Original quantitative research – Exploring and visualizing the small-area-level socioeconomic factors, alcohol availability and built environment influences of alcohol expenditure for the City of Toronto: a spatial analysis approach

Health Promotion and Chronic Disease Prevention in Canada, Vol 39, No 1, January 2019

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Andrew Leung, MSc ^{Author reference 1}; Jane Law, PhD ^{Author reference 1}, ^{Author reference 2}; Martin Cooke, PhD ^{Author reference 1}, ^{Author reference 3}, ^{Author reference 4}; Scott Leatherdale, PhD ^{Author reference 1}

https://doi.org/10.24095/hpcdp.39.1.02

This article has been peer reviewed.

Correspondence: Andrew Leung, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1; Tel: 647-825-2667; Email: as2leung@uwaterloo.ca.

Abstract

Introduction: Many Canadians continue to drink alcohol in excess of the recommended low-risk guidelines. In this study, we visualized the geographic variation of licensed premises alcohol expenditures in Toronto and examined the effects of area-level socioeconomic characteristics, alcohol availability and built environment influences on alcohol expenditures at the Dissemination Area (DA) level.

Methods: Dissemination Area average total household expenditures on alcohol from licensed premises, from the 2010 Survey of Household Spending, was the main outcome variable. Moran's I and Local Moran's I were used to quantify geographic variation and determine hot spots and cold spots of expenditure. We used DA-level socioeconomic characteristics from the 2006 Census of Canada, and the density of licensed premises and other built environment characteristics from the 2008 DMTI Spatial and 2010 CanMap datasets to predict alcohol expenditures in multivariate spatial regression models.

Results: The results indicated that the most significant area-level predictors of alcohol expenditure were the percentage of individuals in management or finance occupations and the percentage with postsecondary education (one-unit increases associated with 78.6% and 35.0% increases in expenditures respectively). Presence of subway lines in the immediate and neighbouring areas was also significant (one-unit increases resulted in 5% and 28% increases respectively). Alcohol outlet density was also positively associated with alcohol expenditures.

Conclusion: The associations identified between licensed premises alcohol expenditures and small-area-level characteristics highlight the potential importance of small-area-level factors in understanding alcohol use. Understanding the small-area-level characteristics of expenditures and geographic variation of alcohol expenditures may provide avenues for alcohol use reduction initiatives and policies.

Keywords: small-area studies, dissemination areas, spatial analysis, spatial regressions, alcohol expenditure, alcohol use, low-risk drinking guidelines, geographic variation, GIS

Highlights

Alcohol use exceeding low-risk drinking guidelines remains a public health problem. Small-area-level associations with alcohol expenditures have not been examined, leaving potentially significant factors affecting alcohol use unidentified.
Understanding the geographical variation of alcohol expenditures in the city of Toronto can help provide target areas for interventions. Spatial regression models can control for geographical variation when looking for associations with small-area-level characteristics.
This study quantified the level of geographical variation in the licensed premises alcohol expenditures and helped visualize the areas of high expenditures using maps.
Significant positive associations were found for small-area-level socioeconomic and built environment factors.

Introduction

Harmful alcohol use is recognized as one of the main modifiable behavioural risks for noncommunicable disease. Despite the negative consequences, many Canadians continue to drink in excess of the recommended low-risk guidelines, and thus may be at risk for experiencing alcohol-related harms. In Canada, alcohol use was estimated to be responsible for 8953 deaths and 172 255 potential years of life lost in 2005.^Footnote 1 In 2011, the indirect and direct costs of alcohol use were estimated to be $5.3 billion for the province of Ontario.^Footnote 2 ^Footnote 3 ^Footnote 4 These economic and social costs of alcohol use are projected to rise with increasing levels of consumption. ^Footnote 2

Various individual-level factors have also been related to alcohol consumption, including ethnicity, occupation, income, housing and education.^Footnote 5 ^Footnote 6 ^Footnote 7^Footnote 8 There is evidence that the physical and demographic aspects of neighbourhoods can also potentially influence alcohol use.^Footnote 9 ^Footnote 10 These neighbourhood-level factors include area-level socioeconomic factors, characteristics of the built environment and local alcohol availability.^Footnote 11 ^Footnote 12 ^Footnote 13 ^Footnote 14 ^Footnote 15 ^Footnote 16

A number of area-level socioeconomic measures have been found to be associated with alcohol use, although the directions of the effects found are not always consistent. Cerdá and colleagues^Footnote 17 found that models including a measure of deprivation provided better estimates of alcohol use compared to models that omitted measures of deprivation. Studies by Galea et al.^Footnote 18 and Pollack et al.^Footnote 19 found that areas with the lowest levels of neighbourhood deprivation had the highest prevalence of alcohol use. Area-level income and education inequality have also been found to be positively associated with alcohol use.^Footnote 18 ^Footnote 20

In general, the built environment can be defined as the aspects of a physical area that are not characteristics of the people who live there but rather capture the physical resources available in the particular location. ^Footnote 21 Elements of the built environment that might affect alcohol consumption include the presence of public transportation^Footnote 21 ^Footnote 22 and alcohol availability, reflected by the presence of alcohol retailers, restaurants and bars.^Footnote 23 Prior work has also suggested that the physical condition of buildings in a neighbourhood is a significant factor. Bernstein et al.^Footnote 9 identified that rates of heavy drinking were 150% higher among individuals in neighbourhoods characterized by high levels of dilapidation, an effect that was significant in multilevel models that also included individual income and education.

A significant portion of research relating neighbourhood factors to alcohol use has used survey measures that capture alcohol consumption. Although we have self-reported alcohol consumption data collected from self-report surveys, those data potentially suffer from the typical problems of self-reported measures. Alcohol expenditure data, while conceptually close to consumption, have been examined less frequently and provide an alternative way of capturing alcohol use. ^Footnote 5 ^Footnote 24 Expenditure data can also be obtained from self-report surveys, such as the data used in the present analysis. These data are therefore subject to the same limitations, but similar research might be done using alcohol sales data that are reported by liquor authorities as well as retailers.

The goal of our study was to examine the association between geographic and environmental influences on licensed premises alcohol expenditures for the City of Toronto. We used expenditure data on alcohol purchased in licensed premises as the main outcome variable and examined the effects of area-level socioeconomic factors, alcohol availability and built environment influences. We employed small-area-level analysis and multivariate spatial regression models to examine the associations between these area-level factors and reported alcohol expenditures, and we used geographic information systems (GIS) techniques to visualize "hot" and "cold" spots of alcohol expenditure for the City of Toronto.

