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Neighbourhood socioeconomic status and cross-sectional associations with obesity and urinary biomarkers of diet among New York City adults: the heart follow-up study

Objective To determine whether neighbourhood socioeconomic status (SES) is associated with body mass index (BMI), waist circumference (WC) and biomarkers of diet (urinary sodium and potassium excretion). Design A cross-sectional study. Setting The data reported were from the 2010 Heart Follow-up Study, a population-based representative survey of 1645 adults. Participants Community-dwelling diverse residents of New York City nested within 128 neighbourhoods (zip codes). Primary and secondary outcome measures BMI (kg/m2) and WC (inches) were measured during in-home visits, and 24-hour urine sample was collected to measure biomarkers of diet: sodium (mg/day) and potassium (mg/day), with high sodium and low potassium indicative of worse diet quality. Results After adjusting for individual-level characteristics using multilevel linear regressions, low versus high neighbourhood SES tertile was associated with 1.83 kg/m2 higher BMI (95% CI 0.41 to 3.98) and 251 mg/day lower potassium excretion (95% CI −409 to 93) among women only, with no associations among men (P values for neighbourhood SES by sex interactions <0.05). Conclusion Our results suggest that women may be particularly vulnerable to the effects of a socioeconomically disadvantaged neighbourhood. Future neighbourhood research should explore sex differences, as these can inform tailored interventions. Trial registration number NCT01889589; Results.

Introduction Lines 6-15: The objectives were clearly stated and relevant to the field; however the last paragraph in the introduction was confusing. I would recommend restating to read: "The objective of our analysis was to examine the association between neighborhood SES, obesity, and diet quality using data from the Heart Follow-Up Study (HFUS), a population-based study of NYC adult residents. Obesity was measured using body mass index and weight circumference, while diet quality was measured using biomarkers from sodium, potassium, and the sodium to potassium ratio derived from 24-hour urine samples".

Methods
Lines 25-50: I believe the CHS has changed lately to not be such a large sample size. As such in the first paragraph of the methods I would reframe the text to focus on describing only the 2010 CHS and not the CHS as an entity in and of itself.
Lines 25-50: Rather than focus on the design of the CHS (I think it could be simplified to just state it was a complex survey, but including the additional information is not problematic), I would like to see further description about how the HFUS participants in particular were recruited from the CHS.
Lines 43-48: Were there results for the factor scores as treated as a continuous variable? I am concerned about the loss in variation from transforming the raw factor scores into tertiles and am interested in what the initial score results were.
Line 31: There is no need to hyphenate multilevel modeling.
Line 36: What is the justification for your statement of high ICC for the non-sodium outcomes? Most recommendations this reviewer is familiar with (E.g., Hox 2010) focus on anything above 10% to be considerable, although it can be evaluated on a case by case basis. With some of your outcomes being <5% ICC, there does not seem to be a large amount of cluster level variance. Did you consider a single level model with a robust cluster variance estimator? Or were you acutely interested in the variation across neighborhood? The former would be simpler in estimation, the latter however carries with it additional interesting questions. Additionally, nothing is reported in results for the extraneous variation in intercepts and slopes from your hierarchical models.
Lines 45-47: Strange to me to list results (even if they are from unadjusted models) in the methods section. I recommend a more general review of the methods and save unadjusted results for a new paragraph in Results.
Lines 31-54: Regarding your description of the multilevel models, I was surprised to not see any mention of the evaluation of significant variation of random effects (intercepts and/or slopes). If this was not of substantive interest, I would recommend stating why. If they were evaluated, state so.
Lines 51-52: I am unclear if you stratified by sex in only unadjusted or full multilevel models (at least as it is written in the methods).

RESULTS
Line 10: change "ages" to "age" Line 20: write out physical activity instead of PA. The word "guidelines" is also missing from Table 1. Page 15: The reference to table 3 implies that there was an overall significant sex interaction, but I am still unclear if this was in unadjusted or adjusted models. It should be the latter and the effect should be reported, then presenting the stratified models.
Page 15: On a whole I am able to follow your reporting of results, but it may be difficult for the general reader to follow. On the other hand, Table 3 is very well presented. It would help if the narrative in Results tracks back more appropriately to this table. I recommend doing a Model by Model review in the narrative to match Table 3 so it is easier to follow.
Page 17: I am curious as to the larger focus on the unadjusted results, compared to the fully adjusted models. Reporting the unadjusted is an interesting read, but I would expect the larger focus to be on the results from the complete models. Instead, it is relegated to a single sentence in the last part of the paragraph.
Page 17, lines 22-54: This is a good presentation of the literature with similar findings, though it does sort of "presuppose" your research. You cited research from cohort studies showing an association between neighborhood SES and weight, so why try to replicate it with a cross sectional study? It seems after reading this paragraph that the real contribution was your use of urinary biomarkers. To address this, it would be helpful for you to include some information in this paragraph about what else your study brings to the literature on SES and weight. Perhaps the neighborhood SES measures you used were novel? This is a minor issue, but one that I think would be well served to be included.
Page 21, Line 31: change "few limitations" to "a few limitations" Page 21, lines 15-26: as before, some greater discussion/context as to the public health/medical implications of an average .40 and .36 unit higher NaK ratio would be useful, especially for a readership not intimately familiar with urinary biomarkers of diet (which may be pronounces in the BMJ readership) Page 20, Lines 3-20: here you note the limitation I mentioned earlier, about using a multilevel model with a small ICC. It may be worthwhile to run a single level analysis with a robust variance estimator, if only to do due diligence.

