Abstract
Objectives:
Understanding and addressing contributing factors to unmet dental need is an important public health challenge. This study investigated the prevalence of, and factors associated with, self-reported unmet dental need using a nationally representative sample of U.S. adults.
Methods:
This was a cross-sectional study using the Medical Expenditures Panel Survey (MEPS) from 2016. The weighted prevalence of unmet dental need was estimated among individuals aged 18 years or older. Chi-squared and multivariate logit regression with marginal effects (i.e., absolute risk differences) were used to measure the association of unmet dental need with respondent characteristics.
Results:
The prevalence of adults reporting unmet dental need was 6% (95% CI: 5.5 to 6.5). Adults with dental insurance were 1.7 percentage points (95% CI: −2.8 to −0.6) less likely to report unmet dental needs than adults without dental insurance. Those with middle income were 2.3 percentage points (95% CI: 1.2 to 3.4), those with low income were 3.3 percentage points (95% CI: 1.7 to 5.0), and those with poor/negative/near-poor income were 4.2 percentage points (95% CI: 2.7 to 5.7) more likely to report an unmet dental need than adults with high income. Both Hispanics (−1.7 percentage points [95% CI: −2.8 to −0.6]) and non-Hispanic Blacks (−1.1 percentage points [95% CI: −2.1 to −0.1]) were less likely to report an unmet dental need than whites. Smoking, education, general health status, chronic disease, and marital status were also significantly associated with reporting an unmet dental need.
Conclusion:
Future policies should continue to address cost and coverage barriers to adult dental care, as these remain significant barriers to access, particularly for low-income adults. Future research should evaluate the reasons adults report unmet dental need and explore how adults’ judgment of dental need compares to providers’ clinical judgment. Additionally, research that explores how race and ethnicity affect perceptions of unmet dental need is warranted.
Introduction
Adequate and equitable access to dental care continues to be a public health challenge in the United States, particularly for underrepresented racial minorities and those with low income (1–5). Oral diseases, if left untreated, may lead to pain, discomfort, social anxiety, depression, and problems chewing, talking, and smiling (6–12). Individuals without access to adequate dental care are more likely to seek treatment from hospital emergency department visits, which are costly and avoidable (13–17). Despite the importance of maintaining oral health, adults and children continually report unmet dental need at a greater frequency than other types of health services, including medical care, prescription drugs, mental health care, or vision care (18–21). Notably, adults in the United States are more likely to have medical and prescription insurance coverage than dental coverage (18). Public insurance programs for low-income non-elderly adults vary greatly across states and range from providing emergency treatment coverage only to providing extensive coverage for preventive, minor and major restorative dental care (22). Medicare, the U.S. national health insurance program for elderly adults (65 years or older) and the disabled, provides no dental benefits to its’ enrollees (22).
Although more commonly reported, unmet dental need is understudied in comparison to the multiple studies that have investigated unmet medical care need among adults (19,23–25). In 2007, 5.5% of the US population was said to have unmet dental need (20). Notably, the presence of unmet dental need is disproportionately higher among vulnerable populations, such as HIV patients (14 – 49%), pregnant women (25%), and homeless individuals (41%) (26–30). However, gaps remain in identifying and characterizing self-reported unmet dental need among adult populations, particularly in the United States. Prior studies have identified income, education, race, and insurance status to be significantly associated with unmet dental need (26,28,31). However, this evidence is limited in its consideration of a population from a single state or a single disadvantaged group (21,26–31).
This study determined the prevalence of self-reported unmet dental need among adults using a nationally representative sample of the U.S. population. We examined the relationship between individual demographic characteristics, financial resources, and health factors with self-reported unmet dental need. These findings will be relevant to dental providers who aim to address unmet dental needs as part of disease prevention and management, and patient overall well-being. Also, healthcare administrators, policy-makers, and payors may find value in discerning correlates of unmet dental need in their constituencies as they strive to improve the quality of care and overall health of the populations they serve.
Methods
Data for this cross-sectional study came from the 2016 Medical Expenditure Panel Survey (MEPS) Full-Year Consolidated Data File. MEPS is a nationally representative annual survey of individuals and families across the United States, collecting information through interviews on demographic characteristics, health conditions and status, specific health services, and costs (32). Data for this study were derived primarily from the MEPS Household Component (MEPS-HC) and include specific questions about dental services and benefits (32). A subsample of households participating in the previous year’s National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics is drawn for participation in MEPS (32). In 2016, 34,655 persons participated in MEPS-HC (46% response rate) (33). For our analysis, we excluded anyone who was under the age of 18 (n=9,211).
