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Category: Hypothesis

Description: The proposed research revisits an old question in alcohol studies: How does the physical availability of alcohol affect alcohol sales and related problems? Along with lower beverage prices, alcohol researchers have identified greater physical availability of alcohol through retail alcohol outlets as a population health risk.1,2 The theoretical bases for these claims are easily stated; greater "full costs" for alcohol, effected by higher prices or reduced availability, are expected to be related to less sales and fewer problems.3,4 These claims are also empirically well-supported; greater beverage prices and taxes, reduced numbers and densities of outlets, and reduced hours and days of sale have been related to lower sales and fewer problems.3,5 However, while many alcohol researchers agree that there is adequate empirical support for the central tenets of "availability theory," a critical examination of theory and research methods identifies two central concerns with this work: First, from a theoretical perspective, routine social activities can reduce or eliminate convenience costs related to availability; for example, consumers may bundle alcohol purchases with other goods (e.g., groceries) reducing or eliminating convenience costs specific to the purchase of alcohol.6 Routine activities can also alter relationships between sales and problems if problems are at least partially determined by characteristics of places where alcohol is sold and consumers who go to those places (e.g., interpersonal violence at bars).7 Second, from an empirical perspective, the key mediator between availability and problems, alcohol sales, is rarely measured; comprehensive alcohol sales data are seldom available to test the impacts of availability on sales or sales on problems; comprehensive data on alcohol sales data by beverage types are unavailable for all communities in the US. Therefore, at the community level, availability studies and the regulatory efforts that could follow from such studies are theoretically and empirically uninformed. The primary regulatory consequence of this limitation to research efforts is to make it very difficult for community planners and public health practitioners to advocate for appropriate local availability controls. Since the degree to which neighborhood and community availability affects sales is unknown (because sales are not measured) and problems related to outlets may not be completely due to alcohol sales (because routine activities partly determine problems), arguments for local regulation cannot be directed at impacts related to sales per se. It can be argued that routine activities associated with outlets, not sales through outlets, are the cause of ostensibly alcohol related problems and our failure to distinguish the two leads us to erroneously attribute negative consequences to the use of alcohol that lie elsewhere. The proposed studies will strengthen our understanding of relationships between the physical availability of alcohol, alcohol sales, and related problems at the community level using a unique dataset that provides these data at the community postcode level (the Western Australia Alcohol Indicators Database, WAAID), over an extended period of time (1991-2020), from an Australian state with an alcohol retail licensing system similar to many in the US. Including measures of sales through outlets by beverage type, alcohol related morbidity, mortality and crime, Census and other environmental data, WAAID enables us to: (1) Measure the extent to which the physical availability of alcohol (i.e., concentration of different types of alcohol outlets) within neighborhoods and communities affects sales, and (2) Distinguish the degree to which concentrations of alcohol outlets per se, independent of sales made through those outlets, are associated with alcohol related problems. Assessed across approximately 350 urban, suburban, exurban and rural areas, measurements of these relationships will, for the first time, enable us to distinguish sales from outlet effects and their relationships to problems with greater geographic resolution and greater relevance to local regulation of alcohol outlets. We will relate measures of availability through outlets that arises to meet consumer demand (e.g., openings and closings of bars, restaurants and 3 types of off-premises outlets), to sales through those outlets (spirits, low- and high alcohol content beer, and wine), and assess the separable impacts of physical availability vs. alcohol sales on problems in local and surrounding geographic areas. We will do so using Bayesian Gaussian and Poisson spatial partial differential equation models developed during the current Center round that allow us to statistically assess relationships over space and time and estimate ostensibly causal relationships.8 The proposed work is ambitious but feasible, relying upon theoretical and empirical achievements in availability studies over the past 40 years, a systematic approach to modeling availability effects, and the historically deep skills and experiences of the project team. Critically, the project geographic information system (GIS) has been developed and fully supported by our collaborators at the National Drug Research Institute (NDRI), Curtin University, Western Australia. Analysis activities can begin upon onset of the project. The short-term goal of the project is to provide a systematic theoretical description and empirical assessment of the processes by which changes in the physical availability of alcohol are linked to sales and problems across communities in Western Australia. The long-term goal of the project is to provide guidelines by which effective regulatory practices that affect availability, sales and problems in these and communities in the US can be implemented. Hypothesis 1. Alcohol sales will be inversely related to economic costs (e.g., effected through taxes) and directly related to spatial availability (i.e., proximity to outlets). H1a - Most simply, this hypothesis can be tested by regressing total alcohol sales summed across outlet and beverage types over measures of beverage taxes and spatial availability (also averaged across outlet types). Tax effects will be evaluated over time within this panel. Spatial availabilitiy effects will be evaluated over space and time. Since this analysis is uninformed to the extent that beverage taxes and spatial availability are specific to beverage types (e.g., wine taxes, wine stores), analyses of sales and availability by beverage type are the next logical step to take: H1b - High- and low-alcohol beer, wine and spirits sales will be related to measures of taxes and spatial availability (now specifically measured for each outlet type). Since spatial availability measures are specific to postcodes, further elucidation of multiresolution effects related to spatial availability measures is the next step to consider: H1c - High- and low-alcohol beer, wine and spirits sales will be related to measures of taxes and spatial availability detailed at local, nearby and community levels. Finally, it is also feasible to test for cross tax (price) and cross availability (i.e., substitution) effects, H1d - As noted in the econometrics literature examing price and tax effects, increases in prices/taxes for one beverage type may lead to substitution with another type at lower cost3,115,116; the same argument can be made with regard to restrictions on physical availability and analysis models will be further detailed in these respects. Each hypothesis will be tested using constraint and likelihood ratio tests. Assessments of reductions in model uncertainty will be indicated using deviance information and Watanabe Akaike Information Criteria (DIC, WAIC).117,118 All analysis models will include the suite of potentially confounding