Potential causal links of long-term exposure to PM2.5 and its chemical components with the risk of nasopharyngeal carcinoma recurrence: A 10-year cohort study in South China.

There is a lack of evidence from cohort studies on the causal association of long-term exposure to ambient fine particulate matter (PM2.5) and its chemical components with the risk of nasopharyngeal carcinoma (NPC) recurrence. Based on a 10-year prospective cohort of 1184 newly diagnosed NPC patients, we comprehensively evaluated the potential causal links of ambient PM2.5 and its chemical components including black carbon (BC), organic matter (OM), sulfate (SO4 2-), nitrate (NO3 -), and ammonium (NH4 +) with the recurrence risk of NPC using a marginal structural Cox model adjusted with inverse probability weighting. We observed 291 NPC patients experiencing recurrence during the 10-year follow-up and estimated a 33% increased risk of NPC recurrence (hazard ratio [HR]: 1.33, 95% confidence interval [CI]: 1.02-1.74) following each interquartile range (IQR) increase in PM2.5 exposure. Each IQR increment in BC, NH4 +, OM, NO3 -, and SO4 2- was associated with HRs of 1.36 (95%CI: 1.13-1.65), 1.35 (95%CI: 1.07-1.70), 1.33 (95%CI: 1.11-1.59), 1.32 (95%CI: 1.06-1.64), 1.31 (95%CI: 1.08-1.57). The elderly, patients with no family history of cancer, no smoking history, no drinking history, and those with severe conditions may exhibit a greater likelihood of NPC recurrence following exposure to PM2.5 and its chemical components. Additionally, the effect estimates of the five components are greater among patients who were exposed to high concentration than in the full cohort of patients. Our study provides solid evidence for a potential relationship between long-term exposure to PM2.5 and its components and the risk of NPC recurrence.

Residential natural hazard risk and mental health effects.

Mental health effects are frequently reported following natural disasters. However, little is known about effects of living in a hazard-prone region on mental health. We analyzed data from 9,312 Gulf Long-term Follow-up Study participants who completed standardized mental health questionnaires including the Patient Health Questionnaire-9 (depression=score≥10), Generalized Anxiety Disorder Questionnaire-7 (anxiety=score≥10), and Primary Care PTSD Screen (PTSD=score≥3). Geocoded residential addresses were linked to census-tract level natural hazard risk scores estimated using the National Risk Index (NRI). We considered an overall risk score representing 18 natural hazards, and individual scores for hurricanes, heatwaves, coastal flooding and riverine flooding. Log binomial regression estimated prevalence ratios (PR) and 95% confidence intervals (CI) for associations between risk scores (quartiles) and mental health outcomes. Increasing hurricane and coastal flooding scores were associated with all mental health outcomes in a suggestive exposure-response manner. Associations were strongest for PTSD, with PRs for the highest vs. lowest quartile of hurricane and coastal flooding risks of 2.29(1.74-3.01) and 1.59(1.23-2.05), respectively. High heatwave risk was associated with anxiety (PR=1.25(1.12-1.38)) and depression (PR=1.19(1.04-1.36)) and suggestively with PTSD (PR=1.20(0.94-1.52)). Results suggest that living in areas prone to natural disasters is one factor associated with poor mental health status.

Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China.

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 µg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 µg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

Associations Between Ambient Extreme Heat Exposure and Emergency Department Visits Related to Kidney Disease.

