Early Life Conditions, Adverse Life Events, and Chewing Ability at Middle and Later Adulthood

Abstract

Objectives. We sought to determine the extent to which early life conditions and adverse life events impact chewing ability in middle and later adulthood.

Methods. Secondary analyses were conducted based on data from waves 2 and 3 of the Survey of Health, Ageing, and Retirement in Europe (SHARE), collected in the years 2006 to 2009 and encompassing information on current chewing ability and the life history of persons aged 50 years or older from 13 European countries. Logistic regression models were estimated with sequential inclusion of explanatory variables representing living conditions in childhood and adverse life events.

Results. After controlling for current determinants of chewing ability at age 50 years or older, certain childhood and later life course socioeconomic, behavioral, and cognitive factors became evident as correlates of chewing ability at age 50 years or older. Specifically, childhood financial hardship was identified as an early life predictor of chewing ability at age 50 years or older (odds ratio = 1.58; 95% confidence interval = 1.22, 2.06).

Conclusions. Findings suggest a potential enduring impact of early life conditions and adverse life events on oral health in middle and later adulthood and are relevant for public health decision-makers who design strategies for optimal oral health.

Oral diseases remain a major public health issue globally.¹ Not only do they affect nearly 4 billion people worldwide,² but they are also one of the most expensive diseases to treat.¹ Oral health is also recognized as an important component of general health and well-being.^3–8 Chronic diseases are increasingly studied within a life course framework that considers health impairments as a consequence of risk exposure in critical periods of life or accumulated exposure over time. Multiple events can occur throughout early- and later-life stages and pathways may link childhood conditions with health impairments in later life through intermediary adverse events. Models incorporating the entire life course have primarily been used to assess chronic conditions such as coronary heart disease and diabetes^9–11 but their application to oral health remains limited.

Like other chronic diseases, oral diseases result from exposure to various risk factors throughout life.¹² Evidence from birth-cohort studies on life course determinants of oral health suggests that socioeconomic background,^13–16 dental-attendance patterns in early life years,^17,18 and parental oral health^19,20 play an important role in terms of tooth decay, periodontal health, tooth loss, and self-rated oral health at early adulthood. Moreover, it has been shown that caries experience in early life predicts caries occurrence later in life.²¹ Despite robust methodological value and relevant findings, a limitation of birth-cohort studies containing oral health information is that they are age restricted. As these were not initiated before the early 1970s,^22,23 there has not yet been sufficient time to follow up individuals into middle and later adulthood. Consequently, they are not suitable to address research questions in terms of life course influences on oral health at older adulthood. This area of research could be served by longitudinal studies of older adults that contain current oral health-related outcomes and have also collected information on the major life course events and experiences of their participants.

A significant consequence of oral diseases is deteriorating chewing ability. This represents serious functional impairment, interrelates with numerous oral health problems which determine oral health-related quality of life, and impacts on general health through influencing nutritional choices; chewing ability is thus often considered a meaningful marker of oral health, particularly in middle and late life years.^24–29 The study’s objective was to determine the extent to which childhood conditions and adverse life events impact on the chewing ability in middle and late adulthood populations from 13 European countries.

METHODS

This study is based on secondary analysis of data from waves 2 and 3 of the Survey of Health, Ageing, and Retirement in Europe (SHARE) and includes Austria, Belgium, Czech Republic, Denmark, France, Germany, Greece, Italy, Netherlands, Poland, Spain, Sweden, and Switzerland. SHARE contains detailed cross-national information about health, socioeconomic conditions, and family backgrounds of adult populations aged 50 years and older. The initial wave of SHARE was conducted in 2004, followed by wave 2 in 2006 to 2007. Wave 3 (SHARELIFE) was conducted in 2008 to 2009 and collected retrospective information about elements of respondents’ lives, ranging from partners, children, housing and work history to details on health and health care (see Appendix, available as a supplement to this article at http://www.ajph.org, for details about the data collection and sampling methods).^30,31

Our data source provides information about respondents’ chewing ability, assessed through the question “Can you bite and chew on hard foods (such as a firm apple)?” (answering options were “yes” or “no”). We used this binary measure as dependent variable in a series of multivariable logistic regression models to detect the extent to which influences from different stages across the life course impact on chewing ability at age 50 years or older. Based on the rationale that early life conditions and events may adversely impact on later oral health outcomes, the models were built so that they included sequential adjustment for covariates at chronologically different periods in life course, from early life to more recent determinants.