GENERAL COMMENTS
Overall, this is a well written study examining associations between neighborhood socioeconomic condition and various cardiovascular risk factors, including diet quality assessed with sodium and potassium biomarkers. It meets all of the criteria necessary for reporting observational studies and includes objective measures of diet quality, thereby limiting measurement error of this outcome. I have two points that require further clarification. 1. In the Discussion, the authors note that they used zip code level data to characterize neighborhood socioeconomic condition because other geographic boundaries were not available. While this geographic boundary has been used in prior neighborhood research, it is also the case that zip codes span across wide geographic areas that can involve varying levels of socioeconomic condition,and thus potentially introduce heterogeneity or confounding within this 'neighborhood' classification.
The authors need a stronger explanation or evidence for why they believe their analyses adequately represent neighborhood effects. 2. The authors also need to highlight how the present study adds to the literature or differs from previous publications using the same dataset and outcomes (e..g, publication by Yi, SS).
Smaller points: -The abstract needs clarification that urinary sodium and potassium are biomarkers of diet quality.
-Curious if the authors ran a p for trend analysis of the neighborhood tertiles across each outcome? While the t-test would identify differences across categories, the p for trend test could provide additional information on whether the results are showing increasing (decreasing) risk by neighborhood tertile.

VERSION 1 -AUTHOR RESPONSE
Reviewer 1: This is a well written paper as an interesting first step into researching neighborhood poverty and diet.
Overall the paper is clear and the methods are sound. I have listed minor revisions/suggestions and some methodological clarifications requested.

Introduction
Lines 6-15: The objectives were clearly stated and relevant to the field; however the last paragraph in the introduction was confusing. I would recommend restating to read: "The objective of our analysis was to examine the association between neighborhood SES, obesity, and diet quality using data from the Heart Follow-Up Study (HFUS), a population-based study of NYC adult residents. Obesity was measured using body mass index and weight circumference, while diet quality was measured using biomarkers from sodium, potassium, and the sodium to potassium ratio derived from 24-hour urine samples".
Thank you for this. We agree that this language is confusing and as you suggest have edited the paragraph. It now reads: "The objective of our analysis was to examine the association between neighborhood SES, obesity, and diet quality using data from the Heart Follow-Up Study (HFUS), a population-based study of New York City (NYC) adult residents by sex. Obesity was ascertained using measured body mass index (BMI) and waist circumference (WC), and diet quality was ascertained using 24-hour urine derived biomarkers of sodium, potassium, and the sodium to potassium (Na-K) ratio."

Methods
Lines 25-50: I believe the CHS has changed lately to not be such a large sample size. As such in the first paragraph of the methods I would reframe the text to focus on describing only the 2010 CHS and not the CHS as an entity in and of itself. Lines 25-50: Rather than focus on the design of the CHS (I think it could be simplified to just state it was a complex survey, but including the additional information is not problematic), I would like to see further description about how the HFUS participants in particular were recruited from the CHS.
Response: We have now changed the sentence to reflect the 2010 CHS only. We have also removed the description of the CHS design and instead focused on HFUS recruitment. The paragraph now reads as: "Study participants in the HFUS were recruited from the 2010 CHS, a complex survey design telephone parent study of approximately 10,000 New Yorkers conducted by the NYC Health Department.29 From the 2010 CHS, a total of, 6,342 participants were screened for HFUS eligibility with 5,830 deemed eligible (not pregnant, lactating, or undergoing kidney dialysis). Of eligible participants, a total of 1,775 individuals participated in the HFUS." 3. Lines 43-48: Were there results for the factor scores as treated as a continuous variable? I am concerned about the loss in variation from transforming the raw factor scores into tertiles and am interested in what the initial score results were.
Response:This is a great point. Initially, we thought to treat the neighborhood factor as a standardized continuous variable and model it in that manner. However, we decided to model the factor score in tertiles for the following reasons. First, for ease of interpretation i.e. it is more intuitive to compare the highest vs. lowest neighborhood SES tertile than to interpret a one standard deviation increase in neighborhood SES score. Second, and more importantly, we believed that the relationship between neighborhood SES and our outcomes of interest might not be linear (i.e. the jump from a mediocre to poor neighborhood might be worse than the jump from a good to a mediocre neighborhood). We wanted to capture this potentially non-linear relationship and so we decided to use tertiles instead.
In the two mock tables below (Table 3X and Table 4X) we present the results modeling neighborhood SES as a continuous factor. Keep in mind that a 1 standard deviation higher SES score represents a worsening of neighborhood SES. Comparing the results from the mock tables below and those from the original tables in the manuscript, it is clear that using the continuous score masks the non-linear relationship. 5. Line 36: What is the justification for your statement of high ICC for the non-sodium outcomes? Most recommendations this reviewer is familiar with (E.g., Hox 2010) focus on anything above 10% to be considerable, although it can be evaluated on a case by case basis. With some of your outcomes being <5% ICC, there does not seem to be a large amount of cluster level variance. Did you consider a single level model with a robust cluster variance estimator? Or were you acutely interested in the variation across neighborhood? The former would be simpler in estimation, the latter however carries with it additional interesting questions.
Response: Thank you for your comment. We do not believe that the ICCs are high, instead we stated that there was "sufficient neighborhood level clustering for all outcomes but sodium." While it has been suggested that drawing conclusions based on the ICC may have its limitations (Diez Roux, 2004), the authors do agree with you that an ICC of 10% would be preferable. Further, though an ICC of 4.4% or even 6.6% may seem small, we do believe that 4.4% of the total variance in BMI explained by the neighborhood or 6.6% of variance in potassium intake explained by the neighborhood is quite substantial given how highly related these outcomes are to individual behaviors. Further, while we did consider a single level model-which would certainly have simplified the analysis, we were uniquely interested in quantifying the associations of neighborhood SES with anthropometrics and diet after accounting for the neighborhood clustering.
To the reviewer's point we changed the language to reflect that though the ICCs aren't large, we still believe them to be substantial in this population based study and important to investigate (see below): Intraclass correlations were 4.4%, 3.6%, 0.17%, 6.6%, and 8.0% respectively for BMI, WC, sodium, potassium, and Na-K ratio. Though the ICC's are of relatively small magnitude, we were uniquely interested in the associations of neighborhood level SES with anthropometrics and diet quality; and thus for all outcomes but sodium a multilevel model could be justified.28 6. Additionally, nothing is reported in results for the extraneous variation in intercepts and slopes from your hierarchical models.
Response: You make a good point. For this paper, we were not interested in reporting differences in means of BMI or sodium by neighborhood (i.e intercept effects). Furthermore, we did not include random slopes within our models as we aimed for parsimony and we had little theoretical basis to believe that the neighborhood SES effect on our outcomes would differ substantially by the neighborhood.
7. Lines 45-47: Strange to me to list results (even if they are from unadjusted models) in the methods section. I recommend a more general review of the methods and save unadjusted results for a new paragraph in Results.
Response: We removed the results for the neighborhood SES by sex interaction from the statistical analysis section of the methods and inserted it into the Results section. The fourth paragraph of the results now reads: "In unadjusted and fully adjusted models, neighborhood SES by sex interactions were significant for outcomes of WC (p<0.05) and potassium (p<0.05). Consequently, all models were stratified by sex." 8. Lines 31-54: Regarding your description of the multilevel models, I was surprised to not see any mention of the evaluation of significant variation of random effects (intercepts and/or slopes). If this was not of substantive interest, I would recommend stating why. If they were evaluated, state so.
Response: Please see our response to comment 6 above.
9. Lines 51-52: I am unclear if you stratified by sex in only unadjusted or full multilevel models (at least as it is written in the methods).
Response: All models (unadjusted and adjusted) were stratified by sex. We had this previously mentioned in the methods section, but as suggested in an earlier comment, we have now removed it from the methods and included it in the results section: "In unadjusted and fully adjusted models, neighborhood SES by sex interactions were significant for outcomes of WC (p<0.05) and potassium (p<0.05). Consequently, all models were stratified by sex."

