Lifestyle Factors and Chronic Diseases: Application of a Composite Risk Index

Meng, Lingfei; Maskarinec, Gertraud; Lee, James; Kolonel, Laurence N.

doi:10.1006/pmed.1999.0538

Cited by 93 publications

(74 citation statements)

References 14 publications

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“…The evidence indicates that healthy behaviours are associated with lower mortality and a lower risk of chronic disease. 36,37 Having two risk factors as opposed to only one increases the risk of death substantially. 38 Risky behaviours related to lifestyle, always hard to change without suitable interventions, are usually established in childhood and often persist in adulthood.…”

Section: Researchmentioning

confidence: 99%

Risk factors for noncommunicable chronic diseases in women in China: surveillance efforts

Wang

Jiang

et al. 2013

Bull. World Health Organ.

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Section: Researchmentioning

confidence: 99%

Risk factors for noncommunicable chronic diseases in women in China: surveillance efforts

Wang

Jiang

et al. 2013

Bull. World Health Organ.

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“…The fact that some unhealthy behaviours may interact, producing an even greater risk than if the individual risks are added together (11,12) , may be an important issue to consider in health promotion (7) . Furthermore, as Hu pointed out in 2002 (13) , with regard to nutritional epidemiology and the conceptual and methodological limitations of studying a single food or nutrient as a risk factor, we may highlight the following drawbacks to studying a single behavioural risk factor in contrast to studying behavioural patterns.…”

mentioning

confidence: 99%

Behavioural patterns of protective and risk factors for non-communicable diseases in Brazil

Steele

Claro

2013

Public Health Nutr.

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Objective: To identify behavioural patterns of protective and risk factors for non-communicable diseases (NCD) and to explore the association between these patterns and sociodemographic characteristics. Design: Principal component analysis was used to identify behavioural patterns from a list of twelve protective and risk factors for NCD. Linear regression was used to explore the association between the patterns and sociodemographic characteristics. Setting: Participants from the Brazilian Surveillance System of Risk and Protective Factors for NCD through Telephone Interviews (VIGITEL) from the years 2009 and 2010 were included. Subjects: A sample of 108 706 adults was included in the analysis. Results: Two behavioural patterns were identified in the analysis, a 'prudent pattern' and a 'risky pattern'. The first involved mostly protective behaviours, while the second one involved essentially the risky ones. Both the less prudent and the more risky behaviour patterns were concentrated in younger men, with lower education, from the more developed region. Conclusions: Public policies to decrease NCD should be aware of the possible tendency towards behavioural patterns in order to be more effective. Keywords Behavioural patterns Principal component analysis Brazil VIGITEL EpidemiologyNon-communicable diseases (NCD) play an important role in the morbidity and mortality of human populations both worldwide and in Brazil. According to the WHO, NCD were responsible for 58?5 % of all deaths worldwide and for 45?9 % of the global burden of disease in 2001(1) . In Brazil in 2004, NCD were the primary cause of 62?8 % of all deaths with known cause (2) . Furthermore, the proportion of deaths due to NCD in the Brazilian state capitals has more than tripled between the 1930s and the 1990s (3) . It has been estimated that in Brazil, NCD were responsible for 59 % of the total years of life lost due to premature death and for 66?3 % of disability-adjusted life years in 1998 (3)

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“…More precisely, we quantify risk using a composite risk index in a similar manner to Meng et al (1999). We differ from Sumner (2009) in that we use the composite risk index approach to quantify the risk index of subsystem so that the IT system managers identify which systems require enhanced security measures.…”

Section: It Risk Managementmentioning

confidence: 99%

“…Similarly to Meng et al (1999), the module's Risk Factor is a composite risk index. Specifically, it is modelled as the product of its Impact and Probability:…”

Section: Phase 2: Modulesmentioning

confidence: 99%

Identifying Security Risk Modules in Information Systems

Dreyfuss¹,

Giat²

2016

InSITE Conference

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We develop a two-stage model for identifying IT system modules with high security risks. In the first phase, we identify the subsystems that pose the highest risk and which require further investigation. In the next phase, we identify the high-security-risk modules using a more detailed approach. The output of this model helps managers decide on how to invest efficiently in improving the security of their IT system. We describe an application of this model to an IT system in an academic institution in Israel. In the first phase, three of ten subsystems are found to be very risky. In the next phase, we highlight the critical modules within those subsystems. The results of our application in the academic institution indicate that security breaches for the purpose of cheating are a greater threat than other types of security issues.

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Lifestyle Factors and Chronic Diseases: Application of a Composite Risk Index

Cited by 93 publications

References 14 publications

Risk factors for noncommunicable chronic diseases in women in China: surveillance efforts

Risk factors for noncommunicable chronic diseases in women in China: surveillance efforts

Behavioural patterns of protective and risk factors for non-communicable diseases in Brazil

Identifying Security Risk Modules in Information Systems

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