Abstract
The health of prospective parents before conception (i.e., preconception health) has important intergenerational consequences. Although oral health is associated with several reproductive outcomes, it is often absent from preconception health promotion. To generate insights that may inform preconception oral health interventions, we used cross-sectional data from 6,159 US-based participants enrolled in the Pregnancy Study Online, a prospective cohort study of pregnancy planners, to characterize preconception oral health care engagement and self-rated oral health. We used latent class analysis to identify homogenous subgroups (i.e., latent classes) of participants with respect to modifiable risk behaviors and quantified associations between class membership and 3 outcomes—reporting no dental visit within the past year, reporting no dental cleaning within the past year, and self-rated oral health—using log binomial regression models. We identified 3 distinct groups of participants: healthy behavior, high prevalence of healthy behaviors; some risk behavior, higher sugar-sweetened beverage consumption and lower vaccine uptake and multivitamin use; and most risk behavior, high probability of most risk behaviors, including current or former cigarette smoking. The some risk behavior class was more likely to report no dental visit within the past year as compared with the healthy behavior class (prevalence difference [PD] 0.17; 95% confidence interval [95% CI], 0.14 to 0.19). This association strengthened when most risk behavior was compared with healthy behavior (PD, 0.32; 95% CI, 0.28 to 0.36). Similarly, reporting no dental cleaning within the past year was more prevalent among some risk behavior (PD, 0.19; 95% CI, 0.16 to 0.21) and most risk behavior (PD, 0.38; 95% CI, 0.34 to 0.42) as compared with healthy behavior. The pattern was similar for self-rated oral health. Our findings suggest that oral health care engagement and self-rated oral health are associated with other modifiable behaviors in the preconception period. Health promotion efforts in the preconception period must consider oral health care engagement alongside other modifiable health behaviors.
Keywords: preconception care, health risk behaviors, latent class analysis, health promotion, epidemiology
Introduction
The preconception period (i.e., the period preceding conception) has broad potential for effective health promotion (Posner et al. 2008; Verbiest et al. 2023). Preconception health not only encompasses the health of the potential future parents (Moos et al. 2008) but is also believed to affect reproductive outcomes and long-term offspring health (Fleming et al. 2018; Stephenson et al. 2018; Daly et al. 2022). Interventions to promote preconception health, including clinician counseling, have shown promise in improving modifiable health behaviors, such as folic acid supplementation (Oza-Frank et al. 2015), cigarette smoking, and alcohol consumption (Hussein et al. 2016; Sijpkens et al. 2021).
Despite consistent evidence that periodontitis during pregnancy is associated with adverse birth outcomes (e.g., preterm birth, low birthweight; Daalderop et al. 2018) and the fact that periodontitis affects almost 40% of adult US women (Eke et al. 2015), oral health is largely absent from preconception health promotion efforts (Freda et al. 2006; American College of Obstetricians and Gynecologists 2019). Substantial barriers to obtaining dental care exist, particularly in the United States, where many adults lack dental insurance (Nasseh and Vujicic 2016; Tolbert et al. 2023). Pregnant people have additional barriers to obtaining dental care, including a high burden of medical appointments and safety concerns (Detman et al. 2010; Kamalabadi et al. 2023). Preconception oral health promotion may be of particular importance because 1) pregnancy can exacerbate existing periodontal health issues (Gaffield et al. 2001), 2) periodontitis during pregnancy is associated with adverse birth outcomes (Daalderop et al. 2018), and 3) recent research indicates a possible association between preconception periodontitis and reproductive health outcomes (Bond et al. 2021; Bond et al. 2023).
Oral health promotion efforts may be limited by the paucity of information about patterns of oral health care engagement in the preconception period (Boggess and Edelstein 2006). Health behaviors are informed by complex factors, including dispositional attributes, which may yield clustering of health-promoting behaviors (Rodin and Salovey 1989). Modifiable health behaviors, such as smoking and high alcohol use, cluster in the preconception period and in pregnancy (Lange et al. 2015; Dennis et al. 2022; Molenaar et al. 2023), and distinct behavior profiles may be associated with different risks of health outcomes (Molenaar et al. 2023). Identifying homogeneous groups within a population has the potential to provide insights into the health risks of multiple co-occurring behaviors, possibly leading to more informed health behavior interventions (Posner et al. 2008). It is therefore necessary to contextualize preconception oral health behaviors in relation to other health behaviors to identify opportunities for increased efficiency in preconception health promotion interventions.
We aimed to identify homogeneous groups of participants based on patterns of modifiable health behaviors in a cohort of pregnancy planners and quantify associations between group membership and oral health care engagement.
