On variable selection in a semiparametric AFT mixture cure model.

In clinical studies, one often encounters time-to-event data that are subject to right censoring and for which a fraction of the patients under study never experience the event of interest. Such data can be modeled using cure models in survival analysis. In the presence of cure fraction, the mixture cure model is popular, since it allows to model probability to be cured (called the incidence) and the survival function of the uncured individuals (called the latency). In this paper, we develop a variable selection procedure for the incidence and latency parts of a mixture cure model, consisting of a logistic model for the incidence and a semiparametric accelerated failure time model for the latency. We use a penalized likelihood approach, based on adaptive LASSO penalties for each part of the model, and we consider two algorithms for optimizing the criterion function. Extensive simulations are carried out to assess the accuracy of the proposed selection procedure. Finally, we employ the proposed method to a real dataset regarding heart failure patients with left ventricular systolic dysfunction.

Long-Term Exposure to Air Pollution and Risk of Acute Lower Respiratory Infections in the Danish Nurse Cohort.

RATIONALE: Air pollution is a major risk factor for chronic cardiorespiratory diseases, affecting both the immune and respiratory systems' functionality, while the epidemiological evidence on respiratory infections remains sparse. OBJECTIVE: We aimed to assess the association of long-term exposure to ambient air pollution with risk of developing new and recurrent ALRIs that characterized by persistently severe symptoms necessitating hospital contact, and identify the potential susceptible populations by socio-economic status (SES), smoking, physical activity status, overweight, and co-morbidity with chronic lung disease. METHODS: We followed 23,912 female nurses from the Danish Nurse Cohort (> 44 years) from baseline (1993 or 1999) until 2018 for the incident and recurrent ALRIs defined by hospital contact (in-, outpatient, and emergency room) data from the National Patient Register. Residential annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) were modelled using Danish DEHM/UBM/AirGIS system. We used marginal Cox models with time-varying exposures to assess the association of 3-year running-mean air pollution with incident and recurrent ALRIs and examine effect modification by age, socio-economic status (SES), smoking, physical activity, body mass index, and comorbidity with asthma or chronic obstructive pulmonary disease (COPD). RESULTS: During a 21.3 years mean follow-up, 4,746 ALRIs were observed, of which 2,553 were incident. We observed strong positive associations of all three pollutants with incident ALRIs, with hazard ratios and 95% confidence intervals of 1.19 (1.08-1.31) per 2.5 µg/m3 for PM2.5, 1.17 (1.11-1.24) per 8.0 µg/m3 for NO2, and 1.09 (1.05-1.12) per 0.3 µg/m3 for BC, and slightly stronger associations with recurrent ALRIs. Associations were strongest in COPD patients and nurses with low physical activity. CONCLUSION: Long-term exposure to air pollution at low levels was associated with risk of new and recurrent ALRIs, with COPD patients and physically inactive subjects most vulnerable. Primary Source of Funding: This study was supported by the Novo Nordisk Foundation Challenge Programme (NNF17OC0027812).

Harnessing AI to unmask Copenhagen's invisible air pollutants: A study on three ultrafine particle metrics.

Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varies over space within cities, with locations near UFP sources having typically smaller APS. Spatial models for lung deposited surface area (LDSA) within urban areas are limited and currently there is no model for APS in any European city. We collected particle number concentration (PNC), LDSA, and APS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 6 state-owned continuous monitors. We developed 94 predictor variables, and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and APS. The annual mean PNC, LDSA, and APS were, respectively, 5523 pt/cm3, 12.0 µm2/cm3, and 46.1 nm. The final R2 values by random forest (RF) model were 0.93 for PNC, 0.88 for LDSA, and 0.85 for APS. The 10-fold, repeated 10-times cross-validation R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS, respectively. The root mean square error for final RF models were 296 pt/cm3, 0.48 µm2/cm3, and 1.60 nm for PNC, LDSA, and APS, respectively. Traffic-related variables, such as length of major roads within buffers 100-150 m and distance to streets with various speed limits were amongst the highly-ranked predictors for our models. Overall, our ML models achieved high R2 values and low errors, providing insights into UFP exposure in a European city where average PNC is quite low. These hyperlocal predictions can be used to study health effects of UFPs in the Danish Capital.

Ambient air pollutants and breast cancer stage in Tehran, Iran.

