Multimorbidity and tooth loss: Data from Chilean National Health Survey 2016-2017

Multimorbidity and tooth loss: Data from Chilean National Health Survey 2016-2017 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multimorbidity and tooth loss: Data from Chilean National Health Survey 2016-2017 Matías Santos-López, Priscila Gómez, Paula Margozzini, Duniel Ortuño This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4530535/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Nov, 2024 Read the published version in BMC Oral Health → Version 1 posted 16 You are reading this latest preprint version Abstract Background: Oral diseases are a significant global public health challenge. Current evidence indicates that several chronic conditions are individually associated with tooth loss. Currently, people are living with more than one chronic condition, known as multimorbidity. This study aimed to evaluate the association between multimorbidity and tooth loss in the Chilean population, considering the common risk factors for oral and chronic diseases. Methods: Cross-sectional study with secondary data from the latest Chilean National Health Survey (ENS 2016-17). The number of remaining teeth was classified into four groups: functional dentition (≥20 remaining teeth), moderate tooth loss (10 to 19 remaining teeth), severe tooth loss (1 to 9 remaining teeth), and edentulism if there were no remaining teeth. Multimorbidity was defined based on the number of chronic conditions present as a binary variable (MMC≥2) and as a 4-level categorical variable (MMC G0-G3 ). The sample was divided into <65 and ≥65 years for statistical analysis. Mean and SD were calculated for crude and adjusted remaining teeth. Poisson regression models with robust variance, crude and adjusted for sex, age, geographic area, and educational level, were fitted to calculate the prevalence ratio between multimorbidity and tooth loss. Results: The study sample was 4,151 individuals between the ages of 18 and 98. Adults aged <65 years with multimorbidity have a 1.07 times higher prevalence of moderate tooth loss (95% CI 0.84; 1.36), 1.12 times higher prevalence of severe tooth loss (95% CI 0.67; 1.89), and a 0.92 times lower prevalence of edentulism (95% CI 0.39; 2.20). Adults aged ≥65 years with multimorbidity have 1.13 times higher prevalence of moderate tooth loss (95% CI 0.94; 1.37), 1.66 times higher prevalence of severe tooth loss (95% CI 1.04; 2.66), and 1.26 times higher prevalence of edentulism (95% CI 0.76; 2.08). Conclusions: There was an association between multimorbidity and tooth loss in the Chilean population, resulting in a higher prevalence of moderate tooth loss, severe tooth loss and edentulism in those with higher number of chronic diseases. This association was more robust in adults aged ≥65 years. Tooth loss multimorbidity oral health epidemiology. Figures Figure 1 Figure 2 BACKGROUND Oral diseases are a significant global public health challenge [ 1 – 3 ], affecting 45% of worldwide population [ 4 ]. Among them, tooth loss significantly affects people's health, nutritional status, appearance, self-confidence, functionality, and quality of life [ 5 – 7 ]. The World Health Organization (WHO) defines functional dentition as the presence of more than 20 teeth in the mouth, which allows an adequate masticatory function. In Chile, the prevalence of functional dentition is estimated at 85,7% in the group between 35 and 44 years old, and 23,9% in the group between 65 and 74 years old [ 8 ]. Current evidence indicates that several chronic conditions are individually associated with tooth loss, including malnutrition [ 9 ], obesity [ 10 ], cardiovascular diseases [ 11 ], hypertension [ 12 ], diabetes [ 13 ], cognitive impairment [ 14 ] and all-cause mortality [ 15 ]. However, due to the changing epidemiological profile in the world, people are living with more than one chronic condition [ 16 ]. Multimorbidity is the presence of multiple chronic diseases in the same person [ 16 ]. It is also a significant global public health challenge [ 17 ]. Combining various chronic diseases with an ageing population creates a negative feedback loop exacerbating both conditions [ 16 ], leading to reduced quality of life, elevated medical needs, increased medication intake, higher healthcare costs, and more significant mortality [ 18 – 20 ]. In Chile, a country with an advanced stage of population ageing, around 70% of the population over 15 years of age lives with two or more chronic diseases simultaneously [ 21 ]. Therefore, in 2021, the “Estrategia de Cuidado Integral Centrado en las Personas” (ECICEP) was implemented [ 22 ]. This initiative is designed as a framework to overcome the fragmentation of the health system, with the objective of transforming it towards a more individual-centric approach. This study aimed to evaluate the association between multimorbidity and tooth loss in the Chilean population, considering the common risk factors for oral and chronic diseases and the existing association established in the literature. METHODS Study design Cross-sectional study with secondary data from the latest National Health Survey in Chile (ENS 2016-17). The ENS 2016-17 is a national epidemiological surveillance tool focusing on non-communicable diseases, considering 72 health problems and their determinants. This tool targets Chilean adults aged 15 years and older, with national, urban, rural and regional representation. Its sampling method was probabilistic with geographic stratification and multistage. The study included a final sample size of 6,233 participants. Of these, 5,520 received a home visit from a nurse, 5,451 underwent laboratory examinations, and 5,306 underwent oral examinations. Participants aged 17 years or older were included in our study. Figure 1 shows the flow chart of the participant´s inclusion. Tooth loss assessment The number of remaining teeth was assessed by oral examination during ENS 2016-17. The examination was conducted during home visits by trained nurses. The nurses were trained by nine dentists affiliated with the Ministry of Health of Chile (MINSAL). The training program included a theoretical presentation, a practical demonstration, an oral examination practice session, and a final test of 55 questions. The average score obtained was 49.95 ± 2.74, with a kappa coefficient of 0.85 (p-value < 0.01) [ 23 ]. This assessment classified the teeth as functional dentition if more than 20 were remaining, moderate tooth loss if there were between 19 and 10 remaining teeth, severe tooth loss if there were between 1 and 9 remaining teeth, and edentulism if there were no remaining teeth. Multimorbidity assessment This assessment corresponds to the 18 chronic systemic conditions proposed by the Ministry of Health of Chile (MINSAL) in the ECICEP and assessed by self-report in the ENS 2016-17. These included: disability, chronic kidney disease (CKD), fibromyalgia, hypertension, thyroid disorders, myocardial infarction, diabetes mellitus (DM), obesity, advanced CKD, glaucoma, cerebrovascular accident, osteoarthritis, depression, chronic obstructive pulmonary disease (COPD), smoking, asthma, liver disease, coagulation disorders. Multimorbidity in our study was defined as a binary variable (MMC ≥ 2) and as a 4-level categorical variable (MMC G0−G3 ): MMC ≥ 2 corresponds to the coexistence of 2 or more chronic conditions and MMC G0−G3 , according to the ECICEP stratification criteria, where G3 corresponds to 5 or more chronic conditions, G2 to 2–4, G1 to 1, and G0 to have no chronic conditions. Covariates The theoretical framework of the association between tooth loss and multimorbidity was structured using a directed acyclic graph (DAGitty version 3.0), as shown in Fig. 2 . The following covariates were considered in the study: sex (male or female); age, divided into 74 years; geographic area, corresponding to the municipality where the patient lives (urban or rural); and educational level, representing the number of years completed and passed, divided into low (0–8 years), medium (8–12 years), and high (≥ 12 years). Statistical analysis A descriptive analysis was conducted on all variables, including the calculation of prevalence, 95% confidence intervals (95% CI), mean, and SD. All of these point estimates were calculated to the unweighted sample and weighted population. Mean and SD were calculated for crude and adjusted remaining teeth. Prevalence and 95% CI were calculated for moderate tooth loss, severe tooth loss, and edentulism. Poisson regression models with robust variance, crude and adjusted for sex, age, geographic area, and educational level, were fitted to calculate the prevalence ratio (PR) between multimorbidity and tooth loss. Sensitivity analysis was performed by calculating crude and adjusted ORs with logistic regressions for the association between moderate tooth loss, severe tooth loss and edentulism. All statistical analyses were performed in R (version 4.2.0) and RStudio (version 2023.06.1) with the packages survey, srvyr, and nnet. Ethics Data from the ENS 2016-17 are publicly available and anonymized. In any case, the study was approved by the Scientific Ethics Committee of the Universidad de Los Andes (CEC-UC) (CEC2022135). The study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. RESULTS The study sample consisted of 4,151 individuals between 18 and 98 years. In the weighted population, 50.6% of the participants were female. The mean age was of 44.9 ± 17.3 years. 