Social Vulnerability and Biological Aging in New York City: An Electronic Health Records-Based Study

Social Vulnerability and Biological Aging in New York City: An Electronic Health Records-Based Study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Social Vulnerability and Biological Aging in New York City: An Electronic Health Records-Based Study View ORCID Profile Pablo Knobel , View ORCID Profile Elena Colicino , View ORCID Profile Itai Klog , Rachel Litke , View ORCID Profile Kevin Lane , View ORCID Profile Alex Federman , View ORCID Profile Charles Mobbs , View ORCID Profile Maayan Yitshak Sade doi: https://doi.org/10.1101/2024.06.29.24309707 Pablo Knobel 1 Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai , New York, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pablo Knobel Elena Colicino 1 Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai , New York, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elena Colicino Itai Klog 1 Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai , New York, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Itai Klog Rachel Litke 2 Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai , New York, NY, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kevin Lane 3 Department of Environmental Health, Boston University School of Public Health , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kevin Lane Alex Federman 4 Division of General Internal Medicine, Icahn School of Medicine , New York, NY, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alex Federman Charles Mobbs 2 Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai , New York, NY, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Charles Mobbs Maayan Yitshak Sade 1 Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai , New York, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maayan Yitshak Sade For correspondence: maayan.yitshak-sade{at}mssm.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Chronological age is not an accurate predictor of morbidity and mortality risk, as individuals’ aging processes are diverse. Phenotypic age acceleration (PhenoAgeAccel) is a validated biological age measure incorporating chronological age and biomarkers from blood samples commonly used in clinical practice that can better reflect aging-related morbidity and mortality risk. The heterogeneity of age-related decline is not random, as environmental exposures can promote or impede healthy aging. Social Vulnerability Index (SVI) is a composite index accounting for different facets of the social, economic, and demographic environment grouped into four themes: socioeconomic status, household composition and disability, minority status and language, and housing and transportation. We aim to assess the concurrent and combined associations of the four SVI themes on PhenoAgeAccel and the differential effects on disadvantaged groups. We use electronic health records data from 31,913 patients from the Mount Sinai Health System (116,952 person-years) and calculate PhenoAge for years with available laboratory results (2011-2022). PhenoAge is calculated as a weighted linear combination of lab results and PhenoAgeAccel is the differential between PhenoAge and chronological age. A decile increase in the mixture of SVI dimensions was associated with an increase of 0.23 years (95% CI: 0.21, 0.25) in PhenoAgeAccel. The socioeconomic status dimension was the main driver of the association, accounting for 61% of the weight. Interaction models revealed a more substantial detrimental association for women and racial and ethnic minorities with differences in leading SVI themes. These findings suggest that neighborhood-level social vulnerability increases the biological age of its residents, increasing morbidity and mortality risks. Socioeconomic status has the larger detrimental role amongst the different facets of social environment. 1. Introduction The proportion of individuals ≥ 65 years old is projected to grow from 1:11 to 1:6 of the global population by the year 2050 1 . Biologically, aging results from the buildup of diverse molecular and cellular damage that leads to declining physical and mental capacities. Over time, this decline translates into increased morbidity and mortality risk 2 . Thus, higher life expectancy can result in years spent in ill health instead of healthy aging 3 . However, chronological age is not a perfect measure of risk, as individuals’ aging processes are diverse 4 : some individuals become frail and dependent, while others remain entirely autonomous. Biological age can better reflect aging-related