RATIONALE & OBJECTIVE: Extreme heat exposure is associated with multiple diseases. However, our current understanding of the specific impact of extreme heat exposure on kidney disease is limited. STUDY DESIGN: Case-crossover study. SETTING & PARTICIPANTS: 1,114,322 emergency department (ED) visits with a principal diagnosis of kidney disease were identified in New York state, 2005-2013. EXPOSURE: Extreme heat exposure was defined as when the daily temperature exceeded the 90th percentile temperature of that month during the study period in the county. OUTCOME: ED visits with a principal diagnosis of kidney disease and its subtypes (ICD-9 [International Classification of Diseases, Ninth Revision] codes 580-599, 788). ANALYTICAL APPROACH: Extreme heat exposure on the ED visit days was compared with extreme heat exposure on control days using a conditional logistic regression model, controlling for humidity, air pollutants, and holidays. The excess risk of kidney disease was calculated for a week (lag days 0-6) after extreme heat exposure during the warm season (May through September). We also stratified our estimates by sociodemographic characteristics. RESULTS: Extreme heat exposure was associated with a 1.7% (lag day 0) to 3.1% (lag day 2) higher risk of ED visits related to kidney disease; this association was stronger with a greater number of extreme heat exposure days in the previous week. The association with extreme heat exposure lasted for an entire week and was stronger in the transitional months (ie, May and September; excess rates ranged from 1.8% to 5.1%) rather than the summer months (June through August; excess rates ranged from 1.5% to 2.7%). The strength of association was greater among those with ED visits related to acute kidney injury, kidney stones, and urinary tract infections. Age and sex may modify the association between extreme heat exposure and ED visits. LIMITATIONS: Individual exposure to heat-how long people were outside or whether they had access to air conditioning-was unknown. CONCLUSIONS: Extreme heat exposure was significantly associated with a dose-dependent greater risk of ED visits for kidney disease.

Environmental exposure disparities in ultrafine particles and PM2.5 by urbanicity and socio-demographics in New York state, 2013-2020.

BACKGROUND: The spatiotemporal and demographic disparities in exposure to ultrafine particles (UFP; number concentrations of particulate matter (PM) with diameter ≤0.1 µm), a key subcomponent of fine aerosols (PM2.5; mass concentrations of PM ≤ 2.5 µm), have not been well studied. OBJECTIVE: To quantify and compare the aerosol pollutant exposure disparities for UFP and PM2.5 by socio-demographic factors in New York State (NYS). METHODS: Ambient atmospheric UFP and PM2.5 were quantified using a global three-dimensional model of chemical transport with state-of-the-science aerosol microphysical processes validated extensively with observations. We matched these to U.S. census demographic data for varied spatial scales (state, county, county subdivision) and derived population-weighted aerosol exposure estimates. Aerosol exposure disparities for each demographic and socioeconomic (SES) indicator, with a focus on race-ethnicity and income, were quantified for the period 2013-2020. RESULTS: The average NYS resident was exposed to 4451 #·cm-3 UFP and 7.87 µg·m-3 PM2.5 in 2013-2020, but minority race-ethnicity groups were invariably exposed to greater daily aerosol pollution (UFP: +75.0% & PM2.5: +16.2%). UFP has increased since 2017 and is temporally and seasonally out-of-phase with PM2.5. Race-ethnicity exposure disparities for PM2.5 have declined over time; by -6% from 2013 to 2017 and plateaued thereafter despite its decreasing concentrations. In contrast, these disparities have increased (+12.5-13.5%) for UFP. The aerosol pollution exposure disparities were the highest for low-income minorities and were more amplified for UFP than PM2.5. DISCUSSION: We identified large disparities in aerosol pollution exposure by urbanization level and socio-demographics in NYS residents. Jurisdictions with higher proportions of race-ethnicity minorities, low-income residents, and greater urbanization were disproportionately exposed to higher concentrations of UFP and PM2.5 than other NYS residents. These race-ethnicity exposure disparities were much larger, more disproportionate, and unabating over time for UFP compared to PM2.5 across various income strata and levels of urbanicity.

Triggering of ST-elevation myocardial infarction by ultrafine particles in New York: Changes following Tier 3 vehicle introduction.

BACKGROUND: Previously, we found increased rates of ST-elevation myocardial infarction (STEMI) associated with increased ultrafine particle (UFP; <100 nm) concentrations in the previous few hours in Rochester, New York. Relative rates were higher after air quality policies and a recession reduced pollutant concentrations (2014-2016 versus 2005-2013), suggesting PM composition had changed and the same PM mass concentration had become more toxic. Tier 3 light duty vehicles, which should produce less primary organic aerosols and oxidizable gaseous compounds, likely making PM less toxic, were introduced in 2017. Thus, we hypothesized we would observe a lower relative STEMI rate in 2017-2019 than 2014-2016. METHODS: Using STEMI events treated at the University of Rochester Medical Center (2014-2019), UFP and other pollutants measured in Rochester, a case-crossover design, and conditional logistic regression models, we estimated the rate of STEMI associated with increased UFP and other pollutants in the previous hours and days in the 2014-2016 and 2017-2019 periods. RESULTS: An increased rate of STEMI was associated with each 3111 particles/cm3 increase in UFP concentration in the previous hour in 2014-2016 (lag hour 0: OR = 1.22; 95% CI = 1.06, 1.39), but not in 2017-2019 (OR = 0.94; 95% CI = 0.80, 1.10). There were similar patterns for black carbon, UFP11-50nm, and UFP51-100nm. In contrast, increased rates of STEMI were associated with each 0.6 ppb increase in SO2 concentration in the previous 120 h in both periods (2014-2016: OR = 1.26, 95% CI = 1.03, 1.55; 2017-2019: OR = 1.21, 95% CI = 0.87, 1.68). CONCLUSIONS: Greater rates of STEMI were associated with short term increases in concentrations of UFP and other motor vehicle related pollutants before Tier 3 introduction (2014-2016), but not afterwards (2017-2019). This change may be due to changes in PM composition after Tier 3 introduction, as well as to increased exposure misclassification and greater underestimation of effects from 2017 to 2019.

