Determinants of dental absenteeism among young children in California: A logistic regression analysis of CHIS 2017–2022

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While prior scholarship has shown that missed days due to dental issues relate to substantial losses of learning time and district-level educational funding, few studies have explored predictors of this outcome. It remains unclear whether certain groups of children or school districts disproportionately bear the burden of dental absenteeism. Methods We leverage data from the California Health Interview Survey (CHIS) from 2017–2022 to construct a complete-case sample of n = 8,470 California children (aged 5–11). We first conduct χ² tests with Bonferroni adjustments to assess which characteristics are associated with missed school days due to dental issues. We then estimate four logistic regression models that successively adding demographic, insurance coverage, dental health access, and general health variables. Finally, we calculate and visualize estimated probabilities based on youth characteristics. Results In χ² tests, significant predictors included being in a household with unmarried caregivers, general health status, and being unable to afford needed dental care in the last year. In logistic regression models, characteristics that significantly predicted higher odds were: being 7–8 years old (relative to 5–6), being Hispanic or White (relative to Asian), living in a household with unmarried caregivers, and being unable to afford needed dental care in the last year. Being in very good general or excellent general health (relative to fair/poor general health) significantly predicted lower odds. Conclusions Even after adjusting for insurance coverage and income, financial barriers remain major drivers of missed school days due to dental issues among young school children in California. Policymakers should address these barriers to improve dental care access and promote health and educational equity. Missed school days School absenteeism Pediatric oral health Racial disparities Health disparities Socioeconomic determinants Household characteristics Accessibility California Health Interview Survey Logistic regression Figures Figure 1 Background Children are more susceptible to many oral health issues than adults, including dental caries 1 . Like health issues generally, dental health issues, can significantly impact attendance, motivation, and academic performance 2 – 4 through many routes. Dental caries and dental pain may affect sleep quality, thereby impacting learning and cognition 5 . When children cannot access needed care, small challenges can grow more extreme, leading to severe pain and medical responses that can keep children out of school. 35% of American children currently lack dental health benefits, leaving them at risk of delaying needed care 6 . The link between oral health and attendance is implied by research showing that fluoride varnish and school-based sealant programs directly correlate to reduced absences 7 . More directly, children with poor oral health are nearly three times more likely to miss school because of dental pain compared to their healthier peers 8 , 9 . In California, while the prevalence of dental caries among children has decreased over the past twenty years, it remains one of the most chronic yet preventable health care needs 1 , 10 . These enduring dental health issues have educational and financial impacts. A nationwide study utilizing 2008 National Health Interview Survey data estimated that, each school year, about 124 million school hours are lost due to dental visits, and 34.4 million are lost due to unexpected dental issues 11 . Financial implications emerge in California, as the state allocates funding to school districts based on their average daily attendance (ADA), which represents the average number of students present in school each day 12 . Districts where large shares of students miss school due to dental issues thus suffer measurable financial losses. Each year, approximately 350,000 California youth miss school due to dental problems, leading to over 850,000 missed school days and approximately $ 60 million in wasted school district resources 10 . Little work at the intersection of dental health and educational outcomes has focused on younger children, for whom learning losses may be uniquely impactful, especially in California. In elementary school, children build foundational numeracy, literacy, and socio-emotional skills. 13 As such, frequent absenteeism in these early grades can have profound impacts on long-term academic achievement, executive functioning, and socio-emotional development 14 . Students who are chronically absent in kindergarten are much less likely to hit a variety of critical reading and math milestones 15 , 16 , and students who fail to achieve key milestones (such as third-grade reading proficiency) are as many as four times less likely to graduate from high school 17 . The foregoing suggests that missed school days due to dental issues likely cause immediate and long-term educational harms, as well as financial harms to school districts that educate students who miss school because of dental issues. Importantly, the academic and financial burdens generated by pediatric oral health challenges are unlikely to be evenly distributed across populations and contexts. Research documents persistent disparities in tooth decay and untreated decay by race and socioeconomic status 18 and enduring racial and socioeconomic segregation across scholastic contexts 19 , 20 . Quantifying which populations (and, therefore, which kinds of schools) are most impacted by lost school days due to dental-related reasons is necessary to understand the structural impact of dental disease burden holistically and to identify potential pathways to combat preventable health challenges and health inequities. The present study In this study, we utilize 2017–2022 data from the California Health Interview Survey to identify significant correlates of missing school due to dental issues, identify populations impacted by losses to both learning time and ADA-based funding, and identify means of ameliorating impacts and inequities related to missed school days due to dental issues. Methods Study Population This study utilized data from the California Health Interview Survey (CHIS), the largest state health survey in the United States 21 , to ascertain the burden of missed school days pertaining to dental health and various population characteristics. We merge two-year survey files from 2017–2018, 2019–2020, and 2021–2022. Our sample includes 8,470 children aged 5–11 whose caregivers provided data on all relevant study measures. Measurement Our outcome of interest, missed days of school due to dental issues, was determined by caregiver responses to the survey question “During the past 12 months, did your child miss any time from school because of a dental problem? Do not count time missed for cleaning or a check-up,” wherein possible responses were “Yes ,” “No,” and “Not Applicable” if a child was not in school or did not have a response. We recode this variable to create a dichotomous indicator (0,1) of missed days due to dental issues. We measured the relationship between missed days due to dental issues and several covariates that capture characteristics historically associated with higher risk of dental issues and school absences. Variables can be roughly grouped into four categories: demographics, insurance coverage, dental health access, and general health. Demographic predictors include age (5–6, 7–8, 9–11), race (Asian, Black, Hispanic, White, Other Race), income as percentage of the federal poverty level (0–99%, 100–199%, 200–299%, 300% or more), education level of parents (less than 9th grade, 9th -11th grade, high school graduate, some college, associates or vocational, bachelors or some graduate school, Masters, PhD or equivalent), household type (married caregivers or unmarried caregivers), region (urban or rural), language spoken at home (English only, English and another language, no English), and citizenship (Born in the US or naturalized / non-citizen). Insurance coverage included three options: currently uninsured, currently insured but uninsured at some point during the past year, and insured throughout the past year. Dental care access was measured by whether the child could not afford needed dental care in the past year (no, yes). And general health was assessed by four categories: fair / poor, good, very good, and excellent. Analytic plan Data analysis was conducted in STATA version 18. We first summarized all variables used in our models (see Table 1 ). Next, we conduct χ² tests with Bonferroni adjustments for thirteen tests (α = .0038) to assess if each variable is predictive of missed days due to dental issues (see Table 2 ). We then estimate four logistic regression models with robust standard errors which successively add demographic, insurance coverage, dental health access, and general health variables as predictors (see Table 3 ). Demographic predictors include race, which is categorized in these data as Asian, Black, Hispanic, White, or Other. Table 1 Sample characteristics Variable Category N % Gender Male 4,345 51.3 Female 4,125 48.7 Region Urban 7,032 83 Rural 1,438 17 Income level (as percent federal poverty) 0–99% FPL 1,007 11.9 100–199% FPL 1,295 15.3 200–299% FPL 1,014 12 ≥ 300% FPL 5,154 60.9 Insurance Status Currently uninsured 122 1.4 Uninsured any of past 12 months 109 1.3 Insured all of past 12 months 8,239 97.3 Family Type Married caregivers 6,367 75.2 Unmarried caregivers 2,103 24.8 Education Level of Adult No formal education or grades 1–8 283 3.3 Grade 9–11 276 3.3 Grade 12 / HS Diploma 1,099 13 Some college 903 10.7 AA / AS / Vocational 1,108 13.1 BA / BS / Some grad school 2,555 30.2 MA / MS 1,547 18.3 PhD / equivalent 699 8.3 Age of Child (years) 5–6 1,785 21.1 7–8 2,060 24.3 9–11 4,625 54.6 Citizenship Status of Child US-born 8,061 95.2 Naturalized / Non-citizen 409 4.8 Household Size 2–3 2,234 26.4 4 3,355 39.6 5+ 2,881 34 Race Asian 1,184 14 Black 344 4.1 Hispanic 1,802 21.3 White 3,854 45.5 Other 1,286 15.2 Language at Home English only 4,831 57 English and another 2,560 30.2 No English 1,079 12.7 Missed School Due Dental Problems (Last 12 