JAMA Health Forum – Health Policy, Health Care Reform, Health Affairs | JAMA Health Forum | JAMA Network
[Skip to Navigation]
Sign In
Figure 1.  Within-Family Association Between Net Worth and Longevity
Within-Family Association Between Net Worth and Longevity

The survival curves represent 2 family members whose net worth differed by approximately $139 000 at Midlife in the United States (MIDUS) study wave 1; this amount corresponded to within-family net worth deviation scores that are ±0.5 SD from the mean deviation score of 0.00. The survival curves were adjusted for family-level mean net worth as well as participant age at wave 1 of the MIDUS study, race/ethnicity, sex, history of cancer or heart disease, health behaviors, and parental educational level.

Figure 2.  Hazard Ratios (HRs) and 95% CIs for the Net Worth Estimate Across Survival Models
Hazard Ratios (HRs) and 95% CIs for the Net Worth Estimate Across Survival Models

In each model, the HR reflects the decrease in hazard associated with a $50 000 increase in net worth. The squares represent the HR estimates, and the lines represent the 95% CIs. DZ indicates dizygotic; MZ, monozygotic.

Table 1.  Descriptive Statistics of the Analysis Variables
Descriptive Statistics of the Analysis Variables
Table 2.  Cox Proportional Hazards Regression for Survival Models
Cox Proportional Hazards Regression for Survival Models
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Views 21,772
    Original Investigation
    July 23, 2021

    Association of Wealth With Longevity in US Adults at Midlife

    Author Affiliations
    • 1Institute for Policy Research, Northwestern University, Evanston, Illinois
    • 2Department of Psychology, Northwestern University, Evanston, Illinois
    • 3Department of Psychology, University of Illinois Urbana-Champaign, Urbana
    • 4Department of Psychology, West Virginia University, Morgantown
    • 5Department of Psychological Sciences, Purdue University, West Lafayette, Indiana
    • 6Department of Psychology, University of Minnesota, Minneapolis
    • 7Department of Medical Social Sciences, Northwestern University, Evanston, Illinois
    JAMA Health Forum. 2021;2(7):e211652. doi:10.1001/jamahealthforum.2021.1652
    Key Points

    Question  Is net worth at midlife associated with all-cause mortality?

    Findings  In this cohort study of 5414 participants in the Midlife in the United States study, those who had accumulated a higher net worth by midlife had significantly lower mortality risk over the subsequent 24 years. In sibling and twin comparison models that controlled for shared early life experiences and genetic influence, the association between net worth and longevity was similar in magnitude.

    Meaning  Net worth at midlife was associated with longevity among adults in the study, and this association is unlikely to be merely an artifact of early experiences or heritable traits shared by families.


    Importance  Wealthy adults tend to live longer than those with less wealth. However, a challenge in this area of research has been the reduction of potential confounding by factors associated with the early environment and heritable traits, which could simultaneously affect socioeconomic circumstances in adulthood and health across the life course.

    Objective  To identify the association between net worth at midlife and subsequent all-cause mortality in individuals as well as within siblings and twin pairs.

    Design, Setting, and Participants  This cohort study conducted a series of analyses using data from the Midlife in the United States (MIDUS) study, an ongoing national study of health and aging. The sample included adults (unrelated individuals, full siblings, and dizygotic and monozygotic twins) aged 20 to 75 years, who participated in wave 1 of the MIDUS study, which occurred from 1994 to 1996. The analyses were conducted between November 16, 2019, and May 18, 2021.

    Exposures  Self-reported net worth (total financial assets minus liabilities) at midlife (the middle years of life).

    Main Outcomes and Measures  All-cause mortality was tracked over nearly 24 years of follow-up, with a censor date of October 31, 2018. Survival models tested the association between net worth and all-cause mortality. Discordant sibling and twin analyses compared longevity within siblings and twin pairs who, given their shared early experiences and genetic backgrounds, were matched on these factors.

    Results  The full sample comprised 5414 participants, who had a mean (SD) age of 46.7 (12.7) years and included 2766 women (51.1%). Higher net worth was associated with lower mortality risk (hazard ratio [HR], 0.95; 95% CI, 0.94-0.97; P < .001). Among siblings and twin pairs specifically (n = 2490), a similar within-family association was observed between higher net worth and lower mortality (HR, 0.94; 95% CI, 0.91-0.97; P = .001), suggesting that the sibling or twin with more wealth tended to live longer than their co-sibling or co-twin with less wealth. When separate estimates were performed for the subsamples of siblings (HR, 0.94; 95% CI, 0.90-0.97; P = .002), dizygotic twins (HR, 0.94; 95% CI, 0.86-1.02; P = .19), and monozygotic twins (HR, 0.95; 95% CI, 0.87-1.04; P = .34), the within-family estimates of the net worth–mortality association were similar, although the precision of estimates was reduced among twins.