Methods

Unit of analysis

The unit of analysis for the study was the Dissemination Area (DA), which is the smallest Statistics Canada geographic unit that covers all of Canada, with each DA containing 400 to 700 persons.^Footnote 25 Socioeconomic and built environment variables are more homogenous within DAs than within larger geographic areas, and using a small spatial scale allows for identification of spatial patterns that might have been masked at larger geographic scales. Figure 1 shows the 3685 Dissemination Areas that make up the City of Toronto from the 2006 Census of Canada. ^Footnote 26

**Figure 1. Dissemination Area map of Toronto, Canada, with major highways**

Dissemination area code	Area (m²)
35200002	49667
35200003	46914
35200004	50041
35200005	43884
35200006	56847
35200007	77433
35200009	63518
35200010	35646
35200012	47781
35200013	7433
35200018	176140
35200028	125402
35200029	43488
35200030	96772
35200031	229689
35200032	229134
35200033	94177
35200034	613878
35200035	113623
35200036	40227
35200037	238808
35200039	63111
35200041	223430
35200042	150303
35200043	793807
35200044	26785
35200046	92810
35200047	58090
35200048	101692
35200049	79389
35200050	570609
35200051	67551
35200052	167251
35200053	49203
35200054	111001
35200055	97703
35200056	884592
35200057	26054
35200058	25305
35200062	142383
35200064	143132
35200065	60905
35200066	80891
35200067	101890
35200068	56421
35200069	129931
35200070	70982
35200071	52229
35200072	113438
35200073	55634
35200074	56304
35200078	165432
35200079	100420
35200080	229923
35200082	50751
35200085	82369
35200086	116339
35200087	182707
35200088	108957
35200089	157005
35200090	132487
35200091	69334
35200092	182254
35200093	141232
35200094	2190786
35200095	74922
35200096	28137
35200097	32989
35200099	35037
35200100	83249
35200101	161140
35200102	260767
35200103	57079
35200104	210304
35200105	185485
35200106	277315
35200107	40183
35200108	66781
35200109	159630
35200110	86579
35200111	58610
35200112	130922
35200114	172042
35200115	124045
35200116	72567
35200117	104289
35200118	68168
35200119	35221
35200120	191194
35200121	62974
35200123	153982
35200124	147220
35200125	67190
35200126	50144
35200127	102623
35200128	450421
35200133	202428
35200134	143144
35200135	56481
35200136	55031
35200137	83286
35200138	31190
35200139	1812014
35200140	1639425
35200141	130794
35200142	63487
35200143	126864
35200144	493518
35200145	44691
35200146	67982
35200147	136398
35200148	128205
35200149	92873
35200150	101075
35200161	82179
35200162	104343
35200163	172829
35200164	86367
35200165	103319
35200166	194925
35200167	308919
35200173	54840
35200174	110107
35200175	193870
35200176	58900
35200177	323426
35200178	108352
35200179	405121
35200180	134782
35200181	54483
35200182	44553
35200183	133494
35200186	85924
35200187	85595
35200188	64581
35200189	113300
35200190	89901
35200193	153096
35200196	313061
35200197	106855
35200198	128948
35200199	176050
35200200	196106
35200201	142079
35200202	195794
35200203	197700
35200204	136720
35200205	105039
35200206	145445
35200207	73368
35200208	118170
35200209	60111
35200210	175904
35200211	58166
35200212	98285
35200213	180123
35200214	189085
35200215	180674
35200216	276476
35200217	138693
35200218	503786
35200219	148727
35200220	234088
35200221	797423
35200222	67298
35200223	109449
35200224	190268
35200232	65472
35200233	126430
35200234	91528
35200235	158154
35200236	95307
35200237	84144
35200238	248311
35200239	79571
35200242	132236
35200243	101060
35200244	100140
35200245	100843
35200246	116276
35200249	94087
35200250	87693
35200251	94219
35200252	145440
35200253	254898
35200254	272528
35200255	225592
35200256	115487
35200260	152697
35200261	98497
35200262	89093
35200263	82824
35200264	78834
35200265	146166
35200266	157772
35200267	133107
35200268	43114
35200269	90240
35200270	1268399
35200271	98715
35200272	146682
35200273	49963
35200275	142655
35200278	146481
35200279	157522
35200280	57982
35200281	69427
35200284	44668
35200288	107830
35200289	284280
35200290	178069
35200291	178682
35200292	185550
35200293	253217
35200294	204318
35200297	57325
35200298	212097
35200300	48761
35200301	184183
35200302	309362
35200303	49576
35200304	256173
35200305	165054
35200306	158322
35200307	42965
35200308	240836
35200309	209931
35200310	93819
35200313	288512
35200315	87063
35200316	43987
35200317	36856
35200318	30698
35200319	58954
35200320	71363
35200321	378125
35200322	106212
35200323	218348
35200324	158046
35200325	28660
35200326	169408
35200327	44075
35200328	143375
35200329	488386
35200330	540533
35200331	131776
35200332	335135
35200333	162225
35200334	134317
35200335	147141
35200336	137739
35200337	127610
35200338	134565
35200339	69169
35200352	61060
35200355	183335
35200357	164389
35200358	132756
35200359	131663
35200360	128269
35200361	117841
35200362	153134
35200363	212737
35200364	192007
35200366	117542
35200368	70677
35200370	302688
35200371	346175
35200372	177992
35200373	226197
35200374	153689
35200375	33069
35200376	576409
35200377	368288
35200378	124073
35200379	204570
35200380	177915
35200381	121928
35200382	117422
35200383	131709
35200384	210698
35200385	93660
35200386	465791
35200387	137147
35200388	211676
35200389	1750950
35200391	115361
35200392	99679
35200393	692392
35200394	209495
35200395	250326
35200396	143399
35200397	342651
35200400	238499
35200401	150592
35200402	147853
35200404	51392
35200405	163428
35200406	79067
35200407	132750
35200408	145846
35200409	59925
35200410	50624
35200411	87150
35200412	82461
35200413	82914
35200414	104703
35200415	287587
35200416	181794
35200417	340641
35200418	49377
35200420	149876
35200421	70202
35200422	226320
35200423	55163
35200426	89153
35200427	109504
35200428	85478
35200429	49921
35200430	187977
35200431	212124
35200432	122441
35200433	84332
35200434	137637
35200435	114058
35200436	110241
35200437	143313
35200438	199642
35200439	101367
35200440	100970
35200442	63498
35200443	75530
35200444	116505
35200445	223899
35200449	82550
35200450	162175
35200451	77378
35200452	1103130
35200453	285136
35200454	281035
35200455	119763
35200456	62149
35200459	107046
35200460	34926
35200463	60600
35200464	105190
35200465	134671
35200466	121494
35200467	148102
35200468	850406
35200469	126850
35200470	131466
35200471	48963
35200472	98990
35200475	230038
35200478	105031
35200479	105900
35200480	90694
35200481	74964
35200482	72041
35200483	100530
35200484	34926
35200488	76422
35200489	35890
35200490	74581
35200491	129799
35200492	60534
35200493	74334
35200494	73933
35200495	169202
35200497	33008
35200498	124092
35200499	109898
35200505	50501
35200506	75268
35200510	57364
35200511	65223
35200512	59694
35200513	58726
35200514	62004
35200515	107465
35200516	60104
35200517	75326
35200518	53241
35200519	57301
35200520	84123
35200521	87995
35200522	88070
35200523	158405
35200524	115962
35200525	173978
35200529	283122
35200531	166428
35200532	111778
35200533	72951
35200534	164156
35200535	68066
35200536	143527
35200538	275073
35200539	45913
35200540	15064
35200541	185419
35200542	16971
35200545	127070
35200561	711066
35200562	93642
35200563	114898
35200564	98254
35200565	115020
35200566	87692
35200567	96285
35200568	73475
35200569	67462
35200570	49047
35200571	61029
35200572	56532
35200573	63277
35200574	63629
35200575	174780
35200576	131169
35200577	61461
35200578	40781
35200579	84158
35200580	51527
35200581	42442
35200582	92162
35200583	75912
35200584	62123
35200585	40505
35200586	53942
35200587	67246
35200588	97908
35200589	66366
35200590	46680
35200591	77250
35200593	55998
35200594	93196
35200595	44318
35200596	97260
35200597	44803
35200598	68988
35200599	71944
35200600	50080
35200601	57308
35200602	63798
35200603	90596
35200604	68182
35200605	74336
35200606	51907
35200610	32976
35200611	72138
35200612	76004
35200639	77434
35200640	102531
35200641	28773
35200642	137705
35200643	135519
35200644	325143
35200645	78250
35200646	75607
35200647	78528
35200648	38039
35200649	67472
35200650	93256
35200651	75211
35200652	74729
35200653	45926
35200654	326118
35200655	87305
35200656	40495
35200657	98582
35200658	199220
35200659	77113
35200660	145000
35200661	31544
35200662	56631
35200663	32828
35200664	78169
35200665	96330
35200666	87762
35200667	45744
35200668	31856
35200669	33653
35200670	52774
35200671	44057
35200672	46304
35200673	28043
35200674	114418
35200675	91715
35200676	85767
35200677	58981
35200678	205478
35200679	56034
35200680	73332
35200681	57351
35200682	48513
35200683	158012
35200686	115474
35200687	42254
35200688	68794