Our primary outcome variable was a binary variable indicating self-reported delayed or unmet dental need. Specifically, participants in the survey were asked two questions about whether they were 1) unable to get or 2) were delayed in getting “dental care, tests, or treatments they or a dentist believed necessary” for any reason in the preceding 12 months. Similar to previous analyses of MEPS, we coded responses as “Yes” (to either question) or “No” (to both questions) (20,34).
We selected our independent variables utilizing the conceptual framework of Andersen’s Behavioral Model for Health Services Use and previous literature on dental care utilization (35).https://paperpile.com/c/4MBN9X/cKeC+ZBw9+Sroz+Gshl+XIKF The Behavioral Model of Health Services Use by Andersen is a framework intended to explain contextual and individual factors (including predisposing characteristics, enabling resources, and need for care) associated with equitable access, utilization, and outcomes of health care (36,37).https://paperpile.com/c/4MBN9X/fAcW+efYP Under Andersen’s Behavioral Model, predisposing characteristics include gender, age, race/ethnicity, marital status, smoking status, and education level; enabling resources include income, health insurance status, and dental insurance status. To measure need for health services, we included self-reported chronic diseases/conditions and general health status. Specifically, we created binary variables for gender, marital status (currently married or not married), smoking status (current smoker, not currently smoking), private dental insurance coverage (yes/no), and whether (yes, no) the individual self-reported having a chronic disease or condition (diabetes, high cholesterol, high blood pressure, cardiovascular disease, respiratory disease, or arthritis). We specifically selected conditions associated with poor oral health (38–40). Categorical variables included age (ages 18–44, 45–64, 65–75, and 76 and above), educational level (less than a high school degree, high school degree, or some form or college), income as a percentage of the federal poverty level (negative/poor/near-poor income (less than 125%), low income (125% to less than 200%), middle income (200% to less than 400%), and high income (greater than or equal to 400%), race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other/multi-racial), general health status (excellent & very good health, good health, fair & poor health), and health insurance status (uninsured, private health insurance, public health insurance, Medicare only, Medicare and private health insurance, and dual-eligible Medicare and Medicaid).
Complete information on each variable contained within MEPS, along with missing data, has been previously reported (33). There were no missing values for the following independent variables: gender, age, race/ethnicity, income, and health insurance coverage. Missing values did exist for current smoker status, marital status, education level, private dental insurance status, general health status, and self-reported comorbidities. Imputing missing values in analyses, particularly variables that are likely not missing at random (such as smoking status, which may be missing due to social desirability bias among respondents), may produce more bias in results (41). Similar to previous studies’ methodology using MEPS, we chose to exclude adults with missing values for independent variables from any analyses (42). Therefore, a total of 4,404 cases were excluded (4,231 missing current smoking status (16.6%), 171 missing education level (0.67%), and 2 missing dental insurance status (<0.01%)).
Unweighted and weighted descriptive statistics of participant characteristics were calculated. The prevalence of unmet dental need for the entire sample was estimated. Next, we determined bivariate relationships between our composite binary indicator of unmet dental need and each independent variable (respondents’ predisposing factors, enabling resources, and need for health services) using χ2 tests. We further calculated the marginal effects of each independent variable included in the multivariate logistic regression model. Because odds ratios cannot be compared across different samples, and for ease of interpretation, we calculated the marginal effects of each independent variable (43,44). The marginal effects can be interpreted as the absolute risk difference attributable to each characteristic, holding all other covariates at the mean. Corresponding 95% confidence intervals were also calculated. Finally, among adults who had unmet dental need, we report the reasons they believed they experienced unmet dental need within the preceding 12 months.
We accounted for the complex sampling design by using person-level sample weights and strata provided by MEPS, which are designed to account for MEPS differential sampling probabilities and nonresponse (45). All data were analyzed using StataSE, Version 16 (College Station, Texas) (46). Findings were considered statistically significant if p-values for coefficients were below 0.05.