    Conclusions and Relevance  This cohort study found that wealth accumulation at midlife was associated with longevity in US adults. Discordant sibling analyses suggested that this association is unlikely to be simply an artifact of early experiences or heritable characteristics shared by families.


    Socioeconomic disparities in life expectancy are substantial in size.1-3 Financial wealth or net worth, which is the value of an individual’s assets (such as savings, real estate, and vehicles) minus liabilities,4,5 is directly associated with longevity4,6-9 and, in some studies, has been found to be more strongly associated with mortality than other indicators of socioeconomic status, such as occupational prestige, educational attainment,7 and income.6 However, a challenge in this area of research has been eliminating or minimizing the potential for confounding by the early environment and heritable traits, either of which could simultaneously affect socioeconomic conditions in adulthood and health in the course of life.10

    Discordant sibling designs allow for the identification and control of such confounders. Full siblings who were raised in the same family share much of their early rearing environment and are genetically related to one another. Thus, in sibling-comparison studies, factors that are shared between siblings are controlled.11,12 Twin comparisons provide an even greater control of family-level early-life confounding and, in the case of monozygotic (MZ) twins, control for all heritable genetic factors.13,14 Previous research found that discordance in occupational prestige was associated with cardiovascular risk15 and overall mortality16; twins with lower-prestige jobs had worse health on both outcomes compared with their co-twins with higher-prestige jobs. This pattern suggests that socioeconomic disparities in health are affected by experiential factors in adulthood over and above any potential confounders that involve the siblings’ shared early environment and genetic characteristics. In other discordant sibling and twin analyses, educational attainment16-22 and composite measures of adult socioeconomic position23 also have been associated with better adult health outcomes18,22,23 and longevity.16,17,19-21 However, results from these and other studies that used different methods do suggest these associations may be partially explained by shared family-level environmental factors17-19,21,22 or genetic predispositions.10,18,23

    Comparatively little attention has been given to wealth disparities, a potentially important oversight because wealth inequality is far greater and growing at a faster rate than income inequality in the United States.24,25 At the individual level, those with greater wealth are better able to access health-promoting resources (eg, medical care, safe places to exercise, and fresh foods). Wealthy individuals also have more protection from economic shocks, such as job loss, unexpected health care expenses, or other financial crises. The association between wealth and longevity has become more relevant in the past year because of the economic fallout from the COVID-19 pandemic, which has disproportionately affected the financial security of low-income26 and older workers.27

    In this cohort study, we used a discordant sibling design to conservatively estimate the association between wealth and longevity. Specifically, we aimed to identify the association between net worth at midlife (the middle years of life) and subsequent all-cause mortality in individuals as well as within siblings and twin pairs. We posed 2 research questions. First, was wealth accumulation at midlife associated with longevity over a nearly 24-year follow-up? Consistent with previous work,4,6-9,28 we expected that higher wealth accumulation would be associated with increased longevity. Second, was the wealth-longevity association present over and above controls for family and heritable factors that could confound this association? That is, was there evidence of a within-family association between wealth and longevity among siblings and twins in the same family? Alternatively, was the wealth-longevity association primarily driven by common factors at the family level, presumably involving early experiences and/or heritable factors?


    The analyses in this cohort study were conducted between November 16, 2019, and May 18, 2021, and were preregistered on the Open Science Framework29 on November 15, 2019. The preregistration included hypotheses, decision rules for inclusion, rationale for covariates, and statistical plan. Deviations from the original analysis plan are detailed in eMethods 3 in the Supplement. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.30 The institutional review boards of the University of Wisconsin-Madison and Harvard Medical School approved the Midlife in the United States (MIDUS) study procedures. Participants in the MIDUS study provided oral informed consent. The institutional review board approval and informed consent for the MIDUS study extend to the present study, which used publicly available MIDUS data, including all mortality data up to 2016.

    Participants and Analysis Sample

    The data used were obtained from the MIDUS31 study, an ongoing national study of health and aging that began in 1994. In the present study, we used data from wave 1 of the MIDUS study (MIDUS 1), which were collected in 1994 to 1996 from 7108 adults aged 20 to 75 years. These participants were recruited through a nationally representative random-digit dialing sampling strategy that included subsamples of siblings and twins. Participants who provided oral informed consent completed a telephone-assisted survey and a mailed self-administered questionnaire.

