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.

Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques.

BACKGROUND: Although PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies. OBJECTIVE: This study aimed to predict PM2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables. METHODS: We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations. RESULTS: Our predictive models demonstrate excellent in-sample model performance, as indicated by high R2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM2.5 concentrations over time is observed in Eastern Texas. IMPACT STATEMENT: We utilized machine learning approaches to predict PM2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R2 > 0.9).

A review of machine learning applications in life cycle assessment studies.

Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA. Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML. This review analyzes forty studies reporting quantitative assessment with a combination of LCA and ML methods. We found that ML approaches have been used for generating life cycle inventories, computing characterization factors, estimating life cycle impacts, and supporting life cycle interpretation. Most of the reviewed studies employed a single ML method, with artificial neural networks (ANNs) as the most frequently applied approach. Both supervised and unsupervised ML techniques were used in LCA studies. For studies using supervised ML, training datasets were derived from diverse sources, such as literature, lab experiments, existing databases, and model simulations. Over 70 % of these reviewed studies trained ML models with less than 1500 sample datasets. Although these reviewed studies showed that ML approaches help improve prediction accuracy, pattern discovery and computational efficiency, multiple areas deserve further research. First, continuous data collection and compilation is needed to support more reliable ML and LCA modeling. Second, future studies should report sufficient details regarding the selection criteria for ML models and present model uncertainty analysis. Third, incorporating deep learning models into LCA holds promise to further improve life cycle inventory and impact assessment. Finally, the complexity of current environmental challenges calls for interdisciplinary collaborative research to achieve deep integration of ML into LCA to support sustainable development.

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.

Measuring students' exposure to particulate matter (PM) pollution across microenvironments and seasons using personal air monitors.

Particulate matter (PM) pollution is a significant concern in public health, yet children's exposure is not adequately characterized. This study evaluated PM exposures among primary school-aged children in NYS across different microenvironments. This study helps fill existing knowledge gaps by characterizing PM exposure among this population across seasons and microenvironments. Sixty students were recruited from randomly selected public primary schools representing various socioeconomic statuses. Individual real-time exposure to PM2.5 was measured continuously using AirBeam personal monitors for 48 h. Children were consistently exposed to higher PM2.5 concentrations in the fall (median: fall = 2.84, spring = 2.31, winter = 0.90 µg/m3). At school, 2.19% of PM2.5 measurements exceeded the EPA annual fine particle standard, 12 µg/m3 (winter = 7.38%, fall = 2.39%, spring = 1.38%). In classrooms, PM1-4 concentrations were higher in spring and overnight, while PM7-10 concentrations were higher in fall and school hours. At home, 37.2% of fall measurements exceeded EPA standards (spring = 10.39%, winter = 4.37%). Overall, PM2.5 levels in classrooms and during transportation never rose above the EPA standard for any significant length of time. However, PM2.5 levels routinely exceeded these standards at home, in the fall, and the evening. More extensive studies are needed to confirm these results.

Identifying joint impacts of sun radiation, temperature, humidity, and rain duration on triggering mental disorders using a high-resolution weather monitoring system.

BACKGROUND: Mental disorders (MDs) are behavioral or mental patterns that cause significant distress or impairment of personal functioning. Previously, temperature has been linked to MDs, but most studies suffered from exposure misclassification due to limited monitoring sites. We aimed to assess whether multiple meteorological factors could jointly trigger MD-related emergency department (ED) visits in warm season, using a highly dense weather monitoring system. METHODS: We conducted a time-stratified, case-crossover study. MDs-related ED visits (primary diagnosis) from May-October 2017-2018 were obtained from New York State (NYS) discharge database. We obtained solar radiation (SR), relative humidity (RH), temperature, heat index (HI), and rainfall from Mesonet, a real-time monitoring system spaced about 17 miles (126 stations) across NYS. We used conditional logistic regression to assess the weather-MD associations. RESULTS: For each interquartile range (IQR) increase, both SR (excess risk (ER): 4.9%, 95% CI: 3.2-6.7%) and RH (ER: 4.0%, 95% CI: 2.6-5.4%) showed the largest risk for MD-related ED visits at lag 0-9 days. While temperature presented a short-term risk (highest ER at lag 0-2 days: 3.7%, 95% CI: 2.5-4.9%), HI increased risk over a two-week period (ER range: 3.7-4.5%), and rainfall hours showed an inverse association with MDs (ER: -0.5%, 95% CI: 0.9-(-0.1)%). Additionally, we observed stronger association of SR, RH, temperature, and HI in September and October. Combination of high SR, RH, and temperature displayed the largest increase in MDs (ER: 7.49%, 95% CI: 3.95-11.15%). The weather-MD association was stronger for psychoactive substance usage, mood disorders, adult behavior disorders, males, Hispanics, African Americans, individuals aged 46-65, or Medicare patients. CONCLUSIONS: Hot and humid weather, especially the joint effect of high sun radiation, temperature and relative humidity showed the highest risk of MD diseases. We found stronger weather-MD associations in summer transitional months, males, and minority groups. These findings also need further confirmation.