morbidity and mortality risk 5 . Phenotypic age (PhenoAge) is a validated biological age measure incorporating chronological age and biomarkers from blood samples commonly used in clinical practice. Phenotypic age Acceleration (PhenoAgeAccel) measures the degree to which a person is younger or older than their chronological age 6 and predicts the risk of morbidity and mortality better than chronological age 6 , 7 . The heterogeneity of age-related decline is not random, as environmental exposures can promote or impede healthy aging: the neighborhood someone lives in has been linked to many aging-related outcomes 8 . For example, the socioeconomic, racial, and ethnic composition of the neighborhood of residence has been associated with increased odds of frailty 9 , and negative neighborhood perception is associated with reduced physical activity and increased sedentary time 10 . Safety is crucial for seniors’ well-being 11 , with associations between self-reported safety and improved self-reported health 12 . Further, socioeconomic status (e.g., education level, household income) is linked to inequalities in aging-related outcomes and life expectancy 13 . The Center for Disease Control and Prevention (CDC) developed the Social Vulnerability Index (SVI) as a composite index to account for the socioeconomic environment 14 . The SVI encompasses social factors organized into four themes: socioeconomic status, household composition and disability, minority status and language, and housing and transportation. The four themes of the index represent different facets of the social environment that are always present simultaneously. However, social environment facets are usually studied independently. These independent analysis raises questions about the combined effects of these exposures and their relative contributions. Concurrent time and space-varying exposures might influence each other’s impact on aging. Therefore, only considering a single exposure may underestimate or overestimate its association, without accounting for potential effect addition, amplification, or confounding. Social environment exposures are unequally distributed across the US, creating exposure and aging disparities among communities and individuals. Women, as well as racial and ethnic minorities, experience worse aging processes in comparison to their male or non-Hispanic White counterparts 16 . Disadvantaged groups thus suffer from a “double jeopardy”: the combination of harmful neighborhood-level exposures and personal-level stressors 17 . These neighborhood-level exposures can influence individual behaviors, and individual characteristics can change how neighborhoods are perceived and interacted with, potentially leading to larger exposure and aging disparities 18 . In this study, we assess the concurrent and combined associations of the four themes of SVI on PhenoAgeAccel using an exposure-mixture approach. We leverage 11 years of electronic health records (EHR) data from the Mount Sinai Health System (MSHS). Additionally, we evaluate the differential effects by sex as well as race and ethnicity. 2. Methods Study Population This retrospective cohort study includes New York City residents who were 65 years or older and have been treated at the MSHS between 2011 and 2022. Data was obtained though the Mount Sinai Data Warehouse. We excluded individuals without valid address information and person-years with no recorded measures of the biomarkers needed to calculate PhenoAgeAccell. This study was approved by the Institutional Review Board of Mount Sinai (STUDY 22-01400), and a waiver of informed consent was granted. Exposure assessment The SVI encompasses social factors (details on each theme can be found in Table S1) extracted from the US Census and American Community Survey calculated for each non-zero population census tract, an administrative boundary which aim to be demographically homogeneous and have around 4,000 inhabitants 15 . All the variables are weighted equally, as are the four themes. SVI and each theme range from zero to one, with higher values indicating greater vulnerability 19 . Addresses in the MSHS are updated in every patient encounter in which the patient reports a change of address. We linked SVI to participants annually based on the last reported address on file. We calculated SVI using findSVI 20 and tidycensus 21 in R 22 . PhenoAgeAccell calculation We derived PhenoAge for each person-year from laboratory results using the formula described by Levine et al 23 . PhenoAge is calculated as a weighted linear combination of lab results transformed into