The Gerontologist 44:48-57 (2004)
© 2004 The Gerontological Society of America
Do Out-of-Pocket Health Expenditures Rise With Age Among Older Americans?
Susan T. Stewart, PhD1
Correspondence: Address correspondence to Susan T. Stewart, Harvard Interfaculty Program for Health Systems Improvement and the National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA 02138-5398. E-mail: susan_stewart{at}harvard.edu
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Abstract
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Purpose: Relationships are examined between age and out-of-pocket costs for different health goods and services among the older population. Design and Methods: Age patterns in health service use and out-of-pocket costs are examined by use of the 1990 Elderly Health Supplement to the Panel Study of Income Dynamics (N = 1,031, age 66+). Multivariate regression is used to examine how age effects are mediated by health, insurance, and socioeconomic variables. Results: Although long-term care expenditures increased with age, out-of-pocket costs for most other services did not. Total out-of-pocket costs increased with age only when nursing home costs were included. Increases with age in hospital and prescription costs were explained by declining health. Patterns of service use suggested reduced access to discretionary care among the oldest old. Implications: Although expenditures did not increase with age for most services, the high personal cost for nursing home care among the oldest old underlines the need for increased efforts to support them in the community. Greater spending by those in poor health highlights the importance of preventing age-related health conditions and their complications. Improved access to discretionary care among the oldest old may help to reduce the need for care in higher cost settings. The high prevalence of out-of-pocket prescription spending across the age range provides impetus for current efforts to reduce these costs.
Key Words: Spending • Cost • Care • Utilization • Medicare • Elderly
Older Americans can face burdensome out-of-pocket costs for Medicare deductibles, coinsurance, and goods and services not covered by Medicare. However, it is not clear whether these costs rise with age among the older population. In studies of total health care costs, those 65 and over are often considered as a single group or are stratified into groups that do not adequately distinguish the younger old from the oldest old (as noted by Perls, 1997; Torrey, 1989). Studies that have looked at out-of-pocket costs among the elderly population have often looked at combined costs for a number of services and have found that costs rise with age but plateau or decline in the oldest age groups (AARP Public Policy Institute & The Lewin Group, 1997; Crystal, Johnson, Harman, Sambamoorthi, & Kumar, 2000; Moon, 1992; Perls & Wood, 1996).
However, in order to provide a complete picture, we must look at spending on different types of health goods and services separately, because spending may increase with age for some services and decline for others. For example, whereas long-term care costs are consistently found to increase with age, findings for acute care costs are mixed (Freiman, 1998; Kington, Rogowski, & Lillard, 1995; Meerding, Polder, Bonneux, Koopmanschap, & van der Maas, 1998; Perls & Wood, 1996; Roos, Shapiro, & Tate, 1989).
This article provides a detailed picture of age patterns in out-of-pocket health spending in a sample of older Americans. In addition to total out-of-pocket costs, expenditures and utilization in different service categories are analyzed by age. Although the main focus of the article is on out-of-pocket payments, age patterns in the burden of total health costs as a portion of income are also examined. Because the oldest old as a group have substantially lower socioeconomic status (SES) than the young old (Atkins, 1989), costs may be more burdensome for the oldest old even if they do not increase with age. For example, Crystal and colleagues (2000) found that those aged 85 and older spent a significantly higher percentage of their income on total out-of-pocket health costs than those aged 65–74.
Multivariate models are also used in the current study to examine the mechanisms through which age has an effect on out-of-pocket health costs. Although descriptive studies may find changes in spending with age, it is important to examine whether these effects are explained by demographic, health, and socioeconomic variables that can differ by age and are known to be associated with health care use and costs (Andersen, 1968; Andersen & Newman, 1973). Poor health is typically a strong predictor of health care utilization among older adults (i.e., Eppig & Poisal, 1997; Linden, Horgas, Gilberg, & Steinhagen-Thiessen, 1997; Verbrugge, 1995), and it may be expected to account for age increases in out-of-pocket costs. However, poor health can also be a result of inadequate use of preventive care, in which case costs could be higher among healthier elderly adults who use more preventive services. In either case, insurance coverage supplemental to Medicare is a central determinant of the personal costs that will be incurred when care is used, and the type of coverage is also an important factor related to utilization (Hurd McGarry, 1997; Wolfe Goddeeris, 1991). Education, income, and wealth are also positively associated with health care use and out-of-pocket health costs among the elderly population (see, e.g., Cartwright, Hu, & Huang, 1992; Holahany & Zedlewski, 1992). Lower SES among the oldest old can particularly affect access to services considered more "discretionary," such as dental and preventive care (Andersen, 1968; Wolinsky et al., 1990), and age differences in out-of-pocket spending that are explained by SES may indicate inequities in access to care. Identification of the factors that underlie age effects should help elderly advocates to better address the needs of those with the potential for high out-of-pocket costs.