54.8% had a medium educational level (8–12 years), and 89.3% lived in an urban area. Concerning multimorbidity, 54.9% had two or more chronic diseases. The mean number of remaining teeth was 22.5 ± 8.6. The prevalence of moderate tooth loss was 25.4%, severe tooth loss was 6.9%, and edentulous was 4.8%. Table 1 presents the characteristics of the unweighted sample and the weighted population. Table 1 Characteristics of the sample and population. Chilean National Health Survey 2016-17 (n = 4,151). Total (n = 4,151) < 65 years (n = 3,131) ≥ 65 years (n = 1,020) Variables Unweighted sample Weighted population Unweighted sample Weighted population Unweighted sample Weighted population Sex Female 63.4% 50.6% 63.2% 49.9% 64.2% 54.8% Male 36.6% 49.4% 36.8% 50.1% 35.8% 45.2% Age Mean ± SD Age Groups 50.0 ± 18.3 44.9 ± 17.3 42.3 ± 13.6 40.2 ± 13.6 73.8 ± 6.8 73.9 ± 6.8 17–24 10.4% 14.8% 13.8% 17.1% - - 25–44 29.7% 38.3% 39.3% 44.4% - - 45–64 35.3% 33.2% 46.9% 38.5% - - 65–74 14.6% 8.2% - - 59.3% 59.7% > 75 10.0% 5.5% - - 40.7% 40.3% Educational Level Low ( 12 years) 22.6% 28.3% 27.0% 30.5% 9.1% 14.9% Geographic Area Urban 83.8% 89.3% 85.1% 89.7% 80.1% 86.8% Rural 16.2% 10.7% 14.9% 10.3% 19.9% 13.2% Multimorbidity No 39.1% 45.1% 46.1% 49.4% 17.5% 17.6% Yes (≥ 2) 60.9% 54.9% 53.9% 50.6% 82.5% 82.4% Multimorbidity G0 14.4% 15.9% 17.8% 18.0% 4.0% 2.8% G1 24.7% 29.2% 28.3% 31.4% 13.5% 14.8% G2 44.9% 43.0% 43.2% 41.8% 50.0% 50.9% G3 16.0% 11.9% 10.7% 8.8% 32.5% 31.5% Number of Remaining Teeth Mean ± SD 19.8 ± 9.8 22.5 ± 8.6 23.1 ± 7.6 24.3 ± 6.8 9.6 ± 8.8 10.8 ± 9.2 Tooth loss Moderate tooth loss 37.5% 25.4% 23.4% 17.3% 80.9% 76.1% Severe tooth loss 11.7% 6.9% 6.2% 3.8% 28.5% 26.4% Edentulism 8.3% 4.8% 2.3% 1.7% 26.7% 24.0% Obesity (35.9%), smoking (32.9%) and hypertension (30.7%) were the most prevalent chronic diseases. When the sample was divided by age, the highest prevalences were obesity (36.1%), smoking (36.0%) and osteoarthritis (26.4%) among those < 65 years, and hypertension (78.9%), obesity (35.2%) and osteoarthritis (34.3%) among ≥ 65 years. Table 2 presents the prevalence of chronic diseases in the unweighted sample and the weighted population. Table 2 Prevalence of morbidities of the sample and population. Chilean National Health Survey 2016-17 (n = 4,151). Total (n = 4,151) < 65 years (n = 3,131) ≥ 65 years (n = 1,020) Morbidity Unweighted sample Weighted population Unweighted sample Weighted population Unweighted sample Weighted population Disability 1.6% 0.9% 0.1% 0.1% 6.1% 5.8% CKD 5.1% 3.1% 0.9% 0.8% 17.8% 17.5% Fibromyalgia 5.8% 5.3% 5.5% 4.9% 6.7% 8.2% Hypertension 38.9% 30.7% 26.0% 23.0% 78.4% 78.9% Thyroid Disorders 9.9% 6.8% 8.4% 5.7% 14.4% 13.3% Myocardial infarction 5.0% 3.3% 2.7% 2.0% 12.2% 11.5% DM 16.3% 11.8% 11.8% 9.1% 30.0% 29.0% Obesity 38.0% 35.9% 38.2% 36.1% 37.5% 35.2% Advanced CKD 0.5% 0.5% 0.3% 0.2% 1.2% 2.3% Glaucoma 2.8% 3.3% 1.7% 1.0% 6.3% 5.6% Cerebrovascular Accident 8.0% 6.5% 5.9% 5.0% 14.3% 15.9% Osteoarthritis 27.0% 27.5% 25.5% 26.4% 31.7% 34.3% Depression 12.7% 16.5% 14.3% 17.7% 7.8% 8.7% COPD 2.7% 1.8% 1.5% 1.0% 6.5% 7.3% Smoking 29.1% 32.9% 34.6% 36.0% 12.1% 13.8% Asthma 6.2% 5.3% 5.6% 4.9% 8.2% 7.6% Liver Disease 6.0% 5.0% 5.7% 4.5% 7.0% 8.6% Coagulation Disorders 2.3% 2.1% 1.2% 1.4% 5.7% 6.9% Table 3 presents the mean number of remaining teeth in < 65 years compared with adults ≥ 65 years and in adults with multimorbidity (≥ 2/G2, G3) compared with those without multimorbidity (≤ 1/G0, G1). Adults aged < 65 with multimorbidity (two or more chronic diseases) have a mean of 24.6 ± 0.3 remaining teeth (95% CI 24.0; 25.3). On the other hand, individuals aged ≥ 65 have a mean of 13.8 ± 0.9 (95% CI 12.1; 15.6). Adults aged < 65 in G3 have a mean of 22.9 ± 0.7 remaining teeth (95% CI 21.4; 24.4), with 2.1 teeth less than G0 individuals. Otherwise, individuals aged ≥ 65 in the G3 group have a mean of 13.6 ± 1.1 remaining teeth (95% CI 11.5; 15.8), 2.0 teeth less than individuals in the G0 group. Table 3 Weighted mean number of remaining teeth in Chilean population according to multimorbidity level. Chilean National Health Survey 2016-17 (n = 4,151). Total (n = 4,151) < 65 years (n = 3,131) ≥ 65 years (n = 1,020) Multimorbidity Mean ± SD (IC 95%) Mean ± SD (IC 95%) Mean ± SD (IC 95%) No (≤ 1) 22.6 ± 0.3 [21.9; 23.3] 24.9 ± 0.3 [24.3; 25.5] 13.8 ± 0.8 [12.2; 15.4] Yes (≥ 2) 22.4 ± 0.3 [21.8; 23.1] 24.6 ± 0.3 [24.0; 25.3] 13.8 ± 0.9 [12.1; 15.6] G0 22.8 ± 0.4 [22.0; 23.5] 25.0 ± 0.4 [24.3; 25.7] 15.6 ± 1.7 [12.4; 18.8] G1 22.6 ± 0.4 [21.8; 23.3] 24.8 ± 0.4 [24.1; 25.5] 13.3 ± 0.9 [11.5; 15.1] G2 22.7 ± 0.3 [22.1; 23.4] 24.9 ± 0.3 [24.3; 25.6] 13.8 ± 0.9 [12.0; 15.6] G3 21.1 ± 0.6 [20.0; 22.3] 22.9 ± 0.7 [21.4; 24.4] 13.6 ± 1.1 [11.5; 15.8] Table 4 presents the prevalence ratios (PRs) for adults aged < 65 years and ≥ 65 years with multimorbidity (≥ 2) compared to those without multimorbidity (≤ 1). Adults aged < 65 years with multimorbidity have a 1.07 times higher prevalence of moderate tooth loss (95% CI 0.84; 1.36), a 1.12 times higher prevalence of severe tooth loss (95% CI 0.67; 1.89), and a 0.92 times lower prevalence of edentulism (95% CI 0.39; 2.20). In contrast, adults aged ≥ 65 years with multimorbidity have a 1.13 times higher prevalence of moderate tooth loss (95% CI 0.94; 1.37), a 1.66 times higher prevalence of severe tooth loss (95% CI 1.04; 2.66), and 1.26 times higher prevalence of edentulism (95% CI 0.76; 2.08). Results in terms of the OR for these comparisons were similar to those estimated with PR (Table S1 ). Table 4 Prevalence ratios of tooth loss in Chilean population according to multimorbidity level. Chilean National Health Survey 2016-17 (n = 4,151). < 65 years (n = 3,131) ≥ 65 years (n = 1,020) Category Multimorbidity PR Crude [95% CI] PR Adjusted a [95% CI] PR Crude [95% CI] PR Adjusted a [95% CI] Moderate tooth loss No (≤ 1) 1 1 1 1 Yes (≥ 2) 2.20 [1.67–2.89] 1.07 [0.84–1.36] 0.96 [0.86–1.06] 1.13 [0.94–1.37] Severe tooth loss No (≤ 1) 1 1 1 1 Yes (≥ 2) 2.52 [1.47–4.33] 1.12 [0.67–1.89] 1.39 [0.89–2.19] 1.66 [1.04–2.66] Edentulism No (≤ 1) 1 1 1 1 Yes (≥ 2) 2.50 [1.03–6.05] 0.92 [0.39–2.20] 0.85 [0.58–1.26] 1.26 [0.76–2.08] a. The model was adjusted for age, sex, educational level and residential zone. PR: prevalence ratio. 95% CI: confidence interval. Table 5 presents the PRs for adults aged < 65 years and ≥ 65 years with multimorbidity (G2-G3) compared with those without multimorbidity (G0-G1). Individuals aged < 65 years with multimorbidity G3 have a 1.76 times higher prevalence of moderate tooth loss (95% CI 1.12; 2.77), 2.55 times higher prevalence of severe tooth loss (95% CI 1.02; 6.36), and 1.30 times higher prevalence of edentulism (95% CI 0.41; 4.14), compared to G0. On the contrary, adults aged ≥ 65 years with multimorbidity G3 had a 1.09 times higher prevalence of moderate tooth loss (95% CI 0.84; 1.40), 1.03 times higher prevalence of severe tooth loss (95% CI 0.45; 2.34) and 1.29 times higher prevalence of edentulism (95% CI 0.56; 2.97), compared to those with G0. Results in terms of the OR for these comparisons were similar to those estimated with PR (Table S2). Table 5 Prevalence ratios of tooth loss in Chilean population according to multimorbidity level (G0-G3). Chilean National Health Survey 2016-17 (n = 4,151). < 65 years (n = 3,131) ≥ 65 years (n = 1,020) Category Multimorbidity PR Crude [95% CI] PR Adjusted b [95% CI] PR Crude [95% CI] PR Adjusted b [95% CI] Moderate tooth loss G0 1 1 1 1 G1 1.65 [0.99–2.73] 1.65 [1.09–2.51] 0.95 [0.67–1.33] 1.17 [0.90–1.53] G2 2.57 [1.63–4.08] 1.44 [0.98–2.10] 1.07 [0.83–1.37] 1.09 [0.85–1.39] G3 5.62 [3.40–9.30] 1.76 [1.12–2.77] 1.10 [0.85–1.44] 1.09 [0.84–1.40] Severe tooth loss G0 1 1 1 1 G1 1.87 [0.71–4.94] 2.03 [0.82–5.03] 0.67 [0.27–1.71] 0.87 [0.34–2.20] G2 2.90 [1.22–6.91] 1.59 [0.71–3.54] 1.32 [0.60–2.93] 1.37 [0.60–3.10] G3 8.70 [3.49–21.66] 2.55 [1.02–6.36] 1.02 [0.46–2.26] 1.03 [0.45–2.34] Edentulism G0 1 1 1 1 G1 2.15 [0.61–7.59] 2.85 [0.81–10.03] 1.36 [0.48–3.83] 1.44 [0.61–3.40] G2 4.05 [1.27–12.94] 2.10 [0.66–6.67] 1.41 [0.54–3.67] 1.09 [0.47–2.49] G3 5.64 [1.81–17.54] 1.30 [0.41–4.14] 2.01 [0.79–5.13] 1.29 [0.56–2.97] b. The model was adjusted for age, sex, educational level and residential zone. PR: prevalence ratio. 