    Mortality data through October 31, 2018, were collected by the MIDUS study team at the University of Wisconsin-Madison. Of the 7108 cases from MIDUS 1, 7017 were considered for inclusion in the current analysis. We selected cases with complete data on mortality status, net worth, and relevant covariates (n = 5414). These cases included 2675 individuals (designated here as singletons) who did not have a sibling or co-twin in the MIDUS study, 1282 nontwin full siblings, 864 dizygotic (DZ) twins, and 593 MZ twins. Twins or siblings who had complete data and who were in a pair with a co-sibling or co-twin who were missing data were included in the full analytic sample. However, they were excluded from subsequent discordant twin or sibling analyses, resulting in 1214 nontwin full siblings, 740 DZ twins, and 536 MZ twins in the discordant twin or sibling analyses. The eMethods 1 in the Supplement provides details on the eligibility criteria and missing data.


    At MIDUS 1, participants responded to the following question: “Suppose you (and your spouse or partner) cashed in all your checking and savings accounts, stocks and bonds, real estate, sold your home, your vehicles, and all your valuable possessions. Then suppose you put that money toward paying off your mortgage and all your other loans, debts, and credit cards. Would you have any money left over after paying your debts or would you still owe money?” Participants reported how much that amount would be, using binned response categories that specified ranges of dollar amounts. Amount of money owed was truncated at a value of $0. Net worth that exceeded $1 million was truncated at that value.

    Covariates included parental educational level and participant age, self-reported race/ethnicity (analyzed here as non-White vs White), sex (female vs male), history of cancer or heart disease as diagnosed by a medical doctor, and status of ever smoking cigarettes regularly or consuming alcohol regularly (the eMethods 1 in the Supplement provides coding details). At MIDUS 1, participants self-reported on their race. Response options included White, Black and/or African American, Native American or Aleutian Islander, Asian or Pacific Islander, multiracial, or other race. Because of the low number of individuals who identified as a race other than White, we dichotomized race categories into non-White vs White. In addition, data on self-identification as Hispanic or Latino were not available at MIDUS 1.

    Mortality follow-up was completed by the MIDUS study team at the University of Wisconsin-Madison. Date of death was obtained from various sources, including relative responses, other informant reports, newspaper or online obituaries, and the National Death Index (using the 15th day of the month, rather than the exact day of death, to maintain participant confidentiality). Survival time was the number of years between the date when the MIDUS 1 self-administered questionnaires were returned to the MIDUS study team (1994 to 1996) and the date of death; if the participant was alive, the censor date was October 31, 2018, which was the date of the MIDUS study team’s latest mortality update.

    Statistical Analysis

    A series of Cox proportional hazards regression models for survival analyses were estimated using Stata, version 16 (StataCorp LLC).32 In model 1, we tested the individual-level association between wealth and longevity by conducting survival analysis among all cases who had complete data on the analysis variables and by using robust SEs to account for dependence among family members.

    Next, we estimated survival models only within the subsample of twins and nontwin siblings. In model 2, siblings and twins were treated as individuals, but a shared frailty was included to estimate dependence among family members. In model 3, we estimated the within-family association between wealth and longevity by calculating the family-level mean net worth (among families that had ≥2 members with complete data) and subsequently calculating the difference between each individual’s net worth and their family’s mean net worth. This between-within method is a common approach to fixed-effects modeling.33,34 When applied in this way, the between-within method allowed us to compare siblings or twins in the same family to one another and thus to control for all unmeasured shared family-level variables consistent with the discordant sibling design.13,14,35 In survival analysis, the between-within method has been shown to provide similar estimates to more common approaches for co-sibling or co-twin control (eg, conditional likelihood methods like stratified Cox regression) and has been observed to be optimal statistically.34

    To further disambiguate environmental vs genetic influences, we tested whether within-family associations between net worth and longevity varied across the nontwin sibling, MZ twin, and DZ twin subsamples. We included a pair of 2-way interaction terms crossing the net worth mean deviation scores with dummy codes for DZ twins or MZ twins. A Wald test was used to measure the equality of the within-family net worth coefficient across siblings, DZ twins, and MZ twins. We estimated separate survival models for nontwin siblings, DZ twins, and MZ twins (models 4-6). A significant within-family association (2-sided P < .05) that was observed among nontwin siblings but not among DZ or MZ twin pairs would suggest residual confounding by early life factors because twins share a closer prenatal and postnatal environment than nontwin siblings. A within-family association that was observed among both siblings and DZ twin pairs but not among MZ twin pairs would suggest genetic confounding. Sensitivity analyses addressed the skewed distribution of net worth and tested the possibility of nonlinear associations between net worth and longevity. Sensitivity analyses also clarified the role of preexisting health problems and considered other model specifications.

