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Introduction to the management station
Published in Sukhpreet Singh Dubb, Core Surgical Training Interviews, 2020
The attributable risk represents the risk that is due to the exposure being investigated. It demonstrates how much greater the frequency of a disease in an exposure group is compared to an unexposed. This importantly assumes that the exposure is causal for a disease.
The Cerebral Palsies
Published in Michele Kiely, Reproductive and Perinatal Epidemiology, 2019
Susser et al.16 have pointed out that the strength of associations between specific factors and the risk of cerebral palsy can be expressed in two different ways. One is the relative risk of cerebral palsy after being exposed to a specific factor compared with not being so exposed. The second is the population attributable risk — namely the proportion of cases attributable to that factor only, after allowing for the frequency with which the factor was present in exposed but unaffected individuals.
Statistics You Need
Published in Saif Aldeen Saleh AlRyalat, Shaher Momani, A Beginner's Guide to Using Open Access Data, 2019
The cohort type of study follows a group of patients with certain risk factors to observe disease. It assesses a single risk factor that may be related to many diseases. The outcomes expected from this type of study are relative risk (RR), attributable risk (AR), and incidence (as discussed later). Relative risk (RR) is expressed by the comparative probability question, “How much more likely is the exposed person going to get the disease compared to the nonexposed?” Attributable risk (AR) addresses the comparative probability question, “How many more cases in an exposed group compared to nonexposed group?” (Bigby, 2000).
Breaking down barriers for prescribing and using hormone therapy for the treatment of menopausal symptoms: an experts’ perspective
Published in Expert Review of Clinical Pharmacology, 2023
Serge Rozenberg, Nick Panay, Marco Gambacciani, Antonio Cano, Sarah Gray, Katrin Schaudig
The existence of misleading information and out-of-date data about breast cancer or cardiovascular disease (CVD) in scientific and nonscientific journals has resulted in an increasing concern among many physicians, leading to excessive fear of prescribing. There is a misperception that MHT increases all cancers, while some cancer risks are not increased or even decreased in some patients (e.g. colon cancer, endometrium cancer in obese women) [5,18]. Moreover, the risk of breast cancer is overestimated both by HCPs and by patients. In this line, it is of high importance to differentiate between relative risk and attributable risk, since only the latter is an expression of the added risk for a patient [8,19]. Relative risk estimates should, therefore, not be used when communicating with patients. As an example, the attributable risk of breast cancer in 1000 women using MHT for 5 years was 20 additional cases study from the Breast Cancer Collaborative Group using estrogen-progestogen regimen and five extra cases while using estrogen-only -therapy [8].
How much would low- and middle-income countries benefit from addressing the key risk factors of road traffic injuries?
Published in International Journal of Injury Control and Safety Promotion, 2020
Kavi Bhalla, Dinesh Mohan, Brian O’Neill
We operationalize this framework in a Road Safety “Calculator” programmed in Stata. The Calculator is a simple analytical tool that can be used by researchers and policy makers to assess how various road safety interventions affect road traffic deaths and injuries. The overall framework of the Calculator is illustrated in Figure 1. It has two key modules. The first module (Figure 2) estimates the population-level burden of injuries (years of life lost, YLLs, years lived with disability, YLDs, and disability adjusted years of life lost, DALYs) using a simplified version of the methodology used by the GBD study while retaining the features most important for estimating the burden of injuries. We have described details of this module in a previous publication (Bhalla & Harrison, 2016). The effect of interventions (or modifying a risk factor) is assessed using an attributable-risk module (Figure 3), which implements Equations 1 & 2, and computes the expected proportional reduction in mortality and burden if the exposure to a risk factor was reduced to an alternative (counterfactual) distribution.
Estimating the avoidable burden and population attributable fraction of human risk factors of road traffic injuries in iran: application of penalization, bias reduction and sparse data analysis
Published in International Journal of Injury Control and Safety Promotion, 2019
Mahmood Bakhtiyari, Mohammad Reza Mehmandar, Mehdi Khezeli, Arman Latifi, Touraj Ahmadi Jouybari, Mohammad Ali Mansournia
According Table 4, the highest population attributable fraction, when keeping other risk factors constant in the society, was related to fatigue and drowsiness, and followed by overspeeding. This means that there was a 25% decrease in the deaths of drivers after eliminating the risk factor of fatigue and drowsiness in the drivers’ population. Furthermore, the total attributable risk of the most important risk factors studied in the research was about 56%. This means that appropriate review and legislation for these risk factors would decrease half of such deaths. Given the fact that fastening seatbelt by driver or passenger was not associated with the deaths of pedestrians, it appears necessary to eliminate 18 cases of accident, in which pedestrians were involved, from the analyses. After crossing out these cases and disregarding 4 deaths occurred among pedestrians, the total common impact of risk factors increased to 60%.