Study of effect modifiers of genetically predicted CETP reduction.

Genetic variants in drug targets can be used to predict the long-term, on-target effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of genetically predicted lower CETP levels on biomarkers and cardiovascular outcomes. We found sex and body mass index (BMI) to be modifiers of the association between genetically predicted lower CETP and lipid biomarkers in UK Biobank participants. Female sex and lower BMI were associated with higher high-density lipoprotein cholesterol and lower low-density lipoprotein cholesterol for the same genetically predicted reduction in CETP concentration. We found that sex also modulated the effect of genetically lower CETP on cholesterol efflux capacity in samples from the Montreal Heart Institute Biobank. However, these modifying effects did not extend to sex differences in cardiovascular outcomes in our data. Our results provide insight into the clinical effects of CETP inhibitors in the presence of effect modification based on genetic data. The approach can support precision medicine applications and help assess the external validity of clinical trials.

Clinical Predictors and Outcomes of Invasive Anal Cancer for People With HIV in an Inception Cohort.

BACKGROUND: Due to the heterogeneity of risk for invasive anal cancer (IAC) among people with human immunodeficiency virus (PWH), we investigated predictors of IAC and described outcomes among those with a cancer diagnosis. METHODS: Using a longitudinal inception cohort of anal cancer screening, we evaluated risk factors and outcome probabilities for incident IAC in Cox models. Screening included anal cytology and digital anorectal examination, and, if results of either were abnormal, high-resolution anoscopy. RESULTS: Between 30 November 2006 and 3 March 2021, a total of 8139 PWH received care at the University of California, San Diego, with 4105 individuals undergoing screening and subsequently followed up over a median of 5.5 years. Anal cancer developed in 33 of them. IAC was more likely to develop in patients with anal high-grade squamous intraepithelial lesions (aHSILs) on initial or subsequent follow-up cytology (hazard ratio, 4.54) and a nadir CD4 cell count ≤200/µL (2.99). The joint effect of aHSILs and nadir CD4 cell count ≤200/µL amplified the hazard of IAC by 9-fold compared with the absence of both. PWH with time-updated cytology aHSIL and CD4 cell counts ≤200/µL had 5- and 10-year probabilities of IAC of 3.40% and 4.27%, respectively. Twelve individuals with cancer died, 7 (21% of the total 33) due to cancer progression, and they had clinical stage IIIA or higher cancer at initial diagnosis. CONCLUSIONS: PWH with both aHSIL and a nadir CD4 cell count ≤200/µL have the highest risk of IAC. PWH who died due to IAC progression had clinical stage IIIA cancer or higher at diagnosis, highlighting the importance of early diagnosis through high-resolution anoscopic screening.

Psychosocial factors, health behaviors and risk of cancer incidence: Testing interaction and effect modification in an individual participant data meta-analysis.

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.

School racial/ethnic composition, effect modification by caring teacher/staff presence, and mid-/late-life depressive symptoms: findings from the Study of Healthy Aging in African Americans.

For Black students in the United States, attending schools with a higher proportion of White students is associated with worse mental and physical health outcomes in adolescence/early adulthood. To our knowledge, no prior studies have evaluated the association between school racial/ethnic composition from kindergarten through grade 12 and later-life mental health. In a cohort of Black adults aged ≥50 years in Northern California who retrospectively reported (2017-2020) school racial/ethnic composition for grades 1, 6, 9, and 12, we assessed the association between attending a school with mostly Black students versus not and mid-/late-life depressive symptoms (8-item Patient-Reported Outcomes Measurement Information System (PROMIS) depression score, standardized to the 2000 US adult population) using age-, sex/gender-, southern US birth-, and parental education-adjusted generalized estimating equations, and assessed effect modification by the presence of a caring teacher/staff member. Levels of later-life depressive symptoms were lower among those who attended schools with mostly Black students in grades 1 and 6 (ß = -0.12 [95% CI, -0.23 to 0.00] and ß = -0.11 [95% CI, -0.22 to 0.00], respectively). In grade 6, this difference was larger for students without an adult at school who cared about them (ß = -0.29 [95% CI, -0.51 to -0.07] vs ß = -0.04 [95% CI, -0.17 to 0.09]). Among Black Americans, experiencing early schooling with mostly Black students may have later-life mental health benefits; this protective association appears more important for students without the presence of caring teachers/staff. This article is part of a Special Collection on Mental Health.

