The influence of living conditions and individual behaviors on the oral-systemic disease connection: a cross-sectional analysis.

OBJECTIVES: To determine the extent to which living conditions and individual behaviors influence the association between oral health status and systemic disease outcomes in Ontario, Canada's most populated province. METHODS: A secondary data analysis of Ontario data from the Canadian Community. Health Survey 2013/14 was undertaken. Separate analyses were conducted for participants aged 35-59 years (n = 11,858) and 60+ years (n = 11,273). A series of regression models were constructed to examine the association between self-reported oral health status and systemic disease outcomes (arthritis, diabetes, hypertension, heart disease, chronic obstructive pulmonary disease, and stroke). Models were adjusted by proxies of living conditions (income, education, ethnicity, country of birth, employment, and food security) and individual behaviors (smoking status, alcohol use, tooth brushing, life stress, physical activity, sense of belonging). Percent attenuation between models was calculated to determine the extent of the living condition-behavior impact. RESULTS: In both age groups, the prevalence of arthritis and high blood pressure was the highest, followed by heart disease. There was variation in percent attenuation by age group and outcome. Among participants aged 35-59 years, living conditions had a greater impact on the oral-systemic relationship, while individual behaviors played a greater role in this association among adults aged 60+ years. CONCLUSION: There is an association between oral and systemic diseases; however, after accounting for living conditions and individual behaviors, this relationship was attenuated. This highlights the need to address upstream and midstream factors that are common to oral and systemic conditions.

Providing dental insurance can positively impact oral health outcomes in Ontario.

BACKGROUND: Universal coverage for dental care is a topical policy debate across Canada, but the impact of dental insurance on improving oral health-related outcomes remains empirically unexplored in this population. METHODS: We used data on individuals 12 years of age and older from the Canadian Community Health Survey 2013-2014 to estimate the marginal effects (ME) of having dental insurance in Ontario, Canada's most populated province (n = 42,553 representing 11,682,112 Ontarians). ME were derived from multi-variable logistic regression models for dental visiting behaviour and oral health status outcomes. We also investigated the ME of insurance across income, education and age subgroups. RESULTS: Having dental insurance increased the proportion of participants who visited the dentist in the past year (56.6 to 79.4%, ME: 22.8, 95% confidence interval (CI): 20.9-24.7) and who reported very good or excellent oral health (48.3 to 57.9%, ME: 9.6, 95%CI: 7.6-11.5). Compared to the highest income group, having dental insurance had a greater ME for the lowest income groups for dental visiting behaviour: dental visit in the past 12 months (ME highest: 17.9; 95% CI: 15.9-19.8 vs. ME lowest: 27.2; 95% CI: 25.0-29.3) and visiting a dentist only for emergencies (ME highest: -11.5; 95% CI: - 13.2 to - 9.9 vs. ME lowest: -27.2; 95% CI: - 29.5 to - 24.8). CONCLUSIONS: Findings suggest that dental insurance is associated with improved dental visiting behaviours and oral health status outcomes. Policymakers could consider universal dental coverage as a means to support financially vulnerable populations and to reduce oral health disparities between the rich and the poor.

Comparing human resource planning models in dentistry: A case study using Canadian Armed Forces dental clinics.

OBJECTIVES: To compare two methods of allocating general dentists to Canadian Armed Forces (CAF) dental detachments: a dentist-to-population ratio model and a needs-based model. METHODS: Data obtained from CAF sources were analysed to compare models. Times assigned to treatment plan procedures were used as a proxy for treatment needs. Full-time equivalents (FTEs) were used as an indicator for the number of dentists allocated to each detachment. FTE values were adjusted for military dentists to account for time spent on compulsory nonclinical duties. The paired-samples t test was used to assess differences between the models for all clinics (dental detachments) and by clinic size. RESULTS: The dentist-to-population ratio model for the CAF population (n=68 183) estimated an allocation of 83.25 FTE general dentists to CAF dental detachments. Based on a systematic sample of the CAF population (n=2226), the needs-based model estimated the requirement for 64.71 FTE general dentists. The average difference between models was 0.71 FTE (SE=0.273), which was statistically significant (P=0.015). In terms of differences by clinic size, differences were more pronounced in clinics serving more than 4000 CAF personnel (2.63 FTEs, SE=0.613, P=0.008). CONCLUSIONS: The findings reveal differences between estimation models of <1 FTE, with higher estimates produced from the dentist-to-population ratio model. A larger difference was found in clinics with larger populations. The perceived overestimation of dental human resource requirements suggests that changing to a needs-based model may result in cost savings.