This study aimed to examine the impacts of single and multiple air pollutants (AP) on the severity of breast cancer (BC). Data of 1148 diagnosed BC cases (2008-2016) were obtained from the Cancer Research Center and private oncologist offices in Tehran, Iran. Ambient PM10, SO2, NO, NO2, NOX, benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene, and BTEX data were obtained from previously developed land use regression models. Associations between pollutants and stage of BC were assessed by multinomial logistic regression models. An increase of 10 µg/m3 in ethylbenzene, o-xylene, m-xylene, and 10 ppb of NO corresponded to 10.41 (95% CI 1.32-82.41), 4.07 (1.46-11.33), 2.89 (1.08-7.73) and 1.08 (1.00-1.15) increase in the odds of stage I versus non-invasive BC, respectively. Benzene (OR, odds ratio = 1.16, 95% CI 1.01-1.33) and o-xylene (OR = 1.18, 1.02-1.38) were associated with increased odds of incidence of BC stages III & IV versus non-invasive stages. BC stage I and stage III&IV in women living in low SES areas was associated with significantly higher levels of benzene, ethylbenzene, o-xylene, and m-xylene. The highest multiple-air-pollutants quartile was associated with a higher odds of stage I BC (OR = 3.16) in patients under 50 years old. This study provides evidence that exposure to AP is associated with increased BC stage at diagnosis, especially under premenopause age.

Stringency of COVID-19 Containment Response Policies and Air Quality Changes: A Global Analysis across 1851 Cities.

The COVID-19 containment response policies (CRPs) had a major impact on air quality (AQ). These CRPs have been time-varying and location-specific. So far, despite having numerous studies on the effect of COVID-19 lockdown on AQ, a knowledge gap remains on the association between stringency of CRPs and AQ changes across the world, regions, nations, and cities. Here, we show that globally across 1851 cities (each more than 300 000 people) in 149 countries, after controlling for the impacts of relevant covariates (e.g., meteorology), Sentinel-5P satellite-observed nitrogen dioxide (NO2) levels decreased by 4.9% (95% CI: 2.2, 7.6%) during lockdowns following stringent CRPs compared to pre-CRPs. The NO2 levels did not change significantly during moderate CRPs and even increased during mild CRPs by 2.3% (95% CI: 0.7, 4.0%), which was 6.8% (95% CI: 2.0, 12.0%) across Europe and Central Asia, possibly due to population avoidance of public transportation in favor of private transportation. Among 1768 cities implementing stringent CRPs, we observed the most NO2 reduction in more populated and polluted cities. Our results demonstrate that AQ improved when and where stringent COVID-19 CRPs were implemented, changed less under moderate CRPs, and even deteriorated under mild CRPs. These changes were location-, region-, and CRP-specific.

Multiple air pollutants exposure and leukaemia incidence in Tehran, Iran from 2010 to 2016: a retrospective cohort study.

OBJECTIVE: Leukaemia is one of the most common cancers and may be associated with exposure to environmental carcinogens, especially outdoor air pollutants. The objective of this study was to investigate the association of ambient air pollution and leukaemia in Tehran, Iran. DESIGN: In this retrospective cohort study, data about the residential district of leukaemia cases diagnosed from 2010 to 2016 were inquired from the Ministry of Health cancer database. Data from a previous study were used to determine long-term average exposure to different air pollutants in 22 districts of Tehran. Latent profile analysis (LPA) was used to classify pollutants in two exposure profiles. The association between air pollutants and leukaemia incidence was analysed by negative binomial regression. SETTING: Twenty-two districts of Tehran megacity. PARTICIPANTS: Patients with leukaemia. OUTCOME MEASURES: The outcome variables were incidence rate ratios (IRR) of acute myeloid and lymphoid leukaemia across the districts of Tehran. RESULTS: The districts with higher concentrations for all pollutants were near the city centre. The IRR was positive but non-significant for most of the air pollutants. However, annual mean NOx was directly and significantly associated with total leukaemia incidence in the fully adjusted model (IRR (95% CI): 1.03 (1.003 to 1.06) per 10 ppb increase). Based on LPA, districts with a higher multiple air-pollutants profile were also associated with higher leukaemia incidence (IRR (95% CI): 1.003 (0.99 to 1.007) per 1 ppb increase). CONCLUSIONS: Our study shows that districts with higher air pollution (nitrogen oxides and multipollutants) have higher incidence rates of leukaemia in Tehran, Iran. This study warrants conducting further research with individual human data and better control of confounding.