months) No 8,194 96.7 Yes 276 3.3 Could Not Afford Needed Dental Care (Last 12 months) No 7,887 93.1 Yes 583 6.9 General Health Fair / Poor 187 2.2 Good 862 10.2 Very Good 2,372 28 Excellent 5,049 59.6 Table 2 Bivariate Associations Between Sample Characteristics and Missed School Days Due to Dental Problems (Sorted by p-Value) among California youth (n = 8,470) Predictor χ² df p-value Significant at Bonferroni adjusted α = 0.0038 Insurance status 0.71 2 0.701 No Citizenship status 0.58 1 0.446 No Language spoken at home 1.98 2 0.372 No Household size 5.85 2 0.054 No Gender 4.55 1 0.033 No Urban vs. rural 4.59 1 0.032 No Adult educational attainment 14.88 7 0.038 No Race 9.81 4 0.044 No Age 8.87 2 0.012 No Income 12.34 3 0.006 No Married vs. unmarried caregivers 12.02 1 0.001 Yes General health status 29.37 3 < 0.001 Yes Could not afford needed dental care 30.92 1 < 0.001 Yes Table 3 Logistic Regression Models Predicting Missed School Days Due to Dental Problems Model 1 Model 2 Model 3 Model 4 Age (Reference: 5–6 years old) 7–8 years old 1.464* 1.464* 1.475* 1.463* (.262) (.262) (.265) (.263) 9–11 years old 1.014 1.015 1.025 0.993 (.169) (.169) (.171) (.166) Race (Reference: Asian) Black 1.480 1.491 1.481 1.500 (.576) (.581) (.578) (.585) Hispanic 1.705* 1.716* 1.716* 1.681* (.432) (.435) (.434) (.426) White 1.665* 1.673* 1.707* 1.735* (.406) (.408) (.421) (.427) Other race 1.589 1.599 1.582 1.568 (.425) (.428) (.426) (.422) Income (Reference: 0–99% FPL) 100–199% FPL 1.162 1.167 1.163 1.197 (.246) (.247) (.247) (.258) 200–299% FPL 0.904 0.907 0.921 0.980 (.222) (.222) (.226) (.245) 300% FPL + 0.936 0.937 0.983 1.063 (.195) (.195) (.204) (.228) Education (Reference: < 9th Grade) 9th − 11th Grade 0.795 0.797 0.797 0.847 (.362) (.362) (.363) (.385) 12th Grade 1.280 1.283 1.234 1.320 (.444) (.444) (.429) (.458) Some College 0.978 0.986 0.920 1.010 (.371) (.373) (.349) (.385) Associates or Vocational 1.098 1.103 1.041 1.146 (.409) (.410) (.388) (.428) Bachelors / Some Grad 0.903 0.905 0.872 0.990 (.329) (.329) (.318) (.364) Masters 0.836 0.836 0.798 0.901 (.324) (.322) (.309) (.351) PhD or equivalent 1.124 1.127 1.090 1.246 (.470) (.470) (.457) (.527) Household type (Reference: Married) Unmarried 1.401* 1.400* 1.367* 1.327* (.199) (.199) (.197) (.192) Region (Reference: Urban) Rural 1.305 1.309 1.313 1.318 (.205) (.205) (.207) (.209) Language at home (Reference: English only) English and other language 1.128 1.131 1.126 1.111 (.195) (.196) (.196) (.193) No English 1.267 1.276 1.292 1.250 (.310) (.313) (.321) (.313) Citizenship (Reference: U.S. born) Naturalized or non-citizen 1.358 1.370 1.344 1.357 (.393) (.398) (.393) (.399) Insurance (Reference = Currently uninsured) Uninsured any prior point, last 12 months 0.575 0.597 0.613 (.507) (.507) (.530) Insured all of the last 12 months 1.175 1.525 1.461 (.602) (.791) (.768) Couldn’t get needed dental (Reference: No) Yes 2.439*** 2.304*** (.428) (.407) General health (Reference: Fair or Poor) Good 0.583 (.181) Very good 0.479* (.138) Excellent 0.369*** (.106) Observations 8,470 8,470 8,470 8,470 Note. Exponentiated coefficients; Robust standard errors in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001 Aligning with emerging best practices, we intentionally select our racial reference group rather than defaulting to having the reference group be White. 22 We select Asian as Asian youth have lower risks of many dental issues than other racial populations, and this group appears first alphabetically. 23 We include race as a predictor not because we view race a marker of intrinsic risk, but rather because we believe a multitude of structural risk factors that shape dental and educational outcomes which are patterned along racial lines. Resulting coefficients should thus be interpreted as indicating how the mix of structural risk factors that shape the experiences of members of a given race relate to missed days due to dental issues. Missed days due to dental health is arguably a rare outcome (occurring in just over three percent of our sample). While our models technically fall outside of the threshold for rare outcomes established by prior literature (as we have more than ten events per predictor), 24 we nonetheless include robust standard errors to reduce sensitivity to heteroskedasticity and model misspecification. 25 , 26 We note that results are functionally identical if we estimate a Firth logistic regression, 27 another approach often taken by methodologists facing rare outcomes (see Supplement). To facilitate comprehension and visualization, we use our final, fully saturated logistic regression model to estimate marginal, post-adjustment probabilities (and 95% confidence intervals) of missed days due to dental issues based on variables that were significant in our final model (see Table 4 and Fig. 1). Table 4 Estimated probability of missed school days due to dental issues for children with varied characteristics that were statistically significant predictors in Model 4 Characteristic Estimated Probability (Standard Error) [95% Confidence Interval] Age of Child 5–6 years old 0.0295 (0.0040) [0.0216, 0.0374] 7–8 years old 0.0424 (0.0044) [0.0338, 0.0511] 9–11 years old 0.0293 (0.0025) [0.0245, 0.0342] Race Asian 0.0209 (0.0044) [0.0123, 0.0295] Black 0.0310 (0.0095) [0.0124, 0.0495] Hispanic 0.0345 (0.0046) [0.0255, 0.0436] White 0.0356 (0.0036) [0.0286, 0.0426] Other Race 0.0323 (0.0047) [0.0231, 0.0415] Household Type Married caregivers 0.0300 (0.0022) [0.0256, 0.0344] Unmarried caregivers 0.0393 (0.0043) [0.0309, 0.0478] Could not Afford Needed Dental Care No 0.0299 (0.0019) [0.0261, 0.0337] Yes 0.0658 (0.0099) [0.0464, 0.0852] General Health Fair or Poor 0.0705 (0.0174) [0.0364, 0.1045] Good 0.0426 (0.0068) [0.0293, 0.0560] Very Good 0.0353 (0.0037) [0.0280, 0.0426] Excellent 0.0275 (0.0024) [0.0229, 0.0322] Results As depicted in Table 1 , about half of our sample (48.7%) was female and a minority (17.0%) lived in rural areas. Unequal percentages were 5–6 years old (21.1%), 6–7 years old (24.3%), and 9–11 years old (54.6%). The majority (60.9%) were in the highest income category (with incomes at or above three times the federal poverty limit), and approximately equal percentages were in each of the three lower income brackets. The vast majority (97.3%) were insured for each of the past twelve months, with just over one percent being currently uninsured or currently insured but having been uninsured at some point in the last year. Most (75.2%) had married caregivers. While some (6.6%) of their adults had not graduated high school, the vast majority (93.4%) had, with over half (56.8%) having earned a Bachelor’s degree, and about a quarter (26.6%) having earned a Master’s. The vast majority were born in the US (95.2%), and varied percentages lived in households with 2–3 (26.4%), 4 (39.6%) or 5 or more people (34%). A plurality were White (45.5%), followed by Hispanic (21.3%), Other Race (15.2%), Asian (14.0%), and Black (4.1%). Most were in households that spoke English exclusively (57.0%), but many were in multilingual households that spoke English (30.2%) or in households where English was not a language of communication (12.7%). At the time of the survey, in the prior year, modest percentages had missed school due to dental issues (3.3%) or been unable to afford needed dental care (6.9%). Most rated their general health as excellent (59.6%), followed by very good (28%), good (10.2%), and fair / poor (2.2%). Table 1 here In χ² tests with Bonferroni adjustment for 13 tests (α = 0.0038), most variables were not significant predictors of missed days due to dental issues, including insurance status, citizenship status, language spoken at home, household size, gender, region (urban vs. rural), education of adult, race, age, and income. Three variables were significant predictors of missed days: caregiver marital status (χ²(1) = 12.02, P = 0.001), general health status (χ²(3) = 29.37, P < 0.001), and whether they could not afford needed dental care in the past year (χ²(1) = 30.92, P < 0.001). See Table 2 for all χ² test results. Table 2 here The results of the logistic regression models were similar (see Table 3 ). Across models, most demographic variables were not significantly predictive of missed days due to dental issues, including income, parent education, region, language spoken at home, citizenship status, and insurance coverage. Results were functionally identical across models. We report Model 4 results here as this model included all predictors. In model 4, children aged 7–8 years had higher odds of missed days due to dental issues compared with children aged 5–6 years (OR 1.46, 95% CI 1.03–2.08, P = 0.035). Relative to Asian children, Hispanic children (OR 1.68, 95% CI 1.02–2.76, P = 0.041) and White children (OR 1.73, 95% CI 1.07–2.81, P = 0.025) also had higher odds. Children with unmarried (versus married) caregivers had higher odds (OR 1.33, 95% CI 1.00–1.76, P = 0.050), as did those who faced access barriers due to being unable to afford needed dental care in the past year (OR 2.30, 95% CI 1.63–3.26, P < 0.001). Finally, compared to children with fair or poor general health, those with very good (OR 0.48, 95% CI 0.27–0.84, P = 0.011) and excellent health (OR 0.37, 95% CI 0.21–0.65, P < 0.001) had lower odds. Table 3 here Focusing on groups defined by variables that were significant predictors in our logistic regression models, we find that predicted probabilities of missed days due to dental issues were materially distinct for youth with different characteristics. Here, we depict predicted probabilities from our fully saturated model (Model 4), which included all predictors, and we present these probabilities as percentages to aid comprehension. As depicted in Table 4 and Fig. 1, when compared to 5–6 year olds, 7–8 year olds were 1.4 times more likely (3.0% vs. 4.2%) to have missed school days due to dental problems. Compared to Asian students, Black and White students were both 1.7 times more likely (2.1% vs. 3.5% and 3.6%, respectively). Compared to those with married caregivers, those with unmarried caregivers were 1.3 times more likely (3.0% vs. 3.9%). Those who indicated being unable to afford dental care their child needed were 2.2 times more likely (3.0% vs. 6.6%). Compared to those in excellent general health, children with good health