Impact of mental health problems on self-perceived oral health needs in a Medicaid population.

Health Promotion and Treatment

Impact of Mental Health Problems on Self-Perceived Oral Health Needs in a Medicaid Population

Evaluation & the Health Professions 2015, Vol. 38(1) 73-93 ª The Author(s) 2014 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0163278714537271 ehp.sagepub.com

Roger A. Boothroyd1 and Annie Ware2

Abstract The purpose of this study was to examine the prevalence of self-perceived oral health needs among Medicaid-enrolled adults with and without mental health problems and to identify factors predictive of enrollees’ perceived oral health needs. The study involved a secondary analysis of 1,721 respondents to the Florida Health Services Survey. Contrary to the previous research, the findings from this study indicated that respondents with mental health problems (52.9%) did not differ significantly in their dental needs compared to those who did not have mental health problems (49.3%). The results from a logistic regression suggested that after controlling for demographic characteristics, substance abuse problems and functional needs increased the likelihood of self-reported oral health needs, whereas the receipt of Supplemental Security Income (SSI) was associated with decreased dental needs. The prevalence of unmet dental

1

Louis de la Parte Florida Mental Health Institute, University of South Florida, Tampa, FL, USA Western Medical University, Ontario, CA, USA

2

Corresponding Author: Roger A. Boothroyd, Louis de la Parte Florida Mental Health Institute, MHC 2718, University of South Florida, 13301 Bruce B. Downs Blvd., Tampa, FL 33612, USA. Email: [email protected]

Downloaded from ehp.sagepub.com at HOWARD UNIV UNDERGRAD LIBRARY on March 10, 2015

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Evaluation & the Health Professions 38(1)

needs among these respondents (i.e., 23%) supported the conclusion that a significant gap exists in the accessibility of oral health services among this population. Keywords mental health, oral health, Medicaid, adults, mail survey

Background Nationally, approximately one-in-five people have untreated dental needs (Dye, Li, & Beltra´n-Aguilar, 2012). The first-ever Surgeon General’s report on oral health characterized these diseases as the ‘‘silent epidemic,’’ stressing these diseases are progressive, cumulative, and become increasingly complex over time (U.S. Department of Health and Human Services, 2000). This report further noted that dental diseases affect peoples’ economic productivity and compromise their ability to work. In response, oral health indicators are prominently featured in Health People 2020 (U.S. Department of Health and Human Services, 2010) that serves as a foundation for prevention efforts for our nation. Prior research has suggested that persons with mental illnesses are at increased risk for oral health problems (Armstrong, 1994; Cormack & Jenkins, 1999; Stiefel et al., 1990). For example, Andrews (1982) and Hellstrom (1977) found that individuals with anorexia nervosa and bulimia had increased oral health problems associated with frequent vomiting and regurgitation. Additionally, xerostomia associated with many pharmacological interventions has been implicated as a contributing factor for increased dental needs among persons with mental illness (Friedlander & Marder, 2002). Barnes et al. (1988) examined the dental care needs of hospitalized adults with mental health disorders and found that 33% had extensive unmet oral health needs. They also found that the extent of patients’ needs varied depending on several demographic and clinical factors such as age, gender, race/ethnicity, and diagnosis. In short male and minority patients had greater oral health needs compared to female and Caucasian patients. Older patients had fewer oral health needs than that of younger patients, whereas individuals diagnosed with schizophrenia had more dental needs than those diagnosed with affective disorders. More recently Janarhanan, Vohen, Kim, and Rizvi (2011) found significant differences between mental health and community older adult