RESULTS
10. Line 10: change "ages" to "age" Response: Thank you, we have made this change.
11. Line 20: write out physical activity instead of PA. The word "guidelines" is also missing from Table  1.
Response: Thank you, we have made these changes.
12. Page 15: The reference to table 3 implies that there was an overall significant sex interaction, but I am still unclear if this was in unadjusted or adjusted models. It should be the latter and the effect should be reported, then presenting the stratified models.
Response: As is mentioned in the results section (previously methods section), we tested for the interaction in fully adjusted models and found a significant neighborhood SES by sex interaction for the outcomes of waist circumference and potassium. We have clarified the language to reflect this (please see statement below which has been moved to the results section -table 3).
"In unadjusted and fully adjusted models, neighborhood SES by sex interactions were significant for outcomes of WC (p<0.05) and potassium (p<0.05). Consequently, all models were stratified by sex." Finally, as outlined in the introduction section, given our a priori hypothesis that the neighborhood effect would differ by sex, on the basis of similar findings from non multilevel studies, we were uniquely interested in the sex specific estimates and hence only reported these results rather than focusing on the interaction effect. The test for interaction further served as a data driven justification for our stratified results which were primarily driven by theory and prior literature.
13. Page 15: On a whole I am able to follow your reporting of results, but it may be difficult for the general reader to follow. On the other hand, Table 3 is very well presented. It would help if the narrative in results tracks back more appropriately to this table. I recommend doing a Model by Model review in the narrative to match Table 3 so it is easier to follow.
Response: Thank you for pointing this out. We have reorganized table 3 and 4 so that it more closely follows the patterns of prior tables. For example, the main column heading is now sex (rather than outcome). The sub-column heading is now the outcome of interest. This should improve the narrative of the text in relation to the table since we first describe the results in men and then move on to women. We edited the text accordingly to reflect this flow (see below in relation to updated tables 3 and 4).
Among men, results from unadjusted and fully adjusted multilevel models showed that neighborhood SES was not associated with individual-level BMI or WC (Table 3) Among men, living in a low vs. high SES neighborhood was significantly associated with 403 mg/day lower potassium excretion (95% CI: -628, -178) and 0.40 (95% CI: 0.13, 0.66) unit higher Na-K ratio in unadjusted models only; P for trend was significant (P < 0.05) for both outcomes in Model 1 (Table 4). From fully adjusted multilevel models among men, middle vs. high SES neighborhood was significantly associated with 313 mg higher potassium excretion (95% CI: 9, 618); all other associations among men were null. Among women, from unadjusted multilevel models, living in a low vs. high or middle vs. high SES neighborhood was significantly associated with 426 mg/day (95% CI: -614, -238) and 425 mg/day (95% CI: -604, -245) lower potassium excretion; P for trend < 0.05. Likewise low vs. high SES neighborhood was associated with 0.36 (95% CI: 0.16, 0.56) units higher Na-K ratio among women; P for trend < 0.05. From fully adjusted models among women, living in a low vs. high or middle vs. high SES neighborhood remained significantly associated with 251 mg/day (95% CI: -409, -93) and 330 mg/day (95% CI: -501, -159) lower potassium excretion; P for trend < 0.05.
14. Page 17: I am curious as to the larger focus on the unadjusted results, compared to the fully adjusted models. Reporting the unadjusted is an interesting read, but I would expect the larger focus to be on the results from the complete models. Instead, it is relegated to a single sentence in the last part of the paragraph.
Response: We have now shortened the description of the unadjusted results and expanded the description of the fully adjusted results to bring more balance to the presentation of these results. This paragraph now reads: "Among men, living in a low vs. high SES neighborhood was significantly associated with 403 mg/day lower potassium excretion (95% CI: -628, -178) and 0.40 (95% CI: 0.13, 0.66) unit higher Na-K ratio in unadjusted models only; P for trend was significant (P < 0.05) for both outcomes in Model 1 ( 15. Page 18: There are some fairly large ranges in the confidence intervals for your potassium measures in Table 4. I suspect this is due to the fact that you are looking at an interaction effect here, but it would be useful to talk a little bit about this in the discussion. Response: Yes, we agree. Dietary measures in general tend to have a high variance and this was exacerbated by our stratification of the results. We have added this to the limitations paragraph (see below): "The current research has a few limitations that are worth noting. First, while our study was population based and representative of non-institutionalized NYC adults, our results may not necessarily be extrapolated to other geographical locations given the uniqueness of NYC neighborhoods. We also relied on zip codes to define neighborhoods, though zip codes are commonly utilized to define neighborhoods, smaller geographic units such as census blocks exist but were not accessible. Additionally, 24-hour urine measures reflect sodium and potassium intake during the previous day and may not necessarily be indicative of habitual sodium and potassium consumption. Further though we were adequately powered to test for interaction by sex, stratification by sex resulted in smaller sample sizes and notably limited the precision our estimates-particularly for our dietary factors of sodium and potassium which already had large variances. Finally, the HFUS was cross-sectional, thus any observed associations may reflect self-selection of certain individuals into certain neighborhoods rather than the effect of a neighborhood on an individual's health. Despite such limitations, the study possesses noteworthy strengths. Our measure of neighborhood SES was rich as it utilized several SES domains. Most notably, our outcomes were objectively measured and therefore subject to less measurement error. This is particularly true of our dietary measures, the HFUS study is the only population-based representative study in the US to use the gold-standard of 24-hour urine to measure sodium and potassium intake." DISCUSSION 16. Page 17, lines 2-17: What is the real world implication of some of these beta coefficients? E.g., in Model 4 for potassium (the only outcome where neighborhood SES is significantly related in fully adjusted models), what does a decrease in K for women and an increase in Men by about 300 or so mean?
