the population for SEQ is likely to maintain a spatially-dispersed growth pattern, whilst the employment is likely to follow a more polycentric distribution focused around the new activity centres. Overall, the thesis demonstrates that the spatial disaggregation method can be applied to supplement the regional forecasts to seek a deeper understanding of the future urban growth patterns. The development, application and validation of the spatial disaggregation methods will enhance the planner’s toolbox whilst responding to the data issues to inform urban planning and future development in a region.

Health studies often rely on aggregated instead of individual-level data to protect patient privacy. However, aggregated data are subject to the modifiable areal unit problem (MAUP), meaning results of statistical analyses may differ depending on the data's scale and areal unit. Past studies have suggested MAUP is context-specific and analyzing multiple spatial scales may provide richer understandings of examined phenomena. More research is needed to understand the role of scale and areal unit in health-related analyses. This study examines associations between surface mining and birth outcomes from 1989 to 2015 in Central Appalachia at the individual; postal; county; and county-sized, non-administrative scales. Evidence from previous studies suggests associations exist between health outcomes and county-level measures of mining activity. This is the first study to examine associations between mining and birth outcomes at more spatially refined exposure estimates. We identified surface mines using Landsat imagery and geocoded birth records. Airsheds, used to quantify the influence area of potential airborne pollutants from surface mining activity, were built using HYSPLIT4. The frequency values of each airshed that intersected each geocoded birth record were summed. These cumulative frequency airshed values were then aggregated. Finally, we implemented multiple regression models, each at a different scale, to examine associations between airsheds and birth outcomes. Results suggest MAUP has minimal impacts on the statistical results of examining associations between surface mining and birth outcomes in Central Appalachia. Results also indicate surface mining is significantly associated with preterm birth and reduced birthweight at each scale. / Master of Science / Health studies often rely on data that has been grouped together within political boundaries (e.g. counties) instead of individual-level data to protect patient privacy. However, results from analyses using grouped data may differ depending on the data's scale and areal unit, which describes the modifiable areal unit problem (MAUP). Past studies have suggested MAUP is specific to the situation being analyzed and examining multiple scales may provide richer understandings of the situation. More research is needed to understand the role of scale and areal unit choice in health-related analyses. This study examines associations between surface mining and birth outcomes from 1989 to 2015 in Central Appalachia at the individual; postal; county; and county-sized, non-administrative scales. Evidence from previous studies suggests associations exist between health outcomes and county-level measures of mining activity. This is the first study to examine associations between mining and birth outcomes at finer scales. Surface mines were identified using satellite images, and we identified the locations of birth records using the mother's home address. Airsheds, used to determine the influence area of airborne pollutants from surface mining activity, were created. We then used statistical models, to examine associations between airsheds and birth outcomes at four spatial scales. Results suggest MAUP has minimal impacts on the statistical results of examining associations between surface mining and birth outcomes in Central Appalachia. Results also indicate surface mining is significantly associated with preterm birth and decreased birthweight in grams at each scale.