were 1.5 times, and children with fair / poor health were 2.6 times more likely, to have missed school due to dental issues. Table 4 here Figure 1 here Discussion This California-based study aimed to identify predictors of missed school days due to dental issues, given the documented educational and financial impacts of this outcome. Surprisingly, and in contrast to a prior study using national data 11 , we saw no evidence that either income level or insurance coverage were predictive of missed days due to dental issues after adjustment for other demographic predictors. However—and in accord with one older, national study 9 —even controlling for these (and other demographic) variables, being unable to afford needed dental care was predictive of missed days due to dental issues. Taken together, these findings echo that of other scholarship that distinguishes insurance coverage and access to care 28 —a distinction that may be even more pronounced in states, like California, where nominal coverage does not eliminate out-of-pocket costs. Findings also suggest that even when families have insurance coverage the out-of-pocket costs for their children’s dental care may be prohibitive that they are, at times, forced to forego needed dental care. When they do, it may invite risks of severe oral health issues that can lead to missed school time. Future research should seek to better understand which children are experiencing these dental access issues and how they can be ameliorated. For example, these challenges could be more pronounced among low-income families who rely on governmental insurance plans that may limit the care they can receive. Or they could be more extreme among families in middle-income bands who lack access both to government provided care and to private wealth and thus may rely on lower premium plans that feature coverage restrictions that prove harmful. Certain other variables were also predictive of missed days, including: age (7–8), race (Hispanic or White), household marital status (unmarried), and general health. Future research could explore why 7–8 year olds are more likely to miss school due to dental issues than 5–6 or 9–11 year, exploring possibilities such as vulnerability from mixed dentation (when baby and adult teeth are both present), changes to diet stemming from increased food independence and / or changes in social habits, and changes in oral hygiene habits due to less parental support in oral care routines. That Hispanic youth are more likely to miss school due to dental issues is concerning, given research indicating that Hispanic youth face elevated risks of both absenteeism and chronic absenteeism 29 . Given these results and the way ADA determines educational budgets, school districts that educate large shares of Hispanic students likely face more pronounced dental-health related financial hardships, presenting equity issues that future research could explore. This study provides more evidence 30 that, even after controlling for correlated markers of socioeconomic disadvantage, children with unmarried caregivers are at higher risk of facing educational challenges. Future research could interrogate the mechanism of the link between caregiver marital status and missed days due to dental issues, exploring, for example, whether households with married caregivers are able to harness more collective time to supervise oral hygiene routines 31 and / or proactively address dental needs in ways that protect against missed days due to dental issues. Finally, we find that youth whose parents rate them as healthier are less likely to miss school due to dental issues, a finding that accords with other research demonstrating how health advantage (and disadvantage) can compound 32 , 33 . These findings suggest that caregivers of youth who face non-dental health challenges should take steps to protect their oral health and educational thriving to avoid steeper general health declines, and that caregivers of youth who face dental health challenges should take steps to address them to avoid general health declines. Dental hygienists and other allied oral health professionals play a vital role in providing preventive dental care for underserved children in school-based settings through oral health screening and preventive dental programs. 9 Not all schools are capable of implementing these programs due to limitations in their budgets and their access to care professionals; however, the State of California and local county agencies should consider allocating funding to establish school-based oral health initiatives in economically disadvantaged schools. This approach would ensure that vulnerable, low-income children receive prompt access to preventive dental care. Furthermore, relying on a single dental workforce model might not be enough to improve children’s oral health in schools. 34 Non-dental personnel, such as school nurses or community health workers, can also play a role in implementing preventive dental programs in school settings. This study is not without limitations. First, our data are cross-sectional and our models, while appropriate to the data, are correlational in nature. We thus caution against causal interpretations of these findings, and encourage future research leveraging experimental methods to evaluate whether, for example, interventions designed to reduce costs of dental care yield measurable reductions in missed days due to dental issues. Second, while CHIS leverages a representative sampling frame, our complete-case analysis uses a subset of the CHIS data. To the extent that data were not missing completely at random, our estimates may be less representative of the state. While not a perfect salve, we note that our final model results were nearly identical in data leveraging imputed values (see Supplement) with the only meaningful distinction being that racial categories (White and Hispanic) were only marginally significantly predictive (p < .1) in the logistic regression that used imputed data. Third, our analysis focuses on California 5-11-year-olds and some results may therefore not be generalizable to different aged youth, or youth in states with distinct demographic compositions or dental insurance coverage paradigms. Still, we believe the core mechanisms we identify—particularly financial barriers despite insurance—likely generalize to myriad U.S. contexts. Given this limitation, however, future research could explore other geographic contexts with distinct demographic compositions (e.g., midwestern or southern states) and populations (e.g., teens). Fourth, data limitations precluded our ability to analyze the experiences of certain racial groups, including American Indian / Alaska Native and Pacific Islander populations. Future research with larger datasets could explore experiences for these populations. Finally, CHIS data stems from parents’ answers to questions about their children and is thus only as accurate as parents’ awareness of their children’s experiences. This study also has notable strengths. CHIS data provide a wealth of variables that potentially relate to dental health outcomes. While very few national and regional surveys collect information on missed school days due to illness, CHIS appears to be the only population-level survey that explicitly records data on missed school days due to “dental issues,” which we regard as a strength of this study. Because CHIS data are designed to be representative of California, they afford opportunities to explore dental and educational outcomes in a context that features racial and socioeconomic diversity. Our specific analysis sample (which is constructed by pooling six years of CHIS data) is well powered to estimate a variety of relationships. Our focus on young children (5–11) enables exploration of potential early drivers of educational inequity that could compound over a child’s lifetime. Finally, our use of multiple statistical techniques (χ², confounder adjusted logistic regression with robust standard errors, Firth logistic regression, and marginal post-estimation and visualization of predicted probabilities) empowers a harmony of rigorous analyses and intuitive visualizations. Future research could build on these analyses by replicating them among older children, exploring regional differences in missed days due to dental issues, and quantifying how disparities in missed days due to dental issues and the distribution of students across school districts contribute to inequities in school resources. Conclusions The uneven distribution of young children missing school due to dental issues has implications for health and educational equity. Missed days during these sensitive periods can contribute to long-term losses to learning and developmental losses. Finding ways to help caregivers overcome documented financial obstacles to accessing necessary dental care thus emerges as a pressing policy priority in California. Abbreviations χ² Chi–squared test/statistic ADA Average Daily Attendance CHIS California Health Interview Survey DF Degrees of Freedom CI Confidence Interval OR Odds Ratio STATA Statistical Software (version 18 mentioned) Declarations Ethics approval and consent to participate This research leveraged publicly available data and thus does not qualify as human subjects research. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the Center for Health Policy Research but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Center for Health Policy Research. In addition, interested parties can apply to access these data here: https://healthpolicy.ucla.edu/our-work/public-use-files/two-year-public-use-files-pufs. STATA code for cleaning, architecting, and analyzing is available upon request. Competing interests Vinodh Bhoopathi serves as an Associate Editor for the BMC Oral Health Journal. The other three authors declare that they have no competing interests. Funding Sean Darling-Hammond was supported by a National Institute of Health grant (LRP0000029873). Vinodh Bhoopathi was supported by a Health Resources and Services Administration grant (T12HP46110). Authors' contributions SDH and VB developed the research concept. SDH accessed the data. SDH and AW prepared initial versions of the data, and SDH prepared subsequent versions. SDH and AW conducted the initial analyses, and SDH conducted all subsequent analyses and produced all tables and figures. VB, AW, and AJ wrote the first draft of the introduction. SDH wrote the first draft of the methods, results, discussion, conclusion, and abstract, and produced the first full draft of the manuscript. All authors contributed to revisions and approved the final manuscript. Acknowledgements Not applicable. References National Institutes of Health. Oral Health in America: Advances and Challenges. National Institutes of Health; 2021. https://www.nidcr.nih.gov/research/oralhealthinamerica . Centers for Disease Control and Prevention. Healthy Schools. Healthy Schools. June 26. 