Model 1 sought to depict childhood influences and includes socioeconomic position (SEP), environmental determinants, cognitive skills and behavioral factors. More specifically, the model included the following explanatory variables:

• number of rooms per household member during childhood (a count variable): SEP determinant;

• having more than 25 books in the household during childhood (yes/no): proxy for scholarly culture and SEP determinant³²;

• running water supply in childhood household (yes/no): an environmental determinant that is also closely linked to behavioral factors, such as oral hygiene practices;

• childhood math skills (self-assessment: much worse; worse; similar to; better than; or much better than that of peers). This parameter may partly reflect cognitive ability and also represents skills which are important for the formation of oral health literacy³³;

• started regular dental visits in childhood (yes/no), as a behavioral factor;

• experienced financial hardship in childhood (yes/no): SEP determinant of extreme monetary shortage in childhood household; and

• experienced a period of hunger in childhood (yes/no): determinant of nutritional deficiency in childhood.

Sequentially, in model 2 we additionally introduced 2 explanatory variables for major adverse events that the respondent may have experienced at any point in his or her adult life:

• financial hardship after childhood (yes/no), to detect the impact of later severe monetary shortage; and

• a period of hunger after childhood (yes/no), to detect the impact of later nutritional deficiency.

Finally, model 3 additionally controlled for the following parameters which represent conditions at the time of interview (age ≥ 50 years):

• Net monthly household income (in tertiles, for each country of residence): Indicates the respondents’ current relative SEP within country of residence. Based on the assumption that an individual’s well-being depends mostly on relative rather than absolute income,³⁴ it also allows for cross-country comparability.

• Not wearing dentures (yes/no): This oral health related parameter refers to denture wearing at the time of interview (no distinction between fixed or removable dentures).

• Current dental attendance (yes/no): This measures whether the respondent had visited a dentist within the past 12 months.

• Self-rated general health (poor, fair, good, very good, or excellent): We included this global rating of general health because there is strong evidence on the associations between general and oral health.^3–8

• Grip strength (of the dominant hand, in kg): We included this measurement to depict the current level of functioning ability and lack of frailty.

We adjusted all models for respondent’s age and gender and also included country dummies to respectively control for demographic and cross-country influences on chewing ability. We also controlled for whether respondents were born before 1946 or after to take account of potential influences of World War II. To test the robustness of our results, we examined the impact of introducing additional explanatory variables for the “duration of hunger period” and the “duration of financial hardship.” Moreover, in an attempt to explore the extent to which cross-country differences in chewing ability may be attributable to differences in the country-specific distribution of risk factors, we examined the distribution of risk factors and associated chewing abilities for the 2 countries with highest and lowest level of chewing ability; we also ran separate regression analyses for persons with and persons without dentures at age 50 years or older (Appendix). Data analysis was carried out in STATA/SE version 12.0 (StataCorp, College Station, TX).

RESULTS

Table 1 presents descriptive statistics of the explanatory variables. Mean age of respondents was 67 years (more than half were born before 1946) and 53% were women. 41% of respondents consistently visited the dentist since childhood; 32% had more than 25 books, 67% had running water in their childhood household, and the average number of rooms per person was 0.7. Sixteen percent of respondents reported having had worse and 34% better math skills than peers. During childhood, 8% of respondents had experienced a period of hunger and 3% financial hardship. After childhood, 3% had experienced a hunger period and 33% financial hardship. The average duration of financial hardship was about 4 years and that of hunger period less than 1 year. In terms of current health-related measures, 54% of respondents had recently gone to the dentist, 40% wore dentures, and 37% rated their current general health as less than good. Average grip strength was 33 kilograms. There was considerable variation by country in the proportion of adults reporting ability to chew hard foods (Table 2). Sweden had the highest proportion of respondents that reported being able to bite and chew hard foods (94%) and Poland the lowest (72%).