Multiple air pollutant exposure and lung cancer in Tehran, Iran.

Lung cancer is the most rapidly increasing malignancy worldwide with an estimated 2.1 million cancer cases in the latest, 2018 World Health Organization (WHO) report. The objective of this study was to investigate the association of air pollution and lung cancer, in Tehran, Iran. Residential area information of the latest registered lung cancer cases that were diagnosed between 2014 and 2016 (N = 1,850) were inquired from the population-based cancer registry of Tehran. Long-term average exposure to PM10, SO2, NO, NO2, NOX, benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene (BTEX), and BTEX in 22 districts of Tehran were estimated using land use regression models. Latent profile analysis (LPA) was used to generate multi-pollutant exposure profiles. Negative binomial regression analysis was used to examine the association between air pollutants and lung cancer incidence. The districts with higher concentrations for all pollutants were mostly in downtown and around the railway station. Districts with a higher concentration for NOx (IRR = 1.05, for each 10 unit increase in air pollutant), benzene (IRR = 3.86), toluene (IRR = 1.50), ethylbenzene (IRR = 5.16), p-xylene (IRR = 9.41), o-xylene (IRR = 7.93), m-xylene (IRR = 2.63) and TBTEX (IRR = 1.21) were significantly associated with higher lung cancer incidence. Districts with a higher multiple air-pollution profile were also associated with more lung cancer incidence (IRR = 1.01). Our study shows a positive association between air pollution and lung cancer incidence. This association was stronger for, respectively, p-xylene, o-xylene, ethylbenzene, benzene, m-xylene and toluene.

Annual and seasonal spatial models for nitrogen oxides in Tehran, Iran.

Very few land use regression (LUR) models have been developed for megacities in low- and middle-income countries, but such models are needed to facilitate epidemiologic research on air pollution. We developed annual and seasonal LUR models for ambient oxides of nitrogen (NO, NO2, and NOX) in the Middle Eastern city of Tehran, Iran, using 2010 data from 23 fixed monitoring stations. A novel systematic algorithm was developed for spatial modeling. The R(2) values for the LUR models ranged from 0.69 to 0.78 for NO, 0.64 to 0.75 for NO2, and 0.61 to 0.79 for NOx. The most predictive variables were: distance to the traffic access control zone; distance to primary schools; green space; official areas; bridges; and slope. The annual average concentrations of all pollutants were high, approaching those reported for megacities in Asia. At 1000 randomly-selected locations the correlations between cooler and warmer season estimates were 0.64 for NO, 0.58 for NOX, and 0.30 for NO2. Seasonal differences in spatial patterns of pollution are likely driven by differences in source contributions and meteorology. These models provide a basis for understanding long-term exposures and chronic health effects of air pollution in Tehran, where such research has been limited.

Vitamin D Deficiency in Pregnant Women and Their Neonates.

BACKGROUND & OBJECTIVE: Vitamin D deficiency during pregnancy is a worldwide problem. Studies have reported prevalence ranged 18-84% in pregnant women. Receiving adequate calcium and vitamin D during pregnancy period is necessary for calcium homeostasis, fetal growth and bone mineralization. This study was aimed to determine the prevalence of vitamin D deficiency in pregnant women and their neonates in Shahroud city in the northeast Iran. METHODS: In this cross-sectional study, 284 pregnant women and their neonates referred to Fatemiyeh Hospital of Shahroud were included. Blood samples of mothers and umbilical cords were collected during the delivery and were sent to laboratory in order to measure calcium and 25-hydroxy vitamin D. FINDINGS: Amounts of Vitamin D insufficiency (20-30 ng/mL) and deficiency (<20 ng/mL) in (mothers, neonates) were found to be (60.2%, 48.9%) and (1.1%, 2.5%) respectively. Calcium deficiency (<8.5 mg/dL) was present in 33.5% of mothers and 25% of neonates. There was a weak correlation between maternal serum and cord blood 25-hydroxy vitamin D (r=0.12, p=0.053). CONCLUSION: More than half of the mothers and their neonates had some degrees of vitamin D deficiency. It is recommended to evaluate the nutritional status of vitamin D in pregnant women along with public health interventions to be carried out.