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.

Residential Proximity to Biorefinery Sources of Air Pollution and Respiratory Diseases in New York State.

Understanding potential health risks associated with biofuel production is critical to sustainably combating energy insecurity and climate change. However, the specific health impacts associated with biorefinery-related emissions are not yet well characterized. We evaluated the relationship between respiratory emergency department (ED) visits (2011-2015) and residential exposure to biorefineries by comparing 15 biorefinery sites to 15 control areas across New York (NY) State. We further examined these associations by biorefinery types (e.g., corn, wood, or soybean), seasons, and lower respiratory disease subtypes. We measured biorefinery exposure using residential proximity in a cross-sectional study and estimation of biorefinery emission via AERMOD-simulated modeling. After controlling for multiple confounders, we consistently found that respiratory ED visit rates among residents living within 10 km of biorefineries were significantly higher (rate ratios (RRs) range from 1.03 to 3.64) than those in control areas across our two types of exposure indices. This relationship held across biorefinery types (higher in corn and soybean biorefineries), seasons (higher in spring and winter), air pollutant types (highest for NO2), and respiratory subtypes (highest for emphysema). Further research is needed to confirm our findings.

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.

Application of data science methods to identify school and home risk factors for asthma and allergy-related symptoms among children in New York.

OBJECTIVES: Few studies have comprehensively assessed multiple environmental exposures affecting children's health. This study applied machine-learning methods to evaluate how indoor environmental conditions at home and school contribute to asthma and allergy-related symptoms. METHODS: We randomly selected 10 public schools representing different socioeconomic statuses in New York State (2017-2019) and distributed questionnaires to students to collect health status and home-and school-environmental exposures. Indoor air quality was measured at school, and ambient particle exposures (PM2.5 and components) were measured using real-time personal monitors for 48 h. We used random forest model to identify the most important risk factors for asthma and allergy-related symptoms, and decision tree for visualizing the inter-relationships among the multiple risk factors with the health outcomes. RESULTS: The top contributing factors identified for asthma were family rhinitis history (relative importance: 10.40%), plant pollen trigger (5.48%); bedroom carpet (3.58%); environmental tobacco smoke (ETS) trigger symptom (2.98%); and ETS exposure (2.56%). For allergy-related symptoms, plant pollen trigger (10.88%), higher paternal education (7.33%), bedroom carpet (5.28%), family rhinitis history (4.78%), and higher maternal education (4.25%) were the strongest contributing factors. Conversely, primary heating with hot water radiator was negatively (-6.86%) associated with asthma symptoms. Younger children (<9 years old) with family history of rhinitis and carpeting in the bedroom were the prominent combined risk factors for asthma. Children jointly exposed to pollen, solvents, and carpeting in their home tended to have greater risks of allergy-related symptoms, even without family history of rhinitis. CONCLUSION: Family rhinitis history, bedroom carpet, and pollen triggers were the most important risk factors for both asthma and allergy-related symptoms. Our new findings included that hot-water radiator was related to reduced asthma symptoms, and the combination of young age, rhinitis history, and bedroom carpeting was related to increased asthma symptoms. Further studies are needed to confirm our findings.

Emergency Food Provision for Children and Families during the COVID-19 Pandemic: Examples from Five U.S. Cities.

As lockdown and school closure policies were implemented in response to the coronavirus, the federal government provided funding and relaxed its rules to support emergency food provision, but not guidance on best practices for effectiveness. Accordingly, cities developed a diverse patchwork of emergency feeding programs. This article uses qualitative data to provide insight into emergency food provision developed in five cities to serve children and families. Based on our qualitative analysis, we find that the effectiveness of local approaches appears to depend on: (i) cross-sector collaboration, (ii) supply chains, and (iii) addressing gaps in service to increased risk populations.

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.

Identifying and evaluating school environmental health indicators.

Children's health, attendance, and academic performance may be affected by school environmental hazards. While prior studies evaluated home environment and health, few have evaluated indicators of school in-/outdoor environment and health. This study addresses this knowledge gap by systematically reviewing and evaluating outdoor and indoor indicators of school environment and student's health and performance in New York State (NYS). We also evaluate statistical methodologies to address highly correlated indicators and integrate multiple exposures. Multiple school environmental indicators were identified from various existing NYS datasets. We summarized data sources, completeness, geographic and temporal coverage, and data quality for each indicator. Each indicator was evaluated by scientific basis/relevance, analytic soundness/feasibility, and interpretation/utility, and validated using objective NYS data. Finally, advanced variable selection methods were described and discussed. We have identified and evaluated multiple school environmental health indicators. It was found that mold and moisture problems, ventilation problems, ambient ozone, and PM2.5 levels are among the top priorities of school environmental issues/indicators in NYS, which were also consistent while using NYS data. Choice of best variable selection method should be made based on the research questions and data characteristics. The school environmental health indicators identified, and variable selection methods evaluated, in this study could be used by other researchers to help school officials and policy makers initiate prevention programs.

