Association of tooth location, occlusal support and chewing ability with cognitive decline and incident dementia.

AIM: Emerging evidence suggests association of tooth loss with impaired cognition. However, the differential effects of anterior versus posterior tooth loss, occlusal support loss and chewing ability are not considered comprehensively. MATERIALS AND METHODS: We conducted cross-sectional (N = 4036) and longitudinal analyses (N = 2787) on data from Health 2000 and 2011 Surveys for associations of posterior occlusal support loss, anterior versus posterior tooth loss, and chewing ability with baseline cognition and 11-year cognitive decline. Additionally, 15-year incident dementia risk was investigated (N = 4073). RESULTS: After considering relevant confounders and potential reverse causality bias, posterior occlusal support loss significantly increased dementia risk across all categories indicative of posterior occlusal support loss (hazard ratios [HRs] between 1.99 and 2.89). Bilateral inadequate posterior occlusal support was associated with 11-year decline in overall cognition (odds ratio [OR] = 1.48:1.00-2.19), and unilateral inadequate posterior occlusal support with total immediate (OR = 1.62:1.14-2.30) and delayed recall decline (OR = 1.45:1.03-2.05). Moreover, posterior tooth loss was associated with dementia (HR = 2.23:1.27-3.91) and chewing ability with total immediate decline (OR = 1.80:1.04-3.13). CONCLUSIONS: Posterior tooth and occlusal support loss significantly increases dementia risk. The impact of posterior occlusal support loss appears to be dose-dependent, and this effect is distinct from that of dentures. Dental healthcare services should be particularly attentive to the state of posterior dentition. Further studies exploring possible mechanisms are warranted.

Association of tooth count with cognitive decline and dementia in the Finnish adult population.

AIM: To evaluate whether tooth loss is associated with cognitive decline and incident dementia. MATERIALS AND METHODS: We analysed data from the Finnish population-based Health 2000 and follow-up Health 2011 surveys (participants aged ≥30 years and without dementia at baseline; N = 5506 at baseline and 3426 at 11-year follow-up). Dementia diagnoses until 2015 were ascertained from national registers (N = 5542). Tooth count was dichotomized as adequate (≥20) versus tooth loss (<20). Tooth loss was further stratified into 10-19 teeth, 1-9 teeth and edentulism. Upper and lower jaws were also considered separately. Baseline cognitive test scores were dichotomized by median as high versus low, and 11-year change as decline versus no decline. RESULTS: Tooth loss (<20) was associated with lower baseline overall cognition (odds ratio [OR] = 1.21, 95% confidence interval [CI] = 1.03-1.43), 11-year cognitive decline (OR = 1.30, 95% CI = 1.05-1.70) and higher 15-year dementia risk (hazard ratio = 1.52, 95% CI = 1.15-2.02) after adjusting for multiple confounders. After adjustment for dentures, associations became non-significant, except for 10-19 teeth remaining and dementia. Results were similar after considering reverse causality bias; however, 10-19 teeth remaining was significantly associated with 11-year cognitive decline even after adjustment for dentures. No jaw-specific differences were observed. CONCLUSIONS: Tooth loss adversely impacts the risk of cognitive decline and dementia. The role of dentures should be further explored.

Periodontal health, cognitive decline, and dementia: A systematic review and meta-analysis of longitudinal studies.

BACKGROUND: Emerging evidence indicates that poor periodontal health adversely impacts cognition. This review examined the available longitudinal evidence concerning the effect of poor periodontal health on cognitive decline and dementia. METHODS: Comprehensive literature search was conducted on five electronic databases for relevant studies published until April 2022. Longitudinal studies having periodontal health as exposure and cognitive decline and/or dementia as outcomes were considered. Random effects pooled estimates and 95% confidence intervals were generated (pooled odds ratio for cognitive decline and hazards ratio for dementia) to assess whether poor periodontal health increases the risk of cognitive decline and dementia. Heterogeneity between studies was estimated by I2 and the quality of available evidence was assessed through quality assessment criteria. RESULTS: Adopted search strategy produced 2132 studies for cognitive decline and 2023 for dementia, from which 47 studies (24 for cognitive decline and 23 for dementia) were included in this review. Poor periodontal health (reflected by having periodontitis, tooth loss, deep periodontal pockets, or alveolar bone loss) was associated with both cognitive decline (OR = 1.23; 1.05-1.44) and dementia (HR = 1.21; 1.07-1.38). Further analysis, based on measures of periodontal assessment, found tooth loss to independently increase the risk of both cognitive decline (OR = 1.23; 1.09-1.39) and dementia (HR = 1.13; 1.04-1.23). Stratified analysis based on the extent of tooth loss indicated partial tooth loss to be important for cognitive decline (OR = 1.50; 1.02-2.23) and complete tooth loss for dementia (HR = 1.23; 1.05-1.45). However, the overall quality of evidence was low, and associations were at least partly due to reverse causality. CONCLUSIONS: Poor periodontal health and tooth loss appear to increase the risk of both cognitive decline and dementia. However, the available evidence is limited (e.g., highly heterogenous, lacking robust methodology) to draw firm conclusions. Further well-designed studies involving standardized periodontal and cognitive health assessment and addressing reverse causality are highly warranted.

