Heat Waves and Adverse Health Events Among Dually Eligible Individuals 65 Years and Older.

Importance: Extensive research has found the detrimental health effects of heat waves. However, a critical gap exists in understanding their association with adverse health events among older dually eligible individuals, who may be particularly susceptible to heat waves.

Objective: To assess the association between heat waves and adverse health events among dually eligible individuals 65 years and older.

Spatiotemporal variability of the association between greenspace exposure and depression in older adults in South Korea.

Background: A number of studies based on young to middle aged adult and child samples have found that exposure to greenspace and bluespace can have a positive impact on mental health and well-being. However, there is limited research among older adults and the extant studies have provided mixed results. The present study was designed to examine how the association between these forms of exposure and depressive symptoms among older adults varies as a function of different spatially and temporally resolved exposure metrics.

Differential effects of air pollution exposure on mental health: Historical redlining in New York State.

Growing evidence suggests that ambient air pollution has adverse effects on mental health, yet our understanding of its unequal impact remains limited, especially in areas with historical redlining practices. This study investigates whether the impact of daily fluctuations in ambient air pollutant levels on emergency room (ER) visits for mental disorders (MDs) varies across neighborhoods affected by redlining. Furthermore, we explored how demographic characteristics and ambient temperature may modify the effects of air pollution.

Delayed effects of air pollution on public bike-sharing system use in Seoul, South Korea: A time series analysis.

Background: As a complementary means to urban public transit systems, public bike-sharing provides a green and active mode of sustainable mobility, while reducing carbon-dioxide emissions and promoting health. There has been increasing interest in factors affecting bike-sharing usage, but little is known about the effect of ambient air pollution.

Method: To assess the short-term impact of daily exposure to multiple air pollutants (PM, PM, NO, and O) on the public bike-sharing system (PBS) usage in Seoul, South Korea (2018-2021), we applied a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model (DLNM).

Examining the geographical distribution of air pollution disparities across different racial and ethnic groups: Incorporating workplace addresses.

Background: Most previous studies on air pollution exposure disparities among racial and ethnic groups in the US have been limited to residence-based exposure and have given little consideration to population mobility and spatial patterns of residences, workplaces, and air pollution. This study aimed to examine air pollution exposure disparities by racial and ethnic groups while explicitly accounting for both the work-related activity of the population and localized spatial patterns of residential segregation, clustering of workplaces, and variability of air pollutant concentration.

Method: In the present study, we assessed population-level exposure to air pollution using tabulated residence and workplace addresses of formally employed workers from LEHD Origin-Destination Employment Statistics (LODES) data at the census tract level across eight Metropolitan Statistical Areas (MSAs).

Role of geographic characteristics in the spatial cluster detection of cancer: Evidence in South Korea, 1999-2013.

Background: Identification of high-risk areas of cancer, referred to as spatial clusters, can inform targeted policies for cancer control. Although cancer cluster detection could be affected by various geographic characteristics including sociodemographic and environmental factors which impacts could also vary over time, studies accounting for such influence remain limited. This study aims to assess the role of geographic characteristics in the spatial cluster detection for lung and stomach cancer over an extended period.

A gap-filling hybrid approach for hourly PM prediction at high spatial resolution from multi-sourced AOD data.

Despite a growing interest in the satellite derived estimation of ground-level PM concentrations, modeling hourly PM levels at high spatial resolution with complete coverage for a large study domain remains a challenge. The primary modeling challenges lie in the presence of missing data in aerosol optical depth (AOD) and the limited data resolution for a single-platformed satellite AOD product. To address these issues, we developed a gap-filling hybrid approach to estimate full coverage hourly ground-level PM concentrations at a high spatial resolution of 1 km using multi-platformed and multi-scale satellite derived AOD products.

The impact of fire-specific PM calibration on health effect analyses.

The quantification of PM concentrations solely stemming from both wildfire and prescribed burns (hereafter referred to as 'fire') is viable using the Community Multiscale Air Quality (CMAQ), although CMAQ outputs are subject to biases and uncertainties. To reduce the biases in CMAQ-based outputs, we propose a two-stage calibration strategy that improves the accuracy of CMAQ-based fire PM estimates. First, we calibrated CMAQ-based non-fire PM to ground PM observations retrieved during non-fire days using an ensemble-based model.

