The Best Measure of a Person's or Family's Wealth Is

Am J Public Health. 2011 May; 101(5): 939–947.

Assessing Alternative Measures of Wealth in Health Inquiry

Catherine Cubbin, PhD, Craig Pollack, Dr., MPH, Brian Flaherty, PhD, Marking Hayward, PhD, Ayesha Sania, MPH, Donna Vallone, PhD, and Paula Braveman, MD, MPH

Abstract

Objectives. We assessed whether it would be feasible to replace the standard measure of cyberspace worth with simpler measures of wealth in population-based studies examining associations between wealth and health.

Methods. We used information from the 2004 Survey of Consumer Finances (respondents aged 25–64 years) and the 2004 Wellness and Retirement Survey (respondents anile 50 years or older) to construct logistic regression models relating wealth to health status and smoking. For our wealth measure, nosotros used the standard measure of cyberspace worth equally well as nine simpler measures of wealth, and we compared results amid the 10 models.

Results. In both data sets and for both health indicators, models using simpler wealth measures generated conclusions about the clan between wealth and health that were like to the conclusions generated past models using net worth. The magnitude and significance of the odds ratios were similar for the covariates in multivariate models, and the model-fit statistics for models using these simpler measures were similar to those for models using cyberspace worth.

Conclusions. Our findings suggest that simpler measures of wealth may exist acceptable in population-based studies of health.

In health research, the term "wealth" refers to total financial resources clustered over a lifetime, every bit opposed to "income," which refers to the capital letter obtained during a specified period of time (e.g., annual earnings in dollars).¹ Wealth may buffer the effects of temporary depression income, equally in the result of illness or unemployment. Compared with income, wealth may better reflect long-term family resources and hence resources bachelor across an individual's lifetime.ⁱⁱ Wealth may be particularly of import for the health of the elderly, whose incomes typically drop dramatically following retirement,³ and for racial/ethnic disparities in health, considering differences in wealth by racial/indigenous group are far greater than are the corresponding differences in income.²

A systematic review of the literature has found that greater wealth is associated with amend health, even later adjustment for other socioeconomic factors, such as income and educational attainment.^1,four–11 Moreover, these findings of positive correlations between wealth and health were about consistent when studies used detailed wealth measures based on multiple questions on assets (e.g., savings, dwelling house, retirement) and debts (e.g., mortgage, loans) instead of single questions on wealth (e.g., home ownership). The review also plant that racial/ethnic disparities in wellness generally decreased afterwards adjustment for wealth.

Despite the conceptual and empirical rationales for including wealth in research on health, population-based health surveys (such as the National Health Interview Survey) and vital statistics data generally take either poor measures of wealth or none at all. This deficiency is not surprising, given the difficulty of collecting wealth data. The topic of wealth is considered to exist sensitive, drove of reliable data is laborious, and the values of assets and debts vary over fourth dimension and may require professional appraisement. Conversely, population-based surveys with detailed wealth measures typically contain little information on health. Thus, current population-based data sources present significant barriers to studies of the relationships between wealth and health.

Standard wealth measures are based on multiple detailed questions; for instance, the Survey of Consumer Finances (SCF) assesses 25 different classes of assets and debts that measure net worth. Simpler approaches to wealth measurement could do good population-based health research by making it more viable to include such measures in important public health surveys. Building on earlier piece of work on the measurement of socioeconomic status and position,^one,2,12,13 nosotros assessed whether simplified measures of wealth could be used in health research to reasonably approximate standard wealth measures. We used population-based data from the SCF and the Health and Retirement Survey (HRS) to appraise (1) correlations betwixt 9 simpler measures of wealth and the standard wealth measure out cyberspace worth, which requires multiple detailed questions on avails and debts; and (2) the results of models relating wealth to self-reported health status and cigarette smoking, with comparisons betwixt models that measured wealth as net worth and models using simpler measures of wealth.

METHODS

We used the 2004 SCF and the 2004 HRS in our analyses because these surveys provide detailed measures of net worth every bit well as indicators of health. The SCF, sponsored past the US Federal Reserve Board, is intended to provide a detailed picture of the finances of noninstitutionalized families in the Us. A multistage area-probability sample is surveyed forth with a supplemental sample of primarily wealthy families. In 2004, the response charge per unit for the area-probability sample was seventy% (n = 3007), and the response rate for the supplemental sample was thirty% (due north = 1515).