Could differential under-reporting of loneliness between men and women bias the gender-specific association between loneliness duration and rate of memory decline? A probabilistic bias analysis of effect modification.

Gender is an observed effect modifier of the association between loneliness and memory aging. However, this effect modification may be a result of information bias due to differential loneliness under-reporting by gender. We applied probabilistic bias analyses to examine whether effect modification of the loneliness-memory decline relationship by gender is retained under three simulation scenarios with various magnitudes of differential loneliness under-reporting between men and women. Data were from biennial interviews with adults aged 50+ in the US Health and Retirement Study from 1996-2016 (5,646 women and 3,386 men). Loneliness status (yes vs. no) was measured from 1996-2004 using the CES-D loneliness item and memory was measured from 2004-2016. Simulated sensitivity and specificity of the loneliness measure were informed by a validation study using the UCLA Loneliness Scale as a gold standard. The likelihood of observing effect modification by gender was higher than 90% in all simulations, although the likelihood reduced with an increasing difference in magnitude of the loneliness under-reporting between men and women. The gender difference in loneliness under-reporting did not meaningfully affect the observed effect modification by gender in our simulations. Our simulation approach may be promising to quantify potential information bias in effect modification analyses.

Combined impact of dynamic air pollution and transportation noise on cardiometabolic disorders in San Diego County.

Air pollution and noise exposure may synergistically contribute to increased cardiometabolic disorders; however, few studies have examined this potential interaction nor considered exposures beyond residential location. This study investigates the combined impact of dynamic air pollution and transportation noise on cardiometabolic disorders in San Diego County. Using the Community of Mine Study (2014-2017), 602 ethnically diverse participants were assessed for obesity, dyslipidemia, hypertension, and metabolic syndrome (MetS) using anthropometric measurements and biomarkers from blood samples. Time-weighted measures of exposure to PM2.5, NO2, road and aircraft noise were calculated using global positioning system (GPS) mobility data and Kernel Density Estimation. Generalized estimating equation models were used to analyze associations. Interactions were assessed on the multiplicative and additive scales using relative excess risk due to interaction (RERI). We found that air pollution and noise interact to affect metabolic disorders on both multiplicative and additive scales. The effect of noise on obesity and MetS was higher when air pollution was higher. The RERI of aircraft noise and NO2 on obesity and MetS were 0.13 (95%CI 0.03, 0.22) and 0.13 (95%CI 0.02, 0.25), respectively. This finding suggests that aircraft noise and air pollution may have synergistic effects on obesity and MetS.

Estimating Additive Interaction in Two-Stage Individual Participant Data Meta-Analysis.

Individual participant data (IPD) meta-analysis provides important opportunities to study interaction and effect modification for which individual studies often lack power. While previous meta-analyses have commonly focused on multiplicative interaction, additive interaction holds greater relevance for public health and may in certain contexts better reflect biological interaction. Methodological literature on interaction in IPD meta-analysis does not cover additive interaction for models including binary or time-to-event outcomes. We aimed to describe how the Relative Excess Risk due to Interaction (RERI) and other measures of additive interaction or effect modification can be validly estimated within two-stage IPD meta-analysis. First, we explain why direct pooling of study-level RERI estimates may lead to invalid results. Next, we propose a three-step procedure to estimate additive interaction: 1) estimate effects of both exposures and their product term on the outcome within each individual study; 2) pool study-specific estimates using multivariate meta-analysis; 3) estimate an overall RERI and 95% confidence interval based on the pooled effect estimates. We illustrate this procedure by investigating interaction between depression and smoking and risk of smoking-related cancers using data from the PSYchosocial factors and Cancer (PSY-CA) consortium. We discuss implications of this procedure, including the application in meta-analysis based on published data.

Identifying predictors of resilience to stressors in single-arm studies of pre-post change.

