Revision as of 01:34, 15 February 2009

Comments from IJBNPA reviewers, my responses in bold

Revised draft due March 23, 2009

Reviewer 1 Comments for Author

• I have no major concerns with the manuscript. The addition of the arterial road density was a nice addition to the general approach

Minor issues requiring attention:

• Need to clarify the “King County WA” refers to King County in the State of Washington in the US. This should be clarified in the Title and the body of the text

  done

• Page 10 “ These findings are consistent with those studies in the US [3, 11] and the UK [22] that linked fast food restaurant density with area-based measures of low socioeconomic status.” would be more complete if it read “These findings are consistent with those studies in the US [3, 11], the UK [22] and Australia [21] that linked fast food restaurant density with area-based measures of low socioeconomic status.”

  done

• Page 12 (top): Clarify the reference to “MAUP”

  done, added some additional text

Reviewer 2 Comments for Author

Evaluating the contributions of arterial road density to neighborhood fast food density is relatively novel.

Major issues requiring attention:

• The background is not particularly well written, especially the first 3 paragraphs. It is not presented in a logical sequence and thus the main points are obscured. Sentence seem to be misplaced/out of order (e.g., why is the last sentence include in the paragraph on socioeconomic variations in fast food density?) Further, the literature review is “off” – there are key studies missing (e.g., a national study on fast food availability by Powell) and some of the citations are not correct. (For the literature review, see the new Larson article in Am J Prev Med 2009 for a review and then see article mentioned within.) For example, the Detroit study examined supermarkets; there is no citation for the Los Angeles study
•  : [correct; deleted this reference];
• and the cited Powell 2006 and Moore study do not directly measure “access to fresh nutritious foods, including high quality produce.” Some statement seem somewhat contradictory (e.g., fast foods being clustered in more densely populated neighborhoods and in commercially zoned areas – perhaps both are possible but it is not clear how as written).

• The hypotheses are severely underdeveloped. For example, how are area socioeconomic status and arterial road density related? Might road density mediate the effect of area SES (I don’t know)? Neighborhood racial composition is not included in the “hypothesis”; yet, it is included as one of three independent variables.
• There are a number of problems in the methods section, especially in the description of the measures. (A strength is the use of parcel polygon centroids to calculate distances between residential dwelling units and fast food outlets.)
• First, based on a list from the public health department, how did you determine whether outlets met your definition of a fast food restaurant? You may know for local and national chain but what about independent restaurants?

  We will redo with the data from BALANCE food sources

• Second, the descriptions of your variables require improvement. Follow a format that includes variable, conceptual definition, and operational definition.

• For example median household income should be identified as your measure of area socioeconomic status (as written in your hypothesis). How was “race aggregated into a dichotomous variable” – you do not state here that you used percent white and not clear how you dichotomized that variable.

  We considered both race and median household income SES variables (now we state "SES (income and race) data at the census tract level..."). Also it was not clear in the original manuscript how we recoded race data. We simply added all nonwhite resident counts per tract to obtain a total number of nonwhite residents. Divided by total residents per tract gave us % nonwhite residents per tract. This was indeed not "dichotomizing" and should be more clear in the revision: "Because of the low proportions of some of the racial subgroups in the county, race was aggregated into a single variable representing the percent of nonwhite residents per tract."

• (Plus, how did you obtain income and race data from TIGER/Line data sets?)

  We stated "Income and race data at the census tract level were obtained from the 2000 US Census SF3 and TIGER/Line data sets," which indicates that income and race data were obtained from the combination of census data (for tabular data) and TIGER/Line (for GIS layer data). We clarify this in the revision, "SES (income and race) data at the census tract level, were obtained from the 2000 US Census SF3 and tract boundaries were obtained from TIGER/Line data sets."

• For the road variable, you should define the different road classes and provide a lay description of these different road classes.

  These are standard FHWA classes; we have clarified that we did not develop these classes. A reference is not added for the FHWA classification system.

• Here you should also indicate this is density per area, rather than population. The outcome variable is not well described. You should clearly state here that you are measuring density both in terms of population and geographic area. Moreover, while perhaps a disciplinary difference, your description of “offsets” and “normalizing” variables is unclear. Are you referring to the same or a different approach?

• The presentation of the results is also very difficult to follow. Consistency in language is important. Can you use “minor” or “local” consistency?

  changed to all "local" or "minor" to "local/minor" and "freeway/arterial" for all occurrences

  Please check how you refer to your density measures – they are called “area-normalized density,” “population-normalized density,” “density,” “density per km2,” “fast food restaurants per capita,” etc. You refer to the racial composition measure as “percent white,” “minority status,” “percent nonwhite,” etc. (There are also formatting issues in this section which I assume are not the author’s fault.) In some places, I am not sure how the authors reached a conclusion (e.g., page 9, “arterial road density was the best predictor of fast food density” and “arterial road density was only minimally associated” with the other two independent variables).

• Points in the discussion are not well-supported. I am not convinced that use of road density measures would change our understanding of the relationship between neighborhood demographics and fast food availability. (My reading of this literature is that findings are mixed.) It may be that low income or minority neighborhood have more fast food restaurants because they have more major roads but this does not change the observation (when found) that they have more. In fact, road density may explain the association. Also, the last paragraph is a large leap from your study (and the overall point is obscured, in my opinion).

Minor issues requiring attention:

• There are a number of typographical and grammatical errors that detract from the manuscript’s quality.

• Street network distance is preferred over Euclidean distance. Please use network distance of justify the use of Euclidean distance.

• Why did you convert parcels for properties within multiple residential units to multiple records?

• For the data analysis, why estimate three separate models – why not just present model 3?