Abstract
Objective
We studied the association between the socioeconomic status (SES), tooth loss, and oral health–related quality of life (OHRQoL) in an adult cohort in western China. As socioeconomic inequalities in oral health are often neglected in oral health promotion. we aimed to verify the impact of SES on tooth loss and OHRQoL.
Methods
In all, 348 participants aged 60 years and older were selected for this study. Relationships amongst SES, tooth loss, and OHRQoL were identified by using a structural equation model (SEM).
Results
In the final sample, 312 people were included, and the response rate was 89.7%. The bias-corrected 95% confidence intervals of the total, direct, and indirect effects were (−0.267 to 0.475), (−0.489 to 0.185), and (0.088 to 0.450), respectively. The comparative fit index of SEM was 0.943. The model showed that their SES directly affected tooth loss in the elderly population. This indirectly affects their oral health–related quality of life. The numbers of natural teeth and occlusal units (with standardised path coefficients of 0.79 and 0.74, respectively) were found to be the most significant factors relating to tooth loss.
Conclusion
SES affected the oral health–related quality of life in elderly people through tooth loss in a Chinese study population. Our data suggest that improvements in the social and economic environments are a primary measure that should be implmented to prevent tooth loss and improve the OHRQoL.
Key words: Socioeconomic status, Tooth loss, Oral health–related quality of life, Structured equation model, Elderly population
Introduction
The health of the elderly worldwide population is an ever-growing concern. Tooth loss is one of the most common oral problems associated with ageing.1 It directly results in a reduction of chewing ability2 and can affect oral health and quality of life of the elderly.3, 4, 5 Studies have found that tooth loss is an important factor associated with the reduction of oral health–related quality of life (OHRQoL).6 Moreover, it has been shown that the lower numbers of natural teeth and fewer occluding tooth pairs may lead to a lower OHRQoL.7,8 The dentition defects and edentulous jaws in the elderly have become a recent global public health concern .9,10 Furthermore, there is a very high prevalance of edentulousness in China. The results of the 4th National Oral Health Survey in 2015, in China, showed that only 18.3% of people in the 65–74 age group had complete dentition (excluding third molars). In addition, 47.7% of this age group had unrepaired missing teeth.11 Hence, it is necessary to undertake preventive strategies to reduce tooth loss in the elderly population. The ideal way to achieve this goal is to tackle the root causes of this problem, and several reports cite socioeconomic status as the fundamental cause of poor oral health as well as the root cause of disease occurrence.12
We tested this hypothesis in Guangxi, which is a remote and mountainous region of China with a relatively poor population. The condition of tooth loss amongst the elderly was worse than in other regions of the country. We aim to verify that the socioeconomic inequalities in oral health can be impacted by the socioeconomic status of the elderly as regards tooth loss and OHRQoL. At present, most studies in this field in China are associated with the impact of oral diseases on OHRQoL, with relatively few that describe the socioeconomic factors involved. These studies pointed to some factors associated with socioeconomic status that are related to OHRQoL, and these include an increased income and better oral health services.13,14 Some studies which were mostly found in regions other than China have shown a clear association between socioeconomic status and the prevalence and severity of oral diseases.15,16 A Brazilian study showed that its poor socioenvironmental determinants and lower individual socioeconomic status remained associated with lower OHRQoL, even after adjusting for sociodemographic and oral clinical indicator variables.17 In previous studies, traditional statistical methods were mostly used, which could only simply explore the intuitive influencing factors of OHRQoL and tooth loss. However, structural equation models (SEMs) can overcome these limitations by simultaneously testing multiple variables in the model and allowing for errors in both independent as well as dependent variables.18
Therefore, in this study, the mediating effect test of the SEM was used to conduct a comprehensive analysis of the potential influencing factors and the correlations between various factors of OHRQoL. In addition, we wanted to verify whether socioeconomic status can affect tooth loss and OHRQoL. This study will help to provide a basis for making future policies that will benefit the health of an ageing community.
