Introductiond-scholarship.pitt.edu/25683/1/Christopher_P_2015_8_MPH.docx · Web viewObjective: This study examines the public health significance of recent legislature change which

APPLICATION OF NEGATIVE BINOMIAL AND LINEAR REGRESSION ANALYSIS

IN ASSESSING SPATIAL AND TEMPORAL TRENDS OF ALCOHOL OUTLET DENSITY AND AGGRAVATED ASSAULT IN THE CITY OF PITTSBURGH

BETWEEN 2000 AND 2012

by

Phyon K. Christopher

BS, Mathematics, University of the Virgin Islands, 2005

Submitted to the Graduate Faculty of

Epidemiology

Graduate School of Public Health in partial fulfillment

of the requirements for the degree of

Master of Public Health

University of Pittsburgh

2015

ii

UNIVERSITY OF PITTSBURGH

GRADUATE SCHOOL OF PUBLIC HEALTH

This essay is submitted

by

Phyon K. Christopher

on

July 30, 2015

and approved by

Essay Advisor:Anthony Fabio, PhD, MPH ______________________________________Assistant ProfessorDepartment of EpidemiologyGraduate School of Public HealthUniversity of Pittsburgh

Essay Reader:Tiffany L. Gary-Webb, PhD, MHS ______________________________________Associate ProfessorDepartment of Behavioral and Community Health SciencesGraduate School of Public HealthUniversity of Pittsburgh

iii

Copyright © by Phyon K. Christopher

2015

ABSTRACT

Objective: This study examines the public health significance of recent legislature change which

allows for the acquisition of a wider variety of alcohol retail licenses. The temporal trend in

alcohol retail density and its effect on aggravated assault in the city of Pittsburgh is assessed by

comparing data from 2000 and 2012. Previous studies have concluded that there is an association

between alcohol outlet density (AOD) and violence, but many of these studies are inconsistent

with some reporting an association between AOD and violence and others reporting no increase

in risk of violence.

Methods: Data for this study was geocoded to determine alcohol outlet density within Pittsburgh

from InfoUSA and Dun&Bradstreet; population and demographic data was geocoded from the

US Census Bureau; neighborhood level rates of violence were calculated from data provided by

the Pittsburgh police department; and data pertaining to 11 neighborhood characteristics were

extracted from Summary File 1 and Summary File 3 of the 2000 and 2010 US Census. The

association between AOD and violence was assessed using negative binomial regression; the

temporal relationship was assessed using linear regression modeling. As a proxy for commercial

activity, “restaurant outlet density” (restaurants) was added to the negative binomial models to

check for robustness.

iv

Anthony Fabio, PhD, MPH

APPLICATION OF NEGATIVE BINOMIAL AND LINEAR REGRESSION

ANALYSIS IN ASSESSING SPATIAL AND TEMPORAL TRENDS OF ALCOHOL

OUTLET DENSITY AND AGGRAVATED ASSAULT IN THE CITY OF

PITTSBURGH BETWEEN 2000 AND 2012

Phyon Christopher, MPH

University of Pittsburgh, 2015

Results: The model building process produced 16 results: 8 for data where the rates were divided

per capita and 8 where the rates were divided per square miles. In 2000 all models produced the

same result. Initially they were significant and became non-significant once “restaurants” was

added to the model. In 2012 almost all models remained significant after adding restaurants

except for the off-premise alcohol retail density per square miles rates in 2012. In 2012 some of

the models also changed from positive to negative association after adding restaurants to the

models. The linear models all showed that there was no association between the change in AOD

and the change in aggravated assault.

Conclusions: The results of this study suggest a weak or no association between AOD and

aggravated assault. When models are created without “restaurants” added, all models produce

relative risk values approximately equal to 1 which shows that AOD does not impact the risk of

violence. After restaurants are added to the model almost all predictors of interest become non-

significant. These two results confirm that the association either isn’t present or is not as strong

as previous literature reported.

v

TABLE OF CONTENTS

PREFACE....................................................................................................................................XI

1.0 INTRODUCTION.........................................................................................................1

2.0 METHODS....................................................................................................................4

2.1 DATA SOURCES.................................................................................................4

2.2 DATA ANALYSIS................................................................................................7

2.3 CONCEPTUAL FRAMEWORK.......................................................................8

3.0 RESULTS.......................................................................................................................9

3.1 MODELS FOR POPULATION RATES 2000..................................................9

3.2 MODELS FOR POPULATION RATES 2012................................................11

3.3 MODELS FOR CHANGE IN POPULATION RATES.................................13

3.4 MODELS FOR SQUARE MILES RATES 2000............................................15

3.5 MODELS FOR SQUARE MILES RATES 2012............................................17

3.6 MODELS FOR CHANGE IN SQUARE MILES RATES.............................19

4.0 DISCUSSION..............................................................................................................21

4.1 EFFECT SIZE....................................................................................................21

4.2 CONFOUNDING...............................................................................................22

4.3 TEMPORAL RELATIONSHIP.......................................................................23

5.0 CONCLUSION............................................................................................................25

vi

5.1 COMPARISON TO OTHER LITERATURE.................................................26

5.2 LIMITATIONS...................................................................................................27

APPENDIX: SUPPLEMENTARY TABLE..............................................................................29

BIBLIOGRAPHY........................................................................................................................32

vii

LIST OF TABLES

Table 1. Negative Binomial Regression Using Density of On-premise Alcohol Retail per Capita

to Predict Aggravated Assault in 2000 while Controlling for Socio Economic Covariates..........10