units of years using two parametric proportional hazard models 23 . We defined PhenoAgeAcell, our outcome of interest, as the differential between chronological age and PhenoAge. We averaged all the available measures of each required biomarker for each year. Most required biomarkers are routinely drawn: albumin, creatinine, glucose, mean cell volume, alkaline phosphatase, red cell distribution width, and white blood cell count. However, C-reactive protein and lymphocyte percent might be drawn routinely but are often obtained when a concern arises for infections, hematologic, liver, or renal disease. To avoid selection bias by limiting our population to a sicker group that has these two tests performed, we imputed C-reactive protein (90.83%) and lymphocyte percent (24.06%). We used the predictive mean matching (pmm) method from the mice 24 package in R because it guarantees imputations within the range of observed data and has been shown to have low root mean square error, fast computation time, and minimal challenges in implementation 25 . Statistical Analysis We used quantile g computation (qGcomp) 26 to assess the combined association of the four SVI themes, and assess the relative contribution of each theme to PhenoAgeAccel. This approach is an extension of the g-computation method and estimates the overall association of the mixture with the outcome and outputs a weight with direction for each exposure in the mixture, allowing us to identify the most highly weighted contributors for each association direction. Exposures contributing to the mixture are combined into a supervised weighted index, defined by deciles of the exposures. The weighted contribution of each exposure is estimated relative to the contribution of the other exposure effects. The overall mixture effect is interpreted as the expected change in PhenoAgeAccel associated with increasing all exposures by one decile simultaneously 27 . We adjusted our models for sex (male, female), race and ethnicity combined (American Indian or Alaska Native, Asian, Black or African-American, Hispanic, Native Hawaiian or Pacific Islander, Other, White) and type of insurance (Medicaid, Medicare, Other, Private insurance, Self-pay). To estimate a time-varying baseline risk, we adjusted the model for an indicator variable of year of observation 28 . We assessed the potential interaction of the exposures with sex (male vs female), as well as race and ethnicity (non-Hispanic White vs other racial and ethnic groups). To evaluate the potential effect of imputing C-reactive protein and lyphocites we repeated the qGcomp analysis including only person-years without any imputed lab results as sensitivity analysis. We used the qgcomp 26 and qgcompint 29 packages in R 22 . 3. Results We included 116,952 person-years from 31,913 participants. The average participant’s age was 70.21 years. One-third of the participants were male, almost half were white, and a quarter were Hispanic. Variability at baseline in the socioeconomic status and household characteristics is higher than in the other CDC-SVI themes ( Table 1 ). Correlations amongst SVI themes range from 0.84 in racial and ethnicity minority status and socioeconomic status to 0.10 for household characteristics and household type and transportation, the later having consistently lower correlations than the rest across comparisons (Figure S1). Our stratified analysis by sex and race and ethnicity looked at different subsets of the population. When stratifying the sample by sex, there is a higher percentage of Hispanics and Blacks and a lower percentage of Whites amongst women compared to men. Age, insurance, and SVI values remain similar. When stratifying the sample by race, there is a higher percentage of men, fewer people covered by Medicaid, and more people covered by Medicare amongst Whites compared to the other groups. SVI values are consistently lower amongst Whites (Table S2). View this table: View inline View popup Download powerpoint Table 1: Descriptive statistics for the study population at baseline. In our qGcomp analysis, a decile increase in the mixture of SVI dimensions was associated with an increase of 0.23 years (95% CI: 0.21, 0.25) in PhenoAgeAccell. The socioeconomic status dimension was the main driver of the association, accounting for 61% of the weight, followed by household composition and racial and ethnic minority status. Housing type and transportation had an insubstantial protective role ( Figure 1 ). Download figure Open in new tab Figure 1: Weights representing the proportion of the positive and negative partial effect in a