Developing a novel index for neighborhood social determinants of cardiovascular diseases in the CARDIA study

Domain: biology
Published: 2026-03-31
Editorial outcome: major_revision

Abstract
Neighborhood social determinants of health have been shown to influence cardiovascular risk and the development of cardiovascular disease, but few studies have been done about early-life exposure as well as the combined effect of multiple neighborhood social determinants of health measures. Each 1-standard-deviation increase in the neighborhood social determinants of health index is associated with increased risk of coronary artery calcification at the year 7, year 15, and year 20 visits (Odds Ratios range from 1.15 to 1.17). The area under the Receiver Operating Characteristics curve ranges between 0.718 (year 7) and 0.719 (year 20). Compared to White participants, Black participants have a significant association between neighborhood social determinants of health indices and coronary artery calcification at Y15 and at Y20. In conclusion, higher early adulthood neighborhood social determinants of health indices are associated with higher middle-aged coronary artery calcification risk; this association is significant in Black adults at older age compared with White adults.

Introduction
Cardiovascular disease (CVD) is the foremost cause of mortality in both the United States and worldwide.1 Social determinants of health (SDH) have been shown to influence cardiovascular risk and the development of CVD.2, 3, 4 SDH may be measured at the individual level or at the contextual level by characteristics of the neighborhood in which an individual lives. Individual-level SDH includes economic stability, educational attainment, occupation, and access to healthcare, while neighborhood-level SDH includes neighborhood deprivation measured by factors such as the proportion of residences owned versus rented, the density of fast-food restaurants, or the mean levels of education and income for families residing in the neighborhood. Both the individual- and the contextual-level factors are known to affect health, well-being, and quality of life,5 playing a substantial role in the increased risk of CVD.6 Neighborhood SES indices using census tract variables comprising neighborhood income, occupation, employment, and wealth have demonstrated that people with lower neighborhood SES levels had up to 3.1 times higher risk of developing CVD compared to those with higher neighborhood SES levels.7, 8

Various approaches have been proposed to integrate SDH and evaluate their associations with adverse outcomes. 9, 10, 11 For example, the Area Deprivation Index (ADI) from the Neighborhood Atlas offered a measure of neighborhood disadvantage metrics.9 The Social Vulnerability Index (SVI), developed by the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR), was constructed using 15 variables from the U.S. Census.10 A Neighborhood Socioeconomic Deprivation Score (NDS) was also developed to evaluate the association between neighborhood SES and health outcomes.11 Factors such as tract-level census indicators of socioeconomic status, including household income and education level, were used in calculating the score. However, all these indices have shown lower effect estimates for specific diseases. Through the combination of conventional regression models and machine-learning methods, the Boosted regression tree (BRT) model might offer a better way to estimate associations for diseases like CVD. 12 BRT models accommodate complex linear and nonlinear responses to the community-level exposures and covariates while being relatively insensitive to collinearity problems.13 Because BRT models are based on a flexible binary-split algorithm, BRT models are able to generate robust estimates despite outliers or missing values commonly observed from community survey data.13 A previous study applied BRT models to individual SDH factors and found significant associations with left ventricular mass index (LVMI).14 However, neighborhood SDH captures population-level and contextual influences that are not reflected by individual factors and may be more relevant for informing public health interventions and policy.

The Coronary Artery Risk Development in Young Adults (CARDIA) study, with longitudinal measures of social determinants of health from young adulthood and subclinical atherosclerosis in midlife, offers a unique framework to explore the links between neighborhood social determinants of health (nSDH) and subclinical atherosclerosis.15 Thus, we aimed to quantify the association between nSDH factors and midlife subclinical cardiovascular disease – coronary artery calcification (CAC).

Results
Table 1 shows descriptive analyses of participant characteristics at the Y25 visit. CAC measurements were available for 3499 CARDIA participants. The study participants were 56.6% female, 46.9% Black, and the average age was 50 ± 4 (SD) years at the Y25 visit. The participants lived in neighborhoods where, on average, 12% of the population did not have a high school diploma, and 23% had a household income of less than 150% of the federal poverty level. Additionally, over 60% of the population were not employed in professional or management occupations, and 10% were unemployed. We assessed the trend of risk factors as well as the nSDH factors over the repeated years of follow-up. During this follow-up, the proportion of current smokers among the study participants decreased from 30.4% at year 0 (Y0) to 17.1% at year 25 (Y25) while the mean BMI increased from 24.5 (Y0) to 30.2 (Y25) (Supplemental Figure 1). For the nSDH factors, both the proportion of the population with education less than high school and with a non-professional occupation decreased during follow-up. Median household income and rent as well as aggregate house value increased during the follow-up period. Total crime rate decreased during follow-up from 7439 per 100,000 at Y0 to 3474 per 100,000 at Y25 (Supplemental Figure 2).