Table 2. Negative Binomial Regression Using Density of Off-premise Alcohol Retail per Capita


Table 3. Negative Binomial Regression Using Density of On-premise Alcohol Retail per Capita


Table 4. Negative Binomial Regression Using Density of Off-premise Alcohol Retail per Capita


Table 5. Linear Regression Using Change in Density of On-premise Alcohol Retail per Capita to

Predict Change in Aggravated Assault while Controlling for the Change in Socio Economic

Covariates......................................................................................................................................14

Table 6. Linear Regression Using Change in Density of Off-premise Alcohol Retail per Capita to

Predict Change in Aggravated Assault while Controlling for the Change in Socio Economic

Covariates......................................................................................................................................14

Table 7. Negative Binomial Regression Using Density of On-premise Alcohol Retail per Square

Mile to Predict Aggravated Assault in 2000 while Controlling for Socio Economic Covariates. 15

Table 8. Negative Binomial Regression Using Density of Off-premise Alcohol Retail per Square

Mile to Predict Aggravated Assault for 2000 while Controlling for Socio Economic Covariates16

viii

Table 9. Negative Binomial Regression Using Density of On-premise Alcohol Retail per Square

Mile to Predict Aggravated Assault in 2012 while Controlling for Socio Economic Covariates. 17

Table 10. Negative Binomial Regression Using Density of Off-premise Alcohol Retail per

Square Mile to Predict Aggravated Assault in 2012 while Controlling for Socio Economic

Covariates......................................................................................................................................18

Table 11. : Linear Regression Using Change in Density of On-premise Alcohol Retail per Square

Mile to Predict Change in Aggravated Assault while Controlling for the Change in Socio

Economic Covariates.....................................................................................................................19

Table 12. Linear Regression Using Change in Density of Off-premise Alcohol Retail per Square

Mile to Predict Change in Aggravated Assault while Controlling for the Change in Socio

Economic Covariates.....................................................................................................................20

Table 13. Basic descriptive statistics for selected demographic characteristics at aggregate county

level................................................................................................................................................30

ix

LIST OF FIGURES

Figure 1. Conceptual Framework Displaying Casual Pathway between Predictors, the

Confounder and the Outcome..........................................................................................................8

x

PREFACE

Working on this research has been a great experience and it couldn’t have been done without the

guidance and patience of my mentor and advisor Dr. Anthony Fabio. Even when I turned things

in late he was always positive and kept me motivated to finish. I would also like to take time to

thank my second reader Dr. Tiffany L. Gary-Webb for reading my paper on such short notices

and providing great feedback. I wouldn’t have been able to improve this document so drastically

without her help. Thank you both for your help and your patience.

xi

1.0 INTRODUCTION

Violence is not randomly distributed, but tends to cluster in certain neighborhoods with

certain characteristics such as high levels of unemployment, low levels of family income,

poverty, female headed households and low high school graduations rates – disadvantaged

neighborhoods 7-9,27,36. Ecologic studies at the neighborhood level suggest that availability of

alcohol is related to violence, injury, traffic crashes, drunk-driving offenses, cirrhosis mortality,

assaultive violence, sexually transmitted diseases and liquor law violations 39. Violence is

intimately linked with alcohol consumption. Studies have shown that “alcohol use directly

affects cognitive and physical functions” and can “reduce self-control, the ability to process

incoming information, assess risks, increase emotional liability, increase impulsivity, and certain

drinkers are more likely to resort to violence in confrontation” 42. Every year about 3 million

violent crimes occur where the offender is perceived to be inebriated. These crimes include rape,

sexual assault, robbery, and aggravated and simple assault 8.

For years Pennsylvania has been very conservative with alcohol retail with licenses

limited based on county populations. State stores owned by the Pennsylvania Control Board were

the only places that sold wines and spirits while malt beverages and beer were sold in cases by

licensed beer retailors. In 2013 legislature passed which allows for the acquisition of a broader

range of alcohol retail license from a wider group of retailers2,3,26. Beer distributors are able to

acquire licenses to sell wines, spirits, and six pack beers in addition to the cases of beers which

1

were previously the only thing sold. New areas such as restaurants and supermarkets are now

allowed to sell beer and wine. Convenient stores with eat-in areas can also sell take-out beer 51.

This increase in alcohol retail has implications for an increase in violence with some studies

claiming alcohol outlet density (AOD) as a major contributor to violence while advocating for

limiting alcohol outlet density 47,48,49. For this study, we define AOD as the number of liquor

licenses existing within an established geographic area and this includes on-premise alcohol

retailers (this includes taverns, nightclubs, hotels and theaters) and off-premise retailers (state

liquor stores and beer distributors).