Results
A total of 21,040 adults were included in this 2016 cross-sectional analysis. Descriptive statistics of the sample’s characteristics are presented in Table 1. Briefly, 64.0% were non-Hispanic White, 59% had private health insurance, 59% had a self-reported chronic disease, 52.2% and 42.1% had at least one dental visit within the year. The prevalence of adults reporting an unmet dental need was 6.0% (95% CI: 5.5 to 6.5). Table 1 also reports the bivariate relationships between self-reported unmet dental need and individual-level predisposing factors, enabling resources, and need.
Table 1:
Sample description according to characteristics of adults in the United States and reported unmet dental need (N=21,040)
| Characteristic | Unweighted Count | Total Sample Weighted % (95% CIa) | Reported unmet dental need Weighted % (95% CIa) |
|---|---|---|---|
| Reported unmet dental need | 1,332 | 6.0 (5.5 to 6.5) | 6.0 (5.5 to 6.5) |
| Predisposing Characteristics | |||
| Gender | |||
| Female | 11,427 | 52.2 (51.5 to 52.8) | 6.4 (5.8 to 7.1) |
| Male | 9,613 | 47.8 (47.1 to 48.5) | 5.5 (4.9 to 6.1) |
| Ages | |||
| 18–44 | 9,841 | 45.1 (43.8 to 46.4) | 5.6 (4.9 to 6.3) |
| 45–64 | 7,134 | 33.8 (32.9 to 34.8) | 6.9 (6.2 to 7.7) |
| 65–75 | 2,524 | 13.2 (12.4 to 13.9) | 5.8 (4.9 to 7.0) |
| 76 and above | 1,541 | 7.9 (7.3 to 8.5) | 4.4 (3.3 to 5.8) |
| Race/Ethnicity | |||
| Non-Hispanic White | 9,079 | 64.0 (61.9 to 66.0) | 5.9 (5.3 to 6.6) |
| Non-Hispanic Black | 3,775 | 11.6 (10.5 to 12.9) | 6.9 (5.9 to 8.0) |
| Hispanic | 6,004 | 15.7 (14.1 to 17.4) | 5.1 (4.4 to 5.9) |
| Other/Multi-racial | 2,182 | 8.7 (7.6 to 9.8) | 6.9 (5.6 to 8.4) |
| Current Smoker | |||
| Yes | 2,995 | 14.2 (13.3 to 15.1) | 10.7 (9.4 to 12.2) |
| No | 18,045 | 85.8 (84.9 to 86.7) | 5.2 (4.7 to 5.7) |
| Married | |||
| Yes | 10,180 | 52.9 (51.6 to 54.1) | 4.3 (3.7 to 4.9) |
| No | 10,860 | 47.1 (45.9 to 48.4) | 7.9 (7.2 to 8.6) |
| Education | |||
| Less than High School | 4,124 | 12.6 (11.8 to 13.5) | 7.1 (5.9 to 8.4) |
| High School/GED | 9,973 | 47.2 (45.9 to 48.5) | 6.2 (5.6 to 6.9) |
| Bachelors/Other | 5,045 | 28.8 (27.7 to 29.9) | 5.5 (4.8 to 6.4) |
| Masters/Doctorate | 1,898 | 11.4 (10.6 to 12.2) | 4.8 (3.6 to 6.3) |
| Enabling Resources | |||
| Incomeb | |||
| Poor/Negative/Near Poor | 4,858 | 15.0 (14.0 to 16.0) | 11.5 (10.1 to 13.1) |
| Low Income | 3,309 | 12.0 (11.4 to 12.7) | 8.3 (7.0 to 9.8) |
| Middle Income | 6,136 | 28.8 (27.8 to 29.9) | 6.0 (5.2 to 6.9) |
| High Income | 6,737 | 44.2 (42.6 to 45.7) | 3.4 (2.8 to 4.1) |
| Health insurance coverage | |||
| Uninsured | 2,585 | 8.1 (7.5 to 8.9) | 7.7 (6.0 to 9.7) |
| Private | 10,668 | 59.0 (57.5 to 60.4) | 4.7 (4.1 to 5.3) |
| Public | 3,787 | 12.1 (11.2 to 13.1) | 12.2 (10.7 to 13.9) |
| Medicare | 1,416 | 7.1 (6.5 to 7.8) | 6.1 (4.7 to 7.8) |
| Dual-eligible Medicare and Medicaid | 702 | 2.2 (2.0 to 2.5) | 11.5 (8.8 to 14.8) |
| Medicare & Private | 1,882 | 11.5 (10.7 to 12.2) | 3.6 (2.8 to 4.7) |
| Private Dental insurance | |||
| Yes | 7,803 | 43.8 (42.6 to 45.7) | 3.9 (3.3 to 4.5) |
| No | 13,237 | 56.2 (54.3 to 57.4) | 7.6 (7.0 to 8.3) |
| Need | |||
| General health status | |||
| Excellent or very good health | 7,775 | 42.3 (41.0 to 43.5) | 3.4 (2.9 to 4.1) |
| Good health | 5,473 | 21.8 (20.9 to 22.7) | 11.1 (10.0 to 12.4) |
| Poor or fair health | 7,792 | 35.9 (35.0 to 36.9) | 5.8 (5.1 to 6.7) |
| Self-reported chronic disease | |||
| Yes | 12,151 | 59.0 (58.0 to 60.0) | 7.1 (6.5 to 7.8) |
| No | 8,889 | 41.0 (40.0 to 42.0) | 4.3 (3.7 to 5.0) |
Source: Medical Expenditure Panel Survey (MEPS), 2016
Weighted to population levels using sampling weights from the MEPS.