Data sources

We used data from four different sources. Licensed premises alcohol expenditures were taken from the 2010 Survey of Household Spending. ^Footnote 27 The 2008 DMTI Spatial Enhanced Points of Interest dataset^Footnote 28 provided licensed premises outlet locations, and 2010 CanMap Route Logistics data^Footnote 29 provided built environment data (subway lines, highways and land-use). The 2006 Census of Canada data^Footnote 26 provided DA-level socioeconomic characteristics. These characteristics were collected by the census "2B long form," which was distributed to 20% of the population in 2006. In this study, we used the 2006 census because of changes to the 2011 census and to the National Household Survey (NHS), which replaced the long form in 2011. In particular, because the NHS was made voluntary, Statistics Canada did not release the socioeconomic characteristics of some DAs due to nonresponse. ^Footnote 30 In order to conduct the analysis at the Dissemination Area level, this study used the 2006 data.

Measures

The main outcome variable was the DA average total household alcohol expenditure per year from licensed premises in 2010. These data were collected using diary and short-period recall questions. ^Footnote 27 We applied a log transformation to correct for skewness.

Consistent with previous alcohol use and expenditure studies, ^Footnote 5 ^Footnote 6 ^Footnote 7 ^Footnote 8,^Footnote 31 we examined a number of relevant socioeconomic variables as predictors of licensed premises expenditure, including neighbourhood ethnic composition, visible minority concentration, occupation, income, neighbourhood deprivation, housing and education. These were taken from the 2006 census data.

We measured area ethnic composition using the percentage of the DA population reporting Black, Chinese, South Asian and Filipino ethnicities. The concentration of nonvisible minorities was measured using the percentage of the DA population identifying as "not a member of a visible minority." We defined occupation as the percentage of employed DA population reporting working in management, business, finance and administration. This variable was created by combining several groups of occupations according to the 2001 National Occupational Classification for Statistics (NOCS) definition.^Footnote 32

We used two measures of area income: median household after-tax income and the average income from government transfer payments. Education was defined as the proportion of the DA population that reported having completed one of the following qualifications: registered apprenticeship, trades certificate or diploma, college, CEGEP, other non-university certificate or diploma and university certificate, degree or diploma.

Three measures captured the nature of housing in each DA. These were the proportion of dwelling types that were apartment buildings with fewer than five floors, the proportion of single detached houses and the proportion of row (attached) houses. We included the presence of subway lines in a neighbourhood as the number of subway intercepts (access points) within the DA. Spatially "lagged" subway intercepts indicated the presence of subway lines in an adjacent DA.

Alcohol outlet data were retrieved from the 2008 DMTI Enhanced Points of Interest dataset.^Footnote 2 ^Footnote 8

We measured local alcohol availability by the densities of two types of restaurant in each DA. "Primary drinking restaurants" were those whose main business was serving alcohol, while "restaurants" were those with a dual focus on serving both food and alcohol. The densities of these two types of business were calculated by dividing the number of restaurants by the area of the DA in square kilometres. Spatially "lagged" versions of these variables captured the effect of outlet density in neighbouring DAs. ^Footnote 33

Analyses: global spatial autocorrelation, local cluster analysis procedure and multivariate spatial regression

We first calculated Global Moran's I statistic to quantify the average level of spatial autocorrelation and to test the null hypothesis that the alcohol expenditure levels in DAs were fully independent of expenditure in adjacent DAs.^Footnote 34 We then calculated Local Moran's I measures to identify clusters of hot spots and cold spots, where hot spots were clusters of adjacent DAs with similarly high levels of licensed premises expenditures and cold spots were clusters with low levels of licensed premises expenditures. Clustering techniques were carried out using GeoDa v1.6.7.^Footnote 35 High resolution maps were created in ArcGIS 10.3 (Environmental Systems Research Institute, Inc., Redlands, CA, USA).

Consistent with the approach used by Pridemore and Grubesic,^Footnote 36 Grubesic et al.^Footnote 24 and Zhu, Gorman and Horel,^Footnote 37 we used multivariate spatial regressions to estimate the effects of the area-level factors on area-level alcohol expenditure. In the presence of positive spatial autocorrelation, ordinary least squares (OLS) regression can result in biased and inefficient parameter estimates.^Footnote 37 Spatial regression models have been developed to address this problematic effect and have been widely employed in spatial econometrics.^Footnote 38

Spatial regression models include at least one additional variable, known as a spatial autoregressive term, to control for geographical variation. We estimated four main spatial regression models: a spatial lag regression model (also known as spatial autoregressive model or SAR), a spatial error regression model (SEM), a spatial Durbin model (SDM) and a spatial Durbin error model (SDEM).^Footnote 14, ^Footnote 34,^Footnote 38 ^Footnote 39 ^Footnote 40 ^Footnote 41 ^Footnote 42 In the spatial lag model, values of the dependent variable (y) for a unit (i) are assumed to be directly influenced by the values of y in neighbouring units.^Footnote 14, ^Footnote 43 In order to account for this effect, a spatially lagged dependent variable ( pWy) is included as an explanatory variable (Figure 2). Alternatively, the SEM incorporates geographical variation by adding a spatial autoregressive error term as a dependent variable (Figure 2). The SDM and SDEM are extensions of the spatial lag and spatial error models respectively, as these models are the same as their counterparts, except for an additional spatial autoregressive term for independent variables (Figure 2).

