Main Page/Research/Papers/fast food and arterials/AJPH/comments

reviewer	comment	PMH response
1	"you imply that once road density is considered SES no longer matters, but you do not consider that SES is also highly related to road density, both poverty and Fast Food are attracted to high density road/highway locations."
1	"Your findings also do not conflict with previous work that suggests that access to fast food is a factor in obesity prevalence; in fact your study supports this hypothesis."
1	"Page 6 describe how distance from residence to fast food is determined – crow fly or network distance"	will clarify that this is Euclidean distance
1	"Level of poverty (7-8%) with such a low portion of the sample in this category would it be better to use median household income rather than % below poverty in your models? (see page 9)"
1	"Page 8 Table 3 and the text do not match, the last line on page 8 says ‘percent non white was again not significantly associated, but in the table % non white for all tracts was listed at p=.046. Also, should pop density be included in the table, it is described in the text as if it were also in the table?
1	"Discussion I do not follow your argument in the last paragraph (page 11) ‘Physical access to fast food measured by proximity or density may not be the most important determinant of fast food use’ I agree it is not the most important factor, but your study indirectly supports the hypothesis that access IS an important factor. Your study supports the hypothesis that amount of traffic passing by a location is an important factor in determining location of fast food outlet. It also supports that idea that more affluent people live in less dense locations further away from fast food (and nearer white table cloth restaurants?)"
1	"Table 4 Should you explain the implications of your findings that %non white is significant in both models and in the opposite direction than what was expected and found in the Pearson correlations (table 3)"
2	"By the way, what kind of correlation is there between freeway/arterial density and SES?"
2	"The authors should think about the difference between analysis that aims to explain why a situation exists and analysis that aims to describe a situation that might help to explain outcomes of interest. By incorporating freeway/arterial density, they seem to be aiming for the former. But this is a less important question. If there are greater concentrations of fast food outlets in low SES areas, we need to understand the implications of this situation and find ways to respond to it, regardless of the cause."
2	"The one concern I have methodologically is in the use of census tracts for measuring fast food density. The question is whether the density within one’s own census tract is a good measure of one’s access to fast food, when density can vary considerably from tract to tract. If the fast food outlets near me, for example, happen to be on the other side of the arterial street used as a boundary for my census tract, I might have 0 density, while the tract right next door has a high density. An alternative is to use an average density for the tract and its adjacent tracts, weighted or unweighted. This approach presents its own challenges, and doesn’t guarantee a more accurate result, but the authors need to discuss the possibility that the definition of areas (census tracts, in this case) can influence the results. This is an example of the modifiable area unit problem (MAUP), on which there is a rather substantial literature."
2	"I was also curious about the measure of distance from each dwelling to the nearest fast food restaurant. This is a much better measure of accessibility than average density for a tract. Yet the authors seem to use this solely for descriptive purposes. Is there some way to use this measure as a dependent variable? What if you use the average distance for all residences within a tract, rather than tract density? This would largely eliminate the problem with the census tract density measure noted above."
3	"Table 2 reports the descriptive statistics for the variables used in the analysis reports two fast food density variables: restaurants per square mile and restaurants per 1000 persons. It is not clear which of these was used as the dependent variable in the reported models. Also, this leads to the question: Do the results differ depending on the rate variable that is used?"
3	"OLS regression is not the most appropriate estimation method for the reported models. Of the 373 census tracts in King County, WA, 196 do not have a fast food restaurant. The first set of OLS models reported in Table 4 include all tracts. Therefore, over half of the observations in these first 3 models have “0” for the dependent variable. OLS regression is not the most appropriate technique in this instance. The large number of zeroes in the dependent variable likely leads to violations of OLS assumptions. It is more appropriate to treat the dependent variables as counts of the number of fast food restaurants and estimate a countdata model. More appropriate estimation techniques include: Poisson regression, zero-inflated Poisson regression, and negative binomial regression."
3	"It is not clear what is gained by estimating a set of models for only tracts with fast food restaurants. By sampling on the dependent variable, and thereby excluding over half of the observations, the utility of these models is not clear. At least provide a more complete justification for doing this. There is still concern that OLS may not be the most appropriate model and that a count-data model would be more appropriate for these models, as well. Justification needs to be provided for using OLS in this context."
3	"Residual diagnostics should be reported for the OLS models. Examine the normality of the residuals (this is especially important due to the relatively small number of observations and for what is likely skewed dependent variables). While Pearson’s r is reported for some variables to assess collinearity, this is not a sufficient diagnostic in a multivariate context. Instead, report variance inflation factors (VIFs) or tolerance statistics."
3	"It appears that correlation coefficients were first used to reduce the number of variables to be included in the model. Avoid this stepwise-like approach. Instead, identify the variables that are relevant based on previous research, estimate the models with these, then maybe reduce the model based on concerns such as collinearity."
3	"Not sure what the utility is of Table 3. Why report correlation coefficients for independent variables with the dependent variable when multivariate models are also reported?"
3	"Be careful in asserting the amount of variation in the dependent variable accounted for by the inclusion of the SES variables. While the R2 value increases a small amount between models #2 and #3, it is not appropriate to imply that the SES variables account for little variation. Although the author does not state this directly, the discussion of the change in R2 between the two models could be interpreted this way. I suspect that arterial and freeway density is correlated with the SES variables. Thus, all that can be stated is that the unique influence of the SES variables is small. It cannot be assumed that arterial and freeway density accounts for all of the variance in the dependent variable identified by the R2 in model #2."
3	"Does the title make sense? Explaining more fully how “fast cars” relates to the analysis."
3	"Are Figures 1-3 necessary? If so, they should be discussed more fully in the manuscript."