Methods
Sampling
Elderly people aged 60 and older in Xixiangtang District, Nanning, Guangxi, were enrolled in the study from January 2019 to December 2019. Elderly people with unclear thinking, an inability to answer questions accurately, physical disabilities, and an inability to cooperate with examinations were excluded. Each participated one-on-one with the questioner before the inquiry started and was asked to sign the form, and these were collected together with the questionnaires after completion.
The sample size was estimated according to the formula , where the deff was 1.5. If the confidence level was 95% on both sides, then u = 1.96. The estimated rate of sample size p was 47.7% of the national 65–74 age group for missing teeth in 2015. The allowable sampling error was 15% p and the theoretical sample size was 280 persons.
Two streets were randomly selected from 10 streets in Xixiangtang District, Nanning City, Guangxi, and 6 communities were selected from 23 subordinate communities. The list of eligible elderly people in each community was created and they were divided into 2 groups consisting of men and women; they were subsequently digitally encoded. After entering their data on a computer, 348 people were randomly selected, of whom half were men. This study protocol was reviewed and approved by the Ethics Committee of Guangxi Medical University, approval number 2019005. Written informed consent was obtained from participants to participate in the study prior to their participation. The data generated and analysed during this study are summarised in this article. Further inquiries can be directed to the corresponding author.
Dental examination
Dental examinations were conducted in the community. Dental examinations were conducted by one person and recorded by another person. The dental examiners were the authors and 2 other dental clinical physicians, all of whom have been engaged in oral clinical work for more than 10 years. The recorders were graduate students in the field of dentistry who had received unified training and qualification. They used disposable masks, probes, and tweezers for examination under a head-mounted light source. Tooth loss included the following variables: the number of natural teeth (NT) referred to the number of natural teeth retained in the mouth, excluding residual roots and loose teeth with degree III. The number of NT plus replaced teeth (NPRT) referred to the total number of NT and those artificially retained in the mouth. The number of occluding pairs (OPs) referred to the number of occluding pairs of NT. The number of occluding pairs of NT plus replaced teeth (OPRs) referred to the pairs of maxillary and mandibular teeth in contact during centric occlusions. These include NT and fixed bridge bodies, removable dentures, as well as fixed denture abutment teeth.
When recording the number of OPRs, the removable dentures were placed on the inspection teeth to contact the lower jaw teeth and acted as the reference standard. The number of OPs referred to either one anterior tooth or one canine or one premolar or half molar (either mesial or distal).
Questionnaire
The questionnaires were conducted in a community setting by three investigators who were graduate students majoring in dentistry. The questionnaires were in Chinese (paper format) and were conducted face-to-face by asking the participant each item individually.
The general information collected included age, gender, and marital status. The data regarding socioeconomic status included education background, preretirement occupation, current personal income, and preretirement income. OHRQoL was evaluated using the General Oral Health Assessment Index (GOHAI) scale, which has been translated and validated in Chinese. The scale covered 12 items in 3 dimensions, including physical functions, pain and discomfort in the mouth, as well as psychological function. The 5-grade score was used. Very often = 1 point, often = 2 points, sometimes = 3 points, rarely = 4 points, and none = 5 points. Each question was scored 0 to 5 points, and the total score of the 12 items was added. The higher the total score, the better the OHRQoL of the individual.
Quality control
One of the authors were responsible for the development of research checklists and questionnaires as well as for the training of dental examinations. Experienced teachers in epidemic investigation in our research room were responsible for the training of questioning with respect to the questionnaire. After 2 rounds of training and pre-investigations, the questioning methods and standards of dental examinations were unified.
The three examiners involved all had received a unified training protocol. The kappa value of the standard consistency test was 0.92, and the inspection results were found to be completely reliable. Epidata 3.1 software was used for data entry, and the double entry and double core pair systems were used to ensure data accuracy.
