Oral Health Equity and Unmet Dental Care Needs in a Population-Based Sample: Findings From the Survey of the Health of Wisconsin

Abstract

Objectives. We used objective oral health screening and survey data to explore individual-, psychosocial-, and community-level predictors of oral health status in a statewide population of adults.

Methods. We examined oral health status in a sample of 1453 adult Wisconsin residents who participated in the Survey of the Health of Wisconsin Oral Health Screening project, conducted with the Wisconsin Department of Health Services during 2010.

Results. We found significant disparities in oral health status across all individual-, psychosocial-, and community-level predictors. More than 15% of participants had untreated cavities, and 20% did not receive needed oral health care. Individuals who self-reported unmet need for dental care were 4 times as likely to have untreated cavities as were those who did not report such a need, after controlling for sociodemographic and behavioral factors.

Conclusions. Our results suggested that costs were a primary predictor of access to care and poor oral health status. The results underscored the role that primary care, in conjunction with dental health care providers, could play in promoting oral health care, particularly in reducing barriers (e.g., the costs associated with unmet dental care) and promoting preventive health behaviors (e.g., teeth brushing).

Oral health is an essential and integral component of overall health, yet unmet health care needs and poor oral health are pervasive. Poor oral health care is associated with increased use of medical services, increased risk for several chronic conditions (including heart disease and diabetes),^1,2 as well as reduced quality of life and employment opportunities.^3,4 There is growing momentum both nationally and internationally for increased understanding and use of a more holistic social-ecological perspective to understanding and addressing oral health disparities.⁵ With the implementation of the Affordable Care Act and the potential for increased access to care among adults, there is a unique and novel opportunity to improve equity in oral health care and outcomes.

Spurred by a 2001 surgeon general’s report in which poor oral health was described as a “Silent Epidemic” sweeping the nation, the Institute of Medicine convened experts to develop a vision for improved oral health care into the future. A 2011 Institute of Medicine report suggested that oral health care should be integrated into an overall model of health care delivery, with an emphasis on the primary care setting.⁶ National recommendations suggested that addressing oral health disparities would require a more fundamental shift toward viewing oral health as a medical issue. Improving access to primary care that includes education and training on the importance of oral health care during medical visits might be a solution to improving oral health equity.^7,8

Elucidating the true magnitude of oral health disparities and unmet needs, including the complex network of population-level predictors, is often limited. A 2010 report by the World Health Organization identified several research gaps, including understanding the social determinants and modifiable risk factors for poor oral health.⁵ Although many risk factors are well-established (age, gender, race/ethnicity, and access to care),^9–13 others, such as psychosocial determinants and behaviors, are not as well understood.

Few, if any, population-based studies have included objective oral health screenings. Oral health screenings of children have been the national benchmark for tracking disparities among children for quite some time, but no analogous nationwide program exists for adults. The Association for State and Territorial Dental Directors (ASTDD) has developed tools, such as the Basic Screening Survey (BSS) protocol, for use in adults. However, access to representative population-based studies of adults is not often feasible or cost effective for most state-based programs. Consequently, most prevalence estimates of unmet oral health needs are based on self-reported, telephone-based surveys that do not include data on predictors, such as tooth brushing, psychosocial factors, and community-level data.

With national shifts in overall health insurance coverage, baseline data and information are needed to support evaluation of the impact of this “natural experiment” on oral health disparities.^6,8 Changes in baseline estimates of oral health disparities and predictors could be followed over time for program evaluation and to guide policy efforts to achieve oral health equity at a time when significant changes to access to primary care are happening. Despite these urgent calls, to date, most oral health surveillance among adult populations rely on subjective survey data, and the true magnitude of poor oral health and multilevel view of determinants at a community level are lacking.

