RESEARCH AND PRACTICE
The Relationship Between Gun Ownership and Stranger and Nonstranger Firearm Homicide Rates in the United States, 1981–2010 Michael Siegel, MD, MPH, Yamrot Negussie, Sarah Vanture, Jane Pleskunas, Craig S. Ross, PhD, MBA, and Charles King III, JD, PhD
Firearms cause more than 31 000 deaths annually in the United States.1 Since the tragic shooting of 20 children and 7 adults in Newtown, Connecticut, in 2012, several states have enacted or debated legislation to restrict the availability of firearms.2 Some gun rights advocates have argued that restricting the availability of guns might cause harm by removing an effective deterrent to crime.3-5 Lott, for example, has suggested that reducing the number of guns held by law-abiding citizens might increase homicides because “it would be easier for criminals to prey on the weakest citizens, who would find it more difficult to defend themselves.”5(p11) Understanding whether increased gun ownership increases or decreases homicides is essential to inform public policy regarding measures to address firearm violence. Of particular interest is the question of whether higher gun ownership is associated with lower rates of stranger homicide (i.e., homicide committed by a person unknown to the victim) because such a relationship is consistent with the hypothesis that increased household ownership of guns deters violent crime by strangers who might otherwise have killed the potential victim. Multiple cross-sectional studies have demonstrated a correlation between higher gun ownership at the state level and higher overall state-specific rates of firearm homicide.6-18 Most recently, we reported a strong and robust relationship between estimated gun ownership in the 50 states and firearm homicide rates over the period 1981 to 2010, while controlling for 20 potential state-level confounding variables.19 None of these studies distinguished between stranger and nonstranger homicides. We are not aware of any published studies that have examined the relationship between gun ownership and stranger versus nonstranger homicide rates.
Objectives. We examined the relationship between gun ownership and stranger versus nonstranger homicide rates. Methods. Using data from the Supplemental Homicide Reports of the Federal Bureau of Investigation’s Uniform Crime Reports for all 50 states for 1981 to 2010, we modeled stranger and nonstranger homicide rates as a function of state-level gun ownership, measured by a proxy, controlling for potential confounders. We used a negative binomial regression model with fixed effects for year, accounting for clustering of observations among states by using generalized estimating equations. Results. We found no robust, statistically significant correlation between gun ownership and stranger firearm homicide rates. However, we found a positive and significant association between gun ownership and nonstranger firearm homicide rates. The incidence rate ratio for nonstranger firearm homicide rate associated with gun ownership was 1.014 (95% confidence interval = 1.009, 1.019). Conclusions. Our findings challenge the argument that gun ownership deters violent crime, in particular, homicides. (Am J Public Health. 2014;104:1912–1919. doi:10.2105/AJPH.2014.302042)
Although the US Department of Justice regularly provides national statistics on rates of stranger versus nonstranger homicide,20,21 we are aware of no published studies that report state-specific data on stranger versus nonstranger homicide. Understanding state-specific patterns of victimization in terms of the relationship between homicide victims and offenders, and identifying the trends in these patterns, would inform state efforts to reduce homicide rates.22 Several studies have examined the relationship between homicide victims and offenders in specific settings, such as national youth homicides22; national homicides23,24; Allegheny County, Pennsylvania, homicides25; adolescent homicides in North Carolina26; homicides in Contra Costa County, California27; and national homicides among intimate partners.28 Nationally, between 1980 and 2008, of the homicides for which the relationship between victim and offender was known (63.1% of all homicides), approximately one fifth (21.9%) were stranger homicides.20 We are not aware
1912 | Research and Practice | Peer Reviewed | Siegel et al.
of any published data on how this may vary among states. In this article, we report and analyze stranger and nonstranger homicides at the state level during the period 1981 to 2010 and examine the relationship between those rates and state-specific household gun ownership during the same years. To the best of our knowledge, ours is the first study to report state-specific data on stranger and nonstranger homicide rates and examine the relationship between state-level gun ownership and stranger and nonstranger homicide rates, while controlling for differences in a wide range of state-level factors associated with homicide.
METHODS Using a panel of annual data for 1981 to 2010 for each of the 50 states, we modeled the stranger and nonstranger firearm homicide rates in a given year for a given state as a function of gun ownership, measured by a proxy, in that state during that year, while
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controlling for factors that might confound the association. We used a negative binomial regression model with fixed effects for each year. We accounted for clustering of observations among states using generalized estimating equations. We obtained stranger and nonstranger homicide data from the Supplemental Homicide Reports (SHRs) of the Federal Bureau of Investigation’s Uniform Crime Reports (UCRs).29-32 Although a substantial number of SHR records are missing data on the victim---offender relationship, a multiply imputed version of the data set exists in which this relationship is imputed for all cases.33-35 To assess whether our analysis was sensitive either to the large amount of missing data or to the imputation of missing data, we conducted a sensitivity analysis, using both the original and the imputed data. Specifically, we analyzed 3 different
versions of the SHR data set: (1) the original data set, in which 34% of records are missing data on the nature of the victim---offender relationship,29 (2) an augmented data set, which included 5 sets of imputed data for any missing victim---offender relationships,33-35 and (3) a multiply imputed data set with an additional weighting adjustment for incomplete reporting of homicides.33-35 These data sets are summarized in Table 1.
Variables and Data Sources Outcome variable. The outcome variable was the annual stranger or nonstranger firearm homicide rate in a given state. We adopted the Bureau of Justice Studies’ definitions of stranger and nonstranger homicide.36 Stranger homicide is when the victim did not know the offender or knew the offender only by sight. Nonstranger homicide is
when the victim “is either related to, well known to, or casually acquainted with the victim.”36 Data source. The only national data source that records homicide victim---offender relationships is the Federal Bureau of Investigation’s UCR SHRs.29-32 The SHRs have collected victim---offender relationship data consistently since 1975.30 The SHRs provide detailed data on nearly all homicides (murders and nonnegligent manslaughters) in the United States. Data are collected by local reporting agencies throughout the country, and SHR variable definitions have remained constant since 1981. Data are provided for each of the 50 states for the entire study period, with missing data for just several years in a few states (Table 1). We obtained the 1981 to 201029 UCR SHR data set from the Inter-University Consortium for Political and Social Research (Ann Arbor, MI).
TABLE 1—Data Sets Used to Estimate Stranger and Nonstranger Homicide Rates: All 50 US States, 1981–2010
Data Set SHR
Agency and Description As part of the Uniform Crime
Homicides Included Murder, nonnegligent
Specification
States and Years Included
Crude rates No weighting
50 states (1981–2010);
Reporting system, the FBI compiles data filed by
manslaughter, negligent manslaughter, and
missing data for AL (1999), FL (1988-1991,
local law enforcement
justifiable homicide
1996-2010), IA (1991),
agencies for homicides
KS (1994–1999), KY
occurring within their
(1988), ME (1991, 1992),
jurisdictions
MT (1987, 1993, 1994,
Records with Missing Data for Victim–Offender Relationship, % 34.0
1996), NH (1997), WI (1998) MI-SHR
SHR data files with 5 sets of imputed data for missing
Murder and nonnegligent
Crude rates No weighting
manslaughter
1464 records Same as SHR
0.0
1464 records
items, produced by Fox (School of Criminology and Criminal Justice, Northeastern University) MI-SHR/W
SHR data files with 5 sets of imputed data for missing items, produced by Fox
Murder and nonnegligent manslaughter
Crude rates Weighted to match annual, state-level FBI homicide counts
Same as SHR, but also
0.0
missing data for IL (1984, 1985), KS (1993, 2000),
(School of Criminology
KY (1987), MT (1982,
and Criminal Justice,
1986, 1990, 1998), NE
Northeastern University)
(1994, 2005, 2007, 2008), NM (1981), VT (1983) 1449 records
Note. FBI = Federal Bureau of Investigation; MI-SHR = multiply imputed SHRs; MI-SHR/W = multiply imputed SHRs/weighted; SHR = Supplemental Homicide Reports.
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Missing data on victim---offender relationship. The SHR is limited by missing data on the victim---offender relationship in a substantial proportion of homicides. During 1981 to 2010, 34% of the homicides in the SHR were missing data on the nature of the victim--offender relationship. The most common reason for missing data is that a homicide is not witnessed or the case is not solved.22 These data were not missing at random. Unsolved homicides are more likely to involve a stranger as offender.37 Discarding cases with missing data could therefore introduce a bias.37 Rigorous efforts have been made to deal with the missing data problem in the SHRs, focusing on imputation of the victim---offender relationship.37-45 Fox and Swatt38 developed a multiple imputation approach for missing items in the SHRs. This approach is generally viewed as the strategy of choice, and Fox has implemented it for all SHR data files from 1976 through 2011. Files that cover the period until 2007 are available through the Inter-University Consortium for Political and Social Research.33 Fox kindly provided us with updated multiply imputed files that cover the years through 2010.35 All these files included 5 sets of data with imputed missing values.34 We used all 5 imputed data sets to derive the value for the victim---offender relationship, using weights provided in the data set.34 We subsequently refer to this data set as the multiply imputed SHRs (MI-SHRs). Missing homicide data. The SHR data set has an additional limitation. Not all local law enforcement agencies complete the supplemental reports, yielding missing data that could lead to an underestimation of homicide rates.39 The SHR data set includes approximately 90% of all homicides.39 This unit missingness can be addressed by applying weights that adjust each state- and year-specific estimate up to the overall number of homicides reported in the UCR for that state and year.39 In addition to compiling a data set that includes multiply imputed values, we also compiled a data set that included these multiply imputed values adjusted for unit missingness by applying weights provided by Fox in the data set.35 These weights adjusted estimates to reflect state homicide totals.34 We subsequently refer to this data set as multiply imputed SHR/weighted (MI-SHR/W).