COVID-19 Symptoms and Deaths among Healthcare Workers, United States.

We evaluated whether demographics and COVID-19 symptoms predicted COVID-19 deaths among healthcare workers (HCWs) in the United States by comparing COVID-19 deaths in HCWs with 3 control groups (HCW nondeaths, non-HCW deaths, and non-HCW nondeaths) using a case-control design. We obtained patient-level data of 33 variables reported during January 1, 2020-October 12, 2021, in all US states. We used logistic regression analysis while controlling for confounders. We found that persons who were >50 years of age, male, Black, or Asian experienced significantly more deaths than matched controls. In addition, HCWs who died had higher risks for the most severe clinical indicators. We also found that the most indicative symptoms were preexisting medical conditions, shortness of breath, fever, cough, and gastrointestinal symptoms. In summary, minority, male, and older HCWs had greater risk for COVID-19 death. Severe clinical indicators and specific symptoms may predict COVID-19-related deaths among HCWs.

Development of a Prognostic Model for Predicting Multiple Sclerosis After Optic Neuritis: A Secondary Analysis of Data From the Optic Neuritis Treatment Trial.

BACKGROUND: Optic neuritis can be the initial manifestation of multiple sclerosis (MS). The purpose of this study was to develop a prognostic model for predicting the risk of MS development among patients with optic neuritis. METHODS: The data from 388 patients with optic neuritis were retrieved from the Optic Neuritis Treatment Trial (ONTT). Cox proportional hazards regression analysis was used to develop a prognostic model. The performance of the model was assessed by using Harrell's C-index and calibration curves. The rates of MS development were estimated using the Kaplan-Meier method. RESULTS: Among the enrolled subjects, a total of 154 (39.7%) patients developed clinically definite MS during a median follow-up period of 15.8 years (interquartile range, 7.2-16.9 years). The factors associated with the development of MS were the presence of brain lesions as on baseline MRI, previous nonspecific neurologic symptoms, commencing low-dose corticosteroids treatment, ocular pain, and absence of optic disc/peripapillary hemorrhage. After incorporating these 5 factors into the prognostic model, a C-index of 0.72 (95% confidence interval [CI], 0.69-0.76) and good calibration curves were obtained. The C-index of the model was significantly higher than the C-indexes of any single factor (P < 0.001 in all cases). The model was able to stratify the ONTT patient cohort into 3 risk groups with significantly different intergroup rates of developing MS (rates for developing MS within a 15-year period: high-risk group, 75.7% [95% CI, 65.6%-82.9%], intermediate-risk group, 44.7% [95% CI, 31.4%-55.4%]; and low-risk group, 20.8% [95% CI, 14.2%-26.8%]; log-rank P < 0.001). CONCLUSIONS: This prognostic model had a better prediction ability when compared with the standard practice that relies solely on using brain lesions on MRI. It can, therefore, help guide decision-making to initiate earlier disease-modifying therapy for patients with optic neuritis at risk of developing MS.

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.

APS (Age, Platelets, 2D Shear-Wave Elastography) Score Predicts Hepatocellular Carcinoma in Chronic Hepatitis B.