Assessing exposure to household air pollution in children under five: A scoping review.

Understanding the differences in the approaches used to assess household air pollution (HAP) is crucial for evaluating HAP-related health effects and interpreting the effectiveness of stove-fuel interventions. Our review aims to understand how exposure to HAP from solid fuels was measured in epidemiological studies in children under five. We conducted a search of PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Global Health Library, Web of Science, and CINAHL to identify English-language research articles published between January 1, 2000 and April 30, 2022.

Imputation of missing time-activity data with long-term gaps: A multi-scale residual CNN-LSTM network model.

Despite the increasing availability and spatial granularity of individuals' time-activity (TA) data, the missing data problem, particularly long-term gaps, remains as a major limitation of TA data as a primary source of human mobility studies. In the present study, we propose a two-step imputation method to address the missing TA data with long-term gaps, based on both efficient representation of TA patterns and high regularity in TA data. The method consists of two steps: (1) the continuous bag-of-words word2vec model to convert daily TA sequences into a low-dimensional numerical representation to reduce complexity; (2) a multi-scale residual Convolutional Neural Network (CNN)-stacked Long Short-Term Memory (LSTM) model to capture multi-scale temporal dependencies across historical observations and to predict the missing TAs.

Using GPS-enabled mobile phones to evaluate the associations between human mobility changes and the onset of influenza illness.

Due to the challenges in data collection, there are few studies examining how individuals' routine mobility patterns change when they experience influenza-like symptoms (ILS). In the present study, we aimed to assess the association between changes in routine mobility and ILS using mobile phone-based GPS traces and self-reported surveys from 1,155 participants over the 2016-2017 influenza season. We used a set of mobility metrics to capture individuals' routine mobility patterns and matched their weekly ILS survey responses.

Exposure to urban green space may both promote and harm mental health in socially vulnerable neighborhoods: A neighborhood-scale analysis in New York City.

Background: There is growing evidence that exposure to green space can impact mental health, but these effects may be context dependent. We hypothesized that associations between residential green space and mental health can be modified by social vulnerability.

Method: We conducted an ecological cross-sectional analysis to evaluate the effects of green space exposure on mental disorder related emergency room (ER) visits in New York City at the level of census tract.

Geostatistical linkage of national demographic and health survey data: a case study of Tanzania.

Background: When Service Provision Assessment (SPA) surveys on primary health service delivery are combined with the nationally representative household survey-Demographic and Health Survey (DHS), they can provide key information on the access, utilization, and equity of health service availability in low- and middle-income countries. However, existing linkage methods have been established only at aggregate levels due to known limitations of the survey datasets.

Methods: For the linkage of two data sets at a disaggregated level, we developed a geostatistical approach where SPA limitations are explicitly accounted for by identifying the sites where health facilities might be present but not included in SPA surveys.

Selenium inhibits growth of trastuzumab-resistant human breast cancer cells via downregulation of Akt and beclin-1.

The response rate to treatment with trastuzumab (Tz), a recombinant humanized anti-HER2 monoclonal antibody, is only 12-34% despite demonstrated effectiveness on improving the survival of patients with HER2-positive breast cancers. Selenium has an antitumor effect against cancer cells and can play a cytoprotective role on normal cells. This study investigated the effect of selenium on HER2-positive breast cancer cells and the mechanism in relation to the response of the cells to Tz.

Image Registration of F-FDG PET/CT Using the MotionFree Algorithm and CT Protocols through Phantom Study and Clinical Evaluation.

We evaluated the benefits of the MotionFree algorithm through phantom and patient studies. The various sizes of phantom and vacuum vials were linked to RPM moving with or without MotionFree application. A total of 600 patients were divided into six groups by breathing protocols and CT scanning time.

Association between extreme temperatures and emergency room visits related to mental disorders: A multi-region time-series study in New York, USA.

Background: There is growing evidence suggesting that extreme temperatures have an impact on mental disorders. We aimed to explore the effect of extreme temperatures on emergency room (ER) visits for mental health disorders using 2.8 million records from New York State, USA (2009-2016), and to examine potential effect modifications by individuals' age, sex, and race/ethnicity through a stratified analysis to determine if certain populations are more susceptible.