One of the main theoretical hypotheses of the association between AOD and violence is

the routine activity theory 1,28. The theory states that a crime occurs when there is an accessible

target, the absence of capable guardians (law enforcement) that could intervene and the presence

of a motivated offender. It is suggested that areas of high AOD attract motivated offenders, have

high rates of accessible targets, and lower rates of capable guardians. This, along with social

disorganization theory is thought to decrease social control in areas in which people congregate

to purchase alcohol 4,18,36. Just as areas with high AOD attract motivated offenders, have high

rates of accessible targets, and lower rates of capable guardians, areas of high commercial

activity exhibit these same traits. Because both of these contribute to an increase in violent

crimes and because they are both related to one another it is important to evaluate how model

building is affected by the inclusion of commercial activity as a confounder. These theoretical

underpinnings are based on the geographic area in which these interactions can occur, in which

the location is where the incident took place and relies on accurate measurements and analysis of

the neighborhood.

2

There are a number of studies which examine whether AOD within or around a

neighborhood is related to higher rates of violence however the results are inconsistent across the

studies. Although some studies found an association 13,37,43, many did not find an association 12, 38

or mentioned it would take an unrealistic increase in alcohol retailers to see an effect 44. There

was one study which reported finding an association within univariate models, but not within

multivariate models 25. Few studies assessed the temporal association, and limit any assessment

of causality. To understand the variations in the current available literature, this study was

constructed to assess the following specific aims: 1.) Assess the cross-sectional association with

AOD and violence within Pittsburgh during two time periods – 2000 and 2012; 2.) Evaluate the

temporal association of alcohol retail density on the incidence of violence as they change over

time; 3.) Assess the presence of confounding due to commercial activity and determine the effect

size of the results.

3

2.0 METHODS

2.1 DATA SOURCES

The sample for this study consisted of 91 well-defined neighborhood tracts with stable

boundaries defined by mixture of historical ward boundaries and boundaries decided by the City

Planning Commission. Each neighborhood consists of either one or two census tracts. Census

tracts have been used as the unit of analysis in previous studies in this area of research 20, and are

considered by some to be the most appropriate administrative boundary to use in assessing such

neighborhood effects 22 therefore for this study census tracts were used for analysis. The change

in geography of census tracts between successive censuses is a major barrier for longitudinal

analysis as during each successive census, tracts may be split or consolidated from the previous

boundaries. Census tracts boundaries were therefore standardized to the 2000 census.

The primary exposure was AOD at the neighborhood level. Part of the study evaluated

AOD as a rate calculated by summing the number of establishments within each census tract for

each category of alcohol establishment and dividing based on per capita (per 10,000 residents)

based on data generated from the US census. Using the same data, AOD rates were also

calculated by summing the number of establishments within each census tract for each category

of alcohol establishment and dividing based on square miles (per 10 square miles). On-premise

outlets were defined as drinking places and included the standard industrial classification (SIC)

4

code of 5183 and off-premise the SIC code of 5181 (wholesale beer) and 5181 (Wholesale wines

and distilled alcoholic beverages).

Two commercial databases were used, InfoUSA (http://www.infousa.com/) and

Dun&Bradstreet (D&B) (http://www.dnb.com/), based on data generated in 2009 and 2003 for

Allegheny county, PA to measure and calculate AOD. Details for this process have been

described elsewhere 29. InfoUSA and D&B are sales and marketing companies that provide data

of businesses in the USA and Canada. In order to standardize the neighborhood boundaries as

mentioned above, the Longitudinal Tract Data Base (LTDB) was used, which provides a

backwards LTDB in which data provided in 2010 tract boundaries can be estimated within 2000

boundaries (Russel Sage Foundation, 2010). Types of establishments and associated

classifications are based on the standard industrial classification and North American Industry

Classification (NAICS) taxonomy.

The 2000 (the average of the data from 2000 to 2003) and the 2012 (the average of the

data from 2009 to 2012) US Census population and demographic data included the number of

residents per census tract to calculate density measures (e.g. density of grocery stores within a

geographic region) were also used. All establishments were geocoded based on local data

provided by the Allegheny County Geographic Information System (GIS) department. The local,

reference database included a combination of streets and rooftops, which produce higher match

rates and positional accuracy (Roongpiboonsopit and Karimi, 2010). ArcGIS version 10 (ESRI,

Redlands, USA) was used to geocode establishments. Spelling sensitivity was set at 70,

minimum candidate score at 10 and minimum match score at 60. Output included XY

coordinates, which were spatially matched to census tracts using year 2000 government census

shape files. The 2003 data was successfully geocode at 87.2% (n = 6,849; 1,008 were unmatched

5

to address points in the geocoding reference file) of the establishments from and 83.1% (n =

3,316; 672 were unmatched) for InfoUSA. The data from 2012 was successfully geocoded at

86.1% (n = 5,073; 817 were unmatched) of the D&B establishments and 84.9% (n = 3,679; 656

were unmatched) from InfoUSA.

Neighborhood outcome rates of violence for 2000 and 2012 were calculated from data

provided by the Pittsburgh police department. All aggravated assaults and robberies reported to

the police are geocoded to a Pittsburgh neighborhood. For police data, definitions were based on

the FBI’s Uniform Crime Reporting (UCR) Programs definition. Violent crimes are defined in

the UCR Program as those offenses that involve force or threat of force. The primary outcome of

interest for this study was aggravated assault as determined by the police report. In the analysis,

the neighborhood-level violent crime rate was calculated as crimes per 10,000 persons and per

10 square miles.

