Fruit and Vegetable Consumption, Food Acquisition Behavior, and Food Insecurity Before and After the COVID-19 Vaccine Rollout.

OBJECTIVES: To assess changes in food acquisition behavior, food insecurity, and dietary behavior and identify factors associated with fruit and vegetable (FV) consumption during the transitional period (before and after the initial vaccine rollout for all adults) of the COVID-19 pandemic. DESIGN: Successive independent samples design. Online surveys were conducted from October 2020 to February 2021 (time 1, before the vaccine rollout) and from October 2021 to December 2021 (time 2, after the vaccine rollout). Descriptive analysis examined changes in food sources, food security, and daily FV consumption in cup equivalents (CEs) from time 1 to time 2. A multivariable logistic regression analysis examined factors associated with FV consumption. SETTING: The Capital Region of New York State. PARTICIPANTS: 1553 adults 18 years of age and older. MAIN OUTCOME MEASURE: Meeting the 2020-2025 MyPlate daily FV consumption recommendations. RESULTS: There were statistically significant (P < .05) increases in the use of supermarkets, eat-in restaurants, farmers' markets, and convenience stores from time 1 to time 2. Food insecurity (40.1% vs 39.4%) and FV consumption (2.6 CE vs 2.4 CE) slightly declined but not significantly. Home food procurement such as gardening and foraging (OR, 1.61; 95% CI, 1.08-2.37) and shopping at food co-op/health food stores (OR, 1.64; 95% CI, 1.07-2.49) were significantly associated with the FV outcome, and these relationships were not modified by food security status. CONCLUSIONS: The present study highlights the importance of food sources in understanding adult dietary behavior during the transitional period of the pandemic. Continuing efforts to monitor access to food sources, food insecurity, and dietary behavior are warranted as various COVID-related emergency food assistance measures have expired.

Long-term exposure to particulate matter and COPD mortality: Insights from causal inference methods based on a large population cohort in southern China.

BACKGROUND: Evidence of the association between long-term exposure to particulate matter (PM) and chronic obstructive pulmonary disease (COPD) mortality from large population-based cohort study is limited and often suffers from residual confounding issues with traditional statistical methods. We hereby assessed the casual relationship between long-term PM (PM2.5, PM10 and PM10-2.5) exposure and COPD mortality in a large cohort of Chinese adults using state-of-the-art causal inference approaches. METHODS: A total of 580,757 participants in southern China were enrolled in a prospective cohort study from 2009 to 2015 and followed up until December 2020. Exposures to PM at each residential address were obtained from the Long-term Gap-free High-resolution Air Pollutant Concentration dataset. Marginal structural Cox models were used to investigate the association between COPD mortality and annual average exposure levels of PM exposure. RESULTS: During an average follow-up of 8.0 years, 2250 COPD-related deaths occurred. Under a set of causal inference assumptions, the hazard ratio (HR) for COPD mortality was estimated to be 1.046 (95 % confidence interval: 1.034-1057), 1.037 (1.028-1.047), and 1.032 (1.006-1.058) for each 1-µg/m3 increase in annual average concentrations of PM2.5, PM10, and PM10-2.5 respectively. Additionally, the detrimental effects appeared to be more pronounced among the elderly (age ≥ 65) and inactive participants. The effect estimates of PM2.5, PM10, and PM10-2.5 tend to be greater among participants who were generally exposed to PM10 concentrations below 70 µg/m3 than that among the general population. CONCLUSION: Our results support causal links between long-term PM exposure and COPD mortality, highlighting the urgency for more effective strategies to reduce PM exposure, with particular attention on protecting potentially vulnerable groups.

How neighborhood environment modified the effects of power outages on multiple health outcomes in New York state?