Dependent variables
The main dependent variables that affect the OHRQoLof the elderly are as follows: total score of GOHAI, physical function, pain and discomfort in the mouth, and psychological function. The dependent variables that affect tooth loss in the elderly include NT, NPRT, OPs, and OPRs.
Independent variables
Education background, preretirement occupation, current personal income, and preretirement income were considered as socioeconomic status indicators. Education background was grouped into 3 categories: low level (primary school and below), middle level (junior high school), and high level (technical secondary school, high school, junior college, and above) according to the Chinese education system.Current personal income was grouped into 3 categories: low level (below ¥2000/month), middle level (¥2000–4000/month), and high level (above ¥4000/month). Preretirement income was grouped into 2 categories: low level (¥2000/month and below) and high level (above ¥2000/month). Classification of income was based on pre-survey results and local per capita income.
Tooth loss could serve as both a dependent variable of socioeconomic status and an independent variable that affected the health-related quality of life of elderly people.
Statistical analysis
SPSS v24.0 (IBM) was used for data statistical analysis. The data obtained from the participants were tested for normality by using the Shapiro–Wilk test. The number of teeth lost and GOHAI scale were nonnormally distributed, and these were expressed as medians (M) and interquartile ranges (Q). In univariate analysis, Kruskal–Wallis and Mann–Whitney tests were used to analyse the relationships between socioeconomic status and tooth loss as well as the relationship between socioeconomic status and OHRQoL. The relationship between tooth loss and OHRQoL was also analysed.
Multifactor analysis uses analysis of a moment structure (AMOS) system modelling, and the bootstrap method for path analysis was used to verify the direct and indirect impact of socioeconomic status, tooth loss, and OHRQoL. These criteria were used for the successful fitting of the model.19 The results from the model included the lower and upper limits of the bootstrap confidence intervals corrected for the bias of the mediating effects. If the confidence interval of the indirect effect did not contain a value of 0, then the mediating effect was assumed to be significant.
Results
In all, 348 people participated in this study; 23 of them refused to take part in this study. Thirteen questionnaires and dental examination forms were incomplete, missing, or logically incorrect, leaving 312 valid questionnaires and examination forms. Therefore, 312 people were included in this study, with an effective response rate of 89.7%. The average age of the study participants was 66 years, with 107 (34.3%), 154 (49.4%), and 51 (16.3%) aged 60–64, 65–74, and older than 74, respectively. There were 126 males (40.4%) and 186 females (59.6%). Married people were the majority, accounting for 81.1%. The vast majority of this population had already retired, with only 8.3% continuing to work.
The relationship between socioeconomic status and tooth loss
The participants with low education had fewer NT than those with high education (P < .017). The number of NT for those with high or low education was not different for the group with middle education. There were significant associations between education,preretirement occupations ,and OPRs (P < .05), and manual workers had fewer OPRs than office workers (Table 1).
Table 1.
The relationships between socioeconomic status and tooth loss (N = 312).