The HRS is a nationally representative information set with an overall response rate of 86% in 2004 (n = 20 129). The HRS, sponsored by the National Institute on Aging and the Social Security Administration, is intended to provide a detailed motion-picture show of health, financial, and other characteristics of the aging population in the Us. For both data sets, imputation techniques were used for missing information, and survey weights were used to adjust for sampling probabilities. SCF researchers imputed all missing data by means of a multiple imputation procedure yielding 5 values for each missing value, to guess the distribution of the missing data. For missing data in the HRS, nosotros relied on the RAND HRS imputations, which first imputed ownership of a detail nugget or debt (using logistic regression models), so brackets (using ordered logit models), then verbal amounts (using either a nearest neighbor approach or a Tobit approach; http://world wide web.rand.org/labor/aging/dataprod.html#randhrs). Further details about sample design and methodology are available for the SCF (http://www.federalreserve.gov/pubs/oss/oss2/method.html) and the HRS (http://hrsonline.isr.umich.edu/index.php?p=sdesign).

Head-of-household respondents aged 25 to 64 years (SCF) or 50 years or older (HRS) who identified every bit (one) Blackness, non-Hispanic; (2) Hispanic; or (3) White, non-Hispanic were included in the analytic samples (n = 3310 for the SCF and n = xi 847 for the HRS). For households with a couple, the SCF defined the caput of household as the male in mixed-gender households or the older individual in same-gender households. The HRS analytic sample did not include persons residing in nursing homes or surveys without a fiscal respondent—the person designated to respond household-level financial questions.

Definitions for each asset and debt included in each survey are listed in Appendix A (available as a supplement to the online version of this commodity at http://www.ajph.org). The SCF contains more avails and debts in split categories than the HRS does, only both surveys are comprehensive. For example, the SCF asks nigh checking accounts, savings accounts, and coin market place accounts separately, but those 3 categories are combined in the HRS. Typically, researchers measure wealth every bit internet worth; following this approach, we calculated net worth by adding the dollar value of all assets minus the value of all debts. We as well calculated 9 simplified measures of net worth. The first 5 of these are based on dollar values, equally follows:

Assets: the condiment value of all assets;
Prevalent assets/debts: items that were reported to be owned by a large percentage of the full sample (at least 25%). The SCF counted checking account, savings account, retirement funds, vehicles, and primary residence as assets, and counted mortgage, credit card residue, and installment loans every bit debts. The HRS asked about a similar list: checking, savings, and coin market account; common funds and stocks; retirement funds; vehicles; and primary residence every bit avails and mortgage and other debt/credit bill of fare balance as debts. Calculation the value of assets minus the value of debts for this subset is the "prevalent avails/debts" measure of wealth;
Highest-proportion avails/debts: Although a large proportion of individuals may own a particular asset/debt, its value may be high or low with respect to a person'southward overall internet worth. We therefore defined highest-proportion items as the avails that, on average, accounted for more than 10% of an individual'southward overall assets, and the debts that accounted for more than 10% of an individual's debt. In the SCF, these assets were vehicles, retirement funds, and principal residence, and the debts were mortgage, credit card residuum, and installment loans. For the HRS, the highest-proportion assets were checking, savings, and coin market accounts; vehicles; and primary residence, and the highest-proportion debts were mortgage and other debt/credit card balance. Adding the value of avails minus the value of debts for this subset is the "highest-proportion assets/debts" measure of wealth;
Prevalent assets: the value of the about prevalent assets;
Highest-proportion assets: the value of the highest-proportion assets;

Respondents may exist more willing to bespeak ownership of an nugget or debt rather than the bodily dollar amounts, which may fluctuate over time. We therefore created an additional 4 indices in parallel with four of the dollar-based measures. If a respondent owned an asset, information technology was scored +1; if a respondent owned a debt, it was scored −ane; if a respondent did not own an asset/debt, information technology was scored 0.
Prevalent assets/debts index: the sum of these items (range −three to 5 in the SCF; range −2 to 5 in the HRS);
Highest-proportion assets/debts alphabetize: the sum of these items (range −3 to three in the SCF; range −ii to 3 in the HRS);
Prevalent assets index: an index of the most prevalent assets (range 0–v);
Highest-proportion assets index: a calibration of the highest-proportion assets (range 0–3).

Dependent Variables and Covariates

Nosotros examined 2 dependent variables in the analyses: self-reported health, which corresponds closely with objective clinical assessments of an individual's overall health¹⁴; and whether the respondent is a current smoker, given the prominent continuing of smoking as a crusade of disease and mortality. Health status was measured on a 4-point (SCF) or 5-point (HRS) Likert calibration, dichotomized equally fair or poor health versus improve health. Current smoking was measured similarly in the 2 survey instruments, with the questions "Practise you currently smoke?" (SCF) and "Do you fume cigarettes now?" (HRS), with "yep" and "no" as responses.

We included age, gender, race/ethnicity (not-Hispanic Black, Hispanic, or not-Hispanic White), marital status (married or partnered, previously married, or never married), and family size as covariates in the analyses. In the HRS, census region was as well included to business relationship for regional variations in cost of living; geographic identifiers were not available in the public-employ SCF information set. Educational attainment was classified into 4 categories: less than high schoolhouse, high school graduate or general equivalency diploma, some college, or college graduate and above. Annual household income from all sources was determined on a pretax basis and was log-transformed.