sociodemographic measures from the Western Australia Census and Environmental covariates (see Section C.2). Impacts of outlet regulations (Section C.2) on sales will be assessed in subsequent analyses, providing an indication of the degree to which different regulations affected sales, but also the degree to which different regulations may have altered relationships of economic and physical availability measures to sales. Results of analyses using long and short data frames for state and community subsets will be compared in order to assess (a) impacts of covariates available for the short frame on estimated effects from the long frame and (b) divergences between analyses of all state postcodes vs those representing community level effects (which differ in spatial resolution). (These will be repeated for tests of all study hypotheses and will not be further discussed.) In general, alcohol taxes which affect prices are considered unaffected by demand and, for that reason, useful statistical instruments by which to assess economic availability effects.115,116 We will use the numerous changes in Western Australia taxes that affect alcohol prices for this purpose (Section C.2) and, although retail prices are not always well indexed by wholesale prices93,96,119,120, we will compare estimates to models using wholesale prices differentiated by outlet and beverage types. It is also plausible that the numbers of outlets that arise across postcodes are affected by demand. Fortunately, statistical instruments for physical availability have been identified in previous research (e.g., land and structure rents, population, income, zoning regulations57,121) and may be used to help identify availability effects.122,123 Hypothesis 2. Impacts of spatial availability on sales will be greatest for outlets which do not combine sales with other goods (e.g., bars and liquor outlets). Tests of this hypothesis follow directly from those conducted for Hypothesis 1 and can be executed within the same analytic framework. Specifically, the two parameters related to spatial availability measures for bars and liquor stores should be greater than those for restaurants and supermarkets since the latter are routinely accessed for food purchases independent of alcohol. These tests are possible because all measures of spatial availability are on the same metric. A corollary prediction here is that restrictions on spatial availability through outlets which do not combine sales with other goods will be related to greater sales through alternative outlets; sales through these outlets will be substituted by purchases through others with greater spatial availability. Hypothesis 3. Availability through outlets and sales through outlets will be separately associated with incidence of problems related to use (this will be especially the case for violent assaults that are related to human interactions in on-premises establishments). Nine problem outcomes will be considered in these analyses: hospitalizations/separations related to abuse and dependence, cirrhosis, assault injuries, suicide/self-inflicted wounds, MVCs, domestic and partner violence, other unintentional injury accidents, and crime incidents related to violent assaults and impaired driving. H3a: Each problem will be regressed over global measures of ethanol sales and spatial availability summarized across beverage and outlet types. H3b: Each problem will be regressed over specific measures of ethanol sales by beverage type and specific measures of spatial availability by outlet type. H3c: Impacts of sales and spatial availability effects will then be detailed at local, nearby and community levels. Testing protocols will follow those specified for Hypotheses 1a-1d and each of the 9 analysis models will include the suite of confounding sociodemographic and environmental covariates identified in Section C.2 and controls for economic availability effects. Critical statistical comparisons of parameters representing outlet effects will be made between models with and without ethanol sales at each stage; these provide a measure of mis-specification bias due to the omission of sales data in the estimation of outlet effects. We expect sales to substantially mediate but not eliminate effects related to spatial availability. Results of these models will be compared with those that treat spatial densities as confounding measures (i.e., marking for otherwise unmeasured area effects) and assess impacts of counts of opened vs closed outlets on problems over time.11 Hypothesis 4. Availability and sales through outlets will be heterogeneously related to problems, varying by outlet and beverage type across three different spatial scales: local postcode, nearby postcodes, and community level. As noted in Sections A.4 and A.5, we expect effects of economic and spatial availability on problems to be quite heterogeneous. The social processes underlying the etiologies of different problem outcomes can be very different (Section A.4) and affect different outcomes in different ways conditional upon measured and unmeasured local, nearby and community-level factors (Section A.5). For these reasons, we expect that the economic and spatial availability parameters estimated for local, nearby and community areas (Hypothesis 3) will statistically differ between equations. Spatial seemingly unrelated regression models can be used to assess differences in parameters across correlated equations (e.g., spsure models in R124) and provide the software necessary to ascertain large-scale structural differences related to economic and spatial availability effects called for here. We have implemented spatial multiple equation models with correlated errors in R-INLA when testing for structural differences in models predicting weekday vs weekend crime rates.125 We will implement similar multiple equation SPDE models and test for structural differences between 2 equations predicting 2 crime outcomes (violence, drink driving) and up to 7 equations predicting 7 commonly measured hospital diagnostic discharges/separations. While these methods are cumbersome to express, their goal is simple: We wish to provide single tests of whether parameters related to physical and spatial availability (a) differ in association with 2 or more problem outcomes (2 statistical tests; one for economic and another for spatial availability measures) and (b) differ when measuring local, nearby and community impacts (2 more tests). The first tests index the degrees to which measures of availability are differentially associated with problem outcomes. The second tests index the degrees to which effects differ across spatial areas (multiresolution effects). Again, results of these models will be compared to those treating spatial densities as confounding measures and assess impacts of counts of opened vs closed outlets on problems over time.11 Approach to Research Questions The proposed work relies upon pre-existing and annually updated population data made available to this project through NDRI WAAID and advanced spatial statistical models and methods developed for analyses of project data at PRC. WAAID will be the source of geocoded postcode data provided in aggregate form under a subcontract through NDRI to Dr. Chikritzhs and Mr. Gillmore. Dr. Chikritzhs and Mr. Gillmore will also aide in the development of specific measurement and analysis procedures suitable to these data, support and inform all project analyses, and support or lead publications emerging from this project. Data through 2020 will be available to PRC at the initiation of the proposed project and updated annually thereafter. Here we assume that data only through 2020 will be available. C.1. Western Australia. While we do not propose direct statistical comparisons to any US state, to assure the relevance of the proposed study to US states and communities we note that Western Australian populations, alcohol