quantile g-computation model assessing the associations between the four SVI themes and PhenoAgeAccell. Darker shading indicates a stronger partial effect. Decile increase in all exposure was associated with an increase of 0.23 (95% CI: 0.21, 0.25) in PhenoAgeAccell. Interaction models revealed an effect modification in the association between the SVI mixture and PhenoAgeAccell by sex. For females, a decile increase in the mixture of SVI dimensions was associated with an increase of 0.27 years (95% CI: 0.25, 0.29), mainly driven by Socioeconomic status (71%). For males a decile increase with association with an increase of 0.13 years (0.10, 0.16), mainly driven by Racial and Ethnic Minority Status (43%), Socioeconomic Status (29%), and Household Characteristics (27%). Interaction models also revealed an effect modification by race and ethnicity when comparing non-Hispanic White to the rest of the participants. For non-Hispanic Whites a decile increase was associated with an increase of 0.12 years (0.09, 0.15), driven by Racial and Ethnic Minority Status (55%). For the other racial and ethnic groups a decile increase was associated with an increase of 0.34 years (0.32, 0.36), driven by Socioeconomic Status (61%) ( Figure 2 ). Download figure Open in new tab Figure 2: Weights representing the proportion of the positive and negative partial effect in stratified quantile g-computation models assessing the associations between the four SVI themes and PhenoAgeAccell. A: For men. Decile increase in all exposure associated with an increase of 0.13 (95% CI: 0.10, 0.16) in PhenoAgeAccell. B: For women. 0.27 (0.25, 0.29) C: For non-Hispanic Whites. 0.12 (0.09, 0.15). D: For other racial and ethnic groups 0.34 (0.32, 0.36). Darker shading indicates a stronger association in the direction. Sensitivity analysis limiting the sample to those person-years without imputations (5853 participants and 8670 person-years) revealed slightly stronger associations compared to the complete imputed subset. The population was similar to the complete sample (Table S3). A decile increase in the mixture of SVI dimensions was associated with an increase of 0.26 years (95% CI: 0.19, 0.33) in PhenoAgeAccell. Interaction models with the subset data showcased similar increases (Table S4). 4. Discussion Our findings show that higher neighborhood-level social vulnerability is associated with an increase in PhenoAgeAccell. In other words, the social vulnerability of the participants’ neighborhood of residence increased their biological age, therefore increasing their morbidity and mortality risk. The Socioeconomic Status theme is the primary driver of the association, while housing type and transportation had an insubstantial role. Interaction models revealed stronger associations among women and among racial and ethnic minorities. Our results contribute to the growing body of evidence linking social vulnerability to diverse health outcomes, particularly aging outcomes. However, our study is the first to investigate the associations between social vulnerability and biological aging. In a cross-sectional US-wide analysis, county-level SVI was associated with an increase in cardiovascular disease-related mortality rate 30 . A Medicare-based analysis found higher risks of diverse postoperative surgical outcomes associated with SVI 31 . In a Canada-based cohort, self-reported social vulnerability has been associated with increased risk of mortality 32 , cognitive decline 33 , and cognitive function 34 in older adults. In a European-based cohort, self-reported social vulnerability was associated with an increased risk of frailty 35 . Utilizing the overall SVI score—or any index or score that combines different aspects of social vulnerability—allows researchers to navigate the complex correlation structure of social, economic, and demographic variables, thus avoiding issues of collinearity and interpretation challenges 36 . However, the overall SVI score might oversimplify the underlying factors linking social vulnerability and biological aging 37 . Our mixture approach enables us to evaluate the effects of the different themes simultaneously, as they occur in real life. Doing so, we obtain a closer measure of the role effects of each theme, informing on how to best target policy to reduce the overall negative effect 38 . Our results highlighted neighborhood socioeconomic status as the main driver of the association, followed by household characteristics, and finally, racial and ethnic minority status. Housing type and transportation did not have a substnatial role. The socioeconomic status