**Figure 2. Spatial regression models for calculating average total alcohol expenditures levels for licensed premises in Toronto, Canada, Dissemination Areas, 2010.**

Our procedure began with descriptive statistics and correlation analysis to describe the distribution of the dataset and to identify issues of multicollinearity. Preliminary bivariate OLS regressions suggested positive spatial autocorrelation, therefore we calculated bivariate regressions using an SEM. We then included statistically significant explanatory variables in a multivariate SEM. The number of insignificant explanatory variables was reduced with backwards stepwise regression (α = 0.10). We tested four different spatial regression models, with the best-fitting model determined by the highest log-likelihood value. All regression models were estimatedusing the software package R version 3.2.3. ^Footnote 44

Results

Descriptive statistics

In 2010, the average annual household alcohol expenditure in licensed premises was $337.51 (range: $47.54 to $2963.02). Table 1 provides descriptive statistics for licensed premises expenditure and the area-level explanatory variables.

**Table 1. Table of descriptive statistics for all outcome and explanatory variables, Dissemination Areas in Toronto, Canada, 2010**
Variable	N	Minimum	Maximum	Mean	Standard Deviation
Expenditures in LP	3512	47.54	2963.02	337.51	291.01
Log transformed LP	3512	3.86	7.99	5.56	0.70
% nonvisible minority	3512	0.00	100.00	59.72	26.62
% ethnicity Filipino	3512	0.00	60.86	3.11	5.38
% employed in management	3512	0.07	0.93	0.53	0.15
Median household income—after tax	3512	11776.00	335039.00	56303.79	25201.81
% income from transfer payments	3512	0.00	56.30	11.17	7.37
% apartments < 5 floors	3512	0.00	100.00	19.04	23.95
% apartments—duplex	3512	0.00	61.39	6.60	7.92
% row houses	3512	0.00	100.00	5.62	14.00
% single detached	3512	0.00	100.00	39.87	34.50
% postsecondary education	3512	0.00	0.94	0.48	0.16
Subway intercept	3512	0.00	1.00	0.06	0.23
Lagged subway intercept	3512	0.00	1.00	0.07	0.16
Primary drinking restaurant density by DA	3512	0.00	116.78	1.10	6.46
Lagged primary drinking restaurant density by DA	3512	0.00	38.79	1.13	3.43
Eating restaurant density by DA	3512	0.00	826.78	21.54	59.30
Lagged eating restaurant density by DA	3512	0.00	493.35	21.53	41.11
Abbreviations: DA, Dissemination Area; LP, licensed premises. Notes: A lagged variable considers the effect of a variable of interest (i.e., alcohol expenditures) in neighbouring areas to a specific DA. For example, for a DA called DA1, if DA2 and DA3 are considered neighbours, their average total alcohol expenditure values can be averaged and included as a spatially-lagged independent variable. What constitutes a neighbour is dependent on the contiguity definition (i.e., rook criterion: sharing a common edge or queen criterion: sharing a common edge or a common vertex), and the resulting neighbourhood structure is stored as a spatial weight matrix. Lagged restaurant density: The effect of restaurant densities in areas immediately adjacent to a DA. Lagged subway intercept: The effect of the presence of subways in areas immediately adjacent to a DA.

Spatial autocorrelation

For licensed premises alcohol expenditure, the Global Moran's I value was 0.634 and highly significant (p =.001) (Figure 3), indicating a high degree of positive spatial autocorrelation. A total of three hot spots and three cold spots were identified for alcohol expenditures, using Local Moran's I (Figure 4).