Response: We have now added language that should help contextualize these findings. The discussion surrounding potassium now reads: "Unlike with sodium, our findings showed significant associations between neighborhood SES and 24hour urinary potassium excretion, an objective indicator of fruit and vegetable consumption37 and healthy diet.21 As potassium is an important nutrient that helps lower blood pressure,38 the strength of these results cannot be underscored; a 251 mg/day difference is substantial-especially on a population wide basis-considering recommended intake should be 4700 mg/day.39 Importantly, our findings are consistent with other studies,11,12,35,40,41 yet mostly using self-reported fruit and vegetable consumption. For example, findings from the National Health Nutrition and Examination survey showed that higher neighborhood SES was associated with increased fruit and vegetable intake.11 Likewise, findings from the New York City Community Health Survey have shown that residing in a neighborhood of low vs. high SES was associated with reporting lower fruit and vegetable intake.12 Prior studies have also linked other neighborhood characteristics, such as neighborhood retail environment, to individual diet quality, including potassium.40 For example, among participants of the Japan Dietetic Students' Study for Nutrition and Biomarkers Study Group, neighborhood availability of supermarkets was associated with higher urinary potassium excretion.13 Finally, previous HFUS findings showed that neighborhood poverty was not associated with 24 hour urinary excretion of potassium.19 However, this study used different methods which likely accounted for the discrepant findings. For example, the current analysis includes more neighborhood level units (i.e. 128 vs. 42 neighborhoods), a different neighborhood SES construct comprised of multiple dimensions of SES rather than just poverty, and sex stratified models. Had we used overall rather than sex-stratified models, associations for potassium would have been null." 17. Page 17, lines 22-54: This is a good presentation of the literature with similar findings, though it does sort of "presuppose" your research. You cited research from cohort studies showing an association between neighborhood SES and weight, so why try to replicate it with a cross sectional study? It seems after reading this paragraph that the real contribution was your use of urinary biomarkers. To address this, it would be helpful for you to include some information in this paragraph about what else your study brings to the literature on SES and weight. Perhaps the neighborhood SES measures you used were novel? This is a minor issue, but one that I think would be well served to be included.
Response: Thank you for this point. As you suggest, we believe that the novelty in our results with regards to BMI and waist circumference are related to: 1) our measure of neighborhood SES which was a composite score, and 2) our explicit testing of an interaction with sex which we felt was missing from prior studies. We have updated the discussion to highlight these points: A number of studies have pointed to an association between neighborhood SES and measures of obesity.6,7,9,33 For example, findings from the Dallas Heart Study, a multiethnic cohort, showed that moving from a higher to a lower SES neighborhood was associated with weight gain.7 Likewise, among women of the Black Women's Health Study, lower neighborhood SES was associated with weight gain over 10 years.34 Though, neither of these studies focused on differences by sex. In our multiethnic cohort of NYC adult residents-using a more comprehensive measure of neighborhood SES, we too found that living in a low SES neighborhood was associated with measures of obesity such as higher BMI, and the association was only present in women.
Prior work has shown individual-level SES to be more strongly associated with obesity in women than in men.1 However, neighborhood effects on measures of obesity by sex yielded mixed findings.22-25 For example, among participants of the 1986 American's Changing Lives Study, neighborhood poverty was associated with higher BMI among women but not men.24 Results from the Multi-Ethnic Study of Atherosclerosis found no association between the social environment and BMI among women.25 Further studies exploring associations between the neighborhood environment and obesity, and whether or not these associations vary across different groups, are warranted to better understand the impacts of residing in low SES neighborhoods and to guide the design of more tailored and comprehensive interventions.
18. Page 21, Line 31: change "few limitations" to "a few limitations" Response: Thank you, we have now made this change.
19. Page 21, lines 15-26: as before, some greater discussion/context as to the public health/medical implications of an average .40 and .36 unit higher NaK ratio would be useful, especially for a readership not intimately familiar with urinary biomarkers of diet (which may be pronounces in the BMJ readership) Response: We have now added language in the discussion to help contextualize the findings for Na-K ratio: "Finally, our study found that residing in a neighborhood of low vs. high SES was associated with a 0.40 and 0.36 unit higher Na-K ratio in men and women respectively, in unadjusted models. Individuals consuming USDA recommendations39 for sodium (< 2300 mg/day) and potassium (≥4700 mg/day) would have an Na-K ratio of 0.49. Thus differences in the order of 0.4 in magnitude are substantial; though these findings were not significant in adjusted models. Though limited studies exist, these findings are somewhat consistent with a study of Japanese women showing that low vs. high neighborhood SES was associated with higher Na-K ratio, adjusting for only survey year, living status, and region of residence.35" 20. Page 20, Lines 3-20: here you note the limitation I mentioned earlier, about using a multilevel model with a small ICC. It may be worthwhile to run a single level analysis with a robust variance estimator, if only to do due diligence.