domain was previously associated with increased cognitive decline 39 and function 40 , self-reported health, and mortality amongst older adults 41 . Household structure has been previously linked to elderly mortality 42 and informal care 43 . Additionally, neighborhood racial and ethnic minority status has been associated with cognitive decline 44 . In the cross-sectional US-wide study amongst the general population, SVI’s socioeconomic status and racial and ethnic minority status had the strongest associations with personal-level social determinants of health 45 . Neighborhood effects can vary from person to person based on the interaction between neighborhood conditions and personal attributes and behavious 46 . We found females to be more susceptible to social vulnerability related accelerated aging compared to males. Moreover, the weight of the themes was substantially different, with socioeconomic status driving the association for females and it being split between socioeconomic status, household characteristics as well as racial and ethnic minority status in men. Similar to our study, a 158 participant study in Detroit found stronger associations between neighborhood quality and PhenoAgeAccell in females than in males, and a study in the UK also found stronger associations between neighborhood quality and self-reported health for women than for men 48 . The differential contribution of the themes to the overall effect, as observed in our study, can be explained by different mechanisms. Men and women might perceive their environment differently, interact with their environment differently, and their vulnerability to the environment might be different 49 . This compounds with sex being a significant factor in wellbeing and survival, with differing impacts on men and women 50 . Comparable results were obtained when stratifying by race and ethnicity. Social vulnerability was associated with a smaller PhenoAgeAccell increase in non-Hispanic White than on the other racial and ethnic group. Similarly, an analysis using the Health and Retirement Study cohort reported stronger associations between neighborhood characteristics and telomere length in Black individuals than White individuals 53 . Moreover, the association in non-Hispanic Whites is led by racial and ethnic minority status theme, but socioeconomic status leads in the other group. These differences can again be explained by differences in perception, interaction, and vulnerability, and compounds with race and ethnicity minority groups often experiencing worse health outcomes compared to non-Hispanic Whites. Black 54 , 55 and Hispanic 56 , 57 individuals often experience worse aging outcomes compared to non-Hispanic Whites. In summary, individual-level characteristics can increase vulnerability while influencing the detrimental effects of neighborhood-level exposures, potentially combining into detrimental effects stronger than their separate ones. This study is not without limitations. The formula for PhenoAge includes blood biomarkers that are not routinely drawn in the MSHS, potentially introducing selection bias. We have minimized this bias by imputing C-reactive protein and Lymphocytes when those are the only two biomarkers missing. Moreover, the analysis being based on EHR data could reduce the sample’s representativeness. However, we include all available biomarkers – both inpatient and outpatient – and we have limited our sample to 65 years or older, a subpopulation who are more likely to visit the health system for regular check-ups. Lastly, our results regarding housing type and transportation might be linked to transportation and housing features specific to New York City. Therefore, our results might not be generalizable to other community types (i.e. rural) where transportation and resource access is more limited. However, these distinct transportation and housing features are common amongst most US metropolises. 