TABLE 1—

Descriptive Statistics of Explanatory Variables: Survey of Health, Ageing, and Retirement in Europe, 2006–2009

Variable	Mean (SD) or %	Range	No.
Age, y	66.64 (9.52)	53–99	16 624
Female	52.89	0 (no)–1 (yes)	16 624
Regular dental visits since childhood	40.94	0 (no)–1 (yes)	16 624
> 25 books in childhood household	31.52	0 (no)–1 (yes)	16 624
No. rooms/person in childhood household	0.70 (0.40)	0.03–7	16 624
Running water supply in childhood household	66.64	0 (no)–1 (yes)	16 624
Childhood math skills
Much worse than peers	2.90	0 (no)–1 (yes)	16 624
Worse than peers	12.56	0 (no)–1 (yes)	16 624
Similar to peers	51.04	0 (no)–1 (yes)	16 624
Better than peers	23.83	0 (no)–1 (yes)	16 624
Much better than peers	9.67	0 (no)–1 (yes)	16 624
No financial hardship during childhood	97.53	0 (no)–1 (yes)	16 624
No financial hardship after childhood	66.20	0 (no)–1 (yes)	16 624
No hunger period during childhood	91.84	0 (no)–1 (yes)	16 624
No hunger period after childhood	96.99	0 (no)–1 (yes)	16 624
Duration of financial hardship, y	3.68 (8.71)	0–89	16 541
Duration of hunger period, y	0.53 (2.10)	0–43	16 611
Recent dental attendance (age ≥ 50 y)	53.91	0 (no)–1 (yes)	16 624
No dentures (age ≥ 50 y)	59.80	0 (no)–1 (yes)	16 624
Current general health (age ≥ 50 y)
Poor	9.23	0 (no)–1 (yes)	16 624
Fair	27.46	0 (no)–1 (yes)	16 624
Good	41.14	0 (no)–1 (yes)	16 624
Very good	14.97	0 (no)–1 (yes)	16 624
Excellent	7.20	0 (no)–1 (yes)	16 624
Grip strength, kg (age ≥ 50 y)	33.14 (12.20)	0–80	16 624
Net monthly household income tertilea (age ≥ 50 y)
First	33.28	0 (no)–1 (yes)	16 624
Second	32.68	0 (no)–1 (yes)	16 624
Third	34.04	0 (no)–1 (yes)	16 624
Born before 1946	55.46	0 (no)–1 (yes)	16 624
Born in 1946 or later	44.54	0 (no)–1 (yes)	16 624

Note. Results are weighted based on respondents with complete records of all variables used in the study sample.

^aBased on within-country income distribution.

TABLE 2—

Population Proportions of Persons Who Can Bite and Chew on Hard Food: Survey of Health, Ageing, and Retirement in Europe, 2006–2009

Country	Able to Chew Hard Food, %
Austria (n = 537)	82.6
Germany (n = 1216)	81.3
Sweden (n = 1362)	94.4
Netherlands (n = 1401)	87.0
Spain (n = 771)	81.1
Italy (n = 1709)	77.3
France (n = 1393)	81.5
Denmark (n = 1677)	82.4
Greece (n = 1211)	80.6
Switzerland (n = 904)	88.7
Belgium (n = 1897)	75.3
Czech Republic (n = 1207)	77.0
Poland (n = 1339)	71.5

Note. Results are weighted based on respondents with complete records of all variables used in the study sample.

Table 3 shows results from logistic regression analyses with sequential inclusion of explanatory variables in chronological groups. Model 1 exclusively includes explanatory variables reflecting childhood conditions. Regular dental attendance during childhood was associated with 1.17 (95% confidence interval [CI] = 1.07, 1.28) times higher odds of good chewing ability at age 50 years or older. Respondents that had more than 25 books in their childhood household were 1.29 (95% CI = 1.18, 1.41) times more likely to have good chewing ability at age 50 years or older compared with those with fewer books. Persons who had running water supply in their childhood household were 1.13 (95% CI = 1.04, 1.22) times more likely to have good chewing ability at age 50 years or older. For each additional room per person in their childhood household, respondents were 1.13 (95% CI = 1.02, 1.26) times more likely to have good chewing ability at age 50 years or older. In comparison with persons who reported having had much worse childhood math skills than their peers, respondents were more likely to have good chewing ability at age 50 years or older if reporting that their childhood math skills were similar (odds ratio [OR] = 1.31; 95% CI = 1.07, 1.61), better (OR = 1.45; 95% CI = 1.17, 1.79) or much better than their peers (OR = 1.61; 95% CI = 1.28, 2.03). Moreover, persons who had not experienced a financial hardship during childhood were 1.62 (95% CI = 1.32, 1.98) times more likely to have good chewing ability at age 50 years or older.