Confidence Interval calculated as β ± (1.96 X standard error)
Income reported as a percentage of the Federal Poverty Line (FPL). Negative/poor/near poor income indicates FPL less than 125%, low income indicates FPL 125% to less than 200%, middle income indicates FPL 200% to less than 400%, and high income indicates FPL greater than or equal to 400%.
The marginal effects of each independent variable on the prevalence of reporting unmet dental need were estimated and presented in Table 2. Those with middle income were 2.3 percentage points (95% CI: 1.2 to 3.4), those with low income were 3.3 percentage points (95% CI: 1.7 to 5.0), and those with poor/negative/near-poor income were 4.2 percentage points (95% CI: 2.7 to 5.7) more likely to report an unmet dental need than adults with high income. Adults with dental insurance were 1.7 percentage points (95% CI: −2.8 to −0.6) less likely to report unmet dental needs than adults without dental insurance. Adults reporting overall “good health” were 5.0 percentage points (95% CI: 3.6 to 6.4) more likely, and those reporting “poor or fair health” were 1.9 percentage points (95% CI: 0.9 to 2.9) more likely to report an unmet dental need, than adults who reported “excellent or very good” general health. Both Hispanics (−1.7 percentage points [95% CI: −2.8 to −0.6]) and non-Hispanic Blacks (−1.1 percentage points [95% CI: −2.1 to −0.1]) were less likely to report an unmet dental need than whites. Smoking, education, general health status, chronic disease, and marital status were also significantly associated with reporting an unmet dental need.
Table 2:
Marginal effect of factors associated with unmet dental need among U.S. adults, 2016 (N=21,040)
| Reported unmet dental need | ||
|---|---|---|
| Characteristics | dy/dxa | CIb |
| Predisposing Characteristics | ||
| Female | .005 | −.002 to .012 |
| Age | ||
| (18–44)c | ||
| 45–64 | .006 | −.005 to .017 |
| 65–75 | −.004 | −.065 to .057 |
| 76 or above | −.024 | −.070 to .022 |
| Race/Ethnicity | ||
| (Non-Hispanic White)c | ||
| Non-Hispanic Black* | −.011 | −.021 to −.001 |
| Hispanic** | −.017 | −.028 to −.006 |
| Other/Multi-racial | .008 | −.007 to .025 |
| Current Smoker** | .024 | .012 to .037 |
| Married** | −.023 | −.031 to −.014 |
| Education | ||
| (Less than High School)c | ||
| High School/GED | .008 | −.001 to .017 |
| Bachelor’s/Other*** | .024 | .011 to .038 |
| Master’s/Doctorate** | .032 | .010 to .054 |
| Enabling Resources | ||
| Incomed | ||
| Poor/Negative/Near Poor*** | .042 | .027 to .057 |
| Low Income*** | .033 | .017 to .050 |
| Middle Income*** | .023 | .012 to .034 |
| (High Income)c | ||
| Health insurance coverage | ||
| (Uninsured)c | ||
| Private | −.007 | −.025 to .010 |
| Public | .003 | −.015 to .020 |
| Medicare | −.009 | −.070 to .051 |
| Dual-eligible Medicare and Medicaid | .010 | −.067 to .087 |
| Medicare & Private | −.024 | −.072 to .024 |
| Private Dental insurance** | −.017 | −.028 to −.007 |
| Need | ||
| General health status | ||
| (Excellent or very good health)c | ||
| Good health*** | .048 | .034 to .062 |
| Poor or fair health*** | .019 | .009 to .029 |
| Self-reported chronic disease*** | .018 | .009 to .027 |
Source: Medical Expenditure Panel Survey (MEPS), 2016
Weighted to population levels using sampling weights from the MEPS.
