- Email: [email protected]
Private dental coverage
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Private dental coverage Who has it and how does it influence dental visits and expenditures? RICHARD J. MANSKI, D.D.S., M.B.A., Ph.D.; MARK D. MACEK, D.D.S., Dr.P.H.; JOHN F. MOELLER, Ph.D.
or many Americans, having dental insurance coverage may be an important influence on their decision to use dental care. During the last 40 years, interest in and demand for dental insurance has increased considerably. The number of enrollees in dental insurance plans has increased from about 4.5 million people in 1967 to more than 100 million people by 1990.1-4 Today, dental insurance is considered to be a popular and sought-after fringe benefit among many employees. Key According to the National Association of demographic Dental Plans, approximately 150 million Americans have some form of dental and socioecocare coverage.1 nomic status Dental insurance has been shown to variables were be an important factor in people’s deciassociated with sions to seek and use dental care serv5-13 dental care use, ices. According to the 1989 National independent of Health Interview Survey, or NHIS, while 70 percent of all people with private dental dental insurance had at least one dental insurance visit during 1989, only 50 percent of coverage. people without dental insurance reported at least one visit during the same period.5 Dental insurance also has been shown to affect dental provider treatment patterns.12,13 For instance, Mueller and Monheit13 found that having coverage doubles the likelihood that a user will obtain bridgework and increases by 38 percent the probability that a user will receive a crown. According to Conrad and colleagues10 and Mueller and Monheit,13 while increased coverage among elderly patients did not result in uniform increased costs, it did result in a substitution in services provided. Since the effect of having insurance is to reduce the perceived price of care, it is
F
ABSTRACT Background. Dental insurance has had a significant impact on dentistry and dental care use. Dental insurance coverage may influence people’s decisions to use dental care. During 1996, 42.9 percent of all dental expenditures were paid by private dental insurance. Methods. The focus of this analysis is on private dental coverage, use and expenditures for the U.S. civilian community-based population during 1996. The authors provide national estimates for the population with private dental coverage, the population with a dental visit, mean number of dental visits per year and mean total expenditures for several socioeconomic and demographic categories during 1996, using Medical Expenditure Panel Survey, or MEPS, data. Results. Poor and low-income people were less likely to have private dental coverage than were people with higher incomes. People without coverage at all income levels were less likely to report a dental visit than were people with coverage. When they controlled for coverage, the authors found that education at any income level did not appear to affect the likelihood of people’s having multiple visits or higher expenditures. Conclusion. People with private coverage are more likely to visit a dentist, have a greater number of visits and have higher expenditures than are those without coverage. Private dental insurance coverage, however, is not the only determinant of dental care use. MEPS data also show that other factors play key roles. Comprehensive strategies designed to improve dental care use should keep each of these determinants in mind. Practice Implications. While dentists may have a limited ability to influence people to seek care initially, they may be in a better position to influence the amount of care patients obtain, thereby helping make sure that patients receive the care that they need and want.
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TABLE 1
U.S. POPULATION WITH PRIVATE DENTAL COVERAGE, PERCENTAGE WITH VISITS, AND MEAN VISITS AND TOTAL EXPENDITURES, UNITED STATES, 1996.* TOTAL POPULATION (000s)
% POPULATION WITH DENTAL VISIT (SE†)
MEAN NUMBER OF VISITS‡ (SE)
MEAN TOTAL EXPENDITURES‡ (SE)
% POPULATION WITH PRIVATE DENTAL COVERAGE (SE)
TOTAL
268,905
42.9 (0.7)
2.55 (0.3)
373.8 (9.6)
51.4 (0.8)
Age (Years) 18 and younger 19 to 44 45 to 64 65 and older
75,326 105,318 54,217 34,050
42.5 41.1 48.8 40.3
2.74 2.20 2.70 2.92
374.8 321.3 425.1 438.0
52.3 56.5 58.4 23.0
Sex Male Female
131,527 137,379
39.7 (0.8) 46.0 (0.7)
2.48 (0.5) 2.61 (0.4)
361.7 (14.9) 383.8 (12.6)
51.7 (0.9) 51.3 (0.8)
33,668
26.1 (0.1)
2.05 (0.8)
299.5 (31.8)
42.3 (1.7)
29,979 205,258
28.5 (0.1) 47.8 (0.8)
2.22 (0.7) 2.63 (0.4)
317.2 (23.8) 385.4 (10.7)
34.2 (1.9) 55.5 (0.8)
38,298 53,406 88,262 88,939
26.2 28.8 44.9 56.6
2.11 2.37 2.56 2.69
267.9 282.3 384.0 414.9
14.4 32.9 60.5 69.6
49,025
23.6 (0.9)
2.28 (0.8)
309.6 (25.3)
20.8 (1.1)
150,047
42.3 (0.8)
2.48 (0.4)
373.7 (14.0)
53.3 (0.8)
67,139
59.3 (0.1)
2.74 (0.6)
394.1 (16.9)
71.2 (1.0)
120,841
44.4 (0.9)
2.67 (0.5)
433.4 (16.1)
55.4 (1.0)
92,992
44.8 (1.3)
2.49 (0.5)
343.7 (14.5)
53.5 (1.4)
51,640
39.0 (1.6)
2.35 (0.7)
277.8 (16.3)
39.8 (1.7)
POPULATION CHARACTERISTIC
Race and Ethnicity Non-Hispanic black Hispanic White§ Family Income by Poverty Status ¶ Poor Low income Middle income High income Education # Some or no school High school graduate College graduate Rural/Urban** Large metropolitan Small metropolitan Nonmetropolitan
(1.0) (0.7) (1.0) (1.4)
(1.1) (1.0) (0.9) (1.0)
not surprising that patients with coverage might substitute higher-cost treatment options in place of lower-cost, and perhaps less-preferred, alternatives.7 Dental insurance has had a profound effect on dentistry and dental care use. Insurance payments for dental care on behalf of enrollees are significant. During 1996, approximately 43 percent of all dental expenditures ($18 billion) were paid to dental providers from private insurance.14 We conducted this study to further examine the status of private dental insurance and the impact of private dental insurance coverage on 1552
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(0.7) (0.4) (0.6) (0.9)
(0.8) (0.8) (0.6) (0.5)
(23.3) (12.4) (16.2) (27.2)
(21.9) (14.6) (17.4) (16.4)
(1.2) (0.9) (1.1) (1.1)
(1.0) (1.3) (0.9) (1.0)
the use of and expenditures for dental care services. To do this, we analyzed data from the 1996 Medical Expenditure Panel Survey, or MEPS.15 METHODS
The U.S. Department of Health and Human Services has sponsored the administration of several national expenditure surveys since 1977. The 1996 MEPS is the third in a series of nationally representative health surveys of the U.S. community-based population sponsored by the Agency for Healthcare Research and Quality (formerly the Agency for Health Care Policy and
T R E N D S
TABLE 1 (CONTINUED)
U.S. POPULATION WITH PRIVATE DENTAL COVERAGE, PERCENTAGE WITH VISITS, AND MEAN VISITS AND TOTAL EXPENDITURES, UNITED STATES, 1996.* % POPULATION WITH AT LEAST ONE VISIT (SE)
MEAN NUMBER OF VISITS‡ (SE)
MEAN TOTAL EXPENDITURES‡ (SE)
Population With Private Dental Coverage
Population Without Private Dental Coverage
Population With Private Dental Coverage
Population Without Private Dental Coverage
Population With Private Dental Coverage
Population Without Private Dental Coverage
56.6 (0.8)
28.6 (0.8)
2.65 (.04)
2.42 (.06)
417.2 (12.3)
298.7 (13.6)
55.8 53.9 60.6 65.0
28.1 23.8 32.6 33.9
2.94 2.28 2.76 3.25
2.61 1.97 2.51 2.72
438.6 355.4 459.8 554.0
286.0 223.8 331.0 369.6
(1.3) (1.0) (1.1) (2.7)
(1.6) (0.9) (1.6) (1.5)
(.09) (.05) (.07) (.15)
(.16) (.08) (.10) (.11)
(30.4) (15.6) (19.5) (56.0)
(36.6) (19.3) (28.4) (28.7)
52.7 (1.0) 60.3 (0.9)
25.7 (1.0) 31.6 (0.9)
2.57 (.05) 2.71 (.05)
2.34 (.09) 2.48 (.07)
408.6 (20.0) 424.5 (16.0)
275.2 (18.2) 317.6 (19.6)
37.1 (1.9)
15.2 (1.3)
2.24 (.11)
1.87 (.11)
336.9 (46.1)
309.2 (52.6)
46.3 (1.9) 59.9 (0.9)
16.7 (1.1) 33.0 (1.0)
2.28 (.11) 2.70 (.04)
2.30 (.13) 2.48 (.07)
386.3 (37.5) 425.6 (13.4)
279.1 (30.7) 299.5 (14.8)
42.8 43.3 54.6 63.2
19.5 21.6 30.1 41.6
2.56 2.48 2.58 2.74
2.07 2.31 2.50 2.53
338.9 345.4 417.3 435.9
300.0 238.0 289.2 343.9
(3.0) (2.0) (1.0) (1.1)
(1.3) (1.1) (1.2) (1.5)
(.21) (.11) (.07) (.05)
(.11) (.12) (.10) (.10)
(41.5) (24.3) (21.9) (18.7)
(45.1) (22.4) (21.4) (28.4)
37.0 (1.8)
18.2 (1.1)
2.40 (.12)
2.37 (.13)
420.0 (56.6)
292.9 (33.0)
53.5 (1.0)
29.6 (1.0)
2.57 (.06)
2.34 (.07)
424.5 (18.2)
278.0 (17.2)
66.2 (1.1)
42.7 (1.9)
2.78 (.06)
2.61 (.12)
407.61 (19.1)
345.4 (30.5)
57.0 (1.0)
28.5 (1.3)
2.71 (.06)
2.67 (.10)
471.6 (20.8)
360.7 (24.9)
57.8 (1.6)
30.3 (1.6)
2.63 (.06)
2.26 (.10)
387.3 (17.0)
264.3 (20.9)
55.3 (1.8)
28.1 (1.7)
2.46 (.10)
2.25 (.10)
310.8 (20.6)
248.7 (23.0)
Source: Cohen.15 SE: Standard error. For people with a visit. Includes all other racial/ethnic groups not shown separately. Poor: incomes below 100 percent of the federal poverty level, or FPL. Low income: incomes 100 to 199 percent of the FPL. Middle income: incomes from 200 to 399 percent of the FPL. High income: incomes 400 percent of the FPL and higher. # For people 18 years of age and younger, refers to parent’s education. ** Large metropolitan: central counties of areas with 1 million people or more. Small metropolitan: other metropolitan counties. Nonmetropolitan: nonmetropolitan counties either adjacent or not adjacent to urban areas. * † ‡ § ¶
