Background & Aims
Non-odontogenic oral pain is prevalent in the elderly population (1) and has huge impacts on individuals’ quality of life (2). Non-odontogenic oral pain is usually persistent and accompanied with psychosocial distress and sleep-related problems, which further complicate pain management (3). The relationship between non-odontogenic oral pain and objective laboratory measurements, however, has not been well documented.
This study aimed to identify the correlates of non-odontogenic oral pain in an elderly non-dentate population and subgroup the population based on the pattern of oral pain and its correlates.
Methods
Completely non-dentate individuals, between 60 and 79 years of age, were identified from the publicly available dataset collected from 2017 to 2020 March (pre-COVID-19 pandemic) as part of the National Health and Nutrition Examination Survey (NHANES) (4). We retrieved subjects’ features including demographic, questionnaire, examination and laboratory data. Association/correlation between oral pain and 46 other features were determined using appropriate statistical tests. Significant difference was recognised when p value <0.05. Clustering was performed to identify subgroups based on the pattern of oral pain and its correlated features. Python version 3.8 was used for cluster analysis, which was performed using the K-prototypes algorithm, a distance- and matching dissimilarity-based clustering method appropriate for data with mixed numerical and categorical features (5). We used the open-source K-prototypes algorithm from the kmodes module (https://pypi.org/project/kmodes/).
Results
A total of 333 completely non-dentate subjects were identified from the dataset. The average age was 69 years (SD=5.6) and 55% were male.
The Pearson’s chi-squared test showed that having been told to take daily low-dose aspirin was significantly associated with oral pain (p=0.02).
The Spearman’s test showed that oral pain was positively correlated with depression (p=0.01) and daytime sleepiness (p=0.02). Oral pain was negatively correlated with diastolic blood pressure (p=0.025), red blood cell count (p=0.003), haemoglobin (p=0.009) and haematocrit (p=0.009).
Six was determined by the elbow method as the most appropriate number of clusters. Among the six clusters, one cluster with high degree of oral pain concomitant with mild depression, moderate daytime sleepiness and having been told to take daily low-dose aspirin was identified (n=32). The red blood cell count (4,600 cells/microL), haemoglobin (13.4 g/dL) and haematocrit (40.5%) of this cluster were below the normative median.
Conclusions
This study utilised a data mining approach to identify correlated features of oral pain and their patterns in a population of elderly edentulous subjects. Approximately 10% of the non-dentate elderly population reported having experienced a high degree of oral pain. Subjects with oral pain were more likely to have been told to take daily low-dose aspirin and have some degree of depression and daytime sleepiness. They, however, tended to have lower diastolic blood pressure, red blood cell count, haemoglobin and haematocrit.
References
1.Derafshi R, Rezazadeh F, Ghapanchi J, Basandeh Sharif D, Farzin M. Prevalence of Chronic Orofacial Pain in Elderly Patients Referred to Shiraz Dental School From 2005 to 2017. Anesth Pain Med. 2019;9(6):e91182.
2.Shueb SS, Nixdorf DR, John MT, Alonso BF, Durham J. What is the impact of acute and chronic orofacial pain on quality of life? J Dent. 2015;43(10):1203-10.
3.Galli F, Lodi G, Sardella A, Vegni E. Role of psychological factors in burning mouth syndrome: A systematic review and meta-analysis. Cephalalgia. 2017;37(3):265-77.
4.National Center for Health Statistics. National Health and Nutrition Examination Survey: NHANES 2017-March 2020 Pre-pandemic 2021 [Available from: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?Cycle=2017-2020.
5.Preud’homme G, Duarte K, Dalleau K, Lacomblez C, Bresso E, Smaïl-Tabbone M, et al. Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark. Sci Rep. 2021;11(1):4202.