Classification of stranger and nonstranger homicides. We classified homicide cases with the following victim---offender relationships as nonstranger homicides: husband, wife, common-law husband, common-law wife, mother, father, son, daughter, brother, sister, in-law, stepfather, stepmother, stepson, stepdaughter, other family, neighbor, acquaintance, boyfriend, girlfriend, ex-husband, ex-wife, employee, employer, friend, homosexual relationship, and other known. We coded homicide cases in which the victim---offender relationship was characterized as “stranger” as stranger homicides. We excluded cases in which the relationship was unknown. In imputing the victim---offender relationship, Fox condensed the possible values into 4 categories: intimate partner, other family, friend or acquaintance, and stranger.34 We coded the stranger category as stranger homicide and the other 3 categories as nonstranger homicide. Types of homicide and calculation of homicide rates. The SHR includes all types of homicide, including murder, nonnegligent manslaughter, negligent manslaughter, and justifiable homicide but excludes deaths from the September 11, 2001, terrorist attacks. The imputed SHR data sets (MI-SHR and MI-SHR/W) included only 1 category of homicide: murder and nonnegligent manslaughter. All 3 data sets provided homicide counts. To calculate homicide rates, we divided annual, state-specific homicide counts by the state population for a given year obtained from the US Census. Table 1 summarizes the 3 data sets used in this study. Main predictor and control variables. The main predictor variable was the annual prevalence of household firearm ownership in a given state, measured by a proxy. Because no annual survey assessed the prevalence of household firearm ownership in all 50 states during the entire study period, we used a wellestablished proxy: the percentage of suicides committed using a firearm (firearm suicides divided by all suicides, or FS/S). This proxy has been extensively validated in the literature4,6,9,16,46-50 and has been determined to be the best of the many earlier proxies that have been tested. 46 The ratio of firearm suicides to all suicides has been shown to correlate highly with survey measures of
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household firearm ownership, 4,6,9,46-50 including state-specific measures of firearm ownership, 9,46 and has been widely used as a proxy for state-specific gun availability in previous studies. 6-9,11,15,16,19,49-52 Although the FS/S proxy has been shown to correlate highly with survey measures of state-level household firearm ownership, its correlation with survey-measured gun ownership during the 3 years it was measured in all 50 states (Behavioral Risk Factors Surveillance System, 2001, 2002, and 2004) was 0.80.19 Recently, we developed a new proxy measure that improves the correlation with surveymeasured gun ownership in the Behavioral Risk Factors Surveillance System during 2001, 2002, and 2004 from 0.80 to 0.95.53 This new proxy measure incorporates a state’s hunting license rate in addition to FS/S.53 In this study, we used both the traditional (FS/S) proxy and our new proxy. Following our earlier approach,19 we controlled for the following state-level factors that have been identified as related to homicide rates and might also be related to firearm ownership rates: proportion of young adults (aged 15---29 years), proportion of young men (aged 15---29 years), proportion of Blacks, proportion of Hispanics, level of urbanization, educational attainment, poverty status, unemployment, median household income, income inequality (the Gini ratio), per capita alcohol consumption, nonhomicide violent crime rate (aggravated assault, robbery, forcible rape), nonviolent (property) crime rate (burglary, larceny or theft, and motor vehicle theft), hate crime rate, divorce rate, region, incarceration rate, and suicide rate.
Model and Statistical Analysis Because the outcome variables—homicide rates—were not normally distributed but skewed and overdispersed (the variance was greater than the mean), we modeled the outcome using a negative binomial model following the approach used in previous studies.7-9,13,19,50,54-56 Estimation of the overdispersion parameter confirmed our choice of a negative binomial model over a Poisson model,57 following Miller et al.7 Clustering in our data might have arisen in 2 ways: by year (30 levels) and by state (50 levels). We entered year as a fixed effect in the
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TABLE 2—Average Proportion of Firearm Homicides Committed by Strangers, by State and Decade: United States, 1981–2010 State
1981–1990, %
1991–2000, %
2001–2010, %
Entire Study Period, %
Alabama
12.7
23.5
22.4
19.4
Alaska
17.6
25.5
18.9
20.7
Arizona
25.3
36.7
39.7
33.9
Arkansas
12.4
17.4
18.8
16.2
California Colorado
28.9 23.9
33.8 25.3
36.4 25.2
33.0 24.8
Connecticut
23.5
30.3
29.0
27.6
Delaware
12.5
25.6
26.7
21.6
Florida
21.7
25.5
NA
23.1
Georgia
16.2
23.7
27.2
22.4
Hawaii
15.9
22.3
26.9
21.7
Idaho
20.2
10.6
8.6
13.1
Illinois Indiana
27.4 23.0
33.8 24.6
34.7 23.2
32.3 23.6
Iowa
15.3
22.2
14.8
17.2
Kansas
23.7
25.6
23.2
23.7
Kentucky
11.5
19.3
22.4
18.2
Louisiana
26.5
24.3
25.1
25.3
8.5
6.6
9.6
8.3
Maryland
24.5
26.2
30.8
27.2
Massachusetts Michigan
28.4 22.1
27.5 25.4
29.1 25.6
28.3 24.4
Minnesota
20.6
25.0
23.0
22.9
Mississippi
12.7
18.1
16.7
15.8
Missouri
22.6
26.3
23.9
24.3
Montana
21.3
2.3
8.5
10.3
Nebraska
19.7
14.7
26.7
19.9
Nevada
22.4
26.3
32.3
27.0
New Hampshire New Jersey
14.6 29.0
6.5 30.3
17.6 30.5
13.1 29.9
New Mexico
18.8
27.3
24.2
23.6
New York
38.7
36.2
33.4
36.1
North Carolina
13.4
21.5
26.8
20.6
North Dakota
13.7
12.4
6.5
10.9
Ohio
23.7
28.0
26.2
26.0
Oklahoma
15.8
18.7
23.6
19.4
Oregon Pennsylvania
18.1 22.8
20.9 29.6
16.5 23.7
18.5 25.4
Rhode Island
19.8
27.6
36.1
27.8
South Carolina
13.6
22.6
24.0
20.1
South Dakota
28.9
12.4
8.7
16.8
Tennessee
17.1
22.1
26.4
21.9
Texas
21.6
28.2
34.1
28.0
Utah
19.2
26.8
21.5
22.5
Vermont Virginia
6.3 18.1
6.7 21.2
22.4 23.3
11.6 20.9
Maine
RESULTS
Continued
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regression model, which allowed us to control for nationwide secular changes that might affect firearm homicide rates. To account for clustering of observations among states, we used a generalized estimating equations approach.58 This procedure accounts for correlation of data within state clusters, avoiding a type 1 error that would be introduced if this correlation were ignored.59 We used an exchangeable (compound symmetry) working correlation matrix to model the correlation among observations within states. We used robust variance estimators (the Huber-White sandwich estimator of variance) to produce consistent point estimates58,60 and standard errors58,60,61 even if the working correlation matrix was misspecified. Our approach follows that of Miller et al.,50 who used a generalized estimating equations approach to account for clustering by region in their study of the impact of gun ownership on suicide rates. Our primary aim was to examine the relationship between gun prevalence and homicide rates while controlling for all identified potential confounding variables. We first ran a full model that included all variables, regardless of their contribution to the model. To develop a final, more parsimonious model, we first entered all variables with Wald test P < .1 on bivariate analyses into 1 model. We then iteratively deleted variables found not to be significant in the presence of the other variables, assessing the significance of each variable using a Wald test at a significance level of .05. Finally, we added each of the excluded variables into the model, 1 at a time, to assess whether it became significant when included in a model with the other variables. We included fixed effects for year and clustering by state in all models. We conducted all analyses using the xtnbreg procedure in STATA version 12 (StataCorp, College Station, TX).
