407 resultados para preventive dental care


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Early diagnosis of melanoma leads to the best prognosis for patients and may be more likely achieved when those who are at high risk for melanoma undergo regular and systematic monitoring. However, many people rarely or never see a dermatologist. Risk prediction models (recently reviewed by Usher-Smith et al ) could assist to triage people into preventive care appropriate for their risk profile. Most risk prediction models contain measures of phenotype including skin, eye and hair colour as well as genetic mutations. Almost all also contain the number and size of naevi, as well as the presence of naevi with atypical features which are independently associated with melanoma risk. In the absence of formal population-based screening programs for melanoma in most countries worldwide, people with high risk phenotypes may need to consider regular monitoring or self-monitoring of their naevi , especially since the vast majority of melanomas are found by people themselves or their friend and relatives. Another group of patients that will require regular monitoring are patients who have been successfully treated for their first melanoma, whose risk to develop a second melanoma is greatly increased . In a US study of 89,515 melanoma survivors those with a previous diagnosis of melanoma had a 9-fold increased risk of developing subsequent melanoma compared with the general population, equating to a rate of 3.76 per 1000 person-years, while in an Australian study, risk of subsequent melanoma was 6 per 1000 person-years. Regular follow-up is therefore essential for melanoma survivors, especially during the first few years after initial melanoma diagnosis.

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For frail older people, admission to hospital is an opportunity to review the indications for specific medications. This research investigates prescribing for 206 older people discharged into residential aged care facilities from 11 acute care hospitals in Australia. Patients had multiple comorbidities (mean 6), high levels of dependency, and were prescribed a mean of 7.2 regular medications at admission to hospital and 8.1 medications on discharge, with hyper-polypharmacy (≥10 drugs) increasing from 24.3% to 32.5%. Many drugs were preventive medications whose time until benefit was likely to exceed the expected lifespan. In summary, frail patients continue to be exposed to extensive polypharmacy and medications with uncertain risk–benefit ratio.

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Background Expenditure on dental and oral health services in Australia is $3.4 billion AUD annually. This is the sixth highest health cost and accounts for 7 % of total national health expenditure. Approximately 49 % of Australian children aged 6 years have caries experience in their deciduous teeth and this is rising. The aetiology of dental caries involves a complex interplay of individual, behavioural, social, economic, political and environmental conditions, and there is increasing interest in genetic predisposition and epigenetic modification. Methods The Oral Health Sub-study; a cross sectional study of a birth cohort began in November 2012 by examining mothers and their children who were six years old by the time of initiation of the study, which is ongoing. Data from detailed questionnaires of families from birth onwards and data on mothers’ knowledge, attitudes and practices towards oral health collected at the time of clinical examination are used. Subjects’ height, weight and mid-waist circumference are taken and Body Mass Index (BMI) computed, using an electronic Bio-Impedance balance. Dental caries experience is scored using the International Caries Detection and Assessment System (ICDAS). Saliva is collected for physiological measures. Salivary Deoxyribose Nucleic Acid (DNA) is extracted for genetic studies including epigenetics using the SeqCap Epi Enrichment Kit. Targets of interest are being confirmed by pyrosequencing to identify potential epigenetic markers of caries risk. Discussion This study will examine a wide range of potential determinants for childhood dental caries and evaluate inter-relationships amongst them. The findings will provide an evidence base to plan and implement improved preventive strategies.

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The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.

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A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.