control systems, drinking and related problems are comparable to those seen in the US. Selected only on the basis of comparable population size, we use New Mexico as a comparison. In 2016, the population of Western Australia was about 2.4M people with household incomes of about AU$83K (US$61K; 0.74 exchange rate), comparable to the state of New Mexico (2.1M people, US$50K; US 2020 Census estimates). Percent native or indigenous (3% vs 11%) and White (92% vs 74%) population, percent married (49% vs 45%), employment (87% vs 92%), and households with internet connections (85% vs 75%) were roughly comparable, as were estimates of gross domestic product (AU$261B or US$193B vs US$105B). Western Australia has a much larger land area (977K vs 121K square miles) but populations are similarly distributed across post/ZIP code areas (387 postcodes vs 427 ZIPcodes). The majority of persons reside in metropolitan areas (e.g., Stirling, Wanneroo, Joondalup, and Swan in Western Australia and Albuquerque, Las Cruces, Rio Rancho, and Santa Fe in New Mexico, all with more than 80K people each). New Mexico and Western Australia license alcohol production, wholesale distribution and retail sales through outlets (vs states which monopolize aspects of production or sales67). Western Australia licenses retail sales through on- or off-premises establishments, small bars (fewer than 120 person capacity), “hotels” which often serve as bars (called such for historical reasons) and taverns, restaurants, wine, liquor, and grocery stores (“supermarkets”). Supermarkets must have a separate liquor store license and the outlet must be located either outside the market or (in only a small number of cases) within a cordoned-off area. Small convenience stores cannot sell alcohol. New Mexico has a similar license structure for retail sales, but with no constraint on the size of bars and sales allowed within grocery and convenience stores. Both states require responsible beverage service training for managers and servers, allow conditional use permits for local control, have social host laws prohibiting provision of alcohol at parties among underage drinkers, require permits for special events (e.g., fairs), allow sales on all days and over similar hours, and allow home deliveries of alcohol.68,69 The minimum legal drinking age is lower in Western Australia (18 vs 21) with a lower BAC limit for impaired driving (0.05 vs 0.08 for adults). Characteristic of the US alcohol market, license densities are greater in New Mexico than Western Australia (off-premise: ~1/2000 vs ~1/4500 persons). Evidence from survey studies shows that drinking patterns and use of drinking contexts are generally comparable between US and Australian populations with own homes the most frequently used drinking context and parties and bars the predominant contexts for heavy drinking.35,70,71 Finally, Australian and US population studies observe very much the same effects relating measures of availability through outlets (absent of controls for sales) to all problems so far examined (interpersonal violence72-74 vs 75-77; intimate partner violence78,79 vs 16,80,81; AMVCs17,82 vs 23,83,84). C.2. Project GIS. Census year-specific postcode polygon maps (shape files that delineate postcode boundaries) are maintained for all years and updated annually by the Australia Bureau of Statistics. These include SA1 Census areas that are smaller than US Census block groups and comprised of about 400 people (US block groups average about 1500 people). Annual estimates of social, demographic and economic characteristics of populations are available from the Australia Bureau of Statistics. As these data become available, they are aggregated to year-specific postcode maps by NDRI staff with SA1 data mapped to postcodes using SA1 centroids (greater than 96% of households are correctly located using this procedure). Postcode routes, like ZIPcodes in the US, are defined for the purpose of mail delivery with route sizes are responsive to population density Routes are limited by numbers of persons and businesses to whom mail is delivered and, thus, much smaller in urban areas.85 Postcodes are nested within "communities", here defined as Australian "Local Goverment Areas" (LGAs are governed towns, cities or districts) that are also represented by one or more postcodes. Out of 137 LGAs in 2020, 40 were represented by 5 or more postcodes (30 in 1991) and 4 were represented by 10 or more postcodes (8 in 1991). Data frames will consist of one long frame that runs from 1991 through 2020 (30 years) and includes locations of outlets, alcohol sales, general population characteristics, crime and hospitalizations/separations, and one short frame that will run from 2003 through 2020 (18 years) that includes these data elements, emergency department presentations, commercial business data and information on extended trading permits (see accompanying table). Both panels include a defined "community subset" of 30 LGAs comprised of 5 or more postcodes in 1991 (see Section C.3). There were 295 Western Australian postcodes in 1991, 311 in 2003, and 387 in 2020. There will be about 10,230 analysis units in the long data frame and 6,138 in the short data frame (about 6,300 and 3,780 in the community subset). All GIS work is conducted in ArcGIS86 with Geostatistical Analysis, Network Analyst, and Spatial Analyst extensions, Python open source packages Fiona, Shapely, PyProj, GeoPandas, PySAL, and proprietary Python scripts that integrate applications across datasets and sources. Data will be updated through 2020 at the beginning and through 2025 at the conclusion of the project. The project GIS allows us to assess distance, density, and topological relationships among postcodes over time. However, area mismatches will arise across years due to changes in shape, size and numbers of postcodes. Although postcodes comprehensively cover the study area, they are gerrymandered in different ways from-year-to-year with resulting impacts on estimated statistical effects (biases due to "unit misalgnments", see Section C.3). While current empirical work indicates that the impacts of misalignment on marginal effects are modest, at best, impacts on local area estimates (e.g., effects estimates for suburban areas where changes are greatest) can be substantial.15,83,87,88 We provide a means of addressing unit misalignments below, but note here that sensitivity tests will be used to bracket effects estimates using a common analysis frame (e.g., postcode areas defined in an early year) and analyses of "concurrent" postcode areas; postcodes which are identically defined from-one-year-to-the-next. Spatial Availability. The opening and closing datea and locationa of "active" alcohol outlets in Western Australia are provided in licensing data. "Active" licenses are those open and selling to customers. Locations are geocoded to street addresses with a geocoding rate better than 99%. Spatial availability indices specific to each of 6 outlet types (small bars, hotels/taverns, restaurants, wine stores, liquor stores, and supermarkets) will be constructed for each postcode in every year using Cumulative Opportunity indices calculated over 5 nearest outlets (Cp5; summed inverse distances from each postcode's SA1 population weighted centroid to the 5 nearest outlets).89 As Cp5 approaches zero, distances become large and opportunities to purchase alcohol low. Cp5 is a preferred spatial density measure that provides estimates of availability in both dense and sparsely populated areas (e.g., where alcohol may only be available in nearby postcodes).48 Within any year, the opening or closing of a new outlet takes place against the background of overall spatial availability effects and can be treated in a quasi-experimental analytic framework.11 Effects related to each opening/closing in year t can be measured in relation to spatial availability effects through