Analysis

Nosotros calculated descriptive statistics, including sociodemographic characteristics of the samples and the prevalence and median values of avails and debts. Nosotros and then examined correlations between internet worth and each of the simplified wealth measures. Nosotros estimated a series of logistic regression models for each dependent variable and wealth measure. For these models, each wealth measure was categorized into quartiles (for measures based on dollar values) or 4 roughly equal groups (for measures based on summary indices). The base model included simply a single wealth measure. Next, the demographic model added age, historic period squared, gender, race/ethnicity, marital status, family unit size, and region (HRS merely). The full model added instruction and income to the demographic model. To compare model fit beyond the full models, each with a different wealth mensurate, we examined the pct differences in the Somers' D, Akaike information criterion (AIC), and Bayesian data criterion (BIC) statistics, comparison the net-worth model to those with a simplified wealth measure.

RESULTS

Tabular array 1 presents weighted prevalences and median values for each nugget and debt. In both information sets, the large majority of respondents owned a checking account (in the HRS, this measure was combined with buying of a savings or money market account), a vehicle, and a master residence. More respondents had retirement funds in the younger SCF population than in the older HRS population. Beyond all assets, the highest value was contained in the respondents' primary residence ($165 000 in the SCF, $150 000 in the HRS). Mortgage on a primary residence was the most common and largest debt for both data sets, followed by other loans and credit card balances in the SCF sample and other debt or credit carte balances in the HRS sample. Debt accumulation was higher in the younger population (SCF) than in the older population (HRS).

Table 1

Prevalences and Median Values of Assets and Debts: Survey of Consumer Finances and Health and Retirement Survey, United States, 2004

Survey of Consumer Finances			Health and Retirement Survey
	Prevalence, %	Median Value, $^a		Prevalence, %	Median Value, $^a
Assets
Checking account^b	83	1500	Checking/savings/coin market accounts^b ^c	87	7500
Savings business relationship^b	49	3000	Certificates of deposit/savings bonds	20	16 000
Money market place account	21	7000	Mutual funds/stocks^b	31	60 000
Telephone call account	two	thirteen 000	Bonds	7	39 000
Certificates of deposit/	ten	12 000	Retirement funds^b	39	49 000
Savings bonds	20	800	Vehicles^b ^c	84	10 000
Common funds	15	35 000	Master residence^b ^c	78	150 000
Stocks	21	12 000	Other residential real estate	13	seventy 000
Bonds	ii	30 000	Nonresidential existent manor	15	81 000
Retirement funds^b ^c	56	35 000	Concern	eleven	100 000
Life insurance	23	6000	Other nonfinancial avails/other savings	17	20 000
Other managed accounts	6	36 000
Other financial assets	eleven	3800
Vehicles^b ^c	88	16 000
Primary residence^b ^c	69	165 000
Other residential real estate	thirteen	94 500
Nonresidential real estate	8	55 000
Business concern	fourteen	102 000
Other nonfinancial assets	8	15 000
Debts
Mortgage, principal residence^b ^c	57	99 000	Mortgage, main residence^b ^c	33	73 000
Other residential property debt	5	87 000	Mortgage, secondary residence	three	59 000
Other line of credit	2	3000	Other residential belongings debt	11	22 000
Credit card rest^b ^c	52	2400	Other debt/credit menu rest^b ^c	31	5000
Other debt	9	4000
Other installment loans^b ^c	53	12 000

Table 2 presents descriptive characteristics of the sample populations, including median values for the various wealth measures. The low proportion of women in the SCF reflects the fact that the household respondent is specified as the man in a household that includes both a adult female and a man. Although the overall median value of avails was only somewhat college in the HRS, median debt amongst the HRS respondents was much lower, resulting in the net worth among the older HRS population being almost 2 times higher than in the SCF sample. Contributing to this pattern, homeownership was higher in the HRS sample than in the SCF sample.

TABLE 2

Demographic, Socioeconomic, Wealth, and Health Characteristics of Samples: Survey of Consumer Finances and Health and Retirement Survey, United States, 2004