TABLE 3—

Multivariate Regression Analysis on Ability to Bite and Chew on Hard Foods: Survey of Health, Ageing, and Retirement in Europe, 2006–2009

Variable	Model 1 (n = 22 034), OR (95% CI)	Model 2 (n = 22 034), OR (95% CI)	Model 3 (n = 16 920), OR (95% CI)
Regular dental visits since childhood
No (Ref)	1.000	1.000	1.000
Yes	1.170*** (1.074, 1.275)	1.156*** (1.061, 1.260)	1.108* (1.003, 1.224)
Books in childhood household
≤ 25 (Ref)	1.000	1.000	1.000
> 25	1.286*** (1.176, 1.406)	1.288*** (1.178, 1.409)	1.122* (1.011, 1.246)
No. rooms/person in childhood household	1.130* (1.016, 1.256)	1.112* (1.001, 1.236)	1.102 (0.973, 1.248)
Running water in childhood household
No (Ref)	1.000	1.000	1.000
Yes	1.126** (1.037, 1.222)	1.129** (1.040, 1.226)	1.061 (0.964, 1.168)
Childhood math skills
Much worse than peers (Ref)	1.000	1.000	1.000
Worse than peers	1.190 (0.953, 1.485)	1.186 (0.950, 1.481)	1.173 (0.899, 1.530)
Similar to peers	1.310* (1.066, 1.610)	1.290* (1.050, 1.586)	1.277 (0.998, 1.636)
Better than peers	1.446** (1.167, 1.791)	1.418** (1.145, 1.757)	1.297* (1.004, 1.675)
Much better than peers	1.611*** (1.277, 2.032)	1.583*** (1.254, 1.997)	1.495** (1.134, 1.969)
Childhood financial hardship
Yes (Ref)	1.000	1.000	1.000
No	1.618*** (1.320, 1.983)	1.745*** (1.421. 2.144)	1.583** (1.218, 2.056)
Childhood hunger period
Yes (Ref)	1.000	1.000	1.000
No	1.148 (0.997, 1.323)	1.123 (0.974, 1.295)	1.104 (0.932, 1.307)
Financial hardship after childhood
Yes (Ref)		1.000	1.000
No		1.255*** (1.162, 1.355)	1.160** (1.061, 1.268)
Hunger period after childhood
Yes (Ref)		1.000	1.000
No		1.264 (0.998, 1.599)	1.194 (0.912, 1.563)
Recent dental attendance (aged ≥ 50 y)
No (Ref)			1.000
Yes			1.167** (1.066, 1.279)
Dentures (aged ≥ 50 y)
Yes (Ref)			1.000
No			2.384*** (2.174, 2.615)
General health
Poor (Ref)			1.000
Fair			1.500*** (1.297, 1.734)
Good			1.926*** (1.666, 2.227)
Very good			2.007*** (1.691, 2.382)
Excellent			1.899*** (1.545, 2.335)
Grip strength, kg (dominant hand)			1.013*** (1.008, 1.019)
Income tertile
Lower (Ref)			1.000
Middle			1.154** (1.044, 1.276)
Upper			1.199** (1.076, 1.337)

Note. CI = confidence interval; OR = odds ratio. All models include control variables for age, gender, country dummy variables, and a dummy variable which distinguishes between being born before or after 1946.

*P < .05; **P < .01; ***P < .001.

Model 2 additionally included major lifetime influences after childhood (Table 3). Participants that had not experienced a financial hardship after childhood had 1.26 (95% CI = 1.16, 1.36) times higher probability of good chewing ability at age 50 years or older compared with those that did have such a negative experience. The estimates for childhood influences in model 2 retained their significance, though the respective estimates were slightly lower than in model 1 for regular dental attendance during childhood, number of rooms per household member, and childhood math skills.