Representative estimates of COVID-19 infection fatality rates from four locations in India: cross-sectional study.

OBJECTIVES: To estimate age-specific and sex-specific mortality risk among all SARS-CoV-2 infections in four settings in India, a major lower-middle-income country and to compare age trends in mortality with similar estimates in high-income countries. DESIGN: Cross-sectional study. SETTING: India, multiple regions representing combined population >150 million. PARTICIPANTS: Aggregate infection counts were drawn from four large population-representative prevalence/seroprevalence surveys. Data on corresponding number of deaths were drawn from official government reports of confirmed SARS-CoV-2 deaths. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was age-specific and sex-specific infection fatality rate (IFR), estimated as the number of confirmed deaths per infection. The secondary outcome was the slope of the IFR-by-age function, representing increased risk associated with age. RESULTS: Among males aged 50-89, measured IFR was 0.12% in Karnataka (95% CI 0.09% to 0.15%), 0.42% in Tamil Nadu (95% CI 0.39% to 0.45%), 0.53% in Mumbai (95% CI 0.52% to 0.54%) and an imprecise 5.64% (95% CI 0% to 11.16%) among migrants returning to Bihar. Estimated IFR was approximately twice as high for males as for females, heterogeneous across contexts and rose less dramatically at older ages compared with similar studies in high-income countries. CONCLUSIONS: Estimated age-specific IFRs during the first wave varied substantially across India. While estimated IFRs in Mumbai, Karnataka and Tamil Nadu were considerably lower than comparable estimates from high-income countries, adjustment for under-reporting based on crude estimates of excess mortality puts them almost exactly equal with higher-income country benchmarks. In a marginalised migrant population, estimated IFRs were much higher than in other contexts around the world. Estimated IFRs suggest that the elderly in India are at an advantage relative to peers in high-income countries. Our findings suggest that the standard estimation approach may substantially underestimate IFR in low-income settings due to under-reporting of COVID-19 deaths, and that COVID-19 IFRs may be similar in low-income and high-income settings.

COVID-19 mortality effects of underlying health conditions in India: a modelling study.

OBJECTIVE: To model how known COVID-19 comorbidities affect mortality rates and the age distribution of mortality in a large lower-middle-income country (India), and to identify which health conditions drive differences with high-income countries. DESIGN: Modelling study. SETTING: England and India. PARTICIPANTS: Individual data were obtained from the fourth round of the District Level Household Survey and Annual Health Survey in India, and aggregate data were obtained from the Health Survey for England and the Global Burden of Disease, Risk Factors and Injuries Studies. MAIN OUTCOME MEASURES: The primary outcome was the modelled age-specific mortality in each country due to each COVID-19 mortality risk factor (diabetes, hypertension, obesity and respiratory illness, among others). The change in overall mortality and in the share of deaths under age 60 from the combination of risk factors was estimated in each country. RESULTS: Relative to England, Indians have higher rates of diabetes (10.6% vs 8.5%) and chronic respiratory disease (4.8% vs 2.5%), and lower rates of obesity (4.4% vs 27.9%), chronic heart disease (4.4% vs 5.9%) and cancer (0.3% vs 2.8%). Population COVID-19 mortality in India, relative to England, is most increased by uncontrolled diabetes (+5.67%) and chronic respiratory disease (+1.88%), and most reduced by obesity (-5.47%), cancer (-3.65%) and chronic heart disease (-1.20%). Comorbidities were associated with a 6.26% lower risk of mortality in India compared with England. Demographics and population health explain a third of the difference in share of deaths under age 60 between the two countries. CONCLUSIONS: Known COVID-19 health risk factors are not expected to have a large effect on mortality or its age distribution in India relative to England. The high share of COVID-19 deaths from people under age 60 in low- and middle-income countries (LMICs) remains unexplained. Understanding the mortality risk associated with health conditions prevalent in LMICs, such as malnutrition and HIV/AIDS, is essential for understanding differential mortality.