Background: Although power outage (PO) is one of the most important consequences of increasing weather extremes and the health impact of POs has been reported previously, studies on the neighborhood environment underlying the population vulnerability in such situations are limited. This study aimed to identify dominant neighborhood environmental predictors which modified the impact of POs on multiple health outcomes in New York State. Methods: We applied a two-stage approach. In the first stage, we used time series analysis to determine the impact of POs (versus non-PO periods) on multiple health outcomes in each power operating division in New York State, 2001-2013. In the second stage, we classified divisions as risk-elevated and non-elevated, then developed predictive models for the elevation status based on 36 neighborhood environmental factors using random forest and gradient boosted trees. Results: Consistent across different outcomes, we found predictors representing greater urbanization, particularly, the proportion of residents having access to public transportation (importance ranging from 4.9-15.6%), population density (3.3-16.1%), per capita income (2.3-10.7%), and the density of public infrastructure (0.8-8.5%), were associated with a higher possibility of risk elevation following power outages. Additionally, the percent of minority (-6.3-27.9%) and those with limited English (2.2-8.1%), the percent of sandy soil (6.5-11.8%), and average soil temperature (3.0-15.7%) were also dominant predictors for multiple outcomes. Spatial hotspots of vulnerability generally were located surrounding New York City and in the northwest, the pattern of which was consistent with socioeconomic status. Conclusion: Population vulnerability during power outages was dominated by neighborhood environmental factors representing greater urbanization.

The immediate effects of winter storms and power outages on multiple health outcomes and the time windows of vulnerability.

BACKGROUND: While most prior research has focused on extreme heat, few assessed the immediate health effects of winter storms and associated power outages (PO), although severe storms have become more frequent. This study evaluates the joint and independent health effects of winter storms and PO, snow versus ice-storm, effects by time window (peak timing, winter/transitional months) and the impacts on critical care indicators including numbers of comorbidity, procedure, length of stay and cost. METHODS: We use distributed lag nonlinear models to assess the impacts of winter storm/PO on hospitalizations due to cardiovascular, lower respiratory diseases (LRD), respiratory infections, food/water-borne diseases (FWBD) and injuries in New York State on 0-6 lag days following storm/PO compared with non-storm/non-PO periods (references), while controlling for time-varying factors and PM2.5. The storm-related hospitalizations are described by time window. We also calculate changes in critical care indicators between the storm/PO and control periods. RESULTS: We found the joint effects of storm/PO are the strongest (risk ratios (RR) range: 1.01-1.90), followed by that of storm alone (1.02-1.39), but not during PO alone. Ice storms have stronger impacts (RRs: 1.04-3.15) than snowstorms (RRs: 1.03-2.21). The storm/PO-health associations, which occur immediately, and some last a whole week, are stronger in FWBD, October/November, and peak between 3:00-8:00 p.m. Comorbidity and medical costs significantly increase after storm/PO. CONCLUSION: Winter storms increase multiple diseases, comorbidity and medical costs, especially when accompanied by PO or ice storms. Early warnings and prevention may be critical in the transitional months and afternoon rush hours.

Power Outage: An Ignored Risk Factor for COPD Exacerbations.

BACKGROUND: COPD is the third leading cause of death in the United States, with 16 million Americans currently experiencing difficulty with breathing. Power outages could be life-threatening for those relying on electricity. However, significant gaps remain in understanding the potential impact of power outages on COPD exacerbations. RESEARCH QUESTION: The goal of this study was to determine how power outages affect COPD exacerbations. STUDY DESIGN AND METHODS: Using distributed lag nonlinear models controlling for time-varying confounders, the hospitalization rate during a power outage was compared vs non-outage periods to determine the rate ratio (RR) for COPD and its subtypes at each of 0 to 6 lag days in New York State from 2001 to 2013. Stratified analyses were conducted according to sociodemographic characteristics, season, and clinical severity; changes were investigated in numerous critical medical indicators, including length of stay, hospital cost, the number of comorbidities, and therapeutic procedures between the two periods. RESULTS: The RR of COPD hospitalization following power outages ranged from 1.03 to 1.39 across lag days. The risk was strongest at lag0 and lag1 days and lasted significantly for 7 days. Associations were stronger for the subgroup with acute bronchitis (RR, 1.08-1.69) than for cases of acute exacerbation (RR, 1.03-1.40). Compared with non-outage periods, the outage period was observed to be $4.67 thousand greater in hospital cost and 1.38 greater in the number of comorbidities per case. The average cost (or number of comorbidities) was elevated in all groups stratified according to cost (or number of comorbidities). In contrast, changes in the average length of stay (-0.43 day) and the average number of therapeutic procedures (-0.09) were subtle. INTERPRETATION: Power outages were associated with a significantly elevated rate of COPD hospitalization, as well as greater costs and number of comorbidities. The average cost and number of comorbidities were elevated in all clinical severity groups.