5. Conclusions Our study shows that higher social vulnerability is associated with an increase in biological age measured as Phenotypic Age Acceleration. We observed that the association is led by socioeconomic status, followed by household characteristics and racial and ethnic minority status. At the same time, housing type and transportation seem to have an irrelevant role. These observed associations were especially pronounced amongst women and racial and ethnic minority groups. Our findings suggest that the social environment plays an important role in healthy aging, pinpoint which facets of the social environment are the more relevant, and that vulnerability is differential amongst population groups. Social environments are often represented by a single metric or index that fails to capture the complexity of a city’s social, economic, and demographic landscape. This makes the design and implementation of policies more difficult, especially when the specific needs of the most vulnerable populations are not considered and those needs are population-specific. Data Availability All data produced in the present study are available upon reasonable request to the authors. Acknowledgments This study was supported by the Mount Sinai transdisciplinary center on early environmental exposures (P30 ES023515) and through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai. References 1. ↵ United Nations DoEaSA , Population Division World Population Prospects 2022: Summary of Results . 2022 . 2. ↵ Tosato M , Zamboni V , Ferrini A , Cesari M. The aging process and potential interventions to extend life expectancy . Clinical interventions in aging 2007 ; 2 ( 3 ): 401 – 412 . OpenUrl 3. ↵ WHO . Decade of healthy ageing: Baseline report . World Health Organization Geneva ; 2020 . 4. ↵ Lowsky DJ , Olshansky SJ , Bhattacharya J , Goldman DP . Heterogeneity in healthy aging . Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 2014 ; 69 ( 6 ): 640 – 649 . OpenUrl 5. ↵ Sprott RL . Biomarkers of aging . Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 1999 ; 54 ( 11 ): B464 – B465 . OpenUrl 6. ↵ Liu Z , Kuo P-L , Horvath S , Crimmins E , Ferrucci L , Levine M. A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: a cohort study . PLoS medicine 2018 ; 15 ( 12 ): e1002718 . OpenUrl 7. ↵ Ma Q , Li B-L , Yang L , et al. Association between Phenotypic Age and Mortality in Patients with Multivessel Coronary Artery Disease . Disease markers 2022 ; 2022 . 8. ↵ Arcaya MC , Tucker-Seeley RD , Kim R , Schnake-Mahl A , So M , Subramanian S. Research on neighborhood effects on health in the United States: a systematic review of study characteristics . Social science & medicine 2016 ; 168 : 16 – 29 . OpenUrl 9. ↵ Fritz H , Cutchin MP , Gharib J , Haryadi N , Patel M , Patel N. Neighborhood characteristics and frailty: a scoping review . The Gerontologist 2020 ; 60 ( 4 ): e270 – e285 . OpenUrl 10. ↵ Claudel SE , Shiroma EJ , Harris TB , et al. Cross-sectional associations of neighborhood perception, physical activity, and sedentary time in community-dwelling, socioeconomically diverse adults . Frontiers in public health 2019 ; 7 : 256 . OpenUrl 11. ↵ Tong CE , Sims-Gould J , Martin-Matthews A. Types and patterns of safety concerns in home care: client and family caregiver perspectives . International Journal for Quality in Health Care 2016 ; 28 ( 2 ): 214 – 220 . OpenUrl CrossRef PubMed 12. ↵ Cain CL , Wallace SP , Ponce NA . Helpfulness, trust, and safety of neighborhoods: Social capital, household income, and self-reported health of older adults . The Gerontologist 2018 ; 58 ( 1 ): 4 – 14 . OpenUrl 13. ↵ Wu Y-T , Daskalopoulou C , Terrera GM , et al. Education and wealth inequalities in healthy ageing in eight harmonised cohorts in the ATHLOS consortium: a population-based study . The Lancet Public Health 2020 ; 5 ( 7 ): e386 – e394 . OpenUrl 14. ↵ CDC . SVI Documentation 2018 . ( https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2018.html ). 15. ↵ U.S. Census Bureau . Census Regions and Divisions of the United States . 16. ↵ Moen P , Spencer D. Converging divergences in age, gender, health, and well-being: Strategic selection in the third age . Handbook of aging and the social sciences : Elsevier ; 2006 : 127 – 144 . 17. ↵ Justice TE . Research, education, and health policy needs . Proceedings of the National Academy 1999 . 18. ↵ Koohsari MJ , Sugiyama T , Sahlqvist S , Mavoa S , Hadgraft N , Owen N. Neighborhood environmental attributes and adults’ sedentary behaviors: review and research agenda . Preventive medicine 2015 ; 77 : 141 – 149 . OpenUrl CrossRef PubMed 19. ↵ Flanagan BE , Gregory EW , Hallisey EJ , Heitgerd JL , Lewis B. A social vulnerability index for disaster management . Journal of homeland security and emergency management 2011 ; 8 ( 1 ):0000102202154773551792. 20. ↵ Xu H. findSVI: Calculate Social Vulnerability Index for Communities . 2023 . 21. ↵ Walker K , Herman M. tidycensus: Load US Census Boundary and Attribute Data as ‘tidyverse’ and ‘sf’-Ready Data Frames . 2024 . 22. ↵ R Developement Core Team . R: A language and environment for statistical computing .: R Foundation for Statistical Computing ; 2010 . 