Research and, before that, the National Center for Health Services Research). MEPS collects health care expenditure, use and payment source data, as well as socioeconomic, demographic and health insurance data similar to its previous surveys. It differs from the National Medical Expen-
diture Survey and the National Medical Care Expenditure Survey in that data on household respondents in each panel are collected for two consecutive years and the survey is fielded continuously (that is, a new panel is selected every day).15 JADA, Vol. 133, November 2002
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T R E N D S
The sample for the 1996 MEPS was 21,571 people in 10,500 households who participated in NHIS. To collect health expenditure and use data for 1996, field researchers interviewed each MEPS household in person three times over an 18-month period; the third round of interviews were conducted between February and May 1997.15 The combined full-year 1996 response rate of the MEPS sample through the third round was 70 percent.15 In this analysis, we focus on dental coverage and the effect of coverage on dental care use in 1996 for the U.S. noninstitutionalized population. Specifically, we provide national estimates for dental coverage status, percentage of population with a dental visit, number of visits per person for those who had a visit, and mean total expenditure for people with a visit by coverage for each of several socioeconomic and demographic categories during 1996. We also conducted multivariate analyses to determine if the bivariate relationships shown in Table 1 would persist in a multivariate statistical model. We also assessed the relative impact of the socioeconomic and demographic variables on dental coverage status and utilization. Specifically, we estimated logistic regression models for dental coverage status (defined as = 1, if covered any time during the year; = 0, otherwise) and dental use (defined as = 1, if one or more dental visits during the year; = 0, otherwise). We estimated the impact of socioeconomic and demographic variables for age (18 years and younger, 19-44 years, 45-64 years and 65 years and older), sex, race/ethnicity (non-Hispanic black, Hispanic and white), family income (low income, middle income and high income), education and rural/urban status for the likelihood of having coverage. We then estimated the impact of coverage—controlling for age, sex, race/ethnicity, family income, education and rural/urban status—with two-part models for the likelihood of having a dental visit, the natural logarithm of the number of visits for those with a visit and the natural logarithm of expenditures for those with a visit.16,17 Economic models of demand developed by Grossman18 and Phelps and Newhouse19 support the empirical specification of noncoverage variables in the model. To minimize the effect of nonprivate or public coverage, we truncated regression models to include people with incomes greater than 100 percent of the federal poverty level, or FPL. 1554
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The variable for dental coverage indicates if a participant was eligible to receive or actually received private payments on their own behalf for dental care obtained during 1996. More specifically, a person is considered to have dental coverage if he or she had a self-report or proxy report of private dental coverage at any time during 1996. A person also is considered to have dental coverage if he or she had a self-report or proxy report of a private insurance payment for dental care received at any time during 1996. While this measure of private dental coverage is more inclusive than a simple self-report of coverage, it is less likely to miss covered participants who fail to report coverage and is similar to methods used in other studies, including a recent study of Medicare beneficiaries and drug coverage.20 We obtained coverage from the Health Insurance Section of the Household Component of MEPS. Private dental coverage refers to coverage offered as a supplement to medical coverage or as coverage obtained from an unrelated and separate plan. Distinctions between these types of dental coverage offerings are not discernible with these data. Additionally, since some medical coverage plans may include dental benefits that were not reported by respondents, these estimates may understate somewhat the actual number of people with coverage. To ensure sufficient numbers to produce reliable national estimates, we combined sociodemographic variable categories when necessary. We computed all estimates and statistics reported taking into account the complex sampling design of MEPS with the use of a software package (SUDAAN, release 6.40, Research Triangle Institute, Research Triangle Park, N.C.).21 RESULTS
There were 21,571 participants in the 1996 MEPS, representing approximately 268 million noninstitutionalized U.S. citizens. Of these, 51 percent were female (n = 11,282), 39 percent were nonwhite (n = 8,323), 58 percent were between the ages of 19 and 64 (n = 12,427), and 81 percent were in families with incomes above the FPL (n = 17,415). Figure 1 shows the percentage of the population with coverage according to family income level. Figure 2 displays the percentage of the population with a dental visit for those with and without coverage by family income level. People in poor and low-income families were less likely
100
70
80
61 60
33
40
14
20 0
Poor
Low Income
Middle Income
High Income
INCOME LEVEL Figure 1. U.S. population with private dental coverage by income, 1996. The population includes people in families with negative incomes. Poor: incomes below 100 percent of the federal poverty level, or FPL. Low income: incomes 100 to 199 percent of the FPL. Middle income: incomes 200 to 399 percent of the FPL. High income: incomes 400 percent of the FPL and higher. Source: Cohen.15
PERCENTAGE OF POPULATION WITH A DENTAL VISIT
(P < .05) to have dental coverage than were people in families with higher incomes. For those with coverage, people in poor and low-income families were the least likely (P > .05) to report a dental visit among all income groups. At each family income level, people without coverage were less likely (P > .05) to report a dental visit than were people with coverage. Table 1 shows private dental coverage status, the percentage of the population with a visit, the mean number of visits per person for those with a visit and mean total expenditures for people with a visit by dental coverage status and selected population characteristics. Overall, 51.4 percent of all people (n = 138,377,780) had private dental coverage in 1996. Of those with coverage, 56.6 percent (n = 78,255,354) reported at least one dental visit during 1996, while substantially fewer (P < .05) people (28.6 percent, n = 110,212,706) without coverage reported a visit. Among those with at least one visit, people with coverage reported a higher number of visits per year than did people without coverage (P < .05, 2.65 vs. 2.42, respectively). Among those with coverage, the mean total expenditure for those with dental expenditures was substantially larger than for those without coverage (P < .05, $417.20 vs. $298.70, respectively). We noted differences in coverage rates for the race, income, age and education categories, but sex did not appear to affect the likelihood (P > .05) of coverage. Nonwhites, people in poorer families and the elderly (those 65 years of age and older) were less likely (P < .05) to have dental coverage than were whites, younger people or people in families with higher incomes. In addition, college graduates were more likely (P < .05) to have coverage than were high school graduates. Both college graduates and high-school graduates were more likely (P < .05) to have coverage than were people with less formal education (less than highschool education). For each demographic and socioeconomic category, people with coverage were much more likely (P < .05) to have a dental visit than were people without coverage. The effect of coverage on visits per person and mean dental expenditures were mixed. For instance, coverage did not have an effect (P > .05) on visits per person in families at low, middle or high income levels and for children (18 years of age and younger), college graduates, Hispanics, people with limited formal education (less than high-school graduate) or people
PERCENTAGE OF POPULATION WITH COVERAGE
T R E N D S
100
80
63 55
60
43
43
42
40
30 20
22
20
0 Poor
Low Income
Middle Income
High Income
INCOME LEVEL With Private Dental Coverage
Without Private Dental Coverage
Figure 2. U.S. population with a dental visit, by income and private dental coverage, 1996. The population includes people in families with negative incomes. Poor: incomes below 100 percent of the federal poverty level, or FPL. Low income: incomes 100 to 199 percent of the FPL. Middle income: incomes 200 to 399 percent of the FPL. High income: incomes 400 percent of the FPL and higher. Source: Cohen.15
who resided in large metropolitan areas or nonmetropolitan areas. For other demographic and socioeconomic categories, people with coverage reported more (P < .05) visits per year than did people without coverage. Coverage did not have JADA, Vol. 133, November 2002
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TABLE 2