Over the entire study period, the average proportion of firearm homicides that were committed by strangers throughout the United States was 21.9% (Table 2). This proportion varied widely, from a low of 8.3% in Maine to a high of 36.1% in New York. The average proportion of firearm homicides committed by strangers increased slightly over the study
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TABLE 2—Continued Washington
19.9
23.4
21.6
21.6
West Virginia
8.3
12.6
10.6
10.5
Wisconsin
20.7
24.4
33.3
26.2
Wyoming
15.0
12.5
9.2
12.2
United States
19.6
22.7
23.5
21.9
Note. NA = not available. Data from Federal Bureau of Investigation Uniform Crime Reports, Supplemental Homicide Report data files, with 5 sets of imputed data for missing items, produced by Fox.35
period, from 19.6% in 1981 to 1990 to 23.5% in 2001 to 2010. Average household gun ownership during the study period, as measured by the new
proxy, ranged from a low of 12.5% in Hawaii to a high of 73.8% in Wyoming (Table A, available as a supplement to the online version of this article at http://www.ajph.org). Average
TABLE 3—Results of Full Model Effects of Gun Ownership on Homicide Rates: United States, 1981–2010 Outcome and Predictor Variables
SHR, IRRa (95% CI)
MI-SHR, IRRa (95% CI)
MI-SHR/W, IRRa (95% CI)
Old gun ownership proxy: FS/S Firearm homicide rate
1.008* (1.002, 1.014)
1.007* (1.001, 1.013)
1.008* (1.003, 1.013)
1.002 (0.999, 1.006)
1.003 (0.999, 1.006)
1.004* (1.000, 1.007)b
1.005* (1.001, 1.009)
1.005* (1.001, 1.009)
1.006* (1.002, 1.009)
1.002 (0.995, 1.009)
1.001 (0.996, 1.006)
1.001 (0.996, 1.006)
Nonstranger homicide rate Stranger firearm homicide rate
1.007* (1.002, 1.013) 1.006 (0.998, 1.015)
1.006* (1.002, 1.011) 1.003 (0.996, 1.009)
1.007* (1.004, 1.011) 1.003 (0.997, 1.009)
Nonstranger firearm homicide rate
1.013* (1.006, 1.021)
1.009* (1.003, 1.015)
1.010* (1.004, 1.015)
Nonfirearm homicide rate Total homicide rate Stranger homicide rate
New gun ownership proxy: weighted average of FS/S and hunting licenses Firearm homicide rate Nonfirearm homicide rate Total homicide rate
1.010* (1.003, 1.016)
1.009* (1.003, 1.016)
1.012* (1.006, 1.017)
1.000 (0.994, 1.005)
1.000 (0.995, 1.005)
1.003 (1.000, 1.007)c
d
e
1.005* (1.000, 1.010)
1.005* (1.000, 1.010)
1.008* (1.005, 1.012)
Stranger homicide rate
1.008 (0.998, 1.019)
1.007 (0.997, 1.016)
1.009* (1.001, 1.017)
Nonstranger homicide rate Stranger firearm homicide rate
1.004 (0.997, 1.012) 1.011 (1.000, 1.022)g
1.005* (1.000, 1.010)f 1.008 (0.997, 1.019)
1.009* (1.005, 1.012) 1.009 (0.999, 1.019)
1.009* (1.001, 1.017)
1.010* (1.004, 1.016)
1.012* (1.008, 1.017)
Nonstranger firearm homicide rate
Note. CI = confidence interval; FS/S = firearm suicides divided by all suicides; IRR = incidence rate ratio; MI-SHR = multiply imputed Supplemental Homicide Reports, unweighted; MI-SHR/W = multiply imputed Supplemental Homicide Reports, weighted to match state-level Federal Bureau of Investigation homicide counts; SHR = Supplemental Homicide Reports. Full model includes fixed effects for year, is adjusted for clustering within states, and controls for percentage of young (ages 15–29), percentage of young men, percentage Black, percentage Hispanic, poverty, unemployment, household income, educational attainment, income inequality, level of urbanization, alcohol consumption, violent crime rate, nonviolent crime rate, hate crime rate, divorce rate, region, incarceration rate, and suicide rate. a The IRR measures the percentage change in the outcome variable for each increase of 1 percentage point in the statespecific household gun ownership proxy. For example, an IRR of 1.012 indicates a 1.2% increase in the homicide rate for each 1 percentage point increase in the gun ownership proxy. b P = .031. c P = .079. d P = .034. e P = .043. f P = .035. g P = .056. *Parameter estimate is statistically significant at a = .05 (95% CI does not overlap 1.000). When the 95% CI appears to overlap 1.000 because of rounding, the P value is reported.
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stranger firearm homicide rates ranged from 0.1 per 100 000 in Iowa, Maine, New Hampshire, North Dakota, South Dakota, and Vermont to 2.7 per 100 000 in Louisiana. Average nonstranger firearm homicide rates ranged from 0.6 per 100 000 in New Hampshire and North Dakota to 7.9 per 100 000 in Louisiana. We first validated the findings of our earlier article,19 which found an increased rate of firearm homicides associated with higher firearm ownership. In all models, whether full or final, whether using FS/S or the new proxy, and using all 3 data sets, we found a significant positive association between firearm ownership and firearm homicide rates (Tables 3 and 4). In the final model using the new proxy and the MI-SHR/W data set, the incidence rate ratio was 1.013 (95% confidence interval [CI] =1.007, 1.018), indicating that for every 1 percentage point increase in the gun ownership proxy, the firearm homicide rate increased by 1.3%. In 8 of the 12 models, the association between gun ownership and nonfirearm homicide rates was not significant; however, we found a significant positive association between gun ownership and nonfirearm homicide rates in 4 of the models and a positive association between gun ownership and total homicide rate that was significant in all but 1 of the 12 models. Of the 12 models, 11 showed no significant relationship between gun ownership and stranger homicide rates (Tables 3 and 4). The association between gun ownership and nonstranger homicide rates in 11 of the 12 models was, however, positive and significant. For the final model using the new proxy and the MI-SHR/W data set, the incidence rate ratio for nonstranger homicide rate associated with gun ownership was 1.009 (95% CI = 1.005, 1.013), indicating that for each 1 percentage point increase in the gun ownership proxy, nonstranger homicide rates increased by 0.9%. Our models consistently failed to uncover a robust, statistically significant relationship between gun ownership and stranger firearm homicide rates (Tables 3 and 4). All models, however, showed a positive and significant association between gun ownership and nonstranger firearm homicide rates. For the final model using the new proxy and the MI-SHR/W data set, the incidence rate ratio for nonstranger firearm homicide rate associated with gun ownership was 1.014 (95% CI = 1.009,
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TABLE 4—Results of Final Model Effects of Gun Ownership on Homicide Rates: United States, 1981–2010 Outcome and Predictor Variables
SHR, IRRa (95% CI)
MI-SHR, IRRa (95% CI)
MI-SHR/W, IRRa (95% CI)
Old gun ownership proxy: FS/S Firearm homicide rate
1.008* (1.002, 1.015)
1.009* (1.003, 1.015)
Nonfirearm homicide rate
1.004* (1.000, 1.007)b
1.003 (1.000, 1.006)c
1.004* (1.001, 1.007)
Total homicide rate
1.005* (1.000, 1.010)d
1.006* (1.001, 1.011)
1.007* (1.002, 1.011)
Stranger homicide rate Nonstranger homicide rate
1.004 (0.996, 1.012) 1.017* (1.010, 1.023)
0.999 (0.994, 1.004) 1.008* (1.003, 1.013)
0.999 (0.994, 1.005) 1.009* (1.005, 1.013)
Stranger firearm homicide rate Nonstranger firearm homicide rate
1.009* (1.002, 1.015)
1.007 (0.999, 1.015)
1.001 (0.993, 1.009)
1.001 (0.994, 1.009)
1.021* (1.013, 1.028)
1.012* (1.005, 1.019)
1.012* (1.006, 1.018)
New gun ownership proxy: weighted average of FS/S and hunting licenses Firearm homicide rate Nonfirearm homicide rate Total homicide rate Stranger homicide rate Nonstranger homicide rate Stranger firearm homicide rate Nonstranger firearm homicide rate
1.009* (1.003, 1.016)
1.009* (1.003, 1.016)
1.013* (1.007, 1.018)
1.002 (0.997, 1.007)
1.001 (0.996, 1.006)
1.004* (1.001, 1.007)
1.004 (0.999, 1.008)
1.005* (1.001, 1.011)
1.008* (1.004, 1.012)
1.006 (0.996, 1.017) 1.009* (1.001, 1.016)
1.003 (0.995, 1.011) 1.006* (1.001, 1.011)
1.005 (0.997, 1.013) 1.009* (1.005, 1.013)
1.008 (0.998, 1.019)
1.004 (0.993, 1.014)
1.007 (0.997, 1.017)
1.014* (1.006, 1.021)
1.013* (1.006, 1.019)
1.014* (1.009, 1.019)
Note. CI = confidence interval; FS/S = firearm suicides divided by all suicides; IRR = incidence rate ratio; MI-SHR = multiply imputed Supplemental Homicide Reports, unweighted; MI-SHR/W = multiply imputed Supplemental Homicide Reports, weighted to match state-level Federal Bureau of Investigation homicide counts; SHR = Supplemental Homicide Reports. Final model includes fixed effects for year, is adjusted for clustering within states, and controls for only those state-level covariates significant in the model at a P < .05 level. a The IRR measures the percentage change in the outcome variable for each increase of 1 percentage point in the statespecific household gun ownership proxy. For example, an IRR of 1.012 indicates a 1.2% increase in the homicide rate for each 1 percentage point increase in the gun ownership proxy. b P = .033. c P = .096. d P = .032. *Parameter estimate is statistically significant at a = .05 (95% CI does not overlap 1.000). When the 95% CI appears to overlap 1.000 because of rounding, the P value is reported.
1.019), indicating that for each 1 percentage point increase in the gun ownership proxy, nonstranger firearm homicide rates increased by 1.4%. Another way to express the magnitude of the association between increases in gun ownership and increases in firearm homicide rates is to calculate the change in firearm homicide rates associated with a 1 standard deviation increase in the gun ownership proxy (SD = 13.8%). In our final model, using the new proxy and the MI-SHR/W data set, a 1 standard deviation increase in gun ownership was associated with a 21.1% increase in the nonstranger firearm homicide rate.