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samples associated with the percentage of respondents who stated that they had oral health needs. In the mental health sample, 41% of the individuals reported dental needs compared to only 23% in the community sample. Similarly, Heaton, Manci, Grembowski, Armfield, and Milgrom (2013) reported that individuals with mental illness were twice as likely to report unmet dental needs (11%) compared to those without mental health needs (5.3%). They concluded that the higher level of unmet need among persons with mental illness suggests that current dental services are not adequate for this population. Given this evi¨ stman (2009) emphasized dence, Persson, Axtelius, So¨derfelt, and O the need to more closely examine the dental needs of individuals with mental disorders. In addition, Sweet, Damiano, Rivera, Kuthy, and Heller (2005) found that privately insured individuals were significantly more likely to receive dental care relative to those insured by Medicaid. Given that Medicaid is the single largest payer of mental health services (Mark et al., 2007) and that Medicaid serves a disproportionate number of persons diagnosed with mental illnesses (McAlpine & Mechanic, 2000), it is important to assess the oral health needs of persons with mental health problems enrolled in the Medicaid system. In Florida, Medicaid only reimburses for a limited number of adult (i.e., 21 years of age and older) dental services (Agency for Health Care Administration, 2011) when provided by a dentist enrolled in Medicaid. These services are restricted to acute emergency dental procedures to alleviate pain or infection, dentures, and denture-related procedures. Specific dental services include comprehensive oral evaluation for determining the need for dentures or for acute emergency services, denture-related procedures, full dentures and removable partial dentures, incision and drainage of an abscess, necessary radiographs to make a diagnosis, and problemfocused oral evaluation. In addition, adult Medicaid recipients are responsible for a 5% coinsurance charge for all procedures. According to a 2006 report, there were 1,479 Medicaid dental providers to serve 2,284,892 Medicaid enrollees on July 1, 2006. Importantly, six Florida counties had no Medicaid-enrolled dentists and nine counties had only one dentist (Manning, n.d.). The goal of this study was to estimate the prevalence of self-perceived oral health needs and service use among Florida Medicaid-enrolled adults with and without mental health problems. In addition, the study sought to identify demographic and clinical factors that were predictive of enrollees’ self-perceived dental needs.


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Method Data Source/Sample The sample used in this secondary analysis consisted of 1,721 adult (ages 21 to 66) Medicaid enrollees who participated in the Florida Health Services Survey (FHSS) at least once between 2000 and 2005. This survey was conducted as part of a larger study examining over multiple years the impact of Medicaid mental health managed care on enrollees’ access to care. The mailings were conducted over multiple years in different regions of the state to coincide with the rollout of Medicaid managed care in that region. During each year, potential respondents were identified through a stratified (health care plan, eligibility status, gender, and race/ethnicity) random sampling procedure from all Florida Medicaid enrollees within targeted geographical regions of the state. Throughout the years, respondents were paid from US$5 to US$10, for completing the questionnaire. During this time, over 7,183 different Medicaidenrolled adults were surveyed.

Measures The adult version of the Florida Health Services Survey contained a number of previously developed and psychometrically validated measures commonly used in behavioral health services research. The measures used in this secondary analysis are described in detail subsequently. Self-perceived oral health needs and service use. Of particular interest in this secondary analysis were enrollees’ responses to two self-reported questions related to oral health. Respondents’ self-perceived oral health needs were assessed using a single ‘‘Yes/No’’ item that asked respondents if they had ‘‘needed dental services in the past 6 months.’’ Respondents’ use of dental services was assessed using a second single ‘‘Yes/No’’ item that asked them whether they ‘‘used dental services in the past 6 months.’’ Colorado Symptom Index (CSI). The CSI (Shern et al., 1994) was used to measure psychiatric symptoms and is consistent with the National Institute of Mental Health task force criteria for outcome measures (Ciarlo, Edwards, Kiresuk, et al., 1981). Although two versions of the CSI exist, the version reported on in this article is the 14-item version. The CSI is a brief respondent self-report measure of psychiatric symptoms in which they report the frequency of various psychiatric symptoms they have experienced


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during the past month. In this study, respondents used a 5-point Likert-type scale: 1 ¼ not at all, 2 ¼ once during the month, 3 ¼ several times during the month, 4 ¼ several times a week, and 5 ¼ at least every day. It takes about 5 min to complete the CSI. Sample items include ‘‘How often have you felt nervous, tense, worried, frustrated, or afraid?’’ ‘‘How often have your voices, thoughts, or feelings interfered with your doing things?’’ and ‘‘How often have you felt like hurting or killing yourself?’’ Scores on this version of the CSI could range from 14 to 70, with higher scores being indicative of respondents reporting more frequent psychiatric symptoms. Boothroyd and Chen (2008) reported that the CSI had excellent internal consistency (.92) and test–retest reliability (.71) in a Medicaid population. They reported that the evidence of the CSI’s validity was strong; CSI scores differentiated among individuals with and without mental health services needs and were significantly negatively correlated with functioning. Simple Screening Instrument–Substance Abuse (SSI-SA). The SSI-SA (Center for Substance Abuse Treatment [CSAT], 1994) was used to measure substance abuse symptoms. As previously described, the SSI-SA was designed by an expert panel convened by CSAT to encompass a broad spectrum of signs and symptoms associated with substance use disorders and to screen for these disorders. Sample items on the SSI-SA include ‘‘Have you tried to cut down or quit drinking or using alcohol or other drugs?’’ ‘‘Have you gone to anyone for help because of your drinking or drug use?’’ ‘‘Do you spend a lot of time thinking about or trying to get alcohol or other drugs?’’ and ‘‘Have any of your family members ever had a drinking or drug problem?’’ Fourteen of the SSI-SA items are scored, yielding a scoring range of 0 to 14; and a cutoff score of ‘‘4’’ designates the presence of substance abuse problems that warrant further assessment. The psychometric properties of the SSI-SA have been examined and found to be good when the measure is administered by mail to a Medicaid population as was the case in this study (Boothroyd, Peters, Armstrong, Rynearson-Moody, & Caudy, 2013). The SSI-SA demonstrated good internal consistency (a ¼ .85) and good convergent validity with self-reported assessments of previous 30-day alcohol and drug use, functional needs, and overall quality of life, and adequately discriminated between persons with varying levels of SSI-SA scores and severity classifications and self-reported substance abuse needs. Receiver–operating characteristic (ROC) analysis of the SSI-SA indicated good psychometric properties, with overall accuracy of .90, specificity of .90, and sensitivity of .82.