**Figure 3. Global Moran's I statistic scatterplot for licensed premises alcohol expenditures, Toronto, Canada, 2010**

Average total alcohol expenditures in licensed premises in a dissemination area (X-axis)	Average total alcohol expenditures of dissemination areas defined as neighbours of the dissemination area in the X-axis (Y-axis)
-1.3244958	-0.7311616
-0.5565847	-0.2821021
0.7484117	-0.561567
-0.4401517	-0.2340585
-0.4677544	0.0137321
0.1743977	-0.0776286
0.155859	0.0776642
0.7556098	-0.0652036
0.4330423	-0.1902973
0.5315923	-0.5895153
-0.4100418	-0.1175573
-0.1643978	-0.3436025
-0.0707168	-0.05484
-0.3747086	-0.2436877
-0.5189426	-0.2641581
-0.2968323	-0.3698181
-0.5201365	-0.3037615
-0.4614725	-0.5201365
-0.4464165	-0.6243217
0.2216988	-0.4464165
-0.9890296	-0.7303166
-0.9429963	-0.8028589
-0.5681462	-0.7075986
-0.7163044	-0.6049392
-0.6810559	0.0824936
0.5901899	-0.5606038
-0.5684756	-0.4834614
-0.3108754	-0.6560255
-0.4841236	-0.3813899
-0.3703255	-0.3705025
-0.8730304	-0.2834671
-0.4262108	-0.2094445
-0.3240842	-0.3222016
-1.2287034	-0.1847554
-0.5390517	-0.4955411
-0.3988015	-0.5491229
-0.7842328	-0.4524045
-0.195189	-0.7839936
-0.7837545	-0.480767
-0.59105	-0.6924452
-0.5093482	-0.4009305
-0.3240148	-0.5924088
-0.9884781	-0.1114004
0.0903522	-0.844179
-0.6998799	0.0903522
-0.6712933	-0.9884781
0.2145993	0.3039231
0.76225	0.0428777
-0.128844	0.3104882
-0.2849519	-0.0626733
0.312677	0.1047885
0.4945289	-0.1139647
0.1386903	0.0445593
0.0637042	0.2448223
0.0413564	0.1641143
0.3047135	-0.1624655
0.1468748	-0.3075306
0.3357355	-0.0571378
0.4288097	0.1044273
0.2783632	0.2145993
0.3176208	0.3087214
0.1616191	0.1777143
-0.1284801	0.1894732
0.6480834	-0.0193006
0.0554045	-0.0028342
0.0708482	0.0480428
-0.0749752	0.1435128
-0.0164182	0.1307323
-0.0531171	0.0436334
0.3604169	-0.052065
-0.1217047	0.0250974
0.1259446	-0.1264832
-0.7765988	-0.0863915
-0.248833	-0.438786
-0.1009731	-0.1366544
0.3152623	0.0436201
0.0803711	0.090694
0.3161973	-0.3214889
0.4720231	0.0855335
0.5485417	0.237196
-0.1172456	0.3803104
0.4016404	0.089197
-0.0518517	0.5786251
0.0399009	0.1701346
0.0448007	0.0960307
-0.1469716	0.0847475
0.1516734	-0.0444126
0.0422183	0.1552018
0.0639164	0.2342408
0.4895272	0.0495393
-0.5225272	0.2720426
0.7178403	-0.2125162
0.1249792	0.1408771
0.5188541	0.0448316
0.1115977	0.4170582
-0.3586784	0.4748991
0.267447	-0.3953525
-0.4923005	-0.4364992
0.0995403	-0.4865046
0.1203455	-0.2390313
-0.1795853	0.0376481
-0.4284503	-0.164828
0.0371475	-0.4254983
-0.6279919	-0.1960157
-0.1532504	-0.1026266
0.5500556	-0.244573
-0.091841	0.0672951
-0.5359086	0.0577542
-0.6059328	-0.1434093
-0.8463101	-0.2924447
-0.750101	-0.4858879
0.6623571	-0.3268007
0.0601986	-0.3336616
-0.4984049	-0.1049518
0.1550129	-0.1123939
-1.4711892	-0.0947693
-0.8898191	-0.2463557
0.2398821	0.3750903
0.3350769	-0.0906453
0.226586	0.3521215
0.3215281	-0.0552289
0.3530178	-0.0430798
0.2166995	-0.005804
0.2758479	0.008239
0.307358	-0.0595899
0.5852524	0.1201041
-0.2228936	0.4327504
0.5111024	0.1189034
-1.3410301	-0.2851757
-1.0663123	-0.6867797
-0.5837078	-0.8748987
-0.9217836	-0.8016275
-0.9198898	-0.7386667
-0.7486069	-0.5340846
-0.8621816	-0.5100688
0.58396	-0.3987974
-0.3736296	-0.4396273
0.6133237	0.0657895
0.4080845	-0.2712853
0.1652819	0.1966436
-0.9585735	0.2554814
0.8002209	-0.5250317
0.1471251	-0.083043
0.6761094	-0.0345744
0.0599326	-0.3304153
0.0503801	-0.0533546
0.6631615	-0.1368215
0.3263091	0.2561983
0.2553798	0.1680942
0.1166687	-0.0090739
0.2542186	-0.119578
-1.2917145	-0.5134366
-1.2788355	-1.0681203
-0.4626961	-0.3387018
0.6510834	-0.8700435
0.0884762	-0.4375655
-0.6143248	-0.4453634
-0.8339755	-0.6744412
-1.2477278	-1.2771008
-1.1808908	-0.5550273
0.5746571	0.3074219
0.2673088	0.3666832
0.041949	0.33705
-2.3626358	-0.9200282
0.1393448	-0.5066628
-0.0226578	0.0645623
0.6369105	0.365531
0.8333684	0.6055981
0.0898835	-0.59179
0.8269246	0.575223
-0.7975941	0.9664733
0.353625	0.5259753
-1.3778578	-0.6661348
-0.2288632	0.0026857
-1.0561039	-0.0134616
0.1282797	0.0655139
0.67701	0.0380479
0.4727017	0.0987507
-0.2280181	0.1688418
0.0109719	0.0105206
-0.1645843	-0.0530631
0.0578568	-0.0812599
0.4672629	0.0394906
-0.1445782	0.053452
-1.4023558	-0.4942573
0.0749072	-0.6328622
0.0482367	-0.3557034
-1.0331688	-0.8174755
-0.6705858	-0.7226165
-0.535828	-0.8106633
-0.9124437	-0.3168784
-0.7868185	-0.4645603
0.2113956	-0.9153344
-0.983524	-0.9051535
0.1532693	-0.3778278
0.4013888	-0.4487063
-0.8133246	-0.4699032
-1.053563	-0.1102416
-0.2964234	-0.3472513
0.3561812	-0.1230269
0.0318962	0.3047155
0.4614011	-0.3575688
-0.2433053	0.0202561
1.0957635	-0.4957264
0.1467746	-0.252703
-0.32798	-0.2553584
-0.8341736	-0.5542516
-0.8564356	-0.1530651
-0.4126255	-0.2567462
0.0903522	-0.5795975
-0.8337773	-0.8178541
-0.6235358	-0.2851782
0.9396656	-0.494346
-1.1742243	-1.0492404
1.0864689	0.0317458
1.5114914	0.6163217
0.9229825	-0.4433508
-1.3803179	-0.532115
-1.2641992	-0.593714
-0.8120559	-0.9238126
0.5395396	-0.1265395
0.1473253	-0.3836703
0.5409868	0.517224
0.2037637	-0.1323429
1.0250063	-0.1786321
-0.6457817	-0.0244142
-1.4724239	-0.2195492
1.0283461	-0.4437914
0.6947682	-0.1612502
0.2297089	0.7830267
0.8712853	0.1023821
-0.5785571	-0.3982577
-0.7458095	-0.4546139
-0.9093091	-0.1260667
-0.4215194	-0.2878956
-0.7311656	-0.4030336
-0.7683426	-0.3198965
0.3482381	0.1984044
-1.1829095	-0.1563162
-1.2960788	-0.45523
0.4524016	-0.595832
