scholarly journals Empirical Assessment of Alternative Methods for Identifying Seasonality in Observational Healthcare Data

2021 ◽  
Author(s):  
Anthony Molinaro ◽  
Frank DeFalco
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Observational Data ◽  
Health Data ◽  
Alternative Methods ◽  
Healthcare Data ◽  
Diagnosis Codes ◽  
Biological Event ◽  
Level Of Significance ◽  
Automated Methods

Abstract BackgroundSeasonality classification is a well-known and important part of time series analysis. Understanding the seasonality of a biological event can contribute to an improved understanding of its causes and help guide appropriate responses. Observational data, however, are not comprised of biological events, but timestamped diagnosis codes the combination of which (along with additional requirements) are used as proxies for biological events. As there exist different methods for determining the seasonality of a time series, it is necessary to know if these methods exhibit concordance. In this study we seek to determine the concordance of these methods by applying them to time series derived from diagnosis codes in observational data. Methods: We compared 8 methods for determining the seasonality of a time series at three levels of significance (0.01, 0.05, and 0.1), against 10 observational health databases. We evaluated 61,467 time series at each level of significance, totaling 184,401 evaluations. Results: Methods of binary seasonality classification when applied to time series derived from diagnosis codes in observational health data produce inconsistent results. Across all databases and levels of significance, concordance ranged from 20.2% to 40.2%.Conclusion: The results indicate that researchers relying on automated methods to assess the seasonality of time series derived from diagnosis codes in observational data should be aware that the methods are not interchangeable. Seasonality determination is highly dependent on the method chosen.

scholarly journals Synthetic control methodology as a tool for evaluating population-level health interventions

2018 ◽  
Vol 72 (8) ◽  
pp. 673-678 ◽  
Author(s):  
Janet Bouttell ◽  
Peter Craig ◽  
James Lewsey ◽  
Mark Robinson ◽  
Frank Popham
Keyword(s):  
Public Health ◽  
Time Series ◽  
Time Series Analysis ◽  
Observational Data ◽  
Control Method ◽  
Health Interventions ◽  
Synthetic Control ◽  
Public Health Interventions ◽  
Synthetic Control Method ◽  
Control Units

BackgroundMany public health interventions cannot be evaluated using randomised controlled trials so they rely on the assessment of observational data. Techniques for evaluating public health interventions using observational data include interrupted time series analysis, panel data regression-based approaches, regression discontinuity and instrumental variable approaches. The inclusion of a counterfactual improves causal inference for approaches based on time series analysis, but the selection of a suitable counterfactual or control area can be problematic. The synthetic control method builds a counterfactual using a weighted combination of potential control units.MethodsWe explain the synthetic control method, summarise its use in health research to date, set out its advantages, assumptions and limitations and describe its implementation through a case study of life expectancy following German reunification.ResultsAdvantages of the synthetic control method are that it offers an approach suitable when there is a small number of treated units and control units and it does not rely on parallel preimplementation trends like difference in difference methods. The credibility of the result relies on achieving a good preimplementation fit for the outcome of interest between treated unit and synthetic control. If a good preimplementation fit is established over an extended period of time, a discrepancy in the outcome variable following the intervention can be interpreted as an intervention effect. It is critical that the synthetic control is built from a pool of potential controls that are similar to the treated unit. There is currently no consensus on what constitutes a ‘good fit’ or how to judge similarity. Traditional statistical inference is not appropriate with this approach, although alternatives are available. From our review, we noted that the synthetic control method has been underused in public health.ConclusionsSynthetic control methods are a valuable addition to the range of approaches for evaluating public health interventions when randomisation is impractical. They deserve to be more widely applied, ideally in combination with other methods so that the dependence of findings on particular assumptions can be assessed.

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.

Dynamical Peculiarities of Concatenational Changes in Functional Cardiovascular Parameters

2018 ◽  
Vol 3 (82) ◽  
Author(s):  
Eurelija Venskaitytė ◽  
Jonas Poderys ◽  
Tadas Česnaitis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Functional State ◽  
The Body ◽  
Functional Changes ◽  
High Intensity Training ◽  
Series Analysis ◽  
Greco Roman ◽  
St Segment ◽  
Standard Lead

Research  background  and  hypothesis.  Traditional  time  series  analysis  techniques,  which  are  also  used  for the analysis of cardiovascular signals, do not reveal the relationship between the  changes in the indices recorded associated with the multiscale and chaotic structure of the tested object, which allows establishing short-and long-term structural and functional changes.Research aim was to reveal the dynamical peculiarities of interactions of cardiovascular system indices while evaluating the functional state of track-and-field athletes and Greco-Roman wrestlers.Research methods. Twenty two subjects participated in the study, their average age of 23.5 ± 1.7 years. During the study standard 12 lead electrocardiograms (ECG) were recorded. The following ECG parameters were used in the study: duration of RR interval taken from the II standard lead, duration of QRS complex, duration of JT interval and amplitude of ST segment taken from the V standard lead.Research  results.  Significant  differences  were  found  between  inter-parametric  connections  of  ST  segment amplitude and JT interval duration at the pre and post-training testing. Observed changes at different hierarchical levels of the body systems revealed inadequate cardiac metabolic processes, leading to changes in the metabolic rate of the myocardium and reflected in the dynamics of all investigated interactions.Discussion and conclusions. It has been found that peculiarities of the interactions of ECG indices interactions show the exposure of the  functional changes in the body at the onset of the workload. The alterations of the functional state of the body and the signs of fatigue, after athletes performed two high intensity training sessions per day, can be assessed using the approach of the evaluation of interactions between functional variables. Therefore the evaluation of the interactions of physiological signals by using time series analysis methods is suitable for the observation of these processes and the functional state of the body.Keywords: electrocardiogram, time series, functional state.

Sign in / Sign up

Export Citation Format

Share Document