95% CI: confidence interval. DISCUSSION In the present study, multimorbidity is associated with tooth loss, specifically, with a higher prevalence of moderate tooth loss, severe tooth loss and edentulism in < 65 years and ≥ 65 years groups. However, this association is more consistent in adults aged ≥ 65 years. This one is the first study to analyze this relationship in Chile. There is limited evidence analyzing the association between multimorbidity and tooth loss, but our findings are consistent with previous studies in other populations. Bomfim et al. analyzed data from the 2019 National Health Survey of Brazil, which included 88,531 individuals aged 18 years and older. Multimorbidity was the main exposure, categorized into two groups, having 2 or 3 comorbidities, based on 13 self-reported chronic diseases: hypertension, diabetes, depression, back problems, mental problems, asthma, arthritis, cancer, heart problems, stroke, chronic obstructive pulmonary disease, chronic kidney disease, and work-related musculoskeletal disorder. Tooth loss was the main outcome, and it was classified into functional dentition and severe tooth loss. They reported that Brazilian adults with multimorbidity have a higher chance of severe tooth loss and a lower chance of functional dentition [ 24 ]. Zhang et al. 2022 assessed the association between multimorbidity and tooth loss in U.S. adults. They performed a secondary data analysis using the US 2012 Behavioral Risk Factor Surveillance System (BRFSS), a national cross-sectional telephone survey studying health conditions and health behaviors, including 471,107 US adults. Multimorbidity was a dichotomous variable that was defined as having at least two of the eight self-reported chronic diseases: diabetes, heart disease, stroke, arthritis, cancer, COPD, kidney disease, and asthma. Tooth loss was grouped into four categories: zero tooth loss, one to five tooth loss, six or more tooth loss, and edentulous. They found that people with multimorbidity are more likely to be edentulous than those with one or no chronic disease [ 25 ]. We found that 54.9% of the Chilean population had multimorbidity. This prevalence is higher than those reported by Garin et al. for China (45.1%) and Ghana (48.3%), similar to India (57.9%), and lower than South Africa (63.4%) and Mexico (63.9%) [ 26 ]. When stratifying the population by age, 50.6% of adults aged < 65 and 82.4% of those aged ≥ 65 years had multimorbidity. These prevalences are higher than those found by Bomfim et al. in the Brazilian population, where 19.3% of adults aged < 60 years and 50.9% of those aged ≥ 60 years had two or more chronic diseases [ 24 ]. The mean number of remaining teeth for adults aged < 65 years was 24.3 ± 6.8, and for adults aged ≥ 65 years was 10.8 ± 9.2. In both groups, a higher mean number of remaining teeth was observed in individuals without multimorbidity (≤ 1/G0-G1) compared to individuals with multimorbidity (≥ 2/G2-G3). No studies evaluating the association between multimorbidity and the mean number of remaining teeth were found to compare these results. Among the biological plausibility to explain the association between multimorbidity and tooth loss, the main one is inflammation. Graves et al. propose that systemic diseases modify the host response to oral bacteria, leading to increased inflammation than under healthy conditions. [ 27 ]. This effect has been explored in the interactions of diabetes mellitus with periodontal disease, where pro-inflammatory mediators lead to tissue inflammation and modification of the immune response, increasing the susceptibility of oral tissues to destruction [ 28 ]. Rheumatoid arthritis also modifies the oral microbiota, increasing the levels of cytokines in the periodontium and saliva, leading to increased periodontal destruction [ 27 ]. A second possible mechanism is linked to the effects of the medications consumed by individuals with multimorbidity. Ucuncu et al. mention that the utilization of inhaled medicines such as corticosteroids and ß-mimetics by individuals with asthma and COPD modify the oral environment, making it drier, more cariogenic and leading to tooth loss [ 29 ]. This study has strengths and limitations that should be recognized. First, the limitations are that the ENS 2016-17 is a cross-sectional study, which cannot determine the directionality of the association between multimorbidity and tooth loss. The evidence supports hypothesizing the bi-directionality between multimorbidity and tooth loss, but it should be investigated with cohort and longitudinal studies. Second, the chronic diseases that compose the multimorbidity variable are self-reported, so they are susceptible to bias at the time of the study—finally, the presence of residual confounding due to unmeasured or unidentified confounding variables. Despite those above, it is possible to mention the following as strengths. Firstly, it is highlighted that the ENS 2016-17 was used, representing the distribution of diseases and their determinants in the Chilean population [ 30 ]. Second, tooth loss was the outcome measure, a complex indicator that reflects the accumulation of oral diseases during life, influenced by biological, social and cultural factors [ 31 , 32 ]. Finally, even though there are different definitions of multimorbidity, two definitions were used, one widely accepted with a cut-off in two or more conditions [ 33 ], and one aligned with the objectives of MINSAL's ECICEP, with population divided into four categories: High Risk, with ≥ 5 chronic conditions (G3); Moderate Risk, with 2 to 4 chronic conditions (G2); Mild Risk, with one chronic condition (G1); and No Risk, with no chronic conditions (G0). ECICEP is the new way of organizing Chile's health care and population care. Generates a classification of risk in different strata, which allows health teams to program their interventions for each group, improving the management of chronic conditions in the population [ 21 ]. In this context, in November 2023, MINSAL approved the first program for periodontal treatment for people with diabetes mellitus between 35 and 54 years of age based on the fact that incorporating periodontal treatments in patients with multimorbidity can contribute to reducing the levels of glycosylated hemoglobin in adults with diabetes mellitus [ 34 ]. The inclusion of a definition of multimorbidity aligned with the ECICEP allows these findings to be used for future public policies in Chile, as the already mentioned periodontal treatment program. Conclusion There was an association between multimorbidity and tooth loss in the Chilean population, resulting in a higher prevalence of moderate tooth loss, severe tooth loss and edentulism in those with higher number of chronic diseases. This association was more robust in adults aged ≥ 65 years. It is necessary to generate new strategies that considering the multimorbidity burden to prioritize health care for those at higher risk and thus be able to improve the oral and systemic health of the population. Abbreviations ENS 2016-17: Chilean National Health Survey 2016-17. ECICEP: Estrategia de Cuidado Integral Centrado en las Personas. MINSAL: Ministry of Health of Chile. Declarations Ethics approval and consent to participate: This study was nested in the NHS 2016-2017, whose protocols and written informed consent were approved by the Scientific Ethics Committee of the Faculty of Medicine of Pontificia Universidad Católica de Chile (CEC-MedUC, Project number 16-019). Also, out study was approved by the Scientific Ethics Committee of the Universidad de Los Andes (CEC-UC) (CEC2022135). Consent for publication: Not applicable. Availability of data and material: The datasets generated and analyzed during the current study are available in the Population Survey repository of the Department of Epidemiology of the Ministry of Health of the Government of Chile, http://epi.minsal.cl/encuestas-poblacionales/ Competing interests: The authors declare that they have no competing interests. Funding: FAI Universidad de los Andes. Authors’ contributions: MS and DO designed the study. MS and DO collected the data. MS was responsible for the statistical analysis and for drafting the manuscript. DO, PG and PM edited the manuscript. All authors read and approved the final manuscript. Acknowledgments : We thank to the ENS 2016-17’s participants. Also, Alvaro Passi for the data analysis contribution. References World Health Organization. Global oral health status report: towards universal health coverage for oral health by 2030. Vol. 57, Dental Abstracts. 2022. 120 p. 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Nutrition [Internet]. 2012;28(7–8):779–84. Available from: http://dx.doi.org/10.1016/j.nut.2011.11.011 Peng J, Song J, Han J, Chen Z, Yin X, Zhu J, et al. The relationship between tooth loss and mortality from all causes, cardiovascular diseases, and coronary heart disease in the general population: Systematic review and dose–response meta-analysis of prospective cohort studies. Biosci Rep. 2019;39(1):1–19. Gerritsen AE, Allen PF, Witter DJ, Bronkhorst EM, Creugers NHJ. Tooth loss and oral health-related quality of life: A systematic review and meta-analysis. Health Qual Life Outcomes [Internet]. 2010;8(1):126. Available from: http://www.hqlo.com/content/8/1/126 Urzua I, Mendoza C, Arteaga O, Rodríguez G, Cabello R, Faleiros S, et al. Dental caries prevalence and tooth loss in chilean adult population: First national dental examination survey. Int J Dent. 2012;2012. Toniazzo MP, Amorim P de SA, Muniz FWMG, Weidlich P. Relationship of nutritional status and oral health in elderly: Systematic review with meta-analysis. Clin Nutr [Internet]. 2018;37(3):824–30. Available from: https://doi.org/10.1016/j.clnu.2017.03.014 Nascimento GG, Leite FRM, Conceição DA, Ferrúa CP, Singh A, Demarco FF. Is there a relationship between obesity and tooth loss and edentulism? A systematic review and meta-analysis. Obes Rev. 2016;17(7):587–98. Dietrich T, Webb I, Stenhouse L, Pattni A, Ready D, Wanyonyi KL, et al. Evidence summary: The relationship between oral and cardiovascular disease. Br Dent J. 2017;222(5):381–5. Shin HS. Association between the number of teeth and hypertension in a study based on 13,561 participants. J Periodontol. 