Many older adults experience a major stressor at some point in their lives. The ability to recover well after a major stressor is known as resilience. An important goal of geriatric research is to identify factors that influence resilience to stressors. Studies of resilience in older adults are typically conducted with a single-arm where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to mathematical coupling and regression to the mean (RTM). We develop a method to correct the bias. We extend the method to include covariates. Our approach considers a counterfactual control group and involves sensitivity analyses to evaluate different settings of control group parameters. Only minimal distributional assumptions are required. Simulation studies demonstrate the validity of the method. We illustrate the method using a large, registry of older adults (N  =7239) who underwent total knee replacement (TKR). We demonstrate how external data can be utilized to constrain the sensitivity analysis. Naive analyses implicated several treatment effect modifiers including baseline function, age, body-mass index (BMI), gender, number of comorbidities, income, and race. Corrected analysis revealed that baseline (pre-stressor) function was not strongly linked to recovery after TKR and among the covariates, only age and number of comorbidities were consistently and negatively associated with post-stressor recovery in all functional domains. Correction of mathematical coupling and RTM is necessary for drawing valid inferences regarding the effect of covariates and baseline status on pre-post change. Our method provides a simple estimator to this end.

Designing three-level cluster randomized trials to assess treatment effect heterogeneity.

Cluster randomized trials often exhibit a three-level structure with participants nested in subclusters such as health care providers, and subclusters nested in clusters such as clinics. While the average treatment effect has been the primary focus in planning three-level randomized trials, interest is growing in understanding whether the treatment effect varies among prespecified patient subpopulations, such as those defined by demographics or baseline clinical characteristics. In this article, we derive novel analytical design formulas based on the asymptotic covariance matrix for powering confirmatory analyses of treatment effect heterogeneity in three-level trials, that are broadly applicable to the evaluation of cluster-level, subcluster-level, and participant-level effect modifiers and to designs where randomization can be carried out at any level. We characterize a nested exchangeable correlation structure for both the effect modifier and the outcome conditional on the effect modifier, and generate new insights from a study design perspective for conducting analyses of treatment effect heterogeneity based on a linear mixed analysis of covariance model. A simulation study is conducted to validate our new methods and two real-world trial examples are used for illustrations.

Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity.

Stepped wedge design is a popular research design that enables a rigorous evaluation of candidate interventions by using a staggered cluster randomization strategy. While analytical methods were developed for designing stepped wedge trials, the prior focus has been solely on testing for the average treatment effect. With a growing interest on formal evaluation of the heterogeneity of treatment effects across patient subpopulations, trial planning efforts need appropriate methods to accurately identify sample sizes or design configurations that can generate evidence for both the average treatment effect and variations in subgroup treatment effects. To fill in that important gap, this article derives novel variance formulas for confirmatory analyses of treatment effect heterogeneity, that are applicable to both cross-sectional and closed-cohort stepped wedge designs. We additionally point out that the same framework can be used for more efficient average treatment effect analyses via covariate adjustment, and allows the use of familiar power formulas for average treatment effect analyses to proceed. Our results further sheds light on optimal design allocations of clusters to maximize the weighted precision for assessing both the average and heterogeneous treatment effects. We apply the new methods to the Lumbar Imaging with Reporting of Epidemiology Trial, and carry out a simulation study to validate our new methods.

Associations of triglyceride-glucose index with hyperuricemia among Royal Thai Army personnel.

BACKGROUND: Hyperuricemia has placed an immense burden on the global healthcare system. Studies have discovered a close correlation between serum uric acid (SUA) and insulin resistance (IR). The objective of this investigation is to examine the association between the triglyceride-glucose (TyG) index, a simple surrogate for IR, and the presence of hyperuricemia. METHODS: Between 2017 and 2021, an epidemiologic study was conducted on Royal Thai Army (RTA) personnel aged 35-60 years, involving a total of 231,286 participants. In the study, hyperuricemia was defined as a SUA level of 7 mg/dL and 6 mg/dL among male and female participants, respectively. Using linear regression analysis and logistic regression analysis, the association between the TyG index and SUA was determined. RESULTS: A positive relationship was demonstrated between the TyG index and the SUA. Overall, SUA increased by 0.32 per unit of TyG index growth (95% CI: 0.31-0.32). In comparison with the first quartile, employees in the fourth TyG quartile had a greater likelihood of having hyperuricemia [adjusted odds ratio (AOR): 2.45, 95% CI: 2.38-2.52]. Effect modification by obesity on the association between the TyG index and SUA was observed (P-interaction < 0.001). Among individuals with obesity, compared with the first TyG index quartile, the AOR for hyperuricemia was 2.15 (95% CI: 2.06-2.25) and 2.14 (95% CI: 1.81-2.53) for the fourth quartile of the TyG index for males and females, respectively. However, for nonobese personnel, in comparison to the top quartile of the TyG index, the AOR for hyperuricemia was 2.73 (95% CI:2.61-2.84) and 5.03 (95% CI: 4.03-6.29) for the fourth quartile of the TyG index for males and females, respectively. Personnel in the fourth TyG index quartile revealed that the prevalence of hyperuricemia reached 44.2%. CONCLUSION: A robust positive association between the TyG index and SUA was illustrated among active-duty RTA personnel. Obesity was identified as a modifier influencing this relationship. Furthermore, individuals in the fourth quarter of the TyG index, regardless of their obesity status, could be considered appropriate candidates for screening SUA levels.