23. Levine ME , Lu AT , Quach A , et al. An epigenetic biomarker of aging for lifespan and healthspan . Aging (Albany NY) 2018 ; 10 ( 4 ): 573 . OpenUrl 24. ↵ Buuren Sv , Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R . Journal of Statistical Software 2011 ; 45 ( 3 ): 1 – 67 . DOI: 10.18637/jss.v045.i03 . OpenUrl CrossRef 25. ↵ Shaw C , Wu Y , Zimmerman SC , et al. Comparison of Imputation Strategies for Incomplete Longitudinal Data in Life-Course Epidemiology . American Journal of Epidemiology 2023 ; 192 ( 12 ): 2075 – 2084 . OpenUrl 26. ↵ Keil A. qgcomp: Quantile G-Computation . 2023 . 27. Keil AP , Buckley JP , O’Brien KM , Ferguson KK , Zhao S , White AJ . A quantile-based g-computation approach to addressing the effects of exposure mixtures . Environmental health perspectives 2020 ; 128 ( 4 ): 047004 . OpenUrl 28. ↵ Whitehead J. Fitting Cox’s regression model to survival data using GLIM . Journal of the Royal Statistical Society: Series C (Applied Statistics) 1980 ; 29 ( 3 ): 268 – 275 . OpenUrl 29. ↵ Keil A. qgcompint: Quantile G-Computation Extensions for Effect Measure Modification . 2022 . 30. ↵ Khan SU , Javed Z , Lone AN , et al. Social vulnerability and premature cardiovascular mortality among US counties, 2014 to 2018 . Circulation 2021 ; 144 ( 16 ): 1272 – 1279 . OpenUrl CrossRef 31. ↵ Paro A , Hyer JM , Diaz A , Tsilimigras DI , Pawlik TM . Profiles in social vulnerability: the association of social determinants of health with postoperative surgical outcomes . Surgery 2021 ; 170 ( 6 ): 1777 – 1784 . OpenUrl 32. ↵ Andrew MK , Mitnitski AB , Rockwood K. Social vulnerability, frailty and mortality in elderly people . PloS one 2008 ; 3 ( 5 ): e2232 . OpenUrl CrossRef PubMed 33. ↵ Andrew MK , Rockwood K. Social vulnerability predicts cognitive decline in a prospective cohort of older Canadians . Alzheimer’s & Dementia 2010 ; 6 ( 4 ): 319 - 325 . e1. OpenUrl 34. ↵ Andrew MK , Fisk JD , Rockwood K. Social vulnerability and prefrontal cortical function in elderly people: a report from the Canadian Study of Health and Aging . International Psychogeriatrics 2011 ; 23 ( 3 ): 450 – 458 . OpenUrl CrossRef PubMed 35. ↵ Wallace LM , Theou O , Pena F , Rockwood K , Andrew MK . Social vulnerability as a predictor of mortality and disability: cross-country differences in the survey of health, aging, and retirement in Europe (SHARE) . Aging Clinical and Experimental Research 2015 ; 27 : 365 – 372 . OpenUrl CrossRef 36. ↵ Fuchs VR . Social determinants of health: caveats and nuances . Jama 2017 ; 317 ( 1 ): 25 – 26 . OpenUrl CrossRef 37. ↵ Kolak M , Bhatt J , Park YH , Padrón NA , Molefe A. Quantification of neighborhood-level social determinants of health in the continental United States . JAMA network open 2020 ; 3 ( 1 ): e1919928 – e1919928 . OpenUrl 38. ↵ Adler NE , Cutler DM , Fielding JE , et al. Addressing social determinants of health and health disparities: A vital direction for health and health care . NAM Perspectives 2016 . 39. ↵ Meyer OL , Mungas D , King J , et al. Neighborhood socioeconomic status and cognitive trajectories in a diverse longitudinal cohort . Clinical gerontologist 2018 ; 41 ( 1 ): 82 – 93 . OpenUrl CrossRef 40. ↵ Rosso AL , Flatt JD , Carlson MC , et al. Neighborhood socioeconomic status and cognitive function in late life . American journal of epidemiology 2016 ; 183 ( 12 ): 1088 – 1097 . OpenUrl CrossRef PubMed 41. ↵ Yao L , Robert SA . The contributions of race, individual socioeconomic status, and neighborhood socioeconomic context on the self-rated health trajectories and mortality of older adults . Research on Aging 2008 ; 30 ( 2 ): 251 – 273 . OpenUrl CrossRef Web of Science 42. ↵ Rahman MO . Age and gender variation in the impact of household structure on elderly mortality . International journal of epidemiology 1999 ; 28 ( 3 ): 485 – 491 . OpenUrl CrossRef PubMed Web of Science 43. ↵ Peek MK , Coward RT , Peek CW . Race, aging, and care: Can differences in family and household structure account for race variations in informal care? Research on Aging 2000 ; 22 ( 2 ): 117 – 142 . OpenUrl CrossRef Web of Science 44. ↵ Meyer OL , Besser L , Mitsova D , et al. Neighborhood racial/ethnic segregation and cognitive decline in older adults . Social Science & Medicine 2021 ; 284 : 114226 . OpenUrl 45. ↵ Brignone E , LeJeune K , Mihalko AE , Shannon AL , Sinoway LI . Self-Reported Social Determinants of Health and Area-Level Social Vulnerability . JAMA Network Open 2024 ; 7 ( 5 ): e2412109 – e2412109 . OpenUrl 46. ↵ Jencks C , Mayer SE . The social consequences of growing up in a poor neighborhood . Inner-city poverty in the United States 1990 ; 111 : 186 . OpenUrl 47. Martin CL , Ward-Caviness CK , Dhingra R , et al. Neighborhood environment, social cohesion, and epigenetic aging . Aging (Albany NY) 2021 ; 13 ( 6 ): 7883 . OpenUrl 48. ↵ Stafford M , Cummins S , Macintyre S , Ellaway A , Marmot M. Gender differences in the associations between health and neighbourhood environment . Social science & medicine 2005 ; 60 ( 8 ): 1681 – 1692 . OpenUrl 49. ↵ Ellaway A , Macintyre S. Women in their place: gender and perceptions of neighborhoods and health in the West of Scotland: Anne Ellaway and Sally Macintyre . Geographies of women’s health : Routledge ; 2012 : 277 – 293 . 