year t-1. Alcohol Sales. Alcohol sales through outlets are provided by Western Australia Mental Health Commission and made available to NDRI on request. These data are provided in aggregate form by postcode and currently measure sales for 4 beverage types (high- and low-alcohol beer, wine, and spirits) and 6 outlet types (small bars, hotels/taverns, restaurants, wine and liquor stores, supermarkets). For analysis purposes, these 24 sales measures are converted to units pure ethanol using standard procedures.90-92 Sales data can be recompiled at NDRI's request to reflect other aspects of the beverage market (e.g., sales by quality class). Economic Availability. Costs of beverages purchased from wholesalers are also available for all alcohol sales. Dollar amounts within beverage-by-outlet types are summed and adjusted by the Western Australia Consumer Price Index to estimate annual wholesale prices. These are divided by total quantities of pure alcohol sold in each year to measure the average wholesale price per liter of pure ethanol; a significant cross-correlation of r=0.636 related wholesale to retail price estimates over 40 quarters of data (~2001-2010).93 Western Australia also introduced 5 tax changes affected wholesale and retail alcohol prices over the past three decades: (1) Biannual increases in beer and spirits taxes indexed to the consumer price index (CPI). (2) A 29% ad valorem Wine Equalization Tax (WET) in 2000. (3) A spirits Ready-to-Drink (RTD) tax on pre-mixed spirit beverages and Alcopops (sweetened drinks) in 2000. (4) A 10% goods and services tax on all alcoholic beverages in 2000. (5) Another Alcopops Tax introduced in 2008.133 There is accumulating evidence that alcohol taxes are directly and wholly passed through to consumers within one year of any tax hike.94-96 Impacts of these covarying measures that affect economic availability will be included in models assessing impacts related to physical availability (Section C.4). Outlet Regulations. Other regulations intended to affect sales through outlets include, first, the designation of 58 "restricted" and "dry" areas beginning in 1992 (currently there are 25 "dry" and 33 "restricted" areas within 32 postcodes) and, second, "extended trading provisions" that allow extended trading hours for outlets on a license-by-license basis (133 ETPs within 60 postcodes issued by 2015). "Restricted" areas limit trading hours, takeaway sales, types and volumes sold. "Dry" areas preclude sales. Importantly, no LGA is entirely restricted or dry. Uniquely, although hotels have functioned as taverns since the 19th century, "small bars", limited to 120 person capacity, were introduced in 2006. Alcohol Related Morbidity and Mortality. Hospital "separations" (discharges in US parlance), emergency department "presentations" (admissions), and deaths are geocoded to household residence. Diagnoses associated with each separation/presentation/death are identified by diagnostic codes using either the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) or, since 1998, ICD-10-CM. We will count diagnostic incidents and deaths for all patients 18 years of age and older. Hospital separations have several advantages when measuring health outcomes: (1) Counts of hospital separations and presentations at public hospitals within the Australian medicare system constitute a census of all hospital encounters. (2) They focus on severe cases that have been vetted using uniform standards and consistent ICD coding conditions.97 (3) They are accurate, validated and widely used to estimate diagnostic incidence and prevalence.98,99 (4) There are sufficient cases to support estimates for small population areas (e.g., areas comparable to US plain states).100 Presenting ICD-9 codes more familiar to US researchers, we will focus upon alcohol abuse and dependence (ICD-9 codes 305.0, 303.0), alcoholic cirrhosis (571.1-571.3), and injuries related to assault (E960-E969), suicide or self-inflicted wounds (E950-E959), motor vehicle accidents (E810- E819), domestic and partner violence (E967), and all other unintentional injury accidents (E800-E807, E850-E858, E860-E869, E880-E888, E900-E909, E910-E915, E916-E928, E929, E980-E989). Alcohol etiologic fractions have been calculated for each of these outcomes101,102 and, while ICD-9 diagnostic codes were used in the US to 2015, they were adopted for hospital separations in Western Australia in 1999 with codes cross-walked at that time103,104 in a manner comparable to procedures adopted in the US.105 We have considerable experience with these population level data (e.g., cirrhosis106; assaults107; suicide108; partner violence16) and will document and statistically control for impacts, if any, of diagnostic coding on tests of study hypotheses. Crime. Violent assault and drink driving (i.e., impaired driving) incidents with or without arrests are reported annually for postcodes by Western Australia police agencies. Incidents are located by address or intersection/milepost of report/arrest. Drink driving counts are contaminated by arrest incidents related to drink driving patrols ("booze buses") but these can be distinguished in available data.82 Police activity (i.e., field officers engaged in patrol activities) is reported and has been demonstrated as a confounder of measures of domestic violence and protective custody incidents, but not assault or drink driving incidents.93 Similar controls work well to assess confounding levels of enforcement effort in US studies.109 Western Australian Census Data. The Australian census is conducted every five years and annual census estimates are available by year for SA1 units reaggregated to postcodes. Data include population, employment and housing characteristics important in previous studies of alcohol sales and related problems: population size by age, gender and ethnicity, income, impoverishment, full- and part-time employment, unemployment, and residential housing characteristics (e.g., dwelling structure, tenure type, landlord type, number of bedrooms, and unoccupied housing).110 Commercial Business Data. Importantly, the commercial business data allow us to identify commercial centers with substantial retail activity, foot and motor vehicle traffic, important routine activities confounders. Environmental Data. Road network data identify transportation links between postcode areas and provide supplementary measures of roadway characteristics related to crashes. Roadway systems, geographically represented in the MWRA “Main Roads Western Australia” GIS, link rural areas, populations, businesses, and outlets and provide a better spatial framework for availability measures. For this reason, roadway distance will be used as the metric by which to calculate Cp5. The connectivity of postcodes in rural areas is also of importance since adjacent postcode boundaries may have no or few connecting roadways. Six roadway characteristics have also been identified as confounding predictors of AMVCs, total roadway length, percent highways, average speed, percent "T" intersections, curviness, and fragmentation.84 Planning and zoning data are available for LGAs and will be used to measure proportions of land area dedicated to retail trade, residential housing, and other commercial and recreational activities.111 Public transportation networks identify increased accessibility across some urban places, may reduce the need to drive and, subsequently, the incidence of motor vehicle crashes related to use. They may also lower costs of transportation to nighttime entertainment districts with associated risks for other alcohol related problems. Conjoint bus and rail stops between postcodes will be used to identify these links. Sex as a Biological Variable. While sex, gender and ethnicity are measures that will be included in all analyses and assessed as study covariates, outcome data stratified by these variables are not available in WAAID