Spatially and Temporally Explicit Life Cycle Environmental Impacts of Soybean Production in the U.S. Midwest.

Understanding spatially and temporally explicit life cycle environmental impacts is critical for designing sustainable supply chains for biofuel and animal sectors. However, annual life cycle environmental impacts of crop production at county scale across mutiple years are lacking. To address this knowledge gap, this study used a combination of Environmental Policy Integrated Climate and process-based life cycle assessment models to quantify life cycle global warming (GWP), eutrophication (EU) and acidification (AD) impacts of soybean production in nearly 1000 Midwest counties yr-1 over 9 years. Sequentially, a machine learning approach was applied to identify the top influential factors among soil, climate, and farming practices, which drive the spatial and temporal heterogeneity of life cycle environmental impacts. The results indicated that significant variations existed in life cycle GWP, EU, and AD among counties and across years. Life cycle GWP impacts ranged from -11.4 to 22.0 kg CO2-eq kg soybean-1, whereas life cycle EU and AD impacts varied by factors of 302 and 44, respectively. Nitrogen application rates, temperature in March and soil texture were the top influencing factors for life cycle GWP impacts. In contrast, soil organic content and nitrogen application rate were the top influencing factors for life cycle EU and AD impacts.

Assessing Health Impacts of Conventional Centralized and Emerging Resource Recovery-Oriented Decentralized Water Systems.

Energy shortage and climate change call for sustainable water and wastewater infrastructure capable of simultaneously recovering energy, mitigating greenhouse gas emissions, and protecting public health. Although energy and greenhouse gas emissions of water and wastewater infrastructure are extensively studied, the human health impacts of innovative infrastructure designed under the principles of decentralization and resource recovery are not fully understood. In order to fill this knowledge gap, this study assesses and compares the health impacts of three representative systems by integrating life cycle and microbial risk assessment approaches. This study found that the decentralized system options, such as on-site septic tank and composting or urine diverting toilets, presented much lower life cycle cancer and noncancer impacts than the centralized system. The microbial risks of decentralized systems options were also lower than those of the centralized system. Moreover, life cycle cancer and noncancer impacts contributed to approximately 95% of total health impacts, while microbial risks were associated with the remaining 5%. Additionally, the variability and sensitivity assessment indicated that reducing energy use of wastewater treatment and water distribution is effective in mitigating total health damages of the centralized system, while reducing energy use of water treatment is effective in mitigating total health damages of the decentralized systems.

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.

How community vulnerability factors jointly affect multiple health outcomes after catastrophic storms.

BACKGROUND: While previous studies uncovered individual vulnerabilities to health risks during catastrophic storms, few evaluated the population vulnerability which is more important for identifying areas in greatest need of intervention. OBJECTIVES: We assessed the association between community factors and multiple health outcomes, and developed a community vulnerability index. METHODS: We retained emergency department visits for several health conditions from the 2005-2014 New York Statewide Planning and Research Cooperative System. We developed distributed lag nonlinear models at each spatial cluster across eight counties in downstate New York to evaluate the health risk associated with Superstorm Sandy (10/28/2012-11/9/2012) compared to the same period in other years, then defined census tracts in clusters with an elevated risk as "risk-elevated communities", and all others as "unelevated". We used machine-learning techniques to regress the risk elevation status against community factors to determine the contribution of each factor on population vulnerability, and developed a community vulnerability index (CVI). RESULTS: Overall, community factors had positive contributions to increased community vulnerabilities to Sandy-related substance abuse (91.35%), injuries (70.51%), cardiovascular diseases (8.01%), and mental disorders (2.71%) but reversely contributed to respiratory diseases (-34.73%). The contribution of low per capita income (max: 22.08%), the percentage of residents living in group quarters (max: 31.39%), the percentage of areas prone to flooding (max: 38.45%), and the percentage of green coverage (max: 29.73%) tended to be larger than other factors. The CVI based on these factors achieved an accuracy of 0.73-0.90 across outcomes. CONCLUSIONS: Our findings suggested that substance abuse was the most sensitive disease susceptible to less optimal community indicators, whereas respiratory diseases were higher in communities with better social environment. The percentage of residents in group quarters and areas prone to flooding were among dominant predictors for community vulnerabilities. The CVI based on these factors has an appropriate predictive performance.