0.7414422	-0.3410895
-1.1884754	-0.2692014
-1.0389826	0.2559529
1.0971344	0.2345681
0.48617	0.8670224
0.2430232	0.2441903
0.4413477	0.7692819
0.4414294	0.6240085
1.4637684	0.8665414
0.857882	1.301702
1.412124	0.7368545
0.2255909	0.5631851
0.3918777	0.3138759
0.5264491	0.7874852
0.6226323	0.5671504
-0.024518	0.1171598
0.6412481	0.3228401
-1.3004564	0.3060888
0.7865845	0.686307
1.3067395	0.9637519
0.9666704	0.9824272
0.886332	1.3729928
1.6486206	0.7442113
1.5699405	0.9290413
1.0862606	1.0692542
-0.6544102	0.5078685
3.0785846	1.1422715
2.254389	1.3621086
1.6841014	1.5771838
1.4936282	1.5083693
1.0124743	1.724154
1.2825364	1.0124743
1.3894972	1.6629278
0.9459692	1.4404386
1.7158219	1.2513059
1.4089014	1.1248807
1.5539416	0.918809
0.0213901	1.0446346
1.4408269	1.0486259
0.2721355	1.0076863
1.5595193	1.0100042
1.4166442	1.5001731
0.4984158	1.6916329
1.4860338	0.8292704
0.8253013	1.5135035
0.99098	0.7373482
1.2215601	0.6141322
0.8746394	0.8047287
1.2470586	1.0138075
1.502217	0.7247827
0.2662495	1.0805634
-1.2592646	-0.0493172
0.9722367	-0.0618049
0.7480491	0.555153
-0.3908469	0.0146647
-1.007238	-0.121552
-0.942035	-0.6619118
-0.5310063	-0.6566295
-0.2678141	-0.9570704
-0.7600393	-0.5943496
-0.4999744	-0.8939717
0.8458006	-0.8022065
-0.7656955	-0.368824
-1.065614	-0.5107407
-0.8103012	-0.2361635
-0.6294517	-0.5469206
-1.4998613	-0.033371
-0.5935618	-0.5964015
-0.7467414	-0.9924042
-1.2088986	-0.6050686
0.6183463	-0.5698499
-0.3832256	-0.2289288
-0.531247	-0.7353225
-1.2281822	-1.1314595
-1.4314747	-0.7834854
-0.7015952	-0.9043048
-0.5778939	-0.5003037
-0.2997667	-0.5794043
-0.4463409	-0.2491081
-0.0485213	-0.2596461
-1.5425111	-1.0111442
-0.9568465	-0.6017025
0.1500756	-0.9625961
0.2498441	-0.132072
0.3463653	0.124143
0.098039	0.0790895
0.9973679	0.1000097
0.2570503	0.1407813
-0.096044	0.1006983
-0.366528	-0.0137512
-0.1217651	0.2394277
0.4303315	0.2034871
0.0515576	0.1428551
0.0620587	0.1930928
-0.125633	0.0434931
2.6574508	1.2924584
0.8791895	0.9912156
1.092499	1.0178207
-0.2714262	0.7462568
0.5700016	0.4068548
-0.2214049	0.0803137
0.1318258	-0.0334379
1.4244277	0.3232957
0.7653796	0.4193003
-1.1658408	-0.0504203
-1.0187927	-0.56824
-1.1424519	-0.63293
-1.5366863	-0.3051089
-0.5180677	-0.5379426
-0.9462054	-0.2785607
0.0518251	-0.8356571
-0.9674581	-0.8493237
0.2616804	0.1734175
-0.9000496	-0.9113349
1.2444473	0.7890632
0.2698391	0.779835
0.0632267	0.5957493
1.355383	1.2093064
-1.3176853	-0.6887319
-1.2708946	-0.6065714
-1.4236916	-0.3853079
-0.7497274	-0.7916177
1.290107	0.6089561
-1.2392999	-0.9709945
-0.8106909	-1.0550826
-0.7914267	-0.6070322
0.8461393	-0.6189319
1.0282917	-0.6749082
-0.3473367	-0.5997897
-0.4056969	-0.6756283
-0.9614929	-0.7235381
-0.8286369	-0.6749333
-1.2440345	-0.94103
-0.6951058	-1.1575944
-0.8208621	-0.7455984
-0.9532906	-0.7230565
-0.6187524	-0.88288
-1.3382142	-0.717066
-0.1983684	-0.7702449
-0.78022	-0.7980538
-0.0713577	-0.7542076
-0.7771699	-0.3340246
-0.1548552	-0.5795521
-0.5979257	-0.4036547
0.1091841	-0.9133659
-1.3190726	-0.2590041
-0.6682891	-0.7060766
-1.695834	-0.8745823
-0.6259335	-0.6614548
-0.9299135	-0.6125662
-0.7087494	-0.8398252
-0.499268	-0.9603801
-0.6892724	-0.7151772
-1.5627572	-0.7992614
-0.8870416	-0.9277473
-0.5157638	-0.3246259
-0.7393034	-0.3895867
0.4057864	-0.7455505
-0.9188386	-0.6994948
-1.7793839	-0.8971594
-0.7796476	-0.7445241
-0.6082167	-0.784282
-0.4421866	-0.8665935
-0.9591137	-0.7033357
-1.0456229	-0.6469749
-0.4663732	-0.971236
-0.8534206	-0.6013255
-0.9160046	-0.9238125
-1.301553	-0.0528273
-0.1292687	-0.5819797
-0.1181484	-0.4604623
0.0586024	-1.1957071
-1.1131695	-0.3070379
0.0399009	-0.7199869
-0.8297227	-0.5755328
-0.8193907	-0.3881234
-0.6336676	-0.7108711
-0.5720209	-0.5058821
-0.6348744	-0.7567313
-1.1612319	-0.2948523
-0.8418216	-0.4799735
0.056098	-0.7558888
-1.0586491	-0.3160907
-0.9685453	-0.3906942
-0.6038214	-0.5121118
-0.7400454	-0.3421705
-0.3395908	-0.2784728
-0.7881611	-0.1723005
0.7128839	-0.2011745
0.189982	-0.0707262
0.0613681	-0.4652339
-0.5218095	-0.4001461
-0.3983624	-0.1441047
-1.1486026	-0.3228492
-1.0632887	-0.398345
-1.2402192	-0.4630435
-0.2003431	-1.2172683
-1.1943174	-0.8607526
0.7210343	-1.2270573
-0.0353893	-0.1546498
-0.5468176	0.0791615
0.3420442	-0.3191582
-0.2841407	-0.0780171
-0.4794718	0.0167353
0.1630556	-0.2209646
-0.0496114	-0.1300832
-0.010429	0.0267596
0.3584303	0.141551
0.0860756	0.1230173
-0.1990688	0.2835573
0.3431366	0.0535259
0.4890538	0.2906095
-0.0980018	0.0793039
0.0542302	-0.0253898
0.1468748	-0.0367418
0.1618174	0.4767795
0.0516646	0.5075137
0.1284826	0.4435052
1.7204883	0.2119692
0.469665	0.530993
-0.028868	0.1931219
0.291327	0.1367309
-0.2435682	0.0109822
0.3915398	0.0194972
0.5435348	0.1086111
-0.1160428	0.1278158
0.1648369	0.0280104
0.3709948	0.0158467
-0.071008	0.0505603
0.1613714	0.0722784
0.6403958	0.0231181
0.1845783	0.2036865
0.0362286	0.0926665
0.0007547	0.1374892
0.9676609	-0.0770713
-0.4165469	0.0315804
0.0087689	-0.2295662
-0.0308497	0.0260795
0.0425414	-0.2827266
0.0522529	-0.202891
0.1616191	-0.2468776
0.4128362	-0.4868652
0.1927488	-0.1378395
-0.2138567	0.1780176
0.1926519	0.063313
0.2125439	-0.3442197
-0.7478604	-0.129818
-1.1774908	-1.3386895
-1.2507688	-0.832507
-0.3793939	-0.1442466
-0.5304449	0.0516576
0.1638475	-0.169612
-0.4009985	0.1011058
-0.4030521	0.2164612
0.1996189	0.5357519
0.4186502	0.5318252
-0.0916637	0.1946011
0.3639515	0.5915708
-0.1106427	0.3861069