LOGISTIC REGRESSION ANALYSIS OF THE PROBABILITY OF HAVING PRIVATE DENTAL CARE COVERAGE, POPULATION WITH A DENTAL VISIT, NATURAL LOGARITHM OF VISITS AND NATURAL LOGARITHM OF EXPENDITURES DURING 1996.* POPULATION CHARACTERISTIC
LOGISTIC REGRESSION ESTIMATES Population With Coverage †
Population With at Least One Visit
Natural Logarithm of Number of Visits
Natural Logarithm of Expenditures
β Coefficient
P Value
β Coefficient
P Value
β Coefficient
P Value
β Coefficient
P Value
-0.2763
.0179
-1.2950
< .0001
0.5481
< .0001
4.5438
< .0001
NA§ NA
NA NA
0.9538 0.0000
< .0001 .0000
0.1025 0.0000
< .0001 .0000
0.3546 0.0000
< .0001 .0000
Age (years) 18 and younger‡ 19-44 45-64 65 and older
0.0000 -0.1124 -0.1743 -1.5452
.0000 .0181 .0073 < .0001
0.0000 -0.1559 0.1124 0.3873
.0000 .0016 .0462 < .0001
0.0000 -0.1369 0.0415 0.1712
.0000 < .0001 .1317 < .0001
0.0000 0.0311 0.3187 0.4642
.0000 .442 < .0001 < .0001
Sex Male‡ Female
0.0000 0.1635
.0000 < .0001
0.0000 0.3560
.0000 < .0001
0.0000 0.0687
.0000 < .0001
0.0000 0.0528
.0000 .0966
-0.0228
.8017
-0.7294
< .0001
-0.1368
< .0001
-0.1380
.0154
-0.2960 0.0000
.0020 .0000
-0.3117 0.0000
< .0001 .0000
-0.1163 0.0000
.0002 .0000
-0.1554 0.0000
.0056 .0000
-1.1885 -0.2062 0.0000
< .0001 .0006 .0000
-0.5714 -0.2503 0.0000
< .0001 < .0001 .0000
-0.0568 -0.0261 0.0000
.0499 .2437 .0000
-0.1083 -0.0256 0.0000
.0187 .4992 .0000
0.0000
.0000
0.0000
.0000
0.0000
.0000
0.0000
.0000
< .0001
0.5731
< .0000
0.0158
.6331
-0.0078
.8899
1.1554
< .0001
1.0001
< .0001
0.0468
.1807
-0.0003
.9958
0.5196
< .0001
0.0521
.4307
0.0775
.0076
0.3244
< .0001
< .0001
0.1168
.1192
0.0437
.1380
0.1463
.0013
.0000
0.0000
.0000
0.0000
.0000
0.0000
.0000
Intercept Coverage Covered Not covered‡
Race and Ethnicity Non-Hispanic black Hispanic White‡¶ Family Income by Poverty Status # Low income Middle income High income‡ Education** Some or no school‡ High school graduate College graduate Rural/Urban †† Large metropolitan Small metropolitan Nonmetropolitan‡
0.7814
0.4735 0.0000
Source: Cohen.15 Population without private coverage may include people with public coverage. Reference group. NA: Not applicable. Includes all other racial/ethnic groups not shown separately. Low income: incomes 100 to 199 percent of the federal poverty level, or FPL. Middle income: incomes 200 to 399 percent of the FPL. High income: incomes 400 percent of the FPL and higher. ** For people 18 years of age and younger, refers to parent’s education. †† Large metropolitan: central counties of areas with 1 million people or more. Small metropolitan: other metropolitan counties. Nonmetropolitan: nonmetropolitan counties either adjacent or not adjacent to urban areas. * † ‡ § ¶ #
an effect (P > .05) on mean dental expenditures for college graduates, non-Hispanic blacks, people at the poor family income level, or people who resided in nonmetropolitan areas. For other demographic and socioeconomic categories, people 1556
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with coverage had higher (P < .05) mean total expenditures than did people without coverage. Table 2 shows the results of the logistic regression analyses for the probability of having private dental care coverage (second and third columns)
T R E N D S
and for the probability of having at least one dental visit during the year (fourth and fifth columns). Table 2 also shows the ordinary least squares, or OLS, regression results for the natural logarithm of dental visits for people with at least one visit (sixth and seventh columns) and for the natural logarithm of expenditures for people with expenditures (eighth and ninth columns) during 1996. The explanatory variables in the coverage equation in the second and third columns include age, sex, race and ethnicity, family income level, education and rural/urban status. The other three equations in Table 2 contain the same explanatory variables, as well as a coverage status variable added to each of these models. We restricted Table 2 to people in families with incomes of 100 percent of the FPL or more, while we made estimations in Table 1 without this restriction. Results in the dental coverage equation in the second and third columns of Table 2 show that people aged 19 to 44 years, 45 to 64 years and 65 years and older were less likely to have coverage than were people aged 18 years and younger. In Table 1, without controlling for other influences on coverage, only the elderly had lower coverage than did the other age groups. Table 2 shows that females, people in families with more income, people with more education and people residing in metropolitan areas were more likely to have coverage than were males, people in families with less income, people with less education and people residing in nonmetropolitan areas. Table 1 mirrored these results, except that it showed no sex effect on coverage. In the logistic equation for the probability of a dental visit in the fourth and fifth columns of Table 2, we found that people with dental coverage were more likely (P < .05) to have a dental visit than were people without coverage. This result, which we found in Table 1 without controlling for other factors, is confirmed to hold after controls. In addition, people aged 45 to 64 years and 65 years and older were more likely (P < .05) to have had a dental visit than were people 18 years of age and younger. Females and college graduates were more likely to have had a dental visit than were males and people with limited formal education (less than high-school graduate). On the other hand, people aged 19 to 44 years, non-Hispanic blacks, Hispanics and people in families at middle or lower income levels were less likely (P < .05) to have a dental visit than
were people 18 years of age and younger, whites and people in families with high income. Rural/urban status did not add to the precision of this model. The sixth and seventh columns in Table 2 show the OLS regression for the natural logarithm of the number of dental visits during 1996 for people with at least one visit during the MEPS survey year. Controlling for other factors, people with dental coverage who visited the dentist at least once (P < .05) during the year had more dental visits during the year than did people who were dental service users without coverage. People who visited the dentist at least once during the year and who were aged 65 years and older had more dental visits than did dental users aged 18 years and younger. Women and people residing in large metropolitan areas (P < .05) who visited the dentist at least once during the year had more dental visits than did men and people residing in nonmetropolitan areas who used dental services. On the other hand, people who visited the dentist at least once during the year, were aged 19 to 44 years, were non-Hispanic black or Hispanic, and in families with low incomes had fewer dental visits than did dental users aged 18 years and younger, whites and people in families with high incomes. The eighth and ninth columns of Table 2 show the OLS regression estimates for the natural logarithm of dental expenditures for people with dental service expenditures during 1996. Controlling for other factors, this equation confirms the Table 1 result that among people who incurred dental expenses during the year those with dental coverage had higher dental expenditures than did those without coverage. In addition, dental users in the age groups 45 to 64 years and 65 years and older (P < .05) spent more on dental services than did people in the group aged 18 years and younger. People residing in metropolitan areas (P < .05) had higher dental expenditures than did people residing in nonmetropolitan areas. On the other hand, non-Hispanic blacks, Hispanics and people in families with low incomes had lower dental expenditures than did whites and people in families with high incomes. DISCUSSION
While these data and analyses are useful, they do have limitations. For instance, self-reporting of data is less accurate than data collected by observation or by dental record abstraction, and analyJADA, Vol. 133, November 2002
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T R E N D S