DISCUSSION To the best of our knowledge, this is the first published article to examine the relationship
between the prevalence of gun ownership and stranger versus nonstranger homicide rates at the state level. Although we found no robust, statistically significant association between gun ownership and stranger homicide rates, higher gun ownership was significantly associated with increased nonstranger homicide rates. This was true for both firearm nonstranger homicide rates and total nonstranger homicide rates. On the basis of our final model with the new proxy and the imputed, weighted data, the analysis indicated that for each 1 standard deviation increase in the gun ownership proxy, a state’s nonstranger firearm homicide rate increased by 21.1%. These findings appear to contravene the argument that gun ownership protects against stranger homicide because states with higher household gun ownership did not experience lower stranger homicide rates. We found that
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higher gun ownership, although not associated with lower stranger homicide rates, was associated with higher nonstranger homicide rates. Because we used a different data set—the UCRs rather than the Centers for Disease Control and Prevention’s vital statistics records—our findings corroborate the results of previous research showing a positive and significant relationship between higher gun ownership at the state level and increased firearm homicide rates.6-19 Consistent with previous research, we found that gun ownership is related not only to increased firearm homicide rates but also to increased total homicide rates. Our study revealed that over the past 3 decades, only approximately one fifth of firearm homicides were committed by strangers. The overall percentage of homicides that were stranger homicides in our data (21.9%) is consistent with the Bureau of Justice Studies’ estimate that 21.9% of homicides committed between 1980 and 2003 were stranger homicides.20 This result has major implications for the strategies that can be used to reduce firearm homicides. Despite widespread media attention to mass shootings committed by estranged people, the majority of homicides are committed by individuals known to the victims. Cross-sectional studies of the relationship between the prevalence of firearms and homicide rates are only 1 line of evidence relevant to the evaluation of the effect of firearms on morbidity and mortality. Findings from other types of studies—such as those examining the relationship between individual gun ownership and risk of firearm injury or death—must also be considered. A recent meta-analysis of such studies reported that people with a gun in the household are at increased risk for firearm homicide or suicide.62 Our research has limitations. The data sources we relied on are limited by incomplete reporting as well as missing information on the victim---offender relationship in a substantial proportion of reported cases. To minimize these limitations, we implemented a multiple imputation approach for the victim---offender relationship and adjusted our estimates to reflect the total number of homicides. Although we used panel data, this study is essentially cross-sectional because most of the variation in gun ownership is between states, not within states over time, which introduces
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the possibility of reverse causation: people may be more likely to acquire firearms when they observe higher rates of homicide. Further research that uses longitudinal designs is needed to determine the direction of causality. Another limitation is that this research relied on proxy rather than survey measurements of household gun ownership as a result of the lack of data on firearms ownership at the state level. Nevertheless, the correlation between the new proxy and survey-measured gun ownership at the state level was 0.95.53 In conclusion, this article is the first to our knowledge to report that a higher proportion of household gun ownership at the state level is associated with statistically significant increased rates of nonstranger total and firearm homicides. By contrast, we found no robust, statistically significant association between household gun ownership and stranger homicides. Our findings thus challenge the argument that gun ownership deters violent crime, in particular, homicides. j
About the Authors Michael Siegel, Yamrot Negussie, Sarah Vanture, and Jane Pleskunas are with the Department of Community Health Sciences, Boston University School of Public Health, Boston, MA. Craig S. Ross is with Virtual Media Resources, Natick, MA. Charles King III is with Greylock McKinnon Associates, Cambridge, MA, and Pleiades Consulting Group, Lincoln, MA. Correspondence should be sent to Michael Siegel, MD, MPH, Department of Community Health Sciences, Boston University School of Public Health, 801 Massachusetts Avenue, 3rd Floor, Boston, MA 02118 (e-mail: mbsiegel@ bu.edu). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted April 10, 2014.
Contributors M. Siegel obtained the data. M. Siegel, Y. Negussie, S. Vanture, and J. Pleskunas analyzed the data. All authors conceptualized and designed the study, interpreted the results, wrote the article, and critically reviewed and commented on the article.
Acknowledgments We gratefully acknowledge the assistance of James Alan Fox, PhD, the Lipman Family Professor of Criminology, Law and Public Policy at the School of Criminology and Criminal Justice at Northeastern University, who kindly provided the Multiply-Imputed Supplementary Homicide Reports File, 1976---2011, including the data sets and a codebook.
References 1. Centers for Disease Control and Prevention. Webbased injury statistics query and reporting systems: fatal injury reports. Available at: http://www.cdc.gov/injury/ wisqars/fatal_injury_reports.html. Accessed December 17, 2013. 2. Kirk C. Did your state pass any gun control laws since Newtown? The 20 states that passed 41 new state laws to restrict guns or improve background checks. Available at: http://www.slate.com/articles/news_ and_politics/crime/2013/12/gun_control_laws_ by_state_mapped.html. Accessed December 17, 2013. 3. National Rifle Association. A post-Heller, postMcDonald question: does an increase in handguns increase crime? Fairfax, VA: National Rifle Association, Institute for Legislative Action; 2010. Available at: http://www.nraila.org/news-issues/fact-sheets/2010/apost-heller,-post-mcdonald-questionb.aspx?s=guns +lower+crime&st=&ps=. Accessed December 17, 2013. 4. Kleck G. Targeting Guns: Firearms and Their Control. New York, NY: Aldine de Gruyter; 1997. 5. Lott JR Jr. More Guns, Less Crime: Understanding Crime and Gun Control Laws. 3rd ed. Chicago, IL: University of Chicago Press; 2010. 6. Cook PJ, Ludwig J. The social costs of gun ownership. J Public Econ. 2006;90(1-2):379---391. 7. Miller M, Azrael D, Hemenway D. Rates of household firearm ownership and homicide across US regions and states, 1988---1997. Am J Public Health. 2002;92 (12):1988---1993. 8. Miller M, Azrael D, Hemenway D. Firearm availability and suicide, homicide, and unintentional firearm deaths among women. J Urban Health. 2002;79(1):26---38. 9. Miller M, Azrael D, Hemenway D. Firearm availability and unintentional firearm deaths, suicide, and homicide among 5---14 year olds. J Trauma. 2002;52 (2):267---274. 10. Seitz ST. Firearms, homicides, and gun control effectiveness. Law Soc Rev. 1972;6(4):595---614. 11. Lester D. Firearm availability and the incidence of suicide and homicide. Acta Psychiatr Belg. 1988;88(56):387---393. 12. Lester D. Firearm deaths in the United States and gun availability. Am J Public Health. 1993;83(11):1642. 13. Miller M, Hemenway D, Azrael D. State-level homicide victimization rates in the US in relation to survey measures of household firearm ownership, 2001--2003. Soc Sci Med. 2007;64(3):656---664. 14. Murnan J, Dake JA, Price JH. Association of selected risk factors with variation in child and adolescent firearm mortality by state. J Sch Health. 2004;74(8):335---340. 15. Rosenfeld R, Baumer E, Messner SF. Social trust, firearm prevalence, and homicide. Ann Epidemiol. 2007;17(2):119---125. 16. Ruddell R, Mays GL. State background checks and firearms homicides. J Crim Justice. 2005;33(2):127---136.
Human Participant Protection
17. Fleegler EW, Lee LK, Monuteaux MC, Hemenway D, Mannix R. Firearm legislation and firearm-related fatalities in the United States. JAMA Intern Med. 2013;173(9):732---740.
Institutional review board approval was not needed for this study because secondary data sources were used.
18. Hepburn LM, Hemenway D. Firearm availability and homicide: a review of the literature. Aggress Violent Behav. 2004;9(4):417---440.