Finally, data pertaining to 11 neighborhood characteristics were extracted from Summary

File 1 and Summary File 3 of the 2000 and 2012 US Census (US Census Bureau, 2003). These

variables were chosen as they had been used in previous studies of alcohol availability and

violent crime 11, 22, 24, as well as studies of violent crime in urban neighborhoods7-10, 43. These data

included racial composition (total population that was non-White), total population size, sex

(total population that was male), unemployment (total population 16 years and over in labor

force who were unemployed), median family income, median home income, poverty (percentage

of households who made an income below the poverty level in 1999 and percentage of

households with public assistance income), family instability (percentage of female-headed

households), and education (percentage of the population older than 25 years with at least a high

school diploma and percentage of the population older than 25 years with a Bachelor's degree).

6

2.2 DATA ANALYSIS

All analyses were conducted using SAS 9.4 and began with summary and descriptive

statistics which can be found in Table 1. Initially the data was acquired as counts, but were

transformed to rates for analysis. In order to evaluate the differences between 2000 and 2012 two

techniques were executed: first, cross-sectional models were created for the two time periods;

second, change variables were created by taking the difference between 2012 and 2000 for each

predictor and from these predictors linear regression models were created. For the cross-sectional

models, negative binomial regression with a log link was used in order to protect against the

effect of excessive zeroes in the data. Following the example of previous studies, backward

stepwise selection regression was used to remove variables that did not fit a predetermined p-

value of <0.2; the predictor of interest remained in the model regardless of its p-value and was

controlled for by 11 demographic variables which were mentioned previously 5,43.

It was suspected that there was some confounding with the increase in commercial

activity in Pittsburgh so after final models were built; the covariate “restaurant outlet density”

was added to the model to check for the robustness of the predictor of interest 45. It was suspected

that if the Routine Activities Theory is taken into account the increase in commercial activity fits

well for explaining the presence of crime in many areas of Pittsburgh as focus on infrastructure is

encouraging growth in commercial activity 1, 28. With this in mind, the presence of AOD is

expected to increase in areas where commercial activity has increased and along with this, likely

offenders are presented with suitable targets in areas where people are more likely to congregate.

As mentioned previously, change variables were calculated by subtracting their 2000

values from their 2012 values; the change for the outcome of interest was also calculated in the

7

same way and backward stepwise selection linear regression was used. After the final models

were produced influential points were identified using Cook’s distance 21, 46. Each identified point

was considered for removal and if removed was replaced with a missing value. This was done

one influential point at a time stepwise between each replacement. Two census tracts were highly

influential and were determined to be mostly commercialized with little to no residential areas

and thus were different from the other neighborhoods in the data set; because of this they were

removed from the model so that the data was reflected in a more accurate way 21. After

influential points were removed, models were created again using backward stepwise selection

regression starting from the full model.

2.3 CONCEPTUAL FRAMEWORK

Figure 1. Conceptual Framework Displaying Casual Pathway between Predictors, the Confounder and the

Outcome

8

Alcohol Outlet Density

Demographic Variables

Commercial Activity Aggravated Assault

3.0 RESULTS

There were 91 tracts in this study for which summery statistics were generated as seen in

Table 1. The average population across all tracts was 3,717 in 2000 and 3,405 in 2012. There

was an increase in median family and home income from 2000 to 2012, which indicates positive

economic development across all census tracts. The number of high school graduates and

population with at least a bachelor’s degree increased slightly from 2000 to 2012. The mean total

population using public assistance income decreased from 211 in 2000 to 178 in 2012. The

average total population below the poverty level decreased from 560 in 2000 to 521 in 2012.

Unemployment also decreased from 180 in 2000 to 166 in 2012. The average on-premise AOD

increased from 2000 to 2012 (31 to 40). Once rates were created by dividing per square miles

there were differences in the average on-premise AOD which decreased slightly from 2000 to

2012 (16 to 10). The average and minimum off-premise alcohol retail density decreased, but not

significantly from 2000 to 2012 and the maximum decreased slightly from 23 to 16.

3.1 MODELS FOR POPULATION RATES 2000

The negative binomial regression model for predicting aggravated assault in 2000

revealed that on-premise alcohol retail density (β= 0.0062; p-value< 0.0001), female headed

households (β= 0.0243; p-value= 0.0016), having an income below the poverty level (β= -

9

0.0258; p-value= 0.0003) and households on public assistance (β= 0.0961; p-value< 0.0001) are

all significant predictors. The predictor of interest was significant before restaurant density was

added to the model, but became not significant after it was added to the model (β= 0.0021; p-

value< 0.2006).

Table 1. Negative Binomial Regression Using Density of On-premise Alcohol Retail per Capita to Predict

Aggravated Assault in 2000 while Controlling for Socio Economic Covariates

Model for On-Premise

Restaurant Outlet Density Added with On-Premise

Variable Estimate (β)

P Value

Estimate (β) P Value

Intercept 3.1245 <0.0001 2.9297 <0.0001

Density of On-Premise Alcohol Establishments

0.0062 <0.0001 0.0021 0.2006

Female householder, No Husband Present (%)

0.0243 0.0016 0.0410 <0.0001

Income Below Poverty Level (%) -0.0258 0.0003 -0.0060 0.4557

Households with Public Assistance Income (%)

0.0961 <0.0001 0.0183 0.3737

Restaurant Establishment - - 0.0020 <0.0001


revealed that off-premise alcohol retail density (β= 0.0207; p-value< 0.0001), total population

(β= -0.0002; p-value< 0.0001), population which identify as black (β= -0.0002; p-value=

0.0019), having an income below the poverty level (β= -0.0206; p-value= 0.0033) and

households on public assistance (β= 0.0722; p-value< 0.0001) are all significant predictors. The

10

predictor of interest was significant before restaurant density was added to the model, but

became not significant after it was added to the model (β= -0.0031; p-value< 0.6352).