Background Two-dimensional (2D) shear-wave elastography (SWE) has been considered to be useful in predicting hepatocellular carcinoma (HCC) development in patients with chronic hepatitis B (CHB). Purpose To develop a risk model using 2D SWE to predict HCC in patients with CHB and to compare its predictive value with that of other models. Materials and Methods Patients with biopsy-proven CHB who underwent US and 2D SWE between April 2011 and December 2015 were enrolled in this study. After 2D SWE and biopsy were performed, the patients received regular follow-up for the detection of HCC. The scoring system was developed by dividing the parameters of the Cox proportional hazards model by the smallest parameter and simplifying the assigned points to integers. The predictive performance of the new score was compared with that of other scores. Results Among the 654 patients (mean age, 37 years; range, 30-43 years; 510 men), 26 developed HCC. The variables of age, platelet count, and liver stiffness measurement at 2D SWE were weighted to develop the so-called APS score, with a cutoff of 60 showing the best discrimination for HCC risk. The APS score (area under the receiver operating characteristic curve [AUC], 0.89) was superior to that of the Chinese University HCC prediction score constructed from age, albumin level, bilirubin level, hepatitis B virus (HBV) DNA level, and cirrhosis (AUC, 0.70; P = .005) and slightly higher than that of the guide with age, gender, HBV DNA level, core promoter mutations, and cirrhosis, or GAG-HCC score (AUC, 0.82; P = .052). In patients who underwent transient elastography, the AUC of the APS score was 0.79, compared with 0.82 for the modified risk estimation for HCC in CHB, or mREACH-B, score (P = .05). The APS score performed better in patients regardless of whether antiviral treatment was used, inflammation grade was low or high, or alanine aminotransferase levels were normal or high (all P > .05). Conclusion The APS score based on only the patient's baseline liver stiffness measurement at two-dimensional shear-wave elastography, age, and platelet count is valuable for predicting hepatocellular carcinoma in patients with chronic hepatitis B. © RSNA, 2021 Online supplemental material is available for this article.

Mobile Texting and Lay Health Supporters to Improve Schizophrenia Care in a Resource-Poor Community in Rural China (LEAN Trial): Randomized Controlled Trial Extended Implementation.

BACKGROUND: Schizophrenia is a severe and disabling condition that presents a dire health equity challenge. Our initial 6-month trial (previously reported) using mobile texting and lay health supporters, called LEAN, significantly improved medication adherence from 0.48 to 0.61 (adjusted mean 0.11, 95% CI 0.03 to 0.20, P=.007) for adults with schizophrenia living in a resource-poor village in rural China. OBJECTIVE: We explored the effectiveness of our texting program in improving participants' medication adherence, functioning, and symptoms in an extended implementation of the intervention after its initial phase. METHODS: In an approximated stepped-wedge wait-list design randomized controlled trial, 277 community-dwelling villagers with schizophrenia were assigned 1:1 in phase 1 into intervention and wait-list control groups. The intervention group received (1) lay health supporters (medication or care supervisors), (2) e-platform (mobile-texting reminders and education message) access, (3) a token gift for positive behavioral changes, and (4) integration with the existing government community-mental health program (the 686 Program) while the wait-listed control group initially only received the 686 Program. Subsequently (in the extended period), both groups received the LEAN intervention plus the 686 Program. The primary outcome was antipsychotic medication adherence (percentage of dosages taken over the past month assessed by unannounced home-based pill counts). The secondary outcomes were symptoms measured during visits to 686 Program psychiatrists using the Clinical Global Impression scale for schizophrenia and functioning measured by trained student assessors using the World Health Organization Disability Assessment Schedule 2.0. Other outcomes included data routinely collected in the 686 Program system (refill records, rehospitalization due to schizophrenia, death for any reason, suicide, wandering, and violent behaviors). We used intention-to-treat analysis and missing data were imputed. A generalized estimating equation model was used to assess program effects on antipsychotics medication adherence, symptoms, and functioning. RESULTS: Antipsychotics medication adherence improved from 0.48 in the control period to 0.58 in the extended intervention period (adjusted mean difference 0.11, 95% CI 0.04 to 0.19; P=.004). We also noted an improvement in symptoms (adjusted mean difference -0.26, 95% CI -0.50 to -0.02; P=.04; Cohen d effect size 0.20) and a reduction in rehospitalization (0.37, 95% CI 0.18 to 0.76; P=.007; number-needed-to-treat 8.05, 95% CI 4.61 to 21.41). There was no improvement in functioning (adjusted mean difference 0.02, 95% CI -0.01 to 0.06; P=.18; Cohen d effect size 0.04). CONCLUSIONS: In an extended implementation, our intervention featuring mobile texting messages and lay health workers in a resource-poor community setting was more effective than the 686 Program alone in improving medication adherence, improving symptoms, and reducing rehospitalization. TRIAL REGISTRATION: Chinese Clinical Trial Registry; ChiCTR-ICR-15006053 https://tinyurl.com/y5hk8vng.