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Data
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Data are from the 1990 Elderly Health Supplement to the Panel Study of Income Dynamics (PSID), which is a longitudinal survey of almost 5,000 U.S. households interviewed since 1968 (Hill, 1992). In 1990, sample members aged 65 and older provided supplemental information on health service use and costs by use of a telephone questionnaire, completed by 99% of eligible households. Additional information regarding health status and insurance coverage was collected by use of a mail-in questionnaire sent to those aged 50 and older. Of the 1,490 eligible respondents aged 65 and older, 82% completed both the telephone and mail-in questionnaires. The present analyses are restricted to the 1,032 of these respondents who were aged 66 or older (to ensure that Medicare beneficiaries had been covered for the entire year prior to the interview for which health expenditures were reported). Sample sizes for specific types of out-of-pocket costs ranged from 895 to 1,026, because for each service a small portion of respondents failed to report whether the service was used or they did not provide an indication of the amount spent. The percentage excluded as a result of missing data ranged from 0.5 to 6.2 across spending categories. For all but two types of spending, those set to missing were not significantly different from responders on measures of age, health, insurance coverage, and income. The two exceptions were older age and worse self-rated health among those missing for hospital and prescription medication costs.
Analyses were weighted to compensate for unequal probabilities of selection, selective attrition from the panel from 1968 to 1990, nonresponse to the PSID in 1990, and nonresponse to the mail-in survey.
Measures
Out-of-Pocket Costs
Out-of-pocket health costs were reported for the year preceding the survey, in eight main categories: hospital care; outpatient surgery; physician, therapist, or ER visits; prescription medications; dental care; equipment; nursing home stays; and home health care. For each service used, respondents answered a series of questions regarding costs and sources of payment, beginning with out-of-pocket costs. The cost of insurance premiums was reported on the mail-in questionnaire and calculated for 1 year. Total out-of-pocket costs were calculated by adding reported out-of-pocket costs for all services.
Although PSID members were originally selected from the noninstitutionalized population, older members who had left a PSID family to go to a nursing home were recontacted for interviews in 1990. Thus, nursing home expenditures represent either short-term stays or the beginning of long-term stays that had not ended by the time the survey took place. Nursing home stays were reported by 5% of respondents (n = 49), and 3% of respondents had been in the institution for the whole year.
Out-of-pocket costs for couples were reported separately for each member by the household head. For each spending category, a small portion of respondents failed to report an exact amount but indicated in subsequent unfolding questions that their spending fell within a certain range. These respondents were assigned an imputed score equal to the mean costs of those who reported an exact amount in that range. (For insurance premium expenditures there were no unfolding questions, and for all respondents missing on these costs a hot-deck method was used to impute an amount based on gender, race, and whether insurance was obtained through an employer.) The percentage with imputed data ranged from 0.1% to 5.9% across the spending categories. For physician, hospital, and dental costs, those imputed were slightly but significantly older and reported worse health than those providing an exact amount. The results section describes the effects of adding a binary variable to a supplemental set of regression analyses to indicate those for whom costs were imputed.
Although the extent of reporting bias for out-of-pocket costs in these surveys is not known, PSID members were experienced in responding to financial survey questions, and the self-reports of other items such as income have been shown to be of high quality (Hill, 1992). The structure of the questions and the use of unfolding brackets may have reduced the underreporting that has been a concern in research on health care use (Eppig & Chulis, 1997; Glandon, Counte, & Tancredi 1992; Jobe et al., 1990; Moeller & Mathiowetz, 1991). Mean out-of-pocket costs per user in the PSID for physician or equipment ($371), dental care ($300), and prescription medications ($313) compare favorably with the 1992 Medicare Current Beneficiary Survey (MCBS), where these costs were $303, $291, and $315, respectively (Laschober & Olin, 1996).