ses of data from different survey sources historically have resulted in varying national estimates.22 In addition, the specification of the dental coverage variable is a function of a report of coverage, a report of payment for dental care by a third party or both. Also, adverse selection may overstate the effect of dental coverage on use and expenditures should people needing extensive services self-select into coverage. This effect, however, may be mitigated in the MEPS data to the extent that most dental coverage is made available through employer-sponsored plans that offer little, if any, choice of plans. Finally, individual coverage plans may vary considerably in their degree of benefit generosity. Findings from previous studies have shown that the demand for dental services is related positively to the generosity of dental coverage.12,13 On the other hand, these data are useful and comprehensive and provide estimates that are nationally representative. As such, MEPS data are unique and provide important information from which dental visits and expenditures can be compared and analyzed in the context of dental care coverage. While the specification for dental coverage has limitations, its formulation is based on previously used and accepted methods, provides an acceptable nationally representative measure of dental care coverage, and makes possible valuable analyses and comparisons.8 Several studies9-13 have shown that the presence of private dental insurance coverage is positively associated with having a dental visit. The current study is one of the first to simultaneously describe the association between presence of private dental insurance and three important health practice measures: the probability of at least one dental visit during the year, the number of dental visits during the last year and expenditures. By looking at these three health practice measures side by side, we are able to describe the interrelation between dental care use and expenditures, across selected demographic and socioeconomic status, or SES, variables. Several studies9-13 have shown that the presence of private dental insurance coverage is positively associated with having a dental visit. MEPS data supported these findings; however, they also demonstrated that among people with a dental visit in the last year, private dental insurance coverage is associated with a greater number of dental visits per year and higher expenditures, when we controlled for selected 1558
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demographic and SES factors. These findings are important, because only 51.4 percent of Americans have private coverage. Our investigation also demonstrated that key demographic and SES variables were associated with dental care use, independent of private dental insurance coverage. For example, people with lower SES, as measured by education level and income level, were significantly less likely to have visited a dentist in the last year than were people with higher education levels or income levels. In addition, females were more likely to have visited a dentist than were males, and whites were more likely to have visited a dentist than were non-Hispanic blacks or Hispanics, when we controlled for insurance coverage and other covariates. Among those who had visited the dentist in the last year, MEPS also showed differences in the number of visits and level of expenditures across demographic and SES variables, independent of private dental insurance coverage. For example, people at a low income level made fewer visits to the dentist and had lower expenditures than did people at a high income level, regardless of insurance coverage. In addition, whites had more dental visits and higher expenditures than did non-Hispanic blacks or Hispanics. Finally, although females had more dental visits than did males, there were no differences in expenditures between the two sexes. Age-specific and rural- or urban-specific associations were more complex. Compared with people aged 18 years and younger, adults aged 19 to 44 years were less likely to have visited a dentist in the last year, and people 45 years and older were more likely to have visited a dentist in the last year, when we controlled for private dental insurance coverage. Among people with a dental visit in the last year and compared with people aged 18 years and younger, adults aged 19 to 44 years had fewer visits, whereas adults aged 65 years and older had more visits, independent of insurance coverage. By contrast, adults aged 45 years and older had higher expenditures than did people aged 18 years and younger. Regarding rural- or urban-specific associations, we found no differences in the probability of a dental visit in the last year across categories, when we controlled for private dental insurance coverage. People residing in large metropolitan areas, however, had more dental visits than did those residing in nonmetropolitan areas, and people
T R E N D S
The authors thank Joel Cohen, Ph.D., and Alan Monheit, Ph.D., for their comments on an earlier draft of this manuscript, as well as Devi Katikineni of Social and Scientific Systems, Bethesda, Md., for providing skillful computer programming support.
Dr. Manski is a professor, Department of Oral Health Care Delivery, Dental School, University of Maryland, Baltimore, and a senior scholar, Center for Cost and Financing Studies, Agency for Healthcare Research and Quality, Rockville, Md. Address reprint requests to Dr. Manski at Department of Oral Health Care Delivery, Dental School, University of Maryland, 666 West Baltimore St., Baltimore, Md. 21201, e-mail “Manski@Dental. umaryland.edu”.
Dr. Macek is an assistant professor, Department of Oral Health Care Delivery Dental School, University of Maryland, Baltimore.
Dr. Moeller is a health economist, Center for Cost and Financing Studies, Agency for Healthcare Research and Quality, Rockville, Md.
residing in small metropolitan and large metropolitan areas had higher expenditures than did those residing in nonmetropolitan areas. Despite differences across income levels, we found no statistically significant differences in number of visits or expenditures across education levels. These findings imply that the financial elements of SES play a more influential role in dental care use and costs than do the educational components of SES, at least for those with a dental visit in the last year. Future studies could investigate these possibilities in greater detail. CONCLUSION
Although presence of private dental insurance coverage is strongly associated with receipt of care, it is clear from this investigation that demographic and SES factors also must play a role. Programs designed to improve dental care use or control expenditures must take into consideration each of these determinants to be successful. Private dental insurance coverage is important, but it is only one piece of a complex puzzle. ■ Dr. Manski was supported for this investigation by the Agency for Healthcare Research and Quality, Rockville, Md. The views expressed in this article are those of the authors, and no official endorsement by Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services is intended or should be inferred.
1. Exhibit I: industry at a glance from 1994 through 1999. NADP 2000 dental benefits report: Enrollment and networks. Dallas: National Association of Dental Plans; 2000:5. 2. Source book on health insurance data, 1986-1987. Washington: Health Insurance Association of America; 1987. 3. Source book on health insurance data, 1990. Washington: Health Insurance Association of America; 1990. 4. 1984/85 Dental statistics handbook. Chicago: American Dental Association, Bureau of Economic and Behavioral Research; 1984. 5. Bloom B, Gift HC, Jack SS. Dental services and oral health. Vital Health Stat 10 1992;183:1-95. 6. Zatz M, Landay M, LeDell JD. Dental benefits and reimbursements. Dent Clin North Am 1987;31(2):193-207. 7. Feldstein PJ. Health care economics. 3rd ed. New York: Wiley; 1988. 8. Mueller CD, Schur CL, Paramore LC. Access to dental care in the United States. JADA 1998;129:429-37. 9. Eklund SA, Pittman JL, Smith RC. Trends in dental care among insured Americans: 1980 to 1995. JADA 1997;128:171-8. 10. Conrad DA, Milgrom P, Kiyak A. Insurance plan effects on dental provider treatment patterns for elderly patients. Med Care 1984; 22(5):430-46. 11. Manski RJ, Edelstein BL, Moeller JF. The impact of insurance coverage on children’s dental visits and expenditures, 1996 JADA 2001;132;1137-45. 12. Manning WG, Bailit HL, Benjamin B, Newhouse JP. The demand for dental care: evidence from a randomized trial in health insurance. JADA, 1985;110:895-902. 13. Mueller CD, Monheit AC. Insurance coverage and the demand for dental care: results for non-aged white adults. J Health Econ 1988;7(1):59-72. 14. Cohen JW, Machlin SR, Zuvekas SH, et al. Health care expense in the United States, 1996. Rockville, Md.: Agency for Healthcare Research and Quality; 2000. MEPS research findings 12. AHRQ publication 01-0009. 15. Cohen J. Design and methods of the Medical Expenditure Panel Survey household component. Rockville, Md.: U.S. Dept. of Health and Human Services, Public Health Service, Agency for Health Care Policy and Research; 1997. AHCPR publication 97-0026. 16. Duan N. A comparison of alternative models for the demand for medical care. Santa Monica, Calif.: Rand Corporation; 1982. 17. Manning WG, Newhouse JP, Duan N, et al. A two-part model of demand for medical care: preliminary results from the health insurance study. In: Van der Gaag J, Perlman M, eds. Health, economics, and health economics: Proceedings of the World Congress on Health Economics, Leiden, Netherlands, September 1980. Amsterdam, Netherlands: North-Holland; 1981:103-23. 18. Grossman M. The demand for health: A theoretical and empirical investigation. New York: National Bureau of Economic Research; 1972. 19. Phelps CE, Newhouse JP. Coinsurance and the demand for medical services. Santa Monica, Calif.: The Rand Corp; 1974. 20. Poisal JA, Chulis GS. Medicare beneficiaries and drug coverage. Health Aff (Millwood) 2000;19(2):248-56. 21. Shah BV, Barnwell BG, Bieler GS. SUDAAN. Users manual. Software for analysis of correlated data: user’s manual, release 6.40. Research Triangle Park, N.C.: Research Triangle Institute; 1995. 22. Macek MD, Manski RJ, Vargas CM, Moeller JF. Comparing oral health care utilization estimates in the United States across three nationally representative surveys. Health Serv Res 2002;37:499-521.