dy/dx – Marginal effects are the absolute risk difference attributable for each characteristic, holding all other covariates at the mean.
Confidence Interval calculated as β ± (1.96 X standard error)
Reference categories in parentheses
Income reported as a percentage of the Federal Poverty Line. (FPL). Negative/poor/near poor income indicates FPL less than 125%, low income indicates FPL 125% to less than 200%, middle income indicates FPL 200% to less than 400%, and high income indicates FPL greater than or equal to 400%.
p<0.05
p<0.01
p<0.001
Table 3 presents the reasons adults believed contributed to their unmet dental need within the preceding 12 months. Cost and lack of insurance coverage were among the most common reasons (56.1% and 11.9%, respectively). Other reasons included time constraints (5.0%) and problems getting to the dentist’s office (3.1%).
Table 3:
Self-reported reasons as to why adults were unable to get, or were delayed in getting, dental care, tests, or treatments the person or dentist believed necessary” (n=1,332)
| Self-reported reasons for unmet dental need | Total unweighted response count | Weighted % |
|---|---|---|
| Could not afford care | 790 | 56.1% |
| Other | 176 | 16.4% |
| Insurance coverage would not approve/cover/pay | 168 | 11.9% |
| Did not have time or took too long | 67 | 5.2% |
| Problems getting to doctor’s office | 36 | 3.1% |
| Could not get time off work | 35 | 3.0% |
| Don’t know where to get care | 31 | 2.5% |
| Doctor refused family health insurance plan | 20 | 1.4% |
| Was refused services | 6 | 0.3% |
| Could not get child care | 2 | 0.1% |
| Different language | 1 | (<0.1%) |
Source: Medical Expenditure Panel Survey (MEPS), 2016
Discussion
We sought to determine the prevalence of, and factors associated with, unmet dental need among adults. We observed that 6.0% (95% CI: 5.5 to 6.5) of adults in the United States experienced self-reported unmet dental needs. Factors associated with a lower prevalence of unmet dental need include being Hispanic, being married, and having private dental insurance. In contrast, factors associated with a higher prevalence of reporting an unmet dental need among adults include lower income, poorer health, and higher education.
Our study analyzed self-reported survey data and therefore results are subject to bias. Our outcome, which is subjective, combined responses from two survey questions which asked whether an individual was 1) unable to get dental treatment or 2) delayed in getting dental treatment. Although these survey questions measured two different things, unmet dental needs are unlikely to improve on their own without professional interventions. Thus, delayed care is similar to unmet need, but measured in a shorter time frame. Nonetheless, our conflated measure may not represent actual clinical need for dental care. Further, the individual may be unaware that they need dental care. MEPS does not conduct a professional assessment of dental health; therefore we are unable to assess whether respondents can accurately judge their need for care. Nevertheless, dental problems tend to be more easily assessed than other needs because of the visibility and pain associated with common oral diseases and conditions, such as broken teeth or tooth decay. Given that data were self-reported, our results are also subject to recall and social desirability bias. While the response rate (46%) to this survey is consistent with previous MEPS survey years (47), it is low. We further excluded any adults who were missing data on key independent characteristics. In particular, our results may be subject to bias from missing data on smoking status, which resulted in the exclusion of approximately 17% of our sample. This study is cross-sectional and therefore we are unable to determine whether the relationships observed hold over time. We used appropriate weights to account for nonresponse, however these limitations may have affected the generalizability of the findings to the wider target population. Despite these limitations, this study is the first to analyze self-reported unmet dental need using nationally representative data while controlling for several demographic, socioeconomic, and health factors.