Demographic, Health, and Socioeconomic Variables
Demographic variables controlled in multivariate analyses are gender, race (Black vs. White), and urban versus rural residence. As a result of regional differences in the cost of medical care, there is also a control variable reflecting annual Medicare expenditures per beneficiary in the state where the respondent lived. Measures of health include self-reported health (1 = poor, 5 = excellent), functional ability (on an 8-point scale reflecting difficulty performing eight activities of daily living), and each of the following reported medical conditions: cancer, diabetes, high blood pressure, heart disease, stroke, lung disease, digestive conditions, and arthritis. (Deafness and sight problems are controlled in predictions of equipment costs, and current smoking status is controlled as a measure of oral health when dental expenditures are predicted.)
For the effects of insurance coverage supplemental to Medicare to be captured, mutually exclusive dummy variables are used to represent those with employer-sponsored insurance, those in a prepaid health plan (HMO), those dually eligible for Medicaid, and those with only fee-for-service (FFS) Medicare coverage. Those with privately purchased supplemental (Medigap) insurance are the majority and are used as the referent group. An exogenous variable reflecting Medicaid eligibility was created by applying federal eligibility criteria for Supplemental Security Income (SSI) to each individual's or couple's reported income and wealth. Those who did not indicate an insurance type on the mail-in survey were included by use of a dummy variable and set to zero on all insurance types. Separate variables indicating coverage by dental insurance, prescription insurance, and long-term care insurance are also included in analyses of these services.
SES is represented by income, wealth, and education. Wealth is considered in addition to income because elderly persons may draw on accumulated assets to pay for health care (Hurd, 1989). Each financial variable is the sum of amounts reported in numerous detailed questions (in the 1990 PSID and in a 1989 "wealth supplement" to the PSID), and they are logged to attenuate the effects of outliers. Education is reported continuously in years.
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Methods
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Descriptive results are depicted graphically by use of a 3-year moving average in which the mean shown for each year of age represents the mean for all respondents at that age as well as those 1 year younger and 1 year older. (In addition to subduing the effect of outliers, this technique provides an adequate sample size in situations where there are only a few respondents of a particular age. The last data point on each chart shows the mean for those age 93 and older; all data points represent at least 5 respondents.) In multivariate analyses, ordinary least squares (OLS) regression is used to predict the level of spending, and cost variables are logged to attenuate the effect of outliers. For specific services used by only a portion of respondents, a two-part model is used, with probit analysis predicting use and OLS regression predicting logged costs among users. Age and other demographic variables are entered into regression models first, and then health, insurance, and SES variables are added in three subsequent steps. In order to test for significant nonlinear effects, parallel regression analyses are run with 5-year age groups rather than a continuous age variable.
Descriptive Results
Table 1 shows the characteristics of the sample. Patterns of service use and out-of-pocket spending are shown by age in Figures 1 to 7. Graphs depict out-of-pocket costs for all respondents, including those who had zero out-of-pocket costs because they did not use the service or it was completely covered by insurance. As shown in Figure 1, total out-of-pocket costs rose dramatically at the oldest ages when the costs of nursing home care were included. (Age patterns were very similar when insurance premiums were included in total out-of-pocket costs, but this is not shown.) However, these age increases are strongly affected by high outliers at the oldest ages that had nursing home expenditures. The lower line in Figure 1 shows that, when the costs of long-term care were excluded, total out-of-pocket costs did not generally increase with age. Thus, although only a small portion of respondents used long-term care, age increases in total out-of-pocket health costs are driven by the high cost of long-term care relative to other services and by the concentration of long-term care users among the oldest old. This is further illustrated in Figures 2 and 3, which show increases at the oldest ages in rates of use and out-of-pocket costs for home care and nursing home care.
Figures 2 and 3 also show that hospital use and costs increased somewhat with age, though costs dropped somewhat at the middle of the age range and at the oldest ages. For physician visits and dental care, there was a general pattern of decline with age in both use and out-of-pocket costs, as shown in Figures 4 and 5, respectively. (The exceptions were some rises in utilization at the oldest ages, and an increase in dental costs among some in the middle of the age range). Figure 4 also shows that prescription medication use increased somewhat with age, but it was high across the age range. Figure 5 shows that prescription costs varied across the age range, increasing slightly and then dropping at the oldest ages. Figures 6 and 7 depict no clear age patterns in use and costs for outpatient surgery and equipment. Although equipment use rose somewhat with age, equipment and surgery costs varied across the age range.