Prenatal exposure to air pollution and BWGA Z-score: Modifying effects of placenta leukocyte telomere length and infant sex.

BACKGROUND: Air pollutants, such as fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3), have been associated with adverse birth outcomes, including low birth weight, often exhibiting sex-specific effects. However, the modifying effect of placental telomere length (TL), reflecting cumulative lifetime oxidative stress in mothers, remains unexplored. METHOD: Using data from a Northeastern U.S. birth cohort (n = 306), we employed linear regression and weighted quantile sum models to assess trimester-average air pollution exposures and birth weight for gestational age (BWGA) z-scores. Placental TL, categorized by median split, was considered as an effect modifier. Interactions among air pollutants, placental TL, infant sex, and BWGA z-score were evaluated. RESULTS: Without placental TL as a modifier, only 1st trimester O3 was significantly associated with BWGA z-scores (coefficient: 0.33, 95% CI: 0.03, 0.63). In models considering TL interactions, a significant modifying effect was observed between 3rd trimester NO2 and BWGA z-scores (interaction p-value = 0.02). Specifically, a one interquartile range (1-IQR) increase in 3rd trimester NO2 was linked to a 0.28 (95% CI: 0.06, 0.52) change in BWGA z-score among shorter placental TL group, with no significant association among longer TL group. Among male infants, there were significant associations between 3rd trimester PM2.5 exposure and BWGA z-scores in the longer TL group (coefficient: -0.34, 95% CI: -0.61, -0.02), and between 1st trimester O3 exposure and BWGA z-scores among males in the shorter TL group (coefficient: 0.59, 95% CI: 0.06, 1.08). For females, only a negative association in 2nd trimester mixture model was observed within the longer TL group (coefficient: -0.10, 95% CI: -0.21, -0.01). CONCLUSION: These findings highlight the need to consider the complex interactions among prenatal air pollutant exposures, placental TL, and fetal sex to better elucidate those at greatest risk for adverse birth outcomes.

Risk of dementia due to Co-exposure to air pollution and neighborhood disadvantage.

BACKGROUND: Co-exposure to air pollution and neighborhood disadvantage may influence cognition decline. We tested these associations in the context of dementia risk. METHODS: We leveraged a cohort of adults ≥65 years (n = 5397) enrolled from 2011 to 2018 in the National Health and Aging Trends Study (NHATS). Particulate matter (PM) ≤ 10 µm in diameter, PM ≤ 2.5 µm in diameter, carbon monoxide, nitric oxide, and nitrogen dioxide - and neighborhood disadvantage were tested for joint associations with dementia risk. Pollutant concentrations at the 2010 census tract level were assigned using the US Environmental Protection Agency's Community Multiscale Air Quality Modeling System. Neighborhood disadvantage was defined using the tract Social Deprivation Index (SDI). Dementia was determined through self- or proxy-report or scores indicative of "probable dementia" according to NHATS screening tools. Joint effects of air pollutants and SDI were tested using quantile g-computation Cox proportional hazards models. We also stratified joint air pollution effects across SDI tertiles. Analyses adjusted for age at enrollment, sex, education, partner status, urbanicity, income, race and ethnicity, years at residence, census segregation, and census region. RESULTS: SDI score (aHR = 1.08; 95% CI 0.96, 1.22), joint air pollution (aHR = 1.03, 95% CI 0.92, 1.16) and joint SDI with air pollution (aHR = 1.04, 95% CI 0.89, 1.22) were not associated with dementia risk. After accounting for competing risk of death, joint SDI with air pollution was not associated with dementia risk (aHR = 1.06; 95% CI 0.87, 1.29). In stratified models, joint air pollution was associated with greater risk of dementia at high (aHR = 1.19; 95% CI 0.87, 1.63), but not at medium or low SDI. CONCLUSION: Air pollution was associated with greater dementia risk in disadvantaged areas after accounting for competing risks. Air pollution associations with dementia incidence may be attenuated when other risk factors are more prominent in disadvantaged neighborhoods.