	SCF (n = 3310)	HRS (northward = xi 847)
Age, y, %
25–49	66	N/A
50–64	34	54
65–74	Due north/A	23
75+	N/A	23
Gender, %
Women	25	47
Men	75	53
Race/ethnicity, %
Black, non-Hispanic	xv	11
Hispanic	11	7
White, non-Hispanic	74	82
Marital status, %
Never married	19	6
Separated/divorced/widowed	27	43
Married/living as married	54	51
Family size, median (range)	2 (1–10)	2 (i–15)
Region, %
Northeast	North/A	18
Midwest	N/A	26
South	Northward/A	37
W	N/A	xix
Educational attainment, %
< High schoolhouse	11	17
Loftier schoolhouse graduate/GED	30	35
Some college	19	24
College graduate	40	24
Almanac income, $1000, median (range)	50 (0–105 070)	36 (0–3532)
Wealth, $thousand, median (range)
Assets	173 (0–744 677)	201 (0–77 225)
Debts	47 (0–43 117)	1 (0–2400)
Net worth	86 (–455–714 677)	158 (–2246–77 225)
Prevalent assets/debts^a	65 (–15 294–75 832)	130 (–2280–77 175)
Highest-proportion assets/debts^b	58 (–59 298–7563)	100 (–2388–12 185)
Prevalent assets^c	149 (0–82 142)	169 (0–77 175)
Highest-proportion avails^d	143 (0–82 028)	133 (0–12 185)
Prevalent assets/debts index^e	two (–ii–5)	3 (–1–v)
Highest-proportion avails/debts index^f	0 (–ii–3)	2 (–1–iii)
Prevalent assets index^g	iv (0–five)	three (0–5)
Highest-proportion assets index^h	two (0–three)	3 (0–3)
Wellness, %
Off-white/poor health status	21	27
Current smoker	26	17

Correlations between net worth and the other ix wealth measures were more often than not moderate (non shown, bachelor upon request), ranging from 0.43 to 0.67 in the SCF, except for the correlation with total assets (near 1.00), and ranging from 0.47 to 0.73 in the HRS, except for the correlations with prevalent assets/debts (0.93), assets (nearly ane.00), and prevalent avails (0.93). The very loftier correlations between the avails-only measures and net worth suggest that it may non be necessary to include debts in a measure of wealth.

Table three presents the results of the logistic regression models for the relationships of cyberspace worth with off-white/poor health status and electric current smoker. In the full SCF model, respondents in the lowest quartile of wealth had odds of fair/poor health that were nearly v times (odds ratio [OR] = 4.98) higher than those for respondents in the highest quartile, and a clear gradient was observed. Differences between Hispanics and not-Hispanic Whites in the likelihood of having off-white/poor health were no longer meaning in the full model. Differences between non-Hispanic Blacks and non-Hispanic Whites in the likelihood of having fair/poor health were not significantly different in either model. Lower educational activity and income were significant predictors of having off-white/poor health in the full model. We establish similar results for the relationship betwixt net worth and off-white/poor health for the HRS information, although the magnitude of the association was smaller than that constitute for the SCF information. Racial/indigenous disparities persisted in the full HRS model, with non-Hispanic Blacks and Hispanics having higher odds of off-white/poor health status than did not-Hispanic Whites.

TABLE 3

Crude and Adjusted Associations of Net Worth With Fair/Poor Wellness Condition and Electric current Smoking: Survey of Consumer Finances and Health and Retirement Survey, United States, 2004