| Variable | NT |
NPRT |
OPs |
OPRs |
||||
|---|---|---|---|---|---|---|---|---|
| M(Q) | P value | M(Q) | P value | M(Q) | P value | M(Q) | P value | |
| Education | .020 | .209 | <.001 | .747 | ||||
| Low (primary school and below) | 25.0 (8.0) | 27.0 (3.0) | 11.0 (3.0) | 16.0 (5.00) | ||||
| Middle (junior high school) | 26.0 (6.0) | 27.0 (3.0) | 14.0 (9.3) | 16.0 (5.00) | ||||
| High (high school and above) | 26.0 (3.0) | 27.0 (2.0) | 15.0 (6.9) | 16.0 (4.00) | ||||
| Preretirement occupation | .326 | .771 | .002 | .110 | ||||
| Office worker | 26.0 (4.0) | 27.0 (2.0) | 15.0 (7.0) | 16.0 (4.00) | ||||
| Manual laborer | 26.0 (6.0) | 27.0 (3.0) | 12.5 (9.5) | 16.0 (5.50) | ||||
| Current personal income | .698 | .079 | .237 | .045 | ||||
| Low (<¥2000) | 26.0 (6.0) | 27.0 (2.0) | 13.5 (8.4) | 17.0 (5.5) | ||||
| Medium (¥2000-4000) | 26.0 (6.0) | 27.0 (3.0) | 13.0 (9.0) | 15.5 (6.5) | ||||
| High (>¥4000) | 26.0 (3.8) | 27.0 (2.0) | 15.0 (8.0) | 16.0 (4.0) | ||||
| Preretirement income | .248 | .573 | .069 | .851 | ||||
| Low (¥2000 and below) | 26.0 (6.0) | 27.0 (3.0) | 12.5 (8.5) | 16.0 (5.5) | ||||
| High (>¥2000) | 26.0 (4.0) | 27.0 (3.0) | 15.0 (8.3) | 16.0 (4.3) | ||||
Kruskal–Wallis and Mann–Whitney tests were used.
$1=¥7.28.
NT, number of natural teeth; NPRT, natural teeth + replaced teeth; OPs, occluding pairs of natural teeth; OPRs, occluding pairs of natural + replaced teeth.
The relationship between socioeconomic status and OHRQoL
The scores of physical function in primary school and below were lower than that in junior high school, which meant that the OHRQoL associated with chewing was poor (P < .01). Those with high income before retirement had low scores with respect to psychological discomfort (P < .05). There was no statistical difference between current personal income, preretirement occupation, and GOHAI scores (Table 2).
Table 2.
The relationship between oral health–related quality of life and selected sociodemographic factors and the number of teeth (N = 312).
| Variable(n) | Total score |
Physical function |
Pain and discomfort in the mouth |
Psychological function |
||||
|---|---|---|---|---|---|---|---|---|
| M(Q) | P value | M(Q) | P value | M(Q) | P value | M(Q) | P value | |
| Education | .310 | .040 | .135 | .230 | ||||
| Low (99) | 54.0 (12.0) | 13.0 (6.0) | 10.0 (4.0) | 19.0 (4.0) | ||||
| Medium (93) | 55.0 (10.0) | 15.0 (4.5) | 10.0 (4.0) | 18.0 (4.0) | ||||
| High (120) | 52.5 (8.0) | 14.0 (4.0) | 10.0 (1.0) | 18.0 (5.0) | ||||
| Preretirement occupation | .67 | .956 | .341 | .223 | ||||
| Office workers (113) | 53.0 (8.0) | 14.0 (5.0) | 10.0 (2.0) | 18.0 (4.0) | ||||
| Manual workers (199) | 54.0 (10.0) | 14.0 (5.0) | 10.0 (4.0) | 18.0 (4.0) | ||||
| Current personal income | .782 | .570 | .506 | .104 | ||||
| Low (120) | 53.5 (10.0) | 14.0 (5.0) | 10.0 (4.0) | 18.0 (3.0) | ||||
| Medium (128) | 54.0 (10.0) | 14.0 (5.0) | 10.0 (4.0) | 18.0 (5.0) | ||||
| High (64) | 53.5 (6.8) | 14.0 (4.0) | 10.0 (2.0) | 18.0 (3.0) | ||||
| Preretirement income | .294 | .877 | .312 | .015 | ||||
| Low (211) | 54.0 (10.0) | 14.0 (5.0) | 10.0 (4.0) | 18.0 (4.0) | ||||
| High (101) | 53.0 (8.0) | 14.0 (5.0) | 10.0 (2.0) | 17.0 (4.0) | ||||
