Hypothetical interventions to prevent stroke: an application of the parametric g-formula to a healthy middle-aged population

Abstract

The effects of interventions on multiple lifestyle and metabolic risk factors, initiated in midlife or later in a healthy population, on the long-term risk of first-ever stroke is not known. A particular methodological challenge in observational studies is to estimate the unbiased effect of a time-varying exposure in presence of time-varying confounders, if those confounders are affected by prior exposure. In such cases, the parametric g-formula can be applied to estimate an unbiased effect. We applied the parametric g-formula to estimate the 18-years (1994-2012) cumulative stroke risk under different scenarios of hypothetical interventions on levels of blood pressure, cholesterol, weight, physical activity, smoking and alcohol intake; and compared these to the observed scenario, to calculate the population risk ratios and risk differences. Among 14,796 eligible participants in the prospective, population-based Tromsø study (baseline mean age 46.1 years, 51% women), the observed 18-years stroke risk was 5.9%. A feasible joint hypothetical intervention on six lifestyle and metabolic risk factors would reduce the 18-year stroke risk by 32% (95% confidence interval 16, 44). A combination of more intensive interventions reduced the estimated 18-years stroke risk by 64% (95% confidence interval 40, 80). Blood pressure reduction and quitting smoking significantly reduced the risk when applied separately.

<p>Estimation of Generalized Impact Fraction and Population Attributable Fraction of Hypertension Based on JNC-IV and 2017 ACC/AHA Guidelines for Cardiovascular Diseases Using Parametric G-Formula: Tehran Lipid and Glucose Study (TLGS)</p>

“…Therefore, the preventive effect of removing or decreasing HTN in this study might have been under-or overestimated. However, despite this limitation, the findings were not very different from the results of the studies by Vangen-Lønne 50 and Jain et al 52 that controlled timevarying confounders for estimating the PAF of HTN for stroke. Second, this study did not control some confounders such as alcohol consumption and nutritional patterns, which challenges the "limited bias" assumption for PAF estimation.…”

“…The parametric G-formula can provide valid PAF or GIF estimates by overcoming the limitations of the conventional methods for PAF and GIF estimation through generating a counterfactual population and using appropriate models. 48 , 49 Vangen-Lønne et al 50 applied the parametric G-formula to investigate the effect of joint interventions for complete or partial elimination of stroke risk factors on the 18-year cumulative stroke risk. Their findings showed that the risk of stroke would be reduced by 28% if SBP decreased to less than 140 mmHg in all individuals.…”

Advancing mediation analysis in occupational health research

“…Moreover, some have argued that controlled direct effects are much more policy-relevant because they estimate the proportion of the total effect of the exposure on the outcome that could be eliminated by a specific intervention on the mediator (13,14). Although controlled direct effects can still be estimated in the presence of exposure-induced mediator-outcome confounders, this does require more sophisticated methods, such as marginal structural models (15,16) or the g-formula (17). Albeit still infrequently used, these approaches offer an important new toolbox for health research and are often easily implemented in standard statistical software (18).…”

The Impact of Unemployment on Antidepressant Purchasing

“…Furthermore, our natural course scenario of covariate set 2 closely approximates empirical antidepressant purchases, and all other time-varying covariates, which indicates that our models are not grossly misspecified. 5,16 Antidepressant purchases do not perfectly indicate the presence of depression. In Finland, only about a third of those with major depression or anxiety disorder received antidepressant treatment.…”

“…13 The g-formula is one of the few methods that can be used to model complex longitudinal dependencies, including time-varying and intermediate confounding. [14][15][16] The g-formula has been used before to model the complex interrelationships between unemployment and antidepressant purchasing, an indicator of diagnosis of depression. 5 An earlier study found that, among young adults entering the labor market for the first time, reducing unemployment is estimated to result in less antidepressant purchasing, especially for low-educated men.…”