Methods
Study Sample
This descriptive cross-sectional study is based on data from the baseline questionnaire of the Pregnancy Study Online (PRESTO), a preconception cohort study (for detailed cohort methodology, see Wise et al. 2015). In brief, eligible participants identified as female, were attempting pregnancy with a male partner without fertility treatments, were aged 21 to 45 y, and were residents of the United States or Canada. Recruitment occurred online via social media advertising, as well as by posted flyers in health care offices and word of mouth. At enrollment, participants completed a questionnaire regarding health history, modifiable health behaviors, and sociodemographic characteristics. Participants completed follow-up questionnaires every 8 wk until a pregnancy was reported or 12 mo had passed, whichever came first. Participants reporting a pregnancy completed additional questionnaires. In April 2019, we selected questions from validated measures for population-level periodontitis surveillance developed by the Centers for Disease Control and Prevention and the American Academy of Periodontology (Eke et al. 2013) for the enrollment questionnaire. At the time that the questions were introduced, they were available to every participant active in the study on each one’s next questionnaire. To be included in this analysis, participants must have been active (i.e., still completing questionnaires, including pregnancy or postpartum questionnaires) on or after April 2019 through August 2023 and completed at least 1 oral health question. For our primary analysis, we restricted our sample to participants residing in the United States because of differences in the availability of safety-net oral health care services (Chari et al. 2022). Our analytic sample comprised 6,159 US participants (Fig.). To evaluate the robustness of our findings, we conducted external replication using data from 1,184 Canadian participants.
Figure.
Sample selection, Pregnancy Study Online (PRESTO), 2018 to 2023. Since 2013, PRESTO has been enrolling pregnancy planners residing in the United States or Canada. In April 2019, questions about oral health were added to the enrollment questionnaire. The term “questions” in this figure refers to these questions.
Participants provided informed consent online, and the study protocol was reviewed and approved by the Boston University Medical Campus Institutional Review Board. This study conforms to STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology; von Elm et al. 2007).
Modifiable Health Behaviors
Latent class analysis (LCA) identifies groups of individuals who are similar based on responses to selected indicators (Nylund-Gibson and Choi 2018; see the Methodological Appendix for details). For indicators, we selected 7 modifiable health behaviors previously identified as targets for preconception health interventions (ACOG Committee on Practice Bulletins 2005; Freda et al. 2006; Verbiest et al. 2023). These were weekly servings of sugar-sweetened beverage consumption in the past month (0, ≤1, >1 to <7, ≥7), weekly alcoholic drink consumption in the past month (0, 1 to 6, 7 to 13, ≥14), current use of any form of prenatal or multivitamin (yes/no), and cigarette smoking status (current, former, never). We additionally included average weekly physical activity in the past year, estimated by calculating a participant’s average metabolic-equivalent hours per week (<10, 10 to <20, 20 to <40, ≥40) over the past year, using the reported frequency of various physical activities (Ainsworth et al. 2000). Two indicators represented engagement in recommended health care: receiving a flu immunization in the past year (yes/no) and how many times the participant reported seeing a primary care physician in the past year (0, 1, 2 or 3, ≥4).
Outcomes
Outcomes reflected preconception oral health care engagement and self-rated oral health status. Recent dental visit was captured by responses to the question “How long has it been since you last visited a dentist or a dental clinic for any reason? Include visits to dental specialists, such as orthodontists.” A recent dental cleaning was captured by the question “How long has it been since you had your teeth cleaned by a dentist or dental hygienist?” Response options to both questions were Within the past year, 1–2 years ago, 3–4 years ago, and 5 or more years ago. We categorized responses as Within the past year vs Not within the past year. Finally, self-rated oral health status was assessed by asking, “Overall, how would you rate the health of your teeth and gums?” Response options were excellent, very good, good, fair, and poor and were categorized as fair/poor vs excellent/very good/good for analysis. All variables were self-reported on the baseline questionnaire.
Analytic Strategy
We used unconditional LCA with the expectation-maximization algorithm to identify latent classes (i.e., homogeneous groups of participants) with respect to selected indicators (i.e., modifiable health behaviors) using PROC LCA in SAS (Lanza et al. 2007). Missingness was assumed to be at random and was minimal for all indicators (≤0.2%). We identified the optimal number of classes by sequentially increasing the number of classes (k), beginning with k = 1 until model nonidentification was concluded (k = 6). To account for the fact that there are often multiple maximum likelihood values possible for a given data set (i.e., local maxima), we calculated 100 iterations of the LCA with randomly generated starting seed values. We identified the most common solution (i.e., the model that was most commonly identified across the 100 iterations or global maxima) as the maximum likelihood solution (Lanza et al. 2007).
To select the appropriate number of classes, we compared relative measures of model fit indices across each iteration. Specifically, we evaluated Akaike information criterion, bayesian information criterion, sample size–adjusted bayesian information criterion, and consistent Akaike information criterion (Nylund-Gibson and Choi 2018). In conjunction with comparing fit statistics across models, we evaluated model interpretability, evaluating whether the classes were distinguishable and no class was diminutive (i.e., <1%; Lanza et al. 2007). After identifying a final model, we calculated entropy and average posterior probabilities (i.e., the probability of membership in each class for each participant) to assess model classification diagnostics using a threshold of 0.7 to conclude acceptable model classification accuracy (Maysn 2013).
To assess whether class membership was associated with preconception oral health, we used binomial models to calculate crude prevalence differences (PDs) comparing the prevalence of our outcomes of interest (dental visit for any reason in the past year, dental cleaning in the past year, and excellent/very good/good oral health) across classes, assigning class membership using the highest posterior probability (Spiegelman and Hertzmark 2005). To account for the fact that class assignment is not a fixed quality, we additionally created 100,000 simulations of our original sample, in which we assigned class membership using the posterior probabilities calculated from the LCA model, an adaptation of the pseudoclass technique (Bandeen-Roche et al. 1997). We then used unadjusted binomial models to calculate crude PDs relating class membership to the outcomes across all simulations. These models were not adjusted for potential confounders because this is a descriptive study primarily designed to inform health interventions. Inappropriate adjustment may distort the magnitude of associations (Zalla et al. 2021); therefore, we follow established recommendations to present crude associations in a descriptive context (Lesko et al. 2022). We summarized the results of these simulations by using the median of the resulting prevalence ratios as a point estimate and the 2.5th and 97.5th percentiles of the distribution of prevalence ratios as a simulation interval (SI).