and potential subgroup differences across study outcomes cannot be further examined in this study. C.3. Statistical Analysis Framework. Numbers of postcode areas in WA grew from 295 in 1991 to 387 in 2020 with about 12% expected to change location, shape, or size each year.107 Representing different subpopulations in successive years, "unit misalignments" bias effects estimates and preclude longitudinal analyses using postcode/ZIPcode data. We developed Bayesian space-time models to statistically adjust for misalignment bias87,88 and have since improved this approach using more efficient Bayesian spatial stochastic partial differential equation models implemented in R-INLA (SPDEs).112-114 Impacts of unit misalignments are represented as changing locations of data elements (e.g., postcode centroids) on a Gaussian spatial random field underlying each SPDE model.8 The approach (a) provides a more realistic assessment of spatial relationships (i.e., continuous spatial autocovariances), (b) statistically adjusts for impacts of misalignments, and (c) allows direct modeling of temporal autoregressive effects. SPDE models represent spatial relationships among data elements using a finite element mesh that links nodes (i.e., postcode centroids) to one another in geographic space. The WAAID GIS has all geographic information needed to define the mesh with suitable projections (e.g., Australian Albers, EPSG:3577). Matern autocovariance functions measure spatial autocovariances along the mesh and enable assessments of temporal autocorrelated measurement error and temporal lag effects. Enforcing consistency across spatial analyses of project data, a common mesh will be defined for all analyses. SPDE error components will include i.i.d. unit effects, effects specific to the estimation of Matern covariance functions, multilevel error components related to multiresolution effects56, and controls for heteroskedasticity that arise due to structural components of analysis models shown in the figure accompanying Section A.5 (1). Across all analysis models, control covariates will include economic and sociodemographic measures and concurrent changes in alcohol beverage taxes and other alcohol control laws. Hypotheses will be tested within one of four data frames, long (30-year) or short (18-year) within a "full set" (Nfs~350) or "community subset" (Ncs~210) of postcode areas. Analyses of the full set will measure effects across all populations and the entire geography of Western Australia replicating methods used in many geospatial studies of alcohol problems. Analyses of the community subset will measure "local", "nearby" and "community" effects specific to 30 communities (LGAs) composed of 5 or more postcode areas. Analyses of the community subset will include data from buffered areas around communities (e.g., spatial availability measures) in order to ease concerns about edge effects. "Local" area effects are those in which independent and dependent variables are jointly measured within postcodes. "Nearby" effects are those in which a postcode dependent measure is related to independent measures summarized across adjacent postcodes (spatial lag effects). "Community" effects are those in which a postcode dependent measure is related to independent variables summarized across community postcodes. While not as highly resolved as studies which rely upon Census block group (or, within Australia, SA1) areas, analyses of data from the community subset should enable us to distinguish local, from nearby and community effects enabling more precise assessments of relationships between availability, sales and problems. Local and nearby measures must be centered on nearby and community measures to insure efficiency and interpretability of these multiresolution effects.56 Wishing to keep to otherwise standard analytic approaches to analyses of alcohol sales and problem data, semi-logarithmic Gaussian SPDEs will be applied to the assessment of alcohol sales in relation to measures of economic, physical and legal availability (e.g., beverage taxes, spatial availability, and regulatory policies). Poisson SPDEs will be applied to the assessment of problem incidence. References 1Campbell, C.A., Hahn, R.A., Elder, R., Brewer, R., Chattopadhyay, S., Fielding, P. (2009) The effectiveness of limiting alcohol outlet density as a means of reducing excessive alcohol consumption and alcohol-related harms. American Journal of Preventive Medicine, 37, 556-569. 2Popova, S., Giesbrecht, N., Bekmuradov, D., and Patra, J. (2009) Hours and days of sale and density of alcohol outlets: Impacts on alcohol consumption and damage: A systematic review. Alcohol and Alcoholism, 44, 500-516. 3Chaloupka, F.J., Grossman, M. and Saffer, H. (1998) The effects of price on the consequences of alcohol use and abuse. In: Galanter, M., Ed. Recent Developments in Alcoholism: Vol. 14, The Consequences of Alcohol. New York: Plenum Press, pp. 331–346. 4Grossman, M. (1988) Health economics of prevention of alcohol-related problems. Paper presented at the Workshop on Health Economics of Prevention and Treatment of Alcohol-Related Problems, National Institute on Alcohol Abuse and Alcoholism, Washington, DC. 5Gruenewald, P.J. (2011) Regulating Availability: How access to alcohol affects drinking and problems in youth and adults. Alcohol Research & Health 34(2):248-256. PMC3860569 6Frankeberger, J., Gruenewald, P.J. and Mair, C. (2021) Dual use of off-premise outlets for alcohol and grocery purchases: Results from the East Bay Neighborhoods Study, In press, Journal of Studies on Alcohol and Drugs. 7Graham, K. and Homel, R. (2009) Raising the Bar. New York: Taylor and Francis. 8Sumetsky, N., Mair, C., Anderson, S. and Gruenewald, P.J. (2020) A spatial partial differential equation approach to addressing unit misalignments in Bayesian Poisson space-time models. Spatial and Spatio-temporal Epidemiology. 9Freisthler, B., Lipperman-Kreda, S., Bersamin, M. and Gruenewald, P.J. (2014) Tracking the when, where, and with whom of alcohol use: Integrating ecological momentary assessment and geospatial data to examine reisk for alcohol-related problems. Alcohol Research: Current Reviews, 36, 1, 29-38. 10Texas Attorney General's Office (2016) Wal-Mart Stores, Inc., et al. v. Texas Alcoholic Beverage commission, et al. Civil Action No. 1:15-cv-00134-RP. 11Gruenewald, P.J., Sumetsky, N. Gaidus, A., Ponicki, W.R., Lee, J.P. and Mair, C. (2021a) Assessing the impacts of alcohol outlets on crime as a natural experiment: Agglomeration, churning and spatial effects. In press, Addiction. 12Gmel, G., Holmes, J., and Studer, J. (2015) Are alcohol outlet densities strongly associated with alcohol-related outcomes? A critical review of recent evidence. Drug and Alcohol Review, 35, 40-54. 13Morrison, C., Cerda, M., Gorman, D., Gruenewald, P., Mair, C., Naimi, T., Scribner, R., Stockwell, T., Toomey, T. and Wieczorek, W. (2015) Commentary on Gmel et al., (2015): Are alcohol outlet densities strongly associated with alcohol-related outcomes? A critical review of recent evidence. Drug and Alcohol Review, 35, 55-57. PMC4826631 14Gruenewald, P.J. (2016) Expert Witness Report of: Paul J. Gruenewald, Ph.D. March 18, 2016 Wal-Mart Stores, Inc., et al. v. Texas Alcoholic Beverage Commission, et al. Civil Action No. 1:15-cv-00134-RP. 15Mair, C., Gruenewald, P., Ponicki, W. and Remer, L. (2013) Varying impacts of alcohol outlet densities on violent assaults: Explaining differences across neighborhoods. Journal of Studies on Alcohol and Drugs, 74, 50-58. PMC3517264 16Cunradi, C.B., Mair, C., Ponicki, W. and Remer, L. (2012) Alcohol outlet density and intimate partner violence-related emergency department visits. Alcoholism: Clinical and Experimental Research, 36, 847-853. 