We aimed to address these gaps using data gathered as a result of a unique partnership that was established between the state of Wisconsin Department of Health Services (DHS) Oral Health Program and the University of Wisconsin Survey of the Health of Wisconsin (SHOW). The partnership facilitated integration of an objective oral health screening using the BSS protocol into an existing population-based examination survey. Our goals were to identify who was at greatest risk for poor oral health in Wisconsin and to examine how social-ecological and individual factors, such as health behaviors and mental health, focusing on some of the often overlooked social determinants, predict adverse oral health outcomes (i.e., cavities) or unmet oral health care needs. Furthermore, with pending changes in access to primary care, we sought to explicitly examine the role that access to oral health care has on oral health status and health equity among a representative sample of Wisconsin adults.

METHODS

We used data collected as part of the SHOW Oral Health Screening project. SHOW is a household-based examination survey that collects data from a representative sample of Wisconsin residents. SHOW methods were described in detail by Nieto et al. in 2010.¹⁴ To summarize, households were selected using a 2-stage cluster approach in which census block groups served as the primary sampling units, followed by all residential households. All adult residents of households (aged 21–74 years) who were not on active military duty, currently under supervision by state or federal office of corrections, nor mentally incapable were invited to participate. As previously described by Nieto et al., participant demographic characteristics matched those of Wisconsin for all age, gender, income, and race categories postweighting and stratification.¹⁴

In-person interviews, self-administered and computer-assisted questionnaires, along with a physical examination and biospecimen collection, were used to gather individual health and determinant data. Subject matters covered a range of topics from health care access and utilization, current health history, self-reported measures of oral health care status and needs, mental health, physical activity and diet, to perceptions of neighborhood characteristics and sleep quality. Questionnaires were developed based on previous National Health and Nutrition Examination Survey questionnaires or standardized and validated instruments (e.g., Short Form-12).^15,16 Household addresses were also geocoded for linkage with existing contextual data, including community-level measures of the social and physical environment. A total of 2479 state residents participated in SHOW between 2008 and 2012.

In 2010, the Wisconsin DHS funded the addition of the ASTDD BSS to the SHOW protocol as an ancillary study. Before any data collection, Wisconsin DHS staff used protocols and materials available through the ASTDD to train and calibrate SHOW field staff in conducting the BSS, which included more than 10 hours of training in local oral health clinics. Wisconsin DHS staff approved oral health screening of SHOW staff before going into the field. Regular trainings were held throughout the data collection period to ensure continued calibration and quality control. We included data on 1435 participants who participated in the BSS screening and completed all SHOW interviews for our analysis.

Outcomes

Oral health status and unmet oral health care needs were assessed using the BSS objective oral screening and self-report interview-based data. Objective measures included observed untreated cavities and assessment of urgent dental care needs. Untreated cavities were coded as 1 for yes, and 0 for no, based on visible loss of tooth structure at the enamel surface and discoloration of the cavity walls during visual inspection using a disposable hand-held mirror and flashlight. Treatment urgency was defined at 3 levels: (1) urgent dental care needed if sign or symptoms, including pain, infection, swelling, or soft-tissue ulceration were identified, and participant reported symptoms lasting more than 2 weeks; (2) early dental care needed if dental caries were present without signs or symptoms, bleeding gums were present, or red soft tissue areas or ill-fitting dentures were observed; and (3) no obvious problem in any of the preceding symptoms were not detected.

Subjective measures of oral health status included self-reported measures of mouth pain and unmet dental care needs. Unmet dental need was measured based on participant responses to the question: “During the past 12 months was there a time when you needed dental care but did not get it at the time?” Individuals who answered in the affirmative were coded as experiencing unmet dental needs. Among those reporting yes to unmet needs, a question on reasons for unmet needs included the following options: could not afford costs, no health insurance or health insurance did not cover sufficient amount of costs, did not want to pay costs, fear or dislike of dentists, too busy, dentist office was not convenient or too far away, perception that the oral health issue was not serious and would go away on its own, or other reasons. Mouth pain was assessed by questionnaire based on self-report of “any aching anywhere in your mouth over the last 12 months.” Individuals were classified as having frequent mouth pain if they reported experiencing painful aching very often, fairly often, or occasionally.