Pearson correlation between the nSDH factors was assessed. Factors like proportion of population of nonwhite race, having education less than high school, not participating in a professional/management occupation, being unemployed and income <150% federal poverty level were highly correlated (Pearson correlation coefficient r > 0.50). And these factors were negatively correlated with neighborhood-level median household income, gross rent, and house value (Pearson correlation coefficient r < -0.30) (Supplemental Table 1).

Figure 1 shows the contribution of nSDH factors at each visit on the risk of CAC at year 25 using the BRT model. During the visits, the contribution of poverty decreased while the contribution of food facilities increased during the follow-up period (Data value shown in Supplemental Table 2). Figure 2 shows the separate contribution of nSDH factors at the year 7, 15, and 20 visits on the risk of CAC at year 25. Among these factors, median gross rent at year 7, proportion of population with non-professional occupation at year 15, and number of food facilities were the three factors that contributed most to the risk of CAC at each corresponding visit (14.91%, 14.29% and 19.73% separately).

Table 2 shows the association of year 0 to year 25 nSDH indices with the risk of CAC at year 25. Each one SD increase in nSDH indices was associated with increased risk for CAC for the year 7 visit (OR 1.17, 95% CI 1.05-1.32), year 15 visit (OR 1.15, 95% CI 1.02-1.30) and year 20 visit (OR 1.17, 95% CI 1.03-1.32). The AUC ranged between 0.718 (year 7) and 0.719 (year 20). The nSDH indices were associated with CAC in White participants at year 7 while in Black participants at years 15 and 20 (Table 3). Similar results were shown after further adjusting for TC/HDL ratio, systolic blood pressure, and diabetes (Supplemental Tables 3 & 4). We further adjusted for dietary intake as a sensitivity analysis and got similar results (Supplemental Table 5). nSDH indices performed better compared to NDS score, with NDS score being associated with CAC at year 7 (OR 0.866, 95%CI 0.765-0.981), and the association in other years was not significant (Supplemental Table 6).

Methods
Study population
The CARDIA Study is a multicenter, longitudinal, population-based cohort of 5,115 Black or White men and women, who were 18 to 30 years at baseline (year : 1985 to 1986) and recruited from 4 urban areas (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and Oakland, California).15 Within each center, the sample was designed to have approximately equal numbers of participants by sex, race (Black vs White), age (18 to 24 years vs 25 to 30 years), and education (high school graduate or less, vs beyond high school). Eight follow-up examinations have been conducted through year 30 (2015 to 2016), with 71% of the surviving cohort attending the year 30 (Y30) examination.15, 31 To assess the presence of subclinical coronary artery disease, 3499 participants with available chest computed tomography (CT) scan for CAC measures at the year 25 (2010-2011) visit were included in the study. All examinations were approved by the institutional review boards at each participating institution (including University of Alabama at Birmingham, Northwestern University, University of Minnesota and Kaiser Foundation Research Institute), and all participants gave written informed consent.

Neighborhood SDH (nSDH) and personal characteristics measures
nSDH measures
Neighborhood-level SDH (nSDH) factors were measured from each available CARDIA visit (Y0, 7, 10, 15, 20, 25). nSDH factors were obtained and compiled from U.S. Census data (1980, 1990, 2000, and 2010) and two five-year estimates from the American Community Survey (2005-2009 and 2010-2014) using geocoded addresses of CARDIA participants.32, 33 Various types of variables were extracted: Census tract-level variables: percent population of a race other than white; percent population with less than a high school diploma; proportion with a blue-collar job; proportion older than age 16 in the workforce without a job; proportion below 150% of the poverty level; median household income; median gross rent; median house value and total crime rate per 100,000 people. Historic physical activity and Food facility data were acquired from the Dun and Bradstreet database (a commercial listing of US businesses) and the Environmental Systems Research Institute's StreetMap product including: 1) count of physical activity facilities located within a 3‐km Euclidean buffer including community and public physical activity facilities and public parks; 2) count of food outlets within a 3‐km Euclidean buffer including grocery store/supermarket, specialty shop and restaurant. 34

