Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records

An Update on the Interdisciplinary Dental Care Approach for Geriatric Diabetic Patients

Diabetes mellitus is a prevalent health issue escalating worldwide that gives rise to numerous problems. Periodontal disorders are recognized as the sixth consequence associated with diabetes mellitus. Research shows that dental health affects overall health, and this knowledge is changing the dental field. The correct choice of glucose goal levels and the optimal selection of glucose-lowering medications are determined by a comprehensive geriatric assessment, an estimate of life expectancy, and a rationale for therapy at regular intervals in elderly diabetics. This article provides an overview of the correlation between diabetes and oral health, with a specific emphasis on xerostomia, periodontal disease, and dental caries. Thus, dentists play a significant role within the allied health profession by contributing to the provision of oral care for those diagnosed with diabetes, with a special focus on geriatric patients.

Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England

BACKGROUND

Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions.

METHODS AND FINDINGS

We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results.

CONCLUSIONS

Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.