    Primary models were reestimated as stratified Cox regressions using Stata32 (eMethods 2 in the Supplement) and as multilevel Cox regressions using Mplus (Muthén & Muthén) (eTables 3-5 in the Supplement).36 A 2-sided P < .05 was considered statistically significant.


    A total of 5414 participants from MIDUS 1 were included in the full analysis sample. These participants had a mean (SD) age of 46.7 (12.7) years; 2766 were women (51.1%), 2648 were men (48.9%), and 4927 (91.0%) self-identified as White individuals. Participants had a mean (SD) net worth of $122 153 ($209 537; median, $32 500). Of these participants, 675 (12.5%) had been diagnosed with a heart problem and 381 (7%) had a cancer diagnosis. In addition, 2790 participants (51.5%) reported ever having smoked cigarettes regularly and 2301 participants (42.5%) reported having used alcohol regularly. Table 1 displays the descriptive statistics, by subsamples of singletons, nontwin siblings, DZ twins, and MZ twins. By the censor date (October 31, 2018), 1010 individuals (18.7%) in the full sample had died. Fifty percent of siblings and twins were in families whose members differed by less than or equal to $87 500 in net worth at MIDUS 1 (interquartile range=$212 500).

    Cohort Analyses

    Survival models were run as gamma shared frailty Cox regressions, stratified Cox regressions, and multilevel Cox regressions. Regardless of the model used, the estimates were similar (eMethods 2 and 3 in the Supplement). Thus, results from the shared frailty Cox regressions are described herein.

    Results from the full sample (model 1; n = 5414; 1010 deaths) are presented in Table 2. After covariate adjustment, net worth was inversely associated with mortality (HR, 0.95; 95% CI, 0.94-0.97; P < .001) such that the hazard was 5% lower for every additional $50 000 of net worth accumulated at midlife. In the subsample of siblings and twins (model 2; n = 2490; 421 deaths), the net worth HR was similar (HR, 0.95; 95% CI, 0.93-0.97; P < .001).

    Between-Within Models

    Similar to models 1 and 2, the between-within model among siblings and twins (model 3) suggested that net worth was inversely associated with mortality within families (HR, 0.94; 95% CI, 0.91-0.97; P = .001). These patterns suggested that a sibling or twin who had accumulated more net worth by midlife tended to live longer than their co-siblings or co-twin with less net worth. Figure 1 depicts survival curves that were split at values above and below 0.5 of an SD from the mean deviation net worth score (mean of 0.00). In Figure 1, the net worth–mortality association is conditional on the shared frailty, thus the lines represent the estimated survival of 2 family members whose net worth differed by 0.5 SD or $139 000 and who were approximately 47 years of age at MIDUS 1 (with all other variables held at their means). A difference of $139 000 in net worth was associated with a 13% relative decrease in the probability of death nearly 24 years later, favoring the family member with a higher net worth; given the low base rate of mortality in the sample, this decrease translated into more than a 1% absolute difference in survival. The interactions between sibling type (ie, nontwin siblings, DZ twins, and MZ twins) and net worth deviation score were not significant, and the Wald test indicated that differences in the within-family HRs across sibling types were no greater than chance (χ2(2) = 0.05; P = .97).

    Separate survival models were estimated for nontwin siblings (model 4), DZ twins (model 5), and MZ twins (model 6). Consistent with the Wald test, the within-family net worth estimates for each of the 3 subsamples were similar (nontwin siblings: HR, 0.94 [95% CI, 0.90-0.97; P = .002]; DZ twins: HR, 0.94 [95% CI, 0.86-1.02; P = .19]; MZ twins: HR, 0.95 [95% CI, 0.87-1.04; P = .34]). These HRs were also similar in magnitude to the net worth HR observed in the individual-level analyses (models 1 and 2). Among nontwin siblings, the P value for the within-family net worth estimate was significant at P < .05, although the P values were not significant in the DZ twin and MZ twin subsamples, likely because of the substantial decrease in the sample size and the number of deaths within the DZ twin (n = 118 decedents) and MZ twin (n = 79 decedents) subsamples. Figure 2 depicts the HRs and 95% CIs for the net worth estimate in the full sample (model 1) and sibling and twin subsample (model 2), the within-family comparison in the sibling and twin subsample (model 3), and the within-sibling and within-twin comparisons (models 4, 5, and 6).

