Time-series analysis of Campylobacter incidence in Switzerland

SUMMARY Campylobacteriosis has been the most common food-associated notifiable infectious disease in Switzerland since 1995. Contact with and ingestion of raw or undercooked broilers are considered the dominant risk factors for infection. In this study, we investigated the temporal relationship between the disease incidence in humans and the prevalence of Campylobacter in broilers in Switzerland from 2008 to 2012. We use a time-series approach to describe the pattern of the disease by incorporating seasonal effects and autocorrelation. The analysis shows that prevalence of Campylobacter in broilers, with a 2-week lag, has a significant impact on disease incidence in humans. Therefore Campylobacter cases in humans can be partly explained by contagion through broiler meat. We also found a strong autoregressive effect in human illness, and a significant increase of illness during Christmas and New Year's holidays. In a final analysis, we corrected for the sampling error of prevalence in broilers and the results gave similar conclusions.

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

Abstract: Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics and oxygenation in tissue. Changes in light-coupling due to movement of the subject can cause movement artifacts (MAs) in the recorded signals. Several methods have been developed so far that facilitate the detection and reduction of MAs in the data. However, due to fixed parameter values (e.g., global threshold) none of these methods are perfectly suitable for long-term (i.e., hours) recordings or were not time-effective when applied to large datasets. We aimed to overcome these limitations by automation, i.e., data adaptive thresholding specifically designed for long-term measurements, and by introducing a stable long-term signal reconstruction. Our new technique (“acceleration-based movement artifact reduction algorithm”, AMARA) is based on combining two methods: the “movement artifact reduction algorithm” (MARA, Scholkmann et al. Phys. Meas. 2010, 31, 649–662), and the “accelerometer-based motion artifact removal” (ABAMAR, Virtanen et al. J. Biomed. Opt. 2011, 16, 087005). We describe AMARA in detail and report about successful validation of the algorithm using empirical NIRS data, measured over the prefrontal cortex in adolescents during sleep. In addition, we compared the performance of AMARA to that of MARA and ABAMAR based on validation data.

The Moderating Role of Electoral Pressure on Congressional Ideological Polarization: A Cross-Sectional Time-Series Analysis

Reelection and self-interest are recurring themes in the study of our congressional leaders. To date, many studies have already been done on the trends between elections, party affiliation, and voting behavior in Congress. However, because a plethora of data has been collected on both elections and congressional voting, the ability to draw a connection between the two provides a very reasonable prospect. This project analyzes whether voting shifts in congressional elections have an effect on congressional voting. Will a congressman become ideologically more polarized when his electoral margins increase? Essentially, this paper assumes that all congressmen are ideologically polarized, and it is elections which serve to reel congressmen back toward the ideological middle. The election and ideological data for this study, which spans from the 56th to the 107th Congress, finds statistically significant relationships between these two variables. In fact, congressman pay attention to election returns when voting in Congress. When broken down by party, Democrats are more exhibitive of this phenomenon, which suggest that Democrats may be more likely to intrinsically follow the popular model of representation. Meanwhile, it can be hypothesized that insignificant results for Republicans indicate that Republicans may follow a trustee model of representation.

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^

Implementation of active surveillance cultures and associated infection control practices for the reduction of methicillin resistant Staphylococcus aureus in the healthcare setting: An interrupted time series analysis

OBJECTIVE. To determine the effectiveness of active surveillance cultures and associated infection control practices on the incidence of methicillin resistant Staphylococcus aureus (MRSA) in the acute care setting. DESIGN. A historical analysis of existing clinical data utilizing an interrupted time series design. ^ SETTING AND PARTICIPANTS. Patients admitted to a 260-bed tertiary care facility in Houston, TX between January 2005 through December 2010. ^ INTERVENTION. Infection control practices, including enhanced barrier precautions, compulsive hand hygiene, disinfection and environmental cleaning, and executive ownership and education, were simultaneously introduced during a 5-month intervention implementation period culminating with the implementation of active surveillance screening. Beginning June 2007, all high risk patients were cultured for MRSA nasal carriage within 48 hours of admission. Segmented Poisson regression was used to test the significance of the difference in incidence of healthcare-associated MRSA during the 29-month pre-intervention period compared to the 43-month post-intervention period. ^ RESULTS. A total of 9,957 of 11,095 high-risk patients (89.7%) were screened for MRSA carriage during the intervention period. Active surveillance cultures identified 1,330 MRSA-positive patients (13.4%) contributing to an admission prevalence of 17.5% in high-risk patients. The mean rate of healthcare-associated MRSA infection and colonization decreased from 1.1 per 1,000 patient-days in the pre-intervention period to 0.36 per 1,000 patient-days in the post-intervention period (P<0.001). The effect of the intervention in association with the percentage of S. aureus isolates susceptible to oxicillin were shown to be statistically significantly associated with the incidence of MRSA infection and colonization (IRR = 0.50, 95% CI = 0.31-0.80 and IRR = 0.004, 95% CI = 0.00003-0.40, respectively). ^ CONCLUSIONS. It can be concluded that aggressively targeting patients at high risk for colonization of MRSA with active surveillance cultures and associated infection control practices as part of a multifaceted, hospital-wide intervention is effective in reducing the incidence of healthcare-associated MRSA.^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^