2024. Accessed December 13, 2025. https://www.cdc.gov/healthy-schools/about/index.html Basch CE. Healthier students are better learners: a missing link in school reforms to close the achievement gap. J Sch Health. 2011;81(10):593–8. 10.1111/j.1746-1561.2011.00632.x . Guarnizo-Herreño CC, Lyu W, Wehby GL. Children’s Oral Health and Academic Performance: Evidence of a Persisting Relationship over the Last Decade in the United States. J Pediatr. 2019;209:183–e1892. 10.1016/j.jpeds.2019.01.045 . Quadri MFA, Ahmad B. The Mediation Pathway Linking Dental Caries and Academic Performance in Children. Caries Res. 2025;59(1):1–10. 10.1159/000540883 . Borrell LN, Reynolds JC, Fleming E, Shah PD. Access to dental insurance and oral health inequities in the United States. Community Dent Oral Epidemiol. 2023;51(4):615–20. 10.1111/cdoe.12848 . Ruff RR, Senthi S, Susser SR, Tsutsui A. Oral health, academic performance, and school absenteeism in children and adolescents: A systematic review and meta-analysis. J Am Dent Assoc 1939. 2019;150(2):111–e1214. 10.1016/j.adaj.2018.09.023 . Jackson SL, Vann WF, Kotch JB, Pahel BT, Lee JY. Impact of Poor Oral Health on Children’s School Attendance and Performance. Am J Public Health. 2011;101(10):1900–6. 10.2105/AJPH.2010.200915 . Agaku IT, Olutola BG, Adisa AO, Obadan EM, Vardavas CI. Association between unmet dental needs and school absenteeism because of illness or injury among U.S. school children and adolescents aged 6–17 years, 2011–2012. Prev Med. 2015;72:83–8. 10.1016/j.ypmed.2014.12.037 . Hashmi S. Children’s Oral Health Matters for School Success. California Department of Public Health; 2025. https://oralhealthsupport.ucsf.edu/media/8336 . Naavaal S, Kelekar U. School Hours Lost Due to Acute/Unplanned Dental Care. Health Behav Policy Rev. 2018;5(2):66–73. 10.14485/HBPR.5.2.7 . Hahnel C, Baumgardner C. Student Count Options for School Funding: Trade-Offs and Policy Alternatives for California. Policy Analysis for California Education; 2022. edpolicyinca.org/publications/studentcount-options-school-funding . Chien N, Harbin V, Goldhagen S, Lippman L, Walker KE. Encouraging the Development of Key Life Skills in Elementary School-Age Children: A Literature Review and Recommendations to the Tauck Family Foundation. Working Paper. Publication #2012-28 . Child Trends; 2012. Ansari A, Gottfried MA. The grade-level and cumulative outcomes of absenteeism during elementary school. Child Dev. 2021;92(4):e548–64. 10.1111/cdev.13555 . Chang HN, Romero M. Present, Engaged, and Accounted For: The Critical Importance of Addressing Chronic Absence in the Early Grades. National Center for Children in Poverty; 2008. https://www.nccp.org/wp-content/uploads/2008/09/text_837.pdf . Anderson S, Romm K. Absenteeism across the Early Elementary Grades: The Role of Time, Gender, and Socioeconomic Status. Elem Sch J. 2020;121(2):179–96. 10.1086/711053 . Hernandez DJ. Double Jeopardy: How Third-Grade Reading Skills and Poverty Influence High School Graduation. Annie E. Casey Foundation; 2011. https://files.eric.ed.gov/fulltext/ED518818.pdf . Darsie B, Conroy S, Kumar J. Oral Health Status of Children: Results of the 2018–2019 California Third Grade Smile Survey. J Calif Dent Assoc. 2021;49(5):331–6. 10.1080/19424396.2021.12222711 . U.S. Government Accountability Office. K–12 Education: Student Population Has Significantly Diversified, but Many Schools Remain Divided Along Racial, Ethnic, and Economic Lines . U.S. Government Accountability Office https://www.gao.gov/assets/gao-22-104737.pdf Owens A, Reardon SF, Kalogrides D, Jang H, Tom T. Trends in Racial/Ethnic and Economic School Segregation, 1991–2020 . The Segregation Index; 2022. https://edopportunity.org/papers/Trends-in-Racial-Ethnic-Segregation_Rnd6.pdf UCLA Center for Health Policy Research. California Health Interview Survey (CHIS). https://healthpolicy.ucla.edu/chis Castillo W, Gillborn S. Leveraging QuantCrit to expose and challenge systemic racism in educational psychology. Educ Psychol. 2025;60(4):301–16. 10.1080/00461520.2025.2554592 . Dye BA, Mitnik GL, Iafolla TJ, Vargas CM. Trends in dental caries in children and adolescents according to poverty status in the United States from 1999 through 2004 and from 2011 through 2014. J Am Dent Assoc 1939. 2017;148(8):550–e5657. 10.1016/j.adaj.2017.04.013 . Vittinghoff E, McCulloch CE. Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression. Accessed December 17, 2025. https://dx.doi.org/10.1093/aje/kwk052 Michael G. Misspecified Discrete Choice Models and Huber-White Standard Errors. J Econom Methods. 2019;8(1):1–17. White HA, Heteroskedasticity-Consistent. Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica. 1980;48(4):817–38. 10.2307/1912934 . Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21(16):2409–19. 10.1002/sim.1047 . DeVoe JE, Baez A, Angier H, Krois L, Edlund C, Carney PA. Insurance + Access ≠ Health Care: Typology of Barriers to Health Care Access for Low-Income Families. Ann Fam Med. 2007;5(6):511–8. 10.1370/afm.748 . Maynard BR, Vaughn MG, Nelson EJ, Salas-Wright CP, Heyne DA, Kremer KP. Truancy in the United States: Examining temporal trends and correlates by race, age, and gender. Child Youth Serv Rev. 2017;81:188–96. 10.1016/j.childyouth.2017.08.008 . Guetto R, Panichella N. Family arrangements and children’s educational outcomes: Heterogeneous penalties in upper-secondary school. Demogr Res. 2019;40:1015–46. Alos-Rullan V. Households’ age, country of birth, and marital status, stronger predictor variables than education in the prevalence of dental sealants, restorations, and caries among US children 5–19 years of age, NHANES 2005–2010. BMC Oral Health. 2019;19(1):195. 10.1186/s12903-019-0896-0 . Limo L, Nicholson K, Stranges S, Gomaa N. Suboptimal Oral Health, Multimorbidity, and Access to Dental Care. JDR Clin Transl Res. 2024;9(1suppl):S13–22. 10.1177/23800844241273760 . Watt RG, Serban S. Multimorbidity: a challenge and opportunity for the dental profession. Br Dent J. 2020;229(5):282–6. 10.1038/s41415-020-2056-y . Institute of Medicine, National Research Consel of the National Academies. Improving Access to Oral Health Care for Vulnerable and Underserved Populations. 2011:13116. 10.17226/13116 Additional Declarations Competing interest reported. Vinodh Bhoopathi serves as an Associate Editor for the BMC Oral Health Journal. The other three authors declare that they have no competing interests. Supplementary Files DeterminantsofdentalabsenteeismSupplement12.19.25.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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09:32:36","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138541,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8389565/v1/9c8bdb4273c35b2fb093b702.html"},{"id":99219958,"identity":"7ddb9bb7-4c8c-41bf-b927-929a8a266564","added_by":"auto","created_at":"2025-12-30 09:32:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134189,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated probability of missed school days due to dental issues for children with varied characteristics that were statistically significant predictors in Model 4 (with 95% confidence intervals)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8389565/v1/7c5f846f928493da230518a5.png"},{"id":109612415,"identity":"86c06d47-45eb-4701-a621-774b95544ac2","added_by":"auto","created_at":"2026-05-20 07:56:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":558868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8389565/v1/cad64204-0c96-422d-85ca-94cce1650fe3.pdf"},{"id":99219957,"identity":"bd40f4ff-3d49-41b2-aba5-2194866407ec","added_by":"auto","created_at":"2025-12-30 09:32:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":29965,"visible":true,"origin":"","legend":"","description":"","filename":"DeterminantsofdentalabsenteeismSupplement12.19.25.docx","url":"https://assets-eu.researchsquare.com/files/rs-8389565/v1/292130637adae8b4e0ddd4bf.docx"}],"financialInterests":"Competing interest reported. Vinodh Bhoopathi serves as an Associate Editor for the BMC Oral Health Journal. The other three authors declare that they have no competing interests.","formattedTitle":"Determinants of dental absenteeism among young children in California: A logistic regression analysis of CHIS 2017–2022","fulltext":[{"header":"Background","content":"\u003cp\u003eChildren are more susceptible to many oral health issues than adults, including dental caries\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Like health issues generally, dental health issues, can significantly impact attendance, motivation, and academic performance\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e through many routes. Dental caries and dental pain may affect sleep quality, thereby impacting learning and cognition\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. When children cannot access needed care, small challenges can grow more extreme, leading to severe pain and medical responses that can keep children out of school. 