    Sensitivity Analyses

    The eMethods 2 and eTables 1-5 in the Supplement present the results of the sensitivity analyses conducted in the combined sibling and twin subsample. Briefly, these analyses found that the observed wealth-longevity association (1) was not an artifact of the right-skewed distribution of net worth, (2) had a roughly linear shape for most of the sample, and (3) was not explained by lifestyle practices and health conditions at MIDUS 1. For example, when net worth was recoded into deciles, the within-family association between net worth and mortality remained statistically significant (HR, 0.92; 95% CI, 0.87-0.96; P = .001). In addition, spline models (eTable 1 in the Supplement) suggested that the HR for the net worth–mortality association was similar for approximately 90% of participants in the full sample. Moreover, when restricting the full sample to only the siblings and twin pairs who were free of previous cancer or heart disease, the within-family association between net worth and mortality remained statistically significant (HR, 0.94; 95% CI, 0.90-0.98; P = .01). The eMethods 2 in the Supplement provides a complete description of all sensitivity analyses.


    In contrast to most previous studies that examined associations between socioeconomic position and longevity using between-person analytic approaches, the present cohort study examined within-family associations between wealth and longevity— a more conservative test of the hypothesis because it implicitly controlled for all factors (eg, early experience and heritable characteristics) shared by siblings and twins. Findings from the within-family analyses suggested that wealth accumulation at midlife may be associated with longevity among adults in the United States. We believe that investigations such as the kind we conducted are important because of the near impossibility of performing an experimental study of the wealth-longevity association. Findings from this study also converged with the results from other studies that used different methods. For example, an analysis found that mortality risk among retired stockholders increased in the years after wealth shocks (ie, acute reductions in wealth) owing to exogenous stock market fluctuations.28

    Across models, the associations observed between net worth and longevity were modest: we observed a 1% absolute difference in the probability of survival after nearly 24 years between family members who differed by approximately $139 000 in net worth at midlife. However, the MIDUS sample was relatively young (mean age of approximately 70 years) with relatively low mortality (18.7% of the full sample had died by the censor date). Thus, follow-up analyses are needed to ascertain whether the magnitude of these associations would change as mortality increases.

    The associations between net worth and longevity in the DZ and MZ twin pairs were similar in magnitude to the associations observed in nontwin siblings. However, when the analyses were separated into subsamples of siblings, DZ twins, and MZ twins, the within-family net worth associations among DZ twins and MZ twins were not statistically significant. This finding was likely the result of lower power and reduced precision in the twin subsamples, as reflected in the wide CIs around the within-family estimates. However, it could also suggest the presence of confounding by shared environmental features. Other studies have discussed the difficulty in distinguishing null associations from false-negative results in sibling study designs when power varies widely between subsamples.37

    The findings of this study suggest that the association between wealth at midlife and longevity is unlikely to be a simple artifact of environmental and heritable characteristics shared by siblings and twins. Moreover, the findings suggest that policies to support individuals’ ability to accrue wealth and to achieve financial security in adulthood could have considerable health benefits. These findings should be interpreted through a broader societal lens. The US ranks first in economic inequality among high-income nations.38 Over the past 30 years, the wealth gap between the high-income and low-income people in the US has widened through policies and practices that have diverted a substantial and increasing share of wealth from the lower- and middle-income groups to the affluent group.24,38 Such redistribution may have implications for longevity patterns in the coming decades. Policies to reduce the wealth gap, if implemented, could be expected to generate substantial returns to public health.


    This study has several limitations. First, although the discordant sibling design, as we have modeled it, reduced confounding by shared environmental and heritable characteristics, it cannot elucidate whether wealth itself is a causal actor or a marker of other nonshared factors (eg, self-regulatory capacity and cognitive ability) and nonshared experiences (eg, social, educational, and professional trajectories) that covary with and/or contribute to both wealth and health. Thus, a causal interpretation of these findings is not warranted. We did consider some possible nonshared confounders in adulthood that were related to lifestyle and disease, however. Sensitivity analyses restricted to participants in good midlife health suggested that these variables had a modest role in the wealth-longevity association.

    Second, at MIDUS 1, a single self-reported questionnaire item was used to measure net worth, which may have introduced error into estimates of net worth. In addition, most participants in the MIDUS 1 sample self-identified as White individuals; thus, the estimates we reported may be less generalizable to underrepresented racial/ethnic groups. This limitation is particularly relevant for present purposes given the substantial and persistent racial disparities in household wealth in the US.39

    Third, although we observed evidence of within-family associations between wealth and longevity, the mechanisms involved were not identified. Wealth is likely to be a distal factor in longevity, acting through more proximal mechanisms (eg, stress and lifestyle) to affect biological processes involved in disease as well as access to medical care and other health-promoting resources. This interpretation is consistent with the way other studies have conceptualized socioeconomic status as a fundamental cause of health disparities.40,41 From this perspective, having wealth allows individuals numerous opportunities to invest resources into the many proximal factors that promote health and longevity.