| NT | <.001 | <.001 | .698 | .010 | ||||
| 26-28 | 55.0 (7.0) | 19.0 (3.0) | 13.0 (3.0) | 23.0 (3.0) | ||||
| 21-25 | 52.0 (10.0) | 18.0 (5.0) | 13.0 (3.0) | 22.0 (5.0) | ||||
| 0-20 | 51.0 (11.0) | 15.0 (5.0) | 13.0 (4.0) | 22.0 (4.0) | ||||
| OPs | .008 | <.001 | .235 | .068 | ||||
| 16-18 | 55.0 (8.0) | 19.0 (3.0) | 13.0 (3.0) | 23.0 (3.0) | ||||
| 11-15 | 52.0 (10.0) | 18.0 (5.0) | 13.0 (3.0) | 23.0 (5.0) | ||||
| 0-10 | 53.0 (9.5) | 17.0 (6.0) | 13.0 (4.0) | 23.0 (4.0) | ||||
| NPRT | .002 | .001 | .075 | .034 | ||||
| 26-28 | 54.0 (9.0) | 18.0 (2.0) | 13.0 (3.0) | 23.0 (4.0) | ||||
| 21-25 | 52.0 (11.5) | 17.0 (6.0) | 12.0 (3.0) | 23.0 (5.0) | ||||
| 0-20 | 49.0 (9.8) | 14.5 (6.0) | 13.0 (1.5) | 22.0 (3.5) | ||||
| OPRs | .004 | .018 | .031 | .074 | ||||
| 16-18 | 54.0 (9.0) | 18.0 (4.0) | 13.0 (3.0) | 23.0 (4.0) | ||||
| 11-15 | 52.0 (10.0) | 17.0 (6.0) | 13.0 (3.0) | 23.0 (5.0) | ||||
| 0-10 | 53.0 (10.3) | 17.5 (6.0) | 13.0 (3.3) | 22.5 (5.0) | ||||
NT, number of natural teeth; NPRT, natural teeth + replaced teeth; OPs, occluding pairs of natural teeth; OPRs, occluding pairs of natural + replaced teeth.
Kruskal–Wallis and Mann–Whitney tests were used.
Note: Bold values means statistical significance at p < 0.05.
The relationship between tooth loss and OHRQoL
The more NT, the higher the OHRQoL (P < .05). The participants in the group with the largest number of NPRT, OPs, and OPRs had the best OHRQoL, which was reflected in the total GOHAI scores and the physical function dimension (PFD; P < .05). In general, the fewer OPs and NPRT, the lower the PFD scores and the worse the OHRQoL. The participants with OPRs in the middle group had the lowest OHRQoL, and this was reflected in the total GOHAI scores and the PFD (P < .01; Table 2).
SEM model analysis
Using this model, we obtained: χ2/df = 2.749, comparative fit index = 0.943 (this ranges from 0.0 to 1.0 and the closer it is to 0.95, the more it fit), Tucker–Lewis index = 0.920 (a value >0.90 is considered acceptable), and root mean square error of approximation = 0.076 (this is a “badness-of-fit” index where 0 indicates the perfect fit and higher values indicate the lack of fit). These data conformed to the general model fitting standards, with good model fitting and reasonable and reliable results.
Path analysis
By mediating the effect path, it can be seen that socioeconomic status has a significant positive impact on tooth loss (unstandardized coefficients (unstd) = 1.112, z = 2.559, P < .05); that is, the better the socioeconomic status, the more teeth you have. Tooth loss had a significant positive impact on OHRQoL (unstd = 0.202, z = 5.212, P < .001); that is, the more teeth you have, the higher your OHRQoL. Socioeconomic status had no significant impact on OHRQoL (unstd = −0.148, z = −0.709, P > .05; Table 3).
Table 3.
Significance tests of the path data.
| Route | Std | Unstd | SE | z value | P | ||
|---|---|---|---|---|---|---|---|
| Tooth loss | <— | Socioeconomic status | 0.182 | 1.112 | 0.435 | 2.559 | .005 |
| OHRQoL | <— | Tooth loss | 0.404 | 0.202 | 0.039 | 5.212 | <.001 |
| OHRQoL | <— | Socioeconomic status | −0.049 | −0.148 | 0.209 | −0.709 | .567 |
OHRQoL, oral health–related quality of life.