Individual characteristics measures
Participants’ BMI was calculated as weight in kilograms divided by height in meters squared. Cigarette smoking status was classified as never, ever, or current; The CARDIA Physical Activity History questionnaire was used to estimate participants’ weekly leisure, occupational, and household physical exertion over the past 12 months. 35

CT measure of CAC
To measure subclinical coronary artery disease, participants in the CARDIA study underwent a multidetector computed tomography (CT) chest scan at Y25 using a standardized protocol.36 A standard protocol for non-contrast CT scan was used to measure CAC 36 and a single CT scan was performed at Y25. The CT images were electronically transmitted to the central CT reading center located at Wake Forest University School of Medicine and measured by experienced image analysts. The Agatston score was calculated corrected for slice thickness, with a minimum calcification area of 1.87 mm2 and attenuation threshold of 130 or more Hounsfield units on a dedicated computer workstation (TeraRecon).37 Robustness of the CAC score has been published.38, 39, 40 Incident CAC was defined as CAC score > 0.

Data availability
CARDIA data are available upon request through the CARDIA Coordinating Center. Researchers must submit a Data Set Request / Intent to Analyze Form (https://sites.uab.edu/cardia/files/2025/12/Data_Set_Request-Intent_to_Analyze_Form_2025-07-10.docx) describing the proposed aims, analytic plan, and requested variables; detailed requirements are outlined in the form. Data access requests must be submitted by a requester who is responsible for ensuring that all aspects of data use comply with the terms of the National Heart, Lung and Blood Institute (NHLBI) Research Materials Distribution Agreement (RMDA) and applicable institutional policies. Requests are reviewed for scientific merit, participant confidentiality, and non-duplication of ongoing projects; access may be denied if proposals overlap with active analyses or do not comply with CARDIA data use policies. More details are available at the CARDIA website (https://www.cardia.dopm.uab.edu). Support from the CARDIA Coordinating Center may be requested by sending an email to CARDIAdataquestions@uabmc.edu.

Code availability
The code used in this study has been made publicly available on GitHub (https://github.com/taogao2017/INSDH-Index) and can be accessed without restriction.