Response: Yes you make a valid point, we did consider running a single level model. We ran these results with a robust variance estimator in SUDAAN (please refer to tables 3Y and 4Y below). As you can see, particularly with respect to the BMI estimates, there are substantial differences between the single level and multilevel estimates. Since we are interested in the neighborhood level effects after accounting for the clustering, we will maintain our initial models. As we mentioned in an earlier response, we have now added a new reference (Diez Roux, 2004) which suggests caution when interpreting the magnitude of ICC estimates. Please leave your comments for the authors below Overall, this is a well written study examining associations between neighborhood socioeconomic condition and various cardiovascular risk factors, including diet quality assessed with sodium and potassium biomarkers. It meets all of the criteria necessary for reporting observational studies and includes objective measures of diet quality, thereby limiting measurement error of this outcome. I have two points that require further clarification.
1. In the Discussion, the authors note that they used zip code level data to characterize neighborhood socioeconomic condition because other geographic boundaries were not available. While this geographic boundary has been used in prior neighborhood research, it is also the case that zip codes span across wide geographic areas that can involve varying levels of socioeconomic condition, and thus potentially introduce heterogeneity or confounding within this 'neighborhood' classification. The authors need a stronger explanation or evidence for why they believe their analyses adequately represent neighborhood effects.
Response: Thank you, you make an excellent point. We agree that zip codes may introduce heterogeneity in the measure. However, we still believe that a zip code is an adequate representation for a NYC neighborhood for the following two reasons: 1) While generally zip codes cover larger geographic areas, in the context of NYC, zip codes cover smaller geographic regions than other locations due to how densely populated the city is.
2) Any heterogeneity introduced would likely results in a non-differential measurement error biasing our results towards the null; despite this, we still found significant level neighborhood effects. We also have adjusted for individual-level compositional factors belonging to those neighborhoods which would address this issue.
We have now discussed this more thoroughly in the updated discussion/limitations section: "The current research has a few limitations that are worth noting. First, while our study was population based and representative of non-institutionalized NYC adults, our results may not necessarily be extrapolated to other geographical locations given the uniqueness of NYC neighborhoods. Further, we relied on zip codes to define neighborhoods; zip codes may encompass a more heterogeneous SES composition and therefore introduce heterogeneity into the measure. Despite this, we believe the use of zip code is appropriate for the following reasons: 1) NYC is a densely populated area and so zip codes encompass much smaller geographical bounds than in other locations, and 2) any heterogeneity introduced in our measure would likely bias results towards the null." 2. The authors also need to highlight how the present study adds to the literature or differs from previous publications using the same dataset and outcomes (e..g, publication by Yi, SS).
Response: Thank you for pointing this out. We originally had language in regarding the discrepant findings and removed for the sake of space. However, we agree and feel that this is an important comment. As such we have added the following language in the discussion section which reads: "Finally, previous HFUS findings showed that neighborhood poverty was not associated with 24 hour urinary excretion of potassium.19 However, this study used different methods which likely accounted for the discrepant findings. For example, the current analysis includes more neighborhood level units (i.e. 128 vs. 42 neighborhoods), a different neighborhood SES construct comprised of multiple dimensions of SES rather than just poverty, and sex stratified models. Had we used overall rather than sex-stratified models, associations for potassium would have been null." 3. Smaller points: -The abstract needs clarification that urinary sodium and potassium are biomarkers of diet quality.
Response: Thank you for pointing this out. We have added text to the abstract to reflect this point: Objective: To determine whether neighborhood socio-economic status (SES) is associated with body mass index (BMI), waist circumference, and biomarkers of diet (urinary sodium and potassium excretion Response: Thank you for this comment. We have now added the p for trend analyses to Tables 3 and  Tables 4 (please see the table footnotes). Additionally we have edited the results to reflect this new test, see updated results section below: In unadjusted and fully adjusted models, neighborhood SES by sex interactions were significant for outcomes of WC (P <0.05) and potassium (P <0.05), all subsequent models were then stratified by sex. Among men, results from unadjusted and fully adjusted multilevel models showed that neighborhood SES was not associated with individual-level BMI or WC ( Among men, living in a low vs. high SES neighborhood was significantly associated with 403 mg/day lower potassium excretion (95% CI: -628, -178) and 0.40 (95% CI: 0.13, 0.66) unit higher Na-K ratio in unadjusted models only; P for trend was significant (P < 0.05) for both outcomes in Model 1 (Table 4). From fully adjusted multilevel models among men, middle vs. high SES neighborhood was significantly associated with 313 mg higher potassium excretion (95% CI: 9, 618); all other associations among men were null. Among women, from unadjusted multilevel models, living in a low vs. high or middle vs. high SES neighborhood was significantly associated with 426 mg/day (95% CI: -614, -238) and 425 mg/day (95% CI: -604, -245) lower potassium excretion; P for trend < 0.05. Likewise low vs. high SES neighborhood was associated with 0.36 (95% CI: 0.16, 0.56) units higher Na-K ratio among women; P for trend < 0.05. From fully adjusted models among women, living in a low vs. high or middle vs. high SES neighborhood remained significantly associated with 251 mg/day (95% CI: -409, -93) and 330 mg/day (95% CI: -501, -159) lower potassium excretion; P for trend < 0.05.