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Private dental coverage Who has it and how does it influence dental visits and expenditures? RICHARD J. MANSKI, D.D.S., M.B.A., Ph.D.; MARK D. MACEK, D.D.S., Dr.P.H.; JOHN F. MOELLER, Ph.D.
or many Americans, having dental insurance coverage may be an important influence on their decision to use dental care. During the last 40 years, interest in and demand for dental insurance has increased considerably. The number of enrollees in dental insurance plans has increased from about 4.5 million people in 1967 to more than 100 million people by 1990.1-4 Today, dental insurance is considered to be a popular and sought-after fringe benefit among many employees. Key According to the National Association of demographic Dental Plans, approximately 150 million Americans have some form of dental and socioecocare coverage.1 nomic status Dental insurance has been shown to variables were be an important factor in people’s deciassociated with sions to seek and use dental care serv5-13 dental care use, ices. According to the 1989 National independent of Health Interview Survey, or NHIS, while 70 percent of all people with private dental dental insurance had at least one dental insurance visit during 1989, only 50 percent of coverage. people without dental insurance reported at least one visit during the same period.5 Dental insurance also has been shown to affect dental provider treatment patterns.12,13 For instance, Mueller and Monheit13 found that having coverage doubles the likelihood that a user will obtain bridgework and increases by 38 percent the probability that a user will receive a crown. According to Conrad and colleagues10 and Mueller and Monheit,13 while increased coverage among elderly patients did not result in uniform increased costs, it did result in a substitution in services provided. Since the effect of having insurance is to reduce the perceived price of care, it is
F
ABSTRACT Background. Dental insurance has had a significant impact on dentistry and dental care use. Dental insurance coverage may influence people’s decisions to use dental care. During 1996, 42.9 percent of all dental expenditures were paid by private dental insurance. Methods. The focus of this analysis is on private dental coverage, use and expenditures for the U.S. civilian community-based population during 1996. The authors provide national estimates for the population with private dental coverage, the population with a dental visit, mean number of dental visits per year and mean total expenditures for several socioeconomic and demographic categories during 1996, using Medical Expenditure Panel Survey, or MEPS, data. Results. Poor and low-income people were less likely to have private dental coverage than were people with higher incomes. People without coverage at all income levels were less likely to report a dental visit than were people with coverage. When they controlled for coverage, the authors found that education at any income level did not appear to affect the likelihood of people’s having multiple visits or higher expenditures. Conclusion. People with private coverage are more likely to visit a dentist, have a greater number of visits and have higher expenditures than are those without coverage. Private dental insurance coverage, however, is not the only determinant of dental care use. MEPS data also show that other factors play key roles. Comprehensive strategies designed to improve dental care use should keep each of these determinants in mind. Practice Implications. While dentists may have a limited ability to influence people to seek care initially, they may be in a better position to influence the amount of care patients obtain, thereby helping make sure that patients receive the care that they need and want.
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TABLE 1
U.S. POPULATION WITH PRIVATE DENTAL COVERAGE, PERCENTAGE WITH VISITS, AND MEAN VISITS AND TOTAL EXPENDITURES, UNITED STATES, 1996.* TOTAL POPULATION (000s)
% POPULATION WITH DENTAL VISIT (SE†)
MEAN NUMBER OF VISITS‡ (SE)
MEAN TOTAL EXPENDITURES‡ (SE)
% POPULATION WITH PRIVATE DENTAL COVERAGE (SE)
TOTAL
268,905
42.9 (0.7)
2.55 (0.3)
373.8 (9.6)
51.4 (0.8)
Age (Years) 18 and younger 19 to 44 45 to 64 65 and older
75,326 105,318 54,217 34,050
42.5 41.1 48.8 40.3
2.74 2.20 2.70 2.92
374.8 321.3 425.1 438.0
52.3 56.5 58.4 23.0
Sex Male Female
131,527 137,379
39.7 (0.8) 46.0 (0.7)
2.48 (0.5) 2.61 (0.4)
361.7 (14.9) 383.8 (12.6)
51.7 (0.9) 51.3 (0.8)
33,668
26.1 (0.1)
2.05 (0.8)
299.5 (31.8)
42.3 (1.7)
29,979 205,258
28.5 (0.1) 47.8 (0.8)
2.22 (0.7) 2.63 (0.4)
317.2 (23.8) 385.4 (10.7)
34.2 (1.9) 55.5 (0.8)
38,298 53,406 88,262 88,939
26.2 28.8 44.9 56.6
2.11 2.37 2.56 2.69
267.9 282.3 384.0 414.9
14.4 32.9 60.5 69.6
49,025
23.6 (0.9)
2.28 (0.8)
309.6 (25.3)
20.8 (1.1)
150,047
42.3 (0.8)
2.48 (0.4)
373.7 (14.0)
53.3 (0.8)
67,139
59.3 (0.1)
2.74 (0.6)
394.1 (16.9)
71.2 (1.0)
120,841
44.4 (0.9)
2.67 (0.5)
433.4 (16.1)
55.4 (1.0)
92,992
44.8 (1.3)
2.49 (0.5)
343.7 (14.5)
53.5 (1.4)
51,640
39.0 (1.6)
2.35 (0.7)
277.8 (16.3)
39.8 (1.7)
POPULATION CHARACTERISTIC
Race and Ethnicity Non-Hispanic black Hispanic White§ Family Income by Poverty Status ¶ Poor Low income Middle income High income Education # Some or no school High school graduate College graduate Rural/Urban** Large metropolitan Small metropolitan Nonmetropolitan
(1.0) (0.7) (1.0) (1.4)
(1.1) (1.0) (0.9) (1.0)
not surprising that patients with coverage might substitute higher-cost treatment options in place of lower-cost, and perhaps less-preferred, alternatives.7 Dental insurance has had a profound effect on dentistry and dental care use. Insurance payments for dental care on behalf of enrollees are significant. During 1996, approximately 43 percent of all dental expenditures ($18 billion) were paid to dental providers from private insurance.14 We conducted this study to further examine the status of private dental insurance and the impact of private dental insurance coverage on 1552
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(0.7) (0.4) (0.6) (0.9)
(0.8) (0.8) (0.6) (0.5)
(23.3) (12.4) (16.2) (27.2)
(21.9) (14.6) (17.4) (16.4)
(1.2) (0.9) (1.1) (1.1)
(1.0) (1.3) (0.9) (1.0)
the use of and expenditures for dental care services. To do this, we analyzed data from the 1996 Medical Expenditure Panel Survey, or MEPS.15 METHODS
The U.S. Department of Health and Human Services has sponsored the administration of several national expenditure surveys since 1977. The 1996 MEPS is the third in a series of nationally representative health surveys of the U.S. community-based population sponsored by the Agency for Healthcare Research and Quality (formerly the Agency for Health Care Policy and
T R E N D S
TABLE 1 (CONTINUED)
U.S. POPULATION WITH PRIVATE DENTAL COVERAGE, PERCENTAGE WITH VISITS, AND MEAN VISITS AND TOTAL EXPENDITURES, UNITED STATES, 1996.* % POPULATION WITH AT LEAST ONE VISIT (SE)
MEAN NUMBER OF VISITS‡ (SE)
MEAN TOTAL EXPENDITURES‡ (SE)
Population With Private Dental Coverage
Population Without Private Dental Coverage
Population With Private Dental Coverage
Population Without Private Dental Coverage
Population With Private Dental Coverage
Population Without Private Dental Coverage
56.6 (0.8)
28.6 (0.8)
2.65 (.04)
2.42 (.06)
417.2 (12.3)
298.7 (13.6)
55.8 53.9 60.6 65.0
28.1 23.8 32.6 33.9
2.94 2.28 2.76 3.25
2.61 1.97 2.51 2.72
438.6 355.4 459.8 554.0
286.0 223.8 331.0 369.6
(1.3) (1.0) (1.1) (2.7)
(1.6) (0.9) (1.6) (1.5)
(.09) (.05) (.07) (.15)
(.16) (.08) (.10) (.11)
(30.4) (15.6) (19.5) (56.0)
(36.6) (19.3) (28.4) (28.7)
52.7 (1.0) 60.3 (0.9)
25.7 (1.0) 31.6 (0.9)
2.57 (.05) 2.71 (.05)
2.34 (.09) 2.48 (.07)
408.6 (20.0) 424.5 (16.0)
275.2 (18.2) 317.6 (19.6)
37.1 (1.9)
15.2 (1.3)
2.24 (.11)
1.87 (.11)
336.9 (46.1)
309.2 (52.6)
46.3 (1.9) 59.9 (0.9)
16.7 (1.1) 33.0 (1.0)
2.28 (.11) 2.70 (.04)
2.30 (.13) 2.48 (.07)
386.3 (37.5) 425.6 (13.4)
279.1 (30.7) 299.5 (14.8)
42.8 43.3 54.6 63.2
19.5 21.6 30.1 41.6
2.56 2.48 2.58 2.74
2.07 2.31 2.50 2.53
338.9 345.4 417.3 435.9
300.0 238.0 289.2 343.9
(3.0) (2.0) (1.0) (1.1)
(1.3) (1.1) (1.2) (1.5)
(.21) (.11) (.07) (.05)
(.11) (.12) (.10) (.10)
(41.5) (24.3) (21.9) (18.7)
(45.1) (22.4) (21.4) (28.4)
37.0 (1.8)
18.2 (1.1)
2.40 (.12)
2.37 (.13)
420.0 (56.6)
292.9 (33.0)
53.5 (1.0)
29.6 (1.0)
2.57 (.06)
2.34 (.07)
424.5 (18.2)
278.0 (17.2)
66.2 (1.1)
42.7 (1.9)
2.78 (.06)
2.61 (.12)
407.61 (19.1)
345.4 (30.5)
57.0 (1.0)
28.5 (1.3)
2.71 (.06)
2.67 (.10)
471.6 (20.8)
360.7 (24.9)
57.8 (1.6)
30.3 (1.6)
2.63 (.06)
2.26 (.10)
387.3 (17.0)
264.3 (20.9)
55.3 (1.8)
28.1 (1.7)
2.46 (.10)
2.25 (.10)
310.8 (20.6)
248.7 (23.0)
Source: Cohen.15 SE: Standard error. For people with a visit. Includes all other racial/ethnic groups not shown separately. Poor: incomes below 100 percent of the federal poverty level, or FPL. Low income: incomes 100 to 199 percent of the FPL. Middle income: incomes from 200 to 399 percent of the FPL. High income: incomes 400 percent of the FPL and higher. # For people 18 years of age and younger, refers to parent’s education. ** Large metropolitan: central counties of areas with 1 million people or more. Small metropolitan: other metropolitan counties. Nonmetropolitan: nonmetropolitan counties either adjacent or not adjacent to urban areas. * † ‡ § ¶
