Population attributable fraction

Mansournia, Mohammad Alì; Altman, Douglas G.

doi:10.1136/bmj.k757

Cited by 297 publications

(305 citation statements)

References 10 publications

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“…The Cox regression was adjusted for birth year, country of birth and childhood BMI at 8 years of age. The PAF was calculated using the formula PAF = p e (1 − 1/HR), where p e is the prevalence of exposure to pubertal timing below the median among cases of type 2 diabetes [27]. Kaplan-Meier survival and cumulative incidence plots and the test for proportionality were performed in R version 3.4.2 using the 'survival' and 'rms' packages [24,28,29] and all other analyses were performed in SPSS statistics (version 25, IBM, Armonk, NY, USA).…”

Section: Discussionmentioning

confidence: 99%

Early puberty and risk for type 2 diabetes in men

et al. 2020

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Aims/hypothesis The association between pubertal timing and type 2 diabetes, independent of prepubertal BMI, is not fully understood. The aim of the present study was to evaluate the association between pubertal timing and risk of adult type 2 diabetes, independent of prepubertal BMI, in Swedish men. Methods We included 30,697 men who had data for BMI at age 8 and 20 years and age at Peak Height Velocity (PHV), an objective assessment of pubertal timing, available from the BMI Epidemiology Study Gothenburg (BEST Gothenburg), Sweden. Information on type 2 diabetes (n = 1851) was retrieved from the Swedish National Patient Register. HRs and 95% CIs were estimated by Cox regression analysis. We observed violations of the assumption of proportional hazards for the association between age at PHV and the risk of type 2 diabetes and therefore split the follow-up period at the median age of type 2 diabetes diagnosis (57.2 years of age) to define early (≤57.2 years) and late (>57.2 years) type 2 diabetes diagnosis. Results Age at PHV was inversely associated with both early (HR 1.28 per year decrease in age at PHV, 95% CI 1.21, 1.36) and late (HR 1.13, 95% CI 1.06, 1.19) type 2 diabetes. After adjustment for childhood BMI, the associations between age at PHVand both early (HR 1.24, 95% CI 1.17, 1.31) and late (HR 1.11, 95% CI 1.05, 1.17) type 2 diabetes were similar. Moreover, early age at PHV predicted insulin treatment of type 2 diabetes (OR 1.25 per year decrease in age at PHV, 95% CI 1.17, 1.33). Assuming a higher risk among those with an age at PHV below the median, the population attributable factor indicates that 15% fewer of the diagnosed individuals would have developed type 2 diabetes had they not reached puberty early. Conclusions/interpretation These findings indicate that early puberty may be a novel independent risk factor for type 2 diabetes.

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Section: Discussionmentioning

confidence: 99%

Early puberty and risk for type 2 diabetes in men

et al. 2020

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“…PAF is defined as the fraction of all cases of a disease or condition in a population which is attributable to the exposure. 23 As the GBR contains almost every birth in Georgia and is representative of the whole population, the current study gave us the opportunity to calculate PAF. Statistical analysis was performed using the statistical package STATA (StataCorp, College Station, TX, USA) version 15.0.…”

Section: Discussionmentioning

confidence: 99%

<p>Unattended Pregnancies and Perinatal Mortality in Georgia</p>

Manjavidze¹,

Rylander²,

Skjeldestad³

et al. 2020

RMHP

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Introduction: The majority of pregnant women in Georgia attend the free-of-charge, national antenatal care (ANC) programme, but over 5% of pregnancies in the country are unattended. Moreover, Georgia has one of the highest perinatal mortality (PM) rates in Europe (11.7/1000 births). Purpose: To assess the association between unattended pregnancies and the risk of PM. Methods: Data were extracted from the Georgian Birth Registry (GBR) and the national vital registration system. All mothers who had singleton births and delivered in medical facilities in Georgia in 2017-2018 were included in the study and categorised into attended pregnancies (at least one ANC visit during pregnancy) and unattended pregnancies (no ANC visits during pregnancy). After exclusions, the study sample included 101,663 women and their newborns, of which 1186 were either stillborn or died within 7 days. Logistic regression analysis was used to assess the effect of unattended pregnancies on PM. Results: During the study period, the PM rate was 12.9/1000 births. In total, 5.6% of women had unattended pregnancies. The odds of PM among women with unattended pregnancies were more than double those among women with attended pregnancies (odds ratio=2.21, [95% confidence interval: 1.81-2.70]). Multiparous women with higher education and who resided/delivered outside of Tbilisi were significantly less likely to experience PM. Conclusion: The risk of PM doubled among women with unattended pregnancies. Six percent of PM cases were attributable to unattended pregnancies. Targeting women with previous unattended pregnancies will likely reduce the PM rate in Georgia.

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“…The population attributable fraction (PAF; sometimes PAR, population attributable risk) provides this information;5 see sample PAF calculation in Boxes 1 and 2. The PAF can be seen as the proportion of a population's incidence of a given disease that can be accounted for by exposure to the risk factor of interest, and is distinct from the attributable fraction which is simply the risk difference divided by the risk among the exposed.…”

Section: Population Attributable Fractionmentioning

confidence: 99%

Impact of your results: Beyond the relative risk

Siegerink

Rohmann

2018

Research and Practice in Thrombosis and Haemostasis

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Universally, reporting guidelines emphasize the importance of using point estimates that indicate the strength of an effect. A single statement of the presence (or absence) of “statistical significance” and/or a P value alone do not provide sufficient information. Instead, an estimate of relative risk with a corresponding confidence interval should be routinely provided. Unfortunately, the context of the reported relative risk is often omitted, thereby hampering the readers’ understanding of the impact of the results. Additionally, commonly used binary outcomes might not be sensitive enough to fully convey the clinical relevance of an intervention or risk factor. This tutorial underlines the role of the context of results presented in clinical research papers. It also provides suggestions of meaningful ways to illustrate the impact of your own results by going beyond the relative risk.

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Population attributable fraction

Cited by 297 publications

References 10 publications

Early puberty and risk for type 2 diabetes in men

Early puberty and risk for type 2 diabetes in men

<p>Unattended Pregnancies and Perinatal Mortality in Georgia</p>

Impact of your results: Beyond the relative risk

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