Ultrafine Particles and Hospital Visits for Chronic Lower Respiratory Diseases in New York State.

Rationale: Exposure to particulate matter is associated with various adverse health outcomes. Ultrafine particles (UFPs; diameter <0.1 µm) are a unique public health challenge because of their size. However, limited studies have examined their impacts on human health, especially across seasons and demographic characteristics. Objectives: To evaluate the effect of UFP exposure on the risk of visiting the emergency department (ED) for a chronic lower respiratory disease (CLRD) in New York State in 2013-2018. Methods: We used a case-crossover design and conditional logistic regression to estimate how UFP exposure led to CLRD-related ED visits. GEOS-Chem Advanced Particle Microphysics, a state-of-the-art chemical transport model with a size-resolved particle microphysics model, generated air pollution simulation data. We then matched UFP exposure estimates to geocoded health records for asthma, bronchiectasis, chronic bronchitis, emphysema, unspecified bronchitis, and other chronic airway obstructions in New York State from 2013 through 2018. In addition, we assessed interactions with age, ethnicity, race, sex, meteorological factors, and season. Results: Each 1-(interquartile range [IQR]) increase in UFP exposure led to a 0.37% increased risk of a respiratory-related ED visit on lag 0-0, or the day of the ED visits, (95% confidence interval [CI], 0.23-0.52%) and a 1.81% increase on lag 0-6, or 6 days before the ED visit, (95% CI, 1.58-2.03%). The highest risk was in the emphysema subtype (lag 0-5, 4.18%; 95% CI, 0.16-8.37%), followed by asthma (lag 0-6, 2.00%), chronic bronchitis (lag 0-6, 1.78%), other chronic airway obstructions (lag 0-6, 1.60%), and unspecified bronchitis (lag 0-6, 1.49%). We also found significant interactions between UFP health impacts and season (Fall, 3.29%), temperature (<90th percentile, 2.27%), relative humidity (>90th percentile, 4.63%), age (children aged <18 yr, 3.19%), and sex (men, 2.06%) on lag 0-6. Conclusions: In this study, UFP exposure increased CLRD-related ED visits across all seasons and demographic characteristics, yet these associations varied according to various factors, which requires more research.

The Joint Effects of Thunderstorms and Power Outages on Respiratory-Related Emergency Visits and Modifying and Mediating Factors of This Relationship.

BACKGROUND: While limited studies have evaluated the health impacts of thunderstorms and power outages (POs) separately, few have assessed their joint effects. We aimed to investigate the individual and joint effects of both thunderstorms and POs on respiratory diseases, to identify disparities by demographics, and to examine the modifications and mediations by meteorological factors and air pollution. METHODS: Distributed lag nonlinear models were used to examine exposures during three periods (i.e., days with both thunderstorms and POs, thunderstorms only, and POs only) in relation to emergency department visits for respiratory diseases (2005-2018) compared to controls (no thunderstorm/no PO) in New York State (NYS) while controlling for confounders. Interactions between thunderstorms and weather factors or air pollutants on health were assessed. The disparities by demographics and seasons and the mediative effects by particulate matter with aerodynamic diameter ≤2.5µm (PM2.5) and relative humidity (RH) were also evaluated. RESULTS: Thunderstorms and POs were independently associated with total and six subtypes of respiratory diseases in NYS [highest risk ratio (RR) = 1.12; 95% confidence interval (CI): 1.08, 1.17], but the impact was stronger when they co-occurred (highest RR = 1.44; 95% CI: 1.22, 1.70), especially during grass weed, ragweed, and tree pollen seasons. The stronger thunderstorm/PO joint effects were observed on chronic obstructive pulmonary diseases, bronchitis, and asthma (lasted 0-10 d) and were higher among residents who lived in rural areas, were uninsured, were of Hispanic ethnicity, were 6-17 or over 65 years old, and during spring and summer. The number of comorbidities was significantly higher by 0.299-0.782/case. Extreme cold/heat, high RH, PM2.5, and ozone concentrations significantly modified the thunderstorm-health effect on both multiplicative and additive scales. Over 35% of the thunderstorm effects were mediated by PM2.5 and RH. CONCLUSION: Thunderstorms accompanied by POs showed the strongest respiratory effects. There were large disparities in thunderstorm-health associations by demographics. Meteorological factors and air pollution levels modified and mediated the thunderstorm-health effects. https://doi.org/10.1289/EHP13237.