LCA External Replication
LCA external replication was carried out via the Canadian data set. We conducted this analysis as confirmatory, rather than exploratory, by using the optimal solution of classes in the US data and inspecting model diagnostics and interpretability. We evaluated the associations between class membership and oral health outcomes in the Canadian data set.
Results
Sample characteristics are presented in Table 1 stratified by country of residence. Participants had a mean age of 30 y. The majority had an educational attainment of at least a college degree, were non-Hispanic White, and reported a high level of household income (≥$100,000). Eighty-nine percent had dental insurance. Most participants reported health-promoting behaviors, with at least 80% reporting consuming ≤6 weekly alcoholic drinks, never smoking cigarettes, currently taking a multivitamin or folate supplement, and having seen a primary care provider in the past year. Oral health care engagement was slightly lower, with 68% citing dental visits for any reason and 65% indicating a dental cleaning in the past year.
Table 1.
Characteristics of the Sample by Country of Residence, PRESTO (2018 to 2023).
| Sample, No. (%) | |||
|---|---|---|---|
| Characteristic | Total (N = 7,343) | US (n = 6,159) | Canadian (n = 1,184) |
| Sociodemographic | |||
| Age, y, mean (SD) | 30.9 (4.2) | 31 (4.3) | 31 (3.9) |
| Participant education, y | |||
| <12 | 66 (0.9) | 50 (0.8) | 16 (1.4) |
| 12 | 382 (5.2) | 328 (5.3) | 54 (4.6) |
| 13 to 15 | 1,395 (19.0) | 1,166 (18.9) | 229 (19.3) |
| 16 | 2,350 (32.0) | 1,897 (30.8) | 453 (38.3) |
| ≥17 | 3,150 (42.9) | 2,718 (44.1) | 432 (36.5) |
| Race and ethnicity | |||
| Hispanic | 555 (7.6) | 521 (8.5) | 34 (2.9) |
| Non-Hispanic multiracial | 278 (3.8) | 227 (3.7) | 51 (4.3) |
| Non-Hispanic Black | 265 (3.6) | 251 (4.1) | 14 (1.2) |
| Non-Hispanic Asian/Pacific Islander | 172 (2.3) | 131 (2.1) | 41 (3.5) |
| Non-Hispanic American Indian/Alaskan Native | 22 (0.3) | 10 (0.2) | 12 (1.0) |
| Non-Hispanic White | 6,002 (81.7) | 4,987 (81.0) | 1,015 (85.7) |
| Non-Hispanic other/missing race | 49 (0.7) | 32 (0.5) | 17 (1.4) |
| Household income, US$ | |||
| <15,000 | 127 (1.8) | 115 (1.9) | 12 (1.1) |
| 15,000 to 24,999 | 245 (3.4) | 221 (3.7) | 24 (2.2) |
| 25,000 to 49,999 | 798 (11.2) | 696 (11.6) | 102 (9.2) |
| 50,000 to 74,999 | 1,083 (15.2) | 882 (14.7) | 201 (18.1) |
| 75,000 to 99,999 | 1,216 (17.1) | 999 (16.6) | 217 (19.5) |
| 100,000 to 124,999 | 1,149 (16.1) | 921 (15.3) | 228 (20.5) |
| 125,000 to 149,999 | 785 (11.0) | 623 (10.4) | 162 (14.6) |
| 150,000 to 199,999 | 875 (12.3) | 757 (12.6) | 118 (10.6) |
| ≥200,000 | 845 (11.9) | 796 (13.2) | 49 (4.4) |
| Missing | 220 | 149 | 71 |
| Dental insurance | |||
| Yes | 6,331 (86.2) | 5,292 (86.0) | 1,039 (87.8) |
| No | 1,010 (13.8) | 865 (14.1) | 15 (12.3) |
| Health insurance | |||
| Private | 6,067 (82.7) | 5,407 (87.8) | 660 (55.7) |
| Government | 1,132 (15.4) | 608 (9.9) | 524 (44.3) |
| Out of pocket | 142 (1.9) | 142 (2.3) | 0 |
| Missing | 2 | 2 | |
| Currently employed | |||
| Yes | 6,349 (87.1) | 5,290 (86.5) | 1,059 (90.1) |
| No | 939 (12.9) | 823 (13.5) | 116 (9.9) |
| Missing | 55 | 46 | 9 |
| Married | |||
| Yes | 6,183 (84.3) | 5,307 (86.2) | 876 (74.1) |
| No | 1,155 (15.7) | 848 (13.8) | 307 (26.0) |
| Missing | 5 | 4 | 1 |
| Parity | |||
| Parous | 2,464 (33.6) | 2,133 (34.6) | 331 (28.0) |
| Nulliparous | 4,879 (66.4) | 4,026 (65.4) | 853 (72.0) |
| Modifiable health behaviors | |||
| Alcohol consumption, drinks/wk | |||
| 0 | 2,461 (33.6) | 2,089 (34.0) | 372 (31.5) |
| 1 to 6 | 4,045 (55.2) | 3,367 (54.8) | 678 (57.3) |
| 7 to 13 | 662 (9.0) | 553 (9.0) | 109 (9.2) |