Research and, before that, the National Center for Health Services Research). MEPS collects health care expenditure, use and payment source data, as well as socioeconomic, demographic and health insurance data similar to its previous surveys. It differs from the National Medical Expen-
diture Survey and the National Medical Care Expenditure Survey in that data on household respondents in each panel are collected for two consecutive years and the survey is fielded continuously (that is, a new panel is selected every day).15 JADA, Vol. 133, November 2002
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T R E N D S
The sample for the 1996 MEPS was 21,571 people in 10,500 households who participated in NHIS. To collect health expenditure and use data for 1996, field researchers interviewed each MEPS household in person three times over an 18-month period; the third round of interviews were conducted between February and May 1997.15 The combined full-year 1996 response rate of the MEPS sample through the third round was 70 percent.15 In this analysis, we focus on dental coverage and the effect of coverage on dental care use in 1996 for the U.S. noninstitutionalized population. Specifically, we provide national estimates for dental coverage status, percentage of population with a dental visit, number of visits per person for those who had a visit, and mean total expenditure for people with a visit by coverage for each of several socioeconomic and demographic categories during 1996. We also conducted multivariate analyses to determine if the bivariate relationships shown in Table 1 would persist in a multivariate statistical model. We also assessed the relative impact of the socioeconomic and demographic variables on dental coverage status and utilization. Specifically, we estimated logistic regression models for dental coverage status (defined as = 1, if covered any time during the year; = 0, otherwise) and dental use (defined as = 1, if one or more dental visits during the year; = 0, otherwise). We estimated the impact of socioeconomic and demographic variables for age (18 years and younger, 19-44 years, 45-64 years and 65 years and older), sex, race/ethnicity (non-Hispanic black, Hispanic and white), family income (low income, middle income and high income), education and rural/urban status for the likelihood of having coverage. We then estimated the impact of coverage—controlling for age, sex, race/ethnicity, family income, education and rural/urban status—with two-part models for the likelihood of having a dental visit, the natural logarithm of the number of visits for those with a visit and the natural logarithm of expenditures for those with a visit.16,17 Economic models of demand developed by Grossman18 and Phelps and Newhouse19 support the empirical specification of noncoverage variables in the model. To minimize the effect of nonprivate or public coverage, we truncated regression models to include people with incomes greater than 100 percent of the federal poverty level, or FPL. 1554
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The variable for dental coverage indicates if a participant was eligible to receive or actually received private payments on their own behalf for dental care obtained during 1996. More specifically, a person is considered to have dental coverage if he or she had a self-report or proxy report of private dental coverage at any time during 1996. A person also is considered to have dental coverage if he or she had a self-report or proxy report of a private insurance payment for dental care received at any time during 1996. While this measure of private dental coverage is more inclusive than a simple self-report of coverage, it is less likely to miss covered participants who fail to report coverage and is similar to methods used in other studies, including a recent study of Medicare beneficiaries and drug coverage.20 We obtained coverage from the Health Insurance Section of the Household Component of MEPS. Private dental coverage refers to coverage offered as a supplement to medical coverage or as coverage obtained from an unrelated and separate plan. Distinctions between these types of dental coverage offerings are not discernible with these data. Additionally, since some medical coverage plans may include dental benefits that were not reported by respondents, these estimates may understate somewhat the actual number of people with coverage. To ensure sufficient numbers to produce reliable national estimates, we combined sociodemographic variable categories when necessary. We computed all estimates and statistics reported taking into account the complex sampling design of MEPS with the use of a software package (SUDAAN, release 6.40, Research Triangle Institute, Research Triangle Park, N.C.).21 RESULTS
There were 21,571 participants in the 1996 MEPS, representing approximately 268 million noninstitutionalized U.S. citizens. Of these, 51 percent were female (n = 11,282), 39 percent were nonwhite (n = 8,323), 58 percent were between the ages of 19 and 64 (n = 12,427), and 81 percent were in families with incomes above the FPL (n = 17,415). Figure 1 shows the percentage of the population with coverage according to family income level. Figure 2 displays the percentage of the population with a dental visit for those with and without coverage by family income level. People in poor and low-income families were less likely
100
70
80
61 60
33
40
14
20 0
Poor
Low Income
Middle Income
High Income
INCOME LEVEL Figure 1. U.S. population with private dental coverage by income, 1996. The population includes people in families with negative incomes. Poor: incomes below 100 percent of the federal poverty level, or FPL. Low income: incomes 100 to 199 percent of the FPL. Middle income: incomes 200 to 399 percent of the FPL. High income: incomes 400 percent of the FPL and higher. Source: Cohen.15
PERCENTAGE OF POPULATION WITH A DENTAL VISIT
(P < .05) to have dental coverage than were people in families with higher incomes. For those with coverage, people in poor and low-income families were the least likely (P > .05) to report a dental visit among all income groups. At each family income level, people without coverage were less likely (P > .05) to report a dental visit than were people with coverage. Table 1 shows private dental coverage status, the percentage of the population with a visit, the mean number of visits per person for those with a visit and mean total expenditures for people with a visit by dental coverage status and selected population characteristics. Overall, 51.4 percent of all people (n = 138,377,780) had private dental coverage in 1996. Of those with coverage, 56.6 percent (n = 78,255,354) reported at least one dental visit during 1996, while substantially fewer (P < .05) people (28.6 percent, n = 110,212,706) without coverage reported a visit. Among those with at least one visit, people with coverage reported a higher number of visits per year than did people without coverage (P < .05, 2.65 vs. 2.42, respectively). Among those with coverage, the mean total expenditure for those with dental expenditures was substantially larger than for those without coverage (P < .05, $417.20 vs. $298.70, respectively). We noted differences in coverage rates for the race, income, age and education categories, but sex did not appear to affect the likelihood (P > .05) of coverage. Nonwhites, people in poorer families and the elderly (those 65 years of age and older) were less likely (P < .05) to have dental coverage than were whites, younger people or people in families with higher incomes. In addition, college graduates were more likely (P < .05) to have coverage than were high school graduates. Both college graduates and high-school graduates were more likely (P < .05) to have coverage than were people with less formal education (less than highschool education). For each demographic and socioeconomic category, people with coverage were much more likely (P < .05) to have a dental visit than were people without coverage. The effect of coverage on visits per person and mean dental expenditures were mixed. For instance, coverage did not have an effect (P > .05) on visits per person in families at low, middle or high income levels and for children (18 years of age and younger), college graduates, Hispanics, people with limited formal education (less than high-school graduate) or people
PERCENTAGE OF POPULATION WITH COVERAGE
T R E N D S
100
80
63 55
60
43
43
42
40
30 20
22
20
0 Poor
Low Income
Middle Income
High Income
INCOME LEVEL With Private Dental Coverage
Without Private Dental Coverage
Figure 2. U.S. population with a dental visit, by income and private dental coverage, 1996. The population includes people in families with negative incomes. Poor: incomes below 100 percent of the federal poverty level, or FPL. Low income: incomes 100 to 199 percent of the FPL. Middle income: incomes 200 to 399 percent of the FPL. High income: incomes 400 percent of the FPL and higher. Source: Cohen.15
who resided in large metropolitan areas or nonmetropolitan areas. For other demographic and socioeconomic categories, people with coverage reported more (P < .05) visits per year than did people without coverage. Coverage did not have JADA, Vol. 133, November 2002
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TABLE 2