Statistical analysis
Participant characteristics were summarized using mean ± standard deviation for continuous variables and count with percentage for categorical variables. Pearson correlation between nSDH factors was assessed. Boosted Regression Tree (BRT) models were used to regress incident CAC risk status against nSDH to evaluate the contribution of each factor.12, 13 As a boosted version of the traditional regression tree model, the BRT model was performed using the “gbm” function in the “dismo” package in “R (version 4.2.2)” software. In the BRT model, the learning rate and tree complexity were set to be 0.001 and 5, respectively, as used in previous studies.12, 13 A contribution was determined for each predictor according to the number of times it was selected as one of the “optimal” predictors. Contribution can also be interpreted as the relative influence (%) of each predictor. The relative influence of each variable is scaled so that the sum adds to 100, with higher numbers indicating stronger influence on the response. The contribution of the nSDH factors was compared for each CARDIA visit. For each nSDH factor, the change of its contribution to the risk of CAC during the CARDIA visits was assessed. Each nSDH factor with its contribution to CAC was then weighted and summed to develop the neighborhood social determinants of health index.12 (Appendix A) We standardized the index at each visit using the visit-specific standard deviation to facilitate comparison of effects across study years. We evaluated the accuracy of the index with the Receiver Operating Characteristics (ROC) curve. The models were adjusted for multiple comparisons using the Benjamini-Hochberg FDR procedure. All models were adjusted for study center, age, sex, race, body‑mass index, smoking status, alcohol consumption, and physical activity. We further adjusted for TC/HDL ratio, systolic blood pressure and diabetes. As a sensitivity analysis, dietary intake was adjusted for. All analyses (with the exception of the BRT models) were performed using SAS (version 9.4, SAS Institute). Two-sided tests were applied throughout, and p-values < 0.05 were considered statistically significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This research investigates the association between neighborhood social determinants of health (nSDH) and the risk of coronary artery calcification (CAC) in middle age, with a focus on racial disparities.\nThe study used data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a longitudinal cohort of 5,115 Black and White participants followed for 25 years.\nResearchers created an nSDH index using boosted regression tree models incorporating various census tract-level variables (race, education, employment, poverty, housing, food and physical activity facilities, crime).\nThe primary outcome measure was the presence of CAC at the year 25 visit, assessed using computed tomography (CT) scans.\nIncrease in the nSDH index was significantly associated with an increased risk of CAC at years 7, 15, and 20. The association was stronger in Black participants compared to White participants at years 15 and 20.\nAmong the nSDH factors, gross rent, non-professional occupations, and the number of food facilities were particularly influential in predicting CAC risk. Adjusting for other factors, such as TC/HDL ratio, systolic blood pressure and diabetes, supported the primary findings.\nThe results suffer of unmeasured confounding factors as healthcare access that might have influence the observed associations.\nHowever, the study highlights the importance of considering social determinants of health when assessing cardiovascular risk and addressing health disparities.", "reviewer_2": "While studies examining the role of neighborhood factors in relation to CVD are very important, this study is missing key components that would strengthen the analyses and discussion.\nMethods:\nLine 125 - the description of the neighborhood marker is too vague. Some of the data sources for the neighborhood factors are provided, such as the US Census data. However, it is not clear whether the physical activity facilities data are based on valid sources or whether they include greenspace and parks. The same is true for food facilities - what types of facilities are included and what is the source of the data? This is critical to compare this study to other studies depending on if they use similar data sets.\nHow was the neighborhood marker actually calculated? There needs to be much more explanation in the methods. The marker itself should also be in Table 1.\nThe authors need to consider additional covariates, such as also adjusting for dietary intake.\nResults:\nThere should be a comparison of the analyses to analyses done with traditional neighborhood deprivation measures (such as SVI and ADI). It would be nice to see if this derived neighborhood metric actually performs better.\nTable 1:\nIt would be helpful to see baseline characteristics by years of follow-up to see how risk factors changed over years. It would also be helpful to see how the individual parts included in the neighborhood marker change over the years and have some discussion of this in the results.\nTable 2 - does this need to be a main table? This could be supplemental. It also needs p-values as the strength of the correlation is good and informative but should also be significant\nTable 3: Multiple comparison adjustment is also important and should be done.\nSupplemental Figure 1 - this could be a main figure and the differences in the contribution of each neighborhood component should be quantified if possible.\nFigure 1 –There needs to be a much better description on how this analysis was done and what this figure represents in the figure heading and in the text.\nDiscussion\nLine 222-225 should include a discussion of structural policies that are upstream causes of worse cardiovascular health in the Black community in the U.S.\nIn the discussion, the findings need to be put into context with studies on other neighborhood measures and CAC, including those that have been done in Cardia before. for instance, food environment and CAC score has been examined and should be discussed:\nhttps://www.ahajournals.org/doi/10.1161/JAHA.118.010586. The discussion needs to be more about integrating their findings with those found in the literature and making it clear what is unique about their findings and why does this study move the field forward.", "reviewer_3": "Comments:\n1. I am concerned about the comparison between Black and White participants wherein the authors state the association is stronger in Black and White participants. The authors did not do a statistical test to compare the odds ratios. Furthermore, the discussion in this manner detracts from the importance that SDOH are the drivers of health outcomes and instead puts a stronger emphasis on race. Additionally since the OR are relatively small I think that they should avoid this discussion and instead highlight that nSDH associates with risk. See below for other considerations which may be more informative.