The mediation effect test
The bootstrap confidence interval for the deviation correction of the total and direct effects from the socioeconomic status to OHRQoL was (−0.267 to 0.475) and (−0.489 to 0.185), respectively. Both of these contain zero, indicating that the total and the effects were not significant. The bootstrap confidence interval for the deviation correction of the indirect effect was (0.088 to 0.450) and does not contain zero. This indicated that the mediating effect was significant. This means that tooth loss played a significant and complete mediating role between socioeconomic status and OHRQoL. Therefore, the socioeconomic status indirectly affected the OHRQoL of the elderly population by affecting tooth loss (Table 4).
Table 4.
The mediation effect test based on the bootstrap method
| Effect | Estimated value | Product of coefficient |
Bias-corrected 95% CI |
||
|---|---|---|---|---|---|
| SE | Z | Lower | Upper | ||
| Total effect | |||||
| Socioeconomic status–>OHRQoL | 0.076 | 0.228 | 0.333 | −0.267 | 0.475 |
| Direct effect | |||||
| Socioeconomic status –>OHRQoL | −0.148 | 0.206 | −0.718 | −0.489 | 0.185 |
| Indirect effect | |||||
| Socioeconomic status–>OHRQoL | 0.224 | 0.104 | 2.154 | 0.088 | 0.450 |
The factor contribution of each observation variable to the latent variable was sorted as follows according to their size. The number of NT and OPs (factor contributions of 0.79 and 0.74, respectively) were the most significant influencing factors amongst the latent variables for tooth loss (shown in the Figure).
Fig.
The model adaptability indicators associated with this study.
Discussion
This study found that education and occupation had significant effects on the retention of NT by individuals in a community. In addition, the number of NT possessed by an elderly person with low education and who was a manual worker was markedly smaller than that of someone who was highly educated with a white-collar job, although the actual income earned had no effect on tooth loss. A previous study on elderly Chinese residents showed that the number of teeth lost was related to age, sex, education level, and oral behaviour habits but was not associated with pension and type of residence.9 A Brazilian study found similar results.20 Our study is basically consistent with those results. When NT are used as the evaluation index of tooth loss, although the socioeconomic status was not statistically different, the risk of tooth loss presents as a gradation with respect to lower socioeconomic status. However, when the occluding pairs of natural posterior teeth is used as the evaluation index of tooth loss, the risk of tooth loss in the elderly gradually increases with the occupation and educational background of the person. The risk of tooth loss of low-educated manual workers was greater than that of those who were highly educated office workers. The data show that both occupation and education impact the number of functional teeth retained, which is consistent with reports on the analysis of other factors affecting the number of missing teeth.21,22
The results of GOHAI PFD show that people with junior high school education scored the highest and those with primary school education and below scored the lowest. This indicated that people with junior high school education had higher OHRQoL in terms of chewing function than people with primary school education and below. Two other studies7,23 have also confirmed that there is a clear correlation between social status and quality of life in older persons. For example, the lower the education level, the greater the impact of oral-associated health on their daily lives.24
This study found that there were 4 indicators of tooth loss that were significantly related to OHRQoL. However, although the relationship between the number of NPRT and OHRQoL had an impact, it did not show a gradient performance, and the quality of life of those in the middle group of teeth was the worst. When using NT as the evaluation index, the gradient changes (ie, the more natural teeth a person has, the better OHRQoL, and the more teeth missing from an individual, the worse OHRQoL, which is consistent with a previous Chinese report).25 This may reflect that different evaluation indicators may have different impacts. When the NPRT are used as the tooth loss indicator, this indicates that there were dentures in the study participant at that time. One review found that removable partial dentures rehabilitation appear to improve OHRQoL in the short term up to 6 months, with a very low level of certainty.26 Assuming that the dentures were of high quality, they must be able to restore oral-related functions and improve OHRQoL. However, poor prosthetics may cause a series of complications such as traumatic ulcers, secondary caries, endodontic diseases, periapical diseases, and food impaction. In addition, restorations with poor appearance would also have different degrees of impact on the social psychology of the individual, thereby reducing their OHRQoL.27