17Morrison, C, Ponicki, W.R., Gruenewald, P.J., Wiebe, D.J. and Smith, K. (2016b) Spatial relationships between alcohol-related road crashes and retail alcohol availability. Drug and Alcohol Dependence,l 162, 241-244. PMC4833595 18Stockwell, T., Zhao, J., Macdonald, S., Pakula, B., Gruenewald, P.J. and Holder, H.D. (2009) Changes in per capita alcohol sales during the partial privatization of British Columbia's retail alcohol monopoly 2003-2008: a multi-level local area analysis. Addiction, 104, 1827-1836. 19Stockwell, T., Zhao, J., Macdonald, S., Vallance, K., Gruenewald, P.J., Ponicki, W.R., Holder, H. and Treno, A. (2011b) Impact on alcohol-related mortality of a rapid rise in the density of private liquor outlets in British Columbia: a local area multi-level analysis. Addiction, 107, 1218-1226. 20Stockwell, T., Auld, M.C., Zhao, J. and Martin, G. (2011a) Does minimum pricing reduce alcohol consumption? The experience of a Canadian province. Addiction, 107, 912-920. 21Stockwell, T., Zhao, J., Marzell, M., Gruenewald, P.J., MacDonals, S., Ponicki, W.R. and Martin, G. (2015) Relationships between minimum alcohol pricing and crime during the partial privatization of a Canadian government alcohol monopoly. Journal of Studies on Alcohol and Drugs, 76, 628-634. 22Rossow, I. and Norstrom, T. (2012) The impact of small changes in bar closing hours on violence. The Norwegian experince from 18 cities. Addiction, 107, 530-537. 23Lipton, R., Gaidus, A., Ponicki, W.R., and Gruenewald, P.J. (2018) Space-Time analyses of alcohol outlets and related motor vehicle crashes. Alcoholism: Clinical and Experimental Research, 42, 1113-1121. PMC5984166 24Marcus, J. and Siedler, T. (2015) Reducing binge drinking? The effect of a ban on late-night off-premise alcohol sales on alcohol-related hospital stays in Germany, IZA Discussion Papers, No. 8763, Institute for the Study of Labor (IZA), Bonn. 25Meany, B., Berning, J. and Smith, T. (2018) The effect of sunday alcohol sales bans on teen drinking in Georgia. Applied Economics Perspectives and Policy, 40, 461-481. 26Pliakas, T., Lock, K., Jones, A., Aalders, S. and Egan, M. (2018) Getting shops to voluntarily stop sellling cheap, stron geers and ciders: A time-series analysis evaluating impacts on alcohol availability and purchasing. , 41, 110-118. 27Hobday, M., Chikritzhs, T., Liang, W. and Meuleners, L. (2015) The effect of alcohol outlets, sales and trading hours on alcohol-related injuries presenting at emergency departments in Perth, Australia, from 2002 to 2010. Addiction, 110, 1901-1909. 28Holmes, J. and Meier, P.S. (2015) Commentary on Hobday et al. (2015): Inconsistent results beneath consistent conclusions – the need for a new approach to analyzing alcohol availability. Addiction, 110, 1910-1911. 29Haughwout, S.P. and Slater, M.E. (2018) Alcohol Epidemiologic Data System (AEDS), CSR, Incorporated, Contract No. HHSN275201300016C, National Institute on Alcohol Abuse and Alcoholism, Division of Epidemiology and Prevention Research. 30Iowa Alcohol Beverages Division (2021) Iowa Data. https://data.iowa.gov/Sales-Distribution/Iowa-Liquor-Sales/m3tr-qhgy. Accessed October, 19, 2021. 31Barnett, S.B.L., Coe, N.B., Harris, J.R. and Basu, A. (2020) Washington's privatization of liquor: Effects on household alcohol purchases from initiative 1183. Addiction, 115, 681-689. 32Byrnes, J., Shakeshaft, A., Petrie, D. and Doran, C. (2013) Can harms associated with high-intensity drinking be reduced by increasing the price of alcohol? Drug and Alcohol Review, 32, 27-30. 33Halonen, J.I., et al., Proximity of off-premise alcohol outlets and heavy alcohol consumption: A cohort study. Drug and Alcohol Dependence, 2013. 132(1-2): p. 295-300. 34Subbaraman, M.S., Mulia, N., Kerr, W.C., Patterson, D., Karriker-Jaffe, K.J. and Greenfield, T.K. (2020) Relationships betwween US state alcohol policies and alcohol outcomes: Differences by gender and race/ethnicity. Addiction, 115, 1285-1294. 35Gruenewald, P.J., Remer, L.R. and LaScala, E.A. (2013) Testing a social ecological model of alcohol use: The California 50 city study. Addiction, 109, 736-745. PMC4106302 36Brenner, A.B., et al., (2015) Longitudinal associations of neighborhood socioeconomic characteristics and alcohol availability on drinking: Results from the Multi-Ethnic Study of Atherosclerosis (MESA). Social Science & Medicine, 145, 17-25. 37White, V., Azar, D., Faulkner, A., Coomber, K., Durkin, S., Livingston, M., Chikritzhs, T. Room, R., and Wakefield, M. (2018) Adolescents' alcohol use and strength of policy relating to youth access, trading hours and driving under the influence: Findings from Australia. Addiction, 113, 1030-1042. 38Friesen, E., Kurdyak, P., Jewett, R., Smith, B., Hobin, E., Tanuseputro, P., and Myran, D. (2021) Associations between the physical availability of alcohol and alcohol use: Regional variation across 15 major cities in Ontario, Canada. Addiction, In press. 39Kerr, W.C. and Greenfield, T.K. (2007) Distribution of alcohol consulmption and expenditures and the impact of improved measurement on coverage of alcohol sales in the 2000 National Alcohool Survey. Alcoholism: Clinical and Experimental Research, 31, 1714-1722. 40Hadfield, P. (2009) Nightlife and Crime: Social Order and Governance In International Perspective. New York: Oxford University Press. 41Kypri, K. and Livingston, M. (2020) Incidence of assault in Sydney, Australia, throughout 5 years of alcohol trading hour rstrictions: Controlled before-and-after study. Addiction, 115, 2045-2054. 42Han, D. and Gorman, D.M. (2013) Evaluating the effects of the introduction of off-sale alcohol outlets on ciolent crime. Alcohol and Alcoholism, 48, 370-374. 43Gorman, D.M., Ponicki, W.R., Zheng, Q., Han, D., Gruenewald, P.J., and Gaidus, A. (2018) Violent crime redistribution in a city following a substantial increase in the number of off-sale alcohol outlets: A Bayesian analysis. Drug and Alcohol Review, 37, 348-355. PMC6231714 44Freisthler, B., Gruenewald, P.J., Remer, L.G., Lery, B. and Needell, B. (2007) Exploring the spatial dynamics of alcohol outlets and child protective services referrals, substantiations, and foster care entries. Child Maltreatment, 12, 114-124. 45Gruenewald, P.J., Grube, J.W., Saltz, R. and Paschall, M.J. (2018a) Environmental Approaches to Prevention: Communities and Contexts. In S.C. Miller, D.A. Fiellin, R.N. Rosenthal, R. Saitz (Eds.), Principles of Addiction Medicine: The Essentials. Chevy Chase, MD: American Society of Addiction Medicine. 46Single, E. (1988) The availability theory of alcohol related problems. In C.D. Chaudron & D.A. Wilkinson, Eds., Theories on Alcoholism, pps. 325-351, Toronto: Addiction Research Foundation. 47Stockwell, T. and Gruenewald, P.J. (2004) Controls on the physical availability of alcohol. In The Essential Handbook of Treatment and Prevention of Alcohol Problems, pps. 213-233, N. Heather and T. Stockwell, Eds., New York: John Wiley. 48Sachs, J.J., Brewer, R., Mesnick, J., Holt, J., Zhang, X., Kanny, D., Elder, R. and Gruenewald, P. (2019) Measuring alcohol outlet density: an overview for public health practitioners. In press, Journal of Public Health Management & Practice. PMC Journal – In Process 49Cohen, L.E. and Felson, M. (1979) Social change and crime trends: A routine activity approach. American Sociological Review, 44, 588-608. 50Mair, C., Sumetsky, N., Lee, J., Gruenewald, P.J. and Torso-Orkis, L. (2021) Features of off-premise alcohol outlets, neighborhood conditions and violent crime: The East Bay Neighbohoods study. In review. 51Gruenewald, P.J. (2007) The spatial ecology of alcohol problems: Niche theory and assortative drinking. Addiction, 102(6): 870-888. 52Sumetsky, N., Gruenewald, P.J., Lipperman-Kreda, S., Lee, J.P. and Mair, C. (2021) Alcohol use frequencies and associated problems across drinking contexts. In press, Journal of Studies on Alcohol and Drugs. PMC Journal - In Process 53Waller, L.A. and Godfrey, C. A. (2004) Applied Spatial Statistics for Public Health Data. New York: Wiley-Interscience. 