REVIEWER
Ryan Richard Ruff New York University, USA REVIEW RETURNED 09-Oct-2017

GENERAL COMMENTS
The authors have sufficiently responded to all previous suggestions or requests for clarification and the paper will be a good contribution to the literature.

Sandra Echeverria
City University of New York Graduate School of Public Heath and Health Sciences REVIEW RETURNED 21-Oct-2017

GENERAL COMMENTS
This study adds to the literature on neighborhood-health associations by using participant assesses measures of weight and biomarkers of diet quality as the study outcomes. The paper would benefit from a few clarifications noted below. ABSTRACT -The authors should note how sodium and potassium are measured to be able to interpret findings (i.e., lower potassium indicates worse diet quality). BACKGROUND -The authors should provide a rationale for sex as an effect measure modifier. Are women hypothesized to have worse neighborhoodhealth associations relative to men? Why might this be the case? METHODS -The authors should describe why both BMI and WC are being investigated. How is the study design enhanced by considering these two outcomes separately as measures of obesity? -The construction of the 'neighborhood SES' was poorly described. It seems to involve census data and data captured in the survey from study participants (i.e., perception of safety from crime). However, this is not described. For example, what year was used for the census data? The neighborhood score was also not defined. Given that the items were measured on different scales, I imagine that they were standardized?

DISCUSSION
-Although there were statistically significant differences across neighborhood tertiles for BMI, WC, and potassium, it would be helpful for the authors to translate these findings to public health or clinical significance. For example, what is the recommended daily intake of sodium? What was the average intake of the study population? Was it high to begin with and hence why there was no observed difference by neighborhood condition? What about potassium? How can these results be interpreted for future policy or public health programs? -Similar to the above, the explanation on sex differences observed was not well developed. What may explain these findings? The authors note that women spend more time at home and in their neighborhoods, but how does this relate to diet quality? There could be several potential mechanisms at play and the authors should elaborate further.

VERSION 2 -AUTHOR RESPONSE
Reviewer 1: The authors have sufficiently responded to all previous suggestions or requests for clarification and the paper will be a good contribution to the literature.