Les villes sont des systèmes complexes que les modèles urbains peuvent aider à comprendre. Des modèles les plus simplistes aux modèles les plus sophistiqués, la modélisation urbaine a permis de mieux comprendre la question urbaine et ses implications sociétales. Dans ce contexte, les modèles peuvent avoir une valeur-ajoutée appréciable dans le processus de décision publique. Encore faut-il que ces modèles deviennent pratiques et répondent aux contraintes opérationnelles de la chaîne de décision. Dans ce sens, peu de recherches s’est intéressée à la question de praticité des modèles urbains et leur utilisation en situation opérationnelle. À ce jour, les modèles urbains standard qui reposent sur une description agrégée de l’espace sont parmi les approches de modélisation les plus opérationnelles et aussi les plus répandues. De par sa relative praticité, cette approche standard est attractive et simple à mettre en oeuvre. Toutefois, l’agrégation spatiale peut aussi être une source de biais statistiques préjudiciables à la qualité de la modélisation. C’est en particulier, le cas des modèles intégrés Transport-Urbanisme ou des modèles de transport à quatre étapes.La présente thèse a pour objectif d’étudier la question de l’agrégation spatiale dans les modèles transport et plus particulièrement dans les modèles d’affectation des déplacements. Les modèles d’affectation servent à calculer les temps de parcours et les conditions de déplacement sous congestion, présents et futurs, des personnes et des marchandises. Ils servent aussi à calculer les accessibilités nécessaires aux modèles d’usage des sols dont les modèles de choix de localisation des ménages et des entreprises. Toute erreur ou biais dans l’affectation des déplacements peut compromettre la validité et la qualité globales de la modélisation. Dans ce cadre, une attention particulière doit être allouée au problème d’agrégation spatiale dans les modèles d’affectation. Dans ces modèles, l’agrégation spatiale consiste à regrouper les observations individuelles enutilisant une description agrégée de l’espace, i.e. des zones. Par nature, l’utilisation d’une description agrégée à la place d’une représentation continue engendre une omission de l’information et de sa variabilité et donc un biais statistique dans la modélisation. C’est le cas par exemple avec l’utilisation des connecteurs de zones ou avec l’omission des trafics intrazones dans les modèles d’affectation.En reposant sur les zones comme unité spatiale de base, les modèles de transport recourent à l’utilisation des connecteurs de zones pour relier les centroïdes de zones au réseau de transport. Les connecteurs sont des liens fictifs qui modélisent les conditions moyennes d’entrée et de sortie du réseau de transport. Pour ce faire, la majorité des modèles de transport reposent sur une méthode simpliste sujette au problème d’agrégation spatiale. La présente thèse examine en détail l’impact de cette description simpliste sur les résultats et la qualité d’un modèle d’affectation des déplacements en transports en commun. Cette thèse propose aussi une nouvelle méthode de modélisation des connecteurs de zones afin de s’affranchir partiellement du biaisd’agrégation spatiale dans la modélisation des conditions d’accès au réseau des transports en commun.L’utilisation des zones comme unité spatiale de base a aussi pour conséquence l’omission des trafics intrazones de l’affectation des déplacements. Les trafics intrazones ont pour origine et pour destination la même zone et de ce fait ne sont pas pris en compte par les modèles standard d’affectation. Cette omission a souvent été ignorée et son impact sur la qualité de la modélisation demeure non évalué. Cette thèse développe une méthode stochastique pour l’évaluation de cet impact... / Cities are complex systems that urban models can help to comprehend. From simplistic models to more sophisticated ones, urban models have pushed forward our understanding the urban phenomenon and its intricacies. In this context, models can be of great value to policy makers providing that these tools become practical. In this regard, research has put little emphasis on the practicality of urban models and their use under operational conditions.To date, urban models which rely on spatial aggregation are the closest possibility to come to practical models. For this reason, the spatially aggregated modeling framework is widely used. This framework is relatively practical when compared to other modeling frameworks like microsimulation. Nevertheless, spatial aggregation is a serious source of bias in these models. This is especially the case of Land-Use and Transport Interaction (LUTI) models and more particularly of Four Step Models.The current PhD is committed to the study of spatial aggregation issues in traffic assignment models. Traffic assignment is responsable for the computation of travel times and travel conditions of present and future travel demand. Accessibility measurement, which is at the core of LUTI models, is tightly dependent on traffic assignment modeling and outcomes. Any bias in traffic assignment is likely to corrupt the overall modeling framework. In this context, a special attention is to be paid to spatial aggregation in traffic assignment models.In traffic assignment, spatial aggregation consists in grouping observations using zones or traffic analysis zones instead of using a continuous representation of space. By design, aggregation bears an implicit omission in data variability and thus a potential bias if this omission is not random. This is the case with the definition of centroid connectors and the omission of intrazonal demand in traffic assignment. With the use of zones as the basic spatial units, transport models require the use of centroid connectors to attach zones to the transportation network. Centroid connectors are introduced to model average access and egress conditions to and from the network. Nevertheless, average accessibility conditions are found to be too crude to render accurately accessibility conditions as encountered by trip makers. The current PhD explores the extent of the impact of this spatial aggregation bias in the case of transit models and suggests a new modeling strategy to overcome such modeling errors.The use of zones as spatial units induces a loss of intrazonal data. The omission of intrazonal trips in traffic assignment models is an example of such omission. This research introduces an uncertainty framework to study the statistical impact of ignoring intrazonal trips in traffic assignment models. Findings from this research are used to design new assignment strategies that are more robust towards the omission bias and more generally towards the spatial aggregation bias.

This dissertation examined risk factors for hypertensive disorders of pregnancy (HDP) — specifically whether neighborhood socioeconomic deprivation exacerbates individual socioeconomic disadvantage (deprivation amplification) to increase the likelihood of developing HDP. To select the optimal areal unit at which to investigate HDP, geographic proxies for neighborhoods were explored. A thematic review qualitatively examined nontraditional neighborhood boundaries identified through internet sources. Data from 2008–2012 Miami-Dade County, Florida birth records (n=121,421) and the U.S. Census Bureau were used for the remaining analyses. Ordinary least squares (OLS) and geographically weighted regression (GWR) analysis empirically compared the proportion of HDP prevalence explained by six areal units: census block groups, census tracts, ZIP code tabulation areas (ZCTAs), and three types of natural neighborhood — census units clustered based on an eight-item Neighborhood Deprivation Index. Multilevel logistic regression examined relationships between HDP, neighborhood deprivation, and individual-level factors. Odds ratios (OR) and adjusted odds ratios (aOR) were calculated. The thematic review found 22 potential alternatives to census boundaries developed through techniques such as crowd-sourcing and qualitative research. In the sensitivity analysis, census tracts aggregated at the scale of ZCTAs performed twice as well as any other model (GWR2 = 0.27) and were used as the Aim 3 unit of analysis. In the multilevel logistic regression, HDP was associated with moderate (aOR=1.13; CI: 1.05, 1.21) and high neighborhood deprivation (aOR=1.16; CI: 1.07, 1.26). Compared with mothers with private insurance, uninsured women (aOR=1.69; CI: 1.56, 1.84) and Medicaid recipients (aOR=1.12; CI: 1.05, 1.18) had higher HDP odds. Non-Hispanic Black women’s HDP odds were 1.58 times those of non-Hispanic White women. Cross-level interactions — between neighborhood deprivation and educational attainment and neighborhood deprivation and insurance status — did not reach statistical significance. Private sector neighborhood boundaries hold promise for developing new public health tools. Because they are relatively easy to generate from census data, natural neighborhoods may balance tradition and innovation. While no evidence of deprivation amplification was found, results suggested that individual-level and neighborhood deprivation are HDP risk factors. Interventions that target expectant mothers in deprived neighborhoods — particularly non-Hispanic Black and Hispanic women who lack health insurance — may help reduce HDP prevalence and disparities.