50. ↵ Adams KB , Leibbrandt S , Moon H. A critical review of the literature on social and leisure activity and wellbeing in later life . Ageing & Society 2011 ; 31 ( 4 ): 683 – 712 . OpenUrl CrossRef Web of Science 51. Lennartsson C , Silverstein M. Does engagement with life enhance survival of elderly people in Sweden? The role of social and leisure activities . The Journals of Gerontology Series B: Psychological Sciences and Social Sciences 2001 ; 56 ( 6 ): S335 – S342 . OpenUrl CrossRef PubMed Web of Science 52. Warr P , Butcher V , Robertson I. Activity and psychological well-being in older people . Aging & mental health 2004 ; 8 ( 2 ): 172 – 183 . OpenUrl 53. ↵ Thierry AD . Association between telomere length and neighborhood characteristics by race and region in US midlife and older adults . Health & place 2020 ; 62 : 102272 . OpenUrl 54. ↵ Blackwell DL , Lucas JW , Clarke TC . Summary health statistics for US adults: national health interview survey, 2012 . Vital and health statistics Series 10, Data from the National Health Survey 2014 ( 260 ): 1 – 161 . 55. ↵ Fuller-Thomson E , Nuru-Jeter A , Minkler M , Guralnik JM . Black—White disparities in disability among older Americans: Further untangling the role of race and socioeconomic status . Journal of aging and health 2009 ; 21 ( 5 ): 677 – 698 . OpenUrl CrossRef PubMed Web of Science 56. ↵ Markides KS , Coreil J , Rogers LP . Aging and health among southwestern Hispanics . Aging and health: Perspectives on gender, race, ethnicity and class 1989 : 177 – 210 . 57. ↵ Markides KS , Eschbach K , Ray LA , Peek MK . Census disability rates among older people by race/ethnicity and type of Hispanic origin . The health of aging Hispanics: The Mexican-origin population : Springer ; 2007 : 26 – 39 . View the discussion thread. Back to top Previous Next Posted June 30, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Social Vulnerability and Biological Aging in New York City: An Electronic Health Records-Based Study Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Social Vulnerability and Biological Aging in New York City: An Electronic Health Records-Based Study Pablo Knobel , Elena Colicino , Itai Klog , Rachel Litke , Kevin Lane , Alex Federman , Charles Mobbs , Maayan Yitshak Sade medRxiv 2024.06.29.24309707; doi: https://doi.org/10.1101/2024.06.29.24309707 Share This Article: Copy Citation Tools Social Vulnerability and Biological Aging in New York City: An Electronic Health Records-Based Study Pablo Knobel , Elena Colicino , Itai Klog , Rachel Litke , Kevin Lane , Alex Federman , Charles Mobbs , Maayan Yitshak Sade medRxiv 2024.06.29.24309707; doi: https://doi.org/10.1101/2024.06.29.24309707 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Epidemiology Subject Areas All Articles Addiction Medicine (574) Allergy and Immunology (865) Anesthesia (304) Cardiovascular Medicine (4460) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (611) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15251) Forensic Medicine (31) Gastroenterology (1132) Genetic and Genomic Medicine (6621) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4564) Health Policy (1372) Health Systems and Quality Improvement (1617) Hematology (544) HIV/AIDS (1272) Infectious Diseases (except HIV/AIDS) (15938) Intensive Care and Critical Care Medicine (1107) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6642) Nursing (346) Nutrition (1001) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3350) Ophthalmology (981) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1698) Pharmacology and Therapeutics (694) Primary Care Research (714) Psychiatry and Clinical Psychology (5464) Public and Global Health (9259) Radiology and Imaging (2212) Rehabilitation Medicine and Physical Therapy (1372) Respiratory Medicine (1198) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (533) Surgery (715) Toxicology (100) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0398b30896b73f0',t:'MTc4MDEwMDExOQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();