Effect of extreme temperatures on daily emergency room visits for mental disorders.

Relatively few studies investigated the effects of extreme temperatures (both heat and cold) on mental health (ICD-9: 290-319; ICD-10: F00-F99) and the potential effect modifications by individuals' age, sex, and race. We aimed to explore the effect of extreme temperatures of both heat and cold on the emergency room (ER) visits for mental health disorders, and conducted a stratified analysis to identify possible susceptible population in Erie and Niagara counties, NY, USA. To assess the short-term impacts of daily maximum temperature on ER visits related to mental disorders (2009-2015), we applied a quasi-Poisson generalized linear model combined with a distributed lag non-linear model (DLNM).

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM.

The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields.

Ground PM prediction using imputed MAIAC AOD with uncertainty quantification.

Satellite-derived aerosol optical depth (AOD) has been widely used to predict ground-level fine particulate matter (PM) concentrations, although its utility can be limited due to missing values. Despite recent attempts to address this issue by imputing missing satellite AOD values, the uncertainty associated with the AOD imputation and its impacts on PM predictions have been understudied. To fill this gap, we developed a missing data imputation model for the AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and PM prediction models using several machine learning methods.

DST-Predict: Predicting Individual Mobility Patterns From Mobile Phone GPS Data.

Predicting spatial behaviors of an individual (e.g., frequent visits to specific locations) is important to improve our understanding of the complexity of human mobility patterns, and to capture anomalous behaviors in an individual's spatial movements, which can be particularly useful in situations such as those induced by the COVID-19 pandemic.

Quality of hybrid location data drawn from GPS-enabled mobile phones: Does it matter?

Despite their increasing popularity in human mobility studies, few studies have investigated the geo-spatial quality of GPS-enabled mobile phone data in which phone location is determined by special queries designed to collect location data with predetermined sampling intervals (hereafter "active mobile phone data"). We focus on two key issues in active mobile phone data-systematic gaps in tracking records and positioning uncertainty-and investigate their effects on human mobility pattern analyses. To address gaps in records, we develop an imputation strategy that utilizes local environment information, such as parcel boundaries, and recording time intervals.

Sex specific effect of ATPase inhibitory factor 1 on body weight: studies in high fat diet induced obese mice and genetic association studies in humans.

Background: Based on the metabolic effect of exogenous ATPase inhibitory factor 1 (IF1) on glucose metabolism, we tested whether IF1 treatment is effective in ameliorating weight gain and whether its effects are sex specific.

Methods: HFD-fed C57BL/6 mice were treated with IF1 (5 mg/kg body weight, injected intraperitoneally). The underlying mechanisms of effect of IF1 on body weight were investigated in vitro and in vivo.

Semen Cuscutae Administration Improves Hepatic Lipid Metabolism and Adiposity in High Fat Diet-Induced Obese Mice.

Since arginase has been shown to compete with nitric oxide (NO) synthase, emerging evidence has reported that arginase inhibition improves obesity by increasing NO production. Semen cuscutae (SC), which is a well-known Chinese medicine, has multiple biological functions such as anti-oxidant function and immune regulation. In this study, we investigated whether the SC as a natural arginase inhibitor influences hepatic lipid abnormalities and whole-body adiposity in high-fat diet (HFD)-induced obese mice.

ATP synthase inhibitory factor 1 (IF1), a novel myokine, regulates glucose metabolism by AMPK and Akt dual pathways.

ATPase inhibitory factor 1 (IF1) is an ATP synthase-interacting protein that suppresses the hydrolysis activity of ATP synthase. In this study, we observed that the expression of IF1 was up-regulated in response to electrical pulse stimulation of skeletal muscle cells and in exercized mice and healthy men. IF1 stimulates glucose uptake AMPK in skeletal muscle cells and primary cultured myoblasts.

Air pollution dispersion from biomass stoves to neighboring homes in Mirpur, Dhaka, Bangladesh.

Background: Indoor air pollution, including fine particulate matter (PM) and carbon monoxide (CO), is a major risk factor for pneumonia and other respiratory diseases. Biomass-burning cookstoves are major contributors to PM and CO concentrations. However, high concentrations of PM (> 1000 μg/m) have been observed in homes in Dhaka, Bangladesh that do not burn biomass.