Model 3 additionally included current determinants of chewing ability (Table 3). Compared with those in the lowest tertile of household income, respondents in the middle and higher income tertile were more likely to report good chewing ability, but there was no evidence for significant differences between the middle and higher groups. Furthermore, having visited a dentist in the past 12 months and not wearing dentures were associated with higher probability for good chewing ability (OR = 1.17; 95% CI = 1.07, 1.28 for dental attendance; OR = 2.38; 95% CI = 2.17, 2.62 for not wearing dentures). In comparison with poor general health, participants reporting fair (OR = 1.50; 95% CI = 1.30, 1.73), good (OR = 1.93; 95% CI = 1.67, 2.23), very good (OR = 2.01; 95% CI = 1.69, 2.38) or excellent general health (OR = 1.90; 95% CI = 1.55, 2.34) had significantly higher probability to have good chewing ability. Moreover, the likelihood of good chewing ability increases significantly by 1% for every additional kilogram of grip strength. There were also changes from model 2 in the estimates for childhood influences and major life events. Having had running water supply in childhood and number of rooms per childhood household member were no longer significant, as the estimates for the associations of childhood regular dental attendance (OR = 1.11; 95% CI = 1.00, 1.22), financial hardship in childhood (OR = 1.58; 95% CI = 1.22, 2.06) and later life years (OR = 1.16; 95% CI = 1.06, 1.27), and having had more than 25 books in childhood household (OR = 1.12; 95% CI = 1.01, 1.25) with chewing ability were more modest in the final model. Finally, the impact of childhood math skills on chewing ability persists, albeit statistical significance refers to comparing the categories “better than peers” (OR = 1.30; 95% CI = 1.00, 1.68) and “much better than peers” (OR = 1.50; 95% CI = 1.13, 1.97) against the category “much worse than peers.”

When introducing additional explanatory variables for “duration of hunger period” and “duration of financial hardship,” regression results remained robust. Both these variables were not significantly associated with the outcome and their inclusion in the models changed neither the significance nor the direction of the associations between the other parameters and chewing ability (results not shown).

DISCUSSION

Based on survey data from 13 European countries, our findings indicate that some childhood SEP determinants (particularly experience of financial hardship), childhood regular dental attendance, and specific cognitive skills in early life years, as well as having experienced financial hardship after childhood, were significant predictors of chewing ability in middle and later adulthood. These associations were significant even after controlling for current determinants of chewing ability. Overall, the strongest predictors of chewing ability at age 50 years or older were denture wearing and general health at age 50 years or older.

Our findings highlight the potential role of early life conditions and adverse life events for achieving good oral function throughout the life course. Establishing regular dental attendance patterns from childhood was associated with better chewing ability at age 50 years or older. Although the general relevance of regular dental attendance for oral health has been previously shown,^13,17,35 our results suggest that dental attendance in early life years may have an impact on chewing ability at middle and late adulthood. Interestingly, this association was only partly explained upon introduction of control variables for current health and living conditions, including current dental attendance patterns. Because there is evidence on the persistence of dental attendance patterns throughout the life course,^18,36,37 this may suggest that dental attendance in childhood can determine later dental visiting patterns and as such has a cumulative impact on oral well-being throughout the life cycle.

In addition, our results coincide with previous studies showing that childhood socioeconomic background influences oral health outcomes in later life years.^13,14,16,38 We applied different childhood socioeconomic position proxy measures, with the number of books in the childhood household demonstrating consistently significant influence on oral well-being in middle and later adulthood. One potential pathway could be that a book-oriented scholarly culture at home endows children with useful skills for learning at school (most importantly reading) and thus influences educational attainment.³² Conversely, lower educational attainment has been shown to correlate with oral health–compromising behaviors.^39–41 The magnitude of the estimate for number of books in the childhood household remained stable upon inclusion of control variables for adverse life events after childhood (financial hardship, hunger period). The additional inclusion of control variables for current health and living conditions resulted in expectedly weaker—though still significant—effect of childhood SEP on chewing ability at age 50 years or older because we also included current household income and there is evidence of an association between number of books in childhood and earnings potential later in life.⁴²

A third childhood parameter examined in this study was availability of running water in the childhood household. From a life course perspective on oral health, running water supply may be relevant for at least 3 reasons. First, not having had running water could be interpreted as an indicator of material deprivation. Second, lack of availability of running water may have determined the possibilities for activities like regular tooth brushing, thereby impeding oral hygiene practice. Third, availability of running water may also be an important factor for prevention of diseases and maintenance of good general health. Our results provided some evidence for this pathway because the lack of availability of running water in childhood was not significantly associated with the outcome once current determinants were entered in the model, including self-rated general health which in turn was strongly associated with chewing difficulty.