Modification of the association between cold spells and cardiovascular disease by changes in natural gas prices: A nationwide time-series study.

BACKGROUND: Although the association between low ambient temperature and cardiovascular disease (CVD) is well-established, the effect of winter energy prices on this association remains unknown. AIM: This ecological study aimed to investigate whether changes in natural gas prices (NGP), which account for a significant portion of winter energy prices in Korea, affect the association of cold spells with hospital admissions and mortality due to CVD. METHODS: Data from the National Health Insurance Service and Statistics Korea were used to determine the daily number of hospital admissions and mortality rates associated with CVD. From January 2012 to February 2017, the NGP continually increased by 24.1%. From January 2012 to December 2014 the NGP continually decreased by 32.6% owing to the Korea Gas Corporation's management decisions, independent of external socioeconomic factors. We investigated the differences in the associations between cold spells and CVD-related outcomes in price-increasing and price-decreasing periods using a Poisson regression with a distributed lag nonlinear model. Cold spells were assessed on two consecutive days at the 5th percentile of the temperature for each region. RESULTS: The meteorological factors and air pollution levels were similar between the two periods. The association between cold spells and hospital admissions due to CVD was stronger during the price-increasing period than during the price-decreasing period [ratio of cumulative relative risk (RRR) = 1.71, 95% confidence interval (CI): 1.31-2.22]. The difference in the association with mortality due to CVD between the two periods was not significant, although the point estimate remained >1 (RRR = 1.11, 95% CI: 0.90-1.38). CONCLUSIONS: Changes in energy prices may modify the cold spell-related CVD risk, possibly by inducing behavioral changes to manage energy expenditure. Policymakers should take into account the potential public health implications of changes in energy prices, alongside their economic effects.

Greenness mitigate cause-specific mortality associated with air pollutants in ischemic and hemorrhagic stroke patients: An ecological health cohort study.

BACKGROUND: Air pollution is one of the most serious environmental risks to mortality of stroke. However, there exists a noteworthy knowledge gap concerning the different stroke subtypes, causes of death, the susceptibility of stroke patient, and the role of greenness in this context. METHODS: We analyzed data from an ecological health cohort, which included 334,261 patients aged ≥40 years with stroke (comprising 288,490 ischemic stroke and 45,771 hemorrhagic stroke) during the period 2013-2019. We used Cox proportional hazards models with time-varying exposure to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the associations of annual average fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) with both all-cause and cause-specific mortality. Additionally, we conducted analyses to examine the effect modification by greenness and identify potential susceptibility factors through subgroup analyses. RESULT: In multivariable-adjusted models, long-term exposure to PM2.5 and NO2 was associated with increased risk of all-cause mortality (HR: 1.038, 95% CI: 1.029-1.047 for PM2.5; HR: 1.055, 95% CI: 1.026-1.085 for NO2, per 10 µg/m3, for ischemic stroke patients; similar for hemorrhagic stroke patients). Gradually increasing effect sizes were shown for CVD mortality and stroke mortality. The HRs of mortality were slightly weaker with high versus low vegetation exposure. Cumulative exposures increased the HRs of pollutant-related mortality, and greater greenness decreased this risk. Two subtypes of stroke patients exhibited diverse patterns of benefit. CONCLUSION: Increasing residential greenness attenuates the increased risk of mortality with different patterns due to chronic air pollutants for ischemic and hemorrhagic stroke, offering valuable insights for precise tertiary stroke prevention strategies.

Modification of heat-related effects on mortality by air pollution concentration, at small-area level, in the Attica prefecture, Greece.