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SF-12. The SF-12 (Ware, Kosinski, & Keller, 1995) examines eight health concepts including physical functioning, role limitations due to physical health problems, bodily pain, general health, vitality (energy/fatigue), social functioning, and role limitations due to physical or emotional problems (psychological distress and psychological well-being). The first four health concepts indicate physical health status and the remaining four concepts indicate mental health status. In a general population, the mean score on each component is around 50, with scores of 40–49 indicating mild disability, scores of 30–39 indicating moderate disability, and scores below 30 indicating severe disability. The measure had a good reported test–retest reliability (.89 for physical health and .76 for mental health) over 2 weeks. The median validity estimate for the physical health component was .67, while the median validity estimate for the mental health component was .97 (Ware, Kosinski, & Keller, 1996). In addition, Salyers, Bosworth, Swanson, Lamb-Pagone, and Osher (2000) found the SF-12 had a good test–retest reliability over 3 to 9 days ( physical health ¼ .73 and mental health ¼ .80). The authors concluded the SF-12 also had discriminant validity as it distinguished a population of adults diagnosed with severe mental illnesses from a general population, and good convergent validity given that scores were related to other physical and mental health indices in predicted ways. Functional needs. The level of functioning was assessed using a summed score based on 13 survey items from the Florida Health Services Survey that identified activities for which respondents reported needing assistance (i.e., stated ‘‘yes’’ that they needed help with the activity) during the past month. Sample questions included needing help with ‘‘personal things like grooming, bathing, or dressing,’’ ‘‘managing money or budgeting,’’ and ‘‘relationships with a boyfriend, girlfriend, or your husband or your wife.’’ Respondents’ scores on this scale could range from 0, indicating no assistance was needed in any area, to 13 indicating that assistance was needed in each area. Thus, higher scores are indicative of poorer functioning. The a reliability of the scores obtained from these 13 items was .80, indicating the scale has adequate internal consistency.

Procedures All procedures, correspondence, and protocols were reviewed and approved by the University of South Florida’s Social/Behavioral Institutional Review Board prior to the mailing and collection of any survey data. A highly systematic and structured approach to survey design and follow-up


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were used, similar to those recommended by Dillman (1978) and Salant and Dillman (1994). In total, five separate mailings were conducted. The first mailing consisted of a prenotification postcard informing the Medicaid enrollees who were sampled, that we were conducting a study examining their health care services, and that they would receive a questionnaire in the mail in about a week. One week later, a second mailing was conducted. This mailing included a personalized cover letter and questionnaire, in both English and Spanish, an explanation of the purpose of the study, and that respondents would be paid (the amount varied from US$5.00 to US$10.00 over the 7 years) for returning a completed questionnaire, and information about the days and hours of operation of the toll-free telephone number for any inquiry regarding the study. A preaddressed stamped return envelope was also included in the second mailing. One week later, a postcard reminder was sent to each person who had not yet responded. This reminder emphasized the importance of the study and again included information on the toll-free telephone number they could call. Two weeks after the postcard reminder was mailed, a fourth mailing containing a cover letter, questionnaire in both English and Spanish, and a preaddressed stamped return envelope was mailed to each nonrespondent. Finally, 4 weeks later, a fifth mailing was sent via certified mail to individuals who still had not responded. As with the second and fourth mailing, enrollees received a personalized cover letter, both English and Spanish questionnaire, and a preaddressed, stamped return envelope. These mailing procedures were based on the findings of a feasibility study conducted to assess the validity of using mail survey procedures with a Medicaid population. The findings from this feasibility study are summarized in Boothroyd and Shern (1998).