Correlates and Confounders

Sociodemographic predictors included data on age, race/ethnicity (non-White vs non-Hispanic White), marital status (never married, married, and divorced/separated/widowed), educational attainment (high school graduate or less, some college, or college graduate or beyond), and health insurance status (defined as no insurance, any public insurance, and private insurance only). Individual economic hardship was based on total family income and according to poverty status (< 100%, 100%–199%, 200%–399%, and ≥ 400% of the federal poverty level).

Preventive oral health behaviors included self-reported frequency of teeth brushing (most or everyday vs never, rarely, or some days), number of times of teeth brushing per day on days when teeth are brushed (≥ 2 times vs 1 time or does not apply), and frequency of flossing (most everyday vs never, some days, or rarely).

Psychosocial correlates examined included positive self-reported depression, anxiety, and stress, each dichotomized at or above normal or as mild cutpoints established using the 3 scales of the 42-item Depression Anxiety and Stress Scales instrument.¹⁷ Each of the 3 Depression Anxiety and Stress Scales can been categorized for purposes of interpretation into 5 ordinal categories (normal, mild, moderate, severe, extremely severe).^17,18

Community-level correlates, including socioeconomic status and density of primary care providers, were also explored. Census block group–level estimates of socioeconomic status were characterized using the economic hardship index, which is a composite measure derived from 2000 US Census data.^19,20 The economic hardship index includes a metric for crowded housing, poverty status, employment, education, dependency, and individual annual income. Census block groups were ranked based on these indicators and assigned a tertile of economic hardship (low, medium, or high). County-level prevalence of primary care providers (estimates provided as the total number of primary care providers listed by the Wisconsin Medical Society and divided by total 2000 census estimates of population) were also included.

We conducted all statistical analyses using SAS version 9.0 (SAS Institute, Cary, NC) and STATA version 11 (StataCorp, College Station, TX). Univariate analyses included estimates of both crude and adjusted values of the prevalence of poor oral health (percentage of untreated cavities) and unmet needs measures. We used Proc-survey commands with pre- and poststratification sampling weights to account for sampling design, and we included adjustments for clustering and nonresponse to ensure the sample was representative of Wisconsin adults. We used multivariable logistic regression to examine associations between correlates of poor oral health and the adjusted odds of untreated cavities, treatment urgency (any or urgent vs none), frequency of mouth pain in the last year, and any unmet dental need. We examined 4 models with increasing adjustments: model 1 included sociodemographics, model 2 added oral health behaviors, model 3 included psychosocial factors, and model 4 added the community-level economic hardship index.

RESULTS

Overall oral health screening rates were high, with more than 85% of individuals interviewed for participation in the main SHOW study agreeing to also participate in the oral health screening ancillary study. Table 1 presents the study population demographic characteristics and the prevalence of oral health status indicators, including the screening-based measures of untreated cavities and need for urgent dental care, as well as self-reported estimates of frequent mouth pain and unmet dental care needs.

TABLE 1—

Sociodemographic Characteristics and Prevalence of Adverse Oral Health Status and Unmet Needs: Oral Health Equity and Unmet Dental Care Needs in a Population-Based Sample, Wisconsin, 2010

