Tooth Loss and Associated Factors in Mexican Older Adults in Nursing Homes: A Multicenter Cross-Sectional Study.
Jesús Alberto Rocha-OrtizSandra Manuela Tepox-PugaSocorro Aída Borges-YáñezMartha Mendoza-RodríguezMauricio Escoffié-RamírezMirna Minaya-SánchezJuan Fernando Casanova-RosadoAlejandro José Casanova-RosadoAmérica Patrícia Pontigo-LoyolaCarlo Eduardo Medina-SolísPublished in: BioMed research international (2023)
The objective of this study was to determine the experience of tooth loss and associated factors in older adults and elderly residing in nursing homes. A cross-sectional study was conducted in Mexican older adults and elderly aged ≥60 years living in four nursing homes (two in Mexico City, Mexico: one in Cuernavaca, Morelos, and one in Oaxaca, Oaxaca). The data were collected at the facility (home nursing) by two dentists in 2019. To determine the number of tooth loss and DMFT, a clinical oral examination was performed. In addition, a questionnaire was applied to determine diverse independent variables (demographic, socioeconomic, and behavioral). The analysis was performed using nonparametric tests and negative binomial regression ( p < 0.05). 257 subjects were included. The mean age was 81.25 ± 9.02 years, and 60.7% were women. The mean number of lost teeth was 18.78 ± 9.05 (women = 19.43 ± 8.59 and men = 17.77 ± 9.68; p > 0.05). In the multivariate negative binomial regression model, it was found that, for each one-year increase in age, the mean tooth loss increased 0.92% ( p < 0.05). In current smokers ( p < 0.01) and in those who brush their teeth < 2 times a day ( p < 0.01), the average of tooth loss increased 22.04% and 61.46%, respectively. The experience of tooth loss in Mexican older adults and elderly was high. Demographic (age) and habit of behavior (tobacco use and less frequent tooth brushing) were associated with increased tooth loss. It is important to promote oral health programs for institutionalized older adults.
Keyphrases
- physical activity
- polycystic ovary syndrome
- middle aged
- healthcare
- mental health
- oral health
- clinical trial
- risk factors
- type diabetes
- public health
- metabolic syndrome
- machine learning
- smoking cessation
- insulin resistance
- quality improvement
- deep learning
- skeletal muscle
- pregnancy outcomes
- electronic health record
- big data