We observed that the prevalence of unmet dental need (6.0%) has increased from previous analyses of MEPS (5.5%) in 2007 (20), although this change is small and is unlikely to be relevant from a policy or public health perspective. Our observed prevalence of unmet dental need was higher than analyses of the NHIS (4.4%) (48) and the average prevalence among European countries (4.1%) (49). NHIS only collects information on unmet dental need due to cost barriers, whereas MEPS inquires about unmet and delayed dental care due to any reason, such as time and child care constraints. But similar to European countries and other studies, cost was found to be the most common reason adults report any unmet dental need (18,49). Given that low income and poor adults were significantly more likely to report a dental need, expanding dental coverage and/or coverage generosity within US public dental insurance programs (such as Medicaid) may reduce unmet dental need (50–52). Recent research found that expanding Medicaid dental coverage and providing more extensive dental benefits to low-income adults increases dental care use and reduces the number of preventable non-traumatic dental visits to emergency department rooms (53–55). More specifically, expanding Medicaid coverage to poor adults significantly increases use of preventive dental care and major dental treatments (53). We found having dental insurance was associated with a lower prevalence of reporting an unmet dental need among adults. Adult dental benefits may reduce unmet need by providing adults, particularly those with low-income, access to the major dental treatments and routine care they need but are otherwise unable to afford or obtain. Future studies should examine whether Medicaid expansions have affected unmet dental need among poor adults, as we all as other health outcomes beyond dental utilization.
Although unexpected, this current study found that having education beyond high school was associated with an increased likelihood of reporting an unmet dental need. This may be because those with more education possess greater oral health literacy and understand what care they are lacking, or it may be because these individuals are more likely to be employed and face time constraints to getting needed or recommended care (56,57). We also found Hispanics and non-Hispanic Blacks were less likely to report unmet dental need. These relationships stayed consistent regardless of how we built the predictive model. Previous research on unmet health needs has found that certain minority groups, such as Hispanics, are less likely to perceive unmet health needs (19,58,59). Although these groups tend to have lower socioeconomic status, higher uninsurance rates, and use fewer dental and health care services than whites, it may be that certain minority groups have lower expectations or perceived need for care or they may have a stronger belief in their ability to access care when needed (19,59). Further, whites are more likely to describe themselves as “care-seekers” than Hispanics and Blacks, and may feel more empowered than minorities to speak up when their needs are not met (19,59).
Somewhat counterintuitively, adults with “good” health had the greatest likelihood of unmet dental need. It may be that some adults report suboptimal overall health because of poor oral health or perceived need for dental care. However, the reverse could also be true – that those with less than “excellent or very good” overall health experience unmet dental need because they and their providers may be more focused on addressing any underlying medical issues and less aware or attentive to their dental care during that time. This study found that those with a chronic disease were more likely to report an unmet dental need. Other national data analyses have found that adults with chronic disease are more likely to have clinically untreated caries or periodontal disease (60). Unmet dental needs may result from the effort, time, and finances required to manage chronic medical conditions. Chronic diseases, such as diabetes and cardiovascular disease, are correlated with poor oral health and may, in turn, lead to greater unmet dental need, although the mechanisms behind these relationships remain unknown (61–65).
Additional research is needed to understand the specific reasons adults believe they have an unmet dental need and how their judgment compares with a dental provider’s clinical judgment. Future research should also explore how race and ethnicity affect perceptions of unmet dental need. Finally, future work should also explore policy interventions which can effectively reduce self-reported unmet dental need in populations.
Conclusion
Future policies should continue to address cost and coverage barriers to adult dental care, as these remain significant barriers to access, particularly for low-income adults. Future research should evaluate the reasons adults self-report unmet dental need and explore how adults’ judgment of dental need compares to providers’ clinical judgment. Additionally, research that explores how race and ethnicity affect perceptions of unmet dental need is warranted.
Acknowledgements:
Research reported in this publication was supported in part by the National Library of Medicine of the National Institutes of Health under award number T15LM012502. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Library of Medicine.
Footnotes
All authors report no conflicts of interest.
Contributor Information
Heather Taylor, Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, 1050 Wishard Blvd. Room 5184, Indianapolis IN 46202-2872.
Ann M. Holmes, Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, 1050 Wishard Blvd. RG 5138, Indianapolis, IN 46202.
Justin Blackburn, Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, 1050 Wishard Ave, Room 5194, Indianapolis, IN 46202.
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