LOGISTIC REGRESSION ANALYSIS OF THE PROBABILITY OF HAVING PRIVATE DENTAL CARE COVERAGE, POPULATION WITH A DENTAL VISIT, NATURAL LOGARITHM OF VISITS AND NATURAL LOGARITHM OF EXPENDITURES DURING 1996.* POPULATION CHARACTERISTIC
LOGISTIC REGRESSION ESTIMATES Population With Coverage †
Population With at Least One Visit
Natural Logarithm of Number of Visits
Natural Logarithm of Expenditures
β Coefficient
P Value
β Coefficient
P Value
β Coefficient
P Value
β Coefficient
P Value
-0.2763
.0179
-1.2950
< .0001
0.5481
< .0001
4.5438
< .0001
NA§ NA
NA NA
0.9538 0.0000
< .0001 .0000
0.1025 0.0000
< .0001 .0000
0.3546 0.0000
< .0001 .0000
Age (years) 18 and younger‡ 19-44 45-64 65 and older
0.0000 -0.1124 -0.1743 -1.5452
.0000 .0181 .0073 < .0001
0.0000 -0.1559 0.1124 0.3873
.0000 .0016 .0462 < .0001
0.0000 -0.1369 0.0415 0.1712
.0000 < .0001 .1317 < .0001
0.0000 0.0311 0.3187 0.4642
.0000 .442 < .0001 < .0001
Sex Male‡ Female
0.0000 0.1635
.0000 < .0001
0.0000 0.3560
.0000 < .0001
0.0000 0.0687
.0000 < .0001
0.0000 0.0528
.0000 .0966
-0.0228
.8017
-0.7294
< .0001
-0.1368
< .0001
-0.1380
.0154
-0.2960 0.0000
.0020 .0000
-0.3117 0.0000
< .0001 .0000
-0.1163 0.0000
.0002 .0000
-0.1554 0.0000
.0056 .0000
-1.1885 -0.2062 0.0000
< .0001 .0006 .0000
-0.5714 -0.2503 0.0000
< .0001 < .0001 .0000
-0.0568 -0.0261 0.0000
.0499 .2437 .0000
-0.1083 -0.0256 0.0000
.0187 .4992 .0000
0.0000
.0000
0.0000
.0000
0.0000
.0000
0.0000
.0000
< .0001
0.5731
< .0000
0.0158
.6331
-0.0078
.8899
1.1554
< .0001
1.0001
< .0001
0.0468
.1807
-0.0003
.9958
0.5196
< .0001
0.0521
.4307
0.0775
.0076
0.3244
< .0001
< .0001
0.1168
.1192
0.0437
.1380
0.1463
.0013
.0000
0.0000
.0000
0.0000
.0000
0.0000
.0000
Intercept Coverage Covered Not covered‡
Race and Ethnicity Non-Hispanic black Hispanic White‡¶ Family Income by Poverty Status # Low income Middle income High income‡ Education** Some or no school‡ High school graduate College graduate Rural/Urban †† Large metropolitan Small metropolitan Nonmetropolitan‡
0.7814
0.4735 0.0000
Source: Cohen.15 Population without private coverage may include people with public coverage. Reference group. NA: Not applicable. Includes all other racial/ethnic groups not shown separately. Low income: incomes 100 to 199 percent of the federal poverty level, or FPL. Middle income: incomes 200 to 399 percent of the FPL. High income: incomes 400 percent of the FPL and higher. ** For people 18 years of age and younger, refers to parent’s education. †† Large metropolitan: central counties of areas with 1 million people or more. Small metropolitan: other metropolitan counties. Nonmetropolitan: nonmetropolitan counties either adjacent or not adjacent to urban areas. * † ‡ § ¶ #
an effect (P > .05) on mean dental expenditures for college graduates, non-Hispanic blacks, people at the poor family income level, or people who resided in nonmetropolitan areas. For other demographic and socioeconomic categories, people 1556
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with coverage had higher (P < .05) mean total expenditures than did people without coverage. Table 2 shows the results of the logistic regression analyses for the probability of having private dental care coverage (second and third columns)
T R E N D S
and for the probability of having at least one dental visit during the year (fourth and fifth columns). Table 2 also shows the ordinary least squares, or OLS, regression results for the natural logarithm of dental visits for people with at least one visit (sixth and seventh columns) and for the natural logarithm of expenditures for people with expenditures (eighth and ninth columns) during 1996. The explanatory variables in the coverage equation in the second and third columns include age, sex, race and ethnicity, family income level, education and rural/urban status. The other three equations in Table 2 contain the same explanatory variables, as well as a coverage status variable added to each of these models. We restricted Table 2 to people in families with incomes of 100 percent of the FPL or more, while we made estimations in Table 1 without this restriction. Results in the dental coverage equation in the second and third columns of Table 2 show that people aged 19 to 44 years, 45 to 64 years and 65 years and older were less likely to have coverage than were people aged 18 years and younger. In Table 1, without controlling for other influences on coverage, only the elderly had lower coverage than did the other age groups. Table 2 shows that females, people in families with more income, people with more education and people residing in metropolitan areas were more likely to have coverage than were males, people in families with less income, people with less education and people residing in nonmetropolitan areas. Table 1 mirrored these results, except that it showed no sex effect on coverage. In the logistic equation for the probability of a dental visit in the fourth and fifth columns of Table 2, we found that people with dental coverage were more likely (P < .05) to have a dental visit than were people without coverage. This result, which we found in Table 1 without controlling for other factors, is confirmed to hold after controls. In addition, people aged 45 to 64 years and 65 years and older were more likely (P < .05) to have had a dental visit than were people 18 years of age and younger. Females and college graduates were more likely to have had a dental visit than were males and people with limited formal education (less than high-school graduate). On the other hand, people aged 19 to 44 years, non-Hispanic blacks, Hispanics and people in families at middle or lower income levels were less likely (P < .05) to have a dental visit than
were people 18 years of age and younger, whites and people in families with high income. Rural/urban status did not add to the precision of this model. The sixth and seventh columns in Table 2 show the OLS regression for the natural logarithm of the number of dental visits during 1996 for people with at least one visit during the MEPS survey year. Controlling for other factors, people with dental coverage who visited the dentist at least once (P < .05) during the year had more dental visits during the year than did people who were dental service users without coverage. People who visited the dentist at least once during the year and who were aged 65 years and older had more dental visits than did dental users aged 18 years and younger. Women and people residing in large metropolitan areas (P < .05) who visited the dentist at least once during the year had more dental visits than did men and people residing in nonmetropolitan areas who used dental services. On the other hand, people who visited the dentist at least once during the year, were aged 19 to 44 years, were non-Hispanic black or Hispanic, and in families with low incomes had fewer dental visits than did dental users aged 18 years and younger, whites and people in families with high incomes. The eighth and ninth columns of Table 2 show the OLS regression estimates for the natural logarithm of dental expenditures for people with dental service expenditures during 1996. Controlling for other factors, this equation confirms the Table 1 result that among people who incurred dental expenses during the year those with dental coverage had higher dental expenditures than did those without coverage. In addition, dental users in the age groups 45 to 64 years and 65 years and older (P < .05) spent more on dental services than did people in the group aged 18 years and younger. People residing in metropolitan areas (P < .05) had higher dental expenditures than did people residing in nonmetropolitan areas. On the other hand, non-Hispanic blacks, Hispanics and people in families with low incomes had lower dental expenditures than did whites and people in families with high incomes. DISCUSSION
While these data and analyses are useful, they do have limitations. For instance, self-reporting of data is less accurate than data collected by observation or by dental record abstraction, and analyJADA, Vol. 133, November 2002
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T R E N D S
ses of data from different survey sources historically have resulted in varying national estimates.22 In addition, the specification of the dental coverage variable is a function of a report of coverage, a report of payment for dental care by a third party or both. Also, adverse selection may overstate the effect of dental coverage on use and expenditures should people needing extensive services self-select into coverage. This effect, however, may be mitigated in the MEPS data to the extent that most dental coverage is made available through employer-sponsored plans that offer little, if any, choice of plans. Finally, individual coverage plans may vary considerably in their degree of benefit generosity. Findings from previous studies have shown that the demand for dental services is related positively to the generosity of dental coverage.12,13 On the other hand, these data are useful and comprehensive and provide estimates that are nationally representative. As such, MEPS data are unique and provide important information from which dental visits and expenditures can be compared and analyzed in the context of dental care coverage. While the specification for dental coverage has limitations, its formulation is based on previously used and accepted methods, provides an acceptable nationally representative measure of dental care coverage, and makes possible valuable analyses and comparisons.8 Several studies9-13 have shown that the presence of private dental insurance coverage is positively associated with having a dental visit. The current study is one of the first to simultaneously describe the association between presence of private dental insurance and three important health practice measures: the probability of at least one dental visit during the year, the number of dental visits during the last year and expenditures. By looking at these three health practice measures side by side, we are able to describe the interrelation between dental care use and expenditures, across selected demographic and socioeconomic status, or SES, variables. Several studies9-13 have shown that the presence of private dental insurance coverage is positively associated with having a dental visit. MEPS data supported these findings; however, they also demonstrated that among people with a dental visit in the last year, private dental insurance coverage is associated with a greater number of dental visits per year and higher expenditures, when we controlled for selected 1558