\n2. To build further on the above comment the author should show the association between the other variables and CAC. It would be interesting to see the association between race and CAC before and after adding in nSDH to the model. In other work these types of analyses have shown an attenuation of the association between race and clinical outcomes, which is a powerful statement regarding the underlying reasons for health inequities across race groups. In this context it might be also important to conduct a formal analysis of whether nSDH mediates or moderates the association between race and CAC.\n3. The measure CAC presence as the outcome is acceptable. The authors should show the distribution of CAC scores in the analysis.\n4. How do they deal with missingness given that there may be biases due to type of missingness? they should consider an imputed analysis."}, "author_response": "We appreciate the reviewer’s insightful comment regarding potential unmeasured confounding by healthcare access. Unfortunately, individual- or neighborhood-level healthcare access data were not available in the CARDIA dataset for the study period that includes 1980s and 1990s. We acknowledge this as a limitation and have added a statement in the discussion of the manuscript to reflect this point as follows.\n“Second, lack of data related to accessibility to healthcare may also affect the assessment of the association in our study.”\nDespite this, we believe our findings remain robust given the consistency of associations observed across time points and after adjusting for multiple individual- and neighborhood-level factors. Future research incorporating healthcare access metrics would be valuable to further elucidate the pathways linking neighborhood environments to subclinical cardiovascular disease.\n\nResponse: We have added the description of the physical activity as well as the food facilities in the manuscript as follows: “Historic data on physical activity and food facilities were acquired from the Dun and Bradstreet database (a commercial listing of US businesses) and the Environmental Systems Research Institute's StreetMap product including: 1) count of physical activity facilities located within a 3‐km Euclidean buffer including community and public physical activity facilities and public parks; 2) count of food outlets within a 3‐km Euclidean buffer including grocery store/supermarket, specialty shop and restaurant.”(Park et al., 2024)\n\nResponse: A detailed description of the BRT method was added in the manuscript as follows. Also nSDH index at Year 25 has been added in Table 1.\n“As a boosted version of the traditional regression tree model, the BRT model was performed using the “gbm” function in the “dismo” package in “R (version 4.2.2)” software. In the BRT model, the learning rate and tree complex were set to be 0.001 and 5, respectively, as same as used in previous studies.”\nAppendix A. Method for the Calculation of the nSDH Index at CARDIA Visits\n“The BRT model was employed using the “gbm” function in the “dismo” package in “R (version 4.2.2)” software. In the BRT model, the learning rate and tree complex were set to 0.001 and 5, respectively. Then, each selected SDH, along with its contribution (including the direction of the contribution) to CAC as obtained from the BRT models, was weighted and summed to develop the neighborhood social determinants of health index. For the nSDH factors, a neighborhood with less than a median percentage population with not white race, not graduated from high school, income less than 150% Federal Poverty Level, without professional/management occupation or unemployed, was coded as 0 and otherwise coded as 1; a neighborhood with less than the median count of food facilities, physical activity facilities within a 3‐km Euclidean buffer, was coded as 0 and otherwise coded as 1; a neighborhood with less than median total crime rate, gross rent, household income or house value, was coded as 0 and otherwise coded as 1; The calculated index was then standardized at each visit using the visit-specific standard deviation to make the effects more comparable among the study visits.”\n\nResponse: We added a sensitivity analysis with the dietary score as suggested and found the results were robust. After adjusting for dietary intake, nSDH indices were significantly associated with year 25 CAC (for nSDH at year 7, OR 1.21,95%CI (1.04 and 1.40); for nSDH at year 15, OR 1.19 , 95%CI (1.03 and 1.39); for nSDH at year 20, OR 1.25, 95%CI (1.07 and 1.46)), while before adjusting for dietary intake all of the year 7, year 15 and year 20 results were significant (year 7, OR 1.17,95% CI (1.05 and 1.32); year 15, OR 1.15, 95%CI (1.02 and 1.30); year 20, OR 1.17, 95%CI (1.03 and 1.32). We also added the results of adjusting for dietary intake in the manuscript as the supplemental Table 5.\n\nResponse: We have done an additional analysis by comparing our new index to the traditional neighborhood deprivation score (NDS)(Boone-Heinonen et al., 2011). The NDS score was derived from several tract-level census indicators of SES: median household income, proportion of the population below the 150% federal poverty level, proportion of the population with less than a high school education, and proportion of the population with a college degree. We compared the results of nSDH index and NDS score, and nSDH index performed better. Only year 7 NDS score was significantly associated with year 25 CAC (OR 0.866, 95%CI (0.765, 0.981), as shown in the table below, while, for nSDH index, all of the year 7, year 15, and year 20 results were significant (year 7, OR 1.17,95% CI (1.05 and 1.32); year 15, OR 1.15, 95%CI (1.02 and 1.30); year 20, OR 1.17, 95%CI (1.03 and 1.32)).\n\nResponse: We have added baseline characteristics as suggested. In supplemental Figure 1, we showed the change in risk factors and in supplemental Figure 2, we showed the change of nSDH factors over the years of follow-up. We added the results in the results part and discussed the trend of the changes in the discussion part.