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19. Siegel M, Ross CS, King C III. The relationship between gun ownership and firearm homicide rates in the United States, 1981-2010. Am J Public Health. 2013; 103(11): 2098---2105. 20. Cooper A, Smith EL. Homicide Trends in the United States, 1980-2008: Annual Rates for 2009 and 2010. Washington, DC: US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics; 2011. 21. Fox JA, Zawitz MW. Homicide Trends in the United States. Washington, DC: US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics; 2007. 22. Moskowitz H, Laraque D, Doucette JT. Relationships of US youth homicide victims and their offenders, 19761999. Arch Pediatr Adolesc Med. 2005;159(4):356--361. 23. Kellermann AL, Mercy JA. Men, women, and murder: gender-specific differences in rates of fatal violence and victimization. J Trauma. 1992;33(1):1---5. 24. Karch DL, Logan J, Patel N. Surveillance for violent deaths—National Violent Death Reporting System, 16 states, 2008. MMWR Surveill Summ. 2011;60(10):1---49. 25. Smith AT Jr, Kuller LH, Perper JA, Brent DA, Moritz G, Costantino JP. Epidemiology of homicide in Allegheny County, Pennsylvania, between 1966---1974 and 1984--1993. Prev Med. 1998;27(3):452---460. 26. Coyne-Beasley T, Schoenbach VJ, Herman-Giddens ME. The epidemiology of adolescent homicide in North Carolina from 1990 to 1995. Arch Pediatr Adolesc Med. 1999;153(4):349---356. 27. Pratt C, Deosaransingh K. Gender differences in homicide in Contra Costa County, California: 19821993. Am J Prev Med. 1997;13(6 suppl):19---24. 28. Paulozzi LJ, Saltzman LE, Thompson MP, Holmgreen P. Surveillance for homicide among intimate partners—United States, 1981-1998. MMWR Surveill Summ. 2001;50(SS03):1---15. 29. National Archive of Criminal Justice Data. Uniform Crime Reporting Program Data Series: Supplemental Homicide Reports, 1981---2010. Ann Arbor, MI: InterUniversity Consortium for Political and Social Research. Available at: http://www.icpsr.umich.edu/icpsrweb/ NACJD/series/00057. Accessed December 17, 2013. 30. Shields RT, Ward BW. Comparison of the National Violent Death Reporting System and Supplementary Homicide Report: potential benefits of integration. Justice Res Policy. 2008;10(2):67---97. 31. Barber C, Hemenway D, Hochstadt J, Azrael D. Underestimates of unintentional firearm fatalities: comparing Supplementary Homicide Report data with the National Vital Statistics System. Inj Prev. 2002;8(3): 252---256. 32. Loftin C, McDowall D, Fetzer MD. A comparison of SHR and Vital Statistics homicide estimates for US cities. J Contemp Crim Justice. 2008;24(1):4---17. 33. Fox JA, Swatt ML. Uniform Crime Reports [United States]: Supplementary Homicide Reports with Multiple Imputation, Cumulative Files 1976-2007. Ann Arbor, MI: Inter-University Consortium for Political and Social Research; 2009. Available at: http://www.icpsr.umich.edu/ icpsrweb/ICPSR/studies/24801. Accessed December 17, 2013. 34. Fox JA, Swatt ML. Uniform Crime Reports [United States]: Supplementary Homicide Reports with Multiple Imputation, Cumulative Files 1976-2007 (Codebook). Ann Arbor, MI: Inter-University Consortium for Political
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and Social Research; 2009. Available at: http://www. icpsr.umich.edu/cgi-bin/file?comp=none&study= 24801&ds=0&file_id=971334. Accessed December 17, 2013. 35. Fox JA. Multiply-Imputed Supplementary Homicide Reports File, 1976-2011. Boston, MA: Northeastern University, School of Criminology and Criminal Justice; 2013. 36. Bureau of Justice Statistics. Stranger and nonstranger crime. Available at: http://www.bjs.gov/index. cfm?ty=tp&tid=941. Accessed December 17, 2013. 37. Wadsworth T, Roberts JM Jr. When missing data are not missing: a new approach to evaluating Supplemental Homicide Report imputation strategies. Criminology. 2008;46(4):841---870. 38. Fox JA, Swatt ML. Multiple imputation of the Supplementary Homicide Reports, 1976---2005. J Quant Criminol. 2009;25(1):51---77. 39. Fox JA. Missing data problems in the SHR: imputing offender and relationship characteristics. Homicide Stud. 2004;8(3):214---254. 40. Riedel M, Regoeczi WC. Missing data in homicide research. Homicide Stud. 2004;8(3):163---192. 41. Addington LA. The effect of NIBRS reporting on item missing data in murder cases. Homicide Stud. 2004;8(3):193---213. 42. Flewelling RL. A nonparametric imputation approach for dealing with missing variables in SHR data. Homicide Stud. 2004;8(3):255---266. 43. Messner SF, Deane G, Beaulieu M. A logmultiplicative association model for allocating homicides with unknown victim-offender relationships. Criminology. 2002;40(2):457---480.
54. Sen B, Panjamapirom A. State background checks for gun purchase and firearm deaths: an explanatory study. Prev Med. 2012;55(4):346---350. 55. Miller M, Azrael D, Hepburn L, Hemenway D, Lippmann SJ. The association between changes in household firearm ownership and rates of suicide in the United States, 1981---2002. Inj Prev. 2006;12(3):178-- 182. 56. Sumner SA, Layde PM, Guse CE. Firearm death rates and association with level of firearm purchase background check. Am J Prev Med. 2008;35(1):1---6. 57. Lawless JF. Negative binomial and mixed Poisson regression. Can J Stat. 1987;15(3):209---225. 58. Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13---22. 59. Hedeker D, Gibbons RD, Flay BR. Random-effects regression models for clustered data with an example from smoking prevention research. J Consult Clin Psychol. 1994;62(4):757---765. 60. Pendergast JF, Gange SJ, Newton MA, Lindstrom MJ, Palta M, Fisher MR. A survey of methods for analyzing clustered binary response data. Int Stat Rev. 1996;64 (1):89---118. 61. Horton NJ, Lipsitz SR. Review of software to fit generalized estimating equation regression models. Am Stat. 1999;53(5):160---169. 62. Anglemyer A, Horvath T, Rutherford G. The accessibility of firearms and risk for suicide and homicide victimization among household members: a systematic review and meta-analysis. Ann Intern Med. 2014;160 (2):101---110.
44. Pampel FC, Williams KR. Intimacy and homicide: compensating for missing data in the SHR. Criminology. 2000;38(2):661---680. 45. Williams KR, Flewelling RL. Family, acquaintance and stranger homicide: alternate procedures for rate calculations. Criminology. 1987;25(3):543---560. 46. Azrael D, Cook PJ, Miller M. State and local prevalence of firearms ownership: measurement, structure, and trends. J Quant Criminol. 2004;20(1): 43---62. 47. Kleck G. Measures of gun ownership levels for macro-level crime and violence research. J Res Crime Delinq. 2004;41(1):3---36. 48. Kleck G. Point Blank: Guns and Violence in America. New York, NY: Aldine de Gruyter; 1991. 49. Hoskin AW. Armed Americans: the impact of firearm availability on national homicide rates. Justice Q. 2001;18(3):569---592. 50. Miller M, Azrael D, Hemenway D. Household firearm ownership and suicide rates in the United States. Epidemiology. 2002;13(5):517---524. 51. Kennedy BP, Kawachi I, Prothrow-Stith D, Lochner K, Gupta V. Social capital, income inequality, and firearm violent crime. Soc Sci Med. 1998;47(1):7---17. 52. Price JH, Thompson AJ, Dake JA. Factors associated with state variations in homicide, suicide, and unintentional firearm deaths. J Community Health. 2004;29 (4):271---283. 53. Siegel M, Ross CS, King C. A new proxy measure for state-level gun ownership in studies of firearm injury prevention. Inj Prev. 2014; 20(3): 204---207.
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The Relationship Between Gun Ownership and Stranger and Nonstranger Firearm Homicide Rates in the United States, 1981–2010 Michael Siegel, MD, MPH, Yamrot Negussie, Sarah Vanture, Jane Pleskunas, Craig S. Ross, PhD, MBA, and Charles King III, JD, PhD
Firearms cause more than 31 000 deaths annually in the United States.1 Since the tragic shooting of 20 children and 7 adults in Newtown, Connecticut, in 2012, several states have enacted or debated legislation to restrict the availability of firearms.2 Some gun rights advocates have argued that restricting the availability of guns might cause harm by removing an effective deterrent to crime.3-5 Lott, for example, has suggested that reducing the number of guns held by law-abiding citizens might increase homicides because “it would be easier for criminals to prey on the weakest citizens, who would find it more difficult to defend themselves.”5(p11) Understanding whether increased gun ownership increases or decreases homicides is essential to inform public policy regarding measures to address firearm violence. Of particular interest is the question of whether higher gun ownership is associated with lower rates of stranger homicide (i.e., homicide committed by a person unknown to the victim) because such a relationship is consistent with the hypothesis that increased household ownership of guns deters violent crime by strangers who might otherwise have killed the potential victim. Multiple cross-sectional studies have demonstrated a correlation between higher gun ownership at the state level and higher overall state-specific rates of firearm homicide.6-18 Most recently, we reported a strong and robust relationship between estimated gun ownership in the 50 states and firearm homicide rates over the period 1981 to 2010, while controlling for 20 potential state-level confounding variables.19 None of these studies distinguished between stranger and nonstranger homicides. We are not aware of any published studies that have examined the relationship between gun ownership and stranger versus nonstranger homicide rates.
Objectives. We examined the relationship between gun ownership and stranger versus nonstranger homicide rates. Methods. Using data from the Supplemental Homicide Reports of the Federal Bureau of Investigation’s Uniform Crime Reports for all 50 states for 1981 to 2010, we modeled stranger and nonstranger homicide rates as a function of state-level gun ownership, measured by a proxy, controlling for potential confounders. We used a negative binomial regression model with fixed effects for year, accounting for clustering of observations among states by using generalized estimating equations. Results. We found no robust, statistically significant correlation between gun ownership and stranger firearm homicide rates. However, we found a positive and significant association between gun ownership and nonstranger firearm homicide rates. The incidence rate ratio for nonstranger firearm homicide rate associated with gun ownership was 1.014 (95% confidence interval = 1.009, 1.019). Conclusions. Our findings challenge the argument that gun ownership deters violent crime, in particular, homicides. (Am J Public Health. 2014;104:1912–1919. doi:10.2105/AJPH.2014.302042)
Although the US Department of Justice regularly provides national statistics on rates of stranger versus nonstranger homicide,20,21 we are aware of no published studies that report state-specific data on stranger versus nonstranger homicide. Understanding state-specific patterns of victimization in terms of the relationship between homicide victims and offenders, and identifying the trends in these patterns, would inform state efforts to reduce homicide rates.22 Several studies have examined the relationship between homicide victims and offenders in specific settings, such as national youth homicides22; national homicides23,24; Allegheny County, Pennsylvania, homicides25; adolescent homicides in North Carolina26; homicides in Contra Costa County, California27; and national homicides among intimate partners.28 Nationally, between 1980 and 2008, of the homicides for which the relationship between victim and offender was known (63.1% of all homicides), approximately one fifth (21.9%) were stranger homicides.20 We are not aware
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of any published data on how this may vary among states. In this article, we report and analyze stranger and nonstranger homicides at the state level during the period 1981 to 2010 and examine the relationship between those rates and state-specific household gun ownership during the same years. To the best of our knowledge, ours is the first study to report state-specific data on stranger and nonstranger homicide rates and examine the relationship between state-level gun ownership and stranger and nonstranger homicide rates, while controlling for differences in a wide range of state-level factors associated with homicide.