2018;89(4):397–406. Patel MH, Kumar J V, Moss ME. Diabetes and tooth loss: an analysis of data from the National Health and Nutrition Examination Survey, 2003-2004. J Am Dent Assoc. 2013;140(5):478–85. Gao W, Wang X, Wang X, Cai Y, Luan Q. Association of cognitive function with tooth loss and mitochondrial variation in adult subjects: a community-based study in Beijing, China. Oral Dis. 2016;22(7):697–702. Peng J, Song J, Han J, Chen Z, Yin X, Zhu J, et al. The relationship between tooth loss and mortality from all causes, cardiovascular diseases, and coronary heart disease in the general population: systematic review and dose-response meta-analysis of prospective cohort studies. Biosci Rep. 2019 Jan;39(1). Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: A systematic review of the literature. Ageing Res Rev [Internet]. 2011;10(4):430–9. Available from: http://dx.doi.org/10.1016/j.arr.2011.03.003 Moffat K, Mercer SW. Challenges of managing people with multimorbidity in today’s healthcare systems. BMC Fam Pr [Internet]. 2015;16(1):15–7. Available from: http://dx.doi.org/10.1186/s12875-015-0344-4 De Carvalho Yokota RT, Van Der Heyden J, Johanna Nusselder W, Robine JM, Tafforeau J, Deboosere P, et al. Impact of chronic conditions and multimorbidity on the disability burden in the older population in Belgium. J Gerontol A Biol Sci Med Sci. 2016;71(7):903–9. Fabbri E, An Y, Zoli M, Simonsick EM, Guralnik JM, Bandinelli S, et al. Aging and the burden of multimorbidity: Associations with inflammatory and anabolic hormonal biomarkers. J Gerontol A Biol Sci Med Sci. 2015;70(1):63–70. Zamorano P, Muñoz P, Espinoza M, Tellez A, Varela T, Suarez F, et al. Impact of a high-risk multimorbidity integrated care implemented at the public health system in Chile. PLoS One. 2022;17(1 January 2022):1–12. Vargas I, Barros X, Fernández MJ, Mayol M. Rediseño en el abordaje de personas con multimorbilidad crónica: desde la fragmentación al cuidado integral centrado en las personas. Rev Med Clin Condes [Internet]. 2021;32(4):400–13. Available from: https://www.elsevier.es/es-revista-revista-medica-clinica-las-condes-202-articulo-rediseno-el-abordaje-personas-con-S0716864021000651 Ministerio de Salud. Estrategia de cuidado integral centrado en las personas para la promoción, prevención y manejo de la cronicidad en contexto de multimorbilidad [Internet]. 2021. Available from: https://www.minsal.cl/wp-content/uploads/2021/06/Marco-operativo_-Estrategia-de-cuidado-integral-centrado-en-las-personas.pdf Margozzini P, Berríos R, Cantarutti C, Veliz C, Ortuno D. Validity of the self-reported number of teeth in Chilean adults. BMC Oral Health. 2019;19(1):1–10. Bomfim RA, Cascaes AM, de Oliveira C. Multimorbidity and tooth loss: the Brazilian National Health Survey, 2019. BMC Public Health [Internet]. 2021;21(1):2311. Available from: https://doi.org/10.1186/s12889-021-12392-2 Zhang Y, Leveille SG, Shi L. Multiple Chronic Diseases Associated With Tooth Loss Among the US Adult Population. Front Big Data. 2022;5(July):1–10. Garin N, Koyanagi A, Chatterji S, Tyrovolas S, Olaya B, Leonardi M, et al. Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study. J Gerontol A Biol Sci Med Sci. 71(2):205–14. Graves DT, Corrêa JD, Silva TA. The Oral Microbiota Is Modified by Systemic Diseases. J Dent Res. 2019;98(2):148–56. Taylor JJ, Preshaw PM, Lalla E. A review of the evidence for pathogenic mechanisms that may link periodontitis and diabetes. J Clin Periodontol. 40(Suppl 14). Ucuncu MY, Topcuoglu N, Kulekci G, Ucuncu MK, Erelel M, Gokce YB. A comparative evaluation of the effects of respiratory diseases on dental caries. BMC Oral Health. 2024;24(1):1–10. Margozzini P, Passi Á. Encuesta Nacional de Salud, ENS 2016-2017: un aporte a la planificación sanitaria y políticas públicas en Chile. ARS med. 2018;43(1):30–4. Haworth S, Shungin D, Kwak SY, Kim HY, West NX, Thomas SJ, et al. Tooth loss is a complex measure of oral disease: Determinants and methodological considerations. Community Dent Oral Epidemiol. 2018;46:555–62. Roberto LL, Silveira MF, De Paula AMB, Ferreira E Ferreira E, Martins AMEDBL, Haikal DS ana. Contextual and individual determinants of tooth loss in adults: a multilevel study. BMC Oral Health. 2020;20(1):1–10. Johnston MC, Crilly M, Black C, Prescott GJ, Mercer SW. Defining and measuring multimorbidity: a systematic review of systematic reviews. Eur J Public Heal. 2019 Feb;29(1):182–9. Facultad de Odontología - Universidad de Chile. Aprobada atención periodontal para pacientes con Diabetes Mellitus [Internet]. 2023. Available from: https://odontologia.uchile.cl/noticias/211184/aprobada-atencion-periodontal-para-pacientes-con-diabetes-mellitus Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4530535","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317465034,"identity":"b13516c2-5100-41e6-bcc5-b5569f4936ca","order_by":0,"name":"Matías Santos-López","email":"","orcid":"","institution":"Universidad de Los Andes","correspondingAuthor":false,"prefix":"","firstName":"Matías","middleName":"","lastName":"Santos-López","suffix":""},{"id":317465035,"identity":"017838d2-d2ad-48a0-9efb-68009c511615","order_by":1,"name":"Priscila Gómez","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Priscila","middleName":"","lastName":"Gómez","suffix":""},{"id":317465036,"identity":"6f6bba37-8cbf-48cd-b66a-6f1a50bd238f","order_by":2,"name":"Paula Margozzini","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Margozzini","suffix":""},{"id":317465037,"identity":"2e15bcdd-98e2-4cbb-af3c-3cc1d72589fc","order_by":3,"name":"Duniel Ortuño","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYDCCAxBKDkRIgAg+IrQwNgApY7gWNmK1JDYQrYXveO/zx7w7bNI3HD978MaHPwzyBLVInjlu2Mx7Ji13w5m8ZMuZbQyGbYS0GNxIY2zmbTucu+FAjpk0bwNDAkFbYFrSDc6/MZP+84cELQkGN4C2AP1OWIvkmWOMM+e2pRnOvPHG2LK3TYKwX/iOtzF8eNtmI893Psfwxo8/NvL8hLSgAwlSNYyCUTAKRsEowAYAxbs+QzS9jGMAAAAASUVORK5CYII=","orcid":"","institution":"Universidad de Los Andes","correspondingAuthor":true,"prefix":"","firstName":"Duniel","middleName":"","lastName":"Ortuño","suffix":""}],"badges":[],"createdAt":"2024-06-05 00:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4530535/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4530535/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12903-024-05184-8","type":"published","date":"2024-11-20T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59523472,"identity":"2bdd5237-b7fe-42e1-805e-5049543bf8ac","added_by":"auto","created_at":"2024-07-02 20:41:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46425,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participant’s inclusion.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4530535/v1/8c9296f87646f6226c1fb0f0.jpg"},{"id":59523471,"identity":"4ab763e6-a2ac-4fbd-9c01-9541fba8876c","added_by":"auto","created_at":"2024-07-02 20:41:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41079,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized association between of tooth loss and multimorbidity.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4530535/v1/04d15a286cd3a5a1c2ee229e.jpg"},{"id":69834973,"identity":"170709d9-882f-424d-a738-1b4c62c1b9ad","added_by":"auto","created_at":"2024-11-25 16:11:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":931410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4530535/v1/478e45e7-5927-4614-93ad-b44d21b8ae6b.pdf"},{"id":59523473,"identity":"e74b33ae-61e9-4a28-98bb-37e2e107b926","added_by":"auto","created_at":"2024-07-02 20:41:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26768,"visible":true,"origin":"","legend":"","description":"","filename":"Suppldrob.docx","url":"https://assets-eu.researchsquare.com/files/rs-4530535/v1/25591a92ee6471bc86b1d088.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multimorbidity and tooth loss: Data from Chilean National Health Survey 2016-2017","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eOral diseases are a significant global public health challenge [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], affecting 45% of worldwide population [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among them, tooth loss significantly affects people's health, nutritional status, appearance, self-confidence, functionality, and quality of life [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe World Health Organization (WHO) defines functional dentition as the presence of more than 20 teeth in the mouth, which allows an adequate masticatory function. In Chile, the prevalence of functional dentition is estimated at 85,7% in the group between 35 and 44 years old, and 23,9% in the group between 65 and 74 years old [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent evidence indicates that several chronic conditions are individually associated with tooth loss, including malnutrition [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], obesity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], cardiovascular diseases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], hypertension [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], diabetes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], cognitive impairment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and all-cause mortality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, due to the changing epidemiological profile in the world, people are living with more than one chronic condition [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultimorbidity is the presence of multiple chronic diseases in the same person [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It is also a significant global public health challenge [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Combining various chronic diseases with an ageing population creates a negative feedback loop exacerbating both conditions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], leading to reduced quality of life, elevated medical needs, increased medication intake, higher healthcare costs, and more significant mortality [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Chile, a country with an advanced stage of population ageing, around 70% of the population over 15 years of age lives with two or more chronic diseases simultaneously [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, in 2021, the \u0026ldquo;Estrategia de Cuidado Integral Centrado en las Personas\u0026rdquo; (ECICEP) was implemented [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This initiative is designed as a framework to overcome the fragmentation of the health system, with the objective of transforming it towards a more individual-centric approach.