By using univariate analysis, it can be seen that the OHRQoL of the people in the middle group with the number of OPs was lower than that of those in the lowest group, which seems to be a contradictory result. However, from the GOHAI dimensional analysis, it can be seen that the fewer the OPs, the lower the scores of physical function, which means the worst chewing ability in these individuals. However, it does not appear that the people with the middle NT and OPs had the worst PFD, because the OHRQoL is a multidimensional evaluation system. It is possible that their quality of life will be affected by pain and psychosocial factors, leading to a certain deviation in the overall evaluation of OHRQoL. Amongst the 3 dimensions for evaluating OHRQoL, the influence of tooth loss on functional limitations and psychological discomfort was particularly obvious, although the pain discomfort dimension has no effect. It is well known that oral pain is mainly caused by caries, periodontal disease, and mucosal disease, so tooth loss has no direct relationship with oral pain. Therefore, the impact of tooth loss on OHRQoL is mainly manifested through functional limitations and psychological discomfort. This shows that tooth loss has a self-evident impact on elderly person's well-being, reflecting their psychological and social status within a society.
Amongst oral diseases, caries and periodontal disease are the 2 main causes of tooth loss, and the best way to prevent them is to solve the problems by using an “upstream” approach to their initiation. By this, we mean that we should be looking for the root causes of the disease. The behaviour of individuals is shaped by the societies and economies in which they operate. A poor childhood, overcrowded living conditions, and unemployment are all detrimental to health and healthy behaviour. In the final analysis, health is determined by socioeconomic factors. A person's birth situation, growth, life, work, housing, income, occupation, life, and the economic policy of the country one lives in can all be regarded as “social risk factors for diseases.”28 The results of the study also verified this. Improvements in the socioeconomic status of a population, trying to balance the polarisation within a society, and eliminating health inequalities by formulating relevant policies to increase income for the elderly are all worthy goals.
Strengths and limitations
A major strength of our study was that it not only clearly and accurately measured that tooth loss played a complete mediating role between socioeconomic status and OHRQoL by using SEM path analysis and the mediation effect test but also accurately estimated the contributions of the associated factors as well as the measurement of the possible errors involved. It also clarified that the number of NT and the number of teeth occlusions were the decisive factors in tooth loss. This study provides few observational variable indicators for SEMs, and it is not comprehensive enough in reflecting the relationship amongst socioeconomic status, tooth loss, and OHRQoL.
Conclusions
The socioeconomic status of elderly is likely to affect their OHRQoL through tooth loss. Our data form a Chinese cohort suggest that improvements in the social and economic environments are the primary measures to prevent tooth loss and improve the OHRQoL. In essence, equity in education and employment for citizens in a country would be beneficial for all. In addition, the inclusion of oral health services in general medical insurance programs would benefit the elderly to improve their oral conditions. This study shows the power of using simple assessments to gauge the impact of health in a population and should encourage similar larger studies.
Author contributions
Yanjun Lyu was responsible for the conception, design, data collection, and drafting of the work. Shaoyong Chen was responsible for the drafting of the manuscript and data analyses. Andi Li and Tingting Zhang were responsible for the data collection and data analyses. Xiaojuan Zeng performed critical revisions of the manuscript. Suren Rao Sooranna was responsible for the revisions of the manuscript.
Conflict of interest
None disclosed.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Footnotes
SOCIOECONOMIC STATUS AND TOOTH LOSS IMPACT
Author Suren Rao Sooranna is also based at the following institution: Life Science and Clinical Research Center Youjiang Medical University for Nationalities 18 Zhongshan Road II Baise 533000 Guangxi China.
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