BMC Public Health 2009, 9:199 http://www.biomedcentral.com/1471-2458/9/199 from police jurisdictions, which do not correspond exactly with county boundaries, a second variable that adjusted for the discrepancy by distance-based weighting between police jurisdiction centriods and county centro- ids was included in the model. Also, the covariates included a variable for urbanization defined according to a modification of the rural-urban continuum classifica- tion (mRUC) scheme [20-22] designed by the U.S. Department of Agriculture.[23] This variable initially included 11 categories that were collapsed to four catego- ries for parsimony during preliminary analyses: major cit- ies (mRUC 1: central counties of one million population or more); other cities (mRUC 2: central counties of metro- politan areas of one million population or more); sub- urbs (mRUC 3–5: fringe counties of metropolitan areas of one million population or more, ranging to counties in metropolitan areas of fewer than 250,000 population); and small towns (mRUC 6–11: urban counties with a population of 20,000 or more adjacent to a metropolitan area, ranging to completely rural counties of less than 2,500 urban population not adjacent to a metro area). Each variable was measured annually except for urbaniza- tion, persons below the poverty level, female-headed households, persons living alone, persons married, and persons college-educated. These variables were based on data available either decennially and/or for certain inter- censal years. Intercensal years without data were linearly interpolated or forecast based on known values. Addition- ally, a linear trend for year was included to account for temporal variability. Given that the availability of firearms in one county may depend on how many FFLs exist in surrounding counties, a variable was included to reflect the prevalence of FFLs in counties surrounding the index county, weighted by the inverse of the squared rectilinear distance between county population-weighted centriods. The leniency of state fire- arm laws in each county was controlled for as well. This was done by coding each county according to a classifica- tion scheme published annually since 1997. [24-29] The scheme uses an integer scale ranging from 0 to 100, with 0 representing maximum restrictiveness and 100 repre- senting maximum leniency, to reflect state laws pertaining to the acquisition, ownership, and transportation of fire- arms and ammunition. Leniency scores were assigned sep- arately to each county for each year. The leniency scores used for 1997 through 1999 were taken directly from pub- lished information. We linearly extrapolated leniency scores for the years 1993 through 1996 based on the val- ues published for the period 1997 through 2002. Finally, a variable containing county-level annual nongun homi- cide rates (defined as ICD-9 codes E960–E964 and E965.5–E967.9 for 1993–1998 and ICD-10 codes X85– X92 and X96–Y08 for 1999) was derived for inclusion in the final statistical model. This was done to explore the possibility that such an adjustment could help control for a latent homicidal tendency at the county level and isolate the contribution of FFLs to gun homicide. Statistical Analysis Plots were generated to assess annual prevalence rates of FFLs and rates of homicide in the U.S. Negative binomial generalized linear regression conducted at the county level was used to estimate the association between the prevalence of FFLs and rates of gun homicide in U.S. counties during the 1993–1999 study period. Model coef- ficients were converted into incidence rate ratios (RR), and the result of the final statistical model are presented. Correlation stemming from use of multiple data years was accounted for with generalized estimating equations under a working independence correlation matrix. Spear- man correlation coefficients and variance inflation factors were used to identify multicollinearity and for diagnostic purposes. Also, variation by year and urbanization, for reasons discussed below, was then investigated by restrict- ing analyses to individual data years and using interaction terms to permit the relation between the prevalence of FFLs and rates of gun homicide to vary across counties according to their size (i.e., on the rural-urban contin- uum). The variable used to represent a linear trend for year was excluded from the year-specific models. The adjusted RR estimates derived from these models are pre- sented in a summary table. To gain insight into how well the FFL prevalence variable may be serving as a way to proxy gun availability in coun- ties of each grouping, the proportion of suicides commit- ted with firearms (FS/S) – which is considered an accurate proxy for household gun availability and has been studied in state-level and region-level analyses [10,14,15] – was computed within each county by year and modeled in place of our FFL variable. Spearman correlation coeffi- cients were also calculated to determine how well FS/S was correlated with the FFL prevalence rate within coun- ties of each grouping. Results In 1993, 17,984 gun homicides and 6,141 nongun homi- cides occurred in the U.S. At that time a total of 253,314 gun manufacturers, gun stores and individuals held active licenses to sell firearms (i.e., FFLs). The annual incidence rates of gun and nongun homicide and the annual preva- lence rates of FFLs in the U.S. from 1993 to 1999 are shown in Figure 1. Rates of gun homicide dropped con- siderably over this period, ranging from 3.98 per 100,000 in 1993 to 2.55 per 100,000 in 1999. By contrast, rates of nongun homicide fluctuated little, ranging from a high in 1993 of 1.52 per 100,000 to a low in 1999 of 1.11 per 100,000. The prevalence of FFLs decreased even more dra- matically than did gun homicide, ranging from 1.99 per Page 3 of 10 (page number not for citation purposes)