| ≥14 | 164 (2.2) | 140 (2.3) | 24 (2.0) |
| Missing | 11 | 10 | 1 |
| Sugar-sweetened beverage consumption, servings/wk | |||
| 0 | 3,206 (43.8) | 2,633 (42.8) | 537 (48.5) |
| ≤1 | 1,221 (16.7) | 1,029 (16.7) | 192 (16.3) |
| >1 to <7 | 1,930 (26.3) | 1,624 (26.4) | 306 (25.9) |
| ≥7 | 970 (13.2) | 860 (14.0) | 110 (9.3) |
| Missing | 16 | 13 | 3 |
| Total MET h/wk | |||
| <10 | 1,080 (14.7) | 946 (15.4) | 134 (11.3) |
| 10 to 19 | 1,605 (21.9) | 1,379 (22.4) | 226 (19.1) |
| 20 to 39 | 2,511 (34.2) | 2,099 (34.1) | 412 (34.8) |
| ≥40 | 2,141 (29.2) | 1,729 (28.1) | 412 (34.8) |
| Missing | 6 | 6 | |
| Received a flu vaccination in the past year | |||
| Yes | 4,342 (59.1) | 3,777 (61.3) | 565 (47.7) |
| No | 3,001 (40.9) | 2,382 (38.7) | 619 (52.3) |
| Cigarette smoking | |||
| Never smoker | 6,007 (81.9) | 5,053 (82.1) | 954 (80.6) |
| Former smoker | 796 (10.9) | 651 (10.6) | 145 (12.3) |
| Current occasional smoker | 181 (2.5) | 161 (2.6) | 20 (1.7) |
| Current regular smoker | 355 (4.8) | 291 (4.7) | 64 (5.4) |
| Missing | 4 | 3 | 1 |
| Currently taking multivitamin | |||
| Yes | 5,935 (80.8) | 5,033 (81.7) | 902 (76.2) |
| No | 1,408 (19.2) | 1,126 (18.3) | 282 (23.8) |
| During the past year, approximately how many times did you visit your family physician or primary care provider? | |||
| None | 999 (13.6) | 875 (14.2) | 124 (10.5) |
| Once | 2,401 (32.7) | 2,140 (34.8) | 261 (22.0) |
| 2 or 3 | 2,852 (38.9) | 2,331 (37.9) | 521 (44.0) |
| 4 or 5 | 694 (9.5) | 532 (8.6) | 162 (13.7) |
| ≥6 | 396 (5.4) | 280 (4.6) | 116 (9.8) |
| Missing | 1 | 1 | |
| Oral health engagement and self-rated oral health | |||
| How long has it been since you last visited a dentist or dental clinic for any reason? | |||
| Within the past year | 5,014 (68.3) | 4,174 (67.8) | 840 (71.0) |
| 1 to 2 y ago | 1,301 (17.7) | 1,091 (17.7) | 210 (17.7) |
| 3 to 4 y ago | 534 (7.3) | 457 (7.4) | 77 (6.5) |
| ≥5 y ago | 492 (6.7) | 435 (7.1) | 57 (4.8) |
| Missing | 2 | 2 | |
| How long has it been since you had your teeth cleaned by a dentist/hygienist? | |||
| Within the past year | 4,773 (65.0) | 3,972 (64.5) | 801 (67.7) |
| 1 to 2 y ago | 1,350 (18.4) | 1,134 (18.4) | 217 (18.2) |
| 3 to 4 y ago | 597 (8.1) | 512 (8.3) | 85 (7.2) |
| ≥5 y ago | 622 (8.5) | 540 (8.8) | 82 (6.9) |
| Missing | 1 | 1 | |
| How would you rate the health of your teeth and gums? | |||
| Excellent | 1,605 (21.9) | 1,333 (21.6) | 272 (23.0) |
| Very good | 2,803 (38.2) | 2,298 (37.3) | 505 (42.7) |
| Good | 1,912 (26.0) | 1,631 (26.5) | 281 (23.7) |
| Fair | 807 (11.0) | 706 (11.5) | 101 (8.5) |
| Poor | 216 (2.9) | 191 (3.1) | 25 (2.1) |
| Comorbidities | |||
| Diabetes | |||
| Yes | 135 (1.8) | 117 (1.9) | 18 (1.5) |
| No | 7,208 (98.2) | 6,042 (98.1) | 1,166 (98.5) |
| Depression | |||
| Yes | 2,027 (27.6) | 1,754 (28.5) | 273 (23.1) |
| No | 5,316 (72.4) | 4,405 (71.5) | 911 (77.0) |
| Anxiety | |||
| Yes | 2,283 (31.1) | 1,962 (31.9) | 321 (27.1) |
| No | 5,060 (68.9) | 4,197 (68.1) | 863 (72.9) |
| PTSD | |||
| Yes | 504 (6.9) | 442 (7.2) | 62 (5.2) |
| No | 6,839 (93.1) | 5,717 (92.8) | 1,122 (94.8) |
| Polycystic ovarian syndrome | |||
| Yes | 809 (11.0) | 698 (11.3) | 111 (9.4) |
| No | 6,534 (89.0) | 5,461 (88.7) | 1,073 (90.6) |
| Endometriosis | |||
| Yes | 263 (3.6) | 221 (3.6) | 42 (3.6) |
| No | 7,080 (96.4) | 5,938 (96.4) | 1,142 (96.5) |
| Body mass index, kg/m2 | |||
| <18.5 | 126 (1.7) | 105 (1.7) | 21 (1.8) |
| 18.5 to 24 | 3,028 (41.3) | 2,512 (40.8) | 516 (43.7) |
| 25 to 29 | 1,718 (23.4) | 1,421 (23.1) | 297 (25.1) |
| ≥30 | 2,465 (33.6) | 2,117 (34.4) | 348 (29.4) |
| Missing | 6 | 4 | 2 |
MET, metabolic equivalent; PRESTO, Pregnancy Study Online; PTSD, posttraumatic stress disorder.
