Substantial racial and ethnic disparities in hypertension and dementia exist in the United States. We evaluated the effect of maintaining systolic blood pressure (SBP) below clinical thresholds on dementia incidence.
We included 6806 Multi-Ethnic Study of Atherosclerosis participants (44 to 84 years old). We implemented the parametric g-formula to simulate the hypothetical interventions to reduce SBP below 120 and 140 mmHg over time, accounting for time-varying confounding. We estimated risk ratios (RRs) and risk differences for dementia incidence at 19 years.
The RRs (95% confidence intervals [CIs]) comparing an intervention reducing SBP below 120 mmHg to no intervention were 0.93 (0.87 to 0.99) for total sample, 0.95 (0.88 to 1.02) for White, 0.90 (0.79 to 1.02) for Black, 0.90 (0.78 to 1.05) for Latino, and 1.16 (0.83 to 1.55) for Chinese American participants. Results for lowering SBP below 140 mmHg and with death as competing event were attenuated.
The reduction of SBP below 120 mmHg over time has modest effects on reducing dementia incidence. More work is needed to understand the heterogeneity across racial and ethnic groups.
There is a potential beneficial effect in lowering SBP to reduce the risk of dementia, which may vary by race and ethnicity. The percentage of participants who would need intervention on blood pressure to meet clinical thresholds is greater for Black and Latino communities. Results are sensitive to the way that death is specified in the research question and analysis.

Assessing population-level effects of vaccines and other infectious disease prevention measures is important to the field of public health. In infectious disease studies, one person\'s treatment may affect another individual\'s outcome, that is, there may be interference between units. For example, the use of bed nets to prevent malaria by one individual may have an indirect effect on other individuals living in close proximity. In some settings, individuals may form groups or clusters where interference only occurs within groups, that is, there is partial interference. Inverse probability weighted estimators have previously been developed for observational studies with partial interference. Unfortunately, these estimators are not well suited for studies with large clusters. Therefore, in this paper, the parametric g-formula is extended to allow for partial interference. G-formula estimators are proposed for overall effects, effects when treated, and effects when untreated. The proposed estimators can accommodate large clusters and do not suffer from the g-null paradox that may occur in the absence of interference. The large sample properties of the proposed estimators are derived assuming no unmeasured confounders and that the partial interference takes a particular form (referred to as \'weak stratified interference\'). Simulation studies are presented demonstrating the finite-sample performance of the proposed estimators. The Demographic and Health Survey from the Democratic Republic of the Congo is then analyzed using the proposed g-formula estimators to assess the effects of bed net use on malaria.