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the association between multimorbidity and tooth loss in the Chilean population, considering the common risk factors for oral and chronic diseases and the existing association established in the literature.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eCross-sectional study with secondary data from the latest National Health Survey in Chile (ENS 2016-17). The ENS 2016-17 is a national epidemiological surveillance tool focusing on non-communicable diseases, considering 72 health problems and their determinants. This tool targets Chilean adults aged 15 years and older, with national, urban, rural and regional representation. Its sampling method was probabilistic with geographic stratification and multistage. The study included a final sample size of 6,233 participants. Of these, 5,520 received a home visit from a nurse, 5,451 underwent laboratory examinations, and 5,306 underwent oral examinations. Participants aged 17 years or older were included in our study. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flow chart of the participant\u0026acute;s inclusion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTooth loss assessment\u003c/h2\u003e \u003cp\u003eThe number of remaining teeth was assessed by oral examination during ENS 2016-17. The examination was conducted during home visits by trained nurses. The nurses were trained by nine dentists affiliated with the Ministry of Health of Chile (MINSAL). The training program included a theoretical presentation, a practical demonstration, an oral examination practice session, and a final test of 55 questions. The average score obtained was 49.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74, with a kappa coefficient of 0.85 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis assessment classified the teeth as functional dentition if more than 20 were remaining, moderate tooth loss if there were between 19 and 10 remaining teeth, severe tooth loss if there were between 1 and 9 remaining teeth, and edentulism if there were no remaining teeth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMultimorbidity assessment\u003c/h2\u003e \u003cp\u003eThis assessment corresponds to the 18 chronic systemic conditions proposed by the Ministry of Health of Chile (MINSAL) in the \u003cem\u003eECICEP\u003c/em\u003e and assessed by self-report in the ENS 2016-17. These included: disability, chronic kidney disease (CKD), fibromyalgia, hypertension, thyroid disorders, myocardial infarction, diabetes mellitus (DM), obesity, advanced CKD, glaucoma, cerebrovascular accident, osteoarthritis, depression, chronic obstructive pulmonary disease (COPD), smoking, asthma, liver disease, coagulation disorders.\u003c/p\u003e \u003cp\u003eMultimorbidity in our study was defined as a binary variable (MMC\u0026thinsp;\u0026ge;\u0026thinsp;2) and as a 4-level categorical variable (MMC\u003csub\u003eG0\u0026minus;G3\u003c/sub\u003e): MMC\u0026thinsp;\u0026ge;\u0026thinsp;2 corresponds to the coexistence of 2 or more chronic conditions and MMC\u003csub\u003eG0\u0026minus;G3\u003c/sub\u003e, according to the ECICEP stratification criteria, where G3 corresponds to 5 or more chronic conditions, G2 to 2\u0026ndash;4, G1 to 1, and G0 to have no chronic conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eThe theoretical framework of the association between tooth loss and multimorbidity was structured using a directed acyclic graph (DAGitty version 3.0), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The following covariates were considered in the study: sex (male or female); age, divided into \u0026lt;\u0026thinsp;65 and \u0026ge;\u0026thinsp;65 years, and secondarily into 17\u0026ndash;24, 25\u0026ndash;44, 45\u0026ndash;64, 65\u0026ndash;74, and \u0026gt;\u0026thinsp;74 years; geographic area, corresponding to the municipality where the patient lives (urban or rural); and educational level, representing the number of years completed and passed, divided into low (0\u0026ndash;8 years), medium (8\u0026ndash;12 years), and high (\u0026ge;\u0026thinsp;12 years).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA descriptive analysis was conducted on all variables, including the calculation of prevalence, 95% confidence intervals (95% CI), mean, and SD. All of these point estimates were calculated to the unweighted sample and weighted population. Mean and SD were calculated for crude and adjusted remaining teeth.\u003c/p\u003e \u003cp\u003ePrevalence and 95% CI were calculated for moderate tooth loss, severe tooth loss, and edentulism. Poisson regression models with robust variance, crude and adjusted for sex, age, geographic area, and educational level, were fitted to calculate the prevalence ratio (PR) between multimorbidity and tooth loss. Sensitivity analysis was performed by calculating crude and adjusted ORs with logistic regressions for the association between moderate tooth loss, severe tooth loss and edentulism. All statistical analyses were performed in R (version 4.2.0) and RStudio (version 2023.06.1) with the packages survey, srvyr, and nnet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003eData from the ENS 2016-17 are publicly available and anonymized. In any case, the study was approved by the Scientific Ethics Committee of the Universidad de Los Andes (CEC-UC) (CEC2022135). The study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe study sample consisted of 4,151 individuals between 18 and 98 years. In the weighted population, 50.6% of the participants were female. The mean age was of 44.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3 years. 54.8% had a medium educational level (8\u0026ndash;12 years), and 89.3% lived in an urban area. Concerning multimorbidity, 54.9% had two or more chronic diseases. The mean number of remaining teeth was 22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6. The prevalence of moderate tooth loss was 25.4%, severe tooth loss was 6.9%, and edentulous was 4.8%. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the characteristics of the unweighted sample and the weighted population.\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\u003eCharacteristics of the sample and population. Chilean National Health Survey 2016-17 (n\u0026thinsp;=\u0026thinsp;4,151).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,151)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,131)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,020)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnweighted\u003c/p\u003e \u003cp\u003esample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnweighted\u003c/p\u003e \u003cp\u003esample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnweighted\u003c/p\u003e \u003cp\u003esample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWeighted population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003e\u003cb\u003eAge Groups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Level\u003c/b\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (\u0026lt;\u0026thinsp;8 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium (8\u0026ndash;12 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;12 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographic Area\u003c/b\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultimorbidity\u003c/b\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e39.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultimorbidity\u003c/b\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Remaining Teeth\u003c/b\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTooth loss\u003c/b\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate tooth loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere tooth loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdentulism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.0%\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\u003eObesity (35.9%), smoking (32.9%) and hypertension (30.7%) were the most prevalent chronic diseases. When the sample was divided by age, the highest prevalences were obesity (36.1%), smoking (36.0%) and osteoarthritis (26.4%) among those\u0026thinsp;\u0026lt;\u0026thinsp;65 years, and hypertension (78.9%), obesity (35.2%) and osteoarthritis (34.3%) among \u0026ge;\u0026thinsp;65 years. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the prevalence of chronic diseases in the unweighted sample and the weighted population.\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\u003ePrevalence of morbidities of the sample and population. Chilean National Health Survey 2016-17 (n\u0026thinsp;=\u0026thinsp;4,151).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,151)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,131)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,020)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnweighted\u003c/p\u003e \u003cp\u003esample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnweighted\u003c/p\u003e \u003cp\u003esample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnweighted\u003c/p\u003e \u003cp\u003esample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWeighted population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibromyalgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid Disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced CKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlaucoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular\u003c/p\u003e \u003cp\u003eAccident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulation\u003c/p\u003e \u003cp\u003eDisorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.9%\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the mean number of remaining teeth in \u0026lt;\u0026thinsp;65 years compared with adults\u0026thinsp;\u0026ge;\u0026thinsp;65 years and in adults with multimorbidity (\u0026ge;\u0026thinsp;2/G2, G3) compared with those without multimorbidity (\u0026le;\u0026thinsp;1/G0, G1).\u003c/p\u003e \u003cp\u003eAdults aged\u0026thinsp;\u0026lt;\u0026thinsp;65 with multimorbidity (two or more chronic diseases) have a mean of 24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 remaining teeth (95% CI 24.0; 25.3). On the other hand, individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 have a mean of 13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (95% CI 12.1; 15.6). Adults aged\u0026thinsp;\u0026lt;\u0026thinsp;65 in G3 have a mean of 22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 remaining teeth (95% CI 21.4; 24.4), with 2.1 teeth less than G0 individuals. Otherwise, individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 in the G3 group have a mean of 13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 remaining teeth (95% CI 11.5; 15.8), 2.0 teeth less than individuals in the G0 group.\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\u003eWeighted mean number of remaining teeth in Chilean population according to multimorbidity level. Chilean National Health Survey 2016-17 (n\u0026thinsp;=\u0026thinsp;4,151).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,151)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,131)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,020)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultimorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e(IC 95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e(IC 95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e(IC 95%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNo (\u0026le;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[21.9; 23.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[24.3; 25.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[12.2; 15.4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eYes (\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[21.8; 23.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[24.0; 25.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[12.1; 15.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eG0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[22.0; 23.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[24.3; 25.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[12.4; 18.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[21.8; 23.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[24.1; 25.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[11.5; 15.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[22.1; 23.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[24.3; 25.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[12.0; 15.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[20.0; 22.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[21.4; 24.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[11.5; 15.8]\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the prevalence ratios (PRs) for adults aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years and \u0026ge;\u0026thinsp;65 years with multimorbidity (\u0026ge;\u0026thinsp;2) compared to those without multimorbidity (\u0026le;\u0026thinsp;1). Adults aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years with multimorbidity have a 1.07 times higher prevalence of moderate tooth loss (95% CI 0.84; 1.36), a 1.12 times higher prevalence of severe tooth loss (95% CI 0.67; 1.89), and a 0.92 times lower prevalence of edentulism (95% CI 0.39; 2.20). In contrast, adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years with multimorbidity have a 1.13 times higher prevalence of moderate tooth loss (95% CI 0.94; 1.37), a 1.66 times higher prevalence of severe tooth loss (95% CI 1.04; 2.66), and 1.26 times higher prevalence of edentulism (95% CI 0.76; 2.08). Results in terms of the OR for these comparisons were similar to those estimated with PR (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\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\u003ePrevalence ratios of tooth loss in Chilean population according to multimorbidity level. Chilean National Health Survey 2016-17 (n\u0026thinsp;=\u0026thinsp;4,151).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,131)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,020)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultimorbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR Crude\u003c/p\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePR Adjusted\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR Crude\u003c/p\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePR Adjusted\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e[95% CI]\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\u003eModerate tooth loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (\u0026le;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20 [1.67\u0026ndash;2.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 [0.84\u0026ndash;1.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 [0.86\u0026ndash;1.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13 [0.94\u0026ndash;1.37]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSevere tooth loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (\u0026le;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.52 [1.47\u0026ndash;4.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 [0.67\u0026ndash;1.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39 [0.89\u0026ndash;2.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66 [1.04\u0026ndash;2.66]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEdentulism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (\u0026le;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50 [1.03\u0026ndash;6.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92 [0.39\u0026ndash;2.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85 [0.58\u0026ndash;1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26 [0.76\u0026ndash;2.08]\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\u003ea. The model was adjusted for age, sex, educational level and residential zone. PR: prevalence ratio. 95% CI: confidence interval.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the PRs for adults aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years and \u0026ge;\u0026thinsp;65 years with multimorbidity (G2-G3) compared with those without multimorbidity (G0-G1). Individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years with multimorbidity G3 have a 1.76 times higher prevalence of moderate tooth loss (95% CI 1.12; 2.77), 2.55 times higher prevalence of severe tooth loss (95% CI 1.02; 6.36), and 1.30 times higher prevalence of edentulism (95% CI 0.41; 4.14), compared to G0. On the contrary, adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years with multimorbidity G3 had a 1.09 times higher prevalence of moderate tooth loss (95% CI 0.84; 1.40), 1.03 times higher prevalence of severe tooth loss (95% CI 0.45; 2.34) and 1.29 times higher prevalence of edentulism (95% CI 0.56; 2.97), compared to those with G0. Results in terms of the OR for these comparisons were similar to those estimated with PR (Table S2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence ratios of tooth loss in Chilean population according to multimorbidity level (G0-G3). Chilean National Health Survey 2016-17 (n\u0026thinsp;=\u0026thinsp;4,151).