35% of American children currently lack dental health benefits, leaving them at risk of delaying needed care\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The link between oral health and attendance is implied by research showing that fluoride varnish and school-based sealant programs directly correlate to reduced absences\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. More directly, children with poor oral health are nearly three times more likely to miss school because of dental pain compared to their healthier peers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn California, while the prevalence of dental caries among children has decreased over the past twenty years, it remains one of the most chronic yet preventable health care needs\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These enduring dental health issues have educational and financial impacts. A nationwide study utilizing 2008 National Health Interview Survey data estimated that, each school year, about 124\u0026nbsp;million school hours are lost due to dental visits, and 34.4\u0026nbsp;million are lost due to unexpected dental issues\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Financial implications emerge in California, as the state allocates funding to school districts based on their average daily attendance (ADA), which represents the average number of students present in school each day\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Districts where large shares of students miss school due to dental issues thus suffer measurable financial losses. Each year, approximately 350,000 California youth miss school due to dental problems, leading to over 850,000 missed school days and approximately \u003cspan\u003e$\u003c/span\u003e60\u0026nbsp;million in wasted school district resources\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLittle work at the intersection of dental health and educational outcomes has focused on younger children, for whom learning losses may be uniquely impactful, especially in California. In elementary school, children build foundational numeracy, literacy, and socio-emotional skills.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e As such, frequent absenteeism in these early grades can have profound impacts on long-term academic achievement, executive functioning, and socio-emotional development\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Students who are chronically absent in kindergarten are much less likely to hit a variety of critical reading and math milestones\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and students who fail to achieve key milestones (such as third-grade reading proficiency) are as many as four times less likely to graduate from high school\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe foregoing suggests that missed school days due to dental issues likely cause immediate and long-term educational harms, as well as financial harms to school districts that educate students who miss school because of dental issues. Importantly, the academic and financial burdens generated by pediatric oral health challenges are unlikely to be evenly distributed across populations and contexts. Research documents persistent disparities in tooth decay and untreated decay by race and socioeconomic status\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and enduring racial and socioeconomic segregation across scholastic contexts\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Quantifying which populations (and, therefore, which kinds of schools) are most impacted by lost school days due to dental-related reasons is necessary to understand the structural impact of dental disease burden holistically and to identify potential pathways to combat preventable health challenges and health inequities.\u003c/p\u003e\n\u003ch3\u003eThe present study\u003c/h3\u003e\n\u003cp\u003eIn this study, we utilize 2017\u0026ndash;2022 data from the California Health Interview Survey to identify significant correlates of missing school due to dental issues, identify populations impacted by losses to both learning time and ADA-based funding, and identify means of ameliorating impacts and inequities related to missed school days due to dental issues.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis study utilized data from the California Health Interview Survey (CHIS), the largest state health survey in the United States\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, to ascertain the burden of missed school days pertaining to dental health and various population characteristics. We merge two-year survey files from 2017\u0026ndash;2018, 2019\u0026ndash;2020, and 2021\u0026ndash;2022. Our sample includes 8,470 children aged 5\u0026ndash;11 whose caregivers provided data on all relevant study measures.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement\u003c/h3\u003e\n\u003cp\u003eOur outcome of interest, missed days of school due to dental issues, was determined by caregiver responses to the survey question \u0026ldquo;During the past 12 months, did your child miss any time from school because of a dental problem? Do not count time missed for cleaning or a check-up,\u0026rdquo; wherein possible responses were \u0026ldquo;Yes ,\u0026rdquo; \u0026ldquo;No,\u0026rdquo; and \u0026ldquo;Not Applicable\u0026rdquo; if a child was not in school or did not have a response. We recode this variable to create a dichotomous indicator (0,1) of missed days due to dental issues.\u003c/p\u003e \u003cp\u003eWe measured the relationship between missed days due to dental issues and several covariates that capture characteristics historically associated with higher risk of dental issues and school absences. Variables can be roughly grouped into four categories: demographics, insurance coverage, dental health access, and general health. Demographic predictors include age (5\u0026ndash;6, 7\u0026ndash;8, 9\u0026ndash;11), race (Asian, Black, Hispanic, White, Other Race), income as percentage of the federal poverty level (0\u0026ndash;99%, 100\u0026ndash;199%, 200\u0026ndash;299%, 300% or more), education level of parents (less than 9th grade, 9th -11th grade, high school graduate, some college, associates or vocational, bachelors or some graduate school, Masters, PhD or equivalent), household type (married caregivers or unmarried caregivers), region (urban or rural), language spoken at home (English only, English and another language, no English), and citizenship (Born in the US or naturalized / non-citizen). Insurance coverage included three options: currently uninsured, currently insured but uninsured at some point during the past year, and insured throughout the past year. Dental care access was measured by whether the child could not afford needed dental care in the past year (no, yes). And general health was assessed by four categories: fair / poor, good, very good, and excellent.\u003c/p\u003e\n\u003ch3\u003eAnalytic plan\u003c/h3\u003e\n\u003cp\u003eData analysis was conducted in STATA version 18. We first summarized all variables used in our models (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Next, we conduct χ\u0026sup2; tests with Bonferroni adjustments for thirteen tests (α\u0026thinsp;=\u0026thinsp;.0038) to assess if each variable is predictive of missed days due to dental issues (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We then estimate four logistic regression models with robust standard errors which successively add demographic, insurance coverage, dental health access, and general health variables as predictors (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Demographic predictors include race, which is categorized in these data as Asian, Black, Hispanic, White, or Other.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIncome level (as percent federal poverty)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;99% FPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;199% FPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;299% FPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;300% FPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInsurance Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrently uninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUninsured any of past 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsured all of past 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried caregivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried caregivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducation Level of Adult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education or grades 1\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 9\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 12 / HS Diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSome college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA / AS / Vocational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBA / BS / Some grad school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMA / MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhD / equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge of Child (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCitizenship Status of Child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUS-born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaturalized / Non-citizen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLanguage at Home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish and another\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo English\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMissed School Due Dental Problems (Last 12 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCould Not Afford Needed Dental Care (Last 12 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eGeneral Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFair / Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate Associations Between Sample Characteristics and Missed School Days Due to Dental Problems (Sorted by p-Value) among California youth (n\u0026thinsp;=\u0026thinsp;8,470)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant at Bonferroni adjusted α\u0026thinsp;=\u0026thinsp;0.0038\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitizenship status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage spoken at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban vs. rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdult educational attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried vs. unmarried caregivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral health status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCould not afford needed dental care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression Models Predicting Missed School Days Due to Dental Problems\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAge (Reference: 5\u0026ndash;6 years old)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7\u0026ndash;8 