	Rough Model, OR (95% CI)		Demographic Model, OR (95% CI)		Full Model, OR (95% CI)
	SCF	HRS	SCF OR (95% CI)	HRS OR (95% CI)	SCF OR (95% CI)	HRS OR (95% CI)
Fair/poor wellness
Age			0.95 (0.88, 1.03)	ane.08** (1.03, one.14)	0.94 (0.87, 1.02)	1.04 (0.99, one.10)
Age squared^a			1.00** (1.00, 1.00)	1.00* (1.00, 1.00)	1.00 (1.00, ane.00)	i.00 (1.00, 1.00)
Men			1.12 (0.85, 1.46)	0.97 (0.87, i.09)	one.11 (0.85, i.47)	0.94 (0.84, i.05)
Blackness, non-Hispanic			0.95 (0.72, ane.26)	1.33*** (1.15, one.54)	0.85 (0.64, i.15)	1.16* (1.00, ane.35)
Hispanic			1.90*** (1.41, ii.57)	2.thirty*** (1.93, two.75)	ane.37 (0.99, ane.88)	ane.59** (1.32, 1.92)
Previously married			i.37* (1.02, 1.84)	1.xx** (one.06, 1.37)	i.13 (0.84, 1.54)	0.99 (0.86, 1.14)
Never married			1.35 (0.97, i.88)	ane.08 (0.84, 1.38)	1.23 (0.88, one.73)	0.87 (0.68, i.12)
Family unit size			ane.00 (0.92, 1.08)	1.08** (one.03, 1.14)	0.98 (0.90, 1.06)	i.02 (0.97, 1.07)
Northeast			Northward/A	1.03 (0.88, one.xx)	Northward/A	0.99 (0.85, ane.17)
Midwest			N/A	1.twenty* (1.04, i.39)	N/A	one.16 (0.99, 1.34)
South			N/A	1.08 (0.91, 1.28)	N/A	one.13 (0.95, ane.35)
< High schoolhouse					ii.91*** (two.04, 4.15)	two.96*** (2.46, 3.58)
Loftier school/GED					2.06*** (ane.58, 2.69)	1.67*** (1.41, one.98)
Some college					ane.53** (1.13, two.07)	i.52*** (1.27, 1.81)
Income (log)					0.eighty*** (0.74, 0.87)	0.82*** (0.77, 0.86)
Net worth
Quartile 1 (lowest)	vii.53*** (5.83, 9.74)	6.12*** (5.thirty, 7.08)	12.27*** (8.74, 17.22)	5.21*** (4.43, half-dozen.13)	iv.98*** (3.42, 7.24)	iii.13*** (2.62, 3.73)
Quartile 2	4.xix*** (iii.19, 5.51)	two.93*** (2.53, 3.39)	six.38*** (4.63, 8.80)	2.62*** (two.25, 3.06)	three.23*** (ii.29, 4.57)	1.85*** (1.58, 2.17)
Quartile 3	2.29*** (1.68, iii.eleven)	1.54*** (1.32, 1.80)	2.88*** (2.08, 4.00)	1.46*** (1.25, 1.71)	one.64** (ane.16, 2.31)	1.20* (one.02, 1.41)
Current smoker
Age			1.09* (1.01, 1.17)	1.28*** (1.17, 1.twoscore)	1.08* (1.01, 1.16)	ane.24*** (ane.13, one.34)
Age squared^a			1.00* (i.00, one.00)	1.00*** (1.00, i.00)	i.00* (1.00, 1.00)	one.00*** (1.00, one.00)
Men			1.79*** (one.39, 2.xxx)	0.81** (0.71, 0.93)	1.72*** (1.33, 2.23)	0.77*** (0.67, 0.88)
Black, non-Hispanic			0.71* (0.54, 0.93)	0.84* (0.70, 1.00)	0.65** (0.49, 0.85)	0.77** (0.64, 0.92)
Hispanic			0.53*** (0.39, 0.73)	0.61*** (0.48, 0.79)	0.39*** (0.28, 0.54)	0.48*** (0.37, 0.62)
Previously married			1.85*** (i.42, ii.42)	1.75*** (1.49, 2.05)	1.63*** (i.25, 2.15)	ane.65*** (1.twoscore, 1.93)
Never married			1.seventy*** (1.26, 2.28)	1.08 (0.79, ane.47)	i.68*** (one.25, 2.27)	i.04 (0.76, i.43)
Family size			0.98 (0.91, one.06)	1.01 (0.95, i.07)	0.95 (0.88, one.03)	1.00 (0.95, i.06)
Northeast			N/A	0.93 (0.76, 1.12)	N/A	0.90 (0.74, 1.10)
Midwest			N/A	0.96 (0.81, 1.15)	Due north/A	0.95 (0.79, i.13)
South			Due north/A	0.78* (0.63, 0.97)	N/A	0.82 (0.66, 1.02)
< High schoolhouse					3.41*** (2.43, 4.78)	two.81*** (two.21, 3.59)
Loftier school/GED					iii.06*** (2.41, iii.87)	two.17*** (one.76, 2.68)
Some higher					ii.03*** (1.55, 2.65)	one.90*** (one.53, 1.36)
Income (log)					0.94 (0.87, ane.00)	0.94** (0.xc, 0.98)
Internet worth
Quartile 1 (everyman)	five.05*** (4.01, half-dozen.36)	4.21*** (iii.52, 5.03)	five.86*** (four.36, 7.88)	three.78*** (3.08, 4.63)	2.85*** (2.02, four.03)	ii.58*** (2.07, 3.21)
Quartile 2	iii.81*** (2.97, 4.xc)	ii.33*** (1.92, two.82)	iii.99*** (2.99, 5.32)	2.21*** (1.83, two.73)	ii.26*** (1.64, 3.11)	1.67*** (1.36, 2.06)
Quartile three	2.29*** (1.76, 2.99)	1.66*** (1.37, two.03)	two.33*** (1.77, 3.07)	1.63*** (1.33, two.00)	i.47* (1.09, 1.99)	1.40*** (1.14, one.72)

In the models predicting current smoking, all 3 quartiles of net worth were statistically significant, and results reflected a gradient pattern in both data sets. Overall, these models revealed remarkably similar results, with the exception of gender (men had higher odds of smoking than did women in the SCF but lower odds of smoking than did women in the HRS), marital status (persons who were never married had higher odds of smoking than did married persons in the SCF but not in the HRS), and income (which was significant in the HRS but not in the SCF).