Table 2. Negative Binomial Regression Using Density of Off-premise Alcohol Retail per Capita to Predict


Model for Off-Premise

Restaurant Outlet Density Added with Off-Premise


P Value


Intercept 4.2017 <0.0001 3.9372 <0.0001

Density of Off-Premise Alcohol Establishments

0.0207 <0.0001 -0.0031 0.6352

Total Population -0.0002 <0.0001 -0.0001 <0.0001

Black or African American Alone 0.0002 0.0019 0.0003 <0.0001

Income Below Poverty Level (%) -0.0206 0.0033 0.0017 0.8251


0.0722 <0.0001 0.0155 0.3584


3.2 MODELS FOR POPULATION RATES 2012


revealed that on-premise alcohol retail density (β= 0.0044; p-value< 0.0001), total population

(β= -0.0001; p-value< 0.0001), population which identify as black (β= 0.0003; p-value= 0.0009),

female headed households (β= -0.0317; p-value= 0.0223), having an income below the poverty

level (β= 0.0161; p-value= 0.0414) and households on public assistance (β= 0.0529; p-value=

11

0.0114) are all significant predictors. The predictor of interest was significant before and after

restaurant density was added to the model (β= -0.0019; p-value= 0.0243).

Table 3. Negative Binomial Regression Using Density of On-premise Alcohol Retail per Capita to Predict


Model for On-Premise

Restaurant Outlet Density Added with On-Premise


P Value


Intercept 3.9644 <0.0001 3.7157 <0.0001

Density of On-Premise Alcohol Establishments

0.0044 <0.0001 -0.0019 0.0243




-0.0317 0.0223 -0.0231 0.0410

Income Below Poverty Level (%) 0.0161 0.0414 0.0165 0.0088


0.0529 0.0114 0.0477 0.0041

Restaurant Establishments - - 0.0025 <0.0001


revealed that off-premise alcohol retail density (β= 0.0188; p-value< 0.0001), total population


female headed households (β= -0.0331; p-value= 0.0228) and households on public assistance

(β= 0.0494; p-value= 0.0259) are all significant predictors. The predictor of interest was

12

significant before and after restaurant density was added to the model (β= -0.0224; p-value=

0.0001).

Table 4. Negative Binomial Regression Using Density of Off-premise Alcohol Retail per Capita to Predict


Model for Off-Premise

Restaurant Outlet Density Added with Off-Premise


P Value


Intercept 4.2735 <0.0001 3.6011 <0.0001

Density of Off-Premise Alcohol Establishments

0.0188 <0.0001 -0.0224 <0.0001




-0.0331 0.0228 -0.0224 0.0332

Income Below Poverty Level (%) 0.0129 0.1174 0.0189 0.0015


0.0494 0.0259 0.0477 0.0020


3.3 MODELS FOR CHANGE IN POPULATION RATES

From the linear regression models, the values for on-premise and off-premise alcohol

retail density were not significant with values β= 0.03761, p-value= 0.8311 and β= 1.87664, p-

value= 0.0893 respectively. In regards to the temporal relationship between the predictors and

13

the outcome, these values indicate that there is no significant association between the change in

the predictors of interest and the change in the outcome.

Table 5. Linear Regression Using Change in Density of On-premise Alcohol Retail per Capita to Predict

Change in Aggravated Assault while Controlling for the Change in Socio Economic Covariates

Variable Estimate (β) P Value

Intercept -2.03949 0.7517

Change in Density of On-Premise Alcohol Establishments 0.03761 0.8311

Change in Total Population -0.01227 0.1931

Change in Black or African American Alone 0.01251 0.1805

Change in Female householder, No Husband Present (%) -0.98752 0.1457

Change in Population 25 years and over who are High School Graduate or more (%)

0.94768 0.0413

Change in Population 25 Years and Over with Bachelor's Degree (%) -1.29794 0.1042

Table 6. Linear Regression Using Change in Density of Off-premise Alcohol Retail per Capita to Predict

Change in Aggravated Assault while Controlling for the Change in Socio Economic Covariates


P Value

Intercept 1.9438 0.7426

Change in Density of Off-Premise Alcohol Establishments 1.87664 0.0893


1.15797 0.0062


14

3.4 MODELS FOR SQUARE MILES RATES 2000


revealed that on-premise alcohol retail density (β= 0.0138; p-value= 0.0002), total population


population over 16 years old who are unemployed (β= -0.0007; p-value= 0.0588 [marginally

significant]), population who at least hold a high school diploma (β= 0.0296; p-value= 0.0008)

and households on public assistance (β= 0.0670; p-value< 0.0001) are all significant predictors.

The predictor of interest was significant before restaurant density was added to the model, but

became not significant after it was added to the model (β= 0.0034; p-value= 0.2037).