BACKGROUND: The independent effects of short-term exposure to increased air temperature and air pollution on mortality are well-documented. There is some evidence indicating that elevated concentrations of air pollutants may lead to increased heat-related mortality, but this evidence is not consistent. Most of these effects have been documented through time-series studies using city-wide data, rather than at a finer spatial level. In our study, we examined the possible modification of the heat effects on total and cause-specific mortality by air pollution at municipality level in the Attica region, Greece, during the warm period of the years 2000 to 2016. METHODS: A municipality-specific over-dispersed Poisson regression model during the warm season (May-September) was used to investigate the heat effects on mortality and their modification by air pollution. We used the two-day average of the daily mean temperature and daily mean PM10, NO2 and 8 hour-max ozone (O3), derived from models, in each municipality as exposures. A bivariate tensor smoother was applied for temperature and each pollutant alternatively, by municipality. Α random-effects meta-analysis was used to obtain pooled estimates of the heat effects at different pollution levels. Heterogeneity of the between-levels differences of the heat effects was evaluated with a Q-test. RESULTS: A rise in mean temperature from the 75th to the 99th percentile of the municipality-specific temperature distribution resulted in an increase in total mortality of 12.4% (95% Confidence Interval (CI):7.76-17.24) on low PM10 days, and 21.25% (95% CI: 17.83-24.76) on high PM10 days. The increase on mortality was 10.09% (95% CI: - 5.62- 28.41) on low ozone days, and 14.95% (95% CI: 10.79-19.27) on high ozone days. For cause-specific mortality an increasing trend of the heat effects with increasing PM10 and ozone levels was also observed. An inconsistent pattern was observed for the modification of the heat effects by NO2, with higher heat effects estimated in the lower level of the pollutant. CONCLUSIONS: Our results support the evidence of elevated heat effects on mortality at higher levels of PM10 and 8 h max O3. Under climate change, any policy targeted at lowering air pollution levels will yield significant public health benefits.

Impact of short-term exposure to air pollution on natural mortality and vulnerable populations: a multi-city case-crossover analysis in Belgium.

BACKGROUND: The adverse effect of air pollution on mortality is well documented worldwide but the identification of more vulnerable populations at higher risk of death is still limited. The aim of this study was to evaluate the association between natural mortality (overall and cause-specific) and short-term exposure to five air pollutants (PM2.5, PM10, NO2, O3 and black carbon) and identify potential vulnerable populations in Belgium. METHODS: We used a time-stratified case-crossover design with conditional logistic regressions to assess the relationship between mortality and air pollution in the nine largest Belgian agglomerations. Then, we performed a random-effect meta-analysis of the pooled results and described the global air pollution-mortality association. We carried out stratified analyses by individual characteristics (sex, age, employment, hospitalization days and chronic preexisting health conditions), living environment (levels of population density, built-up areas) and season of death to identify effect modifiers of the association. RESULTS: The study included 304,754 natural deaths registered between 2010 and 2015. We found percentage increases for overall natural mortality associated with 10 µg/m3 increases of air pollution levels of 0.6% (95% CI: 0.2%, 1.0%) for PM2.5, 0.4% (0.1%, 0.8%) for PM10, 0.5% (-0.2%, 1.1%) for O3, 1.0% (0.3%, 1.7%) for NO2 and 7.1% (-0.1%, 14.8%) for black carbon. There was also evidence for increases of cardiovascular and respiratory mortality. We did not find effect modification by individual characteristics (sex, age, employment, hospitalization days). However, this study suggested differences in risk of death for people with preexisting conditions (thrombosis, cardiovascular diseases, asthma, diabetes and thyroid affections), season of death (May-September vs October-April) and levels of built-up area in the neighborhood (for NO2). CONCLUSIONS: This work provided evidence for the adverse health effects of air pollution and contributed to the identification of specific population groups. These findings can help to better define public-health interventions and prevention strategies.

Modification of the PM2.5- and extreme heat-mortality relationships by historical redlining: a case-crossover study in thirteen U.S. states.