Residential air pollution, greenspace, and adverse mental health outcomes in the U.S. Gulf Long-term Follow-up Study.

Air pollution and greenness are environmental determinants of mental health, though existing evidence typically considers each exposure in isolation. We evaluated relationships between co-occurring air pollution and greenspace levels and depression and anxiety. We estimated cross-sectional associations among 9015 Gulf Long-term Follow-up Study participants living in the southeastern U.S. who completed the Patient Health Questionnaire-9 (depression: score ≥ 10) and Generalized Anxiety Disorder Questionnaire-7 (anxiety: score ≥ 10). Participant residential addresses were linked to annual average concentrations of particulate matter (1 km PM2.5) and nitrogen dioxide (1 km NO2), as well as satellite-based greenness (2 km Enhanced Vegetation Index (EVI)). We used adjusted log-binomial regression to estimate prevalence ratios (PR) and 95 % confidence intervals (CI) for associations between exposures (quartiles) and depression and anxiety. In mutually adjusted models (simultaneously modeling PM2.5, NO2, and EVI), the highest quartile of PM2.5 was associated with increased prevalence of depression (PR = 1.17, 95 % CI: 1.06-1.29), whereas the highest quartile of greenness was inversely associated with depression (PR = 0.89, 95 % CI: 0.80-0.99). Joint exposure to greenness mitigated the impact of PM2.5 on depression (PRPM only = 1.20, 95 % CI: 1.06-1.36; PRPM+green = 0.98, 95 % CI: 0.83-1.16) and anxiety (PRPM only = 1.10, 95 % CI: 1.00-1.22; PRPM+green = 0.95, 95 % CI: 0.83-1.09) overall and in subgroup analyses. Observed associations were stronger in urbanized areas and among nonwhite participants, and varied by neighborhood deprivation. NO2 exposure was not independently associated with depression or anxiety in this population. Relationships between PM2.5, greenness, and depression were strongest in the presence of characteristics that are highly correlated with lower socioeconomic status, underscoring the need to consider mental health as an environmental justice issue.

Association between solar radiation and mood disorders among Gulf Coast residents.

BACKGROUND: Climate factors such as solar radiation could contribute to mood disorders, but evidence of associations between exposure to solar radiation and mood disorders is mixed and varies by region. OBJECTIVE: To evaluate the association of solar radiation with depression and distress among residents living in U.S. Gulf states. METHODS: We enrolled home-visit participants in the Gulf Long-Term Follow-up Study who completed validated screening questionnaires for depression (Patient Health Questionnaire-9, N = 10,217) and distress (Kessler Psychological Distress Questionnaire, N = 8,765) for the previous 2 weeks. Solar radiation estimates from the Daymet database (1-km grid) were linked to residential addresses. Average solar radiation exposures in the seven (SRAD7), 14 (SRAD14), and 30 days (SRAD30) before the home visit were calculated and categorized into quartiles (Q1-Q4). We used generalized linear mixed models to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for associations between solar radiation and depression/distress. RESULTS: Higher levels of SRAD7 were non-monotonically inversely associated with depression [PRVs.Q1 (95%CI): Q2 = 0.81 (0.68, 0.97), Q3 = 0.80 (0.65, 0.99), Q4 = 0.88 (0.69, 1.15)] and distress [PRVs.Q1 (95%CI): Q2 = 0.76 (0.58, 0.99), Q3 = 0.77 (0.57, 1.06), Q4 = 0.84 (0.58, 1.22)]. Elevated SRAD14 and SRAD30 appeared to be associated with decreasing PRs of distress. For example, for SRAD14, PRs were 0.86 (0.63-1.19), 0.80 (0.55-1.18), and 0.75 (0.48-1.17) for Q2-4 versus Q1. Associations with SRAD7 varied somewhat, though not significantly, by season with increasing PRs of distress in spring and summer and decreasing PRs of depression and distress in fall. IMPACT STATEMENT: Previous research suffered from exposure misclassification, which impacts the validity of their conclusions. By leveraging high-resolution datasets and Gulf Long-term Follow-up Cohort, our findings support an association between increased solar radiation and fewer symptoms of mood disorders.