    This cohort study found an association between wealth at midlife and longevity, and this association is unlikely to be merely an artifact of environmental and heritable characteristics shared by families. Policies that support individuals' ability to accrue wealth and achieve financial security could have considerable health benefits. In addition, policies to reduce the wealth gap may generate substantial returns to public health.

    Back to top
    Article Information

    Accepted for Publication: May 24, 2021.

    Published: July 23, 2021. doi:10.1001/jamahealthforum.2021.1652

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Finegood ED et al. JAMA Health Forum.

    Corresponding Author: Eric D. Finegood, PhD, Institute for Policy Research, Northwestern University, 1801 Maple Ave, Ste 2450, Evanston, IL 60201 (eric.finegood@northwestern.edu).

    Author Contributions: Dr Finegood had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Finegood, Turiano, Krueger, Mroczek, Miller.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Finegood, Turiano, Mroczek, Miller.

    Critical revision of the manuscript for important intellectual content: Finegood, Briley, Turiano, Freedman, South, Krueger, Chen, Mroczek.

    Statistical analysis: Finegood, Briley, Turiano, Freedman, Mroczek.

    Obtained funding: Krueger, Mroczek.

    Administrative, technical, or material support: Miller.

    Supervision: Chen, Mroczek, Miller.

    Conflict of Interest Disclosures: Dr Krueger reported receiving grants from the National Institute on Aging (NIA) during the conduct of the study and outside the submitted work. Dr Miller reported receiving grants from the National Institutes of Health (NIH) outside the submitted work. No other disclosures were reported.

    Funding/Support: Dr Finegood was supported by grant F32HL146005 from the National Heart, Lung, and Blood Institute of the NIH. Dr Briley was supported by a Jacobs Foundation Research Fellowship. Dr Turiano was supported by the West Virginia Prevention Research Center and Cooperative Agreement No. 1-U48-DP-005004 from the Centers for Disease Control and Prevention. Dr Freedman was supported by grant F32HD100076 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the NIH. Dr Krueger was supported by grants R01AG053217 and U19AG051426 from the NIH. Dr Mroczek was supported by grants R01 AG018436, R01 AG067622, RF1 AG064006, and U19AG051426 from the NIA and grant P30 AG05998 from the Claude D. Pepper Older Adults Independence Center. Dr Miller was supported by grant R01 MD011749 from the National Institute on Minority Health and Health Disparities and grant P50 DA051361 from the National Institute on Drug Abuse.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: The content is solely the responsibility of the authors and does not represent the official views of the funders.

    Additional Information: The statistical code used in this study is available upon request to the first author. MIDUS 1 data are publicly available at the Inter-university Consortium for Political and Social Research (https://www.icpsr.umich.edu/web/ICPSR/series/203). As of this writing, the 2018 mortality update data are not publicly available.