The burden of total out-of-pocket spending as a portion of household income increased with age in a manner similar to total out-of-pocket expenditures; this is not shown graphically. When nursing home and home care costs were excluded, out-of-pocket burden did not increase with age but showed a slight curvilinear pattern, with the lowest burden at the youngest and oldest ages.
Multivariate Results
Age coefficients from hierarchical regressions are shown in Tables 2 and 3. (Coefficients for other variables added to the model at each step are not shown because of space considerations, but these can be seen for selected equations in the appendix. Estimates of variance inflation, shown in Table 1 of the appendix, indicate that multicollinearity was low and did not likely interfere with the interpretation of results.) Results for services for which a two-part model was used are shown in Table 3. For outpatient surgery, equipment, physician visits, and total out-of-pocket costs including insurance, age was not a significant predictor of use or costs in any hierarchical models, and coefficients are not shown.
As shown in Table 2, age was a significant positive predictor of total out-of-pocket costs excluding insurance premiums until health was controlled, and then reemerged as significant when SES was controlled. This indicates that age was positively related to costs, but that this effect had been suppressed by the negative relationship between age and SES. Out-of-pocket prescription medication costs increased significantly with age until health was controlled.
As shown in Table 3, dental care use declined significantly with age (as found previously with this data by Kington et al., 1995); however, age was no longer significant when SES was controlled. For hospital care, age lost significance as a positive predictor of use when health was controlled. For nursing home care, the effect of age on use remained significant in all models, whereas age was not a significant predictor of spending among users. Age was a positive predictor of home care use until SES was controlled.
In parallel regression analyses using 5-year age groups rather than a continuous age variable, there were no significant curvilinear effects, and results are not shown. However, analyses did reveal accelerated effects of age in the oldest age groups; nursing home and home care use increased more in each successive age group, whereas dental costs deceased more in older age groups. (As an example, results for nonlinear age effects in dental care use are shown in Table 2 of the appendix).
In parallel analyses to examine the effects of imputation, a binary variable indicating those for whom costs were imputed was positive and significant for all but dental and nursing home costs. This indicates that those with higher levels of spending had greater difficulty reporting the exact amount spent. Most age effects were unaffected when those who were imputed were controlled for, with the following exceptions: For physician visits, age became significant as a negative predictor of costs in the first two models. Similarly, among users of hospital care, age became significant as a negative predictor of costs in the first model. Before imputation was controlled for, these effects had been marginally significant (p <.10). Their significance increased as a result of a slightly higher mean age among those who were imputed (74.7 vs. 74.1 years for physician costs; 75.2 vs. 74.1 years for hospital costs), and slightly higher costs among those who were imputed. Finally, for prescription drugs, age became nonsignificant as a positive predictor of costs in the first model (p =.07). This indicates that a small portion of the age-related increase in prescription costs was explained by a positive (but nonsignificant) relationship between age and having imputed medication costs.
Design effects that were due to variability in sampling weights were calculated by use of a jackknife replication technique. As shown in Table 1 of the appendix, most of these estimates were close to 1.0, indicating a relatively small bias in the standard errors. The design effect for age indicates that standard errors for age effects were underestimated by approximately 17%. When adjusted for this, most age effects remain unchanged; two weaker effects that become nonsignificant are the age increase in prescription medication costs and the reemergence of age as a predictor of total costs when SES is controlled.
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Discussion
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This study examined whether out-of-pocket health expenditures for different services increase with age among the older population, and how the relationship between age and expenditures is mediated by other variables. There was limited evidence for age increases in health care use and spending among the older population. Long-term care expenditures increased dramatically with age, consistent with previous findings (Meerding et al., 1998; Roos et al., 1989). However, age was not associated with greater use or out-of-pocket costs for the majority of services.
Hospital and prescription medication costs did increase with age until health was controlled for (and these effects may have been stronger in the absence of missing data, because persons for whom these types of costs were missing were slightly older and reported worse health). The finding that these age effects were explained by declining health highlights the importance of preventive health behaviors and disease management to protect against high health costs as one grows older (Hodgson & Cohen, 1999). They also emphasize the need for increased research on the prevention and control of age-related health conditions, particularly those that lead to long-term disability (Schneider, 1999; Verbrugge, 1995).