Reviewer 2:
This study adds to the literature on neighborhood-health associations by using participant assesses measures of weight and biomarkers of diet quality as the study outcomes. The paper would benefit from a few clarifications noted below.
ABSTRACT -The authors should note how sodium and potassium are measured to be able to interpret findings (i.e., lower potassium indicates worse diet quality).
Response: This is a good point, we have updated the abstract accordingly and note this in the "primary and secondary outcome measures" section of the abstract copied below: Objective: To determine whether neighborhood socio-economic status (SES) is associated with body mass index (BMI), waist circumference, and biomarkers of diet (urinary sodium and potassium excretion). Design: A cross-sectional study. Setting: The data reported were from the 2010 Heart Follow Up Study, a population based representative survey of 1,645 adults. Participants: Community dwelling diverse residents of New York City nested within 128 neighborhoods (zip codes). Primary and secondary outcome measures: BMI (kg/m2) and waist circumference (inches) were measured during in-home visits, and 24-hour urine was collected to measure biomarkers of diet: sodium (mg/day) and potassium (mg/day), with high sodium and low potassium indicative of worse diet quality.
Results: After adjusting for individual-level characteristics using multilevel linear regressions, low vs. high neighborhood SES tertile was associated with 1.83 kg/m2 higher body mass index (95% CI: 0.41, 3.98) and 251 mg/day lower potassium excretion (95% CI: -409,-93) among women only, with no associations among men (p-values for neighborhood SES by sex interactions <0.05). Conclusion: Our results suggest women may be particularly vulnerable to the effects of a socioeconomically disadvantaged neighborhood. Future neighborhood research should explore sex differences, as these can inform tailored interventions.
BACKGROUND -The authors should provide a rationale for sex as an effect measure modifier. Are women hypothesized to have worse neighborhood-health associations relative to men? Why might this be the case?
Response: Thank you for pointing this out. We have now added language in the introduction explicitly stating that some studies have shown associations of SES with obesity among women but not men, and that this has not been fully explored in the neighborhood literature. We hope that this provides a better justification for why we look at this association by sex. We focus on potential reasons for this finding in the discussion which we have expanded in response to this comment. Please see the edited introduction copied below: Beyond individual-level mechanisms, a growing body of research suggests that neighborhood characteristics, such as neighborhood safety and neighborhood SES, may also influence obesity6-9 and diet quality.10-14 For example, findings from the landmark Moving To Opportunity study showed that altering the socioeconomic environment by relocating into a higher income neighborhood was associated with a lower prevalence of obesity15 and improved physical health outcomes in youth girls but not boys.16 Studies10-12,17 pointing to a relationship between the neighborhood environment and diet quality have mainly used subjective measures of diet such as healthy eating indices or selfreported fruit and vegetable intake which can be prone to measurement error.18 To our knowledge, only two previous studies of neighborhood and diet13,19 have included objectively measured biomarkers of diet quality such as sodium and potassium.20,21 Furthermore, it is suggested that the impact of SES on health might differ by sex, with a stronger association among women. For example, a number of studies have linked poor SES to higher rates of obesity in women only or to a greater extent.1,22,23 Yet, the relationships between the neighborhood socioeconomic environment with obesity and diet quality are seldom explored by sex and results have been mixed.16,24-29 METHODS -The authors should describe why both BMI and WC are being investigated. How is the study design enhanced by considering these two outcomes separately as measures of obesity?
Your point is well taken. BMI is a measure of body mass in relation to height which does not take into account body fat distribution. WC on the other hand is measure of central body fat adiposity. So while both are measure of fat, they are informative in different ways. We added language in the methods section and in the discussion to speak to your point: Methods: For obesity, we considered two outcome measures: BMI as a measure of total fat, and waist circumference (WC), a strong determinant of metabolic disease risk,33,34 as a measure of central adiposity. During in-home visits, HFUS participants' weight and height was recorded without shoes. BMI was calculated as measured weight in kilograms divided by measured height in meters-squared. WC was measured in inches as waist girth at the top of the lateral border of the right ilium.
Discussion: Despite such limitations, the study possesses noteworthy strengths. Our measure of neighborhood SES was rich as it utilized several SES domains. Additionally, we utilized two measures of obesity: BMI, as a measure of total fat, and WC as a measure of central adiposity.34 Both BMI and WC have respective limitations: with BMI unable to account for body fat distribution or muscle mass, and WC unable to account for height.34 Given the limitations of each measure on its own, the consistency of our results across both measures added strength to the findings. Also notable, our outcomes were objectively measured and therefore subject to less measurement error. This is particularly true of our dietary measures, the HFUS study is the first population-based representative study in the US to use the gold-standard of 24-hour urine to measure sodium and potassium intake.
-The construction of the 'neighborhood SES' was poorly described. It seems to involve census data and data captured in the survey from study participants (i.e., perception of safety from crime). However, this is not described. For example, what year was used for the census data? The neighborhood score was also not defined. Given that the items were measured on different scales, I imagine that they were standardized?
Response: Thank you for pointing this out. We see how this can be confusing and hope to provide some clarity. We did not use any census data to derive the estimates. Instead we aggregated individual level responses to derive neighborhood proportions. We would like to highlight to the reviewers that this survey is cross-sectional, population based, and weighted to be representative of the city as a whole.
The items used to create the neighborhood score are all measured on the same scale (i.e. they are all proportions ranging from 0% to a possible 100%. For example, we state that the neighborhood level variables are: -proportion of households in the neighborhood with income 100% below the FPL -proportion of individuals in the neighborhood who are unemployed -proportion of individuals in the neighborhood with less than a HS education -proportion of individuals in the neighborhood who report living in an unsafe neighborhood Therefore we did not standardize these measures. We made note of this range in the discussion (underlined below). We reframed the "individual level measures" and "neighborhood SES" paragraphs to provide more clarity. Please see these sections copy pasted below: Other individual-level measures Through survey questionnaires, HFUS participants reported their age in age groups (18-24, 25-44, 45-64, or 65+ years), sex, and race/ethnicity (white non-hispanic, black non-hispanic, hispanic, asian, or other). Participants reported family size as the number of individuals per household, and also reported whether their household income from all sources was less than 100%, 100 -199%, 200 -299%, 300 -399%, 400 -499%, 500 -599%, or 600% or more of the Federal Poverty Level (FPL). For reference, the FPL in 2010 for a household of four people was $22,050.35 Participants also reported their educational attainment defined as less than high school (HS), HS graduate, some college, or college graduate or more. Employment status was recorded and defined as employed, unemployed, or not in the labor force. Participants also answered a series of questions about their physical activity which were used to calculate their total minutes of moderate and vigorous physical activity.36 Participants who reported an average of 150 moderate or 75 vigorous minutes of physical activity per week were considered to have met 2008 physical activity guidelines.37 Participants were also asked to rate the safety of their neighborhoods. Neighborhood safety was reported in response to the question of "How safe from crime do you consider your neighborhood to be" with responses including "extremely safe," "quite safe," "slightly safe," or "not safe at all"; answers were then dichotomized into two categories: an unsafe neighborhood ("slightly safe" or "not safe at all" responses) vs. a safe neighborhood ("extremely safe" or "quite safe" responses).
Neighborhood SES Neighborhoods were defined according to zip codes which were retrieved from participants' addresses. Individual level responses for household poverty level, educational attainment, employment, and perceived neighborhood safety (all defined above) were aggregated by neighborhood to create neighborhood level variables for: proportion of households in the neighborhood with income 100% below the FPL, proportion of individuals in the neighborhood who are unemployed, proportion of individuals in the neighborhood with less than a HS education, and proportion of individuals in the neighborhood who report living in an unsafe neighborhood. All neighborhood level variables were expressed as a proportion with a potential range of 0% to 100%. Then, using the principle factor method, we created a neighborhood SES factor score using these neighborhood level variables; all neighborhood level variables met a loading threshold criteria of 0.3. Finally, we created tertiles from the neighborhood SES score to further characterize neighborhoods as having low SES (disadvantageous), middle SES, or high SES (advantageous).