METHODS Using a panel of annual data for 1981 to 2010 for each of the 50 states, we modeled the stranger and nonstranger firearm homicide rates in a given year for a given state as a function of gun ownership, measured by a proxy, in that state during that year, while
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controlling for factors that might confound the association. We used a negative binomial regression model with fixed effects for each year. We accounted for clustering of observations among states using generalized estimating equations. We obtained stranger and nonstranger homicide data from the Supplemental Homicide Reports (SHRs) of the Federal Bureau of Investigation’s Uniform Crime Reports (UCRs).29-32 Although a substantial number of SHR records are missing data on the victim---offender relationship, a multiply imputed version of the data set exists in which this relationship is imputed for all cases.33-35 To assess whether our analysis was sensitive either to the large amount of missing data or to the imputation of missing data, we conducted a sensitivity analysis, using both the original and the imputed data. Specifically, we analyzed 3 different
versions of the SHR data set: (1) the original data set, in which 34% of records are missing data on the nature of the victim---offender relationship,29 (2) an augmented data set, which included 5 sets of imputed data for any missing victim---offender relationships,33-35 and (3) a multiply imputed data set with an additional weighting adjustment for incomplete reporting of homicides.33-35 These data sets are summarized in Table 1.
Variables and Data Sources Outcome variable. The outcome variable was the annual stranger or nonstranger firearm homicide rate in a given state. We adopted the Bureau of Justice Studies’ definitions of stranger and nonstranger homicide.36 Stranger homicide is when the victim did not know the offender or knew the offender only by sight. Nonstranger homicide is
when the victim “is either related to, well known to, or casually acquainted with the victim.”36 Data source. The only national data source that records homicide victim---offender relationships is the Federal Bureau of Investigation’s UCR SHRs.29-32 The SHRs have collected victim---offender relationship data consistently since 1975.30 The SHRs provide detailed data on nearly all homicides (murders and nonnegligent manslaughters) in the United States. Data are collected by local reporting agencies throughout the country, and SHR variable definitions have remained constant since 1981. Data are provided for each of the 50 states for the entire study period, with missing data for just several years in a few states (Table 1). We obtained the 1981 to 201029 UCR SHR data set from the Inter-University Consortium for Political and Social Research (Ann Arbor, MI).
TABLE 1—Data Sets Used to Estimate Stranger and Nonstranger Homicide Rates: All 50 US States, 1981–2010
Data Set SHR
Agency and Description As part of the Uniform Crime
Homicides Included Murder, nonnegligent
Specification
States and Years Included
Crude rates No weighting
50 states (1981–2010);
Reporting system, the FBI compiles data filed by
manslaughter, negligent manslaughter, and
missing data for AL (1999), FL (1988-1991,
local law enforcement
justifiable homicide
1996-2010), IA (1991),
agencies for homicides
KS (1994–1999), KY
occurring within their
(1988), ME (1991, 1992),
jurisdictions
MT (1987, 1993, 1994,
Records with Missing Data for Victim–Offender Relationship, % 34.0
1996), NH (1997), WI (1998) MI-SHR
SHR data files with 5 sets of imputed data for missing
Murder and nonnegligent
Crude rates No weighting
manslaughter
1464 records Same as SHR
0.0
1464 records
items, produced by Fox (School of Criminology and Criminal Justice, Northeastern University) MI-SHR/W
SHR data files with 5 sets of imputed data for missing items, produced by Fox
Murder and nonnegligent manslaughter
Crude rates Weighted to match annual, state-level FBI homicide counts
Same as SHR, but also
0.0
missing data for IL (1984, 1985), KS (1993, 2000),
(School of Criminology
KY (1987), MT (1982,
and Criminal Justice,
1986, 1990, 1998), NE
Northeastern University)
(1994, 2005, 2007, 2008), NM (1981), VT (1983) 1449 records
Note. FBI = Federal Bureau of Investigation; MI-SHR = multiply imputed SHRs; MI-SHR/W = multiply imputed SHRs/weighted; SHR = Supplemental Homicide Reports.
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Missing data on victim---offender relationship. The SHR is limited by missing data on the victim---offender relationship in a substantial proportion of homicides. During 1981 to 2010, 34% of the homicides in the SHR were missing data on the nature of the victim--offender relationship. The most common reason for missing data is that a homicide is not witnessed or the case is not solved.22 These data were not missing at random. Unsolved homicides are more likely to involve a stranger as offender.37 Discarding cases with missing data could therefore introduce a bias.37 Rigorous efforts have been made to deal with the missing data problem in the SHRs, focusing on imputation of the victim---offender relationship.37-45 Fox and Swatt38 developed a multiple imputation approach for missing items in the SHRs. This approach is generally viewed as the strategy of choice, and Fox has implemented it for all SHR data files from 1976 through 2011. Files that cover the period until 2007 are available through the Inter-University Consortium for Political and Social Research.33 Fox kindly provided us with updated multiply imputed files that cover the years through 2010.35 All these files included 5 sets of data with imputed missing values.34 We used all 5 imputed data sets to derive the value for the victim---offender relationship, using weights provided in the data set.34 We subsequently refer to this data set as the multiply imputed SHRs (MI-SHRs). Missing homicide data. The SHR data set has an additional limitation. Not all local law enforcement agencies complete the supplemental reports, yielding missing data that could lead to an underestimation of homicide rates.39 The SHR data set includes approximately 90% of all homicides.39 This unit missingness can be addressed by applying weights that adjust each state- and year-specific estimate up to the overall number of homicides reported in the UCR for that state and year.39 In addition to compiling a data set that includes multiply imputed values, we also compiled a data set that included these multiply imputed values adjusted for unit missingness by applying weights provided by Fox in the data set.35 These weights adjusted estimates to reflect state homicide totals.34 We subsequently refer to this data set as multiply imputed SHR/weighted (MI-SHR/W).
Classification of stranger and nonstranger homicides. We classified homicide cases with the following victim---offender relationships as nonstranger homicides: husband, wife, common-law husband, common-law wife, mother, father, son, daughter, brother, sister, in-law, stepfather, stepmother, stepson, stepdaughter, other family, neighbor, acquaintance, boyfriend, girlfriend, ex-husband, ex-wife, employee, employer, friend, homosexual relationship, and other known. We coded homicide cases in which the victim---offender relationship was characterized as “stranger” as stranger homicides. We excluded cases in which the relationship was unknown. In imputing the victim---offender relationship, Fox condensed the possible values into 4 categories: intimate partner, other family, friend or acquaintance, and stranger.34 We coded the stranger category as stranger homicide and the other 3 categories as nonstranger homicide. Types of homicide and calculation of homicide rates. The SHR includes all types of homicide, including murder, nonnegligent manslaughter, negligent manslaughter, and justifiable homicide but excludes deaths from the September 11, 2001, terrorist attacks. The imputed SHR data sets (MI-SHR and MI-SHR/W) included only 1 category of homicide: murder and nonnegligent manslaughter. All 3 data sets provided homicide counts. To calculate homicide rates, we divided annual, state-specific homicide counts by the state population for a given year obtained from the US Census. Table 1 summarizes the 3 data sets used in this study. Main predictor and control variables. The main predictor variable was the annual prevalence of household firearm ownership in a given state, measured by a proxy. Because no annual survey assessed the prevalence of household firearm ownership in all 50 states during the entire study period, we used a wellestablished proxy: the percentage of suicides committed using a firearm (firearm suicides divided by all suicides, or FS/S). This proxy has been extensively validated in the literature4,6,9,16,46-50 and has been determined to be the best of the many earlier proxies that have been tested. 46 The ratio of firearm suicides to all suicides has been shown to correlate highly with survey measures of
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household firearm ownership, 4,6,9,46-50 including state-specific measures of firearm ownership, 9,46 and has been widely used as a proxy for state-specific gun availability in previous studies. 6-9,11,15,16,19,49-52 Although the FS/S proxy has been shown to correlate highly with survey measures of state-level household firearm ownership, its correlation with survey-measured gun ownership during the 3 years it was measured in all 50 states (Behavioral Risk Factors Surveillance System, 2001, 2002, and 2004) was 0.80.19 Recently, we developed a new proxy measure that improves the correlation with surveymeasured gun ownership in the Behavioral Risk Factors Surveillance System during 2001, 2002, and 2004 from 0.80 to 0.95.53 This new proxy measure incorporates a state’s hunting license rate in addition to FS/S.53 In this study, we used both the traditional (FS/S) proxy and our new proxy. Following our earlier approach,19 we controlled for the following state-level factors that have been identified as related to homicide rates and might also be related to firearm ownership rates: proportion of young adults (aged 15---29 years), proportion of young men (aged 15---29 years), proportion of Blacks, proportion of Hispanics, level of urbanization, educational attainment, poverty status, unemployment, median household income, income inequality (the Gini ratio), per capita alcohol consumption, nonhomicide violent crime rate (aggravated assault, robbery, forcible rape), nonviolent (property) crime rate (burglary, larceny or theft, and motor vehicle theft), hate crime rate, divorce rate, region, incarceration rate, and suicide rate.