54Banerjee, A., LaScala, E.A., Gruenewald, P.J., Freisthler, B., Treno, A. & Remer, L. (2008) Social disorganization, alcohol and other drug markets, and violence: A space-time model of community structure. In: Yonette F. Thomas, Douglas Richardson & Ivan Cheung. (Eds.), Geography and Drug Addiction. Pp. 117-130. New York: Springer. 55Cerda, M., Wheeler-Martin, K., Bruzelius, E., Ponicki, W.R., Gruenewald, P.J., Mauro, c., Crystal, S., Davis, C., Keyes, K., Hasin, D., Rudolph, K., Martins, S. (2021) Spatiotemporal analysis of the association between pain management clinic laws and opioid prescribing and overdose deaths. In press, American Journal of Epidemiology. PMC Journal - In Process 56Mair, C., Sumetsky, N., Gaidus, A., Gruenewald, P.J. and Ponicki, W. (2020) Multi-resolution analyses of neighborhood correlates of crime: Smaller is not better. American Journal of Epidemiology, in press. PMC Journal – In Process 57Morrison, C., Gruenewald, P.J. and Ponicki, W.R. (2016a) Race, ethnicity and exposure to alcohol outlets. Journal of Studies on Alcohol and Drugs, 77(1), 68-76. PMC4711321 58Lee, J.P., Ponicki, W.R., Mair, C.F., Gruenewald, P. and Ghanem, L. (2020) What explains the concentration of off-premise alcohol outlets in Black neighborhoods? In press, Social Science and Medicine - Population Health. PMC Journal - In Process 59Mair, C., Sumetsky, N., Gruenewald, P.J., and Lee, J.P. (2020) Microecological relationships between area income, off-premise alcohol outlet density, drinking patterns and alcohol use disorders: The East Bay Neighborhoods Study. In press, Alcoholism: Clinical and Experimental Research. PMC Journal – In Process PMID: 32573798 60Gruenewald, P.J., Millar, A.B., Treno, A.J., Ponicki, W.R., Yang, Z. & Roeper, P. (1996) The geography of availability and driving after drinking. Addiction 91(7): 967-983. PMID: 8688823 61Liang, W. and chikritzhs, T. (2011) Revealing the link between licensed outlets and violence: counting venues versus measuring alcohol availability. Drug and Alcohol Review, 30, 524-535. 62Kerr, W.C., Williams, E., Ye, Y., Subbaraman, M.S. and Greenfield, T.K. (2018) Survey estimates of changes in alcohol use patterns following the 2012 privatization of the Washington liquor monopoly. Alcohol and Alcoholism, 53, 470-476. 63Myran, D.T., Chen, J.T., Giesbrecht, N., and Rees, V.W. (2019) The association between alcohol access and alcohol-attributable emergency department visits in Ontario, Canada. Addiction, 114, 1183-1191. 64Lardier, D.T., Opara, I., Lin, Y., Roach, E., Herrera, A., Garcia-Reid, P. and Reid, R.J. (2021) A spatial analysis of alcohol outlet density type, abandoned properties, and police calls on aggravated assault rates in a northeastern US city. Substance Use and Misuse, 56, 1527-1535. 65LeSage, J.P. and Pace, R.K. (2009) Introduction to Spatial Econometrics. Boca Raton: CRC Press. 66Gruenewald, P.J., Freisthler, B., Remer, L.G., LaScala, E.A. and Treno, A.J. (2006) Ecological models of alcohol outlets and violent assaults: Crime potentials and geospatial analysis. Addiction 101(5): 666-677. 67Nelson, T.F., Xuan, Z., Babor, T.F., Brewer, R.D., Chaloupka, F.J., Gruenewald, P.J., Holder, H., Klitzner, M., Mosher, J.F., Ramirez, R.L., Reynolds, R., Toomey, T.L., Churchill, V. and Naimi, T.S. (2013) Efficacy and strength of evidence of U.S. alcohol control policies. American Journal of Preventive Medicine, 45, 19-28. PMC3708657 68Chikritzhs, T. (2009) Australia. In Hadfield, P. (Ed) Nightlife and Crime: Social Order and Governance in International Perspective. Oxford: Oxford University Press. 69National Institute on Alcohol Abuse and Alcoholism (2021) Alcohol Policy Information System (APIS). https://alcoholpolicy.niaaa.nih.gov, accessed September 9, 2021. 70Callinan, S., Livingston, M., Room, R. and Dietze, P. (2016) Drinking contexts and alcohol consumption: How much alcohol is consumed in different Australian locations? Journal of Studies on Alcohol and Drugs, 77, 612-619. 71Dietze, P.M., Livingston, M., Callinan, S. and Room, R. (2014) The big night out: What happens on the most recent heavy drinking occasion among young Victorian risky drinkers? Drug and Alcohol Review, 33, 346-353. 72Stevenson, R.J., Lind, B. and Weatherburn, D. (1999) The relationship between alcohol sales and assault in New South Wales, Australia. Addiction, 94, 397-410. 73Livingston, M. (2008) Alcohol outlet density and assault: A spatial analysis. Addiction, 103, 619-628. 74Morrison, C., Smith, K., Gruenewald, P.J., Ponicki, W.R., Lee, J.P. and Cameron, P. (2016c) Relating off-premises alcohol outlet density to intentional and unintentional injuries. Addiction, 111, 56-64. 75Scribner, R.A., MacKinnon, D.P. and Dwyer, J.H. (1995) The risk of assaultive violence and alcohol availability in Los Angeles county. American Journal of Public Health, 85, 335-340. 76Toomey, T.L., Erickson, D.J., Carlin, B.P., Lenk, K.M., Quick, H.S., Jones, A.M., and Harwood, E.M. (2012) The association between density of alcohol establishments and violent crime within urban neighborhoods. Alcoholism: Clinical and Experimental Research, 36, 1468-1473. 77Zhang, X., Hatcher, B., Clarkson, L., Holt, J., Bagchi, S., Kanny, d. and Brewer, R.D. (2015) Changes in density of on-premises alcohol outlets and impact on violent crime. Atlanta, Georgia, 1997-2007. Preventing Chronic Disease, 12, 140317. https://doi.org/10.5888/pcd12.140317 78Livingston, M. (2010) The ecology of domestic violence: The role of alcohol outlet density. Geospatial Health, 5, 139. https://doi.org/10.4081/gh.2010.194 79Livingston, M. (2011) A longitudinal analysis of alcohol outlet density and domestic violence: Alcohol outlet density and domestic violence. Addiction, 106, 919-925. 80Cunradi, C.B., Mair, C., Ponicki, W. and Remer, L. (2011) Alcohol outlets, neighborhood characteristics, and intimate partner violence: Ecological analysis of a California city. Journal of Urban Health, 88, 191-200. 81McKinney, C.M., Caetano, r., Harris, T.R. and Ebama, M.S. (2009) Alcohol availability and intimate partner violence among US couples. Alcoholism: Cliinical and Experimental Research, 33, 169-176. 82Chikritzhs, T. and Stockwell, T. (2006) The impact of later trading hours for hotels on levels of impaired driver road crashes and driver breath alcohol levels. Addiction, 101, 1254-1264. 83Ponicki, W.R., Gruenewald, P.J. and Remer, L.G. (2013) Spatial panel analyses of alcohol outlets and motor vehicle crashes in California: 1999-2008. Accident Analysis and Prevention, 55, 135-143. PMC4207645 84Lipton, R., Banerjee, A., Ponicki, W.R., Gruenewald, P.J. and Morrison, C. (2021) Impacts of confounding roadway characteristics on estimates of associations between alcohol outlet densities and alcohol-related motor vehicle crashes. Drug and Alcohol Review, 40, 239-246. 85Australian Bureau of Statistics (2011) Australian Statistical Geography Standard (ASGS): Volume 3 – Non ABS Structures, July 2011 (cat. no. 1270.0.55.003). 86Environmental Systems Research Institute (2016) ArcGIS V10.4. ESRI http://desktop.arcgis.com/en/arcmap/. 87Zhu L, Waller LA, Ma J. (2012) Spatial-temporal disease mapping of illicit drug abuse or dependence in the presence of misaligned ZIP codes. GeoJournal. Available at: http:// www.springerlink.com/content/r823l22137l6r505/ fulltext.pdf. Accessed August 16, 2019. 88Gruenewald, P.J., Ponicki, W.R., Remer, L.G., Waller, L.A., Zhu, L., and Gorman, D.M. (2012) Mapping the spread of methamphetamine abuse in California from 1995 to 2008. American Journal of Public Health, 103, 1262-1270. PMC3682593 89Van Meter, E., Lawson, A.B., Colabianchi, N., Nichols, M., Hibbert, J., Porter, D. and Liese, A.D. (2012) Spatial accessibility and availability measures and statistical properties in the food environment. Spatial and Spatiotemporal Epidemiology, 2, 35-47. 90Loxley, W., Chikritzhs, T. and Catalano, P. (2011) National Alcohol Sales Data Project Stage Two: Final Report. Perth (AUST): Curtin University of Technology, National Drug Research Institute. 