DISCUSSION
-Although there were statistically significant differences across neighborhood tertiles for BMI, WC, and potassium, it would be helpful for the authors to translate these findings to public health or clinical significance. For example, what is the recommended daily intake of sodium? What was the average intake of the study population? Was it high to begin with and hence why there was no observed difference by neighborhood condition?
Response: Thank you for this comment. We have added language to discussion stating the recommended intake for sodium is less than 2300 mg/day. Table 2 includes estimates for mean sodium overall (3,240 mg/day) and by tertile of neighborhood ses score among both men and women.
We have now explicitly stated this in the discussion.
Finally, while we cannot state whether sodium was "high to begin with" given the cross-sectional design of this study, we did add language which highlights how ubiquitous sodium is and that it is difficult for individuals to control intake. Please see the updated discussion paragraph copied below: Low neighborhood SES was not associated with urinary sodium excretion in men or women. Given the low intraclass correlation coefficient for sodium, indicative of no neighborhood level sodium clustering, our null findings were expected. Our results are in accordance with previous findings from the HFUS cohort showing no association between neighborhood-level poverty and individual-level sodium intake.19 Similarly, results from the Japan Dietetic Students' Study for Nutrition and Biomarkers Study Group, a Japanese cohort of young women showed no association between neighborhood SES and 24-hour urinary sodium excretion.40 These results may point to the ubiquity of sodium in the US food supply,41 such that everyone is exposed regardless of the SES of the neighborhood they live in. In fact, it is estimated that approximately 80% of sodium consumed is derived from prepackaged, and restaurant foods;41,42 therefore limiting individual ability to control sodium intake. In the current study, daily sodium intake overall was 3,240 mg/day, well exceeding 2015 US Department of Agriculture recommendations of no more than 2,300 mg per day;43 this was true among all tertiles of neighborhood SES.
Comment: What about potassium? How can these results be interpreted for future policy or public health programs?
Response: For potassium we have previously noted in our manuscript that the current USDA recommendation is at least 4700 mg per day. We added language stating that the overall mean potassium intake in this study was 2,182 mg/day and also added a sentence of potential public health implications. Please see our edits to the discussion potassium paragraph underlined and copied below: Unlike with sodium, our findings showed significant associations between neighborhood SES and 24hour urinary potassium excretion, an objective indicator of fruit and vegetable consumption44 and healthy diet.21 As potassium is an important nutrient that helps lower blood pressure,45 the strength of these results cannot be underscored; a 251 mg/day difference is substantial-especially on a population wide basis-considering recommended intake should be 4700 mg/day,43 and that mean potassium intake overall was only 2,182 mg/day. Though we did not directly assess reasons for neighborhood differences in potassium intake, these findings have important public health implications and highlight that certain neighborhoods may require additional intervention (i.e. access or affordability of fruits and vegetables). Importantly, our findings are consistent with other studies,11,12,40,46,47 yet mostly using self-reported fruit and vegetable consumption. For example, findings from the National Health Nutrition and Examination survey showed that higher neighborhood SES was associated with increased fruit and vegetable intake.11 Likewise, findings from the New York City Community Health Survey have shown that residing in a neighborhood of low vs. high SES was associated with reporting lower fruit and vegetable intake.12 Prior studies have also linked other neighborhood characteristics, such as neighborhood retail environment, to individual diet quality, including potassium.46 For example, among participants of the Japan Dietetic Students' Study for Nutrition and Biomarkers Study Group, neighborhood availability of supermarkets was associated with higher urinary potassium excretion.13 Finally, previous HFUS findings showed that neighborhood poverty was not associated with 24 hour urinary excretion of potassium.19 However, this study used different methods which likely accounted for the discrepant findings. For example, the current analysis includes more neighborhood level units (i.e. 128 vs. 42 neighborhoods), a different neighborhood SES construct comprised of multiple dimensions of SES rather than just poverty, and sex stratified models. Had we used overall rather than sex-stratified models, associations for potassium would have been null.
Comment: Similar to the above, the explanation on sex differences observed was not well developed. What may explain these findings? The authors note that women spend more time at home and in their neighborhoods, but how does this relate to diet quality? There could be several potential mechanisms at play and the authors should elaborate further.
Response: Again, thank you for pointing this out. We have now expanded our discussion of the sex differences which we observed. We make the point that neighborhood SES is often correlated with the neighborhood food environment and that women are more likely than men to grocery shop (which can be related to dietary quality). This may partially explain why we see an association among women but not men. Please see edits to the discussion underlined and copied below: Previous studies have signaled that the associations between individual-level SES and fruit and vegetable consumption may vary by sex.44 However, to our knowledge, no prior studies in the US have formally assessed whether the relationship of neighborhood-level SES and diet quality, measured objectively-such as 24-hour urinary potassium excretion, varies by sex. In our study, we found neighborhood SES to be associated with potassium excretion among women but not men. It has been hypothesized that in general, neighborhood level effects might be stronger for women than men considering women may spend more time in the home and within their neighborhoods.48 Further, it has been proposed that the neighborhood food environment (often correlated with neighborhood SES)49 may drive differences in diet quality.50 With women more likely to be primary grocery shoppers, it is perhaps not surprising that associations between the neighborhood environment and diet quality are more pronounced among women.