Model and Statistical Analysis Because the outcome variables—homicide rates—were not normally distributed but skewed and overdispersed (the variance was greater than the mean), we modeled the outcome using a negative binomial model following the approach used in previous studies.7-9,13,19,50,54-56 Estimation of the overdispersion parameter confirmed our choice of a negative binomial model over a Poisson model,57 following Miller et al.7 Clustering in our data might have arisen in 2 ways: by year (30 levels) and by state (50 levels). We entered year as a fixed effect in the
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TABLE 2—Average Proportion of Firearm Homicides Committed by Strangers, by State and Decade: United States, 1981–2010 State
1981–1990, %
1991–2000, %
2001–2010, %
Entire Study Period, %
Alabama
12.7
23.5
22.4
19.4
Alaska
17.6
25.5
18.9
20.7
Arizona
25.3
36.7
39.7
33.9
Arkansas
12.4
17.4
18.8
16.2
California Colorado
28.9 23.9
33.8 25.3
36.4 25.2
33.0 24.8
Connecticut
23.5
30.3
29.0
27.6
Delaware
12.5
25.6
26.7
21.6
Florida
21.7
25.5
NA
23.1
Georgia
16.2
23.7
27.2
22.4
Hawaii
15.9
22.3
26.9
21.7
Idaho
20.2
10.6
8.6
13.1
Illinois Indiana
27.4 23.0
33.8 24.6
34.7 23.2
32.3 23.6
Iowa
15.3
22.2
14.8
17.2
Kansas
23.7
25.6
23.2
23.7
Kentucky
11.5
19.3
22.4
18.2
Louisiana
26.5
24.3
25.1
25.3
8.5
6.6
9.6
8.3
Maryland
24.5
26.2
30.8
27.2
Massachusetts Michigan
28.4 22.1
27.5 25.4
29.1 25.6
28.3 24.4
Minnesota
20.6
25.0
23.0
22.9
Mississippi
12.7
18.1
16.7
15.8
Missouri
22.6
26.3
23.9
24.3
Montana
21.3
2.3
8.5
10.3
Nebraska
19.7
14.7
26.7
19.9
Nevada
22.4
26.3
32.3
27.0
New Hampshire New Jersey
14.6 29.0
6.5 30.3
17.6 30.5
13.1 29.9
New Mexico
18.8
27.3
24.2
23.6
New York
38.7
36.2
33.4
36.1
North Carolina
13.4
21.5
26.8
20.6
North Dakota
13.7
12.4
6.5
10.9
Ohio
23.7
28.0
26.2
26.0
Oklahoma
15.8
18.7
23.6
19.4
Oregon Pennsylvania
18.1 22.8
20.9 29.6
16.5 23.7
18.5 25.4
Rhode Island
19.8
27.6
36.1
27.8
South Carolina
13.6
22.6
24.0
20.1
South Dakota
28.9
12.4
8.7
16.8
Tennessee
17.1
22.1
26.4
21.9
Texas
21.6
28.2
34.1
28.0
Utah
19.2
26.8
21.5
22.5
Vermont Virginia
6.3 18.1
6.7 21.2
22.4 23.3
11.6 20.9
Maine
RESULTS
Continued
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regression model, which allowed us to control for nationwide secular changes that might affect firearm homicide rates. To account for clustering of observations among states, we used a generalized estimating equations approach.58 This procedure accounts for correlation of data within state clusters, avoiding a type 1 error that would be introduced if this correlation were ignored.59 We used an exchangeable (compound symmetry) working correlation matrix to model the correlation among observations within states. We used robust variance estimators (the Huber-White sandwich estimator of variance) to produce consistent point estimates58,60 and standard errors58,60,61 even if the working correlation matrix was misspecified. Our approach follows that of Miller et al.,50 who used a generalized estimating equations approach to account for clustering by region in their study of the impact of gun ownership on suicide rates. Our primary aim was to examine the relationship between gun prevalence and homicide rates while controlling for all identified potential confounding variables. We first ran a full model that included all variables, regardless of their contribution to the model. To develop a final, more parsimonious model, we first entered all variables with Wald test P < .1 on bivariate analyses into 1 model. We then iteratively deleted variables found not to be significant in the presence of the other variables, assessing the significance of each variable using a Wald test at a significance level of .05. Finally, we added each of the excluded variables into the model, 1 at a time, to assess whether it became significant when included in a model with the other variables. We included fixed effects for year and clustering by state in all models. We conducted all analyses using the xtnbreg procedure in STATA version 12 (StataCorp, College Station, TX).
Over the entire study period, the average proportion of firearm homicides that were committed by strangers throughout the United States was 21.9% (Table 2). This proportion varied widely, from a low of 8.3% in Maine to a high of 36.1% in New York. The average proportion of firearm homicides committed by strangers increased slightly over the study
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TABLE 2—Continued Washington
19.9
23.4
21.6
21.6
West Virginia
8.3
12.6
10.6
10.5
Wisconsin
20.7
24.4
33.3
26.2
Wyoming
15.0
12.5
9.2
12.2
United States
19.6
22.7
23.5
21.9
Note. NA = not available. Data from Federal Bureau of Investigation Uniform Crime Reports, Supplemental Homicide Report data files, with 5 sets of imputed data for missing items, produced by Fox.35
period, from 19.6% in 1981 to 1990 to 23.5% in 2001 to 2010. Average household gun ownership during the study period, as measured by the new
proxy, ranged from a low of 12.5% in Hawaii to a high of 73.8% in Wyoming (Table A, available as a supplement to the online version of this article at http://www.ajph.org). Average
TABLE 3—Results of Full Model Effects of Gun Ownership on Homicide Rates: United States, 1981–2010 Outcome and Predictor Variables
SHR, IRRa (95% CI)
MI-SHR, IRRa (95% CI)
MI-SHR/W, IRRa (95% CI)
Old gun ownership proxy: FS/S Firearm homicide rate
1.008* (1.002, 1.014)
1.007* (1.001, 1.013)
1.008* (1.003, 1.013)
1.002 (0.999, 1.006)
1.003 (0.999, 1.006)
1.004* (1.000, 1.007)b
1.005* (1.001, 1.009)
1.005* (1.001, 1.009)
1.006* (1.002, 1.009)
1.002 (0.995, 1.009)
1.001 (0.996, 1.006)
1.001 (0.996, 1.006)
Nonstranger homicide rate Stranger firearm homicide rate
1.007* (1.002, 1.013) 1.006 (0.998, 1.015)
1.006* (1.002, 1.011) 1.003 (0.996, 1.009)
1.007* (1.004, 1.011) 1.003 (0.997, 1.009)
Nonstranger firearm homicide rate
1.013* (1.006, 1.021)
1.009* (1.003, 1.015)
1.010* (1.004, 1.015)
Nonfirearm homicide rate Total homicide rate Stranger homicide rate
New gun ownership proxy: weighted average of FS/S and hunting licenses Firearm homicide rate Nonfirearm homicide rate Total homicide rate
1.010* (1.003, 1.016)
1.009* (1.003, 1.016)
1.012* (1.006, 1.017)
1.000 (0.994, 1.005)
1.000 (0.995, 1.005)
1.003 (1.000, 1.007)c
d
e
1.005* (1.000, 1.010)
1.005* (1.000, 1.010)
1.008* (1.005, 1.012)
Stranger homicide rate
1.008 (0.998, 1.019)
1.007 (0.997, 1.016)
1.009* (1.001, 1.017)
Nonstranger homicide rate Stranger firearm homicide rate
1.004 (0.997, 1.012) 1.011 (1.000, 1.022)g
1.005* (1.000, 1.010)f 1.008 (0.997, 1.019)
1.009* (1.005, 1.012) 1.009 (0.999, 1.019)
1.009* (1.001, 1.017)
1.010* (1.004, 1.016)
1.012* (1.008, 1.017)
Nonstranger firearm homicide rate
Note. CI = confidence interval; FS/S = firearm suicides divided by all suicides; IRR = incidence rate ratio; MI-SHR = multiply imputed Supplemental Homicide Reports, unweighted; MI-SHR/W = multiply imputed Supplemental Homicide Reports, weighted to match state-level Federal Bureau of Investigation homicide counts; SHR = Supplemental Homicide Reports. Full model includes fixed effects for year, is adjusted for clustering within states, and controls for percentage of young (ages 15–29), percentage of young men, percentage Black, percentage Hispanic, poverty, unemployment, household income, educational attainment, income inequality, level of urbanization, alcohol consumption, violent crime rate, nonviolent crime rate, hate crime rate, divorce rate, region, incarceration rate, and suicide rate. a The IRR measures the percentage change in the outcome variable for each increase of 1 percentage point in the statespecific household gun ownership proxy. For example, an IRR of 1.012 indicates a 1.2% increase in the homicide rate for each 1 percentage point increase in the gun ownership proxy. b P = .031. c P = .079. d P = .034. e P = .043. f P = .035. g P = .056. *Parameter estimate is statistically significant at a = .05 (95% CI does not overlap 1.000). When the 95% CI appears to overlap 1.000 because of rounding, the P value is reported.
