Objective

The purpose of this study was to model weight trajectories over a 5-year time period (2012-2016) and their association with behavioural and psychosocial characteristics and health care-related experiences using data from the Patient Outcomes Research to Advance Learning (PORTAL) overweight/obesity cohort.

Methods

Weight trajectories for each eligible patient in the PORTAL overweight/obesity cohort (n = 2864) were identified first using growth modelling; trajectories were then grouped using a hierarchical cluster analysis. Weight trajectory clusters that emerged were compared on demographics, and predictors of cluster membership were examined. Clusters were also compared on responses to a survey assessing health behaviours, quality of life, and health care experience completed in 2015 by 49% of the total sample (n = 1391).

Results

Seven distinct weight trajectory clusters were identified: (a) significant weight loss then maintenance; (b) higher stable weight; (c) moderate stable weight; (d) steady weight loss then relapse; (e) weight gain then weight loss; (f) steady weight gain then maintenance; and (g) lower stable weight. Age, sex, race/ethnicity, and body mass index at baseline predicted patient's weight trajectory (P < .001). Over two thirds of patients maintained their weight over the 5-year period. Significant weight loss then maintenance, weight gain then weight loss, and higher stable weight patients were more likely to report receiving weight counselling from their provider. Patients in the significant weight loss then maintenance and lower stable weight clusters were more likely to be physically active than the other clusters.

Conclusion

Findings suggest variability in patterns of weight change among adults with overweight or obesity who have access to health care and that these patterns differ on demographic, behavioural and psychosocial factors, and health care experience.

Background

Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models.

Methods

We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM_2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM_0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients.

Findings

We found significant associations between COVID-19 death and several air pollution exposures, including: PM_2.5 mass, PM_0.1 mass, PM_2.5 nitrates, PM_2.5 elemental carbon, PM_2.5 on-road diesel, and PM_2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM_2.5 mass and PM_2.5 nitrate to HR ∼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects.

Interpretation

Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.