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.464*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.464*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.475*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.463*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.263)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9\u0026ndash;11 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.166)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRace (Reference: Asian)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.576)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.578)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.585)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.705*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.716*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.716*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.681*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.426)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.665*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.673*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.707*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.735*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.408)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.421)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.427)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.428)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.422)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eIncome (Reference: 0\u0026ndash;99% FPL)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u0026ndash;199% FPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.258)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e200\u0026ndash;299% FPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.222)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.222)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.245)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e300% FPL +\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.228)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eEducation (Reference: \u0026lt; 9th Grade)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9th \u0026minus;\u0026thinsp;11th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.362)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.362)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.385)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.458)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSome College\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.385)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAssociates or Vocational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.428)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBachelors / Some Grad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.364)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.309)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.351)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhD or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.457)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.527)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHousehold type\u003c/p\u003e \u003cp\u003e(Reference: Married)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.401*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.400*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.367*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.327*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.192)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003cp\u003e(Reference: Urban)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.209)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLanguage at home (Reference: English only)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnglish and other language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.193)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo English\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.313)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.313)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCitizenship\u003c/p\u003e \u003cp\u003e(Reference: U.S. born)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNaturalized or non-citizen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.398)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.399)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eInsurance\u003c/p\u003e \u003cp\u003e(Reference\u0026thinsp;=\u0026thinsp;Currently uninsured)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUninsured any prior point, last 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.530)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInsured all of the last 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.602)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.768)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCouldn\u0026rsquo;t get needed dental\u003c/p\u003e \u003cp\u003e(Reference: No)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.439***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.304***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.428)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.407)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eGeneral health (Reference: Fair or Poor)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.181)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.479*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.138)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.369***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.106)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Exponentiated coefficients; Robust standard errors in parentheses.\u003c/p\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAligning with emerging best practices, we intentionally select our racial reference group rather than defaulting to having the reference group be White.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e We select Asian as Asian youth have lower risks of many dental issues than other racial populations, and this group appears first alphabetically.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e We include race as a predictor not because we view race a marker of intrinsic risk, but rather because we believe a multitude of structural risk factors that shape dental and educational outcomes which are patterned along racial lines. Resulting coefficients should thus be interpreted as indicating how the mix of structural risk factors that shape the experiences of members of a given race relate to missed days due to dental issues.\u003c/p\u003e \u003cp\u003eMissed days due to dental health is arguably a rare outcome (occurring in just over three percent of our sample). While our models technically fall outside of the threshold for rare outcomes established by prior literature (as we have more than ten events per predictor),\u003csup\u003e24\u003c/sup\u003e we nonetheless include robust standard errors to reduce sensitivity to heteroskedasticity and model misspecification.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e We note that results are functionally identical if we estimate a Firth logistic regression,\u003csup\u003e27\u003c/sup\u003e another approach often taken by methodologists facing rare outcomes (see Supplement).\u003c/p\u003e \u003cp\u003eTo facilitate comprehension and visualization, we use our final, fully saturated logistic regression model to estimate marginal, post-adjustment probabilities (and 95% confidence intervals) of missed days due to dental issues based on variables that were significant in our final model (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated probability of missed school days due to dental issues for children with varied characteristics that were statistically significant predictors in Model 4\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated Probability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Standard Error)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[95% Confidence Interval]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge of Child\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;6 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0216, 0.0374]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;8 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0338, 0.0511]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;11 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0245, 0.0342]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRace\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0123, 0.0295]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0124, 0.0495]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0255, 0.0436]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0286, 0.0426]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0231, 0.0415]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHousehold Type\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried caregivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0256, 0.0344]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried caregivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0309, 0.0478]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCould not Afford Needed Dental Care\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0261, 0.0337]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0464, 0.0852]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGeneral Health\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair or Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0364, 0.1045]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0293, 0.0560]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0280, 0.0426]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.0229, 0.0322]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Results","content":"\u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, about half of our sample (48.7%) was female and a minority (17.0%) lived in rural areas. Unequal percentages were 5\u0026ndash;6 years old (21.1%), 6\u0026ndash;7 years old (24.3%), and 9\u0026ndash;11 years old (54.6%). The majority (60.9%) were in the highest income category (with incomes at or above three times the federal poverty limit), and approximately equal percentages were in each of the three lower income brackets. The vast majority (97.3%) were insured for each of the past twelve months, with just over one percent being currently uninsured or currently insured but having been uninsured at some point in the last year. Most (75.2%) had married caregivers. While some (6.6%) of their adults had not graduated high school, the vast majority (93.4%) had, with over half (56.8%) having earned a Bachelor\u0026rsquo;s degree, and about a quarter (26.6%) having earned a Master\u0026rsquo;s. The vast majority were born in the US (95.2%), and varied percentages lived in households with 2\u0026ndash;3 (26.4%), 4 (39.6%) or 5 or more people (34%). A plurality were White (45.5%), followed by Hispanic (21.3%), Other Race (15.2%), Asian (14.0%), and Black (4.1%). Most were in households that spoke English exclusively (57.0%), but many were in multilingual households that spoke English (30.2%) or in households where English was not a language of communication (12.7%). At the time of the survey, in the prior year, modest percentages had missed school due to dental issues (3.3%) or been unable to afford needed dental care (6.9%). Most rated their general health as excellent (59.6%), followed by very good (28%), good (10.2%), and fair / poor (2.2%).