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demographic and SES factors. These findings are important, because only 51.4 percent of Americans have private coverage. Our investigation also demonstrated that key demographic and SES variables were associated with dental care use, independent of private dental insurance coverage. For example, people with lower SES, as measured by education level and income level, were significantly less likely to have visited a dentist in the last year than were people with higher education levels or income levels. In addition, females were more likely to have visited a dentist than were males, and whites were more likely to have visited a dentist than were non-Hispanic blacks or Hispanics, when we controlled for insurance coverage and other covariates. Among those who had visited the dentist in the last year, MEPS also showed differences in the number of visits and level of expenditures across demographic and SES variables, independent of private dental insurance coverage. For example, people at a low income level made fewer visits to the dentist and had lower expenditures than did people at a high income level, regardless of insurance coverage. In addition, whites had more dental visits and higher expenditures than did non-Hispanic blacks or Hispanics. Finally, although females had more dental visits than did males, there were no differences in expenditures between the two sexes. Age-specific and rural- or urban-specific associations were more complex. Compared with people aged 18 years and younger, adults aged 19 to 44 years were less likely to have visited a dentist in the last year, and people 45 years and older were more likely to have visited a dentist in the last year, when we controlled for private dental insurance coverage. Among people with a dental visit in the last year and compared with people aged 18 years and younger, adults aged 19 to 44 years had fewer visits, whereas adults aged 65 years and older had more visits, independent of insurance coverage. By contrast, adults aged 45 years and older had higher expenditures than did people aged 18 years and younger. Regarding rural- or urban-specific associations, we found no differences in the probability of a dental visit in the last year across categories, when we controlled for private dental insurance coverage. People residing in large metropolitan areas, however, had more dental visits than did those residing in nonmetropolitan areas, and people
T R E N D S
The authors thank Joel Cohen, Ph.D., and Alan Monheit, Ph.D., for their comments on an earlier draft of this manuscript, as well as Devi Katikineni of Social and Scientific Systems, Bethesda, Md., for providing skillful computer programming support.
Dr. Manski is a professor, Department of Oral Health Care Delivery, Dental School, University of Maryland, Baltimore, and a senior scholar, Center for Cost and Financing Studies, Agency for Healthcare Research and Quality, Rockville, Md. Address reprint requests to Dr. Manski at Department of Oral Health Care Delivery, Dental School, University of Maryland, 666 West Baltimore St., Baltimore, Md. 21201, e-mail “Manski@Dental. umaryland.edu”.
Dr. Macek is an assistant professor, Department of Oral Health Care Delivery Dental School, University of Maryland, Baltimore.
Dr. Moeller is a health economist, Center for Cost and Financing Studies, Agency for Healthcare Research and Quality, Rockville, Md.
residing in small metropolitan and large metropolitan areas had higher expenditures than did those residing in nonmetropolitan areas. Despite differences across income levels, we found no statistically significant differences in number of visits or expenditures across education levels. These findings imply that the financial elements of SES play a more influential role in dental care use and costs than do the educational components of SES, at least for those with a dental visit in the last year. Future studies could investigate these possibilities in greater detail. CONCLUSION
Although presence of private dental insurance coverage is strongly associated with receipt of care, it is clear from this investigation that demographic and SES factors also must play a role. Programs designed to improve dental care use or control expenditures must take into consideration each of these determinants to be successful. Private dental insurance coverage is important, but it is only one piece of a complex puzzle. ■ Dr. Manski was supported for this investigation by the Agency for Healthcare Research and Quality, Rockville, Md. The views expressed in this article are those of the authors, and no official endorsement by Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services is intended or should be inferred.
1. Exhibit I: industry at a glance from 1994 through 1999. NADP 2000 dental benefits report: Enrollment and networks. Dallas: National Association of Dental Plans; 2000:5. 2. Source book on health insurance data, 1986-1987. Washington: Health Insurance Association of America; 1987. 3. Source book on health insurance data, 1990. Washington: Health Insurance Association of America; 1990. 4. 1984/85 Dental statistics handbook. Chicago: American Dental Association, Bureau of Economic and Behavioral Research; 1984. 5. Bloom B, Gift HC, Jack SS. Dental services and oral health. Vital Health Stat 10 1992;183:1-95. 6. Zatz M, Landay M, LeDell JD. Dental benefits and reimbursements. Dent Clin North Am 1987;31(2):193-207. 7. Feldstein PJ. Health care economics. 3rd ed. New York: Wiley; 1988. 8. Mueller CD, Schur CL, Paramore LC. Access to dental care in the United States. JADA 1998;129:429-37. 9. Eklund SA, Pittman JL, Smith RC. Trends in dental care among insured Americans: 1980 to 1995. JADA 1997;128:171-8. 10. Conrad DA, Milgrom P, Kiyak A. Insurance plan effects on dental provider treatment patterns for elderly patients. Med Care 1984; 22(5):430-46. 11. Manski RJ, Edelstein BL, Moeller JF. The impact of insurance coverage on children’s dental visits and expenditures, 1996 JADA 2001;132;1137-45. 12. Manning WG, Bailit HL, Benjamin B, Newhouse JP. The demand for dental care: evidence from a randomized trial in health insurance. JADA, 1985;110:895-902. 13. Mueller CD, Monheit AC. Insurance coverage and the demand for dental care: results for non-aged white adults. J Health Econ 1988;7(1):59-72. 14. Cohen JW, Machlin SR, Zuvekas SH, et al. Health care expense in the United States, 1996. Rockville, Md.: Agency for Healthcare Research and Quality; 2000. MEPS research findings 12. AHRQ publication 01-0009. 15. Cohen J. Design and methods of the Medical Expenditure Panel Survey household component. Rockville, Md.: U.S. Dept. of Health and Human Services, Public Health Service, Agency for Health Care Policy and Research; 1997. AHCPR publication 97-0026. 16. Duan N. A comparison of alternative models for the demand for medical care. Santa Monica, Calif.: Rand Corporation; 1982. 17. Manning WG, Newhouse JP, Duan N, et al. A two-part model of demand for medical care: preliminary results from the health insurance study. In: Van der Gaag J, Perlman M, eds. Health, economics, and health economics: Proceedings of the World Congress on Health Economics, Leiden, Netherlands, September 1980. Amsterdam, Netherlands: North-Holland; 1981:103-23. 18. Grossman M. The demand for health: A theoretical and empirical investigation. New York: National Bureau of Economic Research; 1972. 19. Phelps CE, Newhouse JP. Coinsurance and the demand for medical services. Santa Monica, Calif.: The Rand Corp; 1974. 20. Poisal JA, Chulis GS. Medicare beneficiaries and drug coverage. Health Aff (Millwood) 2000;19(2):248-56. 21. Shah BV, Barnwell BG, Bieler GS. SUDAAN. Users manual. Software for analysis of correlated data: user’s manual, release 6.40. Research Triangle Park, N.C.: Research Triangle Institute; 1995. 22. Macek MD, Manski RJ, Vargas CM, Moeller JF. Comparing oral health care utilization estimates in the United States across three nationally representative surveys. Health Serv Res 2002;37:499-521.
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