		Screening-Based		Self-Report
Variables	Total (n = 1453)	Untreated Cavities (n = 216), %a	Need Any Dental Treatment (n = 227), %	Frequent Mouth Pain (n = 347), %	Unmet Dental Need (n = 274), %
Total population		15.1	16.0	24.6	20.6
Mean age, y	45.7	42.1	42.4	44.4	42.4
Gender				***
Male	49.7	16.0	16.1	19.7	19.9
Female	50.3	14.3	15.9	29.4	21.3
Race/ethnicity		*	**	***	***
Non-White	13.0	22.6	25.2	36.8	41.3
Non-Hispanic White	87.0	14.0	14.6	22.8	17.5
Marital status		*	***	**	***
Never married	18.4	20.9	24.1	31.6	30.0
Divorced/separated/widowed	16.4	18.3	18.9	28.7	31.6
Married, lives with partner	65.3	12.7	13.0	21.6	15.2
Education		***	***	***	***
≤ high school graduate	28.1	24.3	25.6	32.2	24.1
Some college	41.1	14.0	15.6	24.8	23.7
≥ college	30.7	8.2	7.8	17.4	13.3
Health insurance		***	***	***	***
None	8.5	32.8	34.5	33.6	47.7
Any public insurance	29.0	19.8	22.9	31.0	29.4
Private insurance only	62.4	10.6	10.3	20.4	12.8
Family income		***	***	***	***
< 100% FPL	10.7	32.0	34.1	41.2	38.0
100%–199% FPL	17.5	21.4	24.1	32.0	34.3
200%–399% FPL	30.3	14.1	15.4	24.9	21.6
≥ 400% FPL	37.5	7.1	6.6	16.1	9.4
Unknown	4.0	24.9	24.9	25.1	11.7

Note. FPL = federal poverty level.

^aAll percentages were calculated using weighted values.

*P < .05; **P < .01; ***P < .001.

Based on the BSS, an estimated 15.1% of Wisconsin residents were found to have untreated cavities. The proportion of untreated cavities varied by sociodemographic characteristics and was greatest among those with a high school education or less (24.3%), non-Whites (22.6%), single individuals (20.9%), those with no health insurance (32.8%), and family income less than 100% of the federal poverty line (32.0%). Similar trends were reported for unmet health care needs, with more than 47.7% of individuals without health insurance reporting unmet dental needs. This trend was compared with 29.4% of individuals with any public insurance, including both Medicaid recipients and those with Medicare alone or in combination with private insurance and 12.8% with private insurance only reporting unmet dental needs (Table 1).

Table 2 shows the relationship between behavioral correlates and the prevalence of adverse oral health outcomes among study participants. Individuals who reported never, rarely, or sometimes brushing their teeth had the highest prevalence of all 4 indicators of adverse outcomes, including 43.5% with unmet dental care needs, 45.2% with untreated cavities, 46.3% needing urgent dental care, and 44.4% reporting frequent mouth pain. Prevalence of these adverse indicators was less than half (range = 13.7%–23.3%) for individuals who brushed their teeth most or every day and were significantly different according to the self-reported estimates of frequency of brushing or flossing teeth. Individuals who reported brushing their teeth most or every day reported half the symptoms (range = 13.7%–23.3%) compared with those who did not report this behavior.

TABLE 2—

Behaviors, Psychosocial, and Community Level Characteristics Correlates of Adverse Oral Health Status Prevalence and Unmet Needs: Oral Health Equity and Unmet Dental Care Needs in a Population-Based Sample, Wisconsin, 2010