BACKGROUND: Redlining has been associated with worse health outcomes and various environmental disparities, separately, but little is known of the interaction between these two factors, if any. We aimed to estimate whether living in a historically-redlined area modifies the effects of exposures to ambient PM2.5 and extreme heat on mortality by non-external causes. METHODS: We merged 8,884,733 adult mortality records from thirteen state departments of public health with scanned and georeferenced Home Owners Loan Corporation (HOLC) maps from the University of Richmond, daily average PM2.5 from a sophisticated prediction model on a 1-km grid, and daily temperature and vapor pressure from the Daymet V4 1-km grid. A case-crossover approach was used to assess modification of the effects of ambient PM2.5 and extreme heat exposures by redlining and control for all fixed and slow-varying factors by design. Multiple moving averages of PM2.5 and duration-aware analyses of extreme heat were used to assess the most vulnerable time windows. RESULTS: We found significant statistical interactions between living in a redlined area and exposures to both ambient PM2.5 and extreme heat. Individuals who lived in redlined areas had an interaction odds ratio for mortality of 1.0093 (95% confidence interval [CI]: 1.0084, 1.0101) for each 10 µg m-3 increase in same-day ambient PM2.5 compared to individuals who did not live in redlined areas. For extreme heat, the interaction odds ratio was 1.0218 (95% CI 1.0031, 1.0408). CONCLUSIONS: Living in areas that were historically-redlined in the 1930's increases the effects of exposures to both PM2.5 and extreme heat on mortality by non-external causes, suggesting that interventions to reduce environmental health disparities can be more effective by also considering the social context of an area and how to reduce disparities there. Further study is required to ascertain the specific pathways through which this effect modification operates and to develop interventions that can contribute to health equity for individuals living in these areas.

Does Self-Reported BMI Modify the Association Between Stroke and Depressive Symptoms?

BACKGROUND: Depressive symptoms are common in stroke survivors. While obesity has been associated with stroke and depression, its influence on the association between stroke and depressive symptoms is unknown. METHODS: Cross-sectional data from 2015 to 2016 Canadian Community Health Survey was used. History of stroke was self-reported and our outcome of interest was depressive symptoms in the prior 2 weeks, measured using the 9-item Patient Health Questionnaire. Self-reported body mass index (BMI) was modeled as cubic spline terms to allow for nonlinear associations. We used multivariable logistic regression to evaluate the association between stroke and depressive symptoms and added an interaction term to evaluate the modifying effect of BMI. RESULTS: Of the 47,521 participants, 694 (1.0%) had a stroke and 3314 (6.5%) had depressive symptoms. Those with stroke had a higher odds of depressive symptoms than those without (aOR = 3.13, 95% CI 2.48, 3.93). BMI did not modify the stroke-depressive symptoms association (P interaction = 0.242) despite the observed variation in stroke-depressive symptoms association across BMI categories,: normal BMI [18.5-25 kg/m2] (aOR† = 3.91, 95% CI 2.45, 6.11), overweight [25-30 kg/m2] (aOR† = 2.63, 95% CI 1.58, 4.20), and obese [>30 kg/m2] (aOR† = 2.76, 95% CI 1.92, 3.94). Similar results were found when depressive symptoms were modeled as a continuous measure. CONCLUSION: The association between stroke and depressive symptoms is not modified by BMI, needing additional work to understand the role of obesity on depression after stroke.

The association of sexual minority status and bullying victimization is modified by sex and grade: findings from a nationally representative sample.

BACKGROUND: Sexual minority status is associated with face-to-face bullying and cyberbullying victimization. However, limited studies have investigated whether such a relationship differs by sex or grade in a nationally representative sample. METHODS: We concatenated the national high school data from the Youth Risk Behavior Surveillance System (YRBSS) chronologically from 2015 to 2019, resulting in a sample of 32,542 high school students. We constructed models with the interaction term between sexual minority status and biological sex assigned at birth to test the effect modification by sex on both the multiplicative and additive scales. A similar method was used to test the effect modification by grade. RESULTS: Among heterosexual students, females had a higher odds of being bullied than males, while among sexual minority students, males had a higher odds of being bullied. The effect modification by sex was significant on both the multiplicative and additive scales. We also found a decreasing trend of bullying victimization as the grade increased among both heterosexual and sexual minority students. The effect modification by the grade was significant on both the multiplicative and the additive scale. CONCLUSIONS: Teachers and public health workers should consider the difference in sex and grade when designing prevention programs to help sexual minority students.