    Marmot  M.  Social determinants of health inequalities.   Lancet. 2005;365(9464):1099-1104. doi:10.1016/S0140-6736(05)71146-6 PubMedGoogle ScholarCrossref
    Adler  NE, Rehkopf  DHUS.  U.S. disparities in health: descriptions, causes, and mechanisms.   Annu Rev Public Health. 2008;29:235-252. doi:10.1146/annurev.publhealth.29.020907.090852 PubMedGoogle ScholarCrossref
    Chetty  R, Stepner  M, Abraham  S,  et al.  The association between income and life expectancy in the United States, 2001-2014.   JAMA. 2016;315(16):1750-1766. doi:10.1001/jama.2016.4226 PubMedGoogle ScholarCrossref
    Pollack  CE, Chideya  S, Cubbin  C, Williams  B, Dekker  M, Braveman  P.  Should health studies measure wealth? A systematic review.   Am J Prev Med. 2007;33(3):250-264. doi:10.1016/j.amepre.2007.04.033 PubMedGoogle ScholarCrossref
    Wolfe  JD, Baker  EH, Scarinci  IC.  Wealth and obesity among US adults entering midlife.   Obesity (Silver Spring). 2019;27(12):2067-2075. doi:10.1002/oby.22625 PubMedGoogle ScholarCrossref
    Demakakos  P, Biddulph  JP, Bobak  M, Marmot  MG.  Wealth and mortality at older ages: a prospective cohort study.   J Epidemiol Community Health. 2016;70(4):346-353. doi:10.1136/jech-2015-206173 PubMedGoogle ScholarCrossref
    Daly  MC, Duncan  GJ, McDonough  P, Williams  DR.  Optimal indicators of socioeconomic status for health research.   Am J Public Health. 2002;92(7):1151-1157. doi:10.2105/AJPH.92.7.1151 PubMedGoogle Scholar
    Hajat  A, Kaufman  JS, Rose  KM, Siddiqi  A, Thomas  JC.  Long-term effects of wealth on mortality and self-rated health status.   Am J Epidemiol. 2011;173(2):192-200. doi:10.1093/aje/kwq348 PubMedGoogle ScholarCrossref
    Pool  LR, Burgard  SA, Needham  BL, Elliott  MR, Langa  KM, Mendes de Leon  CF.  Association of a negative wealth shock with all-cause mortality in middle-aged and older adults in the United States.   JAMA. 2018;319(13):1341-1350. doi:10.1001/jama.2018.2055 PubMedGoogle ScholarCrossref
    Hill  WD, Davies  NM, Ritchie  SJ,  et al.  Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income.   Nat Commun. 2019;10(1):5741. doi:10.1038/s41467-019-13585-5 PubMedGoogle ScholarCrossref
    Donovan  SJ, Susser  E.  Commentary: advent of sibling designs.   Int J Epidemiol. 2011;40(2):345-349. doi:10.1093/ije/dyr057 PubMedGoogle ScholarCrossref
    Lawlor  DA, Mishra  GD, eds.  Family Matters: Designing, Analysing and Understanding Family Based Studies in Life Course Epidemiology. Oxford University Press; 2009. doi:10.1093/acprof:oso/9780199231034.001.0001
    Carlin  JB, Gurrin  LC, Sterne  JAC, Morley  R, Dwyer  T.  Regression models for twin studies: a critical review.   Int J Epidemiol. 2005;34(5):1089-1099. doi:10.1093/ije/dyi153 PubMedGoogle ScholarCrossref
    Turkheimer  E, Harden  KP. Behavior genetic research methods: testing quasi-causal hypotheses using multivariate twin data. In: Reis  HT, Judd  CM, eds.  Handbook of Research Methods in Social and Personality Psychology. 2nd ed. Cambridge University Press; 2014:159-187.
    Krieger  N, Chen  JT, Coull  BA, Selby  JV.  Lifetime socioeconomic position and twins’ health: an analysis of 308 pairs of United States women twins.   PLoS Med. 2005;2(7):e162. doi:10.1371/journal.pmed.0020162 PubMedGoogle Scholar
    Ericsson  M, Pedersen  NL, Johansson  ALV, Fors  S, Dahl Aslan  AK.  Life-course socioeconomic differences and social mobility in preventable and non-preventable mortality: a study of Swedish twins.   Int J Epidemiol. 2019;48(5):1701-1709. doi:10.1093/ije/dyz042 PubMedGoogle ScholarCrossref
    Søndergaard  G, Mortensen  LH, Nybo Andersen  AM,  et al.  Does shared family background influence the impact of educational differences on early mortality?   Am J Epidemiol. 2012;176(8):675-683. doi:10.1093/aje/kws230 PubMedGoogle ScholarCrossref
    Fujiwara  T, Kawachi  I.  Is education causally related to better health? a twin fixed-effect study in the USA.   Int J Epidemiol. 2009;38(5):1310-1322. doi:10.1093/ije/dyp226 PubMedGoogle ScholarCrossref
    Næss  O, Hoff  DA, Lawlor  D, Mortensen  LH.  Education and adult cause-specific mortality—examining the impact of family factors shared by 871 367 Norwegian siblings.   Int J Epidemiol. 2012;41(6):1683-1691. doi:10.1093/ije/dys143 PubMedGoogle ScholarCrossref