The effect of residential greenness on cardiovascular mortality from a large cohort in South China: An in-depth analysis of effect modification by multiple demographic and lifestyle characteristics.

BACKGROUND: The potential for residential greenness to improve cardiovascular health through both physical and psychological mechanisms is well recognized. However, evidence from rapidly urbanizing developing countries and cohort-based causal inference approaches, remains limited. We aim to examine the effect of residential greenness and time to cardiovascular mortality in South China. METHODS: We utilized data from a community-based population survey involving 748,209 participants at baseline from 2009 to 2015, followed up until 2020. Residential greenness exposure was assessed by the annual Normalized Difference Vegetation Index (NDVI) in the 500 m radius of each participant's residence. We used time-varying proportional hazard Cox models coupled with inverse probability weighting to fit marginal structural models and obtain hazard ratios (HRs) for cardiovascular disease (CVD) mortality after adjusting for confounders. Multiple effect modifiers on both additive and multiplicative scales were further explored. RESULTS: A total of 15,139 CVD-related deaths were identified during a median of 7.9 years of follow-up. A protective effect was found between higher greenness exposure and reduced CVD mortality, with a 9.3 % lower rate of total CVD mortality (HR 0.907, 95 % CI 0.859-0.957) based on a 0.1 increase in annual average NDVI. Demographic (age, marital status) and lifestyle factors (smoking, drinking status) were found to modify the association between residential greenness and CVD mortality (all P interaction values < 0.05 or 95 %CI for RERI excluded the value 0). Notably, this effect was more pronounced among older adults, married, and individuals having healthier lifestyles, indicating a greater benefit from greenness for these subgroups. CONCLUSIONS: Our findings support a causal link between increased residential greenness exposure and a reduced risk of CVD mortality in South China with marked heterogenous effects, which has public health implications for cultivating greener urban environments to mitigate the impact of CVD within the context of rapid urbanization.

Association of long-term exposure to PM2.5 and its chemical components with the reduced quality of sleep.

Poor sleep quality is a widespread concern. While the influence of particle exposure on sleep disturbances has received considerable attention, research exploring other dimensions of sleep quality and the chemical components of the particles remains limited. We employed a marginal structural model to explore the association of long-term exposure to PM2.5 and its chemical components with poor sleep quality. The odds ratio (95 % CI) for poor sleep quality was 1.335 (1.292-1.378), 1.097 (1.080-1.113), 1.137 (1.100-1.174), 1.197 (1.156-1.240), and 1.124 (1.107-1.140) per IQR increase in the concentration of PM2.5, SO42-, NO3-, NH4+, and BC, respectively. The score (and 95 % CI) of sleep latency, use of sleep medication, habitual sleep efficiency, subjective sleep quality, and daytime dysfunction were affected by PM2.5, with an increase of 0.059 (0.050-0.069), 0.054 (0.049-0.059), 0.011 (0.008-0.014), 0.011 (0.005-0.018), and 0.026 (0.018-0.034) per IQR increase in PM2.5 concentrations, respectively. This study supports the association of long-term exposure to PM2.5 and its chemical components with poor sleep quality.

Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports.