Analysis Classification of individuals as having various clinical conditions (i.e., mental health, physical health, and substance abuse) was based on responses to the status measures and using recommended cutoff scores. Respondents classified as having mental health problems was based on a cutoff score of 30 or higher on the CSI as recommended by Boothroyd and Chen (2008). Respondents classified as having physical health problems was based on SF-12 scores of 40 or lower (i.e., 1 standard deviation [SD] below the mean). Those deemed to have substance abuse problems was based on a cutoff score on the SSI-SA as recommended by the CSAT (1994). Overall missing data were present on 6.4% of the 20,652 data points used in this analysis (12 variables 1,721 respondents). Missing data ranged from


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a low of 0% on various demographic variables (e.g., gender and age) to a high of 20% on the marital status variable. If a condition could not be verified, it was assumed that the condition did not exist. For example, for the 27 respondents for whom a mental health need could not be verified, it was assumed that a mental health condition did not exist. For these respondents data were imputed to indicate that they did not have a mental health need. For the nonclinical variables (e.g., marital status), a similar strategy was employed. For example, if a respondent being married could not be verified, it was assumed that the respondent was not married. Given that list wise deletion of cases in analysis frequently results in a substantial reduction in the sample size available for the analysis and can introduce bias when the subsample of missing cases represented are not representative of the original sample. Descriptive statistics were used to estimate the prevalence of respondents’ oral health needs and their access to dental services. Chi-square analyses were used to examine the bivariate relationships between the predictor variables and reported oral health needs. A binary logistic regression was performed to assess which predictors were associated with self-perceived oral health needs. Two additional binary logistic regressions were conducted with the respondents with mental health needs (N ¼ 418) to further explore which predictors were associated with self-perceived oral health needs among this subpopulation. To assess the impact of the method of imputation used in this study, a sensitivity analysis was performed by conducting two binary regression predicting perceived dental health needs and perceived dental health needs among adults with mental health problems without any data imputation. In addition, given that a primary goal of the analysis was to compare the self-perceived oral health needs of respondents with and without mental health problems, a power analysis was conducted to determine if adequate statistical power existed to reasonably make this comparison. The results of this analysis indicated that with a sample size of 425 respondents per group, there would be 78% power to detect significance if the difference in proportions of respondents reporting unmet oral health needs between the two groups approximated 8%.

Results Survey Response Rates The survey response rates varied across years. The overall unadjusted response rate during this 6-year period was 24%. When adjusted for


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Table 1. Comparison of Survey Respondents and Nonrespondents. Respondents (N ¼ 1,721) Characteristics Gender Male Female Race/ethnicity White Black Hispanic Other Eligibility status SSI TANF Age Mean SD Range

Nonrespondents (N ¼ 5,462)

n

%

n

%

689 1,032

40.0 60.0

2,669 2,793

48.9 51.1

566 577 155 423

32.9 33.5 8.9 24.6

1,471 1,870 484 1,637

26.9 34.2 9.0 30.0

956 765

55.5 44.5

2,563 2,899

46.9 33.1

p .001

.001

.001

.001 40.62 12.48 20–66

36.93 11.95 20–65

Note. SD ¼ standard deviation; SSI ¼ supplemental security income; TANF ¼ temporary assistance for needy families.

incorrect addresses, individuals who were no longer Medicaid eligible, and those who were deceased, the response rates ranged between 35% and 45% across years. Although lower than desired, these response rates are consistent with those rates previously reported in the literature from other studies of Medicaid populations (Bebe, Davern, McAlpine, Call, & Rockwood, 2005; Barrilleaux, Phillips, & Stream, 1995; Brown, & Nederend, 1997; Gibson, Koepsell, Diehr, & Hale, 1999).

Survey Respondents and Nonrespondents As shown in Table 1, the average age of the 1,721 adult survey respondents was 40.6 years (SD ¼ 12.48 years); age ranged from 20 to 66. Respondents were more likely to be female (60%). Respondent race/ethnicity was fairly evenly distributed; one third were White, 33.5% were Black/African American, and 33.6% were Hispanic or other (mostly Hispanic). Approximately 56% were receiving Supplemental Security Income (SSI). As is also shown in Table 1, significant differences existed in the demographic characteristic of survey respondents and nonrespondents.