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stranger firearm homicide rates ranged from 0.1 per 100 000 in Iowa, Maine, New Hampshire, North Dakota, South Dakota, and Vermont to 2.7 per 100 000 in Louisiana. Average nonstranger firearm homicide rates ranged from 0.6 per 100 000 in New Hampshire and North Dakota to 7.9 per 100 000 in Louisiana. We first validated the findings of our earlier article,19 which found an increased rate of firearm homicides associated with higher firearm ownership. In all models, whether full or final, whether using FS/S or the new proxy, and using all 3 data sets, we found a significant positive association between firearm ownership and firearm homicide rates (Tables 3 and 4). In the final model using the new proxy and the MI-SHR/W data set, the incidence rate ratio was 1.013 (95% confidence interval [CI] =1.007, 1.018), indicating that for every 1 percentage point increase in the gun ownership proxy, the firearm homicide rate increased by 1.3%. In 8 of the 12 models, the association between gun ownership and nonfirearm homicide rates was not significant; however, we found a significant positive association between gun ownership and nonfirearm homicide rates in 4 of the models and a positive association between gun ownership and total homicide rate that was significant in all but 1 of the 12 models. Of the 12 models, 11 showed no significant relationship between gun ownership and stranger homicide rates (Tables 3 and 4). The association between gun ownership and nonstranger homicide rates in 11 of the 12 models was, however, positive and significant. For the final model using the new proxy and the MI-SHR/W data set, the incidence rate ratio for nonstranger homicide rate associated with gun ownership was 1.009 (95% CI = 1.005, 1.013), indicating that for each 1 percentage point increase in the gun ownership proxy, nonstranger homicide rates increased by 0.9%. Our models consistently failed to uncover a robust, statistically significant relationship between gun ownership and stranger firearm homicide rates (Tables 3 and 4). All models, however, showed a positive and significant association between gun ownership and nonstranger firearm homicide rates. For the final model using the new proxy and the MI-SHR/W data set, the incidence rate ratio for nonstranger firearm homicide rate associated with gun ownership was 1.014 (95% CI = 1.009,
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TABLE 4—Results of Final Model Effects of Gun Ownership on Homicide Rates: United States, 1981–2010 Outcome and Predictor Variables
SHR, IRRa (95% CI)
MI-SHR, IRRa (95% CI)
MI-SHR/W, IRRa (95% CI)
Old gun ownership proxy: FS/S Firearm homicide rate
1.008* (1.002, 1.015)
1.009* (1.003, 1.015)
Nonfirearm homicide rate
1.004* (1.000, 1.007)b
1.003 (1.000, 1.006)c
1.004* (1.001, 1.007)
Total homicide rate
1.005* (1.000, 1.010)d
1.006* (1.001, 1.011)
1.007* (1.002, 1.011)
Stranger homicide rate Nonstranger homicide rate
1.004 (0.996, 1.012) 1.017* (1.010, 1.023)
0.999 (0.994, 1.004) 1.008* (1.003, 1.013)
0.999 (0.994, 1.005) 1.009* (1.005, 1.013)
Stranger firearm homicide rate Nonstranger firearm homicide rate
1.009* (1.002, 1.015)
1.007 (0.999, 1.015)
1.001 (0.993, 1.009)
1.001 (0.994, 1.009)
1.021* (1.013, 1.028)
1.012* (1.005, 1.019)
1.012* (1.006, 1.018)
New gun ownership proxy: weighted average of FS/S and hunting licenses Firearm homicide rate Nonfirearm homicide rate Total homicide rate Stranger homicide rate Nonstranger homicide rate Stranger firearm homicide rate Nonstranger firearm homicide rate
1.009* (1.003, 1.016)
1.009* (1.003, 1.016)
1.013* (1.007, 1.018)
1.002 (0.997, 1.007)
1.001 (0.996, 1.006)
1.004* (1.001, 1.007)
1.004 (0.999, 1.008)
1.005* (1.001, 1.011)
1.008* (1.004, 1.012)
1.006 (0.996, 1.017) 1.009* (1.001, 1.016)
1.003 (0.995, 1.011) 1.006* (1.001, 1.011)
1.005 (0.997, 1.013) 1.009* (1.005, 1.013)
1.008 (0.998, 1.019)
1.004 (0.993, 1.014)
1.007 (0.997, 1.017)
1.014* (1.006, 1.021)
1.013* (1.006, 1.019)
1.014* (1.009, 1.019)
Note. CI = confidence interval; FS/S = firearm suicides divided by all suicides; IRR = incidence rate ratio; MI-SHR = multiply imputed Supplemental Homicide Reports, unweighted; MI-SHR/W = multiply imputed Supplemental Homicide Reports, weighted to match state-level Federal Bureau of Investigation homicide counts; SHR = Supplemental Homicide Reports. Final model includes fixed effects for year, is adjusted for clustering within states, and controls for only those state-level covariates significant in the model at a P < .05 level. a The IRR measures the percentage change in the outcome variable for each increase of 1 percentage point in the statespecific household gun ownership proxy. For example, an IRR of 1.012 indicates a 1.2% increase in the homicide rate for each 1 percentage point increase in the gun ownership proxy. b P = .033. c P = .096. d P = .032. *Parameter estimate is statistically significant at a = .05 (95% CI does not overlap 1.000). When the 95% CI appears to overlap 1.000 because of rounding, the P value is reported.
1.019), indicating that for each 1 percentage point increase in the gun ownership proxy, nonstranger firearm homicide rates increased by 1.4%. Another way to express the magnitude of the association between increases in gun ownership and increases in firearm homicide rates is to calculate the change in firearm homicide rates associated with a 1 standard deviation increase in the gun ownership proxy (SD = 13.8%). In our final model, using the new proxy and the MI-SHR/W data set, a 1 standard deviation increase in gun ownership was associated with a 21.1% increase in the nonstranger firearm homicide rate.
DISCUSSION To the best of our knowledge, this is the first published article to examine the relationship
between the prevalence of gun ownership and stranger versus nonstranger homicide rates at the state level. Although we found no robust, statistically significant association between gun ownership and stranger homicide rates, higher gun ownership was significantly associated with increased nonstranger homicide rates. This was true for both firearm nonstranger homicide rates and total nonstranger homicide rates. On the basis of our final model with the new proxy and the imputed, weighted data, the analysis indicated that for each 1 standard deviation increase in the gun ownership proxy, a state’s nonstranger firearm homicide rate increased by 21.1%. These findings appear to contravene the argument that gun ownership protects against stranger homicide because states with higher household gun ownership did not experience lower stranger homicide rates. We found that
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higher gun ownership, although not associated with lower stranger homicide rates, was associated with higher nonstranger homicide rates. Because we used a different data set—the UCRs rather than the Centers for Disease Control and Prevention’s vital statistics records—our findings corroborate the results of previous research showing a positive and significant relationship between higher gun ownership at the state level and increased firearm homicide rates.6-19 Consistent with previous research, we found that gun ownership is related not only to increased firearm homicide rates but also to increased total homicide rates. Our study revealed that over the past 3 decades, only approximately one fifth of firearm homicides were committed by strangers. The overall percentage of homicides that were stranger homicides in our data (21.9%) is consistent with the Bureau of Justice Studies’ estimate that 21.9% of homicides committed between 1980 and 2003 were stranger homicides.20 This result has major implications for the strategies that can be used to reduce firearm homicides. Despite widespread media attention to mass shootings committed by estranged people, the majority of homicides are committed by individuals known to the victims. Cross-sectional studies of the relationship between the prevalence of firearms and homicide rates are only 1 line of evidence relevant to the evaluation of the effect of firearms on morbidity and mortality. Findings from other types of studies—such as those examining the relationship between individual gun ownership and risk of firearm injury or death—must also be considered. A recent meta-analysis of such studies reported that people with a gun in the household are at increased risk for firearm homicide or suicide.62 Our research has limitations. The data sources we relied on are limited by incomplete reporting as well as missing information on the victim---offender relationship in a substantial proportion of reported cases. To minimize these limitations, we implemented a multiple imputation approach for the victim---offender relationship and adjusted our estimates to reflect the total number of homicides. Although we used panel data, this study is essentially cross-sectional because most of the variation in gun ownership is between states, not within states over time, which introduces
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the possibility of reverse causation: people may be more likely to acquire firearms when they observe higher rates of homicide. Further research that uses longitudinal designs is needed to determine the direction of causality. Another limitation is that this research relied on proxy rather than survey measurements of household gun ownership as a result of the lack of data on firearms ownership at the state level. Nevertheless, the correlation between the new proxy and survey-measured gun ownership at the state level was 0.95.53 In conclusion, this article is the first to our knowledge to report that a higher proportion of household gun ownership at the state level is associated with statistically significant increased rates of nonstranger total and firearm homicides. By contrast, we found no robust, statistically significant association between household gun ownership and stranger homicides. Our findings thus challenge the argument that gun ownership deters violent crime, in particular, homicides. j
About the Authors Michael Siegel, Yamrot Negussie, Sarah Vanture, and Jane Pleskunas are with the Department of Community Health Sciences, Boston University School of Public Health, Boston, MA. Craig S. Ross is with Virtual Media Resources, Natick, MA. Charles King III is with Greylock McKinnon Associates, Cambridge, MA, and Pleiades Consulting Group, Lincoln, MA. Correspondence should be sent to Michael Siegel, MD, MPH, Department of Community Health Sciences, Boston University School of Public Health, 801 Massachusetts Avenue, 3rd Floor, Boston, MA 02118 (e-mail: mbsiegel@ bu.edu). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted April 10, 2014.
Contributors M. Siegel obtained the data. M. Siegel, Y. Negussie, S. Vanture, and J. Pleskunas analyzed the data. All authors conceptualized and designed the study, interpreted the results, wrote the article, and critically reviewed and commented on the article.
Acknowledgments We gratefully acknowledge the assistance of James Alan Fox, PhD, the Lipman Family Professor of Criminology, Law and Public Policy at the School of Criminology and Criminal Justice at Northeastern University, who kindly provided the Multiply-Imputed Supplementary Homicide Reports File, 1976---2011, including the data sets and a codebook.
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