BACKGROUND: Development of distant metastasis (DM) is a major concern during treatment of nasopharyngeal carcinoma (NPC). However, studies have demonstrated improved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy. Therefore, precise prediction of metastasis in patients with NPC is crucial. AIM: To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging (MRI) reports. METHODS: This retrospective study included 792 patients with non-distant metastatic NPC. A total of 469 imaging variables were obtained from detailed MRI reports. Data were stratified and randomly split into training (50%) and testing sets. Gradient boosting tree (GBT) models were built and used to select variables for predicting DM. A full model comprising all variables and a reduced model with the top-five variables were built. Model performance was assessed by area under the curve (AUC). RESULTS: Among the 792 patients, 94 developed DM during follow-up. The number of metastatic cervical nodes (30.9%), tumor invasion in the posterior half of the nasal cavity (9.7%), two sides of the pharyngeal recess (6.2%), tubal torus (3.3%), and single side of the parapharyngeal space (2.7%) were the top-five contributors for predicting DM, based on their relative importance in GBT models. The testing AUC of the full model was 0.75 (95% confidence interval [CI]: 0.69-0.82). The testing AUC of the reduced model was 0.75 (95%CI: 0.68-0.82). For the whole dataset, the full (AUC = 0.76, 95%CI: 0.72-0.82) and reduced models (AUC = 0.76, 95%CI: 0.71-0.81) outperformed the tumor node-staging system (AUC = 0.67, 95%CI: 0.61-0.73). CONCLUSION: The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC. The number of metastatic cervical nodes was identified as the principal contributing variable.

Residential greenness and chronic obstructive pulmonary disease in a large cohort in southern China: Potential causal links, risk trajectories, and mediation pathways.

INTRODUCTION: Residential greenness may influence COPD mortality, but the causal links, risk trajectories, and mediation pathways between them remain poorly understood. OBJECTIVES: We aim to comprehensively identify the potential causal links, characterize the dynamic progression of hospitalization or posthospital risk, and quantify mediation effects between greenness and COPD. METHODS: This study was conducted using a community-based cohort enrolling individuals aged ≥ 18 years in southern China from January 1, 2009 to December 31, 2015. Greenness was characterized by normalized difference vegetation index (NDVI) around participants' residential addresses. We applied doubly robust Cox proportional hazards model, multi-state model, and multiple mediation method, to investigate the potential causal links, risk trajectories among baseline, COPD hospitalization, first readmission due to COPD or COPD-related complications, and all-cause death, as well as the multiple mediation pathways (particulate matter [PM], temperature, body mass index [BMI] and physical activity) connecting greenness exposure to COPD mortality. RESULTS: Our final analysis included 581,785 participants (52.52% female; average age: 48.36 [Standard Deviation (SD): 17.56]). Each interquartile range (IQR: 0.06) increase in NDVI was associated with a reduced COPD mortality risk, yielding a hazard ratio (HR) of 0.88 (95 % CI: 0.81, 0.96). Furthermore, we observed per IQR (0.04) increase in NDVI was inversely associated with the risk of multiple transitions (baseline - COPD hospitalization, baseline - death, and readmission - death risks), especially a declined risk of all-cause death after readmission (HR = 0.66 [95 %CI: 0.44, 0.99]). Within the observed association between greenness and COPD mortality, three mediators were identified, namely PM, temperature, and BMI (HR for the total indirect effect: 0.773 [95 % CI: 0.703, 0.851]), with PM showing the highest mediating effect. CONCLUSIONS: Our findings revealed greenness may be a beneficial factor for COPD morbidity, prognosis, and mortality. This protective effect is primarily attributed to the reduction in PM concentration.

Potential causal links and mediation pathway between urban greenness and lung cancer mortality: Result from a large cohort (2009 to 2020).

Urban greenness, as a vital component of the urban environment, plays a critical role in mitigating the adverse effects of rapid urbanization and supporting urban sustainability. However, the causal links between urban greenness and lung cancer mortality and its potential causal pathway remain poorly understood. Based on a prospective community-based cohort with 581,785 adult participants in southern China, we applied a doubly robust Cox proportional hazard model to estimate the causal associations between urban greenness exposure and lung cancer mortality. A general multiple mediation analysis method was utilized to further assess the potential mediating roles of various factors including particulate matter (PM1, PM2.5-1, and PM10-2.5), temperature, physical activity, and body mass index (BMI). We observed that each interquartile range (IQR: 0.06) increment in greenness exposure was inversely associated with lung cancer mortality, with a hazard ratio (HR) of 0.89 (95 % CI: 0.83, 0.96). The relationship between greenness and lung cancer mortality might be partially mediated by particulate matter, temperature, and physical activity, yielding a total indirect effect of 0.826 (95 % CI: 0.769, 0.887) for each IQR increase in greenness exposure. Notably, the protective effect of greenness against lung cancer mortality could be achieved primarily by reducing the particulate matter concentration.