		Screening-Based		Self-Report
Variables	Total (n = 1453), %a	Untreated Cavities (n = 216), %	Need Any Dental Treatment (n = 227), %	Frequent Mouth Pain (n = 347), %	Unmet Dental Need (n = 274), %
Oral health behaviors
Frequency of teeth brushing		***	***	**	***
Most or every d	90.6	13.7	14.5	23.3	18.7
Never/rarely/some d	5.1	45.2	46.3	44.4	43.5
Unknown or does not apply	4.4	9.0	12.0	27.5	34.1
Teeth brushing per d			*		***
≥ 2 times	59.0	13.3	13.7	22.8	16.7
1 time	35.9	18.1	19.5	26.7	25.0
Unknown or does not apply	5.1	14.5	18.2	30.1	34.7
Frequency of teeth flossing		**	**		***
Most or every d	38.8	11.2	12.0	23.5	15.7
Never/rarely/some d	55.6	18.6	19.4	25.2	23.1
Unknown or does not apply	5.5	8.3	10.6	26.6	30.5
Psychosocial factors
Self-reported depression		**	***	***	***
Normal	81.9	13.6	13.8	21.4	17.8
Mild	7.5	19.3	24.4	33.3	31.7
Moderate/severe/extremely severe	10.6	24.2	27.4	43.2	34.7
Self-reported anxiety		***	***	***	***
Normal	89.3	13.6	14.1	21.7	18.4
Mild	3.7	23.1	25.1	34.2	29.0
Moderate/severe/extremely severe	7.0	30.3	36.1	57.0	44.0
Self-reported stress		*	***	***	***
Normal	86.9	14.1	14.5	22.4	18.6
Mild	6.5	17.5	21.1	31.8	31.5
Moderate/severe/extremely severe	6.6	26.3	31.4	46.0	36.7
Contextual factors
Community economic hardship		***	***	*	***
Least hardship (bottom 25th percentile)	26.2	8.9	10.4	20.0	18.2
Median hardship	48.9	14.3	14.2	24.1	17.8
Most hardship (top 25th percentile)	24.9	23.3	25.5	30.3	28.7
PCPs in county					**
Fewer PCPs (bottom 25th percentile)	19.5	14.5	14.3	21.5	15.5
Median PCPs	48.7	15.8	17.0	23.7	20.1
Most PCPs (top 25th percentile)	31.7	14.5	15.6	27.9	24.7

Note. FPL = federal poverty level; PCP = primary care physicians.

^aAll percentages were calculated using weighted values.

*P < .05; **P < .01; ***P < .001.

Oral health status also varied according to psychosocial factors (Table 2). The highest prevalence of each of the 4 oral health indicators was significantly higher among individuals with moderate, severe, or extremely severe depression, and high anxiety and stress levels compared with those with mild or no psychosocial conditions. Prevalence of all 4 poor oral health indicators was also greater based on census block group estimates of economic hardship. County-level density of primary care providers did not significantly change based on estimates of untreated cavities or needs for oral health care, but the prevalence of self-reported estimates of unmet oral health care needs increased with increasing primary care provider density (Table 2).

Results from the full multivariable logistic regression (model 4) presented in Table 3 show that after adjustment for sociodemographic, psychosocial, and contextual factors, unmet oral health care needs was the greatest predictor of poor oral health, including having untreated cavities (odds ratio [OR] = 4.06; 95% confidence interval [CI] = 2.64, 6.26), needing urgent dental care (OR = 3.68; 95% CI = 2.31, 5.87), and experiencing mouth pain in the last 12 months (OR = 3.53; 95% CI = 2.63, 4.75). Overall psychosocial predictors did not appear to statistically increase odds of any untreated cavities, need for urgent dental care, or unmet dental needs after adjusting for sociodemographic characteristics, behaviors, access to care, and other contextual factors. Although not significant, an increased odds of frequent mouth pain (OR = 2.85; 95% CI = 0.80, 5.93) was observed for individuals with moderate to severe anxiety compared with those without anxiety after adjusting for all other variables in the models. Individuals in the top quartile for economic hardship compared with the bottom quartile had increased odds (OR = 1.82; 95% CI = 1.02, 3.26) of having untreated cavities, but no other significant increases were observed.

TABLE 3—

Odds of Any Untreated Cavities, Needing Urgent Dental Care, and Frequent Mouth Pain During the Last Year: Oral Health Equity and Unmet Dental Care Needs in a Population-Based Sample, Wisconsin, 2010