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Table 2. Predictors of Perceived Dental Needs.a Model

Demographic predictors (n ¼ 6) Over 45 (1)b Female (1) White non-Hispanic (1) Married (1) More than high school (1) Working full or part time (1) Clinical predictors (n ¼ 6) In poor health (1) Mental health need (1) Substance abuse problem (1) Used drugs in past 30 days (1) Had functional needs (1) Receiving SSI (1) Model summary

B

Wald p

OR

OR95

.445 .062 .062 .178 .244 .140

.001 .566 .562 .159 .021 .424

0.64 1.06 0.94 0.84 1.28 1.15

0.51–0.81 0.86–1.31 0.76–1.16 0.65–1.07 1.04–1.57 0.82–1.62

.279 .024 .723 .187 .966 .476

.015 .884 .001 .398 .001 .001

1.32 1.02 2.06 0.83 2.63 0.62

1.06–1.65 0.79–1.29 1.47–2.88 0.54–1.28 2.10–3.29 0.48–.80

2(12, N ¼ 1,721) ¼ 151.40; p ⬍ .001 2 Log-likelihood ¼ 2,234.39; p ⬍ .001 Nagelkerke’s pseudo R2 ¼ .112 61.7% of cases correctly classified

Note. SSI ¼ Supplemental Security Income; dependent variable ¼ perceived dental need (1). a N ¼ 1,721. b (1) Coding of the dichotomized predictor variables.

Respondents were significantly more likely to be female, w2(1, N ¼ 7,183) ¼ 40.99, p ⬍ .001, older t(2,784.12)1 ¼ 10.80, p ⬍ .001, and White and less likely to be ‘‘other,’’ w2(3, N ¼ 7,183) ¼ 29.86, p ⬍ .001, and receiving SSI (in contrast to temporary assistance for needy families [TANF]), w2(1, N ¼ 7,183) ¼ 38.96, p ⬍ .001. The significant differences in the demographic characteristics of respondents and nonrespondents raise some concerns related to the representativeness of the respondents to the enrolled Medicaid population.

Perceived Oral Health Needs, Service Use, and Unmet Needs Overall, 50.2% of the respondents reported having an oral health need within the 6 months prior to responding to the survey. Bivariate results indicated that respondents with mental health needs did not differ significantly in their reported oral health needs (52.9%) compared to respondents without


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mental health needs (49.3%), w2(1, N ¼ 1,721) ¼ 1.57, p ¼ n.s. Collectively 30.5% of the respondents reported they had used dental services within the 6 months prior to responding to the survey. No significant difference was found in the use of dental services between respondents with (35.2%) and without (29.1%) mental health needs, w2(1, N ¼ 1,721) ¼ 3.41, p ¼ n.s. Finally 23.0% of the respondents reported an ‘‘unmet oral health need’’ (i.e., the percentage of respondents reporting an oral health need who did not access an oral health service). Again, no significant difference was found in unmet dental needs between respondents with (21.8%) and without (23.3%) mental health needs, w2(1, N ¼ 1,721) ¼ .44, p ¼ n.s.

Predictors of Oral Health Needs Six dichotomized demographic factors and six dichotomized clinical factors were entered into a binary logistic regression to identify which factors were predictive of respondents’ self-perceived oral health needs. The overall regression model was significant, w2(12, N ¼ 1,721) ¼ 151.40; p ⬍ .001. The 2 log-likelihood ratio test was 2,234.39, p ⬍ .001, indicating that the full model reflected an improvement in fit compared to the null model. Examination of Nagelkerke’s pseudo R2 suggested the relationship between the predictors and the prediction was not very strong (R2 ¼ .112). The regression model correctly classified 61.7% of the respondents with respect to their dental needs, an increase of 10.5% over chance. Inspection of individual predictor variables revealed two demographic and four clinical factors were significantly associated with self-reported dental needs (see Table 2). Being over 45 significantly reduced respondents’ likelihood of reporting oral health needs in the past 6 months ( p ⬍ .001) by 34% (odds ratio [OR] ¼.64) while having more than a high school education significantly increased ( p ⬍ .001, OR ¼ 1.28) the likelihood of dental needs. The remaining four demographic factors (i.e., being female, White–non-Hispanic, married, and working full or part time) were not significantly associated with respondent dental needs. In terms of the clinical factors examined, respondents in poor health ( p ¼ .015, OR ¼ 1.32), having substance abuse problems ( p ⬍ .001, OR ¼ 2.06), and/or functional needs ( p ⬍ .001, OR ¼ 2.63) were significantly more likely to report dental needs in the past 6 months. Examination of the ORs indicated that respondents with functional needs were nearly 2½ times more likely to report oral health needs, while those with substance abuse problems were twice as likely.