Table iv presents the associations between health status, smoking condition, and the 10 wealth measures from the full models. Similar to the models already described, these full models were adjusted for age, gender, race/ethnicity, marital status, family unit size, region (for the HRS), education, and income. The first row reflects the same ORs and confidence intervals (CIs) for internet worth that were shown in the total models in Table 3. Each subsequent row substitutes a different wealth mensurate for net worth. For the SCF models predicting fair/poor health and current smoking condition, all 3 categories of wealth are statistically pregnant in about models. In general, the wealth measures based on dollar values had roughly like ORs compared with the net-worth model. For example, those in the lowest quartile of wealth—measured equally either internet worth or whatsoever of the dollar-value wealth measures—were approximately three to 6 times as likely as were those in the highest wealth quartile to report off-white/poor wellness for both samples. Similarly, those in the lowest wealth quartile using any of the dollar-value measures were approximately two to 3 times as likely as were those in the highest wealth quartile to report current smoking in both samples.

Tabular array 4

Associations of Wealth Measures With Fair/Poor Health Condition and Electric current Smoking: Survey of Consumer Finances and Health and Retirement Survey, Us, 2004

	Fair/Poor Health Status		Electric current Smoking
	SCF OR (95% CI)	HRS OR (95% CI)	SCF OR (95% CI)	HRS OR (95% CI)
Dollar-based measures
Net worth
Quartile 1 (everyman)	4.98*** (three.42, vii.24)	3.13*** (two.62, 3.73)	2.85*** (ii.02, 4.03)	2.58*** (2.07, iii.21)
Quartile ii	three.23*** (2.29, 4.57)	one.85*** (1.58, 2.17)	2.26*** (ane.64, 3.xi)	1.67*** (1.36, 2.06)
Quartile three	1.64** (1.xvi, 2.31)	1.twenty* (1.02, 1.41)	1.47* (1.09, 1.99)	1.40*** (i.fourteen, 1.72)
Assets
Quartile one (lowest)	v.77*** (3.92, 8.49)	3.24*** (ii.70, three.90)	3.04*** (two.15, 4.29)	2.80*** (two.23, 3.52)
Quartile 2	3.47*** (two.45, iv.92)	i.96*** (1.66, 2.31)	2.31*** (1.70, 3.xiv)	i.75*** (1.41, ii.17)
Quartile 3	1.56* (1.08, two.25)	1.24* (1.05, ane.46)	1.49** (one.10, 2.02)	1.52*** (1.24, 1.87)
Prevalent assets/debts^a
Quartile ane (everyman)	4.threescore*** (3.15, six.72)	3.16*** (2.65, 3.76)	3.24*** (2.33, 4.51)	2.65*** (2.13, 3.thirty)
Quartile 2	2.88*** (two.01, 4.fourteen)	1.86*** (1.59, 2.xix)	ii.49*** (1.82, three.41)	1.66*** (i.35, ii.06)
Quartile 3	ane.50* (i.05, 2.14)	1.28** (1.09, i.51)	1.69*** (one.25, two.27)	i.threescore*** (i.31, 1.97)
Highest-proportion assets/debts^b
Quartile 1 (everyman)	4.26*** (2.97, half dozen.16)	2.75*** (two.32, 3.25)	2.97*** (2.13, 4.15)	2.xiv*** (1.74, 2.63)
Quartile 2	2.71*** (1.90, 3.88)	1.69*** (i.44, 1.97)	two.45*** (1.81, iii.33)	1.46*** (i.19, 1.78)
Quartile 3	1.47* (ane.04, 2.08)	1.18*** (one.01, 1.38)	1.72*** (1.27, two.32)	one.13 (0.93, 1.39)
Prevalent assets^c
Quartile 1 (everyman)	5.86*** (3.96, 8.67)	iii.16*** (two.65, three.78)	3.41*** (2.39, 4.86)	2.72*** (ii.18, 3.40)
Quartile 2	iii.86*** (2.68, v.54)	1.92*** (1.63, 2.26)	ii.40*** (1.76, 3.27)	i.seventy*** (1.38, two.12)
Quartile three	1.57* (1.09, two.26)	one.33*** (1.13, 1.57)	ane.69*** (1.25, 2.29)	1.46*** (ane.xix, 1.eighty)
Highest-proportion assets^d
Quartile i (everyman)	5.29*** (3.60, 7.78)	2.80*** (ii.36, 3.33)	3.40*** (2.39, 4.84)	2.35*** (1.ninety, 2.90)
Quartile 2	iii.58*** (two.47, 5.nineteen)	1.73*** (1.47, two.03)	2.44*** (1.79, 3.32)	1.59*** (ane.29, 1.96)
Quartile 3	1.47* (1.00, 2.15)	ane.xxx*** (one.11, 1.53)	1.fourscore*** (1.33, 2.44)	1.21 (0.99, 1.48)
Index-based measures
Prevalent assets/debts alphabetize^eastward
Low	2.12*** (1.52, two.95)	2.55*** (2.16, 3.01)	1.52** (ane.11, 2.08)	1.94*** (1.58, 2.38)
Depression/moderate	ane.61** (i.21, 2.14)	1.53*** (1.31, 1.80)	1.53** (i.xviii, 1.98)	one.72*** (i.41, 2.09)
Moderate/high	i.31 (1.00, ane.71)	i.35*** (one.sixteen, ane.57)	ane.28* (one.01, ane.62)	1.42*** (ane.16, one.74)
Highest-proportion assets/debts index^f
Low	one.89*** (1.30, 2.75)	2.41*** (1.96, 2.97)	1.27*** (0.91, 1.78)	1.43** (1.12, ane.83)
Low/moderate	ane.61** (i.nineteen, 2.18)	ane.56*** (1.35, 1.81)	ane.39* (1.06, i.83)	1.40*** (1.17, 1.68)
Moderate/loftier	1.35 (0.97, 1.84)	i.fifteen* (one.01, 1.31)	1.26 (0.96, one.66)	ane.22* (1.03, 1.45)
Prevalent assets index^thou
Low	2.49*** (i.77, iii.fifty)	iii.31*** (2.69, 4.08)	ane.95*** (1.44, two.66)	3.00*** (ii.32, 3.89)
Low/moderate	ane.69** (1.22, 2.32)	ane.87*** (ane.55, two.27)	1.73*** (1.31, 2.29)	1.87*** (i.47, ii.39)
Moderate/high	1.22 (0.89, 1.67)	1.46*** (i.xx, 1.77)	ane.39* (one.06, 1.82)	ane.53*** (1.20, 1.95)
Highest-proportion avails index^h
Depression	three.47*** (2.27, 5.31)	2.67*** (2.04, 3.48)	one.80** (1.20, ii.seventy)	2.06*** (ane.53, 2.fourscore)
Depression/moderate	2.34*** (i.73, three.18)	ii.26*** (i.xc, 2.70)	1.78*** (1.36, 2.35)	i.80*** (1.45, 2.24)
Moderate/high	1.66** (1.28, 2.16)	1.61*** (1.42, 1.82)	1.44** (1.15, one.82)	ane.52*** (1.thirty, 1.78)