Model fit indices are plotted by number of classes modeled in Appendix Figure 1 and displayed in Appendix Table 1. Fit indices suggested that a 2- or 3-class model could be appropriate, and we ultimately selected a 3-class model as the optimal solution, given class interpretability. The addition of the third class made conceptual sense, insofar as it differentiated a group with a higher prevalence of all risk behaviors and was not rare (i.e., <1% of the sample; Lanza et al. 2007; Nylund-Gibson and Choi 2018). The entropy for the selected model was 0.54; however, all average posterior class probabilities were >0.7 (0.79 for class 1, 0.71 for class 2, and 0.87 for class 3), thus suggesting good classification diagnostics of the model (Maysn 2013).
Table 2 presents 1) class proportions representing the expected proportion of the sample in each class and 2) item-response probabilities representing the probability of endorsing an item given membership in a certain class. The largest class (class proportion, 50%), which we termed healthy behavior, was mainly characterized by being more physically active and more likely to consume no sugar-sweetened beverages, to have received a flu vaccination, and to be taking a prenatal or multivitamin than the other 2 classes. We termed the second class some risk behavior (class proportion, 37%), and it was differentiated from healthy behavior by being more likely to have higher levels of sugar-sweetened beverage consumption and no flu vaccination or multivitamin use. Participants in the third class (class proportion, 12%), which we termed most risk behavior, had the highest probability of being current smokers (55%), as compared with healthy behavior (2%) and some risk behavior (0%). The 3 classes also differed across other indicators. Most risk behavior had the lowest probability of reporting flu vaccination (28% vs 48% for some risk behavior and 79% for healthy behavior), taking a vitamin (53% vs 76% for some risk behavior and 93% for healthy behavior), and drinking no sugar-sweetened beverages (16% vs 27% for some risk behavior and 61% for healthy behavior).
Table 2.
Item-Response Probabilities for 3-Class Model in the US Sample (n = 6,159).
| Class 1: Healthy Behavior | Class 2: Some Risk Behavior | Class 3: Most Risk Behavior | |
|---|---|---|---|
| Class membership proportion | 51 | 37 | 12 |
| Physical activity, MET h/wk | |||
| <10 | 7 | 24 | 26 |
| 10 to 19 | 16 | 29 | 30 |
| 20 to 39 | 40 | 30 | 24 |
| ≥40 | 38 | 18 | 20 |
| Sugar to sweetened beverage consumption, drinks/wk | |||
| ≥7 | 2 | 20 | 45 |
| 1 to <7 | 18 | 36 | 33 |
| <1 | 19 | 16 | 6 |
| None | 61 | 27 | 16 |
| Alcohol consumption, drinks/wk | |||
| ≥14 | 1 | 1 | 8 |
| 7 to 13 | 12 | 3 | 13 |
| 1 to 6 | 64 | 47 | 37 |
| None | 22 | 48 | 42 |
| Smoking status | |||
| Current smoker | 2 | 0 | 55 |
| Former smoker | 8 | 11 | 22 |
| Never smoker | 91 | 89 | 24 |
| PCP visit in the past year | |||
| None | 11 | 18 | 18 |
| Once | 43 | 28 | 20 |
| 2 or 3 times | 39 | 36 | 36 |
| ≥4 | 6 | 18 | 26 |
| Flu vaccination | |||
| Yes | 79 | 48 | 28 |
| No | 21 | 52 | 72 |
| Vitamin | |||
| Yes | 93 | 76 | 53 |
| No | 7 | 24 | 47 |
Data are presented as percentages. Item-response probability refers to the probability of endorsing each latent class indicator given class membership.
MET, metabolic equivalent; PCP, primary care physician.
Table 3 shows stark differences in sociodemographic characteristics across classes, with participants in the healthy behavior class reporting higher income and educational attainment, as well as being more likely to be employed and married and have health and dental insurance. We observed a consistent pattern across all medical comorbidities evaluated, such as anxiety, depression, or posttraumatic stress disorder, where the healthy behavior class had the lowest prevalence, followed by some risk behavior and finally most risk behavior.
Table 3.