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Table 3. Predictors of Perceived Dental Needs Among Adults With Mental Health Needs.a Model

Demographic predictors (n ¼ 6) Over 45 (1)b Female (1) White non-Hispanic (1) Married (1) More than high school (1) Working full or part time (1) Clinical predictors (n ¼ 6) In poor health (1) Substance abuse problem (1) Used drugs in past 30 days (1) Had functional needs (1) Receiving SSI (1) Model summary

B

Wald p

OR

OR95

.417 .227 .148 .649 .291 1.28

.078 .301 .513 .046 .206 .052

0.66 0.80 1.16 0.52 1.34 3.56

0.42–1.05 0.52–1.23 0.74–1.81 0.28–0.99 0.85–2.10 0.99–13.09

.657 1.29 .921 .167 .384

.008 .000 .047 .000 .074

1.93 3.63 0.40 1.18 0.52

1.19–3.14 1.84–7.17 0.16–0.99 1.10–1.27 0.38–1.20

2(11, N ¼ 418) ¼ 60.62; p ⬍ .001 2 Log-likelihood ¼ 517.47; p ⬍ .001 Nagelkerke’s pseudo R2 ¼ .180 65.8% of cases correctly classified

Note. SSI ¼ Supplemental Security Income; dependent variable ¼ perceived dental need (1). a N ¼ 418. b (1) Coding of the dichotomized predictor variables.

In contrast, respondents receiving SSI ( p ⬍ .001, OR ¼ .62) had a decreased likelihood of oral health needs. Having mental health needs or using drugs within the past 30 days were not significant predictors of oral health needs.

Predictors of Oral Health Needs Among Adults With Mental Health Needs A second binary logistic regression was conducted to identify those variables associated with respondents’ self-perceived oral health needs among adults with mental health needs (see Table 3). Eleven of the 12 predictor variables included in the previous logistic regression were entered into the model. The mental health variable was excluded because all of the respondents in this subgroup had mental health needs. The overall regression model was significant, w2(11, N ¼ 418) ¼ 60.62; p ⬍ .001. The 2


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log-likelihood ratio test was 517.47, p ⬍ .001, indicating that the full model reflected an improvement in fit compared to the null model. Examination of Nagelkerke’s pseudo R2 suggested the relationship between the predictors and the prediction was not very strong (R2 ¼ .180). The regression model correctly classified 65.8% of the respondents with respect to their dental needs, an increase of 10.5% over chance. The only significant demographic predictor was being married which increased the likelihood of a respondent reporting a dental need ( p ⬍ .05, OR ¼ .52). As was observed with the full sample, respondents in poor health ( p ¼ .008, OR ¼ 1.93), having substance abuse problems ( p ⬍ .001, OR ¼ 3.63), and/or functional needs ( p ⬍ .001, OR ¼ 1.18) were significantly more likely to report dental needs in the past 6 months. Surprisingly, having used drugs in the past 30 days was associated with reduced self-perceived oral health needs ( p ⬍ .05, OR ¼ .40). In contrast to the original model, receiving SSI and having more than a high school education were not associated with self-perceived dental needs.

Predictors of Unmet Oral Health Needs Among Adults With Mental Health Needs A third binary logistic regression was conducted to identify those variables associated with respondents’ unmet oral health needs among adults with mental health needs. As shown in Table 4, the same 11 predictor variables used in the previous subgroup analysis were included in the model. The overall regression model was significant, w2(11, N ¼ 418) ¼ 47.80; p ⬍ .001. The 2 log-likelihood ratio test was 390.25, p ⬍ .001, indicating that the full model reflected an improvement in fit compared to the null model. Examination of Nagelkerke’s pseudo R2 suggested the relationship between the predictors and the prediction was not very strong (R2 ¼ .166). The regression model correctly classified 79.7% of the respondents with respect to their unmet dental needs, a negligible increase over chance. The only significant demographic predictor in this model was having more than a high school education which increased the likelihood of respondents reporting an unmet oral health need ( p ⬍ .01, OR ¼ 2.03). In terms of clinical predictors, respondents in poor health ( p ¼ .01, OR ¼ 2.25), those with substance abuse problems ( p ⬍ .05, OR ¼ 2.34), having functional needs ( p ⬍ .001, OR ¼ 1.16) also increased the likelihood of respondents reporting unmet oral health needs. Receiving SSI significantly decreased the likelihood of respondents reporting unmet oral health needs ( p ⬍ .05, OR ¼ .48). The remaining predictors were not significant.