By contrast, the measures based on summary indices tended to have lower ORs than did the net-worth SCF model (although CIs overlapped in some cases). However, the magnitude and significance of wealth–wellness associations appeared more than consistent in HRS information, regardless of the wealth mensurate used. Across both samples, measures that used assets merely generally appeared to have higher ORs than did measures that used avails and debts (although CIs overlapped in most all cases). The other covariates were more often than not robust in magnitude and significance across the wealth models (information bachelor as a supplement to the online version of this commodity at http://www.ajph.org).

Comparing the percentage differences in model-fit statistics (Somers' D, AIC, BIC) from the internet-worth full SCF model to the other 9 full wealth measure models yielded comparable fits for both health indicators (not shown, bachelor upon request). To illustrate, the Somers' D statistic was 0.570 in the internet-worth model for off-white/poor wellness status; Somers' D statistics were all inside 4% of that number for the other models, with the measures based on summary indices having higher percentage differences (3%–four%) than did the measures based on dollar values (1%–ii%). The AIC and BIC statistics for fair/poor health were within 3% of the AIC and 2% of the BIC statistics in the net-worth model (AIC = 2606 and BIC = 2704 in the cyberspace-worth model). Again, the measures based on summary indices had higher per centum differences than did the measures based on dollar values. A like design in fit statistics was found for smoking in SCF, with Somers' D statistics within 4%, AIC statistics within 3%, and BIC statistics within 1.5% of those found in the net-worth model. Patterns were similar in the HRS information, with Somers' D statistics inside iv%, AIC statistics within 1.5%, and BIC statistics within 0.5% for both fair/poor health status and smoking.

DISCUSSION

Wealth has repeatedly been shown to be a potent and robust predictor of wellness, after controlling for both income and education.¹ Many studies measure wealth by using the sum of the value in dollars of all fiscal assets minus debts. Multiple detailed items are required for this measure, then wealth has not been included in almost health surveys because of infinite limitations and respondent brunt. Our findings suggest that simpler measures of wealth may be useful for assessing this important variable when information technology is not feasible to use the standard approach.

In both samples, when nosotros used whatsoever single simpler measure nosotros reached conclusions about the association betwixt wealth and health that were like to those we reached when nosotros used net worth. Furthermore, the magnitude and significance of the ORs estimating effects on health were similar not only for the wealth measure merely too for the covariates in multivariate models. Finally, when we used whatever of the simpler measures, the model-fit statistics were very close to those in the internet-worth model (i.e., within four%). Findings were more consequent in the HRS than in the SCF.