Characteristics across Classes in the US Sample (n = 6,159).
| Class 1: Healthy Behavior | Class 2: Some Risk Behavior | Class 3: Most Risk Behavior | ||||
|---|---|---|---|---|---|---|
| No. | % | No. | % | No. | % | |
| Class prevalence | 3,340 | 54.2 | 2,266 | 36.8 | 553 | 9.0 |
| Age, y, mean (SD) | 31.4 | 3.8 | 30.3 | 4.7 | 30.8 | 5.0 |
| Income, US $ | ||||||
| <15,000 | 13 | 0.4 | 57 | 2.5 | 45 | 8.1 |
| 15,000 to 24,999 | 27 | 0.8 | 122 | 5.4 | 72 | 13.0 |
| 25,000 to 49,999 | 166 | 5.0 | 395 | 17.4 | 135 | 24.4 |
| 50,000 to 74,999 | 318 | 9.5 | 454 | 20.0 | 110 | 19.9 |
| 75,000 to 99,999 | 505 | 15.1 | 421 | 18.6 | 73 | 13.2 |
| 100,000 to 124,999 | 559 | 16.7 | 318 | 14.0 | 44 | 8.0 |
| 125,000 to 149,999 | 450 | 13.5 | 157 | 6.9 | 16 | 2.9 |
| 150,000 to 199,999 | 593 | 17.8 | 144 | 6.4 | 20 | 3.6 |
| ≥200,000 | 665 | 19.9 | 120 | 5.3 | 11 | 2.0 |
| Health insurance | ||||||
| Private | 3,241 | 97.0 | 1,849 | 81.6 | 317 | 57.3 |
| Government | 84 | 2.5 | 332 | 14.7 | 192 | 34.7 |
| Out of pocket | 14 | 0.4 | 84 | 3.7 | 44 | 8.0 |
| Dental insurance | ||||||
| Yes | 3,073 | 92.0 | 415 | 18.3 | 370 | 66.9 |
| No | 267 | 8.0 | 1,849 | 81.7 | 183 | 33.1 |
| Missing | 2 | |||||
| Race and ethnicity | ||||||
| Hispanic | 226 | 6.8 | 238 | 10.5 | 57 | 10.3 |
| Non-Hispanic multiracial | 111 | 3.3 | 94 | 4.1 | 22 | 4.0 |
| Non-Hispanic Black | 68 | 2.0 | 140 | 6.2 | 43 | 7.8 |
| Non-Hispanic Asian/Pacific Islander | 79 | 2.4 | 46 | 2.0 | 6 | 1.1 |
| Non-Hispanic American Indian/Alaskan Native | 3 | 0.1 | 5 | 0.2 | 2 | 0.4 |
| Non-Hispanic White | 2,829 | 84.7 | 1,738 | 76.7 | 420 | 75.9 |
| Non-Hispanic other/missing race | 24 | 0.7 | 5 | 0.2 | 3 | 0.5 |
| Currently employed | ||||||
| Yes | 3,088 | 92.5 | 1,814 | 80.1% | 388 | 70.2 |
| No | 246 | 7.4 | 424 | 18.7% | 153 | 27.7 |
| Education | ||||||
| <12 | 1 | 0.0 | 15 | 0.7 | 34 | 6.1 |
| 12 | 44 | 1.3 | 180 | 7.9 | 104 | 18.8 |
| 13 to 15 | 268 | 8.0 | 656 | 28.9 | 242 | 43.8 |
| 16 | 1,051 | 31.5 | 739 | 32.6 | 107 | 19.3 |
| ≥17 | 1,976 | 59.2 | 676 | 29.8 | 66 | 11.9 |
| BMI, kg/m2 | ||||||
| <18.5 | 52 | 1.6 | 36 | 1.6 | 17 | 3.1 |
| 18.5 to 24 | 1,716 | 51.4 | 669 | 29.5 | 127 | 23.0 |
| 25 to 29 | 807 | 24.2 | 499 | 22.0 | 115 | 20.8 |
| ≥30 | 763 | 22.9 | 1,061 | 46.8 | 293 | 53.1 |
| Missing | 2 | 1 | 1 | |||
| Married | ||||||
| Yes | 3,085 | 92.4 | 1,865 | 82.3 | 357 | 64.6 |
| No | 255 | 7.6 | 398 | 17.6 | 195 | 35.3 |
| Anxiety | ||||||
| Yes | 983 | 29.4 | 761 | 33.6 | 218 | 39.4 |
| No | 2,357 | 70.6 | 1,505 | 66.4 | 335 | 60.6 |
| Depression | ||||||
| Yes | 801 | 24.0 | 719 | 31.7 | 234 | 42.3 |
| No | 2,539 | 76.0 | 1,547 | 68.3 | 319 | 57.7 |
| PTSD | ||||||
| Yes | 156 | 4.7 | 199 | 8.8 | 87 | 15.7 |
| No | 3,184 | 95.3 | 2,067 | 91.2 | 466 | 84.3 |
| Diabetes | ||||||
| Yes | 36 | 1.1 | 59 | 2.6 | 22 | 4.0 |
| No | 3,304 | 98.9 | 2,207 | 97.4 | 531 | 96.0 |
| Parity | ||||||
| Parous | 965 | 28.9 | 908 | 40.1 | 260 | 47.0 |
| Nulliparous | 2,375 | 71.1 | 1,358 | 59.9 | 293 | 53.0 |
| How long has it been since you last visited a dentist or dental clinic for any reason? | ||||||
| Within the past year | 2,563 | 76.8 | 1,364 | 60.2 | 247 | 44.7 |
| 1 to 2 y ago | 493 | 14.8 | 449 | 19.8 | 149 | 26.9 |
| 3 to 4 y ago | 160 | 4.8 | 228 | 10.1 | 69 | 12.5 |
| ≥5 y ago | 123 | 3.7 | 224 | 9.9 | 88 | 15.9 |
| Missing | 1 | |||||
| How long has it been since you had your teeth cleaned by a dentist/hygienist? | ||||||
| Within the past year | 2,497 | 74.8 | 1,271 | 56.1 | 204 | 36.9 |
| 1 to 2 y ago | 522 | 15.6 | 466 | 20.6 | 146 | 26.4 |
| 3 to 4 y ago | 182 | 5.5 | 242 | 10.7 | 88 | 15.9 |
| ≥5 years ago | 139 | 4.2 | 286 | 12.6 | 115 | 20.8 |
| Missing | 1 | |||||
| How would you rate the health of your teeth and gums? | ||||||
| Excellent | 895 | 26.8 | 387 | 17.1 | 51 | 9.2 |
| Very good | 1,443 | 43.2 | 733 | 32.4 | 122 | 22.1 |
| Good | 772 | 23.1 | 699 | 30.9 | 160 | 28.9 |
| Fair | 206 | 6.2 | 356 | 15.7 | 144 | 26.0 |
| Poor | 24 | 0.7 | 91 | 4.0 | 76 | 13.7 |
Individual class membership assigned by highest posterior probability.
