Association between non-registration of chronic kidney disease and mortality and cardiovascular outcome: a time-to-event analysis of retrospective primary care data.

OBJECTIVE: Patients with impaired kidney function and increased albuminuria are at risk of developing cardiovascular disease (CVD). Previous research has revealed that a substantial proportion of patients with chronic kidney disease (CKD) do not get a registered diagnosis in the electronic health record of the general practitioner. The aim of this study was to investigate the association between non-registration of CKD and all-cause mortality and cardiovascular outcome. DESIGN AND SETTING: A retrospective study in primary care. METHODS: The analyses were carried out in the INTEGO database, a general practice-based morbidity registration network in Flanders, Belgium. The study used INTEGO data from the year 2018 for all patients ≥18 years old, including 10 551 patients. To assess the risk of mortality and CVD, a time-to-event analysis was performed. Cox proportional hazard model was used to evaluate the association between non-registration and incidence of all-cause mortality and cardiovascular events with mortality as a competing risk. Subgroup analyses were performed for estimated glomerular filtration rate stages (3A, 3B, 4 and 5). Multiple imputation was done following the methodology of Mamouris et al. RESULTS: Mortality was higher in patients with non-registered CKD compared with patients with registered CKD (HR 1.29, 95% CI 1.19 to 1.41). Non-registration of CKD was not associated with an increased risk for the development of CVD (HR 0.92, 95% CI 0.77 to 1.11). CONCLUSION: An association between non-registration and all-cause mortality was identified, although no such association was apparent for CVD.

Determining factors associated with cholera disease in Ethiopia using Bayesian hierarchical modeling.

BACKGROUND: Cholera is a diarrheal disease caused by infection of the intestine with the gram-negative bacteria Vibrio cholera. It is caused by the ingestion of food or water and infected all age groups. This study aimed at identifying risk factors associated with cholera disease in Ethiopia using the Bayesian hierarchical model. METHODS: The study was conducted in Ethiopia across regions and this study used secondary data obtained from the Ethiopian public health institute. Latent Gaussian models were used in this study; which is a group of models that contains most statistical models used in practice. The posterior marginal distribution of the Latent Gaussian models with different priors is determined by R-Integrated Nested Laplace Approximation. RESULTS: There were 2790 cholera patients in Ethiopia across the regions. There were 81.61% of patients are survived from cholera outbreak disease and the rest 18.39% have died. There was 39% variation across the region in Ethiopia. Latent Gaussian models including random and fixed effects with standard priors were the best model to fit the data based on deviance. The odds of surviving from cholera outbreak disease for inpatient status are 0.609 times less than the outpatient status. CONCLUSIONS: The authors conclude that the fitted latent Gaussian models indicate the predictor variables; admission status, aged between 15 and 44, another sick person in a family, dehydration status, oral rehydration salt, intravenous, and antibiotics were significantly associated with cholera outbreak disease.