Plots revealed curvilinear age trends in spending for some services, consistent with some previous descriptive findings of a plateau or decline in spending at the oldest ages (AARP Public Policy Institute & The Lewin Group, 1997; Crystal et al., 2000; Moon, 1992; Perls & Wood, 1996). However, in regression analyses, the only significant nonlinear age effects were greater increases in nursing home and home care use in older age groups, and a more dramatic decrease in dental care use at the oldest ages.
There were no significant age increases in the use of physician visits, outpatient surgery, or equipment, despite worsening reported health with age. Findings were similar when parallel analyses were run in another data set, the second wave of AHEAD (Asset and Health Dynamics of the Oldest Old), a survey of more than 6,000 Americans aged 70 and older that began in 1993 (Soldo, Hurd, Rodgers, & Wallace, 1997). This survey showed the same dramatic age increases in total out-of-pocket costs when nursing home costs were included, whereas prescription costs did not increase with age, and there was a significant decline with age in spending for ambulatory services (physician and emergency room visits, outpatient surgery, and dental care). These patterns of use and spending were not explained by better insurance coverage, and they provide some evidence for reduced access to discretionary health services with age. It may be that the older old received a lower intensity of services, went to lower cost providers, or needed fewer services as a result of a hardiness that helped them to survive to very old age. However, use may also have been restricted because of a reduced ability to pay, as suggested by the SES effect seen for dental care. In addition, the pattern of lower ambulatory and higher hospital spending among the oldest old suggests that improving access to discretionary, preventive care may ultimately help to reduce their health costs by reducing the need for care in more costly settings.
The burden of total out-of-pocket costs as a portion of income increased with age as hypothesized, but only when the costs of long-term care were included. Thus, higher out-of-pocket health burden among the oldest old is due primarily to age-related increases in the use of nursing home care, which is the service associated with the highest out-of-pocket costs (Rice, 1989; Rice & Gabel, 1986). It is important to note that age remained significant as a positive predictor of nursing home and home care use even after health was controlled for. This indicates that other physiological or social factors related to advanced age are important determinants of the use of long-term care. The oldest old may have higher utilization because they are more physiologically and psychosocially frail and are thus less able to rely on informal health services (Wolinsky & Johnson, 1991). The likelihood of having a living spouse or capable child who can provide informal care in the home also decreases with age. To the extent that nursing home use among the oldest old is explained by such social factors rather than health, increased efforts to support the oldest old in the community may prevent unnecessary institutionalization among persons in this group.
It is also noteworthy that age increases in home care use were explained by lower SES among the oldest old, with greater use among lower income elderly persons. Because home care often follows a hospital stay, this is likely related to greater hospital use by lower income elderly persons, and it may be a result of earlier barriers in access to preventive care, as already discussed. The comprehensive coverage of home health care under Medicare meant that cost did not act as a barrier to access for this service in the way it may have for others. It may also be that higher income elderly people were able to make home modifications or purchase other services or equipment that helped to prevent the need for formal home health care (Allen, Foster, & Berg, 2001).
Finally, prescription drug use was high across the age range, and medications were the most expensive health cost for many elderly people. This reflects the fact that prescription medications are increasingly becoming the preferred form of treatment for the diseases affecting older adults (Duka, 1999), and drug costs have risen rapidly, often much faster than inflation (Haddad, 1999). The prescription drug benefit in the recently passed Medicare bill (November 2003) will help to defray a portion of these expenditures. Consumers can also reduce their costs through the use of generic drugs and through participation in savings programs offered by state governments, pharmacies, and drug manufacturers. Efforts to increase awareness of such savings strategies are an important means of addressing the growing problem of high prescription costs.
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Appendix: Full Hierarchical Regression Results for Selected Measures of Out-of-Pocket Spending Table 1. Hierarchical OLS Regression Results for Logged Total Out-of-Pocket Expenditures.
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Footnotes
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This research was supported in part by a University of Southern California Haynes Foundation Fellowship, and by funding from the Andrus Gerontology Center Associates. The author thanks Eileen Crimmins for her comments on the manuscript. Helpful comments on earlier drafts of this work were also provided by Jeff McCombs and Kathleen Wilber, as well as by the editor and two anonymous reviewers. Thanks are also extended to Steve Heeringa for help with the estimation of design effects. 
1 Leonard Davis School of Gerontology, Andrus Gerontology Center, University of Southern California, Los Angeles.
Decision Editor: Laurence G. Branch, PhD
Received for publication June 21, 2002.
Accepted for publication October 23, 2002.
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Abstract
Data