    Lundborg  P, Lyttkens  CH, Nystedt  P.  The effect of schooling on mortality: new evidence from 50,000 Swedish twins.   Demography. 2016;53(4):1135-1168. doi:10.1007/s13524-016-0489-3 PubMedGoogle ScholarCrossref
    Madsen  M, Andersen  AM, Christensen  K, Andersen  PK, Osler  M.  Does educational status impact adult mortality in Denmark? A twin approach.   Am J Epidemiol. 2010;172(2):225-234. doi:10.1093/aje/kwq072 PubMedGoogle ScholarCrossref
    Madsen  M, Andersen  PK, Gerster  M, Andersen  AM, Christensen  K, Osler  M.  Are the educational differences in incidence of cardiovascular disease explained by underlying familial factors? A twin study.   Soc Sci Med. 2014;118:182-190. doi:10.1016/j.socscimed.2014.04.016 PubMedGoogle ScholarCrossref
    Osler  M, McGue  M, Christensen  K.  Socioeconomic position and twins’ health: a life-course analysis of 1266 pairs of middle-aged Danish twins.   Int J Epidemiol. 2007;36(1):77-83. doi:10.1093/ije/dyl266 PubMedGoogle ScholarCrossref
    World Inequality Lab. World Inequality Report 2018. Accessed July 27, 2020. https://wir2018.wid.world
    Menasce Horowitz J, Igielnik R, Kochhar R. Most Americans say there is too much economic inequality in the U.S., but fewer than half call it a top priority. January 9, 2020. Accessed October 14, 2020. https://www.pewsocialtrends.org/2020/01/09/most-americans-say-there-is-too-much-economic-inequality-in-the-u-s-but-fewer-than-half-call-it-a-top-priority/
    Powell  JH. Recent Economic Developments and the Challenges Ahead: Speech at the National Association for Business Economics Virtual Annual Meeting. October 6, 2020. Accessed December 28, 2020. https://www.federalreserve.gov/newsevents/speech/powell20201006a.htm
    Cook  FL, Weiss  E. COVID-19 is hitting older workers like a sledgehammer—here’s what Congress can do. The Hill. May 1, 2020. Accessed June 9, 2020. https://thehill.com/opinion/finance/495541-covid-19-is-hitting-older-workers-like-a-sledge-hammer-heres-what-congress
    Schwandt  H.  Wealth shocks and health outcomes: evidence from stock market fluctuations.   Am Econ J Appl Econ. 2018;10(4):349-377. doi:10.1257/app.20140499 Google ScholarCrossref
    Parsing genetic and environmental influences on longevity: A twin and sibling study in MIDUS. Open Science Framework. Registered November 15, 2019. Accessed June 17, 2021. https://osf.io/zyedp
    von Elm  E, Altman  DG, Egger  M, Pocock  SJ, Gøtzsche  PC, Vandenbroucke  JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   PLoS Med. 2007;4(10):e296. doi:10.1371/journal.pmed.0040296 PubMedGoogle Scholar
    Brim  OG, Ryff  CD, Kessler  RC eds.  How Healthy Are We? A National Study of Well-Being at Midlife. University of Chicago Press; 2004.
    StataCorp.  Stata Statistical Software: Release 16. StataCorp LLC; 2019.
    Allison  PD.  Fixed Effects Regression Models. Sage Publications, Inc; 2009. doi:10.4135/9781412993869
    Sjölander  A, Lichtenstein  P, Larsson  H, Pawitan  Y.  Between-within models for survival analysis.   Stat Med. 2013;32(18):3067-3076. doi:10.1002/sim.5767 PubMedGoogle ScholarCrossref
    McGue  M, Osler  M, Christensen  K.  Causal inference and observational research: the utility of twins.   Perspect Psychol Sci. 2010;5(5):546-556. doi:10.1177/1745691610383511 PubMedGoogle ScholarCrossref
    Muthén  LK, Muthén  BO. Mplus User’s Guide. 8 ed. Muthén & Muthén; 1998-2017.
    Gilman  SE, Loucks  EB.  Another casualty of sibling fixed-effects analysis of education and health: an informative null, or null information?   Soc Sci Med. 2014;118:191-193. doi:10.1016/j.socscimed.2014.06.029 PubMedGoogle ScholarCrossref
    Stiglitz  JE.  The Price of Inequality: How Today’s Divided Society Endangers Our Future. W W Norton & Co; 2012.
    Sykes  BL, Maroto  M.  A wealth of inequalities: mass incarceration, employment, and racial disparities in U.S. household wealth, 1996 to 2011.   Russell Sage Found J Soc Sci. 2016;2(6):129-152. doi:10.7758/rsf.2016.2.6.07Google Scholar
    Phelan  JC, Link  BG, Diez-Roux  A, Kawachi  I, Levin  B.  “Fundamental causes” of social inequalities in mortality: a test of the theory.   J Health Soc Behav. 2004;45(3):265-285. doi:10.1177/002214650404500303 PubMedGoogle ScholarCrossref
    Link  BG, Phelan  J.  Social conditions as fundamental causes of disease.   J Health Soc Behav. 1995;Spec No:80-94. doi:10.2307/2626958 PubMedGoogle Scholar