91Chikritzhs, T., Allsop, S., Moodie, A.R., and Hall, W.D. (2010) Per capita alcohol consumption in Australia: Will the real trend please step forward? Medical Journal of Australia, 193,1-4. 92Distilled Industry Business Council of Australia (2006) Alcohol Tax in Australia. South Melbourne (AUST): DSICA. 93Symons, M., Gray, D., Chikritzhs, Skov, S., Saggers, S., Boffa, J. and Low, J. (2012) A longitudinal study of influences on alcohol consumption and related harm in Central Australia: With a particular emphasis on the role of price. National Drug Research Institute, Curtin University, Perth, Western Australia. 94Siegel, M., Grundman, J., DeJong, W., Naimi, T.S., King, C., Albers, A.B., Williams, R.S., and Jernigan, D.H. (2013) State-specific liquor excise taxes and retail prices in eight U.S. states, 2012. Substance Abuse, 34, 415-421. 95Ngo, A. and Chaloupka, F. (2019) The pass-through of alcohol taxes to prices in OECD countries. 8th Conference of the American Society of Health Economists, Washington, DC, June 23-26. 96Ally, A.K., Meng, Y., Chakraborty, R., Dobson, P.W., Seaton, J.S., Holmes, J., Angus, C., Guo, Y., Hill-McManus, D., Brennan, A. and Meier, P.S. (2014) Alcohol tax pass-through across the product and price range: Do retailers treat cheap alcohol differently? Addiction, 109, 1994-2002. 97Unick, G.J., et al., (2013) Intertwined Epidemics: National Demographic Trends in Hospitalizations for Heroin- and Opioid-Related Overdoses, 1993–2009. PLoS ONE, 8(2): p. 1-8. 98Hasegawa, K., et al., (2013a) Childhood asthma hospitalizations in the United States, 2000-2009. The Journal Of Pediatrics, 163(4): p. 1127-33.e3. 99Hasegawa, K., et al., (2013b) Trends in Bronchiolitis Hospitalizations in the United States, 2000–2009. Pediatrics, 132(1): p. 28-36. 100Ponicki, W.R., Henderson, J., Gaidus, A., Gruenewald, P.J., Lee, J.P., Moore, R.S., Davids, S., and Tilson, N. (2018) Spatial epidemiology of alcohol and drug-related health problems among Northern Plains American Indians: Nebraska and South Dakota, 2007-2012. Alcoholism: Clinical and Experimental Research, 42, 578-588. PMC5832572 101Single, E., et al., (2000) The relative risks and etiologic fractions of different causes of death and disease attributable to alcohol, tobacco and illicit drug use in Canada. CMAJ: Canadian Medical Association Journal, Journal De L'association Medicale Canadienne. 162(12): p. 1669-1675. 102Rehm, J. (2011) The Risks Associated With Alcohol Use and Alcoholism. Alcohol Research & Health. 34(2): p. 135-143. 103Chikritzhs, T., Unwin, L., Codde, J., Catalano, P. and Stockwell, T.R. (2002) Alcohol-related codes: Mapping ICD-9 to ICD-10. National Drug Research Institute. Curtin University of Technology, Perth, Western Australia, ISBN:1 74067 1872. 104Chikritzhs, T., Catalano, P., Stockwell, T.R., Donath, S., Ngo, H.T., Young, D.J. and Matthews, S. (2003) Australian Alcohol Indicators, 1990-2001; patterns of alcohol use and related harms for Australian states and territories. National Drug Research Institute, Perth, Western Australia. 105Centers for Medicare and Medicaid Services. (2019) General Equivalence Mappings. https://www.cms.gov/Medicare/Coding/ICD10/downloads/ICD-10_GEM_fact_sheet.pdf 106Ponicki, W.R. and P.J. Gruenewald (2006) The impact of alcohol taxation on liver cirrhosis mortality. Journal Of Studies On Alcohol. 67(6): p. 934-938. 107Gruenewald, P.J. and Remer, L. (2006) Changes in outlet densities affect violence rates. Alcoholism: Clinical and Experimental Research, 30, 1184-1193. 108Johnson, F.W., P.J. Gruenewald, and L.G. Remer (2009) Suicide and alcohol: do outlets play a role? Alcoholism, Clinical And Experimental Research. 33(12): p. 2124-2133. 109Caetano, R., Vaeth, P. A., Gruenewald, P. J., Ponicki, W. R., Kaplan, Z., & Annechino, R. (2021). Proximity to the US/Mexico border, alcohol outlet density and population-based sociodemographic correlates of spatially aggregated violent crimes in California. Annals of Epidemiology, 58, 42-47. 110Australian Bureau of Satistics (2021) Housing Variables. https://www.abs.gov.au/statistics/standards/housing-variables/latest-release#summary 111Western Australia Department of Planning, Lands and Heritage (2021) Local Planning Strategies, Schemes and Structure Plans. Local Planning Strategies, Schemes and Structure Plans (www.wa.gov.au), Accessed October 19, 2021. 112Blangiardo, M. and Cameletti, M. (2015) Spatial and SpatioTemporal Bayesian Models with R-INLA. Chichester, UK: John Wiley & Sons, Ltd. 113Bakka, H., Rue, H., Fuglstad, G.-A., Riebler, A., Bolin, D., ILlian, J., Krainski, E., Simpson, D. and Lindgren, F. (2018) Spatial modeling with R-INLA: A review. arXiv:1802.06350v2 [stat.ME] 8 May 2018 114Krainski, E.T., Gomez-Rubio, V., Bakka, H., Lenzi, A., Castro-Camilo, D., Simpson, D., Lindgren, F. and Rue, H. (2019) Advanced Spatial Modeling with Stochastic Partial Differential Equations using R and INLA. Boca Raton, FL: CRC Press. 115Young, D.J. and Bielinska-Kwapisz, A. (2003) Alcohol consumption, beverage prices and measurement error. Journal of Studies on Alcohol, 64, 235-238. 116Cook, P. (2007) Paying the Tab: The Costs and Benefits of Alcohol Control. Princeton: Princeton University Press. 117McElreath, R. (2016) Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Boca Raton, FL: CRC Press. 118Burnham, K.P. and Anderson, D.R. (2010) Model Selection and Multimodel Inference. New York: Springer. 119Li, L. and Sexton, R.J. (2005) Retailer pricing strategies for differentiated products: The case of lettuce and bagged salads. Department of Agricultural and Resource Economics, University of California, Davis. 120Hosken, D. and Reiffen, D. (2004a) Patterns of retail price variation. The RAND Journal of Economics, 35, 128-146. 121Wen, H. and Goodman, A.C. (2013) Relationship between urban land price and housing price: Evidence from 21 provincial capitals in China. Habitat International, 40, 9-17. 122Greene, W.H. (2012) Econometric Analysis, 7th Edition. New York: Prentice Hall. 123Woolridge, J.M. (2010) Econometric Analysis of Cross Section and Panel Data. Boston, Massachusetts: MIT Press. 124Minguez, R., Lopez, F.A., and Mur, J. (2019) spsur: An R Package for Spatial Seemingly Unrelated Regressions. https://cran.r-project.org/web/packages/sp;sur/vignettes/spsur-vignette.html. 125Sumetsky, N., Mair, C., Ponicki, W.R. and Gruenewald, P.J. (2021) Microtemporal analyses of violence related to alcohol outlets; Weekends, weeknights, daytime and nighttime. In press, Drug and Alcohol Review. 126Hill, E., A. Allen, and L.A. Waller, Recent development of disease mapping and detection clusters: A comparison of focused score tests and Bayesian hierarchical models for detecting spatial disease clustering. Bulletin of National Institute of Public Health (Japan), 1999. 48: p. 102-112. 127Hoff, P., A First Course in Bayesian Statistical Methods. 2009, New York: Springer. 128Wikle, C.K. (2003) Hierarchical Models in Environmental Science. International Statistical Review. 71, 181-199. 129Cressie, N. and Wikle, C.K. (2011) Statistics for Spatio-Temporal Data. New York: Wiley. 130Gruenewald, P.J., LaScala, E.A., and Ponicki, W.R. (2018) Identifying the population sources of drinking and alcohol impaired driving: An assessment of context specific drinking risks. Journal of Studies on Alcohol and Drugs, 79, 702-709. PMC6240005 131Hodges, J. and Reich, B. (2010) Adding spatially-correlated errors can mess up the fixed effect you love. University of Minnesota Division of Biostatistics; Technical Report No. 2010-002. http:/www.biostat.umn.edu/ftp/pub/2010/rr2010-002.pdf 132Paciorek, C.J. (2010) The importance of scale for spatial-confounding bias and precision of spatial regression estimators. Statistical Science, 25, 107-125. 133Commonwealth of Australia, Parliamentary Budget Office (2015). Alcohol Taxation in Australia. Report No. 03/2015. Canberra, Commonwealth of Australia.