**Figure 4. Hot spot and cold spot map for licensed premises alcohol expenditures by Dissemination Area in Toronto, Canada, 2010**

Dissemination area code	Cold/Hot
35200018	Cold
35200028	Cold
35200029	Cold
35200037	Cold
35200181	Cold
35200183	Cold
35200186	Cold
35200232	Cold
35200270	Cold
35200271	Cold
35200313	Cold
35200410	Cold
35200221	Cold
35200233	Cold
35200234	Cold
35200235	Cold
35200239	Cold
35200267	Cold
35200272	Cold
35200281	Cold
35200284	Cold
35200405	Cold
35200407	Cold
35200409	Cold
35200422	Cold
35200430	Cold
35200435	Cold
35200468	Cold
35200469	Cold
35200470	Cold
35200475	Cold
35200641	Cold
35200939	Cold
35200944	Cold
35200969	Cold
35200999	Cold
35200411	Cold
35200414	Cold
35200418	Cold
35200420	Cold
35200432	Cold
35200433	Cold
35200434	Cold
35200436	Cold
35200437	Cold
35200438	Cold
35200439	Cold
35200440	Cold
35200442	Cold
35200444	Cold
35200449	Cold
35200450	Cold
35200453	Cold
35200459	Cold
35200460	Cold
35200463	Cold
35200465	Cold
35200466	Cold
35200467	Cold
35200471	Cold
35200472	Cold
35200483	Cold
35200484	Cold
35200488	Cold
35200489	Cold
35200490	Cold
35200491	Cold
35200493	Cold
35200495	Cold
35200513	Cold
35200515	Cold
35200540	Cold
35200541	Cold
35200639	Cold
35200640	Cold
35200805	Cold
35200882	Cold
35200883	Cold
35200884	Cold
35200891	Cold
35200929	Cold
35200931	Cold
35200933	Cold
35200934	Cold
35200935	Cold
35200936	Cold
35200937	Cold
35200938	Cold
35200940	Cold
35200941	Cold
35200942	Cold
35200943	Cold
35200945	Cold
35200946	Cold
35200949	Cold
35200950	Cold
35200956	Cold
35200962	Cold
35200965	Cold
35200966	Cold
35200967	Cold
35200974	Cold
35200995	Cold
35200997	Cold
35201000	Cold
35201001	Cold
35201002	Cold
35201003	Cold
35201004	Cold
35201005	Cold
35201205	Cold
35201206	Cold
35201209	Cold
35201213	Cold
35201244	Cold
35201381	Cold
35201122	Cold
35201123	Cold
35201125	Cold
35201126	Cold
35201127	Cold
35201128	Cold
35201131	Cold
35201132	Cold
35201133	Cold
35201134	Cold
35201140	Cold
35201141	Cold
35201142	Cold
35201143	Cold
35201144	Cold
35201145	Cold
35201146	Cold
35201147	Cold
35201148	Cold
35201151	Cold
35201158	Cold
35201159	Cold
35201160	Cold
35201161	Cold
35201162	Cold
35201163	Cold
35201164	Cold
35201166	Cold
35201167	Cold
35201168	Cold
35201169	Cold
35201170	Cold
35201171	Cold
35201172	Cold
35201173	Cold
35201174	Cold
35201175	Cold
35201176	Cold
35201177	Cold
35201179	Cold
35201180	Cold
35201182	Cold
35201185	Cold
35201186	Cold
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Spatial regression results

For the licensed premises expenditure, the SDEM model had the largest log-likelihood, indicating the best fit. The model coefficients and significance of coefficients are provided in Table 2.

**Table 2. Spatial Durbin error model regression results for licensed premises expenditures in Toronto, Canada, Dissemination Areas, 2010: coefficients and significance**
Variable	Coefficient
Socioeconomic variables
Percentage nonvisible minority	0.004^{Table Note ***}
Percentage of Filipino ethnicity	−0.003^{Table Note *}
Percentage employed in management occupations	0.580^{Table Note ***}
Median household income, after tax	0.00001^{Table Note ***}
Percentage of income from transfer payments	−0.018^{Table Note ***}
Percentage of apartments < 5 floors	0.001^{Table Note ***}
Percentage of single detached houses	0.002^{Table Note ***}
Percentage of row houses	0.005^{Table Note ***}
Percentage postsecondary education	0.300^{Table Note ***}
Built environment variables
Subway intercepts	0.049
Lagged subway intercepts	0.249^{Table Note ***}
Primary drinking restaurant density by DA (km²)	0.001
Lagged primary drinking restaurant density by DA (km ²)	0.007^{Table Note *}
Restaurant density by DA (km²)	0.0003^{Table Note *}
Lagged restaurant density by DA (km²)	0.001^{Table Note **}
Constant	4.506^{Table Note ***}
Spatial autoregressive error term
Lambda	0.639^{Table Note ***}
Data source: Statistics Canada. Survey of Household Spending (SHS): detailed information for 2013 [Internet]. Ottawa (ON): Statistics Canada; 2013 [modified 2017 Dec 12; cited 2015 Aug 7]. Available from: http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=3508 Abbreviation: DA, Dissemination Area. Notes: A lagged variable considers the effect of a variable of interest (i.e., alcohol expenditures) in neighbouring areas to a specific DA. For example, for a DA called DA1, if DA2 and DA3 are considered neighbours, their average total alcohol expenditure values can be averaged and included as a spatially-lagged independent variable. What constitutes a neighbour is dependent on the contiguity definition (i.e., rook criterion: sharing a common edge or queen criterion: sharing a common edge or a common vertex), and the resulting neighbourhood structure is stored as a spatial weight matrix. Lagged restaurant density: The effect of restaurant densities in areas immediately adjacent to a DA. Lagged subway intercept: The effect of the presence of subways in areas immediately adjacent to a DA. Table Note 2 p <.05. Return to table note * referrer p <.01. Return to table note referrer p <.001. Return to table note * referrer

Socioeconomic and demographic variables

The percentage of people reporting Filipino ethnicity was the only significant ethnicity variable in the final model. A one-unit increase in the percentage of Filipino ethnicity was associated with a decrease in alcohol expenditures of 0.28%. The percentage that was nonvisible minority was positive and significant, with a one-unit increase associated with an increase in alcohol expenditures of 0.44%.

The percentage of employed residents working in management and administration was significantly associated with alcohol expenditures, with a one-unit increase leading to a predicted increase in expenditures of 78.57%. Median after-tax income was also found to have a positive association, as a one-dollar increase was found to increase predicted expenditures by 0.0006%. Conversely, the percentage of income from transfer payments was negatively associated with alcohol expenditures, as a one percentage point increase was associated with a decrease in expenditures by 1.74%. Postsecondary education had a significantly positive effect on expenditures, with a one-unit increase in the proportion of a DA with postsecondary qualifications associated with an increase in predicted expenditures of 35.00%.

The percentage of apartments with fewer than five floors, the percentage of single-detached houses and the percentage of row houses were all positively associated with alcohol expenditure. The associated expenditure increases from a one-unit increase were 0.13%, 0.23% and 0.48% respectively.

Built environment variables

Both the presence of subways and geographically lagged subway presence (the presence of subways in neighbouring areas) were positively associated with alcohol expenditures. A one-unit increase in subway intercepts was found to increase expenditures by 5.01%. Having subways in adjacent areas (lagged subways) resulted in an increase to expenditures by 28.28%. The density of primary drinking restaurants and lagged primary drinking restaurants were also both positively associated with alcohol expenditures, as one-unit increases resulted in 0.06% and 0.73% increases in expenditure respectively. Finally, both restaurant density and lagged restaurant density were positively associated with alcohol expenditure. A one-unit increase in restaurant density led to an increase in expenditures by 0.03%. As for lagged restaurant density, a one-unit increase was associated with an increase in expenditures by 0.10%.

Discussion

Our study found that significant associations existed between area-level socioeconomic variables and licensed alcohol expenditures. Most notably, the proportions of residents in management occupations and with postsecondary degrees were highly positively associated with alcohol expenditures. For public health promotion, this suggests that campaigns such as The Centre for Addiction and Mental Health's (CAMH) "Rethink Your Drinking" might be tailored to target these sociodemographic groups or the establishments that they frequent. It is possible that many drinking establishments in Toronto target clientele from management and administrative occupations, as they may have more disposable income.

This study also found a positive association between the presence of subways and area-level alcohol expenditure. Previous studies have acknowledged the possibility that increased access by public transportation to areas with high alcohol outlet densities could lead to greater alcohol consumption.^Footnote 19 The findings in this study lend their support to this notion, as having a subway line in the same DA led to an average increase of 5.00% in alcohol expenditures. Furthermore, having subway lines in neighbouring areas was also related to higher alcohol expenditures. For policy makers, this could serve as an additional consideration in recommending alcohol outlet densities.