BMI, body mass index; PTSD, posttraumatic stress disorder.
The prevalence of a dental visit >1 year ago was 23.3% among the healthy behavior class, as compared with 39.8% for some risk behavior and 55.3% for most risk behavior. The prevalence of a cleaning >1 y ago was 25.2% for the healthy behavior class, as opposed to 43.9% of some risk behavior and 63.1% of most risk behavior. For self-rated oral health, only 6.9% of those assigned to the healthy behavior class reported fair or poor oral health, in contrast to 19.7% of some risk behavior and 39.7% of most risk behavior.
Participants’ class membership was associated with preconception oral health and oral health care seeking regardless of the method of class assignment (Table 4). Those in the some risk behavior and most risk behavior classes were more likely than the healthy behavior class to report no dental cleaning within the past year (PD, 0.19 [95% CI, 0.16 to 0.21]; PD, 0.38 [95% CI, 0.34 to 0.42], respectively). Associations were similar, but slightly attenuated, in simulations. Similar trends in PDs were observed for reporting no dental visit for any reason within the past year and fair or poor self-rated oral health.
Table 4.
Associations between Class Membership and Oral Health–Related Outcomes in the US Sample (n = 6,159).
| Class Assignment Based on Highest Posterior Probability, PD (95% CI) | Class Assignment Randomly Simulated, PD (95% SI) a | |||
|---|---|---|---|---|
| Some Risk Behavior vs Healthy Behavior | Most Risk Behavior vs Healthy Behavior | Some Risk Behavior vs Healthy Behavior | Most Risk Behavior vs Healthy Behavior | |
| Last dental visit for any reason | ||||
| ≤1 y | Ref | Ref | Ref | Ref |
| >1 y | 0.17 (0.14 to 0.19) | 0.32 (0.28 to 0.36) | 0.12 (0.10 to 0.14) | 0.27 (0.25 to 0.29) |
| Last dental cleaning | ||||
| ≤1 y | Ref | Ref | Ref | Ref |
| >1 y | 0.19 (0.16 to 0.21) | 0.38 (0.34 to 0.42) | 0.14 (0.12 to 0.16) | 0.32 (0.30 to 0.35) |
| Current self-rated oral health | ||||
| Excellent, very good, good | Ref | Ref | Ref | Ref |
| Fair, poor | 0.13 (0.11 to 0.15) | 0.33 (0.29 to 0.37) | 0.09 (0.08 to 0.11) | 0.28 (0.25 to 0.29) |
PD, prevalence difference; Ref, reference; SI, simulation interval.
Class assignment simulated by a random draw from the posterior probability of class assignment for each individual across 100,000 simulations.
When we evaluated the 3-class solution using the Canadian data set, we observed similar class profiles (for item-response probabilities, see Appendix Table 2). The largest class (class proportion, 75%) was characterized by healthy behaviors. The corresponding some risk behavior class (class proportion, 15%) was characterized by a higher probability of low physical activity and high sugar-sweetened beverage consumption. The third class (class proportion, 10%) was characterized by more risk behaviors, with those in this class having probabilities of 44% and 56% for being current and former smokers, respectively. The associations of class membership with oral health care engagement and status were similar to those in the US sample, though the PDs were smaller in magnitude (Appendix Table 3).
Discussion
In a preconception population of pregnancy planners in the United States, we identified distinct groups (i.e., classes) of participants with modifiable risk behaviors that were associated with oral health care engagement. The 3 classes were as follows: one with a high probability of health-promoting behaviors, a second defined by higher probability of sugar-sweetened beverage consumption and no flu immunization or vitamin use, and a third with higher probability of less healthy behaviors across all indicators. Importantly, these classes were meaningfully descriptively associated with preconception oral health care engagement. We observed a pattern in which oral health care engagement decreased as the class risk profile increased (i.e., subgroups characterized by a higher frequency of risk behaviors had lower oral health care engagement).