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here\u003c/p\u003e \u003cp\u003eIn χ\u0026sup2; tests with Bonferroni adjustment for 13 tests (α\u0026thinsp;=\u0026thinsp;0.0038), most variables were not significant predictors of missed days due to dental issues, including insurance status, citizenship status, language spoken at home, household size, gender, region (urban vs. rural), education of adult, race, age, and income. Three variables were significant predictors of missed days: caregiver marital status (χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;12.02, P\u0026thinsp;=\u0026thinsp;0.001), general health status (χ\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;29.37, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and whether they could not afford needed dental care in the past year (χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;30.92, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for all χ\u0026sup2; test results.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here\u003c/p\u003e \u003cp\u003eThe results of the logistic regression models were similar (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Across models, most demographic variables were not significantly predictive of missed days due to dental issues, including income, parent education, region, language spoken at home, citizenship status, and insurance coverage. Results were functionally identical across models. We report Model 4 results here as this model included all predictors.\u003c/p\u003e \u003cp\u003eIn model 4, children aged 7\u0026ndash;8 years had higher odds of missed days due to dental issues compared with children aged 5\u0026ndash;6 years (OR 1.46, 95% CI 1.03\u0026ndash;2.08, P\u0026thinsp;=\u0026thinsp;0.035). Relative to Asian children, Hispanic children (OR 1.68, 95% CI 1.02\u0026ndash;2.76, P\u0026thinsp;=\u0026thinsp;0.041) and White children (OR 1.73, 95% CI 1.07\u0026ndash;2.81, P\u0026thinsp;=\u0026thinsp;0.025) also had higher odds. Children with unmarried (versus married) caregivers had higher odds (OR 1.33, 95% CI 1.00\u0026ndash;1.76, P\u0026thinsp;=\u0026thinsp;0.050), as did those who faced access barriers due to being unable to afford needed dental care in the past year (OR 2.30, 95% CI 1.63\u0026ndash;3.26, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Finally, compared to children with fair or poor general health, those with very good (OR 0.48, 95% CI 0.27\u0026ndash;0.84, P\u0026thinsp;=\u0026thinsp;0.011) and excellent health (OR 0.37, 95% CI 0.21\u0026ndash;0.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had lower odds.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here\u003c/p\u003e \u003cp\u003eFocusing on groups defined by variables that were significant predictors in our logistic regression models, we find that predicted probabilities of missed days due to dental issues were materially distinct for youth with different characteristics. Here, we depict predicted probabilities from our fully saturated model (Model 4), which included all predictors, and we present these probabilities as percentages to aid comprehension. As depicted in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;1, when compared to 5\u0026ndash;6 year olds, 7\u0026ndash;8 year olds were 1.4 times more likely (3.0% vs. 4.2%) to have missed school days due to dental problems. Compared to Asian students, Black and White students were both 1.7 times more likely (2.1% vs. 3.5% and 3.6%, respectively). Compared to those with married caregivers, those with unmarried caregivers were 1.3 times more likely (3.0% vs. 3.9%). Those who indicated being unable to afford dental care their child needed were 2.2 times more likely (3.0% vs. 6.6%). Compared to those in excellent general health, children with good health were 1.5 times, and children with fair / poor health were 2.6 times more likely, to have missed school due to dental issues.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here\u003c/p\u003e \u003cp\u003eFigure 1 here\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis California-based study aimed to identify predictors of missed school days due to dental issues, given the documented educational and financial impacts of this outcome. Surprisingly, and in contrast to a prior study using national data\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, we saw no evidence that either income level or insurance coverage were predictive of missed days due to dental issues after adjustment for other demographic predictors. However\u0026mdash;and in accord with one older, national study\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u0026mdash;even controlling for these (and other demographic) variables, being unable to afford needed dental care was predictive of missed days due to dental issues.\u003c/p\u003e \u003cp\u003eTaken together, these findings echo that of other scholarship that distinguishes insurance coverage and access to care\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u0026mdash;a distinction that may be even more pronounced in states, like California, where nominal coverage does not eliminate out-of-pocket costs. Findings also suggest that even when families have insurance coverage the out-of-pocket costs for their children\u0026rsquo;s dental care may be prohibitive that they are, at times, forced to forego needed dental care. When they do, it may invite risks of severe oral health issues that can lead to missed school time. Future research should seek to better understand which children are experiencing these dental access issues and how they can be ameliorated. For example, these challenges could be more pronounced among low-income families who rely on governmental insurance plans that may limit the care they can receive. Or they could be more extreme among families in middle-income bands who lack access both to government provided care and to private wealth and thus may rely on lower premium plans that feature coverage restrictions that prove harmful.\u003c/p\u003e \u003cp\u003eCertain other variables were also predictive of missed days, including: age (7\u0026ndash;8), race (Hispanic or White), household marital status (unmarried), and general health. Future research could explore why 7\u0026ndash;8 year olds are more likely to miss school due to dental issues than 5\u0026ndash;6 or 9\u0026ndash;11 year, exploring possibilities such as vulnerability from mixed dentation (when baby and adult teeth are both present), changes to diet stemming from increased food independence and / or changes in social habits, and changes in oral hygiene habits due to less parental support in oral care routines.\u003c/p\u003e \u003cp\u003eThat Hispanic youth are more likely to miss school due to dental issues is concerning, given research indicating that Hispanic youth face elevated risks of both absenteeism and chronic absenteeism\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Given these results and the way ADA determines educational budgets, school districts that educate large shares of Hispanic students likely face more pronounced dental-health related financial hardships, presenting equity issues that future research could explore.\u003c/p\u003e \u003cp\u003eThis study provides more evidence\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e that, even after controlling for correlated markers of socioeconomic disadvantage, children with unmarried caregivers are at higher risk of facing educational challenges. Future research could interrogate the mechanism of the link between caregiver marital status and missed days due to dental issues, exploring, for example, whether households with married caregivers are able to harness more collective time to supervise oral hygiene routines\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and / or proactively address dental needs in ways that protect against missed days due to dental issues.\u003c/p\u003e \u003cp\u003eFinally, we find that youth whose parents rate them as healthier are less likely to miss school due to dental issues, a finding that accords with other research demonstrating how health advantage (and disadvantage) can compound\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These findings suggest that caregivers of youth who face non-dental health challenges should take steps to protect their oral health and educational thriving to avoid steeper general health declines, and that caregivers of youth who face dental health challenges should take steps to address them to avoid general health declines.\u003c/p\u003e \u003cp\u003eDental hygienists and other allied oral health professionals play a vital role in providing preventive dental care for underserved children in school-based settings through oral health screening and preventive dental programs.