scholarly journals On adjustment for seasonality and time trend when estimating linear associations between long-term exposure and health outcomes in time-series studies

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
H. Kim ◽  
M.L. Bell
Keyword(s):  
Time Series ◽  
Health Outcomes ◽  
Time Trend ◽  
Time Series Studies

scholarly journals Alternative adjustment for seasonality and long-term time-trend in time-series analysis for long-term environmental exposures and disease counts

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Honghyok Kim ◽  
Jong-Tae Lee ◽  
Kelvin C. Fong ◽  
Michelle L. Bell
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Health Outcomes ◽  
Time Trend ◽  
Environmental Epidemiology ◽  
Real Data ◽  
Alternative Methods ◽  
Individual Level ◽  
Series Analysis

Abstract Background Time-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology. In this analysis, adjustment for seasonality and long-term time-trend is crucial to obtain valid findings. When applying this analysis for long-term exposure (e.g., months, years) of which effects are usually studied via survival analysis with individual-level longitudinal data, unlike its application for short-term exposure (e.g., days, weeks), a standard adjustment method for seasonality and long-term time-trend can extremely inflate standard error of coefficient estimates of the effects. Given that individual-level longitudinal data are difficult to construct and often available to limited populations, if this inflation of standard error can be solved, rich case-only data over regions and countries would be very useful to test a variety of research hypotheses considering unique local contexts. Methods We discuss adjustment methods for seasonality and time-trend used in time-series analysis in environmental epidemiology and explain why standard errors can be inflated. We suggest alternative methods to solve this problem. We conduct simulation analyses based on real data for Seoul, South Korea, 2002–2013, and time-series analysis using real data for seven major South Korean cities, 2006–2013 to identify whether the association between long-term exposure and health outcomes can be estimated via time-series analysis with alternative adjustment methods. Results Simulation analyses and real-data analysis confirmed that frequently used adjustment methods such as a spline function of a variable representing time extremely inflate standard errors of estimates for associations between long-term exposure and health outcomes. Instead, alternative methods such as a combination of functions of variables representing time can make sufficient adjustment with efficiency. Conclusions Our findings suggest that time-series analysis with case-only data can be applied for estimating long-term exposure effects. Rich case-only data such as death certificates and hospitalization records combined with repeated measurements of environmental determinants across countries would have high potentials for investigating the effects of long-term exposure on health outcomes allowing for unique contexts of local populations.

scholarly journals Air pollution, SARS-CoV-2 transmission, and COVID-19 outcomes: A state-of-the-science review of a rapidly evolving research area

2020 ◽  
Author(s):  
Anushka Bhaskar ◽  
Jay Chandra ◽  
Danielle Braun ◽  
Jacqueline Cellini ◽  
Francesca Dominici
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Health Outcomes ◽  
Study Design ◽  
Air Pollutant ◽  
Short Term ◽  
Cross Sectional ◽  
Short And Long Term ◽  
Long Term Studies

Background: As the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis. Objectives: To conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes. Method: We searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design. Results: 27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes. Discussion: We discuss methodological challenges and highlight additional research areas based on our findings. Challenges include data quality issues, ecological study design limitations, improved adjustment for confounders, exposure errors related to spatial resolution, geographic variability in testing, mitigation measures and pandemic stage, clustering of health outcomes, and a lack of publicly available data and code.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

scholarly journals Long-Term Physical Health Outcomes of Resettled Refugee Populations in the United States: A Scoping Review

2021 ◽  
Author(s):  
Gayathri S. Kumar ◽  
Jenna A. Beeler ◽  
Emma E. Seagle ◽  
Emily S. Jentes
Keyword(s):  
Health Outcomes ◽  
Physical Health ◽  
Public Health Practice ◽  
The United States ◽  
Inclusion Criteria ◽  
Physical Health Outcomes ◽  
Increased Risk ◽  
The Us ◽  
Bhutanese Refugee

AbstractSeveral studies describe the health of recently resettled refugee populations in the US beyond the first 8 months after arrival. This review summarizes the results of these studies. Scientific articles from five databases published from January 2008 to March 2019 were reviewed. Articles were included if study subjects included any of the top five US resettlement populations during 2008–2018 and if data described long-term physical health outcomes beyond the first 8 months after arrival in the US. Thirty-three studies met the inclusion criteria (1.5%). Refugee adults had higher odds of having a chronic disease compared with non-refugee immigrant adults, and an increased risk for diabetes compared with US-born controls. The most commonly reported chronic diseases among Iraqi, Somali, and Bhutanese refugee adults included diabetes and hypertension. Clinicians should consider screening and evaluating for chronic conditions in the early resettlement period. Further evaluations can build a more comprehensive, long-term health profile of resettled refugees to inform public health practice.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

scholarly journals Comparing the Efficacy of Multidisciplinary Assessment and Treatment, or Acceptance and Commitment Therapy, with Treatment as Usual on Health Outcomes in Women on Long-Term Sick Leave—A Randomised Controlled Trial

2021 ◽  
Vol 18 (4) ◽  
pp. 1754
Author(s):  
Anna Finnes ◽  
Ingrid Anderzén ◽  
Ronnie Pingel ◽  
JoAnne Dahl ◽  
Linnea Molin ◽  
...  
Keyword(s):  
Sick Leave ◽  
Health Outcomes ◽  
Acceptance And Commitment Therapy ◽  
Significant Interaction ◽  
Treatment As Usual ◽  
Multidisciplinary Assessment ◽  
Commitment Therapy ◽  
Acceptance And Commitment ◽  
Assessment And Treatment

Background: Chronic pain and mental disorders are common reasons for long term sick leave. The study objective was to evaluate the efficacy of a multidisciplinary assessment and treatment program including acceptance and commitment therapy (TEAM) and stand-alone acceptance and commitment therapy (ACT), compared with treatment as usual (Control) on health outcomes in women on long-term sick leave. Method: Participants (n = 308), women of working age on long term sick leave due to musculoskeletal pain and/or common mental disorders, were randomized to TEAM (n = 102), ACT (n = 102) or Control (n = 104). Participants in the multidisciplinary assessment treatment program received ACT, but also medical assessment, occupational therapy and social counselling. The second intervention included ACT only. Health outcomes were assessed over 12 months using adjusted linear mixed models. The results showed significant interaction effects for both ACT and TEAM compared with Control in anxiety (ACT [p < 0.05]; TEAM [p < 0.001]), depression (ACT [p < 0.001]; TEAM [p < 0.001]) and general well-being (ACT [p < 0.05]; TEAM [p < 0.001]). For self-rated pain, there was a significant interaction effect in favour of ACT (p < 0.05), and for satisfaction with life in favour of TEAM (p < 0.001). Conclusion: Both ACT alone and multidisciplinary assessment and treatment including ACT were superior to treatment as usual in clinical outcomes.

Sign in / Sign up

Export Citation Format

Share Document