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,131)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,020)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultimorbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR Crude\u003c/p\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePR Adjusted\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR Crude\u003c/p\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePR Adjusted\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e[95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eModerate tooth loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65 [0.99\u0026ndash;2.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65 [1.09\u0026ndash;2.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 [0.67\u0026ndash;1.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17 [0.90\u0026ndash;1.53]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.57 [1.63\u0026ndash;4.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44 [0.98\u0026ndash;2.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07 [0.83\u0026ndash;1.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09 [0.85\u0026ndash;1.39]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.62 [3.40\u0026ndash;9.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76 [1.12\u0026ndash;2.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10 [0.85\u0026ndash;1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09 [0.84\u0026ndash;1.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSevere tooth loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87 [0.71\u0026ndash;4.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.03 [0.82\u0026ndash;5.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67 [0.27\u0026ndash;1.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87 [0.34\u0026ndash;2.20]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.90 [1.22\u0026ndash;6.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 [0.71\u0026ndash;3.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32 [0.60\u0026ndash;2.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37 [0.60\u0026ndash;3.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.70 [3.49\u0026ndash;21.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.55 [1.02\u0026ndash;6.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 [0.46\u0026ndash;2.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03 [0.45\u0026ndash;2.34]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEdentulism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.15 [0.61\u0026ndash;7.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.85 [0.81\u0026ndash;10.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36 [0.48\u0026ndash;3.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.44 [0.61\u0026ndash;3.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.05 [1.27\u0026ndash;12.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10 [0.66\u0026ndash;6.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41 [0.54\u0026ndash;3.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09 [0.47\u0026ndash;2.49]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.64 [1.81\u0026ndash;17.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 [0.41\u0026ndash;4.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01 [0.79\u0026ndash;5.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29 [0.56\u0026ndash;2.97]\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\u003eb. The model was adjusted for age, sex, educational level and residential zone. PR: prevalence ratio. 95% CI: confidence interval.\u003c/p\u003e "},{"header":"DISCUSSION","content":"\u003cp\u003eIn the present study, multimorbidity is associated with tooth loss, specifically, with a higher prevalence of moderate tooth loss, severe tooth loss and edentulism in \u0026lt;\u0026thinsp;65 years and \u0026ge;\u0026thinsp;65 years groups. However, this association is more consistent in adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. This one is the first study to analyze this relationship in Chile.\u003c/p\u003e \u003cp\u003eThere is limited evidence analyzing the association between multimorbidity and tooth loss, but our findings are consistent with previous studies in other populations. Bomfim et al. analyzed data from the 2019 National Health Survey of Brazil, which included 88,531 individuals aged 18 years and older. Multimorbidity was the main exposure, categorized into two groups, having 2 or 3 comorbidities, based on 13 self-reported chronic diseases: hypertension, diabetes, depression, back problems, mental problems, asthma, arthritis, cancer, heart problems, stroke, chronic obstructive pulmonary disease, chronic kidney disease, and work-related musculoskeletal disorder. Tooth loss was the main outcome, and it was classified into functional dentition and severe tooth loss. They reported that Brazilian adults with multimorbidity have a higher chance of severe tooth loss and a lower chance of functional dentition [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Zhang et al. 2022 assessed the association between multimorbidity and tooth loss in U.S. adults. They performed a secondary data analysis using the US 2012 Behavioral Risk Factor Surveillance System (BRFSS), a national cross-sectional telephone survey studying health conditions and health behaviors, including 471,107 US adults. Multimorbidity was a dichotomous variable that was defined as having at least two of the eight self-reported chronic diseases: diabetes, heart disease, stroke, arthritis, cancer, COPD, kidney disease, and asthma. Tooth loss was grouped into four categories: zero tooth loss, one to five tooth loss, six or more tooth loss, and edentulous. They found that people with multimorbidity are more likely to be edentulous than those with one or no chronic disease [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe found that 54.9% of the Chilean population had multimorbidity. This prevalence is higher than those reported by Garin et al. for China (45.1%) and Ghana (48.3%), similar to India (57.9%), and lower than South Africa (63.4%) and Mexico (63.9%) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. When stratifying the population by age, 50.6% of adults aged\u0026thinsp;\u0026lt;\u0026thinsp;65 and 82.4% of those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years had multimorbidity. These prevalences are higher than those found by Bomfim et al. in the Brazilian population, where 19.3% of adults aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years and 50.9% of those aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years had two or more chronic diseases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mean number of remaining teeth for adults aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years was 24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8, and for adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years was 10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2. In both groups, a higher mean number of remaining teeth was observed in individuals without multimorbidity (\u0026le;\u0026thinsp;1/G0-G1) compared to individuals with multimorbidity (\u0026ge;\u0026thinsp;2/G2-G3). No studies evaluating the association between multimorbidity and the mean number of remaining teeth were found to compare these results.\u003c/p\u003e \u003cp\u003eAmong the biological plausibility to explain the association between multimorbidity and tooth loss, the main one is inflammation. Graves et al. propose that systemic diseases modify the host response to oral bacteria, leading to increased inflammation than under healthy conditions. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This effect has been explored in the interactions of diabetes mellitus with periodontal disease, where pro-inflammatory mediators lead to tissue inflammation and modification of the immune response, increasing the susceptibility of oral tissues to destruction [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Rheumatoid arthritis also modifies the oral microbiota, increasing the levels of cytokines in the periodontium and saliva, leading to increased periodontal destruction [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A second possible mechanism is linked to the effects of the medications consumed by individuals with multimorbidity. Ucuncu et al. mention that the utilization of inhaled medicines such as corticosteroids and \u0026szlig;-mimetics by individuals with asthma and COPD modify the oral environment, making it drier, more cariogenic and leading to tooth loss [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has strengths and limitations that should be recognized. First, the limitations are that the ENS 2016-17 is a cross-sectional study, which cannot determine the directionality of the association between multimorbidity and tooth loss. The evidence supports hypothesizing the bi-directionality between multimorbidity and tooth loss, but it should be investigated with cohort and longitudinal studies. Second, the chronic diseases that compose the multimorbidity variable are self-reported, so they are susceptible to bias at the time of the study\u0026mdash;finally, the presence of residual confounding due to unmeasured or unidentified confounding variables.