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Not all schools are capable of implementing these programs due to limitations in their budgets and their access to care professionals; however, the State of California and local county agencies should consider allocating funding to establish school-based oral health initiatives in economically disadvantaged schools. This approach would ensure that vulnerable, low-income children receive prompt access to preventive dental care. Furthermore, relying on a single dental workforce model might not be enough to improve children\u0026rsquo;s oral health in schools.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Non-dental personnel, such as school nurses or community health workers, can also play a role in implementing preventive dental programs in school settings.\u003c/p\u003e \u003cp\u003eThis study is not without limitations. First, our data are cross-sectional and our models, while appropriate to the data, are correlational in nature. We thus caution against causal interpretations of these findings, and encourage future research leveraging experimental methods to evaluate whether, for example, interventions designed to reduce costs of dental care yield measurable reductions in missed days due to dental issues. Second, while CHIS leverages a representative sampling frame, our complete-case analysis uses a subset of the CHIS data. To the extent that data were not missing completely at random, our estimates may be less representative of the state. While not a perfect salve, we note that our final model results were nearly identical in data leveraging imputed values (see Supplement) with the only meaningful distinction being that racial categories (White and Hispanic) were only marginally significantly predictive (p\u0026thinsp;\u0026lt;\u0026thinsp;.1) in the logistic regression that used imputed data.\u003c/p\u003e \u003cp\u003eThird, our analysis focuses on California 5-11-year-olds and some results may therefore not be generalizable to different aged youth, or youth in states with distinct demographic compositions or dental insurance coverage paradigms. Still, we believe the core mechanisms we identify\u0026mdash;particularly financial barriers despite insurance\u0026mdash;likely generalize to myriad U.S. contexts. Given this limitation, however, future research could explore other geographic contexts with distinct demographic compositions (e.g., midwestern or southern states) and populations (e.g., teens). Fourth, data limitations precluded our ability to analyze the experiences of certain racial groups, including American Indian / Alaska Native and Pacific Islander populations. Future research with larger datasets could explore experiences for these populations. Finally, CHIS data stems from parents\u0026rsquo; answers to questions about their children and is thus only as accurate as parents\u0026rsquo; awareness of their children\u0026rsquo;s experiences.\u003c/p\u003e \u003cp\u003eThis study also has notable strengths. CHIS data provide a wealth of variables that potentially relate to dental health outcomes. While very few national and regional surveys collect information on missed school days due to illness, CHIS appears to be the only population-level survey that explicitly records data on missed school days due to \u0026ldquo;dental issues,\u0026rdquo; which we regard as a strength of this study. Because CHIS data are designed to be representative of California, they afford opportunities to explore dental and educational outcomes in a context that features racial and socioeconomic diversity. Our specific analysis sample (which is constructed by pooling six years of CHIS data) is well powered to estimate a variety of relationships. Our focus on young children (5\u0026ndash;11) enables exploration of potential early drivers of educational inequity that could compound over a child\u0026rsquo;s lifetime. Finally, our use of multiple statistical techniques (χ\u0026sup2;, confounder adjusted logistic regression with robust standard errors, Firth logistic regression, and marginal post-estimation and visualization of predicted probabilities) empowers a harmony of rigorous analyses and intuitive visualizations.\u003c/p\u003e \u003cp\u003eFuture research could build on these analyses by replicating them among older children, exploring regional differences in missed days due to dental issues, and quantifying how disparities in missed days due to dental issues and the distribution of students across school districts contribute to inequities in school resources.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe uneven distribution of young children missing school due to dental issues has implications for health and educational equity. Missed days during these sensitive periods can contribute to long-term losses to learning and developmental losses. Finding ways to help caregivers overcome documented financial obstacles to accessing necessary dental care thus emerges as a pressing policy priority in California.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eχ\u0026sup2;\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChi\u0026ndash;squared test/statistic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAverage Daily Attendance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalifornia Health Interview Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDegrees of Freedom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTATA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Software (version 18 mentioned)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis research leveraged publicly available data and thus does not qualify as human subjects research.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Center for Health Policy Research but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Center for Health Policy Research. In addition, interested parties can apply to access these data here: https://healthpolicy.ucla.edu/our-work/public-use-files/two-year-public-use-files-pufs. STATA code for cleaning, architecting, and analyzing is available upon request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eVinodh Bhoopathi serves as an Associate Editor for the BMC Oral Health Journal. The other three authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eSean Darling-Hammond was supported by a National Institute of Health grant (LRP0000029873). Vinodh Bhoopathi was supported by a Health Resources and Services Administration grant (T12HP46110).\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eSDH and VB developed the research concept. SDH accessed the data. SDH and AW prepared initial versions of the data, and SDH prepared subsequent versions. SDH and AW conducted the initial analyses, and SDH conducted all subsequent analyses and produced all tables and figures. VB, AW, and AJ wrote the first draft of the introduction. SDH wrote the first draft of the methods, results, discussion, conclusion, and abstract, and produced the first full draft of the manuscript. All authors contributed to revisions and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNational Institutes of Health. Oral Health in America: Advances and Challenges. 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Improving Access to Oral Health Care for Vulnerable and Underserved Populations. 2011:13116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17226/13116\u003c/span\u003e\u003cspan address=\"10.17226/13116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Missed school days, School absenteeism, Pediatric oral health, Racial disparities, Health disparities, Socioeconomic determinants, Household characteristics, Accessibility, California Health Interview Survey, Logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-8389565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8389565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnnually, hundreds of thousands of children miss school due to dental issues. While prior scholarship has shown that missed days due to dental issues relate to substantial losses of learning time and district-level educational funding, few studies have explored predictors of this outcome. It remains unclear whether certain groups of children or school districts disproportionately bear the burden of dental absenteeism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe leverage data from the California Health Interview Survey (CHIS) from 2017–2022 to construct a complete-case sample of n = 8,470 California children (aged 5–11). We first conduct χ² tests with Bonferroni adjustments to assess which characteristics are associated with missed school days due to dental issues. We then estimate four logistic regression models that successively adding demographic, insurance coverage, dental health access, and general health variables. Finally, we calculate and visualize estimated probabilities based on youth characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn χ² tests, significant predictors included being in a household with unmarried caregivers, general health status, and being unable to afford needed dental care in the last year. In logistic regression models, characteristics that significantly predicted higher odds were: being 7–8 years old (relative to 5–6), being Hispanic or White (relative to Asian), living in a household with unmarried caregivers, and being unable to afford needed dental care in the last year. Being in very good general or excellent general health (relative to fair/poor general health) significantly predicted lower odds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEven after adjusting for insurance coverage and income, financial barriers remain major drivers of missed school days due to dental issues among young school children in California. Policymakers should address these barriers to improve dental care access and promote health and educational equity.\u003c/p\u003e","manuscriptTitle":"Determinants of dental absenteeism among young children in California: A logistic regression analysis of CHIS 2017–2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 09:32:27","doi":"10.21203/rs.3.rs-8389565/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80cdb467-5deb-4107-a82d-cd126342b87f","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-20T07:39:47+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T07:56:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 09:32:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8389565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8389565","identity":"rs-8389565","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}