The density of primary drinking restaurants and other restaurants increased expenditures slightly. The direction of association for this finding agrees with previous work by Gruenewald, Ponicki and Holder^Footnote 16 who found that, independent of alcoholic beverage pricing and while controlling for sales and availability, physical availability in the form of alcohol outlets increased the sales of alcoholic beverages.

Several systematic reviews of international alcohol outlet density literature have found considerable evidence of a significant positive association between alcohol outlet density and consumption. Livingston et al.^Footnote 45 examined literature from North America, the UK and Nordic countries, including cross-sectional studies, natural experiments and time-series experiments. The majority of these studies found significant positive associations between alcohol outlet density and alcohol consumption. In another review, Campbell et al.^Footnote 46 also reported that studies generally found outlet density to be associated with increased alcohol consumption, and showed that alcohol bans and changes to licensing arrangements for alcohol can be effective strategies. Popova et al. ^Footnote 47 conducted a systematic review with a focus on both the density of outlets and the hours of sale and their relation to consumption and property damage. Similarly, a majority of the studies reported a positive association between alcohol outlet density and higher overall alcohol consumption.

Several studies have examined the effect of alcohol outlet density and retail arrangements on alcohol consumption in Canada. Xie et al., examining Canadian data from 1968 to 1986, found a significant association between reductions in the density of off-premises sales outlets and reduction in alcohol consumption.^Footnote 46 Trolldal conducted a time-series analysis of the relationship between alcohol sales and availability in four Canadian provinces; however, he did not find a significant positive association. ^Footnote 45 Trolldal also examined the impact of allowing wine sales in grocery stores in Quebec and alcohol retail privatization in Alberta. He found the change in Quebec led to a 10% increase in sales of wine and a smaller increase in total alcohol sales.^Footnote 46 In Alberta, privatization was found to have resulted in a permanent increase in the sale of spirits only.

It may be that some of the variability in the results of the outlet density–consumption relationship is due to that relationship being a nonlinear one. Livingston et al.^Footnote 48 distinguish between two different effects of increasing alcohol outlet density on alcohol-related harms: a proximity effect and an amenity effect. The proximity effect hypothesizes that the effect of outlet density on consumption is reduced at higher density levels, and that outlet density eventually reaches a saturation point. As a result, the corresponding levels of alcohol-related harms from consumption will also plateau. Conversely, the amenity effect postulates that as alcohol outlet density increases, alcohol-related harms increase at an accelerating rate, as more drinkers are brought into closer proximity with each other.

Future research is needed to improve our understanding of these possible relationships. However, the present study does provide evidence of the clustering of both alcohol sales and consumption, which we think can help inform decisions regarding the number and distribution of alcohol sales licenses. In the future, critical values for outlet density could be determined, in order to mitigate alcohol-related harms.^Footnote 46,^Footnote 48,^Footnote 49

Strengths and limitations

This study, using a novel approach, was able to replicate the impact of alcohol availability and income on expenditure observed using more traditional methods. To our knowledge, this was the first study to quantify and visualize the spatial structure of alcohol expenditure and examine associations between expenditures and area-level characteristics in the City of Toronto. It demonstrates the application of spatial methods and use of alcohol expenditure data in the Canadian context.

One of the limitations of this study is the use of DA characteristics in 2006 to predict 2010 licensed premises expenditures. These were the only data available at the time of the study; however, it is hoped that future studies will be able to use area data that are closer in time to the expenditure data. For small-area studies, another primary problem is the issue of the "modifiable area unit problem" (MAUP), in which analyses at different spatial scales result in differing associations between explanatory variables and the outcome variable.^Footnote 13 For this study, the Dissemination Area was chosen as the unit of analysis to balance data availability, limiting MAUP and finding local patterns that have applicability to policy efforts. Another possible limitation is the risk of the ecological fallacy, in which associations observed at a group level are used to make causal inferences at an individual level^Footnote 50, ^Footnote 51 leading to erroneous conclusions. ^Footnote 52, ^Footnote 53 It is, therefore, important to remember that the findings here are only applicable for understanding alcohol expenditure in Toronto at the DA level. Furthermore, it should also be kept in mind that our results are based on cross-sectional data and causal inferences should not be made. Future studies could make use of spatial multilevel models to allow findings to be generalizable to the individual level. Time-series analyses could also be done to address the cross-sectional nature of our study.

Finally, the use of sociodemographic characteristics of residents of a DA to predict alcohol expenditures in the same DA assumes that purchases are made in the area of residence. It is likely that household expenditures often include purchases made in other areas, and this is particularly true because of the small size of this geographic classification and the fact that people travel for work. To fully account for this would require data on the location of expenditures and household characteristics, which are presently unavailable. However, some purchasing activity in other DAs can be statistically accounted for by the spatial Durbin error model, as the spatially lagged alcohol outlet density variables capture the effects of alcohol outlet density in adjacent DAs on the average expenditures in a local DA. The positive associations found indicate that purchases at outlets outside of a local DA have a significant impact on the average expenditures within that DA. The significant spatial error term (Lambda) in the models might also be the result of purchases in adjacent DAs increasing expenditure levels within the DA of residence. In the absence of more in-depth data, one way to overcome this disconnect between place of residence and place of retail behaviour is to employ "gravity models," such as the kernel density estimation method, to better account for retail behaviour in external DAs affecting the expenditures in a local DA. ^Footnote 54

Conclusion

The results of this study suggest that both area-level socioeconomic factors and built environment variables may be related to levels of alcohol expenditure. The results corroborated the findings of previous individual-level studies of socioeconomic correlates of alcohol use, and also found associations between small-area-level alcohol expenditure and socioeconomic characteristics for the first time. We also identified significant built environment associations with alcohol expenditures, which underscore the importance of examining contextual factors as significant influences of health behaviours. Moreover, this study adds considerably to the current understanding of alcohol expenditures by recognizing geographic influences. The findings in this study demonstrate the utility of a spatial analysis approach for understanding alcohol use, and furthermore, highlight how the spatial methods used may help municipalities more effectively formulate alcohol-use reduction strategies to assist in reducing alcohol-related harms. Future research will benefit from this spatial understanding of licensed premised alcohol expenditures and can explore other avenues, such as examining additional built environment variables and applying models that explicitly address area-level effects.

Acknowledgements

The authors would like to thank Dr. Philemon Ho-Yan Leung for his general advice and help in proofreading and editing the paper. Additionally, they would like to thank Dr. Jianhua Zhao for technical writing assistance to address the reviewers' feedback, and Heather Hofstetter for helping to proofread and edit the manuscript.

Conflicts of interest

The authors state that they have no competing interests in this work.

Authors' contributions and statement

AL, JL, MC, and SL all proposed and planned the analysis. AL conducted the analysis and wrote the first draft. AL, JL, MC, and SL commented and edited subsequent drafts.

The content and views expressed in this article are those of the authors and do not necessarily reflect those of the Government of Canada.

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