\u003c/p\u003e \u003cp\u003eDespite those above, it is possible to mention the following as strengths. Firstly, it is highlighted that the ENS 2016-17 was used, representing the distribution of diseases and their determinants in the Chilean population [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Second, tooth loss was the outcome measure, a complex indicator that reflects the accumulation of oral diseases during life, influenced by biological, social and cultural factors [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Finally, even though there are different definitions of multimorbidity, two definitions were used, one widely accepted with a cut-off in two or more conditions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and one aligned with the objectives of MINSAL's ECICEP, with population divided into four categories: High Risk, with \u0026ge;\u0026thinsp;5 chronic conditions (G3); Moderate Risk, with 2 to 4 chronic conditions (G2); Mild Risk, with one chronic condition (G1); and No Risk, with no chronic conditions (G0).\u003c/p\u003e \u003cp\u003e\u003cem\u003eECICEP\u003c/em\u003e is the new way of organizing Chile's health care and population care. Generates a classification of risk in different strata, which allows health teams to program their interventions for each group, improving the management of chronic conditions in the population [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this context, in November 2023, MINSAL approved the first program for periodontal treatment for people with diabetes mellitus between 35 and 54 years of age based on the fact that incorporating periodontal treatments in patients with multimorbidity can contribute to reducing the levels of glycosylated hemoglobin in adults with diabetes mellitus [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The inclusion of a definition of multimorbidity aligned with the ECICEP allows these findings to be used for future public policies in Chile, as the already mentioned periodontal treatment program.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThere was an association between multimorbidity and tooth loss in the Chilean population, resulting in a higher prevalence of moderate tooth loss, severe tooth loss and edentulism in those with higher number of chronic diseases. This association was more robust in adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. It is necessary to generate new strategies that considering the multimorbidity burden to prioritize health care for those at higher risk and thus be able to improve the oral and systemic health of the population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eENS 2016-17: Chilean National Health Survey 2016-17.\u003c/p\u003e\n\u003cp\u003eECICEP: Estrategia de Cuidado Integral Centrado en las Personas.\u003c/p\u003e\n\u003cp\u003eMINSAL: Ministry of Health of Chile.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis study was nested in the NHS 2016-2017, whose protocols and written informed consent were approved by the Scientific Ethics Committee of the Faculty of Medicine of Pontificia Universidad Cat\u0026oacute;lica de Chile (CEC-MedUC, Project number 16-019). Also, out study was\u0026nbsp;approved by the Scientific Ethics Committee of the Universidad de Los Andes (CEC-UC) (CEC2022135).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e\u0026nbsp; The datasets generated and analyzed during the current study are available in the Population Survey repository of the Department of Epidemiology of the Ministry of Health of the Government of Chile, http://epi.minsal.cl/encuestas-poblacionales/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e \u0026nbsp;FAI Universidad de los Andes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e \u0026nbsp;MS and DO designed the study. MS and DO collected the data. MS was responsible for the statistical analysis and for drafting the manuscript. DO, PG and PM edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgments\u003cstrong\u003e:\u003c/strong\u003e\u003c/u\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eWe thank to the ENS 2016-17\u0026rsquo;s participants. Also, Alvaro Passi for the data analysis contribution.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Global oral health status report: towards universal health coverage for oral health by 2030. Vol. 57, Dental Abstracts. 2022. 120 p. \u003c/li\u003e\n\u003cli\u003eKyu HH, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. 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Aging and the burden of multimorbidity: Associations with inflammatory and anabolic hormonal biomarkers. J Gerontol A Biol Sci Med Sci. 2015;70(1):63\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eZamorano P, Mu\u0026ntilde;oz P, Espinoza M, Tellez A, Varela T, Suarez F, et al. Impact of a high-risk multimorbidity integrated care implemented at the public health system in Chile. PLoS One. 2022;17(1 January 2022):1\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eVargas I, Barros X, Fern\u0026aacute;ndez MJ, Mayol M. Redise\u0026ntilde;o en el abordaje de personas con multimorbilidad cr\u0026oacute;nica: desde la fragmentaci\u0026oacute;n al cuidado integral centrado en las personas. Rev Med Clin Condes [Internet]. 2021;32(4):400\u0026ndash;13. Available from: https://www.elsevier.es/es-revista-revista-medica-clinica-las-condes-202-articulo-rediseno-el-abordaje-personas-con-S0716864021000651\u003c/li\u003e\n\u003cli\u003eMinisterio de Salud. 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Encuesta Nacional de Salud, ENS 2016-2017: un aporte a la planificaci\u0026oacute;n sanitaria y pol\u0026iacute;ticas p\u0026uacute;blicas en Chile. ARS med. 2018;43(1):30\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eHaworth S, Shungin D, Kwak SY, Kim HY, West NX, Thomas SJ, et al. Tooth loss is a complex measure of oral disease: Determinants and methodological considerations. Community Dent Oral Epidemiol. 2018;46:555\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eRoberto LL, Silveira MF, De Paula AMB, Ferreira E Ferreira E, Martins AMEDBL, Haikal DS ana. Contextual and individual determinants of tooth loss in adults: a multilevel study. BMC Oral Health. 2020;20(1):1\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eJohnston MC, Crilly M, Black C, Prescott GJ, Mercer SW. Defining and measuring multimorbidity: a systematic review of systematic reviews. Eur J Public Heal. 2019 Feb;29(1):182\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eFacultad de Odontolog\u0026iacute;a - Universidad de Chile. Aprobada atenci\u0026oacute;n periodontal para pacientes con Diabetes Mellitus [Internet]. 2023. Available from: https://odontologia.uchile.cl/noticias/211184/aprobada-atencion-periodontal-para-pacientes-con-diabetes-mellitus\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tooth loss, multimorbidity, oral health, epidemiology.","lastPublishedDoi":"10.21203/rs.3.rs-4530535/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4530535/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eOral diseases are a significant global public health challenge. Current evidence indicates that several chronic conditions are individually associated with tooth loss. Currently, people are living with more than one chronic condition, known as multimorbidity. This study aimed to evaluate the association between multimorbidity and tooth loss in the Chilean population, considering the common risk factors for oral and chronic diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eCross-sectional study with secondary data from the latest Chilean National Health Survey (ENS 2016-17). The number of remaining teeth was classified into four groups: functional dentition (≥20 remaining teeth), moderate tooth loss (10 to 19 remaining teeth), severe tooth loss (1 to 9 remaining teeth), and edentulism if there were no remaining teeth. Multimorbidity was defined based on the number of chronic conditions present as a binary variable (MMC≥2) and as a 4-level categorical variable (MMC\u003csub\u003eG0-G3\u003c/sub\u003e). The sample was divided into \u0026lt;65 and ≥65 years for statistical analysis. Mean and SD were calculated for crude and adjusted remaining teeth. Poisson regression models with robust variance, crude and adjusted for sex, age, geographic area, and educational level, were fitted to calculate the prevalence ratio between multimorbidity and tooth loss.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study sample was 4,151 individuals between the ages of 18 and 98. Adults aged \u0026lt;65 years with multimorbidity have a 1.07 times higher prevalence of moderate tooth loss (95% CI 0.84; 1.36), 1.12 times higher prevalence of severe tooth loss (95% CI 0.67; 1.89), and a 0.92 times lower prevalence of edentulism (95% CI 0.39; 2.20). Adults aged ≥65 years with multimorbidity have 1.13 times higher prevalence of moderate tooth loss (95% CI 0.94; 1.37), 1.66 times higher prevalence of severe tooth loss (95% CI 1.04; 2.66), and 1.26 times higher prevalence of edentulism (95% CI 0.76; 2.08).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThere was an association between multimorbidity and tooth loss in the Chilean population, resulting in a higher prevalence of moderate tooth loss, severe tooth loss and edentulism in those with higher number of chronic diseases. 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