Predictor	Any Untreated Cavities, AORa (95% CI)	Treatment Urgency (Any or Urgent), AOR (95% CI)	Frequent Mouth Pain in Last Year, AOR (95% CI)	Unmet Dental Need, AOR (95% CI)
Any unmet dental need
No (Ref)	1.00	1.00	1.00	1.00
Yes	4.06 (2.64, 6.26)	3.68 (2.31, 5.87)	3.53 (2.63, 4.75)	…
Sociodemographic characteristics
Age (per 10 y)	0.85 (0.73, 0.99)	0.89 (0.76, 1.04)	1.00 (0.89, 1.12)	0.84 (0.74, 0.95)
Gender (male vs female)	0.93 (0.63, 1.31)	0.79 (0.56, 1.11)	0.50 (0.37, 0.68)	0.75 (0.54, 1.05)
Race/ethnicity
Non-Hispanic White (Ref)	1.00	1.00	1.00	1.00
Non-White	0.90 (0.46, 1.77)	0.94 (0.50, 1.76)	1.11 (0.65, 1.90)	2.19 (1.44, 3.32)
Marital status
Married, lives with partner (Ref)	1.00	1.00	1.00	1.00
Never married	0.75 (0.42, 1.32)	0.93 (0.58, 1.51)	1.15 (0.69, 1.90)	0.99 (0.62, 1.59)
Divorced/separated/widowed	0.98 (0.64, 1.49)	0.87 (0.58, 1.31)	0.86 (0.60, 1.24)	1.66 91.17, 2.35
Education
≤ high school graduate	2.09 (1.20, 3.66)	2.23 (1.31, 3.80)	1.64 (1.12, 2.41)	0.94 (0.57, 1.57)
Some college	1.09 (0.63, 1.88)	1.27 (0.72, 2.21)	1.18 (0.84, 1.67)	1.11 (0.72, 1.71)
≥ college (Ref)	1.00	1.00	1.00	1.00
Health insurance
Private insurance (Ref)	1.00	1.00	1.00	1.00
None	1.89 (1.07, 3.35)	2.03 (1.13, 3.64)	0.91 (0.60, 1.39)	3.95 (2.16, 7.21)
Any public insurance	1.32 (0.82, 2.13)	1.56 (0.98, 2.49)	0.94 (0.64, 1.38)	2.02 (1.41, 2.91)
Family income
< 100% FPL	2.13 (0.96, 4.77)	2.17 (1.04, 4.52)	1.69 (0.96, 2.99)	1.75 (0.98, 3.16)
100%–199% FPL	1.30 (0.64, 2.65)	1.52 (0.76, 3.02)	1.24 (0.79, 1.96)	2.08 (1.32, 3.28)
200%–399% FPL	1.32 (0.76, 2.30)	1.50 (0.91, 2.55)	1.26 (0.88, 1.79)	1.94 (1.20, 3.13)
≥ 400% FPL (Ref)	1.00	1.00	1.00	1.00
Unknown	2.87 (1.24, 6.60)	2.73 (1.20, 6.21)	1.16 (0.59, 2.26)	0.57 (0.21, 1.58)
Oral health behaviors
Frequency of teeth brushing
Most or every d (Ref)	1.00	1.00	1.00	1.00
Never/rarely/some d	2.89 (1.54, 5.43)	2.49 (1.26, 4.92)	1.80 (0.84, 3.84)	2.10 (1.02, 4.30)
Unknown or does not apply	0.84 (0.12, 6.05)	0.74 (0.12, 4.75)	0.79 (0.16, 3.89)	1.31 (0.09, 18.70)
Teeth brushing per d
≥ 2 times (Ref)	1.00	1.00	1.00	1.00
1 time	0.95 (0.63, 1.44)	1.05 (0.72, 1.54)	1.05 (0.77, 1.42)	1.38 (1.00, 1.91)
Frequency of teeth flossing
Most or every d (Ref)	1.00	1.00	1.00	1.00
Never/rarely/some d	1.32 (0.86, 2.01)	1.29 (0.90, 1.85)	0.93 (0.70, 1.25)	1.20 (0.87, 1.65)
Unknown or does not apply	0.27 (0.05, 1.45)	0.23 (0.04, 1.28)	0.66 (0.26, 1.68)	0.62 (0.15, 2.56)
Psychosocial factors
Self-reported depression
None (Ref)	1.00	1.00	1.00	1.00
Mild	0.92 (0.47, 1.81)	1.26 (0.73, 2.16)	1.39 (0.87, 2.23)	1.44 (0.88, 2.35)
Moderate to extremely severe	1.20 (0.58, 2.51)	1.25 (0.60, 2.60)	1.59 (0.95, 2.65)	1.29 (0.65, 2.55)
Self-reported anxiety
None (Ref)	1.00	1.00	1.00	1.00
Mild	1.17 (0.48, 2.86)	1.13 (0.46, 2.76)	1.27 (0.65, 2.50)	1.09 (0.54, 2.21)
Moderate to extremely severe	1.48 (0.70, 3.12)	1.65 (0.82, 3.331)	2.85 (0.80, 5.93)	1.74 (0.78, 3.86)
Self-reported stress
None (Ref)	1.00	1.00	1.00	1.00
Mild	0.63 (0.26, 1.51)	0.79 (0.35, 1.75)	0.73 (0.42, 1.28)	1.27 (0.62, 2.59)
Moderate to extremely severe	0.71 (0.33, 1.55)	0.85 (0.40, 1.80)	0.84 (0.41, 1.70)	0.86 (0.44, 1.67)
Contextual factors
Community economic hardship
Least hardship (Ref)	1.00	1.00	1.00	1.00
Median hardship	1.35 (0.79, 2.30)	1.03 (0.55, 1.91)	1.24 (0.87, 1.76)	0.90 (0.58, 1.39)
Most hardship	1.82 (1.02, 3.26)	1.61 (0.86, 2.99)	1.20 (0.79, 1.81)	1.07 (0.69, 1.67)
PCPs in county
Fewer PCPs (Ref)	1.00	1.00	1.00	1.00
Median PCPs	0.89 (0.54, 1.45)	1.02 (0.60, 1.72)	0.94 (0.66, 1.36)	1.21 (0.85, 1.72)
Most PCPs	0.77 (0.40, 1.50)	0.80 (0.43, 1.52)	1.17 (0.77, 1.78)	1.47 (0.98, 2.19)