\n\nResponse: We changed Table 2 into Supplemental Table 1 and added the p values in the table as suggested. All P values for the correlation were less than 0.0001.\n\nResponse: Multiple comparison has been adjusted as below:\n[Table data omitted for brevity]\nTable 2. Association of Y0 to Y25 normalized nSDH index and Y25 CAC adjusted for multiple comparison with Benjamini-Hochberg Procedure *\n\nResponse: We changed the supplemental Figure 1 to the main Figure 1. We also added supplemental Table 2 for the contribution of each nSDH factor at each CARDIA visit.\n\nResponse: With the new Figure 1 added, the original Figure 1 is now Figure 2. We added the description of the analysis and the results in the method and results parts.\n“As a boosted version of the traditional regression tree model, the BRT model was performed using the “gbm” function in the “dismo” package in “R (version 4.2.2)” software. In the BRT model, the learning rate and tree complex were set to be 0.001 and 5, respectively, the same as used in previous studies. A contribution was determined for each predictor according to the number of times it was selected as one of the “optimal” predictors. Contribution can also be interpreted as the relative influence (%) of each predictor. The relative influence of each variable is scaled so that the sum adds to 100, with higher numbers indicating a stronger influence on the response. The contribution of the nSDH factors was compared for each CARDIA visit. Also, for each nSDH factor, the change in its contribution to the risk of CAC during the CARDIA visits was assessed.“\n\nResponse: We added the discussion of structural policies as the upstream causes of worse cardiovascular health in the Black community as follows.\n“Also, structural policies like residential segregation and redlining (disinvestment in Black neighborhoods and increased neighborhood violence and stress) and environmental racism (higher exposure to pollutants) could explain the racial difference in the association between nSDH factors and cardiovascular diseases. (Deo et al., 2023; Liu et al., 2024)”\n\nResponse: We have included previous studies in the discussion as follows.\n“A study in CARDIA also showed that living in neighborhoods with greater availability to “unhealthy” food outlets (fast‐food chain restaurants and convenience stores) was associated with risk of developing CAC. (Kelman et al., 2019) On the other hand, another study in MESA found that greater access to neighborhood healthy food resources slowed the development of coronary atherosclerosis.(Wing et al., 2017)”\n\nResponse: We thank the reviewer for this important comment. We agree that the central message of our study is the significant association between neighborhood social determinants of health (nSDH) and cardiovascular risk, and we have revised the manuscript to emphasize this point more clearly.\nAs suggested, we performed a statistical test to compare the odds ratios between Black and White adults and found that the difference was not statistically significant. Accordingly, we have removed language suggesting that the association is \"stronger\" in either group.\nTo address the reviewer’s concern, we have reframed the discussion to focus on the overarching impact of adverse neighborhood conditions on coronary artery calcification risk and have clarified that race in this study reflects differential exposure to such conditions rather than being a biological or causal determinant.\n\nResponse: We assessed the association between race (Black vs. White) and CAC and did not find a statistically significant association (OR 1.18, 95%CI (0.95, 1.46), P=0.14). Since race is not significantly associated with CAC, we could not conduct mediation analysis using nSDH index as the mediator.\n\nResponse: At year 25, there are 1041 cases of CAC (29.8%) among the 3499 study participants. Given the fact that the distribution of CAC score is right-skewed, we reported the distribution of CAC score by adding the median and IQR of CAC scores in Table 1.\n\nResponse: For the personal risk factors, we used simple (mean) imputation for imputing missing data. For the neighborhood SDH factors, since they were derived from Census data, we do not have the problem of missing data."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Thank you for the extensive edits to the manuscript. There are a couple additional clarifications.\n1. Regarding the nSDH, it seems like the directionality of the individual measures differs where 0 represents more favorable environments for some of the measures but not for others. Does that affect the results if all of the directionalities are kept the same?\n2. The comparison data to NDS should be included in the paper and not just in the reviewer response.", "reviewer_4": "congratulations on your manuscript . This is a very timely topic.\nYour adjustments have made this work meaningful."}, "author_response": "Response: Thank you for the insightful comment. Our developed index accounts for both the magnitude and direction of each individual measure’s contribution to CAC as estimated by the BRT models. A positive direction indicates higher CAC risk, while a negative direction indicates lower risk. This approach ensures consistency across measures and aligns with the reviewer’s suggestion of assigning values based on whether the environment is favorable or unfavorable. Therefore, differences in directionality are explicitly incorporated into the index and do not compromise the results.\n\nResponse: Thank you for the suggestion. The comparison with NDS has been incorporated into the manuscript in the introduction and results parts as below and included into the manuscript as the new supplemental table 6.\nA Neighborhood Socioeconomic Deprivation Score (NDS) was also developed to evaluate the association between neighborhood SES and health outcomes. Factors such as tract-level census indicators of socioeconomic status, including household income and education level, were used in calculating the score. nSDH indices performed better compared to NDS score, with NDS score was associated with CAC at year 7 (OR 0.866, 95%CI 0.765-0.981) and the association in other years were not significant (Supplemental Table 6).\n\nResponse: Thank you for your encouraging comments. We are pleased that the revisions have strengthened the clarity and relevance of the manuscript."}, {"round": 3, "reviewer_comments": {"reviewer_2": "Thank you for the additional edits. I have no further suggestions."}, "author_response": ""}]

Decision letter