Our findings also add a new perspective to the literature on the association between cognitive skills and oral health.^43–45 We found evidence for a gradient in terms of mathematical skills (i.e. respondents with relatively good math skills during childhood had better chewing abilities at age 50 years or older than did respondents with worse math skills). The magnitude of this association declined gradually upon inclusion of control variables for adverse life events and current health and living conditions, but the gradient was still evident in the fully controlled model. Math skills have not only been shown to be strong predictors of employment and earning opportunities⁴⁶ (which could influence oral health indirectly) but to also reflect the ability to perform common day-to-day activities.³³ As such, childhood math skills may relate to oral health literacy (i.e., the capacity to obtain, process, and understand oral health information and services needed to make appropriate health decisions).⁴³

Evidence also supports the role of adverse life events (in and after childhood) on chewing ability at age 50 years or older. Financial hardship, particularly in childhood, seems to play a more important role for oral health in middle and late adulthood than having been exposed to a hunger period. An interpretation of this finding may be that a financial hardship may not only impact on nutrition but may also increase psychosocial stress and affect oral health behaviors, thus fostering susceptibility to oral diseases.⁴⁷ Contrarily, a hunger period may indicate a temporary change in the amount or quality of available food and have less impact if other factors relevant for the onset of oral diseases remain unchanged. Our data indeed indicated that the average duration of hunger periods is shorter than that of financial hardships.

Strengths and Limitations

A particular strength of our analyses is that, by inclusion of control variables for current general and oral health (age ≥ 50 years), we could examine the long-lasting impact of early life conditions on current oral health. Associations of regular dental attendance in childhood and number of books in childhood on chewing ability were less robust in the fully controlled model and barely remained within the range of statistical significance. The strongest and most robust childhood predictor of chewing ability at age 50 years or older was experience of childhood financial hardship. Other strong predictors were experience of financial hardship after childhood and, as expected, the current determinants (dental attendance, denture wearing, general health, grip strength, and income). The strongest predictor of chewing ability overall was denture wearing for which previous literature has reported considerable differences between elderly populations in Europe.⁴⁸ Notwithstanding that not all results for the impact of childhood conditions may be considered perfectly robust, they yet suggest that early life conditions may have a long-lasting and significant effect on chewing ability in middle and later adulthood, even after controlling for current status and health determinants. This seems congruent with earlier evidence on general health outcomes⁴⁹ and may have relevant public health and policy implications because it indicates the importance of early life interventions to achieve good oral health throughout the entire life span.

Some limitations of our study should be noted. First, SHARELIFE data are based on a retrospective survey and may be prone to recall and reporting bias. However, a recent article⁵⁰ suggests that respondents remember well their health status and living conditions in early life years. Second, our analysis is determined by data availability and therefore cannot fully explore other specific exposures and the causal pathways through which early life conditions impact on oral health in later life, such as poor oral health care in the adulthood or unfavorable oral hygiene and dietary patterns. In particular, we found some evidence for cross-country differences in chewing ability being only partially attributable to cross-country differences in the frequency of risk factors considered in our study (Appendix). This suggests that differences in chewing ability between countries may be partly determined by factors not measured in this study. For example, previous evidence has shown that oral diseases follow trajectory patterns and disease occurrence in early life years implies higher oral health risks in subsequent life years.²¹ Because our database could not provide information on respondents’ oral health status in earlier life years, cross-country variations in chewing ability at age 50 years or older could also reflect country-specific risk trajectories that originated and evolved earlier in life. Similarly, our models did not control for other factors, which may shape cross-country differences in chewing ability such as persons’ value attached to good oral health or availability and accessibility to dental care. Despite these limitations, the results of the present study can still be considered meaningful, not least because our regression models include country dummies to account for unobservable country-specific characteristics. Third, our outcome measure (chewing ability) is important in its own right but may be considered only a proxy variable for oral health. In that respect, availability of oral health status data (such as number of teeth) would have allowed for a more comprehensive evaluation of the influence of early life conditions in oral health in older adulthood. However, as there currently is no other longitudinal multicountry data source available, SHARE provides a unique opportunity for analyzing the impact of life course conditions on oral health-related outcomes in older adulthood.

Conclusions

The present study is the first to our knowledge to investigate the impact of early life conditions on chewing ability in middle and later adulthood on basis of representative data from 13 European countries. Our results suggest a potential long-lasting impact of early life socioeconomic, behavioral, and cognitive factors, as well as adverse life events on oral health in middle and later adulthood. This may be relevant information for public health decision-makers who aim to design optimized life course strategies for better oral health. In line with relevant general health literature,^49,51,52 investment in early life seems to be important for oral health as well.