Alcohol Intake and Blood Pressure Levels: A Dose-Response Meta-Analysis of Nonexperimental Cohort Studies.

BACKGROUND: Alcohol consumption may increase blood pressure but the details of the relationship are incomplete, particularly for the association at low levels of alcohol consumption, and no meta-analyses are available for nonexperimental cohort studies. METHODS: We performed a systematic search of longitudinal studies in healthy adults that reported on the association between alcohol intake and blood pressure. Our end points were the mean differences over time of systolic (SBP) and diastolic blood pressure (DBP), plotted according to baseline alcohol intake, by using a dose-response 1-stage meta-analytic methodology. RESULTS: Seven studies, with 19 548 participants and a median follow-up of 5.3 years (range, 4-12 years), were included in the analysis. We observed a substantially linear positive association between baseline alcohol intake and changes over time in SBP and DBP, with no suggestion of an exposure-effect threshold. Overall, average SBP was 1.25 and 4.90 mm Hg higher for 12 or 48 grams of daily alcohol consumption, compared with no consumption. The corresponding differences for DBP were 1.14 and 3.10 mm Hg. Subgroup analyses by sex showed an almost linear association between baseline alcohol intake and SBP changes in both men and women, and for DBP in men while in women we identified an inverted U-shaped association. Alcohol consumption was positively associated with blood pressure changes in both Asians and North Americans, apart from DBP in the latter group. CONCLUSIONS: Our results suggest the association between alcohol consumption and SBP is direct and linear with no evidence of a threshold for the association, while for DBP the association is modified by sex and geographic location.

The association between first and second wave COVID-19 mortality in Italy.

BACKGROUND: The relation between the magnitude of successive waves of the COVID-19 outbreak within the same communities could be useful in predicting the scope of new outbreaks. METHODS: We investigated the extent to which COVID-19 mortality in Italy during the second wave was related to first wave mortality within the same provinces. We compared data on province-specific COVID-19 2020 mortality in two time periods, corresponding to the first wave (February 24-June 30, 2020) and to the second wave (September 1-December 31, 2020), using cubic spline regression. RESULTS: For provinces with the lowest crude mortality rate in the first wave (February-June), i.e. < 22 cases/100,000/month, mortality in the second wave (September-December) was positively associated with mortality during the first wave. In provinces with mortality greater than 22/100,000/month during the first wave, higher mortality in the first wave was associated with a lower second wave mortality. Results were similar when the analysis was censored at October 2020, before the implementation of region-specific measures against the outbreak. Neither vaccination nor variant spread had any role during the study period. CONCLUSIONS: These findings indicate that provinces with the most severe initial COVID-19 outbreaks, as assessed through mortality data, faced milder second waves.

SARS-CoV-2 infection incidence during the first and second COVID-19 waves in Italy.

We assessed the relation between COVID-19 waves in Italy, which was severely affected during the pandemic. We evaluated the hypothesis that a larger impact from the first wave (February-May 2020) predicts a smaller peak during the second wave (September-October 2020), in the absence of local changes in public health interventions and area-specific differences in time trends of environmental parameters. Based on publicly available data on province-specific SARS-CoV-2 infections and both crude and multivariable cubic spline regression models, we found that for provinces with the lowest incidence rates in the first wave, the incidence in the second wave increased roughly in proportion with the incidence in the first wave until an incidence of about 500-600 cases/100,000 in the first wave. Above that value, provinces with higher incidences in the first wave experienced lower incidences in the second wave. It appears that a comparatively high cumulative incidence of infection, even if far below theoretical thresholds required for herd immunity, may provide noticeable protection during the second wave. We speculate that, if real, the mechanism for this pattern could be depletion of most susceptible individuals and of superspreaders in the first wave. A population learning effect regarding cautious behavior could have also contributed. Since no area-specific variation of the national policy against the SARS-CoV-2 outbreak was allowed until early November 2020, neither individual behaviors nor established or purported environmental risk factors of COVID-19, such as air pollution and meteorological factors, are likely to have confounded the inverse trends we observed in infection incidence over time.