Table 7. Negative Binomial Regression Using Density of On-premise Alcohol Retail per Square Mile to

Predict Aggravated Assault in 2000 while Controlling for Socio Economic Covariates

Model for On-premise

Restaurant Outlet Density Added with On-premise


P Value


Intercept 1.6801 0.0285 5.1259 <0.0001

Density of On-premise Alcohol Establishments (per square mile)

0.0138 0.0002 0.0034 0.2037



Population 16 Years and Older Unemployed

-0.0007 0.0588 -0.0003 0.3063

Populations 25 years and over whom are High School Graduate or more (%)

0.0296 0.0008 -0.0149 0.1414


0.0670 <0.0001 0.0096 0.4638


15

The binomial regression model for predicting aggravated assault in 2000 revealed that

off-premise alcohol retail density (β= 0.0672; p-value= 0.0049), total population (β= -0.0002; p-

value< 0.0001), population which identify as black (β= 0.0002; p-value= 0.0158), population

who at least hold a high school diploma (β= 0.0242; p-value= 0.0055) and households on public

assistance (β= 0.0592; p-value< 0.0001) are all significant predictors The predictor of interest

was significant before restaurant density was added to the model, but became not significant

after it was added to the model (β= 0.0098; p-value= 0.5393).

Table 8. Negative Binomial Regression Using Density of Off-premise Alcohol Retail per Square Mile to

Predict Aggravated Assault for 2000 while Controlling for Socio Economic Covariates

Model for Off-premise

Restaurant Outlet Density Added with Off-premise


P Value


Intercept 2.2586 0.0029 5.4030 <0.0001

Density of Off-premise Alcohol Establishments

0.0672 0.0049 0.0098 0.5393




-0.0007 0.0676 -0.0003 0.3417


0.0242 0.0055 -0.0181 0.0646


0.0592 <0.0001 0.0061 0.6306


16

3.5 MODELS FOR SQUARE MILES RATES 2012


revealed that on-premise alcohol retail density (β= 0.0317; p-value< 0.0001), total population


population who hold a bachelor’s degree (β= -0.0435; p-value< 0.0001) and households on

public assistance (β= 0.0570; p-value= 0.0151) are all significant predictors. The predictor of

interest was significant before restaurant density was added to the model and remained

significant after restaurant was added to the model (β= 0.0107; p-value= 0.0104).

Table 9. Negative Binomial Regression Using Density of On-premise Alcohol Retail per Square Mile to


Model for On-premise

Restaurant Outlet Density Added with On-premise


P Value


Intercept 3.6914 0.0008 3.8567 <0.0001

Density of On-premise Alcohol Establishments

0.0317 <0.0001 0.0107 0.0104


Black or African American Alone 0.0002 0.0236 0.0003 0.0017


-0.0277 0.0749 -0.0078 0.5070


0.0169 0.1162 0.0034 0.7232

Total Population 25 Years and Over with Bachelor's Degree

-0.0435 <0.0001 -0.0159 0.1075


0.0570 0.0151 0.0485 0.0034


17


revealed that off-premise alcohol retail density (β= 0.1167; p-value= 0.0023), total population


female headed households (β= -0.0371; p-value= 0.0222), population who hold a bachelor’s

degree (β= -0.0438; p-value< 0.0001) and households on public assistance (β= 0.0581; p-value=

0.0245) are all significant predictors. The predictor of interest was significant before restaurant

density was added to the model, but became not significant after it was added to the model (β=

0.0192; p-value= 0.4625).

Table 10. Negative Binomial Regression Using Density of Off-premise Alcohol Retail per Square Mile to


Model for Off-premise

Restaurant Outlet Density Added with Off-premise


P Value


Intercept 4.2118 0.0003 4.0724 <0.0001

Density of Off-premise Alcohol Establishments

0.1167 0.0023 0.0192 0.4625


Black or African American Alone 0.0003 0.0364 0.0003 0.0045


-0.0371 0.0222 -0.0087 0.4710


0.0155 0.1714 0.0009 0.9262


-0.0438 <0.0001 -0.0122 0.2339


0.0581 0.0245 0.0476 0.0056


18

3.6 MODELS FOR CHANGE IN SQUARE MILES RATES

The values produced from the linear regression models show that both on-premise and

off-premise alcohol retail density were not significant with values β= 0.32286, p-value= 0.2017

and β= 2.47946; p-value= 0.1075 respectively. Exactly like the models created using the per-

capita data, the values in the next two tables indicate that there is no significant association

between the change in the predictors of interest and the change in the outcome.

Table 11. Linear Regression Using Change in Density of On-premise Alcohol Retail per Square Mile to

Predict Change in Aggravated Assault while Controlling for the Change in Socio Economic Covariates


Intercept -0.98213 0.8761

Change in Density of On-premise Alcohol Establishments 0.32286 0.2017





1.00769 0.0272


Table 12. Linear Regression Using Change in Density of Off-premise Alcohol Retail per Square Mile to

Predict Change in Aggravated Assault while Controlling for the Change in Socio Economic Covariates

19


Intercept -0.30326 0.9616

Change in Density of Off-premise Alcohol Establishments 2.47946 0.1075





0.96243 0.0322


20

4.0 DISCUSSION

4.1 EFFECT SIZE

Of the studies found during the literature review there were only 5 which reported an

effect size 12,25,37,43,44. Of those five, three used R2 as their measure while the other two used

relative risk and Akaike information criterion (AIC). Of the different methods used to describe

the effect size, the most useful measure is relative risk because R2 and AIC are not real measures

of effect since they only describe how much variability in the outcome is predicted by the model.