Our results align with prior research that has evaluated the clustering of health behaviors in the preconception period. A study of Canadian pregnancy planners or recently pregnant people reported that less healthy behaviors clustered, such as low physical activity and poor diet (Dennis et al. 2022). Like our findings, less healthy behaviors clustered with markers of lower socioeconomic status, although the authors did not assess oral health or oral health care engagement. A study that used LCA to create subgroups of preconception women reported clustering of risk behaviors such as high alcohol consumption, low physical activity, and tobacco use (Molenaar et al. 2023). This study also noted that risk behaviors were more common among individuals of lower socioeconomic status.
Our findings may be useful in resource allocation in the context of preconception health promotion activities. We found lower oral health care engagement in the some risk behavior and most risk behavior classes but higher prevalence of behaviors strongly associated with worse oral health (i.e., smoking, high sugar-sweetened beverage consumption). Despite these risk behaviors and the fact that people in these classes were more likely to self-report their oral health as fair or poor, they were less engaged in oral health care than the healthy behavior class. This lack of engagement did not necessarily correspond to a lack of engagement in health care overall, because these classes were more likely than the healthy behavior class to have a high frequency of primary care visits. Our results suggest a co-occurrence of oral health–relevant risk behaviors, lower socioeconomic status, worse mental health, and lower engagement with oral health care in the preconception period. Future studies and public health interventions aimed at supporting health overall and oral health specifically in the preconception period should consider the interplay of these factors. Promotion of preconception oral health is important because pregnancy hormones can exacerbate existing periodontal disease (Gaffield et al. 2001) and because preconception periodontal health may be associated with adverse reproductive outcomes (Bond et al. 2021; Bond et al. 2023).
Strengths of our study include the large preconception cohort with detailed information on many modifiable risk behaviors relevant to reproductive health outcomes. We were able to repeat our analysis among Canadian participants as a strategy to independently replicate our findings, although there were differences in some indicators, which might be due to differences in the underlying distributions between US and Canadian samples. The item-response probabilities for never smokers in the most risk behavior class also varied between US and Canadian samples (0.24 vs 0, respectively), which may indicate more heterogeneity in the US sample of never smokers. We used simulation methods to incorporate uncertainty related to class assignment when evaluating the relationship between class membership and outcomes, which is an improvement over class assignment based on the highest posterior probability (Lanza and Rhoades 2013). Our study did have important limitations, notably the potential for latent class indicator or outcome misclassification due to the self-report nature of the data. We also included a limited number of latent class indicator variables, focusing on modifiable health behaviors. Our oral health care engagement questions are based on validated questionnaires (Eke et al. 2013) but are not necessarily reflective of recommended engagement (i.e., cleaning every 6 mo). Finally, our results may not be broadly generalizable. Our study population consisted of pregnancy planners, or people attempting pregnancy but not yet pregnant. Because almost half the pregnancies in the United States are unplanned (Finer and Zolna 2016), our findings may not be applicable to many people who become pregnant. However, our findings could still be useful for the development of interventions targeted to pregnancy planners, such as preconception health counseling, which has been demonstrated to influence some preconception behaviors (Hussein et al. 2016; Sijpkens et al. 2021).
We report that modifiable risk behaviors clustered in a preconception population of pregnancy planners and that these groups were related to oral health and oral health care engagement. Because the preconception period is critical for overall health (Stephenson et al. 2018) and preconception health interventions have shown promise (Hussein et al. 2016; Sijpkens et al. 2021), oral health care engagement should be considered alongside other modifiable health behaviors in the development of preconception health promotion activities.
Author Contributions
J.C. Bond, contributed to conception, design, data analysis and interpretation, drafted and critically revised manuscript; M.A. Simancas-Pallares, K. Divaris, contributed to design, data analysis and interpretation, critically revised the manuscript; R.I. Garcia, contributed to data interpretation, critically revised the manuscript; M.P. Fox, contributed to conception, data interpretation, critically revised the manuscript; L.A. Wise, contributed to conception, data acquisition and interpretation, critically revised the manuscript; B. Heaton, contributed to conception, design, data acquisition, analysis, and interpretation, critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.
Supplemental Material
Supplemental material, sj-docx-1-jdr-10.1177_00220345251325216 for Preconception Oral Health Is Associated with Modifiable Health Behaviors by J.C. Bond, M.A. Simancas-Pallares, K. Divaris, R.I. Garcia, M.P. Fox, L.A. Wise and B. Heaton in Journal of Dental Research
Footnotes
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the National Institute for Dental and Craniofacial Research (F31DE031969) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD086742 and R21HD072326).
ORCID iDs: J.C. Bond 
https://orcid.org/0000-0002-2988-7755
K. Divaris
https://orcid.org/0000-0003-1290-7251
B. Heaton
https://orcid.org/0000-0002-3097-4717
A supplemental appendix to this article is available online.
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Supplementary Materials
Supplemental material, sj-docx-1-jdr-10.1177_00220345251325216 for Preconception Oral Health Is Associated with Modifiable Health Behaviors by J.C. Bond, M.A. Simancas-Pallares, K. Divaris, R.I. Garcia, M.P. Fox, L.A. Wise and B. Heaton in Journal of Dental Research