1 straightforward approach to simplifying wealth measures is to assess assets only, every bit opposed to assets and debts, given that asset-just measures produced similar point estimates and model fit to those produced by the net-worth model. Inclusion of most prevalent or highest debts in improver to assets increases the response burden and may produce a more conservative signal judge. If one were to focus on assets only, taking the SCF as an instance, the simpler measure of about prevalent avails would reduce the number of asset classes from nineteen to 5. Using the highest-proportion assets would reduce the classes further to only those assessing retirement funds, primary residence, and vehicles.

It may be appealing to use fifty-fifty simpler measures consisting of indices based on whether a respondent owns a detail asset blazon, but our findings suggest that using indices may underestimate the wealth effect on wellness, at least for nonelderly adults. However, the results using indices in the HRS data were more consistent, indicating that the simpler yes or no summary index measure might be adequate. Although abode ownership (without estimated value) produced like covariate estimates and model fit, this measure out is express considering it precludes examination of a slope result between wealth and health. We too tested a measure of housing value that compared those in the highest quartile of housing values with nonhomeowners and those in the everyman 2 quartiles of housing values, and we did non find meaning gradient effects (data not shown, available upon asking).

Ane might have expected a stronger relationship between wealth and wellness in the older age grouping because of the accumulation of wealth over the lifetime and the usually experienced loss of income during retirement. However, the magnitude of the association betwixt wealth and income with respect to health was like for the 2 historic period groups and tended to be college in the SCF than it was in the HRS. This finding highlights the importance of measuring wealth when examining social disparities in wellness throughout the life span. Cohort effects and mortality selection at the older ages in the HRS probable play a role in the smaller gradient. Some evidence exists for the mortality-selection statement: In sensitivity analyses among the HRS respondents aged fifty to 64 years only, we found that the wealth associations for both health indicators were stronger than for all respondents aged 50 years or older, although the confidence intervals overlapped.

Nosotros recommend replicating these analyses using a range of different health indicators, within and beyond different population subgroups. For example, wealth is known to vary greatly according to gender¹⁵ and race/ethnicity.^2,sixteen,17 In addition, further work should examine the event of thresholds for selecting assets/debts. The thresholds for the most prevalent assets/debts (25%) and highest-proportion assets/debts (x%) were chosen on the basis of the samples' distributions, in an endeavor to subtract response burden. Unlike results may mayhap be obtained with different cut points. Nonetheless, taking the SCF as an instance, the same assets and debts would be chosen even with a cut signal of l%. Alternatively, choosing 15% as the threshold would add v boosted assets measures that would significantly increase respondent burden. Careful consideration should exist given to choosing a threshold that is meaningful in relation to the information source while as well minimizing respondent burden.

This written report has multiple limitations. Kickoff, it is based on cantankerous-sectional information; therefore, we are unable to determine causality in the association between wealth and health. Second, wealth is time-varying (although likely more stable than income). Because we measured wealth at merely one moment in time, we are unable to fully examine life-form socioeconomic status. All the same, the awarding of this work would probably exist almost useful in cantankerous-sectional surveillance systems. 3rd, we used net worth as the aureate standard even though it suffers from unknown measurement fault and may not be optimally measured past a elementary sum of assets minus debts. Fourth, we classified wealth measures into quartiles to allow for known nonlinearities in the relationship betwixt wealth and health. Using different functional forms may reveal different results. We generated simplified measures empirically; however, at that place may be policy-relevant reasons for including specific measures in particular studies. Finally, the measure of electric current smoking is simplistic; a measure based on more detailed smoking behaviors (e.g., taking into account dependence, duration, cessation, or frequency) would have been preferable.

Our findings suggest that simpler measures of wealth can be used in wellness research when it is not feasible to use internet worth, which has been the standard. Our results advise that for nonelderly adults, the simplest measure is the value of the principal residence, vehicles, and retirement funds. Similarly, for older adults, the simplest measure out is the value of the primary residence, vehicles, and checking, savings, and coin marketplace accounts. These simpler measures significantly reduce the number of items to exist assessed, which thereby reduces respondent brunt, data-drove time, and associated costs. We recommend that additional research be conducted to examine other indicators, thresholds, life stages, and demographic subgroups to determine whether these results have broader generalizability.

Acknowledgments

This commodity was made possible by an award from the American Legacy Foundation.

The authors wish to acknowledge institutional support from the Population Research Eye (grant 5 R24 HD042849) and the Heart for Social Work Research at the University of Texas at Austin. Nosotros also thank Lou Mariano for statistical communication and Adriane Clomax and Tara Powell for their assistance with preparation of the article.

Human Participant Protection

The institutional review lath at the Academy of Texas at Austin designated this written report as exempt from the requirement for protocol approval because it used publicly available secondary data.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3076388/