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Table 4. Unmet Dental Needs Among Adults With Mental Health Needs.a Model

Demographic predictors (n ¼ 6) Over 45 (1)b Female (1) White non-Hispanic (1) Married (1) More than high school (1) Working full or part time (1) Clinical predictors (n ¼ 6) In poor health (1) Substance abuse problem (1) Used drugs in past 30 days (1) Had functional needs (1) Receiving SSI (1) Model summary

B

Wald p

OR

OR95

.434 .166 .167 .265 .709 .090

.127 .531 .543 .448 .007 .885

0.65 0.85 1.18 0.76 2.03 1.09

0.37–1.13 0.50–1.42 0.69–2.02 0.36–1.62 1.21–3.41 0.32–3.68

.811 .849 .010 .147 .733

.008 .012 .983 .000 .021

2.25 2.34 0.99 1.16 0.48

1.23–4.11 1.21–4.53 0.41–2.41 1.07–1.26 0.26–0.89

2(11, N ¼ 418) ¼ 47.80; p ⬍ .001 2 Log-likelihood ¼ 390.25; p ⬍ .001 Nagelkerke’s pseudo R2 ¼ .166 79.7% of cases correctly classified

Note. SSI ¼ Supplemental Security Income; dependent variable ¼ unmet dental need (1). a N ¼ 418. b (1) Coding of the dichotomized predictor variables.

Sensitivity Analysis A binary regression predicting perceived dental health needs among adults with mental health problems was performed without any data imputation. The overall N for this analysis was reduced to 110 cases from 418 cases included with the imputed data (results not presented). The pattern of predictors remained similar to the results obtained with the imputed (i.e., relative strength of the predictors and the direction of the ORs). The reduced statistical power resulted in two variables (i.e., being married and having substance abuse problem) not reaching the conventional level of significance but approximating significance ( p ¼ .065 and p ¼ .064, respectively). As in the model with the imputed data, being in poor health and having functional needs remained strong, significant predictors of perceived oral health needs. One notable difference was that the predictor having used drugs in the past 30 days. The results of a second binary regression conducted without imputed data predicting perceived dental needs showed


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the strength and direction of the predictors to be similar to the regression conducted with the imputed data. Several variables remained significant (i.e., more than high school education and having functional needs), while several variables failed to reach a convention level of significance but were close (i.e., having a substance abuse problem, receiving SSI). One meaningful difference was that being White–non-Hispanic emerged as a significant predictor in the model without the imputed data. Again the reduced power associated with the decrease in sample size from 1,721 to 502 likely contributed to this.

Discussion Contrary to the previous studies that found persons with mental health problems were more likely to have oral health needs (Armstrong, 1994; Cormack & Jenkins, 1999; Heaton, Manci, Grembowski, Armfield, & Milgrom, 2013; Stiefel et al., 1990), mental health needs were not associated with an increased likelihood of reporting dental needs in this analysis. This might be partially attributed to the fact that all respondents were in the Medicaid program and thus has some, albeit limited access to dental health services (Agency for Health Care Administration, 2011). Our findings regarding the frequency of use of dental services and the fact that there were no differences between individuals with and without a mental illness in the use of dental services are relatively consistent with the findings of Heaton et al. (2013), given the different time sampling periods across studies. Heaton et al. (2013) found that approximately 44% had at least one dental visit in the past year, whereas we found that approximately 33% had used dental services in the past 6 months. In terms of unmet dental needs, we found a meaningfully higher level of self-reported unmet needs among our Medicaid adult sample (23%) compared to Heaton et al. (2013), that is, 7% in a nationally representative sample of adults. This is likely due to the fact that Medicaid offers very limited oral health care coverage and in Heaton’s (2013) sample, approximately 42% of the respondents had dental insurance. In contract to Heaton et al. (2013), we found no significant differences in the level of unmet dental needs reported by adults with and without mental health issues (22% vs. 23%, respectively), whereas Heaton et al. (2013) did find significant differences (11% vs. 5%, respectively). Despite these differences, we agree with Heaton et al. (2013) that the level of unmet need indicates current access to dental services is not adequately meeting existing needs.