Note. AOR = adjusted odds ratio; CI = confidence interval; FPL = federal poverty level; PCP = primary care physician.

^aAll odds ratios are adjusted for factors in the table.

Figure 1 presents the multiple reasons reported for unmet dental health care needs in the population. Reasons ranged from costs and limited insurance, to inconvenience and fear of dentists. More than 58% of participants indicated they could not afford costs. The majority of individuals who had unmet dental needs also reported either a lack of insurance (44.9%), or that their insurance did not cover needed care (14.6%). A small proportion of individuals who reported unmet needs stated it was because they did not like or feared dentists (16.5%). Other reasons reported among respondents included perceptions that they were too busy or convenience was lacking, including that the distance to facilities was too great (Figure 1).

FIGURE 1—

Reasons for unmet dental care need among Survey of the Health of Wisconsin participants: Oral Health Equity and Unmet Dental Care Needs in a Population-Based Sample, 2010

Cost was also identified as the primary reason for reporting unmet dental care needs across population subgroups stratified by sociodemographic factors and behaviors (data not shown). Approximately 86% of individuals with no health insurance identified costs as the number one reason for unmet dental care needs compared with 46% of individuals on private health insurance. Similarly, 82% of individuals who never, rarely, or sometimes brushed their teeth compared with 56% of individuals who regularly brushed their teeth identified costs as the primary reason for unmet dental care needs.

DISCUSSION

Understanding the complex and multilayered determinants of oral health status is an important first step in designing and targeting resources for improving population health and achieving health equity.^1,21–23 Few studies have explored predictors of oral health status in a geographically diverse statewide adult population. We confirmed previous research that suggested significant disparities exist according to both subjective and objective indicators of oral health,^11–13,24 as well as that access to care was a primary predictor of adverse outcomes, even after adjusting for individual behaviors, psychosocial factors, and community-level economics.^10,13,25 Costs of obtaining health care were also a significant predictor of unmet dental need, which suggested that reducing cost barriers by improving dental insurance access and other measures to reduce costs to consumers are necessary. Our results also suggested that efforts aimed at increasing education and awareness regarding the importance of preventive oral health behaviors in a primary care setting (e.g., teeth brushing) might be a promising avenue to improve oral health care. Efforts to test this approach based on national recommendations to integrate oral health into overall health care are needed.⁶