Sonoelectrochemical mineralization of perfluorooctanoic acid using Ti/PbO 2 anode assessed by response surface methodology.

BACKGROUND: Perfluorocarboxylic acids (PFCAs) are emerging pollutant and classified as fully fluorinated hydrocarbons containing a carboxylic group. PFCAs show intensively resistance against chemical and biological degradation due to their strong C-F bond. The Sonoelectrochemical mineralization of the synthetic aqueous solution of the perfluorooctanoic acid (PFOA) on Ti/PbO2 anode was investigated using the response surface methodology based on a central composite design with three variables: current density, pH, and supporting electrolyte concentration. METHODS: The defluorination ratio of PFOA was determined as an indicator of PFOA mineralization. Fluoride ion concentration was measured with an ion chromatograph unit. The Ti/PbO2 electrode was prepared using the electrochemical deposition method. The ultrasonic frequency was 20 kHz. RESULTS: The optimum conditions for PFOA mineralization in synthetic solution were electrolyte concentration, pH, and current density of 94 mM, 2, and 83.64 mA/cm(2), respectively. The results indicated that the most effective factor for PFOA mineralization was current density. Furthermore, the PFOA defluorination efficiency significantly enhanced with increasing current density. Under optimum conditions, the maximum mineralization of PFOA was 95.48 % after 90 min of sonoelectrolysis. CONCLUSIONS: Sonoelectrolysis was found to be a more effective technique for mineralization of an environmentally persistent compound.

Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran.

The Middle Eastern city of Tehran, Iran has poor air quality compared with cities of similar size in Europe and North America. Spatial annual and seasonal patterns of SO2 and PM10 concentrations were estimated using land use regression (LUR) methods applied to data from 21 air quality monitoring stations. A systematic algorithm for LUR model building was developed to select variables based on (1) consistency with a priori assumptions about the assumed directions of the effects, (2) a p-value of <0.1 for each predictor, (3) improvements to the leave-one-out cross-validation (LOOCV) R(2), (4) a multicollinearity index called the variance inflation factor, and (5) a grouped (leave-25%-out) cross-validation (GCV) for final model. In addition, several new predictive variables and variable types were explored. The annual mean concentrations of SO2 and PM10 across the stations were 38 ppb and 100.8 µg/m(3), respectively. The R(2) values ranged from 0.69 to 0.84 for SO2 models and from 0.62 to 0.67 for PM10 models. The LOOCV and GCV R(2) values ranged, respectively, from 0.40 to 0.56 and 0.40 to 0.50 for the SO2 models; they were 0.48 to 0.57 and 0.50 to 0.55, respectively, for the PM10 models. There were clear differences between the SO2 and PM10 models, but the warmer and cooler season models were consistent with the annual models for both pollutants. Although there was limited similarity between the SO2 and PM10 predictive variables, measures of street density and proximity to airport or air cargo facilities were consistent across both pollutants. In 2010, the entire population of Tehran lived in areas where the World Health Organization guidelines for 24-hour mean SO2 (7 ppb) and annual average PM10 (20 µg/m(3)) were exceeded.

Drinking water fluoride and blood pressure? An environmental study.

The relationship between intakes of fluoride (F) from drinking water and blood pressure has not yet been reported. We examined the relationship of F in ground water resources (GWRs) of Iran with the blood pressure of Iranian population in an ecologic study. The mean F data of the GWRs (as a surrogate for F levels in drinking water) were derived from a previously conducted study. The hypertension prevalence and the mean of systolic and diastolic blood pressures (SBP & DBP) of Iranian population by different provinces and genders were also derived from the provincial report of non-communicable disease risk factor surveillance of Iran. Statistically significant positive correlations were found between the mean concentrations of F in the GWRs and the hypertension prevalence of males (r = 0.48, p = 0.007), females (r = 0.36, p = 0.048), and overall (r = 0.495, p = 0.005). Also, statistically significant positive correlations between the mean concentrations of F in the GWRs and the mean SBP of males (r = 0.431, p = 0.018), and a borderline correlation with females (r = 0.352, p = 0.057) were found. In conclusion, we found the increase of hypertension prevalence and the SBP mean with the increase of F level in the GWRs of Iranian population.