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Given the established link of oral health diseases to other systemic diseases (Slavkin & Baum, 2000), it was not surprising that in each analysis conducted, respondents who were in poorer health or had substance abuse problems had an increased likelihood of reporting self-perceived dental needs. This was also true for the mental health subpopulation analysis. Interestingly, within the mental health subpopulation, receipt of SSI was not significantly associated with self-perceived oral health needs. Given that respondents with mental health needs who receive SSI are more likely to be engaged in the health care system due to their disability, this could result in their oral health needs being more readily identified and thus increase the likelihood of receiving dental services and reducing unmet oral health needs. The increased likelihood of perceived dental needs among individuals needing functional assistance might be due to the fact that they were found to be significantly associated with both poorer health status and substance abuse problems, increasing the likelihood of dental needs.

Limitations There are several limitations to this study that should be noted. First the survey data used in this reanalysis are cross-sectional in nature and thus the temporal nature of the predictors and dependent variables are unknown. This limits the conclusions that can be drawn regarding the association or possible causality because the presence of predictors and perceived oral health needs was measured simultaneously, making it difficult to determine if some predictors (e.g., mental health need) actually preceded the outcome variable (i.e., perceived oral health need). Second, the survey data are old and although no significant oral health policy changes have occurred associated with Medicaid-funded dental services since these data were collected, other factors may have influenced the current prevalence of oral health care needs today. Third, the low response rate coupled with the significant differences found between respondents and nonrespondents can be the cause for concern. Nonresponse can have two effects on the study findings. First is that it reduces the sample size. Although this in and of itself may result in misleading or incorrect study conclusions, the reduced sample size does decrease the precision of estimate and increase the confidence interval around these estimates. A second and likely more serious impact of nonresponse is that specific subgroups of respondents are under- or overrepresented in the findings. This is the case observed in this study. To the


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extent that these subgroups differ with respect to their variables under examination in this study (e.g., perceived oral health needs and health status conditions), then the estimates are likely to be biased, that is, the prevalence estimates of oral health need may be over- or underestimated. This will limit the generalizabilty of these findings and the prevalence estimates will not be representative of the larger Florida Medicaid population from which this sample was drawn. Fourth, oral health needs and the clinical status classifications were based on respondent self-reports and thus classifications likely have a degree of unreliability. Although some studies have raised concerns regarding the use of self-reported health status measures (e.g., Zajacova & Dowd, 2011), the assessments used in this study have well-established psychometric properties and have been frequently used in other studies. This being said, Clark and Ryan (2006) found that respondents are less likely to use extreme options in self-report questionnaires. Given this, respondents may have underreported their actual health-related conditions. Finally, the imputation of missing data potentially increases the error within the data. Overall 6.4% of the status indicators that could not be verified were assumed not to be present, thus adding noise to the data and likely making it more difficult to find associations between the predictor and the outcome variables. Although the sensitivity analyses conducted revealed a similar pattern with respect to the strength and direction of the predictors, the reduce power resulted in several predictors that previously emerged as significant using the imputed data no longer reaching a conventional level of significance.

Conclusions The findings from this study failed to confirm those of the previous research which have concluded that individuals with mental health needs were more likely to report oral health needs compared to respondents without mental health needs. However, despite the fact that the prevalence of report of oral health needs was similar among groups, the overall rate of unmet dental needs among this sample of Medicaid enrollees (i.e., 23%) suggests a significant gap exists between respondents’ perceived oral health needs and their accessibility to oral health services. This is consistent with the finding from other studies. For example, Janarhanan et al. (2011) found significant differences in mental health (41%) and community (23%) comparison groups associated with the percentage of respondents who stated that they had oral health needs. Heaton et al. (2013) reported that people with mental illness were two times more likely to


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report unmet dental needs (11%) than people who did not have mental health illness (5.3%). Heaton et al. (2013) concluded that the higher level of unmet need among people with mental illness suggests that current dental services are not adequate. Finally, Sweet et al. (2005) reported that privately insured enrollees were significantly more likely than Medicaid enrollees to use dental services. We believe the findings from this study, although simplistic and understating the complexity of the problem, support Persson et al.’s (2009) conclusion calling for a more focused examination of the oral health needs of individuals within the mental health population as well as the Medicaid population in general. Authors’ Note At the time this study was completed, the author Annie Ware was a student at the University of South Florida.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded in part by the University of South Florida Office of Undergraduate Research and through a contract with the Florida Agency for Health Care Administration (MED-078 Amendment #7).

Note 1. Degrees of freedom adjusted for unequal variance.

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