R2 and AIC don’t describe the magnitude of the relationship between the predictor(s) of interest

and the outcome. Relative risk however is a well-documented method for measuring the

magnitude of an association between an exposed and non-exposed group 52-56.

For comparison reasons, relative risk (RR) was calculated for the predictors of interest

by exponentiation of their beta values (exponential formula being eβ). From this study’s

population based rates in 2000 it was found that the RR of aggravated assault occurring in an

area with on-premise outlet density was 1.006 which indicates that there is almost no association.

After commercial activity was added to the model the predictor of interest became not

significant. This result indicates that when not taking the confounding factor into consideration,

the effect of AOD in an area does not have a meaningful effect.

21

Similar results were found for all models in 2000 where the largest relative risk was

1.069 from the off-premise model for square mile rates. This result indicates that an area with

off-premise AOD would have an approximately 7% higher risk of aggravated assaults than an

area without AOD. Even as the largest relative risk value, the magnitude of this effect is not very

large and once adjusted for commercial activity became non-significant. In 2012, the largest

relative risk was 1.124 from the off-premise model for square mile rates. This indicates that an

area with off-premise AOD would have a 12% higher risk of aggravated assault than an area

without AOD. This value might indicate a meaningful effect depending on how risky a safe

neighborhood is expected to be. With that said, after adding commercial activity to the model

this value is no longer significant. Compared to the paper with relative risk, this result appears to

be consistent as Zhu Li, et al. found similar results in their paper and concluded that alcohol

outlet density is not strongly associated with violent crime, but rather activity around illicit drug

markets presents a much stronger association 44.

4.2 CONFOUNDING

As with previous studies the results from this study show positive associations when

commercial activity is initially ignored. However, taking into account a potential confounder had

a major impact on the results. The results discussed in the previous section briefly describes

some of the effects of adjusting for commercial activity as a potential confounder on the results

with many of the models showing AOD as significant before commercial activity is included, but

not significant once commercial activity is included. In 2000 the results of the all the models -

22

whether they are based on population rates or square mile rates - show that once commercial

activity is adjusted for, AOD is not a significant predictor for aggravated assault. In 2012 the

potential confounding affects the results in a different way. Every model in 2012 (except one)

remains significant and showed a positive association before adding restaurants to the models,

but the models for population rates showed a change in the direction of association once

commercial activity was adjusted for. Possible confounding in the 2000 data was confirmed

when the predictors of interest changed from significant to non-significant; in 2012 instead of a

change in significance, there were changes in direction of association. These changes all seem to

confirm that commercial activity was a potential confounder. Many previous studies found a

positive association between AOD and violence, but failed to take any confounding factors into

consideration. It is possible that other potential confounders exist and should be considered for

model building.

4.3 TEMPORAL RELATIONSHIP

To understand the temporal relationship of AOD it was important to evaluate the change

in AOD as it related to the change in aggravated assault while controlling for the change in other

factors. During the linear model building process, change in AOD remained non-significant.

What was found was that as AOD changed over time, it did not significantly influence the

change in aggravated assault over time as the p-value remained greater than 0.05. This is

important to note since as mentioned previously some studies suggested that reducing AOD will

reduce violence rates in an area over time. Based on the comparison of results from the cross

sectional portion of this study to the temporal portion, cross sectional models are not fair

23

assessments of AODs effect on violence over time as there may be a significant positive

association within every time period, but using a change over time model it’s possible that those

same studies would find that those associations aren’t significantly related to a change in

violence over time. With that said, showing cross-sectional analysis of the association of AOD

on violence is not enough to make a decision about reducing violence over crime by reducing

AOD.

24

5.0 CONCLUSION

Although there are many studies that concluded that there is an association between AOD

and violence, this study was not able to find a significant association. Three key aspects were

discussed: effect size, potential confounding and the temporal relationship between AOD and

violence. Looking at effect size, it was noted that the majority of risk ratios were approximately

equal to 1 which indicates that there is approximately no association between AOD and violence.

Even if the models produced larger relative risk values, the majority of models produced a

predictor of interest that was no longer significant once commercial activity was added to the

model. With this in mind it might be possible that there are other confounding factors that were

unaccounted for or that may be present in other areas that are not present in Pittsburgh and vice

versa. For example, commercial activity might not act as a confounder in more rural areas where

AOD is present. Future studies should try to consider the possibility of confounding and adjust

for it. The reporting of effect size in a standardized way is also important. It was difficult to

compare the effect size found in this study with other studies because the majority used R-

squared which is not a very useful indicator of effect size. There was only one study that reported

relative risk which is a great indicator. This study could have gone further by comparing the

effect sizes found here to other studies in order to assess how much of an impact AOD has on

violence, but with the current information available this task may be conducted in the future.

