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Validation of a new predictive risk model: measuring the impact of the major modifiable risks of death for patients and populations.

Lim SS, Carnahan E, Nelson EC, Gillespie CW, Mokdad AH, Murray CJ, Fisher ES - Popul Health Metr (2015)

Bottom Line: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions.We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006.The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health.

View Article: PubMed Central - PubMed

Affiliation: Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA 98121 USA.

ABSTRACT

Background: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions.

Methods: We used data on the distribution of exposure to 12 major behavioral and biometric risk factors inthe US population, mortality rates by cause, and estimates of the proportional hazards of risk factor exposure from published systematic reviews to develop a risk prediction model that estimates an adult's 10 year mortality risk compared to a population with optimum risk factors. We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006.

Results: Predicted risk showed good discrimination with an area under the receiver operating characteristic (ROC) curve of 0.84 (standard error 0.01) for women and 0.84 (SE 0.01) for men. Across deciles of predicted risk, mortality was accurately predicted in men ((Χ (2) statistic = 12.3 for men, p=0.196) but slightly overpredicted in the highest decile among women (Χ (2) statistic = 22.8, p=0.002). Mortality risk was highly concentrated; for example, among those age 30-44 years, 5.1 % (95 % CI 4.1 % - 6.0 %) of the male and 5.9 % (95 % CI 4.8 % - 6.9 %) of the female population accounted for 25 % of the risk of death.

Conclusion: The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health.

No MeSH data available.


Related in: MedlinePlus

Distribution of avoidable risk of mortality in the United States by age and sex (NHANES 2003–2010)
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Fig4: Distribution of avoidable risk of mortality in the United States by age and sex (NHANES 2003–2010)

Mentions: The avoidable risk of death is heavily concentrated in a relatively small fraction of the population, particularly at younger ages (Fig. 4). Among males aged 30 to 44 years, 5.1 % (95 % CI 4.1–6.0 %) of males account for 25 % of the avoidable risk of death in that age group. This is similar in females aged 30 to 44 years, with 5.9 % (95 % CI 4.8–6.9 %) of females accounting for 25 % of the avoidable risk of death. In general, as the population ages, this fraction tends to increase. For example, among males aged 70 to 79, 13.9 % (95 % CI 11.1–16.6 %) of males account for 25 % of avoidable mortality risk in that age group, and among females aged 70 to 79 years, 11.7 % (95 % CI 9.2–14.1 %) of females account for 25 % of avoidable mortality risk in that age group.Fig. 4


Validation of a new predictive risk model: measuring the impact of the major modifiable risks of death for patients and populations.

Lim SS, Carnahan E, Nelson EC, Gillespie CW, Mokdad AH, Murray CJ, Fisher ES - Popul Health Metr (2015)

Distribution of avoidable risk of mortality in the United States by age and sex (NHANES 2003–2010)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4591717&req=5

Fig4: Distribution of avoidable risk of mortality in the United States by age and sex (NHANES 2003–2010)
Mentions: The avoidable risk of death is heavily concentrated in a relatively small fraction of the population, particularly at younger ages (Fig. 4). Among males aged 30 to 44 years, 5.1 % (95 % CI 4.1–6.0 %) of males account for 25 % of the avoidable risk of death in that age group. This is similar in females aged 30 to 44 years, with 5.9 % (95 % CI 4.8–6.9 %) of females accounting for 25 % of the avoidable risk of death. In general, as the population ages, this fraction tends to increase. For example, among males aged 70 to 79, 13.9 % (95 % CI 11.1–16.6 %) of males account for 25 % of avoidable mortality risk in that age group, and among females aged 70 to 79 years, 11.7 % (95 % CI 9.2–14.1 %) of females account for 25 % of avoidable mortality risk in that age group.Fig. 4

Bottom Line: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions.We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006.The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health.

View Article: PubMed Central - PubMed

Affiliation: Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Ave., Suite 600, Seattle, WA 98121 USA.

ABSTRACT

Background: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions.

Methods: We used data on the distribution of exposure to 12 major behavioral and biometric risk factors inthe US population, mortality rates by cause, and estimates of the proportional hazards of risk factor exposure from published systematic reviews to develop a risk prediction model that estimates an adult's 10 year mortality risk compared to a population with optimum risk factors. We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006.

Results: Predicted risk showed good discrimination with an area under the receiver operating characteristic (ROC) curve of 0.84 (standard error 0.01) for women and 0.84 (SE 0.01) for men. Across deciles of predicted risk, mortality was accurately predicted in men ((Χ (2) statistic = 12.3 for men, p=0.196) but slightly overpredicted in the highest decile among women (Χ (2) statistic = 22.8, p=0.002). Mortality risk was highly concentrated; for example, among those age 30-44 years, 5.1 % (95 % CI 4.1 % - 6.0 %) of the male and 5.9 % (95 % CI 4.8 % - 6.9 %) of the female population accounted for 25 % of the risk of death.

Conclusion: The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health.

No MeSH data available.


Related in: MedlinePlus