One particular strength of our study was that oral health status was assessed across several different objectively and subjectively measured indicators. We were unaware of any other state-based studies that measured oral health status using the ASTDD objective screening protocol. Although the BSS protocol was developed by ASTDD, it does not provide a comprehensive set of measures, such as decayed missing filled teeth or decayed missing filled surfaces. It was more feasible to train field surveyors who were not dental professionals to reliably collect the objective oral health screening data using this standardized protocol. Few, if any, statewide surveillance systems have the breadth of both oral screening examination data and detailed self-reported information available to estimate the oral health status in the population. The extensive network of state-based oral health programs is limited to objective oral health screening in school-aged children and self-reported survey based data for adults. Our results found that predictors of oral health status and disparities were consistent across the several measures used in this study, suggesting that data gathered from other surveys that did not include an objective measure of oral health status might be fairly accurate predictors of the magnitude of oral health care problems facing the United States today. At the same time, our findings suggested psychosocial factors might be important confounders to consider in addressing oral health disparities when estimating oral health status using self-reported data alone. Although not significant, the findings that both depression and anxiety severity appeared to be potentially associated with increased odds of reporting frequent mouth pain, but less so with other indicators of poor oral health status, suggested that mental health could influence the estimate of self-rated oral health.

Study Limitations

Although this study had a number of strengths, some limitations existed. We were not able to longitudinally track the impacts of access to care and unmet needs on participants’ overall oral health status over time. Furthermore, the objective screening for oral health care needs relied on a visual inspection and likely underestimated the true burden and severity of oral health care needs in the population. A more rigorous oral health examination that includes radiographs would be more precise, but would likely be too costly to gather information in this population-based sample.

Although our study took an important first step to understanding the complex web and underlying mechanisms and predictors of oral health status in this population, some factors were not addressed. We looked at the density of medical providers as an indicator for access to health care in general, but we did not specifically look at the density of oral health care providers. Exploring how the density of oral health care providers relative to preventive care providers affects access to care, costs for obtaining care, and outcomes might be an important future direction. In addition, we did not adjust for environmental factors, such as consumption of fluoridated water, use of fluoride toothpaste or mouth rinse, and access to topical fluoride treatments as predictors. Additional analyses to understand the role of fluoride in predicting oral health status in this population are needed. Other important behavioral factors related to overall health and health status, including diet and physical activity, might also be important predictors of oral health status and provide additional opportunities for understanding how oral health inequities are related to overall health status in this population.^2,26 Finally, although dental insurance might be a more robust predictor of oral health care utilization than medical insurance, in our analyses, we were only able to control for the latter. Moreover, we were able to ask about perceived barriers to oral health care, including costs and insurance issues, but we were not able to explicitly address the role of dental coverage within this study.

Conclusions

Our study findings suggested that oral health disparities are pervasive and costs in accessing care appear to be primary predictors of unmet health care needs and barriers to achieving good oral health. Although the current Affordable Care Act does not include provisions for reducing these oral health care costs, data suggest that costs are a primary barrier to achieving health equity; policies to address these costs are needed. In the absence of expanding oral health care coverage, increasing oral health prevention in primary medical care settings might be a way to begin to address and improve oral health care for adults. Furthermore, many of the most cost-effective strategies for preventing poor oral health might be dependent on improving behavioral, lifestyle, and community-level social changes; however, where and how to target resources for these approaches remains somewhat uncertain. For most states and communities, a true population-based prevalence and determinants of adult oral health status are unknown. Many programs rely on national surveys for understanding adult oral health, and more granular state- and community-based approaches to oral health surveillance and research are needed to identify cost-effective solutions for prevention and treatment of oral health conditions.^11,24,25 It will be important to continue monitoring the impacts of health care reform within this population to identify additional barriers and solutions to achieving oral health equity.