25

Another important study design future papers should consider is the temporal relationship

of AOD on violence. There were only a few papers that took the temporal relationship into

consideration. As policy aimed at reducing violence is developed, it is important to guide

decisions using the results of research. As it stands, most research provides cross sectional results

which isn’t as useful as temporal results such as those produced from models containing change

variables. It might be possible to reduce violence from alcohol consumption in a more fruitful

way if future research also concluded that AOD is not a major contributor. As AOD increases in

Pittsburgh the results of this study may change in the future, but this study must conclude that it

is not significantly nor practically related to aggravated assault.

5.1 COMPARISON TO OTHER LITERATURE

Previous studies applied various model building methods to evaluate the effects of

alcohol density on violence. Many focused on the geographic association of violence and the

environmental context that drinking occurred in such as demographic factors and types of

alcohol retail establishments. The common theme shared within the literature was the evaluation

of the spatial relationship between AOD and violence. The conclusion of these research varied

depending on what other factors were considered as the data was evaluated. In the Brower et al.

paper, the relationship between the congregation of students and the time of day was considered

for evaluation 57. In the paper by Graham et al. they looked at sexual activity and bar location as

it factored into the relationship between AOD and violence 14. Zhu et al. looked at the association

between illicit drug markets and violence and compared that to AOD and violence 44. What most

26

of these papers concluded is that other factors play a larger role in the incidence of violent

activity than alcohol outlet density. For instance, Graham et al. found that aggression was more

severe at suburban locations rather than locations where there was a lot of entertainment such as

clubs and bars (commercial activity). Comparing this result to the results of this paper, it appears

that social constructs in an area do indeed influence the way AOD relates to violence. In this

research we evaluated commercial activity as a potential confounder and believe that although

it’s not a direct confirmation, this research supports their findings that areas where there are

entertainment such as clubs, bars or restaurants (commercial activities) there is an interaction

between those and AOD which simultaneously impact violence.

5.2 LIMITATIONS

There were several study limitations. Population density and square miles were not

evenly distributed and so some areas had more of an influence on the outcome than others. There

are some areas in Pittsburgh that have almost no residential areas and so for model building two

of those areas were removed. The points removed were not only influential points with a lot of

leverage; they were also areas with no residential areas, but a lot of alcohol outlet retail and

commercial activity. The approach to interpreting the longitudinal effect of AOD on violence

was also a major limitation. Actual longitudinal data was not used for model building so instead

the difference between the two years was taken and modeled. The use of change variables can

only act as a slight description of the temporal relationship between AOD and violence. It tells us

that there is no significant association between the change in the predictor as it relates to the

change in the outcome. If there was a significant association, it would be impossible to describe

27

what the change meant; as AOD increased we wouldn’t be able to tell if violence increased or

decreased – we would only be able to say that it changed over time along with AOD.

28

APPENDIX: SUPPLEMENTARY TABLE

29

Table 13. Basic descriptive statistics for selected demographic characteristics at aggregate county level

2000 2012 Change

Variable Min Max Mean SD Min Max Mean SD Min Max Mean SD

Median Household Income 7,440

642,660

12,1117

13,3326

9,375 831,268 160,701

17,5017

-14,7422

24,5901

38,306

57,982

Median Family Income 0 991,320

161,323

187,538

10,148

1,524,784

234,268

290,686

-34,213

752,168

69,406

121,263


28 100 78 11 0 100 87 13 -100 72 8 17

Total Populations 25 years and over whom are High School Graduate or more

20 13,489 3,017 2,924 0 14,426 3,053 3,070 -712 1,776 36 475

Population 25 Years and Over with Bachelor's Degree (%)

0 34 11 7 0 49 15 9 -20 24 3 6


0 4,097 508 765 0 4,671 598 834 -566 863 90 211

Total Population 41 14,507 3,717 3,272 0 14,826 3,405 3,247 -1,418 1,117 -313 488

Male Population 20 6,923 1,768 1,607 0 7,340 1,636 1,622 -848 1,119 -132 308

Black or African American Alone 1 4,980 1,008 1,193 0 4,368 876 937 -1,640 1,419 -132 516


0 57 8 9 0 36 7 8 -57 30 1 9

Total Households with Public Assistance Income

0 917 211 191 0 607 178 167 -430 272 -33 125

30

Income Below Poverty Level (%) 0 100 19 18 0 100 19 18 -100 92 0.1746

18

Total with Income Below Poverty Level

0 1,967 560 492 0 1,967 521 486 -1417 1182 -39 364


0 2,283 180 294 0 591 166 146 -1880 385 -14 250


2 71 20 13 0 71 18 14 -37 29 -2 8

Total Female householder, No Husband Present

5 2,154 634 518 0 1,725 502 448 -974 364 -133 230

Density of On-Premise Alcohol Establishments (per capita)

0 902 31 100 0 2,105 40 227 -472 2,105 8 231

Density of Off-Premise Alcohol Establishments (per capita)

0 150 5 16 0 263 5 29 -68 263 0.5660

29

Density of On Premise Alcohol Establishments (per square mile)

0 227 16 32 0 142 10 21 .93 36 -6 17

Density of Off Premise Alcohol Establishments (per square mile)

0 23 3 5 0 16 2 3 -11 6 -1 3

Total Restaurant Establishments 0 2,068 100 317 0 2,857 106 400 - - - -

31

Table 13 continued

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Documents

Introductiond-scholarship.pitt.edu/25683/1/Christopher_P_2015_8_MPH.docx · Web viewObjective: This study examines the public health significance of recent legislature change which