Sources of exposure to urinary phthalates among couples undergoing infertility treatment.

Phthalates have been implicated as reproductive toxicants in animal models and in human populations. This study examined associations between potential exposure sources and urinary phthalate metabolite concentrations among women (n = 56) and their male partners (n = 43) undergoing in vitro fertilization (IVF). On the day of oocyte retrieval, participants provided urine samples and completed questionnaires detailing use of personal care products (PCPs), and consumption of medications, foods, and beverages in the preceding 24 h. Urine was analyzed for MEP, MBP, MPP, MHxP, MEHP, MEHHP, MECPP, MiNP, MiDP, MCHP, and MBzP, via liquid chromatography-tandem mass spectrometry. We employed principal component analysis (PCA) to summarize exposure sources and regression models to estimate associations between exposure patterns and urinary phthalate metabolites, adjusted for confounding variables. Among women, application of more body washes and eye creams, and consumption of more supplements, was associated with greater urinary MECPP [relative difference = 1.36 (95% CI: 1.28, 1.45)] and the molar sum of DEHP metabolites, including MEHP, MEHHP, and MECPP [∑DEHP; 1.26 (95% CI: 1.17, 1.34)]. Among men, consumption of more supplements and allergy medications was associated with greater urinary MECPP, MEHHP, and ∑DEHP [relative difference = 1.13 (95% CI: 1.02, 1.23)] concentrations. Identifying differences in sources of phthalate exposure may help clinicians to intervene to reduce exposure as part of a comprehensive strategy to help improve IVF outcomes.

Projecting life-cycle environmental impacts of corn production in the U.S. Midwest under future climate scenarios using a machine learning approach.

Climate change is exacerbating environmental pollution from crop production. Spatially and temporally explicit estimates of life-cycle environmental impacts are therefore needed for suggesting location and time relevant environmental mitigations strategies. Emission factors and process-based mechanism models are popular approaches used to estimate life-cycle environmental impacts. However, emission factors are often incapable of describing spatial and temporal heterogeneity of agricultural emissions, whereas process-based mechanistic models, capable of capturing the heterogeneity, tend to be very complicated and time-consuming. Efficient prediction of life-cycle environmental impacts from agricultural production is lacking. This study develops a rapid predictive model to quantify life-cycle global warming (GW) and eutrophication (EU) impacts of corn production using a novel machine learning approach. We used the boosted regression tree (BRT) model to estimate future life-cycle environmental impacts of corn production in U.S. Midwest counties under four emissions scenarios for years 2022-2100. Results from BRT models indicate that the cross-validation (R2) for predicting life cycle GW and EU impacts ranged from 0.78 to 0.82, respectively. Furthermore, results show that future life-cycle GW and EU impacts of corn production will increase in magnitude under all four emissions scenarios, with the highest environmental impacts shown under the high-emissions scenario. Moreover, this study found that changes in precipitation and temperature played a significant role in influencing the spatial heterogeneity in all life-cycle impacts across Midwest counties. The BRT model results indicate that machine learning can be a useful tool for predicting spatially and temporally explicit future life-cycle environmental impacts associated with corn production under different climate scenarios.